Bacterial resistance to antibiotics is a growing health problem that is projected to cause more deaths than cancer by 2050. Consequently, novel antibiotics are urgently needed. Since more than half of the available antibiotics target the structurally conserved bacterial ribosomes, factors involved in protein synthesis are thus prime targets for the development of novel antibiotics. However, experimental identification of these potential antibiotic target proteins can be labor-intensive and challenging, as these proteins are likely to be poorly characterized and specific to few bacteria. Here, we use a bioinformatics approach to identify novel components of protein synthesis. In order to identify these novel proteins, we established a Large-Scale Transcriptomic Analysis Pipeline in Crowd (LSTrAP-Crowd), where 285 individuals processed 26 terabytes of RNA-sequencing data of the 17 most notorious bacterial pathogens. In total, the crowd processed 26,269 RNA-seq experiments and used the data to construct gene co-expression networks, which were used to identify more than a hundred uncharacterized genes that were transcriptionally associated with protein synthesis. We provide the identity of these genes together with the processed gene expression data. We identified genes related to protein synthesis in common bacterial pathogens and thus provide a resource of potential antibiotic development targets for experimental validation. The data can be used to explore additional vulnerabilities of bacteria, while our approach demonstrates how the processing of gene expression data can be easily crowd-sourced.

中文翻译：

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LSTrAP-Crowd：通过人群来源的RNA测序数据分析预测细菌核糖体的新成分。

细菌对抗生素的耐药性是一个日益严重的健康问题，预计到2050年其死亡人数将比癌症多。因此，迫切需要新型抗生素。由于超过一半的可用抗生素靶向结构保守的细菌核糖体，因此蛋白质合成中涉及的因子是开发新型抗生素的主要靶标。但是，对这些潜在的抗生素靶蛋白的实验鉴定可能是劳动密集型且具有挑战性的，因为这些蛋白的特性可能很差且对少数细菌具有特异性。在这里，我们使用生物信息学方法来识别蛋白质合成的新成分。为了鉴定这些新型蛋白质，我们建立了人群中的大规模转录组学分析管道（LSTrAP-Crowd），其中285个人处理了17种最臭名昭著的细菌病原体的26 TB的RNA测序数据。人群总共处理了26,269个RNA-seq实验，并使用这些数据构建了基因共表达网络，该网络用于鉴定与蛋白质合成转录相关的一百多个未表征的基因。我们提供这些基因的身份以及已处理的基因表达数据。我们确定了常见细菌病原体中与蛋白质合成相关的基因，从而为实验验证提供了潜在的抗生素开发目标资源。数据可用于探索细菌的其他脆弱性，而我们的方法证明了如何轻松地众包基因表达数据的处理。