Mitochondrial quality control is essential for maintaining cellular and tissue homeostasis. Mitophagy, the selective autophagic removal of damaged mitochondria, is a central component of this process, and defects in mitophagy are increasingly linked to neurodegeneration, cardiovascular disease, cancer, and inherited mitochondrial disorders. Ubiquitin-dependent tagging of outer mitochondrial membrane proteins is a major mechanism for marking damaged mitochondria for clearance; however, recent advances reveal that mitochondrial deubiquitinases (DUBs) shape ubiquitin signaling at damaged mitochondria, thereby influencing the efficiency and selectivity of mitochondrial turnover. Moreover, DUBs are emerging as context-dependent editors of the mitochondrial ubiquitin code that link mitophagy to disease pathogenesis and therapeutic intervention. Here, we synthesize current understanding of mitochondrial DUBs in physiology and disease and discuss emerging pharmacological strategies to guide the development of mitophagy-targeted therapeutics.
Yang Xu, Bahareh Behrouz, Andrew Lee et al.· TIPS - Trends in Pharmacolog...· 0 citations
Metastasis remains the leading cause of cancer-related mortality, yet predicting future metastasis is a major clinical challenge due to the lack of validated biomarkers and effective assessment methods. Here, we present EmitGCL, a deep-learning framework that accurately predicts future metastasis and its corresponding biomarkers. Based on a comprehensive benchmarking comparison, EmitGCL outperforms other computational tools across six cancer types from seven cohorts of patients with superior sensitivity and specificity. It captures occult metastatic cells in a patient with a lymph node-negative breast cancer, who was declared to have no evidence of disease by conventional imaging methods but was later confirmed to have metastatic disease. Notably, EmitGCL identifies HSP90AA1 and HSP90AB1 as predictable biomarkers for future breast cancer metastasis, which we validate by in-vitro pharmacological inhibition of HSP90 that reduced breast cancer cell migration and further support across five independent cohorts of patients (n = 420). Furthermore, we demonstrate YY1 transcription factor as a key driver of breast cancer metastasis, which we corroborate with in-silico, CRISPR-based migration assays, and in vivo mouse lung colonization experiments, suggesting that YY1 is a potential therapeutic target for further investigation. Predicting future metastases remains a major clinical challenge. Here, the authors develop EmitGCL, a deep-learning framework to predict metastasis and related biomarkers using cancer single-cell sequencing data, enabling and validating the discovery of occult metastases and breast cancer metastasis biomarkers.
Xiaoying Wang, Maoteng Duan, Anthony J. Snyder et al.· Nature Communications· 0 citations
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