In silico and in vitro approaches allow the identification of the Prosystemin molecular network

Tomato Prosystemin (ProSys), the precursor of Systemin, a small peptidic hormone, is produced at very low concentration in unchallenged plants, while its expression greatly increases in response to several different stressors triggering an array of defence responses. The molecular mechanisms that underpin such a wide array of defence barriers are not fully understood and are likely correlated with the intrinsically disordered (ID) structure of the protein. ID proteins interact with different protein partners forming complexes involved in the modulation of different biological mechanisms. Here we describe the ProSys-protein network that shed light on the molecular mechanisms underpinning ProSys associated defence responses. Three different approaches were used. In silico prediction resulted in 98 direct interactors, most clustering in phytohormone biosynthesis, transcription factors and signal transduction gene classes. The network shows the central role of ProSys during defence responses, that reflects its role as central hub. In vitro ProSys interactors, identified by Affinity Purification-Mass Spectrometry (AP-MS), revealed over three hundred protein partners, while Bimolecular Fluorescent Complementation (BiFC) experiments validated in vivo some interactors predicted in silico and in vitro. Our results demonstrate that ProSys interacts with several proteins and reveal new key molecular events in the ProSys-dependent defence response of tomato plant

Metabolomics should be deployed in the identification and characterization of gene-edited crops

Abstract Gene editing techniques are currently revolutionizing biology allowing far greater precision than previous mutagenic and transgenic approaches. They are becoming applicable to a wide range of plant species and biological processes. Gene editing can rapidly improve a range of crop traits including disease resistance, abiotic stress tolerance, yield, nutritional quality and additional consumer traits. Unlike transgenic approaches, however, it is not facile to forensically detect gene-editing events at the molecular level, as no foreign DNA exists in the elite line. These limitations in molecular detection approaches are likely to focus more attention on the products generated from the technology, than the process per se. Rapid advances in sequencing and genome assembly increasingly facilitate genome sequencing as a means of characterizing new varieties generated by gene editing techniques. Nevertheless, subtle edits such as single base changes or small deletions may be difficult to distinguish from normal variation within a genotype. Given these emerging scenarios, downstream ‘omics’ technologies reflective of edited affects, such as metabolomics, need to be utilized in a more prominent manner to fully assess compositional changes in novel foodstuffs. To achieve this goal, metabolomics or “non-targeted metabolite analysis” needs to make significant advances to deliver greater representation across the metabolome. With the emergence of new edited crop varieties we advocate; (i) concerted efforts in the advancement of ‘omics’ technologies such as metabolomics and (ii) redress the use of the technology in the regulatory assessment for metabolically-engineered biotech crops

Mapping the Arabidopsis Metabolic Landscape by Untargeted Metabolomics at Different Environmental Conditions

Metabolic genome-wide association studies (mGWAS), whereupon metabolite levels are regarded as traits, can help unravel the genetic basis of metabolic networks. A total of 309 Arabidopsis accessions were grown under two independent environmental conditions (control and stress) and subjected to untargeted LC-MS-based metabolomic profiling; levels of the obtained hydrophilic metabolites were used in GWAS. Our two-condition-based GWAS for more than 3000 semi-polar metabolites resulted in the detection of 123 highly resolved metabolite quantitative trait loci (p ≤ 1.0E-08), 24.39% of which were environment-specific. Interestingly, differently from natural variation in Arabidopsis primary metabolites, which tends to be controlled by a large number of small-effect loci, we found several major large-effect loci alongside a vast number of small-effect loci controlling variation of secondary metabolites. The two-condition-based GWAS was followed by integration with network-derived metabolite-transcript correlations using a time-course stress experiment. Through this integrative approach, we selected 70 key candidate associations between structural genes and metabolites, and experimentally validated eight novel associations, two of them showing differential genetic regulation in the two environments studied. We demonstrate the power of combining large-scale untargeted metabolomics-based GWAS with time-course-derived networks both performed under different abiotic environments for identifying metabolite-gene associations, providing novel global insights into the metabolic landscape of Arabidopsis. By combining large-scale untargeted metabolomics-based GWAS and network analysis with environmental stress-driven perturbations of metabolic homeostasis, this system-wide study provides new global insights into the metabolic landscape of Arabidopsis, using a strategy that could readily be extended to other plant species.</p

Mass spectrometry-based metabolomics:a guide for annotation, quantification and best reporting practices

Mass spectrometry-based metabolomics approaches can enable detection and quantification of many thousands of metabolite features simultaneously. However, compound identification and reliable quantification are greatly complicated owing to the chemical complexity and dynamic range of the metabolome. Simultaneous quantification of many metabolites within complex mixtures can additionally be complicated by ion suppression, fragmentation and the presence of isomers. Here we present guidelines covering sample preparation, replication and randomization, quantification, recovery and recombination, ion suppression and peak misidentification, as a means to enable high-quality reporting of liquid chromatography– and gas chromatography–mass spectrometry-based metabolomics-derived data.</p

Relationships of leaf net photosynthesis, stomatal conductance, and mesophyll conductance to primary metabolism: a multi-species meta-analysis approach

Plant metabolism drives plant development and plant-environment responses, and data readouts from this cellular level could provide insights in the underlying molecular processes. Existing studies have already related key in vivo leaf gas-exchange parameters with structural traits and nutrient components across multiple species. However, insights in the relationships of leaf gas-exchange with leaf primary metabolism are still limited. We investigated these relationships through a multispecies meta-analysis approach based on data sets from 17 published studies describing net photosynthesis (A) and stomatal (gs) and mesophyll (gm) conductances, alongside the 53 data profiles from primary metabolism of 14 species grown in different experiments. Modeling results highlighted the conserved patterns between the different species. Consideration of species-specific effects increased the explanatory power of the models for some metabolites, including Glc-6-P, Fru-6-P, malate, fumarate, Xyl, and ribose. Significant relationships of A with sugars and phosphorylated intermediates were observed. While gs was related to sugars, organic acids, myo-inositol, and shikimate, gm showed a more complex pattern in comparison to the two other traits. Some metabolites, such as malate and Man, appeared in the models for both conductances, suggesting a metabolic coregulation between gs and gm The resulting statistical models provide the first hints for coregulation patterns involving primary metabolism plus leaf water and carbon balances that are conserved across plant species, as well as species-specific trends that can be used to determine new biotechnological targets for crop improvement.Fil: Gago, Jorge. Universidad de las Islas Baleares; EspañaFil: Menezes Daloso, Danilo. Universidad de las Islas Baleares; EspañaFil: Figueroa, Carlos Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Agrobiotecnología del Litoral. Universidad Nacional del Litoral. Instituto de Agrobiotecnología del Litoral; Argentina. Universidad de las Islas Baleares; EspañaFil: Flexas, Jaume. Universidad de las Islas Baleares; EspañaFil: Fernie, Alisdair Robert. Universidad de las Islas Baleares; EspañaFil: Nikoloski, Zoran. Universidad de las Islas Baleares; Españ