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Multi-cohort analysis of depression-associated gut bacteria sheds insight on bacterial biomarkers across populations

Cellular and Molecular Life Sciences volume 80, Article number: 9 (2023) Cite this article

Abstract

Gut microbes are associated with the development of depression based on extensive evidence. However, previous studies have led to conflicting reports on this association, posing challenges to the application of gut bacteria in the diagnostics and treatment of depression. To minimise heterogenicity in data analysis, the present meta-analysis adopted a standardised bioinformatics and statistical pipeline to analyse 16S rRNA sequences of 1827 samples from eight different cohorts. Although changes in the overall bacterial community were identified by our meta-analysis, depressive-correlated changes in alpha-diversity were absent. Enrichment of Bacteroidetes, Parabacteroides, Barnesiella, Bacteroides, and Bacteroides vulgatus, along with depletion in Firmicutes, Dialister, Oscillospiraceae UCG 003 and UCG 002, and Bacteroides plebeius, were observed in depressive-associated bacteria. By contrast, elevated L-glutamine degradation, and reduced L-glutamate and L-isoleucine biosynthesis were identified in depressive-associated microbiomes. After systemically reviewing the data of these collected cohorts, we have established a bacterial classifier to identify depressive symptoms with AUC 0.834 and 0.685 in the training and external validation dataset, respectively. Moreover, a low-risk bacterial cluster for depressive symptoms was identified, which was represented by a lower abundance of Escherichia-Shigella, and a higher abundance of Faecalibacterium, Oscillospiraceae UCG 002, Ruminococcus, and Christensenellaceae R.7 group.

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Data availability

Full documentation of metadata and raw sequences were downloaded from the original papers (Supplementary Methods). The main scripts used in the current study are available on GitHub (https://github.com/suishal/Multi-cohort_depressive).

References

  1. (WHO) WHO (2021) Depression. In. https://www.who.int/news-room/fact-sheets/detail/depression

  2. Eisendrath SJ, Cole SA, Christensen JF, Gutnick D, Cole MR, Feldman MD (2014) Depression. In: Feldman MD, Christensen JF, Satterfield JM (eds) Behavioral medicine: a guide for clinical practice, 4e. McGraw-Hill Education, New York, NY

    Google Scholar 

  3. Smith K (2014) Mental health: a world of depression. Nature 515(7526):180–181. https://doi.org/10.1038/515180a

    Article  CAS  Google Scholar 

  4. Wakefield JC, Schmitz MF, Baer JCJAJOP (2010) Does the DSM-IV clinical significance criterion for major depression reduce false positives? Evidence from the national comorbidity survey replication. Am J Psychiatry 167(3):298–304

    Article  Google Scholar 

  5. Horowitz M, Wilcock M (2022) Newer generation antidepressants and withdrawal effects: reconsidering the role of antidepressants and helping patients to stop. Drug Ther Bull 60(1):7–12. https://doi.org/10.1136/dtb.2020.000080

    Article  Google Scholar 

  6. Backhed F, Ding H, Wang T, Hooper LV, Koh GY, Nagy A, Semenkovich CF, Gordon JI (2004) The gut microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci U S A 101(44):15718–15723. https://doi.org/10.1073/pnas.0407076101

    Article  CAS  Google Scholar 

  7. Fan Y, Pedersen O (2021) Gut microbiota in human metabolic health and disease. Nat Rev Microbiol 19(1):55–71. https://doi.org/10.1038/s41579-020-0433-9

    Article  CAS  Google Scholar 

  8. Sudo N, Chida Y, Aiba Y, Sonoda J, Oyama N, Yu XN, Kubo C, Koga Y (2004) Postnatal microbial colonization programs the hypothalamic-pituitary-adrenal system for stress response in mice. J Physiol 558(Pt 1):263–275. https://doi.org/10.1113/jphysiol.2004.063388

    Article  CAS  Google Scholar 

  9. Simpson CA, Diaz-Arteche C, Eliby D, Schwartz OS, Simmons JG, Cowan CSM (2021) The gut microbiota in anxiety and depression – a systematic review. Clin Psychol Rev 83:101943. https://doi.org/10.1016/j.cpr.2020.101943

    Article  Google Scholar 

  10. McGuinness AJ, Davis JA, Dawson SL, Loughman A, Collier F, O’Hely M, Simpson CA, Green J, Marx W, Hair C, Guest G, Mohebbi M, Berk M, Stupart D, Watters D, Jacka FN (2022) A systematic review of gut microbiota composition in observational studies of major depressive disorder, bipolar disorder and schizophrenia. Mol Psychiatry. https://doi.org/10.1038/s41380-022-01456-310.1038/s41380-022-01456-3

    Article  Google Scholar 

  11. Dai Z, Coker OO, Nakatsu G, Wu WKK, Zhao L, Chen Z, Chan FKL, Kristiansen K, Sung JJY, Wong SH, Yu J (2018) Multi-cohort analysis of colorectal cancer metagenome identified altered bacteria across populations and universal bacterial markers. Microbiome 6(1):70. https://doi.org/10.1186/s40168-018-0451-2

    Article  Google Scholar 

  12. Mo Z, Huang P, Yang C, Xiao S, Zhang G, Ling F, Li L (2020) Meta-analysis of 16S rRNA microbial data identified distinctive and predictive microbiota dysbiosis in colorectal carcinoma adjacent tissue. mSystems. https://doi.org/10.1128/mSystems.00138-20

    Article  Google Scholar 

  13. Chakrabarti A, Geurts L, Hoyles L, Iozzo P, Kraneveld AD, La Fata G, Miani M, Patterson E, Pot B, Shortt C, Vauzour D (2022) The microbiota-gut-brain axis: pathways to better brain health. Perspectives on what we know, what we need to investigate and how to put knowledge into practice. Cell Mol Life Sci 79(2):80. https://doi.org/10.1007/s00018-021-04060-w

    Article  CAS  Google Scholar 

  14. Peter J, Fournier C, Durdevic M, Knoblich L, Keip B, Dejaco C, Trauner M, Moser G (2018) A microbial signature of psychological distress in irritable bowel syndrome. Psychosom Med 80(8):698–709. https://doi.org/10.1097/PSY.0000000000000630

    Article  Google Scholar 

  15. Hu S, Li A, Huang T, Lai J, Li J, Sublette ME, Lu H, Lu Q, Du Y, Hu Z, Ng CH, Zhang H, Lu J, Mou T, Lu S, Wang D, Duan J, Hu J, Huang M, Wei N, Zhou W, Ruan L, Li MD, Xu Y (2019) Gut microbiota changes in patients with bipolar depression. Adv Sci. https://doi.org/10.1002/advs.20190075210.1002/advs.201900752

    Article  Google Scholar 

  16. Valles-Colomer M, Falony G, Darzi Y, Tigchelaar EF, Wang J, Tito RY, Schiweck C, Kurilshikov A, Joossens M, Wijmenga C, Claes S, Van Oudenhove L, Zhernakova A, Vieira-Silva S, Raes J (2019) The neuroactive potential of the human gut microbiota in quality of life and depression. Nat Microbiol 4(4):623–632. https://doi.org/10.1038/s41564-018-0337-x

    Article  CAS  Google Scholar 

  17. Kleiman SC, Watson HJ, Bulik-Sullivan EC, Huh EY, Tarantino LM, Bulik CM, Carroll IM (2015) The intestinal microbiota in acute anorexia nervosa and during renourishment: relationship to depression, anxiety, and eating disorder psychopathology. Psychosom Med 77(9):969–981. https://doi.org/10.1097/psy.0000000000000247

    Article  Google Scholar 

  18. Vinberg M, Ottesen NM, Meluken I, Sorensen N, Pedersen O, Kessing LV, Miskowiak KW (2019) Remitted affective disorders and high familial risk of affective disorders associate with aberrant intestinal microbiota. Acta Psychiatr Scand 139(2):174–184. https://doi.org/10.1111/acps.12976

    Article  CAS  Google Scholar 

  19. Strandwitz P, Kim KH, Terekhova D, Liu JK, Sharma A, Levering J, McDonald D, Dietrich D, Ramadhar TR, Lekbua A, Mroue N, Liston C, Stewart EJ, Dubin MJ, Zengler K, Knight R, Gilbert JA, Clardy J, Lewis K (2019) GABA-modulating bacteria of the human gut microbiota. Nat Microbiol 4(3):396–403. https://doi.org/10.1038/s41564-018-0307-3

    Article  CAS  Google Scholar 

  20. Nikolova VL, Hall MRB, Hall LJ, Cleare AJ, Stone JM, Young AH (2021) Perturbations in gut microbiota composition in psychiatric disorders: a review and meta-analysis. JAMA Psychiat 78(12):1343–1354. https://doi.org/10.1001/jamapsychiatry.2021.2573

    Article  Google Scholar 

  21. Sanada K, Nakajima S, Kurokawa S, Barceló-Soler A, Ikuse D, Hirata A, Yoshizawa A, Tomizawa Y, Salas-Valero M, Noda Y, Mimura M, Iwanami A, Kishimoto T (2020) Gut microbiota and major depressive disorder: a systematic review and meta-analysis. J Affect Disord 266:1–13. https://doi.org/10.1016/j.jad.2020.01.102

    Article  CAS  Google Scholar 

  22. Sterne JA, Sutton AJ, Ioannidis JP, Terrin N, Jones DR, Lau J, Carpenter J, Rucker G, Harbord RM, Schmid CH, Tetzlaff J, Deeks JJ, Peters J, Macaskill P, Schwarzer G, Duval S, Altman DG, Moher D, Higgins JP (2011) Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ 343:d4002. https://doi.org/10.1136/bmj.d4002

    Article  Google Scholar 

  23. McDonald D, Hyde E, Debelius JW, Morton JT, Gonzalez A, Ackermann G, Aksenov AA, Behsaz B, Brennan C, Chen Y, DeRight GL, Dorrestein PC, Dunn RR, Fahimipour AK, Gaffney J, Gilbert JA, Gogul G, Green JL, Hugenholtz P, Humphrey G, Huttenhower C, Jackson MA, Janssen S, Jeste DV, Jiang L, Kelley ST, Knights D, Kosciolek T, Ladau J, Leach J, Marotz C, Meleshko D, Melnik AV, Metcalf JL, Mohimani H, Montassier E, Navas-Molina J, Nguyen TT, Peddada S, Pevzner P, Pollard KS, Rahnavard G, Robbins-Pianka A, Sangwan N, Shorenstein J, Smarr L, Song SJ, Spector T, Swafford AD, Thackray VG et al (2018) American gut: an open platform for citizen science microbiome research. mSystems. https://doi.org/10.1128/mSystems.00031-18

    Article  Google Scholar 

  24. Hamilton M (1960) A rating scale for depression. J Neurol Neurosurg Psychiatry 23:56–62. https://doi.org/10.1136/jnnp.23.1.56

    Article  CAS  Google Scholar 

  25. Zhou Y, Chen C, Yu H, Yang Z (2020) Fecal microbiota changes in patients with postpartum depressive disorder. Front Cell Infect Microbiol 10:567268. https://doi.org/10.3389/fcimb.2020.567268

    Article  CAS  Google Scholar 

  26. Mohsen A, Park J, Chen YA, Kawashima H, Mizuguchi K (2019) Impact of quality trimming on the efficiency of reads joining and diversity analysis of Illumina paired-end reads in the context of QIIME1 and QIIME2 microbiome analysis frameworks. BMC Bioinformatics 20(1):581. https://doi.org/10.1186/s12859-019-3187-5

    Article  Google Scholar 

  27. Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, Huttenhower C, Langille MGI (2020) PICRUSt2 for prediction of metagenome functions. Nat Biotechnol 38(6):685–688. https://doi.org/10.1038/s41587-020-0548-6

    Article  CAS  Google Scholar 

  28. Caspi R, Billington R, Fulcher CA, Keseler IM, Kothari A, Krummenacker M, Latendresse M, Midford PE, Ong Q, Ong WK, Paley S, Subhraveti P, Karp PD (2018) The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res 46(D1):D633–D639. https://doi.org/10.1093/nar/gkx935

    Article  CAS  Google Scholar 

  29. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev 10(1):89. https://doi.org/10.1186/s13643-021-01626-4

    Article  Google Scholar 

  30. Borgo F, Riva A, Benetti A, Casiraghi MC, Bertelli S, Garbossa S, Anselmetti S, Scarone S, Pontiroli AE, Morace G, Borghi E (2017) Microbiota in anorexia nervosa: the triangle between bacterial species, metabolites and psychological tests. PLoS One. https://doi.org/10.1371/journal.pone.0179739

    Article  Google Scholar 

  31. Schreiner P, Yilmaz B, Rossel JB, Franc Y, Misselwitz B, Scharl M, Zeitz J, Frei P, Greuter T, Vavricka SR, Pittet V, Siebenhüner A, Juillerat P, Känel RV, Macpherson AJ, Rogler G, Biedermann L (2019) Vegetarian or gluten-free diets in patients with inflammatory bowel disease are associated with lower psychological well-being and a different gut microbiota, but no beneficial effects on the course of the disease. United Eur Gastroenterol J 7(6):767–781. https://doi.org/10.1177/2050640619841249

    Article  Google Scholar 

  32. Downes J, Munson M, Wade WG (2003) Dialister invisus sp. nov., isolated from the human oral cavity. Int J Syst Evol Microbiol 53(Pt 6):1937–40. https://doi.org/10.1099/ijs.0.02640-0

    Article  CAS  Google Scholar 

  33. Whitman WB, Rainey F, Kämpfer P, Trujillo M, Chun J, DeVos P, Hedlund B, Dedysh S, Nedashkovskaya O (2016) Bergey’s manual of systematics of archaea and bacteria. Wiley, Hoboken, NJ

    Google Scholar 

  34. Huang W, Hu W, Cai L, Zeng G, Fang W, Dai X, Ye Q, Chen X, Zhang J (2021) Acetate supplementation produces antidepressant-like effect via enhanced histone acetylation. J Affect Disord 281:51–60. https://doi.org/10.1016/j.jad.2020.11.121

    Article  CAS  Google Scholar 

  35. Karnib N, El-Ghandour R, El Hayek L, Nasrallah P, Khalifeh M, Barmo N, Jabre V, Ibrahim P, Bilen M, Stephan JS, Holson EB, Ratan RR, Sleiman SF (2019) Lactate is an antidepressant that mediates resilience to stress by modulating the hippocampal levels and activity of histone deacetylases. Neuropsychopharmacology 44(6):1152–1162. https://doi.org/10.1038/s41386-019-0313-z

    Article  CAS  Google Scholar 

  36. Zhu F, Ju Y, Wang W, Wang Q, Guo R, Ma Q, Sun Q, Fan Y, Xie Y, Yang Z, Jie Z, Zhao B, Xiao L, Yang L, Zhang T, Feng J, Guo L, He X, Chen Y, Chen C, Gao C, Xu X, Yang H, Wang J, Dang Y, Madsen L, Brix S, Kristiansen K, Jia H, Ma X (2020) Metagenome-wide association of gut microbiome features for schizophrenia. Nat Commun 11(1):1612. https://doi.org/10.1038/s41467-020-15457-9

    Article  CAS  Google Scholar 

  37. Otaru N, Ye K, Mujezinovic D, Berchtold L, Constancias F, Cornejo FA, Krzystek A, de Wouters T, Braegger C, Lacroix C, Pugin B (2021) GABA production by human intestinal Bacteroides spp.: prevalence, regulation, and role in acid stress tolerance. Front Microbiol. https://doi.org/10.3389/fmicb.2021.656895

    Article  Google Scholar 

  38. Luscher B, Shen Q, Sahir N (2011) The GABAergic deficit hypothesis of major depressive disorder. Mol Psychiatry 16(4):383–406. https://doi.org/10.1038/mp.2010.120

    Article  CAS  Google Scholar 

  39. Deng Y, Zhou M, Wang J, Yao J, Yu J, Liu W, Wu L, Wang J, Gao R (2021) Involvement of the microbiota-gut-brain axis in chronic restraint stress: disturbances of the kynurenine metabolic pathway in both the gut and brain. Gut Microbes 13(1):1–16. https://doi.org/10.1080/19490976.2020.1869501

    Article  CAS  Google Scholar 

  40. Gomez-Nguyen A, Basson AR, Dark-Fleury L, Hsu K, Osme A, Menghini P, Pizarro TT, Cominelli F (2021) Parabacteroides distasonis induces depressive-like behavior in a mouse model of Crohn’s disease. Brain Behav Immun 98:245–250. https://doi.org/10.1016/j.bbi.2021.08.218

    Article  CAS  Google Scholar 

  41. Haller D, Holt L, Kim SC, Schwabe RF, Sartor RB, Jobin C (2003) Transforming growth factor-β1 inhibits non-pathogenic gramnegative bacteria-induced NF-κB recruitment to the interleukin-6 gene promoter in intestinal epithelial cells through modulation of histone acetylation*. J Biol Chem 278(26):23851–23860. https://doi.org/10.1074/jbc.M300075200

    Article  CAS  Google Scholar 

  42. Guo Y, Xie J-P, Deng K, Li X, Yuan Y, Xuan Q, Xie J, He X-M, Wang Q, Li J-J, Luo H-R (2019) Prophylactic effects of bifidobacterium adolescentis on anxiety and depression-like phenotypes after chronic stress: a role of the gut microbiota-inflammation axis. Front Behav Neurosci 13:126–126. https://doi.org/10.3389/fnbeh.2019.00126

    Article  CAS  Google Scholar 

  43. Koochakpoor G, Salari-Moghaddam A, Keshteli AH, Afshar H, Esmaillzadeh A, Adibi P (2021) Dietary intake of branched-chain amino acids in relation to depression, anxiety and psychological distress. Nutr J 20(1):11. https://doi.org/10.1186/s12937-021-00670-z

    Article  CAS  Google Scholar 

  44. Wirbel J, Zych K, Essex M, Karcher N, Kartal E, Salazar G, Bork P, Sunagawa S, Zeller G (2021) Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox. Genome Biol 22(1):93. https://doi.org/10.1186/s13059-021-02306-1

    Article  Google Scholar 

  45. Vujkovic-Cvijin I, Sklar J, Jiang L, Natarajan L, Knight R, Belkaid Y (2020) Host variables confound gut microbiota studies of human disease. Nature 587(7834):448–454. https://doi.org/10.1038/s41586-020-2881-9

    Article  CAS  Google Scholar 

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Acknowledgements

The authors would like to thank Prof. Johannes Peter, Prof. Jeroen Raes, Prof. Ian M. Carroll, Dr Bahtiyar Yilmaz, Dr Nikolaj Sørensen, the AGP cohort, and the FGFP cohort, who provided the sequence data and related metadata for this paper, as well as Dr Kanchana Poonsuk for study selection in the systematic review and Matthew Wong for proofreading and reviewing the manuscript.

Funding

This research was funded by a Early Career Scheme grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (RGC/ECS Project No. 27117022) and a Commissioned Research Grant from the Health and Medical Research Fund (HMRF Ref. No.: CFS-HKU2) to HMT.

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Authors and Affiliations

  1. HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China

    Suisha Liang, Zhen Ye Sin, Zhang Xi & Roberto Bruzzone

  2. The Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China

    Zhen Ye Sin, Zhang Xi & Hein M. Tun

  3. Microbiota I-Center (MagIC), Hong Kong SAR, China

    Zhen Ye Sin, Juelei Yu, Shilin Zhao, Zhang Xi & Hein M. Tun

  4. Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China

    Zhen Ye Sin, Juelei Yu, Shilin Zhao, Zhang Xi & Hein M. Tun

  5. Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China

    Juelei Yu & Shilin Zhao

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  1. Suisha Liang
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Contributions

SL and ZS searched, selected, and extracted the data from published papers. SL analysed data and drafted the manuscript. ZS, SZ, XZ, JY, RB and HT commented on the study and revised the manuscript. HT supervised the study. All authors read and approved the final manuscript.

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Correspondence to Hein M. Tun.

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Liang, S., Sin, Z.Y., Yu, J. et al. Multi-cohort analysis of depression-associated gut bacteria sheds insight on bacterial biomarkers across populations. Cell. Mol. Life Sci. 80, 9 (2023). https://doi.org/10.1007/s00018-022-04650-2

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  • DOI: https://doi.org/10.1007/s00018-022-04650-2

Keywords

  • Gut-brain axis
  • Depression
  • Gut bacteria
  • Multi-cohort
  • Bacteria-based identification