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Application of GWAS summary data and drug-induced gene expression profiles of neural progenitor cells in psychiatric drug prioritization analysis

Molecular Psychiatry (2024)Cite this article

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Abstract

Common psychiatric disorders constitute one of the most substantial healthcare burdens worldwide. However, drug development in psychiatry remains hampered partially due to the lack of approaches to estimating drugs that can simultaneously modulate the expression of a nontrivial fraction of disease susceptibility genes. We proposed a new drug prioritization strategy under the framework of our previously proposed phenotype-associated tissues estimation approach (DESE) by investigating the drugs’ selective perturbation effect on disease susceptibility genes. Based on the genome-wide association study summary data and drug-induced gene expression profiles of neural progenitor cells, we applied this strategy to prioritize candidate drugs for schizophrenia, depression and bipolar I disorder and identified several known therapeutic drugs among the top-ranked drug candidates. Also, our results revealed that the disease susceptibility genes involved in the selective gene perturbation analysis were enriched with many biologically sensible function terms and interacted with known therapeutic drugs. Our results suggested that selective gene perturbation analysis could be a promising starting point to prioritize biologically sensible drug candidates under the “one drug, multiple targets” paradigm for the drug development of common psychiatric disorders.

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Fig. 1: Overview of the strategy to prioritize the drug candidates that can significantly perturb the expression of disease susceptibility genes.
Fig. 2: The pairwise transcriptomic response correlation of drug-concentration combinations tested on Control NPC and SCZ NPC lines.
Fig. 3: The characterization of the cell lines in CMap 2 and the associations between cell lines and common psychiatry disorders.
Fig. 4: The functional annotation of the disease susceptibility genes and the comparison of therapeutic drug induced gene expression changes between potential susceptibility and non-susceptibility genes.

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Data availability

The GWAS summary statistics data of the three common psychiatric disorders were downloaded from the Psychiatric Genomics Consortium (https://www.med.unc.edu/pgc), and the GWAS summary statistics data of the two non-psychiatric disorders were accessed from the GWAS Catalog under accession IDs GCST90132223(RA) and GCST003116(CAD). The drug-induced gene expression profiles in Readhead et al.’s study and LINCS (2017 release) can be freely downloaded from the GEO database (GEO ID: GSE119291 and GSE92742). The information of synapse function and synapse gene enrichment is publicly available in SynGO (https://www.syngoportal.org/). The annotations of drug-gene interaction terms are publicly available in Drug Gene Interaction (DGIdb v5.0) database at https://www.dgidb.org/. The information on FDA-approved drugs for common psychiatric disorders was extracted from DrugBank 5.1.1, which can be freely downloaded from https://go.drugbank.com/releases/5-1-1/downloads/all-full-database with a simple registration for academic users.

Code availability

S-PrediXcan was freely downloaded from https://github.com/hakyimlab/MetaXcan. The DESE and drug selective perturbation analysis was performed based on our integrative software platform, KGGSEE (https://pmglab.top/kggsee/download/lib/v1/kggsee.jar, based on Java 8). The detailed manual of our strategy can be found at https://kggsee.readthedocs.io/en/latest/detailed_document.html#dese-for-drug-prioritization-analysis. The main customized scripts used in drug perturbation analysis are on GitHub (https://github.com/pmglab/Drug-prioritization-based-on-DESE, version ID: ee597de).

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Acknowledgements

We thank the GTEx Consortium and 1000 Genomes Projects for providing access to the valuable data. We also appreciate the contributors and authors of the Psychiatric Genomics Consortium for sharing their valuable GWAS summary statistics.

Funding

This work was funded by the National Natural Science Foundation of China (32170637 and 32300500) and the Guangdong project (2017GC010644).

Author information

Authors and Affiliations

  1. Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China

    Xiangyi Li, Chao Xue, Zheng Zhu, Xuegao Yu, Qi Yang & Miaoxin Li

  2. Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Guangzhou, 510080, Guangdong, China

    Xiangyi Li & Miaoxin Li

  3. Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China

    Liqian Cui

  4. Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, Guangzhou, 510080, Guangdong, China

    Liqian Cui

  5. National Key Clinical Department and Key Discipline of Neurology, Guangzhou, 510080, Guangdong, China

    Liqian Cui

  6. Research Center of Medical Sciences, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangdong, Guangzhou, 510080, China

    Miaoxin Li

  7. Center for Precision Medicine, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China

    Miaoxin Li

  8. Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, 519000, Guangdong, China

    Miaoxin Li

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Contributions

Conceptualization: M.L., X.L., L.C.; Methodology: M.L., X.L., C.X., L.C., Z.Z., X.Y.; Investigation: M.L., X.L.; Visualization: M.L., X.L.; Funding acquisition: M.L., C.X.; Project administration: M.L., L.C., X.L.; Supervision: M.L., L.C.; Writing – original draft: X.L., M.L.; Writing – review and editing: X.L., M.L., L.C., Z.Z., C.X., X.Y., Q.Y. All authors read and approved the final manuscript.

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Correspondence to Liqian Cui or Miaoxin Li.

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Li, X., Xue, C., Zhu, Z. et al. Application of GWAS summary data and drug-induced gene expression profiles of neural progenitor cells in psychiatric drug prioritization analysis. Mol Psychiatry (2024). https://doi.org/10.1038/s41380-024-02660-z

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