ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis

Hyperammonemia induces programmed liver cell death

Hyperammonemia is common in liver cirrhosis and causally associated with hepatic encephalopathy development. Little is known about its hepatotoxic effects, which we aimed to characterize in this study. In a mouse model of chronic hyperammonemia without preexisting liver disease, we observed development of liver fibrogenesis and necroptotic cell death. Hyperammonemia also induced dysregulation of its main metabolic pathway, the urea cycle, as reflected by down-regulation of urea cycle enzyme protein expression and accumulation of its metabolites. Inhibition of receptor-interacting serine/threonine-protein kinase 1 (RIPK1) and its upstream inducer Toll-like receptor 4 (TLR4) protected against liver injury and further hyperammonemia. In clinically relevant rodent models of hyperammonemia (genetic ornithine transcarbamylase deficiency and bile duct ligation-induced cirrhosis), TLR4 inhibition reduced circulating ammonia. In conclusion, hyperammonemia induces liver fibrogenesis and RIPK1-mediated cell death, which is associated with urea cycle dysfunction. Inhibition of RIPK1 and TLR4 protects against hyperammonemia-induced liver injury and are potential therapeutic targets for hyperammonemia and chronic liver disease progression.

Impaired unsaturated fatty acid elongation alters mitochondrial function and accelerates metabolic dysfunction-associated steatohepatitis progression

BACKGROUND AND AIMS

Although qualitative and quantitative alterations in liver Polyunsaturated Fatty Acids (PUFAs) are observed in MASH in humans, a causal relationship of PUFAs biosynthetic pathways is yet to be clarified. ELOVL5, an essential enzyme in PUFA elongation regulates hepatic triglyceride metabolism. Nonetheless, the long-term consequences of elongase disruption, particularly in murine models of MASH, have not been evaluated.

APPROACH & RESULTS

In humans, transcriptomic data indicated that PUFAs biosynthesis enzymes and notably ELOVL5 were induced during MASH progression. Moreover, gene module association determination revealed that ELOVL5 expression was associated with mitochondrial function in both humans and mice. WT and Elovl5-deficient mice were fed a high-fat, high-sucrose (HF/HS) diet for four months. Elovl5 deficiency led to limited systemic metabolic alterations but significant hepatic phenotype was observed in Elovl5-/- mice after the HF/HS diet, including hepatomegaly, pronounced macrovesicular and microvesicular steatosis, hepatocyte ballooning, immune cell infiltration, and fibrosis. Lipid analysis confirmed hepatic triglyceride accumulation and a reshaping of FA profile. Transcriptomic analysis indicated significant upregulation of genes involved in immune cell recruitment and fibrosis, and downregulation of genes involved in oxidative phosphorylation in Elovl5-/- mice. Alterations of FA oxidation and energy metabolism were confirmed by non-targeted metabolomic approach. Analysis of mitochondrial function in Elovl5-/- mice showed morphological alterations, qualitative cardiolipin changes with an enrichment in species containing shorter unsaturated FAs, and decreased activity of I and III respiratory chain complexes.

CONCLUSION

Enhanced susceptibility to diet-induced MASH and fibrosis in Elovl5-/- mice is intricately associated with disruptions in mitochondrial homeostasis, stemming from a profound reshaping of mitochondrial lipids, notably cardiolipins.

Effective data visualization strategies in untargeted metabolomics

Covering: 2014 to 2023 for metabolomics, 2002 to 2023 for information visualizationLC-MS/MS-based untargeted metabolomics is a rapidly developing research field spawning increasing numbers of computational metabolomics tools assisting researchers with their complex data processing, analysis, and interpretation tasks. In this article, we review the entire untargeted metabolomics workflow from the perspective of information visualization, visual analytics and visual data integration. Data visualization is a crucial step at every stage of the metabolomics workflow, where it provides core components of data inspection, evaluation, and sharing capabilities. However, due to the large number of available data analysis tools and corresponding visualization components, it is hard for both users and developers to get an overview of what is already available and which tools are suitable for their analysis. In addition, there is little cross-pollination between the fields of data visualization and metabolomics, leaving visual tools to be designed in a secondary and mostly ad hoc fashion. With this review, we aim to bridge the gap between the fields of untargeted metabolomics and data visualization. First, we introduce data visualization to the untargeted metabolomics field as a topic worthy of its own dedicated research, and provide a primer on cutting-edge visualization research into data visualization for both researchers as well as developers active in metabolomics. We extend this primer with a discussion of best practices for data visualization as they have emerged from data visualization studies. Second, we provide a practical roadmap to the visual tool landscape and its use within the untargeted metabolomics field. Here, for several computational analysis stages within the untargeted metabolomics workflow, we provide an overview of commonly used visual strategies with practical examples. In this context, we will also outline promising areas for further research and development. We end the review with a set of recommendations for developers and users on how to make the best use of visualizations for more effective and transparent communication of results.

Personalized Medicine Approach to Proteomics and Metabolomics of Cytochrome P450 Enzymes: A Narrative Review

Cytochrome P450 enzymes (CYPs) represent a diverse family of heme-thiolate proteins involved in the metabolism of a wide range of endogenous compounds and xenobiotics. In recent years, proteomics and metabolomics have been used to obtain a comprehensive insight into the role of CYPs in health and disease aspects. The objective of the present work is to better understand the status of proteomics and metabolomics in CYP research in optimizing therapeutics and patient safety from a personalized medicine approach. The literature used in this narrative review was procured by electronic search of PubMed, Medline, Embase, and Google Scholar databases. The following keywords were used in combination to identify related literature: "proteomics," "metabolomics," "cytochrome P450," "drug metabolism," "disease conditions," "proteome," "liquid chromatography-mass spectrometry," "integration," "metabolites," "pathological conditions." We reviewed studies that utilized proteomics and metabolomics approaches to explore the multifaceted roles of CYPs in identifying disease markers and determining the contribution of CYP enzymes in developing treatment strategies. The applications of various cutting-edge analytical techniques, including liquid chromatography-mass spectrometry, nuclear magnetic resonance, and bioinformatics analyses in CYP proteomics and metabolomics studies, have been highlighted. The identification of CYP enzymes through metabolomics and/or proteomics in various disease conditions provides key information in the diagnostic and therapeutic landscape. Leveraging both proteomics and metabolomics presents a powerful approach for an exhaustive exploration of the multifaceted roles played by CYP enzymes in personalized medicine. Proteomics and metabolomics have enabled researchers to unravel the complex connection between CYP enzymes and metabolic markers associated with specific diseases. As technology and methodologies evolve, an integrated approach promises to further elucidate the role of CYPs in human health and disease, potentially ushering in a new era of personalized medicine.

Anti-hyperuricemia effect of Clerodendranthus spicatus: a molecular biology study combined with metabolomics

Hyperuricemia (HUA), a metabolic disease caused by excessive production or decreased excretion of uric acid (UA), has been reported to be closely associated with a variety of UA transporters. Clerodendranthus spicatus (C. spicatus) is an herbal widely used in China for the treatment of HUA. However, the mechanism has not been clarified. Here, the rat model of HUA was induced via 10% fructose. The levels of biochemical indicators, including UA, xanthine oxidase (XOD), adenosine deaminase (ADA), blood urea nitrogen (BUN), and creatinine (Cre), were measured. Western blotting was applied to explore its effect on renal UA transporters, such as urate transporter1 (URAT1), glucose transporter 9 (GLUT9), and ATP-binding cassette super-family G member 2 (ABCG2). Furthermore, the effect of C. spicatus on plasma metabolites was identified by metabolomics. Our results showed that C. spicatus could significantly reduce the serum levels of UA, XOD, ADA and Cre, and improve the renal pathological changes in HUA rats. Meanwhile, C. spicatus significantly inhibited the expression of URAT1 and GLUT9, while increased the expression of ABCG2 in a dose-dependent manner. Metabolomics showed that 13 components, including 1-Palmitoyl-2-Arachidonoyl-sn-glycero-3-PE, Tyr-Leu and N-cis-15-Tetracosenoyl-C18-sphingosine, were identified as potential biomarkers for the UA-lowering effect of C. spicatus. In addition, pathway enrichment analysis revealed that arginine biosynthesis, biosynthesis of amino acids, pyrimidine metabolism and other metabolic pathways might be involved in the protection of C. spicatus against HUA. This study is the first to explore the mechanism of anti-HUA of C. spicatus through molecular biology and metabolomics analysis, which provides new ideas for the treatment of HUA.

Comparison of reversed-phase, hydrophilic interaction, and porous graphitic carbon chromatography columns for an untargeted toxicometabolomics study in pooled human liver microsomes, rat urine, and rat plasma

INTRODUCTION

Untargeted metabolomics studies are expected to cover a wide range of compound classes with high chemical diversity and complexity. Thus, optimizing (pre-)analytical parameters such as the analytical liquid chromatography (LC) column is crucial and the selection of the column depends primarily on the study purpose.

OBJECTIVES

The current investigation aimed to compare six different analytical columns. First, by comparing the chromatographic resolution of selected compounds. Second, on the outcome of an untargeted toxicometabolomics study using pooled human liver microsomes (pHLM), rat plasma, and rat urine as matrices.

METHODS

Separation and analysis were performed using three different reversed-phase (Phenyl-Hexyl, BEH C18, and Gold C18), two hydrophilic interaction chromatography (HILIC) (ammonium-sulfonic acid and sulfobetaine), and one porous graphitic carbon (PGC) columns coupled to high-resolution mass spectrometry (HRMS). Their impact was evaluated based on the column performance and the size of feature count, amongst others.

RESULTS

All three reversed-phase columns showed a similar performance, whereas the PGC column was superior to both HILIC columns at least for polar compounds. Comparing the size of feature count across all datasets, most features were detected using the Phenyl-Hexyl or sulfobetaine column. Considering the matrices, most significant features were detected in urine and pHLM after using the sulfobetaine and in plasma after using the ammonium-sulfonic acid column.

CONCLUSION

The results underline that the outcome of this untargeted toxicometabolomic study LC-HRMS metabolomic study was highly influenced by the analytical column, with the Phenyl-Hexyl or sulfobetaine column being the most suitable. However, column selection may also depend on the investigated compounds as well as on the investigated matrix.

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

BACKGROUND AND AIMS

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

Improving Peptide-Level Mass Spectrometry Analysis via Double Competition

The analysis of shotgun proteomics data often involves generating lists of inferred peptide-spectrum matches (PSMs) and/or of peptides. The canonical approach for generating these discovery lists is by controlling the false discovery rate (FDR), most commonly through target-decoy competition (TDC). At the PSM level, TDC is implemented by competing each spectrum's best-scoring target (real) peptide match with its best match against a decoy database. This PSM-level procedure can be adapted to the peptide level by selecting the top-scoring PSM per peptide prior to FDR estimation. Here, we first highlight and empirically augment a little known previous work by He et al., which showed that TDC-based PSM-level FDR estimates can be liberally biased. We thus propose that researchers instead focus on peptide-level analysis. We then investigate three ways to carry out peptide-level TDC and show that the most common method ("PSM-only") offers the lowest statistical power in practice. An alternative approach that carries out a double competition, first at the PSM and then at the peptide level ("PSM-and-peptide"), is the most powerful method, yielding an average increase of 17% more discovered peptides at 1% FDR threshold relative to the PSM-only method.