Host genetic regulation of human gut microbial structural variation.
Journal
Nature
ISSN: 1476-4687
Titre abrégé: Nature
Pays: England
ID NLM: 0410462
Informations de publication
Date de publication:
03 Jan 2024
03 Jan 2024
Historique:
received:
22
12
2022
accepted:
23
11
2023
medline:
4
1
2024
pubmed:
4
1
2024
entrez:
3
1
2024
Statut:
aheadofprint
Résumé
Although the impact of host genetics on gut microbial diversity and the abundance of specific taxa is well established
Identifiants
pubmed: 38172637
doi: 10.1038/s41586-023-06893-w
pii: 10.1038/s41586-023-06893-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Investigateurs
Raul Aguirre-Gamboa
(R)
Patrick Deelen
(P)
Lude Franke
(L)
Jan A Kuivenhoven
(JA)
Ilja M Nolte
(IM)
Serena Sanna
(S)
Harold Snieder
(H)
Morris A Swertz
(MA)
Peter M Visscher
(PM)
Judith M Vonk
(JM)
Informations de copyright
© 2024. The Author(s).
Références
Sanna, S., Kurilshikov, A., van der Graaf, A., Fu, J. & Zhernakova, A. Challenges and future directions for studying effects of host genetics on the gut microbiome. Nat. Genet. 54, 100–106 (2022).
pubmed: 35115688
doi: 10.1038/s41588-021-00983-z
Lopera-Maya, E. A. et al. Effect of host genetics on the gut microbiome in 7,738 participants of the Dutch Microbiome Project. Nat. Genet. 54, 143–151 (2022).
pubmed: 35115690
doi: 10.1038/s41588-021-00992-y
Kurilshikov, A. et al. Large-scale association analyses identify host factors influencing human gut microbiome composition. Nat. Genet. 53, 156–165 (2021).
pubmed: 33462485
pmcid: 8515199
doi: 10.1038/s41588-020-00763-1
Wang, J. et al. Genome-wide association analysis identifies variation in vitamin D receptor and other host factors influencing the gut microbiota. Nat. Genet. 48, 1396–1406 (2016).
pubmed: 27723756
pmcid: 5626933
doi: 10.1038/ng.3695
Turpin, W. et al. Association of host genome with intestinal microbial composition in a large healthy cohort. Nat. Genet. 48, 1413–1417 (2016).
pubmed: 27694960
doi: 10.1038/ng.3693
Rühlemann, M. C. et al. Genome-wide association study in 8,956 German individuals identifies influence of ABO histo-blood groups on gut microbiome. Nat. Genet. 53, 147–155 (2021).
pubmed: 33462482
doi: 10.1038/s41588-020-00747-1
Bolte, L. A. et al. Long-term dietary patterns are associated with pro-inflammatory and anti-inflammatory features of the gut microbiome. Gut 70, 1287–1298 (2021).
pubmed: 33811041
doi: 10.1136/gutjnl-2020-322670
Gacesa, R. et al. Environmental factors shaping the gut microbiome in a Dutch population. Nature 604, 732–739 (2022).
pubmed: 35418674
doi: 10.1038/s41586-022-04567-7
Asnicar, F. et al. Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals. Nat. Med. 27, 321–332 (2021).
pubmed: 33432175
pmcid: 8353542
doi: 10.1038/s41591-020-01183-8
Chen, L. et al. Influence of the microbiome, diet and genetics on inter-individual variation in the human plasma metabolome. Nat. Med. 28, 2333–2343 (2022).
Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).
pubmed: 26590418
doi: 10.1016/j.cell.2015.11.001
Zheng, D., Liwinski, T. & Elinav, E. Interaction between microbiota and immunity in health and disease. Cell Res. 30, 492–506 (2020).
Wu, H.-J. & Wu, E. The role of gut microbiota in immune homeostasis and autoimmunity. Gut Microbes 3, 4–14 (2012).
pubmed: 22356853
pmcid: 3337124
doi: 10.4161/gmic.19320
Alberdi, A., Andersen, S. B., Limborg, M. T., Dunn, R. R. & Gilbert, M. T. P. Disentangling host–microbiota complexity through hologenomics. Nat. Rev. Genet. 23, 281–297 (2022).
Brune, A. Symbiotic digestion of lignocellulose in termite guts. Nat. Rev. Microbiol. 12, 168–180 (2014).
pubmed: 24487819
doi: 10.1038/nrmicro3182
Walter, J., Britton, R. A. & Roos, S. Host-microbial symbiosis in the vertebrate gastrointestinal tract and the Lactobacillus reuteri paradigm. Proc. Natl Acad. Sci. USA 108, 4645–4652 (2011).
pubmed: 20615995
doi: 10.1073/pnas.1000099107
Suzuki, T. A. et al. Codiversification of gut microbiota with humans. Science 377, 1328–1332 (2022).
pubmed: 36108023
doi: 10.1126/science.abm7759
Ferreiro, A., Crook, N., Gasparrini, A. J. & Dantas, G. Multiscale evolutionary dynamics of host-associated microbiomes. Cell 172, 1216–1227 (2018).
pubmed: 29522743
pmcid: 5846202
doi: 10.1016/j.cell.2018.02.015
Aras, R. A., Kang, J., Tschumi, A. I., Harasaki, Y. & Blaser, M. J. Extensive repetitive DNA facilitates prokaryotic genome plasticity. Proc. Natl Acad. Sci. USA 100, 13579–13584 (2003).
pubmed: 14593200
pmcid: 263856
doi: 10.1073/pnas.1735481100
Zeevi, D. et al. Structural variation in the gut microbiome associates with host health. Nature 568, 43–48 (2019).
pubmed: 30918406
doi: 10.1038/s41586-019-1065-y
Wang, D. et al. Characterization of gut microbial structural variations as determinants of human bile acid metabolism. Cell Host Microbe 29, 1802–1814 (2021).
pubmed: 34847370
doi: 10.1016/j.chom.2021.11.003
Chen, L. et al. The long-term genetic stability and individual specificity of the human gut microbiome. Cell 184, 2302–2315 (2021).
pubmed: 33838112
doi: 10.1016/j.cell.2021.03.024
Ansari, M. A. et al. Genome-to-genome analysis highlights the effect of the human innate and adaptive immune systems on the hepatitis C virus. Nat. Genet. 49, 666–673 (2017).
pubmed: 28394351
pmcid: 5873514
doi: 10.1038/ng.3835
Sheppard, S. K. et al. Genome-wide association study identifies vitamin B5 biosynthesis as a host specificity factor in Campylobacter. Proc. Natl Acad. Sci. USA 110, 11923–11927 (2013).
pubmed: 23818615
pmcid: 3718156
doi: 10.1073/pnas.1305559110
Zhernakova, A. et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569 (2016).
pubmed: 27126040
pmcid: 5240844
doi: 10.1126/science.aad3369
ter Horst, R. et al. Host and environmental factors influencing individual human cytokine responses. Cell 167, 1111–1124 (2016).
pubmed: 27814508
pmcid: 5787854
doi: 10.1016/j.cell.2016.10.018
Qin, Y. et al. Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort. Nat. Genet. 54, 134–142 (2022).
pubmed: 35115689
pmcid: 9883041
doi: 10.1038/s41588-021-00991-z
Stražar, M. et al. Gut microbiome-mediated metabolism effects on immunity in rural and urban African populations. Nat. Commun. 12, 4845 (2021).
pubmed: 34381036
pmcid: 8357928
doi: 10.1038/s41467-021-25213-2
Rahfeld, P. et al. An enzymatic pathway in the human gut microbiome that converts A to universal O type blood. Nat. Microbiol. 4, 1475–1485 (2019).
pubmed: 31182795
doi: 10.1038/s41564-019-0469-7
Rahfeld, P. & Withers, S. G. Toward universal donor blood: enzymatic conversion of A and B to O type. J. Biol. Chem. 295, 325–334 (2020).
pubmed: 31792054
doi: 10.1074/jbc.REV119.008164
Liu, Q. P. et al. Bacterial glycosidases for the production of universal red blood cells. Nat. Biotechnol. 25, 454–464 (2007).
pubmed: 17401360
doi: 10.1038/nbt1298
Paixão, L. et al. Host glycan sugar-specific pathways in Streptococcus pneumonia: galactose as a key sugar in colonisation and infection. PLoS ONE 10, e0121042 (2015).
pubmed: 25826206
pmcid: 4380338
doi: 10.1371/journal.pone.0121042
Almeida, A. et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat. Biotechnol. 39, 105–114 (2020).
pubmed: 32690973
pmcid: 7801254
doi: 10.1038/s41587-020-0603-3
Goodrich, J. K. et al. Genetic determinants of the gut microbiome in UK twins. Cell Host Microbe 19, 731–743 (2016).
pubmed: 27173935
pmcid: 4915943
doi: 10.1016/j.chom.2016.04.017
Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).
pubmed: 29489753
doi: 10.1038/nature25973
Hughes, D. A. et al. Genome-wide associations of human gut microbiome variation and implications for causal inference analyses. Nat. Microbiol. 5, 1079–1087 (2020).
pubmed: 32572223
pmcid: 7610462
doi: 10.1038/s41564-020-0743-8
Xu, F. et al. The interplay between host genetics and the gut microbiome reveals common and distinct microbiome features for complex human diseases. Microbiome 8, 145 (2020).
pubmed: 33032658
pmcid: 7545574
doi: 10.1186/s40168-020-00923-9
Liu, X. et al. A genome-wide association study for gut metagenome in Chinese adults illuminates complex diseases. Cell Discov. 7, 9 (2021).
pubmed: 33563976
pmcid: 7873036
doi: 10.1038/s41421-020-00239-w
Yang, H. et al. ABO genotype alters the gut microbiota by regulating GalNAc levels in pigs. Nature 606, 358–367 (2022).
Bhattacharjee, S., Banerjee, M. & Pal, R. ABO blood groups and severe outcomes in COVID-19: a meta-analysis. Postgrad. Med. J. 98, e136–e137 (2022).
pubmed: 35232874
doi: 10.1136/postgradmedj-2020-139248
Murugananthan, K. et al. Blood group AB is associated with severe forms of dengue virus infection. Virusdisease 29, 103–105 (2018).
pubmed: 29607366
pmcid: 5877852
doi: 10.1007/s13337-018-0426-8
Anstee, D. J. The relationship between blood groups and disease. Blood 115, 4635–4643 (2010).
pubmed: 20308598
doi: 10.1182/blood-2010-01-261859
Ahluwalia, T. S. et al. FUT2–ABO epistasis increases the risk of early childhood asthma and Streptococcus pneumoniae respiratory illnesses. Nat. Commun. 11, 6398 (2020).
pubmed: 33328473
pmcid: 7744576
doi: 10.1038/s41467-020-19814-6
Paré, G. et al. Novel association of ABO histo-blood group antigen with soluble ICAM-1: results of a genome-wide association study of 6,578 women. PLoS Genet. 4, e1000118 (2008).
pubmed: 18604267
pmcid: 2432033
doi: 10.1371/journal.pgen.1000118
Chen, Z., Yang, S.-H., Xu, H. & Li, J.-J. ABO blood group system and the coronary artery disease: an updated systematic review and meta-analysis. Sci. Rep. 6, 23250 (2016).
pubmed: 26988722
pmcid: 4796869
doi: 10.1038/srep23250
Zhernakova, D. V. et al. Individual variations in cardiovascular-disease-related protein levels are driven by genetics and gut microbiome. Nat. Genet. 50, 1524–1532 (2018).
pubmed: 30250126
pmcid: 6241851
doi: 10.1038/s41588-018-0224-7
Shalon, D. et al. Profiling the human intestinal environment under physiological conditions. Nature 617, 581–591 (2023).
pubmed: 37165188
pmcid: 10191855
doi: 10.1038/s41586-023-05989-7
Scholtens, S. et al. Cohort profile: LifeLines, a three-generation cohort study and biobank. Int. J. Epidemiol. 44, 1172–1180 (2015).
pubmed: 25502107
doi: 10.1093/ije/dyu229
McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).
pubmed: 27548312
pmcid: 5388176
doi: 10.1038/ng.3643
Tigchelaar, E. F. et al. Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the northern Netherlands: study design and baseline characteristics. BMJ Open 5, e006772 (2015).
pubmed: 26319774
pmcid: 4554905
doi: 10.1136/bmjopen-2014-006772
Schirmer, M. et al. Linking the human gut microbiome to inflammatory cytokine production capacity. Cell 167, 1897 (2016).
pubmed: 27984736
doi: 10.1016/j.cell.2016.11.046
Li, Y. et al. A functional genomics approach to understand variation in cytokine production in humans. Cell 167, 1099–1110 (2016).
pubmed: 27814507
doi: 10.1016/j.cell.2016.10.017
The Genome of the Netherlands Consortium. Whole-genome sequence variation, population structure and demographic history of the Dutch population. Nat. Genet. 46, 818–825 (2014).
doi: 10.1038/ng.3021
Kurilshikov, A. et al. Gut microbial associations to plasma metabolites linked to cardiovascular phenotypes and risk. Circ. Res. 124, 1808–1820 (2019).
pubmed: 30971183
doi: 10.1161/CIRCRESAHA.118.314642
Chen, L. et al. Genetic and microbial associations to plasma and fecal bile acids in obesity relate to plasma lipids and liver fat content. Cell Rep. 33, 108212 (2020).
pubmed: 33027657
doi: 10.1016/j.celrep.2020.108212
Boahen, C. K. et al. A functional genomics approach in Tanzanian population identifies distinct genetic regulators of cytokine production compared to European population. Am. J. Hum. Genet. 109, 471–485 (2022).
pubmed: 35167808
pmcid: 8948159
doi: 10.1016/j.ajhg.2022.01.014
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
pubmed: 22388286
pmcid: 3322381
doi: 10.1038/nmeth.1923
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
pubmed: 24695404
pmcid: 4103590
doi: 10.1093/bioinformatics/btu170
Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).
pubmed: 31779668
pmcid: 6883579
doi: 10.1186/s13059-019-1891-0
Lu, J., Breitwieser, F. P., Thielen, P. & Salzberg, S. L. Bracken: estimating species abundance in metagenomics data. PeerJ Comput. Sci. 3, e104 (2017).
doi: 10.7717/peerj-cs.104
Beghini, F. et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. eLife 10, e65088 (2021).
pubmed: 33944776
pmcid: 8096432
doi: 10.7554/eLife.65088
Mende, D. R. et al. proGenomes: a resource for consistent functional and taxonomic annotations of prokaryotic genomes. Nucleic Acids Res. 45, D529–D534 (2017).
pubmed: 28053165
doi: 10.1093/nar/gkw989
Lloyd-Price, J. et al. Strains, functions and dynamics in the expanded Human Microbiome Project. Nature 550, 61–66 (2017).
pubmed: 28953883
pmcid: 5831082
doi: 10.1038/nature23889
The Integrative HMP (iHMP) Research Network Consortium. The Integrative Human Microbiome Project. Nature 569, 641–648 (2019).
doi: 10.1038/s41586-019-1238-8
Mars, R. A. T. et al. Longitudinal multi-omics reveals subset-specific mechanisms underlying irritable bowel syndrome. Cell 182, 1460–1473 (2020).
pubmed: 32916129
pmcid: 8109273
doi: 10.1016/j.cell.2020.08.007
Schirmer, M. et al. Linking the human gut microbiome to inflammatory cytokine production capacity. Cell 167, 1125–1136 (2016).
pubmed: 27814509
pmcid: 5131922
doi: 10.1016/j.cell.2016.10.020
Ter Horst, R. et al. Sex-specific regulation of inflammation and metabolic syndrome in obesity. Arter. Thromb. Vasc. Biol. 40, 1787–1800 (2019).
doi: 10.1161/ATVBAHA.120.314508
Imhann, F. et al. The 1000IBD project: multi-omics data of 1000 inflammatory bowel disease patients; data release 1. BMC Gastroenterol. 19, 5 (2019).
pubmed: 30621600
pmcid: 6325838
doi: 10.1186/s12876-018-0917-5
Zhang, Y. et al. Gut dysbiosis associates with cytokine production capacity in viral-suppressed people living with HIV. Front. Cell. Infect. Microbiol. 13, 1202035 (2023).
pubmed: 37583444
pmcid: 10425223
doi: 10.3389/fcimb.2023.1202035
Agresti, A. in An Introduction to Categorical Data Analysis Ch. 5, 137–172 (Wiley, 2007).
Zaitlen, N., Paşaniuc, B., Gur, T., Ziv, E. & Halperin, E. Leveraging genetic variability across populations for the identification of causal variants. Am. J. Hum. Genet. 86, 23 (2010).
pubmed: 20085711
pmcid: 2801753
doi: 10.1016/j.ajhg.2009.11.016
Jiang, L. et al. A resource-efficient tool for mixed model association analysis of large-scale data. Nat. Genet. 51, 1749–1755 (2019).
pubmed: 31768069
doi: 10.1038/s41588-019-0530-8
Jiang, L., Zheng, Z., Fang, H. & Yang, J. A generalized linear mixed model association tool for biobank-scale data. Nat. Genet. 53, 1616–1621 (2021).
pubmed: 34737426
doi: 10.1038/s41588-021-00954-4
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
pubmed: 20616382
pmcid: 2922887
doi: 10.1093/bioinformatics/btq340
Greer, J. B. et al. ABO blood group and chronic pancreatitis risk in the NAPS2 cohort. Pancreas 40, 1188–1194 (2011).
pubmed: 21792085
pmcid: 3195943
doi: 10.1097/MPA.0b013e3182232975
Weiss, F. U. et al. Fucosyltransferase 2 (FUT2) non-secretor status and blood group B are associated with elevated serum lipase activity in asymptomatic subjects, and an increased risk for chronic pancreatitis: a genetic association study. Gut 64, 646–656 (2015).
pubmed: 25028398
doi: 10.1136/gutjnl-2014-306930
Kim, J., Na, S.-I., Kim, D. & Chun, J. UBCG2: up-to-date bacterial core genes and pipeline for phylogenomic analysis. J. Microbiol. 59, 609–615 (2021).
pubmed: 34052993
doi: 10.1007/s12275-021-1231-4
Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).
pubmed: 24451623
pmcid: 3998144
doi: 10.1093/bioinformatics/btu033
Kaas, R. S., Leekitcharoenphon, P., Aarestrup, F. M. & Lund, O. Solving the problem of comparing whole bacterial genomes across different sequencing platforms. PLoS ONE 9, e104984 (2014).
pubmed: 25110940
pmcid: 4128722
doi: 10.1371/journal.pone.0104984
Xu, S. et al. Ggtree: a serialized data object for visualization of a phylogenetic tree and annotation data. iMeta 1, e56 (2022).
doi: 10.1002/imt2.56
Waack, S. et al. Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models. BMC Bioinform. 7, 142 (2006).
doi: 10.1186/1471-2105-7-142
Bertelli, C. & Brinkman, F. S. L. Improved genomic island predictions with IslandPath-DIMOB. Bioinformatics 34, 2161–2167 (2018).
pubmed: 29905770
pmcid: 6022643
doi: 10.1093/bioinformatics/bty095
Bertelli, C. et al. IslandViewer 4: expanded prediction of genomic islands for larger-scale datasets. Nucleic Acids Res. 45, W30–W35 (2017).
pubmed: 28472413
pmcid: 5570257
doi: 10.1093/nar/gkx343
Ruiz-Perez, C. A., Conrad, R. E. & Konstantinidis, K. T. MicrobeAnnotator: a user-friendly, comprehensive functional annotation pipeline for microbial genomes. BMC Bioinform. 22, 11 (2021).
doi: 10.1186/s12859-020-03940-5
Schwengers, O. et al. Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identification. Microb. Genom. 7, 000685 (2021).
pubmed: 34739369
pmcid: 8743544
Drula, E. et al. The carbohydrate-active enzyme database: functions and literature. Nucleic Acids Res. 50, D571–D577 (2022).
pubmed: 34850161
doi: 10.1093/nar/gkab1045
Gertz, E. M., Yu, Y.-K., Agarwala, R., Schäffer, A. A. & Altschul, S. F. Composition-based statistics and translated nucleotide searches: improving the TBLASTN module of BLAST. BMC Biol. 4, 41 (2006).
pubmed: 17156431
pmcid: 1779365
doi: 10.1186/1741-7007-4-41
Kaminski, J. et al. High-specificity targeted functional profiling in microbial communities with ShortBRED. PLoS Comput. Biol. 11, e1004557 (2015).
pubmed: 26682918
pmcid: 4684307
doi: 10.1371/journal.pcbi.1004557
Lopez-Siles, M. et al. Cultured representatives of two major phylogroups of human colonic Faecalibacterium prausnitzii can utilize pectin, uronic acids, and host-derived substrates for growth. Appl. Environ. Microbiol. 78, 420–428 (2012).
pubmed: 22101049
pmcid: 3255724
doi: 10.1128/AEM.06858-11