bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2020‒09‒20
28 papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh

  1. Amino Acids. 2020 Sep 15.
      The identification of metabolic pathways and the core metabolites provide novel molecular targets for the prevention and treatment of diseases. Diabetes is often accompanied with multiple metabolic disorders including hyperglycemia and dyslipidemia. Analysis of the variances of plasma metabolites is critical for identifying potential therapeutic targets for diabetes. In the current study, non-diabetic subjects with normal glucose tolerance and diabetics (age 40-60 years; n = 42 per group) were selected and plasma samples were analyzed by GC-MS for various metabolites profiling followed by network analysis. Our study identified 24 differential metabolites that were mainly enriched in protein synthesis, lipid and amino acid metabolism. Furthermore, we applied the correlation network analysis on these differential metabolites in fatty acid and amino acid metabolism and identified glycerol, alanine and serine as the hub metabolites in diabetic group. In addition, we measured the activities of enzymes in gluconeogenesis and amino acid metabolism and found significant higher activities of fructose 1,6-bisphosphatase, pyruvate carboxylase, lactate dehydrogenase, aspartate aminotransferase and alanine aminotransferase in diabetic patients. In contrast, the enzyme activities of glycolysis pathway (e.g., hexokinase, phosphofructokinase and pyruvate kinase) and TCA cycle (e.g., isocitrate dehydrogenase, succinate dehydrogenase, fumarate hydratase and malate dehydrogenase) were reduced in diabetes. Together, our studies showed that the linoleic acid and amino acid metabolism were the most affected metabolic pathways and glycerol, alanine and serine could play critical role in diabetes. The integration of network analysis and metabolic data could provide novel molecular targets or biomarkers for diabetes.
    Keywords:  Diabetes; Disorders of amino acid and fatty acid metabolism; GC–MS; Metabolomics and correlation network
  2. Int J Mol Sci. 2020 Sep 16. pii: E6799. [Epub ahead of print]21(18):
      Primary liver cancer is predicted to be the sixth most common cancer and the fourth leading cause of cancer mortality worldwide. Recent studies identified nonalcoholic fatty liver disease (NAFLD) as the underlying cause in 13-38.2% of patients with hepatocellular carcinoma unrelated to viral hepatitis and alcohol abuse. NAFLD progresses to nonalcoholic steatohepatitis (NASH), which increases the risk for the development of liver fibrosis, cirrhosis, and hepatocellular carcinoma. NAFLD is characterized by dysregulation of lipid metabolism. In addition, lipid metabolism is effected not only in NAFLD, but also in a broad range of chronic liver diseases and tumor development. Cancer cells manipulate a variety of metabolic pathways, including lipid metabolism, in order to build up their own cellular components. Identifying tumor dependencies on lipid metabolism would provide options for novel targeting strategies. This review article summarizes the research evidence on metabolic reprogramming and focuses on lipid metabolism in NAFLD, NASH, fibrosis, and cancer. As alternative routes of acetyl-CoA production for fatty acid synthesis, topics on glutamine and acetate metabolism are included. Further, studies on small compound inhibitors targeting lipid metabolism are discussed. Understanding reprogramming strategies in liver diseases, as well as the visualization of the metabolism reprogramming networks, could uncover novel therapeutic options.
    Keywords:  NAFLD; NASH; acetate metabolism; glutamine metabolism; hepatocellular carcinoma; lipid metabolism; liver fibrosis
  3. Methods Mol Biol. 2021 ;2179 327-340
      The critical role of metabolism in facilitating cancer cell growth and survival has been demonstrated by a combination of methods including, but not limited to, genomic sequencing, transcriptomic and proteomic analyses, measurements of radio-labelled substrate flux and the high throughput measurement of oxidative metabolism in unlabelled live cells using the Seahorse Extracellular Flux (XF) technology. These studies have revealed that tumour cells exhibit a dynamic metabolic plasticity, using numerous pathways including both glycolysis and mitochondrial oxidative phosphorylation (OXPHOS) to support cell proliferation, energy production and the synthesis of biomass. These advanced technologies have also demonstrated metabolic differences between cancer cell types, between molecular subtypes within cancers and between cell states. This has been exemplified by examining the transitions of cancer cells between epithelial and mesenchymal phenotypes, referred to as epithelial-mesenchymal plasticity (EMP). A growing number of studies are demonstrating significant metabolic alterations associated with these transitions, such as increased use of glycolysis by triple negative breast cancers (TNBC) or glutamine addiction in lung cancer. Models of EMP, including invasive cell lines and xenografts, isolated circulating tumour cells and metastatic tissue have been used to examine EMP metabolism. Understanding the metabolism supporting molecular and cellular plasticity and increased metastatic capacity may reveal metabolic vulnerabilities that can be therapeutically exploited. This chapter describes protocols for using the Seahorse Extracellular Flux Analyzer (XFe96), which simultaneously performs real-time monitoring of oxidative phosphorylation and glycolysis in living cells. As an example, we compare the metabolic profiles generated from two breast cancer sublines that reflect epithelial and mesenchymal phenotypes, respectively. We use this example to show how the methodology described can generate bioenergetic results that in turn can be correlated to EMP phenotypes. Normalisation of bioenergetic studies should be considered with respect to cell number, and to potential differences in mitochondrial mass, itself being an important bioenergetics endpoint.
    Keywords:  Cellular bioenergetics; Epithelial-to-mesenchymal plasticity; Extracellular acidification; Glycolysis; Metabolic phenotype; Metabolism; Mitochondrial CMxRos; Mitochondrial RedFM; Oxidative phosphorylation; Oxygen consumption; Respiration; Seahorse Extracellular Flux Analyzer
  4. Anal Methods. 2020 May 14. 12(18): 2355-2362
      To better understand the mechanism of hyperlipidemia and discover potential biomarkers, we have used targeted metabolomics to analyze eight amino acid profiles of control and hyperlipidemia rats by a liquid chromatography-mass spectrometry method. With high fat diet, the concentrations of serum of total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and apolipoprotein B (ApoB) were increased by 666.7%, 99.0%, 61.7% and 51.0%, whereas the concentrations of high-density lipoprotein cholesterol (HDL-C) and apolipoprotein A-I (ApoA-I) were decreased by 46.3% and 58.9%. The concentrations of alanine, arginine, lysine, methionine, serine, tyrosine and valine in hyperlipidemia rats were significantly decreased by 21.8%, 19.72%, 26.5%, 19.6%, 48.7%, 19.8% and 24.91%, while there was no striking change in threonine. Combined with experimental results and previous literature, we inferred that alanine and serine were gradually disordered and subsequently generated abundant acetyl-CoA through pyruvate, which resulted in energy metabolism deficiency. Furthermore, Spearman correlation analysis shows that TC was negatively associated with methionine (r = -0.640, p < 0.05), suggesting that the lowered level of methionine caused by the homocysteine pathway enhances absorption and synthesis of TC. Meanwhile, the reduction of tyrosine demonstrated that rapid metabolism of cholesterol in vivo was caused by high levels of exogenous cholesterol. Furthermore, the observed ApoB and lysine changes indicated that lysine was largely incorporated into ApoB particles during the disease process. In addition, the levels of arginine, SOD and MDA reflected the behavior of oxidative stress. Finally, the metabolism fluctuation of valine demonstrated that abnormal lipid metabolism could cause abnormal glucose metabolism. In general, disordered energy metabolism, lipid metabolism, glucose metabolism and elevated oxidative stress were important characteristics of metabolic perturbations in hyperlipidemia. Herein, the discovery of biomarkers and the biological explanations mentioned above could be used to analyze the pathogenesis of hyperlipidemia through metabolic pathways, and these results could play an important role in assisting the clinical diagnosis of hyperlipidemia.
  5. Mol Cell Oncol. 2020 ;7(4): 1761242
      Energy stress disturbs cellular homeostasis and induces cell death. Our recent study revealed that ferroptosis (a non-apoptotic cell death) is an energy-requiring process, and energy stress-mediated activation of adenosine monophosphate-activated protein kinase (AMPK) inhibits ferroptosis. Mechanistically, AMPK regulates ferroptosis through acetyl-CoA carboxylase (ACC) and polyunsaturated fatty acid (PUFA) biosynthesis.
    Keywords:  AMP-activated protein kinase; Energy stress; acetyl-CoA carboxylase; ferroptosis; polyunsaturated fatty acid
  6. Exp Mol Med. 2020 Sep 17.
      As knowledge of cell metabolism has advanced, glutamine has been considered an important amino acid that supplies carbon and nitrogen to fuel biosynthesis. A recent study provided a new perspective on mitochondrial glutamine metabolism, offering mechanistic insights into metabolic adaptation during tumor hypoxia, the emergence of drug resistance, and glutaminolysis-induced metabolic reprogramming and presenting metabolic strategies to target glutamine metabolism in cancer cells. In this review, we introduce the various biosynthetic and bioenergetic roles of glutamine based on the compartmentalization of glutamine metabolism to explain why cells exhibit metabolic reliance on glutamine. Additionally, we examined whether glutamine derivatives contribute to epigenetic regulation associated with tumorigenesis. In addition, in discussing glutamine transporters, we propose a metabolic target for therapeutic intervention in cancer.
  7. Nature. 2020 Sep 16.
      Ferroptosis-an iron-dependent, non-apoptotic cell death process-is involved in various degenerative diseases and represents a targetable susceptibility in certain cancers1. The ferroptosis-susceptible cell state can either pre-exist in cells that arise from certain lineages or be acquired during cell-state transitions2-5. However, precisely how susceptibility to ferroptosis is dynamically regulated remains poorly understood. Here we use genome-wide CRISPR-Cas9 suppressor screens to identify the oxidative organelles peroxisomes as critical contributors to ferroptosis sensitivity in human renal and ovarian carcinoma cells. Using lipidomic profiling we show that peroxisomes contribute to ferroptosis by synthesizing polyunsaturated ether phospholipids (PUFA-ePLs), which act as substrates for lipid peroxidation that, in turn, results in the induction of ferroptosis. Carcinoma cells that are initially sensitive to ferroptosis can switch to a ferroptosis-resistant state in vivo in mice, which is associated with extensive downregulation of PUFA-ePLs. We further find that the pro-ferroptotic role of PUFA-ePLs can be extended beyond neoplastic cells to other cell types, including neurons and cardiomyocytes. Together, our work reveals roles for the peroxisome-ether-phospholipid axis in driving susceptibility to and evasion from ferroptosis, highlights PUFA-ePL as a distinct functional lipid class that is dynamically regulated during cell-state transitions, and suggests multiple regulatory nodes for therapeutic interventions in diseases that involve ferroptosis.
  8. Cell Rep. 2020 Sep 15. pii: S2211-1247(20)31121-9. [Epub ahead of print]32(11): 108132
      Gene and protein expression data provide useful resources for understanding brain function, but little is known about the lipid composition of the brain. Here, we perform quantitative shotgun lipidomics, which enables a cell-type-resolved assessment of the mouse brain lipid composition. We quantify around 700 lipid species and evaluate lipid features including fatty acyl chain length, hydroxylation, and number of acyl chain double bonds, thereby identifying cell-type- and brain-region-specific lipid profiles in adult mice, as well as in aged mice, in apolipoprotein-E-deficient mice, in a model of Alzheimer's disease, and in mice fed different diets. We also integrate lipid with protein expression profiles to predict lipid pathways enriched in specific cell types, such as fatty acid β-oxidation in astrocytes and sphingolipid metabolism in microglia. This resource complements existing brain atlases of gene and protein expression and may be useful for understanding the role of lipids in brain function.
    Keywords:  aging; central nervous system; glia; lipidomics; lipids; neurons
  9. Mol Brain. 2020 Sep 14. 13(1): 125
      Frontotemporal dementia (FTD) is amongst the most prevalent early onset dementias and even though it is clinically, pathologically and genetically heterogeneous, a crucial involvement of metabolic perturbations in FTD pathology is being recognized. However, changes in metabolism at the cellular level, implicated in FTD and in neurodegeneration in general, are still poorly understood. Here we generate induced human pluripotent stem cells (hiPSCs) from patients carrying mutations in CHMP2B (FTD3) and isogenic controls generated via CRISPR/Cas9 gene editing with subsequent neuronal and glial differentiation and characterization. FTD3 neurons show a dysregulation of glutamate-glutamine related metabolic pathways mapped by 13C-labelling coupled to mass spectrometry. FTD3 astrocytes show increased uptake of glutamate whilst glutamate metabolism is largely maintained. Using quantitative proteomics and live-cell metabolic analyses, we elucidate molecular determinants and functional alterations of neuronal and glial energy metabolism in FTD3. Importantly, correction of the mutations rescues such pathological phenotypes. Notably, these findings implicate dysregulation of key enzymes crucial for glutamate-glutamine homeostasis in FTD3 pathogenesis which may underlie vulnerability to neurodegeneration. Neurons derived from human induced pluripotent stem cells (hiPSCs) of patients carrying mutations in CHMP2B (FTD3) display major metabolic alterations compared to CRISPR/Cas9 generated isogenic controls. Using quantitative proteomics, 13C-labelling coupled to mass spectrometry metabolic mapping and seahorse analyses, molecular determinants and functional alterations of neuronal and astrocytic energy metabolism in FTD3 were characterized. Our findings implicate dysregulation of glutamate-glutamine homeostasis in FTD3 pathogenesis. In addition, FTD3 neurons recapitulate glucose hypometabolism observed in FTD patient brains. The impaired mitochondria function found here is concordant with disturbed TCA cycle activity and decreased glycolysis in FTD3 neurons. FTD3 neuronal glutamine hypermetabolism is associated with up-regulation of PAG expression and, possibly, ROS production. Distinct compartments of glutamate metabolism can be suggested for the FTD3 neurons. Endogenous glutamate generated from glutamine via PAG may enter the TCA cycle via AAT (left side of neuron) while exogenous glutamate taken up from the extracellular space may be incorporated into the TCA cycle via GDH (right side of the neuron) FTD3 astrocytic glutamate uptake is upregulated whilst glutamate metabolism is largely maintained. Finally, pharmacological reversal of glutamate hypometabolism manifesting from decreased GDH expression should be explored as a novel therapeutic intervention for treating FTD3.
    Keywords:  CHMP2B; FTD3; GC-MS; GDH; GS; Glucose metabolism; Glutamate; Glutamine; PAG; iPSC-derived neuron
  10. Nat Commun. 2020 Sep 17. 11(1): 4684
      Cancer cells have a characteristic metabolism, mostly caused by alterations in signal transduction networks rather than mutations in metabolic enzymes. For metabolic drugs to be cancer-selective, signaling alterations need to be identified that confer a druggable vulnerability. Here, we demonstrate that many tumor cells with an acquired cancer drug resistance exhibit increased sensitivity to mechanistically distinct inhibitors of cancer metabolism. We demonstrate that this metabolic vulnerability is driven by mTORC1, which promotes resistance to chemotherapy and targeted cancer drugs, but simultaneously suppresses autophagy. We show that autophagy is essential for tumor cells to cope with therapeutic perturbation of metabolism and that mTORC1-mediated suppression of autophagy is required and sufficient for generating a metabolic vulnerability leading to energy crisis and apoptosis. Our study links mTOR-induced cancer drug resistance to autophagy defects as a cause of a metabolic liability and opens a therapeutic window for the treatment of otherwise therapy-refractory tumor patients.
  11. Methods Mol Biol. 2021 ;2194 291-300
      Bile acids are important end products of cholesterol metabolism, having been shown to serve as signaling molecules and intermediates between the host and the gut microbiota. Here we describe a robust and accurate method using ultrahigh-pressure liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS) for the quantification of bile acids in stool/cecal and tissue samples.
    Keywords:  Bile acid; Metabolomics; Quantitation; UHPLC-MS/MS
  12. Mol Cell Proteomics. 2020 Sep 16. pii: mcp.R120.002267. [Epub ahead of print]
      This review covers recent developments in glycosaminoglycan (GAG) analysis via mass spectrometry (MS). GAGs participate in a variety of biological functions, including cellular communication, wound healing, and anticoagulation, and are important targets for structural characterization. GAGs exhibit a diverse range of structural features due to the variety of O- and N-sulfation modifications and uronic acid C-5 epimerization that can occur, making their analysis a challenging target. Mass spectrometry approaches to the structure assignment of GAGs have been widely investigated, and new methodologies remain the subject of development. Advances in sample preparation, tandem MS techniques (MS/MS), on-line separations and automated analysis software have advanced the field of GAG analysis. These recent developments have led to remarkable improvements in the precision and time efficiency for the structural characterization of GAGs.
    Keywords:  Fourier Transform MS; Glycoproteins*; Glycosaminoglycans; Mass Spectrometry; Structural Biology*; Tandem Mass Spectrometry
  13. Mol Omics. 2020 Sep 18.
      Dried blood spots (DBS) and dried milk spots (DMS) represent convenient matrices for collecting and storing human samples. However, the use of these sample types for researching lipid metabolism remains relatively poorly explored, and especially unclear is the efficiency of lipid extraction in the context of high throughput, untargeted lipidomics. A visual inspection of punched DBSs after standard extraction suggests that the samples remain largely intact. DMSs comprise a dense aggregate of milk fat globules on one side of the card, suggesting that part of the lipid fraction may be physically inaccessible. This led us to the hypothesis that decoagulating may facilitate lipid extraction from both DBSs and DMSs. We tested this hypothesis using a mixture of strong chaeotropes (guanidine and thiourea) in both DBS and DMS in the context of high throughput lipidomics (96/384w plate). Extraction of lipids from DMSs was tested with established extractions and one novel solvent mixture in a high throughput format. We found that exposure of DBSs to chaeotropes facilitated collection of the lipid fraction but was ineffective for DMSs. The lipid fraction of DMSs was best isolated without water, using a mixture of xylene/methanol/isopropanol (1 : 2 : 4). We conclude that decoagulation is essential for efficient extraction of lipids from DBSs and that a non-aqueous procedure using a spectrum of solvents is the best procedure for extracting lipids from DMSs. These methods represent convenient steps that are compatible with the sample structure and type, and with high throughput lipidomics.
  14. J Exp Clin Cancer Res. 2020 Sep 14. 39(1): 185
      Molecular oxygen (O2) is a universal electron acceptor that is eventually synthesized into ATP in the mitochondrial respiratory chain of all metazoans. Therefore, hypoxia biology has become an organizational principle of cell evolution, metabolism and pathology. Hypoxia-inducible factor (HIF) mediates tumour cells to produce a series of glucose metabolism adaptations including the regulation of glucose catabolism, glycogen metabolism and the biological oxidation of glucose to hypoxia. Since HIF can regulate the energy metabolism of cancer cells and promote the survival of cancer cells, targeting HIF or HIF mediated metabolic enzymes may become one of the potential treatment methods for cancer. In this review, we summarize the established and recently discovered autonomous molecular mechanisms that can induce cell reprogramming of hypoxic glucose metabolism in tumors and explore opportunities for targeted therapy.
    Keywords:  Glucose; Hypoxia; Metabolic reprogramming; Tumour
  15. Nat Methods. 2020 Sep 14.
      MassIVE.quant is a repository infrastructure and data resource for reproducible quantitative mass spectrometry-based proteomics, which is compatible with all mass spectrometry data acquisition types and computational analysis tools. A branch structure enables MassIVE.quant to systematically store raw experimental data, metadata of the experimental design, scripts of the quantitative analysis workflow, intermediate input and output files, as well as alternative reanalyses of the same dataset.
  16. Breast Cancer Res Treat. 2020 Sep 12.
      BACKGROUNDS: Triple negative breast cancer (TNBC) is a heterogeneous disease with more aggressive clinical courses than other subtypes of breast cancer. In this study, we performed high-resolution mass spectrometry-based quantitative proteomics with TNBC clinical tissue specimens to explore the early and sensitive diagnostic signatures and potential therapeutic targets for TNBC patients.METHODS: We performed an iTRAQ labeling coupled LC-MS/MS approach to explore the global proteome in tumor tissues and corresponding para-tumor tissues from 24 patients with grade I-II and grade III primary TNBC. Relative peptide quantification and protein identification were performed by Proteome Discoverer™ software with Mascot search engine. Differentially expressed proteins were analyzed by bioinformatic analyses, including GO function classification annotation and KEGG enrichment analysis. Pathway analyses for protein-protein interactions and upstream regulations of differentially expressed candidates were performed by Ingenuity Pathway Analysis (IPA) software.
    RESULTS: Totally, 5401 unique proteins were identified and quantified in different stage of TNBCs. 845 proteins were changed in patients with grade I or II TNBC, among which 304 were up-regulated and 541 were down-regulated. Meanwhile, for patients with grade III TNBC, 358 proteins were increased and 651 proteins were decreased. Comparing to para-cancerous tissues, various signaling pathways and metabolic processes, including PPAR pathways, PI3K-Akt pathway, one-carbon metabolism, amino acid synthesis, and lipid metabolism were activated in TNBC cancer tissues. Death receptor signaling was significantly activated in grade I-II TNBCs, however, remarkably inhibited in grade III TNBCs. Western blot experiments were conducted to validate expression levels of CYCS, HMGA1 and XIAP with samples from individual patients.
    CONCLUSIONS: Overall, our proteomic data presented precise quantification of potential signatures, signaling pathways, regulatory networks, and characteristic differences in each clinicopathological subgroup. The proteome provides complementary information for TNBC accurate subtype classification and therapeutic targets research.
    Keywords:  Death receptor signaling; Lipid metabolism; Quantitative proteomics; Triple negative breast cancer
  17. Anal Chem. 2020 Sep 12.
      Local lipid variations in tissues are readily revealed with mass spectrometry imaging (MSI) methods and resulting lipid distributions serve as bioanalytical signatures to reveal cell- or tissue-specific lipids. Comprehensive MSI lipid mapping requires measurements in both ion polarities. Additionally, structural lipid characterization is necessary to link lipid structure to lipid function. Whereas some structural elements of lipids are readily derived from high-resolution mass spectrometry (MS) and tandem-MS (MSn), the localization of C=C double bonds (DBs) requires specialized fragmentation and/or functionalization methods. In this work, we identify a multifunctional matrix-assisted laser desorption/ionization (MALDI) matrix for spatially-resolved lipidomics investigations that reacts with lipids in Paternò-Büchi (PB) reactions during laser irradiation facilitating DB position assignment and allows dual polarity high-resolution MALDI-MSI and MALDI MS2I studies. By screening twelve compounds for improved ionization efficiency in positive/negative ion mode and PB functionalization yield compared to the previously introduced reactive MALDI matrix benzophenone, 2-benzoylpyridine (BzPy) is identified as the best candidate. The multifunctional character of the new matrix enables DB localization of authentic standards belonging to twelve lipid classes and helps to assign 133/58 lipid features in positive/negative ion mode from mouse cerebellum tissue. The analytical capabilities of BzPy as a multifunctional MALDI-MSI matrix are demonstrated by imaging endogenous and PB-functionalized lipids in mouse kidney sections with 7 µm lateral resolution in both ion modes. Tracking diagnostic lipid DB position fragment ions in mouse pancreas tissue with down to 10 µm pixel size allows to identify islets of Langerhans associated lipid isomer upregulation and depletion.
  18. Biochemistry. 2020 Sep 15.
      Cancer cells are highly dependent on different metabolic pathways to sustain their survival, growth and proliferation. Lipid metabolism supplies not only energetic needs of the cells but also provides the raw material for cellular growth and the signalling molecules for many oncogenic pathways. Mainly processed in the liver, lipids play an essential role in the physiology of this organ and string the pathological progression of many diseases such as metabolic syndrome and hepatocellular carcinoma (HCC). The progression of HCC is associated with inflammation and complex metabolic reprogramming, and its prognosis remains poor because of the lack of effective therapies despite many years of dedicated research. Defects in hepatic lipid metabolism induce abnormal gene expression and rewire many cellular pathways involved in oncogenesis and metastasis, implying that interfering with lipid metabolism within the tumour and surrounding microenvironment may be a novel therapeutic approach for treating liver cancer patients. Therefore, this review focuses on the latest advances in drugs targeting the lipid metabolism and leading to promising outcome in preclinical studies and some ongoing clinical trials.
  19. Cell Physiol Biochem. 2020 Sep 19. 54(5): 917-927
      BACKGROUND/AIMS: Glutamine is the most abundant amino acid in the body and has a metabolic role as a precursor for protein, amino sugar and nucleotide synthesis. After glucose, glutamine is the main source of energy in cells and has recently been shown to be an important carbon source for de novo lipogenesis. Glutamine is synthesized by the enzyme glutamine synthetase, a mitochondrial enzyme that is active during adipocyte differentiation suggesting a regulatory role in this process. The aim of our study was therefore to investigate whether glutamine status impacts on the differentiation of adipocytes and lipid droplet accumulation.METHODS: Mouse mesenchymal stem cells (MSCs) were submitted to glutamine deprivation (i.e. glutamine-free adipogenic medium in conjunction with irreversible glutamine synthetase inhibitor, methionine sulfoximine - MSO) during differentiation and their response was compared with MSCs differentiated in glutamine-supplemented medium (5, 10 and 20 mM). Differentiated MSCs were assessed for lipid content using Oil Red O (ORO) staining and gene expression was analysed by qPCR. Intracellular glutamine levels were determined using a colorimetric assay, while extracellular glutamine was measured using liquid chromatography-mass spectrometry (LC-MS).
    RESULTS: Glutamine deprivation largely abolished adipogenic differentiation and lipid droplet formation. This was accompanied with a reduction in intracellular glutamine concentration, and downregulation of gene expression for classical adipogenic markers including PPARγ. Furthermore, glutamine restriction suppressed isocitrate dehydrogenase 1 (IDH1) gene expression, an enzyme which produces citrate for lipid synthesis. In contrast, glutamine supplementation promoted adipogenic differentiation in a dose-dependent manner.
    CONCLUSION: These results suggest that the glutamine pathway may have a previously over-looked role in adipogenesis. The underlying mechanism involved the glutamine-IDH1 pathway and could represent a potential therapeutic strategy to treat excessive lipid accumulation and thus obesity.
    Keywords:  Glutamine; Adipogenesis; Lipogenesis; Methionine sulfoximine
  20. Anal Chem. 2020 Sep 15.
      MRMkit is an open-source software package designed for automated data processing of large-scale targeted mass spectrometry-based metabolomics data. With increasing automation of sample preparation for LC-MS analysis, a challenging next step is to fully automate the workflow to process raw data and assure the quality of measurements in large-scale analysis settings. MRMkit capitalizes on the richness of large-sample data in capturing peak shapes and interference patterns of individual transitions across many samples and deliver fully automated, reproducible peak integration results in a scalable and time-efficient manner. In addition to fast and accurate peak integration, the tool also provides reliable data normalization functions and quality metrics along with visualizations for fast data quality evaluation. MRMkit learns the retention time offset patterns by user specified compound class and makes recommendations for peak picking in multi-modal ion chromatograms. In summary, MRMkit offers highly consistent and scalable data processing capacity to targeted metabolomics, substantially curtailing the time required to produce the final quantification results after the actual LC-MS analysis.
  21. Nat Commun. 2020 09 16. 11(1): 4653
      Cancer cells demand excess nutrients to support their proliferation, but how tumours exploit extracellular amino acids during systemic metabolic perturbations remain incompletely understood. Here, we use a Drosophila model of high-sugar diet (HSD)-enhanced tumourigenesis to uncover a systemic host-tumour metabolic circuit that supports tumour growth. We demonstrate coordinate induction of systemic muscle wasting with tumour-autonomous Yorkie-mediated SLC36-family amino acid transporter expression as a proline-scavenging programme to drive tumourigenesis. We identify Indole-3-propionic acid as an optimal amino acid derivative to rationally target the proline-dependency of tumour growth. Insights from this whole-animal Drosophila model provide a powerful approach towards the identification and therapeutic exploitation of the amino acid vulnerabilities of tumourigenesis in the context of a perturbed systemic metabolic network.
  22. Anal Chem. 2020 Sep 14.
      With recent advances in analytical chemistry, LC-HRMS/MS has become an essential tool for metabolite discovery and detection. Even if most common drug transformations have already been extensively described, manual search of drug metabolites in LC-HRMS/MS datasets is still a common practice in toxicology laboratories, complicating metabolite discovery. Furthermore, the availability of free open-source software for metabolite discovery is still limited. In this article, we present MetIDfyR, an opensource and cross-platform R package for in-silico drug phase I/II biotransformations prediction and mass-spectrometric data mining. MetIDfyR has proven efficacy for advanced metabolite identification in semi-complex and complex mixtures in in-vitro or in-vivo drug studies and is freely available at
  23. Mol Ther. 2020 Sep 02. pii: S1525-0016(20)30457-3. [Epub ahead of print]
      System xc- cystine/glutamate antiporter, composed of a light-chain subunit (xCT, SLC7A11) and a heavy-chain subunit (CD98hc, SLC3A2), is mainly responsible for the cellular uptake of cystine in exchange for intracellular glutamate. In recent years, the xCT molecule has been found to play an important role in tumor growth, progression, metastasis, and multidrug resistance in various types of cancer. Interestingly, xCT also exhibits an essential function in regulating tumor-associated ferroptosis. Despite significant progress in targeting the system xc- transporter in cancer treatment, the underlying mechanisms still remain elusive. It is also unclear why solid tumors are more sensitive to xCT inhibitors such as sulfasalazine, as compared to hematological malignancies. This review mainly focuses on the role of xCT cystine/glutamate transporter in regard to tumor growth, chemoresistance, tumor-selective ferroptosis, and the mechanisms regulating xCT gene expression. The potential therapeutic implications of targeting the system xc- and its combination with chemotherapeutic agents or immunotherapy to suppress tumor growth and overcome drug resistance are also discussed.
    Keywords:  ROS; ferroptosis; system x(c)(−); xCT
  24. Curr Protoc Bioinformatics. 2020 Sep;71(1): e105
      The Perseus software provides a comprehensive framework for the statistical analysis of large-scale quantitative proteomics data, also in combination with other omics dimensions. Rapid developments in proteomics technology and the ever-growing diversity of biological studies increasingly require the flexibility to incorporate computational methods designed by the user. Here, we present the new functionality of Perseus to integrate self-made plugins written in C#, R, or Python. The user-written codes will be fully integrated into the Perseus data analysis workflow as custom activities. This also makes language-specific R and Python libraries from CRAN (, Bioconductor (, PyPI (, and Anaconda ( accessible in Perseus. The different available approaches are explained in detail in this article. To facilitate the distribution of user-developed plugins among users, we have created a plugin repository for community sharing and filled it with the examples provided in this article and a collection of already existing and more extensive plugins. © 2020 The Authors. Basic Protocol 1: Basic steps for R plugins Support Protocol 1: R plugins with additional arguments Basic Protocol 2: Basic steps for python plugins Support Protocol 2: Python plugins with additional arguments Basic Protocol 3: Basic steps and construction of C# plugins Basic Protocol 4: Basic steps of construction and connection for R plugins with C# interface Support Protocol 4: Advanced example of R Plugin with C# interface: UMAP Basic Protocol 5: Basic steps of construction and connection for python plugins with C# interface Support Protocol 5: Advanced example of python plugin with C# interface: UMAP Support Protocol 6: A basic workflow for the analysis of label-free quantification proteomics data using perseus.
    Keywords:  MaxQuant; Perseus; omics data analysis; plugin development; quantitative proteomics
  25. Proteomics. 2020 Sep 16. e1900335
      Proteomics, the study of all the proteins in biological systems, is becoming a data-rich science. Protein sequences and structures have been comprehensively catalogued in online databases. With the recent advancements of the tandem mass spectrometry (MS) technology, protein expression and post-translational modifications (PTMs) can be studied in a variety of biological systems at the global scale. Sophisticated computational algorithms are needed to translate the vast amount of data into novel biological insights. Deep learning automatically extracts data representations at high levels of abstraction from data, and it thrives in data-rich research scientific domains. Here, we provide a comprehensive overview of deep learning applications in proteomics including retention time prediction, MS/MS spectrum prediction, de novo peptide sequencing, PTM prediction, major histocompatibility complex-peptide binding affinity prediction, and protein structure prediction. We also discuss limitations and the future directions of deep learning in proteomics. We hope this review will provide readers an overview of deep learning and how it can be used to analyze proteomics data. This article is protected by copyright. All rights reserved.
    Keywords:  bioinformatics; deep learning; proteomics
  26. J Proteome Res. 2020 Sep 14.
      Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is an increasingly powerful tool for studying proteins in the context of disease. As technological advances in instrumentation and data analysis have enabled deeper profiling of proteomes and peptidomes, the need for a rigorous, standardized approach to validate individual peptide-spectrum matches (PSMs) has emerged. To address this need, we developed a novel and broadly-applicable workflow: PSM Validation with Internal Standards (P-VIS). In this approach, the fragmentation spectrum and chromatographic retention time of a peptide within a biological sample are compared with those of a synthetic version of the putative peptide sequence match. Similarity measurements obtained for a panel of internal standard peptides are then used to calculate a prediction interval for valid matches. If the observed degree of similarity between the biological and the synthetic peptide falls within this prediction interval, the match is considered valid. P-VIS enables systematic and objective assessment of the validity of individual PSMs, providing a measurable degree of confidence when identifying peptides by mass spectrometry.
  27. J Proteome Res. 2020 Sep 13.
      Prostate cancer (PCa) is a hormone-dependent tumour characterized by an extremely heterogeneous prognosis. Despite recent advances in partially uncovering some of the biological processes involved in its progression, there is still an urgent need for identifying more accurate and specific prognostic procedures to differentiate between disease stages. In this context, targeted approaches, focused on mapping dysregulated metabolic pathways, could play a critical role in identifying the mechanisms driving tumorigenesis and metastasis. In this study, a targeted analysis of the NMR-based metabolomic profile of PCa patients with different tumour grades, guided by transcriptomics profiles associated with their stages, was performed. Serum and urine samples were collected from 73 PCa patients. Samples were classified according to their Gleason Score (GS) into low-GS (GS<7) and high-GS PCa (GS≥7) groups. A total of 36 metabolic pathways were found to be dysregulated in the comparison between different PCa grades. Particularly, the levels of glucose, glycine and 1-methlynicotinamide, metabolites involved in energy metabolism and nucleotide synthesis, were significantly altered between both groups of patients. These results underscore the potential of targeted metabolomic profiling to characterize relevant metabolic changes involved in the progression of this neoplastic process.
  28. Anal Bioanal Chem. 2020 Sep 18.
      Oxylipins are highly bioactive lipid mediators derived from polyunsaturated fatty acids (PUFAs) and have fundamental roles in a diverse set of homeostatic and inflammatory processes. Current targeted methods of analyzing oxylipins require long runtimes and laborious sample preparation, limiting their application to epidemiological studies. Here, we report the development of an online solid-phase extraction-liquid chromatography-triple quadrupole mass spectrometry (online SPE-LC-MS/MS) method to quantify 49 non-esterified oxylipins and PUFAs, including prostanoids, leukotrienes, lipoxins, resolvins, hydroxy PUFAs, epoxy PUFAs, and their PUFA precursors, in 50-μL samples of human serum. The new method was validated in terms of linearity, lower limits of quantification, recovery, precision, and matrix effects. The limits of quantification were in the range of 0.18 to 9 pg for oxylipins. A single 11.5-min analysis enabled the accurate (80-120% recovery), precise, and reproducible (RSD < 15%) quantification of 32 analytes at three spiked concentrations (0.1, 1, 5 ng/mL), demonstrating the suitability of this method for large-scale epidemiological studies. We successfully applied it to rapidly analyze a total of 565 serum samples from prediabetic and healthy individuals in a nested case-control panel study. Oxylipin concentrations were quantified within a range similar to those of previously published articles. Application of this approach to both healthy and prediabetic subjects found that several circulating hydroxy PUFAs, including LTB4, 12-HEPE, 15(S)-HETE, and 17-HDHA, were negatively associated with fasting glucose levels, indicating decreased anti-inflammatory activity and impaired glucose tolerance in diabetes progression. This new approach provides a means for high-throughput analyses of non-esterified oxylipins for epidemiological studies and will help unravel the intricate interactions of the oxylipin cascade and accelerate our understanding of the biological regulation of these important lipid mediators in human disease.
    Keywords:  Hydroxy PUFAs; LC-MS/MS; Online SPE; Oxylipins; Prediabetes