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Type 2 diabetes is a complex, multifactorial disease with varying presentation and underlying pathophysiology. Recent studies using data-driven cluster analysis have led to a stratification of type 2 diabetes into novel subgroups based on six clinical measurements. Whether these subgroups truly correspond to the underlying phenotypic differences is nevertheless unclear. Here, we apply an unsupervised, data-driven clustering method (Similarity Network Fusion) to characterize type 2 diabetes in two independent cohorts involving 1,134 subjects in total based on integrated plasma lipidomics and peptidomics data without pre-selection. Logistic regression was then used to explore clustering based on ≥ 180 circulating lipids and 1,195 protein biomarkers, alongside clinical signatures. Two subgroups were identified, one of which associated with elevated C-peptide levels, diabetic complications and more severe insulin resistance compared to the other. GWAS analysis against 403 type 2 diabetes risk variants revealed associations of several SNPs with clusters and altered molecular profiles. We thus demonstrate that heterogeneity in type 2 diabetes can be captured by circulating omics alone using an unsupervised bottom-up approach. Such multiomics signatures could reflect pathological mechanisms underlying type 2 diabetes and thus may help inform on precision medicine approaches to disease management.

Introduction Fine lavender and lavandin are perfume and medicinal plants widely cultivated in Provence (south-east of France) for their essential oil and for the production of honey. The lavender monocultures are affected by a severe decline in France, due to the propagation of the yellow decline disease. This disease is caused by the Stolbur phytoplasma, a bacterium transmitted by a sap-sucking insect, Hyalesthes obsoletus. Objectives Main objective was to develop an untargeted metabolomic approach to highlight changes in the volatiles organic compounds produced by asymptomatic (“healthy”) and symptomatic (“infected”) plants. Methods Samples from fine lavender and lavandin aerial parts were collected in the field, then stored volatile compounds were extracted in the laboratory by ultrasound assisted extraction and analyzed by gas chromatography–mass spectrometry. Afterwards, multivariate statistical analysis has been performed using principal component analysis and partial least square regression as well as a multiblock statistical analysis by orthogonal partial least squares discriminant analysis to compare VOCs from fine lavender (variety 7713) and lavandin (variety abrial).Results Fifty stored VOCs, mainly oxygenated monoterpenes, from lavender and lavandin leaves and blooming head of the inflorescences were identified. Metabolism of several compounds were found to be altered by yellow decline such as β-farnesene overexpressed by symptomatic plants. Conclusion The developed metabolomic approach allowed the identification of specific volatile compounds produced by asymptomatic and symptomatic plants in a complex field environment.

Most research on human pancreatic islets is conducted on samples obtained from normoglycaemic or diseased brain-dead donors and thus cannot accurately describe the molecular changes of pancreatic islet beta cells as they progress towards a state of deficient insulin secretion in type 2 diabetes (T2D). Here, we conduct a comprehensive multi-omics analysis of pancreatic islets obtained from metabolically profiled pancreatectomized living human donors stratified along the glycemic continuum, from normoglycemia to T2D. We find that islet pools isolated from surgical samples by laser-capture microdissection display remarkably more heterogeneous transcriptomic and proteomic profiles in patients with diabetes than in non-diabetic controls. The differential regulation of islet gene expression is already observed in prediabetic individuals with impaired glucose tolerance. Our findings demonstrate a progressive, but disharmonic, remodelling of mature beta cells, challenging current hypotheses of linear trajectories toward precursor or transdifferentiation stages in T2D. Furthermore, through integration of islet transcriptomics with preoperative blood plasma lipidomics, we define the relative importance of gene coexpression modules and lipids that are positively or negatively associated with HbA1c levels, pointing to potential prognostic markers. Wigger, Barovic and Brunner et al. perform a multidimensional analysis of islets from metabolically characterized patients who had undergone pancreatectomy, observing remarkable heterogeneity between samples from individuals with type 2 diabetes, thus arguing against models of linear beta-cell dedifferentiation in diabetes.

Cerebrospinal fluid (CSF) is a key body fluid that maintains the homeostasis in central nervous system (CNS). As a biofluid whose content reflects the brain metabolic activity, the CSF is analyzed in the context of neurological diseases and is rarely collected from healthy subjects. For this reason, the metabolite variation associated with general phenotypic characteristics such as gender and age have hardly ever been studied. Here we present the hydrophilic interaction liquid chromatography-high resolution mass spectrometry (HILIC-HRMS) data as a result of untargeted metabolomics analysis of a cohort of elderly cognitively healthy volunteers ( n = 32). 146 unambiguously identified water soluble metabolites (using accurate mass, retention time and MS/MS matching against spectral libraries) were measured and their abundances across all the subjects depending on their gender are provided in this article. Data tables are available at https://data.mendeley.com/datasets/c73xtsd4s5/1. it's published on mendeley, the DOI is DOI:10.17632/c73xtsd4s5.1. The data presented in this article are related to the research article entitled "A global HILIC-MS approach to measure polar human cerebrospinal fluid metabolome: Exploring gender-associated variation in a cohort of elderly cognitively healthy subjects" (Gallart-Ayala et al., 2018, In press).

We have deployed a multi-omics approach in large cohorts of patients with existing type 2 diabetes to identify biomarkers for disease progression across three molecular classes, metabolites, lipids and proteins. A Cox regression analysis for association with time to insulin requirement in 2,973 patients in the DCS, ANDIS and GoDARTS cohorts identified homocitrulline, isoleucine and 2-aminoadipic acid, as well as the bile acids glycocholic and taurocholic acids, as predictive of more rapid deterioration. Increased levels of eight triacylglycerol species, and lowered levels of the sphingomyelin SM 42:2;2 were also predictive of disease progression. Of ∼1,300 proteins examined in two cohorts, levels of GDF-15/MIC1, IL-18RA, CRELD1, NogoR, FAS, and ENPP7 were associated with faster progression, whilst SMAC/DIABLO, COTL1, SPOCK1 and HEMK2 predicted lower progression rates. Strikingly, identified proteins and lipids were also associated with diabetes incidence and prevalence in external replication cohorts. Implicating roles in disease compensation, NogoR/RTN4R improved glucose tolerance in high fat-fed mice and tended to improved insulin signalling in liver cells whilst IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. Conversely, high NogoR levels led to islet cell apoptosis. This comprehensive, multi-disciplinary approach thus identifies novel biomarkers with potential prognostic utility, provides evidence for new disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.

Objectives To find plasma biomarkers prognostic of type 2 diabetes, which could also inform on pancreatic β-cell deregulations or defects in the function of insulin target tissues. Methods We conducted a systems biology approach to characterize the plasma lipidomes of C57Bl/6J, DBA/2J, and BALB/cJ mice under different nutritional conditions, as well as their pancreatic islet and liver transcriptomes. We searched for correlations between plasma lipids and tissue gene expression modules. Results We identified strong correlation between plasma triacylglycerols (TAGs) and islet gene modules that comprise key regulators of glucose- and lipid-regulated insulin secretion and of the insulin signaling pathway, the two top hits were Gck and Abhd6 for negative and positive correlations, respectively. Correlations were also found between sphingomyelins and islet gene modules that overlapped in part with the gene modules correlated with TAGs. In the liver, the gene module most strongly correlated with plasma TAGs was enriched in mRNAs encoding fatty acid and carnitine transporters as well as multiple enzymes of the β-oxidation pathway. In humans, plasma TAGs also correlated with the expression of several of the same key regulators of insulin secretion and the insulin signaling pathway identified in mice. This cross-species comparative analysis further led to the identification of PITPNC1 as a candidate regulator of glucose-stimulated insulin secretion. Conclusion TAGs emerge as biomarkers of a liver-to-β-cell axis that links hepatic β-oxidation to β-cell functional mass and insulin secretion., Highlights • Plasma triacylglycerols correlated with genes controlling β-cell mass and function. • Plasma triacylglycerols correlated with genes controlling liver β-oxidation. • In humans, triacylglycerols also correlated with key regulators of insulin secretion. • Mouse and human data identified PITPNC1 as a candidate regulator of insulin secretion. • Triacylglycerols are biomarkers of the liver-to-β-cell axis and β-cell function.

Type 2 diabetes is a multifactorial disease with multiple underlying aetiologies. To address this heterogeneity, investigators of a previous study clustered people with diabetes according to five diabetes subtypes. The aim of the current study is to investigate the etiology of these clusters by comparing their molecular signatures. In three independent cohorts, in total 15,940 individuals were clustered based on five clinical characteristics. In a subset, genetic (N = 12,828), metabolomic (N = 2,945), lipidomic (N = 2,593), and proteomic (N = 1,170) data were obtained in plasma. For each data type, each cluster was compared with the other four clusters as the reference. The insulin-resistant cluster showed the most distinct molecular signature, with higher branched-chain amino acid, diacylglycerol, and triacylglycerol levels and aberrant protein levels in plasma were enriched for proteins in the intracellular PI3K/Akt pathway. The obese cluster showed higher levels of cytokines. The mild diabetes cluster with high HDL showed the most beneficial molecular profile with effects opposite of those seen in the insulin-resistant cluster. This study shows that clustering people with type 2 diabetes can identify underlying molecular mechanisms related to pancreatic islets, liver, and adipose tissue metabolism. This provides novel biological insights into the diverse aetiological processes that would not be evident when type 2 diabetes is viewed as a homogeneous disease.

Aims/hypothesisFive clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes (T2D). In the current study we replicate and cross-validate these T2D clusters in three large cohorts using readily measured variables in the clinic.MethodsIn this cross-sectional study, 15,940 individuals were clustered based on age, BMI, HbA1c, random or fasting C-peptide and HDL in three independent cohorts. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort’s cluster centres.ResultsFive distinct T2D clusters were identified and mapped back to the original four ANDIS clusters. Using C-peptide and HDL instead of HOMA-B and HOMA-S three of the clusters mapped with high sensitivity (80.6 – 90.7%) to the previously identified Severe Insulin Deficient (SIDD), Severe insulin resistant (SIRD) and Obese (MOD) clusters. The previously described ANDIS MARD cluster could be mapped to the two milder groups in our study – one characterised by high HDL, and the other having not any extreme characteristic (MDH cluster). When these two milder groups were combined they mapped well to the previously labelled MARD cluster (sensitivity 79.4%). In the cross-validation between cohorts, particularly the SIDD and MDH cluster cross-validated well with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity ranging from 36.1% to 92.3% where individuals shifted from SIRD to MD and vice versa.Conclusions/interpretationClusters based on C-peptide instead of HOMA measures result in clusters that resemble those based on HOMA measures, especially for SIDD, SIRD and MOD. By adding HDL, the MARD cluster based upon HOMA measures resulted in the current clustering in two clusters with one cluster having high HDL levels. Cross-validation between cohorts showed generally a good resemblance between cohorts. Together, our results show that the clustering based on clinical variables readily measured in the clinic (age, HbA1c, HDL, BMI and C-peptide) results in informative clusters that are representative of the original ANDIS clusters and stable across cohorts.

Existing studies do not sufficiently describe the molecular changes of pancreatic islet beta cells leading to their deficient insulin secretion in type 2 diabetes (T2D). Here we address this deficiency with a comprehensive multi-omics analysis of metabolically profiled pancreatectomized living human donors stratified along the glycemic continuum from normoglycemia to T2D. Islet pools isolated from surgical samples by laser-capture microdissection had remarkably heterogeneous transcriptomic and proteomic profiles in diabetics, but not in non-diabetic controls. Transcriptomics analysis of this unique cohort revealed islet genes already dysregulated in prediabetic individuals with impaired glucose tolerance. Our findings demonstrate a progressive but disharmonic remodeling of mature beta cells, challenging current hypotheses of linear trajectories toward precursor or trans-differentiation stages in T2D. Further, integration of islet transcriptomics and pre-operative blood plasma lipidomics data enabled us to define the relative importance of gene co-expression modules and lipids positively or negatively associated with HbA1c levels, pointing to potential prognostic markers.

MetaNetX/MNXref is a reconciliation of metabolites and biochemical reactions providing cross-links between major public biochemistry and Genome-Scale Metabolic Network (GSMN) databases. The new release brings several improvements with respect to the quality of the reconciliation, with particular attention dedicated to preserving the intrinsic properties of GSMN models. The MetaNetX website (https://www.metanetx.org/) provides access to the full database and online services. A major improvement is for mapping of user-provided GSMNs to MXNref, which now provides diagnostic messages about model content. In addition to the website and flat files, the resource can now be accessed through a SPARQL endpoint (https://rdf.metanetx.org).

The standard treatment for neovascular age-related macular degeneration (nAMD) consists of intravitreal anti-vascular endothelial growth factors (VEGF). However, for some patients, even maximal anti-VEGF treatment does not entirely suppress exudative activity. The goal of this study was to identify molecular biomarkers in nAMD with incomplete response to anti-VEGF treatment. Aqueous humor (AH) samples were collected from three groups of patients: 17 patients with nAMD responding incompletely to anti-VEGF (18 eyes), 17 patients affected by nAMD with normal treatment response (21 eyes), and 16 control patients without any retinopathy (16 eyes). Proteomic and multiplex analyses were performed on these samples. Proteomic analyses showed that nAMD patients with incomplete anti-VEGF response displayed an increased inflammatory response, complement activation, cytolysis, protein-lipid complex, and vasculature development pathways. Multiplex analyses revealed a significant increase of soluble vascular cell adhesion molecule-1 (sVCAM-1) [ p = 0.001], interleukin-6 (IL-6) [ p = 0.009], bioactive interleukin-12 (IL-12p40) [ p = 0.03], plasminogen activator inhibitor type 1 (PAI-1) [ p = 0.004], and hepatocyte growth factor (HGF) [ p = 0.004] levels in incomplete responders in comparison to normal responders. Interestingly, the same biomarkers showed a high intercorrelation with r2 values between 0.58 and 0.94. In addition, we confirmed by AlphaLISA the increase of sVCAM-1 [ p < 0.0001] and IL-6 [ p = 0.043] in the incomplete responder group. Incomplete responders in nAMD are associated with activated angiogenic and inflammatory pathways. The residual exudative activity of nAMD despite maximal anti-VEGF treatment may be related to both angiogenic and inflammatory responses requiring specific adjuvant therapy. Data are available via ProteomeXchange with identifier PXD02247

Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems.

Background Metabolic alterations, related to cerebral glucose metabolism, brain insulin resistance, and age-induced mitochondrial dysfunction, play an important role in Alzheimer’s disease (AD) on both the systemic and central nervous system level. To study the extent and significance of these alterations in AD, quantitative metabolomics was applied to plasma and cerebrospinal fluid (CSF) from clinically well-characterized AD patients and cognitively healthy control subjects. The observed metabolic alterations were associated with core pathological processes of AD to investigate their relation with amyloid pathology and tau-related neurodegeneration. Methods In a case-control study of clinical and biomarker-confirmed AD patients (n = 40) and cognitively healthy controls without cerebral AD pathology (n = 34) with paired plasma and CSF samples, we performed metabolic profiling, i.e., untargeted metabolomics and targeted quantification. Targeted quantification focused on identified deregulated pathways highlighted in the untargeted assay, i.e. the TCA cycle, and its anaplerotic pathways, as well as the neuroactive tryptophan and kynurenine pathway. Results Concentrations of several TCA cycle and beta-oxidation intermediates were higher in plasma of AD patients, whilst amino acid concentrations were significantly lower. Similar alterations in these energy metabolism intermediates were observed in CSF, together with higher concentrations of creatinine, which were strongly correlated with blood-brain barrier permeability. Alterations of several amino acids were associated with CSF Amyloidβ1–42. The tryptophan catabolites, kynurenic acid and quinolinic acid, showed significantly higher concentrations in CSF of AD patients, which, together with other tryptophan pathway intermediates, were correlated with either CSF Amyloidβ1–42, or tau and phosphorylated Tau-181. Conclusions This study revealed AD-associated systemic dysregulation of nutrient sensing and oxidation and CNS-specific alterations in the neuroactive tryptophan pathway and (phospho)creatine degradation. The specific association of amino acids and tryptophan catabolites with AD CSF biomarkers suggests a close relationship with core AD pathology. Our findings warrant validation in independent, larger cohort studies as well as further investigation of factors such as gender and APOE genotype, as well as of other groups, such as preclinical AD, to identify metabolic alterations as potential intervention targets.

Acylcarnitines and amino acids are key players in energy metabolism; however, analytical methods for comprehensive and straightforward quantitative profiling of these metabolites, without derivatization or use of ion-pairing agents, are lacking. We therefore developed a hydrophilic interaction chromatography (HILIC)-based high-resolution mass spectrometry (HRMS) method for the simultaneous quantification of acylcarnitines and amino acids in a single run, while taking advantage of HRMS data acquired in full-scan mode to screen for additional derivatives and other polar metabolites. A single-step metabolite extraction with internal standard mixture (in methanol) warranted high-throughput sample preparation whose applicability was demonstrated on a panel of human biofluids (i.e., blood plasma, CSF, and urine) and brain tissue. Method accuracy was within 90-106% of validated NIST reference plasma concentrations for the panel of measured amino acids. Amino acid and acylcarnitine extraction recoveries were 87-100% on average, depending on the concentration range spiked. The coefficient of variation (CV) was 1-10% and 1-25% for intra- and interday measurements, respectively, with the highest CVs for the metabolites at the limit of quantification, depending on the biofluid. Acylcarnitine and amino acid signatures or chemical composition barcodes of the different biofluids and human brain tissue were acquired and biofluid- and tissue-associated differences were discussed in the context of their respective physiological roles. Significant differences were observed in the amino acid profiles, whereas acylcarnitine composition did not show biofluid-characteristic or brain region-specific pattern. The retrospective exploration of full-scan all-ion-fragmentation data allowed us to extract the information on unsaturated and hydroxylated acylcarnitine species, amines, and purine and pyrimidine metabolites. This merged targeted and untargeted approach provides an innovative strategy for simultaneous and comprehensive assessment of acylcarnitine and amino acid metabolism in clinical research studies using relevant biofluids and tissue extracts.

Data needed to reproduce the results from the manuscript "Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models" byNoushin Hadadi, Vikash Pandey, Anush Chiappino-Pepe, Marian Morales, Hector Gallart-Ayala, Florence Mehl, Julijana Ivanisevic, Vladimir Sentchilo and Jan R. van der Meer. The dataset includes: Models folder that contains TFA reconstructions/models as well as REMI models where we integrated different type of constraints based on gene expression data and metabolomics data using the REMI method. We provide scripts and simulation data so that one can reproduce the reported results. Scripts folder contains scripts for generating models, alternative analysis, and biomass building blocks (BBBs) analysis. Stationary vs exponential comparison Sand vs liquid comparison BBB Analysis Simdata folder contains the mat files of alternative solutions obtained from REMi models, BBB analysis for exponential vs stationary and sand vs liquid as well as the edgR analysis results of the differentially expressed genes.

Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems.

Background The metabolic syndrome is strongly associated with gout and hyperuricemia in man. Patients with gout commonly report acute flares after eating particular foods and it is suspected that metabolic changes, apart from serum urate levels, influence the triggering of the inflammatory response to MSU crystals. Objectives By studying the metabolic changes that impact on the response to MSU crystals in a murine model of gout, we hope to identify the dietary effect on metabolites that are associated with the inflammatory response to MSU crystals. Methods Male mice (C57Bl/6) were fed either a normal diet (ND) or high fat diet HFD (DIO D12492) for >6 weeks. During the last 4 weeks of feeding, half the mice in each group were treated with antibiotics (Enrofloxacin 0.25% + co-amoxicillin) to induce a germ-free state. At day 0, mice were injected i-p with 1mg of MSU crystals. Mice were sacrificed 6h after i-p injection. Serum, plasma and peritoneal exudate samples were collected at sacrifice and plasma obtained 3 weeks before sacrifice. Untargeted and targeted liquid chromatography – mass spectrometry based-metabolomics approach was performed on serum and plasma samples. IL1 and IL6 were measured in serum and peritoneal exudates by ELISA. Animal experimental authorization was obtained for these experiments. Results Mice fed a HFD had a greater serum and peritoneal IL6 response to MSU. In an untargeted analysis, multiple metabolic alterations were observed related to diet. Targeted analyses of sphingolipids were performed. Both diet and inflammation (MSU injection) altered plasma sphingolipid levels. No effect of antibiotics was observed. MSU injection induced marked changes in the sphingolipid profile (38.2% of explained variance), while diet had a lesser effect (16.2% of explained variance). Mice fed an HFD had significantly higher serum IL6 before and peritoneal IL6 levels after MSU injection than mice fed a ND. DhCer C16:0 and Cer C16:0 levels decreased significantly after MSU injection. Pre-MSU levels of DhCer C16:0 and Cer C16:0 showed significantly negative correlations with IL6p levels after i-p injection in HFD animals. Conclusion The sphingolipid profile was significantly changed by diet and inflammation. In mice given a HFD, the levels of particular ceramides were significantly correlated with subsequent inflammatory response as assessed by IL6 secretion, suggesting that they may play a role in modulating the inflammatory response to MSU. These same ceramides and their derivatives have been linked to susceptibility to diabetes and cardiovascular diseases1,2. Further investigation of these sphingolipids in gout in man is warranted. References [1] Wigger L, et al. Cell Rep. 2017 Feb 28;18(9):2269-2279. doi: 10.1016/j.celrep.2017.02.019 [2] Summers SA. Cell Metab. 2018 Feb 6;27(2):276-280. doi: 10.1016/j.cmet.2017.12.003 Disclosure of Interests Alexander So Consultant for: SOBI, Grunenthal, Speakers bureau: Grunenthal, Florence Mehl: None declared, Nicola Harris: None declared, Veronique Chobaz: None declared, Hector Gallart-Ayala: None declared

The detection of testosterone abuse in sports is routinely achieved through the 'steroidal module' of the Athlete Biological Passport by GC-MS(/MS) quantification of selected endogenous anabolic androgenic steroids (EAAS) from athletes' urines. To overcome some limitations of the "urinary steroid profile" such as the presence of confounding factors (ethnicity, enzyme polymorphism, bacterial contamination, and ethanol), ultrahigh performance liquid chromatography (UHPLC) measurements of blood concentrations of testosterone, its major metabolites, and precursors could represent an interesting and complementary strategy. In this work, two UHPLC-MS/MS methods were developed for the quantification of testosterone and related compounds in human serum, including major progestogens, corticoids, and estrogens. The validated methods were then used for the analyses of serum samples collected from 19 healthy male volunteers after oral and transdermal testosterone administration. Results from unsupervised multiway analysis allowed variations of target analytes to be assessed simultaneously over a 96-h time period. Except for alteration of concentration values due to the circadian rhythm, which concerns mainly corticosteroids, DHEA, and progesterone, significant variations linked to the oral and transdermal testosterone administration were observed for testosterone, DHT, and androstenedione. As a second step of analysis, the longitudinal monitoring of these biomarkers using intra-individual thresholds showed, in comparison to urine, significant improvements in the detection of testosterone administration, especially for volunteers with del/del genotype for phase II UGT2B17 enzyme, not sensitive to the main urinary marker, T/E ratio. A substantial extension of the detection window after transdermal testosterone administration was also observed in serum matrix. The longitudinal follow-up proposed in this study represents a first example of 'blood steroid profile' in doping control analysis, which can be proposed in the future as a complement to the 'urinary module' for improving steroid abuse detection capabilities.

Cerebrospinal fluid (CSF) is a key body fluid that maintains the homeostasis in central nervous system (CNS). As a biofluid whose content reflects the brain metabolic activity, the CSF has been profiled in the context of neurological diseases to provide novel insights into the disease mechanisms. However, a global high-throughput approach to measure a broad diversity of polar metabolites present in CSF is lacking. Although still perceived as challenging and less reproducible, hydrophilic interaction liquid chromatography (HILIC) has recently evolved to offer the unprecedented coverage capacity of water-soluble metabolome. Here, we present a global HILIC high-resolution mass spectrometry-based (HRMS) approach that combines the profiling in acidic pH ESI (+) and basic pH ESI (-) mode to extend the coverage of CSF polar metabolome. This approach allowed us to annotate and measure a broad range of central carbon metabolites (implicated in glycolysis, TCA cycle, nucleotide, amino acid and fatty acid metabolism) in CSF collected from cognitively healthy elderly volunteers (n = 32), using a single extraction method. Metabolite annotation was achieved using the accurate mass, RT and MS/MS criteria, allowing for the characterization of 146 measurable metabolites. Exploration of characterized individual CSF profiles allowed for a discovery of intriguing gender-associated differences, with significantly higher acylcarnitine levels in men and higher taurine levels women. With this case study, we demonstrate the value of combined HILIC ESI ± HRMS profiling to assess CSF metabolome in clinical research studies.

Citrus cold pressed oils are of great importance to the flavour and fragrance industry. Because of their high added value, careful attention must be paid to ensure the oils’ genuineness and authenticity. Characterising their chemical complexity in a holistic perspective constitutes a potent way to relate specific compounds to the organoleptic properties of interest and to assess their quality. In this context, a complete characterisation using untargeted metabolomics represents an analytical challenge. The present study takes advantage of multiblock data modelling to integrate heterogeneous signals collected from GC-FID, 1H-NMR, UHPLC-TOF/MS- (negative mode) and UHPLC-TOF/MS+ (positive mode) platforms to obtain a complete characterisation of 64 samples of cold pressed lemon oil (CPLO). Two statistical approaches (MB-PLS-DA and Consensus OPLS-DA) were used to classify the samples according to their extraction processes [i.e. Sfumatrice, Food Machinery Corporation Inline Extractor (FMC), Brown Oil Extractor (BOE), Pelatrice, or mixed FMC + Pelatrice]. Furthermore, the multiblock strategy allows links between variables from different analytical methods to be drawn easily and facilitates the identification of compounds. Because citrus oils extracted using the Sfumatrice process constitute the reference quality from an organoleptic point of view, these samples were characterised thoroughly and are reported to contain less fatty acids but more sesquiterpenes and furocoumarins compared with products obtained using other extraction processes.

The detailed characterization of cold-pressed lemon oils (CPLOs) is of great importance for the flavor and fragrance (FF) industry. Since a control of authenticity by standard analytical techniques can be bypassed using elaborated adulterated oils to pretend a higher quality, a combination of advanced orthogonal methods has been developed. The present study describes a combined metabolomic approach based on UHPLC-TOF-MS profiling and (1)H NMR fingerprinting to highlight metabolite differences on a set of representative samples used in the FF industry. A new protocol was set up and adapted to the use of CPLO residues. Multivariate analysis based on both fingerprinting methods showed significant chemical variations between Argentinian and Italian samples. Discriminating markers identified in mixtures belong to furocoumarins, flavonoids, terpenoids and fatty acids. Quantitative NMR revealed low citropten and high bergamottin content in Italian samples. The developed metabolomic approach applied to CPLO residues gives some new perspectives for authenticity assessment.

γ -Lactones are aromatic molecules which constitute the aroma or flavour of many natural products. Functionalized γ -lactones have attracted attention in recent years due to their synthetic importance as building blocks in bioactive natural product synthesis. Furthermore the δ -hydroxy- γ -lactone motif is the core structure of bioactive natural products. To synthesise lactones, an attractive method consists of subjecting β , γ - or γ , δ -unsaturated ester to a Sharpless asymmetric dihydroxylation. Hemisynthesis of such lactones was here realised in two steps from a monoterpenic alcohol, linalool. First, the allylic terpenol was submitted to a Johnson–Claisen rearrangement to produce γ , δ -unsaturated esters. These γ , δ -unsaturated esters were then subjected to an asymmetric dihydroxylation to give the corresponding γ , δ -unsaturated dihydroxyesters which can directly cyclise to the δ -hydroxy- γ -lactones. Five new products were isolated and characterised by 1D- and 2D-NMR. The odour evaluation of lactones and their precursors revealed interesting properties.

Two monoterpenols, perillyl alcohol and nerol, have been converted into their γ,δ-unsaturated ester derivatives following a modified process of microwave-assisted ortho ester Claisen rearrangement. The yields obtained (>90%) are better than those previously obtained. The optimized process needs less reaction time (5 min), smaller amount of reagent, and no solvent.

The goal of this study was to evaluate the combination of powerful chromatographic methods and compact single quadrupole MS device for simple in vitro cytochrome P450 (CYP) inhibition assay, instead of more expensive triple quadrupole MS/MS detectors. For this purpose, two modern chromatographic approaches (ultra-high pressure liquid chromatography (UHPLC) and ultra-high performance supercritical fluid chromatography (UHPSFC)) were tested in combination with simple MS detector. To show the applicability for an in vitro CYP-mediated metabolism assay using the cocktail approach, a method was first developed in UHPLC–MS to separate a mixture of 8 probe substrates and 8 CYP-specific metabolites. A screening procedure was initially applied to determine the best combination of a column, an organic modifier and a mobile-phase pH, followed by fine tuning of the conditions ( i.e. , gradient profile, temperature and pH) using HPLC modelling software. A similar sequential method development procedure was also evaluated for UHPSFC–MS. For method development, where peak tracking is necessary, the use of single quadrupole MS was found to be extremely valuable for following the investigated analytes. Ultimately, a baseline separation of the 16 compounds was achieved in both UHPLC–MS and UHPSFC–MS with an analysis time of less than 7 min. In a second series of experiments, sensitivity was evaluated, and LOQ values were between 2 and 100 ng/mL in UHPLC–MS, while they ranged from 2 to 200 ng/mL in UHPSFC–MS. Based on the concentrations employed for the current in vitro phase I metabolism assay, these LOQ values were appropriate for this type of application. Finally, the two analytical methods were applied to in vitro CYP-dependent metabolism testing. Two well-known phytochemical inhibitors, yohimbine and resveratrol, were investigated, and reliable conclusions were drawn from these experiments with both UHPLC–MS and UHPSFC–MS. At the end, the proposed strategy of optimized chromatography combined with simple MS device has been shown to potentially compete with the widely used combination of generic chromatography and highly selective MS/MS device for simple in vitro CYP inhibition assays. In addition, our analytical method may be easier to use in a routine environment; the instrument cost is significantly reduced and the two developed methods fit for purpose.

International audience; Nutmeg, provided by tree native to Moluccas, is widely interesting for Indonesia essential oil production.21 hydrocarbon and oxygenated compounds are controlled by industrial perfumers in nutmeg samples.Samples of nutmeg essential oils (n = 26) from different geographic origins (Sanger, Siau and Minahasa forthe Sulawesi region and Ambon and Ternate for little islands of the Molucca region) show high variationof concentrations of these compounds. The evolution of composition during the extraction of essentialoil was studied and has shown the importance of this parameter for the nutmeg essential oil quality.Chemometric treatment of gas chromatographic–mass spectrometry (GC–MS) data was assessed forthe differentiation of geographical origin and maturity of the nutmeg. Results were compared to thoseobtained by MID-IR analysis. Chemometric MID-IR spectra treatments conduce to similar results thanthose obtained by GC–MS which is a time consuming analytical technique. Therefore, MID-IR associatedto chemometric analyses constitutes a robust and helpful fast method for authentication of nutmegessential oils.

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.

A series of twelve analogs carrying fluoro, chloro, bromo and iodo halogens on the ortho, meta and para positions of a benzoyloxytropane skeleton were synthesized by a simple acylation of 8-methyl-8-aza-bicyclo[3.2.1]octan- 3α-ol by halogenobenzoyl chlorides. The compounds were evaluated in vitro against Plasmodium falciparum (P. f.), Trypanosoma brucei brucei (T. b. b.), Trypanosoma cruzi (T. c.) and Leishmania infantum (L. i.). This study shows that the presence of a halogenated atom and its position on the aromatic ring are important for in vitro activity. Compounds 4 (IC50 = 3.6 µM), 8 (IC50 = 6.7 µM), 5 (IC50 = 8.1 µM) and 7 (IC50 = 9.5 µM) were found the most active against P. f., whereas compounds 12 (IC50 = 5.1 µM), 11 (IC50 = 5.6 µM) and 9 (IC50 = 5.8 µM) exhibited the most pronounced activity against T. b. b. This series of compounds can be considered as non-toxic to the human cell line MRC-5.

Purpose To evaluate the chemical contamination of surfaces by cytotoxic agents during preparation of injectable chemotherapies in hospital pharmacies. Methods 526 wipe samples collected in 24 Swiss hospital pharmacies were analysed using a validated liquid chromatography–mass spectrometry/mass spectrometry method able to quantify 10 cytotoxic agents: cytarabine, gemcitabine, cyclophosphamide, ifosfamide, methotrexate, etoposide phosphate, irinotecan, doxorubicin, epirubicin and vincristine. Information on chemotherapies produced, equipment and production processes used were collected from all the hospital pharmacies on a voluntary basis in order to investigate their association with contamination rates. Results In two pharmacies, no trace of the 10 cytotoxic agents was detected. Chemical contamination was found in the other 22 hospital pharmacies, with combined total contamination of the 10 cytotoxic agents ranging from 8 ng to more than 41 000 ng per sample. Most contaminated samples came from inside biosafety cabinets, but some came from other clean room areas and logistics rooms. Statistically significant associations were observed between contamination rates and sampling locations, the number of chemotherapies prepared per year and types of cleaning solutions used. Conclusions This study demonstrated that most of the hospital pharmacies tested had some contamination of surfaces by different cytotoxic agents. Even if highest levels of contamination were mainly detected inside biosafety cabinets, technicians were also exposed to cytotoxic agents detected in logistical and storage areas. Protective measures should therefore be maintained or even reinforced in these areas in order to limit technicians' risks of exposure when handling cytotoxic products.

The goal of this study was to evaluate the combination of powerful chromatographic methods and com-pact single quadrupole MS device for simple in vitro cytochrome P450 (CYP) inhibition assay, insteadof more expensive triple quadrupole MS/MS detectors. For this purpose, two modern chromatographicapproaches (ultra-high pressure liquid chromatography (UHPLC) and ultra-high performance supercrit-ical fluid chromatography (UHPSFC)) were tested in combination with simple MS detector. To show theapplicability for an in vitro CYP-mediated metabolism assay using the cocktail approach, a method wasfirst developed in UHPLC–MS to separate a mixture of 8 probe substrates and 8 CYP-specific metabolites.A screening procedure was initially applied to determine the best combination of a column, an organicmodifier and a mobile-phase pH, followed by fine tuning of the conditions (i.e., gradient profile, temper-ature and pH) using HPLC modelling software. A similar sequential method development procedure wasalso evaluated for UHPSFC–MS. For method development, where peak tracking is necessary, the use ofsingle quadrupole MS was found to be extremely valuable for following the investigated analytes. Ulti-mately, a baseline separation of the 16 compounds was achieved in both UHPLC–MS and UHPSFC–MSwith an analysis time of less than 7 min. In a second series of experiments, sensitivity was evaluated,and LOQ values were between 2 and 100 ng/mL in UHPLC–MS, while they ranged from 2 to 200 ng/mLin UHPSFC–MS. Based on the concentrations employed for the current in vitro phase I metabolism assay,these LOQ values were appropriate for this type of application. Finally, the two analytical methods wereapplied to in vitro CYP-dependent metabolism testing. Two well-known phytochemical inhibitors, yohim-bine and resveratrol, were investigated, and reliable conclusions were drawn from these experimentswith both UHPLC–MS and UHPSFC–MS. At the end, the proposed strategy of optimized chromatographycombined with simple MS device has been shown to potentially compete with the widely used combi-nation of generic chromatography and highly selective MS/MS device for simple in vitro CYP inhibitionassays. In addition, our analytical method may be easier to use in a routine environment; the instrumentcost is significantly reduced and the two developed methods fit for purpose.