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Biology subjects

Daga, A.

Publications and source records attributed to Daga, A..

3 recordsLinked to original sources

Synergic microRNAs suppress human glioblastoma progression by modulating clinically relevant targets

Glioblastoma (GBM) is a highly aggressive brain tumor characterized by therapy-resistant glioma stem-like cells (GSCs) and extensive infiltration into surrounding brain tissue. MicroRNAs (miRNAs) are post-transcriptional regulators of oncogenic pathways, but their tumor-suppressive function is frequently lost in GBM. This study explores a multimodal therapeutic approach by restoring a combination of miRNAs to exploit their synergistic effects against GBM. Using patient-derived GBM cells cultured under stem cell-permissive conditions, we demonstrate that miRNA restoration reduces tumor growth, limits invasiveness, stemness and enhances sensitivity to temozolomide. In vivo studies in an orthotopic xenograft mouse model of GBM confirm the therapeutic efficacy and low toxicity of the nanoformulated miRNAs, following local injection. Multi-omics and computational analyses on different GBM subtypes reveal that these miRNAs synergistically suppress tumor-promoting extracellular matrix interactions, particularly through the collagen pathway, and downregulate genes associated with GBM progression. The identified miRNA targets correlate with glioma grade and poor patient prognosis, further underscoring their therapeutic potential. These findings highlight the promise of combinatorial miRNA therapy as a novel strategy for GBM treatment and suggest new molecular targets for theragnostic development.

cancer biology↗

LEP-AD: Language Embedding of Proteins and Attention to Drugs predicts drug target interactions

AO_SCPLOWBSTRACTC_SCPLOWPredicting drug-target interactions is a tremendous challenge for drug development and lead optimization. Recent advances include training algorithms to learn drug-target interactions from data and molecular simulations. Here we utilize Evolutionary Scale Modeling (ESM-2) models to establish a Transformer protein language model for drug-target interaction predictions. Our architecture, LEP-AD, combines pre-trained ESM-2 and Transformer-GCN models predicting binding affinity values. We report new best-in-class state-of-the-art results compared to competing methods such as SimBoost, DeepCPI, Attention-DTA, GraphDTA, and more using multiple datasets, including Davis, KIBA, DTC, Metz, ToxCast, and STITCH. Finally, we find that a pre-trained model with embedding of proteins (the LED-AD) outperforms a model using an explicit alpha-fold 3D representation of proteins (e.g., LEP-AD supervised by Alphafold). The LEP-AD model scales favorably in performance with the size of training data. Code available at https://github.com/adaga06/LEP-AD

bioinformatics↗

Dynamic constriction and fission of ER membranes by reticulon

The endoplasmic reticulum (ER) is a continuous cell-wide membrane network. Network formation has been widely associated with proteins producing membrane curvature and fusion, such as reticulons and atlastin. Regulated network fragmentation, occurring in different physiological contexts, is less understood. We found that the ER network has an embedded fragmentation mechanism based upon the ability of reticulons to produce fission of elongating network branches. In Drosophila, fission is counterbalanced by atlastin-driven fusion, with their imbalance leading to ER fragmentation. Live imaging of ER network dynamics upon ectopic expression of Drosophila reticulon linked fission to augmented membrane friction. Consistently, in vitro analysis revealed that purified reticulon produced velocity-dependent constriction and fission of lipid nanotubes pulled from a flat reservoir membrane. Fission occurred at elongation rates and pulling force ranges intrinsic to the ER network, thus suggesting a novel principle of organelle morphology regulation where the dynamic balance between fusion and fission is governed by membrane motility.

cell biology↗