The present study was performed to identify QTL for muscle traits, with IMF and WHC being the main traits of interest. It was unclear whether IMF-influencing genes would be different from genes responsible for the distribution and quantity of normal adipose tissue (Tanomura et al. 2002
). From our results that showed different QTL for IMF and total fat mass, we conclude that there are indeed different genetic determinants that control total fat mass and IMF. The highly significant QTL for total fat mass on Chr 1 in our cross was also seen in the scans for IMF of the M. longissimus
but had no significant effects (P
< 0.37) and did not affect IMF in the M. quadriceps
. This QTL likely acts predominantly on fat accumulation in the adipose but not in the muscle tissue. The total fat mass QTL on Chr 1 () overlapped with a suggestive QTL (P
< 0.10) for fasting blood glucose levels. It cannot be excluded that this chromosomal region is a major regulator of both glucose utilization and fat metabolism in our cross. It has been shown repeatedly that increased adiposity is linked to insulin resistance and higher blood glucose levels as a result of impaired glucose clearance (Kahn et al. 2006
). In addition, high intramuscular triglyceride levels, which are highly correlated with high body fat mass in our cross, inhibit the insulin-signaling cascade and lower glucose uptake (Chadt et al. 2008
; Powell et al. 2004
; Schmitz-Peiffer 2000
; Tanomura et al. 2002
). Both high fasting blood glucose levels and increased IMF were observed in BMMI806 mice. Different genes in the QTL region could contribute to the QTL effect. One interesting candidate gene in the QTL region on Chr 1 at 67 Mb is the long-chain acyl-CoA-dehydrogenase gene (Acadl
), which is directly involved in the first step of β
oxidation by converting acyl-CoA from fatty acids into Δ2
-trans-enoyl-CoA. Decreased oxidation of mitochondrial fatty acid was shown in Acadl
-deficient mice (Zhang et al. 2007
). It seems possible that impaired fatty acid utilization could result in increased fat mass but also in high IMF, especially since skeleton muscles lack de novo lipogenesis (Eaton 2002
). Interestingly, data from the Mouse Diversity Array showed three SNPs to be different in this gene between BMMI806 and BMMI816 mice. The first SNP is located in an intron and the second and the third SNPs are located within exons and code synonymously. However, since it cannot be ruled out that adjacent base pairs are also different leading to a nonsynonymous coding, Acadl
seems to be a good candidate gene that deserves further attention.
As mentioned, it is known from studies in humans and mice that muscular triglycerides and certain lipid species inside the muscle fibers have similar metabolic effects, e.g., on the glucose uptake (Ebeling et al. 1998
; Pan et al. 1997
; Phillips et al. 1996
; Yu et al. 2002
). To some extent, the biological function of muscle lipid stores seems to be similar in different species in terms of lipids as an energy fuel and their role in homeostasis. However, the respective patterns of fatty acids might be slightly different in different species and can even be manipulated (Ludden et al. 2009
; Wood et al. 2004
). In our study we measured the total fat content within the muscle and found differences between parental mouse lines.
Several fat metabolism genes are located in the QTL region on Chr 7 that affects IMF, e.g., the lipase gene (Lipe
) and the apolipo-protein E gene (Apoe
) (Hansson et al. 2005
; Hunt et al. 2006
), which might be responsible for the high IMF phenotype of the BMMI806. However, both genes are located in regions that are noninformative on the measured SNP level.
Still, there might be sequence alterations in these regions that remained undetected by the Mouse Diversity Array. Another interesting candidate gene in this region is thymoma viral proto-oncogene 2 (Akt2
) at 28 Mb. Akt2
is involved in the insulin-dependent regulation of lipid metabolism and triglyceride storage (Leavens et al. 2009
). There is a SNP marker in this gene that differs between the parental lines, and the BMMI806 animals have high fasting blood glucose levels that might also indicate a certain insulin resistance. This shows that including information about regions that are identical by descent between the parental lines can help to narrow down the list of candidate genes in a QTL, but additional data, such as from RNA expression or sequencing, are needed to finally confirm candidate genes ().
The comparison of syntenic regions between mice and other mammals via the Ensembl database revealed supporting evidence for the presented data. The murine QTL region for IMF on Chr 7 is syntenic with regions on the Sus scrofa
chromosomes (SSC) SSC6 (25–43 Mb) and SSC14 (138–140 Mb) (Birney et al. 2004
; Rohrer et al. 1996
). In the syntenic regions on SSC6, one QTL for IMF and five additional fat-related QTL have been identified in pigs (de Koning et al. 2000
; Liu et al. 2008
; Paszek et al. 2001
), while the small region on SSC14 is located in a region where two fat-related QTL have been found (Dragos-Wendrich et al. 2003
; Knott et al. 1998
). Therefore, it seems possible that genes in these syntenic regions have similar functions in both species. The presented mouse model, which is simplified in its genomic structure, could support the identification of genes that might affect IMF in pigs.
The second objective of this study was the identification of QTL for WHC. One QTL for this trait that was specific for the M. quadriceps
was identified on Chr 2. The M. longissimus
did not show differences in WHC between the parental lines. The particular fiber type composition of the two different muscles could be responsible for differences in WHC and IMF. Depending on the ratio of oxidative and glycogenic fiber types, the metabolism of the tissue can be different (Armstrong and Phelps 1984
; Hamalainen and Pette 1993
; Rehfeldt et al. 2010
). Moreover, a correlation existed between glycogen and WHC, since higher glycogen content was also associated with higher drip loss. It is known that high glycogen storage impairs the WHC of the tissue. With postmortem hypoxia, glycogen degradation results in an increased accumulation of lactate, which among other factors lowers cellular pH values. The timing and extent of this lactate accumulation depend on the amount of stored glycogen. The concomitant pH drop might alter the structural integrity of the muscle fibers’ cellular scaffold and membrane proteins, which results in an increased loss of cell form and cell fluid (Bee et al. 2007
; Choe et al. 2008
). The parental BMMI816 line showed higher glycogen content and inferior WHC.
For the lactate content of the M. longissimus, a significant, positive correlation was found with the WHC in the M. longissimus in males, and with the pH 24 h after dissection in females. Since the M. longissimus samples, in which the glycogen and lactate contents were measured, were taken immediately after exsanguinations, it is possible that hypoxia did not last long enough to observe stronger correlations of lactate and glycogen with carcass pH. However, the results yet support the model of postmortem scaffold destruction and concomitant loss of tissue fluid due to increased tissue acidity that is influenced by the levels of muscular glycogen and lactate at the time of death. For the QTL for WHC on Chr 2 there was a syntenic region on SSC17 (13–37 Mb) in which no WHC-related QTL was found.
Comparative genomics and interspecies research are used to transfer the information obtained from our mouse study into the field of livestock research to which it is supposed to contribute. Using syntenic regions in the context of QTL studies can thereby provide additional information about the plausibility of identified QTL. However, it must be considered that the chance of finding syntenic regions between mouse and pig on a chromosome that contains any QTL is high, depending on the number of QTL and the size of the respective QTL intervals in both species. This is the case, e.g., for IMF and WHC (as drip loss), for which 15 of the 19 porcine chromosomes contain several significant QTL. Therefore, chances are relatively high to find a QTL for IMF on any of the pig chromosomes. However, for the murine IMF QTL on Chr 7 (17–31 Mb), only two syntenic regions exist, one on SSC6 (25–43 Mb) and one on SSC14 (138–140 Mb), but only the region on SSC6 contains a QTL for IMF. More than 50% of all murine genes are identified as having orthologs in pigs, which does not prove but indicate many putative similar gene functions (BioMart, Ensembl). Comparing mouse data to the results from pigs contributes additional information not only about orthology but also about the actual functions of conserved genes in both species.
Both mouse lines used in this study are hypermuscular and share large portions of their genomes. Nevertheless, they show large differences in their phenotypes. The increased body weights of the BMMI lines reflect the selection response during the historic breeding process. It appeared reasonable to examine these lines as models for livestock, since commercially used races of pigs or cattle are generally larger than their wild ancestors, as are the BMMI lines compared to wild-type mice. By generating a G3 population, we expected narrower QTL intervals but had to use a relatively more complicated analysis. Breeding additional generations will probably enable us to fine map these regions better. The QTL identified in the G3 in this study provide evidence to look for interesting alterations in the BMMI genome that explain the different phenotypes. Since the lines are related and have most haplotypes in common, we also expect fewer differences in the DNA sequence between the lines as compared to unrelated lines. This circumstance also could decrease the number of putative candidate genes in subsequent sequencing and expression studies.