High‐fat diet and oral infection induced type 2 diabetes and obesity development under different genetic backgrounds

Abstract

Background Type 2 diabetes (T2D) is an adult‐onset and obese form of diabetes caused by an interplay between genetic, epigenetic, and environmental components. Here, we have assessed a cohort of 11 genetically different collaborative cross (CC) mouse lines comprised of both sexes for T2D and obesity developments in response to oral infection and high‐fat diet (HFD) challenges. Methods Mice were fed with either the HFD or the standard chow diet (control group) for 12 weeks starting at the age of 8 weeks. At week 5 of the experiment, half of the mice of each diet group were infected with Porphyromonas gingivalis and Fusobacterium nucleatum bacteria strains. Throughout the 12‐week experimental period, body weight (BW) was recorded biweekly, and intraperitoneal glucose tolerance tests were performed at weeks 6 and 12 of the experiment to evaluate the glucose tolerance status of mice. Results Statistical analysis has shown the significance of phenotypic variations between the CC lines, which have different genetic backgrounds and sex effects in different experimental groups. The heritability of the studied phenotypes was estimated and ranged between 0.45 and 0.85. We applied machine learning methods to make an early call for T2D and its prognosis. The results showed that classification with random forest could reach the highest accuracy classification (ACC = 0.91) when all the attributes were used. Conclusion Using sex, diet, infection status, initial BW, and area under the curve (AUC) at week 6, we could classify the final phenotypes/outcomes at the end stage of the experiment (at 12 weeks).

The Complexity of Skeletal Transverse Dimension: From Diagnosis, Management, and Treatment Strategies to the Application of Collaborative Cross (CC) Mouse Model

“…The resource comprises a diverse range of recombinant inbred (RI) lines that were produced from a genetically heterogeneous collection of eight progenitor strains. It is specifically created for the examination of complex traits, suggesting greater power than previously published techniques [ 88 , 89 ]. Due to the evolutionary divergence, the conclusive CC mouse group comprises a notably higher genetic diversity than other mouse models [ 90 , 91 ].…”

Towards system genetics analysis of head and neck squamous cell carcinoma using the mouse model, cellular platform, and clinical human data

“…This resource consists of a wide group of recombinant inbred (RI) strains developed from a genetically heterogeneous collection of eight founder strains and expressly intended for complicated trait analysis, with more power than previously described methodologies. [87][88][89][90] Five popular laboratory strains, A/J, C57BL/6J, 129S1/SvImJ, NOD/LtJ, NZO/HiLtJ, and three wild-derived strains, CAST/Ei, PWK/PhJ, and WSB, represent the eight founder strains (Figure 2).…”

“…The impetus for developing a high genetic diversity mouse reference population resulted in the creation of the CC mouse model. This resource consists of a wide group of recombinant inbred (RI) strains developed from a genetically heterogeneous collection of eight founder strains and expressly intended for complicated trait analysis, with more power than previously described methodologies 87–90 …”

Identifying genetic susceptibility to Aspergillus fumigatus infection using collaborative cross mice and RNA‐Seq approach

“…25 Combining the whole genome sequence data of the eight founders of the CC population 26 with the advantage of the merge analysis, 21 it is possible to refine the QTL map interval to a small genomic region and identify strong candidate genes. [27][28][29][30][31][32] Searching for candidate genes within the fine-mapped QTL regions can reveal genes involved in T-cell regulation.…”

“…The availability of genotyping data across all CC lines has opened the way to analyses of QTL in a variety of complex traits, such as behavioral and morphological phenotypes, [18][19][20][29][30][31][32][33] and immunological functions. 34,35 Referring to these genome data makes it possible to map chromosomal regions and genes associated with aspergillosis.…”