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Cited by 22Computational Drug Discovery MethodsGenetic Associations and EpidemiologyBioinformatics and Genomic Networks

Integration of genetically regulated gene expression and pharmacological library provides therapeutic drug candidates

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Konuma, Takahiro, Ogawa, Kotaro, Okada, Yukinori·2021·Human Molecular Genetics
DOI 10.1093/hmg/ddab049PMC PMC7928862
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Abstract

Approaches toward new therapeutics using disease genomics, such as genome-wide association study (GWAS), are anticipated. Here, we developed Trans-Phar [integration of transcriptome-wide association study (TWAS) and pharmacological database], achieving in silico screening of compounds from a large-scale pharmacological database (L1000 Connectivity Map), which have inverse expression profiles compared with tissue-specific genetically regulated gene expression. Firstly we confirmed the statistical robustness by the application of the null GWAS data and enrichment in the true-positive drug–disease relationships by the application of UK-Biobank GWAS summary statistics in broad disease categories, then we applied the GWAS summary statistics of large-scale European meta-analysis (17 traits; naverage = 201 849) and the hospitalized COVID-19 (n = 900 687), which has urgent need for drug development. We detected potential therapeutic compounds as well as anisomycin in schizophrenia (false discovery rate (FDR)-q = 0.056) and verapamil in hospitalized COVID-19 (FDR-q = 0.068) as top-associated compounds. This approach could be effective in disease genomics-driven drug development.

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  • Integrated analysis of genetically regulated and compound-perturbed gene expression revealed significant enrichment for CNS-acting compounds and identified chromatin remodeling via histone deacetylase inhibitors as a potential therapeutic strategy for slowing progression.

    “Integrated analysis of genetically regulated and compound-perturbed gene expression28 revealed significant enrichment for CNS-acting compounds and, along with an alternative locus-based approach, identified chromatin remodeling via histone deacetylase inhibitors as a potential therapeutic strategy for slowing progression”

    From Locus for severity implicates CNS resilience in progression of multiple sclerosis (2023) · cites this paper as [28]

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Research works printed but not linked: 34

  1. [1]Visscher, P.M., Wray, N.R., Zhang, Q., Sklar, P., McCarthy, M.I., Brown, M.A. and Yang, J. (2017) 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet., 101, 5– 22. 28686856 PMC5501872
  2. [3]King, E.A., Davis, J.W. and Degner, J.F. (2019) Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval. PLoS Genet., 15, e1008489. 31830040 PMC6907751
  3. [4]Okada, Y., Wu, D., Trynka, G., Raj, T., Terao, C., Ikari, K., Kochi, Y., Ohmura, K., Suzuki, A., Yoshida, S. et al. (2014) Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature, 506, 376– 381. 24390342 PMC3944098
  4. [5]Sakaue, S. and Okada, Y. (2019) GREP: genome for REPositioning drugs. Bioinformatics, 35, 3821– 3823. 30859178 PMC6761931
  5. [6]Fang, H., The ULTRA-DD Consortium, Hans, D.W., Knezevic, B., Burnham, K.L., Osgood, J., Sanniti, A., Lara, A.L., Kasela, S., Cesco, S.D. et al. (2019) A genetics-led approach defines the drug target landscape of 30 immune-related traits. Nat. Genet., 51, 1082– 1091. 31253980 PMC7124888
  6. [7]Farh, K.K., Marson, A., Zhu, J., Kleinewietfeld, M., Housley, W.J., Beik, S., Shoresh, N., Whitton, H., Ryan, R.J.H., Shishkin, A.A. et al. (2015) Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature, 518, 337– 343. 25363779 PMC4336207
  7. [8]Boyle, E.A., Li, Y.I. and Pritchard, J.K. (2017) An expanded view of complex traits: from polygenic to Omnigenic. Cell, 169, 1177– 1186. 28622505 PMC5536862
  8. [9]Cano-Gamez, E. and Trynka, G. (2020) From GWAS to function: using functional genomics to identify the mechanisms underlying complex diseases. Front. Genet., 11, 424. 32477401 PMC7237642
  9. [10]GTEx Consortium (2017) Genetic effects on gene expression across human tissues. Nature, 550, 204– 213. 29022597 PMC5776756
  10. [11]Wainberg, M., Sinnott-Armstrong, N., Mancuso, N., Barbeira, A.N., Knowles, D.A., Golan, D., Ermel, R., Ruusalepp, A., Quertermous, T., Hao, K. et al. (2019) Opportunities and challenges for transcriptome-wide association studies. Nat. Genet., 51, 592– 599. 30926968 PMC6777347
  11. [12]Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Phenninx, B.W., Jansen, R., De Geus, E.J., Boomsma, D.I., Wright, F.A. et al. (2018) Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet., 48, 245– 252. PMC4767558 26854917
  12. [13]Gusev, A., Mancuso, N., Won, H., Kousi, M., Finucane, H.K., Reshef, Y., Song, L., Safi, A., Schizophrenia Working Group of the Psychiatric Genomics Consortium, McCarroll, S. et al. (2018) Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat. Genet., 50, 538– 548. 29632383 PMC5942893
  13. [14]Mancuso, N., Shi, H., Goddard, P., Kichaev, G., Gusev, A. and Pasaniuc, B. (2017) Integrating gene expression with summary association statistics to identify genes associated with 30 complex traits. Am. J. Hum. Genet., 100, 473– 487. 28238358 PMC5339290
  14. [15]Sirota, M., Dudley, J.T., Kim, J., Chiang, A.P., Morgan, A.A., Sweet-Cordero, A., Sage, J. and Butte, A.J. (2011) Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci. Transl. Med., 3, 96ra77. PMC3502016 21849665
  15. [17]Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L.T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J. et al. (2018) The UK Biobank resource with deep phenotyping and genomic data. Nature, 562, 203– 209. 30305743 PMC6786975
  16. [18]Canela-Xandri, O., Rawlik, K. and Tenesa, A. (2018) An atlas of genetic associations in UK Biobank. Nat. Genet., 50, 1593– 1599. 30349118 PMC6707814
  17. [19]1000 Genomes Project Consortium (2015) A global reference for human genetic variation. Nature, 526, 68– 74. 26432245 PMC4750478
  18. [20]Bulik-Sullivan, B., Finucane, H.K., Anttila, V., Gusev, A., Day, F.R., Loh, P., Repro Gen Consortium, Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Duncan, L. et al. (2015) An atlas of genetic correlations across human diseases and traits. Nat. Genet., 47, 1236– 1241. 26414676 PMC4797329
  19. [21]Bulik-Sullivan, B.K., Loh, P., Finucane, H.K., Ripke, S., Yang, J., Schizophrenia Working Group of the Psychiatric Genomics Consortium, Patterson, N., Daly, M.J., Price, A.L. and Neale, B.M. (2015) LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet., 47, 291– 295. 25642630 PMC4495769
  20. [22]Gaulton, A., Hersey, A., Nowotka, M., Bento, A.P., Chambers, J., Mendez, D., Mutowo, P., Atkinson, F., Bellis, L.J., Cibrián-Uhalte, E. et al. (2017) The ChEMBL database in 2017. Nucleic Acids Res., 45, D945– D954. 27899562 PMC5210557
  21. [23]Wang, Y., Zhang, S., Li, F., Zhou, Y., Zhang, Y., Wang, Z., Zhang, R., Zhu, J., Ren, Y., Tan, Y. et al. (2020) Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics. Nucleic Acids Res., 48, D1031– D1041. 31691823 PMC7145558
  22. [24]Cohen, H., Kaplan, Z., Matar, M.A., Loewenthal, U., Kozlovsky, N. and Zohar, J. (2006) Anisomycin, a protein synthesis inhibitor, disrupts traumatic memory consolidation and attenuates posttraumatic stress response in rats. Biol. Phychiatry, 60, 767– 776. 16735032
  23. [25]Hong, S., Qian, H., Zhao, P., Bazzy-Asaad, A. and Xia, Y. (2007) Anisomycin protects cortical neurons from prolonged hypoxia with differential regulation of p38 and ERK. Brain Res., 1149, 76– 86. 17391655 PMC1937507
  24. [26]Salata, C., Calistri, A., Parolin, C., Baritussio, A. and Palù, G. (2017) Antiviral activity of cationic amphiphilic drugs. Expert Rev. Anti-Infect. Ther., 15, 483– 492. 28286997 PMC7103695
  25. [27]Gonzalez-Rey, E., Anderson, P., González, M.A., Rico, L., Büscher, D. and Deigado, M. (2009) Human adult stem cells derived from adipose tissue protect against experimental colitis and sepsis. Gut, 58, 929– 939. 19136511
  26. [28]Nejad-Moghaddam, A., Panahi, Y., Alitappeh, M.A., Borna, H., Sholrgozar, M.A. and Ghanei, M. (2015) Therapeutic potential of mesenchymal stem cells for the treatment of airway remodeling in pulmonary diseases. Iran. J. Allergy Asthma Immunol., 14, 552– 568. 26725553
  27. [29]Castro, L.L., Kitoko, J.Z., Xisto, D.G., Olsen, P.C., Guedes, H.L.M., Morales, M.M., Lopes-Pacheco, M., Cruz, F.F. and Rocco, P.R.M. (2020) Multiple doses of adipose tissue-derived mesenchymal stromal cells induce immunosuppression in experimental asthma. Stem Cells Transl. Med., 9, 250– 260. 31746562 PMC6988761
  28. [30]Kornicka, K., Marycz, K., Marędziak, M., Tomaszewski, K.A. and Nicpoń, J. (2017) The effects of the DNA methyltranfserases inhibitor 5-azacitidine on ageing, oxidative stress and DNA methylation of adipose derived stem cells. J. Cell. Mol. Med., 21, 387– 401. 27998022 PMC5264131
  29. [31]Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature, 511, 421– 427. 25056061 PMC4112379
  30. [32]Pardiñas, A.F., Holmans, P., Pocklington, A.J., Escott-Price, V., Ripke, S., Carrera, N., Legge, S.E., Bishop, S., Cameron, D., Hamshere, M.L. et al. (2018) Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet., 50, 381– 389. 29483656 PMC5918692
  31. [33]Pers, T.H., Timshel, P., Ripke, S., Lent, S., Sullivan, P.F., O’Donovan, M.C., Franke, L., Hirschhorn, J.N. and Schizophrenia Working Group of the Psychiatric Genomics Consortium (2016) Comprehensive analysis of schizophrenia-associated loci highlights ion channel pathways and biologically plausible candidate causal genes. Hum. Mol. Genet., 25, 1247– 1254. 26755824 PMC4764200
  32. [34]Lichtstein, D., Ilani, A., Rosen, H., Horesh, N., Singh, S.V., Buzaglo, N. and Hodes, A. (2018) Na+, K+-ATPase signaling and bipolar disorder. Int. J. Mol. Sci., 19, 2314. PMC6121236 30087257
  33. [35]Mancuso, N., Freund, M.K., Johnson, R., Shi, H., Kichaev, G., Gusev, A. and Pasaniuc, B. (2019) Probabilistic fine-mapping of transcriptome-wide association studies. Nat. Genet., 51, 675– 682. 30926970 PMC6619422
  34. [36]Chang, C.C., Chow, C.C., Tellier, L.C., Vattikuti, S., Purcell, S.M. and Lee, J.J. (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience, 4, 7. 25722852 PMC4342193

Cited by

Sorted by citations

Authors
Konuma, T., Ogawa, K., & Okada, Y.
Affiliations
  • Japan Tobacco (Japan) (JP)
  • The University of Osaka (JP)
  • Institute for Open and Transdisciplinary Research Initiatives (JP)
Corresponding author
Okada, Yukinori
ORCID
Venue
Human Molecular Genetics
Publisher
Oxford University Press (OUP)
Published
2021-02-01
Volume
30
Issue
3-4
Pages
294-304
ISSN
  • 0964-6906 (ISSN-L)
  • 1460-2083
DOI
10.1093/hmg/ddab049
PMC
PMC7928862
OpenAlex
W3128727642
Cited by
22
References
2
Citations by year
2026: 2 · 2025: 3 · 2024: 1 · 2023: 9 · 2022: 5 · 2021: 2
FWCI
2.23
Type
journal article
Language
en
Open access
Licence
Locations
  • Human Molecular Genetics (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Funders
  • Osaka University
  • Takeda Science Foundation
  • Japan Society for the Promotion of Science
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
transcriptome-wide association study, in silico drug screening, therapeutic drug candidates, COVID-19 pandemic, schizophrenia, anisomycin, verapamil, gene expression profiles, drug-disease relationships
MeSH terms
Drug Development, COVID-19 Drug Treatment, Anisomycin, Pharmaceutical Preparations, Gene Expression Regulation, Humans, Schizophrenia, Software, Verapamil, Gene Expression Profiling, Genomics, Databases, Genetic, Genome-Wide Association Study, Transcriptome, Databases, Pharmaceutical

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