دورية أكاديمية

Gut virome profiling identifies a widespread bacteriophage family associated with metabolic syndrome.

التفاصيل البيبلوغرافية
العنوان: Gut virome profiling identifies a widespread bacteriophage family associated with metabolic syndrome.
المؤلفون: de Jonge PA; Departments of Internal and Experimental Vascular Medicine, Amsterdam University Medical Centers, Location AMC, Amsterdam, the Netherlands.; Amsterdam Gastroenterology Endocrinology Metabolism, Endocrinology, metabolism and nutrition, Amsterdam, the Netherlands.; Amsterdam Cardiovascular Sciences, Diabetes & Metabolism, Amsterdam, the Netherlands., Wortelboer K; Departments of Internal and Experimental Vascular Medicine, Amsterdam University Medical Centers, Location AMC, Amsterdam, the Netherlands.; Amsterdam Gastroenterology Endocrinology Metabolism, Endocrinology, metabolism and nutrition, Amsterdam, the Netherlands.; Amsterdam Cardiovascular Sciences, Diabetes & Metabolism, Amsterdam, the Netherlands., Scheithauer TPM; Departments of Internal and Experimental Vascular Medicine, Amsterdam University Medical Centers, Location AMC, Amsterdam, the Netherlands.; Amsterdam Gastroenterology Endocrinology Metabolism, Endocrinology, metabolism and nutrition, Amsterdam, the Netherlands.; Amsterdam Cardiovascular Sciences, Diabetes & Metabolism, Amsterdam, the Netherlands., van den Born BH; Departments of Internal and Experimental Vascular Medicine, Amsterdam University Medical Centers, Location AMC, Amsterdam, the Netherlands.; Amsterdam Gastroenterology Endocrinology Metabolism, Endocrinology, metabolism and nutrition, Amsterdam, the Netherlands.; Amsterdam Cardiovascular Sciences, Diabetes & Metabolism, Amsterdam, the Netherlands., Zwinderman AH; Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centers, Location AMC, University of Amsterdam, Amsterdam, the Netherlands., Nobrega FL; School of Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK., Dutilh BE; Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Utrecht, the Netherlands.; Institute of Biodiversity, Faculty of Biological Sciences, Cluster of Excellence Balance of the Microverse, Friedrich-Schiller-University Jena, Jena, Germany., Nieuwdorp M; Departments of Internal and Experimental Vascular Medicine, Amsterdam University Medical Centers, Location AMC, Amsterdam, the Netherlands.; Amsterdam Gastroenterology Endocrinology Metabolism, Endocrinology, metabolism and nutrition, Amsterdam, the Netherlands.; Amsterdam Cardiovascular Sciences, Diabetes & Metabolism, Amsterdam, the Netherlands., Herrema H; Departments of Internal and Experimental Vascular Medicine, Amsterdam University Medical Centers, Location AMC, Amsterdam, the Netherlands. h.j.herrema@amsterdamumc.nl.; Amsterdam Gastroenterology Endocrinology Metabolism, Endocrinology, metabolism and nutrition, Amsterdam, the Netherlands. h.j.herrema@amsterdamumc.nl.; Amsterdam Cardiovascular Sciences, Diabetes & Metabolism, Amsterdam, the Netherlands. h.j.herrema@amsterdamumc.nl.
المصدر: Nature communications [Nat Commun] 2022 Jun 23; Vol. 13 (1), pp. 3594. Date of Electronic Publication: 2022 Jun 23.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE
أسماء مطبوعة: Original Publication: [London] : Nature Pub. Group
مواضيع طبية MeSH: Bacteriophages*/genetics , Cardiovascular Diseases* , Metabolic Syndrome*, Bacteria/genetics ; Ecosystem ; Humans ; Virome/genetics
مستخلص: There is significant interest in altering the course of cardiometabolic disease development via gut microbiomes. Nevertheless, the highly abundant phage members of the complex gut ecosystem -which impact gut bacteria- remain understudied. Here, we show gut virome changes associated with metabolic syndrome (MetS), a highly prevalent clinical condition preceding cardiometabolic disease, in 196 participants by combined sequencing of bulk whole genome and virus like particle communities. MetS gut viromes exhibit decreased richness and diversity. They are enriched in phages infecting Streptococcaceae and Bacteroidaceae and depleted in those infecting Bifidobacteriaceae. Differential abundance analysis identifies eighteen viral clusters (VCs) as significantly associated with either MetS or healthy viromes. Among these are a MetS-associated Roseburia VC that is related to healthy control-associated Faecalibacterium and Oscillibacter VCs. Further analysis of these VCs revealed the Candidatus Heliusviridae, a highly widespread gut phage lineage found in 90+% of participants. The identification of the temperate Ca. Heliusviridae provides a starting point to studies of phage effects on gut bacteria and the role that this plays in MetS.
(© 2022. The Author(s).)
References: Belkaid, Y. & Hand, T. W. Role of the microbiota in immunity and inflammation. Cell 157, 121–141 (2014). (PMID: 24679531405676510.1016/j.cell.2014.03.011)
Rastelli, M., Cani, P. D. & Knauf, C. The gut microbiome influences host endocrine functions. Endocr. Rev. 40, 1271–1284 (2019). (PMID: 3108189610.1210/er.2018-00280)
Gurung, M. et al. Role of gut microbiota in type 2 diabetes pathophysiology. EBioMedicine 51, 102590 (2020). (PMID: 31901868694816310.1016/j.ebiom.2019.11.051)
Lang, S. & Schnabl, B. Microbiota and fatty liver disease—the known, the unknown, and the future. Cell Host Microbe 28, 233–244 (2020). (PMID: 32791115746784110.1016/j.chom.2020.07.007)
Frank, D. N. et al. Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases. Proc. Natl Acad. Sci. USA 104, 13780–13785 (2007). (PMID: 17699621195945910.1073/pnas.0706625104)
Clooney, A. G. et al. Whole-virome analysis sheds light on viral dark matter in inflammatory bowel disease. Cell Host Microbe 26, 764–778.e5 (2019). (PMID: 3175776810.1016/j.chom.2019.10.009)
Norman, J. M. et al. Disease-specific alterations in the enteric virome in inflammatory bowel disease. Cell 160, 447–460 (2015). (PMID: 25619688431252010.1016/j.cell.2015.01.002)
Campbell, D. E. et al. Infection with bacteroides phage BV01 alters the host transcriptome and bile acid metabolism in a common human gut microbe. Cell Rep. 32, 108142 (2020). (PMID: 32937127835420510.1016/j.celrep.2020.108142)
Oh, J.-H. et al. Dietary fructose and microbiota-derived short-chain fatty acids promote bacteriophage production in the gut symbiont Lactobacillus reuteri. Cell Host Microbe 25, 273–284.e6 (2019). (PMID: 3065890610.1016/j.chom.2018.11.016)
Reyes, A. et al. Gut DNA viromes of Malawian twins discordant for severe acute malnutrition. Proc. Natl Acad. Sci. USA 112, 11941–11946 (2015). (PMID: 26351661458684210.1073/pnas.1514285112)
Ma, Y., You, X., Mai, G., Tokuyasu, T. & Liu, C. A human gut phage catalog correlates the gut phageome with type 2 diabetes. Microbiome 6, 1–12 (2018). (PMID: 10.1186/s40168-018-0410-y)
De Sordi, L., Lourenço, M. & Debarbieux, L. The battle within: interactions of bacteriophages and bacteria in the gastrointestinal tract. Cell Host Microbe 25, 210–218 (2019). (PMID: 3076353510.1016/j.chom.2019.01.018)
Paez-Espino, D. et al. Uncovering Earth’s virome. Nature 536, 425–430 (2016). (PMID: 2753303410.1038/nature19094)
Gregory, A. C. et al. The gut virome database reveals age-dependent patterns of virome diversity in the human gut. Cell Host Microbe 28, 724–740.e8 (2020). (PMID: 32841606744339710.1016/j.chom.2020.08.003)
Dutilh, B. E. et al. A highly abundant bacteriophage discovered in the unknown sequences of human faecal metagenomes. Nat. Commun. 5, 4498 (2014). (PMID: 2505811610.1038/ncomms5498)
Yutin, N. et al. Discovery of an expansive bacteriophage family that includes the most abundant viruses from the human gut. Nat. Microbiol. 3, 38–46 (2018). (PMID: 2913388210.1038/s41564-017-0053-y)
O’Neill, S. & O’Driscoll, L. Metabolic syndrome: a closer look at the growing epidemic and its associated pathologies. Obes. Rev. 16, 1–12 (2015). (PMID: 2540754010.1111/obr.12229)
Dabke, K., Hendrick, G. & Devkota, S. The gut microbiome and metabolic syndrome. J. Clin. Investig. 129, 4050–4057 (2019). (PMID: 31573550676323910.1172/JCI129194)
Mazidi, M., Rezaie, P., Kengne, A. P., Mobarhan, M. G. & Ferns, G. A. Gut microbiome and metabolic syndrome. Diabetes Metab. Syndr. Clin. Res. Rev. 10, S150–S157 (2016). (PMID: 10.1016/j.dsx.2016.01.024)
Fujisaka, S. et al. Diet, genetics, and the gut microbiome drive dynamic changes in plasma metabolites. Cell Rep. 22, 3072–3086 (2018). (PMID: 29539432588054310.1016/j.celrep.2018.02.060)
Ussar, S. et al. Interactions between gut microbiota, host genetics and diet modulate the predisposition to obesity and metabolic syndrome. Cell Metab. 22, 516–530 (2015). (PMID: 26299453457050210.1016/j.cmet.2015.07.007)
Haro, C. et al. The gut microbial community in metabolic syndrome patients is modified by diet. J. Nutr. Biochem. 27, 27–31 (2016). (PMID: 2637602710.1016/j.jnutbio.2015.08.011)
Bikel, S. et al. Gut dsDNA virome shows diversity and richness alterations associated with childhood obesity and metabolic syndrome. iScience 24, 102900 (2021). (PMID: 34409269836120810.1016/j.isci.2021.102900)
DeBoer, M. D. Assessing and managing the metabolic syndrome in children and adolescents. Nutrients 11, 1788 (2019). (PMID: 672365110.3390/nu11081788)
Shkoporov, A. N. & Hill, C. Bacteriophages of the human gut: the “known unknown” of the microbiome. Cell Host Microbe 25, 195–209 (2019). (PMID: 3076353410.1016/j.chom.2019.01.017)
Snijder, M. B. et al. Cohort profile: the healthy life in an urban setting (HELIUS) study in Amsterdam, the Netherlands. BMJ Open 7, 1–11 (2017). (PMID: 10.1136/bmjopen-2017-017873)
Bin Jang, H. et al. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat. Biotechnol. 37, 632–639 (2019). (PMID: 10.1038/s41587-019-0100-8)
Manrique, P. et al. Healthy human gut phageome. Proc. Natl Acad. Sci. USA 113, 10400–10405 (2016). (PMID: 27573828502746810.1073/pnas.1601060113)
Nayfach, S. et al. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat. Biotechnol. 39, 578–585 (2021). (PMID: 3334969910.1038/s41587-020-00774-7)
Roux, S., Emerson, J. B., Eloe-Fadrosh, E. A. & Sullivan, M. B. Benchmarking viromics: An in silico evaluation of metagenome-enabled estimates of viral community composition and diversity. PeerJ 2017, 1–26 (2017).
Crovesy, L., Masterson, D. & Rosado, E. L. Profile of the gut microbiota of adults with obesity: a systematic review. Eur. J. Clin. Nutr. 74, 1251–1262 (2020). (PMID: 3223122610.1038/s41430-020-0607-6)
Camarillo-Guerrero, L. F., Almeida, A., Rangel-Pineros, G., Finn, R. D. & Lawley, T. D. Massive expansion of human gut bacteriophage diversity. Cell 184, 1098–1109.e9 (2021). (PMID: 33606979789589710.1016/j.cell.2021.01.029)
Lin, H. & Peddada, S. Das Analysis of compositions of microbiomes with bias correction. Nat. Commun. 11, 1–11 (2020). (PMID: 10.1038/s41467-020-17041-7)
Aron-Wisnewsky, J. et al. Gut microbiota and human NAFLD: disentangling microbial signatures from metabolic disorders. Nat. Rev. Gastroenterol. Hepatol. 17, 279–297 (2020). (PMID: 3215247810.1038/s41575-020-0269-9)
Hryckowian, A. J. et al. Bacteroides thetaiotaomicron-Infecting Bacteriophage Isolates Inform Sequence-Based Host Range Predictions. Cell Host Microbe 28, 371–379.e5 (2020). (PMID: 32652063804501210.1016/j.chom.2020.06.011)
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). (PMID: 33594055788686010.1038/s41467-021-21350-w)
Von Meijenfeldt, F. A. B., Arkhipova, K., Cambuy, D. D., Coutinho, F. H. & Dutilh, B. E. Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT. Genome Biol. 20, 1–14 (2019).
Hedzet, S., Rupnik, M. & Accetto, T. Novel Siphoviridae Bacteriophages Infecting Bacteroides uniformis Contain Diversity Generating Retroelement. Microorganisms 9, 892 (2021). (PMID: 33919474814347710.3390/microorganisms9050892)
Tisza, M. J. & Buck, C. B. A catalog of tens of thousands of viruses from human metagenomes reveals hidden associations with chronic diseases. Proc. Natl Acad. Sci. USA 118, e2023202118 (2021). (PMID: 34083435820180310.1073/pnas.2023202118)
Van Den Abbeele, P. et al. Butyrate-producing Clostridium cluster XIVa species specifically colonize mucins in an in vitro gut model. ISME J. 7, 949–961 (2013). (PMID: 2323528710.1038/ismej.2012.158)
Lavigne, R., Seto, D., Mahadevan, P., Ackermann, H. W. & Kropinski, A. M. Unifying classical and molecular taxonomic classification: analysis of the Podoviridae using BLASTP-based tools. Res. Microbiol. 159, 406–414 (2008). (PMID: 1855566910.1016/j.resmic.2008.03.005)
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.e6 (2018). (PMID: 3044931610.1016/j.chom.2018.10.002)
Turner, D., Kropinski, A. M. & Adriaenssens, E. M. A roadmap for genome-based phage taxonomy. Viruses 13, 1–10 (2021). (PMID: 10.3390/v13030506)
Han, M., Yang, P., Zhong, C. & Ning, K. The human gut virome in hypertension. Front. Microbiol. 9, 1–10 (2018). (PMID: 10.3389/fmicb.2018.03150)
Lloyd-Price, J. et al. Strains, functions and dynamics in the expanded Human Microbiome Project. Nature 550, 61–66 (2017). (PMID: 28953883583108210.1038/nature23889)
Cornuault, J. K. et al. The enemy from within: a prophage of Roseburia intestinalis systematically turns lytic in the mouse gut, driving bacterial adaptation by CRISPR spacer acquisition. ISME J. 14, 771–787 (2020). (PMID: 3182724710.1038/s41396-019-0566-x)
Walther, B., Karl, J. P., Booth, S. L. & Boyaval, P. Menaquinones, bacteria, and the food supply: the relevance of dairy and fermented food products to vitamin K requirements. Adv. Nutr. 4, 463–473 (2013). (PMID: 23858094394182510.3945/an.113.003855)
Moreno-Gallego, J. L. et al. Virome diversity correlates with intestinal microbiome diversity in adult monozygotic twins. Cell Host Microbe 25, 261–272.e5 (2019). (PMID: 30763537641108510.1016/j.chom.2019.01.019)
Zmora, N., Suez, J. & Elinav, E. You are what you eat: diet, health and the gut microbiota. Nat. Rev. Gastroenterol. Hepatol. 16, 35–56 (2019). (PMID: 3026290110.1038/s41575-018-0061-2)
Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016). (PMID: 2712603910.1126/science.aad3503)
Zhernakova, A. et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569 (2016). (PMID: 27126040524084410.1126/science.aad3369)
Minot, S. et al. The human gut virome: Inter-individual variation and dynamic response to diet. Genome Res. 21, 1616–1625 (2011). (PMID: 21880779320227910.1101/gr.122705.111)
Rodriguez-Valera, F. et al. Explaining microbial population genomics through phage predation. Nat. Rev. Microbiol. 7, 828–836 (2009). (PMID: 1983448110.1038/nrmicro2235)
Koskella, B. & Brockhurst, M. A. Bacteria–phage coevolution as a driver of ecological and evolutionary processes in microbial communities. FEMS Microbiol. Rev. 38, 916–931 (2014). (PMID: 2461756910.1111/1574-6976.12072)
Lourenço, M. et al. The spatial heterogeneity of the gut limits predation and fosters coexistence of bacteria and bacteriophages. Cell Host Microbe 28, 390–401.e5 (2020). (PMID: 3261509010.1016/j.chom.2020.06.002)
Hatfull, G. F. Dark matter of the biosphere: the amazing world of bacteriophage diversity. J. Virol. 89, 8107–8110 (2015). (PMID: 26018169452425410.1128/JVI.01340-15)
Edwards, R. A., McNair, K., Faust, K., Raes, J. & Dutilh, B. E. Computational approaches to predict bacteriophage-host relationships. FEMS Microbiol. Rev. 40, 258–272 (2016). (PMID: 2665753710.1093/femsre/fuv048)
Burstein, D. et al. Major bacterial lineages are essentially devoid of CRISPR-Cas viral defence systems. Nat. Commun. 7, 10613 (2016). (PMID: 26837824474296110.1038/ncomms10613)
Džunková, M. et al. Defining the human gut host–phage network through single-cell viral tagging. Nat. Microbiol. 4, 2192–2203 (2019). (PMID: 3138400010.1038/s41564-019-0526-2)
de Jonge, P. A. et al. Adsorption sequencing as a rapid method to link environmental bacteriophages to hosts. iScience 23, 101439 (2020). (PMID: 32823052745225110.1016/j.isci.2020.101439)
Hatfull, G. F. Actinobacteriophages: genomics, dynamics, and applications. Annu. Rev. Virol. 7, 37–61 (2020). (PMID: 32991269801033210.1146/annurev-virology-122019-070009)
Ridaura, V. K. et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 341, 1241214 (2013). (PMID: 2400939710.1126/science.1241214)
David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014). (PMID: 2433621710.1038/nature12820)
De Filippis, F. et al. Distinct genetic and functional traits of human intestinal Prevotella copri strains are associated with different habitual diets. Cell Host Microbe 25, 444–453.e3 (2019). (PMID: 3079926410.1016/j.chom.2019.01.004)
Shkoporov, A. N. et al. ΦCrAss001 represents the most abundant bacteriophage family in the human gut and infects Bacteroides intestinalis. Nat. Commun. 9, 4781 (2018). (PMID: 30429469623596910.1038/s41467-018-07225-7)
Koonin, E. V. & Yutin, N. The crAss-like phage group: how metagenomics reshaped the human virome. Trends Microbiol. 28, 349–359 (2020). (PMID: 3229861310.1016/j.tim.2020.01.010)
Edwards, R. A. et al. Global phylogeography and ancient evolution of the widespread human gut virus crAssphage. Nat. Microbiol. 4, 1727–1736 (2019). (PMID: 31285584744097110.1038/s41564-019-0494-6)
Garmaeva, S. et al. Studying the gut virome in the metagenomic era: challenges and perspectives. BMC Biol. 17, 1–14 (2019). (PMID: 10.1186/s12915-019-0704-y)
Zhao, L. et al. Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes. Science 359, 1151–1156 (2018). (PMID: 2959004610.1126/science.aao5774)
Narita, M. The gut microbiome as a target for prevention of allergic diseases. Jpn. J. Allergol. 69, 19–22 (2020).
De La Cuesta-Zuluaga, J. et al. Metformin is associated with higher relative abundance of mucin-degrading akkermansia muciniphila and several short-chain fatty acid-producing microbiota in the gut. Diabetes Care 40, 54–62 (2017). (PMID: 2799900210.2337/dc16-1324)
Gazitúa, M. C. et al. Potential virus-mediated nitrogen cycling in oxygen-depleted oceanic waters. ISME J. 15, 981–998 (2021). (PMID: 3319980810.1038/s41396-020-00825-6)
Sharon, I. et al. Photosystem I gene cassettes are present in marine virus genomes. Nature 461, 258–262 (2009). (PMID: 19710652460514410.1038/nature08284)
Deschasaux, M. et al. Depicting the composition of gut microbiota in a population with varied ethnic origins but shared geography. Nat. Med. 24, 1526–1531 (2018). (PMID: 3015071710.1038/s41591-018-0160-1)
Mobini, R. et al. Metabolic effects of Lactobacillus reuteri DSM 17938 in people with type 2 diabetes: A randomized controlled trial. Diabetes Obes. Metab. 19, 579–589 (2017). (PMID: 2800910610.1111/dom.12861)
Alberti, K. G. M. M. et al. Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 120, 1640–1645 (2009). (PMID: 1980565410.1161/CIRCULATIONAHA.109.192644)
Garmaeva, S. et al. Stability of the human gut virome and effect of gluten-free diet. Cell Rep. 35, 109132 (2021). (PMID: 3401065110.1016/j.celrep.2021.109132)
Shkoporov, A. N. et al. Reproducible protocols for metagenomic analysis of human faecal phageomes. Microbiome 6, 68 (2018). (PMID: 29631623589201110.1186/s40168-018-0446-z)
Chen, S., Zhou, Y., Chen, Y. & Gu, J. Fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018). (PMID: 30423086612928110.1093/bioinformatics/bty560)
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012). (PMID: 22388286332238110.1038/nmeth.1923)
Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. MetaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017). (PMID: 28298430541177710.1101/gr.213959.116)
Pagès H, Aboyoun P, Gentleman R, D. S. Biostrings: efficient manipulation of biological strings. (2020).
Gregory, A. C. et al. Marine DNA viral macro- and microdiversity from pole to pole. Cell 177, 1109–1123.e14 (2019). (PMID: 31031001652505810.1016/j.cell.2019.03.040)
Roux, S., Enault, F., Hurwitz, B. L. & Sullivan, M. B. VirSorter: mining viral signal from microbial genomic data. PeerJ 3, e985 (2015). (PMID: 26038737445102610.7717/peerj.985)
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 11, 119 (2010). (PMID: 10.1186/1471-2105-11-119)
Verhaar, B. J. H. et al. Associations between gut microbiota, faecal short-chain fatty acids, and blood pressure across ethnic groups: the HELIUS study. Eur. Heart J. 41, 4259–4267 (2020). (PMID: 32869053772464110.1093/eurheartj/ehaa704)
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009). (PMID: 19505943272300210.1093/bioinformatics/btp352)
Quinlan, A. R. BEDTools: the Swiss-Army tool for genome feature analysis. Curr. Protoc. Bioinformatics 2014, 11.12.1-11.12.34 (2014).
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013). (PMID: 23630581363253010.1371/journal.pone.0061217)
Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003). (PMID: 10.1111/j.1654-1103.2003.tb02228.x)
Biswas, A., Staals, R. H. J., Morales, S. E., Fineran, P. C. & Brown, C. M. CRISPRDetect: a flexible algorithm to define CRISPR arrays. BMC Genomics 17, 1–14 (2016). (PMID: 10.1186/s12864-016-2627-0)
Nobrega, F. L., Walinga, H., Dutilh, B. E. & Brouns, S. J. J. J. Prophages are associated with extensive CRISPR–Cas auto-immunity. Nucleic Acids Res. 48, 12074–12084 (2020). (PMID: 33219687770804810.1093/nar/gkaa1071)
Wattam, A. R. et al. PATRIC, the bacterial bioinformatics database and analysis resource. Nucleic Acids Res. 42, 581–591 (2014). (PMID: 10.1093/nar/gkt1099)
Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinform. 10, 421 (2009). (PMID: 10.1186/1471-2105-10-421)
Pruitt, K. D., Tatusova, T. & Maglott, D. R. NCBI reference sequences (RefSeq): A curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 35, 61–65 (2007). (PMID: 10.1093/nar/gkl842)
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Front. Microbiol. 8, 1–6 (2017). (PMID: 10.3389/fmicb.2017.02224)
Sullivan, M. J., Petty, N. K. & Beatson, S. A. Easyfig: a genome comparison visualizer. Bioinformatics 27, 1009–1010 (2011). (PMID: 21278367306567910.1093/bioinformatics/btr039)
Seemann, T. Prokka: Rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014). (PMID: 2464206310.1093/bioinformatics/btu153)
Jones, P. et al. InterProScan 5: Genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014). (PMID: 24451626399814210.1093/bioinformatics/btu031)
Johnson, M. et al. NCBI BLAST: a better web interface. Nucleic Acids Res. 36, 5–9 (2008). (PMID: 10.1093/nar/gkn201)
Sievers, F. & Higgins, D. G. Clustal Omega for making accurate alignments of many protein sequences. Protein Sci. 27, 135–145 (2018). (PMID: 2888448510.1002/pro.3290)
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). (PMID: 19505945271234410.1093/bioinformatics/btp348)
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 (2015). (PMID: 2537143010.1093/molbev/msu300)
Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017). (PMID: 28481363545324510.1038/nmeth.4285)
Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018). (PMID: 2907790410.1093/molbev/msx281)
Zhou, X., Shen, X. X., Hittinger, C. T. & Rokas, A. Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets. Mol. Biol. Evol. 35, 486–503 (2018). (PMID: 2917747410.1093/molbev/msx302)
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2014). (PMID: 2540200710.1038/nmeth.3176)
تواريخ الأحداث: Date Created: 20220623 Date Completed: 20220627 Latest Revision: 20221114
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC9226167
DOI: 10.1038/s41467-022-31390-5
PMID: 35739117
قاعدة البيانات: MEDLINE
الوصف
تدمد:2041-1723
DOI:10.1038/s41467-022-31390-5