The mitochondrial genome is a fundamental component of the eukaryotic domain of life, encoding for several important subunits of the respiratory chain, the main energy production system in cells. The processes by means of which mitochondrial DNA (mtDNA) replicates, expresses itself and evolves have been explored over the years, although various aspects are still debated. In this review, we present several key points in modern research on the role of evolutionary forces in affecting mitochondrial genomes in Metazoa. In particular, we assemble the main data on their evolution, describing the contributions of mutational pressure, purifying, and adaptive selection, and how they are related. We also provide data on the evolutionary fate of the mitochondrial synonymous variation, related to the nonsynonymous variation, in comparison with the pattern detected in the nucleus. Elevated mutational pressure characterizes the evolution of the mitochondrial synonymous variation, whereas purging selection, physiologically due to phenomena such as cell atresia and intracellular mtDNA selection, guarantees coding sequence functionality. This enables mitochondrial adaptive mutations to emerge and fix in the population, promoting mitonuclear coevolution.

A remarkable degree of genetic variation has been found in the protein-encoding regions of DNA through deep sequencing of samples obtained from thousands of subjects from several populations. Approximately half of the 20 000 single nucleotide polymorphisms present, even in normal healthy subjects, are nonsynonymous amino acid substitutions that could potentially affect protein function. The greatest challenges currently facing investigators are data interpretation and the development of strategies to identify the few gene-coding variants that actually cause or confer susceptibility to disease. A confusing array of options is available to address this problem. Unfortunately, the overall accuracy of these tools at ultraconserved positions is low, and predictions generated by current computational tools may mislead researchers involved in downstream experimental and clinical studies. First, we have presented an updated review of these tools and their primary functionalities, focusing on those that are naturally prone to analyze massive variant sets, to infer some interesting similarities among their results. Additionally, we have evaluated the prediction congruency for real whole-exome sequencing data in a proof-of-concept study on some of these web-based tools.

Mitochondrial DNA carries a tiny, but fundamental portion of the eukaryotic genetic code. As its nuclear counterpart, it is susceptible to point mutations. Their level of pathogenicity has been assessed for the newly discovered mutations only, leaving some degree of uncertainty on the potential impact of the unknown mutations. Here we present Mitochondrial mutation Impact (MitImpact), a queryable lightweight web interface to a reasoned collection of structurally and evolutionary annotated pathogenicity predictions, obtained by assembling pre-computed with on-the-fly-computed sets of pathogenicity estimations, for all the possible mitochondrial missense variants. It presents itself as a resource for fast and reliable evaluation of gene-specific susceptibility of unknown and verified amino acid changes. MitImpact is freely available at http://bioinformatics.css-mendel.it/ (tools section). ©2014 Wiley Periodicals, Inc.

Joubert syndrome is a clinically and genetically heterogeneous ciliopathy characterized by a typical cerebellar and brainstem malformation (the “molar tooth sign”), and variable multiorgan involvement. To date, 24 genes have been found mutated in Joubert syndrome, of which 13 also cause Meckel syndrome, a lethal ciliopathy with kidney, liver and skeletal involvement. Here we describe four patients with mild Joubert phenotypes who carry pathogenic mutations in either MKS1 or B9D1, two genes previously implicated only in Meckel syndrome.

24,189 are all the possible non-synonymous amino acid changes potentially affecting the human mitochondrial DNA. Only a tiny subset was functionally evaluated with certainty so far, while the pathogenicity of the vast majority was only assessed in-silico by software predictors. Since these tools proved to be rather incongruent, we have designed and implemented APOGEE, a machine-learning algorithm that outperforms all existing prediction methods in estimating the harmfulness of mitochondrial non-synonymous genome variations. We provide a detailed description of the underlying algorithm, of the selected and manually curated training and test sets of variants, as well as of its classification ability. The mitochondrion is an organelle floating in the cytoplasm of almost all eukaryotic cells. Its primary function is to generate energy. It contains an independent DNA (mtDNA), which is inherited maternally in many organisms. This DNA is highly susceptible to mutations since it does not possess the robust DNA repair mechanisms proper of the nuclear DNA. Mutations in the mtDNA were associated to several inherited and acquired mitochondrial diseases, including Alzheimer and Parkinson diseases, and cancer. The assessment of the mutation-disease causal link is an onerous task. It requires important laboratory skills/equipment and, often, an animal facility, which are not always available to any laboratory altogether. More and more often, one falls back on software solutions that rely on structural and functional characteristics of proteins to predict the putative harmfulness of a mutation. Many have been implemented and tested on the nuclear proteins, but only a few were finely tuned to the “neglected genome”. Our work not only presents APOGEE, a machine-learning-based predictor that outperforms all existing predictors in reliability and sensitivity, but it makes freely available the APOGEE’s predictions for all the mitochondrial missense mutations in MitImpact.

The programmed death 1 receptor (PD-1) and its ligand (PD-L1) are key molecules of immune checkpoint mechanisms in cancer and actually represent one of the main targets of immunotherapy. The predictive and prognostic values of PD-L1 expression alone in cancer patients is currently under debate due to the methodological assessment of PD-L1 expression and its temporal variations. Better detailed studies about the molecular basis of immunotherapy biomarkers are necessary. Here we summarize the current knowledge of PD-L1 gene modifications at genetic and epigenetic levels in different tumors, thus highlighting their reported correlation with cellular processes and potential impact on patient outcomes.