Functional dynamics of bacterial species in the mouse gut microbiome revealed by metagenomic and metatranscriptomic analyses
Autoři:
Youn Wook Chung aff001; Ho-Jin Gwak aff003; Sungmin Moon aff002; Mina Rho aff003; Ji-Hwan Ryu aff002
Působiště autorů:
The Airway Mucus Institute, Yonsei University College of Medicine, Seoul, Korea
aff001; Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Korea
aff002; Department of Computer Science and Engineering, Hanyang University, Seoul, Korea
aff003; Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Korea
aff004; Department of Biomedical Informatics, Hanyang University, Seoul, Korea
aff005
Vyšlo v časopise:
PLoS ONE 15(1)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0227886
Souhrn
Background
Microbial communities of the mouse gut have been extensively studied; however, their functional roles and regulation are yet to be elucidated. Metagenomic and metatranscriptomic analyses may allow us a comprehensive profiling of bacterial composition and functions of the complex gut microbiota. The present study aimed to investigate the active functions of the microbial communities in the murine cecum by analyzing both metagenomic and metatranscriptomic data on specific bacterial species within the microbial communities, in addition to the whole microbiome.
Results
Bacterial composition of the healthy mouse gut microbiome was profiled using the following three different approaches: 16S rRNA-based profiling based on amplicon and shotgun sequencing data, and genome-based profiling based on shotgun sequencing data. Consistently, Bacteroidetes, Firmicutes, and Deferribacteres emerged as the major phyla. Based on NCBI taxonomy, Muribaculaceae, Lachnospiraceae, and Deferribacteraceae were the predominant families identified in each phylum. The genes for carbohydrate metabolism were upregulated in Muribaculaceae, while genes for cofactors and vitamin metabolism and amino acid metabolism were upregulated in Deferribacteraceae. The genes for translation were commonly enhanced in all three families. Notably, combined analysis of metagenomic and metatranscriptomic sequencing data revealed that the functions of translation and metabolism were largely upregulated in all three families in the mouse gut environment. The ratio of the genes in the metagenome and their expression in the metatranscriptome indicated higher expression of carbohydrate metabolism in Muribaculum, Duncaniella, and Mucispirillum.
Conclusions
We demonstrated a fundamental methodology for linking genomic and transcriptomic datasets to examine functional activities of specific bacterial species in a complicated microbial environment. We investigated the normal flora of the mouse gut using three different approaches and identified Muribaculaceae, Lachnospiraceae, and Deferribacteraceae as the predominant families. The functional distribution of these families was reflected in the entire microbiome. By comparing the metagenomic and metatranscriptomic data, we found that the expression rates differed for different functional categories in the mouse gut environment. Application of these methods to track microbial transcription in individuals over time, or before and after administration of a specific stimulus will significantly facilitate future development of diagnostics and treatments.
Klíčová slova:
Bacteria – Carbohydrate metabolism – Functional genomics – Mammalian genomics – Metagenomics – Microbiome – Mouse models – Shotgun sequencing
Zdroje
1. Schloss PD, Handelsman J. Biotechnological prospects from metagenomics. Curr Opin Biotechnol. 2003;14(3):303–10. doi: 10.1016/s0958-1669(03)00067-3 12849784.
2. Breitbart M, Hewson I, Felts B, Mahaffy JM, Nulton J, Salamon P, et al. Metagenomic analyses of an uncultured viral community from human feces. J Bacteriol. 2003;185(20):6220–3. doi: 10.1128/JB.185.20.6220-6223.2003 14526037; PubMed Central PMCID: PMC225035.
3. Turnbaugh PJ, Quince C, Faith JJ, McHardy AC, Yatsunenko T, Niazi F, et al. Organismal, genetic, and transcriptional variation in the deeply sequenced gut microbiomes of identical twins. Proc Natl Acad Sci U S A. 2010;107(16):7503–8. doi: 10.1073/pnas.1002355107 20363958; PubMed Central PMCID: PMC2867707.
4. Gilbert JA, Field D, Swift P, Thomas S, Cummings D, Temperton B, et al. The taxonomic and functional diversity of microbes at a temperate coastal site: a 'multi-omic' study of seasonal and diel temporal variation. PLoS One. 2010;5(11):e15545. doi: 10.1371/journal.pone.0015545 21124740; PubMed Central PMCID: PMC2993967.
5. Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464(7285):59–65. doi: 10.1038/nature08821 20203603; PubMed Central PMCID: PMC3779803.
6. Yooseph S, Sutton G, Rusch DB, Halpern AL, Williamson SJ, Remington K, et al. The Sorcerer II Global Ocean Sampling expedition: expanding the universe of protein families. PLoS Biol. 2007;5(3):e16. doi: 10.1371/journal.pbio.0050016 17355171; PubMed Central PMCID: PMC1821046.
7. He S, Kunin V, Haynes M, Martin HG, Ivanova N, Rohwer F, et al. Metatranscriptomic array analysis of 'Candidatus Accumulibacter phosphatis'-enriched enhanced biological phosphorus removal sludge. Environ Microbiol. 2010;12(5):1205–17. doi: 10.1111/j.1462-2920.2010.02163.x 20148930.
8. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30. doi: 10.1093/nar/28.1.27 10592173; PubMed Central PMCID: PMC102409.
9. Jorth P, Turner KH, Gumus P, Nizam N, Buduneli N, Whiteley M. Metatranscriptomics of the human oral microbiome during health and disease. MBio. 2014;5(2):e01012–14. doi: 10.1128/mBio.01012-14 24692635; PubMed Central PMCID: PMC3977359.
10. Meyer F, Paarmann D, D'Souza M, Olson R, Glass EM, Kubal M, et al. The metagenomics RAST server—a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics. 2008;9:386. doi: 10.1186/1471-2105-9-386 18803844; PubMed Central PMCID: PMC2563014.
11. Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, et al. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 2006;34(Database issue):D354–7. doi: 10.1093/nar/gkj102 16381885; PubMed Central PMCID: PMC1347464.
12. Booijink CC, Boekhorst J, Zoetendal EG, Smidt H, Kleerebezem M, de Vos WM. Metatranscriptome analysis of the human fecal microbiota reveals subject-specific expression profiles, with genes encoding proteins involved in carbohydrate metabolism being dominantly expressed. Appl Environ Microbiol. 2010;76(16):5533–40. doi: 10.1128/AEM.00502-10 20562280; PubMed Central PMCID: PMC2918960.
13. Gosalbes MJ, Durban A, Pignatelli M, Abellan JJ, Jimenez-Hernandez N, Perez-Cobas AE, et al. Metatranscriptomic approach to analyze the functional human gut microbiota. PLoS One. 2011;6(3):e17447. doi: 10.1371/journal.pone.0017447 21408168; PubMed Central PMCID: PMC3050895.
14. Petersen LM, Bautista EJ, Nguyen H, Hanson BM, Chen L, Lek SH, et al. Community characteristics of the gut microbiomes of competitive cyclists. Microbiome. 2017;5(1):98. doi: 10.1186/s40168-017-0320-4 28797298; PubMed Central PMCID: PMC5553673.
15. Franzosa EA, Morgan XC, Segata N, Waldron L, Reyes J, Earl AM, et al. Relating the metatranscriptome and metagenome of the human gut. Proc Natl Acad Sci U S A. 2014;111(22):E2329–38. doi: 10.1073/pnas.1319284111 24843156; PubMed Central PMCID: PMC4050606.
16. Duran-Pinedo AE, Yost S, Frias-Lopez J. Small RNA Transcriptome of the Oral Microbiome during Periodontitis Progression. Appl Environ Microbiol. 2015;81(19):6688–99. doi: 10.1128/AEM.01782-15 26187962; PubMed Central PMCID: PMC4561685.
17. Yost S, Duran-Pinedo AE, Teles R, Krishnan K, Frias-Lopez J. Functional signatures of oral dysbiosis during periodontitis progression revealed by microbial metatranscriptome analysis. Genome Med. 2015;7(1):27. doi: 10.1186/s13073-015-0153-3 25918553; PubMed Central PMCID: PMC4410737.
18. Solbiati J, Frias-Lopez J. Metatranscriptome of the Oral Microbiome in Health and Disease. J Dent Res. 2018;97(5):492–500. doi: 10.1177/0022034518761644 29518346; PubMed Central PMCID: PMC5958373.
19. Lee SW, Kuan CS, Wu LS, Weng JT. Metagenome and Metatranscriptome Profiling of Moderate and Severe COPD Sputum in Taiwanese Han Males. PLoS One. 2016;11(7):e0159066. doi: 10.1371/journal.pone.0159066 27428540; PubMed Central PMCID: PMC4948834.
20. Maurice CF, Haiser HJ, Turnbaugh PJ. Xenobiotics shape the physiology and gene expression of the active human gut microbiome. Cell. 2013;152(1–2):39–50. doi: 10.1016/j.cell.2012.10.052 23332745; PubMed Central PMCID: PMC3552296.
21. Ursell LK, Knight R. Xenobiotics and the human gut microbiome: metatranscriptomics reveal the active players. Cell Metab. 2013;17(3):317–8. doi: 10.1016/j.cmet.2013.02.013 23473028; PubMed Central PMCID: PMC3641660.
22. McNulty NP, Yatsunenko T, Hsiao A, Faith JJ, Muegge BD, Goodman AL, et al. The impact of a consortium of fermented milk strains on the gut microbiome of gnotobiotic mice and monozygotic twins. Sci Transl Med. 2011;3(106):106ra. doi: 10.1126/scitranslmed.3002701 22030749; PubMed Central PMCID: PMC3303609.
23. Benitez-Paez A, Belda-Ferre P, Simon-Soro A, Mira A. Microbiota diversity and gene expression dynamics in human oral biofilms. BMC Genomics. 2014;15:311. doi: 10.1186/1471-2164-15-311 24767457; PubMed Central PMCID: PMC4234424.
24. Xiong X, Frank DN, Robertson CE, Hung SS, Markle J, Canty AJ, et al. Generation and analysis of a mouse intestinal metatranscriptome through Illumina based RNA-sequencing. PLoS One. 2012;7(4):e36009. doi: 10.1371/journal.pone.0036009 22558305; PubMed Central PMCID: PMC3338770.
25. Xiao L, Feng Q, Liang S, Sonne SB, Xia Z, Qiu X, et al. A catalog of the mouse gut metagenome. Nat Biotechnol. 2015;33(10):1103–8. doi: 10.1038/nbt.3353 26414350.
26. Tatusov RL, Galperin MY, Natale DA, Koonin EV. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 2000;28(1):33–6. doi: 10.1093/nar/28.1.33 10592175; PubMed Central PMCID: PMC102395.
27. Jiang Y, Xiong X, Danska J, Parkinson J. Metatranscriptomic analysis of diverse microbial communities reveals core metabolic pathways and microbiome-specific functionality. Microbiome. 2016;4:2. doi: 10.1186/s40168-015-0146-x 26757703; PubMed Central PMCID: PMC4710996.
28. Xiong X, Bales ES, Ir D, Robertson CE, McManaman JL, Frank DN, et al. Perilipin-2 modulates dietary fat-induced microbial global gene expression profiles in the mouse intestine. Microbiome. 2017;5(1):117. doi: 10.1186/s40168-017-0327-x 28877764; PubMed Central PMCID: PMC5588750.
29. Just S, Mondot S, Ecker J, Wegner K, Rath E, Gau L, et al. The gut microbiota drives the impact of bile acids and fat source in diet on mouse metabolism. Microbiome. 2018;6(1):134. doi: 10.1186/s40168-018-0510-8 30071904; PubMed Central PMCID: PMC6091023.
30. Jenior ML, Leslie JL, Young VB, Schloss PD. Clostridium difficile Alters the Structure and Metabolism of Distinct Cecal Microbiomes during Initial Infection To Promote Sustained Colonization. mSphere. 2018;3(3). doi: 10.1128/mSphere.00261-18 29950381; PubMed Central PMCID: PMC6021602.
31. Hugenholtz F, Davids M, Schwarz J, Muller M, Tome D, Schaap P, et al. Metatranscriptome analysis of the microbial fermentation of dietary milk proteins in the murine gut. PLoS One. 2018;13(4):e0194066. doi: 10.1371/journal.pone.0194066 29664912; PubMed Central PMCID: PMC5903625.
32. Hibberd MC, Wu M, Rodionov DA, Li X, Cheng J, Griffin NW, et al. The effects of micronutrient deficiencies on bacterial species from the human gut microbiota. Sci Transl Med. 2017;9(390). doi: 10.1126/scitranslmed.aal4069 28515336; PubMed Central PMCID: PMC5524138.
33. Daniel SG, Ball CL, Besselsen DG, Doetschman T, Hurwitz BL. Functional Changes in the Gut Microbiome Contribute to Transforming Growth Factor beta-Deficient Colon Cancer. mSystems. 2017;2(5). doi: 10.1128/mSystems.00065-17 28951889; PubMed Central PMCID: PMC5613170.
34. Velmurugan G, Ramprasath T, Swaminathan K, Mithieux G, Rajendhran J, Dhivakar M, et al. Gut microbial degradation of organophosphate insecticides-induces glucose intolerance via gluconeogenesis. Genome Biol. 2017;18(1):8. doi: 10.1186/s13059-016-1134-6 28115022; PubMed Central PMCID: PMC5260025.
35. Corridoni D, Rodriguez-Palacios A, Di Stefano G, Di Martino L, Antonopoulos DA, Chang EB, et al. Genetic deletion of the bacterial sensor NOD2 improves murine Crohn's disease-like ileitis independent of functional dysbiosis. Mucosal Immunol. 2017;10(4):971–82. doi: 10.1038/mi.2016.98 27848951; PubMed Central PMCID: PMC5433921.
36. Schwab C, Tveit AT, Schleper C, Urich T. Gene expression of lactobacilli in murine forestomach biofilms. Microb Biotechnol. 2014;7(4):347–59. doi: 10.1111/1751-7915.12126 24702817; PubMed Central PMCID: PMC4241727.
37. Berry D, Schwab C, Milinovich G, Reichert J, Ben Mahfoudh K, Decker T, et al. Phylotype-level 16S rRNA analysis reveals new bacterial indicators of health state in acute murine colitis. ISME J. 2012;6(11):2091–106. doi: 10.1038/ismej.2012.39 22572638; PubMed Central PMCID: PMC3475367.
38. Versluis D, D'Andrea MM, Ramiro Garcia J, Leimena MM, Hugenholtz F, Zhang J, et al. Mining microbial metatranscriptomes for expression of antibiotic resistance genes under natural conditions. Sci Rep. 2015;5:11981. doi: 10.1038/srep11981 26153129; PubMed Central PMCID: PMC4495384.
39. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9(4):357–9. doi: 10.1038/nmeth.1923 22388286; PubMed Central PMCID: PMC3322381.
40. Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31(10):1674–6. doi: 10.1093/bioinformatics/btv033 25609793.
41. Rho M, Tang H, Ye Y. FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res. 2010;38(20):e191. doi: 10.1093/nar/gkq747 20805240; PubMed Central PMCID: PMC2978382.
42. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73(16):5261–7. doi: 10.1128/AEM.00062-07 17586664; PubMed Central PMCID: PMC1950982.
43. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nature Methods. 2015;12(1):59–60. WOS:000347668600019. doi: 10.1038/nmeth.3176 25402007
Článek vyšel v časopise
PLOS One
2020 Číslo 1
- Tisícileté topoly, mokří psi, stárnoucí kočky a ospalé octomilky – „jednohubky“ z výzkumu 2024/41
- Jaké jsou aktuální trendy v léčbě karcinomu slinivky?
- Může hubnutí souviset s vyšším rizikem nádorových onemocnění?
- Menstruační krev má značný diagnostický potenciál, mimo jiné u diabetu
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
Nejčtenější v tomto čísle
- Severity of misophonia symptoms is associated with worse cognitive control when exposed to misophonia trigger sounds
- Chemical analysis of snus products from the United States and northern Europe
- Calcium dobesilate reduces VEGF signaling by interfering with heparan sulfate binding site and protects from vascular complications in diabetic mice
- Effect of Lactobacillus acidophilus D2/CSL (CECT 4529) supplementation in drinking water on chicken crop and caeca microbiome
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy