Wastewater-based Surveillance Reveals Incomplete Gut Microbiome Recovery Following Easing of COVID-19 Restrictions in Wuhan, China

Wastewater-based Surveillance Reveals Incomplete Gut Microbiome Recovery Following Easing of COVID-19 Restrictions in Wuhan, China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Wastewater-based Surveillance Reveals Incomplete Gut Microbiome Recovery Following Easing of COVID-19 Restrictions in Wuhan, China Chaoqi Chen, Mengqi Zhang, Jinyuan Xue, Yue Wang, Xiqing Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6822143/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract While SARS-CoV-2 infection is known to disrupt the gut microbiome, its population-level impact and long-term recovery trajectory remain poorly understood. Here, we applied longitudinal wastewater metagenomics in Wuhan, China, to track the compositional and functional dynamics of the gut microbiome in a large urban population over a 15-month period. Within three months following the easing of the “zero-COVID” policy, a marked decline in microbial diversity and depletion of beneficial commensals were observed. Meanwhile, the abundance of fermentative taxa (e.g., Bifidobacterium ) increased significantly, suggesting an early-stage compensatory response. Functional pathway analysis revealed elevated fermentation and suppressed biosynthesis (e.g., amino acids), indicating incomplete functional compensation. In parallel, both clinically confirmed pathogens and gut-resident pathobionts (e.g., Clostridioide , Enterococcus , Eggerthella ) expanded, along with sustained increases in antibiotic resistance and virulence genes. By month 15, both community composition and functional profiles of commensal taxa largely converged toward pre-policy change baselines. Although taxonomic profiles largely recovered, the elevation of virulence-associated features persisted, suggesting lasting impacts on microbiome-associated health risks. Notably, the diversity of clinically relevant antibiotic resistance genes within commensal taxa increased markedly at the final time point, suggesting a late-stage enrichment of latent resistance reservoirs. Together, these findings reveal an incomplete microbiome recovery, with structural restoration uncoupled from sustained functional disruption. This study provides the first wastewater-based evidence of large-scale gut microbiome restructuring in response to large-scale SARS-CoV-2 infection, highlighting the utility of wastewater metagenomics in revealing hidden public health effects. Earth and environmental sciences/Environmental sciences/Environmental impact Earth and environmental sciences/Environmental sciences/Environmental chemistry/Environmental monitoring wastewater-based surveillance gut microbiome SARS-CoV-2 antibiotic resistance Wuhan Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Since its emergence in late 2019, the COVID-19 pandemic has profoundly impacted global public health systems 1 . Beyond its acute virological effects, SARS-CoV-2 has disrupted human immune function, medication usage patterns, and daily life behaviors on a global scale 2 , 3 , 4 . Increasing evidence indicates that SARS-CoV-2 has led to notable disruptions in the human gut microbiome, a key regulator of host immunity, metabolism, and inflammation 5 . These shifts may arise from a combination of infection-induced immune responses and indirect influences, such as the widespread use of antivirals and antibiotics 6 . Gut microbiome disturbances, including reduced diversity, depletion of beneficial short-chain fatty acid-producing bacteria, and functional dysbiosis, have been linked to chronic inflammation, metabolic syndrome, and impaired immune homeostasis 7 . Post-acute COVID-19 syndrome (PACS), characterized by persistent fatigue, respiratory issues, and cognitive symptoms, has also been associated with prolonged gut dysbiosis in certain individuals 8 . Despite increasing attention, understanding of the dynamics of the gut microbiome after SARS-CoV-2 infection remains limited. Most existing studies focus on short-term, small-scale clinical cohorts, and their findings often lack consistency. For instance, Zuo et al. analyzed the gut microbiome of 15 COVID-19 patients and 15 healthy controls, revealing a significant increase in opportunistic pathogens (e.g., Clostridium Hathaway ) and a persistent reduction in beneficial taxa (e.g., Faecalibacterium prausnitzii , Bifidobacterium ), even after viral clearance 9 . In contrast, other studies observed partial or paradoxical recovery, with specific beneficial taxa exceeding levels observed in healthy controls. For example, Wu et al. analyzed the gut microbiome of 53 COVID-19 patients and 76 healthy controls and found a significant increase in beneficial taxa (e.g., Lactobacillus ) during hospitalization 10 . These discrepancies may stem from small sample sizes, different treatment histories (e.g., antibiotic usage), and wide heterogeneity in host background factors (age, comorbidities, diet, genetics, etc.). Moreover, these studies offer only cross-sectional or short-term observations from individual stool samples, lacking longitudinal insight into population-level microbiome recovery dynamics. In recent years, wastewater-based surveillance has emerged as a novel and promising approach for non-invasive monitoring of population health dynamics 11 . By analyzing human-associated microbial and genetic material excreted into sewage, wastewater-based surveillance enables real-time, community-level surveillance of gut microbial dynamics at the population scale. Compared to clinical sampling, it enables unbiased monitoring of microbiome diversity, function, and resistance by capturing data from both symptomatic and asymptomatic individuals. For example, Newton et al. analyzed municipal wastewater from 71 U.S. cities and found that microbial diversity in wastewater was three times higher than that in individual stool samples 12 . They also observed substantially lower variation across wastewater compared to individual fecal samples, supporting the use of wastewater as a stable and reproducible proxy for community-level gut microbiome dynamics. More recently, comprehensive metagenomic assembly of wastewater samples from multiple European cities recovered over 2300 metagenome-assembled genomes, many of which were previously undescribed 13 . The study also revealed shifts in community structures linked to human gut origins and antimicrobial resistance profiles, further highlighting the power of wastewater-based surveillance to explore population-level gut microbiome dynamics. In the context of COVID-19, most wastewater-based surveillance studies have focused on tracking pharmaceutical residues or SARS-CoV-2 RNA. For instance, a recent study in Eastern China quantified antibiotic usage before and after the easing of the zero-COVID policy and reported a 5- to 37-fold increase in consumption during the post-policy surge, consistent with prescription data 14 . Another study monitored SARS-CoV-2 RNA and variants in wastewater across the U.S. during the 2022 and 2023 winter and demonstrated that wastewater surveillance could detect changes in viral circulation 30 to 46 days earlier than clinical testing and accurately track emerging variants 15 . While these findings reinforce the value of wastewater-based surveillance as a sensitive early warning system in the post-emergency pandemic period, the analysis was limited to chemical or viral targets and did not investigate broader gut microbiome recovery. To the best of our knowledge, there were only two studies in the literature that have explored the wastewater microbiome associated with COVID-19. Gallardo-Escárate et al. profiled the microbial communities in wastewater from a penitentiary, residential care home, and healthcare facilities using full-length 16S rRNA sequencing 16 . They found that microbiome shifts, particularly the enrichment of gut bacteria linked to gastrointestinal manifestations of COVID-19, preceded SARS-CoV-2 detection in sewage, suggesting that the gut microbiome may serve as an early indicator for community outbreaks. However, this study focused on taxonomic profiling and lacked functional analyses. Brumfield et al. combined DNA metagenomics, RNA transcriptomics, and targeted SARS-CoV-2 sequencing to analyze composite samples collected from downstream of a sewage collection system and detected viral mutations and microbial signatures associated with infection 17 . Although this study demonstrated the utility of multi-omics approaches for characterizing microbial and viral markers during a COVID-19 surge, its analysis was constrained to a brief outbreak period and involved samples from a single sewer catchment. Moreover, both studies only focused on the gut microbial indicators for the COVID-19 outbreak. The gut microbiome resilience and recovery, particularly following large-scale public health interventions (e.g., the easing of restrictions and the following infection surge), were not explored. To address these knowledge gaps, we analyzed municipal wastewater samples in Wuhan, the megacity where the first case of COVID-19 in China was reported. Sampling covered five strategically selected time points (Fig. 1a): a pre-policy-change baseline during the strict “zero-COVID” period and four subsequent stages of post-policy change and full societal reopening. The large-scale SAR-CoV-2 infections provided a unique opportunity to track temporal dynamics in the population-level gut microbiome. We hypothesized that these shifts would elicit a measurable taxonomic and functional response, followed by a recovery process involving community restructuring and potential functional or pathogenic capacity with implications for long-term public health. This study provides the first population-scale assessment for post-pandemic gut microbiome recovery using wastewater metagenomics and lays a foundation for integrating microbial health metrics into future public health surveillance. 2. Materials and methods 2.1 Sample collection Municipal wastewater samples were collected from up to 29 wastewater treatment plants (WWTPs) distributed across Wuhan, a megacity with a population of approximately 13.7 million. Each WWTP serves between 22,000 and 270,000 residents and primarily receives domestic wastewater. Due to constraints related to site access and resource availability, the number of WWTPs sampled varied across the five collection periods: 27 in Spring 2021 (zero-COVID period), 18 in Spring 2023, 23 in Summer 2023, 14 in Winter 2023, and 26 in Spring 2024. These WWTPs collectively covered approximately 70% to nearly the entire residential population of Wuhan at each time point. Detailed information for each sampling site is provided in Supplementary Table S1 and Fig. S1 . All samples were collected from environmental wastewater streams and contained no identifiable human data. The study did not involve human participants or animals and was exempt from institutional ethics review. To ensure comparability across time points and minimize technical variation, all procedures for sample collection, transport, and processing followed strictly standardized laboratory protocols. All sampling events were conducted during dry weather to eliminate the influence of precipitation. During each 24-hour sampling period, influent samples were collected hourly using TC-8000D autosamplers (Qingdao Suyuan Environmental Protection Equipment Co., China) with 4°C refrigeration chambers. Equal volumes from each hourly subsample were combined into a daily composite. From each composite, a 400 mL aliquot was immediately transferred to sterile plastic bottles with ice packs and transported to the laboratory under cold chain conditions. Three consecutive daily composites were pooled in equal volumes to generate a final 100 mL sample per site and time point. Each final sample was immediately subjected to vacuum filtration through a 0.22 µm mixed cellulose ester membrane (Jinteng Co., China) using sintered glass funnels to concentrate microbial biomass. The filter membranes were then transferred into 15 mL sterile centrifuge tubes and stored at -80°C until metagenomic DNA extraction and sequencing. 2.2 DNA Extraction and Metagenomic Sequencing Sample processing, DNA extraction, library preparation, and sequencing were performed using standardized protocols across all time points. Microbial DNA was extracted from the filter membranes using the MagPure Stool DNA Extraction Kit (Magen Biotechnology Co., Guangzhou, China), following the manufacturer’s protocol. The quality and concentration of the extracted DNA were evaluated using 1% agarose gel electrophoresis and spectrophotometry. Samples meeting quality criteria were sent to BGI Tech (Shenzhen, China) for sequencing. Shotgun metagenomic sequencing libraries were prepared using standard protocols. Genomic DNA was fragmented to approximately 350 bp, followed by end repair, A-tailing, adapter ligation, and PCR amplification. Libraries were purified, size-selected, and quantified prior to sequencing. Paired-end sequencing (2 × 150 bp) was performed on the Illumina NovaSeq 6000 platform at BGI Tech (Shenzhen, China), generating approximately 10 GB of data per sample. All raw sequencing data have been deposited in the Genome Sequence Archive in the National Genomics Data Center, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (accession number: CRA024822). The data are publicly accessible at https://ngdc.cncb.ac.cn/gsa . Sequencing and quality control statistics are presented in Supplementary Table S2. 2.3 Taxonomic and functional annotation All bioinformatic analyses were conducted using unified workflows (Fig. S2) to ensure methodological consistency between sampling batches. Detailed bioinformatics procedures are provided in Supplementary Text S1. Briefly, raw sequencing data were quality-controlled using Fastp (Version 0.23.2). Taxonomic annotation was performed using Kraken2 (Version 2.0.8-beta) with the PlusPFP database 18 . Rarefaction curves demonstrated that taxonomic richness reached saturation within the first 100,000 reads, whereas each sample yielded over 30 million high-quality clean reads, confirming sufficient sequencing depth for comprehensive microbial profiling (Fig. S3). Annotated species were cross-referenced against the Unified Human Gastrointestinal Protein (UHGP) catalog 19 and further curated to exclude ubiquitous environmental bacteria not typically associated with the human gut. Microbial species were classified as either commensals or clinically confirmed pathogenic. The commensal group includes both probiotics (beneficial microbes) and pathobionts (microbes that are typically harmless but capable of causing disease under certain conditions). The clinically confirmed pathogenic group includes species recognized as human pathogens in PHI-base 20 . Only species with demonstrated relevance to the human gut microbiome were retained for downstream analysis. A curated list of genera, their constituent species, and supporting references used to determine gut relevance is provided in Supplementary Table S3. Clean reads were assembled de novo using MEGAHIT (Version 1.2.9) 21 . Assembly statistics, including contig counts and length metrics, are shown in Supplementary Table S4. Open reading frames (ORFs) were predicted from the assembled contigs using Prodigal (Version 2.6.3) 22 . The predicted gene and protein sequences were clustered and de-replicated using CD-HIT (Version 4.8.1) to generate a non-redundant gene catalog and a non-redundant protein catalog 23 , 24 . Functional annotation was performed using HUMAnN2 (Version 2.8.2) to identify microbial metabolic pathways 25 . Annotated pathways were grouped into subclass-level functional categories defined in the MetaCyc Metabolic Pathway Database (MetaCyc). Functional categories and their constituent pathways are provided in Supplementary Table S5. Virulence factors were identified using the VFDB core database 26 and categorized into functional classes according to the VFDB's hierarchical classification scheme. Antibiotic resistance genes (ARGs) were annotated using ARGs-OAP (Version 3.2.4) with the SARG database (Version 3.2.1-L) 27 . ARG-carrying contigs were aligned against the NCBI NR database (Version 2024-02) using DIAMOND (Version 2.0.15) to identify the host 28 . ARGs were classified as pathogen-associated if their predicted hosts were recognized as human pathogens in the PHI-base and grouped by resistance class. ARGs whose predicted hosts matched taxa with demonstrated gut relevance were classified as commensal-associated. Commensal-associated ARGs were grouped into clinically relevant resistance genes, defined as those conferring resistance to last-resort antibiotics, as previously described 29 , and other ARGs detected in non-pathogenic gut commensals. A list of clinically relevant commensal-associated ARGs detected in this study is provided in Supplementary Table S6. 2.4 Visualization and Statistical Analysis Data visualization was performed using the R packages vegan 30 and ggplot2 31 , as well as Origin (Version 2021). To ensure comparability across sequencing batches, taxonomic and functional data were normalized to relative abundances using consistent methods detailed below. For taxonomic profiling, relative abundances were calculated by dividing read counts by the total per sample. Alpha diversity was assessed using the Shannon index at the species level. Community structural differences were visualized using non-metric multidimensional scaling (NMDS) of Bray–Curtis dissimilarity, with group differences tested using permutational multivariate analysis of variance (PERMANOVA). Fold changes relative to the Spring 2021 baseline were log₂-transformed for heatmap visualization. Functional pathway, virulence gene, and ARG abundances were normalized to reads per kilobase per million mapped reads (RPKM), then converted to relative abundances by total-sum scaling. For downstream visualization and comparative analysis, the top nine most represented functional categories comprising at least eight underlying MetaCyc pathways were plotted. For virulence genes, pathway-level abundances were first aggregated by category, and those detected in more than 50% of the samples were selected for visualization. Shannon diversity and NMDS were applied at the resistance class level to assess variation in pathogen- and commensal-associated ARG profiles across groups and to minimize ordination stress due to sparsity at the species level. Statistical analysis was conducted using JMP (Version 16.0). Group comparisons were performed using non-parametric Steel tests, with each post-policy change group compared against the Spring 2021 baseline. A significance level of 0.05 was used. Spearman rank correlation was used to evaluate associations between commensal taxa and functional pathways and between pathogenic taxa and virulence factors. 3. Results 3.1 Dynamics of gut-associated microbial community The composition of gut-associated microbial communities exhibited notable temporal shifts following the easing of COVID-19 restrictions (Fig. 1b). At the phylum level, Bacteroidota, Bacillota, and Actinomycetota dominated across all time points. Notably, compared to the baseline in Spring 2021, Actinomycetota abundance showed a marked increase in relative abundance during Spring 2023 (rising from 25–69%), followed by a decline to 29% by Spring 2024. In contrast, Bacteroidota abundance, initially comprising the largest proportion (39%), declined sharply to 8% in Spring 2023, then partially recovered to 29% by Spring 2024. Similarly, Bacillota decreased from 29% in Spring 2021 to 19% in Spring 2023, followed by a recovery to 33% by Spring 2024. Shannon diversity of the commensal gut microbiota was significantly reduced in Spring 2023, three months after the easing of the zero-COVID policy, compared to the baseline in Spring 2021 (p < 0.001), with greater variability across samples (Fig. 1c). Diversity remained significantly reduced in both Summer 2023 (p < 0.05) and Winter 2023 (p < 0.01). By Spring 2024, diversity levels returned to values comparable to the baseline. Commensal gut microbial communities exhibited distinct clustering across sampling stages (stress = 0.119, Fig. 1d). In particular, the Spring 2023 samples formed a significantly distinct cluster compared to the Spring 2021 baseline (PERMANOVA, p < 0.05). Samples from Summer 2023 and Winter 2023 occupied intermediate clusters that partially overlapped with the Spring 2021 baseline. In spring 2024, the commensal gut microbiota profiles were recovered to a configuration comparable to the pre-policy change baseline. In contrast to the commensal gut microbiota, the Shannon index of clinically confirmed pathogen communities increased significantly in Spring 2023 (p < 0.01), Summer 2023 (p < 0.01), and Winter 2023 (p < 0.01), compared to the baseline in Spring 2021 (Fig. 1e). By Spring 2024, diversity levels returned to values comparable to the baseline. Clinically confirmed pathogen communities exhibited substantial compositional overlap across all sampling stages (stress = 0.144; Fig. 1f). However, samples from Spring 2023 showed greater dispersion, with multiple points falling outside the baseline confidence ellipse. In contrast, Summer 2023, Winter 2023, and Spring 2024 samples clustered more closely. 3.2 Fold changes in commensal and clinically confirmed pathogenic taxa Among the commensal taxa (including both beneficial microbes and pathobionts), Eggerthella , Bifidobacterium , and Collinsella showed the most prominent increases in Spring 2023 (by 3.6-, 3.5-, and 2.0-fold, respectively), compared to the Spring 2021 baseline (Fig. 2a). Eggerthella , a gut-resident genus with potential inflammatory relevance, remained significantly elevated throughout the observation period, with fold increases of 2.5, 1.7, and 1.7 in Summer 2023, Winter 2023, and Spring 2024, respectively. Bifidobacterium and Collinsella returned to baseline levels by Spring 2024 Conversely, 21 commensal taxa showed significant reductions in Spring 2023, with fold decreases ranging from 1.3- to 8.4-fold compared to the Spring 2021 baseline. Among these, the abundance of four genera ( Bacteroides , Parabacteroides , Odoribacter , Butyricimonas ) remained persistently and significantly at low levels through Spring 2024. Among the clinically confirmed pathogenic taxa (Fig. 2b), Clostridioides , Enterococcus , Streptococcus , and Staphylococcus showed significant increases in Spring 2023, with fold changes ranging from 1.3 to 2.4. By Spring 2024, the relative abundances of all these genera returned to baseline levels, except for Clostridioides , which remained 1.3-fold higher than baseline. In contrast, Klebsiella and Enterobacter exhibited significant reductions in Spring 2023 by 1.9- and 1.4-fold, respectively. Other pathogenic genera exhibited no consistent directional shifts. 3.3 Functional pathways of gut microbial community Pathways associated with core biosynthesis and redox balance showed early and marked suppression. Amino acid biosynthesis (Fig. 3a) declined significantly in Spring 2023 (p < 0.01) and remained suppressed through Summer 2023 (p < 0.001), followed by partial recovery in Winter 2023. A similar pattern was observed for cofactor, carrier, and vitamin biosynthesis (Fig. 3b), which showed a significant reduction in Summer 2023 (p < 0.01). In contrast, nucleoside and nucleotide degradation (Fig. 3c) exhibited a significant and persistent increase from Spring 2023 through Spring 2024 (all p < 0.05). Fermentation activity (Fig. 3d) increased significantly in Spring 2023 ( p < 0.05 ). Nucleoside and nucleotide biosynthesis (Fig. 3e) increased significantly in Summer 2023 (p < 0.001). Other pathways, such as amino acid degradation (Fig. 3f), carbohydrate biosynthesis (Fig. 3g), carbohydrate degradation (Fig. 3h), and fatty acid and lipid biosynthesis (Fig. 3i) remained stable across all time points. Correlation analysis further revealed that the depletion of commensal genera (e.g., Bacteroides ) was associated with suppressed amino acid biosynthetic, while Bifidobacterium , Collinsella , and Eggrthella showed strong positive correlations with fermentative pathways (Fig. S4). 3.4 Virulence pathway of pathogen A significant and sustained reduction was observed in adherence functions (Fig. 4a), which declined sharply in Spring 2023 and remained significantly lower through Spring 2024 (p < 0.001). Similarly, biofilm-associated functions (Fig. 4b) showed significant reductions in Summer 2023 and Spring 2024 (p < 0.05). Motility-related functions (Fig. 4c) also declined significantly in Spring 2023, Summer 2023, and Spring 2024 (p < 0.01 to p < 0.001). Antimicrobial activity and competitive advantage functions (Fig. 4d) also decreased significantly in Spring 2023 and Spring 2024 (p < 0.05 ). In contrast, several virulence-associated categories exhibited elevated levels. A progressive and significant rise was observed in effector delivery systems (Fig. 4e), with relative abundance increasing from Spring 2023 through Spring 2024 (p < 0.05 to p < 0.001). Similarly, regulatory functions (Fig. 4f) were significantly elevated throughout the post-policy change period (p < 0.001). Immune modulation pathways (Fig. 4g) also increased significantly in Spring and Summer 2023 (p < 0.001) and remained elevated in Spring 2024 (p < 0.01). Nutritional and metabolic virulence factors (Fig. 4h) increased significantly in Spring 2024 (p < 0.001). In contrast, stress survival mechanisms (Fig. 4i) remained stable across all time points, showing no significant temporal change. Together, these trends suggest a functional shift in virulence strategies. The increased abundance of immune modulation, effector delivery, and metabolic virulence were positively correlated with clinically confirmed pathogens such as Chloridoides and Enterococcus (Fig. S5), supporting their potential role in driving pathogenic functions during community restructuring. 3.5 Antibiotic resistance genes The RPKM-normalized abundance of ARGs exhibited a marked increase in Spring 2023, coinciding with the post-easing infection surge (Fig. 5a). Total ARG abundance increased approximately 3.0-fold compared to the pre-policy change baseline (Spring 2021), driven by widespread increases across both pathogen- and non-pathogen-associated sources. ARGs associated with non-pathogens increased by 6.1-fold, while ARGs associated with pathogens increased by 1.7-fold. Among ARG-carrying pathogens, sharp increases were observed for Clostridioides (7.8-fold), Enterococcus (7.8-fold), and Streptococcus (13.8-fold). By Spring 2024, total ARG abundance had returned to a near-baseline level. The class-level composition of ARGs revealed dynamic shifts between pathogen- and commensal-associated sources over time (Fig. 5b). For pathogen-associated ARGs, multidrug resistance (PATH-Multidrug), macrolide–lincosamide–streptogramin resistance (PATH-MLS), and β-lactam resistance genes (PATH-Beta-lactam) all showed a marked decline in Spring 2023. While PATH-Multidrug returned to near-baseline levels by Spring 2024, PATH-Beta-lactam exhibited a persistent downward trend, declining from 10% in Spring 2021 to 3% in Spring 2024. In contrast, commensal-associated multidrug resistance genes (COMM-Multidrug) showed a marked increase at the final time point, rising from 2% in Spring 2021 to 15% in Spring 2024. The Shannon diversity of pathogen-associated ARGs increased significantly following the easing of COVID-19 restrictions (Fig. 5c). Compared to the pre-policy change baseline in Spring 2021, diversity increased sharply in Spring 2023 (p < 0.001), remained elevated in Summer (p < 0.001) and Winter 2023 (p < 0.01). By Spring 2024, the diversity had returned to the level of baseline. The overall composition of pathogen-associated resistance genes remained relatively stable. Samples from different stages exhibited substantial overlap, and no clear temporal clustering was observed (Stress = 0.171; Fig. 5d). Notably, the Shannon diversity of commensal-associated ARGs showed a significant increase in Spring 2023 (p < 0.05) and Spring 2024 (p < 0.001; Fig. 5e). Spring 2024 samples exhibited a noticeable shift to earlier time points (Stress = 0.134; Fig. 5f). 4. Discussion 4.1 Early microbiome disturbance and compensatory shifts (within the first three months) Substantial shifts in gut microbial community structure following the easing of the “zero-COVID” policy indicate that the large-scale SAR-CoV-2 infections triggered widespread and persistent disruption to the gut microbiome at the population level. Within the first three months, there was a marked decline in commensal microbial alpha diversity, accompanied by a significant reduction of 21 commensal genera, including well-established beneficial taxa such as Bacteroides, Parabacteroides, Odoribacter , and Butyricimonas . These findings align with multiple clinical studies reporting COVID-19-associated dysbiosis, particularly characterized by the depletion of beneficial taxa 32 , 33 , 34 . Interestingly, we observed a pronounced enrichment of Bifidobacterium , which may reflect a transient compensatory response. Members of this genus are known to support mucosal barrier integrity and modulate inflammation. An increase in their abundance may well be a sign of an early gut microbiome restoration. Depletion of beneficial taxa, including Bifidobacterium , has been documented 35 , which contradicts to our results. This discrepancy may be due to differences in disease severity, sampling time, and probiotic use. Clinical cohorts often include hospitalized, severe cases with substantial dysbiosis, whereas our wastewater-based approach reflects the broader population, including mild or asymptomatic infections. Moreover, our sampling at three months post-infection may capture a recovery phase beyond the acute stage, and widespread probiotic use during the pandemic may have contributed to Bifidobacterium enrichment in the community. Furthermore, our results were consistent with the observations by Yeoh et al. 36 , who reported that although Bifidobacterium levels were reduced during the acute phase of infection, Bifidobacterium dentium became significantly enriched one month after hospital discharge, surpassing levels in uninfected controls. This post-acute rebound corroborates our observation of elevated Bifidobacterium abundance around three months post-infection at the population level. This early-phase microbial community shift was accompanied by a pronounced change in functional pathways. Fermentative pathways were significantly elevated, likely driven by the expansion of taxa such as Bifidobacterium . Early fermentation products like acetate and other short-chain fatty acids are known to have immunomodulatory effects that could influence recovery. Our data suggest that while many normal commensals were depleted, certain fermentative microbes proliferated as an initial compensatory response. However, we observed significant suppression of core biosynthetic functions, including amino acid biosynthesis and vitamin biosynthesis. These pathways are critical for maintaining redox balance, cellular metabolism, and micronutrient availability and are typically supported by commensal taxa such as Bacteroides . Their decline further corroborates the widespread depletion of beneficial gut bacteria and suggests that, despite the compensatory increase in fermentation, the overall metabolic capacity of the gut microbiome remained functionally impaired during the early post-infection period. In addition, we also observed blooms of clinically confirmed pathogenic, including Clostridioides difficile and Enterococcus faecalis , as well as pathobiont such as Eggerthella . Significant enrichment of Eggerthella and Enterococcus has been reported in COVID-19 patients, accompanied by decreased microbial diversity and increased pathobiont burden 37 . This pathogenic expansion was paralleled by an increase in virulence-associated gene functions (including immune modulation, regulatory activity, and effector delivery systems), suggesting a microbial adaptation toward immune evasion during this vulnerable period. Both commensal-associated and pathogen-associated ARGs were significantly elevated. Interestingly, this occurred alongside a decline in functions related to adhesion, motility, and interbacterial antagonism, indicating that while direct colonization and competition potential weakened, resistance traits were retained or even enhanced. These observations suggest that antimicrobial resistance risk may extend beyond overt pathogens, reflecting a broader and potentially more persistent threat within the gut microbiome following large-scale infection. 4.2 Long-Term Community and Functional Recovery (up to 15 months) Over the ensuing 15 months, the gut microbiome showed substantial recovery in overall community structure. Alpha diversity and community composition rebounded towards pre-infection baselines by around 15 months post-infection. The relative abundance of many commensal microbiota returned to a level comparable to the baseline in Spring 2021. This indicates that the core microbial diversity lost during acute COVID-19 was largely restored over time. However, not all commensals fully recovered, as four genera commonly associated with a healthy gut ( Parabacteroides , Bacteroides , Odoribacter , and Butyricimonas ) remained persistently and significantly reduced through Spring 2024. This sustained depletion indicates that, despite the overall structural recovery, certain beneficial taxa may be more vulnerable to long-term disruption. In parallel, despite this taxonomic recovery, several pathogen-associated taxa remained persistently elevated even at 15 months post-infection. For example, Clostridioides difficile , Enterococcus faecalis , and Eggerthella were still present at levels higher than the baseline in Spring 2024. The relative abundance of microbial virulence genes also remained elevated in the long term, implying that the gut microbial community continued to carry extra immunogenic and pathogenic potential well into the recovery period. A dysbiosis marked by opportunistic pathogens and excess virulence factors can promote a pro-inflammatory milieu and compromise the gut barrier in post-acute COVID-19 syndrome 38 . This suggests that even with a recovered microbiota composition, functional risks related to inflammation and vulnerability may persist. Notably, commensal-associated genes conferring multidrug resistance showed a marked increase in Spring 2024, contributing to elevated ARG diversity and a more distinct resistome profile. While previous studies have reported transient ARG enrichment within six months post-infection 39 , our results extended this phenomenon to 15 months post-infection and revealed that and revealed a delayed yet notable accumulation of clinically relevant ARGs within gut commensals. This study provides population-level evidence that commensals may increasingly harbor resistance traits following a large-scale public health disruption. These non-pathogenic microbes may act as a long-term reservoir of resistance traits, which may facilitate horizontal gene transfer to opportunistic or pathogenic taxa, thereby elevating the risk of treatment failure and complicating infection control efforts at the population level. 4.3 Implication and Future perspective This study offers a population-level, metagenome-based longitudinal analysis of gut microbiome recovery following widespread SARS-CoV-2 infection. Unlike previous clinical studies focusing on individual cases with short follow-up periods, our approach leverages wastewater-based surveillance to capture broader community-level trends. Deep metagenomic sequencing enabled simultaneous tracking of taxonomic and functional dynamics, revealing both compositional shifts and changes in metabolic activity, virulence, and antibiotic resistance. The sharp decline in microbial diversity and key metabolic functions during the early stage suggests a period of elevated population-level vulnerability. The depletion of beneficial commensals, along with disruptions in pathways related to amino acid and vitamin synthesis, may compromise immune regulation and gut barrier integrity, thereby increasing susceptibility to health risks 40 . While many commensal taxa and their associated metabolic functions showed substantial long-term restoration, several key genera, particularly Bacteroides , remained persistently depleted. Bacteroides constitute one of the most abundant and functionally pivotal genera in the human gut microbiota, playing central roles in dietary glycan degradation, vitamin biosynthesis, and immune modulation 41 . The apparent functional recovery may be driven by compensatory activity from taxa less favorable to host health. For instance, vitamin biosynthesis can be maintained by opportunistic microbes under dysbiotic conditions 42 , and specific pathogens can activate amino acid biosynthesis to expand in the presence of the microbiota 43 . Furthermore, virulence potential remained elevated beyond the initial short-term disruption, indicating that microbiome recovery remains incomplete. Persistent pathogenic activity may continue to pose long-term public health risks. In addition, the accumulation of clinically relevant ARG within commensal microbes following the post-policy change period is of particular concern. Further investigation is warranted to understand how persistent microbial imbalances and functional alterations contribute to long-term health vulnerabilities. In addition, our findings highlight the importance of sustained attention and coordinated responses across multiple levels. At the individual level, adopting microbiome-supportive behaviors remains essential. Increasing dietary fiber intake, avoiding unnecessary antibiotic use, and maintaining gut health awareness may help promote microbial resilience. At the clinical level, delayed or incomplete microbiome recovery following widespread SARS-CoV-2 infection requires attention. Monitoring gut-related symptoms, incorporating microbiome-informed strategies into long-term care, and supporting vulnerable individuals with nutritional or probiotic interventions may help mitigate subclinical consequences. At the policy level, expanding wastewater-based surveillance to include functional microbiome indicators, such as virulence and resistance genes, may enhance early detection of subclinical dysbiosis and emerging microbial risks. Embedding these approaches into routine environmental health frameworks could improve public health challenges preparedness. In addition, governments may promote community-level microbiome restoration through public health education, dietary policy, and equitable access to gut-supportive interventions like fiber-enriched foods, prebiotics, or probiotics. Together, these efforts may enhance long-term population resilience against future microbiome-related health challenges. 5. Conclusion This study provides population-scale, metagenomic evidence that SARS-CoV-2 infection led to long-lasting disruptions in the human gut microbiome, with delayed recovery of commensal structure and persistent functional abnormalities. By leveraging wastewater-based surveillance, we highlight a scalable, non-invasive approach to monitor microbial health at the community level and detect emerging risks such as antibiotic resistance and virulence persistence. These insights offer new directions for microbiome-informed public health strategies in the post-pandemic era. Declarations Acknowledgments This work was supported by the National Science Foundation of China (Grant No. 42277469 and No. 41877508). References Haileamlak A (2021) The impact of COVID-19 on health and health systems. Ethiop J Health Sci 31:1073 Knell G, Robertson MC, Dooley EE, Burford K, Mendez KS (2020) Health behavior changes during COVID-19 pandemic and subsequent stay-at-home orders. Int J Environ Res Public Health 17:6268 Guo J et al (2024) SARS-CoV-2 Nsp7 plays a role in cognitive dysfunction by impairing synaptic plasticity. Front Neurosci 18:1490099 Le-Dang M-A et al (2024) Impact of COVID-19 on patterns of drug utilization: A case study at national hospital. PLoS ONE 19:e0297187 Zhang J et al (2023) Changes of gut microbiota under different nutritional methods in elderly patients with severe COVID-19 and their relationship with prognosis. Front Immunol 14:1260112 Vodnar D-C et al (2020) Coronavirus disease (COVID-19) caused by (SARS-CoV-2) infections: A real challenge for human gut microbiota. Front Cell Infect Microbiol 10:575559 Sultan S et al (2021) Metabolic influences of gut microbiota dysbiosis on inflammatory bowel disease. Front Physiol 12:715506 Nalbandian A et al (2021) Post-acute COVID-19 syndrome. Nat Med 27:601–615 Zuo T et al (2020) Alterations in Gut Microbiota of Patients With COVID-19 During Time of Hospitalization. Gastroenterology 159:944–955 Wu Y et al (2021) Altered oral and gut microbiota and its association with SARS-CoV-2 viral load in COVID-19 patients during hospitalization. Npj Biofilms Microbiomes 7:1–9 Singer AC et al (2023) A world of wastewater-based epidemiology. Nat Water 1:408–415 Newton RJ et al (2015) Sewage reflects the microbiomes of human populations. MBio 6 Becsei Á et al (2024) Time-series sewage metagenomics distinguishes seasonal, human-derived and environmental microbial communities potentially allowing source-attributed surveillance. Nat Commun 15:7551 Zang J et al (2024) Impact of easing COVID-19 restrictions on antibiotic usage in eastern China using wastewater-based epidemiology. Nat Commun 15:1–12 Jimenez BS et al (2023) Wastewater surveillance in the COVID-19 post-emergency pandemic period: A promising approach to monitor and predict SARS-CoV-2 surges and evolution. Heliyon 9:e22356 Gallardo-Escárate C et al (2021) The wastewater microbiome: A novel insight for COVID-19 surveillance. Sci Total Environ 764:142867 Brumfield KD et al (2022) Microbiome analysis for wastewater surveillance during COVID-19. MBio . 13, e00591 Wood DE, Lu J, Langmead B (2019) Improved metagenomic analysis with Kraken 2. Genome Biol 20:1–13 Almeida A et al (2021) A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat Biotechnol 39:105–114 Urban M et al (2020) PHI-base: The pathogen–host interactions database. Nucleic Acids Res 48:D613–D620 Li D et al (2015) MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31:1674–1676 Hyatt D et al (2010) Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform 11:1–11 Li W, Godzik A (2006) Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22:1658–1659 Fu L, Niu B, Zhu Z, Wu S, Li W (2012) CD-HIT: Accelerated for clustering the next-generation sequencing data. Bioinformatics 28:3150–3152 Franzosa EA et al (2018) Species-level functional profiling of metagenomes and metatranscriptomes. Nat Methods 15:962–968 Chen L et al (2005) VFDB: A reference database for bacterial virulence factors. Nucleic Acids Res 33:D325–D328 Yin X et al (2023) ARGs-OAP v3. 0: Antibiotic-resistance gene database curation and analysis pipeline optimization. Eng 27:234–241 Buchfink B, Reuter K, Drost H-G (2021) Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat Methods 18:366–368 Chen C et al (2019) Effect of antibiotic use and composting on antibiotic resistance gene abundance and resistome risks of soils receiving manure-derived amendments. Environ Int 128:233–243 Oksanen J et al (2025) vegan: Community Ecology Package. R package version 2.6–10 Wickham H (2016) ggplot2: Elegant Graphics for Data Analysis. Springer, New York He F et al (2021) Fecal multi-omics analysis reveals diverse molecular alterations of gut ecosystem in COVID-19 patients. Anal Chim Acta 1180:338881 Liu Q et al (2022) Multi-kingdom gut microbiota analyses define COVID-19 severity and post-acute COVID-19 syndrome. Nat Commun 13:1–11 Zhang D et al (2023) Gastrointestinal symptoms of long COVID-19 related to the ectopic colonization of specific bacteria that move between the upper and lower alimentary tract and alterations in serum metabolites. BMC Med 21:1–20 Su Q et al (2024) The gut microbiome associates with phenotypic manifestations of post-acute COVID-19 syndrome. Cell Host Microbe 32:651–660 Yeoh YK et al (2021) Gut microbiota composition reflects disease severity and dysfunctional immune responses in patients with COVID-19. Gut 70:698–706 Agudelo C et al (2025) Enterococcus and eggerthella species are enriched in the gut microbiomes of COVID-19 cases in uganda. Gut Pathog 17:1–13 Smail SW et al (2025) Microbiome dysbiosis in SARS-CoV-2 infection: Implication for pathophysiology and management strategies of COVID-19. Front Cell Infect Microbiol 15:1537456 Su Q et al (2022) Antibiotics and probiotics impact gut antimicrobial resistance gene reservoir in COVID-19 patients. Gut Microbes 14:2128603 Clemente JC, Ursell LK, Parfrey LW, Knight R (2012) The impact of the gut microbiota on human health: An integrative view. Cell 148:1258–1270 Zafar H, Saier MH Jr (2021) Gut bacteroides species in health and disease. Gut Microbes 13:1848158 Tarracchini C et al (2024) Exploring the vitamin biosynthesis landscape of the human gut microbiota. mSystems 9:e00929–e00924 Caballero-Flores G, Pickard JM, Fukuda S, Inohara N, Núñez G (2020) An enteric pathogen subverts colonization resistance by evading competition for amino acids in the gut. Cell Host Microbe 28:526–533 Additional Declarations There is NO Competing Interest. Supplementary Files Graphicabstract.pdf Graphic abstract Supportinginformation.docx Supporting information Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6822143","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":485727277,"identity":"a89c151a-bb39-4b56-a114-fd4f7fbc5391","order_by":0,"name":"Chaoqi Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDACCSBmbAASzMwHmMEiB4jXwpZAqhYGHgPitPDPbj724OcOOzm+4zzfpAvbGOT4biQwfi7AZ8mdY+mGvWeSjSUP826TntnGYCx5I4FZegYeLQYSOWbSjG3MiRuAWm7ztjEkbriRwMbMg1dL/jeglnqgFp5nIC31RGjJYQNqOQzSwgbSkmBASIvEjTQzyd6240C/sJn/5jknYTjzzMNmaXxa+GckP5P42VYtx3f+8GNjnjIbeb7jyQc/49OCAAcgtjLAooloLaNgFIyCUTAKMAEASVVIwlUZCa0AAAAASUVORK5CYII=","orcid":"","institution":"Wuhan University","correspondingAuthor":true,"prefix":"","firstName":"Chaoqi","middleName":"","lastName":"Chen","suffix":""},{"id":485727279,"identity":"f1d82ed7-758a-4fcb-beb6-60d98a722205","order_by":1,"name":"Mengqi Zhang","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Mengqi","middleName":"","lastName":"Zhang","suffix":""},{"id":485727281,"identity":"4c3715e7-782f-4b3f-bf2d-1a6e8cf592e2","order_by":2,"name":"Jinyuan Xue","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Jinyuan","middleName":"","lastName":"Xue","suffix":""},{"id":485727282,"identity":"aac7444d-c0b3-4fca-9f4e-0074568122c6","order_by":3,"name":"Yue Wang","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Wang","suffix":""},{"id":485727287,"identity":"e78f2b1f-71cb-40e3-8024-4f82e250778d","order_by":4,"name":"Xiqing Li","email":"","orcid":"https://orcid.org/0000-0002-3162-5068","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Xiqing","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-06-04 15:52:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6822143/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6822143/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88788478,"identity":"f357478f-42a4-489e-b2bd-88e645156f34","added_by":"auto","created_at":"2025-08-11 12:19:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":98565,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal dynamics of gut microbial diversity and composition following the easing of the “zero-COVID” policy in China.(a) COVID-19 case timeline in China (2021–2025) with sampling points (red dots) and the easing of the “zero-COVID” policy (yellow asterisk). (b) Phylum-level composition across time points. (c) Shannon diversity of gut commensal microbiota. (d) NMDS based on commensal taxa. (e) Shannon diversity of clinically confirmed pathogens. (f) NMDS based on pathogenic taxa. Significance in panels (c) and (e) was tested against the Spring 2021 baseline using the Steel test with control (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001). Figure 1***********bceafd\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6822143/v1/10cebf10e5bf5239fd8de2bc.jpg"},{"id":88789890,"identity":"e0a6de48-a202-41d4-8a2a-b61f3180ee4e","added_by":"auto","created_at":"2025-08-11 12:35:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":114511,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal dynamics of commensal and pathogenic gut microbial taxa following the easing of COVID-19 restrictions. (a) Fold changes in relative abundance for commensal taxa across five time points from Spring 2021 to Spring 2024. (b) Fold changes in clinically confirmed pathogenic taxa over the same period.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6822143/v1/0685d2008b97f68465b5448b.jpg"},{"id":88788480,"identity":"fd38b2b2-a7be-461a-83ab-7fd4e39e8c69","added_by":"auto","created_at":"2025-08-11 12:19:46","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102544,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of functional gene pathways across five time points. Pathways include (a) Amino Acid Biosynthesis, (b) Cofactor, Carrier, and Vitamin Biosynthesis, (c) Nucleoside and Nucleotide Degradation, (d) Fermentation, (e) Nucleoside and Nucleotide Biosynthesis, (f) Amino Acid Degradation, (g) Carbohydrate Biosynthesis, (h) Carbohydrate Degradation, and (i) Fatty Acid and Lipid Biosynthesis. Significance was tested against the Spring 2021 baseline using the Steel–Dwass test (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6822143/v1/dd91098484200795765066ab.jpg"},{"id":88789479,"identity":"f7ae307b-b1bf-4455-808a-5fab28e5f9d8","added_by":"auto","created_at":"2025-08-11 12:27:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":92471,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of virulence-associated gene functions across five time points. Functional categories include: (a) Adherence, (b) Biofilm, (c) Motility, (d) Antimicrobial activity/Competitive advantage, (e) Effector delivery system, (f) Regulation, (g) Immune modulation, (h) Nutritional/Metabolic factor, and (i) Stress survival. Significance was tested against the Spring 2021 baseline using the Steel test with control (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6822143/v1/10567d3910e8e53e48a00174.jpg"},{"id":88788483,"identity":"b0cac060-0c8c-40bd-aabc-9f4ff66a7dae","added_by":"auto","created_at":"2025-08-11 12:19:46","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":81899,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal dynamics of the antibiotic resistome in the human gut microbiome from Spring 2021 to Spring 2024. (a) Total RPKM of ARG-carrying genera, separated by pathogenic (colored) and non-pathogenic (grey) hosts. (b) Relative abundance (%) of ARG classes, grouped by commensal (COMM) and pathogenic (PATH) hosts and by resistance type. (c) Shannon diversity index of pathogen-associated ARGs. (d) NMDS based on pathogen-associated ARG profiles. (e) Shannon diversity index of commensal-associated ARGs. (f) NMDS based on commensal-associated ARG profiles. Significance in panels (c) and (e) was tested against the Spring 2021 baseline using the Steel test with control (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6822143/v1/1a65e3c8a1648a903943e9f9.jpg"},{"id":88790779,"identity":"02720f95-fd09-4c95-ad4d-3a8df79f1d99","added_by":"auto","created_at":"2025-08-11 12:43:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1190902,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6822143/v1/0c9097b5-6e86-44dc-85a0-35d62f6a5954.pdf"},{"id":88789477,"identity":"fa7e9401-2720-4c89-8d79-758438c6f9b4","added_by":"auto","created_at":"2025-08-11 12:27:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":41434,"visible":true,"origin":"","legend":"Graphic abstract","description":"","filename":"Graphicabstract.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6822143/v1/e887974ebc55bff209fec635.pdf"},{"id":88788484,"identity":"720c5e28-a945-4904-b987-d351914f3dc3","added_by":"auto","created_at":"2025-08-11 12:19:46","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":633068,"visible":true,"origin":"","legend":"Supporting information","description":"","filename":"Supportinginformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6822143/v1/96facc835868bfd6d590d6cc.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Wastewater-based Surveillance Reveals Incomplete Gut Microbiome Recovery Following Easing of COVID-19 Restrictions in Wuhan, China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSince its emergence in late 2019, the COVID-19 pandemic has profoundly impacted global public health systems\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Beyond its acute virological effects, SARS-CoV-2 has disrupted human immune function, medication usage patterns, and daily life behaviors on a global scale\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Increasing evidence indicates that SARS-CoV-2 has led to notable disruptions in the human gut microbiome, a key regulator of host immunity, metabolism, and inflammation\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. These shifts may arise from a combination of infection-induced immune responses and indirect influences, such as the widespread use of antivirals and antibiotics\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Gut microbiome disturbances, including reduced diversity, depletion of beneficial short-chain fatty acid-producing bacteria, and functional dysbiosis, have been linked to chronic inflammation, metabolic syndrome, and impaired immune homeostasis\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Post-acute COVID-19 syndrome (PACS), characterized by persistent fatigue, respiratory issues, and cognitive symptoms, has also been associated with prolonged gut dysbiosis in certain individuals\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite increasing attention, understanding of the dynamics of the gut microbiome after SARS-CoV-2 infection remains limited. Most existing studies focus on short-term, small-scale clinical cohorts, and their findings often lack consistency. For instance, Zuo et al. analyzed the gut microbiome of 15 COVID-19 patients and 15 healthy controls, revealing a significant increase in opportunistic pathogens (e.g., \u003cem\u003eClostridium Hathaway\u003c/em\u003e) and a persistent reduction in beneficial taxa (e.g., \u003cem\u003eFaecalibacterium prausnitzii\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e), even after viral clearance\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In contrast, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eother\u003c/span\u003e studies observed partial or paradoxical recovery, with specific beneficial taxa exceeding levels observed in healthy controls. For example, Wu et al. analyzed the gut microbiome of 53 COVID-19 patients and 76 healthy controls and found a significant increase in beneficial taxa (e.g., \u003cem\u003eLactobacillus\u003c/em\u003e) during hospitalization\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. These discrepancies may stem from small sample sizes, different treatment histories (e.g., antibiotic usage), and wide heterogeneity in host background factors (age, comorbidities, diet, genetics, etc.). Moreover, these studies offer only cross-sectional or short-term observations from individual stool samples, lacking longitudinal insight into population-level microbiome recovery dynamics.\u003c/p\u003e\u003cp\u003eIn recent years, wastewater-based surveillance has emerged as a novel and promising approach for non-invasive monitoring of population health dynamics\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. By analyzing human-associated microbial and genetic material excreted into sewage, wastewater-based surveillance enables real-time, community-level surveillance of gut microbial dynamics at the population scale. Compared to clinical sampling, it enables unbiased monitoring of microbiome diversity, function, and resistance by capturing data from both symptomatic and asymptomatic individuals. For example, Newton et al. analyzed municipal wastewater from 71 U.S. cities and found that microbial diversity in wastewater was three times higher than that in individual stool samples\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. They also observed substantially lower variation across wastewater compared to individual fecal samples, supporting the use of wastewater as a stable and reproducible proxy for community-level gut microbiome dynamics. More recently, comprehensive metagenomic assembly of wastewater samples from multiple European cities recovered over 2300 metagenome-assembled genomes, many of which were previously undescribed\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The study also revealed shifts in community structures linked to human gut origins and antimicrobial resistance profiles, further highlighting the power of wastewater-based surveillance to explore population-level gut microbiome dynamics.\u003c/p\u003e\u003cp\u003eIn the context of COVID-19, most wastewater-based surveillance studies have focused on tracking pharmaceutical residues or SARS-CoV-2 RNA. For instance, a recent study in Eastern China quantified antibiotic usage before and after the easing of the zero-COVID policy and reported a 5- to 37-fold increase in consumption during the post-policy surge, consistent with prescription data\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Another study monitored SARS-CoV-2 RNA and variants in wastewater across the U.S. during the 2022 and 2023 winter and demonstrated that wastewater surveillance could detect changes in viral circulation 30 to 46 days earlier than clinical testing and accurately track emerging variants\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. While these findings reinforce the value of wastewater-based surveillance as a sensitive early warning system in the post-emergency pandemic period, the analysis was limited to chemical or viral targets and did not investigate broader gut microbiome recovery.\u003c/p\u003e\u003cp\u003eTo the best of our knowledge, there were only two studies in the literature that have explored the wastewater microbiome associated with COVID-19. Gallardo-Esc\u0026aacute;rate et al. profiled the microbial communities in wastewater from a penitentiary, residential care home, and healthcare facilities using full-length 16S rRNA sequencing\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. They found that microbiome shifts, particularly the enrichment of gut bacteria linked to gastrointestinal manifestations of COVID-19, preceded SARS-CoV-2 detection in sewage, suggesting that the gut microbiome may serve as an early indicator for community outbreaks. However, this study focused on taxonomic profiling and lacked functional analyses. Brumfield et al. combined DNA metagenomics, RNA transcriptomics, and targeted SARS-CoV-2 sequencing to analyze composite samples collected from downstream of a sewage collection system and detected viral mutations and microbial signatures associated with infection\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Although this study demonstrated the utility of multi-omics approaches for characterizing microbial and viral markers during a COVID-19 surge, its analysis was constrained to a brief outbreak period and involved samples from a single sewer catchment. Moreover, both studies only focused on the gut microbial indicators for the COVID-19 outbreak. The gut microbiome resilience and recovery, particularly following large-scale public health interventions (e.g., the easing of restrictions and the following infection surge), were not explored.\u003c/p\u003e\u003cp\u003eTo address these knowledge gaps, we analyzed municipal wastewater samples in Wuhan, the megacity where the first case of COVID-19 in China was reported. Sampling covered five strategically selected time points (Fig.\u0026nbsp;1a): a pre-policy-change baseline during the strict \u0026ldquo;zero-COVID\u0026rdquo; period and four subsequent stages of post-policy change and full societal reopening. The large-scale SAR-CoV-2 infections provided a unique opportunity to track temporal dynamics in the population-level gut microbiome. We hypothesized that these shifts would elicit a measurable taxonomic and functional response, followed by a recovery process involving community restructuring and potential functional or pathogenic capacity with implications for long-term public health. This study provides the first population-scale assessment for post-pandemic gut microbiome recovery using wastewater metagenomics and lays a foundation for integrating microbial health metrics into future public health surveillance.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Sample collection\u003c/h2\u003e\u003cp\u003eMunicipal wastewater samples were collected from up to 29 wastewater treatment plants (WWTPs) distributed across Wuhan, a megacity with a population of approximately 13.7\u0026nbsp;million. Each WWTP serves between 22,000 and 270,000 residents and primarily receives domestic wastewater. Due to constraints related to site access and resource availability, the number of WWTPs sampled varied across the five collection periods: 27 in Spring 2021 (zero-COVID period), 18 in Spring 2023, 23 in Summer 2023, 14 in Winter 2023, and 26 in Spring 2024. These WWTPs collectively covered approximately 70% to nearly the entire residential population of Wuhan at each time point. Detailed information for each sampling site is provided in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. All samples were collected from environmental wastewater streams and contained no identifiable human data. The study did not involve human participants or animals and was exempt from institutional ethics review.\u003c/p\u003e\u003cp\u003eTo ensure comparability across time points and minimize technical variation, all procedures for sample collection, transport, and processing followed strictly standardized laboratory protocols. All sampling events were conducted during dry weather to eliminate the influence of precipitation. During each 24-hour sampling period, influent samples were collected hourly using TC-8000D autosamplers (Qingdao Suyuan Environmental Protection Equipment Co., China) with 4\u0026deg;C refrigeration chambers. Equal volumes from each hourly subsample were combined into a daily composite. From each composite, a 400 mL aliquot was immediately transferred to sterile plastic bottles with ice packs and transported to the laboratory under cold chain conditions. Three consecutive daily composites were pooled in equal volumes to generate a final 100 mL sample per site and time point. Each final sample was immediately subjected to vacuum filtration through a 0.22 \u0026micro;m mixed cellulose ester membrane (Jinteng Co., China) using sintered glass funnels to concentrate microbial biomass. The filter membranes were then transferred into 15 mL sterile centrifuge tubes and stored at -80\u0026deg;C until metagenomic DNA extraction and sequencing.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 DNA Extraction and Metagenomic Sequencing\u003c/h2\u003e\u003cp\u003eSample processing, DNA extraction, library preparation, and sequencing were performed using standardized protocols across all time points. Microbial DNA was extracted from the filter membranes using the MagPure Stool DNA Extraction Kit (Magen Biotechnology Co., Guangzhou, China), following the manufacturer\u0026rsquo;s protocol. The quality and concentration of the extracted DNA were evaluated using 1% agarose gel electrophoresis and spectrophotometry. Samples meeting quality criteria were sent to BGI Tech (Shenzhen, China) for sequencing.\u003c/p\u003e\u003cp\u003eShotgun metagenomic sequencing libraries were prepared using standard protocols. Genomic DNA was fragmented to approximately 350 bp, followed by end repair, A-tailing, adapter ligation, and PCR amplification. Libraries were purified, size-selected, and quantified prior to sequencing. Paired-end sequencing (2 \u0026times; 150 bp) was performed on the Illumina NovaSeq 6000 platform at BGI Tech (Shenzhen, China), generating approximately 10 GB of data per sample. All raw sequencing data have been deposited in the Genome Sequence Archive in the National Genomics Data Center, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (accession number: CRA024822). The data are publicly accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngdc.cncb.ac.cn/gsa\u003c/span\u003e\u003cspan address=\"https://ngdc.cncb.ac.cn/gsa\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Sequencing and quality control statistics are presented in Supplementary Table S2.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Taxonomic and functional annotation\u003c/h2\u003e\u003cp\u003eAll bioinformatic analyses were conducted using unified workflows (Fig. S2) to ensure methodological consistency between sampling batches. Detailed bioinformatics procedures are provided in Supplementary Text S1.\u003c/p\u003e\u003cp\u003eBriefly, raw sequencing data were quality-controlled using Fastp (Version 0.23.2). Taxonomic annotation was performed using Kraken2 (Version 2.0.8-beta) with the PlusPFP database\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Rarefaction curves demonstrated that taxonomic richness reached saturation within the first 100,000 reads, whereas each sample yielded over 30\u0026nbsp;million high-quality clean reads, confirming sufficient sequencing depth for comprehensive microbial profiling (Fig. S3).\u003c/p\u003e\u003cp\u003eAnnotated species were cross-referenced against the Unified Human Gastrointestinal Protein (UHGP) catalog\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and further curated to exclude ubiquitous environmental bacteria not typically associated with the human gut. Microbial species were classified as either commensals or clinically confirmed pathogenic. The commensal group includes both probiotics (beneficial microbes) and pathobionts (microbes that are typically harmless but capable of causing disease under certain conditions). The clinically confirmed pathogenic group includes species recognized as human pathogens in PHI-base\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Only species with demonstrated relevance to the human gut microbiome were retained for downstream analysis. A curated list of genera, their constituent species, and supporting references used to determine gut relevance is provided in Supplementary Table S3.\u003c/p\u003e\u003cp\u003eClean reads were assembled de novo using MEGAHIT (Version 1.2.9)\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Assembly statistics, including contig counts and length metrics, are shown in Supplementary Table S4. Open reading frames (ORFs) were predicted from the assembled contigs using Prodigal (Version 2.6.3)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The predicted gene and protein sequences were clustered and de-replicated using CD-HIT (Version 4.8.1) to generate a non-redundant gene catalog and a non-redundant protein catalog\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Functional annotation was performed using HUMAnN2 (Version 2.8.2) to identify microbial metabolic pathways\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Annotated pathways were grouped into subclass-level functional categories defined in the MetaCyc Metabolic Pathway Database (MetaCyc). Functional categories and their constituent pathways are provided in Supplementary Table S5. Virulence factors were identified using the VFDB core database\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e and categorized into functional classes according to the VFDB's hierarchical classification scheme.\u003c/p\u003e\u003cp\u003eAntibiotic resistance genes (ARGs) were annotated using ARGs-OAP (Version 3.2.4) with the SARG database (Version 3.2.1-L) \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. ARG-carrying contigs were aligned against the NCBI NR database (Version 2024-02) using DIAMOND (Version 2.0.15) to identify the host\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. ARGs were classified as pathogen-associated if their predicted hosts were recognized as human pathogens in the PHI-base and grouped by resistance class. ARGs whose predicted hosts matched taxa with demonstrated gut relevance were classified as commensal-associated. Commensal-associated ARGs were grouped into clinically relevant resistance genes, defined as those conferring resistance to last-resort antibiotics, as previously described\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, and other ARGs detected in non-pathogenic gut commensals. A list of clinically relevant commensal-associated ARGs detected in this study is provided in Supplementary Table S6.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Visualization and Statistical Analysis\u003c/h2\u003e\u003cp\u003eData visualization was performed using the R packages \u003cem\u003evegan\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003eand \u003cem\u003eggplot2\u003c/em\u003e \u003csup\u003e31\u003c/sup\u003e, as well as Origin (Version 2021). To ensure comparability across sequencing batches, taxonomic and functional data were normalized to relative abundances using consistent methods detailed below.\u003c/p\u003e\u003cp\u003eFor taxonomic profiling, relative abundances were calculated by dividing read counts by the total per sample. Alpha diversity was assessed using the Shannon index at the species level. Community structural differences were visualized using non-metric multidimensional scaling (NMDS) of Bray\u0026ndash;Curtis dissimilarity, with group differences tested using permutational multivariate analysis of variance (PERMANOVA). Fold changes relative to the Spring 2021 baseline were log₂-transformed for heatmap visualization.\u003c/p\u003e\u003cp\u003eFunctional pathway, virulence gene, and ARG abundances were normalized to reads per kilobase per million mapped reads (RPKM), then converted to relative abundances by total-sum scaling. For downstream visualization and comparative analysis, the top nine most represented functional categories comprising at least eight underlying MetaCyc pathways were plotted. For virulence genes, pathway-level abundances were first aggregated by category, and those detected in more than 50% of the samples were selected for visualization.\u003c/p\u003e\u003cp\u003eShannon diversity and NMDS were applied at the resistance class level to assess variation in pathogen- and commensal-associated ARG profiles across groups and to minimize ordination stress due to sparsity at the species level.\u003c/p\u003e\u003cp\u003eStatistical analysis was conducted using JMP (Version 16.0). Group comparisons were performed using non-parametric Steel tests, with each post-policy change group compared against the Spring 2021 baseline. A significance level of 0.05 was used. Spearman rank correlation was used to evaluate associations between commensal taxa and functional pathways and between pathogenic taxa and virulence factors.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Dynamics of gut-associated microbial community\u003c/h2\u003e\u003cp\u003eThe composition of gut-associated microbial communities exhibited notable temporal shifts following the easing of COVID-19 restrictions (Fig.\u0026nbsp;1b). At the phylum level, Bacteroidota, Bacillota, and Actinomycetota dominated across all time points. Notably, compared to the baseline in Spring 2021, Actinomycetota abundance showed a marked increase in relative abundance during Spring 2023 (rising from 25\u0026ndash;69%), followed by a decline to 29% by Spring 2024. In contrast, Bacteroidota abundance, initially comprising the largest proportion (39%), declined sharply to 8% in Spring 2023, then partially recovered to 29% by Spring 2024. Similarly, Bacillota decreased from 29% in Spring 2021 to 19% in Spring 2023, followed by a recovery to 33% by Spring 2024.\u003c/p\u003e\u003cp\u003eShannon diversity of the commensal gut microbiota was significantly reduced in Spring 2023, three months after the easing of the zero-COVID policy, compared to the baseline in Spring 2021 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with greater variability across samples (Fig.\u0026nbsp;1c). Diversity remained significantly reduced in both Summer 2023 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and Winter 2023 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). By Spring 2024, diversity levels returned to values comparable to the baseline.\u003c/p\u003e\u003cp\u003eCommensal gut microbial communities exhibited distinct clustering across sampling stages (stress\u0026thinsp;=\u0026thinsp;0.119, Fig.\u0026nbsp;1d). In particular, the Spring 2023 samples formed a significantly distinct cluster compared to the Spring 2021 baseline (PERMANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Samples from Summer 2023 and Winter 2023 occupied intermediate clusters that partially overlapped with the Spring 2021 baseline. In spring 2024, the commensal gut microbiota profiles were recovered to a configuration comparable to the pre-policy change baseline.\u003c/p\u003e\u003cp\u003eIn contrast to the commensal gut microbiota, the Shannon index of clinically confirmed pathogen communities increased significantly in Spring 2023 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), Summer 2023 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and Winter 2023 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), compared to the baseline in Spring 2021 (Fig.\u0026nbsp;1e). By Spring 2024, diversity levels returned to values comparable to the baseline. Clinically confirmed pathogen communities exhibited substantial compositional overlap across all sampling stages (stress\u0026thinsp;=\u0026thinsp;0.144; Fig.\u0026nbsp;1f). However, samples from Spring 2023 showed greater dispersion, with multiple points falling outside the baseline confidence ellipse. In contrast, Summer 2023, Winter 2023, and Spring 2024 samples clustered more closely.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Fold changes in commensal and clinically confirmed pathogenic taxa\u003c/h2\u003e\u003cp\u003eAmong the commensal taxa (including both beneficial microbes and pathobionts), \u003cem\u003eEggerthella\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, and \u003cem\u003eCollinsella\u003c/em\u003e showed the most prominent increases in Spring 2023 (by 3.6-, 3.5-, and 2.0-fold, respectively), compared to the Spring 2021 baseline (Fig.\u0026nbsp;2a). \u003cem\u003eEggerthella\u003c/em\u003e, a gut-resident genus with potential inflammatory relevance, remained significantly elevated throughout the observation period, with fold increases of 2.5, 1.7, and 1.7 in Summer 2023, Winter 2023, and Spring 2024, respectively. \u003cem\u003eBifidobacterium\u003c/em\u003e and \u003cem\u003eCollinsella\u003c/em\u003e returned to baseline levels by Spring 2024\u003c/p\u003e\u003cp\u003eConversely, 21 commensal taxa showed significant reductions in Spring 2023, with fold decreases ranging from 1.3- to 8.4-fold compared to the Spring 2021 baseline. Among these, the abundance of four genera (\u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eParabacteroides\u003c/em\u003e, \u003cem\u003eOdoribacter\u003c/em\u003e, \u003cem\u003eButyricimonas\u003c/em\u003e) remained persistently and significantly at low levels through Spring 2024.\u003c/p\u003e\u003cp\u003eAmong the clinically confirmed pathogenic taxa (Fig.\u0026nbsp;2b), \u003cem\u003eClostridioides\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, and \u003cem\u003eStaphylococcus\u003c/em\u003e showed significant increases in Spring 2023, with fold changes ranging from 1.3 to 2.4. By Spring 2024, the relative abundances of all these genera returned to baseline levels, except for \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eClostridioides\u003c/span\u003e, which remained 1.3-fold higher than baseline. In contrast, \u003cem\u003eKlebsiella\u003c/em\u003e and \u003cem\u003eEnterobacter\u003c/em\u003e exhibited significant reductions in Spring 2023 by 1.9- and 1.4-fold, respectively. Other pathogenic genera exhibited no consistent directional shifts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Functional pathways of gut microbial community\u003c/h2\u003e\u003cp\u003ePathways associated with core biosynthesis and redox balance showed early and marked suppression. Amino acid biosynthesis (Fig.\u0026nbsp;3a) declined significantly in Spring 2023 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and remained suppressed through Summer 2023 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by partial recovery in Winter 2023. A similar pattern was observed for cofactor, carrier, and vitamin biosynthesis (Fig.\u0026nbsp;3b), which showed a significant reduction in Summer 2023 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003eIn contrast, nucleoside and nucleotide degradation (Fig.\u0026nbsp;3c) exhibited a significant and persistent increase from Spring 2023 through Spring 2024 (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Fermentation activity (Fig.\u0026nbsp;3d) increased significantly in Spring 2023 (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). Nucleoside and nucleotide biosynthesis (Fig.\u0026nbsp;3e) increased significantly in Summer 2023 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Other pathways, such as amino acid degradation (Fig.\u0026nbsp;3f), carbohydrate biosynthesis (Fig.\u0026nbsp;3g), carbohydrate degradation (Fig.\u0026nbsp;3h), and fatty acid and lipid biosynthesis (Fig.\u0026nbsp;3i) remained stable across all time points.\u003c/p\u003e\u003cp\u003eCorrelation analysis further revealed that the depletion of commensal genera (e.g., \u003cem\u003eBacteroides\u003c/em\u003e) was associated with suppressed amino acid biosynthetic, while \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eCollinsella\u003c/em\u003e, and \u003cem\u003eEggrthella\u003c/em\u003e showed strong positive correlations with fermentative pathways (Fig. S4).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Virulence pathway of pathogen\u003c/h2\u003e\u003cp\u003eA significant and sustained reduction was observed in adherence functions (Fig.\u0026nbsp;4a), which declined sharply in Spring 2023 and remained significantly lower through Spring 2024 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, biofilm-associated functions (Fig.\u0026nbsp;4b) showed significant reductions in Summer 2023 and Spring 2024 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Motility-related functions (Fig.\u0026nbsp;4c) also declined significantly in Spring 2023, Summer 2023, and Spring 2024 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 to p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Antimicrobial activity and competitive advantage functions (Fig.\u0026nbsp;4d) also decreased significantly in Spring 2023 and Spring 2024 (p\u0026thinsp;\u003cem\u003e\u0026lt;\u0026thinsp;0.05\u003c/em\u003e).\u003c/p\u003e\u003cp\u003eIn contrast, several virulence-associated categories exhibited elevated levels. A progressive and significant rise was observed in effector delivery systems (Fig.\u0026nbsp;4e), with relative abundance increasing from Spring 2023 through Spring 2024 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, regulatory functions (Fig.\u0026nbsp;4f) were significantly elevated throughout the post-policy change period (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Immune modulation pathways (Fig.\u0026nbsp;4g) also increased significantly in Spring and Summer 2023 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and remained elevated in Spring 2024 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Nutritional and metabolic virulence factors (Fig.\u0026nbsp;4h) increased significantly in Spring 2024 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, stress survival mechanisms (Fig.\u0026nbsp;4i) remained stable across all time points, showing no significant temporal change.\u003c/p\u003e\u003cp\u003eTogether, these trends suggest a functional shift in virulence strategies. The increased abundance of immune modulation, effector delivery, and metabolic virulence were positively correlated with clinically confirmed pathogens such as \u003cem\u003eChloridoides\u003c/em\u003e and \u003cem\u003eEnterococcus\u003c/em\u003e (Fig. S5), supporting their potential role in driving pathogenic functions during community restructuring.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Antibiotic resistance genes\u003c/h2\u003e\u003cp\u003eThe RPKM-normalized abundance of ARGs exhibited a marked increase in Spring 2023, coinciding with the post-easing infection surge (Fig.\u0026nbsp;5a). Total ARG abundance increased approximately 3.0-fold compared to the pre-policy change baseline (Spring 2021), driven by widespread increases across both pathogen- and non-pathogen-associated sources. ARGs associated with non-pathogens increased by 6.1-fold, while ARGs associated with pathogens increased by 1.7-fold. Among ARG-carrying pathogens, sharp increases were observed for \u003cem\u003eClostridioides\u003c/em\u003e (7.8-fold), \u003cem\u003eEnterococcus\u003c/em\u003e (7.8-fold), and \u003cem\u003eStreptococcus\u003c/em\u003e (13.8-fold). By Spring 2024, total ARG abundance had returned to a near-baseline level.\u003c/p\u003e\u003cp\u003eThe class-level composition of ARGs revealed dynamic shifts between pathogen- and commensal-associated sources over time (Fig.\u0026nbsp;5b). For pathogen-associated ARGs, multidrug resistance (PATH-Multidrug), macrolide\u0026ndash;lincosamide\u0026ndash;streptogramin resistance (PATH-MLS), and β-lactam resistance genes (PATH-Beta-lactam) all showed a marked decline in Spring 2023. While PATH-Multidrug returned to near-baseline levels by Spring 2024, PATH-Beta-lactam exhibited a persistent downward trend, declining from 10% in Spring 2021 to 3% in Spring 2024. In contrast, commensal-associated multidrug resistance genes (COMM-Multidrug) showed a marked increase at the final time point, rising from 2% in Spring 2021 to 15% in Spring 2024.\u003c/p\u003e\u003cp\u003eThe Shannon diversity of pathogen-associated ARGs increased significantly following the easing of COVID-19 restrictions (Fig.\u0026nbsp;5c). Compared to the pre-policy change baseline in Spring 2021, diversity increased sharply in Spring 2023 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), remained elevated in Summer (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Winter 2023 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). By Spring 2024, the diversity had returned to the level of baseline. The overall composition of pathogen-associated resistance genes remained relatively stable. Samples from different stages exhibited substantial overlap, and no clear temporal clustering was observed (Stress\u0026thinsp;=\u0026thinsp;0.171; Fig.\u0026nbsp;5d). Notably, the Shannon diversity of commensal-associated ARGs showed a significant increase in Spring 2023 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and Spring 2024 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;5e). Spring 2024 samples exhibited a noticeable shift to earlier time points (Stress\u0026thinsp;=\u0026thinsp;0.134; Fig.\u0026nbsp;5f).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Early microbiome disturbance and compensatory shifts (within the first three months)\u003c/h2\u003e\u003cp\u003eSubstantial shifts in gut microbial community structure following the easing of the \u0026ldquo;zero-COVID\u0026rdquo; policy indicate that the large-scale SAR-CoV-2 infections triggered widespread and persistent disruption to the gut microbiome at the population level. Within the first three months, there was a marked decline in commensal microbial alpha diversity, accompanied by a significant reduction of 21 commensal genera, including well-established beneficial taxa such as \u003cem\u003eBacteroides, Parabacteroides, Odoribacter\u003c/em\u003e, and \u003cem\u003eButyricimonas\u003c/em\u003e. These findings align with multiple clinical studies reporting COVID-19-associated dysbiosis, particularly characterized by the depletion of beneficial taxa\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eInterestingly, we observed a pronounced enrichment of \u003cem\u003eBifidobacterium\u003c/em\u003e, which may reflect a transient compensatory response. Members of this genus are known to support mucosal barrier integrity and modulate inflammation. An increase in their abundance may well be a sign of an early gut microbiome restoration. Depletion of beneficial taxa, including \u003cem\u003eBifidobacterium\u003c/em\u003e, has been documented\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, which contradicts to our results. This discrepancy may be due to differences in disease severity, sampling time, and probiotic use. Clinical cohorts often include hospitalized, severe cases with substantial dysbiosis, whereas our wastewater-based approach reflects the broader population, including mild or asymptomatic infections.\u003c/p\u003e\u003cp\u003eMoreover, our sampling at three months post-infection may capture a recovery phase beyond the acute stage, and widespread probiotic use during the pandemic may have contributed to \u003cem\u003eBifidobacterium\u003c/em\u003e enrichment in the community. Furthermore, our results were consistent with the observations by Yeoh et al.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, who reported that although \u003cem\u003eBifidobacterium\u003c/em\u003e levels were reduced during the acute phase of infection, \u003cem\u003eBifidobacterium dentium\u003c/em\u003e became significantly enriched one month after hospital discharge, surpassing levels in uninfected controls. This post-acute rebound corroborates our observation of elevated \u003cem\u003eBifidobacterium\u003c/em\u003e abundance around three months post-infection at the population level.\u003c/p\u003e\u003cp\u003eThis early-phase microbial community shift was accompanied by a pronounced change in functional pathways. Fermentative pathways were significantly elevated, likely driven by the expansion of taxa such as \u003cem\u003eBifidobacterium\u003c/em\u003e. Early fermentation products like acetate and other short-chain fatty acids are known to have immunomodulatory effects that could influence recovery. Our data suggest that while many normal commensals were depleted, certain fermentative microbes proliferated as an initial compensatory response.\u003c/p\u003e\u003cp\u003eHowever, we observed significant suppression of core biosynthetic functions, including amino acid biosynthesis and vitamin biosynthesis. These pathways are critical for maintaining redox balance, cellular metabolism, and micronutrient availability and are typically supported by commensal taxa such as \u003cem\u003eBacteroides\u003c/em\u003e. Their decline further corroborates the widespread depletion of beneficial gut bacteria and suggests that, despite the compensatory increase in fermentation, the overall metabolic capacity of the gut microbiome remained functionally impaired during the early post-infection period.\u003c/p\u003e\u003cp\u003eIn addition, we also observed blooms of clinically confirmed pathogenic, including \u003cem\u003eClostridioides difficile\u003c/em\u003e and \u003cem\u003eEnterococcus faecalis\u003c/em\u003e, as well as pathobiont such as \u003cem\u003eEggerthella\u003c/em\u003e. Significant enrichment of \u003cem\u003eEggerthella\u003c/em\u003e and \u003cem\u003eEnterococcus\u003c/em\u003e has been reported in COVID-19 patients, accompanied by decreased microbial diversity and increased pathobiont burden\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. This pathogenic expansion was paralleled by an increase in virulence-associated gene functions (including immune modulation, regulatory activity, and effector delivery systems), suggesting a microbial adaptation toward immune evasion during this vulnerable period. Both commensal-associated and pathogen-associated ARGs were significantly elevated. Interestingly, this occurred alongside a decline in functions related to adhesion, motility, and interbacterial antagonism, indicating that while direct colonization and competition potential weakened, resistance traits were retained or even enhanced. These observations suggest that antimicrobial resistance risk may extend beyond overt pathogens, reflecting a broader and potentially more persistent threat within the gut microbiome following large-scale infection.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Long-Term Community and Functional Recovery (up to 15 months)\u003c/h2\u003e\u003cp\u003eOver the ensuing 15 months, the gut microbiome showed substantial recovery in overall community structure. Alpha diversity and community composition rebounded towards pre-infection baselines by around 15 months post-infection. The relative abundance of many commensal microbiota returned to a level comparable to the baseline in Spring 2021. This indicates that the core microbial diversity lost during acute COVID-19 was largely restored over time. However, not all commensals fully recovered, as four genera commonly associated with a healthy gut (\u003cem\u003eParabacteroides\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eOdoribacter\u003c/em\u003e, and \u003cem\u003eButyricimonas\u003c/em\u003e) remained persistently and significantly reduced through Spring 2024. This sustained depletion indicates that, despite the overall structural recovery, certain beneficial taxa may be more vulnerable to long-term disruption.\u003c/p\u003e\u003cp\u003eIn parallel, despite this taxonomic recovery, several pathogen-associated taxa remained persistently elevated even at 15 months post-infection. For example, \u003cem\u003eClostridioides difficile\u003c/em\u003e, \u003cem\u003eEnterococcus faecalis\u003c/em\u003e, and \u003cem\u003eEggerthella\u003c/em\u003e were still present at levels higher than the baseline in Spring 2024. The relative abundance of microbial virulence genes also remained elevated in the long term, implying that the gut microbial community continued to carry extra immunogenic and pathogenic potential well into the recovery period. A dysbiosis marked by opportunistic pathogens and excess virulence factors can promote a pro-inflammatory milieu and compromise the gut barrier in post-acute COVID-19 syndrome\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. This suggests that even with a recovered microbiota composition, functional risks related to inflammation and vulnerability may persist.\u003c/p\u003e\u003cp\u003eNotably, commensal-associated genes conferring multidrug resistance showed a marked increase in Spring 2024, contributing to elevated ARG diversity and a more distinct resistome profile. While previous studies have reported transient ARG enrichment within six months post-infection\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, our results extended this phenomenon to 15 months post-infection and revealed that and revealed a delayed yet notable accumulation of clinically relevant ARGs within gut commensals. This study provides population-level evidence that commensals may increasingly harbor resistance traits following a large-scale public health disruption. These non-pathogenic microbes may act as a long-term reservoir of resistance traits, which may facilitate horizontal gene transfer to opportunistic or pathogenic taxa, thereby elevating the risk of treatment failure and complicating infection control efforts at the population level.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Implication and Future perspective\u003c/h2\u003e\u003cp\u003eThis study offers a population-level, metagenome-based longitudinal analysis of gut microbiome recovery following widespread SARS-CoV-2 infection. Unlike previous clinical studies focusing on individual cases with short follow-up periods, our approach leverages wastewater-based surveillance to capture broader community-level trends. Deep metagenomic sequencing enabled simultaneous tracking of taxonomic and functional dynamics, revealing both compositional shifts and changes in metabolic activity, virulence, and antibiotic resistance.\u003c/p\u003e\u003cp\u003eThe sharp decline in microbial diversity and key metabolic functions during the early stage suggests a period of elevated population-level vulnerability. The depletion of beneficial commensals, along with disruptions in pathways related to amino acid and vitamin synthesis, may compromise immune regulation and gut barrier integrity, thereby increasing susceptibility to health risks\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. While many commensal taxa and their associated metabolic functions showed substantial long-term restoration, several key genera, particularly \u003cem\u003eBacteroides\u003c/em\u003e, remained persistently depleted. \u003cem\u003eBacteroides\u003c/em\u003e constitute one of the most abundant and functionally pivotal genera in the human gut microbiota, playing central roles in dietary glycan degradation, vitamin biosynthesis, and immune modulation\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The apparent functional recovery may be driven by compensatory activity from taxa less favorable to host health. For instance, vitamin biosynthesis can be maintained by opportunistic microbes under dysbiotic conditions\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, and specific pathogens can activate amino acid biosynthesis to expand in the presence of the microbiota\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFurthermore, virulence potential remained elevated beyond the initial short-term disruption, indicating that microbiome recovery remains incomplete. Persistent pathogenic activity may continue to pose long-term public health risks. In addition, the accumulation of clinically relevant ARG within commensal microbes following the post-policy change period is of particular concern. Further investigation is warranted to understand how persistent microbial imbalances and functional alterations contribute to long-term health vulnerabilities.\u003c/p\u003e\u003cp\u003eIn addition, our findings highlight the importance of sustained attention and coordinated responses across multiple levels. At the individual level, adopting microbiome-supportive behaviors remains essential. Increasing dietary fiber intake, avoiding unnecessary antibiotic use, and maintaining gut health awareness may help promote microbial resilience. At the clinical level, delayed or incomplete microbiome recovery following widespread SARS-CoV-2 infection requires attention. Monitoring gut-related symptoms, incorporating microbiome-informed strategies into long-term care, and supporting vulnerable individuals with nutritional or probiotic interventions may help mitigate subclinical consequences. At the policy level, expanding wastewater-based surveillance to include functional microbiome indicators, such as virulence and resistance genes, may enhance early detection of subclinical dysbiosis and emerging microbial risks. Embedding these approaches into routine environmental health frameworks could improve public health challenges preparedness. In addition, governments may promote community-level microbiome restoration through public health education, dietary policy, and equitable access to gut-supportive interventions like fiber-enriched foods, prebiotics, or probiotics. Together, these efforts may enhance long-term population resilience against future microbiome-related health challenges.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study provides population-scale, metagenomic evidence that SARS-CoV-2 infection led to long-lasting disruptions in the human gut microbiome, with delayed recovery of commensal structure and persistent functional abnormalities. By leveraging wastewater-based surveillance, we highlight a scalable, non-invasive approach to monitor microbial health at the community level and detect emerging risks such as antibiotic resistance and virulence persistence. These insights offer new directions for microbiome-informed public health strategies in the post-pandemic era.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThis work was supported by the National Science Foundation of China (Grant No. 42277469 and No. 41877508).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHaileamlak A (2021) The impact of COVID-19 on health and health systems. 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Cell Host Microbe 28:526\u0026ndash;533\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"wastewater-based surveillance, gut microbiome, SARS-CoV-2, antibiotic resistance, Wuhan","lastPublishedDoi":"10.21203/rs.3.rs-6822143/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6822143/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhile SARS-CoV-2 infection is known to disrupt the gut microbiome, its population-level impact and long-term recovery trajectory remain poorly understood. Here, we applied longitudinal wastewater metagenomics in Wuhan, China, to track the compositional and functional dynamics of the gut microbiome in a large urban population over a 15-month period. Within three months following the easing of the \u0026ldquo;zero-COVID\u0026rdquo; policy, a marked decline in microbial diversity and depletion of beneficial commensals were observed. Meanwhile, the abundance of fermentative taxa (e.g., \u003cem\u003eBifidobacterium\u003c/em\u003e) increased significantly, suggesting an early-stage compensatory response. Functional pathway analysis revealed elevated fermentation and suppressed biosynthesis (e.g., amino acids), indicating incomplete functional compensation. In parallel, both clinically confirmed pathogens and gut-resident pathobionts (e.g., \u003cem\u003eClostridioide\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003eEggerthella\u003c/em\u003e) expanded, along with sustained increases in antibiotic resistance and virulence genes. By month 15, both community composition and functional profiles of commensal taxa largely converged toward pre-policy change baselines. Although taxonomic profiles largely recovered, the elevation of virulence-associated features persisted, suggesting lasting impacts on microbiome-associated health risks. Notably, the diversity of clinically relevant antibiotic resistance genes within commensal taxa increased markedly at the final time point, suggesting a late-stage enrichment of latent resistance reservoirs. Together, these findings reveal an incomplete microbiome recovery, with structural restoration uncoupled from sustained functional disruption. This study provides the first wastewater-based evidence of large-scale gut microbiome restructuring in response to large-scale SARS-CoV-2 infection, highlighting the utility of wastewater metagenomics in revealing hidden public health effects.\u003c/p\u003e","manuscriptTitle":"Wastewater-based Surveillance Reveals Incomplete Gut Microbiome Recovery Following Easing of COVID-19 Restrictions in Wuhan, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-11 12:19:41","doi":"10.21203/rs.3.rs-6822143/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6f647cf6-d84d-4c80-a68a-bfdac068bf77","owner":[],"postedDate":"August 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":51562791,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":51562792,"name":"Earth and environmental sciences/Environmental sciences/Environmental chemistry/Environmental monitoring"}],"tags":[],"updatedAt":"2025-08-11T12:19:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-11 12:19:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6822143","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6822143","identity":"rs-6822143","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}