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Disentangling the ecology of bacterial communities in cnidarian holobionts
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Disentangling the ecology of bacterial communities in cnidarian holobionts
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COPYRIGHT 2023 EMILY GABRIELA AGUIRRE
DISENTANGLING THE ECOLOGY OF BACTERIAL
COMMUNITIES IN CNIDARIAN HOLOBIONTS
by
Emily Gabriela Aguirre
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOLOGY (MARINE BIOLOGY AND BIOLOGICAL OCEANOGRAPHY))
AUGUST 2023
ii
DEDICATION
I have encountered exceptionally bright and intelligent individuals, not only within the confines
of academic institutions, but also weaving through the streets and embedded in the community.
What sets us apart is opportunity and some luck. I dedicate this accomplishment to them. May
the next generation appreciate, benefit, and thrive from the opportunities their parents were not
fortunate to have!
I extend this dedication to my daughter, Xochitl Aguirre, who has been by my side
throughout this educational journey from the very start. I aspire to shatter the generational curse,
while also serving as a positive role model for her. I have given it my best with the tools I have
(which have accumulated throughout the years) and will continue to do so until the end.
iii
ACKNOWLEDGEMENTS
Above all, I want to express my heartfelt gratitude to my advisor, Carly D. Kenkel. The success
of a student can be gauged by their productivity and overall well-being in their academic
surroundings. Carly has demonstrated unwavering dedication in maintaining a healthy lab
environment, showing respect to her students, and generously sharing her knowledge and
connections with us. I am truly grateful to Carly for giving me an opportunity and being my
biggest supporter throughout my tenure in this program, encouraging my potential even in the
face of skepticism from others. When I joined her lab, Carly welcomed me with open arms and
provided a fresh start after a previous lab experience that hindered my growth as a scientific
researcher. She allowed me the freedom to blossom and explore my own path. Carly embodies
what should be the true essence of a modern professor: brilliant, compassionate, and supportive.
I cannot emphasize enough how thankful I am to have fortuitously ended up in her lab and to
have received her guidance and support.
I would also like to thank my co-advisor John Heidelberg and qualifying/dissertation
committee members: Sergio Sañudo-Willhelmy, David Hutchins, Ebrahim Zandi, Sergey
Nuzhdin, James Boedicker and Cameron Thrash. I truly believe that my work was elevated due
to their solid feedback and thought-provoking questions, which shaped my research approach.
During this Ph.D. journey, I was extremely lucky to have collaborated with some brilliant
and supportive minds. I am extremely grateful for their willingness to teach me new skills and
view me as a colleague. I would like to thank the folks at MOTE Marine Lab (Summerland Key
in Florida, USA), Cory Krediet (Eckerd College), Rusty Lansford (Children’s Hospital Los
Angeles and USC Keck), Clay C.C. Wang (USC Dept. of Chemistry and USC Mann School of
Pharmacy), and Chris Rabot (USC Mann School of Pharmacy).
iv
Next, I would like to acknowledge past and current members of Carly’s awesome CEE
Lab. Many insightful conversations and interesting lab times were had! I wish them all the best
in their current and future endeavors and hope our paths may cross again in the future. A special
thanks to my trusty undergraduate student, Marissa J. Fine. Without her, my last two chapters
would not have been possible. During the tedious process of counting algal symbiont cells, her
patience and determination truly stood out. I wish her the best and watch out for her, she will
become the best environmental lawyer out there!
Obtaining a Ph.D. is not a solitary endeavor, but rather a collaborative effort involving
individuals who come and go, as well as those who stay longer, all of whom contribute valuable
support throughout the entire journey. For this reason, I would like to acknowledge the MORE
Programs at California State University, Los Angeles, who provided funding for my first
undergraduate research endeavors. I would like to thank my first long-term, undergrad research
mentor Sunil Mangalassary and the former director of the MORE-MARC, Vicki Kubo-
Anderson.
Additionally, I would like to thank the folks running the After School Education and
Safety (ASES) Program, who provided low-cost, after school enrichment to my daughter while I
pursued my undergraduate and graduate studies. Let me be clear that without this taxpayer
funded program, completion of my education would have been impossible due to the exorbitant
cost of childcare in the USA and the lack of parental support for students with families in
American higher-ed institutions.
Finally, I would like to also express my heartfelt thanks to my dear friends, who have
become my chosen family. Thank you for bearing with me and supporting me all these years. I
would like to especially thank Melody Aleman, Dwane Burgess, Sandi Amodeo-Burgess,
v
Johanna Rivera, Brenda Rojas, Julie Ollison, Gerid Ollison, Olivia Santiago, Cass Santiago,
Eulalia Lucas, the Lucas-Gonzalez family and Xochitl Aguirre for their presence in my life.
vi
TABLE OF CONTENTS
Dedication ...................................................................................................................................... ii
Acknowledgements ....................................................................................................................... iii
List Of Tables ............................................................................................................................ viii
List Of Figures .............................................................................................................................. ix
Abstract ...........................................................................................................................................x
Chapter 1: Introduction ……………………………………………………………...………... 1
1.1 Corals and their microbes ..............................................................................................1
1.2 Internal factors that may play a role in shaping the cnidarian microbiome ...................2
1.3 Study systems.................................................................................................................3
1.4 Thesis outline .................................................................................................................4
Chapter 2: Host-specific epibiomes of distinct Acropora cervicornis genotypes persist after
field transplantation...................................................................................................................... 6
Summary of contribution ....................................................................................................6
2.1 Abstract .........................................................................................................................7
2.2 Introduction ...................................................................................................................8
2.3 Methods........................................................................................................................10
2.4 Results ..........................................................................................................................14
2.5 Discussion ...................................................................................................................19
2.6 Acknowledgements .....................................................................................................28
2.7 Conflict of interest ......................................................................................................28
2.8 Data accessibility ........................................................................................................28
Chapter 2 Supplementary File ..........................................................................................29
Chapter 3: Abundance of Oligoflexales bacteria is associated with algal symbiont density
independent of thermal stress in Aiptasia anemones ...............................................................44
Summary of contribution ..................................................................................................44
3.1 Abstract .......................................................................................................................45
3.2 Introduction .................................................................................................................46
3.3 Materials and methods ................................................................................................49
3.4 Results .........................................................................................................................56
3.5 Discussion ...................................................................................................................61
3.6 Acknowledgements .....................................................................................................71
3.7 Conflict of interest ......................................................................................................71
vii
3.8 Data accessibility ........................................................................................................71
Chapter 3 Supplementary File ..........................................................................................72
Chapter 4: Symbiodiniaceae- Roseibium establish a stable coculture and exhibit synergistic
growth effects ..............................................................................................................................84
Summary of contribution ..................................................................................................84
4.1 Abstract .......................................................................................................................85
4.2 Introduction .................................................................................................................86
4.3 Materials and methods ................................................................................................89
4.4 Results .......................................................................................................................102
4.5 Discussion .................................................................................................................117
4.6 Acknowledgements ...................................................................................................127
4.7 Conflict of interest ....................................................................................................128
4.8 Data accessibility ......................................................................................................128
Chapter 4 Supplementary File ........................................................................................129
Chapter 5: Conclusion ..............................................................................................................133
References ..................................................................................................................................136
Appendix: Complete genome of Roseibium sp. strain Sym1, a bacterial associate of
Symbiodinium linucheae, the microalgal symbiont of the anemone Aiptasia ......................157
Summary of Contribution ...............................................................................................157
Abstract ...........................................................................................................................157
Announcement ................................................................................................................158
Data availability statement ..............................................................................................159
Acknowledgements .........................................................................................................159
viii
LIST OF TABLES
Table S2.1 Map coordinates of A.cervicornis transplant sites .................................................35
Table S2.2 Sample pipeline for each A. cervicornis genotype ...............................................36
Table S2.3 DADA2, ASV 16S rDNA reads sample pipeline (A. cervicornis) .......................37
Table S2.4 Alpha-diversity statistical output for A. cervicornis mucus samples ...................37
Table S2.5 Beta-diversity statistical output for A. cervicornis mucus samples ......................38
Table S2.6 ANOVA and Tukey HSD output for A. cervicornis mucus samples ...................39
Table S2.7 NCBI BLAST Results for MD3-55 sequences .....................................................41
Table S2.8 Wilcoxon rank sum test for comparison of MD3-55 ............................................43
Table S3.1 DADA2 and Phyloseq 16S rDNA reads pipeline (Aiptasia) ................................79
Table S3.2 NCBI BLAST matches to Oligoflexales ..............................................................79
Table S3.3 Symbiont to host (S/H) or Aiptasia/ S. linucheae ratio sample pipeline ..............79
Table S3.4 Ct values for S/H ratios (Aiptasia/ S. linucheae) ..................................................80
Table S3.5 Alpha diversity statistical output for Aiptasia samples ........................................81
Table S3.6 Beta diversity statistical output for Aiptasia samples ...........................................81
Table S3.7 Statistical output for linear mixed effects model (Aiptasia) .................................82
Table S3.8 Pearson’s correlation output for correlation matrix in Fig. S3.4 ..........................83
Table 4.1 RNA-reads, SE pipeline for S. linucheae SSA01 ...............................................100
Table 4.2 SE, RNA-reads, pipeline for Roseibium sp. Sym1 .............................................101
Table 4.3 PE, RNA-reads, pipeline for Roseibium sp. Sym1 .............................................102
Table 4.4 Sample pipeline for Roseibium sp. Sym1 DGE analysis ....................................103
Table 4.5 Statistical output for SSA01 growth ...................................................................104
Table 4.6 Welch’s t-test for SSA01 ....................................................................................104
Table 4.7 Post-hoc t-test for SSA01 ...................................................................................106
Table 4.8 Statistical output for SSA01 growth (three subculturation trials) .......................107
Table 4.9 Post-hoc t-test for SSA01 (three subculturation trials) .......................................108
Table 4.10 Statistical output for Roseibium sp. Sym1 ..........................................................109
Table 4.11 Post-hoc t-test for Roseibium sp. Sym1, grouped by time and treatment ...........109
Table 4.12 Post-hoc t-test for Roseibium sp. Sym1, grouped by time and sample type ....... 110
Table 4.13 Rank-based GO MWU test results for SSA01 ....................................................113
Table 4.14 BLAST matches of metH and metE genes to SSA01 .........................................113
ix
LIST OF FIGURES
Figure 2.1 Beta diversity in A. cervicornis samples ...............................................................16
Figure 2.2 Core microbiome of A. cervicornis mucus samples ..............................................17
Figure 2.3 MD3-55 ASV distribution .....................................................................................19
Figure S2.1 Relative abundance of taxa in A. cervicornis genotypes .......................................30
Figure S2.2 Alpha-diversity in A. cervicornis genotypes .........................................................31
Figure S2.3 Relative abundance of taxa in A. cervicornis genotypes vs site ............................32
Figure S2.4 Phylogeny of MD3-55 ...........................................................................................33
Figure S2.5 Mitochondria ASV counts .....................................................................................34
Figure 3.1 Study design for chapter 3 .....................................................................................50
Figure 3.2 Alpha and beta diversity microbiome differences of Aiptasia ..............................58
Figure 3.3 Differential heat trees ............................................................................................59
Figure 3.4 Oligoflexales in the microbiome of Aiptasia .........................................................62
Figure S3.1 Relative abundance of taxa, by class, in Aiptasia .................................................73
Figure S3.2 Relative abundance of most abundant genera in Aiptasia .....................................74
Figure S3.3 Symbiont to host ratio (S/H) in Aiptasia anemones ..............................................75
Figure S3.4 Correlation matrix of a set of variables in the Aiptasia dataset ............................76
Figure S3.5 Phylogenetic visualization of Oligoflexales ASV by treatment ............................77
Figure S3.6 Phylogenetic analysis of Oligoflexales .................................................................78
Figure 4.1 Study design for chapter 4 .....................................................................................92
Figure 4.2 Growth dynamics of S. linucheae SSA01 ...........................................................105
Figure 4.3 Growth dynamics of Roseibium sp. Sym1 ..........................................................111
Figure 4.4 PCA and heatmap of differentially expressed genes in SSA01 ..........................112
Figure 4.5 Plot of normalized counts for most differentially expressed genes in SSA01 ....114
Figure 4.6 PCA and heatmap of differentially expressed genes in Roseibium sp. Sym1 .....115
Figure S4.1 Growth dynamics of SSA01 over three subculturing trials in L1-B12 .................130
Figure S4.2 Plot of normalized counts for BHMT/metH genes in SSA01 .............................131
Figure S4.3 Plot of normalized counts for B12 biosynthesis in Roseibium sp. Sym1 .............132
x
ABSTRACT
Multicellular organisms, such as humans, plants, and invertebrates, depend on symbiosis with
microbes for metabolic cooperation and exchange. Although symbioses are generally modeled
and studied as two-way interactions, multipartite symbioses have been increasingly shown to be
both ecologically and evolutionarily relevant. While many studies have identified changes in the
microbiome of symbiotic cnidarians (invertebrates hosting photosynthetic dinoflagellate algal
endosymbionts) it is unclear whether changes in bacterial communities are modulated by
environmental or internal host factors, like host genetics or microalgal symbiont type, and
absence/presence of the symbiont. My dissertation research aimed to unravel the effects of
internal factors.
The second chapter of my thesis highlighted the connection between host specificity and
specific microbial communities in the mucus of the endangered acroporid coral, Acropora
cervicornis. The coral surface mucus, which acts as a protective barrier and contributes to
nutrient cycling, exhibits high bacterial diversity and is presumed to be influenced by the
surrounding environment, yet the influence of the host genotype on the epibiome is not well
understood. My research aimed to determine the contributions of host genotype, environment,
and their combination to the maintenance of epibiomes over time. Distinct epibiome signatures
were observed among different genotypes of A. cervicornis, primarily influenced by the relative
abundance of the ubiquitous Rickettsiales bacterial symbiont MD3-55. Although there were
slight changes in bacterial communities of surviving outplants after one year of field
transplantation, the epibiomes of A. cervicornis were distinctly shaped by the host genotype.
Next, the third chapter explored the link between microalgal symbiont abundance and
bacterial communities. In this chapter, I aimed to differentiate the effects of heat-stress and
xi
symbiont density on microbial communities, while accounting for host and symbiont genetic
diversity. I used the emerging model organism, Aiptasia clonal strain CC7 that hosts
Symbiodinium linucheae, and compared the microbiomes of symbiotic anemones undergoing
mild bleaching with those of aposymbiotic anemones that lacked the typical microbes associated
with Symbiodinium. My research showed that fluctuations in the rare microbiome occurred after
heat stress and symbiont loss. I also observed a correlation between the density of S. linucheae in
Aiptasia and the abundance of Oligoflexales bacteria, suggesting these bacteria may be primary
symbionts of S.linucheae.
Lastly, the fourth chapter dives into the role of bacteria in the cnidarian-algal symbiosis
and the mechanisms underlying Symbiodiniaceae-bacteria interactions. Bacteria may provide
essential nutrients, vitamins, and antimicrobial products to the cnidarian holobiont but the
mechanisms of exchange are unknown. B-vitamins, produced by prokaryotes, play crucial roles
in metabolic reactions, and many eukaryotic algae, including dinoflagellates, rely on them. The
dependence on B12 by Symbiodiniaceae dinoflagellates has previously been observed but
remains poorly documented. In this chapter, I established a stable coculture system between a
B12-producer, Roseibium sp. Sym1, and the microalgal symbiont of Aiptasia, Symbiodinium
linucheae strain SSA01. The objective here was to examine the possible reliance of SSA01 on
Roseibium sp. Sym1 for its B12 needs by comparing growth dynamics and transcriptomic
variations between monocultures and cocultures in B12-depleted media.
1
CHAPTER 1: INTRODUCTION
1.1 Corals and their microbes
It is essential to understand the influence of multi-partner associations on the health of marine
invertebrates, given they are highly vulnerable to the effects of climate change (Mather 2013;
Lam et al. 2020). Corals, which are invertebrate animals (Phylum: Cnidaria) that harbor
intracellular populations of dinoflagellate algae in the family Symbiodiniaceae and other
microbial associates (Pandolfi et al. 2003), are already experiencing alarming population
declines, which are predicted to worsen with climate change (Allemand and Osborn 2019;
Kleypas and Kleypas 2019). Coral reefs around the world are declining, due to bleaching caused
by climate change and other anthropogenic factors (Sharp and Ritchie 2012; Suggett and Smith
2020). Coral bleaching is the disruption of the mutualistic association between corals and their
endosymbiotic algal (dinoflagellates, family Symbiodiniaceae) partners. Bleaching can be caused
by environmental and biological stressors, such as increased temperatures or microbial
pathogens, and can ultimately result in colony death and collapse of reefs (Suggett and Smith
2020). In addition to algal symbionts, other cnidarian-associated microbes are also thought to
play important roles in host health, resilience, and disease (Nyholm and Graf 2012; Thompson et
al. 2014; Peixoto et al. 2017). Metagenomic surveys across cnidarian taxa have revealed diverse
and complex host-specific microbial communities, yet most studies continue to only examine
changes in the cnidarian-algal relationship. Bacterial associates may be important drivers in the
persistence of cnidarian-algal mutualisms, but the mechanisms governing these interkingdom
associations remain unclear (Ritchie 2012a; Sharp and Ritchie 2012; Wegley Kelly et al. 2018;
Matthews et al. 2020a). While the field has accumulated an exciting amount of metagenomic
data (Vega Thurber et al. 2009; Hernandez-Agreda et al. 2017), this data is limited to informing
2
only on microbial diversity rather than function, as microbial taxa may be phylogenetically
different but serve the same function, or phylogenetically similar but serve different functions.
Therefore, adopting a metaorganism approach (merging metagenomic studies with microscopy,
host physiology/genotype, lab experiments with isolates and biochemical data) is imperative to
understanding inter-organismal interactions between coral-associated bacteria, endosymbiotic
algae and their hosts (Jaspers et al. 2019). My thesis incorporates aspects of the metaorganism
approach to disentangle the microbial ecology of bacterial communities in cnidarian holobionts,
defined herein as the ecological unit of the host and its microbes.
1.2 Internal factors that may play a role in shaping the cnidarian microbiome
1.2.1 Host-genotype
Host genotype can influence resilience (Morikawa and Palumbi 2019; Drury 2020), stress
tolerance (Ladd et al. 2017; Wright et al. 2017) and bleaching (Thomas and Palumbi 2017) in
corals. Additionally, host genotype also affects microbial acquisition in threatened stony
Scleractinian corals (Bernasconi et al. 2019b; Glasl et al. 2019; Marchioro et al. 2020; Miller et
al. 2020). Despite recent advances, genotype and bacterial dynamics remain poorly characterized
in stony coral epibiomes (surface and mucus), yet may provide useful information for restoration
and management efforts since bacterial communities are known to play a role in health of corals
(Krediet et al. 2013; Peixoto et al. 2017; Rosado et al. 2019; van Oppen and Blackall 2019). This
is crucial in the quest to identify disease-resistant and disease-susceptible genotypes, within
species, that may benefit from more specialized restoration approaches.
1.2.2 Influence of the algal symbiont on the cnidarian microbiome
3
The cnidarian-algal bipartite symbiosis is better understood than the potential multipartite
symbiosis between cnidarians, algae and bacteria (Ritchie 2012a; Sharp and Ritchie 2012;
Bourne et al. 2016; van Oppen and Blackall 2019; Barno et al. 2021). However, high microbial
diversity poses a challenge in disentangling the nature of coral-bacterial interactions (Blackall et
al. 2015) and identifying primary (obligatory) vs secondary (facultative) bacterial symbionts in
both cnidarians and algal endosymbionts (Ritchie 2012a). Furthermore, the identity of the algal
endosymbiont introduces another multi-level factor to account for (Bernasconi et al. 2019a), as
there is evidence of potential co-occurrence between specific algal endosymbionts and bacterial
families present within the cnidarian holobiont (Olson et al. 2009; Littman et al. 2010; Rouzé et
al. 2016; Herrera et al. 2017; Buerger et al. 2022). The algal phycosphere within the host remains
poorly understood but recent studies have highlighted its potential significance in the holobiont
(Matthews et al. 2020a; Garrido et al. 2021). Evidence suggests that microbes of free-living
dinoflagellates may play a role in carbon, nitrogen and sulfur cycling (Gutierrez et al. 2012;
Hatton et al. 2012; Buchan et al. 2014; Lin et al. 2021), stress tolerance (Camp et al. 2020) and
conceivably even horizontal gene transfer (Wisecaver et al. 2013). Additionally, dinoflagellates
also putatively depend on metabolites produced by prokaryotes, which they cannot obtain from
the cnidarian host, such as vitamin B12 (Lin et al. 2022) and siderophores for iron acquisition
(Amin et al. 2009). However, it is unclear how resource exchange between algae and bacteria are
modulated in-hospite (Matthews et al. 2020a; Garrido et al. 2021), and unfortunately, there are
no tractable algal-bacterial coculture systems to experimentally investigate this within a
cnidarian holobiont.
1.3 Study systems
4
Overall, my thesis aims to investigate how host genotype and algal symbiont abundance
influence the microbiome of cnidarians, using a non-model organism and an emerging model
system. Firstly, the staghorn coral, Acropora cervicornis, was chosen as the study system for Ch.
2 because it is an ecologically relevant reef-builder and threatened Caribbean coral (Baums
2008; Young et al. 2012). This charismatic coral is the focus of numerous restoration initiatives
and continuous scientific investigations (Cunning et al. 2021; Million et al. 2022; Moulding
2022; Banaszak et al. 2023). It is an excellent choice as a non-model organism for my research,
due to its practical applications in the real world. Chapter 3 was approached using a tractable
cnidarian model system, the Anthozoan anemone, Aiptasia (sensu, Exaiptasia pallida), a close
relative of reef-building corals. Aiptasia is easy to maintain, exhibits symbiotic life strategies like
corals, and can reproduce asexually (Baumgarten et al. 2015; Weis 2019). It can also be
experimentally bleached (rendered free of symbiotic algae) without increasing mortality in
laboratory conditions which makes it easily manipulable (Lehnert et al. 2014). The specific
model organism for this chapter, Aiptasia strain CC7, harbors the same Symbiodiniaceae
(dinoflagellate endosymbiont) clade as some corals (Starzak et al. 2014; Baumgarten et al. 2015),
which makes our findings transferable to coral systems. I used the dinoflagellate endosymbiont,
Symbiodinium linucheae, derived from Aiptasia strain CC7 (Xiang et al. 2013), to conduct the
algal-bacterial experiments in Chapter 4 because it is an endosymbiont of both corals and
anemones. Additionally, it can be cultured axenically and clonally (Xiang et al. 2013), which
makes it ideal for controlled laboratory experiments.
1.4 Thesis outline
My dissertation work explored the ecology of microbes in the cnidarian holobiont and centered
around the following questions:
5
1. Is host-genotype relevant to the recruitment and long-term maintenance of microbes in
the coral epibiome?
2. Are some bacterial associates of cnidarians potentially primary symbionts of the algal
dinoflagellate endosymbiont instead?
3. What genetic mechanisms are implicated in the possible exchange of B-vitamins between
bacteria and the algal dinoflagellate endosymbiont?
My first study (Chapter 2) tackled the first question through an investigation of the host and
environmental influence on the microbiome of A. cervicornis. I identified the core microbiome
and assessed the environmental response of the epibiome, among and within genotypes,
following a one-year transplantation study. Next, in chapter 3, I investigated the second thesis
question, by conducting a thermal stress study on aposymbiotic and symbiotic anemones. The
objective was to differentiate microbial patterns resulting from thermal stress from those due to
loss of dinoflagellate symbionts, in order to identify possible primary bacterial symbionts of
dinoflagellates. Lastly, Chapter 4 focused on the third question, where I established a long-term
algal-bacterial coculture system to quantify growth and gene expression of both organisms in
B12-depleted conditions.
6
CHAPTER 2: HOST-SPECIFIC EPIBIOMES OF DISTINCT
ACROPORA CERVICORNIS GENOTYPES PERSIST AFTER
FIELD TRANSPLANTATION
Emily G. Aguirre
1*
, Wyatt C. Million
1
, Erich Bartels
2
, Cory J. Krediet
3
, Carly D. Kenkel
1
1
Department of Biological Sciences, University of Southern California, 3616 Trousdale
Parkway, Los Angeles, CA 90089, United States of America
2
Elizabeth Moore International Center for Coral Reef Research & Restoration, Mote Marine
Laboratory, 24244 Overseas Hwy, Summerland Key, FL 33042, United States
3
Department of Marine Science, Eckerd College, 4200 54th Avenue South, St. Petersburg, FL
33711, United States of America
This chapter appears as published in Coral Reefs (2022) 41:265–276.
SUMMARY OF CONTRIBUTION
Carly D Kenkel (CDK) and Cory J Krediet (CJK) conceived and designed the field experiment
and obtained funding before I joined CDK's Cnidarian Evolutionary Ecology Lab. All authors
contributed to the sample collection. CJK and I, Emily Aguirre, performed DNA extractions. I
also completed library preparation and all bioinformatic and statistical analyses and wrote the
first draft of the manuscript. All authors contributed to revisions and approved the final
manuscript.
7
2.1 ABSTRACT
Microbiome studies across taxa have established the influence of host genotype on microbial
recruitment and maintenance. However, research exploring host-specific epibionts in
scleractinian corals is scant and the influence of intraspecific differences across environments
remains unclear. Here, we studied ten Acropora cervicornis genotypes to investigate the relative
roles of host genotype and environment in structuring the epibiome. Coral mucus was sampled in
a common garden nursery from replicate ramets of distinct genotypes (T0). Coral fragment
replicates (n=3) of each genotype were then transplanted to nine different field sites in the Lower
Florida Keys and mucus was again sampled one year later from surviving ramets (T12). 16S
rRNA amplicon sequencing was used to assess microbial composition, richness, and beta-
diversity. The most abundant and consistent amplicon sequencing variants (ASVs) in all samples
belonged to Midichloriaceae (MD3-55 genus) and Cyanobacteria (Synechococccus). The relative
abundances of these bacterial taxa varied consistently between genotypes whereas neither the
composition nor taxonomic relative abundance were significantly different among field sites.
Interestingly, several high MD3-55 hosting genotypes showed rapid diversification and an
increase in MD3-55 following transplantation. Overall, our results indicate healthy A. cervicornis
genotypes retain distinct epibiome signatures through time, suggesting a strong host component.
Lastly, our results show that differences in MD3-55 abundances can be consistently detected in
the epibiome of distinct host-genotypes of A. cervicornis. As this organism (sensu Aquarickettsia
rohweri) has been implicated as a marker of disease resistance, this finding reinforces the
potential use of microbial indicators in reef restoration efforts via non-invasive surface/mucus
sampling.
8
2.2 INTRODUCTION
Microbiome studies across taxa link host specificity to distinct microbial ecotypes, most
famously in humans (Kolde et al. 2018; Lynch and Hsiao 2019), plants (Wagner et al. 2016),
insects (Vogel and Moran 2011) and recently in acroporid corals (Glasl et al. 2019). Mounting
evidence has established the diversity and importance of bacterial associates in corals (Bourne et
al. 2016; Sweet and Bulling 2017; van Oppen and Blackall 2019). However, dissecting the
influence of host genotype on microbial recruitment and maintenance in corals remains a
challenge due to holobiont (host and its collective microbial associates) diversity (Blackall et al.
2015) and microhabitat niche distinctions (e.g., surface mucus, tissue, and skeleton in
scleractinian coral) (Apprill et al. 2016). Despite these challenges, investigations into host-
specific bacterial associates in corals can help partition the effect of genotype on the microbiome
and its relationship to holobiont fitness and disease.
Acroporid coral microbiomes are known to vary among conspecific hosts but knowledge
about the combined effect of environment and genotype on the stability of microbiomes (Glasl et
al. 2019; Marchioro et al. 2020; Miller et al. 2020) and its link to holobiont resilience is limited.
Previously, (Wright et al. 2017) challenged Acropora millepora genotypes with pathogenic
Vibrio spp. to determine if coral disease was a response to an etiological agent or to a weakened
holobiont. Disease resistant genotypes were largely unaffected by Vibrio spp., and gene
expression resembled that of healthy non-inoculated corals, suggesting that coral disease results
from an unfavorable combination of genotype and environment. Similarly, A. cervicornis
genotypes exhibit distinct tissue microbial signatures (Klinges et al. 2020; Miller et al. 2020)
differentiated by varying abundances of a Rickettsiales coral symbiont and presumed to be an
indicator of disease susceptibility during thermal stress (Klinges et al. 2020). The influence of
9
the environment on distinct A. tenuis genotypes has also been studied in experimental
manipulations, suggesting distinct host-genotype specific microbial composition, irrespective of
single stress or combined stress treatments (e.g., reduced salinity, thermal stress, elevated pCO2
and presence of a macroalgae competitor (Glasl et al. 2019). Taken together, this suggests that
acroporid genotypes exhibit unique microbiome signatures, but it is unclear if these genotype-
specific microbiomes are maintained through time in natural reef environments. This is relevant
as reef environments and their associated microbiomes are changing at unprecedented rates as a
result of climate change and human impact (Hughes et al. 2003; Ainsworth et al. 2010).
Investigating the influence of host-specificity and environment is foundational to unraveling
microbially-mediated processes underpinning the maintenance of holobiont health in conspecific
hosts.
The coral surface mucus is an ideal microhabitat to explore genotype and environment
dynamics due to its function as a defensive barrier between coral epithelia and the environment,
and its putative role in preventing/causing disease (Sutherland et al. 2004; Brown and Bythell
2005; Krediet et al. 2013). Coral mucus composition varies among coral species (Meikle et al.
1988), but generally it is a polysaccharide protein lipid complex, and can be viewed as a
secretory product with multiple functions (Crossland 1987; Coffroth 1990; Wild et al. 2004).
Because of its rich organic composition, mucus hosts the highest bacterial diversity (Garren and
Azam 2010) and contributes to nutrient cycling in the holobiont (Wild et al. 2004). Surface and
mucus microbiomes (epibiomes) are presumed to be influenced by the surrounding environment
(Kooperman et al. 2007; Pollock et al. 2014; McDevitt-Irwin et al. 2017). Therefore, thorough
epibiome characterizations in coral holobionts are pivotal as reefs respond to oceanic changes
due to climate change (Ritchie 2006). Recent research exploring the intersection between
10
environment and acroporid epibiomes yielded novel insights in A. tenuis and A. millepora,
showing that epibiomes were very different from tissue microbiomes, and shared a similar
microbial composition as the surrounding seawater (Marchioro et al. 2020). However, host-
genotype responses, in the epibiomes of acroporids, to variable environments remain under-
explored (Marchioro et al. 2020; Miller et al. 2020).
A fundamental understanding of genotype and environment dynamics in coral epibiomes
can aid restoration efforts since bacterial communities are implicated in coral health and
holobiont resistance (Krediet et al. 2013; Peixoto et al. 2017; Rosado et al. 2019; van Oppen and
Blackall 2019). Here, we aimed to address this knowledge gap for the staghorn coral, Acropora
cervicornis, an ecologically relevant and endangered Caribbean coral. Our goal was to determine
the extent of host genotype, environment, or the combination in the maintenance of epibiomes
over time. To do this, we assessed the environmental response of the epibiome among and within
A. cervicornis genotypes following transplantation to novel reef environments. We found that A.
cervicornis genotypes exhibited distinct epibiome signatures, driven by relative abundance of
MD3-55, a ubiquitous Rickettsiales bacterial symbiont. Biodiversity of bacterial communities
changed slightly in surviving outplants, regardless of the ultimate reef site, following one year of
field transplantation. We also observed an increase of MD3-55 in several high MD3-55 hosting
genotypes. Despite changes in MD3-55 relative abundance, ultimately the epibiomes of distinct
A. cervicornis genotypes were significantly shaped by the host.
2.3 METHODS
2.3.1 Study Overview
11
We sampled mucus from replicate fragments (n=30) of ten known coral genotypes from Mote
Marine Laboratory’s in situ nursery (24° 33' 45.288" N, 81° 24' 0.288" W) in April 2018.
Fragments from all ten genotypes were propagated long-term on mid-water structures (coral
“trees”) for at least 5 years, and then mounted on concrete discs and attached to benthic modules
in preparation for transplantation at least two weeks prior to mucus sample collection. The
samples were prepared for 16S rRNA amplicon sequencing to assess alpha-diversity between
fragments of the same genet and beta-diversity between genotypes (T0). Following nursery
sampling, replicate ramets (n=3) of each genet were transplanted to nine different field sites
(Table S2.1) in the Lower Florida Keys in April 2018. Concrete discs were attached to the reef
substrate with marine epoxy. Metal tags were used to identify colonies for future surveys. We
returned to the sites in April 2019 to again collect mucus samples in order to assess changes in
the epibiotic microbiome of surviving ramets (T12).
2.3.2 Mucus Sampling and 16S rRNA Sequencing
Epibiome samples were obtained by agitating the coral surface with a 10 mL syringe to stimulate
mucus production (Ritchie 2006). Mucus samples were then transferred to 15 mL Eppendorf
tubes and frozen at -20°C until processing. Background filtered seawater (SW) controls were
obtained by filtering duplicate 1L seawater samples through a 1.0µm pore-size, 47mm
polycarbonate filter (Whatman International, Ltd., England), and size fractionated through a 0.2
µm pore-size filter, to capture bacteria between 1- 0.2µm and frozen at -20°C until processing.
The samples were prepared for processing by thawing and centrifuging for 30 minutes,
where only the bottom, heavier fraction (~2-3 mL) containing the mucus was concentrated and
the seawater supernatant was discarded. DNA was extracted from all samples using the DNeasy
12
PowerBiofilm Kit (Qiagen, Hilden, Germany), followed by targeted amplification of the V4
region of the 16S rRNA gene using the Earth Microbiome Project protocols (Thompson et al.
2017) along with the 515F-806R primer set (Caporaso et al. 2011; Parada et al. 2016). Unique
Illumina barcodes were incorporated in a second round of PCR and samples were pooled in
equimolar amounts for sequencing of paired 250-bp reads on the Illumina MiSeq v2 PE 250
platform (Admera Health, USA).
2.3.3 Bioinformatic and Data Analysis
We were able to amplify and successfully sequence 128 samples from the initial timepoint (T0)
and 112 samples from the one-year timepoint (T12). The contrasting difference between the
number of samples collected and those amplified was due to low biomass input for several
samples and low yield in subsequent DNA extractions (Table S2.2). Resulting paired-end reads
were demultiplexed and quality checked using FastQC (Andrews 2010). Amplicon sequencing
variants (ASVs) were called using DADA2 (Callahan et al. 2016) in R (R Core Team 2020),
using the default standard filtering parameters (truncLen=c(240,160),maxN=0, maxEE=c(2,2),
truncQ=2, rm.phix=TRUE, compress=TRUE, multithread=TRUE) and constructed into a
sequence table. Taxonomy was assigned using the naive Bayesian classifier method (Wang et al.
2007) in conjunction with the Silva SSU training data for DADA2, version 138
(https://zenodo.org/record/3731176#.YZiP_NDMKUk). Statistical analyses and visualizations
were conducted in R (R Core Team, 2020). The arrayQualityMetrics and DESeq2 R packages
(Kauffmann et al. 2009; Love et al. 2014) were used to screen for outlier samples. The
compositional nature of data generated by high-throughput sequencing requires normalization
techniques to transform the data into a symmetrical dataset (Gloor et al. 2017; Weiss et al. 2017),
13
therefore we rarefied to an even read depth (5,000 reads), and samples <5,000 reads were
discarded (Table S2.3). After rarefaction, our dataset contained 8,026,016 reads.
Sequence removal of mitochondria and eukaryotes, rarefaction, relative abundance
visualizations, alpha-diversity (Shannon’s Index) and beta-diversity plots were generated using
Phyloseq (McMurdie and Holmes 2013) and ggplot2 (Wickham 2016). To determine consistent
bacterial taxa, we conducted core microbiome analysis in the Microbiome package (Lahti and
Shetty 2019) using a detection limit of 0.001% in > 60% of the samples (prevalence threshold).
Cyanobiaceae (Synechococcales) and Midichloriaceae (Rickettsiales) were identified as the two
dominant amplicon sequencing variants in all T0 coral samples. We queried our Midichloriaceae
ASVs with that of two published sequences of coral-associated Midichloriaceae: MD3-55 in the
NCBI database and a full-length 16S rRNA sequence of Candidatus Aquarickettsia rohweri
(Klinges et al. 2019). The former were also queried against 16S rRNA gene sequences of the
Rickettsiales order (12 families) and one non-Rickettsiales representative in Alphaproteobacteria
was chosen as the outgroup (Caulobacter mirabilis). Phylogenetic analysis was carried out by
aligning all sequences using the MUSCLE algorithm (Edgar 2004). The aligned sequences were
used to construct a maximum likelihood phylogeny with ultrafast bootstrap (1000 bootstrap
replicates) using IQTREE (Kalyaanamoorthy et al. 2017; Minh et al. 2020) and visualized on the
Interactive Tree of Life interface (https://itol.embl.de/itol.cgi).
We conducted multivariate analyses to test observed dissimilarities between (beta-
diversity) microbial communities of genotypes hosting low abundances of MD3-55 versus
genotypes hosting high abundances of MD3-55, across timepoints using the vegan package
(Oksanen et al. 2019). Differences in groups were visualized by principal coordinate analysis
(PCoA) using the weighted-Unifrac metric (Fig. 2.1a) and statistical differences were determined
14
using the non-parametric tests, analysis of similarities (ANOSIM) and permutational multivariate
analysis of variance (PERMANOVA). First, homogeneity of group dispersions was tested in
pairwise comparisons, followed by the appropriate non-parametric test. If uneven group
dispersion was prevalent, the ANOSIM test was applied. Pairwise comparisons with even group
homogeneity were tested using PERMANOVA. ASVs with <5 counts in at least 1 of the
samples were excluded from relative abundance visualizations, beta-diversity, and multivariate
analyses.
2.4 RESULTS
2.4.1 A. cervicornis epibiome composition
We obtained 9,695,036 reads from 240 samples (coral and seawater controls). A total of
8,104,340 reads and 21,554 ASVs (Table S2.3) were retained after filtering for mitochondria,
chloroplast and protistan variants. Samples containing less than 5,000 reads were removed and
the remaining 191 samples (Table S2.3) were subsequently rarefied to the minimum even read
depth of 5,000 reads, resulting in 16,850 ASVs (Table S2.3).
The predominant phyla identified in the dataset were Proteobacteria (56%),
Cyanobacteria (26%), Bacteroidetes (10%), Actinobacteria (4%) and Spirochaetes (1%). All
other phyla were present in abundances < 1% (Fig. S2.1). Alpha-diversity (calculated using all
ASVs) was high in all genotypes, but highest in SW samples (Fig. S2.2). Alpha-diversity also
increased when coral were transplanted to field sites (T12) resulting in statistical differences
between the two timepoints (Wilcoxon, p = 0.03, Table S2.4). Differences in beta-diversity were
observed between the nursery SW samples and nursery coral samples (ANOSIM R= 0.786, p =
0.001). However, we were only able to compare the bacterial composition of coral mucus and
15
background water samples for two sites in the field (T12). Beta dispersion tests for SW samples
from Bahia Honda (BH, n=2) and Big Pine (BP, n=2) sites were conducted against the coral
samples from their respective sites and found to be evenly dispersed and significantly different
for both BH (BETADISPER, p=0.9, PERMANOVA, p= 0.02) and BP (BETADISPER, p=0.734;
PERMANOVA, p=0.03) (Table S2.5). Additionally, no significant differences in beta-diversity
were detected in pairwise comparisons of background seawater controls (nursery, BP and BH,
Table S2.5).
Alpha-diversity assessments of the epibiome, using Shannon’s index, showed differences
among genotypes (ANOVA, F = 3.5, p = 0.0006). Post-hoc analyses (Tukey multiple
comparison of means) detected three significant differences among pairwise comparisons:
genotypes G44-G36 (adjusted p < 0.02), G44-G41 (adjusted p < 0.04) and G7-G44 (adjusted p <
0.04) (Table S2.6).
2.4.2 A. cervicornis epibiomes exhibit distinct genotype signatures
A. cervicornis reared in a common garden nursery environment (T0) exhibited distinct signatures,
in the overall epibiome by host genotype that largely persisted after one year of transplantation to
novel field environments (T12) (Fig. 2.1a). The top 100 most abundant ASVs that were present in
the coral samples were Synechococcus (Cyanobacteria) and MD3-55 (Rickettsiales) (Fig. 2.1b),
with genotypes 3, 7 and 36 hosting low abundances of MD3-55 and the remaining genotypes
hosting high MD3-55 abundances. Low MD3-55 hosts (G3, G7 and G36) differed from high
MD3-55 hosts (all other genotypes, ANOSIM R
= 0.627, p < 0.001) but microbial community
composition was similar and not statistically different when MD3-55 ASVs were removed
(ANOSIM, R = 0.016, p = 0.291).
16
Figure 2.1. Beta-diversity and most abundant taxa present in all samples. (a) Principal coordinates
analysis (PCoA) on a weighted- Unifrac metric, used to visualize differences between Acropora
cervicornis genotypes (G3, G7, G36, G41, G1, G13, G31, G44, G50, G62), site (colors) and timepoint
(circles= T 0 and triangles=T 12). (b) Relative abundance of the top 100 ASVs in the dataset: MD3-55
(Midichloriaceae family, Rickettsiales order) and Synechococcus CC9902 (Synechococcaceae family,
Cyanobacteria class) by genotype and background SW (labeled as N: Nursery, BP: Big Pine, BH: Bahia
Honda and LK: Looe Key).
2.4.3 A. cervicornis epibiomes exhibit temporal changes but not among-site differences
Temporal changes in the epibiome composition of the ten genotypes were also assessed, and
while the groups (T0 versus T12) were evenly dispersed (BETADISPER, p=0.294) beta-diversity
between the two sampling timepoints was significantly different (PERMANOVA, 𝑅 2
=0.163,
p=0.001, Fig. 2.1a). A. cervicornis epibiomes did not differ among transplant sites in terms of
alpha-diversity (Kruskal Wallis test, p=0.95, Table S2.4) or composition at the phylum level
(Fig. S2.3).
2.4.4 Rickettsiales and Cyanobacteria are consistent members of the A. cervicornis epibiome
Taxa that were consistently present in the epibiome across sampling timepoints (T0 and T12),
were assessed via core microbiota analysis using the microbiome package in R on the filtered
dataset. Out of the 6,966 analyzed ASVs, only 190 ASVs were consistent in > 60% of the
17
Figure 2.2. Heatmap displaying core taxa detected in samples at both timepoints, at a prevalence of >
60%, and in frequencies of compositional relative abundance (light yellow = low to dark gray = high,
gradient scale). Analysis was conducted with the microbiome package in R, using non-rarefied data. The
two genera detected were also the most abundant in the dataset, MD3-55 and Synechococcus CC9902.
samples (Fig. 2.2). The detected 190 ASVs consisted of 2 bacterial taxa, MD3-55 (Rickettsiales)
and Synechococcus CC9902 (Cyanobacteria) (Fig. 2.2).
2.4.5 ASVs of the ubiquitous intracellular symbiont, MD3-55 (sensu A. rohweri), increased one-
year post-outplant
Phylogenetic classification placed most of our MD3-55 sequences, 99% of the time in 1000
bootstrap replicates (Fig. S2.4), within the same node (ranging in % similarity of 94-100% of the
18
V4/V5 region of the 16S rRNA region, Table S2.7) as a Rickettsiales symbiont, Candidatus A.
rohweri, that is known to affiliate with A. cervicornis hosts (Klinges et al. 2019; Baker et al.
2021). The version of the SILVA database (version 138) which was used to assign taxonomy to
our dataset has not updated the taxonomy of the Midichloriaceae family (genus MD3-55) to the
newly proposed Candidatus A. rohweri (Klinges et al. 2019), therefore we will refer to these
Rickettsiales as MD3-55.
While the pattern of low and high MD3-55 hosting A. cervicornis genotypes remained
largely consistent over time, MD3-55 significantly increased in abundance, on average, one year
post transplantation (p = 2.2e-16) (Fig. 2.3a, Table S2.8). This pattern was particularly evident
for genotypes G13, G44, G50 and G62 (Fig. 2.3a). Additionally, MD3-55 16S rRNA gene
sequences diversified after 12 months, irrespective of outplant site, increasing from 70 distinct
ASVs in T0 to 124 distinct ASVs in T12 samples (Fig. 2.3b). Of these, 57 variants of the MD3-55
population were observed at both timepoints (T0 and T12), while 13 variants disappeared, and 67
new variants were detected after 12 months (Fig. 2.3c).
To assess whether this increase in MD3-55 abundance was a real biological signal or an
artifact of differential host tissue contamination, we tabulated the abundance of mitochondrial
amplicons after pruning known protists from the dataset. Mitochondrial reads were higher in
samples originating from the nursery than they were in the T12 field samples (Fig. S2.5), yet the
opposite pattern was observed for MD3-55 (Fig. 2.3a).
19
Figure 2.3. MD3-55 ASV distribution in the epibiomes of A. cervicornis genotypes. (a) Raw counts of
MD3-55 in rarefied data, subset by genotypes. The x-axis denotes timepoints (T 0 and T 12) and transplant
sites (BH: Bahia Honda, BP: Big Pine, DL: Dave’s Ledge, EDR: Eastern Dry Rocks, ES: Eastern Sambo,
LK: Looe Key, MR32: Marker 32, MS: Maryland Shoals, N: Nursery, WS: Western Sambo) are
differentiated by color. (b) Venn diagram of distinct and shared MD3-55 ASVs at T 0 (nursery) and T 12
(all transplant field sites, one year later). ASVs diversified following transplantation (67 distinct and 57
shared with the nursery samples), but 13 ASVs unique to the nursery were not detected at the transplant
sites. (c) Heatmap of MD3-55 ASVs by transplant site (arranged from west to east) using non-metric
multidimensional scaling and Bray-Curtis dissimilarity index. Each row represents a distinct MD3-55
ASV. Abundance is denoted by intensity of color, ranging from no abundance= white to high abundance
= dark green.
2.5 DISCUSSION
Microbial communities are spatially organized between coral compartments and highly diverse
(Sweet et al. 2011; Apprill et al. 2016; Hernandez-Agreda et al. 2017). Host-genotype specificity
of the microbiome has been recently explored in acroporids but is still under-researched.
Previous work on the tissue and mucus microbiome of various Acropora spp. has shown
persistent host-genotype differences (Chu and Vollmer 2016; Glasl et al. 2019; Rosales et al.
2019). Although the epibiome of distinct A. cervicornis genotypes has been recently
20
characterized (Miller et al. 2020), we show that genotype-specific differences in the mucus
microbiome of A. cervicornis are largely maintained across space and time. The most abundant
bacterial associates of the A. cervicornis microbial epibiotic community, both in the common
garden nursery and following transplantation to nine distinct field sites, were members of the
Midichloriaceae (MD3-55) and Synechococcaceae (Synechococcus). All other taxa (Fig. S2.1)
were present in abundances of ≤ 10%, and their taxonomic composition did not seem to alter in
transplants (Fig. S2.1), yet microbial signatures characteristic of low hosting MD3-55 genotypes
remained (Fig. 2.1b). Finally, MD3-55 symbionts were detected in the epibiome and increased in
ASV counts over time in genotypes hosting initial (T0) high abundances.
2.5.1 A. cervicornis epibiome retained genotype-specific signatures following transplantation
Site-specific differences in coral microbiomes are well documented (Rohwer et al. 2002; Guppy
and Bythell 2006; Ziegler et al. 2019), and previous studies suggest reasonable flexibility of the
acroporid microbiome under environmental change (Bourne et al. 2008; Grottoli et al. 2018;
Ziegler et al. 2019). Coral mucus is susceptible to environmental effects (Li et al. 2015; Pollock
et al. 2018; Marchioro et al. 2020), therefore we expected A. cervicornis epibiomes to be
partially influenced by surrounding environmental parameters or geography (Littman et al. 2009;
Leite et al. 2018; Epstein et al. 2019). However, neither richness nor the microbiome
composition at the phylum level were significantly altered in A. cervicornis genotypes following
transplantation to different environments (Table S2.4, Fig. S2.3). (Marchioro et al. 2020) found
that environmental parameters explained less of the variation in mucus microbiomes (10%) than
that of microbial communities in the surrounding seawater (32% of variation). Similarly, (Guppy
and Bythell 2006) did not find strong correlations between environmental variables and bacterial
21
structure of the mucus which led them to conclude that environmental influence was modulated
by host intraspecific differences. Taken together, these results suggest host genotype may be a
determining factor in structuring the epibiome of healthy corals, and that the environment may
play a lesser role on mucus microbiomes than previously assumed.
Despite the strong signal of genotype, environmental influence on the epibiome cannot be
completely ruled out, as different clustering patterns were observed for genotypes sampled in the
nursery versus samples obtained from reefs, regardless of transplant site (Table S2.5, Fig. 2.1a).
An increase in ASV richness was also observed in some genotypes after one year (Table S2.4),
although the overall taxonomic composition remained the same. This pattern could be reflective
of unique environmental differences in the nursery location, as it is a sand bottom habitat, but we
did not resample genotypes in the nursery at T12 to test this hypothesis. Divergent clustering of
the nursery-derived samples may also be related to climate variation between years as nursery
and field samples were collected one year apart (April 2018 vs. April 2019). Transplants were
visually monitored every three months for one year and no bleaching or disease was observed.
Healthy coral tissue microbiomes can remain stable through time in coral species (Dunphy et al.
2019). The stability of the coral epibiome is unknown, but it is also possible that transplantation
resulted in some level of dysbiosis leading to divergent microbiome composition regardless of
the ultimate destination. Further research should integrate seasonal sampling in a time-series
framework (2+ years) at nursery and field locations to disentangle spatial from temporal
differences in coral and seawater microbiome variation and stability.
Additionally, the influence of microbial associations in near-coral seawater (seawater
located within 5cm of corals) may play an important role in maintaining healthy, stable coral
epibiomes through time in addition to influencing the microbiome of the surrounding seawater
22
(Shashar et al. 1996; Weber et al. 2019). Weber et al. (2019) demonstrated that the coral
ecosphere contains a taxonomically distinct, species-specific microbiome compared to that of
seawater > 1 m away from the reef. Our mucus samples were taken with syringes from the
surface of the coral and most likely included near-coral seawater. SW (nursery, BP and BH)
controls resembled the microbial structure of low MD3-55 hosting genotypes (Fig. S2.1),
however they also displayed significant differences from the microbial communities of the coral
mucus (Table S2.5), with the seawater containing a higher relative abundance of Bacteroidetes
and Actinobacteria. It is also possible that the microbial structure of our SW samples was
influenced by the corals. Mucus shedding is a natural phenomenon in corals (Brown and Bythell
2005) and can be deployed during stressed conditions like increased UV exposure (Teai et al.
1998) and pathogen regulation (Ritchie 2006) but has also been linked to holobiont health via
regulation of microbial communities (Glasl et al. 2016). It is possible that exuded dissolved
organic matter from the mucus was present in the seawater (Silveira et al. 2017; Weber et al.
2019) and influenced the microbial structure of our SW samples since seawater was collected > 1
m of the focal corals. This may explain why the only distinguishing microbial signature between
corals and seawater the presence/absence of MD3-55 was (Fig. 2.1b). Lastly, microbial beta-
diversity in the seawater did not significantly differ among sites (Table S2.5) despite the one-
year difference in sampling time between the nursery and BH/BP sites. The sites are ~ 8 km
apart and the nursery is closest to LK (Table S2.1). The similarities in seawater microbiomes
indicate that variation in physicochemical conditions did not significantly affect the local
microbiota between sites. Additionally, we did not observe drastic variation in the microbial
structure of A. cervicornis epibiomes by site (other than differences in the presence/absence of
specific MD3-55 ASVs), only genotype effects (Fig. 2.1a). To assess the extent of environmental
23
effects on the mucus microbiome of A. cervicornis genotypes, and vice-versa, follow-up studies
should monitor physicochemical parameters during sampling and sample seawater at least one
meter away from the reef.
2.5.2 MD3-55 in the epibiome
The putative bacterial symbiont, MD3-55, is ubiquitously present in A. cervicornis, and has been
documented in Florida USA, Panama, Puerto Rico and the Cayman Islands (Casas et al. 2004;
Miller et al. 2014, 2020; Gignoux-Wolfsohn and Vollmer 2015; Godoy-Vitorino et al. 2017;
Rosales et al. 2019; Gignoux-Wolfsohn et al. 2020). Our findings point to MD3-55 as the driving
factor distinguishing genotype-specific epibiomes, similar to those signatures reported for A.
cervicornis tissue samples (Klinges et al. 2020; Miller et al. 2020; Baker et al. 2021). No
significant differences were observed among genotypes when MD3-55 ASVs were removed
from the samples (Table S2.5). MD3-55 were previously visualized (Gignoux-Wolfsohn et al.
2020), and are known to lack basic metabolic pathways, strongly supporting an intracellular
lifestyle (Klinges et al. 2019). However, the localization of MD3-55 in A. cervicornis remains
elusive. (Baker et al. 2021) hypothesized A. cervicornis may horizontally transmit MD3-55 to
gametes and juveniles via mucocytes. A histological approach supports this hypothesis as
Rickettsiales-like organisms (RLO) were visualized in the mucocytes of A. cervicornis (Miller et
al. 2014; Gignoux-Wolfsohn et al. 2020). Our study taxonomically identified MD3-55 in the
epibiome, suggesting that those previously identified RLOs located in and near the mucocytes
may be MD3-55 which would indicate that these organisms are not exclusively intracellular.
Although we observed MD3-55 in surface mucus samples, they could have derived from
host tissue contamination. To address this issue, we assessed mitochondrial reads to evaluate
24
whether host tissue contamination increased across sampling timepoints. Mitochondrial reads did
not increase in our T12 data and were lower in the T12 sample set than the T0 dataset (Fig. S2.5),
whereas MD3-55 increased in T12 in G13, G44, G50 and G62 (Fig. 2.3a), which argues against
host tissue contamination. This suggests that MD3-55 may display partial extracellular
inclinations, perhaps to facilitate horizontal transmission. Additionally, MD3-55 is not host
restricted and has been detected in sponges, kelp, ctenophores, and marine sediments (Klinges et
al. 2019). Investigating the abundance and localization of MD3-55 in non-coral hosts residing in
the same habitats as acroporids, and other environmental reservoirs, may be valuable in resolving
the general ecology of MD3-55.
2.5.3 MD3-55 ASVs following transplantation in high MD3-55 hosting genotypes
Recent work has shown that MD3-55 is highly abundant (Klinges et al. 2020; Baker et al. 2021)
in genotypes of A. cervicornis, previously determined to be susceptible to white-band disease
after a bleaching event caused by a temperature stress (Muller et al. 2018). White-band is a
devastating, host-specific disease in A. cervicornis and A. palmata with an unknown etiological
origin, although the putative pathogen is likely bacterial (Casas et al. 2004; Kline and Vollmer
2011; Sweet et al. 2014; Gignoux-Wolfsohn and Vollmer 2015).
Here, we observed an increase in MD3-55 in several high MD3-55 hosting genotypes
following transplantation to novel field sites. While there was some indication that MD3-55
increased in some ramets of low MD3-55 hosting genotypes, these genotypes largely maintained
a persistent signature of low MD3-55 abundance (Fig. 2.3a). Recently, Baker et al. (2021)
indicated A. rohweri populations in A. cervicornis were abundantly high and had greater in situ
replication rates in Florida (USA) compared to those from Belize and the U.S. Virgin Islands.
25
We also observed several nursery MD3-55 ASVs disappear following transplantation, while
others proliferated. Proliferation of particular MD3-55 ASVs also appeared to be site specific
(Fig. 2.3c) suggesting positive selection in certain locations, as previously observed in Florida
(higher abundances) and Caribbean (lower abundances) populations of MD3-55 in A. cervicornis
and A. prolifera (Baker et al. 2021). Higher abundances were attributed to environmental factors,
like higher nutrient stress, which aligns with our observations of host-specific abundance
patterns of MD3-55 out in the field. However, we did not quantify environmental parameters like
dissolved nutrients (nitrogen, phosphorus) or particulate organic carbon, therefore we cannot
conclusively link high MD3-55 abundance patterns in our data to increased nutrient availability
at specific field sites.
We identified 137 MD3-55 ASVs, (Fig. 2.3b) in our ten A. cervicornis genotypes
whereas (Miller et al. 2020) identified 11 MD3-55 ASVs in three genotypes (different from those
in our dataset) using the same 16S rRNA amplifying primers and sequencing protocol. Unlike
Miller et al. (2020), we did not observe MD3-55 ASVs that were associated with a particular
genotype, but rather the ASVs seemed to be distributed sporadically across A. cervicornis
genotypes. These contrasting findings may be due to our higher sample size and number of
genotypes surveyed, in addition to spatiotemporal differences in sampling. In the nursery, we
observed 70 initial ASVs and a diversification of 67 novel ASVs in the field sites after one year
(Fig. 2.3b, c). Baker et al. (2021) showed greater positive selection in genes associated with
ribosomal assembly in MD3-55 strains from Florida, signaling possible speciation across
locations. These findings, along with our study, suggest rapid evolution and diversification in
MD3-55 strains may be happening in Florida field sites. Novel infections in low MD3-55 hosting
genotypes were detected in three different sites, but only in one ramet each in G3, G36 and 2
26
ramets in G7 (Fig. 2.3a). Local acquisition via the surrounding seawater or sediments may be a
possibility, but we observed none to very low abundances of MD3-55 in SW samples of the
nursery and field sites BH and BP (Fig. 2.1b).
2.5.4 Persistence of cyanobacteria in the epibiome
The most abundant taxa, MD3-55 and Synechococcus CC9902 (Fig. 2.1b), in A. cervicornis
epibiomes were also the most stable, with MD3-55 ASVs present in > 60% of the samples
(consistent with the observation of high and low MD3-55 hosting genotypes) and Synechococcus
ASVs present in ~ 90% (consistent in all genotypes) of the samples (Fig. 2.2). This latter finding
is consistent with a recent report on A. tenuis and A. millepora microbiomes where
Synechococcus was documented at higher abundances in the mucus than in the tissue and
surrounding seawater (Marchioro et al. 2020).
While the ecology and functional role of MD3-55 in A. cervicornis may involve
parasitism (Klinges et al. 2019, 2020), it is not well understood. In contrast, the associations
between cyanobacteria and corals are putatively related to nitrogen fixation (Lesser 2004; Lesser
et al. 2007; Lema et al. 2012). Cyanobacteria can establish partnerships with various organisms,
such as other prokaryotes, microbial eukaryotes, and metazoans (Mutalipassi et al. 2021). In
most cases the bulk of the exchanged services involve biologically useful nitrogen (Foster and
O’Mullan 2008). For example, δ15N stable isotope data suggests algal symbionts
(Symbiodiniaceae) preferentially use nitrogen fixed by cyanobacteria, including Synechococcus,
in colonies of the coral, Montastraea cavernosa (Lesser et al. 2007). It is unknown if algal
symbionts of A. cervicornis share a similar nitrogen acquisition strategy, but cyanobacteria
nitrogen-fixers are ubiquitous in the tissue and mucus of acroporids from the Great Barrier Reef
27
and Caribbean (Kvennefors and Roff 2009; Lema et al. 2012; Marchioro et al. 2020; Miller et al.
2020). Other cyanobiont-mediated services have also been identified in healthy corals, like the
exchange of photoprotective compounds in Montastraea cavernosa (Lesser 2004; Lesser et al.
2007). Although some cyanobacteria have been associated as precursors to black-band disease
(BBD) (Frias-Lopez et al. 2003), a bacterial mat that kills and removes healthy tissue and
beneficial bacterial associates from corals (Richardson 1996; Gantar et al. 2011), Synechococcus
species are not linked to BBD (Klaus et al. 2011; Buerger et al. 2016). Given that all our coral
were visibly healthy at the time of sampling and Synechococcus were also present in SW site
samples, the association between A. cervicornis and Synechococcus is likely mutualistic or
commensal. To explore this, future work should investigate host-specific distributions of
cyanobacteria in A. cervicornis and explore the role of cyanobiont-mediated nitrogen in
maintenance of the cnidarian-algal symbiosis.
In summary, understanding the influence of host specificity and the environment on the
maintenance of acroporid epibiomes is pivotal if microbial markers are to be used in reef
restoration (Parkinson et al. 2020). Prior work in A. tenuis suggests that significant genotype
variability may limit the use of microbiome surveys as microbial indicators of coral colony
health (Glasl et al. 2019). Here, we also observe significant and persistent variation in the
composition of the mucus microbiome among genotypes of A. cervicornis, but this finding does
not necessarily preclude the potential utility of microbial indicators. The variation observed in
the epibiomes can be attributed to differences in MD3-55 abundances, highlighting this bacterial
family as a potential indicator taxa. Additionally, MD3-55 was previously deemed as the primary
differentiating “biomarker” in the tissue microbiomes of disease-resistant and disease-susceptible
A. cervicornis genotypes (Klinges et al. 2020). Although MD3-55 was only recently detected in
28
coral epibiomes/coral mucus (Miller et al. 2020), here we show that the presence and abundance
of MD3-55 in A. cervicornis genotypes can also be reliably detected in the epibiome throughout
time. Moreover, high and low infection types are retained through different environmental
exposures over time. This result has utility for reef restoration applications, as non-invasive
sampling of the mucus and surface microbiome of threatened A. cervicornis can potentially
inform on the disease-susceptibility or disease-resistance of restored populations in natural
environments.
2.6 ACKNOWLEDGEMENTS
This study was funded by National Oceanic and Atmospheric Administration Coral Reef
Conservation Program grant NA17NOS4820084 and National Science Foundation Graduate
Research Fellowship Program grant award DGE-1418060. We would like to thank Y. Zhang for
help with collections. A. Clark kindly provided access to a vacuum pump. Thanks to the Florida
Keys National Marine Sanctuary for authorizing this work under FKNMS permits 2015-163-A1
and 2018-035.
2.7 CONFLICT OF INTEREST
The authors have no competing interests to declare.
2.8 DATA ACCESSIBILITY.
Scripts for data analysis used in this project are available at https://github.com/symbiotic-
em/acer_epi_final. Demultiplexed sequences are available at the National Center for
Biotechnology Information (NCBI) Sequence Read Archives (SRA) under accession code:
PRJNA630333.
29
CHAPTER 2: SUPPLEMENTARY FILE
Figures S2.1-S2.5
Tables S2.1-Table S2.8
30
Figure S2.1 Relative abundance of all taxa, by phylum, in A. cervicornis genotypes and background SW
(N: Nursery, BP: Big Pine Shoals, BH: Bahia Honda, LK: Looe Key). The predominant taxa are
Proteobacteria, followed by Cyanobacteria and Actinobacteria. All other taxa were present at < 1%
relative abundance.
31
Figure S2.2 Alpha-diversity of the genotypes and background SW (N: Nursery, BP: Big Pine Shoals,
BH: Bahia Honda, LK: Looe Key) at the nursery (0) and transplant sites (12), measured using Shannon’s
index.
32
Figure S2.3. Relative abundance of all taxa, by phylum, in samples sorted by host genotype and
background SW (BP: Big Pine Shoals, BH: Bahia Honda, LK: Looe Key), and by transplant sites at T 12
only. Differences in relative abundance of phyla can be seen in the genotypes but is indistinguishable if
samples are visualized only by site.
33
Figure S2.4. Phylogenetic classification of our 16S rRNA MD3-55 SILVA-assigned sequences (red text
and circles) with two published 16S rRNA sequences of coral-associated Midichloriaceae, labeled
“MD3.55 16S NCBI” (Genbank accession #FJ425643.1) and “Aquarickettsia rohweri” (Genbank
accession #PRJNA507282, scaffold 109), along with other members of the Rickettsiales (12 families).
Caulobacter mirabilis (Alphaproteobacteria), was chosen as the outgroup representative. Phylogenetic
classification was conducted using IQTREE. Briefly, a maximum likelihood phylogeny with ultrafast
bootstrap (1000 reps.) was applied and tree visualization was done on the Interactive Tree of Life website.
Only one of our sequences (red ASV54) clustered with the “MD3-55 16S NCBI” labeled sequence,
whereas the rest of the MD3-55 ASVs, in this study, clustered with the published A. rohweri 16S rRNA
sequence.
34
Figure S2.5. Raw mitochondria ASV counts (y-axis) on non-rarefied and non-filtered data in mucus
samples collected from the nursery (0) and transplant sites (12). The x-axis shows the tabulated counts for
genotypes and background SW (N: Nursery, BP: Big Pine Shoals, BH: Bahia Honda, LK: Looe Key).
35
Table S2.1. Coordinates of Acropora cervicornis transplant sites and common garden nursery, located off
the lower Florida Keys.
36
Table S2.2. Successfully amplified and filtered (≥ 5,000 reads) sample replicates for each genotype. In
the nursery, thirty replicates of each genotype were initially sampled along with three filtered seawater
samples (SW). For T 12 samples were taken from surviving replicates.
37
Table S2.3. Summary of read filtering in the DADA2 ASV pipeline in all samples prior to
data analysis.
Table S2.4. Alpha-diversity statistical testing output. A Wilcoxon rank sum test was used to conduct a
pairwise comparison of epibiome alpha diversity at T 0 and T 12 in A. cervicornis only. Kruskal-Wallis
Rank sum was used to assess differences in epibiome alpha-diversity between the 9 sites.
38
Table S2.5. Beta-diversity statistical output using non-parametric tests on pairwise comparisons.
Unevenly dispersed groups were tested using ANOSIM. Groups with even dispersion were tested using
PERMANOVA.
39
Table S2.6. ANOVA and Tukey HSD output. Differences in alpha-diversity between genotypes were
assessed by analysis of variance (ANOVA), followed by a Tukey HSD post-hoc test (95% family-wise
confidence level). (* p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001).
40
41
Table S2.7. NCBI BLAST results of 99/138 of MD3-55 SILVA classified sequences from this study
sequences producing significant alignments to A. rohweri of > 93.8% percent identity (“Per. Ident”).
42
43
Table S2.8. A Wilcoxon rank sum test was used to conduct a pairwise comparison of initial (T 0) and
transplant (T 12) MD3-55 reads in A. cervicornis.
44
CHAPTER 3: ABUNDANCE OF OLIGOFLEXALES BACTERIA
IS ASSOCIATED WITH ALGAL SYMBIONT DENSITY
INDEPENDENT OF THERMAL STRESS IN AIPTASIA
ANEMONES
Emily G. Aguirre
1*
, Marissa J. Fine
1
, Carly D. Kenkel
1
1
Department of Biological Sciences, University of Southern California, 3616 Trousdale
Parkway, Los Angeles, CA 90089, United States of America
This chapter is publicly available at the preprint server, bioRxiv,
doi:10.1101/2023.04.14.536969. This chapter is currently in review at Ecology and Evolution, a
Wiley journal.
SUMMARY OF CONTRIBUTION
I, Emily G Aguirre and Dr. Carly D Kenkel (CDK) conceived and designed the lab experiment
and obtained funding. Marissa J Fine (MJF) performed anemone maintenance, aposymbiotic
generation and sampling for the experiment. I completed DNA extractions, library preparation,
qPCR and all bioinformatic and statistical analyses and wrote the first draft of the manuscript.
All authors contributed to revisions and approved the preprint and journal submission.
45
3.1 ABSTRACT
Many multicellular organisms, such as humans, plants, and invertebrates, depend on symbioses
with microbes for metabolic cooperation and exchange. Reef-building corals, an ecologically
important order of invertebrates, are particularly vulnerable to environmental stress in part
because of their nutritive symbiosis with dinoflagellate algae, and yet also benefit from these and
other microbial associations. While coral microbiomes remain difficult to study because of their
complexity, the anemone Aiptasia is emerging as a simplified model. Research has demonstrated
co-occurrences between microbiome composition and the abundance and type of algal symbionts
in cnidarians. However, whether these patterns are the result of general stress-induced shifts or
depletions of algal-associated bacteria remains unclear. Our study aimed to distinguish the effect
of changes in symbiont density and thermal stress on the microbiome of symbiotic Aiptasia
strain CC7 by comparing them with aposymbiotic anemones, depleted of their native symbiont,
Symbiodinium linucheae. Our analysis indicated that overall, thermal stress had the greatest
impact on disrupting the microbiome. We found that three bacterial classes made up most of the
relative abundance (60-85 %) in all samples, but the rare microbiome fluctuated between
symbiotic states and following thermal stress. We also observed that S. linucheae density
correlated with abundance of Oligoflexales, suggesting these bacteria may be primary symbionts
of the dinoflagellate algae. The findings of this study help expand knowledge on prospective
multipartite symbioses in the cnidarian holobiont and how they respond to environmental
disturbance.
46
3.2 INTRODUCTION
Biological organisms, from single cells to ecosystems, are influenced by symbiotic interactions
(Boucher 1985; University of Massachusetts Amherst Massachusetts Lynn Margulis et al. 1991;
Smith and Szathmary 1997; Sachs et al. 2004). Although symbioses are generally modeled and
studied as two-way interactions, multipartite symbioses are also ubiquitous and have been well-
documented in plants (Miransari 2011; Antunes and Goss 2015; Adeniji et al. 2020; Afkhami et
al. 2020), humans (Wahida et al. 2021), and invertebrates (Worthen et al. 2006; Ffrench-
Constant et al. 2007; Chaston and Goodrich-Blair 2010; Stock 2019). These multi-partner
interactions can provide the host with essential metabolites, amino acids and vitamins (Cleveland
et al. 2011; Adeniji et al. 2020). Additionally, they can also confer defense (Zan et al. 2019),
facilitate host morphogenesis (Wichard 2015) and aid resilience under environmental stress
(Gupta et al. 2021; Santoyo et al. 2022).
Understanding the impact of multi-partner associations on the health and survival of
marine invertebrates is crucial given they are among the most susceptible animals to the impacts
of climate change (Mather 2013; Lam et al. 2020). The global decline of corals, which are
cnidarian hosts that harbor intracellular populations of dinoflagellate algae in the family
Symbiodiniaceae and other microbial associates (Pandolfi et al. 2003) is already underway and is
predicted to worsen with climate change (Hoegh-Guldberg et al. 2007; Allemand and Osborn
2019; Kleypas and Kleypas 2019). Bleaching, which involves the expulsion of symbiotic algae
in cnidarians resulting in the loss of color, can be induced by several factors, although elevated
temperature, as noted by (Douglas 2003)), is the most common cause. Yet there is also abundant
variation in coral thermal tolerance evidenced by differences in bleaching among species,
populations, and individuals (Dixon et al. 2015; Thomas et al. 2018; Drury 2020). Collectively,
these observations have led to the "Coral Probiotic Hypothesis” (Reshef et al. 2006) which is
47
based on the notion that symbiotic relationships with bacteria can increase coral resilience
(Peixoto et al. 2017).
The cnidarian-dinoflagellate symbiosis is better characterized in the literature than
potential cnidarian-bacterial symbioses, and it remains unclear whether environmental or internal
host factors, like host genetics or microalgal symbiont type, modulate the composition of
cnidarian microbiomes (Bourne et al. 2016; van Oppen and Blackall 2019; Barno et al. 2021).
One understudied theory postulates that well-known cnidarian-bacterial associates may actually
be primary associates of Symbiodiniaceae, in both free-living and in-hospite states (Ritchie
2012b; Bernasconi et al. 2019a; Matthews et al. 2020b). Global datasets suggest that the identity
of Symbiodiniaceae may contribute to structuring coral and anemone microbiomes (Bernasconi
et al. 2019a). For instance, susceptibility to Vibrio pathogens was higher in Acropora cytherea
corals hosting Symbiodinium than Durusdinium (formerly Clade A and D Symbiodinium,
respectively) (Rouzé et al. 2016). Additionally, the abundance of diazotrophs in Montipora
corals also correlated with algal symbiont type (Olson et al. 2009). Similarly, unique
microbiomes were identified in symbiotic and aposymbiotic Aiptasia anemones (Herrera et al.
2017) indicating presence of the symbiont influences the microbiome. However, insufficient
evidence exists to verify this hypothesis and tracking bacteria in adult corals is nearly
impossible, due to their high bacterial diversity (Blackall et al. 2015), which poses a challenge
for distinguishing obligatory and facultative bacterial symbionts. Furthermore, selectively
eliminating holobiont members empirically is not feasible since reef-building corals cannot
survive without their algal symbionts, who provide vital sugars and nutrients (Weis 2008). The
anemone Aiptasia (Exaiptasia pallida, sensu stricto) is a tractable model for studying the
cnidarian microbiome as it harbors fewer bacterial OTUs, with diversity estimated to be around
48
1-2 orders of magnitude lower than their coral relatives, (Röthig et al. 2016; Herrera et al. 2017).
Aiptasia are easy to maintain, engage in a nutritive symbiosis with Symbiodiniaceae similar to
corals, and can reproduce asexually (Baumgarten et al. 2015; Weis 2019). Unlike their coral
relatives, Aiptasia can be rendered aposymbiotic (free of their dinoflagellate algae) in laboratory
conditions and lack a calcitic skeleton, which facilitates experimental manipulation (Lehnert et
al. 2014).
While associations between microbial community composition and the abundance and/or
diversity of algal endosymbionts is consistent with the hypothesis that some microbes are
primary associates of Symbiodiniaceae, these patterns cannot be distinguished from passive
commensal relationships. Additionally, general stress may be responsible for changes in the host-
photosymbiont relationship, leading to alterations in metabolite production and resulting in
different selective pressures that favor distinct groups of commensals. For example,
Symbiodiniaceae taxa are known to differ in their metabolite production (Camp et al. 2022).
Durusdinium translocates less carbon to hosts than Cladocopium (Cantin et al. 2009) under
thermal stress but Cladocopium translocates more carbon and nitrogen to hosts during non-
stressful conditions (Pernice et al. 2015), which could impact the consortium of microbiota in the
holobiont. The host-photosymbiont relationship can also be affected by mild bleaching, which is
another general stress response (Ortiz et al. 2009). Here, we aimed to distinguish the effects of
the stress response and that of symbiont density on microbial communities while controlling for
host and symbiont genetic diversity. To achieve this, we used the emerging model organism
Aiptasia clonal strain CC7, which harbors Symbiodinium linucheae (Starzak et al. 2014;
Baumgarten et al. 2015) and conducted a comparison between the microbiomes of symbiotic
anemones undergoing mild bleaching and aposymbiotic anemones, which were presumed to lack
49
the microbes typically associated with Symbiodinium.
3.3 MATERIALS AND METHODS
3.3.1 Aiptasia rearing
Aiptasia anemones, clone strain CC7 (obtained from Dr. Cory Krediet, Eckerd College, FL,
USA) were used in this study. Anemones were kept in 0.5 L polycarbonate tanks, filled with 0.2
μm filtered seawater (FSW) from Catalina Island (Catalina Water Co., Long Beach CA, USA)
and maintained at 25°C on a light/dark (14:10 h) cycle under 12-20 µmol photons m -2 s -1.
Animals were maintained in these common garden conditions at USC with weekly feeding
(frozen brine shrimp, Artemia salina) and water changes for 24 months. A subset of Aiptasia
animals were rendered aposymbiotic by menthol-induced bleaching (Matthews et al. 2016) four
months prior to experimental trials. Briefly, a 1.28M menthol solution was prepared (20% w/v in
ethanol) and added to polycarbonate tanks containing FSW for a final concentration of 0.19
mmol l -1. Anemones were placed in the menthol solution for 8 h during the light period of the
14:10, light:dark cycle. The animals were then transferred overnight to tanks containing a final
concentration of 0.10 M DCMU (3-(3,4-dichlorophenyl)-1,1-dimethylurea), an algicide and
photosynthesis inhibitor. This was repeated for four consecutive days. At the end of the fourth
day, anemones were placed in black-out tanks with no light exposure and allowed to recover for
three days with one feeding. After the three-day recovery, the process was repeated once more
and bleaching status was confirmed using a fluorescent microscope. An absence of red
autofluorescence from dinoflagellate chloroplasts was noted and these aposymbiotic anemones
were transferred to black-out tanks filled with 0.2 µm FSW and maintained in the dark for three
months with the same feeding and water change regime as the symbiotic anemone stock.
50
Figure 3.1. Study design for the mild thermal stress experiment. Each treatment condition received 5
tanks (3 tanks x 6 symbiotic anemones and 2 tanks x 6 aposymbiotic anemones; n= 30 per treatment).
Following a 7-day ramping period, the peak temperature exposure continued for 6 days, and sampling
was conducted on the last day. Each anemone was rinsed with filtered seawater (FSW) thrice, deposited
into a microcentrifuge tube and stored at -80 °C until processing. DNA extractions were performed
followed by 16S rRNA amplicon sequencing. Samples with remaining DNA were used in qPCR assays to
determine symbiont (Symbiodinium) to host (Aiptasia) ratios.
3.3.2 Experimental design and sampling
Symbiotic and aposymbiotic anemone of approximately 2.5 cm length were selected for the 6-
day thermal stress experiment. Aposymbiotic Aiptasia were added to the experimental design to
act as a control for distinguishing baseline host stress responses from cnidarian-algal microbiome
shifts. These aposymbiotic anemones were acclimated to the same light/dark (14:10 h) cycle as
symbiotic anemones one week prior to the experiment start date. In addition, all anemones were
starved for 2 weeks prior to sampling to avoid prey (shrimp, Artemia sp.) contamination.
Six 0.5 L tanks containing six symbiotic Aiptasia (n=36) and four 0.5 L tanks containing
six aposymbiotic Aiptasia (n=24) were distributed evenly between experimental conditions (25
°C vs 32 °C, Fig. 3.1) . The treatment temperature was set to 32 °C due to prior observation of
51
slight bleaching and expulsion of Symbiodinium in Aiptasia at this temperature (Perez et al.
2001)). To maintain and manipulate temperature, tanks used in the heat stress treatment were
placed in 10 L bins containing two SL381 submersible water pumps, two 100W aquarium
heaters, a HOBO temperature logger and a digital, waterproof thermometer. Acclimatization of
Aiptasia was reached by exposing them to a gradual temperature ramp over the course of four
days. Temperature was increased from 25 ± 0.5 °C to 27 ±0.5 °C the first day, to 30 ± 0.5 °C
the second day, 32 ± 0.5 °C the third day and 33 ± 0.5 °C on the fourth day. Once Aiptasia were
acclimated to the elevated temperature treatment, treatment was maintained at an average of 32
± 0.5°C for 6 days with FSW changes every 2 days, in both control and heat-stress tanks. At the
end of the 6-day exposure period, each anemone was rinsed three times with FSW in individual
60 x 15 mm petri dishes, placed in a 1.5 mL microcentrifuge tube and frozen at -80 °C, until
processing.
3.3.3 DNA extractions and 16S rDNA sequencing
Individual anemone DNA was extracted by ethanol precipitation as detailed previously
(https://openwetware.org/wiki/Ethanol_precipitation_of_nucleic_acids) with some
modifications. Briefly, animals were placed in sterile, 2 mL polypropylene screw cap tubes
(Merck KGaA , Germany) containing a thin layer of Zi/Si beads (100-500 mm diameter), 200 μL
lysis buffer AP1 from a DNEasy Power Plant Kit (Qiagen, Germany), 2 μL RNAse (100 mg/mL,
stock), and 2 μL Proteinase K (20 mg/mL, stock) and incubated at 55 °C for 10 min in a
temperature controlled water bath. Samples were then homogenized using the Omni bead beater
(Omni International, USA) at 6.3 m/s, for 2 cycles of 30 seconds. The samples were then
centrifuged for 5 min at 14,000 rpm and the proteinase K enzyme was heat-inactivated on a heat
block (78-82 °C) for 3 min. The supernatant was transferred to a 1.5 mL microcentrifuge tube.
52
Ethanol precipitation was performed as described
(https://openwetware.org/wiki/Ethanol_precipitation_of_nucleic_acids) and the pellet was
resuspended in 40 μL elution buffer (GenElute Bacterial Genomic DNA Kit, Merck KGaA,
Germany). DNA was purified using the Zymo DNA Clean & Concentrator Kit (Zymo Research,
USA) following the manufacturer’s instructions.
Amplification of the V5/V6 region of the 16S rRNA gene was done using the 784F and
1061R primer set, which amplifies approximately 277 bp fragments with minor cross-
amplification of host mitochondria and microalgal symbiont chloroplasts (Andersson et al. 2008;
Bayer et al. 2013). Briefly, 50 ng of DNA were amplified in 25 ul reactions using 0.25 μL of
2,000 units/mL Q5 High-Fidelity DNA Polymerase (New England BioLabs Inc., Germany), 0.5
μL of 10 mM each dNTP, 0.25 μL of 10 μM forward and reverse primer each, 0.25 μL BSA
100X (New England BioLabs Inc., Germany), 5.0 μL 5X Q5 PCR Buffer (New England
BioLabs Inc., Germany), 13.5 μL nuclease-free water, and 5 μL of 10 ng/μL DNA. PCR
conditions were as previously described in Bayer et al. (2013) with the following modifications:
26 cycles of denaturation at 95°C for 60 sec and annealing at 55°C for 60 sec. 5 mL of the 16S
rRNA amplified sample was added to a second round of 3-step PCR to incorporate sample-
specific Illumina barcodes using an amplification profile of: 98°C 0:30, (98°C 0:10 | 59°C 0:30 |
72°C 0:30) x 4 cycles, 72°C 2:00. The barcoded samples were pooled in equimolar amounts (1
ng/μL) and sent for 250-bp paired-end sequencing on Illumina’s MiSeq v2 PE 250 platform
(USC Norris Comprehensive Cancer Center Molecular Genomics Core (NCCC), USA).
3.3.4 16S rDNA sequencing and bioinformatic analysis
We successfully extracted DNA and sequenced the 16S rRNA gene of 59 individual anemones
(initial n=60) but seven samples were discarded from the dataset due to low read yields (<
53
2,000). Paired-end reads were demultiplexed by the sequencing facility (USC NCCC Molecular
Genomics Core) and quality checked with FastQC (Andrews, 2010). An amplicon sequencing
variant (ASV) table was was generated with DADA2 (Callahan et al. 2016) in R (R Core Team
2020) using the default filtering parameters (truncLen=c(240,160), maxN=0, maxEE=c(2,2),
truncQ=2, rm.phix=TRUE, compress=TRUE, multithread=FALSE). Taxonomy was assigned
using the Ribosomal Database Project Classifier (Wang et al. 2007) along with the SILVA SSU
version 138.1 database, formatted for DADA2 (DOI/10.5281/zenodo.4587955). Chloroplast and
mitochondria sequences were removed using the R package, Phyloseq (McMurdie and Holmes
2013). The dataset yielded uneven library sizes so samples were rarefied to an even depth of 26,
481 reads (Gloor et al. 2017; Weiss et al. 2017). Following rarefaction, the dataset consisted of 1,
377, 012 reads (Table S3.1). All statistical analyses and visualizations were conducted in R (R
Core Team, 2020). Taxa plots, alpha diversity (Chao1 index), and beta-diversity visualizations
were conducted using the Phyloseq (McMurdie and Holmes 2013), ggplot2 (Wilkinson 2011),
and Microbiome (Lahti and Shetty et al, 2019) packages. Differential tree matrices were
generated and visualized using the Metacoder (Foster et al. 2017) package. Statistical output for
differential heat trees can be found at Zenodo, doi.org/10.5281/zenodo.7693398.
Additional statistical analyses were conducted using the vegan (Dixon 2003) package. A
one-way ANOVA test was conducted on rarefied data, comparing alpha-diversity data (Chao1
scores) between the microbial assemblages of symbiotic and aposymbiotic anemone groups in
different treatments (control vs thermal stress). Post-hoc pairwise comparisons were done using
Tukey’s HSD. Beta-diversity was visualized using a PCoA plot employing the weighted-Unifrac
metric. The adonis2 function in vegan was used to conduct pairwise Permutational Multivariate
Analysis of Variance (PERMANOVA) of microbial assemblage dissimilarities between
54
treatment groups. Adonis2 was used due to even homogeneity of variances in all pairwise
comparisons tested.
As the SILVA SSU v138.1 database only assigned some bacterial taxonomy to Order, we
conducted a separate phylogenetic analysis of ASVs classified in the order Oligoflexales. A
Bioconda (Grüning et al. 2018) environment was used for this analysis. The filtered Phyloseq
dataset was subsetted to include only Oligoflexales reads and resulting ASVs were then
transferred to a fasta file and a standard NCBI nucleotide blast (Sayers et al. 2023) was
performed on the web interface, optimizing for highly similar sequences (megablast). The ASVs
exhibited high quality (Table S3.2) matches to three uncultured bacterium clones, originating
from one microbial survey (Randle et al. 2020) conducted on Aiptasia strain CC7 (GenBank:
MK571601.1/ MK571569.1) and Aiptasia strain H2 (GenBank: MK571216.1), as well as one
uncultured proteobacterium clone (GenBank: FJ425635) found in the microbiome of
scleractinian coral Orbicella (formerly Montastrea) faveolata. We queried our nine Oligoflexales
ASVs and these previously published sequences with 16S rDNA sequences from 2 confirmed
members of the Oligoflexales order (GenBank accession numbers AB540021.2 and
OW948931.1) and ten members (7 families) of the Bdellovibrionota phylum. Sequence
alignment was performed using the MUSCLE algorithm version 5.1 (Edgar 2004) and a
phylogenetic tree was constructed by maximum likelihood with ultrafast bootstrap (n= 1,000
replicates) in IQ-TREE version 2.2.0.3 (Kalyaanamoorthy et al. 2017; Minh et al. 2020). The
resulting phylogenetic tree was visualized and annotated using the integrated web editor
interface, Interactive Tree of Life (ITOL, https://itol.embl.de/).
3.3.5 Symbiont to host (S/H) cell ratio qPCR
To assess the effects of thermal stress on symbiont density in Aiptasia, we analyzed symbiont to
55
host cell ratios (Mieog et al. 2009) using nuclear ribosomal protein L10 as a reference gene for
the host, Aiptasia (Poole et al. 2016) and the actin locus as a target in Symbiodinium (Palacio-
Castro 2019). Nuclear ribosomal protein L10 primers were previously validated for Aiptasia
specificity in qPCR assays (Poole et al. 2016) and used as a reference gene due to stable
expression in Aiptasia (Kitchen and Weis 2017). Previous qPCR assays targeting the actin locus
gene in Symbiodinium sp. (clade A) showed amplification specificity with an estimated copy
number for the actin locus at ~9 per cell (Palacio-Castro 2019). The primers used in this study
for the host were 400nM nrp_L10-F (5’-ACGTTTCTGCCGTGGTGTCCC-3’) and 400 nM
nrp_L10-R (5’-CGGGCAGCTTCAAGGGCTTCA-3’). Primers used for Symbiodinium
symbionts were 300 nM Aact_F (5’-ATGAAGTGCGACGTGGACAT-3’) and 200nM Aact_R
(5’-GGAGGACAGGATGGAGCCT-3’). All qPCR assays were performed on the Agilent
AriaMx qPCR Machine (Agilent, USA). Each reaction totaled 20 μL volumes, using 10 μL
Brilliant III Ultra-fast SYBR qPCR Master Mix (Agilent, USA), 6.1 μL Milli-Q water, 0.8 μL
per primer (forward and reverse, final concentrations listed above), 0.3 μL 1:500 reference dye
(SYBR) and 2 μL of template DNA (concentration range between 5-10 ng/ μL). The thermal
profile was 50 °C 2:00, 95 °C 10:00 (95 °C 0:10 | 60 °C 1:00 | 72°C 0.20) x 40 cycles + melt
curve profile of (95 °C 0:30 | 65 °C 0:30 | 95 °C 0:30) x 1 cycle. Due to low remaining DNA
after 16S rRNA library preparation and sequencing, we used 15 aposymbiotic replicates and 21
symbiotic replicates (Table S3.3) for the qPCR assays. Each sample was assayed in duplicate,
per target primer set. Cycle threshold (Ct) values were calculated by Agilent AriaMx qPCR
machine when the first amplification cycle in a reaction exceeded the fluorescent baseline. All
aposymbiotic anemones in the control (but not those in the thermally stressed samples) exhibited
non-target amplification for the actin primer set, and upon analysis of the melt curve, we noticed
56
a distinct peak between 83-84 °C for these samples, yet all other positive amplification reactions
exhibited a distinct peak between 85-87 °C. We surmised cross-amplification of an Aiptasia
locus in the absence of a Symbiodinium target. Sanger sequencing of the different products
revealed that actin samples with a melt product between 85-86 °C exhibited high quality matches
to the actin gene locus in Symbiodinium spp. (GenBank: AB231899.1, NCBI nr BLAST,
megablast, e-value= 8e-86, bit score= 329) whereas no matches were identified for the lower
melt product samples, likely due to a high abundance of Ns in the sequences. Therefore, actin
reactions exhibiting melt products < 84 °C were assigned a cycle number of 40 to account for
this non-specific amplification (Table S3.4). Ct values were averaged between technical
replicates and symbiont to host ratios were calculated using the formula ((2^ (Ct host)/ (Ct sym))
∗2) based on host/symbiont target ploidy (Aiptasia, host = 2, Symbiodinium, symbiont = 1)
((Cunning and Baker 2013); Palacio-Castro 2019).
3.3.6 S/H ratio statistical analyses
A dataset containing only symbiotic Aiptasia with S/H ratios, their alpha-diversity scores
(Shannon, Chao, Observed, Fisher and Simpson) and select bacterial abundance counts
(Oligoflexales and Staphylococcus) was used to generate a Pearson’s correlation matrix using the
corrplot package in R (Wei and Simko, 2021). The lm command was used for regression analysis
of Oligoflexales abundance counts on S/H ratio in symbiotic anemones. We implemented a
linear mixed effects model with a fixed effect of treatment and a random effect of tank to test
whether S/H ratios were reduced in symbiotic anemones response to heat treatment using the
nlme (Pinheiro and Bates, 2023; (Bates and Pinheiro 1998), and lme4 (Bates et al. 2015)
packages.
3.4 RESULTS
57
3.4.1 Microbial assemblages in Aiptasia
After ASV calling with DADA2, the sequence table yielded 4, 996, 356 reads and after
chloroplast and mitochondria removal, 4, 984, 116 reads and 4, 471 ASVs remained (Table
S3.1). Rarefaction and filtering of taxa occurring at least 3 times in more than 4 samples (the
minimum number of replicates per tank) yielded a final dataset of 1, 338, 082 reads and 774
ASVs. The dataset was dominated by Alphaproteobacteria (66 %), Gammaproteobacteria (19%),
and Bdellovibrionota (4%) (Fig. S3.1). All other taxonomic classes were present in abundances <
3%, except for Oligoflexia, which occurred at 4% relative abundance in symbiotic anemones
under control conditions (Fig. S3.1). The predominant genera in all samples were Cognatishimia,
an unnamed bacterium from the family Rhodobacteraceae and Alcanivorax (Fig. S3.2).
3.4.2 Community dynamics differ by treatment and symbiotic state
Alpha diversity was assessed by estimating species richness using the Chao1 index. Lower
within-sample diversity was observed in symbiotic anemones exposed to heat, but within-sample
diversity was consistent between other treatment groups (Fig. 3.2a). Although alpha diversity
differed between groups on average (ANOVA, p=0.009, Table S3.5), significant differences
were detected for only one pairwise comparison: heat-stressed symbiotic anemones and control
symbiotic anemones (Tukey multiple comparison of means, p=0.016, Table S3.5). A marginal
pairwise difference was detected between symbiotic and aposymbiotic anemones under heat
stress (Tukey multiple comparison of means, p=0.05, Table S3.5).
58
Figure 3.2. Alpha diversity and beta diversity differences of microbial assemblages in Aiptasia. (a) Alpha
diversity index, Chao1, by treatment group. Standard error for Chao1 is represented by the error bars. (b)
Beta differences by weighted Unifrac, principal coordinates of analysis (PCoA) on aposymbiotic samples
(control, dark blue circles vs heat stressed, light blue circles) and symbiotic (control, dark brown triangles
vs heat stressed, light brown triangles).
A principal coordinate analysis (PCoA, weighted Unifrac) also revealed differences in
beta diversity between symbiotic and aposymbiotic pairs in response to treatment (Fig. 3.2b).
Namely, beta diversity in heat-stressed symbiotic anemones converged whereas beta diversity in
control symbiotic anemones did not (PERMANOVA, p= 0.001, Table S3.6), indicating
microbial community composition in symbiotic anemones became more similar to each other
after heat treatment. The opposite pattern was observed in aposymbiotic anemones: convergence
was observed in the control group and divergence in the heat treatment (Fig. 3.2b,
PERMANOVA, p=0.001, Table S3.6). Differences in beta diversity were also observed between
symbiotic and aposymbiotic animals under control conditions (ANOSIM R= 0.28, p = 0.003,
Table S3.6).
We further explored taxa responsible for beta diversity disparities by generating a
differential heat tree and visualizing statistically dissimilar taxa between pairwise comparisons.
We detected differential abundance of several taxa (Wilcoxon Rank Sum tests, FDR-adjusted
59
Figure 3.3. Differential heat trees illustrating pairwise comparisons between groups of interest. Amplicon
data was used to visualize microbial taxonomic composition in Aiptasia, using the R package, Metacoder
(Foster et al. 2017). The bigger tree with taxon labels on the lower left serves as a key for the smaller
pairwise-comparison trees surrounding it a) aposymbiotic control vs aposymbiotic heat, b) aposymbiotic
control vs symbiotic control, c) symbiotic heat-stressed vs aposymbiotic heat-stressed and d) symbiotic
heat-stressed vs symbiotic control. Taxon color (diverging scheme from pink to green) is represented by
log-2 ratio of median proportions of reads observed by treatment group. Significantly differentially
abundant taxa, determined by Wilcoxon rank sum tests followed by FDR correction, colored in pink are
more prominent in the groups shown in the columns and those colored in green are more prominent in the
groups shown on the rows, e.g., Oligoflexales are significantly more abundant in symbiotic control
(green) anemones than symbiotic heat-stressed, but Myxococcota are enriched in symbiotic heat-stressed
60
(pink). Size of tree nodes corresponds to ASV richness, as denoted in the color and size key in the upper
right. Statistical output of differential abundance analysis is archived at Zenodo,
doi.org/10.5281/zenodo.7693398.
p-values < 0.05. Statistical output: Zenodo, doi.org/10.5281/zenodo.7693398) but most
noticeably Firmicutes, Oligoflexales, Oceanospirillales, Planctomycetes and Alteromonadales
(Fig. 3.3). Firmicute abundances were higher in aposymbiotic and symbiotic controls (Fig. 3.3a,
3.3d), and Oligoflexales abundances were highest in symbiotic anemones under control
conditions (Fig. 3.3b). Bacteria from the family Methylophilaceae exhibited a similar pattern as
Oligoflexales but overall abundance was low, and the difference between aposymbiotic and
symbiotic controls was only ~ 100 raw counts. In contrast, Oligoflexales abundances in the
aposymbiotic and symbiotic controls differed by 1-3 orders of magnitude (Fig. 3.4b).
Alteromonodales and Oceanospirillales abundance both decreased in heat-stressed anemones,
regardless of symbiotic state (Fig. 3.3c).
3.4.3 Oligoflexales order are are associated with symbiotic state and are lost under thermal stress
Oligoflexales abundances were elevated in symbiotic anemones under control conditions but
marginally elevated under heat treatment in aposymbiotic anemones relative to aposymbiotic
controls (Fig. 3a, 3b). Total abundance counts clearly displayed this categorical difference as
well (Fig. 3.4b). In addition to these categorical differences in Oligoflexales abundance by
anemone symbiotic state and treatment (Fig. S3.1, S3.3, S3.4b), we also observed quantitative
differences in abundance as a function of algal symbiont density. Symbiont to host (S/H) cell
ratios of symbiotic anemones decreased under thermal stress indicating mild bleaching (p=0.047,
Fig. S3.3, Table S3.7). Total S/H ratios for aposymbiotic anemones averaged around 0 but values
slightly increased in anemones exposed to thermal stress (Fig. S3.3, Table S3.4).
A correlation matrix was built to examine pairwise relationships between the S/H ratio of
61
symbiotic Aiptasia, alpha diversity, and Oligoflexales abundance counts. A moderate correlation
between S/H ratio and the abundance of Oligoflexales bacteria was detected (Pearson’s
Correlation, p=0.013, Fig. S3.4, Table S3.8). Regression analysis verified this positive
correlation, revealing that 53% of the observed variation in Oligoflexales abundance across
samples was explained by differences in S/H ratio (p < 0.001, Fig. 3.4a).
3.4.4 Oligoflexales may diversify under thermal stress
A phylogenetic tree plotting Oligoflexales ASV abundance showed symbiotic anemones in
control conditions initially hosted 9 distinct ASVs but lost 1 after thermal stress (Fig. S3.5). In
contrast, aposymbiotic anemones only harbored 2 ASVs under control conditions, whereas 8
Oligoflexales ASVs were detected in aposymbiotic anemones which experienced thermal stress
(Fig. S3.5). We conducted a more detailed phylogenetic analysis to further investigate
relationships among these distinct ASVs and other Oligoflexales variants identified in prior
studies. Oligoflexales ASVs from this study grouped closely with three uncultured bacterium
clones originating from an unrelated study on Aiptasia clonal strains CC7 and H2 (Randle et al.
2020) and one uncultured proteobacterium clone (GenBank: FJ425635) found in the microbiome
of the scleractinian coral Orbicella (formerly Montastrea) faveolata (Fig. S3.6). Other
Oligoflexales representatives were more distantly related (Fig. S3.6).
3.5 DISCUSSION
Overall, understanding the ecological dynamics of algal-microbe interactions in
cnidarians may be important for developing strategies to mitigate the impacts of climate change
(Matthews et al. 2020b; Frommlet et al. 2015) found that a diverse community of bacteria
62
Figure 3.4. Oligoflexales in the microbiome of Aiptasia. (a) Regression line through the origin and linear
model results testing the relationship between the dependent variable, Oligoflexales bacterial counts (y
axis) and explanatory variable, symbiont to host ratio (S/H) (x axis). (b) Total counts of Oligoflexales in
rarefied data, by treatment group (black circles= aposymbiotic anemones, black triangles= symbiotic
anemones).
facilitated the formation of symbiolites (spheroid, aragonite structures) in ex-hospite
Symbiodiniaceae cultures. This discovery highlighted a unique approach that could potentially
aid in coral calcification within reefs. Similarly, many eukaryotic algae are auxotrophs for the
prokaryote-produced B vitamins, like B12, and must obtain B12 from symbionts or dietary
sources for proper metabolic function and growth (Croft et al. 2006; Helliwell et al. 2011;
Grossman 2016). Furthermore, microbiome and algal symbiont co-occurrences may be a result
of metabolic cooperation, as dinoflagellates may rely on necessary metabolites produced by
bacteria and vice-versa (Cruz-López and Maske 2016; Grossman 2016; Kurihara et al. 2013).
Additional possible functional roles of bacteria associated with Symbiodiniaceae may also span
DMSP production, enhancing iron bioavailability and sulfur cycling (Lawson et al. 2018).
63
Here, we investigated the ecology of microbial communities, associated with Aiptasia,
and how they are affected by presence of the algal symbiont and elevated temperatures. Despite
treatment, Alphaproteobacteria and Gammaproteobacteria remained the predominant microbial
taxa. Differences in beta diversity were observed between symbiotic and aposymbiotic animals
under both control and thermal stress conditions but greater community similarity was observed
among microbial populations in both symbiotic and aposymbiotic anemones exposed to heat
stress. Additionally, elevated temperature decreased species richness in symbiotic anemones.We
also show that Oligoflexales bacteria are part of the rare microbiome in symbiotic anemones but
significantly decreased in abundance following thermal stress. The abundance of Oligoflexales
was positively correlated with higher S/H cell ratio indicating symbiont density, rather than heat
stress per se, impacted their abundance in Aiptasia.
3.5.1 Exploring the cnidarian-algal-bacteria tripartite symbiosis, Oligoflexales as primary
associates of Symbioidiniaceae
We examined the role of symbiotic algae in recruiting microbial taxa that may be specific to the
microbiomes of symbiotic Aiptasia and identified two taxa (Oligoflexales and Methylophilaceae)
that were significantly abundant in symbiotic Aiptasia only and decreased in relative abundance
with symbiont loss both independent of and as a result of thermal stress (Fig. 3.3b, 3.3d, 3.4). We
focused on exploring Oligoflexales because these taxa were present in higher relative
abundances and had been previously reported as associates of Aiptasia (Randle et al. 2020;
Maire et al. 2021). Whereas we could not find a consistent record of Methylophilaceae in
symbiotic Aiptasia. Despite numerous surveys conducted on the Aiptasia microbiome (Röthig et
al. 2016; Herrera et al. 2017; Ahmed et al. 2019; Hartman et al. 2020; Costa et al. 2021),
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Oligoflexales remained undetected until recent research reported their presence in Aiptasia strain
CC7 (Randle et al. 2020) and in the microbiome of Aiptasia acontia in strains AIMS1, AIMS2,
AIMS3, and AIMS4 (Maire et al. 2021). Two factors likely account for these results: (1) the
utilization of identical sequencing primer sets across all three studies, which document the
existence of Oligoflexales, including our own, and (2) the classification of taxonomy based on
the most recent release of the SILVA database (v138, issued in 2019). We chose to use the
784F/1061R primer set because it captures global bacterial diversity while exhibiting low
amplification of chloroplast and mitochondrial host DNA (Bayer et al. 2013) (Andersson et al.
2008; Bayer et al. 2013) (Bayer et al. 2013). Furthermore, as Oligoflexales were recently
recognized as a novel order under the Bdellovibrionota phylum (Nakai et al. 2014; Waite et al.
2020)), older databases may designate them as “unclassified/uncultured bacterial clones”. Based
on our research and the prior studies, we conclude that Oligoflexales are a consistent component
of the symbiotic microbiome in Aiptasia.
Bacteria belonging to the Oligoflexales order are Gram-negative, oligotrophic
spirochaetes, and only one species, Oligoflexus tunisiensis, has been described and isolated
(Nakai et al. 2014, 2016). We conducted a phylogenetic analysis of nine Oligoflexales ASV
sequences against O. tunisiensis and other members of the parent phylum, Bdellovibrionota
(Waite et al. 2020). Our ASVs formed a sister clade to O. tunisiensis and Pseudobacteriovorax
antillogorgiicola, an isolate from gorgonian corals in the family Pseudobacteriovoracacea
(McCauley et al. 2015) (Fig. S3.6). This suggests a close relationship between them. Currently,
Pseudobacteriovoracacea are classified as Bdellovibrionales, but a proposal to reclassify them as
Oligoflexales was submitted (Hahn et al. 2017), which is consistent with our findings grouping
them with O. tunisiensis.
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Although the ecological role of Oligoflexales in symbiotic Aiptasia remains a mystery, it
has previously been suggested to comprise a set of taxa that aid thermotolerance in high salinities
(Randle et al. 2020). The genome sequence of O. tunisiensis also provides clues on possible
metabolic capabilities in Oligoflexales. Nakai et al. (2016) observed an incomplete
denitrification pathway in O. tunisiensis, which resulted in the conversion of nitrate/nitrite
(NO3/NO2) to nitrous oxide (N2O). Heterotrophic bacteria are known to recycle fixed nitrogen
from the environment through denitrification, and those with a complete pathway can reduce
fixed nitrogen to dinitrogen (N2) gas (Knowles 1982). Nitrogen recycling by the host, Aiptasia,
regulates algal symbiotic density (Cui et al. 2019) but bacterially regulated nitrogen may play a
role in maintenance of the cnidarian-algal symbiosis as well, since nitrogen cycling is a hallmark
of reciprocal bacterial association in cnidarian holobionts (Knowlton and Rohwer 2003; Peixoto
et al. 2017). It is unclear if the Oligoflexiales in the present study possess a similar denitrification
pathway, but the genomic evidence from O. tunisiensis suggests that further research is needed to
investigate this possibility.
We observed a higher abundance of Oligoflexales ASVs in symbiotic Aiptasia under
control conditions, compared to symbiotic animals in the heat-stress treatment. In contrast,
aposymbiotic Aiptasia in the control showed only 1 ASV but experienced an increase in diversity
and abundance of ASVs in thermally stressed, aposymbiotic anemones (Fig. S3.5). While
superficially this observation initially appears to contradict the notion that Oligoflexiales are
associates of Symbiodiniaceae, we believe this pattern can be explained by the dynamics of
symbiont maintenance and proliferation in Aiptasia (Jinkerson et al. 2022) and is actually fully
consistent with the primary algal symbiont hypothesis.
Aposymbiotic Aiptasia anemones can retain remnant populations of algal symbionts,
66
even in the absence of light. S. linucheae do not need to photosynthesize to be maintained in
Aiptasia in dark conditions (Jinkerson et al. 2022). Jinkerson et al (2022) found that S. linuchae
did not proliferate in-hospite in the dark, but algal cell density significantly increased after
subsequent transition to light conditions. We theorize that a small, non-detectable (by PCR)
population of algal symbionts remained in the aposymbiotic group, despite continuous darkness
for three months. This S. linucheae population likely proliferated during the combined light and
heat-stress periods as confirmed by the slight increase in S/H ratios in this population (Fig. S3.3,
Table S3.4), in contrast to the aposymbiotic controls only exposed to light. A recent study
showed significantly higher proliferation of S. linucheae in Aiptasia at 32 °C, after 28 days
compared to ambient temperatures (25 °C), suggesting cell division rates initially increased in
response to elevated temperature but then declined after 12 weeks of sustained thermal stress
(Herrera et al. 2021). The slight increase in algal abundance in the aposymbiotic population
subjected to thermal stress was concomitant with an increase and diversification of Oligoflexales
(Fig. 3.4b, Fig. S3.5). Whereas the opposite pattern (Oligoflexales loss) was observed in
symbiotic Aiptasia exposed to heat stress. S/H ratios were lower (Table S3.4, Fig. S3.3) in
symbiotic anemones exposed to thermal stress, and significant differences were observed
between treatments (Table S3.7), indicating symbiont loss. Thermal stress can promote bleaching
in symbiotic cnidarians leading to dysbiosis (Weis 2008; Wooldridge 2009)) but the mechanisms
are not thoroughly established. One theory suggests dysbiosis may occur as a cascade effect that
begins with a decrease in the photosynthetic efficiency of the symbiont, followed by a reduction
in ammonium assimilation by the host, leading to an increase in the available ammonium pool.
This, in turn, stimulates algal growth, and eventually, the host becomes unable to keep up with
the resulting internal metabolic shifts, leading to the expulsion of the symbionts (Cui et al. 2019;
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Rädecker et al. 2021). We hypothesize that heat-stressed, aposymbiotic Aiptasia did not
experience the same metabolic constraints and were able to adequately support growth of algal
populations, leading to slightly higher symbiont abundance and increased abundance/diversity of
Oligoflexales bacteria (Fig. 3.4b, Fig. S3.5).
Lastly, it is a possibility that Oligoflexales observed in aposymbiotic anemones under
heat conditions were environmentally acquired. We used 0.2 µm FSW to maintain our cultures
but previous research has demonstrated that O. tunisiensis is part of the 0.2 µm filtrate culturable
fraction (Nakai et al. 2014). Although O. tunisiensis can reach up to 10 µm in length, and are
between 0.4 - 0.8 µm wide, they can compress through 0.2 µm filter pores. We do not have
morphological data for the Oligoflexales in this study, so we cannot eliminate the possibility of
environmental contamination. However, all animals were clonal propagates and were maintained
using 0.2 µm FSW, originating from the same 20 L tank, yet displayed distinct microbiome
profiles according to treatment (Fig. 3.3). Furthermore, the disparity of Oligoflexales abundances
between treatment groups (Fig. 3.4b) indicates symbiotic-state specificity in control conditions.
Environmentally acquired or not, Oligoflexales may play an important role in the holobiont as
endosymbiotic partners that only associate with cnidarians when microalgal symbionts are
present, as rare members of the symbiotic microbiome. It is possible that Oligoflexales, like rare
members of the coral core microbiome, are widespread in anemone tissue, particularly in close
proximity to algal symbionts (D Ainsworth et al. 2015) and future work should aim to fully
characterize their abundance and distribution.
3.5.2 Microbiome assemblages of aposymbiotic and symbiotic Aiptasia
Microbial assemblages in Aiptasia from wild and cultured populations (including clonal strain
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CC7) in ambient conditions showed consistent similarity at the phylum level but not at lower
taxonomic levels (Brown et al. 2017). In all of our samples, regardless of treatment, Alcanivorax
sp. from the Oceanospirillales order, Cognatishimia sp., and an unclassified bacterium from the
Rhodobacteraceae family from the Rhodobacterales order accounted for around 60-85% of the
total relative abundance (Fig. S3.2). This observation, that 2-3 taxa are numerically dominant in
Aiptasia, contradicts other microbial surveys on Aiptasia that identified a wider range of
taxonomic groups within the same relative abundance range (Röthig et al. 2016; Randle et al.
2020; Curtis et al. 2023). The importance of functional redundancy in shaping microbial
communities in Aiptasia is emphasized by the conflicting results obtained from various studies.
Functional redundancy refers to a diverse range of bacteria with similar capabilities, able to
perform similar functions in the same niche (Louca et al. 2018). Functional redundancy is an
advantageous strategy for ecosystem stability and may play a role in the resilience of hosts, like
corals, facing environmental disturbances (Voolstra and Ziegler 2020).
Prior work conducted by (Röthig et al. 2016; Ahmed et al. 2019; Randle et al. 2020)
showed that aposymbiotic and symbiotic Aiptasia (CC7), hosted distinct microbiomes. Here we
expanded upon this finding by introducing a stressor to assess the response of the aposymbiotic
microbiome and the symbiotic microbiome under thermal stress (Fig. S3.1). Although alpha-
diversity did not differ between symbiotic status (Fig. 3.2a), beta-diversity was dissimilar (Fig.
3.2b, Table S3.6), which prompted us to explore which taxa were responsible for these
observations. We observed a significant difference in the relative abundance of Oligoflexales,
Saccharospirillaceae, Pseudoalteromonadaceae, and Methylophilaceae families between
symbiotic and aposymbiotic animals, with these taxa being more prevalent in the former (Fig.
3.3b). Our findings align with other studies that have demonstrated differences in relative
69
abundance of microbiome composition between aposymbiotic and symbiotic states in strain CC7
(Röthig et al. 2016; Sydnor 2020; Curtis et al. 2023). Nevertheless, the four differentially
abundant taxa we identified were not previously reported, suggesting genotype by environment
and symbiotic state all influence microbial assemblages in Aiptasia.
3.5.3 Microbiome fluctuations caused by heat stress changed the rare microbiome
Here, we aimed to identify microbial taxa that are linked with symbiotic states in a defined
clonal strain of Aiptasia and tracked microbial fluctuations of these taxa following mild
bleaching. The dominant taxa in all Aiptasia were found to be unaffected by both temperature
and symbiotic state. However, the rare microbiome of symbiotic Aiptasia was significantly
affected by temperature stress. In contrast to the other treatment groups, symbiotic Aiptasia
exhibited a reduction in alpha-diversity and beta-diversity convergence after six days of exposure
to heat stress (Fig. 3.2). A decrease in alpha-diversity was also observed in another study
exposing symbiotic Aiptasia CC7 to short-term heat stress (Sydnor 2020), but it appears to be a
temporary phenomenon since a long-term study by (Ahmed et al. 2019)) on Aiptasia CC7,
surveying microbial communities under continuous heat stress (32°C for two years)
demonstrated an increase in both alpha-diversity and number of bacterial taxa relative to paired
controls. Here, symbiotic Aiptasia experienced microbiome restructuring when subjected to heat
stress (Fig. 3.3d) and the resulting assemblage was most similar to that observed in heat-stressed
aposymbiotic anemones (Fig. 3.3c). This suggests that short-term heat stress in Aiptasia may be
the main driver that led to a convergence of microbial communities in both aposymbiotic and
symbiotic animals.
However, possible symbiont gains in the aposymbiotic and symbiont loss in the
70
symbiotic anemones exposed to thermal stress (Fig. S3.3) may also be contributing to the
convergence of microbial communities. Symbiotic anemones exhibited increased relative
abundance of several bacteria taxa, most noticeably Oligoflexales, Saccharospirillaceae, and
Pseudoalteromonadaceae compared to aposymbiotic anemones under control temperatures (Fig.
3.3b). These same taxa increase in relative abundance in heat-stressed aposymbiotic anemones
compared to control aposymbiotic anemones (Fig. 3.3a) until their relative abundance is
indistinguishable from those observed in the symbiotic anemones in response to thermal stress
(Fig. 3.3c). Whereas their abundances decrease in response to heat stress in symbiotic anemones
(Fig. 3d). The most parsimonious explanation for these apparently contradictory changes in
response to heat stress is that it is not thermal stress, but algal symbiont density which influences
patterns of convergence. Additional time-course studies examining repopulation of algal
symbiont communities would provide additional support for this hypothesis.
It is unclear whether changes in the relative abundance or composition of the microbial
community affected the physiology or fitness of aposymbiotic and symbiotic Aiptasia hosts as
we did not conduct any host-specific assays, but we can confirm that no Aiptasia died during the
course of this experiment. While we have identified a strong association between the abundance
of Oligoflexales and algal endosymbionts independent of thermal stress, whether these bacteria
are mutualists or commensals remains unresolved. Additional work on patterns of localization,
metabolic exchange, and spatial and temporal fidelity are needed. But given the tractability of the
Aiptasia model, this represents a promising future study system for investigating multi-partner
symbioses. Understanding how the absence of a member in a multipartite symbiosis impacts the
resilience of other organisms in the holobiont can uncover valuable insights into how symbiotic
organisms respond to environmental challenges.
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3.6 ACKNOWLEDGEMENTS
This study was funded by the National Science Foundation Graduate Research Fellowship
Program grant award DGE-1418060 to EGA and start-up funding from the University of
Southern California to CDK. We would like to thank Dr. Cory J Krediet for providing a set of
clonal Aiptasia CC7 anemones. Special thanks to Dr. Ross Cunning, Dr. Sheila Kitchen and Dr.
Angela Poole for invaluable assistance in determining appropriate qPCR primers. Additionally,
we would like to thank Daniel Olivares-Zambrano for help pooling the libraries before
sequencing and Maria Ruggeri for kindly providing Aiptasia CC7 DNA samples to test the qPCR
primers used to calculate symbiont to host ratios.
3.7 CONFLICT OF INTEREST
The authors declare no competing interests.
3.8 DATA ACCESSIBILITY
Scripts for data analysis and statistical output for differential heat trees generated using the
Metacoder R package are archived at Zenodo, doi.org/10.5281/zenodo.7693398. Demultiplexed
sequences are available at the National Center for Biotechnology Information (NCBI) Sequence
Read Archives (SRA) under accession code: PRJNA929535.
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CHAPTER 3: SUPPLEMENTARY FILE
Figures S3.1-S3.6
Tables S3.1-Table S3.8
73
Figure S3.1. Relative abundance barplots of taxa, by class, in aposymbiotic and symbiotic anemones by
experimental conditions (25 °C vs 32 °C). Alphaproteobacteria, Bdellovibrionota and
Gammaproteobacteria dominate all the samples. Oligoflexia are distinctly present in control symbiotic
anemones.
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Figure S3.2. Relative abundance of the three most abundant genera in all Aiptasia groups: Alcanivorax
belonging to the Oceanospirillales order, Cognatishimia and an unclassified bacterium, both genera are
part of the Rhodobacterales order.
75
Figure S3.3. Symbiont to host ratio (S/H) in Aiptasia anemones. Ribosomal protein L10 was used as a
reference for Aiptasia and actin locus gene for Symbiodinium. In both treatments, aposymbiotic Aiptasia
approximated zero but ratios in symbiotic Aiptasia decreased after mild thermal stress (p= 0.047).
76
Figure S3.4. Correlation matrix showing the strength of interactions between S/H ratio
(“sym_host_ratio”) and a set of variables containing six alpha diversity indices (Fisher, Chao1, Shannon,
Simpson) and Oligoflexales (oligo_cts) counts. S/H ratio displays a significant correlation with one
variable, Oligoflexales abundance.
77
Figure S3.5. Phylogenetic visualization of Oligoflexales ASV abundance per Aiptasia treatment group
(aposymbiotic control, aposymbiotic thermal stress, symbiotic control, and symbiotic thermal stress)
using rarefied and filtered data. A total of 9 ASVs were observed. ASV 4 and ASV 6 were observed in all
samples but differed in abundance. ASV abundance and diversification in aposymbiotic anemones
increased in heat stressed anemones but decreased in symbiotic anemones.
78
Figure S3.6. Oligoflexales phylogenetic analysis. Maximum likelihood phylogeny with ultrafast
bootstrap (n=1,000 replicates) of this study’s (golden color) Oligoflexales ASVs with two published
Oligoflexales sequences (AB540021.2 and OW948931.1), along with four sequences that were close
matches to our ASV sequences (FJ425635.1, MK571216.1, MK571569.1, MK571601.1) and members of
the Bdellovibrionota phylum (Bdellovibrionia and Bacterovoracia). Surprisingly, an uncultured
Oligoflexaceae bacterium (OW948931.1) did not cluster with the other confirmed member of
Oligoflexiales (AB540021.2, Oligoflexus tunisiensis) or presumptive members of Oligoflexiales (ASVs in
this study) but clustered with a Bacteriovorax sp.
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Table S3.1. Read/ASV pipeline using DADA2 and Phyloseq.
Table S3.2. Significant, consistent matches to our 9 queried Oligoflexales ASVs, using standard NCBI
BLAST (blastn suite). GenBank MK571601.1, MK571569.1 and MK571216.1 originate from (Randle et
al. 2020). GenBank FJ425635 derives from an unpublished study on the microbiome of the coral
Orbicella (formerly Montastrea) faveolata.
Table S3.3. Aiptasia samples with sufficient DNA for qPCR analysis to determine S/H ratio. After qPCR
analysis, three “symbiotic” samples were omitted from downstream analysis due to incompatibility with
the 16S rRNA dataset (these three samples produced low Illumina read yields and were filtered from the
sequencing dataset).
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Table S3.4. Average cycle threshold (Ct) values of
samples used for qPCR analysis. Primers L10
(host, Aiptasia) and actin (symbiont, S. linucheae)
were used to calculate S/H ratio using the formula:
(2^ ((Ct host/Ct sym))) * 2
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Table S3.5. Alpha diversity statistical output. A one-way ANOVA test was used to determine alpha
diversity (Chao1 index) differences between groups. Post hoc analysis was performed, using Tukey
multiple comparison of means (95% family-wise confidence level), to assess which groups are different
from the rest.
Table S3.6. Beta diversity statistical output using either the nonparametric test, PERMANOVA for
assessing differences between Aiptasia microbial assemblages (symbiotic control vs symbiotic heat
stressed, aposymbiotic control vs aposymbiotic heat stressed) with even group dispersions or ANOSIM
for microbial assemblages with uneven dispersion (symbiotic control vs aposymbiotic control).
82
Table S3.7. Statistical output of linear mixed effects model, fit by residual maximum likelihood (REML),
to account for random effects (from tank differences between treatment groups).
83
Table S3.8. Pearson’s correlation p-values, corresponding to the correlation matrix on Fig. S3.6.
84
CHAPTER 4: SYMBIODINIACEAE- ROSEIBIUM ESTABLISH A
STABLE COCULTURE AND EXHIBIT SYNERGISTIC
GROWTH EFFECTS
Emily G Aguirre
1
, Marissa J Fine
1
, Tingting Xiang
2
, Carly D Kenkel
1
1
Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
2
Department of Bioengineering, University of California, Riverside, Los Angeles, CA, USA
Keywords: Symbiodinium, Roseibium, Labrenzia, algal phycosphere, cocultures, differential gene
expression, synergistic growth, vitamins, B12, cobalamin, Aiptasia
SUMMARY OF CONTRIBUTION
I, Emily G Aguirre and Dr. Carly D Kenkel (CDK) conceived and designed the lab experiment
and obtained funding. I maintained the cultures and set/ran the experiment. CDK and I conducted
the sampling and flash froze the samples at the final time point. Dr. Tingting Xiang provided the
SSA01 transcriptome used for mapping the RNA-seq reads and provided axenic SSA01. Marissa
J. Fine performed cell counts for all the SSA01 experimental treatments. I performed bacterial
CFU counts, completed RNA extractions, all bioinformatic and statistical analyses and wrote the
first draft of the manuscript. This chapter is currently being prepared for submission to
mSystems, an ASM journal.
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4.1 ABSTRACT
Bacteria potentially play various roles within cnidarian holobionts such as providing essential
micronutrients, antimicrobials, and vitamins. While it is evident that bacterial products are
exchanged, the mechanisms linking these services to the success of the cnidarian-algal symbiosis
remain uncharacterized. To address this gap, we can examine the growth dynamics of algal-
bacterial cocultures in nutrient-depleted conditions. For example, Symbiodinium linucheae, the
algal symbiont of Aiptasia anemones (Exaiptasia diaphana), putatively possesses the cobalamin
(B12)-dependent methionine synthase gene, making it dependent on B12, an essential vitamin
produced by prokaryotes. We previously isolated a bacterial B12-producer, Roseibium, a
fundamental member of the Symbiodiniaceae microbiome, and sequenced its genome to confirm
its metabolic potential for B12 synthesis. Here, we grew axenic cultures of S. linucheae (strain
SSA01) and Roseibium in B12-limited media for 12 weeks, followed by differential gene
expression analysis comparing controls (axenic SSA01 or axenic Roseibium) and coculture
treatments. Although SSA01 exhibited limited transcriptomic differences in coculture, it grew at
higher densities than SSA01 in monocultures, regardless of B12 availability. Roseibium also
achieved higher densities in coculture, and transcriptomic differences suggest it benefits from
associations with SSA01 in low nutrient environments. Taken together, these results suggest
SSA01 may not be true B12 auxotrophs but may benefit in other ways from mutualistic
associations with Roseibium.
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4.2 INTRODUCTION
The cnidarian holobiont is an ecological unit composed of cnidarian hosts (coral, anemones and
jellyfish) that harbor intracellular populations of photosynthetic algae and various microbial
associates, including prokaryotes, viruses, and other single-celled eukaryotic microbes
(Rosenberg et al. 2007; Bosch and McFall-Ngai 2011; Ainsworth et al. 2017; Thurber et al.
2017; Ohdera et al. 2018; Bonacolta et al. 2023). Although the cnidarian-algal symbiosis is
generally discussed as a bipartite interaction between the cnidarian host and dinoflagellate algae
(family, Symbiodiniaceae), there is an increasing recognition that other microbes form persistent
associations (Rosenberg et al. 2007) and can influence holobiont health (Reshef et al. 2006;
Peixoto et al. 2017; Rosado et al. 2019). Little is known about the algal phycosphere inside the
cnidarian holobiont (Garrido et al. 2021). However, there is evidence that it is important. For
example, Actinobacteria and Burkholderiales have been microscopically observed intracellularly
along with Symbiodiniaceae (D Ainsworth et al. 2015), although their functional relevance
remains unknown. Additionally, Symbiodiniaceae are notoriously difficult to grow axenically
and do exhibit a core microbiome across species (Ritchie 2012; Lawson et al. 2018). The
influence of these bacterial players in the persistence of the cnidarian-algal (Symbiodiniaceae)
symbiosis remains to be characterized and metagenomic data suggest many mechanisms
involved in cnidarian-bacteria and Symbiodiniaceae-bacteria interactions are likely yet to be
discovered (Ritchie 2012; Matthews et al. 2020).
Possible roles for bacteria within the cnidarian holobiont may be to provide a steady
supply of micronutrients, vitamins, and antibiotic products (Herndl and Velimirov 1985;
Agostini et al. 2009, 2012); (Raina et al. 2009; Rädecker et al. 2015; Neave et al. 2017; Silveira
et al. 2017). 44% of the he Porites lutea holobiont prokaryotic metagenome-assembled genomes
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(MAGs) encoded genes for the synthesis of the vitamins thiamine (B1) and biotin (B7), while
79% encoded riboflavin (B2) and 6% encoded cobalamin (B12) (Robbins et al. 2019). These
vitamins are crucial for the holobiont as the cnidarian host is unable to produce them
independently. Between 7- 13% of annotated genes in all surveyed genomes of a common coral
bacterial family, Endozoicomonas, were responsible for encoding cofactors, vitamins, prosthetic
groups, and pigments (Neave et al. 2017). These compounds, primarily cofactors and vitamins,
possess the potential for exchange within the holobiont, highlighting their importance. The
genomes of 118 cultured bacteria originating from the coral Pocillopora damicornis revealed a
high abundance of annotated metabolic functions related to the metabolism of cofactors and
vitamins within these bacterial genomes (Li et al. 2022). Furthermore, these compounds are also
required by Symbiodiniaceae as well ((Garrido et al. 2021). Bacterial products are clearly
exchanged, but what are the mechanisms that link these services to the success of the cnidarian-
algal symbiosis?
This multifaceted question can begin to be addressed by focusing on B-vitamins, a
class of mostly prokaryote-produced compounds (Blanche et al. 1992; Croft et al. 2005; Masuko
et al. 2005; Webb et al. 2007). B-vitamins are a set of micronutrients which catalyze essential
metabolic reactions from methionine synthesis (B12) to reactions involving the tricarboxylic acid
(TCA) cycle, Calvin cycle (B1) and reverse TCA cycle (B7) (Croft et al. 2005). Many eukaryotic
algae, including dinoflagellates, exhibit auxotrophy of three major B- vitamins (B1, B7 and B12)
(Croft et al. 2005); (Croft et al. 2006; Sañudo-Wilhelmy et al. 2006, 2014; Helliwell et al. 2011;
Cruz-López and Maske 2016; Grossman 2016). This suggests that high microbial biodiversity in
oligotrophic marine environments, like those many corals reside in, may be sustained by
specialized metabolite exchange involving dissolved micronutrients, like B-vitamins (Kazamia et
88
al. 2012; Grossman 2016; Heal et al. 2017).
B12, in particular, is an essential vitamin that plays a key role in the biosynthesis of
methionine in many eukaryotes (Helliwell et al. 2013). B12 is a cofactor for cobalamin-dependent
methionine synthase, an enzyme encoded by the metH gene, which catalyzes regeneration of
methionine from homocysteine (Foster et al. 1964; Banerjee and Matthews 1990; Banerjee and
Ragsdale 2003; Matthews 2009). In several algal lineages, the metE gene, responsible for
encoding B12-independent methionine synthase, has either been lost or exists as incomplete
copies (Helliwell et al. 2011). This suggests that algae lacking the complete metE gene are likely
dependent on B12 and are auxotrophic for it (Croft et al. 2005). Dinoflagellates have the metH
gene, yet 40/58 species examined possessed an incomplete version of the metE gene, usually
lacking the N-terminal domain, which would result in an inability to properly utilize B12-
independent methionine synthase Lin et al. (2022). The presence of an incomplete metE gene in
dinoflagellate genomes likely accounts for the B12 dependency observed in dinoflagellates (Lin et
al. 2022). However, the prevalent form of methionine synthase found in dinoflagellates, in the
family Symbiodiniaceae, remains uncertain as metH and metE sequences have been published
only for Symbiodinium sp. CCMA128 and Effrenium voratum (Lin et al. 2022). Another process
for converting homocysteine to methionine is through methylation via betaine homocysteine S-
methyltransferase (BHMT), but the mechanism of BHMT has not been widely studied in
organisms other than mammals (Pajares and Pérez-Sala 2006) and it is unclear whether this gene
plays a role on methionine biosynthesis in dinoflagellate algae like Symbiodiniaceae.
To our knowledge, only one study has empirically measured B12 availability in the
cnidarian holobiont and its effect on the growth of the dinoflagellate symbiont, Symbiodinium.
The coelenteric fluid of the stony coral, Galaxea fascicularis, exhibited vitamin B12 levels that
89
were one to two orders of magnitude greater than the vitamin B12 levels found in the surrounding
seawater of the reef (Agostini et al. 2009). The B12 requirement of Symbiodinium sp. (formerly
clade A), the symbiotic partner of G. fascicularis, was further confirmed by cultivating the
symbiont in a medium lacking B12 and treated with antibiotics, with a significant reduction in
growth (Agostini et al. 2009). Despite evidence pointing to high prokaryotic B12 production in
the gastric cavity of G. fascicularis, there have been no follow-up studies attempting to link B12-
vitamin exchange with specific microbes that associate with the cnidarian holobiont. To address
this gap, we previously isolated a putative B12-producer and member of the Symbiodiniaceae
core microbiome (Lawson et al. 2018), Roseibium sp. Sym1 [formerly categorized as Labrenzia
sp., (Hördt et al. 2020; Zhong et al. 2021)], from a xenic, monoclonal culture of Symbiodinium
linucheae, derived from the anemone, Aiptasia (Exaiptasia pallida) (Aguirre et al. 2023). Here,
we investigated growth and gene expression patterns of Roseibium sp. and an axenic strain of
Symbiodinium linucheae SSA01 under B12 limitation. We conducted an evaluation of the growth
of SSA01 and Roseibium in B12-limited media, as cocultures or monocultures, over a period of
12 weeks. At the final 12-week timepoint, RNA samples were collected for differential gene
expression analysis between experimental conditions.
4.3 MATERIALS AND METHODS
4.3.1 S. linucheae SSA01 algal cultures
Cultures of S. linucheae, strain SSA01, a clonal endosymbiotic dinoflagellate originating from
Aiptasia CC7 anemones (Bieri et al. 2016) and axenized as specified in (Xiang et al. 2013), were
obtained from Dr. Xiang and used for the experiment. Seed SSA01 culture was maintained in L1
media (Guillard and Ryther 1962; Guillard and Hargraves 1993). L1 media was prepared using
90
the L1 media Kit (MKL150L, NCMA/Bigelow, USA), but sodium silicate was omitted from the
formulation since silica is not necessary for the growth of Symbiodinium. Additionally, L1
components were diluted in artificial seawater (ASW) (Kester et al. 1967), instead of filtered
natural seawater (FSW). Cultures were grown in 30 mL of media within 50 mL culture tissue
flasks (VWR, USA) at 27°C, on a 14:10 h light-dark cycle under 12-20 µmol photons m
-2
s
-1
.
Prior to the experiment, the initial seed culture was checked for bacterial contamination by
inoculating aliquots into marine R2A agar (Merck KGaA, Germany, prepared using 0.2 μm FSW
and subsequently autoclaved at 121°C for 15 minutes) and incubating the plates at 27°C for up to
5 days. No bacterial growth was observed.
4.3.2 Roseibium sp. Sym1 bacterial cultures
We previously isolated Roseibium sp. Sym 1 from non-axenic, monoclonal cultures of S.
linucheae and originally derived from an Aiptasia CC7 anemone (Aguirre 2022). Roseibium was
reanimated from a cryostock and inoculated into 30 mL of L1, lacking B12 media (L1-B12,
prepared as described below under experimental design) along with an addition of 0.1% glucose
(D-(+)-Glucose BioReagent, Merck KGaA, Germany) to serve as a carbon source. The seed
culture was incubated for 72 h at 27°C. Following a 3-day incubation period, the culture reached
high density and was subsequently used for the experiment.
4.3.3 Experimental design
SSA01-Roseibium cocultures were grown in 30 mL of L1-B12 media on cell culture flasks, for
12 weeks at 27°C on a 14:10 h light-dark cycle under 12-20 µmol photons m
-2
s
-1
(Fig. 4.1). Cell
counts were performed on both SSA01 and Roseibium throughout the 12-week period (see below
91
for methods) and RNA was extracted at the end of the 12 weeks for differential gene expression
analysis (DGE). The experimental design (Fig. 4.1) includes triplicates for each treatment:
1. SSA01 in L1+B12
2. SSA01 in L1-B12
3. Roseibium in L1-B12 (starved, no glucose)
4. Roseibium in L1-B12, 0.1% glucose (high nutrient, for 3 days only)
5. SSA01 + low density (1 x 10
5
cells/mL) Roseibium in L1-B12
6. SSA01 + medium density (1 x 10
6
cells/mL) Roseibium in L1-B12
7. SSA01 + high density (1 x 10
7
cells/mL) Roseibium in L1-B12
8. Low density (1 x 10
5
cells/mL) Roseibium in L1-B12
9. Medium density (1 x 10
6
cells/mL) Roseibium in L1-B12
10. High density (1 x 10
5
cells/mL) Roseibium in L1-B12
For the SSA01-Roseibium cocultures and B12-limited SSA01 and Roseibium
monocultures, the L1-B12 medium was prepared following the method outlined earlier for
SSA01, with the following modifications: the vitamin solution consisting of thiamine (B1), biotin
(B7), and cobalamin (B12) was excluded and substituted with an in-house solution containing
only reagent grade > 99%, B1 and B7 (T4625 and B4639, respectively, from Merck KGaA,
Germany). The final concentrations of B1 and B7 in the medium were 2.96 x 10
-7
M and 2.05 x
10
-9
M, respectively.
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Figure 4.1. Experimental design for the coculture experiment was as follows:(a) SSA01 monocultures
were cultivated in triplicate flasks containing L1+B 12 and L1-B 12 media. SSA01-Roseibium cocultures
with varying densities (low, medium, and high) were grown in triplicate flasks using L1-B 12 media. All
algal cultures were maintained for a duration of 12 weeks. (b) Roseibium monocultures were cultured in
triplicate flasks containing L1-B 12 + 0.1% glucose (high-nutrient) and L1-B 12 (starved) media. Roseibium
cultures with different densities (low, medium, and high) were grown in triplicate flasks using L1-B 12
media. All Roseibium cultures were cultivated for 12 weeks, except for the "high-nutrient" treatment,
which was cultivated for 72 hours due to its rapid growth. (c) Growth curves were plotted for all algal and
bacterial trials over the 12-week duration. At the end of the 12-week period, RNA was extracted from all
cultures for subsequent analysis of differential gene expression.
Initial cell density for SSA01 was measured prior to running the experiment. A 30 mL
SSA01 culture (~10
6
cells/mL) was centrifuged at 6000 rpm for 10 min, at 22°C. The resulting
spent media was carefully decanted, while the pellet was left undisturbed. The pellet was then
washed twice in clean L1-B12 media and reconstituted in L1-B12 media. Aliquots of SSA01
culture were equally distributed into each of the 15 experimental flasks (L1+B12 monoculture, L1
-B12 monoculture, and L1-B12 + varying densities of Roseibium). Likewise, initial, approximate
Roseibium concentration, from the seed culture, was measured as well. The optical density, at
600 nm, was measured using the Synergy H1 multimode reader (Agilent Technologies, USA).
The bacterial culture was diluted to achieve approximate densities of 1 x 10
5
cells/mL (“low
93
density”), 1 x 10
6
cells/mL (“medium density”), and 1 x 10
7
cells/mL (“high density”) in 30 mL
flasks containing L1-B12 media (Fig. 4.1).
4.3.4 Growth curves
Cell densities of SSA01 and Roseibium were measured throughout the 12-week experimental
period. SSA01 cell density was assessed by octuplet hemocytometer counts per flask. The
cultures were agitated by gently pipetting the medium up and down until even cell distribution,
followed by fixing an aliquot of 100 μL in 100 μL 20% formalin in a 1.5 mL microcentrifuge
tube (Eppendorf), resulting in a final concentration of 10% formalin. Samples were stored at
room temperature for subsequent counts. To obtain counts, tubes were vortexed and a 10 μL
aliquot was deposited on Neubauer improved hemocytometer chamber. Cell density was
determined using the following equation: D = (Xr/Vh) * Vs where D= density of algae, Xr= mean
of 8 counts, Vh= volume of (25 small squares) on hemocytometer (1x10-4ml) and Vs= volume of
sample corrected for addition of 20% formalin. Except for the three consecutive monoculture
trials in L1-B12, all SSA01 cultures were initially seeded with SSA01 populations averaging
8x10
3
cells/mL. To consider potential variations and impacts of bacterial densities on the algae,
cocultures were inoculated with Roseibium at low, medium, or high densities.
Roseibium density was determined using CFU counts on marine R2A agar plates. Similar
to sample collection for SSA01, the monocultures and cocultures were gently agitated and
aliquots of 100 μL were diluted to 1:100, 1:000 and 1:1000 in L1-B12 media. Subsequently, 100
μL of a dilution (either 1:100, 1:1000 or 1:10000, depending on the prior week’s growth) were
plated, in duplicates, on marine R2A agar. CFU/mL were determined by using the following
equation D = (X * Y) / Z where D= density of Roseibium, X= average counts from duplicates,
94
Y= dilution plated, Z= volume, in mL, plated.
Even though we washed the cells prior to the experiment to reduce carryover of vitamins
from the seed media, we additionally explored the sustained growth of SSA01 in L1-B12 media
through three consecutive subculturing trials to evaluate the potential influence of carryover
vitamin B12 from the initial trial on the dinoflagellate's growth. Prior to each trial, one SSA01
culture grown in L1-B12 media from the preceding trial was subjected to centrifugation at 6,000
rpm for 10 minutes, and the spent media was decanted. The pelleted culture was subsequently
reconstituted in 1 mL of L1-B12 and utilized to inoculate the next trial, continuing this process
for a total of three trials.
4.3.5 Analysis of growth curve data
Statistical analysis and visualization were conducted in R (R Core Team, 2020). Growth
curve plots were generated using ggplot2 (Wickham 2009) and ggpubr (Kassambara 2023a). To
determine the impact of experimental factors on growth, the rstatix package (Kassambara 2023b)
was employed. Growth was modeled as a function of culture condition or “type” (levels:
monoculture, coculture), Roseibium inoculant density (levels: 1x10
5
, 1x10
6
, 1x10
7
), treatment
(levels: +B12, -B12) and time, using repeated measures ANOVA. For SSA01, culture conditions
and treatment were confounded, such that B12 manipulations were only conducted on
monocultures and inoculant density only tested in cocultures. Therefore, a fully factorial model
could not be applied due to multicollinearity. Instead, a series of two-way models were used. For
SSA01 monocultures, growth was modeled as a two-way interaction (~B12 treatment + time +
treatment:time,). For SSA01 co-cultures, a separate two-way interaction modeled growth as a
function of Roseibium inoculant density and time (~Roseibium density + time + density:time). To
95
test whether growth was altered by culture condition, a third two-way model was generated
which tested the effect of all ‘nutritional’ treatments (levels: +B12, -B12, Roseibium inoculant
density 1x10
5
, 1x10
6
, and 1x10
7
) over time and their interaction (~treatment + time +
treatment:time). Finally, to test whether coculture (levels: “Bacteria”, all inoculant densities
combined into one factor level) or monoculture (levels: +B12, -B12) type had an effect on growth,
we performed pairwise comparisons using a Welch’s two-sample t-test for each timepoint. For
Roseibium, no B12 manipulation was undertaken, and inoculant density was tested in both mono
and cocultures, therefore a three-way model was applied to Roseibium growth as a function of
culture type, inoculant density, time and their interactions (~type + density + time) and post-hoc
t-tests were used to identify significant differences between specific factor levels. Growth data
was not collected for the Roseibium high nutrient treatments.
4.3.6 Gene expression sample collection, RNA extractions, library preparation and sequencing
4.3.6.1 Sample collection
Experimental samples were collected at the 12-week timepoint (excluding the L1-B12
subculturation trials) except for the high nutrient Roseibium samples, which were collected after
3 days. SSA01 and Roseibium monocultures were each gently transferred to 50mL conical
centrifuge tubes and centrifuged at 6,000 rpm for 15 minutes at room temperature. The spent
media was discarded, and the undisturbed pellet was immediately flash frozen in liquid N2 for 5
minutes and then transferred to -80 °C until further processing. SSA01-Roseibium cocultures
were gently agitated, using a sterile 10 mL serological pipet and transferred to an acid-washed,
500 mL filter rig attached to a filtration vacuum pump (<15 PSI) and filtered through a 47mm,
5.0 μm pore-size, polycarbonate filter (Whatman plc, UK), thus collecting Symbiodinium. The
96
filter was immediately placed in a 15 mL conical centrifuge tube, using sterile forceps, and flash
frozen. The rig was acid-washed and rinsed thrice with 0.2 μm D.I H2O. The filtrate was then
passed through a 47 mm, 0.2 μm pore-size, polycarbonate filter (Whatman plc, UK), thus
collecting Roseibium. Similar to the previous step, the 0.2 μm filter was promptly placed in a 15
mL conical centrifuge tube and flash frozen.
4.3.6.2 RNA extractions
RNA extractions for SSA01 were conducted as described in (Rosic and Hoegh-Guldberg 2010),
using a combined TRIzol reagent method (Thermo Fisher Scientific, USA) and RNeasy Mini Kit
(Qiagen, USA) protocol, with some modifications: Samples were thawed in ice, and immediately
transferred into a bead-beating tube containing Zir/Si beads and 1 mL TRI reagent (Merck
KGaA, Germany), a TRIzol equivalent, was used instead. The tubes were then transferred to the
Omni bead beater (Omni International, USA) set to a protocol of 4.0 m/s, for 1 cycle of 1:30.
Samples were allowed to phase-separate for 5 min at room temperature. 200 μL of chloroform
was added, samples were mixed and allowed to stand for 10 min at room temperature. Samples
were then centrifuged at 12,000 x g for 15 min at 4°C. The colorless, upper phase was then
transferred to a sterile 1.5 mL Eppendorf tube and 500 μL of 2-propanol was added and allowed
to phase-separate for 10 min at room temperature. Samples were then centrifuged at 12,000 x g
for 10 min at 4°C. An RNA precipitate was observed at the bottom of each tube. The supernatant
was removed and 700 μL of 75% EtOH was added. At this point, the sample was transferred to a
RNeasy spin column and steps 4-8 of the RNeasy Mini Kit Protocol: Purification of Total RNA
from Plant Cells and Tissues and Filamentous Fungi protocol were performed (RNeasy® Mini
Handbook, Qiagen, USA).
97
RNA extractions for Roseibium were conducted according to RNeasy Mini Kit protocols
for purification of total RNA (RNeasy® Mini Handbook, Supplementary Protocol. Qiagen,
USA). Steps 1-4 of the handbook were omitted and replaced with the following protocol:
Samples were thawed in ice and immediately placed in bead-beating tubes containing Zir/Si
beads and 700 μL of Buffer RLT containing 7 μL of β-mercaptoethanol (β-ME). Bacterial cells
were then bead-beat in the Omni bead beater (Omni International, USA) at 4.0 m/s, for 1 cycle of
1:30. All other kit steps (5-12) were followed as directed.
Total RNA purity was checked spectrophotometrically using the BioTek Take3
microvolume plate (Agilent Technologies, USA) on the Synergy H1 multimode reader, using the
module for RNA concentration.To assess the quality of the RNA, a small volume (2-5 μL) of
each sample was examined using gel electrophoresis. For sequencing purposes, the total RNA
needed to fall within the acceptable range of an A260/A280 ratio between 1.8 and 2.2. The
presence of distinct and robust 18S rRNA bands for SSA01 or 16S rRNA bands for Roseibium
was determined visually, using sensitive SYBR Green dye (Merck KGaA, Germany).
4.3.6.3 Library preparation and sequencing
To enrich the mRNA in total RNA from SSA01-derived samples, the NEB Ultra II Directional
RNA Library Prep Kit (NEB, USA) with poly-A pulldown was employed following the
instructions provided by the manufacturer. One SSA01 sample (coculture, 1x10
6
Roseibium
density, replicate 2) was discarded due to insufficient input RNA, for library preparation and
sequencing. Total RNA from Roseibium-derived samples underwent rRNA depletion using the
NEBNext rRNA depletion kit for bacteria, E7850L (NEB, USA), following the manufacturer's
guidelines. All Roseibium high inoculant density (1x10
7
) coculture replicates were discarded due
98
to insufficient input RNA for library preparation and sequencing. In addition, replicates from the
starved experimental group were pooled together during library preparation due to initial low
RNA input. Library preparation was performed using either ~ 500 ng input RNA (for SSA01) or
~ 6-154 ng input RNA (for Roseibium), according to manufacturer’s instructions, with the
following exceptions for both kit protocols: (a) samples received an 0.8x bead-clean step after
2nd strand synthesis, (b) adapters were diluted 1:30 and samples received an 0.7x bead-clean
after ligation, (c) libraries underwent 14 cycles of amplification and (d) samples received a final,
dual bead-clean of 0.7x and 0.8x to remove residual adapters. Library quantification was done
using a combination of qPCR and microcapillary electrophoresis, using the high sensitivity DNA
assay on the Agilent 2100 Bioanalyzer (Agilent Technologies, USA). Sequencing for SE reads (1
x 100 bp) was done using the NextSeq 2000 P2 (100 cycles) system (Illumina, USA), following
the standard protocol. Due to low yield in the initial run, additional PE reads (2 x 150 bp) were
generated from three Roseibium coculture samples (1x10
6
, medium density triplicates) on the
NovaSeq 6000, flow cell type S4, following the standard protocol. mRNA enrichment, library
preparation and sequencing were conducted by Genomics Core Facility at University of
California, Riverside (USA).
4.3.7 Gene expression- Bioinformatic analyses and statistics
All raw reads for both SSA01 and Roseibium were demultiplexed by the sequencing facility and
quality-checked using FastQC (Andrews, 2010).
4.3.7.1 S. linucheae SSA01 bioinformatic and statistical analysis
The sequencing run yielded a total of 498,689,422 SE raw reads for 14 SSA01 samples.
99
Remaining reads containing adapter sequences were removed using bbduk.sh, part of the
BBTools suite v39.01 (2022) using a reference file with Illumina TruSeq and Nextera adapters.
Next, the sequences were checked for any contaminant rRNA, again using bbduk.sh this time
with 16S and 18S small rRNA sequences, downloaded from the SILVA v128 database, as a
reference file. No contaminant rRNA sequences were identified (Table 4.1). Remaining high
quality reads were mapped to the SSA01 transcriptome (Xiang et al., In prep) using default
parameters in Salmon v1.10.0 (Patro et al. 2017). Briefly, we generated a transcriptome index
and subsequently quantified the reads using the mapping-based mode parameter. Normalized
counts (transcripts per million, TPM) per sample were exported and combined into a counts
table. To determine whether SSA01 possesses the vitamin B12-dependent methionine synthase
gene (metH) or B12-independent methionine synthase (metE), we queried a list of previously
published metH and metE genes of dinoflagellates (Lin et al. 2022) against the reference SSA01
transcriptome (Xiang et al., In prep), using BLAST 2.12.0+ (Zhang et al. 2000; Camacho et al.
2009; Sayers et al. 2022). The above bioinformatic protocols were all performed while using
USC’s Center for Advanced Research Computing (CARC).
DGE analysis was performed using the package DEseq2 (Love et al. 2014) in R v4.2.1 (R
Core Team, 2020). Outlier detection was performed using arrayQualityMetrics (Kauffmann et
al. 2009). The dataset was filtered for transcripts showing at least 1 count in at least 2 samples.
Count data underwent a variance stabilizing transformation (VST) for principal components
analysis (PCA) to visualize overall gene expression patterns in samples. Expression was modeled
as a function of culture type (levels: monoculture and coculture) or type and the effect of B12
treatment (levels: -B12, +B12), as the model ~Type + treatment. Per gene significance was
evaluated using Wald tests. Raw p-values were subjected to a multiple test correction, using the
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Table 4.1. Reads pipeline for S. linucheae SSA01.
Benjamini and Hochberg correction (Benjamini and Hochberg 1995). We considered adjusted p-
values < 0.1 to be statistically significant. Rank-based gene ontology (GO) analysis using the
Mann-Whitney U (MWU) test was performed to identify terms significantly enriched among
upregulated and downregulated genes as described in (Wright et al. 2015) using the softwares R
and perl v5.36.1 (Wall et al., 1994).
4.3.7.2 Roseibium bioinformatics pipeline
The sequencing run yielded 503,274,626 SE reads for the 10 Roseibium samples (Table 4.2).
Adapter sequences and contaminant rRNA were removed as detailed in the SSA01 pipeline for
SE reads, and default parameters for PE reads. The percent range of SE reads kept after rRNA
removal ranged from 1.3-3.5 % in the high nutrient samples, 98.4% in the starved pool and
between 49.4% - 62.31 % in the cocultures. Our previously published Roseibium genome
(Aguirre et al. 2023) was functionally annotated using the JGI-IMG Annotation Pipeline v.5.1.9
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Table 4.2. SE reads pipeline for Roseibium.
(Chen et al. 2019) and the data can be accessed at JGI-GOLD under Project ID: Ga0573226. We
mapped SE reads to this Roseibium genome (Aguirre et al. 2023) concatenated to the SSA01
transcriptome fasta file (Xiang et al., In prep) using default parameters in bowtie2 v2.2.5
(Langmead and Salzberg 2012). Samtools v1.7 (Danecek et al. 2021) was used to convert sam
files to bam files, using default parameters. The bam files were then used to quantify transcript
counts in alignment mode on Salmon v1.10.0 (Patro et al. 2017). Reads originating from the
coculture samples which mapped to the SSA01 transcriptome were removed from the final
counts table. Roseibium-mapped reads kept after removal of SSA01-mapped reads in the
coculture samples ranged between 0.1-0.9 % total reads, from the initial raw reads (Table 4.2).
Because of high rRNA contamination in our samples, we resequenced three coculture samples
(all medium Roseibium inoculant density 1x10
6
replicates), using the NovaSeq 6000 system to
obtain sufficient reads to conduct a thorough DGE analysis. This sequencing run yielded an
additional 380,888,202 PE reads for the three, resequenced coculture samples. Each of the
samples exhibited a sequencing quality score > 30 for this run. Adapter removal, rRNA removal,
genome mapping and quantification of transcript counts was performed as detailed above, except
we used parameters specific for PE sequences, when necessary. Overall, data derived from PE
reads, for the three medium-density cocultures, collectively had higher counts (Table 4.3) than
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Table 4.3. PE reads pipeline for Roseibium.
data from the same SE-sequenced samples. The final dataset used for bacterial DGE analysis in
this study (Table 4.4) was composed of (a) L1-B12 monocultures, 0.1% glucose in triplicate
(derived from SE reads), (b) medium density (1x10
6
) L1-B12 cocultures in triplicate (derived
from PE reads) and (c) L1-B12 monocultures, starved, all replicates pooled during library
preparation (derived from SE reads). Roseibium genes were functionally annotated using
eggNOG-mapper v2.1.9 (Cantalapiedra et al. 2021) for GO terms and subsequent GO analysis.
DGE, outlier detection, visualizations, statistics and rank based GO analysis were performed as
described above in the SSA01 bioinformatics pipeline, with one exception: One DESeq2 model
was used to compare gene expression between monocultures and cocultures (~Type) but the
effects of nutritional enrichment/starvation ( ~Type + treatment) on Roseibium could not be
analyzed due to a lack of statistical power between treatments, due to the limited working dataset
used for gene expression (Table 4.4, DGE table of samples for Roseibium).
4.4 RESULTS
4.4.1 SSA01-Roseibium growth dynamics
We analyzed the growth dynamics of SSA01 and Roseibium individually and in coculture to
assess if they reciprocally enhance each other's growth. All SSA01 cultures demonstrated growth
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Table 4.4. Sample pipeline for DGE analysis of Roseibium sp. Sym1 samples.
over time, but growth rates also differed by treatment and as a function of the treatment by time
interaction (Fig. 4.2, Table 4.5, p<0.001). A rapid logarithmic growth phase was apparent from
weeks 2 to 7, followed by a slower growth rate from weeks 7 to 12, however, SSA01 did not
reach the stationary phase by the end of the 12-week experiment. Nevertheless, by the end of the
experimental monitoring period, SSA01 in coculture achieved significantly higher density than
unsupplemented monocultures, reaching an average of approximately 2.86 x 10⁵ cells/mL
whereas, SSA01 monocultures averaged 1.95 x 10⁵ cells/mL at the final time-point (Fig. 4.2,
Table 4.6; Welch’s t-test, p< 0.01). There were no statistically significant differences in SSA01
growth across SSA01-Roseibium replicates of different bacterial inoculant densities (Table 4.7;
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Table 4.5. Repeated measures ANOVA statistical output for S. linucheae SSA01 growth curves. Here,
“Treatment” refers to B 12 availability and bacterial density (levels: +B 12, -B 12, low density, mid density
and high density ), “time” refers to weeks (levels: 0, 1, 2, 3, 4, 5, 6, 7, 12), Dfn refers to degrees of
freedom and F refers to F-value.
Table 4.6. Welch’s two sample t-test statistical output for SSA01 cultures, showing pairwise comparisons
for data grouped by time (week 12, endpoint) and between group types (levels: +B 12, -B 12, Bacteria) with
Bonferroni adjustments. Here, SSA01 cocultures containing different inoculant densities were grouped
together as “Bacteria '' since no significant differences in SSA01 growth was observed, at the endpoint in
cocultures (see below Table 4.7). The entire statistical output table, including all comparisons for each
timepoint, can be found at Zenodo (doi.org/10.5281/zenodo.8041234).
post-hoc t-test, p.adj=1 for all tested groups). This suggests that the SSA01-Roseibium coculture
can establish a stable relationship within a bacterial inoculation density range of 10
5
-10
7
cells/mL, without exhibiting any detrimental effects.
Surprisingly, SSA01 monocultures in B12-limited media also showed growth after 12
weeks, reaching approximately 1.60 x 10⁵ cells/mL (Fig. 4.2). However, this growth was
significantly lower than cultures in media supplemented with B12, which achieved a final average
density of 2.30 x 10⁵ cells/mL (Table 4.7, post-hoc t-test; No B12 vs B12, p.adj< 0.05) . To
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Figure 4.2. Growth dynamics of S. linucheae SSA01 were examined in coculture with Roseibium bacteria
at low, medium, or high densities in L1-B12 media, as well as in monoculture using L1+B 12 or L1-B 12
media. Cell density was tracked over the 12-week experimental period.
evaluate possible growth effects of B12 carryover in the prior experiment, we conducted three
consecutive subculturation trials on SSA01 monocultures in L1-B12. For the first and second
trials, culture density fluctuated by treatment over time [Fig. S4.1, Table 4.8, treatment:time,
(trial 1, p<0.05, trial 2, p<0.05, trial 3, p>0.5)]. As previously observed (Fig. 4.2), the first trial
also showed slightly elevated growth of L1+B12 monocultures, but a significant pairwise
difference was only detected at week 2 (Fig. S4.1, Table 4.9, post-hoc t-test; Week 2,
p.adj<0.05). No B12 treatment effect on average was observed in the second subculture and by
the third and final subculture, growth only differed as a function of time and no significant
difference in cell density was observed by treatment (Fig. S4.1, Table 4.9).
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Table 4.7. Post-hoc, t-test statistical output for SSA01 cultures, showing pairwise comparisons for data
grouped by time (week 12, endpoint) and between treatments, with Bonferroni adjustments. No
significant differences are observed between bacterial inoculant densities (levels: low, mid and high) in
cocultures but significant differences are observed between treatments (levels: +B 12, -B 12) in
monocultures. The entire statistical output table can be found at Zenodo
(doi.org/10.5281/zenodo.8041234).
Growth of Roseibium differed by culture type, inoculant density, time and their interactions (Fig.
4.3, Table 4.10, ANOVA F=9.4, Type:Treatment:Time p<0.001). During the initial weeks of the
SSA01 logarithmic growth phase (weeks 2-7, as shown in Fig. 4.2), there was a visible decline in
bacterial cell density in cocultures where medium and high densities of Roseibium were present.
However, at the end of the experiment, all SSA01 inoculated Roseibium densities reached an
average of 3 x 10
6
cells/mL, and there were no significant differences observed between the
inoculation treatments in terms of growth in Roseibium (Table 4.11, post-hoc t-test; High-low,
p> 0.1, High-Mid, p>0.5), Low-Mid, p>0.5). From week 8 onwards, there was a notable
resurgence in cocultured bacterial populations (initially inoculated at low, medium, or high
densities) (Fig. 4.3). Although temporal fluctuations were evident between inoculant treatments,
Roseibium exhibited higher density when cocultured (Fig. 4.3, Table 4.12; High, p.adj< 0.05,
Low, p.adj< 0.001, Mid, p.adj< 0.05). In contrast, Roseibium monocultures exhibited an initial
decline in density, and although growth was observed between weeks 6 and 8, densities for all
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Table 4.8. Repeated measures ANOVA statistical output for S. linucheae SSA01 growth curves for three
rounds of -B12 subcultures.“Treatment” refers to B 12 availability (levels: +B 12, -B 12 ) and “time” refers to
weeks (levels: 0, 1, 2, 3, 4, for trial 1, levels: 0, 1, 2, 3, 4, 5, 7, for trial 2 and levels: 0, 3, 6, 8 for trial 3).
treatments plateaued at an average of 7.5 x104 CFU/mL by week 12. Notably, the density of
Roseibium in coculture was approximately two orders of magnitude higher than that of
Roseibium monocultures at the same 12-week timepoint (Fig. 4.3, Table 4.12).
4.4.2 Limited transcriptome differences in SSA01 during coculture with Roseibium
After the removal of adapters, SSA01 samples yielded a total of 496,043,018 reads (35 million
reads per sample on average, Table 4.1). These reads were aligned to 71,056 isogroups
(isogroup= collection of transcripts, equivalent to one gene, hereafter genes), derived from the
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Table 4.9. Post-hoc, t-test statistical output for the 3 rounds of SSA01 subcultures, showing significant
pairwise comparisons for data grouped by time (levels: 0, 1, 2, 3, 4, for trial 1, levels: 0, 1, 2, 3, 4, 5, 7,
for trial 2 and levels: 0, 3, 6, 8 for trial 3) and between treatment groups (levels: +B 12, -B 12 ), with
Bonferroni adjustments.
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Table 4.10. Repeated measures ANOVA statistical output for Roseibium sp.Sym1 growth curves. Here,
“Type” refers to levels: cocultures, monocultures. “Treatment” refers to bacteria density (levels: low, mid
and high) and “time” refers to weeks (levels: 0, 1, 2, 3, 4, 5, 6, 7, 12).
Table 4.11. Post-hoc, t-test statistical output for Roseibium cultures, showing significant pairwise
comparisons for data grouped by time (week 12, endpoint) and between treatments (levels: low, mid and
high), with Bonferroni adjustments. The entire statistical output table can be found at Zenodo
(doi.org/10.5281/zenodo.8041234).
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Table 4.12. Post-hoc t-test, statistical output (conducted after significant three-way interaction for
Roseibium cultures) for significant pairwise comparisons for data grouped by treatment and type (levels:
cocultures, monocultures) between time points with Bonferroni adjustments. Here, “Treatment” refers to
bacteria density (levels: low, mid and high). The entire statistical output table can be found at Zenodo
(doi.org/10.5281/zenodo.8041234).
SSA01 transcriptome (Xiang et al., In prep). Approximately 99% of the clean reads, an average
of 34.7 million reads per sample (Table 4.1), successfully mapped to the SSA01 transcriptome,
indicating sufficient per-sample coverage for further statistical analysis. One outlier sample was
detected (an SSA01 monoculture, L1+B12 replicate) and removed from the dataset prior to DGE
analysis. After filtering the counts in DESeq2 for low expression, 31,522 genes remained in the
dataset.
Principal component analysis (PCA) of variance stabilized count data revealed that PC1
explained 27% of the variance and PC2 explained 11% of the variance among samples (Fig.
4.4a). Samples were largely discriminated along PC1 by the type of sample, with monoculture
and coculture samples clustering separately, with the exception of one SSA01 coculture sample
(a low bacterial density replicate) which clustered closer to the monoculture samples along PC1
but diverged along PC2. Monocultures displayed more dispersion than coculture samples, which
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Figure 4.3. Growth dynamics of Roseibium at low, medium, or high densities were investigated in
coculture with SSA01 algae, cultivated in L1-B 12 media. In addition, the growth of Roseibium
monocultures with low, medium, and high densities, cultured in L1-B12 media under starved conditions,
was tracked to assess whether the bacteria were simply surviving in the medium or experiencing growth
as a result of mutualism with SSA01. Cell density was tracked over the 12-week experimental period.
may be attributable to culture conditions (B12 or no B12 additions).
DGE analysis by culture type revealed significant differential expression of 140 genes
(archived at Zenodo, doi.org/10.5281/zenodo.8041234). Clustering of the top 34 differentially
expressed genes revealed distinct patterns of expression between monocultures and cocultures
(Fig. 4.4b). Notably, cocultures exhibited upregulated expression of chloroplast proteins, while a
downregulation was observed in the expression of a senescence-associated protein. Functional
enrichment of differentially expressed genes using rank-based GO analysis led to the
identification of two significant terms (at a 10% false discovery rate, FDR): GTPase activity
(p.adj< 0.1) and ligase activity (p.adj< 0.1) (Table 4.13).
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Figure 4.4. (a) Principal components analysis was performed on variance-stabilized count data obtained
from S. linucheae SSA01. Symbols denote whether SSA01 was grown in coculture (triangles) or
monoculture (circles). Samples were categorized into groups (b12 vs no b12) and the color scheme was
used to represent these groupings in the analysis. (b) heatmap by gene clustering of the top 34
differentially expressed genes in the DESeq2-generated model (~Type).
Conversely, when conducting DGE analysis using an additive model ~Type+ treatment,
only five genes exhibited significant differences in expression by B12 addition in addition to
culture type. Among these five genes, three were linked to metabolism of photosystem I and
photosystem II and upregulated on average in the absence of B12 irrespective of culture type (Fig.
4.5a, b, c). Whereas the remaining two genes were downregulated on average under B12
limitation but lacked detailed annotation and were only classified as "hypothetical proteins" (Fig.
4.5d, e). This indicates that B12 -limitation affected chloroplast metabolism in SSA01,
chloroplast-associated genes were upregulated in samples cultured in L1-B12 media, regardless of
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Table 4.13. Rank-based gene ontology (GO) term analysis on S. linucheae SSA01 (by model ~Type)
using the Mann-Whitney U (MWU) test, followed by Benjamini-Hochberg correction of p-values.
Table 4.14. BLAST matches S. linucheae SSA01 to cloned metH and metE genes from several
dinoflagellates species, as compiled by Lin et al. (2022).
type (Fig. 4.5).
Surprisingly, expression of 5 genes annotated as putative B12-dependent methionine
synthase (metH) did not differ between B12 culture treatments (Fig. S4.2, b-f), despite the
expectation that S. linucheae are auxotrophic for B12 (Lin et al. 2022), which suggests they may
not be full B12 auxotrophs. To further explore these confounding findings, we performed a blast
of the SSA01 transcriptome for matches against published B12-independent methionine synthase
(metE) and B12-dependent methionine synthase (metH) genes of dinoflagellates (Lin et al. 2022)
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Figure 4.5. Plot of normalized counts for the five most differentially expressed genes in SSA01 returned
by the DESeq2-generated model (~Type + treatment) for SSA01. Genes associated with SSA01
chloroplast metabolism (a) PSI P700 apoprotein A2, p. adjusted=0.0013 (b) PSII CP47 apoprotein, p.
adjusted=0.015, (c) PSII protein D1, p.adjusted=0.019. Two other genes were differentially expressed (d)
and (e) but their function is not annotated. All p-values were adjusted using the Benjamini and Hochberg
correction.
and found significant matches to 3 metH and 4 partial (but incomplete) metE homologs (Table
4.14). Lastly, we searched for the presence of the BHMT gene (which can catalyze the
conversion of homocysteine to methionine without B12) (Pajares and Pérez-Sala 2006), in the
SSA01 transcriptome and surprisingly found one gene in the filtered DGE dataset (out of 2 genes
in the entire SSA01 transcriptome), but this gene was not differentially expressed by either B12
treatment or culture condition (Fig. S4.2a). This could explain the growth phenotype observed in
pure culture regardless of B12 treatment. These findings lead us to conclude that S. linucheae
may not be B12 auxotrophs and potentially not dependent on B12.
4.4.3 Transcriptome differences in Roseibium during coculture with SSA01- preliminary data
We used a reduced dataset, consisting of only 7 samples for DGE analysis (three coculture
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Figure 4.6. (a) Principal components analysis was performed on variance-stabilized count data obtained
from Roseibium sp. Sym1. Symbols denote whether Roseibium was grown in coculture (triangles) or
monoculture (circles). Samples were categorized into groups and the color scheme was used to represent
these groupings in the analysis. (b) heatmap by gene clustering of the top 35 differentially expressed
genes in the DESeq2-generated model (~Type).
samples inoculated at medium density, 1x10
6
three monocultures supplemented with 0.1%
glucose, and one starved monoculture, Table 4.4). After removal of adapter and rRNA
sequences, our dataset yielded a total of 210,229,930 reads. The cleaned reads were aligned to
the concatenated reference [the Roseibium genome from (Aguirre et al. 2023) and SSA01
transcriptome (Xiang et al., In prep). However, after mapping, the three resequenced coculture
samples still presented SSA01 RNA contamination which led to a substantial reduction in the
number of bacterially derived reads available for analysis (Table 4.3 and Table 4.4). Roseibium-
116
only reads mapped to the 6,611 genes in the Roseibium genome but after filtering out low
expression genes, 6,286 genes were retained for further analysis.
PCA of variance-stabilized count data, revealed that 74% of the variance in expression
was explained by PC1, which discriminated cocultures from monocultures, and an additional
11% by PC2 which further differentiated starved and high nutrient monoculture (Fig. 4.6a).
Coculture samples clustered in between starved and high nutrient monoculture samples along
PC2 (Fig. 4.6a). Additionally, samples clustered together by their distinct group (coculture,
monoculture: high nutrient, and monoculture: starved). This strong clustering was also evident in
a heatmap of the top 35 differentially expressed genes by culture type, which revealed strong
upregulation of many ribosomal proteins in cocultures, suggesting active cell growth, in
comparison to the monocultures (Fig. 4.6b). A distinct set of genes was strongly upregulated in
monocultures, but lack of annotation precludes speculation on their functional role (Fig. 4.6b). In
total, DGE analysis for the model ~Type identified 1,812 differentially expressed genes (Padj <
0.1, archived at Zenodo, doi.org/10.5281/zenodo.8041234). Functional enrichment analysis did
not uncover any significant GO terms. However, this lack of significant findings may be
attributed to the limited annotation of the Roseibium genome. We were only able to assign GO
terms to approximately 9% of the genes in the Roseibium genome. Nevertheless, we searched the
list of differentially expressed genes for those involved in B12 biosynthesis. Several genes
encoding for proteins involved in the biosynthesis of B12 (sirohydrochlorin cobaltochelase,
cobalamin adenosyltransferase cobaltochelase, CobN, cobalamin biosynthesis protein CobW and
adenosylcobinamide kinase/ adenosylcobinamide phosphate, CobU) were found to be
significantly upregulated in cocultures (Fig. S4.3).
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4.5 DISCUSSION
Here, we investigated the impact of culturing conditions on growth and transcriptomic responses
of microalgal symbionts of cnidarians, S. linucheae, and a putative bacterial partner, Roseibium,
as well as the role of vitamin B12 in regulating these associations. Associations persisted for 12
weeks and both dinoflagellate and bacteria achieved higher densities when grown in co-culture
than in isolation. We expected B12 limitation to negatively impact growth of S. linucheae, as
previous research suggests that many dinoflagellates rely on B12 for essential metabolic functions
and growth (Croft et al. 2006; Helliwell et al. 2011; Grossman 2016; Lin et al. 2022). However,
our findings on growth dynamics indicate the ability of SSA01 to grow autonomously regardless
of external B12 availability, in ambient temperature and conditions, contradicting the prevailing
assumption that cnidarian associated Symbiodiniaceae are dependent on the presence of vitamin
B12 for growth (Agostini et al. 2009; Matthews et al. 2020). Additionally, our genomic data also
supports this conclusion as we found evidence for the presence of B12 -independent methionine
biosynthesis pathways (metE and BHMT enzyme). Nonetheless, SSA01 may still benefit from
the presence of bacteria, in this case, Roseibium, for the acquisition of other metabolites that
contribute to its enhanced growth and overall fitness. Similarly, Roseibium growth is also
stimulated in coculture, further supported by strong up-regulation of ribosomal proteins which
are a hallmark of cellular growth (Dai and Zhu 2020), which suggests reciprocal nutritional
benefits from Symbiodinium.
4.5.1 S. linucheae- Roseibium cocultures exhibit synergistic growth effects
Our novel laboratory Symbiodinium-Roseibium system displayed stability and synergistic growth
after 12 weeks of cocultivation. While previous studies have examined the growth dynamics
between a cnidarian associated Symbiodiniaceae dinoflagellate and a bacterial culture of a single
118
species through short-term experimental cocultures (Motone et al. 2020; Varasteh et al. 2020),
there is a lack of data regarding long-term, coculture growth dynamics. Here, we show that
axenic dinoflagellate cultures grown in a medium with limited vitamin B 12 exhibited
significantly higher growth when cocultured with Roseibium, compared to dinoflagellate
monocultures grown in B12-depleted media (Table 4.6). The same pattern was evident for
Roseibium but became more evident towards the end of the experimental 12-week trial, where
Roseibium in cocultures outnumbered their monoculture counterparts by 2 orders of magnitude
(Fig. 4.3). However, at the time of sampling, bacteria in coculture were exponentially growing
after plateauing between weeks 6-8. This suggests they experienced a substantial increase in a
carbon source and possibly other metabolites secreted by SSA01 as it presumably approached
stationary phase. A similar phenomenon has been observed in previous experiments involving
cocultures of dinoflagellates and bacteria, where bacterial growth remains restricted until the
dinoflagellate culture reaches its stationary phase (Wang et al. 2014); (Bolch et al. 2017). For
example, growth of bacteria in coculture with the dinoflagellate Gymnodinium catenatum,
exhibited exponential growth only when G. catenatum reached its stationary phase (Bolch et al.
2017). This upwards trend was observed irrespective of the taxonomical origin of the bacterial
associates or whether they had been inoculated individually, in pairs, or in combinations
involving three different taxa (Bolch et al. 2017). Likewise, in Prorocentrum minimum-bacteria
cocultures (grown in -B12 media), growth of the bacterium Dinoroseobacter shibae showed an
exponential pattern only during the stationary phase of P. minimum (Wang et al. 2014). In both
studies, bacteria in coculture with dinoflagellates displayed mutualistic behavior, evidenced by
mutually enhanced growth, until the midpoint of the dinoflagellate's stationary phase, at which
point they transitioned to a pathogenic behavior, indicated by further increases in bacterial cell
119
density and corresponding mortality in the algae, thereby giving rise to the hypothesis that they
mediated algal cell lysis once a sufficient bacterial cell density was increased (Wang et al. 2014);
(Bolch et al. 2017). In contrast, we observed no evidence of pathogenic behavior of Roseibium
towards SSA01. However, our coculture study concluded before reaching the stationary phase of
SSA01 and at an average bacterial cell density of approximately 3 x 10
6
cells/mL. The final
density of Roseibium in our coculture was notably lower, by an order of magnitude, than the
“pathogenic” density threshold in excess of 10
7
cells/mL that was associated with induced
mortality in P. minimum cocultures (Wang et al. (2014)), yet it was at higher density than the
10
4
-10
5
cells/mL threshold that induced algal mortality in G. catenatum cocultures (Bolch et al.
2017). This suggests that our Symbiodinium-Roseibium coculture system is very stable and could
be developed as a model to further explore mutualistic Symbiodiniaceae-bacteria associations.
Future investigations should extend the duration of cocultures and examine the potential for
antagonistic behavior of Roseibium towards Symbiodinium, as the latter reaches its stationary
phase of growth.
4.5.2 Differential gene expression of S. linucheae SSA01 implicates alteration of chloroplasts in
response to B12 limitation
The transcriptomic data indicate SSA01 exhibits a discernible, yet modest reaction to both the
presence of Roseibium and B12-limitation (Fig. 4.4). SSA01 showed differential expression of
140 genes when grown in co vs monoculture conditions [Zenodo
(doi.org/10.5281/zenodo.8041234)]. It is important to note that GO terms were limited in the
SSA01 transcriptome, therefore we recommend further gene enrichment analysis using multiple
databases, such as the Eukaryotic Orthologous Groups (KOG) and Kyoto Encyclopedia of Genes
and Genomes (KEGG). Nevertheless, GO analysis revealed notable enrichments of GTPase
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among downregulated genes and ligase activity among upregulated genes within dinoflagellates
when cultured alongside Roseibium. These enzymatic processes are linked to fundamental
cellular functions, such as replication, but have not been extensively investigated in
dinoflagellates (Roy et al. 2018). It is not known how bacteria influence these processes in
Symbiodinium, but previous studies have proposed various mechanisms to explain their potential
impact. For example, Varasteh et al. (2020) previously isolated a bacterium, Mameliella from a
Symbiodinium suspension and showed increased growth of Symbiodinium in coculture, after
reinoculation, over the course of 12 days. The researchers hypothesized several mechanisms by
which Mameliella contributed to this growth, including production of B-vitamins and secondary
metabolites, and facilitation of horizontal gene transfer due to the presence of GTAs (gene
transfer agents). A separate coculture investigation involving Durusdinium (Symbiodiniaceae,
clade D) and a bacterial isolate from the Flavobacteriaceae clade, proposed that these bacteria
supported Durusdinium growth by producing zeaxanthin, a compound recognized for its
antioxidant properties, especially under thermal stress (Motone et al. 2020).
The additive impact of B12-limitation in addition to these mono vs coculture effects
resulted in significant differential gene expression of only five genes (Fig. 4.5). B12 limitation,
irrespective of culture type (monoculture or coculture), resulted in upregulated expression of
three chloroplast-associated proteins in L1-B12 media and downregulation of two unidentified
hypothetical proteins. Chloroplast-associated genes psaB (PSI P700 apoprotein), psbB (PSII
CP47 apoprotein), and psbA (PSII protein D) showed elevated expression under B12-limited
conditions (Fig. 4.5). These genes are hypothesized to be located within minicircles in
dinoflagellates (Howe et al. 2008), and psbA has been further confirmed to reside within a
minicircle in Symbiodinium spp. (Moore et al. 2003; Takishita et al. 2003). This suggests that
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B12-limitation did not have an effect on SSA01 transcription occurring within the nuclear
genome but rather impacted processes happening outside of it. In dinoflagellates, not all
chloroplast genes are situated within a cellular compartment, as observed in other algae (Howe et
al. 2008). Instead, some are found in minicircle DNA structures resembling plasmids (Howe et
al. 2008). Each minicircle contains either a single gene or a small group of genes, along with an
open reading frame (ORF) (Moore et al. 2003; Takishita et al. 2003; Howe et al. 2008). The
impact of B12-limitation on chloroplast function has previously been investigated in the
unicellular green alga, Euglena gracilis (Carell 1969). Carrell (1969) found that B12-limited
Euglena cultures displayed an increase in protein and chloroplast replication per cell, but not an
increase in nuclear replication, as evidenced by lower cell counts in B12-limited algae by the end
of the experimental trial (Carell 1969). Likewise, our study revealed a decline in cell density
within B12-limited SSA01 monocultures (Fig. 4.2), but not B12-limited cocultures, suggesting
Roseibium were probably supporting SSA01 growth in other ways unrelated to B 12 production.
We did not carry out further investigations to confirm whether the upregulation of these genes
resulted in an increased photosynthetic efficiency of Symbiodinium. Therefore, we encourage
future studies to further test the association between B12-limitation and the expression of
chloroplast minicircles, as this phenomenon remains unexplored in Symbiodinium and may hold
potential significance in other dinoflagellate and algal lineages like Euglena as well.
4.5.3 Positive growth in SSA01 despite B12 limitation and absence of differential metH
expression suggests microcontamination, enhanced storage, or alternate methionine biosynthesis
pathways
Constitutive expression of metH, the gene encoding B12-dependent methionine synthase, was
observed (Fig. S4.2, b-f) in SSA01 in all sample types, which suggests B12 limitation did not
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affect gene expression. To investigate this confounding observation, we examined the potential
influence of residual B12 carry-over as an explanation for the continued positive growth in B 12
limited cultures. This led us to conduct three successive subculturing trials using B12-depleted
media (Fig. S4.1). Surprisingly, by the third trial, the algae were growing at a similar rate as
those cultured in media containing B12, which suggests residual B12 contamination was not
driving this observation. We also considered the possibility of B12 contamination in the media or
the B1/B7 vitamin stock. The ASW was made with Milli-Q (Merck KGaA, Germany) purified
H2O, and compounds labeled as > 99% reagent grade or suitable for cell culture. Similarly, our
in-house vitamin stock was made with > 99% reagent grade vitamins B1 and B7. However, it is
important to note that reagent grade chemicals might contain contaminants at < 1%. These
chemicals do not adhere to the same rigorous standards as trace-metal grade chemicals, and this
could potentially influence experimental trials, especially if cellular B12 requirements are low.
Another potential source of contamination could have come from the L1 media kit (N, P or trace
metals solution) but other studies exploring algal requirements for B12 have used this media for
similar studies without reporting contamination concerns (Wang et al. 2014; Helliwell et al.
2016; Cruz-López et al. 2018; Cooper et al. 2019). Additionally, we considered the possibility of
bacterial contamination in the B12-depleted SSA01 monocultures as a supplementary B12 source.
To monitor this, we regularly plated all the monocultures to check for any signs of bacterial
growth. We did not observe any bacterial growth on these plates during the entire 12-week
duration of the experiment. Additionally, we found no evidence of non SSA01 rRNA genes in
the transcriptome. Therefore, we feel the likelihood of contamination was minimal.
If intracellular B12 requirements of SSA01 are indeed extremely low, it is also possible
that they were able to successfully accumulate enough B12 prior to the experiment, which
123
allowed them to grow for 12 weeks and continue to grow after three consecutive trials in L1-B12.
Direct, precise measurement of intracellular B12 in microalgae has not been possible thus far, due
to the absence of suitable methodology (Sañudo-Wilhelmy et al. 2014), thus the estimated cell
vitamin quota for Symbiodinium remains unknown. Therefore, it is reasonable to speculate that
microcontamination or the accumulation of B12 in Symbiodinium may account for the sustained
expression of metH under B12 limitation. This latter hypothesis is supported by a previous study
in which the green alga Euglena demonstrated a remarkable ability to accumulate B12 at ~ 600
times higher than the minimum estimated cellular requirements for growth (Sarhan et al. 1980).
Similarly, both the alga Chlamydomonas (Watanabe et al. 1991) and Ochromonas were found to
store significantly higher levels of B12 than necessary for growth (Bradbeer 1971). These data
suggest that vitamin accumulation may be common across microalgae from diverse taxonomic
groups, potentially including dinoflagellates like Symbiodinium. More comprehensive work
should be done to definitively rule out B12 contamination in the media components. Additionally,
it is crucial to accurately assess the level of intracellular B12 accumulation in SSA01, which can
be done by using a modified enzyme-linked immunosorbent assay (ELISA) assay as previously
detailed (Zhu et al. 2011) but modified for algal pellets as described by (Nef et al. 2019).
Alternatively, we investigated whether the observed growth and stable transcriptome
profile of SSA01 under B12 limited conditions could be due to the presence and usage of the gene
for B12 independent methionine synthase, metE. We did not find complete homologs of the metE
gene in this study’s transcriptome dataset, only partial homologs (Xiang et al., In prep).
However, no biochemical measures of metE or metH activity were undertaken to confirm this.
Future investigations should consider a comprehensive approach, incorporating measurements of
methionine synthase activity using the rate of methenyltetrahydrofolate (CHþ -THF) production,
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as described in (Xie et al. 2013), along with B12-limitation in the culture media and
transcriptomic analysis.
Our study also revealed the constitutive expression of the BHMT (betaine-homocysteine
methyltransferase) gene (Fig. S4.2a), which was an unforeseen discovery, as we initially focused
only on quantifying the transcription of metE and metH genes. We did not anticipate the
existence of an alternative mechanism that could facilitate the conversion of homocysteine to
methionine in SSA01, as such a mechanism has not been previously reported in the literature for
dinoflagellates. To investigate whether BHMT functions as an alternative mechanism for
regulating homocysteine and increasing methionine levels in SSA01 cells, future research should
focus on mining Symbiodiniaceae genomes for homologs of BHMT. This approach would help
determine if BHMT is a conserved feature among Symbiodinium spp. and shed light on its
potential role in methionine metabolism in SSA01 cells.
Finally, it is currently unknown whether B12-salvage pathways exist in Symbiodinium that
may enable it to synthesize B12 from corrinoid cofactors (precursors to final B12 biosynthesis), as
has been observed in the bacteria Salmonella enterica (Anderson et al. 2008). Anderson et al.
(2008) demonstrated that S. enterica mutants had the ability to aerobically produce complete
corrinoids by importing incomplete corrinoids and incorporating the essential component 5,6-
dimethylbenzimidazole (DMB), resulting in the successful biosynthesis of vitamin B 12.
Naturally, this explanation appears to be the least probable since the biosynthesis of B 12 is known
to be exclusive to prokaryotes. However, it has been observed and verified through biochemical
analysis that cobamide (cyclic tetrapyrrole compounds containing cobalt, including B 12 vitamers)
remodeling of a non-usable cobamide to a usable form, can occur in the eukaryotic microalga
Chlamydomonas reinhardtii (Baum et al. 2020). The genetic mechanism behind this process,
125
however, remains uncharacterized. The reference SSA01 transcriptome used in this study,
contained functional gene annotations for several genes involved in the partial biosynthesis of
cobalamin: (a) cobalamin synthesis protein P47K, (b) CobW, (c) CbiA, (d) CbiX, and (e)
sirohydrochlorin cobaltochelase activity (Xiang et al., In prep). It is unclear whether SSA01
possesses more genes associated with cobalamin biosynthesis in its genome as this reference is
not yet available. However, the existence of these genes does not necessarily indicate they are
functionally linked to B12 biosynthesis in eukaryotes. For example, cobW genes have been
identified in the genomes of dinoflagellates (Zhang et al. 2013; Yu et al. 2020; Song et al. 2022)
and collectively termed, “DinoCBWDs”. Song et al. (2022) conducted growth and expression
studies on the dinoflagellate Karlodinium veneficum and revealed no significant transcriptomic
variation in DinoCBWDs associated with the presence or absence of B12, but DGE occurred
under limited zinc and iron concentrations. The analysis concluded that cobW genes in
dinoflagellates are involved in functions other than B12-related processes, such as zinc
homeostasis and iron regulation (Song et al. 2022).
4.5.4 Transcriptomic differences in Roseibium suggest it benefits from associations with SSA01
in low nutrient environments
Although we were unable to map a substantial amount of mRNA reads to the Roseibium genome
(Table 4.3 and Table 4.4), due to incomplete bacterial ribosomal depletion and residual SSA01
contamination in coculture samples, we were still able to distinguish transcriptomic differences
between treatments in Roseibium, where we observed distinct expression patterns by culture type
(Fig. 4.6a). Most noticeably, a greater proportion of the transcriptome was differentially
regulated, and genes related to growth and ribosomal metabolism were among the most
differentially expressed genes in Roseibium as a function of coculture (Fig. 4.6b), suggesting
active growth was happening at sampling, although it is unclear whether this was due to patterns
126
specific to the mutualistic relationship or a physiological response to enhanced growth. The
application of functional gene enrichment analysis using GO terms on the differentially
expressed genes yielded no statistically significant GO categories. This outcome can be
attributed to the fact that only 9% of the Roseibium genome was annotated with GO terms,
thereby limiting the availability of reference data for this initial analysis. For future
transcriptomic investigations in Roseibium, we suggest functional enrichment analyses utilizing
the COG or KEGG databases as references, instead. These databases offer a greater number of
annotations for Roseibium compared to the GO database, rendering them more suitable for
comprehensive functional enrichment analysis.
Nonetheless, we were able to search the Roseibium genome for genes related to B12
biosynthesis and analyze their transcriptomic profiles in both monoculture and coculture
conditions. We found that in the presence of SSA01, five genes involved in B 12 biosynthesis
exhibited upregulation (Fig. S4.3). Research examining algal-bacterial interactions has shown
that when B12-producing bacteria are introduced to B12 auxotrophic microalgae in a B12-deficient
environment, they can satisfactorily fulfill the algae’s cellular B12 needs (Croft et al. 2005;
Wagner-Döbler et al. 2010; Kazamia et al. 2012; Wang et al. 2014; Cruz-López and Maske
2016; Cruz-López et al. 2018; Cooper et al. 2019; Mansky et al. 2021). However, it is unclear
whether B12 was shared by Roseibium with SSA01 due to the modest differences in SSA01
transcription in the -B12 medium. Not all B12 producers share B12 with their environment and the
relationship may be host and even strain specific. Previously, 33 verified B12 producers were
cocultured alongside the B12 auxotroph, Thalassiosira pseudonana, yet only 54% of these strains
facilitated the growth of T. pseudonana (Sultana et al. 2023). Similarly, (Nef et al. 2022))
showed that two B12-producing bacterial species, Halomonas and Phaeobacter, in coculture, did
127
not adequately fulfill the B12 requirements of algae. Interestingly, although these bacteria
exhibited high B12 production individually, the findings suggested that a greater level of
specificity is necessary for effective B12 sharing between algae and bacteria. We surmise that
Symbiodinium and Roseibium share a longstanding association, supported by the classification of
Roseibium as a core member of the Symbiodinium microbiome (Lawson et al. 2018). However,
to validate this relationship, additional research, specifically a cophylogeny analysis, is
necessary.
In conclusion, we successfully established a reliable coculture system in which
Roseibium facilitated the growth of SSA01 and vice versa, providing a valuable and manageable
model for studying Symbiodiniaceae-bacterial interactions. Additionally, we report long-term
growth dynamics for each organism, establishing a strong basis for achieving mutualistic
stability. While we could not definitively confirm the exchange of B12 between Roseibium and
SSA01 or verify SSA01 is indeed a B12 auxotroph, our results strongly suggest that SSA01 may
not be dependent on B12. Nevertheless, our findings serve as a crucial starting point for delving
into the interactions between Symbiodiniaceae and bacteria, with an emphasis on microbes that
inhabit the cnidarian holobiont.
4.6 ACKNOWLEDGEMENTS
This study was funded by the National Science Foundation Graduate Research Fellowship
Program grant award DGE-1418060 to EGA, and University of Southern California start-up
funds to CDK and USC Dornsife’s Faculty-Led Initiatives in Key Research Areas grant to CDK.
Additionally, we would like to thank Dr. J Cameron Thrash for guidance on the experimental
setup and Holly Stapelfeldt for providing the artificial seawater (ASW) medium. Lastly, we
acknowledge the Center for Advanced Research Computing (CARC) at the University of
128
Southern California for providing computing resources that have contributed to the research
results reported within this publication. URL: https://carc.usc.edu.
4.7 CONFLICT OF INTEREST
The authors declare no competing interests.
4.8 DATA ACCESSIBILITY
Scripts for data analysis are archived at Zenodo (doi.org/10.5281/zenodo.8041234), and also
available on Github. Roseibium sp. Sym1 genome is available at National Center for
Biotechnology Information (NCBI) Sequence Read Archives (SRA) under accession code:
PRJNA884430. The functional annotation for Roseibium sp. Sym1 can be found at JGI-GOLD
under Project ID: Ga0573226. Raw RNA sequences for Symbiodinium linucheae strain SSA01
and Roseibium sp. Sym1 are available upon request. The bacteria, Roseibium sp. Sym1 is
available upon request.
129
CHAPTER 4: SUPPLEMENTARY FILE
Figures S4.1-S4.3
130
Figure S4.1. Growth dynamics of SSA01 monocultures using L1+B12 or L1-B12 media over
three successive subculturing trials to assess the potential impact of residual vitamin B12 carryover on
SSA01 growth. For each trial, cell density was tracked until the culture reached an average > 1 x 10
5
cells/mL, which varied between trials.
131
Figure S4.2. Plot of normalized counts for BHMT and metH, B12-dependent methionine synthase in S.
linucheae SSA01 algae. No significant difference between type (coculture vs monoculture) was found.
132
Figure S4.3. Plot of normalized counts for the five most differentially expressed genes involved in B12
(cobalamin) biosynthesis, generated by the DESeq2 model (~Type) for Roseibium. Genes associated with
biosynthesis (a) sirohydrochlorin cobaltochelase, p. adjusted=0.02 (b) cobalamin adenosyltransferase, p.
adjusted=0.04, (c) cobaltochelase, CobN, p.adjusted=0.1, (d) cobalamin biosynthesis protein CobW,
p.adjusted=0.01 and (e) adenosylcobinamide kinase/ adenosylcobinamide phosphate, CobU,
p.adjusted=0.1. All p-values were adjusted using the Benjamini and Hochberg correction.
133
CHAPTER 5: CONCLUSION
Understanding the impact of multi-partner associations on the health and survival of marine
invertebrates is crucial given they are among the most susceptible animals to the impacts of
climate change. The global decline of corals, which are cnidarian hosts that harbor intracellular
populations of dinoflagellate algae in the family Symbiodiniaceae and other microbial associates,
is already underway and is predicted to worsen with climate change (Pandolfi et al. 2003;
Kleypas and Kleypas 2019). In my thesis, I investigated the modulation of microbiomes in
cnidarians and provided evidence demonstrating how external factors like the environment, as
well as internal factors such as host genetics and the presence of algal endosymbionts, influence
the cnidarian microbiome. My first chapter provided insights into the interplay between host
genotype and environment in shaping the epibiome (mucus and surface) of the critically
endangered and ecologically significant Acropora cervicornis coral. My research revealed the
consistent presence of MD3-55, a bacterial associate of A. cervicornis, in the epibiome over time.
These findings suggest that non-invasive sampling of the A. cervicornis epibiome could
potentially serve as a means to assess disease susceptibility or resistance in natural populations.
To further understand the influence of environmental factors on the mucus microbiome of A.
cervicornis genotypes, and vice versa, future studies should monitor physicochemical parameters
during sampling and collect seawater samples from locations at least one meter away from the
reef. Additionally, investigating the abundance and distribution of MD3-55 in non-coral hosts
inhabiting the same habitats as acroporids, as well as other environmental reservoirs, would
provide valuable insights into the general ecology of MD3-55.
The research comprising my second chapter revealed that the abundance of the rare
microbiome taxon, Oligoflexales, was positively correlated with endosymbiont density in
134
Aiptasia, independent of thermal stress. This discovery suggests that Oligoflexales may serve as
essential partners of Symbiodiniaceae, offering valuable insight into the impact of
endosymbionts on microbial communities within anemones. Oligoflexales represent an
optimistic addition to the roster of potential coral probiotics (Rosado et al. 2019; Blackall et al.
2020; Peixoto et al. 2021), broadening the scope of bacterial options available to enhance coral
health. Further investigations building upon the research presented in this chapter should focus
on comprehensive characterization of the abundance, distribution, and localization of
Oligoflexales within the tissue of anemones. This can be accomplished by attempting to cultivate
this taxon using the methods previously described for another member of this genus (Nakai et al.,
2016), followed by genomic analysis. Once a genome is obtained, specific primers can be
designed for conducting fluorescence in situ hybridization (FISH) microscopy experiments in
Aiptasia tissues. Such efforts will provide deeper insights into the behavior and interaction of
Oligoflexales within anemone systems. The potential impact on the microbial communities of
organisms due to loss of one symbiotic partner in a tripartite symbiosis is an area of research that
could have broader implications for other systems as well. For example, the cnidarian jellyfish,
Cassiopea xamachana, also hosts Symbiodinium and can also be rendered aposymbiotic (Colley
and Trench 1983; Lampert 2016). Like Aiptasia, Cassiopea also experience changes in their
microbial communities in response to symbiont loss as well as temperature fluctuations between
summer and winter (Carabantes et al. 2022), yet it is unknown how the symbiont affects
microbial assemblages in the organism. It would be beneficial to undertake a comparable
investigation, similar to the one I conducted, in different systems such as Cassiopea xamachana
and other emerging cnidarian models, like the facultatively symbiotic coral, Oculina arbuscula
(Rivera and Davies 2021).
135
Lastly, I developed a novel Symbiodiniaceae-bacterial coculture system which will
facilitate the examination of algal-bacterial interactions, particularly in relation to vitamin
availability, allowing us to explore the crucial role of bacteria in promoting the well-being and
vitality of algae both inside and outside cnidarians, thereby broadening our comprehension of
their symbiotic relationships. Future investigations using this system should aim to extend the
duration of cocultures to assess potential antagonistic behavior of bacteria (Roseibium) towards
the algae (Symbiodinium) during the latter's stationary growth phase, while considering other
factors such as thermal stress and nutrient limitations that could induce pathogenesis and disrupt
coculture stability. Additionally, exploring homologs of BHMT in Symbiodinium genomes
would shed light on whether BHMT is a conserved feature among Symbiodinium spp., as well as
its potential contribution to methionine metabolism within this group of organisms. Furthermore,
my work highlighted an association between B12-limitation and chloroplast minicircle expression
in Symbiodinium spp., which may have broader implications for other algal lineages like
Euglena. To gain a deeper understanding of this association, it is worth investigating chloroplast
expression and photosynthetic efficiency in B12-limited cultures of dinoflagellates and other
algae. Although the exchange of B12 between Roseibium and SSA01, and the B12 auxotrophy of
SSA01 could not be conclusively confirmed, my findings provide a valuable foundation to
explore the interactions between Symbiodiniaceae and bacteria, particularly those residing in the
cnidarian holobiont.
136
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APPENDIX:
COMPLETE GENOME OF ROSEIBIUM SP. STRAIN SYM1, A
BACTERIAL ASSOCIATE OF SYMBIODINIUM LINUCHEAE,
THE MICROALGAL SYMBIONT OF THE ANEMONE
AIPTASIA
Emily G. Aguirre
1
, Harold K. Carlson
1
, Carly D. Kenkel
1
1
Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
This appendix appears as published in Microbiol Resource Announcements | ASM Journals
(2023) 12:e0111822
SUMMARY OF CONTRIBUTION
I, Emily G Aguirre and Dr. Carly D Kenkel (CDK) conceived and designed the lab experiment
and obtained funding. Harold K. Carlson (HKC) and I maintained the S. linucheae cultures and
isolated the bacterium, Roseibium sp. Sym1. I performed DNA extractions, completed all
bioinformatic analyses for the genome assembly and wrote the first draft of the manuscript. All
authors (CDK, HKC and I) approved of the final manuscript.
ABSTRACT
We sequenced the genome of Roseibium sp. Sym1, a strain isolated from a monoculture of a
Symbiodiniaceae marine dinoflagellate, Symbiodinium linucheae, a microalgal symbiont of
cnidarians. The completed genome consists of one circular chromosome of 6,694,563 bp and
four plasmids of 192,102 bp, 160,136 bp, 120,881 bp and 89,413 bp.
158
ANNOUNCEMENT
Roseibium species [formerly classified as Labrenzia (Hördt et al. 2020; Zhong et al. 2021)] are
part of the core microbiome of Symbiodiniaceae clades (Lawson et al. 2018). Although several
Roseibium genomes are available, they remain an understudied genus (Rodrigues et al. 2018;
Couceiro et al. 2021; Zhong et al. 2021). We sequenced strain Sym1 to further explore the
metabolic potential of Roseibium in the Symbiodiniaceae phycosphere.
Roseibium sp. Sym1 was isolated (Aguirre 2022a) from the 0.45 μm filtrate slurry of a
three-year, xenic monoculture of Symbiodinium linucheae, an algal symbiont isolated (Aguirre
2022b) from the anemone, Aiptasia (sensu Aiptasia pallida) (Sunagawa et al. 2009). Briefly, the
algal cells were ruptured with 4.5 mm beads and vortexed at full speed for 90 seconds.
Approximately 1 mL of the slurry was passed through a 0.45 µm PES, 30 mm filter into a clean
microcentrifuge tube and subsequently diluted to 1:100 in autoclaved filtered seawater. The
diluted filtrate (100 µL) was then plated on marine Reasoner's 2A agar. A single colony was
picked and subcultured three times, to verify it was a pure isolate. The 16S rRNA gene was
amplified using the primer pair 27F/1492R (Stackebrandt and Goodfellow 1991), followed by
Sanger sequencing (Laragen, Inc, USA) and queried against the NCBI nucleotide database,
BLASTN. The top match was Labrenzia sp. 12b (99.49% identity, GenBank accession
LT629936.1).
Roseibium sp. Sym1 was revived from a cryostock and inoculated into 25 mL of marine
R2A broth. Once high density was achieved, 25 μL of the seed culture was used to inoculate 25
mL of marine R2A broth in triplicate cell culture flasks for 72 hours at 27˚C. Inoculum from one
flask was used for paired-end sequencing and inocula from two flasks was used for long-read
sequencing. Genomic DNA was extracted using GenElute Bacterial Genomic DNA Kit
159
(Millipore Sigma, USA). Paired-end library preparation was constructed using Illumina DNA
Library Prep Kit, and sequenced on the NextSeq 2000 platform, generating an output of
21,815,230 raw short-reads (2 x 151 bp). Oxford Nanopore’s Genomic DNA by Ligation Kit was
used for long-read library preparation (no size selection) and sequenced on the MinION platform
using an R9 flow cell chemistry (R9.4.1), followed by high-accuracy base calling with Guppy
v5.0.16, generating an output of 165,651 raw reads with an N50= 8,039 bp. Library preparations
and sequencing were done by SeqCenter (SeqCenter, USA). Quality control was conducted using
fastp v0.12.4 (Chen et al. 2018) for short-read sequences, and NanoFilt v2.8.0-0 (De Coster et al.
2018) for long-read sequences. The assembly pipeline, Unicycler v.0.5.0 (Wick et al. 2017), was
used to generate a hybrid assembly resulting in five contigs.
Final mean coverage for the genome was estimated to be 55X [using bowtie v0.7.17 and
samtools v1.15.1 (Langmead and Salzberg 2012; Danecek et al. 2021)]. CheckM v1.2.1 (Parks et
al. 2015) determined the genome is 99.58% complete and has a GC content of 61.2%. The hybrid
assembly identified five replicons consisting of one chromosome and four plasmids (rotated and
circularized by Unicycler), totaling to 7,257,095 bp. Default parameters were used for all
software unless stated otherwise.
DATA AVAILABILITY STATEMENT
The hybrid assembly is available at NCBI GenBank under accessions CP114786, CP114787,
CP114788, CP114789, and CP114790. BioProject accession number is PRJNA884430. The code
used to assemble the genome is available at github.com/symbiotic-em.
ACKNOWLEDGMENTS
160
This project was funded by the University of Southern California Dornsife’s Faculty-led
Initiatives in Key Research Areas grant and National Science Foundation Graduate Research
Fellowship Program, award DGE-1418060. We would like to thank Dr. JC Thrash for helpful
discussions on bioinformatic approaches for genome assembly.
Abstract (if available)
Abstract
Multicellular organisms, such as humans, plants, and invertebrates, depend on symbiosis with microbes for metabolic cooperation and exchange. Although symbioses are generally modeled and studied as two-way interactions, multipartite symbioses have been increasingly shown to be both ecologically and evolutionarily relevant. While many studies have identified changes in the microbiome of symbiotic cnidarians (invertebrates hosting photosynthetic dinoflagellate algal endosymbionts) it is unclear whether changes in bacterial communities are modulated by environmental or internal host factors, like host genetics or microalgal symbiont type, and absence/presence of the symbiont. My dissertation research aimed to unravel the effects of internal factors.
The second chapter of my thesis highlighted the connection between host specificity and specific microbial communities in the mucus of the endangered acroporid coral, Acropora cervicornis. The coral surface mucus, which acts as a protective barrier and contributes to nutrient cycling, exhibits high bacterial diversity and is presumed to be influenced by the surrounding environment, yet the influence of the host genotype on the epibiome is not well understood. My research aimed to determine the contributions of host genotype, environment, and their combination to the maintenance of epibiomes over time. Distinct epibiome signatures were observed among different genotypes of A. cervicornis, primarily influenced by the relative abundance of the ubiquitous Rickettsiales bacterial symbiont MD3-55. Although there were slight changes in bacterial communities of surviving outplants after one year of field transplantation, the epibiomes of A. cervicornis were distinctly shaped by the host genotype.
Next, the third chapter explored the link between microalgal symbiont abundance and bacterial communities. In this chapter, I aimed to differentiate the effects of heat-stress and symbiont density on microbial communities, while accounting for host and symbiont genetic diversity. I used the emerging model organism, Aiptasia clonal strain CC7 that hosts Symbiodinium linucheae, and compared the microbiomes of symbiotic anemones undergoing mild bleaching with those of aposymbiotic anemones that lacked the typical microbes associated with Symbiodinium. My research showed that fluctuations in the rare microbiome occurred after heat stress and symbiont loss. I also observed a correlation between the density of S. linucheae in Aiptasia and the abundance of Oligoflexales bacteria, suggesting these bacteria may be primary symbionts of S.linucheae.
Lastly, the fourth chapter dives into the role of bacteria in the cnidarian-algal symbiosis and the mechanisms underlying Symbiodiniaceae-bacteria interactions. Bacteria may provide essential nutrients, vitamins, and antimicrobial products to the cnidarian holobiont but the mechanisms of exchange are unknown. B-vitamins, produced by prokaryotes, play crucial roles in metabolic reactions, and many eukaryotic algae, including dinoflagellates, rely on them. The dependence on B12 by Symbiodiniaceae dinoflagellates has previously been observed but remains poorly documented. In this chapter, I established a stable coculture system between a B12-producer, Roseibium sp. Sym1, and the microalgal symbiont of Aiptasia, Symbiodinium linucheae strain SSA01. The objective here was to examine the possible reliance of SSA01 on Roseibium sp. Sym1 for its B12 needs by comparing growth dynamics and transcriptomic variations between monocultures and cocultures in B12-depleted media.
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Creator
Aguirre, Emily Gabriela
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Core Title
Disentangling the ecology of bacterial communities in cnidarian holobionts
School
College of Letters, Arts and Sciences
Degree
Doctor of Philosophy
Degree Program
Biology (Marine Biology and Biological Oceanography)
Degree Conferral Date
2023-08
Publication Date
07/26/2023
Defense Date
07/12/2023
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Acropora cervicornis,Aiptasia,algal phycosphere,anemone,B12,bacteria,cobalamin,cocultures,coral,differential gene expression,epibiome,Exaiptasia pallida,genotype,Labrenzia,MD3-55,microbial ecology,microbiome,OAI-PMH Harvest,Oligoflexales,Roseibium,Symbiodinium,symbiont-host ratio,symbiosis,symbiotic-state microbiome,Synechococcus,synergistic growth,thermal stress,transcriptome,transplants,Vitamins
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), Thrash, J. Cameron (
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Tags
Acropora cervicornis
Aiptasia
algal phycosphere
anemone
B12
cobalamin
cocultures
coral
differential gene expression
epibiome
Exaiptasia pallida
genotype
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MD3-55
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Synechococcus
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thermal stress
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