bims-mascan 2022-07-31 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2022–07–31
27 papers selected by
Giovanny Rodríguez Blanco, University of Edinburgh

MAVEN2: An Updated Open-Source Mass Spectrometry Exploration Platform.
A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics.
Convolutional Neural Network-Based Compound Fingerprint Prediction for Metabolite Annotation.
Comparative Assessment of Quantification Methods for Tumor Tissue Phosphoproteomics.
Sensitive Detection of Pharmaceutical Drugs and Metabolites in Serum Using Data-Independent Acquisition Mass Spectrometry and Open-Access Data Acquisition Tools.
Improving Phosphoproteomics Profiling Using Data-Independent Mass Spectrometry.
Dietary PUFA Preferably Modify Ethanolamine-Containing Glycerophospholipids of the Human Plasma Lipidome.
Evaluating Peptide Fragment Ion Detection Using Traveling Wave Ion Mobility Spectrometry with Signal-Enhanced MSE (SEMSE).
Lipid Biogeochemistry and Modern Lipidomic Techniques.
Skeletal progenitors preserve proliferation and self-renewal upon inhibition of mitochondrial respiration by rerouting the TCA cycle.
PI3K drives the de novo synthesis of coenzyme A from vitamin B5.
Lipidomic identification of urinary extracellular vesicles for non-alcoholic steatohepatitis diagnosis.
Quantitative Detection of Protein Splice Variants by Selected Reaction Monitoring (SRM) Mass Spectrometry.
The metabolic analysis in human aortic tissues of aortic dissection.
TraVis Pies: A Guide for Stable Isotope Metabolomics Interpretation Using an Intuitive Visualization.
Protocol for establishing a protein-protein interaction network using tandem affinity purification followed by mass spectrometry in mammalian cells.
mTORC1 and mTORC2 Complexes Regulate the Untargeted Metabolomics and Amino Acid Metabolites Profile through Mitochondrial Bioenergetic Functions in Pancreatic Beta Cells.
Reduction of multiple reaction monitoring protein target list using correlation analysis.
Plasma Oxylipins and Their Precursors Are Strongly Associated with COVID-19 Severity and with Immune Response Markers.
Augmentation of MS/MS Libraries with Spectral Interpolation for Improved Identification.
Molecular Network-Based Identification of Tramadol Metabolites in a Fatal Tramadol Poisoning.
Development of a Novel Targeted Metabolomic LC-QqQ-MS Method in Allergic Inflammation.
Enhancement of Proteome Coverage by Ion Mobility Fractionation Coupled to PASEF on a TIMS-QTOF Instrument.
High-Throughput Profiling of Metabolic Phenotypes Using High-Resolution GC-MS.
Rapid nutrient depletion to below the physiological range by cancer cells cultured in Plasmax.
A Protocol for the Acquisition of Comprehensive Proteomics Data from Single Cases Using Formalin-Fixed Paraffin Embedded Sections.
Advances in enrichment methods for mass spectrometry-based proteomics analysis of post-translational modifications.

Metabolites. 2022 Jul 25. pii: 684. [Epub ahead of print]12(8):

MAVEN2: An Updated Open-Source Mass Spectrometry Exploration Platform.

Phillip Seitzer, Bryson Bennett, Eugene Melamud.

  MAVEN, an open-source software program for analysis of LC-MS metabolomics data, was originally released in 2010. As mass spectrometry has advanced in the intervening years, MAVEN has been periodically updated to reflect this advancement. This manuscript describes a major update to the program, MAVEN2, which supports LC-MS/MS analysis of metabolomics and lipidomics samples. We have developed algorithms to support MS/MS spectral matching and efficient search of large-scale fragmentation libraries. We explore the ability of our approach to separate authentic from spurious metabolite identifications using a set of standards spiked into water and yeast backgrounds. To support our improved lipid identification workflow, we introduce a novel in-silico lipidomics library covering major lipid classes and compare searches using our novel library to searches with existing in-silico lipidomics libraries. MAVEN2 source code and cross-platform application installers are freely available for download from GitHub under a GNU permissive license [ver 3], as are the in silico lipidomics libraries and corresponding code repository.

Keywords:  GUI; fragmentation; identification; lipidomics; metabolomics; open-source; software; visualization

DOI:  https://doi.org/10.3390/metabo12080684
Metabolites. 2022 Jun 23. pii: 584. [Epub ahead of print]12(7):

A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics.

Nils Hoffmann, Gerhard Mayer, Canan Has, Dominik Kopczynski, Fadi Al Machot, Dominik Schwudke, Robert Ahrends, Katrin Marcus, Martin Eisenacher, Michael Turewicz.

  Mass spectrometry is a widely used technology to identify and quantify biomolecules such as lipids, metabolites and proteins necessary for biomedical research. In this study, we catalogued freely available software tools, libraries, databases, repositories and resources that support lipidomics data analysis and determined the scope of currently used analytical technologies. Because of the tremendous importance of data interoperability, we assessed the support of standardized data formats in mass spectrometric (MS)-based lipidomics workflows. We included tools in our comparison that support targeted as well as untargeted analysis using direct infusion/shotgun (DI-MS), liquid chromatography-mass spectrometry, ion mobility or MS imaging approaches on MS1 and potentially higher MS levels. As a result, we determined that the Human Proteome Organization-Proteomics Standards Initiative standard data formats, mzML and mzTab-M, are already supported by a substantial number of recent software tools. We further discuss how mzTab-M can serve as a bridge between data acquisition and lipid bioinformatics tools for interpretation, capturing their output and transmitting rich annotated data for downstream processing. However, we identified several challenges of currently available tools and standards. Potential areas for improvement were: adaptation of common nomenclature and standardized reporting to enable high throughput lipidomics and improve its data handling. Finally, we suggest specific areas where tools and repositories need to improve to become FAIRer.

Keywords:  FAIR; bioinformatics; data format; database; lipidomics; mass spectrometry; standardization

DOI:  https://doi.org/10.3390/metabo12070584
Metabolites. 2022 Jun 29. pii: 605. [Epub ahead of print]12(7):

Convolutional Neural Network-Based Compound Fingerprint Prediction for Metabolite Annotation.

Shijinqiu Gao, Hoi Yan Katharine Chau, Kuijun Wang, Hongyu Ao, Rency S Varghese, Habtom W Ressom.

  Metabolite annotation has been a challenging issue especially in untargeted metabolomics studies by liquid chromatography coupled with mass spectrometry (LC-MS). This is in part due to the limitations of publicly available spectral libraries, which consist of tandem mass spectrometry (MS/MS) data acquired from just a fraction of known metabolites. Machine learning provides the opportunity to predict molecular fingerprints based on MS/MS data. The predicted molecular fingerprints can then be used to help rank putative metabolite IDs obtained by using either the precursor mass or the formula of the unknown metabolite. This method is particularly useful to help annotate metabolites whose corresponding MS/MS spectra are missing or cannot be matched with those in accessible spectral libraries. We investigated a convolutional neural network (CNN) for molecular fingerprint prediction based on data acquired by MS/MS. We used more than 680,000 MS/MS spectra obtained from the MoNA repository and NIST 20, representing about 36,000 compounds for training and testing our CNN model. The trained CNN model is implemented as a python package, MetFID. The package is available on GitHub for users to enter their MS/MS spectra and corresponding putative metabolite IDs to obtain ranked lists of metabolites. Better performance is achieved by MetFID in ranking putative metabolite IDs using the CASMI 2016 benchmark dataset compared to two other machine learning-based tools (CSI:FingerID and ChemDistiller).

Keywords:  deep learning; metabolite identification; metabolomics; molecular fingerprint

DOI:  https://doi.org/10.3390/metabo12070605
Anal Chem. 2022 Jul 26.

Comparative Assessment of Quantification Methods for Tumor Tissue Phosphoproteomics.

Yang Zhang, Benjamin Dreyer, Natalia Govorukhina, Alexander M Heberle, Saša Končarević, Christoph Krisp, Christiane A Opitz, Pauline Pfänder, Rainer Bischoff, Hartmut Schlüter, Marcel Kwiatkowski, Kathrin Thedieck, Peter L Horvatovich.

With increasing sensitivity and accuracy in mass spectrometry, the tumor phosphoproteome is getting into reach. However, the selection of quantitation techniques best-suited to the biomedical question and diagnostic requirements remains a trial and error decision as no study has directly compared their performance for tumor tissue phosphoproteomics. We compared label-free quantification (LFQ), spike-in-SILAC (stable isotope labeling by amino acids in cell culture), and tandem mass tag (TMT) isobaric tandem mass tags technology for quantitative phosphosite profiling in tumor tissue. Compared to the classic SILAC method, spike-in-SILAC is not limited to cell culture analysis, making it suitable for quantitative analysis of tumor tissue samples. TMT offered the lowest accuracy and the highest precision and robustness toward different phosphosite abundances and matrices. Spike-in-SILAC offered the best compromise between these features but suffered from a low phosphosite coverage. LFQ offered the lowest precision but the highest number of identifications. Both spike-in-SILAC and LFQ presented susceptibility to matrix effects. Match between run (MBR)-based analysis enhanced the phosphosite coverage across technical replicates in LFQ and spike-in-SILAC but further reduced the precision and robustness of quantification. The choice of quantitative methodology is critical for both study design such as sample size in sample groups and quantified phosphosites and comparison of published cancer phosphoproteomes. Using ovarian cancer tissue as an example, our study builds a resource for the design and analysis of quantitative phosphoproteomic studies in cancer research and diagnostics.

DOI: https://doi.org/10.1021/acs.analchem.2c01036
Pharmaceuticals (Basel). 2022 Jul 21. pii: 901. [Epub ahead of print]15(7):

Sensitive Detection of Pharmaceutical Drugs and Metabolites in Serum Using Data-Independent Acquisition Mass Spectrometry and Open-Access Data Acquisition Tools.

Syed Muhammad Zaki Shah, Arslan Ali, Muhammad Noman Khan, Adeeba Khadim, Mufarreh Asmari, Jalal Uddin, Syed Ghulam Musharraf.

  Data-independent acquisition (DIA) based strategies have been explored in recent years for improving quantitative analysis of metabolites. However, the data analysis is challenging for DIA methods as the resulting spectra are highly multiplexed. Thus, the DIA mode requires advanced software analysis to facilitate the data deconvolution process. We proposed a pipeline for quantitative profiling of pharmaceutical drugs and serum metabolites in DIA mode after comparing the results obtained from full-scan, Data-dependent acquisition (DDA) and DIA modes. using open-access software. Pharmaceutical drugs (10) were pooled in healthy human serum and analysed by LC-ESI-QTOF-MS. MS1 full-scan and Data-dependent (MS2) results were used for identification using MS-DIAL software while deconvolution of MS1/MS2 spectra in DIA mode was achieved by using Skyline software. The results of acquisition methods for quantitative analysis validated the remarkable analytical performance of the constructed workflow, proving it to be a sensitive and reproducible pipeline for biological complex fluids.

Keywords:  MS-DIAL; Perseus; Skyline; data-dependent acquisition; data-independent acquisition; metabolomics

DOI:  https://doi.org/10.3390/ph15070901
J Proteome Res. 2022 Jul 25.

Improving Phosphoproteomics Profiling Using Data-Independent Mass Spectrometry.

Aparna Srinivasan, Justin C Sing, Anne-Claude Gingras, Hannes L Röst.

  Mass spectrometry-based profiling of the phosphoproteome is a powerful method of identifying phosphorylation events at a systems level. Most phosphoproteomics studies have used data-dependent acquisition (DDA) mass spectrometry as their method of choice. In this Perspective, we review some recent studies benchmarking DDA and DIA methods for phosphoproteomics and discuss data analysis options for DIA phosphoproteomics. In order to evaluate the impact of data-dependent and data-independent acquisition (DIA) on identification and quantification, we analyze a previously published phosphopeptide-enriched data set consisting of 10 replicates acquired by DDA and DIA each. We find that though more unique identifications are made in DDA data, phosphopeptides are identified more consistently across replicates in DIA. We further discuss the challenges of identifying chromatographically coeluting phosphopeptide isomers and investigate the impact on reproducibility of identifying high-confidence site-localized phosphopeptides in replicates.

Keywords:  data-independent acquisition; phosphoproteomics

DOI:  https://doi.org/10.1021/acs.jproteome.2c00172
Nutrients. 2022 Jul 26. pii: 3055. [Epub ahead of print]14(15):

Dietary PUFA Preferably Modify Ethanolamine-Containing Glycerophospholipids of the Human Plasma Lipidome.

Christine Dawczynski, Johannes Plagge, Gerhard Jahreis, Gerhard Liebisch, Marcus Höring, Claudine Seeliger, Josef Ecker.

  The content of polyunsaturated fatty acids (PUFA) in complex lipids essentially influences their physicochemical properties and has been linked to health and disease. To investigate the incorporation of dietary PUFA in the human plasma lipidome, we quantified glycerophospholipids (GPL), sphingolipids, and sterols using electrospray ionization coupled to tandem mass spectrometry of plasma samples obtained from a dietary intervention study. Healthy individuals received foods supplemented with different vegetable oils rich in PUFA. These included sunflower, linseed, echium, and microalgae oil as sources of linoleic acid (LA; FA 18:2 n-6), alpha-linolenic acid (ALA; FA 18:3 n-3), stearidonic acid (SDA; FA 18:4 n-3), and docosahexaenoic acid (DHA; FA 22:6 n-3). While LA and ALA did not influence the species profiles of GPL, sphingolipid, and cholesteryl ester drastically, SDA and DHA were integrated primarily in ethanolamine-containing GPL. This significantly altered phosphatidylethanolamine and plasmalogen species composition, especially those with 38-40 carbons and 6 double bonds. We speculate that diets enriched with highly unsaturated FA more efficiently alter plasma GPL acyl chain composition than those containing primarily di- and tri-unsaturated FA, most likely because of their more pronounced deviation of FA composition from typical western diets.

Keywords:  PUFA; lipidomics; phosphatidylethanolamine; plasma; plasmalogen; vegetable oil

DOI:  https://doi.org/10.3390/nu14153055
Anal Chem. 2022 Jul 29.

Evaluating Peptide Fragment Ion Detection Using Traveling Wave Ion Mobility Spectrometry with Signal-Enhanced MSE (SEMSE).

Juan Camilo Rojas Echeverri, Daniela Volke, Sanja Milkovska-Stamenova, Ralf Hoffmann.

The inherent poor sampling of fragment ions in time-of-flight mass analyzers was recently improved for data-dependent acquisition (DDA) by considering their drift times in traveling wave ion mobility spectrometry (TWIMS). Here, we extend this TWIMS-DDA approach to the data-independent acquisition (DIA) mode MSE to improve the signal intensities of fragment ions by providing improved ion beam sampling efficiency, which we termed therefore signal-enhanced MSE (SEMSE). The theoretical expectation that SEMSE improves the number of identified peptides, the number of quantifiable peptides, and the lower limit of quantitation in wideband DIA was evaluated on an electrospray ionisation-ion mobility spectrometry-quadrupole-time-of-flight-MS (ESI-IMS-Q-TOF-MS) (Synapt G2-Si) in comparison to five established TWIMS-DDA and TWIMS-MSE methods with respect to the number of peptide identifications, the spectral quality of supporting peptide spectra matches, and (most importantly) fragment ion signal sensitivity. A comparison of the fragment signals clearly indicated that SEMSE provides 6.8- to 11.5-fold larger peak areas than established MSE techniques. While this clearly shows the advantages of SEMSE, the inherent limitations of the current software tools do not allow using all benefits in routine analyses. As the simultaneous fragmentation of co-eluting peptides limited peptide identification, DDA and MSE data sets were integrated using Skyline.

DOI: https://doi.org/10.1021/acs.analchem.2c00461
Ann Rev Mar Sci. 2022 Jul 25.

Lipid Biogeochemistry and Modern Lipidomic Techniques.

Bethanie R Edwards.

Lipids are structurally diverse biomolecules that serve multiple roles in cells. As such, they are used as biomarkers in the modern ocean and as paleoproxies to explore the geological past. Here, I review lipid geochemistry, biosynthesis, and compartmentalization; the varied uses of lipids as biomarkers; and the evolution of analytical techniques used to measure and characterize lipids. Advancements in high-resolution accurate-mass mass spectrometry have revolutionized the lipidomic and metabolomic fields, both of which are quickly being integrated into marine meta-omic studies. Lipidomics allows us to analyze tens of thousands of features, providing an open analytical window and the ability to quantify unknown compounds that can be structurally elucidated later. However, lipidome annotation is not a trivial matter and represents one of the biggest challenges for oceanographers, owing in part to the lack of marine lipids in current in silico databases and data repositories. A case study reveals the gaps in our knowledge and open opportunities to answer fundamental questions about molecular-level control of chemical reactions and global-scale patterns in the lipidscape. Expected final online publication date for the Annual Review of Marine Science, Volume 15 is January 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

DOI: https://doi.org/10.1146/annurev-marine-040422-094104
Cell Rep. 2022 Jul 26. pii: S2211-1247(22)00907-X. [Epub ahead of print]40(4): 111105

Skeletal progenitors preserve proliferation and self-renewal upon inhibition of mitochondrial respiration by rerouting the TCA cycle.

Guillaume Tournaire, Shauni Loopmans, Steve Stegen, Gianmarco Rinaldi, Guy Eelen, Sophie Torrekens, Karen Moermans, Peter Carmeliet, Bart Ghesquière, Bernard Thienpont, Sarah-Maria Fendt, Nick van Gastel, Geert Carmeliet.

  A functional electron transport chain (ETC) is crucial for supporting bioenergetics and biosynthesis. Accordingly, ETC inhibition decreases proliferation in cancer cells but does not seem to impair stem cell proliferation. However, it remains unclear how stem cells metabolically adapt. In this study, we show that pharmacological inhibition of complex III of the ETC in skeletal stem and progenitor cells induces glycolysis side pathways and reroutes the tricarboxylic acid (TCA) cycle to regenerate NAD+ and preserve cell proliferation. These metabolic changes also culminate in increased succinate and 2-hydroxyglutarate levels that inhibit Ten-eleven translocation (TET) DNA demethylase activity, thereby preserving self-renewal and multilineage potential. Mechanistically, mitochondrial malate dehydrogenase and reverse succinate dehydrogenase activity proved to be essential for the metabolic rewiring in response to ETC inhibition. Together, these data show that the metabolic plasticity of skeletal stem and progenitor cells allows them to bypass ETC blockade and preserve their self-renewal.

Keywords:  CP: Metabolism; CP: Stem cell research; NAD regeneration; TCA rerouting; TET activity; cell-based regenerative medicine; electron transport chain; metabolic plasticity; proliferation; reverse succinate dehydrogenase; self-renewal; skeletal stem cells

DOI:  https://doi.org/10.1016/j.celrep.2022.111105
Nature. 2022 Jul 27.

PI3K drives the de novo synthesis of coenzyme A from vitamin B5.

Christian C Dibble, Samuel A Barritt, Grace E Perry, Evan C Lien, Renee C Geck, Sarah E DuBois-Coyne, David Bartee, Thomas T Zengeya, Emily B Cohen, Min Yuan, Benjamin D Hopkins, Jordan L Meier, John G Clohessy, John M Asara, Lewis C Cantley, Alex Toker.

In response to hormones and growth factors, the class I phosphoinositide-3-kinase (PI3K) signalling network functions as a major regulator of metabolism and growth, governing cellular nutrient uptake, energy generation, reducing cofactor production and macromolecule biosynthesis1. Many of the driver mutations in cancer with the highest recurrence, including in receptor tyrosine kinases, Ras, PTEN and PI3K, pathologically activate PI3K signalling2,3. However, our understanding of the core metabolic program controlled by PI3K is almost certainly incomplete. Here, using mass-spectrometry-based metabolomics and isotope tracing, we show that PI3K signalling stimulates the de novo synthesis of one of the most pivotal metabolic cofactors: coenzyme A (CoA). CoA is the major carrier of activated acyl groups in cells4,5 and is synthesized from cysteine, ATP and the essential nutrient vitamin B5 (also known as pantothenate)6,7. We identify pantothenate kinase 2 (PANK2) and PANK4 as substrates of the PI3K effector kinase AKT8. Although PANK2 is known to catalyse the rate-determining first step of CoA synthesis, we find that the minimally characterized but highly conserved PANK49 is a rate-limiting suppressor of CoA synthesis through its metabolite phosphatase activity. Phosphorylation of PANK4 by AKT relieves this suppression. Ultimately, the PI3K-PANK4 axis regulates the abundance of acetyl-CoA and other acyl-CoAs, CoA-dependent processes such as lipid metabolism and proliferation. We propose that these regulatory mechanisms coordinate cellular CoA supplies with the demands of hormone/growth-factor-driven or oncogene-driven metabolism and growth.

DOI: https://doi.org/10.1038/s41586-022-04984-8
J Nanobiotechnology. 2022 Jul 27. 20(1): 349

Lipidomic identification of urinary extracellular vesicles for non-alcoholic steatohepatitis diagnosis.

Qingfu Zhu, Hengrui Li, Zheng Ao, Hao Xu, Jiaxin Luo, Connor Kaurich, Rui Yang, Pei-Wu Zhu, Sui-Dan Chen, Xiao-Dong Wang, Liang-Jie Tang, Gang Li, Ou-Yang Huang, Ming-Hua Zheng, Hui-Ping Li, Fei Liu.

   BACKGROUND AND AIMS: Non-alcoholic fatty liver disease (NAFLD) is a usual chronic liver disease and lacks non-invasive biomarkers for the clinical diagnosis and prognosis. Extracellular vesicles (EVs), a group of heterogeneous small membrane-bound vesicles, carry proteins and nucleic acids as promising biomarkers for clinical applications, but it has not been well explored on their lipid compositions related to NAFLD studies. Here, we investigate the lipid molecular function of urinary EVs and their potential as biomarkers for non-alcoholic steatohepatitis (NASH) detection.
METHODS: This work includes 43 patients with non-alcoholic fatty liver (NAFL) and 40 patients with NASH. The EVs of urine were isolated and purified using the EXODUS method. The EV lipidomics was performed by LC-MS/MS. We then systematically compare the EV lipidomic profiles of NAFL and NASH patients and reveal the lipid signatures of NASH with the assistance of machine learning.
RESULTS: By lipidomic profiling of urinary EVs, we identify 422 lipids mainly including sterol lipids, fatty acyl lipids, glycerides, glycerophospholipids, and sphingolipids. Via the machine learning and random forest modeling, we obtain a biomarker panel composed of 4 lipid molecules including FFA (18:0), LPC (22:6/0:0), FFA (18:1), and PI (16:0/18:1), that can distinguish NASH with an AUC of 92.3%. These lipid molecules are closely associated with the occurrence and development of NASH.
CONCLUSION: The lack of non-invasive means for diagnosing NASH causes increasing morbidity. We investigate the NAFLD biomarkers from the insights of urinary EVs, and systematically compare the EV lipidomic profiles of NAFL and NASH, which holds the promise to expand the current knowledge of disease pathogenesis and evaluate their role as non-invasive biomarkers for NASH diagnosis and progression.

Keywords:  Lipidomics; Non-alcoholic fatty liver disease; Non-alcoholic steatohepatitis; Urinary extracellular vesicles

DOI:  https://doi.org/10.1186/s12951-022-01540-4
Methods Mol Biol. 2022 ;2537 231-246

Quantitative Detection of Protein Splice Variants by Selected Reaction Monitoring (SRM) Mass Spectrometry.

Alexander Schmidt, Dietmar Schreiner.

  Molecular diversification of the cellular proteome through alternative splicing has emerged as an important biological principle. However, the lack of tools to specifically detect and quantify proteoforms (Smith et al., Nat Methods 10:186-187, 2013) is a major impediment to functional studies. Recently, biological mass spectrometry (MS) has undergone impressive advances (Mann, Nat Rev Mol Cell Biol 17:678, 2016), including the generation of a highly diverse set of biological applications (Aebersold and Mann, Nature 537:347-355, 2016), and has demonstrated to be an essential tool to address many biological questions (Savitski et al., Science 346:1255784, 2014; Rinner et al., Nat Methods 5:315-318, 2008). In particular, targeted LC-MS, with its high selectivity and specificity, is ideally suited for the precise and sensitive quantification of specific proteins and their proteoforms (Picotti and Aebersold, Nat Methods 9:555-566, 2012). We describe in detail the application of this workflow applied to dissect the molecular diversity of the synaptic adhesion proteins and their splicing-derived proteoforms (Schreiner et al., Elife 4:e07794, 2015).

Keywords:  Alternative splicing; Proteoform quantification; Selected reaction monitoring; Stable isotope dilution; Targeted mass spectrometry

DOI:  https://doi.org/10.1007/978-1-0716-2521-7_14
J Clin Lab Anal. 2022 Jul 26. e24623

The metabolic analysis in human aortic tissues of aortic dissection.

Kefeng Zhang, Xudong Pan, Jun Zheng, Yongmin Liu, Lizhong Sun.

   BACKGROUND: The metabolic profile of human aortic tissues is of great importance. Among the analytical platforms utilized in metabolomics, LC-MS provides broad metabolome coverage. The non-targeted metabolomics can comprehensively detect the entire metabolome of an organism and find the metabolic characteristics that have significant changes in the experimental group and the control group and elucidate the metabolic pathway concerning the recognized metabolites. Employing non-targeted metabolomics is helpful to develop biomarkers for disease diagnosis and disease pathology research; for instance, Aortic aneurysm (AA) and Aortic dissection (AD).
AIM: This study sought to describe the non-targeted analysis of 18 aortic tissue samples, comparing between AA and AD.
MATERIAL & METHODS: Our experimental flow included dividing the samples into (AA, nine samples) and (AD, nine samples), SCIEX quadrupole timeofflight tandem mass spectrometer (TripleTOF) 6600+ mass spectrometer data refinement, MetDNA database analysis, and pathway analysis. We performed an initial validation by setting quality control parameters to evaluate the stability of the analysis system during the computer operation. We then used the repeatability of the control samples to examine the stability of the instrument during the entire analysis process to ensure the reliability of the results.
RESULTS: Our study found 138 novel metabolites involved in galactose metabolism.
DISCUSSION: 138 novel metabolites found in this study will be further studied in the future.
CONCLUSION: Our study found 138 novel metabolites between AA and AD, which will provide viable clinical data for future studies aimed to implement galactose markers in aortic tissue analysis.

Keywords:  LC-MS; aortic tissue; galactose pathway; nontarget metabolic analysis

DOI:  https://doi.org/10.1002/jcla.24623
Metabolites. 2022 Jun 25. pii: 593. [Epub ahead of print]12(7):

TraVis Pies: A Guide for Stable Isotope Metabolomics Interpretation Using an Intuitive Visualization.

Sam De Craemer, Karen Driesen, Bart Ghesquière.

  Tracer metabolomics is a powerful technology for the biomedical community to study and understand disease-inflicted metabolic mechanisms. However, the interpretation of tracer metabolomics results is highly technical, as the metabolites' abundances, tracer incorporation and positions on the metabolic map all must be jointly interpreted. The field is currently lacking a structured approach to help less experienced researchers start the interpretation of tracer metabolomics datasets. We propose an approach using an intuitive visualization concept aided by a novel open-source tool, and provide guidelines on how researchers can apply the approach and the visualization tool to their own datasets. Using a showcase experiment, we demonstrate that the visualization approach leads to an intuitive interpretation that can ease researchers into understanding their tracer metabolomics data.

Keywords:  biochemical pathways; data visualization; tracer metabolomics

DOI:  https://doi.org/10.3390/metabo12070593
STAR Protoc. 2022 Sep 16. 3(3): 101569

Protocol for establishing a protein-protein interaction network using tandem affinity purification followed by mass spectrometry in mammalian cells.

Weixiang Bian, Hua Jiang, Shan Feng, Junjie Chen, Wenqi Wang, Xu Li.

  Identification of protein interactors is fundamental to understanding their functions. Here, we describe a modified protocol for tandem affinity purification coupled with mass spectrometry (TAP/MS), which includes two-step purification. We detail the S-, 2×FLAG-, and Streptavidin-Binding Peptide (SBP)- tandem tags (SFB-tag) system for protein purification. This protocol can be used to identify protein interactors and establish a high-confidence protein-protein interaction network based on computational models. This is particularly useful for identifying bona fide interacting proteins for subsequent functional studies. For complete details on the use and execution of this protocol, please refer to Bian et al. (2021).

Keywords:  Bioinformatics; Mass Spectrometry; Molecular Biology; Protein Biochemistry; Proteomics

DOI:  https://doi.org/10.1016/j.xpro.2022.101569
Nutrients. 2022 Jul 22. pii: 3022. [Epub ahead of print]14(15):

mTORC1 and mTORC2 Complexes Regulate the Untargeted Metabolomics and Amino Acid Metabolites Profile through Mitochondrial Bioenergetic Functions in Pancreatic Beta Cells.

Ghada A Soliman, Rinat R Abzalimov, Ye He.

   BACKGROUND: Pancreatic beta cells regulate bioenergetics efficiency and secret insulin in response to glucose and nutrient availability. The mechanistic Target of Rapamycin (mTOR) network orchestrates pancreatic progenitor cell growth and metabolism by nucleating two complexes, mTORC1 and mTORC2.
OBJECTIVE: To determine the impact of mTORC1/mTORC2 inhibition on amino acid metabolism in mouse pancreatic beta cells (Beta-TC-6 cells, ATCC-CRL-11506) using high-resolution metabolomics (HRM) and live-mitochondrial functions.
METHODS: Pancreatic beta TC-6 cells were incubated for 24 h with either: RapaLink-1 (RL); Torin-2 (T); rapamycin (R); metformin (M); a combination of RapaLink-1 and metformin (RLM); Torin-2 and metformin (TM); compared to the control. We applied high-resolution mass spectrometry (HRMS) LC-MS/MS untargeted metabolomics to compare the twenty natural amino acid profiles to the control. In addition, we quantified the bioenergetics dynamics and cellular metabolism by live-cell imaging and the MitoStress Test XF24 (Agilent, Seahorse). The real-time, live-cell approach simultaneously measures the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) to determine cellular respiration and metabolism. Statistical significance was assessed using ANOVA on Ranks and post-hoc Welch t-Tests.
RESULTS: RapaLink-1, Torin-2, and rapamycin decreased L-aspartate levels compared to the control (p = 0.006). Metformin alone did not affect L-aspartate levels. However, L-asparagine levels decreased with all treatment groups compared to the control (p = 0.03). On the contrary, L-glutamate and glycine levels were reduced only by mTORC1/mTORC2 inhibitors RapaLink-1 and Torin-2, but not by rapamycin or metformin. The metabolic activity network model predicted that L-aspartate and AMP interact within the same activity network. Live-cell bioenergetics revealed that ATP production was significantly reduced in RapaLink-1 (122.23 + 33.19), Torin-2 (72.37 + 17.33) treated cells, compared to rapamycin (250.45 + 9.41) and the vehicle control (274.23 + 38.17), p < 0.01. However, non-mitochondrial oxygen consumption was not statistically different between RapaLink-1 (67.17 + 3.52), Torin-2 (55.93 + 8.76), or rapamycin (80.01 + 4.36, p = 0.006).
CONCLUSIONS: Dual mTORC1/mTORC2 inhibition by RapaLink-1 and Torin-2 differentially altered the amino acid profile and decreased mitochondrial respiration compared to rapamycin treatment which only blocks the FRB domain on mTOR. Third-generation mTOR inhibitors may alter the mitochondrial dynamics and reveal a bioenergetics profile that could be targeted to reduce mitochondrial stress.

Keywords:  extra cellular acidification rate (ECAR); high-resolution mass spectrometry (HRMS); mTORC1; mTORC2; mitochondrial stress; oxygen consumption rmassbates (OCR); the internal exposome

DOI:  https://doi.org/10.3390/nu14153022
J Dairy Sci. 2022 Jul 22. pii: S0022-0302(22)00413-1. [Epub ahead of print]

Reduction of multiple reaction monitoring protein target list using correlation analysis.

Holger A Ebhardt, Pierre Ponchon, Konstantinos Theodosiadis, Christophe Fuerer, Marie-Claude Courtet-Compondu, Jonathan O'Regan, Michael Affolter, Yousef Joubran.

  High mass resolution mass spectrometry provides hundreds to thousands of protein identifications per sample, and quantification is typically performed using label-free quantification. However, the gold standard of quantitative proteomics is multiple reaction monitoring (MRM) using triple quadrupole mass spectrometers and stable isotope reference peptides. This raises the question how to reduce a large data set to a small one without losing essential information. Here we present the reduction of such a data set using correlation analysis of bovine dairy ingredients and derived products. We were able to explain the variance in the proteomics data set using only 9 proteins across all major dairy protein classes: caseins, whey, and milk fat globule membrane proteins. We term this method Trinity-MRM. The reproducibility of the protein extraction and Trinity-MRM methods was shown to be below 5% in independent experiments (multi-day single-user and single-day multi-user) using double cream. Further application of this reductionist approach might include screening of large sample cohorts for biologically interesting samples before analysis by high-resolution mass spectrometry or other omics methodologies.

Keywords:  MFGM; MRM; correlation analysis; dairy; quantitative proteomics

DOI:  https://doi.org/10.3168/jds.2021-21647
Metabolites. 2022 Jul 04. pii: 619. [Epub ahead of print]12(7):

Plasma Oxylipins and Their Precursors Are Strongly Associated with COVID-19 Severity and with Immune Response Markers.

Naama Karu, Alida Kindt, Lieke Lamont, Adriaan J van Gammeren, Anton A M Ermens, Amy C Harms, Lutzen Portengen, Roel C H Vermeulen, Willem A Dik, Anton W Langerak, Vincent H J van der Velden, Thomas Hankemeier.

  COVID-19 is characterised by a dysregulated immune response, that involves signalling lipids acting as mediators of the inflammatory process along the innate and adaptive phases. To promote understanding of the disease biochemistry and provide targets for intervention, we applied a range of LC-MS platforms to analyse over 100 plasma samples from patients with varying COVID-19 severity and with detailed clinical information on inflammatory responses (>30 immune markers). The second publication in a series reports the results of quantitative LC-MS/MS profiling of 63 small lipids including oxylipins, free fatty acids, and endocannabinoids. Compared to samples taken from ward patients, intensive care unit (ICU) patients had 2-4-fold lower levels of arachidonic acid (AA) and its cyclooxygenase-derived prostanoids, as well as lipoxygenase derivatives, exhibiting negative correlations with inflammation markers. The same derivatives showed 2-5-fold increases in recovering ward patients, in paired comparison to early hospitalisation. In contrast, ICU patients showed elevated levels of oxylipins derived from poly-unsaturated fatty acids (PUFA) by non-enzymatic peroxidation or activity of soluble epoxide hydrolase (sEH), and these oxylipins positively correlated with markers of macrophage activation. The deficiency in AA enzymatic products and the lack of elevated intermediates of pro-resolving mediating lipids may result from the preference of alternative metabolic conversions rather than diminished stores of PUFA precursors. Supporting this, ICU patients showed 2-to-11-fold higher levels of linoleic acid (LA) and the corresponding fatty acyl glycerols of AA and LA, all strongly correlated with multiple markers of excessive immune response. Our results suggest that the altered oxylipin metabolism disrupts the expected shift from innate immune response to resolution of inflammation.

Keywords:  COVID-19; SARS-CoV-2; cytokine; eicosanoid; inflammation; lipid; metabolomics; oxylipin

DOI:  https://doi.org/10.3390/metabo12070619
J Chem Inf Model. 2022 Jul 29.

Augmentation of MS/MS Libraries with Spectral Interpolation for Improved Identification.

Ethan King, Richard Overstreet, Julia Nguyen, Danielle Ciesielski.

Tandem mass spectrometry (MS/MS) is a primary tool for the identification of small molecules and metabolites where resultant spectra are most commonly identified by matching them with spectra in MS/MS reference libraries. The high degree of variability in MS/MS spectrum acquisition techniques and parameters creates a significant challenge for building standardized reference libraries. Here we present a method to improve the usefulness of existing MS/MS libraries by augmenting available experimental spectra data sets with statistically interpolated spectra at unreported collision energies. We find that highly accurate spectral approximations can be interpolated from as few as three experimental spectra and that the interpolated spectra will be consistent with true spectra gathered from the same instrument as the experimental spectra. Supplementing existing spectral databases with interpolated spectra yields consistent improvements to identification accuracy on a range of instruments and precursor types. Applying this method yields significant improvements (∼10% more spectra correctly identified) on large data sets (2000-10 000 spectra), indicating this is a quick yet adept tool for improving spectral matching in situations where available reference libraries are not yet sufficient. We also find improvements of matching spectra across instrument types (between an Agilent Q-TOF and an Orbitrap Elite), at high collision energies (50-90 eV), and with smaller data sets available through MassBank.

DOI: https://doi.org/10.1021/acs.jcim.2c00620
Metabolites. 2022 Jul 19. pii: 665. [Epub ahead of print]12(7):

Molecular Network-Based Identification of Tramadol Metabolites in a Fatal Tramadol Poisoning.

Romain Magny, Nicolas Auzeil, Bertrand Lefrère, Bruno Mégarbane, Pascal Houzé, Laurence Labat.

  Identification of xenobiotics and their phase I/II metabolites in poisoned patients remains challenging. Systematic approaches using bioinformatic tools are needed to detect all compounds as exhaustively as possible. Here, we aimed to assess an analytical workflow using liquid chromatography coupled to high-resolution mass spectrometry with data processing based on a molecular network to identify tramadol metabolites in urine and plasma in poisoned patients. The generated molecular network from liquid chromatography coupled to high-resolution tandem mass spectrometry data acquired in both positive and negative ion modes allowed for the identification of 25 tramadol metabolites in urine and plasma, including four methylated metabolites that have not been previously reported in humans or in vitro models. While positive ion mode is reliable for generating a network of tramadol metabolites displaying a dimethylamino radical in their structure, negative ion mode was useful to cluster phase II metabolites. In conclusion, the combined use of molecular networks in positive and negative ion modes is a suitable and robust tool to identify a broad range of metabolites in poisoned patients, as shown in a fatal tramadol-poisoned patient.

Keywords:  clinical toxicology; high-resolution tandem mass spectrometry; molecular network; tramadol; untargeted metabolomics

DOI:  https://doi.org/10.3390/metabo12070665
Metabolites. 2022 Jun 25. pii: 592. [Epub ahead of print]12(7):

Development of a Novel Targeted Metabolomic LC-QqQ-MS Method in Allergic Inflammation.

David Obeso, Nuria Contreras, Mariana Dolores-Hernández, Teresa Carrillo, Coral Barbas, María M Escribese, Alma Villaseñor, Domingo Barber.

  The transition from mild to severe allergic phenotypes is still poorly understood and there is an urgent need of incorporating new therapies, accompanied by personalized diagnosis approaches. This work presents the development of a novel targeted metabolomic methodology for the analysis of 36 metabolites related to allergic inflammation, including mostly sphingolipids, lysophospholipids, amino acids, and those of energy metabolism previously identified in non-targeted studies. The methodology consisted of two complementary chromatography methods, HILIC and reversed-phase. These were developed using liquid chromatography, coupled to triple quadrupole mass spectrometry (LC-QqQ-MS) in dynamic multiple reaction monitoring (dMRM) acquisition mode and were validated using ICH guidelines. Serum samples from two clinical models of allergic asthma patients were used for method application, which were as follows: (1) corticosteroid-controlled (ICS, n = 6) versus uncontrolled (UC, n = 4) patients, and immunotherapy-controlled (IT, n = 23) versus biologicals-controlled (BIO, n = 12) patients. The results showed significant differences mainly in lysophospholipids using univariate analyses in both models. Multivariate analysis for model 1 was able to distinguish both groups, while for model 2, the results showed the correct classification of all BIO samples within their group. Thus, this methodology can be of great importance for further understanding the role of these metabolites in allergic diseases as potential biomarkers for disease severity and for predicting patient treatment response.

Keywords:  allergic inflammation; allergy; anaphylaxis; asthma; dynamic multiple reaction monitoring; liquid chromatography coupled to a triple quadrupole mass spectrometry; targeted metabolomics

DOI:  https://doi.org/10.3390/metabo12070592
J Proteome Res. 2022 Jul 24.

Enhancement of Proteome Coverage by Ion Mobility Fractionation Coupled to PASEF on a TIMS-QTOF Instrument.

Jennifer Guergues, Jessica Wohlfahrt, Stanley M Stevens.

  Trapped ion-mobility spectrometry (TIMS) was used to fractionate ions in the gas phase based on their ion mobility (V s/cm2), followed by parallel accumulation-serial fragmentation (PASEF) using a quadrupole time-of-flight instrument to determine the effect on the depth of proteome coverage. TIMS fractionation (up to four gas-phase fractions) coupled to data-dependent acquisition (DDA)-PASEF resulted in the detection of ∼7000 proteins and over 70,000 peptides overall from 200 ng of human (HeLa) cell lysate per injection using a commercial 25 cm ultra high performance liquid chromatography (UHPLC) column with a 90 min gradient. This result corresponded to ∼19 and 30% increases in protein and peptide identifications, respectively, when compared to a default, single-range TIMS DDA-PASEF analysis. Quantitation precision was not affected by TIMS fractionation as demonstrated by the average and median coefficient of variation values that were less than 4% upon label-free quantitation of technical replicates. TIMS fractionation was utilized to generate a DDA-based spectral library for downstream data-independent acquisition (DIA) analysis of lower sample input using a shorter LC gradient. The TIMS-fractionated library, consisting of over 7600 proteins and 82,000 peptides, enabled the identification of ∼4000 and 6600 proteins from 10 and 200 ng of human (HeLa) cell lysate input, respectively, with a 20 min gradient, single-shot DIA analysis. Data are available in ProteomeXchange: identifier PXD033129.

Keywords:  DDA-PASEF; DIA-PASEF; gas-phase fractionation; ion mobility; proteomics; spectral library; timsTOF Pro

DOI:  https://doi.org/10.1021/acs.jproteome.2c00336
Methods Mol Biol. 2022 ;2539 235-260

High-Throughput Profiling of Metabolic Phenotypes Using High-Resolution GC-MS.

Nishikant Wase, Nathan Abshire, Toshihiro Obata.

  Metabolite profiling provides insights into the metabolic signatures, which themselves are considered as phonotypes closely related to the agronomic and phenotypic traits such as yield, nutritional values, stress resistance, and nutrient use efficiency. GC-MS is a sensitive and high-throughput analytical platform and has been proved to be a vital tool for the analysis of primary metabolism to provide an overview of cellular and organismal metabolic status. The potential of GC-MS metabolite profiling as a tool for detecting metabolic changes in plants grown in a high-throughput plant phenotyping platform was explored. In this chapter, we describe an integrated workflow of semi-targeted GC-high-resolution (HR)-time-of-flight (TOF)-MS metabolomics with both the analytical and computational steps, focusing mainly on the sample preparation, GC-HR-TOF-MS analysis part, and data analysis for plant phenotyping efforts.

Keywords:  GC-HR-TOF-MS; Metabolomics; Phenomics; Plant phenotyping

DOI:  https://doi.org/10.1007/978-1-0716-2537-8_19
Am J Physiol Cell Physiol. 2022 Jul 25.

Rapid nutrient depletion to below the physiological range by cancer cells cultured in Plasmax.

Georgina Gardner, Feresteh Moradi, Christopher Moffatt, Meagan Cliche, Bianca Garlisi, John Gratton, Fatima Mehmood, Jeffrey A Stuart.

  Mammalian cell culture is a fundamental tool used to study living cells. Presently, the standard protocol for performing cell culture involves the use of commercial media that contain an excess of nutrients. While this reduces the likelihood of cell starvation, it creates non-physiologic culture conditions that have been shown to 're-wire' cellular metabolism. Recently, researchers have developed new media like Plasmax, formulated to approximate the nutrient composition of human blood plasma. Although this represents an improvement in cell culture practice, physiologic media may be vulnerable to nutrient depletion. In this study we directly addressed this concern by measuring the rates of glucose and amino acid depletion from Plasmax in several cancer cell lines (PC-3, LNCaP, MCF-7, SH-SY5Y) over 48 hours. In all cell lines, depletion of glucose from Plasmax was rapid such that, by 48h, cells were hypoglycemic (<2mM glucose). Most amino acids were similarly rapidly depleted to sub-physiological levels by 48h. In contrast, glucose and most amino acids remained within the physiological range at 24h. When the experiment was done at physiological oxygen (5%) versus standard (18%)with LNCaP cells, no effect on glucose or amino acid consumption was observed. Using RNA sequencing, we show that this nutrient depletion is associated with enrichment of starvation responses, apoptotic signalling, and endoplasmic reticulum stress. A shift from glycolytic metabolism to mitochondrial respiration at 5% O2 was also measured using Seahorse analysis. Taken together, these results exemplify the metabolic considerations for Plasmax, highlighting that cell culture in Plasmax requires daily media exchange.

Keywords:  amino acids; metabolism; metabolomics; physiologic cell culture; physioxia

DOI:  https://doi.org/10.1152/ajpcell.00403.2021
Methods Protoc. 2022 Jul 10. pii: 57. [Epub ahead of print]5(4):

A Protocol for the Acquisition of Comprehensive Proteomics Data from Single Cases Using Formalin-Fixed Paraffin Embedded Sections.

Mitchell Acland, Parul Mittal, Georgia Arentz, Fergus Whitehead, Peter Hoffmann, Manuela Klingler-Hoffmann, Martin K Oehler.

  The molecular analysis of small or rare patient tissue samples is challenging and often limited by available technologies and resources, such as reliable antibodies against a protein of interest. Although targeted approaches provide some insight, here, we describe the workflow of two complementary mass spectrometry approaches, which provide a more comprehensive and non-biased analysis of the molecular features of the tissue of interest. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) generates spatial intensity maps of molecular features, which can be easily correlated with histology. Additionally, liquid chromatography tandem mass spectrometry (LC-MS/MS) can identify and quantify proteins of interest from a consecutive section of the same tissue. Here, we present data from concurrent precancerous lesions from the endometrium and fallopian tube of a single patient. Using this complementary approach, we monitored the abundance of hundreds of proteins within the precancerous and neighboring healthy regions. The method described here represents a useful tool to maximize the number of molecular data acquired from small sample sizes or even from a single case. Our initial data are indicative of a migratory phenotype in these lesions and warrant further research into their malignant capabilities.

Keywords:  LC-MS/MS; MALDI mass spectrometry imaging; endometrial intraepithelial carcinoma; high grade serous ovarian carcinoma; laser capture microdissection; proteomics; serous endometrial carcinoma; serous tubal intraepithelial carcinoma

DOI:  https://doi.org/10.3390/mps5040057
J Chromatogr A. 2022 Jul 19. pii: S0021-9673(22)00545-3. [Epub ahead of print]1678 463352

Advances in enrichment methods for mass spectrometry-based proteomics analysis of post-translational modifications.

Jessica Brandi, Roberta Noberini, Tiziana Bonaldi, Daniela Cecconi.

  Post-translational modifications (PTMs) occur during or after protein biosynthesis and increase the functional diversity of proteome. They comprise phosphorylation, acetylation, methylation, glycosylation, ubiquitination, sumoylation (among many other modifications), and influence all aspects of cell biology. Mass-spectrometry (MS)-based proteomics is the most powerful approach for PTM analysis. Despite this, it is challenging due to low abundance and labile nature of many PTMs. Hence, enrichment of modified peptides is required for MS analysis. This review provides an overview of most common PTMs and a discussion of current enrichment methods for MS-based proteomics analysis. The traditional affinity strategies, including immunoenrichment, chromatography and protein pull-down, are outlined together with their strengths and shortcomings. Moreover, a special attention is paid to chemical enrichment strategies, such as capture by chemoselective probes, metabolic and chemoenzymatic labelling, which are discussed with an emphasis on their recent progress. Finally, the challenges and future trends in the field are discussed.

Keywords:  Chemical enrichment strategies; Enrichment; Immunoaffinity; Mass spectrometry; Post-translational modifications

DOI:  https://doi.org/10.1016/j.chroma.2022.463352