Transcriptomics-driven metabolic pathway analysis reveals similar alterations in lipid metabolism in mouse MASH model and human

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent chronic liver disease worldwide, and can rapidly progress to metabolic dysfunction-associated steatohepatitis (MASH). Accurate preclinical models and methodologies are needed to understand underlying metabolic mechanisms and develop treatment strategies. Through meta-analysis of currently proposed mouse models, we hypothesized that a diet- and chemical-induced MASH model closely resembles the observed lipid metabolism alterations in humans.

METHODS

We developed transcriptomics-driven metabolic pathway analysis (TDMPA), a method to aid in the evaluation of metabolic resemblance. TDMPA uses genome-scale metabolic models to calculate enzymatic reaction perturbations from gene expression data. We performed TDMPA to score and compare metabolic pathway alterations in MASH mouse models to human MASH signatures. We used an already-established WD+CCl4-induced MASH model and performed functional assays and lipidomics to confirm TDMPA findings.

RESULTS

Both human MASH and mouse models exhibit numerous altered metabolic pathways, including triglyceride biosynthesis, fatty acid beta-oxidation, bile acid biosynthesis, cholesterol metabolism, and oxidative phosphorylation. We confirm a significant reduction in mitochondrial functions and bioenergetics, as well as in acylcarnitines for the mouse model. We identify a wide range of lipid species within the most perturbed pathways predicted by TDMPA. Triglycerides, phospholipids, and bile acids are increased significantly in mouse MASH liver, confirming our initial observations.

CONCLUSIONS

We introduce TDMPA, a methodology for evaluating metabolic pathway alterations in metabolic disorders. By comparing metabolic signatures that typify human MASH, we show a good metabolic resemblance of the WD+CCl4 mouse model. Our presented approach provides a valuable tool for defining metabolic space to aid experimental design for assessing metabolism.

Specific GAG ratios in the diagnosis of mucopolysaccharidoses

Mucopolysaccharidoses (MPS) screening is tedious and still performed by analysis of total glycosaminoglycans (GAG) using 1,9-dimethylmethylene blue (DMB) photometric assay, although false positive and negative tests have been reported. Analysis of differentiated GAGs have been pursued classically by gel electrophoresis or more recently by quantitative LC-MS assays. Secondary elevations of GAGs have been reported in urinary tract infections (UTI). In this manuscript, we describe the diagnostic accuracy of urinary GAG measurements by LC-MS for MPS typing in 68 untreated MPS and mucolipidosis (ML) patients, 183 controls and 153 UTI samples. We report age-dependent reference values and cut-offs for chondroitin sulfate (CS), dermatan sulfate (DS), heparan sulfate (HS) and keratan sulfate (KS) and specific GAG ratios. The use of HS/DS ratio in combination to GAG concentrations normalized to creatinine improves the diagnostic accuracy in MPS type I, II, VI and VII. In total 15 samples classified to the wrong MPS type could be correctly assigned using HS/DS ratio. Increased KS/HS ratio in addition to increased KS improves discrimination of MPS type IV by excluding false positives. Some samples of UTI patients showed elevation of specific GAGs, mainly CS, KS and KS/HS ratio and could be misclassified as MPS type IV. Finally, DMB photometric assay performed in MPS and ML samples reveal four false negative tests (sensitivity of 94%). In conclusion, specific GAG ratios in complement to quantitative GAG values obtained by LC-MS enhance discrimination of MPS types. Exclusion of patients with UTI improve diagnostic accuracy in MPS IV but not in other types.

Serum metabolites as biomarkers in systemic sclerosis-associated interstitial lung disease

Systemic sclerosis (SSc) is a severe multi-organ disease with interstitial lung disease (ILD) being the major cause of death. While targeted therapies are emerging, biomarkers for sub-stratifying patients based on individual profiles are lacking. Herein, we investigated how levels of serum metabolites correlated with different stages of SSc and SSc-ILD. Serum samples of patients with SSc without ILD, stable and progressive SSc-ILD as well as of healthy controls (HC) were analysed using liquid targeted tandem mass spectrometry. The best discriminating profile consisted of 4 amino acids (AA) and 3 purine metabolites. L-tyrosine, L-tryptophan, and 1-methyl-adenosine distinguished HC from SSc patients. L-leucine, L-isoleucine, xanthosine, and adenosine monophosphate differentiated between progressing and stable SSc-ILD. In SSc-ILD, both, L-leucine and xanthosine negatively correlated with changes in FVC% predicted. Additionally, xanthosine was negatively correlated with changes in DLco% predicted and positively with the prognostic GAP index. Validation of L-leucine and L-isoleucine by an enzymatic assay confirmed both the sub-stratification of SSc-ILD patients and correlation with lung function and prognosis score. Serum metabolites may have potential as biomarkers for discriminating SSc patients based on the presence and severity of ILD. Confirmation in larger cohorts will be needed to appreciate their value for routine clinical care.

SAT0333 SERUM METABOLITES AS BIOMARKERS IN SYSTEMIC SCLEROSIS-ASSOCIATED INTERSTITIAL LUNG DISEASE

Background:

In fibrotic diseases, metabolic processes are altered with a tendency towards an anabolic state, which is partially reflected in serum. Circulating biomarkers for interstitial lung disease (ILD), the leading cause of death in systemic sclerosis (SSc), are still sparse and not established in routine care.

Objectives:

To assess the potential of serum metabolites as biomarkers for the presence and progression of SSc-ILD.

Methods:

Age and sex matched serum samples of SSc patients from the Zurich cohort and of healthy controls (HC) were analyzed. Progressive SSc-ILD was defined as either a relative decrease in forced vital capacity (FVC) >10%, a decrease in FVC of 5-9% and a concomitant decrease of carbon dioxide diffusion capacity >15%, or an increase of the extent of lung fibrosis on computed tomography from <20% to ≥20% compared to the last visit (mean follow-up interval = 14 months (range = 9-26)). Sera of HC, non-ILD SSc and stable vs. progressive SSc-ILD patients (n = 12 per group; total n = 48) were screened for 110 metabolites by targeted liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Peak areas were analyzed with R 3.6. For univariate analysis, FDR-corrected one-way ANOVA was used. In multivariate group-wise partial least squares discriminant analysis (PLS-DA), variable importance in the projection (VIP) scores ≥2 were considered significant.

Results:

In total, 85 metabolites were detected. Univariate analysis of all groups were suggestive of changes for 1-methyladenosine, L-tryptophan, L-tyrosine, L-leucine and xanthosine (p = 0.077, 0.028, 0.077, 0.028 and 0.032, respectively). In PLS-DA, HCs and SSc patients differed in their levels of L-tyrosine and L-tryptophan, while levels of L-threonine, 3-aminoisobutyric acid, adenosine monophosphate and xanthosine were changed when comparing non-ILD and SSc-ILD patients. Receiver operating curve (ROC) analysis of significant metabolites from uni- and multivariate testing resulted in separation of SSc patients from HCs by L-tyrosine (area under the curve (AUC) = 0.81, 95% confidence interval (CI): 0.67-0.96), L-tryptophan (AUC = 0.86, CI: 0.75-0.97) and 1-methyladenosine (AUC = 0.82, CI: 0.71-0.94). Progressive SSc-ILD patients were separated from stable patients by their levels of L-isoleucine, L-leucine, adenosine monophosphate and xanthosine (AUC = 0.83, 0.85, 0.79 and 0.77; CI: 0.66-1.00, 0.70-1.00, 0.60-0.97 and 0.55-0.99, respectively). Validation of increased values of the branched-chain amino acids L-leucine and L-isoleucine in progressive SSc-ILD vs. stable ILD using an enzymatic assay resulted in similar results as LC-MS/MS analysis, with higher values detected in progressive vs. stable patients (mean = 286.5 and 235.5 nM, respectively; p = 0.005). In ROC analysis (AUC = 0.81, CI: 0.62-1.00), a cut-off value of 250.3 nM separated stable from progressive patients with a sensitivity of 72.7% and a specificity of 83.3%.

Conclusion:

This study in SSc(-ILD) patients suggested alterations in serum metabolite levels corresponding with their current state of disease, indicating the potential use of serum metabolites as discriminating biomarkers upon further confirmation in larger multicenter studies.

Disclosure of Interests:

Chantal Meier: None declared, Katrin Freiburghaus: None declared, Cédric Bovet: None declared, Janine Schniering: None declared, Oliver Distler Grant/research support from: Grants/Research support from Actelion, Bayer, Boehringer Ingelheim, Competitive Drug Development International Ltd. and Mitsubishi Tanabe; he also holds the issued Patent on mir-29 for the treatment of systemic sclerosis (US8247389, EP2331143)., Consultant of: Consultancy fees from Actelion, Acceleron Pharma, AnaMar, Bayer, Baecon Discovery, Blade Therapeutics, Boehringer, CSL Behring, Catenion, ChemomAb, Curzion Pharmaceuticals, Ergonex, Galapagos NV, GSK, Glenmark Pharmaceuticals, Inventiva, Italfarmaco, iQvia, medac, Medscape, Mitsubishi Tanabe Pharma, MSD, Roche, Sanofi and UCB, Speakers bureau: Speaker fees from Actelion, Bayer, Boehringer Ingelheim, Medscape, Pfizer and Roche, Christos Nakas: None declared, Britta Maurer Grant/research support from: AbbVie, Protagen, Novartis, congress support from Pfizer, Roche, Actelion, and MSD, Speakers bureau: Novartis

Effects of Freezing and Thawing Procedures on Selected Clinical Chemistry Parameters in Plasma

Introduction: Measurements from frozen sample collections are important key indicators in clinical studies. It is a prime concern of biobanks and laboratories to minimize preanalytical bias and variance through standardization. In this study, we aimed at assessing the effects of different freezing and thawing conditions on the reproducibility of medical routine parameters from frozen samples. Materials and Methods: In total, 12 pooled samples were generated from leftover lithium heparinized plasma samples from clinical routine testing. Aliquots of the pools were frozen using three freezing methods (in carton box at -80°C, flash freezing in liquid nitrogen, and controlled-rate freezing [CRF]) and stored at -80°C. After 3 days, samples were thawed using two methods (30 minutes at room temperature or water bath at 25°C for 3 minutes). Ten clinical chemistry laboratory parameters were measured before (baseline) and after freeze-thaw treatment: total calcium, potassium, sodium, alanine aminotransferase, lactate dehydrogenase (LDH), lipase, uric acid, albumin, c-reactive protein (CRP), and total protein. We evaluated the influence of the different preanalytical treatments on the test results and compared each condition with nonfrozen baseline measurements. Results: We found no significant differences between freezing methods for all tested parameters. Only LDH was significantly affected by thawing with fast-rate thawing being closer to baseline than slow-rate thawing. Potassium, LDH, lipase, uric acid, albumin, and CRP values were significantly changed after freezing and thawing compared with unfrozen samples. The least prominent changes compared with unfrozen baseline measurements were obtained when a CRF protocol of the local biobank and fast thawing was applied. However, the observed changes between baseline and frozen samples were smaller than the measurement uncertainty for 9 of the 10 parameters. Discussion: Changes introduced through freezing-thawing were small and not of clinical importance. A slight statistically based preference toward results from slow CRF and fast thawing of plasma being closest to unfrozen samples could be supported.

Metabolomics by UHPLC-MS: benefits provided by complementary use of Q-TOF and QQQ for pathway profiling

INTRODUCTION

Non-targeted metabolic profiling using high-resolution mass spectrometry (HRMS) is a standard approach for pathway identification despite technical limitations.

OBJECTIVES

To assess the performance of combining targeted quadrupole (QQQ) analysis with HRMS for in-depth pathway profiling.

METHODS

Serum of exercising patients with type 1 diabetes (T1D) was profiled using targeted and non-targeted assays.

RESULTS

Non-targeted analysis yielded a broad unbiased metabolic profile, targeted analysis increased coverage of purine metabolism (twofold) and TCA cycle (three metabolites).

CONCLUSION

Our screening strategy combined the benefits of the unbiased full-scan HRMS acquisition with the deeper insight into specific pathways by large-scale QQQ analysis.

Chemokine expression in human oral keratinocyte cell lines and keratinized mucosa

Chemoattractant cytokines regulate the immune response within the tissue by recruiting neutrophils and macrophages. These so-called chemokines include a large family of peptide molecules encoded by distinct genes. Their expression is controlled by a variety of microbial and host factors. Among host factors, interleukin-1 (IL-1) is thought to be a key regulator of tissue destruction and mediator of the local immune response. To study its influence on chemokine expression, we used a highly sensitive, semi-quantitative method to assess gene expression at the level of mRNA. RNA was extracted from human oral keratinocyte cell lines after treatment with recombinant human IL-1. To test the method further and possibly establish a chemokine mRNA expression pattern, we also extracted RNA from healthy oral keratinized mucosa. Purified RNA was reverse-transcribed and subsequently amplified in a polymerase chain reaction (RT-PCR) by means of specific primer pairs. Amplified sequences were analyzed by agarose gel electrophoresis, visualized by ethidium bromide staining, transferred to nylon membranes, and hybridized to biotinylated oligonucleotide probes. Detection was achieved by streptavidin-conjugated alkaline phosphatase, a chemiluminescent substrate, and autoradiography. Autoradiographs were analyzed by densitometric measurements. IL-1 stimulation resulted in an increase of the chemokine mRNAs encoding interleukin-8 (IL-8), monocyte chemoattractant protein-1 (MCP-1), and GRO gamma. Macrophage inflammatory protein-1 alpha (MIP-1 alpha) mRNA was not detectable in keratinocytes. In healthy oral mucosa, we found considerable variation between the subjects. Detection of chemokine mRNAs by RT-PCR proved to be sensitive, specific, and fast. It allows for the study of not only cell-line-derived RNA, but also of RNA isolated directly from biopsy material. The latter feature makes this method well-suited for diagnostic purposes.