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Article

Seasonal Surges in Bacterial Diversity along the Coastal Waters of the Eastern Arabian Sea

by
S. Hafza
1,2,
A. Parvathi
1,2,*,
A. S. Pradeep Ram
3,*,
Thampan K. Alok
2,
R. Neeraja
1,
R. Jyothibabu
2 and
G. V. M. Gupta
4
1
Department of Biotechnology, Cochin University of Science and Technology (CUSAT), Kochi 682022, India
2
CSIR—National Institute of Oceanography, Regional Centre, Kochi 682018, India
3
Laboratoire Microorganismes: Génome et Environnement (UMR CNRS 6023), Université Clermont-Auvergne, CEDEX, 63178 Aubière, France
4
Centre for Marine Living Resources and Ecology, Kochi 682508, India
*
Authors to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2024, 12(10), 1796; https://doi.org/10.3390/jmse12101796
Submission received: 15 June 2024 / Revised: 1 October 2024 / Accepted: 3 October 2024 / Published: 9 October 2024
(This article belongs to the Section Marine Biology)
Browse Figures
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Abstract

:
The upwelling phenomenon plays a vital role within marine ecosystems, transporting essential nutrients from the bottom to the surface and boosting biological productivity. However, the bacterial community structure in upwelling zones along the western coast of India (WCI) is understudied. This research systematically examines bacterial diversity across three seasons—pre-monsoon (PR), monsoon (MN), and post-monsoon (PM)—using next-generation sequencing. Our findings show distinct spatial patterns of bacterial communities in the Arabian Sea and demonstrate that ecological variations influence bacterial distribution in this dynamic environment. During MN, the bacterial community exhibited greater species diversity but lower overall abundance compared to PR and PM. Non-Metric MDS cluster analysis revealed a 78% similarity (at order level) between PR and PM, indicating that MN supports unique bacterial diversity. KEGG analysis showed significant seasonal variations in metabolic functions, with increased functional potential during MN. Additionally, Carbohydrate-Active enZymes (CAZymes) analysis revealed distinct seasonal profiles, among which the GH13 enzymes were the most prevalent glycoside hydrolases during MN, predominantly being sucrose phosphorylase and glucosidase, known for breaking down glucan deposits derived from phytoplankton. The CAZymes profiles supported taxonomic and KEGG pathway findings, reinforcing that microbial communities are seasonally distinct and functionally adapted to changing availability of nutrients.

Graphical Abstract

1. Introduction

The Arabian Sea (AS) stands out as one of the most productive marine regions globally, undergoing pronounced seasonal fluctuations in biological activity [1]. During the pre-monsoon (PR) period in the AS, the surface waters remain in an unstable warm state due to high solar radiation [2]. Conversely, the monsoon (MN) season witnesses stable surface waters along the Kochi to Goa regions. The robust southwest monsoon triggers vigorous upwelling along the western Arabian Sea, enriching the photic zone with nutrients from deeper waters and fostering higher productivity [3,4,5,6]. However, during the post-monsoon (PM) period, generally unstable conditions prevail throughout the WCI [7]. Higher chlorophyll a concentrations have been reported during PM and MN with respect to the monsoon [8]. During MN and PM, various mechanisms govern water-column mixing and nutrient upwelling, with a subsequent increase in phytoplankton biomass [9,10,11,12,13,14].
Variations in phytoplankton, nutrients, and other environmental variables dynamically influence the abundance and composition of microbial communities over time and space [15,16,17]. Anthropogenic inputs, such as riverine influx during the monsoon, activities like coastal urbanization, industries, maritime transport, oil extraction and refining, tourism, and aquaculture, affect both the seawater column and marine sediments through benthic-water flux (upwelling processes) [18]. Pollution impacts on coastal microbial communities represent a complex interplay of multiple stressors, including natural and anthropogenic pollutants [19]. These stressors can alter coastal ecosystems, potentially affecting marine microbes and ecosystem functioning negatively [19]. However, investigations into microbial community dynamics are limited, despite their critical importance. Though observations such as Indian JGOFS extensively investigated the Arabian Sea’s biological and chemical properties during the monsoon season, studies were limited in temporal scope and did not thoroughly address the taxonomic composition of microbial communities along the western coast of India in an annual cycle [20]. Understanding microbial populations’ seasonal variance is crucial, considering their pivotal roles in nutrient cycling, sulfate reduction, hydrocarbon degradation, and other ecological processes [21]. On a global scale, studies of microbial communities in upwelling systems have revealed significant correlations between microbial diversity and primary production, underscoring the crucial role of bacteria in organic matter decomposition and transport [22]. Nonetheless, there remains a scarcity of data on microbial community differences and functions along the eastern AS.
This study aims to investigate seasonal shifts in bacterial communities and their functional potential in the coastal areas of AS. Specifically, this study examines the taxonomic composition of the bacterial communities during the pre-monsoon (April–May), monsoon (August), and post-monsoon (January–February) periods along the western coast of India, spanning from 9.951° N to 22.22° N latitude (Figure 1), using shotgun metagenomic sequencing and functional annotation. This study also examines the variations in functional roles of bacterial communities during these three seasons.

2. Materials and Methods

2.1. Sampling Location and Sample Collection

Sampling was carried out during a multidisciplinary oceanographic expedition onboard FORV Sagar Sampada and Sagar Kanya along the WCI (2018–19). The research area spanned from 9.951° N to 22.22° N latitude and 68.940° E to 76.166° E longitude, covering the south—Kochi (L1), central—Goa (L2), and north—Okha (L3) in the eastern Arabian Sea (Figure 1). Each location consisted of 3 samples collected from coastal surface waters as part of MEDAS project of Ministry of Earth Science (MoES) during the summer monsoon—MN (August; FORV SK351), pre-monsoon—PR (April–May; FORV SS375), and post-monsoon—PM (January; FORV SS382). Samples were collected following strict sterility protocols, using Niskin samplers (10 L, Hydrobios, Kiel) (Seabird Scientific, Bellevue, WA, USA) attached to a Conductivity Temperature Depth (CTD) profiler (SBE 19, Seabird Scientific, Bellevue, WA, USA).

2.2. Physiochemical Properties

A CTD profiler equipped with sensors was used to measure temperature, dissolved oxygen (DO), and salinity. The Winkler method was used to estimate dissolved oxygen (DO) in the water samples. The samples were titrated with standard thiosulphate utilizing starch as an endpoint detector after being fixed with 0.5 mL of Winkler reagents [23]. Using an auto analyzer, dissolved inorganic nutrients such as nitrite (NO2), phosphate (PO43−), silicate (SiO4), nitrate (NO3−), and ammonium (NH4+)) were measured (Scalar SAN++, Breda, The Netherlands) [24,25].

2.3. DNA Isolation

Seawater samples (10 L) were collected using a Niskin sampler, which was washed at each sampling point with a diluted HNO3 solution to ensure sterility. The samples were then filtered through 0.22 μm Millipore filter papers (Millipore, MA, USA) to entrap the bacterial population after the sequential filtration of 10L of a water sample through 20-μm and 1-μm pre-filters (Millipore, MA, USA) in technical triplicates for each water sample. Genomic DNA was isolated using the NucleoSpin eDNA Water Kit from Macherey-Nagel. One fourth of the round filter was used to which 2 mL buffer was added to dissolve the filter. DNA was extracted individually from 3 filter papers and the DNA was pooled for sequencing. The filter was treated with proteinase K and eDNA was extracted following the manufacturer’s instructions. The concentration of DNA was checked using a Qubit 3.0 fluorometer (Thermofisher Scientific, Waltham, MA, USA). DNA purity was assessed using a Nanodrop 2000 (Thermofisher Scientific, Waltham, MA, USA).

2.4. Whole Genome Library Preparation and Sequencing

Whole genome Library Preparation was carried out using KAPA HyperPlus Kit (Merck SA, Buenos Aires, Argentina) for all 9 samples (3 samples per season). A 350–500 ng DNA was used for library construction, as per the manufacturers’ protocol. The prepared libraries were quantified with a Qubit 3.0 fluorometer (Thermofisher Scientific, Waltham, MA, USA) using DNA HS assay kit (Thermofisher Scientific, Waltham, MA, USA). The size of the DNA was checked on Agilent 2100 bioanalyzer using an Agilent high sensitivity DNA assay kit. Based on the concentration and size of the fragments, the samples were pooled and pooled libraries were sequenced using a NovaSeq 6000 Sequencing System S4 flow cell (Illumina, Inc., San Diego, CA, USA).

2.5. Downstream Processing

The fastp tool was used to perform adapter trimming on raw sequencing reads to remove artifacts and ensure data quality [26]. FastQC evaluated the quality of the trimmed reads to ensure that the subsequent analyses were based on reliable data. The reconstruction of genome fragments from different organisms by combining reads into contigs was achieved using the MEGAHIT assembler [27]. Taxator-tk and Kraken tools were used for taxonomic classification [28,29]. The Kraken tool utilized the NCBI taxonomy database to create a representation of the taxonomic profiles and the OTU classification is based on de novo assembly. Genes in MAGs were annotated using PROKKA, providing functional insight [30].

2.6. Diversity Analysis and Taxonomy Classification

The KEGG repository is essential for understanding biological systems, from single cells to entire organisms. The KEGG method studies the seven major categories that are as follows: Metabolism Pathways, Genetic Information Processing, Environmental Information Processing, Cellular Processes, Organismal Systems, Human Diseases, and Drug Development. KEGG Level 1 paths provide the highest level in the KEGG PATHWAY database, while KEGG Level 2 provides more specific pathways within each Level 1 category. For KEGG pathway classification studies, high-quality reads were compared against the NCBI-NR-KEGG Database using Diamond tool based on a sequence homology approach [31,32]. Sequences were further annotated with dbCAN metaSever. The reads were compared against CAZy protein sequences using a Diamond blastx tool to analyze the CAZymes [33].

2.7. Statistical Analysis

The alpha diversity was calculated using Shannon and Simpson diversity metrics. For alpha diversity calculation, vegan R package was used [34]. Differential abundance was analyzed with Deseq2 using R0 programming [35]. A Distance-based Linear Model with a Distance-based Redundancy Analysis plot (dbRDA) was carried out to understand the physicochemical and biological disparities between seasons using Primer 7 for Windows software (Plymouth Marine Laboratory, Plymouth, UK) [36]. A Bray–Curtis similarity matrix (4th root transformed to deemphasize the contribution of any one particular dominant cluster) was constructed from the clusters from each location. Additionally, statistical analyses using STAMP G-test (with Yates’ correction) and Fisher’s test underscore significant variation in the diversity between the PR, MN, and PM [37]. Tukey–Kramer post hoc plots ANOVA with Benjamini–Hochberg FDR correction using p-value cutoff < 0.05 and 0.95 confidence were conducted to analyze the significant variation between CAZy profiles and the KEGG functional profile.

3. Results

3.1. Estimation of Physiochemical Parameters

The temperature during the PR and PM (30.51 ± 1.33 °C and 26.42 ± 2.91 °C) was higher compared to MN (23.31 ± 0.79 °C). The highest temperature was observed at Station L1 during PR (31.69 °C). The temperature decreased during MN to 22.42 °C at Station L2 followed by Station L1 (23.58 °C) and Station L3 (23.94 °C). During PM, the temperatures gradually increased (26.42 ± 2.91 °C) (Figure 2a). The overall salinity was higher towards the north (L3, av. 36.56 ± 0.11 and in L2, av. 34.89 ± 0.57), irrespective of seasonal changes. In the south, L1 recorded a lower salinity during all seasons, which was as low as 24.26 during MN (Figure 2b). DO was also observed to be lower during MN in the south (L1: 106.51 µM and L2: 114.91 µM) (Figure 2c). In the case of chlorophyll a, a higher concentration was observed during MN (2.60 ± 1.9mg m−3) compared to PR and PM (1.27 ± 0.14 mg m−3 and 0.60 ± 0.29 mg m−3, respectively) (Figure 2d). Nutrients showed a higher concentration during MN (SiO4:- 22.57 ± 10.35 µM; NO3−:- 7.22 ± 3.67 µM; NO2:- 0.75 ± 0.85 µM; NH4+:- 0.66 ± 0.52 µM) compared to PR and PM (PR: SiO4:- 4.49 ± 1.61 µM; NO3−:- 0.26 ± 0.17 µM; NO2:- 0.41 ± 0.58 µM; NH4+:- 0.27 ± 0.04 µM and PM: SiO4:- 5.28 ± 2.11 µM; NO3−:- 0.21 ± 0.24 µM; NO2:- 0.18 ± 0.21 µM; NH4+:- 0.52 ± 0.25 µM) (Figure 3).

3.2. Bacterial Diversity

The total reads ranged from 27,256,782 to 36,467,020. Using Illumina sequencing technology, for nine samples, an average of 30.67 million raw data were generated where 99.8% reads were retained as high-quality data and the N50 values ranged from 1406 to 2388 (Table S1, Figure S1). The alpha diversity indices showed higher diversity during MN and the least diversity at L2 PR and L1 PM (Figures S2–S4).
After OTUs were categorized at 97% similarity, avg 8102 OTUs (including viruses and archaea) were found. A total of 40 taxa were identified by phylum-level classification of OTUs, with Cyanobacteria and Proteobacteria dominating the metagenomes, followed by Bacteroidetes. Furthermore, in all metagenomes analyzed, Firmicutes, Actinomycetota, Tenericutes, and Planctomycetota had large relative abundances. Upon analyzing the genus-level classification of the OTUs, 2000 genera were identified, with Candidatus pelagibacter, Synechococcus, Candidatus nitrosopelagicus, Prochlorococcus, and Alteromonas showing a dominant presence. However, 21.09 ± 8.9% of the reads were unable to be categorized to any specific genus.
The phylum level analysis indicated Proteobacteria as a dominant phylum in L1 (53.59%) and L3 (55.00%) during PR; conversely, in L2, Cyanobacteria (74.44%) dominated. However, during MN, Proteobacteria emerged as the dominant phyla in L2 (78.73%) and Cyanobacteria declined to 0.86% in L2 and to 2.77% and 4.49% in L1 and L3, respectively (Figure 4). There was an increase in Firmicutes (2.57 to 5.35%) and Actinomycetota (2.36 to 8.36%) during MN in L1 and L2. The abundance of Firmicutes further increased during PM in L1 and L2 (6.91%), whereas Actinomycetota dropped to 3.18%. L3 showed no significant differences in the relative abundance of Firmicutes and Actinomycetota among seasons. There was variation in the relative abundance of Bacteroidetes during PR (6.56%) to MN (12.94%), but this dropped during PM (9.26%) (Figure 4; Table S2).
The classes Cyanophyceae (33.61%), Alphaproteobacteria (30.25%), and Gammaproteobacteria (20.66%) dominated in L1 during PR. The relative abundance of Cyanophyceae was double in L2 compared to other stations during PR (74.44%), followed by Alphaproteobacteria with 11.68%. In L3, Alphaproteobacteria dominated, contributing to 36.44% of the total communities during PR. Following the onset of MN, there was a notable decrease in the relative abundance of Cyanophyceae across all stations, accompanied by a substantial increase in Betaproteobacteria, Flavobacteria, Bacilli, Actinomycetes, Clostridia, Epsilonproteobacteria, and Deltaproteobacteria (Table S3). In PM, Cyanophyceae showed a relative increase to dominance in L1 and L2, along with Alphaproteobacteria. Conversely, in L3, Alpha and Gammaproteobacteria dominated, with no significant changes observed in the levels of other bacterial classes during the transition from PR to MN, except for Flavobacteria, which increased from 7.69% in PR to 14.45% during MN (Figure S5; Table S3)
The group of Synechococcales was predominant during PR, constituting 18.09–74.20% of the community, whereas it was considerably lower in the MN (3.51–0.40%), and its abundance increased again during PM in L1 (53.56%) and L2 (28.33%, whereas L3 retained a similar level as in MN (5.10%). Although Pelagibacterales were present in all three seasons, their abundance varied among stations. In L1, they constituted 13.84% in PR, 12.78% in MN, and 15.46% in PM. But in L2, a relatively lower percentage was observed during PR (8.76%) and it further dropped during MN (2.89%). In both L2 and L3, the highest abundance of Pelagibacteriales was observed during PM (29.19% and 37.92%, respectively). The abundance of Moraxellales was low in MN (1.02 and 1.09%) and PM (0.85 and 0.86%) compared to PR (12.70 and 1.79%) in L1 and L3. While the order Sphingomonadales was present in all three seasons in L1, its abundance was higher in PR (7.34%) compared to MN (5.00%) and PM (0.50%). A similar pattern is observed in L3 as well; a higher abundance was observed in PR (2.89%), but a further reduction in relative abundance was observed in MN (2.07%) and PM (0.83). In contrast, in L2, a significant increase in the relative abundance of Sphingomonadales (6.13%) was observed during MN, while the levels during PR and PM were negligible (<0.7%). The percentage abundance of the Rhodobacterales, Flavobacteriales, Pseudomonadales, Alteromonadales, Hyphomicrobiales, and Burkholderiales orders showed considerable variation across seasons, with a higher abundance in MN, specifically Rhodobacterales and Flavobacteriales in L1; Alteromonadales in L2; and Flavobacteriales in L3 (Figure 5; Table S4).
Synechococcus (32.43%), Candidatus pelagibacter (13.73%), Moraxella (11.70%), and Qipengyuania (3.50%) were the dominant genera in L1 during PR. By the advent of the monsoon, Candidatus pelagibacter (12.62%), Candidatus nitrosopelagicus (5.17%), and Pseudomonas (2.19%) dominated over Synechococcus (1.59%). An observed increase in the relative abundance of genera Marinobacter, Flavobacterium, and Nitrosopumilus was seen during MN. Prochlorococcus (43.69%) dominated the system along with Candidatus pelagibacter (15.31%) and Synechococcus (9.19%) in PM. Synechococcus (73.65%) and Candidatus pelagibacter (8.43%) dominated in L2 during PR. Alteromonas (25.84%), Pseudoalteromonas (4.73%), Qipengyuania (3.61%) and Marinobacter (3.27%) were dominant during MN in L2. Candidatus pelagibacter (28.94%) and Synechococcus (25.91%) co-dominated in L2 during PM. In L3, during all seasons, Candidatus pelagibacter and Synechococcus were consistently present as the dominant genera. Apart from the above-mentioned genera, PR and PM had the presence of Candidatus nitrosopelagicus (3.99%; 1.83%) and Nitrosopumilus (2.02%; 2.41%). On the other hand, MN was much more diverse with Flavobacterium (2.64%) and Pseudomonas (2.17%) followed by Clostridium, Vibrio, Sulfitobacter, and Polaribacter (Table S5). Furthermore, statistical analyses conducted with DESeq2 in R programming at the order and genus taxa levels confirm the diversity variations among PR, MN, and PM. Volcano plots were generated to visually represent the significant differences. The results indicate the downregulation of three orders and the upregulation of two orders during the monsoon, whereas seven orders are downregulated and five orders are upregulated during pre-monsoon (Figure 4, Figure 5 and Figure S6).

3.3. KEGG Analysis

The assessment of functional potential was conducted via KEGG databases using MEGAN. The KEGG analysis unveiled notable disparities among the seasons. KEGG level 1 revealed higher reads on metabolism in MN samples followed by PR (Figure S7). KEGG level 2 analysis supported these results, where the MN samples were functionally more active especially in terms of carbohydrate metabolism, amino acid metabolism, energy metabolism, nucleotide metabolism, metabolism of co-factors and vitamins, lipid metabolism, xenobiotic degradation and metabolism, translation, replication, and repair and membrane transport (Figure 6). The further functional profile was lower in PR and its lowest in PM. Though the seasonal variation was significant, no significant difference between stations was observed functionally during PR and MN. During PM, L3 (north) was functionally more active (in terms of metabolism—carbohydrate, energy, nucleotide, and translation) (Figure 6). Tukey–Kramer post hoc plots ANOVA with Benjamini–Hochberg FDR correction using p-value cutoff < 0.05 and 0.95 confidence identified the significant variation between the KEGG functional profile of MN, PR, and PM (Figures S8 and S9).

3.4. CAZymes

A significant variation in CAZymes distribution was observed between the seasons (Figure 7). An increased abundance of GT4 (disaccharide-glucosyl transferases; retaining mechanism), GH23 (chitinase, peptidoglycan lyase), GH24 (lysozymes), GT25 (galactosyl transferases; inverting mechanism), and GT2 (disaccharide-glucosyl transferases; inverting mechanism) was observed in PR compared to MN. MN was instead observed to have increased levels of AA3_2 (GMC-oxidoreductase family; glycose 1-oxidse), GH13_23 (alpha glycosidase), GH13, GH13_11 (Amylomaltase, Isoamylase), GH13_30 (alpha glucosidases), GH5_11 (Xylan ß-1,4-Xylosidase, Endo ß-1,4 mannanase, etc.), and GT51 (Peptidoglycan glucosyl transferase) (Figure 7c,d). In comparison with PM, the levels of CBM50 (enzymes targeting peptidoglycan/chitin), GH13, and GT51 were higher during MN. Based on the CAZymes profile, principal component analysis (PCA) and heatmap analysis were carried out in which PR, MN+L3PM, and PM were observed to have distinct profiles (Figure 7a,b). Tukey–Kramer post hoc plots ANOVA with Benjamini–Hochberg FDR correction using p-value cutoff < 0.05 and 0.95 confidence demonstrated the above variation between CAZymes profiles (Figure 7).

3.5. Abiotic Factors of the System and Diversity

DistLM-based db-RDA analysis revealed distinct physiochemical and biological disparities in the MN compared to the PR and PM. Cluster analysis based on Bray–Curtis similarity indicated that bacterial communities in PR and PM shared 78% similarity, forming a distinct cluster, whereas the MN community formed a separate cluster with 60% similarity to the others. The cluster analysis derived distinctly delineated variations in prokaryotic diversity (Figure 8). The bacterial community composition analyzed using the DistLM indicated that the influencing factors of the bacterial community were predominantly nutrients, whereas for PR and PM it was temperature, salinity, and DO. Additionally, a higher bacterial diversity was observed in MN compared to PR and PM (Figure 8).

4. Discussion

The present study offers insights into how microorganisms respond and adapt to diverse stressors across different marine environments, as well as shedding light on their coping mechanisms and adaptive strategies. The dataset obtained from three sampling cruises across three transects from south to north examined the bacterial diversity in relation to various physicochemical parameters, including temperature, dissolved oxygen (DO), salinity, and nutrients (nitrate, nitrite, phosphate, and silicate).

4.1. Physicochemical Settings during the Study

There were observed changes in the physicochemical parameters from PR to MN and MN to PM. The PR period is marked by elevated surface temperatures and moderate nutrient levels, except for phosphate, which is lower in the northern region (1.85 µM). Previous studies across the entire Arabian Sea have noted very low phytoplankton biomass and primary production during PR. This is attributed to a uniform mixed layer (<30 m) influenced by increased solar insolation and strong stratification [38]. Consistent with these observations, this study also found lower chlorophyll a levels during PR.
The temperature was considerably higher during PR (30.51 ± 1.34 °C) which was dropped during MN due to monsoonal showers and upwelling. This decline in temperature during MN could be attributed to the presence of upwelled waters, riverine influxes, and precipitation [39,40,41,42,43,44]. A drop in salinity was observed in L1 during MN compared to other stations and seasons. This could possibly be due to riverine fresh water influx, comparatively less saline water inputs, and the low salinity waters transported from the Bay of Bengal [45]. The MN is also characterized by deoxygenation nutrient surge, specifically nitrate, phytoplankton abundance, and chlorophyll a hike [4,46]. Similar to PR, PM is also characterized by a warm temperature and high salinity and a lower chlorophyll a concentration. The nutrients are also very low.

4.2. Taxonomic Distribution during PR

During PR, Proteobacteria was the dominant phylum across all locations, but its abundance varied significantly between L1, L2, and L3. The highest abundance was observed in L3. The dominance of Proteobacteria in marine environments is well-documented [47,48,49,50,51]. They can adapt in most environments mostly due to their ability to utilize wide range of compounds such as carbon, sulfur, aromatics, fatty acids, carbohydrates, etc. [51]. The higher abundance of the phylum Proteobacteria, particularly the class Alphaproteobacteria, in L3 compared to L1 and L2, suggests a location-specific prevalence. A recent study conducted in the marine alphaproteobacterial HIMB59 identified one of the key factors of adaptation as phosphate concentration [52]. An increased phosphate concentration was observed in L3 in particular during the study; could be a factor for this increased abundance. Comparable levels of diversity within the Proteobacteria phylum have been documented in the coastal zone of the Arabian Sea [53] as well as in various other regions globally [54]. Alphaproteobacteria are widely recognized for their diversity and ecological roles in marine environments [55]. In detail, the order Pelagibacterales contributed most significantly to this class in L3. Initially known solely from metagenomic data as the SAR11 clade [56,57], this order is notably small with small genome sizes and limited metabolic functions [58]. Some are oligotrophs, relying on dissolved organic carbon and nitrogen, and are unable to fix carbon or nitrogen themselves. The higher abundance could indicate limited nutrient availability in surface waters during PR, particularly in L3. Such a nutrient limited condition can be observed during this study. Gamma proteobacteria also exhibited variation, with higher abundance in L1 compared to L2 and L3. This higher abundance of Gammaproteobacteria has also been reported previously in SEAS [59]. Gammaproteobacteria included versatile marine taxa with roles in carbon cycling [60]. Betaproteobacteria, Epsilonproteobacteria, and Deltaproteobacteria classes exhibited relatively lower abundances across all locations, with some minor variations between the sampling sites. These classes mostly comprise nitrifying and sulfate reducers [61], and reduced nutrient levels during PR could be the reason for the lower abundance in all stations.
Cyanobacteria are known for their role in primary production and can bloom under nutrient enrichment [62]. The elevated use of nitrogen fertilizers, along with inputs from human and agricultural waste, stormwater runoff, groundwater discharge, and atmospheric deposition, can contribute to increased nitrogen and phosphorus loads. Cyanobacterial growth in coastal waters suggests nutrient co-limitation involving both nitrogen and phosphorus [62]. The higher abundance in L2 could be related to localized factors promoting cyanobacterial growth. Among these, Prochlorococcus thrives in warm oligotrophic waters but is notably absent in eutrophic coastal regions. In the present study, Prochlorococcus flourished during PR and declined in MN where eutrophic conditions existed. Recent research indicates that it originated from an ancestral cyanobacterium through a process involving reductions in cell and genome sizes. Environmental pressures evidently played a significant role in shaping the evolution of Prochlorococcus. Its diminutive size confers advantages for thriving in nutrient-poor environments [63]. Conversely, Synechococcus tends to dominate picocyanobacterial communities in eutrophic coastal areas and mesotrophic open ocean waters [64]. Nutrient data obtained during this study [65] point to a clear mesotrophic condition during PR. Our study aligns with this pattern, indicating a higher abundance of Synechococcus during PR.
Bacteroidetes and Firmicutes exhibited a slight increase in abundance in L3 compared to L1 and L2. Bacteroidetes and Firmicutes play important roles in organic matter degradation and are influenced by nutrient inputs [66]. The increased abundance in L3 might be due to the increased phosphate level at L3 [65]. Flavobacteriia and Bacilli are involved in organic matter degradation and nutrient cycling in marine environments [67]. Actinomycetota, Tenericutes, Planctomycetota, Fusobacteria, Spirochaetes, and Deinococcus-Thermus phylas exhibit relatively lower abundances across all locations, with some minor variations between sampling sites during PR.

4.3. Taxonomic Distribution during PM

Proteobacteria exhibited varying abundances across the stations, with a higher abundance in north experiencing a winter monsoon (L3-PM, 62.38%). Cyanobacteria displayed substantial variability across stations L1 (54.73%), L2 (29.34%), and L3 (6.15%). A reduced abundance towards the north could be due to an unfavorable condition brought out by the winter monsoon. The PM sampling was carried out during January–February, where the north experienced substantial mixing [68]. This influence can be observed in the temperature variance during the study L3-23.06 °C. The order and genus level analysis an abundance of Prochlorococcus in L1 and Synechococcus in L2. The presence of elevated Prochlorococcus abundance observed during the period of post-monsoon (L1 PM) may be attributed to oligotrophic conditions and warmer temperature (28.10 ± 0.31 °C) in the south. To date, there are no reports on the abundance of Prochlorococcus in the coastal waters of Kochi [69]. A similar community structure has been previously observed during the winter monsoon period of the Bay of Bengal, in which Proteobacteria represented the highest diversity and largest fractions [70]. Candidatus nitrosopelagicus and Nitrosopumilus show increased abundance from south to north. The Increased abundance of Candidatus nitrosopelagicus and Nitrosopumilus across the different locations suggests their role in nitrogen cycling, potentially responding to varying nutrient inputs, specifically ammonium [71,72,73]. Flavobacterium, Clostridium and Vibrio display relatively consistent abundances across locations, with slight variations.

4.4. Taxonomic Distribution during MN

The monsoon season (MN) leads to significant shifts in bacterial composition, particularly Cyanophyceae, Alphaproteobacteria, and Gammaproteobacteria. Cyanophyceae (Cyanobacteria) drastically declined during MN, while certain classes like Gammaproteobacteria increased sharply during this period. There was a notable decrease in Synechococcales abundance during MN (0.40–3.51%) compared to PR (74.20–18.09%) and PM (53.56–5.10%) samples, suggesting a shift away from cyanobacterial dominance during the MN. The factors governing the abundance of Synechococcus remain poorly understood, particularly given that even in the most nutrient-depleted regions of the central gyres, population growth rates are often high and not limited [64]. Despite these uncertainties, there is likely a relationship between ambient nitrogen concentrations and Synechococcus abundance [74]. The nitrogen source has a stronger influence on ligand concentration compared to the growth phase, while phosphorus limitation has a more pronounced effect on the cellular and extracellular properties of Synechococcus than nitrogen limitation does [75]. Prochlorococcus is believed to be at least 100 times more abundant than Synechococcus in warm oligotrophic waters [64]. Although there are ambient nutrients available during the MN period, the cyanobacterial population failed to flourish, potentially due to the colder surface water temperatures characteristic of MN due to upwelling. Research conducted in upwelling systems along the east coast of Hainan Island in the South China Sea also revealed a decrease in the abundance of Cyanobacteria in upwelling surface waters, suggesting that upwelling conditions may not be favorable for Cyanobacteria growth [76]. There was a relatively stable abundance of Pelagibacterales across seasons in L1, indicating consistent representation in bacterial communities. A significant increase in Rhodobacterales abundance during MN (L1-MN) potentially reflects the favorable conditions for this order during the monsoon season. Pelagibacterales exhibited relatively stable abundance across the seasons, indicating persistence or resilience in bacterial community representation. Flavobacteriales, Pseudomonadales, Alteromonadales, Hyphomicrobiales, Burkholderiales, Enterobacterales, and Corynebacteriales showed varying degrees of abundance across seasons, highlighting shifts in bacterial community composition. During MN, a notable increase in the abundance of Alteromonadales, particularly Pseudoalteromonas, was observed. These bacteria are commonly found in sea ice and cold waters, recognized for their production of glycolipid-type extracellular polymeric substances (EPS) that exhibit a diverse range of biological activities [77]. This surge in population abundance is perhaps influenced by the temperature of upwelling waters and could be due to plankton during MN [78,79,80]. Reports have documented the presence of this bacterial order even in Arctic ice [81].

4.5. Functional Analysis

The KEGG analysis highlights the increased functional potential during the MN compared to PR and PM. The MN was marked with higher metabolic rates in terms of carbohydrate, energy, and nucleotides as well as amino acids. The higher potential for xenobiotic degradation observed during MN, which was not present in PR and PM, suggests the presence of organisms adapted to the deeper waters during MN [82]. The distinct profiles of CAZyme indicates that the bacterial bioavailable carbohydrates in each season was different, which might contribute to the diversity difference in PR, MN, and PM. This supports the findings presented in taxonomic and KEGG variation. The CAZyme profile of north L3 PM showed similarity to the MN profiles of all stations due to the effect of the north-east monsoon in the north. The GH13 enzymes were the most prevalent glycoside hydrolases (GHase) during MN. The GH13 family is consistently one of the most abundant enzyme families across various metagenomic studies [83,84]. Among the GH13 family detected in the heterotrophic communities associated with this environment were sucrose phosphorylase and glucosidase, known for their involvement in breaking down glucan deposits derived from phytoplankton. In oceanic culture studies from the South China Sea, the majority of genes encoding the GH13 family CAZymes were linked to Rhodobacteraceae and Flavobacteriaceae, which were relatively more abundant during MN (6.93% and 10.62%, respectively). Conversely, in other studies, Alteromonadaceae order bacteria were the predominant ones encoding GH23 family enzymes such as peptidoglycan lyase, which was notably abundant in PR and MN [85]. Additionally, glycosyltransferases (GT) of the GT4 and GT2 families were the most abundant in all datasets. The GH13 and GT4 families encompass CAZymes involved in the uptake and utilization of trehalose. The high prevalence of these CAZymes in oceanic environments could be attributed to trehalose’s characteristics as a stable, non-reducing disaccharide capable of withstanding various pH ranges (3.5–10) and playing a crucial role in energy storage and cellular protection under different stress conditions [86,87]. The glycosyl hydrolase family 5 (GH5) is recognized as one of the largest and diverse families of GH [88]. GH5 enzymes exhibit specificity towards various substrates, such as chitin, mannan, cellulose, Xylan, glucan, and lichenin [89]. The prevalence of the GH5 subfamily 11 was notably high during MN, indicating the presence of chitin, mannan, cellulose, Xylan, glucan, and lichenin in the system. Culture-based studies widely show that GH5 endoglucanases are mostly presented by Gammaproteobacteria [90,91]. The abundance of Gammaproteobacteria was higher during MN in the present study.
To conclude, the MN supported a highly diverse bacterial diversity compared to PR and PM. The changes during the MN could be attributed to several factors:
  • Increased nutrient availability: During the spring inter-monsoon period (PR), the surface layers of the Arabian Sea are nutrient-depleted, but during the summer monsoon, phytoplankton growth is fueled by upwelling events. This upwelling brought on nutrient-rich cold subsurface water into the euphotic zone, with reported 3-fold increases in nitrate compared to surrounding areas. Consequently, high productivity occurs in the Arabian Sea during this season, providing increased organic material for bacterial metabolism [92]. Earlier research conducted in the South China Sea [76], Western Subtropical Pacific [93], and Arabian Sea [94] corroborated these findings.
  • Riverine influxes with anthropogenic inputs: August corresponds to one of the peak riverine runoff months [95]. Our study corroborates previous findings [96,97] as it demonstrates higher nutrient concentrations during the MN period and these could also be anthropogenic driven. Studies shows a 4–6-fold increase in anthropogenic nutrient flux in the past 50 years [98,99]. This is largely attributed to the increased industrialization along the coastline and the rapid development of the agricultural sector in Western Coastal India (WCI). The region is known for cultivating a variety of crops such as rice, bajra, jowar, cotton, millets, and pulses, which have seen significant growth due to the expansion of agricultural activities [100]. This intensification of industrial and agricultural practices near coastal areas has led to various environmental impacts, including increased nutrient runoff [101,102,103]. Research indicates that riverine ecosystems exhibit higher bacterial abundance compared to intertidal and ocean systems [104]. Thus, the riverine runoff during monsoon could positively contribute to the bacterial diversity.
  • Chlorophyll a (Chl a) as a proxy for phytoplankton biomass: High Chl a levels indicate an elevated productivity and organic matter availability, supporting increased bacterial growth [105]. Enhanced levels of nitrates, nitrites, and silicates directly promote phytoplankton growth [106], which contributes significantly during the MN. Such enhanced levels of nutrients were observed during this study. Moreover, upwelling positively influences the phytoplankton blooms [107]; researchers have investigated for decades the relationship between bacteria and algae involving the assimilation and remineralization of phytoplankton-derived organic matter by bacteria [108,109]. All of these attributes point to the conclusion that phytoplankton blooms due to upwelled nutrients could be a reason for higher bacterial diversity during MN in the study region.
  • Deeper water communities: Bacterial communities adapted to deeper waters ascend to the surface during the MN, sustained by optimal conditions created during upwelling—lower temperatures, higher nutrients, and salinity [106]. These communities revert to their native compositions when surface water conditions return to normal physicochemical levels. Recent studies in ocean systems indicate that deeper waters harbor more diverse bacterial community than surface waters [110]. During upwelling events, these diverse communities ascend to the surface and possibly attempt to adapt to the environment.
In summary, the observed high bacterial diversity during the MN season in the western coast of India can be attributed to the interplay of increased nutrient availability, riverine influxes with anthropogenic inputs, elevated phytoplankton biomass, and the influence of deeper water communities brought to the surface during upwelling events. However, to better comprehend the ecological dynamics involved, an in-depth study to analyze the distribution of antimicrobial resistance genes (ARGs) in the Western Coastal India (WCI) region is warranted. ARGs originating from anthropogenic sources due to the use of antibiotics in aquaculture plants in coastal areas may transfer to the marine microbial ecosystem, potentially disrupting its structure and function. Such research would provide crucial insights into how human activities further shape microbial communities and influence ecosystem health.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/jmse12101796/s1. Figure S1: Rarefraction curves of samples; Figure S2: Alpha diversity profiles of sampling stations; Figure S3: NMDS—Beta diversity profiles of sampling stations; Figure S4: Class level classification of bacterial communities during Pre-monsoon (PR), Monsoon (MN) and Post-monsoon (PM).x-axis represent stations, y-axis represents relative percentage abundance; Figure S5: Venn diagram showing shared taxa between sampling locations in 3 different seasons; Figure S6: DeSeq2 differential abundance analysis of order level taxa from (a) pre-monsoon to monsoon and (b) monsoon to post-monsoon (c) pre-monsoon to post-monsoon; of genus level taxa from (d) pre-monsoon to monsoon and (e) monsoon to post-monsoon. Values are on the -log10 scale, 1.3 corresponds to a p-value cutoff of 0.05, 2 to 0.01, 3 to 0.001; Figure S7: KEGG level 1 of the sampled datasets of all seasons; Figure S8: KEGG level 2 of the sampled datasets of pre-monsoon and monsoon (p < 0.05); Figure S9: KEGG level 2 of the sampled datasets of monsoon and post-monsoon (p < 0.05); Table S1: The sequencing statistics of the sampling stations; Table S2: The top 10 dominant phyla present in the stations under study; Table S3: The top 10 dominant Class present in the stations under study; Table S4: The top 10 dominant Order present in the stations under study; Table S5: The top 15 dominant Genus present in the stations under study.

Author Contributions

Conceptualization, S.H. and A.P.; methodology, S.H. and A.P.; software, S.H. and T.K.A.; validation, S.H., A.P. and A.S.P.R.; formal analysis, S.H., R.N. and T.K.A.; investigation, S.H. and A.P.; resources, A.P., A.S.P.R., R.J. and G.V.M.G.; data curation, S.H., R.N. and T.K.A.; writing—original draft preparation, S.H. and A.P.; writing—review and editing, S.H., A.P. and A.S.P.R.; visualization, S.H. and A.P.; supervision, A.P. and A.S.P.R.; project administration, A.P., R.J. and G.V.M.G.; funding acquisition, A.P., R.J. and G.V.M.G. All authors have read and agreed to the published version of the manuscript.

Funding

This study formed a part of the following projects: DST-SERB-Marine microbiome to ascertain the role of microbes in biogeochemical cycling in the Eastern Arabian Sea (SRG/2022/001545) and Marine Ecosystem Dynamics of Eastern Arabian Sea (MEDAS) GAP 3274, funded by the Ministry of Earth Sciences (MoES), Government of India.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

NCBI Sequence Read Archive (SRA) under the bio project number PRJNA1063869.

Acknowledgments

The authors acknowledge the Directors and Scientists-in-Charge of the CSIR-National Institute of Oceanography, India for their facilities and support. The authors express their gratitude to the Secretary of the Ministry of Earth Sciences; the Director and the former Directors, Centre for Marine Living Resources and Ecology; and the Director, National Centre for Coastal Research, Chennai India, for their great support. This study was carried out as part of the project Marine Ecosystem Dynamics of eastern Arabian Sea (MEDAS) funded by MoES, India and the Science and Engineering Research Board (SERB). The data presented are archived at the MoES repository www.incois.gov.in. We also acknowledge all participants of various MEDAS cruises and all colleagues at CSIR-NIO (RC), Kochi for their support and advice. H.S. acknowledges the Council of Scientific and Industrial Research (CSIR), New Delhi, and N.R. acknowledges the Science and Engineering Research Board (SERB) for funding the research fellowship grants. This is the NIO contribution No. 7307; CMLRE contribution No. 192.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Figure 1. Study area in the EAS and sampling locations-L1, L2, and L3.
Figure 1. Study area in the EAS and sampling locations-L1, L2, and L3.
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Figure 2. The distribution of temperature, salinity, DO, and fluorescence (chlorophyll a) during the study period. The x-axis represents stations and the y-axis represents (a) temperature (°C), (b) salinity (psu), (c) dissolved oxygen (µM), and (d) the fluorescence proxy for chlorophyll a concentration expressed in mg/m3.
Figure 2. The distribution of temperature, salinity, DO, and fluorescence (chlorophyll a) during the study period. The x-axis represents stations and the y-axis represents (a) temperature (°C), (b) salinity (psu), (c) dissolved oxygen (µM), and (d) the fluorescence proxy for chlorophyll a concentration expressed in mg/m3.
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Figure 3. The distribution of (a) nitrite (NO2), (b) silicate (SiO4), (c) nitrate (NO3−), (d) ammonium (NH4+), and (e) phosphate (PO43−) during the study period. The x-axis represents the stations and the y-axis represents these chemicals expressed in µM.
Figure 3. The distribution of (a) nitrite (NO2), (b) silicate (SiO4), (c) nitrate (NO3−), (d) ammonium (NH4+), and (e) phosphate (PO43−) during the study period. The x-axis represents the stations and the y-axis represents these chemicals expressed in µM.
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Figure 4. Phylum level classification of bacterial communities during pre-monsoon (PR), monsoon (MN), and post-monsoon (PM). x-axis represents stations; y-axis represents relative percentage abundance.
Figure 4. Phylum level classification of bacterial communities during pre-monsoon (PR), monsoon (MN), and post-monsoon (PM). x-axis represents stations; y-axis represents relative percentage abundance.
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Figure 5. Order-level classification of bacterial communities during pre-monsoon (PR), monsoon (MN), and post-monsoon (PM). The x-axis represents stations, and the y-axis represents relative percentage abundance.
Figure 5. Order-level classification of bacterial communities during pre-monsoon (PR), monsoon (MN), and post-monsoon (PM). The x-axis represents stations, and the y-axis represents relative percentage abundance.
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Figure 6. KEGG level 2 streamgraph of the sampled datasets of all seasons.
Figure 6. KEGG level 2 streamgraph of the sampled datasets of all seasons.
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Figure 7. Distribution of Carbohydrate-Active enZymes (CAZymes) during pre-monsoon (PR), monsoon (MN), and post-monsoon (PM). (a) Principal component analysis (PCA) plot and (b) heatmap showing the distribution of various CAZyme families across the three seasons in the study location. The Tukey–Kramer post hoc plots of significant variation of CAZyme distribution between (c) pre-monsoon to monsoon and (d) monsoon to post-monsoon (p < 0.05).
Figure 7. Distribution of Carbohydrate-Active enZymes (CAZymes) during pre-monsoon (PR), monsoon (MN), and post-monsoon (PM). (a) Principal component analysis (PCA) plot and (b) heatmap showing the distribution of various CAZyme families across the three seasons in the study location. The Tukey–Kramer post hoc plots of significant variation of CAZyme distribution between (c) pre-monsoon to monsoon and (d) monsoon to post-monsoon (p < 0.05).
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Figure 8. DistLM-based db-RDA plot with cluster overlay demonstrating the interrelationship of specific physicochemical parameters (temperature, salinity, dissolved oxygen) and biological parameters (bacterial diversity and abundance) with the bacterial community across different seasons.
Figure 8. DistLM-based db-RDA plot with cluster overlay demonstrating the interrelationship of specific physicochemical parameters (temperature, salinity, dissolved oxygen) and biological parameters (bacterial diversity and abundance) with the bacterial community across different seasons.
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Hafza, S.; Parvathi, A.; Pradeep Ram, A.S.; Alok, T.K.; Neeraja, R.; Jyothibabu, R.; Gupta, G.V.M. Seasonal Surges in Bacterial Diversity along the Coastal Waters of the Eastern Arabian Sea. J. Mar. Sci. Eng. 2024, 12, 1796. https://doi.org/10.3390/jmse12101796

AMA Style

Hafza S, Parvathi A, Pradeep Ram AS, Alok TK, Neeraja R, Jyothibabu R, Gupta GVM. Seasonal Surges in Bacterial Diversity along the Coastal Waters of the Eastern Arabian Sea. Journal of Marine Science and Engineering. 2024; 12(10):1796. https://doi.org/10.3390/jmse12101796

Chicago/Turabian Style

Hafza, S., A. Parvathi, A. S. Pradeep Ram, Thampan K. Alok, R. Neeraja, R. Jyothibabu, and G. V. M. Gupta. 2024. "Seasonal Surges in Bacterial Diversity along the Coastal Waters of the Eastern Arabian Sea" Journal of Marine Science and Engineering 12, no. 10: 1796. https://doi.org/10.3390/jmse12101796

APA Style

Hafza, S., Parvathi, A., Pradeep Ram, A. S., Alok, T. K., Neeraja, R., Jyothibabu, R., & Gupta, G. V. M. (2024). Seasonal Surges in Bacterial Diversity along the Coastal Waters of the Eastern Arabian Sea. Journal of Marine Science and Engineering, 12(10), 1796. https://doi.org/10.3390/jmse12101796

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