Recovering Proteoforms from Cardiovascular Omics Datasets: A Multi-omics Secondary Analysis

项目摘要 包括蛋白质组学和RNA-seq在内的大规模组学技术已成为鉴定 疾病机制和治疗靶点。然而，这些实验在很大程度上没有考虑 “蛋白形式”-由相同基因编码的蛋白变体，例如通过选择性剪接和后剪接。 翻译修饰，可以服务于不同的细胞功能，其分布往往是 在疾病中改变。特别是在心脏中，选择性剪接与广泛的病理性 心力衰竭和心肌病的过程，但目前我们对心脏病的认识很差。 在蛋白质水平上对多种选择性剪接异构体产物的表达状态和分子功能进行了研究。 最近，我们开发并优化了一个计算管道，它可以集成信息 从RNA-seq和蛋白质组学数据中恢复丢失的蛋白质异构体信息。我们 现在的目标是对公开的定量蛋白质组学数据进行有针对性的二次分析， 心脏病的数据存储在永久性的数据库中。具体而言，目标1将（i）确定和量化 心力衰竭和房颤蛋白质组学数据中的选择性剪接异构体，通过使用定制序列 从RNA-seq数据构建的数据库;和（ii）确定AS同种型与 在监管热点的PTM站点，借助可恢复的大规模开放搜索算法， 蛋白质组学数据中的意外PTM。 通过使用我们的管道重新分析现有数据集，我们的目标是提取异构体级别的知识 现有的数据，我们相信将有很大的可能性打开不可预见的途径， 我们的数据库不仅可以为心脏病的研究提供数据，还可以为我们研究界现有的丰富数据资源增值。

PROJECT SUMMARY Large-scale omics techniques including proteomics and RNA-seq have become important tools to identify disease mechanisms and therapeutic targets. However, these experiments have largely not considered “proteoforms” - protein variants coded by the same gene such as through alternative splicing and post- translational modifications that can serve different cellular functions and whose distributions are often permuted in disease. In the heart in particular, alternative splicing is implicated in broad pathological processes in heart failure and cardiomyopathy, but at present we have a poor understanding of the expression status and molecular functions of many alternative splice isoform products at the protein level. Recently we have developed and optimized a computational pipeline which can integrate information from RNA-seq and proteomics data to recover lost protein isoform information from proteomics data. Our goal now is to perform a targeted secondary analysis of publicly available quantitative proteomics data on heart diseases that are housed in persistent data repositories. Specifically, Aim 1 will (i) identify and quantify alternative splice isoforms in heart failure and atrial fibrillation proteomics data, by using custom sequence databases constructed from RNA-seq data; and (ii) determine the intersections between AS isoforms with PTM sites at regulatory hotspots, with the aid of mass-tolerant open-search algorithms that can recover unexpected PTMs in proteomics data. By reanalyzing existing datasets with our pipeline we aim to extract isoform-level knowledge on existing data, which we are confident will have a strong likelihood to open unforeseen avenues into the research of heart diseases, and also add value to the existing rich data resources in our research community.