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From the Jackson Laboratory, Bar Harbor, Me.
This Review is part of a thematic series on New Pathways in HDL Metabolism, which includes the following articles:
Antiinflammatory Properties of HDL
New Insights Into the Regulation of HDL Metabolism and Reverse Cholesterol Transport
Endothelial and Antithrombotic Effects of HDL
Plasma high-density lipoprotein cholesterol (HDL-C) concentrations are genetically determined to a great extent, and quantitative trait locus (QTL) analysis has been used to identify chromosomal regions containing genes regulating HDL-C levels. We discuss new genes found to participate in HDL metabolism. We also summarize 37 mouse and 30 human QTLs for plasma HDL-C levels, finding that all but three of the mouse QTLs have been confirmed by a second cross or a homologous human QTL, that the mouse QTL map is almost saturated because 92% of recently reported QTLs are repeats of those already found, and that 28 of the 30 human QTLs are located in regions homologous to mouse QTLs. This high degree of concordance between mouse and human QTLs suggests that the underlying genes may be the same. Strategies to more rapidly identify genes underlying mouse and human QTLs for HDL-C include focusing on the mouse and using mouse–human homologies, combining crosses, and haplotyping to narrow the region. Sequence analysis and expression studies can distinguish candidate genes consistent across multiple mouse crosses, and testing the candidate genes in human association studies can provide additional evidence for the candidacy of a gene. Together these strategies can accelerate the pace of finding genes that regulate HDL.
Key Words: high-density lipoprotein genetics quantitative trait locus mouse human
Atherosclerosis is the pathological basis for ischemic cardiovascular disease (CVD), the leading cause of morbidity and mortality in the United States and other industrialized nations. Major risk factors for atherosclerosis include high plasma levels of low-density lipoprotein cholesterol (LDL-C), lipoprotein(a), and low levels of plasma high-density lipoprotein cholesterol (HDL-C) levels.1 However, high levels of plasma HDL-C are protective against CVD, as shown by two lines of evidence. First, epidemiological studies have shown an inverse relationship between HDL-C levels and the incidence of CVD. Men and women with HDL-C 6 to 7 mg/dL higher than average have a 20% to 27% decrease in the risk of CVD, and increasing HDL-C level by 1 mg/dL may reduce the risk of CVD by 2% to 3%, independent of plasma LDL-C levels.1 Second, raising plasma HDL-C levels protects against atherosclerosis in mice, rabbits, and humans.2
HDL may reduce atherosclerosis through several different mechanisms,3 including increasing reverse cholesterol transport, inhibiting physical and chemical modifications of LDL and thus reducing foam cell formation, protecting against endothelial dysfunction, inhibiting chronic inflammation by suppressing adhesion molecules and macrophage chemotactic proteins, and reducing arterial lipoprotein retention. Because plasma HDL is largely genetically determined (heritability estimates of HDL-C ranged from 24% to 83%, depending on different twin or family studies, with most studies in the 40% to 60% range4), its regulating genes have been extensively studied in the past few decades. Finding HDL-regulating genes is important in at least two aspects in terms of regulating plasma HDL-C concentrations. First, it has led to studies of new therapies that raise plasma HDL-C levels and/or increase HDL-mediated reverse cholesterol transport. Finding CETP as the causal gene for increased HDL levels in some patients has led to using CETP inhibitors to increase plasma HDL concentrations.2 The discovery of ABCA1 as a key player in cholesterol and phospholipid efflux has led to experiments on targeting this transporter (through specific LXR and PPAR agonists) for increasing HDL-mediated reverse cholesterol transport and reducing atherosclerosis.5 Apolipoprotein A-I (apoA-I) is the major structural protein of HDL and accounts for most of the protective effect of HDL against atherosclerosis. Infusion of phospholipids and apoA-IMilano (a cysteine is substituted for an arginine at position 173 of apoA-I) significantly reduced atheroma volume in coronary arteries of patients with acute coronary syndromes.6 Second, finding HDL-regulating genes has helped understand previously unknown mechanisms of some HDL-regulating drugs. For example, fibrates have been used to decrease plasma LDL and increase plasma HDL levels for a long time, and later it was found that they are agonists for PPAR, a nuclear receptor that regulates genes encoding apoA-I, apoA-II, SR-BI, LPL, apoC-III, and ABCA1 (indirectly through stimulation of LXR).1 Several major HDL-raising drugs currently in clinical use (such as niacin, fibrates, statins) do so in part by decreasing apoA-I catabolism and/or increase apoA-I synthesis.6
In this review, we discuss two approaches that have been used to find HDL-regulating genes: a candidate gene approach and a genome-wide search for new genes by quantitative trait locus (QTL) analysis.
Candidate Genes Regulating HDL Metabolism and Functions
With the identification of some HDL metabolic pathways, the genes involved in the processes have been identified and cloned. In addition, some genes are identified from naturally occurring rare mutations. The genes that are known to regulate HDL metabolisms can be separated into five groups: (1) HDL-associated apolipoproteins; (2) HDL-associated enzymes and transfer proteins; (3) plasma and cell-associated enzymes; (4) cellular receptors, their adaptors, and transporters; and (5) transcription factors (Table 1). Some of the genes regulate HDL-C levels and atherosclerosis. We have summarized many of these genes in our previous review,7 and recent publications have extensively discussed some of them, such as Apoa1, Abca1, SR-BI (Scarb1), cholesterol ester transfer protein (Cetp), endothelial lipase (Lipg), and hepatic lipase; therefore, we limit our discussion to new genes found since the last review, which all belong to the last three groups.
Plasma and Cell-Associated Enzymes
Recent studies suggest that the following four cell-associated enzymes may be candidate genes for HDL metabolism and functions: Dgat1, Pemt, Pcyt1a, and Lipe. Diacylglycerol O-acyltransferase 1 (DGAT1; also called acyl coenzyme A:diacylglycerol acyltransferase) catalyzes the final step in mammalian triglyceride synthesis. The C79T SNP in the 5' noncoding region of DGAT1 is associated with plasma HDL-C levels in females in the Turkish Heart Study population.8 In addition, the 79T construct has 20% to 33% less promoter activity than the 79C construct,8 suggesting that the C79T SNP is functional. Phosphatidylethanolamine N-methyltransferase (PEMT) catalyzes the conversion of phosphatidylethanolamine to phosphatidylcholine in liver. Compared with wild-type controls, Pemt-targeted mutant mice have lower plasma HDL-C levels in females fed chow and in both females and males fed high-fat diet,9 suggesting that PEMT is involved in regulating plasma HDL-C levels. Similarly, liver-specific targeted disruption of phosphate cytidylyltransferase 1 alpha isoform (Pcyt1a), the gene encoding another phosphatidylcholine-synthesizing enzyme, CTP:phosphocholine cytidylyltransferase, leads to decreased plasma HDL-C in mice fed chow, suggesting that PCYT1A regulates plasma HDL-C levels.10 Hormone-sensitive lipase (LIPE) hydrolyzes triacylglycerols, diglycerides, cholesterol esters, and retinyl esters. Lipe-targeted mutant mice have higher plasma HDL-C concentrations than do wild-type controls, possibly as a result of increased lipoprotein lipase activity in cardiac and skeletal muscle.11
Cellular Receptors, Their Adaptors, and Transporters
PDZK1, a PDZ (PSD-95, Drosophila discs-large protein, Zonula occludens protein 1) domain-containing protein, was recently found to regulate plasma HDL-C concentrations. It binds and interacts with the last three carboxyl-terminal amino acids (Arg-Lys-Leu) of SR-BI, and is essential for cell surface expression of SR-BI in mouse liver.12,13 In Pdzk1-targeted mutant mice, SR-BI protein expression is reduced by 95% in the liver and 50% in the proximal intestine, but is unaffected in steroidogenic organs (adrenal, ovary and testis).14 In addition, total and HDL cholesterol are elevated and HDL particle size is increased,14 phenotypes similar to those observed in Scarb1-targeted mutant mice,15 suggesting that loss of hepatic SR-BI is likely the underlying cause. PDZK1, which regulates hepatic SR-BI expression, is in turn regulated by a small PDZK1-associated protein called MAP17 (membrane-associated protein 17).16 Hepatic overexpression of MAP17 in mice results in liver deficiency of PDZK1 and SR-BI and markedly increased plasma HDL-C levels. Metabolic labeling experiments suggest that MAP17 may regulate PDZK1 turnover endogenously.16
Both ABCG1 and ABCG4 have been found to mediate cholesterol efflux to HDL2 and HDL3.17 Another two ABCG family members, ABCG5 and ABCG8, are two neighboring and oppositely oriented genes. The mutation of either causes sitosterolemia, a rare autosomal-recessive disorder characterized by accumulation of plant and animal sterols in blood and tissues.18 ABCG5 and ABCG8 may form a functional complex to decrease the absorption of plant sterols by secreting sterols from gut epithelial cells into the lumen and increasing secretion of hepatic sterols into bile, thereby attenuating dietary sterol accumulation.19,20 Mice overexpressing both human ABCG5 and ABCG8 have a moderate decrease in plasma HDL-C levels on chow,20 consistent with the role of these two genes to decrease dietary cholesterol absorption and increase biliary cholesterol secretion. However, mice with targeted mutation of both Abcg5 and Abcg8 also had a decrease in plasma total cholesterol (mostly HDL-C) levels, reflecting either dietary or genetic differences between mouse and human.19 ABCA7 may also mediate the release of cellular cholesterol and phospholipids to generate HDL. Human ABCA7 mediates the efflux of both cellular cholesterol and phospholipids to apoA-I,21 whereas mouse ABCA7 mediates only phospholipids but not cholesterol efflux to apoA-I.22
Transcription Factors
Transcription factor 1 (Tcf1; also called hepatocyte nuclear factor-1, or Hnf1a) is important in glucose, amino acid, and cholesterol homeostasis. Mutations in TCF1 cause maturity-onset diabetes of the young, type 3 (MODY3). The following evidence suggests that TCF1 is an important regulator of plasma cholesterol (including HDL-C) concentrations. TCF1 represses apoA-I and apoC-III gene expression in HepG2 cells.23 Compared with wild-type mice, Tcf1-targeted mutant mice have hypercholesteremia and increased HDL-C levels.24 HDL particles are abnormally large in the mutant mice, probably reflecting increased HDL cholesterol esterification by LCAT, which has an increased expression in the mutant mice, and a decreased HDL phospholipids hydrolysis by hepatic lipase, whose expression is decreased in the mutant mice.24 In addition, both the G319S25 and I27L26 polymorphisms in TCF1 are significantly associated with plasma HDL-C concentrations in humans.
Hepatic nuclear factor 4 (Hnf4; also called hepatocyte nuclear factor 4 alpha, or nuclear receptor 2A1) activates TCF1-mediated gene activation, while TCF1 suppresses HFN4-dependent transcription.23 Mutations in HNF4 cause MODY1. HNF4 regulates hepatic expression of a number of genes associated with lipoprotein metabolism,27 including genes encoding apoA-I, A-II, A-IV, B, C-II, C-III, E, microsomal triglyceride transfer protein (MTP), cholesterol 7-hydroxylase (CYP7A), SR-BI, and PPAR. Mice with targeted mutation of Hnf4 have a dramatic decrease in LDL and HDL cholesterol levels, and HDL particles in these mice are small and lipid-poor.27
Mammalian NFB (nuclear factor B) is composed of homo- and hetero-dimers of five structurally related and evolutionary conserved proteins: NFB1/p50, NFB2/p52, RelA/p65, RelB and c-Rel. NFB is involved in a variety of diseases including atherosclerosis. Mice with targeted mutation of Nfkb1 (encoding NFB1/p50) have significantly increased plasma concentrations of HDL-C and apoA-I through activation of PPAR in the absence of NFB activity: PPAR binds to peroxisome proliferators response element (PPRE) in apoA-I enhancer and activates apoA-I transcription.28
Among the 54 candidate genes for regulating HDL metabolism and functions (Table 1), eight have been confirmed to be able to regulate plasma HDL levels in both mice and humans (genes encoding apoA-I, CETP, LCAT, LIPC, LIPG, LPL, ABCA1, ABCG5/8), and 17 others play a role in regulating HDL levels in mouse models but have not been confirmed in humans (genes encoding apoA-II, apoA-IV, apoA-V, apoC-III, apoE, PLTP, LIPE, PCYT1A, PEMT, MAP17, PDZK1, SR-BI, HNF4, NFB1, FXR, PPAR and TCF1).
Searching for New Genes Regulating Plasma HDL Cholesterol Levels: QTL Analysis in Mice and Humans
QTLs for HDL-C Concentrations in Mice and Humans
QTL analysis has been used to identify chromosome segments that are responsible for variation of plasma HDL-C levels in both mice and humans. A total of 22 mouse crosses, involving 20 inbred mouse strains, have been used to map HDL-C QTLs: (1) C57BL/6x129; (2) C57BL/6xBALB; (3) C57BL/6xC3H; (4) C57BL/6xCASA; (5) C57BL/6xCAST; (6) C57BL/6xDBA/2; (7) C57BL/6xFVB; (8) C57BL/6xKK; (9) C57BL/6xNZB; (10) C57BL/6xSPRET; (11) CAST x129; (12) DBA/2xCAST; (13) KKxBALB; (14) KKxRR; (15) MRL/MpJxBALB; (16) NZBxSM; (17) PERAxDBA/2; (18) PERA/EixI; (19) RIII x129; (20) SJLxNZO; (21) SJLx129; and (22) SMxA (Table 2). According to the International Committee on Standardized Genetic Nomenclature for Mice (http://www.informatics.jax.org/mgihome/nomen/), QTLs at different chromosome locations, or at the same chromosome location but from different crosses, should be assigned different names. According to this definition, there are 99 HDL-C QTLs, scattered on every somatic chromosome (Table 2; Figure 1). However, if different crosses detect multiple QTLs at the same chromosome location, these QTLs probably have the same underlying gene; therefore, we regard them as the same QTL unless we have evidence that they are not. A total of 37 HDL-C QTLs have been identified according to this latter definition, which we use throughout this review. The 37 unique QTLs are an estimation because the confidence intervals of some are overlapping (for example, those on Chr 5 and Chr 6). Most of the QTLs have been confirmed because they are repeated in multiple crosses; a few have not (for example, Chr 13 and proximal Chr 18) (Table 2).
Figure 1. Composite chromosome map of mouse QTLs for plasma HDL-C levels, mouse concordant regions of human HDL-C QTLs, and candidate genes. A vertical line represents each chromosome, with the centromere at the top. Genetic distances (cM) from the centromere are shown by the scale at the bottom of the figure. Chromosomes are drawn to scale, based on the estimated cM position of the most distally mapped locus in Mouse Genome Informatics (http://www.infomatics.jax.org). HDL-C QTLs are represented by blue (chow diet), yellow (high-fat diet), or hatched (both chow and high-fat diets) bars to the right of each chromosome. Each bar represents a QTL from one cross (sharing the same parental strains, whatever the breeding strategy is) as shown in Table 2. The size of the QTL is determined as 95% confidence interval (CI), or ±10 cM centered around the peak LOD score when the information on CI is not available. Candidate genes are to the left of the chromosomes. Mouse homologous regions of human HDL-C QTLs are represented by red asterisks.
In humans, 21 publications have reported 45 HDL-C QTLs in genome-wide scans in 15 different human populations (Table 3). Some of the HDL-C QTLs are at similar chromosome locations, so they are probably the same QTLs with the same underlying genes. We estimate that 30 unique HDL-C QTLs in humans have been reported in humans. They scatter on all the somatic chromosomes except Chrs 14, 17, 18, 19, and 21 (Figure 2).
Figure 2. Composite chromosome map of human QTLs for plasma HDL-C levels, human concordant regions of mouse HDL-C QTLs, and candidate genes. Chromosomes are drawn to scale, based on the length of each chromosome from Human Genome Browser (http://www.ensembl.org/Homo_sapiens/). HDL-C QTLs are represented by blue bars to the right of each chromosome. Each bar represents a QTL from one population as shown in Table 3. The size of the QTL is determined as 1.5 LOD confidence interval (CI), or ±10 Mb centered around the peak marker when the information on CI is not available. Candidate genes are to the left of the chromosomes. Human homologous regions of mouse HDL-C QTLs that fall within the human HDL-C QTLs are represented by red asterisks.
Mouse and Human HDL-C QTLs Are Concordant
To gain further insight into the relevance of the mouse HDL-C QTL to human studies, we compared HDL-C QTLs found in mice and those found in humans. Of the 37 mouse HDL-C QTLs, mouse homologous regions of human HDL-C QTLs fall within 27 of them (Table 2; Figure 1). However, for 28 of the 30 (93%) human HDL-C QTLs (except for one on distal Chr 2 and one on proximal Chr 5), at least part of their mouse homologous segments are within HDL-C QTLs detected in mouse crosses (Table 3; Figure 2). Because a total of 667 cM (42%) of mouse genome is covered by mouse HDL-C QTLs, by chance mouse homologous regions of 13 (30x42%) human QTLs will fall within mouse QTLs, just by chance. However, this number is significantly different from the actual number of 28 (P<0.0001, Fisher exact test). The result of this comparison implies that many of the QTL for HDL-C levels found in humans have homologous counterparts in mice. Because more mouse HDL-C QTLs have been found, possibly as a result of more extensive studies in mice than in humans, additional human chromosome segments that affect HDL-C concentrations may be predicted by the comparative genomics strategy.
Identifying the Genes Underlying QTLs for Plasma HDL-C Concentrations
Because >90% of human HDL-C QTLs have homologous QTLs in mice, and because finding genes in mice is much more cost-effective in mouse models than in humans, the priority of identifying genes underlying HDL-C QTLs should be in mice. After the genes are identified in mice, their homologous genes can be tested in association studies in human populations.
Since the first two reports on genome-wide scan in mouse crosses to map HDL-C QTLs in 1993,7 22 mouse crosses have been used in mapping HDL-C QTLs, revealing 99 raw QTLs (those detected at the same chromosome location but from different crosses are defined as different QTLs) and 37 unique ones (those detected at the same chromosome location from different crosses are regarded as the same QTL) (Figure 3). It is noteworthy that although nine new mouse crosses have been reported between March 2003 and August 2004 and 31 raw QTLs were detected, only four of them (one on distal Chr 4, two on Chr 10, and one on Chr 13) were unique new QTLs, suggesting that the mouse HDL-C QTL map is close to being saturated, and that our effort should be shifted from making more mapping crosses to identifying the underlying genes for the QTLs we have already had.
Figure 3. Number of mouse crosses used and HDL-C QTLs found by year. According to International Committee on Standardized Genetic Nomenclature for Mice (http://www.informatics.jax.org/mgihome/nomen/gene.shtml#nsqtl), QTLs detected by different crosses, even in the same chromosome location, should be regarded as different QTLs, which we named "Raw QTLs" (blue line). However, when different crosses detected QTLs at the same chromosome location, it is possible that they have the same underlying gene. We therefore summarize them as "unique QTLs" (red line). So far, 22 crosses have been used (green line). Two of them, NZBxSM and B6xC3H, were used in 1993, but were used again in 1995 and 1997, respectively. Therefore, although no additional crosses were added between 1993 and 1997, some new QTLs were found when the same crosses were used again.
Identifying QTL genes has been a challenging and labor-intensive task. Traditionally, QTLs are fine-mapped with a genetic approach using various mouse cross strategies,29 such as overlapping congenic lines, advanced intercross lines, recombinant inbred segregation test, interval specific congenic strains, outbred strains, heterogeneous stocks, recombinant progeny testing, recombinant inbred intercross strains, and selective phenotyping. New genomic and bioinformatics resources, most importantly human and mouse genome sequences and single nucleotide polymorphisms (SNPs) databases, should greatly facilitate this process. The following strategies can be used to narrow HDL-C QTL, and some can even be used to test and prove the underlying genes.
Narrow a QTL by Mouse and Human QTL Comparison
Homologous regions of many human HDL-C QTLs fall within mouse HDL-C QTLs, and vice versa. These homologous QTLs may have the same underlying gene; therefore, overlapping an HDL-C QTL from one species with the homologous regions of the QTL from another species may result in a shortened list of candidate genes in a reduced QTL interval.
SNP and Haplotype Analysis to Narrow a QTL and Test Its Candidate Genes
Recent SNP maps indicate that the genome of common inbred mouse strains is defined by 1- to 2-Mb haplotype blocks, although in some regions the haplotype patterns may be much more complex.30 The haplotype blocks can be used to narrow a QTL because its underlying gene should be in subregions where the parental strains have different haplotypes. When multiple mouse crosses detect HDL-C QTLs at the same or very close chromosomal location, it is probable that these crosses have detected the same QTL with the same underlying gene. Therefore, a QTL interval can be narrowed by comparing the haplotype blocks of all the strains in the crosses that detect the QTL.31 A gene that lies in a region identical by decent in the parental strains that detected the QTL is unlikely to be the causal gene; the true QTL gene should have different SNPs/haplotypes between the parental strains. This approach becomes more powerfully as the number of cross detecting the QTL increases.31
Narrow a QTL by Combining Crosses
Gary Churchill and his colleague Renhua Li at the Jackson Laboratory have developed a statistical method that combines crosses that have identified the same QTL (Li et al, submitted, 2004). In this method, the alleles from multiple crosses are designated according to their direction of effect—either high or low, instead of their strain names. Data from separate crosses can then be combined for re-analysis on the QTL location. Such combining increases the resolution power because both the number of animals and recombinations are increased. This method has been successfully used to narrow the 95% confidence interval of a blood pressure QTL from 42 cM to 18 cM.32
Sequence Analysis to Test the Candidate Genes
A QTL gene should be different between the two parental strains, either in the coding sequences, which change the amino acid sequences and the protein’s functions, or in the regulatory elements, which change the gene expression (at either transcriptional or translational level), or both. It is noteworthy that although sequence differences can detect promising candidates for further functional tests, one cannot eliminate genes solely based on negative findings of sequence variation.
Gene Expression Assay to Reduce the Candidate List
Gene expression studies can help in two ways. First, the genes underlying HDL-C QTLs should be expressed in organs or tissues relevant to HDL metabolism, such as liver, intestine, adipose, and endothelium. Second, if the protein sequence of a QTL gene is the same between the two parental strains, the gene should be expressed differently in the relevant tissues from the two parental strains.
Testing the Candidate Genes by Association Studies in Humans
Once a candidate gene has been identified in the mouse, it is useful to go directly to human association studies for testing SNPs in that candidate and the adjoining genes. This testing of mouse candidate genes in human studies has been useful in identifying genes for obesity,33 diabetes, Graves disease,34 autism,35 atherosclerosis (Wang et al, submitted), and hypertension (DiPetrillo et al, submitted).
Because the mouse map appears to be almost saturated for HDL-C QTLs, we can assume that the regions accounting for most of the variation have been found. To search for the genes, it is most efficient to start with those QTLs that have a human homolog, because it is the human genes we eventually want to identify, and to choose preferentially those that have been found in multiple crosses. By combining crosses and using haplotype analysis, the QTL regions can be narrowed rapidly with in silico techniques. Genes in the narrowed region can be prioritized by using all the parental strains to find those with a sequence difference in the coding region or an expression difference in a relevant tissue that is consistent with the allele effect of the QTL. These candidate genes can then be tested in human populations characterized for HDL-C for an association with the trait. Any gene that is associated with HDL-C in humans can then be tested in the mouse to prove the gene by transgenic or knockout technologies and to study the function of the alternate alleles.
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