Investigating the bacterial response to antimicrobials in kidney organoids.

细菌性尿路感染（uti）非常常见；50%的导管患者会出现尿路感染，占医院获得性感染的40%，33%的健康女性在24岁之前会出现尿路感染。严重的尿路感染会损害肾脏，并可能导致败血症，因此迫切需要更好地了解和治疗这些感染。大肠杆菌是主要病因，30%的尿路感染现在对经典治疗方法复方新诺恶唑具有耐药性，导致环丙沙星或甲氧苄啶等其他抗生素的使用增加。确定如何有效地使用抗生素治疗尿路感染，需要一个现实的尿路感染模型，但没有好的动物模型；老鼠只有在注射大量不现实的细菌剂量时才会感染尿路感染，而猪是最好的模型，也是不现实的。戴维斯实验室开创了肾类器官的生产，可以从人类iPS细胞中制造它们，现在可以制造输尿管。因此，在干细胞衍生的人类类器官中研究细菌对抗菌剂的反应将是一个极好的、可获得的、合乎伦理的模型。El Karoui实验室已经证明，在大肠杆菌中，对环丙沙星和甲氧苄啶的反应，即所谓的SOS反应，在不同的细菌之间是高度不同的，有一种细胞亚群表现出非常高的SOS诱导，这可能导致对这些抗生素的耐受性和耐药性增加。因此，该项目的重点是结合两位导师的专业知识，开发肾脏类器官细菌感染的临床相关模型，并跟踪细菌和肾脏细胞对感染和抗生素治疗的反应。它将细胞和分子生物学与尖端的定量显微镜相结合，对感染过程进行长期成像。定量图像分析将利用基于深度学习的新开发的分割算法。实验结果将用于告知在类器官中抗生素暴露组合下细菌细胞生长的数学模型。

Bacterial urinary tract infections (UTIs) are very common; 50% of catheterized patients develop UTIs, which are 40% of hospital-acquired infections, and 33% of otherwise-healthy women will suffer a UTI by 24. Serious cases of UTIs damage kidneys and can lead to sepsis underscoring the urgent need to better understand and treat these infections. Escherichia coli is the dominant cause, and 30% of UTIs are now resistant to the classic treatment, co-trimoxazole4, leading to increased use of other antibiotics such as ciprofloxacin or trimethoprim.Determining how efficient antibiotic treatments can be used against UTIs, requires a realistic UTI model but there are no good animal models; mice get UTIs only if injected with massive, unrealistic bacterial doses and pigs, the best models, are impractical. The Davies lab pioneered the production of renal organoids and can make them from human iPS cells, and can now make ureters. Therefore, studying the bacterial response to antimicrobials within a stem cell-derived human organoids would be an excellent, accessible, ethical model. The El Karoui lab has shown that in E. coli the response to ciprofloxacin and trimethoprim, called the SOS response, is highly variable between bacteria with a sub-population of cells showing very high SOS induction, probably leading to increased tolerance and resistance to these antibiotics.The project therefore focuses on combining the expertise of both supervisors to develop a clinically relevant model of bacterial infection in kidney organoids and follow the bacterial and kidney cells response to infection and treatment with antibiotics. It will combine cell and molecular biology with cutting-edge quantitative microscopy to image the infection process over time. Quantitative image analysis will make use of newly developed segmentation algorithms based on deep learning. The experimental results will be used to inform mathematical models of bacterial cell growth under combinations of antibiotics exposure in the organoids.