MiRLog and dbmiR: Prioritization and functional annotation tools to study human microRNA sequence variants.

The recent identification of noncoding variants with pathogenic effects suggests that these variations could underlie a significant number of undiagnosed cases. Several computational methods have been developed to predict the functional impact of noncoding variants, but they exhibit only partial concordance and are not integrated with functional annotation resources, making the interpretation of these variants still challenging. MicroRNAs (miRNAs) are small noncoding RNA molecules that act as fine regulators of gene expression and play crucial functions in several biological processes, such as cell proliferation and differentiation. An increasing number of studies demonstrate a significant impact of miRNA single nucleotide variants (SNVs) both in Mendelian diseases and complex traits. To predict the functional effect of miRNA SNVs, we implemented a new meta-predictor, MiRLog, and we integrated it into a comprehensive database, dbmiR, which includes a precompiled list of all possible miRNA allelic SNVs, providing their biological annotations at nucleotide and miRNA levels. MiRLog and dbmiR were used to explore the genetic variability of miRNAs in 15,708 human genomes included in the gnomAD project, finding several ultra-rare SNVs with a potentially deleterious effect on miRNA biogenesis and function representing putative contributors to human phenotypes.

Integrative CUT&Tag-RNA-Seq analysis of histone variant macroH2A1-dependent orchestration of human induced pluripotent stem cell reprogramming.

Aim: Human induced pluripotent stem cells (iPSCs) are inefficiently derived from somatic cells by overexpression of defined transcription factors. Overexpression of H2A histone variant macroH2A1.1, but not macroH2A1.2, leads to increased iPSC reprogramming by unclear mechanisms. Materials & methods: Cleavage under targets and tagmentation (CUT&Tag) allows robust epigenomic profiling of a low cell number. We performed an integrative CUT&Tag-RNA-Seq analysis of macroH2A1-dependent orchestration of iPSCs reprogramming using human endothelial cells. Results: We demonstrate wider genome occupancy, predicted transcription factors binding, and gene expression regulated by macroH2A1.1 during reprogramming, compared to macroH2A1.2. MacroH2A1.1, previously associated with neurodegenerative pathologies, specifically activated ectoderm/neural processes. Conclusion: CUT&Tag and RNA-Seq data integration is a powerful tool to investigate the epigenetic mechanisms occurring during cell reprogramming.

APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants.

Mitochondrial dysfunction has pleiotropic effects and is frequently caused by mitochondrial DNA mutations. However, factors such as significant variability in clinical manifestations make interpreting the pathogenicity of variants in the mitochondrial genome challenging. Here, we present APOGEE 2, a mitochondrially-centered ensemble method designed to improve the accuracy of pathogenicity predictions for interpreting missense mitochondrial variants. Built on the joint consensus recommendations by the American College of Medical Genetics and Genomics/Association for Molecular Pathology, APOGEE 2 features an improved machine learning method and a curated training set for enhanced performance metrics. It offers region-wise assessments of genome fragility and mechanistic analyses of specific amino acids that cause perceptible long-range effects on protein structure. With clinical and research use in mind, APOGEE 2 scores and pathogenicity probabilities are precompiled and available in MitImpact. APOGEE 2's ability to address challenges in interpreting mitochondrial missense variants makes it an essential tool in the field of mitochondrial genetics.

Connecting the dots: A practical evaluation of web-tools for describing protein dynamics as networks.

Protein Structure Networks (PSNs) are a well-known mathematical model for estimation and analysis of the three-dimensional protein structure. Investigating the topological architecture of PSNs may help identify the crucial amino acid residues for protein stability and protein-protein interactions, as well as deduce any possible mutational effects. But because proteins go through conformational changes to give rise to essential biological functions, this has to be done dynamically over time. The most effective method to describe protein dynamics is molecular dynamics simulation, with the most popular software programs for manipulating simulations to infer interaction networks being RING, MD-TASK, and NAPS. Here, we compare the computational approaches used by these three tools-all of which are accessible as web servers-to understand the pathogenicity of missense mutations and talk about their potential applications as well as their advantages and disadvantages.

KDM6A missense variants hamper H3 histone demethylation in lung squamous cell carcinoma.

KDM6A is the disease causative gene of type 2 Kabuki Syndrome, a rare multisystem disease; it is also a known cancer driver gene, with multiple somatic mutations found in a few cancer types. In this study, we looked at eleven missense variants in lung squamous cell carcinoma, one of the most common lung cancer subtypes, to see how they affect the KDM6A catalytic mechanisms. We found that they influence the interaction with histone H3 and the exposure of the trimethylated Lys27, which is critical for wild-type physiological function to varying degrees, by altering the conformational transition.