Environmental Metagenomics, Water Quality and Suggested Remediation Measures of Polluted Waters: A Combined Approach

By Inderjeet Tyagi, Vikas Kumar and Rama Rao Karri

Environmental Metagenomics, Water Quality and Suggested Remediation Measures of Polluted Waters: A Combined Approach is a reference handbook for scientists, engineers and early-career researchers seeking guidance in the areas of water quality, and remediation studies. The comprehensive book, which includes case studies and applications from a range of contributors in the field, offers an essential resource in the science of water quality assessment.

• Includes a range of applications and case studies in wetland, riverine, drinking, and groundwater metagenomics, along with approaches for the remediation of pollutants from wastewater
• Offers the latest updates on environmental metagenomics and its correlation with water environments, remediation measures, and SDGs
• Provides key contributions from global researchers in the fields of water chemistry, environmental science, engineering, and public health

• Language: English
• Publisher: Elsevier Science
• Release date: Mar 26, 2024
• ISBN: 9780443136603

Inderjeet Tyagi

Dr. Inderjeet Tyagi is working as a Scientist at ZSI, Ministry of Environment Forest and Climate Change, Kolkata, India. To his expertise, Dr. Tyagi is working in the field of Wastewater treatment, Water Quality, Environmental Metagenomics, and Environmental Management for the past several years. To his credit, Dr. Tyagi had published 120+ SCI papers with Citation~8600 and an h-index of 51 with a cumulative impact factor >550. More to this, he is leading Five (5) national and international projects related to wastewater and water quality assessment in heavily polluted areas in India. Further, he is also on the reviewer panel of more than 40 international journals belonging to well-known publishers like Elsevier, Nature, Springer Nature, Taylor Francis, ACS, etc. Recently, he was awarded the “India Prime Education Quality Award 2021” for his outstanding contribution to the field of wastewater treatment and Environmental Management. He is a lifetime member of the Indian Science Congress Association (ISCA) and a Member of the International Society of Wetland Scientists (International Chapter). He is the Editor of One Elsevier edited book entitled “Sustainable Materials for Sensing and Remediation of Noxious Pollutants”

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Aqueous ecosystem: Environmental metagenomics, water quality, and possible remediation measures

Inderjeet Tyagia, Kaomud Tyagia, Faheem Ahamadb, Richa Kotharic, and Vikas Kumara

a Centre for DNA Taxonomy, Molecular Systematics Division, Zoological Survey of India, Kolkata, West Bengal, India

b Department of Environmental Science, Keral Verma Subharti College of Science (KVSCOS), Swami Vivekanand Subharti University, Meerut, Uttar Pradesh, India

c Department of Environmental Sciences, Central University of Jammu, Rahya Suchani, (Bagla) Samba, Jammu and Kashmir, India

1.1 Introduction

Water is an essential component for the sustenance of all living organisms. Despite the fact that water encompasses around 71% of the Earth's surface, the availability of clean water suitable for consumption by humans is deteriorating exponentially [1]. The rise of the need for water can be attributed to several factors, including the rapid pace of urbanization, industrial expansion, human settlement in drought-prone regions, alterations in climate patterns, and the growing per capita utilization of water resources [2]. Additionally, detrimental outbreaks (diseases such as COVID-19), play a crucial role in the contamination of natural water resources, therefore presenting significant risks to both the environment and human health [3]. Various pollutants, including heavy metals, organic dyes, pesticides, polycyclic aromatic hydrocarbons (PAHs), persistent organic pollutants (POPs), and endocrine disruptors (EDCs), are consistently released into rivers, wetlands, and adjacent aquatic channels, thereby contaminating the aquatic ecosystem and groundwater through percolation [4]. Several detailed studies about the impact of these inorganic, organic, and emerging pollutants on the degradation of water quality and poor health of aquatic ecosystem are available in the literature [5–7]. These impurities via different interactions got bio accumulated in the food web and transferred to humans thereby resulting in severe health impacts [4]. In addition to the aforementioned pollutants, biological contaminants mainly microbial diversity (pathogens and ARGs) and fecal matter are significant stressors that exert a substantial impact on water quality [8]. These variables serve as key reservoirs of pathogens, including bacteria, protozoans, and viruses, within the aquatic ecosystem. Microbial water quality is a crucial component of water quality as it exhibits a clear correlation with human health, food safety, and ecosystem functions [9]. The composition of microbial communities in aqueous environments is characterized by diversity and dynamic changes. Various factors, including anthropogenic activities, recreational activities, environmental stressors, flow rate, and nutrient alterations, have a significant impact on determining the community structure within a specific ecosystem [10,11]. These factors have been found to increase the presence of fecal-indicating bacteria (FIB), including Escherichia coli and Enterococci, as well as other biological pollutants in water, resulting in a decline in water quality [10,11]. In order to address the crucial issue of the impact of microbiologically contaminated water on human and faunal health, there is an urgent requirement to apply innovative and cost-effective monitoring systems for surveillance. Traditional monitoring technologies rely on cultivation methods and are not effective in dealing with modern microbiologically contaminated water [12]. Another significant drawback of this approach is that 99% of the microbes cannot be cultivated and it is a labor-intensive and time-consuming process [13]. Globally, the scientific community has shown significant interest in metagenomics-based monitoring investigations due to the emergence of advanced technologies [12]. The current availability of next-generation sequencing (NGS) has facilitated the feasibility, cost-effectiveness, and environmental friendliness of monitoring microbiologically contaminated water processes [14,15]. Furthermore, by integrating several techniques such as quantitative polymerase chain reaction (q-PCR), bioinformatics tools, and water quality analysis with metagenomics, researchers can obtain comprehensive insights into the diversity, abundance, water health, and functional significance of biological pollutants in the aquatic ecosystems [12–15]. Additionally, stakeholders have the opportunity to strategically design water and wastewater treatment systems based on the environmental preferences of the metagenome [12–15].

Keeping in view the need of the hour, in the present chapter we had elucidated in detail the protocols for environmental metagenomics that are widely practiced worldwide. Additionally, it provides insights about the role of microbial diversity and community structure in deciphering the health of aquatic ecosystem (water quality, minerals, and environmental variables). Furthermore, it investigates the significance of microbial diversity in bioremediation, biogeochemical and nutrient cycles, specifically focusing on functional and metabolic potentials. Based on the existing research and the experience possessed by the authors, a set of suggestions has been formulated for prospective stakeholders to address the challenges associated with microbiologically polluted water.

1.2 Environmental metagenomics protocols

The application of next-generation sequencing (NGS) tools opens up new scopes for non-invasive monitoring of community structure either it be microbial or eukaryotic along with their functional role in the metabolism, biogeochemical and nutrient cycles in particular aquatic habitat [13,16]. Although with the advent of technologies such as NGS one can decipher the complete prokaryotic communities but in order to avoid contamination and false positives, there are certain significant factors that need to be taken care of such as experimental setup, sample collection, transportation (cold chain), water-sediment filtration, DNA extraction, sequencing errors, library formations, deletion of genomic repeats, chimera removal, quality filtering, computational techniques, selection of accurate statistical tools [13–16], etc. Additionally, the unidentified sequences commonly termed dark matter also play a significant role in the percentage characterization of particular aquatic ecosystem [17,18]. NGS open up new scopes for researchers across the globe to work on the pristine environments for enhancing the baseline database in order to explore the future potential of the ecosystem for socio-economic services such as recreational, cultural, and tourism activities. Fig. 1.1 depicts the challenges (experimental and computational) generally faced by researchers across the globe during targeted amplicon-based (16S rRNA) and whole metagenome sequencing (WMS) [16].

Figure 1.1 Challenges (experimental and computational) generally faced by researchers across the globe during targeted amplicon-based (16S rRNA) and whole metagenome sequencing. (Reproduced with permission from ([16] under Creative Common License)

In this section, the major focus will be on the environmental genomics protocol for the 16S rRNA and whole-genome shotgun sequencing (WGS) with a complete overview from sampling to downstream analysis through selective software and pipelines mentioned in subsequent sections (Fig. 1.2).

Figure 1.2 Schematic overview of most widely used targeted amplicon-based (16S rRNA, 18S rRNA, ITS) and whole metagenomic sequencing approaches [16]. (Free to use under Creative Common License)

1.2.1 Study design and sample collection

Based on the study design, hypothesis, and types of aquatic ecosystems, sample collection for environmental metagenome analysis varies from one ecosystem to another ecosystem in terms of volume, types, frequency, etc. as these factors play significant roles in deciphering the community structure of aquatic ecosystems.

Through different field studies conducted by the Zoological Survey of India (ZSI), Kolkata, we recommend the collection of mixed water-sediment samples or independent water samples and sediment samples from different depths to accurately investigate the microbial community structure along with their functional role in maintaining metabolism, biogeochemical as well as nutrient cycles in the aquatic ecosystem [13,15]. Furthermore, to elucidate the seasonal altercations/climate change, it is recommended to collect the grab samples in triplicate during three distinct seasons (summer-monsoon-winter) or may collect the samples considering pre- and postmonsoon seasons. Moreover, to predict the baseline community structure, one-time sampling in triplicate from the aquatic ecosystem may be carried out. However, this strategy possesses certain limitations such as the size variability of the ecosystem, anthropogenic aspects, etc. [13,15].

In terms of aquatic ecosystems, for rivers, it is advisable to collect triplicate grab samples (∼10–20 L; for polluted rivers: ∼5–10 L) from different depths, for wetland ecosystems, approximately 5–8 L of water needs to be collected for obtaining the detailed information about the microbial diversity present. For drinking water microbiome and groundwater microbiome investigation (located in the same or different geographical locations) it is suggested to collect triplicate samples ∼10–15 L and 5–10 L, respectively using a spatial and temporal approach. The collected samples (river/wetland/drinking/groundwater) were transported to restricted lab space in cold chain with temperature regulated at 0–4°C for filtration using 0.22–0.45 µm filter papers made from different materials such as glass fiber (GF), cellulose nitrate (CN), mixed cellulose ester (MCE), polycarbonate (PC), nylon, polyethersulfone (PES), and cellulose acetate (CA) to capture the DNA and extract it using the standard isolation kits [13,15,19] details mentioned under Section 1.2.2.

Further, to prevent contamination during the collection of samples, it is recommended to maintain an appropriate sample environment, including controlling factors such as temperature, humidity, and other relevant environmental variables [16]. Additionally, it is also recommended to avoid placing the samples in close proximity to potential sources of contamination [16]. Moreover, the use of sterile laboratory resources, such as hand gloves, sanitizers, head covers, and masks, can further mitigate the risk of contamination [20,21]. Thus, proper management and handling of e-DNA samples is essentially required in accurately evaluating the composition and variety of microbial communities present in the particular aqueous environment.

1.2.2 DNA extraction

Primarily two extraction procedures, namely mechanical lysis/bead beating and chemical lysis were widely used across the globe [16]. The filter paper 0.22 or 0.45 µm was first finely chopped into fine pieces and subjected to any of the commercially available DNA isolation kits such as DNeasy PowerWater (Qiagen, Hilden, Germany), XpressPure (MagGenome, India), QIAamp DNA Mini Kit (Qiagen, Hilden, Germany), XpressDNA/RNA (MagGenome, India), QIAamp DNA Stool Mini Kit (Qiagen, Hilden, Germany), MO BIO Power Water Kit (Qiagen, Carlsbad, CA) and MO BIO Power Soil DNA Isolation Kit (Qiagen, Carlsbad, CA) were commercially available across the globe [13,15,22].

1.2.3 DNA sequencing and bioinformatics

1.2.3.1 Target-based amplicon sequencing (16S rRNA)

Target-based amplicon sequencing (16S rRNA) has been widely used for the investigation of microbial diversity in different aquatic ecosystems [13,15,16]. The most prevalent use of the 16S rRNA gene for the classification of known and unknown taxa was due to the encoded 30S subunit in the 70S ribosomal complex and this gene possesses a significant role in various cellular, biogeochemical, and metabolic processes [16]. The 16S gene comprises ∼1500 bp with nine hypervariable regions (V1–V9). For each hypervariable region, there are specific forward reverse primers that are utilized for the classification of bacterial community structure [16]. The details of forward reverse primers along with their sequences and size for each hypervariable region are presented in Table 1.1[23–25]. Based on the primers and targeted hypervariable regions, the final results of the 16S rRNA gene vary significantly and may sometimes lead to misinterpretation [26,27]. Different NGS platforms such as Illumina's MiSeq, Illumina's HiSeq, Oxford Nanopore MinION, and PacBios Sequel with different base pair chemistry have been used for the sequencing of the 16S rRNA gene [23]. Currently, Illumina MiSeq platforms are preferably used as they can sequence up to 600 bp length of nucleotide and can generate robust datasets ranging from 1 to 24 million sequences using the chemistry 2*150, 2*250, and 2*300 base pair [23]. Moreover, the sequencing depth from 50,000 to 100,000 sequences has been recommended for the characterization of microbial diversity using the 16S rRNA gene [28]. The obtained raw sequences were subjected to bioinformatics workflow comprising four steps namely quality-filtering, clustering, taxonomic assignment, and data analysis via different statistical tools [28]. The quality filtering step includes the removal of low-quality or ambiguous taxa, merging of sequences, and removal of chimera [28]. Upon quality filtering, the sequences were subjected to clustering using either the mothur [29] or DADA2 [30] pipelines in QIIME2 [31]. The use of the mothur pipeline resulted in the formation of operational taxonomic units (OTUs) [29], whereas the use of the DADA2 pipeline led to the formation of amplicon sequence variables (ASVs) [30]. The obtained OTUs or ASVs were then taxonomically classified using precustomized databases such as Silva132, Silva v138.1, Greengenes, and RDP. The current database utilized for the taxonomic classification of OTUs or ASVs in QIIME2 is Silva v138.1, as it is one of the most updated database to date. The obtained results (files) OTUs/ASVs table, taxonomic classifier along with metadata were used for the downstream analysis such as taxonomic abundance, alpha and beta diversity, etc. using the online web tool Microbiome Analyst 2.0 [32] as it provides a multiple range for community profiling, functional profiling, and metabolic network visualization for both amplicon and shotgun metagenomics data [32]. Additionally, the functional annotations of bacterial taxa can be predicted using PICRUST2 [33] or TAX4Fun [34]. Fig. 1.3 demonstrates the schematic workflow followed during the target-based amplicon sequencing (16S rRNA).

Table 1.1

aFor archaeal and bacterial reads.

Figure 1.3 Schematic presentation of protocol widely used across the globe for the whole-genome shotgun sequenced (WGS) [35]. (Free to use under Creative Common License)

1.2.3.2 Whole-genome shotgun sequencing

The whole-genome shotgun sequencing (WGS) or whole metagenome sequencing (WMS) approach was mainly recommended where we have to elucidate all the genomes present in the aquatic environment as it provides a complete overview of microbial community structure (archaea, bacteria, virus, plasmids, etc.) and their functional annotations along with antibiotic-resistant genes (ARGs), antimicrobial resistance (AMRs), etc. [35]. In addition to this, using WGS, one can able to obtain draft genomes of the uncultured microorganisms, thereby, opening new scope towards the identification of novel isolates having significant roles in metabolic, cellular, or biogeochemical cycling [36]. As mentioned in Section 1.2.3.1, different sequencing platforms were available for the sequencing of the WGS data but among them, Illumina platforms (MiSeq, HiSeq, and NovaSeq) were majorly preferred [35] due to high accuracy (specially with 150–300 bp) and generation of a large number of sequencing reads (up to 1.5Tb–6 Tb per run) [35]. The raw sequences were subjected to quality filtering and preprocessing involving steps such as sequencing adapters trimming, removing short/low quality reads, and removing reads having N characters based on quality using software such as FASTX, PRINSEQ, Trimmomatic, Sickle, and BBTools [13,16,36]. After quality trimming the paired-end reads were joined using fastq-join [37] or PEAR [38]. Postjoining paired-end reads, two approaches were generally adopted, one assembly approach (most followed) and the second assembly-free approach [35]. In the first approach, the paired-end sequences were assembled using software such as metaSPAdes, MEGAHIT, and IDBA-UD [39–42]. After assembly, the sequences were then binned based on different categories such as taxonomy dependent, semisupervised or unsupervised/taxonomic independent with the aid of software's such as CARMA, Metaphyler, Sort-ITEMS, PhymmBL, TACOA, PhyloPythiaS+Phymm, Metawatt, SCIMM, LikelyBin, MaxBin2, COCACOLA, MetaBAT2, CONCOCT, BMC3C, MetaBMF, MBBC, and Canopy, respectively [35]. The MAGs generated using CheckM or AMBER program from the binned sequences were subjected to taxonomic profiling using MEGAN6/MGS-Fast/MG-RAST/MAGpy/DIAMOND while the strain level classification can be done using software MetaMLST or DESMAN or MetaVSN or StrainPhlAn or PANphlAn [35]. Additionally, the functional annotations can be done using standard software using CARD or BacMet [35]. On the other hand, the assembly-free approach promotes the use of software such as MG-RAST, MEGAN6, CARMA3, Taxator-tk, Kaijou, taxmaps, Kraken, etc. for the taxonomic classification of WGS while functional analyses can be done using DIAMOND, PALADIN, GRASP2, MGS-Fast, FUN4ME, MOCAT2, Shot-MAP, HUMAnN2, Camelian, etc. [35]. Fig. 1.3 demonstrates the schematic workflow of both the approaches, i.e., assembly-based approach and assembly-free approach that may be followed during the WGS studies [35]. Furthermore, in this section, a brief overview of the WGS studies has been provided. For more details about this section, one should consider reading the previously published literature [16,35].

1.3 Linking metagenomics to water quality, biogeochemical cycles, and bioremediation

Microbial communities are of great importance in assessing the overall health of aquatic ecosystems, since they play crucial roles in several aspects like food webs, bioremediation strategies, and biogeochemical cycles that occur within these ecosystems. Understanding the role of microbes in aquatic ecosystems is a subject of great interest. These microorganisms possess the remarkable ability to adapt to various environmental and anthropogenic factors. As they transit from one habitat to another, these microbes play a significant role in regulating nutrient cycles and facilitating the bioremediation of harmful pollutants. This is achieved through the utilization of diverse functional pathways that convert these pollutants into nontoxic products [43]. The interaction between biogeochemical processes and bioremediation activities leads to substantial variations in the water quality of aquatic systems. The health of water, which is crucial for the growth and development of microbial communities, plays a significant role in this regard [43]. When water quality is degraded, it often results in the proliferation of pathogens and AMR microbes. Conversely, in aquatic ecosystems with good water quality, nonpathogenic microbes tend to be more abundant compared to their pathogenic counterparts [9,13,15,43].Fig. 1.4 represents the schematic workflow of linking metagenomics with water quality parameters/environmental variables, biogeochemical cycles, and bioremediation (functional metabolic potential). With the rise of environmental metagenomics approaches, nowadays, one can access the complete microbial community structure present in the aquatic ecosystem and through the chain of algorithm software, we can assess the functional role of these microbes by categorizing them under different categories such as pathogens, degraders, nutrient metabolizers, heavy metal redactors, metabolic activators, AMRs, ARGs, etc. [44,45]. Fig. 1.5 represents the general timeline and developments of environmental metagenomics application in the biological remediation of pollutants such as nitrates, phosphates, ARGs, and heavy metals from the aquatic ecosystem [46–55].

Figure 1.4 Schematic workflow of linking metagenomics with that of water quality parameters/environmental variables, biogeochemical cycles, and bioremediation (functional metabolic potential). (Reproduced with permission from [35] under Creative Common License)

Figure 1.5 General timeline and developments of environmental metagenomics application in the biological remediation of pollutants such as nitrates, phosphates, antibiotic-resistant genes (ARGs), and heavy metals. (Reproduced with permission from [36])

In the environment, inorganic compounds, particularly trace metals, exhibit a high degree of persistence and can undergo solubilization in acidic conditions, hence, facilitating the accelerated absorption of chemical pollutants by microorganisms. Due to the nonbiodegradable nature of the majority of inorganic compounds, the most effective approach for mitigating the presence of these pollutants in the environment is inducing a transformation in the metal's oxidation state. This process aims to convert the metal from a highly toxic and soluble form to a less soluble and less harmful one [44,45]. The investigation of microbial remediation of heavy metals has been the subject of considerable research due to its cost-effectiveness and favorable outcomes [⁴⁴,⁴⁵]. Microbial remediation primarily involves the microbial metabolism-driven reduction of hazardous heavy metal valences to non-toxic or low-toxic levels, with the aim of facilitating resource recycling. Extensive research has been conducted over several decades to investigate the reduction of heavy metals by microbial organisms. However, our understanding of the underlying mechanisms involved in microbial remediation of heavy metals, as well as the response of microbial communities to heavy metal stress, remains constrained. The application of metagenomics has played a crucial role in the substantial progress made in the field of microbial heavy metal clean-up [44]. It was observed that under anoxic conditions and aerated conditions, the toxic form of Cr+6 transformed to the less toxic Cr+3 by the microbes through the functional pathways as previously reported [56]. The mechanism of bioremediation of heavy metals from the aquatic environment involves three steps namely physical absorption through weak Van der Waal's forces followed by the formation of the complex through the interaction of heavy metals and active sites of microbes (cell wall) and finally the conversion of toxic impurities in less toxic products through ion exchange and metabolic pathways [57]. Table 1.2 represents the application of environmental metagenomics in the remediation of heavy metals in different aquatic ecosystems