bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2021–10–24
23 papers selected by
Giovanny Rodríguez Blanco, University of Edinburgh
  1. Lipid Profiling in Cancer Diagnosis with Hand-Held Ambient Mass Spectrometry Probes: Addressing the Late-Stage Performance Concerns.
  2. Recommendations for Good Practice in Mass Spectrometry-Based Lipidomics.
  3. Low glycaemic diets alter lipid metabolism to influence tumour growth.
  4. Nontargeted Serum Lipid Profiling of Nonalcoholic Steatohepatitis by Multisegment Injection-Nonaqueous Capillary Electrophoresis-Mass Spectrometry: A Multiplexed Separation Platform for Resolving Ionic Lipids.
  5. Progress and Challenges in Quantifying Carbonyl-Metabolomic Phenomes with LC-MS/MS.
  6. Molecular response to PARP1 inhibition in ovarian cancer cells as determined by mass spectrometry based proteomics.
  7. Comparison of Kit-Based Metabolomics with Other Methodologies in a Large Cohort, towards Establishing Reference Values.
  8. Transcriptomics and Metabolomics Integration Reveals Redox-Dependent Metabolic Rewiring in Breast Cancer Cells.
  9. Emerging roles of ferroptosis in infectious diseases.
  10. Metabolism-Associated Epigenetic and Immunoepigenetic Reprogramming in Liver Cancer.
  11. Untargeted plasma metabolomics identifies broad metabolic perturbations in glycogen storage disease type I.
  12. The Intersection of Purine and Mitochondrial Metabolism in Cancer.
  13. Defining NASH from a Multi-Omics Systems Biology Perspective.
  14. Rapid preparation of human blood plasma for bottom-up proteomics analysis.
  15. Lipid Profiling of Alzheimer's Disease Brain Highlights Enrichment in Glycerol(phospho)lipid, and Sphingolipid Metabolism.
  16. Local and Systemic Changes in Lipid Profile as Potential Biomarkers for Canine Atopic Dermatitis.
  17. Longitudinal Large-Scale Semiquantitative Proteomic Data Stability Across Multiple Instrument Platforms.
  18. GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control.
  19. Automated Trimethyl Sulfonium Hydroxide Derivatization Method for High-Throughput Fatty Acid Profiling by Gas Chromatography-Mass Spectrometry.
  20. MSCAT: A Machine Learning Assisted Catalog of Metabolomics Software Tools.
  21. Aqueous metabolome of tissue-specific conditional Pten-knockout mouse prostate cancer and TRAMP neuroendocrine carcinoma.
  22. A Complex Metabolic Network Confers Immunosuppressive Functions to Myeloid-Derived Suppressor Cells (MDSCs) within the Tumour Microenvironment.
  23. Vitamin D deficiency promotes accumulation of bioactive lipids and increased endocannabinoid tone in zebrafish.
  1. Metabolites. 2021 Sep 28. pii: 660. [Epub ahead of print]11(10):
    Lipid Profiling in Cancer Diagnosis with Hand-Held Ambient Mass Spectrometry Probes: Addressing the Late-Stage Performance Concerns.
    Lauren Katz, Alessandra Tata, Michael Woolman, Arash Zarrine-Afsar.
      Untargeted lipid fingerprinting with hand-held ambient mass spectrometry (MS) probes without chromatographic separation has shown promise in the rapid characterization of cancers. As human cancers present significant molecular heterogeneities, careful molecular modeling and data validation strategies are required to minimize late-stage performance variations of these models across a large population. This review utilizes parallels from the pitfalls of conventional protein biomarkers in reaching bedside utility and provides recommendations for robust modeling as well as validation strategies that could enable the next logical steps in large scale assessment of the utility of ambient MS profiling for cancer diagnosis. Six recommendations are provided that range from careful initial determination of clinical added value to moving beyond just statistical associations to validate lipid involvements in disease processes mechanistically. Further guidelines for careful selection of suitable samples to capture expected and unexpected intragroup variance are provided and discussed in the context of demographic heterogeneities in the lipidome, further influenced by lifestyle factors, diet, and potential intersect with cancer lipid pathways probed in ambient mass spectrometry profiling studies.
    Keywords:  ambient mass spectrometry; cancer diagnosis with ambient mass spectrometry; lipid profiling; untargeted lipidomics; untargeted metabolomics
    DOI:  https://doi.org/10.3390/metabo11100660
  2. J Lipid Res. 2021 Oct 15. pii: S0022-2275(21)00120-6. [Epub ahead of print] 100138
    Recommendations for Good Practice in Mass Spectrometry-Based Lipidomics.
    Harald C Köfeler, Robert Ahrends, Erin S Baker, Kim Ekroos, Xianlin Han, Nils Hoffmann, Michal Holčapek, Markus R Wenk, Gerhard Liebisch.
      In the last two decades, lipidomics has become one of the fastest expanding scientific disciplines in biomedical research. With an increasing number of new research groups to the field, it is even more important to design guidelines for assuring high standards of data quality. The Lipidomics Standards Initiative (LSI) is a community-based endeavor for the coordination of development of these best practice guidelines in lipidomics, and is embedded within the International Lipidomics Society (ILS). It is the intention of this review to highlight the most quality-relevant aspects of the lipidomics workflow, including preanalytics, sample preparation, mass spectrometry, and lipid species identification and quantitation. Furthermore, this review does not just highlight examples of best practice, but also sheds light on strengths, drawbacks, and pitfalls in the lipidomic analysis workflow. While this review is not designed to be a step-by-step protocol by itself, nor to be dedicated to a specific application of lipidomics, it should nevertheless provide the interested reader with links and original publications to obtain a comprehensive overview concerning the state of the art practices in the field.
    Keywords:  Chromatography; Ion Mobility Spectrometry; LC-MS; Lipid Identification; Lipidomics; Mass Spectrometry; Metabolomics; Phospholipids; Sphingolipids
    DOI:  https://doi.org/10.1016/j.jlr.2021.100138
  3. Nature. 2021 Oct 20.
    Low glycaemic diets alter lipid metabolism to influence tumour growth.
    Evan C Lien, Anna M Westermark, Yin Zhang, Chen Yuan, Zhaoqi Li, Allison N Lau, Kiera M Sapp, Brian M Wolpin, Matthew G Vander Heiden.
      Dietary interventions can change metabolite levels in the tumour microenvironment, which might then affect cancer cell metabolism to alter tumour growth1-5. Although caloric restriction (CR) and a ketogenic diet (KD) are often thought to limit tumour progression by lowering blood glucose and insulin levels6-8, we found that only CR inhibits the growth of select tumour allografts in mice, suggesting that other mechanisms contribute to tumour growth inhibition. A change in nutrient availability observed with CR, but not with KD, is lower lipid levels in the plasma and tumours. Upregulation of stearoyl-CoA desaturase (SCD), which synthesises monounsaturated fatty acids, is required for cancer cells to proliferate in a lipid-depleted environment, and CR also impairs tumour SCD activity to cause an imbalance between unsaturated and saturated fatty acids to slow tumour growth. Enforcing cancer cell SCD expression or raising circulating lipid levels through a higher-fat CR diet confers resistance to the effects of CR. By contrast, although KD also impairs tumour SCD activity, KD-driven increases in lipid availability maintain the unsaturated to saturated fatty acid ratios in tumours, and changing the KD fat composition to increase tumour saturated fatty acid levels cooperates with decreased tumour SCD activity to slow tumour growth. These data suggest that diet-induced mismatches between tumour fatty acid desaturation activity and the availability of specific fatty acid species determine whether low glycaemic diets impair tumour growth.
    DOI:  https://doi.org/10.1038/s41586-021-04049-2
  4. J Proteome Res. 2021 Oct 22.
    Nontargeted Serum Lipid Profiling of Nonalcoholic Steatohepatitis by Multisegment Injection-Nonaqueous Capillary Electrophoresis-Mass Spectrometry: A Multiplexed Separation Platform for Resolving Ionic Lipids.
    Ritchie Ly, Nicholas Ly, Kazunori Sasaki, Makoto Suzuki, Kenjiro Kami, Yoshiaki Ohashi, Philip Britz-McKibbin.
      New methods are needed for global lipid profiling due to the complex chemical structures and diverse physicochemical properties of lipids. Herein we introduce a robust data workflow to unambiguously select lipid features from serum ether extracts by multisegment injection-nonaqueous capillary electrophoresis-mass spectrometry (MSI-NACE-MS). An iterative three-stage screening strategy is developed for nontargeted lipid analyses when using multiplexed electrophoretic separations coupled to an Orbitrap mass analyzer under negative ion mode. This approach enables the credentialing of 270 serum lipid features annotated based on their accurate mass and relative migration time, including 128 ionic lipids reliably measured (median CV ≈ 13%) in most serum samples (>75%) from nonalcoholic steatohepatitis (NASH) patients (n = 85). A mobility map is introduced to classify charged lipid classes over a wide polarity range with selectivity complementary to chromatographic separations, including lysophosphatidic acids, phosphatidylcholines, phosphatidylinositols, phosphatidylethanolamines, and nonesterified fatty acids (NEFAs). Serum lipidome profiles were also used to differentiate high- from low-risk NASH patients using a k-means clustering algorithm, where elevated circulating NEFAs (e.g., palmitic acid) were associated with increased glucose intolerance, more severe liver fibrosis, and greater disease burden. MSI-NACE-MS greatly expands the metabolome coverage of conventional aqueous-based CE-MS protocols and is a promising platform for large-scale lipidomic studies.
    Keywords:  biomarker discovery; human serum; lipidomics; nonalcoholic steatohepatitis; nonaqueous capillary electrophoresis−mass spectrometry
    DOI:  https://doi.org/10.1021/acs.jproteome.1c00682
  5. Molecules. 2021 Oct 12. pii: 6147. [Epub ahead of print]26(20):
    Progress and Challenges in Quantifying Carbonyl-Metabolomic Phenomes with LC-MS/MS.
    Yuting Sun, Huiru Tang, Yulan Wang.
      Carbonyl-containing metabolites widely exist in biological samples and have important physiological functions. Thus, accurate and sensitive quantitative analysis of carbonyl-containing metabolites is crucial to provide insight into metabolic pathways as well as disease mechanisms. Although reversed phase liquid chromatography electrospray ionization mass spectrometry (RPLC-ESI-MS) is widely used due to the powerful separation capability of RPLC and high specificity and sensitivity of MS, but it is often challenging to directly analyze carbonyl-containing metabolites using RPLC-ESI-MS due to the poor ionization efficiency of neutral carbonyl groups in ESI. Modification of carbonyl-containing metabolites by a chemical derivatization strategy can overcome the obstacle of sensitivity; however, it is insufficient to achieve accurate quantification due to instrument drift and matrix effects. The emergence of stable isotope-coded derivatization (ICD) provides a good solution to the problems encountered above. Thus, LC-MS methods that utilize ICD have been applied in metabolomics including quantitative targeted analysis and untargeted profiling analysis. In addition, ICD makes multiplex or multichannel submetabolome analysis possible, which not only reduces instrument running time but also avoids the variation of MS response. In this review, representative derivatization reagents and typical applications in absolute quantification and submetabolome profiling are discussed to highlight the superiority of the ICD strategy for detection of carbonyl-containing metabolites.
    Keywords:  RPLC-ESI-MS; absolute quantification; carbonyl-containing metabolites; stable isotope-coded derivatization; submetabolome profiling
    DOI:  https://doi.org/10.3390/molecules26206147
  6. J Ovarian Res. 2021 Oct 22. 14(1): 140
    Molecular response to PARP1 inhibition in ovarian cancer cells as determined by mass spectrometry based proteomics.
    Alexandra Franz, Fabian Coscia, Ciyue Shen, Lea Charaoui, Matthias Mann, Chris Sander.
       BACKGROUND: Poly (ADP)-ribose polymerase (PARP) inhibitors have entered routine clinical practice for the treatment of high-grade serous ovarian cancer (HGSOC), yet the molecular mechanisms underlying treatment response to PARP1 inhibition (PARP1i) are not fully understood.
    METHODS: Here, we used unbiased mass spectrometry based proteomics with data-driven protein network analysis to systematically characterize how HGSOC cells respond to PARP1i treatment.
    RESULTS: We found that PARP1i leads to pronounced proteomic changes in a diverse set of cellular processes in HGSOC cancer cells, consistent with transcript changes in an independent perturbation dataset. We interpret decreases in the levels of the pro-proliferative transcription factors SP1 and β-catenin and in growth factor signaling as reflecting the anti-proliferative effect of PARP1i; and the strong activation of pro-survival processes NF-κB signaling and lipid metabolism as PARPi-induced adaptive resistance mechanisms. Based on these observations, we nominate several protein targets for therapeutic inhibition in combination with PARP1i. When tested experimentally, the combination of PARPi with an inhibitor of fatty acid synthase (TVB-2640) has a 3-fold synergistic effect and is therefore of particular pre-clinical interest.
    CONCLUSION: Our study improves the current understanding of PARP1 function, highlights the potential that the anti-tumor efficacy of PARP1i may not only rely on DNA damage repair mechanisms and informs on the rational design of PARP1i combination therapies in ovarian cancer.
    Keywords:  Combination therapy; Data-driven protein module discovery; High-grade serous ovarian cancer; Mass spectrometry based proteomics; Molecular response profiling; Molecular signaling pathways; PARP inhibitor resistance; PARP inhibitors; Pathway analysis
    DOI:  https://doi.org/10.1186/s13048-021-00886-x
  7. Metabolites. 2021 Sep 24. pii: 652. [Epub ahead of print]11(10):
    Comparison of Kit-Based Metabolomics with Other Methodologies in a Large Cohort, towards Establishing Reference Values.
    Daisuke Saigusa, Eiji Hishinuma, Naomi Matsukawa, Masatomo Takahashi, Jin Inoue, Shu Tadaka, Ikuko N Motoike, Atsushi Hozawa, Yoshihiro Izumi, Takeshi Bamba, Kengo Kinoshita, Kim Ekroos, Seizo Koshiba, Masayuki Yamamoto.
      Metabolic profiling is an omics approach that can be used to observe phenotypic changes, making it particularly attractive for biomarker discovery. Although several candidate metabolites biomarkers for disease expression have been identified in recent clinical studies, the reference values of healthy subjects have not been established. In particular, the accuracy of concentrations measured by mass spectrometry (MS) is unclear. Therefore, comprehensive metabolic profiling in large-scale cohorts by MS to create a database with reference ranges is essential for evaluating the quality of the discovered biomarkers. In this study, we tested 8700 plasma samples by commercial kit-based metabolomics and separated them into two groups of 6159 and 2541 analyses based on the different ultra-high-performance tandem mass spectrometry (UHPLC-MS/MS) systems. We evaluated the quality of the quantified values of the detected metabolites from the reference materials in the group of 2541 compared with the quantified values from other platforms, such as nuclear magnetic resonance (NMR), supercritical fluid chromatography tandem mass spectrometry (SFC-MS/MS) and UHPLC-Fourier transform mass spectrometry (FTMS). The values of the amino acids were highly correlated with the NMR results, and lipid species such as phosphatidylcholines and ceramides showed good correlation, while the values of triglycerides and cholesterol esters correlated less to the lipidomics analyses performed using SFC-MS/MS and UHPLC-FTMS. The evaluation of the quantified values by MS-based techniques is essential for metabolic profiling in a large-scale cohort.
    Keywords:  SFC-MS/MS; UHPLC-FT/MS; UHPLC-MS/MS; large-scale cohort; lipidomics; mass spectrometry; metabolic profiling; widely-targeted metabolomics
    DOI:  https://doi.org/10.3390/metabo11100652
  8. Cancers (Basel). 2021 Oct 09. pii: 5058. [Epub ahead of print]13(20):
    Transcriptomics and Metabolomics Integration Reveals Redox-Dependent Metabolic Rewiring in Breast Cancer Cells.
    Marcella Bonanomi, Noemi Salmistraro, Giulia Fiscon, Federica Conte, Paola Paci, Valentina Bravatà, Giusi Irma Forte, Tatiana Volpari, Manuela Scorza, Fabrizia Mastroianni, Stefano D'Errico, Elenio Avolio, Gennaro Piccialli, Anna Maria Colangelo, Marco Vanoni, Daniela Gaglio, Lilia Alberghina.
      Rewiring glucose metabolism toward aerobic glycolysis provides cancer cells with a rapid generation of pyruvate, ATP, and NADH, while pyruvate oxidation to lactate guarantees refueling of oxidized NAD+ to sustain glycolysis. CtPB2, an NADH-dependent transcriptional co-regulator, has been proposed to work as an NADH sensor, linking metabolism to epigenetic transcriptional reprogramming. By integrating metabolomics and transcriptomics in a triple-negative human breast cancer cell line, we show that genetic and pharmacological down-regulation of CtBP2 strongly reduces cell proliferation by modulating the redox balance, nucleotide synthesis, ROS generation, and scavenging. Our data highlight the critical role of NADH in controlling the oncogene-dependent crosstalk between metabolism and the epigenetically mediated transcriptional program that sustains energetic and anabolic demands in cancer cells.
    Keywords:  CtBP2; cancer metabolic rewiring; epigenetics; metabolomics integration; transcriptomics
    DOI:  https://doi.org/10.3390/cancers13205058
  9. FEBS J. 2021 Oct 20.
    Emerging roles of ferroptosis in infectious diseases.
    Salimata Bagayoko, Etienne Meunier.
      In living organisms, lipid peroxidation is a continuously occurring cellular process and therefore involved in various physiological and pathological contexts. Among the broad variety of lipids, polyunsaturated fatty acids (PUFA) constitute a major target of oxygenation either when released as mediators by phospholipases or when present in membranous phospholipids. The last decade has seen the characterization of an iron- and lipid peroxidation-dependent cell necrosis, namely ferroptosis that involves the accumulation of peroxidized PUFA-containing phospholipids. Further studies could link ferroptosis in a very large body of (physio)-pathological processes, including cancer, neurodegenerative and metabolic diseases. In this review, we mostly focus on the emerging involvement of lipid peroxidation-driven ferroptosis in infectious diseases, and the immune consequences. We also discuss the putative ability of microbial virulence factors to exploit or to dampen ferroptosis regulatory pathways to their own benefit.
    Keywords:  Lipid peroxidation; ferroptosis; immunity; infections
    DOI:  https://doi.org/10.1111/febs.16244
  10. Cancers (Basel). 2021 Oct 19. pii: 5250. [Epub ahead of print]13(20):
    Metabolism-Associated Epigenetic and Immunoepigenetic Reprogramming in Liver Cancer.
    Chaofan Fan, Shing Kam, Pierluigi Ramadori.
      Metabolic reprogramming and epigenetic changes have been characterized as hallmarks of liver cancer. Independently of etiology, oncogenic pathways as well as the availability of different energetic substrates critically influence cellular metabolism, and the resulting perturbations often cause aberrant epigenetic alterations, not only in cancer cells but also in the hepatic tumor microenvironment. Metabolic intermediates serve as crucial substrates for various epigenetic modulations, from post-translational modification of histones to DNA methylation. In turn, epigenetic changes can alter the expression of metabolic genes supporting on the one hand, the increased energetic demand of cancer cells and, on the other hand, influence the activity of tumor-associated immune cell populations. In this review, we will illustrate the most recent findings about metabolic reprogramming in liver cancer. We will focus on the metabolic changes characterizing the tumor microenvironment and on how these alterations impact on epigenetic mechanisms involved in the malignant progression. Furthermore, we will report our current knowledge about the influence of cancer-specific metabolites on epigenetic reprogramming of immune cells and we will highlight how this favors a tumor-permissive immune environment. Finally, we will review the current strategies to target metabolic and epigenetic pathways and their therapeutic potential in liver cancer, alone or in combinatorial approaches.
    Keywords:  SAM; Warburg effect; acetyl-CoA; combinatorial therapy; guadecitabine; immunometabolism; liver cancer; metformin; resminostat
    DOI:  https://doi.org/10.3390/cancers13205250
  11. J Inherit Metab Dis. 2021 Oct 21.
    Untargeted plasma metabolomics identifies broad metabolic perturbations in glycogen storage disease type I.
    Tamara Mathis, Martin Poms, Harald Köfeler, Matthias Gautschi, Barbara Plecko, Matthias R Baumgartner, Michel Hochuli.
       BACKGROUND: The metabolic defect in glycogen storage disease type I (GSDI) results in fasting hypoglycemia and typical secondary metabolic abnormalities (e.g. hypertriglyceridemia, hyperlactatemia, hyperuricemia). The aim of this study was to assess further perturbations of the metabolic network in GSDI patients under ongoing treatment.
    METHODS: In this prospective observational study, plasma samples of 14 adult patients (11 GSDIa, 3 GSDIb. Mean age 26.4y, range 16-46y) on standard treatment were compared to a cohort of 31 healthy controls utilizing ultra-high performance liquid chromatography (UHPLC) in combination with high resolution tandem mass spectrometry (HR-MS/MS) and subsequent statistical multivariate analysis. In addition, plasma fatty acid profiling was performed by GC/EI-MS.
    RESULTS: The metabolomic profile showed alterations of metabolites in different areas of the metabolic network in both GSD subtypes, including pathways of fuel metabolism and energy generation, lipids and fatty acids, amino acid and methyl-group metabolism, the urea cycle, and purine/pyrimidine metabolism. These alterations were present despite adequate dietary treatment, did not correlate with plasma triglycerides or lactate, both parameters typically used to assess the quality of metabolic control in clinical practice, and were not related to the presence or absence of complications (i.e. nephropathy or liver adenomas).
    CONCLUSION: The metabolic defect of GSDI has profound effects on a variety of metabolic pathways in addition to the known typical abnormalities. These alterations are present despite optimized dietary treatment, which may contribute to the risk of developing long-term complications, an inherent problem of GSDI which appears to be only partly modified by current therapy. This article is protected by copyright. All rights reserved.
    Keywords:  GSD; complications; glycogen storage disorder; lipids; metabolic control; metabolomics
    DOI:  https://doi.org/10.1002/jimd.12451
  12. Cells. 2021 Sep 30. pii: 2603. [Epub ahead of print]10(10):
    The Intersection of Purine and Mitochondrial Metabolism in Cancer.
    Humberto De Vitto, Danushka B Arachchige, Brian C Richardson, Jarrod B French.
      Nucleotides are essential to cell growth and survival, providing cells with building blocks for DNA and RNA, energy carriers, and cofactors. Mitochondria have a critical role in the production of intracellular ATP and participate in the generation of intermediates necessary for biosynthesis of macromolecules such as purines and pyrimidines. In this review, we highlight the role of purine and mitochondrial metabolism in cancer and how their intersection influences cancer progression, especially in ovarian cancer. Additionally, we address the importance of metabolic rewiring in cancer and how the evolving landscape of purine synthesis and mitochondria inhibitors can be potentially exploited for cancer treatment.
    Keywords:  amino acids; cancers; metabolic reprogramming; mitochondrial metabolism; purines
    DOI:  https://doi.org/10.3390/cells10102603
  13. J Clin Med. 2021 Oct 12. pii: 4673. [Epub ahead of print]10(20):
    Defining NASH from a Multi-Omics Systems Biology Perspective.
    Lili Niu, Karolina Sulek, Catherine G Vasilopoulou, Alberto Santos, Nicolai J Wewer Albrechtsen, Simon Rasmussen, Florian Meier, Matthias Mann.
      Non-alcoholic steatohepatitis (NASH) is a chronic liver disease affecting up to 6.5% of the general population. There is no simple definition of NASH, and the molecular mechanism underlying disease pathogenesis remains elusive. Studies applying single omics technologies have enabled a better understanding of the molecular profiles associated with steatosis and hepatic inflammation-the commonly accepted histologic features for diagnosing NASH, as well as the discovery of novel candidate biomarkers. Multi-omics analysis holds great potential to uncover new insights into disease mechanism through integrating multiple layers of molecular information. Despite the technical and computational challenges associated with such efforts, a few pioneering studies have successfully applied multi-omics technologies to investigate NASH. Here, we review the most recent technological developments in mass spectrometry (MS)-based proteomics, metabolomics, and lipidomics. We summarize multi-omics studies and emerging omics biomarkers in NASH and highlight the biological insights gained through these integrated analyses.
    Keywords:  NAFLD; biomarker discovery; liver disease; machine learning; multi-omics; systems biology
    DOI:  https://doi.org/10.3390/jcm10204673
  14. STAR Protoc. 2021 Dec 17. 2(4): 100856
    Rapid preparation of human blood plasma for bottom-up proteomics analysis.
    Evgenia Shishkova, Joshua J Coon.
      This protocol offers step-by-step instructions for preparation of raw blood plasma for liquid chromatography - tandem mass spectrometry (LC-MS/MS) analysis in clinical proteomics studies. The technique is simple, robust, and reproducible, and the entire transformation from plasma proteins to desalted tryptic peptides takes only 3-4 h. The protocol ensures efficient denaturation of native proteases that, in combination with the speediness of the procedure, prevents non-specific and irreproducible cleavage of digested peptides. The protocol can be adopted for large-scale studies and automation. For complete details on the use and execution of this protocol, please refer to Overmyer et al. (2020).
    Keywords:  High Throughput Screening; Mass Spectrometry; Protein Biochemistry; Proteomics
    DOI:  https://doi.org/10.1016/j.xpro.2021.100856
  15. Cells. 2021 Sep 29. pii: 2591. [Epub ahead of print]10(10):
    Lipid Profiling of Alzheimer's Disease Brain Highlights Enrichment in Glycerol(phospho)lipid, and Sphingolipid Metabolism.
    Sumeyya Akyol, Zafer Ugur, Ali Yilmaz, Ilyas Ustun, Santosh Kapil Kumar Gorti, Kyungjoon Oh, Bernadette McGuinness, Peter Passmore, Patrick G Kehoe, Michael E Maddens, Brian D Green, Stewart F Graham.
      Alzheimer's disease (AD) is reported to be closely linked with abnormal lipid metabolism. To gain a more comprehensive understanding of what causes AD and its subsequent development, we profiled the lipidome of postmortem (PM) human brains (neocortex) of people with a range of AD pathology (Braak 0-6). Using high-resolution mass spectrometry, we employed a semi-targeted, fully quantitative lipidomics profiling method (Lipidyzer) to compare the biochemical profiles of brain tissues from persons with mild AD (n = 15) and severe AD (AD; n = 16), and compared them with age-matched, cognitively normal controls (n = 16). Univariate analysis revealed that the concentrations of 420 lipid metabolites significantly (p < 0.05; q < 0.05) differed between AD and controls. A total of 49 lipid metabolites differed between mild AD and controls, and 439 differed between severe AD and mild AD. Interestingly, 13 different subclasses of lipids were significantly perturbed, including neutral lipids, glycerolipids, glycerophospholipids, and sphingolipids. Diacylglycerol (DAG) (14:0/14:0), triacylglycerol (TAG) (58:10/FA20:5), and TAG (48:4/FA18:3) were the most notably altered lipids when AD and control brains were compared (p < 0.05). When we compare mild AD and control brains, phosphatidylethanolamine (PE) (p-18:0/18:1), phosphatidylserine (PS) (18:1/18:2), and PS (14:0/22:6) differed the most (p < 0.05). PE (p-18:0/18:1), DAG (14:0/14:0), and PS (18:1/20:4) were identified as the most significantly perturbed lipids when AD and mild AD brains were compared (p < 0.05). Our analysis provides the most extensive lipid profiling yet undertaken in AD brain tissue and reveals the cumulative perturbation of several lipid pathways with progressive disease pathology. Lipidomics has considerable potential for studying AD etiology and identifying early diagnostic biomarkers.
    Keywords:  Alzheimer’s disease; brain; lipidomics; metabolomics; pathogenesis
    DOI:  https://doi.org/10.3390/cells10102591
  16. Metabolites. 2021 Sep 30. pii: 670. [Epub ahead of print]11(10):
    Local and Systemic Changes in Lipid Profile as Potential Biomarkers for Canine Atopic Dermatitis.
    Jackeline Franco, Bartek Rajwa, Paulo Gomes, Harm HogenEsch.
      Lipids play a critical role in the skin as components of the epidermal barrier and as signaling and antimicrobial molecules. Atopic dermatitis in dogs is associated with changes in the lipid composition of the skin, but whether these precede or follow the onset of dermatitis is unclear. We applied rapid lipid-profiling mass spectrometry to skin and blood of 30 control and 30 atopic dogs. Marked differences in lipid profiles were observed between control, nonlesional, and lesional skin. The lipid composition of blood from control and atopic dogs was different, indicating systemic changes in lipid metabolism. Female and male dogs differed in the degree of changes in the skin and blood lipid profiles. Treatment with oclacitinib or lokivetmab ameliorated the skin condition and caused changes in skin and blood lipids. A set of lipid features of the skin was selected as a biomarker that classified samples as control or atopic dermatitis with 95% accuracy, whereas blood lipids discriminated between control and atopic dogs with 90% accuracy. These data suggest that canine atopic dermatitis is a systemic disease and support the use of rapid lipid profiling to identify novel biomarkers.
    Keywords:  biomarkers; canine atopic dermatitis; diagnostic; disease progression; flow-injection mass-spectrometry; lipidomics; predictive elastic net regression
    DOI:  https://doi.org/10.3390/metabo11100670
  17. J Proteome Res. 2021 Oct 20.
    Longitudinal Large-Scale Semiquantitative Proteomic Data Stability Across Multiple Instrument Platforms.
    Congcong Lu, Tina Glisovic-Aplenc, Kathrin M Bernt, Kevin Nestler, Joseph Cesare, Lusha Cao, Hyoungjoo Lee, Hossein Fazelinia, Asif Chinwalla, Yang Xu, Olga Shestova, Yi Xing, Saar Gill, Mingyao Li, Benjamin Garcia, Richard Aplenc.
      With the rapid developments in mass spectrometry (MS)-based proteomics methods, label-free semiquantitative proteomics has become an increasingly popular tool for profiling global protein abundances in an unbiased manner. However, the reproducibility of these data across time and LC-MS platforms is not well characterized. Here, we evaluate the performance of three LC-MS platforms (Orbitrap Elite, Q Exactive HF, and Orbitrap Fusion) in label-free semiquantitative analysis of cell surface proteins over a six-year period. Sucrose gradient ultracentrifugation was used for surfaceome enrichment, following gel separation for in-depth protein identification. With our established workflow, we consistently detected and reproducibly quantified >2300 putative cell surface proteins in a human acute myeloid leukemia (AML) cell line on all three platforms. To our knowledge this is the first study reporting highly reproducible semiquantitative proteomic data collection of biological replicates across multiple years and LC-MS platforms. These data provide experimental justification for semiquantitative proteomic study designs that are executed over multiyear time intervals and on different platforms. Multiyear and multiplatform experimental designs will likely enable larger scale proteomic studies and facilitate longitudinal proteomic studies by investigators lacking access to high throughput MS facilities. Data are available via ProteomeXchange with identifier PXD022721.
    Keywords:  label-free quantification; large-scale; mass spectrometry; quantitative proteomics; reproducibility; surfaceome; target discovery
    DOI:  https://doi.org/10.1021/acs.jproteome.1c00624
  18. Nat Commun. 2021 Oct 18. 12(1): 6073
    GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control.
    Yi Yang, Guoquan Yan, Siyuan Kong, Mengxi Wu, Pengyuan Yang, Weiqian Cao, Liang Qiao.
      Large-scale profiling of intact glycopeptides is critical but challenging in glycoproteomics. Data independent acquisition (DIA) is an emerging technology with deep proteome coverage and accurate quantitative capability in proteomics studies, but is still in the early stage of development in the field of glycoproteomics. We propose GproDIA, a framework for the proteome-wide characterization of intact glycopeptides from DIA data with comprehensive statistical control by a 2-dimentional false discovery rate approach and a glycoform inference algorithm, enabling accurate identification of intact glycopeptides using wide isolation windows. We further utilize a semi-empirical spectrum prediction strategy to expand the coverage of spectral libraries of glycopeptides. We benchmark our method for N-glycopeptide profiling on DIA data of yeast and human serum samples, demonstrating that DIA with GproDIA outperforms the data-dependent acquisition-based methods for glycoproteomics in terms of capacity and data completeness of identification, as well as accuracy and precision of quantification. We expect that this work can provide a powerful tool for glycoproteomic studies.
    DOI:  https://doi.org/10.1038/s41467-021-26246-3
  19. Molecules. 2021 Oct 15. pii: 6246. [Epub ahead of print]26(20):
    Automated Trimethyl Sulfonium Hydroxide Derivatization Method for High-Throughput Fatty Acid Profiling by Gas Chromatography-Mass Spectrometry.
    Paul Gries, Atul Singh Rathore, Xiyuan Lu, Jennifer Chiou, Yen Bao Huynh, Alessia Lodi, Stefano Tiziani.
      Fatty acid profiling on gas chromatography-mass spectrometry (GC-MS) platforms is typically performed offline by manually derivatizing and analyzing small batches of samples. A GC-MS system with a fully integrated robotic autosampler can significantly improve sample handling, standardize data collection, and reduce the total hands-on time required for sample analysis. In this study, we report an optimized high-throughput GC-MS-based methodology that utilizes trimethyl sulfonium hydroxide (TMSH) as a derivatization reagent to convert fatty acids into fatty acid methyl esters. An automated online derivatization method was developed, in which the robotic autosampler derivatizes each sample individually and injects it into the GC-MS system in a high-throughput manner. This study investigated the robustness of automated TMSH derivatization by comparing fatty acid standards and lipid extracts, derivatized manually in batches and online automatically from four biological matrices. Automated derivatization improved reproducibility in 19 of 33 fatty acid standards, with nearly half of the 33 confirmed fatty acids in biological samples demonstrating improved reproducibility when compared to manually derivatized samples. In summary, we show that the online TMSH-based derivatization methodology is ideal for high-throughput fatty acid analysis, allowing rapid and efficient fatty acid profiling, with reduced sample handling, faster data acquisition, and, ultimately, improved data reproducibility.
    Keywords:  GC–MS; fatty acid profiling; online automated derivatization; trimethyl sulfonium hydroxide
    DOI:  https://doi.org/10.3390/molecules26206246
  20. Metabolites. 2021 Oct 02. pii: 678. [Epub ahead of print]11(10):
    MSCAT: A Machine Learning Assisted Catalog of Metabolomics Software Tools.
    Jonathan Dekermanjian, Wladimir Labeikovsky, Debashis Ghosh, Katerina Kechris.
      The bottleneck for taking full advantage of metabolomics data is often the availability, awareness, and usability of analysis tools. Software tools specifically designed for metabolomics data are being developed at an increasing rate, with hundreds of available tools already in the literature. Many of these tools are open-source and freely available but are very diverse with respect to language, data formats, and stages in the metabolomics pipeline. To help mitigate the challenges of meeting the increasing demand for guidance in choosing analytical tools and coordinating the adoption of best practices for reproducibility, we have designed and built the MSCAT (Metabolomics Software CATalog) database of metabolomics software tools that can be sustainably and continuously updated. This database provides a survey of the landscape of available tools and can assist researchers in their selection of data analysis workflows for metabolomics studies according to their specific needs. We used machine learning (ML) methodology for the purpose of semi-automating the identification of metabolomics software tool names within abstracts. MSCAT searches the literature to find new software tools by implementing a Named Entity Recognition (NER) model based on a neural network model at the sentence level composed of a character-level convolutional neural network (CNN) combined with a bidirectional long-short-term memory (LSTM) layer and a conditional random fields (CRF) layer. The list of potential new tools (and their associated publication) is then forwarded to the database maintainer for the curation of the database entry corresponding to the tool. The end-user interface allows for filtering of tools by multiple characteristics as well as plotting of the aggregate tool data to monitor the metabolomics software landscape.
    Keywords:  database; metabolomics; open-source software; text mining; workflows
    DOI:  https://doi.org/10.3390/metabo11100678
  21. Prostate. 2021 Oct 18.
    Aqueous metabolome of tissue-specific conditional Pten-knockout mouse prostate cancer and TRAMP neuroendocrine carcinoma.
    Sangyub Kim, Li Li, Jinhui Zhang, Cheng Jiang, Junxuan Lü.
       BACKGROUND: Metabolic reprograming is now a recognized hallmark of cancer. The prostate-specific phosphatase and tensin homolog deleted on chromosome 10 (Pten) gene-conditional knockout (KO) mouse carcinogenesis model is highly desirable for studying prostate cancer biology and prevention due to its close resemblance of primary molecular defects and histopathological features of human prostate cancer. We have recently published macromolecular profiling of this model by proteomics and transcriptomics, denoting a preeminence of inflammation and myeloid suppressive immune cell features. Here, we performed metabolomic analyses of Pten-KO prostate versus wild type (WT) counterpart for discernable changes in the aqueous metabolites and contrasted to those in the TRAMP neuroendocrine carcinoma (NECa).
    METHODS: Three matched pairs of tissue-specific conditional Pten-KO mouse prostate and WT prostate of litter/cage-mates at 20-22 weeks of age and three pairs of TRAMP NECa versus WT (28-31 weeks) were profiled for their global aqueous metabolite changes, using hydrophilic interaction liquid chromatography-tandem mass spectrometry.
    RESULTS: The Pten-KO prostate increased purine nucleotide pools, cystathionine, and both reduced and oxidized glutathione (GSH, GSSG), and gluconate/glucuronate species in addition to cholesteryl sulfate and polyamine precursor ornithine. On the contrary, Pten-KO prostate contained diminished pools of glycolytic intermediates and phosphorylcholine derivatives, select amino acids, and their metabolites. Bioinformatic integration revealed a significant shunting of glucose away from glycolysis-citrate cycle and glycerol-lipid genesis to pentose phosphate cycle for NADPH/GSH/GSSG redox and pentose moieties for purine and pyrimidine nucleotides, and glycosylation/glucuronidation. Implicit arginine catabolism to ornithine was consistent with immunosuppression in Pten-KO model. While also increased in cystathionine-GSH/GSSG, purine, and pyrimidine nucleotide pools and glucuronidation at the expense of glycolysis-citrate cycle, the TRAMP NECa increased abundance of many amino acids, methyl donor S-adenosyl-methionine, and intermediates for phospholipids without increasing cholesteryl sulfate or ornithine.
    CONCLUSIONS: The aqueous metabolomic patterns in Pten-KO prostate and TRAMP NECa shared similarities in the greater pools of cystathionine, GSH/GSSG redox pair, and nucleotides and shunting away from glycolysis-citrate cycle in both models. Remarkable metabolic distinctions between them included metabolisms of many amino acids (protein synthesis; arginine-ornithine/immune suppression) and cholesteryl sulfate and methylation donor for epigenetic regulations.
    Keywords:  adenocarcinoma; metabolic biomarkers; neuroendocrine carcinoma; prostate cancer
    DOI:  https://doi.org/10.1002/pros.24256
  22. Cells. 2021 Oct 09. pii: 2700. [Epub ahead of print]10(10):
    A Complex Metabolic Network Confers Immunosuppressive Functions to Myeloid-Derived Suppressor Cells (MDSCs) within the Tumour Microenvironment.
    Francesca Hofer, Gianna Di Sario, Chiara Musiu, Silvia Sartoris, Francesco De Sanctis, Stefano Ugel.
      Myeloid-derived suppressor cells (MDSCs) constitute a plastic and heterogeneous cell population among immune cells within the tumour microenvironment (TME) that support cancer progression and resistance to therapy. During tumour progression, cancer cells modify their metabolism to sustain an increased energy demand to cope with uncontrolled cell proliferation and differentiation. This metabolic reprogramming of cancer establishes competition for nutrients between tumour cells and leukocytes and most importantly, among tumour-infiltrating immune cells. Thus, MDSCs that have emerged as one of the most decisive immune regulators of TME exhibit an increase in glycolysis and fatty acid metabolism and also an upregulation of enzymes that catabolise essential metabolites. This complex metabolic network is not only crucial for MDSC survival and accumulation in the TME but also for enhancing immunosuppressive functions toward immune effectors. In this review, we discuss recent progress in the field of MDSC-associated metabolic pathways that could facilitate therapeutic targeting of these cells during cancer progression.
    Keywords:  cancer; immunometabolism; inflammation; myeloid-derived suppressor cells (MDSC); tumour-microenvironment (TME)
    DOI:  https://doi.org/10.3390/cells10102700
  23. J Lipid Res. 2021 Oct 18. pii: S0022-2275(21)00124-3. [Epub ahead of print] 100142
    Vitamin D deficiency promotes accumulation of bioactive lipids and increased endocannabinoid tone in zebrafish.
    Megan M Knuth, Whitney L Stutts, Morgan M Ritter, Kenneth P Garrard, Seth W Kullman.
      Vitamin D is well known for its traditional role in bone mineral homeostasis; however, recent evidence suggests that vitamin D also plays a significant role in metabolic control. This study served to investigate putative linkages between vitamin D deficiency (VDD) and metabolic disruption of bioactive lipids by mass spectrometry imaging (MSI). Our approach employed infrared-matrix-assisted laser desorption electrospray ionization (IR-MALDESI) MSI for lipid metabolite profiling in 6-month-old zebrafish fed either a vitamin D-deficient (VDD) or a vitamin D-sufficient (VDS) diet. Using a lipidomics pipeline, we found that VDD zebrafish had a greater abundance of bioactive lipids (N-acyls, endocannabinoids (ECs), diacylglycerols/triacylglycerols, bile acids/bile alcohols, and vitamin D derivatives) suggestive of increased endocannabinoid tone compared to VDS zebrafish. Tandem mass spectrometry (MS2) was performed on several differentially expressed metabolites with sufficient ion abundances to aid in structural elucidation and provide additional support for MS1 annotations. To confirm activation of the EC pathways, we subsequently examined expression of genes involved in EC biosynthesis, metabolism, and receptor signaling in adipose tissue and liver from VDD and VDS zebrafish. Gene expression changes were congruent with increased endocannabinoid tone, with VDD zebrafish demonstrating increased synthesis and metabolism of anandamide compared to VDS zebrafish. Taken together, our data suggest that VDD may promote accumulation of bioactive lipids and increased endocannabinoid tone in zebrafish.
    Keywords:  IR-MALDESI; MSI; anandamide; endocannabinoid biosynthesis; ion abundance; lipidomics; lipids; mass spectrometry; metabolomics; nutrition
    DOI:  https://doi.org/10.1016/j.jlr.2021.100142
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