Nanopore metagenomics of plague-focus soils in Ulanqab Plateau, Inner Mongolia: microbial communities, antibiotic resistance, and pathogen-host interactions

Nanopore metagenomics of plague-focus soils in Ulanqab Plateau, Inner Mongolia: microbial communities, antibiotic resistance, and pathogen-host interactions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Nanopore metagenomics of plague-focus soils in Ulanqab Plateau, Inner Mongolia: microbial communities, antibiotic resistance, and pathogen-host interactions Feng Xu, Shoucheng Lei, Hairong Yang, Zhong Yang, Liping Xing, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8201273/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 Aims To conduct the first comprehensive metagenomic analysis of soils from natural plague foci in the Ulanqab Plateau, Inner Mongolia, characterizing the soil microbial communities, profiling the diversity and abundance of antibiotic resistance genes (ARGs), and identifying pathogen-host interaction (PHI) genes with homology to Yersinia pestis. Methods and Results We applied third-generation Oxford Nanopore Technology (ONT) R10 sequencing to soil samples collected from two historic plague foci. High-throughput long-read sequencing enabled detailed characterization of soil microbial communities, functional annotation, and detection of ARGs and PHI genes. The microbial community was dominated by Actinobacteria, Acidobacteria, and Proteobacteria. Functional annotation indicated diverse metabolic capabilities, particularly in amino acid and carbohydrate metabolism. A rich array of ARGs was detected, with vancomycin resistance genes being most prevalent. PHI gene analysis focused specifically on genes annotated to the Y. pestis species revealed abundant homologs of BipA and ZnuC. Although Y. pestis was not detected by metagenomics or qPCR, the presence of Y. pestis-associated PHI gene fragments suggests potential for pathogen persistence. Conclusions Plague-endemic soils in the Ulanqab Plateau are dynamic reservoirs of resistance and virulence determinants. The findings demonstrate the value of advanced long-read metagenomics for environmental pathogen surveillance and risk assessment, highlighting the ecological complexity of these environments and their potential role in maintaining antibiotic resistance and virulence genes. Yersinia pestis Yersinia pestis source site soil metagenomics third-generation sequencing antibiotic resistance genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Impact Statement This study provides the first application of third-generation nanopore sequencing to characterize the soil microbiome of natural plague foci in the Ulanqab Plateau. We reveal these soils as significant reservoirs of antibiotic resistance genes, with vancomycin resistance being particularly prevalent. The detection of Yersinia pestis -associated pathogen-host interaction gene fragments suggests a potential role of these soils in pathogen persistence, even in the absence of detectable Y. pestis . Our findings establish a new framework for environmental monitoring of zoonotic pathogens using long-read metagenomics, with implications for public health surveillance in plague-endemic regions globally. 1. Introduction Plague, caused by the highly pathogenic bacterium Yersinia pestis , remains a major zoonotic threat with a complex natural ecology (Barbieri et al. 2020). Although human cases of plague have declined dramatically due to improved public health measures, there are still natural source areas around the world, including China, where Y. pestis is transmitted among wild rodents and their ectoparasites, and where soil may serve as a potential environmental interface for pathogen persistence and transmission. The potential for Y. pestis transmission and the complex ecology of these natural outbreak sites emphasizes the importance of environmental monitoring, including soil monitoring. Soil not only harbors pathogens, but also influences their evolutionary trajectories through interactions with diverse microbial communities and selective pressures such as antibiotics and heavy metals. Soil in plague-endemic areas is a dynamic interface for the maintenance and spread of Y. pestis (Eisen and Gage 2009). Studies have shown that Y. pestis can survive for long periods of time in soils under natural conditions, which may lead to the resurgence of outbreaks after periods of apparent quiescence (Eisen et al. 2008). The ecological complexity of Y. pestis source areas is further exacerbated by interactions between soil physicochemical properties, vegetation types, and the diversity of resident microbial communities, all of which influence the persistence and pathogenicity of Y. pestis (Dubyanskiy and Yeszhanov 2016). Considerable work has been done on precipitation changes as a factor in causing quiescent plague foci to erupt, as moisture and temperature significantly affect these interactions. Therefore, understanding the structure and function of soil microbial communities in these areas is crucial for elucidating the mechanisms of plague maintenance and developing effective monitoring and control strategies. Traditional culture-based methods for pathogen detection in environmental samples are often limited by low sensitivity and the inability to capture the full range of microbial diversity (especially non-culturable or low abundance organisms) (McConn et al. 2024). The advent of metagenomics sequencing technology has revolutionized the field of environmental microbiology by comprehensively and culture-independently analyzing microbial communities and their functional genes directly from environmental DNA (eDNA) (Pérez-Cobas et al. 2020). Metagenomics not only facilitates the detection of known and novel pathogens, but also allows for the simultaneous characterization of antibiotic resistance genes (ARGs), virulence factors, and pathogen-host interaction (PHI) genes, resulting in a comprehensive understanding of microbial ecosystems and their potential risks (de Nies et al. 2021). Recent studies have applied metagenomics approaches to a variety of environments, including clinical samples, animal repositories, and natural habitats, revealing the ubiquity and diversity of ARGs and highlighting the role of environmental repositories in the spread of resistance (Leigh et al. 2021; Qu et al. 2024). In the context of ARGs, metagenomics sequencing offers unprecedented opportunities to monitor soil microbial communities and associated resistance determinants, to track their spatial and temporal dynamics, and to assess the impact of environmental factors on their persistence and evolution. Third-generation sequencing technologies, particularly Oxford Nanopore Technology (ONT), have further advanced the field of metagenomics by providing long-read length, real-time sequencing capabilities with minimal sample preparation requirements (Espinosa et al. 2024).The ONT R10 platform, with its improved pore structure and chemistry, has improved accuracy in base identification, particularly in homopolymers and repeats that are common in microbial genomes and mobile genetic elements Regions (Sereika et al. 2022). Nanopore sequencing produces ultra-long read lengths that facilitate the assembly of complete genomes and plasmids, enabling the resolution of complex genome structures, detection of structural variation, and accurate annotation of ARGs and pathogen-host interaction (PHI) genes, which encode proteins involved in microbial virulence and host colonization mechanisms. (Jain et al. 2018). Compared to second-generation (short read length) sequencing, nanopore technology excels in capturing full-length sequences of resistance genes and their genetic backgrounds, which is critical for understanding horizontal gene transfer mechanisms and resistance transmission (MacKenzie and Argyropoulos 2023). In addition, the portability and scalability of the ONT device makes it particularly suitable for on-site monitoring in remote or resource-limited areas (e.g., plague-endemic areas) (Oehler et al. 2023). Although the potential importance of soil as an interface for Y. pestis transmission and resistance gene exchange has been recognized, there is still a lack of comprehensive studies integrating high-resolution metagenomic and functional gene analyses of soils from Y. pestis source sites. In this study, we utilized the ONT R10 platform to perform in-depth metagenomic sequencing and functional annotation of soil samples collected from natural plague source sites. The main objectives include: (1) to characterize the taxonomic and functional features of soil microbial communities in plague-endemic areas, with a focus on the diversity and abundance of ARGs and PHI genes; (2) to elucidate the functional features of soil microbial communities in plague-endemic areas; and (3) to assess the potential risks associated with the environmental spread of resistance and pathogenicity determinants. In this study, the third-generation nanopore sequencing system was applied for the first time to the study of soils from Y. pestis -endemic areas, providing new insights into the structure and function of environmental microbial communities, the distribution of resistance and PHI genes, and the ecological factors that influence the persistence and evolution of Y. pestis . By integrating metagenomic, functional, and ecological analyses, this study improves our understanding of the environmental dimensions of plague epidemiology and provides a solid framework for surveillance and risk assessment of emerging infectious diseases in natural repositories. 2. Materials and Methods 2.1༎ Soil sample collection The study area is the Ulanqab Plateau plague focus in Inner Mongolia, characterized by typical plague-focus ecology with widely distributed gerbil hosts and long surveillance history. Two representative sites were selected by integrating 55-year rodent-plague surveillance records with GIS: Soil 1 (historic focus since 1970; rodent plague in 2021) and Soil 2 (newly identified focus; rodent plague in 2022). Coordinates (WGS84) are Soil 1—110.63334°E, 41.19402°N; Soil 2 —110.70092°E, 41.26462°N. At each site, ≥ 10 active shallow gerbil burrows were selected. Site selection was based on 55-year historical records from the National Plague Surveillance System of China, which documents continuous monitoring of rodent plague activity in these specific locations since 1970. Using sterile tools, ~ 100 g soil was scraped from the inner wall around the burrow at 0–20 cm depth per burrow. Equal masses from individual burrows were pooled and thoroughly homogenized to form one composite per site; a 50 g aliquot was transferred to a sterile bag, labeled (site, date), stored at 4°C, and transported to the laboratory for immediate processing. Based on previous rodent plague monitoring data and utilizing geographic information system (GIS) technology, two representative sampling sites (named Soil 1 and Soil 2, respectively) were selected in the study area (see Fig. 1 for GIS locations). At each sampling site, exactly 12 shallow gerbil burrows were identified based on active signs (fresh soil mounds, recent digging activity, and presence of fresh droppings) to ensure they were recently occupied rather than abandoned. Soil was collected from three depth intervals (0–5 cm, 5–15 cm, and 15–20 cm) around each burrow using sterile sampling tools, with approximately 50 g collected from each depth interval per burrow. This resulted in a total of 36 depth-specific samples per site (12 burrows × 3 depths), which were then composited into the two final samples (Soil 1 and Soil 2 ) for metagenomic analysis. Soil 1: Moisture content 8.2% (0–5 cm), 12.1% (5–15 cm), 15.3% (15–20 cm); Temperature 18.5°C (0–5 cm), 16.2°C (5–15 cm), 14.8°C (15–20 cm); Texture: Sandy loam. Soil 2 : Moisture content 6.8% (0–5 cm), 10.5% (5–15 cm), 13.7% (15–20 cm); Temperature 19.1°C (0–5 cm), 17.1°C (5–15 cm), 15.3°C (15–20 cm); Texture: Loamy sand. Pre-sampling climate: The 30 days preceding sampling were characterized by below-average precipitation (15.2 mm vs. historical average of 28.5 mm), moderate temperatures (daily average 16.8°C), and low relative humidity (average 45%). Soil sampling was conducted in September 2024 (Soil 1: September 15, 2024; Soil 2 : September 18, 2024), during the post-summer period when plague activity is typically monitored in this region. To ensure representative samples, soil from multiple burrows at each sampling site was thoroughly mixed to form a composite sample for subsequent analysis. Historical averages for the region: temperature 15.2°C, relative humidity 52%. 2.2༎ DNA extraction and quality control Total genomic DNA was extracted from 0.5 g of each composite soil sample using the DNeasy PowerSoil kit (Qiagen, Germany) according to the manufacturer's instructions with the following modifications to optimize extraction volume and purity: (1) increased bead-beating time from 10 min to 15 min to improve cell lysis efficiency; (2) added an additional wash step with 500 µL of buffer AW2 to reduce humic acid contamination; (3) eluted DNA in 50 µL of buffer AE instead of the recommended 100 µL to increase DNA concentration; and (4) added 2 µL of RNase A (10 mg/mL) during the lysis step to remove RNA contamination. The quality and quantity of extracted DNA was assessed by agarose gel electrophoresis and Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, USA). 16S rRNA gene PCR (QC purpose only). To verify that environmental DNA was amplifiable and to screen for gross PCR inhibition before library preparation, we amplified the bacterial 16S rRNA gene V3-V4 region with primers 27F (5'-AGAGTTTGATCMTGGCTCAG-3') and 1492R (5'-TACGGYTACCTTGTTACGACTT-3') using Phusion High-Fidelity DNA Polymerase (Thermo Fisher Scientific). Amplicons were examined by 1.5% agarose gel electrophoresis. No 16S amplicon data were used for downstream community analyses, which relied on shotgun/long-read metagenomics. 2.3༎ Nanopore R10 Sequencing Library preparation of high-quality DNA samples was performed using the Oxford Nanopore Technology (ONT) Ligation Sequencing Kit (SQK-LSK110). Library construction included DNA repair, end processing, junction ligation and purification steps, all according to the manufacturer's protocol. The prepared libraries were loaded onto an ONT R10.4 flow cell (FLO-MIN112) and sequenced on the PromethION platform (Oxford Nanopore Technologies, UK). Base identification was performed in real time using Guppy (v6.0.1), and only screened read lengths (average Q > 7) were retained for subsequent analysis. 2.4༎ Bioinformatics analysis 2.4.1༎ Data Quality Control and Filtering Raw nanopore reads were filtered using NanoFilt v2.8.0 with parameters --minlen 1000 --minqual 7 (De Coster et al. 2018) and Minimap2 v2.24(Joshi et al. 2023) to remove low-quality sequences, adapters, and host-derived reads. The retained clean reads were used in FASTQ format for subsequent analysis. Soil 1 generated 1,826,148 raw reads (7.44 Gb) and Soil 2 generated 1,721,601 raw reads (7.86 Gb). After quality filtering (Q > 7), Soil 1 and Soil 2 retained 1,329,809 and 1,329,846 clean reads, respectively, corresponding to high-quality data volumes of 7.31 Gb and 7.09 Gb. Average read lengths were 5,371 bp and 5,721 bp for Soil 1 and Soil 2, respectively, with maximum read lengths of 176,475 bp and 171,379 bp. N50 values were 7,496 bp and 7,859 bp, respectively. Further quality control using Minimap2 v2.24 and samtools v1.16 (Danecek et al. 2021) removed host-derived sequences by mapping to the human reference genome (GRCh38.p13). Given the primary focus on environmental microbial communities, removal of human contamination is prioritized. Soil 1 and Soil 2 yielded 1,323,355 and 1,322,168 non-host reads, respectively, with host sequence removal rates of 0.69% and 0.82%. Read length distributions showed the majority of sequences between 4,000 and 12,000 bp, with ultra-long reads exceeding 100,000 bp. 2.4.2༎ Taxonomic Annotation (Metagenome Sequencing Process) Microbial community composition was analyzed using custom R scripts (R v4.2.0) with the vegan package (Marees et al. 2018) for diversity calculations and the phyloseq package (McMurdie and Holmes 2012) for community composition analysis. TPM (Transcripts Per Million) values were calculated for taxonomic abundance normalization using the formula: TPM = (gene_count × 10^6) / (total_gene_count × gene_length). Detailed taxonomic data including raw read counts, TPM values, and coverage for all detected taxa are provided in Supplementary Table S2. 2.4.3༎ Antibiotic resistance gene (ARG) analysis ARGs were identified using the Resistance Gene Identification Tool (RGI) v5.2.1 against the Comprehensive Antibiotic Resistance Database (CARD, v3.2.9) with parameters --alignment_tool BLASTX --identity 80 --coverage 80. Raw gene counts and TPM values for all detected ARG types are provided in Supplementary Table S3 2.4.4. Pathogen-Host Interaction (PHI) Database Annotation Assembled contigs from metagenomic assemblies were compared to the Pathogen-Host Interaction database (PHI-base, v4.12) using BLASTx v2.12.0 to annotate PHI genes, with parameters -evalue 1e-5 -max_target_seqs 10 -num_threads 16. BLASTx searches were performed with parameters -evalue 1e-5 -max_target_seqs 10 -num_threads 16. Homology thresholds were set at ≥ 80% identity over ≥ 80% coverage for gene annotation. PHI gene annotation was performed using BLASTx against the PHI-base database (v4.12) with parameters -evalue 1e-5 -max_target_seqs 10 -num_threads 16. Homology thresholds were set at ≥ 95% identity over ≥ 80% coverage for gene annotation. Only genes meeting these stringent criteria were considered for analysis. TPM values and raw counts for all detected PHI genes are provided in Supplementary Table S4. 2.4.5. Functional Annotation and Enrichment Analysis Functional annotation and enrichment analyses were carried out using eggNOG-mapper v2.1.9 and KEGG databases. GO enrichment analysis was performed using clusterProfiler v4.0 with the background gene set comprising all genes successfully annotated to GO categories from both metagenomes. Statistical significance was assessed using Fisher's exact test with Benjamini-Hochberg False Discovery Rate (FDR) correction. The significance threshold was set at FDR < 0.05. KEGG pathway enrichment was conducted using the same statistical framework with the KEGG database (release 2023-01-15) (Cantalapiedra et al. 2021). 2.4.5༎ Detection of caf1/pla genes by qPCR To preliminarily screen for the presence of Y. pestis in soil samples, quantitative PCR (qPCR) targeting the specific virulence genes caf1 and pla was performed using gene-specific primers. Primers for caf1 were: caf1-F (5'-ATGAAAAAACTTACTGCGGC-3') and caf1-R (5'-TTATTTGCCGTTGCCGTTGC-3'), amplifying a 456 bp fragment. Primers for pla were: pla-F (5'-ATGAAAAAACTTACTGCGGC-3') and pla-R (5'-TTATTTGCCGTTGCCGTTGC-3'), amplifying a 1,017 bp fragment. qPCR reactions were performed in 20 µL volumes containing 10 µL of 2× SYBR Green Master Mix (Applied Biosystems) on a QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems), 0.5 µM of each primer, and 2 µL of template DNA. Cycling conditions were: 95°C for 10 min, followed by 40 cycles of 95°C for 15 s, 55°C for 30 s, and 72°C for 30 s, with fluorescence detection at 72°C. Standard curves were generated using serial dilutions of Y. pestis genomic DNA (10^1 to 10^6 copies/µL) to determine detection limits and quantification. This assay served as an initial screening method to assess whether Y. pestis DNA could be detected in the environmental samples prior to metagenomic analysis. TPM values were calculated for all detected genes to enable abundance comparisons and are provided in supplementary tables. 3. Results 3.1༎ Overview of sequencing data and assembly Clean reads were assembled using metaFlye v2.9.2 with parameters --nano-raw --threads 16 --memory 100. Taxonomic annotation using Kraken2 v2.1.2 and Bracken v2.8 against NCBI nt database (release 2023-10-15) and RefSeq database (release 2023-10-15). Two composite soil samples (Soil 1 and Soil 2 ) from the Ulanqab Plateau plague focus were successfully sequenced using Oxford Nanopore R10 technology. After quality control and host sequence removal, both samples yielded high-quality metagenomic data suitable for downstream analysis (see Materials and Methods 2.4.1 for detailed quality metrics) (Table 1 and Table 2 ). Assembly of the quality-filtered reads produced contigs with N50 values of 7,496 bp and 7,859 bp for Soil 1 and Soil 2, respectively, enabling reliable taxonomic and functional annotation of the soil microbial communities (Fig. 2 ). Table 1 Third-generation sequencing and quality control statistics for soil samples. Sample RawReads RawBases CleanReads CleanBases AvgLen(bp) MaxLen(bp) N50(bp) Soil 1 1,826,148 7,442,453,702 1,329,809 7,310,460,386 5,371 176,475 7,496 Soil 2 1,721,601 7,862,196,029 1,329,846 7,093,782,093 5,721 171,379 7,859 Table 2 Host sequence removal statistics. Sample NoHostReads NoHostBases Host_rate(%) AvgLen(bp) MaxLen(bp) N50(bp) Soil 1 1,323,355 7,092,451,271 0.69 5,359 176,475 7,483 Soil 2 1,322,168 7,565,240,819 0.82 5,721 171,379 7,844 3.2༎ Microbial Community Structure and Diversity Taxonomic annotations at the phylum and order level showed that Actinobacteria , Acidobacteria and Proteobacteria were the dominant phyla in both samples. Among the families, Streptomycetaceae was notably the most abundant, followed by Bradyrhizobiaceae and Pseudomonadaceae (Figs. 3 A, 3 B, Sankey diagram). Bar charts and heat maps further show the relative abundance and clustering of the major taxonomic units, while a venn diagram (Fig. 3 C) indicates that 49 species were shared between the two samples, with 20 species detected only in Soil 1 and 45 species detected only in Soil 1. Notably, Y. pestis was not detected in either sample, which may reflect the current absence of active Y. pestis circulation in the sampled areas, though this does not preclude the presence of the pathogen in wildlife reservoirs. Detailed quantitative data including raw read counts, TPM values, and coverage for all detected taxa are provided in Supplementary Table S2. 3.3 Functional gene annotation COG analysis revealed functional profiles of both soil samples (Fig. 4 A). Soil1 contained 10,679 genes in amino acid transport and metabolism (E) and 7,131 genes in energy production and conversion (C), while Soil2 contained 10,822 genes in amino acid transport and metabolism (E) and 7,133 genes in energy production and conversion (C). Soil1 had 7,655 genes in carbohydrate transport and metabolism (G) and 5,638 genes in cell wall/membrane/envelope biogenesis (M), while Soil2 had 7,796 genes in carbohydrate transport and metabolism (G) and 5,764 genes in cell wall/membrane/envelope biogenesis (M). GO analysis identified functional gene categories in both samples (Fig. 4 B). Soil1 contained 88 genes associated with stress response, 41 genes associated with metal ion transport, 141 genes involved in secondary metabolite biosynthesis, and 67 genes involved in quorum sensing. Soil2 contained 91 genes associated with stress response, 33 genes associated with metal ion transport, 108 genes involved in secondary metabolite biosynthesis, and 72 genes involved in quorum sensing. KEGG pathway analysis identified metabolic pathways in both samples (Fig. 4 C). Soil1 contained 113 genes in glutathione metabolism pathways, 4 genes in oxidative stress response pathways, 449 genes in purine metabolism pathways, and 92 genes in biofilm formation pathways. Soil2 contained 119 genes in glutathione metabolism pathways, 2 genes in oxidative stress response pathways, 452 genes in purine metabolism pathways, and 93 genes in biofilm formation pathways. 3.4༎ Analysis of Antibiotic Resistance Genes (ARGs) A comprehensive analysis revealed a rich and diverse resistome in both soil samples. The stratified bar chart (Fig. 5 ) shows the relative abundance of major ARG types in Soil 1 and Soil 2. Vancomycin resistance genes were the most abundant in both samples. Multidrug, aminoglycoside, and methicillin resistance genes were also common. Other ARG types, such as β-lactam, chloramphenicol, rifamycin, and quinolone resistance genes, were less frequent. Overall ARG profiles were similar at both sites. However, some differences were observed. Soil 1 had a slightly proportion of aminoglycoside and methotrexate resistance genes. Soil 2 showed a abundance of multidrug resistance genes. The detection of multiple ARGs, including those conferring resistance to clinically important antibiotics, highlights the role of plague-endemic soils as reservoirs of antibiotic resistance. Raw gene counts and TPM values for all detected ARG types are provided in Supplementary Table S3. These results suggest that long-term ecological interactions and selective pressures in the Ulanqab Plateau promote ARG accumulation and diversification. The dominance of vancomycin and multidrug resistance genes may reflect both natural and human influences on the local resistome. 3.5༎Annotation of the Pathogen-Host Interaction(PHI) genes in the Y. pestis species Annotation results for the Pathogen-Host Interaction (PHI) database showed that a variety of PHI genes with homology to Y. pestis sequences were present in both Soil Sample 1 (Soil 1) and Soil Sample 2 (Soil 2 ). There were compositional differences in the distribution of PHI genes with homology to Y. pestis sequences between the two sampling sites. A total of 393 PHI genes with homology to Y. pestis sequences were detected only in Soil 1, 447 were detected only in Soil 2, and 49 genes were shared between the two samples. TPM values for these genes are provided in Supplementary Table S4. Further analysis of the functional phenotypes associated with these PHI genes revealed that the majority of genes were associated with reduced virulence phenotypes (indicating that gene disruption reduces virulence, i.e., these genes function as virulence factors), while a relatively small percentage of genes were associated with either unaffected pathogenicity or enhanced virulence (hypervirulence) (Fig. 6 C). Both Soil Sample 1 and Soil Sample 2 were dominated by virulence-reducing genes, while other phenotypes such as loss of pathogenicity and genes with unaffected pathogenicity appeared at much lower frequencies. This distribution indicates that the majority of detected genes are those that, when disrupted, reduce virulence. Analysis of the heat map of the major PHI gene homologs (Fig. 6 B) highlighted several conserved bacterial genes with homology to Y. pestis sequences (≥ 95% identity), including BipA, ZnuC, GlpD , and Pgm , which had abundance (TPM) in both samples. Notably, BipA and ZnuC had the highest expression levels, suggesting that they may be ecologically important in the soil microbiome. Despite some differences in the abundance of these major Y. pestis PHI genes, their distribution in soil sample 1 and soil sample 2 was generally consistent. It should be noted that these genes are conserved across bacterial species and are not Y. pestis-specific markers. The absence of hallmark Y. pestis genes (caf1, pla) in both qPCR and metagenomic datasets further supports that these represent conserved bacterial genes rather than Y. pestis-specific sequences. The detection of PHI gene fragments with homology to Yersinia spp. sequences suggests the presence of conserved bacterial genes rather than Y. pestis-specific virulence factors, particularly given the absence of hallmark plague markers (caf1, pla) in both qPCR and metagenomic analyses. The overall distribution of Y. pestis PHI-annotated genes (Fig. 6 C) further confirmed the high diversity and site-specificity of virulence-related genes in soils from plague-endemic areas. These findings suggest that the soil microbiome harbors a complex and dynamic pool of Y. pestis-associated virulence factors that are detected in these specific samples. It should be noted that ARGs and PHI genes are ubiquitous in environmental microbiomes, and the absence of comparative data from non-plague areas limits our ability to determine if the observed profiles are unique or enriched in plague foci compared to other soil environments. Consistent with the metagenomic screening, qPCR targeting caf1 and pla did not yield detectable amplification in the site-level composites within the predefined Ct threshold. Positive controls amplified as expected, whereas no-template controls remained negative (Supplementary Table S1 ). These results indicate no qPCR-detectable Yersinia pestis signature in the analyzed aliquots. TPM values and raw counts for all detected PHI genes are provided in Supplementary Table S4. 4. Discussion In this study, a comprehensive macro-genomic and functional gene analysis of soil from a natural plague epidemic site in the Ulanchab Plateau, Inner Mongolia, was carried out using third-generation nanopore sequencing technology. The results provide new insights into the taxonomic and functional diversity of soil microbial communities in the plague-endemic area, the distribution and ecological significance of antibiotic resistance genes (ARGs) and pathogen-host interaction (PHI) genes, and the potential environmental risks associated with the persistence and spread of Y. pestis and its associated determinants. Utilizing high-throughput sequencing technology, more than 1.7 million raw reads were generated per sample with N50 values of more than 7 kb, demonstrating the potential of nanopore long-read sequencing technology in facilitating the analysis of environmental metagenomes(Lei and Kumar 2022; De La Cerda et al. 2023). Most of the read lengths ranged from 4000 to 12000bp, and a large number of ultra-long read lengths (> 100kb) were obtained, providing a solid foundation for subsequent assembly and gene annotation(Figure 2 ). These data are consistent with recent reports highlighting the advantages of third-generation sequencing technology in resolving complex microbial communities and mobile genetic elements in environmental samples (Akaçin et al. 2022; Meslier et al. 2022). Taxonomic analyses showed that the dominant phyla in both soil samples were Actinobacteria, Acidobacteria and Ascomycetes, and the major families were Streptomycetaceae, Rhizobacteriaceae and Pseudomonadaceae (Fig. 3 ). The ecological significance of these findings extends beyond simple taxonomic cataloging to understanding the functional dynamics of plague-endemic environments. The predominance of Actinobacteria, particularly Streptomycetaceae, in both samples suggests a soil microbiome adapted to arid conditions and potentially influenced by historical antibiotic production by these bacteria. This composition is consistent with previous studies of soil microbiomes from plague outbreak sites and other arid or semiarid environments, where Actinobacteria and Ascomycetes phyla are prevalent due to their metabolic diversity and resistance to environmental stress (Naylor et al. 2022; He et al. 2023). Venn diagram analysis further highlighted shared and unique taxa, with only 49 species shared by both sampling sites, emphasizing the compositional differences between sampling sites (Fig. 3 ). Functional annotation of the macrogenome revealed a wide range of metabolic and adaptive capabilities (Zhou et al. 2022). COG and GO analyses revealed a high enrichment of genes involved in amino acid transport and metabolism, carbohydrate metabolism, and energy production, reflecting the strong potential of the soil microbiome for nutrient cycling and environmental adaptation (Fig. 4 ). These findings are consistent with the notion that soil microbial communities in natural plague foci are influenced by a combination of biotic and abiotic factors, including soil physicochemical properties, vegetation cover, and host animal activity. KEGG pathway analyses identified active primary and secondary metabolic pathways as well as genes associated with environmental adaptation, xenobiotic matter degradation, and signaling-related genes, suggesting that microbial communities are equipped to cope with environmental fluctuations and anthropogenic impacts (Philippot et al. 2021). Notably, the diversity and abundance of antibiotic resistance genes (ARGs) were higher in soil samples. Vancomycin resistance genes were the most abundant among detected ARGs in both samples, followed by multidrug resistance, aminoglycoside and methotrexate resistance genes (Fig. 5 ). The presence of multiple ARGs, including genes resistant to clinically important antibiotics, suggests potential role of soils in plague-endemic areas as potential reservoirs of antibiotic resistance. (Delgado-Baquerizo et al. 2022; Shi et al. 2022). The distribution of ARGs contained both core and site-specific components, with 49 shared ARGs and a large number of endemic ARGs at each site. This pattern of ARG diversity and distribution is consistent with previous reports that natural environments (especially those with a history of human or animal activity) are important reservoirs and sources of resistance determinants (Zhu et al. 2025). The prevalence of vancomycin and multidrug resistance genes may reflect both natural selection and anthropogenic influences, though comparative data from non-plague areas would be needed to establish baseline resistome profiles (Mühlberg et al. 2020). The ecological and evolutionary significance of ARGs in soils of plague-origin sites is significant (Chen et al. 2022). Although the genetic background of ARGs was not directly assessed in this study, the high diversity and abundance of ARGs were observed in these samples. Future studies should employ metagenome-assembled genome (MAG) binning and plasmid detection to link ARGs and PHI genes to specific microbial hosts and mobile genetic elements(Liao et al. 2024). A key limitation of this study is the lack of biological replicates for robust statistical analysis of community differences, which would require multiple individual burrow-level samples rather than composite samples. Previous studies have shown that environmental selection pressures (e.g., the presence of antibiotics or heavy metals) can promote the persistence and spread of resistance genes in the soil microbiome (Gillieatt and Coleman 2024). The site-specificity of ARGs in the samples of this study may have been influenced by localized differences in soil chemistry, host animal densities, and historical land use, which is consistent with other studies reported for plague outbreak sites and other natural repositories (Zhang et al. 2023). In addition to ARGs, this study specifically analyzed pathogen-host interaction (PHI) genes annotated to the Y. pestis species, providing new insights into the ecological adaptation and virulence potential of this pathogen in soil environments (Fig. 6 ). PHI annotation revealed a wide range of Y. pestis virulence-associated genes with a high degree of site-specificity: 393 endemic Y. pestis PHI genes were present in Soil 1, 447 in Soil 2, and only 49 were shared (Fig. 6 ). The majority of these PHI gene homologs showing sequence similarity to Y. pestis virulence factors were associated with an attenuated virulence phenotype, while a smaller proportion were associated with no effect or enhanced virulence. High abundance of key Y. pestis genes (e.g., BipA and ZnuC) were detected in both samples, indicating their ecological importance in the soil microbiome. BipA is a translation initiation factor associated with stress response and virulence regulation in Y. pestis(Scott Madeleine et al. 2025) , while ZnuC is a key component of the zinc transport system that is critical for nutrient acquisition and virulence expression (Wang et al. 2016; Goh et al. 2021). The prevalence of genes associated with reduced virulence phenotypes indicates that many detected genes are virulence factors that, when disrupted, attenuate pathogenicity, thereby promoting ecological stability and host-microbe coexistence (Goh et al. 2024). Notably, Y. pestis was not detected in this study's samples by both qPCR and metagenomic sequencing. While this result is consistent with the current epidemiological situation in the region, it also highlights the challenge of detecting low abundance pathogens in complex environmental matrices. Previous studies have shown that Y. pestis can survive in soil for long periods of time, but its abundance is often below the detection limit of conventional molecular methods, especially during non-outbreak periods (Eisen et al. 2008). The detection of PHI gene fragments associated with Yersinia spp. suggests that there may be associated virulence factors in the soil microbiome that may contribute to the ecological adaptation and persistence of this group of bacteria (Huang et al. 2023). The results of this study also demonstrate the importance of environmental factors in shaping the structure and function of soil microbial communities in Y. pestis source sites. Soil physicochemical properties (e.g., water content, organic matter, and mineral composition) have been shown to influence the survival and transmission of Y. pestis and other pathogens (Ma et al. 2023). Although transmission of Y. pestis from contaminated soils is possible, it is not likely a major transmission route under natural conditions (Boegler et al. 2012). However, increased soil water content and organic matter can enhance the survival of fleas and their larvae, which are key vectors in the transmission cycle of Y. pestis , while certain metal ions (e.g., calcium, iron, magnesium, and sodium) have been associated with increased pathogenicity and biofilm formation of Y. pestis (Falcón García et al. 2020; Wardinski et al. 2024). Although these environmental variables were not directly measured in this study, differences in microbial community structure and functional gene profiles between sampling sites may reflect potential heterogeneity in soil properties and host animal activity (Weng et al. 2021). This study applies third-generation nanopore sequencing technology to demonstrate the feasibility and advantages of long-read-length metagenomics for environmental pathogen monitoring. The ability to generate ultra-long read lengths and resolve complex genome structures enabled more accurate assembly and annotation of functional genes, including ARGs and PHI genes. This technological advancement is particularly important for monitoring environmental pathogens in natural plague outbreak sites and other remote or resource-limited areas, where rapid and comprehensive assessments of microbial communities and functional determinants are critical for risk assessment and public health interventions (Li et al. 2025). Despite these advances, some limitations remain. A key limitation of this study is the lack of source attribution for detected ARGs and PHI genes to specific taxa or mobile genetic elements, which would require advanced bioinformatics approaches such as MAG binning, plasmid reconstruction, and proximity analysis. A limitation of this study is that we did not fully exploit the unique capabilities of Nanopore sequencing for complete plasmid assembly, pathogenicity island detection, or structural variant analysis. Future studies should implement specialized bioinformatics pipelines to leverage these advantages. Detecting low-abundance pathogens like Y. pestis is still challenging. Limitations of this study include the lack of biological replicates for robust statistical analysis of community separation, which would require multiple individual burrow-level samples rather than composite samples. Future studies should consider targeted enrichment or capture methods to improve sensitivity. Although this study provides a comprehensive snapshot of soil microbial diversity and function, temporal and spatial changes need further investigation. Furthermore, exploring ancient DNA (aDNA) techniques could offer valuable insights into the historical presence and long-term persistence of Y. pestis in these environmental reservoirs, especially when contemporary detection methods are limited by low pathogen abundance. Longitudinal sampling across seasons and microhabitats will help clarify pathogen persistence and transmission dynamics. The ecological and evolutionary relationships among soil microbes, ARGs, and PHI genes are not yet fully understood. Integrating metagenomics, metatranscriptomics, and experimental validation is essential to reveal the mechanisms of resistance and virulence gene maintenance and spread. In summary, this study presents the first high-resolution, long-read metagenomic analysis of soils from a natural plague focus. Future research directions should focus on: (1) longitudinal sampling to capture temporal dynamics of pathogen persistence; (2) integration of metatranscriptomic approaches to assess active gene expression; (3) comparative analysis with non-plague endemic soils to establish baseline resistome profiles; and (4) implementation of advanced bioinformatics pipelines to fully exploit long-read sequencing advantages. We revealed a functionally diverse and ecologically adapted microbial community that serves as a reservoir for resistance and virulence genes. The widespread presence of vancomycin and multidrug resistance genes, the high diversity and site-specificity of PHI genes, and the absence of detectable Y. pestis (while still detecting Y. pestis-associated PHI gene fragments) collectively highlight the complex interactions among environment, microbes, and pathogen persistence in plague-endemic areas, suggesting potential for past presence or very low abundance of the pathogen. These findings are important for environmental monitoring, risk assessment, and the development of targeted interventions for plague and other emerging infectious diseases. Future research should expand sampling in space and time, integrate multi-omics approaches, and develop more sensitive detection methods to further elucidate the ecological and evolutionary dynamics of pathogens and functional genes in natural reservoirs. 5. Conclusion This study used an innovative environmental monitoring approach. Third-generation nanopore long-read sequencing was combined with comprehensive metagenomic and functional gene annotation. This method was applied to the soil microbiome of a natural plague focus in the Ulanqab Plateau. Nanopore sequencing enabled high-resolution, real-time analysis of complex microbial communities. It also allowed precise detection of antibiotic resistance genes and pathogen-host interaction genes. Y. pestis was not detected in the samples. However, multiple resistance genes and virulence gene fragments were found. This highlights the ecological complexity of plague-endemic soils and the potential risk of pathogen persistence. These results demonstrate the value and feasibility of advanced long-read metagenomic technologies for environmental pathogen monitoring. This is especially important in remote or resource-limited areas.The surveillance model developed here offers a scalable and innovative framework for early warning and risk assessment of zoonotic diseases in natural reservoirs. Further research is needed to expand this methodology and clarify the dynamic interactions among soil properties, microbial communities, and environmental pathogens. Declarations Funding We are grateful for funding from the Hohhot Medical and Health Science and Technology Program Project (Project No. Hu Wei Jian Medical-2023073). Authors' Contributions Feng Xu: Conceptualization, Methodology, Funding acquisition, Project administration, Writing - original draft, Writing - review & editing. Shoucheng Lei: Software, Formal analysis, Data curation, Visualization, Writing - original draft. Hairong Yang: Investigation, Resources, Validation. Zhong Yang: Investigation, Resources. Liping Xing: Methodology, Investigation. Zhiliang Jie: Methodology, Investigation. Lanmei Zhou: Validation, Data curation. Yuanyuan Li: Validation, Data curation. Data availability Raw nanopore reads (FASTQ), assemblies, and annotations have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession PRJNA[1362966] and in Zenodo (DOI: 10.5281/zenodo.[1362966]) upon acceptance. I affirm that this article has not been submitted to any other journal and all the authors have consented to submit the manuscript. Conflict of Interest There is no known conflict of interest. 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Nature Communications 16 (1), 4006. doi:10.1038/s41467-025-59019-3 Supplementary Files SupplementaryTables.xlsx 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. 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17:04:11","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":116065,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8201273/v1/2d6081938e014620ca574235.html"},{"id":98441382,"identity":"576de24f-5503-4698-9ea0-93d779439cec","added_by":"auto","created_at":"2025-12-17 17:05:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":966706,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocations of the two soil sampling sites within the officially delineated Ulanqab plague focus (red polygon), Inner Mongolia, China. \u003c/strong\u003eSoil 1 and Soil 2 \u0026nbsp;with WGS84 coordinates and elevation (Soil 1: 110.63334°E, 41.19402°N; Soil 2 : 110.70092°E, 41.26462°N). Geographic data for the Ulanqab plague focus delineation and site coordinates were derived from the National Plague Surveillance System of China and integrated using GIS technology.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8201273/v1/586ab1d09aab9311839b2c35.png"},{"id":98441135,"identity":"a9b5f344-4955-4d61-817b-7b38e60de3f2","added_by":"auto","created_at":"2025-12-17 17:04:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3436925,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRead length distribution for Soil 1 and Soil 2 . (A) Soil 1 showing read counts across length intervals from 0-2000 bp to \u0026gt;40,000 bp; (B) Soil 2 \u0026nbsp;showing similar distribution pattern. X-axis: Read length intervals (bp); Y-axis: Number of reads.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8201273/v1/d9b30aed1dffb3829d8195aa.png"},{"id":98399215,"identity":"9e77a554-7a2c-404c-9141-17ed6bacc5bf","added_by":"auto","created_at":"2025-12-17 11:16:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3972838,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial community structure and diversity of soil samples from the Ulanqab Plateau plague focus.\u003c/strong\u003e (A, B) Sankey diagrams illustrating the taxonomic composition of the soil microbiomes at different taxonomic levels for Soil 1 (A) and Soil 2 (B). The width of each colored bar represents the relative abundance of sequences assigned to that taxon, with numbers indicating the absolute count of sequence counts at each taxonomic level. The diagrams display the hierarchical relationships from domain (left) to species (right), showing the flow of sequences through the taxonomic hierarchy. (C) Venn diagram showing the overlap and detected of microbial species between Soil 1 and Soil 2 . A total of 49 species were shared between both samples, with 20 species unique to Soil 1 and 45 species unique to Soil 2.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8201273/v1/ee5af301ee4879761ecea037.png"},{"id":98399217,"identity":"9dc5602b-e2af-411c-8a6b-1fa61afa381f","added_by":"auto","created_at":"2025-12-17 11:16:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2441639,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional annotation of soil metagenomes showing gene counts for Soil 1 and Soil 2. Bars are paired per category/term/pathway and colored by sample: Soil 1 (blue) and Soil 2 \u0026nbsp;(orange); values on bars indicate gene counts.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8201273/v1/96a42d43a202653bf3019637.png"},{"id":98440024,"identity":"52f0b336-bf79-44d8-88c4-11b55d43dadd","added_by":"auto","created_at":"2025-12-17 17:03:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3135843,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative abundance of major antibiotic resistance gene (ARG) types in Soil 1 and Soil 2 .\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8201273/v1/88654f7414c4a30771d83664.png"},{"id":98399228,"identity":"a5185f0b-ed77-4b9e-a29b-4696d4cd642a","added_by":"auto","created_at":"2025-12-17 11:16:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":6842403,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePHI gene annotation and distribution in soil samples. (A) Bar plot showing the number of unique and shared PHI-annotated genes in Soil 1 and Soil 2 . (B) Heatmap of TPM values for major PHI genes detected in both samples. (C) Bar plot showing the distribution of PHI gene phenotypes in both samples.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8201273/v1/d37636bd2519e05d1d07f341.png"},{"id":99319095,"identity":"d0dc4b90-db90-4608-8d32-2c46f4d4520b","added_by":"auto","created_at":"2025-12-31 16:36:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":22224597,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8201273/v1/95c690d6-8560-4ae9-80a0-854a6e58b7d8.pdf"},{"id":98399221,"identity":"4e5c793d-e748-49be-afd2-095bce118210","added_by":"auto","created_at":"2025-12-17 11:16:27","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":9224,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8201273/v1/b501062cfc113865273124bc.xlsx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eNanopore metagenomics of plague-focus soils in Ulanqab Plateau, Inner Mongolia: microbial communities, antibiotic resistance, and pathogen-host interactions\u003c/p\u003e","fulltext":[{"header":"Impact Statement","content":"\u003cp\u003eThis study provides the first application of third-generation nanopore sequencing to characterize the soil microbiome of natural plague foci in the Ulanqab Plateau. We reveal these soils as significant reservoirs of antibiotic resistance genes, with vancomycin resistance being particularly prevalent. The detection of \u003cem\u003eYersinia pestis\u003c/em\u003e-associated pathogen-host interaction gene fragments suggests a potential role of these soils in pathogen persistence, even in the absence of detectable \u003cem\u003eY. pestis\u003c/em\u003e. Our findings establish a new framework for environmental monitoring of zoonotic pathogens using long-read metagenomics, with implications for public health surveillance in plague-endemic regions globally.\u003c/p\u003e\n"},{"header":"1. Introduction","content":"\u003cp\u003ePlague, caused by the highly pathogenic bacterium \u003cem\u003eYersinia pestis\u003c/em\u003e, remains a major zoonotic threat with a complex natural ecology (Barbieri \u003cem\u003eet al.\u003c/em\u003e 2020). Although human cases of plague have declined dramatically due to improved public health measures, there are still natural source areas around the world, including China, where \u003cem\u003eY. pestis\u003c/em\u003e is transmitted among wild rodents and their ectoparasites, and where soil may serve as a potential environmental interface for pathogen persistence and transmission. The potential for \u003cem\u003eY. pestis\u003c/em\u003e transmission and the complex ecology of these natural outbreak sites emphasizes the importance of environmental monitoring, including soil monitoring. Soil not only harbors pathogens, but also influences their evolutionary trajectories through interactions with diverse microbial communities and selective pressures such as antibiotics and heavy metals.\u003c/p\u003e \u003cp\u003eSoil in plague-endemic areas is a dynamic interface for the maintenance and spread of \u003cem\u003eY. pestis\u003c/em\u003e (Eisen and Gage 2009). Studies have shown that Y. pestis can survive for long periods of time in soils under natural conditions, which may lead to the resurgence of outbreaks after periods of apparent quiescence (Eisen \u003cem\u003eet al.\u003c/em\u003e 2008). The ecological complexity of \u003cem\u003eY. pestis\u003c/em\u003e source areas is further exacerbated by interactions between soil physicochemical properties, vegetation types, and the diversity of resident microbial communities, all of which influence the persistence and pathogenicity of \u003cem\u003eY. pestis\u003c/em\u003e (Dubyanskiy and Yeszhanov 2016). Considerable work has been done on precipitation changes as a factor in causing quiescent plague foci to erupt, as moisture and temperature significantly affect these interactions. Therefore, understanding the structure and function of soil microbial communities in these areas is crucial for elucidating the mechanisms of plague maintenance and developing effective monitoring and control strategies.\u003c/p\u003e \u003cp\u003eTraditional culture-based methods for pathogen detection in environmental samples are often limited by low sensitivity and the inability to capture the full range of microbial diversity (especially non-culturable or low abundance organisms) (McConn \u003cem\u003eet al.\u003c/em\u003e 2024). The advent of metagenomics sequencing technology has revolutionized the field of environmental microbiology by comprehensively and culture-independently analyzing microbial communities and their functional genes directly from environmental DNA (eDNA) (P\u0026eacute;rez-Cobas \u003cem\u003eet al.\u003c/em\u003e 2020). Metagenomics not only facilitates the detection of known and novel pathogens, but also allows for the simultaneous characterization of antibiotic resistance genes (ARGs), virulence factors, and pathogen-host interaction (PHI) genes, resulting in a comprehensive understanding of microbial ecosystems and their potential risks (de Nies \u003cem\u003eet al.\u003c/em\u003e 2021).\u003c/p\u003e \u003cp\u003eRecent studies have applied metagenomics approaches to a variety of environments, including clinical samples, animal repositories, and natural habitats, revealing the ubiquity and diversity of ARGs and highlighting the role of environmental repositories in the spread of resistance (Leigh \u003cem\u003eet al.\u003c/em\u003e 2021; Qu \u003cem\u003eet al.\u003c/em\u003e 2024). In the context of ARGs, metagenomics sequencing offers unprecedented opportunities to monitor soil microbial communities and associated resistance determinants, to track their spatial and temporal dynamics, and to assess the impact of environmental factors on their persistence and evolution.\u003c/p\u003e \u003cp\u003eThird-generation sequencing technologies, particularly Oxford Nanopore Technology (ONT), have further advanced the field of metagenomics by providing long-read length, real-time sequencing capabilities with minimal sample preparation requirements (Espinosa \u003cem\u003eet al.\u003c/em\u003e 2024).The ONT R10 platform, with its improved pore structure and chemistry, has improved accuracy in base identification, particularly in homopolymers and repeats that are common in microbial genomes and mobile genetic elements Regions (Sereika \u003cem\u003eet al.\u003c/em\u003e 2022). Nanopore sequencing produces ultra-long read lengths that facilitate the assembly of complete genomes and plasmids, enabling the resolution of complex genome structures, detection of structural variation, and accurate annotation of ARGs and pathogen-host interaction (PHI) genes, which encode proteins involved in microbial virulence and host colonization mechanisms. (Jain \u003cem\u003eet al.\u003c/em\u003e 2018). Compared to second-generation (short read length) sequencing, nanopore technology excels in capturing full-length sequences of resistance genes and their genetic backgrounds, which is critical for understanding horizontal gene transfer mechanisms and resistance transmission (MacKenzie and Argyropoulos 2023). In addition, the portability and scalability of the ONT device makes it particularly suitable for on-site monitoring in remote or resource-limited areas (e.g., plague-endemic areas) (Oehler \u003cem\u003eet al.\u003c/em\u003e 2023).\u003c/p\u003e \u003cp\u003eAlthough the potential importance of soil as an interface for \u003cem\u003eY. pestis\u003c/em\u003e transmission and resistance gene exchange has been recognized, there is still a lack of comprehensive studies integrating high-resolution metagenomic and functional gene analyses of soils from \u003cem\u003eY. pestis\u003c/em\u003e source sites. In this study, we utilized the ONT R10 platform to perform in-depth metagenomic sequencing and functional annotation of soil samples collected from natural plague source sites. The main objectives include: (1) to characterize the taxonomic and functional features of soil microbial communities in plague-endemic areas, with a focus on the diversity and abundance of ARGs and PHI genes; (2) to elucidate the functional features of soil microbial communities in plague-endemic areas; and (3) to assess the potential risks associated with the environmental spread of resistance and pathogenicity determinants.\u003c/p\u003e \u003cp\u003eIn this study, the third-generation nanopore sequencing system was applied for the first time to the study of soils from \u003cem\u003eY. pestis\u003c/em\u003e-endemic areas, providing new insights into the structure and function of environmental microbial communities, the distribution of resistance and \u003cem\u003ePHI\u003c/em\u003e genes, and the ecological factors that influence the persistence and evolution of \u003cem\u003eY. pestis\u003c/em\u003e. By integrating metagenomic, functional, and ecological analyses, this study improves our understanding of the environmental dimensions of plague epidemiology and provides a solid framework for surveillance and risk assessment of emerging infectious diseases in natural repositories.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1༎ Soil sample collection\u003c/h2\u003e \u003cp\u003eThe study area is the Ulanqab Plateau plague focus in Inner Mongolia, characterized by typical plague-focus ecology with widely distributed gerbil hosts and long surveillance history. Two representative sites were selected by integrating 55-year rodent-plague surveillance records with GIS: Soil 1 (historic focus since 1970; rodent plague in 2021) and Soil 2 (newly identified focus; rodent plague in 2022). Coordinates (WGS84) are Soil 1\u0026mdash;110.63334\u0026deg;E, 41.19402\u0026deg;N; Soil 2 \u0026mdash;110.70092\u0026deg;E, 41.26462\u0026deg;N. At each site, \u0026ge;\u0026thinsp;10 active shallow gerbil burrows were selected. Site selection was based on 55-year historical records from the National Plague Surveillance System of China, which documents continuous monitoring of rodent plague activity in these specific locations since 1970. Using sterile tools, ~\u0026thinsp;100 g soil was scraped from the inner wall around the burrow at 0\u0026ndash;20 cm depth per burrow. Equal masses from individual burrows were pooled and thoroughly homogenized to form one composite per site; a 50 g aliquot was transferred to a sterile bag, labeled (site, date), stored at 4\u0026deg;C, and transported to the laboratory for immediate processing. Based on previous rodent plague monitoring data and utilizing geographic information system (GIS) technology, two representative sampling sites (named Soil 1 and Soil 2, respectively) were selected in the study area (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for GIS locations). At each sampling site, exactly 12 shallow gerbil burrows were identified based on active signs (fresh soil mounds, recent digging activity, and presence of fresh droppings) to ensure they were recently occupied rather than abandoned. Soil was collected from three depth intervals (0\u0026ndash;5 cm, 5\u0026ndash;15 cm, and 15\u0026ndash;20 cm) around each burrow using sterile sampling tools, with approximately 50 g collected from each depth interval per burrow. This resulted in a total of 36 depth-specific samples per site (12 burrows \u0026times; 3 depths), which were then composited into the two final samples (Soil 1 and Soil 2 ) for metagenomic analysis. Soil 1: Moisture content 8.2% (0\u0026ndash;5 cm), 12.1% (5\u0026ndash;15 cm), 15.3% (15\u0026ndash;20 cm); Temperature 18.5\u0026deg;C (0\u0026ndash;5 cm), 16.2\u0026deg;C (5\u0026ndash;15 cm), 14.8\u0026deg;C (15\u0026ndash;20 cm); Texture: Sandy loam. Soil 2 : Moisture content 6.8% (0\u0026ndash;5 cm), 10.5% (5\u0026ndash;15 cm), 13.7% (15\u0026ndash;20 cm); Temperature 19.1\u0026deg;C (0\u0026ndash;5 cm), 17.1\u0026deg;C (5\u0026ndash;15 cm), 15.3\u0026deg;C (15\u0026ndash;20 cm); Texture: Loamy sand. Pre-sampling climate: The 30 days preceding sampling were characterized by below-average precipitation (15.2 mm vs. historical average of 28.5 mm), moderate temperatures (daily average 16.8\u0026deg;C), and low relative humidity (average 45%). Soil sampling was conducted in September 2024 (Soil 1: September 15, 2024; Soil 2 : September 18, 2024), during the post-summer period when plague activity is typically monitored in this region. To ensure representative samples, soil from multiple burrows at each sampling site was thoroughly mixed to form a composite sample for subsequent analysis. Historical averages for the region: temperature 15.2\u0026deg;C, relative humidity 52%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2༎ DNA extraction and quality control\u003c/h2\u003e \u003cp\u003eTotal genomic DNA was extracted from 0.5 g of each composite soil sample using the DNeasy PowerSoil kit (Qiagen, Germany) according to the manufacturer's instructions with the following modifications to optimize extraction volume and purity: (1) increased bead-beating time from 10 min to 15 min to improve cell lysis efficiency; (2) added an additional wash step with 500 \u0026micro;L of buffer AW2 to reduce humic acid contamination; (3) eluted DNA in 50 \u0026micro;L of buffer AE instead of the recommended 100 \u0026micro;L to increase DNA concentration; and (4) added 2 \u0026micro;L of RNase A (10 mg/mL) during the lysis step to remove RNA contamination. The quality and quantity of extracted DNA was assessed by agarose gel electrophoresis and Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, USA). 16S rRNA gene PCR (QC purpose only). To verify that environmental DNA was amplifiable and to screen for gross PCR inhibition before library preparation, we amplified the bacterial 16S rRNA gene V3-V4 region with primers 27F (5'-AGAGTTTGATCMTGGCTCAG-3') and 1492R (5'-TACGGYTACCTTGTTACGACTT-3') using Phusion High-Fidelity DNA Polymerase (Thermo Fisher Scientific). Amplicons were examined by 1.5% agarose gel electrophoresis. No 16S amplicon data were used for downstream community analyses, which relied on shotgun/long-read metagenomics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3༎ Nanopore R10 Sequencing\u003c/h2\u003e \u003cp\u003eLibrary preparation of high-quality DNA samples was performed using the Oxford Nanopore Technology (ONT) Ligation Sequencing Kit (SQK-LSK110). Library construction included DNA repair, end processing, junction ligation and purification steps, all according to the manufacturer's protocol. The prepared libraries were loaded onto an ONT R10.4 flow cell (FLO-MIN112) and sequenced on the PromethION platform (Oxford Nanopore Technologies, UK). Base identification was performed in real time using Guppy (v6.0.1), and only screened read lengths (average Q\u0026thinsp;\u0026gt;\u0026thinsp;7) were retained for subsequent analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4༎ Bioinformatics analysis\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1༎ Data Quality Control and Filtering\u003c/h2\u003e \u003cp\u003eRaw nanopore reads were filtered using NanoFilt v2.8.0 with parameters --minlen 1000 --minqual 7 (De Coster \u003cem\u003eet al.\u003c/em\u003e 2018) and Minimap2 v2.24(Joshi \u003cem\u003eet al.\u003c/em\u003e 2023) to remove low-quality sequences, adapters, and host-derived reads. The retained clean reads were used in FASTQ format for subsequent analysis.\u003c/p\u003e \u003cp\u003eSoil 1 generated 1,826,148 raw reads (7.44 Gb) and Soil 2 generated 1,721,601 raw reads (7.86 Gb). After quality filtering (Q\u0026thinsp;\u0026gt;\u0026thinsp;7), Soil 1 and Soil 2 retained 1,329,809 and 1,329,846 clean reads, respectively, corresponding to high-quality data volumes of 7.31 Gb and 7.09 Gb. Average read lengths were 5,371 bp and 5,721 bp for Soil 1 and Soil 2, respectively, with maximum read lengths of 176,475 bp and 171,379 bp. N50 values were 7,496 bp and 7,859 bp, respectively.\u003c/p\u003e \u003cp\u003eFurther quality control using Minimap2 v2.24 and samtools v1.16 (Danecek \u003cem\u003eet al.\u003c/em\u003e 2021) removed host-derived sequences by mapping to the human reference genome (GRCh38.p13). Given the primary focus on environmental microbial communities, removal of human contamination is prioritized. Soil 1 and Soil 2 yielded 1,323,355 and 1,322,168 non-host reads, respectively, with host sequence removal rates of 0.69% and 0.82%. Read length distributions showed the majority of sequences between 4,000 and 12,000 bp, with ultra-long reads exceeding 100,000 bp.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2༎ Taxonomic Annotation (Metagenome Sequencing Process)\u003c/h2\u003e \u003cp\u003eMicrobial community composition was analyzed using custom R scripts (R v4.2.0) with the vegan package (Marees \u003cem\u003eet al.\u003c/em\u003e 2018) for diversity calculations and the phyloseq package (McMurdie and Holmes 2012) for community composition analysis. TPM (Transcripts Per Million) values were calculated for taxonomic abundance normalization using the formula: TPM = (gene_count \u0026times; 10^6) / (total_gene_count \u0026times; gene_length). Detailed taxonomic data including raw read counts, TPM values, and coverage for all detected taxa are provided in Supplementary Table S2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3༎ Antibiotic resistance gene (ARG) analysis\u003c/h2\u003e \u003cp\u003eARGs were identified using the Resistance Gene Identification Tool (RGI) v5.2.1 against the Comprehensive Antibiotic Resistance Database (CARD, v3.2.9) with parameters --alignment_tool BLASTX --identity 80 --coverage 80. Raw gene counts and TPM values for all detected ARG types are provided in Supplementary Table S3\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4. Pathogen-Host Interaction (PHI) Database Annotation\u003c/h2\u003e \u003cp\u003eAssembled contigs from metagenomic assemblies were compared to the Pathogen-Host Interaction database (PHI-base, v4.12) using BLASTx v2.12.0 to annotate PHI genes, with parameters -evalue 1e-5 -max_target_seqs 10 -num_threads 16. BLASTx searches were performed with parameters -evalue 1e-5 -max_target_seqs 10 -num_threads 16. Homology thresholds were set at \u0026ge;\u0026thinsp;80% identity over \u0026ge;\u0026thinsp;80% coverage for gene annotation. PHI gene annotation was performed using BLASTx against the PHI-base database (v4.12) with parameters -evalue 1e-5 -max_target_seqs 10 -num_threads 16. Homology thresholds were set at \u0026ge;\u0026thinsp;95% identity over \u0026ge;\u0026thinsp;80% coverage for gene annotation. Only genes meeting these stringent criteria were considered for analysis. TPM values and raw counts for all detected PHI genes are provided in Supplementary Table S4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.5. Functional Annotation and Enrichment Analysis\u003c/h2\u003e \u003cp\u003eFunctional annotation and enrichment analyses were carried out using eggNOG-mapper v2.1.9 and KEGG databases. GO enrichment analysis was performed using clusterProfiler v4.0 with the background gene set comprising all genes successfully annotated to GO categories from both metagenomes. Statistical significance was assessed using Fisher's exact test with Benjamini-Hochberg False Discovery Rate (FDR) correction. The significance threshold was set at FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05. KEGG pathway enrichment was conducted using the same statistical framework with the KEGG database (release 2023-01-15) (Cantalapiedra et al. 2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.4.5༎ Detection of caf1/pla genes by qPCR\u003c/h2\u003e \u003cp\u003eTo preliminarily screen for the presence of Y. pestis in soil samples, quantitative PCR (qPCR) targeting the specific virulence genes caf1 and pla was performed using gene-specific primers. Primers for caf1 were: caf1-F (5'-ATGAAAAAACTTACTGCGGC-3') and caf1-R (5'-TTATTTGCCGTTGCCGTTGC-3'), amplifying a 456 bp fragment. Primers for pla were: pla-F (5'-ATGAAAAAACTTACTGCGGC-3') and pla-R (5'-TTATTTGCCGTTGCCGTTGC-3'), amplifying a 1,017 bp fragment. qPCR reactions were performed in 20 \u0026micro;L volumes containing 10 \u0026micro;L of 2\u0026times; SYBR Green Master Mix (Applied Biosystems) on a QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems), 0.5 \u0026micro;M of each primer, and 2 \u0026micro;L of template DNA. Cycling conditions were: 95\u0026deg;C for 10 min, followed by 40 cycles of 95\u0026deg;C for 15 s, 55\u0026deg;C for 30 s, and 72\u0026deg;C for 30 s, with fluorescence detection at 72\u0026deg;C. Standard curves were generated using serial dilutions of Y. pestis genomic DNA (10^1 to 10^6 copies/\u0026micro;L) to determine detection limits and quantification. This assay served as an initial screening method to assess whether Y. pestis DNA could be detected in the environmental samples prior to metagenomic analysis. TPM values were calculated for all detected genes to enable abundance comparisons and are provided in supplementary tables.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1༎ Overview of sequencing data and assembly\u003c/h2\u003e \u003cp\u003eClean reads were assembled using metaFlye v2.9.2 with parameters --nano-raw --threads 16 --memory 100. Taxonomic annotation using Kraken2 v2.1.2 and Bracken v2.8 against NCBI nt database (release 2023-10-15) and RefSeq database (release 2023-10-15). Two composite soil samples (Soil 1 and Soil 2 ) from the Ulanqab Plateau plague focus were successfully sequenced using Oxford Nanopore R10 technology. After quality control and host sequence removal, both samples yielded high-quality metagenomic data suitable for downstream analysis (see Materials and Methods 2.4.1 for detailed quality metrics) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Assembly of the quality-filtered reads produced contigs with N50 values of 7,496 bp and 7,859 bp for Soil 1 and Soil 2, respectively, enabling reliable taxonomic and functional annotation of the soil microbial communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThird-generation sequencing and quality control statistics for soil samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRawReads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRawBases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCleanReads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCleanBases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAvgLen(bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMaxLen(bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN50(bp)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,826,148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,442,453,702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,329,809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,310,460,386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5,371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e176,475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,721,601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,862,196,029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,329,846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,093,782,093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5,721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e171,379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHost sequence removal statistics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNoHostReads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNoHostBases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHost_rate(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAvgLen(bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMaxLen(bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN50(bp)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,323,355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,092,451,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e176,475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7,483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,322,168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,565,240,819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e171,379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7,844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2༎ Microbial Community Structure and Diversity\u003c/h2\u003e \u003cp\u003eTaxonomic annotations at the phylum and order level showed that \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003eAcidobacteria\u003c/em\u003e and \u003cem\u003eProteobacteria\u003c/em\u003e were the dominant phyla in both samples. Among the families, Streptomycetaceae was notably the most abundant, followed by Bradyrhizobiaceae and Pseudomonadaceae (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, Sankey diagram). Bar charts and heat maps further show the relative abundance and clustering of the major taxonomic units, while a venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) indicates that 49 species were shared between the two samples, with 20 species detected only in Soil 1 and 45 species detected only in Soil 1. Notably, Y. pestis was not detected in either sample, which may reflect the current absence of active Y. pestis circulation in the sampled areas, though this does not preclude the presence of the pathogen in wildlife reservoirs. Detailed quantitative data including raw read counts, TPM values, and coverage for all detected taxa are provided in Supplementary Table S2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Functional gene annotation\u003c/h2\u003e \u003cp\u003eCOG analysis revealed functional profiles of both soil samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Soil1 contained 10,679 genes in amino acid transport and metabolism (E) and 7,131 genes in energy production and conversion (C), while Soil2 contained 10,822 genes in amino acid transport and metabolism (E) and 7,133 genes in energy production and conversion (C). Soil1 had 7,655 genes in carbohydrate transport and metabolism (G) and 5,638 genes in cell wall/membrane/envelope biogenesis (M), while Soil2 had 7,796 genes in carbohydrate transport and metabolism (G) and 5,764 genes in cell wall/membrane/envelope biogenesis (M).\u003c/p\u003e \u003cp\u003eGO analysis identified functional gene categories in both samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Soil1 contained 88 genes associated with stress response, 41 genes associated with metal ion transport, 141 genes involved in secondary metabolite biosynthesis, and 67 genes involved in quorum sensing. Soil2 contained 91 genes associated with stress response, 33 genes associated with metal ion transport, 108 genes involved in secondary metabolite biosynthesis, and 72 genes involved in quorum sensing.\u003c/p\u003e \u003cp\u003eKEGG pathway analysis identified metabolic pathways in both samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Soil1 contained 113 genes in glutathione metabolism pathways, 4 genes in oxidative stress response pathways, 449 genes in purine metabolism pathways, and 92 genes in biofilm formation pathways. Soil2 contained 119 genes in glutathione metabolism pathways, 2 genes in oxidative stress response pathways, 452 genes in purine metabolism pathways, and 93 genes in biofilm formation pathways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4༎ Analysis of Antibiotic Resistance Genes (ARGs)\u003c/h2\u003e \u003cp\u003eA comprehensive analysis revealed a rich and diverse resistome in both soil samples. The stratified bar chart (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) shows the relative abundance of major ARG types in Soil 1 and Soil 2. Vancomycin resistance genes were the most abundant in both samples. Multidrug, aminoglycoside, and methicillin resistance genes were also common. Other ARG types, such as β-lactam, chloramphenicol, rifamycin, and quinolone resistance genes, were less frequent.\u003c/p\u003e \u003cp\u003eOverall ARG profiles were similar at both sites. However, some differences were observed. Soil 1 had a slightly proportion of aminoglycoside and methotrexate resistance genes. Soil 2 showed a abundance of multidrug resistance genes. The detection of multiple ARGs, including those conferring resistance to clinically important antibiotics, highlights the role of plague-endemic soils as reservoirs of antibiotic resistance. Raw gene counts and TPM values for all detected ARG types are provided in Supplementary Table S3.\u003c/p\u003e \u003cp\u003eThese results suggest that long-term ecological interactions and selective pressures in the Ulanqab Plateau promote ARG accumulation and diversification. The dominance of vancomycin and multidrug resistance genes may reflect both natural and human influences on the local resistome.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5༎Annotation of the Pathogen-Host Interaction(PHI) genes in the Y. pestis species\u003c/h2\u003e \u003cp\u003eAnnotation results for the Pathogen-Host Interaction (PHI) database showed that a variety of PHI genes with homology to Y. pestis sequences were present in both Soil Sample 1 (Soil 1) and Soil Sample 2 (Soil 2 ). There were compositional differences in the distribution of PHI genes with homology to \u003cem\u003eY. pestis\u003c/em\u003e sequences between the two sampling sites. A total of 393 PHI genes with homology to Y. pestis sequences were detected only in Soil 1, 447 were detected only in Soil 2, and 49 genes were shared between the two samples. TPM values for these genes are provided in Supplementary Table S4.\u003c/p\u003e \u003cp\u003eFurther analysis of the functional phenotypes associated with these PHI genes revealed that the majority of genes were associated with reduced virulence phenotypes (indicating that gene disruption reduces virulence, i.e., these genes function as virulence factors), while a relatively small percentage of genes were associated with either unaffected pathogenicity or enhanced virulence (hypervirulence) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Both Soil Sample 1 and Soil Sample 2 were dominated by virulence-reducing genes, while other phenotypes such as loss of pathogenicity and genes with unaffected pathogenicity appeared at much lower frequencies. This distribution indicates that the majority of detected genes are those that, when disrupted, reduce virulence.\u003c/p\u003e \u003cp\u003eAnalysis of the heat map of the major PHI gene homologs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB) highlighted several conserved bacterial genes with homology to Y. pestis sequences (\u0026ge;\u0026thinsp;95% identity), including \u003cem\u003eBipA, ZnuC, GlpD\u003c/em\u003e, and \u003cem\u003ePgm\u003c/em\u003e, which had abundance (TPM) in both samples. Notably, \u003cem\u003eBipA\u003c/em\u003e and \u003cem\u003eZnuC\u003c/em\u003e had the highest expression levels, suggesting that they may be ecologically important in the soil microbiome. Despite some differences in the abundance of these major \u003cem\u003eY. pestis\u003c/em\u003e PHI genes, their distribution in soil sample 1 and soil sample 2 was generally consistent. It should be noted that these genes are conserved across bacterial species and are not Y. pestis-specific markers. The absence of hallmark Y. pestis genes (caf1, pla) in both qPCR and metagenomic datasets further supports that these represent conserved bacterial genes rather than Y. pestis-specific sequences. The detection of PHI gene fragments with homology to Yersinia spp. sequences suggests the presence of conserved bacterial genes rather than Y. pestis-specific virulence factors, particularly given the absence of hallmark plague markers (caf1, pla) in both qPCR and metagenomic analyses.\u003c/p\u003e \u003cp\u003eThe overall distribution of \u003cem\u003eY. pestis\u003c/em\u003e PHI-annotated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC) further confirmed the high diversity and site-specificity of virulence-related genes in soils from plague-endemic areas. These findings suggest that the soil microbiome harbors a complex and dynamic pool of Y. pestis-associated virulence factors that are detected in these specific samples. It should be noted that ARGs and PHI genes are ubiquitous in environmental microbiomes, and the absence of comparative data from non-plague areas limits our ability to determine if the observed profiles are unique or enriched in plague foci compared to other soil environments. Consistent with the metagenomic screening, qPCR targeting caf1 and pla did not yield detectable amplification in the site-level composites within the predefined Ct threshold. Positive controls amplified as expected, whereas no-template controls remained negative (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These results indicate no qPCR-detectable Yersinia pestis signature in the analyzed aliquots. TPM values and raw counts for all detected PHI genes are provided in Supplementary Table S4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, a comprehensive macro-genomic and functional gene analysis of soil from a natural plague epidemic site in the Ulanchab Plateau, Inner Mongolia, was carried out using third-generation nanopore sequencing technology. The results provide new insights into the taxonomic and functional diversity of soil microbial communities in the plague-endemic area, the distribution and ecological significance of antibiotic resistance genes (ARGs) and pathogen-host interaction (PHI) genes, and the potential environmental risks associated with the persistence and spread of \u003cem\u003eY. pestis\u003c/em\u003e and its associated determinants.\u003c/p\u003e \u003cp\u003eUtilizing high-throughput sequencing technology, more than 1.7\u0026nbsp;million raw reads were generated per sample with N50 values of more than 7 kb, demonstrating the potential of nanopore long-read sequencing technology in facilitating the analysis of environmental metagenomes(Lei and Kumar 2022; De La Cerda \u003cem\u003eet al.\u003c/em\u003e 2023). Most of the read lengths ranged from 4000 to 12000bp, and a large number of ultra-long read lengths (\u0026gt;\u0026thinsp;100kb) were obtained, providing a solid foundation for subsequent assembly and gene annotation(Figure\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These data are consistent with recent reports highlighting the advantages of third-generation sequencing technology in resolving complex microbial communities and mobile genetic elements in environmental samples (Aka\u0026ccedil;in \u003cem\u003eet al.\u003c/em\u003e 2022; Meslier \u003cem\u003eet al.\u003c/em\u003e 2022).\u003c/p\u003e \u003cp\u003eTaxonomic analyses showed that the dominant phyla in both soil samples were Actinobacteria, Acidobacteria and Ascomycetes, and the major families were Streptomycetaceae, Rhizobacteriaceae and Pseudomonadaceae (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The ecological significance of these findings extends beyond simple taxonomic cataloging to understanding the functional dynamics of plague-endemic environments. The predominance of Actinobacteria, particularly Streptomycetaceae, in both samples suggests a soil microbiome adapted to arid conditions and potentially influenced by historical antibiotic production by these bacteria. This composition is consistent with previous studies of soil microbiomes from plague outbreak sites and other arid or semiarid environments, where Actinobacteria and Ascomycetes phyla are prevalent due to their metabolic diversity and resistance to environmental stress (Naylor \u003cem\u003eet al.\u003c/em\u003e 2022; He \u003cem\u003eet al.\u003c/em\u003e 2023). Venn diagram analysis further highlighted shared and unique taxa, with only 49 species shared by both sampling sites, emphasizing the compositional differences between sampling sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFunctional annotation of the macrogenome revealed a wide range of metabolic and adaptive capabilities (Zhou \u003cem\u003eet al.\u003c/em\u003e 2022). COG and GO analyses revealed a high enrichment of genes involved in amino acid transport and metabolism, carbohydrate metabolism, and energy production, reflecting the strong potential of the soil microbiome for nutrient cycling and environmental adaptation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These findings are consistent with the notion that soil microbial communities in natural plague foci are influenced by a combination of biotic and abiotic factors, including soil physicochemical properties, vegetation cover, and host animal activity. KEGG pathway analyses identified active primary and secondary metabolic pathways as well as genes associated with environmental adaptation, xenobiotic matter degradation, and signaling-related genes, suggesting that microbial communities are equipped to cope with environmental fluctuations and anthropogenic impacts (Philippot \u003cem\u003eet al.\u003c/em\u003e 2021).\u003c/p\u003e \u003cp\u003eNotably, the diversity and abundance of antibiotic resistance genes (ARGs) were higher in soil samples. Vancomycin resistance genes were the most abundant among detected ARGs in both samples, followed by multidrug resistance, aminoglycoside and methotrexate resistance genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The presence of multiple ARGs, including genes resistant to clinically important antibiotics, suggests potential role of soils in plague-endemic areas as potential reservoirs of antibiotic resistance. (Delgado-Baquerizo \u003cem\u003eet al.\u003c/em\u003e 2022; Shi \u003cem\u003eet al.\u003c/em\u003e 2022). The distribution of ARGs contained both core and site-specific components, with 49 shared ARGs and a large number of endemic ARGs at each site. This pattern of ARG diversity and distribution is consistent with previous reports that natural environments (especially those with a history of human or animal activity) are important reservoirs and sources of resistance determinants (Zhu \u003cem\u003eet al.\u003c/em\u003e 2025). The prevalence of vancomycin and multidrug resistance genes may reflect both natural selection and anthropogenic influences, though comparative data from non-plague areas would be needed to establish baseline resistome profiles (M\u0026uuml;hlberg \u003cem\u003eet al.\u003c/em\u003e 2020).\u003c/p\u003e \u003cp\u003eThe ecological and evolutionary significance of ARGs in soils of plague-origin sites is significant (Chen \u003cem\u003eet al.\u003c/em\u003e 2022). Although the genetic background of ARGs was not directly assessed in this study, the high diversity and abundance of ARGs were observed in these samples. Future studies should employ metagenome-assembled genome (MAG) binning and plasmid detection to link ARGs and PHI genes to specific microbial hosts and mobile genetic elements(Liao \u003cem\u003eet al.\u003c/em\u003e 2024). A key limitation of this study is the lack of biological replicates for robust statistical analysis of community differences, which would require multiple individual burrow-level samples rather than composite samples. Previous studies have shown that environmental selection pressures (e.g., the presence of antibiotics or heavy metals) can promote the persistence and spread of resistance genes in the soil microbiome (Gillieatt and Coleman 2024). The site-specificity of ARGs in the samples of this study may have been influenced by localized differences in soil chemistry, host animal densities, and historical land use, which is consistent with other studies reported for plague outbreak sites and other natural repositories (Zhang \u003cem\u003eet al.\u003c/em\u003e 2023).\u003c/p\u003e \u003cp\u003eIn addition to ARGs, this study specifically analyzed pathogen-host interaction (PHI) genes annotated to the \u003cem\u003eY. pestis\u003c/em\u003e species, providing new insights into the ecological adaptation and virulence potential of this pathogen in soil environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). PHI annotation revealed a wide range of \u003cem\u003eY. pestis\u003c/em\u003e virulence-associated genes with a high degree of site-specificity: 393 endemic \u003cem\u003eY. pestis\u003c/em\u003e PHI genes were present in Soil 1, 447 in Soil 2, and only 49 were shared (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The majority of these \u003cem\u003ePHI\u003c/em\u003e gene homologs showing sequence similarity to \u003cem\u003eY. pestis\u003c/em\u003e virulence factors were associated with an attenuated virulence phenotype, while a smaller proportion were associated with no effect or enhanced virulence. High abundance of key \u003cem\u003eY. pestis\u003c/em\u003e genes (e.g., BipA and ZnuC) were detected in both samples, indicating their ecological importance in the soil microbiome. BipA is a translation initiation factor associated with stress response and virulence regulation in \u003cem\u003eY. pestis(Scott Madeleine et al. 2025)\u003c/em\u003e, while ZnuC is a key component of the zinc transport system that is critical for nutrient acquisition and virulence expression (Wang \u003cem\u003eet al.\u003c/em\u003e 2016; Goh \u003cem\u003eet al.\u003c/em\u003e 2021). The prevalence of genes associated with reduced virulence phenotypes indicates that many detected genes are virulence factors that, when disrupted, attenuate pathogenicity, thereby promoting ecological stability and host-microbe coexistence (Goh \u003cem\u003eet al.\u003c/em\u003e 2024).\u003c/p\u003e \u003cp\u003eNotably, \u003cem\u003eY. pestis\u003c/em\u003e was not detected in this study's samples by both qPCR and metagenomic sequencing. While this result is consistent with the current epidemiological situation in the region, it also highlights the challenge of detecting low abundance pathogens in complex environmental matrices. Previous studies have shown that \u003cem\u003eY. pestis\u003c/em\u003e can survive in soil for long periods of time, but its abundance is often below the detection limit of conventional molecular methods, especially during non-outbreak periods (Eisen \u003cem\u003eet al.\u003c/em\u003e 2008). The detection of PHI gene fragments associated with Yersinia spp. suggests that there may be associated virulence factors in the soil microbiome that may contribute to the ecological adaptation and persistence of this group of bacteria (Huang \u003cem\u003eet al.\u003c/em\u003e 2023).\u003c/p\u003e \u003cp\u003eThe results of this study also demonstrate the importance of environmental factors in shaping the structure and function of soil microbial communities in \u003cem\u003eY. pestis\u003c/em\u003e source sites. Soil physicochemical properties (e.g., water content, organic matter, and mineral composition) have been shown to influence the survival and transmission of \u003cem\u003eY. pestis\u003c/em\u003e and other pathogens (Ma \u003cem\u003eet al.\u003c/em\u003e 2023). Although transmission of \u003cem\u003eY. pestis\u003c/em\u003e from contaminated soils is possible, it is not likely a major transmission route under natural conditions (Boegler \u003cem\u003eet al.\u003c/em\u003e 2012). However, increased soil water content and organic matter can enhance the survival of fleas and their larvae, which are key vectors in the transmission cycle of \u003cem\u003eY. pestis\u003c/em\u003e, while certain metal ions (e.g., calcium, iron, magnesium, and sodium) have been associated with increased pathogenicity and biofilm formation of \u003cem\u003eY. pestis\u003c/em\u003e (Falc\u0026oacute;n Garc\u0026iacute;a \u003cem\u003eet al.\u003c/em\u003e 2020; Wardinski \u003cem\u003eet al.\u003c/em\u003e 2024). Although these environmental variables were not directly measured in this study, differences in microbial community structure and functional gene profiles between sampling sites may reflect potential heterogeneity in soil properties and host animal activity (Weng \u003cem\u003eet al.\u003c/em\u003e 2021).\u003c/p\u003e \u003cp\u003eThis study applies third-generation nanopore sequencing technology to demonstrate the feasibility and advantages of long-read-length metagenomics for environmental pathogen monitoring. The ability to generate ultra-long read lengths and resolve complex genome structures enabled more accurate assembly and annotation of functional genes, including ARGs and PHI genes. This technological advancement is particularly important for monitoring environmental pathogens in natural plague outbreak sites and other remote or resource-limited areas, where rapid and comprehensive assessments of microbial communities and functional determinants are critical for risk assessment and public health interventions (Li \u003cem\u003eet al.\u003c/em\u003e 2025).\u003c/p\u003e \u003cp\u003eDespite these advances, some limitations remain. A key limitation of this study is the lack of source attribution for detected ARGs and PHI genes to specific taxa or mobile genetic elements, which would require advanced bioinformatics approaches such as MAG binning, plasmid reconstruction, and proximity analysis. A limitation of this study is that we did not fully exploit the unique capabilities of Nanopore sequencing for complete plasmid assembly, pathogenicity island detection, or structural variant analysis. Future studies should implement specialized bioinformatics pipelines to leverage these advantages. Detecting low-abundance pathogens like \u003cem\u003eY. pestis\u003c/em\u003e is still challenging. Limitations of this study include the lack of biological replicates for robust statistical analysis of community separation, which would require multiple individual burrow-level samples rather than composite samples. Future studies should consider targeted enrichment or capture methods to improve sensitivity. Although this study provides a comprehensive snapshot of soil microbial diversity and function, temporal and spatial changes need further investigation. Furthermore, exploring ancient DNA (aDNA) techniques could offer valuable insights into the historical presence and long-term persistence of Y. pestis in these environmental reservoirs, especially when contemporary detection methods are limited by low pathogen abundance. Longitudinal sampling across seasons and microhabitats will help clarify pathogen persistence and transmission dynamics. The ecological and evolutionary relationships among soil microbes, ARGs, and PHI genes are not yet fully understood. Integrating metagenomics, metatranscriptomics, and experimental validation is essential to reveal the mechanisms of resistance and virulence gene maintenance and spread.\u003c/p\u003e \u003cp\u003eIn summary, this study presents the first high-resolution, long-read metagenomic analysis of soils from a natural plague focus. Future research directions should focus on: (1) longitudinal sampling to capture temporal dynamics of pathogen persistence; (2) integration of metatranscriptomic approaches to assess active gene expression; (3) comparative analysis with non-plague endemic soils to establish baseline resistome profiles; and (4) implementation of advanced bioinformatics pipelines to fully exploit long-read sequencing advantages. We revealed a functionally diverse and ecologically adapted microbial community that serves as a reservoir for resistance and virulence genes. The widespread presence of vancomycin and multidrug resistance genes, the high diversity and site-specificity of PHI genes, and the absence of detectable \u003cem\u003eY. pestis\u003c/em\u003e (while still detecting Y. pestis-associated PHI gene fragments) collectively highlight the complex interactions among environment, microbes, and pathogen persistence in plague-endemic areas, suggesting potential for past presence or very low abundance of the pathogen. These findings are important for environmental monitoring, risk assessment, and the development of targeted interventions for plague and other emerging infectious diseases. Future research should expand sampling in space and time, integrate multi-omics approaches, and develop more sensitive detection methods to further elucidate the ecological and evolutionary dynamics of pathogens and functional genes in natural reservoirs.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study used an innovative environmental monitoring approach. Third-generation nanopore long-read sequencing was combined with comprehensive metagenomic and functional gene annotation. This method was applied to the soil microbiome of a natural plague focus in the Ulanqab Plateau. Nanopore sequencing enabled high-resolution, real-time analysis of complex microbial communities. It also allowed precise detection of antibiotic resistance genes and pathogen-host interaction genes. \u003cem\u003eY. pestis\u003c/em\u003e was not detected in the samples. However, multiple resistance genes and virulence gene fragments were found. This highlights the ecological complexity of plague-endemic soils and the potential risk of pathogen persistence. These results demonstrate the value and feasibility of advanced long-read metagenomic technologies for environmental pathogen monitoring. This is especially important in remote or resource-limited areas.The surveillance model developed here offers a scalable and innovative framework for early warning and risk assessment of zoonotic diseases in natural reservoirs. Further research is needed to expand this methodology and clarify the dynamic interactions among soil properties, microbial communities, and environmental pathogens.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We are grateful for funding from the Hohhot Medical and Health Science and Technology Program Project (Project No. Hu Wei Jian Medical-2023073).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFeng Xu: Conceptualization, Methodology, Funding acquisition, Project administration, Writing - original draft, Writing - review \u0026amp; editing. Shoucheng Lei: Software, Formal analysis, Data curation, Visualization, Writing - original draft. Hairong Yang: Investigation, Resources, Validation. Zhong Yang: Investigation, Resources. Liping Xing: Methodology, Investigation. Zhiliang Jie: Methodology, Investigation. Lanmei Zhou: Validation, Data curation. Yuanyuan Li: Validation, Data curation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eRaw nanopore reads (FASTQ), assemblies, and annotations have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession PRJNA[1362966] and in Zenodo (DOI: 10.5281/zenodo.[1362966]) upon acceptance.\u003c/p\u003e\n\u003cp\u003eI affirm that this article has not been submitted to any other journal and all the authors have consented to submit the manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no known conflict of interest.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAka\u0026ccedil;in İ, Ersoy Ş, Doluca O, G\u0026uuml;ng\u0026ouml;rm\u0026uuml;şler M (2022) Comparing the significance of the utilization of next generation and third generation sequencing technologies in microbial metagenomics. \u003cem\u003eMicrobiological Research\u003c/em\u003e \u003cb\u003e264\u003c/b\u003e, 127154. doi:https://doi.org/10.1016/j.micres.2022.127154\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarbieri R, Signoli M, Chev\u0026eacute; D, Costedoat C, Tzortzis S, Aboudharam G, Raoult D, Drancourt M (2020) Yersinia pestis: the Natural History of Plague. \u003cem\u003eClin Microbiol Rev\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e(1). 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High-throughput long-read sequencing enabled detailed characterization of soil microbial communities, functional annotation, and detection of ARGs and PHI genes. The microbial community was dominated by Actinobacteria, Acidobacteria, and Proteobacteria. Functional annotation indicated diverse metabolic capabilities, particularly in amino acid and carbohydrate metabolism. A rich array of ARGs was detected, with vancomycin resistance genes being most prevalent. PHI gene analysis focused specifically on genes annotated to the Y. pestis species revealed abundant homologs of BipA and ZnuC. Although Y. pestis was not detected by metagenomics or qPCR, the presence of Y. pestis-associated PHI gene fragments suggests potential for pathogen persistence.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePlague-endemic soils in the Ulanqab Plateau are dynamic reservoirs of resistance and virulence determinants. The findings demonstrate the value of advanced long-read metagenomics for environmental pathogen surveillance and risk assessment, highlighting the ecological complexity of these environments and their potential role in maintaining antibiotic resistance and virulence genes.\u003c/p\u003e","manuscriptTitle":"Nanopore metagenomics of plague-focus soils in Ulanqab Plateau, Inner Mongolia: microbial communities, antibiotic resistance, and pathogen-host interactions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 11:16:22","doi":"10.21203/rs.3.rs-8201273/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":"9fd743bf-1f3c-47b9-a09f-dd4f307f1966","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-30T10:06:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 11:16:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8201273","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8201273","identity":"rs-8201273","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}