five

Data_Sheet_1_Extreme Drug Tolerance of Mycobacterium abscessus “Persisters”.PDF

收藏
NIAID Data Ecosystem2026-03-11 收录
下载链接:
https://figshare.com/articles/dataset/Data_Sheet_1_Extreme_Drug_Tolerance_of_Mycobacterium_abscessus_Persisters_PDF/11930427
下载链接
链接失效反馈
官方服务:
资源简介:
Persistence of infection despite extensive chemotherapy with antibiotics displaying low MICs is a hallmark of lung disease caused by Mycobacterium abscessus (Mab). Thus, the classical MIC assay is a poor predictor of clinical outcome. Discovery of more efficacious antibiotics requires more predictive in vitro potency assays. As a mycobacterium, Mab is an obligate aerobe and a chemo-organo-heterotroph – it requires oxygen and organic carbon sources for growth. However, bacteria growing in patients can encounter micro-environmental conditions that are different from aerated nutrient-rich broth used to grow planktonic cultures for MIC assays. These in vivo conditions may include oxygen and nutrient limitation which should arrest growth. Furthermore, Mab was shown to grow as biofilms in vivo. Here, we show Mab Bamboo, a clinical isolate we use for Mab drug discovery, can survive oxygen deprivation and nutrient starvation for extended periods of time in non-replicating states and developed an in vitro model where the bacterium grows as biofilm. Using these culture models, we show that non-replicating or biofilm-growing bacteria display tolerance to clinically used anti-Mab antibiotics, consistent with the observed persistence of infection in patients. To demonstrate the utility of the developed “persister” assays for drug discovery, we determined the effect of novel agents targeting membrane functions against these physiological forms of the bacterium and find that these compounds show “anti-persister” activity. In conclusion, we developed in vitro “persister” assays to fill an assay gap in Mab drug discovery compound progression and to enable identification of novel lead compounds showing “anti-persister” activity.
创建时间:
2020-03-04
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作