Metagenomic profiling of antimicrobial resistance in wastewater from metropolitan cities of India

Domain: biology
Published: 2026-03-17
Editorial outcome: minor_revision

Abstract
Wastewater-based surveillance has emerged as a powerful tool for monitoring microbial diversity, antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs). In this study, wastewater samples collected from March 2022 to March 2024 from 19 locations in four metropolitan cities of India were profiled using shotgun metagenomics. Taxonomic abundance and beta diversity analyses revealed significant differences in microbial community compositions, with city-specific clustering; suggesting distinct local environmental influences. However, such distinct clusters were not evident with the ARGs. A high proportion of potentially novel metagenome-assembled genomes (MAGs) (53-70%) were identified on reconstructing the microbial genomes from the metagenomic data. ARGs conferring resistance to antibiotics such as tetracyclines and beta-lactams showed higher association with MGEs in contrast to macrolide resistance genes. Microbial co-occurrence network analysis revealed a city-specific structure and higher contribution of ARGs from specific communities of microbes. These findings underscore the complex interplay between microbial diversity, ARG dissemination, and MGEs in wastewater environments, emphasizing the need for continued surveillance, for designing appropriate mitigation strategies towards curbing the spread of antimicrobial resistance.

Introduction
Antibiotics have been one of the most important therapeutic advances for mankind. However, use and misuse of antibiotics in diverse contexts such as clinical management, agricultural practices, and animal healthcare has forced microbes to develop antimicrobial resistance (AMR)1. AMR is a global concern with an estimated economic burden of US$1 trillion by 2050 (World Bank. Drug-resistant infections: a threat to our economic future)2,3. Antimicrobial (antibiotic) resistance is a natural process in bacteria where, when threatened with stressors arising from anthropogenic activities or otherwise, bacteria develop mutations in their genome to provide better adaptation to the unfavorable external environment4. These mutations can occur in antibiotic resistance genes (ARGs) intrinsic to bacterial genomes or can be acquired through horizontal gene transfer, thereby facilitating the rapid dissemination of resistance across microbial populations5,6. Most clinically relevant ARGs also occur as part of the mobile genetic elements (MGEs), allowing an easier development and transmission of these genes to new hosts, even across taxonomic boundaries, increasing the AMR menace7–10. These MGEs are critical vectors for the horizontal gene transfer (HGT)11,12 of ARGs, facilitating their transfer between bacterial species through mechanisms such as conjugation, transformation, and transduction13. MGEs include plasmids, transposons, integrons, and bacteriophages, all of which contribute to the dynamic exchange of genetic material in microbial communities.

In recent times, wastewater-based surveillance (WWS) has emerged as a powerful tool for monitoring microbial diversity, ARGs and MGEs14–16 providing insights into the taxonomic, resistome and mobilome profiles present in the community. Such information is valuable for public health as it enables tracking pathogenic organisms, early outbreak detection, and assessing the impact of anthropogenic activities on microbial ecosystems17,18. Wastewater environments provide ideal conditions for AMR dissemination through the horizontal transfer of ARGs via MGEs and persistence of multidrug-resistant bacteria (due to high microbial diversity and selective pressure from antibiotic residues19,20), providing a compelling reason for WWS.

In this study, during a two-year period (March 2022 until March 2024), we collected samples from large open wastewater drains, once every month from four geographically diverse metropolitan cities of India to study the taxonomic, resistome and mobilome profiles in these cities. The samples were sequenced using shotgun metagenomics and analyzed. The data generated provides a snapshot of wastewater microbiome and resistome in some of the most densely populated urban centres in the world.

Results
A total of 447 wastewater samples were collected from 19 different sites in four major metropolitan cities of India (Figure 1a). The samples were sequenced on Illumina NovaSeq 6000 platform. The trimmed and filtered read count per sample ranged from 2.74 - 34.64 million paired end reads (Median: 15.54 million paired end reads) (Supplementary Figure 1a and Supplementary data S1). Relative taxonomic and ARG abundances were computed directly from the reads. Subsequently, reads were assembled into contigs, which were then binned into metagenome assembled genomes (MAGs). These MAGs were further analyzed for taxonomic classification, ARG identification, profiling of MGEs and co-abundance network analysis.

Metro cities exhibit distinct taxonomic profiles
Proteobacteria (also known as Pseudomonadota) was the most abundant bacterial phylum across all cities, accounting for 48% to 90% of bacterial reads (Supplementary Figure 1b). Other highly abundant phyla included Actinobacteria, Bacteroidetes (also known as Bacteroidota), and Firmicutes (also known as Bacillota). Eight of the top ten most abundant bacterial phyla were common across all cities. However, phylum Tenericutes (also known as Mycoplasmatota), was notably abundant in Delhi while Phylum Nitrospirae (nitrite oxidizing bacteria) and Kiritimatiellaeota, known to inhabit wastewater21, were abundant in Kolkata. Principal coordinate analysis (PCoA) plot using Aitchison distance between samples based on relative abundance of bacterial species showed distinct city wise clustering (Figure 1b), which was not evident with PCoA plot using bray-curtis distance (Supplementary Figure 1c). Species richness (alpha diversity measuring the number of different taxa observed) was highest in Mumbai and lowest in Kolkata (Supplementary Figure 1d), suggesting the distinct effects of factors like local environment, anthropogenic activities, etc on microbial composition of wastewater. A drop in Simpson diversity was associated with an increased abundance of the genus Pseudomonas (Supplementary Figure 1e).

Among the 10 most abundant bacterial species in each city, though “Uncultured bacterium” (a set of bacterial species unculturable in the laboratory) was the most abundant, they were removed from the figure 1c as it adds limited value to the analysis. Three bacterial species from WHO bacterial priority pathogen list22, were abundant in at least one city. Of these, Escherichia coli was abundant across all cities, whereas Klebsiella pneumoniae was abundant in Chennai and Mumbai while Pseudomonas aeruginosa was abundant in Kolkata (Figure 1c). These pathogens are clinically significant due to their higher propensity to carry resistance against a wide range of antibiotics23. Aeromonas caviae, a bacterium isolated from epizootic guinea pigs and linked with dysentery, was seen in high abundance in Chennai and Mumbai, whereas Prevotella copri, a known human gut bacteria24, was abundant in Kolkata samples.

Many species in the most abundant list were environmentally significant. Sulfurospirillum sp. UCH001, a bacterium previously reported from chloroethene contaminated water in Japan, was abundant across all cities25. Bacteroidales bacterium CF (predicted to ferment lactate and ethanol26) and Cloacibacterium caeni (N2O reduction27) were also abundant consistently. Various species of Pseudomonas, associated with soil (P. alcaligenes), spoiled meat (P. lundensis) and raw milk (P. fragii)28–30 were also observed in high abundance. In contrast to distinct spatial profiles, no consistent trends in the temporal distribution of relative abundances of bacterial species could be observed in the data.

We further looked at the bacterial species that were significantly differentially enriched (Figure 1d and Supplementary data S2) in each city compared to other 3 cities for each collection month. Mumbai had the most enriched species (3401) compared to other cities, mostly when compared to Kolkata (1632) and Delhi (1413). December 2022 had the most enriched species in Mumbai (1021). Kolkata had the most enriched species against Chennai (690), while July 2022 gave the most enriched species (1027). Delhi had the most enrichment against Mumbai (975) and Kolkata (804). Surprisingly Delhi had only 1 enriched specie compared to Kolkata, suggesting a similar taxonomic profile between the two cities. Chennai had the most enriched species against Delhi (1064), with most of them coming from April 2023 (610). Finally we looked at the bacterial species that were enriched in a city when compared to more than 1 city and for more than 2 months. In Chennai we found 6 such species, including Amycolatopsis pretoriensis, Archangium violaceum, Saccharothrix syringae, Sorangium cellulosum and Myxococcus hansupus. In Kolkata there was only Anaeromyxobacter paludicola and none in Delhi. In Mumbai we found multiple species of Aeromonas and Aliarcobacter along with a couple of Sulfurimonas enriched against multiple cities in at least 2 months.

MAG-based analysis reveals several potentially novel bacterial species
13380 metagenome assembled genomes (MAGs) [1304 Kolkata, 2802 Delhi, 3441 Chennai, 5833 Mumbai] were generated from processed reads (see ‘Methods’). After running CheckM2, 5790 medium quality MAGs (Completeness > 50 and Contamination < 10) and 840 high quality MAGs (Completeness > 90 and Contamination < 5) were found. We chose high quality MAGs [121 Kolkata, 134 Delhi, 242 Chennai and 343 Mumbai] for further analysis including taxonomic classification, ARG association and MGE identification. Most MAGs could be taxonomically assigned at the phylum level (93% - 97.7%) but a significantly lower proportion could be classified at the species level (29.2% - 47.1%); suggesting the possible presence of large numbers of potentially novel bacterial species in the wastewater samples analysed (Figure 2a). Chennai had the least (52.9%) while Kolkata had the most (70.8%) unclassified bacterial species. To rule out the effect of MAGs’ quality in taxonomic classification, we compared various quality metrics of unclassified MAGs with MAGs of known bacterial species. Unclassified MAGs were of significantly better quality (Figure 2b) suggesting a higher likelihood of these MAGs representing potentially novel species, a finding similar to the previously reported by Becsei et al.31. The taxonomic classification of MAGs revealed Pseudomonadota (also known as Proteobacteria) and Bacteriodata as the most abundant phyla in all the 4 cities (Figure 2c), consistent with the findings of read-based classification (Supplementary Figure 1b). Other abundant phyla included Campylobacterota (known as Epsilonproteobacteria as part of Proteobacteria), and Bacillota (also known as Firmicutes). At the genus level, multiple genera related to sulfuroxidation32–34 namely Sulfurospirillum and Sulfurovum were abundant in Chennai suggesting a possibility of higher sulfur content in the sites from where the wastewater was collected.

ARG diversity, abundance and trends
Next, we computed ARG abundance by mapping reads to the Comprehensive Antibiotic Resistance Database (CARD) using RGI35,36. Similar to taxonomic analysis, the ARG reads from multiple locations within the city were merged to a single data point per month.

Unlike bacterial species, ARG richness did not show a correlation with the number of sampling sites from the city (Supplementary Figure 2a). PCoA plot using Aitchison distance based on ARG abundance shows the beta diversity between the samples (Figure 3a) where ellipses with 0.95 confidence level around data points from Mumbai and Chennai showed a considerable overlap, whereas Kolkata exhibited the most variation. Similar to species abundance, PCoA plot using bray-curtis distance based on ARG abundance showed considerable overlap between samples from different cities (Supplementary Figure 2b). Among top 10 abundant ARGs from each city (Supplementary Figure 2c), “Bifidobacterium adolescentis rpoB mutants conferring resistance to rifampicin” was most abundant in all the four cities. Other ARGs abundant across all the cities were sul1, conferring resistance to sulfonamide, tet(C) conferring resistance to tetracycline, MexB and mel (macrolide resistance).

The ARG abundance was then summarized to drug classes against which they were conferring resistance. ARGs conferring resistance to macrolide, lincosamide, streptogramin were grouped into MLS class, ARG conferring resistance to penam, cephalosporin, carbapenem, cephamycin, penem and monobactam were grouped into Betalactam class37 and ARG conferring resistance to more than one drug class were grouped into “multi-drug” class. The top 10 drug classes for each city were then plotted temporally (Figure 3b). Multidrug was the most abundant drug class across the cities. Additionally, MLS, tetracycline, rifamycin and aminoglycoside were the other most abundant drug classes. The composition of drug classes clubbed together as ‘multidrug’ seemed to be similar across the cities when they were demultiplexed to individual drug classes (Supplementary Figure 3), the combination of MLS, tetracycline, Beta-lactam and aminocoumarin being the most frequent observation.

Similar to bacterial species, differential abundance analysis was performed for ARGs (Figure 3c and Supplementary data S3). 54 ARGs were differentially abundant in the four cities with Mumbai (41) having the most differentially abundant ARG and Delhi (2) having the least. Barring 1 ARG, none of the differentially abundant ARGs appeared in more than one differential comparison, suggesting that differential abundance of ARGs is a sporadic event.

ARGs on the MAGs and their association with MGEs
Using BacAnt on the 840 MAGs, we identified 167 unique ARGs across four cities (63 Kolkata, 66 Delhi, 69 Chennai, Mumbai 99), conferring resistance to 36 unique drug classes. Comparing proportions of ARG carrying contigs in the MAG, only Kolkata and Mumbai showed significant difference (Supplementary Figure 4a). ARGs present on most contigs were conferring resistance to Macrolide, Mercury and Sulfonamide (Figure 4a). Macrolide, Beta-lactam and Tetracycline were the prominent drug classes present on the contigs in all four cities (Figure 4b).

Since Mobile Genetic Elements (MGEs) are important for horizontal transfer of ARGs, co-occurrence of ARGs with MGEs was investigated. For each drug class, we looked into the percentage of contigs that had MGEs (plasmid and transposon) (Figure 4c). Out of 840 bins (167051 contigs), 42 % (356) bins had ARG genes (49% for Kolkata and Delhi, 42% Mumbai and 35% for Chennai). 57 contigs out of 552 ARG carrying contigs were associated with MGEs (plasmid and transposon). Overall, about 10% of ARG carrying contigs had MGEs and were candidates for horizontal transfer of ARGs (Chennai 6%, Delhi 9%, Chennai 17% and Mumbai 9%). At the drug class level, a higher proportion of macrolide resistant ARGs were found on the contigs however, only a small proportion of these contigs were carrying MGEs. On the contrary, ARGs conferring resistance to mercury had a higher association with MGEs. Other relevant ARGs like those conferring resistance to tetracycline and beta-lactams fall in between these two categories. A city-wise comparison suggests that Kolkata had fewer contigs with both ARGs and MGEs as compared to other cities. There were also many instances where multiple ARGs conferring resistance to a drug class (mercury, erythromycin, macrolide and streptomycin) were present on the same contig (Supplementary Figure 4b).

Comparative analysis of microbial co-occurrence networks
840 high quality MAGs were dereplicated, resulting in 391 non-redundant high-quality MAGs. The reads from samples were mapped back to these MAGs and depths were calculated and bins were filtered for low coverage (Methods). A correlation network of 135 bins was generated for each city separately and communities were generated based on significant association and proximity (Methods). The co-occurrence networks show that each city possesses a structurally unique microbial community organized into distinct modules (Figure 5a, Supplementary data S4). Chennai had the maximum number of communities (15) and Kolkata had the least (9) (Figure 5a). 6 communities with the most number of member nodes from each city were then plotted to look for temporal trends (Figure 5b). The overall phylum-level composition of various communities mirrored the patterns we observed in our MAG analysis (Figure 5e). Several communities showed non-seasonal temporal patterns, such as the spikes observed in C1 and K5 communities, and the opposite trends seen in D2 and M3 (Figure 5b).

We further asked if correlation of the temporal patterns between pairs of communities also matches their phylum composition. Except for a few pairs such as M2-K3 and K6-C6, the temporal patterns of the communities appear to be independent of their taxonomic structure; We observed no significant association between the temporal correlation of a pair of communities and their phylum-level similarity (Figure 5c, Supplementary Figure 5). We next calculated the proportion of MAGs carrying at least one ARG in each community. We observed specific communities (e.g. K5 in Kolkata, C3 and C4 in Chennai, M5 in Mumbai) with a higher proportion of MAGs with ARGs (Figure 5d). Together, our analysis revealed a city-specific community structure, and contribution of specific communities to important functional characteristics such as antimicrobial resistance.

Methods
Sample Collection and DNA Extraction
Wastewater samples were collected from multiple large open wastewater drains in the four major metropolitan cities of India which included Kolkata in the east (2 locations), Mumbai in the west (8 locations), Delhi in the north (4 locations) and Chennai in the south (5 locations) (Figure 1a and Supplementary data S7). Monthly sampling (500 ml) was done by Grab method44,45 over a 2-year period, from March 2022 to March 2024. All the samples were transported in 0.002% bleach (Sodium hypochlorite) solution, maintaining 4°C throughout the transit to CSIR-CCMB. Samples were not collected if there was rain in the preceding 24 hours. 45 mL of the collected wastewater was transferred into a sterile 50 mL conical centrifuge tube containing 4 g of PEG (polyethylene glycol) and 0.9 g of NaCl. The mixture was vortexed thoroughly until the PEG and NaCl were completely dissolved. The samples were then incubated overnight at 4°C. Following incubation, the tubes were centrifuged at 10,621 x g for 30 minutes at 4°C. After centrifugation, the supernatant was discarded, and the resulting pellet was retained for subsequent total metagenomic DNA extraction using the Qiagen AllPrep® PowerViral® DNA/RNA Extraction Kit, as per the manufacturer's protocol. Extracted DNA was quantified by Qubit 1x HS dsDNA assay on Qubit 4.0 instrument (Life Technologies). 100 ng of DNA from each sample was taken for whole genome library preparation.

Shotgun Metagenomic Sequencing
Libraries were prepared using the Illumina DNA Prep (96 Samples, Cat: 20060059) kit with IDT for Illumina Nextera DNA UD Indexes, as per the manufacturer's instructions (Document # 1000000025416 v10). Briefly, the DNA was first tagmented, followed by indexing by amplification and library clean up. The final libraries were quantified using Qubit 1x HS dsDNA assay, normalized, and pooled together by taking equimolar amounts of each library. The quality of the pooled library was checked on an Agilent TapeStation 4200 with a D1000 screen tape. The pooled libraries were then sequenced on the NovaSeq 6000 system (Illumina) with the NovaSeq 6000 S1 Reagent Kit (300 cycles).

Preprocessing and Quality Control of Metagenomic Reads
Paired-end shotgun metagenomic reads were checked for quality using FastQC and adapters and low-quality reads were trimmed using Trimmomatic46. Reads shorter than 75 bases were discarded from further analysis. Trimmed reads were checked for quality again using FastQC.

The processed reads were further analyzed using two different approaches (Supplementary Figure 6). First, reads were directly mapped to “core nt database” or Comprehensive Antibiotic Resistance Database (CARD) database to compute taxonomic and ARG abundance, respectively. Alternatively, reads were assembled in contigs which were further merged to form metagenome assembled genomes (MAGs). These MAGs were further used for taxonomic classification at species level, ARG identification and MGE discovery.

Read-based approach
The wastewater samples are complex and carry DNA from many microorganisms and impurities. It is technically not feasible to resolve sequence data accurately to each microorganism. Though MAG-based approach is likely to be more accurate for species delineation (including yet unknown species) and further ARG association, it results in exclusion of a large proportion of data from analysis. This was evident when our attempts to map back the reads to the MAGs resulted in low mapping percentage. To avoid loss of this usable information, we performed read-based analysis that employs all the reads to estimate species and ARG abundance.

Taxonomic abundance estimation
Trimmed reads were taxonomically classified using Kraken247, using a comprehensive “core nt database” downloaded from kraken2 index (https://benlangmead.github.io/aws-indexes/k2). The abundance at each taxonomic level was estimated using Bracken48. Only bacterial species abundance (most abundant domain) was used in the further analysis. To compare data between the cities, the effect of variable number of collection sites from different cities was normalized. For each city and collection month, the estimated abundance for each bacterial species was summed from all collection sites and normalized by the total estimated abundance of bacteria from all collection sites. For differential abundance analysis (DAA), ALDEx249 was used and paired differential analysis was performed using estimated species abundance data (from bracken), between each pair of cities for all the months separately. Adjusted p-value < 0.05 and log2FoldChange > 1 was used to derive enriched species.

ARG abundance
Resistance Gene Identifier (RGI) was used to estimate ARG abundance using trimmed reads, utilizing the CARD as a reference35,36. ARGs with less than 90 percent identity to reference ARG were discarded. Similar to species abundance, the ARG abundance from multiple collection sites were also normalized for each city and collection month. However, in this case, abundance was normalized by total read count. The ARGs with less than 10 reads per million total reads were also discarded. The DAA for ARG was performed similarly as it was done for the bacterial species.

Alpha and Beta diversity
Combined and normalized abundance data for each collection month and city were used for alpha and beta diversity. Richness, Shannon and Simpson calculated using the “abdiv” R package50 were plotted as alpha diversity metrics. Beta diversities, bray-curtis and aitchison distance, were computed using the “Vegan” R package51. For aitchison, a pseudocount was added which was computed as 0.65% of detection limit52.

Differential abundance estimation
ALDEx249 was used for differential abundance estimation. Differential abundance was computed between each pair of cities for each collection month. The abundance from multiple locations from each city and collection month were used as replicates. Bacterial species or ARG with adjusted p-value < 0.05 and log2 fold change > 1 were considered enriched and plotted in Figure 1d and 3c.

MAG-based approach
Trimmed reads were used for de novo assembly using Megahit (--k-min 35 --k-max 141 --k-step 28 --min-contig-len 200) to generate contigs53. The contigs were further binned together to form MAGs using MetaBAT254 with default parameters. Before running MetaBAT2 the reads were mapped to contigs using minimap255 and “jgi_summarize_bam_contig_depths” script was used to generate depth files from the bam files. Contig files along with the depth file were used to generate MAGs.

Quality of MAGs was assessed using CheckM256 and MAGs with contamination < 5% and completeness > 90% (MIMAG high quality MAGs31,57) were used for further analysis. The high-quality MAGs were taxonomically annotated with Bin Annotation Tool (BAT) using the “20240422_CAT_nr” database58. Sequences of plasmid in the MAGs were identified using GeNomad59. Only contigs with plasmid score >= 0.9 were retained. BacAnt60 was used for ARG and transposable elements annotation using ResDB, TransposonDB, and IntegronDB databases. ARG and transposons with percent identity >= 90 were considered for further analysis.

Microbial co-occurrence network analysis
For network analysis, the high quality MAGs from all the cities were dereplicated using dRep61 to get high quality non-redundant MAGs. A single file was generated with sequences of all the dereplicated MAGs. The trimmed reads from all the samples were mapped back to this single file using minimap255. “Jgi_summarize_bam_contig_depths” script was used to count the depth for each contig. The nucleotide mapped to each contig was generated by multiplying total average depth to contig length. Finally, for each bin, city and collection month, the average nucleotide mapped was recalculated by dividing total nucleotides mapped with total contig length. This gave a depth for each bin for each city and month. The bins with mean average depth across all samples > 0.5 were used for further network generation. For each city, the Pearson correlation was calculated between each pair of bins along with p-value which was further corrected using Benjamini–Hochberg Method. The pair of bins were connected by an edge in the network if the p.adjust < 0.01. The network was generated using the igraph R package62. The community was generated using the “cluster_optimal” function which computes the optimal community structure of a graph, by maximizing the modularity measure over all possible partitions. For temporal trend, 6 communities with maximum member nodes were selected from each city. The temporal abundance was calculated by computing mean abundance of the member nodes, normalized by total read count and multiplied by million. The network nodes were colored according to the phylum and shape signifies if the node was successfully classified at the species level (known) or not (unknown/novel). The phylum composition of chosen six communities were depicted using radar plot. Finally, a correlation plot was generated between all the 24 communities to find if two communities are showing similar temporal trends. We also computed the bray-curtis distance between communities using phyla count for each community to see if communities share similar phyla composition.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "General comments:\nThe findings of this study highlight a substantial proportion of novel metagenome-assembled genomes (MAGs) (55%–71%) reconstructed from wastewater metagenomic data. Additionally, antibiotic resistance genes (ARGs) conferring resistance to clinically important antibiotics such as tetracyclines and beta-lactams demonstrated stronger associations with mobile genetic elements (MGEs) in comparison to macrolide resistance genes. The novelty of this work lies in the identification of these novel MAGs and the characterization of their associated ARGs and MGEs.\nHowever, several methodological and interpretative issues require clarification and additional support to strengthen the manuscript before it can proceed to the second round of review.\n1. Why was Aitchison distance used? How was the statistical significance between the identified clusters assessed?\n2. Line 106: There is an extra space in the sentence: \"different taxa observed) was\" — please remove the additional space.\n3. Line 143: \"Most MAGs could be taxonomically assigned at the phylum level (92%–95%), but a significantly lower proportion could be classified at the species level (29%–45%), suggesting the possible presence of a large number of novel, uncharacterized bacterial species in the wastewater samples analyzed.\"\nThis is a strong claim. Please provide additional supporting evidence, such as novelty thresholds (e.g., ANI/AAI cut-offs), genome completeness, or phylogenomic analysis to substantiate the presence of potentially novel species.\n4. Clarification needed on sequencing parameters:\nPlease specify the sequencing depth, average fragment size, and the total amount of data generated. How many average reads were obtained per sample?\n5. Line 297: What was the rationale for selecting the sampling locations within the cities? Also, please explain why the number of sampling sites varies between cities.\n6. Why was only the grab sampling method used?\nPlease provide justification for this method. Additionally, describe the distance and depth of the sample collection sites from nearby land features or waste disposal points, such as sewage outlets or landfills.\n7. Line 364–365: Why was a threshold of 50% used? Please justify this cut-off based on existing literature, methodological standard, or data distribution.\n8. Clarification on novelty definition: How was novelty of the MAGs determined? Were specific thresholds (e.g., <95% ANI with known references) used to define them as novel?\n9. Were any physicochemical parameters of the wastewater recorded?\nPlease clarify if parameters such as pH, temperature, total hardness, BOD, COD, TDS, or DO were measured during sampling.\n10. L158-159 was the fecal load measured? or is it a speculation?", "reviewer_2": "The manuscript titled “Metagenomic Profiling of Antimicrobial Resistance in Wastewater from Metropolitan Cities of India – a two-year longitudinal study” offers valuable insights into AMR surveillance using metagenomics across urban wastewater systems. However, following minor comments need to be addressed:\nSpecific Comments:\nFigure 1D – Color Coding on Y-axis:\nThe figure legend states that the distribution is “bacteria-wise,” but the color coding used on the Y-axis remains unclear. It is recommended to clearly define what each color represents.\nLine 107 – Alpha Diversity Differences:\nWhile the manuscript attributes differences in alpha diversity solely to the number of sampling sites, other factors such as environmental heterogeneity, physicochemical parameters, temporal variation, and anthropogenic influences could also contribute. Please consider discussing these additional possibilities or provide justification if they were ruled out.\nLine 301 – Use of 1% Bleach in Sample Collection:\nKindly provide a citation supporting the use of 1% bleach for decontaminating the sampling containers. Additionally, it would be helpful to clarify whether residual bleach might impact microbial DNA integrity or downstream analysis, and how this was controlled or validated.\nLine 321 – Read Mapping Details:\nThe manuscript mentions mapping metagenomic reads but does not specify the software/tool used for this step. Please indicate the name and version of the software employed for mapping, along with an appropriate reference, to support reproducibility and methodological transparency.", "reviewer_3": "This manuscript presents a metagenomic study of a two-year wasterwater sampling campaign in four major cities in India. Two different analysis pathways are explored, one based on read mapping and one based on metagenomic assemblies.\nThe paper presents three main findings, 1) the microbiome, but not the resistome clusters on cities, 2) a number of novel MAGs are identified, and 3) mercury, tetracycline and beta-lactam resistance genes are associated with MGEs and macrolide resistance genes are not.\nMajor issues:\nThe first finding, that the microbiome clusters on city level, is not new nor very interesting. Multiple studies over that past 10 years have shown this, most recently and very notably by Becsei et al (2024). It is so well-established that I would find it suspicious if the samples did not cluster on city level. In fact, I am not convinced that the resistome does not also cluster on city level. This conclusion is based entirely on a PCoA (Fig. 3A) which (although correctly based on the non-Euclidean Aitchison metric) does not faithfully present the variance of the data in a low-dimensional Euclidean space (PCA on log-ratio transformed data would be a better choice). The authors also do not report the explained variance (which I suspect is low).\nThe reason why I am not convinced that the resistome doesn’t cluster is that the authors report a number of differentially abundant genes in each city. This is exactly what clustering means! I am fairly certain, that if a PCoA was made on the sub-composition of the differentially abundant genes, strong clustering would be observed. The reason why no clustering is seen is, that the signal is drowned in a background consisting of a large number of genes that are not differentially abundant, but this does not mean that the clustering isn’t there.\nThe authors state in the abstract that “clinically relevant antibiotics such as tetracyclines and beta-lactams showed higher association with MGEs in contrast to macrolide resistance genes”. I would argue that macrolides are considerably more important in human medicine than tetracycline is. Tetracycline is primarily used in livestock and resistance is widespread and common. It is not clear if the authors suggest an epidemiological effect (e.g., that resistance to clinically important drugs get mobilised due to antibiotic usage) or simply just echo the biological fact that some ARGs are mutations in chromosomal genes while others are primarily plasmid borne. The latter is not a particularly novel finding and there is no evidence of the former.\nThe data used in this manuscript and large parts of the analysis is very similar to Becsei et al. (2024). The reported findings are also very similar (e.g., the flaring of pseudomonas resulting in a drop in apparent diversity, clustering on city level, novel MAGs, etc.). Just like in Becsei et al (2024), no apparent temporal effects are found by looking at the fractional abundances over time. Only when Becsei et al. did community detection based on network theory, strong temporal effects were revealed. Based on the Becsei et al treatment, it is fair to say that temporal effects cannot be ruled out based on fractional read-mapping abundances alone. Given that the title of the manuscript is “… - a two-year longitudinal study”, it feels a little lacking that so little effort is made to explore such temporal effects properly.\nMinor issues\nAuthors report that 2179 high quality MAGs were found, 50-70% of which could not be assigned to a known species. In the ‘methods’-section, it is stated that MAGs with <10% contamination and >50% completion were retained. This is typically what we call medium quality MAGs and not high quality, which require <5% contamination and >90% completion.\nReporting “uncultured bacterium” as the most abundant species in all samples is not very useful.\nIs it the same one uncultured bacterium that attracts most of the reads, or is it multiple different bacteria which get lumped together as “uncultured bacterium”? In the latter case it is not the most abundant species, but rather a collection of unknown species, which really does not belong among top-10 abundances. Moreover, if some of these “uncultured bacteria” are indeed abundant on their own, fairly complete MAGs should come out of the assembly analysis. If reads that map to “uncultured bacterium” also map to one of the novel MAGs, it may not be completely novel after all.\nThere are many vague and poorly supported statements throughout the manuscript, for instance, “The composition of drug classes clubbed together as ‘multidrug’ seemed to be similar across the cities”. This is not at all clear to me and it would be nice with a bit more quantitative statements.\nInformation about the sampling is lacking. The authors state that samples are drawn from wastewater drains. What is that? Is it inlet water? or outlet? Are the samples drawn from stagnant water or flowing water? Has the water seen any processing before the samples are taken (mechanical, chemical or otherwise)? Please explain (or reference) the “Grab method”.\nPlease explain (or reference) the “nr database”.\nDESeq2 is generally not appropriate for compositional data (such as metagenomics). DESeq2 expects independent counts and use a negative binomial model which assumes absolute, not relative counts. Metagenomic count data are compositional by nature (e.g. Glover et al 2017), which means that counts are relative and interdependent. The appropriate, compositionally-aware method for differential abundance is either ALDEx2 or ANCOM(-BC).\nMany figures throughout the manuscript are unreadable. Panels 1D and 3C as well as many of the supplementary figures are completely unreadable while the axis labels on 1C, 3B, 2B, and 4D are extremely hard to read.\nLanguage: don’t use ambersand (&) in place of ‘and’.\nWord clouds are not a very useful way of presenting results."}, "author_response": "Response to Reviewers’ Comments\nWe thank the reviewers for their time in assessing our manuscript, and providing us with\nconstructive feedback. We have carefully considered all the points raised and have revised the\nmanuscript accordingly (changes marked in red font). We believe these changes have\nsubstantially improved the quality of our paper. We would like to summarize the key changes\nmade below:\n● Provided justification for using grab sampling and compared its efficacy with that of\ncomposite sampling;\n● Performed experiments to quantify the effect of hypochlorite on DNA degradation and\ntherefore detection of species in metagenomics experiments;\n● Performed a network based community analysis to understand the temporal variation in\nour data;\n● Used high-quality MAGs instead of medium-quality MAGs for all analyses;\n● Used ALDEx2 instead of DESeq2 for identifying differentially abundant species and ARGs;\n● Included details of sequencing parameters and improved several plots significantly.\nA point-by-point response to the comments is provided below in blue font.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nComments and suggestions\nGeneral comments:\nThe findings of this study highlight a substantial proportion of novel metagenome-assembled\ngenomes (MAGs) (55% – 71%) reconstructed from wastewater metagenomic data. Additionally,\nantibiotic resistance genes (ARGs) conferring resistance to clinically important antibiotics such as\ntetracyclines and beta-lactams demonstrated stronger associations with mobile genetic elements\n(MGEs) in comparison to macrolide resistance genes. The novelty of this work lies in the\nidentification of these novel MAGs and the characterization of their associated ARGs and MGEs.\nResponse: We thank the reviewer for their positive comments.\nHowever, several methodological and interpretative issues require clarification and additional\nsupport to strengthen the manuscript before it can proceed to the second round of review.\n1. Why was Aitchison distance used? How was the statistical significance between the identified\nclusters assessed?\nResponse: We had initially used Bray-Curtis distance (Fig R1; now included as the\nSupplementary Figure 1C), however the ellipse with 0.95 confidence level around the data points\nshowed considerable overlap between the cities. Additionally, a recent work by Becsei et al.1\ndemonstrated that Aitchison distance is better in capturing metagenomics diversity which is\ncompositional in nature. Using Aitchison distance resulted in an improvement in the clustering of\ncity specific samples (Fig R2 below and Fig 1B of the revised manuscript). As suggested by the\nreviewer, we further checked the statistical significance between the identified clusters using\nAdonis2 function and found significant differences between the city specific clusters for species\nabundance (Table R1).\nFigure R1: PCoA using Bray-Curtis distance between samples by species abundance\nFigure R2: PCoA using Aitchison distance between samples by species abundance\nChennai Delhi Kolkata Mumbai\nChennai - <0.001 <0.001 <0.001\nDelhi <0.001 - <0.001 <0.001\nKolkata <0.001 <0.001 - <0.001\nMumbai <0.001 <0.001 <0.001 -\nTable R1: Statistical significance between the identified clusters for city level species abundance\nusing Adonis2 function from vegan package in R\n2. Line 106: There is an extra space in the sentence: \"different taxa observed) was\" — please\nremove the additional space.\nResponse: The additional space has been removed.\n3. Line 143: \"Most MAGs could be taxonomically assigned at the phylum level (92%–95%), but a\nsignificantly lower proportion could be classified at the species level (29%–45%), suggesting the\npossible presence of a large number of novel, uncharacterized bacterial species in the wastewater\nsamples analyzed.\"\nThis is a strong claim. Please provide additional supporting evidence, such as novelty thresholds\n(e.g., ANI/AAI cut-offs), genome completeness, or phylogenomic analysis to substantiate\nthe presence of potentially novel species.\nResponse: We thank the reviewer for raising this point. We have now altered the pipeline for the\nMAG based analysis, and this is also reflected in the Methods section (line no. 450 of the revised\nmanuscript). The bins were first checked for quality using checkM2 and the MIMAG high quality\nbins2 (completeness > 90 and contamination < 5) were retained. All the high quality bins from the\nfour cities were further dereplicated using dRep to remove redundancy. During dRep, we skipped\nquality checks by using the ‘--ignoreGenomeQuality’ option.\nFor genome comparison default parameters were used (ANImf, MASH sketch size = 1000). For\ngenome clustering ‘ANI threshold = 0.9 (default)’ and ‘ANI threshold for secondary cluster = 0.98’\nwas used. The bins were classified using the Bin Annotation tool (BAT). We observed that most\nof the bins were classified at the phylum level but very few of these MAGs could be classified at\nthe species level. BAT first predicts ORFs in MAGs using Prodigal which are then classified by\nquerying the NCBI “nr database” using DIAMOND. Finally the MAG is classified based on the\nvoting approach of all classified ORFs. Since MAGs were classified at bacterial phyla but failed\nto be resolved at the species level, and as these are high quality MAGs, we assumed that these\nMAGs could indicate novel bacterial species. Such an assumption is not new, Becsei et al.1 had\nmade a similar assumption in their now published paper. In addition to this, we also compared\nthe MAG statistics to rule out the effect of MAG quality on the classification and found that\nnovel/unknown MAGs were significantly of better quality than the MAGs from known species\n(Figure 2B of the revised manuscript). In order to reflect the assumption, we have now used the\nphrase “potentially novel species” instead of “novel species\" in the manuscript.\n4. Clarification needed on sequencing parameters:\nPlease specify the sequencing depth, average fragment size, and the total amount of data\ngenerated. How many average reads were obtained per sample?\nResponse: The samples were sequenced on Illumina NovaSeq 6000 in paired-end mode, with\nread lengths of 150x2. 447 samples were sequenced with a median read count of 15.54 million\nreads. This information has been provided in Supplementary Figure 1A and Supplementary Table\n1.\n5. Line 297: What was the rationale for selecting the sampling locations within the cities? Also,\nplease explain why the number of sampling sites varies between cities.\nResponse: The following criteria were used for site identification:\n- Main sewage drains flowing in the outskirts of the cities, which covered maximum\npopulation flow, were identified and targeted. The images of a few representative sites are\nprovided in Figure R3 below.\n- Predominantly residential areas were prioritized over commercial areas due to predictable\noutflows from residential locations\n- Drains having industrial discharge were excluded.\nSites meeting the said criteria were further chosen for sampling, which in addition to the logistic\nfeasibility of collecting samples at these locations is the reason for the variation in number of\nsampling sites across the 4 cities. Below are some of the representative images of the sampling\nsites which are large open water drains.\nFigure R3: Representative images of sampling sites.\n6. Why was only the grab sampling method used?\nPlease provide justification for this method. Additionally, describe the distance and depth of the\nsample collection sites from nearby land features or waste disposal points, such as sewage\noutlets or landfills.\nResponse: Grab sampling was utilized due to its logistic feasibility in our setting (especially\nbecause it avoids significant autosampler-associated workload and capital costs). We followed\nthe protocol proposed by Hemalatha et al3, and collected mid-stream samples between 8 AM -10\nAM across all sampling sites in the study, to ensure comparability. These samples were also used\nfor SARS-CoV-2 surveillance. We have now explained the sampling method and included the\nrelevant reference(s) in the manuscript (line no. 373 of the revised manuscript). The sampling\nsites were not near any landfills or sewage outlets. The locations of sampling have been now\nlisted in the supplementary Table 5, and also provided in Table R2 for your reference.\nCity Sampling Location Co-ordinates\nKolkata Kestopur Khal 22°34'54.4\"N 88°29'55.2\"E\nAnandapur Khal 22°30'44.2\"N 88°24'33.0\"E\nDelhi Coronation Pillar 28°43'22.2\"N 77°11'36.5\"E\nYamuna Vihar 28°41'57.1\"N 77°17'20.2\"E\nNajafgarh Drain 28°42'31.5\"N 77°13'09.9\"E\nShahadra Drain 28°39'56.6\"N 77°17'04.4\"E\nChennai\nKodungaiyur\n13°08'17.4\"N 80°15'31.1\"E\nKoyambedu 13°04'06.5\"N 80°12'04.2\"E\nNesapakkam 13°02'08.1\"N 80°11'37.9\"E\nPerungudi 12°57'24.4\"N 80°14'09.5\"E\nAlandur 13°02'15.2\"N 80°11'40.0\"E\nMumbai\nWorli 18°59'31.8\" N 72°48'55.8\" E\nNehru Nagar Kurla 19°03'50.7\" N 72°52'57.4\" E\nMogra Nallah Andheri 19°08'11.2\" N 72°49'48.1\" E\nLBS Marg, Bhandup 19°07'43.2\" N 72°55'30.8\" E\nPratiksha Nagar Wadala 19°00'29.8\" N 72°51'57.5\" E\nKJ Somaiya Ghatkopar 19°04'29.7\" N 72°54'07.1\" E\nVakola 19°04'44.3\" N 72°51'00.3\" E\nMalad 19°07'46.31” N 72°50'11.60\" E\nTable R2: Details and coordinates of the sampling sites from the study, across the 4 cities\n7. Line 364–365: Why was a threshold of 50% used? Please justify this cut-off based on existing\nliterature, methodological standard, or data distribution.\nResponse: Thanks for pointing this out. We have now altered our pipeline to include only MIMAG\nhigh quality MAGs, which are defined as completeness > 90% and contamination < 5% instead\nof medium quality MAGs we used earlier (completeness > 50% and contamination < 10%) in our\nanalysis. Such a quality cutoff has been suggested by Bower et. al.2 and was also used by Becsei\net. al.1 With respect to our data, we retrieved 13380 bins from all the four metro cities. 840 of\nthese bins were of high quality and 5790 of medium quality. After dereplication using dRep, we\ngot 391 high quality and 1998 medium quality non redundant MAGs.\n8. Clarification on novelty definition: How was novelty of the MAGs determined? Were specific\nthresholds (e.g., <95% ANI with known references) used to define them as novel?\nResponse: As clarified in point 3, MAGs that were classified at the phylum level by BAT, but failed\nto classify at the species level were assumed to be either novel or known but uncharacterized\nbacterial species. ANI cutoff was used at the dereplication step using dRep, where MAGs were\nfirst clustered using 0.9 ANI cutoff and 0.98 as secondary clustering threshold. Further, this\napproach was used in earlier studies to define novel/unknown species1. In addition, we have now\nused ‘potentially novel’/ unclassified MAGs instead of novel throughout the manuscript to remove\nany confusion.\n9. Were any physicochemical parameters of the wastewater recorded?\nPlease clarify if parameters such as pH, temperature, total hardness, BOD, COD, TDS, or DO\nwere measured during sampling.\nResponse: Unfortunately, physicochemical parameters were not recorded in this study, a\nlimitation, which has been noted in the revised manuscript.\n10. L158-159 was the fecal load measured? or is it a speculation?\nResponse: It was a speculation and has been now removed from the manuscript.\nReviewer #2 (Remarks to the Author):\nThe manuscript titled “Metagenomic Profiling of Antimicrobial Resistance in Wastewater from\nMetropolitan Cities of India – a two-year longitudinal study” offers valuable insights into AMR\nsurveillance using metagenomics across urban wastewater systems. However, following minor\ncomments need to be addressed:\nResponse: We thank the reviewer for their positive assessment of the manuscript.\nSpecific Comments:\nFigure 1D – Color Coding on Y-axis:\nThe figure legend states that the distribution is “bacteria-wise,” but the color coding used on the\nY-axis remains unclear. It is recommended to clearly define what each color represents.\nResponse: We apologize for the lack of clarity. In the revision, as suggested by another reviewer,\nwe have changed our approach for differential abundance analysis. We are now using ALDEx2\ninstead of DESeq2 which seems to be more appropriate for differential abundance analysis (line\nno. 439 of the revised manuscript). Additionally, we are now comparing cities pairwise for each\ncollection month. Hence this figure has now been removed from the Manuscript.\nLine 107 – Alpha Diversity Differences:\nWhile the manuscript attributes differences in alpha diversity solely to the number of sampling\nsites, other factors such as environmental heterogeneity, physicochemical parameters, temporal\nvariation, and anthropogenic influences could also contribute. Please consider discussing these\nadditional possibilities or provide justification if they were ruled out.\nResponse: We agree with the reviewer that the factors mentioned play a significant role in the\nalpha diversity differences and subsequently mention them in lines 109-110. However, as\nphysicochemical properties were not recorded, we could not study their effect on alpha diversity.\nRegarding temporal variation, we have now performed a network analysis as per the suggestion\nof Reviewer 3, and discuss the temporal variation observed by specific communities across each\ncity. These are provided in lines 256-260.\nLine 301 – Use of 1% Bleach in Sample Collection:\nKindly provide a citation supporting the use of 1% bleach for decontaminating the sampling\ncontainers. Additionally, it would be helpful to clarify whether residual bleach might impact\nmicrobial DNA integrity or downstream analysis, and how this was controlled or validated.\nResponse: Thank you for pointing this out and apologies for our oversight. We would like to\nmention this as an inadvertent error; and the final hypochlorite concentration used was 0.002%\n(we used 1ml of 1% hypochlorite stock solution for 500ml of sample) as per the protocol proposed\nby Hemalatha et al., 20213. This method was chosen because one of the main objectives of our\nproject was also to perform SARS-CoV-2 surveillance in wastewater in our country. The protocol\nwas designed to overcome logistic hurdles for transport of samples from different places,\nespecially in the context of biosafety. This was adapted by other groups in the country for\ngenerating comparable data (https://data.ccmb.res.in/apsi/protocol) for AMR as well.\nIn addition, to understand the effect of bleach on metagenome sequencing, we have performed\nthe sampling with collection in varying concentrations of hypochlorite, including 0.002% and\nanalyzed the same for its effects on the DNA quality/integrity and subsequent metagenomic\nanalysis. Briefly, waste water samples were collected by grab method in triplicates from 2\nlocations without added hypochlorite and hypochlorite added to achieve final concentrations of\n0.001%, 0.002%, 0.02% and 0.2%. Samples without hypochlorite were inactivated using heat and\nlater processed along with the rest of the samples as per the protocol mentioned in the Methods\nsection. One set of grab samples with 0.002% were also stored at RT and another set at 4℃ and\nDNA extraction was done at 24 hrs, 48 hrs and 7 days, to study the effects of duration of storage\nin hypochlorite - on DNA integrity and metagenome sequencing. DNA concentrations were\nmeasured on Qubit fluorometer, DIN values checked using TapeStation (Supplementary Table\nS7) and Metagenomic libraries generated using SOP were sequenced in NovaSeq 6000. Data\nwas processed as described in the Methods section and the read counts, Alpha and Beta diversity\nindices were compared using ANOVA (Figures R4-R9).\nFigure R4: Effect of hypochlorite on DNA integrity, as measured on grab samples collected from\ntwo different locations (CH, PR). At least three samples were collected from each location for\neach concentration. In addition, for the concentration of 0.002%, the concentration used in the\nstudy, we collected triplicates at two different time points during the day. P-values were calculated\nusing ANOVA.\nFigure R5: Impact of sample storage duration on DNA integrity, as measured on grab samples\ncollected from two different locations (CH, PR). At least three samples were collected from each\nlocation for each time point. In addition, for the concentration of 0.002%, the concentration used\nin the study, we collected triplicates at two different time points during the day. P-values were\ncalculated using ANOVA.\nFigure R6: Effect of hypochlorite on bacterial read counts and species richness, as measured on\ngrab samples collected from two different locations (CH, PR). At least three samples were\ncollected from each location for each concentration. In addition, for the concentration of 0.002%,\nthe concentration used in the study, we collected triplicates at two different time points during the\nday. P-values were calculated using ANOVA.\nFigure R7: Effect of hypochlorite concentration on Beta-diversity (Aitchison distance). The shape\nof the dot indicates the sampling locations (CH and PR).\nFigure R8: Impact of sample storage duration on bacterial read counts and species richness, as\nmeasured on grab samples collected from two different locations (CH, PR). At least three samples were\ncollected from each location for each time point. In addition, for the concentration of 0.002%,\nthe concentration used in the study, we collected triplicates at two different time points during the\nday. P-values were calculated using ANOVA\nFigure R9: Effect of sample storage duration on Beta-diversity (Aitchison distance). The shape\nof the dot indicates the sampling locations (CH and PR).\nGrab vs composite sampling\nComposite samples with 0.002% hypochlorite were generated by mixing equal volumes (50ml\neach) of 3 grab samples collected at 1 hour intervals (8 AM, 9 AM, 10 AM) (composite 1 samples)\nand 3 hour intervals (8AM, 11AM, 2PM) (composite 2 samples) respectively, in the laboratory. 45\nml from the resultant mixture was used for further processing and the generated sequencing data\nwas compared with the grab samples with 0.002% bleach (Figure R10).\nFigure R10: Comparisons of DNA concentrations, integrity, bacterial readcounts and species\nrichness (Alpha diversity) differences between grab and composite sampling of two different\nlocations CH and PR. Significance was measured using T-test.\nFrom our experiments, it could be noted that significant degradation of DNA was evident at much\nhigher concentrations (0.2%) of bleach than what was used in the study. The quality metrics\ndeteriorate with time, however, the values of samples with 0.002% hypo were comparable with\nthose without hypochlorite after 7 days of incubation. It was interesting to note that despite these\nchanges, the read counts, alpha and beta diversity was preserved across the samples (no\nstatistically significant difference, Figures R6-R9). DNA degradation, even when it is happening,\nis non selective. Sensitivity of the sequencing technique along with limitations imposed by short\nread sequencing (read length) is probably mitigating the effect of DNA degradation on variability\nin subsequent analysis. In addition, the results from grab vs composite experiment (Figure R10)\nalso indicate no significant differences in the number of species detected in both grab and\ncomposite samples from either of the locations.\nThough the efficacy and reproducibility of the sampling method needs to be further established\nby independent studies, it is probably safe to assume that the concentration of hypochlorite used\nin the study does not affect the detection and relative quantification of species/ ARGs significantly\nand thus, can be an alternative to storing and transporting at -80℃, especially in resource limited\nsettings.\nLine 321 – Read Mapping Details:\nThe manuscript mentions mapping metagenomic reads but does not specify the software/tool\nused for this step. Please indicate the name and version of the software employed for mapping,\nalong with an appropriate reference, to support reproducibility and methodological transparency.\nResponse: We apologise for the omission. The reads were mapped back to the contigs using\n“minimap2” which is then used to generate depth for each contig. Further, we also map the reads\nback to all the dereplicated MAGs, again using “minimap2”. This has now been mentioned in the\n“MAG-based approach” section of Methods and an appropriate reference has been added (line\nno. 460 of the revised manuscript).\nReviewer #3 (Remarks to the Author):\nThis manuscript presents a metagenomic study of a two-year wastewater sampling campaign in\nfour major cities in India. Two different analysis pathways are explored, one based on read\nmapping and one based on metagenomic assemblies. The paper presents three main findings,\n1) the microbiome, but not the resistome clusters on cities, 2) a number of novel MAGs are\nidentified, and 3) mercury, tetracycline and beta-lactam resistance genes are associated with\nMGEs and macrolide resistance genes are not.\nResponse: We thank the reviewer for their detailed assessment of the manuscript and providing\nconstructive feedback.\nMajor issues:\nThe first finding, that the microbiome clusters on city level, is not new nor very interesting. Multiple\nstudies over that past 10 years have shown this, most recently and very notably by Becsei et al\n(2024). It is so well-established that I would find it suspicious if the samples did not cluster on city\nlevel. In fact, I am not convinced that the resistome does not also cluster on city level. This\nconclusion is based entirely on a PCoA (Fig. 3A) which (although correctly based on the non-\nEuclidean Aitchison metric) does not faithfully present the variance of the data in a low-\ndimensional Euclidean space (PCA on log-ratio transformed data would be a better choice).\nResponse: First we would like to thank the reviewer for a detailed comment. We agree with the\nreviewer that microbiome clustering at the city level is not a novel finding. However, the\nimportance of the data coming from this part of the world cannot be re-emphasised. As suggested\nby the reviewer, we have added explained variance to all the PCA/PCoA plots. We have also\ngenerated PCA on the clr transformed data (Figure R11) and that also showed a similar pattern\nas PCoA plot with similar variance explained by PC1 and PC2.\nFigure R11: PCA on clr transformed species abundance\nOn the contrary, the resistome failed to cluster city wise. A probable reason could be the low\nnumber of ARG (166) detected in our samples against a high number of bacterial species (40828).\nFurther, to check for the effect of more abundant species on ARG clustering, we removed 10%\nmost abundant bacterial species/ARG and performed PCoA analysis (Figure R12 and 13\nrespectively). A similar trend was observed even with a subset of the data, suggesting that this\ncould be an inherent pattern for species and ARG abundance.\nFigure R12: PCoA on Aitchison distance after removing 10% most abundant bacterial species\nFigure R13: PCoA on Aitchison distance after removing 10% most abundant ARGs\nThe authors also do not report the explained variance (which I suspect is low).\nResponse: We apologise for the omission and thank the reviewer for pointing out. The explained\nvariance has now been added to all the PCoA/PCA plots.\nThe reason why I am not convinced that the resistome doesn’t cluster is that the authors report a\nnumber of differentially abundant genes in each city. This is exactly what clustering means! I am\nfairly certain, that if a PCoA was made on the sub-composition of the differentially abundant\ngenes, strong clustering would be observed. The reason why no clustering is seen is, that the\nsignal is drowned in a background consisting of a large number of genes that are not differentially\nabundant, but this does not mean that the clustering isn’t there.\nResponse: We thank the reviewer for the comment. We have changed our approach for\ndifferential abundance analysis. Instead of DESeq2, we now use ALDEx2, as suggested by the\nreviewer. Instead of identifying differential abundant (DA) bacterial species/ ARG for each city\nagainst other 3 cities, we now identify DA bacterial species/ARG for each collection month\nbetween each pair of cities. We found 4308 bacterial species that were DA in any one of the\ncomparisons, however only 54 ARGs were found to be DA (Figure R14,15). PCoA on only these\nDA bacterial species resulted in more variance explained by PCo1 and PCo2 for bacterial species\n(Figure R16). However, contrary to the expectation, the clear clustering observed with total\nbacterial species was not observed with DA bacterial species (Figure R17). This suggests that\nDA bacterial species are specific to a month and/or city and not consistent throughout the\ncollection period. Similarly, for DA ARG, the variance explained by PCo1 and PCo2 increased but\nno distinct clustering of city specific samples was observed (Figure R18 and 19).\nFigure R14: DA bacterial species for each collection month between each pair of cities\nFigure R15: DA bacterial ARGs for each collection month between each pair of cities\nFigure R16: PCoA plot for bacterial species using Aitchison distance.\nFigure R17: PCoA plot for DA bacterial species using Aitchison distance.\nFigure R18: PCoA plot for ARGs using Aitchison distance.\nFigure R19: PCoA plot for DA ARG using aitchison distance.\nFindings suggestive of the absence of resistome diversity in the presence of microbial diversity in\nAsian countries were also noted in previous studies4,5. Population density and socioeconomic\nbackgrounds probably play a major role in microbial exposure to similar stressors resulting in\naccumulation of similar ARG signatures.\nThe authors state in the abstract that “clinically relevant antibiotics such as tetracyclines and beta-\nlactams showed higher association with MGEs in contrast to macrolide resistance genes”. I would\nargue that macrolides are considerably more important in human medicine than tetracycline is.\nTetracycline is primarily used in livestock and resistance is widespread and common. It is not\nclear if the authors suggest an epidemiological effect (e.g., that resistance to clinically important\ndrugs get mobilised due to antibiotic usage) or simply just echo the biological fact that some ARGs\nare mutations in chromosomal genes while others are primarily plasmid borne. The latter is not a\nparticularly novel finding and there is no evidence of the former.\nResponse: We again thank the reviewer for the comment and apologise for the misunderstanding.\nWe did not intend to reflect the clinical importance of one antibiotic over the other and therefore\nhave now rephrased the sentence for clarity. We merely report the high abundance of ARGs\nagainst 3 drug classes namely, tetracycline, beta-lactam and macrolides in the Indian wastewater\nsamples. Since there are various contributors of these wastewater such as, human waste,\nindustrial waste, agricultural waste etc., it's not surprising to see ARG against antibiotics used in\nvarious contexts are present in high abundance. However, it was interesting to observe antibiotics\nsuch as beta-lactam and tetracycline have a higher association to MGEs (plasmid + transposons)\nas compared to macrolides, making them more susceptible for horizontal transmission between\nbacterial species.\nThe data used in this manuscript and large parts of the analysis is very similar to Becsei et al.\n(2024). The reported findings are also very similar (e.g., the flaring of pseudomonas resulting in\na drop in apparent diversity, clustering on city level, novel MAGs, etc.). Just like in Becsei et al\n(2024), no apparent temporal effects are found by looking at the fractional abundances over time.\nOnly when Becsei et al. did community detection based on network theory, strong temporal\neffects were revealed. Based on the Becsei et al treatment, it is fair to say that temporal effects\ncannot be ruled out based on fractional read-mapping abundances alone. Given that the title of\nthe manuscript is “… - a two-year longitudinal study”, it feels a little lacking that so little effort is\nmade to explore such temporal effects properly.\nResponse: We thank the reviewer for pointing out this shortcoming of our manuscript. Indeed a\nnumber of analyses were done in accordance with work done by Becsei et al.1 and some of our\nresults do match theirs and this probably demonstrates reproducibility and strength of their\napproach. In our defense though, we have included read based analysis as well to make use of\nmost of the information gathered from metagenomic sequencing and subsequent findings make\nthis manuscript stand apart. We also performed MGE analysis to understand the potential for\nhorizontal transfer of ARG genes in the bacterial community. As suggested by the reviewer, we\nhave now performed the network analysis and the additional insights it provided have thus been\nincluded in a new section (lines 247-271).\nMinor issues\nAuthors report that 2179 high quality MAGs were found, 50-70% of which could not be assigned\nto a known species. In the ‘methods’-section, it is stated that MAGs with <10% contamination and\n>50% completion were retained. This is typically what we call medium quality MAGs and not high\nquality, which require <5% contamination and >90% completion.\nResponse: We agree with the reviewer that using high quality MAGs is better and have now used\nonly high quality MAGs (contamination < 5% and completion > 90%) for all our analysis including\nthe network analysis.\nReporting “uncultured bacterium” as the most abundant species in all samples is not very useful.\nIs it the same one uncultured bacterium that attracts most of the reads, or is it multiple different\nbacteria which get lumped together as “uncultured bacterium”? In the latter case it is not the most\nabundant species, but rather a collection of unknown species, which really does not belong\namong top-10 abundances. Moreover, if some of these “uncultured bacteria” are indeed abundant\non their own, fairly complete MAGs should come out of the assembly analysis. If reads that map\nto “uncultured bacterium” also map to one of the novel MAGs, it may not be completely novel after\nall.\nResponse: We agree with the reviewer that “uncultured bacterium” are not one bacterial species\nbut a set of bacterial species unculturable in the laboratory and hence they have been removed\nfrom the top 10 abundant bacterial species list.\nThere are many vague and poorly supported statements throughout the manuscript, for instance,\n“The composition of drug classes clubbed together as ‘multidrug’ seemed to be similar\nacross the cities”. This is not at all clear to me and it would be nice with a bit more quantitative\nstatements.\nResponse: We apologize for the confusion which arose due to multidrug nomenclature. We have\nnow used nomenclature by Zhang et al.6. ARG conferring resistance to macrolide, lincosamide,\nstreptogramin were grouped into MLS class, ARG conferring resistance to penam, cephalosporin,\ncarbapenem, cephamycin, penem and monobactam were grouped into Betalactam class and\nARG conferring resistance to more than one drug class were grouped into “multi-drug” class (lines\n198 - 201).\nInformation about the sampling is lacking. The authors state that samples are drawn from\nwastewater drains. What is that? Is it inlet water? or outlet? Are the samples drawn from stagnant\nwater or flowing water? Has the water seen any processing before the samples are taken\n(mechanical, chemical or otherwise)? Please explain (or reference) the “Grab method”.\nResponse: Wastewater drains, which are main sewage drains flowing outskirts of the cities were\nmostly identified and targeted, which covered maximum population flow (images of a few\nrepresentative sites are appended below). Those covering predominantly residential areas were\nprioritized over commercial areas due to predictable outflows from residential locations. Those\ndrains which had industrial discharge were excluded. The location information has now been\nprovided in the Supplementary Table S5.\nWastewater collected in hypochlorite was processed for DNA isolation. Grab sampling is an\nacceptable practice for AMR analyses. Sampling in this study was done using this method due to\nits logistic feasibility (especially because it avoids significant autosampler-associated workload\nand capital costs). We have now explained the Grab sampling & included the appropriate\nreference(s) in the Manuscript 7,8.\nPlease explain (or reference) the “nr database”.\nResponse: We sincerely apologize for the error. We have used core-nt database for Kraken2\ndownloaded from https://benlangmead.github.io/aws-indexes/k2. For BAT, we have used NCBI\nnon-redundant database downloaded from\ntbb.bio.uu.nl/tina/CAT_pack_prepare/20240422_CAT_nr.tar.gz. Both the entries are now\ncorrected and source has been added in the manuscript.\nDESeq2 is generally not appropriate for compositional data (such as metagenomics). DESeq2\nexpects independent counts and use a negative binomial model which assumes absolute, not\nrelative counts. Metagenomic count data are compositional by nature (e.g. Glover et al 2017),\nwhich means that counts are relative and interdependent. The appropriate, compositionally-aware\nmethod for differential abundance is either ALDEx2 or ANCOM(-BC).\nResponse: We thank the reviewer for this suggestion, which we have accepted and are now using\nALDEx2 for differentially abundant analysis. Now for each collection month, pairwise city samples\nwere compared for differential abundant analysis. The relevant section is now updated in the\nmanuscript (Line no. 439 of the revised manuscript).\nMany figures throughout the manuscript are unreadable. Panels 1D and 3C as well as many of\nthe supplementary figures are completely unreadable while the axis labels on 1C, 3B, 2B, and 4D\nare extremely hard to read.\nResponse: We apologize for the inconvenience caused. The figures have been updated to the\nbest of our ability and the high resolution images are being submitted for review.\nLanguage: don’t use ambersand (&) in place of ‘and’.\nResponse: The suggested change has been made in the manuscript.\nWord clouds are not a very useful way of presenting results.\nResponse: We agree with the reviewer and word clouds have now been removed.\nReferences\n1. Becsei, Á. et al. Time-series sewage metagenomics distinguishes seasonal, human-\nderived and environmental microbial communities potentially allowing source-attributed\nsurveillance. Nat. Commun. 15, 7551 (2024).\n2. Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and\na metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35,\n725–731 (2017).\n3. Hemalatha, M. et al. Surveillance of SARS-CoV-2 spread using wastewater-based\nepidemiology: Comprehensive study. Sci. Total Environ. 768, 144704 (2021).\n4. Su, J.-Q. et al. Metagenomics of urban sewage identifies an extensively shared antibiotic\nresistome in China. Microbiome 5, 84 (2017).\n5. Hendriksen, R. S. et al. Global monitoring of antimicrobial resistance based on\nmetagenomics analyses of urban sewage. Nat. Commun. 10, 1124 (2019).\n6. Zhang, Z. et al. Assessment of global health risk of antibiotic resistance genes. Nat.\nCommun. 13, 1553 (2022).\n7. Endalamaw, K. et al. Antimicrobial resistance profile of bacteria from hospital wastewater\nat two specialized hospitals in Bahir Dar city, Ethiopia. BMC Microbiol. 24, 525 (2024).\n8. Rajput, V. et al. Metagenomics based longitudinal monitoring of antibiotic resistome and\nmicrobiome in the inlets of wastewater treatment plants in an Indian megacity. Sci. Total\nEnviron. 986, 179691 (2025)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have adequately addressed all the comments raised by the reviewer. I recommend the manuscript for publication.", "reviewer_2": "The authors have addressed most of the reviewer comments satisfactorily. However, the manuscript still lacks consideration of the physicochemical properties of the samples at the time of collection. Parameters such as pH, temperature, salinity, dissolved oxygen, nutrient load, or other relevant environmental factors are known to significantly influence microbial community structure and the distribution of functional traits, including antimicrobial resistance. The absence of these variables limits the interpretation of the results and weakens the ecological context of the study. Incorporating these measurements, or at least discussing their potential influence as a limitation, would substantially strengthen the manuscript.", "reviewer_3": "The authors have addressed all my comments and I have no further concerns."}, "author_response": "Response to Reviewers’ Comments\nWe thank all the reviewers once again for their time in assessing our manuscript, and providing\nus with constructive feedback. Their inputs have resulted in significant improvements to the paper.\nAs previously, the changes made in the manuscript have been marked in red font.\nA point-by-point response to the comments is provided below in blue font.\nREVIEWERS' COMMENTS\nReviewer #2 (Remarks to the Author):\nThe authors have addressed most of the reviewer comments satisfactorily. However, the\nmanuscript still lacks consideration of the physicochemical properties of the samples at the\ntime of collection. Parameters such as pH, temperature, salinity, dissolved oxygen, nutrient\nload, or other relevant environmental factors are known to significantly influence microbial\ncommunity structure and the distribution of functional traits, including antimicrobial\nresistance. The absence of these variables limits the interpretation of the results and weakens\nthe ecological context of the study. Incorporating these measurements, or at least discussing\ntheir potential influence as a limitation, would substantially strengthen the manuscript.\nResponse: We agree with the reviewer regarding the limitation imposed by lack of information on\nphysicochemical properties of the samples in our study. We have now clearly acknowledged this\nlimitation in the discussion section as a separate paragraph before the concluding paragraph."}]

Decision letter