First report on the bacterial community composition, diversity, and functions in Ramsar site of Central Himalayas, Nepal

Wetland bacterial communities are highly sensitive to altered hydrology and the associated change in water physicochemical and biological properties leading to shifts in community composition and diversity, hence affecting the ecological roles. However, relevant studies are lacking in the wetlands of central Himalayas Nepal. Thus, we aimed to explore the variation of bacterial communities, diversity, and ecologic functions in the wet and dry periods of a wetland (designed as Ramsar site, Ramsar no 2257) by using 16S rRNA gene-based Illumina MiSeq sequencing. We reported a pronounced variation in water physicochemical and biological properties (temperature, pH, Chla, DOC, and TN), bacterial diversity, and community composition. Bacterial communities in the dry season harbored significantly higher alpha diversity, while significantly higher richness and abundance were reflected in the wet season. Our results uncovered the effect of nutrients on bacterial abundance, richness, and community composition. Fourteen percent of the total OTUs were shared in two hydrological periods, and the largest portion of unique OTUs (58%) was observed in the dry season. Planctomycetes and Bacteroidetes dominated the wet season exclusive OTUs; meanwhile, Actinobacteria dominated the dry season exclusive OTUs. Bacteria in these wetlands exhibited divergent ecological functions during the dry and wet seasons. By disclosing the variation of water bacterial communities in different hydrologic periods and their relationship with environmental factors, this first-hand work in the Ramsar site of Nepal will develop a baseline dataset for the scientific community that will assist in understanding the wetland’s microbial ecology and biogeography.


Introduction
Wetlands are the most productive ecosystems and biodiversity hot spots and play a pivotal role in the ecological processes supporting biodiversity and maintaining environmental health (Salimi et al., 2021;Zedler & Kercher, 2005). However, wetlands have been intensively degraded around the world due to anthropogenic disturbances in the vicinity, i.e., industrial wastewater, domestic sewage, agricultural runoff, and livestock discharge (Castro González et al., 2022;Yi et al., 2022). Similarly, the change in the hydrological condition of the wetland ecosystem can alter the water's physicochemical properties and nutrients affecting the biogeochemistry of the ecosystem (Salimi et al., 2021;. Aquatic microorganisms as the drivers of nutrient biogeochemical cycling and pollutant degradation regulate the wetland's effective nutrient cycling maintaining the ecological integrity of an aquatic ecosystem (Castro González et al., 2022;Zhi et al., 2015;Zhu et al., 2022). The bacterial community composition of lake wetlands is governed by physicochemical factors, for instance, temperature, pH, water transparency, nutrients (inorganic and organic substrates) (Dai et al., 2016), predation, and lysis (Gurung et al., 2010;Kong et al., 2018), as well as the abundance of grazers and bacteriophages (Sigee, 2005). Due to their small size, short generation period, and high metabolic rate, aquatic bacterial communities are extremely susceptible to environmental changes (Zhu et al., 2022;Fisher et al., 2015;Adhikari et al., 2019) and respond rapidly by altering their composition and diversity. The degree of environmental variation in water and sediment is acknowledged as the major factor that affects aquatic microbial communities in wetlands (B. Wang et al., 2020). The alteration in the physicochemical and biological properties of the water results in a shift in the composition of the bacterial community. This alters the biogeochemistry and function of a wetland to the point where some essential services may be disservices (Salimi et al., 2021). Previous studies focused on bacterial community composition and diversity along the soil, water, and sediment of wetland ecosystems Sun et al., 2019;B. Wang et al., 2020;Yi et al., 2022;. A study in the Poyang lake wetland reflected that drought triggered a decrease in alpha diversity and a shift in community composition (J. Zhao et al., 2022). A study in Xixi National Wetland Park indicated that tourism development considerably affected the wetland water quality resulting in the declination in bacterial alpha diversity and an increase in beta diversity. The authors also noticed the enrichment of typical anti-disturbance taxa (Gammaproteobacteria) and potential pathogens (Bacillus) at the sites under more anthropogenic disturbances (B. Wang et al., 2020). The bacterial community composition of wetland water was mainly driven by total organic carbon (TOC). Another study in the wetlands water of northeast China reported the dominance of Proteobacteria, Actinobacteria, and Bacteroidetes (Sun et al., 2019). Sediment bacterial communities harbored higher alpha diversity than water. Although wetlands are regarded as hotspots for studying microbial ecology, bacterial communities in the wetlands of Nepal have never been studied.
Lake cluster of Pokhara Valley (Ramsar no 2257) is a natural wetland located at the foothills of the Himalayas, Nepal. It was nominated as the Ramsar site of internal importance in 2016 (Paudel et al., 2017). This area receives maximum precipitation in Nepal (Panthi et al., 2015), subjecting to the difference in physical, chemical, and biological properties during the dry and wet period (Fisher et al., 2015;Kistemann et al., 2002;Ren et al., 2019). So, microbiological and physicochemical assessment of the Ramsar site during the dry and wet seasons is of concern. To develop the baseline dataset for the scientific community linking environment and bacterial community, the present study discusses bacterial community composition, diversity, and functions in this wetland. We hypothesize that changing water physicochemical and biological properties in dry and wet periods results in shifting bacterial community composition diversity and ecologic functions. To test this hypothesis, we collected surface water samples from the wetland during late November and late May to represent dry and wet periods, respectively. The objectives of this study are to study (1) the variation of bacterial community composition and diversity in the Ramsar site in the central Himalayas, Nepal, in wet and dry seasons; (2) the environmental factors affecting the bacterial biodiversity; and (3) the ecological functions of bacteria. The findings from the aforementioned objectives would way forward wetland scientists to study wetland ecosystem as ecological processes shaping bacterial communities are crucial for aquatic microbial ecology and biogeography.

Study area
The wetland is located in Pokhara Metropolitan City (PMC) at the foothills of the central Himalayas, Nepal, which is the largest metropolitan of Nepal in terms of area and the second-most populous city of Nepal. The study area was designated as a Ramsar site (Ramsar no 2257) of international importance in 2016 (Paudel et al., 2017), which provides important ecosystem services and hosts significant biodiversity. The study area receives the highest precipitation in Nepal ~ 3500 mm per year (Panthi et al., 2015). The rainy period of the year lasts for 6.8 months, from April to October; meanwhile, the dry period of the year lasts for 5.2 months, from November to March (https:// weath erspa rk. com/s/ 110770/ 1/ Avera ge-Summer-Weath erin-Pokha ra-Nepal).

Sample collection
Samples were collected at the end of May and November to represent samples during the wet and dry seasons, respectively. Water samples were collected from three regions of the Ramsar site (named regions A, B, and C, respectively) ( Fig. 1). A total of 18 samples were collected (nine samples each in the dry and wet seasons). Sampling sites were selected based on logistics. Region A comprises 5 sites, region B comprises three sites, while only one sample was collected from region C, as it is a swampy area, and very risky to collect samples. The samples of the wet period were collected 24 h after precipitation. Of near-surface (about 15 cm under the surface) water, 2.5 L was collected at each sampling site. A triplicate of 2 mL aliquots from each sample was fixed immediately in 1.5% glutaraldehyde for bacterial enumeration. One liter of water was filtered through pre-combusted (400 °C for 4 h) GF/F filters. One hundred milliliters of the filtrate was stored in ambercolored glass bottles (1% hydrochloric acid leached, deionized water rinsed, and combusted) for dissolved organic carbon (DOC) and total nitrogen (TN) analysis (Adhikari et al., 2019). Water samples for DNA extraction were pre-filtered through sterile cheesecloth for retaining substances with a size ≥ 20 μm, including algal biomass, small stones, and plant debris (Crump et al., 1999;Staley et al., 2013). One liter of pre-filtered water sample was passed through a 2-µm polycarbonate membrane (Millipore, USA), and the filter was used for DNA extraction. Water samples and filters were stored in incubators with ice bags to maintain a low temperature during transportation to the laboratory. Soon after arriving at the laboratory, samples were stored at − 80 °C until analysis (within 30 days).

Physicochemical and biological analyses
Water physicochemical properties like temperature, pH, electrical conductivity (EC), total dissolved solids (TDS), and chlorophylla (Chl a) were recorded in situ with a multiprobe Water Quality Sonde (YSI Inc., USA). Concentrations of DOC and TN were measured with a TOC-L (Shimadzu Corp., Japan) following the distributor's protocol. Flow cytometry (Beckman Coulter, Epics Altra II) was used to measure bacterial abundance using SYBR 140 Green I (Molecular Probes) nucleic acid stain at a final concentration of one part in 1.0 × 10 4 (N. P. Adhikari et al., 2019).
DNA extraction, bacterial 16S rRNA amplification, and Illumina MiSeq sequencing Community DNA was extracted from the biomass retained in 0.2-µm membrane filters. Filters were aseptically cut into small pieces (approximately 2 mm) using sterilized scissors and forceps. Other protocols were followed as indicated in the FastDNA® Spin kit (MP Biomedicals, Santa Ana, CA). Spectrophotometry (NanoDrop ND 2000, Thermo Scientific, DE, USA) assessed DNA quality and quantity. Primer pairs for Illumina sequencing were used in Caporaso et al. (2012). The detailed information regarding PCR amplification, gel-purification, and sequencing procedures has been previously explained (Adhikari et al. 2019). The 16S rRNA sequences are uploaded to the NCBI SRA database (BioProject accession number: PRJNA 623,054).
Processing of the sequence data 5′ and 3′ ends of raw sequence data were assembled using Fast Length Adjustment of Short reads (FLASH) software version 1.2.9 (Magoˇc & Salzberg, 2011). Raw FASTQ files were processed with the Quantitative Insights into Microbial Ecology (QIIME) v1.9.0 (Caporaso et al., 2010). Quality control was performed as explained in Adhikari et al. (2019). Chimeric sequences were removed using USEARCH (Edgar, 2010). Bacterial sequences were clustered into Operational Taxonomic Units (OTUs) at 97% pairwise identity using the 'pick_de_novo otus.py' script with the uclust algorithm (Edgar, 2010). QIIME uses the Ribosomal Database Project (RDP) classifier for assigning taxonomic data to each representative sequence (Caporaso et al., 2010).

Statistical analysis
Bacterial alpha diversity estimates, i.e., Shannon diversity index, Pielou's evenness, and Chao1 richness, were calculated with the 'vegan' package in the R-environment (Team R C, 2014). The Kruskal-Wallis test compared the water physicochemical properties and bacterial alpha diversity indices in the dry and wet seasons. It is a non-parametric statistical test used to determine a statistically significant difference between two or more groups, mainly when there is an extreme deviation of data from the normal distribution (MacFarland, 2016;Adhikari et al., 2021). Spearman's rank correlation was used to assess the correlation between two independent groups. The percentage of shared and unique OTUs was calculated using the package 'Venn diagram' in R (Chen & Boutros, 2011). Non-metric multidimensional scaling (NMDS) based on weighted UniFrac distance was used to visualize the pattern in microbial community composition. It is a popular beta-diversity metric used in ecological studies (Wang et al., 2016). Dissimilarity tests like multiresponse permutation procedure (MRPP), analysis of similarities (ANOSIM), and non-parametric multivariate analysis of variance (perMANOVA) with Adonis function were then employed to evaluate the significance of the differences found between rainy and rainless period (Anderson, 2001). Similarity percentage (SIMPER) analysis in PAST v3.06 (Hammer et al., 2001) was used to identify the OTUs responsible for the similarity and dissimilarity of bacterial Page 5 of 15 573 Vol.: (0123456789) community composition. A distance-based multivariate linear model (DISTLM) based on weighted Uni-Frac distance was performed in the DISTLM_for-ward3 program for determining the environmental factors responsible for the variation of bacterial community composition.
For further exploring the complex relationships of water physicochemical properties, nutrients, productivity, and bacterial abundance on the bacterial richness and community composition, we used PLS-PM in the R package 'plspm' (V0.4.7) (Sanchez, 2013). This method is known as the partial least squares approach to structural equation modeling and allows the estimation of complex cause-effect relationships (Wang et al., 2016). Five latent variables were used: water physicochemical properties (the measured water temperature, pH, EC, and TDS), nutrients (DOC and TN), lake primary productivity (Chlorophyll a), bacterial abundance, richness, and community composition. One thousand bootstraps were used to validate the estimates of path coefficients and the coefficients of determination while running PLS-PM. Coefficients represent the direction and strength of the linear relationships between variables or the direct effects. Models with different structures were evaluated using the goodness of fit (GoF) statistic (Wang et al., 2016). The GoF measure accounts for the model quality at both the measurement and the structural models. GoF is calculated as the geometric mean of the average commonality and the average R 2 value. Since it takes into account commonality, this index is more applicable to reflective indicators than to formative indicators in SEM (Sanchez, 2013).
The z-score transformation was used to standardize environmental variables to meet the normality and homogenization of the variance. The statistical analyses and graphical illustrations were performed using R programming unless otherwise indicated (version 4.2.2, R Foundation for Statistical Computing, Vienna, Austria).

Functional analysis
Functional annotation of prokaryotic taxa (FAPRO-TAX) was performed to investigate the potential functions of bacterial communities on the normalized OTU table. It is a promising tool for predicting functional groups, metabolic phenotypes, or ecologically relevant functions of prokaryotes derived from 16S rRNA amplicon sequencing (Sansupa et al., 2021). It is a manually constructed database with a Python script for converting OTU tables into putative functional tables based on the taxa identified in a sample (Louca et al., 2016). Functions predicted in FAPRO-TAX focus on marine and lake biogeochemistry (Louca et al., 2016).

Variation of water physicochemical properties and bacterial abundance in the dry and wet season
The value of water temperature and pH was significantly higher in the wet and dry seasons, respectively. Water temperature ranged from 20.3 °C in the dry season to 31 °C in the wet season; meanwhile, water pH ranged from 6.2 in the wet season to 8.6 in the dry season. The concentrations of water nutrients. i.e., DOC and TN were significantly higher in the wet season and ranged from 0.7-8.9 mg L −1 and 0.2-3.9 mg L −1 , respectively. Furthermore, the concentration of Chla and bacterial abundance also peaked in the wet season. Chla value ranged from 0.5 to 83.4 µg L −1 , while bacterial abundance ranged from 0.07 to 2.99 × 10 6 cells mL −1 (Table S1 and Fig. S1).

Variations in diversity and community composition
After removing chimeric sequences, 753,880 highquality reads were obtained, with 30,395-54,782 sequences (mean = 41,882 ± 6457) in each sample.
Three measures of alpha diversity indices representing diversity (Shannon diversity index), evenness (Pielou's evenness), and richness (Chao1 richness) were used. In study sites, the diversity, evenness, and richness ranged from 5.3 to 8.3, from 0.50 to 0.73, and from 1539 to 4810, respectively (Fig. 2). Values of diversity and evenness were reported to be significantly (p < 0.001) higher in the dry season; meanwhile, the richness was significantly (p < 0.05) higher in the wet season.
Clear separation of samples based on the different seasons was observed in the ordination space of NMDS (Fig. 3a). Dissimilarity tests also confirmed the pattern, showing significantly distinct bacterial composition in May and November bacterial communities (MRPP, ANOSIM, and perMANOVA, all p = 0.001, Table S2). Thus, distinct bacterial communities were obtained during the wet and dry seasons in the Ramsar site of the Central Himalayas. Furthermore, a comparison of bacterial community dissimilarity (Bray-Curtis distance) in two seasons uncovered that bacterial β-diversity in the dry season was significantly higher than that in the wet season (p < 0.001) (Fig. 3b).

Distribution of taxa in the dry and wet season
Out of 16,599 OTUs obtained in this study, a maximum number of unique OTUs were found in the dry season, i.e., 58% (9630). Twenty-eight percent (4652) of the total OTUs were unique in the wet season. Meanwhile, 14% (2317) of the total OTUs were shared in dry and wet seasons (Fig S3).
According to the forward selection of environmental variables (sequential test in DISTLM_forward program, 999 permutations), temperature and TDS were the significant environmental factors explaining the BCC variation. In total, all these factors explained 26% of BCC variation (Table 1). Furthermore, PLS-PM illustrated the direct and indirect effects of different environmental factors on bacterial community composition and richness. Goodness of fit (GoF) statistics values for BCC and richness were 0.54 and 0.52, respectively. GoF values signified the ease of our hypothetical path model. Water physicochemical properties negatively affected nutrients, productivity, and bacterial abundance (i.e., a total effect of − 0.32, − 0.41, and − 0.25, respectively). Meanwhile, nutrients positively affected productivity (a total effect of 0.81) and bacterial abundance (0.65). Wetland productivity showed a positive effect on bacterial abundance (0.23). For bacterial community composition, the total effects of physicochemical properties, nutrients, productivity, and bacterial abundance were 0.13, 0.42, 0.66, and 0.22, respectively. For richness, the total effects of physicochemical properties, nutrients, productivity, and bacterial abundance were 0.34, 0.60, − 0.10, and − 0.20, respectively (Fig. 5).

Potential functions of microbial communities in the dry and wet season
A comprehensive assignment of microbial taxa to function was performed to identify bacterial potential ecological and pathogenic roles in two seasons. The result of predicted functions indicated that the majority of putative functions were enriched for aerobic chemoheterotrophy (16%), chemoheterotrophy (14%), animal parasites or symbionts (9%), aromatic compound degradation (8%), and human pathogens (8%) (Fig. 6). We also noticed that the abundance of bacteria belonging to all the aforementioned putative functions was higher in May. Acinetobacter, Enhydrobacter, Sphingomonas, Pseudomonas, Sphingobium, Aeromicrobium, and Flavobacterium were the most abundant genera associated with aerobic chemoheterotrophy and chemoheterotrophy ( Fig. S6 and S7).

Acinetobacter, Candidatus Xiphinematobacter, and
Clostridium were the predominant genera associated  with animal parasites or symbionts (Fig. S8). Similarly, the dominant genera associated with aromatic compound degradation were Acinetobacter and Rhodococcus (Fig. S9). Bacterial members under the genus Acinetobacter, Clostridium, and Stenotrophomonas showed a higher abundance among the potential human pathogens (Fig. S10). Though the average abundance was comparatively lesser than the five functions mentioned above, the abundance of bacterial genera associated with functions like oxygenic photoautotrophy, photoautotrophy, and phototrophy was higher in the dry season. The genus Synechococcus showed a higher abundance in oxygenic photoautotrophy (Fig. S11). Synechococcus and Rhodoplanes showed a higher abundance in photoautotrophy (Fig. S12). Similarly, bacterial members of the genus Synechococcus and Rhodobacter were more abundant in phototrophy (Fig. S13).

Discussion
Planctomycetes and Actinobacteria dominated the unique OTUs in wet and dry seasons, respectively Our results indicated that Planctomycetes and Actinobacteria dominated the unique OTUs in wet and dry seasons, respectively.
Though bacterial members of Planctomycetes are reported to be ubiquitous, they are generally dominant in freshwater ecosystems, with the abundance ranging from < 1 up to 22% (Andrei et al., 2019). In addition to utilizing a wide range of plant-derived organic substrates, Planctomycetes play a pivotal role in the fractionation of dissolved organic matter in natural water (Tadonléké, 2007). Anammox Planctomycetes are used to remove ammonia from wastewater (Wiegand et al., 2018). A study in Porto, a city in Portugal with a similar anthropogenic influence,  uncovered a higher clone sequence of Planctomycetes in the biofilm of microalgae (Bondoso et al., 2017). The Ramsar site is located in a highly urbanized area of the Central Himalayas, which receives a large amount of municipal wastewater and pollutants. Due to the physiological tolerance of Planctomycetes to heavy metals and their role in wastewater treatment, it is obvious to contribute to the majority of unique OTUs in the wet season. In addition to Planctomycetes, Bacteroidetes also contributed to the high proportion of unique OTUs in spring. Bacteroidetes are fast growers involved in the biodegradation of complex biomolecules (Kirchman, 2002). The genus Flavobacterium was the most abundant within this Phylum. Pioneer study in freshwater microbiology publicized that members of the genus Flavobacterium prefer a copiotroph lifestyle and proliferate in high nutrient conditions (Newton et al., 2011). Our results also supported this fact, as the concentrations of nutrients peaked in May. In November, the majority of the unique OTUs belonged to Actinobacteria. Actinobacteria often belong to the dominant fraction of the heterotrophic bacterioplankton in the freshwater ecosystem (Newton et al., 2011). Their relatively small cell size and high GC content in cell wall structure aid protection from protistan grazing (Hahn et al., 2003). In a limnetic environment, Actinobacteria often reach their maximal abundances in late fall and winter (Allgaier & Grossart, 2006), and their abundance decreases with oxygen limitation and overloading of the nutrients (Shahraki et al., 2021). This study also supports the fact with a higher abundance in the dry season. The high concentration of measured nutrients in the wet season reflected nutrient loading, which resulted in decreased abundance of Actinobacteria. Under the phylum Actinobacteria, clade ACK-M1 showed the highest relative abundance. This clade of typical freshwater bacteria (TFB) was associated with higher pH in freshwater lakes (Lindström et al., 2005). The pH value of the studied sites was significantly higher during the dry season, which may be responsible for the higher abundance of ACK-M1 lineage of phylum Actinobacteria.

Effect of nutrients on bacterial biodiversity
Our result uncovered that water nutrients were the predominant factors affecting all the bacterial biodiversity indices, including abundance, richness, and community composition.
In this study, nutrients positively affected bacterial biodiversity indices (abundance, richness, community composition) and lake productivity, with the highest effect value among all latent variables. DOC and TN were used as the latent variables for nutrients. DOC is the readily available form of carbon in the water column of aquatic ecosystems (Adhikari et al. 2019;Williamson et al., 2008) derived from both autochthonous and allochthonous sources (Farjalla et al., 2006). Autochthonous DOC derived from phytoplankton and aquatic macrophytes is labile and readily assimilated by bacteria (Søndergaard and Theil-Nielsen, 1997;Weiss and Simon, 1999). It could be associated with many dissolved nutrients released in water that inhabiting heterotrophic bacteria can rapidly be used (Landa et al. 2016).
Meanwhile, allochthonous DOC comprising humic substances with a high C/N ratio is associated with lignin components from riparian vegetation (Farjalla et al., 2006;Hedges et al., 1994). Nitrogen is a vital element for all living beings. Having located at the center of the city and surrounded by forest, studied lakes receive DOC from both sources. Nitrogen in aquatic environments is derived from terrestrial landscapes and atmospheric sources. Terrestrial nitrogen sources include domestic, industrial, and agricultural sources (Duce et al., 2008;Xia et al., 2018). In the meantime, atmospheric nitrogen comes via local and long-range transport of nitrogenous pollutants (Boyer et al. 2006). Total nitrogen (TN) is the sum of total Kjeldahl nitrogen (ammonia, nitrite, and nitrate), which are the intermediates of the nitrogen cycle. The positive effect of dissolved nutrients on bacterial richness and abundance is not surprising as we targeted free-living bacteria that rely on dissolved organic matter (Zhao et al., 2017).
Apart from physicochemical parameters and nutrients, the effect of bacterial abundance on bacterial community composition and richness was pronounced, i.e., bacterial abundance as a biological parameter showed a positive and negative impact on bacterial community composition and richness, respectively. Several interactions between bacteria like predation, competition, and mutualism exist (Xu et al., 2018) and such interactions play a pivotal role in controlling community diversity (Needham & Fuhrman, 2016). Bacterial abundance balances growth, and loss rates are regulated by inorganic nutrients, organic substrates, predation, lysis, temperature, and other factors (Gurung et al., 2010); its effect on microbial biodiversity is also expected. A study in an alpine glacier-fed water body of the Tibetan Plateau also uncovered the impact of bacterial abundance on bacterial community composition and OTU richness (Liu et al., 2017).
The result of DISTLM_forward indicated that temperature and TDS significantly explained the variation of bacterial community composition, with a cumulative percentage variation of 26.25. Temperature is often associated with bacterial biodiversity as it directly relates to metabolic rates and the affinity of bacteria to available substrates (Adhikari et al., 2019;Nedwell, 1999). Similarly, TDS measures the sum of all the dissolved ions present in an aqueous medium Kaphle et al., 2021;Khadka & Ramanathan, 2013) that can directly or indirectly affect the bacterial community composition. The value of TDS peaked during autumn due to the concentration of ionic species. Since we targeted FL-bacteria, our result is not surprising.
The high abundance of pathogenic bacteria implies public health attention FAPROTAX-based functional analysis showed metabolic and functional potentials of abundant bacteria in lakes. Furthermore, many inhabiting bacteria were animal parasites and human pathogens, indicating a severe public health threat.
In our study, the abundance of pathogen-associated bacteria was comparatively higher in wet. The region receives maximum precipitation in Nepal (Dahal et al., 2016), and the rainfall starts in April. Previous studies revealed that the influx of contaminated water from streams to lakes and reservoirs could substantially increase pathogen levels (Fisher et al., 2015;Kistemann et al., 2002). As the nearby land is highly influenced by anthropogenic activities like sewage drainage, agricultural practices, urbanization, fishing, boating, and recreation (Paudel et al., 2017), many pathogens can be introduced to the lake.
Acinetobacter, Clostridium, and Stenotrophomonas were the three top genera associated with pathogenic potential. These bacteria have been isolated from diverse habitats. Acinetobacter spp. is an opportunistic pathogen and can potentially cause nosocomial infections like septicemia, pneumonia, meningitis, urinary tract infections, and skin and wound infections in immunocompromised patients (Regalado et al., 2009;Yang et al., 2019). Clostridium spp. is a Gram-positive, spore-forming, and anaerobic bacteria predominantly found in soil. They cause a wide range of infections in humans, i.e., tetanus, food poisoning, and gas gangrene. Stenotrophomonas spp. are environmental bacteria found in soil and aquatic habitats, capable of causing opportunistic infections such as endocarditis, urinary infections, and respiratory infections, including pneumonia in patients with cystic fibrosis (Sánchez, 2015). Thus, the higher abundance of pathogenic bacteria in lakes reflects that the lakes are polluted by pathogens and rapidly transmit diseases to the individuals involved in local activities associated with lake water.
Ecological enlightenment of the study Wetlands are under high pressure due to anthropogenic activities and climate change. Changes in land use patterns and hydrology brought on by climate change will interrupt microbial ecosystems. Microbes are primarily responsible for many biological processes in wetlands, including nutrient cycling, pollutants degradation, greenhouse gas emissions, and others. Despite the promising role of the microbial community in driving wetland ecosystem processes, the fundamental mechanisms by which microbial characteristics affect wetland ecosystem functions in the central Himalayas have not been studied yet.
This first-hand work comprehensively illustrated bacterial diversity and composition (who are there? Who are they? What are there? How are there?) in the dry and wet seasons and explored the potential functions of microbial communities. This will establish a baseline dataset to study how aspects of microbial communities' influence ecosystem multi-functionality such as biogeochemical cycle, carbon sink and source, nitrogen removal, and so on. Furthermore, with this work, it is expected to develop new insights into the development of a wetland ecosystem functioning framework based on microbial features.

Conclusion
The current study is first-hand work regarding the microbiological studies in the Ramsar site of central Himalayas, Nepal, using the high-coverage next-generation sequencing method. This study insight into the structure and distribution of bacterial community composition during the wet and dry seasons in the anthropogenically influenced Ramsar site. Our results characterized the distinct bacterial communities, diversity, and water characteristics (temperature, pH, Chla, DOC, and TN) in wet and dry seasons. Changes in water nutrients and physicochemical properties in different seasons endorsed bacterial abundance, community composition, and diversity variation, and the variation is attributed to different ecological functions. The ecological processes shaping bacterial communities are essential for microbial ecology and biogeography of wetland ecosystems. Therefore, the current study will provide a framework of wetland microbial ecology and critical factors that assist in understanding anthropogenic influence on wetland biodiversity.
Author contribution Study design: Namita Paudel Adhikari. Collection and processing of samples: Subash Adhikari. Experimental work: Namita Paudel Adhikari. Bioinformatics and statistical analysis: Namita Paudel Adhikari, Subash Adhikari. Interpretation of the data: Namita Paudel Adhikari. Drafting and revision of the manuscript: Namita Paudel Adhikari and Subash Adhikari. Data availability All data generated or analyzed during this study are included in this published article (and its supplementary information files).