Analysis of Gene Expression Profiles in the Human Brain Stem, Cerebellum and Cerebral Cortex

The human brain is one of the most mysterious tissues in the body. Our knowledge of the human brain is limited due to the complexity of its structure and the microscopic nature of connections between brain regions and other tissues in the body. In this study, we analyzed the gene expression profiles of three brain regions—the brain stem, cerebellum and cerebral cortex—to identify genes that are differentially expressed among these different brain regions in humans and to obtain a list of robust, region-specific, differentially expressed genes by comparing the expression signatures from different individuals. Feature selection methods, specifically minimum redundancy maximum relevance and incremental feature selection, were employed to analyze the gene expression profiles. Sequential minimal optimization, a machine-learning algorithm, was employed to examine the utility of selected genes. We also performed a literature search, and we discuss the experimental evidence for the important physiological functions of several highly ranked genes, including NR2E1, DAO, and LRRC7, and we give our analyses on a gene (TFAP2B) that have not been investigated or experimentally validated. As a whole, the results of our study will improve our ability to predict and understand genes related to brain regionalization and function.

人脑是人体中最为神秘的组织之一。由于其结构复杂，且脑区与体内其他组织间的连接具有微观特性，我们对人脑的认知仍十分有限。本研究针对三大脑区——脑干（brain stem）、小脑（cerebellum）与大脑皮层（cerebral cortex）的基因表达谱展开分析，旨在筛选出人类不同脑区间存在差异表达的基因，并通过比对不同个体的表达特征，获取一组稳定性强、具有脑区特异性的差异表达基因列表。本研究采用特征选择（feature selection）方法，具体为最小冗余最大相关性（minimum redundancy maximum relevance）与增量特征选择（incremental feature selection），对基因表达谱进行分析。同时采用机器学习算法序列最小优化（sequential minimal optimization），评估筛选出的基因的应用价值。本研究还开展了文献调研，针对NR2E1、DAO与LRRC7等排名靠前的基因，探讨其发挥重要生理功能的实验证据；同时对尚未被研究或未经过实验验证的基因TFAP2B展开分析。总体而言，本研究的结果将提升我们预测与理解与脑区域化及脑功能相关基因的能力。