Biosolids Amendment Enriches Soil Resistome, Virulence Factors, and ESKAPE Pathogens: A Longitudinal Metagenomic Study

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Marie, Catherine Mays, Bing Guo, Tyler S. Radniecki, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6917681/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract The spread of antibiotic resistance poses a significant challenge to public health worldwide. Wastewater treatment utilities are reservoirs of antibiotic-resistant bacteria and antibiotic resistance genes (ARGs). This study investigates the impact of biosolids amendment on the soil microbiome, resistome, virulence factors, and ESKAPE pathogens during carrot cultivation in a replicated greenhouse study. Metagenomic sequencing revealed that biosolids amendment increased the richness of microbial genera, ARGs, and virulence factors in soil. The relative abundance of ESKAPE pathogens, including Enterococcus faecium , Staphylococcus aureus , Klebsiella pneumoniae , Acinetobacter baumanii , Pseudomonas aeruginosa , and Enterobacter spp., was notably higher in biosolids-amended soils. These pathogens persisted throughout the 11-week cultivation period, raising concerns about the potential for horizontal gene transfer and the spread of antibiotic resistance. The study also identified significant co-occurrences between microbial genera and ARGs, which can suggest the possibility of the microbial taxa harboring the co-occurring ARGs. These findings highlight the importance of continued research and monitoring to ensure the safe and effective use of biosolids in agriculture. Biological sciences/Biotechnology/Sequencing/Next generation sequencing Earth and environmental sciences/Environmental sciences Biological sciences/Microbiology/Antimicrobials/Antimicrobial resistance Figures Figure 1 Figure 2 Figure 3 Introduction The increased threat of antibiotic resistance, recently dubbed “The Silent Pandemic”, is a rapidly growing public health concern 1 . Wastewater treatment utilities are one of the main recipients, reservoirs, and resources of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) 2 , 3 . Additionally, wastewater utilities receive a wide variety of antibiotics, metals, and other antimicrobials that can promote the horizontal transfer of ARGs 4 , 5 . In wastewater treatment trains, a large fraction of the ARB, ARGs, and mobile genetic elements end up in the biosolids 6 . In the United States, 31% of biosolids are used as amendments to agricultural soils to improve soil soil fertility ( e.g. , nutrient cycling) and plant defense mechanisms against pathogens 5 , 7 – 9 . Consequently, environments receiving biosolids have been shown to contain a large diversity of ARB (including human pathogens) and ARGs and are considered reservoirs for antibiotic resistance propagation 10 – 13 . Additionally, biosolids contain diverse microorganisms, with large prevalences of Proteobacteria and Bacteroidetes which contain pathogenic species. Accordingly, there are concerns that biosolids land application may increase the prevalence of human pathogens in biosolids-amended soils and further propagate clinical antibiotic resistance. Among the human pathogens of greatest concern are the ESKAPE pathogens: Enterococcus faecium , Staphylococcus aureus , Klebsiella pneumoniae , Acinetobacter baumanii , Pseudomonas aeruginosa , and Enterobacter spp. These pathogens are known for their multidrug resistance and clinical prevalence, posing a direct public health threat 14 . ESKAPE pathogens have been detected in wastewater and receiving environments, including multidrug-resistant Acinetobacter and Pseudomonas aeruginosa , and methicillin-resistant Staphylococcus aureus (MRSA) 15 , 16 . While recent research has reported on ESKAPE taxa in wastewater influent and effluent 17 , 18 , there is not a comprehensive understanding of the fate of these pathogens in environments amended with biosolids. In a broader context, longitudinal studies have examined the impact of biosolids amendment on the soil microbiome and ARG abundance. Many of these studies rely on targeted analyses, such as qPCR, to characterize the soil resistome 11 , 19 , 20 . While qPCR analysis provides an accurate quantitative assessment of the abundances of targeted ARGs, they are limited in their ability to examine the breadth of diversity in the resistome 21 . To address this limitation, metagenomic approaches have been used to characterize changes in the microbiomes and resistomes in soils amended with both untreated and composted manure at the microcosm, greenhouse, and field scales 22 – 24 . These studies take advantage of the vast data acquired from metagenomic analysis and use network analysis and other emerging approaches to determine associations between the microbiome and resistome and evaluate the risk of manure amendment. Metagenomic analyses have also been successfully used to characterize other impacted soils such as those irrigated with wastewater influent, chronically exposed to heavy metals, and amended with antibiotics 25 – 27 . While there have been some metagenomic studies on biosolids-amended soils, they are primarily focused on the biosolids themselves or the impact of biosolids treatment rather than their impact on soil microbiomes and resistomes 12 , 28 – 30 . A recent review of high-throughput sequencing approaches on biosolids soil amendments’ microbial communities highlighted the need for comprehensive longitudinal replicated analysis of biosolids amendments to understand the impacts on soil microbiomes and resistomes 31 . In this paper, shotgun metagenomics was used to characterize the impact of biosolids amendment on the enrichment of the microbiome, resistome, virulence factors, and ESKAPE pathogens in soils during carrot cultivation in a replicated greenhouse study. The relationships within and between the microbiome and the resistome were identified to elucidate co-occurrences between the microbiome and ARGs. Materials and Methods Sample Collection The greenhouse study design has been described in detail previously 32 . Briefly, the soil for cultivation was collected from a commercial agricultural field with no history of wastewater irrigation or biosolids amendment in the Willamette Valley area of Oregon. The soil was air-dried and passed through a 5-mm sieve. Dewatered Class B biosolids were obtained from a conventional wastewater treatment utility in Oregon that uses a conventional activated sludge process. The biosolids were transferred to the laboratory on ice and applied to triplicate pots at a ratio of 70 g per kg of soil. Similarly, triplicate pots of soil – herein referred to as pristine soil – were used as the control treatment. Four germinated carrot seeds were planted in each of the six pots and cultivated for 11 weeks, at which time they were harvested. Deionized water was used to irrigate the plants two to three times per week to maintain a soil moisture content of 80–85% total solids. Soil core samples were collected from the bulk soil using a soil sampler probe (M.K. Rittenhouse & Sons Ltd., St. Catharines, Ontario, Canada) at the time of planting, on week 6 of the study, and at week 11 at the time of harvest. Soil samples were transferred in WhirlPak bags (Nasco, Fort Atkinson, WI) to the lab where they were homogenized by massaging and shaking for 2 min. From each sample, approximately 0.5 g of soil was stored in 50% v/v ethanol at -20°C for microbial analysis. Genomic DNA was extracted using the FastDNA Spin Kit for Soil (MP Biomedicals, Irvine, CA). Extracted DNA was stored at -20°C until submission for sequencing. DNA purification, shotgun sequencing, and quality analysis The concentration and quality of extracted DNA were measured with a Qubit fluorometer (Thermo Fisher Scientific, Waltham, MA). To improve the quality of low-concentration samples, extracted DNA was purified with a ReliaPrep DNA Clean-up and concentration kit (Promega, Madison, WI). Purified DNA was submitted to the Center for Quantitative Life Sciences at Oregon State University. Sequencing libraries were prepared for all samples using the NexteraXT kit (Illumina, San Diego, CA), except for the initial biosolids-amended samples, which were prepared using PrepX (IntegenX, Pleasanton, CA). The three biosolids-amended samples required the PrepX kit because the BioAnalyzer results indicated DNA quality issues when using the NexteraXT kit. Paired-end (2×150 bp) sequencing reads were generated on an Illumina HiSeq 3000 (Illumina, San Diego, CA). Read quality was assessed with FastQC 33 . Subsequently, reads were filtered and trimmed using CutAdapt with quality thresholds of 20 and 15 for forward and reserve reads, respectively, and a minimum length of 36 34 . Reads were again assessed with FastQC to verify quality before subsequent analysis. Metagenomic Analysis The microbial community structure was characterized using Kaiju 35 , which translates the raw metagenomic reads into amino acid sequences in all six possible reading frames and searches for the maximum exact matches in the microbial reference gene database from the National Center for Biotechnology Information (NCBI; Menzel et al., 2016). Those that are matched are then assigned to a taxon in the NCBI taxonomy. Cleaned reads from each sample were then de novo assembled with MEGAHIT 36 using a minimum k value of 59, and a maximum of 159. Assembled reads were processed with ARGs-OAP to annotate ARGs using the SARG database, a non-redundant ARG reference database integrating the Comprehensive Antibiotic Resistance Database (CARD) and the Antibiotic Resistance Database (ARDB) 37 . This hierarchical classification annotated the reads at the type and subtype levels using hidden Markov models. Virulence factors (VFs) in contigs were identified through a BLAST search against the virulence factor database (VFDB), a comprehensive database containing virulence factors from various bacterial pathogens 38 . Statistical Analysis To assess the alpha diversity (richness and evenness) within each treatment (pristine soil and biosolids-amended soil at weeks 0, 6, and 11), the Shannon index and richness for each treatment’s microbiome (species level), resistome, and VFs were calculated in R (version 4.2.2) using the Vegan package (Oksanen, 2024). The impacts of biosolids amendment on the resistome and microbiome diversities were determined by comparing the Shannon indices of pristine samples and those amended with biosolids using the student’s t-test. To determine the differences between sample groups in the microbiome (at the genus level), resistome, and VFs, the beta diversity was determined using the Bray-Curtis dissimilarity and the Vegan package. The variances between treatment groups were determined using the permutational multivariate analysis of variance (PERMANOVA) test Adonis in the Vegan package, which reduces the dimensionality of the data to identify statistically significant differences between biosolids-amended and pristine soil. Hierarchical cluster analysis was used to create heatmaps of the microbial phyla and genera (present above 0.1%) as well as ARGs across samples using the Vegan package. In the heatmaps, dendrograms were generated using Euclidean distances. The relative abundance of ARGs at the subtype level and microbial taxa at the genus level were aggregated. Data was filtered to include microbial genera and ARGs present in at least three samples. To determine co-occurrences within and between the microbiome and resistome, pair-wise Spearman correlations were determined. Statistically correlated ( p < 0.01) ARG subtypes and microbial genera were visualized as a network with the R package igraph 40 . Results and Discussion Microbial Community Diversity and Profile Biosolids generally increase microbial biomass and alter community composition 41 . In our replicated, controlled, greenhouse study, the impact of biosolids amendment on the microbiome diversity was identified using alpha and beta diversity indices (Fig. 1 ). We report that while there were no significant differences in Shannon indices (representing evenness and richness) between pristine and biosolids-amended soils, the latter had significantly higher microbial species richness. Including all time points, there were no significant differences between the Shannon indices of microbial genera in the pristine (4.5 ± 0.2) and biosolids-amended (4.4 ± 0.1) soils (one-sided t-test, p > 0.05; Fig. 1 a). These Shannon indices are generally lower than reports on agricultural soils from Arizona and California (6.0-7.3) but were comparable to those from biosolids from conventional wastewater treatment facilities (3.4–5.3) 42 , 43 . The richness of microbial species, however, was statistically larger in biosolids-amended soils (5,310.7 ± 16.5) compared to pristine (3,959.7 ± 153.3) samples (one-sided t-test, p < 0.001; including all the time points; Fig. 1 d). Over time, this richness declined, suggesting that some bacteria from the biosolids did not naturalize into the soil. In a previous field study with short- and long-term applications of alkaline-treated biosolids, soils with high biosolids application rates showed lower alpha-diversity indices (Chao1 richness, Simpson evenness, and Shannon diversity), whereas lower application rates resulted in increased indices 44 . Another controlled mesocosm study demonstrated similar diversity indices between biosolids-amended soil and the controls after 28 days of crop growth 45 . Similar results of the gradual return from microbiome enrichment in biosolids-amended soils to comparable conditions with control soil have been previously documented 46 . The relative abundances of microbial phyla and genera in each sample were determined to understand the impact of biosolids amendment and cultivation time on the microbial community structure. Overall, our findings show notable shifts, with Bacteroidetes significantly enriched in biosolids-amended soils, while Proteobacteria is the most abundant phylum in both pristine and amended soils. Across all sample types, there was an average of 35.3 ± 1.7% of reads unmapped to any phyla in the database. Of the mapped reads, the most abundant phylum in both pristine and biosolids-amended samples was Proteobacteria throughout the cultivation period (Figure S1 ). Bacteroidetes was the second most abundant phyla in all samples at week 0. Previous studies have also observed these phyla to be dominant in both pristine soils and gut microbiomes (Fierer et al., 2007; Lozupone et al., 2012). Biosolids-amended samples ( n = 9) contained a statistically larger enrichment of the phylum Bacteroidetes (14.5 ± 1.1%) as compared to pristine soil samples ( n = 7; 6.3 ± 1.0%; one-sided t-test, p < 0.01). Bacteroidetes remained the second most abundant phylum in biosolids-amended samples for the duration of the study, while in pristine samples Actinobacteria was the second most dominant phylum during weeks 6 and 11. Because Bacteroidetes are relatively prevalent in both human guts and natural soils, the enrichment of this phylum after soil amendment throughout the cultivation period could be due to their ability to secrete a wide range of carbohydrate-active enzymes capable of targeting diverse and complex glycans present in the soil 49 . While short-term studies show significant changes in microbial communities, long-term impacts may vary 41 , 45 , 50 . We report here distinct differences between pristine and amended soils at the genera level after application. These differences diminished over the 11-week cultivation period, indicating a lasting yet gradually stabilizing impact of biosolids on soil microbial communities. At the genera level, we identified Massilia , Ralstonia , Mucilaginibacter , Nocardioides , Streptomyces , Sphingomonas , and Janthinobacterium (clusters A and C) as the dominant genera in pristine soils at Week 0 (Fig. 2 a). Biosolids amendment, however, altered the microbial communities so that the dominant genera at week 0 included Dechloromonas , Sulfuritalea , Flavobacterium , Candidatu, Accumulibacter , and Pseudomonas (Clusters E and F). The enrichment of Dechloromonas in the biosolids amended soil is consistent with the prevalence of these genera in biosolids and its association with the human microbiome 51 , 52 . By the end of the study ( i.e. , Week 11), the abundance of these notable genera became more similar between pristine and biosolids-amended soils. The main differentiating genera between the two treatments at Week 11 were Ralstonia , Gemmatirosa , and Gemmatimonas (larger abundances in pristine soils) and Rhodanobacter , Dyella , and Thermomonas (larger abundances in biosolids-amended soils). These distinguishing genera between the two treatments at different cultivation points can explain the statistical dissimilarities between the pristine and biosolids-amended soils (PERMANOVA, p < 0.001; Fig. 1 e). The "legacy effects" of biosolids amendments in other studies are marked by increased abundances of specific bacterial groups, such as Streptomycetaceae and Clostridiaceae, which can form resilient, desiccation-resistant spores 19 . These findings demonstrate that the amendment of biosolids to soil increased the richness of microbial species and resulted in statistically larger dissimilarities in microbial genera between the pristine and biosolids-amended soil. We reported the statistically large impact of biosolids amendment on the soil microbial community with some impacts lasting throughout the 11 weeks of cultivation. Additionally, repeated applications of biosolids can exacerbate changes in soil chemical properties 50 . Biosolids amendment generally increases soil organic matter, impacting microbial communities by favoring taxa that thrive in nutrient-rich conditions ( e.g. , Pseudomonadota ) 19 , 45 , 53 . Such changes in microbial composition affect functions like nutrient cycling, with increased bacterial and fungal taxa associated with organic matter decomposition 19 , 44 . Organic amendments, such as biosolids, can influence microbial metabolism, including shifts toward glycolysis and the reductive tricarboxylic acid cycle, supporting microbial growth 44 . Additionally, abiotic factors like soil pH, texture, and organic contaminants ( e.g. , heavy metals) influence microbial responses, with varying effects depending on the soil and biosolids properties 23 , 45 , 53 , 54 . Additional research is needed to further understand the complex interactions between abiotic and biotic factors, their impact on microbial communities in biosolids-amended soils, and their implications for soil health and plant growth. Conclusions This study comprehensively determines the soil microbiome, resistome, virulence factors, and the ESKAPE pathogens impacted by biosolids amendment in a controlled and replicated greenhouse setting. The results from this study demonstrate the persistent impact that biosolids soil amendment has on the receiving soil environment. Biosolids amendment increased the richness of microbial genera, antibiotic resistance genes (ARGs), and virulence factors. In addition, the abundance of the ESKAPE pathogens increased after the amendment of biosolids. While increased microbiome diversity in agricultural soils can suggest ecosystem resiliency and stability resulting in improved soil fertility and crop yield, when paired with increased richness of the resistome, it can lead to increased potential for the horizontal gene transfer of ARGs. The enrichment of the resistome, alongside ESKAPE pathogens and virulence factors following biosolids amendment, indicates the risk of introducing and naturalizing clinically relevant antibiotic-resistant pathogens into the native soil environment. This could result in the proliferation of these pathogens in any future agricultural applications of the soil, such as crop cultivation. Declarations Data availability All sequenced reads can be found in the National Center for Biotechnology Information (NCBI) Sequence Read Archive under Bioproject accession number PRJNA1049878. Author Contribution Statement JSM conducted the sample and data analyses and led the writing of the original draft. CM contributed to the design of the greenhouse study, conducted the greenhouse experiments, and contributed to sample processing. BG contributed to the study design, data analysis, and writing – revision and editing. TR and JWC contributed to funding acquisition, study design, data analysis, and writing – revision and editing. TND led the funding acquisition, study design, and writing – revision and editing. Acknowledgment This work was supported by the USDA National Institute of Food and Agriculture, Agricultural and Food Research Initiative Competitive Program, Agriculture Economics and Rural Communities, grant number: 2018-67017-27631. The funder played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript. Competing interests All authors declare no financial or non-financial competing interests. 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EVOLUTIONARY APPLICATIONS 8, 240–247 (2015). Della-Negra, O. et al. Temporal dynamic of soil microbial communities and antibiotic resistance markers exposed to increasing concentrations of sulfamethoxazole. Environmental Pollution 364, 125306 (2025). Wu, J. et al. Antibiotics and antibiotic resistance genes in agricultural soils: A systematic analysis. Critical Reviews in Environmental Science and Technology 53, 847–864 (2023). Zhu, Y. et al. Changes in bacterial community structure and antibiotic resistance genes in soil in the vicinity of a pharmaceutical factory. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 158, 87–93 (2018). Wang, F. H. et al. Impact of reclaimed water irrigation on antibiotic resistance in public parks, Beijing, China. Environmental Pollution 184, 247–253 (2014). Gatica, J. & Cytryn, E. Impact of treated wastewater irrigation on antibiotic resistance in the soil microbiome. Environmental science and pollution research international 20, 3529–38 (2013). Pan, X. et al. Microbial community and antibiotic resistance gene distribution in food waste, anaerobic digestate, and paddy soil. Science of The Total Environment 889, 164192 (2023). Nguyen, C. C., Hugie, C. N., Kile, M. L. & Navab-Daneshmand, T. Association between heavy metals and antibiotic-resistant human pathogens in environmental reservoirs: A review. Frontiers of Environmental Science & Engineering 13, 46–46 (2019). Roane, T. M. & Kellogg, S. T. Characterization of bacterial communities in heavy metal contaminated soils. Canadian Journal of Microbiology 42, 593–603 (1996). Wang, S. & Wang, H. Adsorption behavior of antibiotic in soil environment: a critical review. Frontiers of Environmental Science & Engineering 9, 565–574 (2015). McMahon, M., Xu, J., Moore, J., Blair, I. & McDowell, D. Environmental stress and antibiotic resistance in food-related pathogens. APPLIED AND ENVIRONMENTAL MICROBIOLOGY 73, 211–217 (2007). Navab-Daneshmand, T., Enayet, S., Gehr, R. & Frigon, D. Bacterial pathogen indicators regrowth and reduced sulphur compounds’ emissions during storage of electro-dewatered biosolids. CHEMOSPHERE 113, 109–115 (2014). Zhu, L. et al. Insights into microbial contamination in multi-type manure-amended soils: The profile of human bacterial pathogens, virulence factor genes and antibiotic resistance genes. Journal of Hazardous Materials 437, 129356 (2022). Martínez José L. & Baquero Fernando. Interactions among Strategies Associated with Bacterial Infection: Pathogenicity, Epidemicity, and Antibiotic Resistance. Clinical Microbiology Reviews 15, 647–679 (2002). Das, S. et al. Genome plasticity as a paradigm of antibiotic resistance spread in ESKAPE pathogens. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 29, 40507–40519 (2022). Kojima, H., Mochizuki, J. & Fukui, M. Sulfuriferula nivalis sp. nov., a sulfur oxidizer isolated from snow and emended description of Sulfuriferula plumbiphila. INTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY 70, 3273–3277 (2020). Forsberg, K. et al. Bacterial phylogeny structures soil resistomes across habitats. NATURE 509, 612-+ (2014). Li, B. et al. Metagenomic and network analysis reveal wide distribution and co-occurrence of environmental antibiotic resistance genes. ISME JOURNAL 9, 2490–2502 (2015). Additional Declarations No competing interests reported. Supplementary Files Ste.MarieetalSI.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Aug, 2025 Reviews received at journal 04 Aug, 2025 Reviews received at journal 28 Jul, 2025 Reviews received at journal 18 Jul, 2025 Reviews received at journal 12 Jul, 2025 Reviewers agreed at journal 12 Jul, 2025 Reviewers agreed at journal 11 Jul, 2025 Reviewers agreed at journal 09 Jul, 2025 Reviewers agreed at journal 08 Jul, 2025 Reviewers invited by journal 07 Jul, 2025 Editor assigned by journal 24 Jun, 2025 Submission checks completed at journal 19 Jun, 2025 First submitted to journal 17 Jun, 2025 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-6917681","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":482484733,"identity":"2df6cfb4-122f-4348-b12f-b5381e839f67","order_by":0,"name":"John Ste. Marie","email":"","orcid":"","institution":"Oregon State University","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"Ste.","lastName":"Marie","suffix":""},{"id":482484734,"identity":"2bab4e32-e7aa-4b68-b399-5203757cb11d","order_by":1,"name":"Catherine Mays","email":"","orcid":"","institution":"Oregon State University","correspondingAuthor":false,"prefix":"","firstName":"Catherine","middleName":"","lastName":"Mays","suffix":""},{"id":482484735,"identity":"ba5bc92b-7530-4b06-8a92-3624ff8c231c","order_by":2,"name":"Bing Guo","email":"","orcid":"","institution":"University of Surrey","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Guo","suffix":""},{"id":482484736,"identity":"8f790e49-3390-45fb-8f85-2bd0f45969e3","order_by":3,"name":"Tyler S. Radniecki","email":"","orcid":"","institution":"Oregon State University","correspondingAuthor":false,"prefix":"","firstName":"Tyler","middleName":"S.","lastName":"Radniecki","suffix":""},{"id":482484737,"identity":"ebf6bd2e-e99d-4cb4-969a-17ce35f70577","order_by":4,"name":"Joy Waite-Cusic","email":"","orcid":"","institution":"Oregon State University","correspondingAuthor":false,"prefix":"","firstName":"Joy","middleName":"","lastName":"Waite-Cusic","suffix":""},{"id":482484738,"identity":"cef22b03-34af-49b1-9f12-2e5d34dd6b22","order_by":5,"name":"Tala Navab-Daneshmand","email":"data:image/png;base64,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","orcid":"","institution":"Oregon State University","correspondingAuthor":true,"prefix":"","firstName":"Tala","middleName":"","lastName":"Navab-Daneshmand","suffix":""}],"badges":[],"createdAt":"2025-06-17 23:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6917681/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6917681/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86342791,"identity":"2623a632-90b9-4f99-82ae-49eb4d501934","added_by":"auto","created_at":"2025-07-09 14:28:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":246752,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha diversity indices (a-c) Shannon index and (d-f) richness for (a) and (d) microbial species, (b) and (e) antibiotic resistance genes (ARGs), and (c) and (f) virulence factors, and ordination of the Bray-Curtis dissimilarity between the (e) microbial genera, (f) ARGs, and (g) virulence factors in pristine and biosolids-amended soils at weeks 0, 6, and 11 in a replicated greenhouse study. In e-g, labels indicate the sampling time (\u003cem\u003ei.e.\u003c/em\u003e, weeks 0, 6, and 11) and the ellipses denote the 95% confidence level for each treatment condition (\u003cem\u003ei.e.\u003c/em\u003e, pristine and biosolids-amended soils).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6917681/v1/79381aa072f3a65eb379e3a2.png"},{"id":86342792,"identity":"43c1b8ba-7ed6-47d0-951b-ba322af05e40","added_by":"auto","created_at":"2025-07-09 14:28:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1373917,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmaps of (a) relative abundance (%) of the top 30 most abundant genera, and (b) abundance of 30 most abundant antibiotic resistance gene (ARG) subtypes (log (gene copies/16S rRNA)) detected in at least three samples in pristine and biosolids-amended soil samples collected at weeks 0, 6, and 11 in a greenhouse study. The dendrogram was generated using Euclidean distance.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6917681/v1/84b08dea6657402d3c32185c.png"},{"id":86343299,"identity":"dfca0c11-7cf8-4642-a6bf-ed18a590500f","added_by":"auto","created_at":"2025-07-09 14:36:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":364725,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between the microbiome (at the genus level present in at least three samples) and resistome in (a) pristine and (b) biosolids-amended soils in collected at weeks 0, 6, and 11 in a greenhouse study. All associations (shown by connections) represent significant Spearman correlations at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01. Grey symbols represent the microbial genera and red symbols indicate antibiotic resistance genotypes.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6917681/v1/7edffd041bc6b9564a2bfc5b.png"},{"id":86344386,"identity":"fa827fe3-fd57-49a1-9744-e1467a53e6d0","added_by":"auto","created_at":"2025-07-09 14:44:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2653838,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6917681/v1/abcfaeb3-8992-4eac-912f-5fe4fba2eb93.pdf"},{"id":86343301,"identity":"888f7058-19b5-4606-987f-ace4cb5bfc48","added_by":"auto","created_at":"2025-07-09 14:36:17","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":594490,"visible":true,"origin":"","legend":"","description":"","filename":"Ste.MarieetalSI.docx","url":"https://assets-eu.researchsquare.com/files/rs-6917681/v1/f1e3c9f7aa2b092881822a2a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Biosolids Amendment Enriches Soil Resistome, Virulence Factors, and ESKAPE Pathogens: A Longitudinal Metagenomic Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe increased threat of antibiotic resistance, recently dubbed \u0026ldquo;The Silent Pandemic\u0026rdquo;, is a rapidly growing public health concern \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Wastewater treatment utilities are one of the main recipients, reservoirs, and resources of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Additionally, wastewater utilities receive a wide variety of antibiotics, metals, and other antimicrobials that can promote the horizontal transfer of ARGs \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In wastewater treatment trains, a large fraction of the ARB, ARGs, and mobile genetic elements end up in the biosolids \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn the United States, 31% of biosolids are used as amendments to agricultural soils to improve soil soil fertility (\u003cem\u003ee.g.\u003c/em\u003e, nutrient cycling) and plant defense mechanisms against pathogens \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Consequently, environments receiving biosolids have been shown to contain a large diversity of ARB (including human pathogens) and ARGs and are considered reservoirs for antibiotic resistance propagation \u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Additionally, biosolids contain diverse microorganisms, with large prevalences of \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eBacteroidetes\u003c/em\u003e which contain pathogenic species. Accordingly, there are concerns that biosolids land application may increase the prevalence of human pathogens in biosolids-amended soils and further propagate clinical antibiotic resistance.\u003c/p\u003e\u003cp\u003eAmong the human pathogens of greatest concern are the ESKAPE pathogens: \u003cem\u003eEnterococcus faecium\u003c/em\u003e, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, \u003cem\u003eAcinetobacter baumanii\u003c/em\u003e, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, and \u003cem\u003eEnterobacter\u003c/em\u003e spp. These pathogens are known for their multidrug resistance and clinical prevalence, posing a direct public health threat \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. ESKAPE pathogens have been detected in wastewater and receiving environments, including multidrug-resistant Acinetobacter and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, and methicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (MRSA) \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. While recent research has reported on ESKAPE taxa in wastewater influent and effluent \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, there is not a comprehensive understanding of the fate of these pathogens in environments amended with biosolids.\u003c/p\u003e\u003cp\u003eIn a broader context, longitudinal studies have examined the impact of biosolids amendment on the soil microbiome and ARG abundance. Many of these studies rely on targeted analyses, such as qPCR, to characterize the soil resistome \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. While qPCR analysis provides an accurate quantitative assessment of the abundances of targeted ARGs, they are limited in their ability to examine the breadth of diversity in the resistome \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo address this limitation, metagenomic approaches have been used to characterize changes in the microbiomes and resistomes in soils amended with both untreated and composted manure at the microcosm, greenhouse, and field scales \u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. These studies take advantage of the vast data acquired from metagenomic analysis and use network analysis and other emerging approaches to determine associations between the microbiome and resistome and evaluate the risk of manure amendment. Metagenomic analyses have also been successfully used to characterize other impacted soils such as those irrigated with wastewater influent, chronically exposed to heavy metals, and amended with antibiotics \u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhile there have been some metagenomic studies on biosolids-amended soils, they are primarily focused on the biosolids themselves or the impact of biosolids treatment rather than their impact on soil microbiomes and resistomes \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. A recent review of high-throughput sequencing approaches on biosolids soil amendments\u0026rsquo; microbial communities highlighted the need for comprehensive longitudinal replicated analysis of biosolids amendments to understand the impacts on soil microbiomes and resistomes \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In this paper, shotgun metagenomics was used to characterize the impact of biosolids amendment on the enrichment of the microbiome, resistome, virulence factors, and ESKAPE pathogens in soils during carrot cultivation in a replicated greenhouse study. The relationships within and between the microbiome and the resistome were identified to elucidate co-occurrences between the microbiome and ARGs.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSample Collection\u003c/h2\u003e\u003cp\u003eThe greenhouse study design has been described in detail previously \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Briefly, the soil for cultivation was collected from a commercial agricultural field with no history of wastewater irrigation or biosolids amendment in the Willamette Valley area of Oregon. The soil was air-dried and passed through a 5-mm sieve. Dewatered Class B biosolids were obtained from a conventional wastewater treatment utility in Oregon that uses a conventional activated sludge process. The biosolids were transferred to the laboratory on ice and applied to triplicate pots at a ratio of 70 g per kg of soil. Similarly, triplicate pots of soil \u0026ndash; herein referred to as pristine soil \u0026ndash; were used as the control treatment. Four germinated carrot seeds were planted in each of the six pots and cultivated for 11 weeks, at which time they were harvested. Deionized water was used to irrigate the plants two to three times per week to maintain a soil moisture content of 80\u0026ndash;85% total solids.\u003c/p\u003e\u003cp\u003eSoil core samples were collected from the bulk soil using a soil sampler probe (M.K. Rittenhouse \u0026amp; Sons Ltd., St. Catharines, Ontario, Canada) at the time of planting, on week 6 of the study, and at week 11 at the time of harvest. Soil samples were transferred in WhirlPak bags (Nasco, Fort Atkinson, WI) to the lab where they were homogenized by massaging and shaking for 2 min. From each sample, approximately 0.5 g of soil was stored in 50% v/v ethanol at -20\u0026deg;C for microbial analysis. Genomic DNA was extracted using the FastDNA Spin Kit for Soil (MP Biomedicals, Irvine, CA). Extracted DNA was stored at -20\u0026deg;C until submission for sequencing.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDNA purification, shotgun sequencing, and quality analysis\u003c/h3\u003e\n\u003cp\u003eThe concentration and quality of extracted DNA were measured with a Qubit fluorometer (Thermo Fisher Scientific, Waltham, MA). To improve the quality of low-concentration samples, extracted DNA was purified with a ReliaPrep DNA Clean-up and concentration kit (Promega, Madison, WI). Purified DNA was submitted to the Center for Quantitative Life Sciences at Oregon State University. Sequencing libraries were prepared for all samples using the NexteraXT kit (Illumina, San Diego, CA), except for the initial biosolids-amended samples, which were prepared using PrepX (IntegenX, Pleasanton, CA). The three biosolids-amended samples required the PrepX kit because the BioAnalyzer results indicated DNA quality issues when using the NexteraXT kit. Paired-end (2\u0026times;150 bp) sequencing reads were generated on an Illumina HiSeq 3000 (Illumina, San Diego, CA). Read quality was assessed with FastQC \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Subsequently, reads were filtered and trimmed using CutAdapt with quality thresholds of 20 and 15 for forward and reserve reads, respectively, and a minimum length of 36 \u003csup\u003e34\u003c/sup\u003e. Reads were again assessed with FastQC to verify quality before subsequent analysis.\u003c/p\u003e\n\u003ch3\u003eMetagenomic Analysis\u003c/h3\u003e\n\u003cp\u003eThe microbial community structure was characterized using Kaiju \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, which translates the raw metagenomic reads into amino acid sequences in all six possible reading frames and searches for the maximum exact matches in the microbial reference gene database from the National Center for Biotechnology Information (NCBI; Menzel et al., 2016). Those that are matched are then assigned to a taxon in the NCBI taxonomy.\u003c/p\u003e\u003cp\u003eCleaned reads from each sample were then \u003cem\u003ede novo\u003c/em\u003e assembled with MEGAHIT \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e using a minimum k value of 59, and a maximum of 159. Assembled reads were processed with ARGs-OAP to annotate ARGs using the SARG database, a non-redundant ARG reference database integrating the Comprehensive Antibiotic Resistance Database (CARD) and the Antibiotic Resistance Database (ARDB) \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. This hierarchical classification annotated the reads at the type and subtype levels using hidden Markov models. Virulence factors (VFs) in contigs were identified through a BLAST search against the virulence factor database (VFDB), a comprehensive database containing virulence factors from various bacterial pathogens \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eTo assess the alpha diversity (richness and evenness) within each treatment (pristine soil and biosolids-amended soil at weeks 0, 6, and 11), the Shannon index and richness for each treatment\u0026rsquo;s microbiome (species level), resistome, and VFs were calculated in R (version 4.2.2) using the Vegan package (Oksanen, 2024). The impacts of biosolids amendment on the resistome and microbiome diversities were determined by comparing the Shannon indices of pristine samples and those amended with biosolids using the student\u0026rsquo;s t-test. To determine the differences between sample groups in the microbiome (at the genus level), resistome, and VFs, the beta diversity was determined using the Bray-Curtis dissimilarity and the Vegan package. The variances between treatment groups were determined using the permutational multivariate analysis of variance (PERMANOVA) test Adonis in the Vegan package, which reduces the dimensionality of the data to identify statistically significant differences between biosolids-amended and pristine soil. Hierarchical cluster analysis was used to create heatmaps of the microbial phyla and genera (present above 0.1%) as well as ARGs across samples using the Vegan package. In the heatmaps, dendrograms were generated using Euclidean distances.\u003c/p\u003e\u003cp\u003eThe relative abundance of ARGs at the subtype level and microbial taxa at the genus level were aggregated. Data was filtered to include microbial genera and ARGs present in at least three samples. To determine co-occurrences within and between the microbiome and resistome, pair-wise Spearman correlations were determined. Statistically correlated (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) ARG subtypes and microbial genera were visualized as a network with the R package igraph \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMicrobial Community Diversity and Profile\u003c/h2\u003e\u003cp\u003eBiosolids generally increase microbial biomass and alter community composition \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. In our replicated, controlled, greenhouse study, the impact of biosolids amendment on the microbiome diversity was identified using alpha and beta diversity indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We report that while there were no significant differences in Shannon indices (representing evenness and richness) between pristine and biosolids-amended soils, the latter had significantly higher microbial species richness. Including all time points, there were no significant differences between the Shannon indices of microbial genera in the pristine (4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2) and biosolids-amended (4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1) soils (one-sided t-test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). These Shannon indices are generally lower than reports on agricultural soils from Arizona and California (6.0-7.3) but were comparable to those from biosolids from conventional wastewater treatment facilities (3.4\u0026ndash;5.3) \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The richness of microbial species, however, was statistically larger in biosolids-amended soils (5,310.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.5) compared to pristine (3,959.7\u0026thinsp;\u0026plusmn;\u0026thinsp;153.3) samples (one-sided t-test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; including all the time points; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Over time, this richness declined, suggesting that some bacteria from the biosolids did not naturalize into the soil. In a previous field study with short- and long-term applications of alkaline-treated biosolids, soils with high biosolids application rates showed lower alpha-diversity indices (Chao1 richness, Simpson evenness, and Shannon diversity), whereas lower application rates resulted in increased indices \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Another controlled mesocosm study demonstrated similar diversity indices between biosolids-amended soil and the controls after 28 days of crop growth \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Similar results of the gradual return from microbiome enrichment in biosolids-amended soils to comparable conditions with control soil have been previously documented \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe relative abundances of microbial phyla and genera in each sample were determined to understand the impact of biosolids amendment and cultivation time on the microbial community structure. Overall, our findings show notable shifts, with \u003cem\u003eBacteroidetes\u003c/em\u003e significantly enriched in biosolids-amended soils, while \u003cem\u003eProteobacteria\u003c/em\u003e is the most abundant phylum in both pristine and amended soils. Across all sample types, there was an average of 35.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7% of reads unmapped to any phyla in the database. Of the mapped reads, the most abundant phylum in both pristine and biosolids-amended samples was \u003cem\u003eProteobacteria\u003c/em\u003e throughout the cultivation period (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). \u003cem\u003eBacteroidetes\u003c/em\u003e was the second most abundant phyla in all samples at week 0. Previous studies have also observed these phyla to be dominant in both pristine soils and gut microbiomes (Fierer et al., 2007; Lozupone et al., 2012). Biosolids-amended samples (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9) contained a statistically larger enrichment of the phylum \u003cem\u003eBacteroidetes\u003c/em\u003e (14.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1%) as compared to pristine soil samples (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7; 6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0%; one-sided t-test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). \u003cem\u003eBacteroidetes\u003c/em\u003e remained the second most abundant phylum in biosolids-amended samples for the duration of the study, while in pristine samples \u003cem\u003eActinobacteria\u003c/em\u003e was the second most dominant phylum during weeks 6 and 11. Because \u003cem\u003eBacteroidetes\u003c/em\u003e are relatively prevalent in both human guts and natural soils, the enrichment of this phylum after soil amendment throughout the cultivation period could be due to their ability to secrete a wide range of carbohydrate-active enzymes capable of targeting diverse and complex glycans present in the soil \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhile short-term studies show significant changes in microbial communities, long-term impacts may vary \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. We report here distinct differences between pristine and amended soils at the genera level after application. These differences diminished over the 11-week cultivation period, indicating a lasting yet gradually stabilizing impact of biosolids on soil microbial communities. At the genera level, we identified \u003cem\u003eMassilia\u003c/em\u003e, \u003cem\u003eRalstonia\u003c/em\u003e, \u003cem\u003eMucilaginibacter\u003c/em\u003e, \u003cem\u003eNocardioides\u003c/em\u003e, \u003cem\u003eStreptomyces\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e, and \u003cem\u003eJanthinobacterium\u003c/em\u003e (clusters A and C) as the dominant genera in pristine soils at Week 0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Biosolids amendment, however, altered the microbial communities so that the dominant genera at week 0 included \u003cem\u003eDechloromonas\u003c/em\u003e, \u003cem\u003eSulfuritalea\u003c/em\u003e, \u003cem\u003eFlavobacterium\u003c/em\u003e, \u003cem\u003eCandidatu, Accumulibacter\u003c/em\u003e, and \u003cem\u003ePseudomonas\u003c/em\u003e (Clusters E and F). The enrichment of \u003cem\u003eDechloromonas\u003c/em\u003e in the biosolids amended soil is consistent with the prevalence of these genera in biosolids and its association with the human microbiome \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. By the end of the study (\u003cem\u003ei.e.\u003c/em\u003e, Week 11), the abundance of these notable genera became more similar between pristine and biosolids-amended soils. The main differentiating genera between the two treatments at Week 11 were \u003cem\u003eRalstonia\u003c/em\u003e, \u003cem\u003eGemmatirosa\u003c/em\u003e, and \u003cem\u003eGemmatimonas\u003c/em\u003e (larger abundances in pristine soils) and \u003cem\u003eRhodanobacter\u003c/em\u003e, \u003cem\u003eDyella\u003c/em\u003e, and \u003cem\u003eThermomonas\u003c/em\u003e (larger abundances in biosolids-amended soils). These distinguishing genera between the two treatments at different cultivation points can explain the statistical dissimilarities between the pristine and biosolids-amended soils (PERMANOVA, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). The \"legacy effects\" of biosolids amendments in other studies are marked by increased abundances of specific bacterial groups, such as Streptomycetaceae and Clostridiaceae, which can form resilient, desiccation-resistant spores \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese findings demonstrate that the amendment of biosolids to soil increased the richness of microbial species and resulted in statistically larger dissimilarities in microbial genera between the pristine and biosolids-amended soil. We reported the statistically large impact of biosolids amendment on the soil microbial community with some impacts lasting throughout the 11 weeks of cultivation. Additionally, repeated applications of biosolids can exacerbate changes in soil chemical properties \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Biosolids amendment generally increases soil organic matter, impacting microbial communities by favoring taxa that thrive in nutrient-rich conditions (\u003cem\u003ee.g.\u003c/em\u003e, \u003cem\u003ePseudomonadota\u003c/em\u003e) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Such changes in microbial composition affect functions like nutrient cycling, with increased bacterial and fungal taxa associated with organic matter decomposition \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Organic amendments, such as biosolids, can influence microbial metabolism, including shifts toward glycolysis and the reductive tricarboxylic acid cycle, supporting microbial growth \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Additionally, abiotic factors like soil pH, texture, and organic contaminants (\u003cem\u003ee.g.\u003c/em\u003e, heavy metals) influence microbial responses, with varying effects depending on the soil and biosolids properties \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Additional research is needed to further understand the complex interactions between abiotic and biotic factors, their impact on microbial communities in biosolids-amended soils, and their implications for soil health and plant growth.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study comprehensively determines the soil microbiome, resistome, virulence factors, and the ESKAPE pathogens impacted by biosolids amendment in a controlled and replicated greenhouse setting. The results from this study demonstrate the persistent impact that biosolids soil amendment has on the receiving soil environment. Biosolids amendment increased the richness of microbial genera, antibiotic resistance genes (ARGs), and virulence factors. In addition, the abundance of the ESKAPE pathogens increased after the amendment of biosolids. While increased microbiome diversity in agricultural soils can suggest ecosystem resiliency and stability resulting in improved soil fertility and crop yield, when paired with increased richness of the resistome, it can lead to increased potential for the horizontal gene transfer of ARGs. The enrichment of the resistome, alongside ESKAPE pathogens and virulence factors following biosolids amendment, indicates the risk of introducing and naturalizing clinically relevant antibiotic-resistant pathogens into the native soil environment. This could result in the proliferation of these pathogens in any future agricultural applications of the soil, such as crop cultivation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll sequenced reads can be found in the National Center for Biotechnology Information (NCBI) Sequence Read Archive under Bioproject accession number PRJNA1049878.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJSM conducted the sample and data analyses and led the writing of the original draft. CM contributed to the design of the greenhouse study, conducted the greenhouse experiments, and contributed to sample processing. BG contributed to the study design, data analysis, and writing – revision and editing. TR and JWC contributed to funding acquisition, study design, data analysis, and writing – revision and editing. TND led the funding acquisition, study design, and writing – revision and editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the USDA National Institute of Food and Agriculture, Agricultural and Food Research Initiative Competitive Program, Agriculture Economics and Rural Communities, grant number: 2018-67017-27631. The funder played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no financial or non-financial competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eG7 Health Ministers. \u003cem\u003eG7 Health Ministers\u0026rsquo; Communiqu\u0026eacute;\u003c/em\u003e. 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Sulfuriferula nivalis sp. nov., a sulfur oxidizer isolated from snow and emended description of Sulfuriferula plumbiphila. \u003cem\u003eINTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY\u003c/em\u003e 70, 3273\u0026ndash;3277 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eForsberg, K. \u003cem\u003eet al.\u003c/em\u003e Bacterial phylogeny structures soil resistomes across habitats. \u003cem\u003eNATURE\u003c/em\u003e 509, 612-+ (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, B. \u003cem\u003eet al.\u003c/em\u003e Metagenomic and network analysis reveal wide distribution and co-occurrence of environmental antibiotic resistance genes. \u003cem\u003eISME JOURNAL\u003c/em\u003e 9, 2490\u0026ndash;2502 (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-antimicrobials-and-resistance","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjamar","sideBox":"Learn more about [npj Antimicrobials and Resistance](http://www.nature.com/npjamar/)","snPcode":"44259","submissionUrl":"https://submission.springernature.com/new-submission/44259/3","title":"npj Antimicrobials and Resistance","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6917681/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6917681/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe spread of antibiotic resistance poses a significant challenge to public health worldwide. Wastewater treatment utilities are reservoirs of antibiotic-resistant bacteria and antibiotic resistance genes (ARGs). This study investigates the impact of biosolids amendment on the soil microbiome, resistome, virulence factors, and ESKAPE pathogens during carrot cultivation in a replicated greenhouse study. Metagenomic sequencing revealed that biosolids amendment increased the richness of microbial genera, ARGs, and virulence factors in soil. The relative abundance of ESKAPE pathogens, including \u003cem\u003eEnterococcus faecium\u003c/em\u003e, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, \u003cem\u003eAcinetobacter baumanii\u003c/em\u003e, \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, and \u003cem\u003eEnterobacter\u003c/em\u003e spp., was notably higher in biosolids-amended soils. These pathogens persisted throughout the 11-week cultivation period, raising concerns about the potential for horizontal gene transfer and the spread of antibiotic resistance. The study also identified significant co-occurrences between microbial genera and ARGs, which can suggest the possibility of the microbial taxa harboring the co-occurring ARGs. These findings highlight the importance of continued research and monitoring to ensure the safe and effective use of biosolids in agriculture.\u003c/p\u003e","manuscriptTitle":"Biosolids Amendment Enriches Soil Resistome, Virulence Factors, and ESKAPE Pathogens: A Longitudinal Metagenomic Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-09 14:28:12","doi":"10.21203/rs.3.rs-6917681/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-17T14:01:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-04T08:42:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-28T12:54:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-18T09:34:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-13T00:50:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221671210631239413420611498260688742627","date":"2025-07-12T13:17:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228010540080845949434441726423753838510","date":"2025-07-11T23:02:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207864953773375349474377696910206025647","date":"2025-07-09T20:43:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248120595905801430381940290559732876388","date":"2025-07-08T15:31:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-07T11:23:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-24T07:43:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-19T17:44:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Antimicrobials and Resistance","date":"2025-06-17T23:17:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-antimicrobials-and-resistance","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjamar","sideBox":"Learn more about [npj Antimicrobials and Resistance](http://www.nature.com/npjamar/)","snPcode":"44259","submissionUrl":"https://submission.springernature.com/new-submission/44259/3","title":"npj Antimicrobials and Resistance","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e05ee5e3-7140-4ea5-9bfd-e8210e346c46","owner":[],"postedDate":"July 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":51229311,"name":"Biological sciences/Biotechnology/Sequencing/Next generation sequencing"},{"id":51229312,"name":"Earth and environmental sciences/Environmental sciences"},{"id":51229313,"name":"Biological sciences/Microbiology/Antimicrobials/Antimicrobial resistance"}],"tags":[],"updatedAt":"2025-11-11T16:35:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-09 14:28:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6917681","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6917681","identity":"rs-6917681","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}