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In recent years, the impact of lipotoxicity on male fertility has received extensive attention, especially on Sertoli cells (SCs). In SCs, energy metabolism is important as disorders of energy metabolism result in infertility eventually. However, the underlying mechanism of lipotoxicity on energy metabolism in SCs remains unknown. Advances in high-throughput metabolomics and lipidomics measurement platforms provide powerful tools to gain insights into complex biological systems. Here, we aimed to explore the potential molecular mechanisms of palmitic acid (PA) regulating energy metabolism in SCs based on metabolomics and lipidomics. The results showed that glucose metabolism-related metabolites were not significantly changed, which suggested that PA treatment had little effect on glucose metabolism and may not influence the normal energy supply from SCs to germ cells. However, fatty acid β-oxidation was inhibited according to accumulation of medium- and long-chain acylcarnitines in cells. In addition, the pool of amino acids and the levels of most individual amino acids involved in the tricarboxylic acid (TCA) cycle were not changed after PA treatment in SCs. Moreover, PA treatment of SCs significantly altered the lipidome, including significant decreases in cardiolipin and glycolipids as well as remarkable increases in ceramide and lysophospholipids, which indicated that mitochondrial function was affected and apoptosis was triggered. The increased apoptosis rate of SCs was verified by elevated protein expression levels of Cleaved Caspase-3 and Bax as well as decreased Bcl-2 protein expression level. Together, these findings indicated that PA may result in mitochondrial dysfunction and increased apoptosis by inhibiting fatty acid β-oxidation of SCs.

ObjectiveDyslipidemia is a hallmark of diabetes mellitus (DM). However, specific lipid molecules closely associated with the initiation and progression of diabetes remain unclear. We used a pseudotargeted lipidomics approach to evaluate the complex lipid changes that occurred long before the diagnosis of type 2 diabetes mellitus (T2DM) and to identify novel lipid markers for screening prediabetes mellitus (PreDM) and T2DM in patients from multiple communities.MethodsFour hundred and eighty-one subjects consisting of T2DM, three subtypes of PreDM, and normal controls (NC) were enrolled as discovery cohort. Serum lipidomic profiles of 481 subjects were analyzed using an ultrahigh performance liquid chromatography-triple quadrupole mass spectrometry (UHPLC-QqQ-MS)-based pseudotargeted lipidomics method. The differential lipid molecules were further validated in an independent case-control study consisting of 150 PreDM, 234 T2DM and 94 NC.ResultsMultivariate discriminative analyses show that lipidomics data have considerable potential for identifying lipidome differences among T2DM, subtypes of PreDM and NC. Statistical associations of lipid (sub)species display significant variations in 11 lipid (sub)species levels for T2DM and distinctive differences in 8 lipid (sub)species levels between prediabetic and normoglycemic individuals, with further differences in 8 lipid (sub)species levels among subtypes of PreDM. Adjusted for sex, age and BMI, only two lipid (sub)species of fatty acid (FA) and phosphatidylcholine (PC) were associated at p< 0.05 for PreDM (all) and subtypes of PreDM. The defined lipid markers not only significantly improve the diagnostic accuracy of PreDM and T2DM but also effectively evaluating the risk of developing into each subtype of PreDM and T2DM when addition of age, sex, BMI, and FPG, respectively.ConclusionsOur findings improve insights into the lipid metabolic complexity and interindividual variations among subtypes of PreDM and T2DM, beyond the well-known differences in dyslipidemia in clinic.

Lipidomics aims to characterize lipid alteration in response to internal or external subtle perturbations in complex biological samples. Lipid abnormality is a major risk factor for many diseases. Large-scale lipidomic studies may offer new insights into the pathophysiological mechanisms of diseases, new opportunities in systems biology, functional biology, and personalized medicine. To this end, a highly efficient and stable lipidomic method is highly in demand. We herein present a rapid and relatively high coverage lipidomic profiling approach based on ultra-high performance liquid chromatography–mass spectrometry by comparing the performance of different chromatographic columns, optimizing the elution gradient and selecting an appropriate data acquisition mode of mass spectra. As a result, a total of 481 lipids were detected from 40 μL serum sample within 13 min, covering 20 common lipid (sub)classes. The developed method was well validated with satisfactory analytical characteristics in linearity, repeatability, stability, and lipid coverage. To show the usefulness, the method was employed to investigate serum lipid profiling of 43 subjects with mild diabetic retinopathy and 44 normal controls, and successfully defined the differential lipids related to diabetic retinopathy. We believe that this rapid method will be beneficial for lipidomic analysis of large-scale clinical samples.

Metabolite and lipid profilings usually need two liquid chromatography-mass spectrometry (LC-MS) methods because of a great polarity difference. A pseudotargeted metabolomics method as a novel emerging approach can integrate the advantages of nontargeted and targeted methods. Here, we aim to establish a comprehensive method for metabolome and lipidome by using a parallel column-based two-dimensional LC (PC-2DLC)-MS and pseudotargeted approach. To simultaneously extract as many polar metabolites and nonpolar lipids as possible, we systematically optimized the sample pretreatment process, and isopropanol/methanol (3:1, v/v) and isopropanol/water (7:3, v/v) were selected as the extraction and reconstitution solvents, respectively. The detected triglycerides significantly increased after the sample pretreatment optimization. Then PC-2DLC coupled with Triple TOF MS was applied to analyze a mixed sample from serum, urine, and liver tissue matrixes. The multiple reaction monitoring (MRM) transitions of the metabolome and lipidome were defined according to the "MRM-Ion Pair Finder" software and lipidomics MRM-transition database, respectively. After verification by QTRAP MS in the scheduled MRM mode, 1609 potential metabolites and lipids corresponding to 1294 MRM transitions, and 847 potential metabolites and lipids corresponding to 687 MRM transitions were detected in positive and negative ion modes, respectively. They range at about 30 orders of magnitude in octanol/water partition coefficient. The pseudotargeted 2DLC-MS method was validated to have good analytical characteristics. As a proof of applicability, sera from type 2 diabetic patients were investigated by the established method. The results indicated that the pseudotargeted 2DLC-MS method is reliable and repeatable and can be used in a metabolomics study.

Metabolic syndrome (MTS) is a cluster of concurrent metabolic abnormal conditions. MTS and its component metabolic diseases are heterogeneous and closely related, making their relationships complicated, thus hindering precision treatment. Methods: We collected seven groups of samples (group a: healthy individuals; group b: obesity; group c: MTS; group d: hyperglycemia, group e: hypertension, group f: hyperlipidemia; group g: type II diabetes, n=7 for each group). We examined the molecular characteristics of each sample by metabolomic, proteomic and peptidomic profiling analysis. The differential molecules (including metabolites, proteins and peptides) between each disease group and the healthy group were recognized by statistical analyses. Furthermore, a two-step clustering workflow which combines multi-omics and clinical information was used to redefine molecularly and clinically differential groups. Meanwhile, molecular, clinical, network and pathway based analyses were used to identify the group-specific biological features. Results: Both shared and disease-specific molecular profiles among the six types of diseases were identified. Meanwhile, the patients were stratified into three distinct groups which were different from original disease definitions but presented significant differences in glucose and lipid metabolism (Group 1: relatively favorable metabolic conditions; Group 2: severe dyslipidemia; Group 3: dysregulated insulin and glucose). Group specific biological signatures were also systematically described. The dyslipidemia group showed higher levels in multiple lipid metabolites like phosphatidylserine and phosphatidylcholine, and showed significant up-regulations in lipid and amino acid metabolism pathways. The glucose dysregulated group showed higher levels in many polypeptides from proteins contributing to immune response. The another group, with better glucose/lipid metabolism ability, showed higher levels in lipid regulating enzymes like the lecithin cholesterol acyltransferase and proteins involved in complement and coagulation cascades. Conclusions: This multi-omics based study provides a general view of the complex relationships and an alternative classification for various metabolic diseases where the cross-talk or compensatory mechanism between the immune and metabolism systems plays a critical role.

The pituitary gland is a small but important organ located in the base of the brain. Although mostly noncancerous, pituitary adenomas (PAs) can cause serious health problems such as headaches, visual field defects, double vision, and hypopituitarism by invasion of regional structures. Nonfunctioning PAs (NFPAs) approximately account for one-third of PAs manifested by no circulating hormone hypersecretion. Lipid reprogramming has been recognized as a hallmark of tumor cells and proven to play a crucial role in tumorigenesis. However, the lipid molecular pathogenesis of NFPAs has remained obscure to date. To uncover lipid alterations that may contribute to the development of NFPAs and define their molecular characteristics, we investigated tissue lipids of patients with NFPAs including eight null cell adenomas (NCAs) and eight oncocytomas (OCMs) and of five normal pituitary glands as the control (Ctrl) using nontargeted lipidomics based on ultrahigh-performance liquid chromatography-Orbitrap Q-Exactive HF mass spectrometry. The lipidomic results were further validated in another set of subjects consisting of 8 NCAs, 10 OCMs, and 6 Ctrls to define crucial lipids discriminating NFPAs from the normal pituitary tumors. Lipidomic analyses revealed that OCM showed more pronounced changes in lipid compositions than NCA and Ctrl. As expected, mitochondria abundant cardiolipins were remarkably increased in OCM, which was accordant with the biochemical evidence of mitochondria hyperplasia in OCM. Significantly increased levels of phospholipids (PLs), especially arachidonic acid (AA)-enriched PLs, were unique characteristics of lipid profiling in OCM vs Ctrl. Our results indicate that AA-PLs may have diagnostic potential for OCM.

Lipids are vital biological molecules and play multiple roles in cellular function of mammalian organisms such as cellular membrane anchoring, signal transduction, material trafficking and energy storage. Driven by the biological significance of lipids, lipidomics has become an emerging science in the field of omics. Lipidome in biological systems consists of hundreds of thousands of individual lipid molecules that possess complex structures, multiple categories, and diverse physicochemical properties assembled by different combinations of polar headgroups and hydrophobic fatty acyl chains. Such structural complexity poses a huge challenge for comprehensive lipidome analysis. Thanks to the great innovations in chromatographic separation techniques and the continuous advances in mass spectrometric detection tools, analytical strategies for lipidomics have been highly diversified so that the depth and breadth of lipidomics have been greatly enhanced. This review will present the current state of mass spectrometry-based analytical strategies including untargeted, targeted and pseudotargeted lipidomics. Recent typical applications of lipidomics in biomarker discovery, pathogenic mechanism and therapeutic strategy are summarized, and the challenges facing to the field of lipidomics are also discussed., Graphical abstract Image 1, Highlights • The diversified mass spectrometry (MS)-based untargeted, targeted and pseudotargeted strategies have greatly enhanced lipidomics applications. • Recent advances in MS techniques allow reliable and sensitive detection of hundreds to thousands of lipids. • The integration of multi-omics analytical techniques has a bright prospect in studying the pathogenic mechanism and potentially targetable therapy. • Comprehensive analysis of the lipidome still faces considerable challenges in analytical techniques.

Lipidomics aims to characterize lipid alteration in response to internal or external subtle perturbations in complex biological samples. Lipid abnormality is a major risk factor for many diseases. Large-scale lipidomic studies may offer new insights into the pathophysiological mechanisms of diseases, new opportunities in systems biology, functional biology, and personalized medicine. To this end, a highly efficient and stable lipidomic method is highly in demand. We herein present a rapid and relatively high coverage lipidomic profiling approach based on ultra-high performance liquid chromatography-mass spectrometry by comparing the performance of different chromatographic columns, optimizing the elution gradient and selecting an appropriate data acquisition mode of mass spectra. As a result, a total of 481 lipids were detected from 40 μL serum sample within 13 min, covering 20 common lipid (sub)classes. The developed method was well validated with satisfactory analytical characteristics in linearity, repeatability, stability, and lipid coverage. To show the usefulness, the method was employed to investigate serum lipid profiling of 43 subjects with mild diabetic retinopathy and 44 normal controls, and successfully defined the differential lipids related to diabetic retinopathy. We believe that this rapid method will be beneficial for lipidomic analysis of large-scale clinical samples.

Carnitines play important roles in fatty acid oxidation and branched chain amino acid metabolism. The disturbance of acylcarnitines is associated with occurrence and development of many diseases. Comprehensive acylcarnitine identification can greatly benefit their targeted detection, following disease differential diagnosis and possible mechanism study. In this study, we developed a novel strategy to identify as many acylcarnitines as possible based on liquid chromatography–high-resolution mass spectrometry (LC–HRMS). The layer–layer progressive strategy first integrated the initial full scan MS/data-dependent MS/MS monitoring (ddMS2) acquisition and the following parallel reaction monitoring (PRM) to analyze a pooled biological sample. Also 733 possible acylcarnitines were identified containing characteristic high-resolution MS/MS features. Further, accurate mass, retention rules, and HRMS/MS information were used to define subclasses and predict undetected acylcarnitine homologues in each subclass, lead...

In a Chinese prospective cohort, 500 patients with new‐onset type 2 diabetes (T2D) within 4.61 years and 500 matched healthy participants are selected as case and control groups, and randomized into discovery and validation sets to discover the metabolite changes before T2D onset and the related diabetogenic loci. A serum metabolomics analysis reveals that 81 metabolites changed significantly before T2D onset. Based on binary logistic regression, eight metabolites are defined as a biomarker panel for T2D prediction. Pipecolinic acid, carnitine C14:0, epinephrine and phosphatidylethanolamine 34:2 are first found associated with future T2D. The addition of the biomarker panel to the clinical markers (BMI, triglycerides, and fasting glucose) significantly improves the predictive ability in the discovery and validation sets, respectively. By associating metabolomics with genomics, a significant correlation (p, In a Chinese prospective cohort, 500 patients with new‐onset type 2 diabetes (T2D) within 4.61 years and 500 matched healthy participants are selected. A serum metabolomics analysis is performed and 81 metabolites are found to significantly change before T2D onset. The new biomarker panel helps to predict T2D. Gene FADS1, CHRM3, and WWOX are found to be associated with T2D‐related metabolites.

Diabetic retinopathy (DR) is the main cause of vision loss or blindness in working age adults worldwide. The lack of effective diagnostic biomarkers for DR leads to unsatisfactory curative treatments. To define potential metabolite biomarkers for DR diagnosis, a multiplatform‐based metabolomics study is performed. In this study, a total of 905 subjects with diabetes without DR (NDR) and with DR at different clinical stages are recruited. Multiplatform metabolomics methods are used to characterize the serum metabolic profiles and to screen and validate the DR biomarkers. Based on the criteria p, Multiplatform metabolomics methods are used to comprehensively investigate the association between the serum metabolic profiles and the onset and progression of diabetic retinopathy (DR) in a large Chinese population, and 12‐HETE and 2‐piperidone are identified as a highly effective marker panel for the detection of DR and early‐stage DR.