Exploring Individual Variability in Drug-Induced Liver Injury (DILI) Responses through Metabolomic Analysis.

Drug-induced liver injury (DILI) is a serious adverse hepatic event presenting diagnostic and prognostic challenges. The clinical categorization of DILI into hepatocellular, cholestatic, or mixed phenotype is based on serum alanine aminotransferase (ALT) and alkaline phosphatase (ALP) values; however, this classification may not capture the full spectrum of DILI subtypes. With this aim, we explored the utility of assessing changes in the plasma metabolomic profiles of 79 DILI patients assessed by the RUCAM (Roussel Uclaf Causality Assessment Method) score to better characterize this condition and compare results obtained with the standard clinical characterization. Through the identification of various metabolites in the plasma (including free and conjugated bile acids and glycerophospholipids), and the integration of this information into predictive models, we were able to evaluate the extent of the hepatocellular or cholestatic phenotype and to assign a numeric value with the contribution of each specific DILI sub-phenotype into the patient's general condition. Additionally, our results showed that metabolomic analysis enabled the monitoring of DILI variability responses to the same drug, the transitions between sub-phenotypes during disease progression, and identified a spectrum of residual DILI metabolic features, which can be overlooked using standard clinical diagnosis during patient follow-up.

Enhancing the accuracy of mid-infrared spectroscopy-based liver steatosis quantification using digital image analysis as a reference.

The assessment of liver steatosis is crucial in both hepatology and liver transplantation (LT) surgery. Steatosis can negatively impact the success of LT. Steatosis is a factor for excluding donated organs for LT, but the increasing demand for transplantable organs has led to the use of organs from marginal donors. The current standard for evaluating steatosis is a semi-quantitative grading based on the visual examination of a hematoxylin and eosin (H&E)-stained liver biopsy, but this method is time-consuming, subjective, and lacks reproducibility. Recent research has shown that infrared (IR) spectroscopy could be used as a real-time quantitative tool to assess steatosis during abdominal surgery. However, the development of IR-based methods has been hindered by the lack of appropriate quantitative reference values. In this study, we developed and validated digital image analysis methods for the quantitation of steatosis in H&E-stained liver sections using univariate and multivariate strategies including linear discriminant analysis (LDA), quadratic DA, logistic regression, partial least squares-DA (PLS-DA), and support vector machines. The analysis of 37 tissue samples with varying grades of steatosis demonstrates that digital image analysis provides accurate and reproducible reference values that improve the performance of IR spectroscopic models for steatosis quantification. A PLS model in the 1810-1052 cm-1 region using first derivative ATR-FTIR spectra provided RMSECV = 0.99%. The gained improvement in accuracy critically enhances the applicability of Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) to support an objective graft evaluation at the operation room, which might be especially relevant in cases of marginal liver donors to avoid unnecessary graft explantation.

Metabolomics-based strategy to assess drug hepatotoxicity and uncover the mechanisms of hepatotoxicity involved.

Toxicity studies, among them hepatotoxicity, are key throughout preclinical stages of drug development to minimise undesired toxic effects that might eventually appear in the course of the clinical use of the new drug. Understanding the mechanism of injury of hepatotoxins is essential to efficiently anticipate their potential risk of toxicity in humans. The use of in vitro models and particularly cultured hepatocytes represents an easy and robust alternative to animal drug hepatotoxicity testing for predicting human risk. Here, we envisage an innovative strategy to identify potential hepatotoxic drugs, quantify the magnitude of the alterations caused, and uncover the mechanisms of toxicity. This strategy is based on the comparative analysis of metabolome changes induced by hepatotoxic and non-hepatotoxic compounds on HepG2 cells, assessed by untargeted mass spectrometry. As a training set, we used 25 hepatotoxic and 4 non-hepatotoxic compounds and incubated HepG2 cells for 24 h at a low and a high concentration (IC10 and IC50) to identify mechanism-related and cytotoxicity related metabolomic biomarkers and to elaborate prediction models accounting for global hepatotoxicity and mechanisms-related toxicity. Thereafter, a second set of 69 chemicals with known predominant mechanisms of toxicity and 18 non-hepatotoxic compounds were analysed at 1, 10, 100 and 1000 µM concentrations from which and based on the magnitude of the alterations caused as compared with non-toxic compounds, we defined a "toxicity index" for each compound. In addition, we extracted from the metabolome data the characteristic signatures for each mechanism of hepatotoxicity. The integration of all this information allowed us to identify specific metabolic patterns and, based on the occurrence of that specific metabolome changes, the models predicted the likeliness of a compound to behave as hepatotoxic and to act through a given toxicity mechanism (i.e., oxidative stress, mitochondrial disruption, apoptosis and steatosis) for each compound and concentration.

A Novel UPLC-MS Metabolomic Analysis-Based Strategy to Monitor the Course and Extent of iPSC Differentiation to Hepatocytes.

Typical protocols to differentiate induced pluripotent stem cells (iPSCs) from hepatocyte-like cells (HLCs) imply complex strategies that include transfection with key hepatic transcription factors and the addition to culture media of nutrients, growth factors, and cytokines. A main constraint to evaluate the hepatic phenotype achieved arises from the way the grade of differentiation is determined. Currently, it relies on the assessment of the expression of a limited number of hepatic gene transcripts, less frequently by assessing certain hepatic metabolic functions, and rarely by the global metabolic performance of differentiated cells. We envisaged a new strategy to assess the extent of differentiation achieved, based on the analysis of the cellular metabolome along the differentiation process and its quantitative comparison with that of primary human hepatocytes (PHHs). To validate our approach, we examined the changes in the metabolome of three iPSC progenies (transfected with/without key transcription factors), cultured in three differentiation media, and compared them to PHHs. Results revealed consistent metabolome changes along differentiation and evidenced the factors that more strongly promote changes in the metabolome. The integrated dissimilarities between the PHHs and HLCs retrieved metabolomes were used as a numerical reference for quantifying the degree of iPSCs differentiation. This newly developed metabolome-analysis approach evidenced its utility in assisting us to select a cell's source, culture conditions, and differentiation media, to achieve better-differentiated HLCs.

Analysis of the Association between Fatigue and the Plasma Lipidomic Profile of Inflammatory Bowel Disease Patients.

Inflammatory bowel disease (IBD) is a chronic, relapsing noninfectious inflammatory condition of the intestinal tract with two main phenotypes, ulcerative colitis (UC) and Crohn's disease (CD), and globally increasing incidence and prevalence. Nearly 80% of the IBD patients with active disease and 50% of those with inactive disease suffer fatigue with significant impairment of their quality of life. Fatigue has been associated with multiple factors in IBD patients but, in most cases, no direct cause can be identified, and risk factors in clinically quiescent IBD are contradictory. Furthermore, as the assessment of fatigue is subjective, there is an unmet clinical need for fatigue biomarkers. In this explorative study, we analyzed the plasma lipidomic profiles of 47 quiescent UC and CD patients (23 fatigued, 24 nonfatigued) using ultraperformance liquid chromatography-time-of-flight mass spectrometry (UPLC-TOFMS). The results showed changes in lipids associated with fatigue and IBD. Significantly decreased levels of phosphatidylcholines, plasmanyls, sphingomyelins, lysophosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, phosphatidylserines, and eicosanoids were observed in patients with fatigue. Network and metabolic pathway analysis indicated a dysregulation of the arachidonic acid and glycerophospholipid metabolisms and the sphingolipid pathway. The protein-metabolite interaction network showed interactions between functionally related metabolites and proteins, displaying 40 disease-associated hidden proteins including ABDH4, GLTP, and LCAT.

Toward Rapid Screening of Liver Grafts at the Operating Room Using Mid-infrared Spectroscopy.

The estimation of steatosis in a liver graft is mandatory prior to liver transplantation, as the risk of graft failure increases with the level of infiltrated fat. However, the assessment of liver steatosis before transplantation is typically based on a qualitative or semiquantitative characterization by visual inspection and palpation and histological analysis. Thus, there is an unmet need for transplantation surgeons to have access to a diagnostic tool enabling an in situ fast classification of grafts prior to extraction. In this study, we have assessed an attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopic method compatible with the requirements of an operation room for the evaluation of the lipid contents in human livers. A set of 20 human liver biopsies obtained from organs intended for transplantation were analyzed by expert pathologists, ATR-FTIR spectroscopy, lipid biochemical analysis, and UPLC-ESI(+/-)TOFMS for lipidomic profiling. Comparative analysis of multisource data showed strong correlations between ATR-FTIR, clinical, and lipidomic information. Results show that ATR-FTIR captures a global picture of the lipid composition of the liver, along with information for the quantification of the triradylglycerol content in liver biopsies. Although the methodology performance needs to be further validated, results support the applicability of ATR-FTIR for the in situ determination of the grade of liver steatosis at the operation room as a fast, quantitative method, as an alternative to the qualitative and subjective pathological examination.

Improving insights from metabolomic functional analysis combining multivariate tools.

BACKGROUND: Metabolomics is a scientific field that relies on the comprehensive analysis of metabolites to provide direct insights into functional processes in biological systems. Metabolomic data provides valuable insights into the functional processes of biological systems, often analyzed through univariate and multivariate approaches, and well as with functional or pathway analysis using different methods such as mummichog. Yet, the integration of results from these sources to aid the interpretation of their biological significance remains challenging. This represents a significant bottleneck limiting the applicability of multivariate analysis of metabolomic data, despite its potential for providing deep biological insights. RESULTS: In this work we propose two straightforward methods to facilitate the interpretation of results from multivariate analysis and functional metabolic analysis using: i) p-values from multivariate tests as input in functional analysis, and ii) cluster-CV to assess the impact on the predictive performance of a multivariate model at the pathway level. Four simulated data sets were analyzed including a data set with no class separation, and three data sets with a statistically significant discrimination between classes by including either univariate, multivariate, or both types of discriminant effects. The data sets were analyzed using univariate tests and OPLS-DA. Furthermore, p-values for each feature estimated by univariate analysis and OPLS-DA were used as input for functional analysis in mummichog. Cluster-CV was then used to assess the effect of detected metabolic pathways on the class separation observed by OPLS-DA. SIGNIFICANCE: Through simulated data, we show how these approaches enhance the interpretation of biological effects driving multivariate models and support the identification of altered pathways not detected by univariate analysis. By providing a deeper understanding of metabolic phenotypes, these methods might improve the biological insights derived from statistical and functional analysis of future or previous studies.

Novel clinical phenotypes, drug categorization, and outcome prediction in drug-induced cholestasis: Analysis of a database of 432 patients developed by literature review and machine learning support.

BACKGROUND: Serum transaminases, alkaline phosphatase and bilirubin are common parameters used for DILI diagnosis, classification, and prognosis. However, the relevance of clinical examination, histopathology and drug chemical properties have not been fully investigated. As cholestasis is a frequent and complex DILI manifestation, our goal was to investigate the relevance of clinical features and drug properties to stratify drug-induced cholestasis (DIC) patients, and to develop a prognosis model to identify patients at risk and high-concern drugs. METHODS: DIC-related articles were searched by keywords and Boolean operators in seven databases. Relevant articles were uploaded onto Sysrev, a machine-learning based platform for article review and data extraction. Demographic, clinical, biochemical, and liver histopathological data were collected. Drug properties were obtained from databases or QSAR modelling. Statistical analyses and logistic regressions were performed. RESULTS: Data from 432 DIC patients associated with 52 drugs were collected. Fibrosis strongly associated with fatality, whereas canalicular paucity and ALP associated with chronicity. Drugs causing cholestasis clustered in three major groups. The pure cholestatic pattern divided into two subphenotypes with differences in prognosis, canalicular paucity, fibrosis, ALP and bilirubin. A predictive model of DIC outcome based on non-invasive parameters and drug properties was developed. Results demonstrate that physicochemical (pKa-a) and pharmacokinetic (bioavailability, CYP2C9) attributes impinged on the DIC phenotype and allowed the identification of high-concern drugs. CONCLUSIONS: We identified novel associations among DIC manifestations and disclosed novel DIC subphenotypes with specific clinical and chemical traits. The developed predictive DIC outcome model could facilitate DIC prognosis in clinical practice and drug categorization.

The assessment of the potential hepatotoxicity of new drugs by in vitro metabolomics.

Drug hepatotoxicity assessment is a relevant issue both in the course of drug development as well as in the post marketing phase. The use of human relevant in vitro models in combination with powerful analytical methods (metabolomic analysis) is a promising approach to anticipate, as well as to understand and investigate the effects and mechanisms of drug hepatotoxicity in man. The metabolic profile analysis of biological liver models treated with hepatotoxins, as compared to that of those treated with non-hepatotoxic compounds, provides useful information for identifying disturbed cellular metabolic reactions, pathways, and networks. This can later be used to anticipate, as well to assess, the potential hepatotoxicity of new compounds. However, the applicability of the metabolomic analysis to assess the hepatotoxicity of drugs is complex and requires careful and systematic work, precise controls, wise data preprocessing and appropriate biological interpretation to make meaningful interpretations and/or predictions of drug hepatotoxicity. This review provides an updated look at recent in vitro studies which used principally mass spectrometry-based metabolomics to evaluate the hepatotoxicity of drugs. It also analyzes the principal drawbacks that still limit its general applicability in safety assessment screenings. We discuss the analytical workflow, essential factors that need to be considered and suggestions to overcome these drawbacks, as well as recent advancements made in this rapidly growing field of research.

Metabolomic Diversity of Human Milk Cells over the Course of Lactation-A Preliminary Study.

Human milk (HM) is a complex biofluid containing a wide cell variety including epithelial cells and leukocytes. However, the cellular compositions and their phenotypic properties over the course of lactation are poorly understood. The aim of this preliminary study was to characterize the cellular metabolome of HM over the course of lactation. Cells were isolated via centrifugation and the cellular fraction was characterized via cytomorphology and immunocytochemical staining. Cell metabolites were extracted and analyzed using ultra-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UPLC-QqTOF-MS) in the positive and negative electrospray ionization modes. Immunocytochemical analysis revealed a high variability of the number of detected cells with relative median abundances of 98% of glandular epithelial cells, 1% of leukocytes, and 1% of keratinocytes. Significant correlations between the milk postnatal age with percentage of epithelial cells and leukocytes, and with total cell count were observed. Results from the Hierarchical Cluster Analysis of immunocytochemical profiles were very similar to those observed in the analysis of the metabolomic profiles. In addition, metabolic pathway analysis showed alterations in seven metabolic pathways correlating with postnatal age. This work paves the way for future investigations on changes in the metabolomic fraction of the cellular compartment of HM.

Targeted cargo delivery in senescent cells using capped mesoporous silica nanoparticles.

Learning to let go with age: Intracellular controlled release of molecules within senescent cells was achieved using mesoporous silica nanoparticles (MSNs) capped with a galacto-oligosaccharide (GOS) to contain the cargo molecules (magenta spheres; see scheme). The GOS is a substrate of the senescent biomarker, senescence-associated ß-galactosidase (SA-ß-gal), and releases the cargo upon entry into SA-ß-gal expressing cells.

The Potential Role of Metabolomics in Drug-Induced Liver Injury (DILI) Assessment.

Drug-induced liver injury (DILI) is one of the most frequent adverse clinical reactions and a relevant cause of morbidity and mortality. Hepatotoxicity is among the major reasons for drug withdrawal during post-market and late development stages, representing a major concern to the pharmaceutical industry. The current biochemical parameters for the detection of DILI are based on enzymes (alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transpeptidase (GGT), alkaline phosphatase (ALP)) and bilirubin serum levels that are not specific of DILI and therefore there is an increasing interest on novel, specific, DILI biomarkers discovery. Metabolomics has emerged as a tool with a great potential for biomarker discovery, especially in disease diagnosis, and assessment of drug toxicity or efficacy. This review summarizes the multistep approaches in DILI biomarker research and discovery based on metabolomics and the principal outcomes from the research performed in this field. For that purpose, we have reviewed the recent scientific literature from PubMed, Web of Science, EMBASE, and PubTator using the terms "metabolomics", "DILI", and "humans". Despite the undoubted contribution of metabolomics to our understanding of the underlying mechanisms of DILI and the identification of promising novel metabolite biomarkers, there are still some inconsistencies and limitations that hinder the translation of these research findings into general clinical practice, probably due to the variability of the methods used as well to the different mechanisms elicited by the DILI causing agent.

Valproate and Short-Chain Fatty Acids Activate Transcription of the Human Vitamin D Receptor Gene through a Proximal GC-Rich DNA Region Containing Two Putative Sp1 Binding Sites.

The vitamin D receptor (VDR) mediates 1,25-dihydroxyvitamin D3 pleiotropic biological actions through transcription regulation of target genes. The expression levels of this ligand-activated nuclear receptor are regulated by multiple mechanisms both at transcriptional and post-transcriptional levels. Vitamin D3 is the natural VDR activator, but other molecules and signaling pathways have also been reported to regulate VDR expression and activity. In this study, we identify valproic acid (VPA) and natural short-chain fatty acids (SCFAs) as novel transcriptional activators of the human VDR (hVDR) gene. We further report a comprehensive characterization of VPA/SCFA-responsive elements in the 5' regulatory region of the hVDR gene. Two alternative promoter DNA regions (of 2.4 and 3.8 kb), as well as subsequent deletion fragments, were cloned in pGL4-LUC reporter vector. Transfection of these constructs in HepG2 and human Upcyte hepatocytes followed by reporter assays demonstrated that a region of 107 bp (from -107 to -1) upstream of the transcription start site in exon 1a is responsible for most of the increase in transcriptional activity in response to VPA/SCFAs. This short DNA region is GC-rich, does not contain an apparent TATA box, and includes two bona fide binding sites for the transcription factor Sp1. Our results substantiate the hypothesis that VPA and SCFAs facilitate the activity of Sp1 on novel Sp1 responsive elements in the hVDR gene, thus promoting VDR upregulation and signaling. Elevated hepatic VDR levels have been associated with liver steatosis and, therefore, our results may have clinical relevance in epileptic pediatric patients on VPA therapy. Our results could also be suggestive of VDR upregulation by SCFAs produced by gut microbiota.

The effect of Holder pasteurization on the lipid and metabolite composition of human milk.

Human milk (HM) is the gold standard for newborn nutrition. When own mother's milk is not sufficiently available, pasteurized donor human milk becomes a valuable alternative. In this study we analyzed the impact of Holder pasteurization (HoP) on the metabolic and lipidomic composition of HM. Metabolomic and lipidomic profiles of twelve paired HM samples were analysed before and after HoP by liquid chromatography-mass spectrometry (MS) and gas chromatography-MS. Lipidomic analysis enabled the annotation of 786 features in HM out of which 289 were significantly altered upon pasteurization. Fatty acid analysis showed a significant decrease of 22 out of 29 detectable fatty acids. The observed changes were associated to five metabolic pathways. Lipid ontology enrichment analysis provided insight into the effect of pasteurization on physical and chemical properties, cellular components, and functions. Future research should focus on nutritional and/or developmental consequences of these changes.

Factors that influence the quality of metabolomics data in in vitro cell toxicity studies: a systematic survey.

REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) is a global strategy and regulation policy of the EU that aims to improve the protection of human health and the environment through the better and earlier identification of the intrinsic properties of chemical substances. It entered into force on 1st June 2007 (EC 1907/2006). REACH and EU policies plead for the use of robust high-throughput "omic" techniques for the in vitro investigation of the toxicity of chemicals that can provide an estimation of their hazards as well as information regarding the underlying mechanisms of toxicity. In agreement with the 3R's principles, cultured cells are nowadays widely used for this purpose, where metabolomics can provide a real-time picture of the metabolic effects caused by exposure of cells to xenobiotics, enabling the estimations about their toxicological hazards. High quality and robust metabolomics data sets are essential for precise and accurate hazard predictions. Currently, the acquisition of consistent and representative metabolomic data is hampered by experimental drawbacks that hinder reproducibility and difficult robust hazard interpretation. Using the differentiated human liver HepG2 cells as model system, and incubating with hepatotoxic (acetaminophen and valproic acid) and non-hepatotoxic compounds (citric acid), we evaluated in-depth the impact of several key experimental factors (namely, cell passage, processing day and storage time, and compound treatment) and instrumental factors (batch effect) on the outcome of an UPLC-MS metabolomic analysis data set. Results showed that processing day and storage time had a significant impact on the retrieved cell's metabolome, while the effect of cell passage was minor. Meta-analysis of results from pathway analysis showed that batch effect corrections and quality control (QC) measures are critical to enable consistent and meaningful estimations of the effects caused by compounds on cells. The quantitative analysis of the changes in metabolic pathways upon bioactive compound treatment remained consistent despite the concurrent causes of metabolomic data variation. Thus, upon appropriate data retrieval and correction and by an innovative metabolic pathway analysis, the metabolic alteration predictions remained conclusive despite the acknowledged sources of variability.

Extracting consistent biological information from functional results of metabolomic pathway analysis using the Mantel's test.

Extraction of meaningful biological information from the vast array of data that metabolomics analyses generate is a major challenge in the field. A variety of computational and visual tools that help to identify changes in metabolic pathways have been proposed including functional analysis and pathway analysis. Meta-analysis of metabolomic data has emerged as a powerful source of information. In this work, the applicability of the Mantel's test for the correlation of functional results from metabolic pathway analysis is shown using experimental and simulated data sets as evaluation examples. The statistical significance of the correlation coefficient can be assessed by permutation testing requiring practically no computation time. The use of the Mantel's test can assist the critical comparison of different phenotypes, studies, methods, platforms, or data preprocessing strategies, as well as help to identify inconsistencies between metabolomic study outcomes, making this algorithm attractive for data interpretation and meta-analysis on a routine basis.

Phosphoproteomic Analysis across the Yeast Life Cycle Reveals Control of Fatty Acyl Chain Length by Phosphorylation of the Fatty Acid Synthase Complex.

The ability to remodel lipid metabolism under changing conditions is pivotal for cellular functionality and homeostasis. Here, we characterize the regulatory landscape of phosphorylation-based signaling events across the life cycle of Saccharomyces cerevisiae and determine its impact on the regulation of lipid metabolism. Our data show that 50 lipid metabolic proteins are differentially phosphorylated as cells transit between different physiological states. To identify functional phosphosites, we devised a strategy where multiple phosphosites are simultaneously mutated into phosphomimetic or phosphodeficient alleles and mutants are phenotyped by in-depth lipidomics flux analysis. This uncovers functional phosphosites in the phosphatidate cytidylyltransferase Cds1, the phosphatidylserine synthase Cho1, and Fas2, the α-subunit of the fatty acid synthase (FAS) complex. Furthermore, we show that the fatty acyl chain length produced by FAS is governed by phosphorylation. Overall, our work demonstrates a vital role for phosphoregulation of lipid metabolism and provides a resource to investigate its molecular underpinnings.

Comparing Targeted vs. Untargeted MS2 Data-Dependent Acquisition for Peak Annotation in LC-MS Metabolomics.

One of the most widely used strategies for metabolite annotation in untargeted LCMS is based on the analysis of MSn spectra acquired using data-dependent acquisition (DDA), where precursor ions are sequentially selected from MS scans based on user-selected criteria. However, the number of MSn spectra that can be acquired during a chromatogram is limited and a trade-off between analytical speed, sensitivity and coverage must be ensured. In this research, we compare four different strategies for automated MS2 DDA, which can be easily implemented in the frame of standard QA/QC workflows for untargeted LC-MS. These strategies consist of (i) DDA in the MS working range; (ii) iterated DDA split into several m/z intervals; (iii) dynamic iterated DDA of (pre)selected potentially informative features; and (iv) dynamic iterated DDA of (pre)annotated metabolic features using a reference database. Their performance was assessed using the analysis of human milk samples as model example by comparing the percentage of LC-MS features selected as the precursor ion for MS2, the number, and class of annotated features, the speed and confidence of feature annotation, and the number of LC runs required.

LC-MS Analyses of Lipid Species in Skeletal Muscle Cells and Tissue.

Liquid chromatography-mass spectrometry (LC-MS) is a widely used methodology for measuring lipids at a global level. Combined with an optimal extraction method LC-MS enables the detection and characterization of a wide range of lipid species even of low abundance. Here, we describe two extraction- and LC-MS-based quantitative analytical methods for lipid, acyl-CoA, and acyl-carnitine analyses from either mouse C2C12 myotubes or mouse skeletal tissue. We also describe the use of 13C16-palmitate and its incorporation into acyl-carnitines to show how stable isotope tracers are metabolized within cells and therefore can be implemented for lipidomic flux analysis.