The evolutionary genomics of pathogen recombination

A pressing problem in studying the evolution of microbial pathogens is to determine the extent to which these genomes recombine. This information is essential for locating pathogenicity loci by using association studies or population genetic approaches. Recombination also complicates the use of phylogenetic approaches to estimate evolutionary parameters such as selection pressures. Reliable methods that detect and estimate the rate of recombination are, therefore, vital. This article reviews the approaches that are available for detecting and estimating recombination in microbial pathogens and how they can be used to understand pathogen evolution and to identify medically relevant loci.     

Mining the genomes of plant pathogenic bacteria: how not to drown in gigabases of sequence

Hundreds of bacterial genomes including the genomes of dozens of plant pathogenic bacteria have been sequenced. These genomes represent an invaluable resource for molecular plant pathologists. In this review, we describe different approaches that can be used for mining bacterial genome sequences and examples of how some of these approaches have been used to analyse plant pathogen genomes so far. We review how genomes can be mined one by one and how comparative genomics of closely related genomes releases the true power of genomics. Databases and tools useful for genome mining that are publicly accessible on the Internet are also described. Finally, the need for new databases and tools to efficiently mine today's plant pathogen genomes and hundreds more in the near future is discussed.     

Detecting balancing selection in genomes: limits and prospects

In spite of the long‐term interest in the process of balancing selection, its frequency in genomes and evolutionary significance remain unclear due to challenges related to its detection. Current statistical approaches based on patterns of variation observed in molecular data suffer from low power and a high incidence of false positives. This raises the question whether balancing selection is rare or is simply difficult to detect. We discuss genetic signatures produced by this mode of selection and review the current approaches used for their identification in genomes. Advantages and disadvantages of the available methods are presented, and areas where improvement is possible are identified. Increased specificity and reduced rate of false positives may be achieved by using a demographic model, applying combinations of tests, appropriate sampling scheme and taking into account intralocus variation in selection pressures. We emphasize novel solutions, recently developed model‐based approaches and good practices that should be implemented in future studies looking for signals of balancing selection. We also draw attention of the readers to the results of recent theoretical studies, which suggest that balancing selection may be ubiquitous but transient, leaving few signatures detectable by existing methods. Testing this new theory may require the development of novel high‐throughput methods extending beyond genomic scans.     

Utilizing natural diversity to evolve protein function: applications towards thermostability

Protein evolution relies on designing a library of sequences that capture meaningful functional diversity in a limited number of protein variants. Several approaches take advantage of the sequence space already explored through natural selection by incorporating sequence diversity available from modern genomes (and their ancestors) when designing these libraries. The success of these approaches is, partly, owing to the fact that modern sequence diversity has already been subjected to evolutionary selective forces and thus the diversity has already been deemed 'fit to survive'. Five of these approaches will be discussed in this review to highlight how protein engineers can use evolutionary sequence history/diversity of homologous proteins in unique ways to design protein libraries.     

Models of the population genetics of transposable elements

Although transposable elements (TEs) have been found in all organisms in which they have been looked for, the ways in which they invade genomes and populations are still a matter of debate. By extending the classical models of population genetics, several approaches have been developed to account for the dynamics of TEs, especially in Drosophila melanogaster . While the formalism of these models is based on simplifications, they enable us to understand better how TEs invade genomes, as a result of multiple evolutionary forces including duplication, deletion, self-regulation, natural selection and genetic drift. The aim of this paper is to review the assumptions and the predictions of these different models by highlighting the importance of the specific characteristics of both the TEs and the hosts, and the host/TE relationships. Then, perspectives in this domain will be discussed.     

Genomic instability in picornaviruses

Picornaviruses are small animal RNA viruses and include wtiological agents of poliomyelitis, foot and mouse disease, hepatitis A, etc. Replication of their genome results in many mutations, which are close in number to a viability threshold. Hence every virus population contains a great variety of genomes and represents a quasispecies. Covalent rearrangements (deletions, insertions, recombination) also contribute to genome variation and arise by replicative and nonreplicative mechanisms, which are still poorly understood. Only a minor fraction of all new changes is fixed during evolution. The fixation is based on two principally different ways of selection: with (positive and negative selection) and without (random selection of nonrepresentative variants) regard to the phenotype. In natural evolution of picornaviruses, the latter way is prevalent, and most fixed mutations are phenotypically neutral. To understand the mechanisms of evolution, it is necessary to evaluate the biological significance of particular genetic changes. Several new approaches to this problem have recently been proposed.     

Genome Instability in Picornaviruses

Picornaviruses are small animal RNA viruses and include etiological agents of poliomyelitis, foot and mouth disease, hepatitis A, etc. Replication of their genome results in many mutations, which are close in number to a viability threshold. Hence every virus population contains a great variety of genomes and represents a quasispecies. Covalent rearrangements (deletions, insertions, recombination) also contribute to genome variation and arise by replicative and nonreplicative mechanisms, which are still poorly understood. Only a minor fraction of all new changes is fixed during evolution. The fixation is based on two principally different ways of selection: with (positive and negative selection) and without (random selection of nonrepresentative variants) regard to the phenotype. In natural evolution of picornaviruses, the latter way is prevalent, and most fixed mutations are phenotypically neutral. To understand the mechanisms of evolution, it is necessary to evaluate the biological significance of particular genetic changes. Several new approaches to this problem have recently been proposed.     

Amino Acid Usage Is Asymmetrically Biased in AT- and GC-Rich Microbial Genomes

Introduction

Genomic base composition ranges from less than 25% AT to more than 85% AT in prokaryotes. Since only a small fraction of prokaryotic genomes is not protein coding even a minor change in genomic base composition will induce profound protein changes. We examined how amino acid and codon frequencies were distributed in over 2000 microbial genomes and how these distributions were affected by base compositional changes. In addition, we wanted to know how genome-wide amino acid usage was biased in the different genomes and how changes to base composition and mutations affected this bias. To carry this out, we used a Generalized Additive Mixed-effects Model (GAMM) to explore non-linear associations and strong data dependences in closely related microbes; principal component analysis (PCA) was used to examine genomic amino acid- and codon frequencies, while the concept of relative entropy was used to analyze genomic mutation rates.

Results

We found that genomic amino acid frequencies carried a stronger phylogenetic signal than codon frequencies, but that this signal was weak compared to that of genomic %AT. Further, in contrast to codon usage bias (CUB), amino acid usage bias (AAUB) was differently distributed in AT- and GC-rich genomes in the sense that AT-rich genomes did not prefer specific amino acids over others to the same extent as GC-rich genomes. AAUB was also associated with relative entropy; genomes with low AAUB contained more random mutations as a consequence of relaxed purifying selection than genomes with higher AAUB.

Conclusion

Genomic base composition has a substantial effect on both amino acid- and codon frequencies in bacterial genomes. While phylogeny influenced amino acid usage more in GC-rich genomes, AT-content was driving amino acid usage in AT-rich genomes. We found the GAMM model to be an excellent tool to analyze the genomic data used in this study.     

Quantification and decomposition of environment‐selection relationships

In nature, selection varies across time in most environments, but we lack an understanding of how specific ecological changes drive this variation. Ecological factors can alter phenotypic selection coefficients through changes in trait distributions or individual mean fitness, even when the trait‐absolute fitness relationship remains constant. We apply and extend a regression‐based approach in a population of Soay sheep (Ovis aries) and suggest metrics of environment‐selection relationships that can be compared across studies. We then introduce a novel method that constructs an environmentally structured fitness function. This allows calculation of full (as in existing approaches) and partial (acting separately through the absolute fitness function slope, mean fitness, and phenotype distribution) sensitivities of selection to an ecological variable. Both approaches show positive overall effects of density on viability selection of lamb mass. However, the second approach demonstrates that this relationship is largely driven by effects of density on mean fitness, rather than on the trait‐fitness relationship slope. If such mechanisms of environmental dependence of selection are common, this could have important implications regarding the frequency of fluctuating selection, and how previous selection inferences relate to longer term evolutionary dynamics.     

The influence of selection on the evolutionary distance estimated from the base changes observed between homologous nucleotide sequences

In most studies of molecular evolution, the nucleotide base at a site is assumed to change with the apparent rate under functional constraint, and the comparison of base changes between homologous genes is thought to yield the evolutionary distance corresponding to the site-average change rate multiplied by the divergence time. However, this view is not sufficiently successful in estimating the divergence time of species, but mostly results in the construction of tree topology without a time-scale. In the present paper, this problem is investigated theoretically by considering that observed base changes are the results of comparing the survivals through selection of mutated bases. In the case of weak selection, the time course of base changes due to mutation and selection can be obtained analytically, leading to a theoretical equation showing how the selection has influence on the evolutionary distance estimated from the enumeration of base changes. This result provides a new method for estimating the divergence time more accurately from the observed base changes by evaluating both the strength of selection and the mutation rate. The validity of this method is verified by analysing the base changes observed at the third codon positions of amino acid residues with four-fold codon degeneracy in the protein genes of mammalian mitochondria; i.e. the ratios of estimated divergence times are fairly well consistent with a series of fossil records of mammals. Throughout this analysis, it is also suggested that the mutation rates in mitochondrial genomes are almost the same in different lineages of mammals and that the lineage-specific base-change rates indicated previously are due to the selection probably arising from the preference of transfer RNAs to codons.     

Mutation and Selection Cause Codon Usage and Bias in Mitochondrial Genomes of Ribbon Worms (Nemertea)

The phenomenon of codon usage bias is known to exist in many genomes and it is mainly determined by mutation and selection. To understand the patterns of codon usage in nemertean mitochondrial genomes, we use bioinformatic approaches to analyze the protein-coding sequences of eight nemertean species. Neutrality analysis did not find a significant correlation between GC12 and GC3. ENc-plot showed a few genes on or close to the expected curve, but the majority of points with low-ENc values are below it. ENc-plot suggested that mutational bias plays a major role in shaping codon usage. The Parity Rule 2 plot (PR2) analysis showed that GC and AT were not used proportionally and we propose that codons containing A or U at third position are used preferentially in nemertean species, regardless of whether corresponding tRNAs are encoded in the mitochondrial DNA. Context-dependent analysis indicated that the nucleotide at the second codon position slightly affects synonymous codon choices. These results suggested that mutational and selection forces are probably acting to codon usage bias in nemertean mitochondrial genomes.     

The temporal dynamics of slightly deleterious mutations in Escherichia coli and Shigella spp

The Shigella are recently emerged clones of Escherichia coli, which have independently adopted an intracellular pathogenic lifestyle. We examined the molecular evolutionary consequences of this niche specialization by comparing the normalized, directional frequency profiles of unique polymorphisms within 2,098 orthologues representing the intersection of five E. coli and four Shigella genomes. We note a surfeit of AT-enriching changes (GC-->AT), transversions, and nonsynonymous changes in the Shigella genomes. By examining these differences within a temporal framework, we conclude that our results are consistent with relaxed or inefficient selection in Shigella owing to a reduced effective population size. Alternative interpretations, and the interesting exception of Shigella sonnei, are discussed. Finally, this analysis lends support to the view that nucleotide composition typically does not lie at mutational equilibrium but that selection plays a role in maintaining a higher GC content than would result solely from mutation bias. This argument sheds light on the enrichment of adenine and thymine in the genomes of bacterial endosymbionts where purifying selection is very weak.     

Viruses Roll the Dice: The Stochastic Behavior of Viral Genome Molecules Accelerates Viral Adaptation at the Cell and Tissue Levels

Recent studies on evolutionarily distant viral groups have shown that the number of viral genomes that establish cell infection after cell-to-cell transmission is unexpectedly small (1–20 genomes). This aspect of viral infection appears to be important for the adaptation and survival of viruses. To clarify how the number of viral genomes that establish cell infection is determined, we developed a simulation model of cell infection for tomato mosaic virus (ToMV), a positive-strand RNA virus. The model showed that stochastic processes that govern the replication or degradation of individual genomes result in the infection by a small number of genomes, while a large number of infectious genomes are introduced in the cell. It also predicted two interesting characteristics regarding cell infection patterns: stochastic variation among cells in the number of viral genomes that establish infection and stochastic inequality in the accumulation of their progenies in each cell. Both characteristics were validated experimentally by inoculating tobacco cells with a library of nucleotide sequence–tagged ToMV and analyzing the viral genomes that accumulated in each cell using a high-throughput sequencer. An additional simulation model revealed that these two characteristics enhance selection during tissue infection. The cell infection model also predicted a mechanism that enhances selection at the cellular level: a small difference in the replication abilities of coinfected variants results in a large difference in individual accumulation via the multiple-round formation of the replication complex (i.e., the replication machinery). Importantly, this predicted effect was observed in vivo. The cell infection model was robust to changes in the parameter values, suggesting that other viruses could adopt similar adaptation mechanisms. Taken together, these data reveal a comprehensive picture of viral infection processes including replication, cell-to-cell transmission, and evolution, which are based on the stochastic behavior of the viral genome molecules in each cell.     

The ways to produce biologically safe marker-free transgenic plants

The review considers the basic strategies used to produce biologically safe marker-free transgenic plants and analyzes their advantages and disadvantages. The systems of positive and negative selection as safer approaches for transformant identification are briefly described. The application of co-transformation, transposition, and site-specific recombination for production of marker-free plants is described. Special attention is paid to novel approaches to create marker-free plants initially containing no selective genes in their genomes.     

Analysis of genes and chromosomes by nonisotopic in situ hybridization

Nonisotopic in situ hybridization is a powerful tool to analyze the organization of complex genomes. Current approaches utilizing this technique for the analysis of linear and spatial genome organizations are presented. Clinical applications of these approaches, which open new avenues for diagnosis of disease-related chromosomal changes, are also discussed.     

Fish mitochondrial genomics: sequence, inheritance and functional variation

Mitochondrial genomic research currently primarily focuses on the analysis and understanding of how mitochondrial mutations produce detrimental phenotypes in humans. Reasons for this focus on negative impacts include the large number of human diseases that are known to result from specific mitochondrial genomes, and the long held belief that mitochondria change only through the accumulation of mutations due to its clonal, maternal inheritance. Recent studies are beginning to challenge these preconceptions and have shown that mitochondrial genomes can have significant positive impacts. Although the number of studies using fishes as models in mitochondrial research is limited, many fish model species provide excellent opportunity for furthering the understanding of mitochondrial genomes, their interactions with the nuclear genome, the potential for understanding the mechanisms of how functional variation effects organisms and how selection for positive functional variation effects population variation.     

Chapter 14: Cancer Genome Analysis

Although there is great promise in the benefits to be obtained by analyzing cancer genomes, numerous challenges hinder different stages of the process, from the problem of sample preparation and the validation of the experimental techniques, to the interpretation of the results. This chapter specifically focuses on the technical issues associated with the bioinformatics analysis of cancer genome data. The main issues addressed are the use of database and software resources, the use of analysis workflows and the presentation of clinically relevant action items. We attempt to aid new developers in the field by describing the different stages of analysis and discussing current approaches, as well as by providing practical advice on how to access and use resources, and how to implement recommendations. Real cases from cancer genome projects are used as examples.

What to Learn in This Chapter

This chapter presents an overview of how cancer genomes can be analyzed, discussing some of the challenges involved and providing practical advice on how to address them. As the primary analysis of experimental data is described elsewhere (sequencing, alignment and variant calling), we will focus on the secondary analysis of the data, i.e., the selection of candidate driver genes, functional interpretation and the presentation of the results. Emphasis is placed on how to build applications that meet the needs of researchers, academics and clinicians. The general features of such applications are laid out, along with advice on their design and implementation. This document should serve as a starter guide for bioinformaticians interested in the analysis of cancer genomes, although we also hope that more experienced bioinformaticians will find interesting solutions to some key technical issues.

This article is part of the “Translational Bioinformatics” collection for PLOS Computational Biology.