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J. Environ. & Sociobiol. : 19(2) : 221-243, 2022

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Received : 12 November, 2022 / Reviewed & Accepted : 12 December, 2022 / Uploaded Online : June, 2023

MICROBIAL ENRICHMENT IN GLOBAL WASTEWATER NICHES UNDER IMPACT OF CLIMATE CHANGE - A COMPUTATIONAL STUDY Souptik Ghosh1*, Nabarun Dawn1*, Souradip Basu2 and Sayak Ganguli1# Graduate Department of Biotechnology, St. Xavier’s College (Autonomous); Kolkata 2Post Graduate Department of Microbiology, St. Xavier’s College (Autonomous); Kolkata Email: [email protected],[email protected], [email protected] *Equal Contribution; # Corresponding author: [email protected] 1Post

ABSTRACT With rise in industrialization and human activities, global warming and eventually climate change concerns are looming large on the face of humankind. Wastewater, being the sink for local microbial community can be assessed and analyzed as an indicator for climate change. Impacts of global warming are far-reaching and cause changes in life processes occurring in Nature, which gets reflected on the abundance of microorganisms detected in the wastewater. Greenhouse gases like Methane and Nitrous oxide, are the major players of global warming. The data used in our research was collected from rural, urban and delta region of India, regions of São Paulo, Brazil, along with domestic and industrial regions of Henan, China. The data were analyzed after thorough metagenomics study. Changes in nutrient cycling, such as carbon and nitrogen cycling, rise of several diseases and antibiotic resistance amongst the microorganisms have been detected. Increase in mean temperature of earth will also cause changes in the life processes of microorganisms and the ones which can survive outside the temperature niche of initial habitat can survive. Thus, by observing these six data sets, we aim to investigate the roles of microbes present in wastewater as an indicator for climate change. Key words: Climate Change, Wastewater, Global warming, Methane, Nitrous oxide


J. Environ. & Sociobiol. : 19(2) : 221-243, 2022, Ghosh et al. INTRODUCTION Climate change and Global Warming is a significant challenge in this era. It disrupts species’ interactions causing the species to either adapt or perish. Microorganisms are present on Earth’s surface from time immemorial (Knauth, 2005) and hence, have undergone the longest periods of evolution by acquiring characteristics to withstand extreme stresses (Konhauser et al., 2003). Earth’s mean temperature is expected to rise by 1.5°C beyond pre-industrial level (IPCC, 2022). With temperature, precipitation patterns alter (Easterling et al., 2000), impacting majorly on carbon cycle (Cox et al., 2000). Soil respiration is a principal carbon exchange pathway between terrestrial ecosystems and atmosphere, which if altered will heavily impact on carbon cycling, ecosystem feedback and responses, and subsequently, cause climate change (Luo and Zhou, 2006). Niche is the part or position of a particular species in a community and its distributional relation based on different environments (Pulliam, 2000). Its ecosystem functioning is majorly reliant on two factors: (i) its structure (Osorio-Olvera et al., 2019) and (ii) microbial community organization (Larkin & Martiny, 2017). Microbes play the role of producers and consumers of Greenhouse Gases (GHGs) by participating in biogeochemical cycles and maintain a balance using feedback responses. GHGs include Nitrous Oxide (N2O), Methane (CH4), Chlorofluorocarbons (CFCs), Carbon dioxide (CO2), and Ozone (O3). They act as a greenhouse by allowing solar heat to penetrate through atmosphere, but blanketing it from returning into space, thus increasing Earth’s surface temperature. Temperature is important in maintaining a balance between carbon consumption and emission. Microbes can mitigate negative influences of climate change due to their versatility in growth and metabolism. Soil runoffs and wastewater containing microbial load, are discharged into drainage system which acts as a microbial sink (Lara-Martín et al., 2020). Microbial bioremediation of polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), pesticides, metals, etc. can reduce water pollution. During stresses, they allot more organicresources to survival pathways, than to growth pathways. Dormancy and cell death are induced for intolerable stresses, causing removal of microbial function (Suzina et al., 2004) and considerable delays in regaining back activity when favorable conditions return (Clein et al., n.d.). As microorganisms die, nutrients like amino acids, inorganic phosphates, etc., are released which can be absorbed by plants or other microbes, for survival, cryptic growth, (Chapman et al., n.d.) or for performing denitrification (Sharma et al., 2006). However, all microbes aren’t beneficial and climate change has aggravated emergence of new diseases due to increased growth and metabolism at increased temperatures. The biggest drawback of climate change is that it may increase AntiMicrobial Resistance (AMR) rate amongstpathogens. A 10°C rise in temperature can increase AMR by up to 4-10%. Mobile resistance genes can undergo horizontal gene transfer (HGT) at increased temperatures, increasing the rate of pathogen growth and transmission (MacFadden et al., 2018). This will in turn increaseantibiotic 222

Microbial enrichment in global wastewater niches under impact of climate.... usageandresult in more bacteria showing AMR (Cabello et al., 2016). Thus, wastewater microbial analysis is a good indicator for local mean temperature and climate change. Table 1. Showing datasets used in current research Abbreviations IR IU ID SP HD HI

Wastewater samples collected from Rural area, India (Set-1) Urban area, India (Set-2) Sundarbans Delta area, India (Set-3) São Paulo, Brazil (Set-4) Henan, China (Domestic) (Set-5) Henan, China (Industrial) (Set-6)

MATERIAL AND METHODS Four web servers were used for our analysis. They are as follows: 1. MetaG: https://www.bioinformatics.uni-muenster.de/tools/metag/index.hbi MetaG is used to obtain data regarding taxonomic assignment from individual reads to whole sample. Reads are supplemented with run-time and parameter data (Wattam et al., 2017). 2. Venny 2.1.0: https://bioinfogp.cnb.csic.es/tools/venny/ This server takes in lists of 2,3,4 datasets to find common elements between them using Venn diagrams. The overlapped regions show common relationships whereas the non-overlapped region show the unique data in the lists. 3. iVikodak Global Mapper: https://web.rniapps.net/iVikodak/global.php iVikodak Global Mapper uses 16S microbiome datasets to suggest microbial environments’ functional profiles (Markowitz et al., 2012). The functions can be performed by the server are Top functions, Core functions, Differentiating functions, Ordination Analysis and Function driven Correlation networks. 4. Microbiome Analyst- Taxon Set Enrichment Analysis (TSEA): https://dev. microbiomeanalyst.ca/MicrobiomeAnalyst/upload/TaxaUpload.xhtml This server is designed to check presence of patterns of biological and ecological importance using user-submitted taxa (Ma et al., 2017). Findings are represented as an interactive network which gives significant taxon set inter-relationships and a table of results is present at the bottom (Merico et al., 2010). A node with a colour intensity based on its P value and size based on number of hits, represents one single taxon set. Taxon sets are linked if their shared hits are >20% of their entire taxa’s sum. RESULTS AND DISCUSSION Fastq files of 6 sets were uploaded into MetaG server setting database as RDP_16S_28S and database profile as Illumina. The details of each dataset andthe abundance of genera have been represented as a Krona chart. 223

J. Environ. & Sociobiol. : 19(2) : 221-243, 2022, Ghosh et al. IR consisted of 45536 bacterial and 5137 archaeal reads, of which Methanothrix (17.3%) and Smithella (9.9%) were most abundant.

Fig. 1. IR-Summary graph

IU consisted of 48471 bacterial and 52 archaeal reads, of which Sulfurospirillum (24.7%) and Cloacibacterium (8.1%) were most abundant.

Fig. 2. IU-Summary graph.


Microbial enrichment in global wastewater niches under impact of climate.... ID consisted of 140071 bacterial and 38 archaeal reads, of which Pseudoalteromonas (49.9%) and Bacillariophyta (10.9%) were most abundant.

Fig. 3. ID-Summary graph.

SP consisted of 181260 bacterial and 83 archaeal reads, of which Lactobacillus (49.9%) were most abundant.

Fig. 4. SP-Summary graph.


J. Environ. & Sociobiol. : 19(2) : 221-243, 2022, Ghosh et al. HD consisted of 51941 bacterial reads, of which Aridibacter (16.1%) and Hyphomicrobium (8.4%) were most abundant.

Fig. 5. HD -Summary graph.

HI consisted of 45642 bacterial reads, of which Meiothermus (34.7%) and Hyphomicrobium (15.4%) were most abundant.

Fig. 6. HI -Summary graph.

Common genera were obtained using Venny 2.1.0, one between Indian samples namely, IR, IU and ID; and the other between foreign samples namely, SP, HD and HI. For Indian samples, 19 (6.4%) are common between them, which are Geobacter, Prevotella, Clostridium sensu stricto, Streptococcus, Escherichia, Propionibacterium, 226

Microbial enrichment in global wastewater niches under impact of climate.... Lactobacillus, Thauera, Acidovorax, Novosphingobium, Burkholderia, Pseudomonas, Sphingomonas, Cloacibacterium, Acinetobacter, Corynebacterium, Rhizobium, Staphylococcus and Rothia. In case of foreign samples, 9 (4.8%) are common between them, which are Thauera, Rhizobium, Diaphorobacter, Hyphomicrobium, Gp3, Pseudomonas, Nitrosomonas, Gemmatimonas and Acidovorax.

Fig. 7. Venn diagrams representing common genera between Indian samples (on left) and foreign samples (on right)

Using the common genera, a heatmap was generated using iVikodak Global mapper (Figures 9-10) to predict the core functions, for Indian and foreign samples.

Fig. 8. Heatmap representing the core functions of IR, IU, and ID, respectively.

Fig. 9. Heatmap representing the core functions of SP, HD, and HI, respectively.


J. Environ. & Sociobiol. : 19(2) : 221-243, 2022, Ghosh et al. Several core functions were predicted of varying likelihood of enrichments, some of which are studied in Table 2. Table 2. Likelihood of core function enrichment in all the samples, where 0 and 1 are the minimum and maximum chances of enrichment, respectively. Likelihood of enrichment

Core functions







Beta-lactam resistance







Vancomycin resistance







Sulfur metabolism







Nitrogen metabolism







Methane metabolism







Carbon fixation pathways in prokaryotes













Benzoate degradation

Function-driven hierarchical microbial network analysis for the microbiome pertaining to Indian and foreign samples were obtained, showing the presence of bi-directional (as red lines) and uni-directional (as blue lines) interactions (Fig. 10).

Fig. 10. Network analysis of (A) Indian samples and (B) foreign samples.

Using Microbiome Analyst, Mixed level Taxon Set Enrichment Analysis was performed using parameter “Host-intrinsic taxon sets”. Enrichment network of nodes (shown in Figures 12-16) and a detailed result table were obtained. No network was obtained for HI. Table 3 shows a summary of major diseases, and their underlying taxon sets responsible. 228

Microbial enrichment in global wastewater niches under impact of climate....

Fig. 11. Set-1: Interactive Disease Network.

Fig. 12. Set-2: Interactive Disease Network.


J. Environ. & Sociobiol. : 19(2) : 221-243, 2022, Ghosh et al.

Fig. 13. Set-3: Interactive Disease Network.

Fig. 14. Set-4: Interactive Disease Network.


Microbial enrichment in global wastewater niches under impact of climate....

Fig. 15. Set-5: Interactive Disease Network.

Table 3. Summary of major diseases caused by microbial genera. SL. No.


Associated Genera



Bacteroides; Alistipes; Phascolarctobacterium; Gemella; Blautia;


Bacterial Vaginosis

Leptotrichia; Lactobacillus; Megasphaera; Olsenella; Prevotella;


Chronic Obstructive Haemophilus; Prevotella; Pseudomonas; Burkholderia; Pulmonary Disease (COPD) Lactobacillus;


Cirrhosis (China)

Streptococcus; Prevotella; Lactobacillus; Fusobacterium; Megasphaera; Haemophilus;


Colorectal cancer

Fusobacterium; Porphyromonas; Prevotella; Dialister; Peptostreptococcus; Gemella;



Brevibacterium; Pseudomonas; Pediococcus;


Maternal Antenatal infection

Arthrobacter; Klebsiella; Acinetobacter; Streptococcus; Pseudoalteromonas;


Pediatric Crohn’s Disease

Bacteroides; Faecalibacterium; Blautia; Ruminococcus;


Type-I Diabetes

Granulicatella; Ruminococcus; Alistipes; Enterobacter; Escherichia; Lactobacillus; Streptococcus;

10 Urogenital schistosomiasis

Megasphaera; Dialister; Acinetobacter; Prevotella; Desulfovibrio; Olsenella; Haemophilus;


J. Environ. & Sociobiol. : 19(2) : 221-243, 2022, Ghosh et al. With rise in temperatures due to GHGaccumulation, microbial community composition is changed and replaced by microbiota which can tolerate those temperatures and thrive by increased microbial decomposition, fermentation, respiration and methanogenesis (E et al., 2018). These factors positively correlate with global warming and increase GHG emission. High abundance of methanogens and methanotrophs, which produce and consume methane respectively, are seen especially in IR, with methanogens being Methanobacterium, Methanobrevibacter, Methanocorpusculum, Methanolinea, Methanosarcina, Methanospirillum and Methanothrix and methylotrophs being Methylocystis, Methylosarcina, Methylotenera, Methyloversatilis. Hyphomicrobium, a methylotroph is present in huge numbers in both the Chinese HI and HD, but not reported in Indian samples, indicating a spatial-temporal role. Together, they maintain a healthy methane balance (Bousquet et al., 2006). The methane cycle pathway is shown in Fig. 17. High incidence in IR may be due to better plant cover in rural areas which augment and regulate this cycle (Kollah et al., 2018). Methanosarcina has been observed to degrade CFC-11 by anoxic reductive dehalogenation (Krone and Thauer, 1992). Methanogens can abiotically degrade rapidly CFC-113 as reported (Lesage et al., 1992) in a landfill leachate. Organohalide-respiring bacteria (OHRB) derive energy from aromatic and/or aliphatic halogenated compounds dehalogenation and reduce their hazardous contamination with groundwater (Jackson, 2004). Geobacter, Desulfuromonas, Anaeromyxobacter, Desulfomonile, and Dehalococcoides found in IR and, Sulfurospirillum and Desulfovibrio found in huge numbers in IU are some of the OHRB found in our research (Hug et al., 2013). Carbon cycle and CO2 release are regulated by atmospheric carbonfixing microbes, by decomposing dead organic material (Crowther et al., 2015). Chemotrophs use inorganicand organic carbon substrates for their own metabolism and energy source, and lower CO2 release and global warming. Bacteria and archaea Fig. 16. Methane cycle involved in wastewater. can convert CO2 to vital sugars. With every 5-10°C rise in temperature, microbial respiration and activity in degrading carbon-based materials increases and more CO2 is emitted (E et al., 2018). With more CO2, global warming rises and in turn, speeds up microbial activity like a 232

Microbial enrichment in global wastewater niches under impact of climate.... vicious cycle. Under anaerobic conditions, microbes produce energy by fermenting carbon compounds. Thiobacillus gets energy from an electron removal Fe-containing compounds to convert carbon. Bacteroides, Clostridium, and Syntrophomonas (found majorly in IR and IU) can collaborate to degrade anaerobically carbon (E et al., 2018). Smithella and Syntrophomonas present in IR imply a multi-trophic interaction between them to produce acetate and butyrate from propionate, and acetate from butyrate, respectively (Puengrang et al., 2020). Paludibacter found in IR and IU is a propionate and acetate-producing bacterium through fermentation (Qiu et al., 2014). Sulfate-reducing bacteria can use sulfate, hydrogen, and different organic and inorganic compounds to respire anaerobically and thrive in environments with wide extremes of pH, temperature, and salinity conditions. They are involved in cycles of carbon and sulfur and play a beneficial role in removing heavy metals and sulfur oxides from the environment. They interact metabolically with methanogens by competing for acetate and hydrogen, in presence of sulfate. In sulfate’s absence, they thrive syntrophically with methanogens. Our dataset shows the presence of sulfate-reducing bacteria like Fig. 17. Sulfur cycle by sulfate-reducing bacteria and Desulfatiglans, Desulfobulbus, its associated methane metabolism. Desulforhabdus, Desulfovibrio, Desulfurivibrio, and Desulfuromonas,especially in IR, and their co-presence with methanogens and methylotrophs implies a possible inter-relationship between carbon, methane, and sulfur cycles in IR wastewater (Muyzer and Stams, 2008). Fig. 17 shows the sulfur cycle. N2O is the most potent GHG. From natural sources, it is emitted from nitrogen cycling by both nitrification and denitrification, contributing about 70% to N2O budget globally (Butterbach-Bahl et al., 2013). Both nitrifying bacteria and methanotrophs produce N2O during nitrification when the pO2 is between 0.1–0.5 kPa. Under strict anaerobic conditions, it is also produced by denitrifying bacteria (Kollah et al., 2018). Climate change disrupts the rate of nitrogen cycling processes and releasing of N2O. N2O production bynitrogen cycleby microbes found from our research are detailed in Fig. 18. Rhizobium is found fairly in all the samples, suggesting its role in symbiotic 233

J. Environ. & Sociobiol. : 19(2) : 221-243, 2022, Ghosh et al. nitrogen fixation (Vitousek et al., 2013). Nitrosomonas and Nitrospira, mostly present in HD and HI, are litho-autotrophic bacteria which oxidize ammonia as sole energy source to nitrites and nitrates by nitrification, and using the CalvinBenson cycle, fix CO2 (Kollah et al., 2018). Other microbes involved in nitrogen cycle are denitrifying bacteria like Acidovorax, Clostridium, Diaphorobacter, Pseudomonas, Rothia, Aridibacter and Thauera (Chen et al., 2019; Gtari et al., 2012; Scholten et al., 1999; Yang et al., 2013; Z-d et al., 2021). Acidovorax, an acetate-utilizing denitrifier (Ginige et al., 2005), is seen in every sample. They are aerobic, chemoorganotrophs, although some strains can anaerobically grow using NO3- as terminal electron acceptor (Willems and Gillis, 2015). Diaphorobacter is a Poly (3-hydroxybutyrate-coFig. 18. N2O production from nitrogen cycle. 3-hydroxyvalerate)-degrading denitrifying bacteria, mostly present in UI and HI (Khan and Hiraishi, 2002). Some denitrifying bacteria can consume N2O and mitigate emissions (Charu Gupta et al., 2014). Under denitrifying conditions, Bacillus, Staphylococcus, Streptococcus, Dechloromonas, Thauera, and Zoogloea can breakdown aromatic hydrocarbons (Joutey et al., 2013; Kouzuma and Watanabe, 2011; Mao et al., 2010; Zhang et al., 2021). Additionally, Thaueracan tolerate saline, alkaline, and high temperature environments, indicating their presence in all the samples (Pal et al., 2018). Novosphingobium, Sphingomonas, Sulfuricurvum, Telmatospirillum and Pseudoalteromonas can degrade PAHs (de Mandal et al., 2019; Hedlund and Staley, 2006; Sohn et al., 2004). Acinetobacter and Pseudomonas found in all the samples, can degrade phenol, benzoate and fluorobenzene, reduce toxicity by converting Cr (VI) to Cr (III), and remove Fe and Mn (Li et al., 2016; Nancharaiah et al., 2010; Ojha et al., 2021). Burkholderia uses 2,3,4,5,6-pentafluorobiphenyl and 4,4´-difluorobiphenyl as its carbon and energy source. It can degrade 2,4-dichlorophenoxyacetic acid (2,4-D), and 2,4,5-trichlorophenoxyacetic acid (2,4,5-T) (Huong et al., 2007; Yousra Turki et al., 2017). Other PCB-degrading bacteria are Ralstonia, Achromobacter, and Comamonas, mostly found in HI (Joutey et al., 2013). Povalibacter found in HD and HI can 234

Microbial enrichment in global wastewater niches under impact of climate.... degrade Polyvinyl alcohols (Nogi et al., 2014). The abundance of these microbes provide evidence that complex hydrocarbons are present in wastewater, which can be biologically degraded by them. Some moderately thermophilic microorganisms have been found, namely Syntrophothermus, Thermoanaerobaculum, Thermodesulfobium, Thermogutta, Thermomonas and Meiothermus, implying that 45-60°C temperatures exist in these areas. Hot untreated industrial wastewater and rising mean temperatures are the main reasons for their growth augmentation (Al-Daghistani et al., 2021; Losey et al., 2013; Mori and Hanada, 2015; Sekiguchi et al., 2000; Slobodkina et al., 2015). Hospital wastewater contains diverse range of Antibiotic Resistance Genes (ARGs), which can be a potential health hazard. Acinetobacter, Burkholderia, Corynebacterium, Escherichia, Staphylocococcus, Streptococcus and Pseudomonas,present in significant amounts, can either acquire ARGs for encoding β-Lactamase, which cleaves the antibiotic β-lactam ring (Bush and Jacoby, 2010), or they can efflux out antibiotics before theirarrival at the target site to produce the desired effect or undergo mutations to downregulate porin genes, which can either restrict access to antibiotic binding sites on cell wall or can acquire genes which changes metabolic pathway and alter the antibiotic binding site (Tenover, 2006). Indiscriminate antibiotic usage along with pollution and climate change are giving rise to drug resistant Vibrio sp. as reported in Indian aquaculture farms (Devi et al., 2009). Extensive antibiotic usage, pollution by biocides and heavy metals, and climate change regulates HGT (Mizuno & Mizushima, 1990). Climate change affects plasmid replicationand increases HGT-related enzyme activity (Tamang et al., 2017). In Vibrio harveyi, transfer of SXT (Hochhut et al., 2001), an Integrative and Conjugative Element (ICE) (Burrus & Waldor, 2004), in presence of ciprofloxacin confers trimethoprim, streptomycin, sulfamethoxazole, and chloramphenicol resistance (Beaber et al., n.d.). Shiga Toxin (STX) prophage induction and STX phage-mediated gene transfer rise in high temperatures with UV irradiation (Tamang et al., 2017). To conclude, human practices andpollution have disrupted microbial community structure and function, causing alteredcarbon and nitrogen cycling. Water surface temperature has risen, facilitating opportunistic microbial respiration and fermentation, producing CO2. Consequently, many vector-borne diseases have emerged. Microbes act as both the source and the sink for GHGs. Introducing genes that can metabolize GHGs can reduce microbial GHG emission. Current agricultural practices need modifications to reduce CH4 and N2O emission. Biofertilizers instead of synthetic nitrogen fertilizers, and denitrifying bacteria that convert N2O into atmospheric N2, can mitigate N2O emission. CH4 uses soil as a sink. High-affinity Methane Oxidation (HAMO) by methanotrophsto mitigate its emission can be enhanced by alternately wetting-drying and draining soil. Hospital wastewater should be treated before discharging. 235

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