Associated transcriptional, brain and clinical variations in schizophrenia

Long-Biao Cui, Shu-Wan Zhao, Ya-Hong Zhang, Kun Chen, Yu-Fei Fu, Ting Qi, Mengya Wang, Jing-Wen Fan, Yue-Wen Gu, Xiao-Fan Liu, Xiao-Sa Li, Wen-Jun Wu, Di Wu, Hua-Ning Wang, Yong Liu, Hong Yin, Martijn P. van den Heuvel, Yongbin Wei
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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. This study examining blood transcriptomic, neuroimaging and clinical data in people with schizophrenia shows a relationship between individual variations in gene transcription, brain structure and cognitive performance.

Abstract Image

精神分裂症的相关转录、大脑和临床变异
了解基因变异与大脑异常之间的关系对于揭示精神分裂症的跨尺度病理生理机制至关重要。这项横断面研究确定了精神分裂症基因表达个体差异的脑结构相关性及其临床意义。我们从 43 名精神分裂症患者的血液样本、磁共振成像扫描和临床评估中收集了 RNA 序列数据,同时还收集了 60 名健康对照者的数据。通过使用 RNA 测序数据,我们发现精神分裂症患者与健康对照组之间在基因水平和同工酶水平的表达均发生了改变(1,836 个基因和 1,104 个同工酶,假发现率调整后 P < 0.05)。我们还发现,不同的基因表达与精神分裂症相关的基因组变异有关(基于全基因组关联研究数据,涉及 76,755 名患者和 243,649 名对照者;回归系数 (β) = 0.211,P = 0.001),以及不同的脑基因表达有关(P <0.001,超几何检验)。结合基因表达和脑成像的多变量相关分析表明,差异表达基因的转录水平与精神分裂症患者认知脑网络额叶和颞叶区域的灰质体积显著相关(P < 0.001, permutation test)。研究结果表明,患者的基因表达、灰质体积和认知能力之间存在明显关联(P = 0.031,置换检验)。我们的研究结果表明,精神分裂症患者的基因组变异与转录组的改变有关,而转录组的改变在宏观脑结构和认知的个体差异中起着一定的作用,这有助于为精神分裂症的评估建立一个全面的多组学标记。这项研究检查了精神分裂症患者的血液转录组、神经影像学和临床数据,结果显示基因转录、大脑结构和认知表现的个体差异之间存在关系。
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