A mechanistic evolutionary model explains the time-dependent pattern of substitution rates in viruses.
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ABSTRACT:
SUBMITTER: Ghafari M
PROVIDER: S-EPMC8585505 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundPhylogenetic analyses reveal probable patterns of divergence of present day organisms from common ancestors. The points of divergence of lineages can be dated if a corresponding historical or fossil record exists. For many species, in particular viruses, such records are rare. Recently, Bayesian phylogenetic analysis using sequences from closely related organisms isolated at different times have been used to calibrate divergences. Phylogenetic analyses depend on the assumption that the average substitution rates that can be calculated from the data apply throughout the course of evolution.ResultsThe present study tests this crucial assumption by charting the kinds of substitutions observed between pairs of sequences with different levels of total substitutions. Datasets of aligned sequences, both viral and non-viral, were assembled. For each pair of sequences in an aligned set, the distribution of nucleotide interchanges and the total number of changes were calculated. Data were binned according to total numbers of changes and plotted. The accumulation of the six possible interchange types in retroelements as a function of distance followed closely the expected hyperbolic relationship. For other datasets, however, significant deviations from this relationship were noted. A rapid initial accumulation of transition interchanges was frequent among the datasets and anomalous changes occurred at specific divergence levels.ConclusionsThe accumulation profiles suggested that substantial changes in frequencies of types of substitutions occur over the course of evolution and that such changes should be considered in evaluating and dating viral phylogenies.
| S-EPMC3026377 | biostudies-literature
Project description:Many factors complicate the estimation of time scales for phylogenetic histories, requiring increasingly complex evolutionary models and inference procedures. The widespread application of molecular clock dating has led to the insight that evolutionary rate estimates may vary with the time frame of measurement. This is particularly well established for rapidly evolving viruses that can accumulate sequence divergence over years or even months. However, this rapid evolution stands at odds with a relatively high degree of conservation of viruses or endogenous virus elements over much longer time scales. Building on recent insights into time-dependent evolutionary rates, we develop a formal and flexible Bayesian statistical inference approach that accommodates rate variation through time. We evaluate the novel molecular clock model on a foamy virus cospeciation history and a lentivirus evolutionary history and compare the performance to other molecular clock models. For both virus examples, we estimate a similarly strong time-dependent effect that implies rates varying over four orders of magnitude. The application of an analogous codon substitution model does not implicate long-term purifying selection as the cause of this effect. However, selection does appear to affect divergence time estimates for the less deep evolutionary history of the Ebolavirus genus. Finally, we explore the application of our approach on woolly mammoth ancient DNA data, which shows a much weaker, but still important, time-dependent rate effect that has a noticeable impact on node age estimates. Future developments aimed at incorporating more complex evolutionary processes will further add to the broad applicability of our approach.
| S-EPMC6657730 | biostudies-literature
Project description:The relative evolutionary rates at individual sites in proteins are informative measures of conservation or adaptation. Often used as evolutionarily aware conservation scores, relative rates reveal key functional or strongly selected residues. Estimating rates in a phylogenetic context requires specifying a protein substitution model, which is typically a phenomenological model trained on a large empirical data set. A strong emphasis has traditionally been placed on selecting the "best-fit" model, with the implicit understanding that suboptimal or otherwise ill-fitting models might bias inferences. However, the pervasiveness and degree of such bias has not been systematically examined. We investigated how model choice impacts site-wise relative rates in a large set of empirical protein alignments. We compared models designed for use on any general protein, models designed for specific domains of life, and the simple equal-rates Jukes Cantor-style model (JC). As expected, information theoretic measures showed overwhelming evidence that some models fit the data decidedly better than others. By contrast, estimates of site-specific evolutionary rates were impressively insensitive to the substitution model used, revealing an unexpected degree of robustness to potential model misspecification. A deeper examination of the fewer than 5% of sites for which model inferences differed in a meaningful way showed that the JC model could uniquely identify rapidly evolving sites that models with empirically derived exchangeabilities failed to detect. We conclude that relative protein rates appear robust to the applied substitution model, and any sensible model of protein evolution, regardless of its fit to the data, should produce broadly consistent evolutionary rates.
| S-EPMC6107055 | biostudies-literature
Project description:Double-stranded (ds) DNA viruses are often described as evolving through long-term codivergent associations with their hosts, a pattern that is expected to be associated with low rates of nucleotide substitution. However, the hypothesis of codivergence between dsDNA viruses and their hosts has rarely been rigorously tested, even though the vast majority of nucleotide substitution rate estimates for dsDNA viruses are based upon this assumption. It is therefore important to estimate the evolutionary rates of dsDNA viruses independent of the assumption of host-virus codivergence. Here, we explore the use of temporally structured sequence data within a Bayesian framework to estimate the evolutionary rates for seven human dsDNA viruses, including variola virus (VARV) (the causative agent of smallpox) and herpes simplex virus-1. Our analyses reveal that although the VARV genome is likely to evolve at a rate of approximately 1 x 10(-5) substitutions/site/year and hence approaching that of many RNA viruses, the evolutionary rates of many other dsDNA viruses remain problematic to estimate. Synthetic data sets were constructed to inform our interpretation of the substitution rates estimated for these dsDNA viruses and the analysis of these demonstrated that given a sequence data set of appropriate length and sampling depth, it is possible to use time-structured analyses to estimate the substitution rates of many dsDNA viruses independently from the assumption of host-virus codivergence. Finally, the discovery that some dsDNA viruses may evolve at rates approaching those of RNA viruses has important implications for our understanding of the long-term evolutionary history and emergence potential of this major group of viruses.
| S-EPMC3107591 | biostudies-literature
Project description:Pithovirus sibericum is a giant (610 Kpb) double-stranded DNA virus discovered in a purportedly 30,000-year-old permafrost sample. A closely related virus, Pithovirus massiliensis, was recently isolated from a sewer in southern France. An initial comparison of these two virus genomes assumed that P. sibericum was directly ancestral to P. massiliensis and gave a maximum evolutionary rate of 2.60 × 10-5 nucleotide substitutions per site per year (subs/site/year). If correct, this would make pithoviruses among the fastest-evolving DNA viruses, with rates close to those seen in some RNA viruses. To help determine whether this unusually high rate is accurate we utilized the well-known negative association between evolutionary rate and genome size in DNA microbes. This revealed that a more plausible rate estimate for Pithovirus evolution is ?2.23 × 10-6 subs/site/year, with even lower estimates obtained if evolutionary rates are assumed to be time-dependent. Hence, we estimate that Pithovirus has evolved at least an order of magnitude more slowly than previously suggested. We then used our new rate estimates to infer a time-scale for Pithovirus evolution. Strikingly, this suggests that these viruses could have diverged at least hundreds of thousands of years ago, and hence have evolved over longer time-scales than previously suggested. We propose that the evolutionary rate and time-scale of pithovirus evolution should be reconsidered in the light of these observations and that future estimates of the rate of giant virus evolution should be carefully examined in the context of their biological plausibility.
| S-EPMC5829572 | biostudies-literature
Project description:Large DNA viruses, including giant mimivirus with a 1.2-Mb genome, exhibit numerous orphan genes possessing no database homologs or genes with homologs solely in close members of the same viral family. Due to their solitary nature, the functions and evolutionary origins of those genes remain obscure. We examined sequence features and evolutionary rates of viral family-specific genes in three nucleo-cytoplasmic large DNA virus (NCLDV) lineages. First, we showed that the proportion of family-specific genes does not correlate with sequence divergence rate. Second, position-dependent nucleotide statistics were similar between family-specific genes and the remaining genes in the genome. Third, we showed that the synonymous-to-nonsynonymous substitution ratios in those viruses are at levels comparable to those estimated for vertebrate proteomes. Thus, the vast majority of family-specific genes do not exhibit an accelerated evolutionary rate, and are thus likely to specify functional polypeptides. On the other hand, these family-specific proteins exhibit several distinct properties: (1) they are shorter, (2) they include a larger fraction of predicted transmembrane proteins, and (3) they are enriched in low-complexity sequences. These results suggest that family-specific genes do not correspond to recent horizontal gene transfer. We propose that their characteristic features are the consequences of the specific evolutionary forces shaping the viral gene repertoires in the context of their parasitic lifestyles.
| S-EPMC1950904 | biostudies-literature
Project description:The high rates of RNA virus evolution are generally attributed to replication with error-prone RNA-dependent RNA polymerases. However, these long-term nucleotide substitution rates span three orders of magnitude and do not correlate well with mutation rates or selection pressures. This substitution rate variation may be explained by differences in virus ecology or intrinsic genomic properties. We generated nucleotide substitution rate estimates for mammalian RNA viruses and compiled comparable published rates, yielding a dataset of 118 substitution rates of structural genes from 51 different species, as well as 40 rates of non-structural genes from 28 species. Through ANCOVA analyses, we evaluated the relationships between these rates and four ecological factors: target cell, transmission route, host range, infection duration; and three genomic properties: genome length, genome sense, genome segmentation. Of these seven factors, we found target cells to be the only significant predictors of viral substitution rates, with tropisms for epithelial cells or neurons (P<0.0001) as the most significant predictors. Further, one-tailed t-tests showed that viruses primarily infecting epithelial cells evolve significantly faster than neurotropic viruses (P<0.0001 and P<0.001 for the structural genes and non-structural genes, respectively). These results provide strong evidence that the fastest evolving mammalian RNA viruses infect cells with the highest turnover rates: the highly proliferative epithelial cells. Estimated viral generation times suggest that epithelial-infecting viruses replicate more quickly than viruses with different cell tropisms. Our results indicate that cell tropism is a key factor in viral evolvability.
| S-EPMC3887100 | biostudies-literature
Project description:BACKGROUND: The activity of a single gene is influenced by the composition of the chromatin in which it is embedded. Nucleosome turnover, conformational dynamics, and covalent histone modifications each induce changes in the structure of chromatin and its affinity for regulatory proteins. The dynamics of histone modifications and the persistence of modification patterns for long periods are still largely unknown. RESULTS: In this study, we present a stochastic mathematical model that describes the molecular mechanisms of histone modification pattern formation along a single gene, with non-phenomenological, physical parameters. We find that diffusion and recruitment properties of histone modifying enzymes together with chromatin connectivity allow for a rich repertoire of stochastic histone modification dynamics and pattern formation. We demonstrate that histone modification patterns at a single gene can be established or removed within a few minutes through diffusion and weak recruitment mechanisms of histone modification spreading. Moreover, we show that strong synergism between diffusion and weak recruitment mechanisms leads to nearly irreversible transitions in histone modification patterns providing stable patterns. In the absence of chromatin connectivity spontaneous and dynamic histone modification boundaries can be formed that are highly unstable, and spontaneous fluctuations cause them to diffuse randomly. Chromatin connectivity destabilizes this synergistic system and introduces bistability, illustrating state switching between opposing modification states of the model gene. The observed bistable long-range and localized pattern formation are critical effectors of gene expression regulation. CONCLUSION: This study illustrates how the cooperative interactions between regulatory proteins and the chromatin state generate complex stochastic dynamics of gene expression regulation.
| S-EPMC4234852 | biostudies-other
Project description:Proteins evolve under a myriad of biophysical selection pressures that collectively control the patterns of amino acid substitutions. These evolutionary pressures are sufficiently consistent over time and across protein families to produce substitution patterns, summarized in global amino acid substitution matrices such as BLOSUM, JTT, WAG, and LG, which can be used to successfully detect homologs, infer phylogenies, and reconstruct ancestral sequences. Although the factors that govern the variation of amino acid substitution rates have received much attention, the influence of thermodynamic stability constraints remains unresolved. Here we develop a simple model to calculate amino acid substitution matrices from evolutionary dynamics controlled by a fitness function that reports on the thermodynamic effects of amino acid mutations in protein structures. This hybrid biophysical and evolutionary model accounts for nucleotide transition/transversion rate bias, multi-nucleotide codon changes, the number of codons per amino acid, and thermodynamic protein stability. We find that our theoretical model accurately recapitulates the complex yet universal pattern observed in common global amino acid substitution matrices used in phylogenetics. These results suggest that selection for thermodynamically stable proteins, coupled with nucleotide mutation bias filtered by the structure of the genetic code, is the primary driver behind the global amino acid substitution patterns observed in proteins throughout the tree of life.
| S-EPMC8442976 | biostudies-literature
Project description:BackgroundSelective pressures at the DNA level shape genes into profiles consisting of patterns of rapidly evolving sites and sites withstanding change. These profiles remain detectable even when protein sequences become extensively diverged. A common task in molecular biology is to infer functional, structural or evolutionary relationships by querying a database using an algorithm. However, problems arise when sequence similarity is low. This study presents an algorithm that uses the evolutionary rate at codon sites, the dN/dS (ω) parameter, coupled to a substitution matrix as an alignment metric for detecting distantly related proteins. The algorithm, called BLOSUM-FIRE couples a newer and improved version of the original FIRE (Functional Inference using Rates of Evolution) algorithm with an amino acid substitution matrix in a dynamic scoring function. The enigmatic hepatitis B virus X protein was used as a test case for BLOSUM-FIRE and its associated database EvoDB.ResultsThe evolutionary rate based approach was coupled with a conventional BLOSUM substitution matrix. The two approaches are combined in a dynamic scoring function, which uses the selective pressure to score aligned residues. The dynamic scoring function is based on a coupled additive approach that scores aligned sites based on the level of conservation inferred from the ω values. Evaluation of the accuracy of this new implementation, BLOSUM-FIRE, using MAFFT alignment as reference alignments has shown that it is more accurate than its predecessor FIRE. Comparison of the alignment quality with widely used algorithms (MUSCLE, T-COFFEE, and CLUSTAL Omega) revealed that the BLOSUM-FIRE algorithm performs as well as conventional algorithms. Its main strength lies in that it provides greater potential for aligning divergent sequences and addresses the problem of low specificity inherent in the original FIRE algorithm. The utility of this algorithm is demonstrated using the Hepatitis B virus X (HBx) protein, a protein of unknown function, as a test case.ConclusionThis study describes the utility of an evolutionary rate based approach coupled to the BLOSUM62 amino acid substitution matrix in inferring protein domain function. We demonstrate that such an approach is robust and performs as well as an array of conventional algorithms.
| S-EPMC4535666 | biostudies-literature