Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A.

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.

Differentially expressed gene (DEG) based protein-protein interaction (PPI) network identifies a spectrum of gene interactome, transcriptome and correlated miRNA in nondisjunction Down syndrome.

Down syndrome, a genetic disorder of known attribution reveals several types of brain abnormalities resulting in mental retardation, inadequacy in speech and memory. In this study, we have presented a consolidative network approach to comprehend the intricacy of the associated genes of Down syndrome. In this analysis, the differentially expressed genes (DEG's) were identified and the central networks were constructed as upregulated and downregulated. Subsequently, GNB5, CDC42, SPTAN1, GNG2, GNAZ, PRKACB, SST, CD44, FGF2, PHLPP1, APP, and FYN were identified as the candidate hub genes by using topological parameters. Later, Fpclass a PPI tool identified WASP gene, a co-expression interacting partner with highest network topology. Moreover, an enhanced enrichment pathway namely Opioid signaling was obtained using ClueGo, depicting the roles of the hub genes in signaling and neuronal mechanisms. The transcriptional regulatory factors and the common miRNA connected to them were identified by using MatInspector and miRTarbase. Later, a regulatory network constructed showed that PLAG, T2FB, CREB, NEUR, and GATA were the most commonly connected transcriptional factors and hsa-miR-122-5p was the most prominent miRNA. In a nutshell, these hub genes and the enriched pathway could help understand at a molecular level and eventually used as therapeutic targets for Down syndrome.