Infectome analysis of bat kidneys from Yunnan province, China, reveals close relatives of Hendra-Nipah viruses and prevalent bacterial and eukaryotic pathogens

Infectome analysis of bat kidneys from Yunnan province, China, reveals close relatives of Hendra-Nipah viruses and prevalent bacterial and eukaryotic pathogens | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Infectome analysis of bat kidneys from Yunnan province, China, reveals close relatives of Hendra-Nipah viruses and prevalent bacterial and eukaryotic pathogens View ORCID Profile Guopeng Kuang , Tian Yang , Weihong Yang , Jing Wang , Hong Pan , Yuanfei Pan , Qin-yu Gou , Wei-chen Wu , Juan Wang , Lifeng Yang , Xi Han , Yao-qing Chen , John-Sebastian Eden , Edward C. Holmes , Mang Shi , Yun Feng doi: https://doi.org/10.1101/2025.01.10.632301 Guopeng Kuang 1 Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute of Endemic Disease Control and Prevention , Dali, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Guopeng Kuang Tian Yang 1 Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute of Endemic Disease Control and Prevention , Dali, China 6 School of Public Health, Dali University , Dali, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Weihong Yang 1 Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute of Endemic Disease Control and Prevention , Dali, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jing Wang 3 National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University , Shenzhen, China 4 State Key Laboratory for Biocontrol, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University , Shenzhen, China 5 Shenzhen Key Laboratory for Systems Medicine in Inflammatory Diseases, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University , Shenzhen, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hong Pan 1 Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute of Endemic Disease Control and Prevention , Dali, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yuanfei Pan 7 Ministry of Education Key Laboratory of Biodiversity Science and Ecological Engineering, School of Life Sciences, Fudan University , Shanghai, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Qin-yu Gou 3 National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University , Shenzhen, China 4 State Key Laboratory for Biocontrol, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University , Shenzhen, China 5 Shenzhen Key Laboratory for Systems Medicine in Inflammatory Diseases, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University , Shenzhen, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wei-chen Wu 3 National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University , Shenzhen, China 4 State Key Laboratory for Biocontrol, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University , Shenzhen, China 5 Shenzhen Key Laboratory for Systems Medicine in Inflammatory Diseases, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University , Shenzhen, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Juan Wang 1 Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute of Endemic Disease Control and Prevention , Dali, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lifeng Yang 1 Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute of Endemic Disease Control and Prevention , Dali, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xi Han 1 Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute of Endemic Disease Control and Prevention , Dali, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yao-qing Chen 8 School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University , Shenzhen, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site John-Sebastian Eden 9 School of Medical Sciences, The University of Sydney , Sydney, New South Wales, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Edward C. Holmes 9 School of Medical Sciences, The University of Sydney , Sydney, New South Wales, Australia 10 Laboratory of Data Discovery for Health Limited , Hong Kong SAR, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mang Shi 3 National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University , Shenzhen, China 4 State Key Laboratory for Biocontrol, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University , Shenzhen, China 5 Shenzhen Key Laboratory for Systems Medicine in Inflammatory Diseases, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University , Shenzhen, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: ynfy428{at}163.com shim23{at}mail.sysu.edu.cn Yun Feng 1 Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute of Endemic Disease Control and Prevention , Dali, China 2 State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University , Beijing, China 6 School of Public Health, Dali University , Dali, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: ynfy428{at}163.com shim23{at}mail.sysu.edu.cn Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Bats are natural reservoirs for a wide range of microorganisms, including many notable zoonotic pathogens. However, the infectome of bat kidneys remains poorly understood. To address this gap, we performed meta-transcriptomic sequencing on kidney tissues from 142 bats, spanning ten species sampled at five locations in Yunnan province, China. This analysis identified 22 viral species, including 20 novel viruses, two of which represented newly discovered henipaviruses closely related to the highly pathogenic Hendra and Nipah viruses. These henipaviruses were found in the kidneys of bats inhabiting an orchard near villages, raising concerns about potential fruit contamination via bat urine and transmission risks to livestock or humans. Additionally, we identified a novel protozoan parasite, tentatively named Klossiella yunnanensis , along with two highly abundant bacterial species, one of which is a newly discovered species— Flavobacterium yunnanensis . These findings broaden our understanding of the bat kidney infectome, underscore critical zoonotic threats, and highlight the need for comprehensive, full-spectrum microbial analyses of previously understudied organs to better assess spillover risks from bat populations. Introduction Bats (order Chiroptera ) are one of the most diverse and abundant groups of mammals, comprising nearly 1,500 species with a near global distribution 1 . Bats are also well-known natural reservoirs for a wide variety of microbial pathogens, a characteristic often attributed to their unique immune systems which maintain a delicate balance between host defenses and immune tolerance to viral infections 2 – 4 . Importantly, bats have been implicated in a number of major emerging disease outbreaks, including Hendra 5 , Nipah 6 , Marburg 7 and Ebola 7 virus disease, severe acute respiratory syndrome (SARS) 8 , Middle East respiratory syndrome (MERS) 8 , and coronavirus disease 2019 (COVID-19) 9 . Indeed, comparative studies indicate that bats harbor a greater diversity of viruses than many other mammalian groups, underscoring their significance for zoonotic disease surveillance 10 . Metagenomic approaches have greatly advanced the characterization of bat viromes, deepening our understanding of the diversity of bat-borne pathogens and their potential role in disease emergence and transmission 11 – 14 . As of October 2024, viral sequences from at least 31 families have been identified in 340 bat species across 111 countries 15 . Bat-borne viruses are transmitted to humans either through direct contact with bats or via so-called “intermediate” hosts, often linked to the ingestion of food or water contaminated with bat saliva, feces, or urine 16 . While much of this research has focused on the bat gut virome, viruses present in other body sites—such as the kidneys—also pose transmission risks. Indeed, zoonotic viruses have been detected in bat kidneys and urine, including henipaviruses 17 – 21 , pararubulaviruses 21 – 26 , and betacoronaviruses 26 . As these kidney-associated pathogens can be excreted through urine they are at heightening risk of human exposure. Beyond viruses, bats harbor a diverse array of bacteria, fungi, and protozoan parasites that infect bats or even humans 27 , 28 . A notable example is the psychrophilic fungus Pseudogymnoascus destructans , which has caused a devastating disease in bats and led to the deaths of millions of animals across eastern North America 28 . Although this fungus is not known to pose a direct threat to humans, disturbances during bat hibernation—such as flying during the day and gathering near cave and mine entrances in winter—may increase human-bat encounters. Additionally, zoonotic bacteria and protozoan parasites, such as members of Leptospira 29 , 30 and Toxoplasma 31 , have been identified in bat kidneys. However, as with viruses, research on the bacteria and eukaryotic pathogens in bat kidneys remain sparse, highlighting a critical gap in our understanding of the diversity of bat pathogens. Yunnan province, located in southwestern China and bordering a number of Southeast Asian countries, is renowned as a hotspot for bat diversity and bat-borne viral pathogens, including close relatives of Marburg virus 32 , SARS-CoV 33 , 34 , and SARS-CoV-2 9 , 35 , 36 . Herein, we utilized a meta-transcriptomics approach to investigate the total infectome—comprising viruses, bacteria, and eukaryotic pathogens—in bat kidneys collected from this geographic region. We further identified and characterized potential human pathogens of notable zoonotic risk and explored interactions between viruses and their protozoan parasite hosts, offering valuable insights into the complexity of the bat kidney infectome. Results Bat species identification Between 2017 and 2021, kidney tissues were sampled from 142 individual bats across five cities/counties in Yunnan province, China ( Fig. 1a and Supplementary Table 1). Species identification was initially performed by recovering partial cytochrome c oxidase I ( COX1) gene sequences using targeted PCR assay and Sanger sequencing. Phylogenetic analysis of full-length COX1 sequences, generated from meta-transcriptomic sequencing, confirmed the presence of ten bat species spanning five genera and three families ( Fig. 1b and Supplementary Table 1). Based on mitochondrial sequences and sampling locations, the samples were pooled into 20 groups for sequencing library constructions, with each group containing 2 to 8 individuals (Supplementary Table 1). Meta-transcriptomic sequencing of total RNA extracted from these pools yielded an average of 56.33 million clean non-rRNA reads, totaling approximately 1.13 billion clean non-rRNA reads. Download figure Open in new tab Fig. 1. Bat kidney sampling and species identification. (a) Map showing the five sampling locations in Yunnan province, China, with pie charts indicating the species composition of the bats sampled at each site. The basemap shapefile used in ArcGIS was obtained from the publicly available GADM data set ( https://gadm.org/download_country.html ). (b) Unrooted phylogenetic tree inferred from full-length COX1 gene sequences of bat kidney samples analyzed in this study. Colors correspond to different bat species, matching the color scheme used in the pie charts. Branch lengths are scaled to the number of nucleotide substitutions per site. Overview of the Yunnan bat kidney infectome Meta-transcriptomic analysis of the bat kidneys identified a diverse microbial community ( Fig. 2 and Supplementary Table 2). Based on our detection criteria (see Methods), microbes were detected in 18 of the 20 analyzed libraries, comprising 0.06% to 1.28% of total clean non-rRNA reads per library ( Fig. 2a ). Two libraries—one from Hipposideros armiger (8 individuals, LS) and another from Rhinolophus stheno (2 individuals, SB)—showed no microbial presence. RNA viruses dominated the microbial community, with 20 species from 12 families identified, as well as one DNA virus, one reverse transcribing virus, two bacterial species, and one eukaryotic species ( Fig. 2b ). Among these, the eukaryote genus Phyllobacterium was the most frequently detected, present in 13 (65%) libraries. Notably, one library contains a total of 12 microbial species, all of which were viruses ( Fig. 2c ). Download figure Open in new tab Fig. 2. Overview of the bat kidney infectome. (a) Numbers of total reads (light blue) and microbial reads (orange) for each library. (b) Count of viral, bacterial, and eukaryotic microbial species detected. (c) Heatmap illustrating the distribution and relative abundance of viral, bacterial, and eukaryotic microbes, represented as RNA abundance (RPM: reads per million non-rRNA reads) in each library. Host species and orders are labeled at the top and color-coded according to their respective categories. Virome of bat kidneys We identified 22 viral species across 12 families in bat kidneys ( Fig. 2 and 3 ). These included six RNA viruses from the Paramyxoviridae , three from the Totiviridae , two each from the Partitiviridae, Solemoviridae , and Narnaviridae , and one each from the Phenuiviridae, Chuviridae, Nodaviridae, Picornaviridae , and Tombusviridae . Additionally, we discovered one DNA virus from the Parvoviridae and one reverse transcribing virus from the Hepadnaviridae ( Fig. 2 , 3). Of these, 20 species (90.91%) spanning 10 families were newly identified per ICTV (International Committee on Virus Taxonomy) species demarcation criteria (Supplementary Table 2). Download figure Open in new tab Fig. 3. Phylogenetic diversity of viruses identified in this study. Phylogenetic trees of viruses from 12 virus families estimated using the maximum likelihood method based on conserved protein sequences (RdRp for RNA viruses, NS1 for Parvoviridae , and DNA polymerase for Hepadnaviridae ). Colored dots on the trees, corresponding to host genera as indicated in the legend, represent viral species identified in this study. Red stars mark members of known mammal-associated viral lineages. All trees are mid-pointed rooted for clarity only with horizontal branch lengths depicting the number of amino acid substitutions per site. Phylogenetic analyses revealed that nine species (40.91%) were related to known mammal-associated viruses, representing one reverse transcribing virus, one DNA virus and seven RNA viruses ( Fig. 3 ). The Paramyxoviridae exhibited the highest diversity, with two species from the genus Henipavirus and four from the genus Jeilongvirus identified. Notably, the two newly identified henipaviruses showed relatively close evolutionary links to two human pathogens—Hendra virus (HeV, 52.23–56.94% identity in the L protein) and Nipah virus (NiV, 52.17–57.03%) ( Fig. 3 ). Interestingly, we identified a hepatotropic virus, denoted Bat hepatitis virus variant YNBS16, in bat kidneys. Phylogenetic analysis revealed that this sequence was closely related to a sequence, ZYPR16 (Clade BtHBV 7), previously identified in bat livers ( Extended Data Fig. 1 ). Download figure Open in new tab Extended Data Fig. 1. Maximum likelihood phylogenetic tree constructed based on amino acid sequences of the DNA polymerase within the genus Orthohepadnavirus . The newly identified virus in this study is marked with a solid red circle. Bat-derived strains and their corresponding clades—determined according to divergence limits used by ICTV as species demarcation criteria—are labeled on the right. There was considerable variation in diversity among viral families ( Fig. 2b , c and 3 ). While the Paramyxoviridae dominated the sampled obtained, in many cases the highest abundance members of this family were not associated with the infection of vertebrates, including Yunnan narnavirus 1 and 2 ( Narnaviridae ) and Yunnan totivirus 1–4 ( Totiviridae ). Yunnan totivirus 1 was especially abundant in pools YNBS16 (RPM = 7393.98) and YNBS17 (RPM = 4171.90) ( Fig. 2c ), indicating that its presence was unlikely due to environmental contamination or dietary origin. Characterization of newly identified henipaviruses Among the viruses identified, we focused on those with potential emergence risks based on their phylogenetic relationship to known high-impact human pathogens, specifically Yunnan bat henipavirus 1 and 2. Of the 20 pooled libraries, one (YNBS03) was positive for Yunnan bat henipavirus 1, while two (YNBS02 and YNBS04) contained reads corresponding to Yunnan bat henipavirus 2. These positive pools were all derived from the kidneys of Rousettus leschenaultii bats inhabiting an orchard near villages in RL (WD) ( Fig. 2c ). Using henipavirus genome sequences assembled from these libraries, primers were designed to further examine individual kidneys through qRT-PCR. The results revealed that one kidney from pool YNBS03 (sample WDBS1745), one from YNBS02 (sample WDBS1733), and two from pool YNBS04 (samples WDBS1762 and WDBS1769) tested positive for henipavirus. Further testing of other organs (heart, liver, lung, gut, and brain) from the same individuals (WDBS1733 and WDBS1745) using qRT-PCR and meta-transcriptomic sequencing confirmed the multi-organ presence of henipaviruses within these bats, with the exception of brain tissues ( Table 1 ). Notably, the kidneys exhibited significantly higher viral abundance compared to other organs, suggesting that they are the primary site of henipavirus replication within the host. View this table: View inline View popup Download powerpoint Table 1. Detection of henipavirus in various organs within individual bats The complete genomes of Yunnan bat henipavirus 1 and 2 were successfully assembled from individual kidney samples WDBS1745 and WDBS1733, achieving mean sequencing depths of 27.99 fold and 1,274.77 fold, respectively ( Fig. 4a ). These two sequences were designated as Yunnan bat henipavirus 1 variant WDBS1745 and Yunnan bat henipavirus 2 variant WDBS1733. The open reading frames (ORFs) and gene arrangements of both viruses were consistent with other members of the genus Henipavirus , with each encoding six proteins ( Fig. 4a ). Download figure Open in new tab Fig. 4. Characterization of the novel henipavirus species examined in this study. (a) Genome organization and sequencing coverage of two novel henipavirus species. Coverage across the full-length genome is displayed, with open reading frames (ORFs) depicted as colored arrows below the coverage plots. Regions confirmed by Sanger sequencing for Yunnan bat henipavirus 1 are marked with a red bar beneath the coverage graph. (b) Maximum likelihood phylogenetic trees estimated using amino acid sequences of each gene within the genus Henipavirus , rooted with J-virus (not shown). Color blocks indicate different species groups, and newly identified viruses are marked with solid red circles. All trees are mid-pointed rooted for clarity only with horizontal branch lengths depicting the number of amino acid substitutions per site. Phylogenetic analysis of all six genes revealed a clear separation between the predominantly rodent-associated and bat/human-associated clades of the genus Henipavirus ( Fig. 4b ). Notably, the newly identified viruses formed distinct lineage, generally grouping with other bat-hosted henipaviruses, including the zoonotic pathogens HeV and NiV, both known for their high mortality rates in humans 37 . Yunnan bat henipavirus 1 was most closely related to HeV and NiV in the N (70.33–71.33% amino acid identity) and L proteins (56.94–57.03% amino acid identity), which underscores its potential risk as an emerging pathogen ( Fig. 4b ). However, the phylogenetic positions of Yunnan bat henipavirus 1 and 2 showed marked variability. In particular, Yunnan bat henipavirus 1 was most closely related to HeV and NiV in the N and L proteins trees, but occupied variable positions in the other trees. Although the bootstrap support for these groupings was generally weak, the topological movement of Yunnan bat henipavirus 1 among the henipaviruses likely reflects the action of recombination. Conversely, the phylogenetic positions of Yunnan bat henipavirus 2 was more consistent across gene trees and also exhibited a greater divergence from other bat henipaviruses. Identification and characterization of bacterial pathogens Our meta-transcriptomic analysis also identified two bacterial taxa with relatively high abundance: Flavobacterium and Phyllobacterium . Phylogenetic analysis revealed that the Flavobacterium species forms a distinct branch closely related to Flavobacterium ammonificans (94.12% nucleic acid identity in the ropB gene) ( Fig. 5a ). This bacterium was tentatively classified as a novel species and named Flavobacterium yunnanensis . Similarly, the Phyllobacterium species was confirmed as Phyllobacterium calauticae based on 97.30% nucleic acid identity and phylogenetic placement of the groEL gene ( Fig. 5b ). Further transcriptomic profiling across all 20 pools demonstrated diverse gene expression patterns for these bacteria ( Fig. 5c ), indicating that they are metabolically active within the bat hosts. Download figure Open in new tab Fig. 5. Gene expression profiles, prevalence and identification of the two bacterial microbes. (a) Maximum likelihood phylogenetic tree of the genus Flavobacterium , constructed using the rpoB gene. (b) Maximum likelihood phylogenetic tree of the genus Phyllobacterium , constructed using the groEL gene. (c) Top 25 expressed genes (measured in RPM) for Flavobacterium yunnanensis and Phyllobacterium calauticae in pools YNBS16 and YNBS01, respectively (left panel), compared with their expression in other positive pools (right panel). Eukaryotic microbe identified in bat kidneys Analysis of genes COX1 and cytochrome b ( cytB ) identified a protozoa pathogen closely related to the Klossiella equi of the family Klossiellidae (phylum Apicomplexa), known to infect the kidney of horses 38 . Phylogenetic and sequence divergence analyses revealed 87.7% nucleotide identity to K. equi (MH203050.1) in the COX1 gene and 91.4% identity in the cytB gene. Based on these findings, the newly identified protozoan was proposed as a novel species, tentatively named Klossiella yunnanensis ( Fig. 6a, b ). Klossiella mitochondrial reads were detected in six pools, exhibiting uneven gene expression levels across different libraries ( Fig. 6c ). Download figure Open in new tab Fig. 6. Identification and characterization of a eukaryotic microbe. (a, b) Phylogenetic trees of Klossiella , estimated using nucleotide sequences of the cox1 gene (a) and cytb gene (b). Colored dots indicate newly identified eukaryotic species, with colors corresponding to host genera. (c) Transcriptomic profiles of the Klossiella mitochondrion, represented as RPM, across positive pools. (d) Spearman’s correlation analysis showing the relationship between the total relative abundance (RPM) of totiviruses, narnaviruses, and Klossiella yunnanensis . Interestingly, members of the viral families Totiviridae and Narnaviridae , known to naturally infect protozoa or fungi, showed co-occurrence with K. yunnanensis ( Fig. 2c ). To explore this relationship, we analyzed the correlation between the relative abundances (in RPM) of these viruses and Klossiella across the six positive pools. Strong positive correlations were observed, with Spearman’s ρ ranging from 0.83 to 0.94 ( p < 0.05) ( Fig. 6d ), suggesting that the viruses in these families are likely hosted by K. yunnanensis rather than by bats. Discussion There have been many studies of the presence of viruses, bacteria, and eukaryotic microbes (i.e., fungi and protozoan parasites) in various bat tissues, including the brain, lung, liver, rectum, feces, urine, throat, and fecal swabs 9 , 32 – 36 , 39 , 40 . In contrast, the kidneys have received comparatively little attention. Our meta-transcriptomic sequencing of bat kidneys revealed a diverse array of microorganisms, shedding light on the broader bat infectome. Although viruses were the predominant microbial group identified, only 9 of the 22 detected viral species were categorized as mammalian viruses. Notably, the mammal-associated viruses identified in the kidneys differed from those identified in the rectal tissues of the same individual bats 36 . These findings align with previous research showing that viruses from different families exhibit marked variation in their organ-specific distribution in bats 14 . As a consequence, these results underscore the importance of adopting a multi-organ approach to comprehensively understand the microbial diversity harbored by bats, particularly for identifying host-microbe interactions. Of particular note, our study identified close relatives of Hendra and Nipah viruses colonizing bat kidneys. Nipah virus (NiV) are lethal pathogens that cause severe diseases in humans, including acute respiratory distress and encephalitis, with a mortality rate of 35-75% 37 , 41 . Similarly, Hendra virus (HeV) has caused multiple fatal outbreaks in humans and horses, including the death of veterinarians 37 . These viruses are naturally hosted by fruit bats ( Pteropus species) and are typically transmitted to humans through bat urine or saliva, often via contamination of food sources 42 , 43 . HeV and NiV were first identified in Australia and Malaysia, respectively, and associated with Pteropus and other bat species 5 , 6 . Herein, we identified two related henipaviruses in Rousettus leschenaultii bats, marking the first detection of henipavirus genomes in bats from China. Previously, antibodies to Nipah or Nipah-like viruses have been reported in bats from multiple regions in China, including Yunnan, Guangdong, Hainan and Hubei provinces, suggesting potential exposure to such viruses 44 . Until now, however, no henipavirus genome sequences had been documented in bats from China. Notably, more distantly related viruses have been discovered in rodents and shrews, including Mojiang virus 45 and Langya virus 46 , with the latter confirmed to infect humans. These findings highlight the significance of continued surveillance and genomic characterization of henipaviruses in bats, which are critical for understanding their potential spillover risk. We also identified at least one bacterial species prevalent in bat kidneys. While the gut microbiota of bats has been extensively studied, less attention has been given to those of other organs, including the kidneys 16 . Previous research identified Leptospira spp. in bat kidney, supporting the hypothesis that bat kidneys may serve as a reservoir for zoonotic Leptospira 47 – 50 , and we previously detected pathogenic Leptospira in bat kidneys using nested PCR in individual tissue samples 51 . However, no Leptospira -associated reads were detected in the meta-transcriptomic sequencing of this study, possibly due to sample pooling which might obscure the detection of low-abundance microbes. Instead, we identified Flavobacterium and Phyllobacterium , of which Phyllobacterium calauticae exhibited relatively high abundance and prevalence ( Fig. 2 and 5 ). Phyllobacterium calauticae is an aerobic, motile bacterium isolated from microaerophilic freshwater sediments, adapted to efficiently utilize oxygen in low-oxygen environments 52 . This is not unprecedented, as Listeria monocytogenes , an environmentally ubiquitous bacterium, has previously been isolated from various wild animals, including bat kidneys 53 , 54 . Previous studies have shown that bats harbor a diverse range of protozoan parasites, some of which are capable of infecting humans 55 . However, research on protozoan parasites present in bat kidneys is limited. Toxoplasma gondii , a zoonotic protozoan parasite, has been detected in bats collected in Yunnan 56 , and herein we identified a protozoan parasite, tentatively named Klossiella yunnanensis , in six (30%) of the libraries. Phylogenetic analysis suggests that K. yunnanensis is closely related to species known to infect horses, K. equi , which is generally considered non-pathogenic but can cause kidney alterations in cases of heavy infection 38 . The pathogenicity of this eukaryotic parasites to humans or even bats remains unclear. In addition, viruses from the families Totiviridae and Narnaviridae 57 , 58 , which are known to infect a wide range of non-vertebrate host types including protozoan parasites, were detected in high abundance in bat kidneys. Spearman’s correlation analysis of relative abundances indicated that these viruses were associated with K. yunnanensis rather than the bat hosts themselves ( Fig. 6d ). This highlights the importance of conducting studies of the total infectome to better elucidate the interactions between viruses within an animal and their potential relationship with the primary host. Our study has several limitations. Uneven sampling across locations and bat species—in which each species was sampled at only one or two sites—complicated our ability to assess virus distribution, compare viral compositions between species, and identify transmission networks. In addition, the analysis of pooled samples with varying numbers of individuals per pool prevented us from determining whether the detected microbes were co-infections within individual bats or from different individuals. The unequal pool sizes may have also influenced the accuracy of microbial quantification. Finally, the lack of a complete genome assembly for the newly discovered eukaryotic parasite, coupled with the absence of a reference genome in existing databases, limited our ability to accurately quantify its abundance. Despite these limitations, our study offers the first comprehensive characterization of the bat kidney infectome, providing a foundation for more effective discovery and characterization of potential bat-borne pathogens. Materials and methods Ethics statement This research, including the specimen collection and processing procedures, was reviewed and approved by the Ethics Committee of the Yunnan Institute of Endemic Disease Control and Prevention (File No. 20160002). All experiments were conducted with the approval of the Biosafety Committee of the same institute. Sample collection Five sampling sites in Yunnan province were selected, denoted RL, ML, SB, LS, and JP ( Fig. 1a ). From 2017 to 2021, sticky nets were deployed around orchards and caves to capture bats, which were promptly removed after being trapped. Initial identification of bat species was performed by experienced field biologists based on morphological characteristics. Captured bats were then transported to the laboratory, euthanized by intracardiac delivery of sodium pentobarbitone, and dissected. Kidney tissues were collected and stored at −80LJ°C until further analysis. Preliminary species identification was confirmed by sequencing the cytochrome c oxidase I ( COX1 ) gene for each specimen 59 . Mammalian species confirmation was achieved using de novo assembled COX1 gene contigs. The final clean cox1 contigs were compared against the database within the BARCODE OF LIFE DATA SYSTEM (BOLDSYSTEMS) 60 , and phylogenetic analyses were conducted using PHYML 3.0 61 for species identification. Meta Dtranscriptomic sequencing Individual tissues were initially organized into sample groups based on species identification and collection location. Specifically, 142 kidney tissues were grouped into 20 libraries, each comprising 2 to 8 individuals (Supplementary Table 1). Total RNA was extracted and purified from each pool using the RNeasy Plus Universal Mini Kit (Qiagen, Germany). RNA libraries were constructed using the Zymo-Seq RiboFree™ Total RNA Library Kit (No. R3003) (Zymo Research, USA), following the manufacturer’s instructions. These libraries were sequenced using paired-end 150LJbp reads on the Illumina NovaSeq 6000 sequencing platform. Characterization of total infectomes Adapter sequences were removed from the sequencing reads, and initial quality control was performed using the pipeline implemented in bbduk.sh ( https://sourceforge.net/projects/bbmap/ ). Duplicate reads were filtered out using cd-hit-dup with default settings 62 . rRNA reads were removed by mapping the processed reads against the SILVA rRNA database (Release 138.1) using Bowtie2 (version 2.3.5.1) in ‘--local’ mode 63 . The remaining high-quality, non-rRNA reads were either (i) directly compared against the non-redundant protein (nr) database using DIAMOND Blastx 64 , or (ii) assembled into contigs using MEGAHIT (version 1.2.8) 65 before comparison against the National Center for Biotechnology Information (NCBI) non-redundant protein (nr) database. An e-value threshold of 1×10 −5 was set to maintain high sensitivity and minimize false positives. For virus identification, contigs identified under the kingdom ‘Viruses’ were extracted, and those shorter than 600LJbp were excluded to ensure the quality of virus genomes. The remaining overlapping contigs were merged into extended viral sequences using the SeqMan program implemented in the Lasergene software package version 7.1 (DNAstar, USA) 66 . Each viral contig was classified at the species level based on the species demarcation criteria established by the ICTV for the respective viral genus 58 . For genera lacking explicit species demarcation criteria, a 90% amino acid identity threshold for the RdRP or replicase protein was applied (Supplementary Table 2). The abundance of these viral contigs was estimated by mapping reads back to the assembled genomes using Bowtie2 version 2.5.2 with ‘--end-to-end’ and ‘--very-fast’ settings. Alignments were sorted and indexed with SAMtools version 1.18 and visualized with Geneious Prime version 2020.2.4 67 , 68 . For bacteria and eukaryotic microbes, we initially utilized MetaPhlAn version 4 to identify potential microbial taxonomy 69 . Complete reference genome sequences of the corresponding genera were subsequently downloaded from GenBank and used as templates for read mapping and gene abundance estimation with Bowtie2 (version 2.5.2) 63 . Highly conserved regions, such as rRNA genes, were excluded from the reference genome sequences before conducting mapping analyses. Based on the mapping results, we generated consensus sequences of marker genes (e.g., rpoB , groEL , recA , and gyrB ), which were then subjected to BLASTn comparisons against the NCBI nucleotide (nt) database to determine microbial taxonomy at the species level. Evolutionary analyses To determine the evolutionary relationships of the newly identified microbes, reference nucleotide/amino acid sequences for microbial taxa in question were downloaded from the NCBI GenBank Database. In all cases, sequences were then aligned using MAFFT 70 , with the 5’ and 3’ unaligned regions (when present) removed manually and ambiguously aligned sequences excluded using TrimAl version 1.5.0 71 . Phylogenetic trees on these data were then estimated using the maximum likelihood method implemented in PHYML 3.0, employing the GTR model of nucleotide substitution and SPR branch swapping 61 . Node support was estimated using an approximate likelihood ratio test using Shimodaira–Hasegawa-like procedures. Characterization of henipaviruses To assess the prevalence of novel henipaviruses in bats and in different organs, real-time quantitative reverse transcription PCR (qRT-PCR) and nested RT-PCR were performed on all individual kidney samples. Specific primers were designed using the virus genome sequences obtained from libraries YNBS02 (Yunnan bat henipavirus 1) and YNBS03 (Yunnan bat henipavirus 2). To investigate viral distributions across various bat organs, PCR detection and individual meta-transcriptomics assays were performed on the brain, heart, liver, kidney, and gut sample of the positive bats (WD1733 and WD1745). However, the library construction for the brain sample from WDBN1733 failed. As the full-length sequence of Yunnan bat henipaviruses 1 was not initially obtained, PCR assays and Sanger sequencing were employed to complete it. The final genome consensus sequences were confirmed by mapping the reads against draft genome sequences, and viral abundance was estimated based on the number of reads mapped to genome 63 . For each complete genomes, potential open reading frames (ORFs) and coding arrangements were predicted using ORFfinder ( https://www.ncbi.nlm.nih.gov/orffinder/ ) and annotated by blastp program ( https://blast.ncbi.nlm.nih.gov/Blast.cgi ). Phylogenetic trees for each gene were estimated following the standard protocol described above. Author Contributions Conceptualization, G.-P.K., T.Y., M.S., and Y.F.; Methodology, G.-P.K., J.W., M.S., and Y.F.; Investigation, G.-P.K., T.Y., W.-H.Y., J.W., H.P., Y.-F.P., W.-C.W., Y.-Q.C., M.S., and Y.F.; Writing – Original Draft, G.-P.K., T.Y., M.S., and Y.F.; Writing – Review and Editing, All authors; Funding Acquisition, M.S., and Y.F.; Resources (sampling), G.-P.K., T.Y., W.-H.Y., H.P., J.W., X.H., L.-F.Y., and Y.F.; Resources (Computational), G.-P.K., T.Y., Y.-F.P., Q.-Y.G, Y.-Q.C., and M.S.; Supervision, J.-S.E., E.C.H., M.S., and Y.F.. Competing interests The authors have declared that no competing interests exist. Additional information Extended data Extended Data Fig. 1 Supplementary information Supplementary Table 1. Information of sample group and RNA library in this study. Supplementary Table 2. Viruses in bat kidneys identified in this study. Acknowledgments This study was funded by grants from the National Key R&D Program of China (2024YFC2607502). Y. F. was supported by Yunnan Revitalization Talent Support Program Top Physician Project (XDYC-MY-2022-0074). M.S. was supported by the National Natural Science Foundation of China (82341118, 32270160), Natural Science Foundation of Guangdong Province of China (2022A1515011854), Shenzhen Science and Technology Program (KQTD20200820145822023), Major Project of Guangzhou National Laboratory (GZNL2023A01001), Guangdong Province “Pearl River Talent Plan” Innovation, Entrepreneurship Team Project (2019ZT08Y464), and the Fund of Shenzhen Key Laboratory (ZDSYS20220606100803007). 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Holmes , Mang Shi , Yun Feng bioRxiv 2025.01.10.632301; doi: https://doi.org/10.1101/2025.01.10.632301 Share This Article: Copy Citation Tools Infectome analysis of bat kidneys from Yunnan province, China, reveals close relatives of Hendra-Nipah viruses and prevalent bacterial and eukaryotic pathogens Guopeng Kuang , Tian Yang , Weihong Yang , Jing Wang , Hong Pan , Yuanfei Pan , Qin-yu Gou , Wei-chen Wu , Juan Wang , Lifeng Yang , Xi Han , Yao-qing Chen , John-Sebastian Eden , Edward C. 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