==== Front mSystems mSystems mSystems mSystems 2379-5077 American Society for Microbiology 1752 N St., N.W., Washington, DC 37272702 00161-23 10.1128/msystems.00161-23 msystems.00161-23 Observation bacteriophagesBacteriophagesGut phageome of the giant panda (Ailuropoda melanoleuca) reveals greater diversity than relative species Lu Juan 1 Wang Haoning 2 Wang Chunmei 3 Zhao Min 1 Hou Rong 4 https://orcid.org/0000-0001-9041-8475 Shen Quan 1 Yang Shixing 1 Ji Likai 1 https://orcid.org/0000-0002-5495-2926 Liu Yuwei 1 Wang Xiaochun 1 Liu Songrui 4 srui_liu@163.com https://orcid.org/0000-0002-5329-6349 Shan Tongling 3 shantongling@shvri.ac.cn https://orcid.org/0000-0002-9352-6153 Zhang Wen 1 z0216wen@yahoo.com 1 Department of Laboratory Medicine, School of Medicine, Jiangsu University , Zhenjiang, Jiangsu, China 2 School of Geography and Tourism, Harbin University , Harbin, Heilongjiang, China 3 Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences , Shanghai, China 4 Chengdu Research Base of Giant Panda Breeding, Sichuan Key Laboratory of Conservation Biology for Endangered Wildlife, Sichuan Academy of Giant Panda , Chengdu, Sichuan, China Editor Rawls John F. Duke University School of Medicine , Durham, North Carolina, USA Address correspondence to Wen Zhang, z0216wen@yahoo.com Address correspondence to Tongling Shan, shantongling@shvri.ac.cn Address correspondence to Songrui Liu, srui_liu@163.com Juan Lu, Haoning Wang, and Chunmei Wang contributed equally to this article. Author order was determined in chronological order of program participation. The authors declare no conflict of interest. 05 6 2023 May-Jun 2023 05 6 2023 8 3 e00161-2315 2 2023 12 4 2023 Copyright © 2023 Lu et al. 2023 Lu et al. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license. ABSTRACT The gut flora is a treasure house of diverse bacteriophages maintaining a harmonious and coexistent relationship with their hosts. The giant panda (Ailuropoda melanoleuca), as a vulnerable endemic species in China, has existed for millions of years and is regarded as a flagship species for biodiversity conservation. And yet, limited studies have analyzed the phage communities in the gut of giant pandas. Using viral metagenomic analysis, the phageomes of giant pandas and other relative species were investigated. Our study explored and compared the composition of phage communities from different animal sources. Giant pandas possessed more diverse and abundant phage communities in the gut compared with other relevant animals. Phylogenetic analyses based on the phage terminase large subunit (TerL) showed that the Caudovirales phages in giant pandas also presented highly genetic diversity. Our study revealed the diversity of phage communities in giant pandas and other relative species, contributing to the health maintenance of giant pandas and laying the groundwork for molecular evolution research of bacteriophages in mammals. IMPORTANCE Gut phageome plays an important role in shaping gut microbiomes by direct interactions with bacteria or indirect influences on the host immune system, potentially regulating host health and disease status. The giant panda (Ailuropoda melanoleuca) is a vulnerable and umbrella species for biodiversity conservation. Our work explored and compared the gut phageome of giant pandas and relative species, contributing to the health maintenance of giant pandas. KEYWORDS viral metagenomics gut phageome diversity giant pandas bacteriophages MOST | National Key Research and Development Program of China (NKPs) 2022YFC2603801 Zhang Wen Chendu Research Base of Giant Panda Breeding 2020CPB-C11 and 2021CPB-B11 Zhang Wen Donghai People's Hospital-School of Medicine UJS Joint Laboratory Foundation no. 20210467 Zhang Wen Jiangsu Provincial Key Research and Development Program (Key Technologies R&D Program of Jiangsu Province) BE2017693 Zhang Wen The Youth Doctoral Scientific Research Foundation of Harbin University HUDF2020105 Wang Haoning cover-dateMay/June 2023 ==== Body pmcOBSERVATION Bacteriophages, as the largest part of the gut virome, play pivotal roles in shaping gut microbiomes by direct interactions with their bacterial hosts or indirect influences on the host immune system (1, 2). A healthy gut generally has a phage–bacteria–host triangular relationship, with fruitful interactions and comparatively balanced structure (3). Bacteriophages that reside in the gastrointestinal tract are characterized by extraordinary stability and high diversity (4, 5). Although gut environments provide a bountiful source of bacteriophage genetic diversity, phages were more unexplored compared with bacteria and eukaryotic viruses (6). Megataxonomy of the virus world was actualized through a uniform standard based on viral hallmark genes (VHGs) that were widely conserved among various groups of viruses and provided a window to explore viral evolutionary relationships (7). Thereafter, the proposal was approved by the International Committee on the Taxonomy of Viruses. Tailed dsDNA phages, for instance, constitute the order Caudovirales, which possess conserved portal proteins and terminase subunits, and the terminase large subunits were generally considered as the VHGs of the order Caudovirales used for the phylogenetic analysis (7). The giant panda (Ailuropoda melanoleuca) is a vulnerable indigenous species in China, and an umbrella species for biodiversity conservation (8). And the giant panda is considered as one of the oldest extant species with a reputation of “living fossils,” dating back to 8 million years ago (8). Owing to the joint efforts from every aspect of society, the protection level of the giant panda was degraded from “endangered” to “vulnerable” in the International Union for Conservation of Nature Red List of threatened species in 2016 (9). However, viral infectious diseases represent a serious health hazard to the giant panda. Several viral pathogens were considered to be a health hazard to giant pandas, such as the canine distemper virus (10), the canine parvovirus (11), and the feline panleukopenia virus (12), bringing the risk of cross-species infection. Nevertheless, bacteriophages, as the most abundant component of the gut virome, still remain to be explored in giant pandas. Therefore, in the present study, we explored the gut phageome of giant pandas and relevant species, and analyzed the genetic diversity of the Caudovirales order based on the terminase large subunits genes through phylogenetic analyses. These findings could reveal the potential interspecific diversity of gut bacteriophages among giant pandas and relative species, contributing to the health maintenance of giant pandas and providing a foundation for research into bacteriophages in the future. To explore the gut phageome of giant pandas and other relevant animals, including bears, red pandas, and musk deer, we conducted viral metagenomics research of bacteriophages in 413 feces samples from giant pandas, 161 from red pandas, 70 from bears, and 85 from musk deer. These samples were pooled into 65 libraries according to animal sources and sample size. After next generation sequencing, the 65 libraries generated a total of 175,261,692 raw sequence reads with an average length of 247 bp and an average proportion of guanine (G) and cytosine (C) in the sequences (GC content [GC%]) of 45.8%. The sequence reads were binned according to barcode and were assembled into larger contigs. In total, 690,673 phage contigs were obtained through de novo assembly within the 65 libraries and alignment against the phage protein database using BLASTx (Table S1). To investigate the constitution and distribution pattern of gut phage communities in different animal groups, a series of comparative analyses were performed. The distribution heatmap of phage communities presented that the phage contigs and singlets reads of giant pandas, red pandas, bears, and musk deer were classified into 10 phage families (Fig. 1A). Thereinto, the phage communities of giant pandas were mainly dominated by Siphoviridae, Podoviridae, Myoviridae, and Drexlerviridae families, whereas the Leviviridae family was distributed sporadically. Unlike giant pandas, the most abundant phage family of red pandas is Leviviridae, and relatively low numbers of reads were classified into Podoviridae, Myoviridae, and Drexlerviridae. In addition, the family Microviridae accounted for the largest percentage of viral reads in the libraries of musk deer, whereas the phage communities of giant pandas possess the least abundant reads in the Microviridae family. Meanwhile, comparison of phage communities among giant pandas and other relevant animal groups through Bray-Curtis analysis of similarities (ANOSIM) and principal coordinate analysis (PCoA) suggested that the difference among groups was statistically significant with P < 0.01 (Fig. 1B and C). Furthermore, the greatest within-group difference was observed in the group of giant pandas, and the least difference was observed in musk deer (P < 0.05) (Fig. S1). FIG 1 Comparison of gut phage communities of giant panda and other relevant animals. (A) Distribution heatmap of gut phage communities. Heatmap representing the reads number of each phage family in exponential form. The names of phage families are presented on the left, and the library names are presented at the bottom. Different animal sources are represented by rectangles with different colors and animal names are indicated on top; (B) principal coordinate analysis plot; (C) Analysis of similarities was performed among four animal groups. Different animal groups are marked with corresponding colors (see color legend). The order Caudovirales represents the largest category of bacteriophages, and the TerL region has high conservativeness of evolution in the Caudovirales order. To phylogenetically analyze phage sequences in the libraries of giant pandas and other animals, 944 sequences with complete coding sequence of phage terminase large subunits (TerL) were obtained after contigs annotation, including 87 sequences from the libraries of bears, 672 from giant pandas, 173 from red pandas, and 12 from musk deer (Table S2). The BLASTx results showed that these phage sequences shared sequence identities with their best matches ranging from 35.88% to 100%, and the length of them ranges from 1,224 bp to 3,090 bp with an average of 1,490 bp. The phylogenetic tree of bacteriophages was constructed based on the 944 TerL protein sequences identified in the study (Fig. 2). The topology structure of the phylogenetic tree revealed that a preponderance of TerL sequences did not cluster with any known families within the Caudovirales order, and formed four unclassified novel clusters among the known clades. Besides, several TerL sequences were phylogenetically clustered with known phage strains in the clades of Drexlerviridae, Podoviridae, Myoviridae, Siphoviridae, Demerecviridae, and so on. The cluster of the Drexlerviridae family is the largest of them, which was dominated by the sequences from giant pandas. Meanwhile, the clade of the Demerecviridae family is composed mainly of sequences from red pandas. And the sequences from musk deer were mainly clustered with bacteriophages in the Siphoviridae family. These results revealed the taxonomic composition and the potential diversity of bacteriophages in giant pandas and other relevant animals. FIG 2 The phylogeny of Caudovirales identified in feces of giant panda and other relevant animals. Bayesian inference tree was established based on amino acid sequences of TerL of Caudovirales. Representative strains of all families in Caudovirales were included. The sequences of TerL found in this study are indicated in different colors according to animal groups (see color legend). Viral groups are marked around the tree with different colors. Auto, Autographiviridae; Chase, Chaseviridae; Sipho, Siphoviridae; Myo, Myoviridae; Here, Herelleviridae. The order Caudovirales was the most essential part of bacteriophages and was ubiquitous not only in the natural environment but in the gut virome of human, which could indirectly interact with the host immune system to regulate health and disease (2, 5, 13). In the present study, the diversity and abundance of phage communities in the gut of giant pandas was significantly higher than other relevant animals, which was dominated by the order Caudovirales, mainly including Siphoviridae, Podoviridae, Myoviridae, etc. Previous research observed a correlation between the abundance of the order Caudovirales and inflammatory bowel disease. Thus, the effects of bacteriophages on intestinal health of giant pandas remain to be studied further (14). Meanwhile, bacteriophages are able to modulate the composition and abundance of gut bacteria, which could also be used for antibiotic therapy to treat bacterial infections (15). In the study, bacteriophages in the Drexlerviridae family of giant pandas were more abundant than that of other relevant animals. And a series of members of the Drexlerviridae family have been recognized as candidate phages in the development of phage therapy (16). Furthermore, phage terminase large subunit (TerL) gene is one of the virus hallmark genes and is broadly conserved in the order Caudovirales (7). The present study also revealed the hidden diversity of bacteriophages in giant pandas through phylogenetic analysis of the TerL gene, consistent with the research on human gut virome (17). Accordingly, although phages were typified by simple structure and tiny size, they owned the greatest abundance and the highest diversity in the gut virome. In addition, our study revealed a certain connection between the composition of phage communities and animal species. Because of the rarity of these animal species and the difficulty of sampling, animal sources and sample sizes are unavoidably limited. Besides, the gut phage communities are also subject to living environment and eating patterns (18). In this study, the timescale of sampling is over 2 yr; the change of diet in different seasons might influence the composition of gut microbiome. Previous research also revealed that environmental factors might play a more significant role in shaping gut microbiota than host genetics (19). Thus, further research would be needed to reveal the potential relationship in a broader and deeper vision. Ultimately, our research reveals the gut phageome of giant pandas and other mammals, providing a foundation for the giant panda protection efforts in the future and contributing a better understanding of the diversity and evolution of bacteriophages in mammals. METHODS Sample collection and preparation During 2018 to 2020, to investigate the phageome of giant pandas and other relevant animals (including bears, red pandas, and musk deer), in total, 729 fecal samples were collected from Sichuan, Wuhan, Chongqing, and Shanghai in China using disposable materials (Table S1). Samples were pooled into 65 sample pools according to sample size and source, including 8 pools for bears, 33 for giant pandas, 16 for red pandas, and 8 for musk deer. Samples were re-suspended in 500 µL Dulbecco’s phosphate-buffered saline and vigorously vortexed for 5 min, following frozen and thawed three times on dry ice. The supernatants were then collected after centrifugation (10 min, 15,000 g) and stored at –80°C until use. Viral metagenomic library construction Supernatant from each sample was pipetted and equally pooled into different sample pools to obtain the final volume of 500 µL. Sample pools were centrifuged at 12,000 g for 5 min at 4°C and then filtered through a 0.45 µm filter (Millipore). The filtrates enriched in viral particles were treated with DNase and RNase to digest unprotected nucleic acid (20 - 22). Then, the remaining total nucleic acid was isolated using QIAamp MinElute Virus Spin Kit (Qiagen) according to the manufacturer’s protocol. The viral nucleic acid samples were subjected to reverse transcription reactions using reverse transcriptase (Super-Script IV, Invitrogen) and 100 µmol of random hexamer primers, followed by a single round of DNA synthesis using Klenow fragment polymerase (New England BioLabs). Overall, 65 libraries were constructed using Nextera XT DNA Sample Preparation Kit (Illumina). All libraries were sequenced on an Illumina NovaSeq 6000 platform (23). Bioinformatics analysis Paired-end reads generated by NovaSeq were debarcoded using vendor software from Illumina. An in-house analysis pipeline running on a 32 nodes Linux cluster was utilized to process the data. Reads were considered duplicates if bases 5–55 were identical and only one random copy of duplicates was kept. Low sequencing quality tails were trimmed using Phred quality score 30 as the threshold. Adaptors were trimmed using the default parameters of VecScreen with specialized parameters designed for adapter removal. Bacterial reads were subtracted by mapping to the bacterial nucleotide sequences from the BLAST non-redundant (nr) database using Bowtie2 v2.2.4. The cleaned reads were de novo assembled by SOAPdenovo2 version r240 using Kmer size 63 with default settings (24). The assembled contigs, along with singlets, were then matched against a customized viral proteome database using BLASTx with an E-value cutoff of <10−5. The virus BLASTx (v.2.2.7) database was compiled using the National Center for Biotechnology Information (NCBI) virus reference proteome and viral proteins sequences from the NCBI nr database. Candidate viral hits are then compared to an in-house non-virus non-redundant (NVNR) protein database with an E-value cutoff of <10−5 to remove false-positive viral hits. The NVNR database was compiled using non-viral protein sequences extracted from the NCBI nr database. Contigs without significant BLASTx similarity to viral proteome database are searched against viral protein families in the vFam database (25) using HMMER3 (26 - 28) to detect remote viral protein similarities. Viral community analysis Composition similarity analysis of the 65 viromes were compared using MEGAN software (v6.21.7) (29) under the compare option. The results were presented using the Unweighted Pair Group Method with PCoA under Bray-Curtis ecological distance matrix with default parameters. ANOSIM was used to compare differences among groups using R v4.0.4 package vegan (v2.5.7). The viral community structure and richness results were visualized in the heatmap which was generated using R v4.0.4 package pheatmap (v1.0.12). Viral sequences extension and annotation Viral contigs were merged using the Low Sensitivity/Fastest parameter in software Geneious v11.1.2 (30). And the individual contig was used as reference for mapping to the raw reads of its original barcode using the Low Sensitivity/Fastest parameter. Putative viral open reading frames (ORFs) were predicted by Geneious v11.1.2 with built-in parameters (minimum size: 300; genetic code: Standard; start codons: ATG) (30) and were checked through BLASTp in NCBI. The annotations of these ORFs were based on comparisons to the Conserved Domain Database with an E-value cutoff of <10−5. Those contigs annotated with phage terminase large subunits (TerL) of Caudovirales were selected, among which identified as complete ORFs were included for further phylogenetic analyses. Phylogenetic analysis Phylogenetic analysis was performed based on the protein sequences of phage terminase large subunits (TerL) identified in this study and protein sequences of reference strains belonging to different families of Caudovirales. These protein sequences were aligned using MUSCLE in MEGA v10.1.8 with the default settings (31). Sites containing more than 50% gaps were temporarily removed from alignments. Bayesian inference trees were then constructed using MrBayes v3.2.7 (32). The Markov chain was run for a maximum of 1 million generations, in which every 50 generations were sampled and the first 25% of Markov chain Monte Carlo samples were discarded as burn-in. Maximum Likelihood tree was also constructed to confirm the Bayesian inference tree using software MEGA v10.1.8 (31). ACKNOWLEDGMENTS This work was supported by the National Key Research and Development Programs of China no. 2022YFC2603801, Chengdu Research Base of Giant Panda Breeding no. 2020CPB-C11 and 2021CPB-B11, Donghai People’s Hospital-School of Medicine UJS Joint Laboratory Foundation no. 20210467, Jiangsu Provincial Key Research and Development Projects no. BE2017693, and the Youth doctoral scientific research foundation of Harbin University, grant number HUDF2020105. W.Z., T.S., and S.L. designed the study and methods. J.L., H.W., and C.W. constructed the libraries. J.L., M.Z., R.H., Q.S., S.Y., L.J., Y.L., X.W., and W.Z. completed the data analysis. The paper first draft was prepared by J.L. and substantially reviewed and revised by all authors. The authors declare no competing interests. DATA AVAILABILITY The raw sequence read data analyzed in this study are available at the NCBI Sequence Read Archive database under the accession numbers listed in Table S1. All viral sequences of phage terminase large subunit (TerL) identified in this study were deposited in the GenBank database under the accession numbers from OP517077 to OP518020 (Table S2). SUPPLEMENTAL MATERIAL The following material is available online at https://doi.org/10.1128/msystems.00161-23. 10.1128/msystems.00161-23.SuF1 Figure S1 msystems.00161-23-s0001.tif Analysis of similarity (ANOSIM) between groups based on the Bray-Curtis distances. Click here for additional data file. 10.1128/msystems.00161-23.SuF2 Table S1 msystems.00161-23-s0002.xlsx Information of sample source and corresponding libraries. Click here for additional data file. 10.1128/msystems.00161-23.SuF3 Table S2 msystems.00161-23-s0003.xlsx Information of viral sequences with phage terminase large subunit (TerL) identified in the present study. Click here for additional data file. ASM does not own the copyrights to Supplemental Material that may be linked to, or accessed through, an article. The authors have granted ASM a non-exclusive, world-wide license to publish the Supplemental Material files. Please contact the corresponding author directly for reuse. ==== Refs REFERENCES 1 Moreno-Gallego JL , Chou S-P , Di Rienzi SC , Goodrich JK , Spector TD , Bell JT , Youngblut ND , Hewson I , Reyes A , Ley RE . 2019. Virome diversity correlates with intestinal microbiome diversity in adult monozygotic twins. Cell Host Microbe 25 :261–272. doi:10.1016/j.chom.2019.01.019 30763537 2 Cadwell K . 2015. The virome in host health and disease. Immunity 42 :805–813. doi:10.1016/j.immuni.2015.05.003 25992857 3 Liang G , Bushman FD . 2021. The human virome: assembly, composition and host interactions. Nat Rev Microbiol 19 :514–527. doi:10.1038/s41579-021-00536-5 33785903 4 Minot S , Bryson A , Chehoud C , Wu GD , Lewis JD , Bushman FD . 2013. Rapid evolution of the human gut virome. Proc Natl Acad Sci USA 110 :12450–12455. doi:10.1073/pnas.1300833110 23836644 5 Shkoporov AN , Clooney AG , Sutton TDS , Ryan FJ , Daly KM , Nolan JA , McDonnell SA , Khokhlova EV , Draper LA , Forde A , Guerin E , Velayudhan V , Ross RP , Hill C . 2019. The human gut virome is highly diverse, stable, and individual specific. Cell Host Microbe 26 :527–541. doi:10.1016/j.chom.2019.09.009 31600503 6 Guerin E , Hill C . 2020. Shining light on human gut bacteriophages. Front Cell Infect Microbiol 10 :481. doi:10.3389/fcimb.2020.00481 33014897 7 Koonin EV , Dolja VV , Krupovic M , Varsani A , Wolf YI , Yutin N , Zerbini FM , Kuhn JH . 2020. Global organization and proposed megataxonomy of the virus world. Microbiol Mol Biol Rev 84 :e00061-19. doi:10.1128/MMBR.00061-19 32132243 8 Wei F , Fan H , Hu Y . 2020. Ailuropoda melanoleuca (Giant panda). Trends Genet 36 :68–69. doi:10.1016/j.tig.2019.09.009 31727389 9 Swaisgood R , Wang D , Wei F . 2016. Ailuropoda melanoleuca. The IUCN red list of threatened species e.T712A121745669. 10 Jin Y , Zhang X , Ma Y , Qiao Y , Liu X , Zhao K , Zhang C , Lin D , Fu X , Xu X , Wang Y , Wang H . 2017. Canine distemper viral infection threatens the giant panda population in China. Oncotarget 8 :113910–113919. doi:10.18632/oncotarget.23042 29371956 11 Guo L , Yang S , Chen S , Zhang Z , Wang C , Hou R , Ren Y , wen X , Cao S , Guo W , Hao Z , Quan Z , Zhang M , Yan Q . 2013. Identification of canine parvovirus with the Q370R point mutation in the VP2 gene from a giant panda (Ailuropoda melanoleuca). Virol J 10 :163. doi:10.1186/1743-422X-10-163 23706032 12 Zhao M , Yue C , Yang Z , Li Y , Zhang D , Zhang J , Yang S , Shen Q , Su X , Qi D , Ma R , Xiao Y , Hou R , Yan X , Li L , Zhou Y , Liu J , Wang X , Wu W , Zhang W , Shan T , Liu S . 2022. Viral metagenomics unveiled extensive communications of viruses within giant PANDAS and their associated organisms in the same ecosystem. Sci Total Environ 820 :153317. doi:10.1016/j.scitotenv.2022.153317 35066043 13 Gregory AC , Zayed AA , Conceição-Neto N , Temperton B , Bolduc B , Alberti A , Ardyna M , Arkhipova K , Carmichael M , Cruaud C , Dimier C , Domínguez-Huerta G , Ferland J , Kandels S , Liu Y , Marec C , Pesant S , Picheral M , Pisarev S , Poulain J , Tremblay J-É , Vik D , Babin M , Bowler C , Culley AI , de Vargas C , Dutilh BE , Iudicone D , Karp-Boss L , Roux S , Sunagawa S , Wincker P , Sullivan MB , Tara Oceans Coordinators . 2019. Marine DNA viral macro- and microdiversity from pole to pole. Cell 177 :1109–1123. doi:10.1016/j.cell.2019.03.040 31031001 14 Norman JM , Handley SA , Baldridge MT , Droit L , Liu CY , Keller BC , Kambal A , Monaco CL , Zhao G , Fleshner P , Stappenbeck TS , McGovern DPB , Keshavarzian A , Mutlu EA , Sauk J , Gevers D , Xavier RJ , Wang D , Parkes M , Virgin HW . 2015. Disease-Specific alterations in the enteric virome in inflammatory bowel disease. Cell 160 :447–460. doi:10.1016/j.cell.2015.01.002 25619688 15 Hermoso JA , García JL , García P . 2007. Taking aim on bacterial pathogens: from phage therapy to enzybiotics. Curr Opin Microbiol 10 :461–472. doi:10.1016/j.mib.2007.08.002 17904412 16 Koonjan S , Seijsing F , Cooper CJ , Nilsson AS . 2020. Infection kinetics and phylogenetic analysis of vB_EcoD_SU57, a virulent T1-like drexlerviridae coliphage. Front Microbiol 11 :565556. doi:10.3389/fmicb.2020.565556 33329423 17 Camarillo-Guerrero LF , Almeida A , Rangel-Pineros G , Finn RD , Lawley TD . 2021. Massive expansion of human gut bacteriophage diversity. Cell 184 :1098–1109. doi:10.1016/j.cell.2021.01.029 33606979 18 Łusiak-Szelachowska M , Weber-Dąbrowska B , Jończyk-Matysiak E , Wojciechowska R , Górski A . 2017. Bacteriophages in the gastrointestinal tract and their implications. Gut Pathog 9 :44. doi:10.1186/s13099-017-0196-7 28811841 19 Rothschild D , Weissbrod O , Barkan E , Kurilshikov A , Korem T , Zeevi D , Costea PI , Godneva A , Kalka IN , Bar N , Shilo S , Lador D , Vila AV , Zmora N , Pevsner-Fischer M , Israeli D , Kosower N , Malka G , Wolf BC , Avnit-Sagi T , Lotan-Pompan M , Weinberger A , Halpern Z , Carmi S , Fu J , Wijmenga C , Zhernakova A , Elinav E , Segal E . 2018. Environment dominates over host genetics in shaping human gut microbiota. Nature 555 :210–215. doi:10.1038/nature25973 29489753 20 Zhang W , Li L , Deng X , Blümel J , Nübling CM , Hunfeld A , Baylis SA , Delwart E . 2016. Viral nucleic acids in human plasma pools. Transfusion 56 :2248–2255. doi:10.1111/trf.13692 27306718 21 Zhang W , Li L , Deng X , Kapusinszky B , Pesavento PA , Delwart E . 2014. Faecal virome of cats in an animal shelter. J Gen Virol. 95 :2553–2564. doi:10.1099/vir.0.069674-0 25078300 22 Zhang W , Yang S , Shan T , Hou R , Liu Z , Li W , Guo L , Wang Y , Chen P , Wang X , Feng F , Wang H , Chen C , Shen Q , Zhou C , Hua X , Cui L , Deng X , Zhang Z , Qi D , Delwart E . 2017. Virome comparisons in wild-diseased and healthy captive giant pandas. Microbiome 5 :90. doi:10.1186/s40168-017-0308-0 28780905 23 Liu Z , Yang S , Wang Y , Shen Q , Yang Y , Deng X , Zhang W , Delwart E . 2016. Identification of a novel human papillomavirus by metagenomic analysis of vaginal swab samples from pregnant women. Virol J 13 :1–7. doi:10.1186/s12985-016-0583-6 26728778 24 Luo R , Liu B , Xie Y , Li Z , Huang W , Yuan J , He G , Chen Y , Pan Q , Liu Y , Tang J , Wu G , Zhang H , Shi Y , Liu Y , Yu C , Wang B , Lu Y , Han C , Cheung DW , Yiu SM , Peng S , Xiaoqian Z , Liu G , Liao X , Li Y , Yang H , Wang J , Lam TW , Wang J . 2012. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. GigaSci 1 . doi:10.1186/2047-217X-1-18 25 Skewes-Cox P , Sharpton TJ , Pollard KS , DeRisi JL . 2014. Profile hidden Markov models for the detection of viruses within metagenomic sequence data. PLoS One 9 :e105067. doi:10.1371/journal.pone.0105067 25140992 26 Eddy SR . 2009. A new generation of homology search tools based on probabilistic inference. Genome Inform 23 :205–211.20180275 27 Finn RD , Clements J , Eddy SR . 2011. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res 39 :W29–W37. doi:10.1093/nar/gkr367 21593126 28 Johnson LS , Eddy SR , Portugaly E . 2010. Hidden Markov model speed Heuristic and Iterative HMM search procedure. BMC Bioinformatics 11 :431. doi:10.1186/1471-2105-11-431 20718988 29 Huson DH , Auch AF , Qi J , Schuster SC . 2007. MEGAN analysis of metagenomic data. Genome Res 17 :377–386. doi:10.1101/gr.5969107 17255551 30 Kearse M , Moir R , Wilson A , Stones-Havas S , Cheung M , Sturrock S , Buxton S , Cooper A , Markowitz S , Duran C , Thierer T , Ashton B , Meintjes P , Drummond A . 2012. Geneious basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28 :1647–1649. doi:10.1093/bioinformatics/bts199 22543367 31 Kumar S , Stecher G , Li M , Knyaz C , Tamura K , Battistuzzi FU . 2018. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 35 :1547–1549. doi:10.1093/molbev/msy096 29722887 32 Ronquist F , Teslenko M , van der Mark P , Ayres DL , Darling A , Höhna S , Larget B , Liu L , Suchard MA , Huelsenbeck JP . 2012. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst Biol 61 :539–542. doi:10.1093/sysbio/sys029 22357727