Tanu Soni

Lipidomic Signature of Progression of Chronic Kidney Disease in the Chronic Renal Insufficiency Cohort

Introduction Human studies report conflicting results on the predictive power of serum lipids on the progression of chronic kidney disease. We aimed to systematically identify the lipids that predict progression to end-stage kidney disease. Methods From the Chronic Renal Insufficiency Cohort, 79 patients with chronic kidney disease stages 2 to 3 who progressed to end-stage kidney disease over 6 years of follow-up were selected and frequency matched by age, sex, race, and diabetes with 121 nonprogressors with less than 25% decline in estimated glomerular filtration rate during the follow-up. The patients were randomly divided into training and test sets. We applied liquid chromatography-mass spectrometry-based lipidomics on visit year 1 samples. Results We identified 510 lipids, of which the top 10 coincided with false discovery threshold of 0.058 in the training set. From the top 10 lipids, the abundance of diacylglycerols and cholesteryl esters was lower, but that of phosphatidic acid 44:4 and monoacylglycerol 16:0 was significantly higher in progressors. Using logistic regression models, a multimarker panel consisting of diacylglycerols and monoacylglycerol independently predicted progression. The c-statistic of the multimarker panel added to the base model consisting of estimated glomerular filtration rate and urine protein-to-creatinine ratio as compared with that of the base model was 0.92 (95% confidence interval: 0.88–0.97) and 0.83 (95% confidence interval: 0.76–0.90, P < 0.01), respectively, an observation that was validated in the test subset. Discussion We conclude that a distinct panel of lipids may improve prediction of progression of chronic kidney disease beyond estimated glomerular filtration rate and urine protein-to-creatinine ratio when added to the base model.

Impaired β-Oxidation and Altered Complex Lipid Fatty Acid Partitioning with Advancing CKD

Studies of lipids in CKD, including ESRD, have been limited to measures of conventional lipid profiles. We aimed to systematically identify 17 different lipid classes and associate the abundance thereof with alterations in acylcarnitines, a metric of β-oxidation, across stages of CKD. From the Clinical Phenotyping Resource and Biobank Core (CPROBE) cohort of 1235 adults, we selected a panel of 214 participants: 36 with stage 1 or 2 CKD, 99 with stage 3 CKD, 61 with stage 4 CKD, and 18 with stage 5 CKD. Among participants, 110 were men (51.4%), 64 were black (29.9%), and 150 were white (70.1%), and the mean (SD) age was 60 (16) years old. We measured plasma lipids and acylcarnitines using liquid chromatography-mass spectrometry. Overall, we identified 330 different lipids across 17 different classes. Compared with earlier stages, stage 5 CKD associated with a higher abundance of saturated C16-C20 free fatty acids (FFAs) and long polyunsaturated complex lipids. Long-chain-to-intermediate-chain acylcarnitine ratio, a marker of efficiency of β-oxidation, exhibited a graded decrease from stage 2 to 5 CKD (P<0.001). Additionally, multiple linear regression revealed that the long-chain-to-intermediate-chain acylcarnitine ratio inversely associated with polyunsaturated long complex lipid subclasses and the C16-C20 FFAs but directly associated with short complex lipids with fewer double bonds. We conclude that increased abundance of saturated C16-C20 FFAs coupled with impaired β-oxidation of FFAs and inverse partitioning into complex lipids may be mechanisms underpinning lipid metabolism changes that typify advancing CKD.

Lipid metabolism is associated with developmental epigenetic programming

Maternal diet and metabolism impact fetal development. Epigenetic reprogramming facilitates fetal adaptation to these in utero cues. To determine if maternal metabolite levels impact infant DNA methylation globally and at growth and development genes, we followed a clinical birth cohort of 40 mother-infant dyads. Targeted metabolomics and quantitative DNA methylation were analyzed in 1st trimester maternal plasma (M1) and delivery maternal plasma (M2) as well as infant umbilical cord blood plasma (CB). We found very long chain fatty acids, medium chain acylcarnitines, and histidine were: (1) stable in maternal plasma from pregnancy to delivery, (2) significantly correlated between M1, M2, and CB, and (3) in the top 10% of maternal metabolites correlating with infant DNA methylation, suggesting maternal metabolites associated with infant DNA methylation are tightly controlled. Global DNA methylation was highly correlated across M1, M2, and CB. Thus, circulating maternal lipids are associated with developmental epigenetic programming, which in turn may impact lifelong health and disease risk. Further studies are required to determine the causal link between maternal plasma lipids and infant DNA methylation patterns.

Effects of fish oil supplementation on prostaglandins in normal and tumor colon tissue: modulation by the lipogenic phenotype of colon tumors ☆

Dietary fish oils have potential for prevention of colon cancer, and yet the mechanisms of action in normal and tumor colon tissues are not well defined. Here we evaluated the impact of the colonic fatty acid milieu on the formation of prostaglandins and other eicosanoids. Distal tumors in rats were chemically induced to model inflammatory colonic carcinogenesis. After 21 weeks of feeding with either a fish oil diet containing an eicosapentaenoic acid/ω-6 fatty acid ratio of 0.4 or a Western fat diet, the relationships between colon fatty acids and prostaglandin E2 (PGE2) concentrations were evaluated. PGE2 is a key proinflammatory mediator in the colon tightly linked with the initiation and progression of colon cancer. The fish oil vs. the Western fat diet resulted in reduced total fatty acid concentrations in serum but not in colon. In the colon, the effects of the fish oil on fatty acids differed in normal and tumor tissue. There were distinct lipodomic patterns consistent with a lipogenic phenotype in tumors. In tumor tissue, the eicosapentaenoic acid/arachidonic acid ratio, cyclooxygenase-2 expression and the mole percent of saturated fatty acids were significant predictors of inter-animal variability in colon PGE2 after accounting for diet. In normal tissues from either control rats or carcinogen-treated rats, only diet was a significant predictor of colon PGE2. These results show that the fatty acid milieu can modulate the efficacy of dietary fish oils for colon cancer prevention, and this could extend to other preventive agents that function by reducing inflammatory stress.

Evaluation of intensity drift correction strategies using MetaboDrift, a normalization tool for multi-batch metabolomics data

In recent years, mass spectrometry-based metabolomics has increasingly been applied to large-scale epidemiological studies of human subjects. However, the successful use of metabolomics in this context is subject to the challenge of detecting biologically significant effects despite substantial intensity drift that often occurs when data are acquired over a long period or in multiple batches. Numerous computational strategies and software tools have been developed to aid in correcting for intensity drift in metabolomics data, but most of these techniques are implemented using command-line driven software and custom scripts which are not accessible to all end users of metabolomics data. Further, it has not yet become routine practice to assess the quantitative accuracy of drift correction against techniques which enable true absolute quantitation such as isotope dilution mass spectrometry. We developed an Excel-based tool, MetaboDrift, to visually evaluate and correct for intensity drift in a multi-batch liquid chromatography - mass spectrometry (LC-MS) metabolomics dataset. The tool enables drift correction based on either quality control (QC) samples analyzed throughout the batches or using QC-sample independent methods. We applied MetaboDrift to an original set of clinical metabolomics data from a mixed-meal tolerance test (MMTT). The performance of the method was evaluated for multiple classes of metabolites by comparison with normalization using isotope-labeled internal standards. QC sample-based intensity drift correction significantly improved correlation with IS-normalized data, and resulted in detection of additional metabolites with significant physiological response to the MMTT. The relative merits of different QC-sample curve fitting strategies are discussed in the context of batch size and drift pattern complexity. Our drift correction tool offers a practical, simplified approach to drift correction and batch combination in large metabolomics studies.

Distinct Lipidomic Landscapes Associated with Clinical Stages of Urothelial Cancer of the Bladder.

BACKGROUND The first global lipidomic profiles associated with urothelial cancer of the bladder (UCB) and its clinical stages associated with progression were identified. OBJECTIVE To identify lipidomic signatures associated with survival and different clinical stages of UCB. DESIGN, SETTING, AND PARTICIPANTS Pathologically confirmed 165 bladder-derived tissues (126 UCB, 39 benign adjacent or normal bladder tissues). UCB tissues included Ta (n=16), T1 (n=30), T2 (n=43), T3 (n=27), and T4 (n=9); lymphovascular invasion (LVI) positive (n=52) and negative (n=69); and lymph node status N0 (n=28), N1 (n=11), N2 (n=9), N3 (n=3), and Nx (n=75). RESULTS AND LIMITATIONS UCB tissues have higher levels of phospholipids and fatty acids, and reduced levels of triglycerides compared with benign tissues. A total of 59 genes associated with altered lipids in UCB strongly correlate with patient survival in an UCB public dataset. Within UCB, there was a progressive decrease in the levels of phosphatidylserine (PS), phosphatidylethanolamines (PEs), and phosphocholines, whereas an increase in the levels of diacylglycerols (DGs) with tumor stage. Transcript and protein expression of phosphatidylserine synthase 1, which converts DGs to PSs, decreased progressively with tumor stage. Levels of DGs and lyso-PEs were significantly elevated in tumors with LVI and lymph node involvement, respectively. Lack of carcinoma in situ and treatment information is the limitation of our study. CONCLUSIONS To date, this is the first study describing the global lipidomic profiles associated with UCB and identifies lipids associated with tumor stages, LVI, and lymph node status. Our data suggest that triglycerides serve as the primary energy source in UCB, while phospholipid alterations could affect membrane structure and/or signaling associated with tumor progression. PATIENT SUMMARY Lipidomic alterations identified in this study set the stage for characterization of pathways associated with these altered lipids that, in turn, could inform the development of first-of-its-kind lipid-based noninvasive biomarkers and novel therapeutic targets for aggressive urothelial cancer of the bladder.

Fatty acid and lipidomic data in normal and tumor colon tissues of rats fed diets with and without fish oil

Data is provided to show the detailed fatty acid and lipidomic composition of normal and tumor rat colon tissues. Rats were fed either a Western fat diet or a fish oil diet, and half the rats from each diet group were treated with chemical carcinogens that induce colon cancer (azoxymethane and dextran sodium sulfate). The data show total fatty acid profiles of sera and of all the colon tissues, namely normal tissue from control rats and both normal and tumor tissues from carcinogen-treated rats, as obtained by gas chromatography with mass spectral detection. Data from lipidomic analyses of a representative subset of the colon tissue samples is also shown in heat maps generated from hierarchical cluster analysis. These data display the utility lipidomic analyses to enhance the interpretation of dietary feeding studies aimed at cancer prevention and support the findings published in the companion paper (Effects of fish oil supplementation on prostaglandins in normal and tumor colon tissue: modulation by the lipogenic phenotype of colon tumors, Djuric et al., 2017 [1]).

Lipidomics and Biomarker Discovery in Kidney Disease

Technological advances in mass spectrometry-based lipidomic platforms have provided the opportunity for comprehensive profiling of lipids in biological samples and shown alterations in the lipidome that occur in metabolic disorders. A lipidomic approach serves as a powerful tool for biomarker discovery and gaining insight to molecular mechanisms of disease, especially when integrated with other -omics platforms (ie, transcriptomics, proteomics, and metabolomics) in the context of systems biology. In this review, we describe the workflow commonly applied to the conduct of lipidomic studies including important aspects of study design, sample preparation, biomarker identification and quantification, and data processing and analysis, as well as crucial considerations in clinical applications. We also review some recent studies of the application of lipidomic platforms that highlight the potential of lipid biomarkers and add to our understanding of the molecular basis of kidney disease.

Associations of the plasma lipidome with mortality in the acute respiratory distress syndrome: a longitudinal cohort study

BackgroundIt is unknown if the plasma lipidome is a useful tool for improving our understanding of the acute respiratory distress syndrome (ARDS). Therefore, we measured the plasma lipidome of individuals with ARDS at two time-points to determine if changes in the plasma lipidome distinguished survivors from non-survivors. We hypothesized that both the absolute concentration and change in concentration over time of plasma lipids are associated with 28-day mortality in this population.MethodsSamples for this longitudinal observational cohort study were collected at multiple tertiary-care academic medical centers as part of a previous multicenter clinical trial. A mass spectrometry shot-gun lipidomic assay was used to quantify the lipidome in plasma samples from 30 individuals. Samples from two different days were analyzed for each subject. After removing lipids with a coefficient of variation > 30%, differences between cohorts were identified using repeated measures analysis of variance. The false discovery rate was used to adjust for multiple comparisons. Relationships between significant compounds were explored using hierarchical clustering of the Pearson correlation coefficients and the magnitude of these relationships was described using receiver operating characteristic curves.ResultsThe mass spectrometry assay reliably measured 359 lipids. After adjusting for multiple comparisons, 90 compounds differed between survivors and non-survivors. Survivors had higher levels for each of these lipids except for five membrane lipids. Glycerolipids, particularly those containing polyunsaturated fatty acid side-chains, represented many of the lipids with higher concentrations in survivors. The change in lipid concentration over time did not differ between survivors and non-survivors.ConclusionsThe concentration of multiple plasma lipids is associated with mortality in this group of critically ill patients with ARDS. Absolute lipid levels provided more information than the change in concentration over time. These findings support future research aimed at integrating lipidomics into critical care medicine.

Shared and distinct lipid-lipid interactions in plasma and affected tissues in a diabetic mouse model

Lipids are ubiquitous metabolites with diverse functions; abnormalities in lipid metabolism appear to be related to complications from multiple diseases, including type 2 diabetes. Through technological advances, the entire lipidome has been characterized and researchers now need computational approaches to better understand lipid network perturbations in different diseases. Using a mouse model of type 2 diabetes with microvascular complications, we examined lipid levels in plasma and in renal, neural, and retinal tissues to identify shared and distinct lipid abnormalities. We used correlation analysis to construct interaction networks in each tissue, to associate changes in lipids with changes in enzymes of lipid metabolism, and to identify overlap of coregulated lipid subclasses between plasma and each tissue to define subclasses of plasma lipids to use as surrogates of tissue lipid metabolism. Lipid metabolism alterations were mostly tissue specific in the kidney, nerve, and retina; no lipid changes correlated between the plasma and all three tissue types. However, alterations in diacylglycerol and in lipids containing arachidonic acid, an inflammatory mediator, were shared among the tissue types, and the highly saturated cholesterol esters were similarly coregulated between plasma and each tissue type in the diabetic mouse. Our results identified several patterns of altered lipid metabolism that may help to identify pathogenic alterations in different tissues and could be used as biomarkers in future research into diabetic microvascular tissue damage.