Marianne Defernez

Gene and Metabolite Regulatory Network Analysis of Early Developing Fruit Tissues Highlights New Candidate Genes for the Control of Tomato Fruit Composition and Development

Variations in early fruit development and composition may have major impacts on the taste and the overall quality of ripe tomato (Solanum lycopersicum) fruit. To get insights into the networks involved in these coordinated processes and to identify key regulatory genes, we explored the transcriptional and metabolic changes in expanding tomato fruit tissues using multivariate analysis and gene-metabolite correlation networks. To this end, we demonstrated and took advantage of the existence of clear structural and compositional differences between expanding mesocarp and locular tissue during fruit development (12–35 d postanthesis). Transcriptome and metabolome analyses were carried out with tomato microarrays and analytical methods including proton nuclear magnetic resonance and liquid chromatography-mass spectrometry, respectively. Pairwise comparisons of metabolite contents and gene expression profiles detected up to 37 direct gene-metabolite correlations involving regulatory genes (e.g. the correlations between glutamine, bZIP, and MYB transcription factors). Correlation network analyses revealed the existence of major hub genes correlated with 10 or more regulatory transcripts and embedded in a large regulatory network. This approach proved to be a valuable strategy for identifying specific subsets of genes implicated in key processes of fruit development and metabolism, which are therefore potential targets for genetic improvement of tomato fruit quality.

THE USE AND MISUSE OF CHEMOMETRICS FOR TREATING CLASSIFICATION PROBLEMS

In this article, we examine the increasing use by analytical chemists of chemometric methods for treating classification problems. The methods considered are principal component analysis (PCA), canonical variates analysis (CVA), discriminant analysis (DA), and discriminant partial least squares (PLS). Overfitting, a potential hazard of multivariate modelling, is illustrated using examples of real and simulated data, and the importance of model validation is discussed.

Factors affecting the robustness of metabolite fingerprinting using 1H NMR spectra

1H NMR spectroscopy is one of the techniques whose potential is currently being explored in the emerging field of metabolomics. It is a non-targeted method, producing signals for all proton-containing chemical species. For crude plant materials the spectra are always complex, with many signals overlapping. Hence a most suitable approach for analysing them is metabolite fingerprinting, which is aimed at highlighting compositional similarities and exploring the overall natural variability in a population of samples. The most commonly used method for this is principal component analysis (PCA), as it allows the whole spectral trace to be analysed and the vast quantity of information to be simplified. In this paper we investigate whether there are factors which may affect the NMR spectra in a way that subsequently decreases the robustness of the metabolite fingerprinting by PCA. Imperfections in the signal registration (i.e. inconsistency of the peak position) are generally detrimental to analysing whole traces by multivariate methods. The sources of such problems are illustrated through specially designed repeatability studies using potato and tomato samples, and the analysis of a tea dataset containing many samples. Careful sample preparation can help to limit peak shifts; for instance here by attempting to control the pH of the extracts. In addition, some compounds are susceptible to interactions affecting their chemical shifts and mathematical alignment of peaks may be necessary. Lastly factors such as resolution can also affect analyses and must be carefully adjusted. Our choice of examples aims to raise awareness of potential problems. We do not question the validity of the NMR approach, but point out those areas where special care may need to be taken.

In vitro digestibility of β-casein and β-lactoglobulin under simulated human gastric and duodenal conditions: A multi-laboratory evaluation

Initially the resistance to digestion of two cows milk allergens, beta-casein, and beta-lactoglobulin (beta-Lg), was compared using a high-protease assay and a low-protease assay in a single laboratory. The low-protease assay represents an alternative standardised protocol mimicking conditions found in the gastrointestinal tract. For the high-protease assay, both proteins were incubated with either pepsin or pancreatin and digestion monitored by sodium dodecyl sulphate-polyacrylamide gel electrophoresis and reverse phase-high performance liquid chromatography. The low-protease assay involved gastroduodenal digestion in the presence or absence of phosphatidylcholine (PC). Both beta-casein and beta-Lg were susceptible to hydrolysis by pepsin and pancreatin in the high-protease assay. In contrast, the kinetics of beta-casein digestion in the low-protease assay were slower, beta-Lg being pepsin resistant. During duodenal digestion, beta-Lg was gradually degraded and addition of PC slowed digestion. Subsequently, the reproducibility of the low-protease assay was assessed in 12 independent laboratories by visual assessment of the gels and densitometric analysis: the inter- and intra-laboratory variability was affected by sampling and electrophoresis method employed. The low-protease assay was shown to be reproducible. Future studies will extend these findings using a broader panel of proteins.

Metabolic footprinting as a tool for discriminating between brewing yeasts

The characterization of industrial yeast strains by examining their metabolic footprints (exometabolomes) was investigated and compared to genome‐based discriminatory methods. A group of nine industrial brewing yeasts was studied by comparing their metabolic footprints, genetic fingerprints and comparative genomic hybridization profiles. Metabolic footprinting was carried out by both direct injection mass spectrometry (DIMS) and gas chromatography time‐of‐flight mass spectrometry (GC–TOF–MS), with data analysed by principal components analysis (PCA) and canonical variates analysis (CVA). The genomic profiles of the nine yeasts were compared by PCR–restriction fragment length polymorphism (PCR–RFLP) analysis, genetic fingerprinting using amplified fragment length polymorphism (AFLP) analysis and microarray comparative genome hybridizations (CGH). Metabolomic and genomic analysis comparison of the nine brewing yeasts identified metabolomics as a powerful tool in separating genotypically and phenotypically similar strains. For some strains discrimination not achieved genomically was observed metabolomically. Copyright

Approaches to understanding the functional architecture of the plant cell wall.

Cell wall polysaccharides are some of the most complex biopolymers known, and yet their functions remain largely mysterious. Advances in imaging methods permit direct visualisation of the molecular architecture of cell walls and the modifications that occur to polymers during growth and development. To address the structural and functional relationships of individual cell wall components, we need to better characterise a broad range of structural and architectural alterations in cell walls, appearing as a consequence of developmental regulation, environmental adaptation or genetic modification. We have developed a rapid method to screen large numbers of plants for a broad range of cell wall phenotypes using Fourier transform infrared microspectroscopy and Principal Component Analysis. We are using model systems to uncover the genes that encode some of the cell-wall-related biosynthetic and hydrolytic enzymes, and structural proteins.

How much is too much? Threshold dose distributions for 5 food allergens

BACKGROUNDnPrecautionary labeling is used to warn consumers of the presence of unintended allergens, but the lack of agreed allergen thresholds can result in confusion and risk taking by patients with food allergy. The lack of data on threshold doses below which subjects are unlikely to react is preventing the development of evidence-based allergen management strategies that are understood by clinician and patient alike.nnnOBJECTIVEnWe sought to define threshold dose distributions for 5 major allergenic foods in the European population.nnnMETHODSnPatients with food allergy were drawn from the EuroPrevall birth cohort, community surveys, and outpatient clinic studies and invited to undergo a food challenge. Low-dose, double-blind, placebo-controlled food challenges were undertaken with commercially available food ingredients (peanut, hazelnut, celery, fish, and shrimp) blinded into commonxa0matrices. Dose distributions were modeled by using interval-censoring survival analysis with 3 parametric approaches.nnnRESULTSnOf the 5 foods used for challenge, 4 produced similar dose distributions, with estimated doses eliciting reactions in 10% of the allergic population (ED10), ranging from 1.6 to 10.1 mg of protein for hazelnut, peanut, and celery with overlapping 95% CIs. ED10 values for fish were somewhat higher (27.3 mg of protein), although the CIs were wide and overlapping between fish and plant foods. Shrimp provided radically different dose distributions, with an ED10 value of 2.5 g of protein.nnnCONCLUSIONnThis evidence base will contribute to the development of reference doses and action levels for allergens in foods below which only the most sensitive subjects might react.

Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study

The metabo-ring initiative brought together five nuclear magnetic resonance instruments (NMR) and 11 different mass spectrometers with the objective of assessing the reliability of untargeted metabolomics approaches in obtaining comparable metabolomics profiles. This was estimated by measuring the proportion of common spectral information extracted from the different LCMS and NMR platforms. Biological samples obtained from 2 different conditions were analysed by the partners using their own in-house protocols. Test #1 examined urine samples from adult volunteers either spiked or not spiked with 32 metabolite standards. Test #2 involved a low biological contrast situation comparing the plasma of rats fed a diet either supplemented or not with vitamin D. The spectral information from each instrument was assembled into separate statistical blocks. Correlations between blocks (e.g., instruments) were examined (RV coefficients) along with the structure of the common spectral information (common components and specific weights analysis). In addition, in Test #1, an outlier individual was blindly introduced, and its identification by the various platforms was evaluated. Despite large differences in the number of spectral features produced after post-processing and the heterogeneity of the analytical conditions and the data treatment, the spectral information both within (NMR and LCMS) and across methods (NMR vs. LCMS) was highly convergent (from 64 to 91xa0% on average). No effect of the LCMS instrumentation (TOF, QTOF, LTQ-Orbitrap) was noted. The outlier individual was best detected and characterised by LCMS instruments. In conclusion, untargeted metabolomics analyses report consistent information within and across instruments of various technologies, even without prior standardisation.

Relatedness of medically important strains of Saccharomyces cerevisiae as revealed by phylogenetics and metabolomics

Ten medically important Saccharomyces strains, comprising six clinical isolates of Saccharomyces cerevisiae and four probiotic strains of Saccharomyces boulardii, were characterized at the genetic and metabolic level and compared with non‐medical, commercial yeast strains used in baking and wine‐making. Strains were compared by genetic fingerprinting using amplified fragment length polymorphism (AFLP) analysis, by ribosomal DNA ITS1 sequencing and by metabolic footprinting using both direct injection mass spectrometry (DIMS) and gas chromatography–time of flight–mass spectrometry (GC–ToF–MS). Overall, the clinical isolates fell into different groupings when compared with the non‐medical strains, with good but not perfect correlation amongst strains at both the genetic and metabolic levels. Probiotic strains of S. boulardii that are used therapeutically to treat human gastro‐intestinal tract disorders showed tight clustering both genetically and metabolically. Metabolomics was found to be of value both as a taxonomic tool and as a means to investigate anomalous links between genotype and phenotype. Key discriminatory metabolites were identified when comparing the three main groups of clinical, probiotic and non‐medical strains and included molecules such as trehalose, myo‐inositol, lactic acid, fumaric acid and glycerol 3‐phosphate. This study confirmed the link between a subset of clinical isolates and baking or probiotic strains but also highlighted that in general the clinical strains were more diverse at both the genomic and metabolic levels. Copyright

Authentication of beef versus horse meat using 60 MHz 1H NMR spectroscopy

Highlights • Demonstration of 60 MHz 1H NMR as a screening tool for distinguishing beef from horse meat.• A simple chloroform extraction combined with a 10 min spectral acquisition time.• A principal components-based authenticity model yielding a ‘beef’ or ‘not-beef’ outcome.