Modeling metabolism in intracellular infections comparing Salmonella and Listeria

我们想建立细胞内感染的代谢模型。我们将比较液泡内策略（沙门氏菌，沙门氏菌血清型鼠伤寒沙门氏菌（S。鼠伤寒杆菌（Typhimurium）革兰氏阴性）与细胞质策略（李斯特菌（Listeria），L.单核细胞增多症，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。在感染期间，这些生物体的代谢与宿主的代谢错综复杂地相互作用和交织。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，代谢的描述允许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）;与Heuner博士（军团菌）、Rudel博士（衣原体）等进一步合作）。此外，我们希望了解感染过程的病理生理学，导致氨基酸通量，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。将生物信息学应用于这些问题还允许开发新的方法和改进的预测（包括用于改进（i）代谢、（ii）代谢网络和（iii）细胞内感染的调节的建模的算法）。所有这些技术、软件和结果将可用于整个SPP。

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.