A scoring approach for multi-platform acquisition in metabolomics

Study on the correlation and difference of qualitative information among three types of UPLC-HRMS and potential generalization in metabolites annotation

The variation of qualitative information among different types of mainstream hyphenated instruments of ultra-performance liquid chromatography coupled to high-resolution mass spectrometry (UPLC-HRMS) makes data sharing and standardization, and further comparison of results consistency in metabolite annotation not easy to attain. In this work, a quantitative study of correlation and difference was first achieved to systematically investigate the variation of retention time (tR), precursor ion (MS1), and product fragment ions (MS2) generated by three typical UPLC-HRMS instruments commonly used in metabolomics area. In terms of the findings of systematic and correlated variation of tR, MS1, and MS2 between different instruments, a computational strategy for integrated metabolite annotation was proposed to reduce the influence of differential ions, which made full use of the characteristic (common) and non-common fragments for scoring assessment. The regular variations of MS2 among three instruments under four collision energy voltages of high, medium, low, and hybrid levels were respectively inspected with three technical replicates at each level. These discoveries could improve general metabolite annotation with a known database and similarity comparison. It should provide the potential for metabolite annotation to generalize qualitative information obtained under different experimental conditions or using instruments from various manufacturers, which is still a big headache in untargeted metabolomics. The mixture of standard compounds and serum samples with the addition of standards were applied to demonstrate the principle and performance of the proposed method. The results showed that it could be an optional strategy for general use in HRMS-based metabolomics to offset the difference in metabolite annotation. It has some potential in untargeted metabolomics.

Development of an Untargeted LC-MS Metabolomics Method with Postcolumn Infusion for Matrix Effect Monitoring in Plasma and Feces

Untargeted metabolomics based on reverse phase LC-MS (RPLC-MS) plays a crucial role in biomarker discovery across physiological and disease states. Standardizing the development process of untargeted methods requires paying attention to critical factors that are under discussed or easily overlooked, such as injection parameters, performance assessment, and matrix effect evaluation. In this study, we developed an untargeted metabolomics method for plasma and fecal samples with the optimization and evaluation of these factors. Our results showed that optimizing the reconstitution solvent and sample injection amount was critical for achieving the balance between metabolites coverage and signal linearity. Method validation with representative stable isotopically labeled standards (SILs) provided insights into the analytical performance evaluation of our method. To tackle the issue of the matrix effect, we implemented a postcolumn infusion (PCI) approach to monitor the overall absolute matrix effect (AME) and relative matrix effect (RME). The monitoring revealed distinct AME and RME profiles in plasma and feces. Comparing RME data obtained for SILs through postextraction spiking with those monitored using PCI compounds demonstrated the comparability of these two methods for RME assessment. Therefore, we applied the PCI approach to predict the RME of 305 target compounds covered in our in-house library and found that targets detected in the negative polarity were more vulnerable to the RME, regardless of the sample matrix. Given the value of this PCI approach in identifying the strengths and weaknesses of our method in terms of the matrix effect, we recommend implementing a PCI approach during method development and applying it routinely in untargeted metabolomics.

A comparison of hydrophilic interaction liquid chromatography and capillary electrophoresis for the metabolomics analysis of human serum

Cationic, anionic, zwitterionic and, partially polar metabolites are very important constituents of blood serum. Several of these metabolites underpin the core metabolism of cells (e.g., Krebs cycle, urea cycle, proteins synthesis, etc.), while others might be considered ancillary but still important to grasp the status of any organism through blood serum analysis. Due to its wide chemical diversity, modern metabolomics analysis of serum is still struggling to provide a complete and comprehensive picture of the polar metabolome, due to the limitations of each specific analytical method. In this study, two metabolomics-based analytical methods using the most successful techniques for polar compounds separation in human serum samples, namely hydrophilic interaction liquid chromatography (HILIC) and capillary electrophoresis (CE), are evaluated, both coupled to a high-resolution time-of-flight mass spectrometer via electrospray ionization (ESI-Q-TOF-MS). The performance of the two methods have been compared using five terms of comparison, three of which are specific to metabolomics, such as (1) compounds' detectability (2) Pezzatti score (Pezzatti et al. 2018), (3) intra-day precision (repeatability), (4) ease of automatic analysis of the data (through a common deconvolution alignment and extrapolation software, MS-DIAL, and (5) time & cost analysis. From this study, HILIC-MS proved to be a better tool for polar metabolome analysis, while CE-MS helped identify some interesting variables that gave it interest in completing metabolome coverage in metabolomics studies. Finally, in this framework, MS-DIAL demonstrates for the first time its ability to process CE data for metabolomics, although it is not designed for it.

Plasma metabolic profiling of patients with tetralogy of fallot

BACKGROUND

Tetralogy of Fallot (TOF) is a common congenital heart disease with high mortality. However, the medical imageology and liquidbiopsy techniques present certain limitations. Thus, this study investigated the plasma metabolic profiles to distinguish key metabolites for early diagnosis of TOF.

METHODS

In total, 69 patients with TOF and 43 normal controls were enrolled for targeted metabolomics based on liquid chromatography-tandem mass spectroscopy (LC-MS/MS). Absolute quantification of metabolites was performed using our standard database. The differentially expressed metabolites (DEMs) were screened by fold change (FC), VIP value and pearson correlation coefficient of OPLS-DA model. Receiver operating characteristic curve (ROC) was used to evaluate predictive ability of DEMs.

RESULTS

Different metabolic profiles were presented between TOF and Normal.The pathway analysis showed that significantly changed metabolites were enriched in nicotinamide and purine metabolism. Many intermediatesproductof purine and amido acid were higher in TOF than in Normal group, while energy substrates and electron carriers were lower in TOF than in Normal group. ROC analysis revealed a high diagnostic value of plasma FAD for differentiating TOF from Normal (AUC = 1).

CONCLUSION

Our study quantitatively characterized plasma metabolites in patients with TOF and may help to develop reliable biomarkers that contribute to the early TOF screening.

Prediction of a Large-Scale Database of Collision Cross-Section and Retention Time Using Machine Learning to Reduce False Positive Annotations in Untargeted Metabolomics

Metabolite identification in untargeted metabolomics is complex, with the risk of false positive annotations. This work aims to use machine learning to successively predict the retention time (Rt) and the collision cross-section (CCS) of an open-access database to accelerate the interpretation of metabolomic results. Standards of metabolites were tested using liquid chromatography coupled with high-resolution mass spectrometry. In CCSBase and QSRR predictor machine learning models, experimental results were used to generate predicted CCS and Rt of the Human Metabolome Database. From 542 standards, 266 and 301 compounds were detected in positive and negative electrospray ionization mode, respectively, corresponding to 380 different metabolites. CCS and Rt were then predicted using machine learning tools for almost 114,000 metabolites. R² score of the linear regression between predicted and measured data achieved 0.938 and 0.898 for CCS and Rt, respectively, demonstrating the models' reliability. A CCS and Rt index filter of mean error ± 2 standard deviations could remove most misidentifications. Its application to data generated from a toxicology study on tobacco cigarettes reduced hits by 76%. Regarding the volume of data produced by metabolomics, the practical workflow provided allows for the implementation of valuable large-scale databases to improve the biological interpretation of metabolomics data.

Liquid chromatography-mass spectrometry metabolite library for metabolomics: Evaluating column suitability using a scoring approach

Untargeted metabolomic studies require an extensive set of analyte (metabolic) information to be obtained from each analyzed sample. Thus, highly selective, and efficient analytical methodologies together with reversed-phase (RP) or hydrophilic interaction liquid chromatography (HILIC) are usually applied in these approaches. Here, we present a performance comparison of five different chromatographic columns (C18, C8, RP Amide, zicHILIC, OH5 HILIC phases) to evaluate their sufficiency of analysis for a large analyte library, consisting of 817 authentic standards. By taking into account experimental chromatographic parameters (i.e. retention time, peak tailing and asymmetry, FWHM, signal-to-noise ratio and peak area and intensity), the proposed column scoring approach provides a simple criterion that may assist analysis in the select of a stationary phase for those metabolites of interest. RPLC methods offered better results regarding metabolic library coverage, while the zicHILIC stationary phase delivered a bigger number of properly eluted compounds. This study demonstrates the importance of choosing the most suitable configuration for the analysis of different metabolic classes.

Digoxin Induces Human Astrocyte Reaction In Vitro

Astrocyte reaction is a complex cellular process involving astrocytes in response to various types of CNS injury and a marker of neurotoxicity. It has been abundantly studied in rodents but relatively poorly in human cells due to limited access to the brain. Astrocytes play important roles in cerebral energy metabolism and are also key players in neuroinflammation. Astroglial metabolic and inflammatory changes have been reported with age, leading to the hypothesis that mitochondrial metabolism and inflammatory responses are interconnected. However, the relationship between energy metabolism and astrocyte reactivity in the context of neurotoxicity is not known. We hypothesized that changes in energy metabolism of astrocytes will be coupled to their activation by xenobiotics. Astrocyte reaction and associated energy metabolic changes were assessed by immunostaining, gene expression, proteomics, metabolomics, and extracellular flux analyses after 24 h of exposure of human ReN-derived astrocytes to digoxin (1-10 µM) or TNFα (30 ng/ml) used as a positive control. Strong astrocytic reaction was observed, accompanied by increased glycolysis at low concentrations of digoxin (0.1 and 0.5 µM) and after TNFα exposure, suggesting that increased glycolysis may be a common feature of reactive astrocytes, independent of the triggering molecule. In conclusion, whether astrocyte activation is triggered by cytokines or a xenobiotic, it is strongly tied to energy metabolism in human ReN-derived astrocytes. Increased glycolysis might be considered as an endpoint to detect astrocyte activation by potentially neurotoxic compounds in vitro. Finally, ReN-derived astrocytes may help to decipher mechanisms of neurotoxicity in ascertaining the ability of chemicals to directly target astrocytes.

Oxidative Stress and Extracellular Matrix Remodeling Are Signature Pathways of Extracellular Vesicles Released upon Morphine Exposure on Human Brain Microvascular Endothelial Cells

Morphine, a commonly used antinociceptive drug in hospitals, is known to cross the blood-brain barrier (BBB) by first passing through brain endothelial cells. Despite its pain-relieving effect, morphine also has detrimental effects, such as the potential induction of redox imbalance in the brain. However, there is still insufficient evidence of these effects on the brain, particularly on the brain endothelial cells and the extracellular vesicles that they naturally release. Indeed, extracellular vesicles (EVs) are nanosized bioparticles produced by almost all cell types and are currently thought to reflect the physiological state of their parent cells. These vesicles have emerged as a promising source of biomarkers by indicating the functional or dysfunctional state of their parent cells and, thus, allowing a better understanding of the biological processes involved in an adverse state. However, there is very little information on the morphine effect on human brain microvascular endothelial cells (HBMECs), and even less on their released EVs. Therefore, the current study aimed at unraveling the detrimental mechanisms of morphine exposure (at 1, 10, 25, 50 and 100 µM) for 24 h on human brain microvascular endothelial cells as well as on their associated EVs. Isolation of EVs was carried out using an affinity-based method. Several orthogonal techniques (NTA, western blotting and proteomics analysis) were used to validate the EVs enrichment, quality and concentration. Data-independent mass spectrometry (DIA-MS)-based proteomics was applied in order to analyze the proteome modulations induced by morphine on HBMECs and EVs. We were able to quantify almost 5500 proteins in HBMECs and 1500 proteins in EVs, of which 256 and 148, respectively, were found to be differentially expressed in at least one condition. Pathway enrichment analysis revealed that the "cell adhesion and extracellular matrix remodeling" process and the "HIF1 pathway", a pathway related to oxidative stress responses, were significantly modulated upon morphine exposure in HBMECs and EVs. Altogether, the combination of proteomics and bioinformatics findings highlighted shared pathways between HBMECs exposed to morphine and their released EVs. These results put forward molecular signatures of morphine-induced toxicity in HBMECs that were also carried by EVs. Therefore, EVs could potentially be regarded as a useful tool to investigate brain endothelial cells dysfunction, and to a different extent, the BBB dysfunction in patient circulation using these "signature pathways".

Metabolomics for exposure assessment and toxicity effects of occupational pollutants: current status and future perspectives

INTRODUCTION

Work-related exposures to harmful agents or factors are associated with an increase in incidence of occupational diseases. These exposures often represent a complex mixture of different stressors, challenging the ability to delineate the mechanisms and risk factors underlying exposure-disease relationships. The use of omics measurement approaches that enable characterization of biological marker patterns provide internal indicators of molecular alterations, which could be used to identify bioeffects following exposure to a toxicant. Metabolomics is the comprehensive analysis of small molecule present in biological samples, and allows identification of potential modes of action and altered pathways by systematic measurement of metabolites.

OBJECTIVES

The aim of this study is to review the application of metabolomics studies for use in occupational health, with a focus on applying metabolomics for exposure monitoring and its relationship to occupational diseases.

METHODS

PubMed, Web of Science, Embase and Scopus electronic databases were systematically searched for relevant studies published up to 2021.

RESULTS

Most of reviewed studies included worker populations exposed to heavy metals such as As, Cd, Pb, Cr, Ni, Mn and organic compounds such as tetrachlorodibenzo-p-dioxin, trichloroethylene, polyfluoroalkyl, acrylamide, polyvinyl chloride. Occupational exposures were associated with changes in metabolites and pathways, and provided novel insight into the relationship between exposure and disease outcomes. The reviewed studies demonstrate that metabolomics provides a powerful ability to identify metabolic phenotypes and bioeffect of occupational exposures.

CONCLUSION

Continued application to worker populations has the potential to enable characterization of thousands of chemical signals in biological samples, which could lead to discovery of new biomarkers of exposure for chemicals, identify possible toxicological mechanisms, and improved understanding of biological effects increasing disease risk associated with occupational exposure.

Metabolomic Effects of the Dietary Inclusion of Hermetia illucens Larva Meal in Tilapia

Black soldier fly (Hermetia illucens) larvae meal have been used as feed protein supplements in fish feed, but few researches have investigated the metabolomic effects of Hermetia illucens larvae meal supplements. Therefore, the metabolic effects on Nile tilapia were investigated by replacing 5%, 10%, and 20% of the dietary soybean meal in the basal diet with Hermetia illucens larvae meal, respectively. This study shows that 20% H. illucens larvae meal feed could promote tilapia average daily gain of upto 5.03 ± 0.18 g (mean ± SEM). It was found that the tricarboxylic acid cycle efficiency was improved by activating the enzymes of mitochondrial isocitrate dehydrogenase, NAD-malate dehydrogenase, succinate dehydrogenase, pyruvate dehydrogenase, and α-ketoglutarate dehydrogenase, which then increased the output of ATP and NADH. Furthermore, amino acid and protein biosynthesis was boosted by enhanced glutamine synthetase and glutamate synthase. In particular, GSH increased with increased H. illucens larvae meal. Unsaturated fatty acid biosynthesis was stimulated by higher levels of fatty acid synthase and acetyl CoA carboxylase. Additionally, there was no significant change in lipase levels. Thus, the higher acetyl Co-A content was primarily involved in fatty acid biosynthesis and energy metabolism. Flavor substances, such as nonanal and 2-methyl-3-furanthiol, also accumulated with the addition of H. illucens larvae meal, which increased the umami taste and meat flavor. Additionally, the flavor of tilapia was improved owing to a decrease in trimethylamine content, which causes an earthy and fishy taste. This study uncovers a previously unknown metabolic effect of dietary H. illucens larvae meal on Nile tilapia.

A Comparative Analysis of Metabolic Profiles of Embryonic Skeletal Muscle from Lantang and Landrace Pigs

Simple Summary

The pig is one of the most important domesticated meat animals. Some studies have revealed that pigs with low meat production show more intense myogenesis at the early stage of embryonic muscle development than pigs with high meat production. Here, by gas chromatography–mass spectrometry GC–MS based metabolomics, we concluded that the nucleotide metabolism and energy metabolism of the longissimuslumborum (LL) were increased in Lantang pigs compared with Landrace pigs, indicating rapid synthesis of nucleic acids and ATP to meet the material and energy requirements of rapid cell proliferation and differentiation in Lantang pigs.

Abstract

Elucidation of the complex regulation of porcine muscle development is key to increasing pork output and improving pork quality. However, the molecular mechanisms involved in early porcine embryonic muscle development in different pig breeds remain largely unknown. Here, GC–MS based metabolomics and metabolomic profiling was used to examine the longissimus lumborum (LL) of the Lantang (LT) and the Landrace (LR) pig at embryonic day 35 (E35). Metabolites showed clear separation between LT and LR, with 40 metabolites having higher abundances in LT and 14 metabolites having lower abundances in LT compared with LR. In addition, these metabolic changes were mainly associated with nucleotide metabolism and energy metabolism, such as purine metabolism, pyrimidine metabolism, the pentose phosphate pathway, and the TCA cycle. More interestingly, the contents of DNA, RNA, and ATP per unit mass of LL tissues were higher in LT, indicating rapid synthesis of nucleic acids and ATP, to meet both the material and energy requirements of rapid cell proliferation and differentiation. Furthermore, enzyme activity associated with the TCA cycle and pentose phosphate pathway, including α-ketoglutaric dehydrogenase (KGDH), malate dehydrogenase (MDH), pyruvate dehydrogenase (PDH), succinate dehydrogenase (SDH), and glucose-6-phosphate dehydrogenase (G6PDH), were higher in LT. Based on these results, we conclude that there are significant differences in nucleotide metabolism and energy metabolism of LL between LT and LR, and we speculate that the enhanced nucleic acid metabolism and energy metabolism in LT can meet the material and energy requirements of rapid cell proliferation and differentiation, making myogenesis more intense in LT compared to LR which might be the metabolic mechanism underlying the distinct skeletal muscle development in the two breeds.

Inside the Alterations of Circulating Metabolome in Antarctica: The Adaptation to Chronic Hypoxia

Although the human body may dynamically adapt to mild and brief oxygen shortages, there is a growing interest in understanding how the metabolic pathways are modified during sustained exposure to chronic hypoxia. Located at an equivalent altitude of approximately 3,800 m asl, the Concordia Station in Antarctica represents an opportunity to study the course of human adaption to mild hypoxia with reduced impact of potentially disturbing variables else than oxygen deprivation. We recruited seven healthy subjects who spent 10 months in the Concordia Station, and collected plasma samples at sea level before departure, and 90 days, 6 months, and 10 months during hypoxia. Samples were analyzed by untargeted liquid chromatography high resolution mass spectrometry to unravel how the non-polar and polar metabolomes are affected. Statistical analyses were performed by clustering the subjects into four groups according to the duration of hypoxia exposure. The non-polar metabolome revealed a modest decrease in the concentration of all the major lipid classes. By contrast, the polar metabolome showed marked alterations in several metabolic pathways, especially those related to amino acids metabolism, with a particular concern of arginine, glutamine, phenylalanine, tryptophan, and tyrosine. Remarkably, all the changes were evident since the first time point and remained unaffected by hypoxia duration (with the exception of a slight return of the non-polar metabolome after 6 months), highlighting a relative inability of the body to compensate them. Finally, we identified a few metabolic pathways that emerged as the main targets of chronic hypoxia.

Structure Elucidation and Mitigation of Endogenous Interferences in LC-MS-Based Metabolic Profiling of Urine

Liquid chromatography-mass spectrometry (LC-MS) is the main workhorse of metabolomics owing to its high degree of analytical sensitivity and specificity when measuring diverse chemistry in complex biological samples. LC-MS-based metabolic profiling of human urine, a biofluid of primary interest for clinical and biobank studies, is not widely considered to be compromised by the presence of endogenous interferences and is often accomplished using a simple "dilute-and-shoot" approach. Yet, it is our experience that broad obscuring signals are routinely observed in LC-MS metabolic profiles and represent interferences that lack consideration in the relevant metabolomics literature. In this work, we chromatographically isolated the interfering metabolites from human urine and unambiguously identified them via de novo structure elucidation as two separate proline-containing dipeptides: N,N,N-trimethyl-l-alanine-l-proline betaine (l,l-TMAP) and N,N-dimethyl-l-proline-l-proline betaine (l,l-DMPP), the latter reported here for the first time. Offline LC-MS/MS, magnetic resonance mass spectrometry (MRMS), and nuclear magnetic resonance (NMR) spectroscopy were essential components of this workflow for the full chemical and spectroscopic characterization of these metabolites and for establishing the coexistence of cis and trans isomers of both dipeptides in solution. Analysis of these definitive structures highlighted intramolecular ionic interactions as responsible for slow interconversion between these isomeric forms resulting in their unusually broad elution profiles. Proposed mitigation strategies, aimed at increasing the quality of LC-MS-based urine metabolomics data, include modification of column temperature and mobile-phase pH to reduce the chromatographic footprint of these dipeptides, thereby reducing their interfering effect on the underlying metabolic profiles. Alternatively, sample dilution and internal standardization methods may be employed to reduce or account for the observed effects of ionization suppression on the metabolic profile.

Current state-of-the-art of separation methods used in LC-MS based metabolomics and lipidomics

Metabolomics deals with the large-scale analysis of metabolites, belonging to numerous compound classes and showing an extremely high chemical diversity and complexity. Lipidomics, being a subcategory of metabolomics, analyzes the cellular lipid species. Both require state-of-the-art analytical methods capable of accessing the underlying chemical complexity. One of the major techniques used for the analysis of metabolites and lipids is Liquid Chromatography-Mass Spectrometry (LC-MS), offering both different selectivities in LC separation and high sensitivity in MS detection. Chromatography can be divided into different modes, based on the properties of the employed separation system. The most popular ones are Reversed-Phase (RP) separation for non- to mid-polar molecules and Hydrophilic Interaction Liquid Chromatography (HILIC) for polar molecules. So far, no single analysis method exists that can cover the entire range of metabolites or lipids, due to the huge chemical diversity. Consequently, different separation methods have been used for different applications and research questions. In this review, we explore the current use of LC-MS in metabolomics and lipidomics. As a proxy, we examined the use of chromatographic methods in the public repositories EBI MetaboLights and NIH Metabolomics Workbench. We extracted 1484 method descriptions, collected separation metadata and generated an overview on the current use of columns, eluents, etc. Based on this overview, we reviewed current practices and identified potential future trends as well as required improvements that may allow us to increase metabolite coverage, throughput or both simultaneously.

Serine Metabolism Tunes Immune Responses To Promote Oreochromis niloticus Survival upon Edwardsiella tarda Infection

Overactive immune response is a critical factor triggering host death upon bacterial infection. However, the mechanism behind the regulation of excessive immune responses is still largely unknown, and the corresponding control and preventive measures are still to be explored. In this study, we find that Nile tilapia, Oreochromis niloticus, that died from Edwardsiella tarda infection had higher levels of immune responses than those that survived. Such immune responses are strongly associated with metabolism that was altered at 6 h postinfection. By gas chromatography-mass spectrometry-based metabolome profiling, we identify glycine, serine, and threonine metabolism as the top three of the most impacted pathways, which were not properly activated in the fish that died. Serine is one of the crucial biomarkers. Exogenous serine can promote O. niloticus survival both as a prophylactic and therapeutic upon E. tarda infection. Our further analysis revealed exogenous serine flux into the glycine, serine, and threonine metabolism and, more importantly, the glutathione metabolism via glycine. The increased glutathione synthesis could downregulate reactive oxygen species. Therefore, these data together suggest that metabolic modulation of immune responses is a potential preventive strategy to control overactive immune responses. IMPORTANCE Bacterial virulence factors are not the only factors responsible for host death. Overactive immune responses, such as cytokine storm, contribute to tissue injury that results in organ failure and ultimately the death of the host. Despite the recent development of anti-inflammation strategies, the way to tune immune responses to an appropriate level is still lacking. We propose that metabolic modulation is a promising approach in tuning immune responses. We find that the metabolomic shift at as early as 6 h postinfection can be predictive of the consequences of infection. Serine is a crucial biomarker whose administration can promote host survival upon bacterial infection either in a prophylactic or therapeutic way. Further analysis demonstrated that exogenous serine promotes the synthesis of glutathione, which downregulates reactive oxygen species to dampen immune responses. Our study exemplifies that the metabolite(s) is a potential therapeutic reagent for overactive immune response during bacterial infection.

Comprehensive Metabolic Signature of Renal Dysplasia in Children. A Multiplatform Metabolomics Concept

Renal dysplasia is a severe congenital abnormality of the kidney parenchyma, which is an important cause of end-stage renal failure in childhood and early adulthood. The diagnosis of renal dysplasia relies on prenatal or postnatal ultrasounds as children show no specific clinical symptoms before chronic kidney disease develops. Prompt diagnosis is important in terms of early introduction of nephroprotection therapy and improved long-term prognosis. Metabolomics was applied to study children with renal dysplasia to provide insight into the changes in biochemical pathways underlying its pathology and in search of early indicators for facilitated diagnosis. The studied cohort consisted of 72 children, 39 with dysplastic kidneys and 33 healthy controls. All subjects underwent comprehensive urine metabolic profiling with the use of gas chromatography and liquid chromatography coupled to mass spectrometry, with two complementary separation modes of the latter. Univariate and multivariate statistical calculations identified a total of nineteen metabolites, differentiating the compared cohorts, independent of their estimated glomerular filtration rate. Seven acylcarnitines, xanthine, and glutamine were downregulated in the urine of renal dysplasia patients. Conversely, renal dysplasia was associated with higher urinary levels of dimethylguanosine, threonic acid or glyceric acid. This is the first metabolomic study of subjects with renal dysplasia. The authors define a characteristic urine metabolic signature in children with dysplastic kidneys, irrespective of renal function, linking the condition with altered fatty acid oxidation, amino acid and purine metabolisms.

Gaining Insights Into Metabolic Networks Using Chemometrics and Bioinformatics: Chronic Kidney Disease as a Clinical Model

Because of its ability to generate biological hypotheses, metabolomics offers an innovative and promising approach in many fields, including clinical research. However, collecting specimens in this setting can be difficult to standardize, especially when groups of patients with different degrees of disease severity are considered. In addition, despite major technological advances, it remains challenging to measure all the compounds defining the metabolic network of a biological system. In this context, the characterization of samples based on several analytical setups is now recognized as an efficient strategy to improve the coverage of metabolic complexity. For this purpose, chemometrics proposes efficient methods to reduce the dimensionality of these complex datasets spread over several matrices, allowing the integration of different sources or structures of metabolic information. Bioinformatics databases and query tools designed to describe and explore metabolic network models offer extremely useful solutions for the contextualization of potential biomarker subsets, enabling mechanistic hypotheses to be considered rather than simple associations. In this study, network principal component analysis was used to investigate samples collected from three cohorts of patients including multiple stages of chronic kidney disease. Metabolic profiles were measured using a combination of four analytical setups involving different separation modes in liquid chromatography coupled to high resolution mass spectrometry. Based on the chemometric model, specific patterns of metabolites, such as N-acetyl amino acids, could be associated with the different subgroups of patients. Further investigation of the metabolic signatures carried out using genome-scale network modeling confirmed both tryptophan metabolism and nucleotide interconversion as relevant pathways potentially associated with disease severity. Metabolic modules composed of chemically adjacent or close compounds of biological relevance were further investigated using carbon transfer reaction paths. Overall, the proposed integrative data analysis strategy allowed deeper insights into the metabolic routes associated with different groups of patients to be gained. Because of their complementary role in the knowledge discovery process, the association of chemometrics and bioinformatics in a common workflow is therefore shown as an efficient methodology to gain meaningful insights in a clinical context.

Neuroinflammatory Response to TNFα and IL1β Cytokines Is Accompanied by an Increase in Glycolysis in Human Astrocytes In Vitro

Astrogliosis has been abundantly studied in rodents but relatively poorly in human cells due to limited access to the brain. Astrocytes play important roles in cerebral energy metabolism, and are also key players in neuroinflammation. Astroglial metabolic and inflammatory changes as a function of age have been reported, leading to the hypothesis that mitochondrial metabolism and inflammatory responses are interconnected in supporting a functional switch of astrocytes from neurotrophic to neurotoxic. This study aimed to explore the metabolic changes occurring in astrocytes during their activation. Astrocytes were derived from human ReN cell neural progenitors and characterized. They were activated by exposure to tumor necrosis factor alpha (TNFα) or interleukin 1β (IL1β) for 24 h. Astrocyte reaction and associated energy metabolic changes were assessed by immunostaining, gene expression, proteomics, metabolomics and extracellular flux analyses. ReN-derived astrocytes reactivity was observed by the modifications of genes and proteins linked to inflammation (cytokines, nuclear factor-kappa B (NFκB), signal transducers and activators of transcription (STATs)) and immune pathways (major histocompatibility complex (MHC) class I). Increased NFκB1, NFκB2 and STAT1 expression, together with decreased STAT3 expression, suggest an activation towards the detrimental pathway. Strong modifications of astrocyte cytoskeleton were observed, including a glial fibrillary acidic protein (GFAP) decrease. Astrogliosis was accompanied by changes in energy metabolism characterized by increased glycolysis and lactate release. Increased glycolysis is reported for the first time during human astrocyte activation. Astrocyte activation is strongly tied to energy metabolism, and a possible association between NFκB signaling and/or MHC class I pathway and glycolysis is suggested.

Approaches in metabolomics for regulatory toxicology applications

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

Evaluation of a nanoflow interface based on the triple-tube coaxial sheath-flow sprayer for capillary electrophoresis-mass spectrometry coupling in metabolomics

The performance of an original CE-MS interface that allows the in-axis positioning of the electrospray with respect to the MS inlet was evaluated. The variations in the geometrical alignment of this configuration in the absence of a nebulizing gas afforded a significant reduction in the sheath-liquid flow rate from 3 µL/min to as low as 300 nL/min. The sheath liquid and BGE were respectively composed of H₂O-iPrOHCH₃COOH 50:50:1 (v/v/v) and 10% acetic acid (pH 2.2). A significant gain in sensitivity was obtained, and it was correlated to the effective mobility of the analytes. Compounds with low mobility values showed a greater sensitivity gain. Special attention was paid to the detection of proteinogenic amino acids. Linear response functions were obtained from 15 ng/mL to 500 ng/mL. The limits of quantification, as low as 34.3 ng/mL, were improved by a factor of up to six compared to the conventional configuration. The in-axis setup was ultimately applied to the absolute quantification of four important amino acids, alanine, tyrosine, methionine and valine, in standard reference material (NIST plasma). The accuracies ranged from 78 to 113%, thus demonstrating the potential of this configuration for metabolomics.

Malate enhances survival of zebrafish against Vibrio alginolyticus infection in the same manner as taurine

Development of low-cost and eco-friendly approaches to fight bacterial pathogens is especially needed in aquaculture. We previously showed that exogenous malate reprograms zebrafish’s metabolome to potentiate zebrafish survival against Vibrio alginolyticus infection. However, the underlying mechanism is unknown. Here, we use GC-MS based metabolomics to identify the malate-triggered metabolic shift. An activated TCA cycle and elevated taurine are identified as the key metabolic pathways and the most crucial biomarker of the reprogrammed metabolome, respectively. Taurine elevation is attributed to the activated TCA cycle, which is further supported by the increased expression of genes in the metabolic pathway of taurine biosynthesis from the isocitrate of the TCA cycle to taurine. Exogenous taurine increases the survival of zebrafish against V. alginolyticus infection as malate did. Moreover, exogenous taurine and malate regulate the expression of innate immunity genes and promote the generation of reactive oxygen species and nitrogen oxide in a similar way. The two metabolites can alleviate the excessive immune response to bacterial challenge, which protects fish from bacterial infection. These results indicate that malate enhances the survival of zebrafish to V. alginolyticus infection via taurine. Thus, our study highlights a metabolic approach to enhance a host’s ability to fight bacterial infection.

Metabolomics study reveals the potential evidence of metabolic reprogramming towards the Warburg effect in precancerous lesions

Background: Most tumors have an enhanced glycolysis flux, even when oxygen is available, called the aerobic glycolysis or the Warburg effect. Metabolic reprogramming promotes cancer progression, and is even related to the tumorigenesis. However, it is not clear whether the observed metabolic changes act as a driver or a bystander in cancer development.

Methods: In this study, the metabolic characteristics of oral precancerous cells and cervical precancerous lesions were analyzed by metabolomics, and the expression of glycolytic enzymes in cervical precancerous lesions was evaluated by RT-PCR and Western blot analysis.

Results: In total, 115 and 23 metabolites with reliable signals were identified in oral cells and cervical tissues, respectively. Based on the metabolome, oral precancerous cell DOK could be clearly separated from normal human oral epithelial cells (HOEC) and oral cancer cells. Four critical differential metabolites (pyruvate, glutamine, methionine and lysine) were identified between DOK and HOEC. Metabolic profiles could clearly distinguish cervical precancerous lesions from normal cervical epithelium and cervical cancer. Compared with normal cervical epithelium, the glucose consumption and lactate production increased in cervical precancerous lesions. The expression of glycolytic enzymes LDHA, HK II and PKM2 showed an increased tendency in cervical precancerous lesions compared with normal cervical epithelium.

Conclusions: Our findings suggest that cell metabolism may be reprogrammed at the early stage of tumorigenesis, implying the contribution of metabolic reprogramming to the development of tumor.

An exploratory approach for an oriented development of an untargeted hydrophilic interaction liquid chromatography-mass spectrometry platform for polar metabolites in biological matrices

The analysis of polar metabolites based on liquid chromatography-mass spectrometry (LC-MS) methods should take into consideration the complexity of interactions in LC columns to be able to cover a broad range of metabolites of key biological pathways. Therefore, in this study, different chromatographic columns were tested for polar metabolites including reversed-phase and hydrophilic interaction liquid chromatography (HILIC) columns. Based on a column screening, two new generations of zwitterionic HILIC columns were selected for further evaluation. A tree-based method optimization was applied to investigate the chromatographic factors affecting the retention mechanisms of polar metabolites with zwitterionic stationary phases. The results were evaluated based on a scoring system which was applied for more than 80 polar metabolites with a high coverage of key human metabolic pathways. The final optimized methods showed high complementarity to analyze a wide range of metabolic classes including amino acids, small peptides, sugars, amino sugars, phosphorylated sugars, organic acids, nucleobases, nucleosides, nucleotides and acylcarnitines. Optimized methods were applied to analyze different biological matrices, including human urine, plasma and liver cell extracts using an untargeted approach. The number of high-quality features (< 30% median relative standard deviation) ranged from 3,755 for urine to 5,402 for the intracellular metabolome of liver cells, showing the potential of the methods for untargeted purposes.

Respiratory tissue-associated commensal bacteria offer therapeutic potential against pneumococcal colonization

Under eubiotic conditions commensal microbes are known to provide a competitive barrier against invading bacterial pathogens in the intestinal tract, on the skin or on the vaginal mucosa. Here, we evaluate the role of lung microbiota in Pneumococcus colonization of the lungs. In eubiosis, the lungs of mice were dominantly colonized by Lactobacillus murinus. Differential analysis of 16S rRNA gene sequencing or L. murinus-specific qPCR of DNA from total organ homogenates vs.broncho alveolar lavages implicated tight association of these bacteria with the host tissue. Pure L. murinus conditioned culture medium inhibited growth and reduced the extension of pneumococcal chains. Growth inhibition in vitro was likely dependent on L. murinus-produced lactic acid, since pH neutralization of the conditioned medium aborted the antibacterial effect. Finally, we demonstrate that L. murinus provides a barrier against pneumococcal colonization in a respiratory dysbiosis model after an influenza A virus infection, when added therapeutically.

Evaluation of Different Tandem MS Acquisition Modes to Support Metabolite Annotation in Human Plasma Using Ultra High-Performance Liquid Chromatography High-Resolution Mass Spectrometry for Untargeted Metabolomics

Ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) is a powerful and essential technique for metabolite annotation in untargeted metabolomic applications. The aim of this study was to evaluate the performance of diverse tandem MS (MS/MS) acquisition modes, i.e., all ion fragmentation (AIF) and data-dependent analysis (DDA), with and without ion mobility spectrometry (IM), to annotate metabolites in human plasma. The influence of the LC separation was also evaluated by comparing the performance of MS/MS acquisition in combination with three complementary chromatographic separation modes: reversed-phase chromatography (RPLC) and hydrophilic interaction chromatography (HILIC) with either an amide (aHILIC) or a zwitterionic (zHILIC) stationary phase. RPLC conditions were first chosen to investigate all the tandem MS modes, and we found out that DDA did not provide a significant additional amount of chemical coverage and that cleaner MS/MS spectra can be obtained by performing AIF acquisitions in combination with IM. Finally, we were able to annotate 338 unique metabolites and demonstrated that zHILIC was a powerful complementary approach to both the RPLC and aHILIC chromatographic modes. Moreover, a better analytical throughput was reached for an almost negligible loss of metabolite coverage when IM-AIF and AIF using ramped instead of fixed collision energies were used.

Exploring blood alterations in chronic kidney disease and haemodialysis using metabolomics

Chronic kidney disease (CKD) is characterized by retention of uremic solutes. Compared to patients with non-dialysis dependent CKD, those requiring haemodialysis (HD) have increased morbidity and mortality. We wished to characterise metabolic patterns in CKD compared to HD patients using metabolomics. Prevalent non-HD CKD KDIGO stage 3b-4 and stage 5 HD outpatients were screened at a single tertiary hospital. Various liquid chromatography approaches hyphenated with mass spectrometry were used to identify 278 metabolites. Unsupervised and supervised data analyses were conducted to characterize metabolic patterns. 69 patients were included in the CKD group and 35 in the HD group. Unsupervised data analysis showed clear clustering of CKD, pre-dialysis (preHD) and post-dialysis (postHD) patients. Supervised data analysis revealed qualitative as well as quantitative differences in individual metabolites profiles between CKD, preHD and postHD states. An original metabolomics framework could discriminate between CKD stages and highlight HD effect based on 278 identified metabolites. Significant differences in metabolic patterns between CKD and HD patients were found overall as well as for specific metabolites. Those findings could explain clinical discrepancies between patients requiring HD and those with earlier stage of CKD.

Evaluation of ion mobility in capillary electrophoresis coupled to mass spectrometry for the identification in metabolomics

Currently, feature annotation remains one of the main challenges in untargeted metabolomics. In this context, the information provided by high-resolution mass spectrometry (HRMS) in addition to accurate mass can improve the quality of metabolite annotation, and MS/MS fragmentation patterns are widely used. Accurate mass and a separation index, such as retention time or effective mobility (μ_eff ), in chromatographic and electrophoretic approaches, respectively, must be used for unequivocal metabolite identification. The possibility of measuring collision cross-section (CCS) values by using ion mobility (IM) is becoming increasingly popular in metabolomic studies thanks to the new generation of IM mass spectrometers. Based on their similar separation mechanisms involving electric field and the size of the compounds, the complementarity of ^DT CCS_N2 and μ_eff needs to be evaluated. In this study, a comparison of ^DT CCS_N2 and μ_eff was achieved in the context of feature identification ability in untargeted metabolomics by capillary zone electrophoresis (CZE) coupled with HRMS. This study confirms the high correlation of ^DT CCS_N2 with the mass of the studied metabolites as well as the orthogonality between accurate mass and μ_eff , making this combination particularly interesting for the identification of several endogenous metabolites. The use of IM-MS remains of great interest for facilitating the annotation of neutral metabolites present in the electroosmotic flow (EOF) that are poorly or not separated by CZE.

Exogenous maltose enhances Zebrafish immunity to levofloxacin-resistant Vibrio alginolyticus

Understanding the interplay between bacterial fitness, antibiotic resistance, host immunity and host metabolism could guide treatment and improve immunity against antibiotic-resistant pathogens. The acquisition of levofloxacin (Lev) resistance affects the fitness of Vibrio alginolyticus in vitro and in vivo. Lev-resistant (Lev-R) V. alginolyticus exhibits slow growth, reduced pathogenicity and greater resistance to killing by the host, Danio rerio (zebrafish), than Lev-sensitive (Lev-S) V. alginolyticus, suggesting that Lev-R V. alginolyticus triggers a weaker innate immune response in D. rerio than Lev-S V. alginolyticus. Differences were detected in the metabolome of D. rerio infected with Lev-S or Lev-R V. alginolyticus. Maltose, a crucial metabolite, is significantly downregulated in D. rerio infected with Lev-R V. alginolyticus, and exogenous maltose enhances the immune response of D. rerio to Lev-R V. alginolyticus, leading to better clearance of the infection. Furthermore, we demonstrate that exogenous maltose stimulates the host production of lysozyme and its binding to Lev-R V. alginolyticus, which depends on bacterial membrane potential. We suggest that exogenous exposure to crucial metabolites could be an effective strategy for treating and/or managing infections with antibiotic-resistant bacteria.

Bacterial cell cycle control by citrate synthase independent of enzymatic activity

Proliferating cells must coordinate central metabolism with the cell cycle. How central energy metabolism regulates bacterial cell cycle functions is not well understood. Our forward genetic selection unearthed the Krebs cycle enzyme citrate synthase (CitA) as a checkpoint regulator controlling the G₁→S transition in the polarized alpha-proteobacterium Caulobacter crescentus, a model for cell cycle regulation and asymmetric cell division. We find that loss of CitA promotes the accumulation of active CtrA, an essential cell cycle transcriptional regulator that maintains cells in G₁-phase, provided that the (p)ppGpp alarmone is present. The enzymatic activity of CitA is dispensable for CtrA control, and functional citrate synthase paralogs cannot replace CitA in promoting S-phase entry. Our evidence suggests that CitA was appropriated specifically to function as a moonlighting enzyme to link central energy metabolism with S-phase entry. Control of the G₁-phase by a central metabolic enzyme may be a common mechanism of cellular regulation.

Applicability of Supercritical fluid chromatography-Mass spectrometry to metabolomics. II-Assessment of a comprehensive library of metabolites and evaluation of biological matrices

In this work, the impact of biological matrices, such as plasma and urine, was evaluated under SFCHRMS in the field of metabolomics. For this purpose, a representative set of 49 metabolites were selected. The assessment of the matrix effects (ME), the impact of biological fluids on the quality of MS/MS spectra and the robustness of the SFCHRMS method were each taken into consideration. The results have highlighted a limited presence of ME in both plasma and urine, with 30% of the metabolites suffering from ME in plasma and 25% in urine, demonstrating a limited sensitivity loss in the presence of matrices. Subsequently, the MS/MS spectra evaluation was performed for further peak annotation. Their analyses have highlighted three different scenarios: 63% of the tested metabolites did not suffer from any interference regardless of the matrix; 21% were negatively impacted in only one matrix and the remaining 16% showed the presence of matrix-belonging compounds interfering in both urine and plasma. Finally, the assessment of retention times stability in the biological samples, has brought into evidence a remarkable robustness of the SFCHRMS method. Average RSD (%) values of retention times for spiked metabolites were equal or below 0.5%, in the two biological fluids over a period of three weeks. In the second part of the work, the evaluation of the Sigma Mass Spectrometry Metabolite Library of Standards containing 597 metabolites, under SFCHRMS conditions was performed. A total detectability of the commercial library up to 66% was reached. Among the families of detected metabolites, large percentages were met for some of them. Highly polar metabolites such as amino acids (87%), nucleosides (85%) and carbohydrates (71%) have demonstrated important success rates, equally for hydrophobic analytes such as steroids (78%) and lipids (71%). On the negative side, very poor performance was found for phosphorylated metabolites, namely phosphate-containing compounds (14%) and nucleotides (31%).

Implementation of liquid chromatography-high resolution mass spectrometry methods for untargeted metabolomic analyses of biological samples: A tutorial

Untargeted metabolomics is now widely recognized as a useful tool for exploring metabolic changes taking place in biological systems under different conditions. By its nature, this is a highly interdisciplinary field of research, and mastering all of the steps comprised in the pipeline can be a challenging task, especially for those researchers new to the topic. In this tutorial, we aim to provide an overview of the most widely adopted methods of performing LC-HRMS-based untargeted metabolomics of biological samples. A detailed protocol is provided in the Supplementary Information for rapidly implementing a basic screening workflow in a laboratory setting. This tutorial covers experimental design, sample preparation and analysis, signal processing and data treatment, and, finally, data analysis and its biological interpretation. Each section is accompanied by up-to-date literature to guide readers through the preparation and optimization of such a workflow, as well as practical information for avoiding or fixing some of the most frequently encountered pitfalls.

Ammonium Fluoride as Suitable Additive for HILIC-Based LC-HRMS Metabolomics

Hydrophilic Interaction Liquid Chromatography (HILIC) chromatography is widely applied in metabolomics as a complementary strategy to reverse phase chromatography. Nevertheless, it still faces several issues in terms of peak shape and compounds ionization, limiting the automatic de-convolution and data semi-quantification performed through dedicated software. A way to improve the chromatographic and ionization performance of a HILIC method is to modify the electrostatic interactions of the analytes with both mobile and stationary phases. In this study, using a ZIC-HILIC chromatographic phase, we evaluated the performance of ammonium fluoride (AF) as additive salt, comparing its performance to ammonium acetate (AA). Three comparative criteria were selected: (1) identification and peak quality of 34 standards following a metabolomics-specific evaluation approach, (2) an intraday repeatability test with real samples and (3) performing two real metabolomics fingerprints with the AF method to evaluate its inter-day repeatability. The AF method showed not only higher ionization efficiency and signal-to-noise ratio but also better repeatability and robustness than the AA approach. A tips and tricks section is then added, aiming at improving method replicability for further users. In conclusion, ammonium fluoride as additive salt presents several advantages and might be considered as a step forward in the application of robust HILIC methods in metabolomics.

Combining the advantages of multilevel and orthogonal partial least squares data analysis for longitudinal metabolomics: Application to kidney transplantation

Kidney transplantation is one of the renal replacement options in patients suffering from end-stage renal disease (ESRD). After a transplant, patient follow-up is essential and is mostly based on immunosuppressive drug levels control, creatinine measurement and kidney biopsy in case of a rejection suspicion. The extensive analysis of metabolite levels offered by metabolomics might improve patient monitoring, help in the surveillance of the restoration of a "normal" renal function and possibly also predict rejection. The longitudinal follow-up of those patients with repeated measurements is useful to understand changes and decide whether an intervention is necessary. The time modality, therefore, constitutes a specific dimension in the data structure, requiring dedicated consideration for proper statistical analysis. The handling of specific data structures in metabolomics has received strong interest in recent years. In this work, we demonstrated the recently developed ANOVA multiblock OPLS (AMOPLS) to efficiently analyse longitudinal metabolomic data by considering the intrinsic experimental design. Indeed, AMOPLS combines the advantages of multilevel approaches and OPLS by separating between and within individual variations using dedicated predictive components, while removing most uncorrelated variations in the orthogonal component(s), thus facilitating interpretation. This modelling approach was applied to a clinical cohort study aiming to evaluate the impact of kidney transplantation over time on the plasma metabolic profile of graft patients and donor volunteers. A dataset of 266 plasma metabolites was identified using an LC-MS multiplatform analytical setup. Two separate AMOPLS models were computed: one for the recipient group and one for the donor group. The results highlighted the benefits of transplantation for recipients and the relatively low impacts on blood metabolites of donor volunteers.

Choosing an Optimal Sample Preparation in Caulobacter crescentus for Untargeted Metabolomics Approaches

Untargeted metabolomics aims to provide a global picture of the metabolites present in the system under study. To this end, making a careful choice of sample preparation is mandatory to obtain reliable and reproducible biological information. In this study, eight different sample preparation techniques were evaluated using Caulobacter crescentus as a model for Gram-negative bacteria. Two cell retrieval systems, two quenching and extraction solvents, and two cell disruption procedures were combined in a full factorial experimental design. To fully exploit the multivariate structure of the generated data, the ANOVA multiblock orthogonal partial least squares (AMOPLS) algorithm was employed to decompose the contribution of each factor studied and their potential interactions for a set of annotated metabolites. All main effects of the factors studied were found to have a significant contribution on the total observed variability. Cell retrieval, quenching and extraction solvent, and cell disrupting mechanism accounted respectively for 27.6%, 8.4%, and 7.0% of the total variability. The reproducibility and metabolome coverage of the sample preparation procedures were then compared and evaluated in terms of relative standard deviation (RSD) on the area for the detected metabolites. The protocol showing the best performance in terms of recovery, versatility, and variability was centrifugation for cell retrieval, using MeOH:H₂O (8:2) as quenching and extraction solvent, and freeze-thaw cycles as the cell disrupting mechanism.

An Integrative Multi-Omics Workflow to Address Multifactorial Toxicology Experiments

Toxicology studies can take advantage of omics approaches to better understand the phenomena underlying the phenotypic alterations induced by different types of exposure to certain toxicants. Nevertheless, in order to analyse the data generated from multifactorial omics studies, dedicated data analysis tools are needed. In this work, we propose a new workflow comprising both factor deconvolution and data integration from multiple analytical platforms. As a case study, 3D neural cell cultures were exposed to trimethyltin (TMT) and the relevance of the culture maturation state, the exposure duration, as well as the TMT concentration were simultaneously studied using a metabolomic approach combining four complementary analytical techniques (reversed-phase LC and hydrophilic interaction LC, hyphenated to mass spectrometry in positive and negative ionization modes). The ANOVA multiblock OPLS (AMOPLS) method allowed us to decompose and quantify the contribution of the different experimental factors on the outcome of the TMT exposure. Results showed that the most important contribution to the overall metabolic variability came from the maturation state and treatment duration. Even though the contribution of TMT effects represented the smallest observed modulation among the three factors, it was highly statistically significant. The MetaCore™ pathway analysis tool revealed TMT-induced alterations in biosynthetic pathways and in neuronal differentiation and signaling processes, with a predominant deleterious effect on GABAergic and glutamatergic neurons. This was confirmed by combining proteomic data, increasing the confidence on the mechanistic understanding of such a toxicant exposure.

Toward a better understanding of chronic kidney disease with complementary chromatographic methods hyphenated with mass spectrometry for improved polar metabolome coverage

The prevalence of chronic kidney disease (CKD) is increasing worldwide. New technical approaches are needed to improve early diagnosis, disease understanding and patient monitoring, and to evaluate new therapies. Metabolomics, as a prime candidate in the field of CKD research, aims to comprehensively analyze the metabolic complexity of biological systems. An extensive analysis of the metabolites contained in biofluids is therefore needed, and the combination of data obtained from multiple analytical platforms constitutes a promising methodological approach. This study presents an original workflow based on complementary chromatographic conditions, reversed-phase and hydrophilic interaction chromatography hyphenated to mass spectrometry to improve the polar metabolome coverage coupled with a univocal metabolite annotation strategy enabling a rapid access to the biological interpretation. This multiplatform workflow was applied in a CKD cohort study to assess plasma metabolic profile modifications related to renal disease. Multivariate analysis of 278 endogenous annotated metabolites enabled patient stratification with respect to CKD stages and helped to generate new biological insights, while also confirming the relevance of tryptophan metabolism pathway in this condition.