Analysis of tRNA gene evolution in bacterial genomes

细菌基因组中 tRNA 基因进化分析

• 批准号: RGPIN-2016-06051
• 负责人: TremblaySavard, Olivier
• 金额: $ 2.26万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=616073
• 关键词: Analysis; tRNA; gene; evolution; bacterial

Transfer RNA (tRNA) genes are among the most important and ancient genes. A large number of studies have addressed tRNA sequence identification, structure prediction and identity elements. However, little is known about the evolution of tRNA genes in terms of number, organization and functional specificity on a genome-wide scale. Having a better understanding of tRNA repertoire evolution is expected to shed light on many important questions related to protein expression, such as the link between tRNA copy number and protein synthesis, the evolution of the genetic code and tRNA functional shift. Moreover, since tRNA genes are dispersed throughout the genome, they represent an interesting subset of genes to study the major evolutionary events that have transformed the genomes. Recently, we inferred the evolutionary history of the tRNA genes in 50 Bacillus strains. We observed that the tRNA genes in Bacillus genomes are mostly affected by duplications and deletions. We also observed large inversions occurring around the axes of replication (origin or terminus). The most interesting and surprising result came from the comparison of the rates of the different types of events in Bacillus and E. coli. The average rate of duplications inferred in Bacillus was 24 times lower than the one that was reported in the E. coli study, while the average rates of losses and inversions were both 12 times lower. These rates were extremely low, suggesting a strong selective pressure acting on tRNA gene repertoires in Bacillus. The goal of my research program is to conduct similar analyses in other bacteria. With the ever-increasing number of bacterial genomes being completely sequenced, there are more and more interesting bacterial genera to analyze: Listeria, Salmonella, Staphylococcus, Streptococcus and Vibrio for example. Comparing the evolution of those other genera with what was inferred in Bacillus and E. coli would help understand if certain genera are clearly more stable than others in terms of the number and the organization of tRNA genes, and if there is a correlation between this stability and the living environment of those different bacteria. However, as most of these newly sequenced bacterial genera exhibit a very high level of divergence compared to Bacillus, new algorithmic methods need to be developed to infer evolutionary histories of tRNA genes in those genomes. In particular, methods taking into account overlapping events and methods considering sequence, genomic context and structural information will be developed for the comparison of two genomes, then will be optimized and generalized to calculate the minimum evolutionary distance in the context of a whole phylogeny. The proposed research program will provide a bi-disciplinary training environment for highly qualified personnel that focuses both on the development of efficient algorithms and the analysis of biological data.

转移RNA(TRNA)基因是最重要、最古老的基因之一。大量研究涉及tRNA序列鉴定、结构预测和识别元件。然而，关于tRNA基因在全基因组范围内的数量、组织和功能特异性方面的进化，人们知之甚少。更好地了解tRNA谱系的进化将有助于揭示与蛋白质表达有关的许多重要问题，如tRNA拷贝数与蛋白质合成之间的联系，遗传密码的进化以及tRNA功能的转变。此外，由于tRNA基因分布在整个基因组中，它们代表了一个有趣的基因子集，用于研究改变了基因组的主要进化事件。 最近，我们推测了50株芽孢杆菌的tRNA基因的进化历史。我们观察到芽孢杆菌基因组中的tRNA基因主要受复制和缺失的影响。我们还观察到围绕复制轴(起始点或终止点)发生的大反转。最有趣和最令人惊讶的结果来自对芽孢杆菌和大肠杆菌中不同类型事件的比率的比较。在芽孢杆菌中推断的平均重复率比在大肠杆菌研究中报告的重复率低24倍，而丢失和倒置的平均比率都低12倍。这些比率极低，表明芽孢杆菌中的tRNA基因库具有很强的选择压力。 我的研究计划的目标是在其他细菌中进行类似的分析。随着越来越多的细菌基因组完成测序，有越来越多的有趣的细菌属需要分析：例如李斯特菌、沙门氏菌、葡萄球菌、链球菌和弧菌。将这些其他属的进化与芽孢杆菌和大肠杆菌中的推断进行比较，将有助于理解某些属在tRNA基因的数量和组织方面是否明显比其他属更稳定，以及这种稳定性与这些不同细菌的生存环境之间是否存在相关性。然而，由于这些新测序的细菌属中的大多数与芽孢杆菌相比表现出非常高的差异性，需要开发新的算法方法来推断这些基因组中tRNA基因的进化历史。特别是，考虑重叠事件的方法和考虑序列、基因组背景和结构信息的方法将被开发用于两个基因组的比较，然后被优化和推广以计算整个系统发育背景下的最小进化距离。 拟议的研究方案将为高素质人员提供一个双学科培训环境，重点是开发有效的算法和分析生物数据。