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The cyanobacterial amino acid beta-N-methylamino-L-alanine perturbs the intermediary metabolism in neonatal rats
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
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2017 (English)In: Amino Acids, ISSN 0939-4451, E-ISSN 1438-2199, Vol. 49, no 5, 905-919 p., 10.1007/s00726-017-2391-8Article in journal (Refereed) Published
Abstract [en]

The neurotoxic amino acid β-N-methylamino-l-alanine (BMAA) is produced by most cyanobacteria. BMAA is considered as a potential health threat because of its putative role in neurodegenerative diseases. We have previously observed cognitive disturbances and morphological brain changes in adult rodents exposed to BMAA during the development. The aim of this study was to characterize changes of major intermediary metabolites in serum following neonatal exposure to BMAA using a non-targeted metabolomic approach. NMR spectroscopy was used to obtain serum metabolic profiles from neonatal rats exposed to BMAA (40, 150, 460mg/kg) or vehicle on postnatal days 9-10. Multivariate data analysis of binned NMR data indicated metabolic pattern differences between the different treatment groups. In particular five metabolites, d-glucose, lactate, 3-hydroxybutyrate, creatine and acetate, were changed in serum of BMAA-treated neonatal rats. These metabolites are associated with changes in energy metabolism and amino acid metabolism. Further statistical analysis disclosed that all the identified serum metabolites in the lowest dose group were significantly (p<0.05) decreased. The neonatal rat model used in this study is so far the only animal model that displays significant biochemical and behavioral effects after a low short-term dose of BMAA. The demonstrated perturbation of intermediary metabolism may contribute to BMAA-induced developmental changes that result in long-term effects on adult brain function.

Place, publisher, year, edition, pages
2017. Vol. 49, no 5, 905-919 p., 10.1007/s00726-017-2391-8
Keyword [en]
β-N-methylamino-L-alanine, cyanobacteria, energy metabolism, neurotoxin, metabolomics, NMR
National Category
Analytical Chemistry Pharmaceutical Sciences
Research subject
Analytical Pharmaceutical Chemistry; Pharmaceutical Science
Identifiers
URN: urn:nbn:se:uu:diva-205735DOI: 10.1016/j.tox.2013.07.010ISI: 000327005300002PubMedID: 23886855OAI: oai:DiVA.org:uu-205735DiVA: diva2:642592
Funder
Swedish Research Council Formas
Available from: 2013-08-22 Created: 2013-08-22 Last updated: 2017-04-21
In thesis
1. Selectivity in NMR and LC-MS Metabolomics: The Importance of Sample Preparation and Separation, and how to Measure Selectivity in LC-MS Metabolomics.
Open this publication in new window or tab >>Selectivity in NMR and LC-MS Metabolomics: The Importance of Sample Preparation and Separation, and how to Measure Selectivity in LC-MS Metabolomics.
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Until now, most metabolomics protocols have been optimized towards high sample throughput and high metabolite coverage, parameters considered to be highly important for identifying influenced biological pathways and to generate as many potential biomarkers as possible. From an analytical point of view this can be troubling, as neither sample throughput nor the number of signals relates to actual quality of the detected signals/metabolites. However, a method’s selectivity for a specific signal/metabolite is often closely associated to the quality of that signal, yet this is a parameter often neglected in metabolomics.

This thesis demonstrates the importance of considering selectivity when developing NMR and LC-MS metabolomics methods, and introduces a novel approach for measuring chromatographic and signal selectivity in LC-MS metabolomics.

Selectivity for various sample preparations and HILIC stationary phases was compared. The choice of sample preparation affected the selectivity in both NMR and LC-MS. For the stationary phases, selectivity differences related primarily to retention differences of unwanted matrix components, e.g. inorganic salts or glycerophospholipids. Metabolites co-eluting with these matrix components often showed an incorrect quantitative signal, due to an influenced ionization efficiency and/or adduct formation.

A novel approach for measuring selectivity in LC-MS metabolomics has been introduced. By dividing the intensity of each feature (a unique mass at a specific retention time) with the total intensity of the co-eluting features, a ratio representing the combined chromatographic (amount of co-elution) and signal (e.g. in-source fragmentation) selectivity is acquired. The calculated co-feature ratios have successfully been used to compare the selectivity of sample preparations and HILIC stationary phases.

In conclusion, standard approaches in metabolomics research might be unwise, as each metabolomics investigation is often unique.  The methods used should be adapted for the research question at hand, primarily based on any key metabolites, as well as the type of sample to be analyzed. Increased selectivity, through proper choice of analytical methods, may reduce the risks of matrix-associated effects and thereby reduce the false positive and false negative discovery rate of any metabolomics investigation.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2017. 40 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 232
Keyword
Metabolomics, NMR, LC-MS, HILIC, UHPLC, Q-ToF, selectivity, co-feature ratio, method evaluation, data evaluation
National Category
Pharmaceutical Sciences Analytical Chemistry
Research subject
Analytical Pharmaceutical Chemistry
Identifiers
urn:nbn:se:uu:diva-318296 (URN)978-91-554-9879-5 (ISBN)
Public defence
2017-05-19, B41, BMC, Husargatan 3, Uppsala, 10:15 (Swedish)
Opponent
Supervisors
Available from: 2017-04-26 Created: 2017-03-30 Last updated: 2017-05-05

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Engskog, Mikael K RKarlsson, OskarHaglöf, JakobElmsjö, AlbertBrittebo, EvaArvidsson, TorbjörnPettersson, Curt

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