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

像所有物种一样，草如玉米、水稻、小麦、燕麦和大麦含有编码信息的基因，这些信息决定了植物发芽、生长和繁殖所合成的所有蛋白质的氨基酸序列。 生物体中的所有基因都使用简单的四个字母（A，C，G，T）来组成三个字母的单词（密码子）来指定蛋白质的每个氨基酸。 这种遗传密码存在冗余，多个相似的密码子通常编码相同的氨基酸。 在DNA中，任何特定物种的基因的总体G+C含量通常非常相似。 然而，在禾本科植物中存在高GC和低GC基因，并且有证据表明高GC和低GC基因可能从DNA转录成RNA，从RNA翻译成蛋白质的方式不同。 该项目将测试来自高GC和低GC基因的蛋白质是否以相同的效率转录和翻译，特别是在应对热，冷和干旱胁迫时是否使用差异调节。 这项工作还将发现密码子使用和蛋白质合成的规则，可用于设计高效蛋白质生产的转基因。 最后，当前的基因预测软件没有考虑到草中发现的高GC和低GC基因。 该项目将创建一个新的基因预测程序，可以更准确地预测草的基因。 更好的基因预测将有利于更大范围的植物研究人员，包括那些研究经济上重要的禾本科植物如玉米、水稻、小麦、燕麦和大麦的研究人员。禾本科植物基因具有双峰GC分布和显著的5'至3' GC梯度。 关于这种变异对基因和蛋白质表达的影响知之甚少。 禾本科植物中高的基因GC含量和强的负5'到3'梯度强烈影响密码子使用偏好。 这是可能的GC偏向基因转换和密码子使用的偏见是重要的，在形成不寻常的GC功能，发现在草基因。 GC偏向的基因转换可能有助于将基因突变移向特定的密码子使用程序，然后对该密码子偏好的选择可以保持基因的GC含量和梯度。将检查水稻中GC偏向的基因转换。 还将在水稻中研究仅在GC含量上不同的天然基因和转基因的转录和翻译特征。翻译效率也可能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，由于禾本科植物基因中GC含量的变化，现有的基因发现者已经遗漏或错误注释了相当数量的禾本科植物基因。 一个新的基因预测工具，占草双峰GC分布将被设计，以提高基因注释在禾本科植物。 该项目的成果将为禾本科植物的基因进化提供新的见解，并为通过基因改造改良作物提供重要指导。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.