SPATIO-TEMPORAL DYNAMIC OF BACTERIAL COMMUNITIES IN THE IROISE SEA

Get Complete Project Material File(s) Now! »

Spatio- temporal dynamic of bacterial communities in the Iroise Sea

Comprendre la dynamique spatiale et temporelle des communautés de microorganismes marins est une étape clé pour mieux appréhender leur rôle dans les écosystèmes. Les structures méso-échelles représentent des zones clés pour étudier ces dynamiques car elles influent directement sur la distribution du plancton et leur activité. Dans ce chapitre, nous avons étudié la dynamique des communautés de bactéries libres dans l’eau, associées au front de Ouessant en mer d’Iroise. Cette structure frontale est caractérisée par des eaux stratifiées typiques au large de Ouessant, avec en surface des eaux chaudes et épuisées en nutriments. Cette stratification n’est pas permise dans les eaux plus côtières, notamment de par l’action des courants de marées, qui peuvent homogénéiser la colonne d’eau sur toute sa hauteur. Dans ces masses d’eau instables, le développement du phytoplancton va être limité par ce mélange constant. A l’interface de ces masses d’eau, le front permet le maintien d’une forte production, par un apport local d’eau enrichie en nutriments. Afin d’étudier comment la présence de ce front pouvait structurer les communautés bactériennes, cinq stations réparties autour du front ont été échantillonnées en Septembre 2014, Mars, Juillet et Septembre 2015, et les communautés ont été caractérisées par le séquençage de leur ADNr 16S. La composition des communautés microbiennes va changer en fonction des différentes masses d’eau présentes, mais pas au niveau de la zone frontale. Par une approche en réseau, nous avons ensuite regroupé les OTUs qui présentaient des dynamiques similaires au niveau de ces masses d’eau en posant l’hypothèse qu’ils partageaient des niches écologiques similaires. Cette approche a mis en évidence l’importance des producteurs primaires qui, de par leur répartition hétérogène autour du front et dans le temps, vont définir des environnements plus ou moins riches en matière organique, et ainsi sélectionner des bactéries hétérotrophes avec des stratégies trophiques différentes. Des bactéries oligotrophes vont être présentes toute l’années et dominer en hivers et dans les eaux profondes au large, lorsqu’il y a peu de développement phytoplanctonique. A l’inverse, différentes populations copiotrophes vont apparaître plus abondantes en été et dans les eaux plus côtières et juste après un bloom intense en Juillet, certaines répondant par des variations en abondance fortes. Ces résultats laissent supposer l’importance de ces communautés bactériennes dans la reminéralisation de la matière organique de la boucle microbienne en mer d’Iroise.
Ocean Frontal systems are widespread hydrological features defining the transition zone between distinct water masses. They are generally of high biological importance as they are often associated with locally enhanced primary production by phytoplankton. However, the composition of bacterial communities in the frontal zone remains poorly understood. In this study, we investigate how a coastal tidal front in Brittany (France) structures the free-living bacterioplankton communities in a spatio-temporal survey across four cruises, five stations and three depths. We used 16S rRNA surveys to compare bacterial community structures across 134 seawater samples and defined groups of co-varying taxa (modules) exhibiting coherent ecological patterns across space and time. Seasonal variations in primary producers, and distribution in the water column appeared as the most salient parameters controlling heterotrophic bacteria. Different dynamics of modules observed in this environment were strongly consistent with a partitioning of heterotrophic bacterioplankton in oligotroph and copiotroph ecological strategies. Overall, this study shows a strong coupling between bacterioplankton communities dynamic, trophic strategies, and seasonal cycles in a complex coastal environment.
Bacteria dominate marine environment in abundance, diversity and activity where they support critical roles in the functioning of marine ecosystems and oceanic biogeochemical cycles (Cotner and Biddanda, 2002; Falkowski et al., 2008; Madsen, 2011). In the coastal environment, they are closely linked to other planktonic organisms (e.g. viruses, phytoplankton and zooplankton) during the recycling of organic matter and inorganic nutrients through the so-called microbial loop (Azam and Malfatti, 2007; Pomeroy et al., 2007). They form complex and highly dynamic assemblage (Giovannoni and Vergin, 2012), with bacterioplankton diversity variations in space and time linked to changes in functional diversity (Galand et al., 2018). Therefore, understanding how the bacterioplankton composition varies in the environment remains one of the central question to understand coastal ecosystem functioning better (Fuhrman et al., 2015).
Organic matter processing implies diverse heterotrophic bacterioplankton among which one could pinpoint members of Bacteriodetes, Roseobacter group or Gammaproteobacteria (9). These taxa contribute to the complexity of the marine ecosystem via different adaptive strategies, owing to the unequal access to their respective resource (10) For instance, heterotrophs are generally distinguished between oligotrophs and copiotrophs that compete at low and high nutrient concentrations respectively (Giovannoni et al., 2014; Koch, 2001). They also present different degrees of ecological specialization, with generalist bacteria able to assimilate a broad variety of substrates, while specialists will compete for a narrow range of nutrients (Mou et al., 2008). Analysis of these ecological traits offer a simplified view of complex microbial communities and has gained interest to understand the dynamic of natural microbial communities better and get an insight of their role in the ecosystem (Haggerty and Dinsdale, 2017; Krause et al., 2014; Raes et al., 2011).
Marine fronts are very common mesoscale features in the ocean and lie at the transition between water masses of different physicochemical characteristics that actively shape the distribution of microbial organisms (phytoplankton, zooplankton and bacteria). Driven by currents and mixing, local nutrients input in the vicinity of the front generally enhance primary and secondary production making frontal zone area of high biological importance (Olson and Backus, 1985) and microbial processing of organic matter (Baltar et al., 2015; Heinänen et al., 1995). However, the bacterial communities composition involved in such dynamic systems remains to investigate (Baltar et al., 2016).
The Ushant Front in the Iroise Sea (Brittany, France) is considered as a model of a coastal tidal front (Le Fèvre, 1986). Its position and characteristics are highly dynamic and influenced by atmospheric forcing and tidal coefficient (Le et al., 2009). It occurs from May to October and leads to contrasted physicochemical environments with higher biomass at the frontal area (Le Fèvre et al., 1983). West of the front, stratification results in warmer oligotrophic surface waters and colder nutrient-rich deeper waters separated by a marked thermocline. East of the front, associated with highly variable conditions, permanently mixed coastal waters are characterized by an unlimited quantity of inorganic nutrients but with highly fluctuating conditions. These contrasted water bodies structure primary producers distribution with the dominance of small phytoplankton and dinoflagellates in surface stratified waters and diatoms in mixed waters (Birrien et al., 1991; GREPMA, 1988; Videau, 1987).
In this study, we tested the hypothesis that such contrasted water mass in physicochemical and biological parameters will strongly drive bacterioplankton communities. Using a network analysis, we defined groups of co-varying bacterial OTUs that present the same dynamic across the samples, suggesting that they possibly share the same ecological niches.

ENVIRONMENTAL SETTINGS

This study was carried out between September 2014 and September 2015 in the Iroise Sea, off Brittany (N.-E. Atlantic), in the vicinity of the Ushant island. In general, Ushant front position and characteristics were estimated using Satellite Surface Temperature (SST) maps (Figure 8) and CTD data collected at the dates of sampling (Figure S 1). The March 2014 sampling took place before the onset of the Ushant front and presented a homogeneous temperature around 10°C across all the stations. High nutrient concentrations at all stations (Si(OH)4: 1.82 to 4.38 µM, Nitrates: 5.88 to 12.13 µM,
Table S 1), low surface chlorophyll a (< 1 µg.L-1 except for Station 1, Figure S 2) and overall low phytoplankton cells counts observed during this cruise ( Table S 1) indicated that sampling occurred prior to the development of the phytoplankton spring blooms. In summer (September 2014, July 2015 and September 2015), SST maps showed a sharp transition between coastal and offshore temperatures confirming the presence of a frontal area. The different observed stratification regimes (Figure S 1) coincided with distinct physicochemical patterns and phytoplanktonic patterns across seasons (Figure 8.B, Table S 1). In late summer (September samples), offshore deep waters shared close characteristics with winter waters (March sample, Temperature: 11.7 to 12.3°C), with few phytoplankton cells, similar concentration of Si(OH)4 (1.61 to 3 µM) and Nitrates (3.99 to 6.41 µM). Conversely, surface waters presented high temperatures (14.7 to 18.2°C), a nutrient depletion (Si(OH)4: 0.02 to 1.65 µM, Nitrates: 0.00 to 0.09 µM) and high phytoplankton cells counts. In early summer (July), the nutrients concentrations were overall lower than in September, probably consumed by the spring bloom coinciding with the onset of the front around May-June. A significant bloom occurred at stations 2 and 3, on the 27th of June, five days before the sampling period as seen in the satellite surface Chla observations (Figure S 2.B).
Figure 8 | Environmental and bacterial communities characteristics of the Iroise Sea for the different cruises. A. Map of the Satellite Sea Surface Temperatures (SST) in the Iroise Sea for each cruise (September 2014 the 8th, March 2015 the 6th, July 2015 the 2nd and September 2015 the 8th), highlighting in the summer cruises the presence of the Ushant front that separates offshore warm stratified waters and coastal cooler mixed waters. The shape correspond to the different stations sampled, with at each time 2 or 3 depths. Surface waters are highly dynamic and some eddies are conspicuous during September 2015, bringing cold water in surface stratified waters. Thus, SST map are not enough to define the frontal area and vertical profiles are needed to characterize the different water masses (Figure S 1) . B. Principal Component Analysis of the environmental characteristics for the different samples based on temperature, inorganic nutrients (Si(OH)4, NO3+NO2, PO4) concentrations and microscopic count of the different phytoplanktonic groups (Diatom, Nanophytoplankton, Cryptohyceae and Dinoflagellate) C. NMDS of the bacterial diversity based on Bray-Curtis dissimilarities among the different cruises, stations and depth (stress = 0.091).

BACTERIAL COMMUNITY DYNAMICS IN THE IROISE SEA

Bacterial diversity structure presented unusual seasonal and spatial patterns, mirroring environmental physicochemical variations represented in the PCA plot of environmental variables (Figure 8.B), as shown in the NMDS ordination plot (Figure 8.C, for each cruise separately see Figure S 3).
In winter, community were highly similar throughout the water column (Permanova test on Bray-Curtis dissimilarities ~ Depth was non-significant, Pr(>f) = 0.225), but presented a coastal to offshore gradient (Permanova test on Bray-Curtis dissimilarities ~ Station was significant, Pr(>f) = 0.001). Conversely, with the onset of stratification in summer, communities were much more heterogeneous and their structure followed the different water masses: deep water communities remained similar to those present in winter (Figure 8.B). In contrast, surface bacterial communities in stratified regimes mostly departed from this typical winter structure, with depth becoming a significant structuring driver (Permanova test Bray-Curtis dissimilarities ~ Depth Pr(>f) = 0.004). This trend was not significant in mixed coastal samples (Permanova test Bray-Curtis dissimilarities ~ Station was not significant, Pr(>f) = 0.222 and 0.434). Besides, similar communities were found in stratified waters in September 2014 and 2015 samples (Figure 8.C) indicating a clear recurring seasonal pattern.
Using LEfSe algorithm (Segata et al., 2011), we found that some stations favored specific biomarker OTUs within each sampling time (Figure S 4). Biomarkers such as OTU1 (Amylibacter) and OTU4 (Planktomarina) were found at the most near-shore station (Station 1) in September 2014 and March 2015 and other biomarkers were found in surface stratified waters in early summer (OTU29 NS4 marine group) and late summer (OTU10 Synechococcus) cruises, but no biomarker was identified for the mixed and frontal stations except in July 2015 at Station 2 with OTU35 (Aliivibrio) and OTU74 (Pseudoalteromonas).
Representation of the main connectivity between the different modules detected with the Louvain algorithm. Each node represent a module, each edge represent the median of the positive correlations between two modules. Only the strongest connection are shown. B. Network visualization of the dataset. Each node represents an OTU, while each edge represent a positive correlation (>0.3) obtained from the covariance matrix calculated with SPIEC-EASI. The node size depends on the abundance of one OTU (in number of sequences) in the entire dataset. The edge size depends on the value of the correlation. Nodes colors represent the OTU affiliation to the different Louvain communities. Network was represented using Gephi and Force Atlas layout algorithm. C. Presentation of the 14 modules detected. Pearson correlations between each module eigengene and the different environmental data are presented in the heatmap. OM: Organic Matter, PAR: Photosynthetically Available Radiation, PON: Particulate Organic Nitrogen, POC: Particulate Organic Carbon. Only the significative correlations (p value > 10-4 ) are shown. The number of OTUs in each module and the relative abundance of each module (in % of all the reads) are detailed.
We further investigated groups of co-varying OTUs that could potentially share the same ecological niches in this contrasted marine environment via a co-occurrence network analysis. In the inferred network, we were able to identify 14 sub-networks (Figure 9) defining groups of OTUs (named modules hereafter) with similar distribution patterns across the entire study. Two modules (5 and 4) were dominant in our dataset and respectively accounted for 32.7% and 20.9% of all sequences. Eight modules represented between 1.6% and 14.2%, and four were rarer with less than 1% in abundance. The OTU taxonomy in each module is summarized in Figure S 5 and Figure 11 presents the distribution of the dominant families among each module.
Since the eigengene can characterize each module, we investigated to which extent module distribution could be correlated (i.e. Pearson correlation) with environmental parameters (Figure 9.C). Correlation patterns partitioned modules into three significant subnetworks. The first one mostly comprises modules 5 and 13 representing 32.7% and 6.8% of the dataset respectively, that correlated positively to inorganic nutrients and negatively to temperature and Particulate Organic Matter (POC, PON) values. Their relative abundance in the different samples showed that they were dominant in oligotrophic waters: together they ranged from 39% at Station 1 to 72% at Station 5 of the late winter communities, and 54% to 59% of the deep stratified waters in September (Figure 10). SAR11 Surface 1 (46.7% of the module sequences), ZD0405 marine group (12%) and SAR86 Clade (6.4%) dominated the main module (i.e. module 5). The module 13 showed a different diversity, dominated by members of Marinimicrobia (15.8%), SAR11 Deep 1 (12%), SAR11 Surface 1 (8.9%) and Salinisphaeraceae (7.9%).

Table of contents :

GENERAL INTRODUCTION
1. MARINE MICROORGANISMS
1.1. Marine bacteria: a recent story
1.2. Key role of planktonic bacteria in marine ecosystems
1.2.1. Role in primary production
1.2.2. Role in Organic Matter recycling and the food web: the microbial loop
1.2.3. Role in global biogeochemical cycles
1.2.3.1. Carbon
1.2.3.2. Nitrogen
1.2.3.3. Sulfur
1.3. The revolution of molecular tools in marine microbial ecology
1.3.1. Accessing diversity using 16S sequencing
1.3.1.1. Methods and limits
1.3.1.2. An unsuspected diversity in the ocean
1.3.2. Metagenomics
1.3.2.1. Methods and limits
1.3.2.2. New capacities discovered
1.4. Marine microbial ecology based on DNA data
1.4.1. Bacterial communities characteristics
1.4.1.1. Free-living and particle-attached bacteria
1.4.1.2. Rare biosphere of sea water
1.4.2. Bacterial communities dynamics
1.4.2.1. Temporal dynamic
1.4.2.2. Spatial dynamic
1.4.2.3. Everything is everywhere
1.4.3. Marine bacteria trophic strategies
2. THE IROISE SEA AND THE BAY OF BREST
2.1. Coastal oceans: a crucial but fragile ecosystem
2.2. The Iroise sea and the Ushant Front
2.3. The Bay of Brest and the SOMLIT station
CHAPTER I SPATIO-TEMPORAL DYNAMIC OF BACTERIAL COMMUNITIES IN THE IROISE SEA
1. RESUME EN FRANÇAIS
2. ABSTRACT
3. INTRODUCTION
4. RESULTS
4.1.1. Environmental settings
4.1.2. Bacterial community dynamics in the Iroise Sea
4.1.3. Description of modules with a specific dynamic, diversity and correlation with environmental parameters
5. DISCUSSION
6. CONCLUSION
7. MATERIAL AND METHODS
7.1. Study site and sampling design
7.2. Nutrients, phytoplankton counts and pigments analysis
7.3. Bacterioplankton communities sampling
7.4. DNA extraction and sequencing
7.5. Bioinformatics analysis
7.6. Statistical analysis
7.7. Network analysis
8. SUPPLEMENTARY DATA
CHAPTER II. TEMPORAL DYNAMIC OF BACTERIAL COMMUNITIES IN THE BAY OF BREST
1. RESUME EN FRANÇAIS
2. ABSTRACT
3. INTRODUCTION
4. MATERIAL AND METHODS
4.1. 1. Study site and sampling strategy
4.2. 2. DNA extraction and sequencing for bacterial diversity
4.3. 3. Bioinformatics analysis
4.4. 4. Statistical analysis
4.5. 5. Looking for general patterns of seasonality
5. RESULTS
5.1. Environmental settings
5.2. Bacterioplankton community’s characteristics
5.3. Seasonality of bacterial communities
6. DISCUSSION
6.1. Seasonal dynamics of bacterial communities
6.2. Break of seasonality: spring blooms
6.3. Late summer communities
6.4. Multiple possible drivers in winter.
7. CONCLUSION
8. SUPPLEMENTARY DATA
CHAPTER III. INTO THE GENOME OF AN ABUNDANT RHODOBACTERACEAE
1. RESUME EN FRANÇAIS
2. ABSTRACT
3. INTRODUCTION
4. MATERIAL AND METHODS
4.1. Study site and sampling
4.2. Libraries preparation for metagenomes
4.3. Reconstruction of Metagenome Assembled Genomes (MAGs)
4.3.1. Sequences quality filtering and assembly
4.3.2. Binning and MAG characterization
4.3.3. Phylogenomic
1. RESULTS AND DISCUSSION
1.1. Recovery of MAGs and identification of the dominant MAG affiliated to the Rhodobacteraceae family
1.2. Genomic characteristics of MAG ID03_00003
1.3. Gene content and putative metabolism
1.4. Taxonomic affiliation and Phylogeny
1.5. Importance of MAG ID03_00003 in the Bay of Brest and the Iroise sea.
2. CONCLUSION
3. SUPPLEMENTARY DATA
DISCUSSION AND PERSPECTIVES
1. THE BREST BAY TIMES SERIES IN THE CONTEXT OF COASTAL GENOMIC OBSERVATORIES
1.1. How to bridge the gap between physical and chemical oceanography and microbial ecology in sub-mesoscale structures?
2. FROM ABIOTIC TO BIOTIC DRIVERS
3. A PERSPECTIVE: BETTER UNDERSTANDING OF ANTHROPIC PRESSURES ON COASTAL ECOSYSTEMS
GENERAL CONCLUSION
REFERENCES

GET THE COMPLETE PROJECT