The transcriptome-wide impact of biological perturbations

生物扰动对转录组的影响

• 批准号: 10672663
• 负责人: Ajay Nadig
• 金额: $ 3.56万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10672663
• 关键词: Affect; Biological; Biological Assay; Biology; Brain; Cell Line; Cell physiology; Chromatin; Chronic Myeloid Leukemia; Clustered Regularly Interspaced Short Palindromic Repeats; Collaborations; Computational Biology; Computer software; Data; Data Set; Disease; Fellowship; Gene Cluster; Gene Deletion; Gene Expression; Gene Expression Profile; Genes; Genetic; Genetic Variation; Genome; Goals; Human; Human Genetics; In Vitro; Individual; Literature; Malignant Neoplasms; Maps; Measures; Mentors; Mentorship; Methodology; Methods; Modeling; Modernization; Mus; Mutation; Neocortex; Neurodevelopmental Disorder; Parameter Estimation; Pathogenicity; Pathologic; Pattern; Phenotype; Repression; Research; Scientist; Shapes; Signal Transduction; Statistical Methods; Stratification; Structure; Testing; Training; Work; base; chronic myeloid leukemia cell; differential expression; experimental study; functional genomics; gene function; genome-wide; improved; in vivo; insight; interest; knock-down; method development; model building; neocortical; novel; open source; response; risk variant; tool; tool development; transcriptome; transcriptome sequencing; transcriptomics

Abstract An important goal in computational biology is to leverage data from high-throughput functional assays to infer the biological consequences of genetic variation. This goal is frequently approached by pairing RNA sequencing and differential expression analysis. Most differential expression methods seek to identify a small number of genes and gene-sets that are affected by a genetic perturbation. However, some genes, such as chromatin regulators, may impact thousands of genes across the transcriptome. These dispersed effects are not captured by existing methods. We will address this methodological gap in the differential expression field by developing a novel statistical tool, and will apply this tool to both normative and disease contexts. In Aim 1, we propose the Transcriptome-wide Impact Model (TIM), a parametric likelihood-based estimator of the overall effect that a perturbation has on the transcriptome. TIM builds on existing differential expression methods, but estimates parameters of the distribution of differential expression effects, rather than individual per-gene effect sizes. This model is also extended to estimate gene-set enrichments and correlation between differential expression signatures. In Aim 2, we aim to apply TIM to a recent Perturb-Seq dataset that perturbs all expressed genes in vitro in a massively parallel manner, enabling us to identify which genes and gene-sets induce the greatest transcriptomic change in human chronic myeloid leukemia cell lines when knocked down. We will also use TIM to identify modules of genes that have similar impact on the transcriptome, and use these modules to annotate genic function. In Aim 3, we will apply TIM to an in vivo Perturb-Seq dataset of 35 neurodevelopmental disorder genes in developing mouse neocortex. Through this Aim, we will stratify neurodevelopmental disorder genes by degree of transcriptome-wide impact, testing the hypothesis that neurodevelopmental-disorder- associated gene expression regulators exert highly dispersed effects on the transcriptome in brain. If true, this finding would raise the intriguing question of whether small, dispersed expression effects can be pathogenic, opening novel avenues for research into neurodevelopmental disorders, as well as many other diseases that are associated with expression regulators (e.g. cancer). We will additionally use TIM to cluster neurodevelopmental disorder genes by similarity of transcriptomic effects, to identify genes with putatively convergent mechanism. Broadly, our model will allow conceptually novel insight to be extracted from differential expression experiments, with applicability to any biological perturbation of interest.

抽象的 计算生物学的一个重要目标是利用高通量功能测定的数据来推断 遗传变异的生物学后果。通过配对RNA测序通常可以实现此目标 和差异表达分析。大多数差异表达方法寻求识别少数 受遗传扰动影响的基因和基因组。但是，某些基因，例如染色质 调节剂可能会影响整个转录组的数千个基因。这些分散的效果未捕获 通过现有方法。我们将通过 开发一种新型的统计工具，并将将此工具应用于规范和疾病环境。在 AIM 1，我们提出了全转录组影响模型（TIM），这是一个基于参数的可能性估计器 扰动对转录组的总体影响。蒂姆建立在现有的微分表达方法的基础上 但是估计差异表达效应的分布的参数，而不是单独的 效应尺寸。该模型还扩展到估计基因 - 富集和差分之间的相关性 表达签名。在AIM 2中，我们的目标是将TIM应用于最近表达的近期witturb-seq数据集 基因以大规模平行的方式体外，使我们能够识别哪些基因和基因诱导 撞倒时人类慢性髓样白血病细胞系中的最大转录组变化。我们也会 使用TIM来识别对转录组有相似影响的基因模块，然后使用这些模块 注释基因函数。在AIM 3中，我们将将TIM应用于35个神经发育的体内witturb-seq数据集 在发展小鼠新皮层中的疾病基因。通过这个目标，我们将分层神经发育障碍 按照整个转录组影响程度的基因，检验了神经发育疾病的假设 相关的基因表达调节剂对大脑转录组产生高度分散的影响。如果是真的，这 发现将提出一个有趣的问题，即小小的，分散的表达效应是否可以是致病性的， 开辟了针对神经发育障碍研究的新型途径，以及许多其他疾病 与表达调节剂有关（例如癌症）。我们还将使用TIM聚集神经发育 通过转录组效应的相似性，疾病基因，以鉴定具有预融合机制的基因。 从广义上讲，我们的模型将允许从概念上的新见解中从差异表达实验中提取出来， 适用于任何感兴趣的生物学扰动。