NSF Postdoctoral Fellowship in Biology FY 2022: Defining a New Paradigm Linking Genetic Variation in Human Population with mRNA Stability

这一行动为NSF 2022财年生物学博士后研究奖学金提供了资金，综合研究调查了基因组、环境和表型之间相互作用的生命规则。该奖学金支持研究员的研究和培训，这些研究员将以创新的方式为生活规则领域做出贡献。个体间基因DNA序列的差异被称为遗传变异。由于DNA测序的进步，科学家们目前对人类遗传变异有了前所未有的了解。基因变异可以导致我们可以看到的个体之间的身体差异，称为表型。然而，它们如何做到这一点的机制还没有完全被理解。这项研究调查了遗传变异如何影响称为信使RNA(MRNAs)的分子。MRNAs是制造蛋白质的指令，这些蛋白质是从DNA复制而来的，并由蛋白质制造机器读取。最近，科学家们发现，一些基因序列使蛋白质制造机器更难读取mRNAs，导致细胞破坏mRNAs。这项研究试图确定人类中导致信使核糖核酸以这种方式破坏的遗传变异，最终导致我们观察到的表型。这项研究的总体目标是了解遗传变异如何通过改变mRNAs的属性来导致表型。这些知识将提高科学家预测基因变异将如何影响个体的能力。为了实现这一目标，该研究员将使用高通量实验和计算方法相结合的方法来定义错义突变在调节信使核糖核酸稳定性中的机制。为了实现这一目标，这项研究将通过结合细胞培养中新的大规模平行敲入试验和机器学习模型来识别改变mRNA稳定性的氨基酸基序。然后，这位研究员将确定新翻译的蛋白质链和核糖体中的关键氨基酸残基，这些氨基酸残基对于使用系统突变来阻止核糖体停滞至关重要。最后，这位研究员将应用预测模型和机械洞察力来定义通过改变翻译和信使核糖核酸稳定性来驱动表型变化的人类遗传变异。这项拟议的研究将定义一种新的机制，通过这种机制，错义变体改变基因表达，并导致分子、细胞和生物的表型。它将为预测人类基因组中单核苷酸变异的功能相关性提供一个框架。此外，该研究员还将接受DNA测序和机器学习方面的最新技术培训。此外，该研究员将指导本科生实习生，并将教授计算生物学的研究生课程模块。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2022, Integrative Research Investigating the Rules of Life Governing Interactions Between Genomes, Environment and Phenotypes. The fellowship supports research and training of the fellow that will contribute to the area of Rules of Life in innovative ways. Differences in the DNA sequence of genes between individuals are known as genetic variants. Scientists currently have unprecedented knowledge of human genetic variants due to advances in DNA sequencing. Genetic variants can cause the physical differences we can see between individuals, known as phenotypes. However, the mechanisms for how they do so are not completely understood.This research investigates how genetic variants impact molecules called messenger RNAs (mRNAs). mRNAs are instructions for making proteins that are copied from DNA and read by protein-making machinery. Recently, scientists discovered that some gene sequences make it harder for protein-making making machinery to read mRNAs, causing the cell to destroy the mRNA. This research seeks to identify genetic variants in humans that cause the mRNA to be destroyed in this way, ultimately causing phenotypes we observe. The overall goal of this research is to understand how genetic variants cause phenotypes by altering the properties of mRNAs. This knowledge will increase scientist’s ability to predict how genetic variants will affect individuals.To achieve this goal, the fellow will use a combination of high-throughput experimental and computational approaches to define the mechanism of missense mutations in regulating mRNA stability. Towards this goal, the research will identify amino acid motifs that alter mRNA stability by combining a novel massively parallel knock-in assay in cell culture and a machine learning model. The fellow will then define critical amino acid residues in the newly-translated protein chain and the ribosome that are essential for ribosome stalling using systematic mutations. Finally, the fellow will apply the predictive models and mechanistic insights to define human genetic variants that drive phenotypic changes by altering translation and mRNA stability. The proposed research will define a novel mechanism by which missense variants alter gene expression and lead to molecular, cellular and organismal phenotypes. It will provide a framework for predicting the functional relevance of single nucleotide variants in the human genome. Additionally, the fellow will gain training in state-of-the art techniques in DNA sequencing and machine learning. Further, the fellow will mentor undergraduate interns, and will teach a graduate course module in computational biology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.