代谢和基因表达模型预测恶臭假单胞菌蛋白质组分配。

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Juan D Tibocha-Bonilla, Vishant Gandhi, Chloe Lieng, Oriane Moyne, Rodrigo Santibáñez-Palominos, Karsten Zengler
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引用次数: 0

摘要

恶臭假单胞菌KT2440, iPpu1676-ME的代谢和基因表达基因组模型(ME-model)提供了生物合成成本和蛋白质组分配的综合表征。与仅代谢模型相比,iPpu1676-ME显著扩展了基因表达、大分子组装和辅因子利用,能够在没有额外约束的情况下进行准确的生长预测。利用RNA测序和核糖体分析数据进行的多组学分析显示,putida具有翻译优先性,烟酰胺生物合成和排队苷代谢等核心途径的翻译效率较高,而次要途径的翻译效率较低。值得注意的是,在多组学数据方面,me模型显著优于m模型,从而验证了其预测能力。因此,iPpu1676-ME为恶臭杆菌的蛋白质组分配提供了有价值的见解,并为了解这种工业相关微生物的资源分配提供了有力的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Model of metabolism and gene expression predicts proteome allocation in Pseudomonas putida.

The genome-scale model of metabolism and gene expression (ME-model) for Pseudomonas putida KT2440, iPpu1676-ME, provides a comprehensive representation of biosynthetic costs and proteome allocation. Compared to a metabolic-only model, iPpu1676-ME significantly expands on gene expression, macromolecular assembly, and cofactor utilization, enabling accurate growth predictions without additional constraints. Multi-omics analysis using RNA sequencing and ribosomal profiling data revealed translational prioritization in P. putida, with core pathways, such as nicotinamide biosynthesis and queuosine metabolism, exhibiting higher translational efficiency, while secondary pathways displayed lower priority. Notably, the ME-model significantly outperformed the M-model in alignment with multi-omics data, thereby validating its predictive capacity. Thus, iPpu1676-ME offers valuable insights into P. putida's proteome allocation and presents a powerful tool for understanding resource allocation in this industrially relevant microorganism.

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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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