Exploring Mechanisms of Bacterial Adaptation to Seasonal Temperature Change

Home , Cryomorphaceae, Desulfobacterales, Vibrionaceae

Cheuk Man Yung

Marine Science and Conservation Duke University

Date:______Approved:

______Dana E Hunt, Supervisor

______Zackary I Johnson

______Jennifer J Wernegreen

______Rachel T Noble

Dissertation submitted in partial fulfillment of the requirements of the degree of Doctor of Philosophy in Marine Science and Conservation in the Graduate school of Duke University

2016

ABSTRACT

Exploring Mechanisms of Bacterial Adaptation to Seasonal Temperature Change

Cheuk Man Yung

Marine Science and Conservation Duke University

Date:______Approved:

______Dana E Hunt, Supervisor

______Zackary I Johnson

______Jennifer J Wernegreen

______Rachel T Noble

An abstract of a dissertation submitted in partial fulfillment of the requirements of the degree of Doctor of Philosophy in Marine Science and Conservation in the Graduate school of Duke University

2016

Abstract

This research examines three potential mechanisms by which bacteria can adapt to different temperatures: changes in strain-level population structure, gene regulation and particle colonization. For the first two mechanisms, I utilize bacterial strains from the Vibrionaceae family due to their ease of culturability, ubiquity in coastal environments and status as a model system for marine bacteria. I first examine vibrio seasonal dynamics in temperate, coastal water and compare the thermal performance of strains that occupy different thermal environments. Our results suggest that there are tradeoffs in adaptation to specific temperatures and that thermal specialization can occur at a very fine phylogenetic scale. The observed thermal specialization over relatively short evolutionary time-scales indicates that few genes or cellular processes may limit expansion to a different thermal niche. I then compare the genomic and transcriptional changes associated with thermal adaptation in closely-related vibrio strains under heat and cold stress. The two vibrio strains have very similar genomes and overall exhibit similar transcriptional profiles in response to temperature stress but their temperature preferences are determined by differential transcriptional responses in shared genes as well as temperature-dependent regulation of unique genes. Finally, I investigate the temporal dynamics of particle-attached and free-living bacterial community in coastal seawater and find that microhabitats exert a stronger forcing on

microbial communities than environmental variability, suggesting that particle- attachment could buffer the impacts of environmental changes and particle-associated communities likely respond to the presence of distinct eukaryotes rather than commonly-measured environmental parameters. Integrating these results will offer new perspectives on the mechanisms by which bacteria respond to seasonal temperature changes as well as potential adaptations to climate change-driven warming of the surface oceans.

Contents

Abstract ...... i

List of Tables ...... vi

List of Figures ...... vii

Acknowledgements ...... xii

1. Introduction ...... 1

1.1 Thermal adaptation in microbe ...... 2

1.2 Responses to temperature at multiple taxonomic levels ...... 4

1.2.1 Genomic and transcriptomic thermal adaptation ...... 5

1.2.2 Strain and population level and thermal adaptation ...... 6

1.2.3 Community level thermal adaptation and ecosystem functions ...... 7

1.3 Overview ...... 9

2. Thermally adaptive tradeoffs in closely related marine bacterial strains ...... 10

2.1 Introduction ...... 10

2.2 Experimental Procedures ...... 15

2.2.1 Sample collection ...... 15

2.2.2 Vibrio culturing and sequencing ...... 16

2.2.3 Statistical analyses ...... 16

2.2.4 Vibrio physiology ...... 18

2.3 Results and Discussion ...... 19

2.3.1 Environmental drivers of seasonal vibrio population dynamics ...... 19

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2.3.2 Thermal performance curves for vibrio isolates ...... 25

2.4 Conclusions ...... 31

2.5 Acknowledgements ...... 32

2.6 Data deposition ...... 32

3. Novel genes and differential gene regulation enable thermal adaptation in closely- related bacteria ...... 34

3.1 Introduction ...... 34

3.2 Experimental Procedures ...... 38

3.2.1 Genome sequencing, assembly and annotation ...... 38

3.2.2 Strains and culture conditions ...... 39

3.2.3 RNA extraction and rRNA depletion ...... 40

3.2.4 RNA sequencing and data analysis ...... 41

3.2.5 Identification and Analysis of Differentially Expressed Genes ...... 42

3.3 Results and Discussion ...... 43

3.3.1 Genome and Transcriptome Comparisons ...... 44

3.3.2 Temperature-responsive transcriptomes ...... 49

3.3.3 Heat stress responses compared to Topt ...... 51

3.3.4 Cold stress responses compared to Topt ...... 59

3.3.5 Similarity between stresses ...... 64

3.4 Conclusions ...... 65

3.5 Data Deposition ...... 66

4. Diverse and temporally-variable particle-associated microbial communities are insensitive to bulk seawater variables ...... 67

4.1 Introduction ...... 67

4.2 Experimental Procedures ...... 70

4.2.1 Sample collection ...... 70

4.2.2 DNA extraction and library preparation ...... 71

4.2.3 Sequence processing ...... 72

4.2.4 Microbial community analysis ...... 73

4.3 Results and Discussion ...... 74

4.4 Conclusions ...... 85

4.5 Acknowledgements ...... 86

4.6 Data Deposition ...... 86

5. Conclusions ...... 87

Appendix ...... 94

References ...... 111

Biography ...... 130

List of Tables

Table 1 Details of the strains selected for growth rate experiments...... 25

Table S1 Complete set of environmental variables used for principle components analysis for the Pivers Island Coastal Observatory time series. Environmental variables marked with asterisks were the constraint set of variables used in canonical correspondence analysis...... 99

Table S2 Summary of the growth rate and OD600 of strains ...... 101

Table S3 Summary of the differentially-expressed shared genes under different temperature conditions ...... 101

Table S4 Summary of the differentially-expressed unique genes under different temperature conditions ...... 101

Table S5 Table showing the pairwise comparisons between the bacterial communities from different size fractions using analysis of similarity (ANOSIM)...... 102

Table S6 Set of environmental parameters used for variation partitioning...... 103

Table S7 Indicator taxa of bacterial communities from each size fraction (Bonferroni corrected P<0.01; average relative abundance >0.05%). The relative library abundance for each indicator phylotype (as percent of library sequences)...... 104

List of Figures

Figure 1 Hypothetical thermal performance curves of bacteria with different temperature preferences. The temperature at which performance is maximum is termed the Topt...... 4

Figure 2 Canonical correspondence analysis (CCA) ordination diagram of axes one and two of the isolated vibrio community at each sampled time point from the Pivers Island Coastal Observatory (PICO) time series. The percent of variation in the vibrio community explained by each axis is in parenthesis after the axis. The constrained set of environmental variables analysed are indicated as vectors: temperature (Temp); projected no-sky daily insolation (Insolation), Silicate, dissolved inorganic carbon (DIC) and Salinity. The vibrio community profiles from each time point are represented as circles; the grey scale of circles' colouring indicates water temperature. Environmental variables marked with asterisks are statistically significant when assessed by the marginal effect of the terms (P < 0.05)...... 20

Figure 3 (A) Thermal distributions and habitat predictions mapped onto Vibrionaceae isolate phylogeny inferred by maximum likelihood analysis of partial hsp60 gene sequences. Projected habitats are identified by colored circles at the parent nodes. A. Maximum likelihood tree of isolate sequences with Escherichia coli K-12 outgroup. The ring of color surrounding the tree indicates the water temperature from which each strain was isolated. Ecological populations predicted by AdaptML are indicated by shaded and numbered clusters if they pass an empirical confidence threshold of 99.99%. Bootstrap confidence values are shown in Figure S2. Exemplar mesophilic, psychrophilic and broad thermal range clades are indicated by red, blue and green shading of clusters respectively. (B) Tree summarizing habitat-associated populations identified by the model and the distribution of each population among 3°C temperature bins. The habitat legend matches the colored circles in (A) and (B) with the habitat distribution inferred by AdaptML. Habitat distributions (legend) are normalized by the total number of counts in each temperature bin to reduce the effects of uneven sampling...... 22

Figure 4 Box and whiskers plot depicting the observed thermal distribution of vibrio isolate 97% sequence identity hsp60 operational taxonomic units (OTUs) containing at least 10 members across sampled temperatures (phylogenetic tree of these OTUs in Figure S3). For each OTU, the thick bar represents the median of the water temperatures when this group was isolated. The box indicates the first quartile (Q1) and third quartile (Q3), and the whiskers indicate the range from Q1 − 1.5 (Q3 − Q1) to Q3 + 1.5 (Q3 − Q1), but not exceeding the range of measured values. Outlier observations beyond the

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whiskers are indicated by open circles. For ease of interpretation, clades are ordered by the median observed environmental temperature...... 24

Figure 5 Maximum growth rate (h−1) of vibrio strains in glucose minimal media batch culture over a range of temperatures. Colored shapes indicate the average maximal observed growth rate at that temperature for three biological replicates per strain; error bars indicate one standard deviation of measured growth rate for these replicates. Black open squares indicate the strain does not grow at that temperature...... 27

Figure 6 Maximum growth rate (h-1) of the warm and cool vibrio strains in glucose minimal media batch culture over a range of temperatures. Colored shapes indicate the average maximal observed growth rate at that temperature for three biological replicates per strain. (light green circle: warm strain, dark green triangle: cool strain); error bars indicate one standard deviation of measured growth rate for these replicates. Black open squares indicate the strain does not grow at that temperature...... 44

Figure 7 (A) Venn diagrams showing the number of shared genes and unique genes (including both genes predicted from NCBI PGAPP and hypothetical transcripts predicted from Rockhopper) as determined by OrthroVenn and reciprocal best BLAST hit between the warm and cool vibrio strains. Numbers do not include paralogous coding sequences. (B &C): Hierarchical clustering of Bray-Curtis dissimilarity of the gene expression profiles of the two closely-related vibrio strains cultured at different temperatures in glucose minimal media. Strains are represented by letters (W: warm strain, C: cool strain), whereas colors refer to the culture conditions (blue: cold stress, green: Temperature of optimal growth (Topt), red: heat stress). Transcriptomic profiles of both strains including considering either all genes (B) or shared genes (C) are displayed. Bootstrap analysis was used to evaluate the stability of clustering topology by resampling 1000 times. Only bootstrap values ≥80% are shown...... 46

Figure 8 Nucleotide identity of differentially expressed genes (gray box plots) compared with non-differentially-expressed genes (white box plots) for both warm and cool strains under all temperature conditions. Error bars indicate the standard deviation of the mean calculated from all genes of all pairwise comparisons. * P<0.05 (Welch’s t-test)...... 49

Figure 9 Bar charts of the number of differentially expressed genes (differentially expressed genes) in each functional category (KEGG). Length of the bar represents the number of DEG enriched in the function category. White bars represent upregulated differentially expressed genes, and gray bars represent the downregulated differentially expressed genes. Top panels: Heat stress vs Topt, Middle panels: Cold stress vs Topt,

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Bottom panels: Hot stress vs Cold stress. Left panels: Warm strain, right panels: Cool strain...... 50

Figure 10 Number of differentially expressed (fold-change absolute value ≥1.5 and p- value <0.00001)) shared and unique genes for cool (C) and warm (W) strains for all pairwise comparisons for three culturing conditions: heat stress, cold stress and Topt...... 52

Figure 11 Scatter plots comparing the expression changes (fold-change absolute value ≥1.5 and p-value <0.00001)) between the warm and cool strains under different temperature conditions for identified orthologs between strains. Red dots indicate genes that are only differentially expressed in the warm strain. Blue dots indicate genes that are only differentially expressed in the cool strain. Green dots indicate genes that are differentially expressed in both strains. Gray dots indicate genes that are not differentially expressed in either strain. Genes unique to each strain are not included. . 53

Figure 12 Volcano plots showing differentially expressed genes (fold-change absolute value ≥1.5 and p-value <0.00001) under thermal stress. The negative log10 transformed p-values test the null hypothesis of no difference in expression levels between different temperature conditions and are plotted against the average log2 fold changes in expression (x-axis). Data for genes that were not classified as differentially expressed are plotted in black. We plotted data for genes that are differentially expressed between different temperature treatment (False-detection-rate adjusted p-value < 1x10-5) and absolute log2fold change (|Log2fold change|) ≥0.58). Blue color represents the differentially-expressed shared genes. Green color represents the differentially- expressed unique genes...... 58

Figure 13 Plot of the Shannon’s diversity for 16S rRNA gene libraries from different seawater size fractions (>63 µm; 63-5 µm; 5-1 µm and <1 µm) for 14 monthly samples from the Pivers Island Coastal Observatory (PICO) time series (July 2013-August 2014). Points represent the mean value and error bars one standard deviation of Shannon’s diversity computed based on five replicates...... 75

Figure 14 (A) Non-metric multidimensional scaling (NMDS) ordination computed based on weighted Bray-Curtis dissimilarity for 16S rRNA gene libraries from >63 µm; 63-5 µm; 5-1 µm and <1 µm seawater size fractions for PICO monthly samples (July 2013- August 2014). Each symbol corresponds to a distinct time point and size fraction. Samples marked with asterisks were visual outliers in March and July 2014. Ellipses represent 95% intervals around centroids for each size fraction. (B) Bar plot showing the average phylum-level contribution for each fraction. Phyla that on average comprise less than 0.05% of the libraries are grouped as "Other Bacteria." ...... 77

Figure 15 Canonical correspondence analysis (CCA) ordination diagram of the first two axes for size-fractionated bacterial community samples from the PICO time series. The percent of the variation in the size-fractionated community explained by each axis is in parentheses after the axis label. The constrained set of environmental variables analyzed are indicated as vectors: temperature; projected no-sky daily insolation (insolation), silicate, pH and number of particles 1-30µm in diameter (particles). The size-fractionated community profiles from each time point are represented as circles; the gradient color of the circles indicates time. Environmental variables marked with asterisks are statistically significant when assessed by marginal effect of the terms (P<0.05). (A) >63 µm; (B) 63-5 µm; (C) 5-1 µm; (D) <1 µm ...... 80

Figure 16 Shared and unique taxa associated with different size fractions (A) percentage of shared and unique operational taxonomic units (OTUs, 97% similarity) between size fractions. For each bar plot, the gray bar in the center represents the number of shared OTUs in the pairwise comparison and the bars on each side represent the number of unique OTUs in the corresponding sample. (B) The percentage of shared and unique sequences between size fractions. For each bar plot, the gray bar in the center represents the number of shared sequences in the pairwise comparison and the bars on each side represent the number of unique sequences in the corresponding sample...... 83

Figure S1 Map showing location of Pivers Island Coastal Observatory (PICO) time series station part of the Albemarle-Pamlico Sound system. (A) Map depicting the United States Eastern Seaboard. (B) Inset depicts detail of the study site at the Beaufort Inlet, which is indicated by an arrow...... 94

Figure S2 Bootstrap, maximum likelihood phylogenetic tree of Vibrionaceae isolates based on partial hsp60 gene sequences with Escherichia coli K-12 outgroup. The ring of colour surrounding the tree indicates the water temperature from which each strain was isolated. Bootstrap values greater than 80% are indicated by a grey circle at the node. .. 95

Figure S3 Maximum likelihood phylogenetic tree of Vibrionaceae isolates based on partial hsp60 gene sequences with Escherichia coli K-12 outgroup. Green circles at the node indicate 97% identity operational taxonomic units containing at least ten members which are used for the box and whiskers plot (Figure 4)...... 96

Figure S4 Maximum likelihood, concatenated partial gene tree (hsp60, adk, mdh) for selected Vibrionaceae isolates used in growth rate studies. Grey dots indicate a bootstrap value greater than 80%. Escherichia coli K-12 was used as the outgroup...... 97

Figure S5 Rarefaction curves for 14 monthly size fractionated samples (>63 µm; 63-5 µm; 5-1 µm; <1 µm) from the PICO time series. OTUs are defined at 97% similarity, shown are the mean and one standard deviation (error bars) of five subsamples...... 97

Figure S6 (A) Temporal changes in the total number of particles (diameter: 1-30 µm) from 1 m depth water at the PICO time series site over 14 months (July 2013-August 2014, except Oct 2013). (B1) Temporal changes in the particle size distributions (diameter: 1-30 µm). (B2) Panel is an inset of panel B1...... 98

Acknowledgements

The completion of this dissertation would not have been possible without the contributions of the support of colleagues, friends and family members. I would like to thank Dana Hunt for her mentorship in scientific thinking and continual encouragement to pursue good science. In addition to Dana, I feel fortunate to have a very supportive committee, including Zackary Johnson, Jennifer Wernegreen and Rachel Noble, who provide insightful advice and constructive comments on my projects.

Thanks Joshua Granek from the Duke Center for the Genomics of Microbial systems for helping me with bioinformatics. I could not have completed all the genomic and transcriptomic analyses without Josh!

I would also like to thank the many other students, post-docs, and technicians, who have worked alongside me in the Marine Microbe Group. Special thanks must go to Christopher Ward, Alyse Larkin, Tiffany Williams, Katy Davis, Yajuan Lin, Sarah

Loftus and Sara Blinebry. I am grateful for their great companionship and inspiring conversation!

The Duke Marine Lab is an amazing community in which I have made lasting friendships and professional connections. Thank you to Sophie Plouviez, Megumi

Shimizu, Tara Essock-Burns, Heather Heenehan, Jerry Moxley, Leslie Acton, Luke

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Fairbanks, Stacy Zhang, Philip Turner, and many others who have made my time as a

PhD student memorable.

Finally, special thanks to my family. I would hot have come this far without their love and support.

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1. Introduction

Microorganisms play important roles in the biogeochemical cycling of elements such as carbon, nitrogen, and phosphorus. The ability of a microbial community to carry out these processes is determined by the organisms present which is in turn shaped by a combination of selection, drift, mutation and stochasticity (Vellend 2010,

Hanson et al. 2012, Nemergut et al. 2013). Yet, a key question in microbial ecology remains: how do microbes respond at the individual and community levels to changes in environmental variables including temperature, pH, nutrient concentrations and oxygen levels. The importance of environmental selection has been established by linking taxa occurrence with environmental conditions through repeated observations, either transects across environmental gradients or at a single location across temporal environmental changes. These observational studies find marine bacterial community composition is correlated with environmental variables including temperature, day length, and nutrient concentrations (Fuhrman et al. 2006, Sapp et al. 2007, Gilbert et al.

2009, 2012). Among these environmental parameters, temperature is consistently one of the most important drivers shaping taxonomic and functional microbial community composition (Fuhrman et al. 2006, Shade et al. 2010, Sunagawa et al. 2015). However, layered upon these annual and geographic temperature gradients is a predicted 2-4.8°C sea surface temperature increase as well as shifts in wind dynamics, currents, upwelling

and nutrient levels due to climate change during the 21st century (Stocker et al. 2014), which may further affect the function and structure of microbial communities.

1.1 Thermal adaptation in microbe

Concern about how climate change will affect marine microbial communities has sparked interest in understanding how microbes occupy different thermal niches

(Thomas et al. 2012). Climate change is predicted to influence both temperature averages and variability (Räisänen 2002). While organisms may be structured by average climate conditions (Thomas et al. 2012), their thermal tolerance is also related to the magnitude of temperature fluctuations (Addo-Bediako et al. 2000); and thus, microorganisms from variable environments (e.g. temperate regions of the oceans) are predicted to be more resilient to temperature fluctuations. Generally, temperature adaptation in microbes is studied by using isolates to measure thermal performance

(growth rate) over a range of temperatures in the lab to yield thermal performance curves. However, this technique requires microbes that can be cultured and assumes that laboratory growth rates reflect responses to environmental conditions. These thermal performance curves are described using three principal traits: maximum growth rate, the growth rate at the optimal temperature; thermal optimum, the temperature that maximizes performance; and thermal niche width, the range of temperatures that permit growth. Bacterial thermal performance curves generally share several common features

including unimodality, negative skewness, and finite breadth. The curves’ negative skewness describes a sharper decline in growth rate above the temperature optima than below (Figure 1); this feature is present in the thermal performance curves of most microbes with the exception of some psychrophiles (Pesciaroli et al. 2012). The negative skewness means that bacteria living at or near their optimal temperature are more sensitive to warming than cooling, due to the proximity to the upper limit of their physiological thermal tolerance. Thus, under many climate change scenarios, in the absence of evolutionary adaptation, one would expect a sharp decline in tropical microbial diversity (Thomas et al. 2012). Although there are general trends related to geographic location, the relationship between temperature and performance can vary widely among taxa, including differences in thermal sensitivity (i.e., the slope of the temperature-growth relationship), and in the maximum, minimum, or optimal temperatures for growth (Figure 1). These variations in thermal performance along with other environmental factors help to determine which taxa are present in a given environment.

Figure 1 Hypothetical thermal performance curves of bacteria with different temperature preferences. The temperature at which performance is maximum is termed the Topt.

1.2 Responses to temperature at multiple taxonomic levels

Microbes can adapt to different thermal conditions through multiple mechanisms, ranging from physiological modification to changes in taxonomic composition. These individual responses and biotic interactions translate to differences in fitness across genotypes within microbial populations and communities. However, linking individual changes due to temperature back to aggregate community-wide responses is challenging. Here, we recognize that multiple processes underlie thermal adaptation and that these processes generally may act at distinct phylogenetic scales.

Below we discuss predicted thermal adaptive responses at the genome to community scales.

1.2.1 Genomic and transcriptomic thermal adaptation

In response to temperature shifts, microbial cells exhibit strong physiological responses, including changes in cell size, elemental stoichiometry, membrane composition and in the translation and transcription machineries (Finkel et al. 2009, Hall et al. 2010, Ying et al. 2015, Bosdriesz et al. 2015). To date, most studies on bacterial thermal adaptation have focused on extremophiles, experimentally evolved strains or opportunistic pathogens (Russell and Fukunaga 1990, Chastanet et al. 2003, Bennett and

Lenski 2007, Tenaillon et al. 2012, Chang et al. 2013); however, this research offers a useful framework for predicting mechanisms of thermal adaptation in environmental bacteria, including changes in gene content, alleles, and expression level. Acquiring new genes through horizontal or phage-mediated transfer may help organisms adapt to environmental stressors including temperature (Ojaimi et al. 2003, Coleman et al. 2006).

Temperature adaptation may also involve changes in existing genes that increase the stability of structural components, for example, higher GC content in double-stranded

RNA regions (Hurst and Merchant 2001) and enrichment of purines on the coding strand, which promotes the stability of tertiary mRNA structures (Wang and Hickey

2002, Singer and Hickey 2003). Selection also favors increased protein stability leading to global shifts in amino acid usage, including a decrease in thermolabile amino acids

(e.g., cysteine and glutamic acid) (Nishio et al. 2003, Hickey and Singer 2004, McDonald

2010), an increase in the (Glutamate + Lysine)/(Glutamine + Histidine) ratio (Farias and

Bonato 2003). Apart from changes in gene content and sequence, bacteria can adapt to distinct temperatures by regulation of intracellular solutes (Roessler and Muller 2001), lipid composition (Hall et al. 2010), and heat shock genes, including chaperones (Van

Derlinden et al. 2008, Mereghetti et al. 2008), sigma factors (Helmann et al. 2001) and membrane proteins (Ojaimi et al. 2003, Hall et al. 2010). We predict that environmental bacteria adopt a number of these strategies to adapt to temperature changes, and accumulation of these genotypic changes will eventually lead to ecological specialization in their thermal niches.

1.2.2 Strain and population level and thermal adaptation

Bacterial responses to temperature can go beyond physiological adaptations and affect composition at the population and community levels. As individual transcriptomic and genomic adaptations translate to differences in fitness, populations within a community or strains within a population that are better competitors at that temperature should prevail (Scheibner et al. 2014). Organisms which are inferior competitors at a given temperature may be retained in the system due to increased fitness at other times (e.g. seasonal temperature change) or other unique capabilities.

Trait-based approaches have demonstrated the trade-off between the ability to take-up resources at low temperatures and respiratory costs at high temperatures (Hall et al.

2008), suggesting we should never observe thermal generalists. Studies have shown

shifts in abundance between bacterial populations with different optimal temperatures and substrate affinities (Ogilvie et al. 1997). In the global ocean, Prochlorococcus strains display different temperature optima and tolerance ranges (Johnson et al. 2006) and

Synechococcus strains also display different thermal niches and physiological capacities to cope with temperature variations (Pittera et al. 2014). Thus one of the initial responses to climate-change induced warming would be shifts in the geographic distribution of existing microbial strains and populations based on thermal preferences.

1.2.3 Community level thermal adaptation and ecosystem functions

However, temperature also limits the metabolic activity of microbial communities in many ecosystems (White et al. 1991, Price and Sowers 2004); thus warming of marine microbial communities usually increases total activity due to enhanced enzyme kinetics (Kirchman et al. 2005, Lopez-Urrutia and Moran 2007).

Further, a strong relationship between bacterial-specific growth rate and temperature was observed in metadata analyses of coastal and marine habitats (White et al. 1991, Dell et al. 2011). As predicted by the metabolic theory of ecology (Brown et al. 2008), studies have found a positive correlation between temperature and both production (Lopez-

Urrutia and Moran 2007) and respiration rates (Vazquez-Dominguez et al. 2007).

Temperature increases may also decrease nutrient availability and thus microbial activity through increased vertical stratification (Sarmiento et al. 1998, Cubasch et al.

2001, Behrenfeld et al. 2006). In addition to altering microbial activity, the microbial community composition would also change in response to warming. A meta-analysis of long-term observations and laboratory manipulations showed that the proportion of small-sized taxa in a community increases with elevated temperature (Daufresne et al.

2009). With increased microbial activity and composition changes, warming can further impact carbon fluxes and trophic transfer. Microcosm experiments show that with 2-

10°C warming, both primary and secondary production increased due to higher physiological rates and changes in trophic structure (Petchey et al. 1999); picophytoplankton and heterotrophic bacteria dominated in the warmed communities while grazer, protists or herbivores were absent. In addition, some models predict increases in small cyanobacteria (Synechococcus and Prochlorococcus) based on projected sea water temperature increases (Flombaum et al. 2013). Since sinking velocity is heavily dependent on cell size, the increasing importance of small primary producers in marine pelagic ecosystems could mean less potential for carbon sequestration in the deep ocean

(Richardson and Jackson 2007). Thus, warming favors small phytoplankton and potentially reduces deep sea carbon sequestration due to higher proportion of picophytoplankton and increased bacterial respiration rates in the epipelagic zone.

1.3 Overview

In my dissertation, I explore potential mechanisms of bacterial thermal adaptation. I have approached studying the mechanisms of thermal adaptation in environmental bacteria at the population, transcriptomic and behavioral levels. In the first two chapters, I focus on vibrios as microbial models because of their ubiquity and culturability, which allowed us to test hypotheses through manipulative experiments.

Moreover, temperature has been shown to influence vibrio strain diversity (Thompson et al. 2005, Keymer et al. 2009). First, I examined seasonal dynamics within the family

Vibrionaceae and link them to laboratory growth characteristics. In my second chapter,

I use these same vibrio strains to identify the genomic and transcriptional changes associated with thermal adaptation in closely-related strains. Finally, at the community level, I examined the temporal dynamics of free-living and particle-attached bacterial communities to determine the potential of particles as a thermal refuge. The work presented in this dissertation is aimed to improve our understanding of how bacteria respond to different thermal environments seasonally and geographically, and ultimately will inform predictions about potential microbial responses to climate change.

2. Thermally adaptive tradeoffs in closely related marine bacterial strains

This chapter has been published as:

Yung, C.M., Vereen, M. K., Herbert, A., Davis, K. M., Yang, J., Kantorowska, A.,

Ward, C. S., Wernegreen, J. J., Johnson, Z. I. and Hunt, D. E. (2015), Thermally

adaptive tradeoffs in closely related marine bacterial strains. Environmental

Microbiology, 17: 2421–2429. doi:10.1111/1462-2920.12714

Author’s contributions:

DEH and CY conceived the study, CY, MKH, KMD, JY, AK, CSW collected field data and samples; CY undertook molecular bench work and growth rate experiment; DEH and CY drafted the manuscript. All authors have read and approved the final manuscript.

2.1 Introduction

Repeated observations of microbial communities from a single location (i.e. time series) or discrete samples across environmental gradients are often used to identify environmental factors and organismal interactions that structure the diversity of aquatic microbial populations (Johnson et al. 2006, Steele et al. 2011, Gilbert et al. 2012).

However, observational techniques are limited in their ability to identify the proximal drivers of microbial diversity due to a number of confounding factors: strong correlations between environmental variables(Gilbert et al. 2012), top down control by predation or viruses, associations with particles and other microscale features (Hunt et al. 2010), or unmeasured environmental variables. Moreover, a taxon observed in a given environment does not imply adaptation to current environmental conditions due to spatial or temporal uncoupling of the environment and populations (Jones and

Lennon 2010, Caporaso et al. 2012, Hunt et al. 2013). Therefore, observation-based predictions of environmental niche specialization are often complemented by laboratory studies of culturable organisms, which can confirm hypotheses and provide additional mechanistic insight. Nevertheless, despite these limitations, field sampling of marine and freshwater microbial communities remains one of the fundamental approaches to study the ecology of uncultivated microbial populations.

In observational studies, temperature is consistently one of the most important variables structuring microbial communities and populations across many different environments (Selje et al. 2004, Johnson et al. 2006, Fuhrman et al. 2006, Sikorski and

Nevo 2007, Fuhrman et al. 2008, Shade et al. 2010), although other variables may be more important globally (Lozupone and Knight 2007). In addition to ease of measurement (and therefore extensive datasets), two characteristics of temperature may drive taxa–temperature relationships in aquatic environments. First, the high specific

heat of water means that large changes in temperatures occur on a time scale longer than the average generation time of microbial populations. Thus, resident microbes are likely to be adapted to in situ temperature conditions as selection persists over time scales long enough to induce changes in microbial population abundances. In contrast, other environmental variables such as nutrients often exhibit pulsed or patchy inputs relative to the generation time of resident microbes (Johnson et al. 2013, Yawata et al. 2014).

Second, genomic specificity to temperature may be distinct from adaptation to other environmental variables. For environmental factors such as nutrients or carbon sources, a horizontally transferred gene or operon could allow for utilization of new resources.

Whereas, temperature adaptation likely involves genome-wide changes as all enzymatic rates are affected by temperature, and growth in a new thermal environment requires that all essential cell processes must function at that temperature. Thus, the evolution of thermal preferences likely differs from that involved in the use of other resources.

Despite these distinct properties of temperature, using observational techniques, it remains difficult to distinguish the effects of temperature from those of other seasonally cycling variables (e.g. light) and phenomena (e.g. seasonal coastal upwelling).

Warming of marine microbial communities generally increases microbial metabolism presumably due to enhanced enzyme kinetics (Fuhrman and Azam 1983,

Kirchman et al. 2005, Lopez-Urrutia and Moran 2007). Both in situ and laboratory studies have shown that progressive warming alters bacterial specific growth rates

(White et al. 1991), secondary production (Rivkin et al. 1996) and respiration rates

(Vazquez-Dominguez et al. 2007). In cultures, growth rates increase with temperature, but only within a certain range, above which growth rates generally decrease rapidly

(Izem and Kingsolver 2005, Chen and Shakhnovich 2010). Laboratory-derived relationships between temperature and growth rate also predict evolutionary tradeoffs where increased fitness at a specific temperature decreases fitness at other temperatures

(Bennett and Lenski 2007, Hall et al. 2010, Chang et al. 2013), although exceptions have been noted (Bennett and Lenski 2007, Knies et al. 2009). Given the temperature sensitivity of bacterial growth and metabolism, it is not surprising that small temperature changes correspond to dramatic shifts in some environmental bacterial populations (Johnson et al. 2006). Yet, in many time series studies, dominant marine microbial populations are present year-round, even though their relative abundance varies (Gilbert et al. 2012, Chow et al. 2013). The ubiquity of dominant taxa suggests these clades may be eurythermic environmental generalists, rather than thermal specialists. Alternately, ubiquitous taxa may be composed of multiple temperature- specialized subtypes that cannot be resolved using standard measures of uncultured microbial diversity [e.g. 16S rRNA gene based operational taxonomic units (OTUs)] as was recently suggested for the marine group SAR11 (Eren et al. 2013). Thus whether ubiquitous taxa are thermal generalists or can be subdivided into specialists remains an open question. Moreover, with growing concerns about climate change-induced

alterations in the oceans, understanding the mechanisms of bacterial thermal adaptation is essential to predicting microbial responses to sea surface temperature changes.

In this study, we explore seasonal partitioning of closely related bacterioplankton focusing how temperature modulates community structure. We have selected the bacterial family Vibrionaceae for further investigation due to their ease of culturability, ubiquity in coastal environments and status as a model system for marine bacteria (Polz et al. 2006). Vibrio are metabolically versatile heterotrophs known for their metabolism of chitin (Hunt et al. 2008a), associations with zooplankton (Turner et al. 2009) (Turner et al. 2009) and the inclusion of a number of facultative marine pathogens (e.g. Vibrio cholerae). In the environment, vibrio abundance is driven by changes in environmental parameters including temperature, salinity, chlorophyll and zooplankton (Kaneko and

Colwell 1973, Randa et al. 2004, Turner et al. 2009, Johnson et al. 2012). Here, we examine vibrio community dynamics at a temperate, coastal site with a large (20°C) seasonal variation in temperature (Johnson et al. 2013). Field observations are corroborated with laboratory-based growth rate studies revealing temperature tradeoffs in growth rates and ranges.

2.2 Experimental Procedures

2.2.1 Sample collection

Water samples were collected as part of the PICO time series in Beaufort, North

Carolina. This site (34°71.81'N, 76°67.08'W) is situated on Pivers Island at the Beaufort

Inlet (Figure S1). Sampling for vibrio isolates was performed biweekly from 30 August

2011 to 2 January 2013. Time series measurements were conducted as previously described (Johnson et al. 2013). Briefly, water was sampled at 10:30 AM local time using a Niskin bottle centred at 1 m. All measurements are replicate samples. Nutrients (NO2,

NO3, PO4, SiOH4) were measured in duplicate on 0.22 µm filtered samples stored at

−80°C until later analysis using an Astoria-Pacific A2 autoanalyser. Salinity was measured using a refractometer (Atago). Spectrophotometric pH measurements using a pH-sensitive dye were collected on a UV-Vis-NIR spectrophotometer (Cary 4000,

Varian). Chlorophyll pigment samples were extracted in 100% methanol and measured using a calibrated Turner 10-AU fluorometer. Oxygen was measured optically using an in situ, calibrated probe (YSI ProODO). Turbidity was measured on discrete samples using a calibrated handheld turbidimeter (Orion AQ4500). Dissolved inorganic carbon was quantified as carbon dioxide using Apollo Scitech DIC analyser AS-C3. Tidal height and wind speed were obtained from NOAA station #8656483 (Beaufort, NC, USA).

Incoming no-sky solar radiation was estimated using the USGS AIR_SEA toolbox for

Matlab.

2.2.2 Vibrio culturing and sequencing

To obtain vibrio isolates, seawater samples were concentrated on a 0.2 µm pore size polyethersulfone membrane filter (Pall Supor-200). Filters were then placed on

Thiosulfate Citrate Bile Sucrose media (Difco) supplemented with 10 g l−1 of NaCl

(TCBS) and incubated at room temperature. Colonies were isolated by streaking three times, alternately on Luria-Bertani with 20 g l−1 of NaCl and TCBS (Thompson et al.

2005). Cultures were preserved in glycerol at −80°C and DNA was extracted using Lyse-

N-Go (Thermo Scientific) and stored at −20°C. Isolates were screened by sequencing the hsp60 gene as previously described (Goh et al. 1996, Hunt et al. 2008b). For selected isolates, the housekeeping genes adk and mdh were sequenced using modified primers as described previously (Preheim et al. 2011). Amplicons were sequenced on an ABI automated sequencer at the Duke Center for Genomic and Computational Biology.

Sequences were manually edited using Sequencher (Genecodes) and manually aligned using Bioedit. This analysis yielded unambiguous alignments (no gaps) for hsp60, adk and mdh of 500, 440 and 426 bp respectively.

2.2.3 Statistical analyses

After editing, hsp60 sequences were obtained for 1803 isolates (average sequences/sample = 59.3, minimum = 35, maximum = 84). We used the furthest neighbour algorithm to cluster hsp60 sequences into 171 OTUs with > 97% identity. We

removed 68 OTUs that were observed a single time from data used for the CCA so that the importance of these low abundance OTUs was not overestimated. CCA was used to identify the environmental factors associated with differences between vibrio community composition at different time points (Terbraak and Verdonschot 1995). Prior to the CCA, the ‘step’ function in the R package vegan (Oksanen et al. 2012) was used to automatically select constraints in the model using forward stepwise selection using

Akaike's information selection criterion with 999 permutation tests at each step. Only the terms that are statistically significant in the stepwise selection were used as the constrained set of parameters to relate the environmental variables to vibrio community composition using the ‘cca’ function in vegan (Oksanen et al. 2012). Statistical significance of the individual environmental variables was assessed using the marginal effect of the terms.

We used MEGA 6 (Tamura et al. 2013) to find the best-fit substitution model and transition/transversion ratio to construct the maximum likelihood tree based on hsp60 gene sequences using using PhyML 3.0 (Guindon et al. 2010). The following parameter settings were used: DNA substitution was modelled using the GTK parameter; estimated proportion of invariable nucleotide sites was 0.48; the gamma distribution parameter was set to 0.6; five gamma rate categories were used and a BIONJ tree was initially used and improved using NNI. Isolates were partitioned into ecologically

cohesive clades according to genetic and ecological similarity using AdaptML (Hunt et al. 2008b) and visualized using the Interactive Tree of Life tool (Letunic and Bork 2007).

2.2.4 Vibrio physiology

Closely related isolates from distinct water temperatures with high similarity among the three sequenced housekeeping genes were analyzed further using thermal performance curves.

For clusters of closely related isolates, the growth rate of triplicate biological replicates was established in glucose minimal media (Tibbles and Rawlings 1994) supplemented with 10 mM ammonium, F/2 trace metals and F/2 vitamins (Guillard

1975) over a range of temperatures (4°C intervals spanning 4–40°C). Cultures were incubated at the designated temperature with constant agitation until entering mid- exponential phase. These cultures were then diluted 1:100 in fresh media and incubated at the designated temperature with constant shaking at 120 r.p.m. The growth rate of each isolate was measured spectrophotometrically using the optical density at 600 nm.

Cultures were determined not to grow at a given temperature if the optical density remained < 0.05 after 2 weeks. For cultures with visible aggregates, subsamples were sonicated at 5 W for 3 s using a Microson Ultrasonic cell disruptor XL-2000 immediately prior to reading the optical density; this treatment was experimentally shown not to change the optical density of non-aggregated samples.

2.3 Results and Discussion

2.3.1 Environmental drivers of seasonal vibrio population dynamics

In order to examine seasonal distributions of closely related bacterioplankton, roughly 100 vibrio isolates were obtained every 2 weeks over 18 months (August 2011–

January 2013) in conjunction with the Pivers Island Coastal Observatory (PICO) sampling, a well studied time series located at the Beaufort Inlet (Beaufort, NC, USA;

Figure S1). This coastal ecosystem is a highly dynamic environment with strong seasonal patterns in variables such as temperature and pH (Johnson et al. 2013). After streaking to isolation, strains were phylogenetically characterized by sequencing a housekeeping gene (hsp60) that offers enhanced resolution over 16S rRNA gene sequencing (Goh et al. 1996, Thompson et al. 2005). This analysis revealed a diverse array of Vibrio and Photobacteria taxa including Vibrio fischeri, Vibrio logei and Vibrio splendidus. Similar to other temperate locations, we observed seasonal variability with distinct vibrio clades present at different times of the year (Kaneko and Colwell 1973,

Thompson et al. 2005). In order to identify the environmental drivers of vibrio community diversity, a constrained set of environmental variables was identified using forward stepwise selection. Environmental variables statistically associated with vibrio community structure (P < 0.05) by forward selection using Akaike's information criterion and 999 permutation tests are daily insolation, water temperature, salinity, silicate and dissolved inorganic carbon (Table S1). These variables were used as the constrained

variable set to identify drivers of vibrio diversity using canonical correspondence analysis (CCA: Figure 2). Among these variables, temperature is the largest driver of vibrio diversity (P = 0.010) followed by insolation (P = 0.016) (Figure 2). Not surprisingly, these two variables are correlated (Pearson's r = 0.739), but the solar maxima precedes thermal maxima by 1 month at this location (Johnson et al. 2013) and both variables independently impact vibrio community structure. Whereas temperature is predicted to directly affect bacterial physiology through enzyme kinetics, light likely indirectly influences vibrio community structure by stimulating primary production. Although the time series is located at the mouth of an estuary and vibrio physiology is linked to salinity (Randa et al. 2004, Materna et al. 2012), salinity did not contribute significantly to vibrio community structure, likely due to the high average value (32 ppt) and low variability (28–34 ppt) at the study site.

constrained set of environmental variables analysed are indicated as vectors: temperature (Temp); projected no-sky daily insolation (Insolation), Silicate, dissolved inorganic carbon (DIC) and Salinity. The vibrio community profiles from each time point are represented as circles; the grey scale of circles' colouring indicates water temperature. Environmental variables marked with asterisks are statistically significant when assessed by the marginal effect of the terms (P < 0.05).

The significance of temperature on vibrio community structure led us to focus on thermal specialization within this group of bacterioplankton. In order to visualize the phylogenetic relationships of the vibrio isolates and their distribution across temperature environments, a maximum likelihood phylogenetic tree based on partial hsp60 gene sequences was constructed (Figure 3A). Mapping the water temperature from which the strains were isolated onto the phylogenetic tree revealed that temperature was visibly non-randomly distributed on the vibrio phylogeny (Figure 3A, outer ring). However, to gain a more quantitative understanding of the thermal distributions of specific clades, we employed AdaptML (Hunt et al. 2008b) to identify potential thermal niches. Isolate sequences were assigned to 3°C temperature bins based on water temperature, and distinct thermal ‘habitats’ were identified statistically as structured distributions across these temperature bins (P < 0.0001). AdaptML identifies the bounds of thermally constrained groups on the phylogenetic tree by assigning clades to ‘habitats’ or projections of the realized thermal niche onto these temperature bins

(Figure 3A, colored dots on branches), allowing identification of differences in temperature-adaptive strategies employed in the vibrio. This approach avoids any a

priori assumptions about the thermal or phylogenetic bounds at which organisms differentiate but does not take into account potential confounding effects between temperature and light or other environmental parameters. This approach robustly

(≥ 98% of the time) identified four thermal habitats corresponding to a mesophilic (HA), a more generalist (HB) and two psychrophilic (HC and HD) distributions. Between the two psychrophiles, HD has a stronger affiliation with water temperatures < 15°C. Although temperature was previously observed to significantly affect vibrio community structure

(Figure 2), this analysis indicates a range of distinct, thermal strategies within the vibrio community in response to seasonal water temperature changes.

parent nodes. A. Maximum likelihood tree of isolate sequences with Escherichia coli K-12 outgroup. The ring of color surrounding the tree indicates the water temperature from which each strain was isolated. Ecological populations predicted by AdaptML are indicated by shaded and numbered clusters if they pass an empirical confidence threshold of 99.99%. Bootstrap confidence values are shown in Figure S2. Exemplar mesophilic, psychrophilic and broad thermal range clades are indicated by red, blue and green shading of clusters respectively. (B) Tree summarizing habitat-associated populations identified by the model and the distribution of each population among 3°C temperature bins. The habitat legend matches the colored circles in (A) and (B) with the habitat distribution inferred by AdaptML. Habitat distributions (legend) are normalized by the total number of counts in each temperature bin to reduce the effects of uneven sampling.

In contrast to the apparent ubiquity of many taxa in previous time series studies, we observed that clades partition largely into mesophilic or psychrophilic thermal distributions across the sampled time points (HA and HC + HD, respectively, in Figure 3).

However, it is worth noting that clades which are truly randomly distributed by temperature would not be assigned to a novel habitat by AdaptML. We refer to groups found at a range of environmental temperatures as ‘broad thermal range’ clades and their existence argues against adaptation of clades to a narrow temperature window. To better visualize clade thermal distributions without the assignment of AdaptML

‘habitats’ and the potential resultant bias against even temperature distributions, vibrio isolates were also clustered into 97% identity hsp60 OTUs (Figure S3). As in the

AdaptML analysis, we observe psychrophiles, mesophiles and broad thermal range clades (Figure 4); however, the prevalence of broad thermal range clades appears higher with this approach. Broad thermal range clades are observed at environmental

temperatures intermediate between mesophiles and psychrophiles and occupy a broader environmental niche (Figure 4). While thermal specialization supports the concept of temperature as a key environmental factor that structures microbial populations and communities, the presence of apparent thermal generalists (broad thermal range clades) suggests that thermal adaptive tradeoffs may not occur in environmental bacteria (Chang et al. 2013). Thus, based on environmental data alone, it is unclear whether clades observed over a wide range of temperatures are thermal generalists or if other factors such as microdiverse ecological specialization drive observed environmental patterns in these groups.

Outlier observations beyond the whiskers are indicated by open circles. For ease of interpretation, clades are ordered by the median observed environmental temperature.

2.3.2 Thermal performance curves for vibrio isolates

To investigate the thermal performance of these strains relative to their environmental distributions and to explore potential thermal adaptive tradeoffs, we characterized the growth rates of a number of representative isolates over a range of temperatures. We chose clades for growth rate analysis which exemplify the three strategies (mesophile, psychrophile and broad thermal range) identified previously

(Table 1).

Table 1 Details of the strains selected for growth rate experiments

Strain Clade † Date of isolation Water temperature at isolation (°C)

Mesophile 1 Clade 18 8/30/2011 27.3 Mesophile 2 Clade 18 6/26/2012 27.6 Mesophile 3 Clade 18 8/29/2012 26.8 Psychrophile 1 Clade 15 12/16/2012 13.6 Psychrophile 2 Clade 15 02/28/2012 12.3 Psychrophile 3 Clade 15 12/5/2012 14.3 Warm 1 Broad thermal range 9/13/2011 27.3 Warm 2 Broad thermal range 9/13/2011 27.3 Intermediate 3 Broad thermal range 11/1/2011 15.6 Intermediate 4 Broad thermal range 11/1/2011 15.6 Cool 5 Broad thermal range 1/3/2012 10.0 Cool 6 Broad thermal range 1/3/2012 10.0 † Clades correspond to designations in Figure 3.

Specifically, the mesophile clade (Figure 3A: clade 18, red shading), most similar by 16S rRNA gene sequence comparisons to Vibrio harveyi ATCC BAA-1116 among the sequenced genomes, was isolated from an environmental temperature range of 21.3–

27.3°C (average = 24.3°C). The psychrophilic clade (Figure 3A: clade 15, blue shading) was identified as V. splendidus by 16S rRNA gene sequence comparison with sequenced genomes and was isolated at temperatures between 9.1°C and 15.6°C (average = 11.6°C).

However, this observed fidelity to specific temperature ranges is not found in all clades.

A number of clades were assigned to the thermally mixed habitat (HB) by AdaptML or were not assigned to a habitat at all, indicating a statistically random distribution across temperature bins. This later group includes the green-shaded clade (Figure 3A) which was identified as most similar to the sequenced genome strain of V. parahaemolyticus

RIMD 2210633 and was isolated from water temperatures between 10°C and 29°C

(average = 20°C). Prior to growth rate studies, we selected at least three isolates per clade and confirmed that the hsp60 phylogeny reflected that of other housekeeping genes (adk, mdh). This analysis revealed high within-clade similarity at all three loci with an average nucleotide identity ≥ 99.8% (Figure S4). To confirm the thermal preferences of the three categories of clades (mesophile, psychrophile, broad thermal range), we examine their thermal performance curves in culture. To evaluate the growth potential of these strains at a range of temperatures, the maximal growth rate of isolates was established in glucose minimal media batch culture over a temperature range from 4°C

to 40°C (Figure 5). Although no strain was shown to grow at temperatures greater than

40°C, several cultures had measurable growth rates at 4°C, indicating that their true thermal minima is below this temperature range. Thus, the upper but not lower limits of growth were established for these culturing conditions. These laboratory culture- based experiments can be used to test field-based predictions of vibrio physiology.

Figure 5 Maximum growth rate (h−1) of vibrio strains in glucose minimal media batch culture over a range of temperatures. Colored shapes indicate the average

maximal observed growth rate at that temperature for three biological replicates per strain; error bars indicate one standard deviation of measured growth rate for these replicates. Black open squares indicate the strain does not grow at that temperature. A. Three strains each from exemplar mesophilic (red circles) and psychrophilic clades (blue triangles), which were the highlighted red and blue clades, respectively, in Figure 3. B. Six strains from the exemplar broad thermal range clade (green in Figure 3) two each isolated from warm (red circles), intermediate (green diamonds) and cool (blue triangles) temperatures with inset showing growth rates from 4°C to 16°C.

In comparing the thermal performance curves of these putatively temperature- differentiated clades, a number of features are readily apparent including differences in the optimum growth temperature (Topt), shape or skewness of the thermal performance curve, temperature range and growth rate (Figure 5). First, similar to previous studies, laboratory measured Topt is higher than the observed environmental distributions of strains likely due to enhanced kinetics (Materna et al. 2012). However, the psychrophilic and mesophilic clades exhibit a 16°C shift in Topt that indicates that observed thermal partitioning in the environment is also evident in laboratory-characterized physiology

(Figure 5A). Second, we noted differences in the shape of the thermal performance curves. The mesophile clade displays a classic ectothermic growth curve with a sharp decline in growth rate above Topt and a long tail at temperatures < Topt (Izem and

Kingsolver 2005, Chen and Shakhnovich 2010). In contrast, the psychrophilic strains exhibit thermal performance curves with a roughly symmetric distribution of growth range above and below Topt and have a wider growth range at temperatures greater than

Topt compared with the mesophilic clade. Third, we observed differences in the bounds of the temperature range, as the psychrophile clade grows at lower temperatures (< 4°C) than the mesophile clade but not at temperatures >28°C where the mesophile grows optimally. However, the upper bounds of the psychrophile clade's growth were higher in culture than in the environment, where it was not observed at temperatures above

16°C. Optimal growth at 20°C suggests that this group should more appropriately be termed psychrotolerant, but we preserve the term psychrophile for clarity of discussion.

Differences in field and laboratory thermal growth range highlight the more permissive growth conditions in rich media as well as the lack of resource competition in culture.

Finally, we observed differences in growth rate; the psychrophilic clade has a lower maximal growth rate than the mesophilic clade at their respective Topts, but below 20°C, psychrophiles exhibit a higher growth rate than the mesophiles (Figure 5A). This suggests a potential ‘cost’ or tradeoff associated with growth at low temperatures that limits either growth range or maximal growth rate. Although each of these clades is represented by three isolates taken from non-sequential sampling points (e.g. ≥ 1 month apart), there is remarkable within-clade similarity in growth rate for isolates (one-way analysis of variance, P > 0.05), suggesting that strains within these clades behave similarly with regards to temperature.

In contrast to the thermal fidelity observed in the mesophile and psychrophile clades, the broad thermal range clade exhibits distinct thermal performance curves for

isolates obtained from different water temperatures (Figure 5B). However, all strains within this clade have similar Topts 28–32°C, intermediate between that of the mesophile and psychrophile clades. Despite this similarity in Topt, there is a temperature-related trend similar to that observed previously: strains isolated from warmer water temperatures (Table 1) grow faster at their Topt. Further, strains isolated from cooler water temperatures display a lower maximal growth rate, but they grow at non- permissive temperatures for strains isolated at warm and intermediate temperatures

(i.e. ≤ 8°C; Figure 5B). Thus, these thermal performance curves show that ‘broad thermal range’ clades are in fact not thermal generalists but rather exhibit thermal specialization at very fine phylogenetic scale that is not resolved by sequencing multiple housekeeping genes (Figure S4). However, clades with optimal growth intermediate between mesophiles and psychrophiles may be genetically poised to fine tune their thermal niche compared with strains with more extreme Topt values.

In addition to shifts in Topt, the shape of the thermal performance curve growth range and growth rate, an interesting temperature-related phenomenon was observed for strains from the psychrophile clade and the cool strains within the broad thermal range clade (strains 5 and 6; Table 1): at temperatures ≤ 20°C, these strains formed visible aggregates, suggesting temperature-mediated lifestyle differentiation. Attachment to nutrient sources, including chitin for vibrios, has previously been shown to protect against thermal cold stress (Amako et al. 1987) and biofilm formation provides many

bacteria resistance to environmental stressors. This temperature-dependent aggregation suggests that lifestyle differentiation, in addition to enzymatic temperature optima, may be a mechanism that vibrio use to buffer the effects of temperature stress. Here, temperature is shown to dictate the environmental range of bacteria. However, thermal adaptation has a cost with apparent tradeoffs even within very closely related strains which argues against the evolution of thermal generalists (Chang et al. 2013).

2.4 Conclusions

This study suggests that there are multiple mechanisms of thermal adaptation within the vibrio community encompassing changes in Topt, the shape of the thermal performance curve, the bounds of the growth range, growth rate and attachment. These findings argue against thermal generalism in ubiquitous clades both at the genus level

(vibrios) and at the strain level; rather clades observed under a range of temperature conditions (broad thermal range clades) exhibited thermal subspecialization at very fine- scale phylogenetic resolution. It remains to be seen if this exquisite thermal adaptation observed in vibrio can be generalized to other prevalent microbial taxa or environmental variables. While thermal adaptive tradeoffs have been explored in Escherichia coli which inhabits a host's relatively constant thermal environment (Bennett and Lenski 2007,

Chang et al. 2013), little information is available for environmental bacteria which are subject to large seasonal temperature changes. These results suggest that there is a

physiological cost to adaption to specific temperatures (particularly cooler temperatures) and that temperature specialization can occur among closely related strains (Figure 5 and Figure S4). Further, the observed thermal specialization over relatively short evolutionary time scales indicates that few genes or cellular processes may limit expansion to a different thermal niche. Characterizing the mechanisms of thermal adaptation is essential to understanding seasonal succession as well as potential microbial responses to larger scale processes like climate change-induced ocean warming.

2.5 Acknowledgements

This research was supported by NSF #OCE1322950 to DEH, NSF #OCE 1416665 to DEH and ZIJ, an NSF graduate fellowship to CSW, and the NSF REU Program. We would like to acknowledge the contributions of the Pivers Island Coastal Observatory sampling team for their assistance with environmental sampling. We would further like to thank Sarah Preheim and Sophie Plouviez for helpful discussion.

2.6 Data deposition

Vibrio partial 16S rRNA, hsp60, mdh and adk gene sequences are deposited in

GenBank under Accession No. KM573823–KM575707. Environmental sampling data are

available as part of the Pivers Island Coastal Observatory (PICO) database available through BCO-DMO Project #2281.

3. Novel genes and differential gene regulation enable thermal adaptation in closely-related bacteria

This chapter will be submitted to peer-reviewed journal with authors:

Cheuk Man Yung, Tiffany C. Williams, Joshua A. Granek, Jennifer J. Wernegreen, Dana

E. Hunt

Author’s contributions:

DEH and CY conceived the study; CY and TCW undertook molecular bench work; CY and JAG undertook bioinformatic analysis; DEH and CY drafted the manuscript.

3.1 Introduction

Across the biogeographic distribution of a species, temperature varies both spatially and temporally; and microbial populations’ responses to this variation can lead to genetic divergence of local populations. Due to the important role of temperature in microbial metabolism and physiology, adaptation to local environmental temperature could have a wide-ranging impact on an organism’s genome (Nakatsu et al. 1998,

Bennett and Lenski 2007, Tenaillon et al. 2012). As temperature-limited growth rates of marine microbes are related to the environmental temperatures where those organisms are observed (Tenaillon et al. 2012) and temperature is one of the key drivers shaping taxonomic and functional microbial community composition in the oceans (Cooper and

Lenski 2010), thermal constraints are likely one of the major factors determining the geographic and ecological ranges of bacteria. However, the genetic basis for thermal tolerance of environmental bacteria has not been well-characterized. Additionally, it is hard to differentiate phenotypic variation driven by trait plasticity from population- level genetic differences; however, genetic variation allows for local adaptation to evolve across environmental gradients.

Much of the evidence for mechanisms of bacterial adaptation comes from laboratory experimental evolution studies, where specific alleles within the population allow cells to grow better at higher temperatures (Haney et al. 1999, Grzymski et al.

2006, Barabote et al. 2009, Ayala-del-Río et al. 2010). Those specific alleles are the results of accumulation of point mutations in protein-coding sequences, which impact protein function or alter the thermal stability of proteins (Berezovsky et al. 2007). Alternations in gene content through horizontal transfer may also allow organisms adapt to novel temperatures as putatively phage-derived genomic islands have been shown to upregulated during stress (Coleman et al. 2006). Compared to genomic changes, gene regulation may be a more flexible way that bacteria adapt to new thermal conditions

(Cooper et al. 2003, Philippe et al. 2007). It is now feasible to examine the relative importance of allelic and transcriptomic changes in thermal adaptation using next generation sequencing to study both genome-wide point mutations and transcriptomic responses to environmental stressors. Recent studies suggested gene regulation and

coding-sequence evolution are highly connected in some E. coli lineages (Vital et al.

2015), but that expression profiles are more variable within closely-related Shewanella strains than pairwise genome comparison (Konstantinidis et al. 2009). These studies provide a useful framework for understanding the evolution of environmental bacteria in response to strong selection through changes in gene content, alleles, and expression.

Until recent developments in sequencing, assessing genetic variation and gene expression during thermal stress in non-model organisms was limited to a few genes including heat shock proteins, known to be involved in thermal stress responses (Van

Derlinden et al. 2008). More recently, studies have taken advantage of microarray technology and next-generation RNA sequencing (RNA-Seq) to assay stress responsiveness in hundreds to thousands of genes. The response of bacteria to thermal stress is more complex than upregulating heat shock proteins, chaperons or sigma factors (Riehle et al. 2005, Rodrigues et al. 2008, Mereghetti et al. 2008) and thermal adaptation includes the expression of genes related to membrane functions, energy metabolism, and cell communication as well as the employment of regulatory small

RNAs (Gierga et al. 2012, Dall’Agnol et al. 2014). With combined genome and transcriptome data, we can now compare the expression levels of paralogs in gene-rich families, identify mutations among copies of orthologous genes found in different populations, and determine the contribution of the unique and shared genes in responding to temperature stress.

Here, we investigate the genetic mechanisms that underlie thermal specialization of closely-related vibrios isolated from coastal water at the Beaufort Inlet (Beaufort, NC,

USA). This location experiences large seasonal temperature fluctuations (annual range of ~20°C), and a clear seasonal shift in vibrio diversity has been observed (Yung et al.

2015). This previous study suggested that the mechanisms of thermal adaptation apparently differ based on evolutionary timescale: shifts in the temperature of maximal growth occur between deeply branching clades but the shape of the thermal performance curve changes on shorter time scales (Yung et al. 2015). The observed thermal specialization in vibrio populations over relatively short evolutionary time scales indicates that few genes or cellular processes may contribute to the differences in thermal performance between populations. In order to understand the molecular mechanisms that underlie adaptation to local thermal regimes in environmental vibrio populations, we employ genomic and transcriptomic approaches to examine transcriptomic changes that occur within strains grown at their thermal optima and under heat and cold stress. Moreover, we compare two closely-related strains with different laboratory thermal preferences to identify in situ evolutionary responses to different thermal environments in genome content and alleles as well as gene expression.

3.2 Experimental Procedures

3.2.1 Genome sequencing, assembly and annotation

Based on a previous study, two vibrio strains Vibrio sp. PID17_43 (Warm) and

Vibrio sp. PID23_8 (Cool) isolated at Pivers Island Costal Observatory Time series were used in this experiment (Yung et al. 2015). Genomic DNA was extracted from batch culture using a Gentra Puregene Yeast/Bact. Kit (QIAGEN). 500bp insert TruSeq libraries were sequenced on the HiSeq2000 (Illumina) using 125bp paired-end reads at the Duke

Center for Genomic and Computational Biology, Durham, NC, USA. 184,933,012 and

177,635,276 paired-end reads of average length 126bp were generated for Vibrio sp.

PID17_43 and PID23-8 respectively. To remove low-quality nucleotides, indexed reads were cleaned by trimming off 5 bases from the 5′ end and 10 bases from the 3′ end using

FASTX trimmer (http://hannonlab.cshl.edu/fastx_toolkit). The reads were then screened for PhiX contamination using Bowtie2 (Ben Langmead and Salzberg 2012). The pre- processed reads were assembled using SPAdes v3.5.0 with read error correction

(Bankevich et al. 2012). The assemblies were assessed using QUAST v3.0 (Gurevich et al. 2013). A total of 54 contigs >2kbp (4.2 Mbp, 45% G+C content, average 2,363x genome coverage) were obtained for Vibrio sp. PID17_43, and 30 contigs >2kbp were obtained for

Vibrio sp. PID23_8 (4.1 Mbp, 45% G+C content, average 2,342x genome coverage). The assembled genome was annotated using the NCBI Prokaryotic Genome Automatic

Annotation Pipeline (PGAAP) (Tatusova et al. 2013). We then used Orthovenn (Wang et

al. 2015) to identify orthologous genes in the two strains with a minimum 70% amino acid identity. We used reciprocal best BLAST hit between the warm and cool vibrio strains to reconfirm the identification of orthologous genes.

3.2.2 Strains and culture conditions

We examined the global transcription for the two vibrio strains at the optimum temperature (Topt) of each strain and under heat stress and cold stress. Temperatures for heat and cold stress were chosen so that cells exhibited the same growth rate (~0.05 hr-1) to minimize the effect of growth rate on gene expression profiles and mRNA stability.

Triplicate biological replicates were cultured in glucose minimal media (Tibbles and

Rawlings 1994) supplemented with 10 mM ammonium, F/2 trace metals and F/2 vitamins (Guillard, 1975) at three temperature (Vibrio sp. PID17_43: 12.5°C, 32°C, 36.5°C;

Vibrio sp. PID23_8: 12°C, 28°C, 36.5°C). Cultures were incubated at the designated temperature with constant agitation until entering mid-exponential phase. These cultures were then diluted 1:100 in fresh media and incubated at the designated temperature with constant shaking at 120 rpm. Cells were harvesting at OD600=0.26-0.29 during early exponential phase. 10mL of cell culture were concentrated on a 0.2 µm polyethersulfone membrane filter (Pall Supor-200) at low vacuum pressure. Filters were then fixed with RNA Protect Bacteria Reagent mixture (Qiagen) for 5 min and then

immediately flash-frozen in liquid nitrogen. Filters were stored <2 weeks at -80°C until

RNA extraction.

3.2.3 RNA extraction and rRNA depletion

Total RNA was extracted from the filter using an RNeasy Mini kit (QIAGEN) according to the standard QIAGEN RNeasy protocol with on-column RNase-free DNase treatment. RNA samples were further DNase treated using the Turbo DNA-free DNase

Kit (Ambion) according to the manufacturer's 'rigorous' protocol. The DNase-treated

RNA was then checked for DNA contamination by PCR with hsp60 gene as previously described (Hunt et al. 2008b). The RNA integrity was monitored by analysis with a

Bioanalyzer (Agilent Technologies). Only samples with an RNA integrity number ≥9.7 were used in further experiments. Ribosomal RNAs (rRNAs) were selectively removed using a previously-developed subtractive hybridization protocol (Stewart et al. 2010) with modifications. In short, gDNA from each strain was used to generate biotin- labeled antisense RNA (aRNA) probes specific to the 16S and 23S rRNA of each strain.

Probes were added to 1 µg total RNA in an estimated 2:1 molar ratio, and two independent hybridization reactions were performed on each sample. Probe-bound

RNAs and excess probe were removed with three rounds of streptavidin-coated magnetic beads (100 µl each; New England Biolabs). mRNA-enriched samples were subsequently tested for residual rRNA and/or probe contamination using the RNA 6000

Pico Kit (Agilent). Samples with >5% rRNA contamination were not used for downstream processes. mRNA samples were quantified using Qubit’s 2.0 fluorometer with Qubit’s RNA High Sensitivity Assay Kit. A minimum of 10ng of enriched mRNA from each sample was sent Duke Center for Genomic and Computational Biology for library preparation and sequencing.

3.2.4 RNA sequencing and data analysis

TruSeq RNA libraries were sequenced using a HiSeq2000 (Illumina) with read lengths of 50nt. During the preprocessing of the Illumina reads, data cleaning required a minimum quality cutoff of Q30 on the leading and trailing bases, and a 4 nt window with a Q25 running quality threshold (Bolger et al. 2014). Any resulting reads shorter than 40 bases were discarded. After screening for PhiX and rRNA contamination using

Bowtie2 (Ben Langmead and Salzberg 2012), we used Rockhopper v2 (McClure et al.

2013) with the following parameters: allowed mismatch= 0.025, minimum seed length=0.33, minimum expression of UTRs and ncRNAs=1, to identify predicted transcripts not previously annotated as genes; this step should improve the identification of non-coding regions including tRNAs and small RNAs. Rockhopper identified 95 hypothetical transcripts in Vibrio sp. PID17_43 and 65 hypothetical transcripts in Vibrio sp. PID23_8. The cleaned Illumina reads were then aligned against the sequenced draft genomes (Bowtie2 indexed) using Bowtie2, allowing one base pair

mismatch. We then used Bowtie2 to map the processed reads (884,455-14,294,519 reads/sample) onto the annotated draft genome, including the hypothetical transcripts identified by Rockhopper.

3.2.5 Identification and Analysis of Differentially Expressed Genes

Read counts for each gene and hypothetical transcript were determined using

HTSeq (Anders et al. 2014). Differentially expressed genes were identified using DEseq2

(Love et al. 2014), a variance-analysis package that was developed to infer statistically significant differences in gene-expression data from high-throughput sequencing. Three biological replicates were included for each growth condition. For each sample, the number of reads per gene was normalized by DEseq2 based on the total number of aligned reads for that sample. The thresholds were false-detection-rate adjusted p-value

<0.00001 and absolute value of fold-change ≥1.5, i.e. |log2fold-change|≥0.58.

Transcriptome profiles of all genes and orthologous genes were compared between strains and between temperatures within a given of strain using the weighted

Bray-Curtis dissimilarity computed using the ‘ecodist’ R package (Goslee and Urban

2007) and visualized using hierarchical clustering with 1000x bootstraps using ‘pvclust’

R package (Smith and Roberts 2003). The sequence divergence between the strains was measured as the average nucleotide identity (Goris et al. 2007) and the nucleotide identity of orthologous genes was calculated using an reciprocal BLAST comparison

between all the coding sequences of the two strains. Welch’s t-test was performed to examine the linkage between nucleotide identity and differentially expressed genes. To obtain a high-level view of the transcriptional response to thermal stresses, we assigned the genes to KEGG categories using KASS (Moriya et al. 2007) based on the bi- directional best hit assignment method, and we tallied the number of normalized counts assigned to each KEGG metabolic category.

3.3 Results and Discussion

Here, we explore mechanisms of thermal specialization in closely-related vibrios.

These strains were previously isolated from coastal seawater adjacent to the Beaufort

Inlet (Beaufort, NC, USA) as part of the Pivers Island Coastal Observatory (Johnson et al.

2013, Yung et al. 2015). We chose two strains with similar sequences in their 16S rRNA genes and housekeeping genes (hsp60, adk, and mdh) that were isolated at different environmental temperatures and exhibit distinct thermal performance in the lab. In order to understand the evolution of thermal adaptation in closely-related strains, we first sequenced the genomes of the two environmental strains (Vibrio sp. PID17_43 and

Vibrio sp. PID23_8), hereafter designated as the warm (Vibrio sp. PID17_43) and cool strains (Vibrio sp. PID23_8) based on their environmental distributions and laboratory thermal performances (Figure 6). Then we sequenced the transcriptomes of each strain grown under three temperature conditions: heat stress, temperature of optimal growth

and cold stress. These experiments will enable us to link genome content, alleles, and transcription to identify mechanisms of thermal adaptation in strains subject to fluctuating environmental pressures.

3.3.1 Genome and Transcriptome Comparisons

Preliminarily, we compared the draft genomes of the warm and cool strains; they share 3372 genes (including predicted genes from NCBI PGAPP and hypothetical transcripts predicted based on the gene expression data), but each strain still harbors a substantial number of unique genes 361 in the warm strain and 280 in cool strain (Figure

7A). However, as these are draft genomes, we cannot absolutely determine the number

of unique or shared genes. Yet, among the shared genes, the average nucleotide identity

(ANI) (Goris et al. 2007) is 99.5%, reiterating that these ecologically-differentiated strains would be grouped as the same “species” by most metrics. However, since these strains were isolated from the environment, and may diverge ecologically on non-temperature axes, we cannot explicitly link genomic differences to thermal adaptation. Thus, we focus on identifying transcriptomic similarities and differences both within a strain cultured at different temperatures and between strains at a given temperature.

stability of clustering topology by resampling 1000 times. Only bootstrap values ≥80% are shown.

We then examined the global transcriptome at the temperature of optimal growth (Topt) of each strain and under heat stress and cold stress (Table S2). For the temperature stress incubations, the growth rate was matched for both strains at 0.05 h−1

(standard deviation=0.003), to reduce the impact of growth rate on gene expression and transcript stability (Table S2). Among the 3954 (warm strain) and 3862 (cool strain) genes, those that met both the following criteria were considered to be differentially expressed: a fold-change absolute value ≥1.5 and a p-value <0.00001 (Table S3 &

Table S4). Biological replicate samples showed very high reproducibility and clustered together (Average Bray-Curtis dissimilarity among replicates= 0.05, SD=0.02)

(Figure 7B). For these isolates, both shared and unique genes are expressed under different temperature conditions (Figure 7B & C). If only orthologous genes are included in the hierarchal clustering, the gene expression profiles group according to the temperature condition rather than the strain (Figure 7B). In addition, the gene expression profiles at heat stress are more similar to the profiles at Topt rather than the profiles at cold stress (Figure 7B). These findings indicate similar strategies to cope with different thermal environments in shared genes. However, when we consider both orthologous and unique genes, the gene expression profiles cluster according to the strain identity (Figure 7B), suggesting that a substantial fractions of the unique genes are

expressed at different temperature conditions. However, most of the unique genes are only identified as hypothetical, so it is difficult to identify the functional role of these genes.

In addition to overall transcriptomic profiles, we wanted to examine the linkage between gene regulation and sequence divergence as gene regulation is coupled with evolution in E. coli (Vital et al. 2015). However, we do not observe the same clear trend in our closely-related vibrio strains (Figure 8); the coding sequence divergence of differentially expressed genes appears to be slightly higher than that of non- differentially expressed genes, but the differences are not statistically significant except when we compare the cool strain gene expression between heat stress and Topt (Welch’s t-test, p<0.05). As many of these sequences differences are synonymous and the two vibrio strains are highly similar, gene expression changes and novel genes appear to drive thermal adaptation rather than allelic differences between strains.

3.3.2 Temperature-responsive transcriptomes

Environmental bacteria encounter a number of stressors including seasonal temperature changes and nutrient limitation. These stresses will likely increase under climate change scenarios that include increases in temperature, ocean acidity, and stratification. From the transcriptional profiles of the two environmental strains at different thermal conditions, we have identified how environmental vibrio strains respond to heat and cold stress.

Figure 9 Bar charts of the number of differentially expressed genes (DEG) in each functional category (KEGG). Length of the bar represents the number of DEG enriched in the function category. White bars represent upregulated differentially expressed genes, and gray bars represent the downregulated differentially expressed genes. Top panels: Heat stress vs Topt, Middle panels: Cold stress vs Topt, Bottom panels: Hot stress vs Cold stress. Left panels: Warm strain, right panels: Cool strain.

To gain a general picture of the functional profiles of cold stress and heat stress responsive genes identified by DESeq, we first use KEGG (Kanehisa et al. 2016) to group genes into functional groups. Although only one-third of the genes can be assigned to a

KEGG functional group (Figure 9), these KEGG profiles allow an overview of metabolic changes. In general, heat stress upregulates genes involved in membrane transport but downregulates metabolism and translation (Figure 9), consistent with a lower growth rate at higher temperatures. Cold stress also downregulates genes related to translation and membrane transport as well as metabolism of cofactors, vitamins and carbon. Cold stress upregulates genes involved in nucleotide and carbohydrate metabolism (Figure 9).

Since we did not observe discernible differences in the thermal responses between the warm and cool strains at a functional level, we wanted to get a better picture of thermal adaptation by examining specific genes rather than whole pathways.

3.3.3 Heat stress responses compared to Topt

Among the orthologous genes, the cool strain (698 differentially expressed genes) has more differentially expressed genes under heat stress than the warm strain (434 differentially expressed genes) (Figure 10 & Figure 11). Although the average absolute fold change of differentially expressed genes is significantly higher in the warm strain

(|log2fold-change|mean=1.13) than the cool strain (|log2fold-change|mean=1.07)

(Welch’s t-test: p<0.05), the higher absolute fold change of warm strain transcripts is

mainly driven by several highly differentially-expressed genes (|log2fold-change|>5).

But in general, heat stress has a more widespread effect on the transcriptome of the cool strain than that of the warm strain.

Figure 10 Number of differentially expressed (fold-change absolute value ≥1.5 and p-value <0.00001)) shared and unique genes for cool (C) and warm (W) strains for all pair-wise comparisons for three culturing conditions: heat stress, cold stress and Topt.

indicate genes that are only differentially expressed in the cool strain. Green dots indicate genes that are differentially expressed in both strains. Gray dots indicate genes that are not differentially expressed in either strain. Genes unique to each strain are not included.

Heat shock proteins (Hsps) and transcription factors are central components of the vibrio heat stress regulatory network. Under heat stress, both strains overexpressed heat shock proteins including hsp20, hsp70 and hsp90; these chaperones and proteases guide protein folding and prevent the degradation of unfolded proteins and irreversible protein aggregation (Thomas and Baneyx 1998, Nonaka et al. 2006). Additionally, the major molecular chaperones including DnaK and GroE (GroES and GroEL) which protect newly-synthesized or stress-denatured polypeptides from misfolding and aggregation were upregulated. ClpB protease was also induced to degrade the misfolded proteins. However, despite similar fitness (growth rate) under heat stress at

36.5 °C, a number of shared genes were only upregulated in the cool strain including a number of known temperature-associated chaperones and enzymes which degrade misfolded proteins (e.g. HtpX, Hsp33, GrpE, DnaJ, HslU, ATPase and HslV peptidase)

(Schröder et al. 1993, Hung et al. 2001, Seong et al. 2002). Although the warm strain did not upregulate these genes, it highly overexpressed a peptidase (220 fold increase versus

T opt), which is also a protease that can break down misfolded proteins. Overall a number of known temperature-responsive genes are induced in both strains under heat stress but additional shared genes are only significantly upregulated in the cool strain,

potentially indicating a requirement for additional mechanisms to respond to temperature-induced protein misfolding.

In addition to known heat-shock genes, regulation of the stress response by transcriptional regulators and sigma factors has been well documented in several mesophilic bacterial species under thermal stress (Helmann et al. 2001, Guisbert et al.

2008). In both strains, transcriptional regulators including TetR and MerR showed significant expression increases during heat stress. Many members of the TetR regulatory family act as repressors on membrane permeability while a few MerR-like regulators that have been shown to activate suboptimal σ70-dependent transcription

(Brown et al. 2003). The alternative sigma factor #32 generally regulates the stress responses but was not upregulated in this study, suggesting that these vibrios have another regulatory pathway for the heat stress and or respond differently to steady-state high temperature compared to a rapid heat shock. In this study, genes encoding #70 and

#38 and the anti sigma E factor were upregulated only in the warm strain. #70 is responsible for transcription in exponential growth (Paget and Helmann 2003, Saecker et al. 2011) while #38 is known to be a general stress response regulator (Yildiz and

Schoolnik 1998, Battesti et al. 2011). The anti sigma E factor blocks #E from acting, suggesting that #E, which is a key cell envelope stress response regulator (Haines-

Menges et al. 2014), does not play an important role in transcriptional regulation in response to heat stress in the warm strain. However, the cool strain did not

differentially express any of the sigma factors compared to growth under optimal conditions, suggesting a less coordinated transcriptional response to heat stress. The differences in sigma factor expression in the two strains may reflect adaptation of these organisms to the thermal variability of their natural habitat.

We also found that several transporters were upregulated under high temperatures in both strains. A zinc ABC transporter permease was highly upregulated

(~64 fold versus T opt) in the warm strain along with the adjacent ABC transporter ATP- binding protein and ABC transporter substrate-binding protein. Additional zinc may be required to produce proteases for the degradation of misfolded proteins or this transporter may be temperature sensitive. A nickel transporter and its two adjacent hypothetical proteins (potentially being the ABC transporter ATP-binding protein and

ABC transporter substrate-binding protein) are also highly upregulated (~128 fold versus T opt). Nickel-containing enzymes are involved important metabolic processes, including the production and consumption of molecular hydrogen, hydrolysis of urea and detoxification of superoxide anion radicals (Eitinger and Mandrand-Berthelot 2000).

In this case, the nickel transporter might be involved in free-radical deactivation as catalase was upregulated. In the transport realm, we also noted that genes comprising the Major Facilitator Superfamily (MFS) of transporters family were upregulated. As

MFS proteins catalyze transport of a wide range of substrates in both directions across the membrane, we could not tell identity which specific substrate was involved.

The gene expression profiles cluster according to the strain identity (Figure 7B) when we consider both orthologous and unique genes, suggesting that novel genes are also important in thermal adaptation. In fact, a significant portion of the novel genes were differentially expressed under temperature stress (Figure 10 & Figure 12). The cool strain (64 out of 215 total) exhibited more differentially expressed novel genes than the warm strain (26 out of 266 total). The warm strain highly expressed its unique copy of catalase but the superoxide dismutase and peroxidase were not upregulated, which suggests that the main sources of free radicals was hydrogen peroxide. The warm strain also overexpressed ViuB, which is a cytoplasmic protein necessary for utilization of the siderophore vibriobactin (Butterton and Calderwood 1994), but we did not identify other genes that are related to iron acquisition in the draft genome. As discussed previously, most of the unique differentially-expressed genes are only identified as hypothetical proteins (

Table S4), even though they may play important roles in thermal adaptation.

Figure 12 Volcano plots showing differentially expressed genes (fold-change absolute value ≥1.5 and p-value <0.00001) under thermal stress. The negative log10

transformed p-values test the null hypothesis of no difference in expression levels between different temperature conditions and are plotted against the average log2 fold changes in expression (x-axis). Data for genes that were not classified as differentially expressed are plotted in black. We plotted data for genes that are differentially expressed between different temperature treatment (False-detection-rate adjusted p-value < 1x10-5) and absolute log2fold change (|Log2fold change|) ≥0.58). Blue color represents the differentially-expressed shared genes. Green color represents the differentially-expressed unique genes.

3.3.4 Cold stress responses compared to Topt

The classical cold-stress response consists of a number of adaptive changes ranging from alterations in membrane composition to global changes in gene expression pattern. In our gene expression analysis, both strains upregulated cold-shock proteins at low temperatures; these proteins possess RNA chaperone activity and destabilize cold- induced mRNA secondary structures thereby facilitating efficient translation (Phadtare

2004). In addition, two genes encoding DEAD/DEAH box helicase were upregulated in both strains, which contribute to cold tolerance as RNA helicases, chaperones and resolve secondary RNA structures, enabling efficient translation and later degradation

(Jarmoskaite and Russell 2011). To maintain efficient transcription, translation and regulation at low temperatures, both strains also expressed ribosome modulation factor to reduce rRNA degradation (Yamagishi et al. 1993). Additionally, both strains also highly expressed the universal stress proteins including UspA, UspB and UspE as well as recombinase which is believed to control the expression of UspA and UspE (Kvint et al. 2003). UspA enhances the rate of cell survival during prolonged exposure to different

stresses, suggesting that it asserts a general “stress endurance” activity (Kvint et al.

2003). Thus both strains respond similarly to cold-stress induction at low temperatures.

Cold temperatures reduce membrane fluidity and permeability (Los et al. 2013).

There are a variety of mechanisms that can alter membrane lipid composition to maintain membrane homeoviscosity in response to temperature change (Thieringer et al.

1998). The conversion of saturated fatty acids into unsaturated fatty acids by desaturases enzymes is one of these pathways (Ray 2006). In our gene expression analyzes, we observed the increased expression of sterol desaturase and cyclopropane- fatty-acyl-phospholipid synthase that might be involved in the unsaturation process.

Low temperature also induced the expression of stress sigma factor RpoS, which has been shown to regulate enzymes involved in the conditional modification of membrane lipid bilayers of cold-adapted cells (Vidovic et al. 2011) as well as regulate proteins involved in the central metabolic pathways under the cold stress in E. coli (Vidovic et al.

2011). We find that RpoS was also induced under heat stress comparing to Topt.

However, the expression of RpoS is four-fold higher under cold stress than heat stress, which suggests that RpoS is particularly important at cooler temperatures. In our study, the significant induction of genes encoding acetyl-CoA acetyltransferase and enoyl-CoA hydratase, which both play key roles in fatty acid synthesis, suggests that altering the fatty acid composition in the membrane is a strategy that vibrios employ to maintain optimal membrane fluidity at low temperatures (Los et al. 2013). Additionally, it has

been demonstrated that during cell envelope biogenesis, there is an increase in outer membrane lipoproteins, which increase connections with the cell wall (Yang et al. 2009).

In our analysis, two genes encoding lipoproteins and a gene encoding a membrane protein were induced in both strains, which may be related to outer membrane synthesis. Our results suggest that membrane component synthesis is activated in the cold conditions, and these changes are likely related to cell envelope remodeling to adapt to low temperatures.

Membrane damage at low temperature leads to the production of reactive oxygen species (ROS), which cause oxidative stress. Additionally, when cells are exposed to low temperature, the rate of enzymatic reactions also declines, leading to a decreased demand for ATP and thus the accumulation of electrons in the respiratory chain (Chattopadhyay 2002). Both situations promote an increase in the production of

ROS, which can damage lipids, proteins, and DNA. The accumulation of reactive oxygen species (ROS) such as superoxide which can be scavenged by superoxide dismutase, peroxidase, and catalase. In both strains, superoxide dismutase and peroxidase are highly expressed. However, the warm strain upregulated its unique copy of catalase more than 16 fold. In an additional indication of oxidation under cold conditions, intracellular level of transcripts that encode thioredoxin reductase, another antioxidant enzyme, were elevated. While psychrophiles have been shown to produce more free radicals at low temperatures (Chattopadhyay et al. 2011), our study suggests

that vibrios also need to counteract oxidative stress due to superoxide during growth at low temperatures.

Although both strains share many of the same strategies to deal with cold, since the cool strain starts to grow faster than the warm strain below 12°C (Yung et al. 2015), we expect that the two strains will have distinct strategies to cope with this stress. From the gene expression data, the warm strain seems to be much more stressed than the cool strain at low temperature. Among the orthologous genes, the warm strain (1407 differentially expressed genes) has differentially more genes than the cool strain (1038 differentially expressed genes) (Figure 10 & Figure 11). In addition, the average absolute fold change of differentially expressed genes is also significantly higher in warm strain

(|log2fold-change|=1.58) than cool strain (|log2fold-change |=1.45) (Welch’s t-test: p<0.001). For example, both strains have five cold shock protein (100% nucleotide identity); the warm strain highly expresses all of them but the cool strain only upregulates three of them. Thus survival under cool temperatures might exert additional metabolic costs in the warm strain. Despite the fact that the warm strain has more differentially expressed genes, there are also genes that are only expressed in the cool strain. For example, only the cool strain upregulates the expression of acyl-CoA desaturase which decreases saturation membrane-lipid fatty acids (Aguilar et al. 1998) to restore membrane fluidity at lower temperatures (Casanueva et al. 2010). This is

potentially one of the main features allowing the cool strain to outperform the warm strain at low temperatures.

Interestingly, the warm strain also highly upregulated genes encoding peptidase

(16 fold versus Topt, zinc transporter (nine fold versus Topt) as well as a nickel transporter

(15 fold versus Topt) under cold stress. Although the fold change of the expression was not as high as that under heat stress, upregulation of these genes suggests that they maybe involved in a more general stress response rather than a temperature-specific response. Moreover, there is high upregulation of a formate transporter (256 fold versus

Topt) as well as genes encoding formate dehydrogenase in the warm strain at cold temperatures. However, pyruvate formate lysase and the pyruvate formate lyase- activating enzyme were not significantly upregulated so the formate is likely not a byproduct of pyruvate metabolism. Although we cannot identify the cause of increased expression of formate transporter in this data set, formate can inhibit expression of acetate-induced proteins including the RpoS regulon (Kirkpatrick et al. 2001). The high expression of formate transporter might increase the metabolic cost of the warm strain and thus compromise the its growth rate at cold temperature.

The cool strain also upregulates the expression of non-coding RNAs including

CsrB and 6S under cold stress, while we did not find CsrB or 6S in the warm strain draft genome. The CsrB family is widespread among bacteria and the CsrB family members has a strong regulatory effect on carbon metabolism, the production of extracellular

products, cell motility, biofilm formation and quorum sensing (Babitzke and Romeo

2007). While 6S appears to interfering with the formation of transcription initiation complexes through the interaction with RNA polymerase holoenzyme containing the housekeeping sigma (Trotochaud and Wassarman 2004, 2006, Gildehaus et al. 2007).

Thus the regulation of non-coding RNA might be other features that differentiate the thermal performance of the warm strain and cool strain at low temperature.

3.3.5 Similarity between stresses

Although heat and cold stress are on the two ends of the thermal stress spectrum, both stresses trigger some similar responses. The Hsp20 and phage-shock proteins (psp) gene exhibited general stress expression response patterns. The psp system has been proposed to perceive membrane stress, signal to the transcription apparatus and then use an ATP-hydrolyzing transcription activator to produce effector proteins to manage the impacts of agents impairing cell membrane function. Other general stress- responsive genes including FAD-dependent oxidoreductase and alanine transporter may help to deal with oxidative stress due to the increase of free radicals under stress.

Cysteine desulfhydrase was found to be induced by all thermal stresses, which might indicate a higher production of H2S from cysteine. H2S further increases the formation of

H2O2 and other ROS (Truong et al. 2008), and thus, the upregulation of cysteine desulfhydrase is another indication that the vibrios experience oxidative stress coupled

with thermal stresses. We also find that gene encoding γ-Glutamylputrescine

Synthetase (PuuA) was upregulated in both strains at both temperature stresses, particularly under cold stress. An elevated concentration of putrescine in E. coli has been observed under various stress conditions (Tkachenko et al. 2001). Putrescine is important for DNA, RNA, and protein synthesis as well as in stabilizing membrane structures (Tabor and Tabor 1985) but high concentrations of putrescine can lead to the inhibition of cell growth and protein synthesis; upregulation of PuuA converts the excess putrescine to a nutrient source by the Puu pathway (Kurihara et al. 2008). Thus the general stress responses common to both temperatures include phage shock protein system, cell membrane repairing, and oxidative response.

3.4 Conclusions

Temperature is a critical parameter determining organismal distributions in diverse environments, the combined approaches used in the present study allowed us to verify that the molecular mechanisms of thermal adaptation primarily manifested as changes in gene expression and differences in gene content in closely-related strains.

Upregulation of genes encoding heat shock proteins, transcription factors, sigma factors and metal transporters are important for the vibrio strains to cope with heat stress. On the other hand, cold stress induced the expression of genes encoding cold shock proteins, membrane proteins, and universal stress proteins. Both stresses induce genes

encoding phage shock proteins and genes related to oxidative stress. Our results show that the two closely related environmental vibrios share strategies to adapt to temperature changes, but they also harbor different novel genes as well as different mechanisms of gene regulation in response to thermal stresses.

3.5 Data Deposition

Draft genome assemblies were deposited in the NCBI whole genome shotgun archive under project ID PRJNA292554. Expression sequence data will be deposited in the NCBI Gene Expression Omnibus.

4. Diverse and temporally-variable particle-associated microbial communities are insensitive to bulk seawater variables

This chapter has been published as:

Yung, C. M., Ward, C. S., Davis, K. M., Johnson, Z. I., & Hunt, D. E. (2016).

Diverse and temporally-variable particle-associated microbial communities are

insensitive to bulk seawater environmental parameters. Applied and

Environmental Microbiology, AEM-00395.

Author’s contributions:

DEH and CY conceived the study, CY, KMD and CSW collected field data and samples;

CY undertook molecular bench work; DEH and CY drafted the manuscript. All authors have read and approved the final manuscript.

4.1 Introduction

There is a growing interest in how microscale features including living phytoplankton cells, phytoplankton exudate, and detrital particles affect bacterial diversity and activity (Gerdts et al. 2013, Yawata et al. 2014, Durham et al. 2015).

Among these microenvironments, particles are relatively well-studied as they can be

readily physically separated from free-living bacteria using filtration. Thus, particles have been identified as an important habitat for estuarine and coastal bacteria, with up to 60% of water-column bacterial cells attached to particles (Crump et al. 1998, Garneau et al. 2009), likely due to nutrients levels up to three orders of magnitude higher than those found in bulk seawater (Blackburn and Fenchel 1999). These high-nutrient microenvironments allow elevated bacterial production and organic matter degradation in a low bulk nutrient background (Azam 1998, Stocker et al. 2008, Stocker 2012).

Moreover, particles with different compositions, e.g. living zooplankton versus exopolysaccharides, may present a diverse range of microenvironments, all of which are distinct from the conditions experienced by free-living cells.

Although free-living and particle-associated bacteria are defined operationally based on filtration properties, these two populations have physiological, genomic, and phylogenetic differences (Karner and Herndl 1992, Delong et al. 1993, Lauro et al. 2009).

Particle-attached bacteria are more metabolically active with higher levels of extracellular enzymes, adhesion proteins and antagonistic compounds (Karner and

Herndl 1992, Smith et al. 1992, Long and Azam 2001, Grossart et al. 2007). Likely to take advantage of compositionally-varied patches of organic matter, particle-associated bacteria generally have larger genomes with a variety of metabolic and regulatory capabilities (Yawata et al. 2014). In contrast, free-living bacteria usually have smaller genomes with streamlined metabolic and regulatory functions that enable efficient

growth in oligotrophic conditions (Polz et al. 2006). Due to this physiological, genomic, and phylogenetic specialization, we might expect differences in particle-attached and free-living bacterial communities.

However, whether particles harbor a distinct microbial community is still controversial, as some studies report a high degree of microenvironmental specialization

(Delong et al. 1993, Acinas et al. 1999, Moeseneder et al. 2001, Allgaier et al. 2007, Smith et al. 2013), while others observe significant overlap between the free-living and attached bacterial communities (Hollibaugh et al. 2000, Ghiglione et al. 2007, Crespo et al. 2013).

Taxa found in both environments could be generalists, but potentially exhibit fine-scale phylogenetic niche partitioning that cannot be resolved at the 16S rRNA gene level

(Hunt et al. 2013). However, associations with specific microenvironments exist in a background of seasonally and episodically changing environmental conditions that also select for specific bacterial communities (Gilbert et al. 2012, Cram et al. 2015, El-Swais et al. 2015). Compared to free-living bacteria, attachment and the high nutrient environment of particles may help to buffer the impacts of changes in bulk seawater environmental parameters like temperature or dissolved nutrients (Huq et al. 1984, Reidl and Klose 2002), potentially resulting in more uniformity in the particle-attached microbial community.

In order to examine variation in particle-attached and free-living marine bacteria over an annual cycle, we sampled microbial communities at the Pivers Island Coastal

Observatory (PICO), a temperate, coastal time-series with strong annual patterns in environmental parameters (Johnson et al. 2013). We measured a suite of environmental variables and collected three sizes of particles as well as free-living bacteria for 14 months. We then assessed the microbial communities in these samples using 16S rRNA gene fragment libraries. This repeated sampling enables us to link bacterial communities and environmental variables to investigate drivers of the observed diversity patterns.

4.2 Experimental Procedures

4.2.1 Sample collection

Water samples were collected as part of the Pivers Island Coastal Observatory

(PICO) time-series adjacent to the Beaufort Inlet, Beaufort, NC, USA (34°71.81’N,

76°67.08’W). Sampling for size-fractionated water was performed monthly from July 3,

2013 to August 6, 2014. Time-series measurements were conducted as previously using water collected with a Niskin bottle centered at 1 m (Johnson et al. 2013, Yung et al.

2015). Methods for determination of water temperature, pH, salinity, dissolved inorganic nutrient concentrations, chlorophyll a concentration, as well as bacterioplankton and phytoplankton abundances were described previously (Johnson et al. 2013, Hunt et al. 2013). All field measurements are replicate samples. The particle size distribution (1-30 µm) was measured using a Coulter Counter (Beckman Coulter

MultiSizer III). To characterize particle-associated and free-living microbial communities, the large-particle-associated (>63 µm), small-particle-associated (63-5 µm), ambiguous (5-1µm) and free-living bacteria (<1 µm) were separated by sequential gravity filtration using a 63 µm plankton net followed by 5 µm (Advantec MFS), 1 µm

(GE Water & Process Technologies), and 0.2 µm (Pall Supor-200) filters. Large particles collected on the 63 µm plankton net from 10 liters of seawater were resuspended in autoclaved artificial seawater and concentrated on 0.22-µm filters. Filtered volumes were chosen such that the filtration rate did not visibly slow during collection and filters were rinsed with autoclaved artificial seawater to remove additional material smaller than the filter pore size. All filters were stored at -80°C until DNA extraction.

4.2.2 DNA extraction and library preparation

We extracted DNA using the Puregene Yeast/Bacteria Kit (Qiagen) according to the manufacturer’s instructions supplemented with three rounds of bead beating (60 seconds each). Microbial communities were characterized using a dual index sequencing approach (Kozich et al. 2013) with the following portions of the primers targeting the V3-V4 region of the bacterial and archaeal 16S rRNA gene: 16S F V3:

CCTACGGGNGGCWSCAG and 16S R V4: GGACTACNVGGGTWTCTAAT (Hugoni et al. 2013). PCR reactions contained 0.4 U of Q5 DNA polymerase (NEB) as well as a final concentration of 200 µM dNTPs, 2 mM MgCl2, and 0.5 µM primers. PCR reactions were

thermocycled using the following protocol: 98°C for 30 sec, and 35 cycles at 98°C for 10 sec, 55°C for 30 sec and 72°C for 30 sec, with a final extension at 72°C for 2 min.

Triplicate reactions per sample were pooled and gel-purified. Paired-end 250 bp sequencing of barcoded amplicons was performed on an Illumina MiSeq running v2 chemistry at the Duke Center for Genomic and Computational Biology.

4.2.3 Sequence processing

We used USEARCH v7 to quality control sequences, merge paired-end reads and then performed OTU clustering using the UPARSE algorithm (Edgar 2013). We first trimmed low-quality bases from the sequences using a 10 nt window with a Q30 running quality threshold. Paired-end sequences with a ≥10 nt overlap and no mismatches were then merged. We performed a final filtering step to discard low- quality merged sequences with a length <400 bp and/or maximum expected error >1, which resulted in 2,358,138 reads. Merged sequences were clustered into OTUs of 98.5% pairwise identity to the centroid, resulting in OTUs in which all members are at least

97% similar. Taxonomies were assigned to the most abundant sequence in each OTU with a confidence threshold score of 0.8 using RDP Classifier (Wang et al. 2007). Rare

OTUs (< 5 total observations) and OTUs matching mitochondrial or plastid sequences based on RDP Classifier assignment were excluded, resulting in 1,479,742 total sequences and 12,373 OTUs. Libraries contained between 10,941 and 42,618 sequences;

to control for uneven sequencing effort, we normalized data by rarefaction to 10,941 sequences per sample.

4.2.4 Microbial community analysis

For each sample, all alpha diversity calculations and error estimates were averaged over five rarefactions, including estimates of Shannon diversity using the

‘vegan’ package in R (Oksanen et al. 2012). Community composition was compared between each pair of samples using the weighted Bray-Curtis dissimilarity in MacQIIME

1.8.0 (Caporaso et al. 2010) and visualized using non-metric multidimensional scaling

(NMDS) ordination using the ‘ggplot2’ R package. Analysis of similarity (ANOSIM) was used to compare the dissimilarity between the bacterial communities from each size fraction (Clarke 1993). Variance partitioning analysis (VPA) assessed the contributions of environmental variables (projected daily insolation, chlorophyll a, turbidity, salinity, temperature, silicate, ammonia, phosphate, and pH) and microhabitats (>63 µm, 63-5

µm, 5-1 µm and <1 µm) to the microbial community variability (Oksanen et al. 2012).

CCA (Canonical Correspondence Analysis) was then used to identify the environmental factors associated with community composition changes (Braak and Verdonschot 1995).

Prior to the CCA, the ‘step’ function in vegan (Oksanen et al. 2012) was used to automatically select constraints in the model using forward stepwise selection using

Akaike's information criterion with 999 permutation tests at each step. We repeated the forward stepwise selection with the bacterial community from each size fraction. As no

terms were statistically significant in the stepwise selection for the >63 µm and 63-5 µm communities, we applied the variable set for the <1 µm fraction (temperature, projected no-sky daily insolation, silicate, pH and particle number) to all four size fractions to using the ‘cca’ function in vegan (Oksanen et al. 2012). To evaluate the significance of the canonical axes, we used a Monte Carlo test with 999 permutations. Statistical significance of the individual environmental variables was assessed using the marginal effect of the terms. We then used the R package 'indicspecies' to determine the strength of the association between taxa and the different size fractions (De Caceres and

Legendre 2009). To focus on key indicator taxa, OTUs were identified that are significantly associated with a particular size fraction (P<0.01) and have an average relative abundance of at least 0.05% across all samples in that size fraction.

4.3 Results and Discussion

Here, we examine the particle-associated and free-living microbial communities from coastal seawater as part of the Pivers Island Coastal Observatory (PICO) time- series. We sampled monthly for over one year (July 2013-August 2014) in order to understand the temporal dynamics of particulate and free-living bacterial communities in relation to environmental variables. We sequentially filtered seawater into fractions which roughly correspond to zooplankton, large particles and debris (>63 µm); particles and phytoplankton (63-5 µm); small particles and large/dividing cells (5-1 µm); and free-

living bacteria (<1 µm) (Hunt et al. 2008b). Although the 5-1 µm size fraction is somewhat ambiguous, it serves to clearly separate truly particle attached (>5 µm) and free-living bacteria. To identify the microbial communities present, we sequenced the

V3-V4 region of the 16S rRNA gene and clustered OTUs at 97% similarity using

UPARSE (Edgar 2013) and rarefied each library to 10,941 reads using QIIME (Caporaso et al. 2010).

Figure 13 Plot of the Shannon’s diversity for 16S rRNA gene libraries from different seawater size fractions (>63 µm; 63-5 µm; 5-1 µm and <1 µm) for 14 monthly samples from the Pivers Island Coastal Observatory (PICO) time series (July 2013- August 2014). Points represent the mean value and error bars one standard deviation of Shannon’s diversity computed based on five replicates.

Over the 14 months of sampling, Shannon’s diversity (H’) was generally highest in the two largest size fractions (>63 and 63-5 µm; Figure 13). Unlike in smaller size fractions (5-1 µm and <1 µm), rarefaction curves do not show saturation in these larger size fractions, suggesting that we did not capture the full diversity of particle-attached communities (Figure S1). Nevertheless, the two particulate size fractions (>63 µm and

63-5 µm) were consistently more diverse than the free-living bacterial community (P <

0.05; ANOVA, Figure 13). The >63 µm (mean H’= 5.92, SD=1.04) and 63-5 µm (mean H’=

6.23, SD=0.35) bacterial communities are also more diverse than whole seawater bacterial communities from three years of weekly sampling at this study site (mean

H’=4.97, SD=0.41; Ward et al, unpublished data). A large portion of bacterial taxa observed in the >63 µm (45.5% of OTUs; 29.9% of sequences) and 63-5 µm (34.0% of

OTUs; 7.9% of sequences) fractions were never detected in the weekly time series. While

85.3% of OTUs and 99.4% of sequences observed in the <1 µm fraction are present in the weekly samples. Thus even long-term sampling may not capture the full diversity of relatively rare particle-associated microbes.

Comparing the bacterial communities over time using non-metric multidimensional scaling (NMDS), the ordination plot shows that samples within each size fraction cluster (Figure 14A); and all size fractions are statistically distinct (Table S1).

Thus, although these size divisions are operational, they harbor distinct microbial communities. As in many coastal sites, the alpha Proteobacteria, gamma Proteobacteria, and Bacteriodetes are the major phyla in all fractions, representing at least 34.8% of total reads in each library (Figure 14B). The free-living communities are dominated by alpha

Proteobacteria (23.8%-55.2% of libraries; Fig 2B), including Rhodobacterales (6.9%-

17.4%) and SAR11 (4.1%-36.2%). While the Proteobacteria in the >63 µm and 63-5 µm

fractions are largely gamma and delta Proteobacteria. Thus despite some overall similarities, marine microenvironments do contain distinct microbial communities.

As noted previously, the temporal variability in both the diversity and composition of the largest size fraction (>63 µm) is much higher than of other microenvironments (Figure 13 and Figure 14); and the within-fraction Bray-Curtis dissimilarity is significantly larger than observed in other size fractions (P<0.001, two- tailed t test). This increased variability might be due to changes in particle abundance or composition (Figure S6) (Karner and Herndl 1992, Bižić-Ionescu et al. 2015). However, we did not find a correlation between the number of particles (1-30 µm) and the community composition, suggesting that particle composition or origin may be more important than abundance. We also note that the 10 L volumes sampled for the >63 µm libraries are much larger than those collected for other size fractions (20-50 mL); thus the

>63 µm libraries integrate over a number of discrete particles and different particle types. Although the other size fractions were sampled at much lower volumes, smaller particles and free-living bacteria are likely more spatially homogeneous, and thus smaller volumes may adequately capture the diversity present in these size fractions

(Padilla et al. 2015). Therefore even though particles within a single sampling point likely represent a broad spectrum of habitats, their microbial communities are also more variable in time than free-living bacterial communities.

Even with high month-to-month variability, the >63 µm fraction exhibited notable outliers in May and July 2014 when the bacterial community composition diverged on the NDMS (indicated with asterisks on Figure 14) and also decreased in

Shannon’s diversity (Figure 13). These time periods did not correspond to any unusual disturbance as measured by our time-series or local weather stations. In fact a large storm in August 2014 which produced high particle loads, decreased salinity, and increased nutrient concentrations did not result in a notably distinct community (Figure

14; Table S2). This lack of particulate community response to a disturbance like a storm, suggests that these divergent communities likely represent changes in particle types

(including organisms) rather than the resuspension of particles from the benthos or export from land. In examining the bacterial communities of these outliers, the most abundant OTU in the May 2014 > 63 µm sample belongs to the genus Staphylococcus

(10% of total sequence reads). This genus is best known for containing human pathogens, but has also been found on copepods (Gerdts et al. 2013). In contrast, the

July 2014 sample contains a dominant OTU from the order Thiohalorhabdales which comprises 23% of the total reads; this group is common on marine particles (Padilla et al.

2015, Ganesh et al. 2015) and has been found in association with sponges (de Voogd et al. 2015), suggesting a potential association with a eukaryotic host. As many families present in the >63 µm fraction were previously detected in the copepod microbiome, including Vibrionaceae, Alteromonadaceae, and Staphylococcaceae (Gerdts et al. 2013), associations with specific copepods or other marine organisms, that vary in their prevalence, could explain the observed patterns. In this system, the associations with specific particle types, likely eukaryotic hosts, rather than with particles with distinct

geographic origins may explain some dramatic changes in the particle-associated bacterial community. However, as microbial community changes are often linked to environmental variables like chlorophyll, temperature etc., we wanted assess the potential of these variables to influence the community composition in different size fractions.

represented as circles; the gradient color of the circles indicates time. Environmental variables marked with asterisks are statistically significant when assessed by marginal effect of the terms (P<0.05). (A) >63 µm; (B) 63-5 µm; (C) 5-1 µm; (D) <1 µm

Although bacterial communities cluster by size fraction (Figure 14A), the microbial community within a given size fraction may respond to environmental variables such as light, temperature and nutrients (Fuhrman et al. 2006, Gilbert et al.

2012, El-Swais et al. 2015). However, across all samples, variance partitioning indicates that microhabitat (50.4% of variation explained) plays a more important role than environmental factors (11.9% of variation explained) in structuring size-fractionated bacterial communities. While measured seawater environmental factors do not have high explanatory power for the variability in size- fractionated bacterial communities, we postulate that free-living bacteria are likely more responsive to bulk-water properties than particle-attached bacteria, due to their exposure to and presumable reliance on these resources. Indeed, much of the microbial community variation in free-living bacteria was explained in the first two canonical correspondence analysis (CCA) axes

(47.8%; Figure 15A); but these CCA axes explain less variability as the size fraction increases (Figure 15B-D). For the <1 µm and 5-1 µm fractions, temperature is the strongest environmental driver of bacterial composition (marginal effect of the terms;

P<0.01). Yet, none of the measured environmental variables was statistically significant for the >63 µm and 63-5 µm fractions. Previous studies have shown that surface

attachment and nutrients may protect bacteria from environmental variability, including temperature extremes (Amako et al. 1987, Wiebe et al. 1992, Crump et al. 1998). Thus, particle-associated communities may be protected from some environmental conditions or respond to factors not measured here including physical substrates, refuges from predation, higher nutrient levels, or unique particulate chemical constituents/conditions not found in the water column. Since the particle-attached bacterial community exhibits high temporal variability despite weakened sensitivity to bulk water environmental parameters, we hypothesize that community composition is most influenced by temporal changes in particle types.

As particle-associated and free-living bacteria communities appear to differentially respond to bulk seawater changes, we wanted to better understand

similarities and differences in the microbial community composition in these habitats.

Figure 16 highlights size fraction-based OTU specialization; across our entire time series, adjacent size fractions have more OTUs in common (28-40%) than non-adjacent fractions

(14%-21%). In addition, when comparing the particle-associated and free-living bacterial fractions, the number of OTUs exclusive to a specific fraction was higher in >63

µm (78%) and 63-5 µm (75%) fractions than in the <1 µm fraction (10-11%; Figure 16).

Thus free-living bacteria are often found in larger size fractions but, particulate-OTUs are not generally found in the smallest size fraction. In a previous study, a large overlap between particle-attached and free-living bacteria was observed: 25% of OTUs comprising 94% of sequence reads were found in both particle-attached and free-living size fractions (Crespo et al. 2013). We similarly observed a higher percentage of sequence reads than OTUs were shared across different size fractions, indicating that the most abundant taxa are shared across different size fractions (Figure 16). OTUs found in multiple size fractions can be explained by several mechanisms: bacterial taxa can live in several habitats or habitats that span multiple size fractions, closely-related taxa can exhibit sub-OTU-level habitat specialization, or sampling artifacts may introduce cells into other size fractions (Hunt et al. 2008b, Yawata et al. 2014, Yung et al. 2015). Our data here, with limited OTU overlap between fractions, suggests that particle-associated bacteria are often microenvironmental specialists but that abundant taxa are often shared between different size fractions. We then identified indicative taxa using

associations between the relative abundance of an OTU and a particular size fraction

(Table S3). Consistent with other studies, many of the indicative OTUs in the free-living fraction (<1 µm) belong to Pelagibacter (SAR11), a group of common, small, free-living bacteria. In contrast, the 63-5µm fraction contains indicative OTUs belonging to the

Planctomycetes, Bacteroidetes and Verrucomicrobia. While, many of the indicative phylotypes in >63 µm size fraction belong to the anoxygenic photosynthetic

Chromatiales and sulfate-reducing Desulfobacterales. The presence of these bacteria, which thrive in reduced environments, suggests the existence of anoxic microzones in larger particles and zooplankton digestive tracts (Alldredge and Cohen 1987). Thus although there is some overlap between the taxa present in each size fraction and changes over time, there are specific taxa which define each size fraction over a seasonal cycle that may offer clues to the lifestyles of taxa in these marine microenvironments.

4.4 Conclusions

Our sequential filtration approach reveals that over an annual cycle, particle- attached bacteria are on average both more diverse and more variable over time than free-living bacteria. However, particle-attached bacterial dynamics are not associated with commonly measured environmental variables, suggesting there are a number of unknown drivers of particle-attached bacterial diversity, potentially including associations with metazoans or other microbes and/or other differences in particle

composition (Dana E Hunt 2015). The high bacterial community variability on large particles (>63 µm) along with taxa that are associated with anoxic conditions and/ or eukaryotes suggests that the specific particulate substrate is important (Preheim et al.

2011, Bižić-Ionescu et al. 2015). Due to the important role of particle-attached bacteria in biogeochemical cycling of carbon and other nutrients, it is critical to understand the dynamics and environmental dependence of bacteria associated with these water- column microenvironments.

4.5 Acknowledgements

We would like to thank the entire PICO team for help with sampling. We would like to thank Patricia Tester and Rance Hardison, for use of and assistance with the

Coulter Counter.

4.6 Data Deposition

Illumina sequencing data was deposited in the NCBI Sequence Read Archive under BioProject Number SRP068349. Environmental metadata is available as part of the

Pivers Island Coastal Observatory (PICO) database through BCO-DMO project # 2281.

5. Conclusions

This dissertation explores potential mechanisms of thermal adaptation in environmental bacteria through a combination of strain-level studies, growth rate experiments, genomics, transcriptomics, and fine-scale spatial community studies. This research offers a useful framework for predicting how marine bacteria respond to different thermal environments seasonally and spatially, including changes in species abundance, and in the genome as gene content, allelic sequences, or transcript expression level. Ultimately, this work will inform predictions about potential microbial responses to climate change.

By incorporating both time series studies and laboratory-based experiments, I demonstrate that vibrio strains isolated at our time series were phylogenetically and physiologically characterized revealing three dominant thermal groups within the vibrio: mesophiles, psychrophiles and broad thermal range clades. Even within a single genus of bacteria (Vibrio), we identified a number of distinct temperature-related environmental distributions. Moreover, the temperatures where these strains were observed in the environment were reflected in their laboratory growth rates; for example, the optimal growth temperature is 16°C higher for a mesophilic clade than a psychrophilic clade. We further studied a clade found over almost the entire yearly temperature range (broad thermal range clade). Within this clade, all strains have

similar optimal growth temperatures but also exhibit temperature-related tradeoffs with faster growth rates for strains isolated from warm waters and broader growth ranges for strains isolated from cooler water temperatures. Additionally, the mechanisms of thermal adaptation apparently differ based on evolutionary time scales: shifts in the temperature of maximal growth occur between deeply branching clades but thermal performance curve shape changes on shorter time scales. Thus, apparently ubiquitous clades are likely not generalists, but contain subclusters with distinct environmental preferences.

The observed thermal specialization in vibrio populations over relatively short evolutionary time scales indicates that few genes or cellular processes may contribute to the differences in thermal performance between populations. In order to understand the molecular mechanisms that underlie adaptation to local thermal regimes in environmental vibrio populations, we employ genomic and transcriptomic approaches to examine transcriptomic changes that occur within strains grown at their thermal optima and under heat and cold stress. Here, I compare two closely-related strains with different laboratory thermal performances to identify in situ evolutionary responses to different thermal environments in genome content and alleles as well as gene expression. Comparative genome analysis shows that the two strains have highly similar genomes. Their orthologous genes have more than 99.5% average nucleotide identity but they still have a number of unique genes. Both strains highly express stress-

related genes and membrane-related genes including heat shock proteins, transcription factors and sigma factors under heat stress, and cold shock proteins, membrane proteins, and universal stress proteins under cold stress. A significant portion of the novel genes were differentially expressed under temperature stress, suggesting they play an important in the differential thermal performance between the two strains. Our results show that the two closely-related environmental vibrios share strategies to adapt to thermal stresses, but they also harbor different novel genes as well as different mechanisms of gene regulation in response to temperature changes.

In addition to thermally-adaptive genomic or transcriptomic changes, behavioral changes, such as surface attachment, are other type of strategy that bacterioplankton could use to cope with temperature changes. We are also interested in particles as there is a growing recognition of the role that these marine microenvironments play as reservoirs of diversity, sites of enhanced biological activity and in facilitating biological interactions. I examine the bacterial community inhabiting the particle-associated microhabitats in coastal seawater at the PICO time series. Partial 16S rRNA gene libraries from monthly samples reveal fundamental differences in microbial community compositions in particle-associated and free-living bacterial communities, which highlight the importance of microhabitat (e.g. particle-associated versus free-living lifestyle). Microhabitat explains more community variability than measured environmental variables. While temperature is only associated with community

changes in the free-living (<1 µm) and 5-1 µm fractions, none of the measured environmental variables are statistically significant in larger particle-associated fractions

(>63 and 63-5 µm). These results, combined with high variability in the particle- associated community especially in the largest size fraction (i.e., >63 µm), suggests that particle composition may be the most important factor instead of temperature in selecting for specific particle-associated bacteria.

This dissertation provides a strong platform for comparative analyses of thermal adaptation of marine bacteria. Most likely, within the bacteria, a number of strategies likely exist to allow survival at different temperature and under fluctuating environmental conditions. To better understand the evolutionary mechanisms associated with thermal adaptation, there have been two major strategies: artificial evolution studies and identifying in situ evolutionary responses. In recent decades, a number of evolution experiments have examined the physiological adaptation and sequence divergence among populations (Nakatsu et al. 1998, Bennett and Lenski 2007,

Tenaillon et al. 2012). These evolution experiments on the thermal-shock or thermal- acclimation response seen in model organisms particularly E. coli have important implications on the global cellular response used by bacteria to survive at temperature extremes (Tenaillon et al. 2012). However, these experimental studies generally evolve populations in temporally constant and spatially uniform environments, and therefore do not incorporate the complexity of the evolution of natural populations including

environmental heterogeneity, biotic interactions and exogenous DNA for horizontal gene transfer. Those studies that included environmental heterogeneity tend to focus on its effect on population dynamics while the role of biotic interactions in the evolutionary process remain unaddressed (Cooper and Lenski 2010). While environmental thermal preference evolution maybe more realistic, identifying specific evolutionary signals related to thermal adaptation in environmental isolates is challenging in light of other ecological differences between strains including uses of different resources and interactions. To date, most observational studies on temperature adaptation in the natural environment have focused on thermal extremophiles (Haney et al. 1999,

Grzymski et al. 2006, Barabote et al. 2009, Ayala-del-Río et al. 2010). With genomic and metagenomic data, we can identify changes in genomes of psychrophiles or thermophiles when they have evolved to adapt to a specific niche of extreme temperature over a long time scale, but these genomes will also contain a number of mutation unrelated to temperature.

Here, we examine the mechanistic basis of temperature adaptation in environmentally isolated closely-related vibrios, which have different thermal performance, by examining both their genomes and transcriptomes. Our study shows that genomic data alone cannot clearly explain subtle thermal performance difference in recently divergent strains when their genomes are highly similar. Therefore, comparative analyses of bacterial adaptation should encompass both genomic and

transcriptomic data. Identifying evolutionary mechanisms that underspin evolution of populations with distinct thermal niches and their thermal adaptation strategies is crucial to understanding the role of adaptation in the microbial populations. Although the diversity in natural bacterioplankton communities is vast, observational studies have shown environments are dominated by a limited number of populations at a given time and that annual changes in community composition are somewhat predictable

(Fuhrman et al. 2006, Gilbert et al. 2009). By identifying adaptation mechanisms and trade-offs in microbial populations, it is possible to increase our understanding of bacterial community adaptation potential in the face of new environmental stressors, that can be applied to the uncultured microbial majority.

This dissertation has made substantial advances in exploring several mechanisms of bacterial thermal adaptation. However, genetic variation and adaptation capacity are not c incorporated into the current predictions of microbial community responses to climate change due to the lack of data. Future work should incorporate the role of genetic diversity and phenotypic plasticity in assessing the evolutionary response of the microbial community to changing climate using experimentally derived data and knowledge of temporal and spatial processes in the ecosystem (Wallenstein and Hall

2012). Taxonomic patterns of plasticity and adaptive capacity can be identified by meta- analysis of existing population-genetic data across clades and laboratory tests of taxa responses to specific climate stresses (Thomas et al. 2012). Integrating population-

genetic and phylogenetic-comparative analyses would aid our understanding of how microbial community structure changes in response to seasonal changes and the role of evolutionary responses in shaping ecosystem functions and services in a warming ocean. Ultimately, we can improve the realism of models describing the ecological and biogeochemical effects of climate change.

Appendix

A B

Figure S6 (A) Temporal changes in the total number of particles (diameter: 1-30 µm) from 1 m depth water at the PICO time series site over 14 months (July 2013- August 2014, except Oct 2013). (B1) Temporal changes in the particle size distributions (diameter: 1-30 µm). (B2) Panel is an inset of panel B1.

Table S1

Date Chlorophyll Water Salinity* Turbidity Dissolved Nitrate Silicate* Urea Ammoniu pH Phosphate Projected no- a (µgL-1) temperature (NTU) Inorganic (µM) (µM) (µM) m (nM) (µM) sky daily at 1m depth* Carbon* insolation* (°C) (µM) (Wm-2d-1) 08/30/2011 10.85 27.3 31 10.25 1926.8 1.321 25.27 0.215 1508 7.913 0.089 407.2 09/13/2011 6.09 27.4 32 7.40 2072.1 ND 4.89 ND 223 7.984 0.092 377.0 09/27/2011 4.81 24.2 28 10.60 1964.2 0.421 36.90 0.258 3584 7.944 0.294 343.4 10/12/2011 6.37 21.3 30 7.53 1982.5 0.046 3.67 ND 473 7.977 0.134 305.7 11/01/2011 4.57 15.6 31 5.80 2021.8 ND 4.74 0.102 200 7.963 0.086 257.5 99 11/22/2011 5.74 17.4 31 5.08 2027.8 0.050 4.90 ND 223 7.927 0.077 217.3

12/16/2011 2.58 13.6 33 1.87 2077.9 ND 3.04 0.061 174 7.953 0.059 195.3 01/03/2012 3.11 10 31 5.35 2096.6 0.121 2.83 ND 74 7.924 ND 199.9 01/17/2012 2.38 9.6 33 3.22 2157.4 0.102 3.58 ND 230 7.887 0.011 215.9 01/31/2012 2.09 11.9 33 3.55 2148.6 0.104 3.41 ND 197 7.881 0.055 241.2 02/14/2012 1.86 9.1 34 1.85 2176.9 0.071 1.40 ND 308 7.894 0.012 273.2 02/28/2012 2.55 12.3 33 2.60 2149.3 0.052 1.43 ND 415 7.898 ND 309.0 03/27/2012 4.21 17.2 34 4.35 2164.6 0.243 1.14 ND 143 7.910 ND 380.3 04/10/2012 3.67 17.5 34 6.05 2108.4 0.127 1.36 ND 145 7.946 0.016 411.3 04/24/2012 3.34 17.7 33.5 7.85 2157.0 0.061 1.00 0.152 170 7.953 0.134 437.4 05/08/2012 3.86 21.8 34 5.26 2157.2 0.135 3.64 0.071 130 7.952 ND 457.7 05/22/2012 5.90 22.3 33 5.25 2119.8 0.028 7.52 ND 124 7.922 0.020 472.0 06/05/2012 5.43 24.2 32 5.62 2149.3 0.076 3.81 0.075 356 7.913 0.038 480.7 06/19/2012 5.28 24.9 32 4.04 2104.2 0.066 8.80 0.115 106 7.907 0.026 483.3 07/03/2012 7.36 27.3 33.5 4.19 2147.6 ND 4.15 0.094 111 7.954 ND 480.4 07/17/2012 6.10 29.1 33 4.26 2135.5 ND 5.52 ND 102 8.007 ND 471.4 07/31/2012 6.31 28.2 34 5.85 2137.8 0.131 7.34 0.089 510 7.950 ND 455.5 08/14/2012 8.24 28.2 34 6.68 2095.7 ND 7.52 ND 44 7.980 0.058 435.0

08/29/2012 8.60 26.8 29.5 8.74 1983.3 0.422 14.06 ND 1824 7.847 0.205 407.7 09/12/2012 11.84 24.7 30 7.65 1986.9 0.062 18.86 ND 50 7.933 0.089 377.6 09/26/2012 7.43 23.1 30 8.54 2048.4 ND 9.14 ND 91 7.914 0.128 344.0 10/10/2012 11.64 21.5 30 5.83 2024.2 ND 11.20 ND 205 7.913 0.095 308.8 10/24/2012 6.83 20.4 31.5 5.58 2091.8 0.228 7.11 ND 49 7.920 0.089 274.3 11/07/2012 5.17 12.7 31 3.43 2014.3 0.048 9.95 ND 96 7.952 0.064 243.0 11/21/2012 5.80 13 27 6.43 1955.4 0.155 11.86 ND 357 7.835 0.088 217.7 12/05/2012 3.45 14.3 34 2.08 1989.4 ND 5.05 ND 13 7.943 0.053 200.9 12/19/2012 4.92 13.6 30 4.46 2036.3 0.040 2.65 ND 59 7.903 0.056 194.8 01/02/2013 4.58 11.4 32 5.38 2061.9 ND 1.61 ND 2 7.932 0.030 199.7 ND = Not detected *Environmental variables marked with asterisks are the constrained set of variables used in the CCA. 100

Table S2 Summary of the growth rate and OD600 of strains

Strain Replicate Incubation !max (h-1) Harvest temperature OD600 Warm strain 1 12.5°C 0.049 0.265 Warm strain 2 12.5°C 0.048 0.263 Warm strain 3 12.5°C 0.048 0.264 Cool strain 1 12°C 0.051 0.264 Cool strain 2 12°C 0.051 0.263 Cool strain 3 12°C 0.054 0.271 Warm strain 1 32°C 1.26 0.267 Warm strain 2 32°C 1.27 0.271 Warm strain 3 32°C 1.31 0.279 Cool strain 1 28°C 0.78 0.288 Cool strain 2 28°C 0.79 0.277 Cool strain 3 28°C 0.81 0.287 Warm strain 1 36.5°C 0.049 0.263 Warm strain 2 36.5°C 0.051 0.266 Warm strain 3 36.5°C 0.051 0.265 Cool strain 1 36.5°C 0.044 0.266 Cool strain 2 36.5°C 0.045 0.264 Cool strain 3 36.5°C 0.046 0.270

Table S3 Summary of the differentially-expressed shared genes under different temperature conditions

(Excel spreadsheets are attached as supplementary data)

Table S4 Summary of the differentially-expressed unique genes under different temperature conditions

(Excel spreadsheets are attached as supplementary data)

101

Table S5 Table showing the pairwise comparisons between the bacterial communities from different size fractions using analysis of similarity (ANOSIM).

Fractions compared R statistic P value

>63 µm, 63-5 µm 0.2183 <0.001

>63 µm, 5-1 µm 0.7512 <0.001

>63 µm, <1 µm 0.8648 <0.001

63-5 µm, 5-1 µm 0.7729 <0.001

63-5 µm, <1 µm 0.9995 <0.001

5-1 µm, <1 µm 0.8816 <0.001

102

Table S6 Set of environmental parameters used for variation partitioning.

Date Projected non- Chlorophyll Turbidity Salinity Water Nitrite Silicate* Ammonium Phosphate pH* Particle (DD/MM/YYY sky daily a (µgL-1) (NTU) temperature (µM) (µM) (nM) (µM) number* Y) insolation* at 1m depth* (1-30 µm L-1) (Wm-2d-1) (°C) 03/07/2013 480.0 6.26 5.80 34.3 27.0 ND 3.63 19 0.064 7.932 2.47E+08 07/08/2013 446.2 5.52 4.44 35.0 26.9 ND 4.52 30 0.031 7.926 2.38E+08 04/09/2013 395.8 7.08 5.28 33.5 27.3 ND 6.39 156 0.065 7.900 2.85E+08 02/10/2013 329.7 6.16 6.51 32.0 22.5 0.133 9.60 69 0.053 7.968 4.99E+08 06/11/2013 241.4 5.19 4.71 33.3 18.4 0.107 2.30 387 0.062 8.082 1.21E+09 04/12/2013 200.3 4.85 4.50 33.4 13.2 0.06 1.94 68 0.035 8.117 4.03E+08 08/01/2014 205.0 3.93 10.20 33.2 7.5 0.071 2.71 39 0.024 8.126 8.02E+08 05/02/2014 253.2 2.49 4.56 33.9 8.6 ND 1.61 13 ND 8.119 8.02E+08

103 05/03/2014 323.4 2.05 4.85 33.4 8.0 0.195 1.74 46 ND 8.118 3.54E+09

01/04/2014 390.8 3.03 4.45 32.7 13.3 ND 1.27 74 0.050 8.076 8.81E+08 07/05/2014 455.8 5.20 9.46 32.8 22.3 ND 2.89 87 ND 7.913 9.79E+08 04/06/2014 480.1 3.34 4.36 34.6 23.6 0.25 1.61 69 ND 7.945 7.82E+08 02/07/2014 480.6 3.79 3.94 34.9 28.1 ND 2.75 21 ND 7.940 6.95E+08 06/08/2014 447.9 5.85 6.56 24.8 26.1 0.77 21.56 3410 0.325 7.714 5.58E+09 ND = Not detected *Environmental variables marked with asterisks are the constrained set of variables used in the CCA.

>63 µm Indicative Taxa

Average relative OTU Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- p- abundance Number Phylum Class Order Family 13 13 13 13 13 13 14 14 14 14 14 14 14 14 value (%)

53 Proteobacteria Gammaproteobacteria Thiohalorhabdales 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.064 0.037 0.055 0.000 0.064 23.435 0.000 0.007 1.690

90 Actinobacteria Acidimicrobiia Acidimicrobiales 0.018 0.000 0.000 0.000 0.000 0.000 0.000 0.494 0.073 0.411 0.000 0.210 8.007 0.055 0.007 0.662

497 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.055 0.338 0.603 0.082 1.499 0.101 0.000 0.027 0.000 0.000 0.000 0.037 0.000 0.009 0.004 0.197

479 Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobulbaceae 0.000 0.009 0.009 0.082 1.919 0.046 0.018 0.311 0.046 0.000 0.000 0.000 0.000 0.000 0.001 0.174

940 Planctomycetes Planctomycetia Pirellulales Pirellulaceae 0.027 0.073 0.000 0.119 0.000 0.146 0.000 0.009 0.119 0.037 1.481 0.000 0.000 0.000 0.003 0.144 104

477 Proteobacteria Alphaproteobacteria Sphingomonadales Erythrobacteraceae 0.000 0.055 0.073 0.000 0.000 0.000 0.000 0.000 0.027 0.027 0.000 0.073 1.088 0.366 0.001 0.122

834 Proteobacteria Alphaproteobacteria Rhodospirillales 0.000 0.000 0.009 0.000 0.000 0.046 0.000 0.000 0.018 0.037 0.000 1.472 0.000 0.000 0.003 0.113

23737 Proteobacteria Gammaproteobacteria Vibrionales Vibrionaceae 0.622 0.000 0.009 0.009 0.027 0.685 0.027 0.000 0.009 0.009 0.000 0.000 0.000 0.000 0.007 0.100

621 Proteobacteria Gammaproteobacteria Chromatiales 0.018 0.000 0.009 0.091 0.475 0.046 0.055 0.018 0.000 0.000 0.448 0.000 0.000 0.128 0.001 0.092

656 Bacteroidetes Bacteroidia Bacteroidales 0.512 0.000 0.000 0.037 0.000 0.448 0.000 0.000 0.174 0.000 0.000 0.000 0.000 0.073 0.005 0.089

670 Verrucomicrobia Verrucomicrobiae Verrucomicrobiales 0.018 0.018 0.000 0.000 0.091 0.064 0.448 0.238 0.210 0.091 0.000 0.000 0.000 0.055 0.001 0.088 Verrucomicrobiaceae

742 Chloroflexi Anaerolineae S0208 0.210 0.018 0.082 0.183 0.009 0.238 0.082 0.000 0.292 0.110 0.000 0.000 0.000 0.000 0.001 0.087

748 Proteobacteria Alphaproteobacteria Rhizobiales Hyphomicrobiaceae 0.347 0.000 0.009 0.000 0.055 0.430 0.000 0.091 0.000 0.027 0.000 0.000 0.000 0.037 0.001 0.071

530 Cyanobacteria Oscillatoriophycideae Oscillatoriales Phormidiaceae 0.000 0.448 0.119 0.228 0.192 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.009 0.071

734 Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobulbaceae 0.000 0.027 0.018 0.201 0.274 0.073 0.000 0.046 0.027 0.192 0.000 0.064 0.000 0.055 0.001 0.070

947 Proteobacteria Gammaproteobacteria Chromatiales 0.082 0.027 0.027 0.000 0.457 0.101 0.009 0.055 0.027 0.027 0.000 0.000 0.000 0.165 0.001 0.070

821 Bacteroidetes Cytophagia Cytophagales Flammeovirgaceae 0.384 0.000 0.000 0.000 0.000 0.165 0.055 0.000 0.292 0.027 0.000 0.009 0.000 0.018 0.001 0.068

1098 Proteobacteria Deltaproteobacteria Myxococcales 0.457 0.000 0.000 0.000 0.000 0.384 0.073 0.000 0.000 0.000 0.000 0.000 0.000 0.027 0.003 0.067

1490 Proteobacteria Alphaproteobacteria Rhodobacterales Hyphomonadaceae 0.000 0.000 0.009 0.000 0.000 0.000 0.000 0.018 0.000 0.201 0.000 0.466 0.210 0.000 0.003 0.065

2653 Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobulbaceae 0.000 0.000 0.000 0.000 0.000 0.027 0.000 0.000 0.018 0.000 0.704 0.137 0.000 0.000 0.002 0.063

1377 Bacteroidetes Flavobacteriia Flavobacteriales Cryomorphaceae 0.000 0.000 0.027 0.000 0.000 0.018 0.018 0.046 0.000 0.009 0.713 0.000 0.000 0.000 0.007 0.059

1194 Proteobacteria Gammaproteobacteria Alteromonadales Alteromonadaceae 0.119 0.000 0.000 0.000 0.128 0.091 0.018 0.000 0.000 0.000 0.000 0.000 0.000 0.448 0.004 0.057

3816 Proteobacteria Alphaproteobacteria Rickettsiales Rickettsiaceae 0.073 0.009 0.457 0.000 0.110 0.055 0.000 0.000 0.009 0.000 0.000 0.000 0.000 0.073 0.004 0.056

1118 Cyanobacteria Nostocophycideae Stigonematales Rivulariaceae 0.018 0.165 0.320 0.000 0.027 0.037 0.174 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.053

63-5 µm Indicative Taxa

Average relative OTU Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- p- abundance Number Phylum Class Order Family 13 13 13 13 13 13 14 14 14 14 14 14 14 14 value (%)

148 Bacteroidetes Saprospirae Saprospirales Saprospiraceae 0.000 1.435 1.289 0.055 0.101 0.055 0.265 0.192 0.037 0.000 0.000 0.000 1.737 0.247 0.001 0.386

237 Proteobacteria Gammaproteobacteria Thiotrichales Piscirickettsiaceae 0.000 0.000 0.000 0.018 0.009 0.155 0.539 0.877 0.695 0.832 0.439 0.238 0.037 0.037 0.001 0.277 105 358 Proteobacteria Gammaproteobacteria Alteromonadales OM60 0.000 0.000 0.000 0.000 0.000 0.146 0.183 0.329 0.676 0.329 0.174 0.174 0.009 0.000 0.001 0.144

754 Verrucomicrobia Opitutae Puniceicoccales Puniceicoccaceae 0.000 0.265 0.210 0.887 0.000 0.009 0.009 0.027 0.000 0.009 0.027 0.000 0.018 0.101 0.001 0.112

369 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.155 0.311 0.439 0.165 0.137 0.046 0.027 0.046 0.000 0.027 0.018 0.018 0.101 0.037 0.001 0.109

867 Cyanobacteria Oscillatoriophycideae Chroococcales 0.000 0.439 0.110 0.219 0.018 0.000 0.018 0.091 0.411 0.091 0.000 0.046 0.000 0.064 0.001 0.108 Gomphosphaeriaceae

539 Cyanobacteria Oscillatoriophycideae Chroococcales Cyanobacteriaceae 0.000 0.009 0.046 0.027 0.009 0.018 0.009 0.859 0.356 0.027 0.046 0.073 0.000 0.000 0.001 0.106

499 Bacteroidetes Saprospirae Saprospirales Saprospiraceae 0.238 0.082 0.027 0.000 0.000 0.082 0.082 0.073 0.000 0.000 0.037 0.146 0.676 0.037 0.001 0.106

405 Bacteroidetes Saprospirae Saprospirales Saprospiraceae 0.101 0.091 0.064 0.101 0.420 0.082 0.027 0.000 0.000 0.000 0.000 0.558 0.000 0.000 0.001 0.103

11138 Bacteroidetes Flavobacteriia Flavobacteriales Cryomorphaceae 0.000 0.055 0.009 0.018 0.128 0.009 0.027 0.101 0.064 0.009 0.521 0.201 0.055 0.000 0.001 0.086

665 Bacteroidetes Saprospirae Saprospirales Saprospiraceae 0.238 0.064 0.073 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.676 0.110 0.000 0.006 0.083

1184 Verrucomicrobia Verrucomicrobiae Verrucomicrobiales 0.082 0.091 0.027 0.027 0.101 0.027 0.101 0.119 0.046 0.082 0.027 0.238 0.009 0.037 0.001 0.072 Verrucomicrobiaceae

1375 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.009 0.128 0.174 0.082 0.046 0.082 0.037 0.037 0.009 0.009 0.073 0.137 0.128 0.000 0.001 0.068

853 Bacteroidetes Rhodothermi Rhodothermales Balneolaceae 0.000 0.009 0.000 0.000 0.000 0.009 0.073 0.119 0.174 0.064 0.027 0.201 0.210 0.009 0.001 0.064

370 Verrucomicrobia Verrucomicrobiae Verrucomicrobiales 0.119 0.283 0.046 0.274 0.027 0.027 0.009 0.000 0.000 0.046 0.009 0.000 0.037 0.000 0.001 0.063 Verrucomicrobiaceae

998 Verrucomicrobia Opitutae 0.073 0.101 0.018 0.027 0.018 0.146 0.073 0.027 0.091 0.091 0.018 0.082 0.073 0.000 0.001 0.060

770 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.128 0.027 0.155 0.018 0.046 0.027 0.000 0.091 0.000 0.064 0.073 0.165 0.009 0.009 0.001 0.058

793 Bacteroidetes BME43 0.000 0.000 0.082 0.018 0.018 0.119 0.091 0.119 0.018 0.046 0.009 0.192 0.073 0.018 0.001 0.057

6855 Proteobacteria Epsilonproteobacteria Campylobacterales Helicobacteraceae 0.046 0.137 0.055 0.018 0.155 0.000 0.000 0.000 0.274 0.009 0.018 0.027 0.009 0.018 0.001 0.055

5-1 µm Indicative Taxa

Average relative OTU Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- p- abundance Number Phylum Class Order Family 13 13 13 13 13 13 14 14 14 14 14 14 14 14 value (%)

23 Proteobacteria Deltaproteobacteria Sva0853 S25_1238 5.575 7.394 2.440 6.892 9.186 0.503 0.896 0.786 0.731 0.146 0.960 3.007 4.415 2.413 0.001 3.239

195 Bacteroidetes Flavobacteriia Flavobacteriales Cryomorphaceae 0.000 0.037 0.119 0.101 0.594 0.027 0.000 0.000 0.046 0.201 1.280 0.941 0.274 0.137 0.001 0.268

16739 Bacteroidetes Flavobacteriia Flavobacteriales Cryomorphaceae 0.347 0.064 0.174 0.137 0.411 0.210 0.046 0.000 0.064 0.676 0.512 0.411 0.027 0.384 0.001 0.247

13855 Verrucomicrobia Verrucomicrobiae Verrucomicrobiales 0.484 0.000 0.000 0.009 0.000 0.411 0.119 0.000 0.000 0.037 1.599 0.494 0.165 0.000 0.002 0.237 Verrucomicrobiaceae

299 Bacteroidetes Rhodothermi Rhodothermales Balneolaceae 0.000 0.027 0.073 0.832 0.064 0.110 0.000 0.000 0.000 0.000 0.000 0.174 1.435 0.101 0.001 0.201 106

396 Verrucomicrobia Pedosphaerae 0.356 0.174 0.283 0.183 0.292 0.000 0.000 0.000 0.000 0.000 0.009 0.402 0.155 0.713 0.001 0.183

164 Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae 0.000 0.000 0.009 0.064 0.055 0.110 0.000 0.393 0.612 0.356 0.055 0.027 0.046 0.037 0.001 0.126

569 Verrucomicrobia Pedosphaerae 0.439 0.265 0.475 0.165 0.219 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.128 0.001 0.121

803 Proteobacteria Gammaproteobacteria Alteromonadales OM60 0.064 0.055 0.073 0.009 0.000 0.484 0.000 0.000 0.137 0.119 0.347 0.055 0.091 0.082 0.001 0.108

400 Bacteroidetes Flavobacteriia Flavobacteriales Cryomorphaceae 0.000 0.110 0.009 0.000 0.082 0.000 0.000 0.000 0.000 0.210 0.018 0.000 0.018 1.060 0.001 0.108

714 Proteobacteria Deltaproteobacteria Myxococcales OM27 0.000 0.146 0.174 0.091 0.128 0.000 0.165 0.000 0.210 0.064 0.201 0.146 0.101 0.064 0.001 0.106

645 Verrucomicrobia Pedosphaerae 0.000 0.219 0.356 0.091 0.101 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.009 0.640 0.005 0.101

838 Proteobacteria Deltaproteobacteria PB19 0.000 0.128 0.594 0.192 0.009 0.000 0.000 0.000 0.146 0.000 0.027 0.091 0.018 0.137 0.001 0.096

4139 Proteobacteria Gammaproteobacteria Pseudomonadales Moraxellaceae 0.000 0.009 0.046 0.000 0.000 0.000 0.439 0.210 0.558 0.000 0.009 0.000 0.000 0.037 0.008 0.093

1254 Cyanobacteria Synechococcophycideae Synechococcales Synechococcaceae 0.018 0.037 0.027 0.420 0.146 0.000 0.000 0.000 0.000 0.000 0.000 0.137 0.466 0.037 0.005 0.092

20997 Bacteroidetes Flavobacteriia Flavobacteriales Cryomorphaceae 0.000 0.000 0.091 0.073 0.658 0.000 0.000 0.000 0.000 0.018 0.009 0.119 0.091 0.027 0.001 0.078

293 Bacteroidetes Saprospirae Saprospirales Saprospiraceae 0.000 0.046 0.037 0.046 0.000 0.000 0.000 0.000 0.567 0.091 0.000 0.238 0.027 0.027 0.001 0.077

558 Proteobacteria Gammaproteobacteria HTCC2188 HTCC2089 0.000 0.000 0.000 0.027 0.000 0.000 0.247 0.155 0.073 0.302 0.165 0.000 0.000 0.018 0.001 0.071

568 Bacteroidetes Flavobacteriia Flavobacteriales Cryomorphaceae 0.082 0.137 0.064 0.018 0.046 0.000 0.000 0.000 0.000 0.000 0.000 0.009 0.055 0.530 0.002 0.067

630 Proteobacteria Deltaproteobacteria Myxococcales OM27 0.000 0.219 0.082 0.320 0.018 0.000 0.000 0.000 0.000 0.000 0.000 0.091 0.155 0.000 0.001 0.063

655 Bacteroidetes Rhodothermi Rhodothermales Rhodothermaceae 0.000 0.000 0.110 0.265 0.018 0.027 0.000 0.000 0.000 0.027 0.009 0.137 0.165 0.091 0.001 0.061

<1 µm Indicative Taxa

Average relative OTU Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- p- abundance Number Phylum Class Order Family 13 13 13 13 13 13 14 14 14 14 14 14 14 14 value (%)

43 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.695 1.599 1.325 0.969 1.069 1.974 1.855 1.435 0.932 0.649 2.221 3.080 0.868 0.247 0.001 1.351

32 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.018 0.000 0.046 0.009 0.082 0.274 1.819 3.565 5.749 2.349 1.408 0.320 0.037 0.055 0.001 1.124

4396 Proteobacteria Alphaproteobacteria Rickettsiales Pelagibacteraceae 1.289 1.124 1.855 2.367 2.404 1.865 0.969 0.905 0.850 0.813 0.457 0.466 0.292 0.055 0.001 1.122

22 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.018 0.000 0.009 0.082 0.302 1.005 2.248 5.740 2.413 2.641 0.868 0.247 0.037 0.055 0.002 1.119

2 Actinobacteria Acidimicrobiia Acidimicrobiales OCS155 1.947 0.859 2.897 2.769 0.713 0.466 0.128 0.073 0.009 0.037 0.073 0.027 0.695 3.354 0.001 1.003 107 45 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.713 0.704 0.585 0.219 0.548 1.216 1.389 0.494 0.631 0.996 1.782 1.353 1.609 0.119 0.001 0.883

50 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 1.746 2.047 1.234 0.823 0.932 0.292 0.055 0.009 0.000 0.000 0.018 0.027 3.702 0.247 0.001 0.795

71 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.402 0.046 0.137 0.201 0.969 2.002 0.713 0.640 0.731 0.576 1.892 2.120 0.439 0.046 0.001 0.780

28601 Proteobacteria Alphaproteobacteria Rickettsiales Pelagibacteraceae 1.033 1.234 1.536 1.225 1.417 1.599 0.292 0.256 0.238 1.124 0.311 0.165 0.356 0.101 0.001 0.778

27858 Actinobacteria Acidimicrobiia Acidimicrobiales OCS155 0.731 0.612 1.782 1.307 1.079 0.622 0.247 0.000 0.000 0.000 0.018 0.018 0.366 1.280 0.001 0.576

4000 Actinobacteria Acidimicrobiia Acidimicrobiales OCS155 0.320 0.000 0.000 0.000 0.009 0.393 1.545 1.462 0.722 1.737 0.576 0.292 0.000 0.018 0.003 0.505

66 SAR406 AB16 Arctic96B-7 A714017 1.956 1.855 0.548 0.594 0.265 0.274 0.009 0.000 0.000 0.000 0.018 0.018 0.877 0.256 0.001 0.477

15710 Actinobacteria Acidimicrobiia Acidimicrobiales OCS155 0.055 0.000 0.000 0.000 0.064 0.082 0.091 0.064 0.119 1.079 4.652 0.311 0.046 0.000 0.006 0.469

20122 Proteobacteria Alphaproteobacteria Rickettsiales Pelagibacteraceae 0.375 0.539 0.603 0.676 1.142 1.801 0.219 0.192 0.055 0.073 0.119 0.210 0.119 0.046 0.001 0.441

24 Proteobacteria Alphaproteobacteria Rickettsiales Pelagibacteraceae 0.064 0.073 0.448 1.362 0.347 0.548 0.256 0.238 1.234 0.439 0.146 0.457 0.009 0.247 0.001 0.419

62 Proteobacteria Alphaproteobacteria Rickettsiales Pelagibacteraceae 0.649 0.558 0.457 0.713 0.475 0.375 0.484 0.137 0.146 0.091 0.101 0.320 0.228 0.292 0.001 0.359

8521 Actinobacteria Acidimicrobiia Acidimicrobiales OCS155 1.106 0.877 0.832 0.777 0.155 0.238 0.192 0.000 0.000 0.000 0.027 0.000 0.311 0.494 0.001 0.358

78 Proteobacteria Gammaproteobacteria Oceanospirillales Halomonadaceae 0.311 1.380 1.042 0.521 0.539 0.027 0.018 0.000 0.000 0.000 0.018 0.018 0.183 0.877 0.001 0.353

1403 Proteobacteria Alphaproteobacteria Rickettsiales Pelagibacteraceae 0.320 0.055 0.503 0.676 1.079 0.841 0.256 0.219 0.073 0.192 0.128 0.119 0.091 0.009 0.001 0.326

5 Actinobacteria Acidimicrobiia Acidimicrobiales OCS155 0.073 0.000 0.000 0.000 0.037 0.292 0.366 0.091 0.137 1.280 1.837 0.329 0.073 0.000 0.002 0.323

108 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.000 0.000 0.009 0.000 0.000 0.110 0.713 1.782 0.640 0.530 0.210 0.174 0.009 0.037 0.001 0.301

24282 Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae 0.009 0.000 0.037 0.046 0.192 0.320 0.238 0.018 0.000 0.018 0.302 0.037 0.055 2.897 0.005 0.298

86 Proteobacteria Gammaproteobacteria Chromatiales 0.951 0.667 0.430 0.192 0.037 0.018 0.000 0.000 0.000 0.000 0.018 0.128 0.210 0.859 0.005 0.251

5410 Actinobacteria Acidimicrobiia Acidimicrobiales C111 0.247 0.174 0.128 0.073 0.128 0.055 0.027 0.018 0.000 0.009 0.612 0.183 0.146 1.627 0.001 0.245

88 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.631 0.804 0.484 0.210 0.366 0.329 0.119 0.000 0.027 0.018 0.000 0.064 0.302 0.037 0.001 0.242

17413 Actinobacteria Acidimicrobiia Acidimicrobiales OCS155 0.146 0.329 0.228 0.201 0.366 0.649 0.283 0.064 0.000 0.018 0.101 0.119 0.192 0.384 0.003 0.220

98 Proteobacteria Gammaproteobacteria Oceanospirillales Halomonadaceae 0.402 0.594 0.457 0.210 0.292 0.137 0.000 0.000 0.027 0.000 0.000 0.018 0.256 0.192 0.003 0.185

13007 SAR406 AB16 Arctic96B-7 A714017 0.210 0.393 0.000 0.009 0.064 0.247 0.521 0.210 0.101 0.110 0.430 0.247 0.027 0.000 0.001 0.183

11602 Proteobacteria Alphaproteobacteria 0.146 0.210 0.174 0.046 0.037 0.183 0.274 0.082 0.137 0.238 0.494 0.183 0.265 0.009 0.001 0.177

20055 Proteobacteria Alphaproteobacteria Rickettsiales Pelagibacteraceae 0.228 0.228 0.292 0.274 0.631 0.356 0.037 0.073 0.027 0.082 0.073 0.000 0.018 0.055 0.001 0.170

58 Proteobacteria Gammaproteobacteria Oceanospirillales SUP05 0.347 1.188 0.402 0.128 0.073 0.009 0.000 0.000 0.000 0.000 0.009 0.037 0.009 0.091 0.003 0.164 108 240 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.073 0.073 0.082 0.101 0.338 0.110 0.055 0.018 0.146 0.000 0.091 0.219 0.228 0.631 0.001 0.155

24075 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.009 0.009 0.000 0.000 0.101 0.393 0.283 0.219 0.448 0.384 0.210 0.055 0.009 0.000 0.001 0.151

201 Proteobacteria Gammaproteobacteria Alteromonadales OM60 0.055 0.192 0.082 0.055 0.283 0.064 0.165 0.027 0.000 0.027 0.073 0.411 0.548 0.128 0.001 0.151

117 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.000 0.384 0.521 0.265 0.265 0.037 0.000 0.000 0.000 0.000 0.000 0.018 0.384 0.210 0.002 0.149

591 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.119 0.411 0.676 0.146 0.137 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.448 0.146 0.001 0.149

113 Proteobacteria Alphaproteobacteria Rhizobiales 0.091 0.311 0.174 0.228 0.219 0.073 0.027 0.018 0.000 0.009 0.037 0.219 0.027 0.603 0.001 0.146

16986 Proteobacteria Betaproteobacteria Methylophilales Methylophilaceae 0.375 0.201 0.037 0.037 0.174 0.192 0.356 0.110 0.128 0.018 0.174 0.082 0.055 0.064 0.001 0.143

149 Proteobacteria Alphaproteobacteria Rickettsiales Pelagibacteraceae 0.137 0.101 0.146 0.228 0.338 0.393 0.265 0.091 0.000 0.055 0.073 0.018 0.046 0.046 0.001 0.138

267 Proteobacteria Betaproteobacteria 0.457 0.366 0.457 0.046 0.101 0.009 0.000 0.000 0.000 0.000 0.000 0.000 0.128 0.366 0.002 0.138

392 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.027 0.119 0.064 0.055 0.064 0.137 0.009 0.055 0.055 0.238 0.356 0.603 0.018 0.018 0.001 0.130

9216 Proteobacteria Alphaproteobacteria Rickettsiales Pelagibacteraceae 0.137 0.165 0.137 0.183 0.201 0.228 0.201 0.165 0.091 0.091 0.018 0.046 0.082 0.009 0.001 0.125

135 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.000 0.000 0.000 0.000 0.009 0.192 0.347 0.338 0.466 0.119 0.146 0.119 0.000 0.000 0.002 0.124

251 Proteobacteria Gammaproteobacteria Thiotrichales Piscirickettsiaceae 0.009 0.009 0.018 0.073 0.027 0.082 0.228 0.302 0.640 0.292 0.009 0.009 0.000 0.000 0.001 0.121

36 Proteobacteria Alphaproteobacteria 0.027 0.018 0.046 0.055 0.146 0.192 0.219 0.256 0.137 0.146 0.174 0.073 0.000 0.128 0.001 0.116

254 Proteobacteria Alphaproteobacteria 0.046 0.009 0.037 0.101 0.183 0.165 0.110 0.027 0.009 0.137 0.256 0.119 0.128 0.174 0.001 0.107

1230 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.119 0.347 0.165 0.082 0.183 0.091 0.027 0.009 0.000 0.009 0.110 0.073 0.192 0.046 0.001 0.104

274 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.091 0.210 0.183 0.064 0.210 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.448 0.201 0.005 0.101

376 Proteobacteria Betaproteobacteria Methylophilales Methylophilaceae 0.009 0.027 0.000 0.009 0.027 0.000 0.201 0.512 0.283 0.073 0.073 0.146 0.018 0.018 0.001 0.100

13492 Proteobacteria Gammaproteobacteria Oceanospirillales Halomonadaceae 0.274 0.247 0.137 0.009 0.137 0.000 0.000 0.000 0.000 0.000 0.009 0.000 0.366 0.137 0.001 0.094

19827 Actinobacteria Acidimicrobiia Acidimicrobiales OCS155 0.128 0.027 0.110 0.027 0.411 0.027 0.009 0.000 0.000 0.000 0.073 0.009 0.073 0.384 0.002 0.091

239 Proteobacteria Gammaproteobacteria Oceanospirillales Halomonadaceae 0.027 0.457 0.247 0.082 0.128 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.302 0.018 0.001 0.090

143 Proteobacteria Gammaproteobacteria Oceanospirillales 0.009 0.055 0.000 0.000 0.018 0.064 0.311 0.356 0.137 0.155 0.000 0.091 0.000 0.000 0.003 0.086

233 Proteobacteria Betaproteobacteria 0.000 0.018 0.000 0.000 0.000 0.037 0.137 0.219 0.119 0.219 0.228 0.165 0.027 0.000 0.001 0.084

116 Actinobacteria Acidimicrobiia Acidimicrobiales C111 0.000 0.000 0.000 0.000 0.064 0.018 0.055 0.055 0.530 0.320 0.000 0.000 0.000 0.119 0.002 0.083

12717 Proteobacteria Gammaproteobacteria Oceanospirillales Halomonadaceae 0.027 0.009 0.082 0.000 0.119 0.110 0.128 0.119 0.064 0.183 0.110 0.073 0.064 0.027 0.001 0.080

2639 Proteobacteria Alphaproteobacteria 0.018 0.009 0.027 0.037 0.027 0.137 0.064 0.018 0.091 0.082 0.338 0.082 0.155 0.000 0.001 0.078 109 4011 Proteobacteria Alphaproteobacteria Rickettsiales Pelagibacteraceae 0.046 0.018 0.091 0.146 0.119 0.101 0.073 0.046 0.055 0.110 0.128 0.037 0.046 0.046 0.001 0.076

28006 Proteobacteria Alphaproteobacteria Rickettsiales Pelagibacteraceae 0.128 0.165 0.174 0.174 0.119 0.110 0.018 0.027 0.009 0.009 0.000 0.073 0.027 0.000 0.001 0.074

5603 Proteobacteria Alphaproteobacteria Rickettsiales Pelagibacteraceae 0.027 0.037 0.000 0.000 0.037 0.274 0.256 0.146 0.046 0.064 0.055 0.073 0.009 0.000 0.001 0.073

16156 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.073 0.174 0.146 0.101 0.091 0.146 0.027 0.009 0.000 0.027 0.027 0.082 0.101 0.009 0.001 0.072

257 Proteobacteria Gammaproteobacteria Thiotrichales Piscirickettsiaceae 0.027 0.027 0.064 0.073 0.073 0.110 0.146 0.091 0.009 0.110 0.192 0.082 0.000 0.009 0.001 0.072

22703 Proteobacteria Alphaproteobacteria Rickettsiales Pelagibacteraceae 0.018 0.018 0.119 0.356 0.082 0.082 0.037 0.037 0.046 0.055 0.027 0.064 0.000 0.027 0.001 0.069

535 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.000 0.000 0.000 0.018 0.009 0.055 0.027 0.046 0.000 0.210 0.265 0.192 0.119 0.027 0.001 0.069

22146 Proteobacteria Alphaproteobacteria Rickettsiales Pelagibacteraceae 0.027 0.009 0.192 0.210 0.210 0.082 0.000 0.037 0.037 0.055 0.037 0.018 0.018 0.009 0.001 0.067

20257 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.000 0.000 0.000 0.000 0.009 0.027 0.155 0.082 0.366 0.110 0.155 0.000 0.000 0.000 0.005 0.065

4870 Actinobacteria Acidimicrobiia Acidimicrobiales OCS155 0.201 0.009 0.101 0.183 0.146 0.009 0.000 0.000 0.000 0.027 0.018 0.037 0.046 0.128 0.001 0.065

273 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.064 0.238 0.055 0.009 0.091 0.027 0.000 0.000 0.000 0.000 0.009 0.037 0.247 0.128 0.004 0.065

1121 Proteobacteria Alphaproteobacteria 0.073 0.018 0.174 0.183 0.091 0.027 0.000 0.009 0.000 0.000 0.064 0.027 0.082 0.128 0.001 0.063

27723 Proteobacteria Alphaproteobacteria Rickettsiales Pelagibacteraceae 0.073 0.073 0.101 0.009 0.055 0.119 0.018 0.091 0.174 0.073 0.000 0.037 0.018 0.000 0.001 0.060

16628 Proteobacteria Alphaproteobacteria 0.091 0.018 0.119 0.073 0.110 0.073 0.027 0.018 0.000 0.009 0.101 0.027 0.128 0.037 0.001 0.059

247 Proteobacteria Alphaproteobacteria 0.037 0.009 0.000 0.000 0.146 0.283 0.119 0.055 0.000 0.046 0.027 0.091 0.009 0.000 0.001 0.059

739 Proteobacteria Betaproteobacteria Burkholderiales Comamonadaceae 0.018 0.073 0.046 0.027 0.073 0.018 0.000 0.000 0.000 0.000 0.000 0.027 0.055 0.475 0.001 0.058

360 Proteobacteria Gammaproteobacteria Oceanospirillales Halomonadaceae 0.037 0.073 0.046 0.009 0.119 0.165 0.018 0.000 0.009 0.000 0.046 0.119 0.110 0.037 0.001 0.056

29372 Proteobacteria Alphaproteobacteria Rickettsiales Pelagibacteraceae 0.110 0.046 0.073 0.073 0.018 0.055 0.000 0.128 0.128 0.055 0.073 0.000 0.018 0.000 0.001 0.055

20963 Proteobacteria Alphaproteobacteria Rickettsiales Pelagibacteraceae 0.027 0.000 0.137 0.155 0.101 0.110 0.055 0.027 0.009 0.037 0.064 0.018 0.000 0.027 0.001 0.055

615 Actinobacteria Acidimicrobiia Acidimicrobiales OCS155 0.110 0.000 0.000 0.000 0.064 0.073 0.064 0.027 0.000 0.128 0.238 0.018 0.009 0.009 0.001 0.053

24868 Bacteroidetes Flavobacteriia Flavobacteriales Flavobacteriaceae 0.055 0.073 0.046 0.037 0.064 0.073 0.037 0.000 0.000 0.027 0.137 0.119 0.009 0.000 0.001 0.050 110

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Biography

Cheuk Man Yung Born: June 21, 1987, Hong Kong

Education Doctor of Philosophy, Marine Science and Conservation (2016) Duke University, Nicholas School of the Environment, Durham, North Carolina, USA

Master of Philosophy, Environemntal Microbiology (2011) The University of Hong Kong, Hong Kong

Bachelor of Science (First class honor), Biology (2009) The Hong Kong University of Science and Technology, Hong Kong

Publications Yung, CM, Ward, CS, Davis, KM, Johnson, ZI, & Hunt, DE (2016) Diverse and temporally- variable particle-associated microbial communities are insensitive to bulk seawater environmental parameters. Applied and Environmental Microbiology, AEM-00395.

Yung CM, Vereen MK, Herbert A, Davis KM, Yang J, Kantorowska A, Ward CS, Wernegreen JJ, Johnson ZI & Hunt DE. (2015) Thermally adaptive tradeoffs in closely- related marine bacterial strains. Environmental Microbiology, 17:2421–2429.

Yung CM, Chan YK, Lacap DC, Perez-Ortega S, de los Rios-Murillo A, Lee CK, Cary SC, and Pointing SB. (2014) Characterization of Chasmoendolithic Community in Miers Valley, McMurdo Dry Valleys, Antarctica. Microbial Ecology, 68:351–359.

Awards and Honors Robert W. Safrit Jr. Fellowship, Duke University (2015) Best poster award in The Human and Environmental Microbiome Symposium (2014) Lawrence E. Blanchard Scholarship, Duke University (2013) Postgraduate Studentship, The University of Hong Kong (2009-2011)

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