RESUMEN
Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the Arylsulfatase A (ARSA) gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models. Notably, a model developed by participants of a genetics and coding bootcamp, trained with standard machine-learning tools in Python, demonstrated superior performance among submissions. Furthermore, the study observed that state-of-the-art deep learning methods provided small but statistically significant improvement in predictive performance compared to less elaborate techniques. These findings underscore the utility of variant effect prediction, and the potential for models trained with modest resources to accurately classify VUS in genetic and clinical research.
RESUMEN
Rhamnolipids produced by P. aeruginosa MR01 were fractionated into mono- and di-rhamnolipids, and their dominant congeners, Rha-C10-C10 and Rha-Rha-C10-C10, were shown by mass spectrometry. Minimum surface tensions and critical micelle concentrations (CMC) were determined as "≃34â¯mN/m; ≃26.17â¯mg/l;" and "≃29â¯mN/m; ≃29.63â¯mg/l" for mono- and di-rhamnolipids, respectively. Spectrophotometry measurements provided a close approximation of CMC. Contact angle and diameter of wet area were determined for rhamnolipid-containing drops on hydrophobic paper to display their capability for alteration of surface wettability. Wet area measurement is a simple, reliable method not requiring a Drop Shape Analyzer. Cell viabilities determined by MTT assay showed a decline in a dose-dependent manner and estimated IC50 values were 25.87⯵g/ml and 31.00⯵g/ml for mono- and di-rhamnolipids treating MCF-7 cells for 48â¯h. Morphological observations using the inverted phase-contrast microscopy and fluorescence microscopy via Hoechst staining revealed the apoptotic characteristics in treated MCF-7 cells. The semi-quantitative RT-PCR method demonstrated that expression of the p53â¯gene in mRNA levels significantly (Pâ¯<â¯0.05) increased when treated with 30⯵g/ml of each rhamnolipid compound for 12â¯h. It can be concluded that rhamnolipids derived from MR01 show significant anticancer potential against MCF-7 cell line and should be further investigated as natural, therapeutic anti-tumor agents.