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  • 1.
    Aftab, Obaid
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Engskog, Mikael
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Haglöf, Jakob
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Elmsjö, Albert
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Arvidsson, Torbjörn
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Pettersson, Curt
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Hammerling, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Gustafsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    NMR spectroscopy based metabolic profiling of drug induced changes in vitro can discriminate between pharmacological classes2014In: Journal of chemical information and modeling, ISSN 1549-9596, Vol. 54, no 11, p. 3251-3258Article in journal (Refereed)
    Abstract [en]

    Drug induced changes in mammalian cell line models have already been extensively profiled at the systemic mRNA level and subsequently used to suggest mechanisms of action for new substances as well as to support drug repurposing, i.e. identifying new potential indications for drugs already licensed for other pharmacotherapy settings. The seminal work in this field, which includes a large database and computational algorithms for pattern matching, is known as the “Connectivity Map” (CMap). The potential of similar exercises at the metabolite level is, however, still largely unexplored. Only recently the first high throughput metabolomic assay pilot study was published, involving screening of metabolic response to a set of 56 kinase inhibitors in a 96-well format. Here we report results from a separately developed metabolic profiling assay, which leverages 1H NMR spectroscopy to the quantification of metabolic changes in the HCT116 colorectal cancer cell line, in response to each of 26 compounds. These agents are distributed across 12 different pharmacological classes covering a broad spectrum of bioactivity. Differential metabolic profiles, inferred from multivariate spectral analysis of 18 spectral bins, allowed clustering of most tested drugs according to their respective pharmacological class. A more advanced supervised analysis, involving one multivariate scattering matrix per pharmacological class and using only 3 spectral bins (three metabolites), showed even more distinct pharmacology-related cluster formations. In conclusion, this kind of relatively fast and inexpensive profiling seems to provide a promising alternative to that afforded by mRNA expression analysis, which is relatively slow and costly. As also indicated by the present pilot study, the resulting metabolic profiles do not seem to provide as information rich signatures as those obtained using systemic mRNA profiling, but the methodology holds strong promise for significant refinement.

  • 2.
    Elmsjö, Albert
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Selectivity in NMR and LC-MS Metabolomics: The Importance of Sample Preparation and Separation, and how to Measure Selectivity in LC-MS Metabolomics.2017Doctoral 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.

    List of papers
    1. The cyanobacterial amino acid beta-N-methylamino-L-alanine perturbs the intermediary metabolism in neonatal rats
    Open this publication in new window or tab >>The cyanobacterial amino acid beta-N-methylamino-L-alanine perturbs the intermediary metabolism in neonatal rats
    Show others...
    2013 (English)In: Amino Acids, ISSN 0939-4451, E-ISSN 1438-2199, Vol. 49, no 5, p. 905-919, article id 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.

    Keywords
    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:nbn:se:uu:diva-205735 (URN)10.1016/j.tox.2013.07.010 (DOI)000327005300002 ()23886855 (PubMedID)
    Funder
    Swedish Research Council Formas
    Available from: 2013-08-22 Created: 2013-08-22 Last updated: 2019-08-12
    2. NMR-based metabolic profiling in healthy individuals overfed different types of fat: links to changes in liver fat accumulation and lean tissue mass.
    Open this publication in new window or tab >>NMR-based metabolic profiling in healthy individuals overfed different types of fat: links to changes in liver fat accumulation and lean tissue mass.
    Show others...
    2015 (English)In: Nutrition & Diabetes, ISSN 2044-4052, E-ISSN 2044-4052, Vol. 5, no 19, p. e182-Article in journal (Refereed) Published
    Abstract [en]

    BACKGROUND: Overeating different dietary fatty acids influence the amount of liver fat stored during weight gain, however, the mechanisms responsible are unclear. We aimed to identify non-lipid metabolites that may differentiate between saturated (SFA) and polyunsaturated fatty acid (PUFA) overfeeding using a non-targeted metabolomic approach. We also investigated the possible relationships between plasma metabolites and body fat accumulation.

    METHODS: In a randomized study (LIPOGAIN study), n=39 healthy individuals were overfed with muffins containing SFA or PUFA. Plasma samples were precipitated with cold acetonitrile and analyzed by nuclear magnetic resonance (NMR) spectroscopy. Pattern recognition techniques were used to overview the data, identify variables contributing to group classification and to correlate metabolites with fat accumulation.

    RESULTS: We previously reported that SFA causes a greater accumulation of liver fat, visceral fat and total body fat, whereas lean tissue levels increases less compared with PUFA, despite comparable weight gain. In this study, lactate and acetate were identified as important contributors to group classification between SFA and PUFA (P<0.05). Furthermore, the fat depots (total body fat, visceral adipose tissue and liver fat) and lean tissue correlated (P(corr)>0.5) all with two or more metabolites (for example, branched amino acids, alanine, acetate and lactate). The metabolite composition differed in a manner that may indicate higher insulin sensitivity after a diet with PUFA compared with SFA, but this needs to be confirmed in future studies.

    CONCLUSION: A non-lipid metabolic profiling approach only identified a few metabolites that differentiated between SFA and PUFA overfeeding. Whether these metabolite changes are involved in depot-specific fat storage and increased lean tissue mass during overeating needs further investigation.

    National Category
    Medical and Health Sciences Nutrition and Dietetics
    Identifiers
    urn:nbn:se:uu:diva-267034 (URN)10.1038/nutd.2015.31 (DOI)000368899900002 ()26479316 (PubMedID)
    Funder
    Swedish Research Council, K2015-54X-22081-04-3Swedish Diabetes Association
    Note

    Rosqvist, Engskog, Haglöf, Riséus and Pettersson contributed equally to this work.

    Available from: 2015-11-17 Created: 2015-11-17 Last updated: 2017-12-01Bibliographically approved
    3. The co-feature ratio, a novel method for the measurement of chromatographic and signal selectivity in LC-MS-based metabolomics.
    Open this publication in new window or tab >>The co-feature ratio, a novel method for the measurement of chromatographic and signal selectivity in LC-MS-based metabolomics.
    Show others...
    2017 (English)In: Analytica Chimica Acta, ISSN 0003-2670, E-ISSN 1873-4324, Vol. 956, p. 40-47Article in journal (Refereed) Published
    Abstract [en]

    Evaluation of analytical procedures, especially in regards to measuring chromatographic and signal selectivity, is highly challenging in untargeted metabolomics. The aim of this study was to suggest a new straightforward approach for a systematic examination of chromatographic and signal selectivity in LC-MS-based metabolomics. By calculating the ratio between each feature and its co-eluting features (the co-features), a measurement of the chromatographic selectivity (i.e. extent of co-elution) as well as the signal selectivity (e.g. amount of adduct formation) of each feature could be acquired, the co-feature ratio. This approach was used to examine possible differences in chromatographic and signal selectivity present in samples exposed to three different sample preparation procedures. The capability of the co-feature ratio was evaluated both in a classical targeted setting using isotope labelled standards as well as without standards in an untargeted setting. For the targeted analysis, several metabolites showed a skewed quantitative signal due to poor chromatographic selectivity and/or poor signal selectivity. Moreover, evaluation of the untargeted approach through multivariate analysis of the co-feature ratios demonstrated the possibility to screen for metabolites displaying poor chromatographic and/or signal selectivity characteristics. We conclude that the co-feature ratio can be a useful tool in the development and evaluation of analytical procedures in LC-MS-based metabolomics investigations. Increased selectivity through proper choice of analytical procedures may decrease the false positive and false negative discovery rate and thereby increase the validity of any metabolomic investigation.

    National Category
    Analytical Chemistry Pharmaceutical Sciences
    Research subject
    Analytical Pharmaceutical Chemistry
    Identifiers
    urn:nbn:se:uu:diva-314239 (URN)10.1016/j.aca.2016.12.022 (DOI)000393252000005 ()28093124 (PubMedID)
    Available from: 2017-01-31 Created: 2017-01-31 Last updated: 2018-01-13Bibliographically approved
    4. Selectivity evaluation using the co-feature ratio in LC/MS metabolomics: comparison of HILIC stationary phases’ performance for the analysis of plasma, urine and cell extracts.
    Open this publication in new window or tab >>Selectivity evaluation using the co-feature ratio in LC/MS metabolomics: comparison of HILIC stationary phases’ performance for the analysis of plasma, urine and cell extracts.
    Show others...
    (English)Manuscript (preprint) (Other academic)
    National Category
    Medicinal Chemistry
    Identifiers
    urn:nbn:se:uu:diva-318089 (URN)
    Available from: 2017-03-24 Created: 2017-03-24 Last updated: 2018-01-13
  • 3.
    Elmsjö, Albert
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Science.
    Haglöf, Jakob
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Science.
    Engskog, Mikael K R
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Science.
    Erngren, Ida
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Science.
    Nestor, Marika
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Radiation Science.
    Arvidsson, Torbjörn
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Science.
    Pettersson, Curt
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Science.
    Method selectivity evaluation using the co-feature ratio in LC/MS metabolomics: Comparison of HILIC stationary phase performance for the analysis of plasma, urine and cell extracts.2018In: Journal of Chromatography A, ISSN 0021-9673, E-ISSN 1873-3778, Vol. 1568, p. 49-56Article in journal (Refereed)
    Abstract [en]

    Evaluation of the chromatographic separation in metabolomics studies has primarily been done using preselected sets of standards or by counting the number of detected features. An alternative approach is to calculate each feature's co-feature ratio, which is a combined selectivity measurement for the separation (i.e. extent of co-elution) and the MS-signal (i.e. adduct formation and in-source fragmentation). The aim of this study was to demonstrate how the selectivity of different HILIC stationary phases can be evaluated using the co-feature ratio approach. The study was based on three sample types; plasma, urine and cell extracts. Samples were analyzed on an UHPLC-ESI-Q-ToF system using an amide, a bare silica and a sulfobetaine stationary phase. For each feature, a co-feature ratio was calculated and used for multivariate analysis of the selectivity differences between the three stationary phases. Unsupervised PCA models indicated that the co-feature ratios were highly dependent on type of stationary phase. For several metabolites a 15-30 fold difference in the co-feature ratio were observed between the stationary phases. Observed selectivity differences related primarily to the retention patterns of unwanted matrix components such as inorganic salts (detected as salt clusters), glycerophospholipids, and polyethylene glycols. These matrix components affected the signal intensity of co-eluting metabolites by interfering with the ionization efficiency and/or their adduct formation. Furthermore, the retention pattern of these matrix components had huge influence on the number of detected features. The co-feature ratio approach has successfully been applied for evaluation of the selectivity performance of three HILIC stationary phases. The co-feature ratio could therefore be used in metabolomics for developing selective methods fit for their purpose, thereby avoiding generic analytical approaches, which are often biased, as type and amount of interfering matrix components are metabolome dependent.

  • 4.
    Elmsjö, Albert
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Haglöf, Jakob
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Engskog, Mikael K. R.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Nestor, Marika
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Radiation Science.
    Arvidsson, Torbjörn
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry. Med Prod Agcy, Uppsala, Sweden.
    Pettersson, Curt
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    The co-feature ratio, a novel method for the measurement of chromatographic and signal selectivity in LC-MS-based metabolomics.2017In: Analytica Chimica Acta, ISSN 0003-2670, E-ISSN 1873-4324, Vol. 956, p. 40-47Article in journal (Refereed)
    Abstract [en]

    Evaluation of analytical procedures, especially in regards to measuring chromatographic and signal selectivity, is highly challenging in untargeted metabolomics. The aim of this study was to suggest a new straightforward approach for a systematic examination of chromatographic and signal selectivity in LC-MS-based metabolomics. By calculating the ratio between each feature and its co-eluting features (the co-features), a measurement of the chromatographic selectivity (i.e. extent of co-elution) as well as the signal selectivity (e.g. amount of adduct formation) of each feature could be acquired, the co-feature ratio. This approach was used to examine possible differences in chromatographic and signal selectivity present in samples exposed to three different sample preparation procedures. The capability of the co-feature ratio was evaluated both in a classical targeted setting using isotope labelled standards as well as without standards in an untargeted setting. For the targeted analysis, several metabolites showed a skewed quantitative signal due to poor chromatographic selectivity and/or poor signal selectivity. Moreover, evaluation of the untargeted approach through multivariate analysis of the co-feature ratios demonstrated the possibility to screen for metabolites displaying poor chromatographic and/or signal selectivity characteristics. We conclude that the co-feature ratio can be a useful tool in the development and evaluation of analytical procedures in LC-MS-based metabolomics investigations. Increased selectivity through proper choice of analytical procedures may decrease the false positive and false negative discovery rate and thereby increase the validity of any metabolomic investigation.

  • 5.
    Elmsjö, Albert
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Rosqvist, Fredrik
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism.
    Engskog, Mikael K R
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Haglöf, Jakob
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Kullberg, Joel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.
    Iggman, David
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism.
    Johansson, Lars
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.
    Ahlström, Håkan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.
    Arvidsson, Torbjörn
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Risérus, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism.
    Pettersson, Curt
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    NMR-based metabolic profiling in healthy individuals overfed different types of fat: links to changes in liver fat accumulation and lean tissue mass.2015In: Nutrition & Diabetes, ISSN 2044-4052, E-ISSN 2044-4052, Vol. 5, no 19, p. e182-Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Overeating different dietary fatty acids influence the amount of liver fat stored during weight gain, however, the mechanisms responsible are unclear. We aimed to identify non-lipid metabolites that may differentiate between saturated (SFA) and polyunsaturated fatty acid (PUFA) overfeeding using a non-targeted metabolomic approach. We also investigated the possible relationships between plasma metabolites and body fat accumulation.

    METHODS: In a randomized study (LIPOGAIN study), n=39 healthy individuals were overfed with muffins containing SFA or PUFA. Plasma samples were precipitated with cold acetonitrile and analyzed by nuclear magnetic resonance (NMR) spectroscopy. Pattern recognition techniques were used to overview the data, identify variables contributing to group classification and to correlate metabolites with fat accumulation.

    RESULTS: We previously reported that SFA causes a greater accumulation of liver fat, visceral fat and total body fat, whereas lean tissue levels increases less compared with PUFA, despite comparable weight gain. In this study, lactate and acetate were identified as important contributors to group classification between SFA and PUFA (P<0.05). Furthermore, the fat depots (total body fat, visceral adipose tissue and liver fat) and lean tissue correlated (P(corr)>0.5) all with two or more metabolites (for example, branched amino acids, alanine, acetate and lactate). The metabolite composition differed in a manner that may indicate higher insulin sensitivity after a diet with PUFA compared with SFA, but this needs to be confirmed in future studies.

    CONCLUSION: A non-lipid metabolic profiling approach only identified a few metabolites that differentiated between SFA and PUFA overfeeding. Whether these metabolite changes are involved in depot-specific fat storage and increased lean tissue mass during overeating needs further investigation.

  • 6.
    Engskog, Mikael K R
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Karlsson, Oskar
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Haglöf, Jakob
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Elmsjö, Albert
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Brittebo, Eva
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Arvidsson, Torbjörn
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Pettersson, Curt
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    The cyanobacterial amino acid beta-N-methylamino-L-alanine perturbs the intermediary metabolism in neonatal rats2013In: Amino Acids, ISSN 0939-4451, E-ISSN 1438-2199, Vol. 49, no 5, p. 905-919, article id 10.1007/s00726-017-2391-8Article in journal (Refereed)
    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.

  • 7. McEwen, Ian
    et al.
    Elmsjö, Albert
    Lehnstrom, Angelica
    Hakkarainen, Birgit
    Johansson, Monika
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Screening of counterfeit corticosteroid in creams and ointments by NMR spectroscopy2012In: Journal of Pharmaceutical and Biomedical Analysis, ISSN 0731-7085, E-ISSN 1873-264X, Vol. 70, p. 245-250Article in journal (Refereed)
    Abstract [en]

    It has been shown that NMR spectroscopy is an effective analytical method to rapidly screen creams and ointments for counterfeit corticosteroids. Extraction and NMR procedures have been developed. Ten over the counter creams and ointments sold in health care shops were screened and two creams were found to contain counterfeited corticosteroids.

1 - 7 of 7
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