Systematic identification of novel µ-proteins in bacteria using ribosome profiling data

使用核糖体分析数据系统鉴定细菌中的新型 µ 蛋白

• 批准号: 378478032
• 负责人: Professorin Dr. Zoya Ignatova
• 金额: --
• 依托单位: Institut für Biochemie und Molekularbiologie
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/378478032?language=en
• 关键词: Systematic; identification; novel; proteins; bacteria

Emerging evidence places short proteins (µ proteins) more centrally in physiological processes. However, their de novo identification is difficult and they are often missed by algorithms searching for new open-reading frames because of the short size of the µ-proteins, often <150 nt or <50 codons. In this project, we seek to address the current void in algorithms/tools for systematic identification of short ORFs (sORFs) in bacteria which encode µ-proteins. We will use the power of two deep-sequencing technologies, RNA-Seq and ribosome profiling, to extract transcriptome-wide expression features of sORFs and use them in designing the algorithm for de novo search of expressed sORFs. These features include translational start, ribosome release score reporting on termination of protein synthesis, and most importantly, the three-nucleotide periodicity in ribosome-protected fragments when truly translating an ORF. To faithfully determine all initiation sites (including non-canonical starts), we will sequence the transcriptome upon inhibition of translation initiation with a newly identified peptide antibiotic. Since bacteria in general employ similar expression rules, it is our vision to train the algorithm with data sets produced in E. coli MG1655 grown at various conditions, and to develop it to work across all bacterial species. Leveraging expression features from RNA-Seq and ribosome profiling, which allow predicting only truly expressed sORFs, is a step forward in detecting sORFs as compared to the algorithms using solely genetic information.µ-proteins might be expressed only under certain growth or environmental conditions. Thus, to address their role in shaping stress response, we aim to probe sORF expression at both translation and transcription level at various stresses (heat, oxidative and osmotic stress) using ribosome profiling and RNA-Seq data sets. We will carry out further characterization of some of these newly discovered µ-proteins (preferably those that are expressed only under certain stress conditions) to elucidate expression on the protein level and interaction partners using genomic tagging for pulldown experiments.

新出现的证据表明，短蛋白（µ蛋白）在生理过程中更为重要。然而，它们的从头鉴定是困难的，并且它们经常被搜索新的开放阅读框的算法错过，因为μ-蛋白质的尺寸很短，通常<150 nt或<50个密码子。在这个项目中，我们试图解决目前在算法/工具中的空白，用于系统识别编码μ蛋白的细菌中的短ORF（sORF）。我们将使用两种深度测序技术，RNA-Seq和核糖体分析，来提取sORF的全转录组表达特征，并将其用于设计表达sORF的从头搜索算法。这些特征包括翻译起始、报告蛋白质合成终止的核糖体释放评分，以及最重要的是，当真正翻译ORF时，核糖体保护片段中的三个核苷酸周期性。为了忠实地确定所有的起始位点（包括非典型的起始位点），我们将在用新鉴定的肽抗生素抑制翻译起始后对转录组进行测序。由于细菌通常采用类似的表达规则，因此我们的愿景是用E. coli MG 1655在各种条件下生长，并使其在所有细菌物种中工作。利用来自RNA-Seq和核糖体分析的表达特征，仅允许预测真正表达的sORF，与仅使用遗传信息的算法相比，在检测sORF方面向前迈出了一步。因此，为了解决它们在形成应激反应中的作用，我们的目标是使用核糖体分析和RNA-Seq数据集在各种应激（热、氧化和渗透应激）下在翻译和转录水平上探测sORF表达。我们将对这些新发现的µ蛋白（最好是那些仅在某些胁迫条件下表达的蛋白）进行进一步表征，以阐明蛋白质水平上的表达和使用基因组标签进行下拉实验的相互作用伴侣。