bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2020‒01‒19
fifty-three papers selected by
Giovanny Rodriguez Blanco
The Beatson Institute for Cancer Research
  1. Analysing central metabolism in ultra-high resolution: At the crossroads of carbon and nitrogen.
  2. Annotation of Specialized Metabolites from High-Throughput and High-Resolution Mass Spectrometry Metabolomics.
  3. Overview of Experimental Methods and Study Design in Metabolomics, and Statistical and Pathway Considerations.
  4. Trapped ion mobility spectrometry and PASEF enable in-depth lipidomics from minimal sample amounts.
  5. Targeted cerebrospinal fluid analysis for inborn errors of metabolism on an LC-MS/MS analysis platform.
  6. Probing Metabolic Changes in IFNγ-Treated Ovarian Cancer Cells.
  7. METLIN: A Tandem Mass Spectral Library of Standards.
  8. High performance chromatography-mass spectrometry (HPLC-MS) Based Lipid Metabolite Profiling of Acromegaly.
  9. Large-scale lipid analysis with C=C location and sn-position isomer resolving power.
  10. Hyphenated mass spectrometry techniques for assessing medication adherence: advantages, challenges, clinical applications and future perspectives.
  11. Analysis of NMR Metabolomics Data.
  12. Integration of proteomics and metabolomics reveals promotion of proliferation by exposure of bisphenol S in human breast epithelial MCF-10A cells.
  13. High-throughput quantitative top-down proteomics.
  14. Combining Charge-Switch Derivatization with Ozone-Induced Dissociation for Fatty Acid Analysis.
  15. Capillary Zone Electrophoresis-Tandem Mass Spectrometry with Activated Ion Electron Transfer Dissociation for Large-scale Top-down Proteomics.
  16. Targeting extracellular nutrient dependencies of cancer cells.
  17. Perspectives on Data Analysis in Metabolomics: Points of Agreement and Disagreement from the 2018 ASMS Fall Workshop.
  18. Deep Intact Proteoform Characterization in Human Cell Lysate Using High-pH and Low-pH Reversed-Phase Liquid Chromatography.
  19. Using the IDEOM Workflow for LCMS-Based Metabolomics Studies of Drug Mechanisms.
  20. Competitive glucose metabolism as a target to boost bladder cancer immunotherapy.
  21. Glucose Favors Lipid Anabolic Metabolism in the Invasive Breast Cancer Cell Line MDA-MB-231.
  22. Metabolic Profiling Reveals a Dependency of Human Metastatic Breast Cancer on Mitochondrial Serine and One-Carbon Unit Metabolism.
  23. Extracting Biological Insight from Untargeted Lipidomics Data.
  24. De Novo Molecular Formula Annotation and Structure Elucidation Using SIRIUS 4.
  25. Overview of Tandem Mass Spectral and Metabolite Databases for Metabolite Identification in Metabolomics.
  26. Metabolomics Data Processing Using XCMS.
  27. Feature-Based Molecular Networking for Metabolite Annotation.
  28. Metabolomics Data Processing Using OpenMS.
  29. MYC regulates fatty acid metabolism through a multigenic program in claudin-low triple negative breast cancer.
  30. Energy Metabolism in Cancer: The Roles of STAT3 and STAT5 in the Regulation of Metabolism-Related Genes.
  31. Dissecting intercellular signaling with mass spectrometry-based proteomics.
  32. Radioisotope-Based Protocol for Determination of Central Carbon Metabolism in T Cells.
  33. Metabolomics Data Preprocessing Using ADAP and MZmine 2.
  34. Differentiating Positional Isomers of Nucleoside Modifications by Higher-Energy Collisional Dissociation Mass Spectrometry (HCD MS).
  35. Mass Spectrometry-Based Metabolomics Analysis of Obese Patients' Blood Plasma.
  36. An Integrated Gene Expression Landscape Profiling Approach to Identify Lung Tumor Endothelial Cell Heterogeneity and Angiogenic Candidates.
  37. BCAT1 promotes proliferation of endometrial cancer cells through reprogrammed BCAA metabolism.
  38. Proteomic analysis of MYB-regulated secretome identifies functional pathways and biomarkers: potential pathobiological and clinical implications.
  39. Protocol for Community-created Public MS/MS Reference Spectra Within the Global Natural Products Social Molecular Networking Infrastructure.
  40. Mammalian ovarian lipid distributions by desorption electrospray ionization-mass spectrometry (DESI-MS) imaging.
  41. Plasma oxylipins and unesterified precursor fatty acids are altered by DHA supplementation in pregnancy: Can they help predict risk of preterm birth?
  42. Perturbations of Lipids and Oxidized Phospholipids in Lipoproteins of Patients with Postmenopausal Osteoporosis Evaluated by Asymmetrical Flow Field-Flow Fractionation and Nanoflow UHPLC-ESI-MS/MS.
  43. Lipidomic profiling of non-mineralized dental plaque and biofilm by untargeted UHPLC-QTOF-MS/MS and SWATH acquisition.
  44. The role of ferroptosis in ionizing radiation-induced cell death and tumor suppression.
  45. Pathway Analysis for Targeted and Untargeted Metabolomics.
  46. Liquid Chromatography Tandem Mass Spectrometry Quantification of 13C-Labeling in Sugars.
  47. A rapid and convenient derivatization method for quantitation of short-chain fatty acids in human feces by ultra-performance liquid chromatography-tandem mass spectrometry.
  48. Detection of Ferroptosis by BODIPY™ 581/591 C11.
  49. Ferroptosis in Cancer Cell Biology.
  50. Methods of the Analysis of Oxylipins in Biological Samples.
  51. Predictive Modeling for Metabolomics Data.
  52. Lipidomic profiling analysis of the phospholipid molecules in SCAP-induced lipid droplet formation in bovine mammary epithelial cells.
  53. Metabolomic Data Exploration and Analysis with the Human Metabolome Database.
  1. Mol Metab. 2019 Dec 19. pii: S2212-8778(19)30953-6. [Epub ahead of print]
    Analysing central metabolism in ultra-high resolution: At the crossroads of carbon and nitrogen.
    Safak Bayram, Susanne Fürst, Martin Forbes, Stefan Kempa.
      BACKGROUND: Cancer cell metabolism can be characterised by adaptive metabolic alterations, which support abnormal proliferative cell growth with high energetic demand. De novo nucleotide biosynthesis is essential for providing nucleotides for RNA and DNA synthesis, and drugs targeting this biosynthetic pathway have proven to be effective anticancer therapeutics. Nevertheless, cancers are often able to circumvent chemotherapeutic interventions and become therapy resistant. Our understanding of the changing metabolic profile of the cancer cell and the mode of action of therapeutics is dependent on technological advances in biochemical analysis.SCOPE OF REVIEW: This review begins with information about carbon- and nitrogen-donating pathways to build purine and pyrimidine moieties in the course of nucleotide biosynthesis. We discuss the application of stable isotope resolved metabolomics to investigate the dynamics of cancer cell metabolism and outline the benefits of high-resolution accurate mass spectrometry, which enables multiple tracer studies.
    CONCLUSION: With the technological advances in mass spectrometry that allow for the analysis of the metabolome in high resolution, the application of stable isotope resolved metabolomics has become an important technique in the investigation of biological processes. The literature in the area of isotope labelling is dominated by 13C tracer studies. Metabolic pathways have to be considered as complex interconnected networks and should be investigated as such. Moving forward to simultaneous tracing of different stable isotopes will help elucidate the interplay between carbon and nitrogen flow and the dynamics of de novo nucleotide biosynthesis within the cell.
    Keywords:  Cancer metabolism; Flux analysis; Isotope resolved metabolomics
    DOI:  https://doi.org/10.1016/j.molmet.2019.12.002
  2. Methods Mol Biol. 2020 ;2104 209-225
    Annotation of Specialized Metabolites from High-Throughput and High-Resolution Mass Spectrometry Metabolomics.
    Thomas Naake, Emmanuel Gaquerel, Alisdair R Fernie.
      High-throughput mass spectrometry (MS) metabolomics profiling of highly complex samples allows the comprehensive detection of hundreds to thousands of metabolites under a given condition and point in time and produces information-rich data sets on known and unknown metabolites. One of the main challenges is the identification and annotation of metabolites from these complex data sets since the number of authentic standards available for specialized metabolites is far lower than an account for the number of mass spectral features. Previously, we reported two novel tools, MetNet and MetCirc, for putative annotation and structural prediction on unknown metabolites using known metabolites as baits. MetNet employs differences between m/z values of MS1 features, which correspond to metabolic transformations, and statistical associations, while MetCirc uses MS/MS features as input and calculates similarity scores of aligned spectra between features to guide the annotation of metabolites. Here, we showcase the use of MetNet and MetCirc to putatively annotate metabolites and provide detailed instructions as to how those can be used. While our case studies are from plants, the tools find equal utility in studies on bacterial, fungal, or mammalian xenobiotic samples.
    Keywords:  Annotation; Metabolic modification; Molecular networking; Plant metabolite; Specialized metabolite; Unknown metabolite
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_12
  3. Methods Mol Biol. 2020 ;2104 1-10
    Overview of Experimental Methods and Study Design in Metabolomics, and Statistical and Pathway Considerations.
    Stephen Barnes.
      Metabolomics has become a powerful tool in biological and clinical investigations. This chapter reviews the technological basis of metabolomics and the considerations in answering biomedical questions. The workflow of metabolomics is explained in the sequence of data processing, quality control, metabolite annotation, statistical analysis, pathway analysis, and multi-omics integration. Reproducibility in both sample analysis and data analysis is key to the scientific progress, and the recommendation is made on reporting standards in publications. This chapter explains the technical aspects of metabolomics in the context of systems biology and applications to human health.
    Keywords:  Annotation; GC-MS; LC-MS; Metabolomics; NMR; Precision medicine; Recommendation; Systems medicine
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_1
  4. Nat Commun. 2020 Jan 16. 11(1): 331
    Trapped ion mobility spectrometry and PASEF enable in-depth lipidomics from minimal sample amounts.
    Catherine G Vasilopoulou, Karolina Sulek, Andreas-David Brunner, Ningombam Sanjib Meitei, Ulrike Schweiger-Hufnagel, Sven W Meyer, Aiko Barsch, Matthias Mann, Florian Meier.
      A comprehensive characterization of the lipidome from limited starting material remains very challenging. Here we report a high-sensitivity lipidomics workflow based on nanoflow liquid chromatography and trapped ion mobility spectrometry (TIMS). Taking advantage of parallel accumulation-serial fragmentation (PASEF), we fragment on average 15 precursors in each of 100 ms TIMS scans, while maintaining the full mobility resolution of co-eluting isomers. The acquisition speed of over 100 Hz allows us to obtain MS/MS spectra of the vast majority of isotope patterns. Analyzing 1 µL of human plasma, PASEF increases the number of identified lipids more than three times over standard TIMS-MS/MS, achieving attomole sensitivity. Building on high intra- and inter-laboratory precision and accuracy of TIMS collisional cross sections (CCS), we compile 1856 lipid CCS values from plasma, liver and cancer cells. Our study establishes PASEF in lipid analysis and paves the way for sensitive, ion mobility-enhanced lipidomics in four dimensions.
    DOI:  https://doi.org/10.1038/s41467-019-14044-x
  5. J Inherit Metab Dis. 2020 Jan 13.
    Targeted cerebrospinal fluid analysis for inborn errors of metabolism on an LC-MS/MS analysis platform.
    Glynis Klinke, Sylvia Richter, Péter Monostori, Brigitte Schmidt-Mader, Angels García-Cazorla, Rafael Artuch, Stine Christ, Thomas Opladen, Georg F Hoffmann, Nenad Blau, Jürgen G Okun.
      BACKGROUND: Laboratory investigations of cerebrospinal fluid (CSF) are essential when suspecting an inborn error of metabolism (IEM) involving neurological features. Available tests are currently performed on different analytical platforms, requiring a large sample volume and long turnaround time, which often delays timely diagnosis. Therefore, it would be preferable to have an "one-instrument" targeted multi-metabolite approach.METHOD: A liquid chromatography-tandem mass spectrometry (LC-MS/MS) platform, based on two different methods for analyzing 38 metabolites using positive and negative electrospray ionization modes, was established. To allow for platform extension, both methods were designed to use the same CSF sample preparation procedure and to be run on the same separation column (ACE C18-PFP).
    RESULTS: Assessment of the LC-MS/MS platform methods was first made by analytical validation, followed by the establishment of literature-based CSF cut-off values and reference ranges, and by the measurement of available samples obtained from patients with confirmed diagnoses of aromatic L-amino acid decarboxylase deficiency, guanidinoacetate methyltransferase deficiency, ornithine aminotransferase deficiency, cerebral folate deficiency and methylenetetrahydrofolate reductase deficiency.
    CONCLUSION: An extendable targeted LC-MS/MS platform was developed for the analysis of multiple metabolites in CSF, thereby distinguishing samples from patients with IEM from non-IEM samples. Reference concentrations for several biomarkers in CSF are provided for the first time. By measurement on a single analytical platform, less sample volume is required (200 μl), diagnostic results are obtained faster, and preanalytical issues are reduced.
    SYNOPSIS: LC-MS/MS platform for CSF analysis consisting of two differentially designed methods This article is protected by copyright. All rights reserved.
    Keywords:  cerebrospinal fluid; inborn errors of metabolism; inherited metabolic diseases; liquid chromatography coupled to tandem mass spectrometry; reference ranges; targeted metabolomics
    DOI:  https://doi.org/10.1002/jimd.12213
  6. Methods Mol Biol. 2020 ;2108 197-207
    Probing Metabolic Changes in IFNγ-Treated Ovarian Cancer Cells.
    Pritpal Kaur, Shreya Nagar, Madhura Bhagwat, Mohammad M Uddin, Yan Zhu, Ales Vancura.
      Interferon-γ (IFNγ) is a pleiotropic cytokine that signals to many different cell types. IFNγ has both antitumor functions and pro-tumorigenic effects and regulates different aspects of cell physiology, including metabolism. Cancer cells undergo a complex rearrangement of metabolic pathways that allows them to satisfy the needs of increased proliferation, and many cancer cells redirect glucose metabolism from oxidative phosphorylation to aerobic glycolysis. In this chapter, we describe a protocol that utilizes the Agilent Seahorse XFp Analyzer to assess mitochondrial respiration and glycolysis in ovarian cancer cells treated with IFNγ.
    Keywords:  Extracellular acidification; Glycolysis; Interferon-γ; Mitochondrial respiration; Ovarian cancer; Oxygen consumption rate
    DOI:  https://doi.org/10.1007/978-1-0716-0247-8_17
  7. Methods Mol Biol. 2020 ;2104 149-163
    METLIN: A Tandem Mass Spectral Library of Standards.
    J Rafael Montenegro-Burke, Carlos Guijas, Gary Siuzdak.
      Untargeted mass spectrometry metabolomics studies rely on accurate databases for the identification of metabolic features. Leveraging unique fragmentation patterns as well as characteristic dissociation routes allows for structural information to be gained for specific metabolites and molecular classes, respectively. Here we describe the evolution of METLIN as a resource for small molecule analysis as well as the tools (e.g., Fragment Similarity Search and Neutral Loss Search) used to query the database and their workflows for the identification of molecular entities. Additionally, we will discuss the functionalities of isoMETLIN, a database of isotopic metabolites, and the latest addition to the METLIN family, METLIN-MRM, which facilitates the analysis of quantitative mass spectrometry data generated with triple quadrupole instrumentation.
    Keywords:  MS/MS spectra; Metabolite identification; Spectral database; Untargeted metabolomics
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_9
  8. J Clin Endocrinol Metab. 2020 Jan 13. pii: dgaa014. [Epub ahead of print]
    High performance chromatography-mass spectrometry (HPLC-MS) Based Lipid Metabolite Profiling of Acromegaly.
    Meng Wang, Shizhe Guo, Min He, Xiaoqing Shao, Lei Feng, Yifei Yu, Wei Gong, Qingfeng Liu, Vladimir Melnikov, Xiaoxue Wang, Zhian He, Lin Jiang, Maohua Chen, Jun Sun, Jianyong Cai, Yao Zhao, Yiming Li, Nicholas A Tritos, Zhiyu Hu, Zhaoyun Zhang.
      CONTEXT: Metabolic disorders, especially dysregulated lipid metabolism, increase the risk of cardiovascular mortality in acromegaly. Previous studies measuring plasma macromolecular lipids have yielded conflicting results.PURPOSE: To explore the plasma lipid metabolite profiles by metabolomics analysis and identify potential metabolites associated with cardiac function in acromegaly.
    METHODS: Plasma was obtained from 80 newly diagnosed, untreated patients with acromegaly and 80 healthy controls. Echocardiography was performed. Based on the results of an oral glucose tolerance test (OGTT), patients were categorized into two groups: normal glucose tolerance (NGT, n=28) and impaired glucose tolerance or diabetes mellitus (IGT/DM, n=52). High performance liquid chromatography-mass spectrometry (HPLC-MS)-based metabolomics analysis was conducted. Data were processed by principal components analysis (PCA), orthogonal partial least square-discriminant analysis (OPLS-DA) and MetaboAnalyst 4.0. Associations between metabolic substances and cardiovascular parameters were also explored.
    RESULTS: Metabolomics uncovered a distinct metabolic pattern between acromegaly and healthy controls and perturbed pathways mainly include glycerophospholipid metabolism, sphingolipid metabolism, as well as linoleic acid metabolism. Collective analysis showed that phosphatidylethanolamine (PE) (22:6/16:0) was positively correlated with LV mass while lysophosphatidylcholine (LysoPC) (16:0) was positively correlated with fractional shortening (FS) and left ventricle ejection fraction (LVEF).
    CONCLUSION: Patients with acromegaly have distinct lipid metabolite profiling while PE (22:6/16:0) and LysoPC (16:0) are correlated with cardiac structure and function, which may contribute to the risk of cardiovascular complications.
    DOI:  https://doi.org/10.1210/clinem/dgaa014
  9. Nat Commun. 2020 Jan 17. 11(1): 375
    Large-scale lipid analysis with C=C location and sn-position isomer resolving power.
    Wenbo Cao, Simin Cheng, Jing Yang, Jiaxin Feng, Wenpeng Zhang, Zishuai Li, Qinhua Chen, Yu Xia, Zheng Ouyang, Xiaoxiao Ma.
      Lipids play a pivotal role in biological processes and lipid analysis by mass spectrometry (MS) has significantly advanced lipidomic studies. While the structure specificity of lipid analysis proves to be critical for studying the biological functions of lipids, current mainstream methods for large-scale lipid analysis can only identify the lipid classes and fatty acyl chains, leaving the C=C location and sn-position unidentified. In this study, combining photochemistry and tandem MS we develop a simple but effective workflow to enable large-scale and near-complete lipid structure characterization with a powerful capability of identifying C=C location(s) and sn-position(s) simultaneously. Quantitation of lipid structure isomers at multiple levels of specificity is achieved and different subtypes of human breast cancer cells are successfully discriminated. Remarkably, human lung cancer tissues can only be distinguished from adjacent normal tissues using quantitative results of both lipid C=C location and sn-position isomers.
    DOI:  https://doi.org/10.1038/s41467-019-14180-4
  10. Clin Chem Lab Med. 2020 Jan 11. pii: /j/cclm.ahead-of-print/cclm-2019-0820/cclm-2019-0820.xml. [Epub ahead of print]
    Hyphenated mass spectrometry techniques for assessing medication adherence: advantages, challenges, clinical applications and future perspectives.
    Sangeeta Tanna, John Ogwu, Graham Lawson.
      Nonadherence to prescribed pharmacotherapy is an understated public health problem globally and is costing many patients their chance to return to good health and healthcare systems billions. Clinicians need an accurate assessment of adherence to medications to aid the clinical decision-making process in the event of poor patient progress and to maximise the patient health outcomes from the drug therapies prescribed. An overview of indirect and direct methods used to measure medication adherence is presented, highlighting the potential for accurate measuring of drugs in biological samples using hyphenated mass spectrometry (MS) techniques to provide healthcare professionals with a reliable evidence base for clinical decision making. In this review we summarise published applications of hyphenated MS techniques for a diverse range of clinical areas demonstrating the rise in the use of such direct methods for assessing medication adherence. Although liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods using plasma, serum and urine samples are the most popular, in recent years increased attention has been given to liquid chromatography high-resolution mass spectrometry (LC-HRMS) methods and alternative biosample matrices including hair, saliva and blood microsamples. The advantages and challenges of using hyphenated MS techniques to address this healthcare problem are also discussed alongside future perspectives.
    Keywords:  bioanalysis; hyphenated techniques; liquid chromatography-high resolution mass spectrometry; liquid chromatography-tandem mass spectrometry; medication adherence; quantitation
    DOI:  https://doi.org/10.1515/cclm-2019-0820
  11. Methods Mol Biol. 2020 ;2104 61-97
    Analysis of NMR Metabolomics Data.
    Wimal Pathmasiri, Kristine Kay, Susan McRitchie, Susan Sumner.
      In this chapter, we summarize data preprocessing and data analysis strategies used for analysis of NMR data for metabolomics studies. Metabolomics consists of the analysis of the low molecular weight compounds in cells, tissues, or biological fluids, and has been used to reveal biomarkers for early disease detection and diagnosis, to monitor interventions, and to provide information on pathway perturbations to inform mechanisms and identifying targets. Metabolic profiling (also termed metabotyping) involves the analysis of hundreds to thousands of molecules using mainly state-of-the-art mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy technologies. While NMR is less sensitive than mass spectrometry, NMR does provide a wealth of complex and information rich metabolite data. NMR data together with the use of conventional statistics, modeling methods, and bioinformatics tools reveals biomarker and mechanistic information. A typical NMR spectrum, with up to 64k data points, of a complex biological fluid or an extract of cells and tissues consists of thousands of sharp signals that are mainly derived from small molecules. In addition, a number of advanced NMR spectroscopic methods are available for extracting information on high molecular weight compounds such as lipids or lipoproteins. There are numerous data preprocessing, data reduction, and analysis methods developed and evolving in the field of NMR metabolomics. Our goal is to provide an extensive summary of NMR data preprocessing and analysis strategies by providing examples and open source and commercially available analysis software and bioinformatics tools.
    Keywords:  Metabolomics; Metabotyping; Multivariate data analysis; NMR; Preprocessing; Quality control
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_5
  12. Sci Total Environ. 2020 Jan 07. pii: S0048-9697(19)36449-6. [Epub ahead of print]712 136453
    Integration of proteomics and metabolomics reveals promotion of proliferation by exposure of bisphenol S in human breast epithelial MCF-10A cells.
    Wei Huang, Lin Zhu, Chao Zhao, Xiangfeng Chen, Zongwei Cai.
      Bisphenol S (BPS) has been reported to have similar estrogenic effects as bisphenol A (BPA). Considering the endocrine disrupting effects of BPS, in this study, we investigated the effects of BPS exposure on normal human breast epithelial cell line MCF-10A by using mass spectrometry (MS)-based metabolomics and quantitative proteomics. We found that exposure to BPS for 24 h altered the proliferation of MCF-10A cells in a hormetic manner with the highest proliferation rate at the dosage of 1 μM. A total of 200 proteins were identified to be significantly changed by 1 μM of BPS exposure. The upregulation of epidermal growth factor receptor (EGFR) and Ras/mTOR-related proteins implied that EGFR-mediated pathways were involved in BPS-induced proliferation of MCF-10A cells. In addition, several proliferation-related protein markers were found to be elevated, such as MKI67 and CDH1, further indicating the promotion of proliferation by low dose of BPS exposure. Besides, 35 endogenous metabolites were found to be significantly changed. The joint pathway analysis of the altered metabolites and proteins suggested changes in pathways of tricarboxylic acid (TCA) cycle, purine metabolism, pyruvate metabolism and lipid metabolism, which were involved in sustaining cell proliferation and cellular signal transduction. Taken together, this study provides insights into the effects and the potential mechanisms of BPS on estrogen receptor α-negative normal breast cell line MCF-10A, broadening our knowledge about the risk of using BPS as the alternative of BPA.
    Keywords:  Bisphenol S; Hormetic effect; Human breast epithelial cell; Metabolomics; Proliferation; Proteomics
    DOI:  https://doi.org/10.1016/j.scitotenv.2019.136453
  13. Mol Omics. 2020 Jan 14.
    High-throughput quantitative top-down proteomics.
    Kellye A Cupp-Sutton, Si Wu.
      Top-down mass spectrometry (MS) analyzes intact proteins at the proteoform level, which allows researchers to better understand the functions of protein modifications. Recently, top-down proteomics has increased in popularity due to advancements in high-resolution mass spectrometers, increased efficiency in liquid chromatography (LC) separation, and advances in data analysis software. Some unique protein proteoforms, which have been distinguished using top-down MS, have even been shown to exhibit marked variation in biological function compared to similar proteoforms. However, the qualitative identification of a particular proteoform may not be enough to determine the biological relevance of that proteoform. Quantitative top-down MS methods have been notably applied to the study of the differing biological functions of protein proteoforms and have allowed researchers to explore proteomes at the proteoform, rather than the peptide, level. Here, we review the top-down MS methods that have been used to quantitatively identify intact proteins, discuss current applications of quantitative top-down MS analysis, and present new areas where quantitative top-down MS analysis may be implemented.
    DOI:  https://doi.org/10.1039/c9mo00154a
  14. J Am Soc Mass Spectrom. 2019 Oct 01. 30(10): 2135-2143
    Combining Charge-Switch Derivatization with Ozone-Induced Dissociation for Fatty Acid Analysis.
    Berwyck L J Poad, David L Marshall, Eva Harazim, Rajesh Gupta, Venkateswara R Narreddula, Reuben S E Young, Eva Duchoslav, J Larry Campbell, James A Broadbent, Josef Cvačka, Todd W Mitchell, Stephen J Blanksby.
      The specific positions of carbon-carbon double bond(s) within an unsaturated fatty acid exert a significant effect on the physical and chemical properties of the lipid that ultimately inform its biological function(s). Contemporary liquid chromatography-mass spectrometry (MS) strategies based on electrospray ionization coupled to tandem MS can easily detect fatty acyl lipids but generally cannot reveal those specific site(s) of unsaturation. Herein, we describe a novel and versatile workflow whereby fatty acids are first converted to fixed charge N-(4-aminomethylphenyl)pyridinium (AMPP) derivatives and subsequently subjected to ozone-induced dissociation (OzID) on a modified triple quadrupole mass spectrometer. The AMPP modification enhances the detection of fatty acids introduced by direct infusion. Fragmentation of the derivatized fatty acids also provides diagnostic fragment ions upon collision-induced dissociation that can be targeted in precursor ion scans to subsequently trigger OzID analyses in an automated data-dependent workflow. It is these OzID analyses that provide unambiguous assignment of carbon-carbon double bond locations in the AMPP-derivatized fatty acids. The performance of this analysis pipeline is assessed in profiling the patterns of unsaturation in fatty acids within the complex biological secretion vernix caseosa. This analysis uncovers significant isomeric diversity within the fatty acid pool of this sample, including a number of hitherto unreported double bond positional isomers that hint at the activity of potentially new metabolic pathways.
    Keywords:  Fatty Acids; Lipids; ozone-induced dissociation; vernix caseosa
  15. J Am Soc Mass Spectrom. 2019 Dec 01. 30(12): 2470-2479
    Capillary Zone Electrophoresis-Tandem Mass Spectrometry with Activated Ion Electron Transfer Dissociation for Large-scale Top-down Proteomics.
    Elijah N McCool, Jean M Lodge, Abdul Rehman Basharat, Xiaowen Liu, Joshua J Coon, Liangliang Sun.
      Capillary zone electrophoresis (CZE)-tandem mass spectrometry (MS/MS) has been recognized as an efficient approach for top-down proteomics recently for its high-capacity separation and highly sensitive detection of proteoforms. However, the commonly used collision-based dissociation methods often cannot provide extensive fragmentation of proteoforms for thorough characterization. Activated ion electron transfer dissociation (AI-ETD), that combines infrared photoactivation concurrent with ETD, has shown better performance for proteoform fragmentation than higher energy-collisional dissociation (HCD) and standard ETD. Here, we present the first application of CZE-AI-ETD on an Orbitrap Fusion Lumos mass spectrometer for large-scale top-down proteomics of Escherichia coli (E. coli) cells. CZE-AI-ETD outperformed CZE-ETD regarding proteoform and protein identifications (IDs). CZE-AI-ETD reached comparable proteoform and protein IDs with CZE-HCD. CZE-AI-ETD tended to generate better expectation values (E values) of proteoforms than CZE-HCD and CZE-ETD, indicating a higher quality of MS/MS spectra from AI-ETD respecting the number of sequence-informative fragment ions generated. CZE-AI-ETD showed great reproducibility regarding the proteoform and protein IDs with relative standard deviations less than 4% and 2% (n = 3). Coupling size exclusion chromatography (SEC) to CZE-AI-ETD identified 3028 proteoforms and 387 proteins from E. coli cells with 1% spectrum level and 5% proteoform-level false discovery rates. The data represents the largest top-down proteomics dataset using the AI-ETD method so far. Single-shot CZE-AI-ETD of one SEC fraction identified 957 proteoforms and 253 proteins. N-terminal truncations, signal peptide cleavage, N-terminal methionine removal, and various post-translational modifications including protein N-terminal acetylation, methylation, S-thiolation, disulfide bonds, and lysine succinylation were detected.
    Keywords:  Activated ion electron transfer dissociation; Capillary zone electrophoresis-tandem mass spectrometry; Disulfide bonds; Escherichia coli; Lysine succinylation; S-thiolation; Top-down proteomics
  16. Mol Metab. 2019 Nov 23. pii: S2212-8778(19)30943-3. [Epub ahead of print]
    Targeting extracellular nutrient dependencies of cancer cells.
    Javier Garcia-Bermudez, Robert T Williams, Rohiverth Guarecuco, Kıvanç Birsoy.
      BACKGROUND: Cancer cells rewire their metabolism to meet the energetic and biosynthetic demands of their high proliferation rates and environment. Metabolic reprogramming of cancer cells may result in strong dependencies on nutrients that could be exploited for therapy. While these dependencies may be in part due to the nutrient environment of tumors, mutations or expression changes in metabolic genes also reprogram metabolic pathways and create addictions to extracellular nutrients.SCOPE OF REVIEW: This review summarizes the major nutrient dependencies of cancer cells focusing on their discovery and potential mechanisms by which metabolites become limiting for tumor growth. We further detail available therapeutic interventions based on these metabolic features and highlight opportunities for restricting nutrient availability as an anti-cancer strategy.
    MAJOR CONCLUSIONS: Strategies to limit nutrients required for tumor growth using dietary interventions or nutrient degrading enzymes have previously been suggested for cancer therapy. The best clinical example of exploiting cancer nutrient dependencies is the treatment of leukemia with l-asparaginase, a first-line chemotherapeutic that depletes serum asparagine. Despite the success of nutrient starvation in blood cancers, it remains unclear whether this approach could be extended to other solid tumors. Systematic studies to identify nutrient dependencies unique to individual tumor types have the potential to discover targets for therapy.
    Keywords:  Amino acids; Asparaginase; Cancer metabolism; Diet; Metabolic therapy; Nutrient dependencies
    DOI:  https://doi.org/10.1016/j.molmet.2019.11.011
  17. J Am Soc Mass Spectrom. 2019 Oct 01. 30(10): 2031-2036
    Perspectives on Data Analysis in Metabolomics: Points of Agreement and Disagreement from the 2018 ASMS Fall Workshop.
    Erin S Baker, Gary J Patti.
      In November 2018, the American Society for Mass Spectrometry hosted the Annual Fall Workshop on informatic methods in metabolomics. The Workshop included sixteen lectures presented by twelve invited speakers. The focus of the talks was untargeted metabolomics performed with liquid chromatography/mass spectrometry. In this review, we highlight five recurring topics that were covered by multiple presenters: (i) data sharing, (ii) artifacts and contaminants, (iii) feature degeneracy, (iv) database organization, and (v) requirements for metabolite identification. Our objective here is to present viewpoints that were widely shared among participants, as well as those in which varying opinions were articulated. We note that most of the presenting speakers employed different data processing software, which underscores the diversity of informatic programs currently being used in metabolomics. We conclude with our thoughts on the potential role of reference datasets as a step towards standardizing data processing methods in metabolomics.
    Keywords:  ASMS Fall Workshop; Informatics; Metabolism; Metabolomics
  18. J Am Soc Mass Spectrom. 2019 Dec 01. 30(12): 2502-2513
    Deep Intact Proteoform Characterization in Human Cell Lysate Using High-pH and Low-pH Reversed-Phase Liquid Chromatography.
    Dahang Yu, Zhe Wang, Kellye A Cupp-Sutton, Xiaowen Liu, Si Wu.
      Post-translational modifications (PTMs) play critical roles in biological processes and have significant effects on the structures and dynamics of proteins. Top-down proteomics methods were developed for and applied to the study of intact proteins and their PTMs in human samples. However, the large dynamic range and complexity of human samples makes the study of human proteins challenging. To address these challenges, we developed a 2D pH RP/RPLC-MS/MS technique that fuses high-resolution separation and intact protein characterization to study the human proteins in HeLa cell lysate. Our results provide a deep coverage of soluble proteins in human cancer cells. Compared to 225 proteoforms from 124 proteins identified when 1D separation was used, 2778 proteoforms from 628 proteins were detected and characterized using our 2D separation method. Many proteoforms with critically functional PTMs including phosphorylation were characterized. Additionally, we present the first detection of intact human GcvH proteoforms with rare modifications such as octanoylation and lipoylation. Overall, the increase in the number of proteoforms identified using 2DLC separation is largely due to the reduction in sample complexity through improved separation resolution, which enables the detection of low-abundance PTM-modified proteoforms. We demonstrate here that 2D pH RP/RPLC is an effective technique to analyze complex protein samples using top-down proteomics.
    Keywords:  Intact proteoforms; Liquid chromatography; Mass spectrometry; RPLC; Top-down proteomics
  19. Methods Mol Biol. 2020 ;2104 419-445
    Using the IDEOM Workflow for LCMS-Based Metabolomics Studies of Drug Mechanisms.
    Anubhav Srivastava, Darren J Creek.
      Rapid advancements in metabolomics technologies have allowed for application of liquid chromatography mass spectrometry (LCMS)-based metabolomics to investigate a wide range of biological questions. In addition to an important role in studies of cellular biochemistry and biomarker discovery, an exciting application of metabolomics is the elucidation of mechanisms of drug action (Creek et al., Antimicrob Agents Chemother 60:6650-6663, 2016; Allman et al., Antimicrob Agents Chemother 60:6635-6649, 2016). Although it is a very useful technique, challenges in raw data processing, extracting useful information out of large noisy datasets, and identifying metabolites with confidence, have meant that metabolomics is still perceived as a highly specialized technology. As a result, metabolomics has not yet achieved the anticipated extent of uptake in laboratories around the world as genomics or transcriptomics. With a view to bring metabolomics within reach of a nonspecialist scientist, here we describe a routine workflow with IDEOM, which is a graphical user interface within Microsoft Excel, which almost all researchers are familiar with. IDEOM consists of custom built algorithms that allow LCMS data processing, automatic noise filtering and identification of metabolite features (Creek et al., Bioinformatics 28:1048-1049, 2012). Its automated interface incorporates advanced LCMS data processing tools, mzMatch and XCMS, and requires R for complete functionality. IDEOM is freely available for all researchers and this chapter will focus on describing the IDEOM workflow for the nonspecialist researcher in the context of studies designed to elucidate mechanisms of drug action.
    Keywords:  Data processing; Drug mechanism; IDEOM; LCMS; Metabolomics; Microsoft Excel; Mode of action
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_21
  20. Nat Rev Urol. 2020 Jan 17.
    Competitive glucose metabolism as a target to boost bladder cancer immunotherapy.
    Julieta Afonso, Lúcio L Santos, Adhemar Longatto-Filho, Fátima Baltazar.
      Bladder cancer - the tenth most frequent cancer worldwide - has a heterogeneous natural history and clinical behaviour. The predominant histological subtype, urothelial bladder carcinoma, is characterized by high recurrence rates, progression and both primary and acquired resistance to platinum-based therapy, which impose a considerable economic burden on health-care systems and have substantial effects on the quality of life and the overall outcomes of patients with bladder cancer. The incidence of urothelial tumours is increasing owing to population growth and ageing, so novel therapeutic options are vital. Based on work by The Cancer Genome Atlas project, which has identified targetable vulnerabilities in bladder cancer, immune checkpoint inhibitors (ICIs) have arisen as an effective alternative for managing advanced disease. However, although ICIs have shown durable responses in a subset of patients with bladder cancer, the overall response rate is only ~15-25%, which increases the demand for biomarkers of response and therapeutic strategies that can overcome resistance to ICIs. In ICI non-responders, cancer cells use effective mechanisms to evade immune cell antitumour activity; the overlapping Warburg effect machinery of cancer and immune cells is a putative determinant of the immunosuppressive phenotype in bladder cancer. This energetic interplay between tumour and immune cells leads to metabolic competition in the tumour ecosystem, limiting nutrient availability and leading to microenvironmental acidosis, which hinders immune cell function. Thus, molecular hallmarks of cancer cell metabolism are potential therapeutic targets, not only to eliminate malignant cells but also to boost the efficacy of immunotherapy. In this sense, integrating the targeting of tumour metabolism into immunotherapy design seems a rational approach to improve the therapeutic efficacy of ICIs.
    DOI:  https://doi.org/10.1038/s41585-019-0263-6
  21. Biology (Basel). 2020 Jan 10. pii: E16. [Epub ahead of print]9(1):
    Glucose Favors Lipid Anabolic Metabolism in the Invasive Breast Cancer Cell Line MDA-MB-231.
    Mª Carmen Ocaña, Beatriz Martínez-Poveda, Ana R Quesada, Miguel Ángel Medina.
      Metabolic reprogramming in tumor cells is considered one of the hallmarks of cancer. Many studies have been carried out in order to elucidate the effects of tumor cell metabolism on invasion and tumor progression. However, little is known about the immediate substrate preference in tumor cells. In this work, we wanted to study this short-time preference using the highly invasive, hormone independent breast cancer cell line MDA-MB-231. By means of Seahorse and uptake experiments, our results point to a preference for glucose. However, although both glucose and glutamine are required for tumor cell proliferation, MDA-MB-231 cells can survive two days in the absence of glucose, but not in the absence of glutamine. On the other hand, the presence of glucose increased palmitate uptake in this cell line, which accumulates in the cytosol instead of going to the plasma membrane. In order to exert this effect, glucose needs to be converted to glycerol-3 phosphate, leading to palmitate metabolism through lipid synthesis, most likely to the synthesis of triacylglycerides. The effect of glucose on the palmitate uptake was also found in other triple-negative, invasive breast cancer cell lines, but not in the non-invasive ones. The results presented in this work suggest an important and specific role of glucose in lipid biosynthesis in triple-negative breast cancer.
    Keywords:  cancer; glucose metabolism; glutamine metabolism; lipid metabolism
    DOI:  https://doi.org/10.3390/biology9010016
  22. Mol Cancer Res. 2020 Jan 15. pii: molcanres.0606.2019. [Epub ahead of print]
    Metabolic Profiling Reveals a Dependency of Human Metastatic Breast Cancer on Mitochondrial Serine and One-Carbon Unit Metabolism.
    Albert M Li, Gregory S Ducker, Yang Li, Jose A Seoane, Yiren Xiao, Stavros Melemenidis, Yiren Zhou, Ling Liu, Sakari Vanharanta, Edward E Graves, Erinn B Rankin, Christina Curtis, Joan Massague, Joshua D Rabinowitz, Craig B Thompson, Jiangbin Ye.
      Breast cancer is the most common cancer among American women and a major cause of mortality. To identify metabolic pathways as potential targets to treat metastatic breast cancer, we performed metabolomics profiling on breast cancer cell line MDA-MB-231 and its tissue-tropic metastatic subclones. Here, we report that these subclones with increased metastatic potential display an altered metabolic profile compared to the parental population. In particular, the mitochondrial serine and one-carbon (1C) unit pathway is upregulated in metastatic subclones. Mechanistically, the mitochondrial serine and 1C unit pathway drives the faster proliferation of subclones through enhanced de novo purine biosynthesis. Inhibition of the first rate-limiting enzyme of the mitochondrial serine and 1C unit pathway, serine hydroxymethyltransferase (SHMT2), potently suppresses proliferation of metastatic subclones in culture and impairs growth of lung metastatic subclones at both primary and metastatic sites in mice. Some human breast cancers exhibit a significant association between the expression of genes in the mitochondrial serine and 1C unit pathway with disease outcome and higher expression of SHMT2 in metastatic tumor tissue compared to primary tumors. In addition to breast cancer, a few other cancer types, such as adrenocortical carcinoma (ACC) and kidney chromophobe cell carcinoma (KICH), also display increased SHMT2 expression during disease progression. Together, these results suggest that mitochondrial serine and 1C unit plays an important role in promoting cancer progression, particularly in late stage cancer. Implications: This study identifies mitochondrial serine and 1C unit metabolism as an important pathway during the progression of a subset of human breast cancers.
    DOI:  https://doi.org/10.1158/1541-7786.MCR-19-0606
  23. Methods Mol Biol. 2020 ;2104 121-137
    Extracting Biological Insight from Untargeted Lipidomics Data.
    Jennifer E Kyle.
      Lipidomics data generated using untargeted mass spectrometry techniques can offer great biological insight to metabolic status and disease diagnoses. As the community's ability to conduct large-scale studies with deep coverage of the lipidome expands, approaches to analyzing untargeted data and extracting biological insight are needed. Currently, the function of most individual lipids are not known; however, meaningful biological information can be extracted. Here, I will describe a step-by-step approach to identify patterns and trends in untargeted mass spectrometry lipidomics data to assist users in extracting information leading to a greater understanding of biological systems.
    Keywords:  Blood plasma; LipidMaps; Lipidome; Lipidomics; Mass spectrometry; Untargeted
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_7
  24. Methods Mol Biol. 2020 ;2104 185-207
    De Novo Molecular Formula Annotation and Structure Elucidation Using SIRIUS 4.
    Marcus Ludwig, Markus Fleischauer, Kai Dührkop, Martin A Hoffmann, Sebastian Böcker.
      SIRIUS 4 is the best-in-class computational tool for metabolite identification from high-resolution tandem mass spectrometry data. It offers de novo molecular formula annotation with outstanding accuracy. When searching fragmentation spectra in a structure database, it reaches over 70% correct identifications. A predicted fingerprint, which indicates the presence or absence of thousands of molecular properties, helps to deduce information about the compound of interest even if it is not contained in any structure database. Here, we present best practices and describe how to leverage the full potential of SIRIUS 4, how to incorporate it into your own workflow, and how it adds value to the analysis of mass spectrometry data beyond spectral library search.
    Keywords:  Annotation; LC–MS/MS; Metabolite identification; Metabolomics; Molecular formula; SIRIUS; Structure prediction
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_11
  25. Methods Mol Biol. 2020 ;2104 139-148
    Overview of Tandem Mass Spectral and Metabolite Databases for Metabolite Identification in Metabolomics.
    Zhangtao Yi, Zheng-Jiang Zhu.
      Liquid chromatography-mass spectrometry (LC-MS) is one of the most popular technologies in metabolomics. The large-scale and unambiguous identification of metabolite structures remains a challenging task in LC-MS based metabolomics. Tandem mass spectral databases provide experimental and in silico MS/MS spectra to facilitate the identification of both known and unknown metabolites, which has become a gold standard method in metabolomics. In addition, metabolite knowledge databases offer valuable biological and pathway information of metabolites. In this chapter, we have briefly reviewed the most common and important tandem mass spectral and metabolite databases, and illustrated how they could be used for metabolite identification.
    Keywords:  Metabolite database; Metabolite identification; Metabolomics; Tandem mass spectrum
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_8
  26. Methods Mol Biol. 2020 ;2104 11-24
    Metabolomics Data Processing Using XCMS.
    Xavier Domingo-Almenara, Gary Siuzdak.
      XCMS is one of the most used software for liquid chromatography-mass spectrometry (LC-MS) data processing and it exists both as an R package and as a cloud-based platform known as XCMS Online. In this chapter, we first overview the nature of LC-MS data to contextualize the need for data processing software. Next, we describe the algorithms used by XCMS and the role that the different user-defined parameters play in the data processing. Finally, we describe the extended capabilities of XCMS Online.
    Keywords:  Data processing; Liquid chromatography; Mass spectrometry; Metabolomics; XCMS
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_2
  27. Methods Mol Biol. 2020 ;2104 227-243
    Feature-Based Molecular Networking for Metabolite Annotation.
    Vanessa V Phelan.
      The Global Natural Product Social Molecular Networking (GNPS) platform leverages tandem mass spectrometry (MS/MS) data for annotation of compounds. Molecular networks aid in the visualization of the chemical space within a metabolomics experiment. Recently, molecular networking has been combined with feature detection methods to yield Feature-Based Molecular Networking (FBMN). FBMN allows for the discrimination of isomers within the molecular network, incorporation of quantitative information generated by the feature detection tools into visualization of the molecular network, and compatibility with forthcoming in silico annotation tools. This chapter provides step-by-step methods for generating a molecular network to annotate microbial natural products using the Global Natural Product Social Molecular Networking (GNPS) Feature-Based Molecular Networking (FBMN) workflow.
    Keywords:  Feature annotation; GNPS; Molecular networking; Natural Products; Secondary metabolism; Specialized metabolites
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_13
  28. Methods Mol Biol. 2020 ;2104 49-60
    Metabolomics Data Processing Using OpenMS.
    Marc Rurik, Oliver Alka, Fabian Aicheler, Oliver Kohlbacher.
      This chapter describes the open-source tool suite OpenMS. OpenMS contains more than 180 tools which can be combined to build complex and flexible data-processing workflows. The broad range of functionality and the interoperability of these tools enable complex, complete, and reproducible data analysis workflows in computational proteomics and metabolomics. We introduce the key concepts of OpenMS and illustrate its capabilities with a complete workflow for the analysis of untargeted metabolomics data, including metabolite quantification and identification.
    Keywords:  Data analysis; Metabolomics; OpenMS; Reproducible science; Workflows
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_4
  29. Br J Cancer. 2020 Jan 16.
    MYC regulates fatty acid metabolism through a multigenic program in claudin-low triple negative breast cancer.
    Jessica C Casciano, Caroline Perry, Adam J Cohen-Nowak, Katelyn D Miller, Johan Vande Voorde, Qifeng Zhang, Susan Chalmers, Mairi E Sandison, Qin Liu, Ann Hedley, Tony McBryan, Hsin-Yao Tang, Nicole Gorman, Thomas Beer, David W Speicher, Peter D Adams, Xuefeng Liu, Richard Schlegel, John G McCarron, Michael J O Wakelam, Eyal Gottlieb, Andrew V Kossenkov, Zachary T Schug.
      BACKGROUND: Recent studies have suggested that fatty acid oxidation (FAO) is a key metabolic pathway for the growth of triple negative breast cancers (TNBCs), particularly those that have high expression of MYC. However, the underlying mechanism by which MYC promotes FAO remains poorly understood.METHODS: We used a combination of metabolomics, transcriptomics, bioinformatics, and microscopy to elucidate a potential mechanism by which MYC regulates FAO in TNBC.
    RESULTS: We propose that MYC induces a multigenic program that involves changes in intracellular calcium signalling and fatty acid metabolism. We determined key roles for fatty acid transporters (CD36), lipases (LPL), and kinases (PDGFRB, CAMKK2, and AMPK) that each contribute to promoting FAO in human mammary epithelial cells that express oncogenic levels of MYC. Bioinformatic analysis further showed that this multigenic program is highly expressed and predicts poor survival in the claudin-low molecular subtype of TNBC, but not other subtypes of TNBCs, suggesting that efforts to target FAO in the clinic may best serve claudin-low TNBC patients.
    CONCLUSION: We identified critical pieces of the FAO machinery that have the potential to be targeted for improved treatment of patients with TNBC, especially the claudin-low molecular subtype.
    DOI:  https://doi.org/10.1038/s41416-019-0711-3
  30. Cancers (Basel). 2020 Jan 03. pii: E124. [Epub ahead of print]12(1):
    Energy Metabolism in Cancer: The Roles of STAT3 and STAT5 in the Regulation of Metabolism-Related Genes.
    Arturo Valle-Mendiola, Isabel Soto-Cruz.
      A central characteristic of many types of cancer is altered energy metabolism processes such as enhanced glucose uptake and glycolysis and decreased oxidative metabolism. The regulation of energy metabolism is an elaborate process involving regulatory proteins such as HIF (pro-metastatic protein), which reduces oxidative metabolism, and some other proteins such as tumour suppressors that promote oxidative phosphorylation. In recent years, it has been demonstrated that signal transducer and activator of transcription (STAT) proteins play a pivotal role in metabolism regulation. STAT3 and STAT5 are essential regulators of cytokine- or growth factor-induced cell survival and proliferation, as well as the crosstalk between STAT signalling and oxidative metabolism. Several reports suggest that the constitutive activation of STAT proteins promotes glycolysis through the transcriptional activation of hypoxia-inducible factors and therefore, the alteration of mitochondrial activity. It seems that STAT proteins function as an integrative centre for different growth and survival signals for energy and respiratory metabolism. This review summarises the functions of STAT3 and STAT5 in the regulation of some metabolism-related genes and the importance of oxygen in the tumour microenvironment to regulate cell metabolism, particularly in the metabolic pathways that are involved in energy production in cancer cells.
    Keywords:  HIF; STAT3; STAT5; Warburg effect; cancer metabolism; transcription factors
    DOI:  https://doi.org/10.3390/cancers12010124
  31. Curr Opin Cell Biol. 2020 Jan 09. pii: S0955-0674(19)30112-7. [Epub ahead of print]63 20-30
    Dissecting intercellular signaling with mass spectrometry-based proteomics.
    Jonathan J Swietlik, Ankit Sinha, Felix Meissner.
      Physiological functions depend on a coordinated interplay of numerous different cell types. Proteins serve as major signaling molecules between cells; however, their comprehensive investigation in physiologically relevant settings has remained challenging. Mass spectrometry (MS)-based shotgun proteomics is emerging as a powerful technology for the systematic analysis of protein-mediated intercellular signaling and regulated post-translational modifications. Here, we discuss recent advancements in cell biological, chemical, and biochemical MS-based approaches for the profiling of cellular messengers released by sending cells, receptors expressed on the cell surface, and their interactions. We highlight methods tailored toward the mapping of dynamic signal transduction mechanisms at cellular interfaces and approaches to dissect communication cell specifically in heterocellular systems. Thereby, MS-based proteomics contributes a unique systems biology perspective for the identification of intercellular signaling pathways deregulated in disease.
    Keywords:  Autocrine; Cell non-autonomous; Heterocellular; Interaction; Intercellular; Ligand; Mass spectrometry; Messenger, Interactions; PTM; Paracrine; Posttranslational modification; Proteomics; Receiver; Receptor; Sender; Signaling
    DOI:  https://doi.org/10.1016/j.ceb.2019.12.002
  32. Methods Mol Biol. 2020 ;2111 257-265
    Radioisotope-Based Protocol for Determination of Central Carbon Metabolism in T Cells.
    Xuyong Chen, John William Sherman, Ruoning Wang.
      T lymphocytes are the major components of the adaptive immune system. It's been known that T cells are able to engage a diverse range of metabolic programs to meet the metabolic demands during their life cycle from early development, activation to functional differentiation. Central carbon metabolic pathways provide energy, reducing power, and biosynthetic precursors to support T cell homeostasis, proliferation, and immune functions. As such, quantitative or semiquantitative analysis of central carbon metabolic flux activities offers mechanistic details, as well as insights into the regulation of metabolic pathways and the impact of changing metabolic programs on T cell life cycle. Global profiling of cellular metabolites by mass spectrometry-based metabolomics and metabolic flux analysis (MFA) using radioactive and nonradioactive/stable isotope approaches are powerful tools for determination of central carbon metabolic pathway activity. Here, we describe in detail the procedure for the radioisotope-based approach of analyzing central carbon metabolic fluxes in T cells.
    Keywords:  Central carbon metabolism; Radioactive isotope; T lymphocytes
    DOI:  https://doi.org/10.1007/978-1-0716-0266-9_20
  33. Methods Mol Biol. 2020 ;2104 25-48
    Metabolomics Data Preprocessing Using ADAP and MZmine 2.
    Xiuxia Du, Aleksandr Smirnov, Tomáš Pluskal, Wei Jia, Susan Sumner.
      The informatics pipeline for making sense of untargeted LC-MS or GC-MS data starts with preprocessing the raw data. Results from data preprocessing undergo statistical analysis and subsequently mapped to metabolic pathways for placing untargeted metabolomics data in the biological context. ADAP is a suite of computational algorithms that has been developed specifically for preprocessing LC-MS and GC-MS data. It consists of two separate computational workflows that extract compound-relevant information from raw LC-MS and GC-MS data, respectively. Computational steps include construction of extracted ion chromatograms, detection of chromatographic peaks, spectral deconvolution, and alignment. The two workflows have been incorporated into the cross-platform and graphical MZmine 2 framework and ADAP-specific graphical user interfaces have been developed for using ADAP with ease. This chapter summarizes the algorithmic principles underlying key steps in the two workflows and illustrates how to apply ADAP to preprocess LC-MS and GC-MS data.
    Keywords:  ADAP; Alignment; Data preprocessing; GC–MS; LC–MS; MZmine 2; Metabolomics; Peak picking; Spectral deconvolution; Visualization
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_3
  34. J Am Soc Mass Spectrom. 2018 Aug 01. 29(8): 1745-1756
    Differentiating Positional Isomers of Nucleoside Modifications by Higher-Energy Collisional Dissociation Mass Spectrometry (HCD MS).
    Manasses Jora, Andrew P Burns, Robert L Ross, Peter A Lobue, Ruoxia Zhao, Cody M Palumbo, Peter A Beal, Balasubrahmanyam Addepalli, Patrick A Limbach.
      The analytical identification of positional isomers (e.g., 3-, N4-, 5-methylcytidine) within the > 160 different post-transcriptional modifications found in RNA can be challenging. Conventional liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) approaches rely on chromatographic separation for accurate identification because the collision-induced dissociation (CID) mass spectra of these isomers nearly exclusively yield identical nucleobase ions (BH2+) from the same molecular ion (MH+). Here, we have explored higher-energy collisional dissociation (HCD) as an alternative fragmentation technique to generate more informative product ions that can be used to differentiate positional isomers. LC-MS/MS of modified nucleosides characterized using HCD led to the creation of structure- and HCD energy-specific fragmentation patterns that generated unique fingerprints, which can be used to identify individual positional isomers even when they cannot be separated chromatographically. While particularly useful for identifying positional isomers, the fingerprinting capabilities enabled by HCD also offer the potential to generate HPLC-independent spectral libraries for the rapid analysis of modified ribonucleosides.
    Keywords:  HCD fragmentation; LC-MS/MS; Nucleoside analysis; Positional isomers; RNA modification
  35. Int J Mol Sci. 2020 Jan 15. pii: E568. [Epub ahead of print]21(2):
    Mass Spectrometry-Based Metabolomics Analysis of Obese Patients' Blood Plasma.
    Petr G Lokhov, Elena E Balashova, Oxana P Trifonova, Dmitry L Maslov, Elena A Ponomarenko, Alexander I Archakov.
      Scientists currently use only a small portion of the information contained in the blood metabolome. The identification of metabolites is a huge challenge because only highly abundant and well-separated compounds can be easily identified in complex samples. However, new approaches that enhance the identification of compounds have emerged; among them, the identification of compounds based on their involvement in a particular biological context is a recent development. In this work, this approach was first applied to identify metabolites in complex samples and, together with metabolite set enrichment analysis, was used for the evaluation of blood plasma from obese patients. The proposed approach was found to provide a statistically sound overview of the biochemical pathways, thus presenting additional information on obesity. Obesity progression was demonstrated to be accompanied by marked alterations in steroidogenesis, androstenedione metabolism, and androgen and estrogen metabolism. The findings of this study suggest that the workflow used for blood analysis is sufficient to demonstrate obesity at the biochemical pathway level as well as to monitor the response to treatment. This workflow is also expected to be suitable for studying other metabolic diseases.
    Keywords:  biological context; blood plasma; mass spectrometry; metabolite identification; metabolite set enrichment analysis; metabolomics; obesity; putatively annotated metabolites
    DOI:  https://doi.org/10.3390/ijms21020568
  36. Cancer Cell. 2020 Jan 13. pii: S1535-6108(19)30571-9. [Epub ahead of print]37(1): 21-36.e13
    An Integrated Gene Expression Landscape Profiling Approach to Identify Lung Tumor Endothelial Cell Heterogeneity and Angiogenic Candidates.
    Jermaine Goveia, Katerina Rohlenova, Federico Taverna, Lucas Treps, Lena-Christin Conradi, Andreas Pircher, Vincent Geldhof, Laura P M H de Rooij, Joanna Kalucka, Liliana Sokol, Melissa García-Caballero, Yingfeng Zheng, Junbin Qian, Laure-Anne Teuwen, Shawez Khan, Bram Boeckx, Els Wauters, Herbert Decaluwé, Paul De Leyn, Johan Vansteenkiste, Birgit Weynand, Xavier Sagaert, Erik Verbeken, Albert Wolthuis, Baki Topal, Wouter Everaert, Hanibal Bohnenberger, Alexander Emmert, Dena Panovska, Frederik De Smet, Frank J T Staal, Rene J Mclaughlin, Francis Impens, Vincenzo Lagani, Stefan Vinckier, Massimiliano Mazzone, Luc Schoonjans, Mieke Dewerchin, Guy Eelen, Tobias K Karakach, Huanming Yang, Jian Wang, Lars Bolund, Lin Lin, Bernard Thienpont, Xuri Li, Diether Lambrechts, Yonglun Luo, Peter Carmeliet.
      Heterogeneity of lung tumor endothelial cell (TEC) phenotypes across patients, species (human/mouse), and models (in vivo/in vitro) remains poorly inventoried at the single-cell level. We single-cell RNA (scRNA)-sequenced 56,771 endothelial cells from human/mouse (peri)-tumoral lung and cultured human lung TECs, and detected 17 known and 16 previously unrecognized phenotypes, including TECs putatively regulating immune surveillance. We resolved the canonical tip TECs into a known migratory tip and a putative basement-membrane remodeling breach phenotype. Tip TEC signatures correlated with patient survival, and tip/breach TECs were most sensitive to vascular endothelial growth factor blockade. Only tip TECs were congruent across species/models and shared conserved markers. Integrated analysis of the scRNA-sequenced data with orthogonal multi-omics and meta-analysis data across different human tumors, validated by functional analysis, identified collagen modification as a candidate angiogenic pathway.
    Keywords:  angiogenesis; anti-angiogenic therapy; cancer; endothelial cells; endothelial heterogeneity; multi-omics; single-cell RNA sequencing
    DOI:  https://doi.org/10.1016/j.ccell.2019.12.001
  37. Int J Clin Exp Pathol. 2018 ;11(12): 5536-5546
    BCAT1 promotes proliferation of endometrial cancer cells through reprogrammed BCAA metabolism.
    Ping Wang, Shouheng Wu, Xiaofeng Zeng, Yaqing Zhang, Ying Zhou, Liuli Su, Wei Lin.
      Branched-chain amino acid aminotransferase 1 (BCAT1) enzyme is an aminotransferase of glutamate and branched-chain amino acids (BCAAs), which is required for survival of various cancers. However, the role of BCAT1 in human endometrial cancer (EC) remains unknown. We analyzed the expression of BCAT1 in endometrial lesions using IHC. After BCAT1 gene knockdown and activity inhibition, cell proliferation, apoptosis, and metabolism were detected using CCK-8 assay, flow cytometry, and LC-MS/MS analysis. We analyzed molecular signature characteristics to understand how BCAT1 promotes cell proliferation. In this study, we demonstrated a significant increase in BCAT1 expression from normal endometrium to atypical endometrial hyperplasia (AEH) and then to EC, and the expression of BCAT1 in EC samples was related to tumor grade, FIGO stage and lymph node metastasis. Next, cell proliferation was markedly inhibited by lentiviral BCAT1 knockdown or Gbp treatment, but this had little effect on apoptosis rate. Further, BCAT1 knockdown resulted in 31.2% and 33.3% decreases in the amount of intracellular isoleucine and leucine produced, respectively, relative to a control. BCAT1 knockdown or activity inhibition resulted in a decrease of pS6K, a downstream target kinase of mTORC1. In conclusion, our study showed that BCAT1 is essential for EC progression and to increase EC cell proliferation through the production of BCAAs to activate the mTORC1 pathway, providing ideas for clinicians to identify metabolism-based targeted approaches for patients with EC.
    Keywords:  BCAT1; LC-MS/MS analysis; cellular metabolism; endometrial cancer
  38. J Proteome Res. 2020 Jan 13.
    Proteomic analysis of MYB-regulated secretome identifies functional pathways and biomarkers: potential pathobiological and clinical implications.
    Haseeb Zubair, Girijesh Kumar Patel, Mohammad Aslam Khan, Shafquat Azim, Asif Zubair, Seema Singh, Sanjeev Kumar Srivastava, Ajay Pratap Singh.
      Earlier we have shown important roles of MYB in pancreatic tumor pathobiology. To better understand the role of MYB in the tumor microenvironment and identify MYB-associated secreted biomarker proteins, we conducted mass spectrometry analysis of the secretome from MYB-modulated and control pancreatic cancer cell lines. We also performed in silico analyses to determine MYB-associated biofunctions, gene networks and altered biological pathways. Our data demonstrated significant modulation (p < 0.05) of 337 secreted proteins in MYB-silenced MiaPaCa cells whereas 282 proteins were differentially present in MYB-overexpressing BxPC3 cells, compared to their respective control cells. Alteration of several phenotypes such as cellular movement, cell death and survival, inflammatory response, protein synthesis etc. was associated with MYB-induced differentially expressed proteins (DEPs) in secretomes. DEPs from MYB-silenced MiaPaCa PC cells were suggestive of the downregulation of genes primarily associated with glucose metabolism, PI3K/AKT signaling and oxidative stress response, among others. DEPs from MYB-overexpressing BxPC3 cells suggested enhanced release of proteins associated with glucose metabolism and cellular motility. We also observed that MYB positively regulated the expression of four proteins with potential biomarker properties, i.e. FLNB, ENO1, ITGB1 and INHBA. Mining of publicly available databases using Oncomine and UALCAN demonstrated that these genes are overexpressed in pancreatic tumors and associated with reduced patient's survival. Altogether, these data provide novel avenues for future investigations on diverse biological functions of MYB, specifically in the tumor microenvironment, and could also be exploited for biomarker development.
    DOI:  https://doi.org/10.1021/acs.jproteome.9b00641
  39. Rapid Commun Mass Spectrom. 2020 Jan 13. e8725
    Protocol for Community-created Public MS/MS Reference Spectra Within the Global Natural Products Social Molecular Networking Infrastructure.
    Fernando Vargas, Kelly C Weldon, Nicole Sikora, Mingxun Wang, Zheng Zhang, Emily C Gentry, Morgan W Panitchpakdi, Andrés Mauricio Caraballo-Rodríguez, Pieter C Dorrestein, Alan K Jarmusch.
      RATIONALE: A major hurdle in identifying chemicals in mass spectrometry experiments is the availability of MS/MS reference spectra in public databases. Currently, scientists purchase databases or use public databases such as Global Natural Product Social Molecular Networking (GNPS). The MSMS-Chooser workflow is an open-source protocol for the creation of MS/MS reference spectra directly in the GNPS infrastructure.METHODS: An MSMS-Chooser Sample Template is provided and completed manually. The MSMS-Chooser Submission File and Sequence Table for data acquisition were programmatically generated. Standards from the Mass Spectrometry Metabolite Library (MSMLS) suspended in a methanol-water (1:1) solution were analyzed. Flow injection on an LC/MS/MS system was used to generate negative and positive mode data using data-dependent acquisition. The MS/MS spectra and Submission File were uploaded to MSMS-Chooser workflow in GNPS for automatic selection of MS/MS spectra.
    RESULTS: Data acquisition and processing required ~2 hours and ~2 min, respectively, per 96-well plate using MSMS-Chooser. Analysis of the MSMLS, over 600 small molecules, using MSMS-Chooser added 889 spectra (including multiple adducts) to the public library in GNPS. Manual validation of one plate indicated accurate selection of MS/MS scans (true positive rate of 0.96 and a true negative rate of 0.99). The MSMS-Chooser output includes a table formatted for inclusion in the GNPS library as well as the ability to directly launch searches via MASST.
    CONCLUSIONS: MSMS-Chooser enables rapid data acquisition, data analysis (selection of MS/MS spectra), and a formatted table for inspection and upload to GNPS. Open file-format data (.mzML or.mzXML) from most mass spectrometry platforms containing MS/MS can be processed using MSMS-Chooser. MSMS-Chooser democratizes the creation of MS/MS reference spectra in GNPS which will improve annotation and strengthen the tools which use the annotation information.
    DOI:  https://doi.org/10.1002/rcm.8725
  40. Anal Bioanal Chem. 2020 Jan 17.
    Mammalian ovarian lipid distributions by desorption electrospray ionization-mass spectrometry (DESI-MS) imaging.
    Fernanda Bertuccez Cordeiro, Alan K Jarmusch, Marisol León, Christina Ramires Ferreira, Valentina Pirro, Livia S Eberlin, Judy Hallett, Maria Angelica Miglino, Robert Graham Cooks.
      Merging optical images of tissue sections with the spatial distributions of molecules seen by imaging mass spectrometry is a powerful approach to better understand the metabolic roles of the mapped molecules. Here, we use histologically friendly desorption electrospray ionization-mass spectrometry (DESI-MS) to map the lipid distribution in tissue sections of ovaries from cows (N = 8), sows (N = 3), and mice (N = 12). Morphologically friendly DESI-MS imaging allows the same sections to be examined for morphological information. Independent of the species, ovarian follicles, corpora lutea, and stroma could be differentiated by principal component analysis, showing that lipid profiles are well conserved among species. As examples of specific findings, arachidonic acid and the phosphatidylinositol PI(38:4), were both found concentrated in the follicles and corpora lutea, structures that promoted ovulation and implantation, respectively. Adrenic acid was spatially located in the corpora lutea, suggesting the importance of this fatty acid in the ovary luteal phase. In summary, lipid information captured by DESI-MS imaging could be related to ovarian structures and data were all conserved among cows, sows, and mice. Further application of DESI-MS imaging to either physiological or pathophysiological models of reproductive conditions will likely expand knowledge of the roles of specific lipids and pathways in ovarian activity and mammalian fertility. Graphical abstract Desorption electrospray ionization-mass spectrometry (DESI-MS) is performed directly from frozen ovarian tissue sections placed onto glass slides. Because the desorption and ionization process of small molecules is so gentle, the tissue architecture is preserved. The sample can then be stained and tissue morphology information can be overlaid with the chemical information obtained by DESI-MS.
    Keywords:  DESI-MS imaging; Folliculogenesis; Lipid; Mass spectrometry; Ovary
    DOI:  https://doi.org/10.1007/s00216-019-02352-6
  41. Prostaglandins Leukot Essent Fatty Acids. 2019 Dec 13. pii: S0952-3278(19)30253-4. [Epub ahead of print]153 102041
    Plasma oxylipins and unesterified precursor fatty acids are altered by DHA supplementation in pregnancy: Can they help predict risk of preterm birth?
    Christopher E Ramsden, Maria Makrides, Zhi-Xin Yuan, Mark S Horowitz, Daisy Zamora, Lisa N Yelland, Karen Best, Jennifer Jensen, Ameer Y Taha, Robert A Gibson.
      Oxidized lipids derived from omega-6 (n-6) and omega-3 (n-3) polyunsaturated fatty acids, collectively known as oxylipins, are bioactive signaling molecules that play diverse roles in human health and disease. Supplementation with n-3 docosahexaenoic acid (DHA) during pregnancy has been reported to decrease the risk of preterm birth in singleton pregnancies, which may be due to effects of DHA supplementation on oxylipins or their precursor n-6 and n-3 fatty acids. There is only limited understanding of the levels and trajectory of changes in plasma oxylipins during pregnancy, effects of DHA supplementation on oxylipins and unesterified fatty acids, and whether and how oxylipins and their unesterified precursor fatty acids influence preterm birth. In the present study we used liquid chromatography-tandem mass spectrometry to profile oxylipins and their precursor fatty acids in the unesterified pool using plasma samples collected from a subset of pregnant Australian women who participated in the ORIP (Omega-3 fats to Reduce the Incidence of Prematurity) study. ORIP is a large randomized controlled trial testing whether daily supplementation with n-3 DHA can reduce the incidence of early preterm birth compared to control. Plasma was collected at study entry (≈pregnancy week 14) and again at ≈week 24, in a subgroup of 48 ORIP participants-12 cases with spontaneous preterm (<37 weeks) birth and 36 matched controls with spontaneous term (≥40 weeks) birth. In the combined preterm and term pregnancies, we observed that in the control group without DHA supplementation unesterified AA and AA-derived oxylipins 12-HETE, 15-HETE and TXB2 declined between weeks 14-24 of pregnancy. Compared to control, DHA supplementation increased unesterified DHA, EPA, and AA, DHA-derived 4-HDHA, 10-HDHA and 19,20-EpDPA, and AA-derived 12-HETE at 24 weeks. In exploratory analysis independent of DHA supplementation, participants with concentrations above the median for 5-lipoxygenase derivatives of AA (5-HETE, Odds Ratio (OR) 8.2; p = 0.014) or DHA (4-HDHA, OR 8.0; p = 0.015) at 14 weeks, or unesterified AA (OR 5.1; p = 0.038) at 24 weeks had higher risk of spontaneous preterm birth. The hypothesis that 5-lipoxygenase-derived oxylipins and unesterified AA could serve as mechanism-based biomarkers predicting spontaneous preterm birth should be evaluated in larger, adequately powered studies.
    Keywords:  Arachidonic; Development; Docosahexaenoic; Linoleic; Oxylipins; Plasma; Preterm
    DOI:  https://doi.org/10.1016/j.plefa.2019.102041
  42. Antioxidants (Basel). 2020 Jan 05. pii: E46. [Epub ahead of print]9(1):
    Perturbations of Lipids and Oxidized Phospholipids in Lipoproteins of Patients with Postmenopausal Osteoporosis Evaluated by Asymmetrical Flow Field-Flow Fractionation and Nanoflow UHPLC-ESI-MS/MS.
    Kang Geun Lee, Gwang Bin Lee, Joon Seon Yang, Myeong Hee Moon.
      Osteoporosis, a degenerative bone disease characterized by reduced bone mass and high risk of fragility, is associated with the alteration of circulating lipids, especially oxidized phospholipids (Ox-PLs). This study evaluated the lipidomic changes in lipoproteins of patients with postmenopausal osteoporosis (PMOp) vs. postmenopausal healthy controls. High-density lipoproteins (HDL) and low-density lipoproteins (LDL) from plasma samples were size-sorted by asymmetrical flow field-flow fractionation (AF4). Lipids from each lipoprotein were analyzed by nanoflow ultrahigh performance liquid chromatography-electrospray ionization-tandem mass spectrometry (nUHPLC-ESI-MS/MS). A significant difference was observed in a subset of lipids, most of which were increased in patients with PMOp, when compared to control. Phosphatidylethanolamine plasmalogen, which plays an antioxidative role, was increased in both lipoproteins (P-16:0/20:4, P-18:0/20:4, and P-18:1/20:4) lysophosphatidic acid 16:0, and six phosphatidylcholines were largely increased in HDL, but triacylglycerols (50:4 and 54:6) and overall ceramide levels were significantly increased only in LDL of patients with PMOp. Further investigation of 33 Ox-PLs showed significant lipid oxidation in PLs with highly unsaturated acyl chains, which were decreased in LDL of patients with PMOp. The present study demonstrated that AF4 with nUHPLC-ESI-MS/MS can be utilized to systematically profile Ox-PLs in the LDL of patients with PMOp.
    Keywords:  asymmetrical flow field-flow fractionation; lipoprotein; nUHPLC–ESI–MS/MS; oxidized lipid; postmenopausal osteoporosis
    DOI:  https://doi.org/10.3390/antiox9010046
  43. Anal Bioanal Chem. 2020 Jan 15.
    Lipidomic profiling of non-mineralized dental plaque and biofilm by untargeted UHPLC-QTOF-MS/MS and SWATH acquisition.
    Bernhard Drotleff, Simon R Roth, Kerstin Henkel, Carlos Calderón, Jörg Schlotterbeck, Merja A Neukamm, Michael Lämmerhofer.
      Dental plaque is a structurally organized biofilm which consists of diverse microbial colonies and extracellular matrix. Its composition may change when pathogenic microorganisms become dominating. Therefore, dental biofilm or plaque has been frequently investigated in the context of oral health and disease. Furthermore, its potential as an alternative matrix for analytical purposes has also been recognized in other disciplines like archeology, food sciences, and forensics. Thus, a careful in-depth characterization of dental plaque is worthwhile. Most of the conducted studies focused on the screening of microbial populations in dental plaque. Their lipid membranes, on the other hand, may significantly impact substance (metabolite) exchange within microbial colonies as well as xenobiotics uptake and incorporation into teeth. Under this umbrella, a comprehensive lipidomic profiling for determination of lipid compositions of in vivo dental plaque samples and of in vitro cultivated biofilm as surrogate matrix to be used for analytical purposes has been performed in this work. An untargeted lipidomics workflow utilizing a ultra-high-performance liquid chromatography (UHPLC)-quadrupole-time-of-flight (QTOF) platform together with comprehensive SWATH (sequential window acquisition of all theoretical fragment ion mass spectra) acquisition and compatible software (MS-DIAL) that comprises a vast lipid library has been adopted to establish an extensive lipidomic fingerprint of dental plaque. The main lipid components in dental plaque were identified as triacylglycerols, followed by cholesterol, cholesteryl esters as well as diacylglycerols, and various phospholipid classes. In vivo plaque is a rare matrix which is usually available in very low amounts. When higher quantities for specific research assays are required, efficient ways to produce an appropriate surrogate matrix are mandatory. A potential surrogate matrix substituting dental plaque was prepared by cultivation of in vitro biofilm from saliva and similarities and differences in the lipidomics profile to in vivo plaque were mapped by statistical evaluation post-analysis. It was discovered that most lipid classes were highly elevated in the in vitro biofilm samples, in particular diacylglycerols, phosphatidylglycerols, and phosphatidylethanolamines (PEs). Furthermore, an overall shift from even-chain lipid species to odd-chain lipids was observed in the cultivated biofilms. On the other hand, even-chain phosphatidylcholines (PCs), lysoPCs, cholesteryl esters, and cholesterol-sulfate were shown to be specifically increased in plaque samples. Graphical abstract.
    Keywords:  Biofilm; Data-independent acquisition; Dental plaque; SWATH; Untargeted lipidomics
    DOI:  https://doi.org/10.1007/s00216-019-02364-2
  44. Cell Res. 2020 Jan 16.
    The role of ferroptosis in ionizing radiation-induced cell death and tumor suppression.
    Guang Lei, Yilei Zhang, Pranavi Koppula, Xiaoguang Liu, Jie Zhang, Steven H Lin, Jaffer A Ajani, Qin Xiao, Zhongxing Liao, Hui Wang, Boyi Gan.
      Ferroptosis, a form of regulated cell death caused by lipid peroxidation, was recently identified as a natural tumor suppression mechanism. Here, we show that ionizing radiation (IR) induces ferroptosis in cancer cells. Mechanistically, IR induces not only reactive oxygen species (ROS) but also the expression of ACSL4, a lipid metabolism enzyme required for ferroptosis, resulting in elevated lipid peroxidation and ferroptosis. ACSL4 ablation largely abolishes IR-induced ferroptosis and promotes radioresistance. IR also induces the expression of ferroptosis inhibitors, including SLC7A11 and GPX4, as an adaptive response. IR- or KEAP1 deficiency-induced SLC7A11 expression promotes radioresistance through inhibiting ferroptosis. Inactivating SLC7A11 or GPX4 with ferroptosis inducers (FINs) sensitizes radioresistant cancer cells and xenograft tumors to IR. Furthermore, radiotherapy induces ferroptosis in cancer patients, and increased ferroptosis correlates with better response and longer survival to radiotherapy in cancer patients. Our study reveals a previously unrecognized link between IR and ferroptosis and indicates that further exploration of the combination of radiotherapy and FINs in cancer treatment is warranted.
    DOI:  https://doi.org/10.1038/s41422-019-0263-3
  45. Methods Mol Biol. 2020 ;2104 387-400
    Pathway Analysis for Targeted and Untargeted Metabolomics.
    Alla Karnovsky, Shuzhao Li.
      Recent advances in analytical techniques, particularly LC-MS, generate increasingly large and complex metabolomics datasets. Pathway analysis tools help place the experimental observations into relevant biological or disease context. This chapter provides an overview of the general concepts and common tools for pathway analysis, including Mummichog for untargeted metabolomics. Examples of pathway mapping, MetScape, and Mummichog are explained. This serves as both a practical tutorial and a timely survey of pathway analysis for label-free metabolomics data.
    Keywords:  MetScape; Metabolic network; Metabolomics; Mummichog; Pathway analysis; Untargeted metabolomics
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_19
  46. Metabolites. 2020 Jan 10. pii: E30. [Epub ahead of print]10(1):
    Liquid Chromatography Tandem Mass Spectrometry Quantification of 13C-Labeling in Sugars.
    Jean-Christophe Cocuron, Zacchary Ross, Ana P Alonso.
      Subcellular compartmentation has been challenging in plant 13C-metabolic flux analysis. Indeed, plant cells are highly compartmented: they contain vacuoles and plastids in addition to the regular organelles found in other eukaryotes. The distinction of reactions between compartments is possible when metabolites are synthesized in a particular compartment or by a unique pathway. Sucrose is an example of such a metabolite: it is specifically produced in the cytosol from glucose 6-phosphate (G6P) and fructose 6-phosphate (F6P). Therefore, determining the 13C-labeling in the fructosyl and glucosyl moieties of sucrose directly informs about the labeling of cytosolic F6P and G6P, respectively. To date, the most commonly used method to monitor sucrose labeling is by nuclear magnetic resonance, which requires substantial amounts of biological sample. This study describes a new methodology that accurately measures the labeling in free sugars using liquid chromatography tandem mass spectrometry (LC-MS/MS). For this purpose, maize embryos were pulsed with [U-13C]-fructose, intracellular sugars were extracted, and their time-course labeling was analyzed by LC-MS/MS. Additionally, extracts were enzymatically treated with hexokinase to remove the soluble hexoses, and then invertase to cleave sucrose into fructose and glucose. Finally, the labeling in the glucosyl and fructosyl moieties of sucrose was determined by LC-MS/MS.
    Keywords:  13C-labeling; 13C-metabolic flux analysis; LC-MS/MS; fructose 6-phosphate; glucose 6-phosphate; hexokinase; invertase; subcellular compartmentation; sucrose
    DOI:  https://doi.org/10.3390/metabo10010030
  47. Rapid Commun Mass Spectrom. 2020 Jan 17. e8730
    A rapid and convenient derivatization method for quantitation of short-chain fatty acids in human feces by ultra-performance liquid chromatography-tandem mass spectrometry.
    Huan Fu, Qing-Li Zhang, Xiao-Wu Huang, Zheng-Hua Ma, Xiao-Li Zheng, Shi-Lin Li, Hao-Nan Duan, Xiao-Chu Sun, Fei-Fei Lin, Li-Juan Zhao, Guo-Sheng Teng, Jia Liu.
      RATIONALE: Short-chain fatty acids (SCFAs) are associated with intestinal microbiota and diseases in humans. SCFAs have a low response in mass spectrometry and in order to increase sensitivity, reduce sample consumption, shorten analysis time, and simplify sample preparation steps, a derivatization method was developed.METHODS: We converted seven SCFAs into amide derivatives with 4-aminomethylquinoline. The reaction took 20 min at room temperature. Analytes were separated on a reversed-phase C18 column and quantitated in positive ion electrospray ionization mode using multiple reaction monitoring. Acetic acid-d4 was used as the stable isotope-labelled surrogate analyte for acetic acid in the working solutions, while the other stable isotope-labelled standards were used as internal standards (ISs).
    RESULTS: Method validation showed that the intra-day and inter-days precision of quantitation for the seven SCFAs over the whole concentration range was ≤ 3.8% (n = 6). The quantitation accuracy ranged from 85.5% to 104.3% (n = 6). Importantly, the collected feces need to be vortexed immediately with ethanol.
    CONCLUSIONS: This study provides a new derivatization method for precise, accurate, and rapid quantification of SCFAs in human feces using ultra-performance liquid chromatography-tandem mass spectrometry. This method successfully determined the concentration of SCFAs in human feces and could assist in the exploration of intestinal microbiota and disease.
    DOI:  https://doi.org/10.1002/rcm.8730
  48. Methods Mol Biol. 2020 ;2108 125-130
    Detection of Ferroptosis by BODIPY™ 581/591 C11.
    Alejandra M Martinez, Ahryun Kim, Wan Seok Yang.
      Ferroptosis is a distinctive form of regulated cell death that is driven by lethal accumulation of lipid peroxides in plasma membranes. Failure to control ferroptosis has been implicated in multiple pathological conditions including cancer development, neurodegeneration, renal injury, ischemia/reperfusion injury, and T-cell immunity. Here we describe a method to detect ferroptosis by determining the amount of lipid peroxides in cellular membranes using BODIPY-C11 probe and flow cytometry. Putative role of ferroptosis in immune modulatory cells can be determined using the same method.
    Keywords:  BODIPY-C11; Erastin; Ferroptosis; Flow cytometry; Lipid peroxides
    DOI:  https://doi.org/10.1007/978-1-0716-0247-8_11
  49. Cancers (Basel). 2020 Jan 09. pii: E164. [Epub ahead of print]12(1):
    Ferroptosis in Cancer Cell Biology.
    Christina M Bebber, Fabienne Müller, Laura Prieto Clemente, Josephine Weber, Silvia von Karstedt.
      A major hallmark of cancer is successful evasion of regulated forms of cell death. Ferroptosis is a recently discovered type of regulated necrosis which, unlike apoptosis or necroptosis, is independent of caspase activity and receptor-interacting protein 1 (RIPK1) kinase activity. Instead, ferroptotic cells die following iron-dependent lipid peroxidation, a process which is antagonised by glutathione peroxidase 4 (GPX4) and ferroptosis suppressor protein 1 (FSP1). Importantly, tumour cells escaping other forms of cell death have been suggested to maintain or acquire sensitivity to ferroptosis. Therefore, therapeutic exploitation of ferroptosis in cancer has received increasing attention. Here, we systematically review current literature on ferroptosis signalling, cross-signalling to cellular metabolism in cancer and a potential role for ferroptosis in tumour suppression and tumour immunology. By summarising current findings on cell biology relevant to ferroptosis in cancer, we aim to point out new conceptual avenues for utilising ferroptosis in systemic treatment approaches for cancer.
    Keywords:  GPX4; cancer; cell death; ferroptosis; inflammation
    DOI:  https://doi.org/10.3390/cancers12010164
  50. Molecules. 2020 Jan 15. pii: E349. [Epub ahead of print]25(2):
    Methods of the Analysis of Oxylipins in Biological Samples.
    Ivan Liakh, Alicja Pakiet, Tomasz Sledzinski, Adriana Mika.
      Oxylipins are derivatives of polyunsaturated fatty acids and due to their important and diverse functions in the body, they have become a popular subject of studies. The main challenge for researchers is their low stability and often very low concentration in samples. Therefore, in recent years there have been developments in the extraction and analysis methods of oxylipins. New approaches in extraction methods were described in our previous review. In turn, the old analysis methods have been replaced by new approaches based on mass spectrometry (MS) coupled with liquid chromatography (LC) and gas chromatography (GC), and the best of these methods allow hundreds of oxylipins to be quantitatively identified. This review presents comparative and comprehensive information on the progress of various methods used by various authors to achieve the best results in the analysis of oxylipins in biological samples.
    Keywords:  GC–MS; HPLC; LC–MS; UHPLC; biological samples; oxylipins
    DOI:  https://doi.org/10.3390/molecules25020349
  51. Methods Mol Biol. 2020 ;2104 313-336
    Predictive Modeling for Metabolomics Data.
    Tusharkanti Ghosh, Weiming Zhang, Debashis Ghosh, Katerina Kechris.
      In recent years, mass spectrometry (MS)-based metabolomics has been extensively applied to characterize biochemical mechanisms, and study physiological processes and phenotypic changes associated with disease. Metabolomics has also been important for identifying biomarkers of interest suitable for clinical diagnosis. For the purpose of predictive modeling, in this chapter, we will review various supervised learning algorithms such as random forest (RF), support vector machine (SVM), and partial least squares-discriminant analysis (PLS-DA). In addition, we will also review feature selection methods for identifying the best combination of metabolites for an accurate predictive model. We conclude with best practices for reproducibility by including internal and external replication, reporting metrics to assess performance, and providing guidelines to avoid overfitting and to deal with imbalanced classes. An analysis of an example data will illustrate the use of different machine learning methods and performance metrics.
    Keywords:  Mass spectrometry; Metabolomics; Performance Metrics; Predictive Modeling; Supervised learning
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_16
  52. Prostaglandins Other Lipid Mediat. 2020 Jan 14. pii: S1098-8823(20)30013-7. [Epub ahead of print] 106420
    Lipidomic profiling analysis of the phospholipid molecules in SCAP-induced lipid droplet formation in bovine mammary epithelial cells.
    Liqiang Han, Kun Pang, Xiu Ling Li, Juan J Loor, Guo Yu Yang, Tengyun Gao.
      The accumulation of lipid droplets (LDs) in the cytoplasm plays an important role in energy balance, membrane synthesis and cell signal transduction. The aim of this study was to investigate the profile of phospholipids after SCAP-induced LD formation in bovine mammary epithelial cells (BMECs). A shRNA-SCAP vector and a SCAP/SREBP vector were used to knock down and overexpress the SCAP gene in BMECs prior to evaluating the effects on LDs using Western blotting, real-time PCR, LD staining and liquid chromatography-tandem mass spectrometry (LC-MS/MS). The average LD diameter was determined following oil red O staining. The overexpression of SCAP increased the abundance of SCD, ACACA and FASN genes and nuclear SREBP1a. In contrast, knocking down SCAP decreased the abundance of the nuclear SREBP1a protein and downregulated the abundance of target genes. Lipid droplet staining revealed that knocking down SCAP reduced LD formation and average LD diameter. In contrast, overexpression of SCAP increased the formation and size of the LDs. The results from an analysis of cellular lipids revealed that phospholipids are the predominant species in the profile of cell lipids. phosphatidylethanolamine (PE) and phosphatidylcholine (PC) are important for determining the size of LDs. The LD formation induced by SCAP gene overexpression and knockdown underscored the role of phospholipids involved in lipid droplet formation and fusion.
    Keywords:  Lipidomics; SCAP; lipid droplets; olipids; phosph
    DOI:  https://doi.org/10.1016/j.prostaglandins.2020.106420
  53. Methods Mol Biol. 2020 ;2104 165-184
    Metabolomic Data Exploration and Analysis with the Human Metabolome Database.
    David S Wishart.
      The Human Metabolome Database (HMDB) is a comprehensive, online, digital database designed to support the analysis and interpretation of metabolomic data acquired from human and/or mammalian metabolomic studies. This chapter covers three methods or protocols pertinent to using the HMDB: (1) understanding the general layout of the HMDB; (2) exploring the contents of a typical HMDB "MetaboCard"; and (3) an example of how HMDB can be used in a metabolomics study on human glioblastoma.
    Keywords:  Data analysis; Database; Disease; Human; Metabolomics
    DOI:  https://doi.org/10.1007/978-1-0716-0239-3_10
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