Untargeted UPLC-MS Profiling Pipeline to Expand Tissue Metabolome Coverage: Application to Cardiovascular Disease.

Metabolic profiling studies aim to achieve broad metabolome coverage in specific biological samples. However, wide metabolome coverage has proven difficult to achieve, mostly because of the diverse physicochemical properties of small molecules, obligating analysts to seek multiplatform and multimethod approaches. Challenges are even greater when it comes to applications to tissue samples, where tissue lysis and metabolite extraction can induce significant systematic variation in composition. We have developed a pipeline for obtaining the aqueous and organic compounds from diseased arterial tissue using two consecutive extractions, followed by a different untargeted UPLC-MS analysis method for each extract. Methods were rationally chosen and optimized to address the different physicochemical properties of each extract: hydrophilic interaction liquid chromatography (HILIC) for the aqueous extract and reversed-phase chromatography for the organic. This pipeline can be generic for tissue analysis as demonstrated by applications to different tissue types. The experimental setup and fast turnaround time of the two methods contributed toward obtaining highly reproducible features with exceptional chromatographic performance (CV % < 0.5%), making this pipeline suitable for metabolic profiling applications. We structurally assigned 226 metabolites from a range of chemical classes (e.g., carnitines, α-amino acids, purines, pyrimidines, phospholipids, sphingolipids, free fatty acids, and glycerolipids) which were mapped to their corresponding pathways, biological functions and known disease mechanisms. The combination of the two untargeted UPLC-MS methods showed high metabolite complementarity. We demonstrate the application of this pipeline to cardiovascular disease, where we show that the analyzed diseased groups (<i>n </i>= 120) of arterial tissue could be distinguished based on their metabolic profiles

Characterizing the breast cancer lipidome and its interaction with the tissue microbiota

Breast cancer is the most diagnosed cancer amongst women worldwide. We have previously shown that there is a breast microbiota which differs between women who have breast cancer and those who are disease-free. To better understand the local biochemical perturbations occurring with disease and the potential contribution of the breast microbiome, lipid profiling was performed on non-tumor breast tissue collected from 19 healthy women and 42 with breast cancer. Here we identified unique lipid signatures between the two groups with greater amounts of lysophosphatidylcholines and oxidized cholesteryl esters in the tissue from women with breast cancer and lower amounts of ceramides, diacylglycerols, phosphatidylcholines, and phosphatidylethanolamines. By integrating these lipid signatures with the breast bacterial profiles, we observed that Gammaproteobacteria and those from the class Bacillus, were negatively correlated with ceramides, lipids with antiproliferative properties. In the healthy tissues, diacylglyerols were positively associated with Acinetobacter, Lactococcus, Corynebacterium, Prevotella and Streptococcus. These bacterial groups were found to possess the genetic potential to synthesize these lipids. The cause-effect relationships of these observations and their contribution to disease patho-mechanisms warrants further investigation for a disease afflicting millions of women around the world.</p

Plasma in HAT patients display different lipid profiles compared with controls.

<p>S-plots of O-PLS-DA model for plasma lipid profiling UPLC-MS features detected in ESI+ mode (A) and ESI- mode (B), whereby each circle represents one feature with a unique combination of m/z and retention time values. Discriminatory features selected surpassed p[<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004200#pntd.0004200.ref001" target="_blank">1</a>] and p(corr)[<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004200#pntd.0004200.ref001" target="_blank">1</a>] threshold criteria, highlighted in red boxes (see <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004200#sec002" target="_blank">Methods</a>). See <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004200#pntd.0004200.s001" target="_blank">S1 Fig</a> for corresponding scores plots. Integrals of features highlighted in the S-plots are shown as bar-chart showing mean averages ± standard error of the mean, for both ESI+ (C) and ESI- (D). Patients levels are shown in red (<i>n</i> = 16) and controls in dark grey (<i>n</i> = 14). Significant differences, as measured via Welch T-test with multiple test correction, are labelled with asterisks, where *** <i>p</i><0.001. Abbreviations: A.U., arbitrary units; LysoPC, lysophosphatidylcholine; PC, phosphatidylcholine.</p

Differences in plasma metabolites between HAT patients and controls detected by NMR.

<p>Bar-charts show relative levels of plasma metabolites that were significantly altered between patients (shown in red, <i>n</i> = 45) and controls (shown in grey, <i>n</i> = 21), as measured by ¹H NMR spectroscopy. (A) Metabolites higher in patients than controls. (B). Metabolites lower in patients than controls. Bars represent group mean average with standard error of the mean as error bars. Significance measured via Welch T-test with multiple test correction, shown as asterisks; * <i>p</i><0.05, ** <i>p</i><0.01, *** <i>p</i><0.001. A.U., arbitrary units; NAG, <i>N</i>-acetyl glycoproteins.</p

Plasma in HAT patients display different ¹H NMR metabolic profiles compared with controls.

<p>PCA model (A) and O-PLS-DA model (B) score plots of plasma ¹H NMR spectra across HAT patients and controls. Each circle represents a spectra from one sample, whereby patients are presented in red (<i>n</i> = 45) and controls in dark grey (<i>n</i> = 21). Abbreviations: R²X, model fit parameter for variation in spectral data; R²Y, model fit parameter for variation in classifier data (for O-PLS-DA); Q², model predictive parameter for spectral data in PCA (Q²X) and for classifier data in O-PLS-DA (Q²Y). Individual component contribution of R²X are shown on the axes as percentage.</p

Chronic treatment with glucagon-like peptide-1 and glucagon receptor co-agonist causes weight loss-independent improvements in hepatic steatosis in mice with diet-induced obesity

Objectives: Co-agonists at the glucagon-like peptide-1 and glucagon receptors (GLP1R/GCGR) show promise as treatments for metabolic dysfunction-associated steatotic liver disease (MASLD). Although most co-agonists to date have been heavily GLP1R-biased, glucagon directly acts on the liver to reduce fat content. The aims of this study were to investigate a GCGR-biased co-agonist as treatment for hepatic steatosis in mice. Methods: Mice with diet-induced obesity (DIO) were treated with Dicretin, a GLP1/GCGR co-agonist with high potency at the GCGR, Semaglutide (GLP1R monoagonist) or food restriction over 24 days, such that their weight loss was matched. Hepatic steatosis, glucose tolerance, hepatic transcriptomics, metabolomics and lipidomics at the end of the study were compared with Vehicle-treated mice. Results: Dicretin lead to superior reduction of hepatic lipid content when compared to Semaglutide or equivalent weight loss by calorie restriction. Markers of glucose tolerance and insulin resistance improved in all treatment groups. Hepatic transcriptomic and metabolomic profiling demonstrated many changes that were unique to Dicretin-treated mice. These include some known targets of glucagon signaling and others with as yet unclear physiological significance. Conclusions: Our study supports the development of GCGR-biased GLP1/GCGR co-agonists for treatment of MASLD and related conditions