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Title: High-confidence assessment of functional impact of human mitochondrial non-synonymous genome variations by APOGEE}
Article:
Abstract:
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.
Introduction:
Assessing the pathogenicity of genome mutations is a notoriously onerous task both in-vitro and in-vivo, and occasionally even unviable because of the paucity of funds or of proper analytical facilities. This is particularly true when dealing with the mitochondrial DNA, which is less studied, although significantly smaller, than the nuclear counterpart[1]. This task was massively faced from a computational point of view though, and a growing number of algorithms and software packages, which elaborate sequence, structural and functional data to yield plausible evaluations of the harmfulness of variant amino acids in the form of pathogenicity scores and categorical, often dichotomous, variables, were implemented and released over time. Their assessments of pathogenicity are actual predictions, whose global congruency was deeply investigated by a few comparative studies[2–7]. Generally, only 60–70% agreement resulted when considering all the possible human non-synonymous variants. No single predictor emerged, neither in terms of classification accuracy, nor of specificity and sensitivity[6]. Similar results were achieved when considering only a subset of 173 validated disease-causing mitochondrial mutations taken from MITOMAP[8]: 64% were deemed possibly or probably damaging by PolyPhen-2, 62% as being high or medium impact variants by MutationAssessor and 61% as being deleterious by PROVEAN. The worst performance was achieved by SIFT, with only 16% of true positives, and by FatHmm that correctly classified only one variant on 173. Even with this subset of validated mutations or with those falling in ultraconserved genomic loci, predictions were broadly incongruent. Reasons for that were ascribed to the intrinsic differences between computational/statistical methods and reference databases, or between training datasets and alignment algorithms[2–7]. These facts drove the development of the so called aggregators or meta-predictors, namely those software packages that yield an evaluation of pathogenicity based on the outcomes of other reference predictors, as well as of databases of nuclear and mitochondrial precomputed predictions[9–12]. Even these were contrasting[2]. Due to their high incongruence and since almost all existing predictors were tailored to the nuclear genome, which is an important contributing factor to their modest classification performance and incongruence, we designed APOGEE. It grounds on three milestones: it feeds third-party predictors with features that are strictly related to the 13 mitochondrial proteins, like multi-alignments and amino acids conservation estimates; its reasoning strategy was tuned on finely curated, non-overlapping, training sets of variations; its predicting model was based on decision tree learning in order to provide investigable rules of pathogenicity.
Discussion:
The proportion of neutral and pathogenic amino acid changing variants that occur in a gene sequence mainly depends on the mutational pressure, genetic drift and both purifying and adaptive selection. These evolutionary mechanisms are generally considered gene-specific, thus making difficult the identification of potential deleterious mutations without any knowledge of the gene-specific level of tolerance to mutations. Therefore, taking into account the evolutionary measure of a gene or of a gene family, meant as the ratio between the non-synonymous and synonymous substitution rates, as calculated in a set of aligned orthologous sequences[15], could dramatically increase the sensitivity of the predictors. A beneficial effect might also be conferred by the RVIS index[16], which determines which nuclear genes are more intolerant to missense mutations. These two indices might provide useful insights in the understanding of the different evolutionary dynamics of genes and will be integrated in future releases of APOGEE. The functional role of each mutant residue is greatly influenced by the co-inherited missense variants within the very same protein or within structurally/functionally associated proteins. This “coevolutionary issue” has been poorly investigated so far, although it is well known that human pathogenic mutations can also be present within other species, without no deleterious effects, because they are probably compensated by co-inherited intra- or inter- gene mutations. Currently, a novel computational strategy has been developed in order to identify human pathogenic mutations that are compensated in extra-specific genomes( Compensated Pathogenic Deviations), i. e., their damaging effects are counterbalanced by other fixed mutations that are absent in humans[17]. A relevant proportion( 3–10%) of human damaging missense mutations has been identified in mammal and vertebrate protein alignments, indicating that compensatory mechanisms exist( sequences are assumed to derive from healthy animal organisms) at different evolutionary ages[17]. The identification of coevolving residue pairs is impeded, at any rate, by the paucity of appropriate experimental data. Knowledge of the ternary and quaternary structures of mitochondrial and nuclear OXPHOS proteins could contribute to resolve the inconsistencies among computational pathogenicity predictions and diseases association[18]. This aspect will also be taken into consideration in the next releases of APOGEE.
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 .