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Identification of four novel QTL linked to the metabolic syndrome in the Berlin Fat Mouse

BACKGROUND: The Berlin Fat Mouse Inbred line (BFMI) is a model for obesity and the metabolic syndrome. This study aimed to identify genetic variants associated with impaired glucose metabolism using the obese lines BFMI861-S1 and BFMI861-S2, which are genetically closely related, but differ in sever...

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Autores principales: Delpero, Manuel, Arends, Danny, Sprechert, Maximilian, Krause, Florian, Kluth, Oliver, Schürmann, Annette, Brockmann, Gudrun A., Hesse, Deike
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8794782/
https://www.ncbi.nlm.nih.gov/pubmed/34689180
http://dx.doi.org/10.1038/s41366-021-00991-3
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author Delpero, Manuel
Arends, Danny
Sprechert, Maximilian
Krause, Florian
Kluth, Oliver
Schürmann, Annette
Brockmann, Gudrun A.
Hesse, Deike
author_facet Delpero, Manuel
Arends, Danny
Sprechert, Maximilian
Krause, Florian
Kluth, Oliver
Schürmann, Annette
Brockmann, Gudrun A.
Hesse, Deike
author_sort Delpero, Manuel
collection PubMed
description BACKGROUND: The Berlin Fat Mouse Inbred line (BFMI) is a model for obesity and the metabolic syndrome. This study aimed to identify genetic variants associated with impaired glucose metabolism using the obese lines BFMI861-S1 and BFMI861-S2, which are genetically closely related, but differ in several traits. BFMI861-S1 is insulin resistant and stores ectopic fat in the liver, whereas BFMI861-S2 is insulin sensitive. METHODS: In generation 10, 397 males of an advanced intercross line (AIL) BFMI861-S1 × BFMI861-S2 were challenged with a high-fat, high-carbohydrate diet and phenotyped over 25 weeks. QTL-analysis was performed after selective genotyping of 200 mice using the GigaMUGA Genotyping Array. Additional 197 males were genotyped for 7 top SNPs in QTL regions. For the prioritization of positional candidate genes whole genome sequencing and gene expression data of the parental lines were used. RESULTS: Overlapping QTL for gonadal adipose tissue weight and blood glucose concentration were detected on chromosome (Chr) 3 (95.8–100.1 Mb), and for gonadal adipose tissue weight, liver weight, and blood glucose concentration on Chr 17 (9.5–26.1 Mb). Causal modeling suggested for Chr 3-QTL direct effects on adipose tissue weight, but indirect effects on blood glucose concentration. Direct effects on adipose tissue weight, liver weight, and blood glucose concentration were suggested for Chr 17-QTL. Prioritized positional candidate genes for the identified QTL were Notch2 and Fmo5 (Chr 3) and Plg and Acat2 (Chr 17). Two additional QTL were detected for gonadal adipose tissue weight on Chr 15 (67.9–74.6 Mb) and for body weight on Chr 16 (3.9–21.4 Mb). CONCLUSIONS: QTL mapping together with a detailed prioritization approach allowed us to identify candidate genes associated with traits of the metabolic syndrome. In addition, we provided evidence for direct and indirect genetic effects on blood glucose concentration in the insulin-resistant mouse line BFMI861-S1.
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spelling pubmed-87947822022-02-07 Identification of four novel QTL linked to the metabolic syndrome in the Berlin Fat Mouse Delpero, Manuel Arends, Danny Sprechert, Maximilian Krause, Florian Kluth, Oliver Schürmann, Annette Brockmann, Gudrun A. Hesse, Deike Int J Obes (Lond) Article BACKGROUND: The Berlin Fat Mouse Inbred line (BFMI) is a model for obesity and the metabolic syndrome. This study aimed to identify genetic variants associated with impaired glucose metabolism using the obese lines BFMI861-S1 and BFMI861-S2, which are genetically closely related, but differ in several traits. BFMI861-S1 is insulin resistant and stores ectopic fat in the liver, whereas BFMI861-S2 is insulin sensitive. METHODS: In generation 10, 397 males of an advanced intercross line (AIL) BFMI861-S1 × BFMI861-S2 were challenged with a high-fat, high-carbohydrate diet and phenotyped over 25 weeks. QTL-analysis was performed after selective genotyping of 200 mice using the GigaMUGA Genotyping Array. Additional 197 males were genotyped for 7 top SNPs in QTL regions. For the prioritization of positional candidate genes whole genome sequencing and gene expression data of the parental lines were used. RESULTS: Overlapping QTL for gonadal adipose tissue weight and blood glucose concentration were detected on chromosome (Chr) 3 (95.8–100.1 Mb), and for gonadal adipose tissue weight, liver weight, and blood glucose concentration on Chr 17 (9.5–26.1 Mb). Causal modeling suggested for Chr 3-QTL direct effects on adipose tissue weight, but indirect effects on blood glucose concentration. Direct effects on adipose tissue weight, liver weight, and blood glucose concentration were suggested for Chr 17-QTL. Prioritized positional candidate genes for the identified QTL were Notch2 and Fmo5 (Chr 3) and Plg and Acat2 (Chr 17). Two additional QTL were detected for gonadal adipose tissue weight on Chr 15 (67.9–74.6 Mb) and for body weight on Chr 16 (3.9–21.4 Mb). CONCLUSIONS: QTL mapping together with a detailed prioritization approach allowed us to identify candidate genes associated with traits of the metabolic syndrome. In addition, we provided evidence for direct and indirect genetic effects on blood glucose concentration in the insulin-resistant mouse line BFMI861-S1. Nature Publishing Group UK 2021-10-23 2022 /pmc/articles/PMC8794782/ /pubmed/34689180 http://dx.doi.org/10.1038/s41366-021-00991-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Delpero, Manuel
Arends, Danny
Sprechert, Maximilian
Krause, Florian
Kluth, Oliver
Schürmann, Annette
Brockmann, Gudrun A.
Hesse, Deike
Identification of four novel QTL linked to the metabolic syndrome in the Berlin Fat Mouse
title Identification of four novel QTL linked to the metabolic syndrome in the Berlin Fat Mouse
title_full Identification of four novel QTL linked to the metabolic syndrome in the Berlin Fat Mouse
title_fullStr Identification of four novel QTL linked to the metabolic syndrome in the Berlin Fat Mouse
title_full_unstemmed Identification of four novel QTL linked to the metabolic syndrome in the Berlin Fat Mouse
title_short Identification of four novel QTL linked to the metabolic syndrome in the Berlin Fat Mouse
title_sort identification of four novel qtl linked to the metabolic syndrome in the berlin fat mouse
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8794782/
https://www.ncbi.nlm.nih.gov/pubmed/34689180
http://dx.doi.org/10.1038/s41366-021-00991-3
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