Modeling metabolism in intracellular infections comparing Salmonella and Listeria

比较沙门氏菌和李斯特菌的细胞内感染代谢建模

• 批准号: 71820928
• 负责人: Professor Dr. Thomas Dandekar
• 金额: --
• 依托单位: Lehrstuhl für Bioinformatik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2008
• 资助国家: 德国
• 起止时间: 2007-12-31 至 2013-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/71820928?language=en
• 关键词: Modeling; metabolism; intracellular; infections; comparing

We want to model metabolism in intracellular infections. We will compare an intra-vacuolar strategy (Salmonella, Salmonella enterica serovar Typhimurium (S. Typhimurium) Gram negative) with a cytoplasmic strategy (Listeria, L. monocytogenes, Gram positive) and examine subsequently whether the results found can be generalized to other bacteria and their infection strategy. During infection, the metabolism of these organisms is intricately interacting and intertwined with the metabolism of the host. Only by this bacterial growth and further infection is possible. The description of metabolism in this situation allows furthermore to identify potential target proteins for antibiotic strategies and where a disturbance of the delicate metabolic interaction of host and parasite could modify or stop the infectious process (intensive collaboration with the bioanalytical platform, in particular PD Eisenreich, and with Prof. Hensel and Bumann (Salmonella), Prof. Fuchs (Listeria); further collaborations with Dr. Heuner (Legionella), Dr. Rudel (Chlamydia) and others). Furthermore, we want to understand the pathophysiology of the infection process, resulting amino acid fluxes, the utilization of different carbohydrate sources as well as metabolic bottle necks. Intervention strategies will be clarified as well as the effect of pathological factors, for instance infection specific transcription factors. In an iterative process new predictions will lead to new experiments and these to further improved models. The application of bioinformatics to these questions allows furthermore the development of new methods and improved predictions (including algorithms for improved modeling of (i) metabolism, (ii) metabolic networks and (iii) regulation of intracellular infection). All these techniques, software and results will be available for the whole SPP.

我们想要模拟细胞内感染的代谢。我们将比较液泡内策略（沙门氏菌、鼠伤寒沙门氏菌血清型（S. Typhimurium）革兰氏阴性）与细胞质策略（李斯特菌、单核细胞增生李斯特氏菌、革兰氏阳性），并随后检查发现的结果是否可以推广到其他细菌及其感染策略。在感染过程中，这些生物体的新陈代谢与宿主的新陈代谢错综复杂地相互作用并交织在一起。只有通过这种细菌生长和进一步感染才有可能。在这种情况下，对代谢的描述可以进一步确定抗生素策略的潜在靶蛋白，以及宿主和寄生虫之间微妙的代谢相互作用的干扰可以改变或停止感染过程（与生物分析平台，特别是 PD Eisenreich 以及 Hensel 和 Bumann 教授（沙门氏菌）、Fuchs 教授（李斯特菌）密切合作；与 Dr. Fuchs 博士（李斯特菌）的进一步合作。 Heuner（军团菌）、Rudel 博士（衣原体）等）。此外，我们希望了解感染过程的病理生理学、产生的氨基酸通量、不同碳水化合物来源的利用以及代谢瓶颈。将阐明干预策略以及病理因素的影响，例如感染特异性转录因子。在迭代过程中，新的预测将导致新的实验，并进一步改进模型。生物信息学在这些问题上的应用进一步促进了新方法的开发和改进的预测（包括用于改进（i）代谢、（ii）代谢网络和（iii）细胞内感染调节的建模的算法）。所有这些技术、软件和结果都将可供整个 SPP 使用。