Lipidomics Datasets Research Articles

Lipidomic analyses in epidemiology.

Clinical lipid measurements have been the mainstay of risk assessment for chronic disease since the Framingham study commenced over 60 years ago. Thousands of subsequent epidemiological studies have provided much insight into the relationship between plasma lipid profiles, health and disease. However, the human lipidome consists of thousands of individual lipid species, and current lipidomic technology presents us with an unprecedented opportunity to measure lipid phenotypes, representing genomic, metabolic, diet and lifestyle-related exposures, in large epidemiological studies. The number of epidemiological studies using lipidomic profiling is increasing and has the potential to provide improved biological and clinical insight into human disease. In this review, we discuss current lipidomic technologies, epidemiological studies using these technologies and the statistical approaches used in the analysis of the resulting data. We highlight the potential of integrating genomic and lipidomic datasets and discuss the future opportunities and challenges in this emerging field.

Computational Modeling of Competitive Metabolism between ω3- and ω6-Polyunsaturated Fatty Acids in Inflammatory Macrophages.

Arachidonic acid (AA), a representative ω6-polyunsaturated fatty acid (PUFA), is a precursor of 2-series prostaglandins (PGs) that play important roles in inflammation, pain, fever, and related disorders including cardiovascular diseases. Eating fish or supplementation with the ω3-PUFAs such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) is widely assumed to be beneficial in preventing cardiovascular diseases. A proposed mechanism for a cardio-protective role of ω3-PUFAs assumes competition between AA and ω3-PUFAs for cyclooxygenases (COX), leading to reduced production of 2-series PGs. In this study, we have used a systems biology approach to integrate existing knowledge and novel high-throughput data that facilitates a quantitative understanding of the molecular mechanism of ω3- and ω6-PUFA metabolism in mammalian cells. We have developed a quantitative computational model of the competitive metabolism of AA and EPA via the COX pathway through a two-step matrix-based approach to estimate the rate constants. This model was developed by using lipidomic data sets that were experimentally obtained from EPA-supplemented ATP-stimulated RAW264.7 macrophages. The resulting model fits the experimental data well for all metabolites and demonstrates that the integrated metabolic and signaling networks and the experimental data are consistent with one another. The robustness of the model was validated through parametric sensitivity and uncertainty analysis. We also validated the model by predicting the results from other independent experiments involving AA- and DHA-supplemented ATP-stimulated RAW264.7 cells using the parameters estimated with EPA. Furthermore, we showed that the higher affinity of EPA binding to COX compared with AA was able to inhibit AA metabolism effectively. Thus, our model captures the essential features of competitive metabolism of ω3- and ω6-PUFAs.

Interaction between dietary lipids and gut microbiota regulates hepatic cholesterol metabolism

The gut microbiota influences many aspects of host metabolism. We have previously shown that the presence of a gut microbiota remodels lipid composition. Here we investigated how interaction between gut microbiota and dietary lipids regulates lipid composition in the liver and plasma, and gene expression in the liver. Germ-free and conventionally raised mice were fed a lard or fish oil diet for 11 weeks. We performed lipidomics analysis of the liver and serum and microarray analysis of the liver. As expected, most of the variation in the lipidomics dataset was induced by the diet, and abundance of most lipid classes differed between mice fed lard and fish oil. However, the gut microbiota also affected lipid composition. The gut microbiota increased hepatic levels of cholesterol and cholesteryl esters in mice fed lard, but not in mice fed fish oil. Serum levels of cholesterol and cholesteryl esters were not affected by the gut microbiota. Genes encoding enzymes involved in cholesterol biosynthesis were downregulated by the gut microbiota in mice fed lard and were expressed at a low level in mice fed fish oil independent of microbial status. In summary, we show that gut microbiota-induced regulation of hepatic cholesterol metabolism is dependent on dietary lipid composition.

Lipidomic dataset of plasma from patients infected with wild type and nef-deficient HIV-1 strain.

Previous in vitro and in vivo studies demonstrated that HIV protein nef plays a key role in impairing cellular and systemic cholesterol metabolism in HIV disease, but clinical support for these findings is lacking. Here we present the data of comparative lipidomic analysis (330 lipid species) of plasma samples from HIV-negative subjects, patients infected with WT HIV-1 strain and patients infected with nef-deficient strain of HIV-1. We determine which effects of HIV on plasma lipidome are explained by the presence of nef. The data can be used to evaluate cardiovascular risk in HIV disease and to assess the role of nef in HIV-induced disturbances in systemic lipid metabolism. The full impact of nef deficiency on lipid and lipoprotein metabolism in HIV-infected patients is presented in the accompanying study “Lipid Metabolism in Patients Infected with Nef-deficient HIV-1 Strain” [1].

The Lipid-Modifying Enzyme SMPDL3B Negatively Regulates Innate Immunity.

SummaryLipid metabolism and receptor-mediated signaling are highly intertwined processes that cooperate to fulfill cellular functions and safeguard cellular homeostasis. Activation of Toll-like receptors (TLRs) leads to a complex cellular response, orchestrating a diverse range of inflammatory events that need to be tightly controlled. Here, we identified the GPI-anchored Sphingomyelin Phosphodiesterase, Acid-Like 3B (SMPDL3B) in a mass spectrometry screening campaign for membrane proteins co-purifying with TLRs. Deficiency of Smpdl3b in macrophages enhanced responsiveness to TLR stimulation and profoundly changed the cellular lipid composition and membrane fluidity. Increased cellular responses could be reverted by re-introducing affected ceramides, functionally linking membrane lipid composition and innate immune signaling. Finally, Smpdl3b-deficient mice displayed an intensified inflammatory response in TLR-dependent peritonitis models, establishing its negative regulatory role in vivo. Taken together, our results identify the membrane-modulating enzyme SMPDL3B as a negative regulator of TLR signaling that functions at the interface of membrane biology and innate immunity.

Lipidomic analysis of plasma samples from women with polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) is a common disorder affecting between 5 and 18 % of females of reproductive age and can be diagnosed based on a combination of clinical, ultrasound and biochemical features, none of which on its own is diagnostic. A lipidomic approach using liquid chromatography coupled with accurate mass high-resolution mass-spectrometry (LC-HRMS) was used to investigate if there were any differences in plasma lipidomic profiles in women with PCOS compared with control women at different stages of menstrual cycle. Plasma samples from 40 women with PCOS and 40 controls aged between 18 and 40 years were analysed in combination with multivariate statistical analyses. Multivariate data analysis (LASSO regression and OPLS-DA) of the sample lipidomics datasets showed a weak prediction model for PCOS versus control samples from the follicular and mid-cycle phases of the menstrual cycle, but a stronger model (specificity 85 % and sensitivity 95 %) for PCOS versus the luteal phase menstrual cycle controls. The PCOS vs luteal phase model showed increased levels of plasma triglycerides and sphingomyelins and decreased levels of lysophosphatidylcholines and phosphatidylethanolamines in PCOS women compared with controls. Lipid biomarkers of PCOS were tentatively identified which may be useful in distinguishing PCOS from controls especially when performed during the menstrual cycle luteal phase.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-014-0726-y) contains supplementary material, which is available to authorized users.

LICRE: unsupervised feature correlation reduction for lipidomics.

Recent advances in high-throughput lipid profiling by liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) have made it possible to quantify hundreds of individual molecular lipid species (e.g. fatty acyls, glycerolipids, glycerophospholipids, sphingolipids) in a single experimental run for hundreds of samples. This enables the lipidome of large cohorts of subjects to be profiled to identify lipid biomarkers significantly associated with disease risk, progression and treatment response. Clinically, these lipid biomarkers can be used to construct classification models for the purpose of disease screening or diagnosis. However, the inclusion of a large number of highly correlated biomarkers within a model may reduce classification performance, unnecessarily inflate associated costs of a diagnosis or a screen and reduce the feasibility of clinical translation. An unsupervised feature reduction approach can reduce feature redundancy in lipidomic biomarkers by limiting the number of highly correlated lipids while retaining informative features to achieve good classification performance for various clinical outcomes. Good predictive models based on a reduced number of biomarkers are also more cost effective and feasible from a clinical translation perspective. The application of LICRE to various lipidomic datasets in diabetes and cardiovascular disease demonstrated superior discrimination in terms of the area under the receiver operator characteristic curve while using fewer lipid markers when predicting various clinical outcomes. The MATLAB implementation of LICRE is available from http://ww2.cs.mu.oz.au/∼gwong/LICRE

Predicting Glycerophosphoinositol Identities in Lipidomic Datasets Using VaLID (Visualization and Phospholipid Identification)—An Online Bioinformatic Search Engine

The capacity to predict and visualize all theoretically possible glycerophospholipid molecular identities present in lipidomic datasets is currently limited. To address this issue, we expanded the search-engine and compositional databases of the online Visualization and Phospholipid Identification (VaLID) bioinformatic tool to include the glycerophosphoinositol superfamily. VaLID v1.0.0 originally allowed exact and average mass libraries of 736,584 individual species from eight phospholipid classes: glycerophosphates, glyceropyrophosphates, glycerophosphocholines, glycerophosphoethanolamines, glycerophosphoglycerols, glycerophosphoglycerophosphates, glycerophosphoserines, and cytidine 5′-diphosphate 1,2-diacyl-sn-glycerols to be searched for any mass to charge value (with adjustable tolerance levels) under a variety of mass spectrometry conditions. Here, we describe an update that now includes all possible glycerophosphoinositols, glycerophosphoinositol monophosphates, glycerophosphoinositol bisphosphates, and glycerophosphoinositol trisphosphates. This update expands the total number of lipid species represented in the VaLID v2.0.0 database to 1,473,168 phospholipids. Each phospholipid can be generated in skeletal representation. A subset of species curated by the Canadian Institutes of Health Research Training Program in Neurodegenerative Lipidomics (CTPNL) team is provided as an array of high-resolution structures. VaLID is freely available and responds to all users through the CTPNL resources web site.

Hierarchical clustering analysis of blood plasma lipidomics profiles from mono- and dizygotic twin families

Twin and family studies are typically used to elucidate the relative contribution of genetic and environmental variation to phenotypic variation. Here, we apply a quantitative genetic method based on hierarchical clustering, to blood plasma lipidomics data obtained in a healthy cohort consisting of 37 monozygotic and 28 dizygotic twin pairs, and 52 of their biological nontwin siblings. Such data are informative of the concentrations of a wide range of lipids in the studied blood samples. An important advantage of hierarchical clustering is that it can be applied to a high-dimensional 'omics' type data, whereas the use of many other quantitative genetic methods for analysis of such data is hampered by the large number of correlated variables. For this study we combined two lipidomics data sets, originating from two different measurement blocks, which we corrected for block effects by 'quantile equating'. In the analysis of the combined data, average similarities of lipidomics profiles were highest between monozygotic (MZ) cotwins, and became progressively lower between dizygotic (DZ) cotwins, among sex-matched nontwin siblings and among sex-matched unrelated participants, respectively. Our results suggest that (1) shared genetic background, shared environment, and similar age contribute to similarities in blood plasma lipidomics profiles among individuals; and (2) that the power of quantitative genetic analyses is enhanced by quantile equating and combination of data sets obtained in different measurement blocks.

A Comparative Lipidomics Platform for Chemotaxonomic Analysis of Mycobacterium tuberculosis

The lipidic envelope of Mycobacterium tuberculosis promotes virulence in many ways, so we developed a lipidomics platform for a broad survey of cell walls. Here we report two new databases (MycoMass, MycoMap), 30 lipid fine maps, and mass spectrometry datasets that comprise a static lipidome. Further, by rapidly regenerating lipidomic datasets during biological processes, comparative lipidomics provides statistically valid, organism-wide comparisons that broadly assess lipid changes during infection or among clinical strains of mycobacteria. Using stringent data filters, we tracked more than 5,000 molecular features in parallel with few or no false-positive molecular discoveries. The low error rates allowed chemotaxonomic analyses of mycobacteria, which describe the extent of chemical change in each strain and identified particular strain-specific molecules for use as biomarkers.

Multivariate multi-way analysis of multi-source data

Motivation: Analysis of variance (ANOVA)-type methods are the default tool for the analysis of data with multiple covariates. These tools have been generalized to the multivariate analysis of high-throughput biological datasets, where the main challenge is the problem of small sample size and high dimensionality. However, the existing multi-way analysis methods are not designed for the currently increasingly important experiments where data is obtained from multiple sources. Common examples of such settings include integrated analysis of metabolic and gene expression profiles, or metabolic profiles from several tissues in our case, in a controlled multi-way experimental setup where disease status, medical treatment, gender and time-series are usual covariates.Results: We extend the applicability area of multivariate, multi-way ANOVA-type methods to multi-source cases by introducing a novel Bayesian model. The method is capable of finding covariate-related dependencies between the sources. It assumes the measurements consist of groups of similarly behaving variables, and estimates the multivariate covariate effects and their interaction effects for the discovered groups of variables. In particular, the method partitions the effects to those shared between the sources and to source-specific ones. The method is specifically designed for datasets with small sample sizes and high dimensionality.We apply the method to a lipidomics dataset from a lung cancer study with two-way experimental setup, where measurements from several tissues with mostly distinct lipids have been taken. The method is also directly applicable to gene expression and proteomics.Availability: An R-implementation is available at http://www.cis.hut.fi/projects/mi/software/multiWayCCA/Contact: ilkka.huopaniemi@tkk.fi; samuel.kaski@tkk.fi

Functional prediction of unidentified lipids using supervised classifiers

Mass spectrometry (MS)-based metabolomics studies often require handling of both identified and unidentified metabolite data. In order to avoid bias in data interpretation, it would be of advantage for the data analysis to include all available data. A practical challenge in exploratory metabolomics analysis is therefore how to interpret the changes related to unidentified peaks. In this paper, we address the challenge by predicting the class membership of unknown peaks by applying and comparing multiple supervised classifiers to selected lipidomics datasets. The employed classifiers include k-nearest neighbours (k-NN), support vector machines (SVM), partial least squares and discriminant analysis (PLS-DA) and Naive Bayes methods which are known to be effective and efficient in predicting the labels for unseen data. Here, the class label predictions are sought for unidentified lipid profiles coming from high throughput global screening in Ultra Performance Liquid Chromatography Mass Spectrometry (UPLCTM/MS) experimental setup. Our investigation reveals that k-NN and SVM classifiers outperform both PLS-DA and Naive Bayes classifiers. Naive Bayes classifier perform poorly among all models and this observation seems logical as lipids are highly co-regulated and do not respect Naive Bayes assumptions of features being conditionally independent given the class. Common label predictions from k-NN and SVM can serve as a good starting point to explore full data and thereby facilitating exploratory studies where label information is critical for the data interpretation.

Systems biology approaches and pathway tools for investigating cardiovascular disease

Systems biology aims to understand the nonlinear interactions of multiple biomolecular components that characterize a living organism. One important aspect of systems biology approaches is to identify the biological pathways or networks that connect the differing elements of a system, and examine how they evolve with temporal and environmental changes. The utility of this method becomes clear when applied to multifactorial diseases with complex etiologies, such as inflammatory-related diseases, herein exemplified by atherosclerosis. In this paper, the initial studies in this discipline are reviewed and examined within the context of the development of the field. In addition, several different software tools are briefly described and a novel application for the KEGG database suite called KegArray is presented. This tool is designed for mapping the results of high-throughput omics studies, including transcriptomics, proteomics and metabolomics data, onto interactive KEGG metabolic pathways. The utility of KegArray is demonstrated using a combined transcriptomics and lipidomics dataset from a published study designed to examine the potential of cholesterol in the diet to influence the inflammatory component in the development of atherosclerosis. These data were mapped onto the KEGG PATHWAY database, with a low cholesterol diet affecting 60 distinct biochemical pathways and a high cholesterol exposure affecting 76 biochemical pathways. A total of 77 pathways were differentially affected between low and high cholesterol diets. The KEGG pathways "Biosynthesis of unsaturated fatty acids" and "Sphingolipid metabolism" evidenced multiple changes in gene/lipid levels between low and high cholesterol treatment, and are discussed in detail. Taken together, this paper provides a brief introduction to systems biology and the applications of pathway mapping to the study of cardiovascular disease, as well as a summary of available tools. Current limitations and future visions of this emerging field are discussed, with the conclusion that combining knowledge from biological pathways and high-throughput omics data will move clinical medicine one step further to individualize medical diagnosis and treatment.

Assessing the performance of statistical validation tools for megavariate metabolomics data

Statistical model validation tools such as cross-validation, jack-knifing model parameters and permutation tests are meant to obtain an objective assessment of the performance and stability of a statistical model. However, little is known about the performance of these tools for megavariate data sets, having, for instance, a number of variables larger than 10 times the number of subjects. The performance is assessed for megavariate metabolomics data, but the conclusions also carry over to proteomics, transcriptomics and many other research areas. Partial least squares discriminant analyses models were built for several LC-MS lipidomic training data sets of various numbers of lean and obese subjects. The training data sets were compared on their modelling performance and their predictability using a 10-fold cross-validation, a permutation test, and test data sets. A wide range of cross-validation error rates was found (from 7.5% to 16.3% for the largest trainings set and from 0% to 60% for the smallest training set) and the error rate increased when the number of subjects decreased. The test error rates varied from 5% to 50%. The smaller the number of subjects compared to the number of variables, the less the outcome of validation tools such as cross-validation, jack-knifing model parameters and permutation tests can be trusted. The result depends crucially on the specific sample of subjects that is used for modelling. The validation tools cannot be used as warning mechanism for problems due to sample size or to representativity of the sampling.

Proliférations chroniquesà grands lymphocytes granuleux

Clinical lipidomics using mass spectrometry (MS) is important to support discovery of biomarkers for diagnosis and understanding the pathophysiology of diseases. Frequently, lipidomics data from clinical studies have large variations among individuals because the human metabolome/lipidome is strongly influenced by genotype, daily activity, diet and gut flora. This inter-personal variability makes data analysis more complex and normally requires a large cohort for robust statistical analysis. Crossover designed experiments treat each subject as his or her own control, thereby reducing the between-subject variability, such that the effects of exposure/treatment are more likely to be identified when using a relatively small number of subjects. This design repeatedly samples an individual when crossing over from one treatment/exposure to another during the course of the study. The acquired datasets have a distinct data structure resulting from repeated longitudinal measurements. A variety of statistical methods are used in published crossover studies, but many appear to ignore the data structure inherent in the experimental design. An appropriate data analysis approach is critical to discovering robust clinical biomarkers. Hereby, we summarize the statistical methodologies suitable for clinical lipidomics studies using crossover design. To help understand and apply these methods to practical cases, we focused on the general concepts of statistical models in the context of analysis of metabolomics data without spending too much effort on mathematical details. Importantly, we aim to evaluate these methods and provide suggestions for data analysis and biomarker discovery. We applied the discussed methods on a MS-based lipidomics dataset from a double-blind random crossover designed clinical dietary intervention study. The strength and potential pitfalls of each method are briefly discussed and a suggestion for analytic workflow proposed.