Dr. rer. nat. Deike Hesse-Wilting

Dr. Hesse-Wilting erforscht die genetischen Grundlagen von Insulinresistenz und Adipositas anhand der Berliner Fettmaus, einem polygenen Tiermodell für metabolische Erkrankungen. Sie nutzt moderne Genotypisierungsmethoden (MiniMUGA-Chips) und statistische Analysen, um Kandidatengene zu identifizieren, die erklären, warum genetisch ähnliche Mausstämme unterschiedliche metabolische Phänotypen aufweisen – einer bleibt trotz Übergewicht stoffwechselgesund, der andere entwickelt Insulinresistenz und Fettleber. Ihre Arbeiten zielen darauf ab, neue therapeutische Angriffspunkte für Typ-2-Diabetes und nicht-alkoholische Fettlebererkrankung (NAFLD) zu identifizieren und könnten pharmazeutischen Unternehmen sowie der öffentlichen Gesundheitsforschung Wege zu präventiven oder therapeutischen Interventionen aufzeigen.

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• Identifikation neuer Kandidatenregionen für Insulinresistenz und Adipositas in einer fortgeschrittenen Kreuzungspopulation aus Berliner Fettmaus und C57BL/6N durch Verwendung von MiniMUGA-Chips und „LMM MQM time series analysis“

  Quelle ↗

  Förderer: Wirtschaftsunternehmen / gewerbliche Wirtschaft Zeitraum: 05/2019 - 12/2028 Projektleitung: Dr. rer. nat. Deike Hesse-Wilting, Prof. Dr. habil. rer. nat. Gudrun A. Brockmann

• Identifikation von neuen Kandidatengenen für Insulinsensitivität und Insulinresistenz in der Berliner Fettmaus

  Quelle ↗

  Förderer: DFG Eigene Stelle (Sachbeihilfe) Zeitraum: 09/2018 - 12/2023 Projektleitung: Dr. rer. nat. Deike Hesse-Wilting

• A deleterious mutation in Acat2 promotes ectopic liver fat storage by disturbing cholesterol metabolism in adipose tissue of the Berlin Fat Mouse

  2023

  Diabetologie und Stoffwechsel · DOI

  Background The Berlin Fat Mouse Inbred (BFMI) lines are polygenic models for obesity. Genetically close sublines BFMI861-S1 (S1) and BFMI861-S2 (S2) were generated. Both are obese, but S1 mice are insulin resistant with increased liver fat, whereas S2 mice are metabolically healthy. Histologically, adipose tissue of S1 contains more larger and less smaller adipocytes. Previously, an association with lower adipose tissue but higher liver weight in S1 was found in a region on chromosome 17. Candidate genes in this region were prioritized using DNA sequence and RNA expression.

• QTL-mapping in the obese Berlin Fat Mouse identifies additional candidate genes for obesity and fatty liver disease

  2022

  Research Square · DOI

  Abstract 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 liver weight, liver triglycerides, and body weight using the obese BFMI sub-line BFMI861-S1. BFMI861-S1 mice are insulin resistant and store ectopic fat in the liver. Methods In generation 10, 58 males and 65 females of the advanced intercross line (AIL) BFMI861-S1xB6N were phenotyped under a standard diet over 20 weeks. QTL analysis was performed after genotyping with the MiniMUGA Genotyping Array. Whole-genome sequencing and gene expression data of the parental lines was used for the prioritization of positional candidate genes. Results Three QTL associated with liver weight, body weight, and subcutaneous adipose tissue (scAT) weight were identified. A highly significant QTL on chromosome (Chr) 1 (157–168 Mb) showed an association with liver weight. A QTL for body weight at 20 weeks was found on Chr 3 (34.1–40 Mb) overlapping with a QTL for scAT weight. In a multiple QTL mapping approach, an additional QTL affecting body weight at 16 weeks was identified on Chr 6 (9.5–26.1 Mb). Considering sequence variants and expression differences, Sec16b and Astn1 were prioritized as top positional candidate genes for the liver weight QTL on Chr 1; Met and Ica1 for the body weight QTL on Chr 6. Interestingly, all top candidate genes have previously been linked with metabolic traits. Conclusions This study shows once more the power of an advanced intercross line for fine mapping. QTL mapping combined with a detailed prioritization approach allowed us to identify additional and plausible candidate genes linked to metabolic traits in the BFMI861-S1xB6N AIL. By reidentifying known candidate genes in a different crossing population the causal link with specific traits is underlined and additional evidence is given for further investigations.