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Electronic Theses and DissertationsUC Riverside

Peer Reviewed

Title:Selective Association Between the Free-Living Nematode Acrobeloides maximus and SoilBacteria

Author:Sedky, Sammy Farid

Acceptance Date:2013

Series:UC Riverside Electronic Theses and Dissertations

Degree:M.S., Genetics, Genomics and BioinformaticsUC Riverside

Advisor(s):De Ley, Paul

Committee:Orwin, Paul, Crowley, David

Permalink:https://escholarship.org/uc/item/14r816m2

Abstract:

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UNIVERSITY OF CALIFORNIA RIVERSIDE

Selective Association Between the Free-Living Nematode Acrobeloides maximus

and Soil Bacteria

A Thesis submitted in partial satisfaction of the requirements for the degree of

Master of Science

in

Genetics, Genomics and Bioinformatics

by

Sammy Farid Sedky

June 2013

Thesis Committee:

Dr. Paul De Ley, Chairperson Dr. Paul Orwin Dr. David Crowley

Copyright by Sammy Farid Sedky

2013

The Thesis of Sammy Farid Sedky is approved:

Committee Chairperson

University of California, Riverside

iv

ACKNOWLEDGMENTS

This master thesis would not have been possible without the help of

several individuals who contributed their time, assistance, and expertise to the

completion of this work:

• To my advisor, Dr. Paul De Ley: Thank you for giving me a lab I could call

home, where I was welcome and where I could grow as a researcher and

scholar. Your assistance in navigating the labyrinth of challenges involved in the

writing and completion of this document has been invaluable. Thank you for your

guidance and support.

• To Dr. Paul Orwin: I will always be grateful for the kind tutelage you provided

during your sabbatical stay at UCR. This study could not have been completed

without your vigilance.

• To Dr. David Crowley: Thank you for allowing us to use Science Lab I 308

to perform most of the wet lab work and for your assistance as a committee

member in the review and editing of this thesis.

• To my colleagues, Dr. JP Baquiran and Brian Thater: It was a blast working

with you both. Thanks for the laughs and good times.

v

UCR DEDICATIONS

• To my friend and protégé, Rosalynn Duong: I could not have chosen a

better undergraduate assistant. Thank you for all your help, the late hours you

put in taking care of the nematodes and bacterial cultures, your assistance with

molecular work, and for most of all sticking with me on this journey.

• To Dr. Irma Tandingan De Ley: Thank you for always being generous with

your time and for your help and technical expertise in the lab.

• To Dr. Cecilia Ritzinger: Thanks for showing me that sometimes we just

need to step back and take a coffee break! Obrigado!

• To the wonderful GGB graduate students: Amanda Hollowell, Yifan Lii,

Stephanie Coffman, Keira Lucas, Hagop Atamian, Patrick Schacht,

Meenakshi Kagda, Tusar Saha, Divya Sain, Ritu Chaudhary, Venkatesh

Sivanandam: I wish you all nothing but the best in your future endeavors! It’s

been a pleasure being your social committee chair these last two years.

• To Dr. Michael Fugate, Dr. John Oross, Dr. Laura Abbot, Dr. Brian

Brady, Dr. Gregory Filatov, Devon Duron Ehnes-Zepeda: Being a TA was one

of the highlights of my graduate career. It was a pleasure to work alongside you

while developing my pedagogical skills.

• To Tiago Pereira and Ran Sun: I always enjoyed your visits to the lab.

Thanks for taking time out of your busy schedules to chat with me. All the best!

vi

DEDICATIONS TO FAMILY AND FRIENDS

• To my loving parents, Soha and Mohamed: Thank you for your endless

encouragement, love, and support. This one’s for you!

• To my aunt and uncle, Siham and Fathy: Thanks for always believing in me

and for your love, encouragement, and support.

• To my sister, Sally: You are never far from my thoughts. Your strength and

courage has always been a source of inspiration.

• To the friends that cheered me on, encouraged me, and believed in me:

Jorge Flores, Ryan Lee, Johnny Tang, Arif Mosavi, Samuel Koo, Deep

Shah, Anand Panchal, Mrunal Patel, Jason Pico, Edgar Alvarado, David

Benitez, Max Flores, Jasmine Flores, Dynasty Carfangnia, Jose Hernandez,

Stephen Clawson: Thank you !!!

vii

TABLE OF CONTENTS

GENERAL INTRODUCTION IMPORTANCE OF NEMATODE- BACTERIA SYMBIOSIS RESEARCH

1

RECENT STUDIES OF NEMATODE-BACTERIAL ASSOCIATIONS IN SOIL MICROBIAL COMMUNITIES

4

Radopholus similis and Wolbachia 4

Entomopathogenic nematodes and γ-Proteobacteria 5

Steinernema carpocapsae and Xenorhabdus nematophila

6

Heterorhabditis bacteriophora and Photorhabdus luminescens

6

Bacillus thuringiensis Cry5B crystal proteins and free living nematodes

7

Caenorhabditis elegans 7

Pristionchus pacificus 8

SYNOPSIS OF THIS THESIS 8 REFERENCES 10 CHAPTER ONE: Survey of the microbiota associated with A. maximus in soil microcosm

ABSTRACT 15 INTRODUCTION 16 MATERIALS AND METHODS 18 Nematode cultivation and harvest 18

Rhizosphere soil collection and exposure 19

Long-term culture of A. maximus 20

Isolation of associated bacteria 20

DNA extraction and PCR analysis 21

16S rRNA gene library construction, sequencing and analysis 22

Preparation for Fluorescence In Situ Hybridization (FISH) 23

FISH Probes 24

FISH- Hybridization, Mounting, Microscopy 24

RESULTS 25 16S rDNA analysis of extracts 25

Co-cultures 26

Isolation and culture 26

Fluorescence In Situ Hybridization 27

DISCUSSION 27 REFERENCES 31 FIGURES AND TABLES 34

viii

CHAPTER TWO: Phylogenetic Analysis of Bacterial Genera Detected At Significant Levels Within The Microbiota Associated With A. maximus

ABSTRACT 41 INTRODUCTION 42 MATERIALS AND METHODS 43 Data Preparation 43

Multiple sequence alignments: T-Coffee, MAFFT, Kalign, and WebPRANK

44

Phylogenetic inference tools: Maximum Likelihood, PhyML, Mr. Bayes

45

LALIGN 45

RESULTS 46 Ochrobactrum sp. clones 46

Chitinophaga sp. clones 46

Pedobacter sp. clones 47

LALIGN plots 47

DISCUSSION 48 Comparison of multiple sequence alignment algorithms 48

Comparison of phylogenetic reconstruction methods 49

Ochrobactrum sp. clones 50

Chitinophaga sp. clones 51

Pedobacter sp. clones 53

LALIGN analysis 54

Requirements for describing a new bacterial species 55

Genotypic approaches 55

Phenotypic approaches 56

Chemotaxonomic approaches 56

FINAL THOUGHTS 57 REFERENCES 58 FIGURES AND TABLES 62 CONCLUDING COMMENTS BACTERIAL COMMUNITY IDENTIFICATION: 16S rRNA GENE MARKER SURVEY VERSUS SHOTGUN METAGENOMICS

88

16S rRNA Gene Marker Surveys 88

Shotgun Metagenomics 90

PCR-amplified 16S rRNA and Pyrosequencing 91

Sequencing Technologies and Future Considerations 92

REFERENCES 95

ix

LIST OF FIGURES

CHAPTER ONE

Figure 1. Soil exposed A. maximus populations were found to be associated with a consistent bacterial population

34

Figure 2A. Ochrobactrum is shown to be the predominate alpha-proteobacteria within A. maximus

35

Figure 2B. Pedobacter is shown to be the primary Spingobacteria within A. maximus, along with a consistent representation of Chitinophaga at a lower level

36

Figure 3A-C. Long term culture experiments appear to show stability at the phylum level (A) but reveal shifts in population as the worm population ages at the genus level (B,C)

37

Figure 4A-F. Fluorescence in situ hybridization was used to identify the presence of both Ochrobactrum sp. (green) and Pedobacter sp. (red) in the gut of formaldehyde fixed A. maximus after recovery from soil microcosm

38

CHAPTER TWO

Figure 1. Ochrobactrum: T-Coffee-PhyML Phylogram 67-68 Figure 2. Ochrobactrum: MAFFT-ML Phylogram 69-70 Figure 3. Ochrobactrum: Kalign-MrBayes Phylogram 71-72 Figure 4. Chitinophaga: T-Coffee-ML Phylogram 73-74 Figure 5. Chitinophaga: MAFFT-ML Phylogram 75-76 Figure 6. Chitinophaga: Kalign-ML Phylogram 77-78 Figure 7. Pedobacter : T-Coffee-PhyML Phylogram 79-80 Figure 8. Pedobacter : MAFFT-PhyML Phylogram 81-82 Figure 9. Pedobacter : Kalign-PhyML Phylogram 83-84 Figure 10A. LALIGN Plot: Ochrobactrum sp. clone Acro231

compared against the consensus sequence of Ochrobactrum clones

85

Figure 10B. LALIGN Plot: Chitinophaga sp. clone Acro210 compared against the consensus sequence of Chitinophaga clones

86

Figure 10C. LALIGN Plot: of Pedobacter sp. clone Acro279 compared against the consensus sequence of Pedobacter clones

87

x

LIST OF TABLES

CHAPTER ONE

Table 1. List Of Sequences Deposited Into GenBank 39

Table 2. Shannon-Wiener Population Statistics for Microcosm Samples

39

Table 3. Identified Isolates from association from A. maximus microcosm and long term culture

40

CHAPTER TWO

Table 1A. Ochrobactrum Sequences Used In Phylogenetic Analyses

62

Table 1B. Pedobacter Sequences Used In Phylogenetic Analyses 62

Table 1C. Chitinophaga Sequences Used In Phylogenetic Analyses 63

Table 2. Pedobacter Settings For Phylogenetic Trees 64

Table 3. Ochrobactrum Settings For Phylogenetic Trees 65

Table 4. Chitinophaga Settings For Phylogenetic Trees 66

1

GENERAL INTRODUCTION

“The soils of our yards, gardens, and fields swarm with thousands of kinds of minute animals and plants of which we know little or nothing. We depend on the soil for our very existence, and it may seem that this fact should have caused us long ago to make ourselves thoroughly acquainted with it and all its inhabitants; yet the truth is otherwise. Here beneath our very feet are microbes, protozoa, fungi, and many other kinds of small organisms, thousands of species, of which we know hardly the first thing beyond the mere fact of their existence. . . . Inhabiting the soil in myriads, hidden behind this veil of ignorance, there is a group of organisms known as nematodes.” (6) Nathan A. Cobb, 1914 IMPORTANCE OF NEMATODE-BACTERIA SYMBIOSIS RESEARCH

Nematodes are among the most abundant metazoans on Earth, inhabiting

terrestrial, marine, and freshwater ecosystems (7). They can be found in

mountains, grasslands, forests, rivers, lakes, oceans and even in inhospitable

environments such as arid deserts and icy tundras. Some parasitize animals and

plants, making their homes within these unsuspecting organisms (25, 28, 44).

Nematodes have evolved a myriad of fascinating survival strategies, allowing

them to adapt to virtually all environments to increase their chances of survival,

especially under adverse conditions. For example, nematodes temporarily enter

different quiescent states in response to environmental stresses, such as

anhydrobiosis in response to desiccation and cryobiosis in response to low

temperatures (27). Furthermore they also utilize several reproductive strategies

ranging from dioecous to protandric hermaphroditism to parthenogenesis (9).

2

Some species are easily culturable bacterial feeders and are therefore highly

suitable for eukaryotic-prokaryotic association studies.

Bacteria are the most abundant and ubiquitous of all organisms found in

nature. Together with nematodes they make up two of the most prominent groups

of organisms in the biosphere. Bacteria and nematodes have long interacted and

evolved alongside one another, resulting in the creation of many different types of

associations, along a continuum ranging from mutualistic and beneficial to

antagonistic and detrimental through for example competition, pathogenesis, and

parasitism (29). There are many free-living nematode species which obtain their

energy entirely or partially from the consumption of bacteria (54). These

nematodes can be non-selective in the bacteria they ingest, such as with

laboratory grown Caenorhabditis elegans, or they can be particular in the

bacteria they choose, allowing them to differentiate between bacteria with

different nutritional qualities and to select for specific genera or species of

bacteria to consume or cultivate (18, 19, 31, 48). This selectivity allows

nematodes to avoid dangerous pathogens while picking up bacteria that promote

development, fecundity, and survival (20, 35, 41). Investigating nematode-

bacteria symbioses can pave the way for comparative analyses with other

eukaryotic host-bacteria associations and will enhance our understanding in the

areas of developmental and evolutionary biology while also leading to

improvements in agriculture, ecology, and health. For example, it will allow for

new insights into the genes implicated in the evolution of symbiosis and those

3

involved in horizontal transfer between bacteria and eukaryotes (11, 12, 53).

Furthermore, it will aid in elucidating the metabolic pathways and chemical

interactions between symbiont and host and can thus lead to the creation of new

drug targets for invasive pathogens (32, 41).

Genetic and molecular tools are being applied to provide new insights into

nematode-bacterial interactions. Metagenomics, the direct sequencing of

environmental samples, has allowed for simultaneous sequencing of symbionts

and their hosts (24). Utilizing the power of the latest genome deep sequencing

platforms, these studies can now be performed on a genome-wide level as

opposed to more traditional gene-centric approaches focusing for example on

16S ribosomal RNA (16S rRNA) based molecular methods. Such high-

throughput molecular tools allow for comprehensive taxonomic and phylogenetic

classification of the diverse bacteria nematodes associate with in soil

communities (45, 46).

The remainder of this discussion will focus on some particularly interesting

recent studies of soil nematode-bacterial associations in which the participating

organisms engage in specific and persistent relationships. Understanding the

mechanisms involved in nematode-bacterial recognition may lead to functional

studies and practical applications through genetic manipulation of these

interactions (38).

4

RECENT STUDIES OF NEMATODE-BACTERIAL ASSOCIATIONS IN SOIL

MICROBIAL COMMUNITIES

Radopholus similis and Wolbachia

Wolbachia are a group of gram negative intracellular α-Proteobacteria in

the order Rickettsiales, the group from which mitochondria and similar organelles

are thought to have originated under the endosymbiotic theory. The latter

postulates symbioses between separate single-celled organisms leading to the

formation of organelles within eukaryotic organisms (50). In 2009, Haegeman et

al. confirmed the presence of a Wolbachia endosymbiont in Radopholus similis, a

burrowing plant-parasitic nematode which attacks the roots of banana plants

(21). This is the first and so far only reported occurrence of Wolbachia being

found in a non-zooparasitic soil-dwelling nematode.

The role Wolbachia plays in R. similis is not yet known. In filarial

nematodes however, Wolbachia is known to be an obligatory mutualist;

eradication of Wolbachia from the host via the antibiotic tetracycline has been

shown to cause adverse effects such as abnormal growth, eventually leading to

death of the nematodes (5, 14, 15, 22, 23). Conversely in arthropods, Wolbachia

takes on the role of a reproductive parasite, imparting a selective advantage to

infected females via feminization, parthenogenesis, male killing, or cytoplasmic

incompatibility, all leading to a decrease of the uninfected portion of the host

population while the endosymbionts are vertically transmitted to the offspring of

5

the infected portion (2, 47, 52). Due to the high infection rate of Wolbachia in R.

similis, it is believed the endosymbiont also provides the nematode with essential

metabolites and is necessary for survival (21), although no studies have as yet

been published documenting the effects on Radopholus of targeted Wolbachia

eradication.

Finding Wolbachia inside R. similis raises many unanswered questions. Do

other plant-parasitic nematodes carry Wolbachia endosymbionts? Can it be

found in nematode species or genera that are easy to culture and maintain in a

laboratory setting? What experiments can be performed to determine the role the

endosymbiont plays inside Radopholus similis?

Entomopathogenic nematodes and γ-Proteobacteria

Entomopathogenic nematodes are soil-dwelling insect parasites that work

in concert with insect-pathogenic bacteria to cause rapid insect death, usually

within 48 hours of infection (10). Model systems are being developed in which

entomopathogenic nematodes and their bacterial symbionts are used as

environmentally-friendly biological control agents against insect pests, providing

an alternative to the spraying of ecologically harmful chemical pesticides into the

environment (8, 4).

6

Steinernema carpocapsae and Xenorhabdus nematophila

Xenorhabdus are gram-negative γ-Proteobacteria in the order

Enterobacteriales (49). The bacterium Xenorhabdus nematophila is a mutualist of

the entomopathogenic nematode Steinernema carpocapsae and a pathogen of

many types of insects including weevils, caterpillars, moths, beetles, fleas, flies,

crickets, and ants (19).

S. carpocapsae ambush and infect highly mobile insects near the soil

surface (3). Once inside, the nematodes ingest the insect’s body liquid and

release X. nematophila into the hemocoel, where together they suppress the

insect’s immune system, leading to its eventual death (40). X. nematophila will

continue to grow and multiply in the insect cadaver, converting the cadaver into

substrates capable of supporting S. carpocapsae reproduction; the resulting new

nematode juveniles will ingest and store the endosymbiont in their digestive tract,

allowing the cycle to repeat (19).

Heterorhabditis bacteriophora and Photorhabdus luminescens

Like Xenorhabdus, Photorhabdus are also gram-negative γ-Proteobacteria

in the order Enterobacteriales (1, 16, 34). The bacterium Photorhabdus

luminescens is an endosymbiont of the entomopathogenic nematode

Heterorhabditis bacteriophora and a pathogen of many insect species (33).

H. bacteriophora typically infects insects that reside deep within the soil

(3). The cycle and mechanism of infection is similar to that utilized by S.

7

carpocapsae and X. nematophila; infected H. bacteriophora invade unsuspecting

insects and release P. luminescens within the insect, causing its death within a

few days (17).

Bacillus thuringiensis Cry5B crystal proteins and free living nematodes

Bacillus thuringiensis (Bt) is a gram positive soil-dwelling bacterium in the

order Bacillales known for producing crystal toxin proteins which are used as a

natural biological pesticide against insects in crop fields (39). Recent studies

have been undertaken to learn about the roles which these bacteria and their

crystal proteins play in the presence of nematodes and in soil microbial

communities (36, 37, 51). The Bt crystal protein, Cry5B has been shown to be

toxic towards Caenorhabditis elegans and Pristionchus pacificus (37). Cry5B

along with other nematicidal Bt crystal proteins have the potential to be utilized

as biological control agents against nematodes which parasitize plants and

animals (26, 51).

Caenorhabditis elegans

Caenorhabditis elegans, is a free-living rhabditid commonly used as a

model organism for genetics research because of its ease to cultivate and

maintain under laboratory conditions. Cry5B has been shown to have adverse

effects on C. elegans; when ingested the Bt toxin causes extensive damage to

the gut, a decrease in progeny production, and leads to death (26).

8

Pristionchus pacificus

Pristionchus pacificus, a free-living diplogastrid and a necromenic

associate of beetles is emerging alongside the popular model C. elegans to

become a commonly used tool in studying evolutionary biology, ecology, and

population genetics (42). Chemosensory studies have shown that P. pacificus

avoids Bt and other insect pathogenic bacteria (36, 37). P. pacificus is

susceptible to the Bt Cry5B crystal protein; when force fed E. coli expressing the

Bt toxin, the nematodes were unable to develop past the L1 larvae stage and

their intestinal tract exhibited signs of thinning, constriction, and degeneration

(51).

SYNOPSIS OF THIS THESIS

In the following chapters we will discuss our work investigating the soil

microbial community associated with the nematode Acrobeloides maximus, a

nonpathogenic bacterial-feeding cephalobid. Cephalobids occur in almost all

soils and are phylogenetically much closer to plant parasitic nematodes than the

model nematodes C. elegans and P. pacificus (7). Moreover, cephalobids have

developed ecological adaptations to desiccation and have slower generation

times at room temperature when compared with C. elegans and P. pacificus (30).

A sealed plate of A. maximus can last in culture well over six months whereas

faster growing species like C. elegans and P. pacificus require subculturing every

few weeks to avoid population collapse (13, 43). These differences make

9

cephalobids like A. maximus a much more suitable model for interaction studies

over C. elegans and P. pacificus. Chapter one discusses the results of our 16S

rRNA gene survey of soil microbiota associated with A. maximus while chapter

two takes a closer phylogenetic look at the bacterial genera detected at

significant levels within the nematode.

10

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25. Lambert K, Bekal S. 2002. Introduction to plant-parasitic nematodes. The Plant Health Instructor. doi: 10.1094/PHI-I-2002-1218-01. 26. Marroquin LD, Elyassnia E, Griffitts JS, Feitelson JS, Aroian RV. 2000. Bacillus thuringiensis (Bt) Toxin Susceptibility and Isolation of Resistance Mutants in the Nematode Caenorhabditis elegans. Genetics. 155(4):1693–1699. 27. McSorley, R. 2003. Adaptations of nematodes to environmental extremes. Florida Entomologist. 86:138-142. 28. Mitreva M, Zarlenga DS, McCarter JP, Jasmer DP. 2007. Parasitic nematodes - from genomes to control. Veterinary Parasitology. 148(1):31-42. 29. Murfin KE, Dillman AR, Foster JM, Bulgheresi S, Slatko BE, Sternberg PW, Goodrich-Blair H. 2012. Nematode-bacterium symbioses--cooperation and conflict revealed in the "omics" age. The Biological Bulletin. 223:85–102. 30. Nicholas WL. 1962. A Study of a Species of Acrobeloides (Cephalobidae) in Laboratory Culture. Nematologica. 8(2):99-109. 31. Ott JA, Novak R, Schiemer F, Hentschel U, Nebelsick M, Polz MF. 1991. Tackling the sulfide gradient: a novel strategy involving marine nematodes and chemoautotrophic ectosymbionts. Marine Ecology. 12:261–279. 32. Pfarr KM, Hoerauf AM. 2006. Antibiotics which target the Wolbachia endosymbionts of filarial parasites: a new strategy for control of filariasis and amelioration of pathology. Mini-Reviews in Medicinal Chemistry. 6(2):203-210. 33. Poinar, GO Jr. 1975. Description and biology of a new insect parasitic rhabditoid Heterorhabditis bacteriophora new genus new species (Rhabditida; Heterorhabditidae new family). Nematologica. 21:463–470. 34. Poinar GO Jr, Thomas GM, Hess R. 1977. Characteristics of the specific bacterium associated with Heterorhabditis bacteriophora (Heterorhabditidae; Rhabditida). Nematologica. 23:97–102. 35. Poinar GO Jr, Hansen EL. 1986. Associations between nematodes and bacteria. Helminthological Abstracts, Series B (Plant Nematology). 55(3):61-81. 36. Rae R, Riebesell M, Dinkelacker I, Wang Q, Herrmann M, Weller AM, Dieterich C, Sommer RJ. 2008. Isolation of naturally associated bacteria of necromenic Pristionchus nematodes and fitness consequences. Journal of Experimental Biology. 211:1927–1936. 37. Rae R, Iatsenko I, Witte H, Sommer RJ. 2010. A subset of naturally isolated Bacillus strains show extreme virulence to the free-living nematodes Caenorhabditis elegans and Pristionchus pacificus. Environmental Microbiology. 12:3007–3021.

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38. Rae R, Witte H, Rödelsperger C, Sommer RJ. 2012. The importance of being regular: Caenorhabditis elegans and Pristionchus pacificus defecation mutants are hypersusceptible to bacterial pathogens. International Journal for Parasitology. 42(8):747-753. 39. Roh JY, Choi JY, Li MS, Jin BR, Je YH. 2007. Bacillus thuringiensis as a specific, safe, and effective tool for insect pest control. Journal of Microbiology and Biotechnology. 17(4): 547–559. 40. Sicard M, Brugirard-Ricaud K, Pages S, Lanois A, Boemare NE, Brehelin M, Givaudan A. 2004. Stages of infection during the tripartite interaction between Xenorhabdus nematophila, its nematode vector, and insect hosts. Applied and Environmental Microbiology. 70:6473-6480. 41. Slatko BE, Taylor MJ, Foster JM. 2010. The Wolbachia endosymbiont as an anti-filarial nematode target. Symbiosis. 51(1):55–65. 42. Sommer RJ, McGaughran A. 2013. The nematode Pristionchus pacificus as a model system for integrative studies in evolutionary biology. Molecular Ecology. 22(9):2380-2393. 43. Stiernagle T. 2006. Maintenance of C. elegans. WormBook, ed. The C. elegans Research Community, WormBook, doi/10.1895/wormbook.1.101.1 http://www.wormbook.org. 44. Stock SP. 2005. Insect-parasitic nematodes: from lab curiosities to model organisms. Journal of Invertebrate Pathology. 89(1):57-66. 45. Stock SP, Bird DM, Ghedin E, Godrich-Blair H. 2011. Abstracts of NEMASYM: The Second Nematode-Bacteria Symbioses Research Coordination Network Meeting. The Journal of Nematology. 43(1): 49–60. 46. Stock SP, Bird DM, Ghedin E, Godrich-Blair H. 2011. Abstracts of NEMASYM: The Third Nematode-Bacteria Symbioses Research Coordination Network Meeting. Symbiosis. 55(3): 97-109. 47. Stouthamer R, Breeuwer JA, Hurst GD. 1999. Wolbachia pipientis: microbial manipulator of arthropod reproduction. Annual Review of Microbiology. 53:71-102. 48. Tan MW, Shapira M. 2011. Genetic and molecular analysis of nematode- microbe interactions. Cellular Microbiology. 13(4):497-507. 49. Thomas GM, Poinar GO Jr. 1979. Xenorhabdus gen. nov., a genus of entornopathogenic, nematophilic bacteria of the family Enterobacteriuceme. International Journal of Systematic Bacteriology. 29:352–360. 50. Thrash JC, Boyd A, Huggett MJ, Grote J, Carini P, Yoder RJ, Robbertse B, Spatafora JW, Rappé MS, Giovannoni SJ. 2011. Phylogenomic evidence for a common ancestor of mitochondria and the SAR11 clade. Scientific Reports. 1(13):1-9. 51. Wei JZ, Hale K, Carta L, Platzer E, Wong C, Fang SC, Aroian RV. 2003. Bacillus thuringiensis crystal proteins that target nematodes. Proceedings of the National Academy of Sciences U S A. 100:2760–2765.

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52. Werren JH, Baldo L, Clark ME. 2008. Wolbachia: master manipulators of invertebrate biology. Nature Reviews Microbiology. 6(10):741-51. 53. Woolfit M, Iturbe-Ormaetxe I, McGraw EA, O'Neill SL. 2009. An Ancient Horizontal Gene Transfer between Mosquito and the Endosymbiotic Bacterium Wolbachia pipientis. Molecular Biology and Evolution. 26(2):367-374. 54. Venette RC, Ferris H. 1998. Influence of bacterial type and density on population growth of bacterial-feeding nematodes. Soil Biology and Biochemistry. 30(7):949–960.

15

CHAPTER ONE:

Survey of the microbiota associated with A. maximus in soil microcosm

ABSTRACT

Microbial populations observed in the microbiome of Acrobeloides

maximus consistently comprised a set of different microorganisms compared to

the microbiome of Caenorhabditis elegans or the metagenome of the soil sample

to which nematodes were exposed. Using 16S rRNA gene amplification, the

bacterial genera Ochrobactrum, Pedobacter, and Chitinophaga were identified at

significant levels within the A. maximus communities only and were found to be

consistently present during each of the three sampling periods over six months.

We were able to isolate and grow colonies of Ochrobactrum and Pedobacter

from the microbiome of the nematode on Miller’s Lysogeny Broth-Miller (LB) and

Yeast Extract (YE) agar plates. We were unable to isolate the associate from the

genus Chitinophaga. The results obtained suggest a possible specific and

persistent association between A. maximus and the two bacterial isolates we

were able to culture.

Using fluorescence in situ hybridization (FISH) with probes specific for

Ochrobactrum and Pedobacter, we were able to stain these bacterial cells and

show that they reside within the intestinal tract of A. maximus. It is possible that

these gut bacteria may be mutualistic by providing A. maximus with essential

metabolites, by aiding the nematode in the digestion of other bacteria, or by

providing it with protection against pathogens.

16

INTRODUCTION

Microbes are ubiquitous within metazoans. Understanding the relationship

between a multicellular organism and its microbiome will allow for a better

understanding of how these interactions have evolved over time and how

conserved these associations are across diverse populations. The biological

interactions that occur within these host-microbe symbioses are complex; the

abundance and diversity of microbes present and the totality of their interactions

between one another and as they relate to their host and environment are often

poorly understood. These host-bacterial associations can be neutral and

facultative, positive in the form of for example mutualistic cooperation or

commensalism, or negative involving for example competition, pathogenesis or

parasitism. Mutualistic microbes provide increased fitness benefits to their host,

whereas those that are pathogenic compromise the well-being of the host and

decrease its fitness. Commensal relationships occur when microbes use their

metazoan host for shelter or as a vehicle for transportation; some microbes even

linger around waiting for the death of their host to use the remains to their

advantage (3, 5, 32, 33).

Studying these networks of interactions will allow for a better

understanding of rhizosphere ecology in which bacteria, fungi, nematodes,

arthropods, and plants associate intimately with one another. Past studies often

lacked analytical focus on the distribution and abundance of organisms that are

17

part of these microbiomes. 16S rRNA based metagenomic studies are beginning

to elucidate the population composition of these microbiomes and the functions

they perform. This is not just important for our scientific understanding, but also

allows for practical applications such as the creation of microbial inoculants and

soil amendments to promote plant health & growth, and the utilization of

nematodes that promote soil nutrient cycling (14, 15, 23). It can also allow for

improvements in the prevention and treatment of microbial disease, for example

by enabling us to identify the mechanisms used by bacterial symbionts to protect

their eukaryotic host. This may in turn allow us to develop molecular tools and

inoculate soils, plants or animals with suppressive organisms to target those

microbes that might otherwise be harmful and pathogenic.

Nematodes are used as laboratory model systems to study microbial

associations. Several important and intricate microbial associations have been

studied among entomopathogenic and filarial nematodes. For example, the

insect-pathogenic bacteria Photorhabdus and Xenorhabdus are necessary

partners for the worm's life cycle in the entomopathogenic nematodes

Heterorhabditis and Steinernema nematodes, respectively (24). In a different

form of mutualism, treatment with the antibiotic tetracycline will eliminate

Wolbachia, an obligatory endosymbiont that provides essential nutrients and

metabolites for filarial nematodes such as Brugia malayi and Litomosoides

sigmodontis (11, 18).

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In this study our efforts focus on the microbiome of Acrobeloides maximus,

a true soil inhabiting member of the nematode family Cephalobidae and a non-

pathogenic free-living bacterivore common in sandy soils that are subject to heat

and protracted desiccation. Using microcosms, we perform a 16S rRNA gene

survey of bacteria of our soil sample and compare that to the bacteria in the

microbiome of A. maximus extracted after exposure to a soil slurry for 24h. A

similar cultivation, exposure, and extraction was performed using monoxenically

grown cultures of Caenorhabditis elegans N2, a free-living bacterivorous

rhabditid specialized in rapid colonization of decomposing fruits and commonly

used as a model organism for molecular genetics research (10 ). For both

species, the nematodes extracted from the soil slurry were maintained on agar

plates supplemented with cholesterol and sampled over a period of two, four, and

six months. These samples were also analyzed using 16S rRNA amplification

and sequencing methods to monitor for changes in the number of species and

abundance of microbes present in the population over time.

MATERIALS AND METHODS

Nematode cultivation and harvest

15 mm and 90 mm petri dishes containing 1.5% nutrient-free agar

supplemented with cholesterol (1 µg/ml) were prepared and seeded with

Escherichia coli S17-1, which contains a constitutive red fluorescent protein

(RFP) expression plasmid for easy detection with fluorescence microscopy.

19

Exogenous cholesterol is necessary for nematode survival; most nematodes are

assumed to be incapable of de novo cholesterol biosynthesis and omission of

cholesterol has been shown to have adverse effects on growth, development,

molting, and brood size in C. elegans (9, 17, 25). A. maximus was grown in a

28°C incubator for a period of at least two weeks and C. elegans was cultivated

at room temperature for up to a week until the nematode populations reached

high density. Worm populations were counted directly and diluted to 104

worms/ml for subsequent soil exposure.

Rhizosphere soil collection and exposure

Rhizospheric soil samples were collected from wild sunflowers at the

California State University San Bernardino (CSUSB) Natural Preserve (San

Bernardino, CA) and placed in 50 ml sterile conical tubes, weighed, and stored at

4o C until processing. A worm slurry was created by rinsing with an adequate

amount of sterile water the agar plates on which nematodes were growing. For

every gram of soil, 1ml of the worm slurry was added to each of three conical

tubes after filling them with approximately 5g of soil each. In the same manner,

we also exposed C. elegans to the same soil in triplicate, and as control three

more tubes received 1 ml of sterile water without nematodes. Samples were

allowed to incubate for 24h at room temperature prior to worm extraction.

Employing a density gradient approach, we recovered the nematodes from

the soil samples: MgSO4 heptahydrate was added to the samples to a

20

concentration of 409 g/L followed by centrifugation at 1800g for 3 minutes (30).

The soil pelleted to the bottom and the above supernatant containing nematodes

was removed and then vacuum filtered using Millipore’s Stericup and Steritop

system through a 0.22 micron polyvinylidene difluoride (PVDF) membrane to

remove external bacterial contaminants. Nematodes were rinsed off the

membrane and collected in 3 ml of sterile water. The remaining soil pellets were

washed twice in sterile water and aliquots were frozen at -20 o C for later DNA

extraction and analysis for our long term culture investigation.

Long-term culture of A. maximus

Recovered nematodes were grown on 1.5% nutrient-free agar

supplemented with cholesterol (1 µg/ml) with no other food sources. The

nematodes were collected for analysis and subcultured at two, four, and six

months. Material from the plates was collected using sterile 10x phosphate buffer

saline (PBS) and washed as previously described to remove free-living bacteria.

Continuing cultures were seeded with 50µl of this washed worm suspension. The

remaining suspension was frozen at -20 o C for subsequent DNA analysis.

Isolation of associated bacteria

Individual nematodes from the initial culture of worms recovered from the

soil slurry were transferred onto petri plates containing 1.5% agar with 5g/L of

yeast extract (YE). The worms were allowed to wander about on the plates for up

21

to 24h, they were then removed and any released bacteria were allowed to grow.

All bacterial isolates were subsequently identified by PCR amplification and

sequencing of their 16s rRNA genes.

DNA extraction and PCR analysis

Using the FastDNA Spin Kit for Soil (Qbiogene), we extracted DNA from

the original rhizosphere soil as well as from soils exposed to A. maximus or C.

elegans and from the long term cultures. With the GenomiPhi V2 DNA

Amplification Kit (GE Healthcare Life Sciences) we individually amplified DNA

from all the samples, which we then used for PCR analysis. Utilizing the 27F (5'-

AGAGTTTGATCMTGGCTCAG-3') and 1492R (5'-GGTTACCTTGTTACGACTT-3') universal

bacteria primers, we next amplified near full length 16S rRNA gene fragments

from our samples (19, 29).

PCR reactions were performed in 50 µl reactions consisting of 25 µl of 1X

concentration of GoTaq Green PCR Master Mix (Promega), 0.5 µm of both 27F

and 1492R primers, and at least 20 ng of template DNA. Thermal cycling

conditions consisted of 5 minutes at 95˚C followed by 30 cycles of 30 sec at

94˚C, 30 sec at 55˚C, 1 min at 72˚C, followed by a final extension step for 10 min

at 72˚C. Gel electrophoresis was performed using a 1% agarose gel stained with

0.5µg/ml 1% ethidium bromide. PCR products were visualized using the BioRad

Gel Doc XR+ System.

22

16S rRNA gene library construction, sequencing and analysis

PCR products were purified using the Wizard SV Gel and PCR Clean-up

System (Promega) and subsequently ligated into the pCR 2.1-TOPO vector and

cloned with the TOPO TA Cloning Kit using One Shot TOP10 Electrocompetent

E. coli. Sets of 94 clones from each of the A. maximus triplicates, 94 clones from

the C. elegans triplicates, and 94 clones from the triplicate controls without

nematodes underwent rolling circle amplification in which the bacterial DNA

inserts within these recombinant plasmids were first amplified then sequenced

(Laragen, CA). In addition, 94 clones from the two, four, and six month long-term

culture studies were also sequenced in one direction, using the M13 forward

primer site in pCR2.1.

Sequences were compiled into FASTA formatted files and aligned against

the prokaryotic multiple sequence alignment (prokMSA) 16S rRNA gene

sequence database by applying the near alignment space termination (NAST)

algorithm (7). The Bellerophon program was used to detect and filter 16S rRNA

gene chimeras (13). The aligned set was then compared against the Ribosomal

Database Project (RDP) phylogeny database (rdp.cme.msu.edu) at 97%

similarity using the classifier on the Greengenes website (greengenes.lbl.gov) (4,

8). The 97% cutoff level is commonly used to differentiate bacterial organisms at

the species level and to distinguish between organisms that may represent

different strains or subgroups (16). To determine operational taxonomic units

(OTUs), SimRank, a general similarity measure was used to find the nearest

23

neighbors to our sequences and calculate divergence (8). Using online

calculators at Chang Bioscience (changbioscience.com), the Shannon-Wiener

diversity index and Shannon-Wiener evenness were calculated based on the

Greengenes assigned OTUs. The sample A. maximus 2 was later resequenced

in the reverse direction to verify error rates. All of our newly generated 16S rRNA

sequences have been deposited into Genbank (Table 1).

Refer to Chapter 2 for phylogenetic analysis and discussion regarding the

identities of the three most interesting bacterial genera found: Ochrobactrum,

Pedobacter, and Chitinophaga.

Preparation for Fluorescence In Situ Hybridization (FISH)

The previously described rhizosphere soil collection and exposure

methods were repeated for fluorescence in situ hybridization (FISH) probing of

exposed worms: worms were extracted after exposure to a soil slurry for 24h,

along with worms collected from agar plates without soil exposure. The FISH

protocol used was largely adopted from the work of Vandekerckhove et al. (31):

Preparation of samples was performed in triplicates. Samples were rinsed twice

in sterile water, fixed for 10 minutes in a 1:1 mixture of glacial acetic acid and

ethanol, rinsed twice for 5 minutes in pure ethanol, transferred to a mixture a 1:1

mixture of methanol and PBS w/Tween 20 (PBT; 50 mM NaCl, 10 mM Na3PO4,

0.1% (wt/vol) Tween 20 [pH 7.4]) for 10 minutes, and fixed for 30 minutes in a

PBT solution containing 1% (vol/vol) paraformaldehyde.

24

FISH Probes

The oligonucleotide probes used were based on probeBase’s 16S rRNA

data (microbial-ecology.net/probebase) and were designed to specifically anneal

to either Pedobacter or Ochrobactrum (21). The probe specific to the Pedobacter

sp., PED672 (5'-GCTACTTGTCACATTCCG-3') (12) was linked with sulfoindocyanine

chromophore Cy3, which fluoresces green and the probe specific to the

Ochrobactrum sp., Ochro3 (5'-AACTCCCCAACGGCTAACAT-3') (6) was linked to Cy5,

which fluoresces red. Each probe was dissolved in TE buffer (10 mM Tris-HCl, 1

mM EDTA [pH 7.4]) to a concentration of 10 μM where 10 μl was used for each

100 μl of the hybridization mixture.

FISH- Hybridization, Mounting, Microscopy

The hybridization mixture consisted of 20 mM Tris-HCl (pH 7.4), 0.02%

(wt/vol) sodium dodecyl sulfate (SDS), 0.9 M NaCl, 5 mM EDTA, 60% (vol/vol)

formamide, and a 1 μM concentration of the two fluorochrome-labeled probes.

Each of the fixed paraformaldehyde samples were added to this hybridization

buffer in sealed conical tubes at 42°C for 3 hours followed by two 30 minute

washes at 42°C in a hybridization buffer containing no formamide. Nematodes

were pipetted out of this wash onto microscope slides. DIC and fluorescence

images from the samples were collected using a Zeiss Axioplan2 equipped with a

CoolSnapFX cooled CCD camera and ImageProExpress 2.0 capture software.

25

RESULTS

16S rDNA analysis of extracts

Nematodes were recovered from soil samples after 24h using a density

gradient extraction, washed to remove free-living bacteria, and examined using

culture-independent 16S rRNA sequencing to taxonomically analyze the bacteria

comprising their microbiomes (Figure 1). Analysis revealed that the A. maximus

triplicates were dominated by α-Proteobacteria and Sphingobacteria, while C.

elegans associates showed a more broad distribution across taxa with no clear

dominant clade. The soil sample to which no nematodes were exposed had a

much higher percentage of bacilli compared to the other samples. Shannon-

Wiener diversity index calculations and analysis of population structures in the

samples showed marked differences among the samples (Table 2). The

α-Proteobacteria clade of the A. maximus triplicates was dominated by the genus

Ochrobactrum, while the Sphingobacteria clade was dominated by the genus

Pedobacter ; these two genera were not detected in the soil samples (data not

shown) and the samples from C. elegans gave no clear indication of any

dominant genera within these two clades (Figure 2). In subsequent experiments,

we were unable to recover transmitted E. coli S17-1 from the initial inoculants of

our samples, and were also unable to observe its red fluorescence via

fluorescence and confocal microscopy (data not shown).

26

Co-cultures

For a period of six months we maintained co-cultures with bacteria from

the soil, with bimonthly collecting and subculturing. Utilizing 16S rRNA methods

we examined the microbiome of a three week old co-culture which showed a

stable population similar in composition to the samples recovered from soil

exposure (Figure 3). Another co-culture was analyzed in the same way, which

again yielded a similar population makeup (data not shown). Over the six month

period we observed a shift away from the initial population structure however

(Fig 3A). Pedobacter and Chitinophaga persisted in the population for up to six

months, with Chitinophaga peaking at four months (Fig 3B) while Ochrobactrum

persisted in the population for up to four months only (Fig 3C). Throughout the

six months, the bacterial population displayed noticeable increases in the

abundance of Sphingobium and a decrease in Brevundimonas.

Isolation and culture

Bacterial isolates were obtained during various time points from the co-

cultures by inoculation onto different kinds of agar media or by isolating individual

nematodes on different media. Applying 16S rRNA gene sequencing we

identified these bacterial isolates (Table 3). Pedobacter was isolated by

transferring nematodes onto fresh agar plates lacking nutrients for bacterial

growth and isolating the microcolonies that formed, while Ochrobactrum isolates

were recovered by transferring individual nematodes onto YE agar. Both

27

Pedobacter and Ochrobactrum isolates were cultivated axenically at 37°C and

had aliquots frozen at -80°C. Attempts to isolate Chitinophaga isolates using

colloidal chitin media were unsuccessful.

Fluorescence In Situ Hybridization

After 24h of soil exposure, nematodes were removed from the soil, and

using FISH, were probed for the presence of Ochrobactrum and Pedobacter.

FISH signals corresponding to both genera were simultaneously identified in the

guts of the nematodes (Fig 4 A -D). Control experiments using cultured strains of

the two bacterial isolates showed no cross reactivity of the probes under fixation

and hybridization conditions and no background staining was observed in worms

fed only E. coli S17-1 (Fig 4E & F).

DISCUSSION

Bacterial isolates from Acrobeloides maximus recovered from soil

microcosms consistently yielded DNA sequences most closely matching with

known sequences of Ochrobactrum, Pedobacter, and Chitinophaga suggesting

that these are clade specific associations. The sequences belonging to these

genera were largely enriched in all A. maximus samples compared to similar

sampling from Caenorhabditis elegans exposed to soil and to direct sampling

from the soil, which showed differing association patterns. Furthermore, these

three bacterial genera persisted in long term co-culture with the nematodes on

28

agar plates where no external nutrients were provided to the nematodes or the

soil microbiota. These associations were adequately stable at the phylum and

subphylum level, but at the class level (alphaproteobacteria and

sphingobacteriia) the populations shifted substantially over time, most likely due

to nutrient consumption and dissipation. Bacterial isolates of Ochrobactrum and

Pedobacter were shown via FISH to colonize the gut of A. maximus.

Other studies describing the microbiota of other nematodes have provided

similar results to our own. Ochrobactrum and Pedobacter were both identified as

genera associated with wild C. elegans (28). In another study, these two genera

were also identified as associates of the soybean root cyst nematode Heterodera

glycines, and those investigators were able to isolate and culture Pedobacter

from soybean cysts (22). Pedobacter was also recently identified as a bacterial

associate of the plant parasitic nematode Bursaphelenchus mucronatus (27).

These investigations along with our own suggest a broad association of

Pedobacter with many nematodes, and that its occurrence is not just specific to

only A. maximus. The replicates we performed during our investigation show that

a quick selection process takes place in the A. maximus microbiome when the

nematodes are introduced to the soil, relative to the soil microbial community

structure. Conversely, it appears from our work and others that laboratory bred C.

elegans are usually unselective in its microbiota, consuming an array of

phylogenetically diverse bacteria which it isolates from the soil (1, 26). It is

important to note the E. coli strain S17-1, used to monoxenically farm both A.

29

maximus and C. elegans before soil inoculation, was quickly displaced by soil

bacteria as a result of nematode exposure to the soil.

The bacterial associations we observed in A. maximus were consistent and

reproducible; however, they were also prone to population shifts in our long term

culture analysis. In our culture based and culture independent tests we were able

to repeatedly recover Ochrobactrum and Pedobacter in association with A.

maximus, and using FISH were able to show that these two genera are localized

primarily in the gut of the nematode. We were unable to find an appropriate FISH

probe to test for the presence of Chitinophaga, and were also unable to culture it

from our enrichments.

Our observations suggest a symbiotic nematode-microbe interaction

between A. maximus and Ochrobactrum and Pedobacter, perhaps involving

mutualism or a phoretic association. These bacteria may for example act as

mutualists by aiding the nematode in the digestion of other microbes and toxins

through secretion of digestive enzymes or they may play a role in protecting the

nematode against attacks by pathogenic bacteria by producing antibiotics.

Research has shown various strains of Pedobacter to be associated with

antifungals and probiotics acting against pathogenic bacteria and fungi (20) and

there have been reports of Ochrobactrum strains protecting entomopathogenic

nematodes from harmful microbes (2). Thus, these two bacterial genera appear

to have potentially widespread associations with a number of taxonomically

different nematode species, suggesting a possibly important evolutionary

30

relationship that warrants further study to elucidate the occurrence and role of

symbiosis in the behavior and ecology of soil nematodes.

31

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13. Huber T, Faulkner G, Hugenholtz P. 2004. Bellerophon: a program to detect chimeric sequences in multiple sequence alignments. Bioinformatics. 20(14):2317-2319. 14. Ingham RE, Trofymow JA, Ingham ER, Coleman DC. 1985. Interactions of bacteria, fungi, and their nematode grazers: Effects on nutrient cycling and plant growth. Ecological Monographs. 55:119–140. 15. Irshad U, Villenave C, Brauman A, Plassard C. 2011. Grazing by nematodes on rhizosphere bacteria enhances nitrate and phosphorus availability to Pinus pinaster seedlings. Soil Biology & Biochemistry. 43:2121–2126. 16. Janda JM, Abbott SL. 2007. 16S rRNA Gene Sequencing for Bacterial Identification in the Diagnostic Laboratory: Pluses, Perils, and Pitfalls. Journal of Clinical Microbiology. 45(9): 2761–2764. 17. Kurzchalia TV, Ward S. 2003. Why do worms need cholesterol? Nature Cell Biology. 5: 684–688. 18. Landmann F, D Voronin, W Sullivan, MJ Taylor. 2011. Anti-filarial Activity of Antibiotic Therapy Is Due to Extensive Apoptosis after Wolbachia Depletion from Filarial Nematodes. PLOS Pathogens. 7(11): e1002351 19. Lane DJ. 1991. 16S/23S rRNA sequencing. Nucleic acid techniques in bacterial systematics. Stackebrandt, E., and Goodfellow, M., eds., John Wiley and Sons. New York, NY.115-175. 20. Lauer A, M Simon, J Banning, B Lam, R Harris. 2008. Diversity of cutaneous bacteria with antifungal activity isolated from female four-toed salamanders. ISME Journal. 2:145-157. 21. Loy A, F Maixner, M Wagner, M Horn. 2007. probeBase—an online resource for rRNA-targeted oligonucleotide probes: new features 2007. Nucleic Acids Research. 35:d800-d804. 22. Nour SM, JR Lawrence, H Zhu, GDW Swerhone, M Welsh, TW Welacky, E Topp. 2003. Bacteria associated with cysts of the soybean cyst nematode (Heterodera glycines). Applied and Environmental Microbiology. 69:607-615. 23. Postma-Blaauw MB, de Vries FT, de Goede RG, Bloem J, Faber JH, Brussaard L. 2005. Within-trophic group interactions of bacterivorous nematode species and their effects on the bacterial community and nitrogen mineralization. Oecologia 142:428-439. 24. Ruby EG. 2008. Symbiotic conversations are revealed under genetic interrogation. Nature Reviews Microbiology. 6:752-762. 25. Shim YH, Chun JH, Lee EY, Paik YK. 2002. Role of cholesterol in germ- line development of Caenorhabditis elegans. Molecular Reproduction and Development. 61:358-366. 26. Tan MW, Shapira M. 2011. Genetic and molecular analysis of nematode- microbe interactions. Cell Microbiology. 13(4):497–507.

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27. Tian X, X Cheng, Z Mao, G Chen, J Yang, B Xie. 2011. Composition of Bacterial Communities Associated with a Plant Parasitic Nematode, Bursaphelenchus mucronatus. Current Microbiology. 6:117-125. 28. Troemel ER, MA Felix, NK Whiteman, A Barriere, FM Ausubel. 2008. Microsporidia are natural intracellular parasites of the nematode Caenorhabditis elegans. PLOS Biology. 6:2736-2752. 29. Turner S, Pryer KM, Miao VPW, Palmer JD. 1999. Investigating deep phylogenetic relationships among cyanobacteria and plastids by small subunit rRNA sequence analysis. Journal of Eukaryotic Microbiology. 46:327-338. 30. van Bezooijen J. 2006. Methods and Techniques for Nematology. Wageningen University. 27-51. 31. Vandekerckhove T, A Coomans, K Cornelis, P Baert, M Gillis. 2002. Use of the Verrucomicrobia-Specific Probe EUB338-III and Fluorescent In Situ Hybridization for Detection of “Candidatus Xiphinematobacter” Cells in Nematode Hosts. Applied and Environmental Microbiology. 68:3121- 3125. 32. Wade WN, Beuchat LR. 2003. Metabiosis of proteolytic moulds and Salmonella in raw, ripe tomatoes. Journal of Applied Microbiology. 95:437–450. 33. Waid J. 1999. Does soil biodiversity depend upon metabiotic activity and influences? Applied Soil Ecology. 13(2):151-158.

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FIGURES AND TABLES

Figure 1. Soil exposed A. maximus populations were found to be associated with a consistent bacterial population. After 24h soil exposure, the A. maximus samples were dominated by α-Proteobacteria and Sphingobacteria, while C. elegans sample was shown to be associated with a broader bacterial distribution. A high percentage of Bacilli is shown to be associated with the bacteria identified from the soil sample.

35

Figure 2A. Within the dominant subgroups the A. maximus populations were consistent and limited compared to the C. elegans associated bacteria which were shown to be more diverse at the genus level. Ochrobactrum is shown to be the predominate alpha-proteobacteria within A. maximus.

36

Figure 2B. Within the dominant subgroups the A. maximus populations were consistent and limited compared to the C. elegans associated bacteria which were shown to be more diverse at the genus level. Pedobacter is shown to be the primary Spingobacteria within A. maximus, along with a consistent representation of Chitinophaga at a lower level.

37

A

B C

Figure 3. Long term culture experiments appear to show stability at the phylum level (A) but reveal shifts in population as the worm population ages at the genus level (B,C). At 6 months post exposure, the initially dominant clades have been supplanted in both the Sphingobacteria (B) and Alpha-Proteobacteria (C).

38

A B

C D

E F Figure 4. Fluorescence in situ hybridization was used to identify the presence of both Ochrobactrum sp. (green) and Pedobacter sp. (red) in the gut of formaldehyde fixed A. maximus after recovery from soil microcosm. Samples from soil microcosms were imaged by DIC (A,C,E) and epifluorescence (B,D,F). A. maximus recovered from soil microcosms (A-D) are compared with A. maximus grown on a monoxenic culture on E. coli DH5α (E,F) to show the specificity of the FISH probes. Scale bar = 10 microns.

39

Table 1. List Of Sequences Deposited Into GenBank Accession

Pedobacter KC110983, KC110977, KC110967, KC110963, KC110947, KC110933, KC110921, KC110919, KC110917, KC110913, KC110909, KC110905, KC110895, KC110984, KC110978, KC110968, KC110966, KC110958, KC110934, KC110904

Ochrobactrum KC110896, KC110901, KC110903, KC110910, KC110914, KC110916, KC110918, KC110923, KC110926, KC110929, KC110931, KC110932, KC110935, KC110936, KC110939, KC110940, KC110941, KC110944, KC110949, KC110951, KC110956, KC110957, KC110960, KC110961, KC110962, KC110970, KC110973, KC110975, KC110976, KC110980, KC110985

Chitinophaga KC110981, KC110971, KC110965, KC110955, KC110943, KC110915, KC110907, KC110897, KC110950, KC110908

Sphingobacteriales bacterium

KC110912, KC110922, KC110946

Niastella

KC110902, KC110979

Other Genera KC110924, KC110969, KC110945, KC110952, KC110959, KC110920, KC110953, KC110982, KC110954, KC110972, KC110898, KC110938, KC110974, KC110906, KC110948, KC110987, KC110899, KC110900, KC110925, KC110930, KC110937, KC110928, KC110911, KC110927, KC110942, KC110964, KC110986

Table 2. Shannon-Wiener Population Statistics for Microcosm Samples

A. maximus 1 A. maximus 2 A. maximus 3 C. elegans Soil

SW Diversity Index

1.78 2.26 1.96 3.41 1.31

SW-Evenness 0.63 0.71 0.64 0.9 0.53

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Table 3. Identified Isolates from association from A. maximus microcosm and long term culture

Accession Isolate name OTUa Source(s) KC687078 Ochrobactrum sp.

W2P 1108 Nematode long term culture

and isolation on YE KC687079 Pseudomonas sp.

JPB-PO4 Massilia sp. PO12

18131396 Nematode long term culture

KC687080 Bacillus sp. JPB-PO2 758 Dead worm in long term culture

KC687081 Pedobacter sp. API 534 Nematode long term culture, oligotrophic medium

KC687082 Brevundimonas sp. JPunk3-2

1066 Acrobeloides stock plate

KC687083 Salmonella sp. JPunk1-2

1607 Nematode associated isolation on YE

KC687084 Achromobacter sp. JPB-PO7

1317 Nematode associated isolation on YE

KC687085 Pseudomonas sp. JPunk3-1

1813 Nematode associated isolation on YE

KC687086 Pseudomonas sp. PO7

1813 Nematode associated isolation on YE

KC687087 Bacillus sp. JPB-PO3 758 Dead worm long term culture Nematode associated isolation on YE

KC687088 Achromobacter sp. JPB-PO7

1317 Nematode associated isolation on YE

a OTU assigned using RDP phylogeny. Isolates assigned to the same OTU were verified to be distinct using direct sequence comparison (bl2seq)

41

CHAPTER TWO:

Phylogenetic Analysis of Bacterial Genera Detected At Significant Levels Within The Microbiota

Associated With A. maximus

ABSTRACT

Using 16S rRNA amplification and sequencing, three bacterial strains

belonging to the genera Ochrobactrum, Pedobacter, and Chitinophaga were

detected at significant levels within cultured Acrobeloides maximus populations

exposed to soil for 24 hours before extraction and re-inoculation on agar. All

three strains were found to persist in re-inoculated A. maximus cultures over a

period of six months. For each strain, sequences from our A. maximus

associated clones along with relevant 16S rRNA sequences from congeners or

related genera available in GenBank were subjected to comprehensive

phylogenetic analyses. Sequences were aligned using the multiple sequence

alignment algorithms implemented in T-Coffee, MAFFT, Kalign, and WebPRANK,

and phylogenetic trees were constructed using Bayesian and maximum-

likelihood methods. Similarities and differences among the different alignment

and tree-building methods suggest tree building methods with T-Coffee

performed more poorly while maximum-likelihood methods with MAFFT and

Kalign produced more mutually consistent topologies that correspond better to

recognized genus boundaries. Inferences are made about the known species

42

that are phylogenetically closest to each of our three strains. Our Ochrobactrum

strains appear to be most closely related to Ochrobactrum tritici, while our

Chitinophaga strains are most similar to a bacterium previously identified as

“Flexibacter cf. sancti.” Our Pedobacter strains seem to be a previously

undescribed sphingobacterium within the genus.

INTRODUCTION

During the last decades of the 20th century, Carl Woese pioneered the idea

of using small subunit ribosomal RNAs (SSU rRNA) as reliable molecular

markers to indicate relatedness among different organisms (45). SSU rRNAs are

thought to be reliable for discerning phylogenetic relatedness because they can

easily be amplified and sequenced directly and are one of the few genes which

can be found within all uni- or multicellular organisms. Furthermore, they encode

for highly conserved ribosomal structures yet usually exhibit sufficient nucleotide

sequence variation to allow for distinction between different species (36).

Sequencing of 16S rRNA has become an established method for identifying

bacteria and a standard tool for discerning their phylogenetic relationships (20,

34).

As discussed in Chapter 1, we used microcosms and performed a 16S

rRNA gene survey of bacteria of our soil sample, and then compared that to the

43

bacteria in the microbiome of Acrobeloides maximus extracted after 24h of

exposure to soil. Clones from our A. maximus sample were sequenced and

generated 16S rRNA sequences from dominant strains belonging among others

to the genera Ochrobactrum, Pedobacter, and Chitinophaga. In this study, a

phylogenetic analysis is undertaken to examine in greater detail how sequences

from our clones of these three bacterial genera relate to known species, and

particularly to clarify whether they most likely represent previously named

species versus as yet unidentified species.

MATERIALS AND METHODS

Data Preparation

Our 16S rRNA amplification via PCR and direct sequencing methods are

described in Chapter 1. Relevant 16S rRNA sequences for each bacterial genus

were gathered from GenBank using NCBI’s Reference Sequence (RefSeq)

database collection as well as the International Nucleotide Sequence Databases

(INSD) and compiled into a FASTA file along with sequences belonging to our

own clones (Table 1 A-C). For purposes of rooting trees and gauging quality of

formal identifications for previously published 16S sequences, outgroup

sequences were also included from genera closely related to each of our three

associates of A. maximus : sequences from the genus Brucella were used for the

44

outgroup for Ochrobactrum; sequences from Chitinophagaceae and

Sphingobacteriales along with sequences from the genera Segetibacter,

Parasegitibacter, and Niastella were used for the outgroup for Chitinophaga; and

sequences from the genera Sphingobacterium and Mucilaginibacter were used

for the outgroup for Pedobacter (Table 1 A-C).

Multiple sequence alignments: T-Coffee, MAFFT, Kalign, and WebPRANK

The following multiple sequence alignment (MSA) tools located on EMBL-

EBI (www.ebi.ac.uk/Tools/msa/) were used to align 16S rRNA sequences for

each genus: T-Coffee, MAFFT, Kalign, and WebPRANK. T-Coffee is an algorithm

that attempts to lessen the faults of other popular progressive alignment methods

by using a consistency based approach (32). By contrast, MAFFT utilizes fast

Fourier transforms to perform rapid alignments and when using more than 60

sequences is shown to be 100 times faster than T-Coffee without sacrificing

accuracy (19). Kalign uses the Wu-Manber string-matching algorithm to perform

quick and accurate sequence alignments (26). WebPRANK is a relatively new

tool which employs a phylogeny-aware progressive algorithm to perform its

sequence alignments (29). Each alignment algorithm ran at its respective default

settings on EMBL-EBI for all alignments.

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Phylogenetic inference tools- Maximum Likelihood, PhyML, Mr. Bayes

Alignment files generated from the MSA tools were examined in MEGA 5, a

software program for molecular evolutionary genetic analysis (38). Maximum

likelihood (ML) analyses were performed in MEGA 5. The sequence alignment,

assembly and analysis software Geneious 6.0.5 (Biomatters Ltd.) incorporates a

MrBayes plugin which was used for Bayesian phylogenetic analyses (16). PhyML

analyses were carried out via the interface of the HIV database website

(hiv.lanl.gov) (13). The settings for substitution models, rate variation, gamma

categories, and branch support methods are presented in Tables 2-4.

LALIGN

LALIGN, a tool on the EMBL-EBI website (www.ebi.ac.uk/Tools/psa/lalign/)

for identifying regions of sequence similarity between two sequences (15) was

used to compare our clones which had noticeably longer branch lengths against

a consensus sequence of the other clones in their respective genus generated

using Consensus Maker v2.0.0 with Kalign on the HIV database website

(hiv.lanl.gov).

46

RESULTS

Ochrobactrum sp. clones

ML, PhyML, and MrBayes trees generated with T-Coffee and WebPRANK

alignments generally did not group the Ochrobactrum clones together;

furthermore, sequences from Ochrobactrum tritici strains did not form a

monophyletic group and were dispersed throughout the trees (Figure 1). The

trees produced using MAFFT and Kalign alignments yielded better results,

clustering the Ochrobactrum clones and the Ochrobactrum tritici sequences

together in their own respective clades, which jointly comprise a monophyletic

group (Figure 2, 3). The ML tree created with MAFFT shows a 99% bootstrap

support value for the monophylum of these two clades (Figure 2). In all trees,

Ochrobactrum sp. clone Acro231 (designated with a blue asterisk) is placed on a

noticeably longer branch than the other Ochrobactrum clones (Figures 1-3).

Chitinophaga sp. clones

Trees constructed with T-Coffee alignments were poorly resolved and

show low branch support values (Figure 4). Trees generated with the other three

sequence alignment tools produced better and similarly comparable results. The

Chitinophaga clones clustered together in their own monophyletic group along

with Flexibacter cf. sancti with 99-100% bootstrap support in the ML and PhyML

trees generated using MAFFT, Kalign, and WebPRANK alignments. ML trees

created with MAFFT and Kalign alignments show identical topology to one

47

another (Figures 5, 6). In a majority of the trees, the Chitinophaga sp. clone

Acro210 (designated with an orange asterisk) has a significantly longer branch

than the other clones (Figures 5, 6). Our Sphingobacteriales clones Acro262 and

Acro263 cluster together in the Segetibacter clade, while our Sphingobacteriales

clone Acro264 is placed in a Niastella clade along with known Niastella

sequences, as well as our Niastella clones Acro224 and 225 (Figures 5, 6).

Pedobacter sp. clones

In all trees, the Pedobacter clones grouped together and formed a sister

clade to Pedobacter africanus strains K1/K2 and NBRC 10065. PhyML trees

were the only ones to separate the Pedobacter strains and the Sphingobacterium

outgroup into distinct clades (Figures 7-9). The topology of the generated

Pedobacter trees is poorly resolved, disallowing clear distinctions or well

supported groupings at the species level. Pedobacter sp. clone Acro279

(designated with a green asterisk) represents a longer branch than the rest of the

Pedobacter clones in nearly all trees (Figures 7-9).

LALIGN plots

Analyses were performed for comparing Ochrobactrum sp. clone Acro231

(blue asterisk), Chitinophaga sp. clone Acro210 (orange asterisk), and

Pedobacter sp. clone Acro279 (green asterisk) against a generated consensus

sequences of their respective clones because they showed longer branch

48

lengths that their counterparts in the phylogenetic trees. A "dot-plot" like graph of

these sequence alignments is reported (Figure 10A-C).

DISCUSSION

Comparison of multiple sequence alignment algorithms

Many factors influence the performance of alignment algorithms, including

the number of sequences used, the length of the sequences, the occurrence and

lengths of insertions & deletions, and the degree of similarity among the

sequences (31). Benchmark criteria have been developed to compare the

performance of MSA algorithms against one another; such as for example

BAliBASE (Benchmark Alignment dataBASE) (bips.ustrasbg.fr/en/Products/

Databases/BAliBASE/) which examines problems commonly associated with

MSAs such as small numbers of sequences, unequal phylogenetic distributions,

internal insertions, repeats, inverted regions and so forth (39, 40, 41). An

alignment benchmark investigation performed by Thompson et al. in 2011

revealed key differences between some of the more popular alignment

algorithms: MAFFT and T-Coffee performed better with more divergent

sequences, whereas Kalign performed better with sequences containing multiple

conserved regions (41). Fletcher and Yang in 2010 performed their own

investigation and showed that WebPRANK consistently outperforms MAFFT in

their branch site test analysis (10).

49

Comparison of phylogenetic reconstruction methods

Computing time continues to be a major factor when comparing different

phylogenetic tree building tools. Standard maximum likelihood analyses with

bootstrapping suffer from the burden of lengthy computational time. Bayesian

inference methods, like MrBayes, tend to perform computationally faster than

standard maximum likelihood methods while still having comparable phylogenetic

accuracy (16, 25). PhyML utilizes approximate likelihood ratio test branch support

methods based on a Shimodaira-Hasegawa-like procedure (aLRT SH-like branch

support methods), making it extremely fast compared to standard maximum

likelihood bootstrapping methods: 1000 bootstrap iterations were used in each of

our standard ML analyses, whereas PhyML-aLRTs only required a single iteration

per analysis (2, 13).

Studies undertaken to compare Bayesian posterior probabilities against

maximum likelihood bootstrap support values show that both are overly

supportive in their measure of phylogenetic accuracy, with Bayesian support

values tending to be higher than values obtained from bootstrapping (6, 9, 27, 43,

44).

50

Ochrobactrum sp. clones

The genus Ochrobactrum is comprised of six species: O. anthropi, O.

intermedium, O. tritici, O. grignonense, O. gallinifaecis, and O. ciceri, which can

be found in plants and their rhizospheres, soil, wastewater, or animal feces (4).

The genus Brucella consists of highly pathogenic strains which infect humans

and livestock (e.g. cattle, sheep, goats, pigs) and is the closest relative to

Ochrobactrum among those available from published sequence databases; it

was therefore used as the outgroup in our phylogenetic analyses (Table 1).

ML, PhyML, and MrBayes trees generated with MAFFT and Kalign

converge on a consensus placing our Ochrobactrum clones and Ochrobactrum

tritici sequences together in their own monophylum (Figure 2 and 3), suggesting

our Ochrobactrum clones are closely related to, or are a new variant of

Ochrobactrum tritici. O. tritici was first found and isolated over a decade ago from

the rhizoplane of wheat (28) and isolates of O. tritici have also been recovered

from wastewater (11).

Some of the O. tritici sequences are very divergent from the others within

the monophylum; most divergent are O. tritici AS1 and the clade comprised of O.

tritici strains TA 93, W19-1, W19-2, A4, and W49-1 (Figures 2 and 3). From our

trees it is reasonable for us to identify our Ochrobactrum clones as being the

same species; it however draws into question whether all the sequences

gathered from GenBank as O. tritici are really one and the same organism.

51

In order to decide whether or not two bacterial strains are the same

species, microbiologists first use 16S rRNA sequence-based phylogeny to check

if bacteria are similar enough to be assigned the same name. Having greater

than 97% 16S rRNA sequence identity similarity is the recommended cut-off for

species delineation (8, 12, 21, 22). There are drawbacks to using 16S rRNA

sequencing to delineate species. The >97% threshold is not universally accepted

and by itself is not sufficient enough for accurate species determination,

moreover 16S rRNA sequencing has poor resolving power for discerning

differences at and below the species level (17). In addition, 16S rRNA genes can

be subject to horizontal gene transfer; this raises a slew of hotbed questions

regarding species concept and phylogenetic reconstruction, including questioning

the validity of 16S rRNA constructed phylogenetic trees (3, 5, 14).

Chitinophaga sp. clones

The genus Chitinophaga was first proposed in 1981 by Sankhobol and

Skerman, and contained only a single species, Chitinophaga pinensis (35). In

2006, Kämpfer et al. proposed reclassifying Cytophaga arvensicola, Flexibacter

filiformis, Flexibacter japonensis, and Flexibacter sancti into the genus

Chitinophaga (18). Thus, the genus now consists of six species: C. pinensis, C.

arvensicola, C. filiformis, C. japonensis, C. sancti, and C. skermanii. Members of

this genus are of special interest because of their ability to degrade the

polysaccharide chitin, a β-1,4-glycosidic linked homopolymer of N-acetyl-D-

52

glucosamine know to be the main component of arthropod exoskeletons and

fungal cell walls (7).

ML and PhyML trees generated using MAFFT, Kalign, and WebPRANK

cluster our Chitinophaga clones and an organism previously identified as

“Flexibacter cf. sancti” (Accession AF181568) together in their own monophylum

(Figure 5 and 6), suggesting our clones are novel strains of this species. From

our trees, it appears that "Flexibacter cf. sancti" is clearly a different organism

from the clade of virtually identical sancti sequences that are presumably

genuine and correctly identified (C. sancti strains NBRC 16033, IFO 15057,

NBRC 15057 and F. sancti strains IFO 16033, IFO 15057, ATCC 23092) (Figure

5 and 6). Most likely "Flexibacter cf. sancti" is an earlier discovery of the same as

yet unnamed Chitinophaga in our experiments to be an associate of A. maximus.

The soil bacterium “Flexibacter cf. sancti” (reclassified as Chitinophaga cf. sancti)

was first discovered as part of a consortium of bacteria comprising a biofilm that

degrades diclofop-methyl, an active ingredient found in many commercial

herbicides, although by itself "Flexibacter cf. sanct" is not known to be a diclofop-

methyl degrader (24).

The trees also group our Sphingobacteriales bacterium clones Acro262

and Acro263 in a Segetibacter specific clade, suggesting the sequences

represent this genus (Figure 5 and 6). The trees moreover group all the Niastella

sequences together into their own clade, which includes our Niastella sp. clones

Acro224 and Acro225. Furthermore, our Sphingobacteriales bacterium clone

53

Acro264 also falls into this Niastella clade, suggesting it too might be a member

of this genus. Parasegetibacter luojiensis RHYL-37 (Accession NR044576) is

placed into our Niastella clade as well, which is not surprising as it was first

classified as being a Niastella sp. in 2008 before being reclassified as

Parasegetibacter in 2009 (46) (Figure 5 and 6).

Pedobacter sp. clones.

The genus Pedobacter was proposed in 1998 and consisted initially of four

species: P. heparinus (formerly Sphingobacterium heparinurn), P. piscium

(formerly S. piscium), P. africanus, and P. saltans (37). As of date, there are no

fewer than 42 named species within the genus: P. africanus, P. agri, P.

alluvionis, P. aquatilis, P. arcticus, P. borealis, P. boryungensis, P. caeni, P.

composti, P. cryoconitis, P. daechungensis, P. daejeonensis, P. duraquae, P.

ginsengisoli, P. glucosidilyticus, P. hartonius, P. heparinus, P. himalayensis, P.

insulae, P. jejuensis, P. koreensis, P. kribbensis, P. kwangyangensis, P. lentus,

P. metabolipauper, P. nyackensis, P. oryzae, P. panaciterrae, P. piscium, P.

rhizospharae, P. roseus, P. saltans, P. sandarakinus, P. soli, P. steynii, P.

suwonensis, P. terrae, P. terricola, P. wanjuense, P. westerhofensis, P.

xinjiangensis, and P. yonginense. This large number of Pedobacter species

makes 16S rRNA phylogenetic analysis difficult and is probably one of the

reasons why the topologies of our Pedobacter trees provide little support for clear

distinctions and groupings at the species level (Figures 7-9). In all the trees our

54

Pedobacter clones cluster together and usually formed sister clades or

monophyla with Pedobacter africanus strains K1/K2 and NBRC 10065 (Figures

7-9). Pedobacter africanus is known to occur in soil and activated sludge in dry

regions, particularly within South Africa (37). There is evidence to suggest that

our Pedobacter clones may be a novel species, but further work will be required

to prove or disprove this assumption.

LALIGN analysis

LALIGN analysis of Ochrobactrum sp. clone Acro231 compared against

the consensus sequence of Ochrobactrum clones shows a 98.6% similarity

among the sequences (Figure 10A). This is above the 97% cutoff and suggests

the longer branch of Ochrobactrum sp. clone Acro231 seen in our trees (Figures

1-3) may be an artifact due to pitfalls intrinsic to the different tree-building

methods used (1).

LALIGN analysis of Chitinophaga sp. clone Acro210 compared against the

consensus sequence of Chitinophaga clones shows a 94.3% similarity (Figure

10B) and the analysis of Pedobacter sp. clone Acro279 compared against the

consensus sequence of Pedobacter clones shows a 94.0% similarity among the

sequences (Figure 10C). Both of these fall slightly below the 97% cuttoff

indicating they are dissimilar from the rest of their respective clones. Further

protocols need to be implemented (discussed below) to determine if they are

truly dissimilar.

55

Requirements for describing a new bacterial species

A combination of phenotypic, genotypic, and chemotaxonomic approaches

is required for a formal description of a new bacterial species. In addition, the

rules for nomenclature and guidelines for publication discussed in the

Bacteriological Code (1990 Revision) (23) must be followed. A multitude of

different diagnostic approaches is required in order to have sufficient descriptive

information to allow for characterization of a novel species.

Genotypic approaches

Use 16S rRNA gene sequencing information and phylogenetic analysis is

the first step to determine the phylogenetic placement of an organism down to at

least the family and genus level. This is commonly followed by a DNA-DNA

hybridization test in order to decide whether two isolates should be considered as

similar or different species. If their 16S rRNA sequences show > 97% identity and

if their genomes show >70% DNA-DNA hybridization the two strains are said to

belong to the same species (14). Multilocus sequence analysis can also be

carried out in which internal fragments of several housekeeping genes are

analyzed to characterize the species (30). Additionally, if the whole genomes of

reference strains are available, then whole genome sequencing of the isolate can

be undertaken. Examining the % G+C content of DNA might be helpful as well;

deviations from expected values may help in elucidating information about the

strain being studied.

56

Phenotypic approaches

Microscopy is the oldest method for obtaining information about the

morphological features of bacteria. Light microscopy may be sufficient for

visualizing and describing the cell shape, size, pigment color, and staining

characteristics. Utilizing different differential staining dyes and techniques such

as Gram staining, can aid in the characterization of the novel species. The

presence and/or absence of certain characteristics structures like flagella,

capsules, polyphosphate granules, and spores also can aid in the description

and identification of a new species. Notes regarding the motility and speed (or

lack thereof) of the bacterium should be recorded as it may be of significance.

Furthermore, it may be necessary to utilize scanning electron microscopy to

show morphology of whole cells or transmission electron microscopy to view

intracellular structures (42).

Physiological characteristics such as pH and temperature range for optimal

growth and its effect on phenotype should be recorded. Cells should be cultured

using various media in nutrient broths and on agar plates to determine carbon

and nitrogen source utilization.

Chemotaxonomic approaches

Chemotaxonomy typically deals with using chemical approaches to study

the composition of different cellular components, such as the cell membrane,

outer cellular layers, and the different organelles residing in the cytoplasm (42). It

57

is common to start with a commercially available cell-based phenotypic assay kits

such as those available from Biolog (Biolog, Inc.) or API (bioMérieux). Common

tests include the oxidase test (to detect cytochrome c oxidases), catalase test (to

determine presence of catalase enzymes using hydrogen peroxide), testing the

strains relationship to oxygen (is the strain aerobic and/or anaerobic?), testing

the strains relationship to carbon dioxide and hydrogen (via manometric

methods), and tests to determine the fatty acid and polar lipid profile of the strain

(33).

FINAL THOUGHTS

Our phylogenetic analysis suggests that two of the three most common

and persistent bacterial associates of A. maximus are unnamed species:

"Flexibacter cf. sancti" appears to be an earlier discovery of the same as yet

unnamed Chitinophaga from our experiments while our Pedobacter strains

appear to be a novel species within the genus. Our Ochrobactrum strains also

appear to be a new variant of Ochrobactrum tritici.

As discussed in the last chapter, the Pedobacter associate of A. maximus

is available in pure culture. This allows for the opportunity for the Pedobacter sp.

to be potentially named and characterized using the discussed phenotypic,

genotypic, and chemotaxonomic approaches.

58

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FIGURES AND TABLES Table 1A. Ochrobactrum Sequences Used In Phylogenetic Analyses

Table 1B. Pedobacter Sequences Used In Phylogenetic Analyses

Clone Sequences Relevant Sequences from GenBank KC110896, KC110901, KC110903, KC110910, KC110914, KC110916, KC110918, KC110923, KC110926, KC110929, KC110931, KC110932, KC110935, KC110936, KC110939, KC110940, KC110941, KC110944, KC110949, KC110951, KC110956, KC110957, KC110960, KC110961, KC110962, KC110970, KC110973, KC110975, KC110976, KC110980, KC110985

AB091758, AB681865, AF508089, AF508090, AF508091, AF508092, AJ242579, AJ864999, AJ865000, AM114403, AM490634, AM490635, AM490636, AY429607, DQ647056, EU999218, GU171382, GU345782, JX010950, JX393006, JX393007, JX393011, NR028902, NR026039, NR042447, U88440 Outgroup sequences: L26166, L26168, L37584, NR042460, NR042462, NR042463, X13695

Clone Sequences Relevant Sequences from GenBank KC110895, KC110904, KC110905, KC110909, KC110913, KC110917, KC110919, KC110921, KC110933, KC110934, KC110947, KC110958, KC110963, KC110966, KC110967, KC110968, KC110977, KC110978, KC110983, KC110984, KC119214, KC157040

AB365796, AB680215, AB680732, AB680733, AB680841, AB681143, AB681144, AB681397, AB682400, AJ438170, AM237384, AM279214, AM279215, AM279217, AM279218, AM491368, EF660750, EF660752, EF693742, EU169155, EU734803, EU834277, FJ263019, GQ225687, GU391547, HM051286, HQ824869, JF915326, JQ236820, JQ342863, JX122142, JX122157, JX133186, JX401449, JX827217, JX869971, JX971539, JX971546, JX971550, JX971552, NR025534, NR025535, NR025536, NR028977, NR041371, NR041374, NR041506, NR041507, NR042133, NR042204, NR042323, NR042446, NR042601, NR042602, NR042603, NR042604, NR042605, NR043538, NR043543, NR043555, NR043665, NR044005, NR044083, NR044218, NR044219, NR044339, NR044380, NR044381, NR044382 Outgroup sequences: AJ459411, NR025537, NR042134, NR042135, NR044404

63

Table 1C. Chitinophaga Sequences Used In Phylogenetic Analyses

Clone Sequences Relevant Sequences from GenBank KC110897, KC110907, KC110908, KC110915, KC110943, KC110950, KC110955, KC110965, KC110971, KC110981

AB078049, AB078066, AB078067, AB078068, AB267723, AB267724, AB278570, AB680728, AB680761, AB680762, AB681008, AB681009, AB681041, AB681043, AB681047, AB681048, AB681049, AB681050, AB681051, AB681052, AB681053, AB682428, AB682429, AF181568, AM237311, AM237312, AM237313, AM237314, AM237315, JQ659521, M62795, NR028638, NR028720, NR040909, NR040917, NR041375, NR041486, NR042512, NR043516, NR044559, NR044560, Outgroup sequences: AB682366, GQ421847, KC110912, KC110922, NR041501, FN665659, FN665660, FN665661, KC110902, KC110979, NR044576, KC110946, AB682649, AB682408, AB682409, NR043672, NR043673

64

Table 2 Pedobacter Settings For Phylogenetic Trees

Multiple Sequence Alignment

Tool

Tree Building

Algorithm Substitution

Model Rates

Variation Gamma

Categories Branch Support

T-Coffee Maximum Likelihood K80

Gamma Distributed with Invariant Sites

5 Bootstrap

(1000 replicates)

T-Coffee PhyML K80 Uniform Rates 4 aLRT (SH-

like supports)

T-Coffee Mr. Bayes HKY85 Gamma

Distributed with Invariant Sites

5 Bootstrap

(1000 replicates)

MAFFT Maximum Likelihood K80

Gamma Distributed with Invariant Sites

5 Bootstrap

(1000 replicates)

MAFFT PhyML K80 Uniform Rates 4 aLRT (SH-

like supports)

MAFFT Mr. Bayes HKY85 Gamma

Distributed with Invariant Sites

5 Bootstrap

(1000 replicates)

Kalign Maximum Likelihood K80

Gamma Distributed with Invariant Sites

5 Bootstrap

(1000 replicates)

Kalign PhyML K80 Uniform Rates 4 aLRT (SH-

like supports)

Kalign Mr. Bayes HKY85 Gamma

Distributed with Invariant Sites

5 Bootstrap

(1000 replicates)

WebPRANK Maximum Likelihood K80

Gamma Distributed with Invariant Sites

5 Bootstrap

(1000 replicates)

WebPRANK PhyML K80 Uniform Rates 4 aLRT (SH-

like supports)

WebPRANK Mr. Bayes HKY85 Gamma

Distributed with Invariant Sites

5 Bootstrap

(1000 replicates)

65

Table 3. Ochrobactrum Settings For Phylogenetic Trees

Multiple Sequence Alignment

Tool

Tree Building

Algorithm Substitution

Model Rates

Variation Gamma

Categories Branch Support

T-Coffee Maximum Likelihood GTR Uniform

Rates 5 Bootstrap

(1000 replicates)

T-Coffee PhyML GTR Uniform Rates 4

aLRT (SH-like

supports)

T-Coffee Mr. Bayes GTR Uniform Rates 5

Bootstrap (1000

replicates)

MAFFT Maximum Likelihood K80 Gamma

Distributed 5 Bootstrap

(1000 replicates)

MAFFT PhyML K80 Uniform Rates 4

aLRT (SH-like

supports)

MAFFT Mr. Bayes HKY85 Gamma Distributed 5

Bootstrap (1000

replicates)

Kalign Maximum Likelihood HKY85 Gamma

Distributed 5 Bootstrap

(1000 replicates)

Kalign PhyML HKY85 Uniform Rates 4

aLRT (SH-like

supports)

Kalign Mr. Bayes HKY85 Gamma Distributed 5

Bootstrap (1000

replicates)

WebPRANK Maximum Likelihood K80 Gamma

Distributed 5 Bootstrap

(1000 replicates)

WebPRANK PhyML K80 Uniform Rates 4

aLRT (SH-like

supports)

WebPRANK Mr. Bayes HKY85 Gamma Distributed 5

Bootstrap (1000

replicates)

66

Table 4. Chitinophaga Settings For Phylogenetic Trees

Multiple Sequence Alignment

Tool

Tree Building

Algorithm Substitution

Model Rates

Variation Gamma

Categories Branch Support

T-Coffee Maximum Likelihood GTR Uniform Rates 5

Bootstrap (1000

replicates)

T-Coffee PhyML GTR Uniform Rates 4 aLRT (SH-

like supports)

T-Coffee Mr. Bayes GTR Uniform Rates 5 Bootstrap

(1000 replicates)

MAFFT Maximum Likelihood K80

Gamma Distributed with Invariant Sites

5 Bootstrap

(1000 replicates)

MAFFT PhyML K80 Uniform Rates 4 aLRT (SH-

like supports)

MAFFT Mr. Bayes HKY85 Gamma

Distributed with Invariant Sites

5 Bootstrap

(1000 replicates)

Kalign Maximum Likelihood K80

Gamma Distributed with Invariant Sites

5 Bootstrap

(1000 replicates)

Kalign PhyML K80 Uniform Rates 4 aLRT (SH-

like supports)

Kalign Mr. Bayes HKY85 Gamma

Distributed with Invariant Sites

5 Bootstrap

(1000 replicates)

WebPRANK Maximum Likelihood K80

Gamma Distributed with Invariant Sites

5 Bootstrap

(1000 replicates)

WebPRANK PhyML K80 Uniform Rates 4 aLRT (SH-

like supports)

WebPRANK Mr. Bayes HKY85 Gamma

Distributed with Invariant Sites

5 Bootstrap

(1000 replicates)

67

68

Figure 1. Ochrobactrum: T-Coffee-PhyML Phylogram

69

70

Figure 2. Ochrobactrum: MAFFT-ML Phylogram

71

72

Figure 3. Ochrobactrum: Kalign-MrBayes Phylogram

73

74

Figure 4. Chitinophaga: T-Coffee-ML Phylogram

75

76

Figure 5. Chitinophaga: MAFFT-ML Phylogram

77

78

Figure 6. Chitinophaga: Kalign-ML Phylogram

79

80

Figure 7. Pedobacter : T-Coffee-PhyML Phylogram

81

82

Figure 8. Pedobacter : MAFFT-PhyML Phylogram

83

84

Figure 9. Pedobacter : Kalign-PhyML Phylogram

85

Waterman-Eggert score: 7002; 1520.4 bits; E(1) < 0; 98.6% identity (98.6% similar) Figure 10A. LALIGN Plot: Ochrobactrum sp. clone Acro231 compared against the consensus sequence of Ochrobactrum clones

86

Waterman-Eggert score: 6666; 1540.9 bits; E(1) < 0; 94.3% identity (94.3% similar) Figure 10B . LALIGN Plot: Chitinophaga sp. clone Acro210 compared against the consensus sequence of Chitinophaga clones

87

Waterman-Eggert score: 6532; 1447.5 bits; E(1) < 0; 94.0% identity (94.0% similar) Figure 10C. LALIGN Plot: of Pedobacter sp. clone Acro279 compared against the consensus sequence of Pedobacter clones

88

CONCLUDING COMMENTS

BACTERIAL COMMUNITY IDENTIFICATION: 16S rRNA GENE MARKER SURVEY VERSUS SHOTGUN METAGENOMICS

The following discussion will compare differences between 16S rRNA gene

marker surveys and shotgun metagenomics, including the advantages, pitfalls,

and considerations to be taken before deciding which method to utilize in a

bacterial community identification study. The primary difference between the two

methods is that the 16S rRNA gene survey focuses on a single gene to identify

members of a bacterial community, whereas metagenomics focuses on the

totality of all genomic information in a sample. The first step involved in any

bacterial community identification project is to strategically plan what questions

are to be answered and the goals to be accomplished, then to decide which of

these methods are most applicable given time considerations and financial

resources available.

16S rRNA Gene Marker Surveys

The 16S rRNA gene is about 1500 nucleotides long and has become the

marker of choice for profiling bacterial communities since the mid-1990s (18).

There are universal primers available that allow for near full-length amplification

of the 16S rRNA gene (14). The advantage of having these universal primers for

amplification is that it permits for simple gene library construction and

89

sequencing, allowing for rapid identification of unknown isolates. A query

sequence can be input into NCBI’s BLAST to yield fast approximate matching or

exact identification without the need for additional laboratory work (1).

Furthermore, separate forward and reverse sequencing reactions are possible

using each PCR primer as well as multiple robust forward or internal primers,

leading to more refined sequence data in terms of both accuracy and ability to

generate robust assemblies of the entire gene.

On the other hand, single gene approaches are hampered by various

limitations. A common problem associated with creating clone libraries of PCR-

amplified 16S rRNA genes is that it may not give an accurate representation of

the microbial community. High species diversity, taxon-specific amplification

problems, and undersampling can lead to some members of the microbial

community not being represented in the clone library (2, 7, 27). Another

drawback is that copy number of the 16S rRNA gene varies between bacterial

species. Species with low abundance of the gene may not be as readily amplified

during PCR as those with a high copy number, creating an amplification bias (9,

24). Despite these minor drawbacks, 16S rRNA gene surveys continue to be

performed to determine the microbial community composition in a variety of

samples and substrates (4, 11, 21).

90

Shotgun Metagenomics

Shotgun metagenomics involves using high throughput sequencing or

massively parallel pyrosequencing to sequence DNA directly from environmental

samples in order to study community structure and microbial biodiversity (5). As

with single-gene methods, shotgun metagenomics allows for the discovery of

bacterial species that cannot be cultured in the lab and as an additional

advantage it can aid in elucidating the genomes of unculturable species provided

those are represented with high abundance in the sample (23). Common

problems associated with shotgun metagenomics are that it applies a general

DNA extraction method to a sample containing many microbial organisms and

that a sample with low biomass may yield insufficient quantities of DNA for proper

analysis, letting rare species go undetected (13).

A limitation of shotgun metagenomics is that it is still very challenging and

cost-prohibitive to sequence microbial communities that are closely associated

with eukayotes, due to large eukaryote genome size and low microbial gene

coding density (13). During post-sequencing it is often difficult to separate

eukaryotic DNA from DNA belonging to the microbial community. This is one of

the reasons why we opted to perform a 16S rRNA survey of the bacterial

community associated with A. maximus rather than take the shotgun

metagenomics approach. In addition, while the wet lab chemistry steps can be

performed more efficiently and much faster relative to total species richness,

shotgun metagenomics presents many downstream bioinformatics challenges in

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processing and robust assembling of short sequencing reads into longer contigs

(19). Chimeric sequences caused by PCR amplification, intracellular sequence

polymorphisms, pseudogenes, sequence contamination, and sequencing errors

continue to pose particular challenges to assembly algorithms and procedures for

assigning contigs to operational taxonomic units (20).

PCR-amplified 16S rRNA and Pyrosequencing

Recently pyrosequencing techniques have been applied to PCR-amplified

16S rRNA, bypassing the need to create traditional clone libraries (13). This low-

cost high-throughput technology is allowing for a rapid increase in the amounts of

16S rRNA being generated. Traditional PCR cloning methods of the past looked

at only a few dozen 16S rRNA sequences, whereas it is now possible to survey

thousands of 16S rRNA sequences at a time (26).

Pyrosequencing technology, such as the 454 GS FLX Titanium XL+

System developed by Roche, allows for generation of about 1,000,000 shotgun

reads per run, produces average read lengths up to 1,000 base pairs, and yields

about 700 Mbs per run. Past problems with pyrosequencing technologies are that

they produce short sequence read lengths ranging from 250 to 500 base pairs;

reads of less than 500 base pairs are insufficient for phylogenetic analysis as

they cannot be used to discern taxnonomic information down to the genus or

species level (24). Other drawbacks of pyrosequencing include the high cost of

reagents and high error rates associated with homopolymer repeats (15). Despite

92

these drawbacks, the long read lengths and deep sequencing capabilities of the

454 GS FLX Titanium XL+ have made it one of the most widely utilized

sequencing technologies (10). Other popular second generation technologies,

like the Illumina HiSeq 2500 are not ideal for 16S rRNA microbial community

analysis. While the Illumina HiSeq 2500 can generate more than ten times the

number of reads per run of the 454 GS FLX Titanium XL+, it suffers from short

read lengths of about 100 nucleotides (3).

Sequencing Technologies and Future Considerations

The field of metagenomics is still in its infancy, however sequencing

technologies and bioinformatics tools are developing at a rapid rate to

accommodate the challenges of obtaining and analyzing large volumes of data in

meaningful ways (8).

As third generation sequencing technologies develop and emerge, second

generation technologies continue to improve in throughput and reliability, while

becoming more cost-efficient. Second generation sequencing technologies

developed by Roche, Illumina, Applied Biosystems, and IonTorrent make use of

many individual cloned DNA fragments which are massively sequenced in

parallel using enzymatic replication, whereas third generation sequencing

technologies being developed by Helicos and Pacific Biosciences utilize single

molecule sequencing methods (16). The Pacific Biosciences single molecule real

time (SMRT) sequencing platform is the first sequencing technology to offer read

93

lengths averaging between 2000 to 3000 base pairs, however it suffers from error

rates much higher than those seen in the second generation technologies,

making it as of this writing not ideal for 16S rRNA microbial community surveys

(6).

If the third generation single molecule sequencing platforms overcome

their high error rate hurdles, they might be able to supersede the second

generation pyrosequencing technologies for microbial community studies. In

addition to the much longer read lengths and bypassing the biases associated

with an enzymatic amplification step, other advantages single molecule

sequencing has over massively parallel sequencing is the ability to sequence the

same DNA molecule multiple times for improved accuracy and the ability to easily

sequence molecules with high GC content, homopolymer regions, and complex

secondary structure (22). Detection of base modifications, methylation, and

single nucleotide polymorphisms can be achieved much more readily with third

generation single molecule sequencing platforms than with second generation

sequencing technologies; recently the methylomes of six different bacteria was

elucidated using SMRT sequencing (17). Ideally, single molecule sequencing

could develop to the point where complete microbial genomes can be

reconstructed from a single fragment library, greatly reducing the time and cost

associated with post-sequencing assembly (12).

Another equally likely possibility is that as second generation

pyrosequencing technologies continue to decrease in cost and increase in

94

throughput, they will make the development of these third generation sequencing

platforms practically moot. Researchers of tomorrow have many considerations

to take into account: the importance of sequencing longer reads, the potential for

sequencing whole DNA strands, and cost-benefit analysis of chasing third and

fourth generation sequencing technologies versus using new improvements

associated with second generation technologies.

95

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