microbial dynamics and dysbiosis in farmed European ...

DTo be or not to bediseased: microbial dynamics anddysbiosis in farmedEuropean seabassand gilthead seabreamDaniela RosadoBiodiversity, Genetics and EvolutionDepartment of Biology2021

AdvisorRaquel Xavier, Auxiliary Researcher, CIBIO-InBIO/FCUP

Co-advisorsMarcos Pérez-Losada, Auxiliary Researcher, CIBIO-InBIO/George Washington UniversityJo Cable, Professor, Cardiff University

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FOREWORD

According to the General Regulation of Doctoral Programs of the University of Porto (number

2, 4th Article) and the Decree Law 74/2006 (Article 31, 24 of March) revised under the Decree

law 230/2009 (14th of September), this thesis includes manuscripts published or in

consideration for publication in peer-reviewed scientific journals. These manuscripts are the

result of collaborations with several co-authors. The candidate declares that he actively

contributed to the ideas and the development of the research work, including the compilation,

analysis, results, discussion and writing as in its current publication form. The candidate was

supported by the National Foundation for Science and Technology (FCT), through a PhD

Grant (PD/BD/117943/2016) and through the I&D Projects (PTDC/MAR-BIO/0902/2014;

PTDC/BIA-MIC/27995/2017), financed by the European Social Fund and by the National

Ministry of Science, Technology and Higher Education (MCTES), through the Operational

Programme Human Capital (POCH), under Portugal 2020, and co financed by the European

Fund of Regional Development (FEDER), through COMPETE – Operational Program for

Competitiveness Factors (POFC). This thesis’ research was developed in the context of the

Doctoral Programme in Biodiversity, Genetics and Evolution (Faculty of Sciences, Univeristy

of Porto). The work was conducted at the Research Centre in Biodiversity and Genetic

Resources - InBIO Associate Laboratory (CIBIO-InBIO).

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ACKNOWLEDGEMENTS

Although doing a PhD can sometimes feel like a lonely road to travel, I am incredibly

privileged by all the support, guidance and companionship that was given to me in what ended

up being one of the best things I’ve ever done in life. I am genuinely proud of all the work done

in the scope of this thesis, and I cannot let it end without acknowledging how grateful I am to

all the people involved. I wouldn’t have been able to do it alone.

To Raquel, the most important person in this thesis, and my personal career hero and

role model. I have never known someone with such a busy life that is, at the same time, able

to always be present. I will be forever tremendously grateful for the day that you thought of me

to enter this microbiome journey with you. Beyond that opportunity, I am grateful to you for

allowing me to always follow my own path and make my own decisions (and mistakes). I have

learned so much through you and because of you. Thank you for the endless conversations,

exchange of ideas, corrections and comments, and thank you for all the laughs. Above all,

thank you for always treating me like a person, and not just a student, and for always being

fair and kind.

To Marcos for all of his wisdom and tremendous patience in passing it on to me. Thank

you for always making time for me, my questions and doubts. Without your enormous help,

this thesis would not have gone far.

To Jo, for all the words of encouragement, kindness and guidance. I always felt very

supported by you. Thank you for being present at any time and for trusting me. You made my

time in Cardiff really pleasant by introducing me to everyone, with coffee breaks and walks on

the park. I will always think of you both as a colleague and as a friend.

To Ricardo, without whom this project wouldn’t be possible. Thank you for taking care

of all the logistics related with sample collection and shipping, for giving us ideas and for

patiently replying to all of my questions and clarifying all my doubts.

Thank you to everyone else who participated, to any extent, in the making of the

scientific articles, especifically Fernando Tavares, Ana Pereira and Pedro Tarroso. I am also

thankful to the people in the lab who initially helped me, including Diana Castro, Susana

Lopes, Sara João and Sofia Mourão.

Thank you to my sweet friends for all the love, support and patience throughout, not

only the difficult times, but also the good ones. Thank you for listening and taking care of me

and making me enjoy life a lot more. Included here, but deserving a special acknowledgement,

are the people from theater school, for one of the most incredible times of my life. Thank you


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for all the conversations, either highly intelectual or completely dumb, for all the laughter and

lessons and, ultimately, for boosting my confidence.

To Dimitra, the personification of love. Thank you for all the talks and shared secrets,

all the nights out and walks together during the day. Thank you for discovering the city of Porto

with me and for being the living proof that it is possible to make good friends for life in

adulthood. Above all, thank you for being my companionship during quarantine, for the daily

yoga classes and for not making me feel alone in the past months. You are one of the greatest

people that walks on this planet and how grateful I am that our paths ever crossed.

I thank profusely to Cristela, my pillar. There was never another friend who believed

so much in me and who cared so much for me. All of your kind words made me grow in ways

that I never thought were possible. Thank you for always speaking the truth, even though you

know I might not like to hear it; that is surely a rare quality. You’ve been with me since the

beginning of my academic life and I just know I can count on you for everything, anytime.

Looking forward to being two old ladies playing cards, drinking G&T, listening to good music

and questioning the meaning of everything.

I am extremely thankful to my family for the biggest support, especially in the last

months. A special thank you to my mommy and daddy, the two strongest and most resilient

people I have ever known. For teaching me how to be confident and for always making me

believe I can do anything I want. For all the help and comfort throughout my academic and

personal life. Thank you both for the unconditional love and support, even though you don’t

really know what I do for a living. To my baby sister Maria, for all the laughter, company and

appreciation; and thank you to her sweet daughter who gave me joy everyday.

I owe a deep sense of gratitude to Inês, my soulmate. Thank you for always being

there for me, listening to me without judgement, and always knowing what I want and need.

Being in sync with you made me through a lot of bad days since you were born. You can’t

imagine how lucky I feel to have my best friend born within my family. I would need to write

another thesis dedicated to how thankful I am to you. With all my heart, thank you.

A colossal thank you to Dr Cecília for all the dedication to my person and for all the

help in my personal growth.

To Gil, the person who has seen it all. Throughout these years you have seen all my

faces and you are, undoubtedly, the most present person and one of the most important. And

I couldn’t finish this list without including Floki and her companionship in all the early morning

or late afternoon walks to clear my head.

Studying the microbiome has made me realise how no living being is truly alone, in all

senses.

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ABSTRACT

Aquaculture is currently the fastest growing food industry that has the capacity to meet

the global food demand. Intensification of aquaculture practices entails several constraints to

its development, most notably by infectious diseases. The mucosal surfaces of fish harbour

microbial communities that play a key role in host health and fitness. In addition to immunity,

other microbial functions benefit the host’s physiology and metabolism. Fish microbiota,

however, is highly variable and influenced by several biotic and abiotic factors. In particular,

host taxonomy, physiology, body site, diet, age, habitat and water parameters govern the

microbial dynamics of fish. Disruptions to favourable conditions can lead to microbial

imbalance, i.e. dysbiosis, affecting fish health and, eventually, leading to disease. Particularly

important in aquaculture are bacterial infections, stress, antibiotic usage and other chemical

treatments, which can induce dysbiosis in farmed fish. Monitoring microbial dynamics and

dysbiosis in farmed fish and their surroundings is paramount to improve aquaculture practices

as well as prevent and mitigate disease. Advances in molecular technology are facilitating

microbiome research, allowing the identification of microbes and the assessment of their

diversity and function.

This doctoral thesis assessed the microbial composition, diversity and structure of the

skin and gill of the European seabass Dicentrarchus labrax and gilthead seabream Sparus

aurata. These are two of the most commercially important farmed finfishes in Europe;

however, their productivity is greatly affected by infectious diseases. In close collaboration

with a fish farm, non-invasive weekly sampling of the external mucosae of both species was

performed between August 2016 and January 2018. I used a metataxomic approach to

sequence the V4 region of the 16S rRNA bacterial gene and analysed differences in the

microbial communities observed under different conditions. I studied the effects of several

biotic and abiotic factors in fish microbiota, including: host taxonomy, body site, age group,

water microbial communities, water temperature, disease and antibiotic treatment.

The first chapter sets the context of this dissertation, presenting a literature review of

aquaculture and microbiomes, enphasizing the microbiome of fish. Additionally, it reviews

microbiome applications in aquaculture settings as well as tools available for microbiome

assessment. Chapters 2 to 5 are presented as scientific papers, whose results interconnect

to reveal comprehensive knowledge about the complex bacterial dynamics and dysbiosis

occurring in farmed European seabass and gilthead seabream. A general discussion of those

results is presented in Chapter 6.

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I identified consistent predominant bacterial taxa in both fish species and tissues,

through time and across host health conditions. The two mucosal tissues presented

differences in diversity and response to external stressors, although they seemed to be

governed by the same factors. Overall, the skin microbiota was more variable and more

susceptible to dysbiosis, while the gill microbiota was more stable, but less resilient to disease

and antibiotic treatment. The microbiota of both tissues was significantly different from that of

the sourrounding waters. Nonetheless, skin and water had a more similar microbial structure

than gill and water. Continuous microbial exchange was high between tissues, also occurring

from the surrounding water.

The European seabass and gilthead seabream harboured differences in microbial

diversity, which were most pronounced in its structure (beta-diversity). Differences in microbial

structure and predicted function were also detected between fish belonging to different age

groups in both species. However, only the seabass showed significant differences in the

microbial composition across age groups. Water temperature governed skin and gill bacterial

dynamics of the European seabass through the year. Changes in the abundance of several

potentially pathogenic genera (Aliivibrio, Pseudomonas, Photobacterium and Vibrio) were also

correlated with changes in temperature. The increase in abundance of one or more of these

potentially pathogenic genera on two occasions seemed to have led to dysbiosis in both

tissues, although disease was not observed. Dysbiosis also occurred as a result of infection

and antibiotic treatment in both adult and juvenile seabass. In the case of adult seabass,

infection by Photobacterium damselae and treatment with oxytetracycline induced asymmetric

responses in the skin and gill microbiota, further demonstrating an effect of body tissue. In

seabass fingerlings, co-infection of Photobacterium damseale subsp. piscicida and Vibrio

harveyi and flumequine treatment led to an increase in diversity in the skin microbiota, a

response less commonly reported for fish microbiota. Short-term recovery assessment in both

age groups indicated that microbial homeostasis was not fully attained after 3 weeks in adults

and 1 week in fingerlings.

This research showed a high variability of the skin and gill microbiota of farmed

European seabass and gilthead seabream through time driven by host related and

environmental factors. Additionally, both disease and antibiotic treatments showed a

detrimental effect in fish microbiota. Monitoring the microbial communities of farmed species

proved to be highly insightful towards identifying biomarkers for health and disease. These

biomarkers can ultimately be the answer to fundamentally improve aquaculture practices.

Keywords: fish microbiota, aquaculture, fish pathogens, dysbiosis, skin, gill, Dicentrarchus

labrax, Sparus aurata, bacteria, 16S rRNA, metataxonomics, water microbiota, ontogenesis,

antibiotics.

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RESUMO

A aquacultura é atualmente a indústria alimentar em maior crescimento com a

capacidade de atender à demanda alimentar global. A intensificação das práticas de

aquacultura acarreta várias limitações ao seu desenvolvimento, em especial devido a

doenças infeciosas. As superfícies mucosas dos peixes abrigam comunidades microbianas

que desempenham um papel fundamental na saúde e aptidão do hospedeiro. Para além da

imunidade, outras funções microbianas beneficiam a fisiologia e metabolismo do hospedeiro.

No entanto, o microbioma dos peixes é altamente variável e influenciado por vários fatores

bióticos e abióticos. Em particular, a taxonomia do hospedeiro, fisiologia, tecido, dieta, idade,

habitat e parâmetros da água, determinam a dinâmica microbiana dos peixes. Alterações às

condições favoráveis podem levar ao desiquilíbrio microbiano, isto é, disbiose, e afetar a

saúde dos peixes, podendo, eventualmente, provocar doença. Particularmente importantes

na aquacultura são as infeções bacterianas, o stress, o uso de antibióticos e outros

tratamentos químicos que podem induzir a disbiose nos peixes. A monitorização das

dinâmicas microbianas e da disbiose nos peixes de piscicultura, bem como do seu ambiente,

é fundamental para melhorar as práticas de aquacultura bem como prevenir e mitigar

doenças. Avanços na tecnologia molecular têm facilitado a investigação na área dos

microbiomas, permitindo a identificação de micróbios e estimar a sua diversidade e função.

Esta tese de doutoramento aferiu a composição, diversidade e estrutura microbiana

da pele e brânquias do robalo Dicentrarchus labrax e dourada Sparus aurata. Estas espécies

são duas das espécies de peixe de cultivo mais importantes comercialmente na Europa; no

entanto, a sua produção é bastante afetada por doenças infeciosas. Em estreita colaboração

com uma piscicultura, amostragem semanal não-invasiva das mucosas externas de ambas

as espécies foi realizada entre Agosto de 2016 e Janeiro de 2018. Usei uma abordagem

metataxonómica para sequenciar a região V4 do gene bacteriano 16S rRNA e analisei as

diferenças nas comunidades microbianas observadas em diferentes condições. Estudei os

efeitos de vários fatores bióticos e abióticos na microbiota dos peixes, incluindo: taxonomia

do hospedeiro, tecido, idade, comunidades microbianas da água, temperatura da água,

doença e tratamento com antibiótico.

O primeiro capítulo contextualiza esta dissertação, apresentado uma revisão da

literatura sobre aquacultura e microbiomas, com um ênfase especial em microbiomas de

peixes. Além disso, analisa as aplicações práticas do estudo do microbioma em ambiente de

aquacultura, bem como as ferramentas disponíveis para a caracterização do microbioma. Os

capítulos 2 a 5 são apresentados como um conjunto de artigos científicos, cujos resultados

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se interligam, revelando um conhecimento abrangente sobre a complexidade das dinâmicas

microbianas e disbiose que ocorreu no robalo e na dourada. Uma discussão geral destes

resultados é apresentada no capítulo 6.

Identifiquei taxa bacterianos consistentemente predominantes nas duas espécies e

tecidos ao longo do tempo, independentemente da condição de saúde dos indivíduos. Os

dois tecidos mucosos apresentaram diferenças na diversidade e resposta a fatores de stress

externos, embora pareçam ser governados pelos mesmos fatores. No geral, a microbiota da

pele foi mais variável e mais susceptível a disbiose, enquanto a microbiota das brânquias foi

mais estável, embora menos resiliente a doença e tratamento com antibiótico. A microbiota

de ambos os tecidos foi significativamente diferente da microbiota da água. No entanto, a

pele apresentou uma estrutura microbiana mais semelhante à da água do que as brânquias.

O intercâmbio microbiano entre tecidos foi elevado, e ocorreu também entre tecidos e a água

circundante.

O robalo e a dourada apresentaram diferenças na diversidade microbiana entre si,

sendo essas diferenças mais pronunciadas a nível da estrutura (beta-diversidade). Diferenças

na estrutura microbiana e possível função também foram detetadas entre grupos de

indivíduos de ambas as espécies que pertenciam a diferentes idades. A temperatura da água

teve uma grande influência nas dinâmicas bacterianas da pele e brânquias do robalo ao longo

de um ano. Alterações na abundância de vários géneros potencialmente patogénicos

(Aliivibrio, Pseudomonas, Photobacterium e Vibrio) também foram correlacionadas com

mudanças na temperatura. O aumento da abundância de um ou mais desses géneros em

duas ocasiões parece ter levado à disbiose nos dois tecidos, embora não tenha sido detetada

nenhuma doença. A disbiose também ocorreu como consequência de infeção e tratamento

com antibióticos, tanto em robalos adultos como em juvenis. No caso do robalo adulto, infeção

provocada por Photobacterium damselae e tratamento com oxytetracyclina induziram

respostas assimétricas na microbiota da pele e brânquias, demonstrando mais uma vez um

efeito do tecido. No robalo juvenil, co-infeção provocada por Photobacterium damseale subsp.

piscicida e Vibrio harveyi e tratamento com flumequina, provocaram um aumento da

diversidade microbiana na pele, uma resposta menos reportada na microbiota de peixes. A

avaliação da recuperação a curto prazo em ambos os grupos etários indicou que a

homeostase microbiana não tinha sido totalmente atingida ao fim de 3 semanas em adultos

e 1 semana em juvenis.

Esta pesquisa mostrou uma alta variabilidade da microbiota da pele e brânquias do

robalo e dourada de aquacultura ao longo do tempo, influenciada por fatores ligados ao

hospedeiro e ao ambiente. Além disso, tanto doenças como tratamentos com antibiótico

mostraram um efeito prejudicial para a microbiota dos peixes. A monitorização das

comunidades microbianas de espécies de peixe de piscicultura provou ser bastante

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informativo para a identificação de biomarcadores de saúde. Estes biomarcadores têm o

potencial de ser a resposta para melhorar as práticas de aquacultura.

Palavras-chave: microbiota de peixe, aquacultura, patógenos de peixe, disbiose, pele,

brânquias, Dicentrarchus labrax, Sparus aurata, bactérias, 16S rRNA, metataxonómica,

microbiota da água, ontogénese, antibióticos.

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Table of Contents

Foreword iii

Acknowledgements iv

Abstract vi

Resumo viii

Table of Contents xi

List of Figures xvi

List of Tables xviii

List of Abbreviations xix

Chapter 1: General introduction 1

1.1 Literature review 1

1.1.1 Aquaculture overview 1

1.1.1.1 European seabass Dicentrarchus labrax and gilthead seabream

Sparus aurata aquaculture 3

1.1.2 Microbiome 4

1.1.3 Microbiome of fish 7

1.1.3.1 Fish microbial dynamics 8

1.1.3.2 Fish microbial imbalance and disease 12

1.1.3.3 Fish microbial function 15

1.1.4 Microbiome applications in aquaculture settings 18

1.1.5 Available tools for microbiome assessment 21

1.1.5.1 Sampling 21

1.1.5.2 Laboratory procedures for culture independent microbial

assessments 22

1.1.5.3 Data analyses 24

1.2 Thesis structure and objectives 27

1.3 References 28

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Chapter 2: Characterization of the skin and gill microbiomes of the

farmed seabass (Dicentrarchus labrax) and seabream (Sparus

aurata) 66

2.1 Abstract 66

2.2 Introduction 67

2.3 Material and methods 68

2.3.1 Sample collection and preparation 68

2.3.1 Data and statistical analyses 69

2.4 Results 70

2.4.1 Taxonomic bacterial composition and core microbiome of seabass and

seabream 70

2.4.2 Microbial diversity 73

2.5 Discussion 75

2.5.1 Core microbiome composition 75

2.5.2 Potential pathogens detected in the core microbiomes 77

2.6 Conclusion 77

2.7 Acknowledgements 78

2.8 References 78

2.9 Supplementary material 87

Chapter 3: Effects of aging on the skin and gill microbiota of farmed

seabass and seabream 88

3.1 Abstract 88

3.2 Introduction 89


3.3.1 Fish species, sampling and preparation 90

3.3.2 Data processing and statistical analysis 92

3.4 Results 94

3.4.1 Microbial diversity across age groups 94

3.4.1.1 Alpha-diversity 94

3.4.1.2 Beta-diversity 97

3.4.1.3 Bacterial taxa 97

3.4.2 Microbial predicted functional diversity across age groups 100

3.4.3 Fish and water microbiota comparisons 102

3.5 Discussion 103

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3.5.1 Microbial diversity across age groups 103

3.5.2 Microbial predicted functional diversity across age groups 105

3.5.3 Fish and water microbiota comparisons 106

3.6 Conclusion 107


3.8 References 108


Chapter 4: Longitudinal sampling of external mucosae in farmed

European seabass reveals the impact of water temperature on

bacterial dynamics 125

4.1 Abstract 125

4.2 Introduction 126


4.3.1 Experimental design, sampling and processing 128

4.3.2 Data processing and statistical analysis 128

4.4 Results 130

4.4.1 Bacterial composition and temporal dynamics of the microbiota 130

4.4.2 Dynamics of potentially pathogenic (PP) genera 134

4.4.3 Effect of water temperature on fish microbiota 135

4.5 Discussion 139

4.5.1 Temporal dynamics of the microbiota 139

4.5.2 Water temperature effects in the diversity of fish microbiota 140

4.5.3 Water temperature effects in the predicted microbiota function 142

4.6 Conclusion 143


4.8 References 143


Chapter 5: Effects of disease and antibiotic treatment on the

microbiota of juvenile and adult seabass 163

Chapter 5.1: Effects of disease, antibiotic treatment and

recovery trajectory on the microbiome of farmed seabass

(Dicentrarchus labrax) 163

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5.1.1 Abstract 163

5.1.2 Introduction 164

5.1.3 Material and methods 165

5.1.3.1 Ethical statement 165

5.1.3.2 Experimental design, sample collection and preparation 165

5.1.3.3 Data and statistical analyses 167

5.1.4 Results 167

5.1.4.1 Gill bacterial composition and diversity 168

5.1.4.2 Skin bacterial composition and diversity 174

5.1.5 Discussion 174

5.1.6 Conclusion 177

5.1.7 Acknowledgements 178

5.1.8 References 178

5.1.9 Supplementary material 187

Chapter 5.2: Monitoring disease and antibiotic treatment in the

skin microbiota of farmed seabass fingerlings 188

5.2.1 Abstract 188

5.2.2 Introduction 188

5.2.3 Material and methods 190

5.2.3.1 Experimental design, sampling and preparations 190

5.2.3.2 Data and statistical analysis 191

5.2.4 Results 192

5.2.4.1 Microbial diversity and composition 192

5.2.4.2 Microbial predicted function 195

5.2.5 Discussion 196

5.2.5.1 Disease effects in skin microbiota of seabass fingerlings: healthy

vs diseased states 196

5.2.5.2 Flumequine effects in skin microbiota of seabass fingerlings:

diseased vs treatment states 198

5.2.5.3 Recovery of the skin microbiota in seabass fingerlings: healthy

vs recovery states 199

5.2.6 Conclusion 199

5.2.7 Acknowledgements 200

5.2.8 References 200

5.2.9 Supplementary material 205

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Chapter 6: General discussion 208

6.1 Variability associated with the skin and gill microbiota in farmed seabass

and seabream 208

6.2 Host related factors shaping the skin and gill microbiota in farmed seabass

and seabream 210

6.3 Water related factors shaping the skin and gill microbiota in farmed seabass

and seabream 211

6.4 Dysbiosis in the skin and gill microbiota of farmed seabass and seabream 212

6.5 Microbial monitoring implications for aquaculture 214

6.6 Future perspectives 216

6.7 Conclusions 217

6.8 References 218

Appendix A: List of additional publications 227

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List of Figures

Chapter 1 Figure 1.1 Pictures of the European seabass Dicentrarchus labrax and the gilthead seabream

Sparus aurata.

Chapter 2 Figure 2.1 Barplots of individual taxonomic composition.

Figure 2.2 Barplots of the core microbiotas.

Figure 2.3 Boxplots of the alpha-diversity values.

Figure 2.4 PCoA plots of the beta-diversity.

Figure S2.1 Rarefaction curves.

Chapter 3 Figure 3.1 Boxplots of the Shannon diversity values in each age group.

Figure 3.2 PCoA plots and boxplots of the Bray-Curtis distance in each age group.

Figure 3.3 Barplots of the most abundant phyla and genera in each age group.

Figure 3.4 LDA score of differently abundant predicted enriched pathways in each age group.

Figure 3.5 Venn diagrams of shared ASVs between tissues and water microbiota in each age

group.

Figure S3.1 Illustrative scheme of the semi-intensive fish farm.

Figure S3.2 Boxplots of the alpha-diversity values for tissue and water microbiota in each age

group.

Figure S3.3 Boxplots of the PD, ACE and Fisher alpha-diversity values in each age group.

Figure S3.4 PCoA plots of the Bray-Curtis distance in each tissue and water microbiota.

Chapter 4 Figure 4.1 Venn diagrams of shared core ASVs between tissues and water microbiota.

Figure 4.2 Alluvial plots of the most abundant potentially pathogenic (PP) genera, and other

PP genera they interact with, per month.

Figure 4.3 Temperature models, and beta- and alpha-diversity estimates, per month.

Figure 4.4 Barplots of microbiota contribution per month.

Figure 4.5 Graph depicting the relative frequency of the differentially enriched potential

pathways during cold and warm months.

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Figure S4.1 Daily average temperature of the water surface throughout the year.

Figure S4.2 Barplots of the most abundant phyla and genera per month.

Chapter 5 Figure 5.1.1 Schematic illustration of the experimental design.

Figure 5.1.2 Boxplots of the alpha-diversity values per state.

Figure 5.1.3 PCoA plots of the beta-diversity.

Figure 5.1.4 Alluvial plots of the most abundant taxa per state.

Figure 5.1.5 Barplots of the core microbiota per state.

Figure S5.1.1 Barplots of individual taxonomic composition.

Figure 5.2.1 Schematic illustration of the experimental design.

Figure 5.2.2 Alpha- and beta-diversity estimates, coloured by state.

Figure 5.2.3 Heatmaps of the most abundant phyla and genera per state.

Figure 5.2.4 Alluvial plots of potentially pathogenic taxa per state.

Figure 5.2.5 LDA score of differently abundant predicted enriched pathways per state.

Figure S5.2.1 Dendrograms of the beta-diversity values based on UniFrac weighted and Bray-

Curtis distances, coloured by state.

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List of Tables

Chapter 2 Table 2.1 Relative proportions of the most abundant ASVs, sequences and core ASVs.

Table 2.2 Significance of diversity indices and relative proportions of dominant taxa.

Chapter 3 Table 3.1 Significance of diversity indices between age groups.

Table 3.2 Significance of beta-diversity and Mantel tests between tissue and water microbiota.

Table S3.1 Relative proportions of the most abundant phyla and genera in each age group.

Table S3.2 Significance of relative proportions of dominant taxa between age groups.

Table S3.3 Significantly enriched predicted pathways in each age group.

Chapter 4 Table 4.1 Significance of the temperature models and season on the diversity indices.

Table 4.2 Significance of the correlation between temperature variables and bacterial

diversity, including the abundance of potentially pathogenic (PP) genera.

Table S4.1 Relative mean proportions of the most abundant phyla and genera per month.

Table S4.2 Core ASVs present in the skin, gill and water microbiota in a year.

Table S4.3 Significance of pairwise alpha- and beta-diversity comparisons across consecutive

months.

Table S4.4 Dynamics of the most abundant potentially pathogenic (PP) genera.

Table S4.5 Significantly enriched potential pathways in the cold and warm months.

Chapter 5 Table 5.1.1 Significance of diversity indices and relative proportions of dominant taxa.

Table 5.1.2 Most abundant ASVs and sequences.

Table 5.2.1 Significance of alpha- and beta-diversity indices between states.

Table S5.2.1 Relative mean proportions of the most abundant phyla and genera per state.

Table S5.2.2 Core ASVs present in each state.

FCUP To be or not to be diseased: microbial dynamics and dysbiosis in farmed European seabass and gilthead seabream xix

List of Abbreviations

°C Degrees Celsius

ACE Abundance-based coverage estimator

ANOVA Analysis of variance

ASV Amplicon sequence variant

ATP Adenosine triphosphate

AXOS Arabinoxylooligosaccharides

bp base pair(s)

ca circa

cm centimeter(s)

CO2 Carbon dioxide

DADA Divise amplicon denoising algorithm

DNA Deoxyribonucleic acid

dpf days post feed

dph days post hatch

FEAST Fast expectation-maximization microbial source tracking

FOS Fructooligosaccharides

g gram(s)

GALT Gut-associated lymphoid tissue

GI Gastrointestinal

GIALT Gill-associated lymphoid tissue

gls Generalized least squares

GOS Galactooligosaccharides

h hour(s)

IMO Isomaltooligosaccharides

IMTA Integrated multi-trophic aquaculture

ITS Internal transcribed spacer

KEEG Kyoto encyclopedia of genes and genomes

Kg Kilogram(s)

FCUP To be or not to be diseased: microbial dynamics and dysbiosis in farmed European seabass and gilthead seabream xx

L Liter(s)

LDA Linear discriminant analysis

LEfSe Linear discriminant analysis effect size

lm linear models

LME Linear mixed effects

m meter(s)

MALT Mucosa associated lymphoid tissue

MAMP Microbe-associated molecular patterns

min minute(s)

mm milimeter(s)

MOS Mannanoligosaccharides

NAD Nicotinamide-Adenine dinucleotide

NALT Nasopharynx-associated lymphoid tissue

nm nanometer

NSTI Nearest sequenced taxon index

OTU Operational taxonomic unit

PCoA Principal coordinates analysis

PCR Polymerase chain reaction

PD Phylogenetic diversity

PERMANOVA Permutational multivariate analysis of variance

PICRUST Phylogenetic investigation of communities by reconstruction of unobserved states

PRR Pattern recognition receptors

QIIME Quantitative insights into microbial ecology

RAS Recirculating aquaculture systems

RNA Ribonucleic acid

rRNA Ribosomal ribonucleic acid

RRPP Randomized residuals in a permutation procedure

SALT Skin-associated lymphoid tissue

scFOS Short-chain fructooligosaccharides

TCA Tricarboxylic acid cycle

TIME Temporal insights into microbial ecology

tns tonne(s)

TOS Trans-galactooligosaccharides

FCUP To be or not to be diseased: microbial dynamics and dysbiosis in farmed European seabass and gilthead seabream xxi

USA United States of America

wph weeks post hatch

XOS Xylooligosaccharides

μm micrometre(s)


1

CHAPTER 1

General introduction

1.1 Literature review

1.1.1 Aquaculture overview Fish is currently one of the main sources of animal protein consumption for humans,

reaching 20.3 kg per capita per year (FAO, 2018). Fish are also an important source of several

essential amino acids, such as lysine and methionine; vitamins, such as vitamin D, A and B;

and minerals, such as calcium, phosphorus, iodine, zinc, iron and selenium (Béné et al., 2015).

Aquaculture accounts for almost half of the global fish supply and is currently the fastest

growing food-supply industry globally, with an estimated value of US$ 232 billion and a

production peak of 171 million tonnes in 2016, from which 80 million tonnes were finfish (FAO,

2018). In order to meet the global demand, arising from the exponential growth of the human

population, production practices are intensifying and ensuring food security is becoming

increasingly challenging (Béné et al., 2015; Fisher et al., 2017).

There are multiple forms of aquaculture in freshwater, brackish and marine

environments (FAO, 2018). Cages and net pens, frequent in marine environments and located

in natural water bodies, are key for rearing Atlantic salmon, the most common marine

aquaculture species (FAO, 2018). Inland aquaculture, usually practiced in freshwater

environments, represented 64.2% of the global farmed fish production, with finfish farming

representing 92.5% of the inland aquaculture in 2016 (FAO, 2018). In inland aquaculture, fish

are commonly grown in ponds, which are usually excavated and require a water supply from

an external source as well as drainage structures (Boyd and McNevin, 2015). Flow through

raceways is also used and is, in principle, similar to ponds, but can harbour higher stock

densities and are positioned in series with water flowing from one unit to another (Boyd and

McNevin, 2015; Soderberg, 1994). A more environmentally friendly method is Recirculating

Aquaculture Systems (RAS), which are closed culture systems that reuse waste water after it

has been filtered and purified (Boyd and McNevin, 2015). A more recent approach, Integrated

Multi-trophic Aquaculture (IMTA), rears multiple species from different trophic levels in a single

system in order to more efficiently take advantage of organic waste (Bouwmeester et al.,

2020). Moreover, aquaculture systems can be classified as extensive, semi-intensive,

intensive or hyper-intensive, with increasing amount of feed, stocking density and water

exchange from the first to the latter (Lane et al., 2014). All production technologies have


2

different profiles of environmental issues that are essential to ensure sustainable aquaculture

development (Lane et al., 2014).

Aquaculture conditions such as stocking densities, low oxygen concentrations, diet or

decreased water quality act as chemical and biological stressors for fish (Ashley, 2007). Stress

is a key factor influencing the health of farmed fish, impacting fish homeostasis by triggering

physiological responses and impairment of the immune system, which has direct

consequences on fish susceptibility to disease (Ashley, 2007; Sneddon et al., 2016). Infectious

diseases are the biggest constraint on aquaculture development, diminishing its economic

value and quality and reducing species growth and survival (FAO, 2018). Infectious agents

enter aquaculture mainly through water, or, to a less extent, through feed, broodstock or others

(Saksida et al., 2014). Although these diseases occur naturally in the wild, aquaculture

broodstock did not coevolve with diseases as wild hosts, which makes them less resistant in

general and makes disease consequences highly unpredictable (Lafferty et al., 2015).

Amongst the multitude of diseases that predominantly infect fish, the majority are attributed to

bacteria, followed by viruses, protists and metazoans (Lafferty et al., 2015). Bacterial diseases

are especially problematic for aquaculture, since bacteria can survive in the aquatic

environment without a host (Pridgeon and Klesius, 2012). Several bacterial genera can be

pathogenic to aquatic animals, mainly the gram-negative Aeromonas, Edwardsiella,

Flavobacterium, Francisella, Photobacterium, Piscirickettsia, Pseudomonas, Tenacibaculum,

Vibrio and Yersinia, and the gram-positive Lactococcus, Renibacterium and Streptococcus

(Austin and Austin, 2016; Toranzo et al., 2005). The pathogenicity and clinical signs caused

by bacterial pathogens are dependent on the stage of the disease and on several host

characteristics, such as species and age group (Toranzo et al., 2005). For example, fish early

life-stages are the most vulnerable to stress and disease, due to a lack of fully functional

immune, digestive and osmotic systems (Rehman et al., 2017). Moreover, certain

environmental variables, such as temperature, photoperiod or precipitation are well known to

affect the physiology and behaviour of animals (Bowden et al., 2007). Especially important in

the aquatic environment, these factors govern the dynamics of many infectious diseases (e.g.,

Mohamad et al., 2019). Hence, perturbations to the host-pathogen-environment balance will

result in disease (Toranzo et al., 2005). In order to develop appropriate measures that can

prevent and control marine diseases, studies must integrate the characteristics of host species

and etiological agents of disease as well as the environmental factors that affect them

(Toranzo et al., 2005).


3

1.1.1.1 European seabass Dicentrarchus labrax and gilthead

seabream Sparus aurata aquaculture The European seabass Dicentrarchus labrax is a fish species from the Moronidae

family with a silvery grey and rather elongated body (Figure 1.1A). The seabass is a slow

growing species with males maturing early between years 2 and 3, while females mature after

3 years (Carillo et al., 1995; Felip et al., 2001). Being an eurytherm (5-28°C) and euryhaline

(3-35‰) species, it mostly lives in coastal marine waters as well as in brackish waters

estuaries and lagoons and can occasionally be found in rivers (FAO, 2016a). Up until the late

60s, the European seabass was typically cultured in coastal lagoons and tidal reservoirs, after

which it started to be massively reared in France and Italy (FAO, 2016a). By the late 70s,

mass-production of the European seabass was implemented across the Mediterranean, being

the first non-salmonid species to be commercially cultured in Europe (FAO, 2016a). Currently,

seabass is farmed in open-water sea cages although production in seawater ponds and

lagoons across the Mediterranean is still very common with a global production of 191,003

tonnes in 2016 (FAO, 2016a).

The gilthead seabream Sparus aurata is a species from the Sparidae family with an

oval, deep and compressed body, and silvery grey colour (Figure 1.1B). The seabream is a

protandrous hermaphrodite, first maturing as male between years 1 and 2 and then as female

in the following 2 to 3 years (FAO, 2016b; Mehanna, 2007). It is also an euryhaline (2-39‰)

and eurytherm (4-26°C) species, being found in marine and brackish waters, for example

coastal lagoons and estuaries (Chervinski, 1984; FAO, 2016b; Kır, 2020). Historically, the

gilthead seabream was extensively cultured in coastal lagoons and saltwater ponds (FAO,

2016b). After the 1980s, when intensive rearing systems were developed and artificial

breeding became successful, this species achieved large-scale production in Spain, Italy and

Greece (FAO, 2016b). Currently it is mostly farmed in coastal ponds and lagoons or in sea

cages and land-based installations across the Mediterranean, with global productions of

185,980 tonnes in 2016 (FAO, 2016b).

Figure 1.1 Seabass (A) and seabream (B). After FAO 2016a, 2016b.


4

Among the 369 finfish species farmed in the world, the European seabass and the

gilthead seabream are considered high-value species, being two of the most important farmed

finfish species traded in Europe (Clarke and Bostock, 2017). Their production, however, is

greatly affected by infectious diseases that account for losses of up to 15% in seabass and of

up to 40% in seabream (Lane et al., 2014). The main bacterial pathogens to which seabass

and seabream are susceptible include Photobacterium damselae, causing photobacteriosis,

Vibrio spp., causing vibriosis, and Tenacibaculum maritimum, causing flexibacteriosis (FAO,

2016a, 2016b; Toranzo et al., 2005). Given the unavoidable constant interaction with aquatic

potential pathogens and the role of the seabass and seabream in the global food economy,

studying their mucosal immune system will help design more efficient prevention and

treatment strategies.

1.1.2 Microbiome More than 300 years ago, Antoine van Leeuwenhoek observed the first host-

associated microbial communities that he scraped from his own teeth and called “animalcules”

(Robinson et al., 2010). Many others followed in the field of microbiology, passing by Pasteur’s

concept of culturing isolated bacteria, Koch’s determination of infectious diseases etiology, to

Staley and Konopka’s “great plate anomaly”, that was based on the observation that most

microbes seen under the microscope could not be cultured (Robinson et al., 2010; Staley and

Konopka, 1985). After several definitions, Berg et al. (2020) recently described the microbiome

as the community of organisms in an environment, including their produced molecules,

structural elements (such as nucleic acids, proteins, lipids and polysaccharides), metabolites,

and molecules produced by coexisting hosts and environmental conditions. All living

microorganisms within the microbiome, namely bacteria, archaea, fungi, algae and small

protists, form the microbiota (Berg et al., 2020). Although many technological and research

advances in the field were made, we are still far from completely grasping the magnitude and

role of the microbial communities associated with any host or environment (Gilbert et al., 2018;

Legrand et al., 2018; Robinson et al., 2010). Nevertheless, the importance of the host-

associated microbial communities to the host health is undeniable (Gilbert et al., 2018;

Legrand et al., 2018; Robinson et al., 2010).

Microbes are one of the major reservoirs of diversity on Earth and have been evolving

for roughly 3.5 billion years (McFall-Ngai et al., 2005). Microbes show an assortment of

lifestyles, being able to live in symbiosis with animals (Smith and Douglas, 1987). Mutualistic

interactions between animals and microbes are one of the most important forms of symbiosis,

having significant ecological and evolutionary effects (McFall-Ngai et al., 2005). The

microbiota of vertebrates has co-evolved with their hosts for almost half a billion years and is


5

now considered the “extended self” (Lloyd-Price et al., 2016). At homeostasis, this symbiotic

relationship provides mutual benefits. While the microbiota benefits from a safe growing

environment, the host is provided with immune, physiologic and metabolic aid (Gilbert et al.,

2018; Martin et al., 2007; Maynard et al., 2012).

The microbiome is one of the most important components of the vertebrate’s immune

system. All higher vertebrates have a complex immune system composed of the innate and

adaptive immune systems (Medzhitov and Janeway, 1998). While the innate immune system

is enrolled in an early response during infection, the adaptive immune system is activated in

the late phase of infection, eliminating pathogens and generating immunological memory

(Mogensen, 2009). The innate immune system is based primarily on physical and chemical

barriers to infection, including the epidermis, ciliated respiratory epithelium, vascular

endothelium, and the mucosal surfaces (Basset et al., 2003). The mucosal surfaces of animals

are part of the mucosal immune system and incorporate the mucosa associated lymphoid

tissues (MALTs), responsible for both symbiont colonization tolerance and protection against

pathogens (Gomez et al., 2013). Within the MALT there is a complex and diverse bacterial

community, the microbiota, that is involved in the mucosal immune system development and

functioning (Salinas and Magadán, 2017). Specifically, the microbiota is the first line of

defense against pathogens, by occupying potential binding sites or by secreting inhibitory

compounds (Basset et al., 2003). In this regard, studying the microbiome is crucial to

understand the microbe-host-pathogen interplays and holds promise to treat and prevent

disease. Besides providing immune benefits, the microbiome also has the ability to increase

the host’s capacity to harvest energy from its diet (e.g., Heintz-Buschart & Wilmes, 2018;

LeBlanc et al., 2013; Turnbaugh et al., 2006), affect lipid metabolism by modifying bile acid

(Martin et al., 2007), repair damaged mucosal barriers (Pull et al., 2005), alter host gene

expression encoding antimicrobial compounds (Cash et al., 2006), increase locomotor activity

(Bäckhed et al., 2007), educate skin T cells to respond to pathogens (Grice and Segre, 2011),

charge nucleotides and synthetize ATP (Huttenhower et al., 2012), prevent overgrowth of

harmful bacteria (Kamada et al., 2013), and maintain a homeostatic pH (Hickey et al., 2012),

just to name a few. Moreover, it is now known that the gut microbiome communicates with the

central nervous system participating in the “gut-brain axis” through microbial-derived

intermediates that either enter the systemic circulation and cross the blood-brain barrier or

interact directly with the immune system to spread signaling (Osadchiy et al., 2019). Insights

into the specific functional contributions of the microbiome opens the door for the use of

specific commensal microbes as therapy (Hall and Robinson, 2021; Mimee et al., 2016; Park

and Im, 2020).

There are several biotic factors that shape the microbiota, including host genotype and

physiology, body site and lifestyle, including diet, as well as abiotic factors, such as


6

temperature and pH (Gilbert et al., 2018; Martin et al., 2007a). The composition and diversity

of the microbiome is based on the microbes available at the time of colonization and the order

in which they colonize, together with the specific characteristics of the colonization habitat

(Martin et al., 2007a). For example, human fetuses do not have a microbiota; consequently,

mode of birth is essential in defining the newborn’s first microbiome, with neonates born

vaginally presenting a different microbiota from those born via Cesarean section (Dominguez-

Bello et al., 2010). Moreover, because the conditions that allow microbial colonization in one

body habitat are not encountered in others, the microbiota will manifest differently in different

ecological niches (Proctor et al., 2019). The human gut microbiota, for example, is significantly

different from the microbiota of the skin, which, in turn, also presents differences depending

on the body site (Costello et al., 2009). Additionally, it is thought that the human microbiome

is highly plastic over time (Caporaso et al., 2011; David et al., 2014), with a great level of

personalization (Flores et al., 2014), which vastly exceeds that of the human genome, to the

point that it might have forensic applications (Fierer et al., 2010; Robinson et al., 2021).

Despite these differences, it is possible to detect certain similarities within habitats or species.

The set of genes that is present across all individuals of a species or habitat is called the core

microbiome (Martin et al., 2007). In sum, the microbiome is composed of the core and the

variable or transient microbiome and is dependent on a wide range of factors (Martin et al.,

2007). Some specific life events that occur on longer time frames, such as aging (Heintz and

Mair, 2014), sexual maturation (Kundu et al., 2017) or reproduction (Koren et al., 2012), also

alter the microbiota and consequently animal-microbial relationships, leading to a state of

“altered symbiosis”.

Changes in conditions can alter microbial composition and diversity switching from

homeostasis to microbial imbalance, i.e., dysbiosis, which impacts the host’s health and

eventually leads to disease (Petersen and Round, 2014). Dysbiosis is the breakdown in the

symbiotic relationship, generally related to one or more major stressors, such as pollutants

(e.g., Jin et al., 2017), infection (e.g., Llewellyn et al., 2017), toxins (e.g., Tsiaoussis et al.,

2019) or climate change (e.g., Greenspan et al., 2020). Dysbiosis can occur through

pathobiont expansion, reduced microbial diversity, loss of beneficial microbes, or a

combination of any of these three events, all of which are linked to disease (Petersen and

Round, 2014). For example, common perturbations to the microbiome are related to obesity

(e.g., Fei & Zhao, 2013), diabetes (e.g., Zhou et al., 2019), inflammatory bowel diseases,

including Crohn’s disease (e.g., Erickson et al., 2012; Frank et al., 2011; Lloyd-Price et al.,

2019), disruption of the host circadian cycle (e.g., Leone et al., 2015), rheumatoid arthritis

(e.g., Zhang et al., 2015), atopic dermatitis (e.g., Kong et al., 2012), psoriasis (e.g.,

Alekseyenko et al., 2013), acne (e.g., Dessinioti & Katsambas, 2010), cardiovascular disease

(e.g., Peng et al., 2018), multiple sclerosis (e.g., Jangi et al., 2016), asthma (e.g., Sokolowska


7

et al., 2018), autism (e.g., Hsiao et al., 2013), Parkinson’s disease (e.g., Sampson et al.,

2016), Alzheimer’s disease (e.g., Jiang et al., 2017), cancer (e.g., Gopalakrishnan et al.,

2018), among many others. To this end, microbiome research is imperative and crucial to

provide new biomarkers of disease (Gilbert et al., 2018). In fact, it has been suggested that

changes in the microbiota can predict disease progression faster and more easily than clinical

symptoms or the presence of pathogenic agents (Woodhams et al., 2020). Although research

has mainly focused on the human microbiome, increasingly more studies have extended the

focus towards other taxa, to the point where the health of the entire Earth’s living organisms

and environments is considered to be dependent on the “smaller majority” of ubiquitous

microorganisms (Blaser et al., 2016).

1.1.3 Microbiome of fish The mucosal immune system of teleost species is a persistent vigilant of the

environment, protecting the host from infection (Salinas and Magadán, 2017). Fish incorporate

four different MALTs, namely the gut-associated lymphoid tissue (GALT), the gill-associated

lymphoid tissue (GIALT), the skin-associated lymphoid tissue (SALT), and the nasopharynx-

associated lymphoid tissue (NALT) (Salinas and Magadán, 2017). A key element of all teleost

MALTs is the mucosal layer, a perfect niche for microbial adherence and growth composed of

mucins, ions and lipids (Gomez et al., 2013). The mucus layer covers the gut, gill, skin and

nasal mucosal surfaces of fish and contains immunological molecules that interact directly

with the commensal microbial communities (Kelly and Salinas, 2017). The microbiome of fish

is thus considered as the first line of defense against pathogens and is likely shaped by the

physicochemical properties of the mucus layer (Kelly and Salinas, 2017).

Being constantly immersed in the aquatic environment, fish are endlessly exposed to

planktonic microbiota that are in contact with their mucosal surfaces (Salinas, 2015). Although

the microbiota of fish is highly diverse, some bacterial taxa dominate across tissues and

species, in particular those from the Phylum Proteobacteria (Legrand et al., 2020b; Llewellyn

et al., 2014). Additionally, the phyla Actinobacteria, Bacteroidetes, Firmicutes and

Fusobacteria are also dominant in the fish microbiota (Legrand et al., 2020b; Llewellyn et al.,

2014). As in other vertebrates, the microbiota of fish is composed of both resident and

transient microbes (Legrand et al., 2020b). While the resident bacteria occur within the

mucosa and are intimately associated with host cells, the transient communities are mostly

free-living bacteria that are part of the fish microbiota for short periods of time (Banerjee and

Kumar Ray, 2017). Resident microbiota is mostly regulated by host factors, while transient

bacteria are mainly influenced by environmental factors (Legrand et al., 2020b). While

perturbations can change microbial composition, it has been proposed that functionally


8

redundant members have the ability to recolonize and preserve microbial functionality (Kelly

and Salinas, 2017). On the other hand, ubiquitous pathogenic bacteria naturally colonize the

water (e.g., Martins et al., 2018) and can also integrate the microbial community of healthy

fish (e.g., Givens et al., 2015). Several stressors can alter the properties of the mucosae (Kelly

and Salinas, 2017), leading to a shift in the abundance of these pathogens, inducing

pathogenicity and leading to dysbiosis and disease (e.g., Hess et al., 2015; Legrand et al.,

2018). In addition, there is no complete distinction between symbionts and pathogens and

high abundances of certain commensals can trigger inflammatory responses (Kelly and

Salinas, 2017).

Bacteria from skin, gills and specially the gastrointestinal (GI) tract have been the focus

of research on fish microbiota, given their role in fish immunity and because these tissues are

the major pathways of pathogen entrance in fish (Merrifield and Rodiles, 2015). Fish microbial

immune response is primarily accomplished through competition between commensals and

potential pathogens for adhesion and nutrients, or through production of secondary

metabolites by the commensal community, such as antimicrobial compounds, that antagonize

potential pathogens (Kelly and Salinas, 2017; Merrifield and Rodiles, 2015; Trivedi, 2012).

The microbiota of fish eggs and larval stages (i.e., whole larvae) has also gained attention

given the importance of the initial steps of microbial colonization (e.g., Abdul Razak et al.,

2019; Bledsoe et al., 2016). Studying the microbiome of fish and their environment will

hopefully promote sustainable aquaculture by preventing and combating pathogens, boosting

host immune response and increasing water quality (Legrand et al., 2020b).

1.1.3.1 Fish microbial dynamics In order to understand microbe-host relationships, research must investigate factors

influencing these dynamics. Such factors could be the result of random dispersal of microbes,

that likely explain the high inter-individual variation presented by fish (Burns et al., 2017); or

of host and environmental selective pressures (Talwar et al., 2018).

Ontogeny is a fundamental and inevitable factor shaping microbial diversity. Fish egg

surfaces are colonized by microbes in the surrounding water (Abdul Razak et al., 2019). As

larvae hatch, these microbes further colonize mucosal tissues, including the GI tract through

water and feed ingestion (e.g., Wilkes Walburn et al., 2019). Until adulthood, fish microbiota

undergoes continuous developmental changes that are stage related. For example, the gut

microbiota of yellowtail kingfish experiences significant changes between 0- and 53-days post

hatch (dph), with significant differences in diversity even between 0- and 3-dph (Wilkes

Walburn et al., 2019). Similarly, the gut microbiota of catfish is also significantly different

between 3-, 65- and 125-dph, but stabilized between 125- and 193-dph (Bledsoe et al., 2016).


9

On the other hand, while the gut microbiota of seabream significantly changes between 5- and

15-dph, no further differences were found between groups up until 71-dph, when the study

ended (Nikouli et al., 2019). A longer term study assessing the gut microbiota of the grass

carp, Chinese perch and Chinese largemouth catfish revealed diversity differences between

all life stages, including larvae from different phases, juveniles and adults (Yan et al., 2016).

On the contrary, the gut microbiota of zebrafish showed no differences between several stages

between 0- and 380-days post feed (dpf), except when fish underwent sexual maturation

between 35- and 75-dpf (Stephens et al., 2016). Lokesh et al. (2017) studied the Atlantic

salmon gut microbiota for 80-weeks post hatch (wph), including several early developmental,

juvenile and adult stages. Differences were found in diversity between all life cycles, with

marked shifts occurring concomitantly with major life events, including hatching and transition

between freshwater and seawater (Lokesh et al., 2019). Additionally, cross-sectional studies

also reported differences in diversity between the gut microbiota of pre-settlement larvae and

post-settlement juveniles/adults of damselfish and cardinalfish (Parris et al., 2016), and

between the skin microbiota of juveniles and adults of two damselfish species (Xavier et al.,

2020). In summary, ontogenetic studies have so far revealed that fish microbiota is highly

dynamic, diversifying with age, and that early life exposure is critical to ensure adult health

and nutrition (Legrand et al., 2020b; Llewellyn et al., 2014). It has been suggested that

microbial colonization is determined by neutral processes in the early life stages, becoming

progressively governed by deterministic processes with age (Talwar et al., 2018). The vast

majority of these studies focused on the gut microbiota and were either cross-sectional or

based on a short time window. Nevertheless, age related microbial differences in fish seems

to be many times linked to important life events (e.g., diet transitions, Wilkes Walburn et al.,

2019; environmental transitions, Lokesh et al., 2019; sexual differentiation, Stephens et al.,

2016) suggesting a compound effect.

In an early study trying to correlate fish microbiota and host genetics, Rawls et al.

(2006) performed reciprocal gut microbial transplants between zebrafish and mice. The results

revealed that, after transplant, although composition was similar to that of the original host,

the abundance of each lineage was more similar to the recipient host (Rawls et al., 2006).

Recently, a study comparing the gill and intestine microbiota of 53 fish reef species (belonging

to 15 families) showed that microbiota composition varied significantly according to host family

(Pratte et al., 2018). Moreover, the authors also saw that similarity between tissues within an

individual was significantly greater than that in the same tissue from different individuals

(Pratte et al., 2018). Other studies also reported microbial differences between species

sharing the same habitat (e.g., 2 fish species reared in the same pond, Li et al., 2014; 2 guppy

ecotypes from the same stream, Sullam et al., 2015; 4 butterflyfish species from the island of

Moorea, Reverter et al., 2017; 44 coral reef species from Mayotte island, Chiarello et al., 2018;


10

13 species within the Mediterranean Sea, Ruiz-Rodríguez et al., 2020; 3 species from

Amazonas, Sylvain et al., 2020). On the other hand, a recent study evaluating the gut

microbiota of blue and channel catfish reported significant differences in structure but not

diversity across fish species or strains (Bledsoe et al., 2018), indicating that host factors,

although predominant, are not straightforward. These results strongly attest for a role of the

host genetics in microbiota assembly and diversification in fish, although the specific

mechanisms through which that happens are still largely unknown.

Diet, or specific diet content, is one of the most straightforward host factors impacting

the gut microbiota, and it can also impact the skin and gill microbiota (Chiarello et al., 2018;

Pratte et al., 2018). Lipids, proteins, vitamins, functional glycomic ingredients and minerals are

linked to gut microbial health that ultimately impact fish metabolism and energy utilization,

performance and immunity (reviewed in Ringø et al., 2016). Several studies have

demonstrated the effect of diet in shaping fish gut microbiota, including the gibel carp (e.g.,

Chen et al., 2014), common carp (e.g., Liu et al., 2014), surgeon fish (e.g., Miyake et al.,

2015), zebrafish (e.g., Koo et al., 2017), gilthead seabream (e.g., Rimoldi et al., 2018),

European seabass (e.g., Pérez-Pascual et al., 2020), rainbow trout (e.g., Michl et al., 2017),

brown trout (e.g., Michl et al., 2019), Atlantic salmon (e.g., Villasante et al., 2019), Nile tilapia

(e.g., Hassaan et al., 2020), among other fish species (e.g., Bolnick et al., 2014). Differences

in microbial composition and diversity in fish gut are mostly related to the nutrient content of

the diet of each fish species, coupled with gut morphology and host phylogeny (Legrand et al.,

2020b). Specifically, gut-colonizing microbes are highly dependent on species-specific

characteristics such as gut size and structure, pH, osmolality, redox potential, as well as diet

content (Merrifield and Ringø, 2014). In this regard, unique bacterial taxa are found in the gut

microbiota of fish with different feeding habits, namely herbivorous, carnivorous, omnivorous

and filter-feeding species (e.g., Li et al., 2014; Liu et al., 2016; Pratte et al., 2018). There is

thus a causal link between fish diet, gut microbiota and fish health. Importantly in aquaculture,

diets fed to farmed fish have negative impacts on gut health, having major consequences in

fish fecundity, growth rate, appetite and susceptibility to disease (Hossain et al., 2018). In this

regard, recent research has focused on gut microbial manipulation by altering or adding

supplements to fish diet (Ringø et al., 2016). An increasing body of literature has exposed the

benefits of probiotics, prebiotics, immunostimulants, medical plant extracts and microalgae in

controlling the microbiota and optimizing fish health in aquaculture (reviewed in Ringø et al.,

2014). Feed supplementation is a central strategy to improve diet quality and thus growth, but

also to mitigate the negative impacts of aquaculture practices, with important outcomes for

health and productivity of fish farms (Egerton et al., 2018; Legrand et al., 2020b; Reverter et

al., 2021).


11

Teleost fish present microbial differences across, as well as within, body habitats.

Studies so far have confirmed differences between the microbiota of distinct tissues in several

fish species, including the rainbow trout (skin, gill, gut and olfactory organ, Lowrey et al., 2015),

Atlantic salmon (gill and intestine, Schmidt et al., 2016), yellowtail kingfish (skin, gill and gut,

Horlick et al., 2020; Legrand et al., 2020a, 2018), grass carp and southern catfish (intestine,

skin and gill, Zhang et al., 2019), rabbitfish (skin, gill, stomach and hindgut, Wu et al., 2020),

among several other fish species (gill and intestines, Pratte et al., 2018; skin, gill and

intestines, Ruiz-Rodríguez et al., 2020; skin and gut, Sylvain et al., 2020; liver and kidney,

Meron et al., 2020). In addition, microbial differences within the same tissue were seen in the

skin of the European seabass and gilthead seabream (Chiarello et al., 2015) and along the

intestine of the grass carp (Yang et al., 2019), rabbitfish (Jones et al., 2018), Asian silver carp

and gizzard shad (Ye et al., 2014). These variations reflect the diverse microbial colonization

patterns due to differences in physicochemical properties of different tissues, such as pH,

oxygen content or nutrient availability (Egerton et al., 2018; Wang et al., 2018). Moreover,

different body niches exert different pressures, for example comprising different MALTs,

attesting for microbial specialization in each tissue (Kelly and Salinas, 2017).

Many studies have reported higher bacterial diversity in water compared to fish skin

(Chiarello et al., 2019, 2018, 2015; Larsen et al., 2015a; Uren Webster et al., 2018; Wu et al.,

2020; Zhang et al., 2019), gills (Pratte et al., 2018; Wu et al., 2020), intestine (Bledsoe et al.,

2016; Parris et al., 2016; Reinhart et al., 2019; Wilkes Walburn et al., 2019; Wu et al., 2020;

Zhang et al., 2018), stomach (Wu et al., 2020) and whole larvae (Nikouli et al., 2019). The

vast majority of studies reported significant differences between the mucosal microbiota of fish

and the water microbial community (Bledsoe et al., 2016; Boutin et al., 2013; Carlson et al.,

2017; Chiarello et al., 2019, 2018, 2015; Larsen et al., 2015a; Legrand et al., 2018; Llewellyn

et al., 2016; Nikouli et al., 2019; Parris et al., 2016; Pratte et al., 2018; Schmidt et al., 2018;

Uren Webster et al., 2018; Wu et al., 2020; Yan et al., 2016; Zhang et al., 2018, 2019).

Nevertheless, compositional similarities, i.e., shared operational taxonomic units (OTUs) or

amplicon sequence variants (ASVs), are sometimes reported between fish and water

microbiota (Bledsoe et al., 2016; Boutin et al., 2013; Llewellyn et al., 2016; Nikouli et al., 2019;

Parris et al., 2016; Pratte et al., 2018; Schmidt et al., 2015; Uren Webster et al., 2018; Wilkes

Walburn et al., 2019; Zhang et al., 2018). This suggests that each body niche selects from a

nested subset of the water microbial communities (Pratte et al., 2018).

Together with host related factors, habitat or season also play an important role in fish

microbial dynamics. Fish skin, gill and gut microbial diversity and structure vary in association

with multiple ecological variables related to specific habitat characteristics, including

geographic distance (Dunn et al., 2020; Pimentel et al., 2017; Sylvain et al., 2020; Yildirimer

and Brown, 2018), habitat type (Jones et al., 2018; Smith et al., 2015; Uren Webster et al.,


12

2018; Xavier et al., 2019) or lake morphology (Smith et al., 2015). There are exceptions

though, for example, the gut microbiota diversity and structure of the European seabass and

gilthead seabream reared in 5 distantly located aquaculture sea cages revealed no influence

of geographic location (Nikouli et al., 2018). Additionally, it has been suggested that the gut

microbiota of some fish species is mostly modulated by host factors (Steury et al., 2019;

Sylvain et al., 2020). Seasonal variation of water temperature characteristics, such as

temperature, pH, ionic composition, dissolved oxygen, conductivity or chlorophyll can also

cause differences in the microbial diversity of fish sampled across different habitats and

seasons (Hovda et al., 2012; Krotman et al., 2020; Minich et al., 2020a; Ornelas-García et al.,

2018; Sylvain et al., 2020; Vasemägi et al., 2017; Zarkasi et al., 2014). Furthermore, microbial

composition seems to reflect fish tolerance to temperature changes (Kokou et al., 2018).

Importantly, microbial differences due to environmental factors could reflect the interaction of

several covarying variables such as habitat and feeding habits (Smith et al., 2015), or even

social behaviour (Xavier et al., 2019).

Fish microbial variation between habitats can be attributed to differential exposure to

microbes or to different evolutive ecological pressures (Smith et al., 2015; Sylvain et al., 2020).

Additionally, microbial changes in fish due to habitat transition were described, including the

gut and skin of Atlantic salmon migrating from freshwater to seawater (Llewellyn et al., 2016;

Lokesh and Kiron, 2016; Rudi et al., 2017), the skin of hatchery-reared common snook

acclimating to the wild (Tarnecki et al., 2019), and the entire microbiota of the Molly

experimentally acclimated to different salinities (Schmidt et al., 2015). Such important events

in the life cycle of many fish species require them to undergo changes in physiology,

geography and diet, ultimately exposing them to different microbial communities from the

surrounding environment (Le and Wang, 2020). A deeper understanding of the role of the

environment in fish microbial dynamics could have important implications for host adaptation

to local selective pressures.

1.1.3.2 Fish microbial imbalance and disease In fish, microbial imbalance (dysbiosis) resulting from changes in the environment

caused by chemicals, antibiotic usage or aquaculture practices can lead to disease (de Bruijn

et al., 2018). In fish farms, several factors, most commonly related to water parameters, cause

stress to fish populations; in particular, hypoxia (Boutin et al., 2013), high ammonia

concentrations (Qi et al., 2017), suboptimal pH (Sylvain et al., 2016) or unfavourable salinity

(Tian et al., 2020; Zhang et al., 2016) can lead to microbial imbalance and susceptibility to

disease. Other fish farm practices also cause microbial imbalances, such as netting and

transfer (Minniti et al., 2017; Tacchi et al., 2015), rearing density (Du et al., 2019; Y. Wang et


13

al., 2019) or starvation (Tran et al., 2018a; Xia et al., 2014). In the majority of the cases,

exposure to such stressors leads to an increase in pathobionts or a decrease in beneficial

microbiota, confirming dysbiosis (Boutin et al., 2013; Minniti et al., 2017; Qi et al., 2017; Tran

et al., 2018a; Y. Wang et al., 2019; Zhang et al., 2016). Importantly, differences in microbial

diversity have been found between farmed and wild species, such as in the gut of fine flounder

(Ramírez and Romero, 2017a), gut, skin and gills of yellowtail kingfish (Legrand et al., 2018;

Ramírez and Romero, 2017b), and the gut of Atlantic salmon (Uren Webster et al., 2018).

Moreover, a study showed that selective breeding for resistant farmed fish species alters gut

but not gill microbiota (Brown et al., 2019). However, whether such changes are linked to

increased susceptibility to disease remains unknown. Cultivation likely exerts pressure on

host-microbe interactions, although more comparative studies are needed to fully understand

this interaction. Still, there is an urgent need to mitigate aquaculture negative impacts on fish

health and studying their microbial communities is proving to answer many questions.

The link between infection and microbial imbalance is undeniable. Many studies have

established a relationship between specific pathogens and dysbiosis in fish. For example, the

impact on microbial diversity was previously described for the skin of Atlantic salmon infected

with Lepeophtheirus salmonis (see Llewellyn et al., 2017) and alphavirus (see Reid et al.,

2017), the gut of brown trout infected with Tetracapsuloides bryosalmonae (see Vasemägi et

al., 2017), the gut of gibel carp infected with herpesvirus (see She et al., 2017), the skin of

rainbow trout infected with Ichthyophthirius multifilis (see Zhang et al., 2018), the gut, skin,

kidney and brain of Asian seabass infected with Tenacibaculum singaporense (see Miyake et

al., 2020), the gut of grass carp with enteric infection (see Tran et al., 2018b), the gut and

stomach of rainbow trout infected with Caligus lacustri (see Parshukov et al., 2019), the buccal

mucosa of the rainbow trout infected with hematopoietic necrosis virus (see Dong et al., 2019),

and the skin of orbicular batfish infected with Tenacibaculum maritimum (see Le Luyer et al.,

2021). Given the serious impact of diseases in aquaculture, studying the effects of infection in

fish microbiota proves useful for understanding pathogen abundance dynamics as well as

providing health status biomarkers (e.g., Legrand et al., 2018; Llewellyn et al., 2017; Reid et

al., 2017; She et al., 2017; Zhang et al., 2018). Additionally, dysbiosis due to disease opens

the door for secondary infections by opportunistic bacteria (e.g., Dong et al., 2019; Llewellyn

et al., 2017; Reid et al., 2017; Zhang et al., 2018). Importantly, infection causing disease in a

specific organ can lead to dysbiosis in other tissues (Legrand et al., 2018). It is, thus,

imperative that host-microbiome-pathogen studies incorporate broader approaches, including

several fish tissues as well as environmental factors.

One of the most important environmental characteristics that is related to microbial

imbalance and disease is water temperature. All bacteria, including ubiquitous pathogens,

have optimal temperature ranges that maximize their growth (Corkrey et al., 2012).


14

Temperature shifts can confer pathogens competitive advantages and create colonization

opportunities (Mouquet et al., 2005). Indeed, positive relationships between temperature and

the abundance of potentially pathogenic genera, have been reported for Vibrio and

Flavobacterium (see Sugita et al., 1989), as well as Photobacterium (see Horlick et al., 2020;

Minich et al., 2020a). Lower temperatures are also correlated with higher incidences of severe

outbreaks of Aliivibrio (e.g., Guijarro et al., 2015; Khider et al., 2018) and Pseudomonas (e.g.,

Huang et al., 2019; Tao et al., 2016). Seasonal distributions of bacterial infections are common

in fish (Bellos et al., 2015; Habiba et al., 2015; Schade et al., 2016), including within

aquaculture settings (Baker-Austin et al., 2013; Eissa et al., 2018; Kayansamruaj et al., 2014;

Matanza and Osorio, 2018).

The most common way to treat diseases in aquaculture is through pharmacological

interventions. Vaccination is one approach, although procedures are costly, cause substantial

stress and most only confer short term immunity (Bakopoulos et al., 2003). Antibiotic treatment

remains the best option, even being sometimes used in a prophylactic manner (Cabello,

2006). Despite their reported negative impacts on animals, it is estimated that antimicrobial

use in food producing animals surpasses human use (Van Boeckel et al., 2017). Several

studies have evaluated the impact on fish microbial balance of several antibiotics, including

rifampicin (Carlson et al., 2017, 2015), olaquindox (He et al., 2017b), streptomycin (Pindling

et al., 2018), sulfamethoxazole (Zhou et al., 2018), florfenicol (E. Wang et al., 2019; Gupta et

al., 2019; Sáenz et al., 2019), oxytetracycline (Almeida et al., 2019b, 2019a; Kim et al., 2019;

López Nadal et al., 2018; Navarrete et al., 2008; Zhou et al., 2018), oxolinic acid (Gupta et al.,

2019), amoxicillin (Kim et al., 2019), and a combination treatment of oxytetracycline,

erythromycin and metronidazole (Legrand et al., 2020a). The vast majority of these studies

also report the enrichment of opportunistic pathogens linked to an increased susceptibility to

secondary infections (Almeida et al., 2019a; Carlson et al., 2017, 2015; He et al., 2017b; Kim

et al., 2019; Legrand et al., 2020a; Navarrete et al., 2008; Sáenz et al., 2019; E. Wang et al.,

2019). Importantly, persistent antibiotic treatment is driving antimicrobial resistance in fish

pathogens (Miller and Harbottle, 2018) and commensal microbes (Hong et al., 2018). For

example, several strains of Photobacterium damselae showed increased resistance to

tetracycline and streptomycin both in wild and farmed fish (Abdel-Aziz et al., 2013; Chiu et al.,

2013; Essam et al., 2016). Additionally, it has been suggested that antibiotic oral

administration increases mobile genetic element exchange of antibiotic resistant genes in fish,

enriching their route of dispersion in aquaculture systems (Sáenz et al., 2019). Thus,

aquaculture environments, including fish, water and biofilms can become reservoirs for

antibiotic resistant genes, posing elevated human, animal and ecosystem threat (Schar et al.,

2020).


15

Several studies reported shifts in microbial composition and diversity caused by

environmental contaminants, including biocides commonly used in aquaculture (potassium

permanganate, Mohammed and Arias, 2015; triclosan, Gaulke et al., 2016; Narrowe et al.,

2015; copper sulfate, Tarnecki et al., 2021), hydrocarbons from crude oil (e.g., Bagi et al.,

2018; DeBofsky et al., 2020), lead (e.g., Zhang et al., 2021), agriculture pesticides (e.g., X.

Wang et al., 2019), microplastics (e.g., Jin et al., 2018; Lu et al., 2019), and nanomaterials

(e.g., titanium dioxide, Chen et al., 2018; molybdenum oxide, Aleshina et al., 2020). Of special

concern, nanomaterials are particles of dimensions between 1 and 100 nm and widely used

in both industrial and consumer products, which inevitably leads to their release to the aquatic

environment (Piccinno et al., 2012; Weir et al., 2012). Several types of nanomaterials are

added to products due to their antimicrobial properties, conferring them the ability to modulate

the microbiota, thus enhancing the concern regarding their effect on commensal microbial

communities (Adamovsky et al., 2018).

1.1.3.3 Fish microbial function Half a billion years of co-evolution between vertebrates and their commensal bacteria

have perfected symbiotic relationships that ultimately benefits fish immunity and physiology

through microbial metabolism (Kelly and Salinas, 2017). Early studies on herbivorous fish

showed that some anaerobic species of the gut microbiota have the ability to help in

fermentative digestion (e.g., Fishelson et al., 1985; Rimmer and Wiebe, 1987). Since then,

several studies have established a circumstantial relationship between host fish physiology

and microbial function. For example, higher plant-fiber intake increased the diversity of

cellulolytic bacteria in grass carp (Li et al., 2016, 2008), while other commensal taxa were

reported to provide assimilable carbon to a wood eating fish (McDonald et al., 2012; Watts et

al., 2013). Carnivorous fish species harbor higher diversity of commensals that produce lipase

and protease, which aids in digestion (Liu et al., 2016). A non-dominant bacterial strain

recovered from the gut of Nile tilapia increased intestinal permeability, lipid accumulation and

triglyceride absorption efficiency when added to the diet (Zhang et al., 2020), while another

study has linked gut microbial diversity with fish metabolism phenotypes (Dvergedal et al.,

2020). Fish microbiota immunity aid may occur through several mechanisms, such as niche

exclusion, resource competition and antibiosis, i.e., production of antibiotic compounds (de

Bruijn et al., 2018; Kelly and Salinas, 2017). For example, commensals are able to compete

with pathogens by producing siderophores with higher affinity for iron, essential for microbial

growth (Ahmed and Holmström, 2014). In the gill microbiota of the Atlantic salmon, several

lactic acid isolates can produce antimicrobial compounds that inhibit the growth of the

pathogen Aeromonas salmonicida (see Ringø and Holzapfel, 2000). Using in vitro assays,


16

another study showed that bacterial isolates from the skin microbiota of the rainbow trout were

able to inhibit the growth of two aquatic fungal pathogens, revealing a skin community with

antifungal properties (Lowrey et al., 2015). In zebrafish, the microbiota enhanced with

probiotics, led to a reduction in the stress response and anxious behaviour (e.g., Davis et al.,

2016). Moreover, gut microbiota was correlated to differentially expressed genes and

physiological parameters in the yellowfin seabream under hypo-osmotic stress, suggesting a

role of the commensals in the fish immune response (Lin et al., 2020).

Studies using gnotobiotic (germ-free) zebrafish have helped further explore the role of

gut microbiota functions. For example, gut microbiota of zebrafish can regulate the expression

of genes related to stimulation of epithelial proliferation (e.g., Cheesman et al., 2011; Rawls

et al., 2006, 2004), promote nutrient metabolism (e.g., Rawls et al., 2004; Semova et al., 2012)

and innate immune response (e.g., Galindo-Villegas et al., 2012; Kanther et al., 2011). On the

other hand, the absence of gut microbiota is related to impaired physiological functions in

zebrafish (e.g., Bates et al., 2006; Rawls et al., 2004).

Although extremely scarce in fish, studies using functional annotation of the

predominant bacterial taxa can offer in-depth insight into fish microbiota functional

composition. The gut microbiota of freshwater carp presented pathways broadly related to

biosynthesis, degradation, energy metabolism and fermentation (Tyagi et al., 2019).

Additionally, aligned to their herbivorous diet, the gut microbiota of freshwater carp also

presented pathways related to the degradation of cellulose and other complex carbohydrates

(Tyagi et al., 2019). In the same study, the authors were able to relate the high prevalence of

Actinobacteria to antibiotic biosynthesis pathways, highlighting a potential as a probiotic (Tyagi

et al., 2019). Another study revealed that functions related to DNA repair and antibiotic

response were enhanced in the feces microbiota of free-living populations of the brown-

marbled grouper when compared to mariculture populations (Hennersdorf et al., 2016).

As microbial diversity is governed by both host and environmental factors, so too is

microbial function. Current inferring tools, based mainly on metabolic information from humans

and their gut microbiota, can be very insightful for predicting which fish microbial functions are

altered by certain factors, although such predictions must be interpreted with caution. For

example, potential pathways of bacterial communities associated with nutrient metabolism,

antimicrobial biosynthesis and virulence factors of pathogens have been inferred from the gut

of two species of catfish (Bledsoe et al., 2018). However, minor differences in the microbial

predicted function were seen between the two species (Bledsoe et al., 2018). An experimental

setting showed that predicted metabolic functions of the gut related to biosynthesis of other

secondary metabolites, metabolism of cofactors and vitamins, and digestive system were

significantly different in perch fed different food ratios (Zha et al., 2018). Additionally,

differences in composition along the intestinal microbiota of grass carp were translated into


17

differential carbohydrate metabolism capacity by the microbiota, which increased along the

intestine (Yang et al., 2019). Regarding habitat, differences in the abundance of lipid and fatty

acid metabolism were found between the gut microbiota of rainbow trout from distinct

phylogeographic distributions (Yildirimer and Brown, 2018). The skin and gut of the South-

west European nase from different populations also showed differences in the predicted

metabolic pathways mainly related to amino acid and energy metabolism (Guivier et al., 2020).

Predation is another factor influencing predicted functional categories of the gut

microbiota of perch related to transport, signaling molecules and interactions, and

environmental adaptation (Zha et al., 2018). A study evaluating microbial differences in the

gut of the yellowtail kingfish of wild and aquaculture origins revealed that fish cultivation led to

differences in microbial predicted functions related to metabolism of cofactors, vitamins, amino

acids and carbohydrates (Ramírez and Romero, 2017b). On the other hand, the gut of Atlantic

salmon from hatcheries presented enhanced microbial predicted functions related to nitrogen

fixation and denitrifying when compared to their wild counterparts (Uren Webster et al., 2018).

Additionally, transition from freshwater to seawater was marked by alterations in the potential

metabolic function of the skin microbiota of the Atlantic salmon, suggesting its ability to

structurally change in order to maintain signaling (Lokesh and Kiron, 2016). Host thermal

tolerance was also linked to differences in the predicted metabolic functions related to cellular

processes and signaling pathways of the gut microbiota of tropical tilapia, suggesting that cold

exposure leads to microbial functional shifts related to stress and cell defense (Kokou et al.,

2018).

Predicted microbial function is altered by disease as was seen in the gut of the Yunlong

grouper, with pathways related to metabolism of terpenoids and polyketides, biosynthesis of

other secondary metabolites and metabolic diseases (under the “human diseases” broader

pathway) upregulated in diseased fish (Ma et al., 2019). In another study, infection with Vibrio

anguillarum induced changes in the predicted pathways involved in infectious diseases,

genetic information processing, and ion channels in the gut microbiota of ayu (Nie et al., 2017).

Of all the negative impacts of antibiotic administration, impaired gut potential function is one,

seen, for example, in the zebrafish exposed to oxytetracycline and sulfamethoxazole (Almeida

et al., 2019a; Zhou et al., 2018). Detailed knowledge of the function capabilities of the fish

microbiota are of great relevance for aquaculture management (Ghanbari et al., 2015). Further

work is necessary to identify fish microbial biomarkers in order to optimize fish microbiota and

mitigate disease (Xiong et al., 2019).


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1.1.4 Microbiome applications in aquaculture settings Given the link between fish, their microbiome and disease, microbial manipulation is a

promising and ecological approach to mitigate fish disease in aquaculture settings (de Bruijn

et al., 2018; Perry et al., 2020). Knowledge of microbial composition, diversity and function will

allow us to steer the microbial communities to select for specific functions (Hoseinifar et al.,

2016). In aquaculture that could be accomplished by using feed additives such as probiotics,

prebiotics and synbiotics (Hoseinifar et al., 2018, 2016).

The term probiotic derives from the Greek words “pro” and “bios”, meaning “pro-life”.

Probiotics are originally defined as “live microorganisms that, when administered in adequate

amounts, confer a health benefit on the host” (Hill et al., 2014). However, given the difference

between terrestrial and aquatic ecosystems, a slightly modified definition for probiotics was

proposed for aquaculture as “any microbial cell provided via the diet or rearing water that

benefits the host fish, fish farmer or fish consumer, which is achieved, in part at least, by

improving the microbial balance of fish” (Merrifield et al., 2010). Probiotics in aquaculture can

include bacteria, bacteriophages, microalgae and yeast (Llewellyn et al., 2014). Probiotics

have different modes of action on disease resistance, such as modulation of immune

parameters, competition for binding sites, production of antibacterial substances or

competition for nutrients (Hoseinifar et al., 2018). Probiotics contain conserved microbe-

associated molecular patterns (MAMPs) that are recognized by pattern recognition receptors

(PRRs) initiating the production of certain molecules, inducing the host immune response

(Bron et al., 2012; Remus et al., 2012). For example, a strain of Psychrobacter sp. was able

to prompt the immune response of the orange-spotted grouper (Sun et al., 2014). Moreover,

probiotics have the ability to bind to the adhesion receptors of the gut mucosa, reducing

pathogen colonization (Chabrillon et al., 2005). Among the most studied probiotics, lactic acid

bacteria are commonly used in aquaculture and include strains of Carnobacteria,

Lactobacillus, Lactococcus, Leuconostoc, Pediococcus, Enterococcus, Vagococcus and

Bacillus genera (Hoseinifar et al., 2018). For instance, the genera Bacillus and Lactobacillus

stimulate inflammatory expression in fish gut (He et al., 2017a), improve digestion and

metabolism (Falcinelli et al., 2015), and regulate genetic components involved in growth and

appetite (Falcinelli et al., 2016; Giorgia et al., 2018). Additionally, Lactobacillus is thought to

be involved in the gut-brain axis, improving learning and memory capacity (Borrelli et al., 2016;

Zang et al., 2019). Some gram-negative bacteria with probiotic activity include strains of

Pseudomonas (e.g., Giri et al., 2012; Vinoj et al., 2015), Aeromonas (e.g., Chi et al., 2014;

Pieters et al., 2008), Shewanella (e.g., Chabrillon et al., 2006; García de la Banda et al., 2012),

Enterobacter (e.g., Burbank et al., 2011; Rodriguez-Estrada et al., 2013) and Roseobacter

(e.g., Planas et al., 2006; Sonnenschein et al., 2020). Probiotics also have the ability to


19

improve water quality, by improving organic matter decomposition or reducing detrimental

elements, metabolic wastes or even pathogenic bacteria (Hai, 2015). Although there are

several constraints to probiotics related with environmental consistency, specificity,

production, storage and transport (Bentzon-Tilia et al., 2016; Borges et al., 2020; Cruz et al.,

2012), several probiotics are currently commercially available (Caipang et al., 2020; Shefat,

2018).

Prebiotics are nutrients that are not digested by the host and they promote growth

and/or activity of one or more beneficial microorganisms (Roberfroid, 2007). They are

fermented by the gut microbiota, leading to the production of short-chain fatty acids that may

be used as energy source by the host (Gibson and Roberfroid, 1995). Several common

prebiotics have been studied in fish, including inulin, fructooligosaccharides (FOS), short-

chain fructooligosaccharides (scFOS), oligofructose, mannanoligosaccharides (MOS), trans-

galactooligosaccharides (TOS), galactooligosaccharides (GOS), xylooligosaccharides (XOS),

arabinoxylooligosaccharides (AXOS) and isomaltooligosaccharides (IMO) (Ringø et al.,

2014). Prebiotics have been effectively tested in a number of fish species, including salmonids

(e.g., Atlantic salmon, Dimitroglou et al., 2011; rainbow trout, Staykov et al., 2007), gadoids

(e.g., Atlantic cod, Lokesh et al., 2012), cyprinids (e.g., common carp, Ebrahimi et al., 2012;

Indian carp, Andrews et al., 2009), sturgeons (e.g., beluga, Razeghi Mansour et al., 2012;

Siberian sturgeon, Mahious et al., 2006), catfishes (e.g., channel catfish, Welker et al., 2007),

among others (e.g., European seabass, Torrecillas et al., 2013; gilthead seabream, Cerezuela

et al., 2013; Nile tilapia, Ibrahem et al., 2010; Senegalese sole, Dimitroglou et al., 2011). The

mode of action of prebiotics includes growth of beneficial bacteria (Merrifield et al., 2010),

increase of fermentation products (Gibson and Roberfroid, 1995), or interaction with pattern

recognition patterns (Torrecillas et al., 2014) that ultimately enhance host health. For instance,

Lactobacillus and Bifidobacterium can be increased in abundance in favour of other less

beneficial bacteria, due to their ability to produce enzymes necessary for the fermentation of

prebiotics (e.g., Hoseinifar et al., 2014). Another example derives from attempts to find dietary

alternatives to the use of fishmeal in farmed fish diets. In this regard, insects, rich in chitin, if

added to fish diet, can act as prebiotics by increasing beneficial commensal bacteria in the gut

(e.g., Bruni et al., 2018). However, these effects are species specific, thus having limited

applications (Ringø et al., 2012). Moreover, there are several factors that influence mode of

action of prebiotics, including source, dosage, rearing conditions, fish species and age and

diet composition (Guerreiro et al., 2018).

Synbiotics are defined as “a mixture of probiotics and prebiotics that beneficially affects

the host by improving the survival and implantation of live microbial dietary supplements in the

gastrointestinal tract, by selectively stimulating the growth and/or by activating the metabolism

of one or a limited number of health-promoting bacteria, and thus improving host welfare”


20

(Gibson and Roberfroid, 1995). Synbiotics are nutritional supplements that can be

administered through feed or baths (Amenyogbe et al., 2020; Cerezuela et al., 2011). For

example, rainbow trout fed with a synbiotic additive that contained the probiotic Enterococcus

faecalis and the prebiotic MOS exhibited increased growth, haematology and immune

response, and resistance to the fish pathogen Aeromonas salmonicida (see Rodriguez-

Estrada et al., 2013). Other synbiotics had similar responses in a number of fish, but the

mechanisms are still largely unknown (reviewed by Huynh et al., 2017). Nevertheless, it is

known that their metabolic processes are able to release enzyme and bioactive products that

promote fish growth. Additionally, they can also promote amino acid production through

microbial metabolism processes that can be utilized as essential nutrients or ultimately

improve gut nutrient absorptive capability (Huynh et al., 2017). Further understanding about

the specific metabolic processes behind the mode of action of synbiotics is needed to increase

implementation in aquaculture (Cerezuela et al., 2011; Huynh et al., 2017).

In aquaculture, it is possible to select for specific microbial communities, either through

directly supplying the system or through environment fertilization (Bentzon-Tilia et al., 2016).

For example, biofloc technology is based on manipulation of microbiota through nutrient

limitation of the microbes that are not able to produce their own food (heterotrophs) in zero-

exchange aquaculture systems (Emerenciano et al., 2013). This technology not only improves

water quality, but leads to the formation of flocs, i.e., bacterial macroaggregates, that can be

eaten by the fish (Emerenciano et al., 2013). Another approach is applied in RAS

(Recirculating Aquaculture Systems) technology, which keeps the water within the system and

reconditions the microbial communities of the rearing water, leading to the biofilm formation of

certain bacteria that are able to remove toxic ammonia and produce nitrate (Espinal and

Matulić, 2019).

Assessing aquaculture microbiota could give insights into changes in specific microbial

communities that are influenced by different chemical parameters, acting as an early warning

indicator of the water quality (Bentzon-Tilia et al., 2016). Additionally, a microbiome-based

monitoring system could also assess undesirable microorganisms such as pathogenic

bacteria, both in water as well as in fish (Bentzon-Tilia et al., 2016; Perry et al., 2020). One

new direction for disease management in aquaculture is to use the detection of dysbiosis in

farmed fish for predicting the onset of disease and as indicator tool for productivity, which can

ultimately be used to optimize aquaculture practices (Infante-Villamil et al., 2020; Perry et al.,

2020).


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1.1.5 Available tools for microbiome assessment With advances in molecular technology, microbiome assessments have become a

practical reality. Currently, many different techniques allow for the identification of the entire

microbiome, as well as its diversity and (predicted) function (Tarnecki et al., 2017).

1.1.5.1 Sampling Designing a microbiome study need to take into consideration the species, the

environment and factors that will be addressed (Knight et al., 2018). Regarding the fish

microbiome, many are the studies that take advantage of collaborations with fish farms and

aquaculture settings (e.g., Minich et al., 2020a; Pimentel et al., 2017; Tarnecki et al., 2019),

while several others are conducted in controlled laboratory environments (e.g., Lokesh and

Kiron, 2016; Pindling et al., 2018; Zhang et al., 2018). Inoculation of germ-free models with

specific bacteria can also be advantageous to create direct links between microbes and

function, although the lack of a homeostatic community context can lead to misinterpretation

of results (Laukens et al., 2015).

In fish, sampling the microbial communities of internal organs is invasive, requiring

animal sacrifice, while assessing the microbiome of external organs can be done non-

invasively. The gut microbiome can be sampled invasively by dissecting the intestine (e.g.,

Xia et al., 2014), or non-invasively, by sampling the microbiome of the feces (e.g., Zarkasi et

al., 2014). Swabbing is the preferred method for microbiome sampling, being least invasive,

and there is little difference to other methods, such as scraping or punch biopsy (Grice et al.,

2008). Increasingly studies are more integrative, assessing the microbiome from different

potential sources; for fish, these include water, feed, sediment, biofilm and nets (e.g., Canada

et al., 2020; León-Zayas et al., 2020; Minich et al., 2020b; Nikouli et al., 2019). Considering

the influence of factors affecting the microbiome such as age, diet, habitat, antibiotic usage

(see section 1.1.3), is also of great importance when planning the study (Kim et al., 2017).

Given the high variability of the microbiome over time, it is important to reflect on

whether a single-time sampling is sufficient or longitudinal sampling is more appropriate (Sinha

et al., 2015). For example, the human gut microbiome displays a circadian rhythm (e.g., Liang

et al., 2014). When cross-sectional design is the only option available, it is paramount to know

if the single time point is representative of the actual microbial diversity and disclose it when it

might not be (Kim et al., 2017). Additionally, appropriate sample size and replicates should be

selected to properly estimate diversity and variability and avoid biases and misinterpretations

(Bharti and Grimm, 2021; Goodrich et al., 2014; Prosser, 2010).

Appropriate transportation and storage are very important in microbiome studies to

preserve the integrity of the microbial DNA/RNA and community composition, and maintain


22

the sample potential for multiple studies, techniques and platforms (Lindahl, 1993; Song et al.,

2016; Sinha et al., 2015). A study evaluating the efficacy of different storage media in the gut

and gill microbiota of rainbow trout showed that storage in 96% ethanol outperforms other

methods in terms of cost, labor, DNA quality and quantity (Hildonen et al., 2019). For

metatranscriptomic analysis, RNAlaterâ buffer is the most efficient to preserve RNA integrity

(Reck et al., 2015). Long term storage temperature can significantly impact results (e.g.,

Carda-Diéguez et al., 2014; Larsen et al., 2015b), especially in communities with lower

diversity (Hill et al., 2016), but is not a problem for short-term DNA storage (e.g., Kim et al.,

2017; Lauber et al., 2010). For RNA studies, however, storage conditions are more critical

(Goodrich et al., 2014). It is recommended to perform DNA/RNA extractions as quickly as

possible after collection and storage (Kuczynski et al., 2012). Importantly, all samples within

a study must have consistent storage conditions (Kim et al., 2017).

1.1.5.2 Laboratory procedures for culture independent microbial

assessments Three fundamental things need to be taken into account regarding laboratory

procedures for microbial assessments: DNA/RNA extraction protocols, PCR conditions, and

sequencing platforms and techniques (Tarnecki et al., 2017). Although extraction protocols

use the same basic steps, different types of cell lysis can be applied, affecting the results (e.g.,

Larsen et al., 2015b; Smith, 2011). Additionally, variation due to different batches of extraction

kits was also previously found (Bushon et al., 2010). Importantly, bias due to extraction

techniques are often confounded by high inter-individual variation observed in some species

(Wagner Mackenzie et al., 2015). Hence, it is important to use the same kit and batch for all

the samples in a study (Kim et al., 2017).

Different approaches using culture independent methods have been used for microbial

characterization (Tarnecki et al., 2017). The most recently developed, more comprehensive

and most commonly used approach nowadays is metataxonomics, which sequences targeted

amplicons of phylogenetically informative markers (Tarnecki et al., 2017). Initially this method

was achieved through Sanger sequencing and was expensive, but the transition to Next

Generation Sequencing technologies allowed for high-throughput and cost-effective detailed

characterization of the microbial communities present in any taxa, as well as in any

environment (Tarnecki et al., 2017). The 16S marker gene is sequenced for bacteria, while

18S is sequenced for eukaryotes and ITS (internal transcribed spacer) for fungi (Kim et al.,

2017). Most commonly used in fish studies, sequencing the 16S rRNA gene allows for the

identification of bacterial isolates (Rosselló-Mora, 2005). The 16S rRNA gene is present in all

living organisms and has the advantage of containing slow evolving regions, useful to design


23

broad-spectrum primers, as well as fast evolving regions, providing powerful classification at

finer taxonomic levels, such as genera (Kuczynski et al., 2012). The 16S rRNA gene

comprises nine variable regions, V1 to V9 (Yarza et al., 2014), with the V4 region being the

most commonly sequenced in fish studies (Tarnecki et al., 2017). Result bias due to the

different regions selected are mainly related to primer choice, which can differentially detect

taxa, both within or between regions (Walker et al., 2015). Different sequence variability

between regions can also lead to dissimilarities in microbial diversity (Engelbrektson et al.,

2010). Prior to sequencing, amplification of the 16S rRNA variable regions is done through

PCR; however, PCR conditions can also lead to potential biases (Tarnecki et al., 2017).

However, a single “best” methodology is yet to be standardized and the best practice is to

consistently apply the same protocol to all samples in a study (Goodrich et al., 2014).

It is possible to sequence all DNA in a sample through shotgun metagenomics

sequencing (Kim et al., 2017). Metagenomics refers to the study of the genetic material

recovered from a particular environment or environmental niche and is dedicated to the

analysis of microbial communities with molecular techniques (Handelsman et al., 1998;

Weisburg et al., 1991). In metagenome sequencing, it is possible to determine which genes

are present and their overall functionalities or pathways (Di Bella et al., 2013). However, this

technique has depth limitations, being challenging to generate high-quality de novo

assemblies, especially in samples that contain large fractions of host DNA, such as mucosal

sites (Kuczynski et al., 2012). Recently emerged techniques in the field of metatranscriptomics

enable characterization of dynamic, context-specific functional profiles of microbial

communities (Shakya et al., 2019). Metatranscriptomics aims to sequence all RNAs in a

sample, determining which genes are being transcribed under particular conditions (Di Bella

et al., 2013), and ultimately can retrieve the entire metatranscriptome. Several bioinformatic

tools are currently available to annotate and map RNA reads to the corresponding metabolic

pathways (Shakya et al., 2019). Metagenomic and metatranscriptomic studies are far more

challenging than amplicon sequence studies, given the fact that coverage is considerably

lower for whole genome sequencing (Di Bella et al., 2013). In order to accurately resolve

differences between samples, high coverage is imperative (Di Bella et al., 2013). Additionally,

different abundances of bacterial taxa and transcription levels of every gene across samples

add extra difficulty to sequence assembly, thus requiring relatively high number of samples,

all with good coverage (Di Bella et al., 2013).

Different sequencing technologies are used in microbial studies, with distinct

characteristics pertaining to read length, sequencing depth, run time, reads per run, relative

cost and error rate (Kuczynski et al., 2012). Different sequencing technologies can also lead

to bias, although bias related to primer choice seems to have greater impact (Tarnecki et al.,

2017). In this regard, third-generation sequencing techniques that do not amplify the DNA and


24

thus, do not require amplification prior to sequencing, are promising in reducing bias and

allowing research standardization (Di Bella et al., 2013).

Especially important during sequencing, positive controls usually include a mixture of

cultured curated organisms, called “mock communities” (Kim et al., 2017; Sinha et al., 2015).

This procedure allows the accuracy of the sequencing to be determined. On the other hand,

there are many possible contamination sources during laboratory procedures, including

human, reagents, kits, dust, or crossover between samples (Kim et al., 2017). A marginal

contamination is virtually inevitable when dealing with microbes, especially with low microbial

biomass samples (Kim et al., 2017). In this regard, negative controls are equally important

during extraction, PCR amplification and sequencing (Kim et al., 2017; Sinha et al., 2015).

These refer to samples without any DNA content, or blanks, and allow identification of false

positives derived from contamination (Kim et al., 2017).

In summary, metataxonomics of the 16S are the most cost effective and popular in

microbiota studies, allowing a metataxonomic analysis (e.g., identification of OTUs/ASVs and

answering which bacteria are present in a sample) (Adamovsky et al., 2018). On the other

hand, metagenomic techniques aim to identify as many genes as possible, and are more

suitable for understanding microbiota dynamics (Adamovsky et al., 2018). Finally,

metatranscriptomic methods aim to retrieve as many RNA transcripts as possible, identifying

differential gene expression and understanding microbiota functionality (Adamovsky et al.,

2018).

1.1.5.3 Data analyses While different methods have been developed to analyze the microbiome, the common

objective is to properly characterize their composition, diversity and function (Goodrich et al.,

2014). The most widely used software options to address microbial data from target amplicon

sequence studies are QIIME (Caporaso et al., 2010), mothur (Schloss et al., 2009) and

DADA2 (Callahan et al., 2016). In metagenomic analyses, software such as IBDA-UD (Peng

et al., 2012), metaSPAdes (Nurk et al., 2017), MEGAHIT (Li et al., 2015), or MetaVelvet

(Namiki et al., 2012) are commonly used. The software SOAPdenovo (Xie et al., 2014) is

generally employed in metatranscriptomic analysis. It is recommended to always use the most

recent version of the software, as they are constantly being updated and improved (Galloway-

Peña and Hanson, 2020).

General bioinformatic pipelines for targeted amplicon sequence studies (e.g., 16S)

encompass quality checks, denoising sequences, clustering sequences into OTUs or ASVs,

inferring taxonomy or phylogeny, and performing multivariate analysis and data visualization

(Kuczynski et al., 2012). In microbiome studies, it is important to perform data filtering, which


25

are sequencing-platform specific (Goodrich et al., 2014). This step will discard sequences that

fail to meet minimum quality requirements, erroneous sequences (e.g., PCR chimaeras), and

those that do not match targeted organisms (Goodrich et al., 2014). It is also common to

remove sequences with single or low reads, as they can be considered contaminants or

sequencing artifacts (Goodrich et al., 2014). To deal with inter-sample differences in biomass

or sequencing depths, normalization can be done by rarefying the OTU/ASV frequency by the

total sample sequence count (Goodrich et al., 2014). However, this approach is not

consensual, as it is not able to detect differentially abundant species and can lead to high

rates of false positives (McMurdie and Holmes, 2014). As such, other approaches, such as

the negative binomial distribution, are preferred (McMurdie and Holmes, 2014).

Clustering sequences into OTUs or ASVs by sequence identity followed by taxonomy

assignment will then provide identification of the bacterial groups in the samples (Goodrich et

al., 2014). In the case of OTUs, this is done through picking algorithms, which can be de novo,

closed reference or open reference, and the chosen method will impact results (Goodrich et

al., 2014). Reference-based methods will cluster sequences, normally using a cut-off point of

97% or higher sequence similarity (Rosselló-Mora, 2005), and compare them against

reference databases that are regularly updated, and thus results can also change between

the taxonomic database versions (Goodrich et al., 2014). On the other hand, ASVs are inferred

de novo without arbitrary thresholds and distinguish sequence variants by as little as one

nucleotide (Callahan et al., 2017). ASVs methods have higher resolution, are better at

discriminating ecological patterns and allow to reliably compare ASVs across studies

(Callahan et al., 2017). Common databases of reference sequences and taxonomies for the

16S rRNA include greengenes (DeSantis et al., 2006), SILVA (Pruesse et al., 2007), and the

Ribosomal Database Project (Cole et al., 2009). Many OTUs/ASVs will be typically identified

to the genus or higher taxonomic levels; species level identification is rare and should always

be prudently considered (Goodrich et al., 2014).

Microbial diversity analysis is performed by comparing species composition and

abundance within and between samples (Lozupone and Knight, 2008). Alpha-diversity

quantifies diversity within individuals and can be compared across sample groups (Knight et

al., 2018). Alpha-diversity metrics can measure species richness (e.g., Chao1 index or Faith’s

phylogenetic diversity), evenness (e.g., Pielou’s evenness), or both (e.g., Shannon or Inverse

Simpson indices). Beta-diversity analyses compare dissimilarities between samples,

generating distance matrices between all pairs of samples (Knight et al., 2018). Beta-diversity

metrics can be qualitative, based on sequence presence or absence (e.g., Jaccard or

unweighted UniFrac) or quantitative, taking into account sequence abundance (e.g., Bray-

Curtis or weighted UniFrac). UniFrac metrics have the advantage of accounting for

phylogenetic distances between sets of taxa (Lozupone and Knight, 2005).


26

Recently, software has been developed to predict microbial functional profile using 16S

rRNA data (Galloway-Peña and Hanson, 2020). Programs such as PICRUSt (Langille et al.,

2013) or Tax4Fun (Aßhauer et al., 2015) use taxa relative abundance to predict the function

of the gene content based on reference genomes. These techniques are highly reliant on

currently available reference genomes, which are biased towards human health

microorganisms (Choi et al., 2017). It is important to notice that these methods do not take

into account actual gene or protein expression, hence results represent a rough estimation

and should be carefully interpreted (Galloway-Peña and Hanson, 2020).

When conducting metagenomic or metatranscriptomic studies, the bioinformatic

pipeline involves assembling sequences into contigs, predicting genes, identifying sequence

function using annotation databases, identifying metabolic pathways, comparing to published

results and finally perform multivariate analysis and visualization (Kuczynski et al., 2012).

Assemblies can be done based on reference genomes, de novo, or a combination of both,

with the first being highly dependent on the availability of reference genomes and quality of

the database (Galloway-Peña and Hanson, 2020). After assembling, genes are identified and

sequence function is annotated through protein sequence homology-based searches against

databases (Galloway-Peña and Hanson, 2020). Sequence reads are mapped to reference

genomes and pathways, such as KEGG (Kanehisa et al., 2019), in order to identify active

microbes and the function of their expressed genes (Galloway-Peña and Hanson, 2020).

Software such as KEGGscape is then able to infer metabolic networks (Nishida et al., 2014).

After microbial assessment, several statistical tools can be implemented to analyze

variation in microbial diversity, taxa abundance or functional components and evaluate overall

patterns of microbial variation with covariates (Galloway-Peña and Hanson, 2020; Mallick et

al., 2017). Statistical methods for microbiome analysis have been developed to specifically

tackle the complex microbiome datasets, such as PERMANOVA analysis to compare

microbial distance matrices (Kelly et al., 2015). Other conventional statistical approaches can

be applied to compare alpha-diversity or abundance variation, such as ANOVA (Sthle and

Wold, 1989) or Kruskal-Wallis tests (Kruskal and Wallis, 1952). These analyses can be

performed using the R packages phyloseq (McMurdie and Holmes, 2013) or vegan (Oksanen

et al., 2008) in target amplicon sequence studies. With metagenomic and metatranscriptomic

data, pathway statistical analysis can be performed, for example, via the pathfindR R package

(Ulgen et al., 2019). Linear discriminant analysis of effect sizes (LEfSe) can be also used to

identify differentially abundant taxa or predicted metabolic pathways (Segata et al., 2011).

Finally, it is important that researchers disclose as much information as possible and

discuss the results taking into account a global improvement and standardization of

microbiome research (Goodrich et al., 2014). Deposition of all sequence files and metadata to

public databases using standard formats is essential (Goodrich et al., 2014).


27

1.2 Thesis structure and objectives The main objectives of this doctoral thesis were to assess the composition, diversity

and structure of the skin and gill microbiota of two commercially important fish species, the

European seabass Dicentrarchus labrax and the gilthead seabream Sparus aurata in an

aquaculture setting. This thesis was developed in collaboration with Piscicultura do Vale da

Lama, a fish farm based in southern Portugal (Ria de Alvor). This is a semi-intensive fish farm

focused on the production of the seabass and seabream. In this farm, grow out phases occur

in exterior ponds, water is captured from the estuary and naturally renewed with the tide (i.e.,

twice a day). My specific goals were to perform baseline characterization of the microbiota of

the skin and gill of juveniles and adults of both species and understand the impact of the

following factors on their microbial dynamics: ontogeny, water temperature, disease and

antibiotic treatment. Towards this goal I used high-throughput amplicon sequencing of the V4

region of the 16S rRNA of bacteria and metataxonomic alpha- and beta-diversity analyses.

Analysis of the predicted function was also applied to some datasets.

The thesis is divided into six chapters. Following this literature review Chapter,

Chapters 2 to 5 are presented as scientific papers. Specifically, Chapters 2, 3 and 4 and

Subchapter 5.1 were published in the international scientific journals Aquaculture, Animal

Microbiome, ISME Communications and Scientific Reports, respectively, whereas Subchapter

5.2 is under review.

Chapter 2 is a baseline characterization of the diversity of the skin and gill microbiota

of the seabass and seabream, assessed by monitoring microbial composition and structure of

these species during 3 winter months (December to February). This study identified the most

abundant phyla and genera and revealed significant differences in the microbial diversity and

structure between tissues and species. It was also possible to identify the presence of several

potentially pathogenic genera within the microbiota of healthy fish.

In Chapter 3 I assessed the effects of aging on the skin and gill microbiota of farmed

seabass and seabream. Three different age groups were studied, including early and late

juveniles and mature adults. Age was seen to shape the microbiota of both species, but varied

for each species. Additionally, the microbiota of the surrounding water was significantly distinct

from the fish microbiota, regardless of the age group.

Chapter 4 comprises a longitudinal study that aimed to characterize the skin and gill

microbiota of seabass and the surrounding water over a period of 12 months, while also

assessing the impact of water temperature on microbial dynamics. Results showed similar

trends in the microbial diversity of both tissues. The microbiota were highly dynamic with

periods where several potentially pathogenic genera were highly abundant in both tissues.

The potential pathogens also exhibited complex interactions with other bacterial genera. The


28

surrounding water temperature influenced fish microbiota, with episodes of dysbiosis

coinciding with warmer months and during transitions between cold/warm months.

Chapter 5 encloses 2 scientific articles, both related to the effects of disease and

antibiotic treatment on the microbiota of seabass. Subchapter 5.1 evaluated the effects of a

natural disease outbreak of Photobacterium damselae and subsequent antibiotic treatment

with oxytetracycline on the gill and skin microbiota of adult seabass. Both infection and

antibiotic treatment caused significant changes in fish microbiota, although such changes

were asymmetrical between tissues. Effects of dysbiosis were more pronounced in the skin

but the gill microbiota presented less resilience and did not return to the diversity levels

observed previous to disease and treatment. Subchapter 5.2 also assessed the effects of a

natural disease outbreak, caused by a coinfection of Photobacterium damselae spp. piscicida

and Vibrio harveyi, this time on the skin of seabass fingerlings. It also analyzed the response

to subsequent antibiotic treatment with flumequine. As in the previous study, both disease and

antibiotic treatment led to dysbiosis, although in this case a significant increase in microbial

diversity was observed. Nevertheless, changes in the abundance of potential pathogenic

genera and other opportunistic taxa confirmed that dysbiosis occurred.

To finalize, Chapter 6 summarizes and discusses all results of this thesis and the future

perspectives based on the outcome of the study of the microbial dynamics and dysbiosis in

farmed European seabass and gilthead seabream.

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CHAPTER 2

Characterization of the skin and gill microbiomes of

the farmed seabass (Dicentrarchus labrax) and

seabream (Sparus aurata)

Daniela Rosado, Marcos Pérez-Losada, Ricardo Severino, Jo Cable,

Raquel Xavier

2019. Aquaculture 500, 57-64. https://doi.org/10.1016/j.aquaculture.2018.09.063.

2.1 Abstract There is substantial evidence showing that the microbiome of teleosts plays a key role

in host health and wellbeing. Aquaculture practices increase the risk of dysbiosis (i.e. microbial

imbalance), which is known to facilitate pathogen infections. The skin and gills are the primary

defense organs against pathogens, thus, characterizing their microbiome composition in

farmed fish is pivotal for detecting potential alterations that may lead to disease susceptibility.

Here, we assessed the skin and gill microbiomes of two of the most important adult fish

species farmed in southern Europe, the seabass and the seabream, during winter months.

We coupled next-generation sequencing (MiSeq) of the 16S rRNA V4 region with the DADA2

bioinformatic pipeline to assess microbial composition and structure. Variation in microbial

alpha-diversity (intra-sample) and taxa proportions were assessed using analysis of variance.

Differences in beta-diversity (between-sample) were tested using permutational multivariate

analysis of variance. Microbiomes of both tissues (n=30 per species) identified 19 bacteria

phyla, dominated by the phyla Proteobacteria (44 - 68%) and Bacteroidetes (15 - 37%); the

families Flavobacteriaceae (11 - 28%), Rhodobacteraeae (4 - 8%) and Vibrionaceae (2 - 17%);

and the genera Rubritalea (4 - 13%), Pseudomonas (4 - 8%) and the NS3a marine group (4 -

12%). Mean relative proportion of these taxa, some alpha-diversity indices and all beta-

diversity distances varied significantly between tissues within and between species. ASVs

belonging to the genera Polaribacter and Vibrio, which include several species that are

pathogenic, were detected in the core microbiomes of seabass or seabream.

Keywords: aquaculture, 16S rRNA, microbiome, pathogens, fish farm


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2.2 Introduction Seminal studies conducted in mammals established a link between the microbiome

and the host’s innate immune response, with implications for host health and wellbeing (see

for example reviews Belkaid and Hand, 2014; Lynch and Pedersen, 2016; Nelson et al., 2014).

Furthermore, differences in microbial composition can account for differential disease

susceptibility in humans (e.g., Börnigen et al., 2013; Dunn et al., 2016; Pérez-Losada et al.,

2018, 2015) and teleosts (reviewed by Kelly and Salinas, 2017). Particularly problematic for

fish, pathogenic bacteria that naturally reside in the aquatic environment can also form part of

their microbiomes (e.g., Borchardt et al., 2003; Califano et al., 2017; Rivas et al., 2011; Rud

et al., 2017) and cause disease if there is a shift in abundance (i.e., dysbiosis) (e.g., Hess et

al., 2015). While the skin microbiome of unstressed fish is dominated by taxa known for their

probiotic and antimicrobial activity, the microbiome of stressed fish is dominated by potential

pathogens (see Boutin et al., 2013). Although mucosal surfaces, such as skin, gills and the

gut, do act as primary barriers to disease (reviewed by Gómez and Balcázar, 2008), they can

be affected by several pathogens (e.g. Aeromonas septicemia, see Balebona et al., 1998;

Doukas et al., 1998), which may cause significant losses. Aquaculture practices also impact

microbial communities in the epidermal mucosa of fish. Overcrowding and low oxygen

concentrations, typical in fish farms, result in host stress and induce dysbiosis in the skin

microbiome, facilitating the proliferation of opportunistic pathogens (e.g., Boutin et al., 2013).

At the same time, infectious diseases that frequently affect farmed fish can also induce

dysbiosis, generally favouring increased abundance of opportunistic bacteria creating

complex feedback mechanisms (e.g., Llewellyn et al., 2017; Reid et al., 2017).

Seabass (Dicentrarchus labrax) and seabream (Sparus aurata) are the two most

important fish species farmed in southern Europe; their productivity, however, is greatly

affected by infectious diseases, which can account for losses of 15% to 40%, respectively

(Lane et al., 2014). Given the role skin and gill microbiomes play in fish innate immunity

(Gourzioti et al., 2016; Pellizzari et al., 2013) and the economic impact of diseases in fish

aquaculture, characterizing the microbiomes of these two fishes is paramount. Additionally,

anthropogenic stressors (e.g., rise of sea temperature and pollution) and farming conditions

(e.g., high densities) aggravate bacterial diseases causing external lesions in skin and gills

(e.g., photobacteriosis and vibriosis) of farmed seabass and seabream (e.g., Avendaño-

Herrera et al., 2006; Bakopoulos et al., 2018; Frans et al., 2011; Gourzioti et al., 2016;

Pellizzari et al., 2013; Weber et al., 2010). To this end, identifying potential fish pathogens

could help to design more efficient prevention and treatment strategies. The first assessment

of the skin microbiome of adult seabass and seabream showed that inter-individual variability

was comparable to interspecific variability (Chiarello et al., 2015). Recently, Tapia-Paniagua


68

et al. (2018) found a reduction in beneficial bacteria from the skin microbiomes of ulcered

compared to healthy seabream. Differences in microbiome diversity of the skin of seabass

have also been assessed in three different fish farms located in Ria de Aveiro, northern

Portugal (Pimentel et al., 2017). Despite high inter-individual variation, microbial composition

was found to act as a unique signature of each individual’s geographic origin (Pimentel et al.,

2017). Although the authors controlled for ontogenetic effects, which affect skin microbiome

(e.g., Sylvain and Derome, 2017), they acknowledged that other factors, such as different

farming practices and probiotic use, may have explained some of the observed differences

(Pimentel et al., 2017). Moreover, previous disease history (e.g., Llewellyn et al., 2017; Reid

et al., 2017; Tapia-Paniagua et al., 2018) and host physiology (e.g., Apprill et al., 2014) may

have influenced the composition of fish skin microbiomes. Other key factor impacting microbial

composition differences between groups is sample size and longitudinal (time) variation

(Knight et al., 2018). Not surprisingly, skin and gill microbiome composition of seabass and

seabream varied greatly between previous studies, as they have been cross-sectional (one

time point) and included few individuals (Chiarello et al., 2015; Pimentel et al., 2017; Tapia-

Paniagua et al., 2018).

In the present study, we monitored the microbiome composition and structure of the

skin and gills of 30 seabass and 30 seabream healthy adults over winter (December to

February) using 16S rRNA next-generation sequencing (MiSeq). Our main aims here were to

characterize the baseline diversity of the skin and gill microbiomes of these two farmed

species and identify potential pathogens or opportunistic bacteria.

2.3 Material and methods

2.3.1 Sample collection and preparation Thirty individuals of both seabass and seabream were collected in 19 of December

2016, 16 of January 2017 and 13 of February 2017 (10 specimens of each species per month)

from a commercial fish farm located in an estuarine environment, the Ria Formosa (Portimão),

southern Portugal. Seabass and seabream sampled were about 2 years old and individuals

weighted on average, 384 g and 318 g. The fish were reared in two separate ponds, at a

density of ca. 4.4 kg/m3 (ca. 130 individual seabass) and 5.2 kg/m3 (ca. 150 individual

seabream), with the same open water circulation systems, thus subjected to the same

environmental conditions. The mean water temperature 30 days before each sampling point

was 16.6 ºC, 15.3 ºC and 14.4 ºC, and the photoperiod for each sampling point was 9 h 35

min, 9 h 54 min and 10 h 45 min, respectively. All fish were fed with the same commercial

feed and they shared the same clinical history. All fish were considered healthy, with no

external lesions and no pathologies detected during the sampling period. Individuals were


69

randomly caught from each tank using a fishing pole, and skin and gill swabs were collected

using tubed sterile dry swabs (Medical Wire & Equipment, UK). Skin samples were taken by

swabbing several times along the right upper lateral part of the fish from head to tail; gill swabs

were taken from the right filaments between the first and second arch. Swabs were

immediately stored at -20ºC until transported on dry ice to the CIBIO laboratory by airmail

where they were kept at -80ºC until processing. DNA from a total of 120 samples (60 skin and

60 gills) was extracted using the PowerSoil DNA Isolation Kit (QIAGEN, Netherlands),

following the manufacturer’s protocol. DNA concentration was measured with the NanoDropTM

2000 Spectrophotometer (Thermo Fisher Scientific, USA) and extractions were sent on dry

ice by airmail to the University of Michigan Medical School (USA) for amplification and

sequencing according to the protocol of Kozich et al. (2013). Each sample was amplified for

the V4 hypervariable region of the 16S rRNA gene (~250 bp), which has been widely used to

characterize microbiomes from vertebrates (Earth Microbiome Project, Gilbert et al., 2014),

including fish (e.g., Carlson et al., 2017; Llewellyn et al., 2014; Thompson et al., 2017; Wang

et al., 2017). Amplicon libraries were sequenced in a single run of the Illumina MiSeq

sequencing platform.

2.3.2 Data and statistical analyses Raw FASTQ files were analyzed using the Quantitative Insights Into Microbial Ecology

2 (QIIME2; release 2018.4) platform. Clean sequences were aligned against the SILVA (132

release) reference database (Quast et al., 2013) with DADA2 pipeline (Callahan et al., 2016).

Samples were rarefied to the minimum read count (9,087) and a feature table containing

amplicon sequence variants (ASVs) was constructed. ASVs with less than 0.01% of reads

across samples were eliminated (Nelson et al., 2014). The core microbiome was assessed for

the skin and gill of seabass and seabream, separately. An ASV was considered as part of the

core microbiome if present in 100% of samples in each group. Rarefaction curves were

performed to examine sampling depth (Figure S2.1).

Microbial taxonomic alpha-diversity (intra-sample) was calculated using Shannon,

ACE, Fisher and Faith’s phylogenetic diversity (PD) indices as implemented in the R package

phyloseq (McMurdie and Holmes, 2013). Species beta-diversity (inter-sample) was estimated

using phylogenetic Unifrac (unweighted and weighted) and Bray-Curtis distances. Dissimilarity

between samples was assessed by principal coordinates analysis (PCoA). Variation in

microbial alpha-diversity and taxa composition were assessed using one-way analysis of

variance (ANOVA). Differences in community composition (beta-diversity) were tested using

permutational multivariate analysis of variance for Unweighted and Weighted Unifrac and

Bray-Curtis indices with 1,000 permutations, as implemented in the adonis function of the R


70

vegan package. In our microbiome statistical analyses we compared i) tissues within each fish

species (skin x gills) and ii) fish species within each tissue (seabass x seabream) – see Table

2.2. We used the three sampling months (December to February) as temporal replicates,

rendering a total of 30 microbiome samples per comparison per tissue. All analyses were

performed in R studio v1.0.143 (RStudio, 2012).

2.4 Results

2.4.1 Taxonomic bacterial composition and core microbiome of

seabass and seabream Approximately 3.2 million raw reads were retrieved (1.7 million for seabass and 1.5

million for seabream) and the number of sequences per sample ranged between 9,087 and

3,537,652. These sequences corresponded to 8,136 unique ASVs, from which ASVs with less

than 0.01% of sequences across all samples and ASVs belonging to Archaea were removed,

resulting in 556 unique ASVs and 3,246,429 sequences. Of the 457 ASVs found in the skin of

the seabass, only 24 were common to all individuals sampled, thus forming the core

microbiome (Table 2.1). Of the 466 ASVs found on the gills of the seabass, only 7 were shared

among all individuals. The same pattern was observed in the seabream, where 15 out of 532

skin ASVs and 2 out of 539 gill ASVs were present in all individuals (Table 2.1). These results

highlight the high inter-individual variability found in both tissues, especially the gills (Table

2.1, Figures 2.1 and 2.2).

Of the total 19 bacteria phyla identified across all samples, Proteobacteria and

Bacteroidetes were the most abundant in both tissues (Figure 2.1, Table 2.1). ASVs from four

(Proteobacteria, Bacteroidetes, Chlamydiae and Verrucomicrobia) of these 19 phyla formed

part of the core microbiome (Figure 2.2). Moreover, the phyla Dependentiae (0.2% of ASVs

and sequences) and Patescibacteria (0.2% of ASV, 0.1% of sequences) were unique to the

microbiome of seabream, while the phyla Spirochaetes (0.2% of ASVs and 0.1% of

sequences) was unique to the gill microbiome of seabass.

The phyla Proteobacteria and Bacteroidetes accounted for 69% to 72% of all ASVs

and 62% to 87% of all sequences in both species and for 50% to 93% of all phyla in the core

microbiomes (Figure 2.2, Table 2.1). It was possible to identify 106 families, from which ASVs

belonging to 16 families formed the core microbiome of both species. Altogether,

Flavobacteriaceae (Bacteroidetes), Rhodobacteraeae (Proteobacteria) and Vibrionaceae

(Proteobacteria) accounted for 19% to 21% of ASVs, 17% to 51% of sequences, and 29% to

50% of all families in the core microbiome of both tissues (Figure 2.2, Table 2.1). From the

117 genera identified, ASVs belonging to 16 of these genera formed the core microbiome of

both species. The NS3a marine group (4% - 12%), Rubritalea (4% - 13%) and Pseudomonas


71

(4% - 8%) were the most abundant genera in the skin and gill of both species (Table 2.1).

Polaribacter (7% - 50%) was highly abundant in both tissues, and Polynucleobacter (14%)

and Vibrio (7%) were highly abundant in the gill of seabass and in the skin of seabream,

respectively (Figure 2.2, Table 2.1).

Table 2.1 Relative abundance (in percentage) of ASVs and sequences belonging to the most abundant taxa in the skin and gill microbiomes of Dicentrarchus labrax (seabass) and Sparus aurata (seabream) and percentage of ASVs that form the core microbiomes (CM). Values are shown for phyla and families with more than 4% of ASVs and sequences and for genera with more than 4% of sequences in any of the analyzed tissues. ASVs were considered as part of the core microbiome of a tissue if present in 100% of the samples in each of the four groups: seabass skin, seabass gill, seabream skin and seabream gill. na stands for “not applicable”, i.e., when a certain taxa was not present in the core microbiome.

Seabass (Dicentrarchus labrax) Seabream (Sparus aurata)

skin gill skin gill

ASVs seqs CM ASVs seqs CM ASVs seqs CM ASVs seqs CM

PHYLUM

Proteobacteria 46 46 50 48 44 57 50 52 60 50 68 na

Bacteroidetes 23 37 42 21 18 29 22 35 33 22 15 50

Unidentified 8 6 na 9 16 na 6 2 na 7 8 na

FAMILY

Flavobacteriaceae 11 28 21 10 13 29 11 26 13 10 11 50

Rhodobacteriaceae 5 7 17 5 6 na 5 8 27 5 4 na

Vibrionaceae 5 8 na 5 4 na 5 17 13 4 2 na

Unidentified 22 11 4 24 23 na 23 10 7 23 22 na

GENUS

NS3a marine group 1 12 13 1 6 14 1 10 7 1 4 na

Rubritalea 1 4 4 1 13 14 0,5 4 7 0,2 4 50

Pseudomonas 1 8 4 2 4 na 1 5 na 2 5 na

Polaribacter 0,5 13 8 0,5 5 14 0,5 11 7 0,5 3 50

Polynucleobacter 1 1 na 2 9 14 2 0,2 na 2 2 na

Vibrio 4 2 na 3 3 na 3 14 7 2 1 na

Unidentified 45 27 33 48 37 na 50 24 27 50 46 na

TOTAL 457 954014 24 466 689678 7 532 662666 15 539 739980 2


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Figure 2.1 Individual taxonomic composition. Distinctive bars represent relative abundances of bacterial phyla of skin and gills of Dicenctrarchus labrax (seabass) and Sparus aurata (seabream).

Figure 2.2 Core microbiota of seabass skin (A), seabass gill (B), seabream skin (C), and seabream gill (D) at phylum, family and genus taxonomic levels.


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2.4.2 Microbial diversity When comparing the alpha-diversity of bacteria between tissues within each species,

significant differences were detected between the skin and gills of seabass (ANOVA, P < 0.05;

Table 2.2, Figure 2.3), but not for seabream (ANOVA, P > 0.05; Table 2.2, Figure 2.3). The

alpha-diversity of the skin microbiome was significantly different between the seabass and the

seabream for all indexes (ANOVA, P < 0.05; Table 2.2), except the Shannon index (ANOVA,

P = 0.4; Table 2.2). On the other hand, the gill microbiomes were similar between species for

all indices except PD (ANOVA, P = 0.03; Table 2.2).

Table 2.2 Significance of diversity indices and relative proportions (in frequency) of dominant taxa for skin and gill within and between Dicentrarchus labrax (seabass) and Sparus aurata (seabream). For each test we report relevant F (alpha-diversity indices and taxa proportions) or R2 (beta-diversity indices) statistic and significance (p). Significant associations are indicated in bold.

Seabass Seabream Skin Gill

Skin x Gill Skin x Gill Seabass x Seabream Seabass x Seabream

Shannon 4.2 (0.04) 3.5 (0.07) 0.6 (0.4) 1.4 (0.3)

ACE 14.8 (3-10) 0.06 (0.8) 14.5 (4-4) 0.009 (0.9)

PD 3.7 (0.06) 0.02 (0.9) 27.8 (2-6) 4.9 (3-2)

Fisher 6.2 (0.02) 0.01 (0.9) 5.5 (2-2) 0.02 (0.9)

Uni Un 0.2 (9-5) 0.2 (9-5) 0.2 (9-5) 0.2 (9-5)

Uni Weigh 0.3 (9-5) 0.3 (9-5) 0.1 (9-5) 0.2 (9-5)

Bray C 0.2 (9-5) 0.2 (9-5) 0.1 (9-5) 0.2 (9-5)

Proteobacteria 1.1 (0.3) 35.6 (2-7) 11.7 (1-3) 83.9 (7-13)

Bacteroidetes 136.3 (2-16) 67.9 (3-11) 2.1 (0.2) 2.4 (0.1)

Flavobacteriaceae 116.9 (2-15) 64.3 (6-11) 3.4 (0.07) 2.6 (0.1)

Rhodobacteriaceae 3.6 (0.06) 23.1 (1-5) 1.5 (0.2) 9.7 (3-3)

Vibrionaceae 1.6 (0.2) 30.7 (8-7) 7.3 (9-3) 3.2 (8-2)

NS3a marine group 73.8 (6-12) 56.9 (4-10) 4.3 (4-2) 8.6 (5-3)

Rubritalea 25.9 (4-6) 0.04 (0.8) 1.7 (0.2) 22.5 (1-5)

Pseudomonas 8.8 (4-3) 0.2 (0.7) 4.3 (4-2) 0.002 (0.9)

Polaribacter 83.1 (9-13) 81.6 (1-12) 4.1 (0.05) 11.5 (1-3)

Polynucleobacter 56.4 (4-10) 11.2 (2-3) 16.4 (2-4) 37.4 (9-8)

Vibrio 0.3 (0.6) 33.7 (3-7) 28.1 (2-6) 1.7 (0.2)


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Figure 2.3 Mean values and standard deviations of Shannon, Faith’s phylogenetic (PD), ACE and Fisher alpha-diversity estimates plotted for the skin and gill microbiomes of Dicentrarchus labrax (seabass) and Sparus aurata (seabream).

Analysis of the PCoA shows that species and tissues within species cluster separately

and that there is a higher variation in the gill microbiomes when compared to the skin (Figure

2.4). There were significant differences in beta- diversity estimates between tissues within

each species and between tissues across species (Adonis, P = 9.9-5 for all; Table 2.2).

Figure 2.4 PCoA plot computed with weighted UniFrac distance. Each dot represents a microbiome sample and is coloured by tissue (skin and gill) within each species (seabass and seabream).


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Mean proportions of bacterial taxa varied between the two fish species and tissues

(Table 2.2). In the seabass, the abundance of Bacteroidetes, Flavobacteriaceae, NS3a marine

group, Rubritalea, Pseudomonas, Polaribacter and Polynucleobacter were significantly

different between the skin and gill (ANOVA, P < 0.05, Table 2.2). In the seabream, the mean

proportion of Proteobacteria, Bacteroidetes, Flavobacteriaceae, Rhodobacteriaceae,

Vibrionaceae, NS3a marine group, Polaribacter, Polynucleobacter and Vibrio varied

significantly (ANOVA, P < 0.05) between the skin and gill microbiomes (Table 2.2). Finally,

Proteobacteria, Vibrionaceae, NS3a marine group, Pseudomonas, Polaribacter,

Polynucleobacter, and Vibrio varied significantly between the skin microbiomes of seabass

and seabream (ANOVA, P < 0.05, Table 2.2), while Proteobacteria, Rhodobacteriaceae,

Vibrionaceae, NS3a marine group, Rubritalea, Polaribacter and Polynucleobacter varied

significantly between their gill microbiomes (ANOVA, P < 0.05, Table 2.2).

2.5 Discussion Characterizing the microbiome composition and structure of the mucosal surfaces of

economically important fish species, such as the seabass and the seabream, is of paramount

importance in order to detect imbalances and prevent potential disease outbreaks in fish

farms. Here, we showed significant differences in both the composition and structure of the

microbial communities residing in the skin and gills of seabass and seabream, which is in line

with previous findings of both fish species (Chiarello et al., 2015). The skin microbiomes were

found to be species-specific as in other fish species (e.g., the stripped mullet, red snapper,

spotted seatrout, sand seatrout, pinfish and Atlantic croaker; Larsen et al., 2013). Despite the

high inter-individual variation, overall, the seabream microbiomes were less diverse than those

of the seabass (Figure 2.3).

2.5.1 Core microbiome composition Proteobacteria (50 - 60%) and Bacteroidetes (29 - 50%, Table 2.1) formed the main

components of the skin and gill microbiomes of seabass and seabream. Proteobacteria is the

most common phylum reported in the skin and gill microbiomes of teleosts (see for example

the review by Llewellyn et al., 2014), including the skin microbiome of seabass and seabream

(Chiarello et al., 2015; Pimentel et al., 2017; Tapia-Paniagua et al., 2018). A predominance of

the phylum Bacteroidetes has also been previously reported in seabass and seabream

(Chiarello et al., 2015; Tapia-Paniagua et al., 2018), as well as in the skin of many other fishes,

such as in the brook char (Boutin et al., 2014), rainbow trout (Lowrey et al., 2015), channel

catfish (Larsen et al., 2014), tambaqui (Sylvain et al., 2016), among others (see Doane et al.,

2017; Larsen et al., 2013, 2015; Legrand et al., 2018; Leonard et al., 2014). The gill


76

microbiome of the bluefin tuna (Valdenegro-Vega et al., 2013), rainbow trout (Lowrey et al.,

2015) and yellowtail kingfish (Legrand et al., 2018) were also found to be dominated by

Bacteroidetes.

In the present study, from the 16 genera identified in the core microbiome of the skin

and gill of adult seabass and seabream, the highest percentage of amplicon sequence variants

(ASVs) belonged to the NS3a marine group, Rubritalea and Pseudomonas genera. Besides

these three, the microbiome of seabass also exhibited an elevated abundance of the genera

Polaribacter, Polynucleobacter and Arcobacter; while the microbiome of seabream included

high abundance of Polaribacter and Vibrio. The genus Pseudomonas has been previously

reported to be highly represented in the skin microbiome of seabass (Pimentel et al., 2017),

cod (Wilson et al., 2008), mosquitofish (Leonard et al., 2014), gulf killifish (Larsen et al., 2015)

and others (see Colwell and Liston, 1932; Horsley, 1977, 1973; Larsen et al., 2013). However,

we found some differences in microbial composition at the genus level in comparison with

previous studies of seabass and seabream; Tapia-Paniagua et al. (2018), for example, found

Staphylococcus and Lactobacillus to be the most abundant in the skin microbiome of

seabream. This in not unexpected since, the skin microbiome of seabass comprises genera

that are unique signatures of specific earth growth ponds, even though these ponds were

geographically close (Pimentel et al., 2017). Besides spatial variation in fish location,

environmental conditions (such as water temperatures and water supply [e.g., Lokesh and

Kiron, 2016; Tapia-Paniagua et al., 2018]), host physiology and even clinical history (Apprill et

al., 2014; Llewellyn et al., 2017) could contribute to explain the observed differences.

Ontogenetic shifts in microbiome composition have been described in several fish

species (e.g., Atlantic salmon, Llewellyn et al., 2016; Zarkasi et al., 2014; Zebrafish, Stephens

et al., 2016; discus, Sylvain and Derome, 2017). The larval microbiome tends to reflect more

the microbial community of the surrounding water (Stephens et al., 2016; Sylvain and Derome,

2017), while adult fish harbour a more adapted and stable microbial community (e.g., Llewellyn

et al., 2016). Califano et al. (2017) even reported an increase in the microbiome composition

of seabream larvae between day 2 and day 34. This pattern, however, is far from being

universal as decreased diversity with age has been reported in other fish species (Stephens

et al., 2016; Yan et al., 2016).

As with most microbiome research, it is important to note any methodological

differences that might explain variation. One of such methodological differences relates to our

skin sampling method; Chiarello et al. (2015) used tissue from different fins, while in the

present study and in Tapia-Paniagua et al. (2018) and Pimentel et al. (2017) we targeted skin

mucous. Lowrey et al. (2015) uncovered high diversity of bacteria in the different dermal layers

of skin, suggesting that mucosal diversity is an underestimation of the actual skin microbial

diversity. Moreover, specifically for this study, the sequenced 16S variable region and the


77

sequencing platform might have impacted taxonomic assignment. We sequenced the V4

region by synthesis (MiSeq), while Chiarello et al. (2015), Pimentel et al. (2017) and Tapia-

Paniagua et al. (2018) used different combinations of other 16S regions and sequencing

platforms (pyrosequencing and sequencing by synthesis). While differences in outputs

provided by different sequencing methods are widely acknowledged (e.g., Frey et al., 2014;

Li et al., 2014), debate regarding the most appropriate region for microbiome studies is still

ongoing (e.g., Guo et al., 2013; Mizrahi-Man et al., 2013). Finally, these results are likely to

have been affected by the different analytical pipelines used to analyze the sequence data -

amplicon sequence variants (ASVs) in this study versus Operational Taxonomic Units (OTUs)

in previous studies.

2.5.2 Potential pathogens detected in the core microbiomes Several ASVs belonging to genera comprising opportunistic and potential pathogenic

bacteria were recovered from the skin and gill core microbiomes of apparently healthy

individuals of seabass and seabream. Polaribacter is one such taxa, recovered from the skin

and gills of both species (Figure 2.2, Table 2.1). This genus is often found in diseased fishes,

being considered opportunistic and colonizing already weakened hosts (Bornø and Linaker,

2015). Species from this genus seem to be common in fish farms and have been reported in

the water and biofilm of recirculating and semiclosed aquaculture systems rearing Atlantic

salmon, turbot and the Senegalese sole (Martins et al., 2013; Rud et al., 2017). The genus

Vibrio, present in the gills of seabass and in both the gills and skin core microbiomes of

seabream, harbours species associated with several diseases in these fish and many are

considered opportunistic pathogens (e.g., Pujalte et al., 2003; Weber et al., 2010). Vibrio

anguillarum and V. alginolyticus, for example, cause skin lesions, and V. splendidus has been

involved in several disease outbreaks (e.g., Frans et al., 2011; Pujalte et al., 2003). V. harveyi

is another important pathogen causing tail rot disease in farmed seabream (Austin and Zhang,

2006; Haldar et al., 2010), comprising many strains that are fatal to seabass (Pujalte et al.,

2003b). However, due to taxonomic assignment limitations, it was not possible to ascertain to

which species these ASVs belonged to and if they are, indeed, pathogenic. If so, the high

prevalence of these ASVs means that, in case of dysbiosis, these bacteria may overgrow and

impact fish health.

2.6 Conclusion The skin and gills of fish are exposed to many pathogens present in the marine and

freshwater environment and represent an important barrier preventing pathogen invasion

(e.g., Trivedi, 2012). The links between microbiome composition and disease resistance are


78

now well established in mammals and teleosts (e.g., Britton and Young, 2014; Gomez et al.,

2013; Gómez and Balcázar, 2008; Kelly and Salinas, 2017), and alterations in the microbiome

often precede the onset of disease (reviewed by Munang’andu et al., 2018). Microbial

imbalance, however, is not easily detected unless baseline information regarding microbiome

composition and structure are established. Here, we describe the skin and gills microbiomes

of farmed healthy seabass and seabream adults through three sampling months, thus yielding

a more robust assessment of the microbiome of these two species. Our results show that

seabass and seabream host different microbiomes despite sharing the same environment.

Furthermore, high levels of intra- and inter-individual variability were found across tissues.

Additionally, several potential pathogens were detected in the core microbiome of both

species, which could lead to potential disease outbreaks during dysbiosis.

2.7 Acknowledgements This work was funded by the European Regional Development Fund (ERDF) through the

COMPETE program and by National Funds through FCT - Foundation for Science and

Technology (project PTDC/MAR-BIO/0902/2014 -POCI01-0145-FEDER-016550; project

POCI-01-0145-FEDER-027995; and by a “Projecto de Investigação Exploratória”:

IF/00764/2013); the Welsh Government and Higher Education Funding Council for Wales

(HEFCW) AquaWales Project through the Sêr Cymru National Research Network for Low

Carbon Energy and Environment (NRNLCEE). DR, MPL and RX are supported by FCT under

the Programa Operacional Potencial Humano – Quadro de Referência Estratégico Nacional

funds from the European Social Fund and Portuguese Ministério da Educação e Ciência (DR

doctoral grant SFRH/BD/117943/2016; MPL: IF/00764/2013; RX: IF/00359/2015).

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2.9 Supplementary material

Figure S2.1 Rarefaction curves representing the number of observed OTUs against sequencing depth for each tissue (skin and gill) for each species (seabass and seabream).


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CHAPTER 3

Effects of aging on the skin and gill microbiota of

farmed seabass and seabream

Daniela Rosado, Marcos Pérez-Losada, Ana Pereira, Ricardo Severino,

Raquel Xavier

2021. Animal Microbiome 3, 10. https://doi.org/10.1186/s42523-020-00072-2.

3.1 Abstract Important changes in microbial composition related to sexual maturation have been

already reported in the gut of several vertebrates including mammals, amphibians and fish.

Such changes in fish are linked to reproduction and growth during developmental stages, diet

transitions and critical life events. We used amplicon (16S rRNA) high-throughput sequencing

to characterize the skin and gill bacterial microbiota of farmed seabass and seabream

belonging to three different developmental age groups: early and late juveniles and mature

adults. We also assessed the impact of the surrounding estuarine water microbiota in shaping

the fish skin and gill microbiota. Microbial diversity, composition and predicted metabolic

functions varied across fish maturity stages. Alpha-diversity in the seabass microbiota varied

significantly between age groups and was higher in older fish. Conversely, in the seabream,

no significant differences were found in alpha-diversity between age groups. Microbial

structure varied significantly across age groups; moreover, high structural variation was also

observed within groups. Different bacterial metabolic pathways were predicted to be enriched

in the microbiota of both species. Finally, we found that the water microbiota was significantly

distinct from the fish microbiota across all the studied age groups, although a high percentage

of ASVs was shared with the skin and gill microbiotas. We report important microbial

differences in composition and potential functionality across different ages of farmed seabass

and seabream. These differences may be related to somatic growth and the onset of sexual

maturation. Importantly, some of the inferred metabolic pathways could enhance the fish

coping mechanisms during stressful conditions. Our results provide new evidence suggesting

that growth and sexual maturation have an important role in shaping the microbiota of the fish

external mucosae and highlight the importance of considering different life stages in microbiota

studies.


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Keywords: Dicentrarchus labrax, Sparus aurata, bacteria, ontogenesis, microbiome

3.2 Introduction Research on animal microbial communities (microbiota) is growing exponentially as

the link between microbiota and host health is strongly validated by emerging evidence (Jin

Song et al., 2019; Kelly and Salinas, 2017; Legrand et al., 2018; Murphy, 2018; Reid et al.,

2017; Ross et al., 2019; Tarnecki et al., 2018; Zhang et al., 2018). Age-related fluctuations in

the microbiota are well studied in humans and are considered as “natural, inevitable and

benign” (Nagpal et al., 2017). Critical microbial changes occur during infancy and old age,

coinciding with stages when the immune system is also more fragile (Nagpal et al., 2017).

Results linking changes in the gut microbiota to reproduction and growth (e.g., monkeys,

Amato et al., 2014) or disease resistance in early life stages (e.g., amphibians, Warne et al.,

2019) have been also found in other vertebrates.

In piscine hosts, most of the microbiota studies related to the effects of age are focused

on the gut (Bledsoe et al., 2016; Llewellyn et al., 2016, 2014; Lokesh et al., 2019; Nikouli et

al., 2019; Parris et al., 2016; Pratte et al., 2018; Stephens et al., 2016; Wilkes Walburn et al.,

2019; Wong et al., 2015; Yan et al., 2016). Some of these studies showed that microbial

communities in the surrounding waters influence the gut microbiota during early life stages,

which becomes increasingly unique with age (Stephens et al., 2016; Wilkes Walburn et al.,

2019). Indeed, initial microbiota colonization in animals is highly dependent on the

environment (e.g., Collado et al., 2015; Frese et al., 2015; Mueller et al., 2015; Quercia et al.,

2019; Villamil et al., 2018). Ecological factors, such as diet transitions (e.g., Wilkes Walburn

et al., 2019) or critical life events (e.g., habitat transition, Lokesh et al., 2019), which in turn

are intrinsically linked to sexual maturation, also play a major role in shaping the fish gut

microbiota. Importantly, most studies testing the role of age on fish microbiota were cross-

sectional and based on a single time point or a short time window (e.g., Llewellyn et al., 2016;

Nikouli et al., 2019; Parris et al., 2016; Wilkes Walburn et al., 2019; Wong et al., 2015). Thus,

given the high susceptibility of the fish microbiota to environmental changes and the high

interindividual microbiota variability (e.g., Larsen et al., 2015; Uren Webster et al., 2020, 2018),

the compound effect of all these factors could be hard to interpret (Thompson et al., 2017).

Fish skin and gills and their associated mucous and microbes form a natural physical

and chemical barrier to pathogens (Dash et al., 2018; Kelly and Salinas, 2017; Trivedi, 2012).

Despite this protective role, little is known about potential host developmental effects on skin

and gill microbiota. Filling out this knowledge gap is particularly important in fish farming,

where diseases are a main concern causing high mortality rates (e.g., Schmidt et al., 2018).

Two previous studies in wild reef fish comparing the gill (Pratte et al., 2018) and skin (Xavier


90

et al., 2020) microbiota of juvenile and mature adult fish from several species, showed a

general pattern of differentiation between life-stages with differences attributed to intraspecific

niche partitioning (Pratte et al., 2018; Xavier et al., 2020). Additionally, increases in body

weight were seen to be associated with an increase in the microbial structure (i.e., beta-

diversity) of the skin and gill microbiota of wild rabbitfish (Wu et al., 2020).

The European seabass (Dicentrarchus labrax) and the gilthead seabream (Sparus

aurata) are two of the most important farmed fish in Europe, with a global production of

191,003 tns and 185,980 tns, respectively, in 2016 (FAO, 2018). The gilthead seabream is a

protandric hermaphrodite, maturing first as males between years 1 and 2, with sex reversal

occurring in the following 2–3 years (Chaoui et al., 2006; Mehanna, 2007; Zohar and Gordin,

1979). The European seabass reaches sexual maturity between years 2 and 3 in males, and

after year 3 in females (Barnabe, 1991; Carillo et al., 1995; Felip et al., 2001). Typically, in

semi-extensive production systems, both fish are reared until they reach their first commercial

size (18–24 months). However, demand for larger fish sizes has been increasing, meaning

that both species can reach sexual maturity before harvest.

Here we used amplicon (16S rRNA) high-throughput sequencing to characterize the

skin and gill bacterial microbiota of farmed seabass and seabream from different ages

(juvenile stages and mature adults). Our main goal was to describe differences in composition,

structure and potential metabolic functions in their microbiota. Additionally, we investigated

the impact of the microbial communities present in the water column on the skin and gill

microbiotas.


3.3.1 Fish species, sampling and preparation Fish were sampled at a semi-intensive open-water farm in the Alvor Estuary (Ria

Formosa, Portimão, Portugal). In this fish farm, seabass and seabream production can take

up to 36 months to reach minimum commercial size, so having a healthy mucosa during this

time is of utter importance. The gilthead seabream is a protandric hermaphrodite, maturing

first as males between years 1 and 2 in the wild, with sex reversal occurring in the following

2–3 years (Chaoui et al., 2006; Mehanna, 2007; Zohar and Gordin, 1979). The European

seabass reaches sexual maturity between years 2 and 3 in males, and after year 3 in females

(Barnabe, 1991; Carillo et al., 1995; Felip et al., 2001). In this particular fish farm, seabass

typically reaches sexual maturity at approximately 275 g, whereas for seabream maturity is

usually attained at 300 g. We monitored the skin and gill microbiota of seabass and seabream

of different age cohorts, including juveniles and adults. Due to commercial restrictions within

the fish farm, sampling was strictly non-invasive and fish could not be dissected to confirm


91

sexual maturation. The categorization of the age group cohorts was based on previous studies

(e.g., Felip et al., 2001; Zohar and Gordin, 1979) and the weight at maturity records available

at the farm.

We collected samples every other week (12 sampling time points) between August

2017 and January 2018 (6 months). We simultaneously sampled three seabass age groups

with approximately 1 year difference. Seabass specimens were categorized as early juveniles

(9 months and an average weight of 22 g at the beginning of the study and 15 months and an

average weight of 76 g at the last sampling point), late juveniles (18 months and an average

weight of 151 g at the beginning of the study and 24 months and an average weight of 277 g

at the last sampling point), and mature adults (32 months and an average weight of 467 g at

the beginning of the study and 38 months and an average weight of 669 g at the last sampling

point). We also simultaneously sampled two seabream cohorts categorized as juveniles (15

months and an average weight of 103 g initially and 21 months and an average weight of 250

g at the last sampling point), and mature adults (31 months and an average weight of 411 g

at the beginning of the study and 37 months and an average weight of 476 g at the last

sampling point). Seabream from an intermediate age-cohort were not available.

Each age group and species was reared in separate but not distant open-water ponds

(maximum 344 m and 380 m apart for seabass and seabream, respectively; Figure S3.1). In

this fish farm, all ponds share the same water inflow, which is taken from a single point in the

estuary. Water in each pond is naturally recycled at each high tide (twice a day) and never

shared between ponds. Hence, fish share roughly the same water quality and environment.

Additionally, fry were bought from commercial hatcheries, which genetic variation is

considered low (Araki and Schmid, 2010).

Fish were caught from each tank using a fishing line, and skin and gill samples were

non-invasively taken using sterile swabs (Medical Wire & Equipment, UK). We swabbed the

right filaments between the first and second arches of the gill and the right upper lateral part

of the fish skin from head to tail. Afterwards fish were released unharmed. We collected water

samples (1 L) from the five different culture ponds at the same time as fish swabbing was

performed, except during the month of December, when no water samples could be collected.

Water samples were filtered through 0.2 μm cellulose nitrate filters on collection day. Swabs

and filters were immediately frozen at − 20 °C and then transported in dry ice to the CIBIO-

InBIO laboratory where they were kept at − 80 °C until processing.

We sampled five fish per week per age group, totaling 60 specimens per species and

age group. We processed 360 seabass samples (60 skin and 60 gills × 3 age groups) plus 29

water samples from their corresponding fishponds. We also processed 240 seabream

samples (60 skin and 60 gills × 2 age groups) plus 16 water samples from their corresponding

fishponds. Seabass and their corresponding water samples were processed using the


92

PowerSoil DNA Isolation Kit (QIAGEN, Netherlands), while seabream and corresponding

water samples were processed using the PureLink Microbiome DNA Purification Kit

(ThermoFisher Scientific, UK). We used two different DNA extraction kits due to supply

shortage at the time of extraction. This technical difference did not impact the goals of our

study since we studied each fish species separately (i.e., mucosa microbiotas are not

compared between fish species). We measured DNA concentration and quality in a

NanoDropTM 2000 Spectrophotometer (ThermoFisher Scientific, USA). DNA extractions were

shipped in dry ice to the University of Michigan Medical School (USA) for amplification and

sequencing according to the protocol of Kozich et al. (2013). Each sample was amplified for

the V4 hyper-variable region of the 16S rRNA gene (~ 250 bp). All amplicon libraries were

pooled and sequenced in a single run of the Illumina MiSeq sequencing platform.

Approximately 8,313,608 and 6,943,265 16S rRNA sequences were retrieved for

seabass and seabream, respectively. The number of sequences per sample ranged from 726

to 46,001 in seabass and from 5145 to 151,713 in seabream. After normalization and removal

of non-bacterial reads, 8724 and 5754 ASVs were assigned to the skin and gill, respectively,

of seabass; while 5308 and 3423 ASVs were assigned to the skin and gill, respectively, of

seabream. A total of 2543 ASVs were retrieved from the water samples collected in seabass

fishponds, while 1440 ASVs were retrieved from the waters of seabream fishponds. Microbial

taxa showing a mean relative proportion ≥ 5% in any group were considered the most

abundant taxa in that group.

3.3.2 Data processing and statistical analysis Raw FASTQ files were denoised using the DADA2 pipeline in R with the parameters

for filtering and trimming being trimLeft = 20, truncLen = c (220,200), maxN = 0, maxEE = c

(2,2), truncQ = 2 (Callahan et al., 2016). We estimated a midpoint rooted tree of ASVs using

the Quantitative Insights Into Microbial Ecology 2 package (QIIME2; release 2019.7). We

constructed a table containing amplicon sequence variants (ASVs) and made taxonomic

inferences against the SILVA (138 release) reference database (Quast et al., 2013). We

normalized ASV abundances using the negative binomial distribution (McMurdie and Holmes,

2014), which accounts for library size differences and biological variability.

Microbial taxonomic alpha-diversity (intra-sample) was calculated using Shannon,

Faith’s phylogenetic diversity (PD), ACE and Simpson 1-D indices as implemented in the R

package phyloseq (McMurdie and Holmes, 2013). We assessed variation in microbial

composition (alpha-diversity) and the mean proportions of the most abundant taxa (≥5% of all

sequences) using Linear Mixed Effects models (LME) with the lmer R package (Gałecki and

Burzykowski, 2013). Since we were interested in assessing whether microbial diversity varied


93

across fish age groups (predictor), we used age groups as a fixed factor and sampling date

(with 12 sampling time points) as a random factor. The final general LME formula was

expressed as: microbial diversity~fish age group + (1|sampling time point). Microbial structure

(beta-diversity) was estimated using phylogenetic Unifrac (unweighted and weighted) and

Bray-Curtis distances. Dissimilarity in microbial structure between samples was visualized

using principal coordinates analysis (PCoA). Additionally, differences in community structure

driven by fish age group were further tested using permutational multivariate analysis of

variance (PERMANOVA) as implemented in the adonis function of the vegan R package

(Oksanen et al., 2008). We used the strata argument to permutate sampling dates and ran

1000 permutations.

Previous fish studies of skin and gill microbiota (e.g., Legrand et al., 2018; Reinhart et

al., 2019; Wu et al., 2020; Zhang et al., 2018), including seabass and seabream (Rosado et

al., 2019a), have shown remarkable differences in microbial composition and structure across

host species and tissues. Although the two species studied here are farmed in the same

location, provided feeds were different throughout the sampling period. For this reason, and

because fish samples were processed using two different DNA extraction kits, we did not

compare mucosa microbiotas between fishes (e.g., Kennedy et al., 2014; Wilkes Walburn et

al., 2019). Additionally, a previous study by our group (Rosado et al., 2019b) showed that

disease and antibiotic treatment in seabass leads to asymmetrical shifts in skin and gill

microbial communities. Therefore, we carried out all our statistical analyses separately for

each fish species and tissue.

Microbial potential metabolic functions were predicted using the metagenomic

Phylogenetic Investigation of Communities by Reconstruction of Unobserved States software

(PICRUSt2) embedded in QIIME2 (Douglas et al., 2019), applying a weighted nearest

sequenced taxon index (NSTI) cutoff of 0.03. Predicted metagenomes were collapsed using

the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway metadata (Kanehisa et al.,

2019). We identified differentially abundant metabolic pathways in the skin and gill microbiota

of seabass and seabream across age groups using linear discriminant analysis (LDA) in

LEfSe, using age groups as classes (Segata et al., 2011). As suggested by the authors, we

used a P-value cut-off of 0.05 and a LDA effect size cut-off of 2 (Segata et al., 2011).

Finally, to assess to what extent water microbial communities shaped skin and gill

microbiota across fish age groups, we estimated the number of shared ASVs between fish

and water microbiota and constructed Venn diagrams in R. We used PERMANOVA and

mantel testes (Mantel, 1967) to assess differences in community structure and correlations

between tissues and water microbiota, respectively, in both species.


94

3.4 Results Skin, gill and water microbial samples from different fish age groups were collected

simultaneously (same day and at approximately the same time) from separate ponds (Figure

S3.1). Three age cohorts were sampled for the seabass, which included fish in their 1st, 2nd

and 3rd year of age; while two age cohorts were sampled for the seabream, which included

fish in their 2nd and 3rd year of age. Since our sampling was non-invasive, to classify fishes

into age groups we coupled available information from the literature (Barnabe, 1991; Chaoui

et al., 2006; Felip et al., 2001; Mehanna, 2007; Zohar and Gordin, 1979) with weight and age

of maturation estimates provided by the fish farm. The three seabass age cohorts were thus

classified as early juveniles, late juveniles and mature adults, respectively; while the two

seabream age cohorts were classified as juveniles and mature adults, respectively – see

Material and methods section for more details. Differences in the average weight estimated

for each age group at the beginning and end of our sampling showed a 245% growth for the

seabass early juvenile group, an 83% growth for the late juvenile group and a 43% growth for

the mature adult group. Similarly, a 143% growth was estimated for the seabream juveniles

and a 16% growth for the mature adults. Descriptive analyses were performed for each age

group separately and comparative statistical analyses were performed between age groups.

3.4.1 Microbial diversity across age groups

3.4.1.1 Alpha-diversity Microbial alpha-diversity was calculated using Shannon, Faith’s phylogenetic diversity

(PD), ACE and Simpson 1-D indices. In general, the skin microbiota showed higher mean

values for the alpha-diversity indices than the gill microbiota across all age groups in both fish

species, except for the Simpson 1-D index in the late juveniles and adults of the seabass

(Figure S3.2). In seabass, the skin and gill microbiota of late juveniles and mature adult fish

presented higher mean values of alpha-diversity than the microbiota of the early juveniles

(Figures 3.1A, S3.3). In seabream, skin and gill microbiotas showed similar alpha-diversity in

both cohorts (Figures 3.1B, S3.3). Linear Mixed Effects (LME) model analysis (diversity~age

group + (1|sampling date)) showed most alpha-diversity estimates varied significantly between

seabass age groups in both tissues. Pairwise comparisons between age groups in seabass

showed significant (p < 0.05) differences in alpha-diversity for almost all the tests comparing

early vs late juveniles and early juveniles vs mature adults (Table 3.1). Seabass late juvenile

vs mature adult alpha-diversity comparisons were never significant (p > 0.05, Table 3.1) for

both fish mucosae. In the seabream, only the Shannon and PD indices of the gill microbiota

varied significantly between juveniles and mature adults (p < 0.04, Table 3.1).


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Figure 3.1 Mean values and standard deviations of Shannon alpha-diversity estimates plotted for the early juveniles/juveniles (green), late juveniles (yellow) and mature adults (orange) of the seabass Dicentrarchus labrax (A) and the seabream Sparus aurata (B) (n = 60 per species x age group x tissue). Pairwise comparisons of alpha-diversity were assessed using Linear Mixed Effect models with age groups as a fixed factor and sampling time as a random factor. Statistically significant differences are denoted with an asterisk and non statistically significant differences with “ns”.


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Table 3.1 Mean alpha-diversity values, and alpha- and beta-diversity comparisons for the skin and gill microbiota of the different age groups of seabass Dicentrarchus labrax and seabream Sparus aurata (n=60 per species x age group x tissue). Variation in alpha-diversity was assessed using Linear Mixed Effect models, with age groups as a fixed factor and sampling time as a random factor. Differences in beta-diversity were assessed using PERMANOVA. For each linear model effect test (alpha-diversity) we report the F statistic and significance (P value) and for each PERMANOVA test (beta-diversity) we report the R2 statistics and significance (P value). Significant differences are indicated in bold. EJ: early juveniles; LJ: late juveniles; MA: mature adults; J: juveniles.

Seabass Seabream Skin Gill Skin Gill Alpha-diversity mean values EJ LJ MA EJ LJ MA J MA J MA

Shannon 3.5±1 4±0.4 3.8±1 3.5±1 3.6±1 3.7±1 3.3±1 3.4±1 2.9±1 3.2±1 PD 20±7 28±11 27±9 19±6 22±8 23±9 19±9 18±8 13±6 15±6 ACE 164±66 239±106 226±83 138±52 159±69 175±73 155±77 145±71 99±49 110±54 Simpson 1-D 18±8 25±8 23±11 20±11 23±10 24±11 15±10 16±9 11±8 14±10 Alpha-diversity comparisons Overall EJ vs LJ LJ vs MA EJ vs MA Overall EJ vs LJ LJ vs MA EJ vs MA J vs MA J vs MA

Shannon 16 (5-7) 6 (0.001) 2 (0.05) -3 (0.003) 3 (0.1) 2 (0.1) -0.2 (0.9) -2 (0.1) 1 (0.3) 5 (0.03) PD 23 (3-9) 6 (1-4) 1 (0.6) -5 (1-4) 7 (0.002) 2 (0.04) -1 (0.5) -4 (0.001) 1 (0.4) 4 (0.04) ACE 17 (2-7) 6 (1-4) 1 (0.6) -5 (1-4) 9 (0.0003) 2 (0.05) -2 (0.2) -4 (0.001) 1 (0.3) 2 (0.2) Simpson 1-D 13 (7-6) 5 (1-4) -2 (0.2) 4 (0.002) 2 (0.1) 2 (0.3) 0.4 (0.9) 2 (0.1) 0.1 (0.8) 4 (0.1) Beta-diversity comparisons Overall EJ vs LJ LJ vs MA EJ vs MA Overall EJ vs LJ LJ vs MA EJ vs MA EJ vs MA EJ vs MA

Unifrac Unweighted 0.04 (9-5) 0.1 (0.001) 0.02 (0.01) 0.1 (0.001) 0.1 (9-5) 0.04

(0.001) 0.1 (0.001) 0.1 (0.001) 0.02 (9-5) 0.03 (9-5) Unifrac Weighted 0.1 (9-5) 0.1 (0.001) 0.1 (0.001) 0.1 (0.001) 0.1 (9-5) 0.02 (0.1) 0.02 (0.2) 0.04 (0.01) 0.01 (0.3) 0.04 (9-5) Bray-Curtis 0.1 (9-5) 0.1 (0.001) 0.1 (0.001) 0.03 (0.02) 0.1 (9-5) 0.1 (0.001) 0.1 (0.001) 0.1 (0.001) 0.02 (0.001) 0.03 (2-4)


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3.4.1.2 Beta-diversity Microbial structure was estimated using phylogenetic UniFrac (unweighted and

weighted) and Bray-Curtis distances. The PERMANOVA analyses of dissimilarity

(diversity~age group, strata = sampling date) showed significant differences between age

groups in both species (p < 0.02, Table 3.1), except for the UniFrac Weighted distance

between the gills of early and late seabass juveniles (p = 0.1, Table 3.1), seabass late juveniles

and mature adults (p = 0.2, Table 3.1), and the skin of juveniles and seabream adults (p = 0.3,

Table 3.1). Moreover, high inter-individual variability within age groups was also observed.

However, differences in beta-diversity dispersion within age groups for the skin and gill

microbiotas of both species were small (Bray-Curtis distance, Figure 3.2).

3.4.1.3 Bacterial taxa Proteobacteria and Bacteroidetes were the most abundant (≥5%) phyla in the skin

(averaging 41 ± 4% and 39 ± 2% of the sequences in seabass and 55 ± 4% and 31 ± 4% in

seabream) and gill (averaging 52 ± 7% and 25 ± 5% in seabass and 69 ± 4% and 12 ± 1% in

seabream) microbiotas of all studied age groups (Table S3.1). The NS3a marine group and a

genus belonging to the Flavobacteriaceae family were the most abundant (≥5%) genera in the

skin (10 ± 1 and 11 ± 2, respectively) and gill (6 ± 1 for both) of all the age groups in seabass;

while Burkholderia-Caballeronia-Paraburkholderia was the most abundant genus in the skin

(17 ± 1) and gill (25 ± 0) of both age groups in seabream (Table S3.1). The most abundant

microbial phyla and genera found in both fish species varied between age groups and tissues

(Figure 3.3, Table S3.1). LME models showed that the relative abundance of all those phyla

was significantly different between age groups, except in the gill microbiota of the seabream,

where the relative abundance of Cyanobacteria did not vary (Table S3.2). LME analyses also

revealed that 100 and 63% of the genera varied between age groups in the skin and gill of the

seabass, respectively, while 40 and 50% varied in the skin and gill of the seabream,

respectively (Table S3.2). Pairwise comparisons of taxa across age groups in seabass yielded

a higher percentage of significant differences between early juveniles and mature adults in

both tissues (100% in the skin and 38% in the gill) than between early and late juveniles (67%

in the skin and 13% in the gill), or between late juveniles and mature adults (0% in the skin

and 25% in the gill) (Table S3.2).


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Figure 3.2 A: PCoA plots computed using Bray-Curtis distances. Each dot represents a microbiome sample and

is colored by age group (green: early juveniles/juveniles; yellow: late juveniles; orange: mature adults) in seabass

Dicentrarchus labrax and seabream Sparus aurata. Ellipses denote a 95% confidence for the age group mean. B:

Bray-Curtis beta-diversity within age groups plotted for the early juveniles/juveniles (green), late juveniles (yellow)

and mature adults (orange) of the seabass Dicentrarchus labrax and the seabream Sparus aurata (n = 60 per

species x age group x tissue).


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Figure 3.3 Most abundant (≥5%) phyla and genera of the seabass Dicentrarchus labrax (A) and the seabream

Sparus aurata (B). Distinct bars represent relative abundance of each taxa for skin, gill and water microbiota of the

studied age group (EJ: early juveniles, LJ: late juveniles, J: Juveniles, and MA: mature adults), labeled to the lowest

taxonomic level possible (n = 60 per species x age group for tissues; n = 10 per species x age group for water).

Unknown genera are identified as u.g.


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3.4.2 Microbial predicted functional diversity across age groups About 462 ± 18 KEGG pathways were inferred in the skin and gill microbiota of the

seabass, while 455 ± 4 pathways were inferred in the skin and gill microbiota of the seabream.

Linear discriminant analysis of the metagenomic predictions performed in LEfSe showed that

different pathways were significantly enriched for each age group in both species (Figure 3.4,

Table S3.3). Overall, there were more enriched pathways in the older age groups (seabass

late juveniles and mature adults of both species) than in the younger age groups of both

species (Figure 3.4, Table S3.3).

While there were no significantly enriched pathways in the skin microbiota of early

juvenile seabass, enriched pathways in the gill microbiota of this age group were related to

metabolic regulator biosynthesis, purine nucleotide degradation, sugar degradation and

fermentation of pyruvate. In the skin microbiota of late juvenile seabass, enriched pathways

were related to thiamine biosynthesis, aldehyde degradation and L-arabinose degradation;

while in the gill microbiota they were related to denitrification, galactose degradation and

nitrogen compound metabolism. In mature seabass, pyrimidine and purine

deoxyribonucleotide de novo biosynthesis were enriched in both tissues. Additionally, the gill

microbiota was also enriched by pathways related to the biosynthesis of chlorophyll, folate,

hemiterpene, L-alanine, L-tyrosine, NAD, secondary metabolite and ubiquinol, chloroaromatic

compound degradation, fermentation to lactate and glycolysis (Figure 3.4, Table S3.3).

In the skin microbiota of seabream juveniles, enriched pathways were related to amine

and polyamine biosynthesis and degradation, choline biosynthesis, and sugar acid and

toluene degradation; whereas in the gill microbiota only pyrimidine and purine

deoxyribonucleotide de novo biosynthesis were identified. The enriched pathways of the

seabream mature adults were related to fatty acid, L-methionine, NAD, palmitate,

palmitoleate, siderophore, stearate and unsaturated fatty acid biosynthesis, pyrimidine and

purine nucleotide salvage, aspartate superpathway and TCA cycle in the skin microbiota;

whereas pyrimidine and purine deoxyribonucleotide de novo biosynthesis, autotrophic CO2

fixation and fermentation of pyruvate were enriched in the gill microbiota (Figure 3.4, Table

S3.3).


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Figure 3.4 LDA score of differentially abundant enriched pathways in the skin and gill microbiota of early juveniles (EJ), late juveniles (LJ), juveniles (J) and mature adults (MA) of seabass Dicentrarchus labrax (A) and seabream Sparus aurata (B). Bios: Biosynthesis; DUA: Degradation/Utilization/Assimilation; GPME: Generator of Precursor Metabolites and Energy; MM: Macromolecule Modification.


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3.4.3 Fish and water microbiota comparisons The microbiota of the farm fishpond waters showed higher alpha-diversity than the skin

and gill microbiota of seabass and seabream for all the indices, except for the Shannon index

in the seabass late juveniles (Figure S3.2). The analyses of dissimilarity between the skin and

gill microbiotas and the water microbiota were statistically significant for all pairwise

comparisons (PERMANOVA, p < 0.001, Table 3.2). Moreover, results from the Mantel tests

revealed a correlation between gill and water microbiota of seabass and seabream across age

groups (p < 0.03, Table 3.2), except in the case of late juvenile seabass (p > 0.05, Table 3.2).

PCoAs showed that the water microbiota clustered more closely to the skin microbiota than to

the gill microbiota in both fishes (Figure S3.4). In both species, the percentage of ASVs shared

between skin and water microbiota, and between gill and water microbiota was very similar

(14% ± 1 and 15% ± 1 of ASVs, respectively) (Figure 3.5).

Table 3.2 Results from pairwise comparisons of beta-diversity and Mantel tests for fish tissues and water per age group for the seabass Dicentrarchus labrax and the seabream Sparus aurata (n = 60 per species x age group for tissues; n = 10 per species x age group for water). For each PERMANOVA test we report the R2 statistics and significance (P value) and for each Mantel test we report the R statistic and significance (P value). Significant differences/associations are indicated in bold. EJ early juveniles, LJ late juveniles, MA mature adults, J juveniles.

UniFrac Unweighted UniFrac Weighted Bray-Curtis Permanova Mantel Permanova Mantel Permanova Mantel

Seabass

EJ Skin vs Water 0.1 (0.001) 0.2 (0.04) 0.1 (0.01) -0.1 (0.9) 0.04 (0.04) 0.1 (0.2) Gill vs Water 0.2 (0.001) 0.5 (1-4) 0.1 (0.001) 0.4 (1-4) 0.2 (0.001) 0.4 (1-4)

LJ Skin vs Water 0.1 (0.001) 0.2 (0.01) 0.1 (0.001) -0.04 (0.7) 0.1 (0.001) 0.1 (0.1) Gill vs Water 0.2 (0.001) 0.5 (1-4) 0.2 (0.001) -0.2 (0.9) 0.2 (0.001) 0.4 (1-4)

MA Skin vs Water 0.1 (0.001) 0.2 (0.1) 0.1 (0.001) -0.1 (0.8) 0.1 (0.002) 0.1 (0.1) Gill vs Water 0.1 (0.001) 0.4 (2-4) 0.1 (0.001) 0.2 (0.03) 0.1 (0.001) 0.2 (0.01)

Seabream J Skin vs Water 0.1 (0.001) 0.2 (0.02) 0.04 (0.01) -0.1 (0.9) 0.1 (0.001) 0.1 (0.1)

Gill vs Water 0.2 (0.001) 0.5 (1-4) 0.1 (0.001) 0.6 (1-4) 0.4 (0.001) 0.6 (1-4) MA Skin vs Water 0.1 (0.001) 0.2 (0.02) 0.03 (0.03) 0.04 (0.3) 0.2 (0.001) 0.2 (0.02)

Gill vs Water 0.1 (0.001) 0.6 (1-4) 0.5 (0.001) 0.7 (1-4) 0.3 (0.001) 0.5 (1-4)


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Figure 3.5 Venn diagrams showing the number and percentage of shared ASVs between skin (yellow), gill (pink) and water (blue) microbiota of the different age groups for the seabass Dicentrarchus labrax (A) and the seabream Sparus aurata (B) (n = 60 per species x age group for tissues; n = 10 per species x age group for water).

3.5 Discussion We characterized the skin and gill microbiota of different age groups of farmed

European seabass and gilthead seabream using 16S rRNA amplicon high-throughput

sequencing. By taking into account potential environmental and seasonal effects, our study

shows that fish age, in particular sexual maturation and growth, impact skin and gill microbial

diversity (Table 3.1; Figure 3.1), composition (Table S3.1; Figures 3.3, 3.5) and predicted

microbial functions (Table S3.3; Figure 3.4).

3.5.1 Microbial diversity across age groups Fish growth and sexual maturation are usually accompanied by extreme

morphological and physiological changes (e.g., Varsamos et al., 2005; Wilkins and Jancsar,

1979). Importantly, some of these changes reported for the skin and gills have been suggested

to also affect their microbiota. For example, changes in epidermal structure derived from

sexual maturation (e.g., increases in the number, size and activity of the mucous cells) have


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been detected in several fish species (e.g., Whitear, 2009; Wilkins and Jancsar, 1979), and

suggested to increase infection rate with Saprolegnia fungus in sea trout and brown trout

(Richards and Pickering, 1978). Likewise, changes in the hormones expressed in the skin alter

the biochemistry of the skin mucous and also potentially affect its microbiota (Roberts and

Bullock, 1980). Fish growth and sexual maturation also impact gill morphology and function in

some fish species. For example, the ability to osmoregulate at different salinities was seen to

increase throughout the developmental stages (larva to juvenile) of seabass (Varsamos et al.,

2005). Additionally, body size was also identified as the main factor affecting morphological

variation in gill rakes and gill pore size in the Silver Carp and Gizzard Shad, suggesting that

the overall filtering ability of these species is related to size and maturation (Walleser et al.,

2014). Importantly, a recent study showed that body weight increase is accompanied by higher

microbial community structuring in the skin and gill of rabbit fish (Wu et al., 2020). We thus

hypothesize that such physiological and morphological changes occurring during fish growth

have led to the changes in microbial diversity, composition and predicted functionality

observed in the present study.

The seabass skin and gill microbiotas of older age groups showed significantly higher

alpha-diversity than those of early juveniles. Additionally, a higher percentage of significant

differences in the relative abundance of the most abundant phyla and genera occurred

between early juveniles and older age groups (67 ± 27% and 55 ± 38% in the skin and gill,

respectively; Table S3.2). This suggests that the skin and gill microbiotas of the seabass were

highly dynamic, diversifying with age. Conversely, the skin and gill microbiotas of the

seabream juveniles and adults showed similar alpha-diversity means, although a high

percentage of the most abundant phyla and genera varied between age groups (70 ± 42%

and 63 ± 18% in the skin and gill, respectively; Table S3.2). Variation in microbiota alpha-

diversity between different age groups has been previously reported for many fish species.

For example, studies on the zebrafish and salmon gut microbiota, have reported differences

between mature and immature life stages; however, those differences also coincide with other

major ecological changes in the fish, such as diet (Stephens et al., 2016) or environment

transitions (Lokesh et al., 2019). Moreover, the relative abundance of predominant bacterial

groups also changed with aging in other fish species (e.g., Lokesh et al., 2019; Pratte et al.,

2018; Stephens et al., 2016).

We detected significant differences in microbial structure across all age groups in both

species. Similar results have been also reported in other fish (e.g., several reef fish species,

Pratte et al., 2018; Xavier et al., 2020, and Salmo salar; Llewellyn et al., 2016), being

particularly evident in longitudinal studies encompassing several months (Lokesh et al., 2019;

Stephens et al., 2016; Yan et al., 2016). High inter-individual variability within age groups was

also previously reported for other fish species (e.g., Pratte et al., 2018; Thompson et al., 2017;


105

Uren Webster et al., 2018). However, our results showed that fish age only explained a low

percentage of the variation in the bacterial community structure (R2 < 0.1). This suggests that

microbial differences between age groups are small at the community level, but clearly

noticeable at the species level, with a high proportion of the predominant bacterial taxa (61 ±

39% and 46 ± 32% in the skin and gill of seabass, respectively; 70 ± 42% and 63 ± 18% in the

skin and gill of the seabream, respectively) changing their abundance with sexual maturation.

Although our statistical models accounted for sampling dates as a random factor, other biotic

and abiotic factors (e.g., variation in the environment and individual weights) could be

responsible for most of the variation observed in community structure (e.g., Llewellyn et al.,

2014; Lokesh et al., 2019; Uren Webster et al., 2018).

3.5.2 Microbial predicted functional diversity across age groups The predicted functional analysis suggests that distinct significantly enriched metabolic

pathways are expressed in skin and gill microbiotas of both fish species across age groups.

Although metabolic information is particularly limited for fish microbiotas, studies on other

vertebrates, mainly in humans and their gut microbiotas, are starting to shed light on the

beneficial outcomes specific microbial metabolic functions have on the host health and

physiology (e.g., Berry and Loy, 2018).

Notwithstanding that present results should be interpreted with caution since

PICRUSt2 analysis is limited by the currently available genomes and biased towards human

health microorganisms (Choi et al., 2017), one could suggest that some of the enriched

metabolic pathways found in our analysis could also improve the seabass and seabream

health and physiology. The protective role of the microbiota is often related to the production

of secondary metabolites that provide chemical defense and mediate bacterial diversity (Kelly

and Salinas, 2017). Secondary metabolites with antimicrobial activity have been previously

isolated from microbial species inhabiting the gut microbiome of fish (Jami et al., 2015). Here,

the biosynthesis of secondary metabolites that have been associated with antimicrobial

activity, including hemiterpene (e.g., Reyes-Jurado et al., 2015), were enriched in mature

adults of both species. Additionally, the biosynthesis of chlorophyll and several amino acids,

herein enriched in older age groups of both fish species, have also been found to be expressed

in the skin and gut of healthy humans (e.g., Barnard et al., 2016; Shao et al., 2017).

Importantly, amino acid biosynthesis was reported in the gut microbiota of grass carp when

fed a protein-deficient diet, suggesting a metabolic role of the gut microbiota towards fish

nutrition (Ni et al., 2014). The biosynthesis of vitamins, here enriched in older age groups of

seabass, has been found beneficial for human skin (e.g., Saxena et al., 2018) and gut mucosa,

including folate and thiamine (Hill, 1997). In addition, polyamines are bacterial metabolites


106

known to have several benefits towards gut mucosa recovery (Tofalo et al., 2019). These

pathways were also enriched in the juveniles of seabream.

It is also worth noticing that some of the enriched metabolic pathways detected in the

present study could be driven by the high environmental variability of the Alvor estuary where

these fish were reared. In estuaries, salinity variations occur on a daily basis due to tides and

pollutants can be prevalent (e.g., Antunes et al., 2007). Biosynthesis of fatty acids and

unsaturated fatty acids were two of the predicted metabolic pathways enriched in the

microbiota of mature seabream. These same pathways have also been enriched in previous

analyses of the skin and gut microbiota in the Atlantic salmon (Dehler et al., 2017; Lokesh and

Kiron, 2016) and in the skin microbiota of the common snook (Tarnecki et al., 2019) when

transitioning between freshwater and seawater. Additionally, two of the predicted metabolic

pathways identified in both fish species were related to degradation of toxic compounds.

Specifically, biodegradation of the highly prevalent toxic pollutants toluene and chloroaromatic

compounds by bacteria is essential to remove them from the environment and to prevent

absorption through the skin and gills in aquatic animals (Fuchs et al., 2011; Patrolecco et al.,

2010; Van Der Meer, 1997).

Following alpha-diversity patterns, fish from older age groups, particularly in the

seabass, had greater enrichment of predicted functions related mainly to the biosynthesis and

degradation of compounds; as well as, to a lesser extent, metabolism and energy cycles. We

then hypothesize that the increase in microbial diversity observed as fish ages leads to wider

functional diversity. This could prove beneficial to those fishes, given the key physiological

modifications older fish groups are experiencing during sexual maturation and growth.

3.5.3 Fish and water microbiota comparisons The water microbiotas of fishponds were significantly distinct and more diverse than

the skin and gill microbiota of both fish species, regardless of their age. It is known that free-

living microbial communities retain higher richness than host-associated communities

(Thompson et al., 2017), with many studies showing a higher bacterial diversity in water

relative to fish skin (Chiarello et al., 2019, 2018, 2015; Larsen et al., 2015; Uren Webster et

al., 2018; Wu et al., 2020), gills (Pratte et al., 2018; Wu et al., 2020), gut (Bledsoe et al., 2016;

Parris et al., 2016; Reinhart et al., 2019; Wilkes Walburn et al., 2019; Zhang et al., 2018),

stomach (Wu et al., 2020), hindgut (Wu et al., 2020) and whole larvae (Nikouli et al., 2019).

Although some studies in fish have shown that the microbial communities found in the water

tend to be recovered in the larval gut microbiota (Stephens et al., 2016; Wilkes Walburn et al.,

2019), others have also shown that water microbiota does not influence directly the microbiota

of the fish mucosa (Bledsoe et al., 2016; Boutin et al., 2013; Carlson et al., 2017; Chiarello et


107

al., 2019, 2018, 2015; Larsen et al., 2015; Legrand et al., 2018; Llewellyn et al., 2016; Nikouli

et al., 2019; Parris et al., 2016; Pratte et al., 2018; Schmidt et al., 2018; Uren Webster et al.,

2018; Wu et al., 2020; Yan et al., 2016; Zhang et al., 2018). Importantly, a previous study of

the skin microbiotas of seabass and seabream also showed significant differences with

planktonic communities (Chiarello et al., 2015). However, in that study only a low number of

Operational Taxonomic Units (3%) was shared between skin and water microbiota; whereas

in the present study higher percentages of ASVs were shared between the skin (14% ± 1) and

the gill (15% ± 1) of both fish species and the surrounding water.

Microbial dissimilarities depicted by PCoAs showed that, although significantly

different, the skin microbiota of both species clustered more closely to the water microbiota

than the gill microbiota. However, only a small percentage of the variation (PC 1 – average

18% ± 2; PC 2 – average 10% ± 1) was explained by this analysis. On the other hand, the

results from the Mantel tests showed a significant (p < 0.03) correlation between the water

and gill microbiotas, but not between the water and skin microbiotas. This suggests that

although both skin and gill are permanently in contact with water, the gill environment may be

more susceptible to variations in the water microbiota.

3.6 Conclusion Skin and gill are important mucosal barriers that protect the fish from the external

environment. They are in permanent contact with the water column and thus prone to

pathogenic bacterioplankton colonization. However, most studies so far investigating microbial

changes related to fish age have either strictly focused on early life stages (i.e., larvae

development) or on the gut microbiota. In the present study, we demonstrate that, to some

extent, changes occurring later in life can also be correlated with aging factors such as growth

and sexual maturation. We uncovered important differences in the diversity, composition and

predicted functionality of the skin and gill microbiotas across age groups of farmed seabass

and seabream. Fish included in this study were exposed to a heterogeneous estuarine

environment, that varies seasonally as well as daily (e.g., salinity and temperature

fluctuations). Hence, although we observed significant differences in microbial community

structure due to age, other biotic and abiotic factors not considered here may have more

deeply structured the fish microbiota. Growth rate decreased drastically with age, being much

higher in juveniles (243 and 83% for early and late seabass juveniles, and 143% in seabream)

relative to adults (43 and 16% in adult seabass and seabream, respectively). We, thus,

conclude that growth and sexual maturation are likely the main drivers of the microbial

differences attributed to age observed here. Overall, our results agree well with other studies

of the skin (Wu et al., 2020; Xavier et al., 2020) and gill (Pratte et al., 2018; Wu et al., 2020)


108

microbiotas of several wild reef fish, suggesting this could be a general pattern across fish.

Our results also highlight the importance of aging in farm fish studies focused on microbial

dysbiosis and disease dynamics.



Technology (project PTDC/ MAR-BIO/0902/2014 -POCI-01-0145-FEDER-016550; project

PTDC/BIA-MIC/27995/2017 POCI-01-0145- FEDER-027995); DR, AP, MP-L and RX were

supported by FCT under the Programa Operacional Potencial Humano – Quadro de

Referência Estratégico Nacional funds from the European Social Fund and Portuguese

Ministério da Educação e Ciência (DR doctoral grant SFRH/ BD/117943/2016; AP doctoral

Grant SFRH/BD/144928/2019; MPL: IF/00764/2013; RX: IF/00359/2015).

3.8 References Amato, K.R., Leigh, S.R., Kent, A., Mackie, R.I., Yeoman, C.J., Stumpf, R.M., Wilson, B.A.,

Nelson, K.E., White, B.A., Garber, P.A., 2014. The role of gut microbes in satisfying the

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3.9 Supplementary material

Figure S3.1 Illustrative scheme of the semi-intensive fish farm where samples were collected. All ponds shared the same inflow of estuarine water (A) and water was never shared between ponds. Each age group and species was reared in separated but not distant open water ponds: 1 - mature adults seabass; 2 - mature adults seabream; 3 - late juveniles seabass; 4 - early juveniles seabass; 5 - early juveniles seabream.


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Figure S3.2 Mean values and standard deviations of Shannon, Faith’s phylogenetic (PD), ACE and Fisher alpha-diversity estimates plotted for skin (yellow), gill (pink) and water (blue) microbiota of the different age groups of the seabass Dicentrarchus labrax (A) and the seabream Sparus aurata (B) (n = 60 per species x age group for tissues; n = 10 per species x age group for water).


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Figure S3.3 Mean values and standard deviations of Faith’s phylogenetic (PD), ACE and Fisher alpha-diversity estimates plotted for the early juveniles/juveniles (green), late juveniles (yellow) and mature adults (orange) of the seabass Dicentrarchus labrax (A) and seabream Sparus aurata (B) (n = 60 per species x age group x tissue). Pairwise comparisons of alpha-diversity were assessed using Linear Mixed Effect models with age groups as a fixed factor and sampling date as a random factor. Statistically significant differences are denoted with an asterisk and non statistically significant differences are denoted with “ns”.


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Table S3.1 Relative mean proportions (%) of the most abundant phyla and genera (≥5%) in the skin and gill microbiota of the different age groups of the seabass Dicentrarchus labrax and the seabream Sparus aurata, and in the water column (n=60 per species x age group for tissues; n=10 per species x age group for water). Taxa with a ≥5% relative mean proportion in a group are indicated in bold. Unknown genera are identified as u.g. Seabass Seabream

Skin Gill Water Skin Gill Water EJ LJ MA EJ LJ MA EJ LJ MA J MA J MA J MA Phyla Bacteroidota 38 42 36 19 26 30 46 45 43 27 35 11 13 46 44 Cyanobacteria - - - - - - - - - 1 1 4 6 2 2 Proteobacteria 47 36 41 61 46 48 39 35 39 59 52 73 65 41 39 Verrucomicrobiota 5 8 5 10 13 7 9 14 10 2 4 2 5 7 11 Genera Burkholderia-Caballeronia-Paraburkholderia

- - - - - - - - - 18 15 25 25 0.1 0.03

Glaciecola 2 2 4 1 1 3 3 2 5 - - - - - - NS3a marine group 11 10 9 5 6 7 13 13 11 7 8 3 3 16 13 Polynucleobacter 3 2 2 4 4 5 0 0 0 - - - - - - Pseudomonas - - - - - - - - - 6 3 1 0.4 0 0.03 Rubritalea 4 5 2 8 9 4 5 9 4 2 3 2 3 4 5 Vibrio - - - - - - - - - 6 7 0.4 0.3 1 0.4 Burkholderiales Incertae Sedis (u.g.) 1 0.01 0.003 7 2 1 0 0 0 0.1 0.1 7 6 0.001 0 Cryomorphaceae (u.g.) 4 3 3 1 1 2 5 4 5 2 3 0.3 0.4 6 6 Flavobacteriaceae (u.g.) 10 13 9 6 8 5 13 14 12 6 9 3 4 12 11 Paracaedibacteraceae (u.g.) - - - - - - - - - 0.3 0.3 3 6 0.003 0.01 Rhodobacteraceae (u.g.) 2 2 2 5 5 2 4 3 4 2 2 1 1 7 7 Burkholderiales (u.g.) 1 0.4 0.1 6 2 1 0 0 0 4 2 12 4 0.004 0 Bacteroidia (u.g.) 1 2 2 2 6 8 0.3 0.3 0.3 - - - - - -


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Table S3.2 Overall and pairwise comparisons of the relative proportions of the most abundant (≥5%) phyla and genera in the skin and gill microbiota of the seabass Dicentrarchus labrax and the seabream Sparus aurata across age groups (n=60 per species x age group x tissue). Variation in taxa proportion was assessed using Linear Mixed Effect models with age group as a fixed factor and sampling time as a random factor. For each linear model effect model test we report the F statistic and significance (P value). Significant differences are indicated in bold. EJ: early juveniles; LJ: late juveniles; MA: mature adults; J: juveniles.

Seabass Seabream Skin Gill Skin Gill Overall EJ vs LJ LJ vs MA EJ vs MA Overall EJ vs LJ LJ vs MA EJ vs MA J vs MA J vs MA Phyla

Bacteroidota 5 (0.01) 2 (0.2) 3 (0.01) 1 (0.4) 25 (4-10) 5 (0.001) 2 (0.05) 7 (0.001) 16 (9-5) 7 (0.01)

Cyanobacteria - - - - - - - - - 4 (0.1) Proteobacteria 18 (2-7) -6 (0.001) 2 (0.05) -4 (0.001) 18 (9-8) -5 (1-5) 1 (0.9) -5 (1-5) 8 (0.01) 19 (3-5) Verrucomicrobiota 13 (7-6) 4 (0.0002) 5 (1-4) 1 (0.8) 10 (0.0001) 3 (0.04) -5 (0.001) -2 (0.1) - 20 (2-5) Total % of phyla variation 100% 67% 100% 33% 100% 100% 33% 67% 100% 75% Genera Burkholderia-Caballeronia-Paraburkholderia - - - - - - - - 1 (0.3) 0.04 (0.9) NS3a marine group 11 (2-5) 4 (1-4) 0.2 (0.9) -4 (0.0001) 0.3 (0.7) 0.1 (0.9) 1 (0.8) 1 (0.7) 2 (0.1) - Polynucleobacter - - - - 2 (0.1) 0.003 (1) 2 (0.2) 2 (0.2) - - Pseudomonas - - - - - - - - 7 (0.01) -

Rubritalea 4 (0.02) 1 (0.8) -2 (0.1) -3 (0.02) 3 (0.05) -2 (0.2) -1 (0.8) -2 (0.04) - -

Vibrio - - - - - - - - 0.3 (0.6) - Burkholderiales Incertae Sedis (u.g.) - - - - 6 (0.01) -3 (0.004) 0.9 (0.6) -2 (0.1) - 1 (0.4) Cryomorphaceae (u.g.) - - - - - - - - - - Flavobacteriaceae (u.g.) 13 (6-6) 4 (0.001) -1 (0.5) -5 (1-4) 0.7 (0.5) 0.1 (0.9) -1 (0.5) -1 (0.6) 7 (0.01) - Paracaedibacteraceae (u.g.) - - - - - - - - - 17 (8-5) Rhodobacteraceae (u.g.) - - - - 6 (0.003) 2 (0.3) 2 (0.1) 3 (0.002) - - Burkholderiales (u.g.) - - - - 11 (5-5) -2 (0.3) 5 (0.001) 3 (0.01) - 15 (0.0002) Bacteroidia (u.g.) - - - - 3 (0.05) 2 (0.2) -2 (0.04) -1 (0.8) - - Total % of genera variation 100% 67% 0% 100% 63% 13% 25% 38% 40% 50%


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Table S3.3 Significantly enriched pathways recovered from the skin and gill of the early juveniles (EJ), late juveniles (LJ) and mature adults (MA) of the seabass Dicentrarchus labrax and the juveniles (J) and mature adults (MA) of the seabream Sparus aurata. LEfSe tests were performed with a P value and LDA score cut-offs of 0.05 and of 2, respectively.

Seabass Seabream

Skin Gill Skin Gill

EJ LJ MA EJ LJ MA J MA J MA

Biosynthesis

Amine and Polyamine biosynthesis (L2) 3

Chlorophyll biosynthesis 2

Choline biosynthesis (L3) 1

Fatty acid biosynthesis (L3) 1

Folate biosynthesis 1

Hemiterpene biosynthesis 2

L-alanine biosynthesis 1

L-methionine biosynthesis 1

L-tyrosine biosynthesis 1

Metabolic regulator biosynthesis (L2) 1

NAD biosynthesis 1 1

Palmitate biosynthesis 1

Palmitoleate biosynthesis 1

Pyrimidine deoxyribonucleotide de novo biosynthesis 1 1 1 1

Purine nucleotide salvage 1

Secondary metabolite biosynthesis (L2) 1

Siderophore biosynthesis (L3) 1

Stearate biosynthesis (L3) 1

Thiamine biosynthesis 2

Ubiquinol biosynthesis 4

Unsaturated Fatty acid biosynthesis 2

Degradation/Utilization/Assimilation

Aldehyde degradation (L2) 1

Amine and Polyamine degradation (L2) 1

Autotrophic CO2 fixation 1

Chloroaromatic compound degradation 1

Denitrification 1

Galactose degradation 1

L-arabinose degradation 1

Nitrogen compound metabolism (L3) 1

Other 1

Purine nucleotide degradation (L3) 1

Sugar degradation (L3) 1

Sugar acid degradation (L3) 1

Toluene degradation (L3) 2

(Continues next page)


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Figure S3.4 PCoA plot computed using Bray-Curtis distances for water, skin and gills microbiota of the seabass Dicentrarchus labrax (A) and the seabream Sparus aurata (B) (n = 60 per species x age group for tissues; n = 10 per species x age group for water). Each dot represents a microbiome sample and is coloured by tissue/origin (skin, gill and water).

Table S3.3 (Continued)

Seabass Seabream

Skin Gill Skin Gill

EJ LJ MA EJ LJ MA J MA J MA

Generation of Precursor Metabolites and Energy

Aspartate superpathway (L2) 1

Fermentation of pyruvate (L3) 1 1

Fermentation to lactate 1

Glycolysis (L2) 2

Other 1 1

TCA cycle (L2) 2

Macromolecule Modification

Nucleic Acid Processing 1

Total 0 4 1 4 3 20 8 14 1 4


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CHAPTER 4

Longitudinal sampling of external mucosae in

farmed European seabass reveals the impact of

water temperature on bacterial dynamics

Daniela Rosado, Raquel Xavier, Jo Cable, Ricardo Severino, Pedro

Tarroso, Marcos Pérez-Losada

2021. ISME Communications. https://doi.org/10.1038/s43705-021-00019-x.

4.1 Abstract Fish microbiota are intrinsically linked to health and fitness, but they are highly variable

and influenced by both biotic and abiotic factors. Water temperature particularly limits bacterial

adhesion and growth, impacting microbial diversity and bacterial infections on the skin and

gills. Aquaculture is heavily affected by infectious diseases, especially in warmer months, and

industry practices often promote stress and microbial dysbiosis, leading to an increased

abundance of potentially pathogenic bacteria. In this regard, fish mucosa health is extremely

important because it provides a primary barrier against pathogens. We used 16 rRNA V4

metataxonomics to characterize the skin and gill microbiota of the European seabass,

Dicentrarchus labrax, and the surrounding water over 12 months, assessing the impact of

water temperature on microbial diversity and function. We show that the microbiota of external

mucosae are highly dynamic with consistent longitudinal trends in taxon diversity. Several

potentially pathogenic genera (Aliivibrio, Photobacterium, Pseudomonas and Vibrio) were

highly abundant, showing complex interactions with other bacterial genera, some of which with

recognized probiotic activity, and were also significantly impacted by changes in temperature.

The surrounding water temperature influenced fish microbial composition, structure and

function over time (days and months). Additionally, dysbiosis was more frequent in warmer

months and during transitions between cold/warm months. We also detected a strong

seasonal effect in the fish microbiota, which is likely to result from the compound action of

several unmeasured environmental factors (e.g., pH, nutrient availability) beyond temperature.

Our results highlight the importance of performing longitudinal studies to assess the impact of

environmental factors on fish microbiotas.


126

Keywords: aquaculture, microbiome, Dicentrarchus labrax, dysbiosis, fish pathogen

4.2 Introduction Cumulative evidence shows that the diversity of commensal microbiota mirror fish

health and that microbial diversity can improve host fitness (Kelly and Salinas, 2017; Legrand

et al., 2018; Zhang et al., 2018). Fish microbial composition and structure are driven by host

related factors, including host taxonomy and ontogeny (Arias et al., 2019; Rosado et al., 2021),

but also physio-chemical properties of the water (e.g., temperature, pH, oxygen and nutrient

concentrations, Duarte et al., 2019; Kokou et al., 2018; Martins et al., 2018; Minich et al.,

2020), which can be highly dynamic (Yukgehnaish et al., 2020). Consequently, the microbiota

of fish external mucosa, i.e., skin and gills, can be highly variable (Chiarello et al., 2015). Water

temperature, in particular, can be pivotal as it can prompt modifications in the composition of

key components of the fish skin, such as mucins and mucosal immunoglobulins, that influence

the microbiota by limiting bacterial adhesion and growth (Chen et al., 2008; Gomez et al.,

2013; Legrand et al., 2020; Solem and Stenvik, 2006). Additionally, bacteria have optimal

temperature ranges that maximize their growth (Corkrey et al., 2012), thereby conferring some

taxa with a competitive advantage and creating colonization opportunities for other taxa during

temperature shifts (Mouquet et al., 2005). Several microbiome studies have reported direct or

indirect temperature-related changes in fish bacterial diversity of the skin, gill or gut in the

puffer fish (Sugita et al., 1989), brown trout (Vasemägi et al., 2017), tilapia (Kokou et al., 2018),

yellowtail kingfish (Horlick et al., 2020), chub mackerel (Minich et al., 2020) and other teleost

species (Krotman et al., 2020). Importantly, differences in host’s tolerance to temperature

seem to be related to microbial composition and their resilience to temperature changes

(Kokou et al., 2018). Such studies were either based on experimental setups to test the effects

of specific temperatures (22° and 26°C, Horlick et al., 2020; 12° and 24°C, Kokou et al., 2018;

10°, 20° and 29°C, Sugita et al., 1989) or on sampling single time points at different localities

(Krotman et al., 2020; Vasemägi et al., 2017); the only exception being Minich et al. (2020)

who conducted a year long study of the chub mackerel microbiota. Hence, the effects on fish

microbiota of natural fluctuations in sea water temperature are still largely understudied,

although there are increasing reports of a positive correlation between temperature and the

abundance of potentially pathogenic genera (e.g., Vibrio and Flavobacterium, Sugita et al.,

1989; and Photobacterium, Horlick et al., 2020; Minich et al., 2020). Indeed, several bacterial

infections in fish have seasonal distributions with higher incidence in warmer months (e.g.,

Bellos et al., 2015; Habiba et al., 2015; Schade et al., 2016). This pattern is particularly notable

in farmed fish (e.g., Baker-Austin et al., 2013; Eissa et al., 2018; Kayansamruaj et al., 2014;


127

Matanza and Osorio, 2018), where the abundance of pathogenic taxa is higher compared to

their wild host counterparts (Tarnecki et al., 2019).

Aquaculture is the fastest growing food-production industry; however, its sustainability

is heavily affected by infectious diseases (Pridgeon and Klesius, 2012). Fish farming practices

are known to promote microbial dysbiosis, which often involves an increase in the abundance

of potentially pathogenic bacteria and a parallel decrease in microbial diversity (e.g., Boutin et

al., 2013). This is critical in fish farms given the predicted increase of diseases due to the

ongoing rise of sea surface temperatures (Baker-Austin et al., 2018, 2017). In this regard, fish

mucosa health is of extreme importance, particularly in the case of the skin and gills, since

both act as primary physical and chemical barriers against pathogens (Beck and Peatman,

2015). Specifically, mucosal microbiota can have an important role in controlling pathogen

abundances either through direct competition or antibiotic production (Dash et al., 2018;

Trivedi, 2012). Indeed, putative bacterial pathogens will integrate within the microbiota of

healthy/asymptomatic fish, where their abundance is controlled by harmless commensal

bacteria (e.g., Califano et al., 2017; Rosado et al., 2019).

The European seabass (Dicentrarchus labrax) is one of the most profitable species

farmed in the Mediterranean region, including southern Portugal (FAO, 2016). Being a

eurythermic (from 5-28°C) and euryhaline (from 3-35%) species, it is traditionally farmed in

outdoor ponds located in protected areas, such as estuaries and coastal lagoons, or in open-

water sea cages (FAO, 2016). As such, farmed fish are subjected to natural fluctuations of

water physio-chemical properties. Among the many diseases reported in European seabass

fish farms, the most frequent have bacterial etiological agents, with vibriosis (caused by Vibrio

spp.) being the most prevalent, followed by photobacteriousis (Photobacterium damselae) and

Tenacibaculosis (Tenacibaculum maritimum) (see Bellos et al., 2015; Muniesa et al., 2020).

Temperature and seasonality are considered the main epizootiological risk factors for severe

disease caused by these pathogens (Bellos et al., 2015).

Here we used 16S rRNA high-throughput sequencing to characterize the composition

and structure of the skin and gill microbiomes of the farmed European seabass and its

surrounding waters over 12 months. We then assessed the impact of water temperature

oscillations measured during different time frames (days, weeks and months prior to sampling)

on microbial diversity and function, as well as the dynamics and effects of potentially

pathogenic bacterial genera in the microbial community.


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4.3.1 Experimental design, sampling and processing European seabass specimens were sampled between February 2017 and January

2018 from a single exterior pond of a fish farm operating in a semi-intensive regime and

located in the Alvor Estuary (Portimão, Portugal). Due to the impossibility of sampling the

same individuals throughout time, a random subset of 5 fish were sampled every fortnight,

totaling 120 fish and 24 sampling time points. Fish were caught using a fishing rod and skin

and gill mucous samples taken immediately using tubed sterile dry swabs (Medical Wire &

Equipment, UK). Water samples (1 L) were concomitantly collected and filtered through 0.2

µm filters, except through December and once in February, due to technical constraints. Fish

diet composition remained stable throughout sampling (SKRETTING/SORGAL), although

pellet size increased from 4 to 6 mm. All sampled fish were considered healthy during the

sampling period based on the lack of visible disease symptoms and mortality fraction recorded

at the sampling pond (i.e., 105 fish, 29% of total density, died in May 2017 from unknown

causes). Fish were not vaccinated against bacterial diseases and no antibiotics were

administered during the sampling period. Water temperature (°C) was measured every 10

seconds in situ throughout the duration of the experiment using a probe placed in the pond at

20 cm deep. The mean daily temperatures ranged between 13.3°C in January (the coldest

month) and 25.1°C in June (the warmest month), with an annual mean temperature of 18.6 ±

2.8°C (Figure S4.1).

Total DNA from 261 samples (120 skin, 120 gill and 21 water filters) was extracted

using the PowerSoil DNA Isolation Kit (QIAGEN, Netherlands). DNA extractions were shipped

on dry ice to the University of Michigan Medical School (USA) for amplification of the V4 hyper-

variable region of the 16S rRNA gene (~250 bp) according to the protocol of Kozich et al.

(2013). All samples were sequenced in a single run on the Illumina MiSeq sequencing

platform.

4.3.2 Data processing and statistical analysis Raw FASTQ files were denoised using the DADA2 pipeline in R vs. 4.0.2 (Callahan et

al., 2016). Microbial composition (alpha-diversity) was calculated using Shannon and Faith’s

phylogenetic diversity (PD) as implemented in the R package phyloseq (McMurdie and

Holmes, 2013). Microbial structure (beta-diversity) was estimated using phylogenetic Unifrac

(unweighted and weighted) distances. Variations in microbial composition and microbial

structure between sampling dates and months were assessed using the Kruskal-Wallis

(Kruskal and Wallis, 1952) and PERMANOVA (Anderson, 2017) tests, as implemented in the

adonis function of the vegan R package (Oksanen et al., 2008). Pairwise comparisons were


129

done between all sampling dates and months using the Wilcox tests with Benjamini-Hochberg

correction (stats package; R Team, 2012) and pairwise adonis (Martinez Arbizu, 2017). No

significant differences were found in bacterial diversity between dates within months (results

not shown). Additionally, dissimilarity in microbial structure between samples was visualized

using Principal Coordinates Analysis (PCoA).

We used the software FEAST (Shenhav et al., 2019) to estimate microbial monthly

exchange between skin, gills and water. This software implements an expectation-

maximization algorithm (Moon, 1996) which infers the fraction of the microbiota (i.e., the sink)

originating from different available sources (see Supplementary File S4.1 for more details).

Genera containing important bacterial pathogens affecting aquaculture were identified

according to the list compiled by Haenen (2017). Several of these potentially pathogenic (PP)

genera showed peaks of abundance throughout the year: Aliivibrio, Photobacterium,

Pseudomonas and Vibrio. Temporal Insights into Microbial Ecology (TIME; Baksi et al., 2018)

was used to visualize the temporal dynamics and predicted microbial competition involving

the most abundant PP genera (≥5%, hereon termed PP genera), as well as to assess their

influence in the community structure throughout months (see Supplementary File S4.1 for

more details).

The impact of temperature and season (coded as a categorical variable) on microbial

alpha-diversity was assessed using linear models (lm), while their impact on microbial beta-

diversity was assessed using permutational multivariate analysis of variance (PERMANOVA)

(see Supplementary File S4.1 for more details). Several temperature variables were derived

from the mean (mn) and standard deviation (SD) of the temperatures measured at different

time frames preceding sampling dates. Briefly, we used temperatures from the 2 and 7 days

before each sampling date (T2_mn / T2_SD and T7_mn / T7_SD). We averaged diversity

values per month since no significant differences within months were found and, consequently,

decreased the impact of serial correlation in the analyses. We assessed the influence of

monthly temperature using samples grouped by month and temperature variables derived

from the periods preceding each of the two sampling points in a given month (T4_mn / T4_SD

and T14_mn / T14_SD). Additionally, we tested the effects of monthly mean temperature and

respective standard deviation (T_month_mn / T_month_SD) and of a broader categorical

time-frame created by dividing the year into cold or warm months based on the daily annual

average temperature (18.6 °C, see Supplementary File S4.1 for more details).

We used generalized least squares models (gls) from the nlme R package (Pinheiro

et al., 2021) to estimate the relation between bacterial diversity and the abundance of PP

genera with temperature. For this test, matrices containing pairwise distances for diversity

metrics or abundance of PP genera were correlated with corresponding matrices of

temperature differences (see Supplementary File S4.1 for more details). Due to the high inter-


130

individual variability of alpha-diversity estimates observed within some dates and months, and

to test whether temperature could prompt such variability, the impact of temperature on the

standard deviations of Shannon (Shannon SD) and Faith's PD (PD SD) indices was also

assessed for skin and gill microbiota using the same set of tests (lm and PERMANOVA, as

well as gls for pairwise matrices).

The metagenomic Phylogenetic Investigation of Communities by Reconstruction of

Unobserved States software (PICRUSt2) embedded in QIIME2 (Douglas et al., 2019) was

used to predict the putative microbial metabolism, applying a weighted nearest taxon index

(NSTI) cutoff of 0.03. Predicted metagenomes were collapsed using the Kyoto Encyclopaedia

of Genes and Genomes (KEGG) Pathway metadata (Kanehisa et al., 2019). We identified

differentially abundant predicted metabolic pathways between cold/warm months in the skin

and gill microbiota using linear discriminant analysis (LDA) in LEfSe (Segata et al., 2011). To

this end, we used a P-value cut-off of 0.05 and a LDA effect size cut-off of 2 (Segata et al.,

2011).

A more detailed description of the Materials and Methods above is provided in

Supplementary File S4.1.

4.4 Results

4.4.1 Bacterial composition and temporal dynamics of the microbiota Six bacterial phyla and 28 genera were categorized as the most abundant taxa (≥5%)

in European seabass skin and gill, and associated water, microbiota throughout the 12 month

study period (Figure S4.2, Table S4.1). Bacteroidota and Proteobacteria were consistently

abundant across all months in the fish microbiota and water, and Verrucomicrobiota was highly

abundant in the water for all months. Glaciecola, NS3a marine group, Pseudoalteromonas,

Psychrobacter, Rubritalea, and an unidentified genus belonging to the Flavobacteriaceae

family were the most abundant genera across all studied microbiota. However, none of the 28

genera were consistently highly abundant across all months in the fish mucosa or water.

Nevertheless, there were a total of 73 core ASVs present in all three studied microbiota

throughout the studied months and from these 44, 42 and 46 ASVs were present in the

microbiota of the skin, gill and water, respectively (Figure 4.1, Table S4.2). The three

microbiota communities analyzed shared 25% of the core ASVs, while skin and gill shared

37%, skin and water shared 43%, and gill and water shared 26% (Figure 4.1). Out of the 73

monthly core ASVs, three belonged to potentially pathogenic (PP) genera: a Photobacterium

ASV in the skin, and two Vibrio ASVs, one present in the skin, and the other in the water (Table

S4.2).


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Monthly estimates of microbial alpha-diversity of the skin and gills followed the same

trends (increase/decrease) throughout the sampling period, showing significant monthly

fluctuations (Kruskal-Wallis, P ≤ 0.05; Table S4.3). Pairwise comparisons showed significant

shifts in alpha-diversity mainly between warm months and between cold to warm or warm to

cold (hereafter termed cold/warm) transitions. Chronologically, significant decreases of

Shannon and Faith’s PD indices in both fish mucosae occurred between March and April

(coinciding with the transition from cold to warm months); this was followed by a significant

increase of the Shannon index between April and May in the skin microbiota and a significant

increase of the Faith PD index between May and June in the gill microbiota; between June

and July both alpha-diversity indices significantly decreased in both mucosae (P ≤ 0.05, Table

S4.3). Additionally, between October and November, significant decreases occurred in the

Shannon index for the skin microbiota and in Faith’s PD for the gill microbiota, coinciding with

the transition between warm to cold months (P ≤ 0.05, Table S4.3). Finally, a significant

increase in the Shannon diversity occurred between November and December in the gill

microbiota (P ≤ 0.05, Table S4.3). It is noteworthy that aforementioned significant decreases

in alpha-diversity occurred in parallel with an increase in the abundance of some PP genera

in the skin in April and in both tissues in November (Figure 4.2).

Figure 4.1 Venn diagram showing the number and percentage of shared core ASVs between the skin and gill

microbiota (N=10 x 12 months x tissue) of the seabass Dicentrarchus labrax and the surrounding water (N=2 x 11

months) throughout the 12 sampling months.

Although microbial structure showed significant differences between months

(PERMANOVA, P ≤ 0.05; Table S4.3), pairwise comparisons showed no significant

differences between consecutive months (corrected P ≥ 0.05, Table S4.3). Moreover, there


132

was no visible clustering of samples per month or per cold/warm months when dissimilarities

were plotted in the PCoAs (Figure 4.3).

Figure 4.2 Alluvial plots of the monthly prevalence of the most abundant potentially pathogenic (PP) genera (A); and alluvial plots of the monthly prevalence of each PP genus and other less abundant PP genera they interact with according to TIME results (B). Results are depicted for the skin and gill microbiota (N=10 x 12 months) of the seabass Dicentrarchus labrax from February 2017 (left) to January 2018 (right). Blue and red bars represent cold and warm months, respectively. Dashed line represents the 5% cutoff, above which genera were considered highly abundant.


133

Figure 4.3 Temperature models (A), and beta-diversity (B) and alpha-diversity (C) estimates. The temperature models were calculated for each sampling date and month. Beta-diversity is represented by PCoA plots computed using Unifrac weighted distance for the skin and gill microbiota (N=10 x 12 months x tissue) of the seabass Dicentrarchus labrax and the surrounding water (N=2 x 11 months) throughout the 12 sampling months. Each dot represents a microbiota sample. Alpha-diversity estimates are represented by boxplots depicting mean values and standard deviations of Shannon and Faith’s PD estimates for the skin and gill microbiota (N=10 x 12 months x tissue) of the seabass Dicentrarchus labrax and the surrounding water (N=2 x 11 months) from February 2017 (left) to January 2018 (right). T_month_mn = mean monthly temperature; T_month_SD = standard deviation of T_month_mn; T14_mn = mean temperature from the 7 days previous to both sampling dates in a given month; T14_SD = standard deviation of T14_mn; T4_mn = mean temperature from the 2 days previous to both sampling dates in a given month; T4_SD = standard deviation of T4_mn; T7_mn = mean temperature from the 7 days previous to a sampling date; T7_SD = standard deviation of T7_mn; T2_mn = mean temperature from the 2 days previous to a sampling date; T2_SD = standard deviation of T2_mn.

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The results from the fast expectation-maximization for microbial source tracking

(FEAST) showed that the dynamics of potential bacterial transferal between the different

available source microbiota (i.e., skin, gill and water) and the tested sink fish mucosae (skin

or gill) varied throughout the months (Figure 4.4). Overall, the skin microbiota was more

variable and dynamic, maintaining only a small fraction of their microbial diversity between

months (mean 13±18%) (Figure 4.4). Unknown sources were the major contributors (mean

51±19%) for the skin microbial community at each given month, followed by the gill microbiota

(21±9%) and the water microbiota (17±15%) (Figure 4.4). Overall, the gill microbiota was more

stable throughout the year than the skin microbiota, with a larger fraction of its bacterial

diversity (36±28%) maintained between months. Similar to the skin microbiota, most of its

composition was attributed to unknown sources (mean 40±23%), whereas the skin microbiota

(mean 19±19%) and water microbiota (mean 8±15%) had a lower contribution to the gill

microbiota (Figure 4.4).

Figure 4.4 Barplots depicting the percentage of microbiota contribution from each source to the microbiota of the skin and gill of the seabass Dicentrarchus labrax per month (February 2017 to January 2018).

4.4.2 Dynamics of potentially pathogenic (PP) genera Several PP genera were highly abundant (i.e., ≥5% of total sequences) in both skin

and gills throughout the year, with the highest incidence of PP genera in the skin microbiota

(Figure 4.2, Table S4.1). Specifically, Aliivibrio, Photobacterium, Pseudomonas and Vibrio

were highly abundant in the skin; while Pseudomonas and Vibrio were highly abundant in the

gill microbiota.

The abundance of the PP genera was correlated with the abundance of several other

genera, with the exception of Pseudomonas in the gill microbiota (Table S4.4). In general,

each of the PP genera detected was correlated with a higher number of other genera in the

skin than in the gill microbiota (Table S4.4). In the skin microbiota, there were mostly negative

correlations between the abundance of PP and other genera, except for Pseudomonas, where

correlations were mostly positive. In the gills, correlations between taxa abundances were

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mostly positive (Table S4.4). The abundances of all PP genera in both tissues, except for

Vibrio in the gill, were affected by other potentially pathogenic genera (Table S4.4). These

interactions consisted mostly of negative correlations, and only the Pseudomonas-Francisella

correlation was positive in both tissues (Pearson Correlation, Table S4.4). Interestingly, during

warm months, peaks in the abundance of PP genera in the skin microbiota did not occur

concomitantly, suggesting higher competition between PP genera in these months (Figure

4.2). Furthermore, in the skin microbiota, both Halobacteriovorax or Sphingomonas, which

contain species with probiotic properties (Chaudhary and Qazi, 2014; Mohseni et al., 2018),

showed a negative interaction with all but one (Pseudomonas) PP genera. In the gill

microbiota, interactions with genera with known probiotic properties were not detected (Table

S4.4).

4.4.3 Effect of water temperature on the fish microbiota There was a significant impact of all the tested temperature variables on at least one

of the alpha-diversity metrics (lm, p ≤ 0.05, Table 4.1), with the exception of T2_SD in the skin

and T14_SD and T7_SD in the gill microbiota (Table 4.1). The effect of water temperature was

more noticeable in the gill microbiota compared to the skin, where the influence of water

temperature was more significant in the standard deviations of the Shannon and PD metrics

than on their mean values (Table 4.1). The influence of all temperature variables was also

denoted in the UniFrac distances, with the exception of T14_SD in the skin and T4_SD in the

gill (Table 4.1). Additionally, seasonality significantly impacted the skin and gill microbiota, as

seen in all models except for the gill PD SD metric when including T7 and T14 variables (p ≤

0.05, Table 4.1). Interestingly, the phylogenetic diversity of the skin microbiota was only

influenced by cold/warm months (Table 4.1).

Table 4.1 Significance of the temperature models and season on the diversity indices in the skin and gill microbiota of the seabass Dicentrarchus labrax. For each test we report F (alpha-diversity) or R2 (beta-diversity) statistic (p). Significant associations are indicated in bold. Models were built using: 1) samples grouped by month; and 2) biweekly samples. Three different sets of models were built used using temperature measured at different time scales: days (T2_mn and T4_mn: mean temperature from the 2 previous days to sampling; T2_SD and T4_SD: standard deviations of mean temperatures observed in the 2 previous days to sampling); weeks (T7_mean, T14_mean and T_month_mean: etc etc); and months (cold/warm: variable with two levels built by grouping months according to the annual mean temperature observed (18.5º). Season was a categorical variable with four levels (spring, summer, autumn, winter) and used as a fixed effect in all models to account for unmeasured environmental changes that may vary seasonally.

Shannon Shannon SD PD PD SD Unifrac

We. Unifrac Un.

Skin Samples

grouped by Month

T4_mn 2.6 (0.1) 14.1 (0.0003) 0.4 (0.5) 23.7 (4-6) 0.1 (9-5) 0.04 (9-5)

T4_SD 0.3 (0.6) 7.2

(0.01) 0.01

(0.9) 15.0

(0.0002) 0.02

(0.1) 0.01

(0.04)

Season 6.6 (0.004) 6.3 (0.001)

12.4 (5-7)

5.9 (0.001)

0.1 (9-5)

0.1 (9-5)


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Table 4.1 (continued)

T14_mn 2.4 (0.1) 22.4 (6-6)

0.6

(0.5) 24.8 (2-6)

0.1 (9-5)

0.04 (9-5)

T14_SD 0.03 (0.9) 18.2 (4-5)

0.01

(0.9)

6.7 (0.01)

0.01

(0.2)

0.01

(0.05)

Season 6.4 (0.001) 7.3 (0.0002)

10.4 (4-6)

4.3 (0.01)

0.1 (9-5)

0.1 (9-5)

T_month_mn 1.4 (0.2)

9.2 (0.003)

0.6

(0.5) 18.7 (3-5)

0.1 (9-5)

0.04 (9-5)

T_month_SD 2.2 (0.1)

23.0 (5-6)

0.6

(0.5) 14.8

(0.0002) 0.1 (2-4)

0.01 (0.01)

Season 9.3 (2-5) 6.7 (0.0003)

13.9 (9-8)

6.6 (0.0004)

0.1 (9-5)

0.1 (9-5)

Cold/Warm 4.4 (0.04) 3.9 (0.04)

4.1 (0.04)

1.1

(0.3) 0.1 (9-5)

0.04 (9-5)

Season 4.9 (0.003) 25.7 (9-13)

3.8 (0.01)

5.9 (0.001)

0.1 (0.003)

0.1 (9-5)

Biweekly data

T2_mn 5.7 (0.02) 6.4 (0.01)

3.8

(0.1) 0.04 (0.8) 0.1 (9-5)

0.04 (9-5)

T2_SD 1.5 (0.2) 1.9

(0.2) 0.8

(0.4) 1.4

(0.2) 0.02

(0.03) 0.01

(0.1)

Season 1.5 (0.03)

8.2 (6-5)

3.5 (0.02)

5.0 (0.003)

0.1 (9-5)

0.1 (9-5)

T7_mn 4.3 (0.04)

6.8 (0.01)

3.4

(0.1) 0.01

(0.9)

0.1 (9-5)

0.1 (9-5)

T7_SD 2.2

(0.1) 10.1

(0.002) 1.7

(0.2) 2.5

(0.1) 0.02

(0.02) 0.01

(0.02)

Season 2.9 (0.04)

5.3 (0.002)

3.2 (0.03)

5.4 (0.002)

0.1 (9-5)

0.1 (9-5)

Gill

Samples grouped by

Month

T4_mn 7.1 (0.001)

59.7 (5-12)

8.9 (0.003)

28.2 (5-7)

0.1 (9-5)

0.03 (9-5)

T4_SD 0.3

(0.6) 28.4 (5-7)

0.2

(0.7) 24.6 (3-6)

0.01

(0.2)

0.01

(0.1)

Season 10.5 (4-6)

13.1 (2-7)

14.3 (6-8)

4.3 (0.01)

0.1 (9-5)

0.1 (9-5)

T14_mn 6.8 (0.01)

75.1 (4-14)

8.4 (0.01)

35.1 (3-8)

0.1 (9-5)

0.04 (9-5)

T14_SD 0.1

(0.7) 2.8

(0.1) 1.9

(0.2)

2.3

(0.1)

0.02 (0.003)

0.01

(0.1)

Season 8.9 (2-5)

13.8 (9-8)

11.0 (2-6)

0.8

(0.5)

0.1 (0.0002)

0.1 (9-5)

T_month_mn

7.4 (0.01)

70.6 (1-13)

9.5 (0.003)

36.6 (2-8)

0.1 (9-5)

0.03 (9-5)

T_month_SD

1.9

(0.2) 55.8 (2-11)

0.6

(0.4) 75.1 (4-14)

0.02 (0.003)

0.01 (0.01)

Season 10.6 (3-6)

10.3 (5-6)

14.5 (5-8)

9.2 (2-5)

0.1 (9-5)

0.1 (9-5)

Cold/Warm 11.8

(0.001) 58.8 (6-12)

12.5 (0.001)

7.5 (0.01)

0.1 (9-5)

0.04 (9-5)

Season 5.9 (0.001)

27.9 (1-13)

7.2 (0.0002)

3.3 (0.02)

0.1 (4-4)

0.1 (9-5)

Biweekly data

T2_mn 10.3 (0.002)

26.0 (1-6)

13.1 (0.001)

1.6

(0.2) 0.1 (9-5)

0.03 (9-5)

T2_SD 0.1

(0.7) 16.4 (10-5)

0.3

(0.6) 15.4

(0.0002) 0.01

(0.3)

0.01 (0.01)

Season 5.4 (0.002)

10.3 (5-6)

5.8 (0.001)

2.8 (0.04)

0.1 (0.0002)

0.1 (9-5)

T7_mn 12.9 (0.001)

41.0 (4-9)

15.6 (0.0001)

0.5

(0.5)

0.1 (9-5)

0.04 (9-5)

T7_SD 0.01

(0.9)

2.0

(0.2)

0.01

(0.9)

1.0

(0.3)

0.01

(0.1)

0.01 (0.02)

Season 10.0 (7-6)

24.2 (3-12)

10.5 (4-6)

1.7

(0.2)

0.1 (9-5)

0.1 (9-5)

Changes in both alpha- and beta-diversity of fish microbiota were significantly

correlated with changes in all temperature variables (P ≤ 0.03; Table 4.2). Evidently, as

depicted in Figure 4.3, there was a positive relation between alpha-diversity and T4_SD in

warm months (i.e., both values increased or decreased between months) and negative in cold

months (i.e., there was an inverse relation between alpha-diversity and T4_SD).

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Overall, there was a significant correlation between changes in abundance of the PP

genera in the skin and gill microbiota and changes in at least one of the temperature variables

(P ≤ 0.05; Table 4.2). Variation in the abundance of Pseudomonas was correlated to all

changes in temperature, independently of the considered time window; while changes in the

abundance of Aliivibrio were correlated to changes to the standard deviation of the mean

temperatures (Table 4.2). Variation in the abundance of Photobacterium was correlated with

long-term changes in temperature, while changes in the abundance of Vibrio were correlated

with changes in mean temperatures in the skin and standard deviations in the gill (Table 4.2).

Table 4.2 Test results from gls models indicating the significance of correlation between temperature variables and bacterial diversity, including the abundance of main (>5% of total reads) potentially pathogenic (PP) genera. Correlation structure was estimated based on distance matrices for both dependent and independent variables using the corMLPE R package (Clarke et al., 2002). For each test we report the F statistic and significance (P value with significant correlations indicated in bold). Months were separated into cold and warm based on whether the mean monthly temperature was lower or higher than the daily annual average (i.e., 18.6°C). T_month_mn: mean monthly temperature; T_month_SD: standard deviation of T_month_mn; T14_mn: mean temperature of the 7 days previous to both sampling dates in a month; T14_SD: standard deviation of T14_mn; T4_mn: mean temperature from the 2 days previous to both sampling dates in a given month; T4_SD: standard deviation of T4_mn; T7_mn: mean temperature from the 7 days previous the sampling date; T7_SD: standard deviation of T7_mn; T2_mn: mean temperature from the 2 days prior to the sampling date; T2_SD: standard deviation of T2_mn.

Months Date

Skin Cold/ warm

months

T_month mn

T_month SD T14_mn T14_S

D T4_mn T4_SD T7_mn T7_SD T2_mn T2_SD

Shannon 21 (0.0001)

21 (0.0001)

48.8 (0.0001)

46.6 (0.001)

28 (0.0001)

55 (0.0001)

96 (0.0001)

29 (0.0001)

31 (0.0001)

38 (0.0001)

7 (0.01)

Shannon SD 21 (0.0001)

102 (0.0001)

367 (0.0001)

224 (0.0001)

940 (0.0001)

183 (0.0001)

663 (0.0001)

3 (0.1)

28 (0.0001)

16 (1-4)

21 (0.0001)

PD 32 (0.0001)

25 (0.0001)

38 (0.0001)

45 (0.0001)

14 (2-4)

58 (0.0001)

72 (0.0001)

22 (0.0001)

36 (0.0001)

27 (0.0001)

1.5 (0.2)

PD SD 6 (0.01)

155 (0.0001)

379 (0.0001)

223 (0.0001)

233 (0.001)

224 (0.0001)

427 (0.0001)

3 (0.1)

28 (0.0001)

16 (1-4)

21 (0.0001)

Unifrac weighted 151 (0.0001)

169 (0.0001)

146 (0.0001)

164 (0.0001)

71 (0.0001)

230 (0.0001)

187 (0.0001)

191 (0.0001)

48 (0.0001)

324 (0.0001)

45 (0.0001)

Unifrac unweighted

532 (0.0001)

562 (0.0001)

450 (0.0001)

653 (0.0001)

102 (0.0001)

673 (0.0001)

369 (0.0001)

704 (0.0001)

196 (0.0001)

634 (0.0001)

18 (0.0001)

Aliivibrio 0.6 (0.4)

0.8 (0.4)

5 (0.02)

3 (0.1)

10 (0.002)

1 (0.3)

5 (0.03)

0.9 (0.4)

5 (0.02)

0.9 (0.4)

2 (0.2)

Photobacterium 2

(0.2) 9

(0.003) 4

(0.04) 16

(0.0001) 18

(0.0001) 7

(0.01) 2

(0.2) 2

(0.1) 0.3

(0.6) 0.6

(0.4) 8

(0.01)

Pseudomonas 306 (0.0001)

564 (0.0001)

248 (0.0001)

736 (0.0001)

37 (0.0001)

711 (0.0001)

90 (0.0001)

839 (0.0001)

19 (0.0001)

850 (0.0001)

3 (0.1)

Vibrio 3 (0.1)

1 (0.3)

1 (0.3)

5 (0.03)

0.7 (0.4)

3 (0.1)

0.7 (0.4)

13 (3-4)

5 (0.02)

9 (0.003)

3 (0.1)

Gill

Shannon 87 (0.0001)

61 (0.0001)

93 (0.0001)

91 (0.0001)

33 (0.0001)

111 (0.0001)

146 (0.0001)

81 (0.0001)

17 (0.0001)

106 (0.0001)

2 (0.2)

Shannon SD 483 (0.0001)

908 (0.0001)

1886 (0.0001)

1379 (0.0001)

302 (0.0001)

1136 (0.0001)

2030 (0.0001)

60 (0.0001)

115 (0.0001)

67 (0.0001)

354 (0.0001)

PD 88 (0.0001)

76 (0.0001)

104 (0.0001)

110 (0.0001)

44 (0.0001)

129 (0.0001)

139 (0.0001)

97 (0.0001)

17 (0.0001)

115 (0.0001)

1 (0.3)

PD SD 182 (0.0001)

820 (0.0001)

3347 (0.0001)

1189 (0.0001)

210 (0.0001)

651 (0.0001)

1773 (0.0001)

60 (0.0001)

115 (0.0001)

67 (0.0001)

354 (0.0001)


230 (0.0001)

148 (0.0001)

225 (0.0001)

89 (0.0001)

265 (0.0001)

140 (0.0001)

244 (0.0001)

30 (0.0001)

255 (0.0001)

24 (0.0001)

Unifrac unweighted

624 (0.0001)

658 (0.0001)

498 (0.0001)

736 (0.0001)

103 (0.0001)

787 (0.0001)

397 (0.0001)

709 (0.0001)

134 (0.0001)

651 (0.0001)

28 (0.0001)


446 (0.0001)

172 (0.0001)

461 (0.0001)

498 (0.0001)

554 (0.0001)

43 (0.0001)

720 (0.0001)

15 (2-4)

1014 (0.0001)

5 (0.03)

Vibrio 0.1 (0.7)

0.02 (0.9)

0.5 (0.5)

0.5 (0.5)

3 (0.1)

0.4 (0.5)

1 (0.3)

3 (0.1)

23 (0.0001)

2 (0.2)

8 (0.004)

Water

Shannon 0.1 (0.8)

4 (0.1)

6 (0.02)

20 (0.0001)

0.3 (0.6)

9 (0.003)

0.3 (0.6)

6 (0.02)

0.1 (0.8)

6 (0.01)

0.1 (0.8)

PD 0.6 (0.4)

0.03 (0.9)

1 (0.2)

0.5 (0.5)

0.6 (0.4)

0.04 (0.9)

0.04 (0.9)

0.004 (0.9)

0.0002 (0.9)

0.4 (0.6)

0.6 (0.4)


3 (0.1)

3 (0.1)

4 (0.1)

1 (0.5)

4 (0.1)

1 (0.3)

2 (0.2)

1 (0.3)

2 (0.2)

0.3 (0.6)

Unifrac unweighted

25 (0.0001)

29 (0.0001)

14 (2-4)

45 (0.0001)

1 (0.3)

39 (0.0001)

6 (0.01)

31 (0.0001)

6 (0.01)

35 (0.0001)

0.1 (0.7)

PICRUSt2 predicted 484 and 477 KEGG pathways in the seabass skin and gill

microbiota, respectively. In the skin microbiota, 84 and 80 predicted pathways were

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differentially abundant in warm and cold months, respectively; while 70 and 78 predicted

pathways were differentially abundant in the gill microbiota during warm and cold months,

respectively (Table S4.5). Interestingly, most of the differently abundant potential pathways

were shared between the skin and gill microbiota in cold (51%) and warm (44%) months.

Predicted pathways related to biosynthesis, degradation/utilization/assimilation and

generation of precursor metabolite and energy were enriched in cold and warm months in the

microbiota of both mucosae (Figure 4.5, Table S4.5). Predicted pathways related to

detoxification and superpathways were only enriched in cold months in both mucosae. In the

skin microbiota, there was a decrease in the frequency of predicted pathways related to

biosynthesis and an increase in degradation/utilization/assimilation related pathways from

warm to cold months (76% to 47% and 13% to 43%, respectively) (Figure 4.5, Table S4.5).

The microbiota of both tissues showed a decrease in the frequency of predicted pathways

related to generation of precursor metabolite and energy from warm to cold months (10% to

5% in the skin; 12% to 1% in the gill) (Figure 4.5, Table S4.5).

Figure 4.5 Relative frequency of the differentially enriched potential pathways in the skin and gill microbiota of the seabass Dicentrarchus labrax during cold and warm months grouped by broader (A) or smaller (B) KEGG categories. Circles are coloured according to broader categories.

At a finer scale, the microbiota of both tissues presented a decrease in frequency of

potential pathways related to amino acid biosynthesis, cell structure biosynthesis, nucleoside

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and nucleotide biosynthesis, and TCA cycle from warm to cold months (Figure 4.5, Table

S4.5). In contrast, potential pathways related to fatty acid and lipid biosynthesis and aromatic

compound degradation increased in frequency from warm to cold months in both tissues. In

the microbiota of both tissues, amine and polyamine degradation, C1 compound utilization

and assimilation and pentose phosphate predicted pathways were only enriched in warm

months; whereas secondary metabolite degradation, antibiotic resistance and the super

pathway of histidine, purine, and pyrimidine biosynthesis predicted pathway categories were

only enriched in the cold months (Figure 4.5, Table S4.5).

4.5 Discussion The skin and gill microbiota of the farmed European seabass were highly dynamic,

being significantly affected by water temperature. Although previous studies reported an effect

of water temperature on fish microbiota, they focused on testing the effects of single

temperatures (Horlick et al., 2020; Kokou et al., 2018; Sugita et al., 1989) or established an

indirect link with temperature based on different geographic locations (Krotman et al., 2020;

Vasemägi et al., 2017). To the best of our knowledge, the only other longitudinal study which

specifically tested the effects of natural fluctuations of sea water temperatures on fish

microbiota was Minich et al.'s (2020) on chub mackerel. In that study, although temperature

significantly affected the skin and gill microbial composition on each sampling date, an effect

on beta-diversity was less clear (Minich et al., 2020). Our results show that mean water

temperature and associated standard deviations occurring at different time scales,

significantly affected the composition, structure and function of the skin and gill microbiota of

the European seabass Dicentrarchus labrax. Additionally, temperature seems to be a factor

triggering dysbiosis.

4.5.1 Temporal dynamics of the microbiota The skin and gill microbiota of farmed European seabass varied significantly over a

12 month period, particularly at lower taxonomic levels (most abundant genera and core

ASVs), while maintaining phylogenetic relatedness at higher taxonomic levels (most abundant

phyla). Similar long-term patterns have been reported in the microbiota of other teleost

species, including the Atlantic charr (gut; Element et al., 2020) and tench (gut; Dulski et al.,

2020), gulf killifish (skin; Larsen et al., 2015) and the largemouth bass, bluegill and spotted

gar (skin and gut; Arias et al., 2019).

The skin and gill microbiomes of healthy European seabass can be very distinct

(Rosado et al., 2019a), responding asymmetrically to disease (photobacteriousis) and with

different recovery trajectories after antibiotic treatment (Rosado et al., 2019b). Here, despite

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such intrinsic differences, the overall bacterial composition of both mucosae followed the same

trends, i.e., concomitantly increasing or decreasing in diversity throughout the year. This

suggests they are likely modulated by the same host and/or environmental factors.

Furthermore, our analysis showed that the dynamics of the skin and gill microbiota are

complex, with only a small portion of the microbiota remaining stable over time (ca. 13% in the

skin and ca. 36% in the gill on average over the 12 months). Skin and particularly the gills

seem to be highly selective habitats, and bacterial variation did not seem to arise due to

substantial outsourcing of bacteria from the water column. This result is in line with previous

studies where water microbiota was shown to have a minor influence on the mucosal

microbiota of adult fish (Bledsoe et al., 2016; Chiarello et al., 2015; Legrand et al., 2018;

Llewellyn et al., 2016; Pratte et al., 2018; Rosado et al., 2021). Instead, bacterial recruitment

from the neighbouring environments seems to be more important. These results suggest that

there is a continuous microbial exchange of communities in the skin and gill, most likely due

to constant abiotic changes in the water environment (e.g., temperature) prompting complex

microbial dynamics.

The high frequency of dysbiotic events, coupled with the dynamics observed in the

abundances of potentially pathogenic (PP) genera in both fish mucosae, seems to be in line

with what has been reported in farmed fish, where stress is often associated with a decrease

in microbial diversity and increased susceptibility to secondary bacterial opportunists (Kelly

and Salinas, 2017; Legrand et al., 2018; Silva et al., 2020). Furthermore, the negative

correlation observed between abundance of PP genera and genera with probiotic properties

confirms direct competition between taxa has an important role in controlling the abundance

of PP genera and potential disease outcome (Legrand et al., 2020). Specifically,

Halobacteriovorax and Sphingomonas had a negative impact on the abundance of Aliivibrio

and Vibrio or Photobacterium, respectively, in the skin microbiota. The probiotic properties of

Halobacteriovorax were previously demonstrated against Vibrio parahaemolyticus in mussels

(Ottaviani et al., 2020). Additionally, Sphingomonas species were described as having

antagonistic properties against the fish pathogen Vibrio anguillarum in roho fingerlings

(Chaudhary and Qazi, 2014).

4.5.2 Water temperature effects in the diversity of fish microbiota Here we show that seasonal patterns, which influence physio-chemical properties of

water and are highly influenced by temperature, contribute significantly to the high variability

of farmed European seabass microbiota composition and structure over a year. Seasonal

changes in the composition and structure of fish microbiota have been reported for the skin

and gut (e.g., Arias et al., 2019; Dulski et al., 2020). However, such studies are commonly

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based on sparsely sampled timepoints (e.g., May, August and November, Arias et al., 2019;

Autumn 2017 and Summer 2018, Dulski et al., 2020). Additionally, the reported effect of

season in the microbiomes of wild fish is mostly linked to differences in prey availability (e.g.,

Ringø et al., 2016) or water properties like chlorophyll a, salinity and temperature (e.g., Minich

et al., 2020). Understanding seasonal variations in fish microbiota can be very important in an

aquaculture context, for example to understand disease dynamics and outbreaks. However,

the lack of longitudinal studies is still hampering such insights.

The bacterial composition and structure of farmed European seabass microbiota were

significantly affected by changes in temperature occurring over both short (two days) and

longer (several months) time frames. This is not surprising given the high sensitivity of bacteria

to temperature (Corkrey et al., 2012). Microbial shifts occurring at small temporal scales, from

just a few hours to days are not unprecedented for fish. Such events occurred, for example,

after cold temperature shock (tropical tilapia, Kokou et al., 2018), acclimation (e.g., to the wild

in the common snook, Tarnecki et al., 2019; from freshwater to seawater in the Atlantic

salmon, Dehler et al., 2017); chemical exposure (e.g., in zebrafish, Gaulke et al., 2016;

channel catfish, Mohammed and Arias, 2015) or parasitism (e.g., in Atlantic salmon, Llewellyn

et al., 2017). In the present work, temperature was a significant factor influencing high variation

in bacterial composition within samples. Differences in bacterial diversity measures between

individuals were significantly correlated with the difference in temperatures experienced by

individuals across short and long time frames. In light of these results, we suggest that

changes in water temperature played a major role in the high temporal variability observed in

the microbiota of several teleost species (e.g., Larsen et al., 2015; Pratte et al., 2018; Uren

Webster et al., 2018), including the European seabass (Rosado et al., 2021, 2019a).

Another emerging trend in our analysis was that the microbiota of the European seabass

was more dynamic during the warmer months and more prone to dysbiosis during cold/warm

transition periods. Microbial diversity significantly decreased on three occasions during the

sampled year, signaling bacterial imbalance. During cold/warm transitions, decreases in

diversity were accompanied by an increase in the abundance of PP genera (i.e., Alivibrio,

Photobacterium or Vibrio), highlighting the opportunistic nature of these bacteria. Seasonal

occurrence of Vibriosis and Photobacteriosis in fish was previously observed (e.g., Bellos et

al., 2015; Habiba et al., 2015), with higher abundances of Vibrio and Photobacterium reported

during warmer months, even if fish remained asymptomatic (e.g., Horlick et al., 2020; Minich

et al., 2020; Sugita et al., 1989). On the other hand, higher incidence of severe outbreaks of

both Aliivibrio (e.g., Guijarro et al., 2015; Khider et al., 2018) and Pseudomonas (e.g., Huang

et al., 2019; Tao et al., 2016) species usually occur at lower temperatures, explaining the high

abundances of these PP genera in cold months. Temperature ranges observed here during

cold/warm transition periods (15-20°C) are known to impact European sea bass homeostasis,

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reducing adaptive immune response and increasing susceptibility to disease (17-23 °C;

Varsamos et al., 2006). Moreover, the adaptive immune response of European seabass is

intrinsically linked to temperature, being delayed at low temperatures (≤18°C) and stimulated

at higher temperatures (≥24°C) (Cecchini and Saroglia, 2002; Varsamos et al., 2006). It is

possible that the cumulative effect of temperature changes occurring at cold/warm transition

periods and changes in host homeostasis could have prompted dysbiosis. Nevertheless,

dysbiotic events did not lead to visible signs of disease. Aspects of fish immunity are also

correlated with age; for example, in rainbow trout, resistance to bacterial infections increases

with host age (Castro et al., 2015). Since the European seabass included in our study were

adults (26 to 32 months), age might explain their apparent resilience to PP genera upgrowth.

4.5.3 Water temperature effects in the predicted microbiota function Changes in microbial composition and structure between cold/warm periods were

accompanied by significant changes in the predicted metabolic function of bacteria. It is

important to note that these results should be interpreted with caution since PICRUSt2

analysis is biased towards human health-related microorganisms (Choi et al., 2017).

Nevertheless, differentially enriched predicted metabolic pathways occurring in the skin and

gill microbiota of the European seabass during warm periods were mostly related to

biosynthesis processes, indicating high energetic expenditure. In particular, predicted

pathways related to amino acid, and nucleoside and nucleotide biosynthesis are considered

essential for bacterial growth (Samant et al., 2008), and were strongly enriched in both

mucosae during this period. Similarly, more predicted pathways related to the generation of

precursor metabolites and energy were enriched in both fish mucosae during warm months.

During the cold period there was a shift, particularly in the skin, where biosynthesis and

metabolic energy generation were reduced and degradation/utilization/assimilation predicted

pathways increased by almost 4-fold. In the gill microbiota, changes between differentially

expressed predicted metabolic pathways were less evident, also pointing to a more stable

microbiota. This suggests that broad time scale temperature shifts elicit microbial composition

changes in order to harbour species with different predicted metabolic functions. Finally, it is

worth noting that antimicrobial resistance (a detoxification mechanism) was significantly

enriched in both tissues only in cold months. Suboptimal temperature conditions can favour

the fitness of antibiotic resistant bacterial strains; for example, non-optimal temperature can

increase the fitness of antibiotic resistant Escherichia coli (see Trindade et al., 2012). Similarly,

increased salinity and decreased pH can select for antibiotic resistant phenotypes in several

bacterial food-related pathogens (McMahon et al., 2007).

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4.6 Conclusion Changes in water temperature have a major impact on teleosts, leading to altered

physiology and behaviour (Alfonso et al., 2020; Almeida et al., 2015; Islam et al., 2020a,

2020b), contributing to parasitism resistance and tolerance (Jackson et al., 2020). We

demonstrate here that water temperature oscillation also impacts the composition, structure

and function of the skin and gill microbiota of farmed European seabass. In particular,

dysbiosis seems to be more frequent in warm months and during cold/warm transitions, and

the abundance of several PP genera is also affected by changes in water temperature.

Furthermore, the microbiota of fish external mucosae behaved in similar ways with regards to

their compositional diversity, suggesting they may be modulated by similar host and

environmental stressors. These results highlight the need for further longitudinal studies

examining the full thermal spectrum experienced by a species, as well as other environmental

factors.



Technology (project PTDC/BIA-MIC/27995/2017 POCI-01-0145- FEDER-027995); DR, MP-L

and RX were supported by FCT under the Programa Operacional Potencial Humano – Quadro

de Referência Estratégico Nacional funds from the European Social Fund and Portuguese

Ministério da Educação e Ciência (DR doctoral grant SFRH/ BD/117943/2016; MP-L:

IF/00764/2013; RX: IF/00359/2015; and 2020.00854.CEECIND; PT:

DL57/2016/CP1440/CT0008).

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4.9 Supplementary material Supplementary File S4.1 – Extended Material and methods Experimental design, sampling and processing

European seabass were sampled in a Portuguese fish farm, from a rearing tank

containing a single age cohort of mature adult seabass in order to reduce any possible

maturation effects which are known to affect studied microbiota (e.g., Rosado et al., 2021).

All fish were 26 months old at the beginning of the study and 38 months old at the last sampling

date. We swabbed the right upper lateral part of the fish skin from head to tail and the right gill

filaments between the first and second arch; fish were then released unharmed. Swabs and

filters were immediately frozen at -20°C and then transported in dry ice to the CIBIO-InBIO

laboratory where they were stored at -80°C until further processing.

Data processing and statistical analysis

DNA extraction kit (n=10) and PCR blanks (n=4) were used to control laboratory

contamination. Additionally, four identical mock communities (ZymoBIOMICS Microbial

Community DNA Standard) were sequenced and analyzed together with our fish samples. All

fish and water samples were successfully amplified and sequenced.

Standard filtering parameters for DADA2 were used, with forward and reverse reads

truncated at 220 nt and 200 nt respectively, and with a maximum of two expected errors per

read. Default settings were also used for amplicon sequence variants (ASV) inference and

chimera detection. Taxonomic inferences were made against the SSU SILVA Release 138

reference database (Quast et al., 2012). Approximately 4 336 483, 2 990 334 and 563 545

16S rRNA sequences were retrieved from the skin and gill microbiotas of commercially farmed

European seabass and associated water samples, respectively. The number of sequences

per sample ranged from 2 033 to 6 150 in the skin, 2 077 to 63 711 in the gill and 15 355 to

47 549 in the water samples. ASV abundances were normalized using the negative binomial

distribution (McMurdie and Holmes, 2014). A midpoint rooted tree of ASVs was estimated

using the Quantitative Insights Into Microbial Ecology 2 package (QIIME2; release 2019.7).

After normalization and removal of non-bacterial reads, 5 559, 4 141 and 1 584 ASVs were

assigned to the skin, gill and water microbiotas, respectively. ASVs present in negative

controls (extraction kits and PCR) that were represented by >1% of the reads were removed

from downstream analysis. These corresponded to <0.3% of the reads present in fish and

water samples. Concomitantly, none of the abundant ASVs (>1%) in the fish samples were

present in the controls. Diversity and bacterial abundances of the mock communities

corresponded to that described by the manufacturer.

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Phyla and genera with mean relative proportion ≥5% of sequence counts were

considered the most abundant in each fish mucosa (skin and gills) and water for each month.

To determine the core microbiota throughout the year, ASVs present in all months were

identified in each mucosa and water samples.

For the fast expectation-maximization for microbial source tracking (FEAST; Shenhav

et al., 2019) analysis, we used a monthly average ASV table as input. Then the skin/gill

microbiota in a given month was considered as the sink with three potential environmental

sources tested: i) the microbiota of the same mucosa obtained from the previous month (to

infer the fraction of the microbiota that remains stable across consecutive months); ii) the

alternative mucosal microbiota obtained in the same month (to infer microbial exchange

between tissues); and iii) the water microbiota obtained in the same month (to infer microbial

recruitment from the surrounding water). Finally, FEAST also reports the fraction of the sink

microbiota that could potentially be attributed to other unknown sources.

To perform the Temporal Insights into Microbial Ecology (TIME; Baksi et al., 2018)

analysis, an ASV table was used as input. Causality networks were inspected to identify

specific genera that were affected by or were responsible for affecting the temporal changes

of the most abundant PP genera. Secondly, Pearson correlation tests were used to determine

whether there was any correlation between PP genera and the other genera; negative

correlations indicating competition.

The standard deviation of Shannon (Shannon SD) and Faith’s PD (PD SD) indices

were calculated using all 5 samples of a given sampling date or using all 10 samples of a

given month, whether the temperature model to use was based on sampling date or monthly

measurements, respectively.

To evaluate the effect of water temperature, we built four different temperature models

encompassing different time windows to test which explained most of the variation in the

studied microbiota. We first used a longer time frame, where a categorical model was created

by dividing the year into cold (November, December, January, February, and March) and

warm (April, May, June, July, August, September and October). A month was considered cold

or warm whether its mean monthly temperature was lower or higher than the daily annual

average (i.e., 18.6°C, Figure S4.1). The second set of models were based on intermediate

time frames and included the mean monthly temperature measurements across all days of a

month and their respective standard deviation (T_month_mn and T_month_SD). We also

tested four shorter frame models, using the mean temperatures and their respective standard

deviations from 2 and 7 days prior to each sampling date (T2_mn / T2_SD and T7_mn /

T7_SD). For monthly analyses we combined these measurements for the two sampling points

of a given month, in a total of 4 and 14 days per month (T4_mn / T4_SD and T14_mn /

T14_SD). Linear models (lm) were used to assess variation in microbial composition and

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included each set of mean and standard deviation temperature models plus season, in order

to account for seasonal variability. Season was categorized into winter (January, February,

March), spring (April, May, June), summer (July, August, September), and fall (October,

November, December). The final general lm formula was expressed as: microbial diversity ~

T_mn + T_SD + Season; or microbial diversity ~ cold/warm + Season. We also analyzed the

relations between diversity and temperature in terms of distance between samples.

Correlation structure between distance matrices was estimated by the generalized least

squares models (gls) We used a maximum likelihood population effects model, as

implemented in corMLPE R package (Clarke et al., 2002), to account for the lack of

independence between pairwise samples in a distance matrix. Distance matrices were

constructed for each continuous temperature variable, alpha-diversity measures and PP

genera abundances using Euclidean distances, while the Gower’s distance (Tuerhong and

Kim, 2014) was used for the categorical temperature variable (cold/warm months). For beta-

diversity, we used the unifrac unweighted and weighted distance matrices.

Bibliography

Baksi, K.D., Kuntal, B.K., Mande, S.S., 2018. ‘TIME’: A Web Application for Obtaining Insights

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Clarke, R.T., Rothery, P., Raybould, A.F., 2002. Confidence limits for regression relationships

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Rosado, D., Pérez-Losada, M., Pereira, A., Severino, R., Xavier, R., 2021. Effects of aging on

the skin and gill microbiota of farmed seabass and seabream. Anim. Microbiome 3.

https://doi.org/10.1186/s42523-020-00072-2

Shenhav, L., Thompson, M., Joseph, T.A., Briscoe, L., Furman, O., Bogumil, D., Mizrahi, I.,

Pe’er, I., Halperin, E., 2019. FEAST: fast expectation-maximization for microbial source

tracking. Nat. Methods 16, 627–632. https://doi.org/10.1038/s41592-019-0431-x

Tuerhong, G., Kim, S.B., 2014. Gower distance-based multivariate control charts for a mixture

of continuous and categorical variables. Expert Syst. Appl. 41, 1701–1707.

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https://doi.org/10.1016/j.eswa.2013.08.068

Figure S4.1 Daily average temperature taken throughout the sampling year. Each dot represents a day and red dots represent the sampling dates. Horizontal grey lines represent annual temperature daily average and standard deviation.

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Figure S4.2 Most abundant phyla and genera present in the skin and gill microbiota (N=10 x 12 months x tissue) of the seabass Dicentrarchus labrax and surrounding water (N=2 x 11 months) from February 2017 (left) to January 2018 (right). Taxa were labelled to the lowest taxonomic level possible; u.g. = unknown genus. Table S4.1 Relative mean proportions (%) of the most abundant phyla and genera (≥ 5%) in the skin and gill microbiota of the seabass Dicentrarchus labrax and in the water column (top, middle and bottom lines, respectively) across months. Taxa with ≥ 5% relative mean proportion in a month are indicated in bold. Unknown genera are identified as u.g.

Phyla Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

Actinobacteriota

1 1 0.5 2 2 8 1 1 1 1 1 1 - - - - - - - - - - - - - - - - - - - - - - - -

Bacteroidota

33 40 16 45 42 31 33 28 43 30 40 37 17 21 19 27 26 20 41 21 26 17 29 34 45 43 35 41 48 40 44 31 41 47 - 47

Cyanobacteria

- - - - - - - - - - - - - - - - - - - - - - - -

0.3 0.2 0.5 2 2 1 0.4 5 1 1 - 0.4

Firmicutes

2 1 10 5 4 1 20 4 2 0.1 1 1 0.2 0.3 10 1 2 2 12 3 2 0.2 0.2 0.1 1 0.1 10 2 0.1 2 0.1 4 0.04 0.1 - 0.1

Proteobacteria

49 39 63 33 36 28 39 52 37 61 39 41 38 41 35 43 40 16 36 62 51 63 47 43 36 45 42 37 38 44 39 51 43 40 - 39

Verrucomicrobiota

7 10 5 10 8 25 2 2 7 3 4 4 26 20 31 24 16 56 3 1 11 8 6 4 12 9 10 14 8 10 11 6 11 7 - 9

Genera

Aliivibrio 0.5 0.3 0.01 0.03 1 0.003 2 1 0.01 7 0.04 0.1 - - - - - - - - - - - - - - - - - - - - - - - -

Bacillus - - - - - - - - - - - -

0.04 0.04 10 0.2 0.03 1 11 2 0.1 0.1 0.1 0.03 - - - - - - - - - - - -


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Table S4.1 (continued)

Cobetia - - - - - - - - - - - - 0 0 0 0 0.01 0 0 10 1 12 0 0 - - - - - - - - - - - -

Escherichia/Shigella 6 0.01 0.003 0.02 0 0.02 0.002 0.04 0.03 0.01 0.002 0.01 - - - - - - - - - - - - - - - - - - - - - - - -

Glaciecola 3 3 0.2 1 3 0.4 2 4 3 2 8 3 1 2 0.3 1 2 0.3 1 7 2 2 5 1 4 3 0.2 1 4 2 3 8 5 4 - 6

Kocuria 0 0.002 0.001 0 0.02 6 1 0.01 0.02 0.01 0.01 0.01 - - - - - - - - - - - - - - - - - - - - - - - -

Litoreibacter - - - - - - - - - - - - - - - - - - - - - - - - 5 5 5 4 2 2 2 1 3 4 - 3

Lysinibacillus 0 0 6 0 0 0 0 0 0.005 0 0.001 0 - - - - - - - - - - - - - - - - - - - - - - - -

Marinobacterium - - - - - - - - - - - - - - - - - - - - - - - -

0.4 0.1 0.2 2 6 4 4 5 7 4 - 4

Marinomonas 0 0.004 0.2 0 0.01 0 0.01 0.01 0 5 0 0 - - - - - - - - - - - - - - - - - - - - - - - -

Methylobacterium-Methylorubrum 0 0.01 0 0.004 0 0.003 0 12 0.05 0 0.01 0 0 0 0 0 0 0.001 0.002 15 0 0 0 0 - - - - - - - - - - - -

NS3a marine group

10 11 5 14 11 6 10 8 9 8 10 10 4 7 6 10 6 3 10 3 6 3 7 5

17 16 7 13 15 10 12 7 10 12 - 15

Paucibacter - - - - - - - - - - - - 0 0 0.1 0.01 0.002 0.2 7 0.2 0 0 0.01 0 - - - - - - - - - - - -

Photobacterium 6 2 14 2 1 0.3 0.4 1 0.5 0.2 1 0.2 - - - - - - - - - - - - - - - - - - - - - - - -

Polaribacter 2 5 1 3 1 5 5 1 1 1 2 5 - - - - - - - - - - - - 2 4 3 1 1 1 3 1 2 1 - 6

Polynucleobacter - - - - - - - - - - - - 7 11 3 4 3 1 3 4 3 3 7 7 - - - - - - - - - - - -

Pseudoalteromonas 0.1 0.2 5 0.1 1 0.01 1 1 1 6 0.2 1

0.04 0.01 0.05 10 1 0.02 0.1 1 0.4 17 0.2 0.1 0 9 13 3 0.01 0.2 0.3 1 0.1 0.03 - 0.5

Pseudomonas 8 5 0.2 3 0.5 0.5 1 3 0.2 0.04 6 9 5 2 0.01 0.04 0.3 0.01 1 4 0.02 0.02 2 3 - - - - - - - - - - - -

Psychrobacter 0.1 0.01 35 0.01 0.04 10 18 4 0.04 0.03 0.02 0.2 0 0 17 0.01 0.01 0.4 0.02 0.1 0.2 0.3 0.001 0.03

0.1 0.03 2 2 0 11 0 11 0.02 0 - 0

Rubritalea 6 7 5 9 5 23 1 0.2 4 2 2 3

25 18 30 23 14 49 2 0.2 7 5 3 3 10 7 9 14 6 9 3 0.4 4 5 - 8

Staphylococcus 2 0 1 0 0.01 0 15 0.3 0.01 0 0.003 0.04 - - - - - - - - - - - - - - - - - - - - - - - -

Vibrio 2 1 0.02 0.5 6 0.1 2 2 6 26 1 2

0.4 1 1 1 3 0.01 2 0.2 8 9 1 3 - - - - - - - - - - - -

Yoonia-Loktanella - - - - - - - - - - - - - - - - - - - - - - - - 4 3 5 4 1 0 0 1 0.1 1 - 5

Cryomorphaceae (u.g.)

3 4 0.3 3 3 0.5 1 1 4 4 5 3 - - - - - - - - - - - - 9 8 2 3 4 3 2 5 6 4 - 8

Flavobacteriaceae (u.g.)

12 11 5 11 7 15 5 3 8 9 10 8 3 4 4 8 6 5 17 1 5 4 5 2

10 9 10 10 12 7 11 5 10 13 - 7

Fokiniaceae (u.g.)

- - - - - - - - - - - - 3 5 2 1 1 0.4 1 1 1 2 3 3 - - - - - - - - - - - -

Rhodobacteraceae (u.g.)

- - - - - - - - - - - - - - - - - - - - - - - - 4 3 4 6 5 3 3 5 6 4 - 4

Bacteroidia (u.g.)

- - - - - - - - - - - - 2 4 3 3 3 8 4 8 5 6 9 15 - - - - - - - - - - - -

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Table S4.2 Core ASVs present in the skin and gill microbiota of the seabass Dicentrarchus labrax and in the surrounding water across months. Unknown genera are identified as u.g.

Taxonomy Skin Gill Water Aureimarina ASV11 ASV11 ASV11

Bacillus ASV29 ASV29 -

Candidatus Actinomarina ASV165 - ASV165

Candidatus Aquiluna ASV96 - ASV96

Candidatus Fritschea - ASV66 -

Candidatus Megaira - ASV126 -

Candidatua Puniceispirillum - - ASV336

Castellaniella - ASV225 -

Crocinitomix ASV77 - ASV74

Fluviicola ASV181 - ASV181

Formosa - - ASV144

Glaciecola ASV6 ASV6 ASV6

Kordiimonas - - ASV56

Litoreibacter ASV13 ASV13 ASV13

Litoricola - ASV140 ASV140

Litorimicrobium ASV60, ASV74, ASV88 ASV60 ASV88

Lutimonas ASV315 - -

Marinobacterium ASV20 ASV20 ASV20

MD3-55 ASV32 ASV32, ASV45 -

NS3a marine group ASV3, ASV7 ASV3, ASV7 ASV3, ASV7

NS4 marine group ASV198 - -

NS5 marine group ASV40 ASV40 ASV212

Paucibacter ASV123 - -

Photobacterium ASV14 - -

Planktomarina - - ASV72

Planktotalea ASV18 ASV18 ASV18

Polaribacter ASV9, ASV87 ASV9 ASV9, ASV87

Polynucleobacter ASV16, ASV25, ASV44 ASV16, ASV25, ASV44 -

Pseudoalteromonas - ASV8 -

Pseudohongiella ASV38 ASV38 ASV38

Roseibacillus ASV8 ASV77 ASV77

Rubritalea ASV1 ASV1 ASV1

SAR92 clade ASV171 - ASV171

Sulfurimonas - - ASV85

Sulfurovum ASV47, ASV100 ASV47, ASV100 ASV47, ASV100, ASV195

Synechococcus CC9902 - - ASV75

Vibrio ASV55 - ASV55

Vicingus - - ASV361

Amoebophilaceae (u.g.) - ASV147 -

Arcobactereaceae (u.g.) - ASV139 -

Bacteroidia (u.g.) ASV4 ASV4, ASV109 ASV209

Burkholderiales Incertae Sedis (u.g.) - ASV63 -

Cellvibrionales (u.g.) - - ASV26

Crocinitomicaceae (u.g.) ASV41 ASV41 ASV41

Cryomorphaceae (u.g.) ASV15 - ASV15, ASV125

Flavobacteriaceae (u.g.) ASV2 ASV2 ASV2

Halieaceae (u.g.) ASV26 ASV26 -

Kordiimonadales (u.g.) ASV28 ASV28 ASV28

Marinimicrobia (SAR406 clade) (u.g.) - - ASV274

Microbacteriaceae (u.g.) - ASV79 -

NS11-12 marine group (u.g.) - - ASV135

NS9 marine group (u.g.) ASV124 - ASV124

Paracaedibacteraceae (u.g.) - ASV35, ASV54 -

Proteobacteria (u.g.) - ASV161 -

Rhodobacteraceae (u.g.) ASV22, ASV52, ASV169 ASV22, ASV69 ASV22, ASV52, ASV169

Rickettsiaceae (u.g.) - ASV51 -

SAR86 clade (u.g.) ASV131 - ASV131, ASV164

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Table S4.3 Alpha- and beta-diversity comparisons for the skin and gill microbiota of the seabass Dicentrarchus labrax across consecutive months. For each Kruskall-Walis test (alpha-diversity) we report the chi-squared value (overall) and significance (P value, overall and pairwise) and for each PERMANOVA test (beta-diversity) we report the R2 statistics and significance (P value). Significant differences are indicated in bold.

Shannon PD Unifrac weighted Unifrac unweighted

Skin

Overall 50 (6-7) 43 (1-5) 0.4 (9-5) 0.2 (9-5) Feb – Mar 0.1 0.2 0.04 (1) 0.04 (1) Mar – Apr 0.0003 0.001 0.4 (0.1) 0.4 (0.1) Apr – May 0.001 0.05 0.4 (0.1) 0.2 (1) May – Jun 0.1 0.2 0.2 (1) 0.1 (1) Jun – Jul 0.0004 0.001 0.3 (0.1) 0.4 (0.1) Jul – Aug 0.9 0.4 0.3 (0.7) 0.1 (1) Aug – Sep 0.8 0.8 0.3 (0.8) 0.02 (1) Sep – Oct 0.1 0.2 0.4 (0.1) 0.3 (0.2) Oct – Nov 0.01 0.3 0.2 (1) 0.1 (1) Nov – Dec 0.3 0.4 0.3 (0.1) 0.1 (1) Dec – Jan 0.9 0.7 0.2 (1) 0.04 (1)

Gill

Overall 50 (7-7) 50 (5-7) 0.3 (9-5) 0.2 (9-5) Feb – Mar 1 0.9 0.2 (1) 0.05 (1) Mar – Apr 0.0002 0.001 0.2 (0.7) 0.3 (0.1) Apr – May 0.1 0.1 0.1 (1) 0.1 (1) May – Jun 0.1 0.04 0.2 (0.5) 0.1 (1) Jun – Jul 0.0001 0.0001 0.1 (1) 0.6 (0.1) Jul – Aug 0.3 0.5 0.1 (1) 0.1 (1) Aug – Sep 0.8 0.9 0.1 (1) 0.02 (1) Sep – Oct 0.3 0.3 0.1 (1) 0.2 (1) Oct – Nov 0.1 0.03 0.1 (1) 0.1 (1) Nov – Dec 0.03 0.1 0.2 (1) 0.1 (1) Dec – Jan 0.6 0.6 0.1 (1) 0.03 (1)

Table S4.4: TIME results showing the dynamics of the most abundant (MA) potentially pathogenic (PP) genera in the skin and gill microbiota of the seabass Dicentrarchus labrax. Most abundant PP genera found across months and the other genera whose abundance is being influenced by (out) or influences (in) their abundance are represented. Pearson correlation coefficients (Corr. Coef.) are included. PP genera are denoted with an asterisk (*) and genera with probiotic properties are denoted with a plus (+).

Tissue MA PP genera Other genera Out In Corr. Coef.

Skin Aliivibrio

Alkalimarinus X -0.3 Alteromonas X -0.1 Arcticiflavibacter X -0.3 Bacteroides X -0.1 Balneola X X 0.8 Candidatus Branchiomonas X -0.1 Candidatus Gortzia X -0.1 Catenococcus X -0.1 Cyanobium PCC-6307 X -0.2 Defluviitaleaceae UCG-011 X -0.2 Endozoicomonas X -0.2 Francisella* X -0.2 Fusobacterium X -0.2 Glaciecola X 0.001 Halobacteriovorax+ X -0.2 Halomonas X -0.03 Hellea X -0.1 Lentimonas X 0.2 Methylobacterium-Methylorubrum X -0.04 Oleispira X 0.2 Paeniglutamicibacter X -0.3 Paracoccus X -0.2 Planococcus X -0.3 Portibacter X -0.2 Poseidonibacter X -0.1 Psychromonas X -0.2 Rhodopirellula X -0.2 Sva0081 sediment group X -0.2 Tenacibaculum* X -0.03


FCUP


160 133

Table S4.4 (continued) Thalassotalea X -0.04

Photobacterium

Acholeplasma X -0.1 Alkalimarinus X -0.3 Amylibacter X X 0.7 Aureispira X -0.2 Candidatus Branchiomonas X -0.1 Cetobacterium X -0.2 Clostridium sensu stricto X -0.1 Defluviitaleaceae UCG-011 X -0.2 Desulforhopalus X -0.3 Devosia X -0.02 Fransicella* X -0.1 Hypnocyclicus X -0.2 Ilumatobacter X -0.01 Labilibacter X -0.1 Lentisphaera X -0.1 Lewinella X -0.2 Litoricola X -0.05 Maribacter X -0.3 Marinobacter X -0.1 Marinoscillum X -0.2 Massilia X 0.04 MD3-55 X 0.03 Moritella X -0.2 OM43 clade X -0.03 Pantoea X -0.1 Parahaliea X -0.2 Phaeocystidibacter X -0.2 Propionigenium X -0.2 Pseudochrobactrum X -0.1 Pseudofulvibacter X X 0.1 fffPseudomonas* X -0.2 Psychrilyobacter X -0.1 Rhodopirellula X -0.2 RS62 marine group X 0.3 Salinirepens X -0.2 Sanguibacter X -0.02 SAR92 clade X -0.2 Sediminispirochaeta X -0.2 SEEP-SRB1 X -0.1 Sphingomonas+ X -0.03 Sulfurimonas X -0.2

Sulfurovum X -0.2 Thiomicrorhabdus X -0.2 Ulvibacter X -0.1 Wenyingzhuangia X -0.2

Pseudomonas

Alkalimarinus X 0.7 Bacteroides X 0.1 BD1-7 clade X 0.2 Candidatus Fritschea X 0.7 Candidatus Omnitrophus X -0.04 Defluviitaleaceae UCG-011 X 0.6 Desulfobulbus X -0.3 Desulforhopalus X 0.2 Francisella* X 0.8 Marinomonas X -0.3 Micrococcus X -0.1 Muricauda X -0.1 Phaeocystidibacter X 0.6 Photobacterium* X -0.2 Psychrilyobacter X 0.2 RBG-16-49-21 X 0.1 Sva0081 sediment group X 0.2 Thiomicrorhabdus X 0.4 Vibrio* X -0.3

Vibrio

Alkalimarinus X -0.2 Alteromonas X -0.1 Arcticiflavibacter X -0.2 Bacteroides X -0.1


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161 133

Table S4.4 (continued) Balneola X X 0.8 Candidatus Branchiomonas X -0.1 Candidatus Gortzia X 0.1 Candidatus Omnitrophus X 0.7 Catenococcus X -0.2 Citreitalea X -0.2 Defluviitaleaceae UCG-011 X -0.1 Endozoicomonas X -0.1 Enterovibrio X -0.1 Francisella* X -0.2 Fusobacterium X -0.2 Glaciecola X 0.02 Halobacteriovorax+ X -0.1 Halomonas X -0.1 Hellea X -0.2 Lentimonas X 0.3 Maritimimonas X -0.2 Methylobacterium-Methylorubrum X -0.1 Muricauda X X 0.5 Oleispira X 0.2 Paeniglutamicibacter X -0.2 Paracoccus X -0.2 Planococcus X -0.2 Portibacter X -0.1 Poseidonibacter X -0.03 Propionigenium X -0.2 Pseudomonas* X -0.3 Psychrilyobacter X -0.1 Psychromonas X -0.2 Rhodopirellula X -0.2 Sva0081 sediment group X -0.1 Tenacibaculum* X -0.1 Thalassotalea X -0.1 Variovorax X -0.1 Wenyingzhuangia X -0.1

Gill

Pseudomonas Candidatus Puniceispirillum X 0.1 Francisella* X 0.1 Psychrilyobacter x -0.01

Vibrio

Caedibacter X 0.04 Candidatus Branchiomonas X -0.03 Glaciecola X 0.02 Labilibacter X 0.6 Thalassotalea X 0.7 Winogradskyella X 0.3

Table S4.5 Significantly enriched potential pathways in the warm and cold months in the skin and gill microbiota of the seabass Dicentrarchus labrax grouped into the KEGG L1 and L2 categories. LEfSe tests were performed with a P value and LDA score cut-offs of 0.05 and of 2, respectively.

Skin Gill

L1 L2 Warm Cold Warm Cold

Biosynthesis

Amine and Polyamine Biosynthesis 0 1 (1%) 3 (4%) 1 (1%)

Amino Acid Biosynthesis 18 (21%) 2 (3%) 13 (19%) 7 (9%)

Aminoacyl-tRNA Charging 1 (1%) - - -

Carbohydrate Biosynthesis 3 (4%) 5 (6%) 3 (4%) 5 (6%)

Cell Structure Biosynthesis 5 (6%) - 4 (6%) 1 (1%)

Cofactor, Carrier, and Vitamin Biosynthesis 14 (17%) 15 (19%) 11 (16%) 13 (17%)

Fatty Acid and Lipid Biosynthesis 1 (1%) 11 (14%) 2 (3%) 13 (17%)

Nucleoside and Nucleotide Biosynthesis 19 (23%) 3 (4%) 8 (11%) 4 (5%)


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Table S4.5 (continued)

Polyprenyl Biosynthesis 1 (1%) - - -

Secondary Metabolite Biosynthesis 2 (2%) - - 4 (5%)

Tetrapyrrole Biosynthesis - - 1 (1%) -

Other Biosynthesis - 1 (1%) - 1 (1%)

Degradation/

Utilization/

Assimilation

Amine and Polyamine Degradation 1 (1%) - 1 (1%) -

Amino Acid Degradation - 4 (5%) 2 (3%) 3 (4%)

Aromatic Compound Degradation 2 (2%) 9 (11%) 2 (3%) 10 (13%)

C1 Compound Utilization and Assimilation 4 (5%) - 3 (4%) -

Carbohydrate Degradation 1 (1%) 5 (6%) 3 (4%) 3 (4%)

Carboxylate Degradation 1 (1%) 5 (6%) 2 (3%) 2 (3%)

Fatty Acid and Lipid Degradation - 1 (1%) 1 (1%) -

Inorganic Nutrient Metabolism 1 (1%) 3 (4%) 2 (3%) 3 (4%)

Nucleoside and Nucleotide Degradation 1 (1%) 1 (1%) 1 (1%) -

Secondary Metabolite Degradation - 5 (6%) - 3 (4%)

Other - 1 (1%) - -

Detoxification Antibiotic Resistance - 1 (1%) - 1 (1%)

Generation of

Precursor

Metabolite and

Energy

Fermentation 2 (2%) 1 (1%) 2 (3%) -

Glycolysis 1 (1%) 2 (3%) 2 (3%) -

Glyoxylate cycle - - - 1 (1%)

Pentose Phosphate Pathways 1 (1%) 0 2 (3%) -

TCA cycle 4 (5%) 1 (1%) 2 (3%) -

Macromolecule

Modification Nucleic Acid Processing 1 (1%) 1 (1%) - 1 (1%)

Superpathways

Superpathway of glyoxylate bypass and TCA - - - 1 (1%)

Superpathway of hexuronide and hexuronate degradation - 1 (1%) - -

Superpathway of histidine, purine, and pyrimidine

biosynthesis - 1 (1%) - 1 (1%)

Total 84

(100%)

80

(100%)

70

(100%)

78

(100%)


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CHAPTER 5

Effects of disease and antibiotic treatment on the

microbiota of juvenile and adult seabass

5.1 Effects of disease, antibiotic treatment and

recovery trajectory on the microbiome of farmed

seabass (Dicentrarchus labrax)

Daniela Rosado, Raquel Xavier, Ricardo Severino, Fernando Tavares, Jo

Cable, Marcos Pérez-Losada

2019. Scientific Reports 9, 18946. https://doi.org/10.1038/s41598-019-55314-4.

5.1.1 Abstract The mucosal surfaces of fish harbour microbial communities that can act as the first-

line of defense against pathogens. Infectious diseases are one of the main constraints to

aquaculture growth leading to huge economic losses. Despite their negative impacts on

microbial diversity and overall fish health, antibiotics are still the method of choice to treat

many such diseases. Here, we use 16 rRNA V4 metataxonomics to study over a 6 week period

the dynamics of the gill and skin microbiomes of farmed seabass before, during and after a

natural disease outbreak and subsequent antibiotic treatment with oxytetracycline.

Photobacterium damselae was identified as the most probable causative agent of disease.

Both infection and antibiotic treatment caused significant, although asymmetrical, changes in

the microbiome composition of the gills and skin. The most dramatic changes in microbial

taxonomic abundance occurred between healthy and diseased fish. Disease led to a decrease

in the bacterial core diversity in the skin, whereas in the gills there was both an increase and

a shift in core diversity. Oxytetracycline caused a decrease in core diversity in the gill and an

increase in the skin. Severe loss of core diversity in fish mucosae demonstrates the disruptive

impact of disease and antibiotic treatment on the microbial communities of healthy fish.

Keywords: aquaculture, 16S rRNA, oxytetracycline, Photobacterium damselae, dysbiosis


164

5.1.2 Introduction Mucosal surfaces of animals harbour microbial communities (i.e., microbiomes), which

can act as the first-line of defense against pathogens, either through competition or production

of antibiotic compounds (Gómez and Balcázar, 2008; Kelly and Salinas, 2017; Trivedi, 2012).

Furthermore, microbiomes are thought to have evolved to optimize the immune response of

each organ and promote homeostasis (Kelly and Salinas, 2017; Lee and Mazmanian, 2010).

Usually a diverse microbiome is associated with healthy phenotypes, but disruptions to this

equilibrium can lead to an increase in abundance of opportunistic pathogens and disease

susceptibility (Mohammed and Arias, 2015; Reid et al., 2017).

Many factors can shape the composition of the fish microbiomes, including host

species (Larsen et al., 2013), stress (Boutin et al., 2013; Zha et al., 2018), diet (Chiarello et

al., 2018), water quality (Galbraith et al., 2018), host physiology (Pratte et al., 2018; Ye et al.,

2014) and infection (Legrand et al., 2018; Llewellyn et al., 2017; Reid et al., 2017). Importantly,

healthy mucosal surfaces, such as the skin and gills, are naturally colonized by pathogens

(Givens et al., 2015; Li et al., 2018; Rosado et al., 2019) from the surrounding waters that can

integrate into the host’s microbial community (Califano et al., 2017; Rud et al., 2017). A shift

in the abundance of such pathogens on the fish mucosae can lead to microbial imbalance (i.e.

dysbiosis) and disease (Hess et al., 2015), which is usually accompanied by a reduction in

bacterial diversity (Legrand et al., 2018; Llewellyn et al., 2014; Reid et al., 2017).

Stress imposed by fish farming conditions can also result in changes in microbiome

composition that may lead to an increase in disease susceptibility (Boutin et al., 2013). As

infectious disease is one of the main constraints to aquaculture growth and profitability, it is

crucial to have a better understanding of the host-symbiont-pathogen nexus. The European

seabass Dicentrarchus labrax is one of the main farmed fish species in southern Europe,

totaling 103.476 tons in landings (10% of global aquaculture production) between 2002 and

2011 (FAO, 2016). This important food resource is susceptible to several bacterial pathogens:

Photobacterium damselae, which causes photobacteriosis, Vibrio spp. causing vibriosis, and

Tenacibaculum maritimum causing tenacibaculosis, just to name a few (Toranzo et al., 2005).

All of these pathogens can induce bacterial septicemia resulting in high mortalities in fish farms

(Faílde et al., 2014; Romalde et al., 2008; Zlotkin et al., 1998). Photobacterium damselae in

particular, is increasingly being reported as the main etiologic agent affecting fish farms

worldwide and has been also described in molluscs, crustaceans and mammals (Labella et

al., 2011; Pedersen et al., 2009; Rivas et al., 2013, 2011; Tao et al., 2018; Terceti et al., 2018,

2016). Control of photobacteriosis in fish farms is challenging, and mortality can reach 60–

80% rates in farmed European seabass (Bakopoulos et al., 2003; Essam et al., 2016).

Although vaccination is available, immunization is still not fully effective (Bakopoulos et al.,


165

2003; Byadgi et al., 2018), and in many cases antibiotic treatment remains the preferred option

to control such pathogens (e.g., Oxytetracycline, Abdel-Aziz et al., 2013; Labella et al., 2011).

Most commonly, the impact of antibiotic use on fish health is assessed through

toxicological studies (Enis Yonar et al., 2011; Rodrigues et al., 2017). The few studies that

have investigated the effects of antibiotics on the microbiome of fish have focused on gut

dysbiosis (Carlson et al., 2017, 2015; Liu et al., 2012; Narrowe et al., 2015; Pindling et al.,

2018; Zhou et al., 2018). Not surprisingly, a decrease in microbial diversity was detected

(Carlson et al., 2017; Zhou et al., 2018), along with an increased susceptibility to secondary

infection, reduced host growth (Carlson et al., 2015; Liu et al., 2012) and higher mortality

(Pindling et al., 2018). Importantly, these studies also reported bacterial pathogens acquiring

resistance after antibiotic treatment, suggesting that farmed fish microbiomes could become

reservoirs for antibiotic resistant genes (Carlson et al., 2017; Liu et al., 2012; Pindling et al.,

2018). In fact, several studies showed an increase in resistance to tetracycline and

streptomycin antibiotics in strains of P. damselae sampled from both wild and farmed fish

hosts (Abdel-Aziz et al., 2013; Chiu et al., 2013; Essam et al., 2016; Nonaka et al., 2012).

In the present study, we characterized the dynamics of the gill and skin microbiomes

of the seabass Dicentrarchus labrax before, during and after a disease outbreak potentially

caused by Photobacterium damselae, and subsequent antibiotic treatment with

oxytetracycline (Rigos and Troisi, 2005). We describe the dysbiosis caused by disease and

antibiotic treatment in microbial diversity of both mucosae over 3 weeks. Towards this aim, we

used 16S rRNA high-throughput sequencing (metataxonomics) and amplicon sequence

variance analysis to examine changes in both alpha- and beta-diversity, as well as differences

in taxa proportion over time.

5.1.3 Material and methods

5.1.3.1 Ethical statement This study monitored a natural infection and subsequent antibiotic treatment as part

of routine procedures in a commercial fish farm. All animals were handled by the fish farm

employees, our sampling through swabbing was non-invasive and fish were released

unharmed with no mortalities observed. According to the Portuguese legislation DL N°

113/2013, our work does not involve animal experimentation and therefore is exempted from

the need of ethical approval.

5.1.3.2 Experimental design, sample collection and preparation Ten individuals of seabass were collected once a week between August 21 and

October 3, 2016, from the same rearing tank in a commercial fish farm located in the estuarine


166

environment of the Ria Formosa (Portimão), southern Portugal. Fish were hatched at

September 26, 2014 and entered the growth facility at March 6, 2015. Fish were kept in an

open water circulation system in a semi-intensive farming facility, where water is supplied to

each tank from the estuary. Fish were kept at a density of ca. 3 kg/m3 corresponding to roughly

100 fish/tank with fish weighting on average 281 g. Given that it was not possible to tag

individual fish and the unlikelihood of re-sampling the same individuals every week, a subset

of samples believed to be representative of the population was chosen, i.e., 10 individuals (~

10%), and for statistical purposes individuals were considered as pseudo-replicates. All fish

were fed with the same commercial feed and they shared the same clinical history. Individuals

were randomly caught using a fishing pole and skin and gill swabs were collected immediately

using tubed sterile dry swabs (Medical Wire & Equipment, UK). Skin samples were taken by

swabbing several times along the right upper lateral part of the fish from head to tail, while gill

swabs were taken from the right filaments between the first and second arch. Due to the non-

invasive nature of our sampling procedure, it was not possible to ascertain the sex of the

individuals sampled; however, we do not expect this to impact our conclusions since, to the

best of our knowledge, no gender bias in microbiome composition has ever been reported for

skin or gill of piscine hosts. Swabs were immediately stored at −20 °C until transported on dry

ice to the CIBIO-InBIO laboratory by airmail where they were kept at −80 °C until further

processing.

To assess gill and skin microbiome dynamics in the seabass during a disease outbreak

and under oxytetracycline treatment after infection, fish were sampled in 4 different states:

healthy, diseased, treatment and recovery (Figure 5.1.1). During the healthy state (August 21

and 29), all fish specimens were considered healthy due to a lack of visible disease symptoms,

such as external lesions or behavioural alterations. On September 8 fish began to die in the

farming tanks, showing symptoms of disease, and treatment with oxytetracycline antibiotic (a

broad-spectrum tetracycline) was initiated, being administrated at 35 g/Kg through commercial

feed for at lasted 8 days. On the same day, smears from spleen and kidney were collected for

culture using Bionor kits DE020, MONO-VA-50 for Vibrio anguillarum and DL020, MONO-Pp-

50 for Photobacterium piscicida. Agglutination essays were not conclusive and, at that stage,

the causative agent of the disease was unknown. We do not have samples from September

8, hence we used the samples from our closest time point, September 5, which we classified

as potentially diseased (i.e., diseased state). Antibiotic treatment lasted until September 16

and fish were sampled on September 12; this sample point corresponded to the treatment

state. Then, three additional time points were sampled (September 19 and 26, and October

3), when fish were no longer dying or presented signs of infection; these three time points

corresponded to the recovery state.


167

Total DNA from 140 fish samples (70 skin and 70 gills) was extracted using the

PowerSoil DNA Isolation Kit (QIAGEN, Netherlands), following the manufacturer’s protocol.

DNA extractions were shipped in dry ice to the University of Michigan Medical School (USA)

for amplification and sequencing on a single run of the Illumina MiSeq platform according to

the protocol of Kozich et al. (2013). Each sample was amplified for the V4 (~250 bp)

hypervariable region of the 16S rRNA gene, using the primers in Caporaso et al. (2011). This

region has been widely used to characterize microbiomes from vertebrates (Earth Microbiome

Project, Gilbert et al., 2014), including fish (Carlson et al., 2017; Llewellyn et al., 2016; Nielsen

et al., 2017; Wang et al., 2017).

5.1.3.3 Data and statistical analyses Raw FASTQ files were analyzed using the Quantitative Insights Into Microbial Ecology

2 (QIIME2; release 2018.4) platform. Clean sequences were aligned against the SILVA (132

release) reference database (Quast et al., 2013) using the DADA2 pipeline (Callahan et al.,

2016). A feature table containing amplicon sequence variants (ASVs) was constructed and

normalized using the negative binomial distribution (McMurdie and Holmes, 2014). The core

microbiome was assessed at the ASV level for the gill and skin of seabass for each state

(healthy, diseased, treatment and recovery) separately. An ASV was considered as part of the

core microbiome if present in 100% of the samples from each state. Core diversity is here

defined as number of ASVs represented in a given group.

Microbial alpha-diversity (intra-sample) was calculated using Shannon, ACE, Fisher

and Faith’s phylogenetic diversity (PD) indices as implemented in the R package phyloseq

(McMurdie and Holmes, 2013). Microbial beta-diversity (inter-sample) was estimated using

phylogenetic Unifrac (unweighted and weighted) and Bray-Curtis distances. Dissimilarity

between samples was assessed by principal coordinates analysis (PCoA). Variation in

microbial alpha-diversity and the mean proportions of the most abundant taxa (with more than

4% of all reads) were assessed using linear models with randomized residuals in a

permutation procedure (RRPP). Differences in community composition (beta-diversity) were

tested using permutational multivariate analysis of variance (PERMANOVA) with 1,000

permutations as implemented in the adonis function of the R vegan package (Oksanen et al.,

2008). All statistical analyses were carried out separately for the gills and skin. All statistical

analyses were performed in R-studio v1.0.143 (Studio, 2012).

5.1.4 Results Approximately, a total of 3.6 million raw reads were generated, while the number of

sequences per sample ranged from 2,354 to 50,564. A total of 6,485 unique ASVs were


168

detected, but after normalization and depletion of Archaea and Algae ASVs, a total of 3,827

ASVs (1,560,279 sequences) and 3,741 ASVs (1,904,115 sequences) were analyzed for the

gill and skin microbiomes, respectively. Taxa showing a mean proportion ≥4% in any state

were considered the most abundant taxa. Analyses of alpha- and beta-diversity showed no

significant differences (P > 0.05; data not shown) between samples from the two healthy time

points as well as between the samples from the three recovery time points (Figure 5.1.1) for

both gill and skin microbiomes. Therefore, in all our subsequent analyses, samples from Aug

21 (Healthy 1) and Aug 29 (Healthy 2) were combined into the “healthy” state; and samples

from Sep 19 (Recovery 1), Sep 26 (Recovery 2) and Oct 3 (Recovery 3) were combined into

the “recovery” state in order to increase sample size (Figure 5.1.1).

Figure 5.1.1 Schematic illustration of the experimental design and health status of each sampling point. Ten fish were sampled for gill and skin microbial communities at each sampling point, totaling 70 fish sampled in this experiment.

5.1.4.1 Gill bacterial composition and diversity No significant differences were detected in alpha-diversity across all states (RRPP,

P > 0.5), with the exception of the Shannon index (RRPP, P = 0.04) (Table 5.1.1, Figure

5.1.2A). There were, however, significant differences between healthy and recovery states for

all alpha-diversity indices (RRPP, P ≤ 0.03; Table 5.1.1). Beta-diversity also varied greatly

between states (PCoA, Figure 5.1.3A), with significant differences both in overall and pairwise

comparisons in almost all the tests (Adonis, P < 0.05; Table 5.1.1).

Bacteroidetes, Proteobacteria and Verrucomicrobia were the most abundant phyla

retrieved from the seabass gill microbiome across states, accounting for 83% to 93% of the

sequences altogether (Table 5.1.2). The most abundant genera were NS3a marine group,

Polaribacter 4, Pseudomonas and Rubritalea, which were present in all four states (Table

5.1.2, Figure S5.1.1A). Other relatively abundant genera were: (a) Polynucleobacter, which

accounted for 4–7% of the sequences in the diseased, treatment and recovery states, but only

0.2% in the healthy state; (b) Stenotrophomonas, represented by 5% of the sequences in the


169

healthy state, and 2–3% in the remainder states; and (c) Photobacterium, which accounted

for 5% of the sequences in the diseased state but ≤1 in all other states (Table 5.1.2, Figure

S5.1.1A).

Table 5.1.1 Microbial diversity and mean relative proportions of dominant taxa in the gill and skin of the seabass Dicentrarchus labrax across all samples and between the four different states (H = Healthy; D = Diseased; T = Treatment; R = Recovery). For each test we report relevant F (alpha-diversity indices and taxa proportions) or R2 (beta-diversity indices) statistic and significance (p). Significant associations are indicated in bold.

GILL SKIN All H vs D D vs T T vs R H vs R All H vs D D vs T T vs R H vs R

Alpha-diversity Shannon 3

(0.04) 19.4

(0.001) 2.6

(0.04) 2.9

(0.1) 19.7

(0.001) 5.1

(0.001) 14.1

(0.002) 28.2

(0.002) 1.6

(0.2) 1.2

(0.3)

ACE 0.9 (0.47)

0.9 (0.4)

0.4 (0.6)

1.7 (0.2)

4.1 (0.05)

10.7 (8-6)

18.2 (0.002)

24.6 (0.002)

2 (0.2)

1.8 (0.2)

PD 2 (0.06)

1.6 (0.2)

0.5 (0.6)

3.1 (0.1)

8.7 (0.01)

6 (0.0001)

21.3 (0.002)

27.7 (0.001)

1.4 (0.3)

3.1 (0.1)

Fisher 1.2 (0.3)

2.2 (0.2)

0.9 (0.4)

2.3 (0.1)

4.9 (0.03)

4.2 (0.002)

16.1 (0.003)

21.8 (0.002)

2 (0.2)

1.2 (0.3)

Beta-diversity

Uni Un 0.2 (9-5)

0.1 (2-4)

0.1 (3-4)

0.1 (4-4)

0.1 (9-5)

0.2 (9-5)

0.1 (9-5)

0.1 (2-4)

0.04 (4-4)

0.1 (9-5)

Uni Weigh 0.5 (9-5)

0.3 (4-4)

1.7 (0.2)

0.1 (0.04)

0.1 (0.03)

0.4 (9-5)

0.4 (2-4)

0.5 (2-4)

0.1 (0.01)

0.1 (0.04)

Bray C 0.4 (9-5)

0.2 (9-5)

0.2 (9-5)

0.1 (0.002)

0.2 (9-5)

0.3 (9-5)

0.2 (2-4)

0.3 (9-5)

0.07 (0.003)

0.1 (9-5)

Phylum

Bacteroidetes 5 (0.003)

11.9 (0.002)

0.5 (0.5)

4.7 (0.04)

2 (0.2)

10 (2-5)

24.9 (3-5)

23.6 (0.0001)

2.2 (0.2)

1.1 (0.3)

Proteobacteria 2.9 (0.04)

5.5 (0.03)

0.3 (0.6)

4.2 (0.04)

0.1 (0.7)

10.4 (1-5)

21.5 (8-5)

29.7 (4-5)

0.3 (0.6)

1.3 (0.3)

Verrucomicrobia 4.9 (0.004)

3.8 (0.1)

1.2 (0.3)

5.3 (0.03)

6 (0.02)

3.8 (0.01)

9.1 (0.005)

10.2 (0.005)

0.3 (0.6)

0.8 (0.4)

Genus

NS3a marine group 5.9 (0.001)

11 (0.003)

6.8 (0.02)

1.9 (0.2)

4.5 (0.04)

8 (0.0001)

11.5 (0.002)

16.7 (0.001)

0.02 (0.9)

5.2 (0.03)

Photobacterium 3.1 (0.03)

3.3 (0.1)

1.7 (0.2)

4.4 (0.04)

3.8 (0.1) - - - - -

Polaribacter 4 7.5 (0.0002)

12.8 (0.001)

5.5 (0.03)

7.6 (0.01)

3.5 (0.1)

9.1 (4-5)

21.6 (7-5)

0.7 (0.4)

6.1 (0.02)

3.3 (0.1)

Polynucleobacter 8.7 (6-5)

75.8 (2-9)

2.1 (0.2)

4 (0.05)

13 (0.001) - - - - -

Pseudoalteromonas - - - - - 7.4 (0.0002)

12.4 (0.002)

6.2 (0.02)

8.6 (0.01)

2.6 (0.1)


7.4 (0.01)

0.2 (0.6)

2 (0.2)

29.1 (2-6)

12.6 (1-6)

18.6 (0.0002)

56.8 (6-7)

1.5 (0.2)

0.9 (0.3)

Rubritalea 4.6 (0.01)

4.4 (0.04)

0.3 (0.6)

3.6 (0.1)

6 (0.02) - - - - -

Stenotrophomonas 9.2 (4-5)

8.5 (0.01)

0.1 (0.8)

1.3 (0.3)

30.3 (1-6)

3.5 (0.02)

7.6 (0.01)

8.8 (0.01)

0.2 (0.7)

0.3 (0.6)

P. damselae damselae

5.3 (0.002)

4.5 (0.04)

2.8 (0.1)

1 (0.3)

10.1 (0.003) - - - - -

Taxa mean proportions varied between states: (i) in healthy versus diseased states 7

taxa increased and 3 decreased; (ii) in diseased versus treatment states 3 taxa increased, 2

decreased and 6 remained almost constant; and (iii) finally, in treatment versus recovery

states 3 taxa decreased, 6 increased and 1 remained constant (Table 5.1.2, Figure 5.1.4A).

The 3 most abundant bacterial phyla and the 8 most abundant genera all varied significantly

(P ≤ 0.04) in their mean proportions across the four studied states (Table 5.1.1, Figure 5.1.4A).

In addition, 9 of these taxa varied significantly (P ≤ 0.04) between healthy and disease states,

whereas 5 varied significantly (P ≤ 0.04) between treatment and recovery states (Table 5.1.1).

Only 2 genera varied significantly (P ≤ 0.03) between disease and treatment states (Table

5.1.1).


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Figure 5.1.2 Mean values and standard deviations of Shannon, Faith’s phylogenetic (PD), ACE and Fisher alpha-diversity estimates plotted for the gill (A) and skin (B) microbiomes of Dicentrarchus labrax (seabass) during the four different states. H1 – Healthy 1; H2 – Healthy 2; D – Diseased; T – Treatment; R1 – Recovery 1; R2 – Recovery 2; R3 – Recovery 3.


171

Figure 5.1.3 PCoA plot computed with weighted Unifrac distance for gill (A) and skin (B). Each dot represents a microbiome sample and is coloured by sampling point.

Of the 55 ASVs recovered from the gill core microbiome across the four states, 21

were present in the healthy state, 26 in the diseased state, 5 in the treatment state and 10 in

the recovery state (Figure 5.1.5A). Four of these ASVs were unique to the healthy state, 11

unique to the diseased state and one to the recovery state (Figure 5.1.5A). Of the 11 unique

ASVs recovered from the gill core microbiome of diseased fish, one was identified as

Photobacterium damselae. There were 8 other ASVs belonging to the Photobacterium genus,

of which 7 were unique to the diseased state and one was found in all four states. This

suggests that P. damselae is the most likely causative agent of the disease in the diseased

fishes.


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Table 5.1.2 Relative proportions of sequences and ASVs belonging to the most abundant (≥4%) phyla and genera in the gill and skin microbiomes of the seabass Dicentrarchus labrax in healthy, diseased, treatment and recovery states. Total number of sequences and ASVs are absolute.

Figure 5.1.4 Alluvial plots of relative frequency of most abundant (>4%) taxa recovered from the gill (A) and skin (B) of the seabass for healthy, diseased, treatment and recovery states.

Sequences (%) ASVs (%) Healthy Diseased Treatment Recovery Healthy Diseased Treatment Recovery

GILL

Phylum Bacteroidetes 30 21 19 26 22 21 21 23 Proteobacteria 51 58 59 52 39 40 42 38 Verrucomicrobia 12 7 5 8 2 2 2 2 Genus NS3a marine group 8 5 8 10 0.3 0.4 0.4 0.3 Photobacterium 0.2 5 0.1 1 0.2 1 0.4 0.4 Polaribacter 4 11 7 4 8 0.4 0.4 0.2 0.2 Polynucleobacter 0.2 4 7 4 1 1 0.5 0.3 Pseudomonas 15 9 9 6 1 1 1 1 Rubritalea 10 5 4 6 0.2 1 0.1 0.2 Stenotrophomonas 5 3 2 2 0.4 0.3 0.2 0.2 TOTAL 24920 17743 25552 23076 1439 978 837 2171

SKIN

Phylum Bacteroidetes 34 19 32 36 24 24 23 25 Proteobacteria 54 72 52 50 39 44 36 38 Verrucomicrobia 5 1 3 4 2 2 2 2 Genus NS3a marine group 9 6 11 11 1 1 1 1 Polaribacter 4 12 5 6 9 0.5 1 0.3 1 Pseudoalteromonas 0.1 1 5 1 0.1 0.4 0.2 0.1 Pseudomonas 25 45 16 22 2 7 1 2 Stenotrophomonas 8 12 6 8 1 2 0.3 1 TOTAL 29110 27438 28598 27453 1433 530 1180 2160


173

Figure 5.1.5 Core microbiota of seabass gill (A) and skin (B) at the ASV level. Distinctive bars represent relative abundance of each ASV for healthy, diseased, treatment and recovery states, labeled to the lowest taxonomic level possible.


174

5.1.4.2 Skin bacterial composition and diversity Alpha-diversity estimates varied significantly across all states (RRPP, P ≤ 0.002; Table

5.1.1, Figure 5.1.2B) and between states in the skin microbiome. They decreased significantly

between healthy and diseased fish and increased significantly between diseased and

treatment states (RRPP, P ≤ 0.003; Table 5.1.1, Figure 5.1.2B). Beta-diversity estimates show

significant differences across all states and between states (Adonis, P < 0.05; Table 5.1.1).

As for the skin microbiome, Bacteroidetes, Proteobacteria and Verrucomicrobia were

the most abundant phyla retrieved across states, accounting for 87% to 93% of the sequences

altogether (Table 5.1.2). The genera NS3a marine group, Polaribacter 4, Pseudomonas and

Stenotrophomonas were the most abundant in all four states (Table 5.1.2, Figure S5.1.1B).

Moreover, Pseudoalteromonas accounted for 5% of the sequences in the treatment state, but

only 0.1–1% in the remaining states (Table 5.1.2, Figure S5.1.1B).

Mean proportions of the bacterial taxa varied significantly between states: (i) in healthy

versus diseased states 4 taxa increased and 4 decreased; (ii) in diseased versus treatment

states 5 taxa increased and 3 decreased; and (iii) in treatment versus recovery states 5 taxa

increased, 2 decreased and 1 remained constant (Table 5.1.2, Figure 5.1.4B). The 3 most

abundant phyla and 5 most abundant genera all varied significantly (P ≤ 0.03) across the four

states (Table 5.1.1, Figure 5.1.4B). All taxa varied significantly between healthy and diseased

states (P ≤ 0.01); all except one varied significantly between diseased and treatment states

(P ≤ 0.02); and 2 genera varied significantly (P ≤ 0.02) between treatment and recovery states

(Table 5.1.1).

A total of 43 ASVs formed the core microbiome of all four states, 17 were present in

the healthy state, 8 were present in the diseased state, 33 in the treatment state and 8 in the

recovery state (Figure 5.1.5B). It is worth noticing that 2 ASVs were unique to the healthy state

and 16 ASVs were unique to the treatment state.

5.1.5 Discussion In this study, we investigated the dynamics of the gill and skin microbiomes in 140

samples of the farmed seabass Dicentrarchus labrax during a natural disease outbreak and

subsequent antibiotic treatment with oxytetracycline. We used high-throughput sequencing

technology to generate 16S rRNA bacterial ASVs and examine changes in microbial

composition and diversity over six weeks. We identified Photobacterium damselae as the most

probable causative agent of disease.

The most abundant taxa found in the gill and skin microbiomes of healthy farmed

seabass (Dicentrarchus labrax) belonged to the Proteobacteria, Bacteroidetes and

Verrucomicrobia phyla. These phyla have been previously described as the most abundant in


175

the gill and skin microbiomes of several teleosts (Boutin et al., 2014; Legrand et al., 2018;

Lowrey et al., 2015; Tapia-Paniagua et al., 2018), including the seabass (Chiarello et al., 2015;

Pimentel et al., 2017; Rosado et al., 2019). At the genus level, the most abundant taxa were

the NS3a marine group, Polaribacter 4, Pseudomonas, and Stenotrophomonas in the gills and

skin (Table 5.1.2, Figure 5.1.4, Figure S5.1.1), and Rubritalea in the gills (Table 5.1.2, Figure

5.1.4, Figure S5.1.1). These results are mainly in accordance with previously described

microbiomes of healthy seabass (Pimentel et al., 2017; Rosado et al., 2019), including fish

retrieved from the same farmed population during winter months (Rosado et al., 2019).

However, one of the most abundant genera in the healthy seabass gill microbiome was

Polynucleobacter (Rosado et al., 2019), which in the present study only accounted for 0.2%

of the sequences in the healthy state, and 4–7% in the other three studied states (Table 5.1.2).

Another compositional difference was the high abundance of Stenotrophomonas found in both

tissues in apparently healthy individuals (Table 5.1.2) in this study, but not in Rosado et al.

(2019). Several environmental factors known to impact microbiome composition, such as

seasonality (de Bruijn et al., 2018; Larsen et al., 2015) and water temperature (Lokesh and

Kiron, 2016), could be driving these differences between our two studies.

The composition and diversity of the gill and skin seabass microbiomes varied

differently during infection. Whereas in the skin there was a significant decrease in alpha-

diversity between healthy and diseased fish, there were no significant differences in the gill

microbiome. An overall decrease in microbial richness was also reported for the skin of Atlantic

salmon as a result of infection with salmonid alphavirus (Reid et al., 2017) and sea lice

(Llewellyn et al., 2017); but interestingly, as in the present study, Legrand et al. (2018) reported

significant differences in microbial richness between the skin of healthy and enteritis-infected

yellowtail kingfish, but not in the gills.

Significant changes in beta-diversity occurred in both gills and skin, showing clear

signs of dysbiosis in both tissues. In the skin microbiome of diseased fish, the abundance of

taxa from the non-pathogenic NS3a marine group and Polaribacter 4 decreased, whereas the

pathogenic Pseudomonas and Stenotrophomonas significantly increased. Pseudomonas spp.

almost doubled their abundance and largely dominated the skin microbiome of diseased fish.

While the genus Stenotrophomonas contains important globally emergent fish pathogens (e.g.

Stenotrophomonas maltophilia; Abraham and Adikesavalu, 2016; Brooke, 2012),

Pseudomonas harbors both opportunist fish pathogens (e.g. P. baetica, P. chlororaphis, Hatai

et al., 1975; López et al., 2012; amongst others, Boutin et al., 2013; Pridgeon and Klesius,

2012) and taxa with known antimicrobial activity against fish pathogens (e.g. Flavobacterium

psychrophilum, Korkea-aho et al., 2011). For example, P. fluorescens is an important

pathogen of carp and salmon (Austin and Austin, 2016; Loch et al., 2012), but is also known

to inhibit the growth of Saprolegnia, an oomycete that causes huge losses in aquaculture (Liu


176

et al., 2015; van West, 2006). Importantly, a ten-fold increase of Pseudoalteromonas, which

was not amongst the most abundant taxa in healthy fish, occurred in the skin of diseased fish.

Species from this genus can inhibit the growth of both Vibrio spp. and Photobacterium

damselae (Lloyd and Pespeni, 2018; Offret et al., 2016; Papaleo et al., 2012; Reid et al., 2017;

Richards et al., 2017), hence an increase of Pseudoalteromonas could lead to a decrease of

the other two genera, as we have seen in the skin microbiomes of seabass transitioning from

healthy to diseased states (from 2% to 0.7% and from 0.3% to 0.2%, respectively). In the gills

of diseased fish, the majority of the most abundant bacterial genera in the healthy state (NS3a

marine group, Polaribacter 4, Pseudomonas, Rubritalea and Stenotrophomonas) decreased

significantly in abundance during infection, with the exception of Polynucleobacter. Amongst

the most abundant taxa in the gill, only Photobacterium spp. was exclusively associated with

diseased fish, where it showed a 25-fold increase. Similarly, all studies addressing the effects

of parasitic infection on fish microbiomes reported significant changes in microbial composition

(Legrand et al., 2018; Llewellyn et al., 2017; Reid et al., 2017). Importantly, all of these studies

reported an increase of potentially pathogenic taxa, which highlights the opportunistic nature

of such pathogens (Legrand et al., 2018; Llewellyn et al., 2017; Reid et al., 2017). Although

Photobacterium damselae was only highly abundant in the diseased gill microbiome, the

tissue with more significant shifts in overall bacterial composition (alpha-diversity) between

healthy and diseased states was the skin. This is not totally unexpected, since it has been

shown that this pathogen can unequally affect the microbiome of distinctive mucosal surfaces

such as the skin and gill (Legrand et al., 2018).

The effects of the disease in the core microbiomes were also significant and again

different between tissues, with a shift of core species in the gill and a decrease of core diversity

in the skin from healthy to diseased states. A shift of the microbial assemblages with

enrichment of specific groups was also described for the gill microbiome of the yellowtail

kingfish as a result of enteritis (Legrand et al., 2018).

Antibiotics administration can negatively impact host physiology in different ways (e.g.,

inhibiting mitochondrial gene expression, Morgun et al., 2015; decreasing enzymatic activity,

Chiu et al., 2013; leading to dysbiosis and the emergence of antibiotic resistant bacteria,

Carlson et al., 2017; Essam et al., 2016; Gaulke et al., 2016; Liu et al., 2012; Pindling et al.,

2018). Specifically, the reported effects of oxytetracycline in the gut microbiome of the Atlantic

salmon showed a clear reduction in taxonomic diversity, becoming almost exclusively

composed of the oxytetracycline resistant Aeromonas spp., which include the salmon

pathogens Aeromonas sobria and A. salmonicida (Navarrete et al., 2008). Similarly, in

zebrafish, long-term exposure (6 weeks) to environmental concentrations of oxytetracycline,

prompted both a decrease in gut microbial diversity and higher mortality when fish were

challenged with the pathogen A. hydrophila (Zhou et al., 2018). The impact of broad-spectrum


177

antibiotics in the skin microbiome of Gambusia affinis have also been assessed (Carlson et

al., 2017, 2015). In this case, the use of rifampicin led to a decrease of diversity in the skin

microbiome after 2.6 days of antibiotic administration. Additionally, as reported for zebrafish

and Atlantic salmon, fish subjected to rifampicin antibiotic administration were more

susceptible to infection due to osmotic stress and exhibited less growth compared to the

control group, an effect that lasted one month after treatment (Carlson et al., 2017, 2015). A

key difference with the present study is that the fish used by Carlson et al. (2017, 2015) were

healthy before antibiotic administration. Importantly, our results showed that skin core diversity

was higher in healthy than in recovery individuals, indicating a negative effect of disease and

antibiotic use.

In the present study, administration of oxytetracycline resulted in a dramatic reduction

of Photobacterium abundance in the gill microbiome, with this genus no longer being one of

the most abundant taxa in the treatment and recovery states. This was expected given the

reported sensitivity of P. damselae to several antibiotics, including oxytetracycline (Abdel-Aziz

et al., 2013). Pseudoalteromonas, however, remained one of the most abundant taxa in the

skin microbiome during treatment perhaps due to the host innate immune response mediated

by the skin microbiome, given the ability of this genus to produce antimicrobial metabolites

that are correlated with host homeostasis (Offret et al., 2016).

Previous studies on the impact of antibiotics on fish skin microbiomes showed that,

even though stabilization of bacterial communities during recovery occurs, neither diversity

nor composition returns to healthy-like values in the short term (after 1 week, Carlson et al.,

2017, 2015). Here the relative frequency of the most abundant taxa found in the skin

microbiome of the seabass during the recovery period, which corresponded to 3 weeks, was

similar to that in healthy individuals (P ≥ 0.1 for all taxa except the NS3a marine group). In the

gill microbiome, however, differences in taxa proportions between the healthy and recovery

states were significant for almost all of the most abundant taxa. Hence, although dysbiosis

due to infection was more noticeable in the skin than in the gill, the microbial communities

present in the skin seem to be more resilient than those of the gill. Importantly, although the

abundance of Photobacterium damselae in the gill seemed to have been controlled through

antibiotic administration, it increased significantly in the recovery state, surpassing its initial

proportion in the healthy state.

5.1.6 Conclusion In summary, the mucosal surfaces of fish, such as the gill and the skin, are constantly

exposed to several pathogens in the aquatic environment and are crucial to prevent and/or

control disease (Trivedi, 2012). It has been shown that both infectious diseases and antibiotic


178

treatment lead to a decrease in microbial diversity, which translates into a decrease in host

immunity (Carlson et al., 2017; Legrand et al., 2018). Here we described microbial changes in

the gill and skin of adult seabass in response to a natural disease outbreak followed by a

succeeding treatment with oxytetracycline. We showed that the gill and skin microbiomes are

highly disturbed by both infection and antibiotic treatment, ultimately decreasing their diversity.

5.1.7 Acknowledgements This work was funded by the European Regional Development Fund (ERDF) through the


Technology (project PTDC/MAR-BIO/0902/2014 -POCI-01-0145-FEDER-016550; project

PTDC/BIA-MIC/27995/2017 POCI-01-0145-FEDER-027995; and by a “Projecto de

Investigação Exploratória”: IF/00764/2013); the Welsh Government and Higher Education

Funding Council for Wales (HEFCW) AquaWales Project through the Sêr Cymru National

Research Network for Low Carbon Energy and Environment (NRN-LCEE). DR, MP-L and RX

were supported by FCT under the Programa Operacional Potencial Humano – Quadro de

Referência Estratégico Nacional funds from the European Social Fund and Portuguese

Ministério da Educação e Ciência (DR doctoral grant SFRH/BD/117943/2016; MPL:

IF/00764/2013; RX: IF/00359/2015).

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5.1.9 Supplementary material

Figure S5.1.1 Individual microbial variation of the most abundant (>5%) genera recovered from the gill (A) and skin (B) of the seabass. Distinctive bars represent relative abundance of each genus.


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5.2 Monitoring disease and antibiotic treatment in

the skin microbiota of farmed seabass fingerlings

Daniela Rosado, Marcos Pérez-Losada, Ricardo Severino, Raquel Xavier

2021. Microbial Ecology. https://doi.org/10.1007/s00248-021-01795-8.

5.2.1 Abstract The microbiota of fish skin, the primary barrier against disease, is highly dynamic and

modulated by several factors. In fish aquaculture, disease outbreaks occur mainly during

early-life stages, with associated high economic losses. Antibiotic treatments sometimes

remain the best option to control bacterial diseases, despite many reported negative impacts

of its use on fish and associated microbiota. Notwithstanding, studies monitoring the effects

of disease and antibiotic treatment on the microbiota of fingerlings are scarce. We sequenced

the bacterial 16S rRNA V4 gene region using a metabarcoding approach to assess the impact

of a mixed infection with Photobacterium damselae ssp. piscicida and Vibrio harveyi and

subsequent antibiotic treatment with flumequine, on the skin microbiota of farmed seabass

(Dicentrarchus labrax) fingerlings. Both infection and antibiotic treatment led to a significant

increase in bacterial diversity and core microbial communities and impacted microbiome

structure. Dysbiosis was confirmed by changes in the abundance of potential pathogenic and

opportunistic bacterial taxa. Skin bacterial metabolic function was also significantly affected

by flumequine administration, suggesting a detriment to fish skin health. Our results add to an

increasing body of literature, showing how fish microbiome response to infection and

antibiotics cannot be easily predicted.

Keywords: Flumequine, Dicentrarchus labrax, fish microbiota, Photobacterium damselae

ssp. piscicida, Vibrio harveyi, dysbiosis

5.2.2 Introduction Commensal microbiota are an essential part of the immune response of animals,

including fish, with a crucial role in disease prevention (Kelly and Salinas, 2017; Trivedi, 2012).

Perturbations to the homeostasis of the commensal organisms is termed dysbiosis and can

occur through three not mutually exclusive events linked to the occurrence of diseases

(Petersen and Round, 2014): i) pathobiont expansion, ii) reduced diversity, iii) and loss of


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beneficial microbes. Most of the microbiome research in vertebrates is focused on the gut

microbiota due to their recognized role in sustaining the gut-brain axis and in disease outcome

(Osadchiy et al., 2019). However, in the case of fish, because pathogens are ubiquitous in the

aquatic environment, fish skin and associated mucus are considered the primary barrier

against diseases, with increasing numbers of studies focusing on the skin microbiota (e.g.,

Legrand et al., 2018; Llewellyn et al., 2017; Rosado et al., 2019). Evidence shows that fish

skin bacteria are highly dynamic, with composition and diversity being sensitive to both biotic

(e.g., ontogeny, Rosado et al., 2021) and abiotic factors (e.g., infection, Reid et al., 2017).

Although aquaculture is the fastest growing food-producing industry, disease

outbreaks due to pathogenic bacteria are one of the biggest constraints for its sustainability

(Borges et al., 2020). The impact of disease on the microbiota of several fish species has been

assessed, albeit being directed mainly at adult populations (e.g., skin and gill, Legrand et al.,

2018; Llewellyn et al., 2017; Reid et al., 2017; Rosado et al., 2019; gut and stomach,

Parshukov et al., 2019; Tran et al., 2018). It is well established that disease often leads to

dysbiosis, through a decrease in bacterial diversity and/or increases in the abundance of

pathogenic taxa other than the main etiological agent of disease (e.g., Le Luyer et al., 2021;

Legrand et al., 2018). Frequently, dysbiosis was also seen to lead to changes in predicted

microbial function (e.g., Llewellyn et al., 2017; Tran et al., 2018; Zhang et al., 2018). Although

disease and mortality incidences in aquaculture are higher on fry or young fingerlings, the

impact of disease on the microbiota during these early life stages is still poorly known (Borges

et al., 2020). Nevertheless, existing reports show that diversity, structure and potential function

of the microbiotas of early-life stages are also affected by disease (skin, Le Luyer et al., 2021;

gut, Vasemägi et al., 2017; gut, Miyake et al., 2020). The European seabass Dicentrarchus

labrax is susceptible to several bacterial pathogens, two of the most threatening being

Photobacterium damselae and Vibrio spp. (FAO, 2016). Both bacteria genera are usually

reported from the skin of healthy fish, and dysbiosis is usually accompanied by increases in

their abundances (e.g., European seabass, Rosado et al., 2021b; perl gentian grouper, Deng

et al., 2020). These pathogens cause photobacteriosis and vibriosis and infections can be

systemic, affecting multiple organs (Essam et al., 2016; Mohamad et al., 2019).

Although vaccines are available against major diseases, procedures are costly and

cause substantial stress and mortality, and they mostly confer short term immunity

(Bakopoulos et al., 2003). For these reasons, antibiotics are still widely used to control disease

in aquaculture. Several studies have reported the negative impacts of antibiotic use on fish,

which include behavioral changes (Almeida et al., 2019a), microbial diversity decrease

(Almeida et al., 2019b, 2019a; Rosado et al., 2019), increased susceptibility to secondary

infections (Carlson et al., 2017), changes in predicted microbial function (Almeida et al.,

2019b) and mortality (Pindling et al., 2018). Flumequine is a fluoroquinolone antibiotic active


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against Gram-negative bacteria and widely used in aquaculture (De Liguoro et al., 2019).

Depending on water temperature, it can persist in fish skin and muscle up to 4-14 days since

the last administration (FAO, 1997). To the best of our knowledge, the impact of flumequine

in the microbiota of fish has never been assessed.

Here, we used a metataxonomic approach (Marchesi and Ravel, 2015) to characterize

the skin microbiota of the seabass Dicentrarchus labrax fingerlings before, during and after a

natural disease outbreak of Photobacterium damselae ssp. piscicida and Vibrio harveyi, and

subsequent antibiotic treatment with flumequine. Our goal was to describe changes in

composition, structure and potential function of the skin microbiota caused by disease and

subsequent antibiotic treatment. We predict that: i) disease will cause dysbiosis through a

decrease in the microbial diversity and core microbiota, while antibiotic treatment will have the

opposite effect; and ii) both disease and antibiotic treatment will lead to changes in the most

abundant bacterial taxa and differences in microbiome structure.

5.2.3 Material and methods

5.2.3.1 Experimental design, sampling and preparation Fish were reared in an open water circulation system in a semi-intensive farming

facility, where water is supplied from the Alvor Estuary (Southern Portugal). Sampled fish

belonged to the same age cohort and were sampled from the same rearing pond. Fish were

6 months old on the first sampling date, weighing on average 57 g, and 7 months old on the

last sampling date, weighing on average 70 g. Individuals were randomly caught using a

fishing pole, and skin samples were taken using tubed sterile swabs (Medical Wire &

Equipment, UK). We swabbed the right upper lateral part of the fish skin from head to tail.

Swabs were immediately stored at -20 °C until transported on dry ice to the CIBIO-InBIO

laboratory where they were kept at -80 °C until processed.

Individuals were collected once a week between August 23 and September 13, 2016,

encompassing four different sampling dates corresponding to four different fish health states:

healthy (N=30), diseased (N=30), treatment (N=30) and recovery (N=15) (Figure 5.2.1).

During the “healthy” state (sampled on August 23), all individuals were considered healthy due

to lack of visible disease symptoms. The second sampling point occurred on August 30, and

on August 31 fish began to die. Hence, samples collected on August 30 were categorized as

“diseased” (although still asymptomatic). Treatment with flumequine antibiotic was initiated on

August 31, being administered at 35 g/tns of fish through commercial feed until September 6.

Bacterial isolates from the liver, kidney and spleen of diseased fish were collected prior to the

start of antibiotic treatment and pathogens were identified via PCR by a commercial company

(Acuipharma, Spain). PCR amplification showed that the etiological agents of infection were


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Photobacterium damselae ssp. piscicida and Vibrio harveyi. Fish were again sampled on the

last day of antibiotic administration (September 6) and these samples were categorized as

“treatment”. A final sampling point occurred on September 13 when fish were asymptomatic,

corresponding to the “recovery” state.

Total DNA from 105 fish samples and 6 controls (extraction kit negative controls) was

extracted using the PowerSoil DNA Isolation Kit (QIAGEN, Netherlands), following the

manufacturer’s protocol. Extraction kit negative controls were pooled into one single sample.

DNA extractions were shipped in dry ice to the University of Michigan Medical School (USA)

for amplification and sequencing on a single run of the Illumina MiSeq platform according to

the protocol of Kozich et al. (2013). Each sample plus 4 PCR blanks and 4 identical mock

communities (ZymoBIOMICS Microbial Community DNA Standard) were amplified and

sequenced for the V4 hyper-variable region of the 16S rRNA gene (~ 250 bp).

In total, 3,389,081 16S rRNA sequences were retrieved for the skin of the seabass

fingerlings, and the number of sequences per sample ranged from 12,891 to 48,211. ASVs

present in negative controls (extraction kit and PCR) were removed from downstream

analysis. After removal of contaminants and non-bacterial sequences, 6,163 ASVs (3,300,989

sequences) were assigned to the skin microbiota of the seabass fingerlings. Diversity and

bacterial abundances of the mock communities corresponded to those described by the

manufacturer. Microbial taxa showing a mean relative proportion ≥ 1% were considered as

part of the most abundant taxa in the microbiota.

5.2.3.2 Data and statistical analyses Raw FASTQ files were denoised using the DADA2 pipeline in R (Callahan et al., 2016).

We estimated a midpoint rooted tree of ASVs using the Quantitative Insights Into Microbial

Ecology 2 package (QIIME2; release 2020.11). We constructed a table containing amplicon

sequence variants (ASVs) and made taxonomic inferences against the SILVA (138 release)

reference database (Quast et al., 2013). We normalized ASV abundances using the negative

binomial distribution, which accounts for library size differences and biological variability

(McMurdie and Holmes, 2014). The core microbiota was assessed at the ASV level for each

health state (i.e., sampling date) separately. An ASV was considered as part of the core

microbiota if present in 100% of the samples from each state.

Microbial alpha-diversity was calculated using Shannon and Faith’s phylogenetic (PD)

diversity indices as implemented in the R phyloseq package (McMurdie and Holmes, 2013).

Additionally, Pielou’s evenness was calculated as implemented in the R microbiome package

(Lahti and Shetty, 2018). Microbiome structure (beta-diversity) was estimated using

phylogenetic UniFrac (weighted and unweighted) and Bray-Curtis distances. We assessed


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variation in microbiome diversity and structure using Kruskal-Wallis and PERMANOVA

(adonis function of the R vegan package, Oksanen et al., 2008), respectively. Dissimilarity

between samples was visually assessed through a principal coordinates analysis (PCoA) and

dendrograms. A heatmap was built to depict changes in the abundance of the most abundant

phyla and genera (≥1% of all reads). All analyses were performed in R-studio v1.2.500.

Predicted bacterial metabolic functions were estimated using the metagenomic

Phylogenetic Investigation of Communities by Reconstruction of Unobserved States software

(PICRUSt2) embedded in QIIME2 (Douglas et al., 2019), applying a weighted nearest

sequenced taxon index (NSTI) cutoff of 0.03. Predicted metagenomes were collapsed using

the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway metadata (Kanehisa et al.,

2019). We used linear discriminant analysis (LDA) in LEfSe to identify differentially abundant

metabolic pathways in the skin microbiota of the seabass fingerlings using state as a class, a

P-value cut-off of 0.05 and a LDA effect size cut-off of 2 (Segata et al., 2011).

5.2.4 Results

5.2.4.1 Microbial diversity and composition Alpha-diversity estimates varied significantly between states (P ≤ 3-3, Table 5.2.1),

with the exception of the PD index between treatment and recovery states, and the Pielou’s

evenness between healthy and recovery states (P = 0.2, Table 5.2.1). There was a significant

increase in diversity and evenness from healthy to diseased states as well as from diseased

to treatment states (Figure 5.2.2). Between treatment and recovery states, a significant

decrease in diversity and evenness occurred (Figure 5.2.2).

Figure 5.2.1 Schematic illustration of the experimental design and health status of each sampling point.

There were a total of 49 bacterial phyla and 926 bacterial genera identified across all

samples. Of those, 6 phyla and 32 genera were found in high abundance in at least one of the

health states (Figure 5.2.3, Table S5.2.1). The abundance of these taxa varied with the onset

of disease and antibiotic treatment (Figure 5.2.3, Table S5.2.1). It is noteworthy that the

abundance of Photobacterium (identified as P. damselae) in the skin remained stable between


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healthy and disease states, having decreased with antibiotic treatment and then again in

recovery (Figure 5.2.3, Figure 5.2.4, Table S5.2.1). On the other hand, the abundance of Vibrio

increased from healthy to the disease state, having decreased with antibiotic treatment and

increased again in recovery (Figure 5.2.3, Figure 5.2.4, Table S5.2.1).

Table 5.2.1 Microbial diversity comparisons for the skin of the seabass Dicentrarchus labrax across all samples and between the four different states. For each Kruskall-Walis test (alpha-diversity) we report the chi-squared value (overall) and significance (P value, overall and pairwise) and for each PERMANOVA test (beta-diversity) we report the R2 statistics (overall) and significance (P value, overall and pairwise). Significant associations are indicated in bold.

Metric Overall Healthy/ Diseased

Diseased/ Treatment

Treatment/ Recovery

Healthy/ Recovery

Shannon 66 (3-14) 4-7 2-6 1-6 4-7 PD 55 (9-12) 6-7 3-3 0.2 3-9 Pielou’s 53 (2-11) 3-3 8-7 1-7 0.2 UniFrac weighted 0.3 (9-5) 7-2 6-3 6-3 0.1 UniFrac unweighted 0.1 (9-5) 6-3 6-3 6-3 6-3 Bray-Curtis 0.3 (9-5) 2-3 1-3 1-3 1-3

Figure 5.2.2 Alpha- and beta-diversity estimates plotted for the skin microbiota of the seabass Dicentrarchus labrax fingerlings during the different states. Alpha-diversity estimates are represented by boxplots depicting mean values and standard deviations of the Shannon, Faith’s phylogenetic (PD) and Pielou’s evenness estimates. Beta-diversity is represented by PCoA plots computed using the UniFrac (weighted and unweighted) and Bray-Curtis distances. Microbial structure based on the UniFrac unweighted distance (Ward method) is further represented by a dendrogram. Each dot in the PCoA and each line in the dendrogram represents a microbiota sample. All graphs are colored by state.

Of the 74 ASVs recovered from the core microbiota across the four states, 6 were

present in the healthy state, 20 in the diseased state, 30 in the treatment state and 67 in the

recovery state (Table S5.2.2). This corresponded to 0.4, 0.7, 1 and 4% of the total ASVs found

in the healthy, diseased, treatment and recovery states, respectively. There were 2 unique

core ASVs in the diseased and treatment states each, while 38 were unique to the recovery

state (Table S5.2.2). Of the 2 unique core ASVs recovered from the microbiota of diseased

fish, one was identified as Photobacterium damselae (Table S5.2.2). Additionally, there were

2 ASVs belonging to Vibrio that were part of the core microbiota during the diseased state, but


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not during the healthy or treatment states. In the recovery state, those two and four other

Vibrio ASVs were part of the core microbiota of the skin.

Figure 5.2.3 Heatmaps of the most abundant (³ 1%) phyla and genera recovered from the skin of the seabass Dicentrarchus labrax fingerlings for healthy, diseased, treatment and recovery states. Unknown genera are labeled as u.g.

Beta-diversity estimates show significant differences between states (p ≤ 6-3, Table

5.2.1), except for the UniFrac weighted distance between healthy and recovery states (P =

0.1, Table 5.2.1). On the other hand, differences between states showed relatively low

dispersal (R < 0.3, Table 5.2.1). Analyses of the PCoA of Bray-Curtis distances showed that

samples from the healthy, treatment and recovery states clustered separately, while no

apparent structural cluster of samples between states was observed from the UniFrac

distances (PCoA, Figure 5.2.2). Moreover, the dendrogram constructed from the UniFrac

unweighted distance showed two main clusters separating healthy fish from the remainder

samples. Within the latter cluster, treatment and recovery states were more closely related

(Figure 5.2.2). Dendrograms constructed from the UniFrac unweighted and Bray-Curtis

distances showed no structure between the health states (Figure S5.2.1).


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Figure 5.2.4 Alluvial plots of relative frequency of key genera recovered from the skin of the seabass Dicentrarchus labrax fingerlings for healthy, diseased, treatment and recovery states. Unknown genera are labeled as u.g.

5.2.4.2 Microbial predicted function There were a total of 478 predicted KEEG pathways in the skin microbiota of the

seabass. Linear discriminant analysis of the metagenomic predictions performed in LEfSe

showed that 3, 5 and 7 different pathways were significantly enriched in the healthy, treatment

and recovery states, respectively (Figure 5.2.5). Interestingly, there were no significantly

enriched pathways in the diseased state. On a broad level, all enriched pathways were related

to either biosynthesis (67% in healthy, 57% in recovery) or to

degradation/utilization/assimilation (33% in healthy, 100% in treatment, 43% in recovery)

categories (Figure 5.2.5). Specifically, enriched metabolic pathways in the healthy state were

related to amino acid degradation and fatty acid and lipid biosynthesis; in the treatment state

enriched pathways were related to carbohydrate degradation and secondary metabolite

degradation; and, finally, in the recovery state enriched pathways were related to carbohydrate

degradation, vitamin biosynthesis, fatty acid biosynthesis, purine nucleotide biosynthesis and

sugar derivative degradation (Figure 5.2.5).


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Figure 5.2.5 LDA scores of differentially enriched pathways in the skin microbiota of the seabass Dicentrarchus labrax fingerlings during healthy, treatment and recovery states. No pathways were differentially enriched during the diseased state. Bios: biosynthesis; DUA: Degradation/Utilization/Assimilation. LEfSe tests were performed with a P value and LDA score cut-offs of 0.05 and of 2, respectively.

5.2.5 Discussion We characterized for the first time the effects of bacterial infection with

Photobacterium damselae ssp. piscicida and Vibrio harveyi and treatment with flumequine in

the skin microbiota of seabass fingerlings. Most of our predictions were confirmed with one

important exception; contrary to our expectations and most of the previous literature, both core

microbiota and microbial diversity increased with the onset of disease. However, dysbiosis

was accompanied by an increase in the abundance of potential pathogenic and opportunistic

taxa.

5.2.5.1 Disease effects in skin microbiota of seabass fingerlings:

healthy vs diseased states Impacts on microbial diversity, richness and evenness caused by infection by bacterial

pathogens and parasites have been described in some fish (in the skin of rainbow trout

infected with Ichthyophthirius multifilis, Zhang et al., 2018; gut of Asian seabass infected with

Tenacibaculum singaporense, Miyake et al., 2020; skin of Atlantic salmon infected with

Lepeophtheirus salmonis, Llewellyn et al., 2017; gut of grass carp with enteric infection, Tran

et al., 2018; gut of brown trout infected with Tetracapsuloides bryosalmonae, Vasemägi et al.,

2017; skin of orbicular batfish infected with Tenacibaculum maritimum, Le Luyer et al., 2021;

gut and stomach of rainbow trout infected with Caligus lacustri, Parshukov et al., 2019; and

skin of adult seabass infected with Photobacterium damselae, Rosado et al., 2019). Dysbiosis

was reported on the vast majority of these studies through decreases in fish microbial diversity


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and increases in pathobionts. Even though an increase in diversity was observed in the

present study, the direction of the changes in the abundance of key microbial taxa indicates

dysbiosis occurred in the skin microbiome of seabass fingerlings. In the present study, Vibrio

(that encompasses one of the etiological agents of infection), and two other unidentified

genera belonging to families with opportunistic taxa, Flavobacteriaceae and Vibrionaceae

(Austin and Austin, 2016), increased their abundance in the diseased state. Another genus

that increased in abundance in diseased fish was Aureispira, previously found to be highly

abundant in the intestinal microbiota of grouper juveniles after iridovirus infections (Joe et al.,

2021). Similar increases in bacterial diversity after disease have been already described in

other fish (e.g., Llewellyn et al., 2017; Tran et al., 2018; Vasemägi et al., 2017; Zhang et al.,

2018), indicating that changes in microbial diversity cannot be readily expected and growth or

decline of specific taxa easily predicted.

Alterations to the core microbiota in response to infection were also previously reported

in the skin of adult European seabass infected with Photobacterium damselae (Rosado et al.,

2019), and in the yellowtail kingfish infected with with enteritis (Legrand et al., 2018). In the

present study, a Photobacterium damselae ASV was 100% prevalent in the diseased state.

However, its mean abundance remained unaltered between the healthy and disease states,

suggesting the skin was only indirectly affected by this pathogen. These results are in line with

our previous study showing that infection caused by P. damselae can lead to dysbiosis of skin

microbiota of farmed seabass despite no increase in abundance (Rosado et al., 2019). Similar

results were also obtained by Legrand et al. (2018), who described skin and gill dysbiosis in

the yellowtail kingfish during enteritis, a gut disease.

Microbiome community structure of the skin microbiota was also significantly affected

by infection. Although samples of diseased fish were collected on the day prior to disease

onset, only a few samples from the diseased state (7 out of 30) clustered within the healthy

group, confirming that significant taxonomic changes had occurred in most individuals

analyzed. These results suggest that some properties of the skin mucous that allowed certain

phylogenetically related taxa to thrive in the skin may have been altered by disease,

consequently affecting resident microbiota. However, despite the increase in microbial

diversity and changes in structure driven by disease, microbial metabolic functions remained

unaltered. This suggests that the increase in diversity observed between healthy and diseased

states was due to colonization by bacteria with similar functions (Kelly and Salinas, 2017).


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5.2.5.2 Flumequine effects in skin microbiota of seabass fingerlings:

diseased vs treatment states In the present study, microbial diversity was observed to increase on the 8th day of

treatment with flumequine. However, as expected, administration of flumequine resulted in a

decrease in abundance of both etiological agents of disease in this study. This is unsurprising

given the reported sensitivity of both species to this antibiotic (Laganà et al., 2011).

Importantly, this treatment led to an increase of potentially harmful Flavobacteriaceae (Austin

and Austin, 2016). Interestingly, the genus Alteromonas, which has been shown to exhibit

antibacterial activity against fish pathogens, including Photobacterium damselae and several

Vibrio spp. (Stevens et al., 2016), and resistance against amoxicillin, erythromycin and

gentamicin (Wu et al., 2019), increased in abundance during antibiotic treatment. Microbial

disruptions have been reported after oxytetracycline and rifampicin treatment in microbiota of

adult fish (e.g., Almeida et al., 2019b, 2019a; Carlson et al., 2017; Rosado et al., 2019), and

after streptomycin, ciprofloxacin or oxytetracycline treatment on earlier life fish stages (e.g.,

zebrafish larvae, López Nadal et al., 2018; Pindling et al., 2018). Although an increase in fish

microbial diversity caused by antibiotic administration is less common, it has been reported

before (e.g., López Nadal et al., 2018; Rosado et al., 2019). Indeed, in the studies where

diversity decreased after antibiotic exposure, there were no pre-existing health conditions. On

the other hand, in this study as well as in Rosado et al. (2019), disease had occurred, and in

the study by López Nadal et al. (2018), fish were immersed with the anti-nutritional compound

saponin before antibiotic treatment. This suggests pre-existing disease/microbiota disruption

and antibiotics may have a compound effect on microbial diversity.

Significant changes in the potential function of the skin microbiota were detected after

antibiotic treatment. Specifically, the degradation of carbohydrates and secondary metabolites

were significantly enriched during antibiotic treatment. However, the production of

carbohydrates and secondary metabolites is linked to the protective role of the microbiota

(e.g., Hooper and Gordon, 2001; Kelly and Salinas, 2017). For example, carbohydrates are

directly related to specific cell-cell adhesion, modulating microbial binding to the mucus

(Brandley and Schnaar, 1986). It is suggested that carbohydrate synthesis by the human

microbiota helps establish symbiosis with microbial commensals and aids pathogenic evasion

(Hooper and Gordon, 2001). Furthermore, production of secondary metabolites is one of the

mechanisms by which commensal microbiota fight against pathogens (Kelly and Salinas,

2017). These results suggest a microbial response to antibiotics, which may ultimately have a

negative effect on fish immunity.


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5.2.5.3 Recovery of the skin microbiota of seabass fingerlings: healthy

vs recovery states Previous studies reported that short-term recovery of the microbiota of fish after

antibiotic treatment does not lead to the diversity levels observed in the healthy state (e.g., 1-

week recovery, Carlson et al., 2017). In the present study, with the exception of Pielou’s

evenness, diversity significantly increased between healthy and recovery states. Importantly,

the abundance of Vibrio increased in the recovery state, indicating microbial balance may not

have been fully obtained.

Microbiome structure was also significantly different between the recovery and healthy

states. However, closely related microbial structuring was found in fish from treatment and

recovery states. Almost half of the enriched metabolic pathways during the recovery state

were related to the same categories of pathways enriched during the treatment state

(carbohydrate and secondary metabolite degradation), with significant differences from the

healthy state. To the best of our knowledge, only the study by Brumlow et al. (2019) has

effectively measured the effects of 3-day antibiotic treatments (Tetracycline and Rifampicin)

in the biochemical profile of the skin of Gambusia affinis. In this study the authors also report

changes in community composition relative to pre-treatment, after 8 day recovery. However,

unlike the present results, where significant changes to microbial function were predicted, the

results of Brumlow et al. (2019) indicated that the biochemical functions of microbiota were

mostly reestablished after the 8 day recovery. Flumequine is a highly persistent antibiotic and

it can take several weeks to be fully depleted from the blood and tissues of fish (Hansen et al.,

2001; Sohlberg et al., 2002). This antibiotic has a slower depletion rate in the skin than in

muscle or liver, and can be present in the skin 20 days after oral administration (Malvisi et al.,

1997). Although a longer time frame would be necessary to evaluate whether full functional

recovery of the skin microbiota does occur, our results highlight the high susceptibility of skin

microbiota to antibiotic exposure.

5.2.6 Conclusion Homeostasis of the microbial communities in the mucosal surfaces of fish is central to

control pathogen abundances (Trivedi, 2012). Here, we describe a dysbiosis episode caused

by a disease outbreak induced by Photobacterium damselae ssp. piscicida and Vibrio harveyi,

and subsequent antibiotic treatment with flumequine. Although, overall, antibiotic treatment

appeared to have a greater impact on the skin microbiota when compared with the infection,

this could be a result of a cumulative effect of both disease and antibiotic treatment. Moreover,

the microbial profile of the fish in the recovery state differed from that of the fish in the healthy

state, as well as their predicted metabolic function. The results from this study highlight that


200

the response of fish commensal bacteria to disease and antibiotics are complex and not easily

predicted.

5.2.7 Acknowledgements This work was funded by the European Regional Development Fund (ERDF) through the


Technology (project PTDC/BIA-MIC/27995/2017 POCI-01-0145- FEDER-027995); DR, MP-L

and RX were supported by FCT under the Programa Operacional Potencial Humano – Quadro

de Referência Estratégico Nacional funds from the European Social Fund and Portuguese

Ministério da Educação e Ciência (DR doctoral grant SFRH/ BD/117943/2016; MP-L:

IF/00764/2013; RX: IF/00359/2015; and 2020.00854.CEECIND).

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5.2.9 Supplementary material Table S5.2.1 Relative mean proportions of sequences (%) belonging to the most abundant (≥ 1%) phyla and genera in the skin microbiota of the seabass Dicentrarchus labrax in healthy (N=30), diseased (N=30), treatment (N=30) and recovery states (N=15). Taxa with ≥ 1% relative mean proportion in any state are indicated in bold. Unknown genera are labeled as u.g.

Healthy Diseased Treatment Recovery Phyla Actinobacteriota 1 0,7 0,6 0,2 Bacteroidota 21 29 37 28 Chloroflexi 0,4 0,4 1 1 Fusobacteriota 0,2 0,3 0,8 1 Proteobacteria 74 63 52 63 Verrucomicrobiota 1 2 4 3 Genera Alkalimarinus 1 1 1 1 Alteromonas 2 2 5 3 Arcticiflavibacter 0,9 1 2 1 Aureispira 0,3 2 0,2 0,4 Fabibacter 0,4 0,5 1 0,3 Formosa 0,2 0,9 1 0,6 Maribacter 0,3 1 2 1 Marinomonas 1 0,9 2 1 NS10 marine group 0,8 3 3 0,5 NS3a marine group 0,5 0,2 0,8 1 Oleispira 8 9 3 13 Photobacterium 1 1 0,2 0,1 Polaribacter 5 3 4 7 Pseudoalteromonas 17 12 12 19 Pseudomonas 19 11 6 1 Psychrobium 1 1 0,2 0,3 Psychrosphaera 1 0,9 2 0,9 Rubritalea 0,3 0,2 0,9 1 Ruegeria 0,2 0,5 1 0,5 Stenotrophomonas 5 3 2 0.4 Tenacibaculum 2 2 1 0,4 Thalassotalea 3 1 2 2 Vibrio 4 5 2 7 Winogradskyella 0,2 0,5 0,9 0,4 Alteromonadaceae (u.g.) 0,1 0,1 2 0,4 Cryomorphaceae (u.g.) 3 2 2 2 Flavobacteriaceae (u.g.) 3 5 6 4 Rhodobacteraceae (u.g.) 0,5 1 1 0,7 Saprospiraceae (u.g.) 0,4 0,8 1 0,6 Vibrionaceae (u.g.) 0,4 0,7 0,1 1 Bacteroidia (u.g.) 0,3 0,5 1 0,7 Gammaproteobacteria (u.g.) 0,3 1 1 0,2

Table S5.2.2 Core ASVs present in the skin microbiota of the seabass Dicentrarchus labrax across states. Unknown genera are labeled as u.g.

Taxonomy Healthy Diseased Treatment Recovery Actibacter - - - ASV48 Alkalimarinus - ASV16 ASV16 ASV16 Alteromonas - ASV7 ASV7 ASV7 Arcticiflavibacter - - ASV19 ASV19; ASV38 Aureispira - - - ASV20 DBS1 - - ASV54 - Fabibacter - - ASV22 ASV22 Formosa - - ASV29 ASV29 Fusobacterium - - - ASV133 Hypnocyclicus - - - ASV99 Labilibacter - - - ASV78 Lacinutrix - - ASV40 ASV40 Lentisphaera - - - ASV36 Leucothrix mucor - - - ASV62 Lewinella - - - ASV50 Maribacter - - ASV32 ASV28; ASV32 Marinicella - - - ASV55 Marinifilum - - ASV59 ASV59 Marinomonas ASV15 ASV15 ASV15 ASV15



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(Table S5.2.2 Continued) Neiella marina - - - ASV142 NS1 marine group - ASV9 ASV9 ASV69 NS3a marine group - - - ASV31 Oceanospirillum - - - ASV68 Oleiphilus - - - ASV85 Oleispira antarctica ASV3 ASV3 ASV3 ASV3 Photobacterium damselae - ASV18 - - Polaribacter - ASV4 ASV4 ASV4 Porticoccus - - - ASV88 Propionigenium - - - ASV102 Pseudoalteromonas ASV1 ASV1 ASV1 ASV1 Pseudoalteromonas phenolica - ASV12 ASV12 ASV12

Pseudomonas ASV14; ASV2 ASV14; ASV2 ASV14;

ASV2 ASV2

Psychrilyobacter atlanticus - - - ASV111 Psychrobium - ASV21 - ASV21 Psychromonas - - - ASV176 Psychrosphaera saromensis - ASV13 ASV13 ASV13 Reinekea - - - ASV94 Rubritalea - - ASV35 ASV35 Ruegeria - - ASV26 ASV26 Salinirepens - - ASV98 - Sanguibacter - ASV24 - - Spongiibacterium - - - ASV57 Stenotrophomonas ASV5 ASV5 ASV5 Tamlana - ASV37 - ASV37 Tenacibaculum - - ASV17 ASV17 Thalassotalea - ASV11 ASV11 ASV11 Thalassotalea agariperforans - - ASV43 ASV43

Vibrio - ASV23; ASV27 - ASV129; ASV23; ASV27; ASV33; ASV63;

ASV82 Vibrio fortis - ASV10 ASV10 ASV10 Vicingus - - - ASV83 Winogradskyella echinorum - - ASV34 ASV34 Alteromonadaceae (u.g.) - - ASV25 ASV25 Cellvibrionaceae (u.g.) - - - ASV47 Cryomorphaceae (u.g.) - - ASV8 ASV8 Flavobacteriaceae (u.g.) - - ASV6 ASV6 Marinifilaceae (u.g.) - - - ASV208 Rhodobacteraceae (u.g.) - - - ASV71 Saprospiraceae (u.g.) - - - ASV97 Spongiibacteraceae (u.g.) - - - ASV156; ASV89 Vibrionaceae (u.g.) - - - ASV30 Kordiimonadales (u.g.) - - - ASV53 Verrucomicrobiales (u.g.) - - - ASV116 Lentisphaeria (u.g.) - - - ASV140; ASV233


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Figure S5.2.1 Microbial structure of the skin microbiota of seabass Dicentrarchus labrax fingerlings based on the UniFrac weighted and Bray-Curtis distances (Ward method). Each line in the dendrogram represent a microbiota sample. All graphs are colored by state (Healthy = green; Diseased = orange; Treatment = yellow; Recovery = purple).


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CHAPTER 6

General Discussion This doctoral thesis was developed in close collaboration with an European seabass

and gilthead seabream farm, located in the south of Portugal. The natural settings of a fish

farm presented several advantages to the study of fish microbiomes in relation to studies

developed in the wild, namely information provided on fish health, age and diet, and to some

extent the environment. The sampling scheme allowed me to take advantage of regular

access to fish, which encompassed weekly sampling between 2016 and 2018 of the skin and

gill microbiota of the seabass and seabream. This allowed me to generate important insights

about the biotic and abiotic factors shaping the microbiota of farmed seabass and seabream.

Specifically, I was able to test the impact of: a) host taxonomy (Chapter 2); b) mucosal tissue

(skin and gill, Chapters 2, 3, 4 and 5.1); c) host age (Chapter 3); d) water temperature

(Chapter 4); and e) water microbial communities (Chapters 3 and 4) on shaping the skin and

gill microbiota of the European seabass Dicentrarchus labrax and gilthead seabream Sparus

aurata. The occurrence of natural bacterial disease outbreaks and consequent antibiotic

treatment in seabass, also allowed me to shed light on the role of infection and antibiotics on

seabass dysbiosis and disease (Chapter 5).

6.1 Variability associated with the skin and gill microbiota in farmed

seabass and seabream The microbial communities of the two studied fish mucosae, skin and gill, presented

differences in diversity (seabass and seabream, Chapters 2 and 3) and response to external

stressors (seabass, Chapter 5.1). Generally, the skin microbiota had higher diversity than the

gill microbiota. Such microbial differences between and within body habitats are well

documented in teleost species (e.g., Legrand et al., 2020a; Lowrey et al., 2015; Pratte et al.,

2018; Zhang et al., 2019). It is known that commensal microbial communities have coevolved

with each body habitat, being highly adapted to specific environment (Lee and Mazmanian,

2010). This adaptation is the result of each niche selective pressures related with physiology

and immunity (Kelly and Salinas, 2017). In fish, these could be the result of different mucosa

associated lymphoid tissues (MALTs, Kelly and Salinas, 2017), physicochemical properties

(Egerton et al., 2018), osmo- and iono-regulatory function (Fu et al., 2010; LeBlanc et al.,

2015), antimicrobial peptides (Noga et al., 2010), mucous histology (Dang et al., 2020), to

mention a few. To the best of my knowledge, there are no comparative studies between fish


209

tissue and microbiome function under homeostasis (e.g., through dual-transcriptomics). I

hypothesize that different physiological requirements within each tissue will translate into

singular microbial communities optimized to provide specific functional needs at each body

site. Nevertheless, the longitudinal analysis of microbial diversity of the skin and gill of the

seabass across one year (in Chapter 4) followed the same trends, suggesting modulation by

the same factors. In that study I observed that under healthy conditions, diversity of the gill

microbiota of the seabass was more stable, i.e., less variable across months, while the skin

microbiota was more variable throughout the year (Chapter 4).

Dysbiosis, observed in Chapters 4 and 5.1, was also more pronounced in the skin

microbiota of the seabass, which presented higher susceptibility to dysbiosis as well as higher

incidences of potentially pathogenic genera. On the other hand, the gill microbiota of the

seabass was less resilient than the skin microbiota in presence of disease and antibiotic

treatment, with significant differences in diversity between healthy and recovery states

(Chapter 5.1). These results suggest that, although the variation observed in the skin

microbiota was higher than in the gill and may underlie a higher susceptibility to dysbiosis, it

may also be responsible for a faster recovery to healthy-like diversity values after microbial

disruption. It has been hypothesized that physiological plasticity, i.e., the ability to change in

response to changes in the environment, confer the microbial community resilience to

disturbances (e.g., Seebacher et al., 2014; Shade et al., 2012). Hence, the skin, perhaps due

to its larger area, offers more opportunities for colonisation by different organisms when

compared to the gill. This could explain its higher bacterial richness and, to some extent, its

increased variability over time due to increasing microbial interactions (e.g., competition). The

differences in microbial response to stressors observed here between tissues can also be a

manifest of the different immunological characteristics of each body niche. For example, a

study showed that cortisol related signatures of stress in Atlantic salmon were related to

changes in the microbial diversity and structure of the feces but not the skin (Uren Webster et

al., 2020). It is known that fish microbial communities regulate host adaptive immune system

and its response, influencing the resilience to stress and disease (Kelly and Salinas, 2017).

Given that microbial diversity is highly adapted to each tissue, it is only logical that its role in

the immune response will also change between body habitats.

Although significantly different, skin microbial structure of both seabass and seabream

were more similar to the water microbial structure, when compared to the gill, especially in

younger age groups (Chapter 3). A recent study showed that skin and gut microbiota interact

more with the surrounding environment, such as water and biofilms formed in fish tanks, than

the gill microbiota (Minich et al., 2020b). Nonetheless, predicted monthly microbial dispersion

was higher between the skin and gill microbiota of adult seabass than between both tissues

and water (Chapter 4). This further suggests that continuous microbial exchange of


210

communities between skin and gill may be creating colonizing opportunities in response to

changes in the environment (Chapter 4).

6.2 Host related factors shaping the skin and gill microbiota in farmed

seabass and seabream Baseline microbial characterization of the skin and gill of farmed seabass and

seabream revealed structural differences between the two species (Chapter 2), corroborating

the effect of host taxonomy on microbial diversity reported for fish (e.g., Larsen et al., 2013;

Pratte et al., 2018; Reverter et al., 2017; Sylvain et al., 2020). However, differences in

microbial composition between seabass and seabream were not seen in the gill. This indicates

that when seabass and seabream share the same environmental conditions, host taxonomy

mostly influences microbiota at the structural level. Similar results have been reported for the

gut microbiota of the phylogenetically related species blue and channel catfish (Bledsoe et al.,

2018). This is likely related to species' traits that exert specific selective pressures. For

example, different species can have distinctive physiological and immune requirements at

homeostasis and respond differently to altered conditions (e.g., Ellison et al., 2020). Despite

the high inter-individual microbial variation commonly observed in fish (e.g., Burns et al., 2017;

Chiarello et al., 2015), and also verified in our studies, seabream microbiota were overall less

diverse than those of the seabass (Chapters 2 and 3). Results also showed that differences

between skin and gill were more pronounced in the seabass, with significant differences in

microbial composition and structure, when compared to the seabream, where only structure

was significantly different (Chapter 2).

The longitudinal study in Chapter 3 showed different effects of aging in seabass and

seabream, although for technical reasons the results for the two species were not directly

comparable. Individuals of farmed seabass and seabream belonging to distinct age groups

(corresponding to different levels of sexual maturation and growth), presented differences in

microbial structure and predicted function. However, only seabass showed significant

differences in microbial composition across age groups, indicating a higher stability of

seabream microbiota during development. The few longitudinal studies evaluating the

ontogenetic effect on teleost microbiota showed that fish experience diversity shifts at different

times of the development depending on the species (e.g., Bledsoe et al., 2016; Lokesh et al.,

2019; Nikouli et al., 2019; Stephens et al., 2016; Wilkes Walburn et al., 2019; Yan et al., 2016).

On the other hand, major life events such as diet transitions (Wilkes Walburn et al., 2019),

environmental transitions (Lokesh et al., 2019), sexual differentiation (Stephens et al., 2016),

or sexual maturation (Chapter 3), seem to be an important factor triggering microbial diversity

shifts through time across teleost species. It is known that sexual maturation in fish is


211

accompanied by physiological changes that will allocate energy from survival to gamete

production and reproductive behavior (Saborido-Rey and Kjesbu, 2005). Growth and sexual

maturation can be characterized by changes in metabolism (e.g., Persson et al., 2005),

morphology (e.g., Walleser et al., 2014), hormones (e.g., Hatef and Unniappan, 2019),

behavior (e.g., Davis and Kassel, 1975), among others. Such extreme changes lead to

alterations in the physicochemical characteristics of the fish tissues (e.g., Saborido-Rey and

Kjesbu, 2005), that can trigger shifts in the microbial communities in order to adapt to the

altered environment as well as functionally aid any supplementary needs. In our study, the

trend of an increased diversity with age observed in the seabass was translated into an

enrichment of the predicted functional diversity. In the seabream, although bacterial diversity

remained seemingly stable throughout development, an increase in functional diversity was

also predicted.

6.3 Water related factors shaping the skin and gill microbiota in farmed

seabass and seabream Besides the effects of host age, significant microbial shifts in fish were also previously

suggested to reflect changes in a multitude of environmental variables, the vast majority

related to water characteristics, such as temperature, pH, ionic composition, dissolved

oxygen, conductivity or chlorophyll a (Hovda et al., 2012; Krotman et al., 2020; Minich et al.,

2020a; Vasemägi et al., 2017; Zarkasi et al., 2014). In fact, in the present thesis a pronounced

effect of water temperature on seabass skin and gill microbial diversity and predicted functions

were seen throughout the year (Chapter 4). Such impact was evident both over short (couple

of days) and long (several weeks) periods of time and was patent in both microbial composition

and structure. Microbial shifts due to temperature occurring at small temporal scales have

been reported for abrupt transitions (e.g., Kokou et al., 2018; Tarnecki et al., 2019), while long

term microbial changes were often associated with season (e.g., Minich et al., 2020a). Albeit

the observed strong effect of temperature, other environmental characteristics might have also

been responsible for the high microbial variability observed in the microbiota of farmed

seabass over the studied year. These results highlight the importance of longitudinal studies

and repeated methodical measurements of environmental variables.

Overall, water microbiota had higher compositional diversity than host associated

communities, independently of their age group (Chapter 3), in accordance to what is usually

described in the literature (Thompson et al., 2017). However, this was not always the case

when analyzing the monthly diversity of the microbiota over a year (Chapter 4). Excluding

months where dysbiosis occurred, there were some months where water microbial diversity

was lower than those of the skin or gill, demonstrating the importance of assessing longitudinal


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variation. This work also demonstrated a significant structural distinction of the water

microbiota from the skin and gill microbiota of the seabass and seabream (Chapter 3), in

agreement with the majority of the studies (e.g., Chiarello et al., 2019; Pratte et al., 2018; Wu

et al., 2020; Zhang et al., 2019). Nevertheless, at least to some extent, microbial communities

of the surrounding water seem to contribute to the microbial diversity of the skin and gill, since

several ASVs were shared between water and all studied microbiotas in both species

(Chapter 3). Similar findings are not uncommon and suggests that body microbiota

composition is a subset selection of the environment microbes (e.g., Nikouli et al., 2019; Pratte

et al., 2018; Zhang et al., 2018). In fact, water microbial communities were found to be one of

the source contributors to the monthly skin and gill microbial diversity of adult seabass;

however, that contribution was the lowest when compared to the other sources (Chapter 4).

On the other hand, it is interesting to notice that the potentially pathogenic genera, whose

increase in abundance promoted dysbiosis in the skin and gill microbiota of the seabass, were

not found in high abundances in the water microbial community (Chapter 4). This suggests

that other potential sources may contribute to the dispersal of potentially pathogenic genera.

One such source might be the biofilms established in rearing tanks, which are known

reservoirs of pathogenic bacteria (e.g., Cai and Arias, 2017; King et al., 2004). Moreover,

biofilms were seen to have greater impact than the surrounding water in mucoase microbial

diversity (Minich et al., 2020b). Additionally, potentially pathogenic genera can enter

aquaculture settings through feed or broodstock, among others (Saksida et al., 2014). On the

other hand, it is known that healthy mucosal surfaces are naturally colonized by potentially

pathogenic bacteria that, under specific environmental conditions, can become pathogenic to

the host (e.g., Givens et al., 2015; Hess et al., 2015; Legrand et al., 2018).

6.4 Dysbiosis in the skin and gill microbiota of farmed seabass and

seabream There were several dysbiotic events observed in the skin and gills of farmed European

seabass populations during the analyzed period. In all but one occasion, dysbiosis was

associated with an increase in the abundance of potentially pathogenic (PP) genera; however,

disease was not always confirmed (Chapter 4 and 5).

In the longitudinal study performed in Chapter 4, despite seabass individuals showing

no signs of disease, three dysbiotic events were evident. Dysbiosis was characterized by a

significant decrease in diversity, which on two occasions was accompanied by an increase in

the abundance of potentially pathogenic genera (Aliivibrio, Pseudomonas, Photobacterium

and Vibrio). Even though pathogenic genera can naturally be part of the healthy microbiota

(e.g., Califano et al., 2017; Givens et al., 2015), such as Vibrio in seabass and seabream


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(Chapter 2), an increase in their abundance can lead to dysbiosis. In Chapter 4, we showed

that changes in the abundance of potentially pathogenic bacteria were also correlated to

changes in the water temperature, indicating that it is a factor inducing dysbiosis. Additionally,

these dysbiotic events occurred in warmer months and during cold/warm transitions, further

demonstrating the importance of changes in water temperature for the onset of such events.

It is especially important that the relationship between temperature and commensal microbial

dynamics is further investigated given the emergence of climate change and ocean warming

(e.g., Baker-Austin et al., 2017). For example, it has been demonstrated that environmental

factors influence the proportion of bacteria with probiotic or pathogenic properties in fish gut

(Zarkasi et al., 2014; Zhao et al., 2020).

Monitoring the skin and gill microbiota of healthy seabass allowed me to understand

how bacterial infection was linked to microbial imbalance in adults (Chapter 5.1) and

fingerlings (Chapter 5.2). Microbial response to disease was asymmetrical between tissues

in the adult population (Chapter 5.1), although representing what constitutes a standard sign

of dysbiosis (Petersen and Round, 2014). Additionally, the increase of abundance of the genus

Pseudoalteromonas in the skin microbial community suggests that this genus could represent

a biomarker for seabass health. The probiotic activity of several Pseudoalteromonas species

has been described against Vibrio spp. and Photobacterium damselae (e.g., Offret et al., 2016;

Richards et al., 2017). Additionally, differences in response between different age groups of

seabass (Chapter 5; Cámara-Ruiz et al., 2021) reinforce a possible ontogenetic effect, likely

related to the development of the immune system. However, it is also likely that different

diseases (photobacteriousis vs vibriosis + photobacteriousis) could cause the observed

differences in response between age groups.

It is important to notice that within the past 5 years no signs of infection were detected

in the seabream populations of the tanks monitored during this thesis. This could attest for a

stronger immune system, including features within the microbiome (e.g., higher stability

throughout time, Chapter 3); however, studies outside the scope of this thesis would be

necessary to fully understand this question.

In both studies included in Chapter 5 treatment with antibiotics, a standard procedure

in fish farms (e.g., Cabello, 2006), was initiated after disease diagnosis. The two antibiotics

used were oxytetracycline in adults (Chapter 5.1) and flumequine in fingerlings (Chapter 5.2).

Results revealed that both antibiotics significantly impacted microbial diversity, with both

oxytetracycline and flumequine leading to an increase in diversity in the skin microbiota

(Chapter 5). On the other hand, oxytetracycline prompted a decrease in core diversity in the

gill microbiota of adult seabass, although differences in composition were not significant

(Chapter 5.1). Differences in response to external stressors are most likely related to

differences in the overall diversity and stability observed between tissues. Overall, gill


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microbial community was more stable than that of the skin. Flumequine treatment also

impacted the predicted metabolic function of the skin microbiota of seabass fingerlings. These

results add to an increasing body of literature showing the harmful effects of antibiotics on fish

microbial homeostasis and consequently, fish health (e.g., Almeida et al., 2019; Carlson et al.,

2017; Kim et al., 2019; Legrand et al., 2020a; Pindling et al., 2018). These studies further

reveal how unpredictable these effects can be and reaffirm the urgent need for alternatives to

antibiotic administration in aquaculture.

Short term microbial recovery of both adults (3 weeks, Chapter 5.1) and fingerlings (1

week, Chapter 5.2) after antibiotic administration was also assessed. Significant differences

in composition and structure between healthy and recovery states were in line with previous

studies revealing the lasting impacts of antibiotic exposure on skin microbial diversity of fish

(e.g., Carlson et al., 2017, 2015). Interestingly, core diversity decreased in adult fish and

increased in fingerlings during recovery, when compared to the core diversity presented by

healthy individuals of both age groups, exhibiting another asymmetrical response that could

be related to development. Additionally, the abundance of the etiological agents of disease in

both studies (Photobacterium, Chapter 5.1 and Vibrio, Chapter 5.2) increased again during

the recovery state, indicating microbial homeostasis had not been reached yet.

6.5 Microbiome monitoring implications for aquaculture The microbiome of fish can be responsible by a great number of beneficial functions,

such as digestion nutrition, growth, reproduction and immunity (Diwan et al., 2021), granting

the host with the phenotypic plasticity needed to adapt to a particular habitat and living

conditions (Rajeev et al., 2021). In aquaculture settings, infectious diseases are one of the

main causes of economic loss with an overexploitation of antimicrobials having developed into

the emergence of antimicrobial resistance (Schar et al., 2020; Rajeev et al., 2021). In this

regard, microbiome research can be explored to optimize fish farm practices, since

commensal microbes play a role in immune protection (Luna et al., 2022; Rajeev et al., 2021).

There are already several preventive and treatment approaches in aquaculture directed

towards microbial modulation, such as the use of probiotics (e.g., Sun et al., 2014), prebiotics

(e.g., Torrecillas et al., 2013), synbiotics (e.g., Rodriguez-Estrada et al., 2013), fecal

microbiota transplant (FMT, e.g., Smith et al., 2017), biofloc technology (e.g., Emerenciano et

al., 2013), or RAS technology (e.g., Espinal and Matulić, 2019) (see Chapter 1 for details).

Thus, it is well established that microbial manipulation is essential to guarantee a profitable

and sustainable aquaculture industry by helping prevent as well as mitigate infectious

diseases (Diwan et al., 2021; Luna et al., 2022; Yılmaz et al., 2021). However, in order to

maximize the efficiency of microbial therapy, it is critical to know what constitutes a healthy


215

commensal community for a species or population and how it can be affected by disturbances

such as disease (Rajeev et al., 2021).

In this thesis, longitudinal monitoring of the skin and gill microbiota of fish revealed

consistent abundant taxa across time, uncovering the predominant bacteria of adult seabass

(Chapters 2, 3 and 4) and seabream (Chapter 2 and 3). Proteobacteria and Bacteroidota

were the dominant phyla, in both fish species and across tissues, time of sampling and health

status, in accordance to what has been described for teleost species (Legrand et al., 2020b;

Llewellyn et al., 2014), including the skin of seabass and seabream (Cámara-Ruiz et al., 2021;

Chiarello et al., 2015; Pimentel et al., 2017; Tapia-Paniagua et al., 2018). Microbial

composition at the genus level was more variable and differed between the sampling periods

analyzed in this thesis. Nevertheless, the genus NS3a marine group was highly abundant in

healthy tissues and species analysed in this thesis. Additionally, Rubritalea and

Polynucleobacter were highly abundant in seabass gill, and Vibrio in seabream skin across

time (Chapters 2, 3 and 4). Despite the variability of abundant genera in time, several ASVs

were 100% prevalent and established the core microbiota of the populations analyzed during

winter (seabass and seabream, Chapter 2) and across one year (seabass, Chapter 4). This

resilient and healthy functional core is composed by genetic strains capable of resisting host

and habitat disturbances (Rajeev et al., 2021).

Monitoring farmed seabass populations also allowed me to explore microbial dynamics

during dysbiotic events (Chapters 4 and 5). For example, I was able to see how the

abundance of potentially pathogenic genera can be correlated with the abundance of potential

probiotics (Chapter 4), or how certain genera increase their abundance during infection

(Chapter 5), representing potential biomarkers. In microbiome research, a biomarker is a

microbial species or a group of species that represent an altered state of homeostasis due to

changes in the biotic and/or abiotic conditions (Llewellyn et al., 2014). For example, the

commensal microbiota will shift depending on host behavioral and physiological response to

stress (Llewellyn et al., 2014). This imbalance of the microbiome, or dysbiosis, is often a

precursor of disease, being crucial to detect the microbial shift through the known biomarkers

before the onset of disease (Llewellyn et al., 2014). In the future, a deeper thorough study of

the complex microbial dynamics occurring during the onset of dysbiosis will be very insightful

to detect all possible microbial biomarkers for stress and disease resistance. Biomarkers can

be further studied to find healthier alternatives to antibiotics for farmed European seabass and

gilthead seabream, such as probiotics.

The results of this thesis supplemented the body of literature revealing not only the

baseline healthy microbial communities of farmed seabass and seabream but also potential

probiotic genera. These results can be taken further to develop alternatives to vaccination

and/or antibiotics to combat infection, helping decrease antimicrobial resistance and


216

production losses. Indeed, such practice is already been recommended to be implemented for

the thriving of the aquaculture industry without putting at risk entire populations of aquatic

animals (Rajeev et al., 2021). Additionally, the results of this thesis regarding fish development

(Chapters 3 and 5) highlight the importance of considering age in fish microbiome studies.

Information regarding the different healthy microbial communities and different microbial

response to infection can refine the research on potential probiotics to be used in aquaculture

settings at each developmental stage. Such extremely valuable information for the successful

application of microbiome research in aquaculture was only unveiled due to the frequent

monitoring of the microbial communities in these farmed populations. Combining

environmental and microbial data using regular monitoring within aquaculture settings coupled

with machine learning algorithms can be very powerful for biomonitoring programs and early

detection of microbial imbalance and prevention of disease (Cordier et al., 2019; Luna et al.,

2022).

6.6 Future perspectives The methodology applied in this thesis to achieve the goals was carefully deliberated

in order to make the most of the available resources. All data were collected non-invasively

and all work presented here is the result of natural occurrences, i.e., no condition was ever

manipulated. Although this strategy might have limited the results in terms of the amount of

information gathered (for example only temperature was regularly measured), it provided

“real” data. Overall, my goals were accomplished and there were very few limitations to sample

collection and sequencing success. On the other hand, in the process of answering the initial

questions, many others germinated.

One of the main caveats in this research was the impossibility to record additional

environmental data besides temperature, that could help explain much of the variability

observed. Fish were reared in an exterior coastal circulation system situated in an estuary,

where water is renewed with every tide and where the environment presents extreme turbidity

and is highly variable throughout the day. For these reasons, specific probes would have been

needed to take accurate, repeated, short-term (e.g., hourly) measurements in order to have

reliable data on other environmental variables. Future work will definitely address these

issues, which will complement our results and help describe to what extent environmental

variables shape microbial diversity. In this regard, it will also allow us to identify other variables

triggering dysbiosis, eventually helping to generate prediction models for dysbiosis in

aquaculture settings. It will also be critical to assess other sources of microbial dispersal,

including potential pathogens, like biofilms, nets, soil or feed. Lastly, in order to evaluate the


217

true resilience of the microbial communities, assessment of the recovery period will need to

be further extended beyond three weeks after infection or antibiotic treatment.

By selecting to collect non-invasive samples, our work also lacked analyses of other

tissues, mainly the gut, as well as other host related factors that could explain microbial

variation, such as sex or weight. Designing experimental studies within the laboratory or in

mesocosm settings under controlled environments will be important to measure other

explanatory factors and compare immune response and dysbiosis. For example, in order to

better understand the effect of ontogeny in response to disease, controlled experiments should

be done using the same aetiological agent in fish populations comprising different age groups.

All data produced in this thesis was based on a metataxonomics approach using the

16S rRNA marker gene, allowing identification of the microbial communities present in the skin

and gill of farmed seabass and seabream under different conditions. However, to gain

additional knowledge regarding the role of factors shaping microbial diversity and function,

such as the ones studied here, and to explore host-microbiome interactions, other

approaches, such as shotgun, metatranscriptomic or dual-transcriptomic sequencing should

be employed.

The microbiota comprises all the microbes (bacteria, archaea, fungi, viruses and

protozoa) living in an environment; however, fish microbiome research has mainly focused

on the bacterial microbiota. In order to fully understand microbe-host-environment

interactions, further studies should also include other microbial organisms and widen the

questions.

6.7 Conclusions The study of the microbiome was initially focused on humans and model organisms,

but recently expanded to many other taxa. This doctoral thesis substantially increased our

knowledge on factors shaping the microbiota of farmed fish as well the factors triggering

dysbiosis. Aquaculture is the most promising food-production industry worldwide that is bound

to accompany human population growth (FAO, 2018). However, there are several constraints

to its further development, the biggest being infectious diseases, urgently requiring improved

practices and health management (Béné et al., 2015; Lafferty et al., 2015). In this regard,

studying the microbiome of fish and the surrounding environment can unveil many answers,

given the role of the microbiome in health and immunity (Kelly and Salinas, 2017; Trivedi,

2012).

Our research showed a high variability of the skin and gill microbiota across time

(Chapters 2-4) governed by host (Chapters 2-3) and environmental (Chapter 4) factors. We

also showed that regular monitoring of the microbial communities of farmed species can yield


218

extremely valuable insights that can ultimately help detect, prevent or mitigate disease

outbreaks in aquaculture settings (Chapter 5). Microbiomes are intimately linked to the

function and health of ecosystems (Sehnal et al., 2021). Due to their sensitivity to

environmental disturbances, ubiquitous nature as well as their high-throughput low cost

sequencing, versatile and informative analysis, microbiomes can be perfect indicators of

health and ecological status of aquatic systems (Sehnal et al., 2021). In this regard, ecosystem

bioassessment and ecological quality status can be done through microbiomes, especially

when other traditional biomonitoring tools are not available or effective (Sehnal et al., 2021).

The questions in this field of research are virtually endless and its potential applications

are fundamental for the future of all living beings. Results from this thesis substantially added

to the existing knowledge and can pave the way to fundamental improvements in the health

of European seabass and gilthead seabream in aquaculture settings.

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FCUP To be or not to be diseased: microbial dynamics and dysbiosis in farmed European seabass and gilthead seabream 227

APPENDIX A

List of additional publications

List of additional publications that are within the scope of fish microbial research and that I

participated in as a co-author.

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