Uncovering the Human Secretome

揭开人类分泌蛋白组的面纱

• 批准号: 9344966
• 负责人: DAVID A. SINCLAIR
• 金额: $ 118.65万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9344966
• 关键词: Algorithms; Amino Acids; Base Pairing; Biological; Biological Response Modifier Therapy; Biology; Code; Communities; Computer Hardware; Computer software; Custom; Data; Disease; Embryonic Development; Endocrine; Generations; Genes; Genetic Databases; Genome; Goals; Hormones; Human; Human Genome; Libraries; Light; Macaca; Machine Learning; Mass Spectrum Analysis; Mathematics; Medicine; Methods; Molecular Profiling; Noise; Nuclear; Pan Genus; Paracrine Communication; Peptides; Pharmaceutical Preparations; Physiological Processes; Process; Proteins; Proteomics; Rare Diseases; Research; Source; Speed; Technology; Therapeutic; Time; Tissues; Work; Yeasts; base; dark matter; drug development; fly; genetic association; genome annotation; improved; interdisciplinary approach; mitochondrial genome; multidisciplinary; novel; peptide hormone; prototype; research and development; screening; terabyte

PROJECT SUMMARY / ABSTRACT Peptide hormones regulate embryonic development and most physiological processes by acting as endocrine or paracrine signals. They are also a rich source of relatively safe medicines to treat both common and rare diseases. Yet finding peptide-coding genes below ~300 base pairs is inherently difficult because they lie within the noise of the genome. Recent multidisciplinary, proteophylogenomic studies in lower species, such as yeast and flies, have uncovered hundreds of new small protein-coding genes called “smORFs”. In humans, recent work on the mitochondrial genome has also uncovered dozens of small peptide hormone genes called MDPs. Based on these and other studies, it is estimated that about 5% of proteins in the human nuclear genome have not yet been discovered, particularly those that encode small peptides below 100 amino acids. It is a well documented but rarely challenged practice to discard large quantities of sequencing and proteomic data because they do not match the annotated human genome. My overarching goal is to discover the human “secretome” and make practical use of it to improve the human condition. Over the past few years, we have developed a unique pipeline of technologies that combines breakthroughs in math, computer hardware and software, proteomics, mass spectrometry, and HTS screening, each of which has been optimized and integrated. Our GeneFinder software modules, based on machine-learning, can process data 100 times faster than traditional methods and rapidly validate small human genes using public and in-house generated databases of genetic and proteomic data. Using the prototype version of the platform that finds conservation between humans, chimp, and macaque, we have discovered thousands of putative peptide-coding genes and validated hundreds of them. We aim to (1) further improve the algorithm to increase its speed and accuracy, (2) improve the genome annotation for thousands of small novel genes, (3) determine their expression profiles in normal and diseased tissues, (4) explore their genetic association with disease loci, and (5) screen the first secretomic library to find hormones with novel biological and therapeutically relevant activities. The data, the software package, and libraries will be made available to the research community. In doing so, we will shed light on the dark matter of the human genome, the parts with the greatest therapeutic potential, thereby helping to steer and accelerate the pace of research and drug development for generations to come.

项目概要/摘要 肽激素通过充当内分泌调节胚胎发育和大多数生理过程 它们也是治疗常见和罕见药物的丰富来源。 然而，寻找低于 300 个碱基对的肽编码基因本质上是困难的，因为它们位于内部。 最近对低等物种（例如酵母）的多学科蛋白质系统基因组研究。 最近，在人类中发现了数百个新的小蛋白质编码基因，称为“smORF”。 对线粒体基因组的研究还发现了数十种称为 MDP 的小肽激素基因。 根据这些和其他研究，估计人类核基因组中大约 5% 的蛋白质具有 尚未被发现，特别是那些编码低于 100 个氨基酸的小肽。 丢弃大量测序和蛋白质组数据的做法有记录但很少受到挑战 因为它们与注释的人类基因组不匹配。我的首要目标是发现人类。 “分泌组”并实际利用它来改善人类状况 在过去的几年里，我们已经做到了。 开发了一系列独特的技术，结合了数学、计算机硬件和 软件、蛋白质组学、质谱和 HTS 筛选，每一项都经过优化和 我们基于机器学习的 GeneFinder 软件模块可以将数据处理速度提高 100 倍。 比传统方法使用公共和内部生成的快速验证人类小基因 使用发现保守性的平台原型版本。 在人类、黑猩猩和猕猴之间，我们发现了数千个假定的肽编码基因， 我们的目标是（1）进一步改进算法以提高其速度和准确性， (2) 改进数千个新小基因的基因组注释，(3) 确定它们的表达谱 在正常和患病组织中，（4）探索它们与疾病位点的遗传关联，以及（5）筛选第一个 分泌组库寻找具有新颖生物和治疗相关活性的激素。 为此，我们将向研究界提供软件包和库。 照亮人类基因组的暗物质，即具有最大治疗潜力的部分，从而帮助 引导和加快子孙后代的研究和药物开发步伐。