Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics

Lipidome of Acinetobacter baumannii antibiotic persister cells

Persister cells constitute a bacterial subpopulation able to survive to high concentrations of antibiotics. This phenotype is temporary and reversible, and thus could be involved in the recurrence of infections and emergence of antibiotic resistance. To better understand how persister cells survive to such high antibiotic concentration, we examined changes in their lipid composition. We thus compared the lipidome of Acinetobacter baumannii ATCC 19606T persister cells formed under ciprofloxacin treatment with the lipidome of control cells grown without antibiotic. Using matrix assisted laser desorption ionisation-Fourier transform ion cyclotron resonance mass spectrometry, we observed a higher abundance of short chains and secondary chains without hydroxylation for lipid A in persister cells. Using liquid chromatography-tandem mass spectrometry, we found that persister cells produced particular phosphatidylglycerols, as LPAGPE and PAGPE, but also lipids with particular acyl chains containing additional hydroxyl group or uncommon di-unsaturation on C18 and C16 acyl chains. In order to determine the impact of these multiple lipidome modifications on membrane fluidity, fluorescence anisotropy assays were performed. They showed an increase of rigidity for the membrane of persister cells, inducing likely a decrease membrane permeability to protect cells during dormancy. Finally, we highlighted that A. baumannii persister cells also produced particular wax esters, composed of two fatty acids and a fatty diol. These uncommon storage lipids are key metabolites allowing a rapid bacterial regrow when antibiotic pressure disappears. These overall changes in persister lipidome may constitute new therapeutic targets to combat these particular dormant cells.

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

Multicentric investigations of the role in the disease severity of accelerated phospholipid changes in COVID-19 patient airway

CONTEXT

The changes in host membrane phospholipids are crucial in airway infection pathogenesis. Phospholipase A2 hydrolyzes host cell membranes, producing lyso-phospholipids and free fatty acids, including arachidonic acid (AA), which contributes significantly to lung inflammation.

AIM

Follow these changes and their evolution from day 1, day 3 to day 7 in airway aspirates of 89 patients with COVID-19-associated acute respiratory distress syndrome and examine whether they correlate with the severity of the disease. The patients were recruited in three French intensive care units. The analysis was conducted from admission to the intensive care unit until the end of the first week of mechanical ventilation.

RESULTS

In the airway aspirates, we found significant increases in the levels of host cell phospholipids, including phosphatidyl-serine and phosphatidyl-ethanolamine, and their corresponding lyso-phospholipids. This was accompanied by increased levels of AA and its inflammatory metabolite prostaglandin E2 (PGE2). Additionally, enhanced levels of ceramides, sphingomyelin, and free cholesterol were observed in these aspirates. These lipids are known to be involved in cell death and/or apoptosis, whereas free cholesterol plays a role in virus entry and replication in host cells. However, there were no significant changes in the levels of dipalmitoyl-phosphatidylcholine, the major surfactant phospholipid. A correlation analysis revealed an association between mortality risk and levels of AA and PGE2, as well as host cell phospholipids.

CONCLUSION

Our findings indicate a correlation between heightened cellular phospholipid modifications and variations in AA and PGE2 with the severity of the disease in patients. Nevertheless, there is no indication of surfactant alteration in the initial phases of the illness.

A Support Vector Machine-Assisted Metabolomics Approach for Non-Targeted Screening of Multi-Class Pesticides and Veterinary Drugs in Maize

The contamination risks of plant-derived foods due to the co-existence of pesticides and veterinary drugs (P&VDs) have not been fully understood. With an increasing number of unexpected P&VDs illegally added to foods, it is essential to develop a non-targeted screening method for P&VDs for their comprehensive risk assessment. In this study, a modified support vector machine (SVM)-assisted metabolomics approach by screening eligible variables to represent marker compounds of 124 multi-class P&VDs in maize was developed based on the results of high-performance liquid chromatography-tandem mass spectrometry. Principal component analysis and orthogonal partial least squares discriminant analysis indicate the existence of variables with obvious inter-group differences, which were further investigated by S-plot plots, permutation tests, and variable importance in projection to obtain eligible variables. Meanwhile, SVM recursive feature elimination under the radial basis function was employed to obtain the weight-squared values of all the variables ranging from large to small for the screening of eligible variables as well. Pairwise t-tests and fold changes of concentration were further employed to confirm these eligible variables to represent marker compounds. The results indicate that 120 out of 124 P&VDs can be identified by the SVM-assisted metabolomics method, while only 109 P&VDs can be found by the metabolomics method alone, implying that SVM can promote the screening accuracy of the metabolomics method. In addition, the method's practicability was validated by the real contaminated maize samples, which provide a bright application prospect in non-targeted screening of contaminants. The limits of detection for 120 P&VDs in maize samples were calculated to be 0.3~1.5 µg/kg.

A multi-omics dataset of the response to early plant polysaccharide ingestion in rabbits

The transition from a milk-based diet to exclusive solid feeding deeply modifies microbiota-host crosstalk. Specifically, early ingestion of plant polysaccharides would be one of the main nutritional components to drive host-microbiota-interaction. To capture the effects of polysaccharides early-life nutrition (starch vs rapidly fermentable fiber) on the holobiont development, we investigated on the one hand the gut bacteriome and metabolome and on the other hand the transcriptome of two host gut tissues. Rabbit model was used to study post-natal co-development of the gut microbiota and its host around weaning transition. The assessment of the microbial composition of the gut appendix together with the caecum was provided for the first time. Gene expression signatures were analyzed along the gut (ileum and caecum) through high-throughput qPCR. The data collected were completed by the analysis of animal growth changes and time-series assessment of blood biomarkers. Those accessible and reusable data could help highlight the gut development dynamics as well as biological adaptation processes at the onset of solid feeding.

Metabolomics Analysis of Rabbit Plasma after Ocular Exposure to Vapors of Sulfur Mustard

Sulfur mustard (SM) is a highly potent alkylating vesicant agent and remains a relevant threat to both civilians and military personnel. The eyes are the most sensitive organ after airborne SM exposure, causing ocular injuries with no antidote or specific therapeutics available. In order to identify relevant biomarkers and to obtain a deeper understanding of the underlying biochemical events, we performed an untargeted metabolomics analysis using liquid chromatography coupled to high-resolution mass spectrometry of plasma samples from New Zealand white rabbits ocularly exposed to vapors of SM. Metabolic profiles (332 unique metabolites) from SM-exposed (n = 16) and unexposed rabbits (n = 8) were compared at different time intervals from 1 to 28 days. The observed time-dependent changes in metabolic profiles highlighted the profound dysregulation of the sulfur amino acids, the phenylalanine, the tyrosine and tryptophan pathway, and the polyamine and purine biosynthesis, which could reflect antioxidant and anti-inflammatory activities. Taurine and 3,4-dihydroxy-phenylalanine (Dopa) seem to be specifically related to SM exposure and correspond well with the different phases of ocular damage, while the dysregulation of adenosine, polyamines, and acylcarnitines might be related to ocular neovascularization. Additionally, neither cysteine, N-acetylcysteine, or guanine SM adducts were detected in the plasma of exposed rabbits at any time point. Overall, our study provides an unprecedented view of the plasma metabolic changes post-SM ocular exposure, which may open up the development of potential new treatment strategies.

Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline

To standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

Utilising a Clinical Metabolomics LC-MS Study to Determine the Integrity of Biological Samples for Statistical Modelling after Long Term -80 °C Storage: A TOFI_Asia Sub-Study

Biological samples of lipids and metabolites degrade after extensive years in -80 °C storage. We aimed to determine if associated multivariate models are also impacted. Prior TOFI_Asia metabolomics studies from our laboratory established multivariate models of metabolic risks associated with ethnic diversity. Therefore, to compare multivariate modelling degradation after years of -80 °C storage, we selected a subset of aged (≥5-years) plasma samples from the TOFI_Asia study to re-analyze via untargeted LC-MS metabolomics. Samples from European Caucasian (n = 28) and Asian Chinese (n = 28) participants were evaluated for ethnic discrimination by partial least squares discriminative analysis (PLS-DA) of lipids and polar metabolites. Both showed a strong discernment between participants ethnicity by features, before (Initial) and after (Aged) 5-years of -80 °C storage. With receiver operator characteristic curves, sparse PLS-DA derived confusion matrix and prediction error rates, a considerable reduction in model integrity was apparent with the Aged polar metabolite model relative to Initial modelling. Ethnicity modelling with lipids maintained predictive integrity in Aged plasma samples, while equivalent polar metabolite models reduced in integrity. Our results indicate that researchers re-evaluating samples for multivariate modelling should consider time at -80 °C when producing predictive metrics from polar metabolites, more so than lipids.

Nanoparticles for inducing Gaucher disease-like damage in cancer cells

Nutrient avidity is one of the most distinctive features of tumours. However, nutrient deprivation has yielded limited clinical benefits. In Gaucher disease, an inherited metabolic disorder, cells produce cholesteryl-glucoside which accumulates in lysosomes and causes cell damage. Here we develop a nanoparticle (AbCholB) to emulate natural-lipoprotein-carried cholesterol and initiate Gaucher disease-like damage in cancer cells. AbCholB is composed of a phenylboronic-acid-modified cholesterol (CholB) and albumin. Cancer cells uptake the nanoparticles into lysosomes, where CholB reacts with glucose and generates a cholesteryl-glucoside-like structure that resists degradation and aggregates into microscale crystals, causing Gaucher disease-like damage in a glucose-dependent manner. In addition, the nutrient-sensing function of mTOR is suppressed. It is observed that normal cells escape severe damage due to their inferior ability to compete for nutrients compared with cancer cells. This work provides a bioinspired strategy to selectively impede the metabolic action of cancer cells by taking advantage of their nutrient avidity.

DIMet: an open-source tool for differential analysis of targeted isotope-labeled metabolomics data

MOTIVATION

Many diseases, such as cancer, are characterized by an alteration of cellular metabolism allowing cells to adapt to changes in the microenvironment. Stable isotope-resolved metabolomics (SIRM) and downstream data analyses are widely used techniques for unraveling cells' metabolic activity to understand the altered functioning of metabolic pathways in the diseased state. While a number of bioinformatic solutions exist for the differential analysis of SIRM data, there is currently no available resource providing a comprehensive toolbox.

RESULTS

In this work, we present DIMet, a one-stop comprehensive tool for differential analysis of targeted tracer data. DIMet accepts metabolite total abundances, isotopologue contributions, and isotopic mean enrichment, and supports differential comparison (pairwise and multi-group), time-series analyses, and labeling profile comparison. Moreover, it integrates transcriptomics and targeted metabolomics data through network-based metabolograms. We illustrate the use of DIMet in real SIRM datasets obtained from Glioblastoma P3 cell-line samples. DIMet is open-source, and is readily available for routine downstream analysis of isotope-labeled targeted metabolomics data, as it can be used both in the command line interface or as a complete toolkit in the public Galaxy Europe and Workfow4Metabolomics web platforms.

AVAILABILITY AND IMPLEMENTATION

DIMet is freely available at https://github.com/cbib/DIMet, and through https://usegalaxy.eu and https://workflow4metabolomics.usegalaxy.fr. All the datasets are available at Zenodo https://zenodo.org/records/10925786.

Comparison of Three Widely Employed Extraction Methods for Metabolomic Analysis of Trypanosoma brucei

Trypanosoma brucei (Tb) is the causative agent of human African trypanosomiasis (HAT), also known as sleeping sickness, which can be fatal if left untreated. An understanding of the parasite's cellular metabolism is vital for the discovery of new antitrypanosomal drugs and for disease eradication. Metabolomics can be used to analyze numerous metabolic pathways described as essential to Tb. brucei but has some limitations linked to the metabolites' physicochemical properties and the extraction process. To develop an optimized method for extracting and analyzing Tb. brucei metabolites, we tested the three most commonly used extraction methods, analyzed the extracts by hydrophilic interaction liquid chromatography high-resolution mass spectrometry (HILIC LC-HRMS), and further evaluated the results using quantitative criteria including the number, intensity, reproducibility, and variability of features, as well as qualitative criteria such as the specific coverage of relevant metabolites. Here, we present the resulting protocols for untargeted metabolomic analysis of Tb. brucei using (HILIC LC-HRMS). © 2024 Wiley Periodicals LLC. Basic Protocol 1: Culture of Trypanosoma brucei brucei parasites Basic Protocol 2: Preparation of samples for metabolomic analysis of Trypanosoma brucei brucei Basic Protocol 3: LC-HRMS-based metabolomic data analysis of Trypanosoma brucei brucei.

Metabolomic changes in adults with status epilepticus: A human case-control study

OBJECTIVE

Status epilepticus (SE) is a life-threatening prolonged epileptic seizure that affects ~40 per 100 000 people yearly worldwide. The persistence of seizures may lead to excitotoxic processes, neuronal loss, and neuroinflammation, resulting in long-term neurocognitive and functional disabilities. A better understanding of the pathophysiological mechanisms underlying SE consequences is crucial for improving SE management and preventing secondary neuronal injury.

METHODS

We conducted a comprehensive untargeted metabolomic analysis, using liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS), on plasma and cerebrospinal fluid (CSF) samples from 78 adult patients with SE and 107 control patients without SE, including 29 with CSF for both groups. The metabolomic fingerprints were compared between patients with SE and controls. Metabolites with differences in relative abundances that could not be attributed to treatment or nutrition provided in the intensive care unit were isolated. Enrichment analysis was performed on these metabolites to identify the most affected pathways.

RESULTS

We identified 76 metabolites in the plasma and 37 in the CSF that exhibited differential expression in patients with SE compared to controls. The enrichment analysis revealed that metabolic dysregulations in patients with SE affected primarily amino acid metabolism (including glutamate, alanine, tryptophan, glycine, and serine metabolism), pyrimidine metabolism, and lipid homeostasis. Specifically, patients with SE had elevated levels of pyruvate, quinolinic acid, and keto butyric acid levels, along with lower levels of arginine, N-acetylaspartylglutamate (NAAG), tryptophan, uracil, and uridine. The tryptophan kynurenine pathway was identified as the most significantly altered in SE, resulting in the overproduction of quinolinic acid, an N-methyl-d-aspartate (NMDA) receptor agonist with pro-inflammatory properties.

SIGNIFICANCE

This study has identified several pathways that may play pivotal roles in SE consequences, such as the tryptophan kynurenine pathway. These findings offer novel perspectives for the development of neuroprotective therapeutics.

BCAA metabolism in pancreatic cancer affects lipid balance by regulating fatty acid import into mitochondria

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has been associated with the host dysmetabolism of branched-chain amino acids (BCAAs), however, the implications for the role of BCAA metabolism in PDAC development or progression are not clear. The mitochondrial catabolism of valine, leucine, and isoleucine is a multistep process leading to the production of short-chain R-CoA species. They can be subsequently exported from mitochondria as short-chain carnitines (SC-CARs), utilized in anabolic pathways, or released from the cells.

METHODS

We examined the specificities of BCAA catabolism and cellular adaptation strategies to BCAA starvation in PDAC cells in vitro. We used metabolomics and lipidomics to quantify major metabolic changes in response to BCAA withdrawal. Using confocal microscopy and flow cytometry we quantified the fluorescence of BODIPY probe and the level of lipid droplets (LDs). We used BODIPY-conjugated palmitate to evaluate transport of fatty acids (FAs) into mitochondria. Also, we have developed a protocol for quantification of SC-CARs, BCAA-derived metabolites.

RESULTS

Using metabolic profiling, we found that BCAA starvation leads to massive triglyceride (TG) synthesis and LD accumulation. This was associated with the suppression of activated FA transport into the mitochondrial matrix. The suppression of FA import into mitochondria was rescued with the inhibitor of the acetyl-CoA carboxylase (ACC) and the activator of AMP kinase (AMPK), which both regulate carnitine palmitoyltransferase 1A (CPT1) activation status.

CONCLUSIONS

Our data suggest that BCAA catabolism is required for the import of long chain carnitines (LC-CARs) into mitochondria, whereas the disruption of this link results in the redirection of activated FAs into TG synthesis and its deposition into LDs. We propose that this mechanism protects cells against mitochondrial overload with LC-CARs and it might be part of the universal reaction to amino acid perturbations during cancer growth, regulating FA handling and storage.

Perturbation and resilience of the gut microbiome up to 3 months after β-lactams exposure in healthy volunteers suggest an important role of microbial β-lactamases

BACKGROUND

Antibiotics notoriously perturb the gut microbiota. We treated healthy volunteers either with cefotaxime or ceftriaxone for 3 days, and collected in each subject 12 faecal samples up to day 90. Using untargeted and targeted phenotypic and genotypic approaches, we studied the changes in the bacterial, phage and fungal components of the microbiota as well as the metabolome and the β-lactamase activity of the stools. This allowed assessing their degrees of perturbation and resilience.

RESULTS

While only two subjects had detectable concentrations of antibiotics in their faeces, suggesting important antibiotic degradation in the gut, the intravenous treatment perturbed very significantly the bacterial and phage microbiota, as well as the composition of the metabolome. In contrast, treatment impact was relatively low on the fungal microbiota. At the end of the surveillance period, we found evidence of resilience across the gut system since most components returned to a state like the initial one, even if the structure of the bacterial microbiota changed and the dynamics of the different components over time were rarely correlated. The observed richness of the antibiotic resistance genes repertoire was significantly reduced up to day 30, while a significant increase in the relative abundance of β-lactamase encoding genes was observed up to day 10, consistent with a concomitant increase in the β-lactamase activity of the microbiota. The level of β-lactamase activity at baseline was positively associated with the resilience of the metabolome content of the stools.

CONCLUSIONS

In healthy adults, antibiotics perturb many components of the microbiota, which return close to the baseline state within 30 days. These data suggest an important role of endogenous β-lactamase-producing anaerobes in protecting the functions of the microbiota by de-activating the antibiotics reaching the colon. Video Abstract.

Multi-omics approach to understand the impact of sun exposure on an in vitro skin ecosystem and evaluate a new broad-spectrum sunscreen

A reconstructed human epidermal model (RHE) colonized with human microbiota and sebum was developed to reproduce the complexity of the skin ecosystem in vitro. The RHE model was exposed to simulated solar radiation (SSR) with or without SPF50+ sunscreen (with UVB, UVA, long-UVA, and visible light protection). Structural identification of discriminant metabolites was acquired by nuclear magnetic resonance and metabolomic fingerprints were identified using reverse phase-ultra high-performance liquid chromatography-high resolution mass spectrometry, followed by pathway enrichment analysis. Over 50 metabolites were significantly altered by SSR (p < 0.05, log2 values), showing high skin oxidative stress (glutathione and purine pathways, urea cycle) and altered skin microbiota (branched-chain amino acid cycle and tryptophan pathway). 16S and internal transcribed spacer rRNA sequencing showed the relative abundance of various bacteria and fungi altered by SSR. This study identified highly accurate metabolomic fingerprints and metagenomic modifications of sun-exposed skin to help elucidate the interactions between the skin and its microbiota. Application of SPF50+ sunscreen protected the skin ecosystem model from the deleterious effects of SSR and preserved the physiological interactions within the skin ecosystem. These innovative technologies could thus be used to evaluate the effectiveness of sunscreen.

From sound check to encore: A journey through high-resolution mass spectrometry-based food analyses and metabolomics

This manuscript presents a comprehensive review of high-resolution mass spectrometry in the field of food analysis and metabolomics. We have followed the historical evolution of metabolomics, its associated techniques and technologies, and its increasing role in food science and research. The review provides a critical comparison and synthesis of tentative identification guidelines proposed for over 15 years, offering a condensed resource for researchers in the field. We have also examined a wide range of recent metabolomics studies, showcasing various methodologies and highlighting key findings as a testimony of the versatility of the field and the possibilities it offers. In doing so, we have also carefully provided a compilation of the software tools that may be employed in this type of studies. The manuscript also explores the prospects of high-resolution mass spectrometry and metabolomics in food science. By covering the history, guidelines, applications, and tools of metabolomics, this review attempts to become a comprehensive guide for researchers in a rapidly evolving field.

First comprehensive untargeted metabolomics study of suramin-treated Trypanosoma brucei: an integrated data analysis workflow from multifactor data modelling to functional analysis

INTRODUCTION

Human African trypanosomiasis, commonly known as sleeping sickness, is a vector-borne parasitic disease prevalent in sub-Saharan Africa and transmitted by the tsetse fly. Suramin, a medication with a long history of clinical use, has demonstrated varied modes of action against Trypanosoma brucei. This study employs a comprehensive workflow to investigate the metabolic effects of suramin on T. brucei, utilizing a multimodal metabolomics approach.

OBJECTIVES

The primary aim of this study is to comprehensively analyze the metabolic impact of suramin on T. brucei using a combined liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance spectroscopy (NMR) approach. Statistical analyses, encompassing multivariate analysis and pathway enrichment analysis, are applied to elucidate significant variations and metabolic changes resulting from suramin treatment.

METHODS

A detailed methodology involving the integration of high-resolution data from LC-MS and NMR techniques is presented. The study conducts a thorough analysis of metabolite profiles in both suramin-treated and control T. brucei brucei samples. Statistical techniques, including ANOVA-simultaneous component analysis (ASCA), principal component analysis (PCA), ANOVA 2 analysis, and bootstrap tests, are employed to discern the effects of suramin treatment on the metabolomics outcomes.

RESULTS

Our investigation reveals substantial differences in metabolic profiles between the control and suramin-treated groups. ASCA and PCA analysis confirm distinct separation between these groups in both MS-negative and NMR analyses. Furthermore, ANOVA 2 analysis and bootstrap tests confirmed the significance of treatment, time, and interaction effects on the metabolomics outcomes. Functional analysis of the data from LC-MS highlighted the impact of treatment on amino-acid, and amino-sugar and nucleotide-sugar metabolism, while time effects were observed on carbon intermediary metabolism (notably glycolysis and di- and tricarboxylic acids of the succinate production pathway and tricarboxylic acid (TCA) cycle).

CONCLUSION

Through the integration of LC-MS and NMR techniques coupled with advanced statistical analyses, this study identifies distinctive metabolic signatures and pathways associated with suramin treatment in T. brucei. These findings contribute to a deeper understanding of the pharmacological impact of suramin and have the potential to inform the development of more efficacious therapeutic strategies against African trypanosomiasis.

Parkinson's disease-derived α-synuclein assemblies combined with chronic-type inflammatory cues promote a neurotoxic microglial phenotype

Parkinson's disease (PD) is a common age-related neurodegenerative disorder characterized by the aggregation of α-Synuclein (αSYN) building up intraneuronal inclusions termed Lewy pathology. Mounting evidence suggests that neuron-released αSYN aggregates could be central to microglial activation, which in turn mounts and orchestrates neuroinflammatory processes potentially harmful to neurons. Therefore, understanding the mechanisms that drive microglial cell activation, polarization and function in PD might have important therapeutic implications. Here, using primary microglia, we investigated the inflammatory potential of pure αSYN fibrils derived from PD patients. We further explored and characterized microglial cell responses to a chronic-type inflammatory stimulation combining PD patient-derived αSYN fibrils (FPD), Tumor necrosis factor-α (TNFα) and prostaglandin E2 (PGE2) (TPFPD). We showed that FPD hold stronger inflammatory potency than pure αSYN fibrils generated de novo. When combined with TNFα and PGE2, FPD polarizes microglia toward a particular functional phenotype departing from FPD-treated cells and featuring lower inflammatory cytokine and higher glutamate release. Whereas metabolomic studies showed that TPFPD-exposed microglia were closely related to classically activated M1 proinflammatory cells, notably with similar tricarboxylic acid cycle disruption, transcriptomic analysis revealed that TPFPD-activated microglia assume a unique molecular signature highlighting upregulation of genes involved in glutathione and iron metabolisms. In particular, TPFPD-specific upregulation of Slc7a11 (which encodes the cystine-glutamate antiporter xCT) was consistent with the increased glutamate response and cytotoxic activity of these cells toward midbrain dopaminergic neurons in vitro. Together, these data further extend the structure-pathological relationship of αSYN fibrillar polymorphs to their innate immune properties and demonstrate that PD-derived αSYN fibrils, TNFα and PGE2 act in concert to drive microglial cell activation toward a specific and highly neurotoxic chronic-type inflammatory phenotype characterized by robust glutamate release and iron retention.

LC-HRMS-Driven Computational Toolbox to Assess Extraction Protocols Dedicated to Untargeted Analysis: How to Ease Analyzing Pesticide-Contaminated Soils?

Metabolomics is a powerful approach that allows for high throughput analysis and the acquisition of large biochemical data. Nonetheless, it still faces several challenging requirements, such as the development of optimal extraction and analytical methods able to respond to its high qualitative and quantitative requisites. Hence, the objective of the present article is to suggest a LC-HRMS-based untargeted profiling approach aiming to provide performant tools that help assess the performance and the quality of extraction methods. It is applied in a herbicide-contaminated soil metabolomics context. The trifactorial experimental design consists of 150 samples issued from five different extraction protocols, two types of soils, and three contamination conditions (contaminated soils with two different formulated herbicides against uncontaminated soils). Four performance and quality criteria are investigated using adapted LC-HRMS-driven computational tools. First, 861 metabolic features are annotated, and then the width of metabolome coverage and quantitative performance of the five different extraction protocols are assessed in all samples using various optimized configurations of heatmaps as well as van Krevelen diagrams. Then, the reproducibility of LC-HRMS profiles issued from the five extractions is studied by two different approaches: Euclidean distances and relative standard deviations. The two methods are examined and compared. Their advantages and limitations are thus discussed. After, the capacity of the different extractions to discriminate between contaminated and uncontaminated soils will be evaluated using orthogonal projections to latent structures-discriminant analysis. Different data scaling parameters are tested, and the results are explored and discussed. All of the suggested computational and visualization tools are performed using public-access platforms or open-source software. They can be readapted by metabolomics developers and users according to their study contexts and fields of application.

MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics

Multiclass metabolomics has become a popular technique for revealing the mechanisms underlying certain physiological processes, different tumor types, or different therapeutic responses. In multiclass metabolomics, it is highly important to uncover the underlying biological information on biosamples by identifying the metabolic markers with the most associations and classifying the different sample classes. The classification problem of multiclass metabolomics is more difficult than that of the binary problem. To date, various methods exist for constructing classification models and identifying metabolic markers consisting of well-established techniques and newly emerging machine learning algorithms. However, how to construct a superior classification model using these methods remains unclear for a given multiclass metabolomic data set. Herein, MultiClassMetabo has been developed for constructing a superior classification model using metabolic markers identified in multiclass metabolomics. MultiClassMetabo can enable online services, including (a) identifying metabolic markers by marker identification methods, (b) constructing classification models by classification methods, and (c) performing a comprehensive assessment from multiple perspectives to construct a superior classification model for multiclass metabolomics. In summary, MultiClassMetabo is distinguished for its capability to construct a superior classification model using the most appropriate method through a comprehensive assessment, which makes it an important complement to other available tools in multiclass metabolomics. MultiClassMetabo can be accessed at http://idrblab.cn/multiclassmetabo/.

Blood Monocyte Phenotype Is A Marker of Cardiovascular Risk in Type 2 Diabetes

BACKGROUND

Diabetes is a major risk factor for atherosclerotic cardiovascular diseases with a 2-fold higher risk of cardiovascular events in people with diabetes compared with those without. Circulating monocytes are inflammatory effector cells involved in both type 2 diabetes (T2D) and atherogenesis.

METHODS

We investigated the relationship between circulating monocytes and cardiovascular risk progression in people with T2D, using phenotypic, transcriptomic, and metabolomic analyses. cardiovascular risk progression was estimated with coronary artery calcium score in a cohort of 672 people with T2D.

RESULTS

Coronary artery calcium score was positively correlated with blood monocyte count and frequency of the classical monocyte subtype. Unsupervised k-means clustering based on monocyte subtype profiles revealed 3 main endotypes of people with T2D at varying risk of cardiovascular events. These observations were confirmed in a validation cohort of 279 T2D participants. The predictive association between monocyte count and major adverse cardiovascular events was validated through an independent prospective cohort of 757 patients with T2D. Integration of monocyte transcriptome analyses and plasma metabolomes showed a disruption of mitochondrial pathways (tricarboxylic acid cycle, oxidative phosphorylation pathway) that underlined a proatherogenic phenotype.

CONCLUSIONS

In this study, we provide evidence that frequency and monocyte phenotypic profile are closely linked to cardiovascular risk in patients with T2D. The assessment of monocyte frequency and count is a valuable predictive marker for risk of cardiovascular events in patients with T2D.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT04353869.

Non-targeted analysis of lipidic extracts by high-resolution mass spectrometry to characterise the chemical exposome: Comparison of four clean-up strategies applied to egg

Biota samples are used to monitor chemical stressors and their impact on the ecosystem and to describe dietary chemical exposure. These complex matrices require an extraction step followed by clean-up to avoid damaging sensitive analytical instruments based on chromatography coupled to mass spectrometry. While interest for non-targeted analysis (NTA) is increasing, there is no versatile or generic sample preparation for a wide range of contaminants suitable for a diversity of biotic matrices. Among the contaminants' variety, persistent contaminants are mostly hydrophobic (mid- to non-polar) and bio-magnify through the lipidic fraction. During their extraction, lipids are generally co-extracted, which may cause matrix effect during the analysis such as hindering the acquired signal. The aim of this study was to evaluate the efficacy of four clean-up methods to selectively remove lipids from extracts prior to NTA. We evaluated (i) gel permeation chromatography (GPC), (ii) Captiva EMR-lipid cartridge (EMR), (iii) sulphuric acid degradation (H2SO4) and (iv) polydimethyl siloxane (PDMS) for their efficiency to remove lipids from hen egg extracts. Gas and liquid chromatography coupled with high-resolution mass spectrometry fitted with either electron ionisation or electrospray ionisation sources operating in positive and negative modes were used to determine the performances of the clean-up methods. A set of 102 chemicals with a wide range of physico-chemical properties that covers the chemical space of mid- to non-polar contaminants, was used to assess and compare recoveries and matrix effects. Matrix effects, that could hinder the mass spectrometer signal, were lower for extracts cleaned-up with H2SO4 than for the ones cleaned-up with PDMS, EMR and GPC. The recoveries were satisfactory for both GPC and EMR while those determined for PDMS and H2SO4 were low due to poor partitioning and degradation/dissociation of the compounds, respectively. The choice of the clean-up methods, among those assessed, should be a compromise that takes into account the matrix under consideration, the levels and the physico-chemical properties of the contaminants.

Untargeted Metabolomics Profiling Reveals Exercise Intensity-Dependent Alterations in Thoroughbred Racehorses' Plasma after Routine Conditioning Sessions

Thoroughbred (TB) racehorses undergo rigorous conditioning programs to optimize their physical and mental capabilities through varied exercise sessions. While conventional investigations focus on limited hematological and biochemical parameters, this field study employed untargeted metabolomics to comprehensively assess metabolic responses triggered by exercise sessions routinely used in TB conditioning. Blood samples were collected pre- and post-exercise from ten racehorses, divided into two groups based on exercise intensity: high intensity (n = 6, gallop at ± 13.38 m/s, 1400 m) and moderate intensity (n = 4, soft canter at ± 7.63 m/s, 2500 m). Intensity was evaluated through monitoring of the speed, heart rate, and lactatemia. Resting and 30 min post-exercise plasma samples were analyzed using ultraperformance liquid chromatography coupled with high-resolution mass spectrometry. Unsupervised principal component analysis revealed exercise-induced metabolome changes, with high-intensity exercise inducing greater alterations. Following high-intensity exercise, 54 metabolites related to amino acid, fatty acid, nucleic acid, and vitamin metabolism were altered versus 23 metabolites, primarily linked to fatty acid and amino acid metabolism, following moderate-intensity exercise. Metabolomics confirmed energy metabolism changes reported by traditional biochemistry studies and highlighted the involvement of lipid and amino acid metabolism during routine exercise and recovery, aspects that had previously been overlooked in TB racehorses.

Aldehyde perception induces specific molecular responses in Laminaria digitata and affects algal consumption by a specialist grazer

In the marine environment, distance signaling based on water-borne cues occurs during interactions between macroalgae and herbivores. In the brown alga Laminaria digitata from North-Atlantic Brittany, oligoalginates elicitation or grazing was shown to induce chemical and transcriptomic regulations, as well as emission of a wide range of volatile aldehydes, but their biological roles as potential defense or warning signals in response to herbivores remain unknown. In this context, bioassays using the limpet Patella pellucida and L. digitata were carried out for determining the effects of algal transient incubation with 4-hydroxyhexenal (4-HHE), 4-hydroxynonenal (4-HNE) and dodecadienal on algal consumption by grazers. Simultaneously, we have developed metabolomic and transcriptomic approaches to study algal molecular responses after treatments of L. digitata with these chemical compounds. The results indicated that, unlike the treatment of the plantlets with 4-HNE or dodecadienal, treatment with 4-HHE decreases algal consumption by herbivores at 100 ng.ml-1 . Moreover, we showed that algal metabolome was significantly modified according to the type of aldehydes, and more specifically the metabolite pathways linked to fatty acid degradation. RNAseq analysis further showed that 4-HHE at 100 ng.ml-1 can activate the regulation of genes related to oxylipin signaling pathways and specific responses, compared to oligoalginates elicitation. As kelp beds constitute complex ecosystems consisting of habitat and food source for marine herbivores, the algal perception of specific aldehydes leading to targeted molecular regulations could have an important biological role on kelps/grazers interactions.

A new statistical workflow (R-packages based) to investigate associations between one variable of interest and the metabolome

INTRODUCTION

In metabolomics, the investigation of associations between the metabolome and one trait of interest is a key research question. However, statistical analyses of such associations are often challenging. Statistical tools enabling resilient verification and clear presentation are therefore highly desired.

OBJECTIVES

Our aim is to provide a contribution for statistical analysis of metabolomics data, offering a widely applicable open-source statistical workflow, which considers the intrinsic complexity of metabolomics data.

METHODS

We combined selected R packages tailored for all properties of heterogeneous metabolomics datasets, where metabolite parameters typically (i) are analyzed in different matrices, (ii) are measured on different analytical platforms with different precision, (iii) are analyzed by targeted as well as non-targeted methods, (iv) are scaled variously, (v) reveal heterogeneous variances, (vi) may be correlated, (vii) may have only few values or values below a detection limit, or (viii) may be incomplete.

RESULTS

The code is shared entirely and freely available. The workflow output is a table of metabolites associated with a trait of interest and a compact plot for high-quality results visualization. The workflow output and its utility are presented by applying it to two previously published datasets: one dataset from our own lab and another dataset taken from the repository MetaboLights.

CONCLUSION

Robustness and benefits of the statistical workflow were clearly demonstrated, and everyone can directly re-use it for analysis of own data.

OpenNAU: An open-source platform for normalizing, analyzing, and visualizing cancer untargeted metabolomics data

Objective

As an important part of metabolomics analysis, untargeted metabolomics has become a powerful tool in the study of tumor mechanisms and the discovery of metabolic markers with high-throughput spectrometric data which also poses great challenges to data analysis, from the extraction of raw data to the identification of differential metabolites. To date, a large number of analytical tools and processes have been developed and constructed to serve untargeted metabolomics research. The different selection of analytical tools and parameter settings lead to varied results of untargeted metabolomics data. Our goal is to establish an easily operated platform and obtain a repeatable analysis result.

Methods

We used the R language basic environment to construct the preprocessing system of the original data and the LAMP (Linux+Apache+MySQL+PHP) architecture to build a cloud mass spectrum data analysis system.

Results

An open-source analysis software for untargeted metabolomics data (openNAU) was constructed. It includes the extraction of raw mass data and quality control for the identification of differential metabolic ion peaks. A reference metabolomics database based on public databases was also constructed.

Conclusions

A complete analysis system platform for untargeted metabolomics was established. This platform provides a complete template interface for the addition and updating of the analysis process, so we can finish complex analyses of untargeted metabolomics with simple human-computer interactions. The source code can be downloaded from https://github.com/zjuRong/openNAU.

Monophasic Variant of Salmonella Typhimurium Infection Affects the Serum Metabolome in Swine

Salmonella is the most relevant foodborne zoonotic agent found in swine, and its presence in French herds is significant. Its carriage is asymptomatic, which makes it difficult to detect during rearing, thus increasing the risk of its presence on pork meat. Studies have shown that enteric infection in animals could be associated with changes in the serum metabolome composition, through the immune response or changes in the digestive microbiota composition. We hypothesized that these changes in the serum metabolome composition could be used as markers for the detection of asymptomatic animals infected by Salmonella. Using untargeted analysis by liquid chromatography coupled with mass spectrometry, we showed that significant differences in the composition of the serum metabolome could be detected between infected or noninfected animals both 1 and 21 days after experimental infection. This serum metabolome composition significantly changed during the 21 days postinfection in the infected animal groups, suggesting an evolution of the impact of infection with time. Despite this evolution, differences in the serum metabolome composition persisted between infected and noninfected animals 21 days after the initial infection. We also showed a possible difference between high-shedding and low-shedding animals 21 days postinfection. Finally, some of the variations in the metabolome were found to be significantly associated with variations of specific members of the fecal microbiota. Thus, excreting and asymptomatic animals, but also high-shedding animals, could be identified on the basis of their serum metabolome composition.

Impaired metabolism predicts coronary artery calcification in women with systemic lupus erythematosus

BACKGROUND

Patients with systemic lupus erythematosus (SLE) exhibit a high risk for cardiovascular diseases (CVD) which is not fully explained by the classical Framingham risk factors. SLE is characterized by major metabolic alterations which can contribute to the elevated prevalence of CVD.

METHODS

A comprehensive analysis of the circulating metabolome and lipidome was conducted in a large cohort of 211 women with SLE who underwent a multi-detector computed tomography scan for quantification of coronary artery calcium (CAC), a robust predictor of coronary heart disease (CHD).

FINDINGS

Beyond traditional risk factors, including age and hypertension, disease activity and duration were independent risk factors for developing CAC in women with SLE. The presence of coronary calcium was associated with major alterations of circulating lipidome dominated by an elevated abundance of ceramides with very long chain fatty acids. Alterations in multiple metabolic pathways, including purine, arginine and proline metabolism, and microbiota-derived metabolites, were also associated with CAC in women with SLE. Logistic regression with bootstrapping of lipidomic and metabolomic variables were used to develop prognostic scores. Strikingly, combining metabolic and lipidomic variables with clinical and biological parameters markedly improved the prediction (area under the curve: 0.887, p < 0.001) of the presence of coronary calcium in women with SLE.

INTERPRETATION

The present study uncovers the contribution of disturbed metabolism to the presence of coronary artery calcium and the associated risk of CHD in SLE. Identification of novel lipid and metabolite biomarkers may help stratifying patients for reducing CVD morbidity and mortality in SLE.

FUNDING

INSERM and Sorbonne Université.

Pipecolate and Taurine are Rat Urinary Biomarkers for Lysine and Threonine Deficiencies

BACKGROUND

The consumption of poor-quality protein increases the risk of essential amino acid (EAA) deficiency, particularly for lysine and threonine. Thus, it is necessary to be able to detect easily EAA deficiency.

OBJECTIVES

The purpose of this study was to develop metabolomic approaches to identify specific biomarkers for an EAA deficiency, such as lysine and threonine.

METHODS

Three experiments were performed on growing rats. In experiment 1, rats were fed for 3 weeks with lysine (L30), or threonine (T53)-deficient gluten diets, or nondeficient gluten diet (LT100) in comparison with the control diet (milk protein, PLT). In experiments 2a and 2b, rats were fed at different concentrations of lysine (L) or threonine (T) deficiency: L/T15, L/T25, L/T40, L/T60, L/T75, P20, L/T100 and L/T170. Twenty-four-hour urine and blood samples from portal vein and vena cava were analyzed using LC-MS. Data from experiment 1 were analyzed by untargeted metabolomic and Independent Component - Discriminant Analysis (ICDA) and data from experiments 2a and 2b by targeted metabolomic and a quantitative Partial Least- Squares (PLS) regression model. Each metabolite identified as significant by PLS or ICDA was then tested by 1-way ANOVA to evaluate the diet effect. A two-phase linear regression analysis was used to determine lysine and threonine requirements.

RESULTS

ICDA and PLS found molecules that discriminated between the different diets. A common metabolite, the pipecolate, was identified in experiments 1 and 2a, confirming that it could be specific to lysine deficiency. Another metabolite, taurine, was found in experiments 1 and 2b, so probably specific to threonine deficiency. Pipecolate or taurine breakpoints obtained give a value closed to the values obtained by growth indicators.

CONCLUSIONS

Our results showed that the EAA deficiencies influenced the metabolome. Specific urinary biomarkers identified could be easily applied to detect EAA deficiency and to determine which AA is deficient.

Untargeted Metabolomics Approach Correlated Enniatin B Mycotoxin Presence in Cereals with Kashin-Beck Disease Endemic Regions of China

Kashin-Beck disease (KBD) is a multifactorial endemic disease that only occurs in specific Asian areas. Mycotoxin contamination, especially from the Fusarium spp., has been considered as one of the environmental risk factors that could provoke chondrocyte and cartilage damage. This study aimed to investigate whether new mycotoxins could be identified in KBD-endemic regions as a potential KBD risk factor. This was investigated on 292 barley samples collected in Tibet during 2009-2016 and 19 wheat samples collected in Inner Mongolia in 2006, as control, from KBD-endemic and non-endemic areas. The LC-HRMS(/MS) data, obtained by a general mycotoxin extraction technic, were interpreted by both untargeted metabolomics and molecular networks, allowing us to identify a discriminating compound, enniatin B, a mycotoxin produced by some Fusarium spp. The presence of Fusarium spp. DNA was detected in KBD-endemic area barley samples. Further studies are required to investigate the role of this mycotoxin in KBD development in vivo.

Metabolomics in archaeological science: A review of their advances and present requirements

Metabolomics, the study of metabolites (small molecules of <1500 daltons), has been posited as a potential tool to explore the past in a comparable manner to other omics, e.g., genomics or proteomics. Archaeologists have used metabolomic approaches for a decade or so, mainly applied to organic residues adhering to archaeological materials. Because of advances in sensitivity, resolution, and the increased availability of different analytical platforms, combined with the low mass/volume required for analysis, metabolomics is now becoming a more feasible choice in the archaeological sector. Additional approaches, as presented by our group, show the versatility of metabolomics as a source of knowledge about the human past when using human osteoarchaeological remains. There is tremendous potential for metabolomics within archaeology, but further efforts are required to position it as a routine technique.

Right in two: capabilities of ion mobility spectrometry for untargeted metabolomics

This mini review focuses on the opportunities provided by current and emerging separation techniques for mass spectrometry metabolomics. The purpose of separation technologies in metabolomics is primarily to reduce complexity of the heterogeneous systems studied, and to provide concentration enrichment by increasing sensitivity towards the quantification of low abundance metabolites. For this reason, a wide variety of separation systems, from column chemistries to solvent compositions and multidimensional separations, have been applied in the field. Multidimensional separations are a common method in both proteomics applications and gas chromatography mass spectrometry, allowing orthogonal separations to further reduce analytical complexity and expand peak capacity. These applications contribute to exponential increases in run times concomitant with first dimension fractionation followed by second dimension separations. Multidimensional liquid chromatography to increase peak capacity in metabolomics, when compared to the potential of running additional samples or replicates and increasing statistical confidence, mean that uptake of these methods has been minimal. In contrast, in the last 15 years there have been significant advances in the resolution and sensitivity of ion mobility spectrometry, to the point where high-resolution separation of analytes based on their collision cross section approaches chromatographic separation, with minimal loss in sensitivity. Additionally, ion mobility separations can be performed on a chromatographic timescale with little reduction in instrument duty cycle. In this review, we compare ion mobility separation to liquid chromatographic separation, highlight the history of the use of ion mobility separations in metabolomics, outline the current state-of-the-art in the field, and discuss the future outlook of the technology. "Where there is one, you're bound to divide it. Right in two", James Maynard Keenan.

Sympathetic nervous activation, mitochondrial dysfunction and outcome in acutely decompensated cirrhosis: the metabolomic prognostic models (CLIF-C MET)

BACKGROUND AND AIMS

Current prognostic scores of patients with acutely decompensated cirrhosis (AD), particularly those with acute-on-chronic liver failure (ACLF), underestimate the risk of mortality. This is probably because systemic inflammation (SI), the major driver of AD/ACLF, is not reflected in the scores. SI induces metabolic changes, which impair delivery of the necessary energy for the immune reaction. This investigation aimed to identify metabolites associated with short-term (28-day) death and to design metabolomic prognostic models.

METHODS

Two prospective multicentre large cohorts from Europe for investigating ACLF and development of ACLF, CANONIC (discovery, n=831) and PREDICT (validation, n=851), were explored by untargeted serum metabolomics to identify and validate metabolites which could allow improved prognostic modelling.

RESULTS

Three prognostic metabolites strongly associated with death were selected to build the models. 4-Hydroxy-3-methoxyphenylglycol sulfate is a norepinephrine derivative, which may be derived from the brainstem response to SI. Additionally, galacturonic acid and hexanoylcarnitine are associated with mitochondrial dysfunction. Model 1 included only these three prognostic metabolites and age. Model 2 was built around 4-hydroxy-3-methoxyphenylglycol sulfate, hexanoylcarnitine, bilirubin, international normalised ratio (INR) and age. In the discovery cohort, both models were more accurate in predicting death within 7, 14 and 28 days after admission compared with MELDNa score (C-index: 0.9267, 0.9002 and 0.8424, and 0.9369, 0.9206 and 0.8529, with model 1 and model 2, respectively). Similar results were found in the validation cohort (C-index: 0.940, 0.834 and 0.791, and 0.947, 0.857 and 0.810, with model 1 and model 2, respectively). Also, in ACLF, model 1 and model 2 outperformed MELDNa 7, 14 and 28 days after admission for prediction of mortality.

CONCLUSIONS

Models including metabolites (CLIF-C MET) reflecting SI, mitochondrial dysfunction and sympathetic system activation are better predictors of short-term mortality than scores based only on organ dysfunction (eg, MELDNa), especially in patients with ACLF.

Untargeted Multiomics Approach Coupling Lipidomics and Metabolomics Profiling Reveals New Insights in Diabetic Retinopathy

Diabetic retinopathy (DR) is a microvascular complication of diabetes mellitus (DM) which is the main cause of vision loss in the working-age population. Currently known risk factors such as age, disease duration, and hemoglobin A1c lack sufficient efficiency to distinguish patients with early stages of DR. A total of 194 plasma samples were collected from patients with type 2 DM and DR (moderate to proliferative (PDR) or control (no or mild DR) matched for age, gender, diabetes duration, HbA1c, and hypertension. Untargeted lipidomic and metabolomic approaches were performed. Partial-least square methods were used to analyze the datasets. Levels of 69 metabolites and 85 lipid species were found to be significantly different in the plasma of DR patients versus controls. Metabolite set enrichment analysis indicated that pathways such as metabolism of branched-chain amino acids (methylglutaryl carnitine p = 0.004), the kynurenine pathway (tryptophan p < 0.001), and microbiota metabolism (p-Cresol sulfate p = 0.004) were among the most enriched deregulated pathways in the DR group. Moreover, Glucose-6-phosphate (p = 0.001) and N-methyl-glutamate (p < 0.001) were upregulated in DR. Subgroup analyses identified a specific signature associated with PDR, macular oedema, and DR associated with chronic kidney disease. Phosphatidylcholines (PCs) were dysregulated, with an increase of alkyl-PCs (PC O-42:5 p < 0.001) in DR, while non-ether PCs (PC 14:0-16:1, p < 0.001; PC 18:2-14:0, p < 0.001) were decreased in the DR group. Through an unbiased multiomics approach, we identified metabolites and lipid species that interestingly discriminate patients with or without DR. These features could be a research basis to identify new potential plasma biomarkers to promote 3P medicine.

Multi-omics quantitative data of tomato fruit unveils regulation modes of least variable metabolites

BACKGROUND

The composition of ripe fruits depends on various metabolites which content evolves greatly throughout fruit development and may be influenced by the environment. The corresponding metabolism regulations have been widely described in tomato during fruit growth and ripening. However, the regulation of other metabolites that do not show large changes in content have scarcely been studied.

RESULTS

We analysed the metabolites of tomato fruits collected on different trusses during fruit development, using complementary analytical strategies. We identified the 22 least variable metabolites, based on their coefficients of variation. We first verified that they had a limited functional link with the least variable proteins and transcripts. We then posited that metabolite contents could be stabilized through complex regulations and combined their data with the quantitative proteome or transcriptome data, using sparse partial-least-square analyses. This showed shared regulations between several metabolites, which interestingly remained linked to early fruit development. We also examined regulations in specific metabolites using correlations with individual proteins and transcripts, which revealed that a stable metabolite does not always correlate with proteins and transcripts of its known related pathways.

CONCLUSIONS

The regulation of the least variable metabolites was then interpreted regarding their roles as hubs in metabolic pathways or as signalling molecules.

Symbiont Identity Impacts the Microbiome and Volatilome of a Model Cnidarian-Dinoflagellate Symbiosis

The symbiosis between cnidarians and dinoflagellates underpins the success of reef-building corals in otherwise nutrient-poor habitats. Alterations to symbiotic state can perturb metabolic homeostasis and thus alter the release of biogenic volatile organic compounds (BVOCs). While BVOCs can play important roles in metabolic regulation and signalling, how the symbiotic state affects BVOC output remains unexplored. We therefore characterised the suite of BVOCs that comprise the volatilome of the sea anemone Exaiptasia diaphana ('Aiptasia') when aposymbiotic and in symbiosis with either its native dinoflagellate symbiont Breviolum minutum or the non-native symbiont Durusdinium trenchii. In parallel, the bacterial community structure in these different symbiotic states was fully characterised to resolve the holobiont microbiome. Based on rRNA analyses, 147 unique amplicon sequence variants (ASVs) were observed across symbiotic states. Furthermore, the microbiomes were distinct across the different symbiotic states: bacteria in the family Vibrionaceae were the most abundant in aposymbiotic anemones; those in the family Crocinitomicaceae were the most abundant in anemones symbiotic with D. trenchii; and anemones symbiotic with B. minutum had the highest proportion of low-abundance ASVs. Across these different holobionts, 142 BVOCs were detected and classified into 17 groups based on their chemical structure, with BVOCs containing multiple functional groups being the most abundant. Isoprene was detected in higher abundance when anemones hosted their native symbiont, and dimethyl sulphide was detected in higher abundance in the volatilome of both Aiptasia-Symbiodiniaceae combinations relative to aposymbiotic anemones. The volatilomes of aposymbiotic anemones and anemones symbiotic with B. minutum were distinct, while the volatilome of anemones symbiotic with D. trenchii overlapped both of the others. Collectively, our results are consistent with previous reports that D. trenchii produces a metabolically sub-optimal symbiosis with Aiptasia, and add to our understanding of how symbiotic cnidarians, including corals, may respond to climate change should they acquire novel dinoflagellate partners.

C/EBPα Confers Dependence to Fatty Acid Anabolic Pathways and Vulnerability to Lipid Oxidative Stress-Induced Ferroptosis in FLT3-Mutant Leukemia

Although transcription factor CCAAT-enhancer binding protein α (C/EBPα) is critical for normal and leukemic differentiation, its role in cell and metabolic homeostasis is largely unknown in cancer. Here, multiomics analyses uncovered a coordinated activation of C/EBPα and Fms-like tyrosine kinase 3 (FLT3) that increased lipid anabolism in vivo and in patients with FLT3-mutant acute myeloid leukemia (AML). Mechanistically, C/EBPα regulated the fatty acid synthase (FASN)-stearoyl-CoA desaturase (SCD) axis to promote fatty acid (FA) biosynthesis and desaturation. We further demonstrated that FLT3 or C/EBPα inactivation decreased monounsaturated FA incorporation to membrane phospholipids through SCD downregulation. Consequently, SCD inhibition enhanced susceptibility to lipid redox stress that was exploited by combining FLT3 and glutathione peroxidase 4 inhibition to trigger lipid oxidative stress, enhancing ferroptotic death of FLT3-mutant AML cells. Altogether, our study reveals a C/EBPα function in lipid homeostasis and adaptation to redox stress, and a previously unreported vulnerability of FLT3-mutant AML to ferroptosis with promising therapeutic application.

SIGNIFICANCE

FLT3 mutations are found in 30% of AML cases and are actionable by tyrosine kinase inhibitors. Here, we discovered that C/EBPα regulates FA biosynthesis and protection from lipid redox stress downstream mutant-FLT3 signaling, which confers a vulnerability to ferroptosis upon FLT3 inhibition with therapeutic potential in AML. This article is highlighted in the In This Issue feature, p. 1501.

Metabolomic profiling of cardiac allografts after controlled circulatory death

BACKGROUND

Assessment of myocardial viability during ex situ heart perfusion (ESHP) is based on the measurement of lactate concentrations. As this provides with limited information, we sought to investigate the metabolic signature associated with donation after circulatory death (DCD) and the impact of ESHP on the myocardial metabolome.

METHODS

Porcine hearts were retrieved either after warm ischemia (DCD group, N = 6); after brain-stem death (BSD group, N = 6); or without DCD nor BSD (Control group, N = 6). Hearts were perfused using normothermic oxygenated blood for 240 minutes. Plasma and myocardial samples were collected respectively every 30 and 60 minutes, and analyzed by an untargeted metabolomic approach using liquid chromatography coupled to high-resolution mass spectrometry.

RESULTS

Median duration of warm ischemia was 23 minutes [19-29] in DCD animals. Lactate level within myocardial biopsies was not significantly different between groups at T0 (p = 0.281), and remained stable over the 4-hour period of ESHP. More than 300 metabolites were detected in plasma and heart biopsy samples. Compared to BSD animals, metabolomics changes involving energy and nucleotide metabolisms were observed in plasma samples of DCD animals before initiation of ESHP, whereas 2 metabolites (inosine monophosphate and methylbutyrate) exhibited concentration changes in biopsy samples. Normalization of DCD metabolic profile was remarkable after 4 hours of ESHP.

CONCLUSION

A specific metabolic profile was observed in DCD hearts, mainly characterized by an increased nucleotide catabolism. DCD and BSD metabolomes proved normalized during ESHP. Complementary investigations are needed to correlate these findings to cardiac performances.

Antibacterial Activities and Life Cycle Stages of Asparagopsis armata: Implications of the Metabolome and Microbiome

The red alga Asparagopsis armata is a species with a haplodiplophasic life cycle alternating between morphologically distinct stages. The species is known for its various biological activities linked to the production of halogenated compounds, which are described as having several roles for the algae such as the control of epiphytic bacterial communities. Several studies have reported differences in targeted halogenated compounds (using gas chromatography-mass spectrometry analysis (GC-MS)) and antibacterial activities between the tetrasporophyte and the gametophyte stages. To enlarge this picture, we analysed the metabolome (using liquid chromatography-mass spectrometry (LC-MS)), the antibacterial activity and the bacterial communities associated with several stages of the life cycle of A. armata: gametophytes, tetrasporophytes and female gametophytes with developed cystocarps. Our results revealed that the relative abundance of several halogenated molecules including dibromoacetic acid and some more halogenated molecules fluctuated depending on the different stages of the algae. The antibacterial activity of the tetrasporophyte extract was significantly higher than that of the extracts of the other two stages. Several highly halogenated compounds, which discriminate algal stages, were identified as candidate molecules responsible for the observed variation in antibacterial activity. The tetrasporophyte also harboured a significantly higher specific bacterial diversity, which is associated with a different bacterial community composition than the other two stages. This study provides elements that could help in understanding the processes that take place throughout the life cycle of A. armata with different potential energy investments between the development of reproductive elements, the production of halogenated molecules and the dynamics of bacterial communities.

Development of a root exudate collection protocol for metabolomics analysis using Nuclear Magnetic Resonance

Large amounts of root exudates are released by plant roots into the soil. Due to their importance in regulating the rhizosphere properties, it is necessary to unravel the precise composition and function of exudates at the root-soil interface. However, obtaining root exudates without inducing artefacts is a difficult task. To analyse the low molecular weight molecules secreted by pea roots, a protocol of root exudate collection was developed to perform a metabolomics analysis using Nuclear Magnetic Resonance (NMR). To date a few NMR studies are dedicated to root exudates. Plant culture, exudates collection and sample preparation methods had thus to be adapted to the NMR approach. Here, pea seedlings were hydroponically grown. The obtained NMR fingerprints show that osmotic stress increases the quantity of the exudates but not their diversity. We therefore selected a protocol reducing the harvest time and using an ionic solvent and applied it to the analysis of faba bean exudates. NMR analysis of the metabolic profiles allowed to discriminate between pea and faba bean according to their exudate composition. This protocol is therefore very promising for studying the composition of root exudates from different plant species as well as their evolution in response to different environmental conditions or pathophysiological events.

Mission Tara Microplastics: a holistic set of protocols and data resources for the field investigation of plastic pollution along the land-sea continuum in Europe

The Tara Microplastics mission was conducted for 7 months to investigate plastic pollution along nine major rivers in Europe-Thames, Elbe, Rhine, Seine, Loire, Garonne, Ebro, Rhone, and Tiber. An extensive suite of sampling protocols was applied at four to five sites on each river along a salinity gradient from the sea and the outer estuary to downstream and upstream of the first heavily populated city. Biophysicochemical parameters including salinity, temperature, irradiance, particulate matter, large and small microplastics (MPs) concentration and composition, prokaryote and microeukaryote richness, and diversity on MPs and in the surrounding waters were routinely measured onboard the French research vessel Tara or from a semi-rigid boat in shallow waters. In addition, macroplastic and microplastic concentrations and composition were determined on river banks and beaches. Finally, cages containing either pristine pieces of plastics in the form of films or granules, and others containing mussels were immersed at each sampling site, 1 month prior to sampling in order to study the metabolic activity of the plastisphere by meta-OMICS and to run toxicity tests and pollutants analyses. Here, we fully described the holistic set of protocols designed for the Mission Tara Microplastics and promoted standard procedures to achieve its ambitious goals: (1) compare traits of plastic pollution among European rivers, (2) provide a baseline of the state of plastic pollution in the Anthropocene, (3) predict their evolution in the frame of the current European initiatives, (4) shed light on the toxicological effects of plastic on aquatic life, (5) model the transport of microplastics from land towards the sea, and (6) investigate the potential impact of pathogen or invasive species rafting on drifting plastics from the land to the sea through riverine systems.

Metabarcoding and Metabolomics Reveal the Effect of the Invasive Alien Tree Miconia calvescens DC. on Soil Diversity on the Tropical Island of Mo'orea (French Polynesia)

Miconia calvescens is a dominant invasive alien tree species that threatens several endemic plants in French Polynesia (South Pacific). While most analyses have been performed at the scale of plant communities, the effects on the rhizosphere have not been described so far. However, this compartment can be involved in plant fitness through inhibitory activities, nutritive exchanges, and communication with other organisms. In particular, it was not known whether M. calvescens forms specific associations with soil organisms or has a specific chemical composition of secondary metabolites. To tackle these issues, the rhizosphere of six plant species was sampled on the tropical island of Mo'orea in French Polynesia at both the seedling and tree stages. The diversity of soil organisms (bacteria, microeukaryotes, and metazoa) and of secondary metabolites was studied using high-throughput technologies (metabarcoding and metabolomics, respectively). We found that trees had higher effects on soil diversity than seedlings. Moreover, M. calvescens showed a specific association with microeukaryotes of the Cryptomycota family at the tree stage. This family was positively correlated with the terpenoids found in the soil. Many terpenoids were also found within the roots of M. calvescens, suggesting that these molecules were probably produced by the plant and favored the presence of Cryptomycota. Both terpenoids and Cryptomycota were thus specific chemicals and biomarkers of M. calvescens. Additional studies must be performed in the future to better understand if they contribute to the success of this invasive tree.

Assessment of local and systemic signature of eosinophilic esophagitis (EoE) in children through multi-omics approaches

Background

Eosinophilic oesophagitis (EoE) is a chronic food allergic disorder limited to oesophageal mucosa whose pathogenesis is still only partially understood. Moreover, its diagnosis and follow-up need repeated endoscopies due to absence of non-invasive validated biomarkers. In the present study, we aimed to deeply describe local immunological and molecular components of EoE in well-phenotyped children, and to identify potential circulating EoE-biomarkers.

Methods

Blood and oesophageal biopsies were collected simultaneously from French children with EoE (n=17) and from control subjects (n=15). Untargeted transcriptomics analysis was performed on mRNA extracted from biopsies using microarrays. In parallel, we performed a comprehensive analysis of immune components on both cellular and soluble extracts obtained from both biopsies and blood, using flow cytometry. Finally, we performed non-targeted plasma metabolomics using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). Uni/multivariate supervised and non-supervised statistical analyses were then conducted to identify significant and discriminant components associated with EoE within local and/or systemic transcriptomics, immunologic and metabolomics datasets. As a proof of concept, we conducted multi-omics data integration to identify a plasmatic signature of EoE.

Results

French children with EoE shared the same transcriptomic signature as US patients. Network visualization of differentially expressed (DE) genes highlighted the major dysregulation of innate and adaptive immune processes, but also of pathways involved in epithelial cells and barrier functions, and in perception of chemical stimuli. Immune analysis of biopsies highlighted EoE is associated with dysregulation of both type (T) 1, T2 and T3 innate and adaptive immunity, in a highly inflammatory milieu. Although an immune signature of EoE was found in blood, untargeted metabolomics more efficiently discriminated children with EoE from control subjects, with dysregulation of vitamin B6 and various amino acids metabolisms. Multi-blocks integration suggested that an EoE plasma signature may be identified by combining metabolomics and cytokines datasets.

Conclusions

Our study strengthens the evidence that EoE results from alterations of the oesophageal epithelium associated with altered immune responses far beyond a simplistic T2 dysregulation. As a proof of concept, combining metabolomics and cytokines data may provide a set of potential plasma biomarkers for EoE diagnosis, which needs to be confirmed on a larger and independent cohort.

Metabolomics meets systems immunology

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

MAW: the reproducible Metabolome Annotation Workflow for untargeted tandem mass spectrometry

Mapping the chemical space of compounds to chemical structures remains a challenge in metabolomics. Despite the advancements in untargeted liquid chromatography-mass spectrometry (LC-MS) to achieve a high-throughput profile of metabolites from complex biological resources, only a small fraction of these metabolites can be annotated with confidence. Many novel computational methods and tools have been developed to enable chemical structure annotation to known and unknown compounds such as in silico generated spectra and molecular networking. Here, we present an automated and reproducible Metabolome Annotation Workflow (MAW) for untargeted metabolomics data to further facilitate and automate the complex annotation by combining tandem mass spectrometry (MS²) input data pre-processing, spectral and compound database matching with computational classification, and in silico annotation. MAW takes the LC-MS² spectra as input and generates a list of putative candidates from spectral and compound databases. The databases are integrated via the R package Spectra and the metabolite annotation tool SIRIUS as part of the R segment of the workflow (MAW-R). The final candidate selection is performed using the cheminformatics tool RDKit in the Python segment (MAW-Py). Furthermore, each feature is assigned a chemical structure and can be imported to a chemical structure similarity network. MAW is following the FAIR (Findable, Accessible, Interoperable, Reusable) principles and has been made available as the docker images, maw-r and maw-py. The source code and documentation are available on GitHub ( https://github.com/zmahnoor14/MAW ). The performance of MAW is evaluated on two case studies. MAW can improve candidate ranking by integrating spectral databases with annotation tools like SIRIUS which contributes to an efficient candidate selection procedure. The results from MAW are also reproducible and traceable, compliant with the FAIR guidelines. Taken together, MAW could greatly facilitate automated metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery.

Exploration of fMRI brain responses to oral sucrose after Roux-en-Y gastric bypass in obese yucatan minipigs in relationship with microbiota and metabolomics profiles

BACKGROUND & AIMS

In most cases, Roux-en-Y gastric bypass (RYGBP) is an efficient intervention to lose weight, change eating behavior and improve metabolic outcomes in obese patients. We hypothesized that weight loss induced by RYGBP in obese Yucatan minipigs would induce specific modifications of the gut-brain axis and neurocognitive responses to oral sucrose stimulation in relationship with food intake control.

METHODS

An integrative study was performed after SHAM (n = 8) or RYGBP (n = 8) surgery to disentangle the physiological, metabolic and neurocognitive mechanisms of RYGBP. BOLD fMRI responses to sucrose stimulations at different concentrations, brain mRNA expression, cecal microbiota, and plasma metabolomics were explored 4 months after surgery and integrated with WGCNA analysis.

RESULTS

We showed that weight loss induced by RYGBP or SHAM modulated differently the frontostriatal responses to oral sucrose stimulation, suggesting a different hedonic treatment and inhibitory control related to palatable food after RYGBP. The expression of brain genes involved in the serotoninergic and cannabinoid systems were impacted by RYGBP. Cecal microbiota was deeply modified and many metabolite features were differentially increased in RYGBP. Data integration with WGCNA identified interactions between key drivers of OTUs and metabolites features linked to RYGBP.

CONCLUSION

This longitudinal study in the obese minipig model illustrates with a systemic and integrative analysis the mid-term consequences of RYGBP on brain mRNA expression, cecal microbiota and plasma metabolites. We confirmed the impact of RYGBP on functional brain responses related to food reward, hedonic evaluation and inhibitory control, which are key factors for the success of anti-obesity therapy and weight loss maintenance.

Deep phenotyping and biomarkers of various dairy fat intakes in an 8-week randomized clinical trial and 2-year swine study

Health effects of dairy fats (DF) are difficult to evaluate, as DF intakes are hard to assess epidemiologically and DF have heterogeneous compositions that influence biological responses. We set out to find biomarkers of DF intake and assess biological response to a summer DF diet (R2), a winter DF diet (R3), and a R3 supplemented with calcium (R4) compared to a plant-fat-based diet (R1) in a randomized clinical trial (n=173) and a 2-year study in mildly metabolically disturbed downsized pigs (n=32). Conventional clinical measures were completed by LC/MS plasma metabolomics/lipidomics. The measured effects were modeled as biological functions to facilitate interpretation. DF intakes in pigs specifically induced a U-shaped metabolic trajectory, reprogramming metabolism to close to its initial status after a one-year turnaround. Twelve lipid species repeatably predicted DF intakes in both pigs and humans (6.6% errors). More broadly, in pigs, quality of DF modulated the time-related biological response (R2: 30 regulated functions, primarily at 6 months; R3: 26 regulated functions, mostly at 6-12 months; R4: 43 regulated functions, mostly at 18 months). Despite this heterogeneity, 9 functions overlapped under all 3 DF diets in both studies, related to a restricted area of amino acids metabolism, cofactors, nucleotides and xenobiotic pathways and the microbiota. In conclusion, over the long-term, DF reprograms metabolism to close to its initial biological status in metabolically-disrupted pigs. Quality of the DF modulates its metabolic influence, although some effects were common to all DF. A resilient signature of DF consumption found in pigs was validated in humans.

Urinary Metabolome Analysis Reveals Potential Microbiota Alteration and Electrophilic Burden Induced by High Red Meat Diet: Results from the French NutriNet-Santé Cohort and an In Vivo Intervention Study in Rats

SCOPE

High red and processed meat consumption is associated with several adverse outcomes such as colorectal cancer and overall global mortality. However, the underlying mechanisms remain debated and need to be elucidated.

METHODS AND RESULTS

Urinary untargeted Liquid Chromatography-Mass Spectrometry (LC-MS) metabolomics data from 240 subjects from the French cohort NutriNet-Santé are analyzed. Individuals are matched and divided into three groups according to their consumption of red and processed meat: high red and processed meat consumers, non-red and processed meat consumers, and at random group. Results are supported by a preclinical experiment where rats are fed either a high red meat or a control diet. Microbiota derived metabolites, in particular indoxyl sulfate and cinnamoylglycine, are found impacted by the high red meat diet in both studies, suggesting a modification of microbiota by the high red/processed meat diet. Rat microbiota sequencing analysis strengthens this observation. Although not evidenced in the human study, rat mercapturic acid profile concomitantly reveals an increased lipid peroxidation induced by high red meat diet.

CONCLUSION

Novel microbiota metabolites are identified as red meat consumption potential biomarkers, suggesting a deleterious effect, which could partly explain the adverse effects associated with high red and processed meat consumption.

Advances in Ultra-High-Resolution Mass Spectrometry for Pharmaceutical Analysis

Pharmaceutical analysis refers to an area of analytical chemistry that deals with active compounds either by themselves (drug substance) or when formulated with excipients (drug product). In a less simplistic way, it can be defined as a complex science involving various disciplines, e.g., drug development, pharmacokinetics, drug metabolism, tissue distribution studies, and environmental contamination analyses. As such, the pharmaceutical analysis covers drug development to its impact on health and the environment. Moreover, due to the need for safe and effective medications, the pharmaceutical industry is one of the most heavily regulated sectors of the global economy. For this reason, powerful analytical instrumentation and efficient methods are required. In the last decades, mass spectrometry has been increasingly used in pharmaceutical analysis both for research aims and routine quality controls. Among different instrumental setups, ultra-high-resolution mass spectrometry with Fourier transform instruments, i.e., Fourier transform ion cyclotron resonance (FTICR) and Orbitrap, gives access to valuable molecular information for pharmaceutical analysis. In fact, thanks to their high resolving power, mass accuracy, and dynamic range, reliable molecular formula assignments or trace analysis in complex mixtures can be obtained. This review summarizes the principles of the two main types of Fourier transform mass spectrometers, and it highlights applications, developments, and future perspectives in pharmaceutical analysis.

Development of an Untargeted Metabolomics Strategy to Study the Metabolic Rewiring of Dendritic Cells upon Lipopolysaccharide Activation

Dendritic cells (DCs) are essential immune cells for defense against external pathogens. Upon activation, DCs undergo profound metabolic alterations whose precise nature remains poorly studied at a large scale and is thus far from being fully understood. The goal of the present work was to develop a reliable and accurate untargeted metabolomics workflow to get a deeper insight into the metabolism of DCs when exposed to an infectious agent (lipopolysaccharide, LPS, was used to mimic bacterial infection). As DCs transition rapidly from a non-adherent to an adherent state upon LPS exposure, one of the leading analytical challenges was to implement a single protocol suitable for getting comparable metabolomic snapshots of those two cellular states. Thus, a thoroughly optimized and robust sample preparation method consisting of a one-pot solvent-assisted method for the simultaneous cell lysis/metabolism quenching and metabolite extraction was first implemented to measure intracellular DC metabolites in an unbiased manner. We also placed special emphasis on metabolome coverage and annotation by using a combination of hydrophilic interaction liquid chromatography and reverse phase columns coupled to high-resolution mass spectrometry in conjunction with an in-house developed spectral database to identify metabolites at a high confidence level. Overall, we were able to characterize up to 171 unique meaningful metabolites in DCs. We then preliminarily compared the metabolic profiles of DCs derived from monocytes of 12 healthy donors upon in vitro LPS activation in a time-course experiment. Interestingly, the resulting data revealed differential and time-dependent activation of some particular metabolic pathways, the most impacted being nucleotides, nucleotide sugars, polyamines pathways, the TCA cycle, and to a lesser extent, the arginine pathway.