The classification of human genetic variants into deleterious and neutral is a challenging issue, whose complexity is rooted in the large variety of biophysical mechanisms that can be responsible for disease conditions. For non-synonymous mutations in structured proteins, one of these is the protein stability change, which can lead to loss of protein structure or function. We developed a stability-driven knowledge-based classifier that uses protein structure, artificial neural networks and solvent accessibility-dependent combinations of statistical potentials to predict whether destabilizing or stabilizing mutations are disease-causing. Our predictor yields a balanced accuracy of 71% in cross validation. As expected, it has a very high positive predictive value of 89%: it predicts with high accuracy the subset of mutations that are deleterious because of stability issues, but is by construction unable of classifying variants that are deleterious for other reasons. Its combination with an evolutionary-based predictor increases the balanced accuracy up to 75%, and allowed predicting more than 1/4 of the variants with 95% positive predictive value. Our method, called SNPMuSiC, can be used with both experimental and modeled structures and compares favorably with other prediction tools on several independent test sets. It constitutes a step towards interpreting variant effects at the molecular scale. SNPMuSiC is freely available at https://soft.dezyme.com/.

DNA acquisition via genetic recombination is considered advantageous as it has the potential to bring together beneficial mutations that emerge independently within a population. Furthermore, recombination is considered to contribute to the maintenance of genome stability by purging slightly deleterious mutations. The prevalence of recombination differs among prokaryotic species and depends on the accessibility of DNA transfer mechanisms. An exceptional example is the human pathogen Mycobacterium tuberculosis (MTB) where no clear transfer mechanisms have been so far characterized and the presence of recombination is questioned. Here, we analyze completely assembled MTB genomes in search for evidence of recombination. We find that putative recombination events are enriched in strains reconstructed by reference-guided assembly and in regions with unreliable alignments. In addition, assembly and alignment artefacts introduce phylogenetic signals that are conflicting the established MTB phylogeny. Our results reveal that the so far reported recombination events in MTB are likely to stem from methodological artefacts. We conclude that no reliable signal of recombination is observed in the currently available MTB genomes. Moreover, our study demonstrates the limitations of reference-guided genome assembly for phylogenetic reconstructions. Rigorously de novo assembled genomes of high quality are mandatory in order to distinguish true evolutionary signal from noise, in particular for low diversity species such as MTB.

In genome evolution, genetic variants are the source of diversity, which natural selection acts upon. Treatment of human tuberculosis (TB) induces a strong selection pressure for the emergence of antibiotic resistance-conferring variants in the infecting Mycobacterium tuberculosis (MTB) strains. MTB evolution in response to treatment has been intensively studied and mainly attributed to point substitutions. However, the frequency and contribution of insertions and deletions (indels) to MTB genome evolution remains poorly understood. Here, we analyzed a multi-drug resistant MTB outbreak for the presence of high-quality indels and substitutions. We find that indels are significantly enriched in genes conferring antibiotic resistance. Furthermore, we show that indels are inherited during the outbreak and follow a molecular clock with an evolutionary rate of 5.37e-9 indels/site/year, which is 23 times lower than the substitution rate. Inherited indels may co-occur with substitutions in genes along related biological pathways; examples are iron storage and resistance to second-line antibiotics. This suggests that epistatic interactions between indels and substitutions affect antibiotic resistance and compensatory evolution in MTB.

The classification of human genetic variants into deleterious and neutral is a challenging issue, whose complexity is rooted in the large variety of biophysical mechanisms that can be responsible for disease conditions. For non-synonymous mutations in structured proteins, one of these is the protein stability change, which can lead to functionality loss. We developed a stabilitydriven knowledge-based classifier that uses protein structure, artificial neural networks and solvent accessibility-dependent combinations of statistical potentials to predict whether destabilizing or stabilizing mutations are disease-causing. Our predictor yields a balanced accuracy of 71% in cross validation. As expected, it has a very high positive predictive value of 89%: it predicts with high accuracy the subset of mutations that are deleterious because of stability issues, but is by construction unable of classifying variants that are deleterious for other reasons. Its combination with an evolutionary-based predictor increases the balanced accuracy up to 75%, and allowed predicting more than 1/4 of the deleterious variants with 95% positive predictive value. Our method, called SNPMuSiC, can be used with both experimental and structural models and compares favorably with other prediction tools on several independent test sets. It constitutes a step towards interpreting variant effects at the molecular scale.