Department of Bioengineering, University of California, San Diego, California, USA.
Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
mSystems. 2024 Nov 19;9(11):e0092324. doi: 10.1128/msystems.00923-24. Epub 2024 Oct 4.
is an important industrial and environmental microorganism known to occupy many niches and produce many compounds of interest. Although it is one of the best-studied organisms, much of this focus including the reconstruction of genome-scale metabolic models has been placed on a few key laboratory strains. Here, we substantially expand these prior models to pan-genome-scale, representing 481 genomes of with 2,315 orthologous gene clusters, 1,874 metabolites, and 2,239 reactions. Furthermore, we incorporate data from carbon utilization experiments for eight strains to refine and validate its metabolic predictions. This comprehensive pan-genome model enables the assessment of strain-to-strain differences related to nutrient utilization, fermentation outputs, robustness, and other metabolic aspects. Using the model and phenotypic predictions, we divide strains into five groups with distinct patterns of behavior that correlate across these features. The pan-genome model offers deep insights into metabolism as it varies across environments and provides an understanding as to how different strains have adapted to dynamic habitats.
As the volume of genomic data and computational power have increased, so has the number of genome-scale metabolic models. These models encapsulate the totality of metabolic functions for a given organism. strain 168 is one of the first bacteria for which a metabolic network was reconstructed. Since then, several updated reconstructions have been generated for this model microorganism. Here, we expand the metabolic model for a single strain into a pan-genome-scale model, which consists of individual models for 481 strains. By evaluating differences between these strains, we identified five distinct groups of strains, allowing for the rapid classification of any particular strain. Furthermore, this classification into five groups aids the rapid identification of suitable strains for any application.
是一种重要的工业和环境微生物，已知占据许多小生境并产生许多感兴趣的化合物。尽管它是研究最多的生物体之一，但包括重建基因组规模代谢模型在内的大部分研究重点都集中在少数几个关键的实验室菌株上。在这里，我们将这些先前的模型大大扩展到全基因组规模，代表了 481 个 的基因组，其中包含 2315 个直系同源基因簇、1874 种代谢物和 2239 种反应。此外，我们还整合了来自 8 个菌株的碳利用实验数据，以优化和验证其代谢预测。这个全面的泛基因组模型使我们能够评估菌株之间与营养利用、发酵产物、鲁棒性和其他代谢方面相关的差异。使用该模型和表型预测，我们将 菌株分为五个组，每个组的行为模式都不同，这些特征相互关联。泛基因组模型深入了解了 代谢在不同环境中的变化，并提供了对不同菌株如何适应动态栖息地的理解。
随着基因组数据量和计算能力的增加，基因组规模代谢模型的数量也在增加。这些模型包含了给定生物体的全部代谢功能。168 株是第一个重建代谢网络的细菌之一。从那时起，针对这个模式微生物已经生成了几个更新的重建。在这里，我们将单个菌株的代谢模型扩展到全基因组规模模型，该模型由 481 个 菌株的个体模型组成。通过评估这些菌株之间的差异，我们确定了五个不同的菌株组，允许快速分类任何特定的菌株。此外，这种分为五个组的分类方法有助于快速识别任何应用的合适菌株。
