Julien Boccard

Antimitotic and antiproliferative activities of chalcones: forward structure-activity relationship

A series of 59 chalcones was prepared and evaluated for the antimitotic effect against K562 leukemia cells. The most active chalcones were evaluated for their antiproliferative activity against a panel of 11 human and murine cell cancer lines. We found that three chalcones were of great interest as potential antimitotic drugs. In vivo safety studies conducted on one of the most active chalcones revealed that the compound was safe, allowing further in vivo antitumor evaluation.

Knowledge discovery in metabolomics: An overview of MS data handling

While metabolomics attempts to comprehensively analyse the small molecules characterising a biological system, MS has been promoted as the gold standard to study the wide chemical diversity and range of concentrations of the metabolome. On the other hand, extracting the relevant information from the overwhelming amount of data generated by modern analytical platforms has become an important issue for knowledge discovery in this research field. The appropriate treatment of such data is therefore of crucial importance in order, for the data, to provide valuable information. The aim of this review is to provide a broad overview of the methodologies developed to handle and process MS metabolomic data, compare the samples and highlight the relevant metabolites, starting from the raw data to the biomarker discovery. As data handling can be further separated into data processing, data pre-treatment and data analysis, recent advances in each of these steps are detailed separately.

UPLC–TOF-MS for plant metabolomics: A sequential approach for wound marker analysis in Arabidopsis thaliana ☆

The model plant Arabidopsis thaliana was studied for the search of new metabolites involved in wound signalling. Diverse LC approaches were considered in terms of efficiency and analysis time and a 7-min gradient on a UPLC-TOF-MS system with a short column was chosen for metabolite fingerprinting. This screening step was designed to allow the comparison of a high number of samples over a wide range of time points after stress induction in positive and negative ionisation modes. Thanks to data treatment, clear discrimination was obtained, providing lists of potential stress-induced ions. In a second step, the fingerprinting conditions were transferred to longer column, providing a higher peak capacity able to demonstrate the presence of isomers among the highlighted compounds.

Optimized liquid chromatography-mass spectrometry approach for the isolation of minor stress biomarkers in plant extracts and their identification by capillary nuclear magnetic resonance.

A LC-MS approach is presented for the isolation of minor key plant biomarkers, in view of their characterization by NMR at the microgram scale. Due to the complexity of plant extracts, the purification of metabolites present in low concentrations is critical. The strategy used relies on the optimization of the chromatographic analysis using ultra-performance liquid chromatography-time-of-flight mass spectrometry (UPLC-TOF-MS), thanks to modelling software. The optimized method is then transferred to semi-preparative LC conditions with MS detection. The approach is illustrated by the isolation of wound-induced jasmonate derivatives revealed by a metabolomic study in Arabidopsis thaliana leaves and their subsequent characterization by capillary NMR (CapNMR).

Analysis of basic compounds by supercritical fluid chromatography: Attempts to improve peak shape and maintain mass spectrometry compatibility

While neutral and acidic compounds are well separated by supercritical fluid chromatography (SFC), basic analytes are more challenging to separate and often problems occur with their peak shapes. Two different methods were explored in the present paper to reduce these problems and maintain compatibility with mass spectrometry (MS). Five different, commercially available 2-ethylpyridine (2-EP) stationary phases were tested without a mobile phase additive using 92 pharmaceutical compounds with basic properties. The kinetic performances of the 5 columns were nearly identical, but the peak shapes of the basic drugs were strongly affected by the stationary phase. The PrincetonSFC 2-EP and Zymor Pegasus 2-EP phases clearly outperformed the other stationary phases, with 77% and 69% of the compounds having Gaussian peaks (and asymmetries between 0.8 and 1.4), respectively. Comparatively, the Waters Viridis Silica 2-EP, Waters Viridis BEH 2-EP and ES industries GreenSep 2-EP phases provided only 52%, 44% and 22% of the compounds with Gaussian peaks, respectively. These differences were attributed to the significant dissimilarities in their silica matrix properties. An alternative strategy was also performed with a hybrid silica stationary phase, Viridis BEH, using 20mM ammonium hydroxide in the mobile phase, which was a mixture of CO(2) and MeOH. With these conditions, 81% of the peaks observed for the basic analytes were Gaussian; however, this value dropped to 17% and 10% in the absence of additive and in the presence of 20mM formic acid, respectively. Finally, the use of a hybrid bare silica stationary phase in the presence of 20mM ammonium hydroxide is quite an interesting solution as this system is compatible with both ultra high performance SFC (UHPSFC) columns packed with sub-2 μm particles and with MS detection. The overall applicability of this system was demonstrated with various mixtures of basic drugs.

Metabolomics reveals herbivore-induced metabolites of resistance and susceptibility in maize leaves and roots

Plants respond to herbivory by reprogramming their metabolism. Most research in this context has focused on locally induced compounds that function as toxins or feeding deterrents. We developed an ultra-high-pressure liquid chromatography time-of-flight mass spectrometry (UHPLC-TOF-MS)-based metabolomics approach to evaluate local and systemic herbivore-induced changes in maize leaves, sap, roots and root exudates without any prior assumptions about their function. Thirty-two differentially regulated compounds were identified from Spodoptera littoralis-infested maize seedlings and isolated for structure assignment by microflow nuclear magnetic resonance (CapNMR). Nine compounds were quantified by a high throughput direct nano-infusion tandem mass spectrometry/mass spectrometry (MS/MS) method. Leaf infestation led to a marked local increase of 1,3-benzoxazin-4-ones, phospholipids, N-hydroxycinnamoyltyramines, azealic acid and tryptophan. Only few changes were found in the root metabolome, but 1,3-benzoxazin-4-ones increased in the vascular sap and root exudates. The role of N-hydroxycinnamoyltyramines in plant-herbivore interactions is unknown, and we therefore tested the effect of the dominating p-coumaroyltyramine on S. littoralis. Unexpectedly, p-coumaroyltyramine was metabolized by the larvae and increased larval growth, possibly by providing additional nitrogen to the insect. Taken together, this study illustrates that herbivore attack leads to the induction of metabolites that can have contrasting effects on herbivore resistance in the leaves and roots.

A consensus orthogonal partial least squares discriminant analysis (OPLS-DA) strategy for multiblock Omics data fusion

Omics approaches have proven their value to provide a broad monitoring of biological systems. However, as no single analytical technique is sufficient to reveal the full biochemical content of complex biological matrices or biofluids, the fusion of information from several data sources has become a decisive issue. Omics studies generate an increasing amount of massive data obtained from different analytical devices. These data are usually high dimensional and extracting knowledge from these multiple blocks is challenging. Appropriate tools are therefore needed to handle these datasets suitably. For that purpose, a generic methodology is proposed by combining the strengths of established data analysis strategies, i.e. multiple kernel learning and OPLS-DA to offer an efficient tool for the fusion of Omics data obtained from multiple sources. Three real case studies are proposed to assess the potential of the method. A first example illustrates the fusion of mass spectrometry-based metabolomic data acquired in both negative and positive electrospray ionisation modes, from leaf samples of the model plant Arabidopsis thaliana. A second dataset involves the classification of wine grape varieties based on polyphenolic extracts analysed by two-dimensional heteronuclear magnetic resonance spectroscopy. A third case study underlines the ability of the method to combine heterogeneous data from systems biology with the analysis of publicly available data related to NCI-60 cancer cell lines from different tissue origins, which include metabolomics, transcriptomics and proteomics. The fusion of Omics data from different sources is expected to provide a more complete view of biological systems. The proposed method was demonstrated as a relevant and widely applicable alternative to handle efficiently the inherent characteristics of multiple Omics data, such as very large numbers of noisy collinear variables.

Metabolite profiling of plant extracts by ultra-high-pressure liquid chromatography at elevated temperature coupled to time-of-flight mass spectrometry

Detailed metabolite profiling of crude plant extracts, mandatory for both quality control and metabolomics purposes, requires high-resolution separation and sensitive detection with a reasonable sample throughput. In this respect, the use of ultra-high-pressure liquid chromatography (UHPLC) working at high temperature (HT) and coupled to time-of-flight mass spectrometry (TOF-MS) was evaluated in the present study in terms of achievable peak capacities for given analysis times. Prior to the analysis of complex mixtures, the effects of TOF-MS detection on peak capacity were evaluated, and a loss of 15-30% compared to UV was observed due to the additional band broadening generated by this detector. Extracts from a model plant Arabidopsis thaliana and from a widely used phytochemical preparation Ginkgo biloba, as well as a standard mixture of representative natural products (NPs), have been analyzed. As expected from the theory, the increase in mobile phase temperature of up to 90 degrees C for the profiling of extracts containing metabolites spread over a large polarity range (e.g., Arabidopsis thaliana) generated similar peak capacities to those obtained at room temperature, but with a 2- to 3-fold reduction in analysis time, demonstrating the power of this approach for such applications. On the other hand, for the analysis of more polar extracts (e.g., Ginkgo biloba), the use of higher temperature was not beneficial, as it induced a significant decrease in retention, and thus resolving power, because of the increase in elution strength. The use of HT-UHPLC-TOF-MS raised the question of NP stability under high temperature conditions. This work demonstrated that no apparent degradation was evidenced at high temperature for a representative mixture of NPs and also for the different metabolites detected in the selected plant extracts.

Differentiation of lemon essential oil based on volatile and non-volatile fractions with various analytical techniques: a metabolomic approach

Due to the importance of citrus lemon oil for the industry, fast and reliable analytical methods that allow the authentication and/or classification of such oil, using the origin of production or extraction process, are necessary. To evaluate the potential of volatile and non-volatile fractions for classification purposes, volatile compounds of cold-pressed lemon oils were analyzed, using GC-FID/MS and FT-MIR, while the non-volatile residues were studied, using FT-MIR, (1)H-NMR and UHPLC-TOF-MS. 64 Lemon oil samples from Argentina, Spain and Italy were considered. Unsupervised and supervised multivariate analyses were sequentially performed on various data blocks obtained by the above techniques. Successful data treatments led to statistically significant models that discriminated and classified cold-pressed lemon oils according to their geographic origin, as well as their production processes. Studying the loadings allowed highlighting of important classes of discriminant variables that corresponded to putative or identified chemical functions and compounds.

Harnessing the complexity of metabolomic data with chemometrics

Because of the ever-increasing number of signals that can be measured within a single run by modern platforms in analytical chemistry, life sciences datasets become not only gradually larger but also more intricate in their structures. Challenges related to making use of this wealth of data include extracting relevant elements within massive amounts of signals possibly spread across different tables, reducing dimensionality, summarising dynamic information in a comprehensible way and displaying it for interpretation purposes. Metabolomics constitutes a representative example of fast-moving research fields taking advantage of recent technological advances to provide extensive sample monitoring. Because of the wide chemical diversity of metabolites, several analytical setups are required to provide a broad coverage of complex samples. The integration and visualisation of multiple highly multivariate datasets constitute key issues for effective analysis leading to valuable biological or chemical knowledge. Additionally, high-order data structures arise from experimental setups involving time-resolved measurements. These data are intrinsically multiway, and classical statistical tools cannot be applied without altering their organisation with the risk of information loss. Dedicated modelling algorithms, able to cope with the inherent properties of these metabolomic datasets, are therefore mandatory for harnessing their complexity and provide relevant information. In that perspective, chemometrics has a central role to play. Copyright