Neurotensin accelerates atherosclerosis and increases circulating levels of short-chain and saturated triglycerides

BACKGROUND AND AIMS

Obesity and type 2 diabetes are significant risk factors for atherosclerotic cardiovascular disease (CVD) worldwide, but the underlying pathophysiological links are poorly understood. Neurotensin (NT), a 13-amino-acid hormone peptide, facilitates intestinal fat absorption and contributes to obesity in mice fed a high-fat diet. Elevated levels of pro-NT (a stable NT precursor produced in equimolar amounts relative to NT) are associated with obesity, type 2 diabetes, and CVD in humans. Whether NT is a causative factor in CVD is unknown.

METHODS

Nt+/+ and Nt-/- mice were either injected with adeno-associated virus encoding PCSK9 mutants or crossed with Ldlr-/- mice and fed a Western diet. Atherosclerotic plaques were analyzed by en face analysis, Oil Red O and CD68 staining. In humans, we evaluated the association between baseline pro-NT and growth of carotid bulb thickness after 16.4 years. Lipidomic profiles were analyzed.

RESULTS

Atherosclerotic plaque formation is attenuated in Nt-deficient mice through mechanisms that are independent of reductions in circulating cholesterol and triglycerides but associated with remodeling of the plasma triglyceride pool. An increasing plasma concentration of pro-NT predicts atherosclerotic events in coronary and cerebral arteries independent of all major traditional risk factors, indicating a strong link between NT and atherosclerosis. This plasma lipid profile analysis confirms the association of pro-NT with remodeling of the plasma triglyceride pool in atherosclerotic events.

CONCLUSIONS

Our findings are the first to directly link NT to increased atherosclerosis and indicate the potential role for NT in preventive and therapeutic strategies for CVD.

Imputation of missing values in lipidomic datasets

Lipidomic data often exhibit missing data points, which can be categorized as missing completely at random (MCAR), missing at random, or missing not at random (MNAR). In order to utilize statistical methods that require complete datasets or to improve the identification of potential effects in statistical comparisons, imputation techniques can be employed. In this study, we investigate commonly used methods such as zero, half-minimum, mean, and median imputation, as well as more advanced techniques such as k-nearest neighbor and random forest imputation. We employ a combination of simulation-based approaches and application to real datasets to assess the performance and effectiveness of these methods. Shotgun lipidomics datasets exhibit high correlations and missing values, often due to low analyte abundance, characterized as MNAR. In this context, k-nearest neighbor approaches based on correlation and truncated normal distributions demonstrate best performance. Importantly, both methods can effectively impute missing values independent of the type of missingness, the determination of which is nearly impossible in practice. The imputation methods still control the type I error rate.

Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study

Introduction

Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised "bottom-up" approach, we attempt to group T2D patients based solely on -omics data generated from plasma.

Methods

Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics.

Results

From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor.

Conclusions

Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.

Genome-wide association analysis of plasma lipidome identifies 495 genetic associations

The human plasma lipidome captures risk for cardiometabolic diseases. To discover new lipid-associated variants and understand the link between lipid species and cardiometabolic disorders, we perform univariate and multivariate genome-wide analyses of 179 lipid species in 7174 Finnish individuals. We fine-map the associated loci, prioritize genes, and examine their disease links in 377,277 FinnGen participants. We identify 495 genome-trait associations in 56 genetic loci including 8 novel loci, with a considerable boost provided by the multivariate analysis. For 26 loci, fine-mapping identifies variants with a high causal probability, including 14 coding variants indicating likely causal genes. A phenome-wide analysis across 953 disease endpoints reveals disease associations for 40 lipid loci. For 11 coronary artery disease risk variants, we detect strong associations with lipid species. Our study demonstrates the power of multivariate genetic analysis in correlated lipidomics data and reveals genetic links between diseases and lipid species beyond the standard lipids.

Identification of biomarkers for glycaemic deterioration in type 2 diabetes

We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates. In an external replication, proteins and lipids are associated with diabetes incidence and prevalence. NogoR/RTN4R injection improved glucose tolerance in high fat-fed male mice but impaired it in male db/db mice. High NogoR levels led to islet cell apoptosis, and IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. This comprehensive, multi-disciplinary approach thus identifies biomarkers with potential prognostic utility, provides evidence for possible disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.

Lipidome- and Genome-Wide Study to Understand Sex Differences in Circulatory Lipids

Background Despite well-recognized differences in the atherosclerotic cardiovascular disease risk between men and women, sex differences in risk factors and sex-specific mechanisms in the pathophysiology of atherosclerotic cardiovascular disease remain poorly understood. Lipid metabolism plays a central role in the development of atherosclerotic cardiovascular disease. Understanding sex differences in lipids and their genetic determinants could provide mechanistic insights into sex differences in atherosclerotic cardiovascular disease and aid in precise risk assessment. Herein, we examined sex differences in plasma lipidome and heterogeneity in genetic influences on lipidome in men and women through sex-stratified genome-wide association analyses. Methods and Results We used data consisting of 179 lipid species measured by shotgun lipidomics in 7266 individuals from the Finnish GeneRISK cohort and sought for replication using independent data from 2045 participants. Significant sex differences in the levels of 141 lipid species were observed (P<7.0×10^-4). Interestingly, 121 lipid species showed significant age-sex interactions, with opposite age-related changes in 39 lipid species. In general, most of the cholesteryl esters, ceramides, lysophospholipids, and glycerides were higher in 45- to 50-year-old men compared with women of same age, but the sex differences narrowed down or reversed with age. We did not observe any major differences in genetic effect in the sex-stratified genome-wide association analyses, which suggests that common genetic variants do not have a major role in sex differences in lipidome. Conclusions Our study provides a comprehensive view of sex differences in circulatory lipids pointing to potential sex differences in lipid metabolism and highlights the need for sex- and age-specific prevention strategies.

Lipidomic risk scores are independent of polygenic risk scores and can predict incidence of diabetes and cardiovascular disease in a large population cohort

Type 2 diabetes (T2D) and cardiovascular disease (CVD) represent significant disease burdens for most societies and susceptibility to these diseases is strongly influenced by diet and lifestyle. Physiological changes associated with T2D or CVD, such has high blood pressure and cholesterol and glucose levels in the blood, are often apparent prior to disease incidence. Here we integrated genetics, lipidomics, and standard clinical diagnostics to assess future T2D and CVD risk for 4,067 participants from a large prospective population-based cohort, the Malmö Diet and Cancer-Cardiovascular Cohort. By training Ridge regression-based machine learning models on the measurements obtained at baseline when the individuals were healthy, we computed several risk scores for T2D and CVD incidence during up to 23 years of follow-up. We used these scores to stratify the participants into risk groups and found that a lipidomics risk score based on the quantification of 184 plasma lipid concentrations resulted in a 168% and 84% increase of the incidence rate in the highest risk group and a 77% and 53% decrease of the incidence rate in lowest risk group for T2D and CVD, respectively, compared to the average case rates of 13.8% and 22.0%. Notably, lipidomic risk correlated only marginally with polygenic risk, indicating that the lipidome and genetic variants may constitute largely independent risk factors for T2D and CVD. Risk stratification was further improved by adding standard clinical variables to the model, resulting in a case rate of 51.0% and 53.3% in the highest risk group for T2D and CVD, respectively. The participants in the highest risk group showed significantly altered lipidome compositions affecting 167 and 157 lipid species for T2D and CVD, respectively. Our results demonstrated that a subset of individuals at high risk for developing T2D or CVD can be identified years before disease incidence. The lipidomic risk, which is derived from only one single mass spectrometric measurement that is cheap and fast, is informative and could extend traditional risk assessment based on clinical assays.

Adverse Effects of Refeeding on the Plasma Lipidome in Young Individuals With Anorexia Nervosa?

OBJECTIVE

Refeeding is the cornerstone of anorexia nervosa (AN) treatment, but little is known regarding the optimal pace and dietary composition or possible adverse effects of current clinical practices. Plasma lipids may be a moderating factor underlying unfavorable refeeding effects in AN, such as an abnormal central body fat distribution. The objective of this study was to analyze the plasma lipidome in the acutely underweight state of AN before and after refeeding.

METHOD

Using high-throughput quantitative mass spectrometry-based shotgun lipidomics, we measured 13 lipid classes and 204 lipid species or subspecies in the plasma of young female patients with acute AN, before (n = 39) and after (n = 23) short-term weight restoration during an intensive inpatient refeeding program (median body mass index [BMI] increase = 26.4%), in comparison to those in healthy control participants (n = 37).

RESULTS

Before inpatient treatment, patients with AN exhibited increased concentrations of cholesterol and several other lipid classes. After refeeding, multiple lipid classes including cholesterol and ceramides, as well as certain ceramide species previously associated with obesity or overfeeding, showed increased concentrations, and a pattern of shorter and more saturated triacylgycerides emerged. A machine learning model trained to predict BMI based on the lipidomic profiles revealed a sizable overprediction in patients with AN after weight restoration.

CONCLUSION

The results point toward a profound lipid dysregulation with similarities to obesity and other features of the metabolic syndrome after short-term weight restoration. Thus, this study provides evidence for possible short-term adverse effects of current refeeding practices on the metabolic state and should inspire more research on nutritional interventions in AN.

Distinct Molecular Signatures of Clinical Clusters in People With Type 2 Diabetes: An IMI-RHAPSODY Study

Type 2 diabetes is a multifactorial disease with multiple underlying aetiologies. To address this heterogeneity, investigators of a previous study clustered people with diabetes according to five diabetes subtypes. The aim of the current study is to investigate the etiology of these clusters by comparing their molecular signatures. In three independent cohorts, in total 15,940 individuals were clustered based on five clinical characteristics. In a subset, genetic (N = 12,828), metabolomic (N = 2,945), lipidomic (N = 2,593), and proteomic (N = 1,170) data were obtained in plasma. For each data type, each cluster was compared with the other four clusters as the reference. The insulin-resistant cluster showed the most distinct molecular signature, with higher branched-chain amino acid, diacylglycerol, and triacylglycerol levels and aberrant protein levels in plasma were enriched for proteins in the intracellular PI3K/Akt pathway. The obese cluster showed higher levels of cytokines. The mild diabetes cluster with high HDL showed the most beneficial molecular profile with effects opposite of those seen in the insulin-resistant cluster. This study shows that clustering people with type 2 diabetes can identify underlying molecular mechanisms related to pancreatic islets, liver, and adipose tissue metabolism. This provides novel biological insights into the diverse aetiological processes that would not be evident when type 2 diabetes is viewed as a homogeneous disease.

Replication and cross-validation of type 2 diabetes subtypes based on clinical variables: an IMI-RHAPSODY study

AIMS/HYPOTHESIS

Five clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes. In the current study we replicate and cross-validate these type 2 diabetes clusters in three large cohorts using variables readily measured in the clinic.

METHODS

In three independent cohorts, in total 15,940 individuals were clustered based on age, BMI, HbA_1c, random or fasting C-peptide, and HDL-cholesterol. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort's cluster centres. Finally, we compared the time to insulin requirement for each cluster.

RESULTS

Five distinct type 2 diabetes clusters were identified and mapped back to the original four All New Diabetics in Scania (ANDIS) clusters. Using C-peptide and HDL-cholesterol instead of HOMA2-B and HOMA2-IR, three of the clusters mapped with high sensitivity (80.6-90.7%) to the previously identified severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD) and mild obesity-related diabetes (MOD) clusters. The previously described ANDIS mild age-related diabetes (MARD) cluster could be mapped to the two milder groups in our study: one characterised by high HDL-cholesterol (mild diabetes with high HDL-cholesterol [MDH] cluster), and the other not having any extreme characteristic (mild diabetes [MD]). When these two milder groups were combined, they mapped well to the previously labelled MARD cluster (sensitivity 79.1%). In the cross-validation between cohorts, particularly the SIDD and MDH clusters cross-validated well, with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity, ranging from 36.1% to 92.3%, where individuals shifted from SIRD to MD and vice versa. People belonging to the SIDD cluster showed the fastest progression towards insulin requirement, while the MDH cluster showed the slowest progression.

CONCLUSIONS/INTERPRETATION

Clusters based on C-peptide instead of HOMA2 measures resemble those based on HOMA2 measures, especially for SIDD, SIRD and MOD. By adding HDL-cholesterol, the MARD cluster based upon HOMA2 measures resulted in the current clustering into two clusters, with one cluster having high HDL levels. Cross-validation between cohorts showed generally a good resemblance between cohorts. Together, our results show that the clustering based on clinical variables readily measured in the clinic (age, HbA_1c, HDL-cholesterol, BMI and C-peptide) results in informative clusters that are representative of the original ANDIS clusters and stable across cohorts. Adding HDL-cholesterol to the clustering resulted in the identification of a cluster with very slow glycaemic deterioration.

Shotgun mass spectrometry-based lipid profiling identifies and distinguishes between chronic inflammatory diseases

Background

Localized stress and cell death in chronic inflammatory diseases may release tissue-specific lipids into the circulation causing the blood plasma lipidome to reflect the type of inflammation. However, deep lipid profiles of major chronic inflammatory diseases have not been compared.

Methods

Plasma lipidomes of patients suffering from two etiologically distinct chronic inflammatory diseases, atherosclerosis-related vascular disease, including cardiovascular (CVD) and ischemic stroke (IS), and systemic lupus erythematosus (SLE), were screened by a top-down shotgun mass spectrometry-based analysis without liquid chromatographic separation and compared to each other and to age-matched controls. Lipid profiling of 596 lipids was performed on a cohort of 427 individuals. Machine learning classifiers based on the plasma lipidomes were used to distinguish the two chronic inflammatory diseases from each other and from the controls.

Findings

Analysis of the lipidomes enabled separation of the studied chronic inflammatory diseases from controls based on independent validation test set classification performance (CVD vs control - Sensitivity: 0.94, Specificity: 0.88; IS vs control - Sensitivity: 1.0, Specificity: 1.0; SLE vs control – Sensitivity: 1, Specificity: 0.93) and from each other (SLE vs CVD ‒ Sensitivity: 0.91, Specificity: 1; IS vs SLE - Sensitivity: 1, Specificity: 0.82). Preliminary linear discriminant analysis plots using all data clearly separated the clinical groups from each other and from the controls, and partially separated CVD severities, as classified into five clinical groups. Dysregulated lipids are partially but not fully counterbalanced by statin treatment.

Interpretation

Dysregulation of the plasma lipidome is characteristic of chronic inflammatory diseases. Lipid profiling accurately identifies the diseases and in the case of CVD also identifies sub-classes.

Funding

Full list of funding sources at the end of the manuscript.

Multi-omics profiling of living human pancreatic islet donors reveals heterogeneous beta cell trajectories towards type 2 diabetes

Most research on human pancreatic islets is conducted on samples obtained from normoglycaemic or diseased brain-dead donors and thus cannot accurately describe the molecular changes of pancreatic islet beta cells as they progress towards a state of deficient insulin secretion in type 2 diabetes (T2D). Here, we conduct a comprehensive multi-omics analysis of pancreatic islets obtained from metabolically profiled pancreatectomized living human donors stratified along the glycemic continuum, from normoglycemia to T2D. We find that islet pools isolated from surgical samples by laser-capture microdissection display remarkably more heterogeneous transcriptomic and proteomic profiles in patients with diabetes than in non-diabetic controls. The differential regulation of islet gene expression is already observed in prediabetic individuals with impaired glucose tolerance. Our findings demonstrate a progressive, but disharmonic, remodelling of mature beta cells, challenging current hypotheses of linear trajectories toward precursor or transdifferentiation stages in T2D. Furthermore, through integration of islet transcriptomics with preoperative blood plasma lipidomics, we define the relative importance of gene coexpression modules and lipids that are positively or negatively associated with HbA1c levels, pointing to potential prognostic markers.

A plasma lipid signature predicts incident coronary artery disease

BACKGROUND

Dyslipidemia is a hallmark of cardiovascular disease but is characterized by crude measurements of triglycerides, HDL- and LDL cholesterol. Lipidomics enables more detailed measurements of plasma lipids, which may help improve risk stratification and understand the pathophysiology of cardiovascular disease.

METHODS

Lipidomics was used to measure 184 lipids in plasma samples from the Malmö Diet and Cancer - Cardiovascular Cohort (N = 3865), taken at baseline examination. During an average follow-up time of 20.3 years, 536 participants developed coronary artery disease (CAD). Least absolute shrinkage and selection operator (LASSO) were applied to Cox proportional hazards models in order to identify plasma lipids that predict CAD.

RESULTS

Eight plasma lipids improved prediction of future CAD on top of traditional cardiovascular risk factors. Principal component analysis of CAD-associated lipids revealed one principal component (PC2) that was associated with risk of future CAD (HR per SD increment =1.46, C·I = 1.35-1.48, P < 0.001). The risk increase for being in the highest quartile of PC2 (HR = 2.33, P < 0.001) was higher than being in the top quartile of systolic blood pressure. Addition of PC2 to traditional risk factors achieved an improvement (2%) in the area under the ROC-curve for CAD events occurring within 10 (P = 0.03), 15 (P = 0.003) and 20 (P = 0.001) years of follow-up respectively.

CONCLUSIONS

A lipid pattern improve CAD prediction above traditional risk factors, highlighting that conventional lipid-measures insufficiently describe dyslipidemia that is present years before CAD. Identifying this hidden dyslipidemia may help motivate lifestyle and pharmacological interventions early enough to reach a substantial reduction in absolute risk.

Plasma lipidomics of monozygotic twins discordant for multiple sclerosis

Blood biomarkers of multiple sclerosis (MS) can provide a better understanding of pathophysiology and enable disease monitoring. Here, we performed quantitative shotgun lipidomics on the plasma of a unique cohort of 73 monozygotic twins discordant for MS. We analyzed 243 lipid species, evaluated lipid features such as fatty acyl chain length and number of acyl chain double bonds, and detected phospholipids that were significantly altered in the plasma of co‐twins with MS compared to their non‐affected siblings. Strikingly, changes were most prominent in ether phosphatidylethanolamines and ether phosphatidylcholines, suggesting a role for altered lipid signaling in the disease.

Shotgun Lipidomics Discovered Diurnal Regulation of Lipid Metabolism Linked to Insulin Sensitivity in Nondiabetic Men

CONTEXT

Meal timing affects metabolic homeostasis and body weight, but how composition and timing of meals affect plasma lipidomics in humans is not well studied.

OBJECTIVE

We used high throughput shotgun plasma lipidomics to investigate effects of timing of carbohydrate and fat intake on lipid metabolism and its relation to glycemic control.

DESIGN

29 nondiabetic men consumed (1) a high-carb test meal (MTT-HC) at 09.00 and a high-fat meal (MTT-HF) at 15.40; or (2) MTT-HF at 09.00 and MTT-HC at 15.40. Blood was sampled before and 180 minutes after completion of each MTT. Subcutaneous adipose tissue (SAT) was collected after overnight fast and both MTTs. Prior to each investigation day, participants consumed a 4-week isocaloric diet of the same composition: (1) high-carb meals until 13.30 and high-fat meals between 16.30 and 22:00 or (2) the inverse order.

RESULTS

12 hour daily lipid patterns showed a complex regulation by both the time of day (67.8%) and meal composition (55.4%). A third of lipids showed a diurnal variation in postprandial responses to the same meal with mostly higher responses in the morning than in the afternoon. Triacylglycerols containing shorter and more saturated fatty acids were enriched in the morning. SAT transcripts involved in fatty acid synthesis and desaturation showed no diurnal variation. Diurnal changes of 7 lipid classes were negatively associated with insulin sensitivity, but not with glucose and insulin response or insulin secretion.

CONCLUSIONS

This study identified postprandial plasma lipid profiles as being strongly affected by meal timing and associated with insulin sensitivity.

CerS6-Derived Sphingolipids Interact with Mff and Promote Mitochondrial Fragmentation in Obesity

Ectopic lipid deposition and altered mitochondrial dynamics contribute to the development of obesity and insulin resistance. However, the mechanistic link between these processes remained unclear. Here we demonstrate that the C_16:0 sphingolipid synthesizing ceramide synthases, CerS5 and CerS6, affect distinct sphingolipid pools and that abrogation of CerS6 but not of CerS5 protects from obesity and insulin resistance. We identify proteins that specifically interact with C_16:0 sphingolipids derived from CerS5 or CerS6. Here, only CerS6-derived C_16:0 sphingolipids bind the mitochondrial fission factor (Mff). CerS6 and Mff deficiency protect from fatty acid-induced mitochondrial fragmentation in vitro, and the two proteins genetically interact in vivo in obesity-induced mitochondrial fragmentation and development of insulin resistance. Our experiments reveal an unprecedented specificity of sphingolipid signaling depending on specific synthesizing enzymes, provide a mechanistic link between hepatic lipid deposition and mitochondrial fragmentation in obesity, and define the CerS6-derived sphingolipid/Mff interaction as a therapeutic target for metabolic diseases.

Plasma Lipidome and Prediction of Type 2 Diabetes in the Population-Based Malmö Diet and Cancer Cohort

OBJECTIVE

Type 2 diabetes mellitus (T2DM) is associated with dyslipidemia, but the detailed alterations in lipid species preceding the disease are largely unknown. We aimed to identify plasma lipids associated with development of T2DM and investigate their associations with lifestyle.

RESEARCH DESIGN AND METHODS

At baseline, 178 lipids were measured by mass spectrometry in 3,668 participants without diabetes from the Malmö Diet and Cancer Study. The population was randomly split into discovery (n = 1,868, including 257 incident cases) and replication (n = 1,800, including 249 incident cases) sets. We used orthogonal projections to latent structures discriminant analyses, extracted a predictive component for T2DM incidence (lipid-PC_DM), and assessed its association with T2DM incidence using Cox regression and lifestyle factors using general linear models.

RESULTS

A T2DM-predictive lipid-PC_DM derived from the discovery set was independently associated with T2DM incidence in the replication set, with hazard ratio (HR) among subjects in the fifth versus first quintile of lipid-PC_DM of 3.7 (95% CI 2.2-6.5). In comparison, the HR of T2DM among obese versus normal weight subjects was 1.8 (95% CI 1.2-2.6). Clinical lipids did not improve T2DM risk prediction, but adding the lipid-PC_DM to all conventional T2DM risk factors increased the area under the receiver operating characteristics curve by 3%. The lipid-PC_DM was also associated with a dietary risk score for T2DM incidence and lower level of physical activity.

CONCLUSIONS

A lifestyle-related lipidomic profile strongly predicts T2DM development beyond current risk factors. Further studies are warranted to test if lifestyle interventions modifying this lipidomic profile can prevent T2DM.

Machine learning of human plasma lipidomes for obesity estimation in a large population cohort

Obesity is associated with changes in the plasma lipids. Although simple lipid quantification is routinely used, plasma lipids are rarely investigated at the level of individual molecules. We aimed at predicting different measures of obesity based on the plasma lipidome in a large population cohort using advanced machine learning modeling. A total of 1,061 participants of the FINRISK 2012 population cohort were randomly chosen, and the levels of 183 plasma lipid species were measured in a novel mass spectrometric shotgun approach. Multiple machine intelligence models were trained to predict obesity estimates, i.e., body mass index (BMI), waist circumference (WC), waist-hip ratio (WHR), and body fat percentage (BFP), and validated in 250 randomly chosen participants of the Malmö Diet and Cancer Cardiovascular Cohort (MDC-CC). Comparison of the different models revealed that the lipidome predicted BFP the best (R2 = 0.73), based on a Lasso model. In this model, the strongest positive and the strongest negative predictor were sphingomyelin molecules, which differ by only 1 double bond, implying the involvement of an unknown desaturase in obesity-related aberrations of lipid metabolism. Moreover, we used this regression to probe the clinically relevant information contained in the plasma lipidome and found that the plasma lipidome also contains information about body fat distribution, because WHR (R2 = 0.65) was predicted more accurately than BMI (R2 = 0.47). These modeling results required full resolution of the lipidome to lipid species level, and the predicting set of biomarkers had to be sufficiently large. The power of the lipidomics association was demonstrated by the finding that the addition of routine clinical laboratory variables, e.g., high-density lipoprotein (HDL)- or low-density lipoprotein (LDL)- cholesterol did not improve the model further. Correlation analyses of the individual lipid species, controlled for age and separated by sex, underscores the multiparametric and lipid species-specific nature of the correlation with the BFP. Lipidomic measurements in combination with machine intelligence modeling contain rich information about body fat amount and distribution beyond traditional clinical assays.

Genetic architecture of human plasma lipidome and its link to cardiovascular disease

Understanding genetic architecture of plasma lipidome could provide better insights into lipid metabolism and its link to cardiovascular diseases (CVDs). Here, we perform genome-wide association analyses of 141 lipid species (n = 2,181 individuals), followed by phenome-wide scans with 25 CVD related phenotypes (n = 511,700 individuals). We identify 35 lipid-species-associated loci (P <5 ×10-8), 10 of which associate with CVD risk including five new loci-COL5A1, GLTPD2, SPTLC3, MBOAT7 and GALNT16 (false discovery rate<0.05). We identify loci for lipid species that are shown to predict CVD e.g., SPTLC3 for CER(d18:1/24:1). We show that lipoprotein lipase (LPL) may more efficiently hydrolyze medium length triacylglycerides (TAGs) than others. Polyunsaturated lipids have highest heritability and genetic correlations, suggesting considerable genetic regulation at fatty acids levels. We find low genetic correlations between traditional lipids and lipid species. Our results show that lipidomic profiles capture information beyond traditional lipids and identify genetic variants modifying lipid levels and risk of CVD.

Coronary Artery Disease Risk and Lipidomic Profiles Are Similar in Hyperlipidemias With Family History and Population-Ascertained Hyperlipidemias

Background

We asked whether, after excluding familial hypercholesterolemia, individuals with high low‐density lipoprotein cholesterol (LDL‐C) or triacylglyceride levels and a family history of the same hyperlipidemia have greater coronary artery disease risk or different lipidomic profiles compared with population‐based hyperlipidemias.

Methods and Results

We determined incident coronary artery disease risk for 755 members of 66 hyperlipidemic families (≥2 first‐degree relatives with similar hyperlipidemia) and 19 644 Finnish FINRISK population study participants. We quantified 151 circulating lipid species from 550 members of 73 hyperlipidemic families and 897 FINRISK participants using mass spectrometric shotgun lipidomics. Familial hypercholesterolemia was excluded using functional LDL receptor testing and genotyping. Hyperlipidemias (LDL‐C or triacylglycerides >90th population percentile) associated with increased coronary artery disease risk in meta‐analysis of the hyperlipidemic families and the population cohort (high LDL‐C: hazard ratio, 1.74 [95% CI, 1.48–2.04]; high triacylglycerides: hazard ratio, 1.38 [95% CI, 1.09–1.74]). Risk estimates were similar in the family and population cohorts also after adjusting for lipid‐lowering medication. In lipidomic profiling, high LDL‐C associated with 108 lipid species, and high triacylglycerides associated with 131 lipid species in either cohort (at 5% false discovery rate; P‐value range 0.038–2.3×10⁻⁵⁶). Lipidomic profiles were highly similar for hyperlipidemic individuals in the families and the population (LDL‐C: r=0.80; triacylglycerides: r=0.96; no lipid species deviated between the cohorts).

Conclusions

Hyperlipidemias with family history conferred similar coronary artery disease risk as population‐based hyperlipidemias. We identified distinct lipidomic profiles associated with high LDL‐C and triacylglycerides. Lipidomic profiles were similar between hyperlipidemias with family history and population‐ascertained hyperlipidemias, providing evidence of similar and overlapping underlying mechanisms.

Lipidomics in Major Depressive Disorder

Omic sciences coupled with novel computational approaches such as machine intelligence offer completely new approaches to major depressive disorder (MDD) research. The complexity of MDD's pathophysiology is being integrated into studies examining MDD's biology within the omic fields. Lipidomics, as a late-comer among other omic fields, is increasingly being recognized in psychiatric research because it has allowed the investigation of global lipid perturbations in patients suffering from MDD and indicated a crucial role of specific patterns of lipid alterations in the development and progression of MDD. Combinatorial lipid-markers with high classification power are being developed in order to assist MDD diagnosis, while rodent models of depression reveal lipidome changes and thereby unveil novel treatment targets for depression. In this systematic review, we provide an overview of current breakthroughs and future trends in the field of lipidomics in MDD research and thereby paving the way for precision medicine in MDD.

Cholesterol is Inefficiently Converted to Cholesteryl Esters in the Blood of Cardiovascular Disease Patients

Shotgun lipidomic analysis of 203 lipids in 13 lipid classes performed on blood plasma of donors who had just suffered an acute coronary syndrome (ACS, n = 74), or an ischemic stroke (IS, n = 21), or who suffer from stable angina pectoris (SAP, n = 78), and an age-matched control cohort (n = 52), showed some of the highest inter-lipid class correlations between cholesteryl esters (CE) and phosphatidylcholines (PC) sharing a common fatty acid. The concentration of lysophospatidylcholine (LPC) and ratios of concentrations of CE to free cholesterol (Chol) were also lower in the CVD cohorts than in the control cohort, indicating a deficient conversion of Chol to CE in the blood plasma in the CVD subjects. A non-equilibrium reaction quotient, Q′, describing the global homeostasis of cholesterol as manifested in the blood plasma was shown to have a value in the CVD cohorts (Q′_ACS = 0.217 ± 0.084; Q′_IS = 0.201 ± 0.084; Q′_SAP = 0.220 ± 0.071) that was about one third less than in the control cohort (Q′_Control = 0.320 ± 0.095, p < 1 × 10⁻⁴), suggesting its potential use as a rapid predictive/diagnostic measure of CVD-related irregularities in cholesterol homeostasis.

Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies

Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics.

Identification of a feedback loop involving β-glucosidase 2 and its product sphingosine sheds light on the molecular mechanisms in Gaucher disease

The lysosomal acid β-glucosidase GBA1 and the non-lysosomal β-glucosidase GBA2 degrade glucosylceramide (GlcCer) to glucose and ceramide in different cellular compartments. Loss of GBA2 activity and the resulting accumulation of GlcCer results in male infertility, whereas mutations in the GBA1 gene and loss of GBA1 activity cause the lipid-storage disorder Gaucher disease. However, the role of GBA2 in Gaucher disease pathology and its relationship to GBA1 is not well understood. Here, we report a GBA1-dependent down-regulation of GBA2 activity in patients with Gaucher disease. Using an experimental approach combining cell biology, biochemistry, and mass spectrometry, we show that sphingosine, the cytotoxic metabolite accumulating in Gaucher cells through the action of GBA2, directly binds to GBA2 and inhibits its activity. We propose a negative feedback loop, in which sphingosine inhibits GBA2 activity in Gaucher cells, preventing further sphingosine accumulation and, thereby, cytotoxicity. Our findings add a new chapter to the understanding of the complex molecular mechanism underlying Gaucher disease and the regulation of β-glucosidase activity in general.

Sphingosine-1-Phosphate Lyase Deficient Cells as a Tool to Study Protein Lipid Interactions

Cell membranes contain hundreds to thousands of individual lipid species that are of structural importance but also specifically interact with proteins. Due to their highly controlled synthesis and role in signaling events sphingolipids are an intensely studied class of lipids. In order to investigate their metabolism and to study proteins interacting with sphingolipids, metabolic labeling based on photoactivatable sphingoid bases is the most straightforward approach. In order to monitor protein-lipid-crosslink products, sphingosine derivatives containing a reporter moiety, such as a radiolabel or a clickable group, are used. In normal cells, degradation of sphingoid bases via action of the checkpoint enzyme sphingosine-1-phosphate lyase occurs at position C2-C3 of the sphingoid base and channels the resulting hexadecenal into the glycerolipid biosynthesis pathway. In case the functionalized sphingosine looses the reporter moiety during its degradation, specificity towards sphingolipid labeling is maintained. In case degradation of a sphingosine derivative does not remove either the photoactivatable or reporter group from the resulting hexadecenal, specificity towards sphingolipid labeling can be achieved by blocking sphingosine-1-phosphate lyase activity and thus preventing sphingosine derivatives to be channeled into the sphingolipid-to-glycerolipid metabolic pathway. Here we report an approach using clustered, regularly interspaced, short palindromic repeats (CRISPR)-associated nuclease Cas9 to create a sphingosine-1-phosphate lyase (SGPL1) HeLa knockout cell line to disrupt the sphingolipid-to-glycerolipid metabolic pathway. We found that the lipid and protein compositions as well as sphingolipid metabolism of SGPL1 knock-out HeLa cells only show little adaptations, which validates these cells as model systems to study transient protein-sphingolipid interactions.

Morphology and Molecular Composition of Purified Bovine Viral Diarrhea Virus Envelope

The family Flaviviridae includes viruses that have different virion structures and morphogenesis mechanisms. Most cellular and molecular studies have been so far performed with viruses of the Hepacivirus and Flavivirus genera. Here, we studied bovine viral diarrhea virus (BVDV), a member of the Pestivirus genus. We set up a method to purify BVDV virions and analyzed their morphology by electron microscopy and their protein and lipid composition by mass spectrometry. Cryo-electron microscopy showed near spherical viral particles displaying an electron-dense capsid surrounded by a phospholipid bilayer with no visible spikes. Most particles had a diameter of 50 nm and about 2% were larger with a diameter of up to 65 nm, suggesting some size flexibility during BVDV morphogenesis. Morphological and biochemical data suggested a low envelope glycoprotein content of BVDV particles, E1 and E2 being apparently less abundant than E^rns. Lipid content of BVDV particles displayed a ~2.3 to 3.5-fold enrichment in cholesterol, sphingomyelin and hexosyl-ceramide, concomitant with a 1.5 to 5-fold reduction of all glycerophospholipid classes, as compared to lipid content of MDBK cells. Although BVDV buds in the endoplasmic reticulum, its lipid content differs from a typical endoplasmic reticulum membrane composition. This suggests that BVDV morphogenesis includes a mechanism of lipid sorting. Functional analyses confirmed the importance of cholesterol and sphingomyelin for BVDV entry. Surprisingly, despite a high cholesterol and sphingolipid content of BVDV envelope, E2 was not found in detergent-resistant membranes. Our results indicate that there are differences between the structure and molecular composition of viral particles of Flaviviruses, Pestiviruses and Hepaciviruses within the Flaviviridae family.

Bovine viral diarrhea virus (BVDV) is the etiologic agent of mucosal disease and bovine viral diarrhea, two economically important diseases of the livestock. BVDV is a member of the Pestivirus genus in the Flaviviridae family, which also includes Hepacivirus and Flavivirus genera. Members of this family share similar genome organization and replication strategies, but differ about their mode of transmission and particle structure. Whereas most studies have been so far performed on viruses of the Hepacivirus and Flavivirus genera, little is known about infectious particles of pestiviruses. In this study, we set up a novel purification method of BVDV infectious particles and analyzed their morphology by cryo-electron microscopy and their molecular composition by mass spectrometry. Our results provide new insights into the structure and biochemical composition of a pestivirus infectious particle, and have implications for research on molecular mechanisms of their morphogenesis and entry.

Bifunctional Sphingosine for Cell-Based Analysis of Protein-Sphingolipid Interactions

Sphingolipids are essential structural components of cellular membranes and are crucial regulators of cellular processes. While current high-throughput approaches allow for the systematic mapping of interactions of soluble proteins with their lipid-binding partners, photo-cross-linking is the only technique that enables for the proteome-wide mapping of integral membrane proteins with their direct lipid environment. Here, we report the synthesis of a photoactivatable and clickable analog of sphingosine (pacSph). When administered to sphingosine-1-phosphate lyase deficient cells, pacSph allows its metabolic fate and the subcellular flux of de novo synthesized sphingolipids to be followed in a time-resolved manner. The chemoproteomic profiling yielded over 180 novel sphingolipid-binding proteins, of which we validated a number, demonstrating the unique value of this technique as a discovery tool. This work provides an important resource for the understanding of the global cellular interplay between sphingolipids and their interacting proteins.

Analysis of transmembrane domains and lipid modified peptides with matrix-assisted laser desorption ionization-time-of-flight mass spectrometry

Protein-lipid interactions within the membrane are difficult to detect with mass spectrometry because of the hydrophobicity of tryptic cleavage peptides on the one hand and the noncovalent nature of the protein-lipid interaction on the other hand. Here we describe a proof-of-principle method capable of resolving hydrophobic and acylated (e.g., myristoylated) peptides by optimizing the steps in a mass spectrometric workflow. We then use this optimized workflow to detect a protein-lipid interaction in vitro within the hydrophobic phase of the membrane that is preserved via a covalent cross-link using a photoactivatable lipid. This approach can also be used to map the site of a protein-lipid interaction as we identify the peptide in contact with the fatty acid part of ceramide in the START domain of the CERT protein.

Cis-Golgi cisternal assembly and biosynthetic activation occur sequentially in plants and algae

The cisternal progression/maturation model of Golgi trafficking predicts that cis-Golgi cisternae are formed de novo on the cis-side of the Golgi. Here we describe structural and functional intermediates of the cis cisterna assembly process in high-pressure frozen algae (Scherffelia dubia, Chlamydomonas reinhardtii) and plants (Arabidopsis thaliana, Dionaea muscipula; Venus flytrap) as determined by electron microscopy, electron tomography and immuno-electron microscopy techniques. Our findings are as follows: (i) The cis-most (C1) Golgi cisternae are generated de novo from cisterna initiators produced by the fusion of 3-5 COPII vesicles in contact with a C2 cis cisterna. (ii) COPII vesicles fuel the growth of the initiators, which then merge into a coherent C1 cisterna. (iii) When a C1 cisterna nucleates its first cisterna initiator it becomes a C2 cisterna. (iv) C2-Cn cis cisternae grow through COPII vesicle fusion. (v) ER-resident proteins are recycled from cis cisternae to the ER via COPIa-type vesicles. (vi) In S. dubia the C2 cisternae are capable of mediating the self-assembly of scale protein complexes. (vii) In plants, ∼90% of native α-mannosidase I localizes to medial Golgi cisternae. (viii) Biochemical activation of cis cisternae appears to coincide with their conversion to medial cisternae via recycling of medial cisterna enzymes. We propose how the different cis cisterna assembly intermediates of plants and algae may actually be related to those present in the ERGIC and in the pre-cis Golgi cisterna layer in mammalian cells.

Flexibility of a eukaryotic lipidome--insights from yeast lipidomics

Mass spectrometry-based shotgun lipidomics has enabled the quantitative and comprehensive assessment of cellular lipid compositions. The yeast Saccharomyces cerevisiae has proven to be a particularly valuable experimental system for studying lipid-related cellular processes. Here, by applying our shotgun lipidomics platform, we investigated the influence of a variety of commonly used growth conditions on the yeast lipidome, including glycerophospholipids, triglycerides, ergosterol as well as complex sphingolipids. This extensive dataset allowed for a quantitative description of the intrinsic flexibility of a eukaryotic lipidome, thereby providing new insights into the adjustments of lipid biosynthetic pathways. In addition, we established a baseline for future lipidomic experiments in yeast. Finally, flexibility of lipidomic features is proposed as a new parameter for the description of the physiological state of an organism.

Quantitative analysis of the lipidomes of the influenza virus envelope and MDCK cell apical membrane

Analysis of the lipid composition of influenza virus–infected cells provides support for the membrane raft-based biogenesis model.

The influenza virus (IFV) acquires its envelope by budding from host cell plasma membranes. Using quantitative shotgun mass spectrometry, we determined the lipidomes of the host Madin–Darby canine kidney cell, its apical membrane, and the IFV budding from it. We found the apical membrane to be enriched in sphingolipids (SPs) and cholesterol, whereas glycerophospholipids were reduced, and storage lipids were depleted compared with the whole-cell membranes. The virus membrane exhibited a further enrichment of SPs and cholesterol compared with the donor membrane at the expense of phosphatidylcholines. Our data are consistent with and extend existing models of membrane raft-based biogenesis of the apical membrane and IFV envelope.

Segregation of sphingolipids and sterols during formation of secretory vesicles at the trans-Golgi network

The trans-Golgi network (TGN) is the major sorting station in the secretory pathway of all eukaryotic cells. How the TGN sorts proteins and lipids to generate the enrichment of sphingolipids and sterols at the plasma membrane is poorly understood. To address this fundamental question in membrane trafficking, we devised an immunoisolation procedure for specific recovery of post-Golgi secretory vesicles transporting a transmembrane raft protein from the TGN to the cell surface in the yeast Saccharomyces cerevisiae. Using a novel quantitative shotgun lipidomics approach, we could demonstrate that TGN sorting selectively enriched ergosterol and sphingolipid species in the immunoisolated secretory vesicles. This finding, for the first time, indicates that the TGN exhibits the capacity to sort membrane lipids. Furthermore, the observation that the immunoisolated vesicles exhibited a higher membrane order than the late Golgi membrane, as measured by C-Laurdan spectrophotometry, strongly suggests that lipid rafts play a role in the TGN-sorting machinery.

Generation of cubic membranes by controlled homotypic interaction of membrane proteins in the endoplasmic reticulum

Cell membranes predominantly consist of lamellar lipid bilayers. When studied in vitro, however, many membrane lipids can exhibit non-lamellar morphologies, often with cubic symmetries. An open issue is how lipid polymorphisms influence organelle and cell shape. Here, we used controlled dimerization of artificial membrane proteins in mammalian tissue culture cells to induce an expansion of the endoplasmic reticulum (ER) with cubic symmetry. Although this observation emphasizes ER architectural plasticity, we found that the changed ER membrane became sequestered into large autophagic vacuoles, positive for the autophagy protein LC3. Autophagy may be targeting irregular membrane shapes and/or aggregated protein. We suggest that membrane morphology can be controlled in cells.

The ISC [corrected] proteins Isa1 and Isa2 are required for the function but not for the de novo synthesis of the Fe/S clusters of biotin synthase in Saccharomyces cerevisiae

The yeast Saccharomyces cerevisiae is able to use some biotin precursors for biotin biosynthesis. Insertion of a sulfur atom into desthiobiotin, the final step in the biosynthetic pathway, is catalyzed by biotin synthase (Bio2). This mitochondrial protein contains two iron-sulfur (Fe/S) clusters that catalyze the reaction and are thought to act as a sulfur donor. To identify new components of biotin metabolism, we performed a genetic screen and found that Isa2, a mitochondrial protein involved in the formation of Fe/S proteins, is necessary for the conversion of desthiobiotin to biotin. Depletion of Isa2 or the related Isa1, however, did not prevent the de novo synthesis of any of the two Fe/S centers of Bio2. In contrast, Fe/S cluster assembly on Bio2 strongly depended on the Isu1 and Isu2 proteins. Both isa mutants contained low levels of Bio2. This phenotype was also found in other mutants impaired in mitochondrial Fe/S protein assembly and in wild-type cells grown under iron limitation. Low Bio2 levels, however, did not cause the inability of isa mutants to utilize desthiobiotin, since this defect was not cured by overexpression of BIO2. Thus, the Isa proteins are crucial for the in vivo function of biotin synthase but not for the de novo synthesis of its Fe/S clusters. Our data demonstrate that the Isa proteins are essential for the catalytic activity of Bio2 in vivo.

Rab10 is involved in basolateral transport in polarized Madin-Darby canine kidney cells

The sorting of newly synthesized membrane proteins to the cell surface is an important mechanism of cell polarity. To identify more of the molecular machinery involved, we investigated the function of the small GTPase Rab10 in polarized epithelial Madin-Darby canine kidney cells. We find that GFP-tagged Rab10 localizes primarily to the Golgi during early cell polarization. Expression of an activated Rab10 mutant inhibits biosynthetic transport from the Golgi and missorts basolateral cargo to the apical membrane. Depletion of Rab10 by RNA interference has only mild effects on biosynthetic transport and epithelial polarization, but simultaneous inhibition of Rab10 and Rab8a more strongly impairs basolateral sorting. These results indicate that Rab10 functions in trafficking from the Golgi at early stages of epithelial polarization, is involved in biosynthetic transport to the basolateral membrane and may co-operate with Rab8.