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Integrative analyses prioritize GNL3 as a risk gene for bipolar disorder

Molecular Psychiatry volume 25, pages 2672–2684 (2020)Cite this article

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Abstract

Genome-wide association studies (GWASs) have identified numerous single nucleotide polymorphisms (SNPs) associated with bipolar disorder (BD), but what the causal variants are and how they contribute to BD is largely unknown. In this study, we used FUMA, a GWAS annotation tool, to pinpoint potential causal variants and genes from the latest BD GWAS findings, and performed integrative analyses, including brain expression quantitative trait loci (eQTL), gene coexpression network, differential gene expression, protein-protein interaction, and brain intermediate phenotype association analysis to identify the functions of a prioritized gene and its connection to BD. Convergent lines of evidence prioritized protein-coding gene G Protein Nucleolar 3 (GNL3) as a BD risk gene, with integrative analyses revealing GNL3’s roles in cell proliferation, neuronal functions, and brain phenotypes. We experimentally revealed that BD-related eQTL SNPs rs10865973, rs12635140, and rs4687644 regulate GNL3 expression using dual luciferase reporter assay and CRISPR interference experiment in human neural progenitor cells. We further identified that GNL3 knockdown and overexpression led to aberrant neuronal proliferation and differentiation, using two-dimensional human neural cell cultures and three-dimensional forebrain organoid model. This study gathers evidence that BD-related genetic variants regulate GNL3 expression which subsequently affects neuronal proliferation and differentiation.

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Fig. 1: Summary results for DLRA and CRISPRi experiments in hNPCs.
Fig. 2: Effects of GNL3 knockdown and overexpression on proliferation and neuron differentiation of hNPCs.
Fig. 3: Effects of GNL3 knockdown and overexpression in human forebrain organoid model.

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Acknowledgements

We thank Ney Alliey-Rodriguez for sharing the BSNIP data and appreciate professor Jun Yao for providing the CRISPRi plasmid. We also thank Tao Zhou for helping take the confocal microscopy images. We appreciate Linlin Chen, Qin Zhao and Chunjiang Zhu for aiding in the karyotype analysis. We also acknowledge Liz Kuney for editing the manuscript. This work was supported by the Natural Science Foundation of China (NSFC) grants (Nos. 31970572, 31871276, 31571312, 81901359, and 81401114), the National Key R&D Project of China grants (Nos. 2016YFC1306000 and 2017YFC0908701), the Natural Science Foundation of Hunan Province grant (No. 2019JJ40404), Innovation-Driven Project of Central South University grants (Nos. 2015CXS034, 2018CX033, and 2020CX003), and NIH grants 1 U01 MH103340-01, 1R01ES024988. Data were generated as part of the PsychENCODE Consortium, supported by U01MH103392, U01MH103365, U01MH103346, U01MH103340, U01MH103339, R21MH109956, R21MH105881, R21MH105853, R21MH103877, R21MH102791, R01MH111721, R01MH110928, R01MH110927, R01MH110926, R01MH110921, R01MH110920, R01MH110905, R01MH109715, R01MH109677, R01MH105898, R01MH105898, R01MH094714, and P50MH106934 awarded to S.A. (Icahn School of Medicine at Mount Sinai), G.C. (Duke University), S.D. (Icahn School of Medicine at Mount Sinai), P.F. (University of Southern California), M.G. (Yale University), D.G. (University of California, Los Angeles), F.G. (Johns Hopkins University), T.M.H. (Lieber Institute for Brain Development), A.J. (Lieber Institute for Brain Development), J.A.K. (University of Southern California), C.L. (SUNY Upstate Medical University), D.P. (Icahn School of Medicine at Mount Sinai), P.R. (Icahn School of Medicine at Mount Sinai), S.S. (University of California, San Francisco), N.S. (Yale University), P.S. (Icahn School of Medicine at Mount Sinai), M.S. (University of California, San Francisco), P.S. (University of North Carolina), F.V. (Yale University), D.W. (Lieber Institute for Brain Development), S.W. (Yale University), K.W. (University of Chicago), J.W. (University of California, San Francisco), and P.Z. (Johns Hopkins University).

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Authors and Affiliations

  1. Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, 541001, Guilin, Guangxi, China

    Qingtuan Meng & Jiawen Wang

  2. Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, 410008, Changsha, Hunan, China

    Qingtuan Meng, Le Wang, Rujia Dai, Zongyao Ren, Sihan Liu, Yan Xia, Yi Jiang, Fangyuan Duan, Kangli Wang, Chunyu Liu & Chao Chen

  3. Guangxi Key Laboratory of Brain and Cognitive Neuroscience, Guilin Medical University, 541001, Guilin, Guangxi, China

    Qingtuan Meng & Jiawen Wang

  4. Department of Neuroscience and Cell Biology, Child Health Institute of New Jersey, , Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, 08901, USA

    Le Wang

  5. Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, 13210, USA

    Rujia Dai, Yan Xia & Chunyu Liu

  6. Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN, 37212, USA

    Yi Jiang

  7. School of Psychology, Shaanxi Normal University, 710000, Xi’an, Shaanxi, China

    Chunyu Liu

  8. National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, 410008, Changsha, Hunan, China

    Chao Chen

  9. Hunan Key Laboratory of Animal Models for Human Diseases, Central South University, 410008, Changsha, Hunan, China

    Chao Chen

  10. Hunan Key Laboratory of Molecular Precision Medicine, Central South University, 410008, Changsha, Hunan, China

    Chao Chen

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Contributions

QM initiated the study and wrote the paper, did the FUMA analysis, CRISPRi experiment, neuron differentiation assay, as well as the human forebrain organoid induction assay. YJ, did the eQTL analysis. LW, ZR, and FD, did the DLRA, cell proliferation and cell cycle experiments. RD, and KW, performed the WGCNA and cell-type deconvolution analysis. YX, performed the association analysis between eSNPs and brain intermediate phenotypes. SL, conducted the differential gene expression analysis. JW, did the immunofluorescence staining analysis. CL, and CC, supervised the study and revised the paper.

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Correspondence to Qingtuan Meng, Chunyu Liu or Chao Chen.

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Meng, Q., Wang, L., Dai, R. et al. Integrative analyses prioritize GNL3 as a risk gene for bipolar disorder. Mol Psychiatry 25, 2672–2684 (2020). https://doi.org/10.1038/s41380-020-00866-5

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