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.
Research details
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.Postdoctoral Fellows: Cheryl Ackert-Bicknell, Ph.D., Annerose Berndt, D.V.M., Ph.D., Magalie Leduc, Ph.D., Zhiguang Su, Ph.D.
Research Assistant II: Lee Bickerstaff, B.S., M.S., Allison Cox, B.S., M.S., Michael Marion, B.S., Susan Sheehan, B.A., M.S., Yuan Shen, M.S., M.D., Ken Walsh, B.S.
Research Assistant I: Cynthia McFarland, Shuqin Xing, M.D.
Colony Coordinator: Harry Whitmore, Fred Rumill
Research Administrative Assistant: Patricia Cherry
Publication listings
(Selected from 2006 to present)
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.
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.
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.
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, (in press).
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, (in press).
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 obes, (in press).
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 ZNB/BlNJ x NZW/LacJ intercross and bioinformatics. J Lipid Res, (in press).
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).
Basso F, Amar MJ, Wagner EM, Vaisman B, Paigen B, Santamarina-Fojo S, Remaley AT. 2006. Enhanced ABCG1 expression increases atherosclerosis in LDLr-KO mice on a western diet. Biochem Biophys Res Comm 351:398-404.
Grindle S, Garganta C, Sheehan S, Gile J, Lapierre A, Whitmore H, Paigen B, DiPetrillo K. 2006. Validation of high-throughput methods for measuring blood urea nitrogen and urinary albumin concentraitons in mic. Comp Med 56(6):482-486.
Ishimori N, Li R, Walsh KA, Korstanje R, Rollins JA, Petkov P, Pletcher MT, Wiltshire T, Donahue LR, Rosen CJ, Beamer WG, Churchill GA, Paigen B. 2006. Quantitataive trait lici that determine bone mineral density in C57BL/6J and 129Sl/SvImJ inbred mice. J Bone Miner Res 21(1):105-112.
Joyce CW, Wagner EM, Basso F, Amar MJ, Freeman LA, Shamburek RD, Knapper CL, Syed J, Wu J, Vaisman BL, Fruchart-Najib J, Billings EM, Paigen B, Remaley AT, Santamarina-Fojo S, Brewer HB Jr. 2006. ABCA1 overexpression in the liver of LDLr-KO mice leads to accumulation of pro-atherogenic lipoproteins and enhanced atherosclerosis. J Biol Chem 281(44):33053-33065.
Li R, Tsaih S-W, Shockley K, Stylianou IM, Wergedal J, Paigen B, Churchill GA. 2006. Structural model analysis of multiple quantitative traits. PLoS Genetics 2(7):e114.
Rollins J, Chen Y, Paigen B, Wang X. 2006. In search of new targets for plasma high-density lipoprotein cholesterol levels: promise of human-mouse comparative genomics. Trends Cardiovasc Med 16:220-234.
Stylianou IM, Korstanje R, Li R, Sheehan S, Paigen B, Churchill GA. 2006. Quantitative trait locus analysis for obesity reveals multiple networks of interacting loci. Mamm Genome 17:22-36.
Stylianou IM, Singh J, Schwartz DA, Paigen. 2006. Comparative genomics of asthma. In: Genetics of Asthma and Chronic Obstructive Pulmonary Disease, Postma DS and Weiss ST, Eds, Chapter 8, pp. 1159-177.
Stylianou IM, Tsaih S-W, DiPetrillo K, Ishimori N, Li R, Paigen B, Churchill G. 2006. Complex genetic architecture revealed by analysis of high-density lipoprotein cholesterol in chromosome substitution strains and F2 crosses. Genetic 174:999-1007.
Wittenburg H, Lyons MA, Li R, Kurtz U, Wang X, Mössner J, Churchill GA, Carey MC, Paigen B. 2006. QTL mapping for genetic determinants of lipoprotein cholesterol levels in combined crosses of inbred mouse strains. J Lipid Res 47:1780-1790.
