Comparative characterization of the infant gut microbiome and their maternal lineage by a multi-omics approach.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
08 Apr 2024
08 Apr 2024
Historique:
received:
29
05
2023
accepted:
22
03
2024
medline:
9
4
2024
pubmed:
9
4
2024
entrez:
8
4
2024
Statut:
epublish
Résumé
The human gut microbiome establishes and matures during infancy, and dysregulation at this stage may lead to pathologies later in life. We conducted a multi-omics study comprising three generations of family members to investigate the early development of the gut microbiota. Fecal samples from 200 individuals, including infants (0-12 months old; 55% females, 45% males) and their respective mothers and grandmothers, were analyzed using two independent metabolomics platforms and metagenomics. For metabolomics, gas chromatography and capillary electrophoresis coupled to mass spectrometry were applied. For metagenomics, both 16S rRNA gene and shotgun sequencing were performed. Here we show that infants greatly vary from their elders in fecal microbiota populations, function, and metabolome. Infants have a less diverse microbiota than adults and present differences in several metabolite classes, such as short- and branched-chain fatty acids, which are associated with shifts in bacterial populations. These findings provide innovative biochemical insights into the shaping of the gut microbiome within the same generational line that could be beneficial in improving childhood health outcomes.
Identifiants
pubmed: 38589361
doi: 10.1038/s41467-024-47182-y
pii: 10.1038/s41467-024-47182-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3004Subventions
Organisme : Ministry of Economy and Competitiveness | Instituto de Salud Carlos III (Institute of Health Carlos III)
ID : PI17/01087
Organisme : Ministry of Economy and Competitiveness | Instituto de Salud Carlos III (Institute of Health Carlos III)
ID : PI19/00044
Organisme : Ministry of Economy and Competitiveness | Instituto de Salud Carlos III (Institute of Health Carlos III)
ID : PI20/01366
Organisme : Ministry of Economy and Competitiveness | Agencia Estatal de Investigación (Spanish Agencia Estatal de Investigación)
ID : PCI2018-092930
Informations de copyright
© 2024. The Author(s).
Références
Zubeldia-Varela, E. et al. Microbiome and allergy: new insights and perspectives. J. Investig. Allergol. Clin. Immunol. 32, 327–344 (2022).
pubmed: 36219547
doi: 10.18176/jiaci.0852
Ruiz‐Ruiz, S. et al. Functional microbiome deficits associated with ageing: chronological age threshold. Aging Cell 19, e13063 (2020).
pubmed: 31730262
doi: 10.1111/acel.13063
Rojo, D. et al. Exploring the human microbiome from multiple perspectives: factors altering its composition and function. FEMS Microbiol. Rev. 41, 453–478 (2017).
pubmed: 28333226
pmcid: 5812509
doi: 10.1093/femsre/fuw046
McBurney, M. I. et al. Establishing what constitutes a healthy human gut microbiome: state of the science, regulatory considerations, and future directions. J. Nutr. 149, 1882–1895 (2019).
pubmed: 31373365
pmcid: 6825832
doi: 10.1093/jn/nxz154
Morin, A. et al. Epigenetic landscape links upper airway microbiota in infancy with allergic rhinitis at 6 years of age. J. Allergy Clin. Immunol. 146, 1358–1366 (2020).
pubmed: 32693091
pmcid: 7821422
doi: 10.1016/j.jaci.2020.07.005
Kemp, K. M., Colson, J., Lorenz, R. G., Maynard, C. L. & Pollock, J. S. Early life stress in mice alters gut microbiota independent of maternal microbiota inheritance. Am. J. Physiol. Regul. Integr. Comp. Physiol. 320, R663–R674 (2021).
pubmed: 33655759
pmcid: 8163610
doi: 10.1152/ajpregu.00072.2020
De Martinis, M., Sirufo, M. M., Viscido, A. & Ginaldi, L. Food allergies and ageing. Int. J. Mol. Sci. 20, 5580 (2019).
pubmed: 31717303
pmcid: 6888073
doi: 10.3390/ijms20225580
Liu, X. et al. Early life Lactobacillus rhamnosus GG colonisation inhibits intestinal tumour formation. Br. J. Cancer 126, 1421–1431 (2022).
pubmed: 35091695
pmcid: 9090826
doi: 10.1038/s41416-021-01562-z
Sun, Y. et al. Prenatal maternal stress exacerbates experimental colitis of offspring in adulthood. Front. Immunol. 12, 700995 (2021).
pubmed: 34804005
pmcid: 8595204
doi: 10.3389/fimmu.2021.700995
Selma-Royo, M. et al. Maternal diet during pregnancy and intestinal markers are associated with early gut microbiota. Eur. J. Nutr. 60, 1429–1442 (2021).
pubmed: 32728880
doi: 10.1007/s00394-020-02337-7
Gomez de Agüero, M. et al. The maternal microbiota drives early postnatal innate immune development. Science 351, 1296–1302 (2016).
pubmed: 26989247
doi: 10.1126/science.aad2571
Jeong, S. Factors influencing development of the infant microbiota: from prenatal period to early infancy. Clin. Exp. Pediatr. 65, 438–447 (2022).
doi: 10.3345/cep.2021.00955
Bogaert, D. et al. Mother-to-infant microbiota transmission and infant microbiota development across multiple body sites. Cell Host Microbe 31, 447–460.e6 (2023).
pubmed: 36893737
doi: 10.1016/j.chom.2023.01.018
Feehily, C. et al. Detailed mapping of Bifidobacterium strain transmission from mother to infant via a dual culture-based and metagenomic approach. Nat. Commun. 14, 3015 (2023).
pubmed: 37230981
pmcid: 10213049
doi: 10.1038/s41467-023-38694-0
Kennedy, K. M. et al. Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies. Nature 613, 639–649 (2023).
pubmed: 36697862
doi: 10.1038/s41586-022-05546-8
Wopereis, H., Oozeer, R., Knipping, K., Belzer, C. & Knol, J. The first thousand days - intestinal microbiology of early life: establishing a symbiosis. Pediatr. Allergy Immunol. Publ. Eur. Soc. Pediatr. Allergy Immunol. 25, 428–438 (2014).
De Leoz, M. L. A. et al. Human milk glycomics and gut microbial genomics in infant feces show a correlation between human milk oligosaccharides and gut microbiota: a proof-of-concept study. J. Proteome Res. 14, 491–502 (2015).
pubmed: 25300177
doi: 10.1021/pr500759e
Costea, P. I. et al. Towards standards for human fecal sample processing in metagenomic studies. Nat. Biotechnol. 35, 1069–1076 (2017).
pubmed: 28967887
doi: 10.1038/nbt.3960
Kishikawa, T. et al. Metagenome-wide association study of gut microbiome revealed novel aetiology of rheumatoid arthritis in the Japanese population. Ann. Rheum. Dis. 79, 103–111 (2020).
pubmed: 31699813
doi: 10.1136/annrheumdis-2019-215743
Asnicar, F. et al. Studying vertical microbiome transmission from mothers to infants by strain-level metagenomic profiling. mSystems 2, e00164–16 (2017).
pubmed: 28144631
pmcid: 5264247
doi: 10.1128/mSystems.00164-16
Vuillermin, P. J. et al. Maternal carriage of Prevotella during pregnancy associates with protection against food allergy in the offspring. Nat. Commun. 11, 1452 (2020).
pubmed: 32210229
pmcid: 7093478
doi: 10.1038/s41467-020-14552-1
Valles-Colomer, M. et al. The person-to-person transmission landscape of the gut and oral microbiomes. Nature 614, 125–135 (2023).
pubmed: 36653448
pmcid: 9892008
doi: 10.1038/s41586-022-05620-1
Djukovic, A. et al. Lactobacillus supports Clostridiales to restrict gut colonization by multidrug-resistant Enterobacteriaceae. Nat. Commun. 13, 5617 (2022).
pubmed: 36153315
pmcid: 9509339
doi: 10.1038/s41467-022-33313-w
Poyet, M. et al. A library of human gut bacterial isolates paired with longitudinal multiomics data enables mechanistic microbiome research. Nat. Med. 25, 1442–1452 (2019).
pubmed: 31477907
doi: 10.1038/s41591-019-0559-3
An, R. et al. Sugar beet pectin supplementation did not alter profiles of fecal microbiota and exhaled breath in healthy young adults and healthy elderly. Nutrients 11, 2193 (2019).
pubmed: 31547291
pmcid: 6770243
doi: 10.3390/nu11092193
Tuikhar, N. et al. Comparative analysis of the gut microbiota in centenarians and young adults shows a common signature across genotypically non-related populations. Mech. Ageing Dev. 179, 23–35 (2019).
pubmed: 30738080
doi: 10.1016/j.mad.2019.02.001
Brink, L. R. et al. Neonatal diet alters fecal microbiota and metabolome profiles at different ages in infants fed breast milk or formula. Am. J. Clin. Nutr. 111, 1190–1202 (2020).
pubmed: 32330237
pmcid: 7266684
doi: 10.1093/ajcn/nqaa076
Conta, G. et al. Longitudinal multi-omics study of a mother-infant dyad from breastfeeding to weaning: an individualized approach to understand the interactions among diet, fecal metabolome and microbiota composition. Front. Mol. Biosci. 8, 688440 (2021).
pubmed: 34671642
pmcid: 8520934
doi: 10.3389/fmolb.2021.688440
Vatanen, T. et al. Mobile genetic elements from the maternal microbiome shape infant gut microbial assembly and metabolism. Cell 185, 4921–4936.e15 (2022).
pubmed: 36563663
pmcid: 9869402
doi: 10.1016/j.cell.2022.11.023
Boulangé, C. L. et al. An extensively hydrolyzed formula supplemented with two human milk oligosaccharides modifies the fecal microbiome and metabolome in infants with cow’s milk protein allergy. Int. J. Mol. Sci. 24, 11422 (2023).
pubmed: 37511184
pmcid: 10379726
doi: 10.3390/ijms241411422
Zhao, L. et al. High throughput and quantitative measurement of microbial metabolome by gas chromatography/mass spectrometry using automated alkyl chloroformate derivatization. Anal. Chem. 89, 5565–5577 (2017).
pubmed: 28437060
pmcid: 5663283
doi: 10.1021/acs.analchem.7b00660
Rey-Stolle, F. et al. Low and high resolution gas chromatography-mass spectrometry for untargeted metabolomics: a tutorial. Anal. Chim. Acta https://doi.org/10.1016/j.aca.2021.339043 (2021).
Xu, J., Zhang, Q.-F., Zheng, J., Yuan, B.-F. & Feng, Y.-Q. Mass spectrometry-based fecal metabolome analysis. TrAC Trends Anal. Chem. 112, 161–174 (2019).
doi: 10.1016/j.trac.2018.12.027
Fernández-García, M. et al. Comprehensive examination of the mouse lung metabolome following Mycobacterium tuberculosis infection using a multiplatform mass spectrometry approach. J. Proteome Res. https://doi.org/10.1021/acs.jproteome.9b00868 (2020).
Mastrangelo, A., Ferrarini, A., Rey-Stolle, F., García, A. & Barbas, C. From sample treatment to biomarker discovery: a tutorial for untargeted metabolomics based on GC-(EI)-Q-MS. Anal. Chim. Acta 900, 21–35 (2015).
pubmed: 26572836
doi: 10.1016/j.aca.2015.10.001
Husek, P. Chloroformates in gas chromatography as general purpose derivatizing agents. J. Chromatogr. B. Biomed. Sci. Appl. 717, 57–91 (1998).
pubmed: 9832240
doi: 10.1016/S0378-4347(98)00136-4
Mojsak, P., Rey-Stolle, F., Parfieniuk, E., Kretowski, A. & Ciborowski, M. The role of gut microbiota (GM) and GM-related metabolites in diabetes and obesity. A review of analytical methods used to measure GM-related metabolites in fecal samples with a focus on metabolites’ derivatization step. J. Pharm. Biomed. Anal. 191, 113617 (2020).
pubmed: 32971497
doi: 10.1016/j.jpba.2020.113617
Shanmuganathan, M. et al. The maternal serum metabolome by multisegment injection-capillary electrophoresis-mass spectrometry: a high-throughput platform and standardized data workflow for large-scale epidemiological studies. Nat. Protoc. 16, 1966–1994 (2021).
pubmed: 33674789
doi: 10.1038/s41596-020-00475-0
Sikorski, C. et al. Serum metabolomic signatures of gestational diabetes in South Asian and white European women. BMJ Open Diabetes Res. Care 10, e002733 (2022).
pubmed: 35450870
pmcid: 9024260
doi: 10.1136/bmjdrc-2021-002733
Bruce, C. Y. et al. The relationship between diet, gut microbiota, and serum metabolome of South Asian infants at 1 year. J. Nutr. 153, 470–482 (2023).
pubmed: 36894240
doi: 10.1016/j.tjnut.2022.12.016
Rafiq, T. et al. Integrative multiomics analysis of infant gut microbiome and serum metabolome reveals key molecular biomarkers of early onset childhood obesity. Heliyon 9, e16651 (2023).
pubmed: 37332914
pmcid: 10272340
doi: 10.1016/j.heliyon.2023.e16651
Bajo-Fernández, M. et al. Structural elucidation of derivatives of polyfunctional metabolites after methyl chloroformate derivatization by high-resolution mass spectrometry gas chromatography. Application to microbiota metabolites. J. Chromatogr. A 1717, 464656 (2024).
pubmed: 38301332
doi: 10.1016/j.chroma.2024.464656
Kuligowski, J., Sánchez-Illana, Á., Sanjuán-Herráez, D., Vento, M. & Quintás, G. Intra-batch effect correction in liquid chromatography-mass spectrometry using quality control samples and support vector regression (QC-SVRC). Analyst 140, 7810–7817 (2015).
pubmed: 26462549
doi: 10.1039/C5AN01638J
Rodríguez-Coira, J. et al. Troubleshooting in large-scale LC-ToF-MS metabolomics analysis: solving complex issues in big cohorts. Metabolites 9, 247 (2019).
pubmed: 31652940
pmcid: 6918290
doi: 10.3390/metabo9110247
Broadhurst, D. et al. Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics 14, 72 (2018).
pubmed: 29805336
pmcid: 5960010
doi: 10.1007/s11306-018-1367-3
Rohart, F., Gautier, B., Singh, A. & Cao, K.-A. L. mixOmics: an R package for ‘omics feature selection and multiple data integration. PLoS Comput. Biol. 13, e1005752 (2017).
pubmed: 29099853
pmcid: 5687754
doi: 10.1371/journal.pcbi.1005752
Singh, A. et al. DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinform. Oxf. Engl. 35, 3055–3062 (2019).
doi: 10.1093/bioinformatics/bty1054
Zhou, Y., Hu, G. & Wang, M. C. Host and microbiota metabolic signals in aging and longevity. Nat. Chem. Biol. 17, 1027–1036 (2021).
pubmed: 34552221
doi: 10.1038/s41589-021-00837-z
Gonzalez-Covarrubias, V. Lipidomics in longevity and healthy aging. Biogerontology 14, 663–672 (2013).
pubmed: 23948799
doi: 10.1007/s10522-013-9450-7
Mutlu, A. S., Duffy, J. & Wang, M. C. Lipid metabolism and lipid signals in aging and longevity. Dev. Cell 56, 1394–1407 (2021).
pubmed: 33891896
pmcid: 8173711
doi: 10.1016/j.devcel.2021.03.034
Albouery, M. et al. Age-related changes in the gut microbiota modify brain lipid composition. Front. Cell. Infect. Microbiol. 9, 444 (2020).
pubmed: 31993375
pmcid: 6970973
doi: 10.3389/fcimb.2019.00444
Sun, N., Youle, R. J. & Finkel, T. The mitochondrial basis of aging. Mol. Cell 61, 654 (2016).
pubmed: 26942670
pmcid: 4779179
doi: 10.1016/j.molcel.2016.01.028
Chaudhari, S. N. & Kipreos, E. T. The energy maintenance theory of aging: maintaining energy metabolism to allow longevity. BioEssays 40, 1800005 (2018).
doi: 10.1002/bies.201800005
Jackson, D. N. & Theiss, A. L. Gut bacteria signaling to mitochondria in intestinal inflammation and cancer. Gut Microbes 11, 285–304 (2020).
pubmed: 30913966
doi: 10.1080/19490976.2019.1592421
Saleh, J., Peyssonnaux, C., Singh, K. K. & Edeas, M. Mitochondria and microbiota dysfunction in COVID-19 pathogenesis. Mitochondrion 54, 1–7 (2020).
pubmed: 32574708
pmcid: 7837003
doi: 10.1016/j.mito.2020.06.008
Sharma, S., Awasthi, A. & Singh, S. Altered gut microbiota and intestinal permeability in Parkinson’s disease: Pathological highlight to management. Neurosci. Lett. 712, 134516 (2019).
pubmed: 31560998
doi: 10.1016/j.neulet.2019.134516
Vezza, T., Abad-Jiménez, Z., Marti-Cabrera, M., Rocha, M. & Víctor, V. M. Microbiota-mitochondria inter-talk: a potential therapeutic strategy in obesity and type 2 diabetes. Antioxid 9, 848 (2020).
doi: 10.3390/antiox9090848
Donohoe, D. R. et al. The microbiome and butyrate regulate energy metabolism and autophagy in the Mammalian colon. Cell Metab. 13, 517–526 (2011).
pubmed: 21531334
pmcid: 3099420
doi: 10.1016/j.cmet.2011.02.018
Altaib, H. et al. Differences in the Concentration of the Fecal Neurotransmitters GABA and Glutamate Are Associated with Microbial Composition among Healthy Human Subjects. Microorganisms 9, 378 (2021).
pubmed: 33668550
pmcid: 7918917
doi: 10.3390/microorganisms9020378
Noronha, A. et al. The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease. Nucleic Acids Res. 47, D614–D624 (2019).
pubmed: 30371894
doi: 10.1093/nar/gky992
Gao, J. et al. Impact of the gut microbiota on intestinal immunity mediated by tryptophan metabolism. Front. Cell. Infect. Microbiol. 8, 13 (2018).
pubmed: 29468141
pmcid: 5808205
doi: 10.3389/fcimb.2018.00013
Rampelli, S. et al. Functional metagenomic profiling of intestinal microbiome in extreme ageing. Aging 5, 902–912 (2013).
pubmed: 24334635
pmcid: 3883706
doi: 10.18632/aging.100623
Sorgdrager, F. J. H., Naudé, P. J. W., Kema, I. P., Nollen, E. A. & De Deyn, P. P. Tryptophan metabolism in inflammaging: from biomarker to therapeutic target. Front. Immunol. 10, 2565 (2019).
pubmed: 31736978
pmcid: 6833926
doi: 10.3389/fimmu.2019.02565
Burger-van Paassen, N. et al. The regulation of intestinal mucin MUC2 expression by short-chain fatty acids: implications for epithelial protection. Biochem. J. 420, 211–219 (2009).
pubmed: 19228118
doi: 10.1042/BJ20082222
Nagpal, R. et al. Gut microbiome and aging: physiological and mechanistic insights. Nutr. Healthy Aging 4, 267–285 (2018).
pubmed: 29951588
pmcid: 6004897
doi: 10.3233/NHA-170030
Fellows, R. et al. Microbiota derived short chain fatty acids promote histone crotonylation in the colon through histone deacetylases. Nat. Commun. 9, 1–15 (2018).
doi: 10.1038/s41467-017-02651-5
Ghosh, T. S., Shanahan, F. & O’Toole, P. W. The gut microbiome as a modulator of healthy ageing. Nat. Rev. Gastroenterol. Hepatol. 19, 565–584 (2022).
pubmed: 35468952
pmcid: 9035980
doi: 10.1038/s41575-022-00605-x
Pascale, A. et al. Microbiota and metabolic diseases. Endocrine 61, 357–371 (2018).
pubmed: 29721802
doi: 10.1007/s12020-018-1605-5
Blanco-Pérez, F. et al. The dietary fiber pectin: health benefits and potential for the treatment of allergies by modulation of gut microbiota. Curr. Allergy Asthma Rep. 21, 43 (2021).
pubmed: 34505973
pmcid: 8433104
doi: 10.1007/s11882-021-01020-z
Li, Y., Faden, H. S. & Zhu, L. The response of the gut microbiota to dietary changes in the first two years of life. Front. Pharmacol. 11, 334 (2020).
pubmed: 32256372
pmcid: 7089920
doi: 10.3389/fphar.2020.00334
Cui, M. et al. Influence of age, sex, and diet on the human fecal metabolome investigated by 1H NMR spectroscopy. J. Proteome Res. 20, 3642–3653 (2021).
pubmed: 34048241
doi: 10.1021/acs.jproteome.1c00220
Rios-Covian, D. et al. An overview on fecal branched short-chain fatty acids along human life and as related with body mass index: associated dietary and anthropometric factors. Front. Microbiol. 11, 973 (2020).
pubmed: 32547507
pmcid: 7271748
doi: 10.3389/fmicb.2020.00973
Houtman, T. A., Eckermann, H. A., Smidt, H. & de Weerth, C. Gut microbiota and BMI throughout childhood: the role of firmicutes, bacteroidetes, and short-chain fatty acid producers. Sci. Rep. 12, 3140 (2022).
pubmed: 35210542
pmcid: 8873392
doi: 10.1038/s41598-022-07176-6
Ragonnaud, E. & Biragyn, A. Gut microbiota as the key controllers of “healthy” aging of elderly people. Immun. Ageing A 18, 2 (2021).
doi: 10.1186/s12979-020-00213-w
Holeček, M. Branched-chain amino acids in health and disease: metabolism, alterations in blood plasma, and as supplements. Nutr. Metab. 15, 1–12 (2018).
doi: 10.1186/s12986-018-0271-1
Taormina, V. M., Unger, A. L., Schiksnis, M. R., Torres-Gonzalez, M. & Kraft, J. Branched-chain fatty acids—an underexplored class of dairy-derived fatty acids. Nutrients 12, 2875 (2020).
pubmed: 32962219
pmcid: 7551613
doi: 10.3390/nu12092875
Dingess, K. A. et al. Branched-chain fatty acid composition of human milk and the impact of maternal diet: the Global Exploration of Human Milk (GEHM) Study. Am. J. Clin. Nutr. 105, 177–184 (2017).
pubmed: 27903517
doi: 10.3945/ajcn.116.132464
Ran-Ressler, R. R., Bae, S., Lawrence, P., Wang, D. H. & Brenna, J. T. Branched-chain fatty acid content of foods and estimated intake in the USA. Br. J. Nutr. 112, 565–572 (2014).
pubmed: 24830474
pmcid: 4381348
doi: 10.1017/S0007114514001081
Mansfeld, J. et al. Branched-chain amino acid catabolism is a conserved regulator of physiological ageing. Nat. Commun. 6, 1–12 (2015).
doi: 10.1038/ncomms10043
Le Couteur, D. G. et al. Branched chain amino acids, aging and age-related health. Ageing Res. Rev. 64, 101198 (2020).
pubmed: 33132154
doi: 10.1016/j.arr.2020.101198
Li, N., Cen, Z., Zhao, Z., Li, Z. & Chen, S. BCAA dysmetabolism in the host and gut microbiome, a key player in the development of obesity and T2DM. Med. Microecol. 16, 100078 (2023).
doi: 10.1016/j.medmic.2023.100078
Minois, N., Carmona-Gutierrez, D. & Madeo, F. Polyamines in aging and disease. Aging 3, 716–732 (2011).
pubmed: 21869457
pmcid: 3184975
doi: 10.18632/aging.100361
Hirano, R., Shirasawa, H. & Kurihara, S. Health-promoting effects of dietary polyamines. Med. Sci. 9, 8 (2021).
Tofalo, R., Cocchi, S. & Suzzi, G. Polyamines and gut microbiota. Front. Nutr. 6, 16 (2019).
pubmed: 30859104
pmcid: 6397830
doi: 10.3389/fnut.2019.00016
Durazzi, F. et al. Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota. Sci. Rep. 11, 3030 (2021).
pubmed: 33542369
pmcid: 7862389
doi: 10.1038/s41598-021-82726-y
Zuo, W. et al. 16S rRNA and metagenomic shotgun sequencing data revealed consistent patterns of gut microbiome signature in pediatric ulcerative colitis. Sci. Rep. 12, 6421 (2022).
pubmed: 35440670
pmcid: 9018687
doi: 10.1038/s41598-022-07995-7
World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA 310, 2191–2194 (2013).
Mera-Berriatua, L. et al. Unravelling the gut microbiota of cow’s milk–allergic infants, their mothers, and their grandmothers. J. Investig. Allergol. Clin. Immunol. 32, 395–398 (2022).
pubmed: 36219548
doi: 10.18176/jiaci.0781
Zubeldia-Varela, E., Barber, D., Barbas, C., Perez-Gordo, M. & Rojo, D. Sample pre-treatment procedures for the omics analysis of human gut microbiota: turning points, tips and tricks for gene sequencing and metabolomics. J. Pharm. Biomed. Anal. 191, 113592 (2020).
pubmed: 32947167
doi: 10.1016/j.jpba.2020.113592
Fiehn, O. Metabolomics by gas chromatography–mass spectrometry: combined targeted and untargeted profiling. Curr. Protoc. Mol. Biol. 114, 30.4.1–30.4.32 (2016).
pubmed: 27038389
doi: 10.1002/0471142727.mb3004s114
Kirwan, J. A. et al. Quality assurance and quality control reporting in untargeted metabolic phenotyping: mQACC recommendations for analytical quality management. Metabolomics 18, 70 (2022).
pubmed: 36029375
pmcid: 9420093
doi: 10.1007/s11306-022-01926-3
Dudzik, D., Barbas-Bernardos, C., García, A. & Barbas, C. Quality assurance procedures for mass spectrometry untargeted metabolomics. a review. J. Pharm. Biomed. Anal. 147, 149–173 (2018).
pubmed: 28823764
doi: 10.1016/j.jpba.2017.07.044
Godzien, J., Alonso-Herranz, V., Barbas, C. & Armitage, E. G. Controlling the quality of metabolomics data: new strategies to get the best out of the QC sample. Metabolomics 11, 518–528 (2015).
doi: 10.1007/s11306-014-0712-4
Armitage, E. G., Godzien, J., Alonso-Herranz, V., López-Gonzálvez, Á. & Barbas, C. Missing value imputation strategies for metabolomics data: General. Electrophoresis 36, 3050–3060 (2015).
pubmed: 26376450
doi: 10.1002/elps.201500352
Isaac, S. et al. Microbiome-mediated fructose depletion restricts murine gut colonization by vancomycin-resistant Enterococcus. Nat. Commun. 13, 7718 (2022).
pubmed: 36513659
pmcid: 9748033
doi: 10.1038/s41467-022-35380-5
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
pubmed: 27214047
pmcid: 4927377
doi: 10.1038/nmeth.3869
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).
pubmed: 17586664
pmcid: 1950982
doi: 10.1128/AEM.00062-07
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).
pubmed: 21702898
pmcid: 3218848
doi: 10.1186/gb-2011-12-6-r60
Oren, A. & Garrity, G. M. Valid publication of the names of forty-two phyla of prokaryotes. Int. J. Syst. Evol. Microbiol. 71, 005056 (2021).
Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).
pubmed: 30423086
pmcid: 6129281
doi: 10.1093/bioinformatics/bty560
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
Tamames, J. & Puente-Sánchez, F. SqueezeMeta, a highly portable, fully automatic metagenomic analysis pipeline. Front. Microbiol. 9, 3349 (2019).
pubmed: 30733714
pmcid: 6353838
doi: 10.3389/fmicb.2018.03349
Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114 (2012).
pubmed: 22080510
doi: 10.1093/nar/gkr988
Mallick, H. et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput. Biol. 17, e1009442 (2021).
pubmed: 34784344
pmcid: 8714082
doi: 10.1371/journal.pcbi.1009442
Haug, K. et al. MetaboLights: a resource evolving in response to the needs of its scientific community. Nucleic Acids Res. 48, D440–D444 (2020).
pubmed: 31691833