Identification of additional body weight QTLs in the Berlin Fat Mouse BFMI861 lines using time series data.

The Berlin Fat Mouse Inbred line (BFMI) is a model for obesity and metabolic syndrome. The sublines BFMI861-S1 and BFMI861-S2 differ in weight despite high genetic similarity and a shared obesity-related locus. This study focused on identifying additional body weight quantitative trait loci (QTLs) by analyzing weekly weight measurements in a male population of the advanced intercross line BFMI861-S1 x BFMI861-S2. QTL analysis, utilizing 200 selectively genotyped mice (GigaMUGA) and 197 males genotyped for top SNPs, revealed a genome-wide significant QTL on Chr 15 (68.46 to 81.40 Mb) for body weight between weeks 9 to 20. Notably, this QTL disappeared (weeks 21 to 23) and reappeared (weeks 24 and 25) coinciding with a diet change. Additionally, a significant body weight QTL on Chr 16 (3.89 to 22.79 Mb) was identified from weeks 6 to 25. Candidate genes, including Gpt, Cbx6, Apol6, Apol8, Sun2 (Chr 15) and Trap1, Rrn3, Mapk1 (Chr 16), were prioritized. This study unveiled two additional body weight QTLs, one of which is novel and responsive to diet changes. These findings illuminate genomic regions influencing weight in BFMI and emphasize the utility of time series data in uncovering novel genetic factors.

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

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. 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. Three QTLs 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. 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.

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 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.

A deletion containing a CTCF-element in intron 8 of the Bbs7 gene is partially responsible for juvenile obesity in the Berlin Fat Mouse.

The Berlin Fat Mouse Inbred (BFMI) line is a model for juvenile obesity. Previous studies on crosses between BFMI and C57Bl/6N (B6N) have identified a recessive defect causing juvenile obesity on chromosome 3 (jObes1). Bbs7 was identified as the most likely candidate gene for the observed effect. Comparative sequence analysis showed a 1578 bp deletion in intron 8 of Bbs7 in BFMI mice. A CTCF-element is located inside this deletion. To investigate the functional effect of this deletion, it was introduced into B6N mice using CRISPR/Cas9. Two mice containing the target deletion were obtained (B6N Bbs7emI8∆1 and Bbs7emI8∆2) and were subsequently mated to BFMI and B6N to generate two families suitable for complementation. Inherited alleles were determined and body composition was measured by quantitative magnetic resonance. Evidence for a partial complementation (13.1-15.1%) of the jObes1 allele by the CRISPR/Cas9 modified B6N Bbs7emI8∆1 and Bbs7emI8∆2 alleles was found. Mice carrying the complementation alleles had a 23-27% higher fat-to-lean ratio compared to animals which have a B6N allele (P(Bbs7emI8∆1) = 4.25 × 10-7; P(Bbs7emI8∆2) = 3.17 × 10-5). Consistent with previous findings, the recessive effect of the BFMI allele was also seen for the B6N Bbs7emI8∆1 and Bbs7emI8∆2 alleles. However, the effect size of the B6N Bbs7emI8∆1 and Bbs7emI8∆2 alleles was smaller than the BFMI allele, and thus showed only a partial complementation. Findings suggest additional variants near Bbs7 in addition to or interacting with the deletion in intron 8.

Clues of in vivo nuclear gene regulation by mitochondrial short non-coding RNAs.

Gene expression involves multiple processes, from transcription to translation to the mature, functional peptide, and it is regulated at multiple levels. Small RNA molecules are known to bind RNA messengers affecting their fate in the cytoplasm (a process generically termed 'RNA interference'). Such small regulatory RNAs are well-known to be originated from the nuclear genome, while the role of mitochondrial genome in RNA interference was largely overlooked. However, evidence is growing that mitochondrial DNA does provide the cell a source of interfering RNAs. Small mitochondrial highly transcribed RNAs (smithRNAs) have been proposed to be transcribed from the mitochondrion and predicted to regulate nuclear genes. Here, for the first time, we show in vivo clues of the activity of two smithRNAs in the Manila clam, Ruditapes philippinarum. Moreover, we show that smithRNAs are present and can be annotated in representatives of the three main bilaterian lineages; in some cases, they were already described and assigned to a small RNA category (e.g., piRNAs) given their biogenesis, while in other cases their biogenesis remains unclear. If mitochondria may affect nuclear gene expression through RNA interference, this opens a plethora of new possibilities for them to interact with the nucleus and makes metazoan mitochondrial DNA a much more complex genome than previously thought.