Antibiotic contamination and antimicrobial resistance dynamics in the urban sewage microbiome in India

Domain: chemistry
Published: 2025-12-29
Editorial outcome: major_revision

Abstract
The emergence and spread of antimicrobial resistance (AMR) in clinically important bacterial pathogens has severely compromised the effectiveness of commonly used antibiotics in healthcare. Acquisition and transmission of AMR genes (ARGs) are often facilitated by sublethal concentrations of antibiotics in microbially dense environments. In this study, we use sewage samples (n = 381) collected from six Indian states between June and December 2023 to assess the concentration of eleven antibiotics, microbial diversity, and ARG richness. We find antibiotics from seven drug classes and detect over 2000 bacterial amplicon sequence variants (ASVs). Metagenomic (n = 220) and isolated genome sequences (n = 305) of aerobic and anaerobic bacterial species identify 82 ARGs associated with 80 mobile genetic elements (MGEs). These MGEs are predominantly present in multidrug-resistant (MDR) bacterial pathogens. Comparative core genome analysis of MDR bacterial isolates (n = 7166) shows strong genetic similarity between sewage-derived strains and clinical pathogens. Our results highlight sewage as a significant reservoir for ARGs, where genetic exchanges occur and facilitate the evolution and spread of AMR pathogens in both community and healthcare settings. Additionally, the dipstick-based assay developed for ARG detection can be used for sewage surveillance in low-resource settings for better understanding of resistance prevalence.

Introduction
Antimicrobial resistance (AMR) is a critical and urgent global health crisis, threatening human, animal, and environmental health due to the increasing prevalence of multidrug-resistant (MDR) bacterial pathogens1. The emergence of MDR microbes is driven by the widespread use of antibiotics across various sectors, which exerts selective pressure that favors resistant variants over susceptible microorganisms2,3. Rapid advances in DNA sequencing technologies have enabled scientists worldwide to decode the metagenomes and whole genomes of diverse microorganisms, facilitating the monitoring of AMR genes (ARGs) and their genetic associations with mobile genetic elements (MGEs). These elements play a crucial role in the rapid spread of resistance through horizontal gene transfer (HGT) among both closely and distantly related microbial species.

The growing need for comprehensive global genomic pathogen surveillance has become increasingly urgent as pathogens continuously evolve. The recent COVID-19 pandemic, one of the most devastating respiratory infectious diseases, highlighted the critical importance of real-time global surveillance systems to detect and track pathogens. Recently, the World Health Organization (WHO) in close collaboration with the Food and Agriculture Organization (FAO) and World Organization of Animal Health (WOAH) has developed the One Health AMR Global Action Plan (GAP), to mitigate the AMR crisis. This tripartite organization (FAO-WOAH-WHO) is tasked to oversee the development and execution of National Action Plans (NAPs) and strategies which aim at reversing AMR4. The WHO is also dedicated to overseeing water, sanitation, and hygiene (WASH) efforts as well as wastewater management, which are crucial for mitigating and reducing the impact of AMR in sewage. An in-depth study on the AMR patterns and burden in sewage and leveraging this data can significantly advance WASH strategies, enabling precise tracking and early detection of AMR pathogens.

Sewage water constitutes a dynamic and complex ecological matrix that plays a pivotal role in the emergence and dissemination of AMR. Characterized by densely populated and diverse microbial communities, this often poorly regulated environment serves as a critical hotspot for HGT, facilitating the exchange of ARGs among bacterial species5. These exchanges are facilitated by MGEs, such as plasmids and transposons, which act as key drivers in the spread of resistance traits. Resistant pathogens originating from sewage systems pose a significant public health risk, with potential transmission to human populations through various pathways such as environmental discharge, agricultural reuse, and contaminated water sources6. In response to the growing need for efficient monitoring tools, we recently applied a high-throughput, culture-independent dipstick-based molecular assay for the rapid detection of bacterial species and viral variants circumventing the need for traditional culturing or genomic sequencing7,8. This approach complements recent advances in genomic and metagenomics surveillance, which have revolutionized our capacity to investigate AMR in complex matrices like wastewater9,10. Integration of next-generation sequencing, quantitative PCR, and meta-transcriptomics now enables high-resolution profiling of microbial communities and resistome dynamics, uncovering not only the breadth of ARGs present but also the molecular mechanisms driving their spread11,12. However, data from Low- and Middle-Income Countries (LMICs), including India, remain limited. In India, pilot studies under national AMR programs are emerging, though coverage remains limited13. Globally, many countries are increasingly implementing wastewater-based surveillance systems that integrate genomic approaches to monitor ARGs and resistant pathogens. This integrated strategy enables early detection of emerging resistance trends and supports evidence-based public health interventions across both clinical and environmental settings. Despite these breakthroughs, significant knowledge gaps remain particularly concerning the environmental and biological factors that modulate HGT and the precise role of MGEs in real-time ARG transmission. Addressing these gaps is vital for developing targeted interventions to curb the environmental spread of AMR. This study supports the One Health Approach and contributes valuable insights into AMR dynamics in resource-constrained environments, aligning with the global trend of wastewater-based surveillance for comprehensive AMR monitoring and informed public health interventions. We utilize a suite of advanced molecular and computational tools to elucidate the drivers and dynamics of AMR propagation in sewage water, contributing to a deeper understanding of this under-explored but globally significant reservoir of resistance.

Results
Quantification of antibiotic contamination in urban sewage using QTRAP mass spectrometer
During June to December 2023, a total of 381 sewage samples were collected from six different states located across the country, including the national capital region (NCR) of India (Supplementary Fig. S1). A total of 102 sewage samples, collected across multiple rounds from various states, were analysed to quantify the concentrations of eleven antibiotics representing seven distinct drug classes using QTRAP mass spectrometer (Fig. 1). To ensure representative coverage across geography and time, 102 samples were selected from the larger set based on spatiotemporal diversity. The calibration curves generated using pure drug compounds were linear across the tested concentration ranges from approximately 0.1 to 500 ng/ml (Supplementary Data S1). The concentrations of tetracycline, amoxicillin, and spectinomycin in sewage were found to be substantially higher (>10 ng/ml) compared to other antibiotics. We observed the highest fluctuation of concentration in spectinomycin and amoxicillin in the sewage among all the eleven antibiotics, depending on the site and date of sample collection. The details of the regression equation, correlation coefficient (R2), limit of detection (LOD), and limit of quantification (LOQ) along with the concentration data of antibiotics generated in this study were included in Supplementary Data S1a–l. Among 11 antibiotics analysed, kanamycin (66.6%), azithromycin (55.8%), and spectinomycin (50%) were most frequently detected above LOQ, however ampicillin, polymyxin-B, and colistin were not detected. Detected concentrations ranged from 0.049 to 18.711 ng/mL, with tetracycline and spectinomycin showing the highest maximum values.

High-throughput monitoring of sewage resistome using dipstick-based molecular assay
Given the widespread presence of multiple antibiotics in sewage across the sites, we expanded our analysis to screen for ARGs (n = 16) using a culture-independent dipstick-based molecular assay7,8. Our findings revealed significant heterogeneity in the prevalence of ARGs across different samples (Fig. 2). Notably, six ARGs - blaTEM, catB, aac(6)-Ib, sul1, ermB and dfrA1 were present in more than 50% of the samples tested in this study. The lowest prevalence was observed for the oqxA, followed by blaKPC, and aadA1 ranging from (1 to 4%). Other prominent ARGs detected including mphA (50%), sul2 (45%), blaOXA (42%), blaCTX-M (39%), and qnrS (38%) (Supplementary Data S2). A comparative assessment employing QTRAP mass spectrometry for quantification of antibiotic residues and dipstick-based assays for ARG detection consistently demonstrated the co-occurrence of antibiotics, including azithromycin, amoxicillin, and kanamycin, with their respective resistance genes (mphA, ermB, blaTEM, blaCTX-M, blaOXA, and aac(6’)-Ib) across both platforms (Supplementary Data S3a).

Metagenomic investigation of urban sewage microbiomes diversity
A total of 220 sewage samples were analysed using metagenomic profiling. Sequencing of the V3–V4 region of the 16S rRNA gene yielded 16,679,255 high-quality reads. The number of non-chimeric reads ranged from a minimum of 25,762 to a maximum of 123,274 after quality filtering with an average read count of 75,815 ± 12,935.56 (Supplementary Data S4). We compared the sewage microbiome profiles between community settings and hospital outlets in Faridabad, Haryana using longitudinal samples collected over seven months. The study included five community sites Badkhal (n = 27), Ballabgarh (n = 27), Dabua colony (n = 27), Parvatiya colony (n = 26), and Sanjay colony (n = 26), and two hospital outlets: BK hospital (n = 21) and ESIC hospital (n = 18). Analysis of alpha diversity indices (Observed, Chao1, Shannon, and Simpson) revealed a significant difference between community and hospital sewage microbiomes in Haryana (BH adjusted p < 0.05) (Fig. 3a). Bray-Curtis distance matrices showed variation captured by 21.3% on the 3D Principal Coordinates Analysis 1 (PCoA1), 14.1% on the PCoA2, and 11.1% on PCoA3 in the ordination plot (Fig. 3b). To validate the patterns observed in the 3D PCoA, we also performed a non-metric multidimensional scaling (NMDS) analysis using Bray-Curtis dissimilarity. The NMDS plot revealed clustering patterns across the seven sites that were broadly consistent with the PCoA results, reinforcing the presence of temporal structure in the microbial communities (Supplementary Fig. S2a). The NMDS stress value was 0.136, indicating a good fit of the data in three dimensions. Permutational Multivariate Analysis of Variance (PERMANOVA) confirmed that Faridabad sites significantly influenced community composition in both analyses (R² = 0.111, p = 0.001). At the genus level, Aliarcobacter was notably elevated across all the collected sites, however, for ESIC hospital (6.3%), its relative abundance was lower compared to BK hospital (20.5%). In hospital outlets, the relative abundance of genera such as Aeromonas, Escherichia and Trichococcus were found to be increased, while Romboustia was lower compared to community settings. Other Gram-negative pathogens like Klebsiella, Acinetobacter, and Pseudomonas were prevalent in both environments (Fig. 3c). Specifically, at the species level, Trichococcus flocculiformis and Aeromonas veronii were more abundant in hospital sewage, while the abundance of Aliarcobacter cryaerophilus remained similar across both settings, with a slight decline at the ESIC outlet (Fig. 3d).

In this study, we collected community sewage samples from various Indian states, including Assam (n = 16), Jharkhand (n = 10), Uttar Pradesh (n = 10), West Bengal (n = 9), and Uttarakhand (n = 4). We then compared these datasets with community-level data from Haryana. Alpha diversity indices like Observed, Chao1, Shannon, and Simpson were calculated and compared across all six states. Haryana exhibited significantly higher alpha diversity than the other five states (Fig. 3e), indicating a richer bacterial diversity in its sewage microbiome. Specifically, the observed species ranged from 192 to 879, Shannon indices from 4.15 to 6.4, and Simpson indices from 0.975 to 0.99. Beta diversity analysis using 3D PCoA and NMDS (Stress-0.141) revealed distinct microbial community structures across sewage samples from six Indian states. The observed geographic clustering was statistically supported by PERMANOVA (R² = 0.188, p = 0.001), highlighting significant state-level variation. (Fig. 3f, Supplementary Fig. S2b). The species with the most assigned sequencing fragments include A. cryaerophilus and Aeromonas caviae across the states. Several other bacteria with known pathogenic potential, including Escherichia coli, Pseudomonas aeruginosa, Enterococcus species, and Acinetobacter baumannii, were also found to be prevalent in sewage samples collected from various states across India (Fig. 3g).

In our shotgun sequencing analysis, we sequenced the metagenomic DNA from a subset of 167 samples, 143 from community source and 24 from hospital outlets, to assess taxonomic composition and diversity metrics. Significant differences in alpha diversity indices (Chao1, Shannon, and Simpson) were observed between the community and hospital settings (Fig. 4a). Bacteroides graminisolvens, A. cryaerophilus and Aeromonas caviae were the most dominant taxa in sewage water across both sequencing strategies. Taxonomic analysis showed a notable enrichment of Aeromonas and Trichococcus in hospital sewage outlets, aligning with findings from amplicon sequencing data (Fig. 4b and c). Additionally, pathogenic bacteria such as E. coli, Klebsiella pneumoniae, various species of Aeromonas and Pseudomonas were commonly detected in sewage wastewater of community and hospital sources in both sequencing approaches (Fig. 4c).

Temporal variations in sewage microbiomes
Longitudinal sampling in Faridabad, Haryana from June to December 2023 revealed temporal shifts in the sewage microbiome. The clustering points in the 3D PCoA plot of each month indicates temporal variation in microbial community composition (R2 = 0.213, p = 0.001). Notably, samples from November and December showed a tendency to cluster separately, suggesting gradual temporal shifts in community composition toward the end of the sampling period. In contrast, samples from June and July were more diffusely distributed, though some degree of grouping was still apparent, pointing to greater variability or transitional dynamics. Samples from August, September, and October displayed moderate overlap, suggesting a period of more gradual change or mixing in community composition (Fig. 5a). These temporal trends were further supported by 3D NMDS analysis, which revealed comparable patterns of month-wise clustering (Supplementary Fig. S2c). Together, these patterns imply a temporal progression in sewage-associated microbial communities, with both distinct and transitional phases observed across the sampling timeline.

The alpha diversity of sewage microbial communities in Faridabad measured by Observed richness (Kruskal-Wallis, p = 1.81e-08), Shannon (p = 1.443e-09), and Simpson (p = 1.703e-09) indices, showed monthly variation. Diversity peaked from June to August and declined from September through December (Fig. 5b–d). We also examined how the abundance of bacterial taxa varied across different months. The relative abundance of A. veronii and T. flocculiformis significantly decreased from June to August, followed by a gradual increase starting in September, reaching higher levels by December. In contrast, Bifidobacterium pseudocatenulatum, Streptococcus equinus, and Bifidobacterium adolescentis were less abundant in July and August but showed an increase in September and October, followed by a decline in November and December. Aliarcobacter ellisi, Aliarcobacter faecis, P. aeruginosa, Tolumonas auensis, and Thauera selenatis were abundant in June through October, with their levels decreasing in November and December. Notably, Pseudomonas lundensis was completely absent from June to October but showed increased abundance in November and December (Fig. 5e).

To elucidate temporal patterns in microbial community structure, we performed a longitudinal, month-wise analysis of the sewage microbiome in Faridabad, with a specific focus on contrasting community and hospital sources, given previously observed significant differences between these settings (Fig. 3a). Month-wise analysis showed significantly higher microbial diversity in community sewage samples compared to the hospital sites. The Observed index was significantly higher in August, September, and October, while Shannon and Simpson indices indicated greater richness and evenness in July, August, and October (Wilcoxon rank-sum test, p < 0.05). No comparison was made for June due to the absence of hospital samples (Supplementary Fig. S3a–c). These findings were further supported by the 3D PCoA, which revealed a clear separation between community and hospital samples across the examined months, indicating distinct microbial community structures influenced by both sampling source and time (R² = 0.2872, p = 0.001) (Supplementary Fig. S3d). The 3D PCoA accounted for a total of 47.7% of the variance across the first three axes, underscoring its robustness in capturing community differences. Similarly, the 3D NMDS analysis echoed these patterns, further validating the temporal and source-based differentiation in microbial composition (Stress = 0.136, R² = 0.2872) (Supplementary Fig. S2d). Next, we analysed the variations in community sewage microbial profiles across different Indian states by comparing them to Haryana using samples collected in December, the month during which all six states were sampled. We observed that A. cryaerophilus was highly prevalent in Haryana, Uttarakhand, and Uttar Pradesh, but present at much lower levels in the other states. In contrast, A. caviae and A. veronii were enriched in Jharkhand and Assam, respectively. E. coli was found to be dominant in Uttarakhand, while its abundance remained low in the other states during the same month. These findings suggest that, despite uniform sampling time, microbial compositions varied substantially across geographic locations, highlighting the influence of region-specific environmental and anthropogenic factors on sewage microbiomes (Supplementary Fig. S3e).

The resistomes diversity and dynamics in urban sewage
We aimed to analyse the resistome profile in both the community and hospital outlets sewage water sites using assembled metagenomic shotgun sequences. A total of 403 resistomes were obtained across 167 samples collected from six Indian states (Supplementary Data S5). Out of these, 248 resistomes are associated with 893 taxonomically resolved metagenome assembled genomes (MAGs) out of 2214 bins. We identified a total of 170 distinct ARGs encoding resistance to 16 drug classes having enzyme mediated or target alteration mechanisms and 78 efflux genes, with varying abundances across the two different sites. The majority of the genes belonged to the beta-lactam, aminoglycoside, macrolide, vancomycin and sulphonamide classes. Notably, among all the 170 ARGs, mphE, msrE, mphA, and mphB from the macrolide class were the most common across the samples, while blaOXA-427, E. coli-blaampC, E. coli-blaampH, blaMOX-6, TEM, were prominent from the beta-lactamase class (Supplementary Data S6a). Additionally, qnrS1 and catQ from the fluoroquinolone and chloramphenicol classes were observed in multiple samples across community and hospital sites. mphE, msrE, qnrS1, and mphA were found at higher levels in community sewage sites, while blaOXA-427, dfrG, IsaE, ermT, qnrB4, blaOXA-12, tetM and tetQ were more prevalent in hospital settings. (Fig. 4d). We identified 78 efflux genes, with CRP being the most common across all samples, while the emrA, emrB, emrR, mexF and tolC gene was more prevalent in hospital settings (Fig. 4e, Supplementary Data S6b). A comparison of ARGs identified through shotgun sequencing and those detected using the dipstick-based assay revealed strong concordance between the two approaches for several ARGs such as blaTEM, blaKPC, blaOXA, dfrA1, sul1, and tetB (Supplementary Data S3b). Further, a correlation analysis was performed to evaluate the relationship between antibiotic residues detected through LC-MS/MS and the abundance of the corresponding resistance genes identified through shotgun metagenomic sequencing. The resulting plot revealed positive correlation between the macrolide antibiotic (azithromycin and erythromycin) residues and the abundance of mph and msr genes, nalidixic acid with qnr and oqxB genes, kanamycin and spectinomycin residues with aad genes, and amoxicillin residues with bla gene (blaOXA) (Supplementary Fig. S4).

Network analyses of bacterial species and their associated ARGs frequently uncover co-occurrence patterns, suggesting which bacterial species are likely to contain or share resistance genes. A network graph depicting bacterial species-ARG association showed clear separation according to species-level taxonomy (Fig. 6). Proteobacterial species such as K. pneumoniae, E. coli, P. aeruginosa, A. baumannii, Enterococcus faecium, and Enterobacter spp. harbored a variety of ARGs. Some ARGs like aac(3)-IId, aph(3”)-Ib, aph(6)-Id, qnrS1, blaTEM, mphA, mphE, msrE and sul2 are shared among them, while others are uniquely seen in their respective species. E. coli, in particular, have the highest number of assigned ARGs and shared many with other Gram-negative bacteria including K. pneumoniae, Proteus mirabilis, A. baumannii, P. aeruginosa, Enterobacter spp. and others (Fig. 6). We also identified colistin resistant genes mcr-3, mcr-3.8, mcr-5, and mcr-9 among several Gram-negative pathogens, including E. coli, Enterobacter mori, E. cloacae and A. veronii. Among Gram-positive bacteria, Enterococcus have the most diverse ARGs, which are shared with other Gram-positive species like Staphylococcus aureus, Bacillus paranthracis, and several Streptococcus species (Fig. 6).

Functional metagenomic analysis of the sewage samples has revealed several pathways potentially involved in the degradation of xenobiotic pollutants. We employed Illumina shotgun sequencing reads to investigate KEGG pathways involved in biodegradation across sewage samples from community and hospital sites in Haryana, extending the comparison to community sewage from other Indian states. A total of 20 KEGG categories associated with various xenobiotic compounds were identified, including aminobenzoate, atrazine, benzoate, caprolactam, chloroalkane, chlorocyclohexane, dioxin, ethylbenzene, furfural, naphthalene, nitrotoluene, polycyclic aromatic hydrocarbons, steroids, toluene, and xylene, along with drug metabolism pathways mediated by cytochrome P450. Among these, the drug metabolism via cytochrome P450 and benzoate degradation pathways were found to be dominant across most sites. In contrast, pathways such as atrazine degradation and chlorocyclohexane/chlorobenzene degradation are present at relatively lower abundances (Supplementary Fig. S5a). While the overall xenobiotic degradation profiles are largely similar between community and hospital samples in Haryana, region-specific differences were evident when comparing across states. Notably, the benzoate degradation pathway was highly enriched in Uttarakhand relative to other regions. In West Bengal, lower abundances of dioxin, atrazine, toluene, polycyclic aromatic hydrocarbon and xylene degradation pathways were observed, whereas drug metabolism and steroid degradation was comparatively enriched (Supplementary Fig. S5b). These findings underscore geographic variability in microbial functional potential for xenobiotic degradation, likely driven by environmental and anthropogenic factors. These variations highlight regional differences in the microbial capacity for xenobiotic degradation in sewage, potentially influenced by local environmental conditions, population lifestyle, industrial discharge, and pollution levels.

Antimicrobial resistance phenotypes of aerobic and anaerobic bacterial taxa isolated from urban sewage
Culturomics analysis of the samples identified a total of 964 bacterial isolates, encompassing both Gram-negative and Gram-positive from sewage water. The bacterial isolates include MDR strains with aerobic and facultative anaerobic metabolic traits. Among the Gram-negative bacteria, notable isolates include E. coli (n = 264), Morganella morganii (n = 127), P. mirabilis (n = 72), K. pneumoniae (n = 70), Enterobacter spp. (n = 22), Pseudomonas spp. (n = 18), and A. baumannii (n = 8). In contrast, the Gram-positive bacteria identified are mostly facultative anaerobes, with E. faecium being the most prevalent (n = 138) (Supplementary Data S7a).

All the aerobic isolates (n = 627) and a subset of facultative anaerobic isolates (n = 54) were subjected to susceptibility testing against 32 antibiotics from 10 different classes (Supplementary Data S7b & S8a). A majority of these isolates (93.8%; n = 639/681) demonstrated resistance to more than 10 antibiotics (Fig. 7a). Notably, Gram-negative ESKAPE pathogens especially K. pneumoniae, A. baumannii, P. aeruginosa and Enterobacter spp. exhibited resistance to multiple antimicrobial classes such as aminoglycosides, beta-lactams, fluoroquinolones, macrolides, and tetracycline. Among Gram-positive bacteria, most E. faecium isolates were resistant to most of the tested antibiotics, including erythromycin and vancomycin (Fig. 7b, Supplementary Data S8b).

In accordance with the WHO’s 2024 guidelines, the study isolates were categorized into critical and high-priority groups. All the A. baumannii study isolates were classified as critical priority pathogens (CPP), due to their resistance to at least one carbapenem antibiotic. Furthermore, among the tested enterobacterial isolates, 69.3% were found to be carbapenem-resistant which includes Enterobacter spp. (78.8%), Citrobacter spp. (72.7%), Proteus spp. (72.2%), K. pneumoniae (70.5%), Morganella spp. (87.8%) and E. coli (57.7%). However, 84.7% of these Enterobacterial isolates exhibited resistance to cephalosporins that are also categorized as CPP. Conversely, the high-priority pathogens identified in this study included vancomycin-resistant E. faecium, which accounted for 18.8% of the tested isolates, and carbapenem-resistant P. aeruginosa, with a concerning resistance rate of 88.8% (Supplementary Data S7c).

Genomic insights into MDR bacterial pathogens isolated from urban sewage
A total of 305 isolates cultured from wastewater and hospital sewage outlets were sequenced and analysed to understand their genetic relatedness and genetic signatures. These isolates belonged to 16 different genera and 31 different species; predominantly the Gram-negative Escherichia (n = 69), Klebsiella (n = 40), Morganella (n = 33), Providencia (n = 31), Citrobacter (n = 29), Proteus (n = 25), and the Gram-positive Enterococcus (n = 22). The overview of all the isolates is shown in the phylogenetic tree based on the 5S, 16S and 23S rRNA sequences (Fig. 8a, Supplementary Data S9a). Global phylogeographical analysis was done to understand the relatedness of the Indian isolates from the sewage and the global representative collection of clinical isolates including E. coli, K. pneumoniae, A. baumannii, P. aeruginosa, E. faecium, and the emerging pathogens M. morganii, P. mirablis, Citrobacter werkmani, and Providencia rettgeri from 2019 to 2023 (Supplementary Data S9b–j). The phylogenetic trees were constructed based on the core-gene SNPs (Fig. 8b–d, Supplementary Fig. S6a–f). The phylogeographical analysis of all the 2769 E. coli isolates (2700 clinical and 69 sewage) showed that these isolates were quite diverse (Fig. 8b) and widely distributed into eight different phylogroups (A, B1, B2, C, D, E, F, and G). However, the isolates obtained from sewage belonged to phylogroups A, B1, B2, C, D, and F and among which B1 (n = 22) and A (n = 20) were the most prevalent. Although B2 isolates were relatively more common in the global clinical cases, this phylogroup was not as prevalent in the Indian clinical cases or the sewage (n = 3). These sewage isolates belonged to 22 different STs/lineages, the predominant were ST167, ST410, ST448, ST648 and ST5869. A few of these isolates that belonged to ST167, ST2083, ST410 and ST448 clustered with the Indian clinical isolates.

The phylogeographical analysis of all 1314 K. pneumoniae isolates (1281 clinical and 33 sewage) showed these isolates to be less diverse and distributed into distinct clades (Fig. 8c). Although the study isolates are distributed on different clades, these isolates clustered closely with the Indian clinical isolates. The most prevalent STs in sewage isolates were ST15, ST37, and ST16 which were also observed to be widely isolated from clinical cases in the US, East and South-East Asia. Although not prevalent, ST11, ST101, ST231 and ST147 found in sewage isolates were also observed in Indian clinical cases. The phylogeographical analysis of 697 A. baumannii isolates (689 clinical and 08 sewage) showed that the isolates are clonal (Fig. 8d). These sewage isolates belonged to ST2 which was found to be the prevalent circulating ST in the clinical settings of India and also in global cases. The P. aeruginosa isolates (774 clinical and 06 sewage) segregated into different clades and the study isolates clustered with the clinical isolates from India, East Asia and North America (Supplementary Fig. S6a). The ST357, one of the dominating STs in the clinical cases of India and East Asia was also observed in sewage. In the case of E. faecium, the isolates (639 clinical and 21 sewage) were quite diverse on the phylogenetic tree (Supplementary Fig. S6b). The most prevalent ST in sewage isolates was ST80 which was also observed to be prevalent in both Indian and global clinical settings. Although most of the study isolates were positioned adjacent to the clinical isolates from Europe and East Asian countries on the tree, few of these sewage isolates clustered with the Indian clinical isolates. The M. morganii isolates (196 clinical and 33 sewage) were clonal forming distinct clades with most isolates clustering with clinical isolates from East Asia, Europe, Oceania and South America (Supplementary Fig. S6c). The P. mirabilis sewage isolates are segregated to different clades clustering with clinical isolates from North America (Supplementary Fig. S6d).

Genetics of antibiotic resistance traits of MDR bacterial pathogens
The analysis revealed a wider range of ARGs conferring resistance to multiple classes of antibiotics, some of which were species-specific while others were shared among different bacteria. Morganella, Citrobacter and Aeromonas showed the presence of limited ARGs when compared with other Gram-negative pathogens such as, Escherichia, Enterobacter, Pseudomonas, Klebsiella and Acinetobacter spp. The resistance encoded was either through enzymatic inactivation, target alteration or antibiotic efflux (Fig. 9, Supplementary Figs. S7–S8). Majority of ARGs encoded resistance to beta-lactams. The blaADC, blaSHV, blaOKP, blaPDC, blaCPH were specific to A. baumannii, K. pneumoniae, Klebsiella quasipneumoniae, P. aeruginosa, and A. veronii, respectively, with almost all the genomes of these species having the specific ARGs. Although not specific to M. morganii, blaDHA was found to be quite prevalent in M. morganii with the presence of one or the other allele in its genome. Except in Enterococcus, the Extended-spectrum beta-lactamase (ESBL), blaOXA was found in all the species. However, the allele varied from species to species, some being organism-specific, such as blaOXA-66 and blaOXA-23 were specific to A. baumannii, blaOXA-50 to P. aeruginosa, and blaOXA-12 to A. veronii. Similarly, the carbapenemase blaNDM-1 was observed in Providencia, Pseudomonas, Klebsiella and Enterobacter, while blaNDM-5 was observed only in E. coli. The most prevalent beta-lactamase genes in E. coli were the ESBLs blaTEM (n = 37/69), followed by blaCTX-M (n = 23/69). Intriguingly, none of the Enterococcus isolates showed the presence of any beta-lactamase genes (Fig. 9, Supplementary Data S10a–i).

The aminoglycoside modifying enzymes (AMEs) viz. aph(3”)-Ib or strA and aph(6)-Id or strB were quite prevalent in Acinetobacter, Pseudomonas, Proteus, Escherichia and Klebsiella. Interestingly, Proteus genomes have more of AMEs compared to other ARGs. While armA was prevalent in A. baumannii, rmts were observed in Pseudomonas and Enterobacterales. The aac(6’)-Ie-aph(2”)-Ia complex and ant(6)-Ia were observed only in E. faecium. The allele aac(6’)-Ii was specific to the Gram-positive Enterococcus and was present in almost all of the E. faecium (n = 20/21). For resistance to macrolides, the msrE and mphE were prevalent in the non-fermenters such as Acinetobacter, Pseudomonas and Enterobacter spp. The mphA was observed more in E. coli and Klebsiella, while mphB was detected in nearly all E. coli (n = 66/69). The msrC was specific to E. faecium and present in almost all isolates (n = 19/21). Resistance to tetracyclines was mostly encoded by efflux genes and amongst tetA and tetB were the most prevalent ones. All the 8 Acinetobacter isolates had the tetB and the adeABC and the adeFGH efflux system genes for resistance towards tetracyclines. The tetE is specific to Aeromonas, tetG to Pseudomonas, tetJ to Proteus, and tetL, tetM and tetU to Enterococcus. Several efflux genes, including those encoding multidrug efflux proteins, were identified in the isolates, with many being species-specific. E. coli had the highest number of efflux genes, followed by P. aeruginosa where nearly all isolates carrying Pseudomonas-specific mex, mux, opm, opr, triA, triB and triC genes confer resistance to multiple drug classes (Fig. 9b). Genes providing resistance to trimethoprim (dfr) were harbored by all the species, with dfrA1 being the most prevalent, majorly observed in Providencia, Proteus, Morganella, and Escherichia. Except for Enterococcus, genes encoding resistance to sulfonamides (sul1 and sul2) were observed in all the species. Enterococcus on the other hand, showed resistance to vancomycin (van genes) (Supplementary Fig. S7).

Comparative analysis of the genomes from sewage pathogens and those from the global clinical isolates revealed notable similarities (Supplementary Data S9a–j). The ESBL blaCTX-M-15 was the most prevalent allele of the blaCTX-M genes in Escherichia, Klebsiella and Morganella in the sewage as well as clinical isolates. While blaNDM-5 was the most prevalent allele of metallo-beta-lactamase in Escherichia in clinical cases and wastewater, blaNDM-1 was the most prevalent allele in Klebsiella and Morganella from clinics and wastewater. The cephalosporin resistant blaDHA-20 was quite prevalent in the clinical and sewage Morganella isolates. However, blaDHA-1 was almost equally prevalent in wastewater, this was not as frequently observed in the clinical cases. Likewise, the blaDHA-17 that was the most prevalent beta-lactamase in the Morganella isolates from clinics, this allele was completely absent in the Morganella from wastewater. Strikingly, the prevalence of strA and strB genes in the sewage samples were identified to be 50% more as compared to the clinical samples. While vanA was the prevalent gene responsible for vancomycin resistance in the global isolates followed by vanB gene, the Indian clinical isolates exclusively had only vanA gene. Interestingly, this was also reflected in the sewage isolates and they only carried the vanA gene.

Genetic linkage of antibiotic resistance genes with mobile genetic elements
Many of these ARGs were found to be associated with MGEs such as, transposons, insertion sequences (IS), and miniature inverted-repeat transposable elements (MITEs). A total of 187 out of 305 genomes showed the association of one or more ARGs with MGEs and IS elements were identified to be the most abundant (Fig. 10a). The species with most number of isolates having ARGs linked with either IS elements or transposons are E. coli (n = 61/69), followed by K. pneumoniae (n = 25/33), M. morganii (n = 20/33), P. mirabilis (n = 14/25), A. baumannii (n = 07/08) and P. putida (n = 06/06). The IS6100 was the predominant IS element found in the study isolates. The most common ARGs that were linked with MGEs are aph(3”)-Ib, aph(6)-Id, ant(3”)-IIa, the dfr genes and the sul genes (Supplementary Data S10j). While in most of the cases the MGEs linked with these ARGs were different across the species, some ARGs were linked with the same MGE across the species. For example, Tn7 is associated with ant(3”)-IIa and dfrA1 in P. mirabilis and M. morganii and blaCTX-M-15 is linked with ISEc9 in E. coli, K. pneumoniae and M. morganii implying inter-species movement of the ARGs by MGEs. In a few isolates of E. coli and M. morganii, the chloramphenicol resistance genes (catI and catB3) were linked with Tn6196. In E. coli, ARGs such as mphA, sul1, blaDHA-1, qnrB4, aadA5 and the dfr genes were associated with IS6100 but in P. putida, IS6100 is associated with mphE, msrE, aph(3”)-Ib and aph(6)-Id. In majority of E. coli isolates that showed linkage of strA, strB and blaTEM-1 with a MGE, the MGE has been IS1133 and/or IS5075 but in A. baumannii, the MGE associated with these two AMEs is ISvsa3. The Gram-positive Enterococcus harbored a completely different set of IS elements and transposons as compared to the Gram-negative species (Fig. 10a).

Further, these isolates were screened for the presence of acquired plasmids through the PlasmidFinder database. The database identified the presence of acquired plasmid genes in Klebsiella, Escherichia, Proteus, Enterobacter, Citrobacter and Enterococcus (Supplementary Data S10k). However, very few isolates showed the presence of ARGs in these plasmids. The beta-lactamase genes blaOXA-181, blaOXA-232, blaOXA-484, and blaCTX-M-15 were associated with the ColKP3 plasmid in E. coli which also harbored many Inc plasmids that carried AMEs such as aph(3”)-Ib, aph(6)-Id and aph(3’)-Ia. Apart from E. coli, a few isolates of Klebsiella, Citrobacter and Enterococcus showed the presence of ARGs with plasmids. Other ARGs carried on Inc plasmids were sul2, tetA, blaCMY-59, and blaTEM-1 (Fig. 10b). IntegronFinder identified integrons in all the species. In many isolates of Acinetobacter, Aeromonas, Citrobacter, Enterobacter, Escherichia, Klebsiella, Morganella, Proteus, Providencia, and Pseudomonas, ARGs were located within these integrons (Supplementary Data S10). Most of these ARGs encoded AMEs such as aadA2 and ant(3”)-IIa, trimethoprim-resistance dfr genes, and the rifamycin-resistance arr-2 gene (Fig. 10c).

Methods
Sewage sampling and measurement of antibiotic concentration using QTRAP mass spectrometer
Wastewater samples (n = 332) were collected from seven different sites across Faridabad, Haryana (North India) including both hospital (n = 82) and community sewage outlets (n = 250) for a duration of seven months from June 2023 to December 2023. Apart from Faridabad, samples (n = 49) were also collected from other parts of India including Assam (North-eastern state), West Bengal & Jharkhand (Eastern states of India), Uttar Pradesh, and Uttarakhand (Northern states of India) (Supplementary Fig. S1). Wastewater samples were collected from publicly accessible sites, and the local authorities were informed prior to sampling. The details of all the sampling sites along with the geographic locations and coordinates are provided in the Supplementary Data S11. The untreated wastewater samples were collected in sterile bottles by grab sampling method during morning hours and immediately transported to the Functional Genomics Laboratory, THSTI, Faridabad, India on ice for further processing44. On reaching the laboratory, the collected 200ml sample was mixed thoroughly and 30ml from it was collected in 50ml sterile falcons and centrifuged at 8000 rpm for 15 min at 4°C. The supernatant was then filtered through a 0.22 µM Millex GV syringe filter (Merck, Darmstadt, Germany) and stored at −80°C for subsequent LC-MS/MS analysis to detect antibiotic residues in sewage water45. Further, the pellet was carefully resuspended in 1.5ml nuclease free water and 100 µl were inoculated in culture broths and incubated both aerobically and anaerobically for culturomics study. From the remaining 1.3ml sample, 1ml was used for metagenomic nucleic acid extraction and subsequent 16S metagenomic sequencing, dipstick assay and shotgun sequencing.

The Sciex Exion LC system paired with the QTRAP 6500+ MS was used for LC-MS/MS analysis of antibiotic residues in sewage water. Samples were filtered through a 0.22 µm Millex GV syringe filter (Merck, Darmstadt, Germany) before analysis. Separation was performed on an Acquity UPLC BEH C18 column at 40°C with a gradient elution of 0.1% formic acid in water (A) and acetonitrile (B), ramping B from 15 to 90% in six minutes at 0.2 mL/min. For electrospray ionization, electrospray voltage used was +5500 V, with a source temperature of 500°C. Transition ranges were determined by initially injecting each commercially purchased pure antibiotic into the LC-MS/MS. One transition per antibiotic with the sharpest peaks was selected for identification purposes and ANALYST 1.7.2 software was used for data analysis46. The method performance characteristics, including the linearity, LOD, LOQ, and coefficient of correlation (R2) were evaluated. Standard calibration curves were obtained using eight different concentrations of each antibiotic across the linear range and was considered acceptable if the coefficient of correlation was more than 0.98 (Supplementary Data S1)47. The LOD and LOQ values, for all the eleven antibiotics, were determined as the concentrations corresponding to signal-to-noise (S/N) ratios of 3:1 and 10:1, respectively18,48.

Isolation of sewage metagenomic DNA and screening of antibiotic resistance genes by dipstick molecular assay
Metagenomic DNA was extracted from sewage samples using the THSTI DNA extraction method49 which includes a series of chemical, physical and mechanical actions to disrupt the cells and release the genomic DNA. The precipitated genomic DNA was washed and resuspended in nuclease-free water followed by resolving the sample in 0.8% agarose and purity check using the Nanodrop One Spectrophotometer (Thermo Fisher Scientific, Wisconsin, USA) (Supplementary Data S4b).

Purified metagenomic DNA was used as a template to detect the presence and prevalence of ARGs in sewage water. Sixteen emerging resistance determinants (Supplementary Data S2) representing different drug classes commonly used in hospital settings or are highly prevalent among bacterial pathogens, were selected for dipstick based detection analysis. Oligonucleotides were initially validated in silico using Clone Manager (Sci-Ed Software), NCBI BLAST and Primer-BLAST to ensure target specificity and to exclude significant cross-hybridization with non-target sequences. Parameters such as secondary structure, primer-dimer formation, and melting temperature compatibility were also evaluated prior to in-vitro testing. Candidate primers were then validated in singleplex and multiplex PCR, and representative amplicons were confirmed by Sanger sequencing to ensure specificity. For dipstick assay, either the forward or reverse primer of each validated set is labeled with a single-stranded tag-linker sequence, complimentary to its respective probe imprinted on the dipstick for hybridization, while the paired primer was labeled with biotin to enable binding with streptavidin-coated latex beads (Tohoku Bio-array, Sendai, Japan). Subsequently, the tagged and biotinylated oligos were used to standardize the multiplex PCR assays for dipstick analysis. Standardized m-PCR assays were translated into C-PAS (4) dipstick test strips (TBA Co., Ltd, Sendai, Japan), in which diluted amplicons were combined with developing solutions containing streptavidin-coated blue-colored latex beads. Immersion of the dipstick in the resulting solution generates a blue color line, indicating the detection of target genes.

16S rRNA gene and shotgun sequencing of sewage metagenome
The metagenomic DNA of the microbiome was amplified for the targeted metagenomics of the 16S rRNA specific V3 to V4 region. The nucleoside nomenclature of the overhang (shown in italics) holding primers for targeting the amplification sites are: Forward Primer: 5’TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG3’, Reverse Primer: 5’GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC3’. The amplicons were cleaned up with the Agencourt AMPure XP paramagnetic beads (Beckman Coulter). Samples were subjected for the library preparation with the Nextera XT Index Kit V2 (Illumina, California, US). The cleaned-up libraries were quantified with Qubit Flex Fluorometer (Thermo Fisher Scientific, Massachusetts, US) and verified for quality analysis with TapeStation 4200 (Agilent, California, US) automated electrophoresis system. Qualified libraries have been normalized to 4 nM concentration and a final concentration of 700 pM pooled library was sequenced in the Nextseq 2000 sequencing platform (Illumina, California, US) with paired end 2×300 cycle chemistry.

For the shotgun sequencing the metagenomic DNA samples were quantified with the Qubit dsDNA BR Assay Kits and HS Assay kits (Thermo Fisher Scientific, Massachusetts, US) in the Qubit Flex Fluorometer. A good quantity of samples with 300ng were tagmented to short fragments using the bead-linked transposome (Illumina, California, US). The library was prepared using the washed fragments with the Illumina DNA prep kit and IDT-Illumina DNA/RNA UD indexes (Illumina, California, US). The library was cleaned up following left-side and right-side size selection double clean up method with the Agencourt AMPure XP paramagnetic beads (Beckman Coulter, California, US) and the library quantity was determined. The TapeStation 4200 (Agilent, California, US) has been used to check the bell-shaped curve formation in the electropherogram of the libraries. Presence of any impurities was cleaned up using the SPRIselect beads (Beckman Coulter, California, US). The libraries were normalized to 4 nM concentration and pooled for sequencing at a final concentration of 750 pM in the Nextseq 2000 sequencing platform (Illumina, California, US). The sequencing chemistry used the paired end 2×150 cycles chemistry in the P2 flow cell (Illumina, California, US) and the data generated for 16s metagenome & shotgun data has been submitted to NCBI database (Bioproject ID: PRJEB81436).

Analysis of bacterial diversity and antibiotic resistance genes in the sewage metagenome
Demultiplexed FASTQ files for Read 1 (R1.fastq) and Read 2 (R2.fastq) of each sample were subjected to initial quality control based on the Fastp (version 0.20.1) reports generated. The trimmed reads were processed using DADA2 package50 (Version 1.16) in R (Version 4.4.1) to generate amplicon sequence variant (ASV) generation. For species-level classification, the NCBI 16S Microbial database was used with blaSTn for alignment51. Only representative sequences with >98% sequence identity in BLAST were selected for species-level annotation. For shotgun data, raw reads were processed and trimmed and the resulting reads were subsequently aligned to the human reference database (GRCh38) using HISAT2. The reads that remained unaligned after HISAT2 were concurrently subjected to taxonomic classification using Kraken 252 and assembled with SPAdes (version 3.13.0) in meta mode53. Bacterial reads were extracted selectively via a custom bash script after Kraken 2. The bacterial diversity within the sewage metagenome was assessed using the phyloseq package in R. Metagenome binning for each sample was carried out with MetaBAT254. Metagenome-assembled genomes (MAGs) were screened for ARGs using the abricate tool with the CARD database, applying default settings (>80% identity, >80% coverage). The contigs with CARD hits were taxonomically classified with Kraken 2, and taxa were evaluated from species to phylum levels. The contigs/scaffolds that met the following criteria were selected for network analysis of bacteria and ARGs: (1) classified as belonging to the superkingdom Bacteria by Kraken 2, (2) contained an ARG as identified by the CARD tool, and (3) had a Kraken 2 taxonomic classification at the species level. Bacteria and ARG nodes connected in the network were visualized using Cytoscape. Additionally, the MAGs were queried against KEGG Orthology (KO) IDs, Clusters of Orthologous Groups (COG), and Gene Ontology (GO) using EggNOG-mapper, with parameters set for >90% identity and >80% coverage55.

Isolation, identification and cultivation of drug-resistant aerobic and facultative anaerobic bacterial species
The sample resuspended in enriched medium, was inoculated into Mueller-Hinton and Brain Heart Infusion (BHI) broth, under selective pressure from five different antibiotics: ampicillin (100 µg/ml), streptomycin (100 µg/ml), nalidixic acid (10 µg/ml), azithromycin (15 µg/ml), and tetracycline (10 µg/ml). Samples demonstrating optimal growth were subsequently processed for culturomics analysis. This involved further culturing on MacConkey and BHI agar to isolate both aerobic and anaerobic organisms. Isolates were identified using 16S rRNA gene Sanger sequencing, and the results were analysed using NCBI database.

Antimicrobial susceptibility testing of bacterial isolates
The susceptibility profiles of a total of 681 aerobic and facultative anaerobic isolates were assessed using the Kirby-Bauer disc diffusion method against a panel of 32 commercially available antibiotic discs. The interpretation of results adhered to the latest guidelines from the Clinical and Laboratory Standards Institute (CLSI) and The European Committee on Antimicrobial Susceptibility Testing (EUCAST)56,57. Reference strains, including E. coli ATCC 25922, A. baumannii ATCC 17978, and K. pneumoniae ATCC 700603, were employed to ensure the accuracy of the experiments. The specific details on antibiotic concentrations and bacterial isolates tested are provided in the Supplementary Data S7.

Whole genome sequencing and bioinformatics analysis
The bacterial isolates confirmed with the Sanger sequencing (Applied Biosystems Genetic Analyzer 3500) were subjected for the whole genome sequencing (WGS). Library preparation involved tagmentation with in vitro transposition resulting DNA fragments. The transposed fragments readily bind to the index adapters (Nextera XT Index kit V2, Illumina). After indexing, the library pool was sequenced at 750 pM following the shotgun sequencing protocol. The raw files were processed using the seqtk tool v1.3-r106 and then merged using PEAR (Paired-End read merger) software. The SPAdes pipeline was employed to assemble the genome from the cleaned paired-end reads53. The quality of the assembled genomes was evaluated using CheckM (version v1.1.3)58. Only genomes meeting the criteria of >90% completeness and <5% contamination were included for subsystem analysis. Annotation was carried out using PROKKA v1.14.6. The whole genome sequences of the bacterial isolates are submitted to the NCBI database (Bioproject ID: PRJEB81436).

Multilocus sequence typing
The sequence type (ST) of the genomes was identified using the MLST finder (https://github.com/tseemann/mlst). The Achtman scheme was used for E. coli (adk, fumC, gyrB, icd, mdh, purA, recA), the Pasteur scheme was used for A. baumannii (cpn60, fusA, gltA, pyrG, recA, rplB, rpoB) and the PubMLST scheme was used for K. pneumoniae (phoE, mdh, tonB, gapA, rpoB, pgi, infB), P. aeruginosa (acsA, areE, guaA, mutL, nuoD, ppsA, trpE) and E. faecium (adk, atpA, ddl, gdh, gyd, pstS, purK)59.

Phylogenetic analysis
The genomes of the study isolates were mapped against the consensus sequences of the multiple copies of the 5S, 16S, 23S rRNA genes of the NCBI reference sequences using Snippy v4.6.0 (https://github.com/tseemann/snippy). For global phylogeographical analysis, the whole genome assemblies of A. baumannii (n = 689), E. coli (n = 2700), K. pneumoniae (n = 1281), P. aeruginosa (n = 774), M. morganii (n = 196), P. mirabilis (n = 513), P. rettgeri (n = 132), C. werkmanii (n = 22), and E. faecium (n = 639) from clinical specimen (Homo sapiens) submitted during 2019–2023 from across the globe were downloaded from NCBI. Only the genomes that passed CheckM criteria with >90% completeness and <5% contamination were included for the analysis58. Alignment of core gene SNPs was generated using Snippy v4.6.0. The maximum likelihood phylogenetic trees were constructed by RAxML v8.2.12 using the GTRGAMMA model60. The trees were visualized using iTOL and annotated with metadata such as year of isolation, geographic location, and sample source61. Clermontyping v20.03 was used to type the E. coli genomes into phylogroups62.

Identification and analysis of antibiotic resistance genes and mobile genetic elements
The CARD database and the PlasmidFinder in the ABRicate tool v1.0.1 were used to predict the AMR genes and plasmids respectively, in the isolates (https://github.com/tseemann/abricate). For all the databases, a filtering criterion of 90% identity and 80% gene coverage was used. The MGEs were identified using the MobileElementFinder v1.1.2 that integrates data from ICEberg, ISFinder and the Tn repository63. The IntegronFinder v2.0.5 was used to identify the presence of integrons in the isolates64.

Statistical analysis
Alpha diversity indices were computed using the ‘estimate_richness’ function from the phyloseq R package. Beta diversity was assessed using Bray-Curtis dissimilarity matrices computed from normalized count data. Principal Coordinates Analysis (PCoA) was conducted in three dimensions using the PCoA() function from the ape package, while Non-metric Multidimensional Scaling (NMDS) was performed using the metaMDS () function from the vegan package. Statistical differences in microbial community composition across sample groups were evaluated using permutational multivariate analysis of variance (PERMANOVA) implemented via the adonis () function in vegan. All analyses were conducted in R (version 4.4.1). Alpha diversity differences among the groups were assessed using the Kruskal Wallis test, followed by Dunn’s post hoc tests with Benjamini-Hochberg (BH) correction for multiple comparisons and boxplots were generated using the ggpubr R package. For pairwise comparisons between two groups, the Wilcoxon rank-sum test was applied. Statistical analysis and microbial composition comparisons among groups were performed using the ampvis2 and ggplot2 R packages. Correlation analysis was done using Spearman correlation in R environment. A p-value of 0.05 or less was considered statistically significant.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors have undertaken a significant study detailing the antibiotic contamination and antimicrobial resistance dynamics that are present in urban sewage from India. The methodology is exhaustively documented and adequate and appropriate QA/QC protocols have been followed. The authors have painstakingly completed a humongous sampling and analytical exercise and must be applauded for their perseverance. However, I have the following concerns that need to be addressed before the manuscript can be deemed ready for publication.\n1. The authors have undertaken the study in six different Indian cities and collected a total of 371 sewage samples. Out of this 100 have been chosen for the antibiotics analysis. Further clarification on the rationale behind the selection of these 100 samples from the 371 samples must be provided by the authors.\n2. Lines 508 to 512 - The authors mention that 332 (250 sewage + 82 hospital) samples were collected from the city of Faridabad and apart from that 39 samples were collected from other parts of India. Details are requested on the sample spread of the 39 samples taken from the other parts of India, including the date of sampling and their coordinates.\n3. A table needs to be provided detailing the geographical location of each of the sampling points along with sampling dates and number of samples collected from each of the sampling location to clarify the sampling spread and whether the coverage is adequate for a longitudinal study. Rationale or justification for Faridabad having the majority of the sampling locations must be provided by the authors and included in the main text.\n4. A spatial map indicating the geographical spread of the sampling locations is essential for informing the readers about the study’s extent of coverage. Both of the above can be included in the supplementary information.\n5. Lines 522 & 523 – The authors mention pure antibiotic but fail to mention the manufacturer and the purity of the standards used for their instrumentation. Details must be provided for readers benefit and for reproducibility.\n6. The figures pack too much information and as a result, the text has become unreadable. Revised higher quality images are requested from the authors with larger and legible fonts in the images for better readability.\nKeeping in view all the above comments, I recommend the manuscript undergo a major revision, especially in terms of addressing the concerns about the sampling spread and also figures requiring significant revision.", "reviewer_2": "The manuscript titled “Antibiotic Contamination and AMR Dynamics in the Urban Sewage Microbiome: Insights from a Longitudinal surveillance” gives a comprehensive analysis of the presence of antibiotic residues, antimicrobial resistance genes and the resistant pathogens in urban sewage in six cities in India. Though the study uses a multi-pronged approach that includes mass spectrometry (QTRAP), dipstick-based molecular assay, 16S rRNA metagenomics, shotgun metagenomics, and bacterial isolate analysis to decipher the AMR scenario in six Indian cities, it fails to provide the direct statistical corelation/ comparison of these parallel approaches. A systematic comparative analysis of these approaches combined with statistics could have provided a strong evidence of the selection pressures driving AMR in the wastewater and could have improved the impact of the study further.\nSpecific Comments\n- In the introduction, the authors should cite the earlier work done on metagenomic surveillance of urban wastewater for AMR and resistant pathogens in India and globally.\nMK Madhukar, N Singh, VR Iyer, DT Sowpati, KB Tallapaka, RK Mishra, SC Moharir, Antimicrobial resistance landscape in a metropolitan city context using open drain wastewater-based metagenomic analysis, Environmental Research Volume 252, Part 1, 1 July 2024, 118556, https://doi.org/10.1016/j.envres.2024.118556\n- Figure numbers are not put on the Figures. This makes it confusing to review the figures.\n- The labelling in Figure 1 should match with the description in the respective figure legend (line no 707- 710). It is not clear how the significance is calculated- what is the control group with which comparison is made to calculate the p value using t test.\n- Line 104 to 108- what was the rationale behind selecting the 100 samples out of the 371? What criteria was applied for selecting those 100 samples?\n- Line 111- the concentration of amoxicillin is not higher than 15 ng/ ml. Needs to be corrected to >10ng/ml.\n- In Figure 2, adding city names in front of sample IDs on Y axis will be more informative.\n- Line 117- the term ‘longitudinal’ is misleading in this context. If the authors want to use the term here, they should show the data of each of the sampling location for the multiple time points. The word ‘longitudinal’ in the title of the manuscript is also misleading.\n- Are the same 100 samples used for QTRAP Mass Spectrometry, used for dipstick-based molecular analysis? If yes, then proper comparative analysis should be performed.\n- Line 131- 132- Mention the mean and standard deviation for number of reads. This will give a sense of the distribution of number of reads across the samples.\n- Line 171- since sampling was done only once from June to December, all the seasons are not covered. Mentioning it ‘seasonal’ variations is not appropriate. Inference about seasonality should be removed throughout the manuscript.\n- Line 172- The microbial diversity varied across different locations (line 133- 143). In that case, pooling the data from these 6 locations for each of the months to analyse the temporal patterns is not appropriate.\n- Line 441- 442- comparative analysis of dipstick data and shotgun metagenomic data should be performed.\n- Line 508- Sewage samples were collected from ‘seven’ cities or ‘six’ cities? At all other places ‘six’ cities is mentioned.\n- Line 507-510- more details on sampling needed. Were the samples collected in replicate for each sampling site for each time point? What was the sample collection method- grab sampling or composite sampling? What measures were taken to ensure that the sampling was unbiased and representative for that site for a particular timepoint? Was 30 ml sample enough to capture the complete picture? More details on sample processing before DNA extraction are needed. Was the sample processed/ concentrated? If yes, what method was used for sample concentration? What was the yield of the DNA? References are needed to support the methodology.\n- Since dipstick assay was standardized in this study only, a comparison with a gold standard assay like RT-PCR would be required to ensure that the assay is efficient and sensitive. Data to support the sensitivity and specificity of the dipstick assay should be provided. The advantage of the assay over RT-PCR and the pros and cons with respect to the sensitivity, specificity and ease of use should be discussed.\nThe study provides insights into the AMR scenario in the urban wastewater through a multi-prong approach. The utilization of chemical, molecular, microbiological and bioinformatic techniques provide a comprehensive picture of AMR scenario. However, the study could have utilized the shotgun metagenomics more effectively to identify the non-bacterial AMR contributors, correlated antibiotic concentrations with ARG abundance, and critically evaluated the methodology. Also a detailed and systematic analysis for at least one year could have provided insights about the seasonal and longitudinal patterns of AMR and pathogens in wastewater.", "reviewer_3": "General comments\nPaul et al. present valuable insights into antibiotic contamination and antimicrobial resistance (AMR) dynamics in the urban sewage microbiome. This well-structured study highlights sewage as a significant reservoir of AMR, integrating data on antibiotic concentrations (11 antibiotics from 7 different classes), 16S rRNA gene sequencing, and ARG richness. Notably, the authors have developed a dipstick-based assay for ARG detection, adding a practical and innovative dimension to the study.\nBy employing both culture-based and sequencing approaches alongside antibiotic analysis, the study provides a comprehensive assessment of AMR in urban sewage. The data are thoroughly analyzed and effectively summarized in well-constructed figures, with ARG quantification and associations with taxa clearly depicted through network analysis.\nMoreover, data from Low- and Middle-Income Countries (LMICs) on this topic are limited, and this study significantly contributes to the understanding of Environmental AMR in these regions. This study aligns with the One Health Approach for AMR. The inclusion of such data would greatly enrich the ongoing global dialogue on AMR and its management in resource-constrained settings. In addition, this study reflects the emerging trend of wastewater-based AMR surveillance, which is gaining traction globally as a cost-effective and comprehensive method for monitoring AMR in urban environments.\nHowever, while the study is robust and methodologically sound, the Introduction and Discussion sections require significant improvement to better contextualize the findings and enhance their broader relevance. Strengthening these sections would further elevate the impact of this important work. Additionally, the dipstick-based assay should be validated to ensure its reliability. Given the emerging importance of wastewater-based epidemiology, it would be fitting to publish the dipstick-based assay as a standalone methods paper rather than including it as a section under methods in the current manuscript. Furthermore, the sample volume collected for analysis is not convincing, and additional clarification or justification is needed for the approach taken.\nThe manuscript may be accepted after major revision, with the suggested improvements addressed to enhance clarity and the overall impact of the study.\nThe specific notes below may provide further clarification, and I hope they are useful to the authors for the revision.\nSpecific comments\nIntroduction:\nIn my opinion, the Introduction can be improved to better explain the need for the study, particularly since the authors imply in the Abstract that they have developed a new assay for the detection of ARGs. However, the current introduction does not adequately address this, and it remains more general in nature. It would benefit from clearer framing to set the premise for the readers, highlighting the significance and novelty of the assay in the context of existing research. Additionally, sewage-ARG related papers from the region have not been cited and incorporating them would provide important context for the study and bring out the novelty of the current study.\nMethods:\nComment 1. The study site should be described in more detail to provide context for the research. This description should include relevant environmental factors and characteristics that may influence the results. Additionally, a map of the study site should be included to visually assist readers in understanding the sampling locations. To enhance transparency and reproducibility, I recommend providing the GPS coordinates of the sampling sites, along with any relevant metadata, in the supplementary materials. This will help to contextualize the study and ensure that others can replicate or build upon the work.\nSewage collection and measurement of antibiotic concentration using QTRAP mass spectrometer:\nComment 2. Were the samples pooled? The volumes don’t add up.\nComment 3. 514 on ice is repeated twice.\nComment 4. The 30 mL sample volume for environmental samples in the antibiotic analysis is relatively low and may not align with the standardized methods typically used in environmental monitoring studies. The article cited worked with spiked water samples, which have different characteristics compared to natural environmental samples. In my opinion, using such a small sample volume for environmental samples may not capture the full range of antibiotic concentrations, leading to less reliable results. A larger sample volume would likely provide more accurate and representative data for antibiotic analysis in environmental settings.\nComment 5. What was the volume of the sample filtered for mass spec analysis?\nIsolation of sewage metagenomic DNA and screening of antibiotic resistance genes by dipstick based assay:\nComment 6. How much volume was used for DNA extraction? The method cited mentions using 35 mL of water for DNA extraction, but if only 30 mL were collected, it seems inconsistent. Could you clarify? See comment 2.\nComment 7. How much volume was used for plating?\nComment 8. Provide the DNA conc. in ng/uL. Since a standard DNA extraction method was not used, it would be necessary to include the gel images and/or DNA purity in the supplementary to support the quality of the extracted DNA.\nComment 9. Mention the type of purity measurement (Nanodrop or Qubit).\nComment 10. What is reference for the said “dipstick” method mentioned? Has this method been validated and published? Please cite the article that describes the method. If the method is novel, it would make more sense to publish it as a separate methods paper with additional experiments and validation to provide a more thorough evaluation of its effectiveness.\nComment 11. The Supplementary Table 2 is not immediately clear, and at first glance, it’s difficult to understand what is being presented. Including a few lines of explanation or a brief legend would help readers better interpret the scheme, especially for those who may wish to adapt this approach in their own work. Providing clarity in this section would enhance its utility and accessibility.\nResults:\nComment 12. A correlation plot between the observed antibiotic concentrations and ARGs/community composition could be considered to verify the relevance of the authors' conclusion regarding the need for tailored antibiotic use. Currently, no attempt was made to correlate the different datasets, and including such an analysis would strengthen the argument by demonstrating a direct relationship between antibiotic presence and resistance gene dynamics.\nComment 13. Line 142 What kind of ordination was performed?\nComment 14. Figure 2 - The beta diversity analysis indicates significant differences between cities, supported by PERMANOVA results (R² = 0.412, p < 0.01). However, the first two axes of the Bray-Curtis PCoA explain only 34.2% of the variance (19% and 15.2%), suggesting that key patterns may exist in higher dimensions. To enhance interpretability, the authors could consider visualizing additional axes (e.g., 3D or scree plot) and exploring alternative ordination methods like NMDS, which may better capture community differences.\nComment 15. Line 162 – What was the basis for subsetting the data? It would be helpful to clarify the criteria or rationale used for selecting the specific subsets of data, as this will provide readers with a better understanding of how the analysis was conducted and ensure the approach is reproducible.\nComment 16. Figure 5 - The PCoA plot provides a useful visualization of microbiome variations across months; however, the first two axes explain only 35% of the variance (21% and 14%), suggesting that key patterns may exist in higher dimensions. To strengthen the analysis, the authors should explore alternative methods like NMDS and assess higher-dimensional variance using a scree plot or 3D PCoA. These could enhance the robustness and clarity of the findings.\nDiscussion:\nIn my opinion, the Discussion is not as intuitive as it could be. Given the significant effort put into sample collection, processing, and data the analysis, the discussion could be improved to explain the observed clustering and seasonal differences more clearly. A more detailed interpretation of these results would help readers better understand the underlying factors driving the patterns observed, providing a stronger connection between the findings and the broader implications of the study."}, "author_response": "Point-by-Point Response to Reviewer Comments\nReviewer #1\nComment# 1: The authors have undertaken a significant study detailing the antibiotic contamination and antimicrobial resistance dynamics that are present in urban sewage from India. The methodology is exhaustively documented and adequate and appropriate QA/QC protocols have been followed. The authors have painstakingly completed a humongous sampling and analytical exercise and must be applauded for their perseverance.\nHowever, I have the following concerns that need to be addressed before the manuscript can be deemed ready for publication.\n1. The authors have undertaken the study in six different Indian cities and collected a total of 371 sewage samples. Out of this 100 have been chosen for the antibiotics analysis. Further clarification on the rationale behind the selection of these 100 samples from the 371 samples must be provided by the authors.\nResponse: We thank the reviewer for their constructive feedback and for acknowledging the scope and methodological rigor of our study. The variation in sample sizes across different analytical components was a result of deliberate methodological planning, guided by both scientific objectives and practical constraints—particularly those associated with high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) used for antibiotic quantification.\nQuantification of 11 Antibiotics in 102 Representative Sewage Samples\nGiven the resource-intensive nature of LC-MS/MS analysis—including costs, instrument time, and sample preparation—conducting antibiotic quantification on all 381 sewage samples was not practical. Instead, a representative subset of 102 samples was strategically selected to maximize analytical efficiency while ensuring robust and meaningful insights. This approach was designed to preserve statistical validity and maximize the representativeness of the dataset across key variables.\nThe selection ensured comprehensive coverage across:\n(i) Geographic diversity – samples were proportionally selected from all six states included in the study,\n5\n(ii) Temporal distribution – samples represented multiple rounds of collection to capture seasonal variability (June to December 2023), and\n(iii) Sewage source types – including community, and hospital-influenced sewage\nThis strategy enabled us to account for spatial and temporal heterogeneity in antibiotic occurrence, aligning with the study’s objective to assess contamination patterns across diverse urban contexts. By ensuring a robust and representative sample for antibiotic analysis, we are confident that the resulting data provide meaningful insights into antibiotic contamination dynamics in Indian urban sewage.\nMicrobial Diversity (n=220):\nTo ensure a comprehensive assessment of microbial diversity, a larger subset of 220 samples was selected from the total of 381 for targeted metagenomic sequencing. This expanded sampling was designed to provide robust spatial and temporal coverage across all six states over the seven-month study period. By incorporating samples from multiple time points and diverse sewage sources, we aimed to capture the heterogeneity in environmental exposures and microbial community structure inherent to urban sewage systems. This approach enhances the statistical power and ecological relevance of the microbiome analysis, allowing for more meaningful interpretation of spatiotemporal patterns and potential public health implications.\nTo achieve higher taxonomic resolution and a more comprehensive characterization of microbial functions related to antibiotic resistance and its dissemination, we have now included shotgun metagenomic sequencing for 167 samples. While this approach is resource-intensive, it offers high-throughput, untargeted insights into both microbial community composition and functional gene profiles. The inclusion of shotgun metagenomics significantly strengthens the metagenomic component of the study by enabling deeper functional and taxonomic analysis beyond the capabilities of targeted approaches.\nIsolated Genome Sequences (n=305):\nThe combination of targeted and shotgun metagenomic approaches provided valuable insights into the structure and functional potential of microbial communities, particularly with respect to antimicrobial resistance (AMR). To further investigate the genetic linkage between antimicrobial resistance genes (ARGs), mobile genetic elements (MGEs), and their association with clinically relevant bacterial species, we additionally isolated multidrug-resistant (MDR) bacteria from sewage samples. These isolates were obtained under both aerobic and anaerobic culture conditions. Whole genome sequencing (WGS) of successfully recovered and high-6\nquality isolates enabled detailed characterization of ARGs, MGEs, and their genomic contexts, offering a complementary, strain-resolved perspective that strengthens our understanding of AMR dissemination dynamics in urban sewage systems.\nComparative Core Genome Analysis (n=7166):\nTo contextualize the study isolates within a global framework, we conducted a comparative core genome analysis using a large dataset comprising 7,166 genomes. This dataset was curated to ensure broad geographic representation and to capture the diversity of resistance alleles. The analysis included all relevant multidrug-resistant (MDR) isolates from our study, supplemented with publicly available clinical genomes (2019–2023), enabling a comprehensive comparison between sewage-derived strains and clinical pathogens. The large sample size enhanced the statistical power and resolution of phylogenetic analyses, facilitating the identification of evolutionary relationships and potential ARG transmission dynamics between environmental and clinical reservoirs.\nIn summary, sample sizes for each analytical component were carefully determined based on technical feasibility, resource availability, and the specific scientific objectives of each dataset.\nCollectively, these complementary approaches provide a robust, multi-layered understanding of the microbial ecology and genetic mechanisms underlying AMR dissemination in urban sewage systems.\nComment# 2. Lines 508 to 512 - The authors mention that 332 (250 sewage + 82 hospital) samples were collected from the city of Faridabad and apart from that 39 samples were collected from other parts of India. Details are requested on the sample spread of the 39 samples taken from the other parts of India, including the date of sampling and their coordinates.\nResponse: We appreciate the reviewer’s valuable suggestion and have complied accordingly.\nA total of 49 samples were collected from regions outside Faridabad, Haryana, bringing the overall number of samples in this study to 381. We apologize for the earlier unintentional error in the reported count, which has now been corrected in the main manuscript. Detailed metadata—including sampling locations, dates, and geographic coordinates—for the 49 samples collected from other parts of India have been provided in Supplementary Table S11.\nThis table offers a comprehensive overview of the spatial and temporal distribution of these samples across multiple Indian states.\nResponse: We sincerely appreciate the reviewer’s constructive suggestion and have addressed it accordingly. In response, we have provided detailed information on the geographical location, sampling dates, and number of samples collected at each site in Supplementary Table S11, which clarifies the spatial and temporal distribution of our sampling efforts.\nRegarding the predominance of sampling sites in Faridabad, the rationale is multifaceted.\nFaridabad, with a population exceeding 1.4 million, is a major industrial hub within the National Capital Region (NCR) of India. The city encompasses a diverse range of wastewater sources, including densely populated urban areas, rapidly expanding peri-urban zones, community sewage, and hospital effluents, many of which lack adequate treatment infrastructure. Furthermore, several sewage sites are connected to the Yamuna River, a critical tributary of the Ganges, which amplifies the potential environmental and public health implications due to downstream contamination risks.\nDespite its significant industrial and environmental importance, Faridabad remains underrepresented in antimicrobial resistance (AMR) surveillance, particularly in environmental studies. Additionally, the proximity of our research institute to Faridabad enabled systematic, precise, and routine longitudinal sampling, which is essential for robust temporal analysis and understanding of AMR dynamics in heterogeneous sewage systems.\nWe have incorporated this detailed justification into the revised manuscript (Lines 484–492) to provide readers with clear context on the sampling strategy and coverage.\nComment #4. A spatial map indicating the geographical spread of the sampling locations is essential for informing the readers about the study’s extent of coverage. Both of the above can be included in the supplementary information.\nResponse: We thank the reviewer for this valuable suggestion. In response, we have developed a comprehensive spatial map illustrating the geographical distribution of all sampling locations across the different cities and states included in the study. This map provides a clear visual representation of the study’s spatial coverage, helping readers better understand the extent and\n8\ndiversity of the sampling effort. The spatial map has been included as Supplementary Figure S7 in the revised manuscript.\nComment #5. Lines 522 & 523 – The authors mention pure antibiotic but fail to mention the manufacturer and the purity of the standards used for their instrumentation. Details must be provided for readers benefit and for reproducibility.\nResponse: We appreciate the reviewer’s insightful query regarding the source and purity of the antibiotic standards used in our study. To ensure analytical rigor and reproducibility, we utilized high-purity standards for all 11 antibiotics analyzed. All standards were procured from Sigma Aldrich (St. Louis, USA) with purities ranging from 95.5% to 99.9%, depending on the compound. Specifically, ampicillin (99.9%), azithromycin (95.5%), nalidixic acid (99.8%), amoxicillin (99.9%), kanamycin (99.9%), erythromycin (99.9%), rifampicin (97.7%), tetracycline (99.9%), spectinomycin (99.9%), polymyxin-B (99.9%), and colistin (99.9%) were employed.\nThese details, including the manufacturer and exact purity for each antibiotic standard, have now been incorporated in the revised manuscript as Supplementary Table S1 to enhance transparency and facilitate reproducibility.\nComment #6. The figures pack too much information and as a result, the text has become unreadable. Revised higher quality images are requested from the authors with larger and legible fonts in the images for better readability.\nKeeping in view all the above comments, I recommend the manuscript undergo a major revision, especially in terms of addressing the concerns about the sampling spread and also figures requiring significant revision.\nResponse: We thank the reviewer for the valuable feedback regarding figure clarity and readability. We acknowledge that some of the original figures contained a large amount of information, which impacted the legibility of text and data presentation. In response, we have revised all figures to enhance image quality and increased font sizes to ensure clear and legible labelling.\nTo further improve readability, we have divided several complex figures into smaller, more focused panels. For example, the phylogeographical analysis figure has been split into two parts: the primary portion is now presented as Figure 8 in the main manuscript, while the\n9\nsupplementary portion has been relocated to Supplementary Figure S4. Similarly, Figure 9 (a–c) has been divided, with panels (a–b) retained in the main text and panel (c) moved to Supplementary Figure S5.\nWe trust these revisions substantially improve the clarity and accessibility of the figures and meet the journal’s standards for presentation.\nReviewer #2\nComment #1. The manuscript titled “Antibiotic Contamination and AMR Dynamics in the Urban Sewage Microbiome: Insights from a Longitudinal surveillance” gives a comprehensive analysis of the presence of antibiotic residues, antimicrobial resistance genes and the resistant pathogens in urban sewage in six cities in India. Though the study uses a multi-pronged approach that includes mass spectrometry (QTRAP), dipstick-based molecular assay, 16S rRNA metagenomics, shotgun metagenomics, and bacterial isolate analysis to decipher the AMR scenario in six Indian cities, it fails to provide the direct statistical correlation/ comparison of these parallel approaches. A systematic comparative analysis of these approaches combined with statistics could have provided strong evidence of the selection pressures driving AMR in the wastewater and could have improved the impact of the study further.\nResponse: We thank the reviewer for this insightful and constructive comment. We would like to clarify that our study indeed incorporates a systematic comparative analysis of the multiple approaches employed. Specifically, mass spectrometry was used to quantify antibiotic concentrations in sewage samples, providing a direct measure of environmental antibiotic contamination. In parallel, dipstick-based molecular assays enabled rapid detection of key antimicrobial resistance genes (ARGs) in the same samples, offering a high-throughput overview of resistance potential. Furthermore, we utilized 16S rRNA and shotgun metagenomic sequencing to comprehensively assess microbial diversity and the functional capacity of the resistome. Shotgun metagenomics also allowed for linkage of ARGs to taxonomic origins and functional pathway analysis. Complementing these, whole-genome sequencing of cultured bacterial isolates provided strain-level resolution and enabled precise identification of genetic contexts of resistance genes, including their co-localization with mobile elements. Crucially, we conducted statistical analysis across these complementary experiments including ARG abundance, and microbial community structure. As suggested by the reviewer, a correlation plot (Supplementary Fig. S2) has been incorporated in the revised\n10\nmanuscript. These analyses offer strong evidence of the selective pressures driving AMR emergence and dissemination in urban wastewater ecosystems. We believe that the integration of multi-omics data, supported by rigorous statistical analysis, enhances the robustness of our findings and provides a comprehensive understanding of AMR dynamics across the six Indian cities included in our longitudinal surveillance.\nComment #2. Specific Comments- In the introduction, the authors should cite the earlier work done on metagenomic surveillance of urban wastewater for AMR and resistant pathogens in India and globally. MK Madhukar, N Singh, VR Iyer, DT Sowpati, KB Tallapaka, RK Mishra, SC Moharir, Antimicrobial resistance landscape in a metropolitan city context using open drain wastewater-based metagenomic analysis, Environmental Research Volume 252, Part 1, 1 July 2024, 118556, https://doi.org/10.1016/j.envres.2024.118556\nResponse: We appreciate the reviewer’s valuable suggestion and have complied accordingly.\nThe cited work has now been appropriately referenced in the revised manuscript, along with an additional recent global study on AMR surveillance in wastewater, to provide broader context and strengthen the discussion (see References 5 and 6). These additions enhance the relevance and framing of our findings within the global landscape of AMR research.\nComment #3. Figure numbers are not put on the Figures. This makes it confusing to review the figures.\nResponse: We thank the reviewer for highlighting this concern. To improve clarity and avoid any confusion, we have now incorporated all figures with their corresponding figure numbers and detailed legends directly beneath each figure at the end of the updated manuscript file. This has been done to ensure easier reference and better alignment with the text for the reader.\nComment #4: The labelling in Figure 1 should match with the description in the respective figure legend (line no 707- 710). It is not clear how the significance is calculated- what is the control group with which comparison is made to calculate the p value using t test.\nResponse: We thank the reviewer for this valuable observation. In response, the figure legend has been revised to ensure consistency with the labelling in Figure 1 and to provide a clearer\n11\nand more accurate description (Lines 938-946).\nRegarding the calculation of statistical significance, we have clarified this in the revised legend as well. The concentrations of individual antibiotics detected across sewage samples were statistically compared to that of ampicillin, which consistently showed undetectable levels by LC/MS-MS in the test samples, while being reliably detected in positive controls. This consistently low or absent detection of ampicillin across all sites served as a baseline reference for comparison. The p-values were calculated using a two-tailed t-test to determine whether the concentrations of other antibiotics were significantly higher than that of ampicillin, indicating their more prominent presence and potential selective pressure in the sampled environments.\nComment #5: Line 104 to 108- what was the rationale behind selecting the 100 samples out of the 371? What criteria was applied for selecting those 100 samples?\nResponse: We kindly refer the reviewer to our response to Comment #1 from Reviewer 1, where we have provided a detailed justification for the variation in sample sizes used for LC-MS/MS-based analysis of antibiotics in sewage samples. The differences in sample numbers were primarily due to logistical constraints and sample quality issues during processing. Nonetheless, we ensured that the selected samples were representative of each site and time point to maintain the integrity and comparability of the dataset.\nComment #6: Line 111- the concentration of amoxicillin is not higher than 15 ng/ ml. Needs to be corrected to >10ng/ml.\nResponse: We appreciate the reviewer’s careful observation. In response to the suggestion, the value has been corrected in the revised manuscript at Line 113-114 to ensure accuracy and consistency with the data presented.\nComment #7: In Figure 2, adding city names in front of sample IDs on Y axis will be more informative.\nResponse: We thank the reviewer for the helpful suggestion. In response, Figure 2 has been updated to include the initials of the respective states from which the samples were collected, making the sample IDs on the Y-axis more informative and easier to interpret. The state-wise\n12\nsample origins and their corresponding initials are as follows:\n• Faridabad, Haryana (HR)\n• Guwahati, Assam (AS)\n• Kolkata, West Bengal (WB)\nThis modification enhances the clarity of geographic representation in the figure.\nComment #8: Line 117- the term ‘longitudinal’ is misleading in this context. If the authors want to use the term here, they should show the data of each of the sampling location for the multiple time points. The word ‘longitudinal’ in the title of the manuscript is also misleading.\nResponse: We thank the reviewer for this thoughtful and important observation. In response, we have removed the term “longitudinal” from the title and relevant sections of the manuscript where its use may have caused ambiguity or appeared inconsistent with the data presentation (see revised Line 120).\nHowever, we respectfully wish to retain the term “longitudinal” in a specific context within the manuscript (Line 188), where it accurately describes the study design and sampling strategy for Faridabad, Haryana. Specifically, sewage samples from seven distinct sites in Faridabad-including five community locations (Badkhal, Ballabgarh, Dabua Colony, Parvatiya Colony, Sanjay Colony) and two hospital-associated sites (BK Hospital and ESIC Hospital)-were collected twice in every week over a continuous seven-month period (June to December 2023). These time-series samples were used across multiple analyses, including mass spectrometry (QTRAP), dipstick-based molecular assays, 16S rRNA metagenomics, and shotgun metagenomics.\nComment #9: Are the same 100 samples used for QTRAP Mass Spectrometry, used for dipstick-based molecular analysis? If yes, then proper comparative analysis should be performed.\nResponse: We thank the reviewer for this important and insightful query. In response, we have carefully analyzed the datasets and confirm that 51 out of the total samples used for QTRAP mass spectrometry-based antibiotic detection were also tested using the dipstick-based molecular assay for ARG detection.\nAs per the reviewer’s suggestion, a proper comparative analysis was conducted using these 51\n13\nmatched samples to evaluate the relationship between the detected antibiotic (QTRAP) and the presence of corresponding antimicrobial resistance genes (dipstick assay). The results of this analysis are now provided in Supplementary Table S3a.\nAdditionally, we have incorporated a detailed description of this comparative analysis in the Results section (Lines 128–133) and further contextualized its significance in the Discussion section (Lines 508–519) of the revised manuscript.\nFurthermore, many of these same samples were also subjected to shotgun metagenomic and 16S rRNA sequencing. This integrative, multi-platform analysis supports our central conclusion that antibiotic contamination in wastewater acts as a key selective pressure driving the enrichment and dissemination of antibiotic resistance genes (ARGs) in the sewage microbiome.\nComment #10: Line 131- 132: Mention the mean and standard deviation for number of reads. This will give a sense of the distribution of number of reads across the samples.\nResponse: We thank the reviewer for the constructive suggestion. In response, we have now included the mean and standard deviation of the read counts to provide a clearer understanding of the sequencing depth across samples. This information has been added to the main manuscript (Lines 137–139) to better reflect the distribution and consistency of the sequencing data.\nComment #11: Line 171- since sampling was done only once from June to December, all the seasons are not covered. Mentioning it ‘seasonal’ variations is not appropriate. Inference about seasonality should be removed throughout the manuscript.\nResponse: We thank the reviewer for this important observation. While the study spanned a 7-month period from June to December 2023-encompassing a substantial portion of seasonal transitions in the region-we acknowledge that the sampling frequency does not allow for robust conclusions on seasonality.\nIn compliance with the reviewer’s suggestion, all references to “seasonal variation” have been removed from the manuscript. Instead, we now present the data as a month-wise comparison, which more accurately reflects the temporal resolution of the sampling. These revisions have\n14\nbeen incorporated in the Results section (Lines 200–210).\nComment #12: Line 172- The microbial diversity varied across different locations (line 133- 143). In that case, pooling the data from these 6 locations for each of the months to analyse the temporal patterns is not appropriate.\nResponse: We thank the reviewer for this important observation and fully agree that microbial diversity is expected to vary across geographically distinct locations. To clarify, the section titled “Temporal variations in sewage microbiome” focuses exclusively on samples collected longitudinally from Faridabad, Haryana over a seven-month period (June to December 2023). No data from other states were pooled for this temporal analysis. This approach allows us to assess within-city temporal dynamics without confounding spatial variability.\nFurthermore, in the revised manuscript, we have expanded our analysis to compare microbial profiles across samples collected from six different states of India—Assam, Uttar Pradesh, Jharkhand, West Bengal, Uttarakhand, and Haryana (Faridabad)—at a common time point. This cross-sectional inter-site comparison highlights spatial diversity while maintaining temporal consistency. The results of this analysis are presented in Supplementary Figure S1e and discussed in the updated text to ensure clarity and appropriate interpretation (Lines 222-231).\nWe hope this resolves the reviewer’s concern.\nComment #13: Line 441- 442- comparative analysis of dipstick data and shotgun metagenomic data should be performed.\nResponse: We thank the reviewer for this valuable suggestion. In response, we have expanded our shotgun metagenomic sequencing dataset by including additional samples, bringing the total number to 167, as now reflected in the revised manuscript. Upon cross-referencing, we identified 75 samples that were common to both the dipstick-based ARG detection assay and shotgun metagenomic analysis.\nA comparative analysis was performed on these overlapping samples to assess the concordance and complementarity between the two methodologies in detecting antimicrobial resistance genes (ARGs). The results of this analysis are presented in Supplementary Table S3b, and a summary of the findings has been included in the revised manuscript (Lines 251–254). This integrated comparison enhances the robustness of our conclusions regarding ARG prevalence and distribution, and underscores the value of using both rapid molecular screening and comprehensive sequencing-based approaches in AMR surveillance.\nResponse: We thank the reviewer for pointing out this inconsistency. To clarify, sewage samples were collected from six different states across India—namely Uttar Pradesh, Uttarakhand, Assam, West Bengal, Jharkhand, and the NCR region of Haryana (Faridabad). Within Faridabad, Haryana, samples were collected from seven distinct sites over a seven-month period (June to December 2023) as part of the longitudinal surveillance effort.\nTo avoid confusion, we have revised the manuscript to consistently refer to these pan-India collection sites as “states” rather than “cities” (see updated text at Lines 40, 106-107, and 159; Table S11). Additionally, to enhance clarity and spatial context, a geographical map illustrating the distribution of sampling locations across the six states has been included in the revised manuscript as Supplementary Figure S7.\nComment #15: Line 507-510- more details on sampling needed. Were the samples collected in replicate for each sampling site for each time point? What was the sample collection method- grab sampling or composite sampling? What measures were taken to ensure that the sampling was unbiased and representative for that site for a particular timepoint? Was 30 ml sample enough to capture the complete picture? More details on sample processing before DNA extraction are needed. Was the sample processed/ concentrated? If yes, what method was used for sample concentration? What was the yield of the DNA? References are needed to support the methodology.\nResponse: We sincerely thank the reviewer for the insightful queries regarding the sampling methodology and sample processing. In response, we have elaborated the details of the sample collection, processing, and DNA extraction protocols in the revised Methods section (Lines 611–622) to ensure transparency and reproducibility. Specifically, wastewater samples were collected using the grab sampling method in sterile bottles during morning hours, consistent with established protocols in similar studies with some modifications (Hendriksen et al., 2019; Tucker et al., 2022). For the longitudinal study in Faridabad, Haryana, samples were collected biweekly (twice in a week) over seven months (June–December 2023) from seven distinct sites. The geographical coordinates and detailed descriptions of all sampling sites across the six states are provided in Supplementary Table S11.\nThese methodological details have been incorporated into the revised manuscript, supported by relevant literature references to validate our approach.\nComment #16: Since dipstick assay was standardized in this study only, a comparison with a gold standard assay like RT-PCR would be required to ensure that the assay is efficient and sensitive. Data to support the sensitivity and specificity of the dipstick assay should be provided. The advantage of the assay over RT-PCR and the pros and cons with respect to the sensitivity, specificity and ease of use should be discussed.\nResponse: We thank the reviewer for this insightful and valuable comment. We fully agree that benchmarking the dipstick assay against a gold standard method such as RT-PCR is important for validating its sensitivity and specificity.\nThe dipstick assay employed in this study was adapted from protocols successfully utilized in our recent publications for detecting bacterial species and viral variants (Talukdar et al., 2024, iScience DOI: 10.1016/j.isci.2024.111238; Paul et al., 2025, Front. Microbiol. DOI: 10.3389/fmicb.2024.1459644), where the assay demonstrated robust performance.\nNotably, in certain instances, the dipstick assay detected resistance genes that were not identified by shotgun sequencing, suggesting potentially higher sensitivity of the assay in these contexts. Conversely, a small number of ARGs identified by shotgun sequencing were not captured by the dipstick assay, likely due to sequence variability affecting probe hybridization efficiency.\nWe have expanded the discussion section (Lines 520-533) to explicitly address the comparative advantages and limitations of the dipstick assay relative to shotgun metagenomics, including considerations of turnaround time, cost-effectiveness, operational simplicity, and suitability for deployment in low-resource settings. While shotgun sequencing remains the comprehensive gold standard, the dipstick assay offers a rapid, scalable, and cost-efficient alternative for targeted ARG surveillance, particularly valuable for large-scale or resource-constrained environments.\nThese additions serve to contextualize the dipstick assay’s utility, sensitivity, and specificity within the broader framework of AMR monitoring technologies.\nComment #17: The study provides insights into the AMR scenario in the urban wastewater through a multi-prong approach. The utilization of chemical, molecular, microbiological and bioinformatic techniques provide a comprehensive picture of AMR scenario. However, the study could have utilized the shotgun metagenomics more effectively to identify the non-bacterial AMR contributors, correlated antibiotic concentrations with ARG abundance, and critically evaluated the methodology. Also a detailed and systematic analysis for at least one year could have provided insights about the seasonal and longitudinal patterns of AMR and pathogens in wastewater.\nResponse: We sincerely appreciate the reviewer’s insightful feedback and recognize the importance of the points raised. Regarding the utilization of shotgun metagenomics to identify non-bacterial contributors to antimicrobial resistance (AMR), we would like to clarify that our study employed a well-established metagenomic DNA extraction protocol optimized primarily for bacterial cells (Bag et al., 2016; doi: 10.1038/srep26775). This method, while effective for\n18\nbacterial lysis, is less efficient in extracting DNA from organisms with more robust cell walls such as fungi and certain protozoa. Consistent with this, our shotgun sequencing data demonstrated that over 95% of reads were bacterial in origin, with minimal representation of non-bacterial taxa.\nIn response to the reviewer’s suggestion, we have expanded the shotgun sequencing dataset to include 167 samples, thereby enriching our analysis of bacterial-associated antimicrobial resistance genes. While this does not fully capture non-bacterial AMR contributors, it significantly strengthens the resolution of bacterial resistome dynamics across diverse urban sewage sites.\nRegarding the temporal scope of our study, we acknowledge that a year-long longitudinal design would offer comprehensive insights into seasonal and temporal patterns of AMR and pathogen prevalence. However, we selected a seven-month period (June to December 2023) to balance technical feasibility, cost-effectiveness, and scientific rigor in establishing baseline data. Importantly, this timeframe encompasses major seasonal phases in Faridabad, including summer, pre-monsoon, monsoon, post-monsoon, autumn, and early winter, allowing us to capture key microbiome dynamics relevant to AMR dissemination.\nWe concur that future work extending over a full calendar year would be valuable to elucidate finer-scale seasonal trends and longitudinal shifts, and we have discussed this limitation (Lines 597-599) and prospect in the revised manuscript.\nReviewer #1\nComment# 1: The authors have undertaken a significant study detailing the antibiotic contamination and antimicrobial resistance dynamics that are present in urban sewage from India. The methodology is exhaustively documented and adequate and appropriate QA/QC protocols have been followed. The authors have painstakingly completed a humongous sampling and analytical exercise and must be applauded for their perseverance.\nHowever, I have the following concerns that need to be addressed before the manuscript can be deemed ready for publication.\n1. The authors have undertaken the study in six different Indian cities and collected a total of 371 sewage samples. Out of this 100 have been chosen for the antibiotics analysis. Further clarification on the rationale behind the selection of these 100 samples from the 371 samples must be provided by the authors.\nResponse: We thank the reviewer for their constructive feedback and for acknowledging the scope and methodological rigor of our study. The variation in sample sizes across different analytical components was a result of deliberate methodological planning, guided by both scientific objectives and practical constraints, particularly those associated with high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) used for antibiotic quantification.\nQuantification of 11 Antibiotics in 102 Representative Sewage Samples\nGiven the resource-intensive nature of LC-MS/MS analysis, including costs, instrument time, and sample preparation, conducting antibiotic quantification on all 381 sewage samples was not practical. Instead, a representative subset of 102 samples was strategically selected to maximize analytical efficiency while ensuring robust and meaningful insights. This approach\n5\nwas designed to preserve statistical validity and maximize the representativeness of the dataset across key variables.\nThe selection ensured comprehensive coverage across:\n(i) Geographic diversity – samples were proportionally selected from all six states included in the study,\n(ii) Temporal distribution – samples represented multiple rounds of collection to capture seasonal variability (June to December 2023), and\n(iii) Sewage source types – including community, and hospital-influenced sewage\nThis strategy enabled us to account for spatial and temporal heterogeneity in antibiotic occurrence, aligning with the study’s objective to assess contamination patterns across diverse urban contexts. By ensuring a robust and representative sample for antibiotic analysis, we are confident that the resulting data provide meaningful insights into antibiotic contamination dynamics in Indian urban sewage.\nMicrobial Diversity (n=220):\nTo ensure a comprehensive assessment of microbial diversity, a larger subset of 220 samples was selected from the total of 381 for targeted metagenomic sequencing. This expanded sampling was designed to provide robust spatial and temporal coverage across all six states over the seven-month study period. By incorporating samples from multiple time points and diverse sewage sources, we aimed to capture the heterogeneity in environmental exposures and microbial community structure inherent to urban sewage systems. This approach enhances the statistical power and ecological relevance of the microbiome analysis, allowing for more meaningful interpretation of spatiotemporal patterns and potential public health implications.\nTo achieve higher taxonomic resolution and a more comprehensive characterization of microbial functions related to antibiotic resistance and its dissemination, we have now included shotgun metagenomic sequencing for 167 samples. While this approach is resource-intensive, it offers high-throughput, untargeted insights into both microbial community composition and functional gene profiles. The inclusion of shotgun metagenomics significantly strengthens the metagenomic component of the study by enabling deeper functional and taxonomic analysis beyond the capabilities of targeted approaches.\nIsolated Genome Sequences (n=305):\nThe combination of targeted and shotgun metagenomic approaches provided valuable insights into the structure and functional potential of microbial communities, particularly with respect\n6\nto antimicrobial resistance (AMR). To further investigate the genetic linkage between antimicrobial resistance genes (ARGs), mobile genetic elements (MGEs), and their association with clinically relevant bacterial species, we additionally isolated multidrug-resistant (MDR) bacteria from sewage samples. These isolates were obtained under both aerobic and anaerobic culture conditions. Whole genome sequencing (WGS) of successfully recovered and high-quality isolates enabled detailed characterization of ARGs, MGEs, and their genomic contexts, offering a complementary, strain-resolved perspective that strengthens our understanding of AMR dissemination dynamics in urban sewage systems.\nComparative Core Genome Analysis (n=7166):\nTo contextualize the study isolates within a global framework, we conducted a comparative core genome analysis using a large dataset comprising 7,166 genomes. This dataset was curated to ensure broad geographic representation and to capture the diversity of resistance alleles. The analysis included all relevant multidrug-resistant (MDR) isolates from our study, supplemented with publicly available clinical genomes (2019–2023), enabling a comprehensive comparison between sewage-derived strains and clinical pathogens. The large sample size enhanced the statistical power and resolution of phylogenetic analyses, facilitating the identification of evolutionary relationships and potential ARG transmission dynamics between environmental and clinical reservoirs.\nIn summary, sample sizes for each analytical component were carefully determined based on technical feasibility, resource availability, and the specific scientific objectives of each dataset.\nCollectively, these complementary approaches provide a robust, multi-layered understanding of the microbial ecology and genetic mechanisms underlying AMR dissemination in urban sewage systems.\nComment# 2. Lines 508 to 512 - The authors mention that 332 (250 sewage + 82 hospital) samples were collected from the city of Faridabad and apart from that 39 samples were collected from other parts of India. Details are requested on the sample spread of the 39 samples taken from the other parts of India, including the date of sampling and their coordinates.\nResponse: We appreciate the reviewer’s valuable suggestion and have complied accordingly.\nA total of 49 samples were collected from regions outside Faridabad, Haryana, bringing the overall number of samples in this study to 381. We apologize for the earlier unintentional error in the reported count, which has now been corrected in the main manuscript (Page No. 20;\nComment# 3. A table needs to be provided detailing the geographical location of each of the sampling points along with sampling dates and number of samples collected from each of the sampling location to clarify the sampling spread and whether the coverage is adequate for a longitudinal study. Rationale or justification for Faridabad having the majority of the sampling locations must be provided by the authors and included in the main text.\nResponse: We sincerely appreciate the reviewer’s constructive suggestion and have addressed it accordingly. In response, we have provided detailed information on the geographical location, sampling dates, and number of samples collected at each site in Supplementary Table S11, which clarifies the spatial and temporal distribution of our sampling efforts.\nRegarding the predominance of sampling sites in Faridabad, the rationale is multifaceted.\nFaridabad, with a population exceeding 1.4 million, is a major industrial hub within the National Capital Region (NCR) of India. The city encompasses a diverse range of wastewater sources, including densely populated urban areas, rapidly expanding peri-urban zones, community sewage, and hospital effluents, many of which lack adequate treatment infrastructure. Furthermore, several sewage sites are connected to the Yamuna River, a critical tributary of the Ganges, which amplifies the potential environmental and public health implications due to downstream contamination risks.\nDespite its significant industrial and environmental importance, Faridabad remains underrepresented in antimicrobial resistance (AMR) surveillance, particularly in environmental studies. Additionally, the proximity of our research institute to Faridabad enabled systematic, precise, and routine longitudinal sampling, which is essential for robust temporal analysis and understanding of AMR dynamics in heterogeneous sewage systems.\nWe have incorporated this detailed justification in the revised manuscript (Page No. 16; Lines 517-525) to provide readers with clear context on the sampling strategy and coverage.\nComment #4. A spatial map indicating the geographical spread of the sampling locations is essential for informing the readers about the study’s extent of coverage. Both of the above can be included in the supplementary information.\nComment #5. Lines 522 & 523 – The authors mention pure antibiotic but fail to mention the manufacturer and the purity of the standards used for their instrumentation. Details must be provided for readers benefit and for reproducibility.\nResponse: We appreciate the reviewer’s insightful query regarding the source and purity of the antibiotic standards used in our study. To ensure analytical rigor and reproducibility, we utilized high-purity standards for all 11 antibiotics analyzed. All standards were procured from Sigma Aldrich (St. Louis, USA) with purities ranging from 95.5% to 99.9%, depending on the compound. Specifically, ampicillin (99.9%), azithromycin (95.5%), nalidixic acid (99.8%), amoxicillin (99.9%), kanamycin (99.9%), erythromycin (99.9%), rifampicin (97.7%), tetracycline (99.9%), spectinomycin (99.9%), polymyxin-B (99.9%), and colistin (99.9%) were employed.\nThese details, including the manufacturer and exact purity for each antibiotic standard, have now been incorporated in the revised manuscript as Supplementary Table S1 to enhance transparency and facilitate reproducibility.\nComment #6. The figures pack too much information and as a result, the text has become unreadable. Revised higher quality images are requested from the authors with larger and legible fonts in the images for better readability.\nKeeping in view all the above comments, I recommend the manuscript undergo a major revision, especially in terms of addressing the concerns about the sampling spread and also figures requiring significant revision.\nResponse: We thank the reviewer for the valuable feedback regarding figure clarity and readability. We acknowledge that some of the original figures contained a large amount of information, which impacted the legibility of text and data presentation. In response, we have\n9\nrevised all figures to enhance image quality and increased font sizes to ensure clear and legible labelling.\nTo further improve readability, we have divided several complex figures into smaller, more focused panels. For example, the phylogeographical analysis figure has been split into two parts: the primary portion is now presented as Figure 8 in the main manuscript, while the supplementary portion has been relocated to Supplementary Fig. S6. Similarly, Figure 9 (a–c) has been divided, with panels (a–b) retained in the main text and part (c) moved to Supplementary Fig. S7.\nWe trust these revisions substantially improve the clarity and accessibility of the figures and meet the journal’s standards for presentation.\nReviewer #2\nComment #1. The manuscript titled “Antibiotic Contamination and AMR Dynamics in the Urban Sewage Microbiome: Insights from a Longitudinal surveillance” gives a comprehensive analysis of the presence of antibiotic residues, antimicrobial resistance genes and the resistant pathogens in urban sewage in six cities in India. Though the study uses a multi-pronged approach that includes mass spectrometry (QTRAP), dipstick-based molecular assay, 16S rRNA metagenomics, shotgun metagenomics, and bacterial isolate analysis to decipher the AMR scenario in six Indian cities, it fails to provide the direct statistical correlation/ comparison of these parallel approaches. A systematic comparative analysis of these approaches combined with statistics could have provided strong evidence of the selection pressures driving AMR in the wastewater and could have improved the impact of the study further.\nResponse: We thank the reviewer for this insightful and constructive comment. We would like to clarify that our study incorporates a systematic comparative analysis of the multiple approaches employed. Specifically, mass spectrometry was used to quantify antibiotic concentrations in sewage samples, providing a direct measure of environmental antibiotic contamination. In parallel, dipstick-based molecular assays enabled rapid detection of key antimicrobial resistance genes (ARGs) in the same samples, offering a high-throughput overview of resistance potential. Furthermore, we utilized 16S rRNA and shotgun metagenomic sequencing to comprehensively assess microbial diversity and the functional\n10\ncapacity of the resistome. Shotgun metagenomics also allowed for linkage of ARGs to taxonomic origins and functional pathway analysis. Complementing these, whole-genome sequencing of cultured bacterial isolates provided strain-level resolution and enabled precise identification of genetic contexts of resistance genes, including their co-localization with mobile elements. Crucially, we conducted statistical analysis across these complementary experiments including ARG abundance, and microbial community structure. As suggested by the reviewer, a correlation plot has been incorporated in the revised manuscript (Supplementary Fig. S4). These analyses offer strong evidence of the selective pressures driving AMR emergence and dissemination in urban wastewater ecosystems. We believe that the integration of multi-omics data, supported by rigorous statistical analysis, enhances the robustness of our findings and provides a comprehensive understanding of AMR dynamics across the six Indian cities included in our longitudinal surveillance.\nComment #2. Specific Comments- In the introduction, the authors should cite the earlier work done on metagenomic surveillance of urban wastewater for AMR and resistant pathogens in India and globally. MK Madhukar, N Singh, VR Iyer, DT Sowpati, KB Tallapaka, RK Mishra, SC Moharir, Antimicrobial resistance landscape in a metropolitan city context using open drain wastewater-based metagenomic analysis, Environmental Research Volume 252, Part 1, 1 July 2024, 118556, https://doi.org/10.1016/j.envres.2024.118556\nResponse: We appreciate the reviewer’s valuable suggestion and have complied accordingly.\nThe cited work has now been appropriately referenced in the revised manuscript, along with an additional recent global study on AMR surveillance in wastewater, to provide broader context and strengthen the discussion (see References 9 and 10). These additions enhance the relevance and framing of our findings within the global landscape of AMR research.\nComment #3. Figure numbers are not put on the Figures. This makes it confusing to review the figures.\nResponse: We thank the reviewer for highlighting this concern. To improve clarity and avoid any confusion, we have now incorporated the figure numbers in all the corresponding figures and detailed legends at the end of the updated manuscript file, however for supplementary figures the legends have been provided in a separate supplementary information file. This has been done to ensure easier reference and better alignment with the text.\nResponse: We thank the reviewer for this valuable observation. In response, the figure legend has been revised to ensure consistency with the labelling in Figure 1 and to provide a clearer and more accurate description (Page No. 31; Lines 1018-1026).\nRegarding the calculation of statistical significance, we have clarified this in the revised legend as well. The concentrations of individual antibiotics detected across sewage samples were statistically compared to that of ampicillin, which consistently showed undetectable levels by LC/MS-MS in the test samples, while being reliably detected in positive controls. This consistently low or absent detection of ampicillin across all sites served as a baseline reference for comparison. The p-values were calculated using a two-tailed t-test to determine whether the concentrations of other antibiotics were significantly higher than that of ampicillin, indicating their more prominent presence and potential selective pressure in the sampled environments.\nComment #5: Line 104 to 108- what was the rationale behind selecting the 100 samples out of the 371? What criteria was applied for selecting those 100 samples?\nResponse: We kindly refer the reviewer to our detailed response to Comment #1 from Reviewer 1, where we have provided a detailed justification for the variation in sample sizes used for LC-MS/MS-based analysis of antibiotics in sewage samples. The differences in sample numbers were primarily due to logistical constraints and sample quality issues during processing. Nonetheless, we ensured that the selected samples were representative of each site and time point to maintain the integrity and comparability of the dataset.\nComment #6: Line 111- the concentration of amoxicillin is not higher than 15 ng/ml. Needs to be corrected to >10ng/ml.\nResponse: We appreciate the reviewer’s careful observation. In response to the suggestion, the value has been corrected in the revised manuscript at Page No. 4; Lines 133-134 to ensure accuracy and consistency with the data presented.\nResponse: We thank the reviewer for the helpful suggestion. In response, Figure 2 has been updated to include the initials of the respective states from which the samples were collected, making the sample IDs on the Y-axis more informative and easier to interpret. The state-wise sample origins and their corresponding initials are as follows:\n• Faridabad, Haryana (HR)\n• Guwahati, Assam (AS)\n• Kolkata, West Bengal (WB)\nThis modification enhances the clarity of geographic representation in the figure.\nComment #8: Line 117- the term ‘longitudinal’ is misleading in this context. If the authors want to use the term here, they should show the data of each of the sampling location for the multiple time points. The word ‘longitudinal’ in the title of the manuscript is also misleading.\nResponse: We thank the reviewer for this thoughtful and important observation. In response, we have removed the term “longitudinal” from the title and relevant sections of the manuscript where its use may have caused ambiguity or appeared inconsistent with the data presentation (see Page No. 5; revised Line 140).\nHowever, we respectfully wish to retain the term “longitudinal” in a specific context within the manuscript (Page No. 7; Line 213), where it accurately describes the study design and sampling strategy for Faridabad, Haryana. Specifically, sewage samples from seven distinct sites in Faridabad-including five community locations (Badkhal, Ballabgarh, Dabua Colony, Parvatiya Colony, Sanjay Colony) and two hospital-associated sites (BK Hospital and ESIC Hospital)-were collected twice in every week over a continuous seven-month period (June to December 2023). These time-series samples were used across multiple analyses, including mass spectrometry (QTRAP), dipstick-based molecular assays, 16S rRNA metagenomics, and shotgun metagenomics.\nComment #9: Are the same 100 samples used for QTRAP Mass Spectrometry, used for dipstick-based molecular analysis? If yes, then proper comparative analysis should be performed.\nAs per the reviewer’s suggestion, a proper comparative analysis was conducted using these 51 matched samples to evaluate the relationship between the detected antibiotic (QTRAP) and the presence of corresponding antimicrobial resistance genes (dipstick assay). The results of this analysis are now provided in Supplementary Table S3a.\nAdditionally, we have incorporated a detailed description of this comparative analysis in the Results section (Page No. 5; Lines 148–153) and further contextualized its significance in the Discussion section (Page No. 17; Lines 544–556) of the revised manuscript.\nFurthermore, many of these same samples were also subjected to shotgun metagenomic and 16S rRNA sequencing. This integrative, multi-platform analysis supports our central conclusion that antibiotic contamination in wastewater acts as a key selective pressure driving the enrichment and dissemination of antibiotic resistance genes (ARGs) in the sewage microbiome.\nComment #10: Line 131- 132: Mention the mean and standard deviation for number of reads. This will give a sense of the distribution of number of reads across the samples.\nResponse: We thank the reviewer for the constructive suggestion. In response, we have now included the mean and standard deviation of the read counts to provide a clearer understanding of the sequencing depth across samples. This information has been added to the main manuscript (Page No. 5; Lines 157–159) to better reflect the distribution and consistency of the sequencing data.\nComment #11: Line 171- since sampling was done only once from June to December, all the seasons are not covered. Mentioning it ‘seasonal’ variations is not appropriate. Inference about seasonality should be removed throughout the manuscript.\nResponse: We thank the reviewer for this important observation. While the study spanned a 7-month period from June to December 2023-encompassing a substantial portion of seasonal transitions in the region-we acknowledge that the sampling frequency does not allow for robust\n14\nconclusions on seasonality.\nIn compliance with the reviewer’s suggestion, all references to “seasonal variation” have been removed from the manuscript. Instead, we now present the data as a month-wise comparison, which more accurately reflects the temporal resolution of the sampling. These revisions have been incorporated in the Results section (Page Nos. 7-8; Lines 229–239).\nComment #12: Line 172- The microbial diversity varied across different locations (line 133- 143). In that case, pooling the data from these 6 locations for each of the months to analyse the temporal patterns is not appropriate.\nResponse: We thank the reviewer for this important observation and fully agree that microbial diversity is expected to vary across geographically distinct locations. To clarify, the section titled “Temporal variations in sewage microbiome” focuses exclusively on samples collected longitudinally from Faridabad, Haryana over a seven-month period (June to December 2023). No data from other states were pooled for this temporal analysis. This approach allows us to assess within-city temporal dynamics without confounding spatial variability.\nFurthermore, in the revised manuscript, we have expanded our analysis to compare microbial profiles across samples collected from six different states of India, Assam, Uttar Pradesh, Jharkhand, West Bengal, Uttarakhand, and Haryana (Faridabad), at a common time point. This cross-sectional inter-site comparison highlights spatial diversity while maintaining temporal consistency. The results of this analysis are presented in Supplementary Fig. S3e and incorporated in the updated result section to ensure clarity and appropriate interpretation (Lines Page No. 8; 255-264).\nWe hope this resolves the reviewer’s concern.\nComment #13: Line 441- 442- comparative analysis of dipstick data and shotgun metagenomic data should be performed.\nResponse: We thank the reviewer for this valuable suggestion. In response, we have expanded our shotgun metagenomic sequencing dataset by including additional samples, bringing the total number to 167, as now reflected in the revised manuscript. Upon cross-referencing, we identified 75 samples that were common to both the dipstick-based ARG detection assay and shotgun metagenomic analysis.\nA comparative analysis was performed on these overlapping samples to assess the concordance and complementarity between the two methodologies in detecting antimicrobial resistance genes (ARGs). The results of this analysis are presented in Supplementary Table\nComment #14: Line 508- Sewage samples were collected from ‘seven’ cities or ‘six’ cities? At all other places ‘six’ cities is mentioned.\nResponse: We thank the reviewer for pointing out this inconsistency. To clarify, sewage samples were collected from six different states across India, namely Uttar Pradesh, Uttarakhand, Assam, West Bengal, Jharkhand, and the NCR region of Haryana (Faridabad). Within Faridabad, Haryana, samples were collected from seven distinct sites over a seven-month period (June to December 2023) as part of the longitudinal surveillance effort.\nTo avoid confusion, we have revised the manuscript to consistently refer to these pan-India collection sites as “states” rather than “cities” (see updated text at Page No. 2; Line 40, Page No. 4; Line 127, and Page No. 6; Line 185; Table S11). Additionally, to enhance clarity and spatial context, a geographical map illustrating the distribution of sampling locations across the six states has been included in the revised manuscript as Supplementary Fig. S1.\nComment #15: Line 507-510- more details on sampling needed. Were the samples collected in replicate for each sampling site for each time point? What was the sample collection method- grab sampling or composite sampling? What measures were taken to ensure that the sampling was unbiased and representative for that site for a particular timepoint? Was 30 ml sample enough to capture the complete picture? More details on sample processing before DNA extraction are needed. Was the sample processed/ concentrated? If yes, what method was used for sample concentration? What was the yield of the DNA? References are needed to support the methodology.\nResponse: We sincerely thank the reviewer for the insightful queries regarding the sampling methodology and sample processing. In response, we have elaborated the details of the sample collection, processing, and DNA extraction protocols in the revised Methods section (Page Nos. 20-21; Lines 671–682) to ensure transparency and reproducibility. Specifically,\n16\nwastewater samples were collected using the grab sampling method in sterile bottles during morning hours, consistent with established protocols in similar studies with some modifications (Hendriksen et al., 2019; Tucker et al., 2022). For the longitudinal study in Faridabad, Haryana, samples were collected biweekly (twice in a week) over seven months (June–December 2023) from seven distinct sites. The geographical coordinates and detailed descriptions of all sampling sites across the six states are provided in Supplementary Table S11.\nAt each time point, approximately 200 ml of untreated wastewater was collected and immediately transported on ice to the laboratory to minimize compositional changes. Upon arrival, the sample was thoroughly mixed, and a 30 ml aliquot was centrifuged at 8,000 rpm for 15 minutes at 4°C to pellet microbial biomass. The supernatant was subsequently filtered through a 0.22 µm Millex GV syringe filter (Merck, Darmstadt, Germany) and stored at −80°C for LC-MS/MS analysis to quantify antibiotic residues. The resulting pellet was resuspended in 1.5 ml of nuclease-free water, with 100 µl inoculated into Brain Heart Infusion (BHI) broth for culturomics under both aerobic and anaerobic conditions. The remaining 1 ml was used for metagenomic nucleic acid extraction, which supported downstream 16S rRNA gene sequencing, dipstick-based ARG detection, and shotgun metagenomic analyses. The DNA yield from the concentrated biomass obtained from 30 ml of sewage was consistently sufficient for all molecular assays and culturomics studies, providing a comprehensive representation of the microbial community and resistome. To ensure unbiased and representative sampling at each site and time point, a fixed sampling point was chosen and consistently sampling was done from this sampling point each time. Further, samples were collected during early morning hours within a defined time window and ensured that there are no atypical activities near the sampling site. Additionally, fresh sterile sample bottles were used at each sampling to ensure that there are no cross-contamination.\nThese methodological details have been incorporated into the revised manuscript, supported by relevant literature references to validate our approach.\nComment #16: Since dipstick assay was standardized in this study only, a comparison with a gold standard assay like RT-PCR would be required to ensure that the assay is efficient and sensitive. Data to support the sensitivity and specificity of the dipstick assay should be provided. The advantage of the assay over RT-PCR and the pros and cons with respect to the sensitivity, specificity and ease of use should be discussed.\nResponse: We thank the reviewer for this insightful and valuable comment. We fully agree\n17\nthat benchmarking the dipstick assay against a gold standard method such as RT-PCR is important for validating its sensitivity and specificity.\nThe dipstick assay employed in this study was adapted from protocols successfully utilized in our recent publications for detecting bacterial species and viral variants (Talukdar et al., 2024, iScience DOI: 10.1016/j.isci.2024.111238; Paul et al., 2025, Front. Microbiol. DOI: 10.3389/fmicb.2024.1459644), where the assay demonstrated robust performance.\nIn alignment with the reviewer’s suggestion, we have now conducted a comprehensive comparative analysis between the dipstick assay results and shotgun metagenomic data derived from the same set of samples (n = 75). This analysis is detailed in Supplementary Table S3b.\nOur findings show that the dipstick assay exhibits high specificity, with substantial concordance to the ARGs detected via shotgun sequencing.\nNotably, in certain instances, the dipstick assay detected resistance genes that were not identified by shotgun sequencing, suggesting potentially higher sensitivity of the assay in these contexts. Conversely, a small number of ARGs identified by shotgun sequencing were not captured by the dipstick assay, likely due to sequence variability affecting probe hybridization efficiency.\nWe have expanded the discussion section (Page Nos. 17-18; Lines 557-574) to explicitly address the comparative advantages and limitations of the dipstick assay relative to shotgun metagenomics, including considerations of turnaround time, cost-effectiveness, operational simplicity, and suitability for deployment in low-resource settings. While shotgun sequencing remains the comprehensive gold standard, the dipstick assay offers a rapid, scalable, and cost-efficient alternative for targeted ARG surveillance, particularly valuable for large-scale or resource-constrained environments.\nThese additions serve to contextualize the dipstick assay’s utility, sensitivity, and specificity within the broader framework of AMR monitoring technologies.\nComment #17: The study provides insights into the AMR scenario in the urban wastewater through a multi-prong approach. The utilization of chemical, molecular, microbiological and bioinformatic techniques provide a comprehensive picture of AMR scenario. However, the study could have utilized the shotgun metagenomics more effectively to identify the non-bacterial AMR contributors, correlated antibiotic concentrations with ARG abundance, and critically evaluated the methodology. Also a detailed and systematic analysis for at least one year could have provided insights about the seasonal and longitudinal patterns of AMR and pathogens in wastewater.\nResponse: We sincerely appreciate the reviewer’s insightful feedback and recognize the\n18\nimportance of the points raised. Regarding the utilization of shotgun metagenomics to identify non-bacterial contributors to antimicrobial resistance (AMR), we would like to clarify that our study employed a well-established metagenomic DNA extraction protocol optimized primarily for bacterial cells (Bag et al., 2016; doi: 10.1038/srep26775). This method, while effective for bacterial lysis, is less efficient in extracting DNA from organisms with more robust cell walls such as fungi and certain protozoa. Consistent with this, our shotgun sequencing data demonstrated that over 95% of reads were bacterial in origin, with minimal representation of non-bacterial taxa.\nIn response to the reviewer’s suggestion, we have expanded the shotgun sequencing dataset to include 167 samples, thereby enriching our analysis of bacterial-associated antimicrobial resistance genes. While this does not fully capture non-bacterial AMR contributors, it significantly strengthens the resolution of bacterial resistome dynamics across diverse urban sewage sites.\nRegarding the temporal scope of our study, we acknowledge that a year-long longitudinal design would offer comprehensive insights into seasonal and temporal patterns of AMR and pathogen prevalence. However, we selected a seven-month period (June to December 2023) to balance technical feasibility, cost-effectiveness, and scientific rigor in establishing baseline data. Importantly, this timeframe encompasses major seasonal phases in Faridabad, including summer, pre-monsoon, monsoon, post-monsoon, autumn, and early winter, allowing us to capture key microbiome dynamics relevant to AMR dissemination.\nWe concur that future work extending over a full calendar year would be valuable to elucidate finer-scale seasonal trends and longitudinal shifts, and we have discussed this limitation (Page No. 20; Lines 657-659) and prospect in the revised manuscript.\nGeneral comments\nPaul et al. present valuable insights into antibiotic contamination and antimicrobial resistance (AMR) dynamics in the urban sewage microbiome. This well-structured study highlights sewage as a significant reservoir of AMR, integrating data on antibiotic concentrations (11 antibiotics from 7 different classes), 16S rRNA gene sequencing, and ARG richness. Notably, the authors have developed a dipstick-based assay for ARG detection, adding a practical and innovative dimension to the study.\nBy employing both culture-based and sequencing approaches alongside antibiotic analysis, the study provides a comprehensive assessment of AMR in urban sewage. The data are thoroughly analyzed and effectively summarized in well-constructed figures, with ARG quantification and associations with taxa clearly depicted through network analysis.\nMoreover, data from Low- and Middle-Income Countries (LMICs) on this topic are limited, and this study significantly contributes to the understanding of Environmental AMR in these regions. This study aligns with the One Health Approach for AMR. The inclusion of such data would greatly enrich the ongoing global dialogue on AMR and its management in resource-constrained settings. In addition, this study reflects the emerging trend of wastewater-based AMR surveillance, which is gaining traction globally as a cost-effective and comprehensive method for monitoring AMR in urban environments. However, while the study is robust and methodologically sound, the Introduction and Discussion sections require significant improvement to better contextualize the findings and enhance their broader relevance. Strengthening these sections would further elevate the impact of this important work. Additionally, the dipstick-based assay should be validated to ensure its reliability. Given the emerging importance of wastewater-based epidemiology, it would be fitting to publish the dipstick-based assay as a standalone methods paper rather than including it as a section under methods in the current manuscript. Furthermore, the sample volume collected for analysis is not convincing, and additional clarification or justification is needed for the approach taken.\nThe manuscript may be accepted after major revision, with the suggested improvements addressed to enhance clarity and the overall impact of the study.\nThe specific notes below may provide further clarification, and I hope they are useful to the authors for the revision.\nResponse: We sincerely appreciate your thoughtful and constructive feedback on our manuscript. We are grateful for your recognition of our study’s comprehensive approach and\n20\nrelevance to global AMR surveillance efforts, especially in LMICs. We have carefully considered your suggestions and made substantial revisions as outlined below:\n1. Revision of Introduction and Discussion sections: We have significantly revised both the Introduction and Discussion sections to better contextualize our findings in the global and regional landscape of AMR, particularly in LMICs. The necessary information has been incorporated into the manuscript (Introduction: Page Nos. 3-4; Lines 93-113, 116-122) and (Discussion: Page No. 16-20; Lines 514-529; 544-556; 557-574; 575-590; 637-645; 657-659).\nDipstick based assay: We appreciate the reviewer’s insightful suggestion regarding the standalone potential of the dipstick-based assay. The dipstick assay, adapted from our previously established protocols for identification of microorganisms and also differentiating the viral variants (Talukdar et al., 2024; Paul et al., 2024), and in this study, we have advanced the technology for identification of ARGs from environmental reservoirs. While, we believe this assay is one of the key strengths of the current study, we have performed a more detailed validation of the assay with shotgun metagenomics. In this regard, we have expanded our shotgun metagenomic sequencing dataset by including additional samples, bringing the total number to 167, as reflected in the revised manuscript (Page Nos. 6-7; Lines 201-203). Upon cross-referencing, we identified 75 samples common to both the dipstick-based ARG detection assay and shotgun metagenomic analysis. A comparative analysis of overlapping samples to understand the concordance between these two approaches in detecting ARGs. The findings are summarized in Supplementary Table S3b, and the information has been included in the revised manuscript (Page No. 9; Lines 284-287).\n2. Response concerning sample volume: We have included a detailed information regarding the total volume of samples collected, volumes used for culture-based study and sequencing analysis in the methodology section of the revised manuscript (Page No. 21; Lines 673-682).\n3. Minor revisions and clarifications: We have carefully addressed the minor, specific suggestions, grammatical errors outlined in the reviewer’s comments which included\n21\nfigure and table clarity, terminology and referencing updates (Page No. 21; Lines 673, Supplementary Table S2, Fig. 8 & 9, Supplementary Fig. S6 & S7).\nWe sincerely hope these revisions satisfactorily address your concerns and enhance the clarity, impact and scientific rigor of the manuscript.\nSpecific comments\nIntroduction:\nIn my opinion, the Introduction can be improved to better explain the need for the study, particularly since the authors imply in the Abstract that they have developed a new assay for the detection of ARGs. However, the current introduction does not adequately address this, and it remains more general in nature. It would benefit from clearer framing to set the premise for the readers, highlighting the significance and novelty of the assay in the context of existing research. Additionally, sewage-ARG related papers from the region have not been cited and incorporating them would provide important context for the study and bring out the novelty of the current study.\nResponse: We thank the reviewer for this insightful comment. In response, we have substantially revised the Introduction section to better articulate the rationale and need for the study, with a clear emphasis on the development and application of the dipstick-based assay for the detection of ARGs (Page Nos. 3-4; Lines 99-103). To strengthen the context and relevance of the study, we have also incorporated and cited several region-specific studies on ARGs in sewage environment, which were previously missing (Page No. 4; Lines 105-113).\nMethods:\nComment 1. The study site should be described in more detail to provide context for the research. This description should include relevant environmental factors and characteristics that may influence the results. Additionally, a map of the study site should be included to visually assist readers in understanding the sampling locations. To enhance transparency and reproducibility, I recommend providing the GPS coordinates of the sampling sites, along with any relevant metadata, in the supplementary materials. This will help to contextualize the study and ensure that others can replicate or build upon the work.\nWe believe these additions will improve the clarity and reproducibility of the study for the researchers.\nSewage collection and measurement of antibiotic concentration using QTRAP mass spectrometer:\nComment 2. Were the samples pooled ? The volumes don’t add up.\nResponse: We appreciate the reviewer’s query regarding the pooling of the samples. The authors would like to mention that the samples collected in this study were not pooled and processed individually. It is also to mention that a total of 200 ml sample was collected from the site, and after mixing thoroughly 30 ml of sample was processed for further downstream experiments (Page No. 21; Lines 673-682).\nComment 3. 514 on ice is repeated twice.\nResponse: We thank the reviewer for pointing out the repetition in the line 514. We have removed the redundant word from the manuscript and rephrased the sentence to improve clarity (Page No. 21; Line 673).\nComment 4. The 30 mL sample volume for environmental samples in the antibiotic analysis is relatively low and may not align with the standardized methods typically used in environmental monitoring studies. The article cited worked with spiked water samples, which have different characteristics compared to natural environmental samples. In my opinion, using such a small sample volume for environmental samples may not capture the full range of antibiotic concentrations, leading to less reliable results. A larger sample volume would likely provide more accurate and representative data for antibiotic analysis in environmental settings.\nWe agree that larger volumes are typically preferred in standardized environmental monitoring protocols, however our study aimed at a targeted and comparative assessment of antibiotic concentrations across multiple samples. Hence, the selected volume was sufficient for reliable detection of antibiotic residues using LC-MS/MS, also this approach ensures comparability and reproducibility, particularly for the targeted objective of the study. We have now expanded the method section and the detailed regarding the sample collection, and their processing have been incorporated in the revised manuscript (Page Nos. 20-21; Lines 671-682).\nComment 5. What was the volume of the sample filtered for mass spec analysis?\nResponse: We appreciate the reviewer’s query. For the mass-spectrometry analysis, we filtered a total of 30 ml of each sample through 0.22 µm Millex GV syringe filters (Merck, Darmstadt, Germany).\nIsolation of sewage metagenomic DNA and screening of antibiotic resistance genes by dipstick-based assay:\nComment 6. How much volume was used for DNA extraction? The method cited mentions using 35 mL of water for DNA extraction, but if only 30 mL were collected, it seems inconsistent. Could you clarify? See comment 2.\nResponse: We appreciate the reviewer’s observation and apologize for the confusion regarding the sample volume. In our study, 200 ml of sewage was collected from each point. From this, 30 ml was aliquoted and processed for metagenomic DNA extraction. While the cited method originally used 35 ml, but we adapted the protocol to our available volume (30 ml) without compromising the efficiency of DNA extraction.\nComment 7. How much volume was used for plating?\nResponse: Reviewer’s query is appreciated. A 30 ml sewage sample was concentrated and resuspended in 1.5 ml of sterile water. For culturing, 100 µl of this concentrated suspension\n24\nwas plated for culturomics study, which correspond to approximately 2 ml of the original sewage sample. The volume details have been incorporated in the revised manuscript (Page No. 21; Lines 678-680).\nComment 8. Provide the DNA conc. in ng/uL. Since a standard DNA extraction method was not used, it would be necessary to include the gel images and/or DNA purity in the supplementary to support the quality of the extracted DNA.\nResponse: We thank the reviewer for the suggestion. The DNA concentration ranged from approximately 55-290 ng/µl, as measured using Nanodrop One spectrophotometer (Thermo Fisher Scientific, Wisconsin, USA). To support the quality of the extracted DNA, we have now included the concentration of each sample along with the purity ratio (A260/A280 and A260/A230) in the Supplementary Table S4b.\nComment 9. Mention the type of purity measurement (Nanodrop or Qubit).\nResponse: We thank the reviewer for the valuable suggestion. DNA purity and concentration were assessed using the Nanodrop One spectrophotometer (Thermo Fisher Scientific, Wisconsin, USA) for purity measurements, and the Qubit Flex fluorometer (Thermo Fisher Scientific, Singapore) particularly prior to sequencing. The necessary information has been incorporated in the manuscript (Page No. 21; Lines 699-700).\nComment 10. What is reference for the said “dipstick” method mentioned? Has this method been validated and published? Please cite the article that describes the method. If the method is novel, it would make more sense to publish it as a separate methods paper with additional experiments and validation to provide a more thorough evaluation of its effectiveness.\nResponse: We thank the reviewer for raising this important point. The dipstick method referred to in this manuscript is based on a previously published and validated platform that we have developed and successfully applied in earlier studies for the detection of bacterial species and viral variants (1. Talukdar et al., 2024, iScience DOI: 10.1016/j.isci.2024.111238; 2. Paul et al., 2025, Front. Microbiol. DOI: 10.3389/fmicb.2024.1459644) where the assay demonstrated robust performance. In the current manuscript, we have adapted this established method for the detection of ARGs in environmental samples, specifically in sewage. This detection assay is an extension of our previously validated dipstick platform. The core platform,\n25\nworkflow, and the detection principles remain consistent with our previously published work, but we have tailored the targeted ARGs and their probe design.\nAs suggested, we have now cited the dipstick references in the Introduction section of the revised manuscript (Page Nos. 3-4; Lines 99-103; Reference Nos: 7 & 8). We have also updated the discussion section of the revised manuscript emphasizing the advantages and limitations of the dipstick assay including considerations of turnaround time, cost-effectiveness, operational simplicity, and suitability for deployment in low-resource settings (Page Nos. 17-18; Lines 557-574).\nComment 11. The Supplementary Table 2 is not immediately clear, and at first glance, it’s difficult to understand what is being presented. Including a few lines of explanation or a brief legend would help readers better interpret the scheme, especially for those who may wish to adapt this approach in their own work. Providing clarity in this section would enhance its utility and accessibility.\nResponse: We appreciate the reviewer’s valuable feedback. To improve the clarity and interpretability of Supplementary Table 2, we have now incorporated a detailed legend which will help the readers to understand the presented information more readily and clearly. The modified legend “Table S2: Detection of antibiotic resistant genes (ARGs) using dipstick-based assay; \"1\" indicates the presence of the resistance gene in the sample, \"0\" denotes the absence of the resistance gene in the sample” have been incorporated in the updated supplementary Table S2 in the revised manuscript.\nResults:\nComment 12. A correlation plot between the observed antibiotic concentrations and ARGs/community composition could be considered to verify the relevance of the authors' conclusion regarding the need for tailored antibiotic use. Currently, no attempt was made to correlate the different datasets, and including such an analysis would strengthen the argument by demonstrating a direct relationship between antibiotic presence and resistance gene dynamics.\nResponse: We thank the reviewer for the valuable suggestion. In response, we have included a correlation analysis plot in the revised manuscript as Supplementary Fig. S4 to evaluate the relationship between the measured antibiotics through LC-MS/MS and the abundance of antimicrobial resistance genes identified. This analysis was performed using spearman\n26\ncorrelation which revealed positive correlation between the macrolide antibiotics and the abundance of mph and msr genes, nalidixic acid with qnr & oqxB genes, kanamycin and spectinomycin residues with aad genes, and amoxicillin residues and bla gene. The relevant details and interpretations have been incorporated into the revised manuscript (Page No. 9; Lines 287-293).\nWe believe that this integrated analysis strengthens our conclusions regarding the role of environmental antibiotic exposure in shaping resistomes and supports the need for tailoring use and context specific antibiotic stewardship strategies.\nComment 13. Line 142 What kind of ordination was performed?\nResponse: We appreciate the reviewer’s query. In response, we would like clarify that two types of ordination analyses viz. 3D Principal Coordinates Analysis (PCoA) and 3D Non-metric Multidimensional Scaling (NMDS) were performed, both based on Bray-Curtis dissimilarity. These methods were employed to robustly visualize and validate the differences in microbial community structure across the samples. We have incorporated the complete details in the Statistical analysis section of the methodology part in the revised manuscript (Page Nos. 25-26; Lines 831-840).\nComment 14. Figure 2 - The beta diversity analysis indicates significant differences between cities, supported by PERMANOVA results (R² = 0.412, p < 0.01). However, the first two axes of the Bray-Curtis PCoA explain only 34.2% of the variance (19% and 15.2%), suggesting that key patterns may exist in higher dimensions. To enhance interpretability, the authors could consider visualizing additional axes (e.g., 3D or scree plot) and exploring alternative ordination methods like NMDS, which may better capture community differences.\nResponse: We thank the reviewer for this valuable and insightful suggestion. In response, we have performed a 3D PCoA to better capture the variation in microbial communities, and the updated results have been incorporated into the main text (Page No. 6; Lines 192-195, Fig 3f). To further strengthen the analysis and provide a more comprehensive comparison, we also conducted a 3D NMDS analysis based on Bray-Curtis dissimilarity, as recommended. The NMDS results and plots have been included in both the main text (Page No. 6; Lines 192-195) and the supplementary materials (Supplementary Fig. S2b).\nAdditionally, to enhance our interpretation of microbial compositional differences across longitudinal sampling sites, we analysed and visualized data from longitudinally collected\n27\nsewage samples from both community and hospital outlets in Faridabad, Haryana. These were assessed using both 3D PCoA and 3D NMDS and the detail finding have been incorporated in the revised manuscript (3D PCoA; Page No. 5; Lines 166-168, Fig. 3b) and (3D NMDS; Page Nos. 5-6; Lines 168-175, Supplementary Fig. S2a).\nComment 15. Line 162 – What was the basis for subsetting the data? It would be helpful to clarify the criteria or rationale used for selecting the specific subsets of data, as this will provide readers with a better understanding of how the analysis was conducted and ensure the approach is reproducible.\nResponse: We thank the reviewer for this valuable feedback. We expanded our shotgun metagenomic sequencing dataset to 167 samples to achieve higher taxonomic resolution and a more comprehensive characterization of microbial functions related to antibiotic resistance and its dissemination. The rationale and relevant information have now been incorporated into the revised manuscript (Page Nos. 6-7; Lines 201-211). The representative subset of these 167 samples was strategically selected to maximize analytical efficiency, ensuring robust and meaningful insights. The selection ensured comprehensive coverage across geographic distribution and temporal heterogeneity of the samples as well as representative from different sewage source types (e.g., community, and hospital-influenced sites).\nThis strategy enabled us to account for spatial and temporal diversity in antibiotic occurrence, aligning with the study’s objective to assess contamination patterns across diverse urban contexts. By ensuring a robust and representative sample, we believe this approach provide meaningful insights, strengthens the study’s goal and enhances its reproducibility.\nComment 16. Figure 5: The PCoA plot provides a useful visualization of microbiome variations across months; however, the first two axes explain only 35% of the variance (21% and 14%), suggesting that key patterns may exist in higher dimensions. To strengthen the analysis, the authors should explore alternative methods like NMDS and assess higher-dimensional variance using a scree plot or 3D PCoA. These could enhance the robustness and clarity of the findings.\nResponse: We sincerely acknowledge the reviewer’s constructive comment. In accordance with the suggestion, we performed a 3D PCoA, and the updated results have been incorporated into the main manuscript (Page No. 7; Lines 214-216, Fig. 5a). To further enhance the robustness and dimensional clarity of our findings, we also carried out a 3D NMDS analysis. The NMDS results, along with their visual representations, are now included in the main text\n28\n(Page No. 7; Lines 222-223) and in the supplementary materials (Supplementary Fig. S2c).\nAdditionally, to better understand temporal shifts in microbial community structure, we conducted a longitudinal, month-wise analysis of the sewage microbiome in Faridabad, with a specific focus on contrasting community versus hospital sources. The observed compositional differences were evaluated using both 3D PCoA (Page No. 8; Lines 248-253; Supplementary Fig. S3d) and 3D NMDS (Page No. 8; Lines 253-255; Supplementary Fig. S2d), and these results have been incorporated into the revised manuscript accordingly.\nDiscussion:\nIn my opinion, the Discussion is not as intuitive as it could be. Given the significant effort put into sample collection, processing, and data the analysis, the discussion could be improved to explain the observed clustering and seasonal differences more clearly. A more detailed interpretation of these results would help readers better understand the underlying factors driving the patterns observed, providing a stronger connection between the findings and the broader implications of the study.\nResponse: We thank the reviewer for this insightful suggestion. In response, we have revised the discussion section to provide a more intuitive and detailed interpretation of the clustering patterns and temporal variations observed in our data (Page No. 18; Lines 575-590).\nFurthermore, we have clarified the broader implications of our findings to strengthen the impact of the study which are as follows:\n• We have incorporated detailed clarification of the sample spread and also the justification behind choosing the Faridabad, Haryana for longitudinal sampling site (Page No. 16; Lines 514-525).\n• We have incorporated a detailed description of the comparative analysis of the antibiotic residues detected in the samples through LC-MS/MS and the ARGs identified by dipstick-based detection assay (Page No. 17; Lines 544-556).\n• We have expanded the discussion section to explicitly address the comprehensive comparison of the dipstick-based assay for detection of ARGs with shotgun metagenomics, including considerations of turnaround time, cost-effectiveness, operational simplicity, and suitability for deployment in low-resource settings (Page Nos. 17-18; Lines 557-574).\n• We have also included detailed insights regarding the use of multi-prong approach to understand the comprehensive picture of AMR scenario in sewage water (Page No. 19-20; Lines 637-645).\nReviewer #1 (Remarks to the Author):\nThe authors have sufficiently addressed all the queries raised and modifications suggested have been incorporated in a satisfactory manner. I recommend the manuscript be accepted for publication.\nResponse: We sincerely thank the reviewer for the positive evaluation of our revised manuscript and for recommending it for publication. We greatly appreciate the precious time and effort, the reviewer has taken to assess our work and strengthen it throughout the review process.\nReviewer #2 (Remarks to the Author):\nThe authors have satisfactorily addressed most of my queries.\nResponse: We are grateful to the reviewer for the positive feedback on our revised manuscript and for acknowledging that the majority of the queries have been satisfactorily addressed. In addition, we have carefully considered few remaining points points and incorporated the necessary clarifications in the updated re-revised manuscript.\nReviewer #3 (Remarks to the Author):\nThe manuscript addresses a relevant topic with potential implications for environmental monitoring and public health. However, in its current form, it suffers from several critical shortcomings that prevent it from making a meaningful contribution to the literature.\nThe overall discussion is weak and largely descriptive, with minimal critical interpretation and very limited comparison with existing data from the same region or from India more broadly, which significantly undermines the contextual relevance of the findings.\nResponse: The author appreciates the reviewer’s comment regarding its potential implication for environmental and public health approach. As per the reviewer’s suggestion, the authors have substantially revised the discussion by critically interpreting the results in the context of occurrence of antibiotic residues in the environment and providing a more critical comparison with previous studies from India and globally (Page Nos. 17-18; Lines 564-590). Specifically, we now discuss the frequent detection of amoxicillin and azithromycin in agreement with prior Indian studies (e.g., Mutiyar and Mittal, 2014; Arun et al., 2022; Akhter et al., 2023;\nOverall, we have revised the discussion to provide a more balanced and informative interpretation by comparing our findings with prior Indian and global studies.\nThe methodology for sample preparation in the antibiotic analysis does not appear to follow standard or validated protocols. No evidence is provided that the current approach was validated, nor are technical or biological replicates described, raising concerns about the robustness and reproducibility of the data. While the manuscript states that eleven antibiotics were analyzed, no information is given on the limits of detection (LOD) or limits of quantification (LOQ), which are critical for assessing the reliability of reported concentrations as the current approach is not a standard one. The discussion of antibiotic residues is largely general and descriptive, without sufficient integration of the study’s own data. For example, the authors do not provide specific values for critical antibiotic in the result or discussion that could support interpretation of environmental or clinical relevance. Explanations for absence or presence of certain antibiotics remain speculative and are not linked back to quantitative data. Similarly, the presentation of the dipstick-based assay is superficial and lacks critical evaluation. The reliability, sensitivity, and known limitations of dipstick methods are not discussed, which is essential for interpreting the validity of the findings.\nResponse: We sincerely appreciate the reviewer’s constructive suggestion and have addressed it accordingly. In our study, sewage water samples were centrifuged and the supernatant filtered through a 0.22 µm syringe filter was used for LC-MS/MS analysis to detect antibiotic residues. While liquid-liquid extraction or solid-phase extraction methods are frequently employed to concentrate antibiotic residues from environmental samples, we performed the direct injection approach to detect and analyse the neat water samples for the presence of antibiotics. This simplified method helped to maintain the integrity of the antibiotics in their original form anticipated to be present in the sewage water sample and also minimizes analyte loss associated with multi-step extraction methods. The details of the sample preparation have been provided\n3\nin the methodology section of the manuscript (Page No. 22-23; Lines 741-746).\nIn our study, technical replicates were included to demonstrate reproducibility of the data, and in addition biological replicates were also represented by longitudinal sewage samples collected independently from the same location at multiple times points. To validate the approach, specifically we have assessed method Linearity (correlation coefficient R² from calibration curve, with concentration range), Precursor ion (m/z) values, and limits of detection (LOD) and limit of quantification (LOQ), and other details have been incorporated in the revised manuscript. As suggested, we agree that reporting the LOD and LOQ is critical for assessing the reliability of the reported antibiotic concentrations. In the revised manuscript, we have now included the LOD and LOQ values for all eleven antibiotics, which were determined based on the signal-to-noise ratio (S/N) of 3:1 and 10:1, respectively following established guidelines. Comprehensive details of the LOD and LOQ values have been incorporated in the revised manuscript (Page No. 5; Lines 139-146 and Supplementary Table 1(l).\nIn response to the reviewer’s comment, regarding the discussion section of antibiotic residue, the authors have addressed it explicitly by linking our explanations with the quantitative data obtained in the study (Page No. 17; Lines 541-557). Specifically, now we have highlighted the highest detection frequency of kanamycin in 66.6% of the samples followed by spectinomycin (50%) and amoxicillin (45.1%) with overall concentration ranging from 0.416-18.711 ng/mL, along with the antibiotics such as erythromycin, tetracycline, nalidixic acid, and rifampicin detected in lower frequency (<15%) in the samples. We have also discussed the absence of ampicillin and polymyxins in the samples, which could be due to the higher degradation and instability in environmental conditions. Additionally, we have strengthened the comparative aspect of antibiotic residues by linking our quantitative findings with previous Indian studies and the details have been incorporated in the revised manuscript (Page No. 17 - 18; Lines 564-581, 584-590).\nAs per the reviewer’s suggestion, we have also expanded the details of the dipstick assay standardization in the methodology section of the revised manuscript (Page No. 23; Lines 760-768). The authors have also revised the discussion elaborating the evaluation of the dipstick assay mainly reliability, sensitivity and its limitation. The detailed information has been incorporated in the discussion section of the revised manuscript (Page No. 18; Lines 604-622).\nWe believe these inclusions in the discussion and methodology section of the manuscript have substantially reduced speculative interpretation, and improve the interpretability of our findings.\nTherefore, I recommend rejection at this stage, with encouragement to submit an improved and more thoroughly revised version.\nResponse: We sincerely thank the reviewer for the constructive and thoughtful evaluation of our manuscript. We fully acknowledge the concerns regarding the methodological rigor, depth of data interpretation, and contextualization with respect to Indian and global studies. In the revised version, we have substantially strengthened the methodological section by\ni) Providing details of the performance characteristics of the LC-MS analysis to detect antibiotic residues including details of linearity, limit of detection (LOD) and limit of quantification (LOQ), coefficient correlation for each antibiotic (Page No. 22; Lines 741-746).\nii) Strengthening the workflow of the molecular based dipstick-assay (Page No. 23; Lines 760-769).\nWe understand that integrating our findings with existing literatures will highlight the novelty and significance. So, as suggested, we have enhanced the discussion by directly comparing our results with relevant Indian and Global studies, highlighting both consistencies and divergences. Furthermore, we have refined the contextual analysis throughout the manuscript to better emphasize the novelty and significance of our findings in the revised manuscript (Page No. 16-17). Also, to strengthen the comparative context of our findings with previous studies, we have incorporated additional references, and are cited as References 15, 17, 19, 28, 39, 47-48 in the revised manuscript.\n6"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have sufficiently addressed all the queries raised and modifications suggested have been incorporated in a satisfactory manner. I recommend the manuscript be accepted for publication.", "reviewer_2": "The authors have satisfactorily addressed most of my queries.", "reviewer_3": "The manuscript addresses a relevant topic with potential implications for environmental monitoring and public health. However, in its current form, it suffers from several critical shortcomings that prevent it from making a meaningful contribution to the literature.\n\nThe overall discussion is weak and largely descriptive, with minimal critical interpretation and very limited comparison with existing data from the same region or from India more broadly, which significantly undermines the contextual relevance of the findings.\n\nThe methodology for sample preparation in the antibiotic analysis does not appear to follow standard or validated protocols. No evidence is provided that the current approach was validated, nor are technical or biological replicates described, raising concerns about the robustness and reproducibility of the data. While the manuscript states that eleven antibiotics were analyzed, no information is given on the limits of detection (LOD) or limits of quantification (LOQ), which are critical for assessing the reliability of reported concentrations as the current approach is not a standard one. The discussion of antibiotic residues is largely general and descriptive, without sufficient integration of the study’s own data. For example, the authors do not provide specific values for critical antibiotic in the result or discussion that could support interpretation of environmental or clinical relevance. Explanations for absence or presence of certain antibiotics remain speculative and are not linked back to quantitative data.\n\nSimilarly, the presentation of the dipstick-based assay is superficial and lacks critical evaluation. The reliability, sensitivity, and known limitations of dipstick methods are not discussed, which is essential for interpreting the validity of the findings.\n\nIn general, although previous reports from India are mentioned, the discussion does not adequately compare current findings with those studies, weakening both the novelty and contextual significance of the results.\n\nIn sum, while the study addresses an important theme, the current version is too limited in methodological rigor, data interpretation, and contextualization to warrant publication. Substantial revisions would be needed to strengthen both the analytical foundation and the scientific discussion.\n\nThe reviewer recognizes that word limits can pose a challenge in providing detail and contextual analysis. Nevertheless, a significantly higher level of rigor, clarity, and integration is required for the manuscript to meet the standards of the journal.\n\nTherefore, I recommend rejection at this stage, with encouragement to submit an improved and more thoroughly revised version."}, "author_response": "Point-by-Point Response to Reviewer Comments\nReviewer #1 (Remarks to the Author):\nThe authors have sufficiently addressed all the queries raised and modifications suggested have been incorporated in a satisfactory manner. I recommend the manuscript be accepted for publication.\nResponse: We sincerely thank the reviewer for the positive evaluation of our revised manuscript and for recommending it for publication. We greatly appreciate the precious time and effort, the reviewer has taken to assess our work and strengthen it throughout the review process.\nReviewer #2 (Remarks to the Author):\nThe authors have satisfactorily addressed most of my queries.\nResponse: We are grateful to the reviewer for the positive feedback on our revised manuscript and for acknowledging that the majority of the queries have been satisfactorily addressed. In addition, we have carefully considered few remaining points points and incorporated the necessary clarifications in the updated re-revised manuscript.\nReviewer #3 (Remarks to the Author):\nThe manuscript addresses a relevant topic with potential implications for environmental monitoring and public health. However, in its current form, it suffers from several critical shortcomings that prevent it from making a meaningful contribution to the literature.\nThe overall discussion is weak and largely descriptive, with minimal critical interpretation and very limited comparison with existing data from the same region or from India more broadly, which significantly undermines the contextual relevance of the findings.\nResponse: The author appreciates the reviewer’s comment regarding its potential implication for environmental and public health approach. As per the reviewer’s suggestion, the authors have substantially revised the discussion by critically interpreting the results in the context of occurrence of antibiotic residues in the environment and providing a more critical comparison with previous studies from India and globally (Page Nos. 17-18; Lines 564-590). Specifically, we now discuss the frequent detection of amoxicillin and azithromycin in agreement with prior Indian studies (e.g., Mutiyar and Mittal, 2014; Arun et al., 2022; Akhter et al., 2023;\nOverall, we have revised the discussion to provide a more balanced and informative interpretation by comparing our findings with prior Indian and global studies.\nThe methodology for sample preparation in the antibiotic analysis does not appear to follow standard or validated protocols. No evidence is provided that the current approach was validated, nor are technical or biological replicates described, raising concerns about the robustness and reproducibility of the data. While the manuscript states that eleven antibiotics were analyzed, no information is given on the limits of detection (LOD) or limits of quantification (LOQ), which are critical for assessing the reliability of reported concentrations as the current approach is not a standard one. The discussion of antibiotic residues is largely general and descriptive, without sufficient integration of the study’s own data. For example, the authors do not provide specific values for critical antibiotic in the result or discussion that could support interpretation of environmental or clinical relevance. Explanations for absence or presence of certain antibiotics remain speculative and are not linked back to quantitative data. Similarly, the presentation of the dipstick-based assay is superficial and lacks critical evaluation. The reliability, sensitivity, and known limitations of dipstick methods are not discussed, which is essential for interpreting the validity of the findings.\nResponse: We sincerely appreciate the reviewer’s constructive suggestion and have addressed it accordingly. In our study, sewage water samples were centrifuged and the supernatant filtered through a 0.22 µm syringe filter was used for LC-MS/MS analysis to detect antibiotic residues. While liquid-liquid extraction or solid-phase extraction methods are frequently employed to concentrate antibiotic residues from environmental samples, we performed the direct injection approach to detect and analyse the neat water samples for the presence of antibiotics. This simplified method helped to maintain the integrity of the antibiotics in their original form anticipated to be present in the sewage water sample and also minimizes analyte loss associated with multi-step extraction methods. The details of the sample preparation have been provided\n3\nin the methodology section of the manuscript (Page No. 22-23; Lines 741-746).\nIn our study, technical replicates were included to demonstrate reproducibility of the data, and in addition biological replicates were also represented by longitudinal sewage samples collected independently from the same location at multiple times points. To validate the approach, specifically we have assessed method Linearity (correlation coefficient R² from calibration curve, with concentration range), Precursor ion (m/z) values, and limits of detection (LOD) and limit of quantification (LOQ), and other details have been incorporated in the revised manuscript. As suggested, we agree that reporting the LOD and LOQ is critical for assessing the reliability of the reported antibiotic concentrations. In the revised manuscript, we have now included the LOD and LOQ values for all eleven antibiotics, which were determined based on the signal-to-noise ratio (S/N) of 3:1 and 10:1, respectively following established guidelines. Comprehensive details of the LOD and LOQ values have been incorporated in the revised manuscript (Page No. 5; Lines 139-146 and Supplementary Table 1(l).\nIn response to the reviewer’s comment, regarding the discussion section of antibiotic residue, the authors have addressed it explicitly by linking our explanations with the quantitative data obtained in the study (Page No. 17; Lines 541-557). Specifically, now we have highlighted the highest detection frequency of kanamycin in 66.6% of the samples followed by spectinomycin (50%) and amoxicillin (45.1%) with overall concentration ranging from 0.416-18.711 ng/mL, along with the antibiotics such as erythromycin, tetracycline, nalidixic acid, and rifampicin detected in lower frequency (<15%) in the samples. We have also discussed the absence of ampicillin and polymyxins in the samples, which could be due to the higher degradation and instability in environmental conditions. Additionally, we have strengthened the comparative aspect of antibiotic residues by linking our quantitative findings with previous Indian studies and the details have been incorporated in the revised manuscript (Page No. 17 - 18; Lines 564-581, 584-590).\nAs per the reviewer’s suggestion, we have also expanded the details of the dipstick assay standardization in the methodology section of the revised manuscript (Page No. 23; Lines 760-768). The authors have also revised the discussion elaborating the evaluation of the dipstick assay mainly reliability, sensitivity and its limitation. The detailed information has been incorporated in the discussion section of the revised manuscript (Page No. 18; Lines 604-622).\nWe believe these inclusions in the discussion and methodology section of the manuscript have substantially reduced speculative interpretation, and improve the interpretability of our findings.\nTherefore, I recommend rejection at this stage, with encouragement to submit an improved and more thoroughly revised version.\nResponse: We sincerely thank the reviewer for the constructive and thoughtful evaluation of our manuscript. We fully acknowledge the concerns regarding the methodological rigor, depth of data interpretation, and contextualization with respect to Indian and global studies. In the revised version, we have substantially strengthened the methodological section by\ni) Providing details of the performance characteristics of the LC-MS analysis to detect antibiotic residues including details of linearity, limit of detection (LOD) and limit of quantification (LOQ), coefficient correlation for each antibiotic (Page No. 22; Lines 741-746).\nii) Strengthening the workflow of the molecular based dipstick-assay (Page No. 23; Lines 760-769).\nWe understand that integrating our findings with existing literatures will highlight the novelty and significance. So, as suggested, we have enhanced the discussion by directly comparing our results with relevant Indian and Global studies, highlighting both consistencies and divergences. Furthermore, we have refined the contextual analysis throughout the manuscript to better emphasize the novelty and significance of our findings in the revised manuscript (Page No. 16-17). Also, to strengthen the comparative context of our findings with previous studies, we have incorporated additional references, and are cited as References 15, 17, 19, 28, 39, 47-48 in the revised manuscript.\n6"}, {"round": 3, "reviewer_comments": {"reviewer_3": "The authors have adequately addressed the comments and incorporated the suggested revisions satisfactorily. I recommend the manuscript for acceptance."}, "author_response": ""}]

Decision letter