bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2023–05–21
25 papers selected by
Giovanny Rodriguez Blanco, University of Edinburgh
  1. Isolation and Identification of Plasma Extracellular Vesicles Protein Biomarkers.
  2. Dual stable isotopes enhance lipidomic studies in bacterial model organism Enterococcus faecalis.
  3. Repeat-Enhancing Featured Ion-Guided Stoichiometry for Identification and Quantification of Direct Infusion Proteome.
  4. Integration of Metabolomic and Proteomic Data to Uncover Actionable Metabolic Pathways.
  5. Impact of acute stress on murine metabolomics and metabolic flux.
  6. Confirmation of Statin and Fibrate Use from Small-Volume Archived Plasma Samples by High-Throughput LC-MS/MS Method.
  7. Analysis of Cancer Cell Line Secretomes: A Complementary Source of Disease-Specific Protein Biomarkers.
  8. Introduction: Cancer Systems and Integrative Biology.
  9. Rewiring of the Host Cell Metabolome and Lipidome during Lytic Gammaherpesvirus Infection Is Essential for Infectious-Virus Production.
  10. IL4i1 and IDO1: oxidases that control a tryptophan metabolic nexus in cancer.
  11. The Ubiquitin-Proteasome System in Tumor Metabolism.
  12. TopFD: A Proteoform Feature Detection Tool for Top-Down Proteomics.
  13. Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism.
  14. Small extracellular vesicle-mediated metabolic reprogramming: from tumors to pre-metastatic niche formation.
  15. Phosphoproteomic Approaches for Identifying Phosphatase and Kinase Substrates.
  16. Untargeted Lipidomics Method for the Discrimination of Five Crab Species by Ultra-High-Performance Liquid Chromatography High-Resolution Mass Spectrometry Combined with Chemometrics.
  17. Microfluidic Devices for Precise Measurements of Cell Directionality Reveal a Role for Glutamine during Cell Migration.
  18. Using mass spectrometry imaging to map fluxes quantitatively in the tumor ecosystem.
  19. Network-centric analysis of co-fractionated protein complex profiles using SECAT.
  20. Uridine-derived ribose fuels glucose-restricted pancreatic cancer.
  21. Comprehensive analysis of alkenones by reversed-phase HPLC-MS with unprecedented selectivity, linearity and sensitivity.
  22. Serine metabolic reprogramming in tumorigenesis, tumor immunity and clinical treatment: Serine metabolic reprogramming in cancer.
  23. Factorial design applied to LC-ESI-QTOF mass spectrometer parameters for untargeted metabolomics.
  24. Emerging roles of nucleotide metabolism in cancer.
  25. UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis.
  1. Methods Mol Biol. 2023 ;2660 207-217
    Isolation and Identification of Plasma Extracellular Vesicles Protein Biomarkers.
    Michelle V Lihon, Marco Hadisurya, Xiaofeng Wu, Anton Iliuk, W Andy Tao.
      Extracellular vesicles (EVs) have emerged as a valuable source for disease biomarkers and an alternative drug delivery system due to their ability to carry cargo and target specific cells. Proper isolation, identification, and analytical strategy are required for evaluating their potential in diagnostics and therapeutics. Here, a method is detailed to isolate plasma EVs and analyze their proteomic profiling, combining EVtrap-based high-recovery EV isolation, phase-transfer surfactant method for protein extraction, and mass spectrometry qualitative and quantitative strategies for EV proteome characterization. The pipeline provides a highly effective EV-based proteome analysis technique that can be applied for EV characterization and evaluation of EV-based diagnosis and therapy.
    Keywords:  Automatic gain control (AGC) strategy; Data-dependent acquisition (DDA); Data-independent acquisition (DIA); Exosomes; Extracellular vesicles (EVs); Extracellular vesicles total recovery and purification (EVtrap); Gas-phase fractionated (GPF) spectral library; Isobaric labeling; Mass spectrometry; Parallel reaction monitoring (PRM); Pharmacokinetics; Plasma; Quantitative proteomics; Spectronaut Pulsar search; Tandem mass tag labeling (TMT labeling)
    DOI:  https://doi.org/10.1007/978-1-0716-3163-8_14
  2. Anal Bioanal Chem. 2023 May 19.
    Dual stable isotopes enhance lipidomic studies in bacterial model organism Enterococcus faecalis.
    Brittni Woodall, Elizabeth M Fozo, Shawn R Campagna.
      Dual stable isotope probes of deuterium oxide and 13C fatty acid were demonstrated to probe the lipid biosynthesis cycle of a Gram-positive bacterium Enterococcus faecalis. As external nutrients and carbon sources often interact with metabolic processes, the use of dual-labeled isotope pools allowed for the simultaneous investigation of both exogenous nutrient incorporation or modification and de novo biosynthesis. Deuterium was utilized to trace de novo fatty acid biosynthesis through solvent-mediated proton transfer during elongation of the carbon chain while 13C-fatty acids were utilized to trace exogenous nutrient metabolism and modification through lipid synthesis. Ultra-high-performance liquid chromatography high-resolution mass spectrometry identified 30 lipid species which incorporated deuterium and/or 13C fatty acid into the membrane. Additionally, MS2 fragments of isolated lipids identified acyl tail position confirming enzymatic activity of PlsY in the incorporation of the 13C fatty acid into membrane lipids.
    Keywords:  13C nutrients; Bioanalytical methods; Deuterium; High resolution mass spectrometry; Isotope tracing; Liquid chromatography
    DOI:  https://doi.org/10.1007/s00216-023-04750-3
  3. J Proteome Res. 2023 May 17.
    Repeat-Enhancing Featured Ion-Guided Stoichiometry for Identification and Quantification of Direct Infusion Proteome.
    Rui Tang, Ge Dai, Qingrun Li, Yan Fang, Haoran Zheng, Rong Zeng.
      The direct infusion-shotgun proteome analysis (DI-SPA) alongside data-independent acquisition mass spectrometry achieved fast proteome identification and quantification without chromatographic separation. However, robust peptide identification and quantification (label and label-free) for the DI-SPA data is still insufficient. We find that in the absence of chromatography, the identification of DI-SPA can be boosted by extending acquisition cycles repeatedly and maximizing the utilization of the featured repetition characteristics, combined with the machine learning-based automatic peptide scoring strategy. Here, we present the repeat-enhancing featured ion-guided stoichiometry (RE-FIGS), a complete and compact solution to (repeated) DI-SPA data. Using our strategy, the peptide identification can be improved above 30% with high reproducibility (70.0%). Notably, the label-free quantification of repeated DI-SPA can be successfully obtained with high accuracy (mean median error, 0.108) and high reproducibility (median error, 0.001). We believe our RE-FIGS method could boost the broad application of the (repeated) DI-SPA method and offer a new choice for proteomic analysis.
    Keywords:  data-independent acquisition; direct infusion-shotgun proteome analysis; label-free quantification; mass spectrometry
    DOI:  https://doi.org/10.1021/acs.jproteome.3c00050
  4. Methods Mol Biol. 2023 ;2660 137-148
    Integration of Metabolomic and Proteomic Data to Uncover Actionable Metabolic Pathways.
    Christian Heckendorf, Benjamin C Blum, Weiwei Lin, Matthew L Lawton, Andrew Emili.
      Mass spectrometry (MS) is an important tool for biological studies because it is capable of interrogating a diversity of biomolecules (proteins, drugs, metabolites) not captured via alternate genomic platforms. Unfortunately, downstream data analysis becomes complicated when attempting to evaluate and integrate measurements of different molecular classes and requires the aggregation of expertise from different relevant disciplines. This complexity represents a significant bottleneck that limits the routine deployment of MS-based multi-omic methods, despite the unmatched biological and functional insight the data can provide. To address this unmet need, our group introduced Omics Notebook as an open-source framework for facilitating exploratory analysis, reporting and integrating MS-based multi-omic data in a way that is automated, reproducible and customizable. By deploying this pipeline, we have devised a framework for researchers to more rapidly identify functional patterns across complex data types and focus on statistically significant and biologically interesting aspects of their multi-omic profiling experiments. This chapter aims to describe a protocol which leverages our publicly accessible tools to analyze and integrate data from high-throughput proteomics and metabolomics experiments and produce reports that will facilitate more impactful research, cross-institutional collaborations, and wider data dissemination.
    Keywords:  Automated pipeline; Biological functional modules; Graphical user interface (GUI); Mass spectrometry; Metabolomics; Multi-omics data integration; Omics Notebook R package; Omics Notebook interface; Pathway enrichment; Principal component analysis (PCA); Proteomics; Proteomics search engines; Systems biology
    DOI:  https://doi.org/10.1007/978-1-0716-3163-8_10
  5. Proc Natl Acad Sci U S A. 2023 05 23. 120(21): e2301215120
    Impact of acute stress on murine metabolomics and metabolic flux.
    Won Dong Lee, Lingfan Liang, Jenna AbuSalim, Connor S R Jankowski, Laith Z Samarah, Michael D Neinast, Joshua D Rabinowitz.
      Plasma metabolite concentrations and labeling enrichments are common measures of organismal metabolism. In mice, blood is often collected by tail snip sampling. Here, we systematically examined the effect of such sampling, relative to gold-standard sampling from an in-dwelling arterial catheter, on plasma metabolomics and stable isotope tracing. We find marked differences between the arterial and tail circulating metabolome, which arise from two major factors: handling stress and sampling site, whose effects were deconvoluted by taking a second arterial sample immediately after tail snip. Pyruvate and lactate were the most stress-sensitive plasma metabolites, rising ~14 and ~5-fold. Both acute handling stress and adrenergic agonists induce extensive, immediate production of lactate, and modest production of many other circulating metabolites, and we provide a reference set of mouse circulatory turnover fluxes with noninvasive arterial sampling to avoid such artifacts. Even in the absence of stress, lactate remains the highest flux circulating metabolite on a molar basis, and most glucose flux into the TCA cycle in fasted mice flows through circulating lactate. Thus, lactate is both a central player in unstressed mammalian metabolism and strongly produced in response to acute stress.
    Keywords:  catecholamine; in vivo; isotope tracing; metabolomics; stress
    DOI:  https://doi.org/10.1073/pnas.2301215120
  6. Int J Mol Sci. 2023 Apr 27. pii: 7931. [Epub ahead of print]24(9):
    Confirmation of Statin and Fibrate Use from Small-Volume Archived Plasma Samples by High-Throughput LC-MS/MS Method.
    Jennifer D Kusovschi, Anna A Ivanova, Michael S Gardner, Robert W McGarrah, William E Kraus, Zsuzsanna Kuklenyik, James L Pirkle, John R Barr.
      Designing studies for lipid-metabolism-related biomarker discovery is challenging because of the high prevalence of various statin and fibrate usage for lipid-lowering therapies. When the statin and fibrate use is determined based on self-reports, patient adherence to the prescribed statin dose regimen remains unknown. A potentially more accurate way to verify a patient's medication adherence is by direct analytical measurements. Current analytical methods are prohibitive because of the limited panel of drugs per test and large sample volume requirement that is not available from archived samples. A 4-min-long method was developed for the detection of seven statins and three fibrates using 10 µL of plasma analyzed via reverse-phase liquid chromatography and tandem mass spectrometry. The method was applied to the analysis of 941 archived plasma samples collected from patients before cardiac catheterization. When statin use was self-reported, statins were detected in 78.6% of the samples. In the case of self-reported atorvastatin use, the agreement with detection was 90.2%. However, when no statin use was reported, 42.4% of the samples had detectable levels of statins, with a similar range of concentrations as the samples from the self-reported statin users. The method is highly applicable in population studies designed for biomarker discovery or diet and lifestyle intervention studies, where the accuracy of statin or fibrate use may strongly affect the statistical evaluation of the biomarker data.
    Keywords:  LC-MS/MS; cardiovascular disease; fibrates; human plasma; mass spectrometry; medical records; statins
    DOI:  https://doi.org/10.3390/ijms24097931
  7. Methods Mol Biol. 2023 ;2645 277-287
    Analysis of Cancer Cell Line Secretomes: A Complementary Source of Disease-Specific Protein Biomarkers.
    Katie Dunphy, Despina Bazou, Paul Dowling.
      Various types of cancer cells enrich or condition the medium that they are cultured in by secreting or shedding proteins and small molecules. These secreted or shed factors are involved in key biological processes, including cellular communication, proliferation, and migration, and are represented by protein families, including cytokines, growth factors, and enzymes. The rapid development of high-resolution mass spectrometry and shotgun strategies for proteome analysis facilitates the identification of these factors in biological models and elucidation of their potential roles in pathophysiology. Hence, the following protocol provides details on how to prepare proteins present in conditioned media for mass spectrometry analysis.
    Keywords:  Biomarkers; Conditioned media; Culture; Metabolites; Proteins; Secretome
    DOI:  https://doi.org/10.1007/978-1-0716-3056-3_18
  8. Methods Mol Biol. 2023 ;2660 1-11
    Introduction: Cancer Systems and Integrative Biology.
    Robert Clarke.
      The insights provided by the holistic approaches of systems and integrative biology offer a means to address the multiple levels of complexity evident in cancer biology. Often focused on the use of large-scale, high-dimensional omics data for discovery in silico, integration with lower-dimensional data and lower-throughput wet laboratory studies allows for the development of a more mechanistic understanding of the control, execution, and operation of complex biological systems. While no single volume can cover all of the advances across this broad and rapidly developing field, we here provide reviews, methods, and detailed protocols for several state-of-the-art approaches to probe cancer biology from an integrative systems perspective. The protocols presented are intended for easy implementation in the laboratory and often offer a clear rationale for their development and application. This introduction provides a very brief description of systems and integrative biology as context for the chapters that follow, with a short overview of each chapter to allow the reader to easily and quickly find those protocols of most interest.
    Keywords:  Cancer; Data analysis; Integrative biology; Methods; Systems biology
    DOI:  https://doi.org/10.1007/978-1-0716-3163-8_1
  9. J Virol. 2023 May 16. e0050623
    Rewiring of the Host Cell Metabolome and Lipidome during Lytic Gammaherpesvirus Infection Is Essential for Infectious-Virus Production.
    Sarah A Clark, Angie Vazquez, Kelsey Furiya, Madeleine K Splattstoesser, Abdullah K Bashmail, Haleigh Schwartz, Makaiya Russell, Shun-Je Bhark, Osvaldo K Moreno, Morgan McGovern, Eric R Owsley, Timothy A Nelson, Erica L Sanchez, Tracie Delgado.
      Oncogenic virus infections are estimated to cause ~15% of all cancers. Two prevalent human oncogenic viruses are members of the gammaherpesvirus family: Epstein-Barr virus (EBV) and Kaposi's sarcoma herpesvirus (KSHV). We use murine herpesvirus 68 (MHV-68), which shares significant homology with KSHV and EBV, as a model system to study gammaherpesvirus lytic replication. Viruses implement distinct metabolic programs to support their life cycle, such as increasing the supply of lipids, amino acids, and nucleotide materials necessary to replicate. Our data define the global changes in the host cell metabolome and lipidome during gammaherpesvirus lytic replication. Our metabolomics analysis found that MHV-68 lytic infection induces glycolysis, glutaminolysis, lipid metabolism, and nucleotide metabolism. We additionally observed an increase in glutamine consumption and glutamine dehydrogenase protein expression. While both glucose and glutamine starvation of host cells decreased viral titers, glutamine starvation led to a greater loss in virion production. Our lipidomics analysis revealed a peak in triacylglycerides early during infection and an increase in free fatty acids and diacylglyceride later in the viral life cycle. Furthermore, we observed an increase in the protein expression of multiple lipogenic enzymes during infection. Interestingly, pharmacological inhibitors of glycolysis or lipogenesis resulted in decreased infectious virus production. Taken together, these results illustrate the global alterations in host cell metabolism during lytic gammaherpesvirus infection, establish essential pathways for viral production, and recommend targeted mechanisms to block viral spread and treat viral induced tumors. IMPORTANCE Viruses are intracellular parasites which lack their own metabolism, so they must hijack host cell metabolic machinery in order to increase the production of energy, proteins, fats, and genetic material necessary to replicate. Using murine herpesvirus 68 (MHV-68) as a model system to understand how similar human gammaherpesviruses cause cancer, we profiled the metabolic changes that occur during lytic MHV-68 infection and replication. We found that MHV-68 infection of host cells increases glucose, glutamine, lipid, and nucleotide metabolic pathways. We also showed inhibition or starvation of glucose, glutamine, or lipid metabolic pathways results in an inhibition of virus production. Ultimately, targeting changes in host cell metabolism due to viral infection can be used to treat gammaherpesvirus-induced cancers and infections in humans.
    Keywords:  EBV; Epstein-Barr virus; KSHV; Kaposi’s sarcoma herpesvirus; MHV-68; gammaherpesvirus; herpesvirus; lipidomics; metabolism; metabolomics; murine herpesvirus 68
    DOI:  https://doi.org/10.1128/jvi.00506-23
  10. J Biol Chem. 2023 May 15. pii: S0021-9258(23)01855-0. [Epub ahead of print] 104827
    IL4i1 and IDO1: oxidases that control a tryptophan metabolic nexus in cancer.
    Leonie Zeitler, Peter J Murray.
      Regulated tryptophan metabolism by immune cells has been associated with the promotion of tolerance and poor outcomes in cancer. The main focus of research has centered on local tryptophan depletion by IDO1, an intracellular heme-dependent oxidase that converts tryptophan to formyl-kynurenine. This is the first step of a complex pathway supplying metabolites for de novo NAD+ biosynthesis, 1-carbon metabolism and a myriad of kynurenine derivatives, of which several act as agonists of the arylhydrocarbon receptor (AhR). Thus, cells that express IDO1 deplete tryptophan while generating downstream metabolites. We now know that another enzyme, the secreted L-amino acid oxidase IL4i1, also generates bioactive metabolites from tryptophan. In tumor microenvironments, IL4i1 and IDO1 have overlapping expression patterns, especially in myeloid cells, suggesting the two enzymes control a network of tryptophan-specific metabolic events. New findings about IL4i1 and IDO1 have shown that both enzymes generate a suite of metabolites that suppress the oxidative cell death ferroptosis. Thus, within inflammatory environments, IL4i1 and IDO1 simultaneously control essential amino acid depletion, AhR activation, suppression of ferroptosis and biosynthesis of key metabolic intermediates. Here, we summarize the recent advances in this field, focusing on IDO1 and IL4i1 in cancer. We speculate that while inhibition of IDO1 remains a viable adjuvant therapy for solid tumors, the overlapping effects of IL4i1 must be accounted for, as potentially both enzymes may need to be inhibited at the same time to produce positive effects in cancer therapy.
    DOI:  https://doi.org/10.1016/j.jbc.2023.104827
  11. Cancers (Basel). 2023 Apr 20. pii: 2385. [Epub ahead of print]15(8):
    The Ubiquitin-Proteasome System in Tumor Metabolism.
    Jie Wang, Yuandi Xiang, Mengqi Fan, Shizhen Fang, Qingquan Hua.
      Metabolic reprogramming, which is considered a hallmark of cancer, can maintain the homeostasis of the tumor environment and promote the proliferation, survival, and metastasis of cancer cells. For instance, increased glucose uptake and high glucose consumption, known as the "Warburg effect," play an essential part in tumor metabolic reprogramming. In addition, fatty acids are harnessed to satisfy the increased requirement for the phospholipid components of biological membranes and energy. Moreover, the anabolism/catabolism of amino acids, such as glutamine, cystine, and serine, provides nitrogen donors for biosynthesis processes, development of the tumor inflammatory environment, and signal transduction. The ubiquitin-proteasome system (UPS) has been widely reported to be involved in various cellular biological activities. A potential role of UPS in the metabolic regulation of tumor cells has also been reported, but the specific regulatory mechanism has not been elucidated. Here, we review the role of ubiquitination and deubiquitination modification on major metabolic enzymes and important signaling pathways in tumor metabolism to inspire new strategies for the clinical treatment of cancer.
    Keywords:  metabolism; signaling pathway; treatment; tumor; ubiquitin–proteasome system modification
    DOI:  https://doi.org/10.3390/cancers15082385
  12. Anal Chem. 2023 May 17.
    TopFD: A Proteoform Feature Detection Tool for Top-Down Proteomics.
    Abdul Rehman Basharat, Yong Zang, Liangliang Sun, Xiaowen Liu.
      Top-down liquid chromatography-mass spectrometry (LC-MS) analyzes intact proteoforms and generates mass spectra containing peaks of proteoforms with various isotopic compositions, charge states, and retention times. An essential step in top-down MS data analysis is proteoform feature detection, which aims to group these peaks into peak sets (features), each containing all peaks of a proteoform. Accurate protein feature detection enhances the accuracy in MS-based proteoform identification and quantification. Here, we present TopFD, a software tool for top-down MS feature detection that integrates algorithms for proteoform feature detection, feature boundary refinement, and machine learning models for proteoform feature evaluation. We performed extensive benchmarking of TopFD, ProMex, FlashDeconv, and Xtract using seven top-down MS data sets and demonstrated that TopFD outperforms other tools in feature accuracy, reproducibility, and feature abundance reproducibility.
    DOI:  https://doi.org/10.1021/acs.analchem.2c05244
  13. Curr Opin Chem Biol. 2023 May 17. pii: S1367-5931(23)00062-5. [Epub ahead of print]75 102324
    Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism.
    Bartosz Jan Bartmanski, Miguel Rocha, Maria Zimmermann-Kogadeeva.
      With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.
    Keywords:  Deep neural networks; Genome-scale models; Machine learning; Metabolic networks; Metabolomics; Microbiota; Multi-omics integration
    DOI:  https://doi.org/10.1016/j.cbpa.2023.102324
  14. Cell Commun Signal. 2023 May 19. 21(1): 116
    Small extracellular vesicle-mediated metabolic reprogramming: from tumors to pre-metastatic niche formation.
    Chuwen Jiang, Zhengting Jiang, Gengyu Sha, Daorong Wang, Dong Tang.
      Metastasis, the spread of a tumor or cancer from the primary site of the body to a secondary site, is a multi-step process in cancer progression, accounting for various obstacles in cancer treatment and most cancer-related deaths. Metabolic reprogramming refers to adaptive metabolic changes that occur in cancer cells in the tumor microenvironment (TME) to enhance their survival ability and metastatic potential. Stromal cell metabolism also changes to stimulate tumor proliferation and metastasis. Metabolic adaptations of tumor and non-tumor cells exist not only in the TME but also in the pre-metastatic niche (PMN), a remote TME conducive for tumor metastasis. As a novel mediator in cell-to-cell communication, small extracellular vesicles (sEVs), which have a diameter of 30-150 nm, reprogram metabolism in stromal and cancer cells within the TME by transferring bioactive substances including proteins, mRNAs and miRNAs (microRNAs). sEVs can be delivered from the primary TME to PMN, affecting PMN formation in stroma rewriting, angiogenesis, immunological suppression and matrix cell metabolism by mediating metabolic reprogramming. Herein, we review the functions of sEVs in cancer cells and the TME, how sEVs facilitate PMN establishment to trigger metastasis via metabolic reprogramming, and the prospective applications of sEVs in tumor diagnosis and treatment. Video Abstract.
    Keywords:  Metabolic reprogramming; Metastasis; PMN; Tumor microenvironment; sEVs
    DOI:  https://doi.org/10.1186/s12964-023-01136-x
  15. Molecules. 2023 Apr 24. pii: 3675. [Epub ahead of print]28(9):
    Phosphoproteomic Approaches for Identifying Phosphatase and Kinase Substrates.
    Andrew G DeMarco, Mark C Hall.
      Protein phosphorylation is a ubiquitous post-translational modification controlled by the opposing activities of protein kinases and phosphatases, which regulate diverse biological processes in all kingdoms of life. One of the key challenges to a complete understanding of phosphoregulatory networks is the unambiguous identification of kinase and phosphatase substrates. Liquid chromatography-coupled mass spectrometry (LC-MS/MS) and associated phosphoproteomic tools enable global surveys of phosphoproteome changes in response to signaling events or perturbation of phosphoregulatory network components. Despite the power of LC-MS/MS, it is still challenging to directly link kinases and phosphatases to specific substrate phosphorylation sites in many experiments. Here, we survey common LC-MS/MS-based phosphoproteomic workflows for identifying protein kinase and phosphatase substrates, noting key advantages and limitations of each. We conclude by discussing the value of inducible degradation technologies coupled with phosphoproteomics as a new approach that overcomes some limitations of current methods for substrate identification of kinases, phosphatases, and other regulatory enzymes.
    Keywords:  PTM; kinase; mass spectrometry; phosphatase; phosphoproteomics; phosphorylation; post-translation modification; quantitative proteomics; substrate identification
    DOI:  https://doi.org/10.3390/molecules28093675
  16. Molecules. 2023 Apr 22. pii: 3653. [Epub ahead of print]28(9):
    Untargeted Lipidomics Method for the Discrimination of Five Crab Species by Ultra-High-Performance Liquid Chromatography High-Resolution Mass Spectrometry Combined with Chemometrics.
    Jiaxu Yao, Jinrui Zhu, Minjie Zhao, Li Zhou, Eric Marchioni.
      In this study, ultra-high-performance liquid chromatography high-resolution accurate mass-mass spectrometry (UHPLC-HRAM/MS) was applied to characterize the lipid profiles of five crab species. A total of 203 lipid molecular species in muscle tissue and 176 in edible viscera were quantified. The results indicate that Cancer pagurus contained high levels of lipids with a docosahexaenoic acid (DHA) and eicosapntemacnioc acid (EPA) structure in the muscle tissue and edible viscera. A partial least squares discriminant analysis (PLS-DA) showed that PE 16:0/22:6, PE P-18:0/20:5, PA 16:0/22:6 and PC 16:0/16:1 could be used as potential biomarkers to discriminate the five kinds of crabs. In addition, some lipids, such as PE 18:0/20:5, PC 16:0/16:1, PE P-18:0/22:6 and SM 12:1;2O/20:0, could be used as characteristic molecules to distinguish between Cancer magister and Cancer pagurus, which are similar in appearance. This study provides a new perspective on discriminating crab species from MS-based lipidomics.
    Keywords:  identification; lipid; mass spectrum; molecular species; quantification; seafood
    DOI:  https://doi.org/10.3390/molecules28093653
  17. Res Sq. 2023 May 03. pii: rs.3.rs-2799430. [Epub ahead of print]
    Microfluidic Devices for Precise Measurements of Cell Directionality Reveal a Role for Glutamine during Cell Migration.
    Nil Gural, Daniel Irimia.
      Cancer cells that migrate from tumors into surrounding tissues are responsible for cancer dissemination through the body. Microfluidic devices have been instrumental in discovering unexpected features of cancer cell migration, including the migration in self-generated gradients and the contributions of cell-cell contact during collective migration. Here, we design microfluidic channels with five successive bifurcations to characterize the directionality of cancer cell migration with high precision. We find that the directional decisions of cancer cells moving through bifurcating channels in response to self-generated epidermal growth factor (EGF) gradients require the presence of glutamine in the culture media. A biophysical model helps quantify the contribution of glucose and glutamine to cancer cell orientation during migration in self-generated gradients. Our study uncovers an unexpected interplay between cancer cell metabolism and cancer cell migration studies and may eventually lead to new ways to delay cancer cell invasion.
    DOI:  https://doi.org/10.21203/rs.3.rs-2799430/v1
  18. Nat Commun. 2023 May 19. 14(1): 2876
    Using mass spectrometry imaging to map fluxes quantitatively in the tumor ecosystem.
    Michaela Schwaiger-Haber, Ethan Stancliffe, Dhanalakshmi S Anbukumar, Blake Sells, Jia Yi, Kevin Cho, Kayla Adkins-Travis, Milan G Chheda, Leah P Shriver, Gary J Patti.
      Tumors are comprised of a multitude of cell types spanning different microenvironments. Mass spectrometry imaging (MSI) has the potential to identify metabolic patterns within the tumor ecosystem and surrounding tissues, but conventional workflows have not yet fully integrated the breadth of experimental techniques in metabolomics. Here, we combine MSI, stable isotope labeling, and a spatial variant of Isotopologue Spectral Analysis to map distributions of metabolite abundances, nutrient contributions, and metabolic turnover fluxes across the brains of mice harboring GL261 glioma, a widely used model for glioblastoma. When integrated with MSI, the combination of ion mobility, desorption electrospray ionization, and matrix assisted laser desorption ionization reveals alterations in multiple anabolic pathways. De novo fatty acid synthesis flux is increased by approximately 3-fold in glioma relative to surrounding healthy tissue. Fatty acid elongation flux is elevated even higher at 8-fold relative to surrounding healthy tissue and highlights the importance of elongase activity in glioma.
    DOI:  https://doi.org/10.1038/s41467-023-38403-x
  19. STAR Protoc. 2023 May 12. pii: S2666-1667(23)00260-5. [Epub ahead of print]4(2): 102293
    Network-centric analysis of co-fractionated protein complex profiles using SECAT.
    Benjamin J Bokor, Darvesh Gorhe, Marko Jovanovic, George Rosenberger.
      The Size-Exclusion Chromatography Analysis Toolkit (SECAT) elucidates protein complex dynamics using co-fractionated bottom-up mass spectrometry (CF-MS) data. Here, we present a protocol for the network-centric analysis and interpretation of CF-MS profiles using SECAT. We describe the technical steps for preprocessing, scoring, semi-supervised machine learning, and quantification, including common pitfalls and their solutions. We further provide guidance for data export, visualization, and the interpretation of SECAT results to discover dysregulated proteins and interactions, supporting new hypotheses and biological insights. For complete details on the use and execution of this protocol, please refer to Rosenberger et al. (2020).1.
    Keywords:  Bioinformatics; Mass Spectrometry; Proteomics
    DOI:  https://doi.org/10.1016/j.xpro.2023.102293
  20. Nature. 2023 May 17.
    Uridine-derived ribose fuels glucose-restricted pancreatic cancer.
    Zeribe C Nwosu, Matthew H Ward, Peter Sajjakulnukit, Pawan Poudel, Chanthirika Ragulan, Steven Kasperek, Megan Radyk, Damien Sutton, Rosa E Menjivar, Anthony Andren, Juan J Apiz-Saab, Zachary Tolstyka, Kristee Brown, Ho-Joon Lee, Lindsey N Dzierozynski, Xi He, Hari Ps, Julia Ugras, Gift Nyamundanda, Li Zhang, Christopher J Halbrook, Eileen S Carpenter, Jiaqi Shi, Leah P Shriver, Gary J Patti, Alexander Muir, Marina Pasca di Magliano, Anguraj Sadanandam, Costas A Lyssiotis.
      Pancreatic ductal adenocarcinoma (PDA) is a lethal disease notoriously resistant to therapy1,2. This is mediated in part by a complex tumour microenvironment3, low vascularity4, and metabolic aberrations5,6. Although altered metabolism drives tumour progression, the spectrum of metabolites used as nutrients by PDA remains largely unknown. Here we identified uridine as a fuel for PDA in glucose-deprived conditions by assessing how more than 175 metabolites impacted metabolic activity in 21 pancreatic cell lines under nutrient restriction. Uridine utilization strongly correlated with the expression of uridine phosphorylase 1 (UPP1), which we demonstrate liberates uridine-derived ribose to fuel central carbon metabolism and thereby support redox balance, survival and proliferation in glucose-restricted PDA cells. In PDA, UPP1 is regulated by KRAS-MAPK signalling and is augmented by nutrient restriction. Consistently, tumours expressed high UPP1 compared with non-tumoural tissues, and UPP1 expression correlated with poor survival in cohorts of patients with PDA. Uridine is available in the tumour microenvironment, and we demonstrated that uridine-derived ribose is actively catabolized in tumours. Finally, UPP1 deletion restricted the ability of PDA cells to use uridine and blunted tumour growth in immunocompetent mouse models. Our data identify uridine utilization as an important compensatory metabolic process in nutrient-deprived PDA cells, suggesting a novel metabolic axis for PDA therapy.
    DOI:  https://doi.org/10.1038/s41586-023-06073-w
  21. Talanta. 2023 May 09. pii: S0039-9140(23)00404-6. [Epub ahead of print]260 124653
    Comprehensive analysis of alkenones by reversed-phase HPLC-MS with unprecedented selectivity, linearity and sensitivity.
    Sian Liao, Xiao-Lei Liu, Katherine E Manz, Kurt D Pennell, Joseph Novak, Ewerton Santos, Yongsong Huang.
      Alkenones are among the most widely used paleotemperature biomarkers. Traditionally, alkenones are analyzed using gas chromatography-flame ionization detector (GC-FID), or GC-chemical ionization-mass spectrometry (GC-CI-MS). However, these methods encounter considerable challenges for samples that exhibit matrix interference or low concentrations, with GC-FID requiring tedious sample preparations and GC-CI-MS suffering from nonlinear response and a narrow linear dynamic range. Here we demonstrate that reversed-phase high pressure liquid chromatography-mass spectrometry (HPLC-MS) methods provide excellent resolution, selectivity, linearity and sensitivity for alkenones in complex matrices. We systematically compared the advantages and limitations of three mass detectors (quadrupole, Orbitrap, and quadrupole-time of flight) and two ionization modes (electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI)) for alkenone analyses. We demonstrate that ESI performs better than APCI as response factors of various unsaturated alkenones are similar. Among the three mass analyzers tested, orbitrap MS provided the lowest limit of detection (0.4, 3.8 and 8.6 pg injected masses for Orbitrap, qTOF and single quadrupole MS, respectively) and the widest linear dynamic range (600, 20 and 30 folds for Orbitrap, qTOF and single quadrupole MS, respectively). Single quadrupole MS operated in ESI mode provides accurate quantification of proxy measurements over a wide range of injection masses, and with its modest instrument cost, represents an ideal method for routine applications. Analysis of global core-top sediment samples confirmed the efficacy of HPLC-MS methods for the detection and quantification of paleotemperature proxies based on alkenones and their superiority over GC-based methods. The analytical method demonstrated in this study should also allow highly sensitive analyses of diverse aliphatic ketones in complex matrices.
    Keywords:  APCI; Alkenones; ESI; Orbitrap; Reversed-phase HPLC
    DOI:  https://doi.org/10.1016/j.talanta.2023.124653
  22. Adv Nutr. 2023 May 13. pii: S2161-8313(23)00312-5. [Epub ahead of print]
    Serine metabolic reprogramming in tumorigenesis, tumor immunity and clinical treatment: Serine metabolic reprogramming in cancer.
    Wang Shunxi, Yuan Xiaoxue, Song Guanbin, Yang Li, Jin Junyu, Liu Wanqian.
      Serine has been recently identified as an essential metabolite for oncogenesis, progression, and adaptive immunity. Influenced by many physiological or tumor environmental factors, the metabolic pathways of serine synthesis, uptake, and usage are heterogeneously reprogrammed and frequently amplified in tumor or tumor-associated cells. The hyperactivation of serine metabolism promotes abnormal cellular nucleotide/protein/lipid synthesis, mitochondrial function, and epigenetic modifications, which drive malignant transformation, unlimited proliferation, metastasis, immunosuppression, and drug resistance of tumor cells. Dietary restriction of serine or phosphoglycerate dehydrogenase (PHGDH) depletion mitigates tumor growth and extends the survival of tumor patients. Correspondingly, these findings triggered a boom in the development of novel therapeutic agents targeting serine metabolism. Here, recent discoveries in the underlying mechanism and cellular function of serine metabolic reprogramming are summarized. The vital role of serine metabolism in oncogenesis, tumor stemness, tumor immunity, and therapeutic resistance is outlined. Finally, some potential tumor therapeutic concepts, strategies, and limitations of targeting the serine metabolic pathway are described in detail. Taken together, this review underscores the importance of serine metabolic reprogramming in tumorigenesis and progression and highlights new opportunities for dietary restriction or selective pharmacological intervention.
    Keywords:  Serine metabolism; Therapeutic targets; Tumor immunity; Tumor stem cell; Tumorigenesis
    DOI:  https://doi.org/10.1016/j.advnut.2023.05.007
  23. Anal Methods. 2023 May 15.
    Factorial design applied to LC-ESI-QTOF mass spectrometer parameters for untargeted metabolomics.
    Olívia Brito de Oliveira Moreira, Jéssica Cordeiro Queiroz de Souza, João Marcos Beraldo Candido, Maria Patrícia do Nascimento, Paula Rocha Chellini, Lúcio Marco de Lemos, Marcone Augusto Leal de Oliveira.
      Investigations of untargeted metabolomics are based on high-quality data acquisition usually from multiplatform systems that include high-resolution mass spectrometry equipment. The comprehensive set of results is used as data entry for bioinformatics and machine learning sciences to access reliable metabolic and biochemical information for clinical, forensic, environmental, and endless applications. In this context, design of experiments is a powerful tool for optimizing data acquisition procedures, using a multivariate approach, which enables the maximization of a high-quality amount of information with reduced number of tests. In this study, we applied a 33 Box-Behnken factorial design with central point triplicate for optimizing the ionization of an HPLC-ESI-QTOF method used for screening urine samples. Nozzle voltage (V), fragmentor voltage (V) and nebulizer pressure (psig) were the factors selected for variation. The response surface methodology was applied in the molecular features extracted at each level, resulting in a statistical model that helps evaluating the synergic interaction between these factors. Together with the qualitative analysis of the resulting total ion chromatograms, we came across a reproducible (6.14% RSD) and highly efficient method for untargeted metabolomics of human urine samples. The proposed method can be useful for applications in several urine-based metabolomics-driven studies, as the factorial design can be applied in the development of any analytical protocol considering different LC-MS setups.
    DOI:  https://doi.org/10.1039/d3ay00094j
  24. Trends Cancer. 2023 May 10. pii: S2405-8033(23)00064-X. [Epub ahead of print]
    Emerging roles of nucleotide metabolism in cancer.
    Diana D Shi, Milan R Savani, Kalil G Abdullah, Samuel K McBrayer.
      Nucleotides are substrates for multiple anabolic pathways, most notably DNA and RNA synthesis. Since nucleotide synthesis inhibitors began to be used for cancer therapy in the 1950s, our understanding of how nucleotides function in tumor cells has evolved, prompting a resurgence of interest in targeting nucleotide metabolism for cancer therapy. In this review, we discuss recent advances that challenge the idea that nucleotides are mere building blocks for the genome and transcriptome and highlight ways that these metabolites support oncogenic signaling, stress resistance, and energy homeostasis in tumor cells. These findings point to a rich network of processes sustained by aberrant nucleotide metabolism in cancer and reveal new therapeutic opportunities.
    Keywords:  metabolism; nucleotides; purines; pyrimidines
    DOI:  https://doi.org/10.1016/j.trecan.2023.04.008
  25. J Cheminform. 2023 May 12. 15(1): 52
    UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis.
    Eftychia E Kontou, Axel Walter, Oliver Alka, Julianus Pfeuffer, Timo Sachsenberg, Omkar S Mohite, Matin Nuhamunada, Oliver Kohlbacher, Tilmann Weber.
      Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for untargeted metabolomics that combines algorithms for data pre-processing, spectral matching, molecular formula and structural predictions, and an integration to the GNPS workflows Feature-Based Molecular Networking and Ion Identity Molecular Networking for downstream analysis. UmetaFlow is implemented as a Snakemake workflow, making it easy to use, scalable, and reproducible. For more interactive computing, visualization, as well as development, the workflow is also implemented in Jupyter notebooks using the Python programming language and a set of Python bindings to the OpenMS algorithms (pyOpenMS). Finally, UmetaFlow is also offered as a web-based Graphical User Interface for parameter optimization and processing of smaller-sized datasets. UmetaFlow was validated with in-house LC-MS/MS datasets of actinomycetes producing known secondary metabolites, as well as commercial standards, and it detected all expected features and accurately annotated 76% of the molecular formulas and 65% of the structures. As a more generic validation, the publicly available MTBLS733 and MTBLS736 datasets were used for benchmarking, and UmetaFlow detected more than 90% of all ground truth features and performed exceptionally well in quantification and discriminating marker selection. We anticipate that UmetaFlow will provide a useful platform for the interpretation of large metabolomics datasets.
    Keywords:  Analysis; High-throughput workflow; Processing; Software; Untargeted metabolomics
    DOI:  https://doi.org/10.1186/s13321-023-00724-w
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