RESEARCH-PGR: Transcriptional and Translational Regulation of Gene Expression by Gene Structure, Codon Usage and tRNAs in Grasses

研究-PGR：草中基因结构、密码子使用和 tRNA 对基因表达的转录和翻译调控

基本信息

批准号：
1740874
负责人：
Kevin Childs
金额：
$ 256.11万
依托单位：
Michigan State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-04-01 至 2024-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1740874&HistoricalAwards=false
关键词：
RESEARCH PGR Transcriptional Translational Regulation

项目摘要

Like all species, grasses such as maize, rice, wheat, oats and barley contain genes encoding information that determines the amino acid sequence of all the proteins that plant synthesizes to germinate, grow and reproduce. All genes in an organism use a simple, four-letter alphabet (A, C, G, T) to make three-letter words (codons) to specify each amino acid of a protein. There is redundancy in this genetic code with multiple, similar codons often coding for the same amino acid. In the DNA, the overall G+C content of genes within any given species is often very similar. However, in grass species there are high-GC and low-GC genes, and there is evidence that high-GC and low-GC genes may be transcribed from DNA into RNA and translated from RNA into protein differently. This project will test whether proteins from high-GC and low-GC genes are transcribed and translated with equal efficiency and, specifically, whether that differential regulation is used when responding to heat, cold and drought stress. This work will also discover rules about codon usage and protein synthesis that can be used to design transgenes for efficient protein production. Finally, current gene prediction software does not account for the high-GC and low-GC genes found in grasses. This project will create a new gene prediction program that will more accurately predict genes in grasses. Better gene prediction will benefit the larger community of plant researchers, including those who work with economically important grasses such as maize, rice, wheat, oats and barley.Grass genes have a bimodal GC distribution and a significant 5' to 3' GC gradient. Little is known about the consequence of such variation on gene and protein expression. High gene GC content and strong negative 5' to 3' gradients in grasses strongly affect codon usage bias. It is possible that both GC biased gene conversion and codon usage bias are important in shaping the unusual GC features that are found in grass genes. GC biased gene conversion may help to move gene mutation towards a particular codon usage program, and then selection on that codon bias may maintain a gene's GC content and gradient. GC biased gene conversion in rice will be examined. Transcriptional and translational characteristics of native and transgenes that differ only in their GC content will also be studied in rice. Translational efficiency may also be affected by regulation of tRNAs in a tissue or condition-specific manner. Therefore, regulation of tRNAs will be examined to determine if changes in tRNA abundances affect protein translational efficiency. Recent published data indicate that due to the variation of GC content in grass genes, a notable number of genes in grasses have been missed or mis-annotated by existing gene finders. A new gene prediction tool that accounts for grass bimodal GC distribution will be designed to improve gene annotation in grasses. The results from this project will provide novel insights into gene evolution in grasses as well as important guidance for crop improvement via genetic modification.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.

像所有物种一样，玉米，大米，小麦，燕麦和大麦等草都包含编码信息，这些基因决定了所有植物合成以发芽，生长和繁殖的所有蛋白质的氨基酸序列。生物体中的所有基因都使用一个简单的四个字母字母（A，C，G，T）来制作三个字母的单词（密码子）来指定蛋白质的每个氨基酸。该遗传密码具有多个相似密码子通常编码相同氨基酸的冗余。在DNA中，任何给定物种中基因的总体G+C含量通常非常相似。但是，在草种中，有高-GC和低型GC基因，并且有证据表明，高-GC和低GC基因可以从DNA转录为RNA，并以不同的方式将RNA转化为蛋白质。该项目将测试来自高-GC和低型GC基因的蛋白质是否以同样的效率转录和翻译，具体而言，是否在响应热，冷和干旱应激时使用了差异调节。这项工作还将发现有关密码子使用和蛋白质合成的规则，这些规则可用于设计转基因以进行有效的蛋白质产生。最后，当前的基因预测软件不能说明草中发现的高GC和低型GC基因。该项目将创建一个新的基因预测程序，该程序将更准确地预测草中的基因。更好的基因预测将使较大的植物研究人员受益，包括那些与经济重要的草（如玉米，大米，小麦，燕麦和大麦）一起工作的人。Grass基因具有双峰GC分布，并具有5'至3'GC梯度。关于这种变异对基因和蛋白质表达的变化的结果知之甚少。高基因GC含量和草中的5'至3'梯度强烈影响密码子使用偏置。 GC偏置的基因转化和密码子使用偏置对于塑造草基基因中发现的异常GC特征很重要。 GC偏置的基因转换可能有助于将基因突变转移到特定的密码子使用程序上，然后对密码子偏置的选择可能会维持基因的GC含量和梯度。将检查大米中的GC偏置基因转化。天然和转基因的转录和翻译特征仅在大米中也将在其GC含量上有所不同。转化效率也可能会受到组织或条件特异性方式的TRNA调节的影响。因此，将检查TRNA的调节，以确定tRNA丰度的变化是否影响蛋白质翻译效率。最近发表的数据表明，由于草基因中GC含量的变化，现有基因发现者遗漏或错误地遗漏了草中的显着基因。一个解释草双峰GC分布的新基因预测工具将旨在改善草中的基因注释。该项目的结果将提供对草中基因进化的新见解，以及通过遗传修饰进行作物改善的重要指导。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估来审查标准的评估值得支持的。