Towards realistic codon models: among site variability and dependency of synonymous and non-synonymous rates

The Complete Mitochondrial Genome and Phylogenetic Analysis of the Freshwater Shellfish Novaculina chinensis (Bivalvia: Pharidae)

Razor clams, belonging to the Pharidae and Solenidae families, are ecologically and economically important; however, very little research has been conducted on the Pharidae family. The genus Novaculina is a marine-derived freshwater lineage, and Novaculina chinensis is a rare freshwater species of the Pharidae family. In order to understand the phylogenetic relationships of N. chinensis, we sequenced the mitochondrial genome of the genus Novaculina, which is 16,262 bp in length and consists of 12 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs), and 2 ribosomal RNA genes (rRNAs). The phylogenetic relationships of 69 Imparidentian mitochondrial genomes (mitogenomes) indicated that N. chineisis is closely related to Sinonovacula constricta of the order Adapedonta. Our study also found that the Ka/Ks ratios of 12 protein-coding genes in the Pharidae family are lower than one, indicating the occurrence of negative purification selection. Morphological observations of the siphons of N. chinensis, Novaculina myanmarensis, and Novaculina gangetica indicate that N. chinensis may be the ancestral clade of the genus Novaculina, which has not been proposed in previous studies. Our study provides useful molecular information on the phylogenetic and evolutionary relationships of Pharidae and also contributes to the conservation and management of the germplasm resources of N. chinensis.

Identification and evolution analysis of YUCCA genes of Medicago sativa and Medicago truncatula and their expression profiles under abiotic stress

The YUCCAs (YUC) are functionally identified flavin-containing monooxidases (FMOs) in plants that act as an important rate-limiting enzyme functioning in the auxin synthesis IPA (indole-3-pyruvic acid) pathway. In this study, 12 MsYUCs and 15 MtYUCs containing characteristic conserved motifs were identified in M. sativa (Medicago sativa L.) and M. truncatula (Medicago truncatula Gaertn.), respectively. Phylogenetic analysis revealed that YUC proteins underwent an evolutionary divergence. Both tandem and segmental duplication events were presented in MsYUC and MtYUC genes. Comparative syntenic maps of M. sativa with M. truncatula, Arabidopsis (Arabidopsis thaliana), or rice (Oryza sativa L.) were constructed to illustrate the evolution relationship of the YUC gene family. A large number of cis-acting elements related to stress response and hormone regulation were revealed in the promoter sequences of MsYUCs. Expression analysis showed that MsYUCs had a tissue-specific, genotype-differential expression and a differential abiotic stress response pattern based on transcriptome data analysis of M. sativa online. In addition, RT-qPCR confirmed that salt stress significantly induced the expression of MsYUC1/MsYUC10 but significantly inhibited MsYUC2/MsYUC3 expression and the expression of MsYUC10/MsYUC11/MsYUC12 was significantly induced by cold treatment. These results could provide valuable information for functional analysis of YUC genes via gene engineering of the auxin synthetic IPA pathway in Medicago.

Genome-Wide Characterization and Sequence Polymorphism Analyses of Glycine max Fibrillin (FBN) Revealed Its Role in Response to Drought Condition

The fibrillin (FBN) gene family is widely distributed in all photosynthetic organisms. Members of this gene family are involved in plant growth and development and their response to various biotic and abiotic stress factors. In this study, 16 members of FBN were identified in Glycine max and characterized by using different bioinformatics tools. Phylogenetic analysis classified FBN genes into seven groups. The presence of stress-related cis-elements in the upstream region of GmFBN highlighted their role in tolerance against abiotic stresses. To further decipher the function, physiochemical properties, conserved motifs, chromosomal localization, subcellular localization, and cis-acting regulatory elements were also analyzed. Gene expression analysis based on FPKM values revealed that GmFBNs greatly enhanced soybean drought tolerance and controlled the expression of several genes involved in drought response, except for GmFBN-4, GmFBN-5, GmFBN-6, GmFBN-7 and GmFBN-9. For high throughput genotyping, an SNP-based CAPS marker was also developed for the GmFBN-15 gene. The CAPS marker differentiated soybean genotypes based on the presence of either the GmFBN-15-G or GmFBN-15-A alleles in the CDS region. Association analysis showed that G. max accessions containing the GmFBN-15-A allele at the respective locus showed higher thousand seed weight compared to accessions containing the GmFBN-15-G allele. This research has provided the basic information to further decipher the function of FBN in soybean.

Beginner's Guide on the Use of PAML to Detect Positive Selection

The CODEML program in the PAML package has been widely used to analyze protein-coding gene sequences to estimate the synonymous and nonsynonymous rates (dS and dN) and to detect positive Darwinian selection driving protein evolution. For users not familiar with molecular evolutionary analysis, the program is known to have a steep learning curve. Here, we provide a step-by-step protocol to illustrate the commonly used tests available in the program, including the branch models, the site models, and the branch-site models, which can be used to detect positive selection driving adaptive protein evolution affecting particular lineages of the species phylogeny, affecting a subset of amino acid residues in the protein, and affecting a subset of sites along prespecified lineages, respectively. A data set of the myxovirus (Mx) genes from ten mammal and two bird species is used as an example. We discuss a new feature in CODEML that allows users to perform positive selection tests for multiple genes for the same set of taxa, as is common in modern genome-sequencing projects. The PAML package is distributed at https://github.com/abacus-gene/paml under the GNU license, with support provided at its discussion site (https://groups.google.com/g/pamlsoftware). Data files used in this protocol are available at https://github.com/abacus-gene/paml-tutorial.

The complete mitochondrial genome of Hemigrapsus sinensis (Brachyura, Grapsoidea, Varunidae) and its phylogenetic position within Grapsoidea

BACKGROUND

In this study, the complete mitogenome of Hemigrapsus sinensis was the first identified and analyzed.

OBJECTIVE

The complete mitochondrial genome of Hemigrapsus sinensis (Brachyura, Grapsoidea, Varunidae) and its phylogenetic position within Grapsoidea.

METHODS

The sample of Hemigrapsus sinensis was collected and DNA was extracted. After sequencing, NOVOPlasty was used for sequence assembly. Annotate sequences with MITOS WebServer, tRNAscan-SE2.0, and NCBI database. MEGA was used for sequence analysis and Phylosuite was used for phylogenetic tree construction. DnaSP was used to calculate Ka/Ks.

RESULTS

This mitochondrial genome shows that it was 15,900 bp and encoded 13 PCGs, 22 tRNA genes, two rRNA genes, and one control region. The genome composition tends to A + T (74.34%) and presents a negative GC-skew (- 0.22) and AT-skew (- 0.03). The PCGs initiation codon was the typical ATN and termination codon was the typical TAN, incomplete T or missing. The ML and BI trees showed that H. sinensis was most closely related to Hemigrapsus and clustered together with the Varunidae. And our phylogenetic trees provide proof that Ocypodoidea and Grapsoidea may be of common origin. Meanwhile, in the phylogenetic tree, parallel mixing of Chiromantes and Orisarma raised doubts over the traditional classification system. Besides, Incomplete Lineage sorting (ILS) was observed in Varunidae. In the subsequent analysis of evolution rate, we found that all of the PCGs (NAD4 was not calculated) had undergone negative selections, indicating the conservation of mitochondrial genes of H. sinensis during the evolution.

CONCLUSION

Therefore, researching the complete mitogenome of H. sinensis would be contributing to molecular taxonomy, phylogenetic relationship, and breeding optimization within the Grapsoidea superfamily.

Next-generation development and application of codon model in evolution

To date, numerous nucleotide, amino acid, and codon substitution models have been developed to estimate the evolutionary history of any sequence/organism in a more comprehensive way. Out of these three, the codon substitution model is the most powerful. These models have been utilized extensively to detect selective pressure on a protein, codon usage bias, ancestral reconstruction and phylogenetic reconstruction. However, due to more computational demanding, in comparison to nucleotide and amino acid substitution models, only a few studies have employed the codon substitution model to understand the heterogeneity of the evolutionary process in a genome-scale analysis. Hence, there is always a question of how to develop more robust but less computationally demanding codon substitution models to get more accurate results. In this review article, the authors attempted to understand the basis of the development of different types of codon-substitution models and how this information can be utilized to develop more robust but less computationally demanding codon substitution models. The codon substitution model enables to detect selection regime under which any gene or gene region is evolving, codon usage bias in any organism or tissue-specific region and phylogenetic relationship between different lineages more accurately than nucleotide and amino acid substitution models. Thus, in the near future, these codon models can be utilized in the field of conservation, breeding and medicine.

Molecular Traits and Functional Analysis of the CLAVATA3/Endosperm Surrounding Region-Related Small Signaling Peptides in Three Species of Gossypium Genus

The CLAVATA3/endosperm surrounding region-related (CLE) small peptides are a group of C-terminally encoded and post-translationally modified signal molecules involved in regulating the growth and development of various plants. However, the function and evolution of these peptides have so far remained elusive in cotton. In this study, 55, 56, and 86 CLE genes were identified in the Gossypium raimondii, Gossypium arboreum, and Gossypium hirsutum genomes, respectively, and all members were divided into seven groups. These groups were distinctly different in their protein characteristics, gene structures, conserved motifs, and multiple sequence alignment. Whole genome or segmental duplications played a significant role in the expansion of the CLE family in cotton, and experienced purifying selection during the long evolutionary process in cotton. Cis-acting regulatory elements and transcript profiling revealed that the CLE genes of cotton exist in different tissues, developmental stages, and respond to abiotic stresses. Protein properties, structure prediction, protein interaction network prediction of GhCLE2, GhCLE33.2, and GhCLE28.1 peptides were, respectively, analyzed. In addition, the overexpression of GhCLE2, GhCLE33.2, or GhCLE28.1 in Arabidopsis, respectively, resulted in a distinctive shrub-like dwarf plant, slightly purple leaves, large rosettes with large malformed leaves, and lack of reproductive growth. This study provides important insights into the evolution of cotton CLEs and delineates the functional conservatism and divergence of CLE genes in the growth and development of cotton.

Synonymous Site-to-Site Substitution Rate Variation Dramatically Inflates False Positive Rates of Selection Analyses: Ignore at Your Own Peril

Most molecular evolutionary studies of natural selection maintain the decades-old assumption that synonymous substitution rate variation (SRV) across sites within genes occurs at levels that are either nonexistent or negligible. However, numerous studies challenge this assumption from a biological perspective and show that SRV is comparable in magnitude to that of nonsynonymous substitution rate variation. We evaluated the impact of this assumption on methods for inferring selection at the molecular level by incorporating SRV into an existing method (BUSTED) for detecting signatures of episodic diversifying selection in genes. Using simulated data we found that failing to account for even moderate levels of SRV in selection testing is likely to produce intolerably high false positive rates. To evaluate the effect of the SRV assumption on actual inferences we compared results of tests with and without the assumption in an empirical analysis of over 13,000 Euteleostomi (bony vertebrate) gene alignments from the Selectome database. This exercise reveals that close to 50% of positive results (i.e., evidence for selection) in empirical analyses disappear when SRV is modeled as part of the statistical analysis and are thus candidates for being false positives. The results from this work add to a growing literature establishing that tests of selection are much more sensitive to certain model assumptions than previously believed.

Large-Scale Comparative Analysis of Codon Models Accounting for Protein and Nucleotide Selection

There are numerous sources of variation in the rate of synonymous substitutions inside genes, such as direct selection on the nucleotide sequence, or mutation rate variation. Yet scans for positive selection rely on codon models which incorporate an assumption of effectively neutral synonymous substitution rate, constant between sites of each gene. Here we perform a large-scale comparison of approaches which incorporate codon substitution rate variation and propose our own simple yet effective modification of existing models. We find strong effects of substitution rate variation on positive selection inference. More than 70% of the genes detected by the classical branch-site model are presumably false positives caused by the incorrect assumption of uniform synonymous substitution rate. We propose a new model which is strongly favored by the data while remaining computationally tractable. With the new model we can capture signatures of nucleotide level selection acting on translation initiation and on splicing sites within the coding region. Finally, we show that rate variation is highest in the highly recombining regions, and we propose that recombination and mutation rate variation, such as high CpG mutation rate, are the two main sources of nucleotide rate variation. Although we detect fewer genes under positive selection in Drosophila than without rate variation, the genes which we detect contain a stronger signal of adaptation of dynein, which could be associated with Wolbachia infection. We provide software to perform positive selection analysis using the new model.

Selection Acting on Genomes

Populations evolve as mutations arise in individual organisms and, through hereditary transmission, may become "fixed" (shared by all individuals) in the population. Most mutations are lethal or have negative fitness consequences for the organism. Others have essentially no effect on organismal fitness and can become fixed through the neutral stochastic process known as random drift. However, mutations may also produce a selective advantage that boosts their chances of reaching fixation. Regions of genomes where new mutations are beneficial, rather than neutral or deleterious, tend to evolve more rapidly due to positive selection. Genes involved in immunity and defense are a well-known example; rapid evolution in these genes presumably occurs because new mutations help organisms to prevail in evolutionary "arms races" with pathogens. In recent years genome-wide scans for selection have enlarged our understanding of the genome evolution of various species. In this chapter, we will focus on methods to detect selection on the genome. In particular, we will discuss probabilistic models and how they have changed with the advent of new genome-wide data now available.

Genome-wide identification and analysis of the CNGC gene family in maize

As one of the non-selective cation channel gene families, the cyclic nucleotide-gated channel (CNGC) gene family plays a vital role in plant physiological processes that are related to signal pathways, plant development, and environmental stresses. However, genome-wide identification and analysis of the CNGC gene family in maize has not yet been undertaken. In the present study, twelve ZmCNGC genes were identified in the maize genome, which were unevenly distributed on chromosomes 1, 2, 4, 5, 6, 7, and 8. They were classified into five major groups: Groups I, II, III, IVa, and IVb. Phylogenetic analysis showed that gramineous plant CNGC genes expanded unequally during evolution. Group IV CNGC genes emerged first, whereas Groups I and II appeared later. Prediction analysis of cis-acting regulatory elements showed that 137 putative cis-elements were related to hormone-response, abiotic stress, and organ development. Furthermore, 120 protein pairs were predicted to interact with the 12 ZmCNGC proteins and other maize proteins. The expression profiles of the ZmCNGC genes were expressed in tissue-specific patterns. These results provide important information that will increase our understanding of the CNGC gene family in maize and other plants.

Increasing the CpG dinucleotide abundance in the HIV-1 genomic RNA inhibits viral replication

BACKGROUND

The human immunodeficiency virus type 1 (HIV-1) structural protein Gag is necessary and sufficient to form viral particles. In addition to encoding the amino acid sequence for Gag, the underlying RNA sequence could encode cis-acting elements or nucleotide biases that are necessary for viral replication. Furthermore, RNA sequences that inhibit viral replication could be suppressed in gag. However, the functional relevance of RNA elements and nucleotide biases that promote or repress HIV-1 replication remain poorly understood.

RESULTS

To characterize if the RNA sequence in gag controls HIV-1 replication, the matrix (MA) region was codon modified, allowing the RNA sequence to be altered without affecting the protein sequence. Codon modification of nucleotides (nt) 22-261 or 22-378 in gag inhibited viral replication by decreasing genomic RNA (gRNA) abundance, gRNA stability, Gag expression, virion production and infectivity. Comparing the effect of these point mutations to deletions of the same region revealed that the mutations inhibited infectious virus production while the deletions did not. This demonstrated that codon modification introduced inhibitory sequences. There is a much lower than expected frequency of CpG dinucleotides in HIV-1 and codon modification introduced a substantial increase in CpG abundance. To determine if they are necessary for inhibition of HIV-1 replication, codons introducing CpG dinucleotides were mutated back to the wild type codon, which restored efficient Gag expression and infectious virion production. To determine if they are sufficient to inhibit viral replication, CpG dinucleotides were inserted into gag in the absence of other changes. The increased CpG dinucleotide content decreased HIV-1 infectivity and viral replication.

CONCLUSIONS

The HIV-1 RNA sequence contains low abundance of CpG dinucleotides. Increasing the abundance of CpG dinucleotides inhibits multiple steps of the viral life cycle, providing a functional explanation for why CpG dinucleotides are suppressed in HIV-1.

Identification of positive selection in genes is greatly improved by using experimentally informed site-specific models

Background

Sites of positive selection are identified by comparing observed evolutionary patterns to those expected under a null model for evolution in the absence of such selection. For protein-coding genes, the most common null model is that nonsynonymous and synonymous mutations fix at equal rates; this unrealistic model has limited power to detect many interesting forms of selection.

Results

I describe a new approach that uses a null model based on experimental measurements of a gene’s site-specific amino-acid preferences generated by deep mutational scanning in the lab. This null model makes it possible to identify both diversifying selection for repeated amino-acid change and differential selection for mutations to amino acids that are unexpected given the measurements made in the lab. I show that this approach identifies sites of adaptive substitutions in four genes (lactamase, Gal4, influenza nucleoprotein, and influenza hemagglutinin) far better than a comparable method that simply compares the rates of nonsynonymous and synonymous substitutions.

Conclusions

As rapid increases in biological data enable increasingly nuanced descriptions of the constraints on individual protein sites, approaches like the one here can improve our ability to identify many interesting forms of selection in natural sequences.

Reviewers

This article was reviewed by Sebastian Maurer-Stroh, Olivier Tenaillon, and Tal Pupko. All three reviewers are members of the Biology Direct editorial board.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-016-0172-z) contains supplementary material, which is available to authorized users.

SENCA: A Multilayered Codon Model to Study the Origins and Dynamics of Codon Usage

Gene sequences are the target of evolution operating at different levels, including the nucleotide, codon, and amino acid levels. Disentangling the impact of those different levels on gene sequences requires developing a probabilistic model with three layers. Here we present SENCA (site evolution of nucleotides, codons, and amino acids), a codon substitution model that separately describes 1) nucleotide processes which apply on all sites of a sequence such as the mutational bias, 2) preferences between synonymous codons, and 3) preferences among amino acids. We argue that most synonymous substitutions are not neutral and that SENCA provides more accurate estimates of selection compared with more classical codon sequence models. We study the forces that drive the genomic content evolution, intraspecifically in the core genome of 21 prokaryotes and interspecifically for five Enterobacteria. We retrieve the existence of a universal mutational bias toward AT, and that taking into account selection on synonymous codon usage has consequences on the measurement of selection on nonsynonymous substitutions. We also confirm that codon usage bias is mostly driven by selection on preferred codons. We propose new summary statistics to measure the relative importance of the different evolutionary processes acting on sequences.

A codon model of nucleotide substitution with selection on synonymous codon usage

The quality of phylogenetic inference made from protein-coding genes depends, in part, on the realism with which the codon substitution process is modeled. Here we propose a new mechanistic model that combines the standard M0 substitution model of Yang (1997) with a simplified model from Gilchrist (2007) that includes selection on synonymous substitutions as a function of codon-specific nonsense error rates. We tested the newly proposed model by applying it to 104 protein-coding genes in brewer's yeast, and compared the fit of the new model to the standard M0 model and to the mutation-selection model of Yang and Nielsen (2008) using the AIC. Our new model provided significantly better fit in approximately 85% of the cases considered for the basic M0 model and in approximately 25% of the cases for the M0 model with estimated codon frequencies, but only in a few cases when the mutation-selection model was considered. However, our model includes a parameter that can be interpreted as a measure of the rate of protein production, and the estimates of this parameter were highly correlated with an independent measure of protein production for the yeast genes considered here. Finally, we found that in some cases the new model led to the preference of a different phylogeny for a subset of the genes considered, indicating that substitution model choice may have an impact on the estimated phylogeny.

The relationship between dN/dS and scaled selection coefficients

Numerous computational methods exist to assess the mode and strength of natural selection in protein-coding sequences, yet how distinct methods relate to one another remains largely unknown. Here, we elucidate the relationship between two widely used phylogenetic modeling frameworks: dN/dS models and mutation-selection (MutSel) models. We derive a mathematical relationship between dN/dS and scaled selection coefficients, the focal parameters of MutSel models, and use this relationship to gain deeper insight into the behaviors, limitations, and applicabilities of these two modeling frameworks. We prove that, if all synonymous changes are neutral, standard MutSel models correspond to dN/dS ≤ 1. However, if synonymous codons differ in fitness, dN/dS can take on arbitrarily high values even if all selection is purifying. Thus, the MutSel modeling framework cannot necessarily accommodate positive, diversifying selection, while dN/dS cannot distinguish between purifying selection on synonymous codons and positive selection on amino acids. We further propose a new benchmarking strategy of dN/dS inferences against MutSel simulations and demonstrate that the widely used Goldman-Yang-style dN/dS models yield substantially biased dN/dS estimates on realistic sequence data. In contrast, the less frequently used Muse-Gaut-style models display much less bias. Strikingly, the least-biased and most precise dN/dS estimates are never found in the models with the best fit to the data, measured through both AIC and BIC scores. Thus, selecting models based on goodness-of-fit criteria can yield poor parameter estimates if the models considered do not precisely correspond to the underlying mechanism that generated the data. In conclusion, establishing mathematical links among modeling frameworks represents a novel, powerful strategy to pinpoint previously unrecognized model limitations and strengths.

A generalized mechanistic codon model

Models of codon evolution have attracted particular interest because of their unique capabilities to detect selection forces and their high fit when applied to sequence evolution. We described here a novel approach for modeling codon evolution, which is based on Kronecker product of matrices. The 61 × 61 codon substitution rate matrix is created using Kronecker product of three 4 × 4 nucleotide substitution matrices, the equilibrium frequency of codons, and the selection rate parameter. The entities of the nucleotide substitution matrices and selection rate are considered as parameters of the model, which are optimized by maximum likelihood. Our fully mechanistic model allows the instantaneous substitution matrix between codons to be fully estimated with only 19 parameters instead of 3,721, by using the biological interdependence existing between positions within codons. We illustrate the properties of our models using computer simulations and assessed its relevance by comparing the AICc measures of our model and other models of codon evolution on simulations and a large range of empirical data sets. We show that our model fits most biological data better compared with the current codon models. Furthermore, the parameters in our model can be interpreted in a similar way as the exchangeability rates found in empirical codon models.

Selection on synonymous codons in mammalian rhodopsins: a possible role in optimizing translational processes

Background

Synonymous codon usage can affect many cellular processes, particularly those associated with translation such as polypeptide elongation and folding, mRNA degradation/stability, and splicing. Highly expressed genes are thought to experience stronger selection pressures on synonymous codons. This should result in codon usage bias even in species with relatively low effective population sizes, like mammals, where synonymous site selection is thought to be weak. Here we use phylogenetic codon-based likelihood models to explore patterns of codon usage bias in a dataset of 18 mammalian rhodopsin sequences, the protein mediating the first step in vision in the eye, and one of the most highly expressed genes in vertebrates. We use these patterns to infer selection pressures on key translational mechanisms including polypeptide elongation, protein folding, mRNA stability, and splicing.

Results

Overall, patterns of selection in mammalian rhodopsin appear to be correlated with post-transcriptional and translational processes. We found significant evidence for selection at synonymous sites using phylogenetic mutation-selection likelihood models, with C-ending codons found to have the highest relative fitness, and to be significantly more abundant at conserved sites. In general, these codons corresponded with the most abundant tRNAs in mammals. We found significant differences in codon usage bias between rhodopsin loops versus helices, though there was no significant difference in mean synonymous substitution rate between these motifs. We also found a significantly higher proportion of GC-ending codons at paired sites in rhodopsin mRNA secondary structure, and significantly lower synonymous mutation rates in putative exonic splicing enhancer (ESE) regions than in non-ESE regions.

Conclusions

By focusing on a single highly expressed gene we both distinguish synonymous codon selection from mutational effects and analytically explore underlying functional mechanisms. Our results suggest that codon bias in mammalian rhodopsin arises from selection to optimally balance high overall translational speed, accuracy, and proper protein folding, especially in structurally complicated regions. Selection at synonymous sites may also be contributing to mRNA stability and splicing efficiency at exonic-splicing-enhancer (ESE) regions. Our results highlight the importance of investigating highly expressed genes in a broader phylogenetic context in order to better understand the evolution of synonymous substitutions.

Phylogenetic Gaussian process model for the inference of functionally important regions in protein tertiary structures

A critical question in biology is the identification of functionally important amino acid sites in proteins. Because functionally important sites are under stronger purifying selection, site-specific substitution rates tend to be lower than usual at these sites. A large number of phylogenetic models have been developed to estimate site-specific substitution rates in proteins and the extraordinarily low substitution rates have been used as evidence of function. Most of the existing tools, e.g. Rate4Site, assume that site-specific substitution rates are independent across sites. However, site-specific substitution rates may be strongly correlated in the protein tertiary structure, since functionally important sites tend to be clustered together to form functional patches. We have developed a new model, GP4Rate, which incorporates the Gaussian process model with the standard phylogenetic model to identify slowly evolved regions in protein tertiary structures. GP4Rate uses the Gaussian process to define a nonparametric prior distribution of site-specific substitution rates, which naturally captures the spatial correlation of substitution rates. Simulations suggest that GP4Rate can potentially estimate site-specific substitution rates with a much higher accuracy than Rate4Site and tends to report slowly evolved regions rather than individual sites. In addition, GP4Rate can estimate the strength of the spatial correlation of substitution rates from the data. By applying GP4Rate to a set of mammalian B7-1 genes, we found a highly conserved region which coincides with experimental evidence. GP4Rate may be a useful tool for the in silico prediction of functionally important regions in the proteins with known structures.

To understand how a protein functions, a critical step is to know which regions in its protein tertiary structure may be functionally important. Functionally important protein regions are typically more conserved than other regions because mutations in these regions are more likely to be deleterious. A number of phylogenetic models have been developed to identify conserved sites or regions in proteins by comparing protein sequences from multiple species. However, most of these methods treat amino acid sites independently and do not consider the spatial clustering of conserved sites in the protein tertiary structure. Therefore, their power of identifying functional protein regions is limited. We develop a new statistical model, GP4Rate, which combines the information from the protein sequences and the protein tertiary structure to infer conserved regions. We demonstrate that GP4Rate outperforms Rate4Site, the most widely used phylogenetic software for inferring functional amino acid sites, via simulations with a case study of B7-1 genes. GP4Rate is a potentially useful tool for guiding mutagenesis experiments or providing insights on the relationship between protein structures and functions.

Quantifying selection against synonymous mutations in HIV-1 env evolution

Intrapatient evolution of human immunodeficiency virus type 1 (HIV-1) is driven by the adaptive immune system resulting in rapid change of HIV-1 proteins. When cytotoxic CD8(+) T cells or neutralizing antibodies target a new epitope, the virus often escapes via nonsynonymous mutations that impair recognition. Synonymous mutations do not affect this interplay and are often assumed to be neutral. We test this assumption by tracking synonymous mutations in longitudinal intrapatient data from the C2-V5 part of the env gene. We find that most synonymous variants are lost even though they often reach high frequencies in the viral population, suggesting a cost to the virus. Using published data from SHAPE (selective 2'-hydroxyl acylation analyzed by primer extension) assays, we find that synonymous mutations that disrupt base pairs in RNA stems flanking the variable loops of gp120 are more likely to be lost than other synonymous changes: these RNA hairpins might be important for HIV-1. Computational modeling indicates that, to be consistent with the data, a large fraction of synonymous mutations in this genomic region need to be deleterious with a cost on the order of 0.002 per day. This weak selection against synonymous substitutions does not result in a strong pattern of conservation in cross-sectional data but slows down the rate of evolution considerably. Our findings are consistent with the notion that large-scale patterns of RNA structure are functionally relevant, whereas the precise base pairing pattern is not.

Synonymous site conservation in the HIV-1 genome

BACKGROUND

Synonymous or silent mutations are usually thought to evolve neutrally. However, accumulating recent evidence has demonstrated that silent mutations may destabilize RNA structures or disrupt cis regulatory motifs superimposed on coding sequences. Such observations suggest the existence of stretches of codon sites that are evolutionary conserved at both DNA-RNA and protein levels. Such stretches may point to functionally important regions within protein coding sequences not necessarily reflecting functional constraints on the amino-acid sequence. The HIV-1 genome is highly compact, and often harbors overlapping functional elements at the protein, RNA, and DNA levels. This superimposition of functions leads to complex selective forces acting on all levels of the genome and proteome. Considering the constraints on HIV-1 to maintain such a highly compact genome, we hypothesized that stretches of synonymous conservation would be common within its genome.

RESULTS

We used a combined computational-experimental approach to detect and characterize regions exhibiting strong purifying selection against synonymous substitutions along the HIV-1 genome. Our methodology is based on advanced probabilistic evolutionary models that explicitly account for synonymous rate variation among sites and rate dependencies among adjacent sites. These models are combined with a randomization procedure to automatically identify the most statistically significant regions of conserved synonymous sites along the genome. Using this procedure we identified 21 conserved regions. Twelve of these are mapped to regions within overlapping genes, seven correlate with known functional elements, while the functions of the remaining four are yet unknown. Among these four regions, we chose the one that deviates most from synonymous rate homogeneity for in-depth computational and experimental characterization. In our assays aiming to quantify viral fitness in both early and late stages of the replication cycle, no differences were observed between the mutated and the wild type virus following the introduction of synonymous mutations.

CONCLUSIONS

The contradiction between the inferred purifying selective forces and the lack of effect of these mutations on viral replication may be explained by the fact that the phenotype was measured in single-cycle infection assays in cell culture. Such a system does not account for the complexity of HIV-1 infections in vivo, which involves multiple infection cycles and interaction with the host immune system.

The interface of protein structure, protein biophysics, and molecular evolution

Abstract The interface of protein structural biology, protein biophysics, molecular evolution, and molecular population genetics forms the foundations for a mechanistic understanding of many aspects of protein biochemistry. Current efforts in interdisciplinary protein modeling are in their infancy and the state-of-the art of such models is described. Beyond the relationship between amino acid substitution and static protein structure, protein function, and corresponding organismal fitness, other considerations are also discussed. More complex mutational processes such as insertion and deletion and domain rearrangements and even circular permutations should be evaluated. The role of intrinsically disordered proteins is still controversial, but may be increasingly important to consider. Protein geometry and protein dynamics as a deviation from static considerations of protein structure are also important. Protein expression level is known to be a major determinant of evolutionary rate and several considerations including selection at the mRNA level and the role of interaction specificity are discussed. Lastly, the relationship between modeling and needed high-throughput experimental data as well as experimental examination of protein evolution using ancestral sequence resurrection and in vitro biochemistry are presented, towards an aim of ultimately generating better models for biological inference and prediction.

Selection on the protein-coding genome

Populations evolve as mutations arise in individual organisms and, through hereditary transmission, may become "fixed" (shared by all individuals) in the population. Most mutations are lethal or have negative fitness consequences for the organism. Others have essentially no effect on organismal fitness and can become fixed through the neutral stochastic process known as random drift. However, mutations may also produce a selective advantage that boosts their chances of reaching fixation. Regions of genes where new mutations are beneficial, rather than neutral or deleterious, tend to evolve more rapidly due to positive selection. Genes involved in immunity and defense are a well-known example; rapid evolution in these genes presumably occurs because new mutations help organisms to prevail in evolutionary "arms races" with pathogens. In recent years, genome-wide scans for selection have enlarged our understanding of the evolution of the protein-coding regions of the various species. In this chapter, we focus on the methods to detect selection in protein-coding genes. In particular, we discuss probabilistic models and how they have changed with the advent of new genome-wide data now available.

Statistics and truth in phylogenomics

Phylogenomics refers to the inference of historical relationships among species using genome-scale sequence data and to the use of phylogenetic analysis to infer protein function in multigene families. With rapidly decreasing sequencing costs, phylogenomics is becoming synonymous with evolutionary analysis of genome-scale and taxonomically densely sampled data sets. In phylogenetic inference applications, this translates into very large data sets that yield evolutionary and functional inferences with extremely small variances and high statistical confidence (P value). However, reports of highly significant P values are increasing even for contrasting phylogenetic hypotheses depending on the evolutionary model and inference method used, making it difficult to establish true relationships. We argue that the assessment of the robustness of results to biological factors, that may systematically mislead (bias) the outcomes of statistical estimation, will be a key to avoiding incorrect phylogenomic inferences. In fact, there is a need for increased emphasis on the magnitude of differences (effect sizes) in addition to the P values of the statistical test of the null hypothesis. On the other hand, the amount of sequence data available will likely always remain inadequate for some phylogenomic applications, for example, those involving episodic positive selection at individual codon positions and in specific lineages. Again, a focus on effect size and biological relevance, rather than the P value, may be warranted. Here, we present a theoretical overview and discuss practical aspects of the interplay between effect sizes, bias, and P values as it relates to the statistical inference of evolutionary truth in phylogenomics.

Proteome-wide evidence for enhanced positive Darwinian selection within intrinsically disordered regions in proteins

BACKGROUND

Understanding the adaptive changes that alter the function of proteins during evolution is an important question for biology and medicine. The increasing number of completely sequenced genomes from closely related organisms, as well as individuals within species, facilitates systematic detection of recent selection events by means of comparative genomics.

RESULTS

We have used genome-wide strain-specific single nucleotide polymorphism data from 64 strains of budding yeast (Saccharomyces cerevisiae or Saccharomyces paradoxus) to determine whether adaptive positive selection is correlated with protein regions showing propensity for different classes of structure conformation. Data from phylogenetic and population genetic analysis of 3,746 gene alignments consistently shows a significantly higher degree of positive Darwinian selection in intrinsically disordered regions of proteins compared to regions of alpha helix, beta sheet or tertiary structure. Evidence of positive selection is significantly enriched in classes of proteins whose functions and molecular mechanisms can be coupled to adaptive processes and these classes tend to have a higher average content of intrinsically unstructured protein regions.

CONCLUSIONS

We suggest that intrinsically disordered protein regions may be important for the production and maintenance of genetic variation with adaptive potential and that they may thus be of central significance for the evolvability of the organism or cell in which they occur.

Evolutionary models accounting for layers of selection in protein-coding genes and their impact on the inference of positive selection

The selective forces acting on a protein-coding gene are commonly inferred using evolutionary codon models by contrasting the rate of nonsynonymous substitutions to the rate of synonymous substitutions. These models usually assume that the synonymous substitution rate, Ks, is homogenous across all sites, which is justified if synonymous sites are free from selection. However, a growing body of evidence indicates that the DNA and RNA levels of protein-coding genes are subject to varying degrees of selective constraints due to various biological functions encoded at these levels. In this paper, we develop evolutionary models that account for these layers of selection by allowing for both among-site variability of substitution rates at the DNA/RNA level (which leads to Ks variability among protein-coding sites) and among-site variability of substitution rates at the protein level (Ka variability). These models are constructed so that positive selection is either allowed or not. This enables statistical testing of positive selection when variability at the DNA/RNA substitution rate is accounted for. Using this methodology, we show that variability of the baseline DNA/RNA substitution rate is a widespread phenomenon in coding sequence data of mammalian genomes, most likely reflecting varying degrees of selection at the DNA and RNA levels. Additionally, we use simulations to examine the impact that accounting for the variability of the baseline DNA/RNA substitution rate has on the inference of positive selection. Our results show that ignoring this variability results in a high rate of erroneous positive-selection inference. Our newly developed model, which accounts for this variability, does not suffer from this problem and hence provides a likelihood framework for the inference of positive selection on a background of variability in the baseline DNA/RNA substitution rate.

Mechanisms of GII.4 norovirus evolution

Since the late 1990s norovirus (NoV) strains belonging to a single genotype (GII.4) have caused at least four global epidemics. To date, the higher epidemiological fitness of the GII.4 strains has been attributed to a faster rate of evolution within the virus capsid, resulting in the ability to escape herd immunity. Four key factors have been proposed to influence the rate of evolution in NoV. These include host receptor recognition, sequence space, duration of herd immunity, and replication kinetics. In this review we discuss recent advancements in our understanding of these four mechanisms in relation to GII.4 evolution.

Estimating the evidence of selection and the reliability of inference in unigenic evolution

Background

Unigenic evolution is a large-scale mutagenesis experiment used to identify residues that are potentially important for protein function. Both currently-used methods for the analysis of unigenic evolution data analyze 'windows' of contiguous sites, a strategy that increases statistical power but incorrectly assumes that functionally-critical sites are contiguous. In addition, both methods require the questionable assumption of asymptotically-large sample size due to the presumption of approximate normality.

Results

We develop a novel approach, termed the Evidence of Selection (EoS), removing the assumption that functionally important sites are adjacent in sequence and and explicitly modelling the effects of limited sample-size. Precise statistical derivations show that the EoS score can be easily interpreted as an expected log-odds-ratio between two competing hypotheses, namely, the hypothetical presence or absence of functional selection for a given site. Using the EoS score, we then develop selection criteria by which functionally-important yet non-adjacent sites can be identified. An approximate power analysis is also developed to estimate the reliability of inference given the data. We validate and demonstrate the the practical utility of our method by analysis of the homing endonuclease I-Bmol, comparing our predictions with the results of existing methods.

Conclusions

Our method is able to assess both the evidence of selection at individual amino acid sites and estimate the reliability of those inferences. Experimental validation with I-Bmol proves its utility to identify functionally-important residues of poorly characterized proteins, demonstrating increased sensitivity over previous methods without loss of specificity. With the ability to guide the selection of precise experimental mutagenesis conditions, our method helps make unigenic analysis a more broadly applicable technique with which to probe protein function.

Availability

Software to compute, plot, and summarize EoS data is available as an open-source package called 'unigenic' for the 'R' programming language at http://www.fernandes.org/txp/article/13/an-analytical-framework-for-unigenic-evolution.

Mutational dynamics of murine angiogenin duplicates

Background

Angiogenin (Ang) is a protein involved in angiogenesis by inducing the formation of blood vessels. The biomedical importance of this protein has come from findings linking mutations in Ang to cancer progression and neurodegenerative diseases. These findings highlight the evolutionary constrain on Ang amino acid sequence. However, previous studies comparing human Angiogenin with homologs from other phylogenetically related organisms have led to the conclusion that Ang presents a striking variability. Whether this variability has an adaptive value per se remains elusive. Understanding why many functional Ang paralogs have been preserved in mouse and rat and identifying functional divergence mutations at these copies may explain the relationship between mutations and function. In spite of the importance of testing this hypothesis from the evolutionarily and biomedical perspectives, this remains yet unaccomplished. Here we test the main mutational dynamics driving the evolution and function of Ang paralogs in mammals.

Results

We analysed the phylogenetic asymmetries between the different Ang gene copies in mouse and rat in the context of vertebrate Ang phylogeny. This analysis shows strong evidence in support of accelerated evolution in some Ang murine copies (mAng). This acceleration is not due to non-functionalisation because constraints on amino acid replacements remain strong. We identify many of the amino acid sites involved in signal localization and nucleotide binding by Ang to have evolved under diversifying selection. Compensatory effects of many of the mutations at these paralogs and their key structural location in or nearby important functional regions support a possible functional shift (functional divergence) in many Ang copies. Similarities between 3D-structural models for mAng copies suggest that their divergence is mainly functional.

Conclusions

We identify the main evolutionary dynamics shaping the variability of Angiogenin in vertebrates and highlight the plasticity of this protein after gene duplication. Our results suggest functional divergence among mAng paralogs. This puts forward mAng as a good system candidate for testing functional plasticity of such an important protein while stresses caution when using mouse as a model to infer the consequences of mutations in the single Ang copy of humans.

The origins of eukaryotic-like proteins in Legionella pneumophila

Legionella pneumophila, the causative agent of Legionnaires' disease, is known to be an intracellular pathogen of multiple species of protozoa and is assumed to have co-evolved with these organisms for millions of years. Genome sequencing of L. pneumophila strains has revealed an abundance of eukaryotic-like proteins (ELPs). Here, we study the evolution of these ELPs, in order to investigate their origin. Thirty-four new ELPs were identified, based on a higher similarity to eukaryotic proteins than to bacterial ones. Phylogenetic analyses demonstrated that both lateral gene transfer from eukaryotic hosts and bacterial genes that became eukaryotic-like by gradual adaptation to the intracellular milieu or gene fragment acquisition, contributed to the existing repertoire of ELPs, which comprise over 3% of the putative proteome of L. pneumophila strains. A PCR survey of 72 L. pneumophila strains showed that most ELPs were conserved in nearly all of these strains, indicating that they are likely to play important roles in this species. Genes of different evolutionary origin have distinct patterns of selection, as reflected by their ratio of a synonymous vs. synonymous mutations. One ELP is common to several strains of Legionella, but outside this genus has homologs only in Acanthamoeba polyphaga mimivirus, indicating that gene exchange involving eukaryotic viruses and intracellular bacterial pathogens may also contribute to the evolution of virulence in either or both of these groups of organisms. Information on selection patterns and eukaryotic-like status was combined as a novel approach to predict type IV secretion system effectors of Legionella, which represent promising targets for future study.

Structural calibration of the rates of amino acid evolution in a search for Darwin in drifting biological systems

In the last two decades, many reports of proteins under positive selection have brought the neutral theory into question. However, the methods used to detect selection have ignored the evolvability of amino acids within proteins, which is fundamental to distinguishing positive selection from the relaxed constraints caused by genetic drift. Disentangling these two counterbalancing forces is essential to test the neutral theory. Here, we calibrate rates of amino acid divergence by using structural information from the full set of crystallized proteins in bacteria. In agreement with previous reports, we show that rates of amino acid evolution correlate negatively with the number of per-amino acid atomic interactions. Calibration of the rates of evolution allows identifying signatures of selection in biological systems that evolve under strong genetic drift, such as endosymbiotic bacteria. Application of this method identifies different rates and dynamics of evolution for highly connected amino acids in the structure compared with sparsely connected ones. We also unearth patterns of Darwinian selection in fundamental cellular proteins in endosymbiotic bacteria including the cochaperonin GroES, ribosomal proteins, proteins involved in cell cycle control, DNA-binding proteins, and proteins involved in DNA replication and repair. This is, to our knowledge, the first attempt to distinguish adaptive evolution from relaxed constraints in biological systems under genetic drift.

Mutation-selection models of coding sequence evolution with site-heterogeneous amino acid fitness profiles

Modeling the interplay between mutation and selection at the molecular level is key to evolutionary studies. To this end, codon-based evolutionary models have been proposed as pertinent means of studying long-range evolutionary patterns and are widely used. However, these approaches have not yet consolidated results from amino acid level phylogenetic studies showing that selection acting on proteins displays strong site-specific effects, which translate into heterogeneous amino acid propensities across the columns of alignments; related codon-level studies have instead focused on either modeling a single selective context for all codon columns, or a separate selective context for each codon column, with the former strategy deemed too simplistic and the latter deemed overparameterized. Here, we integrate recent developments in nonparametric statistical approaches to propose a probabilistic model that accounts for the heterogeneity of amino acid fitness profiles across the coding positions of a gene. We apply the model to a dozen real protein-coding gene alignments and find it to produce biologically plausible inferences, for instance, as pertaining to site-specific amino acid constraints, as well as distributions of scaled selection coefficients. In their account of mutational features as well as the heterogeneous regimes of selection at the amino acid level, the modeling approaches studied here can form a backdrop for several extensions, accounting for other selective features, for variable population size, or for subtleties of mutational features, all with parameterizations couched within population-genetic theory.

Genome-wide functional divergence after the symbiosis of proteobacteria with insects unraveled through a novel computational approach

Symbiosis has been among the most important evolutionary steps to generate biological complexity. The establishment of symbiosis required an intimate metabolic link between biological systems with different complexity levels. The strict endo-cellular symbiotic bacteria of insects are beautiful examples of the metabolic coupling between organisms belonging to different kingdoms, a eukaryote and a prokaryote. The host (eukaryote) provides the endosymbiont (prokaryote) with a stable cellular environment while the endosymbiont supplements the host's diet with essential metabolites. For such communication to take place, endosymbionts' genomes have suffered dramatic modifications and reconfigurations of proteins' functions. Two of the main modifications, loss of genes redundant for endosymbiotic bacteria or the host and bacterial genome streamlining, have been extensively studied. However, no studies have accounted for possible functional shifts in the endosymbiotic proteomes. Here, we develop a simple method to screen genomes for evidence of functional divergence between two species clusters, and we apply it to identify functional shifts in the endosymbiotic proteomes. Despite the strong effects of genetic drift in the endosymbiotic systems, we unexpectedly identified genes to be under stronger selective constraints in endosymbionts of aphids and ants than in their free-living bacterial relatives. These genes are directly involved in supplementing the host's diet with essential metabolites. A test of functional divergence supports a strong relationship between the endosymbiosis and the functional shifts of proteins involved in the metabolic communication with the insect host. The correlation between functional divergence in the endosymbiotic bacterium and the ecological requirements of the host uncovers their intimate biochemical and metabolic communication and provides insights on the role of symbiosis in generating species diversity.

Biological complexity has emerged on earth by the combination of living forms. This combination, called symbiosis, had to overcome the problems caused by the uncoupled metabolisms of the organisms involved. One way to do so was through the loss of genes that were no longer needed for the endosymbiont in the protected cellular environment provided by the host. Another step necessary to adjust both metabolisms was through the change in the function of bacterial proteins to perform new roles in the symbiotic system. In this article, we test such events in symbiotic systems involving an insect and a bacterium by developing a new and simple method to identify proteome-wide functional shifts. Our results show that most of the functional changes occurred at genes involved in metabolic communication with the host and are correlated with the host's ecological traits.

The population genetics of dN/dS

Evolutionary pressures on proteins are often quantified by the ratio of substitution rates at non-synonymous and synonymous sites. The dN/dS ratio was originally developed for application to distantly diverged sequences, the differences among which represent substitutions that have fixed along independent lineages. Nevertheless, the dN/dS measure is often applied to sequences sampled from a single population, the differences among which represent segregating polymorphisms. Here, we study the expected dN/dS ratio for samples drawn from a single population under selection, and we find that in this context, dN/dS is relatively insensitive to the selection coefficient. Moreover, the hallmark signature of positive selection over divergent lineages, dN/dS>1, is violated within a population. For population samples, the relationship between selection and dN/dS does not follow a monotonic function, and so it may be impossible to infer selection pressures from dN/dS. These results have significant implications for the interpretation of dN/dS measurements among population-genetic samples.

Spatial autocorrelation of amino Acid replacement rates in the vasopressin receptor family

Evolutionary rates of sites can be independent of one another or correlated in some fashion. Significant spatial autocorrelation was observed for site amino acid replacement rates in vasopressin receptor family proteins (VPRs). Spatial autocorrelation of rates is the propensity of residues to lie near other residues of similar rate in the folded protein structure. Optimal correlation occurred at a distance suggesting that residues in contact had correlated rates. As another way to study the same phenomenon, VPR was partitioned into >40 x 10 A(3) contiguous spatial clusters for amino acid replacement rate estimation. Partitioning was done without preconception of functional regions of the protein and with a random partition control. Cluster rates exhibited an overdispersed distribution suggesting that rates were not randomly distributed in the spatial partitions. In tests, cluster partitioning improved maximum likelihood and Bayesian likelihood models for VPR evolution. Spatial clusters with outlier rates, or lineage-specific clusters differing in rate, proved to contain VPR features likely to be under selection. Thus the spatial autocorrelation observed is probably not just a statistical finding, but likely has an evolutionary basis in protein function.

Directionality in the evolution of influenza A haemagglutinin

The evolution of haemagglutinin (HA), an important influenza virus antigen, has been the subject of intensive research for more than two decades. Many characteristics of HA's sequence evolution are captured by standard Markov chain substitution models. Such models assign equal fitness to all accessible amino acids at a site. We show, however, that such models strongly underestimate the number of homoplastic amino acid substitutions during the course of HA's evolution, i.e. substitutions that repeatedly give rise to the same amino acid at a site. We develop statistics to detect individual homoplastic events and find that they preferentially occur at positively selected epitopic sites. Our results suggest that the evolution of the influenza A HA, including evolution by positive selection, is strongly affected by the long-term site-specific preferences for individual amino acids.

Shifts in the selection-drift balance drive the evolution and epidemiology of foot-and-mouth disease virus

Foot-and-mouth disease virus (FMDV) is the causative agent of an acute vesicular disease affecting wild and domesticated animals. Despite the economic burden of the disease and all efforts to eradicate it, FMD outbreaks continue to emerge unexpectedly in developed and developing countries. Correlation of the mutational dynamics of the virus with its epidemiology remains unexplored. Analysis of 103 complete genomes representing the seven serotypes shows the important role that selection plays in the genomic evolution of viral isolates for serotypes. We identified selection and relaxed constraints due to genetic drift through analyses of synonymous sites. Finally, we investigated interactions between mutations that showed coevolving patterns and analyzed, based on protein structures, slightly deleterious and compensatory mutational dynamics. Specifically, we demonstrate that structurally exposed capsid proteins present a greater number of adaptive mutations and relaxed selection than nonstructural proteins. Such events have been magnified during the evolution of the southern African virus types (SATs). These shifts in selection-drift balance have generated the great antigenic and genetic diversity observed for SAT serotypes and that are responsible for epizootics on the continent of Africa. The high number of slightly deleterious and compensatory mutations in SAT serotypes in structural proteins is testament to such balance plasticity. The significant accumulation of these coevolving mutations in African serotypes supports their contribution in generating adaptive immune-escaping mutants and in establishing persistent infections. The reverse of this pattern in nonstructural proteins reveals the neutral fixation of mutations in the more widely spread and commonly studied Euro-Asiatic serotypes.

Evolutionary modeling of rate shifts reveals specificity determinants in HIV-1 subtypes

A hallmark of the human immunodeficiency virus 1 (HIV-1) is its rapid rate of evolution within and among its various subtypes. Two complementary hypotheses are suggested to explain the sequence variability among HIV-1 subtypes. The first suggests that the functional constraints at each site remain the same across all subtypes, and the differences among subtypes are a direct reflection of random substitutions, which have occurred during the time elapsed since their divergence. The alternative hypothesis suggests that the functional constraints themselves have evolved, and thus sequence differences among subtypes in some sites reflect shifts in function. To determine the contribution of each of these two alternatives to HIV-1 subtype evolution, we have developed a novel Bayesian method for testing and detecting site-specific rate shifts. The RAte Shift EstimatoR (RASER) method determines whether or not site-specific functional shifts characterize the evolution of a protein and, if so, points to the specific sites and lineages in which these shifts have most likely occurred. Applying RASER to a dataset composed of large samples of HIV-1 sequences from different group M subtypes, we reveal rampant evolutionary shifts throughout the HIV-1 proteome. Most of these rate shifts have occurred during the divergence of the major subtypes, establishing that subtype divergence occurred together with functional diversification. We report further evidence for the emergence of a new sub-subtype, characterized by abundant rate-shifting sites. When focusing on the rate-shifting sites detected, we find that many are associated with known function relating to viral life cycle and drug resistance. Finally, we discuss mechanisms of covariation of rate-shifting sites.

The AIDS epidemic, inflicted by the human immunodeficiency virus (HIV), has already claimed 25 million lives, thus posing a global threat. Since its discovery, several HIV subtypes have emerged, characterized by distinct genomic sequences and variable geographic locations. Here, we investigate the nature of the genetic differences among the subtypes. The neutral theory of evolution suggests that most genetic differences marginally affect the function of the encoded proteins (hence neutral) and thus occur randomly. Alternatively, changes in protein function are reflected by a pattern of nonrandom genetic differences. To address this issue, we developed a computational method, which studies the differences between sequences of different HIV subtypes, and estimates which of the explanations is more likely. Using a large sample of HIV protein sequences, we discovered that part of the variability among the subtypes is not random and possibly reflects different functional constraints imposed on the subtypes during the course of their evolution. An in-depth inspection of these nonrandom changes revealed a correlation with biological traits, such as drug resistance and mechanisms facilitating viral entry into the host cell. Interestingly, nonrandom changes are also characteristic of a viral strain that recently emerged in the former Soviet Union.

Evolutionary pattern of protein architecture in mammal and fruit fly genomes

Mutations, which can alter amino acid constitution, contribute greatly to protein evolution. However, little is reported of their pattern during protein structural evolution. We investigated the distribution of non-synonymous single nucleotide polymorphisms (nsSNPs) and insertions/deletions (indels) along mammal and fruit fly proteins. We found the nsSNPs (and d(N)) and indels increased in protein boundary regions, and this pattern is inversely correlated with the distribution of protein domain density. Additionally, synonymous substitutions (and d(S)) are reduced in 5' and 3' regions, indicating more variable protein boundaries, compared with central interior. All evidence suggests that the inner part of coding sequences (CDSs) is comparatively conserved, whereas the 5' and 3' regions, with higher evolution rates, are more variable. We assumed that due to greater frequencies of nsSNPs and indels in adaptive regions of CDSs it could be easier to ultimately alter, gain, or lose amino acids, thus becoming the front line of protein evolution.

Models of coding sequence evolution

Probabilistic models of sequence evolution are in widespread use in phylogenetics and molecular sequence evolution. These models have become increasingly sophisticated and combined with statistical model comparison techniques have helped to shed light on how genes and proteins evolve. Models of codon evolution have been particularly useful, because, in addition to providing a significant improvement in model realism for protein-coding sequences, codon models can also be designed to test hypotheses about the selective pressures that shape the evolution of the sequences. Such models typically assume a phylogeny and can be used to identify sites or lineages that have evolved adaptively. Recently some of the key assumptions that underlie phylogenetic tests of selection have been questioned, such as the assumption that the rate of synonymous changes is constant across sites or that a single phylogenetic tree can be assumed at all sites for recombining sequences. While some of these issues have been addressed through the development of novel methods, others remain as caveats that need to be considered on a case-by-case basis. Here, we outline the theory of codon models and their application to the detection of positive selection. We review some of the more recent developments that have improved their power and utility, laying a foundation for further advances in the modeling of coding sequence evolution.

Investigating protein-coding sequence evolution with probabilistic codon substitution models

This review is motivated by the true explosion in the number of recent studies both developing and ameliorating probabilistic models of codon evolution. Traditionally parametric, the first codon models focused on estimating the effects of selective pressure on the protein via an explicit parameter in the maximum likelihood framework. Likelihood ratio tests of nested codon models armed the biologists with powerful tools, which provided unambiguous evidence for positive selection in real data. This, in turn, triggered a new wave of methodological developments. The new generation of models views the codon evolution process in a more sophisticated way, relaxing several mathematical assumptions. These models make a greater use of physicochemical amino acid properties, genetic code machinery, and the large amounts of data from the public domain. The overview of the most recent advances on modeling codon evolution is presented here, and a wide range of their applications to real data is discussed. On the downside, availability of a large variety of models, each accounting for various biological factors, increases the margin for misinterpretation; the biological meaning of certain parameters may vary among models, and model selection procedures also deserve greater attention. Solid understanding of the modeling assumptions and their applicability is essential for successful statistical data analysis.

Genomic evolution in a virus under specific selection for host recognition

Genetic variation in viral structural proteins is often explained by evolutionary escape of strong host defenses through processes such as immune evasion, host switching, and tissue tropism. An understanding of the mechanisms driving evolutionary change in virus surface proteins is key to designing effective intervention strategies to disease emergence. This study investigated the predictability of virus genomic evolution in response to highly specific differences in host receptor structure. The bacteriophage PhiX174 was evolved on three E. coli mutant hosts, each differing only by a single sugar group in the lipopolysaccharides, used for phage attachment. Large phage populations were used in order to maximize the amount of sequence space explored by mutation, and thus the potential for parallel evolution. Repeatability was assessed by genome sequencing of multiple isolates from endpoint populations and by fitness of the endpoint population relative to its ancestor. Evolutionary lines showed similar magnitudes of fitness increase between treatments. Only one mutation, occurring in the internal DNA pilot protein H, was completely repeatable, and it appeared to be a necessary stepping stone toward further adaptive change. Substitutions in the surface accessible major capsid protein F appeared to be involved in capsid stability rather than specific interactions with host receptors, suggesting that non-specific alterations to capsid structure could be an important component of adaptation to novel hosts. 33% of mutations were synonymous and showed evidence of selection on codon usage. Lastly, results supported previous findings that evolving populations of small ssDNA viruses may maintain relatively high levels of genetic variation.

Duplications and functional divergence of ADP-glucose pyrophosphorylase genes in plants

Background

ADP-glucose pyrophosphorylase (AGPase), which catalyses a rate limiting step in starch synthesis, is a heterotetramer comprised of two identical large and two identical small subunits in plants. Although the large and small subunits are equally sensitive to activity-altering amino acid changes when expressed in a bacterial system, the overall rate of non-synonymous evolution is ~2.7-fold greater for the large subunit than for the small subunit. Herein, we examine the basis for their different rates of evolution, the number of duplications in both large and small subunit genes and document changes in the patterns of AGPase evolution over time.

Results

We found that the first duplication in the AGPase large subunit family occurred early in the history of land plants, while the earliest small subunit duplication occurred after the divergence of monocots and eudicots. The large subunit also had a larger number of gene duplications than did the small subunit. The ancient duplications in the large subunit family raise concern about the saturation of synonymous substitutions, but estimates of the absolute rate of AGPase evolution were highly correlated with estimates of ω (the non-synonymous to synonymous rate ratio). Both subunits showed evidence for positive selection and relaxation of purifying selection after duplication, but these phenomena could not explain the different evolutionary rates of the two subunits. Instead, evolutionary constraints appear to be permanently relaxed for the large subunit relative to the small subunit. Both subunits exhibit branch-specific patterns of rate variation among sites.

Conclusion

These analyses indicate that the higher evolutionary rate of the plant AGPase large subunit reflects permanent relaxation of constraints relative to the small subunit and they show that the large subunit genes have undergone more gene duplications than small subunit genes. Candidate sites potentially responsible for functional divergence within each of the AGPase subunits were investigated by examining branch-specific patterns of rate variation. We discuss the phenotypes of mutants that alter some candidate sites and strategies for examining candidate sites of presently unknown function.

Pervasive positive selection on duplicated and nonduplicated vertebrate protein coding genes

A stringent branch-site codon model was used to detect positive selection in vertebrate evolution. We show that the test is robust to the large evolutionary distances involved. Positive selection was detected in 77% of 884 genes studied. Most positive selection concerns a few sites on a single branch of the phylogenetic tree: Between 0.9% and 4.7% of sites are affected by positive selection depending on the branches. No functional category was overrepresented among genes under positive selection. Surprisingly, whole genome duplication had no effect on the prevalence of positive selection, whether the fish-specific genome duplication or the two rounds at the origin of vertebrates. Thus positive selection has not been limited to a few gene classes, or to specific evolutionary events such as duplication, but has been pervasive during vertebrate evolution.

Natural selection for nucleotide usage at synonymous and nonsynonymous sites in influenza A virus genes

Influenza A virus is one of the best-studied viruses and a model organism for the study of molecular evolution; in particular, much research has focused on detecting natural selection on influenza virus proteins. Here, we study the dynamics of the synonymous and nonsynonymous nucleotide composition of influenza A virus genes. In several genes, the nucleotide frequencies at synonymous positions drift away from the equilibria predicted from the synonymous substitution matrices. We investigate possible reasons for this unexpected behavior by fitting several regression models. Relaxation toward a mutation-selection equilibrium following a host jump fails to explain the dynamics of the synonymous nucleotide composition, even if we allow for slow temporal changes in the substitution matrix. Instead, we find that deep internal branches of the phylogeny show distinct patterns of nucleotide substitution and that these branches strongly influence the dynamics of nucleotide composition, suggesting that the observed trends are at least in part a result of natural selection acting on synonymous sites. Moreover, we find that the dynamics of the nucleotide composition at synonymous and nonsynonymous sites are highly correlated, providing evidence that even nonsynonymous sites can be influenced by selection pressure for nucleotide composition.