Neutral Theory, Disease Mutations, and Personal Exomes

Some mechanistic underpinnings of molecular adaptations of SARS-COV-2 spike protein by integrating candidate adaptive polymorphisms with protein dynamics

We integrate evolutionary predictions based on the neutral theory of molecular evolution with protein dynamics to generate mechanistic insight into the molecular adaptations of the SARS-COV-2 spike (S) protein. With this approach, we first identified candidate adaptive polymorphisms (CAPs) of the SARS-CoV-2 S protein and assessed the impact of these CAPs through dynamics analysis. Not only have we found that CAPs frequently overlap with well-known functional sites, but also, using several different dynamics-based metrics, we reveal the critical allosteric interplay between SARS-CoV-2 CAPs and the S protein binding sites with the human ACE2 (hACE2) protein. CAPs interact far differently with the hACE2 binding site residues in the open conformation of the S protein compared to the closed form. In particular, the CAP sites control the dynamics of binding residues in the open state, suggesting an allosteric control of hACE2 binding. We also explored the characteristic mutations of different SARS-CoV-2 strains to find dynamic hallmarks and potential effects of future mutations. Our analyses reveal that Delta strain-specific variants have non-additive (i.e., epistatic) interactions with CAP sites, whereas the less pathogenic Omicron strains have mostly additive mutations. Finally, our dynamics-based analysis suggests that the novel mutations observed in the Omicron strain epistatically interact with the CAP sites to help escape antibody binding.

Germline mutations directions are different between introns of the same gene: case study of the gene coding for amyloid-beta precursor protein

Amyloid-beta precursor protein (APP) is highly conserved in mammals. This feature allowed us to compare nucleotide usage biases in fourfold degenerated sites along the length of its coding region for 146 species of mammals and birds in search of fragments with significant deviations. Even though cytosine usage has the highest value in fourfold degenerated sites in APP coding region from all tested placental mammals, in contrast to marsupial mammals with the bias toward thymine usage, the most frequent germline and somatic mutations in human APP coding region are C to T and G to A transitions. The same mutational AT-pressure is characteristic for germline mutations in introns of human APP gene. However, surprisingly, there are several exceptional introns with deviations in germline mutations rates. The most of those introns surround exons with exceptional biases in nucleotide usage in fourfold degenerated sites. Existence of such fragments in exons 4 and 5, as well as in exon 14, can be connected with the presence of lncRNA genes in complementary strand of DNA. Exceptional nucleotide usage bias in exons 16 and 17 that contain a sequence encoding amyloid-beta peptides can be explained either by the presence of yet unmapped lncRNA(s), or by the autonomous expression of a short mRNA that encodes just C-terminal part of the APP providing an alternative source of amyloid-beta peptides. This hypothesis is supported by the increased rate of T to C transitions in introns 16-17 and 17-18 of Human APP gene relatively to other introns.

Parallel decay of vision genes in subterranean water beetles

In the framework of neutral theory of molecular evolution, genes specific to the development and function of eyes in subterranean animals living in permanent darkness are expected to evolve by relaxed selection, ultimately becoming pseudogenes. However, definitive empirical evidence for the role of neutral processes in the loss of vision over evolutionary time remains controversial. In previous studies, we characterized an assemblage of independently-evolved water beetle (Dytiscidae) species from a subterranean archipelago in Western Australia, where parallel vision and eye loss have occurred. Using a combination of transcriptomics and exon capture, we present evidence of parallel coding sequence decay, resulting from the accumulation of frameshift mutations and premature stop codons, in eight phototransduction genes (arrestins, opsins, ninaC and transient receptor potential channel genes) in 32 subterranean species in contrast to surface species, where these genes have open reading frames. Our results provide strong evidence to support neutral evolutionary processes as a major contributing factor to the loss of phototransduction genes in subterranean animals, with the ultimate fate being the irreversible loss of a light detection system.

Phylogenetic and biochemical analysis of calsequestrin structure and association of its variants with cardiac disorders

Calsequestrin is among the most abundant proteins in muscle sarcoplasmic reticulum and displays a high capacity but a low affinity for Ca²⁺ binding. In mammals, calsequestrin is encoded by two genes, CASQ1 and CASQ2, which are expressed almost exclusively in skeletal and cardiac muscles, respectively. Phylogenetic analysis indicates that calsequestrin is an ancient gene in metazoans, and that the duplication of the ancestral calsequestrin gene took place after the emergence of the lancelet. CASQ2 gene variants associated with catecholaminergic polymorphic ventricular tachycardia (CPVT) in humans are positively correlated with a high degree of evolutionary conservation across all calsequestrin homologues. The mutations are distributed in diverse locations of the calsequestrin protein and impart functional diversity but remarkably manifest in a similar phenotype in humans.

Individualized genetic network analysis reveals new therapeutic vulnerabilities in 6,700 cancer genomes

Tumor-specific genomic alterations allow systematic identification of genetic interactions that promote tumorigenesis and tumor vulnerabilities, offering novel strategies for development of targeted therapies for individual patients. We develop an Individualized Network-based Co-Mutation (INCM) methodology by inspecting over 2.5 million nonsynonymous somatic mutations derived from 6,789 tumor exomes across 14 cancer types from The Cancer Genome Atlas. Our INCM analysis reveals a higher genetic interaction burden on the significantly mutated genes, experimentally validated cancer genes, chromosome regulatory factors, and DNA damage repair genes, as compared to human pan-cancer essential genes identified by CRISPR-Cas9 screenings on 324 cancer cell lines. We find that genes involved in the cancer type-specific genetic subnetworks identified by INCM are significantly enriched in established cancer pathways, and the INCM-inferred putative genetic interactions are correlated with patient survival. By analyzing drug pharmacogenomics profiles from the Genomics of Drug Sensitivity in Cancer database, we show that the network-predicted putative genetic interactions (e.g., BRCA2-TP53) are significantly correlated with sensitivity/resistance of multiple therapeutic agents. We experimentally validated that afatinib has the strongest cytotoxic activity on BT474 (IC₅₀ = 55.5 nM, BRCA2 and TP53 co-mutant) compared to MCF7 (IC₅₀ = 7.7 μM, both BRCA2 and TP53 wild type) and MDA-MB-231 (IC₅₀ = 7.9 μM, BRCA2 wild type but TP53 mutant). Finally, drug-target network analysis reveals several potential druggable genetic interactions by targeting tumor vulnerabilities. This study offers a powerful network-based methodology for identification of candidate therapeutic pathways that target tumor vulnerabilities and prioritization of potential pharmacogenomics biomarkers for development of personalized cancer medicine.

Recent efforts to map genetic interactions in tumor cells have suggested that tumor vulnerabilities can be exploited for development of novel targeted therapies. Tumor-specific genomic alterations derived from multi-center cancer genome projects allow identification of genetic interactions that promote tumor vulnerabilities, offering novel strategies for development of targeted cancer therapies. This study develops a novel Individualized Network-based Co-Mutation (termed INCM) methodology for quantifying the putative genetic interactions in cancer. Trained on over 2.5 million nonsynonymous somatic mutations derived from 6,789 tumor exomes across 14 cancer type, we found that genes identified in the cancer type-specific genetic subnetworks were significantly enriched in established cancer pathways. The network-predicted putative genetic interactions are correlated with patient survival. By analyzing drug pharmacogenomics profiles, we showed that the network-predicted putative genetic interactions (e.g., BRCA2-TP53) were significantly correlated with sensitivity/resistance of anticancer drugs (e.g., afatinib) and we experimentally validated it in breast cancer cell lines. Finally, drug-target network analysis reveals several potential druggable genetic interactions (e.g., PIK3CA-PTEN) by targeting tumor vulnerabilities. This study offers a generalizable network-based approach for comprehensive identification of candidate therapeutic pathways that target tumor vulnerabilities and prioritization of potential prognostic and pharmacogenomics biomarkers for development of personalized cancer medicine.

On estimating evolutionary probabilities of population variants

BACKGROUND

The evolutionary probability (EP) of an allele in a DNA or protein sequence predicts evolutionarily permissible (ePerm; EP ≥ 0.05) and forbidden (eForb; EP < 0.05) variants. EP of an allele represents an independent evolutionary expectation of observing an allele in a population based solely on the long-term substitution patterns captured in a multiple sequence alignment. In the neutral theory, EP and population frequencies can be compared to identify neutral and non-neutral alleles. This approach has been used to discover candidate adaptive polymorphisms in humans, which are eForbs segregating with high frequencies. The original method to compute EP requires the evolutionary relationships and divergence times of species in the sequence alignment (a timetree), which are not known with certainty for most datasets. This requirement impedes a general use of the original EP formulation. Here, we present an approach in which the phylogeny and times are inferred from the sequence alignment itself prior to the EP calculation. We evaluate if the modified EP approach produces results that are similar to those from the original method.

RESULTS

We compared EP estimates from the original and the modified approaches by using more than 18,000 protein sequence alignments containing orthologous sequences from 46 vertebrate species. For the original EP calculations, we used species relationships from UCSC and divergence times from TimeTree web resource, and the resulting EP estimates were considered to be the ground truth. We found that the modified approaches produced reasonable EP estimates for HGMD disease missense variant and 1000 Genomes Project missense variant datasets. Our results showed that reliable estimates of EP can be obtained without a priori knowledge of the sequence phylogeny and divergence times. We also found that, in order to obtain robust EP estimates, it is important to assemble a dataset with many sequences, sampling from a diversity of species groups.

CONCLUSION

We conclude that the modified EP approach will be generally applicable for alignments and enable the detection of potentially neutral, deleterious, and adaptive alleles in populations.