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Antti, Henrik
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Publications (10 of 104) Show all publications
Mason, J. E., Lundberg, E., Jonsson, P., Nyström, H., Franklin, O., Lundin, C., . . . Öhlund, D. (2022). A cross-sectional and longitudinal analysis of pre-diagnostic blood plasma biomarkers for early detection of pancreatic cancer. International Journal of Molecular Sciences, 23(21), Article ID 12969.
Open this publication in new window or tab >>A cross-sectional and longitudinal analysis of pre-diagnostic blood plasma biomarkers for early detection of pancreatic cancer
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2022 (English)In: International Journal of Molecular Sciences, ISSN 1661-6596, E-ISSN 1422-0067, Vol. 23, no 21, article id 12969Article in journal (Refereed) Published
Abstract [en]

Pancreatic ductal adenocarcinoma (PDAC) is a major cause of cancer death that typically presents at an advanced stage. No reliable markers for early detection presently exist. The prominent tumor stroma represents a source of circulating biomarkers for use together with cancer cell-derived biomarkers for earlier PDAC diagnosis. CA19-9 and CEA (cancer cell-derived biomarkers), together with endostatin and collagen IV (stroma-derived) were examined alone, or together, by multivariable modelling, using pre-diagnostic plasma samples (n = 259 samples) from the Northern Sweden Health and Disease Study biobank. Serial samples were available for a subgroup of future patients. Marker efficacy for future PDAC case prediction (n = 154 future cases) was examined by both cross-sectional (ROC analysis) and longitudinal analyses. CA19-9 performed well at, and within, six months to diagnosis and multivariable modelling was not superior to CA19-9 alone in cross-sectional analysis. Within six months to diagnosis, CA19-9 (AUC = 0.92) outperformed the multivariable model (AUC = 0.81) at a cross-sectional level. At diagnosis, CA19-9 (AUC = 0.995) and the model (AUC = 0.977) performed similarly. Longitudinal analysis revealed increases in CA19-9 up to two years to diagnosis which indicates a window of opportunity for early detection of PDAC.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
analysis, biomarkers, carcinoma, early detection of cancer, pancreatic ductal, tumor, tumor microenvironment
National Category
Cancer and Oncology
Research subject
Surgery
Identifiers
urn:nbn:se:umu:diva-201220 (URN)10.3390/ijms232112969 (DOI)000881359700001 ()36361759 (PubMedID)2-s2.0-85141870302 (Scopus ID)
Funder
Swedish Research Council, 2017-01531Swedish Research Council, 2016-02990Swedish Research Council, 2019-01690Swedish Research Council, 2017-00650The Kempe Foundations, JCK-1301Swedish Society of Medicine, SLS-890521Swedish Society of Medicine, SLS-786661Region Västerbotten, RV-930167Västerbotten County Council, VLL-643451Västerbotten County Council, VLL-832001Region Västerbotten, RV-583411Region Västerbotten, RV-549731Region Västerbotten, RV-841551Region Västerbotten, 930132Region Västerbotten, RV-930167Cancerforskningsfonden i Norrland, LP20-2257Cancerforskningsfonden i Norrland, LP18-2202Cancerforskningsfonden i Norrland, LP18-2192Cancerforskningsfonden i Norrland, LP21-2298Cancerforskningsfonden i Norrland, LP22-2332Sjöberg FoundationKnut and Alice Wallenberg Foundation, KAW 2015.0114Marianne and Marcus Wallenberg Foundation, MMW 2020.0189Swedish Cancer Society, CAN 2017/332Swedish Cancer Society, CAN 2017/827Swedish Cancer Society, CAN 2011/751Swedish Cancer Society, CAN 2016/643Swedish Cancer Society, 19 0273Swedish Cancer Society, 20 1339
Available from: 2022-12-15 Created: 2022-12-15 Last updated: 2023-06-08Bibliographically approved
Björkblom, B., Wibom, C., Eriksson, M., Bergenheim, A. T., Sjöberg, R. L., Jonsson, P., . . . Melin, B. S. (2022). Distinct metabolic hallmarks of WHO classified adult glioma subtypes. Neuro-Oncology, 24(9), 1454-1468, Article ID noac042.
Open this publication in new window or tab >>Distinct metabolic hallmarks of WHO classified adult glioma subtypes
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2022 (English)In: Neuro-Oncology, ISSN 1522-8517, E-ISSN 1523-5866, Vol. 24, no 9, p. 1454-1468, article id noac042Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Gliomas are complex tumors with several genetic aberrations and diverse metabolic programs contributing to their aggressive phenotypes and poor prognoses. This study defines key metabolic features that can be used to differentiate between glioma subtypes, with potential for improved diagnostics and subtype targeted therapy.

METHODS: Cross-platform global metabolomic profiling coupled with clinical, genetic, and pathological analysis of glioma tissue from 224 tumors - oligodendroglioma (n=31), astrocytoma (n=31) and glioblastoma (n=162) - were performed. Identified metabolic phenotypes were evaluated in accordance with the WHO classification, IDH-mutation, 1p/19q-codeletion, WHO-grading 2-4, and MGMT promoter methylation.

RESULTS: Distinct metabolic phenotypes separate all six analyzed glioma subtypes. IDH-mutated subtypes, expressing 2-hydroxyglutaric acid, were clearly distinguished from IDH-wildtype subtypes. Considerable metabolic heterogeneity outside of the mutated IDH pathway were also evident, with key metabolites being high expression of glycerophosphates, inositols, monosaccharides and sugar alcohols and low levels of sphingosine and lysoglycerophospholipids in IDH-mutants. Among the IDH-mutated subtypes, we observed high levels of amino acids, especially glycine and 2-aminoadipic acid, in grade 4 glioma, and N-acetyl aspartic acid in low-grade astrocytoma and oligodendroglioma. Both IDH-wildtype and mutated oligodendroglioma and glioblastoma were characterized by high levels of acylcarnitines, likely driven by rapid cell growth and hypoxic features. We found elevated levels of 5-HIAA in gliosarcoma and a subtype of oligodendroglioma not yet defined as a specific entity, indicating a previously not described role for the serotonin pathway linked to glioma with bimorphic tissue.

CONCLUSION: Key metabolic differences exist across adult glioma subtypes.

Place, publisher, year, edition, pages
Oxford University Press, 2022
Keywords
Astrocytoma, Glioblastoma, Metabolic reprogramming, Oligodendroglioma, WHO classification
National Category
Cancer and Oncology
Research subject
Molecular Biology; Pathology; Oncology
Identifiers
urn:nbn:se:umu:diva-192529 (URN)10.1093/neuonc/noac042 (DOI)000785708300001 ()35157758 (PubMedID)2-s2.0-85137137374 (Scopus ID)
Funder
Swedish Cancer Society, 2018/390Swedish Cancer Society, 2013/0291Swedish Cancer Society, 19 0370Swedish Research Council, 2019-01566Cancerforskningsfonden i Norrland, AMP17-899Cancerforskningsfonden i Norrland, AMP17- 882Sjöberg Foundation, 2020-01-07-08
Available from: 2022-05-17 Created: 2022-05-17 Last updated: 2023-05-23Bibliographically approved
Dudka, I., Thysell, E., Lundquist, K., Antti, H., Iglesias-Gato, D., Flores-Morales, A., . . . Gröbner, G. (2020). Comprehensive metabolomics analysis of prostate cancer tissue in relation to tumor aggressiveness and TMPRSS2-ERG fusion status. BMC Cancer, 20(1), Article ID 437.
Open this publication in new window or tab >>Comprehensive metabolomics analysis of prostate cancer tissue in relation to tumor aggressiveness and TMPRSS2-ERG fusion status
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2020 (English)In: BMC Cancer, ISSN 1471-2407, E-ISSN 1471-2407, Vol. 20, no 1, article id 437Article in journal (Refereed) Published
Abstract [en]

Background: Prostate cancer (PC) can display very heterogeneous phenotypes ranging from indolent asymptomatic to aggressive lethal forms. Understanding how these PC subtypes vary in their striving for energy and anabolic molecules is of fundamental importance for developing more effective therapies and diagnostics. Here, we carried out an extensive analysis of prostate tissue samples to reveal metabolic alterations during PC development and disease progression and furthermore between TMPRSS2-ERG rearrangement-positive and -negative PC subclasses.

Methods: Comprehensive metabolomics analysis of prostate tissue samples was performed by non-destructive high-resolution magic angle spinning nuclear magnetic resonance (H-1 HR MAS NMR). Subsequently, samples underwent moderate extraction, leaving tissue morphology intact for histopathological characterization. Metabolites in tissue extracts were identified by H-1/P-31 NMR and liquid chromatography-mass spectrometry (LC-MS). These metabolomics profiles were analyzed by chemometric tools and the outcome was further validated using proteomic data from a separate sample cohort.

Results: The obtained metabolite patterns significantly differed between PC and benign tissue and between samples with high and low Gleason score (GS). Five key metabolites (phosphocholine, glutamate, hypoxanthine, arginine and alpha-glucose) were identified, who were sufficient to differentiate between cancer and benign tissue and between high to low GS. In ERG-positive PC, the analysis revealed several acylcarnitines among the increased metabolites together with decreased levels of proteins involved in beta-oxidation; indicating decreased acyl-CoAs oxidation in ERG-positive tumors. The ERG-positive group also showed increased levels of metabolites and proteins involved in purine catabolism; a potential sign of increased DNA damage and oxidative stress.

Conclusions: Our comprehensive metabolomic analysis strongly indicates that ERG-positive PC and ERG-negative PC should be considered as different subtypes of PC; a fact requiring different, sub-type specific treatment strategies for affected patients.

Place, publisher, year, edition, pages
BioMed Central, 2020
Keywords
Metabolomics, Prostate cancer, TMPRSS2-ERG, H-1 HRMAS NMR, Gleason score
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:umu:diva-172522 (URN)10.1186/s12885-020-06908-z (DOI)000536768100003 ()32423389 (PubMedID)2-s2.0-85084897384 (Scopus ID)
Funder
Swedish Research CouncilSwedish Foundation for Strategic Research , RB13-0119The Kempe FoundationsSwedish Cancer SocietyKnut and Alice Wallenberg Foundation
Available from: 2020-06-30 Created: 2020-06-30 Last updated: 2020-06-30Bibliographically approved
Jonsson, P., Antti, H., Späth, F., Melin, B. S. & Björkblom, B. (2020). Identification of Pre-Diagnostic Metabolic Patterns for Glioma Using Subset Analysis of Matched Repeated Time Points. Cancers, 12(11), Article ID 3349.
Open this publication in new window or tab >>Identification of Pre-Diagnostic Metabolic Patterns for Glioma Using Subset Analysis of Matched Repeated Time Points
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2020 (English)In: Cancers, ISSN 2072-6694, Vol. 12, no 11, article id 3349Article in journal (Refereed) Published
Abstract [en]

Simple Summary: Reprogramming of cellular metabolism is a major hallmark of cancer cells, and play an important role in tumor initiation and progression. The aim of our study is to discover circulating early metabolic markers of brain tumors, as discovery and development of reliable predictive molecular markers are needed for precision oncology applications. We use a study design tailored to minimize confounding factors and a novel machine learning and visualization approach (SMART) to identify a panel of 15 interlinked metabolites related to glioma development. The presented SMART strategy facilitates early molecular marker discovery and can be used for many types of molecular data.

Abstract: Here, we present a strategy for early molecular marker pattern detection-Subset analysis of Matched Repeated Time points (SMART)-used in a mass-spectrometry-based metabolomics study of repeated blood samples from future glioma patients and their matched controls. The outcome from SMART is a predictive time span when disease-related changes are detectable, defined by time to diagnosis and time between longitudinal sampling, and visualization of molecular marker patterns related to future disease. For glioma, we detect significant changes in metabolite levels as early as eight years before diagnosis, with longitudinal follow up within seven years. Elevated blood plasma levels of myo-inositol, cysteine, N-acetylglucosamine, creatinine, glycine, proline, erythronic-, 4-hydroxyphenylacetic-, uric-, and aceturic acid were particularly evident in glioma cases. We use data simulation to ensure non-random events and a separate data set for biomarker validation. The latent biomarker, consisting of 15 interlinked and significantly altered metabolites, shows a strong correlation to oxidative metabolism, glutathione biosynthesis and monosaccharide metabolism, linked to known early events in tumor development. This study highlights the benefits of progression pattern analysis and provide a tool for the discovery of early markers of disease.

Place, publisher, year, edition, pages
MDPI, 2020
Keywords
brain tumor, metabolite, metabolic marker pattern, multivariate analysis, blood-based, antioxidant
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:umu:diva-178039 (URN)10.3390/cancers12113349 (DOI)000592910200001 ()33198241 (PubMedID)2-s2.0-85096720832 (Scopus ID)
Funder
Region VästerbottenCancerforskningsfonden i NorrlandSwedish Cancer SocietySwedish Research Council
Available from: 2020-12-30 Created: 2020-12-30 Last updated: 2023-03-24Bibliographically approved
Björkblom, B., Jonsson, P., Tabatabaei, P., Bergström, P., Johansson, M., Asklund, T., . . . Antti, H. (2020). Metabolic response patterns in brain microdialysis fluids and serum during interstitial cisplatin treatment of high-grade glioma. British Journal of Cancer, 122(2), 221-232
Open this publication in new window or tab >>Metabolic response patterns in brain microdialysis fluids and serum during interstitial cisplatin treatment of high-grade glioma
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2020 (English)In: British Journal of Cancer, ISSN 0007-0920, E-ISSN 1532-1827, Vol. 122, no 2, p. 221-232Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: High-grade gliomas are associated with poor prognosis. Tumour heterogeneity and invasiveness create challenges for effective treatment and use of systemically administrated drugs. Furthermore, lack of functional predictive response-assays based on drug efficacy complicates evaluation of early treatment responses.

METHODS: We used microdialysis to deliver cisplatin into the tumour and to monitor levels of metabolic compounds present in the tumour and non-malignant brain tissue adjacent to tumour, before and during treatment. In parallel, we collected serum samples and used multivariate statistics to analyse the metabolic effects.

RESULTS: We found distinct metabolic patterns in the extracellular fluids from tumour compared to non-malignant brain tissue, including high concentrations of a wide range of amino acids, amino acid derivatives and reduced levels of monosaccharides and purine nucleosides. We found that locoregional cisplatin delivery had a strong metabolic effect at the tumour site, resulting in substantial release of glutamic acid, phosphate, and spermidine and a reduction of cysteine levels. In addition, patients with long-time survival displayed different treatment response patterns in both tumour and serum. Longer survival was associated with low tumour levels of lactic acid, glyceric acid, ketoses, creatinine and cysteine. Patients with longer survival displayed lower serum levels of ketohexoses, fatty acid methyl esters, glycerol-3-phosphate and alpha-tocopherol, while elevated phosphate levels were seen in both tumour and serum during treatment.

CONCLUSION: We highlight distinct metabolic patterns associated with high-grade tumour metabolism, and responses to cytotoxic cisplatin treatment.

Place, publisher, year, edition, pages
Nature Publishing Group, 2020
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:umu:diva-167291 (URN)10.1038/s41416-019-0652-x (DOI)000510823600009 ()31819184 (PubMedID)2-s2.0-85076541777 (Scopus ID)
Funder
Swedish Cancer SocietySwedish Research Council
Available from: 2020-01-15 Created: 2020-01-15 Last updated: 2023-03-23Bibliographically approved
Loo, R. L., Chan, Q., Antti, H., Li, J. V., Ashrafian, H., Elliott, P., . . . Wist, J. (2020). Strategy for improved characterization of human metabolic phenotypes using a COmbined Multi-block Principal components Analysis with Statistical Spectroscopy (COMPASS). Bioinformatics, 36(21), 5229-5236
Open this publication in new window or tab >>Strategy for improved characterization of human metabolic phenotypes using a COmbined Multi-block Principal components Analysis with Statistical Spectroscopy (COMPASS)
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2020 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 36, no 21, p. 5229-5236Article in journal (Refereed) Published
Abstract [en]

Motivation: Large-scale population omics data can provide insight into associations between gene-environment interactions and disease. However, existing dimension reduction modelling techniques are often inefficient for extracting detailed information from these complex datasets.

Results: Here, we present an interactive software pipeline for exploratory analyses of population-based nuclear magnetic resonance spectral data using a COmbined Multi-block Principal components Analysis with Statistical Spectroscopy (COMPASS) within the R-library hastaLaVista framework. Principal component analysis models are generated for a sequential series of spectral regions (blocks) to provide more granular detail defining sub-populations within the dataset. Molecular identification of key differentiating signals is subsequently achieved by implementing Statistical TOtal Correlation SpectroscopY on the full spectral data to define feature patterns. Finally, the distributions of cross-correlation of the reference patterns across the spectral dataset are used to provide population statistics for identifying underlying features arising from drug intake, latent diseases and diet. The COMPASS method thus provides an efficient semi-automated approach for screening population datasets.

Place, publisher, year, edition, pages
Oxford University Press, 2020
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:umu:diva-183594 (URN)10.1093/bioinformatics/btaa649 (DOI)000635348000014 ()32692809 (PubMedID)2-s2.0-85105191098 (Scopus ID)
Available from: 2021-05-27 Created: 2021-05-27 Last updated: 2021-09-17Bibliographically approved
Zborayova, K., Antti, H., Blomqvist, L., Flygare, L., Gebre-Medhin, M., Jonsson, J., . . . Zackrisson, B. (2019). Early changes in multiparametric imaging parameters during radiotherapy of squamous carcinoma. Paper presented at 7th International Congress on Innovative Approaches in Head and Neck Oncology (ICHNO), Barcelona, SPAIN, MAR 14-16, 2019.. Radiotherapy and Oncology, 132, 63-63
Open this publication in new window or tab >>Early changes in multiparametric imaging parameters during radiotherapy of squamous carcinoma
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2019 (English)In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 132, p. 63-63Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
Elsevier, 2019
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:umu:diva-158753 (URN)10.1016/S0167-8140(19)30289-0 (DOI)000463820400107 ()
Conference
7th International Congress on Innovative Approaches in Head and Neck Oncology (ICHNO), Barcelona, SPAIN, MAR 14-16, 2019.
Note

Supplement 1.

Available from: 2019-05-15 Created: 2019-05-15 Last updated: 2021-04-16Bibliographically approved
Antti, H. & Sellstedt, M. (2018). Cell-Based Kinetic Target-Guided Synthesis of an Enzyme Inhibitor. ACS Medicinal Chemistry Letters, 9(4), 351-353
Open this publication in new window or tab >>Cell-Based Kinetic Target-Guided Synthesis of an Enzyme Inhibitor
2018 (English)In: ACS Medicinal Chemistry Letters, ISSN 1948-5875, E-ISSN 1948-5875, Vol. 9, no 4, p. 351-353Article in journal (Refereed) Published
Abstract [en]

Finding a new drug candidate for a selected target is an expensive and time-consuming process. Target guided-synthesis, or in situ click chemistry, is a concept where the drug target is used to template the formation of its own inhibitors from reactive building blocks. This could simplify the identification of drug candidates. However, with the exception of one example of an RNA-target, target-guided synthesis has always employed purified targets. This limits the number of targets that can be screened by the method. By applying methods from the field of metabolomics, we demonstrate that target-guided synthesis with protein targets also can be performed directly in cell-based systems. These methods offer new possibilities to conduct screening for drug candidates of difficult protein targets in cellular environments.

Keywords
target-guided synthesis, in situ click chemistry, enzyme catalysis, drug discovery
National Category
Medicinal Chemistry
Identifiers
urn:nbn:se:umu:diva-147832 (URN)10.1021/acsmedchemlett.7b00535 (DOI)000430256200010 ()29670699 (PubMedID)2-s2.0-85045297637 (Scopus ID)
Available from: 2018-05-18 Created: 2018-05-18 Last updated: 2023-03-23Bibliographically approved
Näsström, E., Jonsson, P., Johansson, A., Dongol, S., Karkey, A., Basnyat, B., . . . Baker, S. (2018). Diagnostic metabolite biomarkers of chronic typhoid carriage. PLoS Neglected Tropical Diseases, 12(1), Article ID e0006215.
Open this publication in new window or tab >>Diagnostic metabolite biomarkers of chronic typhoid carriage
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2018 (English)In: PLoS Neglected Tropical Diseases, ISSN 1935-2727, E-ISSN 1935-2735, Vol. 12, no 1, article id e0006215Article in journal (Refereed) Published
Abstract [en]

Background: Salmonella Typhi and Salmonella Paratyphi A are the agents of enteric (typhoid) fever; both can establish chronic carriage in the gallbladder. Chronic Salmonella carriers are typically asymptomatic, intermittently shedding bacteria in the feces, and contributing to disease transmission. Detecting chronic carriers is of public health relevance in areas where enteric fever is endemic, but there are no routinely used methods for prospectively identifying those carrying Salmonella in their gallbladder.

Methodology/Principal findings: Here we aimed to identify biomarkers of Salmonella carriage using metabolite profiling. We performed metabolite profiling on plasma from Nepali patients undergoing cholecystectomy with confirmed S. Typhi or S. Paratyphi A gallbladder carriage (and non-carriage controls) using two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GCxGC-TOFMS) and supervised pattern recognition modeling. We were able to significantly discriminate Salmonella carriage samples from non-carriage control samples. We were also able to detect differential signatures between S. Typhi and S. Paratyphi A carriers. We additionally compared carriage metabolite profiles with profiles generated during acute infection; these data revealed substantial heterogeneity between metabolites associated with acute enteric fever and chronic carriage. Lastly, we found that Salmonella carriers could be significantly distinguished from non-carriage controls using only five metabolites, indicating the potential of these metabolites as diagnostic markers for detecting chronic Salmonella carriers.

Conclusions/Significance: Our novel approach has highlighted the potential of using metabolomics to search for diagnostic markers of chronic Salmonella carriage. We suggest further epidemiological investigations of these potential biomarkers in alternative endemic enteric fever settings.

National Category
Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Identifiers
urn:nbn:se:umu:diva-145618 (URN)10.1371/journal.pntd.0006215 (DOI)000424022700063 ()29373578 (PubMedID)2-s2.0-85041700199 (Scopus ID)
Funder
Swedish Research Council, VR-U 2015-03442
Available from: 2018-04-17 Created: 2018-04-17 Last updated: 2023-03-24Bibliographically approved
Antti, H. & Sellstedt, M. (2018). Metabolic effects of an aspartate aminotransferase-inhibitor on two T-cell lines. PLOS ONE, 13(12), Article ID e0208025.
Open this publication in new window or tab >>Metabolic effects of an aspartate aminotransferase-inhibitor on two T-cell lines
2018 (English)In: PLOS ONE, E-ISSN 1932-6203, Vol. 13, no 12, article id e0208025Article in journal (Refereed) Published
Abstract [en]

An emerging method to help elucidate the mode of action of experimental drugs is to use untargeted metabolomics of cell-systems. The interpretations of such screens are however complex and more examples with inhibitors of known targets are needed. Here two T-cell lines were treated with an inhibitor of aspartate aminotransferase and analyzed with untargeted GC-MS. The interpretation of the data was enhanced by the use of two different cell-lines and supports aspartate aminotransferase as a target. In addition, the data suggest an unexpected off-target effect on glutamate decarboxylase. The results exemplify the potency of metabolomics to provide insight into both mode of action and off-target effects of drug candidates.

Place, publisher, year, edition, pages
PUBLIC LIBRARY SCIENCE, 2018
National Category
Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:umu:diva-154872 (URN)10.1371/journal.pone.0208025 (DOI)000452640900007 ()30532126 (PubMedID)2-s2.0-85058105205 (Scopus ID)
Available from: 2019-01-04 Created: 2019-01-04 Last updated: 2021-06-14Bibliographically approved
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