NSF Postdoctoral Fellowship in Biology FY 2019: The Genomic Basis for Dysregulation of Protein Abundance in Maize

这一行动为NSF国家植物基因组计划2019财年生物学博士后研究奖学金提供了资金。该奖学金支持在东道主实验室为该研究员制定的研究和培训计划，该研究员还提出了扩大生物学参与度的计划。约瑟夫·盖奇的这项研究和培训计划的标题是“玉米蛋白质丰度失调的基因组基础”该奖学金的主办机构是康奈尔大学和华盛顿大学，赞助科学家是Edward S.Buckler博士和Richard Vierstra.DNA序列被翻译成蛋白质，蛋白质是决定有机体大小、形状和健康的重要组成部分。DNA序列中的微小差异可以导致合成蛋白质的量发生巨大变化，但人们对DNA序列中的这些变化如何影响蛋白质数量知之甚少。为了更多地了解DNA序列和蛋白质数量之间的关系，本研究将测量27个不同玉米品种(玉米)产生的数千种不同蛋白质的丰度。基因组序列和基因表达水平将被用来学习DNA序列的差异如何导致不同数量的蛋白质。产生的数据将被用来建立机器学习模型，该模型可以仅从DNA序列预测蛋白质丰度。这项研究的发现将为复杂特征(如生物体的大小、形状或健康)如何由遍布整个基因组的DNA序列的差异控制提供新的见解。更广泛的影响包括指导和培训本科生和研究生，以及参与Skype-A-Science计划(https://skypeascientist.com))，该计划将不同科学、技术和经济领域的科学家与儿童课堂配对，进行问答环节。培训目标包括获得生物信息学、分子遗传学、蛋白质组学、转录组学、机器学习应用和功能基因组学方面的专业知识。蛋白质是表型的关键决定因素。作为分子生物学中心教条的最终步骤，它们的存在和丰度决定了表型状态。到目前为止，还没有发表过关于重要经济作物物种遗传多样性成员之间蛋白质组学差异的研究。这个项目试图建立一个模型，说明罕见和有害的等位基因如何通过改变玉米的蛋白质结构和功能来影响蛋白质的翻译、运输和降解，从而导致蛋白质丰度的失调。这项研究将在玉米功能基因组学和蛋白质组学方面开辟新的天地，将先前存在的基因组数据与新产生的27个不同玉米自交系的时间序列转录数据和蛋白质组特征相结合，生成一个涵盖分子生物学中心教条的所有三个水平的多组数据集：DNA、RNA和蛋白质。由于基因组变异可以通过转录、翻译或两者的失调来影响蛋白质丰度，转录本和蛋白质丰度将一起用于具体评估基因变异如何在全基因组范围内影响翻译。对蛋白质二级结构、溶剂可及性、残基之间的接触和内在无序性的影响将被识别并表征对翻译有影响的遗传变异。这些特征将被用来制定一个总体模型，以描述稀有和有害的变异对蛋白质丰度的影响。为27个不同的玉米自交系产生的所有蛋白质组和转录组数据将对其他项目和研究人员有用，因此，重要的是尽可能地获取和组织所有数据。3的原始读数？核糖核酸测序将存放在NCBI-SRA(https://www.ncbi.nlm.nih.gov/sra)，)上，蛋白质组分析的原始质谱图将在蛋白质交换数据库(http://www.proteomexchange.org/).)上提供文字记录和蛋白质丰度数据将通过DOI提供，并由CyVerse(https://www.cyverse.org/data-store).)托管关键词：基因表达，蛋白质积累失调，测序，蛋白质组学，建模，玉米该奖项反映了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 National Plant Genome Initiative Postdoctoral Research Fellowship in Biology for FY 2019. The fellowship supports a research and training plan in a host laboratory for the Fellow who also presents a plan to broaden participation in biology. The title of the research and training plan for this fellowship to Joseph Gage is "The Genomic Basis for Dysregulation of Protein Abundance in Maize" The host institutions for the fellowship are Cornell University and Washington University and the sponsoring scientists are Drs. Edward S. Buckler and Richard Vierstra.DNA sequences get translated into proteins, which are important components in determining an organism's size, shape, and health. Small differences in DNA sequence can cause large changes in how much of the resulting protein is made, but little is known about how those changes in DNA sequence affect protein quantity. To learn more about the relationship between DNA sequence and protein quantity, this research will measure the abundance of thousands of different proteins produced by twenty-seven diverse varieties of maize (corn). Genome sequences as well as gene expression levels will be used to learn how differences in DNA sequence result in differing amounts of protein. The data generated will be used to build machine learning models that can predict protein abundance from DNA sequence alone. The findings from this study will provide new insights into how complex traits (like an organism's size, shape, or health) are controlled by differences in DNA sequence spread throughout the genome. Broader impacts include mentoring and training undergraduate and graduate students as well as participating in the Skype-A-Scientist program (https://skypeascientist.com) which pairs scientists in various STEM disciplines with classrooms of children for question and answer sessions. Training objectives include obtaining expertise in bioinformatics, molecular genetics, proteomics, transcriptomics, application of machine learning, and functional genomics. Proteins are a crucial determinant of phenotype. As the ultimate step in the central dogma of molecular biology, their presence and abundance determine phenotypic state. To date, there are no published studies of proteomic variability between genetically diverse members of an economic important crop plant species. This project seeks to develop a model of how rare and deleterious alleles cause dysregulation of protein abundance by affecting translation, transport, and degradation via changes to protein structure and function in maize. This research will break new ground in maize functional genomics and proteomics by integrating pre-existing genomic data with newly generated time-series transcriptomic data and proteomic characterization of twenty-seven diverse maize inbred lines to generate a multi-omic dataset that captures all three levels of the central dogma of molecular biology: DNA, RNA, and protein. Since genomic variants can affect protein abundance by dysregulation of either transcription, translation, or both, transcript and protein abundance will be used together to assess specifically how genetic variants impact translation on a genome-wide scale. Genetic variants with influential effects on translation will be identified and characterized for their effects on protein secondary structure, solvent accessibility, contact between residues, and intrinsic disorder. These features will be used to formulate an overarching model to describe the influence of rare and deleterious variants on protein abundance. All proteomic and transcriptomic data generated for the twenty-seven diverse maize inbred lines will be useful for other projects and researchers, and for that reason, it is important that all data be as accessible and organized as possible. Raw reads from 3? RNA sequencing will be deposited on the NCBI-SRA (https://www.ncbi.nlm.nih.gov/sra), and the raw mass spectra from proteomic assays will be made available on the ProteomeXchange database (http://www.proteomexchange.org/). Transcript and protein abundance data will be made available through a DOI and hosted by CyVerse (https://www.cyverse.org/data-store). Keywords: gene expression, dysregulation of protein accumulation, sequencing, proteomics, modeling, maizeThis 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.