Laxman Yetukuri

PPAR gamma 2 Prevents Lipotoxicity by Controlling Adipose Tissue Expandability and Peripheral Lipid Metabolism

Peroxisome proliferator activated receptor gamma 2 (PPARg2) is the nutritionally regulated isoform of PPARg. Ablation of PPARg2 in the ob/ob background, PPARg2−/− Lepob/Lepob (POKO mouse), resulted in decreased fat mass, severe insulin resistance, β-cell failure, and dyslipidaemia. Our results indicate that the PPARg2 isoform plays an important role, mediating adipose tissue expansion in response to positive energy balance. Lipidomic analyses suggest that PPARg2 plays an important antilipotoxic role when induced ectopically in liver and muscle by facilitating deposition of fat as relatively harmless triacylglycerol species and thus preventing accumulation of reactive lipid species. Our data also indicate that PPARg2 may be required for the β-cell hypertrophic adaptive response to insulin resistance. In summary, the PPARg2 isoform prevents lipotoxicity by (a) promoting adipose tissue expansion, (b) increasing the lipid-buffering capacity of peripheral organs, and (c) facilitating the adaptive proliferative response of β-cells to insulin resistance.

Dysregulation of lipid and amino acid metabolism precedes islet autoimmunity in children who later progress to type 1 diabetes

The risk determinants of type 1 diabetes, initiators of autoimmune response, mechanisms regulating progress toward β cell failure, and factors determining time of presentation of clinical diabetes are poorly understood. We investigated changes in the serum metabolome prospectively in children who later progressed to type 1 diabetes. Serum metabolite profiles were compared between sample series drawn from 56 children who progressed to type 1 diabetes and 73 controls who remained nondiabetic and permanently autoantibody negative. Individuals who developed diabetes had reduced serum levels of succinic acid and phosphatidylcholine (PC) at birth, reduced levels of triglycerides and antioxidant ether phospholipids throughout the follow up, and increased levels of proinflammatory lysoPCs several months before seroconversion to autoantibody positivity. The lipid changes were not attributable to HLA-associated genetic risk. The appearance of insulin and glutamic acid decarboxylase autoantibodies was preceded by diminished ketoleucine and elevated glutamic acid. The metabolic profile was partially normalized after the seroconversion. Autoimmunity may thus be a relatively late response to the early metabolic disturbances. Recognition of these preautoimmune alterations may aid in studies of disease pathogenesis and may open a time window for novel type 1 diabetes prevention strategies.

The gut microbiota modulates host energy and lipid metabolism in mice

The gut microbiota has recently been identified as an environmental factor that may promote metabolic diseases. To investigate the effect of gut microbiota on host energy and lipid metabolism, we compared the serum metabolome and the lipidomes of serum, adipose tissue, and liver of conventionally raised (CONV-R) and germ-free mice. The serum metabolome of CONV-R mice was characterized by increased levels of energy metabolites, e.g., pyruvic acid, citric acid, fumaric acid, and malic acid, while levels of cholesterol and fatty acids were reduced. We also showed that the microbiota modified a number of lipid species in the serum, adipose tissue, and liver, with its greatest effect on triglyceride and phosphatidylcholine species. Triglyceride levels were lower in serum but higher in adipose tissue and liver of CONV-R mice, consistent with increased lipid clearance. Our findings show that the gut microbiota affects both host energy and lipid metabolism and highlights its role in the development of metabolic diseases.

Novel Theranostic Opportunities Offered by Characterization of Altered Membrane Lipid Metabolism in Breast Cancer Progression

Activation of lipid metabolism is an early event in carcinogenesis and a central hallmark of many cancers. However, the precise molecular composition of lipids in tumors remains generally poorly characterized. The aim of the present study was to analyze the global lipid profiles of breast cancer, integrate the results to protein expression, and validate the findings by functional experiments. Comprehensive lipidomics was conducted in 267 human breast tissues using ultraperformance liquid chromatography/ mass spectrometry. The products of de novo fatty acid synthesis incorporated into membrane phospholipids, such as palmitate-containing phosphatidylcholines, were increased in tumors as compared with normal breast tissues. These lipids were associated with cancer progression and patient survival, as their concentration was highest in estrogen receptor-negative and grade 3 tumors. In silico transcriptomics database was utilized in investigating the expression of lipid metabolism related genes in breast cancer, and on the basis of these results, the expression of specific proteins was studied by immunohistochemistry. Immunohistochemical analyses showed that several genes regulating lipid metabolism were highly expressed in clinical breast cancer samples and supported also the lipidomics results. Gene silencing experiments with seven genes [ACACA (acetyl-CoA carboxylase α), ELOVL1 (elongation of very long chain fatty acid-like 1), FASN (fatty acid synthase), INSIG1 (insulin-induced gene 1), SCAP (sterol regulatory element-binding protein cleavage-activating protein), SCD (stearoyl-CoA desaturase), and THRSP (thyroid hormone-responsive protein)] indicated that silencing of multiple lipid metabolism-regulating genes reduced the lipidomic profiles and viability of the breast cancer cells. Taken together, our results imply that phospholipids may have diagnostic potential as well as that modulation of their metabolism may provide therapeutic opportunities in breast cancer treatment.

Adipose tissue inflammation and increased ceramide content characterize subjects with high liver fat content independent of obesity.

OBJECTIVE— We sought to determine whether adipose tissue is inflamed in individuals with increased liver fat (LFAT) independently of obesity. RESEARCH DESIGN AND METHODS— A total of 20 nondiabetic, healthy, obese women were divided into normal and high LFAT groups based on their median LFAT level (2.3 ± 0.3 vs. 14.4 ± 2.9%). Surgical subcutaneous adipose tissue biopsies were studied using quantitative PCR, immunohistochemistry, and a lipidomics approach to search for putative mediators of insulin resistance and inflammation. The groups were matched for age and BMI. The high LFAT group had increased insulin (P = 0.0025) and lower HDL cholesterol (P = 0.02) concentrations. RESULTS— Expression levels of the macrophage marker CD68, the chemokines monocyte chemoattractant protein-1 and macrophage inflammatory protein-1α, and plasminogen activator inhibitor-1 were significantly increased, and those of peroxisome proliferator–activated receptor-γ and adiponectin decreased in the high LFAT group. CD68 expression correlated with the number of macrophages and crown-like structures (multiple macrophages fused around dead adipocytes). Concentrations of 154 lipid species in adipose tissue revealed several differences between the groups, with the most striking being increased concentrations of triacylglycerols, particularly long chain, and ceramides, specifically Cer(d18:1/24:1) (P = 0.01), in the high LFAT group. Expression of sphingomyelinases SMPD1 and SMPD3 were also significantly increased in the high compared with normal LFAT group. CONCLUSIONS— Adipose tissue is infiltrated with macrophages, and its content of long-chain triacylglycerols and ceramides is increased in subjects with increased LFAT compared with equally obese subjects with normal LFAT content. Ceramides or their metabolites could contribute to adverse effects of long-chain fatty acids on insulin resistance and inflammation.

Bioinformatics strategies for lipidomics analysis: characterization of obesity related hepatic steatosis

BackgroundLipids are an important and highly diverse class of molecules having structural, energy storage and signaling roles. Modern analytical technologies afford screening of many lipid molecular species in parallel. One of the biggest challenges of lipidomics is elucidation of important pathobiological phenomena from the integration of the large amounts of new data becoming available.ResultsWe present computational and informatics approaches to study lipid molecular profiles in the context of known metabolic pathways and established pathophysiological responses, utilizing information obtained from modern analytical technologies. In order to facilitate identification of lipids, we compute the scaffold of theoretically possible lipids based on known lipid building blocks such as polar head groups and fatty acids. Each compound entry is linked to the available information on lipid pathways and contains the information that can be utilized for its automated identification from high-throughput UPLC/MS-based lipidomics experiments. The utility of our approach is demonstrated by its application to the lipidomic characterization of the fatty liver of the genetically obese insulin resistant ob/ob mouse model. We investigate the changes of correlation structure of the lipidome using multivariate analysis, as well as reconstruct the pathways for specific molecular species of interest using available lipidomic and gene expression data.ConclusionThe methodology presented herein facilitates identification and interpretation of high-throughput lipidomics data. In the context of the ob/ob mouse liver profiling, we have identified the parallel associations between the elevated triacylglycerol levels and the ceramides, as well as the putative activated ceramide-synthesis pathways.

Normalization method for metabolomics data using optimal selection of multiple internal standards

Success of metabolomics as the phenotyping platform largely depends on its ability to detect various sources of biological variability. Removal of platform-specific sources of variability such as systematic error is therefore one of the foremost priorities in data preprocessing. However, chemical diversity of molecular species included in typical metabolic profiling experiments leads to different responses to variations in experimental conditions, making normalization a very demanding task. With the aim to remove unwanted systematic variation, we present an approach that utilizes variability information from multiple internal standard compounds to find optimal normalization factor for each individual molecular species detected by metabolomics approach (NOMIS). We demonstrate the method on mouse liver lipidomic profiles using Ultra Performance Liquid Chromatography coupled to high resolution mass spectrometry, and compare its performance to two commonly utilized normalization methods: normalization by l2 norm and by retention time region specific standard compound profiles. The NOMIS method proved superior in its ability to reduce the effect of systematic error across the full spectrum of metabolite peaks. We also demonstrate that the method can be used to select best combinations of standard compounds for normalization. Depending on experiment design and biological matrix, the NOMIS method is applicable either as a one-step normalization method or as a two-step method where the normalization parameters, influenced by variabilities of internal standard compounds and their correlation to metabolites, are first calculated from a study conducted in repeatability conditions. The method can also be used in analytical development of metabolomics methods by helping to select best combinations of standard compounds for a particular biological matrix and analytical platform.BackgroundSuccess of metabolomics as the phenotyping platform largely depends on its ability to detect various sources of biological variability. Removal of platform-specific sources of variability such as systematic error is therefore one of the foremost priorities in data preprocessing. However, chemical diversity of molecular species included in typical metabolic profiling experiments leads to different responses to variations in experimental conditions, making normalization a very demanding task.ResultsWith the aim to remove unwanted systematic variation, we present an approach that utilizes variability information from multiple internal standard compounds to find optimal normalization factor for each individual molecular species detected by metabolomics approach (NOMIS). We demonstrate the method on mouse liver lipidomic profiles using Ultra Performance Liquid Chromatography coupled to high resolution mass spectrometry, and compare its performance to two commonly utilized normalization methods: normalization by l2 norm and by retention time region specific standard compound profiles. The NOMIS method proved superior in its ability to reduce the effect of systematic error across the full spectrum of metabolite peaks. We also demonstrate that the method can be used to select best combinations of standard compounds for normalization.ConclusionDepending on experiment design and biological matrix, the NOMIS method is applicable either as a one-step normalization method or as a two-step method where the normalization parameters, influenced by variabilities of internal standard compounds and their correlation to metabolites, are first calculated from a study conducted in repeatability conditions. The method can also be used in analytical development of metabolomics methods by helping to select best combinations of standard compounds for a particular biological matrix and analytical platform.

Serum saturated fatty acids containing triacylglycerols are better markers of insulin resistance than total serum triacylglycerol concentrations

Aims/hypothesisThe weak relationship between insulin resistance and total serum triacylglycerols (TGs) could be in part due to heterogeneity of TG molecules and their distribution within different lipoproteins. We determined concentrations of individual TGs and the fatty acid composition of serum and major lipoprotein particles and analysed how changes in different TGs and fatty acid composition are related to features of insulin resistance and abdominal obesity.MethodsWe performed lipidomic analyses of all major lipoprotein fractions using two analytical platforms in 16 individuals, who exhibited a broad range of insulin sensitivity.ResultsWe identified 45 different TGs in serum. Serum TGs containing saturated and monounsaturated fatty acids were positively, while TGs containing essential linoleic acid (18:2 n−6) were negatively correlated with HOMA-IR. Specific serum TGs that correlated positively with HOMA-IR were also significantly positively related to HOMA-IR when measured in very-low-density lipoproteins (VLDLs), intermediate-density lipoproteins (IDLs) and LDL, but not in HDL subfraction 2 (HDL2) or 3 (HDL3). Analyses of proportions of esterified fatty acids within lipoproteins revealed that palmitic acid (16:0) was positively related to HOMA-IR when measured in VLDL, IDL and LDL, but not in HDL2 or HDL3. Monounsaturated palmitoleic (16:1 n−7) and oleic (18:1 n−9) acids were positively related to HOMA-IR when measured in HDL2 and HDL3, but not in VLDL, IDL or LDL. Linoleic acid was negatively related to HOMA-IR in all lipoproteins.Conclusions/interpretationSerum concentrations of specific TGs, such as TG(16:0/16:0/18:1) or TG(16:0/18:1/18:0), may be more precise markers of insulin resistance than total serum TG concentrations.

Composition and lipid spatial distribution of HDL particles in subjects with low and high HDL-cholesterol

A low level of high density lipoprotein cholesterol (HDL-C) is a powerful risk factor for cardiovascular disease. However, despite the reported key role of apolipo-proteins, specifically, apoA-I, in HDL metabolism, lipid molecular composition of HDL particles in subjects with high and low HDL-C levels is currently unknown. Here lipidomics was used to study HDL derived from well-characterized high and low HDL-C subjects. Low HDL-C subjects had elevated triacylglycerols and diminished lysophosphatidylcholines and sphingomyelins. Using information about the lipid composition of HDL particles in these two groups, we reconstituted HDL particles in silico by performing large-scale molecular dynamics simulations. In addition to confirming the measured change in particle size, we found that the changes in lipid composition also induced specific spatial distributions of lipids within the HDL particles, including a higher amount of triacylglycerols at the surface of HDL particles in low HDL-C subjects. Our findings have important implications for understanding HDL metabolism and function. For the first time we demonstrate the power of combining molecular profiling of lipoproteins with dynamic modeling of lipoprotein structure.

Triacylglycerol fatty acid composition in diet-induced weight loss in subjects with abnormal glucose metabolism--the GENOBIN study.

Background The effect of weight loss on different plasma lipid subclasses at the molecular level is unknown. The aim of this study was to examine whether a diet-induced weight reduction result in changes in the extended plasma lipid profiles (lipidome) in subjects with features of metabolic syndrome in a 33-week intervention. Methodology/Principal Findings Plasma samples of 9 subjects in the weight reduction group and 10 subjects in the control group were analyzed using mass spectrometry based lipidomic and fatty acid analyses. Body weight decreased in the weight reduction group by 7.8±2.9% (p<0.01). Most of the serum triacylglycerols and phosphatidylcholines were reduced. The decrease in triacylglycerols affected predominantly the saturated short chain fatty acids. This decrease of saturated short chain fatty acid containing triacylglycerols correlated with the increase of insulin sensitivity. However, levels of several longer chain fatty acids, including arachidonic and docosahexanoic acid, were not affected by weight loss. Levels of other lipids known to be associated with obesity such as sphingolipids and lysophosphatidylcholines were not altered by weight reduction. Conclusions/Significance Diet-induced weight loss caused significant changes in global lipid profiles in subjects with abnormal glucose metabolism. The observed changes may affect insulin sensitivity and glucose metabolism in these subjects. Trial Registration ClinicalTrials.gov NCT00621205