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

越来越多的证据表明，短蛋白(µ蛋白)在生理过程中处于更中心的位置。然而，它们的从头识别是困难的，而且它们经常被搜索新的开放阅读框架的算法遗漏，因为µ-蛋白质的尺寸很短，通常是150个核苷酸或50个密码子。在这个项目中，我们试图解决目前在系统识别细菌中编码µ-蛋白质的短ORF(SORF)的算法/工具方面的空白。我们将利用两种深度测序技术，RNA-Seq和核糖体图谱，提取sORF的转录全组表达特征，并将其用于设计表达sORF从头搜索的算法。这些功能包括翻译开始、报告蛋白质合成终止的核糖体释放分数，以及最重要的是，当真正翻译开放阅读框架时，核糖体保护片段中的三核苷酸周期性。为了准确地确定所有的起始点(包括非规范的起始点)，我们将在用新鉴定的多肽抗生素抑制翻译起始时对转录组进行测序。由于细菌通常使用相似的表达规则，我们的愿景是用在不同条件下生长的大肠杆菌MG1655中产生的数据集来训练算法，并开发它以使其适用于所有细菌物种。利用RNA-Seq和核糖体图谱的表达特征，只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方面向前迈进了一步。微蛋白可能只有在特定的生长或环境条件下才能表达。因此，为了研究它们在形成应激反应中的作用，我们的目标是利用核糖体图谱和RNA-Seq数据集，在翻译和转录水平上探索不同胁迫(热应激、氧化应激和渗透应激)下sORF的表达。我们将对其中一些新发现的µ-蛋白质(最好是那些仅在特定胁迫条件下表达的蛋白质)进行进一步的表征，以利用基因组标签进行下拉实验，以阐明蛋白质水平和相互作用伙伴的表达。

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.