Oxidized glycerophosphatidylcholines in diabetes through non-targeted metabolomics: Their annotation and biological meaning

Oxidised phosphatidylcholine induces sarcolemmal ceramide accumulation and insulin resistance in skeletal muscle

AIMS/HYPOTHESIS

Intracellular ceramide accumulation in specific cellular compartments is a potential mechanism explaining muscle insulin resistance in the pathogenesis of type 2 diabetes. Muscle sarcolemmal ceramide accumulation negatively impacts insulin sensitivity in humans, but the mechanism explaining this localised accumulation is unknown. Previous reports revealed that circulating oxidised LDL is elevated in serum of individuals with obesity and type 2 diabetes. Oxidised phosphatidylcholine, which is present in oxidised LDL, has previously been linked to ceramide pathway activation, and could contribute to localised ceramide accumulation in skeletal muscle. We hypothesised that oxidised phosphatidylcholine inversely correlates with insulin sensitivity in serum, and induces sarcolemmal ceramide accumulation and decreases insulin sensitivity in muscle.

METHODS

We used LC-MS/MS to quantify specific oxidised phosphatidylcholine species in serum from a cross-sectional study of 58 well-characterised individuals spanning the physiological range of insulin sensitivity. We also performed in vitro experiments in rat L6 myotubes interrogating the role of specific oxidised phosphatidylcholine species in promoting sarcolemmal ceramide accumulation, inflammation and insulin resistance in skeletal muscle cells.

RESULTS

Human serum oxidised phosphatidylcholine levels are elevated in individuals with obesity and type 2 diabetes, inversely correlated with insulin sensitivity, and positively correlated with sarcolemmal C18:0 ceramide levels in skeletal muscle. Specific oxidised phosphatidylcholine species, particularly 1-palmitoyl-2-(5-oxovaleroyl)-sn-glycero-3-phosphocholine (POVPC), increase total ceramide and dihydroceramide and decrease total sphingomyelin in the sarcolemma of L6 myotubes by de novo ceramide synthesis and sphingomyelinase activation. POVPC also increases inflammatory signalling and causes insulin resistance in L6 myotubes.

CONCLUSIONS/INTERPRETATION

These data suggest that circulating oxidised phosphatidylcholine species promote ceramide accumulation and decrease insulin sensitivity in muscle, help explain localised sphingolipid accumulation and muscle inflammatory response, and highlight oxidised phosphatidylcholine species as potential targets to combat insulin resistance.

First insight about the ability of specific glycerophospholipids to discriminate non-small cell lung cancer subtypes

Introduction: Discrimination between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) subtypes in non-small cell lung cancer (NSCLC) patients is a significant challenge in oncology. Lipidomics analysis provides a promising approach for this differentiation. Methods: In an accompanying paper, we explored oxPCs levels in a cohort of 200 NSCLC patients. In this research, we utilized liquid chromatography coupled with mass spectrometry (LC-MS) to analyze the lipidomics profile of matching tissue and plasma samples from 25 NSCLC patients, comprising 11 ADC and 14 SCC cases. This study builds upon our previous findings, which highlighted the elevation of oxidised phosphatidylcholines (oxPCs) in NSCLC patients. Results: We identified eight lipid biomarkers that effectively differentiate between ADC and SCC subtypes using an untargeted approach. Notably, we observed a significant increase in plasma LPA 20:4, LPA 18:1, and LPA 18:2 levels in the ADC group compared to the SCC group. Conversely, tumour PC 16:0/18:2, PC 16:0/4:0; CHO, and plasma PC 16:0/18:2; OH, PC 18:0/20:4; OH, PC 16:0/20:4; OOH levels were significantly higher in the ADC group. Discussion: Our study is the first to report that plasma LPA levels can distinguish between ADC and SCC patients in NSCLC, suggesting a potential role for LPAs in NSCLC subtyping. This finding warrants further investigation into the mechanisms underlying these differences and their clinical implications.

Exploration of oxidized phosphocholine profile in non-small-cell lung cancer

Introduction: Lung cancer is one of the most frequently studied types of cancer and represents the most common and lethal neoplasm. Our previous research on non-small cell lung cancer (NSCLC) has revealed deep lipid profile reprogramming and redox status disruption in cancer patients. Lung cell membranes are rich in phospholipids that are susceptible to oxidation, leading to the formation of bioactive oxidized phosphatidylcholines (oxPCs). Persistent and elevated levels of oxPCs have been shown to induce chronic inflammation, leading to detrimental effects. However, recent reports suggest that certain oxPCs possess anti-inflammatory, pro-survival, and endothelial barrier-protective properties. Thus, we aimed to measure the levels of oxPCs in NSCLC patients and investigate their potential role in lung cancer. Methods: To explore the oxPCs profiles in lung cancer, we performed in-depth, multi-level metabolomic analyses of nearly 350 plasma and lung tissue samples from 200 patients with NSCLC, including adenocarcinoma (ADC) and squamous cell carcinoma (SCC), the two most prevalent NSCLC subtypes and COPD patients as a control group. First, we performed oxPC profiling of plasma samples. Second, we analyzed tumor and non-cancerous lung tissues collected during the surgical removal of NSCLC tumors. Because of tumor tissue heterogeneity, subsequent analyses covered the surrounding healthy tissue and peripheral and central tumors. To assess whether the observed phenotypic changes in the patients were associated with measured oxPC levels, metabolomics data were augmented with data from medical records. Results: We observed a predominance of long-chain oxPCs in plasma samples and of short-chain oxPCs in tissue samples from patients with NSCLC. The highest concentration of oxPCs was observed in the central tumor region. ADC patients showed higher levels of oxPCs compared to the control group, than patients with SCC. Conclusion: The detrimental effects associated with the accumulation of short-chain oxPCs suggest that these molecules may have greater therapeutic utility than diagnostic value, especially given that elevated oxPC levels are a hallmark of multiple types of cancer.

Analysis of bone metabolic alterations linked with osteoporosis progression in type 2 diabetic db/db mice

Type 2 diabetes (T2D) is a common chronic disease, characterized by persistent hyperglycemia and insulin resistance. This disorder is associated with decreased bone quality and an elevated risk of bone fractures. However, evidence on the relationship between systemic metabolic change and the development of type 2 diabetic osteoporosis (T2DOP) remains elusive. Herein, we investigate the changes of bone metabolites with bone loss in db/db mice (an animal model of T2DOP exhibited bone loss with age progression), and explore the potential metabolic mechanism underlying type 2 diabetes and osteoporosis. C57BKS male mice were distributed in four groups, consisting six mice in each group: 8w m/m, 24w m/m, 8w db/db and 24w db/db. Bone morphometric and biomechanical parameters of db/db mice were analyzed by micro-CT and materials tester, it was found that 24w db/db mice showed severe bone loss and decreased bone tissue hardness compared with misty/misty littermates. The tibia of misty/misty mice (8 weeks, 24 weeks) and db/db mice (8 weeks, 24 weeks) were screened for differential metabolites by UPLC-Orbitrap MS. Ninety-eight metabolites were identified (35 and 63 metabolites are associated with early staged and late staged, respectively), consisting of amino acids, fatty acyls, and nucleotides. Notably, fatty acyls (such as 18-HEPE, 16(17)-EpDPE, arachidonic acid) and glycerophospholipids (such as phosphocholines (PC) (O-10:1(9E)/0:0), PC (O-16:1(9E)/0:0) [U] and phosphatidylethanolamines (PE) (P-16:0/0:0)) were significantly increased, and metabolites of amino acid pathway (such as l-glutamine, proline, phenylalanine) showed a downregulation trend. Dysregulation of lipid and glutathione pathways is the major contributor to progression of T2DOP in C57BKS mice.

Application of Metabolomics and Traditional Chinese Medicine for Type 2 Diabetes Mellitus Treatment

Diabetes is a major global public health problem with high incidence and case fatality rates. Traditional Chinese medicine (TCM) is used to help manage Type 2 Diabetes Mellitus (T2DM) and has steadily gained international acceptance. Despite being generally accepted in daily practice, the TCM methods and hypotheses for understanding diseases lack applicability in the current scientific characterization systems. To date, there is no systematic evaluation system for TCM in preventing and treating T2DM. Metabonomics is a powerful tool to predict the level of metabolites in vivo, reveal the potential mechanism, and diagnose the physiological state of patients in time to guide the follow-up intervention of T2DM. Notably, metabolomics is also effective in promoting TCM modernization and advancement in personalized medicine. This review provides updated knowledge on applying metabolomics to TCM syndrome differentiation, diagnosis, biomarker discovery, and treatment of T2DM by TCM. Its application in diabetic complications is discussed. The combination of multi-omics and microbiome to fully elucidate the use of TCM to treat T2DM is further envisioned.

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.

Increase in Plasma Oxidized Phosphatidylcholines (OxPCs) in Patients Presenting With ST-Elevation Myocardial Infarction (STEMI)

Objective: ST-segment Elevation Myocardial Infarction (STEMI) occurs as a result of acute occlusion of the coronary artery. Despite successful reperfusion using percutaneous coronary intervention (PCI), a large percentage of myocardial cells die after reperfusion which is recognized as ischemia/reperfusion injury (I/R). Oxidized phosphatidylcholines (OxPCs) are a group of oxidized lipids generated through non-enzymatic oxidation and have pro-inflammatory properties. This study aimed to examine the roles of OxPCs in a clinical setting of myocardial I/R.

Methods: Blood samples were collected from STEMI patients at presentation prior to primary PCI (PPCI) (Isch) and at 4 time-points post-PPCI, including 2 h (R-2 h), 24 h (R-24 h), 48 h (R-48 h), and 30 days (R-30 d) post-PPCI. As controls, blood samples were collected from patients with non-obstructive coronary artery disease after diagnostic coronary angiography. Aspiration thrombectomy was also performed in selected STEMI patients. High-performance lipid chromatography-electrospray mass spectrometry (LC-MS/MS) was used for OxPCs analysis.

Results: Twenty-two distinct OxPC species were identified and quantified in plasma samples in patients presenting with STEMI. These compounds were categorized as fragmented and non-fragmented species. Total levels of OxPCs did not significantly differ between Isch and control groups. However, total levels of fragmented OxPCs increased significantly in the ischemic period compared with controls (Isch: 4.79 ± 0.94, Control: 1.69 ± 0.19 ng/μl of plasma, P < 0.05). Concentrations of non-fragmented OxPCs had significant reductions during ischemia compared to the control group (Isch: 4.84 ± 0.30, Control: 6.6 ± 0.51 ng/μl, P < 0.05). Levels of total OxPCs in patients with STEMI were not significantly different during reperfusion periods. However, fragmented OxPCs levels were elevated at 48 h post-reperfusion and decreased at 30 days following MI, when compared to R-2 h and R-24 h time points (Isch: 4.79 ± 0.94, R-2 h: 5.33 ± 1.17, R-24 h: 5.20 ± 1.1, R-48 h: 4.18 ± 1.07, R-30 d: 1.87 ± 0.31 ng/μl, P < 0.05). Plasma levels of two fragmented OxPCs, namely, POVPC and PONPC were significantly correlated with peak creatine kinase (CK) levels (P < 0.05). As with plasma levels, the dominant OxPC species in coronary aspirated thrombus were fragmented OxPCs, which constituted 77% of total OxPC concentrations.

Conclusion: Biologically active fragmented OxPC were elevated in patients presenting with STEMI when compared to controls. PONPC concentrations were subsequently increased after PPCI resulting in reperfusion. Moreover, levels of POVPC and PONPC were also associated with peak CK levels. Since these molecules are potent stimulators for cardiomyocyte cell death, therapeutics attenuating their activities can result in a novel therapeutic pathway for myocardial salvage for patients undergoing reperfusion therapy.

Structural library and visualization of endogenously oxidized phosphatidylcholines using mass spectrometry-based techniques

Although oxidized phosphatidylcholines (oxPCs) play critical roles in numerous pathological events, the type and production sites of endogenous oxPCs remain unknown because of the lack of structural information and dedicated analytical methods. Herein, a library of 465 oxPCs is constructed using high-resolution mass spectrometry-based non-targeted analytical methods and employed to detect 70 oxPCs in mice with acetaminophen-induced acute liver failure. We show that doubly oxygenated polyunsaturated fatty acid (PUFA)-PCs (PC PUFA;O2), containing epoxy and hydroxide groups, are generated in the early phase of liver injury. Hybridization with in-vivo ¹⁸O labeling and matrix-assisted laser desorption/ionization-tandem MS imaging reveals that PC PUFA;O2 are accumulated in cytochrome P450 2E1-expressing and glutathione-depleted hepatocytes, which are the major sites of liver injury. The developed library and visualization methodology should facilitate the characterization of specific lipid peroxidation events and enhance our understanding of their physiological and pathological significance in lipid peroxidation-related diseases.

Comprehensive Plasma Metabolomic Profile of Patients with Advanced Neuroendocrine Tumors (NETs). Diagnostic and Biological Relevance

Simple Summary

Metabolic flexibility is one of the key hallmarks of cancer and metabolites are the final products of this adaptation, reflecting the aberrant changes of tumors. However, the metabolic plasticity of each cancer type is still unknown, and specifically to date, there are no data on metabolic profile in neuroendocrine tumors. The aim of our retrospective study was to assess the metabolomic profile of NET patients to understand metabolic deregulation in these tumors and identify novel biomarkers with clinical potential. We provided, for the first time, a comprehensive metabolic profile of NET patients and identifies a distinctive metabolic signature in plasma of potential clinical use, selecting a reduced set of metabolites of high diagnostic accuracy. We have identified 32 novel enriched metabolic pathways in NETs related with the TCA cycle, and with arginine, pyruvate or glutathione metabolism, which have distinct implications in oncogenesis and may open innovative avenues of clinical research.

Abstract

Purpose: High-throughput “-omic” technologies have enabled the detailed analysis of metabolic networks in several cancers, but NETs have not been explored to date. We aim to assess the metabolomic profile of NET patients to understand metabolic deregulation in these tumors and identify novel biomarkers with clinical potential. Methods: Plasma samples from 77 NETs and 68 controls were profiled by GC−MS, CE−MS and LC−MS untargeted metabolomics. OPLS-DA was performed to evaluate metabolomic differences. Related pathways were explored using Metaboanalyst 4.0. Finally, ROC and OPLS-DA analyses were performed to select metabolites with biomarker potential. Results: We identified 155 differential compounds between NETs and controls. We have detected an increase of bile acids, sugars, oxidized lipids and oxidized products from arachidonic acid and a decrease of carnitine levels in NETs. MPA/MSEA identified 32 enriched metabolic pathways in NETs related with the TCA cycle and amino acid metabolism. Finally, OPLS-DA and ROC analysis revealed 48 metabolites with diagnostic potential. Conclusions: This study provides, for the first time, a comprehensive metabolic profile of NET patients and identifies a distinctive metabolic signature in plasma of potential clinical use. A reduced set of metabolites of high diagnostic accuracy has been identified. Additionally, new enriched metabolic pathways annotated may open innovative avenues of clinical research.

Clinical and metabolomic predictors of regression to normoglycemia in a population at intermediate cardiometabolic risk

Background

Impaired fasting glucose (IFG) is a prevalent and potentially reversible intermediate stage leading to type 2 diabetes that increases risk for cardiometabolic complications. The identification of clinical and molecular factors associated with the reversal, or regression, from IFG to a normoglycemia state would enable more efficient cardiovascular risk reduction strategies. The aim of this study was to identify clinical and biological predictors of regression to normoglycemia in a non-European population characterized by high rates of type 2 diabetes.

Methods

We conducted a prospective, population-based study among 9637 Mexican individuals using clinical features and plasma metabolites. Among them, 491 subjects were classified as IFG, defined as fasting glucose between 100 and 125 mg/dL at baseline. Regression to normoglycemia was defined by fasting glucose less than 100 mg/dL in the follow-up visit. Plasma metabolites were profiled by Nuclear Magnetic Resonance. Multivariable cox regression models were used to examine the associations of clinical and metabolomic factors with regression to normoglycemia. We assessed the predictive capability of models that included clinical factors alone and models that included clinical factors and prioritized metabolites.

Results

During a median follow-up period of 2.5 years, 22.6% of participants (n = 111) regressed to normoglycemia, and 29.5% progressed to type 2 diabetes (n = 145). The multivariate adjusted relative risk of regression to normoglycemia was 1.10 (95% confidence interval [CI] 1.25 to 1.32) per 10 years of age increase, 0.94 (95% CI 0.91–0.98) per 1 SD increase in BMI, and 0.91 (95% CI 0.88–0.95) per 1 SD increase in fasting glucose. A model including information from age, fasting glucose, and BMI showed a good prediction of regression to normoglycemia (AUC = 0.73 (95% CI 0.66–0.78). The improvement after adding information from prioritized metabolites (TG in large HDL, albumin, and citrate) was non-significant (AUC = 0.74 (95% CI 0.68–0.80), p value = 0.485).

Conclusion

In individuals with IFG, information from three clinical variables easily obtained in the clinical setting showed a good prediction of regression to normoglycemia beyond metabolomic features. Our findings can serve to inform and design future cardiovascular prevention strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-021-01246-1.

Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

A discovery-based lipid profiling study of serum samples from a cohort that included patients with clear cell renal cell carcinoma (ccRCC) stages I, II, III, and IV (n = 112) and controls (n = 52) was performed using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry and machine learning techniques. Multivariate models based on support vector machines and the LASSO variable selection method yielded two discriminant lipid panels for ccRCC detection and early diagnosis. A 16-lipid panel allowed discriminating ccRCC patients from controls with 95.7% accuracy in a training set under cross-validation and 77.1% accuracy in an independent test set. A second model trained to discriminate early (I and II) from late (III and IV) stage ccRCC yielded a panel of 26 compounds that classified stage I patients from an independent test set with 82.1% accuracy. Thirteen species, including cholic acid, undecylenic acid, lauric acid, LPC(16:0/0:0), and PC(18:2/18:2), identified with level 1 exhibited significantly lower levels in samples from ccRCC patients compared to controls. Moreover, 3α-hydroxy-5α-androstan-17-one 3-sulfate, cis-5-dodecenoic acid, arachidonic acid, cis-13-docosenoic acid, PI(16:0/18:1), PC(16:0/18:2), and PC(O-16:0/20:4) contributed to discriminate early from late ccRCC stage patients. The results are auspicious for early ccRCC diagnosis after validation of the panels in larger and different cohorts.

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.

Oxidized lipids in the metabolic profiling of neuroendocrine tumors - Analytical challenges and biological implications

Untargeted metabolomics can be a great tool for exploring new scientific areas; however, wrong metabolite annotation questions the credibility and puts the success of the entire research at risk. Therefore, an effort should be made to improve the quality and robustness of the annotation despite of the challenges, especially when final identification with standards is not possible. Through non-targeted analysis of human plasma samples, from a large cancer cohort study using RP-LC-ESI-QTOF-MS/MS, we have resolved MS/MS annotation through spectral matching, directed to hydroxyeicosatetraenoic acids (HETEs) and, MS/MS structural elucidation for newly annotated oxidized lyso-phosphatidylcholines (oxLPCs). The annotation of unknowns is supported with structural information from fragmentation spectra as well as the fragmentation mechanisms involved, necessarily including data from both polarity modes and different collision energies. In this work, we present evidences that various oxidation products show significant differences between cancer patients and control individuals and we establish a workflow to help identify such modifications. We report here the upregulation of HETEs and oxLPCs in patients with neuroendocrine tumors (NETs). To our knowledge, this is the first attempt to determine HETEs in NETs and one of very few studies where oxLPCs are annotated. The obtained results provide an important insight regarding lipid oxidation in NETs, although their physiological functions still have to be established and require further research.