bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2021–10–03
23 papers selected by
Giovanny Rodríguez Blanco, University of Edinburgh
  1. Enabling High Structural Specificity to Lipidomics by Coupling Photochemical Derivatization with Tandem Mass Spectrometry.
  2. An Extensive Metabolomics Workflow to Discover Cardiotoxin-Induced Molecular Perturbations in Microtissues.
  3. Lipidomic Analysis of Archival Pathology Specimens Identifies Altered Lipid Signatures in Ovarian Clear Cell Carcinoma.
  4. Pre-analytical monitoring and protection of oxidizable lipids in human plasma (vitamin E and ω-3 and ω-6 fatty acids): An update for redox-lipidomics methods.
  5. Clinical Lipidomics: Realizing the potential of lipid profiling.
  6. Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline.
  7. Ferroptosis and Cancer: Complex Relationship and Potential Application of Exosomes.
  8. Lipid Metabolite Biomarkers in Cardiovascular Disease: Discovery and Biomechanism Translation from Human Studies.
  9. Characterization and Determination of 13C-Labeled Nonessential Amino Acids in a 13C5-Glutamine Isotope Tracer Experiment with a Mass Spectrometry Strategy Combining Parallel Reaction Monitoring and Multiple Reaction Monitoring.
  10. Branched-chain amino acid metabolism controls membrane phospholipid structure in Staphylococcus aureus.
  11. Lipidomic Signatures of Changes in Adiposity: A Large Prospective Study of 5849 Adults from the Australian Diabetes, Obesity and Lifestyle Study.
  12. Cross-Platform Evaluation of Commercially Targeted and Untargeted Metabolomics Approaches to Optimize the Investigation of Psychiatric Disease.
  13. Driving Single Cell Proteomics Forward with Innovation.
  14. AlphaMap: an open-source python package for the visual annotation of proteomics data with sequence specific knowledge.
  15. Rapid Automated Annotation and Analysis of N-Glycan Mass Spectrometry Imaging Data Sets Using NGlycDB in METASPACE.
  16. Lipid Droplet-Organelle Contact Sites as Hubs for Fatty Acid Metabolism, Trafficking, and Metabolic Channeling.
  17. Glucose and Amino Acid Metabolic Dependencies Linked to Stemness and Metastasis in Different Aggressive Cancer Types.
  18. HIF-1-Independent Mechanisms Regulating Metabolic Adaptation in Hypoxic Cancer Cells.
  19. JUMPt: Comprehensive Protein Turnover Modeling of In Vivo Pulse SILAC Data by Ordinary Differential Equations.
  20. Reproducible Lipid Alterations in Patient-Derived Breast Cancer Xenograft FFPE Tissue Identified with MALDI MSI for Pre-Clinical and Clinical Application.
  21. Low-Dose Doxycycline Treatment Normalizes Levels of Some Salivary Metabolites Associated with Oral Microbiota in Patients with Primary Sjögren's Syndrome.
  22. The growing need for controlled data access models in clinical proteomics and metabolomics.
  23. Assessment of XCMS Optimization Methods with Machine-Learning Performance.
  1. Acc Chem Res. 2021 Sep 27.
    Enabling High Structural Specificity to Lipidomics by Coupling Photochemical Derivatization with Tandem Mass Spectrometry.
    Xiaoxiao Ma, Wenpeng Zhang, Zishuai Li, Yu Xia, Zheng Ouyang.
      ConspectusLipids have pivotal roles in many biological processes, including energy storage, signal transduction, and plasma membrane formation. A disruption of lipid homeostasis is found to be associated with a range of diseases, such as cardiovascular diseases, diabetes, and cancer. Fundamental lipid biology and disease diagnostics can benefit from monitoring lipid changes in cells, tissues, organs, or the whole biological system. Therefore, it is important to develop lipid analysis tools to achieve comprehensive lipid characterization and quantitation. Over the past two decades, mass spectrometry (MS) has become the method of choice for qualitative and quantitative analyses of lipids, owing to its high sensitivity, multiplexed analysis, and soft ionization features. With the rapid development and adoption of ultrahigh-resolution MS, isobaric lipids can now be routinely resolved. By contrast, the structural characterization and quantitation of isomeric lipids remain an analytical challenge. Although some lipid C═C location or sn-isomers can be resolved by chromatography, ion mobility, or selective ionization approaches, a detailed structural characterization on the lipidome-wide level needs to be achieved.Over the past six years, we have successfully combined the Paternò-Büchi (PB) reaction, which is a UV-promoted photocycloaddition reaction specific to the C═C, with tandem MS (MS/MS) to locate the C═C in lipids and quantify lipid C═C location isomers. The PB reactions have analytical advantages such as a simple experimental setup, rapid lipid C═C derivatization, and highly specific C═C cleavage during PB-MS/MS to produce abundant diagnostic ions. More importantly, without a need of isomer separation or a comparison to authentic standards, PB-MS/MS can be directly applied to identify and quantify a mixture of lipid C═C location isomers, often coexisting with molar ratios sensitive to the biological state of the system. The PB-MS/MS method is compatible with conventional shotgun lipidomics employing a nanoelectrospray ionization or a large-sale lipid structural analysis via liquid chromatography (LC) coupled to any mass spectrometer with tandem MS capability. The PB-MS/MS method is highly versatile, as a variety of PB reagents can be tailored to a broad range of applications. Besides UV-promoted PB reactions, visible-light PB reactions have also been developed to offer more flexibility for a lipid analysis. By using selected PB reagents, the sn-positions of fatty acyls can be resolved together with C═C locations in phospholipids. This method has been used in lipidomic analyses of tissue, blood, and plasma from animal models and clinical samples, demonstrating the potential of using lipid C═C or sn-location isomer ratios for phenotyping and disease diagnostics. Lipid isomer-resolving MS imagings of tissues and single-cell lipid analysis have also been demonstrated by a proper implementation of PB-MS/MS.
    DOI:  https://doi.org/10.1021/acs.accounts.1c00419
  2. Metabolites. 2021 Sep 21. pii: 644. [Epub ahead of print]11(9):
    An Extensive Metabolomics Workflow to Discover Cardiotoxin-Induced Molecular Perturbations in Microtissues.
    Tara J Bowen, Andrew R Hall, Gavin R Lloyd, Ralf J M Weber, Amanda Wilson, Amy Pointon, Mark R Viant.
      Discovering modes of action and predictive biomarkers of drug-induced structural cardiotoxicity offers the potential to improve cardiac safety assessment of lead compounds and enhance preclinical to clinical translation during drug development. Cardiac microtissues are a promising, physiologically relevant, in vitro model, each composed of ca. 500 cells. While untargeted metabolomics is capable of generating hypotheses on toxicological modes of action and discovering metabolic biomarkers, applying this technology to low-biomass microtissues in suspension is experimentally challenging. Thus, we first evaluated a filtration-based approach for harvesting microtissues and assessed the sensitivity and reproducibility of nanoelectrospray direct infusion mass spectrometry (nESI-DIMS) measurements of intracellular extracts, revealing samples consisting of 28 pooled microtissues, harvested by filtration, are suitable for profiling the intracellular metabolome and lipidome. Subsequently, an extensive workflow combining nESI-DIMS untargeted metabolomics and lipidomics of intracellular extracts with ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS/MS) analysis of spent culture medium, to profile the metabolic footprint and quantify drug exposure concentrations, was implemented. Using the synthetic drug and model cardiotoxin sunitinib, time-resolved metabolic and lipid perturbations in cardiac microtissues were investigated, providing valuable data for generating hypotheses on toxicological modes of action and identifying putative biomarkers such as disruption of purine metabolism and perturbation of polyunsaturated fatty acid levels.
    Keywords:  biomarkers; cardiac microtissues; cardiotoxicity; in vitro metabolomics; mode of action; sample harvesting; sensitivity; untargeted toxicokinetics
    DOI:  https://doi.org/10.3390/metabo11090644
  3. Metabolites. 2021 Sep 03. pii: 597. [Epub ahead of print]11(9):
    Lipidomic Analysis of Archival Pathology Specimens Identifies Altered Lipid Signatures in Ovarian Clear Cell Carcinoma.
    Sartaj Ahmad Mir, Soon Boon Justin Wong, Kothandaraman Narasimhan, Chua W L Esther, Shanshan Ji, Bo Burla, Markus R Wenk, David S P Tan, Anne K Bendt.
      Cancer metabolism is associated with the enhanced lipogenesis required for rapid growth and proliferation. However, the magnitude of dysregulation of diverse lipid species still requires significant characterization, particularly in ovarian clear cell carcinoma (OCCC). Here, we have implemented a robust sample preparation workflow together with targeted LC-MS/MS to identify the lipidomic changes in formalin-fixed paraffin-embedded specimens from OCCC compared to tumor-free ovarian tissue. We quantitated 340 lipid species, representing 28 lipid classes. We observed differential regulation of diverse lipid species belonging to several glycerophospholipid classes and trihexosylceramide. A number of unsaturated lipid species were increased in OCCC, whereas saturated lipid species showed a decrease in OCCC compared to the controls. We also carried out total fatty acid analysis and observed an increase in the levels of several unsaturated fatty acids with a concomitant increase in the index of stearoyl-CoA desaturase (SCD) in OCCC. We confirmed the upregulation of SCD (the rate-limiting enzyme for the synthesis of monounsaturated fatty acids) by immunohistochemistry (IHC) assays. Hence, by carrying out a mass spectrometry analysis of archival tissue samples, we were able to provide insights into lipidomic alterations in OCCC.
    Keywords:  archival; clear cell; desaturation; formalin-fixed; lipid profiling; ovarian cancer; unsaturated fatty acids
    DOI:  https://doi.org/10.3390/metabo11090597
  4. Free Radic Biol Med. 2021 Sep 22. pii: S0891-5849(21)00724-3. [Epub ahead of print]176 142-148
    Pre-analytical monitoring and protection of oxidizable lipids in human plasma (vitamin E and ω-3 and ω-6 fatty acids): An update for redox-lipidomics methods.
    Pierangelo Torquato, Danilo Giusepponi, Desirée Bartolini, Carolina Barola, Rita Marinelli, Bartolomeo Sebastiani, Roberta Galarini, Francesco Galli.
      Sample manipulation for storage and storage itself, interfere with the stability of labile lipids in human plasma, including vitamin E (α-tocopherol), polyunsaturated fatty acids (PUFAs), and their enzymatic and free radical-derived oxidation metabolites. This remains a main limit of lipidomics studies that often lack of sufficient standardization and validation at the pre-analytical level. In order to characterize the stability of these lipids in human plasma and to develop a standardized pre-analytical protocol for lipidomics methods, the oxidation metabolites of α-tocopherol, the free form of ω3 and ω6 PUFAs, and some arachidonic acid (AA)-derived eicosanoids were investigated in human plasma during storage at different freezing temperatures. The effect of a protection/defense cocktail of antioxidants and lipoxygenase inhibitors (PD solution) on these lipid parameters was also evaluated. The temperature of storage markedly affected the formation of α-tocopheryl quinone (α-TQ), the main lipoperoxyl radical-derived oxidation metabolite of vitamin E, with the lowest production rate observed in samples stored at -80 °C or in liquid nitrogen. A similar effect of the storage temperature was observed for the free form of the ω-3 species eicosapentaenoic and docosahexaenoic acid, and for the ω-6 AA. Freezing samples at -20 °C resulted in a time-dependent formation of the pro-inflammatory eicosanoid LTB4. The PD solution prevents non-specific alterations of these lipid parameters in samples that are processed for direct analysis and protects from the temperature-dependent modifications of free PUFAs. Combining PD solution and preservation at -80 °C or in liquid nitrogen, resulted in levels of α-TQ and PUFAs that remained stable over 1 month and up to 8 months of storage, respectively. This method paper provides indications for the optimal processing and storage of human plasma utilized in lipidomics studies.
    Keywords:  Arachidonic acid; Leukotriene B4; Lipid peroxidation; Lipoxygenases; α-Tocopherol; α-tocopheryl quinone; ω-3 and ω-6 fatty acids
    DOI:  https://doi.org/10.1016/j.freeradbiomed.2021.09.012
  5. J Lipid Res. 2021 Sep 25. pii: S0022-2275(21)00109-7. [Epub ahead of print] 100127
    Clinical Lipidomics: Realizing the potential of lipid profiling.
    Thomas G Meikle, Kevin Huynh, Corey Giles, Peter J Meikle.
      Dysregulation of lipid metabolism plays a major role in the aetiology and sequelae of inflammatory disorders, cardiometabolic and neurological diseases, and several forms of cancer. Recent advances in lipidomic methodology allow comprehensive lipidomic profiling of clinically relevant biological samples, enabling researchers to associate lipid species and metabolic pathways with disease onset and progression. The resulting data serve not only to advance our fundamental knowledge of the underlying disease process, but also to develop risk assessment models to assist in the diagnosis and management of disease. Currently, clinical applications of in-depth lipidomic profiling are largely limited to the use of research-based protocols in the analysis of clinical trial sample sets. However, we foresee the development of purpose-built clinical platforms designed for continuous operation and clinical integration - assisting healthcare providers with disease risk assessment, diagnosis, and monitoring. Herein, we review the current state of clinical lipidomics and the techniques employed in lipidomic profiling, with a primary focus on mass spectrometry-based analysis. We discuss the prospective design of clinically integrated platforms, including practical considerations for sample collection, storage, and preparation; automation solutions for high-throughput analysis; available software packages, and statistical development of risk assessment models.
    DOI:  https://doi.org/10.1016/j.jlr.2021.100127
  6. Metabolites. 2021 Aug 26. pii: 568. [Epub ahead of print]11(9):
    Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline.
    Brechtje Hoegen, Alan Zammit, Albert Gerritsen, Udo F H Engelke, Steven Castelein, Maartje van de Vorst, Leo A J Kluijtmans, Marleen C D G Huigen, Ron A Wevers, Alain J van Gool, Christian Gilissen, Karlien L M Coene, Purva Kulkarni.
      Inborn errors of metabolism (IEM) are inherited conditions caused by genetic defects in enzymes or cofactors. These defects result in a specific metabolic fingerprint in patient body fluids, showing accumulation of substrate or lack of an end-product of the defective enzymatic step. Untargeted metabolomics has evolved as a high throughput methodology offering a comprehensive readout of this metabolic fingerprint. This makes it a promising tool for diagnostic screening of IEM patients. However, the size and complexity of metabolomics data have posed a challenge in translating this avalanche of information into knowledge, particularly for clinical application. We have previously established next-generation metabolic screening (NGMS) as a metabolomics-based diagnostic tool for analyzing plasma of individual IEM-suspected patients. To fully exploit the clinical potential of NGMS, we present a computational pipeline to streamline the analysis of untargeted metabolomics data. This pipeline allows for time-efficient and reproducible data analysis, compatible with ISO:15189 accredited clinical diagnostics. The pipeline implements a combination of tools embedded in a workflow environment for large-scale clinical metabolomics data analysis. The accompanying graphical user interface aids end-users from a diagnostic laboratory for efficient data interpretation and reporting. We also demonstrate the application of this pipeline with a case study and discuss future prospects.
    Keywords:  bioinformatics pipeline; biomarkers; clinical application; data analysis; inherited metabolic diseases; mass spectrometry; next-generation metabolic screening; untargeted metabolomics
    DOI:  https://doi.org/10.3390/metabo11090568
  7. Front Cell Dev Biol. 2021 ;9 733751
    Ferroptosis and Cancer: Complex Relationship and Potential Application of Exosomes.
    Shuang Wu, Tianye Li, Weiwei Liu, Yongye Huang.
      Cell death induction has become popular as a novel cancer treatment. Ferroptosis, a newly discovered form of cell death, features regulated, iron-dependent accumulation of lipid hydroperoxides. Since this word "ferroptosis" was coined, numerous studies have examined the complex relationship between ferroptosis and cancer. Here, starting from the intrinsic hallmarks of cancer and cell death, we discuss the theoretical basis of cell death induction as a cancer treatment. We review various aspects of the relationship between ferroptosis and cancer, including the genetic basis, epigenetic modification, cancer stem cells, and the tumor microenvironment, to provide information and support for further research on ferroptosis. We also note that exosomes can be applied in ferroptosis-based therapy. These extracellular vesicles can deliver different molecules to modulate cancer cells and cell death pathways. Using exosomes to control ferroptosis occurring in targeted cells is promising for cancer therapy.
    Keywords:  apoptosis; cancer; cell death; exosomes; ferroptosis
    DOI:  https://doi.org/10.3389/fcell.2021.733751
  8. Metabolites. 2021 Sep 14. pii: 621. [Epub ahead of print]11(9):
    Lipid Metabolite Biomarkers in Cardiovascular Disease: Discovery and Biomechanism Translation from Human Studies.
    Peter McGranaghan, Jennifer A Kirwan, Mariel A Garcia-Rivera, Burkert Pieske, Frank Edelmann, Florian Blaschke, Sandeep Appunni, Anshul Saxena, Muni Rubens, Emir Veledar, Tobias Daniel Trippel.
      Lipids represent a valuable target for metabolomic studies since altered lipid metabolism is known to drive the pathological changes in cardiovascular disease (CVD). Metabolomic technologies give us the ability to measure thousands of metabolites providing us with a metabolic fingerprint of individual patients. Metabolomic studies in humans have supported previous findings into the pathomechanisms of CVD, namely atherosclerosis, apoptosis, inflammation, oxidative stress, and insulin resistance. The most widely studied classes of lipid metabolite biomarkers in CVD are phospholipids, sphingolipids/ceramides, glycolipids, cholesterol esters, fatty acids, and acylcarnitines. Technological advancements have enabled novel strategies to discover individual biomarkers or panels that may aid in the diagnosis and prognosis of CVD, with sphingolipids/ceramides as the most promising class of biomarkers thus far. In this review, application of metabolomic profiling for biomarker discovery to aid in the diagnosis and prognosis of CVD as well as metabolic abnormalities in CVD will be discussed with particular emphasis on lipid metabolites.
    Keywords:  biomarkers; cardiovascular disease; heart failure; lipidomics; metabolomics
    DOI:  https://doi.org/10.3390/metabo11090621
  9. Anal Chem. 2021 Sep 27.
    Characterization and Determination of 13C-Labeled Nonessential Amino Acids in a 13C5-Glutamine Isotope Tracer Experiment with a Mass Spectrometry Strategy Combining Parallel Reaction Monitoring and Multiple Reaction Monitoring.
    Yuanyuan Zheng, Hongzhi Zhao, Man Tong, Lin Zhu, Stephanie Ma, Zongwei Cai.
      Isotopic tracer, a powerful technique for metabolic pathway analysis, is currently widely applied in metabolic flux analysis. However, the qualitative and quantitative analyses of 13C-labeled metabolites pose great challenges, especially in complex biological sample matrices. Here, we present an integrated method for the qualitative and quantitative analyses of various isotopologues and isotopomers of 13C-labeled nonessential amino acids (NEAAs) in HepG2 cells incubated with 13C5-glutamine (Gln) based on ultra-high-performance liquid chromatography (UHPLC) coupled with tandem mass spectrometry (MS). First, accurate mass-to-charge (m/z) values of protonated isotopologues and elution time of standards were simultaneously analyzed to characterize 13C-labeled NEAAs by high-resolution Orbitrap MS in the parallel reaction monitoring (PRM) mode. Second, isotopologues and isotopomers of 13C-labeled NEAAs were investigated in HepG2 cells incubated with 13C5-Gln at different time points. Ultimately, a total of 66 multiple reaction monitoring (MRM) transitions were performed by UHPLC coupled with triple quadrupole MS. Among them, 29 MRM transitions were monitored for pure metabolites (unambiguously identified). The other 37 MRM transitions were monitored for mixtures with exactly identical MRM transitions and retention time. The application of targeted profiling of 13C-labeled NEAAs in the dynamic 13C-labeling experiment indicated that the concentration-time profiles of NEAAs were different from each other. The concentrations of most 13C-labeled Gln, Glu, Pro, and Asp altered after 13C5-Gln incubation, indicating that Gln plays a fundamental role in the biosynthesis of Glu, Pro, and Asp. The proposed PRM-MRM combination mode LC-MS approach is expected to provide valuable insights into analyses of isotope-labeled metabolites in isotope tracer experiments.
    DOI:  https://doi.org/10.1021/acs.analchem.1c02554
  10. J Biol Chem. 2021 Sep 27. pii: S0021-9258(21)01058-9. [Epub ahead of print] 101255
    Branched-chain amino acid metabolism controls membrane phospholipid structure in Staphylococcus aureus.
    Matthew W Frank, Sarah G Whaley, Charles O Rock.
      Branched-chain amino acids (primarily isoleucine) are important regulators of virulence and are converted to precursor molecules used to initiate fatty acid synthesis in Staphylococcus aureus. Defining how bacteria control their membrane phospholipid composition is key to understanding their adaptation to different environments. Here, we used mass tracing experiments to show that extracellular isoleucine is preferentially metabolized by the branched-chain ketoacid dehydrogenase complex, in contrast to valine, which is not efficiently converted to isobutyryl-CoA. This selectivity creates a ratio of anteiso:iso C5-CoAs that matches the anteiso:iso ratio in membrane phospholipids, indicating indiscriminate utilization of these precursors by the initiation condensing enzyme FabH. Lipidomics analysis showed that removal of isoleucine and leucine from the medium led to the replacement of phospholipid molecular species containing anteiso/iso 17- and 19-carbon fatty acids with 18- and 20-carbon straight-chain fatty acids. This compositional change is driven by an increase in the acetyl-CoA:C5-CoA ratio, enhancing the utilization of acetyl-CoA by FabH. The acyl carrier protein (ACP) pool normally consists of odd carbon acyl-ACP intermediates, but when branched-chain amino acids are absent from the environment there was a large increase in even carbon acyl-ACP pathway intermediates. The high substrate selectivity of PlsC ensures that, in the presence or absence of extracellular Ile/Leu, the 2-position is occupied by a branched-chain 15-carbon fatty acid. These metabolomic measurements show how the metabolism of isoleucine and leucine, rather than the selectivity of FabH, control the structure of membrane phospholipids.
    Keywords:  Staphylococcus aureus; branched-chain amino acids; fatty acid; fatty acid synthesis; phospholipids
    DOI:  https://doi.org/10.1016/j.jbc.2021.101255
  11. Metabolites. 2021 Sep 21. pii: 646. [Epub ahead of print]11(9):
    Lipidomic Signatures of Changes in Adiposity: A Large Prospective Study of 5849 Adults from the Australian Diabetes, Obesity and Lifestyle Study.
    Habtamu B Beyene, Gavriel Olshansky, Corey Giles, Kevin Huynh, Michelle Cinel, Natalie A Mellett, Adam Alexander T Smith, Jonathan E Shaw, Dianna J Magliano, Peter J Meikle.
      Lipid metabolism is tightly linked to adiposity. Comprehensive lipidomic profiling offers new insights into the dysregulation of lipid metabolism in relation to weight gain. Here, we investigated the relationship of the human plasma lipidome and changes in waist circumference (WC) and body mass index (BMI). Adults (2653 men and 3196 women), 25-95 years old who attended the baseline survey of the Australian Diabetes, Obesity and Lifestyle Study (AusDiab) and the 5-year follow-up were enrolled. A targeted lipidomic approach was used to quantify 706 distinct molecular lipid species in the plasma samples. Multiple linear regression models were used to examine the relationship between the baseline lipidomic profile and changes in WC and BMI. Metabolic scores for change in WC were generated using a ridge regression model. Alkyl-diacylglycerol such as TG(O-50:2) [NL-18:1] displayed the strongest association with change in WC (β-coefficient = 0.125 cm increment per SD increment in baseline lipid level, p = 2.78 × 10-11. Many lipid species containing linoleate (18:2) fatty acids were negatively associated with both WC and BMI gain. Compared to traditional models, multivariate models containing lipid species identify individuals at a greater risk of gaining WC: top quintile relative to bottom quintile (odds ratio, 95% CI = 5.4, 3.8-6.6 for women and 2.3, 1.7-3.0 for men). Our findings define metabolic profiles that characterize individuals at risk of weight gain or WC increase and provide important insight into the biological role of lipids in obesity.
    Keywords:  change in BMI; change in WC; metabolic scores; plasma lipidomics
    DOI:  https://doi.org/10.3390/metabo11090646
  12. Metabolites. 2021 Sep 08. pii: 609. [Epub ahead of print]11(9):
    Cross-Platform Evaluation of Commercially Targeted and Untargeted Metabolomics Approaches to Optimize the Investigation of Psychiatric Disease.
    Lauren E Chaby, Heather C Lasseter, Kévin Contrepois, Reza M Salek, Christoph W Turck, Andrew Thompson, Timothy Vaughan, Magali Haas, Andreas Jeromin.
      Metabolomics methods often encounter trade-offs between quantification accuracy and coverage, with truly comprehensive coverage only attainable through a multitude of complementary assays. Due to the lack of standardization and the variety of metabolomics assays, it is difficult to integrate datasets across studies or assays. To inform metabolomics platform selection, with a focus on posttraumatic stress disorder (PTSD), we review platform use and sample sizes in psychiatric metabolomics studies and then evaluate five prominent metabolomics platforms for coverage and performance, including intra-/inter-assay precision, accuracy, and linearity. We found performance was variable between metabolite classes, but comparable across targeted and untargeted approaches. Within all platforms, precision and accuracy were highly variable across classes, ranging from 0.9-63.2% (coefficient of variation) and 0.6-99.1% for accuracy to reference plasma. Several classes had high inter-assay variance, potentially impeding dissociation of a biological signal, including glycerophospholipids, organooxygen compounds, and fatty acids. Coverage was platform-specific and ranged from 16-70% of PTSD-associated metabolites. Non-overlapping coverage is challenging; however, benefits of applying multiple metabolomics technologies must be weighed against cost, biospecimen availability, platform-specific normative levels, and challenges in merging datasets. Our findings and open-access cross-platform dataset can inform platform selection and dataset integration based on platform-specific coverage breadth/overlap and metabolite-specific performance.
    Keywords:  depression; lipidomics; liquid chromatography–mass spectrometry (LC−MS); mass spectrometry; metabolites; metabolomics; nuclear magnetic resonance (NMR); platform comparison; posttraumatic stress disorder (PTSD); ring trial
    DOI:  https://doi.org/10.3390/metabo11090609
  13. J Proteome Res. 2021 Oct 01.
    Driving Single Cell Proteomics Forward with Innovation.
    Nikolai Slavov.
      Current single-cell mass spectrometry (MS) methods can quantify thousands of peptides per single cell while detecting peptide-like features that may support the quantification of 10-fold more peptides. This 10-fold gain might be attained by innovations in data acquisition and interpretation even while using existing instrumentation. This perspective discusses possible directions for such innovations with the aim to stimulate community efforts for increasing the coverage and quantitative accuracy of single proteomics while simultaneously decreasing missing data. Parallel improvements in instrumentation, sample preparation, and peptide separation will afford additional gains. Together, these synergistic routes for innovation project a rapid growth in the capabilities of MS based single-cell protein analysis. These gains will directly empower applications of single-cell proteomics to biomedical research.
    Keywords:  data acquisition; data interpretation; peptide identity propagation; single-cell proteomics; ultrasensitive proteomics
    DOI:  https://doi.org/10.1021/acs.jproteome.1c00639
  14. Bioinformatics. 2021 Sep 29. pii: btab674. [Epub ahead of print]
    AlphaMap: an open-source python package for the visual annotation of proteomics data with sequence specific knowledge.
    Eugenia Voytik, Isabell Bludau, Sander Willems, Fynn M Hansen, Andreas-David Brunner, Maximilian T Strauss, Matthias Mann.
       SUMMARY: Integrating experimental information across proteomic datasets with the wealth of publicly available sequence annotations is a crucial part in many proteomic studies that currently lacks an automated analysis platform. Here we present AlphaMap, a Python package that facilitates the visual exploration of peptide-level proteomics data. Identified peptides and post-translational modifications in proteomic datasets are mapped to their corresponding protein sequence and visualized together with prior knowledge from UniProt and with expected proteolytic cleavage sites. The functionality of AlphaMap can be accessed via an intuitive graphical user interface or-more flexibly-as a Python package that allows its integration into common analysis workflows for data visualization. AlphaMap produces publication-quality illustrations and can easily be customized to address a given research question.
    AVAILABILITY: AlphaMap is implemented in Python and released under an Apache license. The source code and one-click installers are freely available at https://github.com/MannLabs/alphamap.
    SUPPLEMENTARY INFORMATION: A detailed user guide for AlphaMap is provided as supplementary data.
    DOI:  https://doi.org/10.1093/bioinformatics/btab674
  15. Anal Chem. 2021 Sep 28.
    Rapid Automated Annotation and Analysis of N-Glycan Mass Spectrometry Imaging Data Sets Using NGlycDB in METASPACE.
    Dušan Veličković, Tamara Bečejac, Sergii Mamedov, Kumar Sharma, Namasivayam Ambalavanan, Theodore Alexandrov, Christopher R Anderton.
      Imaging N-glycan spatial distribution in tissues using mass spectrometry imaging (MSI) is emerging as a promising tool in biological and clinical applications. However, there is currently no high-throughput tool for visualization and molecular annotation of N-glycans in MSI data, which significantly slows down data processing and hampers the applicability of this approach. Here, we present how METASPACE, an open-source cloud engine for molecular annotation of MSI data, can be used to automatically annotate, visualize, analyze, and interpret high-resolution mass spectrometry-based spatial N-glycomics data. METASPACE is an emerging tool in spatial metabolomics, but the lack of compatible glycan databases has limited its application for comprehensive N-glycan annotations from MSI data sets. We created NGlycDB, a public database of N-glycans, by adapting available glycan databases. We demonstrate the applicability of NGlycDB in METASPACE by analyzing MALDI-MSI data from formalin-fixed paraffin-embedded (FFPE) human kidney and mouse lung tissue sections. We added NGlycDB to METASPACE for public use, thus, facilitating applications of MSI in glycobiology.
    DOI:  https://doi.org/10.1021/acs.analchem.1c02347
  16. Front Cell Dev Biol. 2021 ;9 726261
    Lipid Droplet-Organelle Contact Sites as Hubs for Fatty Acid Metabolism, Trafficking, and Metabolic Channeling.
    Mike F Renne, Hanaa Hariri.
      Cells prepare for fluctuations in nutrient availability by storing energy in the form of neutral lipids in organelles called Lipid Droplets (LDs). Upon starvation, fatty acids (FAs) released from LDs are trafficked to different cellular compartments to be utilized for membrane biogenesis or as a source of energy. Despite the biochemical pathways being known in detail, the spatio-temporal regulation of FA synthesis, storage, release, and breakdown is not completely understood. Recent studies suggest that FA trafficking and metabolism are facilitated by inter-organelle contact sites that form between LDs and other cellular compartments such as the Endoplasmic Reticulum (ER), mitochondria, peroxisomes, and lysosomes. LD-LD contact sites are also sites where FAs are transferred in a directional manner to support LD growth and expansion. As the storage site of neutral lipids, LDs play a central role in FA homeostasis. In this mini review, we highlight the role of LD contact sites with other organelles in FA trafficking, channeling, and metabolism and discuss the implications for these pathways on cellular lipid and energy homeostasis.
    Keywords:  contact sites; fatty acids; lipid droplets; metabolism; organelles
    DOI:  https://doi.org/10.3389/fcell.2021.726261
  17. Front Pharmacol. 2021 ;12 723798
    Glucose and Amino Acid Metabolic Dependencies Linked to Stemness and Metastasis in Different Aggressive Cancer Types.
    Andrea Chisari, Irene Golán, Sabrina Campisano, Caroline Gélabert, Aristidis Moustakas, Patricia Sancho, Laia Caja.
      Malignant cells are commonly characterised by being capable of invading tissue, growing self-sufficiently and uncontrollably, being insensitive to apoptosis induction and controlling their environment, for example inducing angiogenesis. Amongst them, a subpopulation of cancer cells, called cancer stem cells (CSCs) shows sustained replicative potential, tumor-initiating properties and chemoresistance. These characteristics make CSCs responsible for therapy resistance, tumor relapse and growth in distant organs, causing metastatic dissemination. For these reasons, eliminating CSCs is necessary in order to achieve long-term survival of cancer patients. New insights in cancer metabolism have revealed that cellular metabolism in tumors is highly heterogeneous and that CSCs show specific metabolic traits supporting their unique functionality. Indeed, CSCs adapt differently to the deprivation of specific nutrients that represent potentially targetable vulnerabilities. This review focuses on three of the most aggressive tumor types: pancreatic ductal adenocarcinoma (PDAC), hepatocellular carcinoma (HCC) and glioblastoma (GBM). The aim is to prove whether CSCs from different tumour types share common metabolic requirements and responses to nutrient starvation, by outlining the diverse roles of glucose and amino acids within tumour cells and in the tumour microenvironment, as well as the consequences of their deprivation. Beyond their role in biosynthesis, they serve as energy sources and help maintain redox balance. In addition, glucose and amino acid derivatives contribute to immune responses linked to tumourigenesis and metastasis. Furthermore, potential metabolic liabilities are identified and discussed as targets for therapeutic intervention.
    Keywords:  GBM-glioblastoma multiforme; HCC-hepatocellular carcinoma; PDAC-pancreatic ductal adenocarcinoma; amino acid; cancer stem cell (CSC); glucose; therapy
    DOI:  https://doi.org/10.3389/fphar.2021.723798
  18. Cells. 2021 Sep 09. pii: 2371. [Epub ahead of print]10(9):
    HIF-1-Independent Mechanisms Regulating Metabolic Adaptation in Hypoxic Cancer Cells.
    Shen-Han Lee, Monika Golinska, John R Griffiths.
      In solid tumours, cancer cells exist within hypoxic microenvironments, and their metabolic adaptation to this hypoxia is driven by HIF-1 transcription factor, which is overexpressed in a broad range of human cancers. HIF inhibitors are under pre-clinical investigation and clinical trials, but there is evidence that hypoxic cancer cells can adapt metabolically to HIF-1 inhibition, which would provide a potential route for drug resistance. Here, we review accumulating evidence of such adaptions in carbohydrate and creatine metabolism and other HIF-1-independent mechanisms that might allow cancers to survive hypoxia despite anti-HIF-1 therapy. These include pathways in glucose, glutamine, and lipid metabolism; epigenetic mechanisms; post-translational protein modifications; spatial reorganization of enzymes; signalling pathways such as Myc, PI3K-Akt, 2-hyxdroxyglutarate and AMP-activated protein kinase (AMPK); and activation of the HIF-2 pathway. All of these should be investigated in future work on hypoxia bypass mechanisms in anti-HIF-1 cancer therapy. In principle, agents targeted toward HIF-1β rather than HIF-1α might be advantageous, as both HIF-1 and HIF-2 require HIF-1β for activation. However, HIF-1β is also the aryl hydrocarbon nuclear transporter (ARNT), which has functions in many tissues, so off-target effects should be expected. In general, cancer therapy by HIF inhibition will need careful attention to potential resistance mechanisms.
    Keywords:  2-hydroxyglutarate; AMP-activated protein kinase (AMPK); Myc; cancer metabolism; creatine metabolism; glutamine metabolism; glycolysis; hypoxia; hypoxia-inducible factor-1 (HIF-1); lipid metabolism; phosphatidylinositol 3-kinase (PI3K)
    DOI:  https://doi.org/10.3390/cells10092371
  19. Anal Chem. 2021 Sep 29.
    JUMPt: Comprehensive Protein Turnover Modeling of In Vivo Pulse SILAC Data by Ordinary Differential Equations.
    Surendhar Reddy Chepyala, Xueyan Liu, Ka Yang, Zhiping Wu, Alex M Breuer, Ji-Hoon Cho, Yuxin Li, Ariana Mancieri, Yun Jiao, Hui Zhang, Junmin Peng.
      Recent advances in mass spectrometry (MS)-based proteomics allow the measurement of turnover rates of thousands of proteins using dynamic labeling methods, such as pulse stable isotope labeling by amino acids in cell culture (pSILAC). However, when applying the pSILAC strategy to multicellular animals (e.g., mice), the labeling process is significantly delayed by native amino acids recycled from protein degradation in vivo, raising a challenge of defining accurate protein turnover rates. Here, we report JUMPt, a software package using a novel ordinary differential equation (ODE)-based mathematical model to determine reliable rates of protein degradation. The uniqueness of JUMPt is to consider amino acid recycling and fit the kinetics of the labeling amino acid (e.g., Lys) and whole proteome simultaneously to derive half-lives of individual proteins. Multiple settings in the software are designed to enable simple to comprehensive data inputs for precise analysis of half-lives with flexibility. We examined the software by studying the turnover of thousands of proteins in the pSILAC brain and liver tissues. The results were largely consistent with the proteome turnover measurements from previous studies. The long-lived proteins are enriched in the integral membrane, myelin sheath, and mitochondrion in the brain. In summary, the ODE-based JUMPt software is an effective proteomics tool for analyzing large-scale protein turnover, and the software is publicly available on GitHub (https://github.com/JUMPSuite/JUMPt) to the research community.
    DOI:  https://doi.org/10.1021/acs.analchem.1c02309
  20. Metabolites. 2021 Aug 26. pii: 577. [Epub ahead of print]11(9):
    Reproducible Lipid Alterations in Patient-Derived Breast Cancer Xenograft FFPE Tissue Identified with MALDI MSI for Pre-Clinical and Clinical Application.
    Vanna Denti, Maria K Andersen, Andrew Smith, Anna Mary Bofin, Anna Nordborg, Fulvio Magni, Siver Andreas Moestue, Marco Giampà.
      The association between lipid metabolism and long-term outcomes is relevant for tumor diagnosis and therapy. Archival material such as formalin-fixed and paraffin embedded (FFPE) tissues is a highly valuable resource for this aim as it is linked to long-term clinical follow-up. Therefore, there is a need to develop robust methodologies able to detect lipids in FFPE material and correlate them with clinical outcomes. In this work, lipidic alterations were investigated in patient-derived xenograft of breast cancer by using a matrix-assisted laser desorption ionization mass spectrometry (MALDI MSI) based workflow that included antigen retrieval as a sample preparation step. We evaluated technical reproducibility, spatial metabolic differentiation within tissue compartments, and treatment response induced by a glutaminase inhibitor (CB-839). This protocol shows a good inter-day robustness (CV = 26 ± 12%). Several lipids could reliably distinguish necrotic and tumor regions across the technical replicates. Moreover, this protocol identified distinct alterations in the tissue lipidome of xenograft treated with glutaminase inhibitors. In conclusion, lipidic alterations in FFPE tissue of breast cancer xenograft observed in this study are a step-forward to a robust and reproducible MALDI-MSI based workflow for pre-clinical and clinical applications.
    Keywords:  FFPE tissue; MALDI MSI; breast cancer; diagnosis; lipidomics
    DOI:  https://doi.org/10.3390/metabo11090577
  21. Metabolites. 2021 Sep 03. pii: 595. [Epub ahead of print]11(9):
    Low-Dose Doxycycline Treatment Normalizes Levels of Some Salivary Metabolites Associated with Oral Microbiota in Patients with Primary Sjögren's Syndrome.
    Maria Herrala, Soile Turunen, Kati Hanhineva, Marko Lehtonen, Jopi J W Mikkonen, Hubertus Seitsalo, Reijo Lappalainen, Leo Tjäderhane, Raija K Niemelä, Tuula Salo, Sami Myllymaa, Arja M Kullaa, Olli Kärkkäinen.
      Saliva is a complex oral fluid, and plays a major role in oral health. Primary Sjögren's syndrome (pSS), as an autoimmune disease that typically causes hyposalivation. In the present study, salivary metabolites were studied from stimulated saliva samples (n = 15) of female patients with pSS in a group treated with low-dose doxycycline (LDD), saliva samples (n = 10) of non-treated female patients with pSS, and saliva samples (n = 14) of healthy age-matched females as controls. Saliva samples were analyzed with liquid chromatography mass spectrometry (LC-MS) based on the non-targeted metabolomics method. The saliva metabolite profile differed between pSS patients and the healthy control (HC). In the pSS patients, the LDD treatment normalized saliva levels of several metabolites, including tyrosine glutamine dipeptide, phenylalanine isoleucine dipeptide, valine leucine dipeptide, phenylalanine, pantothenic acid (vitamin B5), urocanic acid, and salivary lipid cholesteryl palmitic acid (CE 16:0), to levels seen in the saliva samples of the HC. In conclusion, the data showed that pSS is associated with an altered saliva metabolite profile compared to the HC and that the LLD treatment normalized levels of several metabolites associated with dysbiosis of oral microbiota in pSS patients. The role of the saliva metabolome in pSS pathology needs to be further studied to clarify if saliva metabolite levels can be used to predict or monitor the progress and treatment of pSS.
    Keywords:  Sjögren’s syndrome; doxycycline; hyposalivation; metabolomics; saliva
    DOI:  https://doi.org/10.3390/metabo11090595
  22. Nat Commun. 2021 Oct 01. 12(1): 5787
    The growing need for controlled data access models in clinical proteomics and metabolomics.
    Thomas M Keane, Claire O'Donovan, Juan Antonio Vizcaíno.
      
    DOI:  https://doi.org/10.1038/s41467-021-26110-4
  23. Anal Chem. 2021 Sep 29.
    Assessment of XCMS Optimization Methods with Machine-Learning Performance.
    Johan Lassen, Kirstine Lykke Nielsen, Mogens Johannsen, Palle Villesen.
      The metabolomics field is under rapid development. In particular, biomarker identification and pathway analysis are growing, as untargeted metabolomics is usable for discovery research. Frequently, new processing and statistical strategies are proposed to accommodate the increasing demand for robust and standardized data. One such algorithm is XCMS, which processes raw data into integrated peaks. Multiple studies have tried to assess the effect of optimizing XCMS parameters, but it is challenging to quantify the quality of the XCMS output. In this study, we investigate the effect of two automated optimization tools (Autotuner and isotopologue parameter optimization (IPO)) using the prediction power of machine learning as a proxy for the quality of the data set. We show that optimized parameters outperform default XCMS settings and that manually chosen parameters by liquid chromatography-mass spectrometry (LC-MS) experts remain the best. Finally, the machine-learning approach of quality assessment is proposed for future evaluations of newly developed optimization methods because its performance directly measures the retained signal upon preprocessing.
    DOI:  https://doi.org/10.1021/acs.analchem.1c02000
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