Mapping genetic variation in enzyme velocity to growth rate phenotype

将酶速度的遗传变异映射到生长速率表型

• 批准号: 10371892
• 负责人: Kimberly Ann Reynolds
• 金额: $ 32.04万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-20 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10371892
• 关键词: Antibiotics; Bacteria; Biochemical Pathway; Biological Models; Biology; Breathing; CRISPR interference; Chromosome Mapping; Complex; DHFR gene; DNA Sequence Alteration; Data; Data Set; Dependence; Dihydrofolate Reductase; Disease; Disease Progression; Dose; Engineering; Environment; Environmental Risk Factor; Enzymes; Escherichia coli; Evolution; Folic Acid; Gene Combinations; Genes; Genetic; Genetic Epistasis; Genetic Variation; Genome; Genotype; Goals; Grant; Growth; Health; Height; Human; Human Genetics; Individual; Knock-out; Knowledge; Laboratories; Libraries; Link; Maps; Mathematics; Measurement; Measures; Mediating; Metabolic; Metabolic Pathway; Metabolism; Methodology; Modeling; Mutagenesis; Mutation; Nutrient; Organism; Pathway interactions; Patients; Performance; Periodicity; Pharmaceutical Preparations; Pharmacology; Phenotype; Play; Reaction; Resistance; Role; Sampling; Scanning; TYMS gene; Techniques; Testing; Theoretical Studies; Thymidylate Synthase; Time; Training; Translating; Trimethoprim; Variant; Work; behavioral phenotyping; cancer therapy; cell behavior; cell growth; cell type; combinatorial; design; disease-causing mutation; environmental intervention; enzyme activity; enzyme pathway; experimental study; folic acid metabolism; gene environment interaction; gene therapy; insight; knock-down; mathematical model; metabolic engineering; mutant; next generation sequencing; personalized medicine; predictive modeling; response; trait

PROJECT SUMMARY/ABSTRACT Over the last thirty years, our capacity to collect genome sequence information has rapidly outpaced our ability to analyze and interpret it. Despite significant efforts to quantitatively relate genotype to phenotype, we struggle to predict classic Mendelian traits like height from human genetic data. In apparently simpler organisms, such as bacteria, we are often unable to predict the effects of even single mutations on growth rate. Indeed, extending our current knowledge of genotype to the understanding, prediction, and control of global cellular behaviors (phenotype) remains a central goal of biology. This problem is made complex by three factors: 1) the mapping between a single gene’s activity and phenotype is non-linear and generally unknown, 2) the mapping is shaped by epistatic interactions between genes, and 3) the mapping is influenced by environmental factors. Given knowledge of the parameters governing these three relationships, we then need a strategy to combine these data into a quantitative model of phenotype. The goal of this grant is to develop exactly such a strategy, by focusing on an experimentally powerful and well defined instantiation of the genotype to phenotype problem: how variation in metabolic enzyme activity influences the growth rate of a unicellular organism (E. coli). We propose a modeling approach in which epistatic relationships between genes and the environment can all be measured and modeled as continuous, dose-dependent phenomena. To parameterize and test this model, we will collect over 100,000 growth rate measurements sampling genetic and environmental variation in folate metabolism, a well-conserved pathway with important roles in human health and disease. These data will be generated using new methodology developed by my laboratory that combines CRISPR interference (CRISPRi), next generation sequencing, and continuous culture to quantitatively measure growth rates for thousands of mutants in parallel under prescribed environmental variation. A small subset of the growth rate data will be used to mathematically constrain our model (~10-20%), and we will evaluate model performance on the remainder. We will also assess the capacity of the model to predict growth rates for higher order combinations of enzyme activity and environmental perturbations not included in the original data set. At completion, we will have established and tested a complete genotype-phenotype mapping relating changes in folate pathway enzyme activities to growth rate. This final model will be of immediate relevance for understanding how variation in folate metabolic enzymes interacts with environmental conditions to influence resistance to common antibiotics (e.g. trimethoprim). More generally, the modeling framework can be translated to map genotype-to-phenotype relationships in other biochemical pathways and cell types. This approach will provide a new strategy for the engineering of biosynthetic pathways, designing personalized therapies, and inferring the growth rate effects of mutations in higher organisms.

项目总结/摘要 在过去的三十年里，我们收集基因组序列信息的能力迅速超过了我们的能力。 分析和解释它。尽管在定量地将基因型与表型联系起来方面做了大量的努力， 从人类基因数据中预测典型的孟德尔特征，比如身高。在明显更简单的生物体中， 作为细菌，我们往往无法预测甚至单个突变对生长速度的影响。的确， 将我们现有的基因型知识扩展到理解、预测和控制全球细胞遗传学， 行为（表型）仍然是生物学的中心目标。这个问题是复杂的三个因素：1） 单个基因的活性和表型之间的映射是非线性的并且通常是未知的，2）映射 基因间的上位性相互作用决定了基因定位的准确性; 3）基因定位受环境因素的影响。 如果知道了控制这三种关系的参数，我们就需要一种策略来联合收割机 将这些数据转化为表型的定量模型。这笔赠款的目标就是制定这样一个战略， 通过集中在实验上强大的和明确定义的基因型到表型的实例， 问题：代谢酶活性的变化如何影响单细胞生物的生长速率（E。 大肠杆菌）。我们提出了一种建模方法，其中基因和环境之间的上位关系 都可以被测量和建模为连续的，剂量依赖的现象。来测试这个 模型，我们将收集超过100，000个生长率测量样本的遗传和环境变异， 叶酸代谢是一种在人类健康和疾病中具有重要作用的保守途径。这些数据 将使用我的实验室开发的新方法产生， （CRISPRi）、下一代测序和连续培养，以定量测量 成千上万的突变体在规定的环境变化下并行。增长率的一小部分 数据将用于数学约束我们的模型（~10-20%），我们将评估模型性能 其余的。我们还将评估模型预测高阶增长率的能力 未包括在原始数据集中的酶活性和环境扰动的组合。在 完成后，我们将建立和测试一个完整的基因型-表型映射相关的变化， 叶酸途径酶活性与生长速度的关系。这一最终模型将直接关系到 了解叶酸代谢酶的变化如何与环境条件相互作用， 对常见抗生素（如甲氧苄啶）的耐药性。更一般地，建模框架可以是 转译以绘制其他生化途径和细胞类型中基因型与表型的关系。这 这种方法将为生物合成途径的工程设计提供一种新的策略， 治疗，并推断高等生物突变的生长率效应。