High-throughput lipidomic profiles sampled with electroporation-based biopsy differentiate healthy skin, cutaneous squamous cell carcinoma, and basal cell carcinoma.

BACKGROUND: The incidence rates of cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC) skin cancers are rising, while the current diagnostic process is time-consuming. We describe the development of a novel approach to high-throughput sampling of tissue lipids using electroporation-based biopsy, termed e-biopsy. We report on the ability of the e-biopsy technique to harvest large amounts of lipids from human skin samples. MATERIALS AND METHODS: Here, 168 lipids were reliably identified from 12 patients providing a total of 13 samples. The extracted lipids were profiled with ultra-performance liquid chromatography and tandem mass spectrometry (UPLC-MS-MS) providing cSCC, BCC, and healthy skin lipidomic profiles. RESULTS: Comparative analysis identified 27 differentially expressed lipids (p < 0.05). The general profile trend is low diglycerides in both cSCC and BCC, high phospholipids in BCC, and high lyso-phospholipids in cSCC compared to healthy skin tissue samples. CONCLUSION: The results contribute to the growing body of knowledge that can potentially lead to novel insights into these skin cancers and demonstrate the potential of the e-biopsy technique for the analysis of lipidomic profiles of human skin tissues.

Meibum Lipidomic Analysis in Evaporative Dry Eye Subjects.

Meibomian Glands (MG) are sebaceous glands responsible for the production of meibum, the main component of the Tear Film Lipid Layer (TFLL). The TFLL facilitates the spread of the tear film over the ocular surface, provides stability and reduces tear evaporation. Alterations in meibum composition lead to different ocular alterations like Meibomian Gland Dysfunction (MGD) and subsequent Evaporative Dry Eye (EDE). The aim of the present study was to investigate the composition and abundance of meibum lipids and their relationship with eyelid margin abnormalities, lipid layer patterns and MG status. The study utilizes a lipidomic approach to identify and quantify lipids in meibum samples using an Elute UHPLC system. This system considered all four dimensions (mass/charge, retention time, ion mobility and intensity) to provide the accurate identification of lipid species. Samples were categorized as healthy or low/no signs of alteration (group 1) or severe signs of alteration or EDE/MGD (group 2). The current investigation found differences in Variable Importance in Projection lipid abundance between both groups for the MGD signs studied. Changes in meibum composition occur and are related to higher scores in eyelid margin hyperaemia, eyelid margin irregularity, MG orifice plugging, MG loss and lipid layer pattern.

Untargeted lipidomics analysis in women with morbid obesity and type 2 diabetes mellitus: A comprehensive study.

There is a phenotype of obese individuals termed metabolically healthy obese that present a reduced cardiometabolic risk. This phenotype offers a valuable model for investigating the mechanisms connecting obesity and metabolic alterations such as Type 2 Diabetes Mellitus (T2DM). Previously, in an untargeted metabolomics analysis in a cohort of morbidly obese women, we observed a different lipid metabolite pattern between metabolically healthy morbid obese individuals and those with associated T2DM. To validate these findings, we have performed a complementary study of lipidomics. In this study, we assessed a liquid chromatography coupled to a mass spectrometer untargeted lipidomic analysis on serum samples from 209 women, 73 normal-weight women (control group) and 136 morbid obese women. From those, 65 metabolically healthy morbid obese and 71 with associated T2DM. In this work, we find elevated levels of ceramides, sphingomyelins, diacyl and triacylglycerols, fatty acids, and phosphoethanolamines in morbid obese vs normal weight. Conversely, decreased levels of acylcarnitines, bile acids, lyso-phosphatidylcholines, phosphatidylcholines (PC), phosphatidylinositols, and phosphoethanolamine PE (O-38:4) were noted. Furthermore, comparing morbid obese women with T2DM vs metabolically healthy MO, a distinct lipid profile emerged, featuring increased levels of metabolites: deoxycholic acid, diacylglycerol DG (36:2), triacylglycerols, phosphatidylcholines, phosphoethanolamines, phosphatidylinositols, and lyso-phosphatidylinositol LPI (16:0). To conclude, analysing both comparatives, we observed decreased levels of deoxycholic acid, PC (34:3), and PE (O-38:4) in morbid obese women vs normal-weight. Conversely, we found elevated levels of these lipids in morbid obese women with T2DM vs metabolically healthy MO. These profiles of metabolites could be explored for the research as potential markers of metabolic risk of T2DM in morbid obese women.

A cell-free nutrient-supplemented perfusate allows four-day ex vivo metabolic preservation of human kidneys.

The growing disparity between the demand for transplants and the available donor supply, coupled with an aging donor population and increasing prevalence of chronic diseases, highlights the urgent need for the development of platforms enabling reconditioning, repair, and regeneration of deceased donor organs. This necessitates the ability to preserve metabolically active kidneys ex vivo for days. However, current kidney normothermic machine perfusion (NMP) approaches allow metabolic preservation only for hours. Here we show that human kidneys discarded for transplantation can be preserved in a metabolically active state up to 4 days when perfused with a cell-free perfusate supplemented with TCA cycle intermediates at subnormothermia (25 °C). Using spatially resolved isotope tracing we demonstrate preserved metabolic fluxes in the kidney microenvironment up to Day 4 of perfusion. Beyond Day 4, significant changes were observed in renal cell populations through spatial lipidomics, and increases in injury markers such as LDH, NGAL and oxidized lipids. Finally, we demonstrate that perfused kidneys maintain functional parameters up to Day 4. Collectively, these findings provide evidence that this approach enables metabolic and functional preservation of human kidneys over multiple days, establishing a solid foundation for future clinical investigations.

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).

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.

Impact of extraction method on the lipids of Himalayan marmot oil with ultrahigh-performance liquid chromatography Q-Exactive Orbitrap mass spectrometry analysis.

RATIONALE: Himalayan marmot oil (SPO) has been used for pharmaceutical purposes for centuries, but its composition is still unclear. The bioactivity of SPO highly depends on the techniques used for its processing. This study focused on the comprehensive lipidomics of SPO, especially on the ones derived from dry rendering, wet rendering, cold pressing, and ultrasound-assisted solvent extraction. METHODS: We performed lipid profiling of SPO acquired by different extraction methods using ultrahigh-performance liquid chromatography Q-Exactive Orbitrap mass spectrometry, and 17 classes of lipids (2 BMPs, 12 LysoPCs, 9 LysoPEs, 41 PCs, 24 PEs, 23 Plasmenyl-PCs, 10 Plasmenyl-PEs, 10 MGs, 63 DGs, 187 TGs, 2 MGDGs, 3 Cer[NDS]s, 22 Cer[NS]s, 2 GlcCer[NS]s, 14 SMs, 14 CEs, and 6 AcylCarnitines) were characterized. RESULTS: Fifty-five lipids were differentially altered (VIP > 1.5, p < 0.05) between the extraction techniques, which can be used as potential biomarkers to differentiate SPO extracted by various methods. Additionally, the contents of oleic acid and arachidic acid were abundant in all samples that may suggest their medicinal values and are conducive to in-depth research. CONCLUSIONS: These findings reveal the alterations of lipid profile and free fatty acid composition in SPO obtained with different extraction methods, providing a theoretical foundation for investigating its important components as functional factors in medicines and cosmetics.

Plasma-based lipidomics reveals potential diagnostic biomarkers for esophageal squamous cell carcinoma: a retrospective study.

Background: Esophageal squamous cell carcinoma (ESCC) is highly prevalent and has a high mortality rate. Traditional diagnostic methods, such as imaging examinations and blood tumor marker tests, are not effective in accurately diagnosing ESCC due to their low sensitivity and specificity. Esophageal endoscopic biopsy, which is considered as the gold standard, is not suitable for screening due to its invasiveness and high cost. Therefore, this study aimed to develop a convenient and low-cost diagnostic method for ESCC using plasma-based lipidomics analysis combined with machine learning (ML) algorithms. Methods: Plasma samples from a total of 40 ESCC patients and 31 healthy controls were used for lipidomics study. Untargeted lipidomics analysis was conducted through liquid chromatography-mass spectrometry (LC-MS) analysis. Differentially expressed lipid features were filtered based on multivariate and univariate analysis, and lipid annotation was performed using MS-DIAL software. Results: A total of 99 differential lipids were identified, with 15 up-regulated lipids and 84 down-regulated lipids, suggesting their potential as diagnostic targets for ESCC. In the single-lipid plasma-based diagnostic model, nine specific lipids (FA 15:4, FA 27:1, FA 28:7, FA 28:0, FA 36:0, FA 39:0, FA 42:0, FA 44:0, and DG 37:7) exhibited excellent diagnostic performance, with an area under the curve (AUC) exceeding 0.99. Furthermore, multiple lipid-based ML models also demonstrated comparable diagnostic ability for ESCC. These findings indicate plasma lipids as a promising diagnostic approach for ESCC.

Roles of glutamic pyruvate transaminase 2 in reprogramming of airway epithelial lipidomic and metabolomic profiles after smoking.

Metabolic abnormalities represent one of the pathological features of chronic obstructive pulmonary disease (COPD). Glutamic pyruvate transaminase 2 (GPT2) is involved in glutamate metabolism and lipid synthesis pathways, whilst the exact roles of GPT2 in the occurrence and development of COPD remains uncertain. This study aims at investigating how GPT2 and the associated genes modulate smoking-induced airway epithelial metabolism and damage by reprogramming lipid synthesis. The circulating or human airway epithelial metabolomic and lipidomic profiles of COPD patients or cell-lines explored with smoking were assessed to elucidate the pivotal roles of GPT2 in reprogramming processes. We found that GPT2 regulate the reprogramming of lipid metabolisms caused by smoking, especially phosphatidylcholine (PC) and triacylglycerol (TAG), along with changes in the expression of lipid metabolism-associated genes. GPT2 modulated cell sensitivities and survival in response to smoking by enhancing mitochondrial functions and maintaining lipid and energy homeostasis. Our findings provide evidence for the involvement of GPT2 in the reprogramming of airway epithelial lipids following smoking, as well as the molecular mechanisms underlying GPT2-mediated regulation, which may offer an alternative of therapeutic strategies for chronic lung diseases.

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%.

Tracing the lipidome in inborn errors of metabolism.

Inborn errors of metabolism (IEM) represent a heterogeneous group of more than 1800 rare disorders, many of which are causing significant childhood morbidity and mortality. More than 100 IEM are linked to dyslipidaemia, but yet our knowledge in connecting genetic information with lipidomic data is limited. Stable isotope tracing studies of the lipid metabolism (STL) provide insights on the dynamic of cellular lipid processes and could thereby facilitate the delineation of underlying metabolic (patho)mechanisms. This mini-review focuses on principles as well as technical limitations of STL and describes potential clinical applications by discussing recently published STL focusing on IEM.

Lipidomic Analysis of Plasma Extracellular Vesicles Derived from Alzheimer's Disease Patients.

Analysis of blood-based indicators of brain health could provide an understanding of early disease mechanisms and pinpoint possible intervention strategies. By examining lipid profiles in extracellular vesicles (EVs), secreted particles from all cells, including astrocytes and neurons, and circulating in clinical samples, important insights regarding the brain's composition can be gained. Herein, a targeted lipidomic analysis was carried out in EVs derived from plasma samples after removal of lipoproteins from individuals with Alzheimer's disease (AD) and healthy controls. Differences were observed for selected lipid species of glycerolipids (GLs), glycerophospholipids (GPLs), lysophospholipids (LPLs) and sphingolipids (SLs) across three distinct EV subpopulations (all-cell origin, derived by immunocapture of CD9, CD81 and CD63; neuronal origin, derived by immunocapture of L1CAM; and astrocytic origin, derived by immunocapture of GLAST). The findings provide new insights into the lipid composition of EVs isolated from plasma samples regarding specific lipid families (MG, DG, Cer, PA, PC, PE, PI, LPI, LPE, LPC), as well as differences between AD and control individuals. This study emphasizes the crucial role of plasma EV lipidomics analysis as a comprehensive approach for identifying biomarkers and biological targets in AD and related disorders, facilitating early diagnosis and potentially informing novel interventions.

Looking into the lipid profile of avocado and byproducts: Using lipidomics to explore value-added compounds.

Consumer priorities in healthy diets and lifestyle boosted the demand for nutritious and functional foods as well as plant-based ingredients. Avocado has become a food trend due to its nutritional and functional values, which in turn is increasing its consumption and production worldwide. Avocado edible portion has a high content of lipids, with the pulp and its oil being rich in monounsaturated fatty acids and essential omega - 3 and omega - 6 polyunsaturated fatty acids (PUFA). These fatty acids are mainly esterified in triacylglycerides, the major lipids in pulp, but also in minor components such as polar lipids (phospholipids and glycolipids). Polar lipids of avocado have been overlooked despite being recently highlighted with functional properties as well. The growth in the industry of avocado products is generating an increased amount of their byproducts, such as seed and peels (nonedible portions), still undervalued. The few studies on avocado byproducts pointed out that they also contain interesting lipids, with seeds particularly rich in polar lipids bearing PUFA, and thus can be reused as a source of add-value phytochemical. Mass spectrometry-based lipidomics approaches appear as an essential tool to unveil the complex lipid signature of avocado and its byproducts, contributing to the recognition of value-added lipids and opening new avenues for their use in novel biotechnological applications. The present review provides an up-to-date overview of the lipid signature from avocado pulp, peel, seed, and its oils.

The sphingolipids change in exosomes from cancer patients and association between exosome release and sphingolipids level based on a pseudotargeted lipidomics method.

The lipid based ESCRT-independent mechanism, which contributes to MVB formation, is one of the crucial procedures in exosome biogenesis. n-SMase is a key lipid metabolism enzyme in this mechanism and can induce the hydrolysis of sphingomyelins (SMs) to ceramides (Cers), thereby promoting the formation of ILVs inside MVBs. Therefore, the regulation of n-SMase can realize the alteration in exosome release. According to the fact that cancer-associated cells have a tendency to release more exosomes than healthy cells, lipid extracts in exosomes from healthy volunteers, HCC and ICC patients were analyzed by a novel pseudotargeted lipidomics method focused on sphingolipids (SLs) to explore whether cancer-related features regulate the release of exosomes through the above pathway. Multivariate analysis based on the SLs expression could distinguish three groups well indicated that the SLs expression among the three groups were different. In cancer groups, two species of critical Cers were up-regulated, denoted as Cer (d18:1_16:0) and Cer (d18:1_18:0), while 55 kinds of SLs were down-regulated, including 40 species of SMs, such as SM (d18:1_16:0), SM (d18:1_18:1) and SM (d18:1_24:0). Meanwhile, several species of SM/Cer exhibited significant down-regulation. This substantial enhancement of the SMs hydrolysis to Cers process during exosome biogenesis suggested that cancer-related features may potentially promote an increase in exosome release through ESCRT-independent mechanism. Moreover, differential SLs have a capability of becoming potential biomarkers for disease diagnosis and classification with an AUC value of 0.9884 or 0.9806 for the comparison between healthy group and HCC or ICC groups, respectively. In addition, an association analysis conducted on the cell lines showed that changes in the SM/Cer contents in cells and their exosomes were negatively correlated with the levels of released exosomes, implied the regulation of exosome release levels can be achieved by modulating n-SMase and subsequent SL expression.

Omics analysis unveils changes in the metabolome and lipidome of Caenorhabditis elegans upon polydopamine exposure.

Polydopamine (PDA) is an insoluble biopolymer with a dark brown-black color that forms through the autoxidation of dopamine. Because of its outstanding biocompatibility and durability, PDA holds enormous promise for various applications, both in the biomedical and non-medical domains. To ensure human safety, protect health, and minimize environmental impacts, the assessment of PDA toxicity is important. In this study, metabolomics and lipidomics assessed the impact of acute PDA exposure on Caenorhabditis elegans (C. elegans). The findings revealed a pronounced perturbation in the metabolome and lipidome of C. elegans at the L4 stage following 24 hours of exposure to 100 µg/mL PDA. The changes in lipid composition varied based on lipid classes. Increased lipid classes included lysophosphatidylethanolamine, triacylglycerides, and fatty acids, while decreased species involved in several sub-classes of glycerophospholipids and sphingolipids. Besides, we detected 37 significantly affected metabolites in the positive and 8 in the negative ion modes due to exposure to PDA in C. elegans. The metabolites most impacted by PDA exposure were associated with purine metabolism, biosynthesis of valine, leucine, and isoleucine; aminoacyl-tRNA biosynthesis; and cysteine and methionine metabolism, along with pantothenate and CoA biosynthesis; the citrate cycle (TCA cycle); and beta-alanine metabolism. In conclusion, PDA exposure may intricately influence the metabolome and lipidome of C. elegans. The combined application of metabolomics and lipidomics offers additional insights into the metabolic perturbations involved in PDA-induced biological effects and presents potential biomarkers for the assessment of PDA safety.

Integrating machine learning and nontargeted plasma lipidomics to explore lipid characteristics of premetabolic syndrome and metabolic syndrome.

Objective: To identify plasma lipid characteristics associated with premetabolic syndrome (pre-MetS) and metabolic syndrome (MetS) and provide biomarkers through machine learning methods. Methods: Plasma lipidomics profiling was conducted using samples from healthy individuals, pre-MetS patients, and MetS patients. Orthogonal partial least squares-discriminant analysis (OPLS-DA) models were employed to identify dysregulated lipids in the comparative groups. Biomarkers were selected using support vector machine recursive feature elimination (SVM-RFE), random forest (rf), and least absolute shrinkage and selection operator (LASSO) regression, and the performance of two biomarker panels was compared across five machine learning models. Results: In the OPLS-DA models, 50 and 89 lipid metabolites were associated with pre-MetS and MetS patients, respectively. Further machine learning identified two sets of plasma metabolites composed of PS(38:3), DG(16:0/18:1), and TG(16:0/14:1/22:6), TG(16:0/18:2/20:4), and TG(14:0/18:2/18:3), which were used as biomarkers for the pre-MetS and MetS discrimination models in this study. Conclusion: In the initial lipidomics analysis of pre-MetS and MetS, we identified relevant lipid features primarily linked to insulin resistance in key biochemical pathways. Biomarker panels composed of lipidomics components can reflect metabolic changes across different stages of MetS, offering valuable insights for the differential diagnosis of pre-MetS and MetS.

Integrated Omics Analysis Uncovers the Culprit behind Exacerbated Atopic Dermatitis in a Diet-Induced Obesity Model.

Atopic dermatitis (AD), a chronic inflammatory skin disease, is exacerbated by obesity, yet the precise linking mechanism remains elusive. This study aimed to elucidate how obesity amplifies AD symptoms. We studied skin samples from three mouse groups: sham control, AD, and high-fat (HF) + AD. The HF + AD mice exhibited more severe AD symptoms than the AD or sham control mice. Skin lipidome analysis revealed noteworthy changes in arachidonic acid (AA) metabolism, including increased expression of pla2g4, a key enzyme in AA generation. Genes for phospholipid transport (Scarb1) and acyltransferase utilizing AA as the acyl donor (Agpat3) were upregulated in HF + AD skin. Associations were observed between AA-containing phospholipids and skin lipids containing AA and its metabolites. Furthermore, imbalanced phospholipid metabolism was identified in the HF + AD mice, marked by excessive activation of the AA and phosphatidic acid (PA)-mediated pathway. This imbalance featured increased expression of Plcb1, Plcg1, and Dgk involved in PA generation, along with a decrease in genes converting PA into diglycerol (DG) and CDP-DG (Lpin1 and cds1). This investigation revealed imbalanced phospholipid metabolism in the skin of HF + AD mice, contributing to the heightened inflammatory response observed in HF + AD, shedding light on potential mechanisms linking obesity to the exacerbation of AD symptoms.

Identification of Plasma Lipid Alterations Associated with Melanoma Metastasis.

The aim of this study was to apply a state-of-the-art quantitative lipidomic profiling platform to uncover lipid alterations predictive of melanoma progression. Our study included 151 melanoma patients; of these, 83 were without metastasis and 68 with metastases. Plasma samples were analyzed using a targeted Lipidyzer™ platform, covering 13 lipid classes and over 1100 lipid species. Following quality control filters, 802 lipid species were included in the subsequent analyses. Total plasma lipid contents were significantly reduced in patients with metastasis. Specifically, levels of two out of the thirteen lipid classes (free fatty acids (FFAs) and lactosylceramides (LCERs)) were significantly decreased in patients with metastasis. Three lipids (CE(12:0), FFA(24:1), and TAG47:2-FA16:1) were identified as more effective predictors of melanoma metastasis than the well-known markers LDH and S100B. Furthermore, the predictive value substantially improved upon combining the lipid markers. We observed an increase in the cumulative levels of five lysophosphatidylcholines (LPC(16:0); LPC(18:0); LPC(18:1); LPC(18:2); LPC(20:4)), each individually associated with an elevated risk of lymph node metastasis but not cutaneous or distant metastasis. Additionally, seventeen lipid molecules were linked to patient survival, four of which (CE(12:0), CE(14:0), CE(15:0), SM(14:0)) overlapped with the lipid panel predicting metastasis. This study represents the first comprehensive investigation of the plasma lipidome of melanoma patients to date. Our findings suggest that plasma lipid profiles may serve as important biomarkers for predicting clinical outcomes of melanoma patients, including the presence of metastasis, and may also serve as indicators of patient survival.

Lipidomics Analysis Unravels Aberrant Lipid Species and Pathways Induced by Zinc Oxide Nanoparticles in Kidney Cells.

Zinc oxide nanoparticles (ZnO NPs) are widely used in versatile applications, from high technology to household products. While numerous studies have examined the toxic gene profile of ZnO NPs across various tissues, the specific lipid species associated with adverse effects and potential biomarkers remain elusive. In this study, we conducted a liquid chromatography-mass spectrometry based lipidomics analysis to uncover potential lipid biomarkers in human kidney cells following treatment with ZnO NPs. Furthermore, we employed lipid pathway enrichment analysis (LIPEA) to elucidate altered lipid-related signaling pathways. Our results demonstrate that ZnO NPs induce cytotoxicity in renal epithelial cells and modulate lipid species; we identified 64 lipids with a fold change (FC) > 2 and p < 0.01 with corrected p < 0.05 in HK2 cells post-treatment with ZnO NPs. Notably, the altered lipids between control HK2 cells and those treated with ZnO NPs were associated with the sphingolipid, autophagy, and glycerophospholipid pathways. This study unveils novel potential lipid biomarkers of ZnO NP nanotoxicity, representing the first lipidomic profiling of ZnO NPs in human renal epithelial cells.

Sources of biases in the in vitro testing of nanomaterials: the role of the biomolecular corona.

The biological fate of nanomaterials (NMs) is driven by specific interactions through which biomolecules, naturally adhering onto their surface, engage with cell membrane receptors and intracellular organelles. The molecular composition of this layer, called the biomolecular corona (BMC), depends on both the physical-chemical features of the NMs and the biological media in which the NMs are dispersed and cells grow. In this work, we demonstrate that the widespread use of 10% fetal bovine serum in an in vitro assay cannot recapitulate the complexity of in vivo systemic administration, with NMs being transported by the blood. For this purpose, we undertook a comparative journey involving proteomics, lipidomics, high throughput multiparametric in vitro screening, and single molecular feature analysis to investigate the molecular details behind this in vivo/in vitro bias. Our work indirectly highlights the need to introduce novel, more physiological-like media closer in composition to human plasma to produce realistic in vitro screening data for NMs. We also aim to set the basis to reduce this in vitro-in vivo mismatch, which currently limits the formulation of NMs for clinical settings.