Quantitative trait locus mapping for atherosclerosis susceptibility

A severe atherosclerosis mouse model on the resistant NOD background

Atherosclerosis is a complex disease affecting arterial blood vessels and blood flow that could result in a variety of life-threatening consequences. Disease models with diverged genomes are necessary for understanding the genetic architecture of this complex disease. Non-obese diabetic (NOD) mice are highly polymorphic and widely used for studies of type 1 diabetes and autoimmunity. Understanding atherosclerosis development in the NOD strain is of particular interest as human atherosclerosis on the diabetic and autoimmune background has not been successfully modeled. In this study, we used CRISPR/Cas9 genome editing to genetically disrupt apolipoprotein E (ApoE) and low-density lipoprotein receptor (LDLR) expression on the pure NOD background, and compared phenotype between single-gene-deleted mice and double-knockout mutants with reference to ApoE-deficient C57BL/6 mice. We found that genetic ablation of Ldlr or Apoe in NOD mice was not sufficient to establish an atherosclerosis model, in contrast to ApoE-deficient C57BL/6 mice fed a high-fat diet (HFD) for over 12 weeks. We further obtained NOD mice deficient in both LDLR and ApoE, and assessed the severity of atherosclerosis and immune response to hyperlipidemia in comparison to ApoE-deficient C57BL/6 mice. Strikingly, the double-knockout NOD mice treated with a HFD developed severe atherosclerosis with aorta narrowed by over 60% by plaques, accompanied by destruction of pancreatic islets and an inflammatory response to hyperlipidemia. Therefore, we succeeded in obtaining a genetic model with severe atherosclerosis on the NOD background, which is highly resistant to the disease. This model is useful for the study of atherosclerosis in the setting of autoimmunity.

Genetic dissection of quantitative trait Loci for hemostasis and thrombosis on mouse chromosomes 11 and 5 using congenic and subcongenic strains

Susceptibility to thrombosis varies in human populations as well as many inbred mouse strains. Only a small portion of this variation has been identified, suggesting that there are unknown modifier genes. The objective of this study was to narrow the quantitative trait locus (QTL) intervals previously identified for hemostasis and thrombosis on mouse distal chromosome 11 (Hmtb6) and on chromosome 5 (Hmtb4 and Hmtb5). In a tail bleeding/rebleeding assay, a reporter assay for hemostasis and thrombosis, subcongenic strain (6A-2) had longer clot stability time than did C57BL/6J (B6) mice but a similar time to the B6-Chr11^A/J consomic mice, confirming the Hmtb6 phenotype. Six congenic and subcongenic strains were constructed for chromosome 5, and the congenic strain, 2A-1, containing the shortest A/J interval (16.6 cM, 26.6 Mbp) in the Hmtb4 region, had prolonged clot stability time compared to B6 mice. In the 3A-2 and CSS-5 mice bleeding time was shorter than for B6, mice confirming the Hmtb5 QTL. An increase in bleeding time was identified in another congenic strain (3A-1) with A/J interval (24.8 cM, 32.9 Mbp) in the proximal region of chromosome 5, confirming a QTL for bleeding previously mapped to that region and designated as Hmtb10. The subcongenic strain 4A-2 with the A/J fragment in the proximal region had a long occlusion time of the carotid artery after ferric chloride injury and reduced dilation after injury to the abdominal aorta compared to B6 mice, suggesting an additional locus in the proximal region, which was designated Hmtb11 (5 cM, 21.4 Mbp). CSS-17 mice crossed with congenic strains, 3A-1 and 3A-2, modified tail bleeding. Using congenic and subcongenic analysis, candidate genes previously identified and novel genes were identified as modifiers of hemostasis and thrombosis in each of the loci Hmtb6, Hmtb4, Hmtb10, and Hmtb11.

A novel mouse model of in-stent restenosis

BACKGROUND AND AIMS

In-stent restenosis (ISR) is the major complication that occurs after percutaneous coronary interventions to facilitate coronary revascularization. Herein we described a simple and cost-effective model, which reproduces important features of ISR in the mouse.

METHODS AND RESULTS

Microvascular bare metal stents were successfully implanted in the abdominal aorta of atherosclerotic ApoE-null mice. Patency of implanted stents was interrogated using ultrasound biomicroscopy. Aortas were harvested at different time points after implantation and processed for histopathological analysis. Thrombus formation was histologically detected after 1 day. Leukocyte adherence and infiltration were evident after 7 days and decreased thereafter. Neointimal formation, neointimal thickness and luminal stenosis simultaneously increased up to 28 days after stent implantation. Using multichannel fluorescence molecular tomography (FMT) for spatiotemporal resolution of MMP activities, we observed that MMP activity in the stented aorta of Apo-E null mice was 2-fold higher than that of wild-type mice. Finally, we compared neointimal formation in response to stenting in two genetically different mouse strains. In-stent neointimas in FVB/NJ mice were 2-fold thicker than in C57BL/6J mice (p=0.002).

CONCLUSION

We have developed a model that can take advantage of the multiple genetic resources available for the mouse to study the mechanisms of in-stent restenosis.

Transcriptome profile of macrophages from atherosclerosis-sensitive and atherosclerosis-resistant mice

We performed a strain intercross between two apoE-deficient mouse strains with a large difference in lesion susceptibility and measured aortic root lesion area in 98 female F(2) progeny. Total RNA was prepared from bone marrow-derived macrophages, and RNA from the five mice with the smallest and largest lesions were used for microarray gene expression profiling. Remarkably, approximately 5% of the 12,288 expressed transcripts were differentially expressed in the atherosclerosis-susceptible and atherosclerosis-resistant bone marrow-derived macrophages (unadjusted p < 0.05), thus defining the transcriptome of macrophages associated with atherosclerosis susceptibility. Using more stringent criteria of twofold or greater change and p < 0.01, 116 and 70 transcripts were overexpressed in lesion-prone and lesion-resistant bone marrow-derived macrophages, respectively. Transcription factor binding site analysis identified two promoter elements that were found more often in the genes overexpressed in the large-lesion group, and one promoter element that was found more often in the small-lesion group. The combination of this expression profiling data with the genetic method of quantitative trait locus mapping should give powerful insights into the genes that affect atherosclerosis susceptibility in mice.

Genetic approaches to autonomic dysreflexia

Autonomic dysreflexia is a potentially life-threatening condition in which episodic hypertension occurs after injuries above the mid-thoracic segments of the spinal cord. Despite the seriousness of this condition, little is known of the molecular mechanisms that lead to its development. The completed sequencing of the mouse genome, its dense genetic map, and the large repository of engineered and spontaneous mouse mutants, make the mouse an ideal model organism in which to study the molecular mechanisms underlying autonomic dysreflexia. We subjected two wild-type strains of mice, 129Sv and C57BL/6, and one spontaneous mouse mutant, Wallerian degeneration slow (Wld s), to spinal cord transection and clip-compression injury. We found that the incidence of autonomic dysreflexia is greatly reduced, compared to spinal cord-transected wild-type mice, in Wld s mice after both injury paradigms and in 129Sv and C57BL/6 that have undergone the clip-compression injury. We also found that the amplitude of the dysreflexic response was greater in cord-compressed 129Sv than in C57BL/6 mice. These results implicate axonal degeneration as an important source of signals that trigger the development of autonomic dysreflexia and are discussed in the context of mouse genetics, interstrain differences and possible molecular mechanisms underlying autonomic dysreflexia after spinal cord injury.

Atherosclerosis susceptibility loci identified from a strain intercross of apolipoprotein E-deficient mice via a high-density genome scan

OBJECTIVE

Apolipoprotein (apo) E-deficient mice are hypercholesterolemic and develop atherosclerosis on low-fat chow diets; however, the genetic background strain has a large effect on atherosclerosis susceptibility. This study aimed to determine the genetic regions associated with strain effects on lesion area.

METHODS AND RESULTS

We performed a strain intercross between atherosclerosis sensitive DBA/2 and atherosclerosis resistant AKR apoE-deficient mice. Aortic root lesion area, total cholesterol, body weights, and complete blood counts were ascertained for 114 male and 95 female F2 progeny. A high-density genome scan was performed using a mouse single nucleotide polymorphism chip yielding 1967 informative polymorphic markers. Quantitative trait locus (QTL) statistical analyses were performed. Novel loci associated with lesion or log lesion area were identified for the female and male F2 cohorts. The atherosclerosis QTLs in female mice reside on chromosomes 15, 5, 3, and 13, and in male mice on chromosomes 17, 18, and 2. QTL were also identified for body weight, total cholesterol, and blood count parameters.

CONCLUSIONS

Loci were identified for atherosclerosis susceptibility in a strain intercross study. The identity of the responsible genes at these loci remains to be determined.

Identifying novel genes for atherosclerosis through mouse-human comparative genetics

Susceptibility to atherosclerosis is determined by both environmental and genetic factors. Its genetic determinants have been studied by use of quantitative-trait-locus (QTL) analysis. So far, 21 atherosclerosis QTLs have been identified in the mouse: 7 in a high-fat-diet model only, 9 in a sensitized model (apolipoprotein E- or LDL [low-density lipoprotein] receptor-deficient mice) only, and 5 in both models, suggesting that different gene sets operate in each model and that a subset operates in both. Among the 27 human atherosclerosis QTLs reported, 17 (63%) are located in regions homologous (concordant) to mouse QTLs, suggesting that these mouse and human atherosclerosis QTLs have the same underlying genes. Therefore, genes regulating human atherosclerosis will be found most efficiently by first finding their orthologs in concordant mouse QTLs. Novel mouse QTL genes will be found most efficiently by using a combination of the following strategies: identifying QTLs in new crosses performed with previously unused parental strains; inducing mutations in large-scale, high-throughput mutagenesis screens; and using new genomic and bioinformatics tools. Once QTL genes are identified in mice, they can be tested in human association studies for their relevance in human atherosclerotic disease.

Quantitative trait loci that determine plasma lipids and obesity in C57BL/6J and 129S1/SvImJ inbred mice

The plasma lipid concentrations and obesity of C57BL/6J (B6) and 129S1/SvImJ (129) inbred mouse strains fed a high-fat diet containing 15% dairy fat, 1% cholesterol, and 0.5% cholic acid differ markedly. To identify the loci controlling these traits, we conducted a quantitative trait loci (QTL) analysis of 294 (B6 x 129) F(2) females fed a high-fat diet for 14 weeks. Non-HDL cholesterol concentrations were affected by five significant loci: Nhdlq1 [chromosome 8, peak centimorgan (cM) 38, logarithm of odds [LOD] 4.4); Nhdlq4 (chromosome 10, cM 70, LOD 4.0); Nhdlq5 (chromosome 6, cM 0) interacting with Nhdlq4; Nhdlq6 (chromosome 7, cM 10) interacting with Nhdlq1; and Nhdlq7 (chromosome 15, cM 0) interacting with Nhdlq4. Triglyceride (TG) concentrations were affected by three significant loci: Tgq1 (chromosome 18, cM 42, LOD 3.2) and Tgq2 (chromosome 9, cM 66) interacting with Tgq3 (chromosome 4, cM 58). Obesity measured by percentage of body fat mass and body mass index was affected by two significant loci: Obq16 (chromosome 8, cM 48, LOD 10.0) interacting with Obq18 (chromosome 9, cM 65). Knowing the genes for these QTL will enhance our understanding of obesity and lipid metabolism.