Infectome analysis of small mammals in Southern China reveals pathogen ecology and emerging risks

Domain: biology
Published: 2025-12-12
Editorial outcome: major_revision

Abstract
Small mammals harbor a diverse array of potentially zoonotic pathogens. To date, however, metagenomics surveys of these species have primarily focused on viruses, with limited attention directed to bacterial and eukaryotic pathogens. Additionally, the ecological determinants of pathogen diversity within these mammals have not been systematically examined. Herein, we employed a metatranscriptomics approach to survey the pathogen infectome—defined as the set of microorganisms infecting the host—across 2408 individual samples, representing lung, spleen, and gut from 858 animals in Guangdong province, China, considering the impact of host species, tissue, season, and geographic location on pathogen diversity. We identified 76 potential pathogen species, comprising 29 RNA viruses, 12 DNA viruses, five bacteria, and 30 eukaryotic pathogens, including 33 that are newly discovered. Distinct tissue tropisms were identified, suggesting varied transmission routes. Individual animals carried an average of one pathogen, with 10 pathogens widely distributed among mammalian orders. Total pathogen richness was largely influenced by geographic region, followed by host species and season, while zoonotic pathogen richness was primarily driven by host species. Collectively, these data provide insights into the structure of the pathogen infectome and the drivers of pathogen diversity and transmission in these key mammalian disease reservoirs.

Introduction
Small mammals, including rodents (order Rodentia) and shrews (order Eulipotyphla), are major reservoirs of zoonotic pathogens that pose significant risks to human health. These pathogens include hemorrhagic fever viruses (such as arenaviruses and hantaviruses), that can have a mortality rate of up to 81%1; plague and fever-causing bacteria (e.g., Yersinia pestis, Rickettsia, and Leptospira), which can lead to serious disease but are only sporadically detected in China2–9; and various endoparasites (e.g., Toxoplasma), that can infect humans but depend on rodents for part of their lifecycle10. Beyond the pathogens they carry, the public health risk posed by small mammals is amplified by their close contact with humans11–14. Indeed, despite significant improvements in hygiene and sanitation over the past two decades, pathogens associated with small mammals can be transmitted through direct animal contact, via their excretions, contaminated water and food, or through blood-sucking fleas and ticks3,9,15–18.

As small mammals can serve as reservoirs for important pathogens and sometimes closely interact with humans, modern pathogen discovery has increasingly focused on these animals. The goal of these studies is to identify the diversity of potential pathogens, although most have concentrated on viruses. Earlier studies using PCR assays with primers designed from conserved regions of viral genomes led to the discovery of numerous new rodent- and shrew-associated viruses within all major families of mammalian viruses, and which often formed host-associated virus clusters on phylogenetic trees19–25. The known diversity of viruses in these hosts was further expanded with the use of viral-particle enrichment metagenomics26–28 and meta-transcriptomics29–32. For example, 206 virus species were identified in a study of 3055 individuals from 50 rodent species, establishing the core virome of these mammals in China26. Similarly, a meta-transcriptomic survey of the shrew lung virome revealed more novel than known viruses33. Notably, some studies also discovered pathogens related to those that cause disease in humans, such as the Mojiang virus—a close relative of the Hendra and Nipah viruses discovered in an abandoned mine shaft26,34. Collectively, these studies provide a broad-scale investigation of the virome in rodents and shrews, expanding our understanding of viral diversity and identifying potential emerging human pathogens.

Large-scale sampling and sequencing has also greatly advanced studies of virus ecology and evolution32,35–37. These studies have compared viral diversity across regions and host species, revealing strong host-virus associations, with geographic factors playing a secondary role in shaping virus diversity26,33,38. Importantly, cross-species virus transmission has also been documented, commonly between different host species and less frequently among host families and orders39,40. Despite these findings, ecological comparison have generally been conducted at a broad scale, often lacking study designs that account for host species, geographic location, temporal distribution, and adequate replication across variables. Moreover, many studies have relied on samples pooled from multiple individuals, sometimes mixing host species within a single sample, thereby complicating the analysis of cross-species virus transmission. Additionally, the focus on viral diversity has overshadowed investigations into the presence and abundance of other pathogen types, such as bacteria and eukaryotic pathogens, leaving major gaps in our understanding of their ecological roles and interactions.

Herein, we describe a large-scale survey of small mammals and their associated pathogens in Guangdong province in southern China. Guangdong is located in the subtropical climate zone and boasts high biodiversity. Historically, Guangdong has been an epicenter for the emergence of SARS and other infectious disease outbreaks of importance41. Additionally, as a major gateway for both domestic and international trade, Guangdong often plays a major role in the spread of infectious diseases. Our study comprises a broad geographic survey conducted in winter, as well as a year-long monthly survey in two cities, encompassing small mammal populations from both domestic and rural settings. We perform meta-transcriptomic sequencing on individual lung, spleen, and gut samples from single animals. With these data in hand, we systematically compare mammalian pathogens across all major microbial groups–RNA viruses, DNA viruses, bacteria, and eukaryotic pathogens—examining their distribution across different organs, host species, geographic locations, and seasons. The data generated provide valuable insights into pathogen diversity under varying conditions, and reveal key factors driving pathogen evolution and transmission.

Results
Large-scale sampling across space, time, hosts, and tissues
We conducted a systematic survey of small mammals and their associated pathogens (i.e., those microbes, including viruses, known or likely to be associated with disease) across Guangdong province, China (Fig. 1a). The survey comprised two parts: the first involved winter-season sampling across nine regions in Guangdong province to compare pathogen diversity among geographic locations, while the second comprised year-round sampling at two specific sites—Anpu and Zhanjiang, both located in southwestern Guangdong and home to regional plague monitoring centers—to assess the impact of seasonal variation (Table 1). In total, 858 individual animals were collected. Sampling was conducted at both agricultural and residential settings within each region. As such, the sampled animals are considered representative of the terrestrial small mammal populations with potential relevance to human exposure.

Cox1 gene analysis identified nine species across six genera: Rattus, Bandicota, Berylmys, Mus, Niviventer, and Suncus, belonging to two mammalian orders—Rodentia and Eulipotyphla (Fig. 1b). R. norvegicus (Rattus norvegicus) was the most commonly sampled species (N = 240), followed by B. indica (Bandicota indica, N = 195), R. tanezumi (Rattus tanezumi, N = 151), S. murinus (Suncus murinus, N = 118), and R. losea (Rattus losea, N = 92). While species composition varied significantly among geographic locations, it remained relatively stable across different seasons (Fig. 1c, d, Table 1).

Microbes and pathogens revealed through meta-transcriptomics
We performed meta-transcriptomic sequencing of the lung, spleen, and gut tissues from all 858 individual animals to capture potential pathogens associated with respiratory, blood-borne, and gastrointestinal transmission routes. As each individual and tissue comprised a separate sequencing library, this resulted in the generation of 2408 sequencing libraries and 10.2 Tbp of total data. The median sequencing depth per library was 42.85 Gbp. From this dataset, we identified over 217 species of eukaryotic viruses, spanning 13 viral RNA supergroups and four DNA viral families (Supplementary Fig. 1). Anelloviridae exhibited the highest detection rate (30.4%), while the greatest viral species richness was observed in the Partiti-Picobirna (n = 68) and Narna-Levi (n = 33) supergroups (Supplementary Fig. 2). For bacteria, Cutibacterium, Pseudomonas, and Cupriavidus were the most prevalent genera in the lung and spleen, whereas Helicobacter, Prevotella, and Bacteroides predominated in the gut (Supplementary Fig. 3).

With respect to known and potential pathogens, we detected 80 species of potential mammalian RNA viruses, 20 potential mammalian DNA viruses, five pathogenic bacterial species, and 30 eukaryotic species associated with or closely related (>95% nucleotide identity at marker genes) to fungal and parasitic pathogens. These pathogens spanned 15 viral, four bacterial, and 17 eukaryotic families. Viruses from the families Picobirnaviridae42,43 and Anelloviridae44,45 were excluded as potential pathogens due to unknown hosts or unspecific pathogenicity. Consequently, the pathogen infectome of the sampled mammalian species, based on the tissues analyzed here, comprised 76 microbial species: 29 RNA viruses, 12 DNA viruses, five bacteria, and 30 eukaryotic pathogens (Fig. 2, Supplementary Figs. 1, 4–7 and Supplementary Data 1–3).

Newly identified pathogens and their positive rate landscape
Among the 41 potential mammalian viral pathogens discovered, 18 were newly identified species, with the remainder representing existing species (Supplementary Figs. 6, 7). Most novel viral pathogens were identified from the RNA virus family Arteriviridae (N = 5) and the DNA virus family Parvoviridae (N = 6). Notably, a novel orbivirus, provisionally named Guangdong rodent orbivirus, was divergent from all known orbiviruses, forming a distinct sister lineage (Supplementary Fig. 6). However, that this virus was identified in lung and spleen at a high abundance (up to 723 RPM) suggests that it is likely to be a bona fide mammalian virus. Additionally, novel members of the bacterial genus Bartonella and eukaryotic genera such as Giardia, Spironucleus, Brachylaima, and Hepatozoon were identified and confirmed through analyses of marker genes, occupying distinct positions on phylogenetic trees (Fig. 2, Supplementary Fig. 5 and Supplementary Data 4).

Pathogen positivity was defined as the proportion of individuals testing positive in at least one of three tissues—lung, spleen, or gut—out of the total number of individuals in each group. Overall, no significant differences in positivity rates were observed among the four pathogen types, although eukaryotic and RNA viral pathogens exhibited significantly higher richness than bacterial and DNA viral pathogens (Supplementary Fig. 8). Among RNA viruses, Guangdong rodent arterivirus 1 had the highest positive rate (9.2%), followed by zoonotic pathogens like Rat hepatitis E virus (9.0%), Wenzhou mammarenavirus (8.7%), Betacoronavirus HKU24 (4.9%), and Seoul orthohantavirus (3.5%). For DNA viruses, parvoviruses—particularly Rat minute virus 2a—exceeded 20% positivity, while adenoviruses were less common (2.8%) (Fig. 2, Supplementary Data 4). Eukaryotic pathogens showed a high overall positivity (40.8%, 259/634), with Pneumocystis (10%) and Angiostrongylus cantonensis (7.6%) among the most common (Fig. 2, Supplementary Data 4). Other notable parasites included Strongyloides, Nippostrongylus, Tritrichomonas, Trypanosoma, and Giardia. Zoonotic species such as Cryptosporidium ubiquitum and Babesia microti were also detected. In comparison, bacterial pathogens were less common, with Bartonella (6%) and Chlamydia (2.4%) being the most common (Fig. 2, Supplementary Data 4).

Tissue tropism suggests multiple transmission pathways
The mammal-associated pathogens identified here were associated with distinct tissue tropisms and potential transmission signatures. The gut exhibited the highest overall pathogen diversity (Fig. 3a, b), particularly for RNA viruses, bacteria, and eukaryotic parasites, while the spleen showed the lowest (Fig. 3c). DNA viral diversity, however, was comparable between the gut and spleen. Despite this, pathogen abundance did not always mirror pathogen diversity. In particular, lungs harbored the highest burden of eukaryotic pathogens, highlighting the importance of respiratory tissues in parasitic infections (Fig. 3c).

Systemic infections were less common: only 25.6% of pathogens were found across all three tissues, with RNA viruses the most common shared agents (Fig. 3d). Most pathogens exhibited clear tissue preferences. Specifically, enteric viruses (e.g., Picornaviridae, Caliciviridae, Coronaviridae) dominated the gut, Parvoviridae and Bartonella were enriched in the spleen, and several zoonotic parasites (e.g., Pneumocystis, Angiostrongylus, Trypanosoma) were concentrated in the lungs (Fig. 3d).

These patterns suggest distinct transmission routes—respiratory, fecal–oral, and circulatory—are shaped by pathogen-specific tissue tropism, although further studies are needed to confirm these pathways. The tissue enrichment of most important zoonotic pathogens (Fig. 3e) further underscores the need to consider tissue when assessing transmission risks.

Frequent host switching in small mammals underscores the risk of pathogen spillover
We first assessed pathogen diversity, measured by species richness, in each individual animal. Accordingly, individual animals carried a median of one pathogen species (range: 0-12) across the three tissue types sampled (Fig. 4a). Notably, based on the detection threshold set in this study (i.e., RPM>1 and additional criteria detailed in the “Methods” section), 30.3% of individual animals showed no detectable pathogens in the tissues sampled. Next, we investigated the factors shaping pathogen composition at the level of individual hosts. Among the variables tested, host phylogenetic relatedness had the strongest effect, accounting for 11.1% of the variation in shared microbial species across samples (Fig. 4b), while other factors had no significant impact. As expected, a significant negative correlation was observed between host genetic distance and the number of shared pathogens (Supplementary Fig. 9), indicating that closely related hosts tend to harbor more similar pathogen communities.

We further examined patterns of pathogen transmission across host species, genera, families, and orders. Cross-species transmission appeared to be the rule rather than the exception, with 65.8% of the pathogens detected showing the ability to cross host species barriers and 12.7% able to infect hosts from multiple mammalian orders (Fig. 4c, d). Notably, all the bacterial species identified were capable of infecting multiple host species, underscoring their broad host adaptability.

Pathogens detected in multiple host orders represent those with the broadest host ranges and, consequently, likely pose an elevated zoonotic risk. We identified 10 such cross-order pathogens, comprising two RNA viruses, three DNA viruses, two bacterial species, and three eukaryotic pathogens (Fig. 4c). Notably, eight of these cross-order pathogens were detected in more than two host species and exhibited consistently high positivity rates (Supplementary Fig. 10), highlighting their potential for widespread transmission. Statistical comparison using Fisher’s exact test revealed no significant differences among pathogen types at the species or genus levels, although rates of cross-order transmission were significantly higher for bacteria than for RNA viruses and eukaryotic pathogens (Fig. 4e).

Environmental and host factors shape pathogen dynamics and transmission trends
We next analyzed the biological and environmental factors that influence total pathogen richness (Fig. 5a). Geographic region explained the largest proportion of variation in pathogen richness (11.3%, Fig. 5b), followed by host species (6.7%, Fig. 5c), the sampling season (6.4%, Fig. 5d), and environmental variables (3.2%), with 72.4% of variation remaining unexplained. Notably, samples from B. indica, along with the towns of Maoming, were associated with higher pathogen richness (Fig. 5c).

We conducted a parallel analysis focusing specifically on known zoonotic pathogens (e.g., Seoul orthohantavirus, Bartonella kosoyi, Angiostrongylus cantonensis amongst others) to assess their immediate public health relevance. In contrast to total pathogen richness, zoonotic pathogen richness was primarily driven by host species (10.9%), with geographic region and season contributing less (2.8% and 0.6%, respectively) (Fig. 5e–h). Hotspot analysis revealed that Angiostrongylus cantonensis was more prevalent in B. indica, particularly in Jieyang and Heyuan, and during autumn (all p<0.01) (Fig. 5i). Seoul orthohantavirus was most frequently detected in R. norvegicus (all p<0.01) and during summer (p<0.05) (Fig. 5i). To further resolve spatial patterns, we controlled for season and host species and compared pathogen positivity rates across locations (Supplementary Fig. 11). This confirmed regional differences and revealed species-specific geographic trends, such as Wenzhou mammarenavirus being more common in B. indica from Anpu (29.3%) and in R. norvegicus from Zhanjiang (30.8%).

Methods
Sample collection
Small mammal samples, comprising rodents (Rodentia) and shrews (Eulipotyphla), were collected in Guangdong province, China, between 2021 and 2022 as part of the Guangdong CDC’s surveillance program for plague and hemorrhagic fever. Sampling was conducted across nine regions—Zhanjiang, Anpu, Maoming, Foshan, Heyuan, Jieyang, Shaoguan, Shenzhen, and Yunfu—each representing different geographical areas and natural habitats within the province. In most regions, collection occurred primarily during the late autumn and winter. However, in Anpu and Zhanjiang, sampling was conducted over the entire 12-month period of 2022.

Each sampling session lasted 3–4 consecutive days, depending on weather conditions, particularly the presence of rain. During each day of sampling, animals were captured using baited cages deployed in both agricultural and residential areas, with 200 cages allocated to each habitat type. A mixture of sweet potato, fried breadsticks, and cooked meat was used as bait to attract rodents. Residential trapping involved placing 2–3 cages at the corners of farmers’ homes, while 2–5 cages were deployed in brushwood or gullies of agricultural areas, with numbers adjusted according to field size. Cages were set at dusk and retrieved the following morning at sunrise. Captured animals were euthanized and then dissected to collect lung, spleen, and gut tissues, which were immediately preserved in RNA Stabilization Solution (ThermoFisher, USA), stored on dry ice, and later transferred to a –80°C freezer.

All protocols for sample collection and processing were reviewed and approved by the Ethics Committee of Sun Yat-sen University (SYSU-IACUC-MED-2021-B0123)

Sample processing, RNA extraction and sequencing
RNA extraction and sequencing were performed on 2408 tissue samples from 858 individual animals. Each sample, approximately ~5–8 mm in size, was homogenized in 600 μl of lysis buffer using a TissueRuptor (Qiagen, Germany), followed by total RNA extraction with the RNeasy Plus Mini Kit (Qiagen, Germany) according to the manufacturer’s protocol. Sequencing libraries were prepared using the MGIEasy RNA Library Prep Kit V3.0 (BGI, China). In brief, RNA was fragmented, reverse-transcribed, and converted into double-stranded cDNA. Unique dual-indexed cDNA molecules were circularized, and rolling-circle replication was employed to generate DNA nanoball (DNB)-based libraries. These libraries were then sequenced on the DNBSEQ T series platform (MGI, China), producing 150-bp paired-end metatranscriptomic reads. The target yield for each sample was 50 Gbp.

Processing of sequencing data
For each sequence data set, the majority of ribosomal RNA (rRNA) reads were initially removed using URMAP (version 1.0.1480)67. Adapters, duplicate and low-quality reads were filtered out using fastp (version 0.20.1, parameters: -q 20, -n 5, -l 50, -y, -c, -D)68. The reads with low complexity were removed using PRINSEQ++ (version 1.2, options: -lc_entropy=0.5 -lc_dust=0.5)69. Residual rRNA reads were further eliminated by mapping to the SILVA rRNA database (Release 138.1)70 using Bowtie2 (version 2.3.5.1)71. Unless otherwise specified, all software was run with default settings.

Molecular identification of host species
The identification of small mammal species was based on de novo assembled contigs containing the cox1 gene sequences. For each sample, open reading frames (ORFs) from the assembled contigs were extracted using ORFfinder (version 0.4.3)72 and compared to cox1 reference sequences from the NCBI RefSeq database using the blastn program (version 2.14.1) with an e-value threshold of 10-10. To ensure accuracy, the core cox1 domain (cd01660) was confirmed using RPSBLAST against the Conserved Domain Database (CDD). Reads were subsequently mapped back to the assembled cox1 sequences to remove assembly errors. To finalize species assignments, a phylogenetic tree incorporating cox1 sequences from this study, along with representative related sequences, was estimated using PHYML 3.0 (version 20120412), employing the GTR+F+Γ nucleotide substitution model with SPR branch-swapping73.

Discovery of viruses and viral pathogens
The remaining clean non-rRNA reads were assembled into contigs using MEGAHIT (version 1.2.8)74 with default settings and a minimum contig length of 300 bp. Assembled contigs were then searched against the NCBI nr database using DIAMOND blastx (version 2.0.14)75 with an e-value cutoff of 10−5 to balance high sensitivity and reduce false positives. Contigs were provisionally categorized based on the NCBI taxonomy of the best-matching protein, and viral-related contigs were extracted. Host-related regions in the viral contigs were removed by aligning them against the NCBI RefSeq genome database using the blastn program (version 2.14.1)76 with an e-value cutoff of 10−10. Viral identities were further confirmed by checking for the presence of specific marker genes: the RNA-dependent RNA polymerase (RdRp) for RNA viruses, the non-structural protein 1 (NS1) for the Parvoviridae, the hexon for the Adenoviridae, the capsid protein (CP) for the Circoviridae, and the ORF1 protein for the Anelloviridae. These marker proteins were then aligned and examined manually to ensure they contained the conserved motif(s) of the corresponding protein. New virus species were determined according to the species demarcation criteria established by the International Committee on Taxonomy of Viruses (ICTV) (Supplementary Data 5). To identify viruses associated with mammalian hosts, phylogenetic analyses were conducted for each virus supergroup (i.e., phylum or class level taxonomic groups). Only viral contigs that clustered within families known to infect mammals77 were classified as mammalian viral pathogens and retained for further analysis.

Discovery of bacterial and eukaryotic pathogens
The remaining contigs were screened against the Conserved Domain Database (CDD) using the rpsblast program (version 2.14.1)76, with an e-value cutoff of 0.01. We targeted contigs containing specific marker genes for identifying eukaryotic microbes, specifically cox1 (cd01663) and EF1a (cd01883), and bacteria, specifically ftsY (TIGR00064), GroEL (TIGR02348), nusG (TIGR00922), rplA (TIGR01169), rplC (TIGR03625), and rpoB (TIGR02013). To facilitate taxonomic identification and to remove false positives, the sequences of these marker genes were then compared against the nt and nr databases with e-values set to 10−10 and 10−5, respectively. For bacterial contigs, reads were mapped back to the homologous gene from the closest relative when applicable, or to the relevant contig if more distantly related, using Bowtie2 (version 2.3.5.1) in ‘end-to-end’ mode71. Species identification for both bacterial and eukaryotic microbes was then conducted through phylogenetic analyses involving these marker genes. Pathogenic microbes were identified based on their relationship to known bacterial and eukaryotic pathogens at the species and genus level.

Quantification of pathogen genomes/transcriptomes
To estimate pathogen abundance, reads were mapped to pathogen genomes (viruses and bacteria) or to a set of marker genes (for eukaryotes) using Bowtie2 (version 2.3.5.1, with end-to-end alignment). Pathogen abundance was measured as the number of reads mapped per million non-rRNA reads (RPM). Two criteria were applied to reduce potential false positives. First, index-hopping, which can occur during high-throughput sequencing when reads are misassigned between samples, was identified using the following rule: if the total read count for a specific virus in a given library was less than 0.1% of the highest read count for that virus in the same sequencing lane, it was considered a false positive due to index-hopping. Second, low-abundance pathogens (RPM<1) and those with low genome or gene coverage (i.e., less than 300 base pairs) were also likely to be false positives and were excluded35,78.

Phylogenetic analyses
To determine the phylogenetic relationships and taxonomy of newly identified pathogens, representative marker proteins or genes related to those identified in this study were downloaded from NCBI/GenBank. Phylogenetic trees were then estimated at the genus or family level. Sequences were first aligned using the L-INS-i algorithm in MAFFT (version 7.520)79. Maximum likelihood (ML) trees were inferred using PhyML 3.0 (version 20120412)73, with the GTR substitution model used for nucleotide sequence alignments and the LG model used for amino acid sequence alignments. To find the optimal tree topology, we employed the default subtree pruning and regrafting (SPR) topology search algorithm and branch length optimization.

Collection and processing of environmental data for epidemiological analyses
To assess how environmental factors shape pathogen diversity and composition, we collected climate, mammal richness, land-use, and Normalized Difference Vegetation Index (NDVI) data for each sampling location from publicly available sources. Climate data were obtained from TerraClimate80, utilizing 14 variables to evaluate their influence on rodent pathogens. Definitions of these variables are available at the TerraClimate website (https://www.climatologylab.org/terraclimate.html). Mammal richness and land-use data were obtained from the International Union for Conservation of Nature (IUCN)81, and China’s Multi-Period Land Use Land Cover Remote Sensing Monitoring Dataset (CNLUCC)82, respectively, while NDVI values were derived from publicly available remote sensing satellite data. To address co-linearity among the climate variables, we performed principal component (PC) analysis. The first three PCs—CPC1, CPC2, and CPC3—were used in subsequent statistical analyses, explaining 57.96%, 16.15%, and 11.40% of the total variance, respectively (cumulatively 85.51%). Based on the projection lengths of raw bioclimatic variables onto these PCs, we interpreted the components as follows:
(i) CPC1 primarily reflects negative correlations with temperature, shortwave radiation, evapotranspiration, and precipitation. Lower CPC1 values indicate higher temperatures and greater water evaporation.
(ii) CPC2 is mainly associated with wind and evapotranspiration. Higher CPC2 values suggest increased water evaporation and lower wind speeds, indicating a harsher thermal environment.
(iii) CPC3 captures precipitation variability, with higher values indicating greater fluctuation in precipitation levels.

Statistical methods
All statistical analyses were conducted using R version 4.3.1.

Assessing environmental and host factors influencing pathogen species richness
To investigate how environmental and host factors influence pathogen species richness, we applied generalized linear models using a negative binomial regression. The factors considered in the analysis included rodent species, environmental characteristics, date, and region of sample collection. Environmental characteristics comprised three principal components as described above (CPC1, CPC2, and CPC3), NDVI, and mammal richness. Model selection was performed based on the Akaike Information Criterion (AIC), evaluating all possible combinations of variables using the MuMIn package in R. The contribution of each variable to model performance was determined by comparing the deviance explained by the full model to that of models where individual variables were removed.

Analysis of pathogen composition and cross-species transmission
We quantified the pathogens shared among animal species and visualized the results using the ComplexUpset package. The pathogen-sharing network was initially constructed using the ggraph package in R, refined through manual adjustments, and visualized using the ggplot2 package. In addition, we investigated the factors influencing pathogen composition (or number of shared pathogens between individual animals) by applying generalized linear models (GLMs), considering host phylogenetic distance, climate variations (Euclidean distance), land use variations, and spatial distance. The effect of each factor was quantified through a model selection process similar to that used in earlier analyses.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Xin and colleagues explore a large dataset of rodent and shrew samples metagenomically, with a study design encompassing geographic, seasonal, host and tissue factors as potential predictors of pathogen composition. The study is notable for covering viral, bacterial and eukaryotic pathogens together which is not common and presents an interesting all-inclusive approach to metagenomic studies.\nThere's very little in terms of significant revisions I'd like to ask for which would include:\n1. I missed the part in the discussion that talks about the inherent uncertainty about the hosts of discovered pathogens. This seems particularly important for microbes found in the gut that could be infecting the last meal of the animal rather than the animal itself. I would say that's a huge caveat for interpreting any metagenomic study that a reader should keep in mind.\n2. While the authors made their data publicly available on CNGB and FigShare, I would ask that the authors submit their data to appropriate NCBI databases (Genbank, SRA, TSA, Bioproject, Biosample, etc.). This is crucial because CNGB is not integrated with tools like BLAST as far as I know, in addition to not having earned the same level of trust that NCBI enjoys in the research community.\n3. Use of TrimAl in phylogenetics. I remain unconvinced that stripping poorly aligned alignment columns adds anything beyond appealing to the human sense of aesthetics and mislead us into a false sense of robustness when we should be taking a step back and reconsidering analysing poor quality alignments in the first place. I would strongly advise the authors to split alignments into taxon sets whose alignments are less ambiguous rather than relying on TrimAl to give the impression that the alignments are fine.\n4. Some computational parts need improvement. Not all software has the versions specified. There's also some interesting, excessive and probably incorrect specifications on PhyML analyses, mainly in terms of the search that was carried out with SPR moves being mentioned which would imply that branch length optimisations and other topology moves were excluded. Some clarification would be good here.\n5. Excluding some viruses from analyses, specifically picobirnas and anellos. I understand the exclusion of picobirnas, on account of suspicions they're bacteriophages but I feel like that should be made explicit with appropriate literature cited, as it's not immediately clear to a naive reader on top of having a long literature footprint. I find the exclusion of anellos more mysterious and I suspect/hope there's some literature I'm not aware of. Either way, the exclusion of both groups should be more transparent and justified.\n6. Most analyses presented in the manuscript explore how pathogen diversity is shaped by various factors but I think one additional formal statistical analysis could enrich the study. The authors mention the numbers of each class of pathogen (RNA/DNA virus, bacteria, eukaryotes) jumping between orders for example, but a formal statistical test exploring relative risks of each pathogen class, even if insignificant, might be a beat addition. This is not a request, just something for the authors to consider.\nMinor notes:\n7. Paragraph starting at line 152: I would suggest adding fractions (e.g. 1/10) for detection rates and binomial confidence intervals for likely prevalence.\n8. This is now a persistent issue for reviewers. Every journal publishing virus-related content should clarify their position regarding the nomenclature put forward by the International Committee on Taxonomy of Viruses (ICTV) soon. Criticism has been leveled against ICTV taxonomy by many members of the research community both in terms of methodological approaches used to justify the deeper classification of viral taxa and, more pertinent to this manuscript, the use of Latin binomial names. I honestly have no idea what \"Orthohantavirus seoulense\" is and any taxonomic rank lower than family is obscure, confusing and might easily change to something equally ridiculous with every new ICTV taxonomy update, leaving this manuscript uninterpretable to future readers, i.e. the exact opposite of what ICTV set out to do. I won't ask the authors to change anything regarding this note, just something for them and the editor to keep in mind going forward.\n9. Related to ICTV issue raised earlier. Authors say they use ICTV species demarcation criteria to determine if a virus is new or not, yet no specific versions of ICTV documents are mentioned (which are likely to change in the future by the way) nor the criteria set out in them.\n10. Also related to ICTV. The terms \"virus\" and \"species\" are used interchangeably throughout the manuscript. Very minor and doesn't require fixing but I'll mention it in case the authors want to be consistent.", "reviewer_2": "In this manuscript, Xin et al. study the infectome of small mammals in Guangdong province to better understand transmission dynamics and key mammalian reservoirs. The authors sampled 858 animals, and in total 2,408 samples were taken. They identify 76 pathogens of which 33 are newly discovered. Although this in itself is an impressive feat, the results are more often than not presented without any conclusions. This makes the manuscript very descriptive (“data-dump”) and this should be improved. The authors should also focus on the rationale for doing a particular analysis. Finally, the work lacks a more human anthropological approach to further bolster the legitimate risk to human health. This would greatly improve the impact of this study.\nMajor comments:\nSection titles should reflect what was found and what it means, not simply a description of what was done.\nL121. What was the rationale for picking Anpu and Zhanjiang for the seasonal sampling?\nL123-129. Does the sampling reflect the species abundance in the region? Were the exact same sites sampled in summer and winter?\nL147-148. How are mammalian vs non-mammalian vs vertebrate associated defined here? Is an arbovirus considered mammalian?\nL141-168. This section is very descriptive, and unclear what it all means. The authors don’t draw any conclusions, but simply dump the data on the reader. Yes, we find pathogens in small mammals, but that is not really surprising. How do these findings compare to the infectome of other animals/mammals?\nL193. “30.3% of the individuals were pathogen-free, based on the detection threshold set in this study”. What was the threshold? Independent of a threshold, this study wasn’t set up to make any claims on the absence of pathogens. Not all body compartments were sampled.\nL194-197. Again, what does this mean? This section is very descriptive.\nL198-207. How identical were the viruses found in multiple hosts? How recently did these cross-species transmissions occur? Can co-speciation be excluded?\nL207. “ suggesting a heightened risk to public health”. Why does this increase risk? The vast majority of cross-species transmission here seems to occur between rats that are relatively closely related. We don’t know anything about the ability of these viruses to infect humans.\nFig 3e and L185-188. How did the authors determine the most likely transmission route? The numbers also don’t add up to 12. So some have multiple likely transmission routes? SARS-CoV-2 is found in human feces as well, even though it is not an epidemiologically relevant transmission route, so I think these findings should be interpreted with caution. In addition, since this study only looks at RNA, we don’t know if they are infectious particles or not.\nDid the authors normalize for differences in tissue size that was used for library preparation? If there are more cells in lung tissue vs gut, the pathogen RPM will automatically go down because more host RNA will be sequenced.\nFig5. Were the sample locations the same in summer and winter?\nL251-252. How do the authors come to the conclusion that the risk is significant?\nMinor comments:\nFig2.Order viruses by abundance and reduce the color coding. I’m all for colors, but 3 different color palettes are just distracting and unnecessary.\nFig 3d. Use an alpha to make the box plots more visible.\nL191-193, Fig 4a. Average doesn’t seem to be the correct metric for a long-tailed distribution.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "General comments\nThe authors present results from a large-scale metatranscriptomic analysis of samples from small mammals in Guangdong, China. Overall, the study presents a valuable description at the diversity of microbes detected in a broad survey of rats, mice and shrews, comparing detection rates among species, locations, and time of year. A major weakness of the manuscript is that the sampling methodology and rationale are not clearly presented. The data is presented as an epidemiological analysis, when more accurately is a survey showing aggregated detection rates of viral, bacterial and parasitic sequences from a variety of small mammals. The sample sizes, presented in a supplemental table, are very small, which limits the ability to estimate prevalence and the power to compare prevalence rates among species and sampling point. By aggregating all samples from a species or tissue type, numbers are improved, but the authors misstate this as prevalence, which in an epidemiological sense is the proportion of infected individuals within a population at a given time. They’re more accurately describing detection rate - the number of positive samples per total samples tested. It’s never clear in the paper whether the denominator is the number of samples or the number of animals that are positive. The detections provide important information, but the data presented don’t support the author’s assessment of risk with regard to zoonotic transmission.\nThe paper would be improved by including prevalence calculations based on sample sets from specific locations and species collected at the same time, and comparing prevalence among species from different sampling points. In terms of sampling methods, it is also unclear why the authors chose only to collect and screen three specific tissue types and not others. I recognize that these studies take a lot of work and resources, but including additional tissues, such as kidneys, for example, would have added a major system also involved in viral excretion to the assessment. Given that more than 800 animals were euthanized, it seems that the study could have done more to maximize the value of each animal used. Lastly, the decision to only include viruses related to families with members known to infect mammals would seem to exclude microbes that could be important to document, even if their pathogenicity is uncertain. This reflects ascertainment bias that limits the power of this metatranscriptomic study. I think the paper would be more impactful if the authors could include the full scope of microbes detected, and then they could emphasize the ones related to known pathogens (they cite a paper that does just this ( ref #45)). Also, viruses related to known pathogens aren’t necessarily pathogenic, and it may be worth including this concept in the discussion. Lastly, a brief discussion of what environmental niche each target host species occupies would help contextualize the findings. In other words, which of these animals is most likely to interact with people (or livestock)? Rats, bandicoots, shrews? And what direction for future studies does this study give? Would authors suggest more targeted epidemiological studies that try to get more accurate prevalence data? Or to characterize how much contact these animals have with people - both would contribute to a more robust understanding of risk of transmission of known or potential zoonotic agents.\nSpecific comments are presented below.\nLine 46: Is it live or spleen? Further in the results spleen is referred to as one of the three tissue types.\nWhy was the urinary system (e.g. kidneys) excluded? Were liver samples tested al all? Results section mentions lung, liver and gut but further analysis includes Spleen - please clarify\nLIne 117: The survey methods should be more detailed. It is not clear how systematic the survey was. How many traps, trapping hours per day/night and how many days / nights were spent at each location or during each season?\n122: this description belongs in methods. How did the authors define residential and agricultural sampling sites? I think a table showing sample dates, location and numbers and environment should be included in the main body of the paper, not the supplement. Without this as a reference, it’s hard to interpret the results and the figures.\nLines: 152-155: Are the prevalences reported describing % of samples with detections, or % of individuals positive within a population? What do the numbers look like when broken down by sample timepoint? Also, what does “relatively high prevalence” refer to? Compared to what? Are there baseline figures for these viruses or parasites from the literature?\nI suggest that the authors generally refrain from subjective language such as “relatively high” prevalence or \"similarly high” prevalence, without statistical substantiation.\nI also suggest that the authors refer to the percentage positive data as detection rates, rather than prevalence to avoid confusion.\nLIne 172: In the abstract, liver, not spleen, is specified as a tissue type. Please clarify.\n212-213: What are the environmental factors mentioned here? Are these the climate variables used in the models, or the environment of the samples (farm vs residential)?\nLINE 214-215: Looking at the figure, there is overlap among all the CIs, suggesting that there isn’t much difference among the data points. I don’t think the authors can conclude that there’s a seasonal effect on infection rates from these data.\n221-222: It’s not clear now the climate data were used in the analysis - no climate specific results seem to be presented other than season of sampling. Figure 5a shows the size of the effect of environmental data - this should be included in the discussion.\nLine 256: rephrase to among the most prevalence pathogens found in this study. If the authors are referring to known prevalence data, they should cite the study.\nLIne 258: Are humans screened for Wenzhou mammarenavirus? How do you know it’s rare? Perhaps it hasn’t been reported because it isn’t tested for.\n277-279: It might be worth mentioning whether these parasites threaten livestock health, since many of the sampled species occur around farms.\nLines 307-311: The authors should discuss the statistical limitations of estimating and comparing infection rates across location and time based on the small sample sizes. Further, although the authors acknowledge the limited scope of including only presumptive pathogens, they do not address why they didn’t include a broader set of data from the metagenomic screening. Similarly, the authors should explain why they did not include a more complete set of tissue types.\nTables / Figures:\nI suggest including a table showing the sampled species, number of individuals by location and date as part of the main paper. It may help the reader interpret the figures.\nFigure 1d - I suggest putting the temporal species composition data in chronological order"}, "author_response": "Major comment\nReviewer #1 Comment #1. I missed the part in the discussion that talks about the inherent uncertainty about the hosts of discovered pathogens. This seems particularly important for microbes found in the gut that could be infecting the last meal of the animal rather than the animal itself. I would say that's a huge caveat for interpreting any metagenomic study that a reader should keep in mind.\nResponse: We thank the reviewer for this thoughtful comment. Our study focused specifically on pathogens infecting mammals, which effectively excludes the majority of microbes associated with other potential sources such as food, parasites, or co-inhabiting microorganisms. However, we acknowledge that in the case of gut samples, it is difficult to entirely exclude the presence of dietary material originating from other mammals, although such occurrences are likely rare. We have revised the relevant text accordingly in lines 329–333, which now reads:\n“Second, although our study focused on mammal-associated viruses—thereby excluding most microbes originating from food, parasites, or co-inhabiting organisms—we cannot entirely exclude the possibility of dietary material from mammalian sources being present in gut samples. This limitation should be taken into account when interpreting the results.”\nReviewer #1 Comment #2. While the authors made their data publicly available on CNGB and FigShare, I would ask that the authors submit their data to appropriate NCBI databases (Genbank, SRA, TSA, Bioproject, Biosample, etc.). This is crucial because CNGB is not integrated with tools like BLAST as far as I know, in addition to not having earned the same level of trust that NCBI enjoys in the research community.\nResponse: We agree. The sequencing data have been deposited in public databases, including placing the raw sequencing reads in the NCBI Sequence Read Archive (SRA) under BioProject ID PRJNA1238520 and the assembled pathogen genome sequences in the NCBI GenBank database under accession numbers PV605111–PV605345. The revised Data Availability section now reads:\n“All sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject ID PRJNA1238520, and in the China National GeneBank (CNGB) under Sequence Archive ID CNP0004378. The assembled pathogen genomic sequences have been submitted to the NCBI GenBank database under accession numbers PV605111–PV605345.”\nReviewer #1 Comment #3. Use of TrimAl in phylogenetics. I remain unconvinced that stripping poorly aligned alignment columns adds anything beyond appealing to the human sense of aesthetics and mislead us into a false sense of robustness when we should be taking a step back and reconsidering analysing poor quality alignments in the first place. I would strongly advise the authors to split alignments into taxon sets whose alignments are less ambiguous rather than relying on TrimAl to give the impression that the alignments are fine.\nResponse: Following the reviewer’s recommendation, we reconstructed the viral family-level phylogeny without trimming low-quality regions from the alignment. The resulting topology was highly similar to the original tree. We have used these updated phylogenies to re-generate Supplementary Fig. 6 and 7.\nReviewer #1 Comment #4. Some computational parts need improvement. Not all software has the versions specified. There's also some interesting, excessive and probably incorrect specifications on PhyML analyses, mainly in terms of the search that was carried out with SPR moves being mentioned which would imply that branch length optimisations and other topology moves were excluded. Some clarification would be good here.\nResponse: Based on reviewer’s recommendation, we have now updated the versions of the software in the revised manuscript. We also thank the reviewer for pointing out the ambiguity in our description of the phylogenetic analysis. To clarify, we used PhyML with both subtree pruning and regrafting (SPR) topology search and branch length optimization (the default options). We have revised the text in the Methods section to clarify this.\nReviewer #1 Comment #5. Excluding some viruses from analyses, specifically picobirnas and anellos. I understand the exclusion of picobirnas, on account of suspicions they're bacteriophages but I feel like that should be made explicit with appropriate literature cited, as it's not immediately clear to a naive reader on top of having a long literature footprint. I find the exclusion of anellos more mysterious and I suspect/hope there's some literature I'm not aware of. Either way, the exclusion of both groups should be more transparent and justified.\nResponse: For picobirnaviruses, Wang et al. (2022) noted in their review that the pathogenicity of these viruses in humans remains unclear. More recent studies further suggest that their true hosts may be microbial species within the gut (Sadiq et al., Virus Evolution, 2024).\nFor anelloviruses, although viral sequences have been detected in multiple tissues, no specific pathogenic effects have been definitively established (Liang et al., Nature Reviews Microbiology, 2021; Kaczorowska et al., FEMS Microbiology Reviews, 2020).\nThese references have now been cited in the revised manuscript to clarify the current understanding of their host specificity and pathogenic potential.\nReviewer #1 Comment #6. Most analyses presented in the manuscript explore how pathogen diversity is shaped by various factors but I think one additional formal statistical analysis could enrich the study. The authors mention the numbers of each class of pathogen (RNA/DNA virus, bacteria, eukaryotes) jumping between orders for example, but a formal statistical test exploring relative risks of each pathogen class, even if insignificant, might be a beat addition. This is not a request, just something for the authors to consider.\nResponse: We agree with the reviewer and have compared the cross-species, genus-level, and order-level host range of different types of pathogens. The corresponding results have been added and can be found in lines 230–233:\n“Statistical comparison using Fisher’s exact test revealed no significant differences among pathogen types at the species or genus levels, although cross-order transmission rates were significantly higher for bacteria than for RNA viruses and eukaryotic pathogens (Fig. 4e).”\nMinor notes:\nReviewer #1 Comment #7. Paragraph starting at line 152: I would suggest adding fractions (e.g. 1/10) for detection rates and binomial confidence intervals for likely prevalence.\nResponse: Changed as suggested.\nReviewer #1 Comment #8. This is now a persistent issue for reviewers. Every journal publishing virus-related content should clarify their position regarding the nomenclature put forward by the International Committee on Taxonomy of Viruses (ICTV) soon. Criticism has been leveled against ICTV taxonomy by many members of the research community both in terms of methodological approaches used to justify the deeper classification of viral taxa and, more pertinent to this manuscript, the use of Latin binomial names. I honestly have no idea what \"Orthohantavirus seoulense\" is and any taxonomic rank lower than family is obscure, confusing and might easily change to something equally ridiculous with every new ICTV taxonomy update, leaving this manuscript uninterpretable to future readers, i.e. the exact opposite of what ICTV set out to do. I won't ask the authors to change anything regarding this note, just something for them and the editor to keep in mind going forward.\nResponse: We fully agree with the reviewer on this point. All virus names have been updated to follow the naming conventions commonly recognized by the virology community. For example, Orthohantavirus seoulense has been changed to Seoul orthohantavirus.\nReviewer #1 Comment #9. Related to ICTV issue raised earlier. Authors say they use ICTV species demarcation criteria to determine if a virus is new or not, yet no specific versions of ICTV documents are mentioned (which are likely to change in the future by the way) nor the criteria set out in them.\nResponse: We have provided information on the version of ICTV documents in Supplementary Table 4.\nReviewer #1 Comment #10. Also related to ICTV. The terms \"virus\" and \"species\" are used interchangeably throughout the manuscript. Very minor and doesn't require fixing but I'll mention it in case the authors want to be consistent.\nResponse: We have revised the text to consistently use the term “species” throughout the manuscript for clarity and uniformity.\nReviewer #2 (Remarks to the Author):\nGeneral Comments:\nReviewer #2 General Comment #1 Although this in itself is an impressive feat, the results are more often than not presented without any conclusions. This makes the manuscript very descriptive (“data-dump”) and this should be improved.\nResponse: We acknowledge that some parts of our conclusions were not clearly articulated, which may have contributed to the impression that the manuscript is largely descriptive. While establishing a baseline of pathogen diversity, abundance, and prevalence is essential for studies of this type, we have extensively revised the manuscript to better emphasize our key findings. This includes streamlining descriptive content and highlighting the main conclusions with clearer interpretive statements.\nReviewer #2 General Comment #2 The authors should also focus on the rationale for doing a particular analysis.\nIn each section of the Results, we have now provided a clear rationale for each analysis, explaining the purpose of performing it or the specific question it aims to address.”\nReviewer #2 General Comment #3 Finally, the work lacks a more human anthropological approach to further bolster the legitimate risk to human health. This would greatly improve the impact of this study.\nResponse: We agree with the reviewer’s suggestion. Accordingly, at the end of the Results section, we have added a dedicated paragraph evaluating the potential threat of known zoonotic pathogens, including their host associations, seasonal and geographic hotspots, and the key ecological drivers behind their distribution. This addition also provides direct insights into strategies for disease control and prevention. The revised text can be found in lines 242–253, as follows:\n“We conducted a parallel analysis focusing specifically on known zoonotic pathogens (e.g., Seoul orthohantavirus, Bartonella kosoyi, Angiostrongylus cantonensis amongst others) to assess their immediate public health relevance. In contrast to total pathogen richness, zoonotic pathogen richness was primarily driven by host species (10.9%), with geographic region and season contributing less (2.8% and 0.6%, respectively) (Fig. 5e-h). Hotspot analysis revealed that A. cantonensis was more prevalent in B. indica, particularly in Jieyang and Heyuan, and during autumn (all p < 0.01). Seoul orthohantavirus was most frequently detected in R. norvegicus (all p < 0.01) and during summer (p < 0.05). To further resolve spatial patterns, we controlled for season and host species and compared pathogen positivity rates across locations (Supplementary Fig. 11). This confirmed regional differences and revealed species-specific geographic trends, such as Wenzhou mammarenavirus being more common in B. indica from Anpu (29.3%) and in R. norvegicus from Zhanjiang (30.8%).”\nMajor comments:\nReviewer #2 Major Comment #1. Section titles should reflect what was found and what it means, not simply a description of what was done.\nResponse: We agree and have changed the section titles accordingly.\nReviewer #2 Major Comment #2. L121. What was the rationale for picking Anpu and Zhanjiang for the seasonal sampling?\nResponse: Anpu and Zhanjiang were selected because they host regional plague monitoring centers, where routine pathogen surveillance is part of their long-term public health responsibilities. Leveraging their existing infrastructure and expertise allowed for systematic and consistent seasonal sampling. We have clarified this point in the manuscript (lines 122–124), which now reads:\n“…while the second comprised year-round sampling at two specific sites —Anpu and Zhanjiang, both located in southwestern Guangdong and home to regional plague monitoring centers—to assess the impact of seasonal variation (Table 1)”\nReviewer #2 Major Comment #3. L123-129. Does the sampling reflect the species abundance in the region? Were the exact same sites sampled in summer and winter?\nResponse: The sampling reflects the natural species abundance in the region, as small mammals were captured using mouse cages that trap animals randomly. The corresponding context has been revised in lines 125-128, to read:\n“Sampling was conducted without bias, covering both agricultural and residential settings within each location. As such, the sampled animals are considered representative of the terrestrial small mammal populations with potential relevance to human exposure.”\nFurthermore, it should be noted that the summer and winter samples were not collected from exactly the same sites, but rather from locations within the same town-level region. The precise latitude and longitude coordinates of each sampling site are provided in the supplementary materials for reference.\nReviewer #2 Major Comment #4. L147-148. How are mammalian vs non-mammalian vs vertebrate associated defined here? Is an arbovirus considered mammalian?\nResponse: Mammalian-associated viruses are defined based on their phylogenetic placement, typically forming monophyletic clusters at the group, genus, or family levels. Arboviruses—such as mosquito-borne flaviviruses, tick-borne flaviviruses, alphaviruses, and phleboviruses—are included in this category, as they also cluster monophyletically and infect mammals.\nReviewer #2 Major Comment #5. L141-168. This section is very descriptive, and unclear what it all means. The authors don’t draw any conclusions, but simply dump the data on the reader. Yes, we find pathogens in small mammals, but that is not really surprising. How do these findings compare to the infectome of other animals/mammals?\nResponse: We agree that this section was overly descriptive. Accordingly, we have substantially revised it to reduce the descriptive content and emphasize the key findings. The revised text can be found in lines 171–186, to read:\n“Pathogen positivity was defined as the proportion of individuals testing positive in at least one of three tissues—lung, spleen, or gut—out of the total number of individuals in each group. Overall, eukaryotic and RNA viral pathogens exhibited significantly higher Richness compared to bacterial and DNA viral pathogens (Supplementary Fig. 8). Among RNA viruses, Guangdong rodent arterivirus 1 had the highest prevalence (9.2%), followed by zoonotic pathogens like Rat hepatitis E virus (9.0%), Wenzhou mammarenavirus (8.7%), Betacoronavirus HKU24 (4.9%), and Seoul orthohantavirus (3.5%). For DNA viruses, parvoviruses—particularly Rat minute virus 2a—exceeded 20%, while adenoviruses were less common (2.8%) (Fig. 2, Table S3). Eukaryotic pathogens showed a high overall prevalence (40.8%, 259/634), with Pneumocystis (10%) and Angiostrongylus cantonensis (7.6%) among the most common (Fig. 2, Supplementary Table 3). Other notable parasites included Strongyloides, Nippostrongylus, Tritrichomonas, Trypanosoma, and Giardia. Zoonotic species such as Cryptosporidium ubiquitum and Babesia microti were also detected. In comparison, bacterial pathogens were less common, with Bartonella (6%) and Chlamydia (2.4%) being the most prevalent (Fig. 2, Supplementary Table 3).”\nReviewer #2 Major Comment #6. L193. “30.3% of the individuals were pathogen-free, based on the detection threshold set in this study”. What was the threshold? Independent of a threshold, this study wasn’t set up to make any claims on the absence of pathogens. Not all body compartments were sampled.\nResponse: Thank you for pointing this out. The detection threshold is now also explicitly stated in the main text. Additionally, the original claim regarding the “absence of pathogens” has been clarified to indicate that it refers only to the three sampled tissue types. This clarification has been incorporated into the revised text in lines 210–212, which now reads:\n“Notably, based on the detection threshold set in this study (i.e., RPM > 1 and additional criteria detailed in the Methods section), 30.3% of individual animals showed no detectable pathogens in the tissues sampled.”\nReviewer #2 Major Comment #7. L194-197. Again, what does this mean? This section is very descriptive.\nPathogens detected across multiple host orders represent those with the broadest host ranges and therefore pose a higher potential risk for cross-species transmission and zoonotic spillover. In response to the reviewer’s comment, we have extensively revised the relevant section to better emphasize the public health implications of broad host range pathogens. The updated text can be found in lines 225–233, which now reads:\n“Pathogens detected in multiple host orders represent those with the broadest host ranges and, consequently, likely pose an elevated zoonotic risk. We identified 10 such cross-order pathogens, comprising two RNA viruses, three DNA viruses, two bacterial species, and three eukaryotic pathogens (Fig. 4c). Notably, eight of these cross-order pathogens were detected in more than two host species and exhibited consistently high positivity rates (Supplementary Fig. 10), highlighting their potential for widespread transmission. Statistical comparison using Fisher’s exact test revealed no significant differences among pathogen types at the species or genus levels, although cross-order transmission rates were significantly higher for bacteria than for RNA viruses and eukaryotic pathogens (Fig. 4e).”\nReviewer #2 Major Comment #8. L198-207. How identical were the viruses found in multiple hosts? How recently did these cross-species transmissions occur? Can co-speciation be excluded?\nResponse: We applied established pathogen species demarcation criteria to determine whether pathogens detected in different hosts belonged to the same species. Accordingly, the viruses identified across multiple host species were classified as the same viral species and exhibited close genetic relatedness. This high degree of similarity suggests that their presence is unlikely to result from host–virus co-divergence.\nReviewer #2 Major Comment #9. L207. “ suggesting a heightened risk to public health”. Why does this increase risk? The vast majority of cross-species transmission here seems to occur between rats that are relatively closely related. We don’t know anything about the ability of these viruses to infect humans.\nResponse: Here, we are referring to “cross-order transmission,” which indicates that a pathogen is shared not only among different host species, but among mammalian hosts belonging to different taxonomic orders. This suggests that the pathogen has the ability to infect a broad range of mammals with distinct genetic backgrounds. As humans belong to a different order from rodents, the ability of a virus to cross host orders implies an elevated risk of zoonotic transmission. We have added this clarification in lines 225–226, which now reads:\n“Pathogens detected in multiple host orders represent those with the broadest host ranges and, consequently, likely pose an elevated zoonotic risk.”\nReviewer #2 Major Comment #10. Fig 3e and L185-188. How did the authors determine the most likely transmission route? The numbers also don’t add up to 12. So some have multiple likely transmission routes? SARS-CoV-2 is found in human feces as well, even though it is not an epidemiologically relevant transmission route, so I think these findings should be interpreted with caution. In addition, since this study only looks at RNA, we don’t know if they are infectious particles or not.\nResponse: In this section, we aimed to illustrate tissue tropism using RNA sequencing data as an indicator of potential sites of active pathogen replication. While the presence of abundant viral RNA in a given tissue may suggest possible transmission routes, we agree with the reviewer that detection alone does not provide definitive evidence of transmission pathways. In response, we have removed the original sentence and revised the wording to take a more cautious tone, as reflected in lines 202–204, which now reads:\n“These patterns suggest distinct transmission routes – respiratory, fecal–oral, and circulatory – are shaped by pathogen-specific tissue tropism, although further studies are needed to confirm these pathways.”\nIn addition, we acknowledge the reviewer’s observation that the numbers did not sum to 12. This discrepancy arose because some pathogens exhibit tropism for multiple tissues, as correctly noted. The related phrase has now been removed.\nReviewer #2 Major Comment #11. Did the authors normalize for differences in tissue size that was used for library preparation? If there are more cells in lung tissue vs gut, the pathogen RPM will automatically go down because more host RNA will be sequenced.\nResponse: We thank the reviewer for raising this important point. For library preparation, we used approximately the same tissue mass for lung, spleen, and gut samples. While we did not quantify the number of host cells in each tissue type, we believe this is unlikely to substantially affect the relative quantification of pathogen RNA. Our approach reflects the abundance of pathogen RNA per unit of tissue, rather than per host cell. Therefore, even if cellular density varies across tissues, the RPM-based comparison still provides a meaningful measure of how much pathogen RNA is present relative to the sampled tissue volume.\nReviewer #2 Major Comment #12. Fig5. Were the sample locations the same in summer and winter?\nResponse: The sampling reflects species abundance in the region, as small mammals were captured using mouse cages that were randomly placed. It should be noted that the summer and winter samples were not collected from the exact same sites, but all sites were located within the same town-level area. The precise latitude and longitude of each sampling site are provided in the Supplementary Table 1.\nReviewer #2 Major Comment #13. L251-252. How do the authors come to the conclusion that the risk is significant?\nResponse: We have revised the text in lines 273–274, which now reads:\n“We identified 14 zoonotic pathogens in the small mammals sampled, although most of the pathogens identified were previously known species.”\nMinor comments:\nReviewer #2 Manor Comment #1. Fig2. Order viruses by abundance and reduce the color coding. I’m all for colors, but 3 different color palettes are just distracting and unnecessary.\nResponse: Changed as suggested.\nReviewer #2 Manor Comment #2. Fig 3d. Use an alpha to make the box plots more visible.\nResponse: The limited visibility in Fig. 3d was likely due to a large number of zero values. To improve clarity and better represent the data distribution, we have replaced the box plot with a violin plot. This change enhances the visualization while retaining key information.\nReviewer #2 Manor Comment #3. L191-193, Fig 4a. Average doesn’t seem to be the correct metric for a long-tailed distribution.\nResponse: Thank you for the insightful comment. As suggested, we have replaced the mean with the median to better represent the central tendency of the long-tailed distribution. The corresponding text and figure have been updated accordingly.\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse: We thank the reviewer and their team for their expertise and valuable feedback.\nReviewer #4 (Remarks to the Author):\nGeneral comments\nReviewer #4 General Comment #1 A major weakness of the manuscript is that the sampling methodology and rationale are not clearly presented. The data is presented as an epidemiological analysis, when more accurately is a survey showing aggregated detection rates of viral, bacterial and parasitic sequences from a variety of small mammals. The sample sizes, presented in a supplemental table, are very small, which limits the ability to estimate prevalence and the power to compare prevalence rates among species and sampling point. By aggregating all samples from a species or tissue type, numbers are improved, but the authors misstate this as prevalence, which in an epidemiological sense is the proportion of infected individuals within a population at a given time. They’re more accurately describing detection rate - the number of positive samples per total samples tested. It’s never clear in the paper whether the denominator is the number of samples or the number of animals that are positive. The detections provide important information, but the data presented don’t support the author’s assessment of risk with regard to zoonotic transmission.\nResponse:\n1. Regarding sampling methodology: We agree that the sampling methodology and rationale were not sufficiently detailed in the original text. To address this, we have expanded the description in the Results section to provide a clearer explanation of the study design and its underlying objectives. The revised text can be found between lines 120-128:\n“The survey comprised two parts: the first involved winter-season sampling across nine regions in Guangdong province to compare pathogen diversity across geographic locations, while the second comprised year-round sampling at two specific sites —Anpu and Zhanjiang, both located in southwestern Guangdong and home to regional plague monitoring centers—to assess the impact of seasonal variation (Table 1). In total, 858 individual animals were collected. Sampling was conducted without bias, covering both agricultural and residential settings within each region. As such, the sampled animals are considered representative of the terrestrial small mammal populations with potential relevance to human exposure.”\nAnd also between lines 138-140:\n“We performed meta-transcriptomic sequencing of the lung, spleen, and gut tissues from all 858 individual animals to capture potential pathogens associated with respiratory, blood-borne, and gastrointestinal transmission routes.”\nFurther technical details regarding the sampling procedure have been included in the Methods section, now revised to read:\n“Each sampling session lasted 3–4 consecutive days, depending on weather conditions, particularly the presence of rain. During each day of sampling, animals were captured using baited cages deployed in both agricultural and residential areas, with 200 cages allocated to each habitat type. Residential trapping involved placing 2–3 cages at the corners of farmers’ homes, while 2–5 cages were deployed in brushwood or gullies of agricultural areas, with numbers adjusted according to field size. Cages were set at dusk and retrieved the following morning at sunrise. Captured animals were euthanized, and were then dissected to collect lung, spleen, and gut tissues, which were immediately preserved in RNA Stabilization Solution (ThermoFisher, USA), stored on dry ice, and later transferred to a –80°C freezer.”\n2. Regarding the sample size: We collected a total of 858 individual animals and performed sequencing on three tissue types per animal, generating 2,574 sequencing libraries (which is a huge number for a metagenomic study). As we surveyed the natural population without targeted bias toward specific host species or regions, the overall sample size is substantial (and the largest of its type). However, we acknowledge the reviewer’s point that, when stratified by species and location, the sample size for certain combinations is relatively limited. We have now addressed this limitation in the ‘Limitations’ paragraph of the Discussion, as follows:\n“Finally, our stratification by species and location leads to relatively small sample sizes for certain combinations. Assessing disease prevalence and associated risks in these specific host groups or regions will therefore require more extensive sampling in future studies.”\nWhile this limitation is to some extent unavoidable, we agree that the rates reported are more accurately described as 'positive rates' rather than 'prevalence rates.' The relevant sections of the manuscript have been revised to reflect this clarification.\n3. Regarding how the “positive rate” is estimated: We apologize for not defining the 'positive rate'. This definition has now been added to the Results section and reads as follows:\n“Pathogen positivity was defined as the proportion of individuals testing positive in at least one of three tissues—lung, spleen, or gut—out of the total individuals in each group.”\nReviewer #4 General Comment #2 The paper would be improved by including prevalence calculations based on sample sets from specific locations and species collected at the same time, and comparing prevalence among species from different sampling points. In terms of sampling methods, it is also unclear why the authors chose only to collect and screen three specific tissue types and not others. I recognize that these studies take a lot of work and resources, but including additional tissues, such as kidneys, for example, would have added a major system also involved in viral excretion to the assessment. Given that more than 800 animals were euthanized, it seems that the study could have done more to maximize the value of each animal used.\nResponse:\n1. Regarding the prevalence calculations: In response to the reviewer’s suggestion, we have compared detection rates across different locations while controlling for time period and host species. The results are now presented in the new Supplementary Fig. 11 and Figure 5 which provides a clearer picture of the spatial patterns of disease prevalence. The Corresponding descriptions can be found in lines 242-253:\n“We conducted a parallel analysis focusing specifically on known zoonotic pathogens (e.g., Seoul orthohantavirus, Bartonella kosoyi, Angiostrongylus cantonensis amongst others) to assess their immediate public health relevance. In contrast to total pathogen richness, zoonotic pathogen richness was primarily driven by host species (10.9%), with geographic region and season contributing less (2.8% and 0.6%, respectively) (Fig. 5e-h). Hotspot analysis revealed that Angiostrongylus cantonensis was more prevalent in B. indica, particularly in Jieyang and Heyuan, and during autumn (all p < 0.01). Seoul orthohantavirus was most frequently detected in R. norvegicus (all p < 0.01) and during summer (p < 0.05). To further resolve spatial patterns, we controlled for season and host species and compared pathogen positivity rates across locations (Supplementary Fig. 11). This confirmed regional differences and revealed species-specific geographic trends, such as Wenzhou mammarenavirus being more common in B. indica from Anpu (29.3%) and in R. norvegicus from Zhanjiang (30.8%).”\n2. Regarding the rationale for selecting only three tissue types for screening: The three selected tissues—lung, gut, and spleen—were chosen to represent major transmission routes: respiratory, digestive, and blood-borne, respectively. We agree that other tissues, such as the kidney (involved in urinary excretion), are also important for understanding viral shedding. In fact, we did collect kidney samples during the study. However, due to limited funding, we focused our analyses on the three primary tissues. We plan to include additional organs, such as the kidney, in future analyses once further resources become available.\nReviewer #4 General Comment #3 Lastly, the decision to only include viruses related to families with members known to infect mammals would seem to exclude microbes that could be important to document, even if their pathogenicity is uncertain. This reflects ascertainment bias that limits the power of this metatranscriptomic study. I think the paper would be more impactful if the authors could include the full scope of microbes detected, and then they could emphasize the ones related to known pathogens (they cite a paper that does just this ( ref #45)). Also, viruses related to known pathogens aren’t necessarily pathogenic, and it may be worth including this concept in the discussion.\nResponse: The primary objective of this study was to investigate pathogen diversity and evolution; therefore, our analyses focus specifically on known and potential pathogens. For viruses, we first identify those classified as vertebrate viruses, and further prioritize those with members known or likely to cause disease. Viral sequences falling outside of vertebrate virus classifications are more likely to be associated with commensal bacteria, parasites, or dietary sources. These are generally not considered pathogens, and in the rare cases where they might be, distinguishing them from non-pathogens is challenging. Moreover, such non-vertebrate viruses likely exhibit ecology distinct from that of vertebrate pathogens, and detailed characterization of these may obscure the core findings of this study. That said, we acknowledge the reviewer’s point that providing an overview of both pathogenic and non-pathogenic microbes contributes to a more comprehensive understanding of the microbial landscape. In response, we have now included an analysis of the full eukaryotic virome and bacterial microbiome, as detailed in lines 142–148, which reads:\n“From this data set, we identified over 217 species of eukaryotic viruses, spanning 13 viral RNA supergroups and four DNA viral families (Supplementary Fig. 1). Anelloviridae exhibited the highest detection rate (30.4%), while the greatest viral species richness was observed in the Partiti-Picobirna (n = 68) and Narna-Levi (n = 33) supergroups (Supplementary Fig. 2). For bacteria, Cutibacterium, Pseudomonas, and Cupriavidus were the most prevalent genera in the lung and spleen, whereas Helicobacter, Prevotella, and Bacteroides predominated in the gut (Supplementary Fig. 3).”\nFurthermore, we agree with the reviewer that “viruses related to known pathogens are not necessarily pathogenic.” This point has been acknowledged and discussed in the revised manuscript (lines 338–340), which now reads:\n“Conversely, viral pathogen diversity may be overestimated, as some viruses classified within pathogenic families or genera may not actually be associated with disease.”\nReviewer #4 General Comment #4 Lastly, a brief discussion of what environmental niche each target host species occupies would help contextualize the findings. In other words, which of these animals is most likely to interact with people (or livestock)? Rats, bandicoots, shrews? And what direction for future studies does this study give? Would authors suggest more targeted epidemiological studies that try to get more accurate prevalence data? Or to characterize how much contact these animals have with people - both would contribute to a more robust understanding of risk of transmission of known or potential zoonotic agents.\nResponse: In response to the reviewer’s suggestions, we have substantially revised the section discussing the public health implications of this study and added the following points to enhance the depth of the discussion (lines 308-314), to read:\n“Nevertheless, of the zoonotic pathogens, both B. indica and R. norvegicus exhibited high prevalence, underscoring that residential areas are not exempt from disease risk. Our analysis also revealed distinct host–geography and host–season interactions, highlighting the need to consider pathogen-specific transmission patterns for targeted surveillance and control. Importantly, as a cross-sectional study, our work necessarily only provides a snapshot of pathogen ecology; longitudinal monitoring is essential to capture temporal dynamics and support early detection of both known and emerging zoonoses.”\nReviewer #4 Specific comments.\nReviewer #4 Comment #1. Line 46: Is it live or spleen? Further in the results spleen is referred to as one of the three tissue types.\nWhy was the urinary system (e.g. kidneys) excluded? Were liver samples tested at all? Results section mentions lung, liver and gut but further analysis includes Spleen - please clarify\nResponse: We apologize for the confusion. Only spleen samples were sequenced—liver samples were not included—and we have corrected the text throughout the manuscript to reflect this. The three tissues selected for sequencing—lung, gut, and spleen—were chosen to represent major transmission routes: respiratory, digestive, and blood-borne, respectively. We agree with the reviewer that additional tissues, such as the kidney (which plays a key role in urinary excretion), are also important for understanding viral shedding. In fact, kidney samples were collected during the study. However, due to limited funding, our sequencing efforts focused on the three primary tissues. We plan to analyze additional organs, including the kidney, in future work as resources allow.\nReviewer #4 Comment #2. LIne 117: The survey methods should be more detailed. It is not clear how systematic the survey was. How many traps, trapping hours per day/night and how many days / nights were spent at each location or during each season?\nResponse: We agree with the reviewer that the survey methodology should be described in greater detail. In response to this suggestion, we have revised the relevant section accordingly. The updated text can be found in lines 359–365, which now reads:\n“Each sampling session lasted 3–4 consecutive days, depending on weather conditions, particularly the presence of rain. During each day of sampling, animals were captured using baited cages deployed in both agricultural and residential areas, with 200 cages allocated to each habitat type. Residential trapping involved placing 2–3 cages at the corners of farmers’ homes, while 2–5 cages were deployed in brushwood or gullies of agricultural areas, with numbers adjusted according to field size. Cages were set at dusk and retrieved the following morning at sunrise.”\nReviewer #4 Comment #3. 122: this description belongs in methods. How did the authors define residential and agricultural sampling sites? I think a table showing sample dates, location and numbers and environment should be included in the main body of the paper, not the supplement. Without this as a reference, it’s hard to interpret the results and the figures.\nResponse: We agree that a clearer description of the terms “residential” and “agricultural” areas is necessary. Accordingly, we have added these definitions in lines 362–363, which now read:\n“Residential trapping involved placing 2–3 cages at the corners of farmers’ homes, while 2–5 cages were deployed in brushwood or gullies of agricultural areas”\nFurthermore, we have added a table summarizing the sample collection dates, locations, sample numbers, and corresponding environments, as suggested by the reviewer.\nReviewer #4 Comment #4. Lines: 152-155: Are the prevalences reported describing % of samples with detections, or % of individuals positive within a population? What do the numbers look like when broken down by sample timepoint? Also, what does “relatively high prevalence” refer to? Compared to what? Are there baseline figures for these viruses or parasites from the literature?\nResponse: “Positivity rate” is actually “the percentage of individuals positive within a defined group”. This definition has now been added to the Results section and reads as follows:\n“Pathogen positivity was defined as the proportion of individuals testing positive in at least one of three tissues—lung, spleen, or gut—out of the total individuals in each group.”\nRegarding reviewer’s question “What do the numbers look like when broken down by sample timepoint?”, we have compared detection rates across different locations while controlling for time period and host species. The results are now presented in the new Supplementary Figure 11, which provides a clearer picture of the spatial patterns of disease prevalence. The corresponding descriptions can be found in lines 249-253:\n“To further resolve spatial patterns, we controlled for season and host species and compared pathogen positivity rates across locations (Supplementary Fig. 11). This confirmed regional differences and revealed species-specific geographic trends, such as Wenzhou mammarenavirus being more common in B. indica from Anpu (29.3%) and in R. norvegicus from Zhanjiang (30.8%).”\nFinally, the term “relatively high prevalence” referred to comparisons with other pathogens detected in this study. However, the corresponding phrasing has now been removed.\nReviewer #4 Comment #5. I suggest that the authors generally refrain from subjective language such as “relatively high” prevalence or \"similarly high” prevalence, without statistical substantiation.\nResponse: We have removed the corresponding statement.\nReviewer #4 Comment #6. I also suggest that the authors refer to the percentage positive data as detection rates, rather than prevalence to avoid confusion.\nResponse: As suggested, we have replaced all instances of “prevalence” with “positivity rate” throughout the manuscript.\n.\nReviewer #4 Comment #7. LIne 172: In the abstract, liver, not spleen, is specified as a tissue type. Please clarify.\nResponse: Thank you for pointing this out. The correct term should be “spleen,” and the error has been corrected in the revised manuscript.\nReviewer #4 Comment #8. 212-213: What are the environmental factors mentioned here? Are these the climate variables used in the models, or the environment of the samples (farm vs residential)?\nResponse: The environmental factors include climate variables, mammalian species richness, and land-use data, all of which were obtained from publicly available databases based on the geographic coordinates of each sampling site. This information has been added to lines 464-473, which now read:\n“To assess how environmental factors shape pathogen diversity and composition, we collected climate, mammal richness, land-use, and Normalized Difference Vegetation Index (NDVI) data for each sampling location from publicly available sources. Climate data were obtained from TerraClimate 79 , utilizing 14 variables to evaluate their influence on rodent pathogens. Definitions of these variables are available at the TerraClimate website (https://www.climatologylab.org/terraclimate.html). Mammal richness and land-use data were obtained from the International Union for Conservation of Nature (IUCN) 80 , and China’s Multi-Period Land Use Land Cover Remote Sensing Monitoring Dataset (CNLUCC)81 , respectively, while NDVI values were derived from publicly available remote sensing satellite data.”\nReviewer #4 Comment #9. LINE 214-215: Looking at the figure, there is overlap among all the CIs, suggesting that there isn’t much difference among the data points. I don’t think the authors can conclude that there’s a seasonal effect on infection rates from these data.\nResponse: The corresponding statement has been removed.\nReviewer #4 Comment #10. 221-222: It’s not clear now the climate data were used in the analysis - no climate specific results seem to be presented other than season of sampling. Figure 5a shows the size of the effect of environmental data - this should be included in the discussion.\nResponse: We did not include detailed analyses of individual climate variables as no clear trends were observed. However, we note that environmental factors, including climate, contributed to explaining the variation in both total pathogen richness and zoonotic pathogen richness at a general level.\nReviewer #4 Comment #11. Line 256: rephrase to among the most prevalence pathogens found in this study. If the authors are referring to known prevalence data, they should cite the study.\nResponse: We apologize for the confusion. The original statement has been revised, to read:\n“…Seoul orthohantavirus, the causative agent, is widespread in the region and constitutes a significant zoonotic threat in Guangdong, with a positivity rate of 3.47%.”\nReviewer #4 Comment #12. LIne 258: Are humans screened for Wenzhou mammarenavirus? How do you know it’s rare? Perhaps it hasn’t been reported because it isn’t tested for.\nResponse: We apologize for the confusion. Our intended message was that the specific disease manifestations caused by Wenzhou mammarenavirus in humans remain unclear. We have revised the text accordingly in lines 281–284, to read:\n“Despite its high positivity rate, the impact of Wenzhou mammarenavirus on humans remains unclear. While human seroprevalence is relatively high, viral RNA is rarely found in typical arenavirus-related illnesses, with the exception of a few respiratory cases 54 .”\nReviewer #4 Comment #13. 277-279: It might be worth mentioning whether these parasites threaten livestock health, since many of the sampled species occur around farms.\nResponse: Added as suggested by the reviewer.\nReviewer #4 Comment #14. Lines 307-311: The authors should discuss the statistical limitations of estimating and comparing infection rates across location and time based on the small sample sizes. Further, although the authors acknowledge the limited scope of including only presumptive pathogens, they do not address why they didn’t include a broader set of data from the metagenomic screening. Similarly, the authors should explain why they did not include a more complete set of tissue types.\nResponse: We have incorporated the reviewer’s concern in the Limitation section of the manuscript, between lines 336-338, to read:\n“….The focus on a limited set of organs also restricts the assessment of zoonotic potential: for instance, liver-specific pathogens like the Chinese liver fluke, endemic to Guangdong, may have been missed 63, 64, 65 . ”\nAnd between lines 344-347, to read:\n“Finally, our stratification by species and location leads to relatively small sample sizes for certain combinations. Assessing disease prevalence and associated risks in these specific host groups or regions will therefore require more extensive sampling.”\nReviewer #4 Comment #15. Tables / Figures:\nI suggest including a table showing the sampled species, number of individuals by location and date as part of the main paper. It may help the reader interpret the figures.\nResponse: In response to the reviewer’s suggestion, we have added Table 1, which provides the relevant information as requested.\nReviewer #4 Comment #16. Figure 1d - I suggest putting the temporal species composition data in chronological order\nResponse: Changed as suggested."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Xin and colleagues have addressed most of the concerns that I had except for one.\nI disagree with the authors' response to the first and most serious issue I raised, namely the claim that one can identify the host of a newly discovered microorganism. In their response, the authors say that they have \"focused specifically on pathogens infecting mammals\", yet the results section of the corrected manuscript still says \"With respect to known and potential pathogens, we detected 80 species of mammalian RNA virus, ...\". Similar claims are repeated later (e.g. \"Among the 100 mammalian viruses discovered, 69 were newly identified species, ...\"). Granted, the figures appear to focus solely on undisputed mammalian pathogens but given the ongoing confusion, may I suggest the authors include a supplementary table that lists the viruses discovered, whether they consider them novel, their identity to nearest relative at nucleotide or amino acid level, corresponding NCBI accessions, status of the organism as a mammalian pathogen or not, and which figures or analyses they've been included or excluded from.\nBeyond this, the authors have done a great job and I thank them, it's a great study.", "reviewer_2": "Minor comments:\nReviewer #2 Major Comment #11. Did the authors normalize for differences in tissue size that was used for library preparation? If there are more cells in lung tissue vs gut, the pathogen RPM will automatically go down because more host RNA will be sequenced.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_5": "The authors have adequately addressed the majority of Reviewer #4's comments. However, a few minor points must be corrected to finalize the manuscript.\nPlease address the following minor points:\n• Line 111: The statement \"systematically compared the full spectrum of pathogens\" is misleading, particularly as the analysis focuses only on vertebrate pathogens as noted by Reviewer #4. Please add a brief statement reflecting this specific scope in both the Abstract and the main text.\n• Line 125: The phrase \"Without bias\" is an overstatement and should be deleted.\n• Line 176: Please replace “Prevalence” with \"positive rate\"\n• Line 361 (Methods section): Please provide specific details regarding the baits used for rodent capture, as the type of bait may introduce bias in the species or individuals captured.\n• Please clarify the discrepancy in the stated number of sequencing libraries. In the Authors’ reply: \"We collected a total of 858 individual animals and performed sequencing on three tissue types per animal, generating 2,574 sequencing libraries...\" Please confirm whether the correct number is 2,574 or 2,408.\n• Tissues Selection: To fully address Reviewer #4's comment regarding the importance of the kidney (as an organ relevant for characterizing pathogens transmitted by urine), please add kidney tissue to the discussion of limitations, alongside the liver."}, "author_response": "Reviewer #1 Comment #1.\nXin and colleagues have addressed most of the concerns that I had except for one.\nI disagree with the authors' response to the first and most serious issue I raised, namely the claim that one can identify the host of a newly discovered microorganism. In their response, the authors say that they have \"focused specifically on pathogens infecting mammals\", yet the results section of the corrected manuscript still says \"With respect to known and potential pathogens, we detected 80 species of mammalian RNA virus, ...\". Similar claims are repeated later (e.g. \"Among the 100 mammalian viruses discovered, 69 were newly identified species, ...\"). Granted, the figures appear to focus solely on undisputed mammalian pathogens but given the ongoing confusion, may I suggest the authors include a supplementary table that lists the viruses discovered, whether they consider them novel, their identity to nearest relative at nucleotide or amino acid level, corresponding NCBI accessions, status of the organism as a mammalian pathogen or not, and which figures or analyses they've been included or excluded from.\nBeyond this, the authors have done a great job and I thank them, it's a great study.\nResponse: We agree with the reviewer that establishing definitive host association for novel pathogen is not always possible in this context. What we did here was to infer potential host associations based on phylogenetic placement, as mammal-associated viruses routinely cluster together phylogenetically. We have therefore toned down the relevant wording so as to describe newly discovered viruses as “potential mammalian pathogens” which we believe is a more accurate description.\nWe also agree that it would be helpful to provide a comprehensive summary of all viruses—both known and novel—along with their closest relatives, inferred host association and pathogenicity (i.e., mammalian viruses or mammalian pathogens), and the figures in which they appear. As requested, we have now included this information in a new Supplementary Table 3.\nReviewer #2 Comment #1. Did the authors normalize for differences in tissue size that was used for library preparation? If there are more cells in lung tissue vs gut, the pathogen RPM will automatically go down because more host RNA will be sequenced. How did the authors account for differences in density in lung tissue and gut tissue for example? Please add in the method section how each tissue sample was quantified.\nResponse: In field conditions, it is not practical to weigh tissue samples prior to RNA extraction, as additional handling increases the risk of RNA degradation, contamination, and processing delays. Therefore, we standardized the input based on approximate volume rather than weight. Specifically, a small piece of tissue roughly the size of a soybean (~5–8 mm in diameter) was used for each extraction, with minor variation depending on the organism and operator.\nThe reviewer is correct that tissue density could, in theory, vary between samples; however, this does not affect normalization in our analysis. Because downstream analyses are based on relative abundance rather than absolute quantification, such variation does not alter the proportional representation of pathogens within each sample.\nWe also agree that this information should be explicitly stated in the Methods section. Accordingly, we have revised the text as follows:\n“Each sample, approximately 5–8 mm in size, was homogenized in 600 μL of lysis buffer using a TissueRuptor (Qiagen, Germany), followed by total RNA extraction with the RNeasy Plus Mini Kit (Qiagen, Germany) according to the manufacturer’s instructions.”\nReviewer #2 Comment #2. L217: “Tissue tropism implies multiple transmission pathways”. Please change “implies” to “suggests”.\nResponse: Changed as suggested.\nReviewer #5 Comment #1. Line 111: The statement \"systematically compared the full spectrum of pathogens\" is misleading, particularly as the analysis focuses only on vertebrate pathogens as noted by Reviewer #4. Please add a brief statement reflecting this specific scope in both the Abstract and the main text.\nResponse: We agree with the reviewer’s comment and have revised the text in lines 117–119 accordingly. The updated sentence now reads as follows:\n“With these data in hand we systematically compared mammalian pathogens across all major microbial groups – RNA viruses, DNA viruses, bacteria, and eukaryotic pathogens – examining their distribution across different organs, host species, geographic locations, and seasons.”\nAdditionally, in response to the reviewer’s suggestion, we have added a brief statement in the abstract to clearly reflect this specific scope in lines 47-51:\n“Herein, we employed a metatranscriptomics approach to survey the pathogen infectome—defined as the set of microorganisms infecting the host—across 2,408 individual samples, representing lung, spleen, and gut from 858 animals in Guangdong province, China, considering the impact of host species, tissue, season, and geographic location on pathogen diversity.”\nReviewer #5 Comment #2. Line 125: The phrase \"Without bias\" is an overstatement and should be deleted.\nResponse: Deleted as suggested.\nReviewer #5 Comment #3. Line 176: Please replace “Prevalence” with \"positive rate\"\nResponse: Changed as suggested.\nReviewer #5 Comment #4. Line 361 (Methods section): Please provide specific details regarding the baits used for rodent capture, as the type of bait may introduce bias in the species or individuals captured.\nResponse: We have revised the text and added details to clearly describe the types of bait used during animal trapping, as follows:\n“A mixture of sweet potato, fried breadstick, and cooked meat was used as bait to attract rodents.”\nReviewer #5 Comment #5. Please clarify the discrepancy in the stated number of sequencing libraries. In the Authors’ reply: \"We collected a total of 858 individual animals and performed sequencing on three tissue types per animal, generating 2,574 sequencing libraries...\" Please confirm whether the correct number is 2,574 or 2,408.\nResponse: We apologize for this mistake. It should be 2,408 libraries.\nReviewer #5 Comment #6. Tissues Selection: To fully address Reviewer #4's comment regarding the importance of the kidney (as an organ relevant for characterizing pathogens transmitted by urine), please add kidney tissue to the discussion of limitations, alongside the liver.\nResponse: We agree, and the discussion on kidney has been added in lines 339-343:\n“The focus on a limited set of organs also restricts the assessment of zoonotic potential: for instance, liver-specific pathogens like the Chinese liver fluke, endemic to Guangdong, may have been missed 63, 64, 65 . Similarly, kidney‐tropic paramyxoviruses, including members of the genus Henipavirus, are also likely to have been missed 66 ”"}]

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