Comparative annotation of viral genomes with non-conserved gene structure

Interspecific adaptation by binary choice at de novo polyomavirus T antigen site through accelerated codon-constrained Val-Ala toggling within an intrinsically disordered region

It is common knowledge that conserved residues evolve slowly. We challenge generality of this central tenet of molecular biology by describing the fast evolution of a conserved nucleotide position that is located in the overlap of two open reading frames (ORFs) of polyomaviruses. The de novo ORF is expressed through either the ALTO protein or the Middle T antigen (MT/ALTO), while the ancestral ORF encodes the N-terminal domain of helicase-containing Large T (LT) antigen. In the latter domain the conserved Cys codon of the LXCXE pRB-binding motif constrains codon evolution in the overlapping MT/ALTO ORF to a binary choice between Val and Ala codons, termed here as codon-constrained Val-Ala (COCO-VA) toggling. We found the rate of COCO-VA toggling to approach the speciation rate and to be significantly accelerated compared to the baseline rate of chance substitution in a large monophyletic lineage including all viruses encoding MT/ALTO and three others. Importantly, the COCO-VA site is located in a short linear motif (SLiM) of an intrinsically disordered region, a typical characteristic of adaptive responders. These findings provide evidence that the COCO-VA toggling is under positive selection in many polyomaviruses, implying its critical role in interspecific adaptation, which is unprecedented for conserved residues.

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.

Is there a twelfth protein-coding gene in the genome of influenza A? A selection-based approach to the detection of overlapping genes in closely related sequences

Protein-coding genes often contain long overlapping open-reading frames (ORFs), which may or may not be functional. Current methods that utilize the signature of purifying selection to detect functional overlapping genes are limited to the analysis of sequences from divergent species, thus rendering them inapplicable to genes found only in closely related sequences. Here, we present a method for the detection of selection signatures on overlapping reading frames by using closely related sequences, and apply the method to several known overlapping genes, and to an overlapping ORF on the negative strand of segment 8 of influenza A virus (NEG8), for which the suggestion has been made that it is functional. We find no evidence that NEG8 is under selection, suggesting that the intact reading frame might be non-functional, although we cannot fully exclude the possibility that the method is not sensitive enough to detect the signature of selection acting on this gene. We present the limitations of the method using known overlapping genes and suggest several approaches to improve it in future studies. Finally, we examine alternative explanations for the sequence conservation of NEG8 in the absence of selection. We show that overlap type and genomic context affect the conservation of intact overlapping ORFs and should therefore be considered in any attempt of estimating the signature of selection in overlapping genes.

Detection of functional overlapping genes: simulation and case studies

As far as protein-coding genes are concerned, there is a non-zero probability that at least one of the five possible overlapping sequences of any gene will contain an open-reading frame (ORF) of a length that may be suitable for coding a functional protein. It is, however, very difficult to determine whether or not such an ORF is functional. Recently, we proposed a method that predicts functionality of an overlapping ORF if it can be shown that it has been subject to purifying selection during its evolution. Here, we use simulation to test this method under several conditions and compare it with the method of Firth and Brown. We found that under most conditions, our method detects functional overlapping genes with higher sensitivity than Firth and Brown's method, while maintaining high specificity. Further, we tested the hypothesis that the two aminoacyl tRNA synthetase classes have originated from a pair of overlapping genes. A central piece of evidence ostensibly supporting this hypothesis is the assertion that an overlapping ORF of a heat-shock protein-70 gene, which exhibits some similarity to class 2 aminoacyl tRNA synthetases, is functional. We found signature of purifying selection only in highly divergent sequences, suggesting that the method yields false-positives in high sequence divergence and that the overlapping ORF is not a functional gene. Finally, we examined three cases of overlap in the human genome. We find varying signatures of purifying selection acting on these overlaps, raising the possibility that two of the overlapping genes may not be functional.

A method for the simultaneous estimation of selection intensities in overlapping genes

Inferring the intensity of positive selection in protein-coding genes is important since it is used to shed light on the process of adaptation. Recently, it has been reported that overlapping genes, which are ubiquitous in all domains of life, seem to exhibit inordinate degrees of positive selection. Here, we present a new method for the simultaneous estimation of selection intensities in overlapping genes. We show that the appearance of positive selection is caused by assuming that selection operates independently on each gene in an overlapping pair, thereby ignoring the unique evolutionary constraints on overlapping coding regions. Our method uses an exact evolutionary model, thereby voiding the need for approximation or intensive computation. We test the method by simulating the evolution of overlapping genes of different types as well as under diverse evolutionary scenarios. Our results indicate that the independent estimation approach leads to the false appearance of positive selection even though the gene is in reality subject to negative selection. Finally, we use our method to estimate selection in two influenza A genes for which positive selection was previously inferred. We find no evidence for positive selection in both cases.

Investigating selection on viruses: a statistical alignment approach

Background

Two problems complicate the study of selection in viral genomes: Firstly, the presence of genes in overlapping reading frames implies that selection in one reading frame can bias our estimates of neutral mutation rates in another reading frame. Secondly, the high mutation rates we are likely to encounter complicate the inference of a reliable alignment of genomes. To address these issues, we develop a model that explicitly models selection in overlapping reading frames. We then integrate this model into a statistical alignment framework, enabling us to estimate selection while explicitly dealing with the uncertainty of individual alignments. We show that in this way we obtain un-biased selection parameters for different genomic regions of interest, and can improve in accuracy compared to using a fixed alignment.

Results

We run a series of simulation studies to gauge how well we do in selection estimation, especially in comparison to the use of a fixed alignment. We show that the standard practice of using a ClustalW alignment can lead to considerable biases and that estimation accuracy increases substantially when explicitly integrating over the uncertainty in inferred alignments. We even manage to compete favourably for general evolutionary distances with an alignment produced by GenAl. We subsequently run our method on HIV2 and Hepatitis B sequences.

Conclusion

We propose that marginalizing over all alignments, as opposed to using a fixed one, should be considered in any parametric inference from divergent sequence data for which the alignments are not known with certainty. Moreover, we discover in HIV2 that double coding regions appear to be under less stringent selection than single coding ones. Additionally, there appears to be evidence for differential selection, where one overlapping reading frame is under positive and the other under negative selection.

Annotation of selection strengths in viral genomes

MOTIVATION

Viral genomes tend to code in overlapping reading frames to maximize informational content. This may result in atypical codon bias and particular evolutionary constraints. Due to the fast mutation rate of viruses, there is additional strong evidence for varying selection between intra- and intergenomic regions. The presence of multiple coding regions complicates the concept of K(a)/K(s) ratio, and thus begs for an alternative approach when investigating selection strengths. Building on the paper by McCauley and Hein, we develop a method for annotating a viral genome coding in overlapping reading frames. We introduce an evolutionary model capable of accounting for varying levels of selection along the genome, and incorporate it into our prior single sequence HMM methodology, extending it now to a phylogenetic HMM. Given an alignment of several homologous viruses to a reference sequence, we may thus achieve an annotation both of coding regions as well as selection strengths, allowing us to investigate different selection patterns and hypotheses.

RESULTS

We illustrate our method by applying it to a multiple alignment of four HIV2 sequences, as well as of three Hepatitis B sequences. We obtain an annotation of the coding regions, as well as a posterior probability for each site of the strength of selection acting on it. From this we may deduce the average posterior selection acting on the different genes. Whilst we are encouraged to see in HIV2, that the known to be conserved genes gag and pol are indeed annotated as such, we also discover several sites of less stringent negative selection within the env gene. To the best of our knowledge, we are the first to subsequently provide a full selection annotation of the Hepatitis B genome by explicitly modelling the evolution within overlapping reading frames, and not relying on simple K(a)/K(s) ratios.