Relationships between Very Low-Density Lipoproteins-Ceramides, -Diacylglycerols, and -Triacylglycerols in Insulin-Resistant Men

This short report describes the relationships between concentrations of ceramides (CER), diacylglycerols (DAG), triacylglycerols (TAG) in very low-density lipoproteins (VLDL) particles, and hepatic lipid accumulation. VLDL particles were isolated from male subjects (n = 12, mean ± SD, age 42.1 ± 5.4 years, BMI 37.4 ± 4.1 kg/m² , ALT 45 ± 21 U/L) and apolipoprotein B100 (apoB100), VLDL-TAG, -CER, and -DAG quantified. The contents of all three lipids were highly correlated with VLDL particle number (r ≥ 0.768, p ≤ 0.003). The molar quantity of VLDL-TAG was 3× that of DAG and 137× that of CER (14,053 ± 5714, 5004 ± 2714, and 105 ± 49 mol/mol apoB100, respectively). Reduced VLDL-CER concentrations were associated with both higher insulin levels (r = -0.645, p = 0.024) and intrahepatic-TAG (r = -0.670, p = 0.017). In fatty liver, the secretion of hepatic TAG, CER, and DAG may be suppressed and contribute to intrahepatic lipotoxicity. The mechanisms by which hepatic-CER and -DAG synthesis and assembly into VLDL is coordinately controlled with TAG will be important in understanding the emerging role of elevated CER contributing to cardiometabolic disease.

Intensive Care Unit Delirium in Surgical Patients Is Associated with Upregulation in Tryptophan Metabolism

INTRODUCTION

In intensive care unit (ICU) patients, delirium is frequent, occurs early in ICU admission, and is associated with poor outcomes. Risk models based on clinical factors have shown variable performance in terms of predictive ability. Identification of a candidate biomarker that associates with delirium may lead to a better understanding of disease mechanism, validation biomarker studies, and the ability to develop targeted interventions for prevention and treatment of delirium. This study analyzed metabolite concentrations early in the course of ICU admission to assess the association with delirium onset.

METHODS

Within 24 hours of ICU admission, blood samples for global and targeted metabolomics analyses in adult surgical ICU patients were collected prospectively. Metabolites were determined using mass spectrometry/ultra-high-pressure liquid chromatography and analyzed in patients with delirium and a group of controls matched on age, sex, and admission Sequential Organ Function Assessment (SOFA) score.

RESULTS

Patients in the study (65 per group) were a mean age of 59 years, had a median SOFA score of 6, and were most commonly admitted to the ICU following major trauma. In the delirium group, median onset of delirium was 3 (interquartile range 1-6) days, and the most common delirium subtype was mixed (56%). Kynurenic acid was significantly increased, and tryptophan concentration was significantly decreased in the delirium group (p=0.04). The ratio of kynurenine-to-tryptophan concentration was significantly higher in the delirium group (p=0.005).

CONCLUSIONS

Evidence of upregulation was found in the tryptophan metabolic pathway in delirious patients because tryptophan concentrations were lower, tryptophan metabolites were higher, and the kynurenine-to-tryptophan ratio was increased. These findings suggest a role of increased inflammation and accumulation of neurotoxic metabolites in the pathogenesis of ICU delirium. Future studies should target this pathway to validate metabolites in the tryptophan pathway as risk biomarkers in patients with ICU delirium.

Environmental lipidomics: understanding the response of organisms and ecosystems to a changing world

BACKGROUND

Understanding the interaction between organisms and the environment is important for predicting and mitigating the effects of global phenomena such as climate change, and the fate, transport, and health effects of anthropogenic pollutants. By understanding organism and ecosystem responses to environmental stressors at the molecular level, mechanisms of toxicity and adaptation can be determined. This information has important implications in human and environmental health, engineering biotechnologies, and understanding the interaction between anthropogenic induced changes and the biosphere. One class of molecules with unique promise for environmental science are lipids; lipids are highly abundant and ubiquitous across nearly all organisms, and lipid profiles often change drastically in response to external stimuli. These changes allow organisms to maintain essential biological functions, for example, membrane fluidity, as they adapt to a changing climate and chemical environment. Lipidomics can help scientists understand the historical and present biofeedback processes in climate change and the biogeochemical processes affecting nutrient cycles. Lipids can also be used to understand how ecosystems respond to historical environmental changes with lipid signatures dating back to hundreds of millions of years, which can help predict similar changes in the future. In addition, lipids are direct targets of environmental stressors, for example, lipids are easily prone to oxidative damage, which occurs during exposure to most toxins.

AIM OF REVIEW

This is the first review to summarize the current efforts to comprehensively measure lipids to better understand the interaction between organisms and their environment. This review focuses on lipidomic applications in the arenas of environmental toxicology and exposure assessment, xenobiotic exposures and health (e.g., obesity), global climate change, and nutrient cycles. Moreover, this review summarizes the use of and the potential for lipidomics in engineering biotechnologies for the remediation of persistent compounds and biofuel production.

KEY SCIENTIFIC CONCEPT

With the preservation of certain lipids across millions of years and our ever-increasing understanding of their diverse biological roles, lipidomic-based approaches provide a unique utility to increase our understanding of the contemporary and historical interactions between organisms, ecosystems, and anthropogenically-induced environmental changes.

Rapid Prostate Cancer Noninvasive Biomarker Screening Using Segmented Flow Mass Spectrometry-Based Untargeted Metabolomics

Spectrometric methods with rapid biomarker detection capacity through untargeted metabolomics are becoming essential in the clinical cancer research. Liquid chromatography-mass spectrometry (LC-MS) is a rapidly developing metabolomic-based biomarker technique due to its high sensitivity, reproducibility, and separation efficiency. However, its translation to clinical diagnostics is often limited due to long data acquisition times (∼20 min/sample) and laborious sample extraction procedures when employed for large-scale metabolomics studies. Here, we developed a segmented flow approach coupled with high-resolution mass spectrometry (SF-HRMS) for untargeted metabolomics, which has the capability to acquire data in less than 1.5 min/sample with robustness and reproducibility relative to LC-HRMS. The SF-HRMS results demonstrate the capability for screening metabolite-based urinary biomarkers associated with prostate cancer (PCa). The study shows that SF-HRMS-based global metabolomics has the potential to evolve into a rapid biomarker screening tool for clinical research.

Lipid Annotator: Towards Accurate Annotation in Non-Targeted Liquid Chromatography High-Resolution Tandem Mass Spectrometry (LC-HRMS/MS) Lipidomics Using A Rapid and User-Friendly Software

Lipidomics has great promise in various applications; however, a major bottleneck in lipidomics is the accurate and comprehensive annotation of high-resolution tandem mass spectral data. While the number of available lipidomics software has drastically increased over the past five years, the reduction of false positives and the realization of obtaining structurally accurate annotations remains a significant challenge. We introduce Lipid Annotator, which is a user-friendly software for lipidomic analysis of data collected by liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS). We validate annotation accuracy against lipid standards and other lipidomics software. Lipid Annotator was integrated into a workflow applying an iterative exclusion MS/MS acquisition strategy to National Institute of Standards and Technology (NIST) SRM 1950 Metabolites in Frozen Human Plasma using reverse phase LC-HRMS/MS. Lipid Annotator, LipidMatch, and MS-DIAL produced consensus annotations at the level of lipid class for 98% and 96% of features detected in positive and negative mode, respectively. Lipid Annotator provides percentages of fatty acyl constituent species and employs scoring algorithms based on probability theory, which is less subjective than the tolerance and weighted match scores commonly used by available software. Lipid Annotator enables analysis of large sample cohorts and improves data-processing throughput as compared to previous lipidomics software.

Method Optimization: Analysis of Benzbromarone and Tolfenamic Acid in Citrus Tissues and Soil Using Liquid Chromatography Coupled With Triple-Quadrupole Mass Spectrometry

Herein, an analytical method was developed for extraction and quantification of benzbromarone and tolfenamic acid in citrus and soil matrices using liquid-liquid extraction followed by liquid chromatography-triple quadrupole-tandem mass spectrometry analysis. The compounds were extracted using 0.1% formic acid in 6:4 ethyl acetate and n-hexane solution, and the analytes were separated using a mixture of 0.1% formic acid in ultrapure water and 0.1% formic acid in acetonitrile as mobile phase. A six-point in-matrix calibration curve was constructed providing good linearity with coefficients of determination R ² ≥ 0.98. The limits of detection and quantification for benzbromarone and tolfenamic acid were 3.0 and 10.0 μg/kg in roots, peel, juice, and soil, and 4.0 and 12.0 μg/kg for leaves samples, respectively. The method yielded excellent recoveries between 81.3 and 101.2%, with relative standard deviation ≤9.5% in the matrices. The developed technique provides a simple and sensitive method for the determination of the chemicals and can be applied to agricultural practices.

Modifications of the urinary metabolome in young women after cranberry juice consumption were revealed using the UHPLC-Q-orbitrap-HRMS-based metabolomics approach

The objectives of this research were to investigate urinary metabolome modifications and discover potential intake biomarkers in young women after cranberry juice consumption. Fifteen female college students were given either cranberry juice or apple juice for three days using a cross-over design. Urine samples were collected before and after juice consumption. The metabolome in the urine was analyzed using UHPLC-Q-orbitrap-HRMS-based metabolomics followed by orthogonal partial least squares-discriminant analyses (OPLS-DA). An S-plot was used to identify discriminant metabolites. Validated OPLS-DA analyses showed that cranberry juice consumption significantly altered the urinary metabolome. Compared to the baseline urine or urine after apple juice consumption, cranberry juice consumption increased urinary excretion of both exogenous and endogenous metabolites. The tentatively identified exogenous metabolites included quinic acid, coumaric acid, 4-hydroxy-5-(hydroxyphenyl)-valeric acid-O-sulphate, 5-(dihydroxyphenyl)-γ-valerolactone sulfate, diphenol glucuronide, 3,4-dihydroxyphenyl propionic acid, 3-(hydroxyphenyl) propionic acid, 4-O-methylgallic acid, trihydroxybenzoic acid and 1,3,5-trimethoxybenzene. Modifications of endogenous metabolites after cranberry juice consumption included the increases in homocitric acid, hippuric acid, 3-hydroxy-3-carboxymethyl-adipic acid, (2)3-isopropylmalate, pimelic acid and N-acetyl-l-glutamate 5-semialdehyde. These metabolites may serve as urinary biomarkers of cranberry juice consumption and contribute to the bioactivities of cranberries against urinary tract infection.

Analysis of Tryptophan Metabolites in Serum Using Wide-Isolation Strategies for UHPLC-HRMS/MS

Current targeted metabolomic workflows are limited by design and thus sacrifice crucial information from a profiling standpoint that could lead to a more fundamental understanding of the metabolic processes of interest. One drawback to performing targeted analysis on ion trapping instruments is the potential for increased variability in analysis when analytes and standards are isolated and trapped individually for fragmentation. In addition, this sequential isolation process increases the duty cycle of the mass spectrometer and reduces the number of points collected across a chromatographic peak. To address this, the use of a wide-isolation window (12 Da) to encompass the target analyte and the isotope standard within a single fragmentation window ensures that fragmentation is consistent when quantitation relies on the ratio of the target to the internal standard. Additionally, the preservation of a faster scan rate ensures that optimal representation of chromatographic peaks is preserved for the purposes of both quantitative and qualitative analyses that require peak integration for statistical analysis. The use of this flexible method is promising in the investigation of pathways that require multiple targets and are highly integrated within the system. Here, we demonstrate the application of this method in a fast ultra-high performance liquid chromatography (UHPLC) analysis to integrate wide-isolation quantitative strategies for high-resolution mass spectrometry (HRMS) combined with profiling qualitative metabolomics for the analysis of tryptophan degradation metabolites in mouse serum. Analysis of tryptophan-deficient states as compared to control in both germ-free or E. coli gut microbiota states was used to quantitate pathway-specific metabolites as well as obtain full profiling information. The quantitative and qualitative results revealed the preservation of the primary pathways of degradation in the kynurenine pathway to potentially produce primary products such as nicotinamide during stress-induced dietary states.

Comparative Proteomic Analysis in Scar-Free Skin Regeneration in Acomys cahirinus and Scarring Mus musculus

The spiny mouse, Acomys cahirinus displays a unique wound healing ability with regeneration of all skin components in a scar-free manner. To identify orchestrators of this regenerative response we have performed proteomic analyses of skin from Acomys and Mus musculus before and after wounding. Of the ~2000 proteins identified many are expressed at similar levels in Acomys and Mus, but there are significant differences. Following wounding in Mus the complement and coagulation cascades, PPAR signaling pathway and ECM-receptor interactions predominate. In Acomys, other pathways predominate including the Wnt, MAPK, the ribosome, proteasome, endocytosis and tight junction pathways. Notable among Acomys specific proteins are several ubiquitin-associated enzymes and kinases, whereas in Mus immuno-modulation proteins characteristic of inflammatory response are unique or more prominent. ECM proteins such as collagens are more highly expressed in Mus, but likely more important is the higher expression of matrix remodeling proteases in Acomys. Another distinctive difference between Acomys and Mus lies in the macrophage-produced arginase 1 is found in Mus whereas arginase 2 is found in Acomys. Thus, we have identified several avenues for experimental approaches whose aim is to reduce the fibrotic response that the typical mammal displays in response to wounding.

Exposure to recurrent hypoglycemia alters hippocampal metabolism in treated streptozotocin-induced diabetic rats

AIMS

Exposure to recurrent hypoglycemia (RH) is common in diabetic patients receiving glucose-lowering therapies and is implicated in causing cognitive impairments. Despite the significant effect of RH on hippocampal function, the underlying mechanisms are currently unknown. Our goal was to determine the effect of RH exposure on hippocampal metabolism in treated streptozotocin-diabetic rats.

METHODS

Hyperglycemia was corrected by insulin pellet implantation. Insulin-treated diabetic (ITD) rats were exposed to mild/moderate RH once a day for 5 consecutive days.

RESULTS

The effect of RH on hippocampal metabolism revealed 65 significantly altered metabolites in the RH group compared with controls. Several significant differences in metabolite levels belonging to major pathways (eg, Krebs cycle, gluconeogenesis, and amino acid metabolism) were discovered in RH-exposed ITD rats when compared to a control group. Key glycolytic enzymes including hexokinase, phosphofructokinase, and pyruvate kinase were affected by RH exposure.

CONCLUSION

Our results demonstrate that the exposure to RH leads to metabolomics alterations in the hippocampus of insulin-treated streptozotocin-diabetic rats. Understanding how RH affects hippocampal metabolism may help attenuate the adverse effects of RH on hippocampal functions.

A joint analysis of metabolomic profiles associated with muscle mass and strength in Caucasian women

Both loss of muscle mass and strength are important sarcopenia-related traits. In this study, we investigated both specific and shared serum metabolites associated with these two traits in 136 Caucasian women using a liquid chromatography-mass spectrometry method. A joint analysis of multivariate traits was used to examine the associations of individual metabolites with muscle mass measured by the body mass index-adjusted appendicular lean mass (ALM/BMI) and muscle strength measured by hand grip strength (HGS). After adjusting for multiple testing, nine metabolites including two amino acids (aspartic acid and glutamic acid) and an amino acid derive (pipecolic acid), one peptide (phenylalanyl-threonine), one carbohydrate (methyl beta-D-glucopyranoside), and four lipids (12S-HETRE, arachidonic acid, 12S-HETE, and glycerophosphocholine) were significant in the joint analysis. Of them, the two amino acids (aspartic acid and glutamic acid) and two lipids (12S-HETRE and 12S-HETE) were associated with both ALM/BMI and HGS, and the other five were only associated with ALM/BMI. The pathway analysis showed the amino acid metabolism pathways (aspartic acid and glutamic acid) might play important roles in the regulation of muscle mass and strength. In conclusion, our study identified novel metabolites associated with sarcopenia-related traits, suggesting novel metabolic pathways for muscle regulation.

Re-modeling of foliar membrane lipids in a seagrass allows for growth in phosphorus-deplete conditions

In this study, we used liquid chromatography high-resolution tandem mass spectrometry to analyze the lipidome of turtlegrass (Thalassia testudinum) leaves with either extremely high phosphorus content or extremely low phosphorus content. Most species of phospholipids were significantly down-regulated in phosphorus-deplete leaves, whereas diacylglyceryltrimethylhomoserine (DGTS), triglycerides (TG), galactolipid digalactosyldiacylglycerol (DGDG), certain species of glucuronosyldiacylglycerols (GlcADG), and certain species of sulfoquinovosyl diacylglycerol (SQDG) were significantly upregulated, accounting for the change in phosphorus content, as well as structural differences in the leaves of plants growing across regions of varying elemental availability. These data suggest that seagrasses are able to modify the phosphorus content in leaf membranes dependent upon environmental availability.

Decoding the Metabolome and Lipidome of Child Malnutrition by Mass Spectrometric Techniques: Present Status and Future Perspectives

Child malnutrition (CM) is a global public health problem. It contributes to poor health in one in four children under five years worldwide and causes serious health problems in children, including stunted, wasted, and overweight growth. These serious public health issues lead to a higher chance of living in poverty in adulthood. Malnutrition is related with reduced economic productivity and increases the serious national and international burden. Currently, there is no meaningful therapeutic intervention of CM, and the use of different therapeutic foods has shown poor outcomes among supplemented malnourished children. The role of metabolites and lipids has been extensively recognized as early determinants of child health, but their contribution in CM and its pathobiology are poorly understood. This perspective provides a most recent update on these aspects. After briefly introducing the disciplines of metabolomics and lipidomics, we describe a mass spectrometry-based metabolic workflow for analysis of both metabolites and lipids and summarize several recent applications of metabolomics and lipidomics in CM. Finally, we discuss the future directions of the field toward the development of meaningful interventions for CM through metabolomics and lipidomics advances.

Impact of matrix effects and ionization efficiency in non-quantitative untargeted metabolomics

INTRODUCTION

LC-MS-based untargeted metabolomics has become increasingly popular due to the vast amount of information gained in a single analysis. Many studies utilize metabolomics to profile metabolic changes in various representative biofluids, tissues, or other sample types. Most analyses are performed measuring changes in the metabolic pool of a single biological matrix due to an altered phenotype, such as disease versus normal. Measurements in such experiments are typically highly reproducible with little variation due to analytical and technological advancements in mass spectrometry. With the expanded application of metabolomics into various non-analytical scientific disciplines, the emergence of studies comparing the signal intensities of specific analytes across different biological matrices (e.g. plasma vs. urine) is becoming more common, but the matrix effect between sample types is often neglected. Additionally, the practice of comparing the signal intensities of different analytes and correlating to relative abundance is also increasingly prevalent, but the response ratio between analytes due to differences in ionization efficiency is not always accounted for in data analysis. This report serves to communicate and raise awareness of these two well-recognized issues to prevent improper data interpretation in the field of metabolomics.

OBJECTIVES

We demonstrate the impact of matrix effects and ionization efficiency with labeled analytical standards in human plasma, serum, and urine and describe how the direct comparison of non-quantitative signal intensities between biofluids, as well as between different analytes in the same biofluid, in untargeted metabolomics is inaccurate without proper response corrections.

METHODS

Human plasma, serum, and urine (n = 4 technical replicates per biofluid) were spiked with a panel of labeled internal standards all at identical concentrations, simultaneously extracted, and analyzed by UHPLC-HRMS. Signal intensities were compared for demonstration of the impact of matrix effects in untargeted metabolomics. A neat mixture of two co-eluting, structurally-similar labeled standards at the same concentration was also analyzed to demonstrate the effect of ionization efficiency on signal intensity.

RESULTS

Despite being spiked at identical concentrations, labeled standards we examined in this study showed significant differences in their signal intensities between biofluids, as well as from each other in the same biofluid, due to matrix effects. Co-eluting standards were also found to yield significantly different signal intensities at identical concentrations due to differences in ionization efficiency.

CONCLUSIONS

Due to the presence of matrix effects in untargeted, non-quantitative metabolomics, the signal intensity of any single analyte cannot be directly compared to the signal intensity of that same analyte (or any other analyte) between any two different matrices. Due to differences in ionization efficiency, the signal intensity of any single analyte cannot be directly compared to the signal intensity of any other analyte, even in the same matrix.

Metabolomic Differentials in Women With and Without Fibromyalgia

A nontargeted plasma metabolomic analysis was conducted to compare differentially expressed metabolites in women with and without fibromyalgia (FM) using data and samples collected from two parent studies in women with FM (n = 20) and comparative data collected from newly recruited age‐matched women (n = 20). Blood plasma samples were analyzed for metabolite content using liquid chromatography mass spectrometry. Consolidation of positive and negative ion mode metabolomics data with fold change (>2 or <0.5) and variable importance of projection scores ≥1 revealed statistically significant metabolites comparing samples from women with and without FM. Metabolite profiles in patients with FM differed from the comparison group in energy, lipid and amino acid metabolites reflecting heightened oxidative stress, inflammation, and tryptophan degradation in patients with FM. Study results may contribute to further identification of unique metabolomic profiles enhancing understanding of the pathophysiology of FM and for the development of effective therapeutic options.

Abstract 5273: Multi-omics approaches reveal potential role for corticosterone in prostate cancer

Prostate cancer (PCa) is the most commonly diagnosed cancer in American men. In 2018, 164,690 new cancer cases and 29,430 cancer deaths are projected to occur in the United States. Currently, there is no efficient diagnostic and safe treatment option for the management of PCa. Using Ultra-High Performance Liquid Chromatography coupled High Resolution Mass Spectrometry (UHPLC-HRMS), we globally profiled more than 1400 metabolites across 79 urine samples related to PCa (19 control, 23 benign prostate hyperplasia (BPH), 11 prostatitis, and 26 PCa samples). Among all, corticosterone was identified as a top differential metabolite. Corticosterone levels were markedly elevated in PCa urine samples and modest but significant elevation of the corticosterone in BPH and prostatitis urine samples compared to controls, with an excellent area under the receiver operating characteristic curve of 0.95, 0.94, and 0.93, respectively. In addition, the expression of corticosterone biosynthetic gene, Steroid 11-beta-hydroxylase (CYP11B1), was elevated in PCa tissues, cell lines, and urine samples, while Corticosteroid 11-beta-dehydrogenase isozyme 2 (HSD11B2), which metabolize corticosterone, were markedly reduced in prostate tumors. With the understanding that androgen signaling is the key factor for prostate cancer, we investigated the role of androgen in regulating CYP11B1 and HSD11B2. We analyzed several androgen treated PCa cell lines derived global gene expression datasets and revealed that treatment with androgen increase in CYP11B1 expression and a concomitant decrease in HSD11B2 expression levels. Interestingly, we uncover that genetic knockdown of Androgen Receptor (AR) diminished CYP11B1 expression and alternatively, increased HSD11B2. Moreover, AR based chromatin occupancy (ChIP-seq) data also suggest direct binding of AR to the promoter of CYP11B1 in multiple PCa cells. The role of corticosterone in PCa is previously unknown. Our multi-omics approaches thus indicate that corticosterone might be potential prognostic marker for PCa. The tumor biology and mechanism through which corticosterone associates with PCa is being investigated in our laboratory.

Citation Format: Iqbal Mahmud, Bongyong Lee, Ranjan Perera, Timothy J. Garrett. Multi-omics approaches reveal potential role for corticosterone in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5273.

Impact of exenatide on mitochondrial lipid metabolism in mice with nonalcoholic steatohepatitis

Exenatide (Exe) is a glucagon-like peptide (GLP)-1 receptor agonist that enhances insulin secretion and is associated with induction of satiety with weight loss. As mitochondrial dysfunction and lipotoxicity are central features of nonalcoholic steatohepatitis (NASH), we tested whether Exe improved mitochondrial function in this setting. We studied C57BL/6J mice fed for 24 weeks either a control- or high-fructose, high-trans-fat (TFD)-diet (i.e., a NASH model previously validated by our laboratory). For the final 8 weeks, mice were treated with Exe (30 µg/kg/day) or vehicle. Mitochondrial metabolism was assessed by infusion of [13C3]propionate, [3,4-13C2]glucose and NMR-based 13C-isotopomer analysis. Exenatide significantly decreased fasting plasma glucose, free fatty acids and triglycerides, as well as adipose tissue insulin resistance. Moreover, Exe reduced 23% hepatic glucose production, 15% tri-carboxylic acid (TCA) cycle flux, 20% anaplerosis and 17% pyruvate cycling resulting in a significant 31% decrease in intrahepatic triglyceride content (P = 0.02). Exenatide improved the lipidomic profile and decreased hepatic lipid byproducts associated with insulin resistance and lipotoxicity, such as diacylglycerols (TFD: 111 ± 13 vs Exe: 64 ± 13 µmol/g protein, P = 0.03) and ceramides (TFD: 1.6 ± 0.1 vs Exe: 1.3 ± 0.1 µmol/g protein, P = 0.03). Exenatide lowered expression of hepatic lipogenic genes (Srebp1C, Cd36) and genes involved in inflammation and fibrosis (Tnfa, Timp1). In conclusion, in a diet-induced mouse model of NASH, Exe ameliorates mitochondrial TCA cycle flux and significantly decreases insulin resistance, steatosis and hepatocyte lipotoxicity. This may have significant clinical implications to the potential mechanism of action of GLP-1 receptor agonists in patients with NASH. Future studies should elucidate the relative contribution of direct vs indirect mechanisms at play.

Software tool for internal standard based normalization of lipids, and effect of data-processing strategies on resulting values

Background

Lipidomics, the comprehensive measurement of lipids within a biological system or substrate, is an emerging field with significant potential for improving clinical diagnosis and our understanding of health and disease. While lipids diverse biological roles contribute to their clinical utility, the diversity of lipid structure and concentrations prove to make lipidomics analytically challenging. Without internal standards to match each lipid species, researchers often apply individual internal standards to a broad range of related lipids. To aid in standardizing and automating this relative quantitation process, we developed LipidMatch Normalizer (LMN) http://secim.ufl.edu/secim-tools/ which can be used in most open source lipidomics workflows.

Results

LMN uses a ranking system (1–3) to assign lipid standards to target analytes. A ranking of 1 signifies that both the lipid class and adduct of the internal standard and target analyte match, while a ranking of 3 signifies that neither the adduct or class match. If multiple internal standards are provided for a lipid class, standards with the closest retention time to the target analyte will be chosen. The user can also signify which lipid classes an internal standard represents, for example indicating that ether-linked phosphatidylcholine can be semi-quantified using phosphatidylcholine. LMN is designed to work with any lipid identification software and feature finding software, and in this study is used to quantify lipids in NIST SRM 1950 human plasma annotated using LipidMatch and MZmine.

Conclusions

LMN can be integrated into an open source workflow which completes all data processing steps including feature finding, annotation, and quantification for LC-MS/MS studies. Using LMN we determined that in certain cases the use of peak height versus peak area, certain adducts, and negative versus positive polarity data can have major effects on the final concentration obtained.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2803-8) contains supplementary material, which is available to authorized users.

Strain-Level Differentiation of Bacteria by Paper Spray Ionization Mass Spectrometry

Paper spray ionization mass spectrometry (PSI-MS) is a relatively new analytical technique allowing for rapid mass spectrometric analysis of biological samples with little or no sample preparation. The expeditious nature of the analysis and minimal requirement for sample preparation make PSI-MS a promising avenue for future clinical assays with one potential application in the identification of different types of bacteria. Although past PSI-MS studies have demonstrated the ability to distinguish between bacteria of different species and morphological classes, achieving within-species strain-level differentiation has never been performed. In this report, we demonstrate the first strain-level bacterial differentiation by PSI-MS with the mammalian intestinal bacterium Oxalobacter formigenes ( Oxf). This novel application holds promising clinical significance as it could be used to differentiate between pathogenic bacteria and their harmless, commensal relatives, saving time and money in clinical diagnostics. Both whole cells and cell lysates of  Oxf strains HC1 and OxWR were analyzed using the Prosolia Velox 360TM PSI source coupled to a Thermo Scientific Q Exactive high-resolution mass spectrometer with a rapid 30 s analytical method. Multivariate statistical analysis followed by examination of significant features provided for and confirmed differentiation between Oxf HC1 and OxWR. We report a panel of strain-exclusive metabolites that could serve as potential strain-indicating biomarkers.

Lipidomics for wildlife disease etiology and biomarker discovery: a case study of pansteatitis outbreak in South Africa

INTRODUCTION

Lipidomics is an emerging field with great promise for biomarker and mechanistic studies due to lipids diverse biological roles. Clinical research applying lipidomics is drastically increasing, with research methods and tools developed for clinical applications equally promising for wildlife studies.

OBJECTIVES

Limited research to date has applied lipidomics, especially of the intact lipidome, to wildlife studies. Therefore, we examine the application of lipidomics for in situ studies on Mozambique tilapia (Oreochromis mossambicus) in Loskop Dam, South Africa. Wide-scale mortality events of aquatic life associated with an environmentally-derived inflammatory disease, pansteatitis, have occurred in this area.

METHODS

The lipidome of adipose tissue (n = 31) and plasma (n = 51) from tilapia collected from Loskop Dam were characterized using state of the art liquid chromatography coupled to high-resolution tandem mass spectrometry.

RESULTS

Lipid profiles reflected pansteatitis severity and were significantly different between diseased and healthy individuals. Over 13 classes of lipids associated with inflammation, cell death, and/or oxidative damage were upregulated in pansteatitis-affected adipose tissue, including ether-lipids, short-chained triglyceride oxidation products, sphingolipids, and acylcarnitines. Ceramides showed a 1000-fold increase in the most affected adipose tissues and were sensitive to disease severity. In plasma, triglycerides were found to be downregulated in pansteatitis-affected tilapia.

CONCLUSION

Intact lipidomics provided useful mechanistic data and possible biomarkers of pansteatitis. Lipids pointed to upregulated inflammatory pathways, and ceramides serve as promising biomarker candidates for pansteatitis. As comprehensive coverage of the lipidome aids in the elucidation of possible disease mechanisms, application of lipidomics could be applied to the understanding of other environmentally-derived inflammatory conditions, such as those caused by obesogens.