Scholarlink

<p>The genome-scale model of metabolism and gene expression (ME-model) for <i>Pseudomonas putida</i> KT2440, <i>i</i>Ppu1676-ME, provides a comprehensive representation of biosynthetic costs and proteome allocation. Compared to a metabolic-only model, <i>i</i>Ppu1676-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 <i>P. putida</i>, 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, <i>i</i>Ppu1676-ME offers valuable insights into <i>P. putida</i>’s proteome allocation and presents a powerful tool for understanding resource allocation in this industrially relevant microorganism.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Model of metabolism and gene expression predicts proteome allocation in Pseudomonas putida

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月24日

查看全文
  • Juan D. Tibocha-Bonilla,
  • Vishant Gandhi,
  • Chloe Lieng,
  • Oriane Moyne,
  • Rodrigo Santibáñez-Palominos,
  • Karsten Zengler

摘要

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.