Lipidomics Research Research Articles

Lipidomics-based deep learning algorithms can enhance prediction of obstructive coronary artery disease

Abstract Background Lipidomics emerge as a promising research field with the potential to help in personalized risk stratification and also improve our understanding on the functional role of individual lipid species in the metabolic perturbations occurring in coronary artery disease (CAD). The existing technological challenges to detect diverse yet structurally similar lipids and their isomers, as well as the need for novel statistical and computational tools to handle the high-dimensional lipidome may be responsible for the reduced clinical translation of the very first lipidomics research outcomes. Methods To that end, this post-hoc analysis of the prospective CorLipid trial aimed to investigate the predictive capability of a lipidomics panel for obstructive CAD risk through an extreme gradient boosting (XGBoost) machine learning (ML) approach. The lipid profiles of 146 individuals with suspected CAD were investigated through liquid chromatography-mass spectrometry. Fasting blood samples were drawn prior to invasive coronary angiography execution. Obstructive CAD was defined as SYNTAX Score (SS) >0 compared to non-obstructive CAD (SS=0). Results Study participants (75.3% male, mean age: 61 ±10.5 years old) were divided into two categories based on the existence of obstructive CAD (54.8% with SS>0 and 45.2% with SS=0). In total, 517 lipid species were identified, from which 290 lipid species were finally quantified in participants’ serum [glycerophospholipids (52.1%), glycerolipids (28.6%) and sphingolipids (19.3%)]. The levels of glycerophospholipid, sphingolipid and glycerolipid classes were significantly different in patients with obstructive CAD. Finally, a ML XGBoost algorithm identified a panel of 17 serum biomarkers (5 sphingolipids, 7 glycerophospholipids, triacylglycerols, galectin-3, glucose, LDL and LDH) as totally sensitive (100% sensitivity, 62.1% specificity and 100% negative predictive value) for the prediction of obstructive CAD. Conclusions These findings provide molecular insights into the role of dysregulated lipid metabolism in the development and progression of CAD while validating the existing body of evidence from similar research studies. Further (ML-based) investigation of lipid metabolism could hold promises for novel therapeutic strategies and improvement of the existing risk stratification schemes.Lipidomics panel importance

Metabolomic and Lipidomic Analysis of Manganese-Associated Parkinsonism: a Case-Control Study in Brescia, Italy.

Excessive Manganese (Mn) exposure is neurotoxic and can cause Mn-Induced Parkinsonism (MnIP), marked by cognitive and motor dysfunction. Although metabolomic and lipidomic research in Parkinsonism (PD) patients exists, it remains limited. This study hypothesizes distinct metabolomic and lipidomic profiles based on exposure status, disease diagnosis, and their interaction. We used a case-control design with a 2×2 factorial framework to investigate the metabolomic and lipidomic alterations associated with Mn exposure and their link to PD. The study population of 97 individuals was divided into four groups: non-exposed controls (n=23), exposed controls (n=25), non-exposed with PD (n=26) and exposed with PD (n=23). Cases, defined by at least two cardinal PD features (excluding vascular, iatrogenic, and traumatic origins), were recruited from movement disorder clinics in four hospitals in Brescia, Northern Italy. Controls, free from neurological or psychiatric conditions, were selected from the same hospitals. Exposed subjects resided in metallurgic regions (Val Camonica and Bagnolo Mella) for at least 8 continuous years, while non-exposed subjects lived in low-exposure areas around Lake Garda and Brescia city. We conducted untargeted analyses of metabolites and lipids in whole blood samples using ultra-high-performance liquid chromatography (UHPLC) and mass spectrometry (MS), followed by statistical analyses including Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA), and Two-Way Analysis of Covariance (ANCOVA). Metabolomic analysis revealed modulation of alanine, aspartate, and glutamate metabolism (Impact=0.05, p=0.001) associated with disease effect; butanoate metabolism (Impact=0.03, p=0.004) with the exposure effect; and vitamin B6 metabolism (Impact=0.08, p=0.03) with the interaction effect. Differential relative abundances in 3-sulfoxy-L-Tyrosine (β=1.12, FDR p<0.001), glycocholic acid (β=0.48, FDR p=0.03), and palmitelaidic acid (β=0.30, FDR p<0.001) were linked to disease, exposure, and interaction effects, respectively. In the lipidome, ferroptosis (Pathway Lipids=11, FDR p=0.03) associated with the disease effect and sphingolipid signaling (Pathway Lipids=9, FDR p=0.04) associated with the interaction effect were significantly altered. Lipid classes triacylglycerols, ceramides, and phosphatidylethanolamines showed differential relative abundances associated with disease, exposure, and interaction effects, respectively. These findings suggest that PD and Mn exposure induce unique metabolomic and lipidomic changes, potentially serving as biomarkers for MnIP and warranting further study.

Current Insights on Lipidomics in Dermatology: A Systematic Review

Inflammatory dermatoses and lipid disturbances are interrelated, especially owing to chronic inflammatory conditions. This study aimed to investigate recent findings about lipidomic and dermatologic diseases as well as on the sampling techniques developed to study lipidomics invivo and analytical and statistical approaches employed. A systematic review was designed using the search algorithm (lipidomics) AND (skin OR dermatology OR stratum corneum OR sebum OR epidermis), following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The literature search identified 1013 references, and finally, only 48 were selected, including a total of 2651 participants with a mean age of 34.13 ± 16.28 years. The dermatologic diseases evaluated were atopic dermatitis, acne, psoriasis, hidradenitis suppurativa, and other skin diseases. Sebutape was the primary sampling technique for lipidomics research. Most of the studies performed untargeted profiling through liquid chromatography with tandem mass spectrometry statistically analyzed with principal component analysis, orthogonal partial least-squares discriminate analysis, heatmap, and volcano plot models. The most consulted databases were LIPIDMAPS Structure Database, MetaboAnalyst, and Human Metabolome Database. A large heterogeneity of lipidomic and lipid metabolism profiles was observed in patients with skin diseases. Skin lipidomic analysis is valuable in exploring skin disease and has ample translational potential.

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.

The lipidomics reporting checklist a framework for transparency of lipidomic experiments and repurposing resource data

The rapid increase in lipidomic studies has led to a collaborative effort within the community to establish standards and criteria for producing, documenting, and disseminating data. Creating a dynamic easy-to-use checklist that condenses key information about lipidomic experiments into common terminology will enhance the field's consistency, comparability, and repeatability. Here, we describe the structure and rationale of the established Lipidomics Minimal Reporting Checklist to increase transparency in lipidomics research.

Lipidomics in Plants Under Abiotic Stress Conditions: An Overview

Lipids are ubiquitous macromolecules that play essential roles in several metabolic processes in plants, such as primary and secondary metabolism, energy storage, and lipid signaling, also being major constituents of membranes. Considering their importance, lipid contents, proportion, and composition are widely modulated in response to environmental conditions, which is even more important under unfavorable conditions such as abiotic stresses. In recent years, technological advances have allowed for the analysis of the global lipid profile, also known as lipidomics, which has emerged as a powerful tool for the comprehensive analysis of the modulation and roles of lipids under different conditions. This review provides a current overview of plant lipidomics research, covering the different lipid classes found in plants, analytical techniques, and the main lipid-related responses under temperature, water, salt, alkali, heavy metal, nutrient deficiency, light, and oxidative stress.

Algal Adaptation to Environmental Stresses: Lipidomics Research

Algal Adaptation to Environmental Stresses: Lipidomics Research

LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis.

In the field of lipidomics, where the complexity of lipid structures and functions presents significant analytical challenges, LipidSig stands out as the first web-based platform providing integrated, comprehensive analysis for efficient data mining of lipidomic datasets. The upgraded LipidSig 2.0 (https://lipidsig.bioinfomics.org/) simplifies the process and empowers researchers to decipher the complex nature of lipids and link lipidomic data to specific characteristics and biological contexts. This tool markedly enhances the efficiency and depth of lipidomic research by autonomously identifying lipid species and assigning 29 comprehensive characteristics upon data entry. LipidSig 2.0 accommodates 24 data processing methods, streamlining diverse lipidomic datasets. The tool's expertise in automating intricate analytical processes, including data preprocessing, lipid ID annotation, differential expression, enrichment analysis, and network analysis, allows researchers to profoundly investigate lipid properties and their biological implications. Additional innovative features, such as the 'Network' function, offer a system biology perspective on lipid interactions, and the 'Multiple Group' analysis aids in examining complex experimental designs. With its comprehensive suite of features for analyzing and visualizing lipid properties, LipidSig 2.0 positions itself as an indispensable tool for advanced lipidomics research, paving the way for new insights into the role of lipids in cellular processes and disease development.

The Neurolipid Atlas: a lipidomics resource for neurodegenerative diseases uncovers cholesterol as a regulator of astrocyte reactivity impaired by ApoE4.

Lipid changes in the brain have been implicated in many neurodegenerative diseases including Alzheimer's Disease (AD), Parkinson's disease and Amyotrophic Lateral Sclerosis. To facilitate comparative lipidomic research across brain-diseases we established a data commons named the Neurolipid Atlas, that we have pre-populated with novel human, mouse and isogenic induced pluripotent stem cell (iPSC)-derived lipidomics data for different brain diseases. We show that iPSC-derived neurons, microglia and astrocytes display distinct lipid profiles that recapitulate in vivo lipotypes. Leveraging multiple datasets, we show that the AD risk gene ApoE4 drives cholesterol ester (CE) accumulation in human astrocytes recapitulating CE accumulation measured in the human AD brain. Multi-omic interrogation of iPSC-derived astrocytes revealed that cholesterol plays a major role in astrocyte interferon-dependent pathways such as the immunoproteasome and major histocompatibility complex (MHC) class I antigen presentation. We show that through enhanced cholesterol esterification ApoE4 suppresses immune activation of astrocytes. Our novel data commons, available at neurolipidatlas.com, provides a user-friendly tool and knowledge base for a better understanding of lipid dyshomeostasis in neurodegenerative diseases.

Targeted and untargeted metabolomics and lipidomics in dried blood microsampling: Recent applications and perspectives.

Blood microsampling (BµS) offers an alternative to conventional methods that use plasma or serum for profiling human health, being minimally invasive and cost effective, especially beneficial for vulnerable populations. We present a non-systematic review that offers a synopsis of the analytical methods, applications and perspectives related to dry blood microsampling in targeted and untargeted metabolomics and lipidomics research in the years 2022 and 2023. BµS shows potential in neonatal and paediatric studies, therapeutic drug monitoring, metabolite screening, biomarker research, sports supervision, clinical disorders studies and forensic toxicology. Notably, dried blood spots and volumetric absorptive microsampling options have been more extensively studied than other volumetric technologies. Therefore, we suggest that a further investigation and application of the volumetric technologies will contribute to the use of BµS as an alternative to conventional methods. Conversely, we support the idea that harmonisation of the analytical methods when using BµS would have a positive impact on its implementation.

The Lipidomic Signature of Glioblastoma: A Promising Frontier in Cancer Research.

Glioblastoma is the most aggressive primary brain malignancy in adults, and has a survival duration of approximately 15 months. First line treatment involves surgical resection, chemotherapy, and radiation, but despite the multi-pronged approach and advances in cancer research, glioblastoma remains devastating with a high mortality rate. Lipidomics is an emerging discipline that studies lipid pathways and characteristics, and is a promising field to understand biochemical mechanisms. In glioblastoma, disrupted lipid homeostasis has been reported in the literature. A thorough understanding of serum lipidomics may offer ways to better understand glioblastoma biomarkers, prognosis, and treatment options. Here, we review the literature, offering future directions for lipidomics research in glioblastomas.

Abstract 1451 Alzheimer's disease is likely a lipid-disorder complication: An example of functional lipidomics research

Abstract 1451 Alzheimer's disease is likely a lipid-disorder complication: An example of functional lipidomics research

Polyphenolics extracts from Ascophyllum nodosum protect against ultraviolet B-induced oxidative damage in mouse retinal Müller cells

Long-term exposure to sunlight, especially ultraviolet (UV), induces eye injury in people. We investigated the protective potential of Polyphenolic extract from Ascophyllum nodosum (PEAn) against UV-B-induced oxidative damage in retinal Müller cells (RMCs). In the UV-B-induced damage model of RMCs, PEAn demonstrated a potent photoprotective effect that reduced oxidative damage and apoptosis. PEAn significantly regulated the concentrations of ROS, Caspase-3, and Calcium ions (Ca2+) in RMCs generated by UV-B. The mitochondrial membrane potential (ΔΨm) and activities of various antioxidant enzymes (SOD, CAT, and GSH-Px) were significantly increased, whereas the MDA was reduced. Lipidomic research in RMCs also revealed that UV-B irradiation significantly altered the lipid compounds in the cells, while PEAn therapy gradually restored normal cells. Both the accumulation of triacylglycerides and the toxicity of the lipids in RMCs were simultaneously reduced by PEAn. The findings indicated that PEAn could be a potential natural photochemical protectant against UV-B-induced oxidative damage.

Zebrafish as a Model for Lipidomics and Similar Investigations.

Zebrafish (Danio rerio) has emerged as a pivotal model organism in vertebrate development research over several decades. Beyond its contributions to developmental biology, zebrafish have increasingly played a crucial role in the field of lipidomics. Lipidomics, a comprehensive analysis of lipids within biological systems, offers profound insights into lipid metabolism and signaling pathways. This chapter explores the zebrafish's unique attributes that make it an ideal candidate for lipidomics studies. With a genome sharing numerous genetic similarities with humans, zebrafish serve as a powerful model for dissecting lipid metabolism and unraveling the complexities of lipid mediator-related diseases. In this chapter, we delve into specific protocols tailored for utilizing zebrafish in lipidomics research and similar investigations. Through a comprehensive exploration of zebrafish as a model organism, this chapter aims to provide researchers with valuable insights and methodologies for advancing lipidomics studies using zebrafish.

From big data to big insights: statistical and bioinformatic approaches for exploring the lipidome.

The goal of lipidomic studies is to provide a broad characterization of cellular lipids present and changing in a sample of interest. Recent lipidomic research has significantly contributed to revealing the multifaceted roles that lipids play in fundamental cellular processes, including signaling, energy storage, and structural support. Furthermore, these findings have shed light on how lipids dynamically respond to various perturbations. Continued advancement in analytical techniques has also led to improved abilities to detect and identify novel lipid species, resulting in increasingly large datasets. Statistical analysis of these datasets can be challenging not only because of their vast size, but also because of the highly correlated data structure that exists due to many lipids belonging to the same metabolic or regulatory pathways. Interpretation of these lipidomic datasets is also hindered by a lack of current biological knowledge for the individual lipids. These limitations can therefore make lipidomic data analysis a daunting task. To address these difficulties and shed light on opportunities and also weaknesses in current tools, we have assembled this review. Here, we illustrate common statistical approaches for finding patterns in lipidomic datasets, including univariate hypothesis testing, unsupervised clustering, supervised classification modeling, and deep learning approaches. We then describe various bioinformatic tools often used to biologically contextualize results of interest. Overall, this review provides a framework for guiding lipidomic data analysis to promote a greater assessment of lipidomic results, while understanding potential advantages and weaknesses along the way.

Isorhynchophylline improves lipid metabolism disorder by mediating a circadian rhythm gene Bmal1 in spontaneously hypertensive rat.

Hypertension is a progressive metabolic disease characterized by circadian regulation of lipid metabolism disorder. Identifying specific lipid components and maintaining circadian homeostasis of lipid metabolism might be a promising therapeutic strategy for hypertension. Isorhynchophylline (IRP) can regulate lipid metabolism; however, the underlying mechanism of IRP in improving lipid metabolism rhythm disorder is still unclear. The lipid circadian biomarkers and abnormal metabolic pathways intervened by IRP were investigated using diurnal lipidomic research methods. The 24-h circadian changes in mRNA and protein expression levels of circadian genes, including Bmal1, Clock, Cry1, Cry2, Per1, and Per2, and lipid metabolism-related factors (PPARα and LPL) were determined using RT-PCR and western blot analyses, respectively. The underlying mechanisms were intensively investigated by inhibiting Bmal1. Molecular docking and drug affinity responsive target stability analyses were performed to assess the binding affinity of IRP and Bmal1. IRP treatment could effectively improve 24-h blood pressure, ameliorate the lipid metabolic rhythm disorder, reverse the expression levels of circadian rhythm genes, and regulate lipid metabolism-related genes (PPARα and LPL) by mediating Bmal1. This study highlighted the potential effects of IRP in maintaining the circadian homeostasis of lipid metabolism and the treatment of hypertension.

Elevated lipid class concentrations in females with anorexia nervosa before and after intensive weight restoration treatment-A lipidomics study.

To study the plasma lipidome of patients with anorexia nervosa (AN) before and after weight restoration treatment and report associations with AN subtypes and oral contraceptive pill (OCP) usage. Quantitative shotgun lipidomics analysis was used to study plasma lipids of 50 female patients with AN before and after weight restoration treatment and 50 healthy female controls (HC). The AN group was assessed with blood samples and questionnaires before and after weight restoration. In total we quantified 260 lipid species representing 26 lipid classes of which 13 lipid class concentrations were elevated in patients with AN at admission compared with HC. Lipid classes remained elevated after weight restoration treatment of 84 days (median; interquartile range 28), and only the concentration of the ceramide lipid class increased between pre- and post-treatment (p = .03), whereas lysophosphatidylcholine (LPC, p = .02), ether-linked Phosphatidylcholine (LPCO, p = .02), and lysophosphatidylethanolamine (LPE, p = .009) decreased. In AN, 13 out of 26 lipid class concentrations were elevated at admission and remained elevated post-treatment. Ceramides increased further between pre- and post-weight restoration treatment, which could be related to the rapid weight gain during re-nutrition. Further research is needed to elucidate the effects of weight restoration treatment on short- and long-term lipid profiles in individuals with AN. Lipidomics research can increase the understanding of AN, a complex and potentially life-threatening eating disorder. By analyzing lipids, or fats, in the body, we can identify biological markers that may inform diagnosis and develop more effective treatments. This research can also shed light on the underlying mechanisms of the disorder, leading to a better understanding of the processes involved in eating behavior.

Investigating Performance of the SLIM-Based High Resolution Ion Mobility Platform for Separation of Isomeric Phosphatidylcholine Species.

Lipids are structurally diverse molecules that play a pivotal role in a plethora of biological processes. However, deciphering the biological roles of the specific lipids is challenging due to the existence of numerous isomers. This high chemical complexity of the lipidome is one of the major challenges in lipidomics research, as the traditional liquid chromatography-mass spectrometry (LC-MS) based approaches are often not powerful enough to resolve these isomeric and isobaric nuances within complex samples. Thus, lipids are uniquely suited to the benefits provided by multidimensional liquid chromatography-ion mobility-mass spectrometry (LC-IM-MS) analysis. However, many forms of lipid isomerism, including double-bond positional isomers and regioisomers, are structurally similar such that their collision cross section (CCS) differences are unresolvable via conventional IM approaches. Here we evaluate the performance of a high resolution ion mobility (HRIM) system based on structures for lossless ion manipulation (SLIM) technology interfaced to a high resolution quadrupole time-of-flight (QTOF) analyzer to address the noted lipidomic isomerism challenge. SLIM implements the traveling wave ion mobility technique along an ∼13 m ion path, providing longer path lengths to enable improved separation of isomeric features. We demonstrate the power of HRIM-MS to dissect isomeric PC standards differing only in double bond (DB) and stereospecific number (SN) positions. The partial separation of protonated DB isomers is significantly enhanced when they are analyzed as metal adducts. For sodium adducts, we achieve close to baseline separation of three different PC 18:1/18:1 isomers with different cis-double bond locations. Similarly, PC 18:1/18:1 (cis-9) can be resolved from the corresponding PC 18:1/18:1 (trans-9) form. The separation capacity is further enhanced when using silver ion doping, enabling the baseline separation of regioisomers that cannot be resolved when measured as sodium adducts. The sensitivity and reproducibility of the approach were assessed, and the performance for more complex mixtures was benchmarked by identifying PC isomers in total brain and liver lipid extracts.

An untargeted serum and urine lipidomics research based on UPLC-MS revealed the lipid alterations on adjuvant-induced arthritis rats.

Rheumatoid arthritis (RA) is a systemic autoimmune disease dominated by chronic inflammatory lesions of peripheral synovial joints. Growing evidence suggests that abnormal lipid metabolism levels contribute to the progression of RA. Although several metabolomics studies have shown abnormality in the RA lipidome, the relationship between the overall lipid metabolites and RA has not been systematically evaluated. In this study, an untargeted lipidomics method based on ultra performance liquid chromatography-mass spectrometry (UPLC-MS) was used to analyze the serum and urine lipidomes of adjuvant-induced arthritis rats to study the characteristics of lipid metabolism changes in the rats and search lipid markers for diagnosing RA. By combining with orthogonal partial least squares discriminant analysis, a total of 52 potential lipid markers were identified, mainly involved in sphingolipid metabolism, glycerophospholipid metabolism, sterol lipid metabolism, glycerolipid metabolism and fatty acid metabolism, which provided crucial insight into lipid metabolism disturbances in RA. Further receiver operating characteristic analysis revealed that the areas under the curve of PC(22:4/16:0), PI(18:1/16:0) and LacCer(d18:1/12:0) from serum and 25-hydroxycholesterol from urine were 0.94, 1.00, 1.00 and 1.00, respectively, indicating the high predictive ability of this method for RA. In this study, our results indicated that a combination of serum and urine analysis can provide a more comprehensive and reliable assessment of RA, and a UPLC-MS-based lipidomics strategy is a powerful tool to search for potential lipid markers associated with RA and explore the pathogenesis of RA.

Assessing Glycosphingolipid Profiles in Human Health and Disease Using Non-Imaging MALDI Mass Spectrometry

Glycosphingolipids (GSLs) are a glycolipid subtype which plays vital roles in numerous biological processes, cell–cell interactions, as well as oncogenesis and ontogenesis. They are ubiquitous molecules found mostly in cell membranes. Abnormal expression of GSLs as well as altered molecular structure have been linked with progression of cancer and metastasis and are involved in the pathophysiology of neurodegenerative, autoimmune, and infectious diseases as well as inherited enzyme defects—glycosphingolipidoses. Matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) plays a leading role in analyzing and characterizing different GSLs, and thus can help to distinguish altered GSL patterns. This review offers insights into the benefits and limitations when using MALDI MS in this field of lipidomic research, with an emphasis on which are the optimal matrices in analyzing GSLs from different tissues (normal and pathological) as well as highlighting GSLs’ particular profiles in various cell cultures, and normal and pathological human tissues obtained by MALDI non-imaging MS (non-IMS). These findings can have implications in further understanding the role of altered GSL expression in various pathological conditions and could be a target for future therapies.