Lipidomics for studying metabolism

Lipidomics of sheep and goat Milk-based infant formulae during in vitro dynamic digestion

Lipid hydrolysis process during IF digestion, particularly the characterization of the lipidome and the resulting lipid breakdown products, has not been thoroughly investigated. This study aimed to compare the lipid hydrolysis profiles during the in vitro dynamic digestion of IFs made from whole sheep and goat milk. Using a lipidomics platform and multivariate statistical analysis, we observed changes in complex lipid levels during digestion. In the gastric compartment, we noted a progressive hydrolysis of triacylglycerols, phosphatidylcholines, and sphingomyelins. Conversely, lipolysis breakdown products like monoacylglycerols (e.g., MG(16:0), MG(18:0)), diacylglycerols, lysophosphatidylcholines (LPC 16:0, LPC 18:1, LPC 18:2), and free fatty acids increased in the intestinal compartment. The lipolysis trends were similar for both types of infant formulas, with long-chain fatty acid triglycerides (C > 46) exhibiting lower digestibility compared to medium-chain fatty acid triglycerides. Overall, these results indicate that sheep milk can be used as an ingredient in the manufacturing of IF.

Multi-omics integration analysis: Tools and applications in environmental toxicology

Nowadays, traditional single-omics study is not enough to explain the causality between molecular alterations and toxicity endpoints for environmental pollutants. With the development of high-throughput sequencing technology and high-resolution mass spectrometry technology, the integrative analysis of multi-omics has become an efficient strategy to understand holistic biological mechanisms and to uncover the regulation network in specific biological processes. This review summarized sample preparation methods, integration analysis tools and the application of multi-omics integration analyses in environmental toxicology field. Currently, omics methods have been widely applied being as the sensitivity of early biological response, especially for low-dose and long-term exposure to environmental pollutants. Integrative omics can reveal the overall changes of genes, proteins, and/or metabolites in the cells, tissues or organisms, which provide new insights into revealing the overall toxicity effects, screening the toxic targets, and exploring the underlying molecular mechanism of pollutants.

Clinical stratification of 1318 Primary Sjögren's Syndrome patients

OBJECTIVE

Primary Sjögren's Syndrome (pSS) is a complex autoimmune disorder characterized by diverse clinical manifestations yet lacking effective therapeutic strategies currently. This study aims to gain a thorough understanding of the clinical landscape of pSS and further delineate its clinical subtypes, thereby enabling the efficient management for pSS.

METHODS

We conducted a cross-sectional observational study of 1318 pSS patients. The pSS patients were categorized and compared based on gender, anti-SSA antibodies, and labial salivary gland biopsies (LGSB). Unsupervised clustering analysis was employed to identify pSS subtypes using systemic involvement among patients. Furthermore, we assessed clinical and biological variances among these subtypes.

RESULTS

Through group comparisons, we observed more pronounced extraglandular manifestations among male patients, SSA-negative group, and those with positive LGSB results. Based on systemic involvement, pSS patients were categorized into four groups. C1 exhibited minimal systemic involvement, lacking hematologic or serologic manifestations, with the lowest ESSDAI scores. C2 presented with serologic changes in all patients, partial joint involvement, and no hematologic systemic manifestations. C3 lacked joint involvement but all members displayed hematologic systemic involvement, with higher rates of renal, cutaneous, and systemic manifestations. C4 encompassed patients with joint and hematologic involvement, displaying the highest ESSDAI scores. The positivity rates of antibodies, immunological parameters, and inflammatory markers exhibited significant differences among the groups. Furthermore, notable variances were observed in the expression of peripheral blood transcriptomic modules among these groups.

CONCLUSION

In this cohort study, we summarized the clinical characteristics of Chinese patients with pSS and identified four distinct subgroups of pSS based on systemic involvement, revealing clinical and molecular disparities that unveil distinct pathobiological endotypes. Our findings hold significant implications for clinical management.

Ten metabolites-based algorithm predicts the future development of type 2 diabetes in Chinese

INTRODUCTION

Type 2 diabetes (T2D) is a heterogeneous metabolic disease with large variations in the relative contributions of insulin resistance and β-cell dysfunction across different glucose tolerance subgroups and ethnicities. A more precise yet feasible approach to categorize risk preceding T2D onset is urgently needed. This study aimed to identify potential metabolic biomarkers that could contribute to the development of T2D and investigate whether their impact on T2D is mediated through insulin resistance and β-cell dysfunction.

METHODS

A non-targeted metabolomic analysis was performed in plasma samples of 196 incident T2D cases and 196 age- and sex-matched non-T2D controls recruited from a long-term prospective Chinese community-based cohort with a follow-up period of ∼ 16 years.

RESULTS

Metabolic profiles revealed profound perturbation of metabolomes before T2D onset. Overall metabolic shifts were strongly associated with insulin resistance rather than β-cell dysfunction. In addition, 188 out of the 578 annotated metabolites were associated with insulin resistance. Bi-directional mediation analysis revealed putative causal relationships among the metabolites, insulin resistance and T2D risk. We built a machine-learning based prediction model, integrating the conventional clinical risk factors (age, BMI, TyG index and 2hG) and 10 metabolites (acetyl-tryptophan, kynurenine, γ-glutamyl-phenylalanine, DG(18:2/22:6), DG(38:7), LPI(18:2), LPC(P-16:0), LPC(P-18:1), LPC(P-20:0) and LPE(P-20:0)) (AUROC = 0.894, 5.6% improvement comparing to the conventional clinical risk model), that successfully predicts the development of T2D.

CONCLUSIONS

Our findings support the notion that the metabolic changes resulting from insulin resistance, rather than β-cell dysfunction, are the primary drivers of T2D in Chinese adults. Metabolomes as a valuable phenotype hold potential clinical utility in the prediction of T2D.

Effects of different processing methods on the lipid composition of seabuckthorn fruit oil based on lipidomics

Employing lipidomics, this study investigated the lipid composition of seabuckthorn fruit oil processed via supercritical CO2 extraction and centrifugal separation. Qualitative analysis showed that a total of 2861 lipid molecules were identified in seabuckthorn fruit oil. Quantitative analysis showed that the content of lipids in seabuckthorn fruit oil extracted by supercritical CO2 extraction (927,539.84 µg/mL) was significantly higher than that in centrifugal-separated seabuckthorn fruit oil (735,717.63 µg/mL), with 17 distinct lipid classes and 215 lipid molecules differentiated through multivariate statistical analysis. Lipid molecules, such as diacylglycerol (DG), ceramides (Cer), monohexosyl ceramide, phosphatidylglycerol, phosphatidylinositol, phosphatidylethanolamine, and monogalactosyl DG, were predominantly found in the oil extracted using supercritical CO2. In contrast, monogalactosyl monoacylglycerol, diglycosyl ceramide, and Cer phosphate were significantly present in the oil extracted by centrifugal separation. These findings contribute new insights into how processing methods affect the quality and composition of seabuckthorn fruit oil and provide a basis for detecting oil adulteration.

Polygenic scores for complex traits are associated with changes in concentration of circulating lipid species

Understanding perturbations in circulating lipid levels that often occur years or decades before clinical symptoms may enhance our understanding of disease mechanisms and provide novel intervention opportunities. Here, we assessed if polygenic scores (PGSs) for complex traits could detect lipid dysfunctions related to the traits and provide new biological insights. We constructed genome-wide PGSs (approximately 1 million genetic variants) for 50 complex traits in 7,169 Finnish individuals with routine clinical lipid profiles and lipidomics measurements (179 lipid species). We identified 678 associations (P < 9.0 × 10-5) involving 26 traits and 142 lipids. Most of these associations were also validated with the actual phenotype measurements where available (89.5% of 181 associations where the trait was available), suggesting that these associations represent early signs of physiological changes of the traits. We detected many known relationships (e.g., PGS for body mass index (BMI) and lysophospholipids, PGS for type 2 diabetes and triacyglycerols) and those that suggested potential target for prevention strategies (e.g., PGS for venous thromboembolism and arachidonic acid). We also found association of PGS for favorable adiposity with increased sphingomyelins levels, suggesting a probable role of sphingomyelins in increased risk for certain disease, e.g., venous thromboembolism as reported previously, in favorable adiposity despite its favorable metabolic effect. Altogether, our study provides a comprehensive characterization of lipidomic alterations in genetic predisposition for a wide range of complex traits. The study also demonstrates potential of PGSs for complex traits to capture early, presymptomatic lipid alterations, highlighting its utility in understanding disease mechanisms and early disease detection.

Lipid profiling identifies modifiable signatures of cardiometabolic risk in children and adolescents with obesity

Pediatric obesity is a progressive, chronic disease that can lead to serious cardiometabolic complications. Here we investigated the peripheral lipidome in 958 children and adolescents with overweight or obesity and 373 with normal weight, in a cross-sectional study. We also implemented a family-based, personalized program to assess the effects of obesity management on 186 children and adolescents in a clinical setting. Using mass spectrometry-based lipidomics, we report an increase in ceramides, alongside a decrease in lysophospholipids and omega-3 fatty acids with obesity metabolism. Ceramides, phosphatidylethanolamines and phosphatidylinositols were associated with insulin resistance and cardiometabolic risk, whereas sphingomyelins showed inverse associations. Additionally, a panel of three lipids predicted hepatic steatosis as effectively as liver enzymes. Lipids partially mediated the association between obesity and cardiometabolic traits. The nonpharmacological management reduced levels of ceramides, phospholipids and triglycerides, indicating that lowering the degree of obesity could partially restore a healthy lipid profile in children and adolescents.

Untargeted Lipidomics Analysis to Discover Lipid Profiles and Biomarkers of Rabbit Acne Model and Reveal Action Mechanism of Isotretinoin

Background

Acne vulgaris (AV), a chronic inflammatory pilosebaceous disorder, affects 80-90% of teenagers. This study aimed to discover lipid profiles and biomarkers of the rabbit ear acne model, and investigate the mechanism of isotretinoin in treating acne at the lipid level.

Methods

Untargeted lipidomic analysis using ultra-high performance liquid chromatography system (UHPLC) coupled to q-extraction plus was performed to identify skin lipid metabolites in blank control (groups C), model group (group M) and isotretinoin group (group T). Multivariate statistical analysis was used to process the lipidomics data.

Results

A total of 43 lipid classes comprising 6989 lipid species were identified from the mass spectrometry data. The orthogonal partial least squares discriminant analysis (OPLS-DA) model demonstrated significant separation in skin lipidomic profiles between group M and group C. With variable influence on projection (VIP) > 1.0 and P-value < 0.05, 299 significantly different lipid metabolites were identified. These lipid metabolites consisted mainly of ceramides (Cer) (53.85%), phosphatidylethanolamines (PE) (9.03%), phosphatidylcholines (PC)(5.35%), and sphingomyelin (SM)(4.01%). Combining with AUC ≥ 0.9 as the elected criteria, Cer (d18;1_24:0), zymosterol (ZyE)(33:5), Cer (t43:1), ZyE (33:6), ZyE (24:7), and ZyE (35:6) have "high" accuracy. Isotretinoin treatment normalized 25 lipid metabolites in the acne model.

Conclusion

Our findings provide new insights into the role of lipid metabolism in the pathogenesis of acne and the action mechanism of isotretinoin.

Fast and broad-coverage lipidomics enabled by ion mobility-mass spectrometry

Aberrant lipid metabolism has been widely recognized as a hallmark of various diseases. However, the comprehensive analysis of distinct lipids is challenging due to the complexity of lipid molecular structures, wide concentration ranges, and numerous isobaric and isomeric lipids. Usually, liquid chromatography-mass spectrometry (LC-MS)-based lipidomics requires a long time for chromatographic separation to achieve optimal separation and selectivity. Ion mobility (IM) adds a new separation dimension to LC-MS, significantly enhancing the coverage, sensitivity, and resolving power. We took advantage of the rapid separation provided by ion mobility and optimized a fast and broad-coverage lipidomics method using the LC-IM-MS technology. The method required only 8 minutes for separation and detected over 1000 lipid molecules in a single analysis of common biological samples. The high reproducibility and accurate quantification of this high-throughput lipidomics method were systematically characterized. We then applied the method to comprehensively measure dysregulated lipid metabolism in patients with colorectal cancer (CRC). Our results revealed 115 significantly changed lipid species between preoperative and postoperative plasma of patients with CRC and also disclosed associated differences in lipid classes such as phosphatidylcholines (PC), sphingomyelins (SM), and triglycerides (TG) regarding carbon number and double bond. Together, a fast and broad-coverage lipidomics method was developed using ion mobility-mass spectrometry. This method is feasible for large-scale clinical lipidomic studies, as it effectively balances the requirements of high-throughput and broad-coverage in clinical studies.

Altered lipidomics biosignatures in schizophrenia: A systematic review

Multiomics approaches have significantly aided the identification of molecular signatures in complex neuropsychiatric disorders. Lipidomics, one of the newest additions in the -omics family, sheds light on lipid profiles and is an emerging methodological tool to study schizophrenia pathobiology, as lipid dysregulation has been repeatedly observed in schizophrenia. In this review, we performed a detailed literature search for lipidomics studies in schizophrenia. Following elaborate inclusion/exclusion criteria, we focused on human studies in schizophrenia and schizophrenia-related diagnoses in brain and blood specimens, including serum plasma, platelets and red blood cells. Eighteen studies fulfilled our inclusion criteria, of which five were conducted in the brain, 12 in peripheral material and one in both. Here, we first provide background on lipidomics and the main lipid categories addressed, review in detail the included literature and look for common lipidomics patterns in brain and the periphery that emerge from these studies. Furthermore, we highlight current limitations in schizophrenia lipidomics research and underline the need for following up on lipidomics results with complementary molecular approaches.

Challenges in Lipidomics Biomarker Identification: Avoiding the Pitfalls and Improving Reproducibility

Identification of features with high levels of confidence in liquid chromatography-mass spectrometry (LC-MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in biomarker identification. In this work, we illustrate the reproducibility gap for two open-access lipidomics platforms, MS DIAL and Lipostar, finding just 14.0% identification agreement when analyzing identical LC-MS spectra using default settings. Whilst the software platforms performed more consistently using fragmentation data, agreement was still only 36.1% for MS2 spectra. This highlights the critical importance of validation across positive and negative LC-MS modes, as well as the manual curation of spectra and lipidomics software outputs, in order to reduce identification errors caused by closely related lipids and co-elution issues. This curation process can be supplemented by data-driven outlier detection in assessing spectral outputs, which is demonstrated here using a novel machine learning approach based on support vector machine regression combined with leave-one-out cross-validation. These steps are essential to reduce the frequency of false positive identifications and close the reproducibility gap, including between software platforms, which, for downstream users such as bioinformaticians and clinicians, can be an underappreciated source of biomarker identification errors.

Improved detection in untargeted lipidomics through silver-doped infrared matrix-assisted laser desorption electrospray ionization

RATIONALE

Silver doping of electrospray is known to increase the abundance of olefinic compounds detected by mass spectrometry. While demonstrated in targeted experiments, this has yet to be investigated in an untargeted study. Utilizing infrared matrix-assisted laser desorption electrospray ionization mass spectrometry imaging (IR-MALDESI-MSI), an untargeted lipidomics experiment on mouse liver was performed to evaluate the advantages of silver-doped electrospray.

METHODS

10 ppm silver nitrate was doped into the IR-MALDESI solvent consisting of 60% acetonitrile and 0.2% formic acid. Using an Orbitrap mass spectrometer in positive ionization mode, MSI was performed, analyzing from m/z 150 to m/z 2000 to capture all lipids with potential silver adducts. The lipids detected in the control and silver-doped electrosprays were compared by annotating using the LIPID MAPS Structural Database and eliminating false positives using the metabolite annotation confidence score.

RESULTS

Silver-doped electrospray allowed for the detection of such ions of lipid molecules as [M + H]+ or [M + NH4]+ and as [M + Ag]+. Among the ions seen as [M + H]+ or [M + NH4]+, the signal was comparable between the control and silver-doped electrosprays. The silver-doped electrospray led to a 10% increase in the number of detected lipids, all of which contained a bay region increasing the interaction between silver and alkenes. Silver preferentially interacted with lipids that did not contain hard bases such as phosphates.

CONCLUSIONS

Silver-doped electrospray enabled detection of 10% more olefinic lipids, all containing bay regions in their putative structures. This technique is valuable for detecting previously unobserved lipids that have the potential to form bay regions, namely fatty acyls, glycerolipids, prenol lipids, and polyketides.

Lipidomics combined with random forest machine learning algorithms to reveal freshness markers for duck eggs during storage in different rearing systems

The differences in lipids in duck eggs between the 2 rearing systems during storage have not been fully studied. Herein, we propose untargeted lipidomics combined with a random forest (RF) algorithm to identify potential marker lipids based on ultra-performance liquid chromatography‒mass spectrometry (UPLPC-MS/MS). A total of 106 and 16 differential lipids (DL) were screened in egg yolk and white, respectively. In yolk, metabolic pathway analysis of DLs revealed that glycerophospholipid metabolism and sphingolipid metabolism were the key metabolic pathways in the traditional free-range system (TFS) during storage, glycosylphosphatidylinositol-anchored biosynthesis and glyceride metabolism were the key pathways in the floor-rearing system (FRS). In egg white, the key pathway in both systems is the biosynthesis of unsaturated fatty acids. Combined with RF algorithm, 12 marker lipids were screened during storage. Therefore, this study elucidates the changes in lipids in duck eggs during storage in 2 rearing systems and provides new ideas for screening marker lipids during storage. This approach is highly important for evaluating the quality of egg and egg products and provides guidance for duck egg production.

Role of inflammatory biomarkers in mediating the effect of lipids on spontaneous intracerebral hemorrhage: a two-step, two-sample Mendelian randomization study

Background

Spontaneous intracerebral hemorrhage (sICH) is a form of stroke with high mortality rates and significant neurological implications for patients. Abnormalities in lipid metabolism have been implicated in various cardiovascular diseases, yet their relationship with sICH remains insufficiently explored, particularly concerning their association with inflammatory factors.

Methods

Employing a two-sample, two-step Mendelian Randomization approach, combined with data from GWAS datasets, to investigate the causal relationship between plasma lipid levels and sICH. Additionally, the role of inflammatory factors in this relationship was examined, and sensitivity analyses were conducted to ensure the robustness of the results.

Results

The results indicate a significant causal relationship between 19 plasma lipid metabolites and sICH. Furthermore, mediation analysis revealed that three distinct lipids, namely Sterol ester (27:1/20:2), Phosphatidylcholine (16:0_20:4), and Sphingomyelin (d34:1), exert their influence on sICH through inflammatory factors. TRAIL (OR: 1.078, 95% CI: 1.016-1.144, p = 0.013) and HGF (OR: 1.131, 95% CI: 1.001-1.279, p = 0.049) were identified as significant mediators.

Conclusion

This study provides new evidence linking abnormalities in lipid metabolism with sICH and elucidates the role of inflammatory factors as mediators. These findings contribute to a better understanding of the pathogenesis of sICH and offer novel insights and therapeutic strategies for its prevention and treatment.

Spatially and temporally probing distinctive glycerophospholipid alterations in Alzheimer's disease mouse brain via high-resolution ion mobility-enabled sn-position resolved lipidomics

Dysregulated glycerophospholipid (GP) metabolism in the brain is associated with the progression of neurodegenerative diseases including Alzheimer's disease (AD). Routine liquid chromatography-mass spectrometry (LC-MS)-based large-scale lipidomic methods often fail to elucidate subtle yet important structural features such as sn-position, hindering the precise interrogation of GP molecules. Leveraging high-resolution demultiplexing (HRdm) ion mobility spectrometry (IMS), we develop a four-dimensional (4D) lipidomic strategy to resolve GP sn-position isomers. We further construct a comprehensive experimental 4D GP database of 498 GPs identified from the mouse brain and an in-depth extended 4D library of 2500 GPs predicted by machine learning, enabling automated profiling of GPs with detailed acyl chain sn-position assignment. Analyzing three mouse brain regions (hippocampus, cerebellum, and cortex), we successfully identify a total of 592 GPs including 130 pairs of sn-position isomers. Further temporal GPs analysis in the three functional brain regions illustrates their metabolic alterations in AD progression.

Plasma lipidomics profiling in predicting the chemo-immunotherapy response in advanced non-small cell lung cancer

Background

Advanced non-small cell lung cancer (NSCLC) presents significant treatment challenges, with chemo-immunotherapy emerging as a promising approach. This study explores the potential of lipidomic biomarkers to predict responses to chemo-immunotherapy in advanced non-small cell lung cancer (NSCLC) patients.

Methods

A prospective analysis was conducted on 68 NSCLC patients undergoing chemo-immunotherapy, divided into disease control (DC) and progressive disease (PD) groups based on treatment response. Pre-treatment serum samples were subjected to lipidomic profiling using liquid chromatography-mass spectrometry (LC-MS). Key predictive lipids (biomarkers) were identified through projection to latent structures discriminant analysis. A biomarker combined model and a clinical combined model were developed to enhance the prediction accuracy. The predictive performances of the clinical combined model in different histological subtypes were also performed.

Results

Six lipids were identified as the key lipids. The expression levels of PC(16:0/18:2), PC(16:0/18:1), PC(16:0/18:0), CE(20:1), and PC(14:0/18:1) were significantly up-regulated. While the expression level of TAG56:7-FA18:2 was significantly down-regulated. The biomarker combined model demonstrated a receiver operating characteristic (ROC) curve of 0.85 (95% CI: 0.75-0.95) in differentiating the PD from the DC. The clinical combined model exhibited an AUC of 0.87 (95% CI: 0.79-0.96) in differentiating the PD from the DC. The clinical combined model demonstrated good discriminability in DC and PD patients in different histological subtypes with the AUC of 0.78 (95% CI: 0.62-0.96), 0.79 (95% CI: 0.64-0.94), and 0.86 (95% CI: 0.52-1.00) in squamous cell carcinoma, large cell carcinoma, and adenocarcinoma subtype, respectively. Pathway analysis revealed the metabolisms of linoleic acid, alpha-linolenic acid, glycerolipid, arachidonic acid, glycerophospholipid, and steroid were implicated in the chemo-immunotherapy response in advanced NSCLC.

Conclusion

Lipidomic profiling presents a highly accurate method for predicting responses to chemo-immunotherapy in patients with advanced NSCLC, offering a potential avenue for personalized treatment strategies.

Integrated Lipidomics and Transcriptomics Analyses Reveal Key Regulators of Fat Deposition in Different Adipose Tissues of Geese (Anser cygnoides)

The fat deposition of different adipose tissues is widely recognized as correlated, with distinct effects on meat quality traits and reproductive performance in poultry. In this study, we utilized lipidomics and transcriptomics analyses to investigate the heterogeneity and regulators of intramuscular fat (IMF), abdominal fat (AF), and subcutaneous fat (SF) in geese. Lipidomic profiling revealed 165, 129, and 77 differential lipid molecules (DLMs) between AF vs. IMF, SF vs. IMF, and SF vs. AF, respectively, with 47 common DLMs identified between AF vs. IMF and SF vs. IMF. Transcriptomic analysis identified 3369, 5758, and 131 differentially expressed genes (DEGs) between AF vs. IMF, SF vs. IMF, and SF vs. AF, respectively, with 2510 common DEGs identified between AF vs. IMF and SF vs. IMF. The KEGG results indicate that DLMs were predominantly enriched in glycerophospholipid and glycerolipid metabolism pathways, while DEGs were primarily enriched in metabolic pathways. Pearson correlation analysis identified FABP4, LPL, PLCB1, DSE, and PDE5A as potential factors influencing fat deposition. This study elucidates the heterogeneity and regulatory factors of different adipose tissues in geese, offering new insights for targeted improvements in goose meat quality and production efficiency.

First Proof of Concept of a Click Inverse Electron Demand Diels-Alder Reaction for Assigning the Regiochemistry of Carbon-Carbon Double Bonds in Untargeted Lipidomics

Lipidomics by high-resolution mass spectrometry (HRMS) has become a prominent tool in clinical chemistry due to the proven connections between lipid dysregulation and the insurgence of pathologies. However, it is difficult to achieve structural characterization beyond the fatty acid level by HRMS, especially when it comes to the regiochemistry of carbon-carbon double bonds, which play a major role in determining the properties of cell membranes. Several approaches have been proposed for elucidating the regiochemistry of double bonds, such as derivatization before MS analysis by photochemical reactions, which have shown great potential for their versatility but have the unavoidable drawback of splitting the MS signal. Among other possible approaches for derivatizing electron-rich double bonds, the emerging inverse-electron-demand Diels-Alder (IEDDA) reaction with tetrazines stands out for its unmatchable kinetics and has found several applications in basic biology and protein imaging. In this study, a catalyst-free click IEDDA reaction was employed for the first time to pinpoint carbon-carbon double bonds in free and conjugated fatty acids. Fatty acid and glycerophospholipid regioisomers were analyzed alone and in combination, demonstrating that the IEDDA reaction had click character and allowed the obtention of diagnostic product ions following MS/MS fragmentation as well as the possibility of performing relative quantitation of lipid regioisomers. The IEDDA protocol was later employed in an untargeted lipidomics study on plasma samples of patients suffering from prostate cancer and benign prostatic conditions, confirming the applicability of the proposed reaction to complex matrices of clinical interest.

Analysis of cantharidin-induced kidney injury and the protective mechanism of resveratrol in mice determined by liquid chromatography/mass spectrometry-based metabonomics

Cantharidin (CTD) is the main active component in the traditional Chinese medicine Mylabris and an effective anti-tumor agent. However, it is relatively toxic and exhibits nephrotoxicity, which limits its clinical use. However, its toxic mechanism is not clear. The toxic effects of CTD exposure on the kidney and the protective effect of resveratrol (RES) were studied in a mouse model, by determination of serum biochemical and renal antioxidant indicators, histopathological and ultrastructural observation, and metabonomics. After CTD exposure, serum uric acid, creatinine, and tissue oxidative stress indicators increased, and the renal glomerular and tubular epithelial cells showed clear pathological damage. Ultrastructure observation revealed marked mitochondrial swelling, endoplasmic reticulum dilation, and the presence of autophagy lysosomes in glomerular epithelial cells. RES ameliorated the renal injury induced by CTD. Metabonomics analysis indicated that CTD can induce apoptosis and oxidative damage in kidney cells, mainly by disrupting sphingolipid and glutathione metabolism, increasing sphingosine and sphingomyelin levels, and decreasing glutathione levels. RES counteracts these effects by regulating renal cell proliferation, the inflammatory response, oxidative stress, and apoptosis, by improving the levels of phosphatidylcholine (PC), LysoPC, and lysophosphatidyl glycerol in the glycerophospholipid metabolism pathway, thereby reducing CTD-induced nephrotoxicity. The mechanisms of CTD-induced renal injury and the protective effect of RES were revealed by metabonomics, providing a basis for evaluating clinical treatment regimens to reduce CTD-induced nephrotoxicity.

Lipids in Equine Airway Inflammation: An Overview of Current Knowledge

Mild-moderate and severe equine asthma (MEA and SEA) are prevalent inflammatory airway conditions affecting horses of numerous breeds and disciplines. Despite extensive research, detailed disease pathophysiology and the differences between MEA and SEA are still not completely understood. Bronchoalveolar lavage fluid cytology, broadly used in clinical practice and in equine asthma research, has limited means to represent the inflammatory status in the lower airways. Lipidomics is a field of science that can be utilized in investigating cellular mechanisms and cell-to-cell interactions. Studies in lipidomics have a broad variety of foci, of which fatty acid and lipid mediator profile analyses and global lipidomics have been implemented in veterinary medicine. As many crucial proinflammatory and proresolving mediators are lipids, lipidomic studies offer an interesting yet largely unexplored means to investigate inflammatory reactions in equine airways. The aim of this review article is to collect and summarize the findings of recent lipidomic studies on equine airway inflammation.

Cosmic kidney disease: an integrated pan-omic, physiological and morphological study into spaceflight-induced renal dysfunction

Missions into Deep Space are planned this decade. Yet the health consequences of exposure to microgravity and galactic cosmic radiation (GCR) over years-long missions on indispensable visceral organs such as the kidney are largely unexplored. We performed biomolecular (epigenomic, transcriptomic, proteomic, epiproteomic, metabolomic, metagenomic), clinical chemistry (electrolytes, endocrinology, biochemistry) and morphometry (histology, 3D imaging, miRNA-ISH, tissue weights) analyses using samples and datasets available from 11 spaceflight-exposed mouse and 5 human, 1 simulated microgravity rat and 4 simulated GCR-exposed mouse missions. We found that spaceflight induces: 1) renal transporter dephosphorylation which may indicate astronauts' increased risk of nephrolithiasis is in part a primary renal phenomenon rather than solely a secondary consequence of bone loss; 2) remodelling of the nephron that results in expansion of distal convoluted tubule size but loss of overall tubule density; 3) renal damage and dysfunction when exposed to a Mars roundtrip dose-equivalent of simulated GCR.

Pseudo-targeted lipidomics insights into lipid discrepancies between yak colostrum and mature milk based on UHPLC-Qtrap-MS

Yak milk is essential to maintain the normal physiological functions of herders in Tibetan areas of China. However, the lipid components of yak colostrum (YC) and mature milk (YM) have not been systematically studied. We employed a quantitative lipidomics to comprehensively describe the alterations in the milk lipid profile of lactating yaks. Herein, totally 851 lipids from 28 lipid subclasses in YC and YM were identified and screened for 43 significantly different lipids (SDLs; variable importance in projection > 1, fold change < 0.5 or > 2 with P < 0.05), with cholesterol ester (CE, 16:0) and triacylglycerol (TAG, 54:6 (20:5), 50:1 (16:0), 56:6 (20:5)) were the potential lipid biomarkers. Fourteen SDLs were modulated downwards, and 29 SDLs were modulated upwards in YM. Moreover, by analyzing lipid metabolic pathways in these SDLs, glycerophospholipid metabolism was the most critical. Our results furnish integral lipid details for evaluating yak milk's nutritional quality.

Lipidomic analysis of facial skin surface lipids in acne in young women

BACKGROUND

Alterations in the secretion and composition of skin surface lipids (SSL) are closely associated with the development of acne. Lipidomics is a useful tool to analyse the SSL of different types of acne. Our previous study found that phosphatidylserine and triacylglycerols dominate SSL changes in male acne and infantile acne, respectively. However, skin surface lipids as well as specific lipids in female acne patients remain to be investigated.

OBJECTIVES

To analyse and compare the SSL profile of acne women and healthy women and to discuss the involvement of differential lipids in acne development.

METHODS

Systematic lipidomics approach (high-throughput UPLC-QTOF-MS technology in combination with multivariate data analysis methods) was used to analyse the variations of SSL between acne and healthy groups.

RESULTS

Analysis revealed significant differences in lipid content and composition between the two groups. Further analysis showed that levels of 13 individual lipids were significantly different and followed the same trend as the main class and subclasses. The largest individual contributor to the subgroup was triglycerides (TG) and phosphatidylinositol (PI). In addition, female acne patients exhibited reduced ceramide chain length (CCL) and increased levels of unsaturated fatty acids (UFAs), The changes of CCL in female acne are identical to male acne.

CONCLUSIONS

There was a significantly higher level of TG and PI in the SSL of female acne patients. A reduction in CCL and an increase in UFAs content might contribute to the reduced skin barrier function in acne patients. The results suggest that female acne may have different pathogenesis than male acne.

In-depth organic mass cytometry reveals differential contents of 3-hydroxybutanoic acid at the single-cell level

Comprehensive single-cell metabolic profiling is critical for revealing phenotypic heterogeneity and elucidating the molecular mechanisms underlying biological processes. However, single-cell metabolomics remains challenging because of the limited metabolite coverage and inability to discriminate isomers. Herein, we establish a single-cell metabolomics platform for in-depth organic mass cytometry. Extended single-cell analysis time guarantees sufficient MS/MS acquisition for metabolite identification and the isomers discrimination while online sampling ensures the high-throughput of the method. The largest number of identified metabolites (approximately 600) are achieved in single cells and fine subtyping of MCF-7 cells is first demonstrated by an investigation on the differential levels of 3-hydroxybutanoic acid among clusters. Single-cell transcriptome analysis reveals differences in the expression of 3-hydroxybutanoic acid downstream antioxidative stress genes, such as metallothionein 2 (MT2A), while a fluorescence-activated cell sorting assay confirms the positive relationship between 3-hydroxybutanoic acid and target proteins; these results suggest that the heterogeneity of 3-hydroxybutanoic acid provides cancer cells with different ability to resist surrounding oxidative stress. Our method paves the way for deep single-cell metabolome profiling and investigations on the physiological and pathological processes that occur during cancer.

Lipid Identification of Biomarkers in Esophageal Squamous Cell Carcinoma by Lipidomic Analysis

Lipids participate in many important biological functions through energy storage, membrane structure stabilization, signal transduction, and molecular recognition. Previous studies have shown that patients with esophageal squamous cell carcinoma (ESCC) have abnormal lipid metabolism. However, studies characterizing lipid metabolism in ESCC patients through lipidomics are limited. Plasma lipid profiles of 65 ESCC patients and 42 healthy controls (HC) were characterized by lipidomics-based ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Single-factor and multi-factor statistical analysis were used to screen the differences in blood lipids between groups, and combined with component ratio analysis and receiver operating characteristic (ROC) curve diagnostic efficiency assessment, to reveal the potential mechanisms and biomarkers of ESCC. There were significant differences in lipid profiles between the ESCC and HC groups. Thirty-six differential lipids (11 up-regulated and 25 down-regulated) were selected based on the criteria of p < .05 and fold change > 1.3 or < 0.77. Glycerophospholipids were the major differential lipids, suggesting that these lipid metabolic pathways exhibit a significant imbalance that may contribute to the development of esophageal squamous cell carcinoma. Among them, the seven candidate biomarkers for esophageal squamous cell carcinoma with the highest diagnostic value are three phosphatidylserine (PS), three fatty acids (FA) and one phosphatidylcholine (PC).

Lipidomic Assessment of the Inhibitory Effect of Standardized Water Extract of Hydrangea serrata (Thunb.) Ser. Leaves during Adipogenesis

Obesity is primarily exacerbated by excessive lipid accumulation during adipogenesis, with triacylglycerol (TG) as a major lipid marker. However, as the association between numerous lipid markers and various health conditions has recently been revealed, investigating the lipid metabolism in detail has become necessary. This study investigates the lipid metabolic effects of Hydrangea serrata (Thunb.) Ser. hot water leaf extract (WHS) on adipogenesis using LC-MS-based lipidomics analysis of undifferentiated, differentiated, and WHS-treated differentiated 3T3-L1 cells. WHS treatment effectively suppressed the elevation of glycerolipids, including TG and DG, and prevented a molecular shift in fatty acyl composition towards long-chain unsaturated fatty acids. This shift also impacted glycerophospholipid metabolism. Additionally, WHS stabilized significant lipid markers such as the PC/PE and LPC/PE ratios, SM, and Cer, which are associated with obesity and related comorbidities. This study suggests that WHS could reduce obesity-related risk factors by regulating lipid markers during adipogenesis. This study is the first to assess the underlying lipidomic mechanisms of the adipogenesis-inhibitory effect of WHS, highlighting its potential in developing natural products for treating obesity and related conditions. Our study provides a new strategy for the development of natural products for the treatment of obesity and related diseases.

Integration of clinical phenoms and metabolomics facilitates precision medicine for lung cancer

Lung cancer is a common malignancy that is frequently associated with systemic metabolic disorders. Early detection is pivotal to survival improvement. Although blood biomarkers have been used in its early diagnosis, missed diagnosis and misdiagnosis still exist due to the heterogeneity of lung cancer. Integration of multiple biomarkers or trans-omics results can improve the accuracy and reliability for lung cancer diagnosis. As metabolic reprogramming is a hallmark of lung cancer, metabolites, specifically lipids might be useful for lung cancer detection, yet systematic characterizations of metabolites in lung cancer are still incipient. The present study profiled the polar metabolome and lipidome in the plasma of lung cancer patients to construct an inclusive metabolomic atlas of lung cancer. A comprehensive analysis of lung cancer was also conducted combining metabolomics with clinical phenotypes. Furthermore, the differences in plasma lipid metabolites were compared and analyzed among different lung cancer subtypes. Alcohols, amides, and peptide metabolites were significantly increased in lung cancer, while carboxylic acids, hydrocarbons, and fatty acids were remarkably decreased. Lipid profiling revealed a significant increase in plasma levels of CER, PE, SM, and TAG in individuals with lung cancer as compared to those in healthy controls. Correlation analysis confirmed the association between a panel of metabolites and TAGs. Clinical trans-omics studies elucidated the complex correlations between lipidomic data and clinical phenotypes. The present study emphasized the clinical importance of lipidomics in lung cancer, which involves the correlation between metabolites and the expressions of other omics, ultimately influencing clinical phenotypes. This novel trans-omics network approach would facilitate the development of precision therapy for lung cancer.

The Plasma Lipidomic Landscape in Patients with Sepsis due to Community-acquired Pneumonia

Rationale: The plasma lipidome has the potential to reflect many facets of the host status during severe infection. Previous work is limited to specific lipid groups or was focused on lipids as prognosticators.Objectives: To map the plasma lipidome during sepsis due to community-acquired pneumonia (CAP) and determine the disease specificity and associations with clinical features.Methods: We analyzed 1,833 lipid species across 33 classes in 169 patients admitted to the ICU with sepsis due to CAP, 51 noninfected ICU patients, and 48 outpatient controls. In a paired analysis, we reanalyzed patients still in the ICU 4 days after admission (n = 82).Measurements and Main Results: A total of 58% of plasma lipids were significantly lower in patients with CAP-attributable sepsis compared with outpatient controls (6% higher, 36% not different). We found strong lipid class-specific associations with disease severity, validated across two external cohorts, and inflammatory biomarkers, in which triacylglycerols, cholesterol esters, and lysophospholipids exhibited the strongest associations. A total of 36% of lipids increased over time, and stratification by survival revealed diverging lipid recovery, which was confirmed in an external cohort; specifically, a 10% increase in cholesterol ester levels was related to a lower odds ratio (0.84; P = 0.006) for 30-day mortality (absolute mortality, 18 of 82). Comparison with noninfected ICU patients delineated a substantial common illness response (57.5%) and a distinct lipidomic signal for patients with CAP-attributable sepsis (37%).Conclusions: Patients with sepsis due to CAP exhibit a time-dependent and partially disease-specific shift in their plasma lipidome that correlates with disease severity and systemic inflammation and is associated with higher mortality.

Understanding the variation in exercise responses to guide personalized physical activity prescriptions

Understanding the factors that contribute to exercise response variation is the first step in achieving the goal of developing personalized exercise prescriptions. This review discusses the key molecular and other mechanistic factors, both extrinsic and intrinsic, that influence exercise responses and health outcomes. Extrinsic characteristics include the timing and dose of exercise, circadian rhythms, sleep habits, dietary interactions, and medication use, whereas intrinsic factors such as sex, age, hormonal status, race/ethnicity, and genetics are also integral. The molecular transducers of exercise (i.e., genomic/epigenomic, proteomic/post-translational, transcriptomic, metabolic/metabolomic, and lipidomic elements) are considered with respect to variability in physiological and health outcomes. Finally, this review highlights the current challenges that impede our ability to develop effective personalized exercise prescriptions. The Molecular Transducers of Physical Activity Consortium (MoTrPAC) aims to fill significant gaps in the understanding of exercise response variability, yet further investigations are needed to address additional health outcomes across all populations.

Lipidomics reveals new lipid-based lung adenocarcinoma early diagnosis model

Lung adenocarcinoma (LUAD) continues to pose a significant mortality risk with a lack of dependable biomarkers for early noninvasive cancer detection. Here, we find that aberrant lipid metabolism is significantly enriched in lung cancer cells. Further, we identified four signature lipids highly associated with LUAD and developed a lipid signature-based scoring model (LSRscore). Evaluation of LSRscore in a discovery cohort reveals a robust predictive capability for LUAD (AUC: 0.972), a result further validated in an independent cohort (AUC: 0.92). We highlight one lipid signature biomarker, PE(18:0/18:1), consistently exhibiting altered levels both in cancer tissue and in plasma of LUAD patients, demonstrating significant predictive power for early-stage LUAD. Transcriptome analysis reveals an association between increased PE(18:0/18:1) levels and dysregulated glycerophospholipid metabolism, which consistently displays strong prognostic value across two LUAD cohorts. The combined utility of LSRscore and PE(18:0/18:1) holds promise for early-stage diagnosis and prognosis of LUAD.

Detection and analysis of triacylglycerol regioisomers via electron activated dissociation (EAD) tandem mass spectrometry

Triacylglycerols (TGs) are important components of human diet. The positional distribution of fatty acids (FAs) on the glycerol backbone affects the chemistry and physical properties of fats. Especially for infants, the structure of TGs plays an important role in the growth and development. However, limited by detecting technology, accurately identifying regioisomers of ABA/AAB and BAC/ABC/ACB type TGs is a significant challenge for human milk utilization and the development of infant formula. For this, we exploit a novel method for identifying the regioisomers of ABA/AAB and BAC/ABC/ACB type TGs within complex lipid mixtures, via used electron activated dissociation (EAD) tandem mass spectrometry. The distribution information of acyl chains at the sn-2 and sn-1/3 positions of glycerol backbone and double bonds in unsaturated FAs can be easily obtained by fragmenting TG ions with energetic electrons (15 eV). Then, the standard curve was established by correlating the peak area intensity of sn-2 characteristic product ion with the content of TG regioisomers standard. These analytical methods successfully enabled the identification and quantification of TG regioisomers in human milk, cow milk, infant formula, palm oil, and sunflower oil. Additionally, the distribution of the double-bond positions of unsaturated FAs in these samples was also identified. Compared to traditional methods, this approach eliminates the need for complex processing and analysis procedures, enabling rapid structural characterization of ABA/AAB and BAC/ABC/ACB type TGs within 17 min. Hence, we provide a rapid and convenient methodology for detecting and analyzing ABA/AAB and BAC/ABC/ACB type TG regioisomers, thereby offering valuable assistance in the development of specialized formulations and facilitating effective process control for ensuring the quality of edible oils and fats.

Exploring the Relationship Among Lipid Profile Changes, Growth, and Reproduction in Folsomia candida Exposed to Teflubenzuron Over Time

The integration of untargeted lipidomics approaches in ecotoxicology has emerged as a strategy to enhance the comprehensiveness of environmental risk assessment. Although current toxicity tests with soil microarthropods focus on species performance, that is, growth, reproduction, and survival, understanding the mechanisms of toxicity across all levels of biological organization, from molecule to community is essential for informed decision-making. Our study focused on the impacts of sublethal concentrations of the insecticide teflubenzuron on the springtail Folsomia candida. Untargeted lipidomics was applied to link changes in growth, reproduction, and the overall stress response with lipid profile changes over various exposure durations. The accumulation of teflubenzuron in organisms exposed to the highest test concentration (0.035 mg a.s. kg-1 soil dry wt) significantly impacted reproductive output without compromising growth. The results suggested a resource allocation shift from reproduction to size maintenance. This hypothesis was supported by lipid shifts on day 7, at which point reductions in triacylglycerol and diacylglycerol content corresponded with decreased offspring production on day 21. The hypermetabolism of fatty acids and N-acylethanolamines on days 2 and 7 of exposure indicated oxidative stress and inflammation in the animals in response to teflubenzuron bioaccumulation, as measured using high-performance liquid chromatography-tandem mass spectrometry. Overall, the changes in lipid profiles in comparison with phenotypic adverse outcomes highlight the potential of lipid analysis as an early-warning tool for reproductive disturbances caused by pesticides in F. candida. Environ Toxicol Chem 2024;00:1-12. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Trans-omic analysis reveals opposite metabolic dysregulation between feeding and fasting in liver associated with obesity

Dysregulation of liver metabolism associated with obesity during feeding and fasting leads to the breakdown of metabolic homeostasis. However, the underlying mechanism remains unknown. Here, we measured multi-omics data in the liver of wild-type and leptin-deficient obese (ob/ob) mice at ad libitum feeding and constructed a differential regulatory trans-omic network of metabolic reactions. We compared the trans-omic network at feeding with that at 16 h fasting constructed in our previous study. Intermediate metabolites in glycolytic and nucleotide metabolism decreased in ob/ob mice at feeding but increased at fasting. Allosteric regulation reversely shifted between feeding and fasting, generally showing activation at feeding while inhibition at fasting in ob/ob mice. Transcriptional regulation was similar between feeding and fasting, generally showing inhibiting transcription factor regulations and activating enzyme protein regulations in ob/ob mice. The opposite metabolic dysregulation between feeding and fasting characterizes breakdown of metabolic homeostasis associated with obesity.

A review of concepts and methods for FTIR imaging of biomarker changes in the post-stroke brain

Stroke represents a core area of study in neurosciences and public health due to its global contribution toward mortality and disability. The intricate pathophysiology of stroke, including ischemic and hemorrhagic events, involves the interruption in oxygen and nutrient delivery to the brain. Disruption of these crucial processes in the central nervous system leads to metabolic dysregulation and cell death. Fourier transform infrared (FTIR) spectroscopy can simultaneously measure total protein and lipid content along with a number of key biomarkers within brain tissue that cannot be observed using conventional techniques. FTIR imaging provides the opportunity to visualize this information in tissue which has not been chemically treated prior to analysis, thus retaining the spatial distribution and in situ chemical information. Here we present a review of FTIR imaging methods for investigating the biomarker responses in the post-stroke brain.

Eicosanoid profiles in an arthritis model: Effects of a soluble epoxide hydrolase inhibitor

Rheumatoid arthritis is a common systemic inflammatory autoimmune disease characterized by damage to joints, inflammation and pain. It is driven by an increase of inflammatory cytokines and lipids mediators such as prostaglandins. Epoxides of polyunsaturated fatty acids (PUFAs) are lipid chemical mediators in a group of regulatory compounds termed eicosanoids. These epoxy fatty acids (EpFA) have resolutive functions but are rapidly metabolized by the soluble epoxide hydrolase enzyme (sEH) into the corresponding diols. The pharmacological inhibition of sEH stabilizes EpFA from hydrolysis, improving their half-lives and biological effects. These anti-inflammatory EpFA, are analgesic in neuropathic and inflammatory pain conditions. Nonetheless, inhibition of sEH on arthritis and the resulting effects on eicosanoids profiles are little explored despite the physiological importance. In this study, we investigated the effect of sEH inhibition on collagen-induced arthritis (CIA) and its impact on the plasma eicosanoid profile. We measured the eicosanoid metabolites by LC-MS/MS-based lipidomic analysis. The treatment with a sEH inhibitor significantly modulated 11 out of 69 eicosanoids, including increased epoxides 12(13)-EpODE, 12(13)-EpOME, 13-oxo-ODE, 15-HEPE, 20-COOH-LTB4 and decreases several diols 15,6-DiHODE, 12,13-DiHOME, 14,15-DiHETrE, 5,6-DiHETrE and 16,17-DiHDPE. Overall the inhibition of sEH in the rheumatoid arthritis model enhanced epoxides generally considered anti-inflammatory or resolutive mediators and decreased several diols with inflammatory features. These findings support the hypothesis that inhibiting the sEH increases systemic EpFA levels, advancing the understanding of the impact of these lipid mediators as therapeutical targets.

LipidSIM: Inferring mechanistic lipid biosynthesis perturbations from lipidomics with a flexible, low-parameter, Markov modeling framework

Lipid metabolism is a complex and dynamic system involving numerous enzymes at the junction of multiple metabolic pathways. Disruption of these pathways leads to systematic dyslipidemia, a hallmark of many pathological developments, such as nonalcoholic steatohepatitis and diabetes. Recent advances in computational tools can provide insights into the dysregulation of lipid biosynthesis, but limitations remain due to the complexity of lipidomic data, limited knowledge of interactions among involved enzymes, and technical challenges in standardizing across different lipid types. Here, we present a low-parameter, biologically interpretable framework named Lipid Synthesis Investigative Markov model (LipidSIM), which models and predicts the source of perturbations in lipid biosynthesis from lipidomic data. LipidSIM achieves this by accounting for the interdependency between the lipid species via the lipid biosynthesis network and generates testable hypotheses regarding changes in lipid biosynthetic reactions. This feature allows the integration of lipidomics with other omics types, such as transcriptomics, to elucidate the direct driving mechanisms of altered lipidomes due to treatments or disease progression. To demonstrate the value of LipidSIM, we first applied it to hepatic lipidomics following Keap1 knockdown and found that changes in mRNA expression of the lipid pathways were consistent with the LipidSIM-predicted fluxes. Second, we used it to study lipidomic changes following intraperitoneal injection of CCl4 to induce fast NAFLD/NASH development and the progression of fibrosis and hepatic cancer. Finally, to show the power of LipidSIM for classifying samples with dyslipidemia, we used a Dgat2-knockdown study dataset. Thus, we show that as it demands no a priori knowledge of enzyme kinetics, LipidSIM is a valuable and intuitive framework for extracting biological insights from complex lipidomic data.

Lipid storage disease in 4 sibling superb birds-of-paradise (Lophorina superba)

Pedigree analysis, clinical, gross, microscopic, ultrastructural, and lipidomic findings in 4 female superb bird-of-paradise (SBOP, Lophorina superba) siblings led to the diagnosis of a primary inherited glycerolipid storage disease. These birds were the offspring of a related breeding pair (inbreeding coefficient = 0.1797) and are the only known SBOPs to display this constellation of lesions. The birds ranged from 0.75 to 4.3 years of age at the time of death. Two birds were euthanized and 1 died naturally due to the disease, and 1 died of head trauma with no prior clinical signs. Macroscopic findings included hepatomegaly and pallor (4/4), cardiac and renal pallor (2/4), and coelomic effusion (1/4). Microscopic examination found marked tissue distortion due to cytoplasmic lipid vacuoles in hepatocytes (4/4), cardiomyocytes (4/4), renal tubular epithelial cells (4/4), parathyroid gland principal cells (2/2), exocrine pancreatic cells (3/3), and the glandular cells of the ventriculus and proventriculus (3/3). Ultrastructurally, the lipids were deposited in single to coalescing or fused droplets lined by an inconspicuous or discontinuous monolayer membrane. Lipidomic profiling found that the cytoplasmic lipid deposits were primarily composed of triacylglycerols. Future work, including sequencing of the SBOP genome and genotyping, will be required to definitively determine the underlying genetic mechanism of this disease.

The shifting lipidomic landscape of blood monocytes and neutrophils during pneumonia

The lipidome of immune cells during infection has remained unexplored, although evidence of the importance of lipids in the context of immunity is mounting. In this study, we performed untargeted lipidomic analysis of blood monocytes and neutrophils from patients hospitalized for pneumonia and age- and sex-matched noninfectious control volunteers. We annotated 521 and 706 lipids in monocytes and neutrophils, respectively, which were normalized to an extensive set of internal standards per lipid class. The cellular lipidomes were profoundly altered in patients, with both common and distinct changes between the cell types. Changes involved every level of the cellular lipidome: differential lipid species, class-wide shifts, and altered saturation patterns. Overall, differential lipids were mainly less abundant in monocytes and more abundant in neutrophils from patients. One month after hospital admission, lipidomic changes were fully resolved in monocytes and partially in neutrophils. Integration of lipidomic and concurrently collected transcriptomic data highlighted altered sphingolipid metabolism in both cell types. Inhibition of ceramide and sphingosine-1-phosphate synthesis in healthy monocytes and neutrophils resulted in blunted cytokine responses upon stimulation with lipopolysaccharide. These data reveal major lipidomic remodeling in immune cells during infection, and link the cellular lipidome to immune functionality.

Lipid fingerprint-based histology accurately classifies nevus, primary melanoma, and metastatic melanoma samples

Probably, the most important factor for the survival of a melanoma patient is early detection and precise diagnosis. Although in most cases these tasks are readily carried out by pathologists and dermatologists, there are still difficult cases in which no consensus among experts is achieved. To deal with such cases, new methodologies are required. Following this motivation, we explore here the use of lipid imaging mass spectrometry as a complementary tool for the aid in the diagnosis. Thus, 53 samples (15 nevus, 24 primary melanomas, and 14 metastasis) were explored with the aid of a mass spectrometer, using negative polarity. The rich lipid fingerprint obtained from the samples allowed us to set up an artificial intelligence-based classification model that achieved 100% of specificity and precision both in training and validation data sets. A deeper analysis of the image data shows that the technique reports important information on the tumor microenvironment that may give invaluable insights in the prognosis of the lesion, with the correct interpretation.

Recent Analytical Methodologies in Lipid Analysis

Lipids represent a large group of biomolecules that are responsible for various functions in organisms. Diseases such as diabetes, chronic inflammation, neurological disorders, or neurodegenerative and cardiovascular diseases can be caused by lipid imbalance. Due to the different stereochemical properties and composition of fatty acyl groups of molecules in most lipid classes, quantification of lipids and development of lipidomic analytical techniques are problematic. Identification of different lipid species from complex matrices is difficult, and therefore individual analytical steps, which include extraction, separation, and detection of lipids, must be chosen properly. This review critically documents recent strategies for lipid analysis from sample pretreatment to instrumental analysis and data interpretation published in the last five years (2019 to 2023). The advantages and disadvantages of various extraction methods are covered. The instrumental analysis step comprises methods for lipid identification and quantification. Mass spectrometry (MS) is the most used technique in lipid analysis, which can be performed by direct infusion MS approach or in combination with suitable separation techniques such as liquid chromatography or gas chromatography. Special attention is also given to the correct evaluation and interpretation of the data obtained from the lipid analyses. Only accurate, precise, robust and reliable analytical strategies are able to bring complex and useful lipidomic information, which may contribute to clarification of some diseases at the molecular level, and may be used as putative biomarkers and/or therapeutic targets.

Dynamic changes in the mouse hepatic lipidome following warm ischemia reperfusion injury

Liver failure secondary to metabolic dysfunction-associated steatotic liver disease (MASLD) has become the most common cause for liver transplantation in many parts of the world. Moreover, the prevalence of MASLD not only increases the demand for liver transplantation, but also limits the supply of suitable donor organs because steatosis predisposes grafts to ischemia-reperfusion injury (IRI). There are currently no pharmacological interventions to limit hepatic IRI because the mechanisms by which steatosis leads to increased injury are unclear. To identify potential novel mediators of IRI, we used liquid chromatography and mass spectrometry to assess temporal changes in the hepatic lipidome in steatotic and non-steatotic livers after warm IRI in mice. Our untargeted analyses revealed distinct differences between the steatotic and non-steatotic response to IRI and highlighted dynamic changes in lipid composition with marked changes in glycerophospholipids. These findings enhance our knowledge of the lipidomic changes that occur following IRI and provide a foundation for future mechanistic studies. A better understanding of the mechanisms underlying such changes will lead to novel therapeutic strategies to combat IRI.

Metabolomics and lipidomics signature in celiac disease: a narrative review

Celiac disease (CD) is a chronic immune-mediated inflammatory disease of the small intestine caused by aberrant immune responses to consumed gluten proteins. CD is diagnosed by a combination of the patients reported symptoms, serologic and endoscopic biopsy evaluation of the small intestine; and adherence to a strict gluten-free diet (GFD) is considered the only available therapeutic approach for this disorder. Novel approaches need to be considered for finding new biomarkers to help this disorder diagnosis and finding a new alternative therapeutic method for this group of patients. Metabolomics and lipidomics are powerful tools to provide highly accurate and sensitive biomarkers. Previous studies indicated a metabolic fingerprint for CD deriving from alterations in gut microflora or intestinal permeability, malabsorption, and energy metabolism. Moreover, since CD is characterized by increased intestinal permeability and due to the importance of membrane lipid components in controlling barrier integrity, conducting lipidomics studies in this disorder is of great importance. In the current study, we tried to provide a critical overview of metabolomic and lipidomic changes in CD.

HILIC/MS quantitation of low-abundant phospholipids and sphingolipids in human plasma and serum: Dysregulation in pancreatic cancer

A new hydrophilic interaction liquid chromatography - mass spectrometry method is developed for low-abundant phospholipids and sphingolipids in human plasma and serum. The optimized method involves the Cogent Silica type C hydride column, the simple sample preparation by protein precipitation, and the removal of highly abundant lipid classes using the postcolumn valve directed to waste during two elution windows. The method allows a highly confident and sensitive identification of low-abundant lipid classes in human plasma (246 lipid species from 24 lipid subclasses) based on mass accuracy and retention dependencies in both polarity modes. The method is validated for quantitation using two internal standards (if available) for each lipid class and applied to human plasma and serum samples obtained from patients with pancreatic ductal adenocarcinoma (PDAC), healthy controls, and NIST SRM 1950. Multivariate data analysis followed by various statistical projection methods is used to determine the most dysregulated lipids. Significant downregulation is observed for lysophospholipids with fatty acyl composition 16:0, 18:0, 18:1, and 18:2. Distinct trends are observed for phosphatidylethanolamines (PE) in relation to the bonding type of fatty acyls, where most PE with acyl bonds are upregulated, while ether/plasmenyl PE are downregulated. For the sphingolipid category, sphingolipids with very long N-acyl chains are downregulated, while sphingolipids with shorter N-acyl chains were upregulated in PDAC. These changes are consistently observed for various classes of sphingolipids, ranging from ceramides to glycosphingolipids, indicating a possible metabolic disorder in ceramide biosynthesis caused by PDAC.

Cellular metabolism: A key player in cancer ferroptosis

Cellular metabolism is the fundamental process by which cells maintain growth and self-renewal. It produces energy, furnishes raw materials, and intermediates for biomolecule synthesis, and modulates enzyme activity to sustain normal cellular functions. Cellular metabolism is the foundation of cellular life processes and plays a regulatory role in various biological functions, including programmed cell death. Ferroptosis is a recently discovered form of iron-dependent programmed cell death. The inhibition of ferroptosis plays a crucial role in tumorigenesis and tumor progression. However, the role of cellular metabolism, particularly glucose and amino acid metabolism, in cancer ferroptosis is not well understood. Here, we reviewed glucose, lipid, amino acid, iron and selenium metabolism involvement in cancer cell ferroptosis to elucidate the impact of different metabolic pathways on this process. Additionally, we provided a detailed overview of agents used to induce cancer ferroptosis. We explained that the metabolism of tumor cells plays a crucial role in maintaining intracellular redox homeostasis and that disrupting the normal metabolic processes in these cells renders them more susceptible to iron-induced cell death, resulting in enhanced tumor cell killing. The combination of ferroptosis inducers and cellular metabolism inhibitors may be a novel approach to future cancer therapy and an important strategy to advance the development of treatments.

Retention time prediction and MRM validation reinforce the biomarker identification of LC-MS based phospholipidomics

Dysfunctional lipid metabolism plays a crucial role in the development and progression of various diseases. Accurate measurement of lipidomes can help uncover the complex interactions between genes, proteins, and lipids in health and diseases. The prediction of retention time (RT) has become increasingly important in both targeted and untargeted metabolomics. However, the potential impact of RT prediction on targeted LC-MS based lipidomics is still not fully understood. Herein, we propose a simplified workflow for predicting RT in phospholipidomics. Our approach involves utilizing the fatty acyl chain length or carbon-carbon double bond (DB) number in combination with multiple reaction monitoring (MRM) validation. We found that our model's predictive capacity for RT was comparable to that of a publicly accessible program (QSRR Automator). Additionally, MRM validation helped in further mitigating the interference in signal recognition. Using this developed workflow, we conducted phospholipidomics of sorafenib resistant hepatocellular carcinoma (HCC) cell lines, namely MHCC97H and Hep3B. Our findings revealed an abundance of monounsaturated fatty acyl (MUFA) or polyunsaturated fatty acyl (PUFA) phospholipids in these cell lines after developing drug resistance. In both cell lines, a total of 29 lipids were found to be co-upregulated and 5 lipids were co-downregulated. Further validation was conducted on seven of the upregulated lipids using an independent dataset, which demonstrates the potential for translation of the established workflow or the lipid biomarkers.

Plasma lipidomic profiling reveals six candidate biomarkers for the prediction of incident stroke in patients with hypertension

INTRODUCTION

The burden of stroke in patients with hypertension is very high, and its prediction is critical.

OBJECTIVES

We aimed to use plasma lipidomics profiling to identify lipid biomarkers for predicting incident stroke in patients with hypertension.

METHODS

This was a nested case-control study. Baseline plasma samples were collected from 30 hypertensive patients with newly developed stroke, 30 matched patients with hypertension, 30 matched patients at high risk of stroke, and 30 matched healthy controls. Lipidomics analysis was performed by ultrahigh-performance liquid chromatography-tandem mass spectrometry, and differential lipid metabolites were screened using multivariate and univariate statistical methods. Machine learning methods (least absolute shrinkage and selection operator, random forest) were used to identify candidate biomarkers for predicting stroke in patients with hypertension.

RESULTS

Co-expression network analysis revealed that the key molecular alterations of the lipid network in stroke implicate glycerophospholipid metabolism and choline metabolism. Six lipid metabolites were identified as candidate biomarkers by multivariate statistical and machine learning methods, namely phosphatidyl choline(40:3p)(rep), cholesteryl ester(20:5), monoglyceride(29:5), triglyceride(18:0p/18:1/18:1), triglyceride(18:1/18:2/21:0) and coenzyme(q9). The combination of these six lipid biomarkers exhibited good diagnostic and predictive ability, as it could indicate a risk of stroke at an early stage in patients with hypertension (area under the curve = 0.870; 95% confidence interval: 0.783-0.957).

CONCLUSIONS

We determined lipidomic signatures associated with future stroke development and identified new lipid biomarkers for predicting stroke in patients with hypertension. The biomarkers have translational potential and thus may serve as blood-based biomarkers for predicting hypertensive stroke.

Features of Metabolite Changes in Disease Evolution in Cholecystolithiasis

BACKGROUND

Cholecystolithiasis is defined as a disease caused by complex and changeable factors. Advanced age, female sex, and a hypercaloric diet rich in carbohydrates and poor in fiber, together with obesity and genetic factors, are the main factors that may predispose people to choledocholithiasis. However, serum biomarkers for the rapid diagnosis of choledocholithiasis remain unclear.

AIMS

This study was designed to explore the pathogenesis of cholecystolithiasis and identify the possible metabolic and lipidomic biomarkers for the diagnosis of the disease.

METHODS

Using UHPLC-MS/MS and GC-MS, we detected the serum of 28 cholecystolithiasis patients and 19 controls. Statistical analysis of multiple variables included Principal Component Analysis (PCA). Visualization of differential metabolites was performed using volcano plots. The screened differential metabolites were further analyzed using clustering heatmaps. The quality of the model was assessed using random forests.

RESULTS

In this study, dramatically altered lipid homeostasis was detected in cholecystolithiasis group. In addition, the levels of short-chain fatty acids and amino acids were noticeably changed in patients with cholecystolithiasis. They detected higher levels of FFA.18.1, FFA.20.1, LPC16.0, and LPC20.1, but lower levels of 1-Methyl-L-histidine and 4-Hydroxyproline. In addition, glycine and L-Tyrosine were higher in choledocholithiasis group. Analyses of metabolic serum in affected patients have the potential to develop an integrated metabolite-based biomarker model that can facilitate the early diagnosis and treatment of the disease.

CONCLUSION

Our results highlight the value of integrating lipid, amino acid, and short-chain fatty acid to explore the pathophysiology of cholecystolithiasis disease, and consequently, improve clinical decision-making.

Mechanism of Obesity-Related Lipotoxicity and Clinical Perspective

The link between cellular exposure to fatty acid species and toxicity phenotypes remains poorly understood. However, structural characterization and functional profiling of human plasma free fatty acids (FFAs) analysis has revealed that FFAs are located either in the toxic cluster or in the cluster that is transcriptionally responsive to lipotoxic stress and creates genetic risk factors. Genome-wide short hairpin RNA screen has identified more than 350 genes modulating lipotoxicity. Hypertrophic adipocytes in obese adipose are both unable to expand further to store excess lipids in the diet and are resistant to the antilipolytic action of insulin. In addition to lipolysis, the inability of packaging the excess lipids into lipid droplets causes circulating fatty acids to reach toxic levels in non-adipose tissues. Deleterious effects of accumulated lipid in non-adipose tissues are known as lipotoxicity. Although triglycerides serve a storage function for long-chain non-esterified fatty acid and their products such as ceramide and diacylglycerols (DAGs), overloading of palmitic acid fraction of saturated fatty acids (SFAs) raises ceramide levels. The excess DAG and ceramide load create harmful effects on multiple organs and systems, inducing chronic inflammation in obesity. Thus, lipotoxic inflammation results in β cells death and pancreatic islets dysfunction. Endoplasmic reticulum stress stimuli induce lipolysis by activating cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA) and extracellular signal-regulated kinase (Erk) 1/2 signaling in adipocytes. However, palmitic acid-induced endoplasmic reticulum stress-c-Jun N-terminal kinase (JNK)-autophagy axis in hypertrophic adipocytes is a pro-survival mechanism against endoplasmic reticulum stress and cell death induced by SFAs. Endoplasmic reticulum-localized acyl-coenzyme A (CoA): glycerol-3-phosphate acyltransferase (GPAT) enzymes are mediators of lipotoxicity, and inhibiting these enzymes has therapeutic potential for lipotoxicity. Lipotoxicity increases the number of autophagosomes, which engulf palmitic acid, and thus suppress the autophagic turnover. Fatty acid desaturation promotes palmitate detoxification and storages into triglycerides. As therapeutic targets of glucolipotoxicity, in addition to caloric restriction and exercise, there are four different pharmacological approaches, which consist of metformin, glucagon-like peptide 1 (GLP-1) receptor agonists, peroxisome proliferator-activated receptor-gamma (PPARγ) ligands thiazolidinediones, and chaperones are still used in clinical practice. Furthermore, induction of the brown fat-like phenotype with the mixture of eicosapentanoic acid and docosahexaenoic acid appears as a potential therapeutic application for treatment of lipotoxicity.

Analysis of Sphingosine and Sphinganine from the Aqueous Humor for Signaling Studies Using Ultrahigh-Performance Liquid Chromatography-Mass Spectrometry

Sphingolipids, including sphingosine and sphinganine, are one of the major classes of lipids. They serve as constituents of cell membranes and lipid rafts and aid in the performance of cell-cell communication and adhesion. Abnormal levels of sphingolipids in the aqueous humor can indicate impaired sphingolipid metabolism and associated ocular pathologies. Sphingolipids can be extracted from the aqueous humor by the methyl-tert-butyl ether (MTBE) lipid extraction method and subsequently analyzed by liquid chromatography-mass spectrometry (LC-MS). This chapter describes a modified protocol for an MTBE lipid extraction from the aqueous humor, followed by analysis with ultrahigh-performance liquid chromatography-mass spectrometry (UHPLC-MS).

Hydroxy fatty acids in the surface Earth system

Lipids are ubiquitous and highly abundant in a wide range of organisms and have been found in various types of environmental media. These molecules play a crucial role as organic tracers by providing a chemical perspective on viewing the material world, as well as offering a wealth of information on metabolic activities. Among the diverse lipid compounds, hydroxy fatty acids (HFAs) with one to multiple hydroxyl groups attached to the carbon chain stand out as important biomarkers for different sources of organic matter. HFAs are widespread in nature and are involved in biotransformation and oxidation processes in living organisms. The unique chemical and physical properties attributed to the hydroxyl group make HFAs ideal biomarkers in biomedicine and environmental toxicology, as well as organic geochemistry. The molecular distribution patterns of HFAs can be unique and diagnostic for a given class of organisms, including animals, plants, and microorganisms. Thus, HFAs can act as a valuable proxy for understanding the ecological relationships between different organisms and their environment. Furthermore, HFAs have numerous industrial applications due to their higher reactivity, viscosity, and solvent miscibility. This review paper integrates the latest research on the sources and chemical analyses of HFAs, as well as their applications in industrial/medicinal production and as biomarkers in environmental studies. This review article also provides insights into the biogeochemical cycles of HFAs in the surface Earth system, highlighting the importance of these compounds in understanding the complex interactions between living organisms and the environment.