Trypanosoma brucei, a parasitic protozoan, is the infectious agent of a deadly disease in humans and livestock known as African trypanosomiasis. TbTim17 is the major component of the TbTIM complex that imports hundreds of nuclear-encoded proteins into the mitochondria. Here, we show that depletion of TbTim17 increased T. brucei tolerance to paraquat, increasing the EC50 by 1.6- to 1.9-fold compared with the wild type. Subsequent analysis revealed that increased reactive oxygen species levels resulting from TbTim17 knockdown preadapt cells to resist paraquat-induced oxidative stress. This is supported by the finding that treating cells with N-acetylcysteine during TbTim17 RNAi induction reduced the paraquat EC50 to wild-type levels. TbTim17 knockdown reduced the levels of several mitochondrial proteins, except SOD A. These cells were also tolerant to heat stress. Interestingly, in wild-type parasites, TbTim17 levels increased under oxidative and heat stress, suggesting it has a protective role under stress. Furthermore, TbTim17 knockdown induces the SLS response, a trypanosome-specific ER stress-response pathway, suggesting a communication between two organelles during stress in T. brucei. Understanding the role of mitochondrial protein translocases in the mitochondrial stress response in T. brucei is critical for elucidating the mechanisms of adaptation to environmental stresses and drug resistance in this parasite.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.