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Mechanism-Based Biomarker Prediction for Low-Grade Inflammation in Liver and Adipose Tissue

Metabolic disorders, such as obesity and type 2 diabetes have a large impact on global health, especially in industrialized countries. Tissue-specific chronic low-grade inflammation is a key contributor to complications in metabolic disorders. To support therapeutic approaches to these complications...

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Autores principales: van Bilsen, Jolanda H. M., van den Brink, Willem, van den Hoek, Anita M., Dulos, Remon, Caspers, Martien P. M., Kleemann, Robert, Wopereis, Suzan, Verschuren, Lars
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8631400/
https://www.ncbi.nlm.nih.gov/pubmed/34858196
http://dx.doi.org/10.3389/fphys.2021.703370
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author van Bilsen, Jolanda H. M.
van den Brink, Willem
van den Hoek, Anita M.
Dulos, Remon
Caspers, Martien P. M.
Kleemann, Robert
Wopereis, Suzan
Verschuren, Lars
author_facet van Bilsen, Jolanda H. M.
van den Brink, Willem
van den Hoek, Anita M.
Dulos, Remon
Caspers, Martien P. M.
Kleemann, Robert
Wopereis, Suzan
Verschuren, Lars
author_sort van Bilsen, Jolanda H. M.
collection PubMed
description Metabolic disorders, such as obesity and type 2 diabetes have a large impact on global health, especially in industrialized countries. Tissue-specific chronic low-grade inflammation is a key contributor to complications in metabolic disorders. To support therapeutic approaches to these complications, it is crucial to gain a deeper understanding of the inflammatory dynamics and to monitor them on the individual level. To this end, blood-based biomarkers reflecting the tissue-specific inflammatory dynamics would be of great value. Here, we describe an in silico approach to select candidate biomarkers for tissue-specific inflammation by using a priori mechanistic knowledge from pathways and tissue-derived molecules. The workflow resulted in a list of candidate markers, in part consisting of literature confirmed biomarkers as well as a set of novel, more innovative biomarkers that reflect inflammation in the liver and adipose tissue. The first step of biomarker verification was on murine tissue gene-level by inducing hepatic inflammation and adipose tissue inflammation through a high-fat diet. Our data showed that in silico predicted hepatic markers had a strong correlation to hepatic inflammation in the absence of a relation to adipose tissue inflammation, while others had a strong correlation to adipose tissue inflammation in the absence of a relation to liver inflammation. Secondly, we evaluated the human translational value by performing a curation step in the literature using studies that describe the regulation of the markers in human, which identified 9 hepatic (such as Serum Amyloid A, Haptoglobin, and Interleukin 18 Binding Protein) and 2 adipose (Resistin and MMP-9) inflammatory biomarkers at the highest level of confirmation. Here, we identified and pre-clinically verified a set of in silico predicted biomarkers for liver and adipose tissue inflammation which can be of great value to study future development of therapeutic/lifestyle interventions to combat metabolic inflammatory complications.
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spelling pubmed-86314002021-12-01 Mechanism-Based Biomarker Prediction for Low-Grade Inflammation in Liver and Adipose Tissue van Bilsen, Jolanda H. M. van den Brink, Willem van den Hoek, Anita M. Dulos, Remon Caspers, Martien P. M. Kleemann, Robert Wopereis, Suzan Verschuren, Lars Front Physiol Physiology Metabolic disorders, such as obesity and type 2 diabetes have a large impact on global health, especially in industrialized countries. Tissue-specific chronic low-grade inflammation is a key contributor to complications in metabolic disorders. To support therapeutic approaches to these complications, it is crucial to gain a deeper understanding of the inflammatory dynamics and to monitor them on the individual level. To this end, blood-based biomarkers reflecting the tissue-specific inflammatory dynamics would be of great value. Here, we describe an in silico approach to select candidate biomarkers for tissue-specific inflammation by using a priori mechanistic knowledge from pathways and tissue-derived molecules. The workflow resulted in a list of candidate markers, in part consisting of literature confirmed biomarkers as well as a set of novel, more innovative biomarkers that reflect inflammation in the liver and adipose tissue. The first step of biomarker verification was on murine tissue gene-level by inducing hepatic inflammation and adipose tissue inflammation through a high-fat diet. Our data showed that in silico predicted hepatic markers had a strong correlation to hepatic inflammation in the absence of a relation to adipose tissue inflammation, while others had a strong correlation to adipose tissue inflammation in the absence of a relation to liver inflammation. Secondly, we evaluated the human translational value by performing a curation step in the literature using studies that describe the regulation of the markers in human, which identified 9 hepatic (such as Serum Amyloid A, Haptoglobin, and Interleukin 18 Binding Protein) and 2 adipose (Resistin and MMP-9) inflammatory biomarkers at the highest level of confirmation. Here, we identified and pre-clinically verified a set of in silico predicted biomarkers for liver and adipose tissue inflammation which can be of great value to study future development of therapeutic/lifestyle interventions to combat metabolic inflammatory complications. Frontiers Media S.A. 2021-11-10 /pmc/articles/PMC8631400/ /pubmed/34858196 http://dx.doi.org/10.3389/fphys.2021.703370 Text en Copyright © 2021 van Bilsen, van den Brink, van den Hoek, Dulos, Caspers, Kleemann, Wopereis and Verschuren. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
van Bilsen, Jolanda H. M.
van den Brink, Willem
van den Hoek, Anita M.
Dulos, Remon
Caspers, Martien P. M.
Kleemann, Robert
Wopereis, Suzan
Verschuren, Lars
Mechanism-Based Biomarker Prediction for Low-Grade Inflammation in Liver and Adipose Tissue
title Mechanism-Based Biomarker Prediction for Low-Grade Inflammation in Liver and Adipose Tissue
title_full Mechanism-Based Biomarker Prediction for Low-Grade Inflammation in Liver and Adipose Tissue
title_fullStr Mechanism-Based Biomarker Prediction for Low-Grade Inflammation in Liver and Adipose Tissue
title_full_unstemmed Mechanism-Based Biomarker Prediction for Low-Grade Inflammation in Liver and Adipose Tissue
title_short Mechanism-Based Biomarker Prediction for Low-Grade Inflammation in Liver and Adipose Tissue
title_sort mechanism-based biomarker prediction for low-grade inflammation in liver and adipose tissue
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8631400/
https://www.ncbi.nlm.nih.gov/pubmed/34858196
http://dx.doi.org/10.3389/fphys.2021.703370
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