bims-mascan 2022-11-13 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2022–11–13
forty papers selected by
Giovanny Rodriguez Blanco, University of Edinburgh

Using SILAC to Develop Quantitative Data-Independent Acquisition (DIA) Proteomic Methods.
Congruence and Complementarity of Differential Mobility Spectrometry and NMR Spectroscopy for Plasma Lipidomics.
Heavy Methyl SILAC Metabolic Labeling of Human Cell Lines for High-Confidence Identification of R/K-Methylated Peptides by High-Resolution Mass Spectrometry.
Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology.
GBM tumors are heterogeneous in their fatty acid metabolism and modulating fatty acid metabolism sensitizes cancer cells derived from recurring GBM tumors to temozolomide.
Advancements in Pulsed Stable Isotope-Resolved Metabolomics.
Metabolic Pathways, Enzymes, and Metabolites: Opportunities in Cancer Therapy.
Alignment and Analysis of a Disparately Acquired Multibatch Metabolomics Study of Maternal Pregnancy Samples.
Isotope-Assisted Metabolic Flux Analysis: A Powerful Technique to Gain New Insights into the Human Metabolome in Health and Disease.
Recent progress in mass spectrometry for single-cell metabolomics.
Phosphotyrosine Profiling Using SILAC.
A Super-SILAC Approach for Profiling Histone Posttranslational Modifications.
Identification of Protein Interaction Partners in Bacteria Using Affinity Purification and SILAC Quantitative Proteomics.
CD1d-dependent rewiring of lipid metabolism in macrophages regulates innate immune responses.
Metabolomics.
SILAC-IodoTMT for Assessment of the Cellular Proteome and Its Redox Status.
Metabolomic Profiling to Understand Chondrocyte Metabolism.
A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism.
Non-Targeted Serum Lipidomics Analysis and Potential Biomarkers of Laryngeal Cancer Based on UHPLC-QTOF-MS.
Targeted Absolute Protein Quantification Using SILAC Internal Standard and Full-Length Protein Calibrators (TAQSI).
Stable Isotope Labeling by Amino Acids and Bioorthogonal Noncanonical Amino Acid Tagging in Cultured Primary Neurons.
Probabilistic model checking of cancer metabolism.
Target enzymes in serine-glycine-one-carbon metabolic pathway for cancer therapy.
Deep Proteome Profiling with Reduced Carryover Using Superficially Porous Microfabricated nanoLC Columns.
Metabolite activation of tumorigenic signaling pathways in the tumor microenvironment.
Esterified Oxylipins: Do They Matter?
Druggable Metabolic Vulnerabilities Are Exposed and Masked during Progression to Castration Resistant Prostate Cancer.
Genetic mapping of maize metabolites using high-throughput mass profiling.
Isotopic Tracer for Absolute Quantification of Metabolites of the Pentose Phosphate Pathway in Bacteria.
Profiling of Secreted Proteins in Serum-Containing Media Using BONCAT and Pulsed SILAC.
Quantification of Protein Palmitoylation by Cysteine-SILAC.
Chemoproteomic Profiling of Geranyl Pyrophosphate-Binding Proteins.
Protocol for CAROM: A machine learning tool to predict post-translational regulation from metabolic signatures.
Fats, Friends or Foes: Investigating the Role of Short- and Medium-Chain Fatty Acids in Alzheimer's Disease.
Metabolic Plasticity of Cancer Stem Cells in Response to Microenvironmental Cues.
SILAC-Based Proteomic Analysis of Meiosis in the Fission Yeast Schizosaccharomyces pombe.
SILAC-Based Quantitative Phosphoproteomics in Yeast.
Post-Translational Modifications by Lipid Metabolites during the DNA Damage Response and Their Role in Cancer.
Lipid Metabolism and Homeostasis in Patients with Neuroendocrine Neoplasms: From Risk Factor to Potential Therapeutic Target.
Development and validation of a simple and rapid HILIC-MS/MS method for the quantification of low-abundant lysoglycerophospholipids in human plasma.

Methods Mol Biol. 2023 ;2603 245-257

Using SILAC to Develop Quantitative Data-Independent Acquisition (DIA) Proteomic Methods.

Ellen P Casavant, Jason Liang, Sumedh Sankhe, W Rodney Mathews, Veronica G Anania.

  Proteins are integral to biological systems and functions. Identifying and quantifying proteins can therefore offer systems-wide insights into protein-protein interactions, cellular signaling, and biological pathway activity. The use of quantitative proteomics has become a method of choice for identifying and quantifying proteins in complex matrices. Proteomics allows researchers to survey hundreds to thousands of proteins in a less biased manner than classical antibody-based protein capture strategies. Typically, discovery approaches have used data-dependent acquisition (DDA) methods, but this approach suffers from stochasticity that compromises quantitation. Recent developments in data-independent acquisition (DIA) proteomics workflows enable proteomic profiling of thousands of samples with increased peak picking consistency making it an excellent candidate for discovering and assessing biomarkers in clinical samples. However, quantitation of peptides from DIA datasets is computationally intensive, and guidelines on how to establish DIA methods are lacking. Method development and optimization require novel tools to visualize and filter DIA datasets appropriately. Here, a protocol and novel script workflow for the optimization of quantitative DIA methods using stable isotope labeling of amino acids in culture (SILAC) are presented. This protocol includes steps for cell growth and labeling, peptide digestion and preparation, and optimization of quantitative DIA methods. In addition, important steps for (1) computational analysis to identify and quantify peptides, (2) data visualizations to identify the linear abundance ranges for all peptides in the sample, and (3) descriptions of how to find high confidence quantitation abundance thresholds are described herein.

Keywords:  Computational analysis; Data-independent acquisition; Proteins quantitation; SILAC

DOI:  https://doi.org/10.1007/978-1-0716-2863-8_20
Metabolites. 2022 Oct 27. pii: 1030. [Epub ahead of print]12(11):

Congruence and Complementarity of Differential Mobility Spectrometry and NMR Spectroscopy for Plasma Lipidomics.

Mohan Ghorasaini, Konstantina Ismini Tsezou, Aswin Verhoeven, Yassene Mohammed, Panayiotis Vlachoyiannopoulos, Emmanuel Mikros, Martin Giera.

  The lipid composition of lipoprotein particles is determinative of their respective formation and function. In turn, the combination and correlation of nuclear magnetic resonance (NMR)-based lipoprotein measurements with mass spectrometry (MS)-based lipidomics is an appealing technological combination for a better understanding of lipid metabolism in health and disease. Here, we developed a combined workflow for subsequent NMR- and MS-based analysis on single sample aliquots of human plasma. We evaluated the quantitative agreement of the two platforms for lipid quantification and benchmarked our combined workflow. We investigated the congruence and complementarity between the platforms in order to facilitate a better understanding of patho-physiological lipoprotein and lipid alterations. We evaluated the correlation and agreement between the platforms. Next, we compared lipid class concentrations between healthy controls and rheumatoid arthritis patient samples to investigate the consensus among the platforms on differentiating the two groups. Finally, we performed correlation analysis between all measured lipoprotein particles and lipid species. We found excellent agreement and correlation (r > 0.8) between the platforms and their respective diagnostic performance. Additionally, we generated correlation maps detailing lipoprotein/lipid interactions and describe disease-relevant correlations.

Keywords:  differential mobility spectrometry; lipid; lipidomics; lipoprotein; mass spectrometry; nuclear magnetic resonance

DOI:  https://doi.org/10.3390/metabo12111030
Methods Mol Biol. 2023 ;2603 173-186

Heavy Methyl SILAC Metabolic Labeling of Human Cell Lines for High-Confidence Identification of R/K-Methylated Peptides by High-Resolution Mass Spectrometry.

Enrico Massignani, Marianna Maniaci, Tiziana Bonaldi.

  Protein methylation is a widespread post-translational modification (PTM) involved in several important biological processes including, but not limited to, RNA splicing, signal transduction, translation, and DNA repair. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is considered today the most versatile and accurate technique to profile PTMs with high precision and proteome-wide depth; however, the identification of protein methylations by MS is still prone to high false discovery rates. In this chapter, we describe the heavy methyl SILAC metabolic labeling strategy that allows high-confidence identification of in vivo methyl-peptides by MS-based proteomics. We provide a general protocol that covers the steps of heavy methyl labeling of cultured cells, protein sample preparation, LC-MS/MS analysis, and downstream computational analysis of the acquired MS data.

Keywords:  FDR; Heavy methyl SILAC; Liquid chromatography-tandem mass spectrometry (LC-MS/MS); Metabolic labeling; Protein methylation; Protein methyltransferases; Proteomics

DOI:  https://doi.org/10.1007/978-1-0716-2863-8_14
Proteomics. 2022 Nov 09. e2200013

Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology.

Christoph B Messner, Vadim Demichev, Ziyue Wang, Johannes Hartl, Georg Kustatscher, Michael Mülleder, Markus Ralser.

  There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. First, many conditions need to be considered to produce generalisable results. For example, biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput proteomics can be applied to capture biological diversity, annotate gene functions or to generate predictive and prognostic models for human disease. This article is protected by copyright. All rights reserved.

Keywords:  Dynamic biological systems; biomarker discovery; data-independent acquisition; gene annotation; precision medicine; proteomics

DOI:  https://doi.org/10.1002/pmic.202200013
Front Oncol. 2022 ;12 988872

GBM tumors are heterogeneous in their fatty acid metabolism and modulating fatty acid metabolism sensitizes cancer cells derived from recurring GBM tumors to temozolomide.

Sweta Parik, Juan Fernández-García, Francesca Lodi, Karen De Vlaminck, Marleen Derweduwe, Steven De Vleeschouwer, Raf Sciot, Wietse Geens, Linqian Weng, Francesca Maria Bosisio, Gabriele Bergers, Johnny Duerinck, Frederick De Smet, Diether Lambrechts, Jo A Van Ginderachter, Sarah-Maria Fendt.

  Glioblastoma is a highly lethal grade of astrocytoma with very low median survival. Despite extensive efforts, there is still a lack of alternatives that might improve these prospects. We uncovered that the chemotherapeutic agent temozolomide impinges on fatty acid synthesis and desaturation in newly diagnosed glioblastoma. This response is, however, blunted in recurring glioblastoma from the same patient. Further, we describe that disrupting cellular fatty acid homeostasis in favor of accumulation of saturated fatty acids such as palmitate synergizes with temozolomide treatment. Pharmacological inhibition of SCD and/or FADS2 allows palmitate accumulation and thus greatly augments temozolomide efficacy. This effect was independent of common GBM prognostic factors and was effective against cancer cells from recurring glioblastoma. In summary, we provide evidence that intracellular accumulation of saturated fatty acids in conjunction with temozolomide based chemotherapy induces death in glioblastoma cells derived from patients.

Keywords:  FADS2; SCD1; fatty acid metabolism; glioblastoma; lipotoxicity; tumor heterogeneity

DOI:  https://doi.org/10.3389/fonc.2022.988872
Handb Exp Pharmacol. 2022 Nov 11.

Advancements in Pulsed Stable Isotope-Resolved Metabolomics.

Martin Forbes, Sabrina Geisberger, Matthias Pietzke, Guido Mastrobuoni, Stefan Kempa.

  The understanding of biochemical processes of metabolism is gained through the measurement of the concentration of intermediates and the rate of metabolite conversion. However, the measurement of metabolite concentrations does not give a full representation of this dynamic system. To understand the kinetics of metabolism, the system must be described and quantified in terms of metabolite flow as a function of time. In order to measure the metabolite flow, or more precisely the metabolic flux through a biological system, substrates of the cell are labelled with stable isotopes. The usage of these substrates by the cell leads to the incorporation of the isotopes into downstream intermediates.The most important metabolic pathways are encompassed in the central carbon metabolism (CCM). According to the Kyoto Encyclopedia of Genes and Genomes (KEGG), the central carbon metabolism "is the most basic aspect of life". It includes all metabolites and enzymatic reactions within: glycolysis and gluconeogenesis, pentose phosphate pathway (PPP), tricarboxylic acid (TCA) cycle, oxidative phosphorylation (OXPHOS), amino acids and nucleotide metabolic pathways. Some molecules are at the crossroad of metabolic pathways, interconnecting diverse metabolic and therefore functional outcomes. Labelling these nodal metabolites and analysing their isotopic composition allows the precise determination of the metabolic flow within the biochemical networks that they are in.Application of stable isotope labelled substrates allows the measurement of metabolic flux through a biochemical pathway. The rapid turnover of metabolites in pathways requires pulse-feeding cells with a labelled substrate. This method allows for the determination of different cell states. For example, the action of a drug from immediate impact until the compensatory response of the metabolic system (cell, organs, organisms). Pulsed labelling is an elegant way to analyse the action of small molecules and drugs and enables the analysis of regulatory metabolic processes in short time scales.

Keywords:  Cancer metabolism; Isotope-resolved metabolomics; Mass spectrometry methods; Metabolic flux analysis

DOI:  https://doi.org/10.1007/164_2022_621
Cancers (Basel). 2022 Oct 27. pii: 5268. [Epub ahead of print]14(21):

Metabolic Pathways, Enzymes, and Metabolites: Opportunities in Cancer Therapy.

Rishabh Kumar, Anurag Mishra, Priyanka Gautam, Zainab Feroz, Sivakumar Vijayaraghavalu, Eviania M Likos, Girish C Shukla, Munish Kumar.

  Metabolic reprogramming enables cancer cells to proliferate and produce tumor biomass under a nutrient-deficient microenvironment and the stress of metabolic waste. A cancer cell adeptly undergoes a variety of adaptations in metabolic pathways and differential expression of metabolic enzyme genes. Metabolic adaptation is mainly determined by the physiological demands of the cancer cell of origin and the host tissue. Numerous metabolic regulators that assist cancer cell proliferation include uncontrolled anabolism/catabolism of glucose metabolism, fatty acids, amino acids metabolism, nucleotide metabolism, tumor suppressor genes, microRNAs, and many regulatory enzymes and genes. Using this paradigm, we review the current understanding of metabolic reprogramming in tumors and discuss the new strategies of cancer metabolomics that can be tapped into for cancer therapeutics.

Keywords:  Warburg effect; amino acid metabolism; cancer metabolism; cancer therapeutics; fatty acid metabolism; glycolysis; microRNA; oncogenes; tumor suppressor genes

DOI:  https://doi.org/10.3390/cancers14215268
J Proteome Res. 2022 Nov 11.

Alignment and Analysis of a Disparately Acquired Multibatch Metabolomics Study of Maternal Pregnancy Samples.

Hani Habra, Maureen Kachman, Vasantha Padmanabhan, Charles Burant, Alla Karnovsky, Jennifer Meijer.

  Untargeted liquid chromatography-mass spectrometry metabolomics studies are typically performed under roughly identical experimental settings. Measurements acquired with different LC-MS protocols or following extended time intervals harbor significant variation in retention times and spectral abundances due to altered chromatographic, spectrometric, and other factors, raising many data analysis challenges. We developed a computational workflow for merging and harmonizing metabolomics data acquired under disparate LC-MS conditions. Plasma metabolite profiles were collected from two sets of maternal subjects three years apart using distinct instruments and LC-MS procedures. Metabolomics features were aligned using metabCombiner to generate lists of compounds detected across all experimental batches. We applied data set-specific normalization methods to remove interbatch and interexperimental variation in spectral intensities, enabling statistical analysis on the assembled data matrix. Bioinformatics analyses revealed large-scale metabolic changes in maternal plasma between the first and third trimesters of pregnancy and between maternal plasma and umbilical cord blood. We observed increases in steroid hormones and free fatty acids from the first trimester to term of gestation, along with decreases in amino acids coupled to increased levels in cord blood. This work demonstrates the viability of integrating nonidentically acquired LC-MS metabolomics data and its utility in unconventional metabolomics study designs.

Keywords:  LC-HRMS; alignment; metabolomics; normalization; partial correlation; plasma; pregnancy

DOI:  https://doi.org/10.1021/acs.jproteome.2c00371
Metabolites. 2022 Nov 04. pii: 1066. [Epub ahead of print]12(11):

Isotope-Assisted Metabolic Flux Analysis: A Powerful Technique to Gain New Insights into the Human Metabolome in Health and Disease.

Bilal Moiz, Andrew Li, Surya Padmanabhan, Ganesh Sriram, Alisa Morss Clyne.

  Cell metabolism represents the coordinated changes in genes, proteins, and metabolites that occur in health and disease. The metabolic fluxome, which includes both intracellular and extracellular metabolic reaction rates (fluxes), therefore provides a powerful, integrated description of cellular phenotype. However, intracellular fluxes cannot be directly measured. Instead, flux quantification requires sophisticated mathematical and computational analysis of data from isotope labeling experiments. In this review, we describe isotope-assisted metabolic flux analysis (iMFA), a rigorous computational approach to fluxome quantification that integrates metabolic network models and experimental data to generate quantitative metabolic flux maps. We highlight practical considerations for implementing iMFA in mammalian models, as well as iMFA applications in in vitro and in vivo studies of physiology and disease. Finally, we identify promising new frontiers in iMFA which may enable us to fully unlock the potential of iMFA in biomedical research.

Keywords:  NMR; fluxomics; isotopic non-stationary metabolic flux analysis; mass spectrometry; metabolic engineering; metabolic flux analysis; metabolomics

DOI:  https://doi.org/10.3390/metabo12111066
Curr Opin Chem Biol. 2022 Nov 05. pii: S1367-5931(22)00111-9. [Epub ahead of print]71 102226

Recent progress in mass spectrometry for single-cell metabolomics.

Xingyu Pan, Huan Yao, Sichun Zhang, Xinrong Zhang.

  Metabolites are the end products of cellular vital activities and can reflect the state of cellular to a certain extent. Rapid change of metabolites and the low abundance of signature metabolites cause difficulties in single-cell detection, which is a great challenge in single-cell metabolomics analysis. Mass spectrometry (MS) is a powerful tool that uniquely suited to detect intracellular small-molecule metabolites and has shown good application in single-cell metabolite analysis. In this mini-review, we describe three types of emerging technologies for MS-based single-cell metabolic analysis in recent years, including nano-ESI-MS based single-cell metabolomics analysis, high-throughput analysis via flow cytometry, and cellular metabolic imaging analysis. These techniques provide a large amount of single-cell metabolic data, allowing the potential of MS in single-cell metabolic analysis is gradually being explored and is of great importance in disease and life science research.

Keywords:  Cytometry; ESI; MADLI; Mass Spectrometry; Nano-ESI; SIMS; Single-cell metabolomics

DOI:  https://doi.org/10.1016/j.cbpa.2022.102226
Methods Mol Biol. 2023 ;2603 117-125

Phosphotyrosine Profiling Using SILAC.

Keshava K Datta, Aditi Chatterjee, Harsha Gowda.

  Tyrosine phosphorylation on proteins is an important posttranslational modification that regulates various processes in cells. Mass spectrometry-based phosphotyrosine profiling can reveal tyrosine kinase signaling activity in cells. Using quantitative proteomics strategies such as stable isotope labeling with amino acids in cell culture (SILAC) allows comparison of tyrosine kinase signaling activity across two to -three different conditions. In this book chapter, we discuss the reagents required and a step-by-step protocol to carry out phosphotyrosine profiling using SILAC.

Keywords:  Kinases; Mass spectrometry; Phosphatases; Phosphopeptides; Phosphorylation; Proteomics; SILAC; Tyrosine

DOI:  https://doi.org/10.1007/978-1-0716-2863-8_9
Methods Mol Biol. 2023 ;2603 87-102

A Super-SILAC Approach for Profiling Histone Posttranslational Modifications.

Roberta Noberini, Elisa Longhi, Tiziana Bonaldi.

  Histone posttranslational modifications (PTMs) play an important role in the regulation of gene expression and have been implicated in a multitude of physiological and pathological processes. During the last decade, mass spectrometry (MS) has emerged as the most accurate and versatile tool to quantitate histone PTMs. Stable-isotope labeling by amino acids in cell culture (SILAC) is an MS-based quantitation strategy involving metabolic labeling of cells, which has been applied to global protein profiling as well as histone PTM analysis. The classical SILAC approach is associated with reduced experimental variability and high quantitation accuracy, but provides limited multiplexing capabilities and can be applied only to actively dividing cells, thus excluding clinical samples. Both limitations are overcome by an evolution of classical SILAC involving the use of a mix of heavy-labeled cell lines as a spike-in standard, known as "super-SILAC". In this chapter, we will provide a detailed description of the optimized protocol used in our laboratory to generate a histone-focused super-SILAC mix and employ it as an internal standard for histone PTM quantitation.

Keywords:  Epigenetics; Histone posttranslational modifications; Mass spectrometry; Super-SILAC

DOI:  https://doi.org/10.1007/978-1-0716-2863-8_7
Methods Mol Biol. 2023 ;2603 31-42

Identification of Protein Interaction Partners in Bacteria Using Affinity Purification and SILAC Quantitative Proteomics.

Monika Kopeckova, Marek Link, Ivona Pavkova.

  Affinity purification, combined with mass spectrometry (AP-MS) is considered a pivotal technique in protein-protein interaction studies enabling systematic detection at near physiological conditions. The addition of a quantitative proteomic method, like SILAC metabolic labeling, allows the elimination of non-specifically bound contaminants which greatly increases the confidence of the identified interaction partners. Compared to eukaryotic cells, the SILAC labeling of bacteria has specificities that must be considered. The protocol presented here describes the labeling of bacterial cultures with stable isotope-labeled amino acids, purification of an affinity-tagged protein, and sample preparation for MS measurement. Finally, we discuss the analysis and interpretation of MS data to identify and select the specific partners interacting with the protein of interest. As an example, this workflow is applied to the discovery of potential interaction partners of glyceraldehyde-3-phosphate dehydrogenase in the gram-negative bacterium Francisella tularensis.

Keywords:  Affinity purification; Bacteria; LC-MS/MS; Protein-protein interactions; SILAC

DOI:  https://doi.org/10.1007/978-1-0716-2863-8_3
Nat Commun. 2022 Nov 07. 13(1): 6723

CD1d-dependent rewiring of lipid metabolism in macrophages regulates innate immune responses.

Phillip M Brailey, Lauren Evans, Juan Carlos López-Rodríguez, Anthony Sinadinos, Victoria Tyrrel, Gavin Kelly, Valerie O'Donnell, Peter Ghazal, Susan John, Patricia Barral.

Alterations in cellular metabolism underpin macrophage activation, yet little is known regarding how key immunological molecules regulate metabolic programs in macrophages. Here we uncover a function for the antigen presenting molecule CD1d in the control of lipid metabolism. We show that CD1d-deficient macrophages exhibit a metabolic reprogramming, with a downregulation of lipid metabolic pathways and an increase in exogenous lipid import. This metabolic rewiring primes macrophages for enhanced responses to innate signals, as CD1d-KO cells show higher signalling and cytokine secretion upon Toll-like receptor stimulation. Mechanistically, CD1d modulates lipid import by controlling the internalization of the lipid transporter CD36, while blocking lipid uptake through CD36 restores metabolic and immune responses in macrophages. Thus, our data reveal CD1d as a key regulator of an inflammatory-metabolic circuit in macrophages, independent of its function in the control of T cell responses.

DOI: https://doi.org/10.1038/s41467-022-34532-x
Nat Biotechnol. 2022 Nov;40(11): 1573

Metabolomics.

DOI: https://doi.org/10.1038/s41587-022-01553-2
Methods Mol Biol. 2023 ;2603 259-268

SILAC-IodoTMT for Assessment of the Cellular Proteome and Its Redox Status.

Marie Vajrychova, Barbora Salovska, Kristyna Pimkova, Ivo Fabrik, Zdenek Hodny.

  Stable isotope labeling by amino acids in cell culture (SILAC) and iodoacetyl tandem mass tag (iodoTMT) are well-implemented mass spectrometry-based approaches for quantification of proteins and for site-mapping of cysteine modification. We describe here a combination of SILAC and iodoTMT to assess ongoing changes in the global proteome and cysteine modification levels using liquid chromatography separation coupled with high-resolution mass spectrometry (LC-MS/MS).

Keywords:  Cysteine; Global proteome; IodoTMT; Liquid chromatography; Mass spectrometry; Redox; SILAC

DOI:  https://doi.org/10.1007/978-1-0716-2863-8_21
Methods Mol Biol. 2023 ;2598 141-156

Metabolomic Profiling to Understand Chondrocyte Metabolism.

Priyanka P Brahmachary, Hope D Welhaven, Ronald K June.

  Metabolism has long been recognized as a critical physiological process necessary to maintain homeostasis in all types of cells including the chondrocytes of articular cartilage. Alterations in metabolism in disease and metabolic adaptation to physiological stimuli such as mechanical loading are increasingly recognized as important for understanding musculoskeletal systems such as synovial joints. Metabolomics is an emerging technique that allows quantitative measurement of thousands of small molecule metabolites that serve as both products and reactants to myriad reactions of cellular biochemistry. This protocol describes procedures to perform metabolomic profiling on chondrocytes and other tissues and fluids within the synovial joint.

Keywords:  Cartilage; Metabolite extraction; Metabolomic profiling; Metabolomics; Osteoarthritis; Synovial fluid

DOI:  https://doi.org/10.1007/978-1-0716-2839-3_11
Transl Pediatr. 2022 Oct;11(10): 1704-1716

A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism.

Ashley Hertzog, Arthavan Selvanathan, Beena Devanapalli, Gladys Ho, Kaustuv Bhattacharya, Adviye Ayper Tolun.

   Background and Objective: Traditional targeted metabolomic investigations identify a pre-defined list of analytes in samples and have been widely used for decades in the diagnosis and monitoring of inborn errors of metabolism (IEMs). Recent technological advances have resulted in the development and maturation of untargeted metabolomics: a holistic, unbiased, analytical approach to detecting metabolic disturbances in human disease. We aim to provide a summary of untargeted metabolomics [focusing on tandem mass spectrometry (MS-MS)] and its application in the field of IEMs.
Methods: Data for this review was identified through a literature search using PubMed, Google Scholar, and personal repositories of articles collected by the authors. Findings are presented within several sections describing the metabolome, the current use of targeted metabolomics in the diagnostic pathway of patients with IEMs, the more recent integration of untargeted metabolomics into clinical care, and the limitations of this newly employed analytical technique.
Key Content and Findings: Untargeted metabolomic investigations are increasingly utilized in screening for rare disorders, improving understanding of cellular and subcellular physiology, discovering novel biomarkers, monitoring therapy, and functionally validating genomic variants. Although the untargeted metabolomic approach has some limitations, this "next generation metabolic screening" platform is becoming increasingly affordable and accessible.
Conclusions: When used in conjunction with genomics and the other promising "-omic" technologies, untargeted metabolomics has the potential to revolutionize the diagnostics of IEMs (and other rare disorders), improving both clinical and health economic outcomes.

Keywords:  Inborn errors of metabolism (IEMs); biomarker; diagnosis; untargeted metabolomics; “-omics”

DOI:  https://doi.org/10.21037/tp-22-105
Metabolites. 2022 Nov 09. pii: 1087. [Epub ahead of print]12(11):

Non-Targeted Serum Lipidomics Analysis and Potential Biomarkers of Laryngeal Cancer Based on UHPLC-QTOF-MS.

Haoyue Wang, Yanbo Luo, Huan Chen, Hongwei Hou, Qingyuan Hu, Min Ji.

  Laryngeal cancer is a common head and neck malignant cancer type. However, effective biomarkers for diagnosis are lacking and pathogenesis is unclear. Lipidomics is a powerful tool for identifying biomarkers and explaining disease mechanisms. Hence, in this study, non-targeted lipidomics based on ultra-performance liquid chromatography-quadrupole time of flight-mass spectrometry (UHPLC-QTOF-MS) were applied to screen the differential lipid metabolites in serum and allowed for exploration of the remodeled lipid metabolism of laryngeal cancer, laryngeal benign tumor patients, and healthy crowds. Multivariate analysis and univariate analysis were combined to screen for differential lipid metabolites among the three groups. The results showed that, across a total of 57 lipid metabolic markers that were screened, the regulation of the lipid metabolism network occurred mainly in phosphatidylcholine (PC), lysophosphatidylcholine (LPC), and sphingomyelin (SM) metabolism. Of note, the concentration levels of sphingolipids 42:2 (SM 42:2) and sphingolipids 42:3 (SM 42:3) correlated with laryngeal cancer progression and were both significantly different among the three groups. Both of them could be considered as potential biomarkers for diagnosis and indicators for monitoring the progression of laryngeal cancer. From the perspective of lipidomics, this study not only revealed the regulatory changes in the lipid metabolism network, but also provided a new possibility for screening biomarkers in laryngeal cancer.

Keywords:  biomarkers; laryngeal cancer; lipid metabolism; lipidomics; liquid chromatography–tandem mass spectrometry; serum

DOI:  https://doi.org/10.3390/metabo12111087
Methods Mol Biol. 2023 ;2603 269-283

Targeted Absolute Protein Quantification Using SILAC Internal Standard and Full-Length Protein Calibrators (TAQSI).

Xinwen Wang, Jian Shi, Hao-Jie Zhu.

  Mass spectrometry (MS)-based proteomics has been increasingly used for targeted absolute protein quantifications in both basic and clinical research. There is a great need to overcome some pitfalls of current MS-based targeted absolute protein quantification methods, such as high inter-assay variability and high cost associated with the use of synthesized isotopic peptides/proteins. Here we describe a targeted absolute protein quantification method utilizing SILAC internal standards and unlabeled full-length protein calibrators (TAQSI). The method has proven accurate, precise, reproducible, and cost-effective. Notably, the method is resistant to the variabilities caused by protein extraction and digestion. Moreover, it avoids measurement errors due to nonsynonymous mutations. This versatile method can be used for determining the absolute expressions of numerous proteins in various biological samples. As a proof-of-concept, this method was successfully applied to absolutely quantitate the protein expressions of carboxylesterase 1 (CES1) in human liver tissues.

Keywords:  Absolute protein quantification; Heavy labeled internal standard; Stable isotope labeling by amino acid in cell culture (SILAC); Targeted proteomics

DOI:  https://doi.org/10.1007/978-1-0716-2863-8_22
Methods Mol Biol. 2023 ;2603 163-171

Stable Isotope Labeling by Amino Acids and Bioorthogonal Noncanonical Amino Acid Tagging in Cultured Primary Neurons.

Guoan Zhang, Katrin Deinhardt, Thomas A Neubert.

  Cultured primary neurons are a well-established model for the study of neuronal function. Conventional stable isotope labeling with amino acids in cell culture (SILAC) requires nearly complete metabolic labeling of proteins and therefore is difficult to apply to cultured primary neurons, which do not divide in culture. In a multiplex SILAC strategy, two different sets of heavy amino acids are used for labeling cells for the different experimental conditions. This allows for straightforward SILAC quantitation using partially labeled cells because the two cell populations are always equally labeled. When combined with bioorthogonal noncanonical amino acid tagging (BONCAT), it allows for comparative proteomic analysis of de novo protein synthesis. Here we describe protocols that utilize the multiplex SILAC labeling strategy for primary cultured neurons to study steady-state and nascent proteomes.

Keywords:  BONLAC; Mass spectrometry; Primary neurons; Proteomics; Quantitation; SILAC

DOI:  https://doi.org/10.1007/978-1-0716-2863-8_13
Sci Rep. 2022 Nov 07. 12(1): 18870

Probabilistic model checking of cancer metabolism.

Meir D Friedenberg, Adrian Lita, Mark R Gilbert, Mioara Larion, Orieta Celiku.

Cancer cell metabolism is often deregulated as a result of adaption to meeting energy and biosynthesis demands of rapid growth or direct mutation of key metabolic enzymes. Better understanding of such deregulation can provide new insights on targetable vulnerabilities, but is complicated by the difficulty in probing cell metabolism at different levels of resolution and under different experimental conditions. We construct computational models of glucose and glutamine metabolism with focus on the effect of IDH1/2-mutations in cancer using a combination of experimental metabolic flux data and patient-derived gene expression data. Our models demonstrate the potential of computational exploration to reveal biologic behavior: they show that an exogenously-mutated IDH1 experimental model utilizes glutamine as an alternative carbon source for lactate production under hypoxia, but does not fully-recapitulate the patient phenotype under normoxia. We also demonstrate the utility of using gene expression data as a proxy for relative differences in metabolic activity. We use the approach of probabilistic model checking and the freely-available Probabilistic Symbolic Model Checker to construct and reason about model behavior.

DOI: https://doi.org/10.1038/s41598-022-21846-5
Int J Cancer. 2022 Nov 08.

Target enzymes in serine-glycine-one-carbon metabolic pathway for cancer therapy.

Wei Sun, Erhu Zhao, Hongjuan Cui.

  Cancer cells selectively take up exogenous serine or synthesize serine via the serine synthesis pathway for conversion into intracellular glycine and one-carbon units for nucleotide biosynthesis. In this process, serine-glycine metabolism and the one-carbon cycle play vital roles, which is named serine-glycine-one-carbon metabolism (SGOC). The SGOC pathway is a metabolic network crucial for tumorigenesis with unexpected complexity and clinical importance. Accumulating evidence has demonstrated that metabolic enzymes in SGOC metabolism play key roles in tumorigenesis, metastasis, and resistance to therapies. In this review, we focus on the involvement of serine and glycine in the folate-mediated one-carbon pathway during cancer progression and highlight the pathways through which cancer cells acquire and use one-carbon units. In addition, we discuss the recently elucidated effects of SGOC (folate cycle) metabolic enzymes in the occurrence and development of tumors and their links to drug resistance. Inhibitors of target enzymes in the SGOC pathway display promise as investigational new drug candidates for the treatment of tumors. This article is protected by copyright. All rights reserved.

Keywords:  cancer therapy; drug resistance; inhibitors; serine-glycine-one-carbon metabolism; target enzymes

DOI:  https://doi.org/10.1002/ijc.34353
Anal Chem. 2022 Nov 10.

Deep Proteome Profiling with Reduced Carryover Using Superficially Porous Microfabricated nanoLC Columns.

Karel Stejskal, Op de Beeck Jeff, Manuel Matzinger, Gerhard Dürnberger, Alexander Boychenko, Paul Jacobs, Karl Mechtler.

In the field of liquid chromatography-mass spectrometry (LC-MS)-based proteomics, increases in the sampling depth and proteome coverage have mainly been accomplished by rapid advances in mass spectrometer technology. The comprehensiveness and quality of the data that can be generated do, however, also depend on the performance provided by nano-liquid chromatography (nanoLC) separations. Proper selection of reversed-phase separation columns can be important to provide the MS instrument with peptides at the highest possible concentration and separated at the highest possible resolution. In the current contribution, we evaluate the use of the prototype generation 2 μPAC nanoLC columns, which use C18-functionalized superficially porous micropillars as a stationary phase. When compared to traditionally used fully porous silica stationary phases, more precursors could be characterized when performing single shot data-dependent LC-MS/MS analyses of a human cell line tryptic digest. Up to 30% more protein groups and 60% more unique peptides were identified for short gradients (10 min) and limited sample amounts (10-100 ng of cell lysate digest). With LC-MS gradient times of 10, 60, 120, and 180 min, respectively, we identified 2252, 6513, 7382, and 8174 protein groups with 25, 500, 1000, and 2000 ng of the sample loaded on the column. Reduction of sample carryover to the next run (up to 2 to 3%) and decreased levels of methionine oxidation (up to 3-fold) were identified as additional figures of merit. When analyzing a disuccinimidyl dibutyric urea-crosslinked synthetic library, 29 to 59 more unique crosslinked peptides could be identified at an experimentally validated false discovery rate of 1-2%.

DOI: https://doi.org/10.1021/acs.analchem.2c01196
Sci Signal. 2022 Nov 08. 15(759): eabj4220

Metabolite activation of tumorigenic signaling pathways in the tumor microenvironment.

Vivien Low, Zhongchi Li, John Blenis.

The role of metabolites exchanged in the tumor microenvironment is largely thought of as fuels to drive the increased biosynthetic and bioenergetic demands of growing tumors. However, this view is shifting as metabolites are increasingly shown to function as signaling molecules that directly regulate oncogenic pathways. Combined with our growing understanding of the essential role of stromal cells, this shift has led to increased interest in how the collective and interconnected metabolome of the tumor microenvironment can drive malignant transformation, epithelial-to-mesenchymal transition, drug resistance, immune evasion, and metastasis. In this review, we discuss how metabolite exchange between tumors and various cell types in the tumor microenvironment-such as fibroblasts, adipocytes, and immune cells-can activate signaling pathways that drive cancer progression.

DOI: https://doi.org/10.1126/scisignal.abj4220
Metabolites. 2022 Oct 22. pii: 1007. [Epub ahead of print]12(11):

Esterified Oxylipins: Do They Matter?

Carmen E Annevelink, Rachel E Walker, Gregory C Shearer.

  Oxylipins are oxygenated metabolites of fatty acids that share several similar biochemical characteristics and functions to fatty acids including transport and trafficking. Oxylipins are most commonly measured in the non-esterified form which can be found in plasma, free or bound to albumin. The non-esterified form, however, reflects only one of the possible pools of oxylipins and is by far the least abundant circulating form of oxylipins. Further, this fraction cannot reliably be extrapolated to the other, more abundant, esterified pool. In cells too, esterified oxylipins are the most abundant form, but are seldom measured and their potential roles in signaling are not well established. In this review, we examine the current literature on experimental oxylipin measurements to describe the lack in reporting the esterified oxylipin pool. We outline the metabolic and experimental importance of esterified oxylipins using well established roles of fatty acid trafficking in non-esterified fatty acids and in esterified form as components of circulating lipoproteins. Finally, we use mathematical modeling to simulate how exchange between cellular esterified and unesterified pools would affect intracellular signaling.. The explicit inclusion of esterified oxylipins along with the non-esterified pool has the potential to convey a more complete assessment of the metabolic consequences of oxylipin trafficking.

Keywords:  esterification; lipoprotein; metabolism; oxylipin

DOI:  https://doi.org/10.3390/metabo12111007
Biomolecules. 2022 Oct 28. pii: 1590. [Epub ahead of print]12(11):

Druggable Metabolic Vulnerabilities Are Exposed and Masked during Progression to Castration Resistant Prostate Cancer.

Stephen Y C Choi, Caroline Fidalgo Ribeiro, Yuzhuo Wang, Massimo Loda, Stephen R Plymate, Takuma Uo.

  There is an urgent need for exploring new actionable targets other than androgen receptor to improve outcome from lethal castration-resistant prostate cancer. Tumor metabolism has reemerged as a hallmark of cancer that drives and supports oncogenesis. In this regard, it is important to understand the relationship between distinctive metabolic features, androgen receptor signaling, genetic drivers in prostate cancer, and the tumor microenvironment (symbiotic and competitive metabolic interactions) to identify metabolic vulnerabilities. We explore the links between metabolism and gene regulation, and thus the unique metabolic signatures that define the malignant phenotypes at given stages of prostate tumor progression. We also provide an overview of current metabolism-based pharmacological strategies to be developed or repurposed for metabolism-based therapeutics for castration-resistant prostate cancer.

Keywords:  Warburg’s effect; androgen receptor; cancer metabolism; drug resistance; fatty acids; lactate; prostate cancer

DOI:  https://doi.org/10.3390/biom12111590
Plant Sci. 2022 Nov 08. pii: S0168-9452(22)00355-7. [Epub ahead of print] 111530

Genetic mapping of maize metabolites using high-throughput mass profiling.

Tzitziki González-Rodríguez, Sergio Pérez-Limón, Héctor Peniche-Pavía, Rubén Rellán-Álvarez, Ruairidh J H Sawers, Robert Winkler.

  Plant metabolites are the basis of human nutrition and have biological relevance in ecology. Farmers selected plants with favorable characteristics since prehistoric times and improved the cultivars, but without knowledge of underlying mechanisms. Understanding the genetic basis of metabolite production can facilitate the successful breeding of plants with augmented nutritional value. To identify genetic factors related to the metabolic composition in maize, we generated mass profiles of 198 recombinant inbred lines (RILs) and their parents (B73 and Mo17) using direct-injection electrospray ionization mass spectrometry (DLI-ESI MS). Mass profiling allowed the correct clustering of samples according to genotype. We quantified 71 mass features from grains and 236 mass features from leaf extracts. For the corresponding ions, we identified tissue-specific metabolic 'Quantitative Trait Loci' (mQTLs) distributed across the maize genome. These genetic regions could regulate multiple metabolite biosynthesis pathways. Our findings demonstrate that DLI-ESI MS has sufficient analytical resolution to map mQTLs. These identified genetic loci will be helpful in metabolite-focused maize breeding. Mass profiling is a powerful tool for detecting mQTLs in maize and enables the high-throughput screening of loci responsible for metabolite biosynthesis.

Keywords:  Biosynthesis; Breeding; Direct mass spectrometry; Genetic mapping; High-throughput; Maize; Metabolic quantitative trait loci; Metabolomics

DOI:  https://doi.org/10.1016/j.plantsci.2022.111530
Metabolites. 2022 Nov 09. pii: 1085. [Epub ahead of print]12(11):

Isotopic Tracer for Absolute Quantification of Metabolites of the Pentose Phosphate Pathway in Bacteria.

Khairunnisa Mohd Kamal, Mohd Hafidz Mahamad Maifiah, Yan Zhu, Nusaibah Abdul Rahim, Yumi Zuhanis Has-Yun Hashim, Muhamad Shirwan Abdullah Sani.

  The pentose phosphate pathway (PPP) plays a key role in many metabolic functions, including the generation of NADPH, biosynthesis of nucleotides, and carbon homeostasis. In particular, the intermediates of PPP have been found to be significantly perturbed in bacterial metabolomic studies. Nonetheless, detailed analysis to gain mechanistic information of PPP metabolism remains limited as most studies are unable to report on the absolute levels of the metabolites. Absolute quantification of metabolites is a prerequisite to study the details of fluxes and its regulations. Isotope tracer or labeling studies are conducted in vivo and in vitro and have significantly improved the analysis and understanding of PPP. Due to the laborious procedure and limitations in the in vivo method, an in vitro approach known as Group Specific Internal Standard Technology (GSIST) has been successfully developed to measure the absolute levels of central carbon metabolism, including PPP. The technique adopts derivatization of an experimental sample and a corresponding internal standard with isotope-coded reagents to provide better precision for accurate identification and absolute quantification. In this review, we highlight bacterial studies that employed isotopic tracers as the tagging agents used for the absolute quantification analysis of PPP metabolites.

Keywords:  absolute quantification; bacterial metabolomics; isotope labeling; metabolomic; pentose phosphate pathway (PPP)

DOI:  https://doi.org/10.3390/metabo12111085
Methods Mol Biol. 2023 ;2603 235-243

Profiling of Secreted Proteins in Serum-Containing Media Using BONCAT and Pulsed SILAC.

Jihye Shin, Cheolju Lee.

  Secreted proteins play pivotal roles in signal transduction and cell-to-cell communication. Despite increasing interest in secretome analysis over the past decade, most studies on this topic have utilized serum-free medium (SFM). However, fetal bovine serum (FBS) is the most widely used serum supplement for cell culture, and secretome analysis using serum-containing medium (SCM) is important to identify proteins secreted under realistic conditions and to understand their physiological roles. In this chapter, we describe a simple and robust protocol based on bioorthogonal non-canonical amino acid tagging (BONCAT) and pulsed stable isotope labeling by amino acids in cell culture (pSILAC), for identification and quantitation of the cell secretome in SCM. In this protocol, the secretome of SFM is compared with that of SCM to confirm the effect of FBS. Additionally, for mass spectrometric data processing, we provide parameters that increase true positives and decrease both false positives and false negatives.

Keywords:  BONCAT; Pulsed SILAC; Quantitative analysis; Secreted proteins; Secretome; Serum-containing medium

DOI:  https://doi.org/10.1007/978-1-0716-2863-8_19
Methods Mol Biol. 2023 ;2603 59-69

Quantification of Protein Palmitoylation by Cysteine-SILAC.

Caiyun Fang, Xiaoqin Zhang, Haojie Lu.

  Cysteine-SILAC enables the detection and quantification of protein S-palmitoylation, an important protein posttranslational modification. Here we describe the cell culture, protein extraction, selective enrichment, mass spectrometry, and data analysis for palmitoylated proteins from cell samples by this method.

Keywords:  Cysteine-SILAC; Mass spectrometry; Metabolic labeling; Protein palmitoylation; Selective enrichment

DOI:  https://doi.org/10.1007/978-1-0716-2863-8_5
Methods Mol Biol. 2023 ;2603 127-138

Chemoproteomic Profiling of Geranyl Pyrophosphate-Binding Proteins.

Rong Cai, Yinsheng Wang.

  Chemical proteomics has been widely applied in the identification and quantification of targeted proteins. Here we describe a chemoproteomic method, in combination with stable isotope labeling by amino acids in cell culture (SILAC), for the proteome-wide profiling of geranyl pyrophosphate (GPP)-binding proteins. After labeling using a desthiobiotin-GPP acyl phosphate probe, desthiobiotin-conjugated peptides of GPP-binding proteins could be enriched from the tryptic digestion products of complex protein mixtures and subsequently identified with liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. To exclude nonspecific binding proteins, we applied SILAC, together with competitive labeling experiments, including high vs. low concentrations of GPP probe, GPP vs. ATP probes, and GPP probe labeling with or without the presence of GPP. Several known or candidate GPP-binding proteins were identified with this method, suggesting the potential application of this method in the study of isoprenoid-interacting proteins and biological functions of isoprenoids.

Keywords:  Acyl phosphate probe; Affinity-based protein profiling; GPP probe; Geranyl pyrophosphate-binding proteins; Quantitative proteomic analysis; SILAC

DOI:  https://doi.org/10.1007/978-1-0716-2863-8_10
STAR Protoc. 2022 Dec 16. 3(4): 101799

Protocol for CAROM: A machine learning tool to predict post-translational regulation from metabolic signatures.

Kirk Smith, Nicole Rhoads, Sriram Chandrasekaran.

  This protocol describes CAROM, a computational tool that combines genome-scale metabolic networks (GEMs) and machine learning to identify enzyme targets of post-translational modifications (PTMs). Condition-specific enzyme and reaction properties are used to predict targets of phosphorylation and acetylation in multiple organisms. CAROM is influenced by the accuracy of GEMs and associated flux-balance analysis (FBA), which generate the inputs of the model. We demonstrate the protocol using multi-omics data from E. coli. For complete details on the use and execution of this protocol, please refer to Smith et al. (2022).

Keywords:  Bioinformatics; Computer sciences; Genomics; Metabolism; Metabolomics; Microbiology; Proteomics; Systems biology

DOI:  https://doi.org/10.1016/j.xpro.2022.101799
Biomedicines. 2022 Nov 01. pii: 2778. [Epub ahead of print]10(11):

Fats, Friends or Foes: Investigating the Role of Short- and Medium-Chain Fatty Acids in Alzheimer's Disease.

Aishat O Ameen, Kristine Freude, Blanca I Aldana.

  Characterising Alzheimer's disease (AD) as a metabolic disorder of the brain is gaining acceptance based on the pathophysiological commonalities between AD and major metabolic disorders. Therefore, metabolic interventions have been explored as a strategy for brain energetic rescue. Amongst these, medium-chain fatty acid (MCFA) supplementations have been reported to rescue the energetic failure in brain cells as well as the cognitive decline in patients. Short-chain fatty acids (SCFA) have also been implicated in AD pathology. Due to the increasing therapeutic interest in metabolic interventions and brain energetic rescue in neurodegenerative disorders, in this review, we first summarise the role of SCFAs and MCFAs in AD. We provide a comparison of the main findings regarding these lipid species in established AD animal models and recently developed human cell-based models of this devastating disorder.

Keywords:  butyrate; decanoic acid; energy metabolism; hiPSC; neurodegeneration; octanoic acid

DOI:  https://doi.org/10.3390/biomedicines10112778
Cancers (Basel). 2022 Oct 29. pii: 5345. [Epub ahead of print]14(21):

Metabolic Plasticity of Cancer Stem Cells in Response to Microenvironmental Cues.

Yunong Xie, Stephanie Ma, Man Tong.

  An increasing body of evidence suggests that cancer stem cells (CSCs) utilize reprogrammed metabolic strategies to adapt to a hostile tumor microenvironment (TME) for survival and stemness maintenance. Such a metabolic alteration in CSCs is facilitated by microenvironmental cues including metabolites such as glucose, amino acids and lipids, and environmental properties such as hypoxic and acidic TME. Similarly, metabolites uptake from the diet exerts critical imprints to the metabolism profile of CSCs and directly influence the maintenance of the CSC population. Moreover, CSCs interact with tumor-infiltrating cells inside the CSC niche to promote cancer stemness, ultimately contributing to tumor development and progression. Understanding the underlying mechanisms of how CSCs employ metabolic plasticity in response to different microenvironmental cues represents a therapeutic opportunity for better cancer treatment.

Keywords:  cancer metabolism; cancer stem cells; plasticity; tumor microenvironment

DOI:  https://doi.org/10.3390/cancers14215345
Methods Mol Biol. 2023 ;2603 19-29

SILAC-Based Proteomic Analysis of Meiosis in the Fission Yeast Schizosaccharomyces pombe.

Dorothea Anrather, Silvia Bagelova Polakova, Lubos Cipak, Juraj Gregan.

  Stable isotope labeling by amino acids in cell culture (SILAC) provides a powerful tool to quantify proteins and posttranslational modifications. Here we describe how to apply SILAC for protein identification and quantification in synchronous meiotic cultures induced by inactivation of the Pat1 kinase in the fission yeast Schizosaccharomyces pombe.

Keywords:  Mass Spectrometry; Meiosis; Pat1; Proteome; SILAC; Schizosaccharomyces pombe; Yeast

DOI:  https://doi.org/10.1007/978-1-0716-2863-8_2
Methods Mol Biol. 2023 ;2603 103-115

SILAC-Based Quantitative Phosphoproteomics in Yeast.

María Luisa Hernáez, Concha Gil.

  In this chapter, detailed procedures for stable isotope labeling with amino acids in cell culture, SILAC labeling of yeast auxotroph, optimization and evaluation of phosphopeptide enrichment, and sample preparation and analysis by high-resolution LC-MS/M, identification of phosphosites, and quantification methods are described.We report methods for the application of double SILAC to yeast using a combination of labeled lysine and labeled arginine.The combination of SILAC-based quantitation with phosphopeptides enrichment by TiO2 in a batch that enables measurement of protein posttranslational modifications is a powerful application to analyze the global phosphoproteome for studies in signaling pathways.

Keywords:  In-solution digestion; Phosphopeptides enrichment; Phosphoproteomics; Q-Exactive; Quantitative Proteomics; SILAC; TiO2

DOI:  https://doi.org/10.1007/978-1-0716-2863-8_8
Biomolecules. 2022 Nov 08. pii: 1655. [Epub ahead of print]12(11):

Post-Translational Modifications by Lipid Metabolites during the DNA Damage Response and Their Role in Cancer.

Guangrong Zhu, Xiangyang Zheng, Zhifeng Wang, Xingzhi Xu.

  Genomic DNA damage occurs as an inevitable consequence of exposure to harmful exogenous and endogenous agents. Therefore, the effective sensing and repair of DNA damage are essential for maintaining genomic stability and cellular homeostasis. Inappropriate responses to DNA damage can lead to genomic instability and, ultimately, cancer. Protein post-translational modifications (PTMs) are a key regulator of the DNA damage response (DDR), and recent progress in mass spectrometry analysis methods has revealed that a wide range of metabolites can serve as donors for PTMs. In this review, we will summarize how the DDR is regulated by lipid metabolite-associated PTMs, including acetylation, S-succinylation, N-myristoylation, palmitoylation, and crotonylation, and the implications for tumorigenesis. We will also discuss potential novel targets for anti-cancer drug development.

Keywords:  DNA damage response; N-myristoylation; S-succinylation; acetylation; cancer; crotonylation; lipid metabolites; palmitoylation; post-translational modification

DOI:  https://doi.org/10.3390/biom12111655
Metabolites. 2022 Nov 02. pii: 1057. [Epub ahead of print]12(11):

Lipid Metabolism and Homeostasis in Patients with Neuroendocrine Neoplasms: From Risk Factor to Potential Therapeutic Target.

Roberta Modica, Anna La Salvia, Alessia Liccardi, Giuseppe Cannavale, Roberto Minotta, Elio Benevento, Antongiulio Faggiano, Annamaria Colao.

  Lipid metabolism is known to be involved in tumorigenesis and disease progression in many common cancer types, including colon, lung, breast and prostate, through modifications of lipid synthesis, storage and catabolism. Furthermore, lipid alterations may arise as a consequence of cancer treatment and may have a role in treatment resistance. Neuroendocrine neoplasms (NENs) are a heterogeneous group of malignancies with increasing incidence, whose mechanisms of cancer initiation and progression are far from being fully understood. Alterations of lipid metabolism may be common across various cancer types, but data about NENs are scattered and heterogeneous. Herein, we provide an overview of the relevant literature on lipid metabolism and alterations in NENs. The available evidence both in basic and clinical research about lipid metabolism in NENs, including therapeutic effects on lipid homeostasis, are summarized. Additionally, the potential of targeting the lipid profile in NEN therapy is also discussed, and areas for further research are proposed.

Keywords:  cancer; cancer therapy; cholesterol; lipid metabolism; metabolic syndrome; neuroendocrine neoplasm; neuroendocrine tumor

DOI:  https://doi.org/10.3390/metabo12111057
Anal Bioanal Chem. 2022 Nov 12.

Development and validation of a simple and rapid HILIC-MS/MS method for the quantification of low-abundant lysoglycerophospholipids in human plasma.

Haonan Li, Yiqun Han, Teng Wang, Wu Chen, Yifan Xu, Ke Gao, Yanwen Wang, Jicheng Gong, Weiju Li, Hongyin Zhang, Junxia Wang, Xinghua Qiu, Tong Zhu.

  Lysoglycerophospholipids (Lyso-GPLs) are an essential class of signaling lipids with potential roles in human diseases, such as cancer, central nervous system diseases, and atherosclerosis. Current methods for the quantification of Lyso-GPLs involve complex sample pretreatment, long analysis times, and insufficient validation, which hinder the research of Lyso-GPLs in human studies, especially for Lyso-GPLs with low abundance in human plasma such as lysophosphatidic acid (LPA), lysophosphatidylinositol (LPI), lysophosphatidylglycerol (LPG), lysophosphatidylserine (LysoPS), lyso-platelet-activating factor (LysoPAF), and cyclic phosphatidic acid (cPA). Herein, we report the development and validation of a simple and rapid liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the quantification of Lyso-GPLs with low abundance in plasma. Protein precipitation using MeOH for Lyso-GPL extraction, quick separation (within 18 min) based on hydrophilic interaction liquid chromatography (HILIC), and sensitive MS detection under dynamic multiple reaction monitoring (dMRM) mode enabled efficient quantification of 22 Lyso-GPLs including 2 cPA, 4 LPG, 11 LPA, 2 LysoPS, and 3 LysoPAF in 50 μL of human plasma. The present method showed good linearity (goodness of fit, 0.99823-0.99995), sensitivity (lower limit of quantification, 0.03-14.06 ng/mL), accuracy (73-117%), precision (coefficient of variation ≤ 28%), carryover (≤ 17%), recovery (80-110%), and stability (83-123%). We applied the method in an epidemiological study and report concentrations of 18 Lyso-GPLs in 567 human plasma samples comparable to those of previous studies. Significant negative associations of LysoPAF C18, LysoPAF C18:1, and LysoPAF C16 with homeostatic model assessment for insulin resistance (HOMA-IR) level were observed; this indicates possible roles of LysoPAF in glucose homeostasis. The application of the present method will improve understanding of the roles of circulating low-abundant Lyso-GPLs in health and diseases.

Keywords:  Diabetes; High-throughput; Lipidomics; Lysophospholipids

DOI:  https://doi.org/10.1007/s00216-022-04421-9