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

• 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

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，诸如玉米、水稻、小麦、燕麦和大麦等禾本科植物含有的基因编码信息决定了植物在发芽、生长和繁殖过程中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一个简单的四字母字母表（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。这种遗传密码中存在冗余，多个相似的密码子通常编码相同的氨基酸。在DNA中，任何给定物种中基因的G+C含量通常是非常相似的。禾本科植物中存在高gc基因和低gc基因，有证据表明，高gc基因和低gc基因从DNA转录成RNA和从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，这些规则可用于设计高效蛋白质生产的转基因。最后，目前的基因预测软件没有考虑草中发现的高gc和低gc基因。这个项目将创建一个新的基因预测程序，将更准确地预测草的基因。更好的基因预测将使更多的植物研究人员受益，包括那些研究玉米、水稻、小麦、燕麦和大麦等具有重要经济意义的牧草的研究人员。禾草基因具有双峰型GC分布和显著的5′~ 3′GC梯度。人们对这种变异对基因和蛋白质表达的影响知之甚少。禾草中较高的基因GC含量和较强的负5′~ 3′梯度强烈影响密码子使用偏向性。可能GC偏倚基因转换和密码子使用偏倚在形成草基因中发现的不寻常的GC特征中都很重要。GC偏倚基因转换可以帮助基因突变向特定密码子使用程序移动，然后选择该密码子偏倚可以维持基因的GC含量和梯度。GC偏置基因在水稻中的转化将被检验。原生基因和转基因基因的转录和翻译特性也将在水稻中进行研究。翻译效率也可能受到组织或特定条件下trna调控的影响。因此，我们将研究tRNA的调控，以确定tRNA丰度的变化是否会影响蛋白质的翻译效率。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注释了草中相当数量的基因。设计一种新的基因预测工具来解释草的双峰GC分布，以提高草的基因注释。该项目的研究结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。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

Statistical analysis of GC-biased gene conversion and recombination hotspots in eukaryotic genomes: a phylogenetic hidden Markov model-based approach

真核基因组中GC偏向基因转换和重组热点的统计分析：基于系统发育隐马尔可夫模型的方法

• DOI: 10.1145/3459930.3469509
• 发表时间: 2021
• 期刊: Proceedings of ACM BCB 2021
• 作者: Gao, Meijun;Liu, Kevin J.
• 通讯作者: Liu, Kevin J.

GingerRoot: A Novel DNA Transposon Encoding Integrase-Related Transposase in Plants and Animals

• DOI: 10.1093/gbe/evz230
• 发表时间: 2019-11-01
• 期刊: GENOME BIOLOGY AND EVOLUTION
• 影响因子: 3.3
• 作者: Cerbin, Stefan;Wai, Ching Man;Jiang, Ning
• 通讯作者: Jiang, Ning

Benchmarking transposable element annotation methods for creation of a streamlined, comprehensive pipeline

• DOI: 10.1186/s13059-019-1905-y
• 发表时间: 2019-12-16
• 期刊: GENOME BIOLOGY
• 影响因子: 12.3
• 作者: Ou, Shujun;Su, Weija;Hufford, Matthew B.
• 通讯作者: Hufford, Matthew B.

Disentangling Population History and Character Evolution among Hybridizing Lineages

解开杂交谱系中的人口历史和性状进化

• DOI: 10.1093/molbev/msaa004
• 发表时间: 2020
• 期刊: Molecular Biology and Evolution
• 影响因子: 10.7
• 作者: Mullen, Sean P;VanKuren, Nicholas W;Zhang, Wei;Nallu, Sumitha;Kristiansen, Evan B;Wuyun, Qiqige;Liu, Kevin;Hill, Ryan I;Briscoe, Adriana D;Kronforst, Marcus R
• 通讯作者: Kronforst, Marcus R

The impact of gene sequence alignment and gene tree estimation error on summary-based species network estimation

基因序列比对和基因树估计误差对基于摘要的物种网络估计的影响

• DOI: 10.1145/3535508.3545559
• 发表时间: 2022
• 期刊: and Health Informatics (BCB ’22
• 作者: Gao, Meijun;Wang, Wei;Liu, Kevin J.
• 通讯作者: Liu, Kevin J.