Stable cores and dynamic peripheries: spatial structuring dominates over temporal turnover in wastewater microbiomes across 16 Swedish cities

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Stosic, Anna J. Székely, Ekaterina Avershina, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7987707/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Wastewater microbial communities integrate biological inputs from human populations, the surrounding environment, and sewer infrastructure. Understanding how spatial and temporal factors shape these communities is essential for ecological interpretation and wastewater-based surveillance. However, the relative contributions of geography and short-term temporal change remain unclear at national scales. Results We analyzed influent wastewater from 16 Swedish wastewater treatment plants collected at two timepoints, in winter (Week 3) and late spring (Week 21) of 2024, using shotgun metagenomic sequencing and compositional data analysis. A median of 24.9 million reads per sample were classified to Bacteria, Viruses, or Archaea, with communities dominated by Bacteria (97.6 percent), followed by Viruses (1.97 percent) and Archaea (0.42 percent). Genus-level diversity increased significantly from winter to spring, with median within-city changes of +10 genera in richness and +0.21 in the Shannon index (p < 0.003). Community composition was strongly structured by geography: city explained 58.3 percent of total variance (p = 0.0003), while week accounted for 3.7 percent (p = 0.106). Within cities, temporal turnover was substantial (median Aitchison distance 37.5; p = 4.8 × 10⁻⁴) but largely confined to peripheral taxa, which accounted for approximately 93 percent of total change. A stable core of 88 genera persisted across both timepoints, decreasing slightly in relative abundance (median change −0.020; p = 0.0021). Geographic distance and population size showed no significant associations with microbial composition. Discussion Swedish wastewater microbiomes are characterized by strong spatial differentiation, stable core communities, and locally variable peripheral turnover. Spatial factors dominate over temporal variation, emphasizing the importance of location-specific baselines for ecological assessment and wastewater-based monitoring. These findings highlight the robustness yet dynamism of urban wastewater ecosystems and provide a foundation for future national surveillance efforts. wastewater metagenomics sewage water microbiome core microbiome temporal turnover spatial structuring Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Wastewater harbors highly diverse microbial communities that integrate inputs from human populations, industrial sources, and the surrounding environment. These communities include gut-associated bacteria, environmental taxa, and opportunistic pathogens, and thus wastewater microbiomes provide a unique window into the intersection of human health and the environment. In recent years, they have gained increasing attention both as ecological systems of interest and as a basis for wastewater-based epidemiology (WBE), where microbial signals are used to monitor population-level health trends.( 1 – 3 ) Previous studies have described the taxonomic composition and functional potential of wastewater microbiomes across different regions.( 4 , 5 ) These consistently highlight pronounced spatial variation, reflecting local environmental conditions, sewer infrastructure, and population inputs.( 6 ) At the same time, temporal changes have been reported, ranging from seasonal shifts to short-term fluctuations.( 7 ) However, the relative importance of spatial versus temporal drivers in shaping wastewater microbial communities remains uncertain.( 8 ) In particular, it is unclear whether short-term temporal changes represent coordinated nationwide patterns or independent, city-specific trajectories. Another open question concerns the balance between community stability and change. Many ecosystems are thought to be anchored by a relatively stable core microbiome of widespread taxa, while more dynamic peripheral members contribute to turnover and variability.( 9 , 10 ) Although this framework is well established for host-associated microbiomes, it has rarely been applied to wastewater communities. Understanding whether temporal restructuring is driven by changes in the stable core or by shifts in the peripheral microbiota is critical for interpreting wastewater data, especially in surveillance contexts where reproducibility and comparability across sites are essential. To address these gaps, we investigated the relative contributions of space, time, and taxonomic structure to wastewater microbial community dynamics using samples from 16 Swedish cities collected at two timepoints. Specifically, we asked: (i) do alpha- and beta-diversity metrics show systematic changes between timepoints, (ii) how much compositional turnover occurs within cities compared to between cities, and (iii) how do stable “core” taxa versus more variable peripheral taxa contribute to observed changes? By combining compositional data analysis with turnover decomposition, we demonstrate that spatial structuring is the dominant factor shaping wastewater microbiomes, whereas temporal changes occur primarily within cities and are largely driven by the peripheral community. These findings highlight the importance of accounting for strong local effects when using wastewater microbiomes for ecological inference and wastewater-based surveillance applications. Materials and Methods Sampling strategy and study design The present study includes sewage water samples collected from 16 wastewater treatment plants (WWTP) across Sweden at two timepoints: Week 3 (W3) and Week 21 (W21) in 2024. The study sites included: Gävle (WWTP: Duvbackens avloppsreningsverk), Göteborg (Ryaverket), Kalmar (Kalmarsundsverket), Karlstad (Sjöstadsverket), Linköping (Nykvarnsverket), Luleå (Uddebo reningsverk), Malmö (Sjölunda reningsverk), and four locations in Stockholm: Bromma (Bromma reningsverk), Grödinge (Himmerfjärdsverket), Henriksdal (Henriksdals reningsverk), and Käppala (Käppalaverket), Umeå (Öns reningsverk), Uppsala (Kungsängsverket), Västerås (Kungsängsverket), Östersund (Gövikens reningsverk), and Östhammar (Alunda avloppsreningsverk). At each site, influent samples were obtained following standardized collection procedures. For most cities, untreated wastewater samples that are representative of a single day were collected by flow compensated samplers at WWTP. Uppsala is the exception, where samples were collected daily, and then combined flow-proportionally into one composite weekly sample for the purpose of analyses. All samples were transported under cooled conditions to the analysis laboratory immediately after collection and processed for nucleic acid extraction within 48 hours of arrival. Extraction and sequencing Total nucleic acids were extracted from 40 mL of sewage water samples using the Maxwell® RSC Enviro TNA Kit (Promega) on the Maxwell® RSC instrument (Promega), following the manufacturer’s instructions with the PureFood GMO and Authentication program, as described previously.( 11 ) Total genomic DNA was quantified using Quant-iT™ PicoGreen™ dsDNA Assay Kit (ThermoFisher Scientific). Shotgun metagenomic sequencing (total DNA) was performed using the Illumina DNA Prep Tagmentation kit (Illumina®) with dual indices, following the manufacturer’s instructions. Individual libraries were sequenced by the paired-end method (2 x 150 base pair) on the Novaseq 6000 S4 platform (Illumina®), with a median output of 32.2 Gb per sample after quality trimming. Sequencing analysis Raw reads were processed using Biopipe ( https://github.com/hpvcenter/biopipe ), a workflow for standardized taxonomic classification of metagenomic data. The pipeline consists of two main steps: (i) bioinformatic preprocessing of sequencing data and (ii) taxonomic classification, followed by downstream analysis of diversity and community composition. Briefly, adapter removal and quality trimming were performed using Trimmomatic (minimum read length: 36 bp, slidingWindow:4:15),( 12 ) and read quality was assessed before and after trimming with FastQC. High-quality reads were screened for human sequences using NextGenMap against the human reference genome (GRCh38),( 13 ) with non-default parameters of > 95% identity over at least 75% of read length. Reads classified as human were removed, and non-human reads were extracted using SAMtools and reconverted to FASTQ format. Taxonomic classification used Kraken2, ( 14 ) (v2.1.2), with a RefSeq database (June 2024 build) including archaea, bacteria, viruses, plasmids, protozoa, fungi, UniVec_Core and two human references (GRCh38 and T2T-CHM13v2.0; human libraries created with --no-mask). Bracken (v2.9) was used on Kraken2 reports to refine abundance estimates to the genus level for downstream analyses. ( 15 ) To ensure computational efficiency, all analyses were executed on an on-premises Hopsworks cluster (version 3.1; https://hopsworks.ai ) running Apache Spark for distributed execution of the Biopipe workflow, enabling parallelized preprocessing and taxonomic classification. Downstream diversity analysis and statistics Before prevalence or compositional calculations, fixed detection thresholds were applied to reduce spurious low-abundance calls: taxa were retained if the relative abundance fraction was ≥ 0.001 and if the number of reads was ≥ 50. Compositional methods used a centered log-ratio (CLR) transformation, with a small pseudocount (ε = 1×10⁻⁶) added before renormalization to avoid undefined log values. Diversity analyses Genus-level richness and Shannon diversity indices (alpha diversity) were calculated for each sample on the renormalized profiles to assess within-sample diversity. Temporal differences between W3 and W21 within each city were evaluated using paired Wilcoxon signed-rank tests. Between-sample community structure (beta diversity) was assessed by transforming relative abundance data with the centered log-ratio (CLR) and calculating Aitchison distances (Euclidean distances on CLR-transformed data). The effects of city (between-city structure) and week (within-city change) were analyzed with PERMANOVA (adonis2, 9,999 permutations), reporting marginal (by-term) tests and a week-only model with city used as permutation strata to respect pairing. In parallel, a presence/absence matrix was constructed at the genus level after applying detection thresholds, and binary Jaccard distances were computed. The same PERMANOVA designs were applied to the Jaccard matrix (by-term city and week; and week-only with strata = city). Principal coordinates analysis (PCoA) was applied to both Aitchison and Jaccard distances for visualization. Differences in community dispersion (homogeneity of multivariate dispersion) between weeks were evaluated with PERMDISP (betadisper + permutation test) for both distance types. Turnover and genus contributions Within-city temporal turnover was quantified as the Aitchison distance between W3 and W21 CLR compositions for each city. The across-city distribution of turnover was summarized by the sample median and a nonparametric bootstrap 95% confidence interval (10,000 resamples), tested against zero with a one-sample Wilcoxon signed-rank test. For each city, ΔCLR (W21 − W3) was computed per genus; squared ΔCLR summed to the city’s squared Aitchison turnover. Genera were ranked by within-city contribution, and each genus’s mean turnover share was summarized across cities. Core and peripheral taxa A stable core genus was defined as any genus present in at least 75% of cities at both weeks. For each city and week, the fractions of community abundance explained by the stable core were calculated. Changes in stable-core abundance (W21 − W3) were tested with paired Wilcoxon signed-rank tests, reporting the Hodges–Lehmann paired median difference and 95% confidence interval. The peripheral fraction was defined as 1 − (stable-core fraction). Within-city turnover was correlated with changes in stable-core, and peripheral fractions using Spearman’s ρ. Prevalence shifts To test W3 to W21 changes in genus prevalence across cities, paired 2×2 tables were constructed for each genus to count city-level gains (absent → present) and losses (present → absent). McNemar’s test was applied per genus, and p-values were adjusted using the Benjamini–Hochberg false discovery rate (BH-FDR). Signed prevalence change was reported as Δ = (gains − losses)/number of cities, alongside FDR-adjusted p-values. Concordance of community configurations Week-specific CLR-PCA scores (up to five components) were calculated for the set of paired cities. Geometric concordance between W3 and W21 ordination configurations was quantified with Procrustes analysis and tested using PROTEST (9,999 permutations). Differential abundance Differential abundance was evaluated using two complementary compositional frameworks. With ANCOM-BC2, genus-by-sample count matrices were built. For the global site effect (adjusted for week), a fixed-effects model (site + week) was fit. For the week effect, a mixed model with a random intercept for city (week + (1|site)) was used, restricting to genera observed in at least two distinct cities to satisfy model identifiability. Multiple testing was controlled with BH-FDR. In ALDEx2 (paired), Dirichlet-Monte Carlo CLR (mc.samples = 128) was applied with a paired W3 vs W21 test at the genus level. BH-FDR was again used for multiple testing correction, and ALDEx2 effect sizes were reported in CLR units. Spatial and population effects Geographic structuring was assessed by correlating pairwise Aitchison dissimilarities with great-circle geographic distances (km) using Mantel tests (Spearman, 9,999 permutations). To compare distance–decay between weeks, we used a paired permutation procedure that shuffled week labels within site-pairs and tested the difference in regression slopes. Multi-scale spatial signal was further evaluated by deriving principal coordinates of neighbor matrices (PCNM) from the geographic distance matrix and fitting distance-based redundancy analyses (dbRDA; capscale) of Aitchison dissimilarities on the subset of positive-eigenvalue PCNM axes (up to five), separately for each week; significance was assessed with permutation ANOVA, and adjusted R² was reported. Furthermore, each site was linked to the number of connected inhabitants, retrieved from previous studies.( 16 ) Connected inhabitants were analyzed on the log₁₀ scale. Associations with community features were evaluated in three ways: (i) for alpha diversity, we fitted ordinary least squares models of richness and Shannon on log₁₀(connected inhabitants) separately by week and, for paired changes, regressed within-city Δ alpha (W21 − W3) on log₁₀(connected inhabitants), reporting Spearman’s ρ alongside the OLS slope and p-value; (ii) for temporal turnover, we related within-city Aitchison distance (W21 vs W3) to log₁₀(connected inhabitants) using linear regression and Spearman correlation; and (iii) at the dissimilarity level, we compared Aitchison distances to pairwise |Δ log₁₀(connected inhabitants)| with Mantel and partial Mantel tests that control for geography, and fitted dbRDA models of Aitchison dissimilarities on log₁₀(connected inhabitants), reporting permutation p-values and adjusted R². Permutation, resampling, and multiple testing Unless otherwise noted, permutation tests used 9,999 permutations with pairing respected where appropriate (strata = city). Bootstrap confidence intervals for medians were generated with 10,000 resamples. Multiple tests across genera were controlled with the BH-FDR procedure. Results Dataset overview Across 32 samples (16 cities × 2 weeks), shotgun metagenomic sequencing yielded a median of 50.0 million high-quality read pairs per sample. After removing human and unclassified reads, Kraken2 assigned a median of 24.9 million reads per sample to Bacteria, Viruses, or Archaea; assignments were dominated by Bacteria (median 97.6%), with smaller fractions of Viruses (1.97%) and Archaea (0.42%). Alpha diversity Across the 16 paired cities, genus-level alpha diversity significantly increased from Week 3 to Week 21. Median within-city change was + 10 genera for richness and + 0.209 for Shannon (paired Wilcoxon W21–W3: richness p = 0.00266, Shannon p = 0.00295, n = 16). The direction of change was broadly consistent across sites, with variable magnitudes by city (Fig. 1 A–B). Beta diversity Community composition differed primarily by city, with little consistent change by week. In PERMANOVA on Aitchison distances, city explained 58.3% of variance (R² = 0.583, p = 0.0003), whereas week accounted for 3.7% and was not significant (R² = 0.037, p = 0.106). A Bray–Curtis sensitivity analysis confirmed the finding (city R² = 0.651, p = 0.0017; week R² = 0.047, p = 0.0609). When the week effect was tested within pairs (permuting within city), it remained non-significant (Aitchison, p = 0.0878). Taken together, samples from the same city are much more similar to each other (even across weeks) than samples from different cities collected in the same week. PCoA of Aitchison distances showed no coherent clustering by city and no nationwide week pattern. Although each city shifted between Week 3 and Week 21, the directions of change varied across sites, consistent with the findings that cities explain variation (Fig. 2 A-B). As shown in Fig. 2 C–D, the presence/absence structure (Jaccard) revealed the same pattern of strong city-level clustering and weak temporal change. Group dispersion changed only modestly. Distances to the week centroid were higher at W21 than W3 for Aitchison (median 30.9 vs 27.2), but the PERMDISP test was borderline (F = 3.61, p = 0.066; Fig. 2 B). For Jaccard, dispersion did not differ (F = 1.80, p = 0.19; Fig. 2 D). Overall, between-city differences dominate, with idiosyncratic within-city shifts over time and no evidence for a synchronized temporal change. Turnover Within-city community composition changed substantially between Week 3 and Week 21. Turnover, quantified as the Aitchison distance between the two weeks’ CLR genus profiles, had a median of 37.5 (bootstrap 95% CI 34.9–45.3), significantly greater than zero (paired Wilcoxon, p = 4.82×10⁻⁴). Magnitudes varied across cities ranging between 31.5 and 52.3 indicating clear reconfiguration within cities over time (Fig. 3 ). Stable core and peripheral fractions A conservative “stable core” was defined as genera present in ≥ 75% of cities at both sampling timepoints. Stable core consisted of 88 genera (intersection of week-specific cores) and the full list of genera with per-week prevalence and mean abundance is shown in Supplementary Table S1 . For each city, the fraction of total relative abundance explained by the stable core was computed for W3 and W21, and the paired change tested (Supplementary Fig. S1 ). The stable-core fraction decreased from W3 to W21 (Hodges–Lehmann paired median Δ = −0.020; 95% CI [-0.0312, -0.0103]; p = 0.00209), indicating a proportional expansion of the peripheral fraction (1 − stable core). Moreover, within-city compositional turnover was not associated with changes in the stable-core fraction. Cities exhibiting greater week-to-week shifts did not systematically lose or gain in core abundance (Spearman’s ρ = 0.10, p = 0.69). A per-city decomposition of turnover into stable-core and peripheral components is provided in Supplementary Table S2 . Across all cities, most of the within-city turnover observed between weeks 3 and 21 was driven by the peripheral fraction rather than the stable core (Fig. 4 ), consistent with the decomposition results (Supplementary Table S2 ). Presence/absence and differential abundance Three complementary signals were evaluated: (i) between-city differences in genus abundance (ANCOM-BC2 global site effect, adjusted for week), (ii) within-city week changes (ANCOM-BC2 mixed model with a random intercept for site and ALDEx2 paired tests), and (iii) changes in presence/absence across weeks (paired McNemar tests per genus). ANCOM-BC2 identified a substantial site effect: 86 of 225 genera (38.2%) were significant for the global site term after FDR correction (q < 0.05; Supplementary Table S3 ). Significant taxa included gut-associated genera (e.g., Bacteroides, Faecalibacterium, Bifidobacterium) and wastewater/environmental genera (e.g., Pseudomonas, Comamonas, Polaromonas), consistent with the strong spatial structure in β-diversity analyses. Using the mixed-effects ANCOM-BC2 model (week + (1|site)), only Butyricimonas showed a significant W21–W3 shift (q = 0.026, log-fold change ≈ − 0.38; decrease at W21), with no other genera passing FDR. In contrast, ALDEx2 (paired) detected 46 of 225 genera (20.4%) with week-associated differences (FDR < 0.05; Supplementary Table S4 ). The discrepancy reflects differing model assumptions (bias-corrected GLM with random intercept vs Dirichlet-Monte Carlo CLR with paired tests) in the presence of a strong site effect; week signals were modest and framework-dependent. Prevalence (presence/absence). Paired McNemar tests found no genera with FDR-significant prevalence changes (all q = 1; n = 16). Spatial and population correlates To test whether geographic distance influenced wastewater microbiomes, Mantel tests were performed between pairwise Aitchison dissimilarities and inter-city distances. No significant association was detected at either timepoint (Week 3: r = − 0.109, p = 0.716; Week 21: r = 0.021, p = 0.414). Distance–decay slopes were shallow and not different between weeks (Week 3 slope = − 0.003; Week 21 slope = + 0.00217; ΔSlope = 0.00516, p = 0.197, Supplementary Fig. S2 ). PCNM-based dbRDA detected no spatial structuring (Week 3 adj. R² = −0.0016, p = 0.489; Week 21 adj. R² = 0.00512, p = 0.430), and Procrustes/PROTEST analyses indicated weak geographic–microbial concordance (Week 3 p = 0.136; Week 21 p = 0.0454). Discussion This nationwide analysis provides one of the first systematic assessments of spatial and temporal dynamics in Swedish wastewater microbiomes based on shotgun metagenomes from 16 wastewater treatment plants sampled twice in 2024 (weeks 3 and 21). The results revealed that spatial variation overwhelmingly outweighed temporal change. PERMANOVA attributed far more variance to city than to collection time point, and PCoA did not reveal a coherent nationwide pattern, converging on the same conclusion: city explained most of the variance in microbial composition, while sampling week contributed little. In other words, geography, not short-term timing, emerges as the primary organizing axis of wastewater microbiomes across Sweden. Temporal change was present but strongly city-specific. Although alpha diversity tended to increase on average, the magnitude and direction of these shifts differed between locations, providing no counterweight to the dominant spatial signal. Each wastewater system appeared to follow its own course rather than exhibiting a coordinated national trend. The resulting within-city turnover was therefore heterogeneous, reflecting local adjustments in microbial composition rather than a unified temporal drift across Sweden. The distribution of change within communities helps explain why spatial structure dominates. The high-prevalence backbone, or stable core, remained largely constant, while the more variable peripheral fraction accounted for nearly all detectable movement. In cities showing greater turnover, this reflected fluctuations within the periphery rather than any systematic restructuring of the core Differential-abundance analyses support this interpretation when viewed in context. ANCOM-BC2 revealed widespread differences between sites but almost no signal of change across weeks. ALDEx2 identified a larger set of week-associated genera; however, these shifts were city-specific and largely confined to the peripheral fraction rather than reflecting a coherent national pattern. Together, both frameworks converge on the same conclusion: wastewater microbiomes are strongly defined by site, with only modest and fragmented week-to-week drift. Spatial correlation analyses further reinforce this view. Neither geographic proximity nor the number of inhabitants connected to each treatment plant explained community similarity. This may indicate that the site effect comprises a more complex set of local determinants, such as industrial inputs, catchment characteristics, or infrastructure, beyond simple geographic distance. Findings froµ this study align with prior work showing the saµe hierarchy, site effects outweigh short-terµ teµporal variation, both within individual sewer networks, where location predicts coµposition and teµporal shifts are asynchronous,( 17 ) and across treatµent plants, where spatial and operational factors surpass seasonality.( 18 ) Our shotgun-based core-periphery pattern also echoes evidence froµ activated sludge in Denµark, where a persistent, abundant core coexisted with a dynaµic peripheral fraction.( 19 ) Saunders and colleagues reported an ~ 63-genus abundant core coµprising ~ 68% of reads alongside strong between-plant differentiation; siµilarly, we observe a stable cross-site core with µost week-to-week µoveµent confined to the periphery, reinforcing the need for site-specific baselines. Despite differences in µethod and µatrix (aµplicon sludge vs. shotgun wastewater), both studies converge on the saµe conclusion: site doµinates week, and teµporal change is concentrated in non-core taxa.( 19 ) A key limitation of this study is its two-timepoint design, which constrains the ability to capture temporal variability and to confirm whether only the peripheral fraction consistently encodes community change. Longitudinal sampling over multiple years and seasons will be essential to test the persistence of this pattern and to determine whether peripheral taxa indeed provide the most sensitive signal of ecological or epidemiological perturbations. Methodological constraints also remain: k-mer–based classifiers such as Kraken2 are prone to misclassification and incomplete taxonomic recovery in complex metagenomes. Future work should therefore integrate improved and better-curated reference databases, complemented by assembly-based and discovery-oriented approaches capable of detecting novel or poorly represented organisms. Such methodological advances, together with denser temporal sampling, will be crucial for refining our understanding of wastewater microbial dynamics and for harnessing the periphery as an early-warning layer in public-health surveillance. Conclusions Swedish wastewater microbiomes are characterized by strong catchment specific structuring, a stable but slightly contracting core, and substantial city-specific peripheral turnover. Together, these findings define a stable yet dynamic microbial landscape in Swedish wastewater and provide a foundation for both ecological research and wastewater-based public-health surveillance. Abbreviations ALDEx2 ANOVA-Like Differential Expression tool 2 ANCOM-BC2 Analysis of Composition of Microbiomes with Bias Correction 2 BH-FDR Benjamini–Hochberg False Discovery Rate bp Base pair CI Confidence interval CLR Centered log-ratio dbRDA Distance-based redundancy analysis DNA Deoxyribonucleic acid FDR False discovery rate Gb Gigabase GRCh38 Genome Reference Consortium Human Build 38 HEAP Human Exposome Assessment Platform IHRC International Human Papillomavirus Reference Center IQR Interquartile range KI Karolinska Institutet OLS Ordinary least squares PCA Principal component analysis PCNM Principal coordinates of neighbor matrices PCoA Principal coordinates analysis PERMANOVA Permutational multivariate analysis of variance PERMDISP Permutational analysis of multivariate dispersions QC Quality control RefSeq Reference Sequence Database RNA Ribonucleic acid R² Coefficient of determination ρ Spearman’s rank correlation coefficient TNA Total nucleic acid W3 Week 3 (winter sampling) W21 Week 21 (late spring sampling) WBE Wastewater-based epidemiology WGS Whole genome sequencing WWTP Wastewater treatment plant Δ Change (difference between two measurements) Declarations Ethics approval and consent to participate This study analyzed composite wastewater samples collected at municipal wastewater treatment plants. No human subjects were directly involved, and no identifiable personal data were used. Consent for publication All authors have read and approved the final version of the manuscript and consent to its publication. Availability of data and materials The datasets supporting the conclusions of this article are available in the BioProject repository under accession number PRJNA1354877 (https://www.ncbi.nlm.nih.gov/sra/PRJNA1354877). All code used for processing and analysis of sequencing data is publicly available at GitHub: https://github.com/hpvcenter/biopipe (Biopipe) and https://github.com/mim86/WW_stat_analysis (downstream analysis). Competing Interests The authors declare that they have no competing interests Funding This project was funded by the Human Exposome Assessment Platform (Project No. 874662) granted by Horizon 2020. Open access funding provided by Karolinska Institute. The funding bodies had no role in the design of the study; in the collection, analysis, and interpretation of data; or in the writing of the manuscript. Author´s contributions Data curation, Formal analysis, Methodology: DM, MS. Investigation, Validation: MS, LSAM. Resources: AS, Conceptualization and project administration: LSAM. Writing – original draft preparation: DM, MS. Writing – review and editing: DM, MS, AS, LSAM. All the authors have read and approved the final manuscript. Acknowledgements We gratefully acknowledge the participating wastewater treatment plants for their efforts in sampling. We also thank the laboratory personnel of the Swedish Environmental Epidemiology Center (SEEC) for performing the sample processing, in particular Filip Petrini for his work preparing the samples for seq uencing. We acknowledge support for sample collection and processing from the SciLifeLab Pandemic Laboratory Preparedness Program (grant number REPLP1:007) and from governmental funding to the Public Health Agency of Sweden under assignment S2024/00187. The authors would like to thank Head of IHRC Joakim Dillner for continuous encouragement and support. References Hendriksen RS, Munk P, Njage P, van Bunnik B, McNally L, Lukjancenko O, et al. Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage. Nat Commun. 2019;10(1):1124. Newton RJ, McLellan SL, Dila DK, Vineis JH, Morrison HG, Eren AM, et al. Sewage reflects the microbiomes of human populations. mBio. 2015;6(2):e02574. Bibby K, Peccia J. Identification of viral pathogen diversity in sewage sludge by metagenome analysis. Environ Sci Technol. 2013;47(4):1945–51. McLellan SL, Fisher JC, Newton RJ. The microbiome of urban waters. Int Microbiol. 2015;18(3):141–9. 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Assessing PMMoV as a faecal marker for wastewater-based surveillance - Insights from Swedish wastewaters and foods. medRxiv. 2025:2025.05.18.25327821. Fierer N, Holland-Moritz H, Alexiev A, Batther H, Dragone NB, Friar L, et al. A Metagenomic Investigation of Spatial and Temporal Changes in Sewage Microbiomes across a University Campus. mSystems. 2022;7(5):e0065122. Wei Z, Liu Y, Feng K, Li S, Wang S, Jin D et al. The divergence between fungal and bacterial communities in seasonal and spatial variations of wastewater treatment plants. Sci Total Environ. 2018;628–9:969–78. Saunders AM, Albertsen M, Vollertsen J, Nielsen PH. The activated sludge ecosystem contains a core community of abundant organisms. ISME J. 2016;10(1):11–20. Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.pdf TableS2stablecoregenus.csv TableS3ANCOMBC2site.csv TableS4aldexressig.csv Cite Share Download PDF Status: Posted 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-7987707","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":556598668,"identity":"4f27e10d-2d62-4b6b-9291-b8a34ee83bd2","order_by":0,"name":"Dhananjay Mukhedkar","email":"","orcid":"","institution":"Karolinska University Hospital Huddinge, Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Dhananjay","middleName":"","lastName":"Mukhedkar","suffix":""},{"id":556598669,"identity":"4a6e74e3-a1d5-4bf0-80b8-0cac913fb325","order_by":1,"name":"Milan S. Stosic","email":"","orcid":"","institution":"Akershus University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Milan","middleName":"S.","lastName":"Stosic","suffix":""},{"id":556598670,"identity":"4758ad02-c717-4439-b17d-e8e51468c456","order_by":2,"name":"Anna J. Székely","email":"","orcid":"","institution":"Swedish University of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"J.","lastName":"Székely","suffix":""},{"id":556598671,"identity":"d31fe2a1-1757-4d44-bd17-fd536d2e7b1a","order_by":3,"name":"Ekaterina Avershina","email":"","orcid":"","institution":"Oslo University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ekaterina","middleName":"","lastName":"Avershina","suffix":""},{"id":556598672,"identity":"78a6472e-95f3-4138-86d4-ab0ce9bd04e3","order_by":4,"name":"Laila Sara 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1","display":"","copyAsset":false,"role":"figure","size":1374375,"visible":true,"origin":"","legend":"\u003cp\u003ePaired changes in genus-level alpha diversity from Week 3 to Week 21 across 16 cities.\u003c/p\u003e\n\u003cp\u003eA) Week-wise distributions with paired city trajectories. Boxplots show the median and the middle 50% of values (whiskers mark range). Colored dots represent cities; dashed lines connect each city from Week 3 to Week 21; open diamonds represent the average. B) Within-city change (Week 21 − Week 3) for each city shown as horizontal lollipops; the dashed vertical line marks zero (no change).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7987707/v1/b226f3911f976f6a650cab3e.png"},{"id":97659061,"identity":"2434881e-3a12-4765-8a9b-a6416f287f31","added_by":"auto","created_at":"2025-12-08 07:35:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1317354,"visible":true,"origin":"","legend":"\u003cp\u003eBeta diversity in wastewater microbiomes across 16 Swedish cities\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A–B)\u003c/strong\u003e Principal coordinate analysis (PCoA) and dispersion of genus-level community profiles based on Aitchison distances. \u003cstrong\u003e(A)\u003c/strong\u003e PCoA shows that samples cluster primarily by city, with paired Week 3 (W3, circles) and Week 21 (W21, triangles) samples connected by dashed lines. Each city exhibits a directional shift between weeks, but the direction and magnitude of change vary across locations. \u003cstrong\u003e(B)\u003c/strong\u003eCommunity dispersion, expressed as distance to the group centroid in Aitchison space. Dispersion increased slightly at Week 21, indicating marginally higher heterogeneity among cities. \u003cstrong\u003e(C–D)\u003c/strong\u003e PCoA and dispersion based on presence/absence structure (Jaccard distances). \u003cstrong\u003e(C)\u003c/strong\u003e The pattern parallels that of the Aitchison-based analysis, showing strong city-level clustering and weak temporal separation. \u003cstrong\u003e(D)\u003c/strong\u003e Dispersion did not differ significantly between weeks, suggesting stable overall community variability.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7987707/v1/74f64b98a6922f0bc0fe70f4.png"},{"id":97659081,"identity":"7d5df3cb-b217-404f-a324-b0768efd94cb","added_by":"auto","created_at":"2025-12-08 07:35:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":286033,"visible":true,"origin":"","legend":"\u003cp\u003ePer-city microbial turnover between Week 3 and Week 21 (GENUS).\u003c/p\u003e\n\u003cp\u003eAitchison distance (CLR Euclidean) for each city; larger values indicate greater within-city compositional change. Dashed vertical line marks zero reference.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7987707/v1/cc6b4d781d5e400a0d7181fe.png"},{"id":97659070,"identity":"6f9fcb25-c960-4f56-8448-1f6f03a69097","added_by":"auto","created_at":"2025-12-08 07:35:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":432602,"visible":true,"origin":"","legend":"\u003cp\u003eWithin-city microbial turnover decomposition (W21 vs W3).\u003c/p\u003e\n\u003cp\u003eProportion of the within-city Aitchison turnover explained by the \u003cem\u003estable core\u003c/em\u003e (genera present in ≥75% of cities at both weeks) and the \u003cem\u003eperipheral\u003c/em\u003e fraction, shown for each city (bars stacked to 100%).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7987707/v1/12a2f7e8b9986159dc18c7f0.png"},{"id":99711758,"identity":"aea3019c-866e-4a09-bb40-5a1119e67b0b","added_by":"auto","created_at":"2026-01-07 13:39:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3696358,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7987707/v1/57121b6d-edbe-413a-b503-dff671edb6ad.pdf"},{"id":97659064,"identity":"46a045af-d728-435b-a5f1-cfdfb625110e","added_by":"auto","created_at":"2025-12-08 07:35:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":656221,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7987707/v1/37b463e8cb4d4786021bf3f7.pdf"},{"id":97659060,"identity":"33c3c70f-6905-4593-a7ec-471d2c458aa4","added_by":"auto","created_at":"2025-12-08 07:35:13","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5703,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2stablecoregenus.csv","url":"https://assets-eu.researchsquare.com/files/rs-7987707/v1/abe3154241654d42e71756b0.csv"},{"id":97672564,"identity":"45d88f06-f562-4cc2-a0fe-0564483827ca","added_by":"auto","created_at":"2025-12-08 09:38:23","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":12822,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3ANCOMBC2site.csv","url":"https://assets-eu.researchsquare.com/files/rs-7987707/v1/9778389f754f26977b6612fd.csv"},{"id":97674927,"identity":"567a75e7-9e96-452a-a74f-7c91264609f5","added_by":"auto","created_at":"2025-12-08 09:44:50","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":28717,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4aldexressig.csv","url":"https://assets-eu.researchsquare.com/files/rs-7987707/v1/79d6af153d8cd34d6bc6d20d.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Stable cores and dynamic peripheries: spatial structuring dominates over temporal turnover in wastewater microbiomes across 16 Swedish cities","fulltext":[{"header":"Background","content":"\u003cp\u003eWastewater harbors highly diverse microbial communities that integrate inputs from human populations, industrial sources, and the surrounding environment. These communities include gut-associated bacteria, environmental taxa, and opportunistic pathogens, and thus wastewater microbiomes provide a unique window into the intersection of human health and the environment. In recent years, they have gained increasing attention both as ecological systems of interest and as a basis for wastewater-based epidemiology (WBE), where microbial signals are used to monitor population-level health trends.(\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003cp\u003ePrevious studies have described the taxonomic composition and functional potential of wastewater microbiomes across different regions.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) These consistently highlight pronounced spatial variation, reflecting local environmental conditions, sewer infrastructure, and population inputs.(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) At the same time, temporal changes have been reported, ranging from seasonal shifts to short-term fluctuations.(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) However, the relative importance of spatial versus temporal drivers in shaping wastewater microbial communities remains uncertain.(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) In particular, it is unclear whether short-term temporal changes represent coordinated nationwide patterns or independent, city-specific trajectories.\u003c/p\u003e\u003cp\u003eAnother open question concerns the balance between community stability and change. Many ecosystems are thought to be anchored by a relatively stable core microbiome of widespread taxa, while more dynamic peripheral members contribute to turnover and variability.(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) Although this framework is well established for host-associated microbiomes, it has rarely been applied to wastewater communities. Understanding whether temporal restructuring is driven by changes in the stable core or by shifts in the peripheral microbiota is critical for interpreting wastewater data, especially in surveillance contexts where reproducibility and comparability across sites are essential.\u003c/p\u003e\u003cp\u003eTo address these gaps, we investigated the relative contributions of space, time, and taxonomic structure to wastewater microbial community dynamics using samples from 16 Swedish cities collected at two timepoints. Specifically, we asked: (i) do alpha- and beta-diversity metrics show systematic changes between timepoints, (ii) how much compositional turnover occurs within cities compared to between cities, and (iii) how do stable \u0026ldquo;core\u0026rdquo; taxa versus more variable peripheral taxa contribute to observed changes? By combining compositional data analysis with turnover decomposition, we demonstrate that spatial structuring is the dominant factor shaping wastewater microbiomes, whereas temporal changes occur primarily within cities and are largely driven by the peripheral community. These findings highlight the importance of accounting for strong local effects when using wastewater microbiomes for ecological inference and wastewater-based surveillance applications.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSampling strategy and study design\u003c/h2\u003e\u003cp\u003eThe present study includes sewage water samples collected from 16 wastewater treatment plants (WWTP) across Sweden at two timepoints: Week 3 (W3) and Week 21 (W21) in 2024. The study sites included: G\u0026auml;vle (WWTP: Duvbackens avloppsreningsverk), G\u0026ouml;teborg (Ryaverket), Kalmar (Kalmarsundsverket), Karlstad (Sj\u0026ouml;stadsverket), Link\u0026ouml;ping (Nykvarnsverket), Lule\u0026aring; (Uddebo reningsverk), Malm\u0026ouml; (Sj\u0026ouml;lunda reningsverk), and four locations in Stockholm: Bromma (Bromma reningsverk), Gr\u0026ouml;dinge (Himmerfj\u0026auml;rdsverket), Henriksdal (Henriksdals reningsverk), and K\u0026auml;ppala (K\u0026auml;ppalaverket), Ume\u0026aring; (\u0026Ouml;ns reningsverk), Uppsala (Kungs\u0026auml;ngsverket), V\u0026auml;ster\u0026aring;s (Kungs\u0026auml;ngsverket), \u0026Ouml;stersund (G\u0026ouml;vikens reningsverk), and \u0026Ouml;sthammar (Alunda avloppsreningsverk).\u003c/p\u003e\u003cp\u003eAt each site, influent samples were obtained following standardized collection procedures. For most cities, untreated wastewater samples that are representative of a single day were collected by flow compensated samplers at WWTP. Uppsala is the exception, where samples were collected daily, and then combined flow-proportionally into one composite weekly sample for the purpose of analyses. All samples were transported under cooled conditions to the analysis laboratory immediately after collection and processed for nucleic acid extraction within 48 hours of arrival.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eExtraction and sequencing\u003c/h3\u003e\n\u003cp\u003eTotal nucleic acids were extracted from 40 mL of sewage water samples using the Maxwell\u0026reg; RSC Enviro TNA Kit (Promega) on the Maxwell\u0026reg; RSC instrument (Promega), following the manufacturer\u0026rsquo;s instructions with the PureFood GMO and Authentication program, as described previously.(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) Total genomic DNA was quantified using Quant-iT\u0026trade; PicoGreen\u0026trade; dsDNA Assay Kit (ThermoFisher Scientific). Shotgun metagenomic sequencing (total DNA) was performed using the Illumina DNA Prep Tagmentation kit (Illumina\u0026reg;) with dual indices, following the manufacturer\u0026rsquo;s instructions. Individual libraries were sequenced by the paired-end method (2 x 150 base pair) on the Novaseq 6000 S4 platform (Illumina\u0026reg;), with a median output of 32.2 Gb per sample after quality trimming.\u003c/p\u003e\n\u003ch3\u003eSequencing analysis\u003c/h3\u003e\n\u003cp\u003eRaw reads were processed using Biopipe (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/hpvcenter/biopipe\u003c/span\u003e\u003cspan address=\"https://github.com/hpvcenter/biopipe\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a workflow for standardized taxonomic classification of metagenomic data. The pipeline consists of two main steps: (i) bioinformatic preprocessing of sequencing data and (ii) taxonomic classification, followed by downstream analysis of diversity and community composition.\u003c/p\u003e\u003cp\u003eBriefly, adapter removal and quality trimming were performed using Trimmomatic (minimum read length: 36 bp, slidingWindow:4:15),(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and read quality was assessed before and after trimming with FastQC. High-quality reads were screened for human sequences using NextGenMap against the human reference genome (GRCh38),(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) with non-default parameters of \u0026gt;\u0026thinsp;95% identity over at least 75% of read length. Reads classified as human were removed, and non-human reads were extracted using SAMtools and reconverted to FASTQ format. Taxonomic classification used Kraken2, (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) (v2.1.2), with a RefSeq database (June 2024 build) including archaea, bacteria, viruses, plasmids, protozoa, fungi, UniVec_Core and two human references (GRCh38 and T2T-CHM13v2.0; human libraries created with --no-mask). Bracken (v2.9) was used on Kraken2 reports to refine abundance estimates to the genus level for downstream analyses. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eTo ensure computational efficiency, all analyses were executed on an on-premises Hopsworks cluster (version 3.1; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hopsworks.ai\u003c/span\u003e\u003cspan address=\"https://hopsworks.ai\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) running Apache Spark for distributed execution of the Biopipe workflow, enabling parallelized preprocessing and taxonomic classification.\u003c/p\u003e\n\u003ch3\u003eDownstream diversity analysis and statistics\u003c/h3\u003e\n\u003cp\u003eBefore prevalence or compositional calculations, fixed detection thresholds were applied to reduce spurious low-abundance calls: taxa were retained if the relative abundance fraction was \u0026ge;\u0026thinsp;0.001 and if the number of reads was \u0026ge;\u0026thinsp;50. Compositional methods used a centered log-ratio (CLR) transformation, with a small pseudocount (ε\u0026thinsp;=\u0026thinsp;1\u0026times;10⁻⁶) added before renormalization to avoid undefined log values.\u003c/p\u003e\n\u003ch3\u003eDiversity analyses\u003c/h3\u003e\n\u003cp\u003eGenus-level richness and Shannon diversity indices (alpha diversity) were calculated for each sample on the renormalized profiles to assess within-sample diversity. Temporal differences between W3 and W21 within each city were evaluated using paired Wilcoxon signed-rank tests.\u003c/p\u003e\u003cp\u003eBetween-sample community structure (beta diversity) was assessed by transforming relative abundance data with the centered log-ratio (CLR) and calculating Aitchison distances (Euclidean distances on CLR-transformed data). The effects of city (between-city structure) and week (within-city change) were analyzed with PERMANOVA (adonis2, 9,999 permutations), reporting marginal (by-term) tests and a week-only model with city used as permutation strata to respect pairing. In parallel, a presence/absence matrix was constructed at the genus level after applying detection thresholds, and binary Jaccard distances were computed. The same PERMANOVA designs were applied to the Jaccard matrix (by-term city and week; and week-only with strata\u0026thinsp;=\u0026thinsp;city). Principal coordinates analysis (PCoA) was applied to both Aitchison and Jaccard distances for visualization. Differences in community dispersion (homogeneity of multivariate dispersion) between weeks were evaluated with PERMDISP (betadisper\u0026thinsp;+\u0026thinsp;permutation test) for both distance types.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eTurnover and genus contributions\u003c/h2\u003e\u003cp\u003eWithin-city temporal turnover was quantified as the Aitchison distance between W3 and W21 CLR compositions for each city. The across-city distribution of turnover was summarized by the sample median and a nonparametric bootstrap 95% confidence interval (10,000 resamples), tested against zero with a one-sample Wilcoxon signed-rank test.\u003c/p\u003e\u003cp\u003eFor each city, ΔCLR (W21\u0026thinsp;\u0026minus;\u0026thinsp;W3) was computed per genus; squared ΔCLR summed to the city\u0026rsquo;s squared Aitchison turnover. Genera were ranked by within-city contribution, and each genus\u0026rsquo;s mean turnover share was summarized across cities.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCore and peripheral taxa\u003c/h3\u003e\n\u003cp\u003eA stable core genus was defined as any genus present in at least 75% of cities at both weeks. For each city and week, the fractions of community abundance explained by the stable core were calculated. Changes in stable-core abundance (W21\u0026thinsp;\u0026minus;\u0026thinsp;W3) were tested with paired Wilcoxon signed-rank tests, reporting the Hodges\u0026ndash;Lehmann paired median difference and 95% confidence interval. The peripheral fraction was defined as 1 \u0026minus; (stable-core fraction). Within-city turnover was correlated with changes in stable-core, and peripheral fractions using Spearman\u0026rsquo;s ρ.\u003c/p\u003e\n\u003ch3\u003ePrevalence shifts\u003c/h3\u003e\n\u003cp\u003eTo test W3 to W21 changes in genus prevalence across cities, paired 2\u0026times;2 tables were constructed for each genus to count city-level gains (absent \u0026rarr; present) and losses (present \u0026rarr; absent). McNemar\u0026rsquo;s test was applied per genus, and p-values were adjusted using the Benjamini\u0026ndash;Hochberg false discovery rate (BH-FDR). Signed prevalence change was reported as Δ = (gains\u0026thinsp;\u0026minus;\u0026thinsp;losses)/number of cities, alongside FDR-adjusted p-values.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eConcordance of community configurations\u003c/h2\u003e\u003cp\u003eWeek-specific CLR-PCA scores (up to five components) were calculated for the set of paired cities. Geometric concordance between W3 and W21 ordination configurations was quantified with Procrustes analysis and tested using PROTEST (9,999 permutations).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDifferential abundance\u003c/h2\u003e\u003cp\u003eDifferential abundance was evaluated using two complementary compositional frameworks. With ANCOM-BC2, genus-by-sample count matrices were built. For the global site effect (adjusted for week), a fixed-effects model (site\u0026thinsp;+\u0026thinsp;week) was fit. For the week effect, a mixed model with a random intercept for city (week + (1|site)) was used, restricting to genera observed in at least two distinct cities to satisfy model identifiability. Multiple testing was controlled with BH-FDR.\u003c/p\u003e\u003cp\u003eIn ALDEx2 (paired), Dirichlet-Monte Carlo CLR (mc.samples\u0026thinsp;=\u0026thinsp;128) was applied with a paired W3 vs W21 test at the genus level. BH-FDR was again used for multiple testing correction, and ALDEx2 effect sizes were reported in CLR units.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSpatial and population effects\u003c/h2\u003e\u003cp\u003eGeographic structuring was assessed by correlating pairwise Aitchison dissimilarities with great-circle geographic distances (km) using Mantel tests (Spearman, 9,999 permutations). To compare distance\u0026ndash;decay between weeks, we used a paired permutation procedure that shuffled week labels within site-pairs and tested the difference in regression slopes. Multi-scale spatial signal was further evaluated by deriving principal coordinates of neighbor matrices (PCNM) from the geographic distance matrix and fitting distance-based redundancy analyses (dbRDA; capscale) of Aitchison dissimilarities on the subset of positive-eigenvalue PCNM axes (up to five), separately for each week; significance was assessed with permutation ANOVA, and adjusted R\u0026sup2; was reported.\u003c/p\u003e\u003cp\u003eFurthermore, each site was linked to the number of connected inhabitants, retrieved from previous studies.(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) Connected inhabitants were analyzed on the log₁₀ scale. Associations with community features were evaluated in three ways: (i) for alpha diversity, we fitted ordinary least squares models of richness and Shannon on log₁₀(connected inhabitants) separately by week and, for paired changes, regressed within-city Δ alpha (W21\u0026thinsp;\u0026minus;\u0026thinsp;W3) on log₁₀(connected inhabitants), reporting Spearman\u0026rsquo;s ρ alongside the OLS slope and p-value; (ii) for temporal turnover, we related within-city Aitchison distance (W21 vs W3) to log₁₀(connected inhabitants) using linear regression and Spearman correlation; and (iii) at the dissimilarity level, we compared Aitchison distances to pairwise |Δ log₁₀(connected inhabitants)| with Mantel and partial Mantel tests that control for geography, and fitted dbRDA models of Aitchison dissimilarities on log₁₀(connected inhabitants), reporting permutation p-values and adjusted R\u0026sup2;.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003ePermutation, resampling, and multiple testing\u003c/h2\u003e\u003cp\u003eUnless otherwise noted, permutation tests used 9,999 permutations with pairing respected where appropriate (strata\u0026thinsp;=\u0026thinsp;city). Bootstrap confidence intervals for medians were generated with 10,000 resamples. Multiple tests across genera were controlled with the BH-FDR procedure.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eDataset overview\u003c/h2\u003e\u003cp\u003eAcross 32 samples (16 cities \u0026times; 2 weeks), shotgun metagenomic sequencing yielded a median of 50.0\u0026nbsp;million high-quality read pairs per sample. After removing human and unclassified reads, Kraken2 assigned a median of 24.9\u0026nbsp;million reads per sample to Bacteria, Viruses, or Archaea; assignments were dominated by Bacteria (median 97.6%), with smaller fractions of Viruses (1.97%) and Archaea (0.42%).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eAlpha diversity\u003c/h2\u003e\u003cp\u003eAcross the 16 paired cities, genus-level alpha diversity significantly increased from Week 3 to Week 21. Median within-city change was +\u0026thinsp;10 genera for richness and +\u0026thinsp;0.209 for Shannon (paired Wilcoxon W21\u0026ndash;W3: richness p\u0026thinsp;=\u0026thinsp;0.00266, Shannon p\u0026thinsp;=\u0026thinsp;0.00295, n\u0026thinsp;=\u0026thinsp;16). The direction of change was broadly consistent across sites, with variable magnitudes by city (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u0026ndash;B).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eBeta diversity\u003c/h2\u003e\u003cp\u003eCommunity composition differed primarily by city, with little consistent change by week. In PERMANOVA on Aitchison distances, city explained 58.3% of variance (R\u0026sup2; = 0.583, p\u0026thinsp;=\u0026thinsp;0.0003), whereas week accounted for 3.7% and was not significant (R\u0026sup2; = 0.037, p\u0026thinsp;=\u0026thinsp;0.106). A Bray\u0026ndash;Curtis sensitivity analysis confirmed the finding (city R\u0026sup2; = 0.651, p\u0026thinsp;=\u0026thinsp;0.0017; week R\u0026sup2; = 0.047, p\u0026thinsp;=\u0026thinsp;0.0609). When the week effect was tested within pairs (permuting within city), it remained non-significant (Aitchison, p\u0026thinsp;=\u0026thinsp;0.0878). Taken together, samples from the same city are much more similar to each other (even across weeks) than samples from different cities collected in the same week.\u003c/p\u003e\u003cp\u003ePCoA of Aitchison distances showed no coherent clustering by city and no nationwide week pattern. Although each city shifted between Week 3 and Week 21, the directions of change varied across sites, consistent with the findings that cities explain variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u0026ndash;D, the presence/absence structure (Jaccard) revealed the same pattern of strong city-level clustering and weak temporal change.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGroup dispersion changed only modestly. Distances to the week centroid were higher at W21 than W3 for Aitchison (median 30.9 vs 27.2), but the PERMDISP test was borderline (F\u0026thinsp;=\u0026thinsp;3.61, p\u0026thinsp;=\u0026thinsp;0.066; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). For Jaccard, dispersion did not differ (F\u0026thinsp;=\u0026thinsp;1.80, p\u0026thinsp;=\u0026thinsp;0.19; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Overall, between-city differences dominate, with idiosyncratic within-city shifts over time and no evidence for a synchronized temporal change.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eTurnover\u003c/h2\u003e\u003cp\u003eWithin-city community composition changed substantially between Week 3 and Week 21. Turnover, quantified as the Aitchison distance between the two weeks\u0026rsquo; CLR genus profiles, had a median of 37.5 (bootstrap 95% CI 34.9\u0026ndash;45.3), significantly greater than zero (paired Wilcoxon, p\u0026thinsp;=\u0026thinsp;4.82\u0026times;10⁻⁴). Magnitudes varied across cities ranging between 31.5 and 52.3 indicating clear reconfiguration within cities over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eStable core and peripheral fractions\u003c/h2\u003e\u003cp\u003eA conservative \u0026ldquo;stable core\u0026rdquo; was defined as genera present in \u0026ge;\u0026thinsp;75% of cities at both sampling timepoints. Stable core consisted of 88 genera (intersection of week-specific cores) and the full list of genera with per-week prevalence and mean abundance is shown in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eFor each city, the fraction of total relative abundance explained by the stable core was computed for W3 and W21, and the paired change tested (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The stable-core fraction decreased from W3 to W21 (Hodges\u0026ndash;Lehmann paired median Δ = \u0026minus;0.020; 95% CI [-0.0312, -0.0103]; p\u0026thinsp;=\u0026thinsp;0.00209), indicating a proportional expansion of the peripheral fraction (1\u0026thinsp;\u0026minus;\u0026thinsp;stable core).\u003c/p\u003e\u003cp\u003eMoreover, within-city compositional turnover was not associated with changes in the stable-core fraction. Cities exhibiting greater week-to-week shifts did not systematically lose or gain in core abundance (Spearman\u0026rsquo;s ρ\u0026thinsp;=\u0026thinsp;0.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.69). A per-city decomposition of turnover into stable-core and peripheral components is provided in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. Across all cities, most of the within-city turnover observed between weeks 3 and 21 was driven by the peripheral fraction rather than the stable core (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), consistent with the decomposition results (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003ePresence/absence and differential abundance\u003c/h2\u003e\u003cp\u003eThree complementary signals were evaluated: (i) between-city differences in genus abundance (ANCOM-BC2 global site effect, adjusted for week), (ii) within-city week changes (ANCOM-BC2 mixed model with a random intercept for site and ALDEx2 paired tests), and (iii) changes in presence/absence across weeks (paired McNemar tests per genus).\u003c/p\u003e\u003cp\u003eANCOM-BC2 identified a substantial site effect: 86 of 225 genera (38.2%) were significant for the global site term after FDR correction (q\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Significant taxa included gut-associated genera (e.g., Bacteroides, Faecalibacterium, Bifidobacterium) and wastewater/environmental genera (e.g., Pseudomonas, Comamonas, Polaromonas), consistent with the strong spatial structure in β-diversity analyses.\u003c/p\u003e\u003cp\u003eUsing the mixed-effects ANCOM-BC2 model (week + (1|site)), only Butyricimonas showed a significant W21\u0026ndash;W3 shift (q\u0026thinsp;=\u0026thinsp;0.026, log-fold change \u0026asymp; \u0026minus;\u0026thinsp;0.38; decrease at W21), with no other genera passing FDR. In contrast, ALDEx2 (paired) detected 46 of 225 genera (20.4%) with week-associated differences (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). The discrepancy reflects differing model assumptions (bias-corrected GLM with random intercept vs Dirichlet-Monte Carlo CLR with paired tests) in the presence of a strong site effect; week signals were modest and framework-dependent.\u003c/p\u003e\u003cp\u003ePrevalence (presence/absence). Paired McNemar tests found no genera with FDR-significant prevalence changes (all q\u0026thinsp;=\u0026thinsp;1; n\u0026thinsp;=\u0026thinsp;16).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eSpatial and population correlates\u003c/h2\u003e\u003cp\u003eTo test whether geographic distance influenced wastewater microbiomes, Mantel tests were performed between pairwise Aitchison dissimilarities and inter-city distances. No significant association was detected at either timepoint (Week 3: r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.109, p\u0026thinsp;=\u0026thinsp;0.716; Week 21: r\u0026thinsp;=\u0026thinsp;0.021, p\u0026thinsp;=\u0026thinsp;0.414). Distance\u0026ndash;decay slopes were shallow and not different between weeks (Week 3 slope\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.003; Week 21 slope\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.00217; ΔSlope\u0026thinsp;=\u0026thinsp;0.00516, p\u0026thinsp;=\u0026thinsp;0.197, Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). PCNM-based dbRDA detected no spatial structuring (Week 3 adj. R\u0026sup2; = \u0026minus;0.0016, p\u0026thinsp;=\u0026thinsp;0.489; Week 21 adj. R\u0026sup2; = 0.00512, p\u0026thinsp;=\u0026thinsp;0.430), and Procrustes/PROTEST analyses indicated weak geographic\u0026ndash;microbial concordance (Week 3 p\u0026thinsp;=\u0026thinsp;0.136; Week 21 p\u0026thinsp;=\u0026thinsp;0.0454).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis nationwide analysis provides one of the first systematic assessments of spatial and temporal dynamics in Swedish wastewater microbiomes based on shotgun metagenomes from 16 wastewater treatment plants sampled twice in 2024 (weeks 3 and 21). The results revealed that spatial variation overwhelmingly outweighed temporal change. PERMANOVA attributed far more variance to city than to collection time point, and PCoA did not reveal a coherent nationwide pattern, converging on the same conclusion: city explained most of the variance in microbial composition, while sampling week contributed little. In other words, geography, not short-term timing, emerges as the primary organizing axis of wastewater microbiomes across Sweden.\u003c/p\u003e\u003cp\u003eTemporal change was present but strongly city-specific. Although alpha diversity tended to increase on average, the magnitude and direction of these shifts differed between locations, providing no counterweight to the dominant spatial signal. Each wastewater system appeared to follow its own course rather than exhibiting a coordinated national trend. The resulting within-city turnover was therefore heterogeneous, reflecting local adjustments in microbial composition rather than a unified temporal drift across Sweden.\u003c/p\u003e\u003cp\u003eThe distribution of change within communities helps explain why spatial structure dominates. The high-prevalence backbone, or stable core, remained largely constant, while the more variable peripheral fraction accounted for nearly all detectable movement. In cities showing greater turnover, this reflected fluctuations within the periphery rather than any systematic restructuring of the core\u003c/p\u003e\u003cp\u003eDifferential-abundance analyses support this interpretation when viewed in context. ANCOM-BC2 revealed widespread differences between sites but almost no signal of change across weeks. ALDEx2 identified a larger set of week-associated genera; however, these shifts were city-specific and largely confined to the peripheral fraction rather than reflecting a coherent national pattern. Together, both frameworks converge on the same conclusion: wastewater microbiomes are strongly defined by site, with only modest and fragmented week-to-week drift. Spatial correlation analyses further reinforce this view. Neither geographic proximity nor the number of inhabitants connected to each treatment plant explained community similarity. This may indicate that the site effect comprises a more complex set of local determinants, such as industrial inputs, catchment characteristics, or infrastructure, beyond simple geographic distance.\u003c/p\u003e\u003cp\u003eFindings fro\u0026micro; this study align with prior work showing the sa\u0026micro;e hierarchy, site effects outweigh short-ter\u0026micro; te\u0026micro;poral variation, both within individual sewer networks, where location predicts co\u0026micro;position and te\u0026micro;poral shifts are asynchronous,(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) and across treat\u0026micro;ent plants, where spatial and operational factors surpass seasonality.(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) Our shotgun-based core-periphery pattern also echoes evidence fro\u0026micro; activated sludge in Den\u0026micro;ark, where a persistent, abundant core coexisted with a dyna\u0026micro;ic peripheral fraction.(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) Saunders and colleagues reported an ~\u0026thinsp;63-genus abundant core co\u0026micro;prising\u0026thinsp;~\u0026thinsp;68% of reads alongside strong between-plant differentiation; si\u0026micro;ilarly, we observe a stable cross-site core with \u0026micro;ost week-to-week \u0026micro;ove\u0026micro;ent confined to the periphery, reinforcing the need for site-specific baselines. Despite differences in \u0026micro;ethod and \u0026micro;atrix (a\u0026micro;plicon sludge vs. shotgun wastewater), both studies converge on the sa\u0026micro;e conclusion: site do\u0026micro;inates week, and te\u0026micro;poral change is concentrated in non-core taxa.(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eA key limitation of this study is its two-timepoint design, which constrains the ability to capture temporal variability and to confirm whether only the peripheral fraction consistently encodes community change. Longitudinal sampling over multiple years and seasons will be essential to test the persistence of this pattern and to determine whether peripheral taxa indeed provide the most sensitive signal of ecological or epidemiological perturbations.\u003c/p\u003e\u003cp\u003eMethodological constraints also remain: k-mer\u0026ndash;based classifiers such as Kraken2 are prone to misclassification and incomplete taxonomic recovery in complex metagenomes. Future work should therefore integrate improved and better-curated reference databases, complemented by assembly-based and discovery-oriented approaches capable of detecting novel or poorly represented organisms. Such methodological advances, together with denser temporal sampling, will be crucial for refining our understanding of wastewater microbial dynamics and for harnessing the periphery as an early-warning layer in public-health surveillance.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eSwedish wastewater microbiomes are characterized by strong catchment specific structuring, a stable but slightly contracting core, and substantial city-specific peripheral turnover. Together, these findings define a stable yet dynamic microbial landscape in Swedish wastewater and provide a foundation for both ecological research and wastewater-based public-health surveillance.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eALDEx2\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eANOVA-Like Differential Expression tool 2\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eANCOM-BC2\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAnalysis of Composition of Microbiomes with Bias Correction 2\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBH-FDR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBenjamini\u0026ndash;Hochberg False Discovery Rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ebp\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBase pair\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCentered log-ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003edbRDA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDistance-based redundancy analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDeoxyribonucleic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFalse discovery rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGb\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGigabase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGRCh38\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGenome Reference Consortium Human Build 38\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHEAP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHuman Exposome Assessment Platform\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIHRC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInternational Human Papillomavirus Reference Center\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterquartile range\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKarolinska Institutet\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOLS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOrdinary least squares\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrincipal component analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCNM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrincipal coordinates of neighbor matrices\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCoA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrincipal coordinates analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePERMANOVA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePermutational multivariate analysis of variance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePERMDISP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePermutational analysis of multivariate dispersions\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eQC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eQuality control\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRefSeq\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReference Sequence Database\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRibonucleic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eR\u0026sup2;\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCoefficient of determination\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eρ\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSpearman\u0026rsquo;s rank correlation coefficient\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTotal nucleic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eW3\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWeek 3 (winter sampling)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eW21\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWeek 21 (late spring sampling)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWBE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWastewater-based epidemiology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWGS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWhole genome sequencing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWWTP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWastewater treatment plant\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eΔ\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChange (difference between two measurements)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study analyzed composite wastewater samples collected at municipal wastewater treatment plants. No human subjects were directly involved, and no identifiable personal data were used.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final version of the manuscript and consent to its publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are available in the BioProject repository under accession number PRJNA1354877 (https://www.ncbi.nlm.nih.gov/sra/PRJNA1354877). All code used for processing and analysis of sequencing data is publicly available at GitHub:\u0026nbsp;\u0026nbsp;https://github.com/hpvcenter/biopipe (Biopipe) and https://github.com/mim86/WW_stat_analysis\u0026nbsp;(downstream analysis).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was funded by the Human Exposome Assessment Platform (Project No. 874662) granted by Horizon 2020. Open access funding provided by Karolinska Institute. The funding bodies had no role in the design of the study; in the collection, analysis, and interpretation of data; or in the writing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026acute;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData curation, Formal analysis, Methodology: DM, MS. Investigation, Validation: MS, LSAM. Resources: AS, Conceptualization and project administration: LSAM. Writing \u0026ndash; original draft preparation: DM, MS. Writing \u0026ndash; review and editing: DM, MS, AS, LSAM. All the authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the participating wastewater treatment plants for their efforts in sampling. We also thank the laboratory personnel of the Swedish Environmental Epidemiology Center (SEEC) for performing the sample processing, in particular Filip Petrini for his work preparing the samples for seq \u0026nbsp; \u0026nbsp;uencing. We acknowledge support for sample collection and processing from the SciLifeLab Pandemic Laboratory Preparedness Program (grant number REPLP1:007) and from governmental funding to the Public Health Agency of Sweden under assignment S2024/00187. The authors would like to thank Head of IHRC Joakim Dillner for continuous encouragement and support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHendriksen RS, Munk P, Njage P, van Bunnik B, McNally L, Lukjancenko O, et al. Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage. Nat Commun. 2019;10(1):1124.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNewton RJ, McLellan SL, Dila DK, Vineis JH, Morrison HG, Eren AM, et al. Sewage reflects the microbiomes of human populations. mBio. 2015;6(2):e02574.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBibby K, Peccia J. Identification of viral pathogen diversity in sewage sludge by metagenome analysis. Environ Sci Technol. 2013;47(4):1945\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcLellan SL, Fisher JC, Newton RJ. The microbiome of urban waters. Int Microbiol. 2015;18(3):141\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu L, Ning D, Zhang B, Li Y, Zhang P, Shan X, et al. Global diversity and biogeography of bacterial communities in wastewater treatment plants. Nat Microbiol. 2019;4(7):1183\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu H, Shi Y, Wang S, Chen C, Pang Z, Zhang G, et al. Geographical and environmental factors influence snow microbial communities in city and suburban areas of Northern China. Curr Res Microb Sci. 2025;9:100459.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJu F, Zhang T. Bacterial assembly and temporal dynamics in activated sludge of a full-scale municipal wastewater treatment plant. ISME J. 2015;9(3):683\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVavourakis CD, Heijnen L, Peters M, Marang L, Ketelaars HAM, Hijnen WAM. Spatial and Temporal Dynamics in Attached and Suspended Bacterial Communities in Three Drinking Water Distribution Systems with Variable Biological Stability. Environ Sci Technol. 2020;54(22):14535\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGonze D, Coyte KZ, Lahti L, Faust K. Microbial communities as dynamical systems. Curr Opin Microbiol. 2018;44:41\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShade A, Handelsman J. Beyond the Venn diagram: the hunt for a core microbiome. Environ Microbiol. 2012;14(1):4\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIsaksson F, Lundy L, Hedstr\u0026ouml;m A, Sz\u0026eacute;kely AJ, Mohamed N. Evaluating the Use of Alternative Normalization Approaches on SARS-CoV-2 Concentrations in Wastewater: Experiences from Two Catchments in Northern Sweden. Environments. 2022;9(3):39.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSedlazeck FJ, Rescheneder P, von Haeseler A. NextGenMap: fast and accurate read mapping in highly polymorphic genomes. Bioinformatics. 2013;29(21):2790\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20(1):257.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: estimating species abundance in metagenomics data. PeerJ Comput Sci. 2017;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePersson S, Varg JE, Coker S, Persson F, Petrini F, Dafalla I et al. Assessing PMMoV as a faecal marker for wastewater-based surveillance - Insights from Swedish wastewaters and foods. medRxiv. 2025:2025.05.18.25327821.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFierer N, Holland-Moritz H, Alexiev A, Batther H, Dragone NB, Friar L, et al. A Metagenomic Investigation of Spatial and Temporal Changes in Sewage Microbiomes across a University Campus. mSystems. 2022;7(5):e0065122.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWei Z, Liu Y, Feng K, Li S, Wang S, Jin D et al. The divergence between fungal and bacterial communities in seasonal and spatial variations of wastewater treatment plants. Sci Total Environ. 2018;628\u0026ndash;9:969\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaunders AM, Albertsen M, Vollertsen J, Nielsen PH. The activated sludge ecosystem contains a core community of abundant organisms. ISME J. 2016;10(1):11\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"wastewater metagenomics, sewage water microbiome, core microbiome, temporal turnover, spatial structuring","lastPublishedDoi":"10.21203/rs.3.rs-7987707/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7987707/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWastewater microbial communities integrate biological inputs from human populations, the surrounding environment, and sewer infrastructure. Understanding how spatial and temporal factors shape these communities is essential for ecological interpretation and wastewater-based surveillance. However, the relative contributions of geography and short-term temporal change remain unclear at national scales.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed influent wastewater from 16 Swedish wastewater treatment plants collected at two timepoints, in winter (Week 3) and late spring (Week 21) of 2024, using shotgun metagenomic sequencing and compositional data analysis. A median of 24.9 million reads per sample were classified to Bacteria, Viruses, or Archaea, with communities dominated by Bacteria (97.6 percent), followed by Viruses (1.97 percent) and Archaea (0.42 percent).\u003c/p\u003e\n\u003cp\u003eGenus-level diversity increased significantly from winter to spring, with median within-city changes of +10 genera in richness and +0.21 in the Shannon index (p \u0026lt; 0.003). Community composition was strongly structured by geography: city explained 58.3 percent of total variance (p = 0.0003), while week accounted for 3.7 percent (p = 0.106). Within cities, temporal turnover was substantial (median Aitchison distance 37.5; p = 4.8 × 10⁻⁴) but largely confined to peripheral taxa, which accounted for approximately 93 percent of total change.\u003c/p\u003e\n\u003cp\u003eA stable core of 88 genera persisted across both timepoints, decreasing slightly in relative abundance (median change −0.020; p = 0.0021). Geographic distance and population size showed no significant associations with microbial composition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSwedish wastewater microbiomes are characterized by strong spatial differentiation, stable core communities, and locally variable peripheral turnover. Spatial factors dominate over temporal variation, emphasizing the importance of location-specific baselines for ecological assessment and wastewater-based monitoring. These findings highlight the robustness yet dynamism of urban wastewater ecosystems and provide a foundation for future national surveillance efforts.\u003c/p\u003e","manuscriptTitle":"Stable cores and dynamic peripheries: spatial structuring dominates over temporal turnover in wastewater microbiomes across 16 Swedish cities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 07:35:08","doi":"10.21203/rs.3.rs-7987707/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff373cf1-bc60-4555-b57c-b13e2120bf0f","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T13:39:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 07:35:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7987707","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7987707","identity":"rs-7987707","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}