2/2-Measuring translational dynamics and the proteome to identify potential brain biomarkers for psychiatric disease

2/2-测量翻译动力学和蛋白质组以识别精神疾病的潜在大脑生物标志物

• 批准号: 9173991
• 负责人: ANDREY RZHETSKY
• 金额: $ 31.6万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-08 至 2020-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9173991
• 关键词: Affect; Antibodies; Binding; Biological Assay; Biological Markers; Bipolar Disorder; Brain; Chicago; Chromatin; Chromosome Mapping; Code; Computer Simulation; Data; Data Analyses; Development; Diagnosis; Disease; Fractionation; Funding; Gene Expression; Genes; Genetic; Genomics; Genotype; Head; Human; Illinois; Label; Lead; Link; Liquid Chromatography; Maps; Mass Spectrum Analysis; Measures; Mental disorders; Messenger RNA; Methods; Molecular; National Institute of Mental Health; Network-based; Nucleotides; Open Reading Frames; Outcome Study; Patients; Peptides; Phenotype; Population; Process; Production; Proteins; Proteome; Proteomics; Quantitative Trait Loci; Regulation; Reporting; Resolution; Ribosomes; Sampling; Schizophrenia; Specificity; System; Techniques; Technology; Testing; Tissues; Transcript; Translating; Translations; Universities; Variant; Work; abstracting; base; brain cell; case control; cell type; density; differential expression; disease diagnosis; disorder risk; frontal lobe; genetic variant; genome wide association study; genome-wide; improved; innovation; neuropsychiatry; next generation; nuclease; population based; protein expression; protein function; rare variant; ribosome profiling; tool; transcriptome

Abstract To further our efforts in identifying the molecular bases of bipolar disorder and schizophrenia, we propose to study protein translation and abundances at the genome-wide level in frontal cortex tissue from 300 brains from patients and healthy controls. We have already amassed an enormous amount of data from these brains, including genotypes, transcriptome profiles and chromatin states. The next step is to look for alterations in protein function in the same brains, since proteins are the ultimate products of gene expression and a critical link between genetic variants and higher order phenotypes, including disease diagnosis. Since proteins are encoded by mRNA transcripts, it appears that protein levels should roughly correlate with transcript levels. However, measured expression levels of mRNAs and their corresponding proteins are often discordant, as are maps of their respective quantitative trait loci. Since we are unable to explain these discrepancies, our picture of molecular changes underlying psychiatric disorders is clearly incomplete. Most previous population-based studies of proteins in neuropsychiatry have been limited to candidate proteins, for which antibodies are already available. For example, in our PsychENCODE project, we are the process of using microwestern arrays to assay ~1000 proteins. In this study, we will use the recently developed technique of ribosome profiling and next-generation proteomics to identify which transcripts are actively being translated in brain and to quantify the abundance of more than 12,000 proteins. Through integrative data analysis, we use the two complementary technologies to detect translational products and to measure their quantitative relationships. Furthermore, these proteins and their translation efficiencies will be assessed for association with disorders. To further improve the specificity of quantification, we will use state-of-the-art deconvolution methods to quantify cell type specific measures of translation efficiency and protein products. This will allow protein translation and abundance in specific major brain cell types to be studied for their changes in affected brains. This study is innovative for being the first genome-wide, population-based study of protein translation and abundance in brains of psychiatric patients. It offers a unique opportunity to fill the gaps between transcriptome and proteome data, and between genetic variants and higher-order phenotypes. It will be a huge step forward in studying the proteins of human brains and the regulatory changes associated with psychiatric disorders, which should ultimately lead to better diagnosis and treatment of these diseases.

抽象的 为了进一步确定躁郁症和精神分裂症的分子碱基，我们建议研究 来自患者的300个大脑的额叶皮质组织的蛋白质翻译和丰度在全基因组水平上 和健康对照。我们已经从这些大脑中积累了大量数据，包括 基因型，转录组轮廓和染色质状态。下一步是寻找蛋白质功能的改变 相同的大脑，因为蛋白质是基因表达的最终产物，遗传之间的关键联系 变体和高阶表型，包括疾病诊断。由于蛋白质是由mRNA编码的 转录本，看来蛋白质水平应与转录水平大致相关。但是，测量 mRNA及其相应蛋白的表达水平通常是不一致的，它们各自的地图也是不一致的 定量性状基因座。由于我们无法解释这些差异，所以我们对分子变化的图片 潜在的精神疾病显然是不完整的。 大多数以前基于人群的神经精神病学蛋白的研究仅限于候选蛋白质，因为 哪些抗体已经可用。例如，在我们的Psychencode项目中，我们是使用的过程 微量阵列分析〜1000个蛋白质。在这项研究中，我们将使用最近开发的技术 核糖体分析和下一代蛋白质组学，以确定在大脑中积极翻译哪些转录本 并量化超过12,000种蛋白质的丰度。通过集成数据分析，我们使用两个 互补的技术检测转化产品并衡量其定量关系。 此外，将评估这些蛋白质及其翻译效率与疾病的关联。到 进一步提高定量的特异性，我们将使用最新的反卷积方法来量化细胞 类型的翻译效率和蛋白质产品的特定度量。这将允许蛋白质翻译和 特定的主要脑细胞类型中的丰富性，以研究其受影响的大脑的变化。 这项研究是对全基因组的第一个基于人群的蛋白质翻译和蛋白质翻译研究和 精神病患者的大脑丰富。它提供了一个独特的机会，可以填补转录组和 蛋白质组数据，遗传变异和高阶表型之间。这将是向前迈出的一大步 研究人大脑的蛋白质以及与精神疾病相关的调节性变化，这些变化 最终应导致更好的诊断和治疗这些疾病。