Quantitative regulatory genomics: networks, cis-regulatory codes, and phenotypic variation

定量调控基因组学：网络、顺式调控密码和表型变异

• 批准号: 10021007
• 负责人: Saurabh Sinha
• 金额: $ 35.7万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-20 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10021007
• 关键词: Antineoplastic Agents; Behavioral; Binding Sites; Biological; Biological Assay; Biology; Cell Line; Cell physiology; Chromatin; Code; Collaborations; Computing Methodologies; Cytotoxic agent; DNA; DNA Sequence; Data; Embryonic Development; Estrogens; Exhibits; Foundations; Gene Expression; Genes; Genetic Polymorphism; Genetic Transcription; Genomics; Genotype; Goals; Individual; Inflammation; Investigation; Knowledge; Link; Logic; Malignant Neoplasms; Methods; Modeling; Modernization; Molecular; Multiomic Data; Patients; Pharmaceutical Preparations; Pharmacogenomics; Phenotype; Property; Regulator Genes; Reporter; Research; Shapes; Statistical Models; Techniques; Transcriptional Regulation; Untranslated RNA; Variant; Work; base; behavioral response; biophysical model; gene interaction; genomic data; insight; machine learning method; malignant breast neoplasm; neurogenomics; programs; reconstruction; response; social; synergism; tool; transcription factor; transcriptomics

How do changes in DNA sequence impact organismal properties? This is a central question of modern biology, and insights into it can help us understand, among other things, why patients respond differently to the same treatment, or why some species exhibit behavioral properties not seen in other species. A major hurdle in solving this ‘genotype-to-phenotype’ problem is our poor knowledge of gene regulatory mechanisms underlying phenotypes and cellular processes, and how those mechanisms are encoded in DNA. It also leads to severe difficulties in prioritizing phenotype-linked non-coding variants (polymorphisms) for further investigation. Driven by these challenges, my lab seeks to develop quantitative frameworks for describing and discovering transcriptional regulatory mechanisms. We have made significant progress towards this goal in two main directions: (1) We have developed detailed biophysical models of the cis-regulatory encoding of gene expression. Using these models we have shown how the regulatory function of transcription factor (TF) binding sites depends on their sequence and DNA shape, as well as their ‘trans-context’, e.g., cellular concentrations of regulators, and ‘cis-context’, e.g., proximity to other TF binding sites and chromatin states. (2) We have devised statistical models to discover TF-gene interactions from transcriptomic data, as well as other types of ‘omics’ data if available. Working closely with biologists, we have applied these models to understand phenotypes such as cytotoxic drug response in cell lines, behavioral response to social encounters, and embryonic development. Building on the strong foundations of our past work, I propose to establish a research program that studies transcriptional regulation holistically at the cis- and trans- levels. Our new pursuits will include: (1) use of our computational, sequence-level models to describe two data-rich mammalian regulatory programs, an experimental collaboration to dissect the cis-regulatory logic of a key inflammation gene using massively parallel reporter assays, and major advances in our modeling techniques; (2) new machine learning methods for reconstructing networks of TF-gene interactions that explain phenotypic differences, integration of cis- and trans-regulatory evidence from multi-omics data, and collaborations to apply these methods in cancer pharmacogenomics and behavioral neurogenomics; (3) a new probabilistic framework to combine traditional statistical scores of a non-coding variant with quantitative predictions of its regulatory impact based on the above-mentioned techniques. Explorations of new forms of synergy among these related goals of network reconstruction, cis-regulatory sequence modeling and variant interpretation will be woven throughout our research program.

DNA 序列的变化如何影响生物体特性？这是现代社会的一个核心问题 生物学及其见解可以帮助我们了解患者反应的原因 与相同的处理方法不同，或者为什么有些物种表现出在其他物种中未见的行为特性 其他物种。解决“基因型到表型”问题的一个主要障碍是我们的知识匮乏 表型和细胞过程背后的基因调控机制的研究，以及这些机制如何 机制被编码在DNA中。这也给确定优先顺序带来了严重困难 表型相关的非编码变异（多态性）以供进一步研究。受这些推动 挑战，我的实验室致力于开发定量框架来描述和 发现转录调控机制。我们已经在这方面取得了重大进展 目标有两个主要方向：（1）我们开发了详细的生物物理模型 基因表达的顺式调节编码。使用这些模型，我们展示了监管如何 转录因子 (TF) 结合位点的功能取决于其序列和 DNA 形状，如 以及它们的“跨环境”，例如调节剂的细胞浓度，以及 “顺式上下文”，例如，与其他 TF 结合位点和染色质状态的接近度。 (2) 我们有 设计了统计模型来从转录组数据中发现 TF 基因相互作用，以及 其他类型的“组学”数据（如果有）。我们与生物学家密切合作，应用了这些 了解表型的模型，例如细胞系中的细胞毒性药物反应、行为反应 社交遭遇和胚胎发育。建立在我们的坚实基础之上 过去的工作，我建议建立一个研究转录调控的研究计划 顺式和反式水平的整体。我们的新追求将包括：（1）利用我们的 计算的序列级模型来描述两个数据丰富的哺乳动物调控程序，一个 实验合作剖析关键炎症基因的顺式调控逻辑 大规模并行报告分析，以及我们建模技术的重大进步； (2)新 用于重建 TF-基因相互作用网络的机器学习方法，解释 表型差异、多组学顺式和反式监管证据的整合 数据以及将这些方法应用于癌症药物基因组学和行为学的合作 神经基因组学； (3) 一个新的概率框架，结合了传统的统计分数 非编码变体，根据其监管影响进行定量预测 上述技术。探索这些相关目标之间新的协同形式 网络重建、顺式调控序列建模和变异解释将交织在一起 贯穿我们的研究计划。