A target enrichment method for gathering phylogenetic information from hundreds of loci: An example from the Compositae.

UnlabelledPremise of the studyThe Compositae (Asteraceae) are a large and diverse family of plants, and the most comprehensive phylogeny to date is a meta-tree based on 10 chloroplast loci that has several major unresolved nodes. We describe the development of an approach that enables the rapid sequencing of large numbers of orthologous nuclear loci to facilitate efficient phylogenomic analyses. •Methods and resultsWe designed a set of sequence capture probes that target conserved orthologous sequences in the Compositae. We also developed a bioinformatic and phylogenetic workflow for processing and analyzing the resulting data. Application of our approach to 15 species from across the Compositae resulted in the production of phylogenetically informative sequence data from 763 loci and the successful reconstruction of known phylogenetic relationships across the family. •ConclusionsThese methods should be of great use to members of the broader Compositae community, and the general approach should also be of use to researchers studying other families

A target enrichment method for gathering Phylogenetic information from hundreds of loci: An example from the Compositae 1

Premise of the study: The Compositae (Asteraceae) are a large and diverse family of plants, and the most comprehensive phylogeny to date is a meta-Tree based on 10 chloroplast loci that has several major unresolved nodes. We describe the development of an approach that enables the rapid sequencing of large numbers of orthologous nuclear loci to facilitate efficient phylogenomic analyses. Methods and Results: We designed a set of sequence capture probes that target conserved orthologous sequences in the Compositae. We also developed a bioinformatic and phylogenetic workfl ow for processing and analyzing the resulting data. Application of our approach to 15 species from across the Compositae resulted in the production of phylogenetically informative sequence data from 763 loci and the successful reconstruction of known phylogenetic relationships across the family. Conclusions: These methods should be of great use to members of the broader Compositae community, and the general approach should also be of use to researchers studying other families

Connectivity in gene coexpression networks negatively correlates with rates of molecular evolution in flowering plants

<div><p>Gene coexpression networks are a useful tool for summarizing transcriptomic data and providing insight into patterns of gene regulation in a variety of species. Though there has been considerable interest in studying the evolution of network topology across species, less attention has been paid to the relationship between network position and patterns of molecular evolution. Here, we generated coexpression networks from publicly available expression data for seven flowering plant taxa (<i>Arabidopsis thaliana</i>, <i>Glycine max</i>, <i>Oryza sativa</i>, <i>Populus</i> spp., <i>Solanum lycopersicum</i>, <i>Vitis</i> spp., and <i>Zea mays</i>) to investigate the relationship between network position and rates of molecular evolution. We found a significant negative correlation between network connectivity and rates of molecular evolution, with more highly connected (i.e., “hub”) genes having significantly lower nonsynonymous substitution rates and <i>dN</i>/<i>dS</i> ratios compared to less highly connected (i.e., “peripheral”) genes across the taxa surveyed. These findings suggest that more centrally located hub genes are, on average, subject to higher levels of evolutionary constraint than are genes located on the periphery of gene coexpression networks. The consistency of this result across disparate taxa suggests that it holds for flowering plants in general, as opposed to being a species-specific phenomenon.</p></div

Linear regression of gene connectivity of seven taxa analyzed.

<p>Taxa: <i>A</i>. <i>thaliana</i>, <i>G</i>. <i>max</i>, <i>Populus spp</i>., <i>S</i>. <i>lycopersicum</i>, <i>Vitis spp</i>., <i>O</i>. <i>sativa</i>, and <i>Z</i>. <i>mays</i>, against (a): non-synonymous substitutions (<i>dN</i>), (b): synonymous substitutions (<i>dS</i>), (c): estimates of adaptive evolution (ω = <i>dN</i>/<i>dS</i>) and (d): number of connections in ortholog comparison. Circles represent genes, while the regression coefficient, represented as Kendall's tau (τ) coefficient, is the dashed line. Significance is indicated by bold text. Note that all significant results except the two marked with an asterisk (*) remained significant after correcting for multiple comparisons (see text for details).</p

Multiple genomic regions influence root morphology and seedling growth in cultivated sunflower (Helianthus annuus L.) under well-watered and water-limited conditions.

With climate change and an ever-increasing human population threatening food security, developing a better understanding of the genetic basis of crop performance under stressful conditions has become increasingly important. Here, we used genome-wide association studies to genetically dissect variation in seedling growth traits in cultivated sunflower (Helianthus annuus L.) under well-watered and water-limited (i.e., osmotic stress) conditions, with a particular focus on root morphology. Water limitation reduced seedling size and produced a shift toward deeper rooting. These effects varied across genotypes, and we identified 13 genomic regions that were associated with traits of interest across the two environments. These regions varied in size from a single marker to 186.2 Mbp and harbored numerous genes, some of which are known to be involved in the plant growth/development as well as the response to osmotic stress. In many cases, these associations corresponded to growth traits where the common allele outperformed the rare variant, suggesting that selection for increased vigor during the evolution of cultivated sunflower might be responsible for the relatively high frequency of these alleles. We also found evidence of pleiotropy across multiple traits, as well as numerous environmentally independent genetic effects. Overall, our results indicate the existence of genetic variation in root morphology and allocation and further suggest that the majority of alleles associated with these traits have consistent effects across environments

Simplified representation of a hypothetical coexpression network.

<p>Node A represents a hub gene while node B represents a peripheral gene. Lines connecting nodes represent network edges, and reflect correlations in expression.</p

Table listing the output of GO term enrichment analysis for each co-expression network module.

Each tab contains a GO term along with over represented and under represented P-values, the number of DEGs found belonging to that category, the total number of genes belonging to that category, the description of the GO term, and the GO ontology category that term belongs to. Each tab corresponds to a different module in the network. (XLSX)</p

Phenotypic means and standard deviations of all measured traits (n = 21).

Phenotypic means and standard deviations of all measured traits (n = 21).</p

Phenotypic trait comparison for control vs. stress scenarios.

In all panels, control is shown in gray, dry-down in blue, PEG in red, salt in yellow, and low-nutrient in green. (A) Boxplot of overall plant performance measured as total biomass. Black horizontal bars indicate median, while white diamonds indicate mean values per treatment. Letters above each box correspond to their post hoc Wilcoxon groupings. (B) Principal component analysis (PCA) for all measured traits (n = 21) illustrated using the first two PCs. (C) PCA of all size-independent traits (n = 10) illustrated using the first two PCs.</p