Amaan Arif and Prachi Srivastava*

Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus 226028, India

*Corresponding Author: Prachi Srivastava, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus 226028, India.

Received: February 24, 2025; Published: June 14, 2025

Abstract

Background: Microbiome-gut-brain axis represents a complex, bidirectional communication network connecting the gastrointestinal tract and its microbial populations with the central nervous system (CNS). This complex system is important for maintaining physiological homeostasis and has significant implications for mental health. The human gut has trillions of microorganisms, collectively termed gut microbiota, which play important roles in digestion, immune function, and production of various metabolites.

Purpose: The present study aims to investigate the communication between gut microbiota and the brain that can occur via multiple pathways: neural (e.g., vagus nerve), endocrine (e.g., hormone production), immune (e.g., inflammation modulation), and metabolic (e.g., production of short-chain fatty acids).

Methods: Artificial Intelligence (AI) has emerged as a powerful tool in interpreting the complexities of the microbiome-gut-brain axis. AI techniques, such as machine learning and deep learning, enable the integration and analysis of large, multifaceted datasets, uncovering patterns and correlations that can be avoided by traditional methods. These techniques enable predictive modelling, biomarker discovery, and understanding of underlying biological mechanisms, enhancing research efficiency and covering the way for personalised therapeutic approaches.

Result: Dysbiosis, or imbalance of gut microbiota, has been linked to mental health disorders such as anxiety, depression, multiple sclerosis, autism spectrum disorders, etc, offering new perspectives on their etiology and potential therapeutic interventions.

Conclusion: The application of AI in microbiome research has provided valuable insights into mental health conditions. AI models have identified specific gut bacteria linked to disease, offered predictive models, and discovered distinct microbiome signatures associated with specific diseases. Integrating AI with microbiome research holds promise for revolutionizing mental health care, offering new diagnostic tools and targeted therapies. Challenges remain, but the potential benefits of AI-driven insights into microbiome-gut-brain interactions are immense and offer hope for innovative treatments and preventative measures to improve mental health outcomes.

Keywords: Microbiome-Gut-Brain Axis; Artificial Intelligence (AI); Microbial Dysbiosis; Predictive Modelling, Biomarker Discovery

References

1. Arneth B. “Gut–brain axis biochemical signalling from the gastrointestinal tract to the central nervous system: gut dysbiosis and altered brain function”. Postgraduate Medical Journal 94 (2018): 446-452.
2. Kasarełło K., et al. “Communication of gut microbiota and brain via immune and neuroendocrine signaling”. Frontiers in Microbiology (2023): 14.
3. Strandwitz P. “Neurotransmitter modulation by the gut microbiota”. Brain Research 1693 (2018): 128-133.
4. Bhatia N., et al. “Gut-Brain Axis and Neurological Disorders-How Microbiomes Affect our Mental Health”. CNS and Neurological Disorders Drug Targets (2022).
5. Abi-Dargham A., et al. “Candidate biomarkers in psychiatric disorders: state of the field. World Psychiatry 22 (2023).
6. Kalia M and Silva J. “Biomarkers of psychiatric diseases: current status and future prospects”. Metabolism: Clinical and Experimental 3 (2015): S11-15.
7. Wilczyńska K and Waszkiewicz N. “Diagnostic Utility of Selected Serum Dementia Biomarkers: Amyloid β-40, Amyloid β-42, Tau Protein, and YKL-40: A Review”. Journal of Clinical Medicine 9 (2020).
8. Chong J., et al. “Blood-based high sensitivity measurements of beta-amyloid and phosphorylated tau as biomarkers of Alzheimer’s disease: a focused review on recent advances”. Journal of Neurology, Neurosurgery, and Psychiatry 92 (2021): 1231-1241.
9. Han H and Liu W. “The coming era of artificial intelligence in biological data science”. BMC Bioinformatics 20 (2022).
10. Chakraborty I., et al. “Artificial Intelligence in Biological Data”. Journal of Information Technology and Software Engineering 7 (2017): 1-6.
11. Akhtar M., et al. “AI in Bioinformatics”. International Journal of Sciences: Basic and Applied Research 56 (2021): 301-311.
12. Steardo L., et al. “Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review”. Frontiers in Psychiatry 11 (2020).
13. Shen H., et al. “Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI”. NeuroImage 49 (2010): 3110-3121.
14. Rosa M., et al. “Sparse network-based models for patient classification using fMRI”. Neuroimage 105 (2013): 493-506.
15. Chang C., et al. “Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer’s Disease”. International Journal of Molecular Sciences (2021): 22.
16. Aqeel A., et al. “A Long Short-Term Memory Biomarker-Based Prediction Framework for Alzheimer’s Disease”. Sensors (Basel, Switzerland) 22 (2022).
17. Hao Z., et al. “Faecalibacterium prausnitzii (ATCC 27766) has preventive and therapeutic effects on chronic unpredictable mild stress-induced depression-like and anxiety-like behavior in rats”. Psychoneuroendocrinology 104 (2019): 132-142.
18. Leylabadlo H., et al. “The critical role of Faecalibacterium prausnitzii in human health: An overview”. Microbial pathogenesis (2020): 104344.
19. Emery D., et al. “16S rRNA Next Generation Sequencing Analysis Shows Bacteria in Alzheimer’s Post-Mortem Brain”. Frontiers in Aging Neuroscience 9 (2017).
20. Zhang J., et al. “Features of Gut Microbiome Associated With Responses to Fecal Microbiota Transplantation for Inflammatory Bowel Disease: A Systematic Review”. Frontiers in Medicine 9 (2022).
21. Dopkins N., et al. “The role of gut microbiome and associated metabolome in the regulation of neuroinflammation in multiple sclerosis and its implications in attenuating chronic inflammation in other inflammatory and autoimmune disorders”. Immunology 154 (2018).
22. Lu Q., et al. “Gut Microbiota in Bipolar Depression and Its Relationship to Brain Function: An Advanced Exploration”. Frontiers in Psychiatry 10 (2019).
23. Simpson C., et al. “The gut microbiota in anxiety and depression-A systematic review”. Clinical Psychology Review 83 (2020): 101943.
24. Aizawa E., et al. “Possible association of Bifidobacterium and Lactobacillus in the gut microbiota of patients with major depressive disorder”. Journal of Affective Disorders 202 (2016): 254-257.
25. Agus A., et al. “Gut Microbiota Regulation of Tryptophan Metabolism in Health and Disease”. Cell Host & Microbe 6 (2018): 716-724.
26. Ho L., et al. “Gut microbiota changes in children with autism spectrum disorder: a systematic review”. Gut Pathogens 12 (2020).
27. Weston B., et al. “An agent-based modeling framework for evaluating hypotheses on risks for developing autism: effects of the gut microbial environment”. Medical Hypotheses 4 (2015): 395-401.
28. Yang J., et al. “Effects of gut microbial-based treatments on gut microbiota, behavioral symptoms, and gastrointestinal symptoms in children with autism spectrum disorder: A systematic review”. Psychiatry Research 293 (2020).
29. Chen Y., et al. “FTACMT study protocol: a multicentre, double-blind, randomised, placebo-controlled trial of faecal microbiota transplantation for autism spectrum disorder”. BMJ Open 12 (2022).
30. Shen T., et al. “The Association Between the Gut Microbiota and Parkinson's Disease, a Meta-Analysis”. Frontiers in Aging Neuroscience 13 (2021).
31. Bedarf J., et al. “Functional implications of microbial and viral gut metagenome changes in early stage L-DOPA-naïve Parkinson’s disease patients”. Genome Medicine 9 (2017).
32. Cattaneo A., et al. “Association of brain amyloidosis with pro-inflammatory gut bacterial taxa and peripheral inflammation markers in cognitively impaired elderly”. Neurobiology of Aging 49 (2017): 60-68.
33. Ait-Belgnaoui A., et al. “Bifidobacterium longum and Lactobacillus helveticus Synergistically Suppress Stress-related Visceral Hypersensitivity Through Hypothalamic-Pituitary-Adrenal Axis Modulation”. Journal of Neurogastroenterology and Motility 24 (2018): 138-146.
34. Cheng S., et al. “Identifying psychiatric disorder-associated gut microbiota using microbiota-related gene set enrichment analysis”. Briefings in Bioinformatics (2019).
35. Cheng R., et al. “Lactobacillus Rhamnosus GG and Bifidobacterium Bifidum TMC3115 Can Affect the Development of Hippocampal Neurons Cultured in Vitro in a Strain-dependent Manner (P20-009-19)”. Current Developments in Nutrition1 (2019).
36. Ohno H. “The impact of metabolites derived from the gut microbiota on immune regulation and diseases”. International Immunology (2020).
37. , et al. “Non-coding RNAs as regulators in epigenetics (Review)”. Oncology Reports 37.1 (2017): 3-9.
38. KaelinW and McKnightS. “Influence of Metabolism on Epigenetics and Disease”. Cell 153 (2013): 56-69.
39. Bind S., et al. “Epigenetic Modification: A Key Tool for Secondary Metabolite Production in Microorganisms”. Frontiers in Microbiology 13 (2022).
40. Reigstad C., et al. “Gut microbes promote colonic serotonin production through an effect of short‐chain fatty acids on enterochromaffin cells”. The FASEB Journal 29 (2015): 1395-1403.
41. Yunes R., et al. “A Multi-strain Potential Probiotic Formulation of GABA-Producing Lactobacillus plantarum 90sk and Bifidobacterium adolescentis 150 with Antidepressant Effects”. Probiotics and Antimicrobial Proteins 12 (2019): 973-979.
42. Lydiard R. “The role of GABA in anxiety disorders”. The Journal of Clinical Psychiatry 3 (2003): 21-27.
43. Hamamah S., et al. “Role of Microbiota-Gut-Brain Axis in Regulating Dopaminergic Signaling.” Biomedicines 10 (2022).
44. Sheng J., et al. “The Hypothalamic-Pituitary-Adrenal Axis: Development, Programming Actions of Hormones, and Maternal-Fetal Interactions”. Frontiers in Behavioral Neuroscience 14 (2021).
45. Tolhurst G., et al. “Short-Chain Fatty Acids Stimulate Glucagon-Like Peptide-1 Secretion via the G-Protein–Coupled Receptor FFAR2. Diabetes 61 (2012): 364-371.
46. Subramanian I., et al. “Multi-omics Data Integration, Interpretation, and Its Application”. Bioinformatics and Biology Insights 14 (2020).
47. Kobak D and Berens P. “The art of using t-SNE for single-cell transcriptomics”. Nature Communications 10 (2018).
48. Kaur H., et al. “Gut microbiome mediated epigenetic regulation of brain disorder and application of machine learning for multi-omics data analysis”. Genome (2020).
49. Brieuc M., et al. “A practical introduction to Random Forest for genetic association studies in ecology and evolution”. Molecular Ecology Resources 18 (2018): 755-766.
50. Pestana D., et al. “A Full Featured Configurable Accelerator for Object Detection With YOLO”. IEEE Access 9 (2021): 75864-75877.
51. Hu M., et al. “IMOVNN: incomplete multi-omics data integration variational neural networks for gut microbiome disease prediction and biomarker identification”. Briefings in Bioinformatics 24 (2023): 6.
52. Marcos-Zambrano L., et al. “Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment”. Frontiers in Microbiology 12 (2021).
53. Jin Y., et al. “The Diversity of Gut Microbiome is Associated With Favorable Responses to Anti-Programmed Death 1 Immunotherapy in Chinese Patients With NSCLC”. Journal of Thoracic Oncology (2019).
54. Gao Y., et al. “Faecalibacterium prausnitzii abrogates intestinal toxicity and promotes tumor immunity to increase the efficacy of dual CTLA-4 and PD-1 checkpoint blockade”. Cancer Research (2023).
55. Cacabelos R., et al. “The role of pharmacogenomics in adverse drug reactions”. Expert Review of Clinical Pharmacology 12 (2019): 407-442.
56. Oh M and Zhang L. “DeepGeni: deep generalized interpretable autoencoder elucidates gut microbiota for better cancer immunotherapy”. Scientific Reports 13 (2021).

Citation

Citation: Amaan Arif and Prachi Srivastava. “Diving into Microbiome Gut-Brain Axis to Predict Biomarkers Through Artificial Intelligence".Acta Scientific Microbiology 8.7 (2025): 07-26.

Copyright

Copyright: © 2025 Amaan Arif and Prachi Srivastava. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.