Multitargeted Internal Calibration for the Quantification of Chronic Kidney Disease-Related Endogenous Metabolites Using Liquid Chromatography-Mass Spectrometry.

Accurate quantitative analysis in liquid chromatography-mass spectrometry (LC-MS) benefits from calibration curves generated in the same matrix as the study sample. In the case of endogenous compound quantification, as no blank matrix exists, the multitargeted internal calibration (MTIC) is an attractive and straightforward approach to avoid the need for extensive matrix similarity evaluation. Its principle is to take advantage of stable isotope labeled (SIL) standards as internal calibrants to simultaneously quantify authentic analytes using a within sample calibration. An MTIC workflow was developed for the simultaneous quantification of metabolites related to chronic kidney disease (CKD) using a volumetric microsampling device to collect 20 µL of serum or plasma, followed by a single-step extraction with acetonitrile/water and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Since a single concentration of internal calibrant is necessary to calculate the study sample concentration, the instrument response function was investigated to determine the best SIL concentration. After validation, the trueness of 16 endogenous analytes in authentic human serum ranged from 72.2 to 116.0%, the repeatability from 1.9 to 11.3%, and the intermediate precision ranged overall from 2.1 to 15.4%. The proposed approach was applied to plasma samples collected from healthy control participants and two patient groups diagnosed with CKD. Results confirmed substantial concentration differences between groups for several analytes, including indoxyl sulfate and cortisone, as well as metabolite enrichment in the kynurenine and indole pathways. Multitargeted methodologies represent a major step toward rapid and straightforward LC-MS/MS absolute quantification of endogenous biomarkers, which could change the paradigm of MS use in clinical laboratories.

Low-polarity untargeted metabolomic profiling as a tool to gain insight into seminal fluid.

INTRODUCTION: A decrease in sperm cell count has been observed along the last several decades, especially in the most developed regions of the world. The use of metabolomics to study the composition of the seminal fluid is a promising approach to gain access to the molecular mechanisms underlying this fact. OBJECTIVES: In the present work, we aimed at relating metabolomic profiles of young healthy men to their semen quality parameters obtained from conventional microscopic analysis. METHODS: An untargeted metabolomics approach focusing on low- to mid-polarity compounds was used to analyze a subset of seminal fluid samples from a cohort of over 2700 young healthy men. RESULTS: Our results show that a broad metabolic profiling comprising several families of compounds (including acyl-carnitines, steroids, and other lipids) can contribute to effectively distinguish samples provided by individuals exhibiting low or high absolute sperm counts. CONCLUSION: A number of metabolites involved in sexual development and function, signaling, and energy metabolism were highlighted as being distinctive of samples coming from either group, proving untargeted metabolomics as a promising tool to better understand the pathophysiological processes responsible for male fertility impairment.

Network principal component analysis: a versatile tool for the investigation of multigroup and multiblock datasets.

MOTIVATION: Complex data structures composed of different groups of observations and blocks of variables are increasingly collected in many domains, including metabolomics. Analysing these high-dimensional data constitutes a challenge, and the objective of this article is to present an original multivariate method capable of explicitly taking into account links between data tables when they involve the same observations and/or variables. For that purpose, an extension of standard principal component analysis called NetPCA was developed. RESULTS: The proposed algorithm was illustrated as an efficient solution for addressing complex multigroup and multiblock datasets. A case study involving the analysis of metabolomic data with different annotation levels and originating from a chronic kidney disease (CKD) study was used to highlight the different aspects and the additional outputs of the method compared to standard PCA. On the one hand, the model parameters allowed an efficient evaluation of each group's influence to be performed. On the other hand, the relative relevance of each block of variables to the model provided decisive information for an objective interpretation of the different metabolic annotation levels. AVAILABILITY AND IMPLEMENTATION: NetPCA is available as a Python package with NumPy dependencies. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Challenges in ESI-MS-based Untargeted Metabolomics.

Untargeted metabolomics is now widely recognized as a useful tool for exploring metabolic changes taking place in biological systems under different conditions. In this article, we aim to provide a short overview of the liquid-phase separation methods hyphenated to MS to perform untargeted metabolomics of biological samples. Each approach is complemented by up-to-date literature to guide readers, as well as practical information for avoiding or fixing some of the most frequently encountered pitfalls. This article covers mainly data acquisition, but a short discussion is provided regarding signal processing and data treatment, as well as data analysis and its biological interpretation in the context of metabolomic studies.

Approaches in metabolomics for regulatory toxicology applications.

Innovative methodological approaches are needed to conduct human health and environmental risk assessments on a growing number of marketed chemicals. Metabolomics is progressively proving its value as an efficient strategy to perform toxicological evaluations of new and existing substances, and it will likely become a key tool to accelerate chemical risk assessments. However, additional guidance with widely accepted and harmonized procedures is needed before metabolomics can be routinely incorporated in decision-making for regulatory purposes. The aim of this review is to provide an overview of metabolomic strategies that have been successfully employed in toxicity assessment as well as the most promising workflows in a regulatory context. First, we provide a general view of the different steps of regulatory toxicology-oriented metabolomics. Emphasis is put on three key elements: robustness of experimental design, choice of analytical platform, and use of adapted data treatment tools. Then, examples in which metabolomics supported regulatory toxicology outputs in different scenarios are reviewed, including chemical grouping, elucidation of mechanisms of toxicity, and determination of points of departure. The overall intention is to provide insights into why and how to plan and conduct metabolomic studies for regulatory toxicology purposes.

Neuroinflammatory Response to TNFα and IL1ß Cytokines Is Accompanied by an Increase in Glycolysis in Human Astrocytes In Vitro.

Astrogliosis has been abundantly studied in rodents but relatively poorly in human cells due to limited access to the brain. Astrocytes play important roles in cerebral energy metabolism, and are also key players in neuroinflammation. Astroglial metabolic and inflammatory changes as a function of age have been reported, leading to the hypothesis that mitochondrial metabolism and inflammatory responses are interconnected in supporting a functional switch of astrocytes from neurotrophic to neurotoxic. This study aimed to explore the metabolic changes occurring in astrocytes during their activation. Astrocytes were derived from human ReN cell neural progenitors and characterized. They were activated by exposure to tumor necrosis factor alpha (TNFα) or interleukin 1ß (IL1ß) for 24 h. Astrocyte reaction and associated energy metabolic changes were assessed by immunostaining, gene expression, proteomics, metabolomics and extracellular flux analyses. ReN-derived astrocytes reactivity was observed by the modifications of genes and proteins linked to inflammation (cytokines, nuclear factor-kappa B (NFκB), signal transducers and activators of transcription (STATs)) and immune pathways (major histocompatibility complex (MHC) class I). Increased NFκB1, NFκB2 and STAT1 expression, together with decreased STAT3 expression, suggest an activation towards the detrimental pathway. Strong modifications of astrocyte cytoskeleton were observed, including a glial fibrillary acidic protein (GFAP) decrease. Astrogliosis was accompanied by changes in energy metabolism characterized by increased glycolysis and lactate release. Increased glycolysis is reported for the first time during human astrocyte activation. Astrocyte activation is strongly tied to energy metabolism, and a possible association between NFκB signaling and/or MHC class I pathway and glycolysis is suggested.

Comprehensive Examination of the Mouse Lung Metabolome Following Mycobacterium tuberculosis Infection Using a Multiplatform Mass Spectrometry Approach.

The mechanisms whereby Mycobacterium tuberculosis (Mtb) rewires the host metabolism in vivo are surprisingly unexplored. Here, we used three high-resolution mass spectrometry platforms to track altered lung metabolic changes associated with Mtb infection of mice. The multiplatform data sets were merged using consensus orthogonal partial least squares-discriminant analysis (cOPLS-DA), an algorithm that allows for the joint interpretation of the results from a single multivariate analysis. We show that Mtb infection triggers a temporal and progressive catabolic state to satisfy the continuously changing energy demand to control infection. This causes dysregulation of metabolic and oxido-reductive pathways culminating in Mtb-associated wasting. Notably, high abundances of trimethylamine-N-oxide (TMAO), produced by the host from the bacterial metabolite trimethylamine upon infection, suggest that Mtb could exploit TMAO as an electron acceptor under anaerobic conditions. Overall, these new pathway alterations advance our understanding of the link between Mtb pathogenesis and metabolic dysregulation and could serve as a foundation for new therapeutic intervention strategies. Mass spectrometry data has been deposited in the Metabolomics Workbench repository (data-set identifier: ST001328).

Processing of NMR and MS metabolomics data using chemometrics methods: a global tool for fungi biotransformation reactions monitoring.

INTRODUCTION: Biotransformation constitutes an important aspect of the drug discovery process, to mimic human metabolism of active principal ingredient but also to generate new chemical entities. Several microorganisms such as fungi are well adapted to transform drug, whether at the stage of screening or for large-scale production. OBJECTIVES: Due to the high chemical complexity of the biotransformation media, it seems attractive to develop new analytical strategies in order to guarantee an adequate monitoring and optimize the production of targeted metabolites or drug candidates. METHODS: The model designed for this purpose concerns the biotransformation of a potential histamine H3 antagonist (S38093) in order to produce phase I metabolites. MS, NMR and chemometrics tools were used to monitor biotransformation reactions. RESULTS: First, a screening of eleven filamentous fungi was carried out by UHPLC-UV-MS and principal component analysis to select the best candidates. Subsequently, MS (tR, m/z) and NMR (1H, JRES) fingerprints associated with Consensus OPLS-DA multiblock approach were used to better understand the bioreaction mechanisms in terms of nutrient consumption and hydroxylated metabolites production. Then an experimental design was set up to optimize the production conditions (pH, kinetic) of these target metabolites. CONCLUSION: This study demonstrates how NMR and MS acquisitions combined with chemometric methods offer an innovative analytical strategy to have a grasp of functionalization mechanisms, and identify metabolites and other compounds (amino acids, nutrients, etc.) in complex biotransformation mixtures.

In vitro models to study insulin and glucocorticoids modulation of trimethyltin (TMT)-induced neuroinflammation and neurodegeneration, and in vivo validation in db/db mice.

Brain susceptibility to a neurotoxic insult may be increased in a compromised health status, such as metabolic syndrome. Both metabolic syndrome and exposure to trimethyltin (TMT) are known to promote neurodegeneration. In combination the two factors may elicit additive or compensatory/regulatory mechanisms. Combined effects of TMT exposure (0.5-1 µM) and mimicked metabolic syndrome-through modulation of insulin and glucocorticoid (GC) levels-were investigated in three models: tridimensional rat brain cell cultures for neuron-glia effects; murine microglial cell line BV-2 for a mechanistic analysis of microglial reactivity; and db/db mice as an in vivo model of metabolic syndrome. In 3D cultures, low insulin condition significantly exacerbated TMT's effect on GABAergic neurons and promoted TMT-induced neuroinflammation, with increased expression of cytokines and of the regulator of intracellular GC activity, 11ß-hydroxysteroid dehydrogenase 1 (11ß-Hsd1). Microglial reactivity increased upon TMT exposure in medium combining low insulin and high GC. These results were corroborated in BV-2 microglial cells where lack of insulin exacerbated the TMT-induced increase in 11ß-Hsd1 expression. Furthermore, TMT-induced microglial reactivity seems to depend on mineralocorticoid receptor activation. In diabetic BKS db mice, a discrete exacerbation of TMT neurotoxic effects on GABAergic neurons was observed, together with an increase of interleukin-6 (IL-6) and of basal 11ß-Hsd1 expression as compared to controls. These results suggest only minor additive effects of the two brain insults, neurotoxicant TMT exposure and metabolic syndrome conditions, where 11ß-Hsd1 appears to play a key role in the regulation of neuroinflammation and of its protective or neurodegenerative consequences.

High-throughput identification of monoclonal antibodies after compounding by UV spectroscopy coupled to chemometrics analysis.

Monoclonal antibodies (mAbs) compounded into the hospital pharmacy are widely used nowadays. Their fast identification after compounding and just before administration to the patient is of paramount importance for quality control at the hospital. This remains challenging due to the high similarity of the structure between mAbs. Analysis of the ultraviolet spectral data of four monoclonal antibodies (cetuximab, rituximab, bevacizumab, and trastuzumab) using unsupervised principal component analysis led us to focus exclusively on the second-derivative spectra. Partial least squares-discriminant analysis (PLS-DA) applied to these data allowed us to build models for predicting which monoclonal antibody was present in a given infusion bag. The calibration of the models was obtained from a k-fold validation. A prediction set from another batch was used to demonstrate the ability of the models to predict well. PLS-DA models performed on the spectra of the region of aromatic amino acid residues presented high ability to predict mAb identity. The region corresponding to the tyrosine residue reached the highest score of good classification with 89 %. To improve the score, standard normal variate (SNV) preprocessing was applied to the spectral data. The quality of the optimized PLS-DA models was enhanced and the region from the tyrosine/tryptophan residues allowed us excellent classification (100 %) of the four mAbs according to the matrix of confusion. The sensitivity and specificity performance parameters assessed this excellent classification. The usefulness of the combination of UV second-derivative spectroscopy to multivariate analysis with SNV preprocessing demonstrated the unambiguous identification of commercially available monoclonal antibodies. Graphical abstract PLS-DA models on the spectra of the region of aromatic amino acid residues allows mAb identification with high prediction.

Longitudinal monitoring of endogenous steroids in human serum by UHPLC-MS/MS as a tool to detect testosterone abuse in sports.

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.

Standardized LC×LC-ELSD Fractionation Procedure for the Identification of Minor Bioactives via the Enzymatic Screening of Natural Extracts.

To identify natural bioactive compounds from complex mixtures such as plant extracts, efficient fractionation for biological screening is mandatory. In this context, a fully automated workflow based on two-dimensional liquid chromatography (2D-LC × LC) was developed, allowing for the production of hundreds of semipure fractions per extract. Moreover, the ELSD response was used for online sample weight estimation and automated concentration normalization for subsequent bioassays. To evaluate the efficiency of this protocol, an enzymatic assay was developed using AMP-activated protein kinase (AMPK). The activation of AMPK by nonactive extracts spiked with biochanin A, a known AMPK activator, was enhanced greatly when the fractionation workflow was applied compared to screening crude spiked extracts. The performance of the workflow was further evaluated on a red clover (Trifolium pratense) extract, which is a natural source of biochanin A. In this case, while the crude extract or 1D chromatography fractions failed to activate AMPK, semipure fractions containing biochanin A were readily localized when produced by the 2D-LC×LC-ELSD workflow. The automated fractionation methodology presented demonstrated high efficiency for the detection of bioactive compounds at low abundance in plant extracts for high-throughput screening. This procedure can be used routinely to populate natural product libraries for biological screening.

Statistical Correlations between HPLC Activity-Based Profiling Results and NMR/MS Microfraction Data to Deconvolute Bioactive Compounds in Mixtures.

Recent approaches in natural product (NP) research are leading toward the discovery of bioactive chemical entities at the microgram level. In comparison to classical large scale bioassay-guided fractionation, the use of LC-MS metabolite profiling in combination with microfractionation for both bioactivity profiling and NMR analysis, allows the identification of bioactive compounds at a very early stage. In that context, this study aims to assess the potential of statistic correlation analysis to enable unambiguous identification of features related to bioactive compounds in mixtures, without the need for complete isolation. For that purpose, a mixture of NPs was microfractionated by rapid small-scale semi-preparative HPLC for proof-of-concept. UHPLC-ESI-TOFMS profiles, micro-flow CapNMR spectra and a cancer chemopreventive assay carried out on every microfraction were analysed by statistical correlations.

Assessing Susceptibility to Epilepsy in Three Rat Strains Using Brain Metabolic Profiling Based on HRMAS NMR Spectroscopy and Chemometrics.

The possibility that a metabolomic approach can inform about the pathophysiology of a given form of epilepsy was addressed. Using chemometric analyses of HRMAS NMR data, we compared several brain structures in three rat strains with different susceptibilities to absence epilepsy: Genetic Absence Epilepsy Rats from Strasbourg (GAERS), Non Epileptic Control rats (NEC), and Wistar rats. Two ages were investigated: 14 days postnatal (P14) before the onset of seizures and 5 month old adults with fully developed seizures (Adults). The relative concentrations of 19 metabolites were assessed using (1)H HRMAS NMR experiments. Univariate and multivariate analyses including multiblock models were used to identify the most discriminant metabolites. A strain-dependent evolution of glutamate, glutamine, scyllo-inositol, alanine, and glutathione was highlighted during cerebral maturation. In Adults, data from somatosensory and motor cortices allowed discrimination between GAERS and NEC rats with higher levels of scyllo-inositol, taurine, and phosphoethanolamine in NEC. This epileptic metabolic phenotype was in accordance with current pathophysiological hypothesis of absence epilepsy (i.e., seizure-generating and control networks) and putative resistance of NEC rats and was observed before seizure onset. This methodology could be very efficient in a clinical context.

Steroidomic Footprinting Based on Ultra-High Performance Liquid Chromatography Coupled with Qualitative and Quantitative High-Resolution Mass Spectrometry for the Evaluation of Endocrine Disrupting Chemicals in H295R Cells.

The screening of endocrine disrupting chemicals (EDCs) that may alter steroidogenesis represents a highly important field mainly due to the numerous pathologies, such as cancer, diabetes, obesity, osteoporosis, and infertility that have been related to impaired steroid-mediated regulation. The adrenal H295R cell model has been validated to study steroidogenesis by the Organization for Economic Co-operation and Development (OECD) guideline. However, this guideline focuses solely on testosterone and estradiol monitoring, hormones not typically produced by the adrenals, hence limiting possible in-depth mechanistic investigations. The present work proposes an untargeted steroidomic footprinting workflow based on ultra-high pressure liquid chromatography (UHPLC) coupled to high-resolution MS for the screening and mechanistic investigations of EDCs in H295R cell supernatants. A suspected EDC, triclocarban (TCC), used in detergents, cosmetics, and personal care products, was selected to demonstrate the efficiency of the reported methodology, allowing the simultaneous assessment of a steroidomic footprint and quantification of a selected subset of steroids in a single analysis. The effects of exposure to increasing TCC concentrations were assessed, and the selection of features with database matching followed by multivariate analysis has led to the selection of the most salient affected steroids. Using correlation analysis, 11 steroids were associated with a high, 18 with a medium, and 8 with a relatively low sensitivity behavior to TCC. Among the candidates, 13 identified steroids were simultaneously quantified, leading to the evaluation and localization of the disruption of steroidogenesis caused by TCC upstream of the formation of pregnenolone. The remaining candidates could be associated with a specific steroid class (progestogens and corticosteroids, or androgens) and represent a specific footprint of steroidogenesis disruption by TCC. This strategy was devised to be compatible with medium/high-throughput screening and could be useful for the mechanistic elucidation of EDCs.

Quantitative monitoring of tamoxifen in human plasma extended to 40 metabolites using liquid-chromatography high-resolution mass spectrometry: new investigation capabilities for clinical pharmacology.

Liquid-chromatography (LC) high-resolution (HR) mass spectrometry (MS) analysis can record HR full scans, a technique of detection that shows comparable selectivity and sensitivity to ion transitions (SRM) performed with triple-quadrupole (TQ)-MS but that allows de facto determination of "all" ions including drug metabolites. This could be of potential utility in in vivo drug metabolism and pharmacovigilance studies in order to have a more comprehensive insight in drug biotransformation profile differences in patients. This simultaneous quantitative and qualitative (Quan/Qual) approach has been tested with 20 patients chronically treated with tamoxifen (TAM). The absolute quantification of TAM and three metabolites in plasma was realized using HR- and TQ-MS and compared. The same LC-HR-MS analysis allowed the identification and relative quantification of 37 additional TAM metabolites. A number of new metabolites were detected in patients' plasma including metabolites identified as didemethyl-trihydroxy-TAM-glucoside and didemethyl-tetrahydroxy-TAM-glucoside conjugates corresponding to TAM with six and seven biotransformation steps, respectively. Multivariate analysis allowed relevant patterns of metabolites and ratios to be associated with TAM administration and CYP2D6 genotype. Two hydroxylated metabolites, α-OH-TAM and 4'-OH-TAM, were newly identified as putative CYP2D6 substrates. The relative quantification was precise (<20 %), and the semiquantitative estimation suggests that metabolite levels are non-negligible. Metabolites could play an important role in drug toxicity, but their impact on drug-related side effects has been partially neglected due to the tremendous effort needed with previous MS technologies. Using present HR-MS, this situation should evolve with the straightforward determination of drug metabolites, enlarging the possibilities in studying inter- and intra-patients drug metabolism variability and related effects.

Comprehensive plasma steroidomics reveals subtle alterations of systemic steroid profile in patients at different stages of prostate cancer disease.

The steroid submetabolome, or steroidome, is of particular interest in prostate cancer (PCa) as the dependence of PCa growth on androgens is well known and has been routinely exploited in treatment for decades. Nevertheless, the community is still far from a comprehensive understanding of steroid involvement in PCa both at the tissue and at systemic level. In this study we used liquid chromatography/high resolution mass spectrometry (LC/HRMS) backed by a dynamic retention time database DynaSTI to obtain a readout on circulating steroids in a cohort reflecting a progression of the PCa. Hence, 60 relevant compounds were annotated in the resulting LC/HRMS data, including 22 unknown steroid isomers therein. Principal component analysis revealed only subtle alterations of the systemic steroidome in the study groups. Next, a supervised approach allowed for a differentiation between the healthy state and any of the stages of the disease. Subsequent clustering of steroid metabolites revealed two groups responsible for this outcome: one consisted primarily of the androgens, whereas another contained corticosterone and its metabolites. The androgen data supported the currently established involvement of a hypothalamic-pituitary-gonadal axis in the development of PCa, whereas biological role of corticosterone remained elusive. On top of that, current results suggested a need for improvement in the dynamic range of the analytical methods to better understand the role of low abundant steroids, as the analysis revealed an involvement of estrogen metabolites. In particular, 2-hydroxyestradiol-3-methylether, one of the compounds present in the disease phenotype, was annotated and reported for the first time in men.

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

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

From fundamentals in calibration to modern methodologies: A tutorial for small molecules quantification in liquid chromatography-mass spectrometry bioanalysis.

Over the last two decades, liquid chromatography coupled to mass-spectrometry (LC‒MS) has become the gold standard to perform qualitative and quantitative analyses of small molecules. When quantitative analysis is developed, an analyst usually refers to international guidelines for analytical method validation. In this context, the design of calibration curves plays a key role in providing accurate results. During recent years and along with instrumental advances, strategies to build calibration curves have dramatically evolved, introducing innovative approaches to improve quantitative precision and throughput. For example, when a labeled standard is available to be spiked directly into the study sample, the concentration of the unlabeled analog can be easily determined using the isotopic pattern deconvolution or the internal calibration approach, eliminating the need for multipoint calibration curves. This tutorial aims to synthetize the advances in LC‒MS quantitative analysis for small molecules in complex matrices, going from fundamental aspects in calibration to modern methodologies and applications. Different work schemes for calibration depending on the sample characteristics (analyte and matrix nature) are distinguished and discussed. Finally, this tutorial outlines the importance of having international guidelines for analytical method validation that agree with the advances in calibration strategies and analytical instrumentation.

A unified strategy to rebalance multifactorial designs with unequal group sizes: application to analysis of variance multiblock orthogonal partial least squares.

BACKGROUND: Adequately handling unbalanced groups remains one of the major challenges for the analysis of multivariate data collected from multifactorial experimental designs. While partial least squares-based methods, such as analysis of variance multiblock orthogonal partial least squares (AMOPLS), can offer better discrimination between factor levels, they can be more heavily affected by this issue, and unbalanced designs of experiments may lead to a substantial confusion of the effects. Even state-of-the-art analysis of variance (ANOVA) decomposition methodologies using general linear models (GLM) lack the ability to efficiently disentangle these sources of variation when combined with AMOPLS. RESULTS: A versatile solution developed as an extension of a prior rebalancing strategy is proposed for the first decomposition step based on ANOVA. This approach has the advantage of yielding an unbiased estimation of the parameters and retaining the within-group variation in the rebalanced design, while preserving the orthogonality of effect matrices, even in presence of unequal group sizes. This property is of utmost importance for model interpretation because it avoids mixing sources of variation related to the different effects in the design. A real case study involving metabolomic data from in vitro toxicological experiments was used to demonstrate the potential of this strategy to handle unequal group sizes using a supervised approach. Primary 3D rat neural cell cultures were exposed to trimethyltin following a multifactorial design of experiments involving three fixed effect factors. SIGNIFICANCE AND NOVELTY: The rebalancing strategy was demonstrated as a novel and potent solution to handle unbalanced experimental designs by offering unbiased parameter estimators and orthogonal submatrices, thus avoiding confusion of the effects and facilitating model interpretation. Moreover, it can be combined with any multivariate method used for the analysis of high-dimensional data collected from multifactorial designs.