A molecular inventory of the faecal microbiomes of 23 marsupial species

A molecular inventory of the faecal microbiomes of 23 marsupial species | 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 A molecular inventory of the faecal microbiomes of 23 marsupial species View ORCID Profile Kate L. Bowerman , Rochelle M. Soo , Pierre-Alain Chaumeil , Michaela D. J. Blyton , Mette Sørensen , Disan Gunbilig , Maika Malig , Moutusee Islam , Julian Zaugg , David L. A. Wood , Ivan Liachko , Benjamin Auch , Mark Morrison , Lutz Krause , Birger Lindberg Møller , Elizabeth H. J. Neilson , Philip Hugenholtz doi: https://doi.org/10.1101/2025.08.31.672981 Kate L. Bowerman 1 Australian Centre for Ecogenomics, School of Chemistry & Molecular Biosciences, The University of Queensland , Brisbane, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kate L. Bowerman For correspondence: k.bowerman{at}uq.edu.au p.hugenholtz{at}uq.edu.au Rochelle M. Soo 1 Australian Centre for Ecogenomics, School of Chemistry & Molecular Biosciences, The University of Queensland , Brisbane, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pierre-Alain Chaumeil 1 Australian Centre for Ecogenomics, School of Chemistry & Molecular Biosciences, The University of Queensland , Brisbane, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michaela D. J. Blyton 1 Australian Centre for Ecogenomics, School of Chemistry & Molecular Biosciences, The University of Queensland , Brisbane, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mette Sørensen 2 Department of Plant and Environmental Sciences, University of Copenhagen , Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Disan Gunbilig 2 Department of Plant and Environmental Sciences, University of Copenhagen , Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maika Malig 4 Phase Genomics Inc. , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Moutusee Islam 4 Phase Genomics Inc. , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Julian Zaugg 1 Australian Centre for Ecogenomics, School of Chemistry & Molecular Biosciences, The University of Queensland , Brisbane, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site David L. A. Wood 3 Microba Life Sciences , Brisbane, Queensland, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ivan Liachko 4 Phase Genomics Inc. , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Benjamin Auch 4 Phase Genomics Inc. , Seattle, WA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mark Morrison 5 Frazer Institute, School of Medicine, The University of Queensland , Brisbane, Queensland, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lutz Krause 3 Microba Life Sciences , Brisbane, Queensland, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Birger Lindberg Møller 2 Department of Plant and Environmental Sciences, University of Copenhagen , Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elizabeth H. J. Neilson 2 Department of Plant and Environmental Sciences, University of Copenhagen , Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site Philip Hugenholtz 1 Australian Centre for Ecogenomics, School of Chemistry & Molecular Biosciences, The University of Queensland , Brisbane, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: k.bowerman{at}uq.edu.au p.hugenholtz{at}uq.edu.au Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Despite the recent expansion of culture-independent analyses of animal faecal microbiomes, many lineages remain understudied. Marsupials represent one such group, where despite their iconic status, direct sequencing-based analyses remain limited. Here we present a metagenomic and metabolomic exploration of the faecal microbiomes of 23 Diprotodontia marsupials, producing a reference set of 3,868 prokaryotic and 12,142 viral metagenome-assembled genomes, the majority (>80%) of which represent novel species. As with other animals, host phylogeny is the primary driver of microbiome composition, including distinct profiles for two eucalyptus folivore specialists (koalas and southern greater gliders), suggesting independent solutions to this challenging diet. Expansion of several bacterial and viral lineages were observed in these and other marsupial hosts that may provide adaptive benefits. Antimicrobial resistance genes were significantly more prevalent in captive than wild animals likely reflecting human interaction. This molecular dataset contributes to our ongoing understanding of animal faecal microbiomes. Impact statement Despite their ecological and evolutionary importance, marsupials remain underrepresented in microbiome research. Here, we present the most extensive faecal microbiome dataset to date for this group, encompassing metagenomic, metabolomic, and proximity ligation data from 23 marsupial species. As in other animals, we find the microbial community structure reflects the host species, and some marsupials carry expanded sets of certain microbial lineages indicative of within-host evolution. This work substantially expands the genomic landscape of host-associated microbes and viruses in a poorly studied mammalian clade. Data summary Raw read data, prokaryotic MAGs ≥50% complete with ≤10% contamination are available via the European Nucleotide Archive under project PRJEB89408. The full set of viral genomes, clustered protein database and metabolite data (raw and processed) are available via https://doi.org/10.48610/14e37e9 . Prokaryotic MAGs are also available via https://figshare.com/s/87443d80817f57aadc16 . Introduction Marsupials are celebrated Australian fauna, however, their microbiomes remain underexplored. Marsupials comprise seven taxonomic orders, of which the Diprotodontia harbour the majority of iconic species including koalas ( Phascolarctus cinereus ), wombats (e.g. Vombatus ursinus ) and kangaroos (e.g. Osphranter rufus ) 1 . However, only 27 of the 151 extant Diprotodontia species 2 have had their faecal microbiomes characterised using culture-independent methods, and only 14 of these have been analysed using metagenomics ( Table S1 ) 3 – 29 . To our knowledge, no other omic techniques have been applied to marsupial microbiomes and no culture-independent analyses of marsupial viral communities have been reported. Here, we applied metagenomics and metabolomics to faecal samples from 23 Diprotodontia species, including two eucalyptus folivore specialists, koalas and southern greater gliders, to contribute to our knowledge of microbial species associated with marsupials. Eucalyptus leaves are an unusual diet due to their low nutrient availability and plethora of complex and often toxic secondary metabolites that deter herbivores, including terpenes, tannins, cyanogenic glycosides, flavonoids and formylated phloroglucinol compounds 30 – 32 . Furthermore, there is a high degree of biochemical variation between leaves of the estimated 150-200 Eucalyptus species that marsupials feed on and koalas and greater gliders have different feeding preferences 31 , 33 – 35 . Here, we find this reflected in their faecal metagenomes and metabolomes indicating independent solutions to mastering this challenging diet. Despite the evolutionary divergence and geographical isolation of marsupials from other mammals over most of their history 36 , prokaryotic taxonomic novelty of the analysed faecal microbiomes was only observed at the genus and species level, suggesting that readily discoverable higher level microbial diversity in the animal gut is reaching saturation. Results and discussion Comparison of marsupial faecal microbiomes To extend our knowledge of the marsupial gastrointestinal microbiome, we characterised 94 faecal samples from 23 host animal species (and one subspecies Trichosurus vulpecula subsp. fuliginosus , golden brushtail possum) via metagenomic and metabolomic analyses ( Tables S2 & S3 ). These species represent 17 genera and 6 families in the order Diprotodontia , of which this study provides the first metagenomic data for 13 species, 9 genera and 1 family ( Fig. 1 ). A further set of 23 faecal samples from nine of these species was investigated using proximity-ligation sequencing to provide links between microbial hosts and their viruses and plasmids 37 ( Table S3 ). Download figure Open in new tab Figure 1 A maximum-likelihood phylogenetic tree of the 23 marsupial species used in this study (subspecies Trichosurus vulpecula fuligonosus was excluded due to the closeness to Trichosurus vulpecula ) and an outgroup ( Dromiciops gliroides ) was generated from a concatenated alignment of nine genes (four chromosomal, three mitochondrial and two ribosomal) 107 . Stars indicate hosts for which this study provides the first metagenomic data. Interlocked circles denote samples sequenced using both standard metagenomic and proximity ligation sequencing. We compared faecal microbiomes based on their taxonomic (prokaryotic and viral), functional (KEGG and CAZy) and metabolomic profiles ( Fig. 2 ). In all cases, host phylogeny (marsupial family) was the primary contributor to variance ranging from 12% (viral taxa) to 31% (CAZymes). This is consistent with previous reports of host phylogeny contributing the most variance to faecal profiles in animals, indicative of phylosymbiosis between animals and their microbiomes 38 – 40 . Download figure Open in new tab Figure 2 Principal component analysis based on a, marker gene-based prokaryotic community b, genome-based prokaryotic community c, genome-based viral community d, metabolomic e, KEGG-based functional and f, CAZy-based functional profiles. Taxonomic relative abundances were centred log-ratio transformed and functional RPKM values were log 2 transformed prior to PCA. Metabolomic data as described in the methods. Samples are coloured based on their marsupial host family. Results of redundancy analysis incorporating host family, diet (grazer, browser, omnivore, fungivore), gut type (hindgut, foregut), location and captivity status displayed per dataset. Sample location (wildlife sanctuary or region where samples were obtained) and diet (grass-dominant grazers, foliage-dominant browsers, omnivores, fungivores) also contributed significantly to microbiome variance ( Fig. 2 ), consistent with other mammals 38 , 41 . Wild/captive status was not significantly correlated with faecal profiles, despite known differences in some marsupial species, such as higher incidence of the bacterial family Muribaculaceae in captive vs wild koalas 10 . This may reflect sampling bias as fewer scat samples were obtained from wild animals in our study (19% wild vs 81% captive). Macropod microbiomes were taxonomically distinct from those of other marsupial species forming a primary divide using principal component analysis ( Fig. 2A-C ), driven by differences in the classes Clostridia and Bacteroidia , or their associated phages ( Fig. S1A-C , Tables S4-S6 ). This may be in part due to macropods being the sole foregut fermenting family in the dataset. However, gut morphology (foregut vs hindgut) did not explain additional variance in the faecal profiles beyond that captured by the host family ( Fig. 2 ) and the distinction was less apparent in functional and metabolomic PCA plots ( Fig. 2D-F , Tables S7-S9 ). The functional profiles of the grazing families Macropodidae and Vombatidae overlapped, particularly evident in the CAZyme profiles where they were separated from other families along the primary component axis ( Fig. 2F ). Multiple xylan and mannan-degrading CAZymes were amongst the top contributors to this separation, likely used for digestion of hemicelluloses that are more abundant in grasses compared to leaves and other browse ( Fig. S1F , Table S9 ). Macropodidae and Vombatidae also harboured the highest faecal microbial diversity, with browsing animals significantly lower ( Fig. S2 ), as seen previously in African herbivores 42 . Download figure Open in new tab Fig. S1. Principal component analysis based on a , marker gene-based prokaryotic community b , genome-based prokaryotic community c , genome-based viral community d , metabolomic e , KEGG-based functional and f , CAZy-based functional profiles. Top 20 features based on Euclidean magnitude across PC1 and PC2 are displayed. Full lists are contained in Tables S4-S9. Download figure Open in new tab Fig. S2. Shannon diversity based on SingleM prokaryotic community profiles. We then compared the faecal profiles of specialist (koala, southern greater glider) and generalist eucalyptus feeders (common and golden brushtail and common ringtail possums) to look for signs of shared adaptations to this challenging diet. Despite similarities in the context of all marsupials investigated ( Fig. 2 ), analysis in isolation revealed taxonomic, functional and metabolomic differences between these groups ( Fig. S3 , Tables S10-S17 ). Multiple eucalypt-derived terpenes positively associated with the specialist feeders ( Fig. S3B , Table S11 ) indicative of the high rates of eucalypt ingestion in these hosts. However, the profiles of the two specialist hosts were distinct. For example, koala faeces were higher in essential oil metabolites and greater glider faeces in wax-derived compounds ( Fig. S3E , Table S15 ), likely reflecting feeding preferences. Koalas favour leaves of the subgenus Symphyomyrtus that are often rich in complex oils and greater gliders prefer the leaves of the subgenus Monocalyptus that have thick wax layers 43 – 45 . Download figure Open in new tab Fig. S3. Principal component analysis of eucalypt consuming marsupials ( a-d ) and eucalypt specialist marsupials ( e-h ) based on a & e, marker gene-based prokaryotic community, b & f , metabolomic, c & e , KEGG-based functional, d & h , CAZy-based functional profiles. Eucalypt consumers includes koalas, greater gliders, common and golden brushtails and common ringtails. Eucalypt specialists include koalas and greater gliders. Top 20 features based on Euclidean magnitude across PC1 and PC2 are displayed. Full lists are contained in Tables S10-S17. Prokaryotic communities of the marsupial gut We individually assembled and binned the 94 standard Illumina shotgun metagenomes (averaging ∼3.2 Gbp per sample) obtaining a total of 908 medium to high quality (≥70% complete, 10 kb) prokaryotic metagenome-assembled genomes (MAGs) ( Table S4 ). An additional 2,960 medium to high quality prokaryotic MAGs were obtained from the 23 proximity ligation metagenomes (averaging ∼56 Gbp per sample). The combined set of 3,868 MAGs (3,828 bacterial and 40 archaeal) represent 2,412 species, 659 genera, 134 families, 59 orders, 27 classes and 19 phyla, of which 1,951 species (80%) and 58 genera (9%) are new to GTDB (release 09-RS220) ( Table S18 ) 46 . The lack of higher taxonomic rank novelty (family and above) is notable especially as this study represents the first metagenomic data for many marsupial species ( Table S1 ), suggesting that microbial diversity recoverable from animal faeces as MAGs is beginning to saturate above the level of species. This is consistent with a recent extensive faecal microbiome survey of goats and sheep that found only 3.7% novelty at the genus level for their MAG dataset, but 84.5% novelty at the species level 47 . Read mapping analysis against the 3,868 marsupial MAG dataset and 85,205 GTDB 09-RS220 species representatives ( see Methods ) 46 revealed that a substantial amount of microbial diversity was not captured in the MAG or reference genome sequences for most marsupial hosts with a median read mapping percentage of 40% (ranging from 5 to 70%; Fig. S4 ). This is comparable to a previous animal faecal microbiome study in which a large proportion of metagenomic reads did not map to reference genomes (21%) 48 suggesting that total diversity in the gut is still far from saturation. Consistent with this inference, a marker gene analysis of the marsupial scat reads using SingleM indicates substantially more prokaryotic microbial diversity remains to be described including 2 novel phyla, 27 novel classes, 59 novel orders and 132 novel families ( Table S19 ). Differences in read recruitment observed between marsupial hosts partly reflect prior metagenomic studies, with the koala and wombat 9 , 10 , 28 having amongst the highest levels of read recruitment ( Fig. S4 ). Download figure Open in new tab Fig. S4. Read recruitment to MAG database incorporating MAGs from the current study ≥70% complete with ≤5% contamination, or study MAGs combined with public genomes selected based on read mapping or marker gene-based profiles. Read mapping proportions based on filtered alignments with ≥95% identity across ≥90% of the read length. Broad marsupial host range species Only 10.8% of marsupial gut prokaryotic species represented by genome sequences were found in two or more marsupial families ( Table S20 ). The most ubiquitous species in our study, found in 35 individual scat samples across all six marsupial families was Escherichia coli . This well-studied microbial species is a facultative anaerobe and common in the lower intestine of mammals, although it typically constitutes only a small percentage of the total prokaryotic community. E. coli is also found widely in the environment 49 suggesting that it likely moves between marsupial hosts, which is supported by its presence in cohabiting individuals at Port Douglas Wildlife Habitat and low incidence at other animal sanctuaries ( Table S20 ). Other broad range species include Phascolarctobacterium faecium and Akkermansia muciniphila detected in five of the six marsupial families investigated ( Table S20 ). P. faecium was originally isolated from koala faeces 50 , but has since been found in a wide range of animal hosts including human 51 , rat 52 and cow 53 . It uses succinate as a primary substrate, likely to be provided by Bacteroides thetaiotaomicron 54 or other Bacteroides species that are also commonly found in marsupials ( Table S20 ). A. muciniphila is a specialist mucin degrader common to many animal species 55 and is likely to play the same role in marsupials. Marsupial host specific taxa The majority of microbial species with genomic representation were lineage specific (89.2%; Table S20 ) consistent with separation of host marsupial families by their taxonomic profiles ( Fig. 2A & B ). Cryptobacteroides species are conspicuous constituents of marsupial grazers particularly in kangaroos, which harbour 48 novel species ( Table S20 ) including a radiation of 37 species indicating a macropod-specific expansion of this genus ( Fig. 3 & 4A ). Cryptobacteroides are recognised for their importance in the rumen where they are inferred to degrade lignocellulose and starch via polysaccharide utilisation loci (PUL) 56 and may play a similar role in kangaroos. Relative to other marsupial Bacteroidota, the Cryptobacteroides species are enriched in starch-, xylan-, β-glucan- and β-mannan-degrading enzymes, indicating their likely importance in grass digestion in kangaroos and wombats ( Table S21 ). Download figure Open in new tab Figure 3 Maximum-likelihood phylogenetic tree of dereplicated bacterial MAGs supplemented with public genomes identified as present in marsupials based on read mapping to the GTDB or profiling with SingleM. Rings indicate (from inside): MAGs from the current study, presence in marsupial families based on read mapping and count of marsupial families bacterial species is present in. Dark orange branches indicate the class SZUA-567 , dark pink the genus Cryptobacteroides and dark blue the genus Eubacterium_I (see Fig. 4 for more detail). Consistent with a previous report 9 , an as-yet-uncultured basal Planctomycetota lineage, SZUA-567 , was identified in koala scat. This family-level lineage is represented by two genera and nine species in our survey and was found solely in koalas ( Table S20 ), suggesting that it was introduced into the koala microbiome after divergence from wombats approximately 35-40 Mya 57 ( Fig. 1 ). Currently, the closest relatives of koala SZUA-567 MAGs have been identified in termite gut microbiomes 58 . Koalas are known to feed on termite nests 59 and SZUA-567 species from koalas and insects are interleaved ( Fig. 4B ) leading us to hypothesise that ingested termites are the source of this lineage in the koala. The specific role of these bacteria and how they managed to colonise the koala gut remain to be determined. Download figure Open in new tab Figure 4 Maximum-likelihood phylogenetic trees of a , Cryptobacteroides b , family SZUA-567 c , Eubacterium_I and d , Crassvirales . Trees in panels a-c incorporate dereplicated bacterial MAGs and public genomes and are inferred from an alignment of GTDB marker proteins 46 . Panel d tree incorporates vOTU representatives and public Crassvirales genomes used in 81 and is based on the alignment of TerL proteins. Boxes to the right of each tree indicate presence of each species in marsupial families based on read mapping or origin of public genomes. An expansion of the Lachnospiraceae genus Eubacterium_I was identified in southern greater gliders with 13 species identified, of which 11 were found exclusively in this host ( Fig. 4C ) and the other two also found in koala scat ( Table S20) 28 . The only characterised representative of this genus, E. ramulus isolated from human faeces, is recognised for its ability to degrade flavonoids 60 , 61 . Since flavonoids are prominent phenolic metabolites in eucalyptus leaves including catechins, isorhamnetin, phloretin, kaempferol and luteolin 62 , 63 , we hypothesize that the role of Eubacterium_I in the greater glider and koala is to metabolise these compounds. This is supported by enrichment of flavonoid modifying enzymes in marsupial Eubacterium_I species relative to other marsupial Lachnospiraceae , including flavone/flavonol reductase ( flr , which initiates flavone and flavonol catabolism in the gut 64 , flavanone/flavanol-cleaving reductase ( fcr ) and chalcone isomerase ( chi ; Table S22 ). DNA viruses of the marsupial gut To assess marsupial gut viromes, we focused our analysis on the subset of 23 host samples sequenced using proximity ligation due to greater sequencing depth ( Table S3 ) and the ability to connect non-integrated viruses to their hosts. A total of 12,142 DNA viral genomes were obtained ranging in size from 18 to 231 kbp (average 48.6 kbp) ( Table S23 ). These represent 8,928 viral species (vOTUs) according to a recommended ANI-based definition 65 ( Table S23 ). Most of these species were represented by a single genome (82%), however 21 vOTUs comprised 10 or more genomes each ( Table S23 ). The great majority (99%) of the vOTUs had no close (species-level) matches in public databases ( Table S24 ). However, at a higher taxonomic level, 98% of the marsupial viral species belong to the class Caudoviricetes ( Table S23 ) consistent with DNA viruses found in many other animal faecal microbiomes 66 – 68 and morphological identification of a great variety of Caudoviricetes ( Myoviridae , Siphoviridae and Podoviridae ) in the gastrointestinal tracts of kangaroos 69 . Prokaryote and marsupial host range Almost a third of the marsupial vOTUs (2,713) could be linked to one or more prokaryotic host species either via proximity ligation (as evidenced by interlinkage reads) and/or by host genome sequences flanking the viral genome ( Table S25 ). Most of these vOTUs (2,405; 89%) were associated with a single host species ( Table S25 ) consistent with the typically narrow host range of cultured phages 70 . Further, culture-independent evidence from human faecal samples using single cell viral tagging found that 98% of the phages targeted only one host species 71 . Together these findings suggest that phages are not significant vectors of gene exchange between different bacterial species in the marsupial gut microbiome and animal gut more generally. Of the 308 vOTUs with links to >1 prokaryotic host species, 42% (129) were genus specific, 31% (96) family specific, 16% (51) order specific, 8% (25) class specific, 2% (5) phylum specific and 1% (2) domain specific ( Table S25 ). The most promiscuous viral species, vOTU_2a, was linked to nine host species belonging to five genera of the family CAG-272 in the order Oscillospirales ( Table S25 ). Another viral species, vOTU_14, appears to be capable of targeting monoderms (a member of the spore-forming Bacillales genus Pradoshia ) and diderms ( Prevotella sp017410365 ), a rarely observed trait amongst phages 72 . The carriage of multiple genes encoding phage tail lysozyme domains in vOTU_14 genomes may assist this virus in breaching different cell envelope types ( Table S26) 73 . The marsupial host range of faecal viral species reflected in large part prokaryotic populations in that most were restricted to single host lineages; 59% (5,282) to a single marsupial host species, 67% (6,001) to a single genus and 96% (8,584) to a single family ( Table S27 ). Indeed, a substantial portion of vOTUs (3,692, 41.4%) were identified in a single faecal sample, suggesting that many are specific to individual animals, consistent with the observed individuality of human gut viral communities 74 , 75 ( Table S27 ). Some of these localised viral populations were linked to bacterial species found in multiple marsupial families (e.g. vOTU_1974 to Bacteroides xylanisolvens and vOTU_7758 to Parabacteroides distasonis ; Tables S20 & S25 ) suggesting that they have not been transferred between marsupial species via their prokaryotic hosts. This is similar to an observation in human faeces where the same bacterial species was targeted by different viruses in different faecal samples 71 . The converse was also found, however, where the broad marsupial host range of a bacterial host was mirrored by their viruses. For example, the only viral species identified in representatives of five marsupial families (vOTU_1924; Table S27 ) was linked to Phascolarctobacterium faecium detected in the same marsupial families ( Table S20 ), suggesting that this virus has been successfully dispersed between marsupial species with its host. Crassvirales are prevalent in macropods Crassvirales are a recently discovered lineage of dsDNA viruses that have gained attention due to their prevalence in the faecal microbiomes of humans and other primates 76 – 78 . However, their presence in other animal hosts appears to be sporadic, with the exception of chickens 79 . Notably, 151 (1.7%) of the marsupial vOTUs were classified as Crassvirales , most of which (91%, 137 vOTUs) belong to macropod clades ( Fig. 4D ; Table S27 ). Consistent with previous reports of a strictly lytic lifestyle, marsupial Crassvirales were not found to be integrated into their prokaryotic hosts (with one potential exception; rk1_vMAG_90 was predicted to be a provirus, however, no host MAG was identified), and lack identifiable integrases and recombinases necessary for lysogenic infection ( Table S26 ) 80 . Only five Crassvirales genomes were linked to identifiable host MAGs, three members of the Bacteroidota (in the genera Alistipes , Barnesiella and Prevotella ), and two Bacillota (both members of the genus Candidatus Coprovivens). This appears to extend the known host range of these viruses, which are thought to exclusively target members of the phylum Bacteroidota 78 , 79 , although CRISPR spacers suggest broader associations, including with members of the Bacillota 81 . Ca. Coprovivens belongs to the as-yet-uncultured Bacilli order RF39 , all members of which are predicted to be host-associated 82 . Therefore, it is possible that Bacteroidota cells simply co-host Ca . Coprovivens and Crassvirales . Antimicrobial resistance potential in marsupials As captive animals comprise a large portion of marsupials in this study, we assessed the antimicrobial resistance (AMR) potential of their scat microbiomes across the dataset to understand whether this was influenced by captivity as is seen in other species species 83 , 84 . We identified 188 types of recognised AMR genes across the 117 marsupial scat samples (3,907 total genes), representing 24 classes of antimicrobial resistance genes ( Table S28 ). Approximately half (47%) of these gene types were identified on potentially mobilisable elements (plasmid or viral contigs), with plasmids carrying ∼20 to 40-fold more AMR genes (1 in ∼1,500 genes) than viral (1 in ∼60,000 genes) or chromosomal sequences (1 in ∼33,000 genes; Fig. S5 ), consistent with the primary role of plasmids in AMR transfer 85 . Plasmid-encoded AMR genes were also more diverse than viral-encoded genes, comprising 17 AMR classes vs 10 for viral sequences ( Table S28 ). Download figure Open in new tab Fig. S5. Count of AMR genes identified on all contigs based on AMRFinder search, excluding singletons. Source indicates the Genomad-predicted sequence type of the contig carrying the representative gene sequence. The most prevalent classes of resistance genes found in marsupial faeces target glycopeptides (93% of captive animals, 86% wild) and ꞵ-lactams (87% captive, 45% wild) ( Table S29 ), broadly reflective of the widespread distribution of these AMR classes 86 . Both of these classes were significantly enriched in captive vs wild animals, as were lower prevalence classes targeting macrolides (80% vs 5%), lincosamides (75% vs 5%), sulfonamides (28% vs 0%) and trimethoprims (20% vs 0%) ( Table S29 ). While captive animals harboured a significantly higher abundance of many AMR classes overall, this effect was host-dependent ( Fig. S6 , Table S29 ). Captive possums, southern greater gliders and koalas (families Phalangeridae , Pseudocheiridae and Phascolarctidae ) typically displayed lower AMR gene abundance than macropods, wombats and small gliding species (families Macropodidae , Vombatidae and Petauridae ), with the exception of one hospitalised common brushtail possum (CBP3) ( Table S30 ). Human intervention via antibiotic treatment is therefore likely driving differences in prevalence of AMR gene classes between captive and wild animals, and between different marsupial families. For example, in macropods, clindamycin (a lincosamide) is used to treat necrobacillosis and co-trimoxazole (a combination of sulfonamide and trimethoprim) to treat wounds and urinary tract infections 87 , 88 . However, other antibiotics such as macrolides are not commonly used to treat marsupials 89 and increased macrolide resistance in captive animals may be due to other factors, such as transfer of antibiotic resistant bacteria from humans and other animals or environmental sources 90 , 91 . Download figure Open in new tab Fig. S6. a PCA based on AMR gene RPKM values. b RPKM sum per AMR class per marsupial faecal sample. Concluding remarks In this study, we present a metagenomic (including proximity ligation data) and metabolomic molecular inventory of the faecal microbiomes of 23 marsupial species, the first such data for 13 of these species, and the first metabolomic and proximity ligation data for any marsupial. The 3,868 assembled medium-to-high quality MAGs include representative genomes for 1,951 prokaryotic species (80%) and 58 genera (9%) new to GTDB 09-RS220. In addition, we present a marsupial viral genome catalogue of 12,142 genomes (8,928 vOTUs), with prokaryote host links for ∼30% of viral species. Combined, these data confirm host family as the primary driver of marsupial faecal taxonomic, functional and metabolomic profiles. Host specificity extends to species with highly specialised, eucalyptus-dominant diets (koalas and southern greater gliders), where their distinct taxonomic and functional profiles support alternative solutions to eucalyptus digestion. We observe host lineage-specific bacterial expansions including Cryptobacteroides species in kangaroos, the Planctomycetota family SZUA-567 in koalas and Eubacterium_I in greater gliders and expanded repertoire of Crassvirales phage in macropod hosts. We expect these data will provide a valuable reference for comparative analyses of other animal faecal and gut microbiome datasets. While this resource expands our knowledge of the marsupial microbiome, many species remain entirely unexplored (122/151 Diprotodontia species), indicating the need for ongoing work in this area. Methods Sample collection Ninety-five faecal samples from 82 captive marsupials were collected from wildlife sanctuaries in South-East Queensland (Lone Pine Koala Sanctuary, Brisbane; David Fleay Wildlife Park, Burleigh Heads; and Currumbin Wildlife Sanctuary, Currumbin) and North Queensland (Wildlife Habitat, Port Douglas and Cairns Tropical Zoo) ( Table S3 ). Twenty-two samples from 16 wild marsupials were collected from areas surrounding Brisbane and Wongabel State Forest, Queensland. Samples were stored in Eppendorf tubes at −80°C prior to downstream processing. DNA extraction and sequencing of Illumina-only samples For the 94 faecal samples sequenced using only Illumina sequencing, DNA extraction and sequencing methods are described in 28 . Briefly, ∼50 mg of faecal sample was added to tubes with 0.7 mm garnet beads (MO BIO, Carlsbad, CA, USA) and suspended in 750 µL Tissue Lysis Buffer (Promega, Madison, WI, USA). Following 10 min of bead-beating at maximum speed, samples were pelleted by centrifugation. Finally, 200 µL of supernatant was used as the input for DNA extraction on a Maxwell 16 Research Instrument with the Maxwell 16 Tissue DNA Purification Kit (Promega, Madison, WI, USA) according to the manufacturer’s instructions. DNA was sequenced with the Nextera XT DNA Sample Prep kit (Illumina, San Diego, CA, USA). Samples were sequenced on the Illumina NextSeq 2000 platform, producing 150 bp paired-end reads at the Australian Centre for Ecogenomics. Proximity ligation sample preparation For the 23 samples sequenced by Phase Genomics using proximity ligation sequencing, faecal pellets were cut in half with a sterile scalpel blade in a laminar flow hood and the centre of the pellet was added to a sterile tube with PG Shield (Phase Genomics, Seattle, WA, USA). For southern greater glider samples, due to the limited amount of available sample, ∼0.5 g of faeces was added to 5 mL of PG Shield. For the remainder of the samples, ∼1 g of faeces was added to 10 mL of PG Shield. Tubes were shaken and sent to Phase Genomics for proximity ligation sequencing using the ProxiMeta Hi-C v4.0 Kit which sequentially cross-links cells, mechanically lyses them, isolates and fragments chromatin, and ligates spatially adjacent DNA ends. After reversing cross-links, the biotin-tagged Hi-C junctions were purified on streptavidin beads to yield DNA ready for paired-end sequencing 92 . Indexed Hi-C libraries were pooled and loaded onto an Illumina NovaSeq X with 2 × 150 bp chemistry. Metagenome-assembled genome recovery For the Illumina-only samples, sequencing adaptors were removed with SeqPurge (v2018_11) 93 and the trimmed reads were assembled into contigs using MetaSpades (v3.15.3) using default settings 94 . The contigs were binned using Aviary (v.0.4.3) with aviary recover in default mode 95 from coverage profiles generated across all samples within each main animal group (kangaroo/wallaby, possum, koala, wombat). For the proximity ligation samples, sequencing reads were processed as described 37 , with assembly performed using MEGAHIT (v1.2.9) 96 and binning performed using the ProxiMeta platform 97 , 98 incorporating both standard shotgun and Hi-C data. Marsupial MAG database MAG completeness and contamination was assessed using CheckM2 (v.1.0.1) 99 . MAGs with a completeness of ≥70%, contamination of ≤5% and an N50 of ≥10 kb were chosen for downstream analysis supplemented with publicly available genomes. Public genomes for inclusion in the genome database were selected from species representative genomes in GTDB 08-RS214 46 , 100 . Selection was based on read mapping values generated using CoverM v0.6.1 101 , filtering for alignments exceeding 95% identity across 90% of the read length. Public genomes achieving ≥0.05% relative abundance and recruiting reads to ≥10% of the genome length were included in the final database (n=1,201). The combined genome set (3,868 MAGs + 1,201 public genomes) was dereplicated using CoverM v0.7.0 101 with the ‘cluster’ command at 95% identity with an alignment fraction of 60%. The ‘Parks2020_reduced’ quality formula was used with genome statistics generated by CheckM2 v1.0.1 99 . MAGs were classified with GTDB-Tk using GTDB release 09-RS220 102 . A maximum likelihood tree was inferred using IQ-TREE v2.2.2.3 103 based on the alignment generated by GTDB-Tk ‘de_novo_wf’, using ModelFinder for model selection within the LG model set. Ultrafast bootstrap approximation was generated from 10,000 replicates. Marsupial host DNA contamination As there were no genomes available for the majority of species in the dataset (genomes currently available for koala, eastern-grey kangaroo, yellow-bellied glider, common ringtail possum, green ringtail possum, common brushtail possum & common wombat), an estimate of host DNA contamination was established via read mapping to a marsupial genome database comprising available genomes and exome sequencing data from all genera in the dataset ( Fig. S7 104 – 106 , Table S31 ). Read mapping was performed using CoverM v0.7.0 101 filtering for alignments exceeding 95% identity across 90% of the read length. Download figure Open in new tab Fig. S7. The relative amount of host animal DNA in the faecal metagenomes was estimated by mapping sequencing reads to publicly available marsupial host genomes, including an exon capture dataset obtained from NCBI Genbank (Benson et al., 2013). As found in other animals, host contamination was low (<4% of total reads; Ong et al., 2022), with one conspicuous outlier from an eastern grey kangaroo (∼17%) that may indicate elevated gut epithelial cells in the feces due to inflammation or other dysbiosis (Jiang et al., 2020). Marsupial host tree Nine genes ( apoB , brca1 , irbp , vwf , cytb , coi , nadh2 , 12S rRNA, 16S rRNA) from the 23 marsupial species used in this study were searched for in the NCBI database ( Table S32 ) 107 . For the three marsupial species that were missing from the NCBI database, the missing genes were identified from the sample metagenome assemblies using BLASTx (v.2.9.0) 108 . The BLAST hit with the highest e-value was extracted from the assemblies using BEDTools (v2.27.1) 109 and the correct open reading frame was identified by translating the nucleotides to amino acids using https://web.expasy.org/translate/ . An NCBI BLASTp search was conducted to determine if the correct gene had been identified. MAFFT (v7.505) 110 in auto mode was used to align the genes. The alignment was trimmed with trimAl (v1.4.1) 111 using the gappyout mode. A phylogenetic tree of the multiple sequence alignment was created using IQ-TREE (v1.6.12) 103 using ModelFinder automatic model selection to identify the best-fit model and 1,000 bootstrap replicates. The concatenated gene tree was visualised in ARB (v6.0.6) 112 and rooted on the outgroup ( Dromiciops gliroides ) before being exported into Adobe Illustrator 2023. Prokaryotic community composition Reads were deduplicated using BBMap v39.01 (sourceforge.net/projects/bbmap/) (dedup.sh; absorbrc=f absorbmatch=t absorbcontainment=f) and quality trimmed using Trimmomatic v0.39 113 (SLIDINGWINDOW:4:15 LEADING:3 TRAILING:3 MINLEN:50) prior to mapping to the compiled genome database (described above). Deduplicated, untrimmed reads were used for marker gene-based community analysis. Illumina reads generated by Phase Genomics were subsampled to 10% of their full depth using SeqKit v2.4.0 114 to align with the read depth of other samples. Genome-based community analysis was performed using relative abundance values generated from read mapping using CoverM v0.7.0 101 . Read alignments were filtered to retain the subset that had ≥95% identity across ≥90% of the read length, excluding alignments to genomes with a minimum covered fraction of ≤10%. Marker gene-based community analysis was based on read classification using SingleM v0.16.0 115 . Per-marker counts were combined using the ‘condense’ function. The bacterial and archaeal community fraction was estimated using SingleM Microbial Fraction 116 . Shannon diversity was calculated from SingleM coverage values using phyloseq v1.48.0 117 . Functional profiling Proteins within assembled contigs were identified using Prodigal v2.6.3 118 and clustered at 90% identity across 80% of protein length using MMseqs2 v14-7e284 119 . Representative protein sequences were functionally annotated using DRAM v1.5.0 120 using the Kofam 121 , Pfam 122 and dbCAN 123 databases accessed December 2022. Sample reads (forward only) were aligned to the protein database using DIAMOND v2.1.0 124 (settings evalue 0.00001, min-score 40, query-cover 80, id 70, max-hsps 1 and max-target-seqs 1), filtering for reads ≥ 140 bp with Seqkit v2.4.0 114 . Reads were also aligned to the single copy marker genes included within the SingleM GTDB release 08-RS214 metapackage 115 using the same settings. RPKM (reads per kilobase per million mapped read) values were calculated for both the protein database and SingleM marker genes, with the mean RPKM value across all SingleM markers used to normalise protein database values on a per sample basis. Normalised RPKM values per protein were summed across equivalent functional annotations and log 2 transformed for PCA and differential abundance analysis. Virus identification, filtering and taxonomic assignment Viral contigs in the proximity ligation assemblies were identified using VIBRANT (v1.2.1) 125 , with contigs carrying both bacterial and viral sequence annotated as prophage where viral sequence comprised ≥50% of the contig length. Viral binning was performed using the ProxiPhage algorithm as described 37 . Briefly viral contigs are clustered into viral MAGs (vMAGs) by merging proximity-ligation signal–based clusters and conventional binning results via a greedy network-collapse algorithm, yielding non-redundant vMAGs that meet minimum Hi-C linkage thresholds. Putative viral hosts were identified based on Hi-C linkage data 37 . To limit false positive interactions, only virus-host linkages supported by ≥2 Hi-C read links, a connectivity ratio of ≥0.1 and copy number meeting a threshold determined by a ROC curve were used 37 . Connections with an average copy count of less than 80% of the highest copy count were also removed. To minimise analysis of short viral fragments, we set a viral genome length threshold of ≥18 kb, which is the recommended minimum length of a member of the Caudoviricetes 126 , the dominant viral class found in gut microbiomes 66 – 68 . GeNomad v.1.8.1 127 was used to assign taxonomy to the viruses using the end-to-end command in default mode. For the vMAGs (viruses with multiple contigs), the contigs were concatenated before running GeNomad. Viral novelty was assessed via BLAST alignment to the NCBI viral catalogue (downloaded August 2024) and the Unified Human Gut Virome Catalog (UHGV, votus_full dataset, accessed February 2025, https://github.com/snayfach/UHGV ), retaining hits with ≥95% identity and ≥80% alignment fraction (of query sequence). Crassvirales validation Candidate terminase (TerL), major capsid protein (MCP) and portal proteins were identified in Crassvirales genomes using HMM profiles generated from published multi-sequence alignments 128 using hmmer v3.3.2 129 . Marsupial proteins of length ≥500 with HMM alignment E-value ≤1e-10 (TerL, portal) or 1e-06 (MCP) were combined with known Crassvirales and outgroup proteins of each type 81 . Sequences were aligned using Mafft v7.490 110 and alignments trimmed using trimAl v1.5.0 111 with the ‘gappyout’ option. Maximum-likelihood trees were inferred using IQ-TREE v2.4.0 103 with the ModelFinder option and bootstrap support approximated from 10,000 ultrafast bootstrap replicates. Viral clustering and community profiling An initial all-vs-all comparison of viral genomes was undertaken using skani (v0.2.2) 130 . The Marker k-mer compression factor parameter (-m) was set to 200 (recommended for viruses), and the --slow preset was applied to improve average nucleotide identity (ANI) accuracy, particularly for highly fragmented assemblies. Genome pairs with ANI ≥ 95% and alignment fraction (AF) ≥ 85% were selected, following the thresholds used in 131 . The selected genome pairs were combined into clusters of viruses with similar ANI. Each cluster was manually inspected to identify and resolve non-robust connections. For each cluster, a representative genome was selected based on its connectivity within the cluster. The genome with the highest number of ANI connections was chosen as the representative. If multiple genomes had the same maximum number of connections, the genome with the highest average ANI across all its connections was selected as the representative. The viral community across all samples was assessed based on a viral database containing all cluster representatives and singleton genomes. Read mapping of all samples to the compiled database was performed using CoverM v0.7.0 101 , filtering alignments ≥95% identity across ≥90% of the read length, with a minimum covered fraction of 30%. Antimicrobial resistance Potential antimicrobial resistance genes were identified across all assembled contigs with AMRFinderPlus (v3.12.8) 132 on proteins identified using Prodigal (v2.6.3) 118 . Hits were filtered for those ≥50% identity across ≥80% protein length. Sequence source of each AMR gene (viral, plasmid or chromosomal) was based on prediction of plasmid/viral sequences for each contig using GeNomad v1.8.1 127 . AMR class abundance was calculated as the sum of RPKM values protein within that class, generated as described above. Differential abundance of AMR classes between wild and captive animals was performed using MaAsLin2 133 with marsupial species as a random effect. Marsupial family comparisons (captive animals only) included location as a random effect. Data were log 2 transformed prior to analysis. Metabolomic profiling Metabolites were extracted from faecal and plant samples using hexane according to 134 . In brief, ∼100 mg of frozen homogenized material was added to 1 mL of hexane, followed by incubation (1 h, 40°C, gentle agitation). Thereafter debris was precipitated by centrifugation (5 min, 2000 x g), and dried overnight (65°C) to obtain dry mass for normalization. The resultant supernatant (1 μL) was analyzed by GC–MS (GCMS-QP 2010 Plus, Shimadzu) using a stationary phase column (HP-5 ms Ultra Inert GC Column, 20 m x 0.18 mm, 0.18 μm, 7 inch cage, Agilent Technologies), with a purge flow of 4 mL min−1 and hydrogen as carrier gas. Injection temperature was 250 °C in splitless mode with a temperature gradient of: 40 °C, 4 min hold; 2 °C min−1 to 120 °C; 30 °C min−1 to 280 °C; 3 min hold at 280 °C. Obtained chromatograms were analyzed using PARADISe 135 . Chromatograms were processed using PARAFAC2-based PARADISe dashboard 135 for simultaneous deconvolution and integration. The tentative identity of individual unknowns was accepted by comparison of their mass-spectral data with those of the NIST11 mass spectral library (NIST) data after comprehensive peak alignment, manual interval selections, and feature detections. Further processing of peak areas was performed using the web portal of MetaboAnalyst (v5.0) 136 . Missing values were replaced with 20% of the minimum value for that compound and compounds with ≥50% missing values were removed. Compounds with low repeatability (relative standard deviation ≥30%) and low variance (10% of total compounds) were also removed. Remaining data were median normalized, log-transformed and auto-scaled prior to analysis in R. Statistical methods and data visualisation SingleM coverage and read mapping-based data (genome-based prokaryotic and viral profiles) in relative abundance format were centred log-ratio transformed prior to PCA and differential abundance analysis. Prokaryotic genome-based profiles were filtered for taxa present in ≥2 samples. Squared Shannon diversity values were compared using lme4 v1.1-35.4 137 and lmerTest 3.1-3 138 with marsupial species as a random effect. RPKM data (KEGG, CAZy, AMR profiles) were log 2 transformed prior to analysis. Metabolomic data preparation is described above. Where multiple samples were obtained from the same animal, only a single sample was retained for statistical analyses. PCA and redundancy analyses were conducted using vegan v2.6-4 (R v4.3.2) 139 . To identify the most influential features in PCA, species scores were extracted and the Euclidean magnitude of each feature across PC1 and PC2 was calculated, and the top 20 features by magnitude were selected for display. Redundancy analysis included marsupial family, diet (grazer, browser, omnivore, fungivore), gut type (hindgut, foregut), location and captivity status as explanatory variables and significance was determined using the ‘anova.cca’ function. Enrichment of functions between groups was assessed using EnrichM v0.6.1 ( https://github.com/geronimp/enrichM ). Plots were created using ggplot2 v3.5.1 140 . Bacterial and viral phylogenetic tree figures were created using iTOL 141 . Author contributions RMS, MM, BLM, EHJN and PH designed the project. RMS, MS, DG, MM, MI, IL, BA and EHJN performed experimental work. RMS, KLB, PAC, DLAW, BA, LK and EHJN performed data analysis. JZ provided data analysis scripts. All authors contributed to data interpretation. RMS, KLB and PH wrote the manuscript and all authors edited and approved the submitted version. RMS and KLB contributed equally to this work. Conflicts of interest IL and BA are employees of Phase Genomics, Inc. Funding information This work was supported by Australian Research Council Discovery Projects DP150104202, awarded to P.H. and M.M, and DP250103673, awarded to P.H. and K.L.B, and partially supported by grant INV-044643 from the Gates Foundation to Phase Genomics. Ethical approval Ethical permission for the collection of all samples was granted by the Animal Ethics Unit, the University of Queensland, Brisbane, Australia, under ANRFA/SCMB/099/14 and 2023/AE000378. Acknowledgements We thank Lone Pine Koala Sanctuary, David Fleay Wildlife Park, Wildlife Habitat Port Douglas, and Cairns Tropical Zoo for providing samples from captive animals and D.S. Teakle and A. Shima for the collection of samples from wild animals. We thank Miriam Shiffman and Emily Hoedt for sample collection and faecal DNA extractions, Alex Hasson for preliminary statistical analysis of the data and Thomas Sicheritz-Pontén for discussions. Funder Information Declared Australian Research Council , DP150104202 , DP250103673 Gates Foundation , INV-044643 Footnotes Repositories: PRJEB89408 References 1. ↵ Beck , R.M.D. in American and Australasian Marsupials: An Evolutionary, Biogeographical, and Ecological Approach. (eds. N.C. Cáceres & C.R. Dickman) 1-66 (Springer International Publishing, Cham; 2022 ). 2. ↵ Burgin , C.J. , Colella , J.P. , Kahn , P.L. & Upham , N.S . How many species of mammals are there? J. Mammal . 99 , 1 – 14 ( 2018 ). OpenUrl CrossRef 3. ↵ Alfano , N. et al. Variation in koala microbiomes within and between individuals: effect of body region and captivity status . Sci. Rep . 5 , 10189 ( 2015 ). 4. Barker , C.J. , Gillett , A. , Polkinghorne , A. & Timms , P . Investigation of the koala ( Phascolarctos cinereus ) hindgut microbiome via 16S pyrosequencing . Vet. Microbiol . 167 , 554 – 564 ( 2013 ). OpenUrl PubMed 5. Bowerman , K.L. et al. Metagenomic analysis of marsupial gut microbiomes provides a genetic basis for the low methane economy . bioRxiv , 2024.2012.2004.626884 ( 2024 ). 6. Brice , K.L. et al. The Koala ( Phascolarctos cinereus ) faecal microbiome differs with diet in a wild population . PeerJ 7 , e6534 ( 2019 ). OpenUrl PubMed 7. Blyton , M.D.J. et al. Faecal inoculations alter the gastrointestinal microbiome and allow dietary expansion in a wild specialist herbivore, the koala . Animal Microbiome 1 , 6 ( 2019 ). 8. Blyton , M.D.J. et al. The koala gut microbiome is largely unaffected by host translocation but rather influences host diet . Front Microbiol 14 , 1085090 ( 2023 ). 9. ↵ Blyton , M.D.J. , Soo , R.M. , Hugenholtz , P. & Moore , B.D . Maternal inheritance of the koala gut microbiome and its compositional and functional maturation during juvenile development . Environ. Microbiol . 24 , 475 – 493 ( 2022 ). OpenUrl CrossRef 10. ↵ Blyton , M.D.J. , Soo , R.M. , Hugenholtz , P. & Moore , B.D . Characterization of the juvenile koala gut microbiome across wild populations . Environ. Microbiol . 24 , 4209 – 4219 ( 2022 ). OpenUrl 11. Chen , J. et al. Animal age affects the gut microbiota and immune system in captive koalas ( Phascolarctos cinereus ) . Microbiol Spectr 11 , e0410122 ( 2023 ). OpenUrl 12. Chhour , K.-L. , Hinds , L.A. , Deane , E.M. & Jacques , N.A . The microbiome of the cloacal openings of the urogenital and anal tracts of the tammar wallaby , Macropus eugenii. Microbiology 154 , 1535 – 1543 ( 2008 ). OpenUrl PubMed 13. Chhour , K.L. , Hinds , L.A. , Jacques , N.A. & Deane , E.M . An observational study of the microbiome of the maternal pouch and saliva of the tammar wallaby , Macropus eugenii, and of the gastrointestinal tract of the pouch young. Microbiology (Reading ) 156 , 798 – 808 ( 2010 ). OpenUrl PubMed 14. Clough , J. , Schwab , S. & Mikac , K . Gut microbiome profiling of the endangered Southern greater glider ( Petauroides volans ) after the 2019–2020 Australian megafire . Animals 13 , 3583 ( 2023 ). OpenUrl PubMed 15. Dahlhausen , K.E. , Doroud , L. , Firl , A.J. , Polkinghorne , A. & Eisen , J.A . Characterization of shifts of koala ( Phascolarctos cinereus ) intestinal microbial communities associated with antibiotic treatment . PeerJ 6 , e4452 ( 2018 ). OpenUrl PubMed 16. Eisenhofer , R. , D’Agnese , E. , Taggart , D. , Carver , S. & Penrose , B . Microbial biogeography of the wombat gastrointestinal tract . PeerJ 10 , e12982 ( 2022 ). OpenUrl PubMed 17. Eisenhofer , R. , Helgen , K.M. & Taggart , D . Signatures of landscape and captivity in the gut microbiota of southern hairy-nosed wombats ( Lasiorhinus latifrons ) . Animal Microbiome 3 , 4 ( 2021 ). 18. Evans , P.N. et al. Community composition and density of methanogens in the foregut of the Tammar wallaby ( Macropus eugenii ) . Appl. Environ. Microbiol . 75 , 2598 – 2602 ( 2009 ). OpenUrl Abstract / FREE Full Text 19. Godwin , S. et al. Investigation of the microbial metabolism of carbon dioxide and hydrogen in the kangaroo foregut by stable isotope probing . ISME J . 8 , 1855 – 1865 ( 2014 ). OpenUrl PubMed 20. Gulino , L.-M. et al. Shedding light on the microbial community of the macropod foregut using 454-amplicon pyrosequencing . PLoS ONE 8 , e61463 ( 2013 ). OpenUrl CrossRef PubMed 21. Klieve , A.V. , Ouwerkerk , D. & Maguire , A.J . Archaea in the foregut of macropod marsupials: PCR and amplicon sequence-based observations . J. Appl. Microbiol . 113 , 1065 – 1075 ( 2012 ). OpenUrl CrossRef PubMed 22. Kondo , K. et al. Association of maternal genetics with the gut microbiome and eucalypt diet selection in captive koalas . PeerJ 12 , e17385 ( 2024 ). OpenUrl PubMed 23. Li , M. et al. Spatial dynamics of the bacterial community structure in the gastrointestinal tract of red kangaroo ( Macropus rufus ) . World J Microbiol Biotechnol 32 , 98 ( 2016 ). 24. Littleford-Colquhoun , B.L. , Weyrich , L.S. , Hohwieler , K. , Cristescu , R. & Frère , C.H . How microbiomes can help inform conservation: landscape characterisation of gut microbiota helps shed light on additional population structure in a specialist folivore . Animal Microbiome 4 , 12 ( 2022 ). 25. O’Dea , C. et al. Microbial diversity profiling of gut microbiota of Macropus giganteus using three hypervariable regions of the bacterial 16S rRNA . Microorganisms 9 , 1721 ( 2021 ). OpenUrl PubMed 26. Oliveros , A. , Terraube , J. , Levengood , A.L. , Powell , D. & Frère , C.H . Influence of scat ageing on the gut microbiome: how old is too old? BMC Genomics 24 , 427 ( 2023 ). 27. Pope , P.B. et al. Adaptation to herbivory by the Tammar wallaby includes bacterial and glycoside hydrolase profiles different from other herbivores . Proc. Natl. Acad. Sci. U. S. A . 107 , 14793 – 14798 ( 2010 ). OpenUrl Abstract / FREE Full Text 28. ↵ Shiffman , M.E. et al. Gene and genome-centric analyses of koala and wombat fecal microbiomes point to metabolic specialization for Eucalyptus digestion . PeerJ 5 , e4075 ( 2017 ). OpenUrl PubMed 29. ↵ Volmer , J.G. et al. Isolation and characterisation of novel Methanocorpusculum species indicates the genus is ancestrally host-associated . BMC Biol . 21 , 59 ( 2023 ). 30. ↵ Marsh , K.J. et al. Genus-wide variation in foliar polyphenolics in eucalypts . Phytochemistry 144 , 197 – 207 ( 2017 ). OpenUrl PubMed 31. ↵ Moore , B.D. , Foley , W.J. , Wallis , I.R. , Cowling , A. & Handasyde , K.A . Eucalyptus foliar chemistry explains selective feeding by koalas . Biol. Lett . 1 , 64 – 67 ( 2005 ). OpenUrl CrossRef PubMed 32. ↵ Chandorkar , N. , Tambe , S. , Amin , P. & Madankar , C . A systematic and comprehensive review on current understanding of the pharmacological actions, molecular mechanisms, and clinical implications of the genus Eucalyptus . Phytomedicine Plus 1 , 100089 ( 2021 ). 33. ↵ Marsh , K.J. , Blyton , M.D.J. , Foley , W.J. & Moore , B.D . Fundamental dietary specialisation explains differential use of resources within a koala population . Oecologia 196 , 795 – 803 ( 2021 ). OpenUrl PubMed 34. Moore , B.D. & Foley , W.J . Tree use by koalas in a chemically complex landscape . Nature 435 , 488 – 490 ( 2005 ). OpenUrl CrossRef PubMed Web of Science 35. ↵ Jensen , L.M. , Wallis , I.R. & Foley , W.J . The relative concentrations of nutrients and toxins dictate feeding by a vertebrate browser, the greater glider Petauroides volans . PLoS ONE 10 , e0121584 ( 2015 ). OpenUrl CrossRef PubMed 36. ↵ Luo , Z.-X. , Yuan , C.-X. , Meng , Q.-J. & Ji , Q . A Jurassic eutherian mammal and divergence of marsupials and placentals . Nature 476 , 442 – 445 ( 2011 ). OpenUrl CrossRef GeoRef PubMed Web of Science 37. ↵ Uritskiy , G. et al. Accurate viral genome reconstruction and host assignment with proximity-ligation sequencing . bioRxiv , 2021.2006.2014.448389 ( 2021 ). 38. ↵ Ley , R.E. et al. Evolution of mammals and their gut microbes . Science 320 , 1647 – 1651 ( 2008 ). OpenUrl Abstract / FREE Full Text 39. McKenzie , V.J. et al. The effects of captivity on the mammalian gut microbiome . Integrative and Comparative Biology 57 , 690 – 704 ( 2017 ). OpenUrl CrossRef PubMed 40. ↵ Nishida , A.H. & Ochman , H . Rates of gut microbiome divergence in mammals . Mol. Ecol . 27 , 1884 – 1897 ( 2018 ). OpenUrl CrossRef PubMed 41. ↵ Youngblut , N.D. et al. Host diet and evolutionary history explain different aspects of gut microbiome diversity among vertebrate clades . Nat. Commun . 10 , 2200 ( 2019 ). OpenUrl CrossRef PubMed 42. ↵ Rojas , C.A. , Ramírez-Barahona , S. , Holekamp , K.E. & Theis , K.R . Host phylogeny and host ecology structure the mammalian gut microbiota at different taxonomic scales . Anim Microbiome 3 , 33 ( 2021 ). 43. ↵ Tucker , D.J. et al. A metabolomic approach to identifying chemical mediators of mammal– plant interactions . J. Chem. Ecol . 36 , 727 – 735 ( 2010 ). OpenUrl PubMed 44. Li , H. , Madden , J.L. & Potts , B.M . Variation in leaf waxes of the Tasmanian Eucalyptus species—I. Subgenus Symphyomyrtus . Biochemical Systematics and Ecology 25 , 631 – 657 ( 1997 ). OpenUrl 45. ↵ Moore , B.D. , Wallis , I.R. , Marsh , K.J. & Foley , W.J . in Conservation of Australia’s Forest Fauna. (ed. D. Lunney) 0 (Royal Zoological Society of New South Wales, 2004 ). 46. ↵ Parks , D.H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life . Nat. Biotechnol . 36 , 996 ( 2018 ). 47. ↵ Zhang , K. et al. Compendium of 5810 genomes of sheep and goat gut microbiomes provides new insights into the glycan and mucin utilization . Microbiome 12 , 104 ( 2024 ). OpenUrl PubMed 48. ↵ Levin , D. et al. Diversity and functional landscapes in the microbiota of animals in the wild . Science 372 , eabb5352 ( 2021 ). OpenUrl Abstract / FREE Full Text 49. ↵ Jang , J. et al. Environmental Escherichia coli : ecology and public health implications-a review . J. Appl. Microbiol . 123 , 570 – 581 ( 2017 ). OpenUrl CrossRef PubMed 50. ↵ Del Dot , T. , Osawa , R. & Stackebrandt , E . Phascolarctobacterium faecium gen. nov, spec. nov., a novel taxon of the sporomusa group of Bacteria . Syst. Appl. Microbiol . 16 , 380 – 384 ( 1993 ). OpenUrl CrossRef 51. ↵ Wu , F. et al. Phascolarctobacterium faecium abundant colonization in human gastrointestinal tract . Exp Ther Med 14 , 3122 – 3126 ( 2017 ). OpenUrl CrossRef PubMed 52. ↵ Wen , Y. et al. Cow, yak, and camel milk diets differentially modulated the systemic immunity and fecal microbiota of rats . Science Bulletin 62 , 405 – 414 ( 2017 ). OpenUrl PubMed 53. ↵ Yang , Z.-D. et al. Isomaltulose exhibits prebiotic activity, and modulates gut microbiota, the production of short chain fatty acids, and secondary bile acids in rats . Molecules 26 , 2464 ( 2021 ). OpenUrl PubMed 54. ↵ Ikeyama , N. et al. Microbial interaction between the succinate-utilizing bacterium Phascolarctobacterium faecium and the gut commensal Bacteroides thetaiotaomicron . Microbiologyopen 9 , e1111 ( 2020 ). OpenUrl CrossRef PubMed 55. ↵ Geerlings , S.Y. et al. Genomic convergence between Akkermansia muciniphila in different mammalian hosts . BMC Microbiol . 21 , 298 ( 2021 ). 56. ↵ Sato , Y. et al. Identification of 146 metagenome-assembled genomes from the rumen microbiome of cattle in Japan . Microbes and Environments 37 , ME22039 ( 2022 ). OpenUrl 57. ↵ Black , K.H. , Price , G.J. , Archer , M. & Hand , S.J . Bearing up well? Understanding the past, present and future of Australia’s koalas . Gondwana Research 25 , 1186 – 1201 ( 2014 ). OpenUrl GeoRef 58. ↵ Salgado , J.F.M. , Hervé , V. , Vera , M.A.G. , Tokuda , G. & Brune , A . Unveiling lignocellulolytic potential: a genomic exploration of bacterial lineages within the termite gut . Microbiome 12 , 201 ( 2024 ). 59. ↵ Melzer , A. , Cristescu , R. , Ellis , W. , FitzGibbon , S. & Manno , G . The habitat and diet of koalas Phascolarctos cinereus in Queensland . Australian Mammalogy 36 , 189 – 199 ( 2014 ). OpenUrl 60. ↵ Schneider , H. , Schwiertz , A. , Collins , M.D. & Blaut , M . Anaerobic transformation of quercetin-3-glucoside by bacteria from the human intestinal tract . Arch. Microbiol . 171 , 81 – 91 ( 1999 ). OpenUrl CrossRef PubMed Web of Science 61. ↵ Simmering , R. , Pforte , H. , Jacobasch , G. & Blaut , M . The growth of the flavonoid-degrading intestinal bacterium, Eubacterium ramulus , is stimulated by dietary flavonoids in vivo . FEMS Microbiol. Ecol . 40 , 243 – 248 ( 2002 ). OpenUrl CrossRef PubMed Web of Science 62. ↵ Abd-Alla , M.F. , El-Negoumy , S.I. , Hosny El-Lakany , M. & Saleh , N.A.M . Flavonoid glycosides and the chemosystematics of Eucalyptus camaldulensisis . Phytochemistry 19 , 2629 – 2632 ( 1980 ). OpenUrl 63. ↵ Dezsi , Ș. et al. Antimicrobial and antioxidant activities and phenolic profile of Eucalyptus globulus Labill. and Corymbia ficifolia (F. Muell.) K.D. Hill & L.A.S. Johnson leaves . Molecules 20 , 4720 – 4734 ( 2015 ). OpenUrl PubMed 64. ↵ Yang , G. et al. Discovery of an ene-reductase for initiating flavone and flavonol catabolism in gut bacteria . Nat Commun 12 , 790 ( 2021 ). 65. ↵ Roux , S. et al. Minimum information about an uncultivated virus genome (MIUViG) . Nat. Biotechnol . 37 , 29 – 37 ( 2019 ). OpenUrl CrossRef PubMed 66. ↵ Liang , G. & Bushman , F.D . The human virome: assembly, composition and host interactions . Nat Rev Micro 19 , 514 – 527 ( 2021 ). OpenUrl 67. Shkoporov , A.N. et al. Viral biogeography of the mammalian gut and parenchymal organs . Nat. Microbiol . 7 , 1301 – 1311 ( 2022 ). OpenUrl PubMed 68. ↵ Moltzau Anderson , J. , Lachnit , T. , Lipinski , S. , Falk-Paulsen , M. & Rosenstiel , P. Impact of antibiotic perturbation on fecal viral communities in mice . G3 Genes|Genomes|Genetics 13 ( 2022 ). 69. ↵ Klieve , A.V . Bacteriophages from the forestomachs of Australian marsupials . Appl Environ Microbiol 57 , 3660 – 3663 ( 1991 ). OpenUrl Abstract / FREE Full Text 70. ↵ de Jonge , P.A. , Nobrega , F.L. , Brouns , S.J.J. & Dutilh , B.E . Molecular and evolutionary determinants of bacteriophage host range . Trends Microbiol . 27 , 51 – 63 ( 2019 ). OpenUrl CrossRef PubMed 71. ↵ Džunková , M. et al. Defining the human gut host–phage network through single-cell viral tagging . Nat. Microbiol . 4 , 2192 – 2203 ( 2019 ). OpenUrl PubMed 72. ↵ Pchelin , I.M. , Smolensky , A.V. , Azarov , D.V. & Goncharov , A.E . Lytic spectra of tailed bacteriophages: A systematic review and meta-analysis . Viruses 16 , 1879 ( 2024 ). OpenUrl PubMed 73. ↵ Fernandes , S. & São-José , C . Enzymes and mechanisms employed by tailed bacteriophages to breach the bacterial cell barriers . Viruses 10 , 396 ( 2018 ). 74. ↵ Moreno-Gallego , J.L. et al. Virome diversity correlates with intestinal microbiome diversity in adult monozygotic twins . Cell Host Microbe 25 , 261 – 272 .e265 ( 2019 ). OpenUrl CrossRef PubMed 75. ↵ Shkoporov , A.N. et al. The human gut virome is highly diverse, stable, and individual specific . Cell Host Microbe 26 , 527 – 541 .e525 ( 2019 ). OpenUrl CrossRef PubMed 76. ↵ Dutilh , B.E. et al. A highly abundant bacteriophage discovered in the unknown sequences of human faecal metagenomes . Nat. Commun . 5 , 4498 ( 2014 ). OpenUrl CrossRef PubMed 77. Guerin , E. et al. Biology and taxonomy of crAss-like bacteriophages, the most abundant virus in the human gut . Cell Host Microbe 24 , 653 – 664 .e656 ( 2018 ). OpenUrl CrossRef PubMed 78. ↵ Edwards , R.A. et al. Global phylogeography and ancient evolution of the widespread human gut virus crAssphage . Nat. Microbiol . 4 , 1727 – 1736 ( 2019 ). OpenUrl PubMed 79. ↵ Ramos-Barbero , M.D. et al. Recruitment of complete crAss-like phage genomes reveals their presence in chicken viromes, few human-specific phages, and lack of universal detection . ISME J . 18 ( 2024 ). 80. ↵ Fogg , P.C. , Rigden , D.J. , Saunders , J.R. , McCarthy , A.J. & Allison , H.E . Characterization of the relationship between integrase, excisionase and antirepressor activities associated with a superinfecting Shiga toxin encoding bacteriophage . Nucleic Acids Res 39 , 2116 – 2129 ( 2011 ). OpenUrl CrossRef PubMed 81. ↵ Yutin , N. et al. Analysis of metagenome-assembled viral genomes from the human gut reveals diverse putative CrAss-like phages with unique genomic features . Nat. Commun . 12 , 1044 ( 2021 ). OpenUrl CrossRef PubMed 82. ↵ Field , C.J. , Bowerman , K.L. & Hugenholtz , P . Multiple independent losses of sporulation and peptidoglycan in the Mycoplasmatales and related orders of the class Bacilli . Microbial Genomics 10 ( 2024 ). 83. ↵ Brisson , L. et al. Comparing antibiotic resistance in free-ranging vs captive African wild herbivores . J. Wildl. Dis . 59 , 224 – 233 ( 2023 ). OpenUrl PubMed 84. ↵ Zhai , J. et al. A comparison of antibiotic resistance genes and mobile genetic elements in wild and captive Himalayan vultures . PeerJ 12 , e17710 ( 2024 ). OpenUrl CrossRef PubMed 85. ↵ Castañeda-Barba , S. , Top , E.M. & Stalder , T . Plasmids, a molecular cornerstone of antimicrobial resistance in the One Health era . Nat. Rev. Microbiol . 22 , 18 – 32 ( 2024 ). OpenUrl CrossRef PubMed 86. ↵ Zhuang , M. et al. Distribution of antibiotic resistance genes in the environment . Environ. Pollut . 285 , 117402 ( 2021 ). 87. ↵ Quesenberry , K.E. , Orcutt , C.J. , Mans , C. & Carpenter , J.W. Ferrets, Rabbits, and Rodents: Clinical Medicine and Surgery, Edn. Fourth Edition . ( 2014 ). 88. ↵ Lewis , J.C. et al. Observations on the treatment of necrobacillosis in wallabies . Br. Vet. J . 145 , 394 – 396 ( 1989 ). OpenUrl PubMed 89. ↵ Mercer , M.A. , Vol. 22/05/2025 (MSD Veterinary Manual, MSD Veterinary Manual ; 2022 ). 90. ↵ Allen , H.K. et al. Call of the wild: antibiotic resistance genes in natural environments . Nat Rev Micro 8 , 251 – 259 ( 2010 ). OpenUrl 91. ↵ Woolhouse , M. , Ward , M. , van Bunnik , B. & Farrar , J . Antimicrobial resistance in humans, livestock and the wider environment . Philosophical Transactions of the Royal Society B: Biological Sciences 370 , 20140083 ( 2015 ). 92. ↵ Burton , J.N. , Liachko , I. , Dunham , M.J. & Shendure , J . Species-level deconvolution of metagenome assemblies with Hi-C-based contact probability maps . G3 Genes|Genomes|Genetics 4 , 1339 – 1346 ( 2014 ). OpenUrl 93. ↵ Sturm , M. , Schroeder , C. & Bauer , P . SeqPurge: highly-sensitive adapter trimming for paired-end NGS data . BMC Bioinformatics 17 , 208 ( 2016 ). 94. ↵ Nurk , S. , Meleshko , D. , Korobeynikov , A. & Pevzner , P.A . metaSPAdes: a new versatile metagenomic assembler . Genome Res . 27 , 824 – 834 ( 2017 ). OpenUrl Abstract / FREE Full Text 95. ↵ Newell , R.J.P. in Newell RJP ( 2024 ). 96. ↵ Li , D. , Liu , C.M. , Luo , R. , Sadakane , K. & Lam , T.W . MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph . Bioinformatics 31 , 1674 – 1676 ( 2015 ). OpenUrl CrossRef PubMed 97. ↵ Press , M.O. et al. Hi-C deconvolution of a human gut microbiome yields high-quality draft genomes and reveals plasmid-genome interactions . bioRxiv, 198713 ( 2017 ). 98. ↵ Stewart , R.D. et al. Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen . Nat. Commun . 9 , 870 ( 2018 ). 99. ↵ Chklovski , A. , Parks , D.H. , Woodcroft , B.J. & Tyson , G.W . CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning . Nat Methods 20 , 1203 – 1212 ( 2023 ). OpenUrl CrossRef PubMed 100. ↵ Parks , D.H. et al. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy . Nucleic Acids Res . 50 , D785 – D794 ( 2021 ). OpenUrl 101. ↵ Aroney , S.T.N. et al. CoverM: read alignment statistics for metagenomics . Bioinformatics 41 ( 2025 ). 102. ↵ Chaumeil , P.A. , Mussig , A.J. , Hugenholtz , P. & Parks , D.H . GTDB-Tk v2: memory friendly classification with the genome taxonomy database . Bioinformatics 38 , 5315 – 5316 ( 2022 ). OpenUrl CrossRef PubMed 103. ↵ Nguyen , L.-T. , Schmidt , H.A. , von Haeseler , A. & Minh , B.Q . IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies . Mol. Biol. Evol . 32 , 268 – 274 ( 2014 ). OpenUrl CrossRef PubMed 104. ↵ Ong , C.T. et al. Evaluation of host depletion and extraction methods for shotgun metagenomic analysis of bovine vaginal samples . Microbiol Spectr 10 , e0041221 ( 2022 ). OpenUrl PubMed 105. Jiang , P. , Lai , S. , Wu , S. , Zhao , X.-M. & Chen , W.-H . Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment . BMC Genomics 21 , 348 ( 2020 ). 106. ↵ Duchêne , D.A. et al. Analysis of phylogenomic tree space resolves relationships among marsupial families . Systematic Biology 67 , 400 – 412 ( 2017 ). OpenUrl CrossRef 107. ↵ May-Collado , L.J. , Kilpatrick , C.W. & Agnarsson , I . Mammals from ‘down under’: a multi-gene species-level phylogeny of marsupial mammals ( Mammalia , Metatheria). PeerJ 3 , e805 ( 2015 ). OpenUrl CrossRef PubMed 108. ↵ Camacho , C. et al. BLAST+: architecture and applications . BMC Bioinformatics 10 , 421 ( 2009 ). OpenUrl CrossRef PubMed 109. ↵ Quinlan , A.R. & Hall , I.M . BEDTools: a flexible suite of utilities for comparing genomic features . Bioinformatics 26 , 841 – 842 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 110. ↵ Katoh , K. & Standley , D.M . MAFFT multiple sequence alignment software version 7: improvements in performance and usability . Mol. Biol. Evol . 30 , 772 – 780 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 111. ↵ Capella-Gutiérrez , S. , Silla-Martínez , J.M. & Gabaldón , T . trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses . Bioinformatics 25 , 1972 – 1973 ( 2009 ). OpenUrl CrossRef PubMed Web of Science 112. ↵ Ludwig , W. et al. ARB: a software environment for sequence data . Nucleic Acids Res . 32 , 1363 – 1371 ( 2004 ). OpenUrl CrossRef PubMed Web of Science 113. ↵ Bolger , A.M. , Lohse , M. & Usadel , B . Trimmomatic: a flexible trimmer for Illumina sequence data . Bioinformatics 30 , 2114 – 2120 ( 2014 ). OpenUrl CrossRef PubMed Web of Science 114. ↵ Shen , W. , Sipos , B. & Zhao , L. SeqKit2: A Swiss army knife for sequence and alignment processing . iMeta 3 , e191 ( 2024 ). OpenUrl CrossRef 115. ↵ Woodcroft , B.J. et al. in bioRxiv ( 2024 ). 116. ↵ Eisenhofer , R. , Alberdi , A. & Woodcroft , B.J. in bioRxiv ( 2024 ). 117. ↵ McMurdie , P.J. & Holmes , S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data . PLoS ONE 8 , e61217 ( 2013 ). OpenUrl CrossRef PubMed 118. ↵ Hyatt , D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification . BMC Bioinformatics 11 , 119 ( 2010 ). 119. ↵ Steinegger , M. & Söding , J . MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets . Nat. Biotechnol . 35 , 1026 – 1028 ( 2017 ). OpenUrl CrossRef PubMed 120. ↵ Shaffer , M. et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function . Nucleic Acids Res . 48 , 8883 – 8900 ( 2020 ). OpenUrl CrossRef PubMed 121. ↵ Aramaki , T. et al. KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold . Bioinformatics 36 , 2251 – 2252 ( 2019 ). OpenUrl 122. ↵ Mistry , J. et al. Pfam: The protein families database in 2021 . Nucleic Acids Res 49 , D412 – d419 ( 2021 ). OpenUrl CrossRef PubMed 123. ↵ Yin , Y. et al. dbCAN: a web resource for automated carbohydrate-active enzyme annotation . Nucleic Acids Res . 40 , W445 – W451 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 124. ↵ Buchfink , B. , Xie , C. & Huson , D.H . Fast and sensitive protein alignment using DIAMOND . Nat. Methods 12 , 59 – 60 ( 2015 ). OpenUrl CrossRef PubMed 125. ↵ Kieft , K. , Zhou , Z. & Anantharaman , K . VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences . Microbiome 8 , 90 ( 2020 ). 126. ↵ Lefkowitz , E.J. et al. Virus taxonomy: the database of the International Committee on Taxonomy of Viruses (ICTV) . Nucleic Acids Res . 46 , D708 – D717 ( 2017 ). OpenUrl CrossRef 127. ↵ Camargo , A.P. et al. Identification of mobile genetic elements with geNomad . Nat Biotechnol 42 , 1303 – 1312 ( 2024 ). OpenUrl CrossRef PubMed 128. ↵ Piedade , G.J. et al. Seasonal dynamics and diversity of Antarctic marine viruses reveal a novel viral seascape . Nat. Commun . 15 , 9192 ( 2024 ). OpenUrl CrossRef PubMed 129. ↵ Mistry , J. , Finn , R.D. , Eddy , S.R. , Bateman , A. & Punta , M . Challenges in homology search: HMMER3 and convergent evolution of coiled-coil regions . Nucleic Acids Res . 41 , e121 ( 2013 ). OpenUrl CrossRef PubMed 130. ↵ Shaw , J. & Yu , Y.W . Fast and robust metagenomic sequence comparison through sparse chaining with skani . Nat. Methods 20 , 1661 – 1665 ( 2023 ). OpenUrl CrossRef PubMed 131. ↵ Nayfach , S. et al. Metagenomic compendium of 189,680 DNA viruses from the human gut microbiome . Nat. Microbiol . 6 , 960 – 970 ( 2021 ). OpenUrl PubMed 132. ↵ Feldgarden , M. et al. AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence . Sci Rep 11 , 12728 ( 2021 ). 133. ↵ Mallick , H. et al. Multivariable association discovery in population-scale meta-omics studies . PLoS Comput. Biol . 17 , e1009442 ( 2021 ). OpenUrl CrossRef PubMed 134. ↵ Sørensen , M. , Rinnan , R. , Woodrow , I. , Møller , B.L. & Neilson , E.H.J . The entangled dynamics of eucalypt leaf and flower volatile emissions . Environmental and Experimental Botany 176 , 104032 ( 2020 ). 135. ↵ Johnsen , L.G. , Skou , P.B. , Khakimov , B. & Bro , R . Gas chromatography – mass spectrometry data processing made easy . J. Chromatogr. A 1503 , 57 – 64 ( 2017 ). OpenUrl CrossRef PubMed 136. ↵ Pang , Z. et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights . Nucleic Acids Res . 49 , W388 – W396 ( 2021 ). OpenUrl CrossRef PubMed 137. ↵ Bates , D. , Mächler , M. , Bolker , B. & Walker , S . Fitting linear mixed-effects models using lme4 . Journal of Statistical Software 67 , 1 – 48 ( 2015 ). OpenUrl CrossRef 138. ↵ Kuznetsova , A. , Brockhoff , P.B. & Christensen , R.H.B . lmerTest package: Tests in linear mixed effects models . Journal of Statistical Software 82 , 1 – 26 ( 2017 ). OpenUrl CrossRef 139. ↵ Oksanen , J. et al. in Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Wagner H ( 2015 ). 140. ↵ Wickham , H. et al. in Wickham H, Chang W, Henry L, Pedersen TL, Takahashi K, Wilke C, Woo K ( 2019 ). 141. ↵ Letunic , I. & Bork , P . Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool . Nucleic Acids Res 52 , W78 – w82 ( 2024 ). OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted September 01, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following A molecular inventory of the faecal microbiomes of 23 marsupial species Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share A molecular inventory of the faecal microbiomes of 23 marsupial species Kate L. Bowerman , Rochelle M. Soo , Pierre-Alain Chaumeil , Michaela D. J. Blyton , Mette Sørensen , Disan Gunbilig , Maika Malig , Moutusee Islam , Julian Zaugg , David L. A. Wood , Ivan Liachko , Benjamin Auch , Mark Morrison , Lutz Krause , Birger Lindberg Møller , Elizabeth H. J. Neilson , Philip Hugenholtz bioRxiv 2025.08.31.672981; doi: https://doi.org/10.1101/2025.08.31.672981 Share This Article: Copy Citation Tools A molecular inventory of the faecal microbiomes of 23 marsupial species Kate L. Bowerman , Rochelle M. Soo , Pierre-Alain Chaumeil , Michaela D. J. Blyton , Mette Sørensen , Disan Gunbilig , Maika Malig , Moutusee Islam , Julian Zaugg , David L. A. Wood , Ivan Liachko , Benjamin Auch , Mark Morrison , Lutz Krause , Birger Lindberg Møller , Elizabeth H. J. Neilson , Philip Hugenholtz bioRxiv 2025.08.31.672981; doi: https://doi.org/10.1101/2025.08.31.672981 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Microbiology Subject Areas All Articles Animal Behavior and Cognition (7633) Biochemistry (17680) Bioengineering (13889) Bioinformatics (41927) Biophysics (21445) Cancer Biology (18585) Cell Biology (25491) Clinical Trials (138) Developmental Biology (13373) Ecology (19897) Epidemiology (2067) Evolutionary Biology (24308) Genetics (15606) Genomics (22494) Immunology (17736) Microbiology (40385) Molecular Biology (17175) Neuroscience (88583) Paleontology (666) Pathology (2830) Pharmacology and Toxicology (4822) Physiology (7641) Plant Biology (15149) Scientific Communication and Education (2045) Synthetic Biology (4293) Systems Biology (9822) Zoology (2271)