The neuroactive potential of the human gut microbiota in quality of life and depression.
Journal
Nature microbiology
ISSN: 2058-5276
Titre abrégé: Nat Microbiol
Pays: England
ID NLM: 101674869
Informations de publication
Date de publication:
04 2019
04 2019
Historique:
received:
05
08
2017
accepted:
05
12
2018
pubmed:
6
2
2019
medline:
30
7
2019
entrez:
6
2
2019
Statut:
ppublish
Résumé
The relationship between gut microbial metabolism and mental health is one of the most intriguing and controversial topics in microbiome research. Bidirectional microbiota-gut-brain communication has mostly been explored in animal models, with human research lagging behind. Large-scale metagenomics studies could facilitate the translational process, but their interpretation is hampered by a lack of dedicated reference databases and tools to study the microbial neuroactive potential. Surveying a large microbiome population cohort (Flemish Gut Flora Project, n = 1,054) with validation in independent data sets (n
Identifiants
pubmed: 30718848
doi: 10.1038/s41564-018-0337-x
pii: 10.1038/s41564-018-0337-x
doi:
Substances chimiques
3,4-Dihydroxyphenylacetic Acid
102-32-9
Dopamine
VTD58H1Z2X
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Pagination
623-632Commentaires et corrections
Type : CommentIn
Type : CommentIn
Type : CommentIn
Références
Cryan, J. F. & Dinan, T. G. Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat. Rev. Neurosci. 13, 701–712 (2012).
pubmed: 22968153
doi: 10.1038/nrn3346
O’Mahony, S. M., Clarke, G., Borre, Y. E., Dinan, T. G. & Cryan, J. F. Serotonin, tryptophan metabolism and the brain–gut–microbiome axis. Behav. Brain. Res. 277, 32–48 (2015).
pubmed: 25078296
doi: 10.1016/j.bbr.2014.07.027
Braniste, V. et al. The gut microbiota influences blood–brain barrier permeability in mice. Sci. Transl. Med. 6, 263ra158 (2014).
pubmed: 25411471
pmcid: 4396848
doi: 10.1126/scitranslmed.3009759
Lyte, M. & Brown, D. R. Evidence for PMAT- and OCT-like biogenic amine transporters in a probiotic strain of Lactobacillus: implications for interkingdom communication within the microbiota–gut–brain axis. PLoS ONE 13, e0191037 (2018).
pubmed: 29324833
pmcid: 5764344
doi: 10.1371/journal.pone.0191037
Mcdonald, D. et al. American Gut: an open platform for citizen science. mSystems 3, e00031-18 (2018).
pubmed: 29795809
pmcid: 5954204
doi: 10.1128/mSystems.00031-18
Naseribafrouei, A. et al. Correlation between the human fecal microbiota and depression. Neurogastroenterol. Motil. 26, 1155–1162 (2014).
pubmed: 24888394
doi: 10.1111/nmo.12378
Jiang, H. et al. Altered fecal microbiota composition in patients with major depressive disorder. Brain Behav. Immun. 48, 186–194 (2015).
pubmed: 25882912
doi: 10.1016/j.bbi.2015.03.016
Zheng, P. et al. Gut microbiome remodeling induces depressive-like behaviors through a pathway mediated by the host’s metabolism. Mol. Psychiatry 21, 786–796 (2016).
pubmed: 27067014
doi: 10.1038/mp.2016.44
Kelly, J. R. et al. Transferring the blues: depression-associated gut microbiota induces neurobehavioural changes in the rat. J. Psychiatr. Res. 82, 109–118 (2016).
pubmed: 27491067
doi: 10.1016/j.jpsychires.2016.07.019
Hill, J. M., Clement, C., Pogue, A. I., Bhattacharjee, S. & Zhao, Y. et al. Pathogenic microbes, the microbiome, and Alzheimer’s disease (AD). Front. Aging Neurosci. 6, 127 (2014).
pubmed: 24982633
pmcid: 4058571
Burokas, A., Moloney, R. D., Dinan, T. G. & Cryan, J. F. Microbiota regulation of the mammalian gut–brain axis. 91, 1–62 (2015).
Falony, G., Vieira-Silva, S. & Raes, J. Richness and ecosystem development across faecal snapshots of the gut microbiota. Nat. Microbiol. 3, 526–528 (2018).
pubmed: 29693658
doi: 10.1038/s41564-018-0143-5
Kelly, J. R. et al. Lost in translation? The potential psychobiotic Lactobacillus rhamnosus (JB-1) fails to modulate stress or cognitive performance in healthy male subjects. Brain Behav. Immun. 61, 50–59 (2017).
pubmed: 27865949
doi: 10.1016/j.bbi.2016.11.018
Bedarf, J. R. et al. Functional implications of microbial and viral gut metagenome changes in early stage L-DOPA-naïve Parkinson’s disease patients. Genome Med. 9, 39 (2017).
pubmed: 28449715
pmcid: 5408370
doi: 10.1186/s13073-017-0428-y
Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).
pubmed: 27126039
doi: 10.1126/science.aad3503
Zhernakova, A. 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
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
Hays, R. D., Sherbourne, C. D. & Mazel, R. M. The RAND 36-Item Health Survey 1.0. Health Econ. 2, 217–227 (1993).
pubmed: 8275167
doi: 10.1002/hec.4730020305
Hays, R. D. & Morales, L. S. The RAND-36 measure of health-related quality of life. Ann. Med. 33, 350–357 (2001).
pubmed: 11491194
doi: 10.3109/07853890109002089
Ware, J. E., Keller, S. D. & Kosinski, M. SF-36: Physical and Mental Health Summary Scales (Health Institute, New England Medical Center, Boston, 1994).
Lewis, S. J. & Heaton, K. W. Stool form scale as a useful guide to intestinal transit time. Scand. J. Gastroenterol. 32, 920–924 (1997).
pubmed: 9299672
doi: 10.3109/00365529709011203
Rivière, A., Selak, M., Lantin, D., Leroy, F. & De Vuyst, L. Bifidobacteria and butyrate-producing colon bacteria: importance and strategies for their stimulation in the human gut. Front. Microbiol. 7, 979 (2016).
pubmed: 27446020
pmcid: 4923077
doi: 10.3389/fmicb.2016.00979
Louis, P., Hold, G. L. & Flint, H. J. The gut microbiota, bacterial metabolites and colorectal cancer. Nat. Rev. Microbiol. 12, 661–672 (2014).
pubmed: 25198138
doi: 10.1038/nrmicro3344
Gevers, D. et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe 15, 382–392 (2014).
pubmed: 24629344
pmcid: 4059512
doi: 10.1016/j.chom.2014.02.005
Li, L. et al. Gut microbes in correlation with mood: case study in a closed experimental human life support system. Neurogastroenterol. Motil. 28, 1233–1240 (2016).
pubmed: 27027909
doi: 10.1111/nmo.12822
Watten, R. G., Syversen, J. L. & Myhrer, T. Quality of life, intelligence and mood. Soc. Indic. Res. 36, 287–299 (1995).
doi: 10.1007/BF01078818
National Collaborating Centre for Mental Health, National Institute for Health and Clinical Excellence, Royal College of Psychiatrists, British Psychological Society Depression: the Treatment and Management of Depression in Adults (British Psychological Society and Royal College of Psychiatrists, London, 2010).
Bruffaerts, R., Bonnewyn, A. & Demyttenaere, K. The epidemiology of depression in Belgium. A review and some reflections for the future [Article in Dutch]. Tijdschr. Psychiatr. 50, 655–665 (2008).
pubmed: 18951344
Maier, L. et al. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature 555, 623–628 (2018).
pubmed: 29555994
pmcid: 6108420
doi: 10.1038/nature25979
Cussotto, S. et al. Differential effects of psychotropic drugs on microbiome composition and gastrointestinal function. Psychopharmacology https://doi.org/10.1007/s00213-018-5006-5 (2018).
pubmed: 30155748
doi: 10.1007/s00213-018-5006-5
Forslund, K. et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528, 262–266 (2015).
pubmed: 26633628
pmcid: 4681099
doi: 10.1038/nature15766
Dinan, T. G., Stanton, C. & Cryan, J. F. Psychobiotics: a novel class of psychotropic. Biol. Psychiatry 74, 720–726 (2013).
pubmed: 23759244
doi: 10.1016/j.biopsych.2013.05.001
Holmes, I., Harris, K. & Quince, C. Dirichlet multinomial mixtures: generative models for microbialmetagenomics. PLoS ONE 7, e30126 (2012).
pubmed: 22319561
pmcid: 3272020
doi: 10.1371/journal.pone.0030126
Vandeputte, D. et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature 551, 507–511 (2017).
pubmed: 29143816
doi: 10.1038/nature24460
Markowitz, V. M. et al. IMG 4 version of the integrated microbial genomes comparative analysis system. Nucleic Acids Res. 42, D560–D567 (2014).
pubmed: 24165883
doi: 10.1093/nar/gkt963
Lyte, M. & Ernst, S. Catecholamine induced growth of gram negative bacteria. Life Sci. 50, 203–212 (1992).
pubmed: 1731173
doi: 10.1016/0024-3205(92)90273-R
McLean, P. G., Borman, R. A. & Lee, K. 5-HT in the enteric nervous system: gut function and neuropharmacology. Trends Neurosci. 30, 9–13 (2007).
pubmed: 17126921
doi: 10.1016/j.tins.2006.11.002
Yano, J. M. et al. Indigenous bacteria from the gut microbiota regulate host serotonin biosynthesis. Cell 161, 264–276 (2015).
pubmed: 25860609
pmcid: 4393509
doi: 10.1016/j.cell.2015.02.047
Tsavkelova, E. A., Klimova, S. Y., Cherdyntseva, T. A. & Netrusov, A. I. Hormones and hormone-like substances of microorganisms: a review [Article in Russian]. Prikl. Biokhim. Mikrobiol. 42, 261–268 (2006).
pubmed: 16878539
Lyte, M. & Cryan, J. F. (eds) Microbial Endocrinology: Interkingdom Signaling in Infectious Disease and Health (Springer, New York, 2014).
Mazzoli, R. & Pessione, E. The neuro-endocrinological role of microbial glutamate and GABA signaling. Front. Microbiol. 7, 1934 (2016).
pmcid: 5127831
doi: 10.3389/fmicb.2016.01934
pubmed: 27965654
Feehily, C., O’Byrne, C. P. & Karatzas, K. A. G. Functional γ-aminobutyrate shunt in Listeria monocytogenes: role in acid tolerance and succinate biosynthesis. Appl. Environ. Microbiol. 79, 74–80 (2013).
pubmed: 23064337
pmcid: 3536111
doi: 10.1128/AEM.02184-12
Nogueira, T. et al. Horizontal gene transfer of the secretome drives the evolution of bacterial cooperation and virulence. Curr. Biol. 19, 1683–1691 (2009).
pubmed: 19800234
pmcid: 2773837
doi: 10.1016/j.cub.2009.08.056
Gao, K. et al. Of the major phenolic acids formed during human microbial fermentation of tea, citrus, and soy flavonoid supplements, only 3,4-dihydroxyphenylacetic acid has antiproliferative activity. J. Nutr. 136, 52–57 (2006).
pubmed: 16365058
doi: 10.1093/jn/136.1.52
Goldstein, D. S., Holmes, C., Lopez, G. J., Wu, T. & Sharabi, Y. Cerebrospinal fluid biomarkers of central dopamine deficiency predict Parkinson’s disease. Parkinsonism Relat. Disord. 50, 108–112 (2018).
pubmed: 29475591
pmcid: 6319386
doi: 10.1016/j.parkreldis.2018.02.023
Bienenstock, J., Forsythe, P., Karimi, K. & Kunze, W. Neuroimmune aspects of food intake. Int. Dairy J. 20, 253–258 (2010).
doi: 10.1016/j.idairyj.2009.12.002
Petty, F. Plasma concentrations of gamma-aminobutyric acid (GABA) and mood disorders: a blood test for manic depressive disease? Clin. Chem. 40, 296–302 (1994).
pubmed: 8313610
doi: 10.1093/clinchem/40.2.296
Inoshita, M. et al. Elevated peripheral blood glutamate levels in major depressive disorder. Neuropsychiatr. Dis. Treat. 14, 945–953 (2018).
pubmed: 29670355
pmcid: 5896673
doi: 10.2147/NDT.S159855
Chow, S., Shao, J. & Wang, H. Sample Size Calculations in Clinical Trial Research (Chapman and Hall, Boca Raton, 2008).
Aaronson, N. K. et al. Translation, validation, and norming of the Dutch language version of the SF-36 Health Survey in community and chronic disease populations. J. Clin. Epidemiol. 51, 1055–1068 (1998).
pubmed: 9817123
doi: 10.1016/S0895-4356(98)00097-3
WHO Collaborating Centre for Drug Statistics Methodology ATC Classification Index with DDDs (WHO, 2017).
American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders: Text Revision 4th edn (American Psychiatric Association, Washington, 2002).
Mujagic, Z. et al. A novel biomarker panel for irritable bowel syndrome and the application in the general population. Sci. Rep. 6, 26420 (2016).
pubmed: 27263852
pmcid: 4893613
doi: 10.1038/srep26420
Tito, R. Y. et al. Brief report: Dialister as a microbial marker of disease activity in spondyloarthritis. Arthritis Rheumatol. 69, 114–121 (2017).
pubmed: 27390077
doi: 10.1002/art.39802
Hildebrand, F., Tadeo, R., Voigt, A. Y., Bork, P. & Raes, J. LotuS: an efficient and user-friendly OTU processing pipeline. Microbiome 2, 30 (2014).
pubmed: 27367037
pmcid: 4179863
doi: 10.1186/2049-2618-2-30
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
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
Schmieder, R. & Edwards, R. Fast identification and removal of sequence contamination from genomic and metagenomic datasets. PLoS ONE 6, e17288 (2011).
pubmed: 21408061
pmcid: 3052304
doi: 10.1371/journal.pone.0017288
Cock, P. J. A. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422–1423 (2009).
pubmed: 19304878
pmcid: 2682512
doi: 10.1093/bioinformatics/btp163
Li, J. et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834–841 (2014).
pubmed: 24997786
doi: 10.1038/nbt.2942
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
pubmed: 19451168
pmcid: 2705234
doi: 10.1093/bioinformatics/btp324
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
doi: 10.1093/bioinformatics/btt656
pubmed: 24227677
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2015).
Oksanen, J. et al. vegan: Community Ecology. R package version 2.4-2 http://CRAN.R-project.org/package=vegan (2017).
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
pubmed: 23630581
pmcid: 3632530
doi: 10.1371/journal.pone.0061217
Gloor, G. B. & Reid, G. Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data. Can. J. Microbiol. 62, 692–703 (2016).
pubmed: 27314511
doi: 10.1139/cjm-2015-0821
Fletcher, T. D. QuantPsyc: Quantitative Psychology Tools. R package version 1.5 http://cran.r-project.org/package=QuantPsyc (2012).
Morgan, M. DirichletMultinomial: Dirichlet-Multinomial Mixture Model Machine Learning for Microbiome Data. Bioconductor version 1.20.0 http://bioconductor.org/packages/release/bioc/html/DirichletMultinomial.html (2017).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, New York, 2009).
Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
doi: 10.1111/j.2041-210X.2011.00169.x
Wei, T. et al. corrplot: Visualization of a Correlation Matrix. R package version 0.77 http://CRAN.R-project.org/package=corrplot (2016).
Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 42, 459–471 (2014).
doi: 10.1093/nar/gkt1103
Kanehisa, M. et al. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 42, D199–D205 (2014).
pubmed: 24214961
doi: 10.1093/nar/gkt1076
Vieira-Silva, S. et al. Species–function relationships shape ecological properties of the human gut microbiome. Nat. Microbiol. 1, 16088 (2016).
pubmed: 27573110
doi: 10.1038/nmicrobiol.2016.88
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).
pubmed: 26476454
doi: 10.1093/nar/gkv1070
Haft, D. H. et al. TIGRFAMs and genome properties in 2013. Nucleic Acids Res. 41, D387–D395 (2013).
pubmed: 23197656
doi: 10.1093/nar/gks1234
Powell, S. et al. eggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges. Nucleic Acids Res. 40, D284–D289 (2012).
pubmed: 22096231
doi: 10.1093/nar/gkr1060
Cheng, F. et al. admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem. Inf. Model. 52, 3099–3105 (2012).
pubmed: 23092397
doi: 10.1021/ci300367a
Federhen, S. The NCBI Taxonomy database. Nucleic Acids Res. 40, D136–D143 (2012).
pubmed: 22139910
doi: 10.1093/nar/gkr1178
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).
doi: 10.1093/nar/gks1219
Asnicar, F., Weingart, G., Tickle, T. L., Huttenhower, C. & Segata, N. Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ 3, e1029 (2015).
pubmed: 26157614
pmcid: 4476132
doi: 10.7717/peerj.1029