Network analysis of toxin production in Clostridioides difficile identifies key metabolic dependencies.
Network analysis of toxin production in Clostridioides difficile identifies key metabolic dependencies.
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DOI：
10.1371/journal.pcbi.1011076
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发表时间：
2023-04
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
艰难梭菌的发病机制是通过其两种毒素蛋白TcdA和TcdB介导的，这两种毒素蛋白诱导肠上皮细胞死亡和炎症。可以改变C。艰难梭菌毒素的生产通过改变细胞外环境中的各种代谢物浓度。然而，目前尚不清楚哪些细胞内代谢途径参与以及它们如何调节毒素产生。为了研究细胞内代谢途径对不同营养环境和毒素产生状态的反应，我们使用先前发表的C.艰难梭菌菌株CD630和CDR20291（iCdG709和iCdR703）。我们使用RIPTiDe算法将公开可用的转录组数据与模型集成，以创建16个独特的上下文化C。代表一系列营养环境和毒素状态的艰难模型。我们使用具有通量采样和影子定价分析的随机森林来识别与毒素状态和环境相关的代谢模式。具体而言，我们发现精氨酸和鸟氨酸的摄取在低毒素状态下特别活跃。此外，精氨酸和鸟氨酸的摄取高度依赖于细胞内脂肪酸和大的聚合物代谢物库。我们还应用了代谢转换算法（MTA）来识别将代谢从高毒素状态转移到低毒素状态的模型扰动。这一分析扩展了我们对C. difficile并识别代谢依赖性，可以利用这些代谢依赖性来减轻疾病的严重程度。艰难梭菌是约73%的医疗保健获得性胃肠道感染的病原体，导致显著的医疗保健负担。它的毒素对毒力至关重要，并在建立C的营养生态位中发挥关键作用。很难具有高毒素产生的高毒力菌株可导致C.艰难梭菌感染（CDI），如进展为假膜性结肠炎、中毒性巨结肠，在某些情况下，死亡。提高我们对这些毒素如何通过其环境和细胞内代谢进行调节的理解，可以使我们能够减弱C。在感染患者中的艰难毒力。因此，我们收集了C.艰难梭菌在16种不同条件下生长，以研究毒素产生如何响应环境变化。我们将这些数据与C. difficile，使我们能够模拟高和低毒素产生状态下的细胞内代谢。我们对代谢和毒素产生的网络分析预测了高和低毒素产生状态下的代谢模式，并为毒素的代谢调节提供了见解。此外，我们的分析突出了可以作为抗毒素靶点的新蛋白质。
Clostridioides difficile pathogenesis is mediated through its two toxin proteins, TcdA and TcdB, which induce intestinal epithelial cell death and inflammation. It is possible to alter C. difficile toxin production by changing various metabolite concentrations within the extracellular environment. However, it is unknown which intracellular metabolic pathways are involved and how they regulate toxin production. To investigate the response of intracellular metabolic pathways to diverse nutritional environments and toxin production states, we use previously published genome-scale metabolic models of C. difficile strains CD630 and CDR20291 (iCdG709 and iCdR703). We integrated publicly available transcriptomic data with the models using the RIPTiDe algorithm to create 16 unique contextualized C. difficile models representing a range of nutritional environments and toxin states. We used Random Forest with flux sampling and shadow pricing analyses to identify metabolic patterns correlated with toxin states and environment. Specifically, we found that arginine and ornithine uptake is particularly active in low toxin states. Additionally, uptake of arginine and ornithine is highly dependent on intracellular fatty acid and large polymer metabolite pools. We also applied the metabolic transformation algorithm (MTA) to identify model perturbations that shift metabolism from a high toxin state to a low toxin state. This analysis expands our understanding of toxin production in C. difficile and identifies metabolic dependencies that could be leveraged to mitigate disease severity. Clostridioides difficile is the causative agent in approximately 73% of healthcare-acquired gastrointestinal infections, resulting in a significant healthcare burden. Its toxins are crucial to virulence and play a key role in establishing a nutritional niche for C. difficile. Highly virulent strains with high toxin production can lead to worse outcomes for patients with C. difficile infection (CDI), such as progression to pseudomembranous colitis, toxic megacolon, and in some cases, death. Improving our understanding of how these toxins are regulated through their environment and intracellular metabolism could allow us to attenuate C. difficile virulence in infected patients. Therefore, we have compiled gene expression data of C. difficile grown in 16 different conditions to investigate how toxin production changes in response to the environment. We have integrated these data with a genome-scale metabolic model of C. difficile, allowing us to simulate the intracellular metabolism in high and low toxin producing states. Our network analysis of metabolism and toxin production predicts metabolic patterns in high and low toxin-producing states and provides insights into metabolic regulation of toxins. Additionally, our analysis highlights new proteins that could serve as anti-toxin targets.
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影响因子：
4.3
作者：
King ZA;Dräger A;Ebrahim A;Sonnenschein N;Lewis NE;Palsson BO
通讯作者：
Palsson BO
影响因子：
4.3
作者：
Jenior, Matthew L.;Moutinho, Thomas J., Jr.;Papin, Jason A.
通讯作者：
Papin, Jason A.
影响因子：
3.1
作者：
Karlsson, S;Lindberg, A;Åkerlund, T
通讯作者：
Åkerlund, T
影响因子：
4.6
作者：
Carlucci, Christian;Jones, Carys S.;Allen-Vercoe, Emma
通讯作者：
Allen-Vercoe, Emma
影响因子：
3.1
作者：
D'Auria, Kevin M.;Kolling, Glynis L.;Papin, Jason A.
通讯作者：
Papin, Jason A.