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Background and AimsPrevious trials evaluating remote ischaemic preconditioning in children undergoing cardiac surgery showed mixed results. We sought to determine whether adequately delivered bilateral preconditioning is cardioprotective in young children, with or without cyanosis, undergoing surgery.MethodsProspective, double-blind, randomised controlled trial at two UK centres. Children aged 3-36 months undergoing tetralogy of Fallot repair or ventricular septal defect closure were randomised in a 1:1 ratio to receive either bilateral preconditioning or sham intervention. Participants were followed up until hospital discharge or 30 days. The primary outcome was area under the curve for high-sensitivity troponin-T in the first 24 hours after surgery, analysed by intention-to-treat. Right atrial biopsies were obtained in selected patients. Trial registration:ISRCTN12923441.ResultsBetween 24 October 2016 and 8 December 2020, 120 eligible children were randomised to receive either bilateral preconditioning (n=60) or sham intervention (n=60). Participants had a median age of 7 months and 42 (35%) were female. The primary outcome, area under the curve for hs-troponin-T was higher in the preconditioning group (mean: 70.0±50.9µg/L/hr, n=56) than in controls (mean: 55.6±30.1µg/L/hr, n=58), p=0.04. Sub-group analyses did not show a differential treatment effect by oxygen saturations (pinteraction=0.25) but showed evidence of differential treatment effect by underlying defect (pinteraction=0.04). Myocardial metabolism, quantified in atrial biopsies, and secondary outcomes were not different between randomised groups.ConclusionsBilateral remote ischemic preconditioning does not attenuate myocardial injury in children undergoing surgical repair for congenital heart defects, and there was evidence of potential harm in unstented tetralogy of Fallot.

The tricarboxylic acid (TCA) cycle, or Krebs cycle, is the central pathway of energy production in eukaryotic cells and plays a key part in aerobic respiration throughout all kingdoms of life. The enzymes involved in this cycle generate the reducing equivalents NADH and FADH2 by a series of enzymatic reactions, which are utilized by the electron transport chain to produce ATP. One of the pivotal enzymes in this cycle is 2-oxoglutarate dehydrogenase complex (OGDHC), which generates NADH by oxidative decarboxylation of 2-oxoglutarate to succinyl-CoA. OGDHC is a megadalton protein complex originally thought to be assembled just from three catalytically active subunits (E1o, E2o, E3). In fungi and animals, however, the protein MRPS36 has more recently been proposed as a putative additional component. Based on extensive XL-MS data obtained from measurements in mice and bovine heart mitochondria, supported by phylogenetic analyses, we provide evidence that MRPS36 is an essential member of OGDHC, albeit only in eukaryotes. Comparative sequence analysis and computational structure predictions reveal that in eukaryotic OGDHC, E2o does not contain the peripheral subunit-binding domain (PSBD), present in bacterial and archaeal E2o’s. We propose that in eukaryotes MRPS36 evolved as an E3 adaptor protein, functionally replacing the PSBD. We further provide a refined structural model of the complete eukaryotic OGDHC containing 16 E1o, 12 E3, and 6 subunits of MRPS36 accommodated around the OGDHC core composed of 24 E2o subunits (3.45 MDa). The model provides new insights into the OGDH complex topology and stipulates putative mechanistic implications.

Acute myeloid leukaemia (AML) creates an immunosuppressive environment to conventional T cells through Arginase 2 (ARG2)-induced arginine depletion. We identify that AML blasts release the acute phase protein serum amyloid A (SAA), which acts in an autocrine manner to upregulate ARG2 expression and activity, and promote AML blast viability. Following in vitro cross-talk invariant natural killer T (iNKT) cells become activated, upregulate mitochondrial capacity, and release IFN-γ. iNKT retain their ability to proliferate and be activated despite the low arginine AML environment, due to the upregulation of Large Neutral Amino Acid Transporter-1 (LAT-1) and Argininosuccinate Synthetase 1 (ASS)-dependent amino acid pathways, resulting in AML cell death. T cell proliferation is restored in vitro and in vivo. The capacity of iNKT cells to restore antigen-specific T cell immunity was similarly demonstrated against myeloid-derived suppressor cells (MDSCs) in wild-type and Jα18−/− syngeneic lymphoma-bearing models in vivo. Thus, stimulation of iNKT cell activity has the potential as an immunotherapy against AML or as an adjunct to boost antigen-specific T cell immunotherapies in haematological or solid cancers.

Spatial separation of ions in the gas-phase, providing information about their size as collisional cross-sections, can readily be achieved through ion mobility. The timsTOF Pro series combines a trapped ion mobility device with a quadrupole, collision-cell and a time-of-flight analyser to enable the analysis of ions at great speed. Here, we show that the timsTOF Pro is capable of physically separating N-glycopeptides from non-modified peptides and producing high-quality fragmentation spectra, both beneficial for glycoproteomics analyses of complex samples. The glycan moieties enlarge the size of glycopeptides compared to non-modified peptides, yielding a clear cluster in the mobilogram that, next to increased dynamic range from the physical separation of glycopeptides and non-modified peptides, can be used to make an effective selection filter for directing the mass spectrometer to analytes of interest. This new approach was applied to selected glycoproteins, human plasma- and neutrophil-derived glycopeptides. We show that the achieved physical separation, combined with the focussing of the mass spectrometer, allows for improved extraction of information from the samples, even at shorter LC gradients of 15 min. We validated our approach on human neutrophil and plasma samples of known make-up, in which we captured the anticipated glycan heterogeneity (paucimannose, phosphomannose, high mannose, hybrid and complex glycans) from plasma and neutrophil samples at the expected abundances. As the method is compatible with off-the-shelve data acquisition routines and data analysis software, it can readily be applied by any laboratory with a timsTOF Pro and is reproducible as demonstrated by a comparison between two laboratories.

Crosslinking mass spectrometry (XL-MS) adds tremendous value to structural biology investigations. Its main strength lies in uncovering structural information in the form of distance constraints between neighboring amino acids of proteins, protein regions and complex samples, which are difficult to assess by other analytical techniques. However, although several approaches have been proposed, interpreting XL-MS data in a structural context has been cumbersome. ChimeraX has gained momentum as a flexible and widely used software package for the visualization of structural data, but is currently lacking functionalities for integration of experimental XL-MS data. Here, we introduce XMAS, a bundle that allows users to load results from several XL-MS search engines directly into ChimeraX and map the information onto protein structures. Besides automatically locating distance constraints on protein structures, XMAS offers the possibility to work with replicate experiments and/or different crosslinkers, and filter this data based on the number of replicates for which a given distance constraint was detected, thereby increasing the data quality. Additionally, we introduce the concept of self-links, which allows easy modeling of homo-dimeric interactions. Its core functionality is extended by the implementation of seamless connections to the HADDOCK suite to streamline otherwise time-consuming tasks in structural modeling pipelines. We demonstrate these key elements of the XMAS bundle by modeling crosslinking data obtained from human fibrin clots. The software is freely available from the ChimeraX toolshed, with an extensive user manual and example datasets.

With ever-increasing amounts of data produced by mass spectrometry (MS) proteomics and metabolomics, and the sheer volume of samples now analyzed, the need for a common open format possessing both file size efficiency and faster read/write speeds has become paramount to drive the next generation of data analysis pipelines. The Proteomics Standards Initiative (PSI) has established a clear and precise XML representation for data interchange, mzML, receiving substantial uptake; nevertheless, storage and file access efficiency has not been the main focus. We propose an HDF5 file format ‘mzMLb’ that is optimised for both read/write speed and storage of the raw mass spectrometry data. We provide extensive validation of write speed, random read speed and storage size, demonstrating a flexible format that with or without compression is faster than all existing approaches in virtually all cases, while with compression, is comparable in size to proprietary vendor file formats. Since our approach uniquely preserves the XML encoding of the metadata, the format implicitly supports future versions of mzML and is straightforward to implement: mzMLb’s design adheres to both HDF5 and NetCDF4 standard implementations, which allows it to be easily utilised by third parties due to their widespread programming language support. A reference implementation within the established ProteoWizard toolkit is provided. Data files used in the paper.

Coral reefs are suffering unprecedented declines in health state on a global scale. Some have suggested that human assisted evolution or assisted gene flow may now be necessary to effectively restore reefs and pre-condition them for future climate change. An understanding of the key metabolic processes in corals, including under stressed conditions, would greatly facilitate the effective application of such interventions. To date, however, there has been little research on corals at this level, particularly regarding studies of the metabolome of Scleractinian corals. Here, the metabolomic profiles [measured using 1H nuclear magnetic resonance spectroscopy (1H NMR) and ultra-high-performance liquid chromatography-mass spectrometry (LC-MS)] of two dominant reef building corals, Acropora hyacinthus and A. millepora, from two distinct geographical locations (Australia and Singapore) were characterized. We assessed how an acute temperature stress (an increase of 3.25°C ± 0.28 from ambient control levels over 8 days), shifted the corals’ baseline metabolomic profiles. Regardless of the profiling method utilized, metabolomic signatures of coral colonies were significantly distinct between coral species, a result supporting previous work. However, this strong species-specific metabolomic signature appeared to mask any changes resulting from the acute heat stress. On closer examination, we were able to discriminate between control and temperature stressed groups using a partial least squares discriminant analysis classification model (PLSDA). However, in all cases “late” components needed to be selected (i.e., 7 and 8 instead of 1 and 2), suggesting any treatment effect was small, relative to other sources of variation. This highlights the importance of pre-characterizing the coral colony metabolomes, and of factoring that knowledge into any experimental design that seeks to understand the apparently subtle metabolic effects of acute heat stress on adult corals. Further research is therefore needed to decouple these apparent individual and species-level metabolomic responses to climate change in corals.

High plasma triacylglyceride levels are known to be associated with increased risk of atherosclerotic cardiovascular disease. Apolipoprotein C-III (apoC-III) is a key regulator of plasma triacylglyceride levels and is associated with hypertriglyceridemia via a number of pathways. There is consistent evidence for an association of cardiovascular events with blood apoC-III level, with support from human genetic studies of APOC3 variants. As such, apoC-III has been recognised as a potential therapeutic target for patients with severe hypertriglyceridaemia with one of the most promising apoC-III-targeting drugs, volanesorsen, having recently progressed through Phase III trials.To exploit a rare loss of function variant in APOC3 (rs138326449) to characterise the potential long-term treatment effects of apoC-III targeting interventions on the metabolome.In a recall-by-genotype study, 115 plasma samples were analysed by UHPLC-MS to acquire non-targeted metabolomics data. The study included samples from 57 adolescents and 33 adults. Overall, 12 985 metabolic features were tested for an association with APOC3 genotype.161 uniquely annotated metabolites were found to be associated with rs138326449(APOC3). The highest proportion of associated metabolites belonged to the acyl-acyl glycerophospholipid and triacylglyceride metabolite classes. In addition to the anticipated (on-target) reduction of metabolites in the triacylglyceride and related classes, carriers of the rare variant exhibited previously unreported increases in levels of a number of metabolites from the acyl-alkyl glycerophospholipid class.Overall, our results suggest that therapies targeting apoC-III may potentially achieve a broad shift in lipid profile that favours better metabolic health.

Metabolic phenotyping of tissues uses metabolomics and lipidomics to measure the relative polar and nonpolar (lipid) metabolite levels in biological samples. This approach aims to understand disease biochemistry and identify biochemical markers of disease. Sample preparation methods must be reproducible, sensitive (high metabolite and lipid yield), and ideally rapid. We evaluated three biphasic methods for polar and nonpolar compound extraction (chloroform/methanol/water, dichloromethane/methanol/water, and methyl tert-butyl ether [MTBE]/methanol/water), a monophasic method for polar compound extraction (acetonitrile/methanol/water), and a monophasic method for nonpolar compound extraction (isopropanol/water). All methods were applied to mammalian heart, kidney, and liver tissues. Polar extracts were analyzed by hydrophilic interaction chromatography (HILIC) ultrahigh-performance liquid chromatography-mass spectrometry (UHPLC-MS) and nonpolar extracts by C18 reversed-phase UHPLC-MS. Method reproducibility and yield were assessed using multiple annotated endogenous compounds (putatively and MS/MS annotated). Monophasic methods had the highest yield and high reproducibility for both polar (positive ion: median relative standard deviation (RSD) < 18%; negative ion: median RSD < 28%) and nonpolar (positive and negative ion: median RSD < 15%) extractions for heart, kidneys, and liver. The polar monophasic method extracted higher levels of lipid than biphasic polar extractions, and these lipids caused minimal detection suppression for other compounds during HILIC UHPLC-MS. The nonpolar monophasic method had similar or greater detection responses of all detected lipid classes compared to biphasic methods (including increased phosphatidylinositol, phosphatidylserine, and cardiolipin responses). Monophasic methods are quicker and simpler than biphasic methods and are therefore most suited for future automation.

Measuring physiochemically diverse molecules (including lipids) which vary significantly in their concentrations poses a great analytical challenge. In untargeted lipidomics studies, reversed phase chromatography coupled with data-dependent MS/MS acquisition (DDA) is frequently applied. The optimal assay should deliver a high number of detected compounds with associated fragmentation data. In this work, we introduce novel 30 and 50 min UHPLC assays utilising lipid separation on a C

Hematological and solid cancers catabolize the semiessential amino acid arginine to drive cell proliferation. However, the resulting low arginine microenvironment also impairs chimeric antigen receptor T cells (CAR-T) cell proliferation, limiting their efficacy in clinical trials against hematological and solid malignancies. T cells are susceptible to the low arginine microenvironment because of the low expression of the arginine resynthesis enzymes argininosuccinate synthase (ASS) and ornithine transcarbamylase (OTC). We demonstrate that T cells can be reengineered to express functional ASS or OTC enzymes, in concert with different chimeric antigen receptors. Enzyme modifications increase CAR-T cell proliferation, with no loss of CAR cytotoxicity or increased exhaustion. In vivo, enzyme-modified CAR-T cells lead to enhanced clearance of leukemia or solid tumor burden, providing the first metabolic modification to enhance CAR-T cell therapies.

Clinical metabolic phenotyping employs metabolomics and lipidomics to detect and measure hundreds to thousands of metabolites and lipids within human samples. This approach aims to identify metabolite and lipid changes between phenotypes (e.g. disease status) that aid understanding of biochemical mechanisms driving the phenotype. Sample preparation is a critical step in clinical metabolic phenotyping: it must be reproducible and give a high extraction yield of metabolites and lipids, and in high-throughput studies it needs to be rapid. Here, we assessed the extraction of polar metabolites from human urine and polar metabolites and lipids from human plasma for analysis by ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) metabolomics and lipidomics. We evaluated several monophasic (urine and plasma) and biphasic (plasma) extractions, and we also tested alterations to (a) solvent–biofluid incubation time and temperature during monophasic extraction, and (b) phase partitioning time during biphasic extraction. Extracts were analysed by three UHPLC-MS assays: (i) hydrophilic interaction chromatography (HILIC) for urine and plasma, (ii) C18 aqueous reversed phase for urine, and (iii) C18 reversed phase for plasma lipids, and the yield and reproducibility of each method was assessed. We measured UHPLC-MS injection reproducibility as well as sample preparation reproducibility to assess sample solvent composition compatibility with UHPLC-MS and to pinpoint the origin of variance within the methods. For HILIC UHPLC-MS plasma and urine analysis, monophasic 50 : 50 methanol : acetonitrile had the most detected putatively-identified polar metabolites with high method reproducibility. This method had the highest lipid yield for plasma extracts analysed by the HILIC method. If lipid removal from the plasma polar HILIC extract is required, then the biphasic methanol/chloroform/water method is recommended. For C18 (aqueous) UHPLC-MS urine analysis, 50 : 50 methanol : water had high reproducibility and yield. For C18 UHPLC-MS plasma lipidomics, monophasic 100% isopropanol had the highest detection response of all annotated lipid classes with high reproducibility. Increasing monophasic incubation time and temperature had little benefit on metabolite and lipid yield and reproducibility for all methods.

Dysregulated cellular metabolism is a hallmark of cancer. As yet, few druggable oncoproteins directly responsible for this hallmark have been identified. Increased fatty acid acquisition allows cancer cells to meet their membrane biogenesis, ATP, and signaling needs. Excess fatty acids suppress growth factor signaling and cause oxidative stress in non-transformed cells, but surprisingly not in cancer cells. Molecules underlying this cancer adaptation may provide new drug targets. Here, we identify Diacylglycerol O-acyltransferase 1 (DGAT1), an enzyme integral to triacylglyceride synthesis and lipid droplet formation, as a frequently up-regulated oncoprotein allowing cancer cells to tolerate excess fatty acids. DGAT1 over-expression alone induced melanoma in zebrafish melanocytes, and co-operated with oncogenic BRAF or NRAS for more rapid melanoma formation. Mechanistically, DGAT1 stimulated melanoma cell growth through sustaining mTOR kinase–S6 kinase signaling and suppressed cell death by tempering fatty acid oxidation, thereby preventing accumulation of reactive oxygen species including lipid peroxides.SIGNIFICANCEWe show that DGAT1 is a bona fide oncoprotein capable of inducing melanoma formation and co-operating with other known drivers of melanoma. DGAT1 facilitates enhanced fatty acid acquisition by melanoma cells through suppressing lipototoxicity. DGAT1 is also critical for maintaining S6K activity required for melanoma cell growth.

Introduction High plasma triacylglyceride levels are known to be associated with increased risk of atherosclerotic cardiovascular disease. Apolipoprotein C-III (apoC-III) is a key regulator of plasma triacylglyceride levels and is associated with hypertriglyceridemia via a number of pathways. There is consistent evidence for an association of cardiovascular events with blood apoC-III level, with support from human genetic studies of APOC3 variants. As such, apoC-III has been recognised as a potential therapeutic target for patients with severe hypertriglyceridaemia with one of the most promising apoC-III-targeting drugs, volanesorsen, having recently progressed through Phase III trials. Objectives To exploit a rare loss of function variant in APOC3 (rs138326449) to characterise the potential long-term treatment effects of apoC-III targeting interventions on the metabolome. Methods In a recall-by-genotype study, 115 plasma samples were analysed by UHPLC-MS to acquire non-targeted metabolomics data. The study included samples from 57 adolescents and 33 adults. Overall, 12 985 metabolic features were tested for an association with APOC3 genotype. Results 144 uniquely annotated metabolites were found to be associated with rs138326449(APOC3). The highest proportion of associated metabolites belonged to the acyl-acyl glycerophospholipid and triacylglyceride metabolite classes. In addition to the anticipated (on-target) reduction of metabolites in the triacylglyceride and related classes, carriers of the rare variant exhibited previously unreported increases in levels of a number of metabolites from the acyl-alkyl glycerophospholipid and ceramide classes. Conclusion Overall, our results suggest that therapies targeting apoC-III may potentially achieve a broad shift in lipid profile that favours better metabolic health.

Characterisation of animal models of diabetic cardiomyopathy may help unravel new molecular targets for therapy. Long-living individuals are protected from the adverse influence of diabetes on the heart, and the transfer of a longevity-associated variant (LAV) of the human BPIFB4 gene protects cardiac function in the db/db mouse model. This study aimed to determine the effect of LAV-BPIFB4 therapy on the metabolic phenotype (ultra-high-performance liquid chromatography-mass spectrometry, UHPLC-MS) and cardiac transcriptome (next-generation RNAseq) in db/db mice. UHPLC-MS showed that 493 cardiac metabolites were differentially modulated in diabetic compared with non-diabetic mice, mainly related to lipid metabolism. Moreover, only 3 out of 63 metabolites influenced by LAV-BPIFB4 therapy in diabetic hearts showed a reversion from the diabetic towards the non-diabetic phenotype. RNAseq showed 60 genes were differentially expressed in hearts of diabetic and non-diabetic mice. The contrast between LAV-BPIFB4- and vehicle-treated diabetic hearts revealed eight genes differentially expressed, mainly associated with mitochondrial and metabolic function. Bioinformatic analysis indicated that LAV-BPIFB4 re-programmed the heart transcriptome and metabolome rather than reverting it to a non-diabetic phenotype. Beside illustrating global metabolic and expressional changes in diabetic heart, our findings pinpoint subtle changes in mitochondrial-related proteins and lipid metabolism that could contribute to LAV-BPIFB4-induced cardio-protection in a murine model of type-2 diabetes.

Summary Implementing and combining methods from a diverse range of R/Bioconductor packages into ‘omics’ data analysis workflows represents a significant challenge in terms of standardization, readability and reproducibility. Here, we present an R/Bioconductor package, named struct (Statistics in R using Class-based Templates), which defines a suite of class-based templates that allows users to develop and implement highly standardized and readable statistical analysis workflows. Struct integrates with the STATistics Ontology to ensure consistent reporting and maximizes semantic interoperability. We also present a toolbox, named structToolbox, which includes an extensive set of commonly used data analysis methods that have been implemented using struct. This toolbox can be used to build data-analysis workflows for metabolomics and other omics technologies. Availability and implementation struct and structToolbox are implemented in R, and are freely available from Bioconductor (http://bioconductor.org/packages/struct and http://bioconductor.org/packages/structToolbox), including documentation and vignettes. Source code is available and maintained at https://github.com/computational-metabolomics.

Measuring physiochemically diverse molecules (including lipids) which vary significantly in their concentrations poses a great analytical challenge. In untargeted lipidomics studies, reversed phase chromatography coupled with data-dependent MS/MS acquisition (DDA) is frequently applied. The optimal assay should deliver a high number of detected compounds with associated fragmentation data. In this work, we introduce novel 30 and 50 min UHPLC assays utilising lipid separation on a C30 stationary phase with a modified DDA strategy using smaller precursor m/z ranges scheduled for different lipid classes across the retention time range (defined as scheduled MS/MS). To evaluate the efficiency of the novel assays, mammalian tissue extracts (lamb liver, kidney and heart) were analysed and data were compared to a 15 min reversed phase C18 assay with multiple traditional DDA injections. The 30 min C30 assay detected double the number of detected compounds compared to the 15 min C18 assay. Applying the scheduled MS/MS DDA strategy with a single injection, a similar number of annotated lipids were reported compared to the traditional DDA strategy applied with five replicate injections on a C18 column. A longer 50 min C30 chromatographic assay did not result in an expected improvement in the chromatographic separation of overlapping isomer peaks compared to the 30 min method but did result in loss of accuracy of peak picking algorithms. We recommend the 30 min C30 assay with scheduled MS/MS acquisition as an efficient tool to analyse complex biological matrices and to annotate lipid species based on MS/MS data.

OBJECTIVES: Concerns about inadvertent chronic excessive vitamin A (VA) intakes due to overly frequent supplementation, fortification and voluntarily fortified products have been raised. Although chronic excessive VA intake can create liver abnormalities, clinically detectable signs of VA toxicity are rare, indicating the need for early biomarkers of tissue damage induced by excessive VA intake. METHODS: To identify early markers of VA toxicity, we induced chronic hypervitaminosis A in pigs (64 pigs, 8 per group) dosed with an oral supplement of retinyl propionate (0 up to 10,000 µg/KgBW) for 17 weeks. To assess the regulatory role of vitamin A in liver metabolism, a microarray analysis was performed to identify genetic regulation in liver tissue. Gene expression data were confirmed using qRT-PCR, and differentially expressed transcripts and pathways were identified using Genespring and Ingenuity Pathway Analysis (IPA). Additionally, two untargeted UPLC-MS assays (HILIC and C(18) reversed phase) were applied to analyse plasma metabolites followed by univariate and multivariate analysis. RESULTS: Metabolomics analysis indicated that between 228 to 949 plasma metabolites were statistically significant between VA treated and control animals. The majority of metabolic changes observed in plasma were lipids, with ceramides, glycerophospholipids, lysoglycerophospholipids, sterol lipids and triacylglycerides enriched in both low and high VA dosed animals. Gene expression analysis confirmed significant changes in lipid metabolism, with pathways in metabolism of terpenoids and membrane lipids significantly increased by 2.4 fold. CONCLUSIONS: The combined analysis of gene expression with untargeted metabolomics data confirm that changes in liver function and lipid metabolism offers an opportunity to develop a biomarker panel to diagnose pre-symptomatic hypervitaminosis A in humans. FUNDING SOURCES: Supported by the Bill and Melinda Gates foundation.

OBJECTIVES: The objective of this study was to assess the impact of high-dose vitamin A (VA) on lipid metabolism. Previously, VA has been shown to enhance fat mobilisation, leading to a reduction in body fat. We hypothesise that hypervitaminosis A will increase expression of genes associated with lipid catabolism. METHODS: To induce chronic hypervitaminosis A, two groups of pigs (n = 8) were fed a commercial diet. The treatment group was additionally dosed, daily, with an oral supplement of retinyl propionate of 10,000 µg/KgBW for 17 weeks. To assess the impact of VA on lipid metabolism, a microarray analysis was performed to identify gene expression in adipose tissue. Differentially expressed transcripts and pathways were identified using Genespring and mapped to human orthologues for Ingenuity Pathway Analysis (IPA); gene fold changes were confirmed using qRT-PCR. Additionally, an untargeted UPLC-MS lipidomic analysis was carried out in serum samples to identify changes in lipd classes and their metabolites. RESULTS: In dosed animals, significant increases in plasma retinol (0.66 μmol/L) and liver retinyl ester concentrations (11.98 μmol/g both P

MS1-based quantitation is performed by direct integration of peptide precursor signal intensity from the MS1 spectra across retention time, based on the assumption that these signals have a linear relationship with abundance across a relatively wide dynamic range. Since ionisation efficiency varies between peptides, only relative abundance changes between biological samples are usually established. Whether each sample is run individually ‘label-free’, or two or three samples multiplexed within each run by a MS1-based labelling technique such as stable isotope labeling by amino acids in cell culture (SILAC), the informatics methods involved are broadly similar. In this chapter we present the key components of such pipelines, including the detection and quantitation of peptide features from the raw data, alignment of chromatographic variations between runs so that corresponding features can be matched, intensity normalisation to correct sample-loading differences and ionisation fluctuations, and methods to combine the peptide-level quantifications for the statistical analysis of differential protein expression across treatment groups. At each stage, the techniques have been designed for robustness against the systematic and random variation inherent in MS data, and errors during the preceding parts of the pipeline.

Motivation: The Rank Product (RP) is a statistical technique widely used to detect differentially expressed features in molecular profiling experiments such as transcriptomics, metabolomics and proteomics studies. An implementation of the RP and the closely related Rank Sum (RS) statistics has been available in the RankProd Bioconductor package for several years. However, several recent advances in the understanding of the statistical foundations of the method have made a complete refactoring of the existing package desirable. Results: We implemented a completely refactored version of the RankProd package, which provides a more principled implementation of the statistics for unpaired datasets. Moreover, the permutation-based P-value estimation methods have been replaced by exact methods, providing faster and more accurate results. Availability and implementation: RankProd 2.0 is available at Bioconductor (https://www.bioconductor.org/packages/devel/bioc/html/RankProd.html) and as part of the mzMatch pipeline (http://www.mzmatch.sourceforge.net). Contact: rainer.breitling@manchester.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

The global analysis of metabolism by liquid chromatography coupled to mass spectrometry is often hampered by a large amount of biological and technical variability. Here, we introduce an experimental and analytical strategy that can produce robust metabolome profiles in the face of this challenge. By applying a new computational approach based on concordance analysis to an extremely large number of analytical replicates, we are able to show that the overexpression of an antisense non-coding RNA targeting glutamine synthetase I results in a major reorganization of the metabolism of Streptomyces coelicolor, the model species of antibiotic-producing bacteria. We identified 97 metabolites with statistically significant reproducible dynamic behavior across the time series. The observed metabolic changes are very rapid, specific and widespread across metabolism, but focus on the nitrogen assimilation pathways. Our results demonstrate the power of highly replicated experimental designs for the robust characterization of metabolite dynamics. The identified global rearrangement of metabolism suggests the usefulness of RNA interference as an efficient strategy to manipulate the physiology of bacteria with wider biotechnological applicability in microorganisms.

Microorganisms depend on their ability to modulate their metabolic composition according to specific circumstances, such as different phases of the growth cycle and circadian rhythms, fluctuations in environmental conditions, as well as experimental perturbations. A thorough understanding of these metabolic adaptations requires the ability to comprehensively identify and quantify the metabolome of bacterial cells in different states. In this review, we present an overview of the diverse metabolomics approaches recently adopted to explore the metabolism of a wide variety of microorganisms. Focusing on a selection of illustrative case studies, we assess the different experimental designs used and explore the major achievements and remaining challenges in the field. We conclude by discussing the important complementary information provided by computational methods such as genome-scale metabolic modeling, which enable an integrated analysis of metabolic state changes in the context of overall cellular physiology.

Metabolomic strategies based on nuclear magnetic resonance (NMR) and liquid chromatography coupled with mass spectrometry (LC/MS) have been developed to obtain metabolite profiles for urine samples excreted by male Goto–Kakizaki (G–K) and Wistar rats from 12–20 weeks of age. Multivariate statistical analysis was applied to the generated data sets. The efficiencies of two software packages for LC/MS data processing, MZmine and XCMS, were compared and gave similar results. The extracted data from both analytical methods were subjected to statistical analysis by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The performance of PLS-DA modeling was compared for both analytical methods and after different data normalization methods were used. The changes in metabolite profile included increased creatinine, glucose, and dimethylamine and decreased creatine, hippurate, formate, phenylalanine, allantoin, fumarate, citrate, acetate, amino acids, and some unidentified metabolites in the urine of G–K rats compared to Wistar rats. The obtained results gave evidence that 1 H NMR procedures produce more information about the identity of metabolites. The multivariate analysis allowed the differentiation of the metabolic profiles related to animal age. In conclusion, the metabolomic studies of G–K rat urine samples provided further insights concerning experimental methodologies for data generation and processing, as well as possible markers for diabetes research.

Antimonial (sodium stibogluconate, SSG) resistance and differentiation have been shown to be closely linked in Leishmania donovani, with SSG-resistant strains showing an increased capacity to generate infectious (metacyclic) forms. This is the first untargeted LC-MS metabolomics study which integrated both phenomena in one experimental design and provided insights into metabolic differences between three clinical L. donovani strains with a similar genetic background but different SSG-susceptibilities. We performed this analysis at different stages during promastigote growth and in the absence or presence of drug pressure. When comparing SSG-resistant and SSG-sensitive strains, a number of metabolic changes appeared to be constitutively present in all growth stages, pointing towards a clear link with SSG-resistance, whereas most metabolic changes were only detected in the stationary stage. These changes reflect the close intertwinement between SSG-resistance and an increased metacyclogenesis in resistant parasites. The metabolic changes suggest that SSG-resistant parasites have (i) an increased capacity for protection against oxidative stress; (ii) a higher fluidity of the plasma membrane; and (iii) a metabolic survival kit to better endure infection. These changes were even more pronounced in a resistant strain kept under Sb(III) drug pressure.

Antimonial (sodium stibogluconate, SSG) resistance and differentiation have been shown to be closely linked in Leishmania donovani, with SSG-resistant strains showing an increased capacity to generate infectious (metacyclic) forms. This is the first untargeted LC-MS metabolomics study which integrated both phenomena in one experimental design and provided insights into metabolic differences between three clinical L. donovani strains with a similar genetic background but different SSG-susceptibilities. We performed this analysis at different stages during promastigote growth and in the absence or presence of drug pressure. When comparing SSG-resistant and SSG-sensitive strains, a number of metabolic changes appeared to be constitutively present in all growth stages, pointing towards a clear link with SSG-resistance, whereas most metabolic changes were only detected in the stationary stage. These changes reflect the close intertwinement between SSG-resistance and an increased metacyclogenesis in resistant parasites. The metabolic changes suggest that SSG-resistant parasites have (i) an increased capacity for protection against oxidative stress; (ii) a higher fluidity of the plasma membrane; and (iii) a metabolic survival kit to better endure infection. These changes were even more pronounced in a resistant strain kept under Sb(III) drug pressure.

Thanks to significant improvements in LC-MS technology, metabolomics is increasingly used as a tool to discriminate the responses of organisms to various stimuli or drugs. In this minireview we discuss all aspects of the LC-MS metabolomics pipeline, using a complex and versatile model organism, Leishmania donovani, as an illustrative example. The benefits of a hyphenated mass spectrometry platform and a detailed overview of the entire experimental pipeline from sampling, sample storage and sample list set-up to LC-MS measurements and the generation of meaningful results with state-of-the-art data-analysis software will be thoroughly discussed. Finally, we also highlight important pitfalls in the processing of LC-MS data and comment on the benefits of implementing metabolomics in a systems biology approach.

Motivation: Stable isotope-labelling experiments have recently gained increasing popularity in metabolomics studies, providing unique insights into the dynamics of metabolic fluxes, beyond the steady-state information gathered by routine mass spectrometry. However, most liquid chromatography–mass spectrometry data analysis software lacks features that enable automated annotation and relative quantification of labelled metabolite peaks. Here, we describe mzMatch–ISO, a new extension to the metabolomics analysis pipeline mzMatch.R. Results: Targeted and untargeted isotope profiling using mzMatch–ISO provides a convenient visual summary of the quality and quantity of labelling for every metabolite through four types of diagnostic plots that show (i) the chromatograms of the isotope peaks of each compound in each sample group; (ii) the ratio of mono-isotopic and labelled peaks indicating the fraction of labelling; (iii) the average peak area of mono-isotopic and labelled peaks in each sample group; and (iv) the trend in the relative amount of labelling in a predetermined isotopomer. To aid further statistical analyses, the values used for generating these plots are also provided as a tab-delimited file. We demonstrate the power and versatility of mzMatch–ISO by analysing a 13C-labelled metabolome dataset from trypanosomal parasites. Availability: mzMatch.R and mzMatch–ISO are available free of charge from http://mzmatch.sourceforge.net and can be used on Linux and Windows platforms running the latest version of R. Contact: rainer.breitling@manchester.ac.uk . Supplementary information: Supplementary data are available at Bioinformatics online

Cancer cells exhibit several unique metabolic phenotypes that are critical for cell growth and proliferation(1). Specifically, they overexpress the M2 isoform of the tightly regulated enzyme pyruvate kinase (PKM2), which controls glycolytic flux, and are highly dependent on de novo biosynthesis of serine and glycine(2). Here we describe a new rheostat-like mechanistic relationship between PKM2 activity and serine biosynthesis. We show that serine can bind to and activate human PKM2, and that PKM2 activity in cells is reduced in response to serine deprivation. This reduction in PKM2 activity shifts cells to a fuel-efficient mode in which more pyruvate is diverted to the mitochondria and more glucose-derived carbon is channelled into serine biosynthesis to support cell proliferation.

The combination of high-resolution LC-MS-based untargeted metabolomics with stable isotope tracing provides a global overview of the cellular fate of precursor metabolites. This methodology enables detection of putative metabolites from biological samples and simultaneous quantification of the pattern and extent of isotope labeling. Labeling of Trypanosoma brucei cell cultures with 50% uniformly C-13-labeled glucose demonstrated incorporation of glucose-derived carbon into 187 of 588 putatively identified metabolites in diverse pathways including carbohydrate, nucleotide, lipid, and amino acid metabolism. Labeling patterns confirmed the metabolic pathways responsible for the biosynthesis of many detected metabolites, and labeling was detected in unexpected metabolites, including two higher sugar phosphates annotated as octulose phosphate and nonulose phosphate. This untargeted approach to stable isotope tracing facilitates the biochemical analysis of known pathways and yields rapid identification of previously unexplored areas of metabolism.

Summary: The application of emerging metabolomics technologies to the comprehensive investigation of cellular biochemistry has been limited by bottlenecks in data processing, particularly noise filtering and metabolite identification. IDEOM provides a user-friendly data processing application that automates filtering and identification of metabolite peaks, paying particular attention to common sources of noise and false identifications generated by liquid chromatography–mass spectrometry (LC–MS) platforms. Building on advanced processing tools such as mzMatch and XCMS, it allows users to run a comprehensive pipeline for data analysis and visualization from a graphical user interface within Microsoft Excel, a familiar program for most biological scientists. Availability and implementation: IDEOM is provided free of charge at http://mzmatch.sourceforge.net/ideom.html, as a macro-enabled spreadsheet (.xlsb). Implementation requires Microsoft Excel (2007 or later). R is also required for full functionality. Contact: michael.barrett@glasgow.ac.uk Supplementary Information: Supplementary data are available at Bioinformatics online.