Investigating the bacterial response to antimicrobials in kidney organoids.

细菌性尿路感染（UTI）非常常见; 50%的导管插入患者发生UTI，占医院获得性感染的40%，33%的其他健康女性将在24岁时患上UTI。严重的尿路感染会损害肾脏，并可能导致败血症，这强调了更好地了解和治疗这些感染的迫切需要。大肠杆菌是主要原因，30%的UTI现在对经典治疗药物复方新诺明耐药，导致其他抗生素如环丙沙星或甲氧苄啶的使用增加。确定如何有效地使用抗生素治疗UTI，需要一个现实的UTI模型，但没有良好的动物模型;老鼠只有在注射大量不切实际的细菌剂量时才会患上尿路感染，而猪，最好的模型，是不切实际的。戴维斯实验室率先生产肾类器官，可以从人类iPS细胞中制造它们，现在可以制造输尿管。因此，研究干细胞衍生的人类类器官内细菌对抗菌剂的反应将是一个优秀的，可访问的，道德模型。El Karoui实验室已经证明，在E.大肠杆菌对环丙沙星和甲氧苄啶的反应，称为SOS反应，在具有显示非常高SOS诱导的细胞亚群的细菌之间是高度可变的，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主管的专业知识，开发一种临床相关的肾脏类器官细菌感染模型，并跟踪细菌和肾脏细胞对感染和抗生素治疗。它将结合联合收割机细胞和分子生物学与尖端的定量显微镜成像感染过程随着时间的推移。定量图像分析将利用新开发的基于深度学习的分割算法。实验结果将用于为类器官中抗生素暴露组合下细菌细胞生长的数学模型提供信息。

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.