Biochemical Activity of 17 Cancer-Associated Variants of DNA Polymerase Kappa Predicted by Electrostatic Properties

Decoding driver and phenotypic genes in cancer: Unveiling the essence behind the phenomenon

Gray hair, widely regarded as a hallmark of aging. While gray hair is associated with aging, reversing this trait through gene targeting does not alter the fundamental biological processes of aging. Similarly, certain oncogenes (such as CXCR4, MMP-related genes, etc.) can serve as markers of tumor behavior, such as malignancy or prognosis, but targeting these genes alone may not lead to tumor regression. We pioneered the name of this class of genes as "phenotypic genes". Historically, cancer genetics research has focused on tumor driver genes, while genes influencing cancer phenotypes have been relatively overlooked. This review explores the critical distinction between driver genes and phenotypic genes in cancer, using the MAPK and PI3K/AKT/mTOR pathways as key examples. We also discuss current research techniques for identifying driver and phenotypic genes, such as whole-genome sequencing (WGS), RNA sequencing (RNA-seq), RNA interference (RNAi), CRISPR-Cas9, and other genomic screening methods, alongside the concept of synthetic lethality in driver genes. The development of these technologies will help develop personalized treatment strategies and precision medicine based on the characteristics of relevant genes. By addressing the gap in discussions on phenotypic genes, this review significantly contributes to clarifying the roles of driver and phenotypic genes, aiming at advancing the field of targeted cancer therapy.

Biochemical Characterization of Disease-Associated Variants of Human Ornithine Transcarbamylase

Human ornithine transcarbamylase deficiency (OTCD) is the most common ureagenesis disorder in the world. OTCD is an X-linked genetic deficiency in which patients experience hyperammonemia to varying degrees depending on the severity of the genetic mutation. More than two-thirds of the known mutations are caused by single nucleotide substitutions. In this paper, partial order optimum likelihood (POOL), a machine learning method, is used to analyze single nucleotide substitutions in OTC with varying disease phenotypes and predicted catalytic efficiencies. Specifically, we used a computed metric, μ4, a measure of the degree of coupling between an ionizable residue and its neighbors, calculated for the catalytic residues, to identify which protein variants were most likely to have impacted catalytic activities. From this analysis, 17 disease-associated variants were selected plus one additional variant, representing a range of μ4 values and POOL ranks. Then μ4 predictions were compared with established bioinformatics tools, SIFT, PolyPhen-2, Provean, FATHMM, MutPred2, and MutationTaster2. The bioinformatics tools predicted that most of these mutations are deleterious. The variants were biochemically characterized using kinetics assays, size exclusion chromatography, and differential scanning fluorimetry. POOL combined with μ4 analysis was able to predict correctly which variants were catalytically hindered in vitro for 17 out of 18 variants. Then by expressing a subset of these proteins in cell culture, mechanisms for disease were proposed. Analysis using μ4 is a complementary method to the sequence-based bioinformatics tools for predicting the effects of mutation on catalytic function.