Mapping genetic variation in enzyme velocity to growth rate phenotype

项目摘要/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 来分析和解释它。尽管我们在基因和表型之间的定量联系上做出了重大努力，但我们仍在努力 从人类基因数据中预测孟德尔人的典型特征，如身高。在表面上更简单的生物体中， 作为细菌，我们经常无法预测即使是单一突变对生长速度的影响。的确， 将我们目前的基因型知识扩展到对全球细胞的理解、预测和控制 行为(表型)仍然是生物学的中心目标。这个问题因三个因素而变得复杂：1) 单个基因的活性和表型之间的映射是非线性的，通常是未知的，2)映射 是由基因间的上位性交互作用形成的，3)定位受环境因素的影响。 在已知支配这三种关系的参数的情况下，我们需要一种策略来组合 将这些数据转化为表型的量化模型。这笔赠款的目标正是为了制定这样的战略， 通过专注于实验上强大的和定义明确的从基因到表型的实例化 问题：代谢酶活性的变化如何影响单细胞生物体的生长速度(E。 Coli.我们提出了一种建模方法，在该方法中基因和环境之间的上位关系 所有这些都可以测量并建模为连续的、剂量依赖的现象。对此进行参数化和测试 模型中，我们将收集超过100,000个增长速度测量数据，对遗传和环境变化进行采样 叶酸代谢是一种保守的代谢途径，在人类健康和疾病中起着重要作用。这些数据 将使用我的实验室开发的结合CRISPR干扰的新方法生成 (CRISPRi)、下一代测序和连续培养，以定量测量 在规定的环境变化下，数千个突变体平行。增长率的一小部分 数据将用于对我们的模型进行数学约束(~10%-20%)，我们将评估模型的性能 剩下的部分。我们还将评估模型预测更高级别增长率的能力。 原始数据集中没有包括的酶活性和环境扰动的组合。在… 完成后，我们将建立并测试一个完整的基因-表型图谱，该图谱与 叶酸途径酶活性对生长速度的影响。这一最终模型将直接与 了解叶酸代谢酶的变化如何与环境条件相互作用以影响 对常见抗生素(如甲氧苄氨嘧啶)的耐药性。更一般地，建模框架可以是 翻译成其他生化途径和细胞类型中的基因型与表型的关系图。这 该方法将为生物合成途径的工程提供一种新的策略，设计个性化的 治疗，以及推断高等生物体中突变的生长率效应。

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.