Lipid Profile after Pharmacologic Discontinuation and Restoration of Menstruation in Women with Endometriosis: A 12-Month Observational Prospective Study.

The lipid profile is affected following menstrual cessation (MC). We aimed to evaluate the effects of goserelin-induced MC and subsequent menstrual restoration (MR) on lipid metabolism. Premenopausal women with histologically verified endometriosis (n = 15) received goserelin monthly for 6 months (6mο), resulting in MC, and were followed-up for another 6 months after MR (12mο). Serum total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), lipoprotein a ([Lp(a)] and lipidomics were measured at baseline, 6mo and 12mo. Shotgun quantitative deep lipidomics were determined at the level of lipid class category, subclass, species, and fatty acyl chain lengths and degree of saturation. TC (p = 0.006), LDL-C (p = 0.028), HDL-C (p = 0.002), and apoA1 (p = 0.013) increased during goserelin-induced MC and remained practically unchanged during MR. TG, apoB, and Lp(a) did not change. From the deep lipidomics analysis, multivariate statistical analysis demonstrated profound alterations in lipid species with MC, whereas no statistically valid models could be fitted for the restoration period. In conclusion, GnRH-analog-induced MC alters lipid profiles at various levels, from standard blood lipid and lipoprotein profiles to several lipid species as detected by lipidomics analysis. Changes largely persist for at least 6 m after MR.

Integrated omics analysis for characterization of the contribution of high fructose corn syrup to non-alcoholic fatty liver disease in obesity.

BACKGROUND: High-Fructose Corn Syrup (HFCS), a sweetener rich in glucose and fructose, is nowadays widely used in beverages and processed foods; its consumption has been correlated to the emergence and progression of Non-Alcoholic Fatty Liver Disease (NAFLD). Nevertheless, the molecular mechanisms by which HFCS impacts hepatic metabolism remain scarce, especially in the context of obesity. Besides, the majority of current studies focuses either on the detrimental role of fructose in hepatic steatosis or compare separately the additive impact of fructose versus glucose in high fat diet-induced NAFLD. AIM: By engaging combined omics approaches, we sought to characterize the role of HFCS in obesity-associated NAFLD and reveal molecular processes, which mediate the exaggeration of steatosis under these conditions. METHODS: Herein, C57BL/6 mice were fed a normal-fat-diet (ND), a high-fat-diet (HFD) or a HFD supplemented with HFCS (HFD-HFCS) and upon examination of their metabolic and NAFLD phenotype, proteomic, lipidomic and metabolomic analyses were conducted to identify HFCS-related molecular alterations of the hepatic metabolic landscape in obesity. RESULTS: Although HFD and HFD-HFCS mice displayed comparable obesity, HFD-HFCS mice showed aggravation of hepatic steatosis, as analysis of the lipid droplet area in liver sections revealed (12,15 % of total section area in HFD vs 22,35 % in HFD-HFCS), increased NAFLD activity score (3,29 in HFD vs 4,86 in HFD-HFCS) and deteriorated hepatic insulin resistance, as compared to the HFD mice. Besides, the hepatic proteome of HFD-HFCS mice was characterized by a marked upregulation of 5 core proteins implicated in de novo lipogenesis (DNL), while an increased phosphatidyl-cholines(PC)/phosphatidyl-ethanolamines(PE) ratio (2.01 in HFD vs 3.04 in HFD-HFCS) was observed in the livers of HFD-HFCS versus HFD mice. Integrated analysis of the omics datasets indicated that Tricarboxylic Acid (TCA) cycle overactivation is likely contributing towards the intensification of steatosis during HFD-HFCS-induced NAFLD. CONCLUSION: Our results imply that HFCS significantly contributes to steatosis aggravation during obesity-related NAFLD, likely deriving from DNL upregulation, accompanied by TCA cycle overactivation and deteriorated hepatic insulin resistance.

Automated Annotation of Untargeted All-Ion Fragmentation LC-MS Metabolomics Data with MetaboAnnotatoR.

Untargeted metabolomics and lipidomics LC-MS experiments produce complex datasets, usually containing tens of thousands of features from thousands of metabolites whose annotation requires additional MS/MS experiments and expert knowledge. All-ion fragmentation (AIF) LC-MS/MS acquisition provides fragmentation data at no additional experimental time cost. However, analysis of such datasets requires reconstruction of parent-fragment relationships and annotation of the resulting pseudo-MS/MS spectra. Here, we propose a novel approach for automated annotation of isotopologues, adducts, and in-source fragments from AIF LC-MS datasets by combining correlation-based parent-fragment linking with molecular fragment matching. Our workflow focuses on a subset of features rather than trying to annotate the full dataset, saving time and simplifying the process. We demonstrate the workflow in three human serum datasets containing 599 features manually annotated by experts. Precision and recall values of 82-92% and 82-85%, respectively, were obtained for features found in the highest-rank scores (1-5). These results equal or outperform those obtained using MS-DIAL software, the current state of the art for AIF data annotation. Further validation for other biological matrices and different instrument types showed variable precision (60-89%) and recall (10-88%) particularly for datasets dominated by nonlipid metabolites. The workflow is freely available as an open-source R package, MetaboAnnotatoR, together with the fragment libraries from Github (https://github.com/gggraca/MetaboAnnotatoR).

Is Current Practice Adhering to Guidelines Proposed for Metabolite Identification in LC-MS Untargeted Metabolomics? A Meta-Analysis of the Literature.

Metabolite identification remains a bottleneck and a still unregulated area in untargeted LC-MS metabolomics. The metabolomics research community and, in particular, the metabolomics standards initiative (MSI) proposed minimum reporting standards for metabolomics including those for reporting metabolite identification as long ago as 2007. Initially, four levels were proposed ranging from level 1 (unambiguously identified analyte) to level 4 (unidentified analyte). This scheme was expanded in 2014, by independent research groups, to give five levels of confidence. Both schemes provided guidance to the researcher and described the logical steps that had to be made to reach a confident reporting level. These guidelines have been presented and discussed extensively, becoming well-known to authors, editors, and reviewers for academic publications. Despite continuous promotion within the metabolomics community, the application of such guidelines is questionable. The scope of this meta-analysis was to systematically review the current LC-MS-based literature and effectively determine the proportion of papers following the proposed guidelines. Also, within the scope of this meta-analysis was the measurement of the actual identification levels reported in the literature, that is to find how many of the published papers really reached full metabolite identification (level 1) and how many papers did not reach this level.

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.

Lipid profiling of mouse intestinal organoids for studying APC mutations.

Inactivating mutations including both germline and somatic mutations in the adenomatous polyposis coli (APC) gene drives most familial and sporadic colorectal cancers. Understanding the metabolic implications of this mutation will aid to establish its wider impact on cellular behaviour and potentially inform clinical decisions. However, to date, alterations in lipid metabolism induced by APC mutations remain unclear. Intestinal organoids have gained widespread popularity in studying colorectal cancer and chemotherapies, because their 3D structure more accurately mimics an in vivo environment. Here, we aimed to investigate intra-cellular lipid disturbances induced by APC gene mutations in intestinal organoids using a reversed-phase ultra-high-performance liquid chromatography mass spectrometry (RP-UHPLC-MS)-based lipid profiling method. Lipids of the organoids grown from either wild-type (WT) or mice with APC mutations (Lgr5-EGFP-IRES-CreERT2Apcfl/fl) were extracted and analysed using RP-UHPLC-MS. Levels of phospholipids (e.g. PC(16:0/16:0), PC(18:1/20:0), PC(38:0), PC(18:1/22:1)), ceramides (e.g. Cer(d18:0/22:0), Cer(d42:0), Cer(d18:1/24:1)) and hexosylceramides (e.g. HexCer(d18:1/16:0), HexCer(d18:1/22:0)) were higher in Apcfl/fl organoids, whereas levels of sphingomyelins (e.g. SM(d18:1/14:0), SM(d18:1/16:0)) were lower compared with WT. These observations indicate that cellular metabolism of sphingomyelin was up-regulated, resulting in the cellular accumulation of ceramides and production of HexCer due to the absence of Apcfl/fl in the organoids. Our observations demonstrated lipid profiling of organoids and provided an enhanced insight into the effects of the APC mutations on lipid metabolism, making for a valuable addition to screening options of the organoid lipidome.

Metabolic Signatures of Gestational Weight Gain and Postpartum Weight Loss in a Lifestyle Intervention Study of Overweight and Obese Women.

BACKGROUND: Overweight and obesity amongst women of reproductive age are increasingly common in developed economies and are shown to adversely affect birth outcomes and both childhood and adulthood health risks in the offspring. Metabolic profiling in conditions of overweight and obesity in pregnancy could potentially be applied to elucidate the molecular basis of the adverse effects of gestational weight gain (GWG) and postpartum weight loss (WL) on future risks for cardiovascular disease (CVD) and other chronic diseases. METHODS: Biofluid samples were collected from 114 ethnically diverse pregnant women with body mass index (BMI) between 25 and 40 kg/m2 from Chicago (US), as part of a randomized lifestyle intervention trial (Maternal Offspring Metabolics: Family Intervention Trial; NCT01631747). At 15 weeks, 35 weeks of gestation, and at 1 year postpartum, the blood plasma lipidome and metabolic profile of urine samples were analyzed by liquid chromatography mass spectrometry (LC-MS) and 1H nuclear magnetic resonance spectroscopy (1H NMR) respectively. RESULTS: Urinary 4-deoxyerythronic acid and 4-deoxythreonic acid were found to be positively correlated to BMI. Seventeen plasma lipids were found to be associated with GWG and 16 lipids were found to be associated with WL, which included phosphatidylinositols (PI), phosphatidylcholines (PC), lysophospholipids (lyso-), sphingomyelins (SM) and ether phosphatidylcholine (PC-O). Three phospholipids found to be positively associated with GWG all contained palmitate side-chains, and amongst the 14 lipids that were negatively associated with GWG, seven were PC-O. Six of eight lipids found to be negatively associated with WL contained an 18:2 fatty acid side-chain. CONCLUSIONS: Maternal obesity was associated with characteristic urine and plasma metabolic phenotypes, and phospholipid profile was found to be associated with both GWG and postpartum WL in metabolically healthy pregnant women with overweight/obesity. Postpartum WL may be linked to the reduction in the intake of linoleic acid/conjugated linoleic acid food sources in our study population.

Tissue Multiplatform-Based Metabolomics/Metabonomics for Enhanced Metabolome Coverage.

The use of tissue as a matrix to elucidate disease pathology or explore intervention comes with several advantages. It allows investigation of the target alteration directly at the focal location and facilitates the detection of molecules that could become elusive after secretion into biofluids. However, tissue metabolomics/metabonomics comes with challenges not encountered in biofluid analyses. Furthermore, tissue heterogeneity does not allow for tissue aliquoting. Here we describe a multiplatform, multi-method workflow which enables metabolic profiling analysis of tissue samples, while it can deliver enhanced metabolome coverage. After applying a dual consecutive extraction (organic followed by aqueous), tissue extracts are analyzed by reversed-phase (RP-) and hydrophilic interaction liquid chromatography (HILIC-) ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS) and nuclear magnetic resonance (NMR) spectroscopy. This pipeline incorporates the required quality control features, enhances versatility, allows provisional aliquoting of tissue extracts for future guided analyses, expands the range of metabolites robustly detected, and supports data integration. It has been successfully employed for the analysis of a wide range of tissue types.

Expanding lipidome coverage using MS/MS-aided untargeted data-independent RP-UPLC-TOF-MSE acquisition.

Lipid function and importance in disease are being rediscovered due to modern advancements in chemical analysis. RP-UPLC-TOF-MSE is now the lipidomics tool of choice and can provide the demanded specificity for detecting the great diversity of the lipidome. It can offer simplicity, rapidity, robustness and high throughputness, without the need for further optimization in current sample preparation protocols. This method can cover the major lipid categories with the ability to detect several corresponding subclasses. It can deliver adequate information for deciphering fatty chain length, unsaturation and regioisomerism. It has enabled the detection of a vast number of lipids, of which more than 250 are reported here. These lipids were detected from applications in a variety of biological matrices and species.

Application of Metabolic Profiling to Abdominal Aortic Aneurysm Research.

Abdominal aortic aneurysm (AAA) is a complex disease posing diagnostic and therapeutic challenges. Metabonomics may aid in the diagnosis of AAA, determination of individualized risk, discovery of therapeutic targets, and improve understanding of pathogenesis. A systematic review of the diversity and outcomes of existing AAA metabonomic research has been performed. Original research studies applying metabonomics to human aneurysmal disease are included. Seven relevant articles were identified: four studies were based on plasma/serum metabolite profiling, and three studies examined aneurysmal tissue. Aminomalonic acid, guanidinosuccinic acid, and glycerol emerge as potential plasma biomarkers of large aneurysm. Lipid profiling improves predictive models of aneurysm presence. Patterns of metabolite variation associated with AAA relate to carbohydrate and lipid metabolism. Perioperative perturbations in metabolites suggest differential systemic inflammatory responses to surgery, generating hypotheses for adjunctive perioperative therapy. Significant limitations include small study sizes, lack of correction for multiple testing false discovery rates, and single time-point sampling. Metabolic profiling carries the potential to identify biomarkers of AAA and elucidate pathways underlying aneurysmal disease. Statistically and methodologically robust studies are required for validation, addressing the hiatus in understanding mechanisms of aneurysm growth and developing effective treatment strategies.

Lessons from Metabonomics on the Neurobiology of Stroke.

The application of metabonomic science to interrogate stroke permits the study of metabolite entities, small enough to cross the blood-brain barrier, that provide insight into neuronal dysfunction, and may serve as reservoirs of biomarker discovery. This systematic review examines the applicability of metabolic profiling in ischemic stroke research. Six human studies utilizing metabolic profiling to analyze biofluids from ischemic stroke patients have been included, employing 1H-NMR and/or mass spectrometry to analyze plasma, serum, and/or urine in a targeted or untargeted fashion. Three are diagnostic studies, and one investigates prognostic biomarkers of stroke recurrence following transient ischemic attack. Two studies focus on metabolic distinguishers of depression or cognitive impairment following stroke. Identified biomarkers from blood and urine predominantly relate to homocysteine and folate, branched chain amino acid, and lipid metabolism. Statistical models are well fitted and reproducible, with excellent validation outcomes, demonstrating the feasibility of metabolic profiling to study a complex disorder with multicausal pathology, such as stroke.

Replication of LC-MS untargeted lipidomics results in patients with calcific coronary disease: An interlaboratory reproducibility study.

BACKGROUND: Recently a lipidomics approach was able to identify perturbed fatty acyl chain (FAC) and sphingolipid moieties that could stratify patients according to the severity of coronary calcification, a form of subclinical atherosclerosis. Nevertheless, these findings have not yet been reproduced before generalising their application. The aim of this study was to evaluate the reproducibility of lipidomics approaches by replicating previous lipidomic findings in groups of patients with calcific coronary artery disease (CCAD). METHODS: Patients were separated into the following groups based on their calcium score (CS); no calcification (CS: 0; n=26), mild calcification (CS: 1-250; n=27) and severe calcification (CS: >250; n=17). Two serum samples were collected from each patient and used for comparative analyses by 2 different laboratories, in different countries and time points using liquid chromatography coupled to mass spectrometry untargeted lipidomics methods. RESULTS: Six identical metabolites differentiated patients with severe coronary artery calcification from those with no calcification were found by both laboratories independently. Additionally, relative intensities from the two analyses demonstrated high correlation coefficients. Phosphatidylcholine moieties with 18-carbon FAC were identified in lower intensities and 20:4 FAC in higher intensities in the serum of diseased group. Moreover, 3 common sphingomyelins were detected. CONCLUSION: This is the first interlaboratory reproducibility study utilising lipidomics applications in general and specifically in patients with CCAD. Lipid profiling applications in patients with CCAD are very reproducible in highly specialised and experienced laboratories and could be applied in clinical practice in order to spare patients diagnostic radiation.

Discovery of Infection Associated Metabolic Markers in Human African Trypanosomiasis.

Human African trypanosomiasis (HAT) remains a major neglected tropical disease in Sub-Saharan Africa. As clinical symptoms are usually non-specific, new diagnostic and prognostic markers are urgently needed to enhance the number of identified cases and optimise treatment. This is particularly important for disease caused by Trypanosoma brucei rhodesiense, where indirect immunodiagnostic approaches have to date been unsuccessful. We have conducted global metabolic profiling of plasma from T.b.rhodesiense HAT patients and endemic controls, using 1H nuclear magnetic resonance (NMR) spectroscopy and ultra-performance liquid chromatography, coupled with mass spectrometry (UPLC-MS) and identified differences in the lipid, amino acid and metabolite profiles. Altogether 16 significantly disease discriminatory metabolite markers were found using NMR, and a further 37 lipid markers via UPLC-MS. These included significantly higher levels of phenylalanine, formate, creatinine, N-acetylated glycoprotein and triglycerides in patients relative to controls. HAT patients also displayed lower concentrations of histidine, sphingomyelins, lysophosphatidylcholines, and several polyunsaturated phosphatidylcholines. While the disease metabolite profile was partially consistent with previous data published in experimental rodent infection, we also found unique lipid and amino acid profile markers highlighting subtle but important differences between the host response to trypanosome infections between animal models and natural human infections. Our results demonstrate the potential of metabolic profiling in the identification of novel diagnostic biomarkers and the elucidation of pathogenetic mechanisms in this disease.

Optimization of metabolite extraction of human vein tissue for ultra performance liquid chromatography-mass spectrometry and nuclear magnetic resonance-based untargeted metabolic profiling.

Human vein tissue is an important matrix to examine when investigating vascular diseases with respect to understanding underlying disease mechanisms. Here, we report the development of an extraction protocol for multi-platform metabolic profiling of human vein tissue. For the first stage of the optimization, two different ratios of methanol/water and 5 organic solvents--namely dichloromethane, chloroform, isopropanol, hexane and methyl tert-butyl ether (MTBE) solutions with methanol--were tested for polar and organic compound extraction, respectively. The extraction output was assessed using (1)H Nuclear Magnetic Resonance (NMR) spectroscopy and a panel of Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS) methodologies. On the basis of the reproducibility of extraction replicates and metabolic coverage, the optimal aqueous (methanol/water) and organic (MTBE/methanol) solvents identified from the first stage were used in a sequential approach for metabolite extraction, altering the order of solvent-mixture addition. The combination of organic metabolite extraction with MTBE/methanol (3 : 1) followed by extraction of polar compounds with methanol/water (1 : 1) was shown to be the best method for extracting metabolites from human vein tissue in terms of reproducibility and number of signals detected and could be used as a single extraction procedure to serve both NMR and UPLC-MS analyses. Molecular classes such as triacylglycerols, phosphatidylcholines, phosphatidylethanolamines, sphingolipids, purines, and pyrimidines were reproducibly extracted. This study enabled an optimal extraction protocol for robust and more comprehensive metabolome coverage for human vein tissue. Many of the physiological and pathological processes affecting the composition of human vein tissue are common to other tissues and hence the extraction method developed in this study can be generically applied.

Perturbations in fatty acid metabolism and apoptosis are manifested in calcific coronary artery disease: An exploratory lipidomic study.

BACKGROUND: Controversy exists concerning the beneficial or harmful effects of the presence of ectopic calcification in the coronary arteries. Additionally, further elucidation of the exact pathophysiological mechanism is needed. In this study, we sought to identify metabolic markers of vascular calcification that could assist in understanding the disease, monitoring its progress and generating hypotheses describing its pathophysiology. METHODS: Untargeted lipid profiling and complementary modeling strategies were employed to compare serum samples from patients with different levels of calcific coronary artery disease (CCAD) based on their calcium score (CS). Subsequently, patients were divided into three groups: no calcification (NC; CS=0; n=26), mild calcification (MC; CS:1-250; n=27) and severe (SC; CS>250; n=17). RESULTS: Phosphatidylcholine levels were found to be significantly altered in the disease states (p=0.001-0.04). Specifically, 18-carbon fatty acyl chain (FAC) phosphatidylcholines were detected in lower levels in the SC group, while 20:4 FAC lipid species were detected in higher concentrations. A statistical trend was observed with phosphatidylcholine lipids in the MC group, showing the same tendency as with the SC group. We also observed several sphingomyelin signals present at lower intensities in SC when compared with NC or MC groups (p=0.000001-0.01). CONCLUSIONS: This is the first lipid profiling study reported in CCAD. Our data demonstrate dysregulations of phosphatidylcholine lipid species, which suggest perturbations in fatty acid elongation/desaturation. The altered levels of the 18-carbon and 20:4 FAC lipids may be indicative of disturbed inflammation homeostasis. The marked sphingomyelin dysregulation in SC is consistent with profound apoptosis as a potential mechanism of CCAD.

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 (n = 120) of arterial tissue could be distinguished based on their metabolic profiles.

Metabolic phenotyping of atherosclerotic plaques reveals latent associations between free cholesterol and ceramide metabolism in atherogenesis.

Current optimum medical treatments have had limited success in the primary prevention of cardiovascular events, underscoring the need for new pharmaceutical targets and enhanced understanding of mechanistic metabolic dysregulation. Here, we use a combination of novel metabolic profiling methodologies, based on ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS) followed by chemometric modeling, data integration, and pathway mapping, to create a systems-level metabolic atlas of atherogenesis. We apply this workflow to compare arterial tissue incorporating plaque lesions to intimal thickening tissue (immediate preplaque stage). We find changes in several metabolite species consistent with well-established pathways in atherosclerosis, such as the cholesterol, purine, pyrimidine, and ceramide pathways. We then illustrate differential levels of previously unassociated lipids to atherogenesis, namely, phosphatidylethanolamine-ceramides (t-test p-values: 3.8 × 10(-6) to 9.8 × 10(-12)). Most importantly, these molecules appear to be interfacing two pathways recognized for their involvement in atherosclerosis: ceramide and cholesterol. Furthermore, we show that ß-oxidation intermediates (i.e., acylcarnitines) manifest a pattern indicating truncation of the process and overall dysregulation of fatty acid metabolism and mitochondrial dysfunction. We develop a metabolic framework that offers the ability to map significant statistical associations between detected biomarkers. These dysregulated molecules and consequent pathway modulations may provide novel targets for pharmacotherapeutic intervention.

Systems biology of human atherosclerosis.

Systems biology describes a holistic and integrative approach to understand physiology and pathology. The "omic" disciplines include genomics, transcriptomics, proteomics, and metabolic profiling (metabonomics and metabolomics). By adopting a stance, which is opposing (yet complimentary) to conventional research techniques, systems biology offers an overview by assessing the "net" biological effect imposed by a disease or nondisease state. There are a number of different organizational levels to be understood, from DNA to protein, metabolites, cells, organs and organisms, even beyond this to an organism's context. Systems biology relies on the existence of "nodes" and "edges." Nodes are the constituent part of the system being studied (eg, proteins in the proteome), while the edges are the way these constituents interact. In future, it will be increasingly important to collaborate, collating data from multiple studies to improve data sets, making them freely available and undertaking integrative analyses.

A closed-tube methylation-sensitive high resolution melting assay (MS-HRMA) for the semi-quantitative determination of CST6 promoter methylation in clinical samples.

BACKGROUND: CST6 promoter is highly methylated in cancer, and its detection can provide important prognostic information in breast cancer patients. The aim of our study was to develop a Methylation-Sensitive High Resolution Melting Analysis (MS-HRMA) assay for the investigation of CST6 promoter methylation. METHODS: We designed primers that amplify both methylated and unmethylated CST6 sequences after sodium bisulfate (SB) treatment and used spiked control samples of fully methylated to unmethylated SB converted genomic DNA to optimize the assay. We first evaluated the assay by analyzing 36 samples (pilot training group) and further analyzed 80 FFPES from operable breast cancer patients (independent group). MS-HRMA assay results for all 116 samples were compared with Methylation-Specific PCR (MSP) and the results were comparable. RESULTS: The developed assay is highly specific and sensitive since it can detect the presence of 1% methylated CST6 sequence and provides additionally a semi-quantitative estimation of CST6 promoter methylation. CST6 promoter was methylated in 39/80 (48.75%) of FFPEs with methylation levels being very different among samples. MS-HRMA and MSP gave comparable results when all samples were analyzed by both assays. CONCLUSIONS: The developed MS-HRMA assay for CST6 promoter methylation is closed tube, highly sensitive, cost-effective, rapid and easy-to-perform. It gives comparable results to MSP in less time, while it offers the advantage of additionally providing an estimation of the level of methylation.

Implementation of molecular phenotyping approaches in the personalized surgical patient journey.

OBJECTIVE: The present review describes commonly employed metabolic profiling platforms and discusses the current and likely future application of these technologies in surgery. BACKGROUND: The metabolic adaptations that occur in response to surgical illness and trauma are incompletely understood. Evaluating these will be critical to the development of personalized surgical health solutions. Metabonomics is an advancing field in systems biology, which provides a means of interrogating these metabolic shifts. METHODS: Recent literature regarding metabolic profiling technologies and their applications in surgical practice are discussed. Future strategies are proposed for the incorporation of these and next-generation technologies in the evaluation of all steps in the patient surgical pathway. RESULTS: Metabolite-based profiling has provided valuable insights into the metabolic irregularities that occur in cancer development and progression across a variety of cancer subclasses including colorectal, breast, prostate, and lung cancers. In addition, metabolic modeling has shown considerable promise in other surgical conditions including trauma and sepsis and in the assessment of pharmacotherapeutic efficacy. DISCUSSION: Metabonomics offers a posttranscriptional view of system activity providing functional information downstream of the genome and proteome. Information at this level will provide the surgeon with a novel means of evaluating major socioeconomic problems such as cancer and sepsis. In addition, the rapid nature of emerging next generation profiling platforms provides a viable means of "real-time" perioperative metabolic assessment and optimization.