Decreasing ganglioside synthesis delays motor and cognitive symptom onset in Spg11 knockout mice.

Biallelic variants in the SPG11 gene account for the most common form of autosomal recessive hereditary spastic paraplegia characterized by motor and cognitive impairment, with currently no therapeutic option. We previously observed in a Spg11 knockout mouse that neurodegeneration is associated with accumulation of gangliosides in lysosomes. To test whether a substrate reduction therapy could be a therapeutic option, we downregulated the key enzyme involved in ganglioside biosynthesis using an AAV-PHP.eB viral vector expressing a miRNA targeting St3gal5. Downregulation of St3gal5 in Spg11 knockout mice prevented the accumulation of gangliosides, delayed the onset of motor and cognitive symptoms, and prevented the upregulation of serum levels of neurofilament light chain, a biomarker widely used in neurodegenerative diseases. Importantly, similar results were observed when Spg11 knockout mice were administrated venglustat, a pharmacological inhibitor of glucosylceramide synthase expected to decrease ganglioside synthesis. Downregulation of St3gal5 or venglustat administration in Spg11 knockout mice strongly decreased the formation of axonal spheroids, previously associated with impaired trafficking. Venglustat had similar effect on cultured human SPG11 neurons. In conclusion, this work identifies the first disease-modifying therapeutic strategy in SPG11, and provides data supporting its relevance for therapeutic testing in SPG11 patients.

Mitochondrial dysfunction governs immunometabolism in leukocytes of patients with acute-on-chronic liver failure.

BACKGROUND & AIMS: Patients with acute-on-chronic liver failure (ACLF) present a systemic hyperinflammatory response associated with increased circulating levels of small-molecule metabolites. To investigate whether these alterations reflect inadequate cell energy output, we assessed mitochondrial morphology and central metabolic pathways with emphasis on the tricarboxylic acid (TCA) cycle in peripheral leukocytes from patients with acutely decompensated (AD) cirrhosis, with and without ACLF. METHODS: The study included samples from patients with AD cirrhosis (108 without and 128 with ACLF) and 41 healthy individuals. Leukocyte mitochondrial ultrastructure was visualized by transmission electron microscopy and cytosolic and mitochondrial metabolic fluxes were determined by assessing NADH/FADH2 production from various substrates. Plasma GDF15 and FGF21 were determined by Luminex and acylcarnitines by LC-MS/MS. Gene expression was analyzed by RNA-sequencing and PCR-based glucose metabolism profiler array. RESULTS: Mitochondrial ultrastructure in patients with advanced cirrhosis was distinguished by cristae rarefication and swelling. The number of mitochondria per leukocyte was higher in patients, accompanied by a reduction in their size. Increased FGF21 and C6:0- and C8:0-carnitine predicted mortality whereas GDF15 strongly correlated with a gene set signature related to leukocyte activation. Metabolic flux analyses revealed increased energy production in mononuclear leukocytes from patients with preferential involvement of extra-mitochondrial pathways, supported by upregulated expression of genes encoding enzymes of the glycolytic and pentose phosphate pathways. In patients with ACLF, mitochondrial function analysis uncovered break-points in the TCA cycle at the isocitrate dehydrogenase and succinate dehydrogenase level, which were bridged by anaplerotic reactions involving glutaminolysis and nucleoside metabolism. CONCLUSIONS: Our findings provide evidence at the cellular, organelle and biochemical levels that severe mitochondrial dysfunction governs immunometabolism in leukocytes from patients with AD cirrhosis and ACLF. LAY SUMMARY: Patients at advanced stages of liver disease have dismal prognosis due to vital organ failures and the lack of treatment options. In this study, we report that the functioning of mitochondria, which are known as the cell powerhouse, is severely impaired in leukocytes of these patients, probably as a consequence of intense inflammation. Mitochondrial dysfunction is therefore a hallmark of advanced liver disease.

Metabolomics in the understanding and management of hepatic encephalopathy.

Metabolomics refers to the study of biological components below 1000 Daltons (Da) involved in metabolic pathways as substrates, products or effectors. According to the interconnected metabolic disturbances that have been described in the pathophysiology of hepatic encephalopathy (HE), this technique appears to be well adapted to study and better delineate the disease. This review will focus on recent advances in metabolomics in the field of HE. Thus, after a brief overview of the general principles of metabolomics, we will discuss metabolomics as a potentially efficient tool for unraveling new HE pathophysiological insights, biomarkers identification, or as a predicting tool for treatment response or outcome prognosis. Finally, we will give our vision on the prospects offered by metabolomics for improving care of HE patients.

Untargeted lipidomics uncovers lipid signatures that distinguish severe from moderate forms of acutely decompensated cirrhosis.

BACKGROUND & AIMS: Acute decompensation (AD) of cirrhosis is a heterogeneous clinical entity associated with moderate mortality. In some patients, this condition develops quickly into the more deadly acute-on-chronic liver failure (ACLF), in which other organs such as the kidneys or brain fail. The aim of this study was to characterize the blood lipidome in a large series of patients with cirrhosis and identify specific signatures associated with AD and ACLF development. METHODS: Serum untargeted lipidomics was performed in 561 patients with AD (518 without and 43 with ACLF) (discovery cohort) and in 265 patients with AD (128 without and 137 with ACLF) in whom serum samples were available to perform repeated measurements during the 28-day follow-up (validation cohort). Analyses were also performed in 78 patients with AD included in a therapeutic albumin trial (43 patients with compensated cirrhosis and 29 healthy individuals). RESULTS: The circulating lipid landscape associated with cirrhosis was characterized by a generalized suppression, which was more manifest during AD and in non-surviving patients. By computing discriminating accuracy and the variable importance projection score for each of the 223 annotated lipids, we identified a sphingomyelin fingerprint specific for AD of cirrhosis and a distinct cholesteryl ester and lysophosphatidylcholine fingerprint for ACLF. Liver dysfunction and infections were the principal net contributors to these fingerprints, which were dynamic and interchangeable between patients with AD whose condition worsened to ACLF and those who improved. Notably, blood lysophosphatidylcholine levels increased in these patients after albumin therapy. CONCLUSIONS: Our findings provide insights into the lipid landscape associated with decompensation of cirrhosis and ACLF progression and identify unique non-invasive diagnostic biomarkers of advanced cirrhosis. LAY SUMMARY: Analysis of lipids in blood from patients with advanced cirrhosis reveals a general suppression of their levels in the circulation of these patients. A specific group of lipids known as sphingomyelins are useful to distinguish between patients with compensated and decompensated cirrhosis. Another group of lipids designated cholesteryl esters further distinguishes patients with decompensated cirrhosis who are at risk of developing organ failures.

Blood metabolomics uncovers inflammation-associated mitochondrial dysfunction as a potential mechanism underlying ACLF.

BACKGROUND & AIMS: Acute-on-chronic liver failure (ACLF), which develops in patients with cirrhosis, is characterized by intense systemic inflammation and organ failure(s). Because systemic inflammation is energetically expensive, its metabolic costs may result in organ dysfunction/failure. Therefore, we aimed to analyze the blood metabolome in patients with cirrhosis, with and without ACLF. METHODS: We performed untargeted metabolomics using liquid chromatography coupled to high-resolution mass spectrometry in serum from 650 patients with AD (acute decompensation of cirrhosis, without ACLF), 181 with ACLF, 43 with compensated cirrhosis, and 29 healthy individuals. RESULTS: Of the 137 annotated metabolites identified, 100 were increased in patients with ACLF of any grade, relative to those with AD, and 38 comprised a distinctive blood metabolite fingerprint for ACLF. Among patients with ACLF, the intensity of the fingerprint increased across ACLF grades, and was similar in patients with kidney failure and in those without, indicating that the fingerprint reflected not only decreased kidney excretion but also altered cell metabolism. The higher the ACLF-associated fingerprint intensity, the higher the plasma levels of inflammatory markers, tumor necrosis factor α, soluble CD206, and soluble CD163. ACLF was characterized by intense proteolysis and lipolysis; amino acid catabolism; extra-mitochondrial glucose metabolism through glycolysis, pentose phosphate, and D-glucuronate pathways; depressed mitochondrial ATP-producing fatty acid ß-oxidation; and extra-mitochondrial amino acid metabolism giving rise to metabotoxins. CONCLUSIONS: In ACLF, intense systemic inflammation is associated with blood metabolite accumulation and profound alterations in major metabolic pathways, in particular inhibition of mitochondrial energy production, which may contribute to the development of organ failures. LAY SUMMARY: Acute-on-chronic liver failure (ACLF), which develops in patients with cirrhosis, is characterized by intense systemic inflammation and organ failure(s). Because systemic inflammation is energetically expensive, its metabolic costs may result in organ dysfunction/failure. We identified a 38-metabolite blood fingerprint specific for ACLF that revealed mitochondrial dysfunction in peripheral organs. This may contribute to organ failures.

A strategy for multimodal data integration: application to biomarkers identification in spinocerebellar ataxia.

The growing number of modalities (e.g. multi-omics, imaging and clinical data) characterizing a given disease provides physicians and statisticians with complementary facets reflecting the disease process but emphasizes the need for novel statistical methods of data analysis able to unify these views. Such data sets are indeed intrinsically structured in blocks, where each block represents a set of variables observed on a group of individuals. Therefore, classical statistical tools cannot be applied without altering their organization, with the risk of information loss. Regularized generalized canonical correlation analysis (RGCCA) and its sparse generalized canonical correlation analysis (SGCCA) counterpart are component-based methods for exploratory analyses of data sets structured in blocks of variables. Rather than operating sequentially on parts of the measurements, the RGCCA/SGCCA-based integrative analysis method aims at summarizing the relevant information between and within the blocks. It processes a priori information defining which blocks are supposed to be linked to one another, thus reflecting hypotheses about the biology underlying the data blocks. It also requires the setting of extra parameters that need to be carefully adjusted.Here, we provide practical guidelines for the use of RGCCA/SGCCA. We also illustrate the flexibility and usefulness of RGCCA/SGCCA on a unique cohort of patients with four genetic subtypes of spinocerebellar ataxia, in which we obtained multiple data sets from brain volumetry and magnetic resonance spectroscopy, and metabolomic and lipidomic analyses. As a first step toward the extraction of multimodal biomarkers, and through the reduction to a few meaningful components and the visualization of relevant variables, we identified possible markers of disease progression.

MetaboRank: network-based recommendation system to interpret and enrich metabolomics results.

Motivation: Metabolomics has shown great potential to improve the understanding of complex diseases, potentially leading to therapeutic target identification. However, no single analytical method allows monitoring all metabolites in a sample, resulting in incomplete metabolic fingerprints. This incompleteness constitutes a stumbling block to interpretation, raising the need for methods that can enrich those fingerprints. We propose MetaboRank, a new solution inspired by social network recommendation systems for the identification of metabolites potentially related to a metabolic fingerprint. Results: MetaboRank method had been used to enrich metabolomics data obtained on cerebrospinal fluid samples from patients suffering from hepatic encephalopathy (HE). MetaboRank successfully recommended metabolites not present in the original fingerprint. The quality of recommendations was evaluated by using literature automatic search, in order to check that recommended metabolites could be related to the disease. Complementary mass spectrometry experiments and raw data analysis were performed to confirm these suggestions. In particular, MetaboRank recommended the overlooked α-ketoglutaramate as a metabolite which should be added to the metabolic fingerprint of HE, thus suggesting that metabolic fingerprints enhancement can provide new insight on complex diseases. Availability and implementation: Method is implemented in the MetExplore server and is available at www.metexplore.fr. A tutorial is available at https://metexplore.toulouse.inra.fr/com/tutorials/MetaboRank/2017-MetaboRank.pdf. Supplementary information: Supplementary data are available at Bioinformatics online.

Orchestration of Tryptophan-Kynurenine Pathway, Acute Decompensation, and Acute-on-Chronic Liver Failure in Cirrhosis.

Systemic inflammation (SI) is involved in the pathogenesis of acute decompensation (AD) and acute-on-chronic liver failure (ACLF) in cirrhosis. In other diseases, SI activates tryptophan (Trp) degradation through the kynurenine pathway (KP), giving rise to metabolites that contribute to multiorgan/system damage and immunosuppression. In the current study, we aimed to characterize the KP in patients with cirrhosis, in whom this pathway is poorly known. The serum levels of Trp, key KP metabolites (kynurenine and kynurenic and quinolinic acids), and cytokines (SI markers) were measured at enrollment in 40 healthy subjects, 39 patients with compensated cirrhosis, 342 with AD (no ACLF) and 180 with ACLF, and repeated in 258 patients during the 28-day follow-up. Urine KP metabolites were measured in 50 patients with ACLF. Serum KP activity was normal in compensated cirrhosis, increased in AD and further increased in ACLF, in parallel with SI; it was remarkably higher in ACLF with kidney failure than in ACLF without kidney failure in the absence of differences in urine KP activity and fractional excretion of KP metabolites. The short-term course of AD and ACLF (worsening, improvement, stable) correlated closely with follow-up changes in serum KP activity. Among patients with AD at enrollment, those with the highest baseline KP activity developed ACLF during follow-up. Among patients who had ACLF at enrollment, those with immune suppression and the highest KP activity, both at baseline, developed nosocomial infections during follow-up. Finally, higher baseline KP activity independently predicted mortality in patients with AD and ACLF. Conclusion: Features of KP activation appear in patients with AD, culminate in patients with ACLF, and may be involved in the pathogenesis of ACLF, clinical course, and mortality.

Improving lipid mapping in Genome Scale Metabolic Networks using ontologies.

INTRODUCTION: To interpret metabolomic and lipidomic profiles, it is necessary to identify the metabolic reactions that connect the measured molecules. This can be achieved by putting them in the context of genome-scale metabolic network reconstructions. However, mapping experimentally measured molecules onto metabolic networks is challenging due to differences in identifiers and level of annotation between data and metabolic networks, especially for lipids. OBJECTIVES: To help linking lipids from lipidomics datasets with lipids in metabolic networks, we developed a new matching method based on the ChEBI ontology. The implementation is freely available as a python library and in MetExplore webserver. METHODS: Our matching method is more flexible than an exact identifier-based correspondence since it allows establishing a link between molecules even if a different level of precision is provided in the dataset and in the metabolic network. For instance, it can associate a generic class of lipids present in the network with the molecular species detailed in the lipidomics dataset. This mapping is based on the computation of a distance between molecules in ChEBI ontology. RESULTS: We applied our method to a chemical library (968 lipids) and an experimental dataset (32 modulated lipids) and showed that using ontology-based mapping improves and facilitates the link with genome scale metabolic networks. Beyond network mapping, the results provide ways for improvements in terms of network curation and lipidomics data annotation. CONCLUSION: This new method being generic, it can be applied to any metabolomics data and therefore improve our comprehension of metabolic modulations.

Separation of biologically relevant isomers on an Orbitrap mass spectrometer using high-resolution drift tube ion mobility and varied drift gas mixtures.

RATIONALE: Atmospheric pressure drift tube ion mobility is a powerful addition to the Orbitrap mass spectrometer enabling direct separation of isomers. Apart from offering high resolving power in a compact design, it also facilitates optimization of the separation gas, as shown here for a series of biologically relevant isomer pairs. METHODS: An Excellims MA3100 High-Resolution Atmospheric Pressure Ion Mobility Spectrometer (HR-IMS) was coupled to a Thermo Scientific™ Q Exactive™ Focus hybrid quadrupole-Orbitrap™ mass spectrometer, using an Excellims Directspray™ Electrospray Ionization source and a gas mixture setup to provide various drift gases (air, CO2 and mixtures). This instrument combination was used to separate isomers of eight pairs of metabolites and gangliosides, optimizing drift gas conditions for best separation of each set. RESULTS: All but one of the isomers pairs provided could be partially or fully separated by the HR-IMS-MS combination using ion mobility drift times. About half of the separated compounds showed significantly better analytical separation when analyzed in a mixture of CO2 and air rather than air or CO2 alone. Resolving power of up to 102 was achieved using the 10 cm atmospheric drift tube ion mobility add-on for the Orbitrap mass spectrometer. CONCLUSIONS: The present analysis demonstrates the usefulness of using atmospheric drift tube IMS on an Orbitrap mass spectrometer to characterize the isomeric composition of samples. It also highlights the potential benefits of being able to quickly optimize the drift gas composition to selectively maximize the mobility difference for isomer separation.

Evaluation of the High-Field Orbitrap Fusion for Compound Annotation in Metabolomics.

Annotation of signals of interest represents a key point in mass spectrometry-based metabolomics studies. The first level of investigation is the elemental composition, which can be deduced from accurately measured masses and isotope patterns. However, accuracy of these two parameters remains to be evaluated on last generation mass spectrometers to determine the level of confidence that can be used during the annotation process. In this context, we evaluated the performance of the Orbitrap Fusion mass spectrometer for the first time and demonstrated huge potential for metabolite annotation via elemental composition determination. This work was performed using a set of 50 standard compounds analyzed under LC/MS conditions in solvent and biological media. Accurate control of the number of trapped ions proved mandatory to avoid space charge effects, ensure sub-ppm mass accuracy (using external calibration), and reliable measurement of isotopic patterns at 500,000 resolution. On the basis of the results, we propose standard optimized experimental conditions for performing robust and accurate untargeted metabolomics on the Orbitrap Fusion at high mass measurement and mass spectral accuracy.

proFIA: a data preprocessing workflow for flow injection analysis coupled to high-resolution mass spectrometry.

MOTIVATION: Flow Injection Analysis coupled to High-Resolution Mass Spectrometry (FIA-HRMS) is a promising approach for high-throughput metabolomics. FIA-HRMS data, however, cannot be preprocessed with current software tools which rely on liquid chromatography separation, or handle low resolution data only. RESULTS: We thus developed the proFIA package, which implements a suite of innovative algorithms to preprocess FIA-HRMS raw files, and generates the table of peak intensities. The workflow consists of 3 steps: (i) noise estimation, peak detection and quantification, (ii) peak grouping across samples and (iii) missing value imputation. In addition, we have implemented a new indicator to quantify the potential alteration of the feature peak shape due to matrix effect. The preprocessing is fast (less than 15 s per file), and the value of the main parameters (ppm and dmz) can be easily inferred from the mass resolution of the instrument. Application to two metabolomics datasets (including spiked serum samples) showed high precision (96%) and recall (98%) compared with manual integration. These results demonstrate that proFIA achieves very efficient and robust detection and quantification of FIA-HRMS data, and opens new opportunities for high-throughput phenotyping. AVAILABILITY AND IMPLEMENTATION: The proFIA software (as well as the plasFIA dataset) is available as an R package on the Bioconductor repository (http://bioconductor.org/packages/proFIA), and as a Galaxy module on the Main Toolshed (https://toolshed.g2.bx.psu.edu), and on the Workflow4Metabolomics online infrastructure (http://workflow4metabolomics.org). CONTACT: alexis.delabriere@cea.fr or etienne.thevenot@cea.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Targeted versus untargeted omics - the CAFSA story.

BACKGROUND: In 2009, untargeted metabolomics led to the delineation of a new clinico-biological entity called cerebellar ataxia with elevated cerebrospinal free sialic acid, or CAFSA. In order to elucidate CAFSA, we applied sequentially targeted and untargeted omic approaches. METHODS AND RESULTS: First, we studied five of the six CAFSA patients initially described. Besides increased CSF free sialic acid concentrations, three patients presented with markedly decreased 5-methyltetrahydrofolate (5-MTHF) CSF concentrations. Exome sequencing identified a homozygous POLG mutation in two affected sisters, but failed to identify a causative gene in the three sporadic patients with high sialic acid but low 5-MTHF. Using targeted mass spectrometry, we confirmed that free sialic acid was increased in the CSF of a third known POLG-mutated patient. We then pursued pathophysiological analyses of CAFSA using mass spectrometry-based metabolomics on CSF from two sporadic CAFSA patients as well as 95 patients with an unexplained encephalopathy and 39 controls. This led to the identification of a common metabotype between the two initial CAFSA patients and three additional patients, including one patient with Kearns-Sayre syndrome. Metabolites of the CSF metabotype were positioned in a reconstruction of the human metabolic network, which highlighted the proximity of the metabotype with acetyl-CoA and carnitine, two key metabolites regulating mitochondrial energy homeostasis. CONCLUSION: Our genetic and metabolomics analyses suggest that CAFSA is a heterogeneous entity related to mitochondrial DNA alterations either through POLG mutations or a mechanism similar to what is observed in Kearns-Sayre syndrome.

Lipoprotein lipase in hypothalamus is a key regulator of body weight gain and glucose homeostasis in mice.

AIMS/HYPOTHESIS: Regulation of energy balance involves the participation of many factors, including nutrients, among which are circulating lipids, acting as peripheral signals informing the central nervous system of the energy status of the organism. It has been shown that neuronal lipoprotein lipase (LPL) participates in the control of energy balance by hydrolysing lipid particles enriched in triacylglycerols. Here, we tested the hypothesis that LPL in the mediobasal hypothalamus (MBH), a well-known nucleus implicated in the regulation of metabolic homeostasis, could also contribute to the regulation of body weight and glucose homeostasis. METHODS: We injected an adeno-associated virus (AAV) expressing Cre-green fluorescent protein into the MBH of Lpl-floxed mice (and wild-type mice) to specifically decrease LPL activity in the MBH. In parallel, we injected an AAV overexpressing Lpl into the MBH of wild-type mice. We then studied energy homeostasis and hypothalamic ceramide content. RESULTS: The partial deletion of Lpl in the MBH in mice led to an increase in body weight compared with controls (37.72 ± 0.7 g vs 28.46 ± 0.12, p < 0.001) associated with a decrease in locomotor activity. These mice developed hyperinsulinaemia and glucose intolerance. This phenotype also displayed reduced expression of Cers1 in the hypothalamus as well as decreased concentration of several C18 species of ceramides and a 3-fold decrease in total ceramide intensity. Conversely, overexpression of Lpl specifically in the MBH induced a decrease in body weight. CONCLUSIONS/INTERPRETATION: Our study shows that LPL in the MBH is an important regulator of body weight and glucose homeostasis.

Mechanisms governing the fragmentation of glycerophospholipids containing choline and ethanolamine polar head groups.

Glycerophospholipids are the major amphiphilic molecules found in the plasma membrane bilayer of all vertebrate cells. Involved in many biological processes, their huge structural diversity and large concentration scale make their thorough characterization extremely difficult in complex biological matrices. Mass spectrometry techniques are now recognized as being among the most powerful methods for the sensitive and comprehensive characterization of lipids. Depending on the experimental conditions used during electrospray ionization mass spectrometry experiments, glycerophospholipids can be detected as different molecular species (e.g. protonated, sodiated species) when analyzed either in positive or negative ionization modes or by direct introduction or hyphenated mass spectrometry-based methods. The observed ionized forms are characteristic of the corresponding phospholipid structures, and their formation is highly influenced by the polar head group. Although the fragmentation behavior of each phospholipid class has already been widely studied under low collision energy, there are no established rules based on charge-induced dissociation mechanisms for explaining the generation of fragment ions. In the present paper, we emphasize the crucial roles played by ion-dipole complexes and salt bridges within charge-induced dissociation processes. Under these conditions, we were able to readily explain almost all the fragment ions obtained under low-energy collision-induced dissociation for particular glycerophospholipids and lysoglycerophospholipids species including glycerophosphatidylcholines and glycerophosphatidylethanolamines. Thus, in addition to providing a basis for a better comprehension of phospholipid fragmentation processes, our work also highlighted some potentially new relevant diagnostic ions to signal the presence of particular lipid species.

Cerebrospinal fluid metabolomics highlights dysregulation of energy metabolism in overt hepatic encephalopathy.

BACKGROUND & AIMS: Hepatic encephalopathy (HE) is a neurological complication observed in patients with liver disease and/or porto-systemic shunt. The proportion of cirrhotic patients developing overt HE is about 20%, and 60-80% of cirrhotic patients exhibit mild cognitive impairment potentially related to minimal HE. However, the pathophysiological mechanisms of HE remain poorly understood. In this context, metabolomics was used to highlight dysfunction of metabolic pathways in cerebrospinal fluid (CSF) samples of patients suffering from HE. METHODS: CSF samples were collected in 27 control patients without any proven neurological disease and 14 patients with symptoms of HE. Plasma samples were obtained from control patients, and from cirrhotic patients with and without HE. Metabolomic analysis was performed using liquid chromatography coupled to high-resolution mass spectrometry. RESULTS: Concentrations of 73 CSF metabolites, including amino acids, acylcarnitines, bile acids and nucleosides, were altered in HE patients. Accumulation of acetylated compounds, which could be due to a defect of the Krebs cycle in HE patients, is reported for the first time. Furthermore, analysis of plasma samples showed that concentrations of metabolites involved in ammonia, amino-acid and energy metabolism are specifically and significantly increased in CSF samples of HE patients. Lastly, several drugs were detected in CSF samples and could partially explain worsening of neurological symptoms for some patients. CONCLUSION: By enabling the simultaneous monitoring of a large set of metabolites in HE patients, CSF metabolomics highlighted alterations of metabolic pathways linked to energy metabolism that were not observed in plasma samples. LAY SUMMARY: CSF metabolomics provides a global picture of altered metabolic pathways in CSF samples of HE patients and highlights alterations of metabolic pathways linked to energy metabolism that are not observed in plasma samples.

High resolution mass spectrometry based techniques at the crossroads of metabolic pathways.

The metabolome is the set of small molecular mass compounds found in biological media, and metabolomics, which refers to as the analysis of metabolome in a given biological condition, deals with the large scale detection and quantification of metabolites in biological media. It is a data driven and multidisciplinary approach combining analytical chemistry for data acquisition, and biostatistics, informatics and biochemistry for mining and interpretation of these data. Since the middle of the 2000s, high resolution mass spectrometry is widely used in metabolomics, mainly because the detection and identification of metabolites are improved compared to low resolution instruments. As the field of HRMS is quickly and permanently evolving, the aim of this work is to review its use in different aspects of metabolomics, including data acquisition, metabolite annotation, identification and quantification. At last, we would like to show that, thanks to their versatility, HRMS instruments are the most appropriate to achieve optimal metabolome coverage, at the border of other omics fields such as lipidomics and glycomics.

Lipidomic analysis of cerebrospinal fluid by mass spectrometry-based methods.

Lipids are natural substances found in all living organisms. Essential to the integrity of cell membranes, they also have many biological functions linked to energy storage and cell signaling, and are involved in a large number of heterogeneous diseases such as cancer, diabetes, neurological disorders, and inherited metabolic diseases. Lipids are challenging to analyze because of their huge structural diversity and numerous species. Up to now, lipid analysis has been achieved by targeted approaches focusing on selected families and relying on extraction protocols and chromatographic methods coupled to various detectors including mass spectrometry. Thanks to the technological improvements achieved in the fields of chromatography, high-resolution mass spectrometry and bioinformatics, it is possible to perform global lipidomic analyses enabling the concomitant detection, identification and relative quantification of many lipid species belonging to different families. The aim of this review is to focus on mass spectrometry-based methods to perform lipid and lipidomic analyses and on their application to the analysis of cerebrospinal fluid.

Impact of Source Conditions on Collision Cross Section Determination by Trapped Ion Mobility Spectrometry.

Collision cross section (CCS) values determined in ion mobility-mass spectrometry (IM-MS) are increasingly employed as additional descriptors in metabolomics studies. CCS values must therefore be reproducible and the causes of deviations must be carefully known and controlled. Here, we analyzed lipid standards by trapped ion mobility spectrometry-mass spectrometry (TIMS-MS) to evaluate the effects of solvent and flow rate in flow injection analysis (FIA), as well as electrospray source parameters including nebulizer gas pressure, drying gas flow rate, and temperature, on the ion mobility and CCS values. The stability of ion mobility experiments was studied over 10 h, which established the need for a delay-time of 20 min to stabilize source parameters (mostly pressure and temperature). Modifications of electrospray source parameters induced shifts of ion mobility peaks and even the occurrence of an additional peak in the ion mobility spectra. This behavior could be essentially explained by ion-solvent cluster formation. Changes in source parameters were also found to impact CCS value measurements, resulting in deviations up to 0.8%. However, internal calibration with the Tune Mix calibrant reduced the CCS deviations to 0.1%. Thus, optimization of source parameters is essential to achieve a good desolvation of lipid ions and avoid misinterpretation of peaks in ion mobility spectra due to solvent effects. This work highlights the importance of internal calibration to ensure interoperable CCS values, usable in metabolomics annotation.