Mitochondria are central actors in diverse physiological phenomena ranging from energy metabolism to stress signaling and immune modulation. Accumulating scientific evidence points to the critical inv Show more
Mitochondria are central actors in diverse physiological phenomena ranging from energy metabolism to stress signaling and immune modulation. Accumulating scientific evidence points to the critical involvement of specific mitochondrial-associated events, including mitochondrial quality control, intercellular mitochondrial transfer, and mitochondrial genetics, in potentiating the metastatic cascade of neoplastic cells. Furthermore, numerous recent studies have consistently emphasized the highly significant role mitochondria play in coordinating the regulation of tumor-infiltrating immune cells and immunotherapeutic interventions. This review provides a comprehensive and rigorous scholarly investigation of this subject matter, exploring the intricate mechanisms by which mitochondria contribute to tumor metastasis and examining the progress of mitochondria-targeted cancer therapies. Show less
Background Hypoxia is a common feature of many solid tumors and causes radiotherapy and immunotherapy resistance. Pharmacological inhibition of oxidative phosphorylation (OXPHOS) has emerged as a the Show more
Background Hypoxia is a common feature of many solid tumors and causes radiotherapy and immunotherapy resistance. Pharmacological inhibition of oxidative phosphorylation (OXPHOS) has emerged as a therapeutic strategy to reduce hypoxia. However, the OXPHOS inhibitors tested in clinical trials caused only moderate responses in hypoxia alleviation or trials were terminated due to dose-limiting toxicities. To improve the therapeutic benefit, FDA approved OXPHOS inhibitors (e.g. atovaquone) were conjugated to triphenylphosphonium (TPP + ) to preferentially target cancer cell’s mitochondria. In this study, we evaluated the hypoxia reducing effects of several mitochondria-targeted OXPHOS inhibitors and compared them to non-mitochondria-targeted OXPHOS inhibitors using newly developed spheroid models for diffusion-limited hypoxia. Methods B16OVA murine melanoma cells and MC38 murine colon cancer cells expressing a HIF-Responsive Element (HRE)-induced Green Fluorescent Protein (GFP) with an oxygen-dependent degradation domain (HRE-eGFP-ODD) were generated to assess diffusion-limited hypoxia dynamics in spheroids. Spheroids were treated with IACS-010759, atovaquone, metformin, tamoxifen or with mitochondria-targeted atovaquone (Mito-ATO), PEGylated mitochondria-targeted atovaquone (Mito-PEG-ATO) or mitochondria-targeted tamoxifen (MitoTam). Hypoxia dynamics were followed and quantified over time using the IncuCyte Zoom Live Cell-Imaging system. Results Hypoxic cores developed in B16OVA.HRE and MC38.HRE spheroids within 24 h hours after seeding. Treatment with IACS-010759, metformin, atovaquone, Mito-PEG-ATO and MitoTam showed a dose-dependent reduction of hypoxia in both B16OVA.HRE and MC38.HRE spheroids. Mito-ATO only alleviated hypoxia in MC38.HRE spheroids while tamoxifen was not able to reduce hypoxia in any of the spheroid models. The mitochondria-targeted OXPHOS inhibitors demonstrated stronger anti-hypoxic effects compared to the non-mito-targeted OXPHOS inhibitors. Conclusions We successfully developed a high-throughput spheroid model in which hypoxia dynamics can be quantified over time. Using this model, we showed that the mitochondria-targeted OXPHOS inhibitors Mito-ATO, Mito-PEG-ATO and MitoTam reduce hypoxia in tumor cells in a dose-dependent manner, potentially sensitizing hypoxic tumor cells for radiotherapy. Supplementary Information The online version contains supplementary material available at 10.1186/s40170-024-00342-6. Show less
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of pr Show more
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings-most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science. Show less
W W Cleland, P A Frey, J A Gerlt · 1998 · The Journal of biological chemistry · American Society for Biochemistry and Molecular Biology · added 2026-04-20