Overview
Most common human diseases are complex, caused by many genes that interact with environmental risk factors. For examples, the genes that cause heart disease interact with a person's diet and exercise regimen, and the genes that cause high blood pressure interact with a person's salt intake and stress level. We use mouse models to study these diseases because mice are genetically well defined, relatively inexpensive to work with, reproduce quickly, are biologically very similar to humans, and their environments can be strictly regulated. Understanding the genetics of quantitative traits in mice helps us understand them in humans. In our laboratory, we are focusing on finding genes that control HDL-cholesterol levels, one of the many traits underlying heart disease risk. If we can identify some of these genes, we will gain insight into gene networks and systems biology. Additionally, we are applying similar techniques to studies of lung disease and the genetics of aging and longevity.
Scientific report
Finding the Genes for Common Diseases
Focus on HDL genes
Many common human diseases are complex, caused by multiple genes that interact with environmental risk factors. Our research centers on the use of mouse models to understand the genetics of human disease. Our current focus is on finding the genes that control high density lipoprotein (HDL)-cholesterol, the good cholesterol. Heart disease risk can be explained in part by the balance between HDL and low density lipoprotein (LDL)-cholesterol, the bad cholesterol. For example, an increase of only 1% in HDL levels decreases the risk of heart disease by 2-3%. However, while several good drugs capable of lowering LDL levels are currently available, therapies to raise HDL levels are quite limited. The most effective means now available for increasing HDL levels are exercise, cessation of smoking, moderate alcohol intake (a glass of wine daily), and consumption of healthy fats (olive oil, avocado, fish). The availability of drugs that could increase HDL levels would be a useful addition to the other therapies, especially for those who already have evidence of heart disease. We have two primary objectives in our focus on identifying genes that control HDL. First, we hope that some of these genes will be good drug targets and thus improve the range of treatment options for heart disease, the major cause of death in the United States. Second, we know very little about systems biology-how genes interact with each other and with the environment to cause the variation in disease risk observed among people. If we can identify some of the genes regulating HDL levels, one of many traits underlying heart disease risk, we will gain some insight into gene networks and systems biology.
Accelerating the pace of finding QTL genes
The first step in finding genes responsible for a quantitative disease is to identify quantitative trait loci (QTL) associated with the disease. Over the past decade, our laboratory has carried out over 25 mouse QTL crosses and found over 250 disease-related QTL. These QTL are relatively large, on the order of 40 megabases (Mb), and contain hundreds of genes. Once a QTL is found, the next step is to narrow the QTL region in order to identify the particular gene or genes regulating the trait. This has traditionally been done using additional mouse crosses. However, the Human Genome Project, the sequencing of the mouse and human genome, and the resources put into developing other genetic resources and tools are changing the way we work. The field of genetics has become increasingly data-rich, with extensive databases and sophisticated tools for data mining and analysis, and we are using these resources to accelerate the pace of gene identification. The statistical and bioinformatics methods that we are using to narrow QTL regions include a statistical technique for combining data from multiple mouse crosses, haplotype analysis, comparative genomics, and haplotype association mapping. Combining these tools often reduces the number of possible genes in a QTL region from several hundred to a manageable list of 5-15. Then we test each of these candidate genes by sequencing and expression tests to determine if the gene differs between the strains that gave the QTL. During the past two years, we have tentatively identified about 12 genes that affect HDL cholesterol.
Using the mouse to find human genes
For years, The Jackson Laboratory has maintained that discovering disease genes in the mouse can hasten their discovery in humans. This year, that dogma has moved much closer to reality, as we have used the power of the mouse model together with the advantages of the human system to identify disease genes. This approach is based on the observation that the QTL for a trait are found in homologous positions in human and mouse. This cross-species concordance has enormous biological and economic consequences: finding disease genes in mice and then confirming them in humans is much simpler, faster, and less expensive than finding them in humans first.
The powerful and cost-effective strategy that we use to identify the genes underlying disease-related QTL is a mouse:human:mouse paradigm. We first search for candidate genes in the mouse using the approaches described above. When we have reduced the list of probable candidates to only 1-4 genes, we test those genes in a hypothesis-driven association study in humans. When we find a human association, we then return to the mouse for additional studies: first, to obtain definitive proof of the gene's involvement in the disease, using genetically engineered mice or other strategies, and second, to carry out functional studies to determine how the gene and its polymorphisms affect the disease phenotype.
Beyond HDL genes
These approaches to finding QTL genes have proven so powerful with HDL genes that we are extending them to other diseases in collaboration with others. The first is a study of lung disease, both asthma and emphysema. We are surveying a set of inbred strains for variation in susceptibility to these diseases, and then we will carry out our QTL analyses combining the new information with that in the literature, find genes for asthma and emphysema in the mouse, and test these in humans. Finally, we are heavily involved with a Jackson-wide study into the genetics of aging (see report on the Integrative Center for Genetics of Aging). We are surveying a large set of strains for differences in longevity and measuring their health and well-being in multiple ways every six months. We hope to gain insight into the genetic basis of longevity.
Lab staff
Principal Investigator: Beverly J. Paigen, Ph.D.
Research Scientist: Ron Korstanje, Ph.D.
Postdoctoral Fellows: Cheryl Ackert-Bicknell, Ph.D., Annerose Berndt, D.V.M., Ph.D., Magalie Leduc, Ph.D.
Research Assistant II: Michael Marion, B.S., Susan Sheehan, B.A., M.S., Ken Walsh, B.S.
Research Assistant I: Christina Caputo, B.S., Cynthia McFarland
Colony Coordinator: Harry Whitmore, Fred Rumill
Research Administrative Assistant: Patricia Cherry
Publication listings
(Selected from 2007 to present)
Cox A, Ackert-Bicknell C, Dumont BL, Ding Y, Tzenova Bell J, Brockmann GA, Wergedal JE, Bult C, Paigen B, Flint J, Tsaih S-W, Churchill GA, Broman KW. 2009. A new standard genetic map for the laboratory mouse. Genetics 182:1-10.
Stylianou IM, Svenson KL, VanOrman SK, Langle Y, Millar JS, Paigen B, Rader DJ. 2009. Novel ENU-induced point mutation in scavenger receptor class B, member 1, results in liver specific loss of SCARB1 protein. PLoS One (In press)
Su Z, Cox A, Shen Y, Stylianou IM, Paigen B. 2009. Farp2 and Stk25 are candidate genes for the HDL cholesterol locus on mouse chromosome 1. Arterioscler Thromb Vasc Biol 29:107-113.
Su Z, Ishimori N, Chen Y, Leiter EH, Churchill GA, Paigen B, Stylianou IM. 2009. Four additional mouse crosses improve the lipid QTL landscape and identify Lipg as a QTL gene. J Lipid Res (In press)
Su Z, Wang X, Tsiah S-W, Zhang A, Cox A, Sheehan S, Paigen B. 2009. Identifying the genetic basis of HDL variation between 129l/SvlmJ and C57BL/6J mice: critical importance for testing HDL candidate genes in targeted mutant mice. J Lipid Res 50:116-125.
Xing S, Tsaih SW, Yuan R, Svenson KL, Jorgenson LM, So M, Paigen BJ, Korstanje R. 2009. Genetic influence on electrocardiogram time intervals and heart rate in aging mice. Am J Physiol Heart Circ Physiol 296(6):H1907-H1913.
Yuan R, Tsaih SW, Petkova SB, Evsikova CM, Xing S, Marion MA, Bogue MA, Mills KD, Bult CL, Rosen CL, Sundberg JP, Harrison DE, Churchill GA, Peters L, Paigen B. 2009. Aging in inbred strains of mice: study design and interim report on median lifespans and circulating IGF1 levels. Aging Cell 8:277-287.
Ackert-Bicknell CL, Demissie S, de Evsikova CM, Hsu YH, Demambro VE, Karasik D, Cupples LA, Ordovas JM, Tucker KL, Cho K, Canalis E, Paigen B, Churchill GA, Forejt J, Beamer WG, Ferrari S, Bouxsein ML, Kiel DP, Rosen CJ. 2008. A PPARG by dietary fat interaction influences bone mass in mice and humans. J Bone Miner Res 23:1398-1408.
Burgess-Herbert SL,Cox A, Paigen B. 2008. Practical applications of the bioinformatics toolbox for narrowing quantitative trait loci. Genetics 180(4):107-113.
Dong J, Ishimori N, Paigen B, Tsutsui H, Fujii S. 2008. Role of modulator recognition factor 2 in adipogenesis and leptin expression in 3T3-L1 cells. Biochem Biophys Res Commun 366(2):551-555.
Doorenbos C, Tsaih S-W, Sheehan S, Ishimori N, Navis G, Churchill G, DiPetrillo K, Korstanje R. 2008. Quantitative trait loci for urinary albumin in crosses between C57BL/6J and A/J inbred mice in the presence and absence of Apoe. Genetics 179:693-699.
Gregorova S, Divina P, Storchova R, Trachtulec Z, Fotopulosova V, Svenson K, Donahue LR, Paigen B, Forejt J. 2008. Mouse consomic strains: Exploiting genetic divergence between Mus m. musculus and Mus m. domesticus subspecies. Genome Res 18:509-515.
Ishimori N, Stylianoi IM, Li R, Korstanje R, Marion MA, Donahue LR, Rosen CJ, Beamer WG, Paigen B, Churchill GA. 2008. Quantitative trait loci for bone mineral density in an SM/J by NZB/BlNJ intercross population and identification of Trps1 as a candidate gene. J Bone Miner Res 23(9):1529-1537.
Korstanje R, Desai J, Lazar G, King B, Rollins J, Spurr M, Joseph J, Kadambi S, Li Y, Cherry A, Matteson PG, Paigen B, Millonig JH. 2008. Quantitative trait loci affecting phenotypic variation in the vacuolated lens mouse mutant, a multigenic mouse model of neural tube defects and cataracts. Physiol Genomics 35:296-304.
Mackiewicz M, Paigen B, Naidoo N, Pack AI. 2008. Analysis of the QTL for sleep homeostasis in mice: Homer1a is a likely candidate. Physiol Genomics 33:91-99.
Matteson P, Desai J, Korstanje R, Lazar G, Borsuk TE. Rollins J, Kadambi S, Joseph J, Radman T. Wink J, Benayed R, Paigen B, Millonig JH. 2008. The orphan G protein coupled receptor, Gpr161, encodes the vaculated lens locus and controls neurulation and lens development. PNAS 105(6):2088-2093.
Petkova SB, Yuan R, Tsaih S-W, Schott W, Roopenian DC, Paigen B. 2008. Genetic influences on immune phenotype revealed strain-specific variations in peripheral blood lineages. Physiol Genomi 34(3):304-314.
Stylianou IM, Affourtit JP, Shockley KR, Wilpan RY, Abdi FA, Bhardwaj S, Rollins J, Churchill GA, Paigen B. 2008. Applying gene expression, proteomics and SNP analysis for complex trait gene identification. Genetics 178(3):1795-1805.
Stylianou IM, Langley SR, Walsh R, Revenu C, Paigen B. 2008. Differences in DBA/1J and DBA/2J reveal lipid QTL genes. J Lipid Res 49:2402-2413.
Su Z, Korstanje R, Tsaih S-W, Paigen B. 2008. Candidate genes for obesity revealed from a C57BL/6J x 129S1/SvlmJ intercross. Int J obesity 32:1180-1189.
Su Z, Tsaih S, Shen Y, Sheehan S, Paigen B. 2008. Candidate genes that control plasma triglyceride, free fatty acid, and glucose identified by an NZB/BlNJ x NZW/LacJ intercross and bioinformatics. J Lipid Res 49:1500-1510.
Su Z, Wang X, Tsiah S-W, Zhang A, Cox A, Sheehan S, Paigen B. 2008. Identifying the genetic basis of HDL variation between 1291/SvImJ and C57BL/6J mice: critical importance for testing HDL candidate genes in targeted mutant mice. J Lipid Res 49:1500-1510.
Svenson KL, Ahituv N, Maganini PA, Savage H, Suetin HR, Paigen B, Peters LL. 2008. A new mouse mutant for the low-density lipoprotein receptor identified using ENU mutagenesis. J Lipid Res 49:2452-2462.
Basso F, Freeman LA, Ko C, Joyce C, Amar MJ, Shamburek RD, Tansey T, Thomas F, Wu J, Paigen B, Remaley AT, Santamarina-Fojo S, Brewer JR HB. 2007. Hepatic ABCG5/G8 overexpression reduces apoB-lipoproteins and atherosclerosis when cholesterol absorption is inhibited. J Lipid Res 48:114-126.
Chen Y, Rollins J, Paigen B, Wang X. 2007. Geetic and genomic insights into the molecular basis of atherosclerosis. Cell Metab 6:164-179.
Freeman L, Amar MJA, Shamburek R, Paigen B, Brewer Jr HB, Santamarina-Fojo S, Gonzalez-Navarro H. 2007. Lipolytic and ligand-binding functions of hepatic lipase protect against atherosclerosis in LDL receptor-deficient mice. J Lipid Res 48:104-113.
Nishihara E, Tsaih S-W, Tsukahara C, Langley S, Sheehan S, DiPetrillo K, Kunita S, Yagami K-i, Churchill GA, Paigen B, Sugiyama F. 2007. Quantitative trait loci associated with blood pressure of metabolic syndrome in the progeny of NZO-HILtJ X C3H/HeJ intercross. Mamm Genome 18:573-583.
Peters LL, Robledo RF, Bult CJ, Churchill GA, Paigen BJ, Svenson KL. 2007. The mouse as a model for human biology: a resource guide for complext trait analysis. Nat Rev Genet 8:58-69.
Sheehan S, Tsaih S-W, King BL, Stanton C, Churchill GA, Paigen B, DiPetrillo. 2007. Genetic analysis of albuminuria in a cross between C57BL/6J and DBA/2J. Am J Physiol Renal Physiol 293:F1649-F1656.
Svenson KL, Von Smith R, Magnani PA, Suetin HR, Paigen B, Naggert JK, Li R, Churchill GA, Peters LL. 2007. Multiple trait measurements in 43 inbred mouse strains captures the phenotypic diversity C of human population. J Appl Physiol 102(6):2369-2378.
Chen Y, Rollins J, Paigen B, Wang X. 2007. Toward understanding the molecular basis of atherosclerosis with genetics and genomics. Cell Metab (In press).
