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Kagan, A.

Publications and source records attributed to Kagan, A..

2 recordsLinked to original sources

A kinome inhibitor screen implicates adhesion and growth factor signaling in cellular recovery after caspase activation

Apoptosis is a common form of regulated cell death and requires cysteine-aspartic proteases called effector caspases. Caspase activation triggers positive feedback, leading to the idea that apoptosis is irreversible. However, we and others have demonstrated that cancer cells can survive effector caspase activation and become more aggressive and drug-resistant as a result. Despite the profound implications of apoptotic reversal, also known as anastasis, for both regenerative medicine and cancer therapy, the molecular pathways that enable cells to survive executioner caspase activation remain largely unmapped. To systematically dissect this phenomenon, we developed a quantitative screening platform that combines inducible caspase activation with kinome-wide pharmacological profiling. This approach uniquely allowed us to: (1) identify pharmacological modulators of post-caspase survival, (2) identify specific kinases regulating post-caspase survival, and (3) distinguish general toxicity from anastasis effects. This approach implicated regulators of cell adhesion and the cytoskeleton, consistent with the known rounding of apoptotic cells and respreading during recovery. Growth factor signaling also emerged from the analysis. In addition to its expected effects on unstressed cells, fetal bovine serum markedly increased anastasis. Some growth factor combinations were more effective than individual ones at recapitulating the serum effect. Similarly, pleiotropic kinase inhibitors were generally more effective than selective ones. Nevertheless, selective Rho kinase inhibition significantly enhanced anastasis whereas Akt inhibition impaired it, suggesting that these kinases serve as central nodes that integrate multiple upstream inputs. Beyond identifying specific kinase targets, our work provides a framework for anti-anastasis therapies that could prevent cancer cell recovery after chemotherapy.

cell biology↗

Finetuning Foundation Models for Temporal Clinical Transcriptomics Data

BackgroundTimeseries clinical transcriptomic datasets offer the opportunity to gain insights into the dynamics of disease mechanisms/treatment responses. However, their utility in uncovering temporal patterns is often limited by high noise levels and small sample sizes. Leveraging foundational gene embeddings and incorporating interaction information can help address these challenges, improve gene network analysis, and enable the detection of subtle changes that drive disease progression or drug response. ResultsWe finetuned gene embeddings from foundation models using healthy tissue gene expression data and used them in temporal GNNs to model gene expression of responder and non-responders to treatment in 3 disease datasets - ulcerative colitis, Crohns disease and psoriasis. Application of our method to these datasets confirmed known mechanisms associated with drug action, and also identified key differences between activated and repressed pathways for responders and non responders including B-Cell activation and mitochondria related activity in ulcerative colitis patients. ConclusionFinetuning gene embeddings from foundation models provide a richer context to model gene expression data compared to using them in their naive state. Even with smaller sample sizes, results from GNN-based temporal models outperform traditional methods by detecting known mechanisms of response and unraveling role of genes and mechanisms not known to be associated with response and non-response. Code AvailabilityCode and data are available in a public GitHub repository - https://github.com/Sanofi-Public/GNN-Timeseries

bioinformatics↗