Meta-analysis defines predominant shared microbial responses in various diseases and a specific inflammatory bowel disease signal.
16S amplicon
Crohn’s disease
Gut microbiome
Meta-analyses
Ulcerative colitis
Journal
Genome biology
ISSN: 1474-760X
Titre abrégé: Genome Biol
Pays: England
ID NLM: 100960660
Informations de publication
Date de publication:
23 02 2022
23 02 2022
Historique:
received:
25
10
2021
accepted:
15
02
2022
entrez:
24
2
2022
pubmed:
25
2
2022
medline:
3
5
2022
Statut:
epublish
Résumé
Gut microbial alteration is implicated in inflammatory bowel disease but is noted in other diseases. Systematic comparison to define similarities and specificities is hampered since most studies focus on a single disease. We develop a pipeline to compare between disease cohorts starting from the raw V4 16S amplicon sequence variants. Including 12,838 subjects, from 59 disease cohorts, we demonstrate a predominant shared signature across diseases, indicating a common bacterial response to different diseases. We show that classifiers trained on one disease cohort predict relatively well other diseases due to this shared signal, and hence, caution should be taken when using such classifiers in real-world scenarios, where diseases are intermixed. Based on this common signature across a large array of diseases, we develop a universal dysbiosis index that successfully differentiates between cases and controls across various diseases and can be used for prioritizing fecal donors and samples with lower disease probability. Finally, we identify a set of IBD-specific bacteria, which can direct mechanistic studies and design of IBD-specific microbial interventions. A robust non-specific general response of the gut microbiome is detected in a large array of diseases. Disease classifiers may confuse between different diseases due to this shared microbial response. Our universal dysbiosis index can be used as a tool to prioritize fecal samples and donors. Finally, the IBD-specific taxa may indicate a more direct association to gut inflammation and disease pathogenesis, and those can be further used as biomarkers and as future targets for interventions.
Sections du résumé
BACKGROUND
Gut microbial alteration is implicated in inflammatory bowel disease but is noted in other diseases. Systematic comparison to define similarities and specificities is hampered since most studies focus on a single disease.
RESULTS
We develop a pipeline to compare between disease cohorts starting from the raw V4 16S amplicon sequence variants. Including 12,838 subjects, from 59 disease cohorts, we demonstrate a predominant shared signature across diseases, indicating a common bacterial response to different diseases. We show that classifiers trained on one disease cohort predict relatively well other diseases due to this shared signal, and hence, caution should be taken when using such classifiers in real-world scenarios, where diseases are intermixed. Based on this common signature across a large array of diseases, we develop a universal dysbiosis index that successfully differentiates between cases and controls across various diseases and can be used for prioritizing fecal donors and samples with lower disease probability. Finally, we identify a set of IBD-specific bacteria, which can direct mechanistic studies and design of IBD-specific microbial interventions.
CONCLUSIONS
A robust non-specific general response of the gut microbiome is detected in a large array of diseases. Disease classifiers may confuse between different diseases due to this shared microbial response. Our universal dysbiosis index can be used as a tool to prioritize fecal samples and donors. Finally, the IBD-specific taxa may indicate a more direct association to gut inflammation and disease pathogenesis, and those can be further used as biomarkers and as future targets for interventions.
Identifiants
pubmed: 35197084
doi: 10.1186/s13059-022-02637-7
pii: 10.1186/s13059-022-02637-7
pmc: PMC8867743
doi:
Types de publication
Journal Article
Meta-Analysis
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
61Subventions
Organisme : NIDDK NIH HHS
ID : P30 DK078392
Pays : United States
Informations de copyright
© 2022. The Author(s).
Références
Genome Med. 2016 Jul 13;8(1):75
pubmed: 27412252
Sci Rep. 2019 Oct 16;9(1):14840
pubmed: 31619731
mBio. 2020 Jun 9;11(3):
pubmed: 32518182
mSystems. 2017 Nov 21;2(6):
pubmed: 29181446
Am J Gastroenterol. 2019 Jul;114(7):1142-1151
pubmed: 30741738
mBio. 2016 Aug 16;7(4):
pubmed: 27531910
Microbiome. 2016 Jun 23;4(1):30
pubmed: 27338587
Microbiome. 2018 May 17;6(1):90
pubmed: 29773078
Nat Commun. 2020 May 15;11(1):2448
pubmed: 32415070
mSystems. 2017 Mar 7;2(2):
pubmed: 28289731
Sci Rep. 2017 Oct 19;7(1):13537
pubmed: 29051531
mSystems. 2017 Mar 7;2(2):
pubmed: 28289733
J Bacteriol. 2015 Apr 27;197(3):2104-2111
pubmed: 25917902
Gastroenterology. 2020 Feb;158(3):610-624.e13
pubmed: 31605691
Nat Biotechnol. 2017 Nov;35(11):1077-1086
pubmed: 28967885
Gut. 2017 May;66(5):813-822
pubmed: 28179361
J Psychiatr Res. 2017 Apr;87:23-29
pubmed: 27988330
NPJ Parkinsons Dis. 2020 Jun 12;6:11
pubmed: 32566740
Sci Rep. 2015 Aug 04;5:12693
pubmed: 26239401
PLoS One. 2017 Feb 21;12(2):e0172605
pubmed: 28222161
Schizophr Res. 2019 Feb;204:23-29
pubmed: 30268819
Diabetes Res Clin Pract. 2018 Oct;144:51-62
pubmed: 30121305
NPJ Biofilms Microbiomes. 2022 Jan 11;8(1):2
pubmed: 35017536
Sci Rep. 2020 Mar 25;10(1):5450
pubmed: 32214153
Nat Commun. 2020 Sep 15;11(1):4635
pubmed: 32934239
Appl Environ Microbiol. 2018 Jan 31;84(4):
pubmed: 29196292
Cell Host Microbe. 2017 Sep 13;22(3):269-278.e3
pubmed: 28910633
mSystems. 2018 Jan 30;3(1):
pubmed: 29404425
Cell Host Microbe. 2013 Sep 11;14(3):329-39
pubmed: 24034618
Nat Biotechnol. 2020 Jun;38(6):685-688
pubmed: 32483366
Nature. 2019 May;569(7758):655-662
pubmed: 31142855
Gut Microbes. 2020 May 3;11(3):453-464
pubmed: 31530087
Infect Immun. 2018 Jun 21;86(7):
pubmed: 29685983
Sci Rep. 2018 Jul 27;8(1):11356
pubmed: 30054529
J Clin Invest. 2014 Aug;124(8):3617-33
pubmed: 25003194
Genome Biol. 2019 Nov 1;20(1):219
pubmed: 31672155
mSystems. 2019 Jan 29;4(1):
pubmed: 30701193
Gut Pathog. 2019 Jan 18;11:1
pubmed: 30675188
Nat Methods. 2015 Mar;12(3):179-85
pubmed: 25719825
Genome Biol. 2021 Mar 30;22(1):93
pubmed: 33785070
Mov Disord. 2017 May;32(5):739-749
pubmed: 28195358
Brain Behav Immun. 2020 Mar;85:120-127
pubmed: 31255682
Sci Rep. 2017 Apr 24;7(1):1088
pubmed: 28439072
Microbiology (Reading). 2010 Aug;156(Pt 8):2271-2282
pubmed: 20488878
J Gastroenterol. 2019 Apr;54(4):347-358
pubmed: 30519748
Gastroenterology. 2008 Feb;134(2):577-94
pubmed: 18242222
Genome Biol. 2011 Jun 24;12(6):R60
pubmed: 21702898
Elife. 2019 Jan 22;8:
pubmed: 30666957
Sci Rep. 2016 May 27;6:26752
pubmed: 27229737
Pathog Dis. 2020 Apr 1;78(3):
pubmed: 32504490
Mucosal Immunol. 2014 Jul;7(4):983-94
pubmed: 24399150
Sci Rep. 2019 Sep 13;9(1):13263
pubmed: 31520001
PLoS One. 2014 Dec 05;9(12):e114277
pubmed: 25479553
Gastroenterology. 2020 Mar;158(4):930-946.e1
pubmed: 31812509
Mov Disord. 2018 Jan;33(1):88-98
pubmed: 28843021
Nat Commun. 2017 Dec 5;8(1):1784
pubmed: 29209090
Cell Host Microbe. 2014 Mar 12;15(3):382-392
pubmed: 24629344
mSystems. 2019 May 14;4(4):
pubmed: 31098399
Nat Biotechnol. 2019 Aug;37(8):852-857
pubmed: 31341288
Ann Am Thorac Soc. 2013 Jun;10(3):179-87
pubmed: 23802813
Cell. 2018 Nov 1;175(4):962-972.e10
pubmed: 30388453