Novel QTLs for HDL levels identified in mice by controlling for Apoa2 allelic effects: confirmation of a chromosome 6 locus in a congenic strain

Loss-of-function mutation in Pcsk1 increases serum APOA1 level and LCAT activity in mice

Background

The convertase subtilisin/kexin family 1 gene (PCSK1) has been associated in various human genetics studies with a wide spectrum of metabolic phenotypes, including early-onset obesity, hyperphagia, diabetes insipidus, and others. Despite the evident influence of PCSK1 on obesity and the known functions of other PCSKs in lipid metabolism, the role of PCSK1 specifically in lipid and cholesterol metabolism remains unclear. This study evaluated the effect of loss of PCSK1 function on high-density lipoprotein (HDL) metabolism in mice.

Results

HDL cholesterol, apolipoprotein A1 (APOA1) levels in serum and liver, and the activities of two enzymes (lecithin-cholesterol acyltransferase, LCAT and phospholipid transfer protein, PLTP) were evaluated in 8-week-old mice with a non-synonymous single nucleotide mutation leading to an amino acid substitution in PCSK1, which results in a loss of protein’s function. Mutant mice had similar serum HDL cholesterol concentration but increased levels of serum total and mature APOA1, and LCAT activity in comparison to controls.

Conclusions

This study presents the first evaluation of the role of PCSK1 in HDL metabolism using a loss-of-function mutant mouse model. Further investigations will be needed to determine the underlying molecular mechanism.

Network analysis in the identification of special mechanisms between small cell lung cancer and non-small cell lung cancer

BACKGROUND

To explore the similar and different pathogenesis between non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC).

METHODS

This study used bioinformatics methods, including functional enrichment analysis, compared the topological features of SCLC and NSCLC in the human protein interaction network in a system aspect, and analyzed the highly intense modules from an integrated network.

RESULTS

This study included 5082 and 2781 significantly different expression genes for NSCLC and SCLC, respectively. The differently expressed genes of NSCLC are mainly distributed in the extracellular region and synapse. By contrast, the genes of SCLC are located in the organelle, macromolecular complex, membrane-enclosed lumen, cell part, envelope, and synapse. Compared with SCLC, the differently expressed genes of NSCLC act in the biological regulation, multicellular organismal process, and viral reproduction and locomotion, which show that NSCLC is more likely to cause a wide range of cancer cell proliferation and virus infection than SCLC. The network topological properties of SCLC and NSCLC are similar, except the average shortest path length, which indicates that most of the genes of the two lung cancers play a similar function in the entire body. The commonly expressed genes show that all of the genes in the module may also cause NSCLC and SCLC, simultaneously.

CONCLUSIONS

The proteins in module will involve the same or similar biological functions and the interactions among them induce the occurrence of lung cancer. Moreover, a potential biomarker of SCLC is the interaction between APIP and apoptotic protease activating factor (APAF)1, which share a common module.

Atherosclerosis susceptibility Loci identified in an extremely atherosclerosis-resistant mouse strain

Background

C3H/HeJ (C3H) mice are extremely resistant to atherosclerosis, especially males. To understand the underlying genetic basis, we performed quantitative trait locus (QTL) analysis on a male F₂ (the second generation from an intercross between 2 inbred strains) cohort derived from an intercross between C3H and C57BL/6 (B6) apolipoprotein E–deficient (Apoe^−/−) mice.

Methods and Results

Two hundred forty‐six male F₂ mice were started on a Western diet at 8 weeks of age and kept on the diet for 5 weeks. Atherosclerotic lesions in the aortic root and fasting plasma lipid levels were measured. One hundred thirty‐four microsatellite markers across the entire genome were genotyped. Four significant QTLs on chromosomes (Chr) 2, 4, 9, and 15 and 4 suggestive loci on Chr1, Chr4, and Chr7 were identified for atherosclerotic lesions. Unexpectedly, the C3H allele was associated with increased lesion formation for 2 of the 4 significant QTLs. Six loci for high‐density lipoprotein (HDL), 6 for non‐HDL cholesterol, and 3 for triglycerides were also identified. The QTL for atherosclerosis on Chr9 replicated Ath29, originally mapped in a female F₂ cohort derived from B6 and C3H Apoe^−/− mice. This locus coincided with a QTL for HDL, and there was a moderate, but statistically significant, correlation between atherosclerotic lesion sizes and plasma HDL cholesterol levels in F₂ mice.

Conclusions

These data indicate that most atherosclerosis susceptibility loci are distinct from those for plasma lipids except for the Chr9 locus, which exerts effect through interactions with HDL.

New quantitative trait loci for carotid atherosclerosis identified in an intercross derived from apolipoprotein E-deficient mouse strains

Carotid atherosclerosis is the primary cause of ischemic stroke. To identify genetic factors contributing to carotid atherosclerosis, we performed quantitative trait locus (QTL) analysis using female mice derived from an intercross between C57BL/6J (B6) and BALB/cJ (BALB) apolipoprotein E (Apoe^−/−) mice. We started 266 F₂ mice on a Western diet at 6 wk of age and fed them the diet for 12 wk. Atherosclerotic lesions in the left carotid bifurcation and plasma lipid levels were measured. We genotyped 130 microsatellite markers across the entire genome. Three significant QTLs, Cath1 on chromosome (Chr) 12, Cath2 on Chr5, and Cath3 on Chr13, and four suggestive QTLs on Chr6, Chr9, Chr17, and Chr18 were identified for carotid lesions. The Chr6 locus replicated a suggestive QTL and was named Cath4. Six QTLs for HDL, three QTLs for non-HDL cholesterol, and three QTLs for triglyceride were found. Of these, a significant QTL for non-HDL on Chr1 at 60.3 cM, named Nhdl13, and a suggestive QTL for HDL on ChrX were new. A significant locus for HDL (Hdlq5) was overlapping with a suggestive locus for carotid lesions on Chr9. A significant correlation between carotid lesion sizes and HDL cholesterol levels was observed in the F₂ population (R = −0.153, P = 0.0133). Thus, we have identified several new QTLs for carotid atherosclerosis and the locus on Chr9 may exert effect through interactions with HDL.

Using bioinformatics and systems genetics to dissect HDL-cholesterol genetics in an MRL/MpJ x SM/J intercross

A higher incidence of coronary artery disease is associated with a lower level of HDL-cholesterol. We searched for genetic loci influencing HDL-cholesterol in F2 mice from a cross between MRL/MpJ and SM/J mice. Quantitative trait loci (QTL) mapping revealed one significant HDL QTL (Apoa2 locus), four suggestive QTL on chromosomes 10, 11, 13, and 18 and four additional QTL on chromosomes 1 proximal, 3, 4, and 7 after adjusting HDL for the strong Apoa2 locus. A novel nonsynonymous polymorphism supports Lipg as the QTL gene for the chromosome 18 QTL, and a difference in Abca1 expression in liver tissue supports it as the QTL gene for the chromosome 4 QTL. Using weighted gene co-expression network analysis, we identified a module that after adjustment for Apoa2, correlated with HDL, was genetically determined by a QTL on chromosome 11, and overlapped with the HDL QTL. A combination of bioinformatics tools and systems genetics helped identify several candidate genes for both the chromosome 11 HDL and module QTL based on differential expression between the parental strains, cis regulation of expression, and causality modeling. We conclude that integrating systems genetics to a more-traditional genetics approach improves the power of complex trait gene identification.

Genetic analysis of atherosclerosis and glucose homeostasis in an intercross between C57BL/6 and BALB/cJ apolipoprotein E-deficient mice

BACKGROUND

Diabetic patients have an increased risk of developing atherosclerosis and related complications compared with nondiabetic individuals. The increased cardiovascular risk associated with diabetes is due in part to genetic variations that influence both glucose homeostasis and atherosclerotic lesion growth. Mouse strains C57BL/6J (B6) and BALB/cJ (BALB) exhibit distinct differences in fasting plasma glucose and atherosclerotic lesion size when deficient in apolipoprotein E (Apoe(-/-)). Quantitative trait locus (QTL) analysis was performed to determine genetic factors influencing the 2 phenotypes.

METHODS AND RESULTS

Female F(2) mice (n=266) were generated from an intercross between B6.Apoe(-/-) and BALB.Apoe(-/-) mice and fed a Western diet for 12 weeks. Atherosclerotic lesions in the aortic root, fasting plasma glucose, and body weight were measured. 130 microsatellite markers across the entire genome were genotyped. Four significant QTLs, Ath1 on chromosome (Chr) 1, Ath41 on Chr2, Ath42 on Chr5, and Ath29 on Chr9, and 1 suggestive QTL on Chr4, were identified for atherosclerotic lesion size. Four significant QTLs, Bglu3 and Bglu12 on Chr1, Bglu13 on Chr5, Bglu15 on Chr12, and 2 suggestive QTLs on Chr9 and Chr15 were identified for fasting glucose levels on the chow diet. Two significant QTLs, Bglu3 and Bglu13, and 1 suggestive locus on Chr8 were identified for fasting glucose on the Western diet. One significant locus on Chr1 and 2 suggestive loci on Chr9 and Chr19 were identified for body weight. Ath1 and Ath42 coincided with Bglu3 and Bglu13, respectively, in the confidence interval.

CONCLUSIONS

We have identified novel QTLs that have major influences on atherosclerotic lesion size and glucose homeostasis. The colocalization of QTLs for atherosclerosis and diabetes suggests possible genetic connections between the 2 diseases.

Quantitative trait loci that control plasma lipid levels in an F2 intercross between C57BL/6J and DDD.Cg-A(y) inbred mouse strains

The objectives of this study were to characterize plasma lipid phenotypes and dissect the genetic basis of plasma lipid levels in an obese DDD.Cg-A(y) mouse strain. Plasma triglyceride (TG) levels were significantly higher in the DDD.Cg-A(y) strain than in the B6.Cg-A(y) strain. In contrast, plasma total-cholesterol (CHO) levels did not substantially differ between the two strains. As a rule, the A(y) allele significantly increased TG levels, but did not increase CHO levels. Quantitative trait locus (QTL) analyses for plasma TG and CHO levels were performed in two types of F(2) female mice [F(2)A(y) (F(2) mice carrying the A(y) allele) and F(2) non- A(y) mice (F(2) mice without the A(y) allele)] produced by crossing C57BL/6J females and DDD.Cg-A(y) males. Single QTL scan identified one significant QTL for TG levels on chromosome 1, and two significant QTLs for CHO levels on chromosomes 1 and 8. When the marker nearest to the QTL on chromosome 1 was used as covariates, four additional significant QTLs for CHO levels were identified on chromosomes 5, 6, and 17 (two loci). In contrast, consideration of the agouti locus genotype as covariates did not detect additional QTLs. DDD.Cg-A(y) showed a low CHO level, although it had Apoa2(b), which was a CHO-increasing allele at the Apoa2 locus. This may have been partly due to the presence of multiple QTLs, which were associated with decreased CHO levels, on chromosome 8.

The mouse QTL map helps interpret human genome-wide association studies for HDL cholesterol

Genome-wide association (GWA) studies represent a powerful strategy for identifying susceptibility genes for complex diseases in human populations but results must be confirmed and replicated. Because of the close homology between mouse and human genomes, the mouse can be used to add evidence to genes suggested by human studies. We used the mouse quantitative trait loci (QTL) map to interpret results from a GWA study for genes associated with plasma HDL cholesterol levels. We first positioned single nucleotide polymorphisms (SNPs) from a human GWA study on the genomic map for mouse HDL QTL. We then used mouse bioinformatics, sequencing, and expression studies to add evidence for one well-known HDL gene (Abca1) and three newly identified genes (Galnt2, Wwox, and Cdh13), thus supporting the results of the human study. For GWA peaks that occur in human haplotype blocks with multiple genes, we examined the homologous regions in the mouse to prioritize the genes using expression, sequencing, and bioinformatics from the mouse model, showing that some genes were unlikely candidates and adding evidence for candidate genes Mvk and Mmab in one haplotype block and Fads1 and Fads2 in the second haplotype block. Our study highlights the value of mouse genetics for evaluating genes found in human GWA studies.

Variations in DNA elucidate molecular networks that cause disease

Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (Lpl), lactamase beta (Lactb) and protein phosphatase 1-like (Ppm1l), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors.

Genetic control of lipids in the mouse cross DU6i x DBA/2

An F(2) pedigree based on the mouse lines DU6i and DBA/2 with extremely different growth and obesity characteristics was generated to search for QTLs affecting serum concentrations of triglycerides (TG), total cholesterol (CHOL), HDL cholesterol (HDL-C), and LDL cholesterol (LDL-C). Compared with many other studies, we searched for spontaneous genetic variants contributing to high lipid levels under a standard breeding diet. Significant QTLs for CHOL were identified on chromosomes 4 and 6, and a female-specific locus on chromosome 3. QTLs for HDL-C were detected on chromosome 11 for both sexes, and on chromosome 1 for females. These QTLs are located in syntenic human regions that have QTLs that have not been previously confirmed in animal studies. LDL-C QTLs have been mapped for both sexes to chromosome 8 and in males on chromosome 13. Epistatic interactions that significantly accounted for the phenotypic variance of HDL-C, CHOL, and LDL-C serum concentrations were also detected with one interaction between chromosomes 8 and 15, accounting for 22% of the observed variance in LDL-C levels. The identified loci coincide in part with regions controlling growth and obesity. Thus, multiple genes or pleiotropic effects may be assumed. The identified QTLs for cholesterol and its transport proteins as subcomponents of risk for coronary heart disease will further improve our understanding of the genetic net controlling plasma lipid concentrations.

Quantitative trait locus analysis of plasma cholesterol levels and body weight by controlling the effects of the Apoa2 allele in mice

Colleagues and I previously performed quantitative trait locus (QTL) analysis on plasma total-cholesterol (T-CHO) levels in C57BL/6J (B6) x RR F2 mice. We identified only one significant QTL (Cq6) on chromosome 1 in a region containing the Apoa2 gene locus, a convincing candidate gene for Cq6. Because Cq6 was a highly significant QTL, we considered that the detection of other potential QTLs might be hindered. In the present study, QTL analysis was performed in B6.KK-Apoa2b N(8) x RR F2 mice [B6.KK-Apoa2b N(8) is a partial congenic strain carrying the Apoa2b allele from the KK strain, and RR also has the Apoa2b allele] by controlling of the effects of the Apoa2 allele, for identifying additional QTLs. Although no significant QTLs were identified, 2 suggestive QTLs were found on chromosomes 2 and 3 in place of the effects of the Apoa2 allele. A significant body weight QTL was identified on chromosome 3 (Bwq7, peak LOD score 5.2); its effect on body weight was not significant in previously analyzed B6 x RR F2 mice. Suggestive body weight QTL that had been identified in B6 x RR F2 mice on chromosome 4 (LOD score 3.8) was not identified in B6.KK-Apoa2b N(8) x RR F2 mice. Thus, contrary to expectation, the genetic control of body weight was also altered significantly by controlling of the effects of the Apoa2 allele. The QTL mapping strategy by controlling of the effects of a major QTL facilitated the identification of additional QTLs.

QTL mapping for genetic determinants of lipoprotein cholesterol levels in combined crosses of inbred mouse strains

To identify additional loci that influence lipoprotein cholesterol levels, we performed quantitative trait locus (QTL) mapping in offspring of PERA/EiJxI/LnJ and PERA/EiJxDBA/2J intercrosses and in a combined data set from both crosses after 8 weeks of consumption of a high fat-diet. Most QTLs identified were concordant with homologous chromosomal regions that were associated with lipoprotein levels in human studies. We detected significant new loci for HDL cholesterol levels on chromosome (Chr) 5 (Hdlq34) and for non-HDL cholesterol levels on Chrs 15 (Nhdlq9) and 16 (Nhdlq10). In addition, the analysis of combined data sets identified a QTL for HDL cholesterol on Chr 17 that was shared between both crosses; lower HDL cholesterol levels were conferred by strain PERA. This QTL colocalized with a shared QTL for cholesterol gallstone formation detected in the same crosses. Haplotype analysis narrowed this QTL, and sequencing of the candidate genes Abcg5 and Abcg8 confirmed shared alleles in strains I/LnJ and DBA/2J that differed from the alleles in strain PERA/EiJ. In conclusion, our analysis furthers the knowledge of genetic determinants of lipoprotein cholesterol levels in inbred mice and substantiates the hypothesis that polymorphisms of Abcg5/Abcg8 contribute to individual variation in both plasma HDL cholesterol levels and susceptibility to cholesterol gallstone formation.

Fine mapping of a QTL region with large effects on growth and fatness on mouse chromosome 2

We combined the use of a congenic line and recombinant progeny testing (RPT) to characterize and fine map a previously identified region of distal mouse chromosome 2 (MMU2) harboring quantitative trait loci (QTL) with large effects on growth and fatness. The congenic line [M16i.B6-(D2Mit306-D2Mit52); MB2] was created using an inbred line (M16i) derived from a line that had undergone long-term selection for rapid weight gain (M16) as the recipient for an approximately 38-cM region on MMU2 from the inbred line C57BL/6J. A large F2 cohort (1,200 mice) originating from a cross between MB2 and M16i was created, and 40 F2 males with defined recombinations within the QTL region were used to produce 665 segregating progeny. Linkage analysis of the F2 population detected QTL with very large effects on body weight, body fat, lean tissue mass, bone mineral density, and liver weight. Confidence intervals of the QTL were narrowed to regions of 1.5-4.5 cM. Analysis of progeny of the recombinant F2 males confirmed the existence of the QTL and further contributed to localization of their map positions. These efforts confirmed the presence of QTL with major effect on MMU2, narrowed the estimated region harboring the QTL from 38 to 12 cM, and further characterized phenotypic effects of the QTL, effectively culminating in a significantly decreased pool of positional candidate genes potentially representing these genes controlling predisposition to growth and fatness.

Use of a dense single nucleotide polymorphism map for in silico mapping in the mouse

Rapid expansion of available data, both phenotypic and genotypic, for multiple strains of mice has enabled the development of new methods to interrogate the mouse genome for functional genetic perturbations. In silico mapping provides an expedient way to associate the natural diversity of phenotypic traits with ancestrally inherited polymorphisms for the purpose of dissecting genetic traits. In mouse, the current single nucleotide polymorphism (SNP) data have lacked the density across the genome and coverage of enough strains to properly achieve this goal. To remedy this, 470,407 allele calls were produced for 10,990 evenly spaced SNP loci across 48 inbred mouse strains. Use of the SNP set with statistical models that considered unique patterns within blocks of three SNPs as an inferred haplotype could successfully map known single gene traits and a cloned quantitative trait gene. Application of this method to high-density lipoprotein and gallstone phenotypes reproduced previously characterized quantitative trait loci (QTL). The inferred haplotype data also facilitates the refinement of QTL regions such that candidate genes can be more easily identified and characterized as shown for adenylate cyclase 7.