Unraveling the potential of microbial communities for lake bioremediation via the metagenomics tool: a review

Environmental microbial ecosystems have great diversity due to their excellent abilities to withstand a broad variety of extreme or unfavorable conditions and their capacity to adapt gradually to unique niches (Edge et al. 2020). Surface water habitats, particularly lakes, foster a high diversity of microbes that are functionally more complex than any other habitats playing an essential role in several biogeochemical activities, thus manifesting a crucial role in sustainable ecosystems (Kuang et al. 2023). The ecological conditions of these systems are highly dynamic due to the changes in their spatial form, biotic characterization, and physicochemical interactions that can lead to quick environmental alterations. Such environmental changes in the lake ecosystems can be monitored by analyzing the various ecological parameters. However, the continuous human interventions and unchecked discharge of anthropogenic (domestic and industrial) wastewater into the lakes have led to progression in the deterioration of lake ecosystems (Kiersztyn et al. 2019; Schöpke & Walko 2022). Consequently, influences on the assembly of microbial communities, biogeochemical cycle fluxes, and unknown interactions between biological entities ranging from habitats to genes have been observed (Coutinho et al. 2018; Grill et al. 2019). Therefore, there is a need to efficiently characterize the lake dynamics to ensure these ecosystems' sustainability (Eissa et al. 2022; Mironova et al. 2022). However, it is impossible to comprehensively understand the functioning of such ecosystems without identifying the responsible organisms where nearly all of the microbes found in natural environments (99%) are difficult to cultivate and, therefore, cannot be used in basic biotechnology research (Bøifot et al. 2020). Since microbial dynamics in polluted waters are of utmost importance and can be used as a predictive measure to know the responses to environmental stressors induced by anthropogenic activities (Jousset et al. 2017; Palit et al. 2022), developing an alternative microbial/biotechnological system providing vision into these specially adapted exclusive microorganisms is highly desirable.

Several different methods have been employed to explore and understand lakes' ecological systems, such as genetics, diversity, interactions of microbes, and metabolic processes; however, the conventional techniques (cultivation dependent) cannot fully provide such information and, therefore, are not comprehensive (Wommack & Ravel 2013). In fact, only 1% of the microbial species in wastewater treatment is artificially cultured, which can easily lead to overlooking crucial metabolic species and the associated pathways (Wu et al. 2019). Given the limited knowledge of the microbial communities that can help transform inorganic and organic compounds into simpler, non-toxic ones, biological treatment of contaminated lakes remained challenging. Cultivation methods provide valuable insights into microbes' physiological properties involved in denitrification, nitrification, phosphate removal, methanogenesis, sulfate reduction, xenobiotic remediation, etc. (Liang et al. 2023). However, due to different factors, such as laboratory prejudices or interspecies metabolic dependency, most microbes present in eutrophic water bodies are not cultivated so far (Chen et al. 2022). Therefore, obtaining information on microbial communities and their functions in biological lake bioremediation more directly, comprehensively, and quickly becomes an urgent issue. In this scenario, the total microbial population assessment could play a pivotal role in overcoming these shortcomings (Fang et al. 2020).

Given the role of various microbes in different metabolic pathways occurring in water bodies, the limited characterization of microbial species in lakes, and the success of metagenomics for identifying uncultured microorganisms along with their metabolic processes, lakes can be comprehensively studied using these pioneering techniques. Such critical information can further aid in understanding, designing, and implementing improved lake rejuvenation strategies (Reichart et al. 2020; Singh et al. 2022). Metagenomics has been extensively applied in the last few decades for environmental research, especially wastewater treatment. From 2017 to 2023, a large number of articles appeared on Google Scholar (∼6,000) for a search on ‘lake,’ ‘remediation,’ and ‘metagenomics’. These studies are mainly research articles focused on metal pollution, phosphorus and nitrogen removal, ARG studies, biogeochemical cycle, and microbial characterization. Significantly, very few review articles have been found showing the impacts of metagenomics studies for lake bioremediation, and none of them are directly linked to strategizing the remediation technologies. Therefore, this review summarizes how metagenomics has been contributing to gaining scientific comprehension in environmental microbiology and lake bioremediation systems. Firstly, the review describes essential milestones in the evolution of metagenomics research and the advances made in the last two decades, followed by conventional approaches and the need for metagenomics for lake systems. Subsequently, metagenomics applications expanding the knowledge about the potential of microbial communities in lake bioremediation have been explained. Furthermore, the review collates the multiple case studies in different parts of the world using metagenomics as a powerful tool for planning lake rejuvenation strategies and exploring the lake micro biota's taxonomic and functional capabilities. Finally, potential approaches and future research directions are also proposed for lake rejuvenation.

The term metagenomics was first defined by Handelsman in 1998 as estimating the total genetic material of any microbial communities, providing microbial and genetic diversity, and metallic processes in a confined environment (Handelsman et al. 1998). Though the term metagenome came into light in 1998, reports of the inability to culture all microbes can be dated back (Kumar et al. 2015). Without approaches to culturing all microorganisms, their genetic potential remained hidden and underutilized for years. Staley & Konopka 1985 collated the existing data on the ‘great plate count anomaly’ and highlighted the unresolved and ignored microbial world that was left unexplored (Staley & Konopka 1985). Later, the fact was supported and provided strong experimental evidence that not all the microorganisms observed in the microscope can be cultured on petri plates or in test tubes with the existing staining procedures (Kumar et al. 2015). During the 1980s, evidence started accumulating, which drew the attention of the scientific community toward the uncultured microbial world, and the belief that the microbial world had been conquered was laid to rest. In 1977, Woese and co-workers proposed that ribosomal RNA or 16S rRNA could be used as a molecular marker or evolutionary chronometer, which changed the whole progression of microbiology (Woese & Fox 1977). At the same time, Sanger's automated sequencing method revolutionized the characterization of the hidden microbial world (Sanger et al. 1977). All these extensive studies on molecular techniques over the past few years further helped characterize the microbial diversity, clearing the fog over the unknown microbial diversity and providing insight into the new uncultured world. The PCR, fluorescent in situ hybridization (FISH), cloning and sequencing of rRNA genes, denaturing and temperature gradient gel electrophoresis (DGGE and TGGE), restriction-fragment length polymorphism, and terminal restriction-fragment length polymorphism (T-RFLP) are some of the remarkable recent advancements that have left an enormous impact (Escobar-Zepeda et al. 2015). The 16S rRNA gene was still a superior phylogenetic marker for microbiota characterization due to multigene quality, its large appropriate size (1,500 bp) suitable for informatics, and universal presence in bacterial cells (Kumar et al. 2015). Although the use of 16S rRNA was a breakthrough, there was still many stones which remained unturned such as knowledge of metabolic and ecological functions of microbial communities. Consequently, certain development related to the gene expression techniques (using gene cloning from total DNA) for given metabolic function associated to microorganisms was performed (Healy et al. 1995). Such curiosity implied the discovery of unknown genes, their functions and metabolic pathways, products which open various gates of opportunity. Initial research efforts were focused on knowing ‘who is there,’ but now the research has transformed to identify core aspects of ‘what they are doing and how exactly they do it’ (Haleyur et al. 2019), which laid the foundation to a new area named ‘metagenomics analysis.’ Pyrosequencing technology is used in metagenomics as an alternative to the conventional di-deoxynucleotide-sanger method for metagenomic DNA sequencing, resulting in the extraction of reliable data on the essential genes involved in environmental decontamination (Ibekwe et al. 2018). Although functional metagenomics is a powerful technique for discovering novel active genes in unculturable microorganisms, it also requires screening subsequent phenotypes by artificially transforming metagenomic DNA in a suitable host to uncover a desired activity/product (Lim et al. 2014; Otto et al. 2020). Shotgun metagenomics, developed initially to obtain complete genomes from pure cultures, has been adapted to the analysis of simple microbial species, enabling researchers to obtain nearly complete genomes of the communities' dominant microorganisms. Nowadays, more advanced NGS techniques such as Roche/454's GS FLX Titanium, Illumina/GAII, Solexa's, or Life/SOLiD APG's three are replacing expensive and time-consuming Sanger sequencing methods (Hakeem et al. 2016). Nanopore is an easy technique based on the principle of translocation of DNA sequence through nanometer size pores by external application of an electric field but is prone to many errors, instability, and tuning of membrane dimensions (Hakeem et al. 2016). NGS techniques produce vast data and significantly reduce the time and expense of sequencing large genomes (Pérez-Cobas et al. 2020). A list of various NGS platforms which have been used in recent times is given in Table 1.

Lakes are biologically active structures that play a vital role in various biogeochemical processes and are a valuable natural resource for human societies. Healthy lake habitats offer safe drinking water, promote marine and terrestrial biodiversity, and provide ecosystem services to the fishing and aquaculture industries. Natural stresses imposed by shifts in seasonal cycles in epilimnion and hypolimnion, as well as anthropogenic stresses imposed by nutrient enrichment (eutrophication) and other contaminants discarded indiscriminately from various activities, affect the microbial population assembly in lakes (Salam 2019). The sudden population flare-up, industrialization, and increased land use have imposed significant stresses on lakes, resulting in extreme contamination (Sicuro et al. 2020). Changes in the microbial population in lakes can negatively impact carbon cycling, nutrient cycling, drinking water outbreaks, beach and shellfish harvesting areas, toxic algal blooms, aquatic plant and animal health, fisheries, and marine habitats (Pal 2020). Changes in invertebrate communities may harm the food chains that keep fish habitat and aquatic habitats intact (Pal 2020). Currently, physical and chemical parameters such as temperature, pH, and nutrient concentrations (e.g., carbon, nitrogen, and phosphorus) are used to determine and track the health of lake ecosystem. Although these variables are significant, they do not identify microbial interactions and their genetic relationship and hence do not provide support in planning a holistic and integrated water management strategy. Microbiological assays that include obtaining and analyzing microbial species in pure isolation culture compromise reproducibility and external variable control (Garza & Dutilh 2015). There is evidence that the number of organisms on isolation plates forming a bacterial lawn was several orders of magnitude lower than the total cell numbers, as assessed by microscopy and other methods (Salam et al. 2018).

Old conventional or culture-dependent techniques have also been applied to monitor the diversity and structure of microbial communities, which in turn can be useful for bioremediation processes such as randomly amplified polymorphic DNA (RAPD) analysis, length heterogeneity (LH)-PCR, DGGE/TGGE,), and terminal-restriction fragment (T-RF) length polymorphism (T-RFLP or sometimes T-RFLP) (Desai et al. 2010).

PCR: PCR has been widely used in molecular biology research due to its non-expensive, easy-to-use, and reliable results for replicating the targeted DNA segment. It is often regarded as a qualitative technique used to detect the absence or presence of targeted DNA. However, the real-time PCR is an advanced version that also tells how much of a specific DNA or gene is present in the environment. For example, it has been used for detecting and quantifying the critical physiological groups of bacteria in environmental samples such as ammonia oxidizers, methane oxidizers, and sulfate reducers by targeting amoA, pmoA, and dsrA genes, respectively (Foti et al. 2007). Another advancement was observed in i.e., LH-PCR for detecting amplicon length variations, but mutation in the genes can produce different results (Rastogi & Sani 2011). One of the significant limitations of LH-PCR was the incapability to low resolution for complex amplicon peaks, leading to underestimation of diversity, which may occur due to similarity in the amplicon length of distinct taxa. Other errors can also arise due to the nonspecific binding of the primers and the requirement of prior sequence information.

Fluorescence In Situ Hybridization: FISH is an easy and straightforward molecular cytogenetic technique requiring fluorescent probes that only bind with the sequence with a high degree of sequence complementarity. This technique has been useful for high-resolution automated analysis of mixed microbial populations when combined with flow cytometry (Rastogi & Sani 2011). However, low signal intensity, background fluorescence, and target inaccessibility are the major problems encountered in the FISH analysis.

Amplified Ribosomal DNA Restriction Analysis (ARDRA): This technique works on the DNA sequence variations in the PCR-amplified 16S rRNA genes. In this process, the amplified PCR product of the environmental DNA sample is generally digested by tetracutter restriction endonucleases, and the obtained restricted fragments are then resolved on agarose or polyacrylamide gels (Bharagava et al. 2018). ARDRA has been used for characterizing microbes in activated sludge during the effluent treatment and biodegradation process (Shah et al. 2014). However, the restriction profiles of complex microbial communities obtained from the ARDRA are sometimes too complicated to be resolved on agarose/ polyacrylamide gel.

Ribosomal Intergenic Spacer Analysis (RISA): This technique involves PCR amplification of intergenic spacer region (ISR) of small (16S) as well as large (23S) ribosomal subunits having variability in nucleotide sequence among microorganisms (Ciesielski et al. 2013). The advanced version of RISA, i.e., ARISA, consists of a fluorescence-labeled forward primer that subsequently helped automatically detect ISR fragments by a laser detector, allowing the simultaneous analysis of multiple samples. The technique has been successfully used to monitor microbial communities during treatment processes such as anaerobic digesters and municipal wastewater treatment plants (WWTPs) (Ciesielski et al. 2013). However, it has been observed that the microbial diversity can be overestimated, leading to discrepancies in the results. Also, the requirement for a large amount of sample DNA, long processing time, and low resolution are major issues of this technique.

DGGE/TGGE: Both DGGE and TGGE are types of electrophoresis that either need chemical gradient or temperature for denaturing the environmental sample as it moves across an acrylamide gel (Desai et al. 2010). These techniques are based on the principle of amplification of the functional gene or rRNA PCR products obtained from microbial communities' DNA and their separation on polyacrylamide gels. DGGE has been used to assess the microbial community structure shifts and characterize functional genes in the contaminated environment. These techniques and PCR have also been used to analyze the hydrocarbon-degrading bacterial communities during bioremediation (Kadali et al. 2015). However, its labor-intensive nature and reduced reproducibility are some of its major limitations (Desai et al. 2010). Furthermore, the difference in melting point of DNA fragments resulting in varying results, limited sequence information, and sequence heterogeneity among multiple rRNA operons are a few other associated limitations (Bharagava et al. 2018).

RAPD Analysis: RAPD uses amplification through PCR with shorter nucleotide sequence as a primer, which anneals at numerous random sites on DNA sequence at low temperature (∼35 °C) (Rastogi & Sani 2011). RAPD produces several PCR amplicons of different lengths in a single reaction and separates them on the gel (agarose of polyacrylamide) based on the genetic complexity of the microbial communities. Although the technique is widely used, it requires highly skilled labor with carefully developed laboratory protocols, as a small change can influence the outcome (Bharagava et al. 2018).

A list of a few other conventional molecular techniques is presented in Table 2, along with their merits and demerits.

All these discussed molecular techniques are culture-dependent, either less accurate or time-consuming and therefore cannot be considered for identifying uncultivable microbes. This implies using advanced methodology, such as the metagenomics approach, giving full-scale characterization of microbial communities' composition, structure, and activity during bioremediation at a contaminated site. Technological advances in metagenomics allow culture- and cloning-independent analysis of lake micro-biome. Although the majority (>90%) of microbial species cannot be cultured using existing laboratory culture techniques, these technological advancements have been a paradigm shift (Simon & Daniel 2011). Earlier, metagenomics was based entirely on amplicon analysis of the 16S ribosomal RNA (rRNA) gene, the most common sequencing method for studying the micro-biome (Jurado et al. 2020). It is noteworthy that surveys of the 16S rRNA gene should not be referred to as metagenomic studies; instead, a study on a single gene used as a taxonomic marker (Reddy & Dubey 2021). The limitations of this technique include annotation, which is based on the assumed connotation of the 16S rRNA gene with taxa, defined as an operational taxonomic unit (OTU). OTUs are typically studied at the phyla or genera level, with species-level analysis considered less accurate (Villegas-Plazas et al. 2019). Furthermore, individual genes are not directly sequenced but predicted using OTUs. Another limitation of taxonomic-based metagenomics is the lack of direct gene identification, which can limit understanding of lake micro-biome due to horizontal gene transfer and numerous bacterial strains. To minimize genetic uncertainty, an intentionally curated DNA pool is subjected to sequencing in a selective metagenomics approach. The most popular selective metagenomics methods are sequence- and function-driven screening (Kori et al. 2019; Suttner et al. 2020). Targeted metagenomics, which generally includes function-based screening, can provide wide coverage and substantial redundancy of sequences for targeted genes (Orschler et al. 2021). Even at low abundances of genetic material, it can reveal unique genome areas directly linked to an ecological role through sequence analysis. For genome assembly and subsequent data analysis, better sequence coverage of the obtained target metagenomics can be advantageous. As a result, researchers are concentrating on ‘targeted metagenomics’ studies, which combine metagenomic library screening with sequencing analysis (Grieb et al. 2020). The metagenomics-driven research increases the understanding of the content and composition of genes for vital ecological processes in microbial communities. Bioinformatics tools have facilitated easy management of the enormous amount of data generated to help revolutionize metagenomics and to provide an analysis platform for high-throughput sequencing technologies (Henry et al. 2020). The conventional approaches rely on amplicon studies for evaluating microbial biodiversity in lakes, indicating that water samples only reveal a part of the biodiversity. This is a crucial limitation of amplicon-based sequencing in environmental assessment, monitoring, and remediation programs (Karnachuk et al. 2020). In contrast, function-driven metagenomics can help to differentiate between what is changing within the microorganisms and variation occurring in lakes. It can also address questions like how credible environmental impact assessments and water quality monitoring patterns are, as well as analyze the water remediation activities based on current microbial biodiversity assessment methods (Yang et al. 2019). Therefore, use of the best knowledge-based method, such as metagenomics, is important to conduct a systematic analysis of the changes occurring in diverse microbial communities in lakes.

Any alteration in the lake due to natural or anthropogenic activities impacts the lake water quality, which can impact microbial communities and their metabolic activities, which also affects biogeochemical processes such as sulfur, nitrogen, phosphorus, and carbon metabolism. Unchecked or untreated release of wastewater or agricultural runoff with high concentrations of contaminants destroys the ecological structure and functions of the lake, which may also lead to eutrophication-like conditions. Biological treatment is a widely used low-cost and environmentally benign technique to remediate eutrophication or similar conditions. Therefore, understanding how microbial metabolism drives lake ecosystems in the presence of contaminants is of utmost importance.

Phosphate-accumulating bacteria are the backbone of biological phosphate removal, the optimization of which can enhance the removal process. It is believed that these bacteria’s biodiversity should be considered for phosphorous removal. However, only a few genera (Tetrasphaera, Accumulibacter, and Thioploca) are known to be cultured in laboratory conditions, which restricts our understanding of the diversity and phylogeny of these bacteria (Qiu et al. 2020). At the same time, unresolved interactions of these microbes with other co-existing microbial species, metabolic functions, and niche differentiation affect the overall phosphorus removal capability. The metagenomic binning technique has been used, which shows that Candidatus Accumulibacter is the principal clade of phosphate-accumulating bacteria (Skennerton et al. 2015). Biodiversity of this clade was improved to seven clades through enrichment with different clades by modifying the laboratory culture and assembling the genome using metagenomic binning technology, thereby increasing the biodiversity (Skennerton et al. 2015). Recently, the community structure of Accumulibacter was adjusted by optimization of parameters and obtained phosphorus removal up to >99% (Kolakovic et al. 2021). Metagenomics also helped distinguish between phosphate-accumulating bacteria and glycogen-accumulating organisms (GAOs), which was previously unclear due to their interaction mechanism, an important factor in phosphorus removal studies – for example, Ca. Propionivibrio had been thought to be a phosphate-accumulating bacteria that favorably coexisted with Ca. Accumulibacter, in a laboratory scale, was found to be a type of glycogen-accumulating organism (Albertsen et al. 2016). The finding not only provided an accurate measure of phosphate-accumulating bacteria but also explored the GAOs diversity, which are an important microbial class for enhanced biological phosphorus removal systems. It is evident that metagenomics increases our understanding of microbial diversity, which can significantly play an important role in designing and adjusting the phosphorus removal performance in the biological removal process.

As already discussed, anthropogenic pollution can severely affect microbial activities critical to nitrogen metabolism and can further complicate nitrogen removal (Guan et al. 2022). Metagenomics has gained attention for understanding the genetic and functional information of microorganisms,which has been extremely useful in determining additional action for the bioremediation of surface water or wastewater. For example, knowledge of microbial diversity involved in denitrification was improved by metagenomics of the partial nitration anammox (PNA) process involving the collection of 23 metagenome-assembled genomes (MAGs) of denitrification microbes and classification in terms of functional genes (Speth et al. 2016). In the metagenomics study, it was observed that the larger granular particles harbor high biodiversity and functional diversity, which later on was correlated with the higher denitrification performance of large granular-based PNA (Chen et al. 2020). The eutrophic lake was found to have higher N removal efficiency than the oligotrophic. Higher nirS:nirK (nitrite reductase) ratios and higher denitrification potential with higher abundances of nosZ gene (N₂O reductase) were detected in an eutrophic lake, which indicated its enhanced capacity for complete denitrification, which could be the possible reasons for improved N removal. The removal rate of ammonical N was found to be higher in Ca. Brocadia in an immobilized aerobic baffled reactor showing higher N removal when compared to the Ca. Jettenia (Zhao et al. 2019). Metagenomics study revealed complete functional genes and higher gene expression associated with N metabolism in Ca. The Brocadia immobilized reactor may be due to its niche advantage and substrate competition promoting transcription, which ultimately led to high removal performance. The above-mentioned studies showed the applications of metagenomics, which have significantly improved our knowledge of structural and functional microbial diversity involved in nitrogen cycling networks. Our improved knowledge of microbial species and metabolic pathways can help us to accurately control microbial diversity for enhanced operational efficiency for lake remediation.

Biotransformation and bioremediation-based remediation approaches of toxic metals have gained massive interest in attaining sustainable development. Approaches based on metagenomics as advanced tools for profiling microbial communities that can potentially eliminate toxic metals from the wastewater have been explored. Several studies have reported the alteration in the functions, structure, and metabolic pathways under the elevated concentration of metal ions (Sharma et al. 2021). In a very interesting study it was observed that the presence of Cu(II) concentration at 5 mg/L in the bioreactor inhibited the biodegradation of organic matter. Later on, with the help of metagenomics, it was revealed that Cu(II) inhibited the expression of genes responsible for the degradation of refractory organic compounds (Zhao et al. 2021). Similarly, metagenomics study helped identifying inhibition of gene expression related to N metabolism reduction of microbial communities for N metabolism in the presence of Cr(VI) contamination, which was the cause of the decrease in the ammonia and total N removal performance (Sun et al. 2019). Physiological and metagenomic analyses showed that the Methylococcaceae and Methylophilaceae families are the principle agents responsible for the self-purification of heavy metals contaminated mine water (Zloty Stok and Kowary mines located in SW Poland) (Drewniak et al. 2016). The findings also revealed that the biofilm formation and heavy metal resistance functions are more desirable in microorganisms engaged in water purification than the ability to utilize heavy metals in the respiratory process (oxidation–reduction). Thus, metagenomics study can also help identify the genes and their function, which can allow for enhanced clean-up of metal-contaminated ecosystems.

Recent studies have reported that WWTPs are hotspots for numerous ARGs. The limited treatment efficacy of WWTPs can increase their concentrations in surface water bodies, and the transfer of ARGs between bacteria can significantly increase the antimicrobial resistance (AMR) in the environment. Metagenomics has been used to detect and analyze functional gene fragments, advancing our research on the formation and distribution of ARGs in water environments (Chen et al. 2022). The ARGs have been found at an elevated concentration in the WWTPs due to the complex ecological environment of WWTPs. Rodriguez et al. observed a similar result wherein metagenomics of different reactors of WWTPs revealed the occurrence of different ARGs in each reactor (Rodríguez et al. 2021). The use of metagenomics also revealed that the majority of ARGs were carried by plasmids, and there was a highest relative abundance of ARGs for plasmids and conjugate transposons, suggesting this is the chief reason for the horizontal transfer of resistance genes (Che et al. 2019). Seasonal ARGs profile was investigated using high-throughput sequencing and metagenomics in sediments and overlying water of Taihu Lake (Bai et al. 2022). The results indicated the occurrence of 11 ARG types and 33 ARG subtypes wherein the bacitracin, multidrug, and sulfonamides resistance genes were highly abundant. ARG hosts were also identified, among which Pseudomonas was highly abundant, which may assist the ARGs propagation. Anaerobic reactor-based study showed a good removal of ARGs and heavy metal resistance genes (MRGs) could be achieved using thermophilic–thermophilic sequences, whereas thermophilic–mesophilic sequence was more appropriate for resistance genes and pathogenic bacteria removal (Jang et al. 2018). On the other hand, a study for testing the efficacy of different reactor setup designs for ARG removal [anaerobic–aerobic sequence (AAS) bioreactor, aerobic reactors, and anaerobic units] revealed that AAS and aerobic setup showed higher removal of ARG-like sequence than the anaerobic units (Christgen et al. 2015). The results also indicated that AAS reactors have promising futuristic applications for removing ARGs from contaminated environmental samples as they require less energy (32% less). These metagenomic studies over time have expanded our knowledge of ARGs in water environments.

Overall, metagenomics has expanded our knowledge, enabling us to understand microbial communities with deeper insight. Metagenomics not only provide us with the structural and functional status of the microbiota in any water body but can also help us to take the necessary step to accelerate the remediation process.

Shotgun metagenomics is the process of sequencing all genes in the species present in a given environmental sample, irrespective of their order (Quince et al. 2017). It is commonly attributed to the numerous benefits of amplicon sequencing, including a much higher taxonomic resolution for distinguishing species strains and improved diversity detection. It can also provide researchers with unparalleled insight into a sample's functional potential without requiring complex taxa or active genes to be targeted (Campanaro et al. 2016; Ranjan et al. 2016). This nonbiased method can detect harmful bacteria and dsDNA viruses in a lake and is needed to produce recyclable water for agricultural and recreational purposes (Wylie et al. 2018). Shotgun metagenomics produces considerably more data than amplicon-based methods, allowing for the creation of MAGs, which are approximate representations of individual genomes. MAGs can distinguish between wastewater treatment ponds regarding functional and taxonomic variations (Ye et al. 2020). Rapid annotation using Subsystems Metagenome Technology called MG-RAST is an automated metagenome analysis program offering quantitative understandings of microbial communities and diversity based on sequenced data. Using different bioinformatics tools, this pipeline can also perform various functions such as protein estimation, quality control, clustering, and similarity-based annotation of metagenomic datasets. These bioinformatics tools help in decrypting metagenomic data produced by sequencing, providing the basic idea of taxonomy and functional capability of polluted water bodies (Meyer et al. 2018). Metagenome sequencing on Lonar Lake (India) was conducted for years in a hypersaline and hyperalkaline soda lake. Results revealed a vast taxonomic diversity and functional differences. The analysis showed the dominance of Proteobacteria, Bacteroidetes, and Firmicutes. The functional analysis depicted antibiotic and metal resistance gene profiles in the lake. The resistome of the lake confirmed acriflavine and fluoroquinolone resistance genes, indicating sewage water contamination in the lake. This study was the basis for understanding antibiotics and metal resistance processes resulting from different anthropogenic activities near the lake. The research suggests that the combined approaches were suitable for preserving the Lonar Lake ecosystem (Chakraborty et al. 2020).

The Pangong Lake, located in the eastern part of Ladakh in India, was explored using bioinformatics tools like MG-RAST (Rathour et al. 2020). This ecosystem is a high-altitude salty water body that represents an extraordinary microbial population pool having the adaptability to survive in stressful conditions. Sequencing this lake through shotgun metagenomics revealed that the lake was full of psychrotolerant and psychrophilic microbial communities. Proteobacteria were the most adequately present phylum, while Methylophaga, Marinobacter and Halomonas, and were present in abundance primarily at the genus level. Nitrogen metabolism, methane metabolism, sulfur reduction, xylene, and benzoate degradation are responsible for the identified enzyme pathways. The metagenome also contained stress response genes responsible for adaptation to various factors such as low temperature, pH, salt tolerance, osmotic stress, and oxidative stress. The researchers compared Pangong Lake metagenome samples to water samples from three different aquatic settings, including freshwater lakes, saline lakes, and marine ecosystems. They used the MG-RAST server against the RefSeq and Subsystem databases. MG-RAST analysis concluded that Pangong Lake was closely related to marine samples at the phylum level and saline samples at the functional level. Statistical analysis using ANOSIM at various taxonomic and functional levels revealed that there was no significant difference in overall composition between ecosystems, but error plots showed a significant difference (Rathour et al. 2020). Shotgun metagenomics was also used to analyze Western India's river system, exposed probable genes occupied in xenobiotics bioremediation and their trailed credible detoxification pathways (Yadav et al. 2021). The study proved that the remediation-related genes and the enzymatic pathways would help microorganisms survive in a polluted and highly nutrient-rich environment. Functional analysis was also done using the Integrated Microbial Genomes and Metagenomes (IMG/M) method, which provided annotation, interpretation, and microbial metagenome datasets distribution. It offered comparative data using computational methods, along with a metagenome-specific analysis. It also consisted of microbial group collective genome samples found in IMG's all-inclusive collection of genomes from all three realms of life, namely genome fragments, plasmids, and viruses (Chen et al. 2019), for the function-based comparative analysis of metagenomic samples, analytical tools allowed studying the relative abundance of various functional families or functional categories, protein families across metagenomic models. The results showed that aerobic and anaerobic catabolism processes were occurring for the degradation of pollutants. Furthermore, taxonomic diversity analysis showed only minor differences in xenobiotic compound transformation potential between cities and non-city water bodies (Palaniappan et al. 2020). This study presented an investigative approach that would serve as a fundamental tool for using metagenome-aided information to plan and design improved bioremediation strategies to decontaminate water bodies.

Meta Genome Analyzer (MEGAN) is a widely used and accepted software for efficiently analyzing large metagenomic datasets (Simon & Daniel 2011). This software is typically preferred for interactive analysis and comparison of taxonomically and functionally metagenomic data. This software facilitates the taxonomic analysis that utilizes the NCBI taxonomy data and functional analysis using mapping of readings in the COG, SEED, and KEGG classifications (Huson et al. 2016). Additionally, a taxonomic and functional comparison of samples can be made using various graphing and visualization techniques called co-occurrence plots (Bieker et al. 2020). MEGAN was recently used to depict the taxonomy and functional characterization of Erhai Lake, a distinctive subtropical lake in the Yunnan-Guizhou Plateau (Pan et al. 2020). It was discovered that Erhai Lake receives a large quantity of nitrogen and phosphate nutrients, which promote the development and spread of resistance genes. High-throughput 16S rRNA sequencing and metagenomic DNA were used to examine the bacterial population profile and antibiotic and MRGs in Erhai Lake. It was observed that Proteobacteria, Nitrospirae, Bacteroidetes, and Firmicutes were the leading microbial groups present. ARGs with elevated relative abundance [bacitracin, macrolide lincosamide-streptogramin (MLS), and tetracycline resistance genes] and multi-metal and arsenic resistance genes. It was concluded that the microbial culture was the critical driver of resistome formation. The abundance of Bacteroidetes and Chloroflexi was related to harsh nutrient contamination status (nitrogen and phosphorus). Furthermore, nitrogen was seen to influence multidrug, rifamycin, and fosfomycin resistance genes. The impact on MRG and ARG abundances of high nutrient levels in the lake was not positive, which showed that this nutritional load selectively stressed resistome.

In terms of identifying the nutrient contamination, metagenomics has produced microbial snapshots of the lake and revealed three different lethal cyanobacterial blooms causing the contamination due to the high nutritional input (Steffen et al. 2012). This initial or starting characterization is vital for such communities to better compare different cyanobacterial blooms. This study used the RefSeq database (MG-RAST) to annotate these metagenomes. While the focus was mainly on the bacterial communities associated with cyanobacterial blooms, viral sequences were also present in these datasets (Randle-Boggis et al. 2016; Chen et al. 2018). Another exciting finding of this study included the occurrence of the mlrC gene in lakes, which was majorly involved in the degradation of microcystin. This comparative study confirmed the ever-increasing trend in microbial ecology. The key observations revealed that functional genes like those that help in nitrogen assimilation appeared to be extra instructive compared to standard 16S rDNA gene analysis (Devarapalli & Kumavath 2015). In this approach, they could quickly identify potential deviating assimilated nitrogen pathways using different bioinformatic tools like Simple Metagenomics Analysis Shell, also known as SmashCommunity. It is a standalone metagenomic annotation plus analysis pipeline sharing design principles for microbial communities. It is appropriate to study data obtained from Sanger or 454 sequencing techniques (Devarapalli & Kumavath 2015). Typically, it supports tools for critical metagenomic tasks such as gene prediction assembly. It can also provide methods for estimating functional compositions and quantitative phylogenetics of metagenomes, comparing their essays, and generating intuitive ocular representations of various analyses. Community Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis is another tool for deciphering metagenomic data (CAMERA). CAMERA is a database-related computational infrastructure offering an advanced web-based platform and a single mechanism for depositing, allocating, analyzing, visualizing, and exchanging data on microbial biology (Dudhagara et al. 2015). It represented that the genomic input of heterotrophic bacteria for nitrogen assimilation was the driving force behind the toxic freshwater blooms.

Shotgun metagenomics sequences administered by Trimmomatic Bowtie2 MEGAHIT v1.1.3 were conducted in China in 2017, which helped to gain metagenomic insights into the microbial communities and antimicrobial resistance gene for the South China Sea (Gao et al. 2017). The draft genomes were annotated using eggNOG-mapper. The findings provided the first metagenomic insights into the microbial population composition in the South China Sea and a broad view of related antibiotic-resistant genes (ARGs). The Unkeshwar hot springs in India were investigated using whole-genome sequencing, and taxonomic classification was performed using MEGAN (Mehetre et al. 2016). The Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for practical annotation in the same study. It was revealed that the Unkeshwar hot springs have a lot of phylogenetic diversity and metabolic potential for biotechnological applications. Similarly, shotgun metagenomic tools such as CosmosID metagenomic software were used in Saudi Arabia to evaluate microbial profiles for lakes, revealing high-performance k-mer algorithm databases (Zaouri et al. 2020). The software exposed that the lake consisted of low alpha diversity for bacteria. Recently, a whole metagenomic approach was used to characterize the microbial biodiversity present in river sediment in USA (Reddington et al. 2020). It was observed that microbial consortia and their ecological functions were unrelated to geographic location. Instead, microbial ecological responses were related to the urban and other anthropogenic effects, as well as changes in taxa manifested over a limited geographic space (Sonthiphand et al. 2019). The sequencing system IlluminaHiseq 2000 was used in a study on metagenomic sequencing for a microbial gene catalog (Lou et al. 2019). Gene taxonomic classification using BLASTP search and MetaGene predicted open reading frames (ORFs). These analyses revealed that the bacitracin resistance genes were the most prevalent ARGs in the study site conducted in South China. A large number of common virulence factors were also discovered in high abundance.

Such information deduced from these studies clearly shows the importance of a metagenomic approach, which is very much needed today for designing better rejuvenation strategies and clean-up of any contaminated target niches. Various other studies have successfully employed metagenomic approaches, and are listed in Table 3.

There are many other online bioinformatics software available that are used to analyze metagenomic datasets (Escobar-Zepeda et al. 2018). Some of these tools are listed in Table 4.

The irrigation precipitation runoff water, if disposed untreated, can pollute the surface water bodies with different contaminants such as heavy metals, concentrated nutrients, or pathogens that severely damage water quality (Maurice et al. 2013; Gaytán et al. 2020). Dissolved oxygen, electrical conductivity, oxidation–reduction potential, hydrogen ion concentrations, dissolved solids, and turbidity are all important factors that affect water bodies and their quality (Mason et al. 2014; Mendes et al. 2014; Pan et al. 2014). As a consequence, it is important to monitor changes in the microbial environment and their direct and indirect effects on water quality. Indeed, metagenomic analysis has been used to determine how anthropogenic activities influence the quality of surface waters, which in turn affect microbial ecosystem structure and, as a result, biome re-structuring (Lefterova et al. 2015; Boulangé et al. 2016; Nakagawa & Fujita 2018; Samarkos et al. 2018). Metagenomics has enabled the scientific community to explore and identify the hidden microbial world's compositional and revealed biochemical functions. It has helped categorize existing diversity with evolutionary and ecological significance and simultaneously revealed the functional capabilities of numerous microbes of diverse environments. The development of various metagenomic branches shows the progression in this field with the rapid and economic feasibility of sequencing assessment. This has led to easy identification of microbes and their associated functions, providing advanced information. Functional metagenomics is crucial because it helps identify various metabolites from unculturable microbes. The advancement of metagenomics is a competent tool to recognize microbial communities, making it more understandable, better informative, multipurpose, and economically feasible.

Characterization and identification of microbial communities through metagenomics is rapid, efficient, and accurate, which also overcomes the lacunas of traditional molecular techniques. Over time, the cost of sequencing has been brought down, due to which technologies like NGS are widely used and popular by environmental researchers. Existence of various computational and bioinformatics tools has enabled the handling and accurate interpretation of huge data in a very informative way. Existing reports tell that there can be certain ways where metagenomics can aid in lake rejuvenation, such as:

• Accurate quantification of species that can aid in fine adjustments to increase or decrease certain species to modulate the reactor performance.

• Identification of different clades of microbial communities directly responsible for specific functions (for example, phosphate-accumulating bacteria). Such information can be further used to augment the existing clade or to enrich it to achieve the targeted functions.

• Information on microbial nutrient cycling networks can help to understand the structuring of microbial ecology related to nutrient cycles and can also help to accurately control microbes and increase operational efficiency (removal process).

• The knowledge gained from metagenomics can elucidate the distribution mechanism of horizontal transfer of resistance genes and metabolic pathways for degradation-related ARGs in water environments. This information is crucial to stop the life-threatening AMR in lakes.

• Understanding microbial communities' response mechanisms under heavy metal stress can help identify which species are resistant to which metal ions. This would help us to optimize the parameters for treatment methodology.

• As a technique, metagenomics functioning can be enhanced by combining it with multiple omics technologies, such as metagenomics, metaproteomics, and metatranscriptomics, for the characterization of communities at the genome, proteomics, and transcriptome levels, resulting in the complete interpretation of the interaction between microbial populations.

There are still many questions that need attention in order to completely formulate effective strategies for lake rejuvenation. For example, (a) complete control of microbial response in a suitable environment to achieve the maximum removal of nitrogen and phosphorus; (b) elucidation of the mechanism of transformation pollutants and the deep knowledge of corresponding functions even at lab-scale studies is still unknown; (c) changes in microbial species, their interactions leading to evolution during the complex environmental sample (wastewater) treatment needs to be explored; (d) impacts of emerging contaminants, mechanisms of attaining gene resistances in natural environment is still needs to be determined; (e) and whether this resistance and its spread is going to be beneficial or harmful as anticipated needs to be determined. In addition, there are other challenges associated with metagenomics, such as requirements on the sample quality, more computational efforts for data analysis, difficulties in statistically analyzing the large, sparse data matrix, and higher costs for targeted monitoring. More research must be carried out to rectify all these issues and challenges.

Information revealed through metagenomics can substantially impact the development of bioaugmentation strategies, such as enriched cultures, and alter rejuvenation parameters to support positive phylogenetic groups or guide metabolic pathways. Adopting such metagenomic strategies would help lake rejuvenation as they can provide a blueprint of microbe-related events occurring in any targeted niche. Taken as a whole, metagenomics and its applications can determine hidden microbes with novel catabolic genes and enzymes from the environmental microorganisms, which in turn would help in designing a knowledge-based approach for bio-stimulation or bioaugmentation strategies, thus providing a novel or improved system for clean-up of contaminated lakes.

S.N. is grateful to the Department of Science and Technology for providing fellowship through the DST-INSPIRE program. Facilities of Environmental Biotechnology and Genomics Division, CSIR-NEERI are gratefully acknowledged. The article is checked for plagiarism using the iThenticate software and recorded in the Knowledge Resource Center, CSIR-NEERI, Nagpur foranti-plagiarism (KRC No.: CSIR-NEERI/KRC/2021/MAY/DRC/2).

No external funding has been received for the current study.

S.N. conceptualized the study, wrote the original draft, and edited. A.K. designed the study, conceptualized, reviewed, edited, and supervised the study.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.