Methods to detect selection on noncoding DNA

Detecting signals of adaptive evolution in grape plastomes with a focus on the Cretaceous-Palaeogene (K/Pg) transition

BACKGROUND AND AIMS

Although plastid genes are widely used in phylogenetic studies, signals of positive selection have been scarcely investigated in the grape family. The plastomes from 91 accessions of Vitaceae were examined to understand the extent to which positive selection is present and to identify which genes are involved. Moreover, the changes through time of genes under episodic positive selection were investigated and the hypothesis of an adaptive process following the Cretaceous-Palaeogene (K/Pg) transition about 66 million years ago was tested.

METHODS

Different codon-substitution models were used to assess pervasive and episodic positive selection events on 70 candidate plastid genes. Divergence times between lineages were estimated and stochastic character mapping analysis was used to simulate variation over time of the genes found to be under episodic positive selection.

KEY RESULTS

A total of 20 plastid genes (29 %) showed positive selection. Among them, 14 genes showed pervasive signatures of positive selection and nine genes showed episodic signatures of positive selection. In particular, four of the nine genes (psbK, rpl20, rpoB, rps11) exhibited a similar pattern showing an increase in the rate of variation close to the K/Pg transition.

CONCLUSION

Multiple analyses have shown that the grape family has experienced ancient and recent positive selection events and that the targeted genes are involved in essential functions such as photosynthesis, self-replication and metabolism. Our results are consistent with the idea that the K/Pg transition has favoured an increased rate of change in some genes. Intense environmental perturbations have influenced the rapid diversification of certain lineages, and new mutations arising on some plastid genes may have been fixed by natural selection over the course of many generations.

Functional analysis of the Vsx2 super-enhancer uncovers distinct cis-regulatory circuits controlling Vsx2 expression during retinogenesis

Vsx2 is a transcription factor essential for retinal proliferation and bipolar cell differentiation, but the molecular mechanisms underlying its developmental roles are unclear. Here, we have profiled VSX2 genomic occupancy during mouse retinogenesis, revealing extensive retinal genetic programs associated with VSX2 during development. VSX2 binds and transactivates its enhancer in association with the transcription factor PAX6. Mice harboring deletions in the Vsx2 regulatory landscape exhibit specific abnormalities in retinal proliferation and in bipolar cell differentiation. In one of those deletions, a complete loss of bipolar cells is associated with a bias towards photoreceptor production. VSX2 occupies cis-regulatory elements nearby genes associated with photoreceptor differentiation and homeostasis in the adult mouse and human retina, including a conserved region nearby Prdm1, a factor implicated in the specification of rod photoreceptors and suppression of bipolar cell fate. VSX2 interacts with the transcription factor OTX2 and can act to suppress OTX2-dependent enhancer transactivation of the Prdm1 enhancer. Taken together, our analyses indicate that Vsx2 expression can be temporally and spatially uncoupled at the enhancer level, and they illuminate important mechanistic insights into how VSX2 is engaged with gene regulatory networks that are essential for retinal proliferation and cell fate acquisition.

KaKs_Calculator 3.0: Calculating Selective Pressure on Coding and Non-coding Sequences

KaKs_Calculator 3.0 is an updated toolkit that is capable of calculating selective pressure on both coding and non-coding sequences. Similar to the nonsynonymous/synonymous substitution rate ratio for coding sequences, selection on non-coding sequences can be quantified as the ratio of non-coding nucleotide substitution rate to synonymous substitution rate of adjacent coding sequences. As testified on empirical data, KaKs_Calculator 3.0 shows effectiveness to detect the strength and mode of selection operated on molecular sequences, accordingly demonstrating its great potential to achieve genome-wide scan of natural selection on diverse sequences and identification of potentially functional elements at a whole-genome scale. The package of KaKs_Calculator 3.0 is freely available for academic use only at https://ngdc.cncb.ac.cn/biocode/tools/BT000001.

Diversification and Functional Evolution of HOX Proteins

Gene duplication and divergence is a major contributor to the generation of morphological diversity and the emergence of novel features in vertebrates during evolution. The availability of sequenced genomes has facilitated our understanding of the evolution of genes and regulatory elements. However, progress in understanding conservation and divergence in the function of proteins has been slow and mainly assessed by comparing protein sequences in combination with in vitro analyses. These approaches help to classify proteins into different families and sub-families, such as distinct types of transcription factors, but how protein function varies within a gene family is less well understood. Some studies have explored the functional evolution of closely related proteins and important insights have begun to emerge. In this review, we will provide a general overview of gene duplication and functional divergence and then focus on the functional evolution of HOX proteins to illustrate evolutionary changes underlying diversification and their role in animal evolution.

Impact of Genetic Variation in Gene Regulatory Sequences: A Population Genomics Perspective

The unprecedented rise of high-throughput sequencing and assay technologies has provided a detailed insight into the non-coding sequences and their potential role as gene expression regulators. These regulatory non-coding sequences are also referred to as cis-regulatory elements (CREs). Genetic variants occurring within CREs have been shown to be associated with altered gene expression and phenotypic changes. Such variants are known to occur spontaneously and ultimately get fixed, due to selection and genetic drift, in natural populations and, in some cases, pave the way for speciation. Hence, the study of genetic variation at CREs has improved our overall understanding of the processes of local adaptation and evolution. Recent advances in high-throughput sequencing and better annotations of CREs have enabled the evaluation of the impact of such variation on gene expression, phenotypic alteration and fitness. Here, we review recent research on the evolution of CREs and concentrate on studies that have investigated genetic variation occurring in these regulatory sequences within the context of population genetics.

Robust inference of positive selection on regulatory sequences in the human brain

A longstanding hypothesis is that divergence between humans and chimpanzees might have been driven more by regulatory level adaptations than by protein sequence adaptations. This has especially been suggested for regulatory adaptations in the evolution of the human brain. We present a new method to detect positive selection on transcription factor binding sites on the basis of measuring predicted affinity change with a machine learning model of binding. Unlike other methods, this approach requires neither defining a priori neutral sites nor detecting accelerated evolution, thus removing major sources of bias. We scanned the signals of positive selection for CTCF binding sites in 29 human and 11 mouse tissues or cell types. We found that human brain-related cell types have the highest proportion of positive selection. This result is consistent with the view that adaptive evolution to gene regulation has played an important role in evolution of the human brain.

Divergent evolution in the genomes of closely related lacertids, Lacerta viridis and L. bilineata, and implications for speciation

BACKGROUND

Lacerta viridis and Lacerta bilineata are sister species of European green lizards (eastern and western clades, respectively) that, until recently, were grouped together as the L. viridis complex. Genetic incompatibilities were observed between lacertid populations through crossing experiments, which led to the delineation of two separate species within the L. viridis complex. The population history of these sister species and processes driving divergence are unknown. We constructed the first high-quality de novo genome assemblies for both L. viridis and L. bilineata through Illumina and PacBio sequencing, with annotation support provided from transcriptome sequencing of several tissues. To estimate gene flow between the two species and identify factors involved in reproductive isolation, we studied their evolutionary history, identified genomic rearrangements, detected signatures of selection on non-coding RNA, and on protein-coding genes.

FINDINGS

Here we show that gene flow was primarily unidirectional from L. bilineata to L. viridis after their split at least 1.15 million years ago. We detected positive selection of the non-coding repertoire; mutations in transcription factors; accumulation of divergence through inversions; selection on genes involved in neural development, reproduction, and behavior, as well as in ultraviolet-response, possibly driven by sexual selection, whose contribution to reproductive isolation between these lacertid species needs to be further evaluated.

CONCLUSION

The combination of short and long sequence reads resulted in one of the most complete lizard genome assemblies. The characterization of a diverse array of genomic features provided valuable insights into the demographic history of divergence among European green lizards, as well as key species differences, some of which are candidates that could have played a role in speciation. In addition, our study generated valuable genomic resources that can be used to address conservation-related issues in lacertids.

Parallel molecular evolution and adaptation in viruses

Parallel molecular evolution is the independent evolution of the same genotype or phenotype from distinct ancestors. The simple genomes and rapid evolution of many viruses mean they are useful model systems for studying parallel evolution by natural selection. Parallel adaptation occurs in the context of several viral behaviours, including cross-species transmission, drug resistance, and host immune escape, and its existence suggests that at least some aspects of virus evolution and emergence are repeatable and predictable. We introduce examples of virus parallel evolution and summarise key concepts. We outline the difficulties in detecting parallel adaptation using virus genomes, with a particular focus on phylogenetic and structural approaches, and we discuss future approaches that may improve our understanding of the phenomenon.

Identifying co-opted transposable elements using comparative epigenomics

The human genome gives rise to different epigenomic landscapes that define each cell type and can be deregulated in disease. Recent efforts by ENCODE, the NIH Roadmap and the International Human Epigenome Consortium (IHEC) have made significant advances towards assembling reference epigenomic maps of various tissues. Notably, these projects have found that approximately 80% of human DNA was biochemically active in at least one epigenomic assay while only approximately 10% of the sequence displayed signs of purifying selection. Given that transposable elements (TEs) make up at least 50% of the human genome and can be actively transcribed or act as regulatory elements either for their own purposes or be co-opted for the benefit of their host; we are interested in exploring their overall contribution to the "functional" genome. Traditional methods used to identify functional DNA have relied on comparative genomics, conservation analysis and low throughput validation assays. To discover co-opted TEs, and distinguish them from noisy genomic elements, we argue that comparative epigenomic methods will also be important.

Translating natural genetic variation to gene expression in a computational model of the Drosophila gap gene regulatory network

Annotating the genotype-phenotype relationship, and developing a proper quantitative description of the relationship, requires understanding the impact of natural genomic variation on gene expression. We apply a sequence-level model of gap gene expression in the early development of Drosophila to analyze single nucleotide polymorphisms (SNPs) in a panel of natural sequenced D. melanogaster lines. Using a thermodynamic modeling framework, we provide both analytical and computational descriptions of how single-nucleotide variants affect gene expression. The analysis reveals that the sequence variants increase (decrease) gene expression if located within binding sites of repressors (activators). We show that the sign of SNP influence (activation or repression) may change in time and space and elucidate the origin of this change in specific examples. The thermodynamic modeling approach predicts non-local and non-linear effects arising from SNPs, and combinations of SNPs, in individual fly genotypes. Simulation of individual fly genotypes using our model reveals that this non-linearity reduces to almost additive inputs from multiple SNPs. Further, we see signatures of the action of purifying selection in the gap gene regulatory regions. To infer the specific targets of purifying selection, we analyze the patterns of polymorphism in the data at two phenotypic levels: the strengths of binding and expression. We find that combinations of SNPs show evidence of being under selective pressure, while individual SNPs do not. The model predicts that SNPs appear to accumulate in the genotypes of the natural population in a way biased towards small increases in activating action on the expression pattern. Taken together, these results provide a systems-level view of how genetic variation translates to the level of gene regulatory networks via combinatorial SNP effects.

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.

Inferring positive selection in humans from genomic data

Adaptation can be described as an evolutionary process that leads to an adjustment of the phenotypes of a population to their environment. In the classical view, new mutations can introduce novel phenotypic features into a population that leave footprints in the genome after fixation, such as selective sweeps. Alternatively, existing genetic variants may become beneficial after an environmental change and increase in frequency. Although they may not reach fixation, they may cause a shift of the optimum of a phenotypic trait controlled by multiple loci. With the availability of polymorphism data from various organisms, including humans and chimpanzees, it has become possible to detect molecular evidence of adaptation and to estimate the strength and target of positive selection. In this review, we discuss the two competing models of adaptation and suitable approaches for detecting the footprints of positive selection on the molecular level.

Nucleotide variation and balancing selection at the Ckma gene in Atlantic cod: analysis with multiple merger coalescent models

High-fecundity organisms, such as Atlantic cod, can withstand substantial natural selection and the entailing genetic load of replacing alleles at a number of loci due to their excess reproductive capacity. High-fecundity organisms may reproduce by sweepstakes leading to highly skewed heavy-tailed offspring distribution. Under such reproduction the Kingman coalescent of binary mergers breaks down and models of multiple merger coalescent are more appropriate. Here we study nucleotide variation at the Ckma (Creatine Kinase Muscle type A) gene in Atlantic cod. The gene shows extreme differentiation between the North (Canada, Greenland, Iceland, Norway, Barents Sea) and the South (Faroe Islands, North-, Baltic-, Celtic-, and Irish Seas) with F_ST > 0.8 between regions whereas neutral loci show no differentiation. This is evidence of natural selection. The protein sequence is conserved by purifying selection whereas silent and non-coding sites show extreme differentiation. The unfolded site-frequency spectrum has three modes, a mode at singleton sites and two high frequency modes at opposite frequencies representing divergent branches of the gene genealogy that is evidence for balancing selection. Analysis with multiple-merger coalescent models can account for the high frequency of singleton sites and indicate reproductive sweepstakes. Coalescent time scales vary with population size and with the inverse of variance in offspring number. Parameter estimates using multiple-merger coalescent models show that times scales are faster than under the Kingman coalescent.

Evidence for widespread positive and negative selection in coding and conserved noncoding regions of Capsella grandiflora

The extent that both positive and negative selection vary across different portions of plant genomes remains poorly understood. Here, we sequence whole genomes of 13 Capsella grandiflora individuals and quantify the amount of selection across the genome. Using an estimate of the distribution of fitness effects, we show that selection is strong in coding regions, but weak in most noncoding regions, with the exception of 5' and 3' untranslated regions (UTRs). However, estimates of selection on noncoding regions conserved across the Brassicaceae family show strong signals of selection. Additionally, we see reductions in neutral diversity around functional substitutions in both coding and conserved noncoding regions, indicating recent selective sweeps at these sites. Finally, using expression data from leaf tissue we show that genes that are more highly expressed experience stronger negative selection but comparable levels of positive selection to lowly expressed genes. Overall, we observe widespread positive and negative selection in coding and regulatory regions, but our results also suggest that both positive and negative selection on plant noncoding sequence are considerably rarer than in animal genomes.

Natural selection on coding and noncoding DNA sequences is associated with virulence genes in a plant pathogenic fungus

Natural selection leaves imprints on DNA, offering the opportunity to identify functionally important regions of the genome. Identifying the genomic regions affected by natural selection within pathogens can aid in the pursuit of effective strategies to control diseases. In this study, we analyzed genome-wide patterns of selection acting on different classes of sequences in a worldwide sample of eight strains of the model plant-pathogenic fungus Colletotrichum graminicola. We found evidence of selective sweeps, balancing selection, and positive selection affecting both protein-coding and noncoding DNA of pathogenicity-related sequences. Genes encoding putative effector proteins and secondary metabolite biosynthetic enzymes show evidence of positive selection acting on the coding sequence, consistent with an Arms Race model of evolution. The 5' untranslated regions (UTRs) of genes coding for effector proteins and genes upregulated during infection show an excess of high-frequency polymorphisms likely the consequence of balancing selection and consistent with the Red Queen hypothesis of evolution acting on these putative regulatory sequences. Based on the findings of this work, we propose that even though adaptive substitutions on coding sequences are important for proteins that interact directly with the host, polymorphisms in the regulatory sequences may confer flexibility of gene expression in the virulence processes of this important plant pathogen.

No gene in the genome makes sense except in the light of evolution

Evolutionary conservation has been an accurate predictor of functional elements across the first decade of metazoan genomics. More recently, there has been a move to define functional elements instead from biochemical annotations. Evolutionary methods are, however, more comprehensive than biochemical approaches can be and can assess quantitatively, especially for subtle effects, how biologically important--how injurious after mutation--different types of elements are. Evolutionary methods are thus critical for understanding the large fraction (up to 10%) of the human genome that does not encode proteins and yet might convey function. These methods can also capture the ephemeral nature of much noncoding functional sequence, with large numbers of functional elements having been gained and lost rapidly along each mammalian lineage. Here, we review how different strengths of purifying selection have impacted on protein-coding and non-protein-coding loci and on transcription factor binding sites in mammalian and fruit fly genomes.

Patterns of Selection in Plant Genomes

Plants show a wide range of variation in mating system, ploidy level, and demographic history, allowing for unique opportunities to investigate the evolutionary and genetic factors affecting genome-wide patterns of positive and negative selection. In this review, we highlight recent progress in our understanding of the extent and nature of selection on plant genomes. We discuss differences in selection as they relate to variation in demography, recombination, mating system, and ploidy. We focus on the population genetic consequences of these factors and argue that, although variation in the magnitude of purifying selection is well documented, quantifying rates of positive selection and disentangling the relative importance of recombination, demography, and ploidy are ongoing challenges. Large-scale comparative studies that examine the relative and joint importance of these processes, combined with explicit models of population history and selection, are key and feasible goals for future work.

A novel test for selection on cis-regulatory elements reveals positive and negative selection acting on mammalian transcriptional enhancers

Measuring natural selection on genomic elements involved in the cis-regulation of gene expression--such as transcriptional enhancers and promoters--is critical for understanding the evolution of genomes, yet it remains a major challenge. Many studies have attempted to detect positive or negative selection in these noncoding elements by searching for those with the fastest or slowest rates of evolution, but this can be problematic. Here, we introduce a new approach to this issue, and demonstrate its utility on three mammalian transcriptional enhancers. Using results from saturation mutagenesis studies of these enhancers, we classified all possible point mutations as upregulating, downregulating, or silent, and determined which of these mutations have occurred on each branch of a phylogeny. Applying a framework analogous to Ka/Ks in protein-coding genes, we measured the strength of selection on upregulating and downregulating mutations, in specific branches as well as entire phylogenies. We discovered distinct modes of selection acting on different enhancers: although all three have experienced negative selection against downregulating mutations, the selection pressures on upregulating mutations vary. In one case, we detected positive selection for upregulation, whereas the other two had no detectable selection on upregulating mutations. Our methodology is applicable to the growing number of saturation mutagenesis data sets, and provides a detailed picture of the mode and strength of natural selection acting on cis-regulatory elements.

Non-coding transcription at cis-regulatory elements: computational and experimental approaches

Mammalian genomes are pervasively transcribed, generating mostly RNAs with no coding potential that display different size, structure and interspecies sequence conservation. A prominent contribution to the ncRNA pool comes from the transcription of cis-regulatory elements, namely promoters, enhancers and locus control regions. While this phenomenon has been extensively documented, possible roles of such ncRNAs in gene regulation are still unclear. Addressing this issue will require experimental strategies dealing with the low abundance of enhancer-templated ncRNAs and aimed at specifically dissecting the relative role of transcription per se vs. RNA products. In this review, we first focus on the identification and characterization of cis-regulatory elements, highlighting the differences between emerging classes of ncRNAs associated to specific chromatin signatures. We then discuss current experimental strategies to dissect the function of nc transcription and computational approaches to the analysis and classification of regulatory sequences identified in next-generation sequencing experiments.