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Realistic biomarkers from plasma extracellular vesicles for detection of beryllium exposure
PURPOSE: Exposures related to beryllium (Be) are an enduring concern among workers in the nuclear weapons and other high-tech industries, calling for regular and rigorous biological monitoring. Conventional biomonitoring of Be in urine is not informative of cumulative exposure nor health outcomes. B...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9489591/ https://www.ncbi.nlm.nih.gov/pubmed/35551477 http://dx.doi.org/10.1007/s00420-022-01871-7 |
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author | Adduri, Raju S. R. Vasireddy, Ravikiran Mroz, Margaret M. Bhakta, Anisha Li, Yang Chen, Zhe Miller, Jeffrey W. Velasco-Alzate, Karen Y. Gopalakrishnan, Vanathi Maier, Lisa A. Li, Li Konduru, Nagarjun V. |
author_facet | Adduri, Raju S. R. Vasireddy, Ravikiran Mroz, Margaret M. Bhakta, Anisha Li, Yang Chen, Zhe Miller, Jeffrey W. Velasco-Alzate, Karen Y. Gopalakrishnan, Vanathi Maier, Lisa A. Li, Li Konduru, Nagarjun V. |
author_sort | Adduri, Raju S. R. |
collection | PubMed |
description | PURPOSE: Exposures related to beryllium (Be) are an enduring concern among workers in the nuclear weapons and other high-tech industries, calling for regular and rigorous biological monitoring. Conventional biomonitoring of Be in urine is not informative of cumulative exposure nor health outcomes. Biomarkers of exposure to Be based on non-invasive biomonitoring could help refine disease risk assessment. In a cohort of workers with Be exposure, we employed blood plasma extracellular vesicles (EVs) to discover novel biomarkers of exposure to Be. METHODS: EVs were isolated from plasma using size-exclusion chromatography and subjected to mass spectrometry-based proteomics. A protein-based classifier was developed using LASSO regression and validated by ELISA. RESULTS: We discovered a dual biomarker signature comprising zymogen granule protein 16B and putative protein FAM10A4 that differentiated between Be-exposed and -unexposed subjects. ELISA-based quantification of the biomarkers in an independent cohort of samples confirmed higher expression of the signature in the Be-exposed group, displaying high predictive accuracy (AUROC = 0.919). Furthermore, the biomarkers efficiently discriminated high- and low-exposure groups (AUROC = 0.749). CONCLUSIONS: This is the first report of EV biomarkers associated with Be exposure and exposure levels. The biomarkers could be implemented in resource-limited settings for Be exposure assessment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00420-022-01871-7. |
format | Online Article Text |
id | pubmed-9489591 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-94895912022-09-22 Realistic biomarkers from plasma extracellular vesicles for detection of beryllium exposure Adduri, Raju S. R. Vasireddy, Ravikiran Mroz, Margaret M. Bhakta, Anisha Li, Yang Chen, Zhe Miller, Jeffrey W. Velasco-Alzate, Karen Y. Gopalakrishnan, Vanathi Maier, Lisa A. Li, Li Konduru, Nagarjun V. Int Arch Occup Environ Health Original Article PURPOSE: Exposures related to beryllium (Be) are an enduring concern among workers in the nuclear weapons and other high-tech industries, calling for regular and rigorous biological monitoring. Conventional biomonitoring of Be in urine is not informative of cumulative exposure nor health outcomes. Biomarkers of exposure to Be based on non-invasive biomonitoring could help refine disease risk assessment. In a cohort of workers with Be exposure, we employed blood plasma extracellular vesicles (EVs) to discover novel biomarkers of exposure to Be. METHODS: EVs were isolated from plasma using size-exclusion chromatography and subjected to mass spectrometry-based proteomics. A protein-based classifier was developed using LASSO regression and validated by ELISA. RESULTS: We discovered a dual biomarker signature comprising zymogen granule protein 16B and putative protein FAM10A4 that differentiated between Be-exposed and -unexposed subjects. ELISA-based quantification of the biomarkers in an independent cohort of samples confirmed higher expression of the signature in the Be-exposed group, displaying high predictive accuracy (AUROC = 0.919). Furthermore, the biomarkers efficiently discriminated high- and low-exposure groups (AUROC = 0.749). CONCLUSIONS: This is the first report of EV biomarkers associated with Be exposure and exposure levels. The biomarkers could be implemented in resource-limited settings for Be exposure assessment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00420-022-01871-7. Springer Berlin Heidelberg 2022-05-12 2022 /pmc/articles/PMC9489591/ /pubmed/35551477 http://dx.doi.org/10.1007/s00420-022-01871-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Adduri, Raju S. R. Vasireddy, Ravikiran Mroz, Margaret M. Bhakta, Anisha Li, Yang Chen, Zhe Miller, Jeffrey W. Velasco-Alzate, Karen Y. Gopalakrishnan, Vanathi Maier, Lisa A. Li, Li Konduru, Nagarjun V. Realistic biomarkers from plasma extracellular vesicles for detection of beryllium exposure |
title | Realistic biomarkers from plasma extracellular vesicles for detection of beryllium exposure |
title_full | Realistic biomarkers from plasma extracellular vesicles for detection of beryllium exposure |
title_fullStr | Realistic biomarkers from plasma extracellular vesicles for detection of beryllium exposure |
title_full_unstemmed | Realistic biomarkers from plasma extracellular vesicles for detection of beryllium exposure |
title_short | Realistic biomarkers from plasma extracellular vesicles for detection of beryllium exposure |
title_sort | realistic biomarkers from plasma extracellular vesicles for detection of beryllium exposure |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9489591/ https://www.ncbi.nlm.nih.gov/pubmed/35551477 http://dx.doi.org/10.1007/s00420-022-01871-7 |
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