Table_1_Expansion of Schizophrenia Gene Network Knowledge Using Machine Learning Selected Signals From Dorsolateral Prefrontal Cortex and Amygdala RNA-seq Data.DOCX

It is widely accepted, given the complex nature of schizophrenia (SCZ) gene networks, that a few or a small number of genes are unlikely to represent the underlying functional pathways responsible for SCZ pathogenesis. Several studies from large cohorts have been performed to search for key SCZ network genes using different analytical approaches, such as differential expression tests, genome-wide association study (GWAS), copy number variations, and differential methylations, or from the analysis of mutations residing in the coding regions of the genome. However, only a small portion (<10%) of candidate genes identified in these studies were considered SCZ disease-associated genes in SCZ pathways. RNA sequencing (RNA-seq) has been a powerful method to detect functional signals. In this study, we used RNA-seq data from the dorsolateral prefrontal cortex (DLPFC) from 254 individuals and RNA-seq data from the amygdala region from 46 individuals. Analysis was performed using machine learning methods, including random forest and factor analysis, to prioritize the numbers of genes from previous SCZ studies. For genes most differentially expressed between SCZ and healthy controls, 18 were added to known SCZ-associated pathways. These include three genes (GNB2, ITPR1, and PLCB2) for the glutamatergic synapse pathway, six genes (P2RX6, EDNRB, GHR, GRID2, TSPO, and S1PR1) for neuroactive ligand–receptor interaction, eight genes (CAMK2G, MAP2K1, RAF1, PDE3A, RRAS2, VAV1, ATP1B2, and GLI3) for the cAMP signaling pathway, and four genes (GNB2, CAMK2G, ITPR1, and PLCB2) for the dopaminergic synapse pathway. Besides the previously established pathways, 103 additional gene interactions were expanded to SCZ-associated networks, which were shared among both the DLPFC and amygdala regions. The novel knowledge of molecular targets gained from this study brings opportunities for a more complete picture of the SCZ pathogenesis. A noticeable fact is that hub genes, in the expanded networks, are not necessary differentially expressed or containing hotspots from GWAS studies, indicating that individual methods, such as differential expression tests, are not enough to identify the underlying SCZ pathways and that more integrative analysis is required to unfold the pathobiology of SCZ.

鉴于精神分裂症（schizophrenia, SCZ）基因网络的复杂特性，学界已达成广泛共识：少数或少量基因难以代表精神分裂症发病机制背后的功能性通路。过往多项基于大型队列的研究，采用不同分析手段探寻关键精神分裂症网络基因，这些手段包括差异表达分析、全基因组关联研究（genome-wide association study, GWAS）、拷贝数变异、差异甲基化，或是针对基因组编码区突变的分析。然而，此类研究中鉴定出的候选基因，仅有不到10%被认定为精神分裂症通路中与疾病相关的基因。RNA测序（RNA sequencing, RNA-seq）是检测功能性信号的有力手段。本研究使用了254名个体的背外侧前额叶皮层（dorsolateral prefrontal cortex, DLPFC）RNA测序数据，以及46名个体的杏仁核区域RNA测序数据。研究采用随机森林、因子分析等机器学习方法，对既往精神分裂症研究中的基因进行优先排序。针对精神分裂症与健康对照间差异表达最显著的基因，本研究将18个基因补充至已知的精神分裂症相关通路中：谷氨酸能突触通路涉及GNB2、ITPR1、PLCB2共3个基因；神经活性配体-受体相互作用通路涉及P2RX6、EDNRB、GHR、GRID2、TSPO、S1PR1共6个基因；环磷酸腺苷（cAMP）信号通路涉及CAMK2G、MAP2K1、RAF1、PDE3A、RRAS2、VAV1、ATP1B2、GLI3共8个基因；多巴胺能突触通路涉及GNB2、CAMK2G、ITPR1、PLCB2共4个基因。除已明确的通路外，本研究还拓展了103个额外的基因相互作用至精神分裂症相关网络中，这些网络在背外侧前额叶皮层与杏仁核区域中均存在共享。本研究获得的新型分子靶点认知，为更全面地解析精神分裂症发病机制提供了契机。值得注意的是，拓展网络中的枢纽基因，既未必存在差异表达，也未必携带GWAS研究中的热点突变，这表明单一方法（如差异表达分析）不足以识别精神分裂症的潜在通路，需开展更多整合性分析以揭示精神分裂症的病理生物学机制。