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TR, S.

Publications and source records attributed to TR, S..

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Dynamic regulation of the Bcl-xL-BAD interaction

BackgroundThe interaction between the anti-apoptotic protein Bcl-xL and the BH3-only sensitizer BAD represents a critical regulatory checkpoint in the intrinsic apoptotic pathway. Although this interaction is known to influence mitochondrial fate, its dynamic regulation and structural determinants in living cells remain poorly understood. Here, we developed a fluorescence lifetime imaging microscopy-based Forster resonance energy transfer (FLIM-FRET) platform to visualize and quantify Bcl-xL-BAD interactions in real-time. MethodsWe developed a quantitative fluorescence lifetime-based FRET (FLIM-FRET) approach to visualize and measure Bcl-xL-BAD interactions in single living glioblastoma cells. Stable GFP/Venus-Bcl-xL and mCherry-BAD FRET pairs were created, followed by acceptor photobleaching FRET, FLIM-FRET, Annexin V-BFP-based apoptosis assays, pharmacological perturbation using BH3 mimetics, and molecular dynamics simulations with MM/GBSA analysis. Statistical significance was assessed using appropriate parametric tests across multiple independent experiments. ResultsUsing this platform, we observed that apoptotic stress markedly enhances the engagement of Bcl-xL and BAD. Increased FRET efficiency coincided with Annexin V positivity and nuclear condensation, indicating that maximal BAD binding reflects a higher level of apoptotic commitment. Structure-function analysis using targeted Bcl-xL mutants revealed distinct binding requirements: disruption of the core hydrophobic groove (Y101K) abolished BAD binding and impaired BH3 mimetic sensitivity, whereas mutation within the BH1 domain (G138A) preserved BAD interaction and sensitivity to BH3 mimetics. Molecular dynamics simulations corroborated these observations by revealing preserved BAD-binding energetics in the G138A mutant, but destabilization in the Y101K mutant. ConclusionsTogether, these findings demonstrate the utility of a live-cell FLIM-FRET platform for resolving protein-protein interactions involving apoptotic proteins at the single-cell level. By linking interaction dynamics, structural determinants, and functional outcomes, this approach provides a broadly applicable framework for studying apoptotic priming, structural tolerance at BCL-2 family interfaces, and cellular responses to BH3-mimetic therapies.

Cell Biology↗

BC-Predict Database: A Curated Resource of Experimentally Validated Markers in Multidrug Resistance in Breast Cancer

BackgroundIn this study, we aim to develop a yearly updatable database that could predict chemotherapeutic drug resistance and overall survival probability in breast cancer patients. Existing drug sensitivity databases depend on correlation-based predictions. In our study, candidates involved in drug resistance are chosen based on cell line validation (overexpression or downregulation or inhibition of candidates) studies, curated manually. Method28,773 mRNA expression signatures from 914 breast cancer patients were extracted from cProsite. 106 of these patients had clinical information and log2 fold change information required for this study. We categorized these patients into deceased and surviving groups from TCGA. To prepare a database that can predict drug resistance and overall survival, we included mRNAs that were over-expressed in at least 80% of the breast cancer patients and mRNAs over-expressed in deceased and surviving groups. In addition, we also reported breast cancer-associated drug resistance candidates which have been reported in cell-line based studies. The database matrix preparation involved an approximate of 15000 manual searches of cell validated studies. (750 candidates x 20 drugs). The database was validated using a publicly available breast cancer patient proteomics data. ResultsOur analysis identified a list of top priority candidates associated with multidrug resistance, categorized based on their resistance to >15 drugs, 5-15 drugs, and 2-4 drugs. Analysis of patient profiles in the database revealed that the number of proteins contributing to drug resistance was high in the poor prognosis category compared to the good prognosis category. ConclusionsOur study highlights the probable gaps in breast cancer drug resistance research, as only a small subset of overexpressed mRNA candidates found in patients are studied in vitro or in vivo experiments focusing on drug resistance. We also identified candidates involved in multidrug resistance, whose role in drug resistance has not been studied in more than 15 drugs. After further validations, this will benefit the clinicians and upcoming CRISPR gene therapeutics.

cancer biology↗