Sanderford, M. | bioRxiv Science

bioRxiv2021

TopHap: Rapid inference of key phylogenetic structures from common haplotypes in large genome collections with limited diversity

MotivationBuilding reliable phylogenies from very large collections of sequences with a limited number of phylogenetically informative sites is challenging because sequencing errors and recurrent/backward mutations interfere with the phylogenetic signal, confounding true evolutionary relationships. Massive global efforts of sequencing genomes and reconstructing the phylogeny of SARS-CoV-2 strains exemplify these difficulties since there are only hundreds of phylogenetically informative sites and millions of genomes. For such datasets, we set out to develop a method for building the phylogenetic tree of genomic haplotypes consisting of positions harboring common variants to improve the signal-to-noise ratio for more accurate phylogenetic inference of resolvable phylogenetic features. ResultsWe present the TopHap approach that determines spatiotemporally common haplotypes of common variants and builds their phylogeny at a fraction of the computational time of traditional methods. To assess topological robustness, we develop a bootstrap resampling strategy that resamples genomes spatiotemporally. The application of TopHap to build a phylogeny of 68,057 genomes (68KG) produced an evolutionary tree of major SARS-CoV-2 haplotypes. This phylogeny is concordant with the mutation tree inferred using the co-occurrence pattern of mutations and recovers key phylogenetic relationships from more traditional analyses. We also evaluated alternative roots of the SARS-CoV-2 phylogeny and found that the earliest sampled genomes in 2019 likely evolved by four mutations of the most recent common ancestor of all SARS-CoV-2 genomes. An application of TopHap to more than 1 million genomes reconstructed the most comprehensive evolutionary relationships of major variants, which confirmed the 68KG phylogeny and provided evolutionary origins of major variants of concern. AvailabilityTopHap is available on the web at https://github.com/SayakaMiura/TopHap. Contacts.kumar@temple.edu

bioinformatics↗

bioRxiv2021

Dynamic coupling of residues within proteins as a mechanistic foundation of many enigmatic pathogenic missense variants

Many pathogenic missense mutations are found in protein positions that are neither well-conserved nor belong to any known functional domains. Consequently, we lack any mechanistic underpinning of dysfunction caused by such mutations. We explored the disruption of allosteric dynamic coupling between these positions and the known functional sites as a possible mechanism for such mutations. In this study, we present an analysis of 144 human enzymes containing 591 pathogenic missense variants, in which allosteric dynamic coupling of mutated positions with known active sites provides insights into a primary biophysical mechanism and evidence of their functional importance. We illustrate this mechanism in a case study of {beta}-Glucocerebrosidase (GCase), which contains 94 Gaucher disease-associated missense variants located some distance away from the active site. An analysis of the conformational dynamics of GCase suggests that mutations on these distal sites cause changes in the flexibility of active site residues despite their distance, indicating a dynamic communication network throughout the protein. The disruption of the long-distance dynamic coupling due to the presence of missense mutations may provide a plausible general mechanistic explanation for biological dysfunction and disease. Author SummaryGenetic diseases occur when mutations to a particular gene cause a gain/loss in function of the related protein. Although several methods based on conservation and protein biochemistry exist to predict which genetic mutations may impact function, many disease causing changes remain unexplained by these metrics. In this study, we propose an explanation for these genetic changes may cause disease. In order to function, important regions of a protein must be able to exhibit collective motion. Through computer simulations, we observed that changing even a single amino acid within a protein can change the protein motion. Notably, disease causing genetic changes tend to alter the motion of regions which are critically important to protein function, even the mutations are far from these critical regions. In addition, we examined the degree that two amino acids within a protein may "couple" to one another, meaning the degree to which motion in one amino acid will affect the other. We found that amino acids which are highly coupled to the active site of a protein are more likely to result in disease if mutated, thereby offering a new tool for predicting genetic disease which incorporates internal protein dynamics.

biophysics↗