Exploring Intraspecific Variation

in the Gut Communities of Western Australian Endemic

Termites (Isoptera, Termitidae) as a Foundation for Future Local

Biofuel Initiatives

Ghislaine Anne Marie Marguerite Platell (née Small)

Bachelor of Science (Microbiology) with Honours

This thesis is presented for the degree of Doctor of Philosophy of

The University of Western Australia

2018

School of Chemistry and Biochemistry Centre for Integrative Bee Research

Australian Research Council Centre of Excellence in Plant Energy Biology

ii

THESIS DECLARATION

I, Ghislaine Platell, certify that:

This thesis has been substantially accomplished during enrolment in the degree.

This thesis does not contain material which has been accepted for the award of any other degree or diploma in my name, in any university or other tertiary institution.

No part of this work will, in the future, be used in a submission in my name, for any other degree or diploma in any university or other tertiary institution without the prior approval of The University of Western Australia and where applicable, any partner institution responsible for the joint-award of this degree.

This thesis does not contain any material previously published or written by another person, except where due reference has been made in the text.

The work(s) are not in any way a violation or infringement of any copyright, trademark, patent, or other rights whatsoever of any person.

The following approvals were obtained from the Western Australia Department of Parks and Wildlife (formerly Department of Environment and Conversation) prior to commencing the relevant work described in this thesis: permits SF009435, SF009982, CE004974 and SF010459.

The work described in this thesis was funded by: a Research Collaboration Award and Discovery Early Career Researcher Award (DE120101117). Some of this research was supported by an Australian Government Research Training Program (RTP) Scholarship.

Part of the 16S rRNA gene sequencing data presented in Chapter 2 was organised by Andreas Brune and Aram Mikaelyan while at the Max Planck Institute for Terrestrial Microbiology, as described in the text. Paul Schmidt (University of Göttingen) undertook the molecular identification of my samples for Chapter 3.

This thesis does not contain work that I have published, nor work under review for publication, although publications are planned from Chapter 2 and 3 in particular and contributions to each chapter are listed on the next page.

Signature:

Date:

iii

CONTRIBUTIONS

Chapter 1 The General Introduction was entirely planned and authored by me and reviewed by all four of my supervisors.

Chapter 2 The experimental design was discussed with my supervisors and Andreas Brune from the Max Planck Institute for Terrestrial Microbiology. I undertook the experimental work under the supervision of Tamara Hartke (field work) and Kate Howell (laboratory work including in house sequencing) and a portion was conducted in Marburg by Aram Mikaelyan. I conducted the data analysis and wrote the chapter, which has been edited by Kate Howell, Tamara Hartke and Boris Baer.

Chapter 3 The experimental design was discussed with my supervisors and Theodore Evans, from UWA. I undertook the experimental work (including in house sequencing supervised by Kate Howell) except for the help of Paul Schmidt (University of Göttingen) who undertook the molecular identification of my samples and Michael Bunce and Nicole White (TrEnD Laboratory, Curtin University) who ran the pilot trnL sequencing. I conducted the data analysis, which included a random sampling program created by Ian Small. I wrote the chapter, which has been edited by Tamara Hartke and Boris Baer.

Chapter 4 I made the originally observation of protists in the higher termites included in this study (2014 dataset) and designed and supervised a short term secondary research project conducted in 2015 by Honours students Erika Eto and Steven Correia. William Orsi (Department of Earth and Environmental Sciences, Palaeontology & Geobiology, Ludwig Maximilian University of Munich) kindly helped identify the protists as flagellates. I wrote the chapter, which has been edited by Kate Howell, Tamara Hartke and Boris Baer.

Chapter 5 The use of cellulose binding probes was first suggested by Harvey Millar and these were prepared by Mitchell Hattie and Keith Stubbs, from UWA. Optimisation of protocols was primarily discussed with Harvey Millar, Catherine Colas des Francs and Ryan Dosselli from UWA. I conducted the work and wrote the chapter which has been edited by Harvey Millar, Boris Baer, Catherine Colas des Francs, Jahmila Parthenay and Kieran Mulroney.

Chapter 6 The General Discussion was planned and authored by me, and edited by Tamara Hartke, Harvey Millar and Boris Baer.

iv

ACKNOWLEDGEMENTS Thank you to my supervisors for supporting me through the ups and downs both in terms of this work and in each of our lives. Boris – thank you for taking on this termite project amongst the bees and for bringing valuable ideas to the project from a different perspective, even after relocating the US. Tamara – thank you for sharing your termite expertise with me; learning about and then falling in love with these fascinating mini social cockroaches has been a massive highlight of my Honours and PhD. Thank you for your continued support from Germany and for your (and my) productive visits. Kate – thank you for passing on laboratory and sequencing knowledge and in particular for teaching me to use the MiSeq. It allowed me to conduct every aspect of this project from the field work, to running the sequencer and the analysis. Harvey – Thank you for sharing your valuable protein knowledge, your experience with theses and your support as my last remaining supervisor at UWA. I would like to thank Andreas, Aram and Niclas for hosting me at the Max Planck Institute for Terrestrial Microbiology for one month, honing my dissection technique, teaching me valuable methods, passing on protocols, the great discussions and their famous DictDb. Thanks for sequencing some of my samples, which allowed me to make valuable comparisons and work on a revised pipeline for my next experiments. Thanks to Theo for the experimental design discussions since moving to Perth. I also want to extend a big thank you to PEB, CIBER and UWA for the space, the equipment and the lovely co-workers. In particular, thank you to Hayden for keeping the computing side of things running as smoothly as possible, to my students Nithin, Claire, Steven and Erika for their involvement and contributions, to Ryan for your help troubleshooting my (many) protein issues, to Ellen for a brilliant example to follow, to Rachael for pipeline discussions, to Karina for the outreach and extracurricular learning opportunities and to Sandi my office mate and Antarctica travel buddy! I must thank my fellow Homeward Bounders from across the globe for the continued contact, support and opportunities. I would love to thank my "maman" and dad which have been involved both as parents and co-workers. It was wonderful learning lab techniques from my mum and analysis tricks from dad. I am grateful for great friends and hobbies to keep me healthy. Thanks to the Games Workshop crew for a fun and rewarding work environment, to my rugby team the Divas for the motivation to exercise and practice my courage. Thank you to Buzz and Summer, my two puppies that were great company while I wrote and napped and last but certainly not least, to Peter, for your support while I have studied all these years.

v

ABBREVIATIONS ABPPs Activity-Based Proteomics Probes ANOVA ANalysis Of VAriance A. obeuntis Amitermes obeuntis B Beelu National Park BLAST Basic Local Alignment Search Tool CMC Carboxymethyl Cellulose CO2 Carbon dioxide COII Cytochrome c Oxidase subunit II C. acinaciformis Coptotermes acinaciformis raffrayi C:N Carbon-to-Nitrogen ratio C+ Aspergillus niger positive control for cellulase dbRDA distance-based RedunDancy Analysis DictDb Dictyopteran gut microbiota reference Database DMF Dimethylformamide DNA Deoxyribonucleic Acid DTT Dithiothreitol EDTA Ethylenediaminetetraacetic Acid EU European Union GH Glycoside Hydrolases GHGs Greenhouse Gases GH2d-ABP Glycoside Hydrolase Activity Based Probe 2d GL Gigalitre GPS Global Positioning System g/L Gram per Litre HEPES 4-(2-HydroxyEthyl)-1-PiperazineEthaneSulfonic acid hr Hour J John Forrest National Park kD Kilodalton M Molar mg Milligram min Minute ml Millilitre ML Megalitre mm Millimetre mM Millimolar MM301 Retsch Mixer Mill Mya Million years ago NaPO4 Sodium Phosphate NDSB201 Non-Detergents Sulfobetaine 201 nls Non-Linear Least-Squares nm Nanometer OTU Operational Taxonomic Unit O. occasus Occasitermes occasus PCoA Principal Coordinate Analysis PCR Polymerase Chain Reaction

vi

PERMANOVA PERmutational Multivariate ANalysis Of VAriance pH Potential of Hydrogen PhD Doctor of Philosophy PMSF Phenylmethylsulfonyl fluoride P3 Proctodeal segment 3 QIIME Quantitative Insights Into Microbial Ecology RE Renewable Energy rRNA Ribosomal Ribonucleic Acid SDS Sodium dodecyl sulfate RT Room Temperature TBTA tris[(1-benzyl-1H-1,2,3-triazol-4- yl)methyl] amine T. reesei Trichoderma reesei T. westraliensis Tumulitermes westraliensis UniFrac Unique Fraction metric US United States USB Universal Serial Bus UV Ultraviolet V Volt V4 V4 hypervariable region of the 16S rRNA gene V3-V4 V3 to V4 region of the 16S rRNA gene W Banyowla Regional Park WA Western Australia x g Times the force of gravity μl Microlitre μM Micromolar ° C Degrees Celsius

vii

ABSTRACT Climate change is arguably the biggest issue facing humanity today. Mitigation

strategies can reduce humankind's net greenhouse gas contributions and in

turn reduce our impact on further climate change. In particular, second

generation biofuel technologies produced from crop and forest residues, have

the benefits of decreased greenhouse gas emissions and low competition with

food needs, as well as the potential to reduce waste streams and improve

agricultural land quality. In Western Australia, the two most promising

substrates are wheat straw and eucalyptus mallee. Biofuels from wheat straw

would recycle crop "waste" that is currently being burnt, hence providing

improvements to two human activities that contribute to climate change.

The biggest barrier to the implementation of biofuel production from these

substrates is the development of a cost-effective cellulose hydrolysis process.

Higher termites (Isoptera, Termitidae) are a promising source of enzymes

because they harbour primarily bacterial consortia that efficiently degrade

various forms of lignocellulose, optimised over evolutionary time. In this

thesis, I have investigated short-term influences on the termite gut

community to determine whether members of the gut population of endemic

higher termites may warrant further study as part of optimised biofuel

production from wheat crop residue in Western Australia.

To my knowledge, I provide the first characterisation of gut communities of

three endemic Western Australian termites with broad diets, higher termites

Tumulitermes westraliensis, Amitermes obeuntis and the lower termite

Coptotermes acinaciformis raffrayi. I propose new standards for experimental

design in the study of termite gut communities, including the use of a

standardised 16S rRNA amplification strategy, increased replication, a

standard species core community definition and analysis method for accurate

core community calculations. My work evaluates potential factors shaping

intraspecific variation in the gut community of higher termites, providing a

viii

greater understanding of the vertically, horizontally and environmentally

acquired components of the bacterial and protistan microbiota and the

generation of intraspecific variation. Protein extraction and visualisation

protocols were optimised with the aim to improve the integrity of enzymes

recovered from termite guts and enhance binding of cellulase-specific probes,

with a future aim to isolate them for further testing.

My project confirms that diet affects termite gut core community composition

and abundance on a short time scale under field conditions. Intraspecific

variation occurring on a short time scale allows the manipulation of the gut

community to target substrates of interest, laying the foundation for future

local biofuel initiatives. Potential future studies are outlined and could focus

on the gut communities or enzymes produced by higher termites feeding

directly on biofuel substrates of interest, such as wheat and/or eucalyptus

mallee in Western Australian Wheatbelt fields.

ix

TABLE OF CONTENTS Thesis Declaration...................................................................................................ii Contributions..........................................................................................................iii Acknowledgements.................................................................................................iv Abbreviations...........................................................................................................v Abstract...................................................................................................................vii Table of Contents....................................................................................................ix

Chapter 1: General Introduction..............................................................................1 1. Renewable energy: a climate change mitigation strategy..................................2 2. Biofuel: a medium term mitigation strategy......................................................4 3. The quest for cellulase enzymes........................................................................10 4. Termites as a source of cellulases......................................................................14 5. Conclusion...........................................................................................................19 References...............................................................................................................21 Chapter 2: Colony Differences in the Gut Communities of Western Australian Termites Tumulitermes westraliensis and Coptotermes acinaciformis raffrayi.....................................................................................................................29 Introduction...........................................................................................................30 Methods..................................................................................................................33 Results.....................................................................................................................40 Discussion................................................................................................................51 References...............................................................................................................59 Supplementary figures...........................................................................................64 Chapter 3: Sampling Intensity, Scope and Scale Significantly Affect Estimation of Dietary Influence on the Gut Communities of Tumulitermes westraliensis and Amitermes obeuntis........................................................................................67 Introduction...........................................................................................................69 Methods..................................................................................................................72 Results.....................................................................................................................81 Discussion..............................................................................................................101 References..............................................................................................................112 Supplementary figures..........................................................................................116 Chapter 4: Observation of Flagellated Protists in the guts of Higher Termites Tumulitermes westraliensis and Amitermes obeuntis Following Rainfall.........137 Introduction..........................................................................................................138 Methods.................................................................................................................141 Results....................................................................................................................142 Discussion..............................................................................................................147 References.............................................................................................................150

x

Chapter 5: Accurate Characterisation of the Complete Termite Cellulase Profile Requires Selective Inhibition of Gut Proteases...................................................153 Introduction..........................................................................................................154 Protocols................................................................................................................157 Experiments...........................................................................................................161 Discussion..............................................................................................................183 References.............................................................................................................188 Chapter 6: General Discussion..............................................................................191 A: How much intraspecific variation exists in the gut communities of Western Australian endemic termites?..............................................................................192 B: How should the core community of a termite species be defined and accurately estimated?...........................................................................................196 C: Can the gut community of local higher termite species be modified in a field-based feeding experiment?.........................................................................199 D: Can enzymes relevant to breaking down a substrate of choice be identified using comparative enzymology?.........................................................................202 Broader Contributions.........................................................................................204 Conclusion............................................................................................................208 References.............................................................................................................210

1

CHAPTER 1: General Introduction

2

Overview:

Climate change is arguably the biggest issue facing humanity today. Mitigation

strategies can reduce humankind's net greenhouse gas contributions, to in

turn reduce our impact on climate change. This review will give an overview of

these issues and then focus on biofuel, a group of renewable energy

technologies that can lower the transport sector's emissions. In particular,

second generation biofuel production can be improved by the development of

a more cost effective cellulose hydrolysis process. Termites have been

identified as a promising source of enzymes or microbes to that end and are

the ultimate focus of this review. There are currently no commercial second

generation biofuel plants in Western Australia and local possibilities will be

explored, although the scope of this thesis remains in studying the gut

communities of local termite species with future possibilities to learn from

them in order to improve biofuel production.

1. Renewable energy: a climate change mitigation strategy

Anthropogenic climate change

The greenhouse effect, a process by which gases in the atmosphere trap heat,

makes the Earth habitable. Human activities such as deforestation, intensive

agriculture and burning of fossil fuels have and continue to increase the

concentration of major greenhouse gases (GHGs) in the atmosphere (Mitchell,

1989). While the naturally dominant GHG water vapour only persists in the

atmosphere for about a week, the major anthropogenic contributor carbon

dioxide (CO2) can persist for several centuries. Water vapour is replenished by

evaporation from the oceans, a process regulated by temperature (Bengtsson,

2010; Allwood et al., 2014). Hence anthropogenic GHG emissions cause an

amplification of warming in direct and indirect ways, leading to climate

change.

3

The scale of the change in Earth’s climate has started a new geological epoch.

Its suggested name "Anthropocene" represents the wide and increasing

impacts of human activity on the planet (Crutzen and Stoermer, 2000).

Climate change is arguably the biggest threat faced by humankind today,

particularly because it compounds with many other issues. For example,

effects of climate change on temperature and rainfall will impact food

production. Agriculture currently contributes about 30% of human GHG

emissions (Smith and Gregory, 2013). So not only will feeding the world's

growing population become more difficult, but producing more food using

current agricultural practices will intensify climate change.

Continuing the present course will lead to a much warmer climate, extensive

sea level rise and species impoverishment, with the risk of a sixth great

extinction event. Working toward a strong and rapid reduction in GHG

emissions should allow the climate to stabilise, although it will remain

significantly warmer than that of the Holocene (Steffen et al., 2016). Mitigation

strategies can be put in place to reduce the effects of climate change, while

adaptation strategies can prepare us for inevitable changes. This introductory

chapter focuses on one strategy for climate change mitigation, the use of

renewable energy, taking inspiration from natural cellulose-degrading systems

to improve energy production.

Renewable energy

Climate change mitigation requires human intervention to reduce the sources

of GHGs or enhance their sinks. One major mitigation strategy is the use of

renewable energy (RE), whose sources are replenished or added to by natural

processes (Allwood et al., 2014). RE also contributes to sustainable social and

economic development and leads to health benefits through the reduction of

air pollution, furthering its positive economic impact (IPCC, 2011; Mathiesen et

al., 2011).

4

The world's dependence on energy continues to increase with world

population and industrialisation, particularly in the developing world

(Wolfram et al., 2012). Non-renewable energy sources, such as oil and coal

cannot fill this need, due to their finite availability as well as the

environmental damage associated with their use. Indeed, half of the world's oil

supply may have already been accessed (an event called peak oil; Höök and

Tang, 2013; Chapman, 2014), underlining the urgent need to develop

replacement technologies. RE can complement and later replace oil to

mitigate both GHG emissions and oil depletion.

Electric vehicles are predicted to dominate future transport because of the

decreasing costs of electric batteries and the growth of renewable electricity

generation (Catenacci et al., 2013; Holland et al., 2016). However batteries

currently face reliability, size and range issues and the large scale deployment

of charging stations may impact existing distribution networks (Sims et al.,

2014; Haddadian et al., 2015). Until that gap is fully bridged, other technologies

are required to replace oil such as biofuels, which can be adopted to varying

extents with the current infrastructure (Fiorese et al., 2013; Sims et al., 2014).

Certain biofuels may play a longer term role in powering heavy machinery,

ships and planes (Buijs et al., 2013; Sims et al., 2014; Bharathiraja et al., 2017).

2. Biofuel: a medium term mitigation strategy

Biofuel overview

Biofuel is fuel derived from biomass, which is living or dead, non-fossilised

organisms or their by-products (Allwood et al., 2014; Brooksbank et al., 2014).

There are various technologies commercially available or in development to

produce biofuel. These are classified into four generations, all or most of

which will soon be required to meet transport fuel demands (Dutta et al.,

2014). Biofuel production does have environmental impacts, the extent of

which depends on the type of biomass, and how it is produced, harvested,

5

transported and processed. Caution needs to be used on a case-by-case basis

to assess the benefits and impacts of biofuel production (Koh and Ghazoul,

2008).

Bioethanol is the most common biofuel produced worldwide, followed by

biodiesel. Global production of bioethanol averaged approximately 103 GL per

year in 2016, 85% of which was produced by the United States (US) and Brazil

(Renewable Fuels Association, 2017). Biodiesel averaged 39 GL per year

(Renewable Fuels Association, 2017). By comparison, oil production averaged

5322 GL per year in 2015 (including crude oil, shale oil, oil sands and natural

gas liquids; BP, 2017). The European Union (EU) is the world's largest biodiesel

producer, with 13.5 GL produced in 2015, making up 80% of the EU's biofuel

production. Bioethanol production was estimated at 5.2 GL across the major

EU contributors in 2015 (Flach et al., 2016). Australia's biofuel production was

estimated at 250 ML of bioethanol and 50 ML of biodiesel in 2016 (Farrell,

2016). While biodiesel can be used in current vehicles with little modification,

bioethanol has shown to be corrosive to existing infrastructure and engines. It

requires specific transport and storage and to be used in most existing vehicles

as a low concentration blend (Jin et al. 2011).

Interest in so-called drop-in fuels that are similar enough to gasoline to be

distributed through existing gasoline infrastructure and be used almost

undiluted in existing engines has therefore grown (International Energy

Agency, 2011; Buijs et al., 2013; Bharathiraja et al., 2017). Biobutanol is a drop-in

fuel that would allow better mileage, less ignition problems, a safer use at high

temperatures, and easier storage and distribution than bioethanol. However

there are concerns over the low production yield of biobutanol from biomass

(Pfromm et al., 2010; Jin et al. 2011). One solution is to chemically convert

bioethanol to biobutanol (Ndaba et al., 2015). Regardless, bioethanol, biodiesel

and biobutanol can be produced from all four generations of biofuel

technology (Dutta et al., 2014) and will be covered in this review.

6

First generation biofuel: an ethical dilemma

First generation bioethanol production involves the fermentation of sugars or

starch, while biodiesel production is based on the esterification of edible oils

(Dutta et al., 2014). The US and Brazil primarily produce bioethanol from corn

and sugarcane respectively (Haq et al., 2016). In the EU, bioethanol is mainly

produced from wheat, corn and sugar beet and first generation biodiesel from

rapeseed oil and palm oil (Flach et al., 2016). In Australia, bioethanol is

produced from wheat starch, red sorghum and molasses from sugar

manufacture, while first generation biodiesel feedstocks include animal fats

(Farrell, 2016).

First generation biofuel production is cost effective but the feedstocks are

expensive and drive up the price of food (Dutta et al., 2014; Gasparatos et al.,

2013). In 2008, an international food crisis was declared as a result of the rising

prices of agricultural commodities, partly due to their use in fuel production

(Schmitz and Kavallari, 2009). Crops raised for first generation biofuel

production require large areas of prime farmland, which leads to deforestation

and therefore impacts biodiversity and GHG emissions (Gasparatos et al.,

2013). First generation biofuels are therefore not a sustainable and ethical

solution to singly meet growing transport fuel demands.

Second generation biofuel: a local solution

Second generation biofuels are fuels produced from non-food feedstocks

(Dutta et al., 2014). As of 2015, 67 biorefineries worldwide were producing

bioethanol, biodiesel, or aviation biofuel using second generation

technologies. Over a third of these were operating on a commercial scale,

while the rest were pilot projects (Nguyen et al., 2017). Second generation

biodiesel is produced from non-edible oil seeds or waste cooking oil using first

generation technology (Dutta et al., 2014; Farrell, 2016). Used cooking oil is the

second most used feedstock for biodiesel production in the EU and is

commonly used in Australia (Flach et al., 2016; Farrell, 2016).

7

Second generation bioethanol or biobutanol are produced from lignocellulose,

the main component of plant cell walls and therefore the most abundant

biomass on Earth (Dutta et al., 2014; Haq et al., 2016). Lignocellulosic

feedstocks include crop and forest residues, organic municipal solid waste and

purposely-grown energy crops such as grasses and short rotation forests

(Dutta et al., 2014). There are 17 operating lignocellulosic bioethanol plants in

the EU, four of which are on a commercial scale (Nguyen et al., 2017). There

are 14 operating lignocellulosic bioethanol plants in the US (9 of which are

commercial), and two pilot plants in Australia (Brooksbank et al., 2014;

Nguyen et al., 2017).

Lignocellulosic material is recalcitrant and more complex technology is

required for its use as a fuel source. The cellulose and hemicellulose

components must first be broken down into glucose to be fermented into

ethanol or butanol (Dutta et al., 2014; Haq et al., 2016). While lignocellulosic

feedstocks are cheap, the pre-treatment and hydrolysis processes required to

break down the substrate are not. Another barrier to widespread

commercialisation of lignocellulosic biofuel is the energy and monetary cost of

transporting biomass to the plant. Except for the case of dedicated energy

crops, the feedstock typically originates from multiple locations (Lange, 2007;

Ho et al., 2014). However, Searcy et al. (2007) showed that the cost of

transporting biomass is more than the cost of transporting its energy products

and that building power plants near the feedstock is a more cost effective

approach. For example, all four lignocellulosic bioethanol plants in Brazil use

sugarcane bagasse from local mills (Nguyen et al., 2017).

A local second generation bioethanol or biobutanol approach for Western

Australia (WA) would likely include wheat residue. WA contributes half of the

wheat production of Australia, with over 10 million tonnes produced in 2015

(Wilkinson, 2017). There are other uses for wheat residue such as animal feed

or bedding (Brooksbank et al., 2014; Giannoccaro et al., 2017). Fields also

8

benefit from the retention of crop stubble and mulched residue to limit

erosion and increase yield (Malinda, 1995; Erenstein, 2002; Brooksbank et al.,

2014). Yet of the 15 million tonnes of residue produced per annum, much of it

is currently being burnt (Taskforce, 2007). A 2014 WA Department of

Agriculture and Food report highlighted ten hubs in WA with the

infrastructure to support bioethanol plants from cereal straw. They estimated

that 149 to 1212 thousand tonnes of cereal straw would be available within 50

km of each of the ten hubs, taking into account alternative straw uses

(Brooksbank et al., 2014). As an example the Beta Renewables plant in

Crescentino is a commercial plant utilising second generation technology with

an input capacity of 200,000 t/y lignocellulosic crop residue including wheat

straw and an output capacity of 40,000 t/y (50.8 ML per year) of ethanol

(European Biofuels Technology Platform, 2016). All but two of the hubs

identified by Brooksbank et al. (2014) exceed the input capacity of the

Crescentino plant, suggesting there are multiple viable options for second

generation plants in Western Australia. Local farmers' interest would need to

be explored in those areas to determine the ultimate success of such a venture

(Giannoccaro et al., 2017).

Eucalyptus mallee has also been investigated as a potential substrate for

biofuel production in Western Australia, with diverse ecological benefits.

Mallee are multibranched short trees that can be harvested every 3-7 years,

providing frequently renewed biomass and carbon sequestration (Wu et al.,

2008; Yu et al., 2015). When planted between wheat fields, their root systems

rectify accumulation of groundwater and by extension the widespread issue of

dryland salinity in the Wheatbelt caused by intensive agriculture (Shepherd et

al., 2014; Yu et al., 2015). In addition, mallee plantations limit wind erosion,

foster local biodiversity (Wu et al., 2008), and are an almost carbon neutral

source of biomass (Yu et al., 2015). Second generation technologies therefore

have the benefits of decreased GHG emissions and decreased competition

with food needs, as well as the potential to reduce waste streams and improve

land quality (Wu et al., 2008; Brooksbank et al., 2014; Dutta et al., 2014).

9

However, further work is required to improve the cost efficiency of the

process. The difficult and costly hydrolysis step will be the focus of section 3 in

this review.

Third generation biofuel: a cost barrier

Third generation biofuel is produced from microalgae (Dutta et al., 2014).

Microalgae are a collection of single cells or simple multicellular organisms

from multiple kingdoms that live in a broad range of humid environments and

use sunlight to produce and store fixed carbon. The ease of cultivation of

microalgae and their higher lipid content than vascular plants make them

attractive for biodiesel production. They can be cultivated on non-arable land

using non-potable water and converted to biodiesel with much less waste than

first and second generation biodiesel feedstocks (Borowitzka and Moheimani,

2013; Leite et al., 2013). They can also be used to produce bioethanol or

biobutanol, in which case the lipid content is not important and different

species can be selected (Borowitzka and Moheimani, 2013; Dutta et al., 2014).

Despite these benefits, there are currently no commercial third generation

biofuel plants in operation, as it remains the most expensive type of biofuel to

produce (Dutta et al., 2014). Algae can be grown in highly controlled but

expensive photobioreactors. Open ponds are a lower cost alternative with a

lower energy input requirement, but have a higher risk of contamination and

lower productivity. Third generation biodiesel production is similar to first

generation technologies, with the addition of expensive harvesting procedures

to concentrate and dry the feedstock (Borowitzka and Moheimani, 2013; Leite

et al., 2013). Algal cultures do not scale very well and algal biodiesel is

projected to play a small part in meeting global energy demand (Borowitzka

and Moheimani, 2013).

10

Fourth generation biofuel and overcoming the cost barrier

Fourth generation biofuel technologies under development involve genetic

modification of microorganisms to improve fuel yield as a means to counteract

the cost barrier to commercialisation (Dutta et al., 2014; Vassilev and

Vassileva, 2016). Other uses for lignocellulosic and algal biomass are being

investigated to generate alternate money streams to justify second and third

generation biofuel production. These include the production of chemicals and

animal feed, as well as waste treatment (Mata et al., 2010; Kircher, 2015). A

combination of biofuels across multiple generations, with a localised

approach, is therefore the only way to meet transport fuel demands

sustainably, ethically and in economically viable ways.

3. The quest for cellulase enzymes

Cellulase: a collection of enzymes

The hydrolysis step of second generation lignocellulosic biofuel production

involves incubating the pre-treated plant fibre with commercial cellulase

enzymes. Three major types of glycoside hydrolases (GH) are required for the

breakdown of cellulose into glucose: endoglucanases, exoglucanases and β-

glucosidases, collectively referred to as cellulases (Figure 1). Naturally

occurring cellulose is insoluble but contains amorphous regions that can be

accessed by endoglucanases and subsequently cleaved. Exoglucanases, tunnel-

shaped enzymes, can then release cellobiose (made up of two glucose

subunits) from the ends of cellulose chains. Endoglucanases thus work

synergistically with exoglucanases by increasing the number of available ends

(Wilson, 2011). Finally, β-glucosidases act on cellobiose to complete the

digestion to glucose. Other accessory enzymes, for example hemicellulases,

aldo-keto reductases and laccases, facilitate this process by increasing access

to the cellulose component of lignocellulose (Coy et al., 2010; Mohanram et al.,

2013; Sethi et al. 2013).

11

Commercialised enzymes are simple mixtures

There are multiple deployment strategies for cellulases found in nature. The

two most common are 1) secretion of individual enzymes with one or multiple

catalytic domains, such as in fungi, bacteria and even termites (Brune, 2014;

Payne et al., 2015, Figure 1A); and 2) cell-bound complexes of up to hundreds

of enzymes called cellulosomes, primarily found in anaerobic bacteria (Bayer

et al., 2004; Artzi et al., 2017, Figure 1B). A rarer strategy involves direct

binding of bacteria to the substrate and importation of partially degraded

chains for further intracellular processing. This strategy has so far only been

described in phylum Fibrobacteres (Wilson, 2011; Ransom-Jones et al., 2012;

Abdul Rahman et al., 2016, Figure 1C). Different strategies can work

synergistically as demonstrated by Resch et al. (2013), suggesting that diversity

is key to an optimised cellulose-degrading approach.

However, most currently available commercial cellulases are mixtures derived

from Trichoderma or Aspergillus fungal species, in particular T. reesei

(Lambertz et al., 2014; Bischof et al., 2016). Fungi produce a high yield of

protein but the enzyme mixture cannot be modified to optimise the

breakdown of different substrates (Lambertz et al., 2014), which is necessary to

increase degradation efficiency (Banerjee et al., 2010; Mohanram et al., 2013).

Research has therefore focused on developing cellulose-degrading consortia,

as these can perform more complex functions, can be modulated to suit

different biofuel feedstocks and are more stable than monocultures (Zuroff

and Curtis, 2012; Brethauer and Studer, 2014). This reinforces the idea that

second generation biofuel production will benefit from an approach optimised

for locally available substrates.

Finding or engineering enzymes

There are two main approaches to optimising cellulase cocktails or consortia:

1) designing or modifying known cellulases and 2) finding more efficient

naturally occurring enzymes (Mohanram et al., 2013). Enzyme engineering can

12

take several forms: rational design by site-directed mutagenesis (Huang et al.,

2012), directed evolution using error-prone PCR (Liang et al., 2011), combining

enzymes into multifunctional chimeras (Morais et al., 2012) or designing

cellulosomes (Moraïs et al., 2010). This review will focus on the second

approach, enzyme discovery, as well as the idea that naturally occurring

enzymes or consortia can be naturally optimised to different substrates. For

example, the cellulosome of Clostridium thermocellum is naturally modified

when the bacterium is exposed to different substrates (Raman et al., 2009).

Novel cellulases can be found in certain free-living bacteria, fungi and protists.

Some animals, most of which are insects, also possess the ability to degrade

cellulose, commonly relying on symbioses with cellulose degrading gut

microbes (Oppert et al., 2010). Termites are particularly successful, and

produce their own (endogenous) cellulases, alongside those expressed by their

gut community (Brune, 2014). Termites therefore harbour consortia that could

be harnessed for more efficient commercial cellulose degradation.

13

Figure 1: The tree known deployment strategies for cellulases include (A)

secretion of individual enzymes, (B) cell-bound complexes of up to hundreds

of enzymes called cellulosomes and (C) direct binding to the substrate and

importation of partially degraded chains. All involve different classes of

cellulases working synergistically. Modelled on Ransom-Jones et al (2012).

14

4. Termites as a source of cellulases

Social ecosystem engineers

Termites are social insects of the Infraorder Isoptera (Krishna et al., 2013),

closely related to wood-dwelling cockroaches within the order Blattodea

(Inward et al., 2007; Figure 2). There are almost 3000 described living species

worldwide, of which only 12% are considered significant pest species (Krishna

et al., 2013). In Australia, there are at least 273 termite species from 41 genera

and 5 families (Krishna et al., 2013). Approximately 64% of these are endemic

to Australia (Eggleton, 2000). Pests species of economic importance have been

preferentially studied; and in the case of Australia include the world's earliest

branch termite lineage, Mastotermes darwiniensis (Veivers et al., 1983; Konig

et al., 2006; Scharf, 2015), the most damaging genus in Australia, Coptotermes

(Calaby and Gay, 1956; Peters and Fitzgerald, 2003; Evans and Gleeson, 2006;

Lee et al., 2015) and several species within the genus Nasutitermes (Hogan et

al., 1988; Lee et al., 2007; Webb and Mcclintock, 2015).

Although they are more renowned as pests, Australian termites primarily play

a key ecological role similar to that of earthworms in the Northern

hemisphere. Both animals are sometimes referred to as 'ecosystem engineers'

because they contribute to soil turnover and aeration, and nutrient cycling

(Lavelle et al., 1997; Black and Okwakol, 1997). Termites are particularly

successful cellulose-degraders, being able to digest up to 99% of the cellulose

they ingest (Esenther and Kirk, 1974). They produce their own (endogenous)

cellulases, but also take advantage of the cellulases expressed by the hundreds

of microorganisms in their intestinal tracts (Brune, 2014). Indeed, a termite

cannot survive on lignocellulose if its gut has been defaunated (Eutick et al.,

1978; Veivers et al., 1983), so their gut community is key to their success as

both pests and ecosystem engineers.

15

The successful higher termites

Termite families are classified as either lower or higher termites (Figure 2).

The lower termites include the basal families Mastotermitidae,

Hodotermitidae, Termopsidae, Kalotermitidae and Rhinotermitidae, which

primarily feed on wood and have gut communities dominated by cellulose-

degrading flagellates and their associated prokaryotes (Cleveland, 1923; Inoue

et al., 2000; Dolan, 2001). The cellulose-degrading flagellates are passed from

generation to generation by proctodeal trophallaxis (anus-to-mouth transfer;

Ohkuma et al., 2009). The gut community is established in the juveniles by

repeated transfer of gut fluid until the third instar, when they can maintain

the larger flagellates and become independent feeders, and again for up to two

weeks following any molt (Nalepa, 2015).

The higher termite family Termitidae appears to have lost these flagellates and

diversified its diet approximately 40-60 million years ago (Mya; Engel et al.,

2009; Bourguignon et al., 2016). This most derived family encompasses over

80% of all living termite species, each more or less specialised to consume

wood, grass, litter, humus, soil or fungus (Jones and Eggleton, 2011; Brune and

Ohkuma, 2011; Bignell, 2011; Poulsen, 2015; Figure 3). These substrates are

broken down by endogenous cellulases in the midgut, which are

complemented by microbial enzymes in the P3 hindgut segment (Brune, 2014;

Figure 3). Higher termites are thought to transmit their bacterial gut

community by trophallaxis (mouth-to-mouth) and coprophagy (excrement

ingestion) as proctodeal trophallaxis has never been reported (Diouf et al.,

2015). It is unclear to date whether the gut community of higher termites has

co-evolved with its host to the same extent as in the lower termites. The

potentially plastic bacterial gut community of the higher termites may thus be

the ideal source of enzymes or consortia suited to breaking down substrates of

interest to the biofuel industry.

16

Figure 2: Phylogenetic relationship of select Australian termite species, as

compared to two species of roaches and a mantis. This phylogeny was created

using Phylogeny.fr (Dereeper et al., 2008) based on cytochrome oxidase

subunit II (COII) sequences from Inward et al. (2007) and this study.

Figure 3: Gut structure and segments of Western Australian endemic higher

termites (A) Tumulitermes westraliensis, which feeds on grass and (B)

Amitermes obeuntis, which feeds on wood and soil. The gut outlines were

obtained by exaggerating the contrast of light microscopy photographs of each

gut, thinning the outline and colouring in the lumen. The two gut structures

are similar except for an enlarged P1 hindgut segment (blue) in A. obeuntis,

which has been found in all soil feeders and is thought to be involved in the

modification of humic acid (Brune, 2014). Cellulases produced by the host are

secreted in the midgut (red); whereas the bulk of the gut community resides

within the P3 hindgut segment (green), where microbial cellulases are

expressed. P2 (pink) is the enteric valve that separates the P1 and P3 hindgut

segments.

17

The importance of the core microbiome

Taxa that are consistently detected across samples from a predefined habitat

are known as the core community and are thought to be critical to the

functioning of that community. This concept allows scientists to define a

baseline community for that habitat and predict responses to perturbation and

manipulation (Shade and Handelsman, 2012). Each member of the core

community is thought to fill a niche in the termite gut that is consistently

present regardless of the location or food source of the host (Bignell et al.,

1980; Benjamino and Graf, 2016). Indeed, Huang et al. (2013) showed that

despite feeding the lower termite Reticulitermes flavipes either grassy or

woody diets, a core microbiome of 65% of commonly recovered taxa

(accounting for 95% of sequences) persisted across all diets. Yet the source of

the core and of the remainder of the gut community remains unclear (Scharf,

2015; Benjamino and Graf, 2016; Shapira, 2016).

Microbiota transplant experiments have shown that gut communities are

assembled primarily based on the available niches in the gut habitat. In

zebrafish, mice, termites and cockroaches, the newly established gut

community is made up of lineages from the community of origin, but their

relative abundances resemble that of the normal gut community of the

recipient host (Rawls et al., 2006; Mikaelyan et al., 2016). This implies that

bacterial phylotypes taken up from the environment or transferred by

nestmates may be amplified in the gut environment. Thus the core

microbiome within a termite species could be the result of passing on

particular phylotypes from generation to generation, consistently collecting

them from the environment or, most likely, a combination of both. Regardless

of its origin, studying the core microbiome indicates which community

members may consistently benefit the host and potential self-sustaining

consortium for industrial applications.

Core microbiomes for the termite gut habitat have been estimated in previous

studies using a variety of approaches, each defining which taxa should be

18

considered core in different ways (Huang et al., 2013; Dietrich et al., 2014;

Otani et al., 2014; Reid et al., 2014; Benjamino and Graf, 2016). Despite these

differences, the core community concept typically is used to draw evolutionary

conclusions about termite-microbe relationships based on a single point in

evolutionary time. The inconsistency of method and interpretation of the core

community concept in the literature affects the quality of these conclusions.

Modulating the gut community

Studying the non-core taxa is a way to measure plasticity in the termite gut

community. Variation in composition and structure (abundance of each

taxon) of gut communities may highlight microbial taxa relevant to a

particular set of conditions. For example, within colony differences are

thought to reflect taxa relevant to the different roles of castes and age groups

(Hongoh et al., 2006; Xiang et al., 2012; Li et al., 2016; Benjamino and Graf,

2016). Reid et al. (2014) found that each colony of the lower termite

Stolotermes ruficeps had a signature gut community, even when collected in

close proximity. They speculated that differences in diet, such as wood species

or degree of decay, may explain the variation. Understanding the extent of and

factors driving natural intraspecific gut community variation can suggest

appropriate levels of variation for commercialised consortia targeting different

substrates.

Changes in the gut community have been measured in response to forced

dietary changes for both lower and higher termites (Tanaka et al., 2006;

Miyata et al., 2007; Husseneder et al., 2009; Huang et al., 2013; Wang et al.,

2016). Boucias et al. (2013) reported no significant change in the gut

community of the lower termite Reticulitermes flavipes laboratory colonies

during a week-long exposure to filter paper and/or pinewood. On the other

hand, Huang et al. (2013) used a six-week time frame with the same termite

species, a greater variety of natural substrates and freshly collected termites.

They reported that the gut community could be differentiated based on the

recalcitrance of the diet, with diet affecting the community composition (with

19

taxa exclusively associated with certain diets) and and stucture (with many

taxa enriched under a particular diet).

Miyata et al. (2007) fed artificial diets to the higher termite Nasutitermes

takasagoensis over a three week period and reported large changes to the gut

community structure depending on the complexity of the diet. Wang et al.

(2016) also found changes following forced feeding of the higher termite

Mironasutitermes shangchengensis with corn stalks or filter paper for up to ten

days. However, they reported that the gut community seemed resilient

because the community composition and structure was most similar to the

control ten days following feeding, as compared to any other time point. For

example, the relative abundance of Spirochaetes was lower in groups fed for

four days as compared to the control, but similar to the control in groups fed

for seven and ten days. On the other hand, Firmicutes, Actinobacteria and

Acidobacteria were more abundant in groups fed for four and seven days, but

similar to the control in groups fed for ten days. Gut community manipulation

in termites requires further study to determine the benefits and duration of

gut community changes following a change in diet. Feeding times of multiple

weeks have generated greater changes in past studies but Wang et al. (2016)

note that keeping higher termites healthy in laboratory conditions is of greater

difficulty than with lower termites. Hence, field based feeding experiments

with substrates of interest may hold the key to optimising bacterial consortia

in higher termites for biofuel applications.

5. Conclusion

Climate change is the biggest issue facing humanity today and renewable

energy is one of the most effective mitigation strategies we can employ. A

combination of technologies will support this goal in the medium term, at

least, and second generation biofuel production is already playing a role. The

further implementation of this technology relies upon the development of

20

more efficient enzyme cocktails or bacterial consortia at a local level. Termites

are a promising source of enzymes or microbes since they successfully feed on

lignocellulose in various forms and therefore evolved highly optimised

cellulose-degrading consortia suited to different substrates. More work is

required to determine whether gut communities are also optimised on shorter

time scales to respond to changes in food sources.

Studying local higher termites with a predominantly bacterial gut population

may therefore lead to optimised bioethanol production from wheat crop

residue in Western Australia. With this information in mind, I studied the

following research questions during my PhD.

A: How much intraspecific variation exists in the gut communities of

Western Australian endemic termites?

Research I conducted to address this is covered in Chapters 2, 3 and 4.

B: How should the core community of a termite species be define and

accurately estimated?

Answers to this question are provided in Chapter 3.

C: Can the gut community of local higher termite species be modified under

field conditions?

Results from a field-based experiment to test this are provided in Chapter 3.

D: Can enzymes relevant to breaking down a substrate of choice be

identified using comparative enzymology?

Optimisation work to that end is presented in Chapter 6.

21

References:

Abdul Rahman, N., Parks, D. H., Vanwonterghem, I., Morrison, M., Tyson, G. W., &

Hugenholtz, P. (2016). A phylogenomic analysis of the bacterial phylum Fibrobacteres. Frontiers in Microbiology, 6, 5.

Allwood, J. M., Bosetti, V., Dubash, N. K., Gómez-Echeverri, L., & von Stechow, C. (2014). Glossary. In O. Edenhofer, R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth et al. (Eds.), Climate change 2014: Mitigation of climate change. Contribution of working group III to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Artzi, L., Bayer, E. A., & Moraïs, S. (2017). Cellulosomes: bacterial nanomachines for dismantling plant polysaccharides. Nature Reviews Microbiology, 15(2), 83-95.

Banerjee, G., Car, S., Scott-Craig, J., Borrusch, M., & Walton, J. (2010). Rapid optimization of enzyme mixtures for deconstruction of diverse pretreatment/biomass feedstock combinations. Biotechnology for Biofuels, 3(1), 22.

Bayer, E. A., Belaich, J.-P., Shoham, Y., & Lamed, R. (2004). The cellulosomes: multienzyme machines for degradation of plant cell wall polysaccharides. Annual Review of Microbiology, 58(1), 521-554.

Bengtsson, L. (2010). The global atmospheric water cycle. Environmental Research Letters.

Benjamino, J., & Graf, J. (2016). Characterization of the core and caste-specific microbiota in the termite, Reticulitermes flavipes. Frontiers in Microbiology, 7, 1.

Bharathiraja, B., Jayamuthunagai, J., Sudharsanaa, T., Bharghavi, A., Praveenkumar, R., Chakravarthy, M. et al. (2017). Biobutanol – an impending biofuel for future: a review on upstream and downstream processing techniques. Renewable and Sustainable Energy Reviews, 68, 788-807.

Bignell, D. E. (2011). Morphology, physiology, biochemistry and functional design of the termite gut: an evolutionary wonderland. In D. E. Bignell, Y. Roisin, & N. Lo (Eds.), Biology of Termites: a Modern Synthesis (pp. 375-412). Netherlands: Springer.

Bignell, D. E., Oskarsson, H., & Anderson, J. M. (1980). Specialization of the Hindgut Wall for the Attachment of Symbiotic Micro-Organisms in a Termite Procubitermes aburiensis (Isoptera, Termitidae, Termitinae). Zoomorphology, 96, 103-112.

Bischof, R. H., Ramoni, J., & Seiboth, B. (2016). Cellulases and beyond: the first 70 years of the enzyme producer Trichoderma reesei. Microbial Cell Factories, 15(1), 106.

Black, H. I. J., & Okwakol, M. J. N. (1997). Agricultural intensification, soil biodiversity and agroecosystem function in the tropics: the role of termites. Applied Soil Ecology, 6(1), 37-53.

Borowitzka, M. A., & Moheimani, N. R. (2013). Sustainable biofuels from algae. Mitigation and Adaptation Strategies for Global Change, 18(1), 13-25.

Boucias, D. G., Cai, Y., Sun, Y., Lietze, V.-U., Sen, R., Raychoudhury, R. et al. (2013). The hindgut lumen prokaryotic microbiota of the termite Reticulitermes flavipes and its responses to dietary lignocellulose composition. Molecular Ecology, 22(7), 1836-1853.

Bourguignon, T., Lo, N., Šobotník, J., Ho, S. Y. W., Iqbal, N., Coissac, E. et al. (2016). Mitochondrial Phylogenomics Resolves the Global Spread of Higher Termites, Ecosystem Engineers of the Tropics. Molecular Biology and Evolution, msw253.

22

BP (2017). BP Statistical Review of World Energy June 2017. Brethauer, S., & Studer, M. H. (2014). Consolidated bioprocessing of lignocellulose by

a microbial consortium. Energy & Environmental Science, 7(4), 1446. Brooksbank, K., Lever, M., Paterson, H., & Weybury, M. (2014). Biomass scoping study

(Bulletin 4862). Department of Agriculture and Food, Western Australia, Perth. Brune, A. (2014). Symbiotic digestion of lignocellulose in termite guts. Nature Reviews

Microbiology, 12(3), 168-180. Brune, A., & Ohkuma, M. (2011). Role of the termite gut microbiota in symbiotic

digestion. In D. E. Bignell, Y. Roisin, & N. Lo (Eds.), Biology of Termites: a Modern Synthesis (pp. 439-475). Netherlands: Springer.

Buijs, N. A., Siewers, V., & Nielsen, J. (2013). Advanced biofuel production by the yeast Saccharomyces cerevisiae. Current Opinion in Chemical Biology, 17(3), 480-488.

Calaby, J. H., & Gay, F. J. (1956). The distribution and biology of the genus Coptotermes (Isoptera) in Western Australia. Australian Journal of Zoology, 4(1), 19-39.

Catenacci, M., Verdolini, E., Bosetti, V., & Fiorese, G. (2013). Going electric: Expert survey on the future of battery technologies for electric vehicles. Energy Policy, 61, 403-413.

Chapman, I. (2014). The end of Peak Oil? Why this topic is still relevant despite recent denials. Energy Policy, 64, 93-101.

Cleveland, L. R. (1923). Symbiosis between termites and their intestinal protozoa. Proceedings of the National Academy of Sciences of the United States of America, 9(12), 424-428.

Coy, M. R., Salem, T. Z., Denton, J. S., Kovaleva, E. S., Liu, Z., Barber, D. S. et al. (2010). Phenol-oxidizing laccases from the termite gut. Insect Biochemistry and Molecular Biology, 40(10), 723-732.

Crutzen, P. J., & Stoermer, E. F. (2000). The Anthropocene. Global Change Newsletter, 41, 17-18.

Dereeper, A., Guignon, V., Blanc, G., Audic, S., Buffet, S., Chevenet, F. et al. (2008). Phylogeny.fr: robust phylogenetic analysis for the non-specialist. Nucleic Acids Research, 36(Web Server issue), W465-9.

Dietrich, C., Köhler, T., & Brune, A. (2014). The cockroach origin of the termite gut microbiota: patterns in bacterial community structure reflect major evolutionary events. Applied and Environmental Microbiology.

Diouf, M., Roy, V., Mora, P., Frechault, S., Lefebvre, T., Hervé, V., Rouland-Lefèvre, C., Miambi, E. (2015). Profiling the succession of bacterial communities throughout the life stages of a higher termite Nasutitermes arborum (Termitidae, Nasutitermitinae) Using 16S rRNA Gene Pyrosequencing. PLoS One, 10, e0140014.

Dolan, M. F. (2001). Speciation of termite gut protists: the role of bacterial symbionts. International Microbiology, 4(4), 203-208.

Dutta, K., Daverey, A., & Lin, J.-G. (2014). Evolution retrospective for alternative fuels: first to fourth generation. Renewable Energy, 69, 114-122.

Eggleton, P. (2000). Global patterns of termite diversity. In T. Abe, D. E. Bignell, & M. Higashi (Eds.), Termites: Evolution, Sociality, Symbioses, Ecology (pp. 25-33). The Netherlands: Kluwer Academic Publishers.

Engel, M. S., Grimaldi, D. A., & Krishna, K. S. (2009). Termites (Isoptera): their phylogeny, classification, and rise to ecological dominance. American Museum Novitiates, 3650, 1-27.

Erenstein, O. (2002). Crop residue mulching in tropical and semi-tropical countries: An evaluation of residue availability and other technological implications. Soil

23

and Tillage Research, 67(2), 115-133. Esenther, G. R., & Kirk, T. K. (1974). Catabolism of aspen sapwood in Reticulitermes

flavipes (Isoptera: Rhinotermitidae). Annals of the Entomological Society of America, 67(6), 989-991.

Eutick, M. L., Veivers, P., O’Brien, R. W., & Slaytor, M. (1978). Dependence of the higher termite, Nasutitermes exitiosus and the lower termite, Coptotermes lacteus on their gut flora. Journal of Insect Physiology, 24(5), 363-368.

European Biofuels Technology Platform (2016). Biofuel Fact Sheet. Evans, T. A., & Gleeson, P. V. (2006). The effect of bait design on bait consumption in

termites (Isoptera: Rhinotermitidae). Bulletin of Entomological Research, 96(1), 85-90.

Fiorese, G., Catenacci, M., Verdolini, E., & Bosetti, V. (2013). Advanced biofuels: future perspectives from an expert elicitation survey. Energy Policy.

Flach, B., Lieberz, S., Rondon, M., Williams, B., & Wilson, C. (2016). EU Biofuels Annual 2016. Global Agricultural Information Network.

Gasparatos, A., Stromberg, P., & Takeuchi, K. (2013). Sustainability impacts of first-generation biofuels. Animal Frontiers, 3(2), 12-26.

Giannoccaro, G., de Gennaro, B. C., De Meo, E., & Prosperi, M. (2017). Assessing farmers’ willingness to supply biomass as energy feedstock: Cereal straw in Apulia (Italy). Energy Economics, 61, 179-185.

Haddadian, G., Khodayar, M., & Shahidehpour, M. (2015). Accelerating the global adoption of electric vehicles: barriers and drivers. The Electricity Journal, 28(10), 53-68.

Haq, F., Ali, H., Shuaib, M., Badshah, M., Hassan, S. W., Munis, M. F. H. et al. (2016). Recent progress in bioethanol production from lignocellulosic materials: A review. International Journal of Green Energy, 13(14), 1413-1441.

Ho, D. P., Ngo, H. H., & Guo, W. (2014). A mini review on renewable sources for biofuel. Bioresource Technology, 169, 742-749.

Hogan, M., Veivers, P. C., Slaytor, M., & Czolij, R. T. (1988). The site of cellulose breakdown in higher termites (Nasutitermes walkeri and Nasutitermes exitiosus). Journal of Insect Physiology, 34(9), 891-899.

Holland, S. P., Mansur, E. T., Muller, N. Z., & Yates, A. J. (2016). Are there environmental benefits from driving electric vehicles? The importance of local factors. American Economic Review, 106(12), 3700-3729.

Hongoh, Y., Ekpornprasit, L., Inoue, T., Moriya, S., Trakulnaleamsai, S., Ohkuma, M., Noparatnaraporn, N., Kudo, T. (2006). Intracolony variation of bacterial gut microbiota among castes and ages in the fungus-growing termite Macrotermes gilvus. Molecular Ecology, 15(2), 505-516.

Höök, M., & Tang, X. (2013). Depletion of fossil fuels and anthropogenic climate change—A review. Energy Policy, 52(0), 797-809.

Huang, J.-W., Cheng, Y.-S., Ko, T.-P., Lin, C.-Y., Lai, H.-L., Chen, C.-C. et al. (2012). Rational design to improve thermostability and specific activity of the truncated Fibrobacter succinogenes 1,3-1,4-β- d-glucanase. Applied Microbiology & Biotechnology, 94(1), 111-121.

Huang, X.-F., Bakker, M., Judd, T., Reardon, K., & Vivanco, J. (2013). Variations in diversity and richness of gut bacterial communities of termites (Reticulitermes flavipes) fed with grassy and woody plant substrates. Microbial Ecology, 65(3), 531-536.

Husseneder, C., Berestecky, J. M., & Grace, J. K. (2009). Changes in composition of culturable bacteria community in the gut of the formosan subterranean termite depending on rearing conditions of the host. Annals of the Entomological

24

Society of America, 102(3), 498-507. Wilkinson, I. (2017). Western Australian wheat industry. Retrieved 5/05/2017,

https://www.agric.wa.gov.au/grains-research-development/western-australian-wheat-industry.

Inoue, T., Kitade, O., Yoshimura, T., & Yamaoka, I. (2000). Symbiotic associations with protists. In T. Abe, D. E. Bignell, & M. Higashi (Eds.), Termites: Evolution, Sociality, Symbiosis, Ecology (pp. 275-288). Dordrecht: Kluwer Academic Publishers.

International Energy Agency (2011). Technology Roadmap, Biofuels for Transport. International Energy Agency (2017). IEA Oil Market Report. Inward, D., Beccaloni, G., & Eggleton, P. (2007). Death of an order: a comprehensive

molecular phylogenetic study confirms that termites are eusocial cockroaches. Biology Letters, 3(3), 331-335.

IPCC. (2011). Summary for Policymakers. In O. Edenhofer, R. Pichs-Madruga, Y. Sokona, K. Seyboth, P. Matschoss, S. Kadner et al. (Eds.), IPCC special report on renewable energy sources and climate change mitigation. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Jin, C., Yao, M., Liu, H., Lee, C.-f. F., & Ji, J. (2011). Progress in the production and application of n-butanol as a biofuel. Renewable and Sustainable Energy Reviews, 15(8), 4080-4106.

Jones, D. T., & Eggleton, P. (2011). Global biogeography of termites: a compilation of sources. In D. E. Bignell, Y. Roisin, & N. Lo (Eds.), Biology of Termites: a Modern Synthesis (pp. 477-498). Netherlands: Springer.

Kircher, M. (2015). Sustainability of biofuels and renewable chemicals production from biomass. Curr Opin Chem Biol, 29, 26-31.

Koh, L. P., & Ghazoul, J. (2008). Biofuels, biodiversity, and people: understanding the conflicts and finding opportunities. Biological conservation, 141(10), 2450-2460.

Konig, H., Li, L., Wenzel, M., & Frohlich, J. (2006). Bacterial ectosymbionts which confer motility: Mixotricha paradoxa from the intestine of the Australian termite Mastotermes darwiniensis. Progress in Molecular & Subcellular Biology, 41, 77-96.

Krishna, K., Grimaldi, D. A., Krishna, V., & Engel, M. S. (2013). Treatise on the Isoptera of the world. Bulletin of the American Museum of Natural History, 1.

Lambertz, C., Garvey, M., Klinger, J., Heesel, D., Klose, H., Fischer, R. et al. (2014). Challenges and advances in the heterologous expression of cellulolytic enzymes: a review. Biotechnol Biofuels, 7(1), 135.

Lange, J.-P. (2007). Lignocellulose conversion: an introduction to chemistry, process and economics. Biofuels, Bioproducts and Biorefining, 1(1), 39-48.

Lavelle, P., Bignell, D., Lepage, M., Wolters, V., Roger, P., Ineson, P. et al. (1997). Soil function in a changing world: the role of invertebrate ecosystem engineers. European Journal of Soil Biology, 33(4), 159-193.

Lee, C.-Y., Charunee, V., & Lenz, M. (2007). Challenges to subterranean termite management of multi-genera faunas in Southeast Asia and Australia. Sociobiology, 50(1), 213-221.

Lee, T. R. C., Cameron, S. L., Evans, T. A., Ho, S. Y. W., & Lo, N. (2015). The origins and radiation of Australian Coptotermes termites: from rainforest to desert dwellers. Molecular Phylogenetics and Evolution, 82, 234-244.

Leite, G. B., Abdelaziz, A. E. M., & Hallenbeck, P. C. (2013). Algal biofuels: challenges and opportunities. Bioresource Technology, 145, 134-141.

Li, H., Dietrich, C., Zhu, N., Mikaelyan, A., Ma, B., Pi, R. et al. (2016). Age polyethism drives community structure of the bacterial gut microbiota in the fungus-

25

cultivating termite Odontotermes formosanus. Environmental Microbiology, 18(5), 1440-1451.

Liang, C., Fioroni, M., Rodríguez-Ropero, F., Xue, Y., Schwaneberg, U., & Ma, Y. (2011). Directed evolution of a thermophilic endoglucanase (Cel5A) into highly active Cel5A variants with an expanded temperature profile. Journal of Biotechnology, 154(1), 46-53.

Malinda, D. K. (1995). Factors in conservation farming that reduce erosion. Australian Journal of Experimental Agriculture, 35(7), 969-978.

Mata, T. M., Martins, A. A., & Caetano, N. S. (2010). Microalgae for biodiesel production and other applications: a review. Renewable and Sustainable Energy Reviews, 14(1), 217-232.

Mathiesen, B. V., Lund, H., & Karlsson, K. (2011). 100% Renewable energy systems, climate mitigation and economic growth. Applied Energy, 88(2), 488-501.

Mikaelyan, A., Thompson, C. L., Hofer, M. J., & Brune, A. (2016). Deterministic assembly of complex bacterial communities in guts of germ-free cockroaches. Appl Environ Microbiol, 82(4), 1256-1263.

Mitchell, J. (1989). The ”greenhouse” effect and global warming. Review of Geophysics. Miyata, R., Noda, N., Tamaki, H., Kinjyo, K., Aoyagi, H., Uchiyama, H., Tanaka, H.

Influence of feed components on symbiotic bacterial community structure in the gut of the wood-feeding higher termite Nasutitermes takasagoensis. Bioscience, Biotechnology, and Biochemistry, 71(5), 1244-1251.

Mohanram, S., Amat, D., Choudhary, J., Arora, A., & Nain, L. (2013). Novel perspectives for evolving enzyme cocktails for lignocellulose hydrolysis in biorefineries. Sustainable Chemical Processes, 1(1), 15.

Moraïs, S., Barak, Y., Caspi, J., Hadar, Y., Lamed, R., Shoham, Y. et al. (2010). Cellulase-xylanase synergy in designer cellulosomes for enhanced degradation of a complex cellulosic substrate. mBio, 1(5), e00285-10.

Morais, S., Barak, Y., Lamed, R., Wilson, D., Xu, Q., Himmel, M. et al. (2012). Paradigmatic status of an endo- and exoglucanase and its effect on crystalline cellulose degradation. Biotechnology for Biofuels, 5(1), 78.

Nalepa, C. A. (2015). Origin of termite eusociality: trophallaxis integrates the social, nutritional, and microbial environments. Ecological Entomology, 40(4), 323-335.

Ndaba, B., Chiyanzu, I., & Marx, S. (2015). n-Butanol derived from biochemical and chemical routes: A review. Biotechnology reports (Amsterdam, Netherlands), 8, 1-9.

Nguyen, Q., Bowyer, J., Howe, J., Bratkovich, S., & Groot…, H. (2017). Global production of second generation biofuels: trends and influences. Dovetail Partners, Inc.

Ohkuma, M., Noda, S., Hongoh, Y., Nalepa, C. A., & Inoue, T. (2009). Inheritance and diversification of symbiotic trichonymphid flagellates from a common ancestor of termites and the cockroach Cryptocercus. Proceedings of the Royal Society B: Biological Sciences, 276(1655), 239-245.

Ohkuma, M. (2003). Termite symbiotic systems: efficient bio-recycling of lignocellulose. Applied Microbiology and Biotechnology, 61(1), 1-9.

Oppert, C., Klingeman, W. E., Willis, J. D., Oppert, B., & Jurat-Fuentes, J. L. (2010). Prospecting for cellulolytic activity in insect digestive fluids. Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology, 155(2), 145-154.

Otani, S., Mikaelyan, A., Nobre, T., Hansen, L. H., Kone, N. A., Sorensen, S. J., Aanen, D.K., Boomsma, J.J., Brune, A. Poulsen, M (2014). Identifying the core microbial community in the gut of fungus-growing termites. Molecular Ecology.

26

Payne, C. M., Knott, B. C., Mayes, H. B., Hansson, H., Himmel, M. E., Sandgren, M. et al. (2015). Fungal cellulases. Chemical Reviews, 115(3), 1308-1448.

Peters, B. C., & Fitzgerald, C. J. (2003). Field evaluation of the bait toxicant chlorfluazuron in eliminating Coptotermes acinaciformis (Froggatt) (Isoptera: Rhinotermitidae). Journal of Economic Entomology, 96(6), 1828-1831.

Pfromm, P. H., Amanor-Boadu, V., Nelson, R., Vadlani, P., & Madl, R. (2010). Bio-butanol vs. bio-ethanol: A technical and economic assessment for corn and switchgrass fermented by yeast or Clostridium acetobutylicum. Biomass and Bioenergy, 34(4), 515-524.

Poulsen, M. (2015). Towards an integrated understanding of the consequences of fungus domestication on the fungus-growing termite gut microbiota: Fungus-growing termite gut microbiota. Environmental Microbiology, 17(8), 2562-2572.

Raman, B., Pan, C., Hurst, G. B., Rodriguez Jr, M., McKeown, C. K., Lankford, P. K. et al. (2009). Impact of pretreated switchgrass and biomass carbohydrates on Clostridium thermocellum ATCC 27405 cellulosome composition: a quantitative proteomic analysis. PLoS ONE, 4(4), 1-13.

Ransom-Jones, E., Jones, D. L., McCarthy, A. J., & McDonald, J. E. (2012). The Fibrobacteres: an Important phylum of cellulose-degrading bacteria. Microbial Ecology, 63, 267-281.

Rawls, J. F., Mahowald, M. A., Ley, R. E., & Gordon, J. I. (2006). Reciprocal gut microbiota transplants from zebrafish and mice to germ-free recipients reveal host habitat selection. Cell, 127(2), 423-433.

Reid, N. M., Addison, S. L., West, M. A., & Lloyd-Jones, G. (2014). The bacterial microbiota of Stolotermes ruficeps (Stolotermitidae), a phylogenetically basal termite endemic to New Zealand. FEMS Microbiology Ecology, 90(3), 678-688.

Renewable Fuels Association, World Fuel Ethanol Production. Retrieved 5/05/2017, http://www.ethanolrfa.org/resources/industry/statistics/.

Resch, M. G., Donohoe, B. S., Baker, J. O., Decker, S. R., Bayer, E. A., Beckham, G. T. et al. (2013). Fungal cellulases and complexed cellulosomal enzymes exhibit synergistic mechanisms in cellulose deconstruction. Energy & Environmental Science, 6, 1858.

Farrell, R. (2016). Australia Biofuels Annual 2016. Global Agricultural Information Network.

Scharf, M. E. (2015). Omic research in termites: an overview and a roadmap. Frontiers in Genetics, 6, 76.

Schmitz, P. M., & Kavallari, A. (2009). Crop plants versus energy plants—On the international food crisis. Bioorganic & Medicinal Chemistry, 17(12), 4020-4021.

Searcy, E., Flynn, P., Ghafoori, E., & Kumar, A. (2007). The relative cost of biomass energy transport. Applied Biochemistry and Biotechnology, 137-140(1-12), 639-652.

Sethi, A., Slack, J. M., Kovaleva, E. S., Buchman, G. W., & Scharf, M. E. (2013). Lignin-associated metagene expression in a lignocellulose-digesting termite. Insect Biochemistry and Molecular Biology, 43, 91-101.

Shade, A., & Handelsman, J. (2012). Beyond the Venn diagram: the hunt for a core microbiome: The hunt for a core microbiome. Environmental Microbiology, 14(1), 4-12.

Shapira, M. (2016). Gut microbiotas and host evolution: scaling up symbiosis. Trends in Ecology & Evolution, 31(7), 539-549.

Shepherd, M., Bartle, J., Lee, D. J., Brawner, J., Bush, D., Turnbull, P. et al. (2014). Eucalypts as a biofuel feedstock. Biofuels, 2(6), 639-657.

Sims, R., Schaeffer, R., Creutzig, F., Cruz-Núñez, X., D’Agosto, M., Dimitriu, D. et al. (2014). Transport. In O. Edenhofer, R. Pichs-Madruga, Y. Sokona, E. Farahani, S.

27

Kadner, K. Seyboth et al. (Eds.), Climate change 2014: mitigation of climate change. contribution of working group III to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Smith, P., & Gregory, P. J. (2013). Climate change and sustainable food production. Proceedings of the Nutrition Society, 72(1), 21-28.

Steffen, W., Leinfelder, R., Zalasiewicz, J., Waters, C. N., Williams, M., Summerhayes, C. et al. (2016). Stratigraphic and Earth system approaches to defining the Anthropocene: defining the Anthropocene. Earth’s Future, 4(8), 324-345.

Tanaka, H., Aoyagi, H., Shiina, S., Doudou, Y., Yoshimura, T., Nakamura, R., Uchiyama, H. (2006). Influence of the diet components on the symbiotic microorganisms community in hindgut of Coptotermes formosanus Shiraki. Applied Microbiology & Biotechnology, 71(6), 907-917.

Tokuda, G., & Watanabe, H. (2007). Hidden cellulases in termites: revision of an old hypothesis. Biology Letters, 3(3), 336-339.

Vassilev, S. V., & Vassileva, C. G. (2016). Composition, properties and challenges of algae biomass for biofuel application: An overview. Fuel, 181, 1-33.

Veivers, P. C., O’Brien, R. W., & Slaytor, M. (1983). Selective defaunation of Mastotermes darwiniensis and its effect on cellulose and starch metabolism. Insect Biochemistry, 13(1), 95-101.

Wang, Y., Su, L., Huang, S., Bo, C., Yang, S., Li, Y. et al. (2016). Diversity and resilience of the wood-feeding higher termite Mironasutitermes shangchengensis gut microbiota in response to temporal and diet variations. Ecology and Evolution, 6(22), 8235-8242.

Webb, G. A., & Mcclintock, C. (2015). Elimination of the mound-building termite, Nasutitermes exitiosus (Isoptera: Termitidae) in South-Eastern Australia using bistrifluron bait. Journal of Economic Entomology, 108(6), 2702-2710.

Western Australia Biofuels Taskforce (2007). Report: April 2007. Wilson, D. B. (2011). Microbial diversity of cellulose hydrolysis. Current Opinion in

Microbiology, 14(3), 259-263. Wolfram, C., Shelef, O., & Gertler, P. (2012). How will energy demand develop in the

developing world? Journal of Economic Perspectives, 26(1), 119-138. Wu, H., Fu, Q., Giles, R., & Bartle, J. (2008). Production of mallee biomass in Western

Australia: energy balance analysis. Energy Fuels, 22(1), 190-198. Xiang, H., Xie, L., Zhang, J., Long, Y., Liu, N., Huang, Y., Wang, Q. (2012).

Intracolonial differences in gut bacterial community between worker and soldier castes of Coptotermes formosanus. Insect Science, 19, 86-95.

Yu, Y., Bartle, J., Mendham, D., & Wu, H. (2015). Site variation in life cycle energy and carbon footprints of mallee biomass production in Western Australia. Energy Fuels, 29(6), 3748-3752.

Zuroff, T. R., & Curtis, W. R. (2012). Developing symbiotic consortia for lignocellulosic biofuel production. Applied Microbiology and Biotechnology, 93(4), 1423-1435.

28

29

CHAPTER 2: Colony Differences in the Gut Communities of Western Australian Termites Tumulitermes westraliensis and Coptotermes acinaciformis raffrayi

30

Foreword:

The gut bacteria of termites are generally thought to be highly conserved, yet

the natural level of intraspecific variation has rarely been studied. In this

chapter I therefore begin to answer my first research question: How much

intraspecific variation exists in the gut communities of Western Australian

endemic termites? Sample replication in termite studies is typically low and

because there are no standardised primer pairs and PCR conditions across the

field, it is unclear whether datasets can be combined for greater explanatory

power. Two 16S rRNA gene amplification strategies were compared to measure

technical variation across datasets collected with different methods, before

comparing two local species to each other and to previous studies conducted

with the same methods.

Introduction:

Termites are eusocial insects that break down lignocellulose in various stages

of humification, with the use of both endogenous cellulases and symbioses

with cellulose-degrading microbes (reviewed by Brune, 2014). There are close

to 3,000 species of termites worldwide and these are typically classified as

either lower or higher termites (Krishna et al., 2013). Lower termites primarily

feed on wood and their gut is dominated by cellulose-degrading flagellates and

their associated prokaryotes (Cleveland, 1923; Inoue et al., 2000; Dolan, 2001).

Higher termites (Termitidae) appear to have lost these flagellates

approximately 60 million years ago, switching to a predominantly prokaryotic

gut community (Ohkuma, 2003; Tokuda and Watanabe, 2007; Brune, 2014).

They have become the most species-rich and ecologically diverse termite

family, containing over 80% of all termite species, each specialised to consume

wood, grass, litter, humus, soil or fungus (Jones and Eggleton, 2011; Brune and

Ohkuma, 2011; Bignell, 2011; Poulsen, 2015).

31

The bacterial gut community of termites is therefore key to their success, but

the drivers of community composition (the diversity of bacterial taxa present)

and community structure (the relative abundance of bacterial taxa) remain

poorly understood. Choosing case studies to examine the sources of natural

intraspecific variation would help to elucidate these community drivers.

Within colony differences may reflect different roles of castes and age groups

(Hongoh et al., 2006; Xiang et al., 2012; Diouf et al., 2015; Li et al., 2016;

Benjamino and Graf, 2016), but intercolony variation has rarely been studied.

To my knowledge, only four studies have discussed colony variation in mature

worker gut communities to date. Hongoh et al. (2005) found that the bacterial

community structure was conserved to some degree within the genera

Reticulitermes (lower termites) and Microcerotermes (higher termites), but

also found significant differences between certain sampling sites and termite

species within each genus. Boucias et al. (2013) found that the Reticulitermes

flavipes colony of origin had a stronger influence on the gut community

composition than the wood or paper diet they provided for a week. Benjamino

and Graf (2016) showed that the gut communities of termites from the same

colony were more similar to each other than between colonies, although they

shared a core community that accounted for most of the reads. Reid et al.

(2014) found that colonies of the lower termite Stolotermes ruficeps had

unique gut communities, even when collected in close proximity. They

speculated that differences in diet, in terms of wood species or humification

state, might explain these differences.

Intraspecies sample replication in termite studies to date is low and it is

unclear whether already published datasets can be combined for greater

explanatory power because there is no standardised method within the field.

Various PCR primers targeting different 16S rRNA gene hypervariable regions

have been used to characterise the gut community of termites (Miyata et al.,

2007; Köhler et al., 2012; He et al., 2013; Abdul Rahman et al., 2015). However,

it is known that the choice of PCR conditions always introduces bias in 16S

datasets (Sipos et al., 2007; Tremblay et al., 2015). I therefore tested two

32

different amplification strategies to measure the effect of such bias on the

recovered gut community to assess the comparability of datasets recovered

with different methods.

To investigate intraspecific variation in gut community composition and

structure, I chose a lower and a higher termite species known to have broad

diets. Coptotermes acinaciformis raffrayi is endemic to Western Australia (Lo

et al., 2006; Lee et al., 2015) and feeds broadly on the humification gradient,

from live to rotten wood (Greaves, 1962; Lee et al., 2015). Tumulitermes

westraliensis is a Western Australian endemic of the subfamily

Nasutitermitinae. It was first classified as a grass-feeder (Hill, 1921; Hill, 1925;

Hill, 1942) and its diet later refined to include dried grass, grass-tree

(Xanthorrhoea) debris and seeds (Abensperg-Traun and Milewski, 1995),

suggesting that T. westraliensis feeds on a wide variety of plant material with

varying degrees of lignification. This study investigates, for the first time, the

gut communities of these two termite species and moreover, is the first

examination of the gut community within the genus Tumulitermes.

The T. westraliensis gut community composition and structure was then

compared to those of previously studied higher termites obtained using the

same method (Dietrich et al., 2014; Mikaelyan et al., 2015a). I hypothesised that

the gut community of T. westraliensis would be colony specific and different

to that of true grass-feeders, reflecting its broader diet. Likewise, I compared

C. acinaciformis raffrayi to other lower termite species (Dietrich et al., 2014),

hypothesising that its gut community would be similar to other broadly

feeding species.

33

Methods:

Sample collection

T. westraliensis and C. acinaciformis raffrayi were collected from two different

areas near Perth, Western Australia (Figure 1). T. westraliensis samples were

collected from three mounds (T1, T2 and T3) in Banyowla Regional Park in the

Perth Hills. A small pickaxe was used to break into mounds and mature

workers, recognisable by their darker head capsules (Figure 2A), were

recovered with soft forceps. For colony T3, triplicate samples were collected

(T3a, T3b, T3c) to measure the level of intracolony variation (V4 dataset only,

see below). Food stores observed in T. westraliensis mounds during sample

collection included varying amounts of dried grass, dead grass tree leaves and

sections of woody stem (Figure 2D). Two C. acinaciformis raffrayi samples

were collected in the Gnangara pine plantation, one from a rotten pine log

(C1) and the other from a sound native tree species log (C2). Each sample was

collected at least 80 m from its nearest conspecific neighbour under the

assumption that these would be separate, independent colonies. Soldiers of

both species were also collected and stored in 70% EtOH to confirm species

identification using morphological characters and COII sequencing (with a

minimum of 99% sequence identity with previously published sequences;

Figure 2B).

Samples were transported to the laboratory in the dark with mound or woody

material from their colony. Dissections were performed on the day of

collection to ensure that the natural gut flora was sampled. Samples from

different colonies were dissected separately using sterilised equipment and

solutions, as follows. Workers were anaesthetised in a petri dish on ice for at

least ten minutes. To prevent contamination of samples during the dissection,

workers were then surfaced sterilised by immersion in a 5% hypochlorite

solution for 1 min, rinsed twice in distilled water and dried on filter paper on

ice (Rosengaus and Traniello, 1997). Whole intestinal tracts were dissected by

34

Fi

gure

1:

Sam

plin

g lo

catio

ns n

ear

Pert

h, W

este

rn A

ustr

alia

(A

). Tu

mul

iterm

es w

estr

alie

nsis

sam

ples

wer

e co

llect

ed i

n Ba

nyow

la

Regi

onal

Par

k (B

) an

d C

opto

term

es a

cina

cifo

rmis

raf

fray

i was

sam

pled

from

the

Gna

ngar

a St

ate

Fore

st (

C).

All

sam

ples

wer

e co

llect

ed

at l

east

80

m a

part

and

are

ass

umed

to

be s

epar

ate

colo

nies

. Red

: T. w

estr

alie

nsis

; yel

low

: C. a

cina

cifo

rmis

raf

fray

i. M

aps

mod

ified

fr

om G

PSV

isua

lizer

.com

.

35

Figure 2: Tumulitermes westraliensis, a Western Australian endemic species historically classified as a grass-feeder. Mature workers (A) with dark head capsules were used in preference to younger workers (marked with an asterisk), here photographed on clay. Soldiers (B) were used for species identification by morphology and COII sequencing. Whole worker guts (C) were pooled to profile the gut community; the gut includes the crop, midgut and hindgut segments (P1 to P5). A typical food store inside a mound (D) shows a mixture of woody (1), grassy (2) and grass tree leaf (3) material.

36

holding the head with forceps and pulling gently at the distal end of the

abdomen using Inox 5 Watchmaker forceps. Each excised intestinal tract (see

Figure 2C for an example) was placed in a 1.5 ml Eppendorf tube containing

100 μl of 1× phosphate buffered saline. The process was repeated to obtain a

pool of 10 guts for each colony.

To confirm species identity, DNA was extracted from whole individual soldiers

using the Zymo Research Tissue & Insect DNA MiniPrep™ Kit and the COII

gene amplified with the primers A-tLeu (5′-ATGGCAGATTAGTGCAATGG-

3′) and B-tLys (5′- GTTTAAGAGACCAGTACTTG-3′) as described by Liu and

Beckenbach (1992). Resulting amplicons were sequenced in both directions by

Macrogen (Korea). Paired-end sequences were aligned and trimmed to obtain

an identical overlapping region. These quality trimmed COII sequences were

compared within a species and to previously sequenced COII genes using the

nucleotide BLAST online tool (National Center for Biotechnology

Information).

Parallel sequencing and data analysis

DNA was extracted from pooled gut samples using a previously established

protocol (Paul et al., 2012) except that the bead-beating step was undertaken

with a Retsch Mixer Mill (MM301) for 3 min as detailed by Delmotte et al.

(2009). The V4 hypervariable region of the 16S rRNA gene (henceforth V4) was

amplified using the V4 primers recommended by Illumina at the time of data

collection in 2013, F515 (5′-GTGCCAGCMGCCGCGGTAA-3′) and R806 (5′-

GGACTACHVGGGTWTCTAAT-3′), as described by Caporaso et al. (2011).

Resulting amplicons were sequenced in-house as paired-end 150 bp reads on

an Illumina MiSeq using a 300 cycle Miseq Reagent Kit v2 according to the

manufacturer’s instructions. Aliquots of each DNA sample representing the 5

colonies (including only the first replicate of T3) were also amplified using V3-

V4 primers 343Fmod (5′-TACGGGWGGCWGCA-3′) and 784Rmod (5′-

GGGTMTCTAATCCBKTT-3′) designed by Köhler et al. (2012) for termite gut

37

community studies (henceforth V3-V4) and sequenced as described by

Mikaelyan et al. (2015a).

Data analysis utilised Usearch's UPARSE pipeline (Edgar, 2013), beginning

with assembling and quality filtering paired-end fastq reads, through to the

creation of an OTU (Operational Taxonomic Unit) table containing raw read

counts for each bacterial group. Quality filtering used the "stringent" method

recommended in Usearch, which removed reads with an expected error above

1. Singleton sequences were removed prior to clustering into OTUs. The

unfiltered trimmed dataset was used when mapping reads to the OTUs to

create an OTU table. This allowed the use of the full dataset while reducing

the likelihood of spurious OTUs.

During the quality-filtering step, the V4 reads were trimmed to 253 bp (the

expected length of the region targeted with the V4 primers), whereas the V3-

V4 reads were trimmed to 400 bp, to maximise read length while maintaining

read quality. The V3-V4 data was quality-filtered alongside 454 and MiSeq

higher termite data from previous studies (Dietrich et al., 2014; Mikaelyan et

al., 2015a) to allow for direct comparisons between the datasets. The V3-V4

dataset was filtered and trimmed a second time to match the V4 region

targeted by the V4 primers, to distinguish amplification bias from bias arising

during the taxonomy assignment step of the data analysis. In this case,

trimming involved removing the first 203 bases and shortening reads to 253

bp. This trimmed V3-V4 dataset was analysed together with the V4 dataset,

separately for each species, and will be hereafter referred to as the combined

V4 dataset.

OTUs were determined de novo for each of the three sub-analyses (V4, V3-V4

and combined V4) but all were clustered at 97% identity to represent bacterial

species. Taxonomy was assigned to the OTU tables in QIIME 1.9 (Caporaso et

al., 2010) using DictDb version 3.0, the curated 16S termite and cockroach

database created by Mikaelyan et al. (2015b), and the RDP classifier (Wang et

38

al., 2007), with a confidence value of 0.8. The datasets were rarefied to

different depths to normalise to the sample with the lowest number of reads in

each sub-analysis, as described below.

Amplification bias

Primer bias was predicted for both amplification strategies using a java script

(available upon request) to search for V4 and V3-V4 primer sequences within

reference sequences in DictDb version 3.0, taking into account all possible

versions of ambiguous bases in the primers but allowing no other mismatches.

The number of matches for each primer pair was compared separately for each

of the phyla represented in the database.

To characterise amplification bias in the biological dataset, the combined V4

dataset was summarised as a genus-level heatmap. This dataset was rarefied to

49,800 reads per sample, then OTUs were picked within each species and

clustered at the genus level using a relational database based on the DictDb

taxonomy assignments. Any OTUs not identified to the genus level were

removed (on average 22% of reads per sample for C. acinaciformis raffrayi and

38% for T. westraliensis). The raw read counts were then all increased by a

value of one (to eliminate zeros) and transformed to log 10 values. A phylum-

level summary was created by averaging across all colonies within each

species and including all reads identified to at least phylum level

(approximately 99% of reads). For ease of visualisation, only the 50 most

abundant genera in the gut of each species were included in the heatmap .

Heatmaps were created with Bioconductor's ComplexHeatmap package in R

(Gu, 2016). Paired t-tests conducted in R (R Development Core Team, 2016)

were used to contrast the abundance of each bacterial genus and phylum

measured using the V4 and V3-V4 methods.

39

Beta diversity

Two beta diversity metrics were applied to each sub-analysis in QIIME to

obtain distance values for each possible pairwise comparison. The metrics

used were unweighted UniFrac (which considers presence/absence of species

and their phylogenetic relationship, hereafter referred to as community

composition) and weighted UniFrac (which takes into account phylogenetic

relationships and relative abundance, hereafter community structure). To

visualise bias introduced by laboratory method in the V4 and the V3-V4

datasets, Principal Coordinate Analysis (PCoA) was conducted on the

combined V4 dataset in QIIME and plotted with ggplot2 (Wickham, 2009).

The V4 dataset containing replicates for colony T3 was rarefied to 500,000

reads per sample and used to measure intraspecific variation. The distance

measures were categorised as a) nestmates (pairs from the same colony; T3

only, n = 3), b) conspecifics (pairs from different colonies within a species, n =

8), or c) heterospecifics (pairs of T. westraliensis and C. acinaciformis raffrayi

colonies; n = 10). The difference in variation was tested within each metric

using the Kruskal-Wallis test and a post-hoc analysis with the Mann-Whitney-

Wilcoxon Test in R.

To compare the gut communities in T. westraliensis and C. acinaciformis

raffrayi to those reported in previous studies, OTUs were picked for the V3-V4

dataset for lower and higher termites separately and each sub-analysis rarefied

to 3,000 reads. PCoA was conducted as described above. For the higher

termites, the humus and litter groups were almost indistinguishable and

therefore consolidated as in Mikaelyan et al. (2015a). Mikaelyan et al. (2015a)

included Trinervitermes sp. with the wood-feeders and excluded Amitermes

meridionalis (from Dietrich et al., 2014). Here, they were both retained in a

separate grass-feeding group for comparison with T. westraliensis. Samples

were coloured by the termite host subfamily and points were shaped as per

their respective diet group (Gontijo and Domingos, 1991; Jones and Eggleton,

2011; Mikaelyan et al., 2015a), with the T. westraliensis samples forming an

40

"unknown" group. Finally, ellipses representing 95% confidence intervals for

each diet group containing at least four samples were superimposed on the

plots.

Alpha Diversity

Alpha diversity analysis was conducted in QIIME. The alpha diversity metrics

used were the number of observed OTUs (a measure of species richness) and

Simpson's Reciprocal Index (a measure of dominance). Rare OTUs have a

relatively low impact on the Index compared to more frequent OTUs, however

the uniformity in abundance caused by excluding rare OTUs would inflate the

Index. Non-parametric t-tests (with 999 Monte Carlo permutations) were

applied to each alpha diversity metric.

This analysis was performed on the combined V4 dataset, to determine the

effect of method choice on the detected bacterial species richness and

diversity. It was also used on all lower and higher termites making up the V3-

V4 dataset, to determine any correlations between diet and the species

richness in the gut.

Results:

The two amplification strategies yielded significantly different levels of

detection for particular bacterial groups in both gut communities, with a more

obvious bias in T. westraliensis. Signature gut communities were detected for

each colony, with greater differences between species, moderate differences

within a species, and minimal differences between nestmates. I found that

colonies of T. westraliensis had similar gut community structure to different

higher termites feeding on a variety of diets.

41

Technical biases in gut community sequencing

Comparison of both primer pairs to DictDb version 3.0 predicted primer bias

for all phyla in the database. Fewer than 640 sequences representing 15 phyla

(1.15% of 55,732 sequences from 62 phyla in DictDb) perfectly matched the V4

and/or V3-V4 primer pairs (Table 1). Within this subset, phylum Tenericutes

had seven matches with the V4 primer set and no matches with the V3-V4

primers. Candidate phylum TM7 had five matches with the V3-V4 primer set

and none with the V4 primers.

In practice, the two amplification strategies yielded significantly different

levels of detection for 14 out of 21 phyla in the gut of T. westraliensis (Figure

3C). When considering all reads identified to at least phylum level, the most

abundant phyla in the T. westraliensis gut were Spirochaetes, Firmicutes,

Bacteroidetes and Fibrobacteres in both datasets. However, Spirochaetes were

detected twice as frequently using the V4 method, while Firmicutes and

Bacteroidetes were over three times more frequent in the V3-V4 dataset. The

relative frequency for Fibrobacteres was not statistically different between the

two methods. Detection differed by more than 10:1 for two phyla, including an

average of 85 reads for Planctomycetes with the V4 primers and only 7 with

V3-V4 and an average of 204 for Synergistetes using V3-V4 primers and only 10

with V4. Cyanobacteria was only detected using the V4 method (46 reads)

while candidate phyla TM7 (958 reads) and SR1 (105) were only recorded in the

V3-V4 dataset (Table S1A; attached as Chapter2_TableS1.xlsx).

In the C. acinaciformis raffrayi gut, four out of 17 phyla were detected

differently between the methods (Figure 3D). The most abundant phyla were

Bacteroidetes, Spirochaetes, Firmicutes (significantly different), Elusimicrobia

(significantly different) and Proteobacteria in both datasets. However,

Spirochaetes was detected more than twice as frequently in the V4 dataset and

Firmicutes almost twice as frequently in the V3-V4 dataset. Elusimicrobia was

recorded four times more frequently in the V3-V4 dataset. Cyanobacteria,

Planctomycetes, Tenericutes were also all detected more frequently in the V4

42

Table 1: Phylum-level table containing numbers of sequences in the DictDb version 3.0 16S curated reference database that contain the forward and reverse primer sequences used in this study (allowing no mismatches and all possible versions of ambiguous bases) of the primer set targetting the V4 region of the 16S gene and the V3-V4 primer set. Phylum Tenericutes is biased against using the V3-V4 primer set, while TM7 is biased against with the V4 primers.

Phylum V4 matches V3-V4 matches Actinobacteria 8 8

Bacteroidetes 210 213

Candidate phylum TG3 2 2

Candidate phylum TM7 0 5

Chlorobi 2 2

Cyanobacteria 1 1

Deferribacteres 2 3

Elusimicrobia 25 21

Fibrobacteres 6 6

Firmicutes 186 191

Planctomycetes 10 10

Proteobacteria 53 51

Spirochaetes 90 89

Synergistetes 29 30

Tenericutes 7 0

Verrucomicrobia 4 7

43

Figure 3: Relative abundance of the 50 most common bacterial genera, clustered by phylum and ordered from most abundant in (A) Tumulitermes westraliensis and (B) Coptotermes acinaciformis raffrayi using two different PCR conditions targeting the V4 or V3-V4 region of the 16S gene. Phylum level differences are summarised in (C) and (D) for each method. Differences in detection rates between the PCR conditions (paired t-tests): * p < 0.05; ** p < 0.01.

44

dataset, the latter two phyla represented by only 2 reads, on average, with the

V3-V4 method. Candidate phyla OD1 (14 reads) and Synergistetes (12) were

only detected using the V4 methods, while phyla SR1 (93 reads) and OP11 (5)

were only detected with the V3-V4 primer pair. TM7 was predominently

detected with the V3-V4 primer pair, resulting in 258 reads as compared to 2

with V4 (Table S1B).

Bias was also apparent at the genus level. Detection of 28 of the 50 genera in

Figure 3A was significantly different between methods in the T. westraliensis

gut, eight of which were highly significantly different (p < 0.01). These, in

order of appearance in Figure 3A, were: Treponema Ia and Treponema Ih

(Spirochaetes); M2PB4-61 termite group and Alisitpes IV (Bacteroidetes); Gut

cluster 7 (Firmicutes); Uncultured genus 2 (Proteobacteria); Subcluster IIIb

(TG3); and Endomicrobium (Elusimicrobia). Treponema Ia was the most

abundant genus in both datasets but was detected on average three times

more frequently in the V4 dataset, making up 39% of reads (Spirochaetes as a

whole made up 80% of V4 reads). M2PB4-61 termite group and Gut cluster 7

were detected five times more frequently using the V3-V4 method. Uncultured

genus 2 was recorded almost seven times, Subcluster IIIb four times and

Endomicrobium nine times more frequently in the V3-V4 dataset (Table S1A).

The vadinBC27 wastewater-sludge group (Bacteroidetes) and Termite

cockroach cluster (Synergistetes) were only detected using the V3-V4 primer

pair (Table S1A).

For C. acinaciformis raffrayi, only six of the 50 total genera represented in

Figure 3B were significantly different between the methods: Candidatus

Armantifilum, BCf9-17 termite group (Bacteroidetes), Termite Cockroach

cluster (Bacteroidetes, p < 0.01), Treponema II (Spirochaetes), Uncultured

genus 12 (Firmicutes, p < 0.01), and Uncultured genus 2 (Proteobacteria). The

gut was always found to be dominated by Candidatus Azobacteroides

(Bacteroidetes) with approximately 50% of reads assigned to this genus using

both methods. Treponema II was detected over three times more frequently in

45

the V4 dataset, while Uncultured genus 2 and Endomicrobium were recorded

four times as often in the V3-V4 dataset. Candidatus Hepatincola

(Proteobacteria) was the only other genus to be detected at least three times

more in one of the datasets, here in V3-V4. Termite cockroach cluster 2

(Planctomycetes) was recorded in the V4 dataset only, while Cluster TG2

(Cyanobacteria) was only detected in the V3-V4 dataset Table S1B).

Despite these differences in abundance, no significant differences were found

with either alpha diversity metric when comparing the two datasets (non-

parametric t-tests with 999 Monte Carlo permutations). A total of 1041

bacterial species were estimated in the T. westraliensis gut using the V3-V4

dataset, versus 982 in the V4 dataset (p = 0.826); and 385 estimated in the C.

acinaciformis raffrayi gut with V3-V4 versus 357 with V4 (p = 0.664). The

Simpson's Reciprocal Index averaged to 55.5 for T. westraliensis in the V3-V4

dataset and 21.3 for the V4 dataset (p = 0.141); the Index averaged 7.0 and 4.9 in

C. acinaciformis raffrayi respectively (p = 0.681).

PCoA was conducted on the combined V4 dataset to visualise any differences

caused by primer choice (Figure S1). With the unweighted UniFrac metric, PC1

explained 56% and 47% of the variation for C. acinaciformis raffrayi (Figure

S1B) and T. westraliensis (Figure S1A) respectively and samples clustered

primarily with their match from the alternate amplification strategy. PC2

separated the primers used, accounting for 45% and 20% of the variation.

With the weighted metric, samples clustered primarily by primer pair, with

PC1 describing 82% and 74% of the variation amongst C. acinaciformis raffrayi

and T. westraliensis samples. PC2 explained 18% and 14% of the variation,

clustering the samples by colony. The T. westraliensis colony 3 biological

replicates (T3a, T3b and T3c) clustered more closely within the V4 dataset

than the first replicate with its identical counterpart in the V3-V4 dataset. In

summary, a similar number of bacterial species was recovered regardless of

which primer pair was used, however the identity and relative abundances of

those bacteria species differed significantly between the two methods.

46

Colony differences

The V4 dataset containing replicates for colony T3 was used to estimate intra-

specific variation. Pairwise comparisons (beta diversity) were categorised as

nestmates, conspecifics or heterospecifics. Differences between the gut

communities were significantly greater for pairs of samples from different

species, followed by those from different colonies (conspecifics) and by

comparisons between nestmates (Kruskal-Wallis test p < 0.001; post-hoc

Mann-Whitney U tests Figure 4).

Intraspecific comparisons using the V3-V4 dataset found significant

differences between T. westraliensis colonies in relative frequencies of the

most abundant genera. The most abundant identified genus in the gut was

always Treponema Ia (Spirochaetes, a phylum associated with cellulose

degradation or accessory functions). However, nearly twice as many reads

were identified as Treponema Ia in colony T1, as compared to colony T3 (Table

S1A). Treponema Ic and If were the second and third most abundant

Spirochaete genera, If being most abundant in T1 and Ic in T3. Colony T2 had

the highest number of reads assigned to Spirochaetes overall (57% of the

data), with many reads belonging to unidentified genera. T1 was the only

colony with no known Candidate Phylum TG3 genera in its top 20 most

abundant . Subcluster IIIb (TG3) was the fourth and eighth most abundant

genus in T2 and T3 respectively, whereas only two reads were detected in T1.

Overall, four known TG3 genera were detected in the T. westraliensis gut

(Subclusters IIIa, IIIb, IVa and IVb). Approximately 1250 reads per sample were

assigned to unknown groups in the cellulose-degrading TG3 candidate

phylum. T2 was assigned most of the reads identified as known TG3 genera

but colony 3 contained the most reads assigned to TG3 overall (Table S1A).

T2 was the only colony with a member of the cellulose-degrading

Fibrobacteres genus (Subcluster Ia) in its top 20, while only one read

belonging to that genus was detected in T3. Fibrobacteres was more abundant

in T2, with two previously known genera, Subcluster Ia and Ib featuring more

47

Figure 4: Average distance (and standard error of the mean) between each pair of samples either between species (heterospecifics, n=10), between colonies (conspecifics, n=8), or within colony T3 (nestmates, n=3). Both unweighted and weighted UniFrac beta diversity metrics indicated a significant difference between sample groupings (Kruskal-Wallis test p = 0.0002; Mann-Whitney-Wilcoxon test results indicated by * p < 0.05; ** p < 0.01; *** p < 0.001).

48

heavily in T2 (with 331 and 216 reads respectively in T2, 22 and three in T1 and

one and zero in T3). Unidentified Fibrobacteres reads (all belonging to the

Termite cluster family within Subphylum 2) were also more highly detected in

T2 with 3627 reads, followed by 71 reads in T3. T1 and T2 have Tannerella

(Bacteroidetes) and Treponema If (Spirochaetes) in second and third whereas

in T3 they rank fifth and eleventh respectively. Treponema Ic (Spirochaetes) is

fifth and fourth in T2 and T3 but tenth in T1, despite T3 recording twice as

many as either of the other two colonies. On the other hand, T1 has

Candidatus Armantifilum (Bacteroidetes) ranked eighth with double the

amount of reads as the other two colonies (where it ranked 14th and 15th;

Table S1).

The distance (beta diversity) values for the C. acinaciformis raffrayi colonies

were of the same magnitude as the T. westraliensis colonies. In the V3-V4

dataset, Gut cluster I (Firmicutes) was the third most abundant genus in C.

acinaciformis colony 1 (C1) with 40 times more reads than in C2.

Dysgonomonas (Bacteroidetes) was detected eight times more frequently in

C1, while Treponema Ig was detected eight times more and Treponema Il 16

times more in C2. C2 had over seven times more Endomicrobium and

Candidatus Hepatincola (Proteobacteria) than C1.

Species comparison

In the PCoA comparing gut community composition and structure of various

higher termite species (Figure 5), samples clustered primarily by diet,

regardless of the metric used (unweighted and weighted UniFrac in Figure 5A

and 5B respectively). The first two axes of the unweighted plot explained 26%

of the observed variation, while those of the weighted plot accounted for 52%

of variation. The metric used affected the relative position of each diet group.

The fungus-cultivating group was separated from all others in terms of

community composition while sharing a similar community structure with the

soil-feeders. The grass-feeders, containing only two species, did not form a

distinct group.

49

The T. westraliensis samples did not fit neatly into any of the feeding groups.

All colonies had a gut community composition comparable to the wood-

feeders and the grass-feeding Trinervitermes sp. (Figure 5A). However, two T.

westraliensis colonies had a similar community structure to the humus/litter

group, while colony T2 and Trinervitermes sp. sat outside of the confidence

interval of the wood-feeding group (Figure 5B). The second grass feeder, A.

meridionalis, had a unique community composition, most similar to the soil

and fungus groups, with more similarity to those groups in terms of

community structure.

Among the higher termites, the humus- and soil-feeding groups have the

highest number of bacterial species in the gut, with an average of 703 and 656

per sample, followed by termites consuming litter (519), fungus (388), grass

(386) and wood (265). The T. westraliensis samples averaged 332 species per

sample. Significantly different comparisons (p < 0.05) were between humus

and fungus, between soil and wood and between T. westraliensis and wood.

The only significantly different values for the Simpson's Reciprocal Index were

comparisons of wood and humus (p = 0.03) and wood and soil (p = 0.02). In

both cases, differences were only significant between the extremes but alpha

diversity values overall correlated with the humification gradient.

In the lower termites, sample clustering was best explained by PC1 in the

unweighted PCoA (explaining 26.8% of the variation) and PC2 in the weighted

PCoA (13.5%). Both separated the samples based on whether the termite hosts

feed broadly on the humification gradient or not. Coptotermes and

Mastotermes formed the bulk of the "broad" feeders group and the drywood,

dampwood and grass-feeding termites clustered together. Reticulitermes was

within the "broad" feeding group but was unique in terms of community

composition, and most similar to the grass feeders in community structure.

The Coptotermes samples shared similar community structures, but C. niger

50

Figure 5: Principle Coordinate Analysis (PCoA) of the (A) unweighted and (B) weighted UniFrac distances between all available higher termite datasets sequenced using the V3-V4 method. Points are coloured by the subfamily of the termite; shape represents the termite's diet. The T. westraliensis diet is listed as "unknown". In (A) the first component (PC1) explains 13.91% of the variation, while the second component (PC2) explains 11.64%. In (B) PC1 explains 38.59% and PC2 13.07%. Ellipses represent 95% confidence intervals for each diet group with at least four samples. T. westraliensis colony 2 (T2) did not cluster with the other T. westraliensis samples in (B).

51

clustered with M. darwiniensis in terms of community composition (Figure

S2).

Alpha diversity analysis revealed significant differences amongst the lower

termites, in terms of the number of OTUs recovered from the guts of termites

from the "broad" (with an average of 83 OTUs per sample) and "narrow"

feeding groups. The drywood and dampwood termites and the grass-feeders

averaged 212, 345 and 188 bacterial species respectively (p = 0.008, non-

parametric t-test with 999 Monte Carlo permutations). The Simpson

Reciprocal Index was also significantly different between the two groups (p =

0.009), with an average of 7 for the "broad" diet and 45 (drywood), 65

(dampwood) and 27 (grass) for the "narrow" diet groups.

Discussion:

The current study has described, for the first time, the influence of primer

choice on estimates of gut community composition and structure in termites.

Using two endemic Western Australian termites, T. westraliensis and C.

acinaciformis raffrayi, I have demonstrated that colonies of both higher and

lower termites have signature gut communities, which limits the applicability

of previous single-colony studies to the question of core gut microbial

communities. I hypothesise that locally available substrates may drive the gut

community structure and composition at a colony level. Larger numbers of

biological replicates and the characterisation of colony specific food sources

would be required to confirm this.

Method comparison highlights the need for a standardised approach

PCR conditions (be it annealing temperature, primer choice or both)

significantly affected termite gut community profiles at both the phylum and

genus level. While certain differences could be predicted from the database

analysis, for example phyla Tenericutes and TM7 which were predicted to be

52

amplified unequally with each primer pair, the differences in the biological

datasets resulting from the two methods was much more extensive.

Overall the V3-V4 method yielded a higher species richness, i.e. more bacterial

groups were detected at the same depth, but genera were more evenly

distributed in the V4 dataset. The dominance of Treponema Ia and the

Spirochaetes in T. westraliensis, which appeared to amplify more efficiently

with the V4 primers, overshadowed lower abundance groups and increased

the Reciprocal Simpson Index for this dataset. The effect was not as

pronounced for the C. acinaciformis raffrayi data, possibly because its gut does

not contain as many bacterial species and because the most abundant genus

was not detected at significantly different levels with each method. The

increased species diversity detected in the V3-V4 dataset could be explained

by the lower annealing temperature recommended with this primer pair by

Köhler et al. (2012), which has been shown to limit some of the bias associated

with PCR (Sipos et al., 2007).

Because certain bacterial groups were only detected with one primer pair or

the other, it is not clear which method provides data that represents the gut

community most accurately. However it is clear that datasets collected with

each method cannot be directly compared and that caution must be exercised

when making generalised statements comparing biological findings across

studies. In the interest of collaboration, researchers should agree on a

standardised method to maximise re-use of publicly available datasets. The

method used on the widest range of termite species (across multiple families

and feeding groups) in the literature to date employs the V3-V4 primers

343Fmod and 784Rmod designed by Köhler et al. (2012) and the following PCR

conditions: initial denaturation for 3 min at 95°C, followed by 26 cycles of 20 s

at 95°C, 20 s at 48°C, and 30 s at 72°C and terminal extension for 3 min at 72°C.

I recommend that future termite gut studies use this amplification strategy

instead of, or in addition to other methods, as a means to standardise results

across the field.

53

Each colony has a signature gut microbiota

I hypothesised that there would be colony differences within each species in

terms of their gut microbiota, due to their broad diets. I did find significant

differences between the gut community composition and structure of

conspecific termites from different colonies, as compared to nestmates.

Previous studies have reported intercolony variation in the genera

Reticulitermes (Boucias et al. 2013; Benjamino and Graf, 2016) and

Microcerotermes (Hongoh et al., 2005), and in Stolotermes ruficeps (Reid et al.,

2014). Here, I extend these findings to T. westraliensis and C. acinaciformis.

Overall, I found that conspecific non-nestmates differed more in the types of

bacteria present than in their relative abundances. This suggests that different

colonies retained a core microbiome typical of the species or area, while

collecting bacteria from their direct environment, which could allow these

termites to optimise the digestion of local substrates (Otani et al., 2014; Brune

and Dietrich, 2015; Benjamino and Graf, 2016). Indeed, Miyata et al. (2007)

showed that altering the components of the diet of Nasutitermes

takasagoensis affected both the composition and structure of the gut

microbiota. In the lower termites, altering diet components or feeding of

different plant species has also been found to affect the gut community

(Tanaka et al., 2006; Husseneder et al., 2009; Huang et al., 2013). There

remains a baseline level of variation in the gut community within a colony,

even when controlling for caste and age, providing further support for the idea

that there are environmental drivers of the gut community in these termites.

In future, including replicate colonies sampled from different sites may reveal

even greater variability. Studying the variation in accessible food sources for

each colony would indicate if these differences could indeed be linked to diet.

Putative functional differences in gut communities support the possibility that

diversity and abundance differences are driven by dietary factors. T.

westraliensis colony 2 (T2) stood out from the other colonies in the weighted

54

PCoA, featuring high levels of known TG3 and Fibrobacteres genera and a

higher variety of abundant Spirochaetes. TG3, Fibrobacteres and certain

Spirochaete genera are associated with wood fibres in Nasutitermes species

(Mikaelyan et al. 2014) and Fibrobacteres and TG3 have both been associated

with cell-bound cellulose-degradation (Sorokin et al., 2014; Abdul et al., 2016),

suggesting a more highly lignified diet. The vast majority of Spirochaetes

belonged to Treponema cluster I, as previously reported in a range of termite

species (Lilburn et al., 1999). Interestingly, genus Treponema Ia, the most

abundant genus in the gut of T. westraliensis has not been associated with

fibre degradation, but rather enhancing cellulose-degradation by other

organisms and using their by-products (Kudo et al., 1987). Treponema Ic and

If, most abundant in T2, have been reported to associate with fibre and are the

most abundant genera in wood-feeding Nasutitermes (Dietrich et al., 2014;

Mikaelyan et al., 2014). This colony also clustered more closely with termite

species feeding on sound substrates (be it wood or grass). I therefore

hypothesise that T2 was consuming less humified and/or more highly lignified

substrate(s) than the other two colonies. Future studies could undertake a

detailed inventory of the storage chambers for each colony to determine

whether there are correlations between the lignification and humification level

of the stored material and the abundance of fibre-associated bacteria in the

gut community.

I found indications of intraspecific colony differences in C. acinaciformis

raffrayi, although replication was very low. The same genera were detected in

both colonies, but colony C1 had more Gut cluster I (Firmicutes) and

Dysgonomonas (Bacteroidetes, capable of varying degrees of cellulose

degradation; Pramono et al., 2015; Sun et al., 2015). More Treponema Ig,

Treponema II, Endomicrobium and Candidatus Hepatincola (Proteobacteria)

was detected in C2. Treponema Ig and II have been linked to protists in other

lower termites (Mikaelyan et al., 2015b; Benjamino and Graf, 2016) and were

not detected in T. westraliensis. Endomicrobium is a common endosymbiont of

lower termite gut protists, but has also been reported as a free-living

55

bacterium both in lower and higher termites (Stingl et al., 2005; Ohkuma et

al., 2007; Zheng and Brune, 2015). The differences in abundance of protist-

associated bacteria would suggest that colony differences in C. acinaciformis

raffrayi might in part be driven by different abundances of gut protists.

Because C1 was collected in rotten wood and C2 was collected in sound wood,

I hypothesise that protist numbers correlate with humification of the diet.

Niche breadth and variability of the gut microbiota of termites

The gut habitat (e.g. pH, oxygen concentration) shapes the microbiota in

universal ways by providing specific niches (Mikaelyan et al. 2017), and this

gut environment has an additive effect with diet on gut community structure,

due to the varying availability of plant fibre and nitrogen along the

humification gradient. Indeed, members of different higher termite

subfamilies share similarities in their gut community structure if they

naturally feed on the same diet (Mikaelyan et al. 2015a). I extended the results

of Mikaelyan et al. (2015a) by analysing their raw data with a different analysis

pipeline to show that diet also correlates with gut community composition.

Since vertical transfer of a symbiotic core microbiome facilitates co-evolution

of the gut community and its host species (Bignell, 2011; Brune and Dietrich,

2015), comparing the gut community composition and structure within and

between species offers a window into the relative contributions of inherited vs

environmentally acquired (ingested) microbes. Such an approach may also

allow us to make inferences about the diet of a species with sparse natural

history data, based on its gut community profile.

There was a correlation between the alpha diversity values for the higher

termites and the point on humification gradient at which they feed, with the

highest species richness and diversity in the guts of humus and soil feeders.

Litter-feeding termites were indistinguishable from the humus group when

comparing beta diversity values, but litter was linked to lower species richness,

presumably due to its less humified state. T. westraliensis had similar alpha

diversity values and gut community composition to the wood- and grass-

56

feeders, but the grouping of two out of three colonies with the humus/litter

group in terms of gut community structure had greater explanatory power. In

addition, the gut of T. westraliensis was shown to be dominated by

Spirochaetes like typical wood and grass feeders and Spirochaetes have been

associated with nitrogen fixation in higher termite guts (Warnecke et al., 2007;

Brune and Ohkuma, 2011; Mikaelyan et al., 2017). This reinforces our

observation that the diet of T. westraliensis may be more similar to litter than

grass, perhaps in terms of variety. However, it suggests that the diet is lower

on the humification gradient than litter, and hence lower in nitrogen. This

also underscores need for more extensive sampling within a species to capture

a more accurate picture of the plasticity of its microbial community and

dietary range.

The gut community of lower termites is shaped additionally by the presence of

cellulose-degrading protists that provide unique niches for bacteria. Certain

lineages of protist and bacteria have been shown to be co-evolving with the

host (Brune and Dietrich, 2015), but environmental uptake and diet still play a

significant role, especially for lower abundance phylotypes (Tai et al., 2015). I

found that the complexity of the diet might be driving the gut community

structure of lower termite guts more than their phylogeny.

Interestingly, lower termites which forage on broader diets were found to have

less diverse gut microbiota than specialist foraging or non-foraging termites at

the same sequencing depth. This result is in contrast to Waidele et al. (2017),

who recovered more OTUs from Reticulitermes (628 OTUs, also included in

this study) than Cryptotermes (476 OTUs, drywood termite closely related to

the one included in this study). They hypothesised that foraging-type species

with broad diets have a higher probability of encountering new

microorganisms, some of which may become resident in the gut.

They attempted to remove transient gut microbes by feeding the termites

sterile pine for six weeks, which should have decreased species richness in the

gut. It should be noted that community composition estimates are affected by

57

sequencing depth and community structure, as more abundant taxa may lead

to low abundance taxa remaining undetected at that depth. My comparative

analysis was conducted at a rarefied depth of 3,000 reads per sample,

compared to a depth of 32,824 reads in Waidele et al. (2017), which likely

influenced our respective results. Recent work applying the same V3-V4

strategy I used included more lower termite species (Bourguignon et al., 2018)

and may help resolve these inconsistencies. There, the sequencing

depth varied between 1,847 reads and 10,395 reads per sample for the lower

termites, so a new meta-analysis could be done with a subset of their data to

avoid rarefying below my current threshold.

Conclusion

I have shown that primer choice influences the estimation of gut community

composition and structure in termites and recommend that a standard

approach be applied using the primers 343Fmod and 784Rmod designed by

Köhler et al. (2012) that target the V3-V4 region of the 16S rRNA gene. The

recommended low annealing temperature would maximise data

reproducibility and utility across research groups.

There were colony differences within both T. westraliensis and C.

acinaciformis raffrayi. While their complex diets may be one explanation for

the existence of these intra-species differences, it also highlights the necessity

of extensive sampling. Future studies on T. westraliensis could quantify the

amount and condition of each plant type found within a mound's food store or

characterise the plant DNA recovered from the gut, to determine whether gut

community differences reflect colony-specific food availability. Replicated

biological sampling is necessary for future metagenomic studies to account for

variability of gut communities within a species and to further understand the

drivers of community structure and composition in termites.

58

The T. westraliensis gut community shares similarities with wood, grass, and

litter feeders, indicating that its diet may be more complex and less

specialised than previously thought. C. acinaciformis raffrayi shared

similarities with other lower termites that eat various species of wood at

different stages of decomposition. This suggests that the complexity of a diet

may have a larger impact on the gut community composition and structure of

termites than the plant species or humification level on their own.

The evolution of the dual-cellulolytic system in termites over 150 million years

allows us to learn from a fine-tuned natural process and to apply it to

renewable energy advancements such as biofuel (reviewed by König et al.,

2013; Ni and Tokuda, 2013; Brune, 2014). This apparent plasticity of the gut

community may allow for the development of cellulase enzyme cocktails

suited to biofuel feedstocks of interest. Bacterial enzymes are well suited to

industrial processes (Gronenberg et al., 2013), placing the spotlight on higher

termites. T. westraliensis' variable gut community makes it a potential model

species to study the drivers that shape the gut community structure in higher

termites, aside from host phylogeny.

59

References Abdul Rahman, N., Parks, D. H., Willner, D. L., Engelbrektson, A. L., Goffredi, S. K.,

Warnecke, F., Scheffrahn, R. H., Hugenholtz, P. (2015). A molecular survey of Australian and North American termite genera indicates that vertical inheritance is the primary force shaping termite gut microbiomes. Microbiome, 3, 5.

Abensperg-Traun, M., & Milewski, A. V. (1995). Abundance and diversity of termites (Isoptera) in imburnt versus burnt vegetation at the Barrens in Mediterranean Western Australia. Australian Journal of Ecology, 20(3), 413-417.

Benjamino, J., & Graf, J. (2016). Characterization of the core and caste-specific microbiota in the termite, Reticulitermes flavipes. Frontiers in Microbiology, 7, 1.

Bignell, D. E. (2011). Morphology, physiology, biochemistry and functional design of the termite gut: an evolutionary wonderland. In D. E. Bignell, Y. Roisin, & N. Lo (Eds.), Biology of Termites: a Modern Synthesis (pp. 375-412). Netherlands: Springer.

Boucias, D. G., Cai, Y., Sun, Y., Lietze, V.-U., Sen, R., Raychoudhury, R. et al. (2013). The hindgut lumen prokaryotic microbiota of the termite Reticulitermes flavipes and its responses to dietary lignocellulose composition. Molecular Ecology, 22(7), 1836-1853.

Bourguignon, T., Lo, N., Dietrich, C., Šobotník, J., Sidek, S., Roisin, Y., Brune, A., Evans, T. A. (2018). Rampant host switching shaped the termite gut microbiome. Current Biology, 28(4), 649-654.e2.

Brune, A. (2014). Symbiotic digestion of lignocellulose in termite guts. Nature Reviews Microbiology, 12(3), 168-180.

Brune, A., & Dietrich, C. (2015). The gut microbiota of termites: digesting the diversity in the light of ecology and evolution. Annual Review of Microbiology, 69(1), 145-166.

Brune, A., & Ohkuma, M. (2011). Role of the termite gut microbiota in symbiotic digestion. In D. E. Bignell, Y. Roisin, & N. Lo (Eds.), Biology of Termites: a Modern Synthesis (pp. 439-475). Netherlands: Springer.

Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Lozupone, C. A., Turnbaugh, P. J. et al. (2011). Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proceedings of the National Academy of Sciences, 108(Supplement 1), 4516-4522.

Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K. et al. (2010). QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 7, 335-336.

Cleveland, L. R. (1923). Symbiosis between termites and their intestinal protozoa. Proceedings of the National Academy of Sciences of the United States of America, 9(12), 424-428.

Colman, D. R., Toolson, E. C., & Takacs-Vesbach, C. D. (2012). Do diet and taxonomy influence insect gut bacterial communities? Molecular Ecology, 21, 5124-5137.

Delmotte, N., Knief, C., Chaffron, S., Innerebner, G., Roschitzki, B., Schlapbach, R. et al. (2009). Community proteogenomics reveals insights into the physiology of phyllosphere bacteria. Proceedings of the National Academy of Sciences, 106(38), 16428-16433.

Dietrich, C., Köhler, T., & Brune, A. (2014). The cockroach origin of the termite gut microbiota: patterns in bacterial community structure reflect major evolutionary events. Applied and Environmental Microbiology.

Diouf, M., Roy, V., Mora, P., Frechault, S., Lefebvre, T., Hervé, V., Rouland-Lefèvre,

60

C., Miambi, E. (2015). Profiling the succession of bacterial communities throughout the life stages of a higher termite Nasutitermes arborum (Termitidae, Nasutitermitinae) Using 16S rRNA Gene Pyrosequencing. PLoS One, 10, e0140014.

Dolan, M. F. (2001). Speciation of termite gut protists: the role of bacterial symbionts. International Microbiology, 4(4), 203-208.

Edgar, R. C. (2013). UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods, 10(10), 996-998.

Gontijo, T. A., & Domingos, D. J. (1991). Guild distribution of some termites from cerrado vegetation in south-east Brazil. Journal of Tropical Ecology, 7(04), 523-529.

Greaves, T. (1962). Studies of foraging galleries and the invasion of living tres by Coptotermes acinaciformis and C. brunneus (Isoptera). Australian Journal of Zoology, 10, 630-651.

Gronenberg, L. S., Marcheschi, R. J., & Liao, J. C. (2013). Next generation biofuel engineering in prokaryotes. Current Opinion in Chemical Biology, 17(3), 462-471.

Gu, Z., Eils, R., & Schlesner, M. (2016). Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics, 32(18), 2847-2849.

He, S., Ivanova, N., Kirton, E., Allgaier, M., Bergin, C., Scheffrahn, R. H. et al. (2013). Comparative metagenomic and metatranscriptomic analysis of hindgut paunch microbiota in wood- and dung-feeding higher termites. PLoS ONE, 8(4), 1-14.

Hill, G. F. (1921). New and rare Australian termites, with notes on their biology. Proceedings of the Linnean Society of New South Wales, 46, 433-456.

Hill, G. F. (1925). Termites of the Australian region. Proceedings of The Royal Society of Victoria, 37, 206-229.

Hill, G. F. (1942). Termites (Isoptera) from the Australian region. Melbourne, Australia: Council for Scientific and Industrial Research.

Hongoh, Y., Ekpornprasit, L., Inoue, T., Moriya, S., Trakulnaleamsai, S., Ohkuma, M., Noparatnaraporn, N., Kudo, T. (2006). Intracolony variation of bacterial gut microbiota among castes and ages in the fungus-growing termite Macrotermes gilvus. Molecular Ecology, 15(2), 505-516.

Hongoh, Y., Deevong, P., Inoue, T., Moriya, S., Trakulnaleamsai, S., Ohkuma, M., Vongkaluang, C., Noparatnaraporn, N., Kudo, T. (2005). Intra- and interspecific comparisons of bacterial diversity and community structure support coevolution of gut microbiota and termite host. Applied & Environmental Microbiology, 71(11), 6590-6599.

Huang, X.-F., Bakker, M., Judd, T., Reardon, K., & Vivanco, J. (2013). Variations in diversity and richness of gut bacterial communities of termites (Reticulitermes flavipes) fed with grassy and woody plant substrates. Microbial Ecology, 65(3), 531-536.

Husseneder, C., Berestecky, J. M., & Grace, J. K. (2009). Changes in composition of culturable bacteria community in the gut of the formosan subterranean termite depending on rearing conditions of the host. Annals of the Entomological Society of America, 102(3), 498-507.

Inoue, T., Kitade, O., Yoshimura, T., & Yamaoka, I. (2000). Symbiotic associations with protists. In T. Abe, D. E. Bignell, & M. Higashi (Eds.), Termites: Evolution, Sociality, Symbiosis, Ecology (pp. 275-288). Dordrecht: Kluwer Academic Publishers.

Jones, D. T., & Eggleton, P. (2011). Global biogeography of termites: a compilation of sources. In D. E. Bignell, Y. Roisin, & N. Lo (Eds.), Biology of Termites: a Modern

61

Synthesis (pp. 477-498). Netherlands: Springer. Köhler, T., Dietrich, C., Scheffrahn, R. H., & Brune, A. (2012). High-resolution analysis

of gut environment and bacterial microbiota reveals functional compartmentation of the gut in wood-feeding higher termites (Nasutitermes spp.). Applied & Environmental Microbiology, 78(13), 4691-4701.

König, H., Li, L., & Fröhlich, J. (2013). The cellulolytic system of the termite gut. Applied Microbiology and Biotechnology, 97(18), 7943-7962.

Krishna, K., Grimaldi, D. A., Krishna, V., & Engel, M. S. (2013). Treatise on the Isoptera of the world. Bulletin of the American Museum of Natural History, 1.

Kudo, H., Cheng, K.-J., & Costerton, J. W. (1987). Interactions between and cellulolytic bacteria in the degradation of straw cellulose. Canadian Journal of Microbiology, 33(3), 244-248.

Lee, T. R. C., Cameron, S. L., Evans, T. A., Ho, S. Y. W., & Lo, N. (2015). The origins and radiation of Australian Coptotermes termites: From rainforest to desert dwellers. Molecular Phylogenetics and Evolution, 82, 234-244.

Li, H., Dietrich, C., Zhu, N., Mikaelyan, A., Ma, B., Pi, R. et al. (2016). Age polyethism drives community structure of the bacterial gut microbiota in the fungus-cultivating termite Odontotermes formosanus. Environmental Microbiology, 18(5), 1440-1451.

Lilburn, Schmidt, & Breznak. (1999). Phylogenetic diversity of termite gut Spirochaetes. Enviromental Microbiology, 1(4), 331-345.

Liu, H., & Beckenbach, A. T. (1992). Evolution of the mitochondrial cytochrome oxidase II gene among 10 orders of insects. Molecular Phylogenetics and Evolution, 1(1), 41-52.

Lo, N., Eldridge, R. H., & Lenz, M. (2006). Phylogeny of Australian Coptotermes (Isoptera: Rhinotermitidae) species inferred from mitochondrial COII sequences. Bulletin of Entomological Research, 96(4), 433-437.

Mikaelyan, A., Dietrich, C., Köhler, T., Poulsen, M., Sillam-Dusses, D., & Brune, A. (2015a). Diet is the primary determinant of bacterial community structure in the guts of higher termites. Molecular Ecology, 24(20), 5284-5295.

Mikaelyan, A., Köhler, T., Lampert, N., Rohland, J., Boga, H., Meuser, K. et al. (2015b). Classifying the bacterial gut microbiota of termites and cockroaches: A curated phylogenetic reference database (DictDb). Systematic and Applied Microbiology, 38(7), 472-482.

Mikaelyan, A., Meuser, K., & Brune, A. (2017). Microenvironmental heterogeneity of gut compartments drives bacterial community structure in wood-and humus-feeding higher termites. FEMS Microbiology Ecology, 93(1), fiw210.

Mikaelyan, A., Strassert, J. F. H., Tokuda, G., & Brune, A. (2014). The fibre‐associated cellulolytic bacterial community in the hindgut of wood‐feeding higher termites (Nasutitermes spp.). Environmental Microbiology, 16(9), 2711-2722.

Miyata, R., Noda, N., Tamaki, H., Kinjyo, K., Aoyagi, H., Uchiyama, H., Tanaka, H. Influence of feed components on symbiotic bacterial community structure in the gut of the wood-feeding higher termite Nasutitermes takasagoensis. Bioscience, Biotechnology, and Biochemistry, 71(5), 1244-1251.

National Center for Biotechnology Information. Basic Local Alignment Search Tool. 2017, from https://blast.ncbi.nlm.nih.gov/Blast.cgi.

Ni, J., & Tokuda, G. (2013). Lignocellulose-degrading enzymes from termites and their symbiotic microbiota. Biotechnology advances, 31(6), 838-850.

Ohkuma, M. (2003). Termite symbiotic systems: efficient bio-recycling of lignocellulose. Applied Microbiology and Biotechnology, 61(1), 1-9.

Ohkuma, M., Sato, T., Noda, S., Ui, S., Kudo, T., & Hongoh, Y. (2007). The candidate

62

phylum ‘Termite Group 1’ of bacteria: phylogenetic diversity, distribution, and endosymbiont members of various gut flagellated protists. FEMS Microbiology Ecology, 60(3), 467-476.

Otani, S., Mikaelyan, A., Nobre, T., Hansen, L. H., Kone, N. A., Sorensen, S. J., Aanen, D.K., Boomsma, J.J., Brune, A. Poulsen, M (2014). Identifying the core microbial community in the gut of fungus-growing termites. Molecular Ecology.

Paul, K., Nonoh, J. O., Mikulski, L., & Brune, A. (2012). “Methanoplasmatales,” Thermoplasmatales-related archaea in termite guts and other environments, are the seventh order of methanogens. Applied and Environmental Microbiology, 78(23), 8245-8253.

Poulsen, M. (2015). Towards an integrated understanding of the consequences of fungus domestication on the fungus-growing termite gut microbiota: fungus-growing termite gut microbiota. Environmental Microbiology, 17(8), 2562-2572.

Pramono, A. K., Sakamoto, M., Iino, T., Hongoh, Y., & Ohkuma, M. (2015). Dysgonomonas termitidis sp. nov., isolated from the gut of the subterranean termite Reticulitermes speratus. International Journal of Systematic and Evolutionary Microbiology, 65(Pt 2), 681-685.

R Development Core Team (2016). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.

Reid, N. M., Addison, S. L., West, M. A., & Lloyd-Jones, G. (2014). The bacterial microbiota of Stolotermes ruficeps (Stolotermitidae), a phylogenetically basal termite endemic to New Zealand. FEMS Microbiology Ecology, 90(3), 678-688.

Rosengaus, R. & Traniello, J. (1997). Pathobiology and disease transmission in dampwood termites Zootermopsis angusticollis (Isoptera: Termopsidae) infected with the fungus Metarhizium anisopliae (Deuteromycotina: Hypomycetes). Sociobiology. 30. 185-195.

Sipos, R., Székely, A. J., Palatinszky, M., Révész, S., Márialigeti, K., & Nikolausz, M. (2007). Effect of primer mismatch, annealing temperature and PCR cycle number on 16S rRNA gene-targetting bacterial community analysis: PCR parameters influencing quantitative bias. FEMS Microbiology Ecology, 60(2), 341-350.

Sorokin, D. Y., Gumerov, V. M., Rakitin, A. L., Beletsky, A. V., Damsté, J. S. S., Muyzer, G. et al. (2014). Genome analysis of gen. nov., sp. nov., a novel extremely haloalkaliphilic anaerobic chitinolytic bacterium from the candidate phylum Termite Group 3 : Chitinolytic bacterium from the candidate phylum TG3. Environmental Microbiology, 16(6), 1549-1565.

Stingl, U., Radek, R., Yang, H., & Brune, A. (2005). “Endomicrobia”: cytoplasmic symbionts of termite gut protozoa form a separate phylum of prokaryotes. Applied and Environmental Microbiology, 71(3), 1473-1479.

Sun, X., Yang, Y., Zhang, N., Shen, Y., & Ni, J. (2015). Draft genome sequence of Dysgonomonas macrotermitis strain JCM 19375T, isolated from the gut of a termite. Genome Announcements, 3(4), e00963-e00915.

Tai, V., James, E. R., Nalepa, C. A., Scheffrahn, R. H., Perlman, S. J., & Keeling, P. J. (2015). The role of host phylogeny varies in shaping microbial diversity in the hindguts of lower termites. Applied and Environmental Microbiology, 81(3), 1059-1070.

Tanaka, H., Aoyagi, H., Shiina, S., Doudou, Y., Yoshimura, T., Nakamura, R., Uchiyama, H. (2006). Influence of the diet components on the symbiotic microorganisms community in hindgut of Coptotermes formosanus Shiraki. Applied Microbiology & Biotechnology, 71(6), 907-917.

Tokuda, G., & Watanabe, H. (2007). Hidden cellulases in termites: revision of an old

63

hypothesis. Biology Letters, 3(3), 336-339. Tremblay, J., Singh, K., Fern, A., Kirton, E. S., He, S., Woyke, T. et al. (2015). Primer

and platform effects on 16S rRNA tag sequencing. Frontiers in Microbiology, 6, 771.

Waidele, L., Korb, J., Voolstra, C. R., Künzel, S., Dedeine, F., & Staubach, F. (2017). differential ecological specificity of protist and bacterial microbiomes across a set of termite species. Frontiers in Microbiology, 8, 5.

Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007). Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied & Environmental Microbiology, 73(16), 5261-5267.

Warnecke, F., Luginbuhl, P., Ivanova, N., Ghassemian, M., Richardson, T. H., Stege, J. T. et al. (2007). Metagenomic and functional analysis of hindgut microbiota of a wood-feeding higher termite. Nature, 450(7169), 560-565.

Watson, J. A. L., & McMahan, E. A. (1978). Polyethism in the Australian harvester termite Drepanotermes (Isoptera, Termitinae). Insectes Sociaux, 25(1), 53-62.

Wickham, H. (2009). ggplot2: Elegant Graphics for Data Analysis. New York: Springer-Verlag.

Xiang, H., Xie, L., Zhang, J., Long, Y., Liu, N., Huang, Y., Wang, Q. (2012). Intracolonial differences in gut bacterial community between worker and soldier castes of Coptotermes formosanus. Insect Science, 19, 86-95.

Zheng, H., & Brune, A. (2015). Complete genome sequence of Endomicrobium proavitum, a free- living relative of the intracellular symbionts of termite gut flagellates (Phylum Elusimicrobia). Genome Announcements, 4(3), e00679-e00615.

64

Figure S1: Principal coordinate analysis for (A) Tumulitermes westraliensis and (B) Coptotermes acinaciformis raffrayi using two different PCR conditions targeting the V4 (blue) or V3-V4 (red) region of the 16S gene. Samples clustered by sequencing methodology as well as by species with both unweighted and weighted UniFrac beta diversity metrics, and biological replicates clustered more closely within each method than technical replicates across methods.

65

Figure S2: Principal coordinate analysis using (A) unweighted and (B) weighted UniFrac metrics for all lower termites included in this study. Samples clustered by the position of the diet on the humification gradient with most "broad" diets (light blue) separating from "narrow" (dampwood, drywood and grassy) diets. Reticulitermes was listed as part of the "broad" feeding group and sat in between the two groups on the weighted plot, and clustered with the grass feeder on the unweighted plot. The Coptotermes samples clustered together in the weighted PCoA, but C. niger clustered with M. darwiniensis in the unweighted analysis.

66

67

CHAPTER 3: Sampling Intensity, Scope and Scale Significantly Affect Estimation of Dietary Influence on the Gut Communities of Tumulitermes westraliensis and Amitermes obeuntis

68

Foreword:

In this chapter, I extend findings from Chapter 2 to further investigate my first

research question: How much intraspecific variation exists in the gut

communities of Western Australian endemic termites? Intraspecific variation in

the guts of two higher termites, Tumulitermes westraliensis and Amitermes

obeuntis, is characterised and I investigate local factors mediating horizontal

and environmental uptake which may contribute to this variation.

I also focus on identifying the portion of the gut community that is

consistently present in the gut of each species and likely to contain organisms

passed on from generation to generation through vertical transfer. This “core

community” has been reported in previous termite studies, but various

different methods have been applied, which may affect interpretation of

results across studies. This inconsistency led to my second research question,

also explored in this chapter: How should the core community of a termite

species be defined and accurately estimated? More specifically, I tested three

aspects related to sampling for termite core microbiome studies: scale, defined

as the geographic distribution of sampling sites; scope, the caste composition

of samples; and sampling intensity, the number of samples taken per unit of

interest.

Further, I report on a food-replacement experiment conducted with wheat and

lucerne straw to determine whether the gut communities become enriched in

taxa better suited to digesting these substrates. This experiment addresses my

third research question: Can the gut community of local higher termite species

be modified under field conditions? The core communities of each species, as

well as their shared core microbiome, were compared by size, abundance and

predicted functions. The results were compared between different sampling

intensities, scales and scopes.

69

Based on these results, the likelihood of vertical acquisition of common gut

community members from a common ancestor versus environmental or

horizontal transfer is then discussed. The source of termite gut community

members is a point of discussion in the literature, but this is the first direct

test of potential contributing factors under field conditions. My contributions

add to the broader discussion in the field, with implications for how we

understand the evolution and ecology of termite microbiomes.

Introduction:

Microbial communities from similar habitats contain consortia of taxa

consistently present in that habitat, a group defined as the core community or

core microbiome, in addition to transient or sample-specific taxa. Members of

the core microbiome are thought to be critical to the functioning of that

community; it is thus the baseline community for a particular habitat, from

which it should be possible to measure responses to perturbation and

manipulation (Shade and Handelsman, 2012). In practice, a core microbiome is

measured by determining which taxa, usually represented by highly similar

genetic sequences (operational taxonomic units, OTUs) are present in most or

all samples representing the habitat of interest (Shade and Handelsman, 2012).

Termite gut core microbiomes have previously been reported in different

termite species (Huang et al., 2013; Dietrich et al., 2014; Otani et al., 2014; Reid

et al., 2014; Benjamino and Graf, 2016). Benjamino and Graf (2016)

hypothesised that each member of the core microbiome occupies a specific

niche in the hindgut of a particular termite species, regardless of the location

or food source of the host. For example, Huang et al. (2013) found that the core

microbiome of the lower termite Reticulitermes flavipes comprised 65% of

common OTUs (95% of sequences) and persisted despite changes in diet to

either grassy or woody substrates. The diets did, however, significantly change

bacterial species richness and the relative abundance of bacterial taxa. The

authors concluded that the core microbiome has a role in lignocellulose

70

degradation and that differences in the recalcitrance of the substrates may

explain differences in the species richness of the gut, with more recalcitrant

food sources requiring more complex communities for efficient breakdown.

Comparison of termite gut microbiome studies is hindered by the fact that

different studies have used different sampling and bioinformatics methods to

search for and define the core microbiome. Previous approaches include 1)

using technical replicates of a single large pooled sample to calculate the core

community of a species (Su et al., 2016); 2) using single or replicated pooled

samples for multiple termite species to calculate a core microbiome across

several termite species (Dietrich et al., 2014; Otani et al., 2014; Abdul Rahman

et al., 2015, Su et al., 2016); 3) using replicate guts or pooled samples from

different colonies of a single termite species to calculate the core microbiome

(Reid et al., 2014; Benjamino and Graf, 2016); 4) using replicate guts from

different castes to calculate a core between each pair of castes in a colony

(Diouf et al., 2018) or 5) using pooled samples from one or more colonies fed

on different diets to infer a core community that persists on all diets (Huang

et al., 2013; Wang et al., 2016). In all of these studies the scope, i.e. caste

composition of samples, was limited to workers, except in Diouf et al. 2018

where five castes of Nasutitermes arborum were considered. The geographic

distribution of sampling sites (scale) was in most cases restricted to a single

colony, or a small portion of the species known range. Sampling intensity, the

number of samples taken per unit of interest, varied from 3 (Su et al., 2016) to

66 (Abdul Rahman et al., 2015). Finally, OTUs included in the core community

were required to be present in 40% (Reid et al., 2014) to 100% (Huang et al.,

2013; Otani et al., 2014; Abdul Rahman et al., 2015; Su et al., 2016; Wang et al.,

2016) of samples in these studies. These differences in sample selection and

bioinformatics methods are compounded by variation in laboratory methods

(see Chapter 2).

In termites, the core community shared across a species is generally thought

to be vertically transmitted from parent colony to new colonies through the

71

winged reproductives since they are the only link between generations of

colonies (Diouf et al., 2018). However the origin of the remainder of the gut

community (hereafter referred to as the non-core community, or in some

cases the group-specific core community) remains unclear. The prevalence of

core taxa across cockroaches, lower (basal) termites and higher (derived)

termites was specific to each host group in Dietrich et al. (2014) but no

evidence of co-speciation was found within each of these host groups,

suggesting that other factors, such as environmental uptake, shape bacterial

community composition within them. However, Otani et al. (2014) recovered a

core microbiome of 42 OTUs common to nine species of fungus-cultivating

termites (Macrotermitinae), which accounted for 56–68% of reads within each

species. The large number of shared taxa suggests vertical inheritance from a

common ancestor. Environmental factors seem to influence the relative

abundances of bacterial groups in these gut communities (i.e. community

structure), which more closely resembled omnivorous cockroaches than other

higher termites (Termitidae). In this case, the obligate association with

Termitomyces, consumed by the termites along with plant material on which

it is cultivated, likely influences the community structure in a consistent

manner.

To explore environmental influences on the gut microbiome and test how

varying numbers and types of samples affect inference of the core community,

I established a feeding experiment at three sites with two co-occurring, and

sometimes co-habiting, higher termite species. My study species have narrow,

nearly overlapping, distribution ranges (see Figure 1A) but different feeding

habits: Tumulitermes westraliensis feeds on dried plant debris stored in its

mound (Chapter 2) while Amitermes obeuntis consumes decaying wood (Perry

et al., 1985; Abensperg-Traun, 1992). I collected 16S gut metabarcoding data on

pooled and individual guts before and after the feeding experiment. The core

microbiome was defined as the OTUs shared by 100% of samples within a

given set of samples either within each species or across species. This cut-off

level was chosen because it is the most common used by previous studies, as

72

well as the most stringent. This data was then used in a sampling simulation

to test the effects of the three aspects of sample selection mentioned above:

scale, the geographic distribution of sampling sites; scope, the caste

composition of samples; and sampling intensity, the number of samples taken

per unit of interest. It was hypothesised that the choice of sampling scale,

scope and intensity all affect the estimation of the gut core microbiome. The

core microbiome calculated across all available samples and castes was used to

estimate a species-wide core community. Core communities were further

calculated and compared across locations, colonies and diets to further

elucidate potential sources of the gut microbial community and functional

differences.

Methods:

Feeding experiment

Six mounds were chosen in each of three locations in the Perth Hills, Western

Australia: John Forrest National Park, Hovea (J); Beelu National Park,

Mundaring (B); Banyowla Regional Park, Wattle Grove (W), covering a small

part of the species' ranges (Figure 1). Selected mounds were undamaged, at

least 2 m from any other mounds and a minimum 20 m from each other.

Where possible, I selected mounds housing both Tumulitermes westraliensis

and Amitermes obeuntis; otherwise the presence of T. westraliensis was

favoured due to its feeding habits. Chosen mounds were sampled as detailed

below prior to establishment of feeding treatments. Loose vegetation was

cleared in a 2 m radius around the mound, leaving exposed soil and live

vegetation. Two mounds per location were randomly assigned to one of three

diet treatments: wheat straw sourced from the monoculture treatment of a

long-term crop rotation trial (provided by Nathan Craig, UWA), lucerne hay

(grown locally, sourced from a pet store) or a control consisting of loose

cleared vegetation from the site (including dried leaves and grass, small sticks,

gumnuts). Twenty-five bags (hand made using UV resistant polyethylene

73

Figure 1: Species ranges (A) for T. westraliensis (pink) and A. obeuntis (orange) obtained from and created in The Atlas Of Living Australia Spatial Portal (https://spatial.ala.org.au/; points outside WA are questionable), with sampling locations in the Perth Hills (purple; B). Mound locations in John Forrest National Park (C), Beelu National Park (D) and Banyowla Regional Park (E), with purple: mound was sampled for T. westraliensis and A. obeuntis; pink: T. westraliensis only sampled from the mound; orange: A. obeuntis only sampled from the mound. Mounds provided with wheat are labelled in yellow, lucerne in green and control colonies in blue. Maps (B-E) created in GPSVisualizer.com and further modified.

74

netting, Diamond Networks, WA) filled with the diet were then secured

evenly across the cleared space. The contents were of similar volume for each

treatment but averaged 20 g for wheat, 30 g for lucerne and 40 g for controls.

After three weeks, termites were again sampled from the mounds and, when

possible, from the bags.

Sample collection

T. westraliensis and A. obeuntis were collected in March (before feeding) and

April 2016 (after feeding). An electric drill was used to create up to three

minimally invasive access points per mound and individuals of all available

castes were collected using a modified portable vacuum cleaner. At the end of

the experiment, a small pickaxe was also used, as necessary, to access greater

numbers of termites. Samples were transported to the laboratory in the dark

with mound material. Dissections were performed on the day of collection to

minimise possible effects of the sampling procedure on the gut flora.

Samples from different colonies were dissected separately using sterilised

equipment and solutions, as follows. Termites were anaesthetised on ice in a

petri dish for at least ten minutes and separated into the following caste

groups: workers (T. westraliensis workers were further separated into mature

workers with dark head capsules, pale young workers, as per Chapter 2 and a

third group of intermediate pigmentation), soldiers, alates (individuals with

fully developed wings) and nymphs (individuals with wing buds).

Whole intestinal tracts were dissected by first cutting off the head with a

scalpel, then holding the thorax and pulling gently at the distal end of the

abdomen using Inox 5 Watchmaker forceps. Each excised intestinal tract was

placed in a 1.5 ml Eppendorf tube containing 100 μl of 1× phosphate buffered

saline. The process was repeated to obtain up to five biological replicates of 10

guts for each caste. All nymph and alate samples were dissected individually

and young T. westraliensis workers were dissected in groups of up to ten, as

75

available. Ten individual T. westraliensis worker guts and three alate guts

collected in 2015 from mound TW1 (John Forrest National Park) and stored at -

20 °C were processed alongside the other samples to increase the total number

of alates and to measure the effect of sample pooling. In total, 260 T.

westraliensis samples (141 before and 119 after the field experiment; 5 alate, 15

nymph, 62 mature worker, 142 soldier and 36 other worker samples) and 61 A.

obeuntis samples (33 before and 28 after feeding; 4 alate, 3 nymph, 53 worker

and 1 soldier sample) were processed.

To confirm species identity, DNA was extracted from termite heads using a

Qiagen DNeasy Blood & Tissue Kit (Hilden, Germany) following the

manufacturer's protocol. All PCR reactions were performed in 25µl volumes

using HotStartMastermix 2x Polymerase (Genaxxon, Ulm, Germany) and the

primers Modified A-tLeu (5'-CAGATAAGTGCATTGGATTT-3') and TK-N-3785

(5'-GTTTAAGAGACCATTACTTA-3') from Dedeine et al. (2016). PCR products

were purified with the Gennaxon Purification Kit (Genaxxon; Ulm, Germany)

and sent to Seqlab (Sequence Laboratories Göttingen, Germany) for Sanger

sequencing. Sequences were assembled and proofread in Geneious version

8.1.8 (Kearse et al., 2012). These quality-trimmed COII sequences were

compared to previously sequenced COII genes using the nucleotide BLAST

online tool (National Center for Biotechnology Information).

Sequencing and data analysis

DNA was extracted from the gut samples using a previously established

protocol (Paul et al., 2012). The V3 and V4 hypervariable regions of the 16S

rRNA gene were amplified using the primers 343Fmod (5′-

TACGGGWGGCWGCA-3′) and 784Rmod (5′-GGGTMTCTAATCCBKTT-3′)

designed by Köhler et al. (2012) and recommended in Chapter 2. Resulting

amplicons were sequenced in-house as paired-end 300 bp reads on an Illumina

MiSeq using a 600 cycle Miseq Reagent Kit v3 and a Nextera XT Index Kit v2

according to the manufacturer’s instructions.

76

The reads were quality filtered as described in detail in Chapter 2 except that

reads were oriented against the DictDb reference database (Mikaelyan et al.,

2015) to reduce spurious OTUs. Reads were trimmed to 400 bp to maximise

read length while maintaining read quality. OTUs were determined de novo

both for each species separately, as well as together to allow direct

comparison; taxonomy was assigned as per Chapter 2. All datasets were

rarefied to an even sequencing depth of 11,400 reads per sample, to match the

sample with the lowest number of reads.

A pilot chloroplast DNA profiling analysis was also performed during this

study using trnL intron primers c and h (Taberlet et al., 2007) on a subsample

of ten gut extracts, to confirm that the termites had fed on the substrates

provided. The reads were quality filtered as in Chapter 2 except that they were

trimmed to 110 bp and OTUs clustered at 99%. A reference database

containing chloroplast genomes from RefSeq (O'Leary et al., 2016) as well as

unpublished choloroplast sequences from the Pilbara region of WA (Nevill et

al., 2017) was used to assign taxonomy to the reads.

Worker ages

To determine if T. westraliensis workers of different ages could be included in

the same downstream analyses, a UniFrac beta diversity analysis (both

unweighted and weighted) was conducted to measure pairwise distances

between the total reads recovered per worker age group per colony. Samples

were collapsed by colony in QIIME, which resulted in summing by OTU all

reads from workers collected during the first time point from each colony. The

beta diversity calculation was done at a depth of 11,400 reads. Distances were

visualised in a Principal Coordinate Analysis (PCoA) and the average distance

within each group (young n = 21 pairs; mature n = 153) and between ages (n =

126) was summarised and compared within each metric using the Kruskal-

Wallis test, followed by Mann-Whitney-Wilcoxon post-hoc tests with fdr

corrected p-values to account for multiple testing in R (version 3.5.0, R Core

Team, 2018).

77

Investigation of sample pooling, sampling intensity, scope and scale

The core microbiome was defined as the OTUs shared by 100% of samples

within a given set of samples either within each species or across species.

Further analysis was conducted in QIIME (version 1.9.1, Caporaso et al., 2010).

Core microbiomes were determined for every available sample in the

biologically relevant sample groupings listed in Table 1 (Biological category).

These samples represent one caste (low scope) and different levels of scale

(single colony, single location, multiple locations) and sampling intensity.

To test for the effect of sampling scope and intensity on the number and

abundance of the core taxa, these biologically relevant core communities were

compared to core communities generated from artificial datasets. 999 artificial

datasets were created containing 1, 2, 3, 5, 10, 20, 30 or 45 randomly selected

samples (Table 1, Random category), using simple random selection without

replacement from the pool of real samples. Note that these artificial datasets

will include multiple identical selections at a sampling intensity of 1 due to the

limited number of real samples in the pool, namely 52 for mature workers and

260 for all castes (including young workers and those in between ages) in T.

westraliensis. This will be increasingly unlikely at higher sampling intensities

(e.g. there are 1326 possible combinations for selections of 2 samples out of

52). Inverse models were fit to each random grouping using Non-Linear Least-

Squares (nls) in R.

To compare the core communities between simulated sampling intensities,

PCoA using unweighted and weighted Unifrac beta diversity metrics was

conducted on a subset of 20 randomly selected core communities per

sampling intensity for both mature workers and all castes. The samples

included in the analysis consisted of the total abundance of each core OTU

from the samples used in the core determination at each sampling intensity.

The average pairwise distances within each level of sampling intensity were

compared using a Kruskal-Wallis test followed by Mann-Whitney-Wilcoxon

78

Table 1: Summary of samples included in core microbiome analysis, before rarefaction Grouping

type Termite species Sample

number Replicates Sample type(s)

Biological T. westraliensis

A. obeuntis

1

1

29

26

Mature workers from the first time point

Biological Both, separately 3 4 Mature workers from a single colony and first time point

Biological Both, separately 5 3 Mature workers from a single location and first time point, collapsed by colony

Biological Both, separately 10 3 Mature workers from a single location and first time point

Biological T. westraliensis

A. obeuntis

29

26

1

1

All mature workers from a the first time point

Biological T. westraliensis

A. obeuntis

52

53

1

1

All mature workers from both time points

Random Both, separately 1 999 Mature workers/All castes

Random Both, separately 2 999 Mature workers/All castes

Random Both, separately 3 999 Mature workers/All castes

Random Both, separately 5 999 Mature workers/All castes

Random Both, separately 10 999 Mature workers/All castes

Random Both, separately 20 999 Mature workers/All castes

Random Both, separately 30 999 Mature workers/All castes

Random Both, separately 45 999 Mature workers/All castes

Random T. westraliensis 1 999 Mature workers and 10%, 30% or 50% soldiers

Random T. westraliensis 2 999 Mature workers and 10%, 30% or 50% soldiers

Random T. westraliensis 3 999 Mature workers and 10%, 30% or 50% soldiers

Random T. westraliensis 5 999 Mature workers and 10%, 30% or 50% soldiers

Random T. westraliensis 10 999 Mature workers and 10%, 30% or 50% soldiers

Random T. westraliensis 20 999 Mature workers and 10%, 30% or 50% soldiers

Random T. westraliensis 30 999 Mature workers and 10%, 30% or 50% soldiers

Random T. westraliensis 45 999 Mature workers and 10%, 30% or 50% soldiers

79

post-hoc tests with fdr corrected p-values. To test the effect of sample type, a

PCoA was used to compare 10 individual T. westraliensis worker guts from

mound TW1 to in-silico-pooled groups of five. Each randomly selected group

of five was offset by another group containing the remaining five samples.

Core Communities

Based on results of our random resampling analysis, only groupings with at

least 20 samples per analysed level were considered to have sufficient

replication to infer reliable core communities. Levels within each category of

interest (species, time point, location or diet) were rarefied to the same

number of samples prior to determination of the core community. The

resulting core microbiomes were compared by the number of OTUs and the

abundance of those taxa. For each species, separate core microbiomes were

calculated for each caste (including only mature workers for T. westraliensis)

individually and in all possible combinations. The number of shared OTUs

was visualised using the R VennDiagram package (version 1.6.20; Chen and

Boutros, 2011). Unreliable core communities obtained with less than 20

samples were greyed out. Pie charts (base R) were used to visualise the relative

number and abundance of OTUs in the core and non-core communities. As

above, all OTUs found in 100% of samples were considered the core

community and in both analyses, taxonomic groups were classified by

functions predicted from the literature (see Table S1 for full functions list and

sources).

Core communities were also calculated across all samples from both termite

species, as well as in triplicate for rarefied datasets including workers from

both species from all locations and within each location, in an attempt to tease

apart vertically transferred from environmentally acquired taxa. Rarefaction

was carried out at 27 samples in each dataset, the number of workers collected

in Beelu National Park, the unit of interest with the smallest number of

available samples. Core community sizes were compared using proportion

tests in R.

80

Effect of diet, location, colony of origin and co-habitation

The rarefied datasets described above were used in distance-based redundancy

analysis (dbRDA), a constrained form of PCoA, using unweighted and

weighted UniFrac beta diversity metrics. dbRDAs and ANOVA-like

permutation tests (PERMANOVA) were both conducted in QIIME (originally

from the R vegan package, Oksanen et al., 2018). Partial distance-based

redundancy analysis (dbRDA), which estimates the individual effects of

crossed factors, was conducted in the R vegan package (version 2.5-2, Oksanen

et al., 2018) to isolate the effects of location and diet, by removing the effect of

the other.

The geographical distance between all pairwise combinations of colonies was

calculated from their GPS coordinates, using the R package geosphere (version

1.5-7, Hijmans, 2017). The Pearson correlations and associated p-values were

calculated in R between physical distances and weighted or unweighted

UniFrac beta diversity distances for worker gut samples collected during the

first time point (natural diet), collapsed by colony in QIIME. The beta

diversity calculation was done at a depth of 11,400 reads as for previous

analyses. Workers from colony TW1 were not included in this analysis since

the GPS coordinates for the mound were not recorded. For each species, the

average pairwise weighted and unweighted UniFrac distances were also

compared a) within colony (pairs collected from the same colony, n = 18 and

23 pairs for T. westraliensis and A. obeuntis respectively), b) within location

(pairs of collapsed colonies from the same location, n = 40 and 23) and c)

between locations (pairs of collapsed colonies from different locations, n = 96

and 55) using a Kruskal-Wallis test followed by Mann-Whitney-Wilcoxon

post-hoc tests with fdr corrected p-values. Workers from colony TW1 were not

included as they were individual samples rather than pooled, although their

average pairwise beta diversity distance was compared to the within-location

average using Mann-Whitney-Wilcoxon tests.

81

Results:

Effect of sampling intensity and scope on the core community

The number of bacterial OTUs was compared in the core communities

obtained from our natural sample groupings to randomly generated groups of

samples of the same size (Figure 2). Increasing the number of samples used to

determine the core community decreased the number of bacterial OTUs in it.

This trend was modelled with an inverse function as y = 675.0/x+150.3 for T.

westraliensis and y = 1282.7/x+194.5 for A. obeuntis. In both cases, the average

core community size approached the asymptote (predicted "real" core

community size of 150 and 195 OTUs respectively) around 20 samples and

crossed it at 30 samples. Thus, for both species a minimum of 20 samples was

required to accurately estimate the core community, despite the gut

microbiome species richness of A. obeuntis being 1.5 times greater than that of

T. westraliensis. In T. westraliensis, where there were more replicates from

each caste, increasing scope by including multiple castes further decreased the

size of the core community (Figure 3A; y = 677.7/x+122.7). Interestingly,

changing the proportion of samples from each caste had no significant effect

on the slope or asymptote (Figure 3B; average equation y = 668.3/x+136.9).

As demonstrated in T. westraliensis, the sampling intensity used to determine

the core community affected the identity and abundance of OTUs in it

(weighted PCoA shown in Figure 4). Increasing sampling intensity also

decreased the variation between the replicate core communities. Indeed, both

when considering workers only or all castes, average UniFrac distances were

found to be highly significantly different by level of sampling intensity

(Kruskal-Wallis test, p < 2.2x10-16). All post-hoc comparisons (Mann-Whitney-

Wilcoxon tests) were highly significant except for pairwise tests between 20,

30 and 45 samples in the weighted analysis including all castes; all p-values are

listed in Table S2). Pooled samples were less variable than single-gut samples

in both bacterial OTU number and abundance, such that pools were always

82

Fi

gure

2: A

vera

ge n

umbe

r of

cor

e O

TUs

± st

anda

rd d

evia

tion

for

the

"Bio

logi

cal"

and

"Ran

dom

mat

ure

wor

kers

" gro

ups

of s

ampl

es li

sted

in T

able

2

for

A)

Tum

ulite

rmes

wes

tral

iens

is a

nd B

) A

mite

rmes

obe

untis

. The

cor

e co

mm

unity

siz

e de

crea

sed

with

incr

ease

d nu

mbe

rs o

f sam

ples

sel

ecte

d an

d w

as m

odel

led

(red

lin

e) w

ith a

n in

vers

e fu

nctio

n A

) y

= 67

5.0/

x+15

0.3

and

B) y

= 1

282.

7/x+

194.

5 ap

proa

chin

g an

asy

mpt

ote

(das

hed

line)

.

A

B

83

Fi

gure

3: A

vera

ge n

umbe

r of

cor

e O

TUs

± st

anda

rd d

evia

tion

for

the

A)

"Ran

dom

all

cast

es"

and

B) "

Rand

om w

orke

rs a

nd s

oldi

ers"

gro

ups

of s

ampl

es li

sted

in

Tabl

e 2

for

Tum

ulit

erm

es w

estr

alie

nsis

. The

cor

e co

mm

unit

y si

ze d

ecre

ased

wit

h in

crea

sed

scop

e (h

ere,

num

ber

of c

aste

s) a

nd w

as m

odel

led

wit

h an

inv

erse

fu

ncti

on f

or t

he r

ando

m w

orke

r da

tase

t (r

ed l

ine,

y =

675

.0/x

+150

.3)

and

in b

lue

for

each

of

the

com

pare

d da

tase

ts A

) y

= 67

7.7/

x+12

2.7

and

B) 1

0%,

y =

666.

7/x+

137.

5; 3

0%, y

= 6

68.3

/x+1

36.1;

50%

, y =

669

.9/x

+137

.0; a

ppro

achi

ng a

n as

ympt

ote

(das

hed

line)

. The

pro

port

ion

of s

ampl

es in

clud

ed fr

om e

ach

cast

e ha

d no

effe

ct s

ince

the

blue

mod

els

in B

) are

not

sig

nific

antl

y di

ffere

nt.

A

B

84 Fi

gure

4:

Prin

cipa

l C

oord

inat

e A

naly

sis

(PC

oA)

show

ing

wei

ghte

d U

niFr

ac d

ista

nces

bet

wee

n co

re c

omm

uniti

es c

alcu

late

d fr

om d

iffer

ent

num

bers

of r

ando

mly

sel

ecte

d sa

mpl

es fr

om th

e po

ol o

f A) m

atur

e T.

wes

tral

iens

is w

orke

rs B

) all

T. w

estr

alie

nsis

cas

tes.

Gro

upin

gs s

epar

ate

alon

g PC

1, ex

plai

ning

43%

of t

he v

aria

tion.

The

gro

ups

wer

e fo

und

to b

e si

gnifi

cant

ly d

iffer

ent

in b

oth

case

s, in

ter

ms

of t

he a

vera

ge p

airw

ise

dist

ance

w

ithin

eac

h gr

oup,

usi

ng a

Kru

skal

-Wal

lis t

est

(p <

2.2

x10-1

6 ). A

ll po

st-h

oc c

ompa

riso

ns (

Man

n-W

hitn

ey-W

ilcox

on t

ests

) w

ere

high

ly s

igni

fican

t ex

cept

for p

airw

ise

test

s in

B) b

etw

een

20, 3

0 an

d 45

sam

ples

.

A

B

Num

ber

of sa

mpl

es

85

more similar to each other than were the single-gut samples (Figure 5). The

average UniFrac distance was significantly lower (Mann-Whitney-Wilcoxon;

unweighted, p = 0.018; weighted, p = 1.912e-06) between pooled samples

collected from each T. westraliensis colony (unweighted, 0.326; weighted,

0.086), as compared to between the TW1 individuals (unweighted, 0.343;

weighted, 0.144).

Species and caste differences in gut and core communities

Gut communities of young workers were found to be distinct from mature

workers in T. westraliensis (Figure 6; Kruskal-Wallis test p < 4.287e-10). Their

gut communities were similar at the phylum level, but young workers had

twice as many Actinobacteria as mature workers, making up 3% of all young

worker reads (Table S3). Nymphs and alates were much more distinct. Overall,

44% of the gut microbiome of alates was Bacteroidetes as compared to 11% in

nymphs and 14-17% in other castes (soldiers and workers of various ages).

Spirochaetes were the most abundant phylum in nymphs, making up 44% of

all reads, but 21% in alates and 27-32% in other castes. Nymphs also had

almost four times more TM7 reads (alates 0.7%, other castes 0.5-0.9%) and 19

times more Chlorobi (alates 0.4%, other castes 0.1-0.3%).

The species-level core communities comprised 56 OTUs for T. westraliensis

and 116 for A. obeuntis (Figure 7A). Within each species, few OTUs (ranging

from 0 OTUs in A. obeuntis workers to 8 OTUs in T. westraliensis workers)

were found to be exclusive to, but universal within a single caste. When

categories with with less than 20 samples were included, such caste-specific

OTUs ranged from 19 OTUs in T. westraliensis nymphs to 84 OTUs in T.

westraliensis alates (Figure 7A).

Each species core was made up of a comparatively small number of highly

abundant taxa, forming about half of OTUs recovered from the gut

community (Figure 7C). The A. obeuntis core community was dominated by

Firmicutes, a phylum known to perform cellulose, hemicellulose and

86

Figure 5: Principal Coordinate Analysis (PCoA) showing weighted UniFrac distances between individual (purple) mature workers from colony TW1, as compared to artificially pooled groups of five (pink).

87

Figure 6: UniFrac beta diversity analysis showing differences between juvenile (with white head capsules) and mature workers (with dark head capsules) in T. westraliensis visualised as A) a weighted Principle Coordinate Analysis (PCoA) where gut communities cluster by age group (juveniles in orange, mature workers in blue), separated along PC2 (11% explained). In B) average distances and standard errors of the mean between each pair of samples collapsed by colony are categorised as juveniles (n=21), mature workers (n=153), or comparisons between juvenile and mature workers (n=126). Both unweighted (purple) and weighted (pink) UniFrac beta diversity metrics indicated significant differences between sample groupings (Kruskal-Wallis test: unweighted p = 8.35e-13; weighted p = 4.29e-10). Stars indicate highly significant Mann-Whitney-Wilcoxon post-hoc p-values (fdr corrected; p < 4.39e-04).

A

B

88

Predicted Functions:

89

Figure 7: Summary of the species level core for T. westraliensis and A. obeuntis. Venn diagrams (A) show that all T. westraliensis major castes (5 alate, 15 nymph, 52 mature worker, 142 soldier samples) shared 56 OTUs and 8 with A. obeuntis samples, which had a core made up of 116 OTUs (4 alate, 3 nymph, 53 worker and 1 soldier sample). Numbers shown in grey were calculated using less than the 20 samples recommended by this study and therefore are not thought to be typical of these castes (note a single A. obeuntis soldier pooled sample was included and hence a soldier core community was not calculated and the total number of OTUs not shared by other castes is shown in grey). Bar charts (B) show the predicted functions for the species core by average abundance within each caste. Pie charts (C) summarise the proportion of OTUs (orange) and reads (grey) made up on average by the species core in each caste.

90

cellobiose degradation (particularly family Ruminococcaceae, Table S1),

followed by taxa of unknown functions (Figure 7B). Compared to other castes,

alates had higher proportions of sulfate-reducing bacteria

(Deltaproteobacteria, Desulfovibrionaceae) and carbohydrate fermenters, and

fewer organisms of unknown functions. In T. westraliensis, a large proportion

of the species core community was of unknown function. This was particularly

pronounced in the alates, where two thirds of the reads could not be assigned

a function. The two major taxa whose functions could not be determined

were Bacteroidetes of the family Porphyromonadaceae, followed by

unclassified Treponema. Of those OTUs which could be assigned to functional

groups, taxa contributing to carbohydrate fermentation and aromatic

compound degradation (Spirochaetes, Treponema Ia) were the most abundant

in all castes except alates, where carbohydrate fermentation was more

common (Bacteroidetes, Rikenellaceae).

Eight OTUs were shared across all 321 samples collected from both species

(Figure 7A). Three of these were identified as members of the Firmicutes

family Ruminococcaceae, thought to contribute to cellulose degradation,

hemicellulose and cellobiose degradation. Other shared OTUs included

another cellulose degrader from Acidobacteria family Acidobacteriaceae, a

sulfate-reducer from family Desulfovibrionaceae (Proteobacteria), and an

aromatic compound degrading Peptococcaceae (Firmicutes). Two OTUs from

family Synergistaceae (Synergistetes) are of unknown function. Significant

differences in overall community composition were found between the

rarefied datasets of co-habiting and non-co-habiting workers within each

species (8 samples per species per group, unweighted Unifrac; dbRDA p-values

< 0.012 and PERMANOVA p-values < 0.009; Tables S4). Co-habiting termites

across both species (n=16) had 28 OTUs in common compared to 25 shared by

non-cohabiting colonies, 20 of which were common to both, which was not

significantly different (proportion test; p = 0.786; TableS4). Non-co-habiting

termites shared five exclusive OTUs as compared to the co-habiting group's

eight.

91

When rarefying to 27 samples with random inclusion of different castes, the

interspecies core community increased to 14-25 OTUs (triplicate core

calculations, Table S5). Similar to the simulation results, the more castes were

included, the smaller the resulting core community. In particular, core sizes of

23 and 25 OTUs were obtained when including four or five T. westraliensis

castes and a single A. obeuntis caste (workers). Adding in a further A. obeuntis

caste (alates) resulted in a drop to 14 OTUs. Randomly selecting 27 worker

samples from both species resulted in core community sizes of 15-18. Core

communities obtained for each location ranged from 14-17 OTUs for John

Forrest National Park, 21-23 for Banyowla Regional Park and 21 OTUs for Beelu

National Park, with 10 OTUs in common for all three. The size of each

location-specific core community was not significantly different to that of any

other location, or to cores calculated from worker samples selected from all

three locations (proportions tests, p-values > 0.2); however up to 2 OTUs were

unique to each location and universal within all replicate cores at those

locations. In Banyowla Regional Park, these belonged to the carbohydrate

fermenting genus Tannerella (family Porphyromonadaceae, phylum

Bacteroidetes) and family Synergistaceae. Members of Ruminococcaceae were

identified in both National Parks and a Lachnospiraceae OTU was additionally

identified in Beelu National Park.

Effect of sampling scale, location and diet

Significant effects of location were found in workers of both species, and in

soldiers of T. westraliensis using partial db-RDA (p-values < 0.036; Figure 8,

Table S6), dbRDA (p-values = 0.001) and PERMANOVA (p-values = 0.001;

Tables S4). Both species showed significant differences between average

UniFrac beta diversity distances among worker samples collected from the

same colony compared to workers collected from different colonies (Figure 9,

Kruskal-Wallis test: p < 6.70e-7) during the first time point. In T. westraliensis

only, there were significant differences detected between workers collected

from the same or different locations in the unweighted analysis only (Mann-

92

Figure 8: Partial distance-based Redundancy Analysis (drRDA) showing the effect of location (with the effect of diet removed) on the gut communities of A) A. obeuntis workers (in two locations where 21 samples were collected), B) T. westraliensis workers (17 samples), T. westraliensis soldiers C) at the first time point (27) and D) across both time points (43). A significant effect of location was found in all cases using PERMANOVA, with the smallest effect found for A) with p = 0.036 and all others p < 0.005.

93

Figure 9: Average UniFrac beta diversity distances and standard errors of the mean between each pair of A) T. westraliensis and B) A. obeutis worker samples, collected from the same colony (A) n = 18 and B) n =23), within a location (samples collapsed by colony, A) n = 40 and B) 23) or from different locations (samples collapsed by colony, A) n = 96 and B) 55). Both unweighted (purple) and weighted (pink) UniFrac beta diversity metrics indicated significant differences between sample groupings (Kruskal-Wallis test: p < 6.70e-7). All Mann-Whitney-Wilcoxon post-hoc tests were significant (fdr corrected; p < 1.29e-05) except for the comparison between within location and between locations in the weighted analysis both species (A) p = 0.23, B) p = 0.73) and in the unweighted analysis in B) (p = 0.63).

A

B

94

Whitney-Wilcoxon; fdr corrected p = 3.70e-07). The relationship between

geographic distance between colonies and beta diversity distances between

worker gut communities was first compared overall, followed by comparisons

within a location and to increasingly distant locations (Table S7). Significant

positive correlations were found in T. westraliensis only, with the strongest

correlation between geographic distance and community composition

(unweighted analysis; r = 0.56, p = 8e-13). When community structure was

considered, the correlation remained significant albeit less strongly positive

(weighted analysis; r = 0.18, p = 0.03). No significant correlations were found

within a location, between mid-range locations or between distant locations,

except for the weighted analysis for mid-range locations in A. obeuntis, where

there was a strong negative correlation (r = -0.549; p = 0.034).

Significant effects of diet were found, but only when including at least half the

minimum recommended number of 20 samples per diet group using partial

db-RDA (p-values < 0.05; Figure 10, Table S6), dbRDA (p-values < 0.03) and

PERMANOVA (p-values < 0.03; Tables S4). However, it could not be

confirmed whether the termites fed on the substrates provided in the pilot

trnL dataset (data not shown). Lucerne could not be differentiated from local

native legumes and wheat was not detected in any of the three A. obeuntis and

one T. westraliensis samples tested expected to contain it. More sequences

remained unidentified in the samples provided with wheat as compared to the

others whereas the grass Family Poaceae, which includes wheat, was identified

in the crop of the “wood-feeding” Occasitermes occasus.

I examined the core communities in search of the taxa causing location and

diet effects (Figures 11 and 12; Tables S8-S17, attached as Chapter3_TablesS8-

S17.pdf). Overall core communities (shared between multiple locations or

diets) always constituted a larger proportion of the gut community in terms of

read abundance than core communities specific to a location or diet (made up

of taxa exclusive but universal to each location or diet). A. obeuntis overall

core communities (Figures 11A, 12A and B; Tables S8-S10) were dominated by

95

organisms classified as cellulose degraders also capable of hemicellulose and

cellobiose degradation. This group, the most abundant of which were family

Ruminococcaceae (Firmicutes), comprised about half of the reads assigned to

the core community. Ruminococcaceae was also the most abundant taxa in

half of the location or diet specific cores, while the other core microbiomes

contained larger proportions of the cellulose degrading Treponema Ic group.

Other core functions included carbohydrate fermentation, aromatic

compound degradation, sulfate reduction, ammonium oxidation and

microbial polymer digestion. Interestingly, one function identified in low

abundance in A. obeuntis only was "protist endosymbiont", due to the

presence of OTUs identified as order Rickettsiales (Alphaproteobcteria). It was

found in most group-specific cores, except in all location-specific cores (Table

S8; attached as Chapter3_TablesS8-S17.pdf) and the natural diet group in the

two diet comparison only (Table S10, i.e. was shared in the natural diet group

when including only six samples; Table S9) .

T. westraliensis overall core communities (for workers see Figures 11B, 12C-D,

Tables S11-S13; for soldiers Figures 11C-D, 12 E-F, Tables S14-S17) contained the

same functional groups as A. obeuntis cores, with cellulose, hemicellulose and

cellobiose degraders (Ruminococcaceae), cellulose degraders (Treponema Ic

and If), carbohydrate fermentation (Rikenellaceae and genus Tannerella,

Porphyromonadaceae) and carbohydrate fermenters (coupled with aromatic

compound degradation; Treponema Ia; the most abundant taxon in all T.

westraliensis overall cores) representing about a quarter of reads of known

functions each. Cellulose degradation coupled with hemicellulose and

cellobiose degradation was typically the most commonly assigned function (by

read abundance) in the group specific cores, due to the presence of large

abundances of Ruminococcaceae and/or Lachnospiraceae, depending on the

location or diet. In soldiers only, in Banyowla Regional Park, the

Lachnospiraceae were in similar abundance to the cellulose degrader

Treponema If. Overall, Ruminococcaceae more commonly dominated, but a

higher relative abundance of Lachnospiraceae was linked to the lucerne diet

96

97

Figure 10: Partial distance-based Redundancy Analysis (drRDA) showing the effect of diet (with the effect of location removed) on the gut communities of A. obeuntis workers A) with two diets where the most samples were collected (15) and B) with six samples available for each diet; T. westraliensis workers C) with six samples available for each diet and D) with wheat and lucerne combined into a single group to increase sample numbers to 10; and finally T. westraliensis soldiers E) with all diets (14 samples) and F) two diets (19). A significant effect of diet was found in all cases using PERMANOVA, except where sample numbers were much lower than recommended. Post-hoc tests were conducted on D) and E) to determine whether introduced diets were significantly different to the control by using subsets of the datasets containing two diets at a time. The combined wheat and lucerne group was found to be significantly different to the control (p = 0.038) in D). In E) wheat was significantly different to the control (p = 0.001), lucerne was found to be marginally insignificant when compared to the control (p = 0.072) and an outlier resulted in an insignificant test result between wheat and lucerne (p = 0.245; data not shown).

98

Predicted Functions:

Figure 11: Barplots showing the abundance (number of reads shown in each plot) of the overall core community determined for each set of rarefied dataset, as well as the core specific to a location, coloured by function. Specifically, the A. obeuntis worker dataset was rarefied to 21 samples in A); the T. westraliensis worker dataset was rarefied to 17 samples (lower sampling intensity than the 20 sample minimum recommended by this study) in B); T. westraliensis soldier datasets were rarefied to 27 samples in C) and 43 in D). Taxonomic groups thought to undertake multiple functions were classified as a separated functional group, so as not to overestimate their abundance.

99

100

Predicted Functions:

Figure 12: Barplots showing the abundance (number of reads shown in each plot) of the overall core community determined for each set of rarefied datasets, as well as the core specific to a diet, coloured by function. Specifically, A. obeuntis worker datasets were rarefied to 15 samples in A) and 6 in B); T. westraliensis worker datasets were rarefied to 10 samples in C) and 6 in D); T. westraliensis soldier datasets were rarefied to 14 samples in E) and 19 in F), all lower sampling intensity than the 20 sample minimum recommended by this study. Taxonomic groups thought to undertake multiple functions were classified as a separated functional group, so as not to overestimate their abundance.

101

and to the soldier caste in general. Interestingly, workers had relatively more

reads from cellulose degrading bacteria (coupled with other functions) on

natural and wheat diets, whereas soldiers had more in the control and lucerne

diets.

Discussion:

Sampling recommendations for reliable conclusions

It had previously been suggested but never tested that increasingly smaller

termite gut core communities would be recovered across increasing

geographic ranges (Benjamino and Graf, 2016). Our comparisons of real and

simulated data at various sampling scales, scopes and intensities, indicate that

all three factors are important considerations. Sampling intensity, the number

of samples analysed per level of the factor of interest, had the largest influence

and is the easiest for researchers to implement. Pooled samples are a better

representation of a colony if the research question focuses on higher level

variation, whereas individuals allow the full capture of intracolony variation

and can increase sampling intensity if the number of available termites is low.

Overall core communities (shared between multiple locations or diets) always

constituted a larger proportion of the gut community in terms of read

abundance than core communities specific to a location or diet (made up of

taxa exclusive but universal to each location or diet). Functional groups in the

overall core had similar relative abundances in each location or diet specific

group, suggesting that there is a large and consistent portion of the gut

community that plays an important role. In the group-specific core

communities, the functional groups were not of consistent abundance,

suggesting that different functional combinations may be better suited to each

set of environmental conditions.

102

Sampling intensity

Sampling intensity had a bigger effect on the number of taxa included in the

core community than any other factor tested, with an inverse relationship

between sampling intensity and core community size. This suggests that

previous studies based on few samples systematically overestimate the number

of core gut community members. Increasing the number of samples used to

determine the core community decreased the number of core OTUs towards

an asymptote that represents a predicted "true" core size. Based on these

findings, a minimum of 20 samples and ideally 30 samples is required to

determine a core community accurately. At this point, the standard deviation

measured from the simulation overlaps with the asymptote indicating it is a

sufficient number of samples to approach the "true" core community size; any

further increase in sampling intensity has minimal impact on the core

community size. Increasing sampling intensity decreased the variation

between core communities, up to about 20 samples, suggesting that core

communities determined with larger numbers of samples also approach the

"true" core community in terms of composition and structure. These

limitations also apply to detecting treatment effects; increasing sampling

intensity returned more significant p-values when testing the effect of diet.

The simple random sampling method used in this chapter can be applied early

in the bioinformatic analysis phase to test whether sufficient sampling

intensity has been reached for the termite species, geographical range, and the

research question of interest.

Sampling scale

Differences in the gut community structure and composition, and the physical

distance between their colonies of origin were significantly positively

correlated in T. westraliensis only. This trend disappeared when comparing

within a location, or between mid-range or distant locations. In A. obeuntis, a

negative correlation was found in the mid-range colonies only. Significant

differences between average UniFrac beta diversity distances among worker

103

samples collected from the same colony as compared to workers collected

from different colonies were found in both species, however. In T.

westraliensis there was a further distinction in gut community composition

between workers collected from the same or different locations. The db-RDA

analysis resulted in significant effects of location in both termite species, even

at low sampling intensities. The core community size decreased from single

colony, to single location to multiple locations in comparison to the variability

expected from the simple random samples. This suggests that the effect of

location is not based solely on physical distance, but stems from other factors

that make a location unique, perhaps soil properties, available food sources,

the presence of heterospecific termites and/or phylogenetic divergence

between the host termites.

Each location and diet had a distinct set of characteristic OTUs which

appeared in every colony from that location or fed that specific diet. This

means that previous studies relying on samples from a single colony or

location likely also overestimate the number of taxa in the termite gut core

community by including taxa exclusive to that location and not shared across

the species. When the sampling area is limited, taxa considered to be part of

the species core community could be vertically inherited or common to the

environments from which the samples originated. By widening the sampling

scale, this study decreased the inclusion of environmentally acquired

organisms. These could be further limited by including samples from the

entire range of the species, although ruling out environmental acquisition may

remain difficult in species with small ranges like many Western Australian

endemics. I therefore recommend that future studies wishing to draw

conclusions about a species’ core gut microbiome include samples from across

the entire species range to minimise the effect of sampling location on the

recovered gut communities.

Microbiota transplant experiments show that gut communities are assembled

primarily around available niches in the gut habitat. In zebrafish with mouse

104

gut flora, mice with zebrafish flora, and cockroaches with mouse or termite

gut flora, the established gut community was made up of lineages from the

community of origin, but their relative abundances resembled those of the

normal gut community of the recipient host (Rawls et al., 2006; Mikaelyan et

al., 2016). Seedorf et al. (2014) found that the transplanted gut community

composition and structure more closely resembled the community of origin if

it was from a gut environment (human, zebrafish and termite gut) as

compared to a non-gut environment (human skin and tongue, soil, and

estuarine microbial mats). In this way, taxa present in the environment may

appear transiently in the gut community or establish and become resident in

the gut habitat when taken up with soil or food. This may be reflected in the

effects of location and diet described in the current study.

Sampling scope

In the inverse models calculated for T. westraliensis, increasing scope by

including multiple castes decreased core community size by 14 to 28 OTUs,

depending on the number of castes included. Changing the proportion of

samples from each caste had no effect. This has implications for the study of

the species core community. Assuming there is a vertically transmitted, co-

evolved portion of the gut community, I would expect it to include few but

abundant vital community members, so including as many castes as possible

(no matter their sampling intensities) more closely approaches the "true"

species core community. Since alates disperse to form new colonies, they must

carry the complete core community in their guts, presumably with minimal

extra taxa, to maximise the success of their future colonies. Unfortunately,

only a few alates were recovered during this study. The low sampling intensity,

a quarter of the recommended 20, suggests that the 84 OTUs found here as

exclusive but universal to that caste in T. westraliensis, is likely an

overestimation. In this study, alate gut communities were found to have lower

species diversity in both termite species than those of other castes (greater

only than soldiers in A. obeutis, although a single soldier sample was tested).

This is in marked contrast to Diouf et al. (2018), who recovered a greater

105

diversity of OTUs from the guts of 10 swarming alates (635 OTUs) than from

other castes of Nasutitermes arborum (244 to 449 OTUs from at least 25

individuals per caste). One possibility is that in this species, these taxa are

present in all castes but in lower abundance in alates, allowing the detection of

more taxa at the same sequencing depth. Future studies should aim to recover

at least 20 alates (or 20 pools) across the range of the species and maximise

sequencing depth to test how closely the alate gut community resembles the

species core community.

The relative abundances of core gut microbial taxa was similar in all castes of

A. obeuntis, but T. westraliensis alates differed from the other castes in terms

of the relative abundances of its core OTUs. In particular, the Bacteroidetes

family Porphyromonadaceae, of unknown function, dominated almost half of

the alate guts, whereas Spirochaetes and Firmicutes were much more

abundant in other castes. Porphyromonadaceae has been reported in various

other higher termites (Köhler, 2012; Dietrich, 2014; Diouf, 2015; Mikaelyan,

2017; Diouf, 2018) but its function has only been discussed in lower termites

where it is an ectobiont of protists, alongside Spirochaetes. There they may

play a role in consuming oxygen to protect their anaerobic hosts (Hongoh et

al., 2007b). Free-living Porphyromonadaceae in cattle are thought to have

varied functions related to cellulose degradation and carbohydrate

fermentation (Ziemer, 2014). Multiple OTUs within this family are shared by

all four major T. westraliensis castes and may contribute to several key

functions that are passed on across generations via the alates. Young workers

(with large bodies and pale heads) were found to harbour a significantly

different gut community to mature workers (with dark head capsules) in T.

westraliensis. I did not test this in A. obeuntis due to the difficulty in

consistently distinguishing worker instars. Li et al. (2016) concluded that

differences in the gut community structure of workers of different ages in the

fungus-cultivating termite Odontotermes formosanus were driven by changes

in both diet and the gut environment. Genera Candidatus Captivus and

Candidatus Hepatincola (order Rickettsiales, class Alphaproteobacteria) were

106

found in multiple A. obeuntis core communities but not in T. westraliensis.

These are typically protist endosymbionts, reported in at least ciliates and

amoebae (Szokoli et al., 2016). They may indicate the presence of protists in

the gut, not linked to any particular diet, which can be confirmed with the use

of eukaryotic primers (Pawlowski et al., 2012; Adl et al., 2014). The presence of

protists in higher termites is further discussed in Chapter 4.

Eight OTUs were shared across all samples collected from both species,

including three members of the lignocellulose-degrading Rumminococcaceae

(Firmicutes) and one member of Acidobacteriaceae (Acidobacteria; cellulose

degrader). A member of the sulfate-reducing family Desulfovibrionaceae

(Proteobacteria) may also consume oxygen and hydrogen, produce acetate and

fix nitrogen (Kuhnigk et al., 1996). Peptococcaceae (Firmicutes) was linked to

aromatic compound degradation and may play a role in the degradation of

lignin (Geib et al., 2008) or plant defensive secondary metabolites (Bennett

and Allsgrove, 1994). Two OTUs from family Synergistaceae (Synergistetes)

were of unknown function. Hongoh et al., (2007a) suggests that members of

this family may ferment amino acids into acetate and other products.

Importantly, cellulose degradation was a function represented in all core

communities (overall and group-specific) and the most common function for

OTUs shared across both species. Hence these taxa are good candidates for

further study. For example, taxon-specific primers could be designed based on

these 16S rRNA sequences to target cellulose degraders of interest with long-

read Pacific Biosciences sequencing (Orr et al., 2018) and recover cellulase

enzyme sequences to be cloned into culturable organims for testing.

The eight OTUs shared in all samples may be vertically transferred, acquired

from a common ancestor. Conversely, up to two OTUs per location were

found to be exclusive to each location but shared by workers from both

species (members of the carbohydrate fermenting genus Tannerella (family

Porphyromonadaceae, phylum Bacteroidetes), family Synergistaceae, and the

lignocellulose degraders Ruminococcaceae and Lachnospiraceae) and are

107

therefore likely to be environmentally acquired. However, due to the relatively

close proximity of the locations, it cannot be ruled out that additional OTUs

shared amongst multiple locations could be environmentally acquired. Indeed,

it is possible that the eight universal OTUs may be widespread taxa acquired

in all locations, since the two termite species share the whole geographic

range included in the study. Recent work suggests that members of

Ruminococcaceae gut cluster 8 (represented by two of these universal OTUs)

are generally environmentally and/or horizontally acquired (Bourguignon et

al., 2018). Significant differences between co-habiting and non-co-habiting

workers of both species demonstrate the strength of external influences on the

termite gut community, with co-habiting workers sharing eight exclusive

OTUs across species. Interestingly, five OTUs were found to be exclusive to

non-co-habiting termites, with a common source being more difficult to infer.

Future studies could investigate more closely related species, with similar

natural diets and partially overlapping geographic ranges to further tease apart

the source of gut community members. For example, Tumulitermes dalbiensis

also collects grass and leaf debris (Abensperg-Traun, 1992); its range

overlaping that of T. westraliensis but more widespread (Watson and Abbey,

1993). T. pastinator , like T. westraliensis, was classified as a grass-feeder by

Hill (1942) and is found in the northern half of Australia (Watson and Abbey,

1993). Taxa shared by all three species are likely to be vertically transferred and

the result of common ancestry, whereas taxa found exclusively in T.

westraliensis and T. dalbiensis where their range overlaps are likely to be

environmentally acquired. Profiling the microbial community from the

surrounding environment and available food sources may also indicate how

much of the gut community is free-living in the environment. However,

previous studies have shown that there is typically little overlap in

environmental and gut communities (Fall et al., 2007) presumably because

taxa found at non-detectable levels in the environment may establish in gut

niches (Seedorf et al. 2014).

108

Diet drives functional shift

I have shown that the termite gut community can be manipulated during

three weeks under field conditions. This method may allow the identification

of key organisms or enzymes relevant to breaking down substrates of interest,

all without introducing potentially traumatic relocation and artificial

conditions to colony members. When sampling intensity was sufficiently high,

a significant difference between diet groups was detected. A portion of the gut

community was exclusive and universal to each feeding group.

Changes in the gut community do not guarantee successful feeding on the

substrates provided, however. I could not confirm that the target colonies fed

on the provided substrates, although A. obeuntis was recovered from multiple

bags. Bags were weighed before and after the experiment, however I consider

this data unreliable due to the uneven loss of smaller food particles through

the nylon netting during transport and the presence of other termite species in

some bags. Any food that may have been collected by T. westraliensis would

be added to full stores in the mound, likely leading to a broadening of the diet

rather than a replacement. Natural food sources were disturbed while setting

up the experiment, so the diet has likely shifted regardless of whether the

colony fed on the substrates provided.

A pilot chloroplast trnL DNA profiling analysis (data not shown) conducted to

confirm that termites fed on the substrates provided lead to inconclusive

results. The termites were certainly not exclusively feeding on the plants of

interest and this diversity of reads and the low resolution of the trnL primers

meant I could not confidently differentiate lucerne from local native legumes.

Wheat was not detected, although more sequences remained unidentified in

samples expected to contain it as compared to the others. The grass Family

Poaceae, which includes wheat, was identified in one sample, the crop of the

“wood-feeding” Occasitermes occasus. This suggests wheat should have been

detected at least at family level, unless the sequences retained for this

substrate were not of sufficient length or quality for identification (Särkinen et

109

al., 2012). Analysing the crop contents and the P3 contents returned different

plant species, perhaps reflecting the time since ingestion or the level of

degradation of the target DNA. Primers targeting longer regions could result

in more accurate identification but may introduce further bias since the target

DNA is from dried material and being digested. One possibility would be to

optimise the DNA extraction protocol for dried plant samples (Särkinen et al.,

2012). Unpublished choloroplast sequences from the Pilbara region of WA

(Nevill et al., 2017) was used in this preliminary analysis; the addition of plants

found in the Perth Hills may help further define the diet of local species and

the success of field-based feeding experiments.

Significant differences between controls and lucerne or wheat-fed termites,

however, suggest each treatment had a different effect. Testing untouched

colonies on a natural diet at the second time point would have helped confirm

whether time, disturbance and the reshuffling of substrates caused by raking

the area around the colony, or the feeding treatment itself was responsible for

the observed changes.

Ruminococcaceae and Lachnospiraceae OTUs (Firmicutes) were commonly

shared across samples within a species and have both been shown to degrade

cellulose, as well as hemicellulose and cellobiose (Thompson et al., 2012),

establishing their ability to attack many components of the termites’ diet and

explaining their abundance in the gut. In fact, Firmicutes was the most

abundant phylum in all T. westraliensis castes except in the reproductives'

guts, where it was second most abundant in nymphs (after Spirochaetes) and

third most abundant in alates (after Bacteroidetes and Spirochaetes). Both

Bacteroidetes and Spirochaetes were primarily linked to carbohydrate

fermentation and Spirochaetes also to cellulose degradation (Mikaelyan et al.,

2014; Dietrich et al., 2014), although many of their functions remained

unknown. Multiple taxa from both phyla have also been linked to nitrogen

fixation (Lilburn, 2001; Inoue et al., 2015), an important contribution in the gut

due to the nitrogen poor diet of most termites and justifying their prevalence.

110

Sulfate reduction was a function found in all core communities. Kuhnigk et al.

(1996) suggested that sulfate-reducing bacteria of the genus Desulfovibrio

played a broader role in the termite gut, since their isolates had very high

respiration rates, consumed oxygen as well as hydrogen, and fixed nitrogen.

Hence they shifted digestive end products from sugars to acetate and

enhanced termites’ nitrogen poor diet. Planctomycetes were also commonly

identified in core microbiomes. Their role in the termite gut is still unknown

since they are all uncultivated lineages. They may degrade microbial polymers

found in decaying wood and humus, or if these are anaerobic organisms, they

could be oxidising ammonium (Köhler et al., 2008).

Due to difficulty in controlling access to other food sources, it is not clear from

these results whether field experiments will lead to the identification of

bacterial taxa specific to a substrate of interest. Future studies on harvesters

like T. westraliensis could focus on the plant species recovered from food

stores. Studies involving species such as A. obeuntis that feed directly at the

source could place the substrate directly on top of the mound to maximise

chances of it being consumed (Lambert and Power, 1999). This method was

successfully trialled during this project with plastic containers containing karri

wood or wheat straw to collect large numbers of A. obeuntis, Occasitermes

occasus or Coptotermes acinaciformis individuals. Evidence of feeding, ie holes

in the wood, was only found in the latter two species; wheat was not trialled

with these species.

Conclusion

A strong effect of location was found in this study, as well as an effect of diet

in a field setting for two Western Australian endemic species: Tumulitermes

westraliensis and Amitermes obeuntis. There is a relatively small number of

OTUs shared by all castes in each species. Together, they comprise

approximately half of reads recovered from the gut community. These OTUs

could form the core community passed on from generation to generation. In

111

future, comparing gut communities of termites within the same genus with

overlapping geographical ranges would help differentiate between vertical

transmission and environmental acquisition of gut community members. The

most abundant recurring taxa typically had multiple functions and cellulose

degradation was the most common function predicted in all core

communities. Widely shared and abundant taxa, as well as those universal but

exclusive to a diet group are good candidates for further genomic study to

isolate enzymes of interest for biofuel production, cloning in culturable hosts

and in-vitro study.

Based on the findings of this study, the following sampling guidelines are

recommended to obtain an "accurate" core community:

Scope: All relevant castes should be included, ie only workers if that is the

scope of the question, or all castes for a total species core community

estimation.

Scale: The maximum relevant range should be included, which could be a

single colony if that is the scope of the question, or multiple locations across

the entire species range for a total species core community estimation.

Intensity: A minimum of 20, ideally 30, samples per level of each unit of

interest should be included. This translates to at least 20 samples in total to

estimate a species core microbiome, or 20 samples per treatment, location or

colony as relevant to the scope of the research question.

112

References Abdul Rahman, N., Parks, D. H., Willner, D. L., Engelbrektson, A. L., Goffredi, S. K.,

Warnecke, F., Scheffrahn, R. H., Hugenholtz, P. (2015). A molecular survey of Australian and North American termite genera indicates that vertical inheritance is the primary force shaping termite gut microbiomes. Microbiome, 3, 5.

Abensperg-Traun, M. (1992). The effects of sheep-grazing on the subterranean termite fauna (Isoptera) of the Western Australian wheatbelt. Australian Journal of Ecology, 17, 425-432.

Adl, S. M., Habura, A., & Eglit, Y. (2014). Amplification primers of SSU rDNA for soil protists. Soil Biology and Biochemistry, 69(0), 328-342.

Benjamino, J., & Graf, J. (2016). Characterization of the core and caste-specific microbiota in the termite, Reticulitermes flavipes. Frontiers in Microbiology, 7, 1.

Bennett, R. N., & Allsgrove, R. M. W. (1994). Secondary metabolites in plant defence mechanisms. New Phytologist, 127, 617-633.

Bourguignon, T., Lo, N., Dietrich, C., Šobotník, J., Sidek, S., Roisin, Y., Brune, A., Evans, T. A. (2018). Rampant host switching shaped the termite gut microbiome. Current Biology, 28(4), 649-654.e2.

Brune, A., & Ohkuma, M. (2011). Role of the termite gut microbiota in symbiotic digestion. In D. E. Bignell, Y. Roisin, & N. Lo (Eds.), Biology of Termites: a Modern Synthesis (pp. 439-475). Netherlands: Springer.

Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K. et al. (2010). QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 7, 335-336.

Chen, H., & Boutros, P. C. (2011). VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics, 12, 35.

Dedeine, F., Dupont, S., Guyot, S., Matsuura, K., Wang, C., Habibpour, B. et al. (2016). Historical biogeography of Reticulitermes termites (Isoptera: Rhinotermitidae) inferred from analyses of mitochondrial and nuclear loci. Molecular Phylogenetics and Evolution, 94(Pt B), 778-790.

Dietrich, C., Köhler, T., & Brune, A. (2014). The cockroach origin of the termite gut microbiota: patterns in bacterial community structure reflect major evolutionary events. Applied and Environmental Microbiology.

Diouf, M., Hervé, V., Mora, P., Robert, A., Frechault, S., Rouland-Lefèvre, C., Miambi, E. (2018). Evidence from the gut microbiota of swarming alates of a vertical transmission of the bacterial symbionts in Nasutitermes arborum (Termitidae, Nasutitermitinae). Antonie Van Leeuwenhoek, 111(4), 573-587.

Diouf, M., Roy, V., Mora, P., Frechault, S., Lefebvre, T., Hervé, V., Rouland-Lefèvre, C., Miambi, E. (2015). Profiling the succession of bacterial communities throughout the life stages of a higher termite Nasutitermes arborum (Termitidae, Nasutitermitinae) Using 16S rRNA Gene Pyrosequencing. PLoS One, 10, e0140014.

Eutick, M. L., Veivers, P., O’Brien, R. W., & Slaytor, M. (1978). Dependence of the higher termite, Nasutitermes exitiosus and the lower termite, Coptotermes lacteus on their gut flora. Journal of Insect Physiology, 24(5), 363-368.

Fall, S., Hamelin, J., Ndiaye, F., Assigbetse, K., Aragno, M., Chotte, J. L. et al. (2007). Differences between bacterial communities in the gut of a soil-feeding termite (Cubitermes niokoloensis) and its mounds. Applied and Environmental Microbiology, 73(16), 5199-5208.

Geib, S. M., Filley, T. R., Hatcher, P. G., Hoover, K., Carlson, J. E., Jimenez-Gasco, M.

113

D. M. et al. (2008). Lignin degradation in wood-feeding insects. Proceedings of the National Academy of Sciences of the United States of America, 105(35), 12932-12937.

Hijmans, R. J. (2017). geosphere: Spherical Trigonometry. R package version 1.5-7. https://CRAN.R-project.org/package=geosphere.

Hongoh, Y., Sato, T., Dolan, M. F., Noda, S., Ui, S., Kudo, T. et al. (2007a). The motility symbiont of the termite gut flagellate Caduceia versatilis is a member of the “Synergistes” group. Applied and Environmental Microbiology, 73(19), 6270-6276.

Hongoh, Y., Sato, T., Noda, S., Ui, S., Kudo, T., & Ohkuma, M. (2007b). Candidatus Symbiothrix dinenymphae: bristle-like Bacteroidales ectosymbionts of termite gut protists. Environmental Microbiology, 9(10), 2631-2635.

Huang, X.-F., Bakker, M., Judd, T., Reardon, K., & Vivanco, J. (2013). Variations in diversity and richness of gut bacterial communities of termites (Reticulitermes flavipes) fed with grassy and woody plant substrates. Microbial Ecology, 65(3), 531-536.

Inoue, J.-i., Oshima, K., Suda, W., Sakamoto, M., Iino, T., Noda, S. et al. (2015). Distribution and evolution of nitrogen fixation genes in the phylum Bacteroidetes. Microbes and environments, 30(1), 44-50.

Kearse, M., Moir, R., Wilson, A., Stones-Havas, S., Cheung, M., Sturrock, S. et al. (2012). Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics, 28(12), 1647-1649.

Köhler, T., Stingl, U., Meuser, K., & Brune, A. (2008). Novel lineages of Planctomycetes densely colonize the alkaline gut of soil-feeding termites (Cubitermes spp.). Environmental Microbiology, 10(5), 1260-1270.

Köhler, T., Dietrich, C., Scheffrahn, R. H., & Brune, A. (2012). High-resolution analysis of gut environment and bacterial microbiota reveals functional compartmentation of the gut in wood-feeding higher termites (Nasutitermes spp.). Applied & Environmental Microbiology, 78(13), 4691-4701.

Kuhnigk, T., Branke, J., Krekeler, D., Cypionka, H., & König, H. (1996). A feasible role of sulfate-reducing bacteria in the termite gut. Systematic and Applied Microbiology, 19(2), 139-149.

Lambert, C., & Power, V. (1999). Termite harvesting procedures. Perth Zoo’s Numbat Breeding Progamme.

Li, H., Dietrich, C., Zhu, N., Mikaelyan, A., Ma, B., Pi, R. et al. (2016). Age polyethism drives community structure of the bacterial gut microbiota in the fungus-cultivating termite Odontotermes formosanus. Environmental Microbiology, 18(5), 1440-1451.

Lilburn, T. G. (2001). Nitrogen fixation by symbiotic and free-living Spirochetes. Science, 292(5526), 2495-2498.

Mikaelyan, A., Köhler, T., Lampert, N., Rohland, J., Boga, H., Meuser, K. et al. (2015). Classifying the bacterial gut microbiota of termites and cockroaches: A curated phylogenetic reference database (DictDb). Systematic and Applied Microbiology, 38(7), 472-482.

Mikaelyan, A., Meuser, K., & Brune, A. (2017). Microenvironmental heterogeneity of gut compartments drives bacterial community structure in wood-and humus-feeding higher termites. FEMS Microbiology Ecology, 93(1), fiw210.

Mikaelyan, A., Strassert, J. F. H., Tokuda, G., & Brune, A. (2014). The fibre‐associated cellulolytic bacterial community in the hindgut of wood‐feeding higher termites (Nasutitermes spp.). Environmental Microbiology, 16(9), 2711-2722.

114

Mikaelyan, A., Thompson, C. L., Hofer, M. J., & Brune, A. (2016). Deterministic assembly of complex bacterial communities in guts of germ-free cockroaches. Applied and Environmental Microbiology, 82(4), 1256-1263.

National Center for Biotechnology Information. Basic Local Alignment Search Tool. 2017, from https://blast.ncbi.nlm.nih.gov/Blast.cgi.

Nevill, P. G., Zhong, X., Tonti-Filippini, J., Byrne, M., Thiele, K., Leeuwen, S. V. et al. (2017). Plastids of the Pilbara. https://s3-ap-southeast-2.amazonaws.com/pilbara-cpt-website/index.html.

Ohkuma, M., & Brune, A. (2011). Diversity, structure, and evolution of the termite gut microbial community. In D. E. Bignell, Y. Roisin, & N. Lo (Eds.), Biology of Termites: a Modern Synthesis (pp. 413-438). Netherlands: Springer.

Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D. et al. (2018). vegan: community ecology package. R package version 2.5-2. https://CRAN.R-project.org/package=vegan.

O’Leary, N. A., Wright, M. W., Brister, J. R., Ciufo, S., Haddad, D., McVeigh, R. et al. (2016). Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res, 44(D1), D733-D745.

Orr, R. J. S., Zhao, S., Klaveness, D., Yabuki, A., Ikeda, K., Watanabe, M. M. et al. (2018). Enigmatic Diphyllatea eukaryotes: culturing and targeted PacBio RS amplicon sequencing reveals a higher order taxonomic diversity and global distribution. BMC Evolutionary Biology, 18(1), 115.

Otani, S., Mikaelyan, A., Nobre, T., Hansen, L. H., Kone, N. A., Sorensen, S. J., Aanen, D.K., Boomsma, J.J., Brune, A. Poulsen, M (2014). Identifying the core microbial community in the gut of fungus-growing termites. Molecular Ecology.

Paul, K., Nonoh, J. O., Mikulski, L., & Brune, A. (2012). “Methanoplasmatales,” Thermoplasmatales-related archaea in termite guts and other environments, are the seventh order of methanogens. Applied and Environmental Microbiology, 78(23), 8245-8253.

Pawlowski, J., Audic, S., Adl, S., Bass, D., Belbahri, L., Berney, C. et al. (2012). CBOL protist working group: barcoding eukaryotic richness beyond the animal, plant, and fungal kingdoms. PLoS. Biol., 10(11).

Perry, D. H., Watson, J. A. L., Buun, S. E., & Black, R. (1985). Guide to the termites (Isoptera) from the extreme south-west of western Australia. Journal of the Royal Society of Western Australia, 67(part 2), 66-78.

R Core Team (2018). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.

Rawls, J. F., Mahowald, M. A., Ley, R. E., & Gordon, J. I. (2006). Reciprocal gut microbiota transplants from zebrafish and mice to germ-free recipients reveal host habitat selection. Cell, 127(2), 423-433.

Reid, N. M., Addison, S. L., West, M. A., & Lloyd-Jones, G. (2014). The bacterial microbiota of Stolotermes ruficeps (Stolotermitidae), a phylogenetically basal termite endemic to New Zealand. FEMS Microbiology Ecology, 90(3), 678-688.

Rosengaus, R. B., Zecher, C. N., Schultheis, K. F., Brucker, R. M., & Bordenstein, S. R. (2011). Disruption of the termite gut microbiota and its prolonged consequences for fitness. Applied and Environmental Microbiology, 77(13), 4303-4312.

Seedorf, H., Griffin, N. W., Ridaura, V. K., Reyes, A., Cheng, J., Rey, F. E. et al. (2014). Bacteria from diverse habitats colonize and compete in the mouse gut. Cell, 159(2), 253-266.

Shade, A., & Handelsman, J. (2012). Beyond the Venn diagram: the hunt for a core microbiome: The hunt for a core microbiome. Environmental Microbiology,

115

14(1), 4-12. Shimada, K., Lo, N., Kitade, O., Wakui, A., & Maekawa, K. (2013). Cellulolytic protist

numbers rise and fall dramatically in termite queens and kings during colony foundation. Eukaryotic Cell, 12(4), 545-550.

Su, L., Yang, L., Huang, S., Su, X., Li, Y., Wang, F. et al. (2016). Comparative gut microbiomes of four species representing the higher and the lower termites. Journal of Insect Science, 16(1).

Szokoli, F., Castelli, M., Sabaneyeva, E., Schrallhammer, M., Krenek, S., Doak, T. G. et al. (2016). Disentangling the taxonomy of Rickettsiales and description of two novel symbionts (”Candidatus Bealeia paramacronuclearis” and “Candidatus Fokinia cryptica”) sharing the cytoplasm of the ciliate protist Paramecium biaurelia. Applied and Environmental Microbiology, 82(24), 7236-7247.

Taberlet, P., Coissac, E., Pompanon, F., Gielly, L., Miquel, C., Valentini, A. et al. (2007). Power and limitations of the chloroplast trnL (UAA) intron for plant DNA barcoding. Nucleic Acids Research, 35(3), e14.

Thompson, C. L., Vier, R., Mikaelyan, A., Wienemann, T., & Brune, A. (2012). ‘Candidatus Arthromitus’ revised: segmented filamentous bacteria in arthropod guts are members of Lachnospiraceae: Arthromitus filaments in arthropod guts. Environmental Microbiology, 14(6), 1454-1465.

Wang, Y., Su, L., Huang, S., Bo, C., Yang, S., Li, Y. et al. (2016). Diversity and resilience of the wood-feeding higher termite Mironasutitermes shangchengensis gut microbiota in response to temporal and diet variations. Ecology and Evolution, 6(22), 8235-8242.

Watson, J. A. L., & Abbey, . H. M. (1993). Atlas of Australian Termites. Ziemer, C. J. (2014). Newly cultured bacteria with broad diversity isolated from eight-

week continuous culture enrichments of cow feces on complex polysaccharides. Applied and Environmental Microbiology, 80(2), 574-585.

116 Ta

ble

S1: F

unct

iona

l gro

ups

pred

icte

d fr

om th

e lit

erat

ure

(with

ref

eren

ces)

for c

ore

taxa

iden

tifie

d in

the

guts

of T

. wes

tral

iens

is a

nd

A. o

beun

tis b

ased

on

taxo

nom

y as

sign

men

ts m

ade

usin

g th

e rd

p cl

assi

fier i

n Q

IIM

E an

d th

e D

ictD

b v3

refe

renc

e da

taba

se

Phy

lum

C

lass

O

rder

Fa

mil

y G

enus

Fu

ncti

on

Sour

ce

Unk

now

n

U

nkno

wn

Aci

doba

cter

ia

Aci

doba

cter

ia

Aci

doba

cter

iale

s A

cido

bact

eria

ceae

U

ncul

ture

d_23

C

ellu

lose

de

grad

atio

n C

ampb

ell

(201

4)

Aci

doba

cter

ia

Hol

opha

gae

Hol

opha

gale

s H

olop

haga

ceae

U

ncul

ture

d_3

Aro

mat

ic

com

poun

d de

grad

atio

n C

ampb

ell

(201

4)

Act

inob

acte

ria

Act

inob

acte

ria

U

nkno

wn

Act

inob

acte

ria

Act

inob

acte

ria

Cor

ioba

cter

iale

s C

orio

bact

eria

ceae

Car

bohy

drat

e fe

rmen

tatio

n G

upta

et a

l. (2

013)

Act

inob

acte

ria

Act

inob

acte

ria

Fran

kial

es

Aci

doth

erm

acea

e A

cido

ther

mus

C

ellu

lose

de

grad

atio

n N

orm

and

et a

l. (2

015)

Act

inob

acte

ria

Act

inob

acte

ria

Mic

roco

ccal

es_1

Sa

ngui

bact

erac

eae

Sang

uiba

cter

C

arbo

hydr

ate

ferm

enta

tion

Schu

man

n &

St

acke

bran

dt

(201

4)

Act

inob

acte

ria

Act

inob

acte

ria

Mic

roco

ccal

es_2

Pr

omic

rom

onos

pora

ceae

X

ylan

imon

as

Cel

lulo

se

degr

adat

ion;

H

emic

ellu

lose

de

grad

atio

n Ri

vas

et a

l. (2

003)

Ba

cter

oide

tes

Unk

now

n

Bact

eroi

dete

s Ba

cter

oidi

a Ba

cter

oida

les

Unk

now

n

Bact

eroi

dete

s Ba

cter

oidi

a Ba

cter

oida

les

Mar

inila

biac

eae

Alk

alifl

exus

C

arbo

hydr

ate

The

Edito

rial

117

ferm

enta

tion

Boar

d (2

015)

Ba

cter

oide

tes

Bact

eroi

dia

Bact

eroi

dale

s M

gMjR

-022

Unk

now

n

Bact

eroi

dete

s Ba

cter

oidi

a Ba

cter

oida

les

Porp

hyro

mon

adac

eae

U

nkno

wn

Hon

goh

et a

l, (2

007)

Bact

eroi

dete

s Ba

cter

oidi

a Ba

cter

oida

les

Porp

hyro

mon

adac

eae_

3 Ta

nner

ella

C

arbo

hydr

ate

ferm

enta

tion

Saka

mot

o et

al.

(201

5)

Bact

eroi

dete

s Ba

cter

oidi

a Ba

cter

oida

les

Porp

hyro

mon

adac

eae_

Cl

uste

r_V

C

andi

datu

s_A

rman

tifilu

m

Unk

now

n H

ongo

h et

al,

(200

7)

Bact

eroi

dete

s Ba

cter

oidi

a Ba

cter

oida

les

Rike

nella

ceae

Car

bohy

drat

e fe

rmen

tatio

n G

raf J

. (20

14),

Lin

et a

l., 2

014

Bact

eroi

dete

s Sp

hing

obac

teri

a Sp

hing

obac

teri

ale

s_1

Unk

now

n K

ämpf

er (2

015)

C

andi

date

_phy

lum

_BD

1_5

Unk

now

n H

anke

et a

l. (2

014)

Can

dida

te_p

hyl

um_O

P11

Cel

lulo

se

degr

adat

ion;

A

rom

atic

co

mpo

und

degr

adat

ion

Yous

sef e

t al.

(201

1)

Can

dida

te_p

hyl

um_S

R1

Sulfa

te re

duct

ion

Drö

ge e

t al.

(200

5);

Dav

is

et a

l. (2

009)

C

andi

date

_phy

lum

_TG

3 Su

bphy

lum

_1

ince

rtae

_sed

is

Term

ite_c

lust

er_I

II

Subc

lust

er_I

IIb

Cel

lulo

se

degr

adat

ion

Abd

ul R

ahm

an

et a

l. (2

016)

C

andi

date

_phy

lum

_TG

3 Su

bphy

lum

_2

ince

rtae

_sed

is

Term

ite_c

lust

er_I

V

Subc

lust

er_I

Vb

Cel

lulo

se

degr

adat

ion

Abd

ul R

ahm

an

et a

l. (2

016)

118 C

andi

date

_phy

lum

_TM

7

U

nkno

wn

C

hlor

obi

Chl

orob

ia

Chl

orob

iale

s O

PB56

Te

rmite

_clu

ster

U

nkno

wn

C

hlor

obi

Chl

orob

ia

Chl

orob

iale

s SJ

A-2

8

Unk

now

n

Chl

orof

lexi

va

dinB

A26

Unk

now

n G

arri

ty e

t al.

(200

1)

Cya

noba

cter

ia

Cya

noba

cter

ia

Cya

noba

cter

iale

s C

0d-2

Nitr

ogen

fixa

tion

Flor

es e

t al.

(201

5)

Cya

noba

cter

ia

Cya

noba

cter

ia

Cya

noba

cter

iale

s M

L635

J-21

C

lust

er_T

G2

Nitr

ogen

fixa

tion

Flor

es e

t al.

(201

5)

Def

erri

bact

eres

D

efer

riba

cter

es

Def

erri

bact

eral

es

Def

erri

bact

erac

eae

Muc

ispi

rillu

m

Unk

now

n

Elus

imic

robi

a El

usim

icro

bia

Elus

imic

robi

ales

Li

neag

e_II

I

Car

bohy

drat

e fe

rmen

tatio

n;

Nitr

ogen

fixa

tion

Zhen

g &

Bru

ne

(201

5)

Elus

imic

robi

a El

usim

icro

bia

Endo

mic

robi

ales

En

dom

icro

biac

eae

Endo

mic

robi

um

Car

bohy

drat

e fe

rmen

tatio

n;

Nitr

ogen

fixa

tion

Zhen

g &

Bru

ne

(201

5)

Fibr

obac

tere

s Su

bphy

lum

_2

Inse

ct_c

lust

er

Cel

lulo

se

degr

adat

ion

Abd

ul R

ahm

an

et a

l. (2

016)

Fi

rmic

utes

U

nkno

wn

Firm

icut

es

Baci

lli

Lact

obac

illal

es

Car

bohy

drat

e fe

rmen

tatio

n Sa

lvet

ti et

al.

(201

3)

Firm

icut

es

Baci

lli

Lact

obac

illal

es

Stre

ptoc

occa

ceae

Car

bohy

drat

e fe

rmen

tatio

n Lo

ry (2

014)

Firm

icut

es

Baci

lli

Lact

obac

illal

es

Stre

ptoc

occa

ceae

La

ctov

um

Car

bohy

drat

e fe

rmen

tatio

n D

rake

(201

4)

119 Fi

rmic

utes

C

lost

ridi

a_1

Clo

stri

dial

es

Unk

now

n

Firm

icut

es

Clo

stri

dia_

1 C

lost

ridi

ales

C

lost

ridi

acea

e_1

Art

hrop

od_c

lust

er

Unk

now

n W

iege

l et a

l. (2

006)

Firm

icut

es

Clo

stri

dia_

1 C

lost

ridi

ales

La

chno

spir

acea

e

Cel

lulo

se

degr

adat

ion

Hem

icel

lulo

se

degr

adat

ion;

C

ello

bios

e de

grad

atio

n Bi

ddle

et a

l. (2

013)

Firm

icut

es

Clo

stri

dia_

1 C

lost

ridi

ales

La

chno

spir

acea

e C

andi

datu

s_A

rthr

om

itus

Cel

lulo

se

degr

adat

ion

Hem

icel

lulo

se

degr

adat

ion;

C

ello

bios

e de

grad

atio

n Th

omps

on e

t al

. (20

12)

Firm

icut

es

Clo

stri

dia_

1 C

lost

ridi

ales

La

chno

spir

acea

e C

atab

acte

r H

emic

ellu

lose

de

grad

atio

n Yo

koya

ma

et a

l. (2

017)

Firm

icut

es

Clo

stri

dia_

1 C

lost

ridi

ales

Ru

min

ococ

cace

ae

Cel

lulo

se

degr

adat

ion

Hem

icel

lulo

se

degr

adat

ion;

C

ello

bios

e de

grad

atio

n Bi

ddle

et a

l. (2

013)

Firm

icut

es

Clo

stri

dia_

1 C

lost

ridi

ales

Ru

min

ococ

cace

ae

Papi

lliba

cter

Aro

mat

ic

com

poun

d de

grad

atio

n D

efno

un e

t al.

(200

0)

120 Fi

rmic

utes

C

lost

ridi

a_1

Clo

stri

dial

es

Rum

inoc

occa

ceae

Ru

min

ococ

cus_

1

Cel

lulo

se

degr

adat

ion

Hem

icel

lulo

se

degr

adat

ion;

C

ello

bios

e de

grad

atio

n Bi

ddle

et a

l. (2

013)

Fi

rmic

utes

C

lost

ridi

a_2

Clo

stri

dial

es_1

U

nkno

wn

Firm

icut

es

Clo

stri

dia_

2 C

lost

ridi

ales

_1

Pept

ococ

cace

ae

Aro

mat

ic

com

poun

d de

grad

atio

n

Her

rman

n et

al.

(201

0); K

e et

al.

(201

1)

Firm

icut

es

Clo

stri

dia_

2 C

lost

ridi

ales

_1

Pept

ococ

cace

ae_2

D

esul

fosp

oros

inus

Car

bohy

drat

e fe

rmen

tatio

n;

Sulfa

te re

duct

ion

Hip

pe (2

015)

Firm

icut

es

Clo

stri

dia_

2 C

lost

ridi

ales

_1

Pept

ococ

cace

ae_3

Pe

loto

mac

ulum

_3

Aro

mat

ic

com

poun

d de

grad

atio

n

Qiu

et a

l. (2

006)

; Rai

ney

(201

5)

Firm

icut

es

Clo

stri

dia_

2 C

lost

ridi

ales

_1

Vei

llone

llace

ae

C

arbo

hydr

ate

ferm

enta

tion

Soko

lova

, T. G

. (2

015)

Firm

icut

es

Clo

stri

dia_

2 C

lost

ridi

ales

_1

Vei

llone

llace

ae

Den

dros

poro

bact

er

Car

bohy

drat

e fe

rmen

tatio

n St

röm

pl (2

015)

Firm

icut

es

Clo

stri

dia_

2 C

lost

ridi

ales

_1

Vei

llone

llace

ae

Ther

mos

inus

C

arbo

hydr

ate

ferm

enta

tion

Soko

lova

. (2

015)

Firm

icut

es

Clo

stri

dia_

2 C

lost

ridi

ales

Sy

ntro

phom

onad

acea

e

Car

bohy

drat

e fe

rmen

tatio

n So

bier

aj &

Bo

one

(200

6)

Firm

icut

es

Clo

stri

dia_

2 C

lost

ridi

ales

Sy

ntro

phom

onad

acea

e Pe

losp

ora

Car

bohy

drat

e fe

rmen

tatio

n So

bier

aj &

Bo

one

(200

6)

121 Fi

rmic

utes

C

lost

ridi

a_3

U

nkno

wn

Firm

icut

es

Clo

stri

dia_

3 C

lost

ridi

ales

Sy

ntro

phom

onad

acea

e D

ethi

obac

ter

Car

bohy

drat

e fe

rmen

tatio

n So

bier

aj &

Bo

one

(200

6)

Firm

icut

es

Erys

ipel

otri

chi

Erys

ipel

otri

chal

es

Erys

ipel

otri

chac

eae

Erys

ipel

othr

ix

Car

bohy

drat

e fe

rmen

tatio

n St

acke

bran

dt

(201

5)

Firm

icut

es

Erys

ipel

otri

chi

Erys

ipel

otri

chal

es

Erys

ipel

otri

chac

eae

Hol

dem

ania

C

arbo

hydr

ate

ferm

enta

tion

Will

ems

(201

5)

Firm

icut

es

Erys

ipel

otri

chi

Erys

ipel

otri

chal

es

Erys

ipel

otri

chac

eae

Turi

ciba

cter

C

arbo

hydr

ate

ferm

enta

tion

Boss

hard

(201

5)

Lent

isph

aera

e Le

ntis

phae

ria

BS5

Unk

now

n

Lent

isph

aera

e Le

ntis

phae

ria

WC

HB1

-25

Unk

now

n

Plan

ctom

ycet

es

Phyc

isph

aera

e

Unk

now

n

Plan

ctom

ycet

es

Plan

ctom

ycet

aci

a Pl

anct

omyc

etal

es

Plan

ctom

ycet

acea

e

Unk

now

n

Plan

ctom

ycet

es

Plan

ctom

ycet

aci

a Pl

anct

omyc

etal

es

Plan

ctom

ycet

acea

e Te

rmite

_coc

kroa

ch_c

lust

er_2

Am

mon

ium

ox

idat

ion;

M

icro

bial

pol

ymer

di

gest

ion

Köh

ler e

t al.

(200

8)

Plan

ctom

ycet

es

vadi

nHA

49

U

nkno

wn

Pr

oteo

bact

eria

U

nkno

wn

Prot

eoba

cter

ia

Alp

hapr

oteo

bact

eria

Unk

now

n

Prot

eoba

cter

ia

Alp

hapr

oteo

bact

eria

Rh

izob

iale

s_2

Brad

yrhi

zobi

acea

e Br

adyr

hizo

bium

_12

Car

bohy

drat

e fe

rmen

tatio

n;

Nitr

ogen

fixa

tion

Kuy

kend

all

(201

5)

Prot

eoba

cter

ia

Alp

hapr

oteo

baRh

izob

iale

s_2

Xan

thob

acte

race

ae

Unc

ultu

red_

1 U

nkno

wn

Ore

n (2

014)

122

cter

ia

Prot

eoba

cter

ia

Alp

hapr

oteo

bact

eria

Rh

odos

piri

llale

s_1

Rhod

ospi

rilla

ceae

U

ncul

ture

d_2

Unk

now

n

Prot

eoba

cter

ia

Alp

hapr

oteo

bact

eria

Rh

odos

piri

llale

s_2

DA

111

U

nkno

wn

Prot

eoba

cter

ia

Alp

hapr

oteo

bact

eria

Ri

cket

tsia

les

Can

dida

tus_

Cap

tivus

C

andi

datu

s_C

aptiv

us

Prot

ist

endo

sym

bion

t Sz

okol

i et a

l (2

016)

Prot

eoba

cter

ia

Alp

hapr

oteo

bact

eria

Ri

cket

tsia

les

Can

dida

tus_

Hep

atin

cola

C

andi

datu

s_H

epat

inc

ola

Prot

ist

endo

sym

bion

t Sz

okol

i et a

l (2

016)

Prot

eoba

cter

ia

Beta

prot

eoba

cter

ia

Burk

hold

eria

les

Unk

now

n

Prot

eoba

cter

ia

Beta

prot

eoba

cter

ia

Burk

hold

eria

les

Oxa

loba

cter

acea

e

Unk

now

n Ba

ldan

i et a

l. (2

014)

Prot

eoba

cter

ia

Beta

prot

eoba

cter

ia

Rhod

ocyc

lale

s Rh

odoc

ycla

ceae

Unk

now

n O

ren

(201

4b)

Prot

eoba

cter

ia

Beta

prot

eoba

cter

ia

SC-I

-84

Inse

ct_c

lust

er_I

I

Unk

now

n

Prot

eoba

cter

ia

Del

tapr

oteo

bac

teri

a Bd

ello

vibr

iona

les

Bact

erio

vora

ceae

Unk

now

n

Prot

eoba

cter

ia

Del

tapr

oteo

bac

teri

a D

esul

farc

ulal

es

Des

ulfa

rcul

acea

e D

esul

farc

ulus

Su

lfate

redu

ctio

n Su

n et

al.

(201

0)

Prot

eoba

cter

ia

Del

tapr

oteo

bac

teri

a D

esul

foba

cter

ales

D

esul

fobu

lbac

eae

Des

ulfo

bulb

us

Sulfa

te re

duct

ion

Kue

ver e

t al.

(201

5)

Prot

eoba

cter

ia

Del

tapr

oteo

bac

teri

a D

esul

fovi

brio

nale

s D

esul

fovi

brio

nace

ae

Su

lfate

redu

ctio

n Su

n et

al.

(201

0)

Prot

eoba

cter

ia

Del

tapr

oteo

bac

Des

ulfo

vibr

iona

leD

esul

fovi

brio

nace

ae

Des

ulfo

vibr

io_5

Su

lfate

redu

ctio

n K

ueve

r et a

l.

123

teri

a s

(201

5b)

Prot

eoba

cter

ia

Del

tapr

oteo

bac

teri

a D

esul

fovi

brio

nale

s D

esul

fovi

brio

nace

ae

Gut

_clu

ster

_2

Sulfa

te re

duct

ion

Kue

ver (

2014

)

Prot

eoba

cter

ia

Del

tapr

oteo

bac

teri

a F-

1404

R

U

nkno

wn

Prot

eoba

cter

ia

Del

tapr

oteo

bac

teri

a Rs

-K70

Te

rmite

_clu

ster

_III

Unk

now

n

Prot

eoba

cter

ia

Del

tapr

oteo

bac

teri

a Sy

ntro

phob

acte

ral

es

Synt

roph

acea

e

Car

bohy

drat

e fe

rmen

tatio

n K

ueve

r (20

14b)

Prot

eoba

cter

ia

Del

tapr

oteo

bac

teri

a Sy

ntro

phob

acte

ral

es

Synt

roph

acea

e Sy

ntro

phus

C

arbo

hydr

ate

ferm

enta

tion

Kue

ver &

Sc

hink

(201

5)

Prot

eoba

cter

ia

TA18

Unk

now

n

Spir

ocha

etes

U

nkno

wn

Sp

iroc

haet

es

Spir

ocha

etes

Unk

now

n

Spir

ocha

etes

Sp

iroc

haet

es

CW

-1_

term

ite_g

roup

U

nkno

wn

Sp

iroc

haet

es

Spir

ocha

etes

Sp

iroc

haet

ales

U

nkno

wn

Spir

ocha

etes

Sp

iroc

haet

es

Spir

ocha

etal

es

Lept

ospi

race

ae

Rs-

H88

_ter

mite

_gro

up

Unk

now

n Pi

card

eau

(201

4)

Spir

ocha

etes

Sp

iroc

haet

es

Spir

ocha

etal

es

Lept

ospi

race

ae

Unc

ultu

red_

3 U

nkno

wn

Cam

pbel

l (2

014b

) Sp

iroc

haet

es

Spir

ocha

etes

Sp

iroc

haet

ales

PL

-11B

10

U

nkno

wn

Spir

ocha

etes

Sp

iroc

haet

es

Spir

ocha

etal

es

Spir

ocha

etac

eae_

Trep

one

ma_

I

Unk

now

n

Spir

ocha

etes

Sp

iroc

haet

es

Spir

ocha

etal

es

Spir

ocha

etac

eae_

Trep

one

ma_

I Tr

epon

ema_

Ia

Car

bohy

drat

e fe

rmen

tatio

n;

Die

tric

h et

al.

(201

4);

124

Aro

mat

ic

com

poun

d de

grad

atio

n

Mik

aely

an e

t al.

(201

5)

Spir

ocha

etes

Sp

iroc

haet

es

Spir

ocha

etal

es

Spir

ocha

etac

eae_

Trep

one

ma_

I Tr

epon

ema_

Ic

Cel

lulo

se

degr

adat

ion

Mik

aely

an e

t al.

(201

4)

Spir

ocha

etes

Sp

iroc

haet

es

Spir

ocha

etal

es

Spir

ocha

etac

eae_

Trep

one

ma_

I Tr

epon

ema_

If C

ellu

lose

de

grad

atio

n M

ikae

lyan

et a

l. (2

014)

Spir

ocha

etes

Sp

iroc

haet

es

Spir

ocha

etal

es

Spir

ocha

etac

eae_

Trep

one

ma_

I Tr

epon

ema_

Ih

Unk

now

n

Spir

ocha

etes

Sp

iroc

haet

es

Spir

ocha

etal

es

Spir

ocha

etac

eae

Spir

ocha

eta

Car

bohy

drat

e fe

rmen

tatio

n C

anal

e-Pa

rola

(1

992)

Syne

rgis

tete

s Sy

nerg

istia

Sy

nerg

ista

les

Syne

rgis

tace

ae

Can

dida

tus_

Tam

mel

la

Unk

now

n

Syne

rgis

tete

s Sy

nerg

istia

Sy

nerg

ista

les

Syne

rgis

tace

ae

Term

ite_c

ockr

oach

_clu

ster

U

nkno

wn

Tene

ricu

tes

Mol

licut

es

RF9

Car

bohy

drat

e fe

rmen

tatio

n Sk

enne

rton

et

al. (

2016

) V

erru

com

icro

bia

Opi

tuta

e O

pitu

tale

s O

pitu

tace

ae

Opi

tutu

s C

arbo

hydr

ate

ferm

enta

tion

Jans

sen

(201

5)

Ver

ruco

mic

robi

a O

pitu

tae

vadi

nHA

64

Term

ite_c

lust

er_I

Unk

now

n

Ver

ruco

mic

robi

a Sp

arto

bact

eria

C

htho

niob

acte

ral

es

Xip

hine

mat

obac

tera

ceae

C

andi

datu

s_X

iphi

nem

atob

acte

r N

emat

ode

sym

bion

t V

ande

kerc

khov

e et

al.

(201

5)

125

Table S2: Kruskal-Wallis and fdr corrected Mann-Whitney-Wilcoxon test results for comparisons between randomly generated datasets of varying sampling intensity. UniFrac beta diversity analysis was conducted on a subset of 20 randomly selected core communities. The average pairwise distances within each level of sampling intensity were found to be highly significant (bolded text) except in the weighted analysis when including all castes and a minimum of 20 samples (p > 0.245).

Test Method Castes Sampling intensities compared P-value

K-W Weighted Workers All < 2.200e-16 K-W Unweighted Workers All < 2.200e-16 K-W Weighted All All < 2.200e-16 K-W Unweighted All All < 2.200e-16 M-W Weighted Workers 2,3 3.080e-16

M-W Weighted Workers 2,5 3.080e-16

M-W Weighted Workers 3,5 2.694e-08

M-W Weighted Workers 10,5 3.080e-16

M-W Weighted Workers 10,20 3.080e-16

M-W Weighted Workers 30,20 3.875e-08

M-W Weighted Workers 30,45 3.080e-16

M-W Unweighted Workers 2,3 3.080e-16

M-W Unweighted Workers 2,5 3.080e-16

M-W Unweighted Workers 3,5 4.129e-08

M-W Unweighted Workers 10,5 3.080e-16

M-W Unweighted Workers 10,20 3.080e-16

M-W Unweighted Workers 30,20 1.934e-14

M-W Unweighted Workers 30,45 3.080e-16

M-W Weighted All 2,3 5.237e-10

M-W Weighted All 2,5 1.760e-15

M-W Weighted All 3,5 4.880e-04

M-W Weighted All 10,5 1.600e-03

M-W Weighted All 10,20 3.026e-14

M-W Weighted All 30,20 0.245

M-W Weighted All 30,45 0.652

M-W Weighted All 20,45 0.391

M-W Unweighted All 2,3 7.700e-16

M-W Unweighted All 2,5 7.700e-16

M-W Unweighted All 3,5 3.626e-05

M-W Unweighted All 10,5 1.020e-11

M-W Unweighted All 10,20 1.026e-05

M-W Unweighted All 30,20 8.871e-07

M-W Unweighted All 30,45 0.037

126

Table S3: Caste differences in T. westraliensis showing percentage of reads from each caste assigned to each bacterial phylum.

Phylum Alate Nymph Soldiers Mature Workers

Other Workers

Young Workers

Acidobacteria 0.51% 0.75% 1.28% 1.26% 1.47% 1.52%

Actinobacteria 0.33% 0.49% 0.66% 1.42% 4.97% 3.11%

Bacteroidetes 44.00% 10.81% 17.18% 14.16% 13.89% 14.99%

Candidate phylum BD1 5 0.00% 0.00% 0.00% 0.01% 0.00% 0.01%

Candidate phylum OP11 0.19% 0.09% 0.10% 0.05% 0.07% 0.05%

Candidate phylum SR1 0.11% 0.25% 0.27% 0.23% 0.27% 0.39%

Candidate phylum TG3 1.88% 0.67% 0.74% 0.82% 1.38% 1.28%

Candidate phylum TM7 0.18% 0.68% 0.68% 0.65% 0.52% 0.86%

Chlorobi 0.02% 0.43% 0.17% 0.25% 0.25% 0.24%

Chloroflexi 0.01% 0.02% 0.08% 0.06% 0.07% 0.07%

Cyanobacteria 0.09% 0.17% 0.21% 0.16% 0.17% 0.26%

Deferribacteres 0.06% 0.06% 0.12% 0.06% 0.05% 0.05%

Elusimicrobia 0.26% 0.18% 0.28% 0.18% 0.17% 0.22%

Fibrobacteres 3.73% 1.33% 0.59% 0.68% 0.86% 0.68%

Firmicutes 19.64% 31.92% 41.12% 38.16% 35.36% 36.75%

Lentisphaerae 0.09% 0.04% 0.04% 0.06% 0.07% 0.06%

Planctomycetes 0.20% 0.20% 0.31% 0.36% 0.38% 0.33%

Proteobacteria 2.55% 3.33% 4.30% 4.31% 4.94% 4.62%

Spirochaetes 20.78% 44.39% 27.09% 31.75% 29.35% 29.38%

Synergistetes 3.57% 2.21% 2.93% 3.52% 3.95% 3.15%

Tenericutes 0.06% 0.16% 0.17% 0.17% 0.19% 0.23%

Verrucomicrobia 0.02% 0.10% 0.09% 0.10% 0.09% 0.14%

Unknown 1.56% 1.45% 0.98% 1.13% 1.16% 1.10%

127 Ta

ble

S4:

Dat

aset

s an

d as

soci

ated

sta

tistic

s by

spe

cies

(A

. ob

eunt

is a

nd T

. w

estr

alie

nsis

) an

d ca

ste

(wor

kers

(W

), so

ldie

rs (

S) o

r al

l ca

stes

(i

nclu

ding

non

-mat

ure

wor

kers

) be

fore

(tim

e po

int 1

) an

d af

ter

(2)

feed

ing.

The

tota

l num

ber

of s

ampl

es c

olle

cted

for

each

dat

aset

(to

tal s

ampl

es)

and

the

num

ber o

f sam

ples

use

d (r

aref

actio

n ba

sed

on lo

wes

t num

ber o

f ava

ilabl

e sa

mpl

es p

er u

nit o

f int

eres

t) is

incl

uded

. Sta

tistic

al te

sts

incl

ude

prop

ortio

n te

sts

com

pari

ng n

umbe

r of

cor

e O

TUs

and

abun

danc

e of

cor

e re

ads

and

two

test

s (P

erm

anov

a an

d db

-RD

A,

wei

ghte

d an

d un

wei

ghte

d, c

ondu

cted

in Q

IIM

E) c

ompa

ring

fact

ors

in t

erm

s of

the

ir U

niFr

ac d

ista

nces

. Mos

t te

sts

wer

e si

gnifi

cant

(si

gnifi

cant

res

ults

in b

old

text

) exc

ept t

hat c

ore

size

s w

ere

gene

rally

con

sist

ent,

sugg

estin

g th

at o

ur r

ecom

men

datio

n to

incl

ude

a m

inim

um o

f 20

sam

ples

doe

s le

ad to

mor

e co

nsis

tent

and

acc

urat

e co

re c

omm

uniti

es.

Spec

ies

Tim

e po

int

Cas

te

Dat

aset

To

tal

sam

ples

R

aref

ied

to

Cor

e O

TUs

Cor

e re

ads

Per

man

ova

(w.)

P

erm

anov

a (u

nw.)

db

-RD

A

(w.)

db

-RD

A

(unw

.)

A

Both

A

ll Lo

catio

n (2

*)

51

24

0.31

0 2.

200e

-16

0.01

5 0.

001

0.01

6 0.

001

A

Both

A

ll Ti

me

61

28

0.

215

2.20

0e-1

6 0.

002

0.00

2 0.

001

0.00

1 A

Bo

th

W

Die

t (2)

41

15

0.

158

2.20

0e-1

6 0.

005

0.00

2 0.

004

0.00

1 A

Bo

th

W

Loca

tion

(2)

53

21

0.05

1 2.

200e

-16

0.02

1 0.

001

0.01

7 0.

001

A

Both

W

Ti

me

53

26

0.

002

2.20

0e-1

6 0.

004

0.00

1 0.

006

0.00

1 A

Bo

th

W

Die

t 53

6

NA

NA

0.13

0 0.

002

0.12

9 0.

004

A

2 W

D

iet

27

6 N

A N

A 0.

081

0.00

3 0.

096

0.00

4 T

2 A

ll D

iet

119

33

0.17

4 2.

200e

-16

0.00

1 0.

001

0.00

1 0.

001

T 2

All

Loca

tion

119

26

0.28

8 2.

200e

-16

0.00

1 0.

001

0.00

1 0.

001

T 2

S D

iet (

2)

55

19

0.53

9 2.

200e

-16

0.03

0 0.

001

0.02

9 0.

001

T 2

W/S

C

aste

78

23

0.

768

1.61

0e-0

8 0.

003

0.01

4 0.

003

0.01

4 T

1 A

ll Lo

catio

n 14

1 43

0.

820

2.20

0e-1

6 0.

001

0.00

1 0.

001

0.00

1 T

1 S

Loca

tion

88

27

0.40

2 2.

200e

-16

0.00

1 0.

001

0.00

1 0.

001

T 1

W/S

C

aste

12

7 39

0.

981

2.20

0e-1

6 0.

001

0.00

1 0.

001

0.00

1

128 T

Both

A

ll D

iet

260

33

0.14

2 2.

200e

-16

0.00

1 0.

001

0.00

1 0.

001

T Bo

th

All

Loca

tion

260

79

0.33

3 2.

200e

-16

0.00

1 0.

001

0.00

1 0.

001

T Bo

th

All

Tim

e

260

119

0.41

2 2.

200e

-16

0.00

3 0.

001

0.00

2 0.

001

T Bo

th

S D

iet

143

14

0.28

1 2.

200e

-16

0.02

9 0.

001

0.03

1 0.

001

T Bo

th

S Lo

catio

n 14

3 43

0.

799

2.14

0e-1

1 0.

001

0.00

1 0.

001

0.00

1 T

Both

S

Tim

e

143

55

0.93

9 2.

200e

-16

0.24

0 0.

025

0.23

9 0.

032

T Bo

th

W

Loca

tion

62

17

0.46

0 2.

200e

-16

0.00

1 0.

001

0.00

1 0.

001

T Bo

th

W

Tim

e

62

23

0.50

1 2.

200e

-16

0.23

9 0.

019

0.23

4 0.

018

T Bo

th

W

Die

t 62

6

NA

NA

0.17

4 0.

003

0.19

0.

005

T Bo

th

W

Die

t (W

+L)†

62

10

NA

NA

0.32

3 0.

002

0.34

2 0.

002

T 2

W

Die

t 23

6

NA

NA

0.14

0 0.

005

0.12

9 0.

006

T 2

W

Die

t (W

+L)

23

10

NA

NA

0.37

8 0.

003

0.38

0.

006

T Bo

th

W/S

C

aste

20

5 62

1.

000

2.20

0e-1

6 0.

001

0.00

1 0.

001

0.00

1 Bo

th

1 A

ll Sp

ecie

s 17

4 33

3.

040e

-4

2.20

0e-1

6 0.

001

0.00

1 0.

001

0.00

1 Bo

th

1 W

Sp

ecie

s 65

26

0.

624

2.20

0e-1

6 0.

001

0.00

1 0.

001

0.00

1 Bo

th

Both

A

ll Sp

ecie

s 32

1 61

1.

086e

-4

0.62

2 0.

001

0.00

1 0.

001

0.00

1

Both

Bo

th

W

Co-

habi

tati

on

34

16

0.78

6 0.

725

0.76

8 0.

444

NA

NA

Bo

th

Both

W

Sp

ecie

s 11

5 53

0.

005

2.20

0e-1

6 0.

001

0.00

1 0.

001

0.00

1

Both

(T)

Both

W

C

o-ha

bita

tion

17

8

NA

NA

0.12

4 0.

009

0.2

70

0.0

12

Both

(A)

Both

W

C

o-ha

bita

tion

17

8

NA

NA

0.22

5 0.

005

0.2

31

0.0

07

*Num

ber

of g

roup

s in

clud

ed to

max

imis

e nu

mbe

r of

sam

ples

in r

aref

ied

grou

ps

† Whe

at a

nd L

ucer

ne d

iet g

roup

s co

mbi

ned

to m

axim

ise

num

ber

of s

ampl

es in

rar

efie

d gr

oups

129 Ta

ble

S5: I

nter

spec

ies

core

sum

mar

y C

aste

Lo

cati

on

Ava

ilab

le s

ampl

es

Rar

efac

tion

nb

Cor

e n

b P

ropo

rtio

n te

sts

All

All

321

3

21

8

All

All

321

5

3 1

1

Beel

u-Ba

nyow

la

All

(6)

All

321

2

7 2

5

0.3

79

All

(5)

All

321

2

7 2

3

Beel

u-JF

A

ll (7

) A

ll 3

21

27

14

0

.49

7

Wor

kers

A

ll 1

15

115

1

0

Bany

owla

-JF

Wor

kers

A

ll 1

15

53

10

0

.26

8

Wor

kers

A

ll 1

15

27

16

Each

loca

tion

com

pare

d to

all

wor

kers

W

orke

rs

All

115

2

7 1

5

Wor

kers

A

ll 1

15

27

18

Wor

kers

Be

elu

27

27

21

0.5

26

Wor

kers

Ba

nyow

la

38

27

21

0.5

65

Wor

kers

Ba

nyow

la

38

27

23

Wor

kers

Ba

nyow

la

38

27

23

Wor

kers

Jo

hn F

orre

st

50

27

17

0.7

37

Wor

kers

Jo

hn F

orre

st

50

27

14

Wor

kers

Jo

hn F

orre

st

50

27

14

130 Ta

ble

S6: S

tati

stic

s su

mm

ary

for

a se

lect

ion

of r

aref

ied

data

sets

(se

e fu

ll se

t in

Tabl

e S4

) to

inve

stig

ate

loca

tion

and

die

t effe

ct

furt

her.

All

test

s in

clud

e a

form

of d

ista

nce

base

d re

dund

ancy

ana

lysi

s (d

b-RD

A)

cond

ucte

d w

ith t

he R

veg

an p

acka

ge u

sing

bo

th u

nwei

ghte

d an

d w

eigh

ted

Uni

Frac

bet

a di

vers

ity d

ista

nce

mat

rice

s pr

oduc

ed in

QII

ME.

Sta

ndar

d db

-RD

As

inve

stig

ate

the

effe

ct o

f lo

catio

n an

d di

et,

whe

reas

par

tial

db-

RDA

s se

ek t

o is

olat

e th

e ef

fect

of

each

one

. Pa

rtia

l db

-RD

As

wer

e al

l si

gnifi

cant

, exc

ept f

or d

iet w

hen

sam

ple

inte

nsiti

es w

ere

less

than

hal

f the

20

sam

ples

rec

omm

ende

d by

this

stu

dy.

Spec

ies

Tim

e po

int

Cas

te

Dat

aset

To

tal

sam

ples

R

aref

ied

to

db-R

DA

(w.)

P

arti

al d

b-R

DA

(w

.)

db-R

DA

(u

nw.)

P

arti

al d

b-R

DA

(u

nw.)

A

Bo

th

W

Die

t (2)

* 41

15

0.

006

0.01

0.

001

0.00

1 A

Bo

th

W

Loc

(2)

53

21

0.00

1 0.

036

0.00

1 0.

001

A

Both

W

D

iet

53

6 0.

314

0.68

0.

033

0.45

3 A

2

W

Die

t 27

6

0.25

6 0.

543

0.06

7 0.

357

T 2

S D

iet (

2)

55

19

0.00

1 0.

048

0.00

1 0.

001

T 1

S Lo

catio

n 88

27

0.

001

0.00

1 0.

001

0.00

1 T

Both

S

Die

t 14

3 14

0.

001

0.00

4 0.

001

0.00

1 T

Both

S

Loca

tion

143

43

0.00

1 0.

001

0.00

1 0.

001

T Bo

th

W

Loca

tion

62

17

0.00

8 0.

005

0.00

1 0.

001

T Bo

th

W

Die

t 62

6

0.00

9 0.

13

0.00

1 0.

002

T Bo

th

W

Die

t (W

+L)†

62

10

0.00

2 0.

032

0.00

1 0.

001

T 2

W

Die

t 23

6

0.01

6 0.

14

0.00

1 0.

003

T 2

W

Die

t (W

+L)

23

10

0.00

2 0.

022

0.00

1 0.

001

*Num

ber o

f gro

ups

incl

uded

to m

axim

ise

num

ber o

f sam

ples

in ra

refie

d gr

oups

† W

heat

and

Luc

erne

die

t gro

ups

com

bine

d to

max

imis

e nu

mbe

r of s

ampl

es in

rare

fied

grou

ps

131 Ta

ble

S7:

Stat

istic

s su

mm

ary

for

the

rela

tions

hip

betw

een

geog

raph

ic d

ista

nce

betw

een

colo

nies

and

the

bet

a di

vers

ity

dist

ance

bet

wee

n w

orke

r gu

t co

mm

uniti

es c

olle

cted

dur

ing

the

first

tim

e po

int.

Thre

e m

ain

grou

ping

s of

com

pari

sons

wer

e ob

tain

ed, o

ne c

onta

inin

g co

mpa

riso

ns w

ithin

a lo

catio

n, fo

llow

ed b

y co

mpa

riso

ns to

incr

easi

ngly

dis

tant

loca

tion

s. S

igni

fican

t po

siti

ve c

orre

latio

ns w

ere

foun

d ac

ross

all

loca

tions

in

T. w

estr

alie

nsis

onl

y, w

ith t

he s

tron

gest

cor

rela

tion

indi

catin

g th

at

incr

ease

d di

stan

ce b

etw

een

colo

nies

was

pri

mar

ily li

nked

to

incr

ease

d di

ffere

nces

in t

erm

s of

the

bac

teri

al s

peci

es p

rese

nt in

th

e gu

t. H

owev

er w

hen

focu

sing

on

indi

vidu

al g

roup

ings

(co

mpa

riso

ns w

ithin

a l

ocat

ion,

bet

wee

n m

id-r

ange

loc

atio

ns o

r di

stan

t loc

atio

ns) n

o si

gnifi

cant

tren

ds w

ere

foun

d.

Spec

ies

Met

ric

Acr

oss

all l

ocat

ions

W

ithi

n lo

cati

on

Bet

wee

n m

id-r

ange

lo

cati

ons

Bet

wee

n di

stan

t lo

cati

ons

Cor

rela

tio

n

P-v

alue

C

orre

lati

on

P

-va

lue

Cor

rela

tion

P

-val

ue

Cor

rela

tion

P

-val

ue

T U

nwei

ghte

d 0.

564

8.88

3e-

13

-0.2

36

0.14

2 -0

.181

0.

291

-0.2

01

0.12

3 T

Wei

ghte

d 0.

183

0.03

3 -0

.004

0.

979

-0.0

94

0.58

7 -0

.192

0.

143

A

Unw

eigh

ted

0.18

6 0.

103

0.00

2 0.

992

-0.4

91

0.06

3 -0

.193

0.

232

A

Wei

ghte

d -0

.047

0.

684

0.25

4 0.

242

-0.5

49

0.03

4 -0

.271

0.

091

132

References Abdul Rahman, N., Parks, D. H., Vanwonterghem, I., Morrison, M., Tyson, G. W., &

Hugenholtz, P. (2016). A phylogenomic analysis of the bacterial phylum Fibrobacteres. Frontiers in Microbiology, 6, 5.

Baldani J.I., Rouws L., Cruz L.M., Olivares F.L., Schmid M., Hartmann A. (2014) The family Oxalobacteraceae. In: Rosenberg E., DeLong E.F., Lory S., Stackebrandt E., Thompson F. (eds) The Prokaryotes. Springer, Berlin, Heidelberg.

Biddle, A., Stewart, L., Blanchard, J., & Leschine, S. (2013). Untangling the genetic basis of fibrolytic specialization by Lachnospiraceae and Ruminococcaceae in diverse gut communities. Diversity, 5(3), 627-640.

Bosshard, P. P. (2015). Turicibacter. In Bergey's manual of systematics of archaea and bacteria (eds W. B. Whitman, F. Rainey, P. Kämpfer, M. Trujillo, J. Chun, P. DeVos, B. Hedlund and S. Dedysh).

Campbell B.J. (2014) The Family Acidobacteriaceae. In: Rosenberg E., DeLong E.F., Lory S., Stackebrandt E., Thompson F. (eds) The Prokaryotes. Springer, Berlin, Heidelberg.

Campbell B.J. (2014b) The Family Acidobacteriaceae. In: Rosenberg E., DeLong E.F., Lory S., Stackebrandt E., Thompson F. (eds) The Prokaryotes. Springer, Berlin, Heidelberg.

Canale-Parola E. (1992) Free-living saccharolytic Spirochetes: The Genus Spirochaeta. In: Balows A., Trüper H.G., Dworkin M., Harder W., Schleifer KH. (eds) The Prokaryotes. Springer, New York, NY.

Davis, J. P., Youssef, N. H., & Elshahed, M. S. (2009). Assessment of the diversity, abundance, and ecological distribution of members of candidate division SR1 reveals a high level of phylogenetic diversity but limited morphotypic diversity. Applied and Environmental Microbiology, 75(12), 4139-4148.

Defnoun, S., Labat, M., Ambrosio, M., Garcia, J.-L., & Patel, B. K. C. (2000). Papillibacter cinnamivorans gen. nov., sp. nov., a cinnamate-transforming bacterium from a shea cake digester. International Journal of Systematic and Evolutionary Microbiology, 50, 1221-1228.

Dietrich, C., Köhler, T., & Brune, A. (2014). The cockroach origin of the termite gut microbiota: patterns in bacterial community structure reflect major evolutionary events. Applied and Environmental Microbiology.

Drake, H. L. (2014). The genus Lactovum. In Lactic acid bacteria (eds W. H. Holzapfel and B. J. Wood).

Dröge, S., Limper, U., Emtiazi, F., Schönig, I., Pavlus, N., Drzyzga, O. et al. (2005). In vitro and in vivo sulfate reduction in the gut contents of the termite Mastotermes darwiniensis and the rose-chafer Pachnoda marginata. The Journal of General and Applied Microbiology, 51(2), 57-64.

Flores, E. , López‐Lozano, A. and Herrero, A. (2015). Nitrogen fixation in the oxygenic phototrophic prokaryotes (Cyanobacteria): the fight against oxygen. In Biological Nitrogen Fixation, F. J. de Bruijn (Ed.).

Garrity G.M., Holt J.G., Castenholz R.W., Pierson B.K., Keppen O.I., Gorlenko V.M. (2001) Phylum BVI. Chloroflexi phy. nov.. In: Boone D.R., Castenholz R.W., Garrity G.M. (eds) Bergey’s manual of systematic bacteriology. Springer, New York, NY.

Graf J. (2014) The Family Rikenellaceae. In: Rosenberg E., DeLong E.F., Lory S., Stackebrandt E., Thompson F. (eds) The Prokaryotes. Springer, Berlin, Heidelberg.

Gupta, R. S., Chen, W. J., Adeolu, M., & Chai, Y. (2013). Molecular signatures for the

133

class Coriobacteriia and its different clades; proposal for division of the class Coriobacteriia into the emended order Coriobacteriales, containing the emended family Coriobacteriaceae and Atopobiaceae fam. nov., and Eggerthellales ord. nov., containing the family Eggerthellaceae fam. nov. International Journal of Systematic and Evolutionary Microbiology, 63(Pt 9), 3379-3397.

Hanke, A., Hamann, E., Sharma, R., Geelhoed, J. S., Hargesheimer, T., Kraft, B. et al. (2014). Recoding of the stop codon UGA to glycine by a BD1-5/SN-2 bacterium and niche partitioning between Alpha- and Gammaproteobacteria in a tidal sediment microbial community naturally selected in a laboratory chemostat. Frontiers in Microbiology, 5, 533.

Herrmann, S., Kleinsteuber, S., Chatzinotas, A., Kuppardt, S., Lueders, T., Richnow, H.-H. et al. (2010). Functional characterization of an anaerobic benzene-degrading enrichment culture by DNA stable isotope probing. Environmental Microbiology, 12(2), 401-411.

Hippe, H. and Stackebrandt, E. (2015). Desulfosporosinus. In Bergey's manual of systematics of archaea and bacteria (eds W. B. Whitman, F. Rainey, P. Kämpfer, M. Trujillo, J. Chun, P. DeVos, B. Hedlund and S. Dedysh).

Hongoh, Y., Sato, T., Noda, S., Ui, S., Kudo, T., & Ohkuma, M. (2007b). Candidatus Symbiothrix dinenymphae: bristle-like Bacteroidales ectosymbionts of termite gut protists. Environmental Microbiology, 9(10), 2631-2635.

Janssen, P. H. (2015). Opitutus. In Bergey's Manual of Systematics of Archaea and Bacteria (eds W. B. Whitman, F. Rainey, P. Kämpfer, M. Trujillo, J. Chun, P. DeVos, B. Hedlund and S. Dedysh).

Kämpfer, P. (2015). Sphingobacteriales ord. nov. In Bergey's manual of systematics of archaea and bacteria (eds W. B. Whitman, F. Rainey, P. Kämpfer, M. Trujillo, J. Chun, P. DeVos, B. Hedlund and S. Dedysh).

Ke, J., Singh, D., & Chen, S. (2011). Aromatic compound degradation by the wood-feeding termite Coptotermes formosanus (Shiraki). International Biodeterioration & Biodegradation, 65(6), 744-756.

Köhler, T., Stingl, U., Meuser, K., & Brune, A. (2008). Novel lineages of Planctomycetes densely colonize the alkaline gut of soil-feeding termites (Cubitermes spp.). Environmental Microbiology, 10(5), 1260-1270.

Kuykendall, L. D. (2015). Bradyrhizobium. In Bergey's Manual of Systematics of Archaea and Bacteria (eds W. B. Whitman, F. Rainey, P. Kämpfer, M. Trujillo, J. Chun, P. DeVos, B. Hedlund and S. Dedysh).

Kuever J. (2014) The Family Desulfovibrionaceae. In: Rosenberg E., DeLong E.F., Lory S., Stackebrandt E., Thompson F. (eds) The Prokaryotes. Springer, Berlin, Heidelberg.

Kuever J. (2014b) The Family Syntrophaceae. In: Rosenberg E., DeLong E.F., Lory S., Stackebrandt E., Thompson F. (eds) The Prokaryotes. Springer, Berlin, Heidelberg.

Kuever, J. , Rainey, F. A. and Widdel, F. (2015). Desulfobulbus. In Bergey's manual of systematics of archaea and bacteria (eds W. B. Whitman, F. Rainey, P. Kämpfer, M. Trujillo, J. Chun, P. DeVos, B. Hedlund and S. Dedysh)

Kuever, J. , Rainey, F. A. and Widdel, F. (2015b). Desulfovibrio. In Bergey's manual of systematics of archaea and bacteria (eds W. B. Whitman, F. Rainey, P. Kämpfer, M. Trujillo, J. Chun, P. DeVos, B. Hedlund and S. Dedysh).

Kuever, J. and Schink, B. (2015). Syntrophus. In Bergey's manual of systematics of archaea and bacteria (eds W. B. Whitman, F. Rainey, P. Kämpfer, M. Trujillo, J. Chun, P. DeVos, B. Hedlund and S. Dedysh).

134

Lin, S. Y., Hsu, Y. H., Liu, Y. C., Hung, M. H., Hameed, A., Lai, W. A. et al. (2014). Rhizobium straminoryzae sp. nov., isolated from the surface of rice straw. International Journal of Systematic and Evolutionary Microbiology, 64(Pt 9), 2962-2968.

Lory S. (2014) The Family Streptococcaceae. In: Rosenberg E., DeLong E.F., Lory S., Stackebrandt E., Thompson F. (eds) The Prokaryotes. Springer, Berlin, Heidelberg.

Mikaelyan, A., Köhler, T., Lampert, N., Rohland, J., Boga, H., Meuser, K. et al. (2015). Classifying the bacterial gut microbiota of termites and cockroaches: A curated phylogenetic reference database (DictDb). Systematic and Applied Microbiology, 38(7), 472-482.

Mikaelyan, A., Strassert, J. F. H., Tokuda, G., & Brune, A. (2014). The fibre‐associated cellulolytic bacterial community in the hindgut of wood‐feeding higher termites (Nasutitermes spp.). Environmental Microbiology, 16(9), 2711-2722.

Normand, P. , Berry, A. and Benson, D. R. (2015). Acidothermus. In Bergey's manual of systematics of archaea and bacteria (eds W. B. Whitman, F. Rainey, P. Kämpfer, M. Trujillo, J. Chun, P. DeVos, B. Hedlund and S. Dedysh).

Oren A. (2014) The Family Xanthobacteraceae. In: Rosenberg E., DeLong E.F., Lory S., Stackebrandt E., Thompson F. (eds) The Prokaryotes. Springer, Berlin, Heidelberg.

Oren A. (2014b) The Family Rhodocyclaceae. In: Rosenberg E., DeLong E.F., Lory S., Stackebrandt E., Thompson F. (eds) The Prokaryotes. Springer, Berlin, Heidelberg.

Picardeau M. (2014) The Family Leptospiraceae. In: Rosenberg E., DeLong E.F., Lory S., Stackebrandt E., Thompson F. (eds) The Prokaryotes. Springer, Berlin, Heidelberg. Qiu, Y.-L., Sekiguchi, Y., Hanada, S., Imachi, H., Tseng, I.-C., Cheng, S.-S. et al.

(2006). Pelotomaculum terephthalicum sp. nov. and Pelotomaculum isophthalicum sp. nov.: two anaerobic bacteria that degrade phthalate isomers in syntrophic association with hydrogenotrophic methanogens. Archives of Microbiology, 185(3), 172-182.

Rainey, F. A. (2015). Pelotomaculum. In Bergey's manual of systematics of archaea and bacteria (eds W. B. Whitman, F. Rainey, P. Kämpfer, M. Trujillo, J. Chun, P. DeVos, B. Hedlund and S. Dedysh).

Rivas, R., Sánchez, M., Trujillo, M. E., Zurdo-Piñeiro, J. L., Mateos, P. F., Martínez-Molina, E. et al. (2003). Xylanimonas cellulosilytica gen. nov., sp. nov., a xylanolytic bacterium isolated from a decayed tree (Ulmus nigra). International Journal of Systematic and Evolutionary Microbiology, 53(Pt 1), 99-103.

Sakamoto, M. , Tanner, A. C. and Benno, Y. (2015). Tannerella. In Bergey's manual of systematics of archaea and bacteria (eds W. B. Whitman, F. Rainey, P. Kämpfer, M. Trujillo, J. Chun, P. DeVos, B. Hedlund and S. Dedysh).

Salvetti, E., Fondi, M., Fani, R., Torriani, S., & Felis, G. E. (2013). Evolution of lactic acid bacteria in the order Lactobacillales as depicted by analysis of glycolysis and pentose phosphate pathways. Systematic and Applied Microbiology, 36(5), 291-305.

Schumann P., Stackebrandt E. (2014) The Families Sanguibacteraceae and Rarobacteraceae. In: Rosenberg E., DeLong E.F., Lory S., Stackebrandt E., Thompson F. (eds) The Prokaryotes. Springer, Berlin, Heidelberg.

Skennerton, C. T., Haroon, M. F., Briegel, A., Shi, J., Jensen, G. J., Tyson, G. W. et al. (2016). Phylogenomic analysis of Candidatus ‘Izimaplasma’ species: free-living

135

representatives from a Tenericutes clade found in methane seeps. ISME Journal, 10(11), 2679-2692.

Sobieraj M., Boone D.R. (2006) Syntrophomonadaceae. In: Dworkin M., Falkow S., Rosenberg E., Schleifer KH., Stackebrandt E. (eds) The Prokaryotes. Springer, New York, NY.

Sokolova, T. G. (2015). Thermosinus. In Bergey's manual of systematics of archaea and bacteria (eds W. B. Whitman, F. Rainey, P. Kämpfer, M. Trujillo, J. Chun, P. DeVos, B. Hedlund and S. Dedysh).

Stackebrandt, E. (2015). Erysipelothrix. In Bergey's manual of systematics of archaea and bacteria (eds W. B. Whitman, F. Rainey, P. Kämpfer, M. Trujillo, J. Chun, P. DeVos, B. Hedlund and S. Dedysh).

Strömpl, C. (2015). Dendrosporobacter. In Bergey's manual of systematics of archaea and bacteria (eds W. B. Whitman, F. Rainey, P. Kämpfer, M. Trujillo, J. Chun, P. DeVos, B. Hedlund and S. Dedysh).

Sun, H., Spring, S., Lapidus, A., Davenport, K., Del Rio, T. G., Tice, H. et al. (2010). Complete genome sequence of Desulfarculus baarsii type strain (2st14T). Standards in Genomic Sciences., 3, 276-284.

Szokoli, F., Castelli, M., Sabaneyeva, E., Schrallhammer, M., Krenek, S., Doak, T. G. et al. (2016). Disentangling the taxonomy of Rickettsiales and description of two novel symbionts (”Candidatus Bealeia paramacronuclearis” and “Candidatus Fokinia cryptica”) sharing the cytoplasm of the ciliate protist Paramecium biaurelia. Applied and Environmental Microbiology, 82(24), 7236-7247.

The Editorial Board (2015). Alkaliflexus . In Bergey's manual of systematics of archaea and bacteria (eds W. B. Whitman, F. Rainey, P. Kämpfer, M. Trujillo, J. Chun, P. DeVos, B. Hedlund and S. Dedysh).

Thompson, C. L., Vier, R., Mikaelyan, A., Wienemann, T., & Brune, A. (2012). ‘Candidatus Arthromitus’ revised: segmented filamentous bacteria in arthropod guts are members of Lachnospiraceae: Arthromitus filaments in arthropod guts. Environmental Microbiology, 14(6), 1454-1465.

Vandekerckhove, T. T., Navarro, J. B., Coomans, A. and Hedlund, B. P. (2015). “Candidatus Xiphinematobacter”. In Bergey's manual of systematics of archaea and bacteria (eds W. B. Whitman, F. Rainey, P. Kämpfer, M. Trujillo, J. Chun, P. DeVos, B. Hedlund and S. Dedysh).

Wiegel, J., Tanner, R., & Rainey, F. A. (2006). An Introduction to the Family Clostridiaceae. The Prokaryotes, 654-678.

Willems, A. (2015). Holdemania. In Bergey's manual of systematics of archaea and bacteria (eds W. B. Whitman, F. Rainey, P. Kämpfer, M. Trujillo, J. Chun, P. DeVos, B. Hedlund and S. Dedysh).

Yokoyama, H. , Wagner, I. D. and Wiegel, J. (2017). Caldicoprobacter. In Bergey's manual of systematics of archaea and bacteria (eds W. B. Whitman, F. Rainey, P. Kämpfer, M. Trujillo, J. Chun, P. DeVos, B. Hedlund and S. Dedysh).

Youssef, N. H., Blainey, P. C., Quake, S. R., & Elshahed, M. S. (2011). Partial genome assembly for a candidate division OP11 single cell from an anoxic spring (Zodletone Spring, Oklahoma). Applied and Environmental Microbiology, 77(21), 7804-7814.

Zheng, H., & Brune, A. (2015). Complete genome sequence of, a free-living relative of the intracellular symbionts of termite gut flagellates (phylum Elusimicrobia). Genome Announcements, 3(4), 11.

136

137

CHAPTER 4: Observation of Flagellated Protists in the guts of Higher Termites Tumulitermes westraliensis and Amitermes obeuntis Following Rainfall

138

Foreword:

I have thus far focused on the bacterial community in the hindgut of termites

and factors that may affect the community structure and composition. One

factor that is usually considered for lower termites only, is the presence of a

protistan community in the gut. It is widely accepted that protists occur in

lower termites, have coevolved with the host and play a role in shaping the gut

community by providing extra niches. There have been multiple historical

sightings in higher termite guts but they remain rarely acknowledged in recent

publications. As part of my first research question – How much intraspecific

variation exists in the gut communities of Western Australian endemic

termites? – the presence of protists was investigated in three local termites

species, including two higher termites.

Introduction:

Phylogenetic studies have shown that termites are closely related to the

cockroaches and should be nested within their order Blattodea (Lo et al., 2000;

Inward et al., 2007; Engel et al., 2009). Furthermore, a symbiosis with

cellulose-degrading flagellated protists developed in a common ancestor to

the lower termites and their sister group, the wood-feeding roaches

(Cryptocercus). These flagellates are transferred vertically to the offspring by

proctodeal trophallaxis (anus-to-mouth) and have been shown to have co-

evolved with their host (Ikeda-Ohtsubo and Brune, 2009; Ohkuma et al., 2009;

Brune and Dietrich, 2015). The establishment of the gut community is

sequential, with the offspring requiring repeated transfer of gut fluid until the

third instar, when they can maintain the larger flagellates and become

independent feeders. In the lower termites, proctodeal trophallaxis is also

important in the two weeks following any molt to re-establish the gut

community (Nalepa, 2015).

139

The flagellate community is crucial to the survival of lower termites on a

cellulose-based diet (Cleveland, 1923; Veivers et al., 1983). The termite secretes

endoglucanases that randomly cleave cellulose chains in the midgut, which

are endocytosed by the larger flagellates in the hindgut. These produce endo-

and exoglucanases, as well as β-glucosidases and accessory enzymes to

synergistically break down cellulose into glucose (Watanabe et al., 1998;

Ohkuma, 2008; Brune, 2014). This product is fermented into acetate within the

protists and taken up by the termite as its primary carbon and energy source

(Leadbetter et al., 1999; Brune, 2014). The flagellates make up the bulk of the

gut volume, creating niches for bacteria to colonise. Bacterial ectosymbionts

coat the protists and confer motility beyond that of the flagella, allowing the

host cell to navigate the currents of the gut and access wood particles

(Ohkuma, 2008). The flagellates have also been shown to produce hydrogen,

used by certain endosymbionts to produce more acetate (Leadbetter et al.,

1999). Protists in lower termites therefore play key roles in metabolism and

shape the bacterial community structure.

These flagellates are generally thought to have been lost in the lineage leading

to the higher termites around 40-60 Mya (family Termitidae; Engel et al.,

2009; Bourguignon et al., 2016). Cellulose degradation is undertaken by the

host and its gut bacteria (Tokuda and Watanabe, 2007; Brune and Dietrich,

2015). Proctodeal trophallaxis has not been reported in the higher termites,

which are thought to share their gut contents by trophallaxis (mouth-to-

mouth) and coprophagy (excrement ingestion; Diouf et al., 2015). The higher

termites underwent a diversification of their diet, with species specialised in

feeding on wood, but also grass, litter, soil or fungus. Termitidae now makes

up more than 70% of all termite species with over 2000 species (Krishna et al.,

2013). The shift from a protist-dominated to a bacterial gut community has

likely contributed to the success of the family Termitidae, in terms of its

diversity and abundance (Engel et al., 2009; Brune, 2014).

140

However, there have been multiple reports of protists found in higher

termites. Cleveland (1923) observed protists in one species of Nasutitermes and

two species of Mirotermes and concluded that protists are strongly linked to

wood-feeding termite species. Kirby (1927) described flagellates and amoebae

in the guts of higher termites. The most abundant were amoebae in the guts of

the wood-feeding Mirotermes. Kirby (1932) also observed three flagellates, one

amoeba and one ciliate species across multiple Amitermes species. Inoue et al.

(1998, unpublished) correlated cellulase activity in the hindgut of the

Australian termite species Ephelotermes melanoma, Macrognathotermes

sunteri and Xylochomitermes melvillensis with the presence of amoeba

(Slaytor, 2000). It was theorised that amoeba were responsible for cellulose-

degradation in certain higher termites. However, for the last decade, the focus

has been on bacterial cellulases (Tokuda and Watanabe, 2007; Brune and

Dietrich, 2015) and the irregular appearance of protists in higher termite guts

has not been extensively investigated.

Rahman et al. (2015) detected protists with molecular methods in four out of

eight pooled samples from the higher termite Gnathamitermes, collected in

Arizona (United States of America). The protist rRNA sequence most closely

resembled that of the ciliate Clevelandella, previously reported as

endosymbionts of wood-feeding roaches (Kidder, 1937; Lynn and Wright,

2013). Metaclevelandella of the same protistan family was first described in the

gut of the higher termite Capritermes incola Wasm (Uttangi and Desai, 1963).

Rahman et al. (2015) concluded that protists may have been retained or

reacquired in low abundance in certain higher termite taxa, but there are only

rare reports of non-flagellated protists in lower termites (Dobell, 1910; de

Mello, 1941).

Protists in higher termites may be low abundance symbionts or transient and

regardless their presence likely impacts the bacterial gut community, either

through functional competition, predation, or through the presence of protist-

associated bacteria. It is therefore important to characterise the function and

141

prevalence of protists in higher termites. In the present study, protists were

observed in the guts of two Western Australian higher termites: Tumulitermes

westraliensis and Amitermes obeuntis. A systematic approach was undertaken

to determine the prevalence of protists in T. westraliensis using light

microscopy.

Methods:

In September 2014, Tumulitermes westraliensis and Amitermes obeuntis

workers were freshly collected from John Forrest National Park (Western

Australia), as described in Chapter 2. Whole guts were prepared as squash

mounts, viewed under a compound light microscope with a 100x oil-

immersion objective lens and filmed with an AM4023X Dino-Eye USB digital

microscope eyepiece. Protists were identified as motile cells with clear

contents, typically larger in size to the gut bacteria (longer than 4 µm).

To determine the prevalence of protists in T. westraliensis, a systematic

approach was used in 2015. The lower termite Coptotermes acinaciformis,

known to harbour flagellates was used as a positive control for the protist

examination method. Three to five T. westraliensis workers were collected

from eight mounds located at two different sites in April and May 2015. Five of

the colonies were located in John Forrest National Park whereas the remaining

three colonies were located at Banyowla Regional Park, Western Australia.

The guts were dissected as described in Chapter 3. The crop was separated

from the remainder of the gut, both prepared as squash mounts and observed

separately. The crop was examined for a minimum of five minutes and the rest

of the gut (referred to as the hindgut hereafter) for a minimum of fifteen

minutes. Protists were filmed as before.

142

Results:

Protists were observed in both A. obeuntis and T. westraliensis individuals

collected in September 2014. Two flagellated oval-shaped protists (one 10 µm

in length and 4 µm in width and the other 5 µm by 3 µm) were found in a

single A. obeuntis worker (Figure 1A, ProtistsAobeSep2014.mp4). An oval

protist (4 µm by 4 µm) containing a dark circular structure was found in

another A. obeuntis individual, as well as a minimum of five elongated protists

(approximately 6-15 µm in length and 1-2 µm in width; Figure 1B).

Oval protists (4-10 µm in length and 3-5 µm in width) were present in multiple

T. westraliensis individuals (Figure 2; ProtistsTwestSep2014.mp4). Two

contained dark rounded structures, arranged in a circular fashion (Figure 2A

and D), while others were clear (Figure 3B and C). Clear elongated protists

(approximately 6-15 µm in length and 1-2 µm in width) were present in large

numbers and protists were found over multiple days in the same batch of T.

westraliensis.

Protists were found in the crop and in the hindgut of the C. acinaciformis

controls in April 2015. No protist activity was examined within either the crop

or the hindgut in 26 T. westraliensis specimens collected in April 2015. No

protists were found in the crop of specimens collected in May 2015. However,

elongated protists (approximately 6-15 µm in length and 1-2 µm in width) were

found in the hindgut of all 11 individuals examined in May (from three

colonies; Figure 3). Two out of five individuals collected from one colony

harboured oval protists as well (4-10 µm in length and 3-5 µm in width; Figures

3 and 4; ProtistsTwestMay2015.mp4). Two oval protists contained dark

rounded structures, but these were scattered throughout the cell (Figure 4A

and B).

143

Figure 1: Protists found in the gut of Amitermes obeuntis in September 2014. Elongated protists are indicated with an 'e' and oval protists with an 'o'. These images are still frames from videos taken using an AM4023X Dino-Eye USB digital microscope eyepiece (see also ProtistsAobeSep2014.mp4). The video contains two clips. A 29 s clip (A) shows two oval protists, unable to move beyond undulations. One protist is 10 µm in length, while the other is less than 5 µm. A flagellum extends from the right extremity of the larger protist. A 30 s clip (B) shows an oval protist containing a single dark structure (4 µm) rotating rapidly on itself and approximately five elongated protists (6-15 µm long) swimming back and forth across the field of view.

144

Figure 2: Protists found in the gut of Tumulitermes westraliensis in September 2014. Elongated protists are indicated with an 'e' and oval protists with an 'o'. These images are still frames from a video recorded using an AM4023X Dino-Eye USB digital microscope eyepiece (see also ProtistsTwestSep2014.mp4). The video is a series of clips. A 35 s clip (A) shows the movements of a 6 µm long oval protist containing dark structures arranged in a circular fashion. The protist is seen rotating and undulating towards the top left corner of the field of view and reverts direction when reaching a solid structure. The clip ends with the protist diving and no longer being visible. Throughout the clip, four to seven clear oval protists (one of similar size and the others less than 5 µm), a large numbers of clear elongated protists and gut bacteria cross the field of view and come in and out of focus, making it difficult to count the cells. A 10 s clip (B) and a 5 s clip (C) show eight and seven clear oval protists respectively (less than 5 µm) and one elongated protist (7 and 15 µm respectively) moving in confined spaces between gut structures. The final 10 s clip (D) shows a single oval protist containing dark structures (arranged in a circular fashion; 6 µm). The protist is unable to move and rotating in place, with the base of a flagellum visible at the top edge of the field of view.

145

Figure 3: Map showing the location of the five T. westraliensis mounds sampled in John Forrest National Park (Western Australia) in April (red) and May (yellow) 2015. One colony was sampled once in each month. The number of individuals sampled per mound varied between three and five as indicated. No protists were found in individuals sampled in April. Elongated protists were recorded in the hindgut of all individuals sampled in May. Oval-shaped protists were found in two out of five workers from one colony sampled in May.

146

Fi

gure

4:

Prot

ists

fou

nd i

n th

e gu

t of

Tum

ulite

rmes

wes

tral

iens

is i

n M

ay 2

015.

Elo

ngat

ed p

rotis

ts a

re i

ndic

ated

with

an

'e' a

nd o

val

prot

ists

with

an

'o'.

Thes

e im

ages

are

stil

l fra

mes

from

vid

eos

take

n us

ing

an A

M40

23X

Din

o-Ey

e U

SB d

igita

l mic

rosc

ope

eyep

iece

(see

al

so P

rotis

tsTw

estM

ay20

15.m

p4).

The

vide

o co

ntai

ns t

hree

clip

s. A

16

s cl

ip (

A)

show

s an

ova

l pr

otis

t co

ntai

ning

sca

tter

ed d

ark

stru

ctur

es (6

µm

). Th

e pr

otis

t is

init

ially

con

stra

ined

, and

then

sw

ims

to th

e le

ft o

f the

fiel

d of

vie

w. A

n el

onga

ted

prot

ist c

onta

inin

g a

sing

le d

ark

stru

ctur

e (6

µm

) cr

osse

s th

e fie

ld o

f vie

w fr

om b

otto

m t

o to

p an

d a

min

imum

of f

our

clea

r el

onga

ted

prot

ists

are

see

n. A

22

s c

lip (

B) s

how

s an

ova

l pro

tist

con

tain

ing

smal

ler

scat

tere

d da

rk s

truc

ture

s (5

µm

). Th

e pr

otis

t is

rot

atin

g ra

pidl

y on

itse

lf. A

fina

l 12

s c

lip (

C)

show

s a

clea

r ov

al p

roti

st (

6 µm

) fir

st r

otat

ing

on i

tsel

f th

en s

wim

min

g to

the

top

rig

ht c

orne

r of

the

fie

ld o

f vi

ew. A

m

inim

um o

f fiv

e el

onga

ted

prot

ists

are

mov

ing

slow

ly o

r re

mai

ning

sta

tiona

ry.

147

Discussion:

Protists were found in the guts of T. westraliensis and A. obeuntis in

September 2014 and in T. westraliensis in May 2015. They were smaller in size

and in lower numbers than in C. acinaciformis. There were two main shapes of

protists observed (oval and elongated), some containing dark rounded

structures. Some of the oval protists were seen to be flagellated and all other

protists are thought to be flagellated because they moved in a similar fashion

(Orsi, personal communication, 11/10/16). The oval-shaped protists were

similar in size and appearance to flagellates described in Amitermes by Kirby

(1932).

The use of an inverted microscope and a higher resolution camera would

confirm the presence of any flagella and cilia. The use of Toluidine blue stain

could establish whether any protists contain plant matter and thus if they are

cellulose-degraders like the lower termite symbionts (Parker et al., 1982). A

molecular approach would be a more systematic and high throughput way of

determining the prevalence of protists in the guts of higher termites. As there

are no universal barcoding primer pair currently available for protists, a

combination of primers or a two-step barcoding approach with universal

eukaryotic primers should be used to investigate the presence and diversity of

protists in higher termites (Pawlowski et al., 2012; Adl et al., 2014).

The protists were not found at all time points or uniformly in all colonies.

Rahman (2015) reported the presence of the ciliate Clevelandella in half of their

Gnathamitermes samples. Clevelandella have been found in cockroach guts,

pointing to a symbiont-host interaction (Kidder, 1937; Lynn and Wright, 2013).

This hypothesis of low-level persistence or reacquisition of protists as

symbionts in higher termites is not supported by this dataset, as it would not

explain the time-dependent presence (or dramatic changes in abundance).

Indeed, protists were found in May 2015 in all three individuals collected from

148

a colony, when they had not been detected in five individuals recovered from

the same colony in April. Another possibility is horizontal transfer of protists

through interactions with lower termites (Radek et al., 2018) or environmental

acquisition for example through soil. Indeed, Clevelandella has since been

detected in soil (Gabilondo et al., 2015). Soil contains many protists which are

often endemic to an area and not well studied (Adl et al., 2014). Using

molecular approaches on gut contents from multiple termite species with

overlapping distributions and the neighbouring soil would help confirm

whether protists in higher termites are environmentally or horizontally

acquired.

In the current study, protist presence seemed to be linked to rainfall. Protists

were first observed in termites collected early in September 2014, following

heavy rain on the 30th August (70.8 mm in Mundaring, Western Australia;

http://www.bom.gov.au/climate/data/). No protists were found in three

colonies sampled in April 2015, when the maximum daily rainfall was 24.0 mm.

Elongated protists were recorded in all three John Forrest National Park

colonies sampled in May and oval protists in one of these colonies, one week

after heavier rain of 42.2 mm. Protists were only detected following the

substantial rain event in the colony sampled twice. Termites examined by

Kirby were collected in Panama in "summer" (1927) and August and in

California in "fall" (1932) and the presence of protozoa was not uniform. All of

these sampling times make up part of the respective locations' typically rainy

seasons, supporting the link between increased rainfall and the observation of

protists in higher termite guts.

Moisture availability is the dominant factor determining the biogeographic

patterns of soil protists (Bates et al., 2013; Adl et al., 2014). In dry weather,

protists may encyst and lay dormant, until the conditions become more

favourable (Adl and Gupta, 2006). Sampling repeatedly from several colonies

over multiple seasons would confirm whether the presence of protists in the

gut is linked to rainfall events and how long they persist in the gut. Such a

149

study would confirm whether the presence of protists in the gut of higher

termites is due to seasonal environmental uptake of active terrestrial protists.

Microscopy would be key to differentiate between active protists and cysts, as

both will be detected with molecular methods (Santos et al., 2015).

This study confirms that at least some higher termites can harbour live

flagellated protists in their guts. The inconsistent nature of protist presence

reported in higher termites in the literature and the current study suggests

that they are likely to be transient, taken up seasonally from the environment

following rain, rather than in a symbiosis with the host. Soil protists can be

cellulose-degrading or bactericidal (Habte and Alexander, 1975; Kramer et al.,

2016), indicating there could be significant impacts caused by the presence of

these protists in higher termite guts through functional competition or

predation. Additionally, certain soil protists have been reported to have

associated bacteria (Bland J. Finlay and Fenchel, 1991, Atef Omara et al., 2017),

which would contribute to a 16S rRNA gene profiling dataset. I did find

protist-associated genera Candidatus Captivus and Candidatus Hepatincola

(order Rickettsiales, class Alphaproteobacteria) in A. obeuntis samples, as

reported in Chapter 3, during a rainy period. The presence of protists may

hence seasonally affect the bacterial community within the gut and should be

taken into account going forward.

150

References: Abdul Rahman, N., Parks, D. H., Willner, D. L., Engelbrektson, A. L., Goffredi, S. K.,

Warnecke, F., Scheffrahn, R. H., Hugenholtz, P. (2015). A molecular survey of Australian and North American termite genera indicates that vertical inheritance is the primary force shaping termite gut microbiomes. Microbiome, 3, 5.

Adl, M. S., & Gupta, V. V. S. R. (2006). Protists in soil ecology and forest nutrient cycling. Canadian Journal of Forest Research, 36(7), 1805-1817.

Adl, S. M., Habura, A., & Eglit, Y. (2014). Amplification primers of SSU rDNA for soil protists. Soil Biology and Biochemistry, 69(0), 328-342.

Bates, S. T., Clemente, J. C., Flores, G. E., Walters, W. A., Parfrey, L. W., Knight, R., Fierer, N. (2013). Global biogeography of highly diverse protistan communities in soil. ISME J, 7(3), 652-659.

Bland J. Finlay, & Fenchel, T. (1991). An anaerobic protozoon, with symbiotic methanogens, living in municipal landfill material. FEMS Microbiology Ecology, 85, 169-180.

Bourguignon, T., Lo, N., Šobotník, J., Ho, S. Y. W., Iqbal, N., Coissac, E. et al. (2016). Mitochondrial Phylogenomics Resolves the Global Spread of Higher Termites, Ecosystem Engineers of the Tropics. Molecular Biology and Evolution, msw253.

Brune, A. (2014). Symbiotic digestion of lignocellulose in termite guts. Nature Reviews Microbiology, 12(3), 168-180.

Brune, A., Dietrich, C. (2015). The gut microbiota of termites: digesting the diversity in the light of ecology and evolution. Annual Review of Microbiology. 69(1). 145-166.

Cleveland, L. R. (1923). Symbiosis between termites and their intestinal protozoa. Proceedings of the National Academy of Sciences of the United States of America, 9(12), 424-428.

de Mello, F. (1941). First record of an amœba parasite of an Indian termite. Proceedings of the Indian Academy of Sciences - Section B, 14, 68-73.

Diouf, M., Roy, V., Mora, P., Frechault, S., Lefebvre, T., Hervé, V., Rouland-Lefèvre, C., Miambi, E. (2015). Profiling the succession of bacterial communities throughout the life stages of a higher termite Nasutitermes arborum (Termitidae, Nasutitermitinae) Using 16S rRNA Gene Pyrosequencing. PLoS One, 10, e0140014.

Dobell, C. (1910). On some parasitic protozoa from Ceylon. Spolia Zeylan, 7, 65-87. Engel, M. S., Grimaldi, D. A., Krishna, K. S. (2009). Termites (Isoptera): their phylogeny, classification, and rise to ecological dominance. American Museum Novitiates, 3650, 1-27. Gabilondo, R., Fernández-Montiel, I., García-Barón, I., & Bécares, E. (2015). The

effects of experimental increases in underground carbon dioxide on edaphic protozoan communities. International Journal of Greenhouse Gas Control, 41, 11-19.

Habte, M., & Alexander, M. (1975). Protozoa as agents responsible for the decline of Xanthomonas campestris in soil. Appl Microbiol, 29(2), 159-164.

Ikeda-Ohtsubo, W., & Brune, A. (2009). Cospeciation of termite gut flagellates and their bacterial endosymbionts: species and ‘Endomicrobium trichonymphae’. Molecular Ecology, 18(2), 332-342.

Inward, D., Beccaloni, G., & Eggleton, P. (2007). Death of an order: a comprehensive molecular phylogenetic study confirms that termites are eusocial cockroaches. Biology Letters, 3(3), 331-335.

151

Kidder, G. W. (1937). The intestinal protozoa of the wood-feeding roach Panesthia. Parasitology, 29(02), 163.

Kirby, H. (1927). Studies on some amoebae from the termite Mirotermes, with notes on some other Protozoa from the Termitidae. Quarterly Journal of Microscopical Science,

Kirby, H. (1932). Protozoa in termites of the genus Amitermes. Parasitology, 24(03), 289.

Kramer, S., Dibbern, D., Moll, J., Huenninghaus, M., Koller, R., Krueger, D. et al. (2016). Resource Partitioning between Bacteria, Fungi, and Protists in the Detritusphere of an Agricultural Soil. Frontiers in Microbiology, 7, 1524.

Krishna, K., Grimaldi, D. A., Krishna, V., & Engel, M. S. (2013). Treatise on the Isoptera of the world. Bulletin of the American Museum of Natural History, 1.

Leadbetter, J. R., Schmidt, T. M., Graber, J. R., & Breznak, J. A. (1999). Acetogenesis from H2 plus CO2 by spirochetes from termite guts. Science, 283(5402), 686-689.

Lo, N., Tokuda, G., Watanabe, H., Rose, H., Slaytor, M., Maekawa, K. et al. (2000). Evidence from multiple gene sequences indicates that termites evolved from wood-feeding cockroaches. Current Biology, 10(13), 801-804.

Lynn, D. H., & Wright, A. D. (2013). Biodiversity and molecular phylogeny of Australian Clevelandella species (class Armophorea, order Clevelandellida, family Clevelandellidae), intestinal endosymbiotic ciliates in the wood-feeding roach Panesthia cribrata Saussure, 1864. Journal of Eukaryotic Microbiology, 60(4), 335-341.

Nalepa, C. A. (2015). Origin of termite eusociality: trophallaxis integrates the social, nutritional, and microbial environments: Origin of termite eusociality. Ecological Entomology, 40(4), 323-335.

Ohkuma, M. (2008). Symbioses of flagellates and prokaryotes in the gut of lower termites. Trends in Microbiology, 16(7), 345-352.

Ohkuma, M., Noda, S., Hongoh, Y., Nalepa, C. A., & Inoue, T. (2009). Inheritance and diversification of symbiotic trichonymphid flagellates from a common ancestor of termites and the cockroach Cryptocercus. Proceedings of the Royal Society B: Biological Sciences, 276(1655), 239-245.

Omar, A., Zhang, Q., Zou, S., & Gong, J. (2017). Morphology and Phylogeny of the Soil Ciliate Metopus yantaiensis n. sp. (Ciliophora, Metopida), with Identification of the Intracellular Bacteria. Journal of Eukaryotic Microbiology, 64, 792-805.

Orsi, W (Department of Earth and Environmental Sciences, Palaeontology & Geobiology, Ludwig Maximilian University of Munich). (11/10/16). Personal communication.

Parker, A. J., Haskins, E. F., & Deyrup-Olsen, I. (1982). Toluidine blue: a simple, effective stain for plant tissues. The American Biology Teacher, 487-489.

Pawlowski, J., Audic, S., Adl, S., Bass, D., Belbahri, L., Berney, C. et al. (2012). CBOL protist working group: barcoding eukaryotic richness beyond the animal, plant, and fungal kingdoms. PLoS. Biology, 10(11).

Radek, R., Meuser, K., Strassert, J. F. H., Arslan, O., Teßmer, A., Šobotník, J. et al. (2018). Exclusive Gut Flagellates of Serritermitidae Suggest a Major Transfaunation Event in Lower Termites: Description of Heliconympha glossotermitis gen. nov. spec. nov. Journal of Eukaryotic Microbiology, 65(1), 77-92. Santos, S. S., Nielsen, T. K., Hansen, L. H., & Winding, A. (2015). Comparison of three DNA extraction methods for recovery of soil protist DNA. Journal of Microbiological Methods, 115, 13-19.

Slaytor, M. (2000). Energy metabolism in the termite and its gut microbiota. In T.

152

Abe, D. E. Bignell, & M. Higashi (Eds.), Termites: Evolution, Sociality, Symbioses, Ecology (pp. 307-332). Dordrecht, The Netherlands: Kluwer Academic Publishers.

Tokuda, G., & Watanabe, H. (2007). Hidden cellulases in termites: revision of an old hypothesis. Biology Letters, 3(3), 336-339.

Uttangi, J. C., & Desai, R. N. (1963). Metaclevelandella termitis, a new genus and species of Heterotrichous ciliate (family Clevelandellidae) found in the Indian termite Capritermes incola Wasm. Parasitology, 53(1-2), 39.

Veivers, P. C., O’Brien, R. W., & Slaytor, M. (1983). Selective defaunation of Mastotermes darwiniensis and its effect on cellulose and starch metabolism. Insect Biochemistry, 13(1), 95-101.

Watanabe, H., Noda, H., Tokuda, G., & Lo, N. (1998). A cellulase gene of termite origin. Nature, 394(6691), 330-331.

153

CHAPTER 5: Accurate Characterisation of the Complete Termite Cellulase Profile Requires Selective Inhibition of Gut Proteases

154

Foreword:

The long term goal of this research is to isolate enzymes from higher termite

guts relevant to second generation biofuel production in Western Australia. A

shorter term goal was to establish if the gut community of local higher

termites can be optimised for breaking down substrates of interest through

feeding experiments and this has been the primary focus of previous thesis

chapters. In this chapter, I begin to answer the final research question – Can

enzymes relevant to breaking down a substrate of choice be identified using

comparative enzymology?

I report on the optimisation of protein extraction and gel based protein

visualisation, enzyme assays and cellulase-binding probe methods for whole

and segmented termite guts. I do not cover in tube cellulase assays or protein

concentration experiments that were also undertaken. The chapter includes a

chronological rationale for each relevant optimisation step to highlight

obstacles to this research, followed by recommendations for future work.

Introduction:

Higher termites are able to efficiently degrade cellulose due to the production

of endogenous enzymes in the midgut and the synergistic action of their gut

bacteria ( reviewed in Brune, 2014). Three major types of glycoside hydrolases

(GHs, collectively referred to as cellulases) are required for the breakdown of

cellulose into glucose. Cellulose is typically insoluble but contains amorphous

regions that can be accessed by endoglucanases (also known as

endocellulases). These cleave the cellulose chain randomly, producing more

ends for exoglucanases (or exocellulases) to act. There are two types of

exoglucanases, allowing these tunnel-shaped enzymes to cleave cellobiose

(made up of two glucose subunits) from either end of the cellulose chain

(reviewed in Wilson, 2011). Finally, β-glucosidase acts on cellobiose to

155

complete the digestion to glucose. Other accessory enzymes, such as

hemicellulases, laccases and aldo-keto reductase, facilitate this process by

increasing access to the cellulose chain ( Coy et al., 2010; Mohanram et al.,

2013; Sethi et al., 2013).

The deployment strategies for these cellulases differ amongst different

organisms. The termite itself secretes soluble endoglucanases in the midgut,

as well as β-glucosidases to break down any resulting cellobiose and prevent

enzyme inhibition (as reviewed by Brune, 2014). The hindgut microbiota

employs a variety of strategies, from secreting soluble enzymes, producing

cell-bound enzymes (typically clustered together in a cellulosome), to binding

directly to the substrate and importing partially degraded chains for further

intracellular processing (Fibrobacteres; Wilson, 2011; Ransom-Jones et al.,

2012). Quantifying and characterising the activity of these enzymes must

therefore be adapted to each enzyme deployment strategy. Indeed, until

methods were developed to target the cell-bound enzymes that dominate the

hindgut of many of the higher termites, the consensus was that endogenous

enzymes drove cellulose digestion in higher termites (Schulz et al., 1986;

Hogan et al., 1988). In 2007, Tokuda and Watanabe showed that cell-bound

cellulases could be accessed following the breakdown of the bacterial cell walls

and centrifugation. This method allows the separation of soluble (so-called

crude extract) and cell-bound (pellet extract) enzymes. Separating the gut

segments also allows for the comparison of the endogenous and symbiont

derived cellulases (O'Brien et al., 1979; McEwen et al., 1980; Tokuda and

Watanabe, 2007).

Cellulase zymography is a commonly used electrophoretic method to visualise

endoglucanase activity on commercially available amorphous cellulose

(carboxymethyl cellulose (CMC); Schwarz et al., 1987; Tokuda and Watanabe,

2007; Oppert et al., 2010). Endoglucanases can be extracted from the resulting

gel in different ways and identified using mass spectrometry: 1) Proteins can

be extracted directly from the zymogram by excising the clear zone that

156

signals cellulase activity (Nakashima et al., 2002; Ni et al., 2014), 2) they can be

extracted following silver staining of the zymogram, allowing the distinction

between proteins of similar sizes (Willis et al., 2010), 3) they can be extracted

from the zymogram using a standard acrylamide gel run in parallel as a guide

(Tarayre et al., 2015), or 4) from the guide acrylamide gel itself (Park et al.,

2002).

Activity-based proteomics probes (ABPPs) can be used to detect and identify

active enzymes of a class of interest. Probes are synthesised to resemble a

substrate or inhibitor of the target enzyme and make use of a reporter group

to visualise or enrich the protein (Stubbs, 2014). Several cellulase-binding

probes have been designed for use in microbial cultures (Chauvigné-Hines et

al., 2012). The use of click chemistry allows for the probes to be coupled with a

fluorescent reporter group for gel visualisation, or biotin for subsequent

protein concentration and identification using mass spectrometry.

I conducted a series of experiments with the aim to visualise cellulases from

the Western Australian termite species Tumulitermes westraliensis and

Amitermes obeuntis and their activity. First, total protein was visualised for

quality control purposes and then the commonly used zymography method

applied, for comparison of endoglucanase activity with previously studied

species. Finally, a cellulase-binding probe method was applied to termite gut

extracts, to allow for the identification of a broad range of cellulases

(Chauvigné-Hines et al., 2012; Anderson et al., 2013).

157

Protocols:

Protocols were optimised throughout the study and relevant versions have

been numbered below. These versions will be referred to in the experiments

section.

Protein Extraction (PE)

PE1: Working on ice, termite gut segments of interest were pooled into 2 mL

bead-beating tubes containing 0.7 g of 0.1 mm zirconia/silica beads and 1 ml

extraction buffer (25 mM Tris pH 8.5, 2% SDS, 10 mM DTT, Roche cOmplete™

Protease Inhibitor Cocktail as instructed). Samples were homogenised with a

FastPrep®-24 (MP Biomedicals) for 45 s at 6.5 m/s, heated at 99° C for 3 min

and centrifuged at 20,000 × g for 10 min at 4° C.

PE2: Working on ice, termite gut segments of interest were pooled into 2 mL

bead-beating tubes containing 0.2 g of 0.1 mm zirconia/silica beads and 200 µl

extraction buffer (25 mM Tris pH 8.5, 2% SDS, 10 mM DTT, Roche cOmplete™

Protease Inhibitor Cocktail as instructed). Phenylmethylsulfonyl fluoride

(PMSF, Sigma Aldrich) was prepared at 100 mM in ethanol and 10 µl added to

samples after the collection of gut segments, as detailed below. Samples were

homogenised with a FastPrep24 for 45 s at 6.5 m/s and centrifuged at 20,000 ×

g for 10 min at 4° C.

PE3: Working on ice, termite gut segments of interest were pooled into 1.5 ml

tubes in 100 µl Tris buffer (pH 7.6, 0.42 µM SDS, as per Chauvigné-Hines et al.

(2012), with Roche cOmplete™ Protease Inhibitor Cocktail). A positive control

for cellulase activity was included, made up of 1 mg/ml cellulase (Aspergillus

niger, Sigma Aldrich) in Tris buffer. 10 µl of 100 mM PMSF was added to each

sample after the dissections were completed. Samples were homogenised with

a micropestle (cleaned between each sample with ethanol) and centrifuged at

20,000 × g for 10 min at 4° C. The supernatant will be referred to as the crude

158

extract and were stored at 4° C until their same-day use (Tokuda et al., 2005).

The pellet was washed three times in Tris buffer, resuspended in 100 µl

CelLytic B (Sigma), vigorously mixed for 15 s and incubated on ice for 10 min.

Samples were centrifuged at 20,000 × g for 10 min at 4° C; the supernatant will

be referred to as the pellet extract (Tokuda and Watanabe, 2007).

PE4: Working on ice, termite gut segments of interest were pooled into 1.5 ml

tubes in 150 µl 50 mM HEPES buffer (pH 7.6, with 1M NDSB201, 20 mM DTT,

50 mM EDTA and 2x Halt™ Protease and Phosphatase Inhibitor Cocktail,

EDTA-free (ThermoFisher Scientific). A positive control for cellulase activity

was included, made up of 30 µg/µl cellulase (Aspergillus niger) in HEPES

buffer. Samples were homogenised with a micropestle (sterilised between each

sample by washing in ethanol) and centrifuged at 20,000 × g for 10 min at 4° C.

The supernatant will be referred to as the crude extract and were stored at 4°

C until their same-day use (Tokuda et al., 2005). The pellet was washed three

times and resuspended in 150 µl of the HEPES buffer with 0.5% Triton x-100.

Homogenates were transferred to bead-beating tubes containing 0.2 g of 0.1

mm zirconia/silica beads and processed three times in a FastPrep24 at 6 m/s

for 40 s, incubating samples on ice for 5 min in between each run. Samples

were centrifuged at 20,000 × g for 5 min at 4° C, transferred to new tubes and

centrifuged once more for 10 min to remove the beads. The supernatant will

be referred to as the pellet extract (Tokuda and Watanabe, 2007).

Protein Precipitation (PP)

PP1: Four volumes of -20° C acetone were added to each extract, and these

were mixed and incubated at -20° C overnight. The next day, they were

centrifuged at 20,000 × g for 20 min at 4° C, the supernatant discarded and the

pellets air-dried. Pellets were resuspended in 30 µL of 2 × Laemmli sample

buffer (0.0625 M Tris pH 6.8; 2 % SDS; 10 % glycerol; 0.001 % Bromophenol

blue) with freshly added 0.4% β-mercaptoethanol (Sigma Aldrich) and heated

at 65° C for 5 min.

159

PP2: Each extract was mixed with 4 volumes methanol, then with 1 volume

chloroform and finally with 2.5 volumes water (All diluents were chilled on ice

prior to use). These were centrifuged at 20,000 × g for 5 min and the aqueous

phase discarded carefully from the top, leaving the proteins in the interphase.

The proteins were mixed once more with 2.5 volumes of methanol and

centrifuged at 20,000 × g for 10 min. The supernatant was discarded and the

pellets air dried and stored at -80 °C until use. Pellets were resuspended in 20

µl 50 mM HEPES, 2% SDS, 1M NDSB201, and 1x Halt™ Protease and

Phosphatase Inhibitor Cocktail, EDTA-free (ThermoFisher Scientific).

Acrylamide gel electrophoresis

Samples in Laemmli sample buffer were centrifuged at 20,000 × g for 3 min at

ambient temperature. Up to 25 µl (avoiding any pellets formed post

centrifugation) were loaded in each well of a 12% acrylamide gel, along with 5

µl of Precision Plus Protein™ Dual Color Standard (BioRad). Gel

electrophoresis was conducted at 90 V until the samples had migrated out of

the stacking gel, whereupon the voltage was increased to 130 V, until the dye

reached the bottom of the gel. For Coomassie staining, SimplyBlue™ SafeStain

(Invitrogen) was applied using the recommended microwave method, which

involves heating the gel 3 x 1 min in distilled water on high and once in the

stain, shaking the gel for 1 min between each stage and 5 min in the stain. The

gel was then rinsed in distilled water on a shaker for several minutes prior to

scanning.

Cellulase zymography

This method was modified from previous studies (Schwarz et al., 1987; Tokuda

and Watanabe, 2007; Oppert et al., 2010). Samples in Laemmli sample buffer

were centrifuged at 20,000 × g for 3 min at RT and 10 µl were loaded in each

well of a 12% acrylamide gel containing 0.1% carboxymethylcellulose (CMC,

Sigma Aldritch), along with 5 µl of MW standard. Gel electrophoresis was

conducted at 90 V until the samples had migrated out of the stacking gel,

160

whereupon the voltage was increased to 130 V, until the dye reached the

bottom of the gel. The gel was washed in distilled water for 5 min, soaked

three times in 0.1 M NaPO4 buffer (pH 6.5) for 20 min, incubated for 60 min in

0.1 M NaPO4 buffer at 28° C. The gel was stained in 1 g/L Congo red solution

(prepared in ethanol) for 30 min and destained in 1M NaCl for 30 min. The

contrast of the stain was increased by adding 5% glacial acetic acid

(Waeonukul, 2007) and the gel photographed on a light box.

Cellulase-binding Probe (CP)

CP1: Aliquots of 30 µl of each enzyme extract were labelled with 2 μl of 75 µM

GH2d-ABP probe (prepared by Mitchell Hattie and Dr Keith Stubbs;

Chauvigné-Hines et al., 2012) and incubated at 60° C for 3 h with mild

agitation (300 rpm) on a Thermomixer Comfort (Eppendorf). Samples were

precipitated with acetone as per protocol PP1. The precipitated samples were

centrifuged at 20,000 × g for 20 min, the pellet dried at ambient temperature

and resuspended in 15 µl Tris pH 7.6 (containing 2% SDS, 50 mM DTT and

Roche cOmplete™ Protease Inhibitor Cocktail). Samples containing probes

were mixed with 6 µl of a labelling mastermix containing Fluor 488-alkyne

(Sigma Aldrich), DTT, TRIS[(1-benzyl-1H-1,2,3-triazol-4-yl)methyl] amine

(TBTA, Sigma Aldrich, prepared in dimethylformamide, DMF) and copper

sulfate as described by Chauvigné-Hines et al. (2012). These were incubated for

1 hr in the dark at ambient temperature. Following gel electrophoresis (see

protocol 1 above, with Coomassie staining deferred) the probe gel was fixed in

40% methanol, 10% acetic acid for 2 × 15 min, rinsed in water and visualised on

a GE Typhoon Trio™ with the green-excitation mode (532 nm) and the 526 nm

filter. The gel was then stained with Coomassie blue.

CP2: Aliquots of 30 µl of each enzyme extract were labelled with 2 μl of 75 µM

GH2d-ABP and incubated at 4° C for 30 min with mild agitation. Samples were

precipitated using protocol PP2. Resuspended samples were mixed with 6 µl of

a labelling mastermix containing Fluor 488-alkyne (Sigma Aldrich), DTT,

tris[(1-benzyl-1H-1,2,3-triazol-4-yl)methyl] amine (TBTA, prepared in DMF)

161

and copper sulfate as described by Chauvigné-Hines et al. (2012). These were

incubated for 30 min in the dark at ambient temperature. Following gel

electrophoresis (see protocol above, with Coomassie blue staining done the

following day) the probe gel was visualised on a GE Typhoon Trio™ with the

green-excitation mode (532 nm) and the 526 nm filter. Finally, gels were fixed

in 40% methanol, 10% acetic acid for 2 × 15 min, rinsed in water overnight and

stained with Coomassie blue.

Experiments:

Below is the optimisation process into visualising termite gut cellulases

undergone throughout this study. The rationale behind each experiment and

the protocols used is followed by each experiment's results, so as to better

explain the reasoning behind the next experiment. The results are discussed

further in a separate section.

Total protein visualisation of termite gut sample extracts

Rationale

Gut samples from a variety of sources were treated minimally and compared at

a total protein level to test standard protein techniques on termite gut samples

and check for any obvious differences between different termite species, diets

(supplied substrates and mound material) or time in captivity.

Methods

Single whole worker guts, in triplicate, were dissected as summarised in Table

1. Total protein was visualised on acrylamide gels stained with Coomassie blue

as per extraction PE1, precipitation PP1 and gel electrophoresis protocols.

162

Table 1: Termite sources and captivity conditions for total gut protein visualisation Sample name

Species Mound Diet Mound material

Duration of captivity

OFP1 Occasitermes occasus

1 Whatman filter paper

None provided

10 days

OW1 Occasitermes occasus

1 Wheat* None provided

10 days

AC1 Amitermes obeuntis

1 Wood chips† Yes 10 weeks

AGT1 Amitermes obeuntis

1 Dead Xanthorrhoea (grass tree) leaves

None provided

10 weeks

AC2 Amitermes obeuntis

2 Wood chips Yes 10 days

AW2 Amitermes obeuntis

2 Wheat None provided

10 days

AFP2 Amitermes obeuntis

2 Whatman filter paper

None provided

10 days

AL2 Amitermes obeuntis

2 Hemp litter‡

None provided

10 days

AC-2 Amitermes obeuntis

2 None provided Yes 10 days

*Wheat was sourced from the monoculture treatment of a long-term crop rotation trial (C:N ratio of 83), provided by Nathan Craig, UWA †Wood chips provided by John McGrath, Future Farm Industries CRC Ltd ‡Litter (Mini-Hemp®) made from hemp hurd was purchased at a pet store Results

The total protein profiles of Amitermes obeuntis and Occasitermes occasus

were very similar, despite the two higher termites not being closely related

(Figure 1). There was a larger number of distinct protein bands appearing in

the 50 to 100 kD range in O. occasus. Modifying the diet of these species over

days or weeks did not appear to affect their total protein profile at this level.

There were inconsistencies across replicates at around 100 kD irrespective of

treatment in A. obeuntis. For example AW2 replicate 1, AC1 replicate 2 and

AGT1 replicate 3 are missing a band present in all other samples, perhaps due

to degradation.

163

Figure 1: Total protein from whole Amitermes obeuntis and Occasitermes occasus guts, following captivity and feeding, as summarised in Table 1. Biological triplicates were processed as described in version 1 of the protein extraction, precipitation and gel electrophoresis protocols. A larger number of proteins appear in the 50 to 100 kD range in O. occasus and a 100 kD band appears inconsistently across replicates in A. obeuntis (missing bands are indicated by arrows).

164

Conclusions

Any differences between the proteomes of termites fed on different diets could

not be identified by visualising total protein profiles in gel and more specific

methods are required. However, they do provide a good method to monitor

overall quality of the extracts and in this case indicated some possible protein

degradation.

Contribution of termite-derived proteins to cellulase profiles

Rationale

This experiment tested whether the addition of Phenylmethylsulfonyl fluoride

(PMSF) would further prevent protein degradation and result in more

consistent protein profiles (without the potentially missing bands seen in the

previous experiment). It also tested whether heating the samples prior to gel

loading was a step that damaged or improved the resulting protein profiles.

Finally, it aimed to compare whole guts to the remaining gutless bodies, as gut

proteomes were expected to contain more cellulases (of interest), but also

more proteases (with potential to disrupt the work).

Methods

Five freshly collected Tumulitermes westraliensis workers were pooled for each

treatment as indicated in Table 2. Protein was extracted as described in

extraction PE2 and 70 µl aliquots from each sample were heated at 99° C for 3

min. Total protein was visualised on acrylamide gels via gel electrophoresis

and cellulase zymography. A positive control for cellulase activity was

included, made up of 1 mg/ml cellulase (Aspergillus niger) in Tris buffer.

165

Table 2: Sample sources and treatments for total protein visualisation and zymography

Sample name

Species Body part Protease inhibitors

Heating

TBPH Tumulitermes westraliensis

Gutless body

cOmplete* and PMSF†

Yes

TBP Tumulitermes westraliensis

Gutless body

cOmplete and PMSF

No

TBRH Tumulitermes westraliensis

Gutless body

cOmplete

Yes

TBR Tumulitermes westraliensis

Gutless body

cOmplete

No

TGPH Tumulitermes westraliensis

Whole gut cOmplete and PMSF

Yes

TGP Tumulitermes westraliensis

Whole gut cOmplete and PMSF

No

TGRH Tumulitermes westraliensis

Whole gut cOmplete

Yes

TGR Tumulitermes westraliensis

Whole gut cOmplete

No

C+ Aspergillus niger‡ n/a none No *Roche cOmplete™ Protease Inhibitor Cocktail (Sigma-Aldrich) †Phenylmethylsulfonyl fluoride (Sigma-Aldrich) ‡ Aspergillus niger cellulase used as a positive control (Sigma-Aldrich)

166

Results

Gutless termite bodies always resulted in more abundant and distinct bands in

their protein profiles than their corresponding whole guts (Figure 2A).

Endoglucanase activity was more abundant in the gut samples, but still

detected in gutless termites (Figure 2B). Heating at 99° C for 3 min prior to gel

electrophoresis resulted in less abundant profiles for gutless termites and less

distinct bands in gut profiles.

Heating also resulted in a major loss of endoglucanase activity. In unheated

samples, proteins just larger than 50 kD produced the bulk of the

endoglucanase activity (see white arrows in Figure 2B). An equivalent band

was present in all total protein profiles, however was more abundant in the

gutless bodies and inconsistent in the gut samples (see black arrows in Figure

2B).

The addition of PMSF did not lead to consistent improvement of the protein

profile. Indeed, proteins larger than 37 kD in TBPH (which included PMSF,

Figure 2A) are in lower abundance than in all other gutless termite samples,

whereas a 50 kD protein in sample TGP (with PMSF) is not visible in TGR (no

PMSF).

167

Figure 2: Total protein (A) and endoglucanase activity (B) from whole guts and gutless bodies of Tumulitermes westraliensis, as summarised in Table 2. Samples were processed as described in version 2 of the protein extraction and version 1 of the gel electrophoresis and cellulase zymography protocols, with and without heating at 99° C for 3 min. Gutless bodies (samples names starting with "TB") have more abundant and distinct bands in their protein profiles than whole guts, whereas endoglucanase activity was more abundant in the gut samples (at approximately 50 kD, see arrows). Heating resulted in less abundant profiles for gutless termites, less distinct bands in gut profiles and a major loss of endoglucanase activity (in (B), no bands are visible in lanes labeled H, which included heated extracts).

168

Conclusions

The comparative quality of the gutless body protein profiles indicated that the

gut extracts were impacted by greater protease activity, not suitably controlled

for by our chosen additives.

Heating samples prior to gel electrophoresis offered no benefits in the context

of this study and was removed from later protocols, in the interest of handling

samples at a minimum.

PMSF did not consistently offer benefits but should not in theory negatively

impact samples and was added in in later experiments as a precaution.

Comparison of total protein and cellulase profiles between species and gut

segments

Rationale

This experiment aimed to visualise T. westraliensis and A. obeuntis gut protein

profiles among different gut segments. The foregut/midgut primarily contains

endogenous enzymes, whereas the hindgut (P1-P5 sections) is dominated by

microbial enzymes (Brune, 2014). Sample sizes were increased to 20 gut

segments per sample, to account for the small gut segment sizes and increase

the amount of protein to visualise.

Methods

Twenty freshly collected T. westraliensis and A. obeuntis gut segments were

pooled for each treatment, as indicated in Table 3. Total protein and

endoglucanase activity (assay conducted in duplicate) was visualised on

acrylamide gels as per extraction PP2 and cellulase zymography. A positive

control for cellulase activity was included, made up of 1 mg/ml cellulase

(Aspergillus niger) in Laemmli sample buffer.

169

Table 3: Gut segment origin for comparison by total protein visualisation and zymography Sample name Species Gut segment

AFM Amitermes obeuntis Foregut/midgut

AH Amitermes obeuntis Hindgut

TFM Tumulitermes westraliensis Foregut/midgut

TH Tumulitermes westraliensis Hindgut

C+ Aspergillus niger _

Results

Separating the guts into foregut/midgut and hindgut (P1-P5) resulted in

distinct profiles for each gut section, which were similar for T. westraliensis

and A. obeuntis (Figure 3A). In both species the foregut/midgut profile is

smeared more heavily and contains few distinct bands, particularly at high

molecular weights.

The cellulase zymogram (Figure 3B) on the other hand identified clear

differences between the two species. A. obeuntis has two major activity bands

in each lane, either side of the 50 kD marker, which do not obviously

correspond with bands of a similar size on the total protein gel. There are

smaller bands (less than 37 kD) that may correspond to those seen in T.

westraliensis. T. westraliensis has a stronger activity in both gut segments,

making the number of bands difficult to determine, however major bands are

smaller than 50 kD.

170

Figure 3: Total protein (A) and endoglucanase activity (B; which includes 2 technical replicates for each treatment) from foregut/midgut and hindgut portions of Tumulitermes westraliensis and Amitermes obeuntis guts (see Table 3), visualised as described in version 2 of the protein extraction and version 1 of the gel electrophoresis and cellulase zymography protocols. T. westraliensis and A. obeuntis have similar total protein profiles, with smears in the foregut/midgut lanes. They have similar sized endoglucanases but these are more active in T. westraliensis.

171

Conclusions

I confirmed that separating guts into foregut/midgut and hindgut sections

provided distinct proteomes. The foregut/midgut section likely contains

greater concentrations of proteases that were not adequately prevented from

affecting the extracts.

Despite the apparent protein degradation, the cellulase zymogram identified

relatively distinct activity bands, specific to each species with endocellulase

activity being stronger in the foregut/midgut and in T. westraliensis, perhaps

due to its more sound and hence more recalcitrant diet. This indicates that the

zymogram method is very sensitive but results likely underestimate the level

of activity in the gut.

Termite cellulase extraction and visualisation using cellulase-binding probes

Rationale

This experiment aimed to replicate the crude and pellet extracts protocol from

Tokuda et al. (2005) to further differentiate microbial proteins as either

secreted or cell bound. A cellulase-binding probe method was also tested to

highlight potential cellulases. Probe GH2d-ABP (Chauvigné-Hines et al., 2012)

was chosen for its affinity to a wide range of GH families and because it was

used by the authors for a subsequent study (Anderson et al., 2013).

Methods

Two replicates of 30 guts each from freshly collected A. obeuntis were divided

into the midgut, the P1 and the P3. Crude and pellet extracts were obtained

(PE3), as summarised in Table 4. CP1 binding and gel electrophoresis protocols

were followed. Aliquots of each enzyme extract were stored following protein

extraction, protein precipitation or following probe binding and precipitation.

Precipitation was deemed required for this protocol to concentrate the

enzyme extracts.

172

Table 4: Sample sources and extract types for cellulase-binding probe test

Sample name

Species Gut segment Enzyme extract

AMC1 Amitermes obeuntis

Midgut Crude

AH1C1 Amitermes obeuntis

P1

Crude

AH3C1 Amitermes obeuntis

P3 Crude

AMC2 Amitermes obeuntis

Midgut Crude

AH1C2 Amitermes obeuntis

P1

Crude

AH3C2 Amitermes obeuntis

P3 Crude

AMPe1 Amitermes obeuntis

Midgut Pellet

AH1Pe1 Amitermes obeuntis

P1

Pellet

AH3Pe1 Amitermes obeuntis

P3 Pellet

AMPe2 Amitermes obeuntis

Midgut Pellet

AH1Pe2 Amitermes obeuntis

P1

Pellet

AH3Pe2 Amitermes obeuntis

P3 Pellet

C+ Aspergillus niger N/A Commercial preparation (soluble extract)

173

Results

The extracts had very similar total protein profiles before and after

precipitation (Figure 4A and B), although the bands look more concentrated

in B. Aliquots of extracts incubated with GH2d-ABP (Figure 4C) have a

different total protein profile after compared to prior the incubation. Select

bands are more visible than they were prior to the incubation, while others

disappear. For example, there is a group of three bands between 37 and 50 kD

in AH3C1 and AH3C2 in Figure 4A that almost entirely disappear in C

(indicated by black brackets in Figure 4A and 4C). Two bands level with these

appear in AMC1 and AMC2 in C (white brackets), as do two thick bands

smaller than 37 kD in the Aspergillus niger control (grey brackets).

The P1 hindgut segment lanes contained only small amounts of protein

(Figure 4A, B and C). The midgut crude extract lanes contained very few

proteins larger than 50 kD. Smearing suggests this may be evidence of protein

degradation. Many of the wells and lanes of the stacking gels can also be seen

to contain (likely aggregated) protein in Figure 4A, B and C.

Fluorescent labelling of proteins bound to GH2d-ABP was largely unsuccessful

with most of the fluorescence detected at the very top or bottom of the gel, as

well as a small amount bound to the largest band featuring in the positive

control, between 50 and 75 kD.

174

Figure 4: Crude and pellet extracts from midgut, P1 and P3 of Amitermes obeuntis (see Table 4 and version 3 of the extraction protocol). Fresh extracts (A), precipitated extracts (B) and extracts incubated with GH2d-ABP cellulase (C, version 1 of cellulase-binding probes protocol) were visualised following version 1 of the gel electrophoresis protocol. The wells can be seen to still contain protein and degradation is likely in most lanes (A, B and C). The total protein profiles in C are different to A and B (differences shown with brackets emcompassing groups of bands). The fluorescence labelling (D, version 1 of cellulase-binding probes protocol) was largely unsuccessful.

175

Conclusions

Many of the wells and lanes of the stacking gels were found to contain protein,

suggesting aggregation. Further additives such as non-detergent

sulphobetaines (NSDB) may assist in solubilisation of these proteins.

The P1 hindgut segment lanes contained only small amounts of protein and

are deemed of lesser interest, at least during the optimisation stages of these

methods.

Fluorescent binding was unsuccessful and the presence of EDTA and DTT

(chelating agents; Krȩżel et al., 2001) in the resuspension buffer likely

inhibited click chemistry with the fluorescent probes by making the required

copper catalyst inert. Removal of these agents may result in further protein

degradation.

Determining the effect of temperature on the use of cellulase-binding probes

Rationale

Non-detergent sulphobetaines (NSDB) have been shown to have a protective

effect against protein denaturation while assisting in protein solubilisation

(Vuillard et al., 1995). Due to persistent issues with potential protein

degradation and aggregation, the addition of the 3-(1-Pyridinio)-1-

propanesulfonate (NDSB201, Sigma Aldritch) and the precipitation of samples

using chloroform/methanol, better suited to membrane-associated proteins

(Wessel and Flügge, 1984) were tested.

Due to the likely effect of EDTA on the click chemistry step, the resuspension

buffer was modified to include 1 × Halt™ Protease and Phosphatase Inhibitor

Cocktail (ThermoFisher Scientific), EDTA-free.

176

In the interest of limiting protein degradation, this experiment aimed to

determine whether the probe binding conditions described by Chauvigné-

Hines et al. (2012) could be conducted in shorter and cooler conditions.

Methods

Cellulase from Aspergillus niger only was used in this experiment and the

temperature and duration of probe binding modulated, as summarised in

Table 5 (10 combinations were chosen to fill a 20 well gel with replication,

starting with the 3 hour, 60°C step described by Chauvigné-Hines et al.). Each

reaction contained 0.2 mg cellulase in 50 mM HEPES, 1M NDSB201 and 1 ×

Halt™ Protease and Phosphatase Inhibitor Cocktail (EDTA-free) and was

labelled with 2 μl of 75 µM GH2d-ABP. Samples were precipitated using PP2

and visualised using CP1.

Table 5: Treatments to find range of successful probe binding to cellulase positive control

Time Temperature (°C) 30 min 1 hr 2 hr 3 hr 30 - - - √ 40 √ √ √ √ 50 √ √ √ √ 60 √ √ √ √ Results

The total protein profile of cellulase from Aspergillus niger was visibly

impacted with increasing temperature, both at 50 and 60° C (Figure 5A and C).

The effect worsened with increasing time at 50° C and was constantly poor at

60° C. Treatments at 40° C did not decline visibly. GH2d-ABP binding

occurred at all temperatures and durations, with the fluorescent profiles

largely matching the Coomassie staining (Figure 5B and D).

177

Figure 5: Cellulase from Aspergillus niger incubated with GH2d-ABP at varying temperatures and durations. Samples were prepared as per the cellulase-binding probe temperature test protocol, precipitated and visualised as described in version 2 of the precipitation protocol and probe optimisation protocol 1. Protein abundance declined with increasing temperature and time. Treatments at 40° C did not visibly alter the protein patterns and GH2d-ABP binding occurred at all temperatures and durations.

178

Conclusions

GH2d-ABP binding occurred at all temperatures and durations, however the

quality of the protein profile decreased with increasing temperature.

Since no difference was noticed between 30 and 40° C, a further experiment

trialed binding at lower temperatures (data not shown). Binding was

successful at 4° C for 30 min, which may allow the maintenance of protein

quality during the experiment.

During this follow up experiment, issues were encountered with the

precipitation of the detergent (SDS) at 4° C. The replacement of SDS with

Triton x-100 was then tested to rectify this.

Finalised cellulase-binding probe protocol

Rationale

Based on the results obtained from the previous experiments, a final protocol

was developed for binding of the GH2d-ABP cellulase-binding probe,

involving Triton x-100 instead of SDS (to prevent precipitation at cold

temperatures) and probe binding at 4° C for 30 min.

Methods

This final protocol was trialled with both T. westraliensis and A. obeuntis over

several days. Up to three replicates with 30 gut segments were used, based on

available harvested material and volume recovered for the pellet extract, as

indicated in Table 6. It involved PE4, CP2 and associated precipitation and gel

electrophoresis protocols.

179

Table 6: Sample sources and extract types used with the final protocol devised during this study Sample name

Species Gut segment

Enzyme extract

Replicates

T1MC Tumulitermes westraliensis Midgut Crude 3 T1H3C Tumulitermes westraliensis P3 Crude 3 T1MPe Tumulitermes westraliensis Midgut Pellet 3

T1H3Pe Tumulitermes westraliensis P3 Pellet 2 T2MC Tumulitermes westraliensis Midgut Crude 2

T2H3C Tumulitermes westraliensis P3 Crude 2

T2MPe Tumulitermes westraliensis Midgut Pellet 1 T2H3Pe Tumulitermes westraliensis P3 Pellet 1

A1MC Amitermes obeuntis Midgut Crude 3

A1H3C Amitermes obeuntis P3 Crude 3 A1MPe Amitermes obeuntis Midgut Pellet 3

A1H3Pe Amitermes obeuntis P3 Pellet 3

A2MC Amitermes obeuntis Midgut Crude 3 A2H3C Amitermes obeuntis P3 Crude 3

A2MPe Amitermes obeuntis Midgut Pellet 3

A2H3Pe Amitermes obeuntis P3 Pellet 3 C+ Aspergillus niger N/A Commercial

preparation One preparation

180

Results

The P3 hindgut segment of Tumulitermes westraliensis contained large

amounts of protein, resulting in an overload of proteins in these lanes. The

fluorescent profiles all contain distinct bands, except for T2H3Pe (E). Across

the two T. westraliensis colonies (Figure 6), the fluorescent profiles (Figure 6B

and E) are more consistent than their associated Coomassie stained profiles (A

and D). Two bands between 75 and 100 kD in T1H3C (A) are not present in

T2H3C (D) and not visible in either under fluorescence. On the other hand,

two bands between 50 and 75 kD in P3 crude extracts in B and E are not clearly

seen in A and D. In the midgut, one band around 25 kD is much more defined

under fluorescence in T1MC (B) and T2MC (E). The same is true of a 50 kD

band in T1MPe (B) and T2MPe (E).

Endoglucanase activity was detected in both gut segments (Figure 6C and F).

Most of the activity was detected in crude extracts and in particular a band

was recorded at 50 kD in at least the P3 crude extract and the midgut pellet

extract. It is not possible to confirm the presence of this band in the midgut

crude extract due to the intense activity seen in this gut segment. Both crude

extracts contain proteins smaller than 50 kD and the hindgut pellet extract

contains a small amount of activity just above 50 kD.

There are proteins potentially responsible for this activity visualised in Figure

6B and E, particularly a distinct band slightly larger than 50 kD in T1H3Pe. In

many cases it is very difficult to match bands across gels, due to the large

number of weak bands and the appearance of the molecular weight marker

under fluorescence and on the cellulase zymogram.

In both Amitermes obeuntis colonies (Figure 7), very few bands were detected

larger than 37 kD in all gut segments both with Coomassie staining and under

fluorescence. In contrast, all of the endoglucanase activity detected was from

proteins larger than 37 kD, around 50 kD (Figure 7C and F).

181

Figure 6: Total protein profiles (A, D) and endoglucanase activity (C, F, cellulase zymography protocol 1) of the crude and pellet extracts from the midgut and P3 of two Tumulitermes westraliensis colonies (see Table 6) recovered as per extraction protocol version 4. (B) and (E) show the fluorescent labelling of GH2d-ABP, as described in probe binding protocol 2 (and associated precipitation and gel electrophoresis protocols).

182

Figure 7: Total protein profiles (A, D) and endoglucanase activity (C, F, cellulase zymography protocol 1) of the crude and pellet extracts from the midgut and P3 of two Amitermes obeuntis colonies (see Table 6) recovered as per extraction protocol version 4. (B) and (E) show the fluorescent labelling of GH2d-ABP, as described in probe binding protocol 2 (and associated precipitation and gel electrophoresis protocols).

183

The fluorescent profile of the second colony (E) was more consistent across

replicates than the first (B), particularly between 25 and 37 kD where two

proteins vary dramatically in abundance.

Conclusions

Concern over the small size of gut segments and protein degradation resulted

in overloading of certain lanes, although the fluorescent probe resulted in

more distinct profiles. These profiles differed slightly from the Coomassie

stained versions, indicating limited specificity of the binding.

In both termite species, more endoglucanase activity was detected in the

midgut, particularly in the crude extracts. This midgut crude extract most

likely represents termite-derived endocellulases and distinct bands from the

hindgut indicate the presence of microbial endoglucanases.

Discussion:

The set of experiments presented in this chapter aimed to apply a cellulase

zymography method, as well as adapt the use of cellulase-binding probe

GH2d-ABP (Chauvigné-Hines et al., 2012) to the Western Australian termite

species Tumulitermes westraliensis and Amitermes obeuntis. The goal is to

ultimately extract proteins of interest from acrylamide gels resulting from

both methods and to identify the cellulases of interest with mass

spectrometry. My work revealed that this aim was challenging to achieve with

high quality extracts, which may have been overlooked by previous studies if

total protein was not visualised (data not shown in previous studies), leading

to a potential underestimation of cellulase capabilities in termites. The issues

encountered are discussed below alongside recommendations for future

studies.

184

The cellulase zymography method was successfully applied to both termite

species, however I found that protein bands could not be reliably excised from

the zymogram or the corresponding Coomassie stained gel. The difficult to

match the activity bands to protein bands was based on three reasons: 1) the

sites of activity overlapped with multiple protein bands; 2) endoglucanase

activity was observed at higher molecular weights than where most visible

protein bands appeared and 3) the molecular weight marker was not always

visible (and did not migrate equally) on the cellulose-containing gel. The high

sensitivity of the enzyme activity staining reaction meant that too low a

concentration of proteins was added to allow elution from the zymogram. One

possible solution may be to heat the samples to partially denature the

endoglucanases, decreasing their activity and allowing more protein to be

loaded. Schwarz et al. (1987) showed that heating clostridial cellulases for 10

min at 70-80° C in the presence of SDS resulted in partial denaturation and

more distinct zymograms. Oppert et al. (2010) heated extracts from various

insects at 70° C for 20 min, and observed reduced smearing as well. They also

reported not detecting protein bands in corresponding Coomassie-stained

gels. In the current study, heating at 99° C for 3 min irreversibly denatured the

cellulases, whereas cellulase activity from marine shipworm bacteria was not

destroyed in harsher conditions (10 min boiling in SDS and ß-

mercaptoethanol; Imam et al., 1993). Further testing is required to optimise

the partial denaturation of the endoglucanases of T. westraliensis and A.

obeuntis in a method similar to that described when testing probe binding

across a temperature range.

Further to point 2) above, it is not clear if endoglucanases were present in

small abundance in the biological samples, or were partially lost during

sample manipulations. Protocol optimisation aimed to minimise protein

aggregation and degradation, but no consistent improvements were achieved

with the addition of protease and phosphatase inhibitor cocktails, PMSF,

NDSB21, Triton x-100, or with heating or chloroform-methanol precipitation.

It could be that not all proteases were inhibited. For example,

185

metalloproteases have been described in termite guts (Sethi et al., 2011; Lima

et al., 2014) and would have still been active following precipitation in

extraction PE4, as EDTA was omitted due to its potential interaction with

fluorescent labelling of the probes. There was no difference in the quality of

the protein profiles following the removal of EDTA with precipitation, but the

potential for other non-inhibited proteases remains. Measurements of

protease activity following incubation of enzyme extracts with several class-

specific inhibitors (Lima et al., 2014) should be undertaken with each termite

species to determine what could cause protein degradation, prior to

investigating the cellulase enzymes.

The zymography method used in this study relied on enzymes renaturing after

migration on a denaturing gel (Schwarz et al., 1987). So the second possible

explanation for the inconsistent protein profiles is that protein degradation is

controlled throughout sample processing, but proteases were able to renature

on the acrylamide gel itself. This is supported by the fact that degradation

seemed to occur primarily in the top half of the gel and always in the midgut

(and occasionally in other gut segments, especially for A. obeuntis). SDS-

resistant proteases have previously been reported and in particular Blumentals

(1990) reported smearing in half of a protease zymogram, caused by a 66 kD

serine protease. In this study, heating at 99° C for 3 min did not seem to

inhibit the proteases, while irreversibly denaturing the cellulases.

Nasutitermes corniger worker proteases have been shown to remain active

following heating at 90° C for 30 min (Lima et al., 2014), suggesting that

heating alone may not be sufficient. Optimising conditions that will

irreversibly denature all enzyme activity in termite guts would allow for a

complete protein profile recovery.

Zymograms showed endoglucanase activity ranged from less than 37 to 50 kD

in T. westraliensis and between 37 and 60 kD in A. obeuntis. Activity bands

ranging from 24 to 40 kD have been reported in a wide range of insect gut

fluids, including the termite Reticulitermes hageni Banks (Oppert et al., 2010).

186

Endoglucanases smaller than 50 kD were reported in the midguts of higher

termites Nasutitermes takasagoensis, N. walkeri and Macrotermes barneyi

(Tokuda and Watanabe, 2007; Wu et al., 2012; Ni et al., 2014). Larger

endoglucanases were reported in the hindguts of N. takasagoensis and N.

walkeri with bands ranging from less than 50 to 120 kD (Tokuda and

Watanabe, 2007). Endoglucanases described in this study are well aligned with

previous findings, particularly in the midgut. Decreasing the protein load or

partial denaturation of the enzymes to limit smearing would confirm the

presence or absence of larger endoglucanases in the hindgut.

Chauvigné-Hines et al. (2012) prescribed harsh conditions of 60° C for 3 hr for

the probe binding. It was shown that probe binding could occur at 4° C over

30 min, to maximise protein quality. A large number of proteins were found to

bind to GH2d-ABP, regardless of the conditions. GHs exist in a wide range of

sizes, from 20 to over 100 kD, if they exist in aggregates (Stütz and Wrodnigg,

2011). The GH profiles obtained in the study could be due to a wide range of

cellulases and other GHs in termite guts, or due to a lack of probe specificity.

This could be determined by adapting the biotin click-chemistry method

described by Chauvigné-Hines et al. (2012) to termite gut extracts to obtain a

proteome and check whether it is primarily made up of GHs.

In summary, I concluded from my experiments that the integrity of the

proteins extracted is the first obstacle that will need to be addressed in the

future to study cellulase activity from termite gut extracts. Active proteases are

likely to result in an underestimated total cellulase activity. In particular, in-

gel assays may underestimate or not detect cellulases that are of similar mass

to proteases that remain active. Optimising a method to partially denature the

endoglucanase activity for more distinct zymograms in combination with a

method to irreversibly denature all enzymes for complete protein profiles

would allow for the largest recovery for endoglucanases of interest. This

strategy is not appropriate for use with cellulase-binding probes, as GHs need

to be in an active form to bind with the probes. A better solution in this case is

187

to test for effective protease inhibitors and use probes with biotin reporter

groups, for protein enrichment and proteomics.

The overarching goal of this work was to identify enzymes of interest to

biofuel production in Western Australia and study their activity. By using

cellulase-binding probes, I sought to identify enzymes in numerous

unsequenced termites and bacterial genomes at once and to study their

abundance and localisation in real time (Stubbs, 2014). Genetic approaches

would then be required to obtain sequences for the most promising enzymes

(for example rapid amplification of cDNA ends or RACE; Ni et al., 2014), which

could then be cloned into culturable organisms (since most organisms in the

gut cannot be cultured; Small et al. 2012) and further tested. This path, if

protein quality can be successfully maintained, would allow targetting of the

most abundant and/or active enzymes, which are of most interest for biofuel

applications.

188

References: Anderson, L. N., Culley, D. E., Hofstad, B. A., Chauvigne-Hines, L. M., Zink, E. M.,

Purvine, S. O., Smith, R. D., Callister, S. J., Magnuson, J. M., Wright, A. T. (2013). Activity-based protein profiling of secreted cellulolytic enzyme activity dynamics in Trichoderma reesei QM6a, NG14, and RUT-C30. Molecular BioSystems, 9(12), 2992-3000.

Blumentals, I. I., Robinson, A. S., & Kelly, R. M. (1990). Characterization of sodium dodecyl sulfate-resistant proteolytic activity in the hyperthermophilic archaebacterium Pyrococcus furiosus. Applied and Environmental Microbiology, 56(7), 1992.

Brune, A. (2014). Symbiotic digestion of lignocellulose in termite guts. Nature Reviews Microbiology, 12(3), 168-180.

Chauvigné-Hines, L. M., Anderson, L. N., Weaver, H. M., Brown, J. N., Koech, P. K., Nicora, C. D., Hofstad, B. A., Smith, R. D., Wilkins, M. J., Callister, S. J., Wright, A. T. (2012). Suite of activity-based probes for cellulose-degrading enzymes. Journal of the American Chemical Society, 134(50), 20521-20532.

Coy, M. R., Salem, T. Z., Denton, J. S., Kovaleva, E. S., Liu, Z., Barber, D. S. et al. (2010). Phenol-oxidizing laccases from the termite gut. Insect Biochemistry and Molecular Biology, 40(10), 723-732.

Hogan, M., Veivers, P. C., Slaytor, M., & Czolij, R. T. (1988). The site of cellulose breakdown in higher termites (Nasutitermes walkeri and Nasutitermes exitiosus). Journal of Insect Physiology, 34(9), 891-899.

Imam, S. H., Greene, R. V., & Hockridge, M. E. (1993). Zymographic analyses of carboxymethylcellulases secreted by the bacterium from wood-boring marine shipworms. Biotechnology Techniques, 7(8), 579-584.

Krȩżel, A., Leśniak, W., Jeżowska-Bojczuk, M., Młynarz, P., Brasuñ, J., Kozłowski, H. et al. (2001). Coordination of heavy metals by dithiothreitol, a commonly used thiol group protectant. Journal of Inorganic Biochemistry, 84(1-2), 77-88.

Lima, T. A., Pontual, E. V., Dornelles, L. P., Amorim, P. K., Sá, R. A., Coelho, L. C. et al. (2014). Digestive enzymes from workers and soldiers of termite Nasutitermes corniger. Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology, 176, 1-8.

McEwen, S. E., Slaytor, M., & O’Brien, R. W. (1980). Cellobiase activity in three species of Australian termites. Insect Biochemistry, 10(5), 563-567.

Mohanram, S., Amat, D., Choudhary, J., Arora, A., & Nain, L. (2013). Novel perspectives for evolving enzyme cocktails for lignocellulose hydrolysis in biorefineries. Sustainable Chemical Processes, 1(1), 15.

Nakashima, K., Watanabe, H., Saitoh, H., & Tokuda, G., Azuma, J.I. (2002). Dual cellulose-digesting system of the wood-feeding termite, Coptotermes formosanus Shiraki. Insect Biochemistry and Molecular Biology, 32(7), 777-784.

Ni, J., Wu, Y., Yun, C., Yu, M., & Shen, Y. (2014). cDNA cloning and heterologous expression of an endo-β-1,4-glucanase from the fungus-growing termite Macrotermes barneyi. Archives of Insect Biochemistry and Physiology, 86(3), 151-164.

O’Brien, G. W., Veivers, P. C., McEwen, S. E., Slaytor, M., & O’Brien, R. W. (1979). The origin and distribution of cellulase in the termites, Nasutitermes exitiosus and Coptotermes lacteus. Insect Biochemistry, 9(6), 619-625.

Oppert, C., Klingeman, W. E., Willis, J. D., Oppert, B., & Jurat-Fuentes, J. L. (2010). Prospecting for cellulolytic activity in insect digestive fluids. Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology, 155(2),

189

145-154. Park, S. G., Kho, C. W., Cho, S., Lee, D. H., Kim, S. H., & Park, B. C. (2002). A

functional proteomic analysis of secreted fibrinolytic enzymes from Bacillus subtilis 168 using a combined method of two-dimensional gel electrophoresis and zymography. Proteomics, 2(2), 206-211.

Ransom-Jones, E., Jones, D. L., McCarthy, A. J., & McDonald, J. E. (2012). The Fibrobacteres: an important phylum of cellulose-degrading bacteria. Microbial Ecology, 63, 267-281.

Schulz, M. W., Slaytor, M., Hogan, M., & O’Brien, R. W. (1986). Components of cellulase from the higher termite, Nasutitermes walkeri. Insect Biochemistry, 16(6), 929-932.

Schwarz, W. H., Bronnenmeier, K., Gräbnitz, F., & Staudenbauer, W. L. (1987). Activity staining of cellulases in polyacrylamide gels containing mixed linkage β-glucans. Analytical Biochemistry, 164(1), 72-77.

Sethi, A., Slack, J. M., Kovaleva, E. S., Buchman, G. W., & Scharf, M. E. (2013). Lignin-associated metagene expression in a lignocellulose-digesting termite. Insect Biochemistry and Molecular Biology, 43, 91-101.

Sethi, A., Xue, Q. G., La Peyre, J. F., Delatte, J., & Husseneder, C. (2011). Dual origin of gut proteases in Formosan subterranean termites (Coptotermes formosanus Shiraki) (Isoptera: Rhinotermitidae). Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, 159(3), 261-267.

Small, G., Hartke, T., Peacock, C., Baer B., & Sutton, D. (2012). Comparative study of two endemic Australian termite species and their symbiotic bacteria. University of Western Australia.

Stubbs, K. A. (2014). Activity-based proteomics probes for carbohydrate-processing enzymes: current trends and future outlook. Carbohydrate research, 390, 9-19.

Stütz, A. E., & Wrodnigg, T. M. (2011). Imino sugars and glycosyl hydrolases: historical context, current aspects, emerging trends. Advances in Carbohydrate Chemistry and Biochemistry, 66, 187-298.

Tarayre, C., Bauwens, J., Brasseur, C., Mattéotti, C., Millet, C., Guiot, P. A. et al. (2015). Isolation and cultivation of xylanolytic and cellulolytic Sarocladium kiliense and Trichoderma virens from the gut of the termite Reticulitermes santonensis. Environmental Science and Pollution Research (international), 22(6), 4369-4382.

Tokuda, G., Lo, N., & Watanabe, H. (2005). Marked variations in patterns of cellulase activity against crystalline‐vs. carboxymethyl‐cellulose in the digestive systems of diverse, wood‐feeding termites. Physiological Entomology, 30(4), 372-380.

Tokuda, G., & Watanabe, H. (2007). Hidden cellulases in termites: revision of an old hypothesis. Biology Letters, 3(3), 336-339.

Vuillard, L., Braun-Breton, C., & Rabilloud, T. (1995). Non-detergent sulphobetaines: a new class of mild solubilization agents for protein purification. Biochemical Journal, 305(Pt 1), 337-343.

Waeonukul, R. , Kya, K. L., Ratanakhanokchai, K. (2007). Multiple cellulases and xylanases from Bacillus circulans B6 during growth on avicel under aerobic condition. Thai Journal for Biotechnology, 8, 27-32.

Wessel, D., & Flügge, U. I. (1984). A method for the quantitative recovery of protein in dilute solution in the presence of detergents and lipids. Analytical biochemistry, 138(1), 141-143.

Willis, J. D., Klingeman, W. E., Oppert, C., Oppert, B., & Jurat-Fuentes, J. L. (2010). Characterization of cellulolytic activity from digestive fluids of Dissosteira carolina (Orthoptera: Acrididae). Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology, 157(3), 267-272.

190

Wilson, D. B. (2011). Microbial diversity of cellulose hydrolysis. Current Opinion in Microbiology, 14(3), 259-263.

Wu, Y., Chi, S., Yun, C., Shen, Y., Tokuda, G., Ni, J. (2012). Molecular cloning and characterization of an endogenous digestive β-glucosidase from the midgut of the fungus-growing termite Macrotermes barneyi. Insect Molecular Biology, 21(6), 604-614.

191

CHAPTER 6: General Discussion

192

As I covered in detail in Chapter 1, higher termites are a promising source of

enzymes or microbes for the production of second-generation lignocellulosic

biofuel, since they efficiently degrade various forms of lignocellulose and have

optimised their primarily bacterial cellulose-degrading consortia over

evolutionary time. In this thesis, I have investigated influences on the gut

community over shorter time scales, to determine whether certain taxa in the

bacterial gut population of endemic higher termites (Termitidae) may warrant

further study as part of optimised biofuel production from wheat crop residue

in Western Australia.

To my knowledge, I provide the first characterisation of gut communities of

three endemic Western Australian termites with broad diets, Tumulitermes

westraliensis, Amitermes obeuntis and Coptotermes acinaciformis raffrayi, as

well as propose standardised techniques for the field of termite gut

microbiology. The findings, implications and limitations of my project are

described below under each of the four research questions, in the context of

improving biofuel production, followed by a discussion on the contributions of

this thesis to the broader understanding of microbial gut communities.

A: How much intraspecific variation exists in the gut communities of

Western Australian endemic termites?

Boucias et al. (2013) and Benjamino and Graf (2016) previously demonstrated

that gut communities of the lower termite Reticulitermes flavipes from the

same colony are more similar to each other than to the gut flora of other

colonies. Reid et al. (2014) found that colonies of the lower termite Stolotermes

ruficeps have unique gut communities, even when collected from colonies in

close proximity to each other, and speculated that differences in diet, such as

wood species or humification state, might explain these differences. In

Chapter 2, I extended these results to two endemic Western Australian

termites, the lower termite C. acinaciformis raffrayi and the higher termite T.

193

westraliensis. I found that colonies of both higher and lower termites have

signature gut communities and suggested that colony differences may be

explained by the complexity of their diets.

There is a correlation between species richness in the guts of higher termites

and the point on the humification gradient at which they feed, with the

highest species richness and diversity in the guts of humus and soil feeders. I

found that the T. westraliensis gut community shared similarities with wood,

grass, and litter feeders, indicating that its diet may be more complex and less

specialised than previously reported by Hill (1942). The gut community of

lower termites is also shaped by the presence of cellulose-degrading protists

that provide unique niches for bacteria (Brune and Dietrich, 2015). My work

uncovered a difference in the abundance of protist-associated bacteria

between colonies of C. acinaciformis raffrayi, suggesting that colony

differences might in part be driven by different abundances of gut protists.

Because one colony was sampled from rotten wood and the other from sound

wood, protist numbers could correlate with humification level of the diet,

although this needs to be tested on a larger dataset. Overall, C. acinaciformis'

gut community composition and structure is similar to other lower termites

that eat various species of wood at different stages of decomposition. I

concluded that locally available substrates may drive gut community structure

and composition at the colony level and that the complexity of a diet, i.e.

number of different food sources, may have a larger impact on gut community

composition and structure in termites than the plant species or humification

level alone. These findings support the idea that the gut community can be

manipulated in the search for optimised consortia to degrade substrates of

interest.

Chapter 2 also highlighted the necessity for extensive sampling to account for

variability of gut communities within a species and to further understand the

drivers of gut microbial community structure and composition in termites. To

that end, Chapter 3 included increased sampling of two higher termites

194

species, T. westraliensis and A. obeuntis, to extend the findings of intraspecific

variation. In particular, Chapter 3 confirmed the finding of intercolony

differences and added findings of caste differences and effects of location and

diet on gut communities. Within colony differences have previously been

investigated between castes of the lower termites C. formosanus (Xiang et al.,

2012) and R. flavipes (Benjamino and Graf, 2016) and between worker age

groups in the higher fungus-cultivating termites Macrotermes gilvus (Hongoh

et al., 2006) and Odontotermes formosanus (Li et al., 2016). Here, caste

differences were described in T. westraliensis and A. obeuntis. Young workers

of T. westraliensis were found to harbour significantly different gut

communities than older workers, the first such reported case in a non fungus-

cultivating species.

In my work, the location of a colony had a strong effect on the gut community

of both termite species and these differences remained significant even at low

sampling intensities. The size of the core community was found to decrease as

sampling intensity increased from single colony, to single location to multiple

locations, in comparison to the variability expected from simple random

sampling. In T. westraliensis, significant differences in gut community

composition were also found between workers collected from the same or

different locations. Hongoh et al. (2005) also found significant differences

between certain sampling sites in the genera Reticulitermes (lower termites)

and Microcerotermes (higher termites). I concluded that the effect of location

is based on various factors that make a location unique, perhaps soil

properties, available food sources, or presence of heterospecifics and their

respective microbial communities.

Chapter 3 also reported the presence of Alphaproteobacteria order

Rickettsiales in most guts of the higher termite A. obeuntis. This group are

known symbionts of protists (Szokoli et al., 2016), suggesting by extrapolation

the presence of their protist hosts. Protists are well characterised in lower

termites and key to their success (Ohkuma and Brune, 2011), while they have

195

been rarely reported in higher termites. Chapter 4 confirmed that at least

some individual T. westraliensis and A. obeuntis workers harbour live

flagellated protists in their guts at certain times. This and other reports of

inconsistent protist presence in guts of higher termites (Cleveland, 1923; Kirby,

1927; Abdul Rahman et al., 2015) suggest that they are likely to be transient,

taken up seasonally from the environment, rather than co-evolving with the

host as in lower termites (Tai et al., 2015; Poinar, 2009). Indeed, soil moisture

content has been found to determine the structure of soil protist communities

(Bates et al., 2013; Adl et al., 2014) and preliminary observations in Chapter 4

suggest they may be taken up following rainfall. This may have a seasonal

effect on the bacterial community and lead to further intraspecific variation. A

more systematic and high throughput molecular approach is needed to

determine the prevalence of protists in the guts of higher termites. As there is

no universal barcoding primer pair currently available for protists, a

combination of primers or a two-step barcoding approach with universal

eukaryotic primers should be used to investigate the presence and diversity of

protists in higher termites (Pawlowski et al., 2012; Adl et al., 2014).

Alongside biological variation, Chapter 2 described the influence of the 16S

rRNA amplication strategy on estimates of gut community composition and

structure in termites. Certain bacterial groups were only detected with one

primer pair or the other, however the V3-V4 method yielded a higher species

richness than the V4 method tested at the same depth. This increased species

diversity could be explained by the lower annealing temperature

recommended with the V3-V4 primer pair by Köhler et al. (2012), which has

been shown to limit some of the bias associated with PCR (Sipos et al., 2007). I

therefore recommend that a standard approach be applied using the primers

343Fmod and 784Rmod designed by (Köhler et al., 2012) to target the V3-V4

region of the 16S rRNA gene, along with a low annealing temperature of 48°C.

This method has already been used on the widest range of termite species

(across all families and many feeding groups) in the literature to date, allowing

broader comparisons (e.g. Bourgignon et al. 2018). If used more broadly, this

196

approach would maximise data reproducibility and utility across research

groups.

B: How should the core community of a termite species be defined and

accurately estimated?

The identification of the core community of a habitat is important, because

those taxa are critical to the functioning of that community (Shade and

Handelsman, 2012). I chose to define the core community as the list of

Operational Taxonomic Units (OTUs) clustered at 97% similarity and present

in 100% of samples in the grouping of interest. One limitation of this approach

is the arbitrary threshold of 97% similarity. Although frequently chosen to

represent a bacterial species, it is not necessarily the best approximation of the

number of bacterial species actually present (Nguyen et al., 2016). Indeed, it

has been shown that even bacteria with identical 16S sequences could

represent multiple species (Antony-Babu et al., 2017) and that many bacterial

species harbour multiple copies of the 16S rRNA gene, which may not all be

identical (Větrovský and Baldrian, 2013). This has implications for the

conclusions drawn about relative abundances of taxa in the gut, as well as

species richness estimations and by extension the size of the core community.

Despite this, low numbers of OTUs were shared across species as compared to

within a species, suggesting that this method is still useful in the investigation

of the factors shaping the presence of OTUs, if not bacterial species, in the gut.

In spite of these limitations, I chose the 97% similarity OTU clustering cut-off

because it has been used in the majority of termite gut meta-barcoding studies

to date (e.g. Otani et al. 2014, Abdul Rahman et al., 2015, Tai et al. 2015,

Benjamino and Graf, 2016, Mikaelyan et al., 2017). In the future, comparing the

clustering of OTUs at 97% to 99% or even 100% is warranted, as

recommended by multiple studies (Nguyen et al., 2016; De Filippis et al., 2018;

Edgar, 2018). As the accessibility of single-molecule real-time sequencing (e.g.

Pacific Biosciences, Oxford Nanopore) or Whole Genome Shotgun Sequencing

197

(WGS) increases, bacterial species can be better distinguished (Franzén et al.,

2015; Ranjan et al., 2016). The caveat is that the datasets would have a different

set of biases than when obtained with V3-V4 primers and Illumina sequencing

(Ross et al., 2013), making comparisons with existing studies more difficult.

The definition of a core community has differed substantially between termite

microbiome studies (Huang et al., 2013; Dietrich et al., 2014; Otani et al., 2014;

Reid et al., 2014; Abdul Rahman et al., 2015, Benjamino and Graf, 2016; Su et al.,

2016; Wang et al., 2016). The caste composition of samples (scope) was usually

limited to workers, the geographic distribution of sampling sites (scale) in

most cases restricted to a single location or small portion of the species known

range, and the number of samples taken per unit of interest (sampling

intensity) varied widely (see Chapter 3 for further details). The recovery cut-

offs for inclusion of a bacterial OTU in the core community varied between

40-100% of samples in these studies. To test the effects of modifying sampling

intensity, scale and scope, a stringent cut-off of 100% for the inclusion of

OTUs in the core community was chosen. Some taxa that are in fact members

of the core community may not have been included if they were below

detection in certain or all samples with the even sequencing depth of 11,400

reads achieved in Chapter 3. However, false positives were excluded by

ensuring that taxa were shared in all samples. The field as a whole would

benefit from a standardised definition of the core community and methods for

its determination and my aim in Chapter 3 was to propose a way forward.

I hypothesised that sampling scale, scope and intensity would all affect core

community estimations. I found that sampling intensity had the largest effect

on the outcome, which is fortunately the easiest factor for researchers to

control. Scale and scope also had significant effects and hence, all three form

important considerations when designing a study. I have shown that a

minimum of 20 samples but ideally 30 per factor of interest should be included

in any core community calculation, in order to draw statistically meaningful

conclusions. I developed a simple random sampling method which future

198

researchers can use on their own data sets to test whether sufficient sampling

intensity has been reached. The many factors contributing to intraspecific

variation (Research Question A, above) highlight how previous studies relying

on samples from a single colony or location have likely overestimated the

number of taxa in the termite gut core community. I therefore recommend

that future studies include samples from the maximum relevant range to

minimise the possibility of spurious results due to unintended location effects.

I found that increasing scope by including multiple castes decreased core

community size but changing the proportion of samples from each caste had

no effect. Hence all relevant castes should be included, even if very few

samples are available for some castes. In practice, this means sampling only

workers if the question is exclusive to them or using all castes for a species

core estimation.

Including all castes in a core community calculation should better represent

the vertically transmitted gut microbiome passed from parent colonies to new

colonies via the alates (founding queen and king), and shared from the

founders to all future colony members by the workers. It should be noted that

soldiers are effectively a dead end, as their gut community is not shared with

others (Noirot and Darlington, 2000). I chose a cautious and more stringent

approach by including them since soldier guts are also inoculated via the

workers (Diouf et al., 2015) and the conditions in soldier guts may foster taxa

that are present but less abundant in workers. I found a core community

shared by all castes, made up of 56 and 116 OTUs for T. westraliensis and A.

obeuntis respectively. In T. westraliensis I had sufficient samples to show that

there is a community of 14 OTUs exclusive and universal to the reproductive

castes (nymphs and alates). Because of the alates' role in vertical transmission,

I would expect the alate gut community to most closely resemble the species

core community. This suggests that alate-exclusive OTUs are likely be present

at low levels in other castes. I typically recovered a smaller number of OTUs

from alates in both termite species than from other castes, but Diouf et al.

(2018) found a higher species diversity in the swarming alate gut compared to

199

any other caste of Nasutitermes arborum. It has been suggested that alates

purge their gut prior to flight (Nutting, 1969), which could result in lower,

more even abundances of gut bacteria and recovery of a greater number of

OTUs at the same sequencing depth. In the future, comparisons of pre- and

post-flight alates will reveal whether they have distinct gut communities and

may provide insights into pre-swarming behaviour.

Taxa exclusive to a location or feeding group are likely to be environmentally

acquired, ingested with food or soil. The most abundant taxa in most core

communities have multiple functions, with cellulose degradation being the

most common function predicted in both species. Taxa exclusive but universal

to termites consuming a diet of interest may degrade it most efficiently, or

more effectively counter the plant's defensive mechanisms (Bennett and

Allsgrove, 1994; Sugio et al., 2015). Such a list is obtained by calculating the

core community for a group feeding on the substrate of interest (including at

least 20 samples, as shown above) and excluding taxa found in the core

community of a larger group. For example, this larger group may be the entire

species (species core community) or termites sampled across multiple feeding

groups, as relevant. These exclusive taxa may in turn be a source of enzymes

well suited to breaking down the substrate of choice and hence have industrial

applications.

C: Can the gut community of local higher termite species be modified in a

field-based feeding experiment?

Feeding experiments have most commonly been conducted with lower

termites and in laboratory conditions (e.g. Tanaka et al., 2006; Husseneder et

al., 2009; Huang et al., 2013), where several reported that altering diet

components or feeding different plant species has been found to affect the gut

community. Miyata et al. (2007) showed that altering the components of the

diet of the higher termite N. takasagoensis in laboratory conditions affected

200

both the composition and structure of the gut microbiota. Wang et al. (2016)

found that the gut community of the wood-feeding higher termite

Mironasutitermes shangchengensis reverted to its original state after feeding

on corn stalks and filter paper for 10 days and noted poor survival of higher

termites in laboratory conditions (with a maximum of 10 days). Preliminary

work conducted during my PhD in which T. westraliensis, A. obeuntis and

Occasitermes occasus were kept under controlled laboratory conditions met

with inconsistent success; survival ranged from a minimum of one day in T.

westraliensis to a maximum of around one month in all species. Activity of the

termites declined over this time and presence of mites increased. Presence of

mites has been linked to deteriorating nest conditions and decreased activity

of termites in other species, including Cryptotermes secundus (Korb & Fuchs,

2006). Due to the limitations of laboratory-based experiments in higher

termites, a field-based study was conducted and described in Chapter 3.

The field study's primary benefit was to minimise the impact of experimental

procedures on the termites, which, in laboratory conditions, could have

resulted in changes in the gut community beyond those induced simply by a

change in diet. A three-week field study resulted in a significant effect of

multiple diet treatments on the gut communities of both species. These

effects, including significant differences between the control diet and lucerne

or wheat-fed termites, could only be detected at greater sampling intensities.

While underscoring the importance of adequate replication in gut microbiome

studies (Research Question B, above), this result demonstrates that

manipulating the gut community under field conditions is possible, potentially

allowing the identification of key organisms or enzymes relevant to breaking

down substrates of interest without relocation and artificial conditions.

However, there are two main limitations to a field-based study. I could not

differentiate between transient and resident gut members (Waidele et al.,

2017) and importantly, confining the termites to the provided food source

remains difficult. In this study, I could not guarantee that the change was

201

solely caused by the provided diets rather than the relocation of natural food

sources during the experimental setup. Future studies should include

additional control colonies that remain undisturbed throughout the

experiment, isolated enough to guarantee they are not affected by the

substrates available at other mounds. Further optimisation of methods to

characterise the actual diet using chloroplast gene sequencing may help to

confirm whether this was successful. Investigating the microbial community

introduced alongside the provided substrates would also be beneficial.

Another approach would be to target species like A. obeuntis that feed directly

at the source by placing the substrate directly on top of the mound to

potentially enhance feeding, as well as providing easy means to collect the

feeding termites (Lambert and Power, 1999).

An approach with more direct applications would be to study higher termites

collected directly from Western Australian Wheatbelt fields (feeding on wheat

or the eucalyptus mallee plantations surrounding the fields; Yu et al., 2015) or

crop residue. The idea to feed termites biofuel feedstocks was reviewed by

Scharf and Boucias (2010) and attempted in Reticulitermes flavipes with corn

stover and soybean residue (Rajarapu et al., 2015; Rajarapu and Scharf, 2017).

They reported a loss of protists likely due to plant defensive mechanisms, as

well as small changes in enzyme expression between treatments. However as

discussed previously, higher termites naturally feed on a wider variety of

substrates and offer primarily bacterial enzymes, which are more applicable to

industrial processes. Wheat and/or eucalyptus mallee would also be more

relevant substrates for a WA specific biofuel approach, warranting further

study.

Overall, it was shown that it is possible to influence the gut community of

local higher termites over a three week period in field conditions.

Environmental uptake may in part explain the effects of diet and location

measured in Chapter 3. Co-habitation, i.e. the two species sharing a mound,

was found to have a significant effect on the gut communities, presumably due

202

to horizontal transfer of taxa between the species, either through ingestion of

heterospecific corpses, shared mound material or faecal matter (Bourguignon

et al, 2018).

D: Can enzymes relevant to breaking down a substrate of choice be

identified using comparative enzymology?

The long-term goal of this work is to identify enzymes of interest to biofuel

production in Western Australia and study their biochemical activity.

Knowing that the microbial gut communities, which produce enzymes of

interest, can be influenced on a short time scale, future studies could focus on

enzyme-related work to lead to more practical applications. In Chapter 5,

protein extraction and visualisation protocols were optimised to improve the

integrity of enzymes recovered from termite guts, an important obstacle that

was not consistently achieved. The biggest challenge was preventing protein

degradation while maintaining cellulase activity. Previous studies on termite

gut cellulases have not reported the effect of proteases, except for Lima et al.

(2014), and zymograms are rarely shown in conjunction with total protein gels.

If denaturation occurred during sample preparation, it is likely that this was

not an issue exclusive to my work and may have gone unnoticed in previous

studies, leading to an underestimation of cellulase abundance and activity in

the guts of termites.

As I have outlined in Chapter 5, the inconsistent protein profiles obtained on

acrylamide gels could also be caused by renaturation of proteases during

electrophoresis, since degradation occurred primarily in the top half of the gel.

Indeed, Blumentals (1990) reported smearing caused by an SDS-resistant

serine protease in the top half of a protease zymogram. In Chapter 5, heating

extracts at 99° C for 3 min did not inhibit the proteases, but did irreversibly

denature the cellulases of interest. Furthermore, Nasutitermes corniger worker

proteases have been shown to remain active following heating at 90° C for 30

203

min (Lima et al., 2014), suggesting that heating alone is not an appropriate

treatment to inactivate termite gut proteases. Further work is required to find

ways to control these proteases without interfering with probe binding or

cellulase activity. One way to achieve this could be to measure protease

activity following incubation of enzyme extracts with several class-specific

inhibitors (Lima et al., 2014), with the aim to optimise a protective cocktail.

The endoglucanases described in Chapter 5 were regardless well aligned with

previous findings. In both T. westraliensis and A. obeuntis, more

endoglucanase activity was detected in the midgut, where the crude extract

should represent termite derived endocellulases (Tokuda et al., 2005; Tokuda

& Watanabe, 2007). Different bands were found in hindgut extracts, indicating

the presence of distinct microbial endoglucanases. Endoglucanase activity

bands ranged from less than 37 to 50 kD in T. westraliensis and between 37

and 60 kD in A. obeuntis, similar to sizes reported in a wide range of insect gut

fluids, including the termite Reticulitermes hageni Banks (24 to 40 kD; Oppert

et al., 2010). Endoglucanases smaller than 50 kD were reported in the midguts

of higher termites Nasutitermes takasagoensis, N. walkeri and Macrotermes

barneyi (Tokuda and Watanabe, 2007; Wu et al., 2012; Ni et al., 2014). Larger

endoglucanases were reported in the hindguts of N. takasagoensis and N.

walkeri with bands ranging from less than 50 to 120 kD (Tokuda and

Watanabe, 2007).

I then focused on cellulase-binding probes to visualise enzymes of interest,

with the aim of isolating them as outlined by Chauvigné-Hines et al. (2012).

Chauvigné-Hines et al. (2012) prescribed harsh conditions of 60° C for 3 hr for

the probe binding step. It was shown in Chapter 5 that GH2d-ABP binding

could occur at 4° C over 30 min, which might be better conditions to maximise

protein integrity. A large number of proteins were found to bind to this probe,

regardless of the conditions. This could indicate the presence of a wide range

of cellulases and glycoside hydrolases (GH) since they have been reported as

ranging from 20 to over 100 kD in aggregate forms (Stütz and Wrodnigg, 2011).

204

Another possibility is a lack of probe specificity, which could be determined by

using biotin reporter groups instead of fluorescent groups (Chauvigné-Hines

et al., 2012) to obtain a proteome and check whether it is primarily made up of

GHs. Genetic approaches would then be required to obtain sequences for the

most promising enzymes, which could then be cloned into culturable

organisms for further testing (Ni et al., 2014).

Another option to circumvent protein degradation issues would be to use

genomic approaches to find candidate enzymes. This could be achieved with

WGS or a more tailored approach would be to design taxon-specific primers

based on 16S rRNA data to target cellulose degraders of interest, followed by

long-read sequencing (Orr et al., 2018). A difficulty with this strategy is that

cellulases have evolved multiple times independently with unrelated

sequences and structures, and hence each cellulase-containing protein family

would need to be searched for independently (Sukharnikov et al., 2011).

Further research would then be necessary to clone sequences of interest into

culturable hosts and test for functionality, optimal conditions and specificity

of the identified cellulases (Willis et al., 2010). In fact metagenome studies

have had a poor record of predicting enzymes recovered in proteomic studies

in termite guts (Scharf, 2015). The appeal of the approach tested in Chapter 5

is that targeted enzymes would be known to be functional and efficient in

vivo, although their optimal conditions, specificity and efficiency in the new

host would still need to be evaluated.

Broader Contributions

In the context of local optimisation of biofuel production from lignocellulose,

my work provides a roadmap for discovering and studying enzymes from

termite guts, and other microbial habitats. My investigation into primer bias

reinforces the need for standardised techniques and increased replication in

the field of gut metagenomics, to allow for valid comparisons between studies.

My stringent core community analysis method allows the most accurate

205

species core community estimation in termites to date, also contributing to

reproducibility across studies. Subseting the gut community into groups

shared by all species members or within groups of interest (colony, location,

diet) facilitates a better understanding of functional roles and eventually

sources of enzymes. Finally, my work into optimising protein extraction and

enzyme visualisation highlighted the need for greater quality control in this

field, to more accurately measure the abundance and activity of enzymes of

interest. These advances change the way past studies are interpreted and will

have an impact on how future research on microbial communities is

conducted.

The gut communities of three endemic Western Australian termites, higher

termites T. westraliensis and A. obeunits and the lower termite C.

acinaciformis raffrayi were described for the first time. I have shown in the

process that termite gut microbiomes are more complex than previously

thought. Bacterial communities specific to each colony, location and diet were

characterised, highlighting the plasticity of the gut community and building

upon the more recent idea that the bacterial community as a whole is not in

fact highly conserved (Brune and Dietrich, 2015; Tai et al., 2015; Bourguignon

et al., 2018). To my knowledge, the alate bacterial gut community has been

investigated in only three other studies to date (Hongoh et al., 2006;

Benjamino and Graf, 2016; Diouf et al. 2018) despite being the vehicle for

vertically transferred organisms. The alate gut community warrants further

study for evidence of co-speciation between gut symbionts and the termite

host. The inconsistent presence of protists in two higher termite species was

supported by both observational and sequencing data in this study, indicating

another, perhaps seasonal, factor shaping the gut community of higher

termites.

My work has implications for how we understand the evolution and ecology of

termite microbiomes, particularly the factors shaping intraspecies variation.

206

The mechanism likely to generate intraspecific differences in the termite gut

community are summarised in Figure 1. A key point is the mixing of vertically

and horizontally acquired organisms to create unique, locally adapted gut

communities. Microbiota transplant experiments have shown that gut

communities are assembled primarily around available niches in the gut

habitat, with the final gut community composed of lineages from the

community of origin, but the community structure resembling the normal gut

community of the recipient host (Rawls et al., 2006; Mikaelyan et al., 2016). An

example niche in a higher termite gut are prominent cuticular spines

extending about halfway the width of the gut lumen described in

Procubitermes aburiensis by Bignell et al. (1980). These are more extensive

than in most other insect species, providing surfaces for the attachment of

microorganisms. Seedorf et al. (2014) found that the transplanted gut

community composition and structure more closely resembled the community

of origin if it was from a gut environment than a non-gut environment,

suggesting that these gut specific niches suit particular taxonomic groups.

Although these transplants were artificial, termites naturally receive

microbiome transplants via trophallaxis every time they moult, because the

lining of the gut is shed (Xing et al., 2013). In lower termites at least, symbionts

are shed ahead of molting and die in the process (Raina et al., 2008; Napela,

2017), so that gut microbes must be obtained from other workers.

Recently, Bourguignon et al. (2018) compared 94 termite species across all six

termite families and all eight Termitidae subfamilies, with a similar

sequencing depth to mine (11,509 reads per sample compared to my 11,400).

They suggest the gut community has been shaped by a ‘‘mixed-mode’’

transmission combining vertical transfer with more extensive horizontal

transfer between termite lineages than previously expected. They compared

representative sequences for "genus level" OTUs to the most closely related

published environmental sequences. Often the termite-derived sequences

clustered together, indicating that most of the taxa had a strong host

specificity for termites and may be vertically or horizontally acquired. Other

207

Fi

gure

1: F

acto

rs in

fluen

cing

ter

mite

gut

com

mun

ity c

ompo

sitio

n an

d le

adin

g to

intr

aspe

cific

var

iatio

n. D

ispe

rsin

g re

prod

uctiv

es in

ocul

ate

the

new

ly f

ound

ed c

olon

y w

ith o

rgan

ism

s br

ough

t fr

om t

heir

par

ent

colo

nies

. Fur

ther

diff

eren

ces

wit

hin

a sp

ecie

s m

ay r

esul

t fr

om e

nvir

onm

enta

l ac

quis

ition

via

con

tact

with

foo

d, s

oil,

mou

nd m

ater

ial,

or h

oriz

onta

l tr

ansf

er b

y co

nsum

ing

hete

rosp

ecifi

c co

rpse

s or

fae

cal

mat

ter.

The

se

acqu

ired

org

anis

ms

may

be

tran

sien

t or

bec

ome

resi

dent

in

the

gut

com

mun

ity.

The

re-e

stab

lishm

ent

of t

he g

ut c

omm

unity

in

mou

lted

indi

vidu

als

may

pro

vide

opp

ortu

nitie

s fo

r co

loni

satio

n of

a g

iven

nic

he b

y a

diffe

rent

mic

robe

. Th

e gu

t co

mm

unity

is

exch

ange

d be

twee

n ge

nera

tions

of c

olon

y m

embe

rs, b

efor

e in

ocul

atin

g di

sper

sing

rep

rodu

ctiv

es. T

his

mix

ing

of v

ertic

ally

tra

nsm

itted

and

env

iron

men

tally

acq

uire

d or

gani

sms

gene

rate

s un

ique

gut

com

mun

ities

in in

divi

dual

s an

d at

the

colo

ny le

vel.

208

clusters were interspersed with environmental sequences suggesting

horizontal transfer with other animal groups or environmental uptake.

Such environmental uptake may in part explain the effects of diet and location

measured in Chapter 3. Diet was shown to influence the gut community both

over evolutionary time (Chapter 2) and on a shorter time scale (Chapter 3).

Co-habitation, which increases the opportunity for horizontal transfer, was

also found to have a significant effect on the gut communities of T.

westraliensis and A. obeuntis in Chapter 3; certain OTUs were shared between

the species only when cohabitating. Taxa present in the environment may

appear transiently in the gut community or establish and be amplified in the

gut habitat when taken up from soil, food or other termites. This mixed-mode

transmission could foster adaptive flexibility of the gut community to the

environmental conditions and food sources of the colony (Shapira, 2016).

Presumably a sample of all gut microbes, whether vertically transferred,

environmentally and/or horizontally acquired, is shared with post-moult

nestmates (Figure 1). All of these organisms, whether established or transient

in the donor, would have the opportunity to establish in the unpopulated

lining of the gut, increasing colony-level differences.

Conclusion

My project confirms that diet affects termite gut core community composition

and abundance on a short time scale under field conditions. Future studies

could focus on the enzymes produced by the gut communities of higher

termites directly feeding on biofuel substrates of interest, e.g. crops or crop

residue in Western Australian Wheatbelt fields, including wheat and/or

eucalyptus mallee. Continued effort is warranted: certain biofuels are the best

renewable energy option currently available for niche applications such as

powering large ships and mining equipment (Sims et al., 2014) and second

generation biofuel production would recycle crop "waste" that is currently

being burnt. Biofuels from wheat straw would thus result in harnessing CO2

209

currently produced from crop residue burning, to diminish the need for

burning of fossil fuels, two activities that contribute to climate change.

I have also proposed new standards for experimental design in the study of

termite gut communities, including the use of a standardised 16S rRNA

amplification strategy, increased replication, a standard species core

community definition and analysis method for accurate calculations. The

communities of three endemic Western Australian termites were described for

the first time and factors shaping intraspecific variation were further

characterised. This has provided a greater understanding of the vertically,

horizontally and environmentally acquired components of the gut community

and the generation of intraspecific variation. Hence, intraspecific variation

occuring on a short time scale allows the manipulation of the gut community

to target substrates of interest, laying the foundation for future local biofuel

initiatives.

210

References Abdul Rahman, N., Parks, D. H., Willner, D. L., Engelbrektson, A. L., Goffredi, S. K.,

Warnecke, F., Scheffrahn, R. H., Hugenholtz, P. (2015). A molecular survey of Australian and North American termite genera indicates that vertical inheritance is the primary force shaping termite gut microbiomes. Microbiome, 3, 5.

Adl, S. M., Habura, A., & Eglit, Y. (2014). Amplification primers of SSU rDNA for soil protists. Soil Biology and Biochemistry, 69(0), 328-342.

Antony-Babu, S., Stien, D., Eparvier, V., Parrot, D., Tomasi, S., & Suzuki, M. T. (2017). Multiple Streptomyces species with distinct secondary metabolomes have identical 16S rRNA gene sequences. Sci Rep, 7(1), 66.

Bates, S. T., Clemente, J. C., Flores, G. E., Walters, W. A., Parfrey, L. W., Knight, R., Fierer, N. (2013). Global biogeography of highly diverse protistan communities in soil. ISME J, 7(3), 652-659.

Benjamino, J., & Graf, J. (2016). Characterization of the core and caste-specific microbiota in the termite, Reticulitermes flavipes. Frontiers in Microbiology, 7, 1.

Bennett, R. N., & Allsgrove, R. M. W. (1994). Secondary metabolites in plant defence mechanisms. New Phytologist, 127, 617-633.

Blumentals, I. I., Robinson, A. S., & Kelly, R. M. (1990). Characterization of sodium dodecyl sulfate-resistant proteolytic activity in the hyperthermophilic archaebacterium Pyrococcus furiosus. Applied and Environmental Microbiology, 56(7), 1992.

Bourguignon, T., Lo, N., Dietrich, C., Šobotník, J., Sidek, S., Roisin, Y., Brune, A., Evans, T. A. (2018). Rampant host switching shaped the termite gut microbiome. Current Biology, 28(4), 649-654.e2.

Brune, A., & Dietrich, C. (2015). The gut microbiota of termites: digesting the diversity in the light of ecology and evolution. Annual Review of Microbiology, 69(1), 145-166.

Chauvigné-Hines, L. M., Anderson, L. N., Weaver, H. M., Brown, J. N., Koech, P. K., Nicora, C. D., Hofstad, B. A., Smith, R. D., Wilkins, M. J., Callister, S. J., Wright, A. T. (2012). Suite of activity-based probes for cellulose-degrading enzymes. Journal of the American Chemical Society, 134(50), 20521-20532.

Cleveland, L. R. (1923). Symbiosis between termites and their intestinal protozoa. Proceedings of the National Academy of Sciences of the United States of America, 9(12), 424-428.

Costa-Leonardo, A. M., Laranjo, L. T., Janei, V., & Haifig, I. (2013). The fat body of termites: functions and stored materials. Journal of Insect Physiology, 59(6), 577-587.

De Filippis, F., Parente, E., Zotta, T., & Ercolini, D. (2018). A comparison of bioinformatic approaches for 16S rRNA gene profiling of food bacterial microbiota. International Journal of Food Microbiology, 265, 9-17.

Dietrich, C., Köhler, T., & Brune, A. (2014). The cockroach origin of the termite gut microbiota: patterns in bacterial community structure reflect major evolutionary events. Applied and Environmental Microbiology.

Diouf, M., Hervé, V., Mora, P., Robert, A., Frechault, S., Rouland-Lefèvre, C., Miambi, E. (2018). Evidence from the gut microbiota of swarming alates of a vertical transmission of the bacterial symbionts in Nasutitermes arborum (Termitidae, Nasutitermitinae). Antonie Van Leeuwenhoek, 111(4), 573-587.

Diouf, M., Roy, V., Mora, P., Frechault, S., Lefebvre, T., Hervé, V., Rouland-Lefèvre, C., Miambi, E. (2015). Profiling the succession of bacterial communities

211

throughout the life stages of a higher termite Nasutitermes arborum (Termitidae, Nasutitermitinae) Using 16S rRNA Gene Pyrosequencing. PLoS One, 10, e0140014.

Edgar, R. C. (2018). Updating the 97% identity threshold for 16S ribosomal RNA OTUs. Bioinformatics, 34(14), 2371-2375.

Franzén, O., Hu, J., Bao, X., Itzkowitz, S. H., Peter, I., & Bashir, A. (2015). Improved OTU-picking using long-read 16S rRNA gene amplicon sequencing and generic hierarchical clustering. Microbiome, 3, 43.

Hill, G. F. (1942). Termites (Isoptera) from the Australian Region. Melbourne, Australia: Council for Scientific and Industrial Research.

Hongoh, Y., Ekpornprasit, L., Inoue, T., Moriya, S., Trakulnaleamsai, S., Ohkuma, M., Noparatnaraporn, N., Kudo, T. (2006). Intracolony variation of bacterial gut microbiota among castes and ages in the fungus-growing termite Macrotermes gilvus. Molecular Ecology, 15(2), 505-516.

Hongoh, Y., Deevong, P., Inoue, T., Moriya, S., Trakulnaleamsai, S., Ohkuma, M., Vongkaluang, C., Noparatnaraporn, N., Kudo, T. (2005). Intra- and interspecific comparisons of bacterial diversity and community structure support coevolution of gut microbiota and termite host. Applied & Environmental Microbiology, 71(11), 6590-6599.

Huang, X.-F., Bakker, M., Judd, T., Reardon, K., & Vivanco, J. (2013). Variations in diversity and richness of gut bacterial communities of termites (Reticulitermes flavipes) fed with grassy and woody plant substrates. Microbial Ecology, 65(3), 531-536.

Husseneder, C., Berestecky, J. M., & Grace, J. K. (2009). Changes in composition of culturable bacteria community in the gut of the formosan subterranean termite depending on rearing conditions of the host. Annals of the Entomological Society of America, 102(3), 498-507.

Kirby, H. (1927). Studies on some amoebae from the termite Mirotermes, with notes on some other Protozoa from the Termitidae. Quarterly Journal of Microscopical Science.

Köhler, T., Dietrich, C., Scheffrahn, R. H., & Brune, A. (2012). High-resolution analysis of gut environment and bacterial microbiota reveals functional compartmentation of the gut in wood-feeding higher termites (Nasutitermes spp.). Applied & Environmental Microbiology, 78(13), 4691-4701.

Korb, J., & Fuchs, A. (2006). Termites and mites - adaptive behavioural responses to infestation? Behaviour, 143(7), 891-907.

Li, H., Dietrich, C., Zhu, N., Mikaelyan, A., Ma, B., Pi, R. et al. (2016). Age polyethism drives community structure of the bacterial gut microbiota in the fungus-cultivating termite Odontotermes formosanus. Environmental Microbiology, 18(5), 1440-1451.

Lima, T. A., Pontual, E. V., Dornelles, L. P., Amorim, P. K., Sá, R. A., Coelho, L. C. et al. (2014). Digestive enzymes from workers and soldiers of termite Nasutitermes corniger. Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology, 176, 1-8.

Mikaelyan, A., Meuser, K., & Brune, A. (2017). Microenvironmental heterogeneity of gut compartments drives bacterial community structure in wood-and humus-feeding higher termites. FEMS Microbiology Ecology, 93(1), fiw210.

Mikaelyan, A., Thompson, C. L., Hofer, M. J., & Brune, A. (2016). Deterministic assembly of complex bacterial communities in guts of germ-free cockroaches. Applied and Environmental Microbiology, 82(4), 1256-1263.

Miyata, R., Noda, N., Tamaki, H., Kinjyo, K., Aoyagi, H., Uchiyama, H., Tanaka, H.

212

Influence of feed components on symbiotic bacterial community structure in the gut of the wood-feeding higher termite Nasutitermes takasagoensis. Bioscience, Biotechnology, and Biochemistry, 71(5), 1244-1251.

Nalepa, C. A. (2017). What kills the hindgut flagellates of lower termites during the host molting cycle. Microorganisms, 5(4).

Nguyen, N. P., Warnow, T., Pop, M., & White, B. (2016). A perspective on 16S rRNA operational taxonomic unit clustering using sequence similarity. NPJ Biofilms Microbiomes, 2, 16004.

Ni, J., Wu, Y., Yun, C., Yu, M., & Shen, Y. (2014). cDNA cloning and heterologous expression of an endo-β-1,4-glucanase from the fungus-growing termite Macrotermes barneyi. Archives of Insect Biochemistry and Physiology, 86(3), 151-164.

Ohkuma, M., & Brune, A. (2011). diversity, structure, and evolution of the termite gut microbial community. In D. E. Bignell, Y. Roisin, & N. Lo (Eds.), Biology of Termites: a Modern Synthesis (pp. 413-438). Netherlands: Springer.

Oppert, C., Klingeman, W. E., Willis, J. D., Oppert, B., & Jurat-Fuentes J. L. (2010). Prospecting for cellulolytic activity in insect digestive fluids. Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology, 155(2), 145-154.

Otani, S., Mikaelyan, A., Nobre, T., Hansen, L. H., Kone, N. A., Sorensen, S. J., Aanen, D.K., Boomsma, J.J., Brune, A. Poulsen, M (2014). Identifying the core microbial community in the gut of fungus-growing termites. Molecular Ecology.

Pawlowski, J., Audic, S., Adl, S., Bass, D., Belbahri, L., Berney, C. et al. (2012). CBOL protist working group: barcoding eukaryotic richness beyond the animal, plant, and fungal kingdoms. PLoS. Biol., 10(11).

Poinar, G. O. (2009). Description of an early Cretaceous termite (Isoptera: Kalotermitidae) and its associated intestinal protozoa, with comments on their co-evolution. Parasites and Vectors, 2(1), 12.

Raina, A., Park, Y. I., & Gelman, D. (2008). Molting in workers of the Formosan subterranean termite Coptotermes formosanus. Journal of Insect Physiology, 54(1), 155-161.

Rajarapu, S. P., & Scharf, M. E. (2017). Saccharification of agricultural lignocellulose feedstocks and protein-level responses by a termite gut-microbe bioreactor. Frontiers in Energy Research, 5, 186.

Rajarapu, S. P., Shreve, J. T., Bhide, K. P., Thimmapuram, J., & Scharf, M. E. (2015). Metatranscriptomic profiles of Eastern subterranean termites, Reticulitermes flavipes (Kollar) fed on second generation feedstocks. BMC Genomics, 16, 332.

Ranjan, R., Rani, A., Metwally, A., McGee, H. S., & Perkins, D. L. (2016). Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. Biochemical and Biophysical Research Communications, 469(4), 967-977.

Rawls, J. F., Mahowald, M. A., Ley, R. E., & Gordon, J. I. (2006). Reciprocal gut microbiota transplants from zebrafish and mice to germ-free recipients reveal host habitat selection. Cell, 127(2), 423-433.

Reid, N. M., Addison, S. L., West, M. A., & Lloyd-Jones, G. (2014). The bacterial microbiota of Stolotermes ruficeps (Stolotermitidae), a phylogenetically basal termite endemic to New Zealand. FEMS Microbiology Ecology, 90(3), 678-688.

Ross, M. G., Russ, C., Costello, M., Hollinger, A., Lennon, N. J., Hegarty, R., Nusbaum, C., Jaffe, D.B. (2013). Characterizing and measuring bias in sequence data. Genome Biology, 14(5), R51.

Scharf, M. E., & Boucias, D. G. (2010). Potential of termite-based biomass pre-

213

treatment strategies for use in bioethanol production: Termite-based biomass pre-treatment strategies. Insect Science, 17(3), 166-174.

Seedorf, H., Griffin, N. W., Ridaura, V. K., Reyes, A., Cheng, J., Rey, F. E. et al. (2014). Bacteria from diverse habitats colonize and compete in the mouse gut. Cell, 159(2), 253-266.

Shade, A., & Handelsman, J. (2012). Beyond the Venn diagram: the hunt for a core microbiome: The hunt for a core microbiome. Environmental Microbiology, 14(1), 4-12.

Scharf, M. E. (2015). Omic research in termites: an overview and a roadmap. Frontiers in Genetics, 6, 76.

Sims, R., Schaeffer, R., Creutzig, F., Cruz-Núñez, X., D’Agosto, M., Dimitriu, D. et al. (2014). Transport. In O. Edenhofer, R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth et al. (Eds.), Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Sipos, R., Székely, A. J., Palatinszky, M., Révész, S., Márialigeti, K., & Nikolausz, M. (2007). Effect of primer mismatch, annealing temperature and PCR cycle number on 16S rRNA gene-targetting bacterial community analysis: PCR parameters influencing quantitative bias. FEMS Microbiology Ecology, 60(2), 341-350.

Su, L., Yang, L., Huang, S., Su, X., Li, Y., Wang, F. et al. (2016). comparative gut microbiomes of four species representing the higher and the lower termites. Journal of Insect Science, 16(1).

Sugio, A., Dubreuil, G., Giron, D., & Simon, J.-C. (2015). Plant-insect interactions under bacterial influence: ecological implications and underlying mechanisms. Journal of Experimental Botany, 66(2), 467-478.

Sukharnikov, L. O., Cantwell, B. J., Podar, M., & Zhulin, I. B. (2011). Cellulases: ambiguous nonhomologous enzymes in a genomic perspective. Trends in Biotechnology, 29(10), 473-479.

Szokoli, F., Castelli, M., Sabaneyeva, E., Schrallhammer, M., Krenek, S., Doak, T. G. et al. (2016). Disentangling the taxonomy of Rickettsiales and description of two novel symbionts (”Candidatus Bealeia paramacronuclearis” and “Candidatus Fokinia cryptica”) sharing the cytoplasm of the ciliate protist Paramecium biaurelia. Applied and Environmental Microbiology, 82(24), 7236-7247.

Tai, V., James, E. R., Nalepa, C. A., Scheffrahn, R. H., Perlman, S. J., & Keeling, P. J. (2015). The role of host phylogeny varies in shaping microbial diversity in the hindguts of lower termites. Applied and Environmental Microbiology, 81(3), 1059-1070.

Tanaka, H., Aoyagi, H., Shiina, S., Doudou, Y., Yoshimura, T., Nakamura, R., Uchiyama, H. (2006). Influence of the diet components on the symbiotic microorganisms community in hindgut of Coptotermes formosanus Shiraki. Applied Microbiology & Biotechnology, 71(6), 907-917.

Tokuda, G., Lo, N., & Watanabe, H. (2005). Marked variations in patterns of cellulase activity against crystalline‐vs. carboxymethyl‐cellulose in the digestive systems of diverse, wood‐feeding termites. Physiological Entomology, 30(4), 372-380.

Tokuda, G., & Watanabe, H. (2007). Hidden cellulases in termites: revision of an old hypothesis. Biology Letters, 3(3), 336-339.

Větrovský, T., & Baldrian, P. (2013). The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS ONE, 8(2), e57923.

214

Waidele, L., Korb, J., Voolstra, C. R., Künzel, S., Dedeine, F., & Staubach, F. (2017). Differential ecological specificity of protist and bacterial microbiomes across a set of termite species. Frontiers in Microbiology, 8, 5.

Wang, Y., Su, L., Huang, S., Bo, C., Yang, S., Li, Y. et al. (2016). Diversity and resilience of the wood-feeding higher termite Mironasutitermes shangchengensis gut microbiota in response to temporal and diet variations. Ecology and Evolution, 6(22), 8235-8242.

Willis, J. D., Oppert, C., & Jurat-Fuentes, J. L. (2010). Methods for discovery and characterization of cellulolytic enzymes from insects. Insect Science, 17(3), 184-198.

Wu, Y., Chi, S., Yun, C., Shen, Y., Tokuda, G., Ni, J. (2012). Molecular cloning and characterization of an endogenous digestive β-glucosidase from the midgut of the fungus-growing termite Macrotermes barneyi. Insect Molecular Biology, 21(6), 604-614.

Yu, Y., Bartle, J., Mendham, D., & Wu, H. (2015). Site variation in life cycle energy and carbon footprints of mallee biomass production in Western Australia. Energy Fuels, 29(6), 3748-3752.

Xiang, H., Xie, L., Zhang, J., Long, Y., Liu, N., Huang, Y., Wang, Q. (2012). Intracolonial differences in gut bacterial community between worker and soldier castes of Coptotermes formosanus. Insect Science, 19, 86-95.

Xing, L., Chouvenc, T., & Su, N.-Y. (2013). Molting process in the Formosan subterranean termite (Isoptera: Rhinotermitidae). Annals of the Entomological Society of America, 106(5), 619-625.