Comparative Transcriptomics Suggests Early Modifications by Vintec® in Grapevine Trunk of Hormonal Signaling and Secondary Metabolism Biosynthesis in Response to Phaeomoniella chlamydospora and Phaeoacremonium minimum.

Given their well-known antifungal abilities, species of the genus Trichoderma are of significant interest in modern agriculture. Recent studies have shown that Trichoderma species can induce plant resistance against different phytopathogens. To further extend this line of investigation, we investigate herein the transcriptomic response of grapevine trunk to Vintec®, which is a Trichoderma atroviride SC1-based commercial formulation for biological control of grapevine trunk diseases and which reduces wood colonization. The aim of the study is to understand whether the biocontrol agent Vintec® modifies the trunk response to Phaeoacremonium minimum and Phaeomoniella chlamydospora, which are two esca-associated fungal pathogens. An analysis of transcriptional regulation identifies clusters of co-regulated genes whose transcriptomic reprogramming in response to infection depends on the absence or presence of Vintec®. On one hand, the results show that Vintec® differentially modulates the expression of putative genes involved in hormonal signaling, especially those involved in auxin signaling. On the other hand, most significant gene expression modifications occur among secondary-metabolism-related genes, especially regarding phenylpropanoid metabolism and stilbene biosynthesis. Taken together, these results suggest that the biocontrol agent Vintec® induces wood responses that counteract disease development.

Modification of Early Response of Vitis vinifera to Pathogens Relating to Esca Disease and Biocontrol Agent Vintec® Revealed By Untargeted Metabolomics on Woody Tissues.

Esca disease is one of the most destructive grapevine trunk diseases. Phaeoacremonium minimum and Phaeomoniella chlamydospora are two of the known fungal pathogens associated with this disease. Today, biocontrol agents against Esca are mainly based on the use of the strain of the mycoparasite fungal genus Trichoderma such as the Vintec® product. The aim of this study was to investigate early response of woody tissues to Esca pathogens and identify metabolites that could be correlated with a biocontrol activity within a complex woody matrix. An untargeted liquid chromatography-high-resolution mass spectrometry metabolomic approach coupled to a spectral similarity network was used to highlight clusters of compounds associated with the plant response to pathogens and biocontrol. Dereplication highlighted the possible role of glycerophospholipids and polyphenol compounds, the latest mainly belonging to stilbenoids. Antifungal activity of some relevant biomarkers, evaluated in vitro on Phaeomoniella chlamydospora and Botrytis cinerea, suggests that some of these compounds can play a role to limit the development of Esca pathogens in planta.

Phenolic profile of a Parma violet unveiled by chemical and fluorescence imaging.

The ability of phenolic compounds to autofluoresce upon illumination by UV or blue light was exploited to explore the nature and distribution of these metabolites within the flower petals, leaves and roots of the violet, Viola alba subsp. dehnhardtii. This was achieved through a dual complementary approach that combined fluorescence microscopy imaging of living intact tissues and chemical extraction of pulverized material. The blue to red fluorescence displayed by living tissues upon illumination was indicative of their richness in phenolic compounds. Phenolic acids were found in all tissues, while flavonoids characterized the aerial part of the plant, anthocyanidins being restricted to the petals. The chemical quantification of phenolics in plant extracts confirmed their tissue-specific distribution and abundance. A key finding was that the spectral signatures obtained through confocal microscopy of endogenous fluorophores in living tissues and their counterpart extracts share the same fluorescence patterns, pointing out the potential of fluorescence imaging of intact organs for a proper estimation of their phenolic content. In addition, this study highlighted a few distinct morphology cell types, in particular foliar-glandular-like structures, and jagged petal cell walls. Altogether, these data provide a comprehensive histochemical localization of phenolics in living tissues of a violet. Converting fluorescence imaging into a chemical imprint indicated that one can rely on fluorescence microscopy of intact living tissues as a rapid, non-destructive means to follow their phenolic imprint under various environmental conditions.

MS-CleanR: A Feature-Filtering Workflow for Untargeted LC-MS Based Metabolomics.

Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) is currently the gold-standard technique to determine the full chemical diversity in biological samples. However, this approach still has many limitations; notably, the difficulty of accurately estimating the number of unique metabolites profiled among the thousands of MS ion signals arising from chromatograms. Here, we describe a new workflow, MS-CleanR, based on the MS-DIAL/MS-FINDER suite, which tackles feature degeneracy and improves annotation rates. We show that implementation of MS-CleanR reduces the number of signals by nearly 80% while retaining 95% of unique metabolite features. Moreover, the annotation results from MS-FINDER can be ranked according to the database chosen by the user, which enhance identification accuracy. Application of MS-CleanR to the analysis of Arabidopsis thaliana grown in three different conditions fostered class separation resulting from multivariate data analysis and led to annotation of 75% of the final features. The full workflow was applied to metabolomic profiles from three strains of the leguminous plant Medicago truncatula that have different susceptibilities to the oomycete pathogen Aphanomyces euteiches. A group of glycosylated triterpenoids overrepresented in resistant lines were identified as candidate compounds conferring pathogen resistance. MS-CleanR is implemented through a Shiny interface for intuitive use by end-users (available at https://github.com/eMetaboHUB/MS-CleanR).

Deciphering the phylogeny of violets based on multiplexed genetic and metabolomic approaches.

Molecular phylogenetics based on nucleotide sequence comparisons has profoundly influenced plant taxonomy. A comprehensive chemotaxonomical approach based on GC-MS and UHPLC-HRMS profiling was evaluated for its ability to characterize a large collection of plants all in the violet family Violaceae (n = 111) and thus decipher the taxonomy. A thorough identification of violets is challenging due to their natural hybridization and phenotypic variability. Phylogenetic inference performed on ribosomal internal transcribed spacer sequences using maximum likelihood and neighbor-joining distance methods allowed the clear identification of 58% of the collection. Metabolomic approaches with multivariate data analysis were performed on SPME/GC-MS chromatograms of volatile compounds emitted by fresh mature flowers and on UHPLC-HRMS/MS leaf extracts for non-volatile compounds. Interestingly, molecular and biochemical approaches provided separate classifications while highlighting several common clusters. The profiling of secondary metabolites was proved most suitable for the classification of hundreds of extracts. The combination of phylogenetic and chemotaxonomic approaches, allowed the classification of 96% of the entire collection. A correlation network revealed specific chemotaxonomic biomarkers, in particular flavonoids, coumarins and cyclotides. Overall, our pioneering approach could be useful to solve misclassification issues within collections of close plant species.

Targeted Dereplication of Microbial Natural Products by High-Resolution MS and Predicted LC Retention Time.

A new strategy for the identification of known compounds in Streptomyces extracts that can be applied in the discovery of natural products is presented. The strategy incorporates screening a database of 5555 natural products including 5098 structures from Streptomyces sp., using a high-throughput LCMS data processing algorithm that utilizes HRMS data and predicted LC retention times (tR) as filters for rapid identification of known compounds in the natural product extract. The database, named StrepDB, contains for each compound the structure, molecular formula, molecular mass, and predicted LC retention time. All identified compounds are annotated and color coded for easier visualization. It is an indirect approach to quickly assess masses (which are not annotated) that may potentially lead to the discovery of new or novel structures. In addition, a spectral database named MbcDB was generated using the ACD/Spectrus DB Platform. MbcDB contains 665 natural products, each with structure, experimental HRESIMS, MS/MS, UV, and NMR spectra. StrepDB was used to screen a mutant Streptomyces albus extract, which led to the identification and isolation of two new compounds, legonmaleimides A and B, the structures of which were elucidated with the aid of MbcDB and spectroscopic techniques. The structures were confirmed by computer-assisted structure elucidation (CASE) methods using ACD/Structure Elucidator Suite. The developed methodology suggests a pipeline approach to the dereplication of extracts and discovery of novel natural products.