REFERENCES

1. Cryan JF, O’Riordan KJ, Cowan CSM, et al. The microbiota-gut-brain axis. Physiol Rev 2019;99:1877-2013.

2. Sukmajaya AC, Lusida MI, Soetjipto, Setiawati Y. Systematic review of gut microbiota and attention-deficit hyperactivity disorder (ADHD). Ann Gen Psychiatry 2021;20:12.

3. Bundgaard-Nielsen C, Knudsen J, Leutscher PDC, et al. Gut microbiota profiles of autism spectrum disorder and attention deficit/hyperactivity disorder: a systematic literature review. Gut Microbes 2020;11:1172-87.

4. Cheung SG, Goldenthal AR, Uhlemann AC, Mann JJ, Miller JM, Sublette ME. Systematic review of gut microbiota and major depression. Front Psychiatry 2019;10:34.

5. Jiang HY, Zhang X, Yu ZH, et al. Altered gut microbiota profile in patients with generalized anxiety disorder. J Psychiatr Res 2018;104:130-6.

6. Carlson AL, Xia K, Azcarate-Peril MA, et al. Infant gut microbiome associated with cognitive development. Biol Psychiatry 2018;83:148-59.

7. Sordillo JE, Korrick S, Laranjo N, et al. Association of the infant gut microbiome with early childhood neurodevelopmental outcomes: an ancillary study to the VDAART randomized clinical trial. JAMA Netw Open 2019;2:e190905.

8. Tamana SK, Tun HM, Konya T, et al. Bacteroides-dominant gut microbiome of late infancy is associated with enhanced neurodevelopment. Gut Microbes 2021;13:1-17.

9. Loughman A, Ponsonby AL, O’Hely M, et al. Gut microbiota composition during infancy and subsequent behavioural outcomes. EBioMedicine 2020;52:102640.

10. Ou Y, Belzer C, Smidt H, de Weerth C. Development of the gut microbiota in healthy children in the first ten years of life: associations with internalizing and externalizing behavior. Gut Microbes 2022;14:2038853.

11. Morais LH, Schreiber HL 4th, Mazmanian SK. The gut microbiota-brain axis in behaviour and brain disorders. Nat Rev Microbiol 2021;19:241-55.

12. Margolis KG, Cryan JF, Mayer EA. The microbiota-gut-brain axis: from motility to mood. Gastroenterology 2021;160:1486-501.

13. Mirzayi C, Renson A, Genomic Standards Consortium, et al. Reporting guidelines for human microbiome research: the STORMS checklist. Nat Med 2021;27:1885-92.

14. VanderWeele TJ. Principles of confounder selection. Eur J Epidemiol 2019;34:211-9.

15. Vujkovic-Cvijin I, Sklar J, Jiang L, Natarajan L, Knight R, Belkaid Y. Host variables confound gut microbiota studies of human disease. Nature 2020;587:448-54.

16. Kraaij R, Schuurmans IK, Radjabzadeh D, et al. The gut microbiome and child mental health: a population-based study. Brain Behav Immun 2023;108:188-96.

17. Valles-Colomer M, Falony G, Darzi Y, et al. The neuroactive potential of the human gut microbiota in quality of life and depression. Nat Microbiol 2019;4:623-32.

18. Minichino A, Jackson MA, Francesconi M, et al. Endocannabinoid system mediates the association between gut-microbial diversity and anhedonia/amotivation in a general population cohort. Mol Psychiatry 2021;26:6269-76.

19. Bosch JA, Nieuwdorp M, Zwinderman AH, et al. The gut microbiota and depressive symptoms across ethnic groups. Nat Commun 2022;13:7129.

20. Gao W, Salzwedel AP, Carlson AL, et al. Gut microbiome and brain functional connectivity in infants-a preliminary study focusing on the amygdala. Psychopharmacology 2019;236:1641-51.

21. Rothenberg SE, Chen Q, Shen J, et al. Neurodevelopment correlates with gut microbiota in a cross-sectional analysis of children at 3 years of age in rural China. Sci Rep 2021;11:7384.

22. Laue HE, Karagas MR, Coker MO, et al. Sex-specific relationships of the infant microbiome and early-childhood behavioral outcomes. Pediatr Res 2022;92:580-91.

23. Guzzardi MA, Ederveen THA, Rizzo F, et al. 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 Immun 2022;100:311-20.

24. van de Wouw M, Wang Y, Workentine ML, et al. Associations between the gut microbiota and internalizing behaviors in preschool children. Psychosom Med 2022;84:159-69.

25. Textor J, Hardt J, Knüppel S. DAGitty: a graphical tool for analyzing causal diagrams. Epidemiology 2011;22:745.

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

27. Eckermann HA, Ou Y, Lahti L, de Weerth C. Can gut microbiota throughout the first 10 years of life predict executive functioning in childhood? Dev Psychobiol 2022;64:e22226.

28. Amrhein V, Greenland S, McShane B. Scientists rise up against statistical significance. Nature 2019;567:305-7.

29. Schober P, Boer C, Schwarte LA. Correlation coefficients: appropriate use and interpretation. Anesth Analg 2018;126:1763-8.

30. Kong M, Zhang H, Cao X, Mao X, Lu Z. Higher level of neutrophil-to-lymphocyte is associated with severe COVID-19. Epidemiol Infect 2020;148:e139.

31. Verkouter I, Noordam R, de Roos A, et al. Adult weight change in relation to visceral fat and liver fat at middle age: The Netherlands epidemiology of obesity study. Int J Obes 2019;43:790-9.

32. Vissing NH, Chawes BL, Rasmussen MA, Bisgaard H. Epidemiology and risk factors of infection in early childhood. Pediatrics 2018;141:e20170933.

33. Xia Y, Sun J, 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, Van de Wiele T, Fouhy F, O’Mahony S, Clarke G, Keane J. Gut microbiome patterns depending on children’s psychosocial stress: reports versus biomarkers. Brain Behav Immun 2019;80:751-62.

35. Liu B, Lin W, Chen S, et al. Gut microbiota as an objective measurement for auxiliary diagnosis of insomnia disorder. Front Microbiol 2019;10:1770.

36. Hu S, Li A, Huang T, et al. Gut microbiota changes in patients with bipolar depression. Adv Sci 2019;6:1900752.

37. Chen JJ, Zheng P, Liu YY, et al. Sex differences in gut microbiota in patients with major depressive disorder. Neuropsychiatr Dis Treat 2018;14:647-55.

38. Li Z, Lai J, Zhang P, et al. Multi-omics analyses of serum metabolome, gut microbiome and brain function reveal dysregulated microbiota-gut-brain axis in bipolar depression. Mol Psychiatry 2022;27:4123-35.

39. Lai WT, Deng WF, Xu SX, et al. Shotgun metagenomics reveals both taxonomic and tryptophan pathway differences of gut microbiota in major depressive disorder patients. Psychol Med 2021;51:90-101.

40. Yıldırım S, Nalbantoğlu ÖU, Bayraktar A, et al. Stratification of the gut microbiota composition landscape across the Alzheimer’s disease continuum in a turkish cohort. mSystems 2022;7:e0000422.

41. Acuña I, Cerdó T, Ruiz A, et al. Infant gut microbiota associated with fine motor skills. Nutrients 2021;13:1673.

42. Zhong H, Penders J, Shi Z, et al. Impact of early events and lifestyle on the gut microbiota and metabolic phenotypes in young school-age children. Microbiome 2019;7:2.

43. Pietrucci D, Cerroni R, Unida V, et al. Dysbiosis of gut microbiota in a selected population of Parkinson’s patients. Parkinsonism Relat Disord 2019;65:124-30.

44. Dong TS, Guan M, Mayer EA, et al. Obesity is associated with a distinct brain-gut microbiome signature that connects Prevotella and Bacteroides to the brain’s reward center. Gut Microbes 2022;14:2051999.

45. Fife DA, D’Onofrio J. Common, uncommon, and novel applications of random forest in psychological research. Behav Res Methods 2023;55:2447-66.

46. Cutler A, Cutler DR, 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, Eckermann HA, de Vos WM, de Weerth C. Does entry to center-based childcare affect gut microbial colonization in young infants? Sci Rep 2020;10:10235.

48. Dunson DB. Commentary: practical advantages of Bayesian analysis of epidemiologic data. Am J Epidemiol 2001;153:1222-6.

49. Segata N, Izard J, Waldron L, et al. Metagenomic biomarker discovery and explanation. Genome Biol 2011;12:R60.

50. Mallick H, Rahnavard A, McIver LJ, et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput Biol 2021;17:e1009442.

51. Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis 2015;26:27663.

52. Fernandes AD, Reid JN, Macklaim JM, McMurrough TA, Edgell DR, Gloor GB. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2014;2:15.

53. Nearing JT, Douglas GM, Hayes MG, et al. Microbiome differential abundance methods produce different results across 38 datasets. Nat Commun 2022;13:342.

54. Kodikara S, Ellul S, Lê Cao KA. Statistical challenges in longitudinal microbiome data analysis. Brief Bioinform 2022;23:bbac273.

55. Hejblum BP, Skinner J, Thiébaut R. Time-course gene set analysis for longitudinal gene expression data. PLoS Comput Biol 2015;11:e1004310.

56. Roswall J, Olsson LM, Kovatcheva-Datchary P, et al. Developmental trajectory of the healthy human gut microbiota during the first 5 years of life. Cell Host Microbe 2021;29:765-76.e3.

57. Sanada K, Nakajima S, Kurokawa S, et al. Gut microbiota and major depressive disorder: a systematic review and meta-analysis. J Affect Disord 2020;266:1-13.

58. Knight R, Vrbanac A, Taylor BC, et al. Best practices for analysing microbiomes. Nat Rev Microbiol 2018;16:410-22.

59. Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome datasets are compositional: and this is not optional. Front Microbiol 2017;8:2224.

60. Barlow JT, Bogatyrev SR, Ismagilov RF. Publisher correction: a quantitative sequencing framework for absolute abundance measurements of mucosal and lumenal microbial communities. Nat Commun 2020;11:3438.

61. Jian C, Luukkonen P, Yki-Järvinen H, Salonen A, Korpela K. Quantitative PCR provides a simple and accessible method for quantitative microbiota profiling. PLoS One 2020;15:e0227285.

62. Vandeputte D, Kathagen G, D’hoe K, et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature 2017;551:507-11.

63. Louca S, Polz MF, Mazel F, et al. Function and functional redundancy in microbial systems. Nat Ecol Evol 2018;2:936-43.

64. Wemheuer F, Taylor JA, Daniel R, et al. Tax4Fun2: prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences. Environ Microbiome 2020;15:11.

65. Douglas GM, Maffei VJ, Zaneveld JR, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol 2020;38:685-8.

66. Douglas GM, Maffei VJ, Zaneveld J, et al. 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, Robeson MS, Hauser LJ, Schadt CW, Gorin AA. PanFP: pangenome-based functional profiles for microbial communities. BMC Res Notes 2015;8:479.

68. Subramanian I, Verma S, Kumar S, Jere A, Anamika K. Multi-omics data integration, interpretation, and its application. Bioinform Biol Insights 2020;14:1177932219899051.

69. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014;15:550.

70. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010;26:139-40.

71. Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015;43:e47.

72. Tyanova S, Temu T, Cox J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat Protoc 2016;11:2301-19.

73. Bruderer R, Bernhardt OM, Gandhi T, et al. Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues. Mol Cell Proteomics 2015;14:1400-10.

74. Zhang J, Xin L, Shan B, et al. PEAKS DB: de novo sequencing assisted database search for sensitive and accurate peptide identification. Mol Cell Proteomics 2012;11:M111.010587.

75. Demichev V, Messner CB, Vernardis SI, Lilley KS, Ralser M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat Methods 2020;17:41-4.

76. Schmid R, Heuckeroth S, Korf A, et al. Integrative analysis of multimodal mass spectrometry data in MZmine 3. Nat Biotechnol 2023;41:447-9.

77. Pang Z, Chong J, Zhou G, et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res 2021;49:W388-96.

78. Shen X, Zhu ZJ. MetFlow: an interactive and integrated workflow for metabolomics data cleaning and differential metabolite discovery. Bioinformatics 2019;35:2870-2.

79. Manzoni C, Kia DA, Vandrovcova J, et al. Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences. Brief Bioinform 2018;19:286-302.

80. Eckburg PB, Bik EM, Bernstein CN, et al. Diversity of the human intestinal microbial flora. Science 2005;308:1635-8.

81. Villa MM, Bloom RJ, Silverman JD, et al. High-throughput isolation and culture of human gut bacteria with droplet microfluidics. bioRxiv 2019.

82. Watterson WJ, Tanyeri M, Watson AR, et al. Droplet-based high-throughput cultivation for accurate screening of antibiotic resistant gut microbes. Elife 2020;9:e56998.

83. Clavel T, Horz HP, Segata N, Vehreschild M. Next steps after 15 stimulating years of human gut microbiome research. Microb Biotechnol 2022;15:164-75.

84. Richard ML, Sokol H. The gut mycobiota: insights into analysis, environmental interactions and role in gastrointestinal diseases. Nat Rev Gastroenterol Hepatol 2019;16:331-45.

85. Borrel G, Brugère JF, Gribaldo S, Schmitz RA, Moissl-Eichinger C. The host-associated archaeome. Nat Rev Microbiol 2020;18:622-36.

86. Neu U, Mainou BA. Virus interactions with bacteria: partners in the infectious dance. PLoS Pathog 2020;16:e1008234.

87. Nagpal J, Cryan JF. Microbiota-brain interactions: moving toward mechanisms in model organisms. Neuron 2021;109:3930-53.

88. Horvath TD, Haidacher SJ, Engevik MA, et al. 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 Protoc 2023;18:490-529.

89. Moysidou CM, Owens RM. Advances in modelling the human microbiome-gut-brain axis in vitro. Biochem Soc Trans 2021;49:187-201.

90. Nestler EJ, Hyman SE. Animal models of neuropsychiatric disorders. Nat Neurosci 2010;13:1161-9.

91. Binda S, Hill C, Johansen E, et al. Criteria to qualify microorganisms as “probiotic” in foods and dietary supplements. Front Microbiol 2020;11:1662.

92. Meyyappan AC, Forth E, Wallace CJK, Milev R. Effect of fecal microbiota transplant on symptoms of psychiatric disorders: a systematic review. BMC Psychiatry 2020;20:299.

93. Secombe KR, Al-Qadami GH, Subramaniam CB, et al. Guidelines for reporting on animal fecal transplantation (GRAFT) studies: recommendations from a systematic review of murine transplantation protocols. Gut Microbes 2021;13:1979878.

Microbiome Research Reports
ISSN 2771-5965 (Online)

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/