Lipid metabolism in sickness and in health: Emerging regulators of lipotoxicity.

Lipids play crucial roles in signal transduction, contribute to the structural integrity of cellular membranes, and regulate energy metabolism. Questions remain as to which lipid species maintain metabolic homeostasis and which disrupt essential cellular functions, leading to metabolic disorders. Here, we discuss recent advances in understanding lipid metabolism with a focus on catabolism, synthesis, and signaling. Technical advances, including functional genomics, metabolomics, lipidomics, lipid-protein interaction maps, and advances in mass spectrometry, have uncovered new ways to prioritize molecular mechanisms mediating lipid function. By reviewing what is known about the distinct effects of specific lipid species in physiological pathways, we provide a framework for understanding newly identified targets regulating lipid homeostasis with implications for ameliorating metabolic diseases.

Lipidomic risk scores are independent of polygenic risk scores and can predict incidence of diabetes and cardiovascular disease in a large population cohort.

Type 2 diabetes (T2D) and cardiovascular disease (CVD) represent significant disease burdens for most societies and susceptibility to these diseases is strongly influenced by diet and lifestyle. Physiological changes associated with T2D or CVD, such has high blood pressure and cholesterol and glucose levels in the blood, are often apparent prior to disease incidence. Here we integrated genetics, lipidomics, and standard clinical diagnostics to assess future T2D and CVD risk for 4,067 participants from a large prospective population-based cohort, the Malmö Diet and Cancer-Cardiovascular Cohort. By training Ridge regression-based machine learning models on the measurements obtained at baseline when the individuals were healthy, we computed several risk scores for T2D and CVD incidence during up to 23 years of follow-up. We used these scores to stratify the participants into risk groups and found that a lipidomics risk score based on the quantification of 184 plasma lipid concentrations resulted in a 168% and 84% increase of the incidence rate in the highest risk group and a 77% and 53% decrease of the incidence rate in lowest risk group for T2D and CVD, respectively, compared to the average case rates of 13.8% and 22.0%. Notably, lipidomic risk correlated only marginally with polygenic risk, indicating that the lipidome and genetic variants may constitute largely independent risk factors for T2D and CVD. Risk stratification was further improved by adding standard clinical variables to the model, resulting in a case rate of 51.0% and 53.3% in the highest risk group for T2D and CVD, respectively. The participants in the highest risk group showed significantly altered lipidome compositions affecting 167 and 157 lipid species for T2D and CVD, respectively. Our results demonstrated that a subset of individuals at high risk for developing T2D or CVD can be identified years before disease incidence. The lipidomic risk, which is derived from only one single mass spectrometric measurement that is cheap and fast, is informative and could extend traditional risk assessment based on clinical assays.

Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline.

To standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

Four-dimensional lipidomics profiling in X-linked adrenoleukodystrophy using trapped ion mobility mass spectrometry.

Lipids play pivotal roles in an extensive range of metabolic and physiological processes. In recent years, the convergence of trapped ion mobility spectrometry and MS has enabled 4D-lipidomics, a highly promising technology for comprehensive lipid analysis. 4D-lipidomics assesses lipid annotations across four distinct dimensions-retention time, collisional cross section, m/z (mass-to-charge ratio), and MS/MS spectra-providing a heightened level of confidence in lipid annotation. These advantages prove particularly valuable when investigating complex disorders involving lipid metabolism, such as adrenoleukodystrophy (ALD). ALD is characterized by the accumulation of very-long-chain fatty acids (VLCFAs) due to pathogenic variants in the ABCD1 gene. A comprehensive 4D-lipidomics strategy of ALD fibroblasts demonstrated significant elevations of various lipids from multiple classes. This indicates that the changes observed in ALD are not confined to a single lipid class and likely impacts a broad spectrum of lipid-mediated physiological processes. Our findings highlight the incorporation of mainly saturated and monounsaturated VLCFA variants into a range of lipid classes, encompassing phosphatidylcholines, triacylglycerols, and cholesterol esters. These include ultra-long-chain fatty acids with a length of up to thirty carbon atoms. Lipid species containing C26:0 and C26:1 were the most frequently detected VLCFA lipids in our study. Furthermore, we report a panel of 121 new candidate biomarkers in fibroblasts, exhibiting significant differentiation between controls and individuals with ALD. In summary, this study demonstrates the capabilities of a 4D-lipid profiling workflow in unraveling novel insights into the intricate lipid modifications associated with metabolic disorders like ALD.

Identification of TFG- and Autophagy-Regulated Proteins and Glycerophospholipids in B Cells.

Autophagy supervises the proteostasis and survival of B lymphocytic cells. Trk-fused gene (TFG) promotes autophagosome-lysosome flux in murine CH12 B cells, as well as their survival. Hence, quantitative proteomics of CH12tfgKO and WT B cells in combination with lysosomal inhibition should identify proteins that are prone to lysosomal degradation and contribute to autophagy and B cell survival. Lysosome inhibition via NH4Cl unexpectedly reduced a number of proteins but increased a large cluster of translational, ribosomal, and mitochondrial proteins, independent of TFG. Hence, we propose a role for lysosomes in ribophagy in B cells. TFG-regulated proteins include CD74, BCL10, or the immunoglobulin JCHAIN. Gene ontology (GO) analysis reveals that proteins regulated by TFG alone, or in concert with lysosomes, localize to mitochondria and membrane-bound organelles. Likewise, TFG regulates the abundance of metabolic enzymes, such as ALDOC and the fatty acid-activating enzyme ACOT9. To test consequently for a function of TFG in lipid metabolism, we performed shotgun lipidomics of glycerophospholipids. Total phosphatidylglycerol is more abundant in CH12tfgKO B cells. Several glycerophospholipid species with similar acyl side chains, such as 36:2 phosphatidylethanolamine and 36:2 phosphatidylinositol, show a dysequilibrium. We suggest a role for TFG in lipid homeostasis, mitochondrial functions, translation, and metabolism in B cells.

Distinctive Lipid Characteristics of Colorectal Cancer Revealed through Non-targeted Lipidomics Analysis of Tongue Coating.

The metabolites and microbiota in tongue coating display distinct characteristics in certain digestive disorders, yet their relationship with colorectal cancer (CRC) remains unexplored. Here, we employed liquid chromatography coupled with tandem mass spectrometry to analyze the lipid composition of tongue coating using a nontargeted approach in 30 individuals with colorectal adenomas (CRA), 32 with CRC, and 30 healthy controls (HC). We identified 21 tongue coating lipids that effectively distinguished CRC from HC (AUC = 0.89), and 9 lipids that differentiated CRC from CRA (AUC = 0.9). Furthermore, we observed significant alterations in the tongue coating lipid composition in the CRC group compared to HC/CRA groups. As the adenoma-cancer sequence progressed, there was an increase in long-chain unsaturated triglycerides (TG) levels and a decrease in phosphatidylethanolamine plasmalogen (PE-P) levels. Furthermore, we noted a positive correlation between N-acyl ornithine (NAOrn), sphingomyelin (SM), and ceramide phosphoethanolamine (PE-Cer), potentially produced by members of the Bacteroidetes phylum. The levels of inflammatory lipid metabolite 12-HETE showed a decreasing trend with colorectal tumor progression, indicating the potential involvement of tongue coating microbiota and tumor immune regulation in early CRC development. Our findings highlight the potential utility of tongue coating lipid analysis as a noninvasive tool for CRC diagnosis.

Metabolic characterization of sphere-derived prostate cancer stem cells reveals aberrant urea cycle in stemness maintenance.

Alteration of cell metabolism is one of the essential characteristics of tumor growth. Cancer stem cells (CSCs) are the initiating cells of tumorigenesis, proliferation, recurrence, and other processes, and play an important role in therapeutic resistance and metastasis. Thus, identification of the metabolic profiles in prostate cancer stem cells (PCSCs) is critical to understanding prostate cancer progression. Using untargeted metabolomics and lipidomics methods, we show distinct metabolic differences between prostate cancer cells and PCSCs. Urea cycle is the most significantly altered metabolic pathway in PCSCs, the key metabolites arginine and proline are evidently elevated. Proline promotes cancer stem-like characteristics via the JAK2/STAT3 signaling pathway. Meanwhile, the enzyme pyrroline-5-carboxylate reductase 1 (PYCR1), which catalyzes the conversion of pyrroline-5-carboxylic acid to proline, is highly expressed in PCSCs, and the inhibition of PYCR1 suppresses the stem-like characteristics of prostate cancer cells and tumor growth. In addition, carnitine and free fatty acid levels are significantly increased, indicating reprogramming of fatty acid metabolism in PCSCs. Reduced sphingolipid levels and increased triglyceride levels are also observed. Collectively, our data illustrate the comprehensive landscape of the metabolic reprogramming of PCSCs and provide potential therapeutic strategies for prostate cancer.

Using Data-Dependent and -Independent Hybrid Acquisitions for Fast Liquid Chromatography-Based Untargeted Lipidomics.

Untargeted lipidomics using liquid chromatography (LC) coupled with tandem mass spectrometry (MS) is essential for large cohort studies. Using a fast LC gradient of less than 10 min for the rapid screening of lipids decreases the annotation rate, because of the lower coverage of the MS/MS spectra caused by the narrow peak width. A systematic procedure is proposed in this study to achieve a high annotation rate in fast LC-based untargeted lipidomics by integrating data-dependent acquisition (DDA) and sequential window acquisition of all-theoretical mass spectrometry data-independent acquisition (SWATH-DIA) techniques using the updated MS-DIAL program. This strategy uses variable SWATH-DIA methods for quality control (QC) samples, which are a mixture of biological samples that were analyzed multiple times to correct the MS signal drift. In contrast, biological samples are analyzed using DDA to facilitate the structural elucidation of lipids using the pure spectrum to the maximum extent. The workflow is demonstrated using an 8.6 min LC gradient, where the QC samples are analyzed using five different SWATH-DIA methods. The use of both DDA and SWATH-DIA achieves a 1.7-fold annotation coverage from publicly available benchmark data obtained using a fast LC-DDA-MS technique and offers 95.3% lipid coverage, as compared to the benchmark data set from a 25 min LC gradient. This study demonstrates that harmonized improvements in analytical conditions and informatics tools provide a comprehensive lipidome in fast LC-based untargeted lipidomics, not only for large-scale studies but also for small-scale experiments, contributing to both clinical applications and basic biology.

A Targeted, Bioinert LC-MS/MS Method for Sensitive, Comprehensive Analysis of Signaling Lipids.

Signaling lipids are key players in cellular processes. Despite their importance, no method currently allows their comprehensive monitoring in one analytical run. Challenges include a wide dynamic range, isomeric and isobaric species, and unwanted interaction along the separation path. Herein, we present a sensitive and robust targeted liquid chromatography-mass spectrometry (LC-MS/MS) approach to overcome these challenges, covering a broad panel of 17 different signaling lipid classes. It involves a simple one-phase sample extraction and lipid analysis using bioinert reversed-phase liquid chromatography coupled to targeted mass spectrometry. The workflow shows excellent sensitivity and repeatability in different biological matrices, enabling the sensitive and robust monitoring of 388 lipids in a single run of only 20 min. To benchmark our workflow, we characterized the human plasma signaling lipidome, quantifying 307 endogenous molecular lipid species. Furthermore, we investigated the signaling lipidome during platelet activation, identifying numerous regulations along important lipid signaling pathways. This highlights the potential of the presented method to investigate signaling lipids in complex biological systems, enabling unprecedentedly comprehensive analysis and direct insight into signaling pathways.

LipiDex 2 Integrates MSn Tree-Based Fragmentation Methods and Quality Control Modules to Improve Discovery Lipidomics.

As lipidomics experiments increase in scale and complexity, data processing tools must support workflows for new liquid chromatography-mass spectrometry (LC-MS) methods while simultaneously supporting quality controls to maximize the confidence in lipid identifications. LipiDex 2 improves lipidomics data processing algorithms from LipiDex 1 and introduces new tools for spectral matching and peak annotation functions, with improvements in speed and user-friendliness. In silico spectral library generation now supports tandem mass spectral (MSn) tree-based fragmentation methods, and the LipiDex 2 workflow fully integrates the fragmentation logic into the data processing steps to enable lipid identification at the appropriate level of structural resolution. Finally, LipiDex 2 features new modules for automated quality control checks that also allow users to visualize data quality in a data dashboard user interface.

Stable-Isotope N-Me Aziridination Enables Accurate Quantitative C═C Isomeric Lipidomics.

Accurate lipid quantification is essential to revealing their roles in physiological and pathological processes. However, difficulties in the structural resolution of lipid isomers hinder their further accurate quantification. To address this challenge, we developed a novel stable-isotope N-Me aziridination strategy that enables simultaneous qualification and quantification of unsaturated lipid isomers. The one-step introduction of the 1-methylaziridine structure not only serves as an activating group for the C═C bond to facilitate positional identification but also as an isotopic inserter to achieve accurate relative quantification. The high performance of this reaction for the identification of unsaturated lipids was verified by large-scale resolution of the C═C positions of 468 lipids in serum. More importantly, by using this bifunctional duplex labeling method, various unsaturated lipids such as fatty acids, phospholipids, glycerides, and cholesterol ester were accurately and individually quantified at the C═C bond isomeric level during the mouse brain ischemia. This study provides a new approach to quantitative structural lipidomics.

Single-Cell Untargeted Lipidomics Using Liquid Chromatography and Data-Dependent Acquisition after Live Cell Selection.

We report the development and validation of an untargeted single-cell lipidomics method based on microflow chromatography coupled to a data-dependent mass spectrometry method for fragmentation-based identification of lipids. Given the absence of single-cell lipid standards, we show how the methodology should be optimized and validated using a dilute cell extract. The methodology is applied to dilute pancreatic cancer and macrophage cell extracts and standards to demonstrate the sensitivity requirements for confident assignment of lipids and classification of the cell type at the single-cell level. The method is then coupled to a system that can provide automated sampling of live, single cells into capillaries under microscope observation. This workflow retains the spatial information and morphology of cells during sampling and highlights the heterogeneity in lipid profiles observed at the single-cell level. The workflow is applied to show changes in single-cell lipid profiles as a response to oxidative stress, coinciding with expanded lipid droplets. This demonstrates that the workflow is sufficiently sensitive to observing changes in lipid profiles in response to a biological stimulus. Understanding how lipids vary in single cells will inform future research into a multitude of biological processes as lipids play important roles in structural, biophysical, energy storage, and signaling functions.

Investigation of the Effects of Labware Contamination on Mass Spectrometry-Based Human Serum Lipidome Analysis.

Polypropylene microcentrifuge tubes (MCTs) are increasingly used in lipidome sample preparation. In the absence of a comprehensive study evaluating ramifications of plasticware utilization in mass spectrometry-based lipidomic analyses, we conducted a systematic analysis to elucidate potential negative effects ascribable to labware contamination in serum lipidomics. During serum lipid extractions, tested glassware introduced 24 labware contaminants. In contrast, Eppendorf polypropylene MCTs contributed 485 contaminant features, many of which could be erroneously putatively identified as lipids via their m/z values. Eppendorf MCTs contamination engendered severe ion-suppression of 40 low abundance serum lipids, while generating mild to modest lipid ion-suppression across a multitude of higher abundance coeluting lipids. Less compatible polypropylene MCTs from an alternative manufacturer introduced a staggering 2,949 contaminant m/z values, severely affecting 75 coeluting serum lipids and causing more frequent and pronounced ion-suppression instances. Furthermore, by performing serum extractions with varied initial volumes, it was ascertained that labware-induced lipid ion-suppression is a dynamic phenomenon, contingent on both lipid and labware contaminant concentrations where low-abundance lipids are disproportionately impacted by coelutes of suppressive contaminants. In addition to lipid ion-suppression, the identification and quantification of 7 fatty acid endogenous serum lipids were compromised by the leaching of structurally identical surfactants from MCTs. MCTs artificially introduced 10 additional primary amides extraneous to serum samples. Utmost caution is imperative in interpreting data concerning primary amides and fatty acids when employing plastic labware. Through this investigation, we aspire to elevate awareness regarding the pernicious impact of labware contamination on lipidome analysis.

MobiLipid: A Tool for Enhancing CCS Quality Control of Ion Mobility-Mass Spectrometry Lipidomics by Internal Standardization.

Ion mobility-mass spectrometry (IM-MS) offers benefits for lipidomics by obtaining IM-derived collision cross sections (CCS), a conditional property of an ion that can enhance lipid identification. While drift tube (DT) IM-MS retains a direct link to the primary experimental method to derive CCS values, other IM technologies rely solely on external CCS calibration, posing challenges due to dissimilar chemical properties between lipids and calibrants. To address this, we introduce MobiLipid, a novel tool facilitating the CCS quality control of IM-MS lipidomics workflows by internal standardization. MobiLipid utilizes a newly established DTCCSN2 library for uniformly (U)13C-labeled lipids, derived from a U13C-labeled yeast extract, containing 377 DTCCSN2 values. This automated open-source R Markdown tool enables internal monitoring and straightforward compensation for CCSN2 biases. It supports lipid class- and adduct-specific CCS corrections, requiring only three U13C-labeled lipids per lipid class-adduct combination across 10 lipid classes without requiring additional external measurements. The applicability of MobiLipid is demonstrated for trapped IM (TIM)-MS measurements of an unlabeled yeast extract spiked with U13C-labeled lipids. Monitoring the CCSN2 biases of TIMCCSN2 values compared to DTCCSN2 library entries utilizing MobiLipid resulted in mean absolute biases of 0.78% and 0.33% in positive and negative ionization mode, respectively. By applying the CCS correction integrated into the tool for the exemplary data set, the mean absolute CCSN2 biases of 10 lipid classes could be reduced to approximately 0%.

Lipidomic analysis of human TANGO2-deficient cells suggests a lipid imbalance as a cause of TANGO2 deficiency disease.

TANGO2 deficiency disease (TDD) is a multisystem disease caused by variants in the TANGO2 gene. Symptoms include neurodevelopmental delays, seizures and potentially lethal metabolic crises and cardiac arrhythmias. While the function of TANGO2 remains elusive, vitamin B5/pantothenic acid supplementation has been shown to alleviate symptoms in a fruit fly model and has also been used with success to treat individuals suffering from TDD. Since vitamin B5 is the precursor to the lipid activator coenzyme A (CoA), we hypothesized that TANGO2-deficient cells would display changes in the lipid profile compared to control and that these changes would be rescued by vitamin B5 supplementation. In addition, the specific changes seen might point to a pathway in which TANGO2 functions. Indeed, we found profound changes in the lipid profile of human TANGO2-deficient cells as well as an increased pool of free fatty acids in both human cells devoid of TANGO2 and Drosophila harboring a previously described TANGO2 loss of function allele. All these changes were reversed upon vitamin B5 supplementation. Pathway analysis showed significant increases in triglyceride as well as in lysophospholipid levels as the top enriched pathways in the absence of TANGO2. Consistent with a defect in triglyceride metabolism, we found changes in lipid droplet numbers and sizes in the absence of TANGO2 compared to control. Our data will allow for comparison between other model systems of TDD and the homing in on critical lipid imbalances that lead to the disease state.

Lipidomics random forest algorithm of seminal plasma is a promising method for enhancing the diagnosis of necrozoospermia.

BACKGROUND: Despite the clear clinical diagnostic criteria for necrozoospermia in andrology, the fundamental mechanisms underlying it remain elusive. This study aims to profile the lipid composition in seminal plasma systematically and to ascertain the potential of lipid biomarkers in the accurate diagnosis of necrozoospermia. It also evaluates the efficacy of a lipidomics-based random forest algorithm model in identifying necrozoospermia. METHODS: Seminal plasma samples were collected from patients diagnosed with necrozoospermia (n = 28) and normozoospermia (n = 28). Liquid chromatography-mass spectrometry (LC-MS) was used to perform lipidomic analysis and identify the underlying biomarkers. A lipid functional enrichment analysis was conducted using the LION lipid ontology database. The top 100 differentially significant lipids were subjected to lipid biomarker examination through random forest machine learning model. RESULTS: Lipidomic analysis identified 46 lipid classes comprising 1267 lipid metabolites in seminal plasma. The top five enriched lipid functions as follows: fatty acid (FA) with ≤ 18 carbons, FA with 16-18 carbons, monounsaturated FA, FA with 18 carbons, and FA with 16 carbons. The top 100 differentially significant lipids were subjected to machine learning analysis and identified 20 feature lipids. The random forest model identified lipids with an area under the curve > 0.8, including LPE(20:4) and TG(4:0_14:1_16:0). CONCLUSIONS: LPE(20:4) and TG(4:0_14:1_16:0), were identified as differential lipids for necrozoospermia. Seminal plasma lipidomic analysis could provide valuable biochemical information for the diagnosis of necrozoospermia, and its combination with conventional sperm analysis may improve the accuracy and reliability of the diagnosis.

Data-dependent and -independent acquisition lipidomics analysis reveals the tissue-dependent effect of metformin on lipid metabolism.

INTRODUCTION: Despite the well-recognized health benefits, the mechanisms and site of action of metformin remains elusive. Metformin-induced global lipidomic changes in plasma of animal models and human subjects have been reported. However, there is a lack of systemic evaluation of metformin-induced lipidomic changes in different tissues. Metformin uptake requires active transporters such as organic cation transporters (OCTs), and hence, it is anticipated that metformin actions are tissue-dependent. In this study, we aim to characterize metformin effects in non-diabetic male mice with a special focus on lipidomics analysis. The findings from this study will help us to better understand the cell-autonomous (direct actions in target cells) or non-cell-autonomous (indirect actions in target cells) mechanisms of metformin and provide insights into the development of more potent yet safe drugs targeting a particular organ instead of systemic metabolism for metabolic regulations without major side effects. OBJECTIVES: To characterize metformin-induced lipidomic alterations in different tissues of non-diabetic male mice and further identify lipids affected by metformin through cell-autonomous or systemic mechanisms based on the correlation between lipid alterations in tissues and the corresponding in-tissue metformin concentrations. METHODS: A dual extraction method involving 80% methanol followed by MTBE (methyl tert-butyl ether) extraction enables the analysis of free fatty acids, polar metabolites, and lipids. Extracts from tissues and plasma of male mice treated with or without metformin in drinking water for 12 days were analyzed using HILIC chromatography coupled to Q Exactive Plus mass spectrometer or reversed-phase liquid chromatography coupled to MS/MS scan workflow (hybrid mode) on LC-Orbitrap Exploris 480 mass spectrometer using biologically relevant lipids-containing inclusion list for data-independent acquisition (DIA), named as BRI-DIA workflow followed by data-dependent acquisition (DDA), to maximum the coverage of lipids and minimize the negative effect of stochasticity of precursor selection on experimental consistency and reproducibility. RESULTS: Lipidomics analysis of 6 mouse tissues and plasma allowed a systemic evaluation of lipidomic changes induced by metformin in different tissues. We observed that (1) the degrees of lipidomic changes induced by metformin treatment overly correlated with tissue concentrations of metformin; (2) the impact on lysophosphatidylcholine (lysoPC) and cardiolipins was positively correlated with tissue concentrations of metformin, while neutral lipids such as triglycerides did not correlate with the corresponding tissue metformin concentrations; (3) increase of intestinal tricarboxylic acid (TCA) cycle intermediates after metformin treatment. CONCLUSION: The data collected in this study from non-diabetic mice with 12-day metformin treatment suggest that the overall metabolic effect of metformin is positively correlated with tissue concentrations and the effect on individual lipid subclass is via both cell-autonomous mechanisms (cardiolipins and lysoPC) and non-cell-autonomous mechanisms (triglycerides).

Lipidomic signatures in patients with early-onset and late-onset Preeclampsia.

BACKGROUND: Preeclampsia is a pregnancy-specific clinical syndrome and can be subdivided into early-onset preeclampsia (EOPE) and late-onset preeclampsia (LOPE) according to the gestational age of delivery. Patients with preeclampsia have aberrant lipid metabolism. This study aims to compare serum lipid profiles of normal pregnant women with EOPE or LOPE and screening potential biomarkers to diagnose EOPE or LOPE. METHODS: Twenty normal pregnant controls (NC), 19 EOPE, and 19 LOPE were recruited in this study. Untargeted lipidomics based on ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to compare their serum lipid profiles. RESULTS: The lipid metabolism profiles significantly differ among the NC, EOPE, and LOPE. Compared to the NC, there were 256 and 275 distinct lipids in the EOPE and LOPE, respectively. Furthermore, there were 42 different lipids between the LOPE and EOPE, of which eight were significantly associated with fetal birth weight and maternal urine protein. The five lipids that both differed in the EOPE and LOPE were DGTS (16:3/16:3), LPC (20:3), LPC (22:6), LPE (22:6), PC (18:5e/4:0), and a combination of them were a potential biomarker for predicting EOPE or LOPE. The receiver operating characteristic analysis revealed that the diagnostic power of the combination for distinguishing the EOPE from the NC and for distinguishing the LOPE from the NC can reach 1.000 and 0.992, respectively. The association between the lipid modules and clinical characteristics of EOPE and LOPE was investigated by the weighted gene co-expression network analysis (WGCNA). The results demonstrated that the main different metabolism pathway between the EOPE and LOPE was enriched in glycerophospholipid metabolism. CONCLUSIONS: Lipid metabolism disorders may be a potential mechanism of the pathogenesis of preeclampsia. Lipid metabolites have the potential to serve as biomarkers in patients with EOPE or LOPE. Furthermore, lipid metabolites correlate with clinical severity indicators for patients with EOPE and LOPE, including fetal birth weight and maternal urine protein levels.

UHPLC-HRMS-based Multiomics to Explore the Potential Mechanisms and Biomarkers for Colorectal Cancer.

BACKGROUND: Understanding the metabolic changes in colorectal cancer (CRC) and exploring potential diagnostic biomarkers is crucial for elucidating its pathogenesis and reducing mortality. Cancer cells are typically derived from cancer tissues and can be easily obtained and cultured. Systematic studies on CRC cells at different stages are still lacking. Additionally, there is a need to validate our previous findings from human serum. METHODS: Ultrahigh-performance liquid chromatography tandem high-resolution mass spectrometry (UHPLC-HRMS)-based metabolomics and lipidomics were employed to comprehensively measure metabolites and lipids in CRC cells at four different stages and serum samples from normal control (NR) and CRC subjects. Univariate and multivariate statistical analyses were applied to select the differential metabolites and lipids between groups. Biomarkers with good diagnostic efficacy for CRC that existed in both cells and serum were screened by the receiver operating characteristic curve (ROC) analysis. Furthermore, potential biomarkers were validated using metabolite standards. RESULTS: Metabolite and lipid profiles differed significantly among CRC cells at stages A, B, C, and D. Dysregulation of glycerophospholipid (GPL), fatty acid (FA), and amino acid (AA) metabolism played a crucial role in the CRC progression, particularly GPL metabolism dominated by phosphatidylcholine (PC). A total of 46 differential metabolites and 29 differential lipids common to the four stages of CRC cells were discovered. Eight metabolites showed the same trends in CRC cells and serum from CRC patients compared to the control groups. Among them, palmitoylcarnitine and sphingosine could serve as potential biomarkers with the values of area under the curve (AUC) more than 0.80 in the serum and cells. Their panel exhibited excellent performance in discriminating CRC cells at different stages from normal cells (AUC = 1.00). CONCLUSIONS: To our knowledge, this is the first research to attempt to validate the results of metabolism studies of serum from CRC patients using cell models. The metabolic disorders of PC, FA, and AA were closely related to the tumorigenesis of CRC, with PC being the more critical factor. The panel composed of palmitoylcarnitine and sphingosine may act as a potential biomarker for the diagnosis of CRC, aiding in its prevention.

Analysis aqueous humor lipid profile of neovascular glaucoma secondary to diabetic retinopathy and lipidomic alteration response to anti-VEGF treatment.

The objective of this study was to examine the lipid spectrum of aqueous humor (AH) in patients with neovascular glaucoma (NVG) secondary to proliferative diabetic retinopathy and to investigate the lipid alteration response to anti-vascular endothelial growth factor (anti-VEGF) treatment. Lipidomic analysis using ultra-high performance liquid chromatography-tandem mass spectrometry was conducted to compare the lipid profiles of the AH in NVG patients with those of a control group. Lipid changes in the AH of NVG patients before and after intravitreal conbercept injections were also evaluated. The identification of lipids showing differential expression was accomplished through both multivariate and univariate analyses. This study included 13 NVG patients and 20 control subjects. Based on LipidSearch software, 639 lipid species across 33 lipid classes were detected in the participants' AH. The combination of univariate and multivariate statistical analyses yielded 53 differentially expressed lipids (VIP >1 and P < 0.05). In addition, 9 lipids were found to be differentially expressed before and after the intravitreal conbercept injections in the NVG patients. Significant alterations in the metabolic pathways of glycerophospholipid and glycerolipid exhibited notable changes. Our results highlighted the lipid changes in patients' AH in relation to the progression of NVG, and indicated that the modified lipids could potentially be utilized as therapeutic targets for NVG.