Selection on the protein-coding genome

Evidence of episodic positive selection in Corynebacterium diphtheriae complex of species and its implementations in identification of drug and vaccine targets

BACKGROUND

Within the pathogenic bacterial species Corynebacterium genus, six species that can produce diphtheria toxin (C. belfantii, C. diphtheriae, C. pseudotuberculosis, C. rouxii, C. silvaticum and C. ulcerans) form a clade referred to as the C. diphtheria complex. These species have been found in humans and other animals, causing diphtheria or other diseases. Here we show the results of a genome scale analysis to identify positive selection in protein-coding genes that may have resulted in the adaptations of these species to their ecological niches and suggest drug and vaccine targets.

METHODS

Forty genomes were sampled to represent species, subspecies or biovars of Corynebacterium. Ten phylogenetic groups were tested for positive selection using the PosiGene pipeline, including species and biovars from the C. diphtheria complex. The detected genes were tested for recombination and had their sequences alignments and homology manually examined. The final genes were investigated for their function and a probable role as vaccine or drug targets.

RESULTS

Nineteen genes were detected in the species C. diphtheriae (two), C. pseudotuberculosis (10), C. rouxii (one), and C. ulcerans (six). Those were found to be involved in defense, translation, energy production, and transport and in the metabolism of carbohydrates, amino acids, nucleotides, and coenzymes. Fourteen were identified as essential genes, and six as virulence factors. Thirteen from the 19 genes were identified as potential drug targets and four as potential vaccine candidates. These genes could be important in the prevention and treatment of the diseases caused by these bacteria.

Divergence in Coding Sequence and Expression of Different Functional Categories of Immune Genes between Two Wild Rodent Species

Differences in immune function between species could be a result of interspecific divergence in coding sequence and/or expression of immune genes. Here, we investigate how the degree of divergence in coding sequence and expression differs between functional categories of immune genes, and if differences between categories occur independently of other factors (expression level, pleiotropy). To this end, we compared spleen transcriptomes of wild-caught yellow-necked mice and bank voles. Immune genes expressed in the spleen were divided into four categories depending on the function of the encoded protein: pattern recognition receptors (PRR); signal transduction proteins; transcription factors; and cyto- and chemokines and their receptors. Genes encoding PRR and cyto-/chemokines had higher sequence divergence than genes encoding signal transduction proteins and transcription factors, even when controlling for potentially confounding factors. Genes encoding PRR also had higher expression divergence than genes encoding signal transduction proteins and transcription factors. There was a positive correlation between expression divergence and coding sequence divergence, in particular for PRR genes. We propose that this is a result of that divergence in PRR coding sequence leads to divergence in PRR expression through positive feedback of PRR ligand binding on PRR expression. When controlling for sequence divergence, expression divergence of PRR genes did not differ from other categories. Taken together, the results indicate that coding sequence divergence of PRR genes is a major cause of differences in immune function between species.

Comparative genome and transcriptome analyses reveal adaptations to opportunistic infections in woody plant degrading pathogens of Botryosphaeriaceae

Botryosphaeriaceae are an important fungal family that cause woody plant diseases worldwide. Recent studies have established a correlation between environmental factors and disease expression; however, less is known about factors that trigger these diseases. The current study reports on the 43.3 Mb de novo genome of Lasiodiplodia theobromae and five other genomes of Botryosphaeriaceae pathogens. Botryosphaeriaceous genomes showed an expansion of gene families associated with cell wall degradation, nutrient uptake, secondary metabolism and membrane transport, which contribute to adaptations for wood degradation. Transcriptome analysis revealed that genes involved in carbohydrate catabolism, pectin, starch and sucrose metabolism, and pentose and glucuronate interconversion pathways were induced during infection. Furthermore, genes in carbohydrate-binding modules, lysine motif domain and the glycosyl hydrolase gene families were induced by high temperature. Among these genes, overexpression of two selected putative lignocellulase genes led to increased virulence in the transformants. These results demonstrate the importance of high temperatures in opportunistic infections. This study also presents a set of Botryosphaeriaceae-specific effectors responsible for the identification of virulence-related pathogen-associated molecular patterns and demonstrates their active participation in suppressing hypersensitive responses. Together, these findings significantly expand our understanding of the determinants of pathogenicity or virulence in Botryosphaeriaceae and provide new insights for developing management strategies against them.

Genome-wide survey of codons under diversifying selection in a highly recombining bacterial species, Helicobacter pylori

Selection has been a central issue in biology in eukaryotes as well as prokaryotes. Inference of selection in recombining bacterial species, compared with clonal ones, has been a challenge. It is not known how codons under diversifying selection are distributed along the chromosome or among functional categories or how frequently such codons are subject to mutual homologous recombination. Here, we explored these questions by analysing genes present in >90% among 29 genomes of Helicobacter pylori, one of the bacterial species with the highest mutation and recombination rates. By a method for recombining sequences, we identified codons under diversifying selection (dN/dS> 1), which were widely distributed and accounted for ∼0.2% of all the codons of the genome. The codons were enriched in genes of host interaction/cell surface and genome maintenance (DNA replication,recombination, repair, and restriction modification system). The encoded amino acid residues were sometimes found adjacent to critical catalytic/binding residues in protein structures.Furthermore, by estimating the intensity of homologous recombination at a single nucleotide level, we found that these codons appear to be more frequently subject to recombination.We expect that the present study provides a new approach to population genomics of selection in recombining prokaryotes.

Optimization of sequence alignments according to the number of sequences vs. number of sites trade-off

Background

Comparative analysis of homologous sequences enables the understanding of evolutionary patterns at the molecular level, unraveling the functional constraints that shaped the underlying genes. Bioinformatic pipelines for comparative sequence analysis typically include procedures for (i) alignment quality assessment and (ii) control of sequence redundancy. An additional, underassessed step is the control of the amount and distribution of missing data in sequence alignments. While the number of sequences available for a given gene typically increases with time, the site-specific coverage of each alignment position remains highly variable because of differences in sequencing and annotation quality, or simply because of biological variation. For any given alignment-based analysis, the selection of sequences thus defines a trade-off between the species representation and the quantity of sites with sufficient coverage to be included in the subsequent analyses.

Results

We introduce an algorithm for the optimization of sequence alignments according to the number of sequences vs. number of sites trade-off. The algorithm uses a guide tree to compute scores for each bipartition of the alignment, allowing the recursive selection of sequence subsets with optimal combinations of sequence and site numbers. By applying our methods to two large data sets of several thousands of gene families, we show that significant site-specific coverage increases can be achieved while controlling for the species representation.

Conclusions

The algorithm introduced in this work allows the control of the distribution of missing data in any sequence alignment by removing sequences to increase the number of sites with a defined minimum coverage. We advocate that our missing data optimization procedure in an important step which should be considered in comparative analysis pipelines, together with alignment quality assessment and control of sampled diversity. An open source C++ implementation is available at http://bioweb.me/physamp.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0619-8) contains supplementary material, which is available to authorized users.

Darwin and Fisher meet at biotech: on the potential of computational molecular evolution in industry

Background

Today computational molecular evolution is a vibrant research field that benefits from the availability of large and complex new generation sequencing data – ranging from full genomes and proteomes to microbiomes, metabolomes and epigenomes. The grounds for this progress were established long before the discovery of the DNA structure. Specifically, Darwin’s theory of evolution by means of natural selection not only remains relevant today, but also provides a solid basis for computational research with a variety of applications. But a long-term progress in biology was ensured by the mathematical sciences, as exemplified by Sir R. Fisher in early 20th century. Now this is true more than ever: The data size and its complexity require biologists to work in close collaboration with experts in computational sciences, modeling and statistics.

Results

Natural selection drives function conservation and adaptation to emerging pathogens or new environments; selection plays key role in immune and resistance systems. Here I focus on computational methods for evaluating selection in molecular sequences, and argue that they have a high potential for applications. Pharma and biotech industries can successfully use this potential, and should take the initiative to enhance their research and development with state of the art bioinformatics approaches.

Conclusions

This review provides a quick guide to the current computational approaches that apply the evolutionary principles of natural selection to real life problems – from drug target validation, vaccine design and protein engineering to applications in agriculture, ecology and conservation.

Less is more: an adaptive branch-site random effects model for efficient detection of episodic diversifying selection

Over the past two decades, comparative sequence analysis using codon-substitution models has been honed into a powerful and popular approach for detecting signatures of natural selection from molecular data. A substantial body of work has focused on developing a class of "branch-site" models which permit selective pressures on sequences, quantified by the ω ratio, to vary among both codon sites and individual branches in the phylogeny. We develop and present a method in this class, adaptive branch-site random effects likelihood (aBSREL), whose key innovation is variable parametric complexity chosen with an information theoretic criterion. By applying models of different complexity to different branches in the phylogeny, aBSREL delivers statistical performance matching or exceeding best-in-class existing approaches, while running an order of magnitude faster. Based on simulated data analysis, we offer guidelines for what extent and strength of diversifying positive selection can be detected reliably and suggest that there is a natural limit on the optimal parametric complexity for "branch-site" models. An aBSREL analysis of 8,893 Euteleostomes gene alignments demonstrates that over 80% of branches in typical gene phylogenies can be adequately modeled with a single ω ratio model, that is, current models are unnecessarily complicated. However, there are a relatively small number of key branches, whose identities are derived from the data using a model selection procedure, for which it is essential to accurately model evolutionary complexity.

PSP: rapid identification of orthologous coding genes under positive selection across multiple closely related prokaryotic genomes

Background

With genomic sequences of many closely related bacterial strains made available by deep sequencing, it is now possible to investigate trends in prokaryotic microevolution. Positive selection is a sub-process of microevolution, in which a particular mutation is favored, causing the allele frequency to continuously shift in one direction. Wide scanning of prokaryotic genomes has shown that positive selection at the molecular level is much more frequent than expected. Genes with significant positive selection may play key roles in bacterial adaption to different environmental pressures. However, selection pressure analyses are computationally intensive and awkward to configure.

Results

Here we describe an open access web server, which is designated as PSP (Positive Selection analysis for Prokaryotic genomes) for performing evolutionary analysis on orthologous coding genes, specially designed for rapid comparison of dozens of closely related prokaryotic genomes. Remarkably, PSP facilitates functional exploration at the multiple levels by assignments and enrichments of KO, GO or COG terms. To illustrate this user-friendly tool, we analyzed Escherichia coli and Bacillus cereus genomes and found that several genes, which play key roles in human infection and antibiotic resistance, show significant evidence of positive selection. PSP is freely available to all users without any login requirement at: http://db-mml.sjtu.edu.cn/PSP/.

Conclusions

PSP ultimately allows researchers to do genome-scale analysis for evolutionary selection across multiple prokaryotic genomes rapidly and easily, and identify the genes undergoing positive selection, which may play key roles in the interactions of host-pathogen and/or environmental adaptation.

The essentials of computational molecular evolution

In this chapter, we give a brief yet self-contained introduction to computational molecular evolution. In particular, we present the emergence of the use of likelihood-based methods, review the standard DNA substitution models, and introduce how model choice operates. We also present recent developments in inferring absolute dates and rates on a phylogeny and show how state-of-the-art models take inspiration from diffusion theory to link population genetics, which traditionally focuses at a taxonomic level under that of species, and molecular evolution.