👤 Tariq Masoodi

The viability of cells and the integrity of the genome depend on the detection and repair of damaged DNA through intricate mechanisms. Cancer treatment employs chemotherapy or radiation therapy to eliminate neoplastic cells by causing substantial damage to their DNA. In many cases, improved DNA repair mechanisms lead to resistance to these medicines; therefore, it is essential to expand efforts to develop drugs that can sensitise cells to these treatments by inhibiting the DNA repair process. Multiple studies have demonstrated a correlation between the overexpression of Apurinic/Apyrimidinic Endonuclease (APE1), the primary mammalian enzyme responsible for excising apurinic or apyrimidinic sites in DNA, and the resistance of cells to cancer therapies; in contrast, APE1 downregulation increases cellular susceptibility to DNA-damaging agents. Thus, the effectiveness of existing therapies can be improved by promoting the targeted sensitization of cancer cells while protecting healthy cells. The current study aims to employ explainable artificial intelligence (XAI) to enhance the accuracy and reliability of machine learning models for the prediction of APE1 inhibitors. Various ML-based regression models are employed to predict the pIC50 value of different medicines. Bayesian optimization and the Permutation Feature Importance (PFI) approach are employed to determine the best hyperparameters of machine learning models and to discover the most significant features for recognizing drug candidates that target APE1 enzymes, respectively. To acquire comprehensive elucidations for the predictive models in our research, two XAI methodologies, namely SHAP and LIME, are used. The SHAP analysis reveals that the features 'C1SP2' and 'ASP-2' are essential in influencing the model's predictions. The SHAP values demonstrate variability for features such as 'maxHBint2' and 'GATS1s,' signifying that their impact is dependent on specific instances within the dataset. The LIME study corroborates these findings, demonstrating that 'C1SP2' and 'ASP-2' are the most significant positive contributors, whereas features like 'SHCHnX,' 'nHdCH2,' and 'GATS1s' result in a decrease in the predicted values. Due to the limited sample size of the APE1 dataset, direct training on this dataset posed challenges in model generalization and reliability. To overcome this limitation, the BACE-1 dataset is leveraged for model training, enabling the ML models to learn from a more extensive and diverse chemical space. Among the tested algorithms, XGBoost demonstrated superior predictive performance, achieving R Show less

Protein ubiquitination is one of the most crucial posttranslational modifications responsible for regulating the stability and activity of proteins involved in homeostatic cellular function. Inconsistencies in the ubiquitination process may lead to tumorigenesis. Ubiquitin-specific peptidases are attractive therapeutic targets in different cancers and are being evaluated for clinical development. Ubiquitin-specific peptidase 37 (USP37) is one of the least studied members of the USP family. USP37 controls numerous aspects of oncogenesis, including stabilizing many different oncoproteins. Recent work highlights the role of USP37 in stimulating the epithelial-mesenchymal transition and metastasis in lung and breast cancer by stabilizing SNAI1 and stimulating the sonic hedgehog pathway, respectively. Several aspects of USP37 biology in cancer cells are yet unclear and are an active area of research. This review emphasizes the importance of USP37 in cancer and how identifying its molecular targets and signalling networks in various cancer types can help advance cancer therapeutics. Show less

Nutrition during development affects risk of future cardiovascular disease. Relatively little is known about whether the amount and type of fat in the maternal diet affect vascular function in the offspring. To investigate this, pregnant and lactating rats were fed either 7%(w/w) or 21%(w/w) fat enriched in either 18:2n-6, trans fatty acids, saturated fatty acids, or fish oil. Their offspring were fed 4%(w/w) soybean oil from weaning until day 77. Type and amount of maternal dietary fat altered acetylcholine (ACh)-mediated vaso-relaxation in offspring aortae and mesenteric arteries, contingent on sex. Amount, but not type, of maternal dietary fat altered phenylephrine (Pe)-induced vasoconstriction in these arteries. Maternal 21% fat diet decreased 20:4n-6 concentration in offspring aortae. We investigated the role of Δ6 and Δ5 desaturases, showing that their inhibition in aortae and mesenteric arteries reduced vasoconstriction, but not vaso-relaxation, and the synthesis of specific pro-constriction eicosanoids. Removal of the aortic endothelium did not alter the effect of inhibition of Δ6 and Δ5 desaturases on Pe-mediated vasoconstriction. Thus arterial smooth muscle 20:4n-6 biosynthesis de novo appears to be important for Pe-mediated vasoconstriction. Next we studied genes encoding these desaturases, finding that maternal 21% fat reduced Fads2 mRNA expression and increased Fads1 in offspring aortae, indicating dysregulation of 20:4n-6 biosynthesis. Methylation at CpG -394 bp 5' to the Fads2 transcription start site predicted its expression. This locus was hypermethylated in offspring of dams fed 21% fat. Pe treatment of aortae for 10 minutes increased Fads2, but not Fads1, mRNA expression (76%; P<0.05). This suggests that Fads2 may be an immediate early gene in the response of aortae to Pe. Thus both amount and type of maternal dietary fat induce altered regulation of vascular tone in offspring though differential effects on vaso-relaxation, and persistent changes in vasoconstriction via epigenetic processes controlling arterial polyunsaturated fatty acid biosynthesis. Show less