1. INTRODUCTION
The Ganga originates from the Gaumukh ice cave in the Gangotri glacier system. Its primary headstream, Bhagirathi, confluences with a superior stream from Badrinath, i.e., Alaknanda in Devprayag, Uttarakhand, forming the Ganga [1,2]. The river flows 2525 km through the plains of Northern India, forming a drainage basin covering 8,61,404 sq. km and comprising 26.2% of the country’s total land area [3,4]. The Ganges, a large basin area with abundant water, contributes to the country’s food security by providing agriculturally fertile land and a significant portion of crop production [5,6]. The British bacteriologist Ernest Hankin first noted the antibacterial properties of Ganga water in 1896 against Vibrio cholera. According to studies, Ganga water does not support the growth of many infectious microorganisms. In another study, the National Environmental Engineering and Research Institute also reported that, as compared to Yamuna and Narmada water, Ganga water contains a substantially greater proportion of bacteriophages. The higher Himalayan stretch of the Ganga had a greatly elevated bacteriophage population. These findings indicate that the Ganga River has a greater bactericidal capacity [7,8]. Though this self-cleansing property of Ganga water is primarily attributed to a large diversity of bacteriophages that are natural predators to many strains of disease-causing bacteria that also cause foul smells and deteriorate the quality of water [9,10].
A recent metagenomic study explored the viral habitats of Ganges, focusing primarily on phage identification and assessment of their diversity. It provided insights into the abundance and diversity of phages, microbial communities, and antibiotic resistance genes in river sediment [11]. Very little is known about the types of microbes that are prevalent in freshwater habitats, except species from the division cyanobacteria, which are rather suitable for cultivation and morphological differentiation. It was thus concluded that there was no apparent distinction between soil bacteria and aquatic bacteria based on culture methods and that bacteria found in groundwater, spring water, and streams are also present in soil [12]. These findings cast doubt on the idea that there is a distinct freshwater bacterial flora. Rivers are diligently explored for novel microbiota because they serve as reservoirs for various microbiomes. Due to their numerous advantages for humans and for every individual in the ecological tier system, these microorganisms are considered significant [13].
In another metagenomic investigation, a microbial community with bioremediation potential in sediment samples from the Ganga and Yamuna rivers was found, where urea ABC transporter, UrtA, UrtD, UrtE, zinc/cadmium/mercury/lead-transporting ATPase, and other protein domains played crucial roles [14]. With the continual inflow of microorganisms from soil, plant, or animal residues, the river ecology interacts and converges with many other ecosystems. Rivers have extraordinarily high levels of microbial diversity because they are continually importing microbial communities from other ecosystems [15-17]. Another metagenomic study indicated a correlation between the composition of the microbiome and pollution, revealing a high abundance of microbial communities with implications for human health. The Ganga river, particularly upstream, showed a highly dynamic microbial structure, highlighting the functional capabilities of these communities [18]. To examine the genetic make-up, functional potential, and ecological dynamics of microbial communities in various contexts, modern metagenomic research applies cutting-edge sequencing technology, bioinformatics tools, and analytical methods. These studies use the most recent advancements in computational biology and genetics to better comprehend the intricate microbial ecosystems [19,20]. The lives of humans, animals, and plants are influenced by the experimental identification and characterization of microorganisms in their niche. However, the procedure is laborious and expensive. Contrarily, as computational approaches are complementary to experimental procedures, employing the NGS in river microbiomes in a specific habitat may be characterized and annotated more quickly [21]. Several previous studies have been done on biological diversity in the river Ganga, with some emphasis on the diversity of diatoms, phytoplankton, green algae, and blue-green algae [22-24]. Due to anthropogenic activity, many disease-causing bacteria have also been reported in the River Ganga, adding to its microbial diversity [25,26].
The river is also a repository for many bacteriophages, fungi, and non-pathogenic species of bacteria [27]. To the best of our knowledge, few significant studies on the metagenomic profile of the sediments from the Ganga River have been conducted so far. This study will assist those involved with developing proactive plans to address the health concerns of those dependent on the river, directly or indirectly, and thus maintain the health of the aquatic ecosystem. The microbial diversity in the Ganga’s flowing water is also thoroughly examined in this study.
2. MATERIALS AND METHODS
2.1. Sample Collection
Water samples were collected from three distinct sites along the river Ganga in Haridwar, Uttarakhand, India; these were then pooled together for further assessments. Before subsequent analysis, the sample was transported at 4°C in 50 ml sterile centrifuge tubes (HiMedia Pvt. Ltd., Mumbai). The sites are primarily chosen based on their relative higher anthropogenic activities.
The sites include Har-ki-Pauri (N 29°57’21.1” E 78°10’15.2”), Chandi Devi Ghat-Eastern Ganga Canal Road (N 29°56’35.2” E 78°1011.6), and Daksheshwer Mahadev Temple, Haridwar (N 29°55’17.5” E 78°08’46.1”).
All samples were collected on the same day. Figure 1 depicts all three locations together with satellite imagery and GPS-included pictures showing the actual locations of the sample collection.
 | Figure 1: Sample collection sites along with satellite and GPS images; (a) Har ki Pauri, (b) Chandi Devi Ghat, (c) Daksheshwer Mahadev temple. [Click here to view] |
2.2. Sample Storage and DNA Extraction
Before undergoing DNA extraction, the sample was kept at −20°C. The Xploregene kit was employed to extract the metagenomic DNA from this sample, and the manufacturer’s instructions were strictly followed. Before being used for the next procedures, the samples containing extracted DNA underwent a GEL check and a nanodrop. The nanodrop readings of 260/280 at a ~ value of 1.8–2 are used to determine the quality of DNA. A substantial quantity of extracted DNA of high quality was used to create the metagenomic library.
2.3. Metagenomic Library Preparation
The DNA from the extracted samples was fragmented into lengths of 600 bp using the KAPPA fragmentation process. After the samples were successfully broken down, they were subjected to end repair and A-tailing using a blend of the enzymes Hypa peep plus ERAT. Subsequently, the adaptor was introduced and ligated to the end-repaired DNA fragments using DNA ligase. Illumina primers were used to carry out library amplification on the adapter-ligated samples.40 ng of extracted DNA is used for amplification, along with 10 pM of each primer. Initial denaturation was performed at 98°C. Further steps involved four complete cycles involving denaturation at 98°C for 15 s, followed by annealing at 60°C for 30 s, and extension at 72°C for 30 s. The final extension was executed at 72°C for 1 min and then stored at 4°C before further analysis. Libraries were purified using Ampure beads and quantitated using the Qubit dsDNA High Sensitivity Assay Kit [28].
2.4. Metagenome Sequencing and Quality Assessment
Raw sample reads were obtained from the popular high-throughput sequencing platform Sanger/Illumina. Its version 1.9 was used to obtain raw sample readings from this popular high-throughput sequencing platform. The quality assessment of the raw fast reads thus obtained from the sample was performed using FastQC v. 0.11.9 using default parameters [29]. After quality evaluation, the raw fastq readings are processed using Fastpv.0.20.1 (parameters: --trim_front1 3 --trim_front2 3 --length_required 50 --correction -- trim_poly_g --qualified_quality_phred 30) [30], and then the reads are once again evaluated for quality using FastQCv.0.11.9.
2.5. Bioinformatics Analysis and Sequence Annotation
For metagenomic sequences, MG-RAST software was used with default parameters as it provided automated quality checking, annotation, comparative analysis, and archiving services [31]. First, reads with incorrect lengths were omitted, and low-quality reads were trimmed out using SolexaQA. The program identified the gene sequences using the machine learning approach (FragGeneScan). To evaluate the probable functions and annotation of the genes, MG-RAST created clusters of proteins with a 90% similarity level using the UCLUST operation in QIIME. The longest sequence from each cluster was picked up for analysis of the identity of the sequence. These sequences were then computed against various databases consisting of ribosomal RNA databases and protein databases providing information on the functional hierarchy, for example, RefSeq, IMG, TrEMBL, KEGG, GenBank, Swiss Prot, PATRIC, eggnog, KO, GO, NOG, and COG. The processed reads were also classified metagenomically using CCmetagen v.1.2.5 and visualized with Krona (CCMetagen-1.2) [32]. The OTU tables thus obtained were used to deduce a phylogenetic tree using NCBI’s common tree file [33] and were plotted using Interactive Tree of Life (iTOL) [34]. Barplots were plotted using Phyloseq R packages [35], and a heatmap was plotted using Ampvis 2 R packages [36].
3. RESULTS AND DISCUSSIONS
3.1. Generation of Sequences
To detect microbial biodiversity, samples from three different sites were pooled and subsequently analyzed using high-throughput next-generation sequencing. The total number of reads for the raw data and the trimmed data, i.e., 8522567 and 82429791, respectively, as well as their read orientation (R1 and R2), average GC% of 58, and length of base pair reads for raw data were found to be 159. A similar finding was also reported by the author, in which the GC% was 53 for samples of sediments collected from the Ganga River at Haridwar, and a striking resemblance was also found between the distribution pattern of GC content and the presence of bacterial diversity grouped at the OTU level [18]. After trimming, a read length of 50–156 was further processed for analysis [Table 1]. For the analysis of taxonomic classification, all the high-quality reads we gained from the sample were taken into consideration.
Table 1: Total numbers of reads for the raw data and the trimmed data.
| Sample name | Read orientation | Raw data | Trimmed reads | ||||
|---|---|---|---|---|---|---|---|
| Average GC (%) | Length (bp) | Total sequences | Average GC (%) | Length (bp) | Total sequences | ||
| Water | R1 | 58 | 159 | 85222567 | 58 | 50-156 | 82429791 |
| R2 | 58 | 159 | 85222567 | 58 | 50-156 | 82429791 | |
3.2. Taxonomical classification of metagenome
Based on the taxonomical classification and functional analysis using MG-RAST and CC Metagen, the diversity was categorized on three levels, i.e., class, order, and family. In the manner of their relative abundance, the top 30 classes are shown in Figure 2a and comprises Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, Actinobacteria (class), Deltaproteobacteria, Deinococci, Flavobacteria, Sphingobacteria, Cytophagia, Bacilli, Planctomycetacia, Clostridia, Bacteroidia, Verrucomicrobiae, Anaerolineae, Methanomicrobia, and Chloroflexi (class), to name a few. Previous studies on the microbial flora from the sediments also indicate Proteobacteria as the most prevalent bacterial flora, followed by Actinobacteria, Firmicutes, and Deinococcus-Thermus [27]. Similar studies also revealed that Proteobacteria, Planctomycetes, Actinobacteria, Bacteroidetes, Verrucomicrobia, Firmicutes, and Cyanobacteria. were highly abundant in Ganga water [36,37]. In a study conducted in Kanpur (India), Pseudomonadota, Actinomycetota, Bacillota, Nitrospirota, Gemmatimonadota, Cyanobacteria, and Acidobacteriota were among the other numerous phyla that were represented [39].
 | Figure 2: (a) Top 30 classes based on their relative abundance. (b) Top 30 orders based on their relative abundance. (c) Top 30 families based of their relative abundances. [Click here to view] |
Among orders the most prevalent ones involve Sphingomonadales, Burkholderiales, Pseudomonadales, Rhodobacterales, Caulobacterales, Actinomycetales, Rhizobiales, Deinococcales, Flavobacteriales, Xanthomonadales, Bdellovibrionales, Sphingobacteriales, Enterobacteriales, Cytophagales, Planctomycetales, Myxococcales, Methylophilales, Rhodocyclales, Bacteroidales, and Rhodospirillales as represented by the bar graph in Figure 2b.
In the case of families [Figure 2c], the prominent ones with the greatest number of reads comprises Sphingomonadaceae, Erythrobacteraceae, Moraxellaceae, Comamonadaceae, and Caulobacteraceae. Rhodobacteraceae, Deinococcaceae, Burkholderiaceae, Xanthomonadaceae, Flavobacteriaceae, Bacteriovoracaceae, Propionibacteriaceae, Enterobacteriaceae, Pseudomonadaceae, Rhizobiaceae, Planctomycetaceae, Methylophilaceae, Bradyrhizobiaceae, and Rhodocyclaceae. Comamonadaceae was the most prevalent family in the three sediment samples from the Ganga stretch near Kanpur, India. Other prominent families were Rhodocyclaceae, Burkholderiaceae, and Sphingomonadaceae [38,39].
3.3. Microbial Diversity at a Glance
Employing a contigLCA algorithm to determine the distribution of taxa and identify a single consensus taxonomic entity for each feature on every distinct sequence has brought forward the diversity at the domain level. The sample contains 118 more sequences in addition to 98.88% processed reads from bacteria (2,366,875), 0.58% from eukaryota (13,851), 0.31% from archaea (7,471), 0.18% from viruses (4,395), and 0.04% from unclassified sequences (1001) [Figure 3].
 | Figure 3: Taxonomic hits distribution at the domain level for sampled water. [Click here to view] |
The total numbers of different species annotations, as a function of the number of sequences sampled, were determined by plotting the rarefaction curve of the annotated species richness. The curve initially exhibited an exponential growth rate when the most common species were found, but as more reads were processed and analyzed, much rarer species in the sample came to light, causing the curve to take the shape of a plateau [Figure 4].
 | Figure 4: Rarefaction curve for the sampled water. [Click here to view] |
Within viruses, 50% of reads belonged to potyviridae, 41% to lambdaviruses, Human endogenous retrovirus K and some unculturable phages comprise 3% of the total share of viral reads each, while human papilomavirus and alphapolyomavirus, together with some other viruses, share a 2% stack each. Among fungi, basidiomycota made up the biggest portion (75%), followed by ascomycota (21%), and uncultured fungi (approximately 4%). In the sediment samples from Kanpur, the predominant phylum was ascomycota and not basidiomycota, with a diverse range of fungal species. The major organisms found were Gibberellazeae, Schizosaccharomyces pombe, Neosartorya fumigata, and Cryptococcus neoformans [39]. In our analysis of the archaeal reads, we found that Euryarchaeota made up 35% of all archaeal reads, uncultured archaeons made up 47%, and other archaeal reads formed 18% of the total archaeal reads. Comparing this with another study in Kanpur, the most prevalent phylum was Euryarchaeota, with Methanosarcinaceae, Halobacteriaceae, and Thermococcaceae being the three most abundant families [Figure 5] [39].
 | Figure 5: Taxonomic classification of viruses, fungi and archaea based on reads. [Click here to view] |
The stacked bar graph shown in [Figure 6] represents the total number of hits across all the previously mentioned databases. These consisted of ribosomal RNA databases, protein databases that provide information on the functional hierarchy, and protein databases. The e-value range is used to color the bars, indicating annotated readings.
 | Figure 6: The graph displays the number of hits in the different databases listed. These include protein databases, protein databases with functional hierarchy information, and ribosomal RNA databases. The bars representing annotated reads are colored by an e-value range. [Click here to view] |
3.4. Relative Abundance
A stacked bar plot was plotted using Phyloseq R packages, showing the average relative abundance for class, order, and family separately [Figure 7]. The stacked bar plots exhibit the microbial diversity within each of these categories. Among the classes, Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria are more prevalent. In order, Sphingomonadales, Burkholderiales, and Pseudomonadales share the largest diversity in terms of average relative abundance. In the case of the families Sphingomonadaceae, Erythrobacteraceae, Moraxellaceae, and Comamonadaceae, they are the most prominent contributors to average relative abundance.
 | Figure 7: Stacked bar plots showing the average relative abundance of each taxa at the levels of class, order and families. [Click here to view] |
When we plotted the extracted high-quality reads using Ampvis2 R packages, a heatmap was generated [Figure 8], visualizing the taxonomic percentage of reads at the phyla level. Proteobacteria with 85.75% share the largest portion of percentage reads, followed by Bacteroidetes (4%), and Actinobacteria with 3.12% stack. Firmicutes and Deinococcus-Thermus also share a major part, with 1.51% and 1.27%, respectively. Verrucomicrobia and Chloroflexi comprise 0.47% each, while Cyanobacteria share 0.6% of the total percentage. Some of the other phyla include Euryarchaeota (0.38%), Chordata (0.13%), Ascomycota (0.07%), Basidiomycota (0.05%), Nitrospirae (0.07%), Spirochaetes (0.12%), Arthropoda (0.03%), Synergistetes (0.04%), Chlorophyta (0.02%), Cnidaria (0.08%), Aquificae (0.06%), Chlamydiae (0.03%), and Nematoda (0.01%).
 | Figure 8: Heatmap at the phyla level showing the percentage read abundance at the phyla level. [Click here to view] |
3.5. Analysis Based on Phylogeny
After subjecting the processed or trimmed reads to multiple sequence alignment and running them through RefDB of MG-RAST, we generated a phylogenetic tree [Figure 9] at the species level of the top 50 species detected in the sample; some of these are Novosphingobium aromaticivorans, Erythrobacter litoralis, Erythrobacter spp. NAP1, Erythrobacter spp. SD-21, Sphingobium japonicum, Acinetobacter baumannii, Citromicrobiumbathyo marinum, Sphingobium chlorophenolicum, Polaromonas spp. JS666, Acidovorax spp. JS42, Rhodopseudomonas palustris, and Acinetobacter spp. ADP1.
 | Figure 9: Phylogenetic tree at the species level based on the top 50 species from the sampled water using MG-RAST reference database. [Click here to view] |
The vast majority of the species on the top 50 list that we extracted were found in two distinct orders, Sphingomonadales and Burkholderiales, which together contain 11 and 13 species, respectively. Sphingopyxis alaskensis, Erythrobacter spp. SD-21, Sphingomonas wittichii, Sphingomonas spp. SKA58, Citromicrobiumbathyo marinum, and Sphingobium indicum belong to the order Sphingomonadales. Burkholderiales is represented by Albidiferax ferrireducens, Acidovorax citrulli, Polaromonas naphthalenivorans, Variovorax paradoxus, Methylibium petroleiphilum, and Alicycliphilus denitrificans.
4. CONCLUSION
The metagenome sample from the Ganga River shows an enriched distribution of numerous organisms from various domains, according to our research. The majority of the discovered creatures are bacteria, while eukaryotes, viruses, and archaea have demonstrated a noteworthy presence. It may be inferred from the phylogenetic analysis of the top 50 species that microbial species have undergone significant evolutionary diversity. A considerable difference in the relative abundance of particular microorganisms is also highlighted in the text. High anthropogenic activities at the sampling locations are also one of the reasons for such a high microbial diversity. Surveillance studies are vital for future enhancement and management strategies. They support explorations of various communities, environmental monitoring, and metagenomic resource utilization for their functional characterization. It would be very valuable for industrial uses where these organisms are needed, as well as for government agencies concerned with the ecological health of the river. In the future, new methods must be developed for the isolation of these organisms for commercial use and to protect and conserve the riverine ecology of the river.
5. ACKNOWLEDGEMENT
The authors wish to thank the Head, Department of Botany and Microbiology, Gurukula Kangri (deemed to be a university), Haridwar, for providing the necessary laboratory facilities.
6. AUTHOR’S CONTRIBUTIONS
AK and SC outlined and wrote the main manuscript text, analyzed the data, and prepared figures and tables. HVC, HC, and RCD reviewed the manuscript and made the necessary corrections. All authors read and approved the final manuscript.
7. FUNDING
There is no funding to report.
8. CONFLICT OF INTERESTS
The authors declare that there is no conflict of interest.
9. Ethical approvals
This study does not involve experiments on animals or human subjects.
10. DATA AVAILABILITY
All data supporting the findings of this study are available within the paper.
11. PUBLISHER’S NOTE
This journal remains neutral with regard to jurisdictional claims in published institutional affiliations.
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