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Associated transcriptional, brain and clinical variations in schizophrenia

Nature Mental Health (2024)Cite this article

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Abstract

Understanding the relationship between genetic variations and brain abnormalities is crucial for uncovering the cross-scale pathophysiological mechanisms underlying schizophrenia. This cross-sectional study identifies brain structural correlates of individual variation in gene expression in schizophrenia and its clinical implication. RNA-sequencing data from blood samples, magnetic resonance imaging scans and clinical assessments were collected from 43 patients with schizophrenia, together with data from 60 healthy controls. Using RNA-sequencing data we show alterations in both gene-level and isoform-level expression between patients with schizophrenia and healthy controls (1,836 genes and 1,104 isoforms, false-discover-rate-adjusted P < 0.05). We also show differential gene expression to be associated with schizophrenia-related genomic variations (based on genome-wide association study data on 76,755 patients and 243,649 controls; regression coefficient (β) = 0.211, P = 0.001) and differential brain gene expression (P < 0.001, hypergeometric test). Multivariate correlation analysis combining gene expression and brain imaging shows that transcriptional levels of differentially expressed genes significantly correlate with gray matter volume in the frontal and temporal regions of cognitive brain networks in patients with schizophrenia (P < 0.001, permutation test). Findings show a significant association between gene expression, gray matter volume and cognitive performance in patients (P = 0.031, permutation test). Our results suggest that genomic variants in individuals with schizophrenia are associated with alterations in the transcriptome, which plays a role in individual variations in macroscale brain structure and cognition, contributing to building a comprehensive, multi-omics marker for the assessment of schizophrenia.

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Fig. 1: Overview of methods.
Fig. 2: DEGs and isoforms.
Fig. 3: Association between gene transcription and brain GMV.
Fig. 4: Association between transcription, GMV and brain connectivity.
Fig. 5: Association between transcription, GMV and performance in the WAIS test.

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Data availability

The tabulated brain volume data and genetic summary statistics are publicly available at https://github.com/CAIMI-WEIGroup/NMH2024_SCZ_DEG_MRI. The raw MRI data are available from the corresponding author upon reasonable request. The raw RNA-seq data of 103 participants are available from the corresponding author upon reasonable request after having all relevant approvals from China’s Ministry of Science and Technology related to the export of genetic information and materials relevant to this work. Data from blood-derived DEGs reported in meta-analyses of microarray data in SCZ were downloaded from ref. 20 (2,145 genes) and ref. 21 (247 genes); gene expression data from brain samples of those with SCZ were downloaded from ref. 6; data from transcriptome-wide association studies in SCZ were downloaded from http://gusevlab.org/projects/chromatinTWAS/; data from PGC (wave 3) were downloaded from ref. 2; data from GWAS summary statistics on SCZ, BD, ADHD, ASD and MDD were downloaded from https://pgc.unc.edu/; data from GWAS summary statistics from insomnia were downloaded from https://cncr.nl/research/summary_statistics/; data of whole-brain gene expression profiles from the Allen Human Brain Atlas were downloaded from https://human.brain-map.org/; and data from GTEx were downloaded from https://gtexportal.org.

Code availability

The code supporting the findings of this study is available at https://github.com/CAIMI-WEIGroup/NMH2024_SCZ_DEG_MRI.

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Acknowledgements

This study received funding from the National Natural Science Foundation of China (grant number 82271949 to L.-B.C., grant number 82202264 to Y.W. and grant number 81974215 to H.-N.W.), China Postdoctoral Science Foundation (grant number 2019TQ0130 to L.-B.C.), Beijing Municipal Natural Science Foundation (grant number 7232341 to Y.W.), Key Research and Development Program of Shaanxi Province (grant number 2023-YBSF-444 to D.W.), Shaanxi Natural Science Foundation (grant number 2022JQ-908 to W.-J.W.), European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement ERC-COG number 101001062 to M.P.v.d.H.) and the Netherlands Organization for Scientific Research (VIDI grant number 452-16-015 to M.P.v.d.H., ALW open grant number ALWOP.179 to M.P.v.d.H. and Gravitation project BRAINSCAPES: A Road map from Neurogenetics to Neurobiology, grant number 024.004.012 to M.P.v.d.H.). Analyses were supported by the High-Performance Computing Platform of BUPT. We thank X.-J. Wang and F. Cao for their continued scientific support.

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  1. These authors contributed equally: Martijn P. van den Heuvel, Yongbin Wei.

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China

    Long-Biao Cui

  2. Shaanxi Provincial Key Laboratory of Clinical Genetics, Fourth Military Medical University, Xi’an, China

    Long-Biao Cui & Kun Chen

  3. Schizophrenia Imaging Lab, Xijing 986 Hospital, Fourth Military Medical University, Xi’an, China

    Long-Biao Cui, Shu-Wan Zhao, Yu-Fei Fu, Jing-Wen Fan, Yue-Wen Gu & Xiao-Fan Liu

  4. Department of Radiology, The Second Medical Center, Chinese PLA General Hospital, Beijing, China

    Long-Biao Cui

  5. Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China

    Shu-Wan Zhao, Ting Qi, Mengya Wang, Yong Liu & Yongbin Wei

  6. Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi’an, China

    Ya-Hong Zhang, Xiao-Sa Li, Wen-Jun Wu, Di Wu & Hua-Ning Wang

  7. Department of Anatomy, Histology and Embryology and K. K. Leung Brain Research Centre, School of Basic Medicine, Fourth Military Medical University, Xi’an, China

    Kun Chen

  8. Department of Neurology, University of California San Francisco, San Francisco, CA, USA

    Ting Qi

  9. Department of Radiology, Xi’an People’s Hospital (Xi’an Fourth Hospital), Xi’an, China

    Hong Yin

  10. Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands

    Martijn P. van den Heuvel & Yongbin Wei

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Contributions

L.-B.C. had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. L.-B.C., S.-W.Z., Y.-H.Z., K.C., Y.-F.F., M.W., J.-W.F., Y.-W.G., X.-F.L., X.-S.L., W.-J.W., D.W., H.-N.W., Y.W. and M.P.v.d.H. were responsible for acquisition, analysis or interpretation of data. L.-B.C., T.Q. and Y.W. were responsible for drafting the article. All authors provided critical revision of the article for important intellectual content. L.-B.C., S.-W.Z. and Y.W. were responsible for statistical analysis. L.-B.C., D.W., H.-N.W., M.P.v.d.H. and Y.W. obtained funding. Y.L., H.Y. and M.P.v.d.H. provided supervision.

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Correspondence to Yongbin Wei.

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Nature Mental Health thanks El Cherif Ibrahim and the other, anonymous reviewers for their contribution to the peer review of this work.

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Supplementary Methods, Results, Figs. 1–6 and references.

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44220_2024_306_MOESM3_ESM.xlsx

Supplementary Table 1 Demographic and clinical data of participants. Supplementary Table 2 DEGs in blood samples from people with SCZ. Supplementary Table 3 Overlaps between DEGs and risk genes reported in TWAS on SCZ. Supplementary Table 4 Gene-set analysis for gene sets involved in GO cellular components. Supplementary Table 5 Gene-set analysis for GWAS-Catalog-reported gene sets. Supplementary Table 6 Differentially expressed transcripts in blood samples from those with SCZ. Supplementary Table 7 Overlaps between DEGs and risk genes derived from GWAS using different SNP-to-gene mapping approaches. Supplementary Table 8 Overlaps between upregulated/downregulated DEGs and risk genes derived from GWAS. Supplementary Table 9 The most important genes contributing to the transcription–GMV association in SCZ revealed by LASSO. Supplementary Table 10 Scanning parameters.

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Cui, LB., Zhao, SW., Zhang, YH. et al. Associated transcriptional, brain and clinical variations in schizophrenia. Nat. Mental Health (2024). https://doi.org/10.1038/s44220-024-00306-1

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