Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA.
Department of Pharmacology, University of California, San Diego, La Jolla, CA, 92093, USA.
Nat Commun. 2024 Jun 19;15(1):5234. doi: 10.1038/s41467-024-49231-y.
It has proved challenging to quantitatively relate the proteome to the transcriptome on a per-gene basis. Recent advances in data analytics have enabled a biologically meaningful modularization of the bacterial transcriptome. We thus investigate whether matched datasets of transcriptomes and proteomes from bacteria under diverse conditions can be modularized in the same way to reveal novel relationships between their compositions. We find that; (1) the modules of the proteome and the transcriptome are comprised of a similar list of gene products, (2) the modules in the proteome often represent combinations of modules from the transcriptome, (3) known transcriptional and post-translational regulation is reflected in differences between two sets of modules, allowing for knowledge-mapping when interpreting module functions, and (4) through statistical modeling, absolute proteome allocation can be inferred from the transcriptome alone. Quantitative and knowledge-based relationships can thus be found at the genome-scale between the proteome and transcriptome in bacteria.
定量地将蛋白质组与基于基因的转录组联系起来一直具有挑战性。数据分析的最新进展使得细菌转录组具有生物学意义的模块化成为可能。因此，我们研究了在不同条件下来自细菌的转录组和蛋白质组的匹配数据集是否可以以相同的方式模块化，以揭示它们组成之间的新关系。我们发现：（1）蛋白质组和转录组的模块包含相似的基因产物列表，（2）蛋白质组中的模块通常代表转录组模块的组合，（3）已知的转录和翻译后调控反映在两组模块之间的差异中，允许在解释模块功能时进行知识映射，（4）通过统计建模，可以仅从转录组推断出绝对蛋白质组分配。因此，可以在细菌的蛋白质组和转录组之间在基因组范围内找到定量和基于知识的关系。
