Untargeted Lipidomics Research Articles

STEM-08. LIPID SATURATION HOMEOSTASIS PRESENTS A TARGETABLE VULNERABILITY IN MYC-AMPLIFIED GROUP 3 MEDULLOBLASTOMA

Abstract Medulloblastoma (MB) is the most common malignant pediatric brain cancer, originating from the cerebellum. Despite advancements in standard of care (SoC), MB remains fatal for 30% of patients. Tumor relapse, spinal metastasis and treatment resistance are the most prevalent in MYC-driven Group 3 MB (G3-MB). Patients surviving SoC are faced with life-long neurocognitive and neurodevelopmental deficits. These issues highlight the urgent need for improved treatment modalities. Reprogramming of cellular lipid metabolism is an emerging hallmark of cancer and may yield novel cancer-specific therapeutic options. Here we explore the lipidome of both G3-MB and its proposed cell of origin, human neural stem cells (hNSCs) by comparing untargeted lipidomics using Liquid-Chromatography-Mass Spectrometry (LC-MS). Comparative analyses revealed a differential abundance of distinct lipid species in G3-MB, with an overall reduced saturation level of fatty acids (FAs). These findings implicate the de novo lipid synthesis (DNL) pathway. We identified the enzymes involved in DNL to be essential for MB survival in our genome-wide CRISPR KO screen. Additionally, mRNA expression of the DNL enzymes increases at relapse in our SoC-adapted murine patient-derived xenograft (PDX) model. Pharmacological and genetic targeting of the DNL enzyme Stearoyl-CoA Desaturase 1 (SCD1) selectively targets G3-MB. Furthermore, small molecule treatment of SCD1 demonstrates efficacy against G3-MB PDX models in vivo. We identified SCD expression as a prognostic marker in Group 3 and 4 MB patients and identified possible pathways for MB treatment. Overall, these findings indicate that SCD1 is a potent target for the treatment of G3-MB.

Tandem Mass Spectrometry in Untargeted Lipidomics: A Case Study of Peripheral Blood Mononuclear Cells

Peripheral blood mononuclear cells (PBMCs), including lymphocytes, are important components of the human immune system. These cells contain a diverse array of lipids, primarily glycerophospholipids (GPs) and sphingolipids (SPs), which play essential roles in cellular structure, signaling, and programmed cell death. This study presents a detailed analysis of GP and SP profiles in human PBMC samples using tandem mass spectrometry (MS/MS). Hydrophilic interaction liquid chromatography (HILIC) and electrospray ionization (ESI) coupled with linear ion-trap MS/MS were employed to investigate the diagnostic fragmentation patterns that aided in determining regiochemistry in complex lipid extracts. Specifically, the study explored the fragmentation patterns of various lipid species, including phosphatidylcholines (PCs), phosphatidylethanolamines (PEs), their plasmalogen and lyso forms, phosphatidylserines (PSs), phosphatidylinositols (PIs), phosphatidylglycerols (PGs), sphingomyelins (SMs), and dihexosylceramides (Hex2Cer). Our comprehensive analysis led to the characterization of over 200 distinct lipid species, significantly expanding our understanding of PBMC lipidome complexity. A freely available spreadsheet tool for simulating MS/MS spectra of GPs is provided, enhancing the accessibility and reproducibility of this research. This study advances our knowledge of PBMC lipidomes and establishes a robust analytical framework for future investigations in lipidomics.

MS2Lipid: A Lipid Subclass Prediction Program Using Machine Learning and Curated Tandem Mass Spectral Data

Background: Untargeted lipidomics using collision-induced dissociation-based tandem mass spectrometry (CID-MS/MS) is essential for biological and clinical applications. However, annotation confidence still relies on manual curation by analytical chemists, despite the development of various software tools for automatic spectral processing based on rule-based fragment annotations. Methods: In this study, we present a novel machine learning model, MS2Lipid, for the prediction of known lipid subclasses from MS/MS queries, providing an orthogonal approach to existing lipidomics software programs in determining the lipid subclass of ion features. We designed a new descriptor, MCH (mode of carbon and hydrogen), to increase the specificity of lipid subclass prediction in nominal mass resolution MS data. Results: The model, trained with 6760 and 6862 manually curated MS/MS spectra for the positive and negative ion modes, respectively, classified queries into one or several of 97 lipid subclasses, achieving an accuracy of 97.4% in the test set. The program was further validated using various datasets from different instruments and curators, with the average accuracy exceeding 87.2%. Using an integrated approach with molecular spectral networking, we demonstrated the utility of MS2Lipid by annotating microbiota-derived esterified bile acids, whose abundance was significantly increased in fecal samples of obese patients in a human cohort study. This suggests that the machine learning model provides an independent criterion for lipid subclass classification, enhancing the annotation of lipid metabolites within known lipid classes. Conclusions: MS2Lipid is a highly accurate machine learning model that enhances lipid subclass annotation from MS/MS data and provides an independent criterion.

Untargeted Lipidomic Profiling of Amniotic Fluid Reveals Dysregulated Lipid Metabolism in Healthy Normal-Weight Mothers with Fetal Macrosomia

Background: Alterations in maternal lipid metabolism have been elucidated by several studies in relation to macrosomia. However, the lipidome of the intrauterine compartment associated with macrosomia, particularly in early pregnancy, remains largely unknown. Objectives: (1) To compare the lipidomic profile of early 2nd trimester amniotic fluid (AF) of healthy mothers with normal body mass index who gave birth to large-for-gestational age (LGA) versus appropriate-for-gestational age (AGA) infants; and (2) to examine if insulin and glucose concentrations in AF were associated with the AF lipidomic profile. Methods: In this nested case–control study, bio-banked AF samples were collected from pregnant women undergoing routine amniocentesis at 12–22 weeks of gestation. A subsample of 15 LGA infants (cases) were contrasted with 15 AGA infants (controls). An untargeted lipidomics analysis using liquid chromatography quadrupole time-of-flight mass spectrometry was conducted. Univariate and multivariate statistical analyses (principal component analysis and partial least-squares discriminant analysis) were used to extract differentially abundant (DA) features with high variable importance in projection (VIP) scores. Results: LGA AF was characterized by elevations of 30 phosphatidic acid species. Among other DA features, sphingomyelin (SM 14:0;O2/20:1) had the highest VIP score and was markedly elevated in LGA AF. Neither insulin nor glucose was associated with 2nd trimester AF lipidomic profiles in these healthy, normal-weight mothers. Conclusion: These findings provide evidence of early dysregulated lipid metabolism in healthy, normal-weight mothers with LGA infants.

Ion Mobility QTOF-MS Untargeted Lipidomics of Human Serum Reveals a Metabolic Fingerprint for GNE Myopathy.

GNE myopathy, also known as hereditary inclusion body myopathy (HIBM), is a rare genetic muscle disorder marked by a gradual onset of muscle weakness in young adults. GNE myopathy (GNEM) is caused by bi-allelic variants in the UDP-N-acetylglucosamine 2-epimerase (UDP-GlcNAc 2-epimerase)/N-acetylmannosamine kinase (ManNAc kinase) gene (GNE), clinically resulting in the loss of ambulation within 10-20 years from the onset of the initial symptoms. The disease's mechanism is poorly understood and non-invasive biomarkers are lacking, hindering effective therapy development. Based on the available evidence, we employed a lipidomic approach to study the serum lipid profile of GNE patients. The multivariate statistical analysis revealed a downregulation of carnitines, as well as of lysophosphatidylcholines, in sera samples derived from GNEM patients. Furthermore, we identified lower levels of sphingomyelins and, concomitantly, high levels of ceramides in serum samples from GNEM patients when compared to control samples derived from healthy donors. Moreover, the GNEM serum samples showed the upregulation of Krebs cycle intermediates, in addition to a decrease in oxaloacetic acid. The correlated data gathered in this study can offer a promising diagnostic panel of complex lipids and polar metabolites that can be used in clinic for GNEM in terms of a metabolic fingerprint measurable in a minimally invasive manner.

Targeted ultra-high performance liquid chromatography-tandem mass spectrometry assay for the quantification of medium-chain phosphatidylcholines in platelets of coronary artery disease patients

In a recent untargeted clinical lipidomics study of platelets of coronary artery disease (CAD) patients, medium-chain phosphatidylcholines (MCPCs) with C8 and C10 fatty acyl residues were found significantly upregulated in the patient group with acute coronary syndrome (ACS) as compared to chronic coronary syndrome (CCS) and healthy controls. To support this finding, this work presents the development and optimization of a targeted UHPLC-QTrap-MS/MS method with multiple reaction monitoring acquisition for the quantitative analysis of MCPCs (PC 10:0/8:0, PC 16:0/8:0, PC 10:0/20:4 and PC 10:0/10:0) in platelets for biomarker validation. A systematic optimization of chromatographic and mass spectrometric parameters was performed. A charged surface hybrid CSH C18 (1.7 µm, 130 Å) column and fine-tuned gradient elution with 2-propanol/acetonitrile and ammonium acetate as additive to the mobile phase was employed in the final method in ESI negative mode. Four selected PC standards (PC 6:0/6:0, PC 8:0/8:0, PC 10:0/10:0 and PC 12:0/12:0), which cover well the carbon and retention rime range of the target analytes, were used for the optimization process and calibration. Quantification was based on matrix-matched calibration with these four selected commercially available MCPC standards as surrogate calibrants and PC 6:0/6:0 (d22) as internal standard. Furthermore, an organic solvent and fatty acyl carbon number-corrected response factor approach gave also accuracies within acceptance limits of bioanalytical validation guidelines and has more generic applicability. Compared to the previous untargeted RPLC-ESI-QTOF-MS/MS method, the optimized targeted UHPLC-QTrap-MS/MS assay showed increased sensitivity and selectivity for the detection of medium-chain PCs in platelet samples of CAD (LOQs in the range of 0.5-5 nmol/L). The method performance parameters indicated its suitability for a future biomarker validation study of MCPCs in platelets.

CG17192 is a Phospholipase That Regulates Signaling Lipids in the Drosophila Gut upon Infection.

The chemoproteomics technique, activity-based protein profiling (ABPP), has proven to be an invaluable tool in assigning functions to enzymes. The serine hydrolase (SH) enzyme superfamily, in particular, has served as an excellent example in displaying the versatility of various ABPP platforms and has resulted in a comprehensive cataloging of the biochemical activities associated within this superfamily. Besides SHs, in mammals, several other enzyme classes have been thoroughly investigated using ABPP platforms. However, the utility of ABPP platforms in fly models remains underexplored. Realizing this knowledge gap, leveraging complementary ABPP platforms, we reported the full array of SH activities during various developmental stages and adult tissues in the fruit fly (Drosophila melanogaster). Following up on this study, using ABPP, we mapped SH activities in adult fruit flies in an infection model and found that a gut-resident lipase CG17192 showed increased activity during infection. To assign a biological function to this uncharacterized lipase, we performed an untargeted lipidomics analysis and found that phosphatidylinositols were significantly elevated when CG17192 was depleted in the adult fruit fly gut. Next, we overexpressed this lipase in insect cells, and using biochemical assays, we show that CG17192 is a secreted enzyme that has phospholipase C (PLC) type activity, with phosphatidylinositol being a preferred substrate. Finally, we show during infection that heightened CG17192 regulates phosphatidylinositol levels and, by doing so, likely modulates signaling pathways in the adult fruit fly gut that might be involved in the resolution of this pathophysiological condition.

Combining metabolomics and machine learning to discover biomarkers for early-stage breast cancer diagnosis.

There is an urgent need for better biomarkers for the detection of early-stage breast cancer. Utilizing untargeted metabolomics and lipidomics in conjunction with advanced data mining approaches for metabolism-centric biomarker discovery and validation may enhance the identification and validation of novel biomarkers for breast cancer screening. In this study, we employed a multimodal omics approach to identify and validate potential biomarkers capable of differentiating between patients with breast cancer and those with benign tumors. Our findings indicated that ether-linked phosphatidylcholine exhibited a significant difference between invasive ductal carcinoma and benign tumors, including cases with inconsistent mammography results. We observed alterations in numerous lipid species, including sphingomyelin, triacylglycerol, and free fatty acids, in the breast cancer group. Furthermore, we identified several dysregulated hydrophilic metabolites in breast cancer, such as glutamate, glycochenodeoxycholate, and dimethyluric acid. Through robust multivariate receiver operating characteristic analysis utilizing machine learning models, either linear support vector machines or random forest models, we successfully distinguished between cancerous and benign cases with promising outcomes. These results emphasize the potential of metabolic biomarkers to complement other criteria in breast cancer screening. Future studies are essential to further validate the metabolic biomarkers identified in our study and to develop assays for clinical applications.

Multiomics Studies on Metabolism Changes in Alcohol-Associated Liver Disease.

Metabolic dysfunction in the liver represents a predominant feature in the early stages of alcohol-associated liver disease (ALD). However, the mechanisms underlying this are only partially understood. To investigate the metabolic characteristics of the liver in ALD, we did a relative quantification of polar metabolites and lipids in the liver of mice with experimental ALD using untargeted metabolomics and untargeted lipidomics. A total of 99 polar metabolites had significant abundance alterations in the livers of alcohol-fed mice. Pathway analysis revealed that amino acid metabolism was the most affected by alcohol in the mouse liver. Metabolites involved in glycolysis and the TCA cycle were decreased, while glycerol 3-phosphate (G3P) and long-chain fatty acids were increased. Relative quantification of lipids unveiled an upregulation of multiple lipid classes, suggesting that alcohol consumption drives metabolism toward lipid synthesis. Results from enzyme expression and activity detection indicated that the decreased activity of mitochondrial glycerol 3-phosphate dehydrogenase contributed to the disordered metabolism.

A Procedure for Solid-Phase Extractions Using Metal-Oxide-Coated Silica Column in Lipidomics.

Lipid enrichment is indispensable for enhancing the coverage of targeted molecules in mass spectrometry (MS)-based lipidomics studies. In this study, we developed a simple stepwise fractionation method using a titanium- and zirconium-dioxide-coated solid-phase extraction (SPE) silica column that separates neutral lipids, phospholipids, and other lipids, including fatty acids (FAs) and glycolipids. Chloroform was used to dissolve the lipids, and neutral lipids, including steryl esters, diacylglycerols, and triacylglycerols, were collected in the loading fraction. Second, methanol with formic acid (99:1, v/v) was used to retrieve FAs, ceramides, and glycolipids, including glycosylated ceramides and glycosylated diacylglycerols, by competing for affinity with the Lewis acid sites on the metal oxide surface. Finally, phospholipids strongly retained via chemoaffinity interactions were eluted using a solution containing 5% ammonia and high water content (45:50 v/v, 2-propanol:water), which canceled the electrostatic and chelating interactions with the SPE column. High average reproducibility of <10% and coverage of ∼100% compared to those of the non-SPE samples were demonstrated by untargeted lipidomics of human plasma and mouse brain, testis, and feces. The advantage of our procedure was showcased by characterizing minor lipid subclasses, including dihexosylceramides containing very long-chain polyunsaturated FA in the testis, monogalactosyl and digalactosyl monoacylglycerols in feces, and acetylated and glycolylated derivatives of gangliosides in the brain that were not detected using conventional solvent extraction methods. Likewise, the value of our method in biology is maximized during glycolipidome profiling in the absence of neutral lipids and phospholipids that cover more than 80% of the chromatographic peaks.

Detailed lipid investigation of edible seaweeds by photochemical derivatization and untargeted lipidomics

Seaweeds are macrophytic algae that have been gaining interest as alternative healthy foods, renewable drug sources, and climate change mitigation agents. In terms of their nutritional value, seaweeds are renowned for their high content of biologically active polyunsaturated fatty acids. However, little is known about the regiochemistry—the geometry and position of carbon–carbon double bonds—of free and conjugated fatty acids in seaweeds. In the present work, a detailed characterization of the seaweed lipidome was achieved based on untargeted HRMS-based analysis and lipid derivatization with a photochemical aza-Paternò–Büchi reaction. A triple-data processing strategy was carried out to achieve high structural detail on the seaweed lipidome, i.e., (i) a first data processing workflow with all samples for aligning peak and statistical analysis that led to the definition of lipid sum compositions (e.g., phosphatidylglycerol (PG) 34:1), (ii) a second data processing workflow in which the samples of each seaweed were processed separately to annotate molecular lipids with known fatty acyl isomerism (e.g., PG 16:0_18:1), and (iii) the annotation of lipid regioisomers following MS/MS annotation of the lipid derivatives obtained following the aza-Paternò–Büchi reaction (e.g., PG 16:0_18:1 ω-9). Once the platform was set up, the lipid extracts from 8 seaweed species from different seaweed families were characterized, describing over 900 different lipid species, and information on the regiochemistry of carbon–carbon double bonds uncovered unknown peculiarities of seaweeds belonging to different families. The overall analytical approach helped to fill a gap in the knowledge of the nutritional composition of seaweeds.Graphical

Untargeted Lipidomics Reveals Novel HDL Metabotypes and Lipid-Clinical Correlates

Plasma high-density lipoprotein (HDL), originally studied for its role in lipid transport, is now appreciated to have wide-ranging biological functions that become defective during disease. While >200 lipids have collectively been detected in HDL, published HDL lipidomic analyses in different diseases have commonly been targeted to prespecified subsets of lipids. Here, we report the results of untargeted lipidomic analysis of HDL isolated from 101 subjects referred for computed tomographic coronary imaging for whom multiple additional clinical and lipoprotein metadata were measured. Unsupervised clustering of the total HDL lipidome revealed that the subjects fell into one of two discrete groups, herein referred to as HDL ‘metabotypes’. Subjects in metabotype 1 were likelier to be female and tended to have a less atherogenic lipoprotein profile, higher HDL cholesterol efflux capacity (CEC), and lower-grade non-calcified burden on coronary imaging than metabotype 2 counterparts. Specific lipids were relatively enriched in metabotype 1 HDL. Linear modeling revealed that several of these lipids were positively associated with CEC, statin use, HDL size, and HDL particle number, and positively correlated with HDL apolipoprotein A-1, suggesting that they may be informative HDL biomarkers. Taken together, we posit a novel, clinically relevant categorization for HDL revealed by systems biology.

A novel long non-coding RNA connects obesity to impaired adipocyte function

BackgroundLong non-coding RNAs (lncRNAs) can perform tasks of key relevance in fat cells, contributing, when defective, to the burden of obesity and its sequelae. Here, scrutiny of adipose tissue transcriptomes before and after bariatric surgery (GSE53378) granted identification of 496 lncRNAs linked to the obese phenotype. Only expression of linc-GALNTL6-4 displayed an average recovery over 2-fold and FDR-adjusted p-value <0.0001 after weight loss. The aim of the present study was to investigate the impact on adipocyte function and potential clinical value of impaired adipose linc-GALNTL6-4 in obese subjects. MethodsWe employed transcriptomic analysis of public dataset GSE199063, and cross validations in two large transversal cohorts to report evidence of a previously unknown association of adipose linc-GALNTL6-4 with obesity. We then performed functional analyses in human adipocyte cultures, genome-wide transcriptomics, and untargeted lipidomics in cell models of loss and gain of function to explore the molecular implications of its associations with obesity and weight loss. ResultsThe expression of linc-GALNTL6-4 in human adipose tissue is adipocyte-specific and co-segregates with obesity, being normalized upon weight loss. This co-segregation is demonstrated in two longitudinal weight loss studies and two cross-sectional samples. While compromised expression of linc-GALNTL6-4 in obese subjects is primarily due to the inflammatory component in the context of obesity, adipogenesis requires the transcriptional upregulation of linc-GALNTL6-4, the expression of which reaches an apex in terminally differentiated adipocytes. Functionally, we demonstrated that the knockdown of linc-GALNTL6-4 impairs adipogenesis, induces alterations in the lipidome, and leads to the downregulation of genes related to cell cycle, while propelling in adipocytes inflammation, impaired fatty acid metabolism, and altered gene expression patterns, including that of apolipoprotein C1 (APOC1). Conversely, the genetic gain of linc-GALNTL6-4 ameliorated differentiation and adipocyte phenotype, putatively by constraining APOC1, also contributing to the metabolism of triglycerides in adipose. ConclusionsCurrent data unveil the unforeseen connection of adipocyte-specific linc-GALNTL6-4 as a modulator of lipid homeostasis challenged by excessive body weight and meta-inflammation.

Early pregnancy serum PFAS are associated with alterations in the maternal lipidome

Per- and polyfluoroalkyl substances (PFAS) have been detected in the blood of humans and animals worldwide. Exposure to some PFAS are associated with multiple adverse pregnancy outcomes. Existing literature has identified a strong association with PFAS exposure and metabolic dysfunction in humans, including modification of lipid metabolism. Using a subset of the Michigan Mother-Infant Pairs cohort (n=95), this study investigated associations between first trimester plasma levels of PFAS and maternal lipids and metabolites in the first trimester (T1), at the time of delivery (T3), and in the infant cord blood (CB) using untargeted shotgun lipidomics and metabolomics. Identifying PFAS-induced alterations in the maternal lipid- or metabolome at specific timepoints may help elucidate windows of susceptibility to adverse pregnancy outcomes. Out of 9 PFAS measured, 7 were detected in at least 20% of samples and were used for further analyses. PFOS and PFHxS were measured at the highest concentrations with medians of 5.76 ng/ml and 3.33 ng/ml, respectively. PFOA, PFNA, and PFDA had lower measured values with medians of < 1.2 ng/mL. PFHxS concentrations were positively associated with monounsaturated sphingomyelins (SMs) in T1 maternal plasma in adjusted models, determined by an adjusted p-value (q) < 0.1. PFHxS was positively associated with saturated and polyunsaturated SMs and inversely associated with saturated diacylglycerols in T1. Following metabolite-specific analysis, two mono-unsaturated diacylglycerols with carbon chain lengths of 32 and 35 were inversely associated with PFHxS in T1. In T3, only the association between PFHxS and SMs remained, but was attenuated. In addition, PFDA was associated with an increase in polyunsaturated plasmenyl-phosphatidylethanolamines in T3. No associations were identified between PFAS and infant cord blood lipids. Continued research into PFAS associated disruptions in lipid metabolism at sensitive stages of gestation may provide insight into the mechanisms that lead to adverse birth and pregnancy outcomes.

Development of LC-FAIMS-MS and its application to lipidomics study of Acinetobacter baumannii infection

The recent advances in mass spectrometry (MS) technologies have enabled comprehensive lipid profiling in biological samples. However, the robustness and efficiency of MS-based lipidomics is compromised by the complexity of biological samples. High-field asymmetric waveform ion mobility spectrometry (FAIMS) is a technology that can continuously transmit one type of ion, independent of the mass-to-charge ratio. Here we present the development and application of LC-FAIMS-MS/MS-based platform for untargeted lipidomics. We used 3 optimally balanced compensation voltages, i.e., 29 V, 34 V and 39 V, to analyze all subclasses of glycerophospholipids. The reproducibility of the method was evaluated using reference standards. The reproducibility of retention times ranged from 0.9% to 1.5% RSD; whereas RSD values of 5%–10% were observed for peak areas. More importantly, the coupling of a FAIMS device can significantly improve the robustness and efficiency. We exploited this NPLC-FAIMS-HRMS to analyze the serum lipid profiles in mice infected intranasally with Acinetobacter baumannii. The temporal profiles of serum lipids after A. baumannii inoculation were obtained for 4 h, 8 h, and 24 h. We found that nearly all ether PC and ether PE lipids were significantly decreased 8 h after inoculation. The resultant volcano plot illustrated the distribution of 28 increased and 28 decreased lipid species in mouse sera 24 h after inoculation. We also found that a single ether PE composition can comprise multiple isomeric structures, and the relative abundance of each isomer could be quantified using the newly developed NPLC-FAIMS-PRM method.

An Untargeted Lipidomics Workflow Incorporating High-Resolution Demultiplexing (HRdm) Drift Tube Ion Mobility-Mass Spectrometry.

Global discovery lipidomics can provide comprehensive chemical information toward understanding the intricacies of metabolic lipid disorders such as dyslipidemia; however, the isomeric complexity of lipid species remains an analytical challenge. Orthogonal separation strategies, such as ion mobility (IM), can be inserted into liquid chromatography-mass spectrometry (LC-MS) untargeted lipidomic workflows for additional isomer separation and high-confidence annotation, and the emergence of high-resolution ion mobility (HRIM) strategies provides marked improvements to the resolving power (Rp > 200) that can differentiate small structural differences characteristic of isomers. One such HRIM strategy, high-resolution demultiplexing (HRdm), utilizes multiplexed drift tube ion mobility spectrometry (DTIMS) with post-acquisition algorithmic deconvolution to access high IM resolutions while retaining the measurement precision inherent to the drift tube technique; however, HRdm has yet to be utilized in untargeted studies. In this manuscript, a proof-of-concept study using ATP10D dysfunctional murine models was investigated to demonstrate the utility of HRdm-incorporated untargeted lipidomic analysis pipelines. Total lipid features were found to increase by 2.5-fold with HRdm compared to demultiplexed DTIMS as a consequence of more isomeric lipids being resolved. An example lipid, PC 36:5, was found to be significantly higher in dysfunctional ATP10D mice with two resolved peaks observed by HRdm that were absent in both the functional ATP10D mice and the standard demultiplexed DTIMS acquisition mode. The benefits of utilizing HRdm for discerning isomeric lipids in untargeted workflows have the potential to enhance our analytical understanding of lipids related to disease complexity and biologically relevant studies.

Integrative metagenomic and lipidomic analyses reveal alterations in children with obesity and after lifestyle intervention.

Despite emerging evidence linking alterations in gut microbiota to childhood obesity, the metabolic mechanisms linking gut microbiota to the lipid profile during childhood obesity and weight loss remain poorly understood. In this study, children with obesity were treated with lifestyle weight loss therapy. Metagenomics association studies and serum untargeted lipidomics analyses were performed in children with obesity and healthy controls before and after weight loss. We identified alterations in gut microbiota associated with childhood obesity, as well as variations in circulating metabolite concentrations. Children with obesity showed significant decreases in the levels of s-Rothia_kristinae and s-Enterobacter_roggenkampii, alongsige elevated levels of s-Clostridiales_bacterium_Marseille-P5551. Following weight loss, the levels of s-Streptococcus_infantarius and s-Leuconostoc_citreum increased by factors of 3.354 and 1.505, respectively, in comparison to their pre-weight loss levels. Correlation analyses indicated a significant positive relationship between ChE(2:0) levels and both with s-Lachnospiraceae_bacterium_TF09-5 and fasting glucose levels. CoQ8 levels were significantly negatively correlated with s-Rothia_kristinae and HOMA-IR. We linked altered gut microbiota and serum lipid levels in children with obesity to clinical indicators, indicating a potential impact on glucose metabolism via lipids. This study contributes to understanding the mechanistic relationship between altered gut microbiota and childhood obesity and weight loss, suggesting gut microbiome as a promising target for intervention. https://www.chictr.org.cn/showproj.html?proj=178971, ChiCTR2300072179.

Evaluation of the effect of dietary supplementation with Allium mongolicum regel bulb powder on the volatile compound and lipid profiles of the longissimus thoracis in Angus calves based on GC–IMS and lipidomic analysis

The effect of A. mongolicum Regel bulb powder (AMRP) supplementation on the flavour of beef from Angus calves has not been investigated thus far. We used GC–IMS and untargeted lipidomics techniques to examine the volatile compound and lipid metabolic profiles and reveal the effects of dietary AMRP supplementation on the flavour of beef. A total of 6 characteristic volatile compounds and 30 key lipid compounds were identified in the AMRP treatment group. AMRP promoted the release of triglycerides and phosphatidylinositols from beef and accelerated the production of volatile compounds such as ethyl acetate, 1-penten-3-one, and tetrahydrofurane, and the production of these three characteristic volatile compounds was significantly correlated with the UFAs in triglycerides according to correlation analysis. In summary, dietary AMRP supplementation had a positive effect on the flavour of beef, and these findings provide a theoretical basis for the development and utilisation of AMRP as a feed additive.

Impact of Phenylketonuria on the Serum Metabolome and Plasma Lipidome: A Study in Early-Treated Patients.

Data suggest that metabolites, other than blood phenylalanine (Phe), better and independently predict clinical outcomes in patients with phenylketonuria (PKU). To find new biomarkers, we compared the results of untargeted lipidomics and metabolomics in treated adult PKU patients to those of matched controls. Samples (lipidomics in EDTA-plasma (22 PKU and 22 controls) and metabolomics in serum (35 PKU and 20 controls)) were analyzed using ultra-high-performance liquid chromatography and high-resolution mass spectrometry. Data were subjected to multivariate (PCA, OPLS-DA) and univariate (Mann-Whitney U test, p < 0.05) analyses. Levels of 33 (of 20,443) lipid features and 56 (of 5885) metabolite features differed statistically between PKU patients and controls. For lipidomics, findings include higher glycerolipids, glycerophospholipids, and sphingolipids species. Significantly lower values were found for sterols and glycerophospholipids species. Seven features had unknown identities. Total triglyceride content was higher. Higher Phe and Phe catabolites, tryptophan derivatives, pantothenic acid, and dipeptides were observed for metabolomics. Ornithine levels were lower. Twenty-six metabolite features were not annotated. This study provides insight into the metabolic phenotype of PKU patients. Additional studies are required to establish whether the observed changes result from PKU itself, diet, and/or an unknown reason.

Untargeted Blood Lipidomics Analysis in Critically Ill Pediatric Patients with Ventilator-Associated Pneumonia: A Pilot Study.

This study aims to explore the diagnostic potential of blood lipid profiles in suspected ventilator-associated pneumonia (VAP). Early detection of VAP remains challenging for clinicians due to subjective clinical criteria and the limited effectiveness of current diagnostic tests. Blood samples from 20 patients, with ages between 6 months and 15 years, were collected at days 1, 3, 6, and 12, and an untargeted lipidomics analysis was performed using a Ultra high Pressure Liquid Chromatography hyphenated with High Resolution Mass Spectrometry UPLC-HRMS (TIMS-TOF/MS) platform. Patients were stratified based on modified pediatric clinical pulmonary index score (mCPIS) into high (mCPIS ≥ 6, n = 12) and low (mCPIS < 6, n = 8) VAP suspicion groups. With the untargeted lipid profiling, we were able to identify 144 lipid species from different lipid groups such as glycerophospholipids, glycerolipids, and sphingolipids, in the blood of children with VAP. Multivariate and univariate statistical analyses revealed a distinct distribution of blood lipid profiles between the studied groups, indicating the potential utility of lipid biomarkers in discriminating VAP presence. Additionally, specific lipids were associated with pharyngeal culture results, notably the presence of Klebsiella pneumoniae and Staphylococcus aureus, underscoring the importance of lipid profiling in identifying the microbial etiology of VAP.