Global and local genomic features together modulate the spontaneous single nucleotide mutation rate.

Spontaneous mutations are evolutionary engines as they generate variants for the evolutionary downstream processes that give rise to speciation and adaptation. Single nucleotide mutations (SNM) are the most abundant type of mutations among them. Here, we perform a meta-analysis to quantify the influence of selected global genomic parameters (genome size, genomic GC content, genomic repeat fraction, number of coding genes, gene count, and strand bias in prokaryotes) and local genomic features (local GC content, repeat content, CpG content and the number of SNM at CpG islands) on spontaneous SNM rates across the tree of life (prokaryotes, unicellular eukaryotes, multicellular eukaryotes) using wild-type sequence data in two different taxon classification systems. We find that the spontaneous SNM rates in our data are correlated with many genomic features in prokaryotes and unicellular eukaryotes irrespective of their sample sizes. On the other hand, only the number of coding genes was correlated with the spontaneous SNM rates in multicellular eukaryotes primarily contributed by vertebrates data. Considering local features, we notice that local GC content and CpG content significantly were correlated with the spontaneous SNM rates in the unicellular eukaryotes, while local repeat fraction is an important feature in prokaryotes and certain specific uni- and multi-cellular eukaryotes. Such predictive features of the spontaneous SNM rates often support non-linear models as the best fit compared to the linear model. We also observe that the strand asymmetry in prokaryotes plays an important role in determining the spontaneous SNM rates but the SNM spectrum does not.

Analysis and Prediction of Pathogen Nucleic Acid Specificity for Toll-like Receptors in Vertebrates.

Identification of key sequence, expression and function related features of nucleic acid-sensing host proteins is of fundamental importance to understand the dynamics of pathogen-specific host responses. To meet this objective, we considered toll-like receptors (TLRs), a representative class of membrane-bound sensor proteins, from 17 vertebrate species covering mammals, birds, reptiles, amphibians, and fishes in this comparative study. We identified the molecular signatures of host TLRs that are responsible for sensing pathogen nucleic acids or other pathogen-associated molecular patterns (PAMPs), and potentially play important roles in host defence mechanism. Interestingly, our findings reveal that such host-specific features are directly related to the strand (single or double) specificity of nucleic acid from pathogens. However, during host-pathogen interactions, such features were unable to explain the pathogenic PAMP (i.e., DNA, RNA or other) selectivity, suggesting a more complex mechanism. Using these features, we developed a number of machine learning models, of which Random Forest achieved a high performance (94.57% accuracy) to predict strand specificity of TLRs from protein-derived features. We applied the trained model to propose strand specificity of some previously uncharacterized distinct fish-specific novel TLRs (TLR18, TLR23, TLR24, TLR25, TLR27).

Special Care Is Needed in Applying Phylogenetic Comparative Methods to Gene Trees with Speciation and Duplication Nodes.

How gene function evolves is a central question of evolutionary biology. It can be investigated by comparing functional genomics results between species and between genes. Most comparative studies of functional genomics have used pairwise comparisons. Yet it has been shown that this can provide biased results, as genes, like species, are phylogenetically related. Phylogenetic comparative methods should be used to correct for this, but they depend on strong assumptions, including unbiased tree estimates relative to the hypothesis being tested. Such methods have recently been used to test the "ortholog conjecture," the hypothesis that functional evolution is faster in paralogs than in orthologs. Although pairwise comparisons of tissue specificity (τ) provided support for the ortholog conjecture, phylogenetic independent contrasts did not. Our reanalysis on the same gene trees identified problems with the time calibration of duplication nodes. We find that the gene trees used suffer from important biases, due to the inclusion of trees with no duplication nodes, to the relative age of speciations and duplications, to systematic differences in branch lengths, and to non-Brownian motion of tissue specificity on many trees. We find that incorrect implementation of phylogenetic method in empirical gene trees with duplications can be problematic. Controlling for biases allows successful use of phylogenetic methods to study the evolution of gene function and provides some support for the ortholog conjecture using three different phylogenetic approaches.

The optimization of mRNA expression level by its intrinsic properties-Insights from codon usage pattern and structural stability of mRNA.

Codon usage bias (CUB) and mRNA structural stability are important intrinsic features of mRNA that correlate positively with mRNA expression level. However, it remains unclear whether the mRNA expression level can be regulated by adjusting these two parameters, influencing the mRNAs' structure. Here we explored the influence of CUB and mRNA structural stability on mRNA expression levels in Saccharomyces cerevisiae, using both wild type and computationally mutated mRNAs. Although in wild type, both CUB and mRNA stability positively regulate the mRNA expression level, any deviation from natural situation breaks such equilibrium. The naturally occurring codon composition is responsible for optimizing the mRNA expression, and under such composition, the mRNA structure having highest stability is selected by nature.

Systematic Analyses and Prediction of Human Drug Side Effect Associated Proteins from the Perspective of Protein Evolution.

Identification of various factors involved in adverse drug reactions in target proteins to develop therapeutic drugs with minimal/no side effect is very important. In this context, we have performed a comparative evolutionary rate analyses between the genes exhibiting drug side-effect(s) (SET) and genes showing no side effect (NSET) with an aim to increase the prediction accuracy of SET/NSET proteins using evolutionary rate determinants. We found that SET proteins are more conserved than the NSET proteins. The rates of evolution between SET and NSET protein primarily depend upon their noncomplex (protein complex association number = 0) forming nature, phylogenetic age, multifunctionality, membrane localization, and transmembrane helix content irrespective of their essentiality, total druggability (total number of drugs/target), m-RNA expression level, and tissue expression breadth. We also introduced two novel terms-killer druggability (number of drugs with killing side effect(s)/target), essential druggability (number of drugs targeting essential proteins/target) to explain the evolutionary rate variation between SET and NSET proteins. Interestingly, we noticed that SET proteins are younger than NSET proteins and multifunctional younger SET proteins are candidates of acquiring killing side effects. We provide evidence that higher killer druggability, multifunctionality, and transmembrane helices support the conservation of SET proteins over NSET proteins in spite of their recent origin. By employing all these entities, our Support Vector Machine model predicts human SET/NSET proteins to a high degree of accuracy (∼86%).

Elucidating the genotype-phenotype relationships and network perturbations of human shared and specific disease genes from an evolutionary perspective.

To date, numerous studies have been attempted to determine the extent of variation in evolutionary rates between human disease and nondisease (ND) genes. In our present study, we have considered human autosomal monogenic (Mendelian) disease genes, which were classified into two groups according to the number of phenotypic defects, that is, specific disease (SPD) gene (one gene: one defect) and shared disease (SHD) gene (one gene: multiple defects). Here, we have compared the evolutionary rates of these two groups of genes, that is, SPD genes and SHD genes with respect to ND genes. We observed that the average evolutionary rates are slow in SHD group, intermediate in SPD group, and fast in ND group. Group-to-group evolutionary rate differences remain statistically significant regardless of their gene expression levels and number of defects. We demonstrated that disease genes are under strong selective constraint if they emerge through edgetic perturbation or drug-induced perturbation of the interactome network, show tissue-restricted expression, and are involved in transmembrane transport. Among all the factors, our regression analyses interestingly suggest the independent effects of 1) drug-induced perturbation and 2) the interaction term of expression breadth and transmembrane transport on protein evolutionary rates. We reasoned that the drug-induced network disruption is a combination of several edgetic perturbations and, thus, has more severe effect on gene phenotypes.

Insights into the evolutionary features of human neurodegenerative diseases.

Comparative analyses between human disease and non-disease genes are of great interest in understanding human disease gene evolution. However, the progression of neurodegenerative diseases (NDD) involving amyloid formation in specific brain regions is still unknown. Therefore, in this study, we mainly focused our analysis on the evolutionary features of human NDD genes with respect to non-disease genes. Here, we observed that human NDD genes are evolutionarily conserved relative to non-disease genes. To elucidate the conserved nature of NDD genes, we incorporated the evolutionary attributes like gene expression level, number of regulatory miRNAs, protein connectivity, intrinsic disorder content and relative aggregation propensity in our analysis. Our studies demonstrate that NDD genes have higher gene expression levels in favor of their lower evolutionary rates. Additionally, we observed that NDD genes have higher number of different regulatory miRNAs target sites and also have higher interaction partners than the non-disease genes. Moreover, miRNA targeted genes are known to have higher disorder content. In contrast, our analysis exclusively established that NDD genes have lower disorder content. In favor of our analysis, we found that NDD gene encoded proteins are enriched with multi interface hubs (party hubs) with lower disorder contents. Since, proteins with higher disorder content need to adapt special structure to reduce their aggregation propensity, NDD proteins found to have elevated relative aggregation propensity (RAP) in support of their lower disorder content. Finally, our categorical regression analysis confirmed the underlined relative dominance of protein connectivity, 3'UTR length, RAP, nature of hubs (singlish/multi interface) and disorder content for such evolutionary rates variation between human NDD genes and non-disease genes.

Understanding the effect of secondary structures and aggregation on human protein folding class evolution.

Using several model organisms it has been shown earlier that protein designability is related to contact density or fraction of buried residues and influence protein evolutionary rates dramatically. Here, using Homo sapiens as a model organism, we have analyzed two main folding classes (all-alpha and all-beta) to examine the factors affecting their evolutionary rates. Since, secondary structures are the most fundamental components of the protein folding classes, we explored the effect of protein secondary structure composition on evolution. Our results show that sheet and helix fractions exhibit positive and negative correlations, respectively, with the rate of protein evolution. On dividing the secondary structure components according to solvent accessibility, linear regression model identified two factors namely buried sheet fraction and relative aggregation propensity. Both these factors together can explain about 13.4% variability in the rate of human protein evolution, while buried sheet residues can alone account to 9.9% variability.