tRNA signatures reveal polyphyletic origins of streamlined SAR11 genomes among the alphaproteobacteria

Phylogenomic analyses are subject to bias from compositional convergence and noise from horizontal gene transfer (HGT). Compositional convergence is a likely cause of controversy regarding phylogeny of the SAR11 group of Alphaproteobacteria that have extremely streamlined, A+T-biased genomes. While careful modeling can reduce artifacts caused by convergence, the most consistent and robust phylogenetic signal in genomes may lie distributed among encoded functional features that govern macromolecular interactions. Here we develop a novel phyloclassification method based on signatures derived from bioinformatically defined tRNA Class-Informative Features (CIFs). tRNA CIFs are enriched for features that underlie tRNA-protein interactions. Using a simple tRNA-CIF-based phyloclassifier, we obtained results consistent with those of bias-corrected whole proteome phylogenomic studies, rejecting monophyly of SAR11 and affiliating most strains with Rhizobiales with strong statistical support. Yet SAR11 and Rickettsiales tRNA genes share distinct patterns of A+T-richness, as expected from their elevated genomic A+T compositions. Using conventional supermatrix methods on total tRNA sequence data, we could recover the artifactual result of a monophyletic SAR11 grouping with Rickettsiales. Thus tRNA CIF-based phyloclassification is more robust to base content convergence than supermatrix phylogenomics on whole tRNA sequences. Also, given the notoriously promiscuous HGT of aminoacyl-tRNA synthetases, tRNA CIF-based phyloclassification may be relatively robust to HGT of network components. We describe how unique features of tRNA-protein interaction networks facilitate the mining of traits governing macromolecular interactions from genomic data, and discuss why interaction-governing traits may be especially useful to solve difficult problems in microbial classification and phylogeny

Developmental and metabolic plasticity of white-skinned grape berries in response to <i>Botrytis cinerea</i> during noble rot

Noble rot results from exceptional infections of ripe grape (Vitis vinifera) berries by Botrytis cinerea. Unlike bunch rot, noble rot promotes favorable changes in grape berries and the accumulation of secondary metabolites that enhance wine grape composition. Noble rot-infected berries of cv Sémillon, a white-skinned variety, were collected over 3 years from a commercial vineyard at the same time that fruit were harvested for botrytized wine production. Using an integrated transcriptomics and metabolomics approach, we demonstrate that noble rot alters the metabolism of cv Sémillon berries by inducing biotic and abiotic stress responses as well as ripening processes. During noble rot, B. cinerea induced the expression of key regulators of ripening-associated pathways, some of which are distinctive to the normal ripening of red-skinned cultivars. Enhancement of phenylpropanoid metabolism, characterized by a restricted flux in white-skinned berries, was a common outcome of noble rot and red-skinned berry ripening. Transcript and metabolite analyses together with enzymatic assays determined that the biosynthesis of anthocyanins is a consistent hallmark of noble rot in cv Sémillon berries. The biosynthesis of terpenes and fatty acid aroma precursors also increased during noble rot. We finally characterized the impact of noble rot in botrytized wines. Altogether, the results of this work demonstrated that noble rot causes a major reprogramming of berry development and metabolism. This desirable interaction between a fruit and a fungus stimulates pathways otherwise inactive in white-skinned berries, leading to a greater accumulation of compounds involved in the unique flavor and aroma of botrytized wines.Facultad de Ciencias Agrarias y Forestale

Developmental and metabolic plasticity of white-skinned grape berries in response to <i>Botrytis cinerea</i> during noble rot

Noble rot results from exceptional infections of ripe grape (Vitis vinifera) berries by Botrytis cinerea. Unlike bunch rot, noble rot promotes favorable changes in grape berries and the accumulation of secondary metabolites that enhance wine grape composition. Noble rot-infected berries of cv Sémillon, a white-skinned variety, were collected over 3 years from a commercial vineyard at the same time that fruit were harvested for botrytized wine production. Using an integrated transcriptomics and metabolomics approach, we demonstrate that noble rot alters the metabolism of cv Sémillon berries by inducing biotic and abiotic stress responses as well as ripening processes. During noble rot, B. cinerea induced the expression of key regulators of ripening-associated pathways, some of which are distinctive to the normal ripening of red-skinned cultivars. Enhancement of phenylpropanoid metabolism, characterized by a restricted flux in white-skinned berries, was a common outcome of noble rot and red-skinned berry ripening. Transcript and metabolite analyses together with enzymatic assays determined that the biosynthesis of anthocyanins is a consistent hallmark of noble rot in cv Sémillon berries. The biosynthesis of terpenes and fatty acid aroma precursors also increased during noble rot. We finally characterized the impact of noble rot in botrytized wines. Altogether, the results of this work demonstrated that noble rot causes a major reprogramming of berry development and metabolism. This desirable interaction between a fruit and a fungus stimulates pathways otherwise inactive in white-skinned berries, leading to a greater accumulation of compounds involved in the unique flavor and aroma of botrytized wines.Facultad de Ciencias Agrarias y Forestale

Distinctive expansion of gene families associated with plant cell wall degradation, secondary metabolism, and nutrient uptake in the genomes of grapevine trunk pathogens

BackgroundTrunk diseases threaten the longevity and productivity of grapevines in all viticulture production systems. They are caused by distantly-related fungi that form chronic wood infections. Variation in wood-decay abilities and production of phytotoxic compounds are thought to contribute to their unique disease symptoms. We recently released the draft sequences of Eutypa lata, Neofusicoccum parvum and Togninia minima, causal agents of Eutypa dieback, Botryosphaeria dieback and Esca, respectively. In this work, we first expanded genomic resources to three important trunk pathogens, Diaporthe ampelina, Diplodia seriata, and Phaeomoniella chlamydospora, causal agents of Phomopsis dieback, Botryosphaeria dieback, and Esca, respectively. Then we integrated all currently-available information into a genome-wide comparative study to identify gene families potentially associated with host colonization and disease development.ResultsThe integration of RNA-seq, comparative and ab initio approaches improved the protein-coding gene prediction in T. minima, whereas shotgun sequencing yielded nearly complete genome drafts of Dia. ampelina, Dip. seriata, and P. chlamydospora. The predicted proteomes of all sequenced trunk pathogens were annotated with a focus on functions likely associated with pathogenesis and virulence, namely (i) wood degradation, (ii) nutrient uptake, and (iii) toxin production. Specific patterns of gene family expansion were described using Computational Analysis of gene Family Evolution, which revealed lineage-specific evolution of distinct mechanisms of virulence, such as specific cell wall oxidative functions and secondary metabolic pathways in N. parvum, Dia. ampelina, and E. lata. Phylogenetically-informed principal component analysis revealed more similar repertoires of expanded functions among species that cause similar symptoms, which in some cases did not reflect phylogenetic relationships, thereby suggesting patterns of convergent evolution.ConclusionsThis study describes the repertoires of putative virulence functions in the genomes of ubiquitous grapevine trunk pathogens. Gene families with significantly faster rates of gene gain can now provide a basis for further studies of in planta gene expression, diversity by genome re-sequencing, and targeted reverse genetic approaches. The functional validation of potential virulence factors will lead to a more comprehensive understanding of the mechanisms of pathogenesis and virulence, which ultimately will enable the development of accurate diagnostic tools and effective disease management

Discovery of Core Biotic Stress Responsive Genes in Arabidopsis by Weighted Gene Co-Expression Network Analysis

<div><p>Intricate signal networks and transcriptional regulators translate the recognition of pathogens into defense responses. In this study, we carried out a gene co-expression analysis of all currently publicly available microarray data, which were generated in experiments that studied the interaction of the model plant <i>Arabidopsis thaliana</i> with microbial pathogens. This work was conducted to identify (i) modules of functionally related co-expressed genes that are differentially expressed in response to multiple biotic stresses, and (ii) hub genes that may function as core regulators of disease responses. Using Weighted Gene Co-expression Network Analysis (WGCNA) we constructed an undirected network leveraging a rich curated expression dataset comprising 272 microarrays that involved microbial infections of Arabidopsis plants with a wide array of fungal and bacterial pathogens with biotrophic, hemibiotrophic, and necrotrophic lifestyles. WGCNA produced a network with scale-free and small-world properties composed of 205 distinct clusters of co-expressed genes. Modules of functionally related co-expressed genes that are differentially regulated in response to multiple pathogens were identified by integrating differential gene expression testing with functional enrichment analyses of gene ontology terms, known disease associated genes, transcriptional regulators, and <i>cis</i>-regulatory elements. The significance of functional enrichments was validated by comparisons with randomly generated networks. Network topology was then analyzed to identify intra- and inter-modular gene hubs. Based on high connectivity, and centrality in meta-modules that are clearly enriched in defense responses, we propose a list of 66 target genes for reverse genetic experiments to further dissect the Arabidopsis immune system. Our results show that statistical-based data trimming prior to network analysis allows the integration of expression datasets generated by different groups, under different experimental conditions and biological systems, into a functionally meaningful co-expression network.</p></div

Leave-One-Out Cross-Validation (LOO-CV) scores of alphaproteobacterial genomes under two different binary phyloclassifiers.

<p>A. Score distribution of genomes under the binary tRNA-CIF-based phyloclassifier as sketched in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi-1003454-g002" target="_blank">Figure 2</a>. The score of a genome in this classifier is the summation of differences in heights of the features of its tRNAs in the RRCH and RSR function logos in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi-1003454-g002" target="_blank">Figure 2</a>. B. Scores under the “zero” total tRNA sequence-based phyloclassifer defined in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#s4" target="_blank">Materials and Methods</a> and conducted as a control. Here the score of a genome is just the sum of log-odds of its tRNA sequences in two class-specific sequence profiles from the RRCH and RSR clades. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi.1003454.s010" target="_blank">Figure S2</a> for alternative treatments of missing data under other methods. Complete source code and data to reproduce these results and those in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi.1003454.s010" target="_blank">Figure S2</a> are in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi.1003454.s002" target="_blank">Dataset S2</a>.</p

Seven-way tRNA-CIF-based phyloclassification of alphaproteobacterial genomes by the default multilayer perceptron in WEKA.

<p>Each test genome classified is assigned a probability of classification into each of the seven alphaproteobacterial clades indicated. Bootstrap support values under resampling of tRNA sites against (left) all tRNA CIFs and (right) CIFs with heights bits and model retraining (100 replicates). All support values correspond to most probable clade as shown except for <i>Stappia</i> and <i>Labrenzia</i> for which they correspond to Rhizobiales. Complete source code and data to produce this figure, including the full WEKA model for classification of other alphaproteobacterial genomes and code to produce such models from scratch, is provided in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi.1003454.s004" target="_blank">Dataset S4</a>.</p

Gene Ontology (GO) term enrichments in modules identified by WGCNA and in randomly generated modules.

<p>(<b>A</b>) Molecular function and (<b>B</b>) biological process GO terms. Line plots represent the number of enriched modules as a function of the number of enriched terms in a module. Red symbols represent the observed counts, and red lines represent the best smooth of the data points. The black lines and symbols represent the mean number of enriched modules for modules with any given number of enriched GO terms in 100 random replicates. The boxplot represents the number of total modules with at least one enriched term in all 100 random permutations of the network. Red symbols represent the number of modules with at least one enriched term in the natural network. Real BP data included eight modules enriched for up to 274 terms, which were not shown in the figure.</p

Function logos of structurally aligned tRNA data as calculated by LOGOFUN [36] for two groups of Alphaproteobacteria and overview of tRNA-CIF-based binary phyloclassification.

<p>Function logos generalize sequence logos. They are the sole means by which we predict tRNA Class-Informative Features (CIFs), which form the basis of the scoring schemes of the classifiers reported in this work. A full derivation of the mathematics of function logos is provided in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi.1003454-Freyhult1" target="_blank">[36]</a>. The tRNA-CIF-based phyloclassifier shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi-1003454-g003" target="_blank">Figure 3A</a> sums differences in heights of features between two function logos for a set of genomically derived tRNAs. Complete source code and data to reproduce the function logos in this figure are in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003454#pcbi.1003454.s001" target="_blank">Dataset S1</a>.</p

Graphical representation of the TOOL meta-module.

<p>Nodes of the unweighted network (TOM > 0.1) are visualized as circles and correlations between nodes as lines (edges). Node size is proportional to degree of unweighted connectivity; the colors of the edge of the nodes correspond to module membership; edge width and opacity are proportional to TOM and adjacency values between the two connected nodes, respectively. The central color of each node is based on the mean expression log₂ fold-change (up-regulation in red, down-regulation in blue) in response to necrotrophs (left) and biotrophs (right). The shape of the network is based on a force-directed graph calculation which creates a shape based on centrality computed with Cytoscape v. 2.8 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118731#pone.0118731.ref029" target="_blank">29</a>]. Intra-modular hub genes are numbered according to their ranked unweighted connectivity.</p