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.

项目摘要 /摘要 肽激素通过充当内分泌来调节胚胎发育和大多数物理过程 或旁分泌信号。它们也是相对安全的药物的丰富来源，可以治疗常见和罕见的药物 疾病。然而，在〜300碱基对以下的辣椒编码基因固有上很困难，因为它们位于 基因组的噪音。较低物种的近期多学科的蛋白质基因组学研究，例如酵母菌 苍蝇已经发现了数百种称为“ smorfs”的新小蛋白质编码基因。在人类中，最近 线粒体基因组上的工作还发现了数十种称为MDP的小胡椒基因基因。 基于这些和其他研究，据估计，人类核基因组中约有5％的蛋白质已 尚未发现，尤其是那些编码小胡椒粉以下的氨基酸以下的小胡椒粉。这是一个井 有记录但很少有挑战的实践来丢弃大量测序和蛋白质组学数据 因为它们与注释的人类基因组不匹配。我的总体目标是发现人类 “秘密”并实际利用它来改善人类状况。在过去的几年中，我们有 开发了独特的技术管道，结合了数学，计算机硬件和 软件，蛋白质组学，质谱和HTS筛选，每个筛选都已优化，并且 融合的。我们的GeneFinder软件模块基于机器学习，可以更快地处理数据 比传统方法，并使用公共和内部生成的迅速验证小型人类基因 遗传和蛋白质组学数据的数据库。使用找到保护的平台的原型版本 在人类，黑猩猩和猕猴之间，我们发现了数千种推定的肽编码基因和 验证了数百个。我们的目标是（1）进一步改善算法以提高其速度和准确性， （2）改善数千种小型基因的基因组注释，（3）确定其表达谱 （4）在正常和解散的组织中探索其与疾病基因座的遗传关联，（5）筛选第一个 秘密图书馆可以找到具有新型生物学和治疗相关活动的激素。数据， 软件包和图书馆将提供给研究社区。这样，我们将脱掉 对人类基因组的暗物质（具有最大治疗潜力的部分）的光线，从而有助于 引导和加速未来几代人的研究和药物开发的速度。