Rodríguez Natalia, Gassó Patricia, Martínez-Pinteño Albert, Segura Àlex-González, Mezquida Gisela, Moreno-Izco Lucia, González-Peñas Javier, Zorrilla Iñaki, Martin Marta, Rodriguez-Jimenez Roberto, Corripio Iluminada, Sarró Salvador, Ibáñez Angela, Butjosa Anna, Contreras Fernando, Bioque Miquel, Cuesta Manuel-Jesús, Parellada Mara, González-Pinto Ana, Berrocoso Esther, Bernardo Miquel, Mas Sergi
Department of Clinical Foundations, Pharmacology Unit, University of Barcelona, Barcelona, Spain.
Institut d'investigacions Biomèdiques August Pi i Sunyer (IDIBAPs), Barcelona, Spain.
Schizophrenia (Heidelb). 2022 Apr 27;8(1):45. doi: 10.1038/s41537-022-00215-1.
A better understanding of schizophrenia subtypes is necessary to stratify the patients according to clinical attributes. To explore the genomic architecture of schizophrenia symptomatology, we analyzed blood co-expression modules and their association with clinical data from patients in remission after a first episode of schizophrenia. In total, 91 participants of the 2EPS project were included. Gene expression was assessed using the Clariom S Human Array. Weighted-gene co-expression network analysis (WGCNA) was applied to identify modules of co-expressed genes and to test its correlation with global functioning, clinical symptomatology, and premorbid adjustment. Among the 25 modules identified, six modules were significantly correlated with clinical data. These modules could be clustered in two groups according to their correlation with clinical data. Hub genes in each group showing overlap with risk genes for schizophrenia were enriched in biological processes related to metabolic processes, regulation of gene expression, cellular localization and protein transport, immune processes, and neurotrophin pathways. Our results indicate that modules with significant associations with clinical data showed overlap with gene sets previously identified in differential gene-expression analysis in brain, indicating that peripheral tissues could reveal pathogenic mechanisms. Hub genes involved in these modules revealed multiple signaling pathways previously related to schizophrenia, which may represent the complex interplay in the pathological mechanisms behind the disease. These genes could represent potential targets for the development of peripheral biomarkers underlying illness traits in clinical remission stages after a first episode of schizophrenia.
为了根据临床特征对患者进行分层，有必要更好地理解精神分裂症的亚型。为了探究精神分裂症症状学的基因组结构，我们分析了血液共表达模块及其与首次发作精神分裂症后缓解期患者临床数据的关联。总共纳入了2EPS项目的91名参与者。使用Clariom S人类阵列评估基因表达。应用加权基因共表达网络分析（WGCNA）来识别共表达基因模块，并测试其与整体功能、临床症状学和病前适应的相关性。在识别出的25个模块中，有6个模块与临床数据显著相关。根据它们与临床数据的相关性，这些模块可以分为两组。每组中与精神分裂症风险基因重叠的枢纽基因在与代谢过程、基因表达调控、细胞定位和蛋白质运输、免疫过程以及神经营养因子途径相关的生物学过程中富集。我们的结果表明，与临床数据有显著关联的模块与先前在大脑差异基因表达分析中确定的基因集有重叠，这表明外周组织可能揭示致病机制。参与这些模块的枢纽基因揭示了先前与精神分裂症相关的多种信号通路，这可能代表了该疾病背后病理机制中的复杂相互作用。这些基因可能代表了首次发作精神分裂症后临床缓解期疾病特征潜在外周生物标志物开发的潜在靶点。
