Methodological recommendations for human microbiota-gut-brain axis research

Abstract

Observational studies have determined numerous correlations between sequence-based gut microbiota data and human mental traits. However, these associations are often inconsistent across studies. This inconsistency is one of the reasons that mechanistic validation studies of the observed correlations are lagging, making it difficult to establish causal associations. The absence of consistent study findings may partially be due to the lack of clear guidelines for identifying confounders of relations between complex microbial communities and mental conditions. Gut microbial complexity also impedes deciphering microbiota-host relations by using a single analytical approach. The aim of the current review is to help solve these problems by providing methodological recommendations for future human microbiota-gut-brain axis research on the selection of confounders, the use of integrative biostatistical methods, and the steps needed to translate correlative findings into causal conclusions.

Keywords

Microbiota-gut-brain axis / gut microbiota / mental development and health / correlation / causation / confounders / statistical analyses

References

[1] Cryan JF,Cowan CSM.The microbiota-gut-brain axis.Physiol Rev2019;99:1877-2013

[2] Sukmajaya AC,Soetjipto .Systematic review of gut microbiota and attention-deficit hyperactivity disorder (ADHD).Ann Gen Psychiatry2021;20:12 PMCID:PMC7888126

[3] Bundgaard-Nielsen C,Leutscher PDC.Gut microbiota profiles of autism spectrum disorder and attention deficit/hyperactivity disorder: a systematic literature review.Gut Microbes2020;11:1172-87 PMCID:PMC7524304

[4] Cheung SG,Uhlemann AC,Miller JM.Systematic review of gut microbiota and major depression.Front Psychiatry2019;10:34 PMCID:PMC6378305

[5] Jiang HY,Yu ZH.Altered gut microbiota profile in patients with generalized anxiety disorder.J Psychiatr Res2018;104:130-6

[6] Carlson AL,Azcarate-Peril MA.Infant gut microbiome associated with cognitive development.Biol Psychiatry2018;83:148-59 PMCID:PMC5724966

[7] Sordillo JE,Laranjo N.Association of the infant gut microbiome with early childhood neurodevelopmental outcomes: an ancillary study to the VDAART randomized clinical trial.JAMA Netw Open2019;2:e190905 PMCID:PMC6583279

[8] Tamana SK,Konya T.Bacteroides-dominant gut microbiome of late infancy is associated with enhanced neurodevelopment.Gut Microbes2021;13:1-17 PMCID:PMC8210878

[9] Loughman A,O’Hely M.Gut microbiota composition during infancy and subsequent behavioural outcomes.EBioMedicine2020;52:102640 PMCID:PMC7016366

[10] Ou Y,Smidt H.Development of the gut microbiota in healthy children in the first ten years of life: associations with internalizing and externalizing behavior.Gut Microbes2022;14:2038853 PMCID:PMC8865293

[11] Morais LH,Mazmanian SK.The gut microbiota-brain axis in behaviour and brain disorders.Nat Rev Microbiol2021;19:241-55

[12] Margolis KG,Mayer EA.The microbiota-gut-brain axis: from motility to mood.Gastroenterology2021;160:1486-501 PMCID:PMC8634751

[13] Mirzayi C,Genomic Standards Consortium.Reporting guidelines for human microbiome research: the STORMS checklist.Nat Med2021;27:1885-92 PMCID:PMC9105086

[14] VanderWeele TJ.Principles of confounder selection.Eur J Epidemiol2019;34:211-9 PMCID:PMC6447501

[15] Vujkovic-Cvijin I,Jiang L,Knight R.Host variables confound gut microbiota studies of human disease.Nature2020;587:448-54 PMCID:PMC7677204

[16] Kraaij R,Radjabzadeh D.The gut microbiome and child mental health: a population-based study.Brain Behav Immun2023;108:188-96 PMCID:PMC7614161

[17] Valles-Colomer M,Darzi Y.The neuroactive potential of the human gut microbiota in quality of life and depression.Nat Microbiol2019;4:623-32

[18] Minichino A,Francesconi M.Endocannabinoid system mediates the association between gut-microbial diversity and anhedonia/amotivation in a general population cohort.Mol Psychiatry2021;26:6269-76 PMCID:PMC8760063

[19] Bosch JA,Zwinderman AH.The gut microbiota and depressive symptoms across ethnic groups.Nat Commun2022;13:7129 PMCID:PMC9726934

[20] Gao W,Carlson AL.Gut microbiome and brain functional connectivity in infants-a preliminary study focusing on the amygdala.Psychopharmacology2019;236:1641-51 PMCID:PMC6599471

[21] Rothenberg SE,Shen J.Neurodevelopment correlates with gut microbiota in a cross-sectional analysis of children at 3 years of age in rural China.Sci Rep2021;11:7384 PMCID:PMC8016964

[22] Laue HE,Coker MO.Sex-specific relationships of the infant microbiome and early-childhood behavioral outcomes.Pediatr Res2022;92:580-91 PMCID:PMC9065210

[23] Guzzardi MA,Rizzo F.Maternal pre-pregnancy overweight and neonatal gut bacterial colonization are associated with cognitive development and gut microbiota composition in pre-school-age offspring.Brain Behav Immun2022;100:311-20

[24] van de Wouw M,Workentine ML.Associations between the gut microbiota and internalizing behaviors in preschool children.Psychosom Med2022;84:159-69

[25] Textor J,Knüppel S.DAGitty: a graphical tool for analyzing causal diagrams.Epidemiology2011;22:745

[26] Cinelli C,Pearl J.A crash course in good and bad controls. Sociol Method Res 2022.

[27] Eckermann HA,Lahti L.Can gut microbiota throughout the first 10 years of life predict executive functioning in childhood?.Dev Psychobiol2022;64:e22226

[28] Amrhein V,McShane B.Scientists rise up against statistical significance.Nature2019;567:305-7

[29] Schober P,Schwarte LA.Correlation coefficients: appropriate use and interpretation.Anesth Analg2018;126:1763-8

[30] Kong M,Cao X,Lu Z.Higher level of neutrophil-to-lymphocyte is associated with severe COVID-19.Epidemiol Infect2020;148:e139 PMCID:PMC7360950

[31] Verkouter I,de Roos A.Adult weight change in relation to visceral fat and liver fat at middle age: The Netherlands epidemiology of obesity study.Int J Obes2019;43:790-9

[32] Vissing NH,Rasmussen MA.Epidemiology and risk factors of infection in early childhood.Pediatrics2018;141:e20170933

[33] Xia Y,Chen DG. Statistical analysis of microbiome data with R. In: ICSA Book Series in Statistics. Springer Singapore; 2018. Available from: https://link.springer.com/book/10.1007/978-981-13-1534-3. [Last accessed on 14 Oct 2023]

[34] Michels N,Fouhy F,Clarke G.Gut microbiome patterns depending on children’s psychosocial stress: reports versus biomarkers.Brain Behav Immun2019;80:751-62

[35] Liu B,Chen S.Gut microbiota as an objective measurement for auxiliary diagnosis of insomnia disorder.Front Microbiol2019;10:1770 PMCID:PMC6701205

[36] Hu S,Huang T.Gut microbiota changes in patients with bipolar depression.Adv Sci2019;6:1900752 PMCID:PMC6662053

[37] Chen JJ,Liu YY.Sex differences in gut microbiota in patients with major depressive disorder.Neuropsychiatr Dis Treat2018;14:647-55 PMCID:PMC5833751

[38] Li Z,Zhang P.Multi-omics analyses of serum metabolome, gut microbiome and brain function reveal dysregulated microbiota-gut-brain axis in bipolar depression.Mol Psychiatry2022;27:4123-35

[39] Lai WT,Xu SX.Shotgun metagenomics reveals both taxonomic and tryptophan pathway differences of gut microbiota in major depressive disorder patients.Psychol Med2021;51:90-101

[40] Yıldırım S,Bayraktar A.Stratification of the gut microbiota composition landscape across the Alzheimer’s disease continuum in a turkish cohort.mSystems2022;7:e0000422 PMCID:PMC8823292

[41] Acuña I,Ruiz A.Infant gut microbiota associated with fine motor skills.Nutrients2021;13:1673 PMCID:PMC8156744

[42] Zhong H,Shi Z.Impact of early events and lifestyle on the gut microbiota and metabolic phenotypes in young school-age children.Microbiome2019;7:2 PMCID:PMC6320620

[43] Pietrucci D,Unida V.Dysbiosis of gut microbiota in a selected population of Parkinson’s patients.Parkinsonism Relat Disord2019;65:124-30

[44] Dong TS,Mayer EA.Obesity is associated with a distinct brain-gut microbiome signature that connects Prevotella and Bacteroides to the brain’s reward center.Gut Microbes2022;14:2051999 PMCID:PMC8942409

[45] Fife DA.Common, uncommon, and novel applications of random forest in psychological research.Behav Res Methods2023;55:2447-66

[46] Cutler A,Stevens JR. Ensemble machine learning. New York, NY: Springer New York; 2012. Available from: https://link.springer.com/10.1007/978-1-4419-9326-7. [Last accessed on 14 Oct 2023]

[47] Hermes GDA,de Vos WM.Does entry to center-based childcare affect gut microbial colonization in young infants?.Sci Rep2020;10:10235 PMCID:PMC7314774

[48] Dunson DB.Commentary: practical advantages of Bayesian analysis of epidemiologic data.Am J Epidemiol2001;153:1222-6

[49] Segata N,Waldron L.Metagenomic biomarker discovery and explanation.Genome Biol2011;12:R60 PMCID:PMC3218848

[50] Mallick H,McIver LJ.Multivariable association discovery in population-scale meta-omics studies.PLoS Comput Biol2021;17:e1009442 PMCID:PMC8714082

[51] Mandal S,White RA,Knight R.Analysis of composition of microbiomes: a novel method for studying microbial composition.Microb Ecol Health Dis2015;26:27663 PMCID:PMC4450248

[52] Fernandes AD,Macklaim JM,Edgell DR.Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis.Microbiome2014;2:15 PMCID:PMC4030730

[53] Nearing JT,Hayes MG.Microbiome differential abundance methods produce different results across 38 datasets.Nat Commun2022;13:342 PMCID:PMC8763921

[54] Kodikara S,Lê Cao KA.Statistical challenges in longitudinal microbiome data analysis.Brief Bioinform2022;23:bbac273 PMCID:PMC9294433

[55] Hejblum BP,Thiébaut R.Time-course gene set analysis for longitudinal gene expression data.PLoS Comput Biol2015;11:e1004310 PMCID:PMC4482329

[56] Roswall J,Kovatcheva-Datchary P.Developmental trajectory of the healthy human gut microbiota during the first 5 years of life.Cell Host Microbe2021;29:765-76.e3

[57] Sanada K,Kurokawa S.Gut microbiota and major depressive disorder: a systematic review and meta-analysis.J Affect Disord2020;266:1-13

[58] Knight R,Taylor BC.Best practices for analysing microbiomes.Nat Rev Microbiol2018;16:410-22

[59] Gloor GB,Pawlowsky-Glahn V.Microbiome datasets are compositional: and this is not optional.Front Microbiol2017;8:2224 PMCID:PMC5695134

[60] Barlow JT,Ismagilov RF.Publisher correction: a quantitative sequencing framework for absolute abundance measurements of mucosal and lumenal microbial communities.Nat Commun2020;11:3438 PMCID:PMC7338468

[61] Jian C,Yki-Järvinen H,Korpela K.Quantitative PCR provides a simple and accessible method for quantitative microbiota profiling.PLoS One2020;15:e0227285 PMCID:PMC6961887

[62] Vandeputte D,D’hoe K.Quantitative microbiome profiling links gut community variation to microbial load.Nature2017;551:507-11

[63] Louca S,Mazel F.Function and functional redundancy in microbial systems.Nat Ecol Evol2018;2:936-43

[64] Wemheuer F,Daniel R.Tax4Fun2: prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences.Environ Microbiome2020;15:11 PMCID:PMC8067651

[65] Douglas GM,Zaneveld JR.PICRUSt2 for prediction of metagenome functions.Nat Biotechnol2020;38:685-8 PMCID:PMC7365738

[66] Douglas GM,Zaneveld J. PICRUSt2: an improved and extensible approach for metagenome inference. bioRxiv 2019. Available from: https://www.biorxiv.org/content/10.1101/672295v1. [Last accessed on 14 Oct 2023].

[67] Jun SR,Hauser LJ,Gorin AA.PanFP: pangenome-based functional profiles for microbial communities.BMC Res Notes2015;8:479 PMCID:PMC4584126

[68] Subramanian I,Kumar S,Anamika K.Multi-omics data integration, interpretation, and its application.Bioinform Biol Insights2020;14:1177932219899051 PMCID:PMC7003173

[69] Love MI,Anders S.Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.Genome Biol2014;15:550 PMCID:PMC4302049

[70] Robinson MD,Smyth GK.edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.Bioinformatics2010;26:139-40 PMCID:PMC2796818

[71] Ritchie ME,Wu D.limma powers differential expression analyses for RNA-sequencing and microarray studies.Nucleic Acids Res2015;43:e47 PMCID:PMC4402510

[72] Tyanova S,Cox J.The MaxQuant computational platform for mass spectrometry-based shotgun proteomics.Nat Protoc2016;11:2301-19

[73] Bruderer R,Gandhi T.Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues.Mol Cell Proteomics2015;14:1400-10 PMCID:PMC4424408

[74] Zhang J,Shan B.PEAKS DB: de novo sequencing assisted database search for sensitive and accurate peptide identification.Mol Cell Proteomics2012;11:M111.010587 PMCID:PMC3322562

[75] Demichev V,Vernardis SI,Ralser M.DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput.Nat Methods2020;17:41-4 PMCID:PMC6949130

[76] Schmid R,Korf A.Integrative analysis of multimodal mass spectrometry data in MZmine 3.Nat Biotechnol2023;41:447-9 PMCID:PMC10496610

[77] Pang Z,Zhou G.MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights.Nucleic Acids Res2021;49:W388-96 PMCID:PMC8265181

[78] Shen X.MetFlow: an interactive and integrated workflow for metabolomics data cleaning and differential metabolite discovery.Bioinformatics2019;35:2870-2

[79] Manzoni C,Vandrovcova J.Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences.Brief Bioinform2018;19:286-302 PMCID:PMC6018996

[80] Eckburg PB,Bernstein CN.Diversity of the human intestinal microbial flora.Science2005;308:1635-8 PMCID:PMC1395357

[81] Villa MM,Silverman JD.High-throughput isolation and culture of human gut bacteria with droplet microfluidics. bioRxiv 2019.

[82] Watterson WJ,Watson AR.Droplet-based high-throughput cultivation for accurate screening of antibiotic resistant gut microbes.Elife2020;9:e56998 PMCID:PMC7351490

[83] Clavel T,Segata N.Next steps after 15 stimulating years of human gut microbiome research.Microb Biotechnol2022;15:164-75 PMCID:PMC8719818

[84] Richard ML.The gut mycobiota: insights into analysis, environmental interactions and role in gastrointestinal diseases.Nat Rev Gastroenterol Hepatol2019;16:331-45

[85] Borrel G,Gribaldo S,Moissl-Eichinger C.The host-associated archaeome.Nat Rev Microbiol2020;18:622-36

[86] Neu U.Virus interactions with bacteria: partners in the infectious dance.PLoS Pathog2020;16:e1008234 PMCID:PMC7012391

[87] Nagpal J.Microbiota-brain interactions: moving toward mechanisms in model organisms.Neuron2021;109:3930-53

[88] Horvath TD,Engevik MA.Interrogation of the mammalian gut-brain axis using LC-MS/MS-based targeted metabolomics with in vitro bacterial and organoid cultures and in vivo gnotobiotic mouse models.Nat Protoc2023;18:490-529

[89] Moysidou CM.Advances in modelling the human microbiome-gut-brain axis in vitro.Biochem Soc Trans2021;49:187-201 PMCID:PMC7924999

[90] Nestler EJ.Animal models of neuropsychiatric disorders.Nat Neurosci2010;13:1161-9 PMCID:PMC3750731

[91] Binda S,Johansen E.Criteria to qualify microorganisms as “probiotic” in foods and dietary supplements.Front Microbiol2020;11:1662 PMCID:PMC7394020

[92] Meyyappan AC, Forth E, Wallace CJK, Milev R. Effect of fecal microbiota transplant on symptoms of psychiatric disorders: a systematic review.BMC Psychiatry2020;20:299 PMCID:PMC7294648

[93] Secombe KR,Subramaniam CB.Guidelines for reporting on animal fecal transplantation (GRAFT) studies: recommendations from a systematic review of murine transplantation protocols.Gut Microbes2021;13:1979878 PMCID:PMC8489962