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Urinary Metabolite Profiling Combined with Computational Analysis Predicts Interstitial Cystitis-Associated Candidate Biomarkers

[Image: see text] Interstitial cystitis/painful bladder syndrome (IC) is a chronic syndrome of unknown etiology that presents with bladder pain, urinary frequency, and urgency. The lack of specific biomarkers and a poor understanding of underlying molecular mechanisms present challenges for disease...

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Autores principales: Wen, He, Lee, Tack, You, Sungyong, Park, Soo-Hwan, Song, Hosook, Eilber, Karyn S., Anger, Jennifer T., Freeman, Michael R., Park, Sunghyouk, Kim, Jayoung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2014
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4286193/
https://www.ncbi.nlm.nih.gov/pubmed/25353990
http://dx.doi.org/10.1021/pr5007729
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author Wen, He
Lee, Tack
You, Sungyong
Park, Soo-Hwan
Song, Hosook
Eilber, Karyn S.
Anger, Jennifer T.
Freeman, Michael R.
Park, Sunghyouk
Kim, Jayoung
author_facet Wen, He
Lee, Tack
You, Sungyong
Park, Soo-Hwan
Song, Hosook
Eilber, Karyn S.
Anger, Jennifer T.
Freeman, Michael R.
Park, Sunghyouk
Kim, Jayoung
author_sort Wen, He
collection PubMed
description [Image: see text] Interstitial cystitis/painful bladder syndrome (IC) is a chronic syndrome of unknown etiology that presents with bladder pain, urinary frequency, and urgency. The lack of specific biomarkers and a poor understanding of underlying molecular mechanisms present challenges for disease diagnosis and therapy. The goals of this study were to identify noninvasive biomarker candidates for IC from urine specimens and to potentially gain new insight into disease mechanisms using a nuclear magnetic resonance (NMR)-based global metabolomics analysis of urine from female IC patients and controls. Principal component analysis (PCA) suggested that the urinary metabolome of IC and controls was clearly different, with 140 NMR peaks significantly altered in IC patients (FDR < 0.05) compared to that in controls. On the basis of strong correlation scores, fifteen metabolite peaks were nominated as the strongest signature of IC. Among those signals that were higher in the IC group, three peaks were annotated as tyramine, the pain-related neuromodulator. Two peaks were annotated as 2-oxoglutarate. Levels of tyramine and 2-oxoglutarate were significantly elevated in urine specimens of IC subjects. An independent analysis using mass spectrometry also showed significantly increased levels of tyramine and 2-oxoglutarate in IC patients compared to controls. Functional studies showed that 2-oxoglutarate, but not tyramine, retarded growth of normal bladder epithelial cells. These preliminary findings suggest that analysis of urine metabolites has promise in biomarker development in the context of IC.
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spelling pubmed-42861932015-10-29 Urinary Metabolite Profiling Combined with Computational Analysis Predicts Interstitial Cystitis-Associated Candidate Biomarkers Wen, He Lee, Tack You, Sungyong Park, Soo-Hwan Song, Hosook Eilber, Karyn S. Anger, Jennifer T. Freeman, Michael R. Park, Sunghyouk Kim, Jayoung J Proteome Res [Image: see text] Interstitial cystitis/painful bladder syndrome (IC) is a chronic syndrome of unknown etiology that presents with bladder pain, urinary frequency, and urgency. The lack of specific biomarkers and a poor understanding of underlying molecular mechanisms present challenges for disease diagnosis and therapy. The goals of this study were to identify noninvasive biomarker candidates for IC from urine specimens and to potentially gain new insight into disease mechanisms using a nuclear magnetic resonance (NMR)-based global metabolomics analysis of urine from female IC patients and controls. Principal component analysis (PCA) suggested that the urinary metabolome of IC and controls was clearly different, with 140 NMR peaks significantly altered in IC patients (FDR < 0.05) compared to that in controls. On the basis of strong correlation scores, fifteen metabolite peaks were nominated as the strongest signature of IC. Among those signals that were higher in the IC group, three peaks were annotated as tyramine, the pain-related neuromodulator. Two peaks were annotated as 2-oxoglutarate. Levels of tyramine and 2-oxoglutarate were significantly elevated in urine specimens of IC subjects. An independent analysis using mass spectrometry also showed significantly increased levels of tyramine and 2-oxoglutarate in IC patients compared to controls. Functional studies showed that 2-oxoglutarate, but not tyramine, retarded growth of normal bladder epithelial cells. These preliminary findings suggest that analysis of urine metabolites has promise in biomarker development in the context of IC. American Chemical Society 2014-10-29 2015-01-02 /pmc/articles/PMC4286193/ /pubmed/25353990 http://dx.doi.org/10.1021/pr5007729 Text en Copyright © 2014 American Chemical Society This is an open access article published under an ACS AuthorChoice License (http://pubs.acs.org/page/policy/authorchoice_termsofuse.html) , which permits copying and redistribution of the article or any adaptations for non-commercial purposes.
spellingShingle Wen, He
Lee, Tack
You, Sungyong
Park, Soo-Hwan
Song, Hosook
Eilber, Karyn S.
Anger, Jennifer T.
Freeman, Michael R.
Park, Sunghyouk
Kim, Jayoung
Urinary Metabolite Profiling Combined with Computational Analysis Predicts Interstitial Cystitis-Associated Candidate Biomarkers
title Urinary Metabolite Profiling Combined with Computational Analysis Predicts Interstitial Cystitis-Associated Candidate Biomarkers
title_full Urinary Metabolite Profiling Combined with Computational Analysis Predicts Interstitial Cystitis-Associated Candidate Biomarkers
title_fullStr Urinary Metabolite Profiling Combined with Computational Analysis Predicts Interstitial Cystitis-Associated Candidate Biomarkers
title_full_unstemmed Urinary Metabolite Profiling Combined with Computational Analysis Predicts Interstitial Cystitis-Associated Candidate Biomarkers
title_short Urinary Metabolite Profiling Combined with Computational Analysis Predicts Interstitial Cystitis-Associated Candidate Biomarkers
title_sort urinary metabolite profiling combined with computational analysis predicts interstitial cystitis-associated candidate biomarkers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4286193/
https://www.ncbi.nlm.nih.gov/pubmed/25353990
http://dx.doi.org/10.1021/pr5007729
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