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Pfeuffer J, Bielow C, Wein S, Jeong K, Netz E, Walter A, Alka O, Nilse L, Colaianni PD, McCloskey D, Kim J, Rosenberger G, Bichmann L, Walzer M, Veit J, Boudaud B, Bernt M, Patikas N, Pilz M, Startek MP, Kutuzova S, Heumos L, Charkow J, Sing JC, Feroz A, Siraj A, Weisser H, Dijkstra TMH, Perez-Riverol Y, Röst H, Kohlbacher O, Sachsenberg T. OpenMS 3 enables reproducible analysis of large-scale mass spectrometry data. Nat Methods 2024; 21:365-367. [PMID: 38366242 DOI: 10.1038/s41592-024-02197-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2024]
Affiliation(s)
- Julianus Pfeuffer
- Algorithmic Bioinformatics, Freie Universität Berlin, Berlin, Germany
- Visual and Data-Centric Computing, Zuse Institute Berlin, Berlin, Germany
| | - Chris Bielow
- Bioinformatics Solution Center, Institut für Mathematik und Informatik, Freie Universität Berlin, Berlin, Germany
| | - Samuel Wein
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tuebingen, Tübingen, Germany
| | - Kyowon Jeong
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tuebingen, Tübingen, Germany
| | - Eugen Netz
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tuebingen, Tübingen, Germany
| | - Axel Walter
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tuebingen, Tübingen, Germany
| | - Oliver Alka
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tuebingen, Tübingen, Germany
| | - Lars Nilse
- Institute of Molecular Medicine and Cell Research, University of Freiburg, Freiburg, Germany
| | | | - Douglas McCloskey
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
- BioMed X Institute, Heidelberg, Germany
| | - Jihyung Kim
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tuebingen, Tübingen, Germany
| | | | - Leon Bichmann
- Yale Center for Systems and Engineering Immunology and Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Mathias Walzer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), , Wellcome Trust Genome Campus, Hinxton, UK
| | - Johannes Veit
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tuebingen, Tübingen, Germany
| | - Bertrand Boudaud
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Matthias Bernt
- Department of Computational Biology, Helmholtz Centre for Environmental Research GmbH-UFZ, Leipzig, Germany
| | - Nikolaos Patikas
- Evergrande Center for Immunologic Diseases Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| | - Matteo Pilz
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tuebingen, Tübingen, Germany
| | - Michał Piotr Startek
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
- Institute for Immunology, University Medical Center of the Johannes-Gutenberg University, Mainz, Germany
| | - Svetlana Kutuzova
- Department of Computer Science/Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Lukas Heumos
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Oberschleißheim, Germany
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center with the CPC-M bioArchive, Helmholtz Zentrum Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Joshua Charkow
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Justin Cyril Sing
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Ayesha Feroz
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tuebingen, Tübingen, Germany
| | - Arslan Siraj
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tuebingen, Tübingen, Germany
| | | | - Tjeerd M H Dijkstra
- Department for Women's Health, University Clinic Tübingen, Tübingen, Germany
- Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen, Germany
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), , Wellcome Trust Genome Campus, Hinxton, UK
| | - Hannes Röst
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Oliver Kohlbacher
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tuebingen, Tübingen, Germany
- Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen, Germany
| | - Timo Sachsenberg
- Applied Bioinformatics, Department of Computer Science, University of Tuebingen, Tübingen, Germany.
- Institute for Bioinformatics and Medical Informatics, University of Tuebingen, Tübingen, Germany.
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2
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Chandel S, Bhattacharya A, Gautam A, Zeng W, Alka O, Sachsenberg T, Gupta GD, Narang RK, Ravichandiran V, Singh R. Investigation of the anti-cancer potential of epoxyazadiradione in neuroblastoma: experimental assays and molecular analysis. J Biomol Struct Dyn 2023:1-19. [PMID: 37753734 DOI: 10.1080/07391102.2023.2262593] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
Neuroblastoma, the most common childhood solid tumor, originates from primitive sympathetic nervous system cells. Epoxyazadiradione (EAD) is a limonoid derived from Azadirachta indica, belonging to the family Meliaceae. In this study, we isolated the EAD from Azadirachta indica seed and studied the anti-cancer potential against neuroblastoma. Herein, EAD demonstrated significant efficacy against neuroblastoma by suppressing cell proliferation, enhancing the rate of apoptosis and cycle arrest at the SubG0 and G2/M phases. EAD enhanced the pro-apoptotic Caspase 3 and Caspase 9 and inhibited the NF-kβ translocation in a dose-dependent manner. In order to identify the specific EAD target, a gel-free quantitative proteomics study on SH-SY5Y cells using Liquid Chromatography with tandem mass spectrometry was done in a dose-dependent manner, followed by detailed bioinformatics analysis to identify effects on protein. Proteomics data identified that Enolase1 and HSP90 were up-regulated in neuroblastoma. EAD inhibited the expression of Enolase1 and HSP90, validated by mRNA expression, immunoblotting, Enolase1 and HSP90 kit and flow-cytometry based bioassay. Molecular docking study, Molecular dynamic simulation, and along with molecular mechanics/Poisson-Boltzmann surface area analysis also suggested that EAD binds at the active site of the proteins and were stable throughout the 100 ns Molecular dynamic simulation study. Overall, this study suggested EAD exhibited anti-cancer activity against neuroblastoma by targeting Enolase1 and HSP90 pathways.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shivani Chandel
- Department of Pharmacognosy, ISF College of Pharmacy, Moga, Punjab, India
| | - Arka Bhattacharya
- Department of Pharmaceutical Chemistry, ISF College of Pharmacy, Moga, Punjab, India
| | - Anupam Gautam
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
- International Max Planck Research School "From Molecules to Organisms", Max Planck Institute for Biology Tübingen, Tübingen, Germany
- Cluster of Excellence: EXC 2124: Controlling Microbes to Fight Infection, University of Tübingen, Tübingen, Germany
| | - Wenhuan Zeng
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
| | - Oliver Alka
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
| | - Timo Sachsenberg
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen, Germany
| | - G D Gupta
- Department of Pharmaceutics, ISF College of Pharmacy, Moga, Punjab, India
| | - Raj Kumar Narang
- Department of Pharmaceutics, ISF College of Pharmacy, Moga, Punjab, India
| | - V Ravichandiran
- Department of Natural Products, National Institute of Pharmaceutical Education and Research, Kolkata, India
| | - Rajveer Singh
- Department of Pharmacognosy, ISF College of Pharmacy, Moga, Punjab, India
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3
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Kontou EE, Walter A, Alka O, Pfeuffer J, Sachsenberg T, Mohite OS, Nuhamunada M, Kohlbacher O, Weber T. UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis. J Cheminform 2023; 15:52. [PMID: 37173725 PMCID: PMC10176759 DOI: 10.1186/s13321-023-00724-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 04/27/2023] [Indexed: 05/15/2023] Open
Abstract
Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for untargeted metabolomics that combines algorithms for data pre-processing, spectral matching, molecular formula and structural predictions, and an integration to the GNPS workflows Feature-Based Molecular Networking and Ion Identity Molecular Networking for downstream analysis. UmetaFlow is implemented as a Snakemake workflow, making it easy to use, scalable, and reproducible. For more interactive computing, visualization, as well as development, the workflow is also implemented in Jupyter notebooks using the Python programming language and a set of Python bindings to the OpenMS algorithms (pyOpenMS). Finally, UmetaFlow is also offered as a web-based Graphical User Interface for parameter optimization and processing of smaller-sized datasets. UmetaFlow was validated with in-house LC-MS/MS datasets of actinomycetes producing known secondary metabolites, as well as commercial standards, and it detected all expected features and accurately annotated 76% of the molecular formulas and 65% of the structures. As a more generic validation, the publicly available MTBLS733 and MTBLS736 datasets were used for benchmarking, and UmetaFlow detected more than 90% of all ground truth features and performed exceptionally well in quantification and discriminating marker selection. We anticipate that UmetaFlow will provide a useful platform for the interpretation of large metabolomics datasets.
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Affiliation(s)
- Eftychia E Kontou
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet Building 220, 2800, Kgs. Lyngby, Denmark
| | - Axel Walter
- Applied Bioinformatics, Department of Computer Science, Eberhard Karls University Tübingen, Sand 14, 72076, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Sand 14, 72076, Tübingen, Germany
| | - Oliver Alka
- Applied Bioinformatics, Department of Computer Science, Eberhard Karls University Tübingen, Sand 14, 72076, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Sand 14, 72076, Tübingen, Germany
| | - Julianus Pfeuffer
- Visual and Data-Centric Computing, Zuse Institute Berlin, Takustr. 7, 14195, Berlin, Germany
- Algorithmic Bioinformatics, Freie Universität Berlin, Takustr. 9, 14195, Berlin, Germany
| | - Timo Sachsenberg
- Applied Bioinformatics, Department of Computer Science, Eberhard Karls University Tübingen, Sand 14, 72076, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Sand 14, 72076, Tübingen, Germany
| | - Omkar S Mohite
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet Building 220, 2800, Kgs. Lyngby, Denmark
| | - Matin Nuhamunada
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet Building 220, 2800, Kgs. Lyngby, Denmark
| | - Oliver Kohlbacher
- Applied Bioinformatics, Department of Computer Science, Eberhard Karls University Tübingen, Sand 14, 72076, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Sand 14, 72076, Tübingen, Germany
- Translational Bioinformatics, University Hospital Tübingen, Schaffhausenstr. 77, 72072, Tübingen, Germany
| | - Tilmann Weber
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet Building 220, 2800, Kgs. Lyngby, Denmark.
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4
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Alka O, Shanthamoorthy P, Witting M, Kleigrewe K, Kohlbacher O, Röst HL. DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics. Nat Commun 2022; 13:1347. [PMID: 35292629 PMCID: PMC8924252 DOI: 10.1038/s41467-022-29006-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 02/18/2022] [Indexed: 11/09/2022] Open
Abstract
The extraction of meaningful biological knowledge from high-throughput mass spectrometry data relies on limiting false discoveries to a manageable amount. For targeted approaches in metabolomics a main challenge is the detection of false positive metabolic features in the low signal-to-noise ranges of data-independent acquisition results and their filtering. Another factor is that the creation of assay libraries for data-independent acquisition analysis and the processing of extracted ion chromatograms have not been automated in metabolomics. Here we present a fully automated open-source workflow for high-throughput metabolomics that combines data-dependent and data-independent acquisition for library generation, analysis, and statistical validation, with rigorous control of the false-discovery rate while matching manual analysis regarding quantification accuracy. Using an experimentally specific data-dependent acquisition library based on reference substances allows for accurate identification of compounds and markers from data-independent acquisition data in low concentrations, facilitating biomarker quantification.
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Affiliation(s)
- Oliver Alka
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen, Germany. .,Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany.
| | - Premy Shanthamoorthy
- Terrence Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - Michael Witting
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, Neuherberg, Germany.,Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, Neuherberg, Germany.,Chair of Analytical Food Chemistry, School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Karin Kleigrewe
- Bavarian Center for Biomolecular Mass Spectrometry, Technical University of Munich, Freising, Germany
| | - Oliver Kohlbacher
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen, Germany.,Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany.,Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen, Germany
| | - Hannes L Röst
- Terrence Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, Canada. .,Department of Computer Science, University of Toronto, Toronto, Canada.
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5
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Morgenstern M, Peikert CD, Lübbert P, Suppanz I, Klemm C, Alka O, Steiert C, Naumenko N, Schendzielorz A, Melchionda L, Mühlhäuser WWD, Knapp B, Busch JD, Stiller SB, Dannenmaier S, Lindau C, Licheva M, Eickhorst C, Galbusera R, Zerbes RM, Ryan MT, Kraft C, Kozjak-Pavlovic V, Drepper F, Dennerlein S, Oeljeklaus S, Pfanner N, Wiedemann N, Warscheid B. Quantitative high-confidence human mitochondrial proteome and its dynamics in cellular context. Cell Metab 2021; 33:2464-2483.e18. [PMID: 34800366 PMCID: PMC8664129 DOI: 10.1016/j.cmet.2021.11.001] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 09/01/2021] [Accepted: 11/01/2021] [Indexed: 12/18/2022]
Abstract
Mitochondria are key organelles for cellular energetics, metabolism, signaling, and quality control and have been linked to various diseases. Different views exist on the composition of the human mitochondrial proteome. We classified >8,000 proteins in mitochondrial preparations of human cells and defined a mitochondrial high-confidence proteome of >1,100 proteins (MitoCoP). We identified interactors of translocases, respiratory chain, and ATP synthase assembly factors. The abundance of MitoCoP proteins covers six orders of magnitude and amounts to 7% of the cellular proteome with the chaperones HSP60-HSP10 being the most abundant mitochondrial proteins. MitoCoP dynamics spans three orders of magnitudes, with half-lives from hours to months, and suggests a rapid regulation of biosynthesis and assembly processes. 460 MitoCoP genes are linked to human diseases with a strong prevalence for the central nervous system and metabolism. MitoCoP will provide a high-confidence resource for placing dynamics, functions, and dysfunctions of mitochondria into the cellular context.
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Affiliation(s)
- Marcel Morgenstern
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Christian D Peikert
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Philipp Lübbert
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany; Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Ida Suppanz
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Cinzia Klemm
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Oliver Alka
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Conny Steiert
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany
| | - Nataliia Naumenko
- Department of Cellular Biochemistry, University Medical Center Göttingen, 37073 Göttingen, Germany
| | - Alexander Schendzielorz
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Laura Melchionda
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany; CIBSS Centre for Integrative Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany
| | - Wignand W D Mühlhäuser
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Bettina Knapp
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Jakob D Busch
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany
| | - Sebastian B Stiller
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany
| | - Stefan Dannenmaier
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Caroline Lindau
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany
| | - Mariya Licheva
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany; Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Christopher Eickhorst
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany; Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany; Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg, Germany
| | - Riccardo Galbusera
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Ralf M Zerbes
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany
| | - Michael T Ryan
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, 3800 Melbourne, VIC, Australia
| | - Claudine Kraft
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany; CIBSS Centre for Integrative Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany
| | - Vera Kozjak-Pavlovic
- Department of Microbiology, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Friedel Drepper
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Sven Dennerlein
- Department of Cellular Biochemistry, University Medical Center Göttingen, 37073 Göttingen, Germany
| | - Silke Oeljeklaus
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany
| | - Nikolaus Pfanner
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany; CIBSS Centre for Integrative Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany; BIOSS Centre for Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany
| | - Nils Wiedemann
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, University of Freiburg, 79104 Freiburg, Germany; CIBSS Centre for Integrative Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany; BIOSS Centre for Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany.
| | - Bettina Warscheid
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany; CIBSS Centre for Integrative Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany; BIOSS Centre for Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany.
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6
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Bichmann L, Gupta S, Rosenberger G, Kuchenbecker L, Sachsenberg T, Ewels P, Alka O, Pfeuffer J, Kohlbacher O, Röst H. DIAproteomics: A Multifunctional Data Analysis Pipeline for Data-Independent Acquisition Proteomics and Peptidomics. J Proteome Res 2021; 20:3758-3766. [PMID: 34153189 DOI: 10.1021/acs.jproteome.1c00123] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Data-independent acquisition (DIA) is becoming a leading analysis method in biomedical mass spectrometry. The main advantages include greater reproducibility and sensitivity and a greater dynamic range compared with data-dependent acquisition (DDA). However, the data analysis is complex and often requires expert knowledge when dealing with large-scale data sets. Here we present DIAproteomics, a multifunctional, automated, high-throughput pipeline implemented in the Nextflow workflow management system that allows one to easily process proteomics and peptidomics DIA data sets on diverse compute infrastructures. The central components are well-established tools such as the OpenSwathWorkflow for the DIA spectral library search and PyProphet for the false discovery rate assessment. In addition, it provides options to generate spectral libraries from existing DDA data and to carry out the retention time and chromatogram alignment. The output includes annotated tables and diagnostic visualizations from the statistical postprocessing and computation of fold-changes across pairwise conditions, predefined in an experimental design. DIAproteomics is well documented open-source software and is available under a permissive license to the scientific community at https://www.openms.de/diaproteomics/.
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Affiliation(s)
- Leon Bichmann
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen 72076, Germany.,Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen 72076, Germany
| | - Shubham Gupta
- Donnelly Center for Biomolecular Research, University of Toronto, Toronto, Ontario ON M5S 3E1, Canada
| | - George Rosenberger
- Department of Systems Biology, Columbia University, New York, New York 10032, United States
| | - Leon Kuchenbecker
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen 72076, Germany
| | - Timo Sachsenberg
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen 72076, Germany
| | - Phil Ewels
- Science for Life Laboratory (SciLifeLab), Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Oliver Alka
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen 72076, Germany
| | - Julianus Pfeuffer
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen 72076, Germany.,Institute for Informatics, Freie Universität Berlin, Berlin 14195, Germany.,Zuse Institute Berlin, Berlin 14195, Germany
| | - Oliver Kohlbacher
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen 72076, Germany.,Institute for Biological and Medical Informatics, University of Tübingen, Tübingen 72076, Germany.,Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen 72076, Germany
| | - Hannes Röst
- Donnelly Center for Biomolecular Research, University of Toronto, Toronto, Ontario ON M5S 3E1, Canada
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7
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Kutuzova S, Colaianni P, Röst H, Sachsenberg T, Alka O, Kohlbacher O, Burla B, Torta F, Schrübbers L, Kristensen M, Nielsen L, Herrgård MJ, McCloskey D. SmartPeak Automates Targeted and Quantitative Metabolomics Data Processing. Anal Chem 2020; 92:15968-15974. [PMID: 33269929 DOI: 10.1021/acs.analchem.0c03421] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Technological advances in high-resolution mass spectrometry (MS) vastly increased the number of samples that can be processed in a life science experiment, as well as volume and complexity of the generated data. To address the bottleneck of high-throughput data processing, we present SmartPeak (https://github.com/AutoFlowResearch/SmartPeak), an application that encapsulates advanced algorithms to enable fast, accurate, and automated processing of capillary electrophoresis-, gas chromatography-, and liquid chromatography (LC)-MS(/MS) data and high-pressure LC data for targeted and semitargeted metabolomics, lipidomics, and fluxomics experiments. The application allows for an approximate 100-fold reduction in the data processing time compared to manual processing while enhancing quality and reproducibility of the results.
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Affiliation(s)
- Svetlana Kutuzova
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Lyngby 2800, Denmark
| | - Pasquale Colaianni
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Lyngby 2800, Denmark
| | - Hannes Röst
- Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, 1 King's College Circle, Toronto M5S 1A8, Canada.,Department of Computer Science, University of Toronto, 40 St. George Street, Toronto M5T 3A1, Canada
| | - Timo Sachsenberg
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Geschwister-Scholl-Platz, Tübingen 72076, Germany.,Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen 72074, Germany
| | - Oliver Alka
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Geschwister-Scholl-Platz, Tübingen 72076, Germany.,Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen 72074, Germany
| | - Oliver Kohlbacher
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Geschwister-Scholl-Platz, Tübingen 72076, Germany.,Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen 72074, Germany.,Biomolecular Interactions, MPI for Developmental Biology, Tübingen 72076, Germany.,Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen 72076, Germany
| | - Bo Burla
- Singapore Lipidomics Incubator (SLING), Life Sciences Institute, National University of Singapore, 21 Lower Kent Ridge Road, Singapore 117456, Singapore
| | - Federico Torta
- Singapore Lipidomics Incubator (SLING), Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 21 Lower Kent Ridge Road, Singapore 117597, Singapore
| | - Lars Schrübbers
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Lyngby 2800, Denmark
| | - Mette Kristensen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Lyngby 2800, Denmark
| | - Lars Nielsen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Lyngby 2800, Denmark
| | - Markus J Herrgård
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Lyngby 2800, Denmark.,BioInnovation Institute, Ole Maaløes Vej 3, Copenhagen N 2830, Denmark
| | - Douglas McCloskey
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet 220, Lyngby 2800, Denmark
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8
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Scheidt T, Alka O, Gonczarowska-Jorge H, Gruber W, Rathje F, Dell’Aica M, Rurik M, Kohlbacher O, Zahedi RP, Aberger F, Huber CG. Phosphoproteomics of short-term hedgehog signaling in human medulloblastoma cells. Cell Commun Signal 2020; 18:99. [PMID: 32576205 PMCID: PMC7310537 DOI: 10.1186/s12964-020-00591-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Accepted: 05/05/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Aberrant hedgehog (HH) signaling is implicated in the development of various cancer entities such as medulloblastoma. Activation of GLI transcription factors was revealed as the driving force upon pathway activation. Increased phosphorylation of essential effectors such as Smoothened (SMO) and GLI proteins by kinases including Protein Kinase A, Casein Kinase 1, and Glycogen Synthase Kinase 3 β controls effector activity, stability and processing. However, a deeper and more comprehensive understanding of phosphorylation in the signal transduction remains unclear, particularly during early response processes involved in SMO activation and preceding GLI target gene regulation. METHODS We applied temporal quantitative phosphoproteomics to reveal phosphorylation dynamics underlying the short-term chemical activation and inhibition of early hedgehog signaling in HH responsive human medulloblastoma cells. Medulloblastoma cells were treated for 5.0 and 15 min with Smoothened Agonist (SAG) to induce and with vismodegib to inhibit the HH pathway. RESULTS Our phosphoproteomic profiling resulted in the quantification of 7700 and 10,000 phosphosites after 5.0 and 15 min treatment, respectively. The data suggest a central role of phosphorylation in the regulation of ciliary assembly, trafficking, and signal transduction already after 5.0 min treatment. ERK/MAPK signaling, besides Protein Kinase A signaling and mTOR signaling, were differentially regulated after short-term treatment. Activation of Polo-like Kinase 1 and inhibition of Casein Kinase 2A1 were characteristic for vismodegib treatment, while SAG treatment induced Aurora Kinase A activity. Distinctive phosphorylation of central players of HH signaling such as SMO, SUFU, GLI2 and GLI3 was observed only after 15 min treatment. CONCLUSIONS This study provides evidence that phosphorylation triggered in response to SMO modulation dictates the localization of hedgehog pathway components within the primary cilium and affects the regulation of the SMO-SUFU-GLI axis. The data are relevant for the development of targeted therapies of HH-associated cancers including sonic HH-type medulloblastoma. A deeper understanding of the mechanisms of action of SMO inhibitors such as vismodegib may lead to the development of compounds causing fewer adverse effects and lower frequencies of drug resistance. Video Abstract.
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Affiliation(s)
- Tamara Scheidt
- Department of Biosciences, Bioanalytical Research Laboratories and Molecular Cancer Research and Tumor Immunology, Cancer Cluster Salzburg, University of Salzburg, Hellbrunner Straße 34, 5020 Salzburg, Austria
| | - Oliver Alka
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Sand 14, 72076 Tübingen, Germany
| | - Humberto Gonczarowska-Jorge
- Leibniz-Institute of Analytical Sciences- ISAS - e.V, Dortmund, Germany
- Present address: CAPES Foundation, Ministry of Education of Brazil, Brasília, DF 70040-020 Brazil
| | - Wolfgang Gruber
- Department of Biosciences, Bioanalytical Research Laboratories and Molecular Cancer Research and Tumor Immunology, Cancer Cluster Salzburg, University of Salzburg, Hellbrunner Straße 34, 5020 Salzburg, Austria
- Present address: EVER Valinject GmbH, 4866 Unterach am Attersee, Austria
| | - Florian Rathje
- Department of Biosciences, Bioanalytical Research Laboratories and Molecular Cancer Research and Tumor Immunology, Cancer Cluster Salzburg, University of Salzburg, Hellbrunner Straße 34, 5020 Salzburg, Austria
| | | | - Marc Rurik
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Sand 14, 72076 Tübingen, Germany
| | - Oliver Kohlbacher
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Sand 14, 72076 Tübingen, Germany
- Biomolecular Interactions, Max Planck Institute for Developmental Biology, Max-Planck-Ring 5, 72076 Tübingen, Germany
- Institute for Translational Bioinformatics, University Hospital Tübingen, Hoppe-Seyler-Str. 9, 72076 Tübingen, Germany
- Applied Bioinformatics, Center for Bioinformatics, University of Tübingen, Sand 14, 72076 Tübingen, Germany
| | - René P. Zahedi
- Leibniz-Institute of Analytical Sciences- ISAS - e.V, Dortmund, Germany
- Gerald Bronfman Department of Oncology, Jewish General Hospital, McGill University, Montreal, Canada
- Segal Cancer Proteomics Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Canada
| | - Fritz Aberger
- Department of Biosciences, Bioanalytical Research Laboratories and Molecular Cancer Research and Tumor Immunology, Cancer Cluster Salzburg, University of Salzburg, Hellbrunner Straße 34, 5020 Salzburg, Austria
| | - Christian G. Huber
- Department of Biosciences, Bioanalytical Research Laboratories and Molecular Cancer Research and Tumor Immunology, Cancer Cluster Salzburg, University of Salzburg, Hellbrunner Straße 34, 5020 Salzburg, Austria
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9
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Abstract
This chapter describes the open-source tool suite OpenMS. OpenMS contains more than 180 tools which can be combined to build complex and flexible data-processing workflows. The broad range of functionality and the interoperability of these tools enable complex, complete, and reproducible data analysis workflows in computational proteomics and metabolomics. We introduce the key concepts of OpenMS and illustrate its capabilities with a complete workflow for the analysis of untargeted metabolomics data, including metabolite quantification and identification.
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Affiliation(s)
- Marc Rurik
- Applied Bioinformatics Group, University of Tübingen, Tübingen, Germany
| | - Oliver Alka
- Applied Bioinformatics Group, University of Tübingen, Tübingen, Germany
| | - Fabian Aicheler
- Applied Bioinformatics Group, University of Tübingen, Tübingen, Germany
| | - Oliver Kohlbacher
- Applied Bioinformatics Group, University of Tübingen, Tübingen, Germany.
- Biomolecular Interactions, Max Planck Institute for Developmental Biology, Tübingen, Germany.
- Quantitative Biology Center, University of Tübingen, Tübingen, Germany.
- Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen, Germany.
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany.
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10
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Hoffmann N, Rein J, Sachsenberg T, Hartler J, Haug K, Mayer G, Alka O, Dayalan S, Pearce JTM, Rocca-Serra P, Qi D, Eisenacher M, Perez-Riverol Y, Vizcaíno JA, Salek RM, Neumann S, Jones AR. mzTab-M: A Data Standard for Sharing Quantitative Results in Mass Spectrometry Metabolomics. Anal Chem 2019; 91:3302-3310. [PMID: 30688441 PMCID: PMC6660005 DOI: 10.1021/acs.analchem.8b04310] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Accepted: 01/28/2019] [Indexed: 12/29/2022]
Abstract
Mass spectrometry (MS) is one of the primary techniques used for large-scale analysis of small molecules in metabolomics studies. To date, there has been little data format standardization in this field, as different software packages export results in different formats represented in XML or plain text, making data sharing, database deposition, and reanalysis highly challenging. Working within the consortia of the Metabolomics Standards Initiative, Proteomics Standards Initiative, and the Metabolomics Society, we have created mzTab-M to act as a common output format from analytical approaches using MS on small molecules. The format has been developed over several years, with input from a wide range of stakeholders. mzTab-M is a simple tab-separated text format, but importantly, the structure is highly standardized through the design of a detailed specification document, tightly coupled to validation software, and a mandatory controlled vocabulary of terms to populate it. The format is able to represent final quantification values from analyses, as well as the evidence trail in terms of features measured directly from MS (e.g., LC-MS, GC-MS, DIMS, etc.) and different types of approaches used to identify molecules. mzTab-M allows for ambiguity in the identification of molecules to be communicated clearly to readers of the files (both people and software). There are several implementations of the format available, and we anticipate widespread adoption in the field.
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Affiliation(s)
- Nils Hoffmann
- Leibniz-Institut
für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Straße 6b, 44227 Dortmund, Germany
| | - Joel Rein
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, United Kingdom
| | - Timo Sachsenberg
- Applied Bioinformatics
Group, Center for Bioinformatics, University
of Tübingen, Sand
14, 72076 Tübingen, Germany
| | - Jürgen Hartler
- Institute of Computational Biotechnology, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria
- Center
for Explorative Lipidomics, BioTechMed-Graz, 8010 Graz, Austria
| | - Kenneth Haug
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Gerhard Mayer
- Medizinisches Proteom Center (MPC), Ruhr-Universität
Bochum, Universitätsstraße
150, D-44801 Bochum, Germany
| | - Oliver Alka
- Applied Bioinformatics
Group, Center for Bioinformatics, University
of Tübingen, Sand
14, 72076 Tübingen, Germany
| | - Saravanan Dayalan
- Metabolomics Australia, The University
of Melbourne, Parkville, Victoria 3010, Australia
| | - Jake T. M. Pearce
- MRC-NIHR National Phenome Centre, Department of Surgery & Cancer, Imperial College London, London SW7 2AZ, United Kingdom
| | - Philippe Rocca-Serra
- University of Oxford, e-Research Centre, 7 Keble Road, Oxford OX1
3QG, United Kingdom
| | - Da Qi
- BGI-Shenzhen, Shenzhen, 518083, People’s Republic of China
- Institute
of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Martin Eisenacher
- Medizinisches Proteom Center (MPC), Ruhr-Universität
Bochum, Universitätsstraße
150, D-44801 Bochum, Germany
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Reza M. Salek
- International Agency for Research on Cancer, 150 cours Albert Thomas, 69008 Lyon, France
| | - Steffen Neumann
- Department
of Stress and Developmental Biology, Leibniz
Institute of Plant Biochemistry, 06120 Halle, Germany
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig Deutscher, Platz
5e, 04103 Leipzig, Germany
| | - Andrew R. Jones
- Institute
of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
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11
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Peters K, Worrich A, Weinhold A, Alka O, Balcke G, Birkemeyer C, Bruelheide H, Calf OW, Dietz S, Dührkop K, Gaquerel E, Heinig U, Kücklich M, Macel M, Müller C, Poeschl Y, Pohnert G, Ristok C, Rodríguez VM, Ruttkies C, Schuman M, Schweiger R, Shahaf N, Steinbeck C, Tortosa M, Treutler H, Ueberschaar N, Velasco P, Weiß BM, Widdig A, Neumann S, Dam NMV. Current Challenges in Plant Eco-Metabolomics. Int J Mol Sci 2018; 19:E1385. [PMID: 29734799 PMCID: PMC5983679 DOI: 10.3390/ijms19051385] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 04/24/2018] [Accepted: 04/25/2018] [Indexed: 12/22/2022] Open
Abstract
The relatively new research discipline of Eco-Metabolomics is the application of metabolomics techniques to ecology with the aim to characterise biochemical interactions of organisms across different spatial and temporal scales. Metabolomics is an untargeted biochemical approach to measure many thousands of metabolites in different species, including plants and animals. Changes in metabolite concentrations can provide mechanistic evidence for biochemical processes that are relevant at ecological scales. These include physiological, phenotypic and morphological responses of plants and communities to environmental changes and also interactions with other organisms. Traditionally, research in biochemistry and ecology comes from two different directions and is performed at distinct spatiotemporal scales. Biochemical studies most often focus on intrinsic processes in individuals at physiological and cellular scales. Generally, they take a bottom-up approach scaling up cellular processes from spatiotemporally fine to coarser scales. Ecological studies usually focus on extrinsic processes acting upon organisms at population and community scales and typically study top-down and bottom-up processes in combination. Eco-Metabolomics is a transdisciplinary research discipline that links biochemistry and ecology and connects the distinct spatiotemporal scales. In this review, we focus on approaches to study chemical and biochemical interactions of plants at various ecological levels, mainly plant⁻organismal interactions, and discuss related examples from other domains. We present recent developments and highlight advancements in Eco-Metabolomics over the last decade from various angles. We further address the five key challenges: (1) complex experimental designs and large variation of metabolite profiles; (2) feature extraction; (3) metabolite identification; (4) statistical analyses; and (5) bioinformatics software tools and workflows. The presented solutions to these challenges will advance connecting the distinct spatiotemporal scales and bridging biochemistry and ecology.
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Affiliation(s)
- Kristian Peters
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
| | - Anja Worrich
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger-Str. 159, 07743 Jena, Germany.
- UFZ-Helmholtz-Centre for Environmental Research, Department Environmental Microbiology, Permoserstraße 15, 04318 Leipzig, Germany.
| | - Alexander Weinhold
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger-Str. 159, 07743 Jena, Germany.
| | - Oliver Alka
- Applied Bioinformatics Group, Center for Bioinformatics, University of Tübingen, Sand 14, 72076 Tübingen, Germany.
| | - Gerd Balcke
- Leibniz Institute of Plant Biochemistry, Cell and Metabolic Biology, Weinberg 3, 06120 Halle (Saale), Germany.
| | - Claudia Birkemeyer
- Institute of Analytical Chemistry, University of Leipzig, Linnéstr. 3, 04103 Leipzig, Germany.
| | - Helge Bruelheide
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Am Kirchtor 1, 06108 Halle (Saale), Germany.
| | - Onno W Calf
- Molecular Interaction Ecology, Institute for Water and Wetland Research (IWWR), Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands.
| | - Sophie Dietz
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
| | - Kai Dührkop
- Department of Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany.
| | - Emmanuel Gaquerel
- Centre for Organismal Studies, Heidelberg University, Im Neuenheimer Feld 360, 69120 Heidelberg, Germany.
| | - Uwe Heinig
- Weizmann Institute of Science, Faculty of Biochemistry, Department of Plant Sciences, 234 Herzl St., P.O. Box 26, Rehovot 7610001, Israel.
| | - Marlen Kücklich
- Institute of Biology, University of Leipzig, Talstraße 33, 04109 Leipzig, Germany.
| | - Mirka Macel
- Molecular Interaction Ecology, Institute for Water and Wetland Research (IWWR), Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands.
| | - Caroline Müller
- Chemical Ecology, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany.
| | - Yvonne Poeschl
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Informatics, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06120 Halle (Saale), Germany.
| | - Georg Pohnert
- Institute of Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743 Jena, Germany.
| | - Christian Ristok
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
| | - Victor Manuel Rodríguez
- Group of Genetics, Breeding and Biochemistry of Brassica, Misión Biológica de Galicia (CSIC), Apartado 28, 36080 Pontevedra, Spain.
| | - Christoph Ruttkies
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
| | - Meredith Schuman
- Department of Molecular Ecology, Max Planck Institute for Chemical Ecology, Hans-Knöll-Straße 8, 07745 Jena, Germany.
| | - Rabea Schweiger
- Chemical Ecology, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany.
| | - Nir Shahaf
- Weizmann Institute of Science, Faculty of Biochemistry, Department of Plant Sciences, 234 Herzl St., P.O. Box 26, Rehovot 7610001, Israel.
| | - Christoph Steinbeck
- Institute of Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743 Jena, Germany.
| | - Maria Tortosa
- Group of Genetics, Breeding and Biochemistry of Brassica, Misión Biológica de Galicia (CSIC), Apartado 28, 36080 Pontevedra, Spain.
| | - Hendrik Treutler
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
| | - Nico Ueberschaar
- Institute of Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743 Jena, Germany.
| | - Pablo Velasco
- Group of Genetics, Breeding and Biochemistry of Brassica, Misión Biológica de Galicia (CSIC), Apartado 28, 36080 Pontevedra, Spain.
| | - Brigitte M Weiß
- Institute of Biology, University of Leipzig, Talstraße 33, 04109 Leipzig, Germany.
| | - Anja Widdig
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Biology, University of Leipzig, Talstraße 33, 04109 Leipzig, Germany.
- Research Group of Primate Kin Selection, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany.
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
| | - Nicole M van Dam
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany.
- Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger-Str. 159, 07743 Jena, Germany.
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12
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Pfeuffer J, Sachsenberg T, Alka O, Walzer M, Fillbrunn A, Nilse L, Schilling O, Reinert K, Kohlbacher O. OpenMS – A platform for reproducible analysis of mass spectrometry data. J Biotechnol 2017; 261:142-148. [DOI: 10.1016/j.jbiotec.2017.05.016] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2017] [Revised: 05/17/2017] [Accepted: 05/22/2017] [Indexed: 10/19/2022]
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