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Lactic acid bacteria and bifidobacteria (LAB & Bifido), isolated from the gastrointestinal tract of Apis mellifera intermissa (BGIT), honey (H), propolis (P) and bee bread (BB) of hives set in different vegetations (wildflowers, caraway, orange blossom, Marrubium vulgare, Eucalyptus and Erica cinerea), were subjected to analysis of their antibacterial potential. Isolates able to inhibit Staphylococcus aureus were selected and identified with MALDI-TOF MS leading to 154 strains representing 12 LAB & Bifido species. Lactiplantibacillus plantarum, Pediococcus pentosaceus and Enterococcus faecalis were predominantly found in all matrices. BGIT showed the highest LAB & Bifido diversity with exclusive occurrences of five species (including Bifidobacterium asteroides and Limosilactobacillus fermentum). Honey was the second origin harboring an important variety of LAB species of which Apilactobacillus kunkeei and Enterococcus mundtii were characteristic of both H and BGIT. Principal components analysis revealed associations between antibacterial activities of LAB & Bifido, matrices and honey bee forage plants. Inhibition trends of S. aureus and Citrobacter freundii were highlighted with: Lactiplantibacillus plantarum from BGIT, P, H of bees feeding on Erica cinerea; Pediococcus pentosaceus from BGIT, P, BB associated with Erica cinerea; and Bifidobacterium asteroidesfrom BGIT/orange blossom system. However, Enterococcus faeciumassociated with BGIT/Eucalyptus system antagonized Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii. Our findings highlighted noteworthy effects of bee forage plants on the antibacterial activity of LAB & Bifido. Our approach could be useful to identify multiple conditions promoting antibacterial potency of LAB & Bifido under the combined effects of feeding plants and living matrices.

Antimicrobial peptides (AMPs) provide large structural libraries of molecules with high variability of constitutional amino acids (AAs). Highlighting structural organization and structure-activity trends in such molecular systems provides key information on structural associations and functional conditions that could usefully help for drug design. This work presents link analyses between minimal inhibitory concentration (MIC) and different types of constitutional AAs of anti-Pseudomonas aeruginosa AMPs. This scope was based on a dataset of 328 published molecules. Regulation levels of AAs in AMPs were statistically ordinated by correspondence analysis helping for classification of the 328 AMPs into nine structurally homogeneous peptide clusters (PCs 1-9) characterized by high/low relative occurrences of different AAs. Within each PC, negative trends between MIC and AAs were highlighted by iterated multiple linear regression models built by bootstrap processes (bagging). MIC decrease was linked to different AAs that varied with PCs: alcohol-type AAs (Thr, Ser) in Cys-rich and low Arg PCs (PCs 1-3); basic AAs (Lys, Arg) in Pro-rich and low Val PCs (PCs 4-8); Trp (heterocyclic AA) in Arg-rich PCs (PCs 6, 7, 9). Aliphatic AAs (more particularly Gly) showed MIC reduction effects in different PCs essentially under interactive forms.

A new simplex-based simulation approach (Spx) was developed to highlight multidirectional and multi-scale relationships between morphometric variables helping to functionally differentiate biological (fish) groups for better stocks definition and monitoring. Application concerned Merluccius hubbsi sampled in 1968–1972 and 2004 in six Southwestern Atlantic areas. Simulation results highlighted negative trends opposing front to back compartments indicating competition for body biomass distribution. However, top and bottom parts within these compartments were positively correlated indicating cooperative processes in favour of target local growth regulation. Positive and negative trends of growth regulation were also highlighted at lower body scale, notably between smaller components constituting body front compartment. On a geographical scale, average regulation levels of same morphometric variables showed monotonic or alternated variations between successive fish groups. This highlighted target and modulated growth regulations governing biomass distribution in different body parts by geographical-dependent ways. Under dynamical aspect (1968–1972 vs. 2004), growth regulation of mouth tended to increase with time leading to conclude on morphometric responses of M. hubbsi to overfishing pressure. Spx results were confirmed by several traditional approaches which showed less integrative aspect.

Saponins in Caryophyllaceae were subjected to many structural elucidations for several decades leading to big datasets of molecules essentially based on four aglycones: quillaic acid (QA), gypsogenin (Gyp), gypsogenic acid (GA) and 16α-OH-gypsogenic acid (16α-OH-GA). Statistical analyses of structural variability of 231 saponins (published between 1971 and 2016) highlighted several diversification trends associating the four aglycones to different glycosylation (Gly) patterns defined by specific, dominant or relatively well-regulated sugars at the carbons C3, C23 and/or C28. The C28 was frankly more glycosylated than C3 in GA and 16α-OH-GA versus comparable Gly levels in Gyp and QA. Moreover, GA and 16α-OH-GA showed higher regulations of glucose than Gyp and QA. Further structural diversification of GA- and 16α-OH-GA-based saponins was provided by specific occurrence, absence or high regulations of other hexoses (Gal, Man), GlcA and/or pentoses (Xyl, Ara) in presence of Glc (dominant sugar). Under Gyp and QA, Gly patterns showed systematic occurrence of 3-Gal with diversification due (i) to presence-absence of Fuc, Rha, Qui (6-deoxyhexoses), Xyl at C3 or C28 or (ii) to high regulations of 3-Glc, 3-GlcA, 3-Ara, 28-Gal and/or 28-Glc. The highlighted structural organization of saponins showed significant associations with several plant genera. At lower biological scale, roots, seeds and aerial parts were revealed to be more or less favorite compartments for the different saponin classes. Finally, a synthesis of published pharmacological activities was made taking into account affiliations of saponins to different aglycones and plant genera. Preliminary average aglycone-glycosylation patterns were established for the most cited activities (cytotoxic, antiproliferative, immunomodulatory).

Background: Saponin metabolism shows high structural variability due to the diversity of aglycones and glycosylations (Gly). Although they represent a potential source of drug design, their metabolism remains misunderstood yet due to insufficient investments in analytical methods. Aims: Bibliographic structural data offer a wide field for extensive statistical analysis, highlighting mechanistic orders governing metabolic diversity. This work presents an original simulation method based on simplex rule for highlighting regulatory mechanisms of metabolism from categorical structural data. Methods: Simulation was applied on a set of 231 saponins of the Caryophyllaceae plant family initially affiliated to four aglycone types: gypsogenin (Gyp), quillaic acid (QA), gypsogenic acid (GA), and 16-OH-gypsogenic acid (16-OH-GA). Molecules were initially characterized by relative glycosylation levels of different carbons. Simplex approach was applied by combining saponins of the four aglycone groups using a complete set of N gradual weightings between structural groups. In silico combinations were applied by randomly sampling representative saponins from the four groups conforming to their weights given by mixture design. Gly profiles of sampled saponins were averaged to calculate a barycentric molecular profile for each mixture. With N mixtures, N barycentric molecules were iteratively calculated by bootstrap, leading to smoothed data from which Gly trends between carbons were highlighted. Results: Sequential, competing and cooperative Gly trends were highlighted according to the types of aglycones, attached saccharides and positions of substituted carbons. Such various conditional Gly trends seemed to be linked to multiple factors, including steric effects, regio-selectivity, enzymatic specificity and enzymatic promiscuity. These simulated results could be helpfully useful in chemical synthesis and drug design. Conclusion: These simulated results could usefully help for chemical syntheses and drug design.

Introduction: This work provides statistical analyses of bibliographic data on pharmacologically evaluated flavonoids from different plant families. By opposition to structural elucidations benefitting from full data aspects, pharmacological evaluations are concerned with partial investigations resulting in sparse information. Methods: The limited data availability was overcome by extensive consideration of several small sets of pharmacologically evaluated flavonoids in several plant taxa, alternatively to the traditional intensive analysis of big dataset of a given metabolic family in a given plant taxon. Statistical analyses were carried out using correspondence analysis, cluster analysis, box plots and fisher exact test to highlight structure-structure, structure-plant and structure-activity trends. Results: Different aglycone types showed opposite trends between hydroxylation (flavonols, anthocyanidins, flavanols), and methoxylation (isoflavones, isoflavanes, neoflavones). Moreover, different carbons showed differential substitution levels in different aglycones: C3 in flavonols, C6, C8 in flavones, flavonols, C2’ in flavanones, C6’ in isoflavanes. Plant families were well differentiated by different relative occurrences of aglycones: flavones in Lamiaceae, flavanones in Rutaceae, neoflavones in Rubiaceae, flavonols in Asteraceae, isoflavones in Fabaceae. Relatively more hydroxylated flavonoids occurred in Asteraceae, Lamiaceae and Fabaceae vs. more methoxylated ones in Rutaceae and Rubiaceae. Concerning structure-activity trends, flavanols and isoflavones were relatively more concerned with anti-diabetic and anti-inflammatory activities, respectively, vs. balanced distribution of flavones. Anti-inflammatory activity showed significant association with substitution position of same chemical groups (OH, OCH3), whereas anti-diabetic activity was revealed to be mainly influenced by the type of chemical groups (positive effect of OH and glycosyls). Conclusion: These results call for regular updates and further investigations.

Cycloartane represents the most common aglycone in saponins of Astragalus genus. These saponins revealed high structural diversity through continuous elucidation of new molecules. Traditionally, saponins’ diversity was updated through reviews listing molecules with respect to their pharmacological activities and/or producing plant species. Beyond the long lists of reviewed molecules, this work provides statistical classification of 178 Astragalus saponins published between 1983 and 2014, leading to different molecular clusters grouping structurally close saponins and separating dissimilar ones. Clustering highlighted hierarchical factors governing structural diversification of cycloartane-based saponins in Astragalus, including rare aglycone forms, rare substitution positions and relative substitution levels of the carbon C3. Rare cycloartane forms were due to different epoxydations occurring at different degrees and positions in the lateral part of cycloartane. In common cycloartane forms, rare saponins were due to rare substituted positions consisting of atypical hydroxylations. Within the most common saponins, several classes were highlighted by the variations of C3-substitution levels in both epoxydated and not epoxydated cycloartane. Structural variations within and between saponin classes provided a strong basis for highlighting significant effects of cycloartane forms and C3-substitution levels on molecular ramification, elongation levels and substitution types (glycosylation and acetylation). Finally, these two key structural factors seemed to influence compartmental distribution of saponins in Astragalus genus.

Objective: To evaluate the total phenolic content and compare the antioxidant activity of various solvent extracts and fractions from the aerial parts of Coronopus didymus through various assays. Methods: Total phenolic content was determined using the Folin-Ciocalteu assay and the in vitro antioxidant activity of a number of different extracts was investigated in a dose-dependent manner with three different methods: the 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) and ferric reducing antioxidant power (FRAP) assays. A flavone was isolated from the most active ethanolic extract with high antioxidant activity using size exclusion chromatography. IC50 values were calculated for the DPPH and ABTS methods. The FRAP activity was assessed in terms of μM Fe (II) equivalent. Results: The phenolic content was found to be highest in the ethanol extract (CDA Et; 47.8 mM GAE) and the lowest in the dichloromethane extract (CDA DCM; 3.13 mM GAE). The ethanol extract showed high radical scavenging activity towards DPPH and ABTS radicals with IC50 values of (7.80 × 102) and (4.32 × 102) μg/mL, respectively. The most active ethanol extract had a FRAP value of 1921.7 μM Fe (II) equivalent. The isolated flavone F10C (5,7,4′-trihydroxy-3′-methoxy flavone) was far more effective for scavenging free radicals in the DPPH and ABTS assays with IC50 of 43.8 and 0.08 μg/mL, than the standard trolox, with IC50 values of 97.5 and 21.1 μg/mL, respectively. In addition, the flavone F10C and the standard ascorbic acid had FRAP values of 1621.7 and 16 038.0 μM Fe (II) equivalents, respectively. Conclusions: The total phenolic content of extracts in decreasing order is ethanol extract (CDA Et) > acetone extract (CDA ACE) > phenolic extract (CDA MW) > n-hexane extract (CDA nHX)> chloroform extract (CDA CHL) > dichloromethane extract (CDA DCM). The ordering of extracts in terms of antioxidant activity from highest to lowest is CDA Et > CDA MW > CDA DCM > CDA CHL > CDA ACE > CDA nHX in DPPH, ABTS and FRAP assays. A significant relationship is found between antioxidant potential and total phenolic content, suggesting that phenolic compounds are the major contributors to the antioxidant activity of C. didymus.

36 pages, 4 figures, 1 table, supplementary material https://doi.org/10.1080/23308249.2019.1678013, Assessment of fish trophic ecology in natural environments is imperative to understand biological and ecological requirements, supporting the management and conservation of populations and environments. There are several methods available to assess fish diets, generically divided in noninvasive approaches, such as underwater observations, and invasive approaches, such as analyses of lipid biomarkers, stable isotopes and stomach contents. Each one has application assumptions and conditions that must be aligned with study objectives and methodological criteria, assuring results robustness and allowing comparability among different studies. This review addresses the advantages and limitations of these methods, highlighting those applied for stomach analysis (SA). This work presents complete updated review concerning qualitative and quantitative methods applied to SA: sixty equations were reviewed comprising four formulae to value stomachs per se and, for stomach content analysis, eight single indices, fifteen composite indices and thirty-three equations related to modeling approaches. The review presents different potentialities associated to these methods, standardizes distinct names applied to a same method, and provides clarifications on confusion sources due to the use of same names in distinct methods, a source of scientific mistakes detected in the specialized literature, The first author thanks Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) (Finance Code 001) for the Doctorate scholarship, and Dr. Ana Maria Gealh (Universidade Estadual de Ponta Grossa) and Dr. José Marcelo Rocha Aranha (Universidade Federal do Paraná) for the initial formation on fish feeding ecology. The second and last authors thank Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the financial support for the research mission in Brazil (Process 453871/2016-0). The last author thanks CNPq for the research grant (Processes 305403/2015-0 and 310451/2018-3) and the Aquaculture and Sustainable Development Post-Graduation Program of UFPR, With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI)

Torilis leptophylla commonly known as bristle fruit hedge parsley is widely acknowledged for its potential to treat liver and gastrointestinal disorders. Till today no scientific evidence has been reported to support this claim. So the present investigation was carried out to get an inclusive picture of volatile phytoconstituents of Torilis leptophylla. Hyphenated chromatographic technique revealed the presence of 11 VOC’s from n-Hexane extract and 5 from chloroform extract. Identified compounds belonged to different classes including terpenes, esters, phenols, hydrocarbons and alcohols. Among alcohols, falcarinol was revealed to be a de-novo antifungal agent not previously reported in the genus Torilis. These analytical/structural chemical investigations highlighted a first metabolic pattern associated with the use of this plant as a folk medicine for the cure of various infectious diseases. Moreover, they ratified that the metabolic pool of phytochemicals can play a noteworthy role in drug discovery and development.

Olive oils represent complex matrices varying from pure to heterogeneous varietal contents. Quantitative analysis of co-occurring components is fundamental for conformity checking and adulteration alerting (fighting) of commercial oils. Proportions of co-occurring components are governed by additive-dilutive processes which obey to simplex rule. Using simplex rule, we developed an original computational approach to predict proportions of different co-occurring oil varieties from quantitative chemical features of blends. The approach consisted in applying a complete set of N mixtures between different olive oil varieties by gradually varying their proportions. The N simulated mixtures were characterized by N average fatty acid (FA) profiles calculated from N combinations of randomly sampled individual profiles. After k iterations of the mixture design, the k sets of N FA average profiles were used as input in a discriminant analysis to predict proportions of co-occurring olive oil varieties in different blends. Illustrative application concerned blends made by three main French mono-varietal virgin olive oils (Aglandau, Grossane and Salonenque) and benefiting from Protected Designation of Origin label. Predictive model was validated on outside blends and showed prediction errors with an order of 10% susceptible of reduction by applying a larger mixture design.

Torilis leptophylla synonymously called bristle fruit hedge parsley belongs to the genus Torilis. It is widely appraised for its folkloric use in Indo-Pak subcontinent for its potential to cope with liver and gastrointestinal diseases. In order to discern the full medicinal prospects of Torilis leptophylla roots, it is required to have a complete picture of its phytoconstituents therefore comprehensive HPLC-MS profiling is hereby reported for the first time that led to the identification of 11 compounds in the roots of Torilis leptophylla including a C-linked glycoside named as Flavone-6-C-β-D-xylopyranosyl-(1®4)-α-L-rhamnopyranosyl-(1®4)-α-L-rhamnopyranosyl-(1®4)-β-D-glucopyranoside. This compound was identified for the first time in this genus through combined application of TLC, PC and HPLC-DAD-ESI-MS analyses. So the present study paves way for establishing the identity of secondary metabolites, metabolomic fingerprinting and provides authentic basis for the use of Torilis leptophylla in in vivo applications. In addition, the metabolic pool of phytochemicals can play a noteworthy role in drug discovery and development.

A mass conservation law-based chemometric approach was developed to extract smoothed processes governing inter- and intra-molecular variability of structural diversity in metabolic pools. The approach consisted of a machine-learning method using simplex rule to calculate a complete set of smoothed barycentric molecules from iterated linear combinations between molecular classes (glycosylation classes). An application to four glycosylation levels (GLs) of Caryophyllaceae saponins highlighted aglycone-dependent variations of glycosylations, especially for gypsogenic acid (GA) which showed high 28-glucosylation levels. Quillaic acid (QA) and gypsogenin (Gyp) showed closer variation ranges of GLs, but differed by relationships between glycosylated carbons toward different sugars. Relative GLs of carbons C3 and C28 showed associative (positive), competitive (negative) or independent (unsensitive) trends conditioned by the aglycone type (GA, Gyp) and molecular (total) GLs (the four classes): 28-glucosylation and 28-xylosylation showed negative global trends in Gyp vs GLs-depending trends in QA. Also, relative levels of 3-galactosylation and 3-xylosylation varied by unsensitive ways in Gyp vs positive trends in QA. These preliminary results revealed higher metabolic tensions (competitions) between considered glycosylations in Gyp vs more associative processes in QA. In conclusion, glycosylations of GA and QA were relatively distant whereas Gyp occupied intermediate position.

International audience; Genus Oenothera includes medicinal plants that are distributed throughout the world and are known since ancient times. Popular indications of different species of this genus include treatment of inflammations, diabetes, microbial infections, ulcers, tumors, kidney and liver problems. The plants of this genus are a botanical source for various pharmaceutically active components like sterols, alkaloids, phenolic acids, flavonoids, triterpe-noids, saponins, biflavonols and tocopherols. This review article is a compilation of chemical composition and biological activities of the various species of the genus Oenothera.

International audience; Metabolisms represent highly organized systems characterized by strong regulations satisfying the mass conservation principle. This makes a whole chemical resource to be competitively shared between several ways at both intra-and inter-molecular scales. Whole resource sharing can be statistically associated with a constant sum-unit constraint which represents the basis of simplex mixture rule. In this work, a new simplex-based simulation approach was developed to learn scaffold information on metabolic processes controlling molecular diversity from a wide set of observed chemical structures. Starting from a dataset of chemical structures classified into p clusters, a machine learning process was applied by linearly combining the p clusters j and randomly sampling a constant number (n) of molecules according to different clusters' weights (wj/w) given by Scheffé's mixture matrix. At the output of mixture design, molecular linear combinations lead to calculate barycentric molecules integrating the characteristics of the different weighted clusters. The N mixtures-design was iterated by bootstrap technique leading to extensive exploration of chemical variability between and within clusters. Finally, the K response matrices issued from K iterated mixture designs were averaged to calculate a smoothed matrix containing scaffold information on regulation processes responsible for molecular diversification at inter- and intra-molecular (atomic) scales. This matrix was used as a backbone for graphical analysis of positive and negative trends between atomic characteristics (chemical substitutions) at both mentioned scales. This new simplex approach was illustrated by cycloartane-based saponins of Astragalus genus by combining three desmosylation clusters characterized by relative glycosylation levels of different aglycones' carbons.

Metabolic pools of biological matrices can be extensively analyzed by NMR. Measured data consist of hundreds of NMR signals with different chemical shifts and intensities representing different metabolites' types and levels, respectively. Relevant predictive NMR signals need to be extracted from the pool using variable selection methods. This paper presents both a review and research on this metabolomics field. After reviews on discriminant potentials and statistical analyses of NMR data in biological fields, the paper presents an original approach to extract a small number of NMR signals in a biological matrix A (BM-A) in order to predict metabolic levels in another biological matrix B (BM-B). Initially, NMR dataset of BM-A was decomposed into several row-column homogeneous blocks using hierarchical cluster analysis (HCA). Then, each block was subjected to a complete set of Jackknifed correspondence analysis (CA) by removing separately each individual (row). Each CA condensed the numerous NMR signals into some principal components (PCs). The different PCs representing the (n - 1) active individuals were used as latent variables in a stepwise multi-linear regression to predict metabolic levels in BM-B. From the built regression model, metabolite level in the outside individual was predicted (for next model validation). >From all the PCs-based regression models resulting from all the jackknifed CA applied on all the individuals, the most contributive NMR signals were identified by their highest absolute contributions to PCs. Finally, these selected NMR signals (measured in BMA) were used to build final population and sub-population regression models predicting metabolite levels in BM-B.

A total of 54 lactic acid bacteria (LAB) were isolated from stored wheat samples sourced from grain silos in North Tunisia. Fifteen representative isolates were identified by 16S rDNA sequencing as Pediococcus pentosaceus, Lactobacillus plantarum, Lactobacillus graminis, Lactobacillus coryniformis and Weissella cibaria. These isolates were screened for antifungal activity in dual culture agar plate assay against eight post-harvest moulds (Penicillium expansum, Penicillium chrysogenum, Penicillium glabrum, Aspergillus flavus, Aspergillus niger, Aspergillus carbonarius, Fusarium graminearum and Alternaria alternata). All LAB showed inhibitory activity against moulds, especially strains of L. plantarum which exhibited a large antifungal spectrum. Moreover, LAB species such as L. plantarum LabN10, L. graminis LabN11 and P. pentosaceus LabN12 showed high inhibitory effects against the ochratoxigenic strain A. carbonarius ANC89. These LAB were also investigated for their ability to reduce A. carbonarius ANC89 biomass and its ochratoxin A (OTA) production on liquid medium at 28 and 37 °C and varied pH conditions. The results indicated that factors such as temperature, pH and bacterial biomass on mixed cultures, has a significant effect on fungal inhibition and OTA production. High percentage of OTA reduction was obtained by L. plantarum and L. graminis (>97%) followed by P. pentosaceus (>81.5%). These findings suggest that in addition to L. plantarum, L. graminis and P. pentosaceus strains may be exploited as a potential OTA detoxifying agent to protect humans and animals health against this toxic metabolite.

Wheat represents a principal ingredient in traditional Tunisian diet including couscous, bread, pasta and biscuits. Northen Tunisia is an important growing area of wheat which after harvest is stored in silos and on farm. The cereal grains can become contaminated by post-harvest moulds during storage in silos under unfavorable conditions leading to a decrease in quality, packing and marketing of wheat. In this study, a mycological survey was undertaken to determine the biodiversity of post-harvest moulds on durum wheat stored in silos localized in five regions of Northern Tunisia and to investigate changes during the storage period. A total of 127 samples were obtained from Oued Mliz, Jendouba, Ksar Mezouar, Mateur and Ghezala silos during 2010–2011 and 2011–2012 wheat seasons. After sampling, seeds were placed on Potato Dextrose Agar medium (PDA) for 7 days of incubation at 28 °C. A total of 6035 strains of filamentous fungi were isolated. The quantitative and qualitative changes on wheat mycoflora during storage were statistically explored by multivariate methods including correspondence and hierarchical cluster analysis. The most predominant post-harvest moulds genera isolated were Alternaria (28%), Fusarium (19%), Penicillium (19%), Aspergillus (14%), Mucor (8%) and Rhizopus (7%). Various genera of fungi imperfecti, including Ulocladium , Geotrichum , Chaetomium , Trichothecium , Paecilomyces , Aureobasidium and Chrysonilia (anamorphic Neurospora ), and the Mucorales genera Lichtheiia and Syncephalastrum accounted for the remainder of about 6% of the total. Statistical data analysis revealed six mycological patterns corresponding to six distinct communities as characterized by the prevalence of different moulds. Such patterns clearly showed different spatio-temporal variability indicating that distribution and evolution of moulds during storage was sensitive to geographic location, year of sampling and short or long-term storage.

Metabolisms represent highly organized systems characterized by strong regulations obeying to mass conservation law. This makes a whole chemical resource to be competitively shared between several biosynthesis components (ways) at both intra-and inter-molecular scales. Statistically, the whole shared-resource principle can be considered under a constant sum-unit constraint which represents the basis of simplex mixture rule. In this work, a new simplex-based chemometric approach was developed to extract scaffold information on different biosynthesis regulation factors responsible for the chemical structural diversity at atomic and molecular scales within a large metabolic system. This approach consisted in combining different (p) molecular clusters into a complete set of N mixtures containing a same constant (n) number of molecules which were randomly sampled by gradually varying the relative weights (nj/n) of combined clusters j. The complete set of N combinations was initially given by the Scheffe’s mixture matrix. At the output of Scheffe’s design, each molecular mixture was represented by a theoretical average (barycentric) molecule which was trained by the characteristics of the different weighted clusters. The mixture design was iterated several (K) times by applying bootstrap technique leading to extensively explore chemical variability between and within clusters. Finally, the K response matrices issued from the K applications of Scheffe’s mixture design were averaged to obtain a smoothed matrix containing scaffold information on different regulation factors responsible for molecular diversification at inter- and intra- (atomic) molecular scales. This matrix was used as a backbone for graphical analysis of different positive and negative trends between atomic characteristics (chemical substitutions) at both mentioned scales. This new simplex approach was illustrated by cycloartane-based saponins of Astragalus genus by considering three desmosylation clusters (mono-, bi- and tridesmoside saponins) characterized by relative glycosylation levels of different aglycones’ carbons.

International audience; Background. Olive oils (OOs) show high chemical variability due to several factors of genetic, environmental and anthropic types. Genetic and environmental factors are responsible for natural compositions and polymorphic diversification resulting in different varietal patterns and phenotypes. Anthropic factors, however, are at the origin of different blends' preparation leading to normative, labelled or adulterated commercial products. Control of complex OO samples requires their (i) characterization by specific markers; (ii) authentication by fingerprint patterns; and (iii) monitoring by traceability analysis.Methods. These quality control and management aims require the use of several multivariate statistical tools: specificity highlighting requires ordination methods; authentication checking calls for classification and pattern recognition methods; traceability analysis implies the use of network-based approaches able to separate or extract mixed information and memorized signals from complex matrices. Results. This chapter presents a review of different chemometrics methods applied for the control of OO variability from metabolic and physical-chemical measured characteristics. The different chemometrics methods are illustrated by different study cases on monovarietal and blended OO originated from different countries.Conclusion. Chemometrics tools offer multiple ways for quantitative evaluations and qualitative control of complex chemical variability of OO in relation to several intrinsic and extrinsic factors.

Complexity of metabolic systems can be undertaken at different scales (metabolites, metabolic pathways, metabolic network map, biological population) and under different aspects (structural, functional, evolutive). To analyse such a complexity, metabolic systems need to be decomposed into different components according to different concepts. Four concepts are presented here consisting in considering metabolic systems as sets of metabolites, chemical reactions, metabolic pathways or successive processes. From a metabolomic dataset, such decompositions are performed using different mathematical methods including correlation, stiochiometric, ordination, classification, combinatorial and kinetic analyses. Correlation analysis detects and quantifies affinities/oppositions between metabolites. Stoichiometric analysis aims to identify the organisation of a metabolic network into different metabolic pathways on the hand, and to quantify/optimize the metabolic flux distribution through the different chemical reactions of the system. Ordination and classification analyses help to identify different metabolic trends and their associated metabolites in order to highlight chemical polymorphism representing different variability poles of the metabolic system. Then, metabolic processes/correlations responsible for such a polymorphism can be extracted in silico by combining metabolic profiles representative of different metabolic trends according to a weighting bootstrap approach. Finally evolution of metabolic processes in time can be analysed by different kinetic/dynamic modelling approaches.

Two new tridesmosidic glycosides of (3β,6α,16β,20R,24S)-20,24-epoxycycloartane-3,6,16,25-tetrol (=cycloastragenol), armatosides I and II (1 and 2, resp.), were isolated from the roots of Astragalus armatus (Fabaceae) as well as the known bidesmosidic glycosides of cycloastragenol, trigonoside II (3) and trojanoside H (4). Their structures were elucidated as (3β,6α,16β,20R,24S)-3-O-(2,3-di-O-acetyl-β-D-xylopyranosyl)-20,24-epoxy-25-O-β-D-glucopyranosyl-6-O-β-D-xylopyranosylcycloartane-3,6,16,25-tetrol (1), and (3β,6α,16β,20R,24S)-3-O-(2-O-acetyl-β-D-xylopyranosyl)-20,24-epoxy-25-O-β-D-glucopyranosyl-6-O-β-D-xylopyranosylcycloartane-3,6,16,25-tetrol (2). These structures were established by extensive NMR and MS analyses and by comparison with literature data.

HPLC analysis of secondary metabolites represents an efficient tool for the studying of plant chemical diversity under different aspects: chemotaxonomy, metabolomics, adaptative responses to ecological factors, etc. Statistical analyses of HPLC databases, e.g. correlation analysis between HPLC peaks, can reliably provide information on the similarity/dissimilarity degrees between the chemical compounds. The similarities, corresponding to positive correlations, can be interpreted in terms of analogies between chemical structures, synchronic metabolisms or co-evolution of two compounds under certain environment conditions, etc. . In terms of metabolism, positive correlations can translate precursor-product relationships between compounds; negative correlations can be indicative of competitive processes between two compounds for a common precursor(s), enzyme(s) or substrate(s). Furthermore, the correlation analysis under a metabolic aspect can help to understand the biochemical origins of an observed polymorphism in a plant species. With the aim of showing this, we present a new approach based on a simplex mixture design, Scheffe matrix, which provides a correlation network making it possible to graphically visualise and to numerically model the metabolic trends between HPLC peaks. The principle of the approach consisted in mixing individual HPLC profiles representative of different phenotypes, then from a complete mixture set, a series of average profiles were calculated to provide a new database with a small variability. Several iterations of the mixture design provided a smoothed final database from which the relationships between the secondary metabolites were graphically and numerically analysed. These relationships were scale-dependent, namely either deterministic or systematic: the first consisted of a monotonic global trend covering the whole variation field of each metabolites’ pair; the second consisted of repetitive monotonic variations which gradually attenuated or intensified along a global trend. This new metabolomic approach was illustrated from 404 individual plants of Astragalus caprinus (Leguminoseae), belonging to four chemical phenotypes (chemotypes) on the basis of flavonoids analysed in their leaves. After smoothing, the relationships between flavonoids were numerically fitted using linear or polynomial models; therefore the co-response coefficients were easily interpreted in terms of metabolic affinities or competitions between flavonoids which would be responsible of the observed chemical polymorphism (the four chemotypes). The statistical validation of the approach was carried out by comparing Pearson correlations to Spearman correlations calculated from the smoothed and the crude HPLC database, respectively. Moreover, the signs of the smoothed relationships were finely supported by analogies and differences between the chemical structures of flavonoids, leading to fluent interpretation in relation to the pathway architecture.

The quantitative assessment of plant diversity and its monitoring with time represent a key environmental issue for management and conservation of natural resources. Assessment of plant diversity could be based on chemical analyses of secondary metabolites (e.g. flavonoids, terpenoids), because of the substantial quantitative and qualitative between-individual variability in such compounds. At a geographical scale, the plant populations become widely dispersed, and their monitoring from numerous routine individual analyses could become restricting. To overcome such constraint, this study develops a multivariate calibration model giving the relative frequency of a particular taxon from a simple high-performance liquid chromatography (HPLC) analysis of a plant mixture. The model was built from a complete set of mixtures combining different taxons, according to an experimental design (Scheffe’s matrix). For each mixture, a reference HPLC pattern was simulated by averaging the individual HPLC profiles of the constitutive taxons. The calibration models, based on Bayesian discriminant analysis (BDA), gave statistical relationships between the contributions of each taxon in mixtures and reference HPLC patterns of these mixtures. Finally, these models were validated on new mixtures by using outside plants. This new biodiversity survey approach is illustrated on four chemical taxons (four chemotypes) of Astragalus caprinus (Fabaceae). The more differentiated the taxon, the better predicted its contributions (in mixtures) were by BDA calibration model. This new approach could be very useful for a global routine survey of plant diversity.

The pharmacokinetics of corticosteroids provides a large set of mathematical models which led to analyse many kinetic profiles corresponding to many clinical and/or physiological situations. In this paper, we present a review on the usefulness, advantages and limits of such models which could find a large application in medicinal chemistry.

A novel oleanane-type triterpene saponin (1) together with two known molecules, soyasapogenol B and astragaloside VIII were isolated from the roots of Astragalus caprinus. Their structural elucidation was performed mainly by 2D NMR techniques (COSY, TOCSY, NOESY, HSQC, HMBC) and mass spectrometry. Compound 1 was determined as 3-O-[alpha-L-rhamnopyranosyl-(1 --> 2)-beta-D-glucuronopyranosyl]-22-O-beta-D-apiofuranosyl-soyasapogenol B.

To be analyzed, the heterogeneity characterizing biological data calls for using appropriate models involving numerous variables. A high variable number could become problematic when one needs to determine a priori the most significant variable combination in order to reduce the inter-individual variability (IIV). Alternatively to multiple introductions of single variables, we propose a single introduction of a multivariate variable. We present cluster analysis as a stratification strategy that combines the initial single covariates to build a multivariate categorical covariate. It is an exploratory multivariate analysis that outlines homogeneous categories of individuals (clusters) according to similarities from the set of covariates. It includes many clustering techniques combining a distance measure and a linkage algorithm, and leading to various stratification patterns. The cluster analysis approach is illustrated by a case study on cortisol kinetics in 82 patients after intravenous bolus administration of synacthen (synthetic corticotropin). Using NONMEM, a basic infusion model was initially achieved for cortisol, and then a classical covariate selection was applied to improve IIV. The best fit was between the elimination rate constant k and the body mass index (BMI), which improved IIV of k. An alternative method is presented consisting in the population into homogeneous and non-overlapping groups by applying a cluster analysis. Such categorization (or clustering) was carried out using Euclidean distance and complete-linkage algorithm. This algorithm gave five dissimilar clusters that differed by increasing BMI, obesity duration, and waist-hip ratio. The dispersion of k according to the five clusters showed three distinctvariation ranges a priori, which corresponded a posteriori(after NONMEM modeling) to three sub-populations of k. After grouping the clusters that had similar variation ranges of k, we obtained three final clusters representing non-obese, intermediate, and extreme obese sub-populations. The pharmacokinetic model based on three clusters was better than the basic model, similar to the classical covariate model, but had a stronger interpretability: It showed that the stimulation and elimination of cortisol were higher in the extreme obese followed by intermediate then non-obese subjects.

In previous work, 14 flavonol glycosides were characterised in the leaves of Astragalus caprinus, an endemic Fabaceae, widely distributed in North West Africa. These compounds included quercetin and kaempferol derivatives, glycosylated by two, three or four sugars with an aliphatic or aromatic acyl moiety. In this paper, we describe the flavonoid patterns of 404 individual plants investigated by HPLC and subjecting the resulting data to cluster analysis. This analysis highlighted four chemotypes associated with a high accumulation of a kaempferol triglycoside, a kaempferol tetraglycoside with its acylated derivatives, two quercetin tetraglycoside derivatives and methylated diglycoside derivatives, respectively. A discriminant analysis validated the chemotaxonomic value of the four chemical polymorphisms. These chemotypes were more or less abundant according to geographical sites which corresponded to different climatic conditions.

A new flavonol tetraglycoside, together with four acylated derivatives, were isolated from the leaves of Astragalus caprinus. Their structures were elucidated by spectroscopic methods, mainly 2D NMR, as kaempferol-3-O-[[beta-D-xylopyranosyl(1--3)-alpha-L-rhamnopyranosyl(1--6)][alpha-L-rhamnopyranosyl(1--2)]]-beta-D-galactopyranoside (1), its 3(Gal)-p-coumaric (2) and 3(Gal)-ferulic (3) esters, and its 4(Gal)-p-coumaric (4) and 4(Gal)-ferulic (5) esters.

The metabolism is a complex system interacting with several intrinsic and extrinsic factors of biological organisms, viz. genome expressions, physiological states, environmental conditions, etc. This multifactorial interaction means that the metabolism works as a reactive and flexible system providing reliable biochemical pictures on the effects of different governing factors. Metabolic flexibility and reliability are linked to conservation laws, constraining the metabolism to a close system linking input (resources) to output (products) signals: any entering signal will be decomposed into weighted parts through different metabolic pathways. This gives to metabolic trends different functional degrees highlighted by different relative levels of metabolites. Sharing the same unit resource, the different metabolic pathways are statistically constrained to be regulated within a simplex space characterized by a unit sum of its components. Output metabolic responses and their inside regulatory processes can be analyzed by using two simplex-based approaches: correspondence analysis (CA) and weighted metabolic profiles analysis (WMPA), respectively. These two approaches are based on two opposite (complementary) principles consisting of decomposition and combination of metabolic variability. In CA, metabolic datasets are decomposed into extreme trends representing elementary components of metabolic polymorphism called metabotypes. In WMPA, iterated combinations between different metabolic components help to extract functional information on their generator backbone system.

Metabolism represents a junction system in biological body receiving cumulated signals from upstream (genome, transcriptome, proteome) and downstream (environment) systems. This median position of the metabolic system makes it to be very sensitive toward internal and external signals resulting in its regulatory role in physiological homeostasy and adaptive responses to endogenous and exogenous factors. These factors have initiation, modulation or pressure effects on the biological organisms and species which adapt, resist or react through different types of metabolisms based on different synthesis and regulation levels of metabolites (Wilson, 2009).

The flexibility of metabolic systems implies a high variability of metabolic profiles linked to different regulation ratios between metabolites. Such regulations are controlled by several interactive metabolic pathways resulting in multidirectional continuums of metabolic profiles. This article presents a new metabolomic approach helping to graphically analyse the flexibility of metabolic regulation systems. Its principle consists in extracting a metabolic backbone from iterative combinations of metabolic profiles representing different metabolic trends. The iterated combinations were performed on the basis of Scheffe matrix then averaged to calculate a response matrix of smoothed metabolic profiles. From such a smoothed matrix, a graphical analysis of relationships between metabolites highlighted different scale-dependent variation paths responsible for the observed metabolic trends. Such a flexibility favouring some metabolites at the expense of others was indirectly checked by a single kinetic approach by considering both the variation of maximal concentrations and the metabolic trends in time. This kinetic approach highlighted a succession of metabolic trends linked to the variation of maximal concentrations in time. Finally, a delayed regulation of a metabolite was highlighted both by the kinetic approach and by a dynamic application of the metabolomic approach. This new approach was illustrated on a dataset of blood concentrations of levodopa and its metabolites analysed in 34 patients at different times.

Population pharmacokinetic (PK) (or pharmacodynamic (PD)) modelling aims to analyse the variability of drug kinetics (or dynamics) between numerous subjects belonging to a population. Such variability includes inter- and intra-individual sources leading to important differences between the variation ranges, the relative concentrations and the global shapes of PK profiles. These various sources of variability suggest that the distance metrics between the subjects can be examined under different aspects. Some subjects are so distant from the majority that they tend to be atypical or outliers. This paper presents three multivariate statistical methods to diagnose the outliers within a full population PK dataset, prior to any modelling step. Each method combined all the concentration-time variables to analyse the differences between patients by referring to a distance criterion: (a) Correspondence analysis (CA) used the chi-square distance to highlight the most atypical profiles in terms of relative concentrations; (b) Mahalanobis distance was calculated to extract PK profiles showing atypical shapes due to atypical variations in concentration; (c) Andrews method combined all the concentration variables into a Fourier transformation to give sine-cosine curves showing the clustering behaviours of subjects under the Euclidean distance criterion. After identification of outlier subjects, these methods can also be used to extract the concentration values which cause the atypical states of the patients. Therefore, the outliers will incorporate different variability sources of the PK dataset according to each method and independently of any PK modelling. Finally, a significant positive trend was found between the number of times outlier concentrations were detected (by one, two or three diagnostics) and the NPDE metrics of these concentrations (after a PK modelling): NPDE were highest when the corresponding concentration was detected by more diagnostics a priori. The application of a priori outlier diagnostics is illustrated here on two PK datasets: stimulated cortisol by synacthen and capecitabine administrated orally.

Four new flavonol 3-O-glycosides were isolated from the leaves of Astragalus caprinus. Their structures were elucidated by spectroscopic methods as rhamnocitrin-3-O-[3-hydroxy-3-methylglutaroyl(1--6)][beta-D-apiofuranosyl(1--2)]-beta-D-galactopyranoside (1), rhamnetin-3-O-[3-hydroxy-3-methylglutaroyl(1--6)][beta-D-apiofuranosyl(1--2)]-beta-D-galactopyranoside (2), kaempferol-3-O-[beta-D-xylopyranosyl(1--3)-alpha-L-rhamnopyranosyl(1--6)]-beta-D-galactopyranoside (3), and quercetin-3-O-[beta-D-xylopyranosyl(1--3)-alpha-L-rhamnopyranosyl(1--6)][beta-D-apiofuranosyl(1--2)]-beta-D-galactopyranoside (4).

Flavonoid glycosides of Astragalus caprinus (Leguminosae) were investigated; more than 30 glycosides were found in leaf material, based on the aglycones kaempferol, quercetin and their methylated derivatives. Among them 14 compounds were found in significant amounts and showed a contrasting distribution. They could be ordered into three groups: polyglycosides, acylated polyglycosides and methylated polyglycosides. The distribution of these compounds was studied within a large collection of individual plants harvested in Tunisia; the results showed a relationship between metabolic trends and ecological diversification.