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Tzanakis K, Nattkemper TW, Niehaus K, Albaum SP. MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data. BMC Bioinformatics 2022; 23:267. [PMID: 35804309 PMCID: PMC9270834 DOI: 10.1186/s12859-022-04793-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 06/14/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Modern mass spectrometry has revolutionized the detection and analysis of metabolites but likewise, let the data skyrocket with repositories for metabolomics data filling up with thousands of datasets. While there are many software tools for the analysis of individual experiments with a few to dozens of chromatograms, we see a demand for a contemporary software solution capable of processing and analyzing hundreds or even thousands of experiments in an integrative manner with standardized workflows. RESULTS Here, we introduce MetHoS as an automated web-based software platform for the processing, storage and analysis of great amounts of mass spectrometry-based metabolomics data sets originating from different metabolomics studies. MetHoS is based on Big Data frameworks to enable parallel processing, distributed storage and distributed analysis of even larger data sets across clusters of computers in a highly scalable manner. It has been designed to allow the processing and analysis of any amount of experiments and samples in an integrative manner. In order to demonstrate the capabilities of MetHoS, thousands of experiments were downloaded from the MetaboLights database and used to perform a large-scale processing, storage and statistical analysis in a proof-of-concept study. CONCLUSIONS MetHoS is suitable for large-scale processing, storage and analysis of metabolomics data aiming at untargeted metabolomic analyses. It is freely available at: https://methos.cebitec.uni-bielefeld.de/ . Users interested in analyzing their own data are encouraged to apply for an account.
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Affiliation(s)
- Konstantinos Tzanakis
- International Research Training Group "Computational Methods for the Analysis of the Diversity and Dynamics of Genomes", Faculty of Technology, Bielefeld University, Bielefeld, Germany.
| | - Tim W Nattkemper
- Biodata Mining Group, Center for Biotechnology (CeBiTec), Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Karsten Niehaus
- Proteome and Metabolome Research, Center for Biotechnology (CeBiTec), Faculty of Biology, Bielefeld University, Bielefeld, Germany
| | - Stefan P Albaum
- Bioinformatics Resource Facility, Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, Germany
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Maria VL, Licha D, Scott-Fordsmand JJ, Huber CG, Amorim MJB. Multiomics assessment in Enchytraeus crypticus exposed to Ag nanomaterials (Ag NM300K) and ions (AgNO 3) - Metabolomics, proteomics (& transcriptomics). ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 286:117571. [PMID: 34438494 DOI: 10.1016/j.envpol.2021.117571] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 06/04/2021] [Accepted: 06/07/2021] [Indexed: 06/13/2023]
Abstract
Silver nanomaterials (AgNMs) are broadly used and among the most studied nanomaterials. The underlying molecular mechanisms (e.g. protein and metabolite response) that precede phenotypical effects have been assessed to a much lesser extent. In this paper, we assess differentially expressed proteins (DEPs) and metabolites (DEMs) by high-throughput (HTP) techniques (HPLC-MS/MS with tandem mass tags, reversed-phase (RP) and hydrophilic interaction liquid chromatography (HILIC) with mass spectrometric detection). In a time series (0, 7, 14 days), the standard soil model Enchytraeus crypticus was exposed to AgNM300K and AgNO3 at the reproduction EC20 and EC50. The impact on proteins/metabolites was clearly larger after 14 days. NM300K caused more upregulated DEPs/DEMs, more so at the EC20, whereas AgNO3 caused a dose response increase of DEPs/DEMs. Similar pathways were activated, although often via opposite regulation (up vs down) of DEPs, hence, dissimilar mechanisms underlie the apical observed impact. Affected pathways included e.g. energy and lipid metabolism and oxidative stress. Uniquely affected by AgNO3 was catalase, malate dehydrogenase and ATP-citrate synthase, and heat shock proteins (HSP70) and ferritin were affected by AgNM300K. The gene expression-based data in Adverse Outcome Pathway was confirmed and additional key events added, e.g. regulation of catalase and heat shock proteins were confirmed to be included. Finally, we observed (as we have seen before) that lower concentration of the NM caused higher biological impact. Data was deposited to ProteomeXchange, identifier PXD024444.
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Affiliation(s)
- Vera L Maria
- Department of Biology, CESAM, University of Aveiro, Aveiro, Portugal.
| | - David Licha
- Department of Biosciences, Bioanalytical Research Labs, University of Salzburg, Salzburg, Austria.
| | | | - Christian G Huber
- Department of Biosciences, Bioanalytical Research Labs, University of Salzburg, Salzburg, Austria.
| | - Mónica J B Amorim
- Department of Biology, CESAM, University of Aveiro, Aveiro, Portugal.
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Untargeted Metabolomics Reveals Molecular Effects of Ketogenic Diet on Healthy and Tumor Xenograft Mouse Models. Int J Mol Sci 2019; 20:ijms20163873. [PMID: 31398922 PMCID: PMC6719192 DOI: 10.3390/ijms20163873] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 08/05/2019] [Accepted: 08/07/2019] [Indexed: 02/07/2023] Open
Abstract
The application of ketogenic diet (KD) (high fat/low carbohydrate/adequate protein) as an auxiliary cancer therapy is a field of growing attention. KD provides sufficient energy supply for healthy cells, while possibly impairing energy production in highly glycolytic tumor cells. Moreover, KD regulates insulin and tumor related growth factors (like insulin growth factor-1, IGF-1). In order to provide molecular evidence for the proposed additional inhibition of tumor growth when combining chemotherapy with KD, we applied untargeted quantitative metabolome analysis on a spontaneous breast cancer xenograft mouse model, using MDA-MB-468 cells. Healthy mice and mice bearing breast cancer xenografts and receiving cyclophosphamide chemotherapy were compared after treatment with control diet and KD. Metabolomic profiling was performed on plasma samples, applying high-performance liquid chromatography coupled to tandem mass spectrometry. Statistical analysis revealed metabolic fingerprints comprising numerous significantly regulated features in the group of mice bearing breast cancer. This fingerprint disappeared after treatment with KD, resulting in recovery to the metabolic status observed in healthy mice receiving control diet. Moreover, amino acid metabolism as well as fatty acid transport were found to be affected by both the tumor and the applied KD. Our results provide clear evidence of a significant molecular effect of adjuvant KD in the context of tumor growth inhibition and suggest additional mechanisms of tumor suppression beyond the proposed constrain in energy supply of tumor cells.
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Pang H, Jia W, Hu Z. Emerging Applications of Metabolomics in Clinical Pharmacology. Clin Pharmacol Ther 2019; 106:544-556. [DOI: 10.1002/cpt.1538] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 05/18/2019] [Indexed: 12/20/2022]
Affiliation(s)
- Huanhuan Pang
- School of Pharmaceutical Sciences Tsinghua University Beijing China
| | - Wei Jia
- Cancer Biology Program University of Hawaii Cancer Center Honolulu Hawaii USA
| | - Zeping Hu
- School of Pharmaceutical Sciences Tsinghua University Beijing China
- Tsinghua‐Peking Joint Center for Life Sciences Tsinghua University Beijing China
- Beijing Frontier Research Center for Biological Structure Tsinghua University Beijing China
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Filtering procedures for untargeted LC-MS metabolomics data. BMC Bioinformatics 2019; 20:334. [PMID: 31200644 PMCID: PMC6570933 DOI: 10.1186/s12859-019-2871-9] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Accepted: 05/02/2019] [Indexed: 12/21/2022] Open
Abstract
Background Untargeted metabolomics datasets contain large proportions of uninformative features that can impede subsequent statistical analysis such as biomarker discovery and metabolic pathway analysis. Thus, there is a need for versatile and data-adaptive methods for filtering data prior to investigating the underlying biological phenomena. Here, we propose a data-adaptive pipeline for filtering metabolomics data that are generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Our data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients. Results Using metabolomics datasets that were generated in our laboratory from samples of human blood, as well as two public LC-MS datasets, we compared our data-adaptive filtering method with traditional methods that rely on non-method specific thresholds. The data-adaptive approach outperformed traditional approaches in terms of removing noisy features and retaining high quality, biologically informative ones. The R code for running the data-adaptive filtering method is provided at https://github.com/courtneyschiffman/Metabolomics-Filtering. Conclusions Our proposed data-adaptive filtering pipeline is intuitive and effectively removes uninformative features from untargeted metabolomics datasets. It is particularly relevant for interrogation of biological phenomena in data derived from complex matrices associated with biospecimens. Electronic supplementary material The online version of this article (10.1186/s12859-019-2871-9) contains supplementary material, which is available to authorized users.
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Maria VL, Licha D, Ranninger C, Scott-Fordsmand JJ, Huber CG, Amorim MJB. The Enchytraeus crypticus stress metabolome – CuO NM case study. Nanotoxicology 2018; 12:766-780. [DOI: 10.1080/17435390.2018.1481237] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Vera L. Maria
- Department of Biology, CESAM, University of Aveiro, Aveiro, Portugal
| | - David Licha
- Biosciences, Bioanalytical Research Labs, University of Salzburg, Salzburg, Austria
| | - Christina Ranninger
- Biosciences, Bioanalytical Research Labs, University of Salzburg, Salzburg, Austria
| | | | - Christian G. Huber
- Biosciences, Bioanalytical Research Labs, University of Salzburg, Salzburg, Austria
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Boyles MSP, Ranninger C, Reischl R, Rurik M, Tessadri R, Kohlbacher O, Duschl A, Huber CG. Copper oxide nanoparticle toxicity profiling using untargeted metabolomics. Part Fibre Toxicol 2016; 13:49. [PMID: 27609141 PMCID: PMC5017021 DOI: 10.1186/s12989-016-0160-6] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Accepted: 08/26/2016] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND The rapidly increasing number of engineered nanoparticles (NPs), and products containing NPs, raises concerns for human exposure and safety. With this increasing, and ever changing, catalogue of NPs it is becoming more difficult to adequately assess the toxic potential of new materials in a timely fashion. It is therefore important to develop methods which can provide high-throughput screening of biological responses. The use of omics technologies, including metabolomics, can play a vital role in this process by providing relatively fast, comprehensive, and cost-effective assessment of cellular responses. These techniques thus provide the opportunity to identify specific toxicity pathways and to generate hypotheses on how to reduce or abolish toxicity. RESULTS We have used untargeted metabolome analysis to determine differentially expressed metabolites in human lung epithelial cells (A549) exposed to copper oxide nanoparticles (CuO NPs). Toxicity hypotheses were then generated based on the affected pathways, and critically tested using more conventional biochemical and cellular assays. CuO NPs induced regulation of metabolites involved in oxidative stress, hypertonic stress, and apoptosis. The involvement of oxidative stress was clarified more easily than apoptosis, which involved control experiments to confirm specific metabolites that could be used as standard markers for apoptosis; based on this we tentatively propose methylnicotinamide as a generic metabolic marker for apoptosis. CONCLUSIONS Our findings are well aligned with the current literature on CuO NP toxicity. We thus believe that untargeted metabolomics profiling is a suitable tool for NP toxicity screening and hypothesis generation.
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Affiliation(s)
- Matthew S. P. Boyles
- Department of Molecular Biology, Division of Allergy and Immunology, University of Salzburg, Hellbrunner Strasse 34, 5020 Salzburg, Austria
| | - Christina Ranninger
- Department of Molecular Biology, Division of Chemistry and Bioanalytics, University of Salzburg, Hellbrunner Strasse 34, 5020 Salzburg, Austria
| | - Roland Reischl
- Department of Molecular Biology, Division of Chemistry and Bioanalytics, University of Salzburg, Hellbrunner Strasse 34, 5020 Salzburg, Austria
| | - Marc Rurik
- Center for Bioinformatics, University of Tübingen, Tübingen, Germany ,Department of Computer Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany
| | - Richard Tessadri
- Faculty of Geo- and Atmospheric Science, Institute of Mineralogy and Petrography, University of Innsbruck, Innrain 52, 6020 Innsbruck, Austria
| | - Oliver Kohlbacher
- Center for Bioinformatics, University of Tübingen, Tübingen, Germany ,Department of Computer Science, University of Tübingen, Sand 14, 72076 Tübingen, Germany ,Quantitative Biology Center, University of Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany ,Faculty of Medicine, University of Tübingen, Geissweg 3, 72076 Tübingen, Germany ,Max Planck Institute for Developmental Biology, Spemannstraße 35, 72076 Tübingen, Germany
| | - Albert Duschl
- Department of Molecular Biology, Division of Allergy and Immunology, University of Salzburg, Hellbrunner Strasse 34, 5020 Salzburg, Austria
| | - Christian G. Huber
- Department of Molecular Biology, Division of Chemistry and Bioanalytics, University of Salzburg, Hellbrunner Strasse 34, 5020 Salzburg, Austria
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Ranninger C, Schmidt LE, Rurik M, Limonciel A, Jennings P, Kohlbacher O, Huber CG. Improving global feature detectabilities through scan range splitting for untargeted metabolomics by high-performance liquid chromatography-Orbitrap mass spectrometry. Anal Chim Acta 2016; 930:13-22. [PMID: 27265900 DOI: 10.1016/j.aca.2016.05.017] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2016] [Revised: 05/13/2016] [Accepted: 05/16/2016] [Indexed: 12/14/2022]
Abstract
Untargeted metabolomics aims at obtaining quantitative information on the highest possible number of low-molecular biomolecules present in a biological sample. Rather small changes in mass spectrometric spectrum acquisition parameters may have a significant influence on the detectabilities of metabolites in untargeted global-scale studies by means of high-performance liquid chromatography-mass spectrometry (HPLC-MS). Employing whole cell lysates of human renal proximal tubule cells, we present a systematic global-scale study of the influence of mass spectrometric scan parameters and post-acquisition data treatment on the number and intensity of metabolites detectable in whole cell lysates. Ion transmission and ion collection efficiencies in an Orbitrap-based mass spectrometer basically depend on the m/z range scanned, which, ideally, requires different instrument settings for the respective mass ranges investigated. Therefore, we split a full scan range of m/z 50-1000 relevant for metabolites into two separate segments (m/z 50-200 and m/z 200-1,000), allowing an independent tuning of the ion transmission parameters for both mass ranges. Three different implementations, involving either scanning from m/z 50-1000 in a single scan, or scanning from m/z 50-200 and from m/z 200-1000 in two alternating scans, or performing two separate HPLC-MS runs with m/z 50-200 and m/z 200-1000 scan ranges were critically assessed. The detected features were subjected to rigorous background filtering and quality control in order to obtain reliable metabolite features for subsequent differential quantification. The most efficient approach in terms of feature number, which forms the basis for statistical analysis, identification, and for generating biological hypotheses, was the separate analysis of two different mass ranges. This lead to an increase in the number of detectable metabolite features, especially in the higher mass range (m/z greater than 400), by 2.5 (negative mode) to 6-fold (positive mode) as compared to analysis involving a single scan range. The total number of features confidently detectable was 560 in positive ion mode, and 436 in negative ion mode.
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Affiliation(s)
- Christina Ranninger
- Department of Molecular Biology, Division of Chemistry and Bioanalytics, University of Salzburg, Salzburg, Austria.
| | - Lukas E Schmidt
- Department of Molecular Biology, Division of Chemistry and Bioanalytics, University of Salzburg, Salzburg, Austria.
| | - Marc Rurik
- Center for Bioinformatics, University of Tübingen, Tübingen, Germany; Department of Computer Science, University of Tübingen, Germany.
| | - Alice Limonciel
- Department of Physiology and Medical Physics, Division of Physiology, Medical University of Innsbruck, Innsbruck, 6020, Austria.
| | - Paul Jennings
- Department of Physiology and Medical Physics, Division of Physiology, Medical University of Innsbruck, Innsbruck, 6020, Austria.
| | - Oliver Kohlbacher
- Center for Bioinformatics, University of Tübingen, Tübingen, Germany; Department of Computer Science, University of Tübingen, Germany; Quantitative Biology Center, University of Tübingen, Germany; Faculty of Medicine, University of Tübingen, Germany; Biomolecular Interactions, Max Planck Institute for Developmental Biology, Tübingen, Germany.
| | - Christian G Huber
- Department of Molecular Biology, Division of Chemistry and Bioanalytics, University of Salzburg, Salzburg, Austria.
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