Exploring the Microbiota-Gut-Brain Axis for Mental Disorders with Knowledge Graphs

Authors

  • Ting Liu Center for Integrative Bioinformatics VU (IBIVU), Vrije Universiteit, Amsterdam, The Netherlands
  • Xueli Pan Knowledge Representation and Reasoning (KR&R) Group, Vrije Universiteit, Amsterdam, The Netherlands
  • Xu Wang
  • K. Anton Feenstra Center for Integrative Bioinformatics VU (IBIVU), Vrije Universiteit, Amsterdam, The Netherlands
  • Jaap Heringa Center for Integrative Bioinformatics VU (IBIVU), Vrije Universiteit, Amsterdam, The Netherlands
  • Zhisheng Huang Knowledge Representation and Reasoning (KR&R) Group, Vrije Universiteit, Amsterdam, The Netherlands

DOI:

https://doi.org/10.2991/jaims.d.201208.001

Keywords:

Microbiota-gut-brain axis, Gut microbiota, Neurotransmitter, Mental disorder, Knowledge graph, Biomedical ontology

Abstract

Gut microbiota has a significant influence on brain-related diseases through the communication routes of the gut-brain axis. Many species of gut microbiota produce a variety of neurotransmitters. In essence, the neurotransmitters are chemicals that influence mood, cognition, and behavior of the host. The relationships between gut microbiota and neurotransmitters has received much attention in medical and biomedical research. However, the integration of the various proposed neurotransmitter signal routes that underpin these relationships has not yet been studied well. To unlock the influence of gut microbiota on mental health via neurotransmitters, the microbiota-gut-brain (MGB) axis, we gather the decentralized results in the existing studies into a structured knowledge base. In this paper, we therefore propose a novel Microbiota Knowledge Graph based on a newly constructed knowledge graph for uncovering the potential associations among gut microbiota, neurotransmitters, and mental disorders which we refer to as MiKG. It includes many interfaces that link to well-known biomedical ontologies, e.g. UMLS, MeSH, KEGG, and SNOMED CT, and is extendable by linking to future ontologies to further exploit the relationships between gut microbiota and neurotransmitters. This paper present MiKG, an effective knowledge graph, that can be used to investigate the MGB axis using the relationships among gut microbiota, neurotransmitters, and mental disorders.

References

D. Vigo, G. Thornicroft, R. Atun, Estimating the true global burden of mental illness, Lancet Psychiatry. 3 (2016), 171–178.

World Health Organization, Mental Disorders Affect One in Four People, World Health Report, World Health Organization, 2001.

J. Lönnqvist, Major psychiatric disorders in suicide and suicide attempters, in: D. Wasserman, C. Wasserman (Eds.), Oxford Textbook of Suicidology and Suicide Prevention: A Global Perspective, Oxford, UK: Oxford University Press, 2009, pp. 275–286.

S. Bachmann, Epidemiology of suicide and the psychiatric perspective, Int. J. Environ. Res. Public Health. 15 (2018), 1425.

M. Thyloth, H. Singh, V. Subramanian, Increasing burden of mental illnesses across the globe: current status, Indian J. Soc. Psychiatry. 32 (2016), 254

V. Patel, S. Saxena, C. Lund, G. Thornicroft, F. Baingana, P. Bolton, et al., The lancet commission on global mental health and sustainable development, Lancet. 392 (2018), 1553–1598.

E.A. Mayer, R. Knight, S.K. Mazmanian, J.F. Cryan, K. Tillisch, Gut microbes and the brain: paradigm shift in neuroscience, J. Neurosci. 34 (2014), 15490–15496.

Y. Wang, L.H. Kasper, The role of microbiome in central nervous system disorders, Brain Behav. Immun. 38 (2014), 1–12.

J.F. Cryan, K.J. O’Riordan, C.S.M. Cowan, K.V. Sandhu, T.F.S. Bastiaanssen, M. Boehme, et al., The microbiota-gut-brain axis, Physiol. Rev. 99 (2019), 1877–2013.

T.C. Fung, C.A. Olson, E.Y. Hsiao, Interactions between the microbiota, immune and nervous systems in health and disease, Nat. Neurosci. 20 (2017), 145.

Y. Li, Y. Hao, B. Zhang, F. Fan, The role of microbiome in insomnia, circadian disturbance and depression, Front. Psychiatry. 9 (2018), 669.

P. Strandwitz, Neurotransmitter modulation by the gut microbiota, Brain Res. 1693 (2018), 128–133.

T. Liu, Z. Huang, Evidence-based analysis of neurotransmitter modulation by gut microbiota, in: H. Wang, S. Siuly, R. Zhou, F. Martin-Sanchez, Y. Zhang, Z. Huang (Eds.), International Conference on Health Information Science, Springer, Cham, Switzerland, 2019, pp. 238–249.

Y. Yang, Z. Huang, Y. Han, X. Hua, W. Tang, Using knowledge graph for analysis of neglected influencing factors of statin-induced myopathy, in: Y. Zeng et al. (Eds.), International Conference on Brain Informatics, Springer, Cham, Switzerland, 2017, pp. 304–311.

A. Santos, A.R. Colaço, A.B. Nielsen, L. Niu, P.E. Geyer, F. Coscia, et al., Clinical knowledge graph integrates proteomics data into clinical decision-making, bioRxiv, 2020.

L. Penev, M. Dimitrova, V. Senderov, G. Zhelezov, T. Georgiev, P. Stoev, K. Simov, Openbiodiv: a knowledge graph for literatureextracted linked open data in biodiversity science, Publications. 7 (2019), 38.

O. Takaki, I. Takeuti, K. Takahashi, N. Izumi, K. Murata, M. Ikeda, K. Hasida, Graphical representation of quality indicators based on medical service ontology, SpringerPlus. 2 (2013), 274.

S. Biswas, P. Mitra, K.S. Rao, Relation prediction of co-morbid diseases using knowledge graph completion, IEEE/ACM Trans. Comput. Biol. Bioinformat. 99 (2019). 1–1.

H. Paulheim, Knowledge graph refinement: a survey of approaches and evaluation methods, Semantic Web. 8 (2017), 489–508.

M. Rotmensch, Y. Halpern, A. Tlimat, S. Horng, D. Sontag, Learning a health knowledge graph from electronic medical records, Sci. Rep. 7 (2017), 5994.

Y. Janssens, J. Nielandt, A. Bronselaer, N. Debunne, F. Verbeke, E. Wynendaele, et al., Disbiome database: linking the microbiome to disease, BMC Microbiol. 18 (2018), 50.

D. Collarana, M. Galkin, I. Traverso-Ribón, C. Lange, M.-E. Vidal, S. Auer, Semantic data integration for knowledge graph construction at query time, in 2017 IEEE 11th International Conference on Semantic Computing (ICSC), IEEE, San Diego, CA, USA, 2017, pp. 109–116.

J. Hastings, W. Ceusters, M. Jensen, K. Mulligan, B. Smith, Representing mental functioning: ontologies for mental health and disease, in Third International Conference on Biomedical Ontology, Graz, Austria, 2012, pp. 1–5. http://ontology.buffalo.edu/smith// articles/ICBO2012/MFO_Hastings.pdf

V. Osadchiy, C.R. Martin, E.A. Mayer, The gut–brain axis and the microbiome: mechanisms and clinical implications. Clin. Gastroenterol. Hepatol. 17 (2019), 322–332.

M. Scriven, T.G. Dinan, J.F. Cryan, M. Wall, Neuropsychiatric disorders: influence of gut microbe to brain signalling, Diseases. 6 (2018), 78.

D. Bzdok, A. Meyer-Lindenberg, Machine learning for precision psychiatry: opportunities and challenges, Biol. Psychiatry Cogn. Neurosci. Neuroimaging. 3 (2018), 223–230.

V.K. Mehta, P.S. Deb, R.D. Subba, Application of computer techniques in medicine, Med. J. Armed Forces India. 50 (1994), 215– 218.

M. Valles-Colomer, G. Falony, Y. Darzi, E.F. Tigchelaar, J. Wang, R.Y. Tito, et al., The neuroactive potential of the human gut microbiota in quality of life and depression, Nat. Microbiol. 4 (2019), 623–632.

P. Zheng, B. Zeng, C. Zhou, M. Liu, Z. Fang, X. Xu, et al., Gut microbiome remodeling induces depressive-like behaviors through a pathway mediated by the hosts metabolism, Mol. Psychiatry. 21 (2016), 786.

J.A. Bravo, P. Forsythe, M.V. Chew, E. Escaravage, H.M. Savignac, T.G. Dinan, J. Bienenstock, J.F. Cryan, Ingestion of lactobacillus strain regulates emotional behavior and central gaba receptor expression in a mouse via the vagus nerve, Proc. Natl. Acad. Sci. 108 (2011), 16050–16055.

W.-H. Liu, H.-L. Chuang, Y.-T. Huang, C.-C. Wu, G.-T. Chou, S. Wang, Y.-C. Tsai, Alteration of behavior and monoamine levels attributable to lactobacillus plantarum ps128 in germ-free mice, Behav. Brain Res. 298 (2016), 202–209.

S.C. Kleiman, H.J. Watson, E.C. Bulik-Sullivan, E.Y. Huh, L.M. Tarantino, C.M. Bulik, I.M. Carroll, The intestinal microbiota in acute anorexia nervosa and during renourishment: relationship to depression, anxiety, and eating disorder psychopathology, Psychosom. Med. 77 (2015), 969.

R.P. Smith, C. Easson, S.M. Lyle, R. Kapoor, C.P. Donnelly, E.J. Davidson, Gut microbiome diversity is associated with sleep physiology in humans, PloS One. 14 (2019), e0222394.

C.E. Schretter, J. Vielmetter, I. Bartos, Z. Marka, S. Marka, S. Argade, S.K. Mazmanian, A gut microbial factor modulates locomotor behaviour in drosophila, Nature. 563 (2018), 402.

T. Liu, K. Anton Feenstra, J. Heringa, Z. Huang, Influence of gut microbiota on mental health via neurotransmitters: areview, J. Artif. Intell. Med. Sci. 1 (2020), 1–14.

F. Özoğul, E. Kuley, Y. Özoğul, İ. Özoğul The function of lactic acid bacteria on biogenic amines production by food-borne pathogens in arginine decarboxylase broth, Food Sci. Technol. Res. 18 (2012), 795–804.

A. Mayr, G. Hinterberger, M.P. Dierich, C. Lass-Flörl, Interaction of serotonin with candida albicans selectively attenuates fungal virulence in vitro, Int. J. Antimicrob. Agents. 26 (2005), 335–337.

Y. Gezginc, I. Akyol, E. Kuley, F. Özogul, Biogenic amines formation in streptococcus thermophilus isolated from home-made natural yogurt, Food Chem. 138 (2013), 655–662.

M.G. Strakhovskaia, E.V. Ivanova, G. Fraĭnkin, Stimulatory effect of serotonin on the growth of the yeast candida guilliermondii and the bacterium streptococcus faecalis, Mikrobiologiia. 62 (1993), 46–49.

M. Lyte, Probiotics function mechanistically as delivery vehicles for neuroactive compounds: microbial endocrinology in the design and use of probiotics, Bioessays. 33 (2011), 574–581.

E.A. Tsavkelova, I.V. Botvinko, V.S. Kudrin, A.V. Oleskin, Detection of neurotransmitter amines in microorganisms with the use of high-performance liquid chromatography, Doklady Biochem. Proc. Acad. Sci. USSR. 372 (2000), 372 115.

V.A. Shishov, T.A. Kirovskaya, V.S. Kudrin, A.V. Oleskin, Amine neuromediators, their precursors, and oxidation products in the culture of escherichia coli k-12, Appl. Biochem. Microbiol. 45 (2009), 494–497.

M. Diaz, B. del Rio, V. Ladero, B. Redruello, M. Fernández, M.C. Martin, M.A. Alvarez, Isolation and typification of histamineproducing lactobacillus vaginalis strains from cheese, Int. J. Food Microbiol. 215 (2015), 117–123.

R.P. Brown, J. John Mann, A clinical perspective on the role of neurotransmitters in mental disorders, Psychiatr. Serv. 36 (1985), 141–150.

S. Jupp, T. Burdett, C. Leroy, H.E. Parkinson, A new ontology lookup service at embl-ebi, in SWAT4LS, Proceedings of SWAT4LS International Conference, Cambridge, UK, 2015, pp. 118–119. http://ceur-ws.org/Vol-1546/paper_29.pdf

G. Fragoso, S. de Coronado, M. Haber, F. Hartel, L. Wright, Overview and utilization of the NCIthesaurus, Int. J. Genomi. 5 (2004), 648–654.

L.M. Schriml, C. Arze, S. Nadendla, Y.-W. Wayne Chang, M. Mazaitis, V. Felix, G. Feng, W.A. Kibbe, Disease ontology: a backbone for disease semantic integration, Nucleic Acids Res. 40 (2012), D940–D946.

I.K. Dhammi, S. Kumar, Medical Subject Headings (MESH) terms, Indian J. Orthopaed. 48 (2014), 443.

J. Kim, T.G.R. Macieira, S.L. Meyer, M. Ansell, R.I. Bjarnadottir, M.B. Smith, Towards implementing snomed ct in nursing practice: a scoping review, Int. J. Med. Informat. 134 (2020), 104035.

M.D. Wilkinson, M. Dumontier, I.J. Aalbersberg, G. Appleton, M. Axton, A. Baak, et al., The fair guiding principles for scientific data management and stewardship, Sci. Data. 3 (2016), 160018.

Z. Huang, J. Yang, F. van Harmelen, Q. Hu, Constructing knowledge graphs of depression, in: S. Siuly et al. (Eds.), Conference on Health Information Science, Springer, Cham, Switzerland, 2017, pp. 149–161.

S. Sang, Z. Yang, X. Liu, L. Wang, H. Lin, J. Wang, M. Dumontier, Gredel: a knowledge graph embedding based method for drug discovery from biomedical literatures, IEEE Access. 7 (2018), 8404–8415.

S. Sakr, G. Al-Naymat, Relational processing of rdf queries: a survey, ACM SIGMOD Record. 38 (2010), 23–28.

O. Bodenreider, The Unified Medical Language System (UMLS): integrating biomedical terminology, Nucleic Acids Res. 32 (2004), D267–D270.

M. Kanehisa, S. Goto, Kegg: kyoto encyclopedia of genes and genomes, Nucleic Acids Res. 28 (2000), 27–30.

M.H. Coletti, H.L. Bleich, Medical subject headings used to search the biomedical literature, J. Am. Med. Informat. Assoc. 8 (2001), 317–323.

L. Stanescu, D. Dan Burdescu, M. Brezovan, C.G. Mihai, Creating New Medical Ontologies for Image Annotation: A Case Study, Springer Science & Business Media, New York, NY, USA, 2011.

R.H. Güting, Graphdb: modeling and querying graphs in databases, in VLDB, Citeseer, VLDB ‘94, Proceedings of the 20th International Conference on Very Large Data Bases, Santiago de Chile, Chile, 1994, vol. 94, pp. 12–15. http://www.vldb.org/conf/ 1994/P297.PDF

M. Arenas, J. Pérez, Querying semantic web data with sparql, in Proceedings of the Thirtieth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, Athens, Greece, 2011, pp. 305–316.

N. Guarino, D. Oberle, S. Staab, What is an ontology?, in: S. Staab, R. Studer (Eds.), Handbook on Ontologies, Springer, Cham, Switzerland, 2009, pp. 1–17.

N. Achich, B. Bouaziz, Ontology visualization: an overview, in: A. Abraham, P. Muhuri, A. Muda, N. Gandhi (Eds.), International Conference on Intelligent Systems Design and Applications, Springer, Cham, Switzerland, 2017, pp. 880–891.

Brachman, H.J. Levesque, R. Fikes, Krypton: integrating terminology and assertion, in AAAI, Proceedings of the National Conference on Artificial Intelligence, Washington, D.C. 1983, vol. 83, pp. 31–35.

M. Schink, P.C. Konturek, E. Tietz, W. Dieterich, T.C. Pinzer, S. Wirtz, M.F. Neurath, Y Zopf, Microbial patterns in patients with histamine intolerance, J. Physiol. Pharmacol. 69 (2018), 579– 593.

B. Pugin, W. Barcik, P. Westermann, A. Heider, M. Wawrzyniak, P. Hellings, C.A. Akdis, L. OMahony, A wide diversity of bacteria from the human gut produces and degrades biogenic amines, Microb. Ecol. Health Dis. 28 (2017), 1353881.

W. Barcik, M. Wawrzyniak, C.A. Akdis, L. OMahony, Immune regulation by histamine and histamine-secreting bacteria, Curr. Opin. Immunol. 48 (2017), 108–113.

S.K. Mohamed, V. Nováček, A. Nounu, Discovering protein drug targets using knowledge graph embeddings, Bioinformatics. 36 (2020), 603–610.

Y. Fang, H. Wang, L. Wang, R. Di, Y. Song, Diagnosis of copd based on a knowledge graph and integrated model, IEEE Access. 7 (2019), 46004–46013.

E. Thursby, N. Juge, Introduction to the human gut microbiota, Biochem. J. 474 (2017), 1823–1836.

M.S.C. Thomas, D. Mareschal, I. Dumontheil, Educational Neuroscience: Development Across the Life Span, Routledge, Abingdon, United Kingdom, 2020.

American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders (DSM-5®), American Psychiatric Publishing, Washington, D.C. 2013.

Published

2021-05-05

How to Cite

1.
Liu T, Pan X, Wang X, Feenstra KA, Heringa J, Huang Z. Exploring the Microbiota-Gut-Brain Axis for Mental Disorders with Knowledge Graphs. JAIMS [Internet]. 2021 May 5 [cited 2024 May 19];1(3-4):30-42. Available from: http://ojs.ais.cn/jaims/article/view/77