Genomic regions controlling corticosterone levels in rats

Genomic Regions Controlling Corticosterone Levels in Rats Marc N. Potenza, Edward S. Brodkin, Bina Joe, Xingguang Luo, Elaine F. Remmers, Ronald L. Wilder, Eric J. Nestler, and Joel Gelernter Background: The identification of genetic factors controlling stress-responsiveness should advance the understanding of susceptibility to psychiatric illness. Methods: Rat strains, F344/NHsd and LEW/NHsd, which differ in measures of stress-responsiveness and behaviors modeling psychiatric disorders, were bred to generate F2 progeny that were used in a quantitative trait loci (QTL) analysis to identify genomic regions influencing late-afternoon corticosterone levels. Results: Regions on chromosomes 4 and 10 previously identified as influencing autoimmune phenomena were the most significant QTL observed, reaching suggestive significance at the genome-wide level. Congenic animals targeting these regions with F344/NHsd deoxyribonucleic acid on a DA/Bkl genomic background demonstrated corticosterone levels approximating those of F344/NHsd rats and differing significantly from DA/Bkl rats. Conclusions: Specific genomic regions influence both corticosterone levels and stress-related disease susceptibility. These findings not only represent the first identification of QTL controlling corticosterone levels but also suggest a mechanism underlying genetic differences in stress-responsiveness. Key Words: Stress, addiction, Fischer 344, Lewis, Dark Agouti (DA), genetics, quantitative trait loci, hypothalamic–pituitary–adrenal axis, congenic, drug dependence

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tress and stress-responsiveness have long been implicated in a variety of neuropsychiatric disorders, including depression, posttraumatic stress disorder, and drug addiction (Jacobsen et al 2001; Parker et al 2003); however, the relationship between stress-related systems and neuropsychiatric disorders remains incompletely understood. This relationship seems particularly complex in the case of addictive disorders, because drug use can lead to stress- or immune-system-related illness (e.g., intravenous drug use and human immunodeficiency virus disease and bacterial endocarditis) (Cooper et al 2003). Drug use has also been associated with autoimmune disease in a manner seemingly unrelated to increased exposure to viral or bacterial pathogens. For example, duration of tobacco smoking has been linked with seropositive rheumatoid arthritis (Stolt et al 2003). Abused drugs, such as opiates and cocaine, have been demonstrated to influence endocrine systems, including adrenal function (Cooper et al 2003). Because genetic contributions to neuropsychiatric disorders including addictions seem substantial (Fu et al 2002), an improved understanding of the genetic contributions to differences in specific components of stressrelated systems has significant implications for neuropsychiatric disorders. Quantitative trait loci (QTL) analysis represents a relatively new methodology for identifying genomic regions contributing

From the Department of Psychiatry (MNP, XL, JG), Yale University School of Medicine, New Haven, Connecticut; Department of Psychiatry and Center for Neurobiology and Behavior (ESB), University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania; National Institute of Arthritis and Musculoskeletal and Skin Diseases (BJ, EFR, RLW), National Institutes of Health, Bethesda, Maryland; and Department of Psychiatry and Center for Basic Neuroscience (EJN), University of Texas Southwestern Medical Center, Dallas, Texas. Address reprint requests to Marc N. Potenza, M.D., Ph.D., Assistant Professor of Psychiatry, Yale University School of Medicine, Connecticut Mental Health Center, Room S-104, 34 Park Street, New Haven, CT 06519. Received July 9, 2003; revised November 4, 2003; accepted November 7, 2003.

0006-3223/04/$30.00 doi:10.1016/j.biopsych.2003.11.005

to specific phenotypes (Bice et al 1998; Remmers et al 1996). Quantitative traits, such as height and weight, can be measured over a continuous spectrum and show interindividual variation. In QTL analyses, animals are bred in a controlled fashion, and quantitative traits are measured and analyzed with respect to the genetic identity of specific chromosomal locations. Specifically, two inbred strains of animals that differ in specific phenotypes are mated, creating F1 progeny that are fixed heterozygotes throughout the genome. F2 progeny, derived from mating of the F1 animals, are generally the focus of QTL analyses. They represent a genetically and behaviorally heterogeneous group with contributions from the two parental strains. Quantitative trait loci analyses are frequently genome-wide and thus provide information on the extent to which specific genomic regions contribute to specific phenotypes. A particular strength of QTL analyses is their ability to identify genomic contributions to phenotypes determined by multiple genes, a feature with particular relevance to complex behavior and neuropsychiatric disorders. Although QTL analyses represent a powerful approach, the regions identified are generally large and often contain multiple candidate genes, and identification of specific genes contributing to the phenotypes of interest requires much further investigation. Inbred rodent strains show differences in hypothalamic– pituitary–adrenal (HPA) axis function (Kosten and Ambrosio 2002) and therefore can be used to investigate genetic contributions to stress-responsiveness. Quantitative trait loci analyses have identified genomic regions influencing stress-related phenomena. For example, studies of Fischer 344 (F344)/Dark Agouti (DA) and F344/Lewis (LEW) rat progeny1 have identified specific 1

The terms Fischer, Lewis, Dark Agouti, Brown Norway, and Stroke Prone Spontaneously Hypertensive Rats are used in this article to link the strains used in the present study to ones used in prior research reports. Rat nomenclature committees have encouraged researchers to use the more meaningful designations (e.g., F344/NHsd) that indicate more precisely the strain and origin of the animals. We note that the Dark Agouti label is actually a misnomer that has been erroneously applied to the DA inbred strain, named for its d blood group allele and its agouti coat color (Festing, http://www.informatics.jax.org/external/festing/rat/ docs/DA.shtml). The more accurate and complete nomenclature for the animals used in the experiments is used and described in greater detail in the Methods and Materials section.

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M.N. Potenza et al genomic regions controlling susceptibility to arthritis and uveitis, respectively (Remmers et al 1996; Sun et al 1999). Fischer 344 and LEW rats differ in late-afternoon plasma corticosterone levels, and these differences might relate to the strains’ distinct responses to stress (Brodkin et al 1998; Kosten and Ambrosio 2002; Nestler et al 1996). These differences seem to hold significance for psychiatric illnesses, including substance use disorders. For example, cocaine administration to F344 and LEW rats resulted in between-strain differences in corticosterone responsiveness (Simar et al 1996). To date, no genomic regions associated with corticosterone levels have been identified in genome-wide analyses in rats. Identification of such regions holds the potential to advance significantly the study of genetic influences of stress pathways in animal models of psychiatric illness and ultimately lead to advances in prevention and treatment of psychiatric disorders. We hypothesized that QTL corresponding to plasma corticosterone levels would overlap with non-major histocompatability QTL previously identified in studies of rat autoimmune susceptibility in F344 and DA rats: specifically, the regions of chromosomes 4, 7, 10, and 8 corresponding to Cia3 through Cia6, respectively (Remmers et al 1996). We predicted that the autoimmune QTL relating to major histocompatability loci would be driven by genetic differences related to major histocompatability factors rather than HPA factors (including corticosterone levels) per se, whereas those seemingly unrelated to major histocompatability factors would more likely be directly related to HPA function. Despite the use of LEW rather than DA rats in the present study, we predicted that these regions would be identified, given the similarities of DA and LEW rats in autoimmune disease susceptibility (Wilder et al 2000) and other biochemical and behavioral parameters (e.g., those related to drug addiction [Brodkin et al 1999]) and the identification of similar genomic regions in QTL underlying uveoretinitis susceptibility in LEW versus F344 rats (Sun et al 1999) and collagen-induced arthritis in DA versus F344 rats (Remmers et al 1996). To test these hypotheses, we first performed a QTL analysis targeting the specific regions and then performed additional studies based on the initial findings. Specifically, an autosome-wide scan was performed to examine whether the identified QTL in the a priori hypothesized regions were the major genetic contributors to the phenotype or whether additional chromosomal regions might be implicated. Next, congenic animals targeting the implicated regions were examined with respect to late-afternoon corticosterone levels.

Methods and Materials Animal Procedures The animal care and use committees at Yale University and the National Institutes of Health (NIH) approved the research performed at each institution, and all work was performed in strict accordance with the NIH “Guide for the Care and Use of Laboratory Animals.” All work involving the QTL analysis was performed at Yale University, and all work with the congenic animals was performed at the NIH. No animals were shipped between these facilities. F344/NHsd and LEW/NHsd rats used for the QTL analyses were maintained and characterized as described previously (Brodkin et al 1998). Fischer 344/NHsd and LEW/NHsd rats were obtained from Harlan-Sprague Dawley (Indianapolis, Indiana) at 35– 45 days of age. F1 progeny were generated by both F344/NHsd (female) ⫻ LEW/NHsd (male) and LEW/NHsd (female) ⫻ F344/NHsd (male) crosses, and F2 inter-

BIOL PSYCHIATRY 2004;55:634 – 641 635 cross progeny were derived from mating of both (F344/NHsd ⫻ LEW/NHsd)F1 ⫻ (F344/NHsd ⫻ LEW/NHsd)F1 and (LEW/NHsd ⫻ F344/NHsd)F1 ⫻ (LEW/NHsd ⫻ F344/NHsd)F1 pairs. F2 progeny were weaned at 21 days of age. Animals were housed in groups of two to four with food (Purina chow) and tap water ad libitum in a temperature-controlled colony with a 12-hour light/ dark cycle (lights on at 7:00 AM). Only male rates were included in this study to limit variation in corticosterone levels associated with the estrus cycle in female animals. Plasma Corticosterone Level Measurement Before measurement of corticosterone levels, a minimum period of 2 weeks of habituation to home cages was used for animals shipped from commercial sources or between different locations within institutions. Late-afternoon (between 3:00 and 5:00 PM) blood samples were obtained, and aliquots of frozen plasma were assayed for corticosterone levels by radioimmunoassay. Blood samples were collected from rats taken individually from their home cages and killed within 40 sec to minimize stress-related changes. Gloves were changed before retrieving the next animal for sacrifice from the home cage, because it has been our groups’ experience that proximity to blood and decapitation increases animals’ corticosterone levels (Brodkin et al 1998). Animal weights at the time of sacrifice averaged 265.0 g (SD ⫽ 26.6; range ⫽ 179 –357). Radioimmunoassay was performed according to manufacturer’s specifications (ICN Biomedicals, Costa Mesa, California). This procedure shows no significant cross-reactivity with other endogenous steroids. Corticosterone levels for the animals used in the QTL analyses and for the congenic animals were obtained at Yale University and the NIH, respectively. At Yale, corticosterone levels were also determined in a pooled sample to provide a comparison to minimize variance due to between-assay variability. Similar pooled samples were not included in studies of the congenic animals, and as such the results focus on findings related to corticosterone levels rather than the corticosterone/(pooled serum sample) values. Deoxyribonucleic Acid Extraction, Purification, Amplification, and Analysis Genomic deoxyribonucleic acid (DNA) was obtained from frozen liver tissue of F344/NHsd and LEW/NHsd parental animals and F2 progeny by alkaline lysis and column purification strategy (Qiagen, Valencia, California; www.qiagen.com). Quality of DNA was assessed by agarose gel electrophoresis. Amplification of DNA was performed with the polymerase chain reaction and primers obtained from Research Genetics (now part of Invitrogen, Carlsbad, California; http://mp.invitrogen.com/ mappairs_rat.php3), the National Institute of Arthritis and Musculoskeletal and Skin Diseases (Bethesda, Maryland; Remmers et al 1996), or Applied Biosystems (ABI, Foster City, California; www. appliedbiosystems.com). Analysis of DNA was performed by size fractionation, either by agarose gel electrophoresis and ethidium bromide visualization, or by acrylamide gel electrophoresis and fluorescence detection with an ABI 377 semiautomated sequencer. Data from these gels were independently read by two researchers and double-entered before analysis. QTL Analysis Quantitative trait loci analysis was performed as described previously (Remmers et al 1996). Out of 298 F2 progeny, including those with phenotypic extremes for corticosterone levels (top and bottom 10%–15%), 188 were analyzed at 178 www.elsevier.com/locate/biopsych

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Table 1. Log Ratio Statistic Scores for Corticosterone Levels at Specific Genomic Locations

Marker D1Rat4 D1Rat7 D1Rat19 D1Arb8 D1Rat256 D1Rat266 D1Mgh7 D1Arb11 D1Rat35 D1Rat215 D1Rat164 D1Rat437 D1Rat67 D1Rat70 D1Rat169 D1Rat76 D1Mgh12 D1Arb25 D1Rat122 D2Rat3 D2Rat182 D2Rat11 D2Mit6 D2Mgh19 D2Rat217 D2Rat34 D2Rat170 D2Rat240 D2Rat62 D2Rat185 D2Rat69 D2Rat168 D3Rat53 D3Rat80 D3Rat75 D3Rat24 D3Rat63 D3Arb12 D3Mgh10 D4Arb14 D4Rat11 D4Arb17 D4Rat153 D4Rat15 D4Rat24

Chromosome

Map Location (cM)

Log Ratio Statistic, Corticosterone

Log Ratio Statistic, Corticosterone/ Serum

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 4 4 4 4 4 4

9.2 12.5 23.8 35.0 28.3 46.8 53.8 57.0 59.4 74.5 83.5 89.1 95.9 106.1 122.0 125.3 133.4 139.0 143.5 .1 6.9 15.1 29.5 35.5 43.5 57.1 68.2 79.7 90.8 97.7 106.8 111.5 4.6 18.9 36.0 49.5 65.2 79.9 86.5 0 18.2 23.0 27.1 29.4 32.8

.1 .1 2.7 3.4 .6 1.7 2.0 1.2 2.4 1.0 .4 .1 .9 .1 1.2 1.8 0 .1 .9 1.0 3.6 2.0 4.6 3.9 3.1 .8 .2 .2 1.3 0 2.2 1.9 1.1 .1 4.2 2.1 2.5 .1 2.9 .1 .7 .4 1.5 3.8 12.1

.1 .2 3.2 3.9 .7 1.0 1.3 1.2 2.9 1.4 .6 .4 .3 .4 2.7 3.2 .4 .6 .1 .7 2.4 1.5 3.4 3.4 3.4 1.0 .9 1.3 1.6 .8 1.7 1.1 2.1 .2 3.5 2.2 2.9 .5 4.3 .1 .2 .1 1.6 5.4 13.6

genetic loci distributed across the rat autosomes. Data were analyzed as described previously with the software programs MAPMAKER/EXP and MAPMAKER/QTL (Remmers et al 1996) and MapManager QT (Manly and Olson 1999). Data presented are from analyses with MapManager QT. Marker map locations from a Stroke Prone Spontaneously Hypertensive Rats x Brown Norway (SHRSPxBN) genetic map as described in the rat genome database (www.rgd.mcw.edu/GENOMESCANNER) were used in the QTL analyses, with study data– derived distances used for six markers (D1Arb8, D1Arb11, D1Arb25, D4Arb17, D12Arb8, and D20 Mgh1) not available from the map. www.elsevier.com/locate/biopsych

Table 1. continued

Marker D4Rat226 D4Arb8 D4Rat33 D4Rat108 D4Rat172 D4Rat40 D4Rat48 D4Rat193 D4Rat60 D4Rat241 D4Rat66 D4Mgh30 D4Rat112 D5Rat121 D5Rat126 D5Rat82 D5Rat10 D5Rat85 D5Rat196 D5Rat30 D5Rat171 D5Rat93 D5Rat49 D6Rat41 D6Rat29 D6Rat133 D6Rat23 D6Rat14 D6Rat160 D6Rat109 D7Rat113 D7Rat37 D7Rat27 D7Rat51 D7Rat22 D7Rat139 D7Rat11 D7Rat81 D7Rat4 D8Rat77 D8Rat55 D8Rat52 D8Rat164 D8Arb6 D8Arb8 D8Rat43 D8Rat36 D8Rat23 D8Rat21

Chromosome

Map Location (cM)

Log Ratio Statistic, Corticosterone

Log Ratio Statistic, Corticosterone/ Serum

4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8

32.9 34.1 40.8 41.9 46.4 48.7 54.3 62.5 71.4 77.0 82.6 86.7 98.8 3.5 16.0 26.0 37.0 48.0 58.1 68.3 78.5 85.2 105.6 20.6 33.0 37.5 46.6 57.8 76.0 85.2 3.1 6.8 22.0 31.0 45.5 52.3 64.7 72.4 80.4 0 8.4 14.3 18.5 23.1 34.6 36.9 41.2 46.0 47.1

7.1 12.4 12.8 10.9 11.9 12.8 7.5 8.5 .8 .5 .7 .7 1.5 2.3 .5 2.0 1.7 .2 .4 1.8 2.4 3.0 3.4 1.4 .4 .8 1.3 2.7 .1 .8 .4 4.2 3.8 4.8 4.9 2.4 .6 .1 .4 0 1.0 .2 0 1.7 .9 3.5 3.0 .3 4.3

8.5 15.6 15.7 13.5 13.3 15.4 9.9 12.0 3.0 1.8 1.7 .4 1.2 1.7 .2 1.7 1.1 .1 .5 3.4 2.9 3.0 4.2 2.2 .7 .7 .4 3.6 .4 .5 .6 3.7 3.4 5.7 6.1 4.1 2.6 .4 1.1 .1 .4 0 .1 2.5 .3 3.1 2.2 .2 3.3

Congenic Animals DA/Bkl and F344/NHsd parental rats were obtained from Bantin & Kingman (Fremont, California) and Harlan-Sprague Dawley, respectively. Animals with defined regions of F344/ NHsd chromosomes upon a DA/Bkl genetic background (DA.F344/Arb congenic rats) were developed through backcrossing techniques, as described and characterized elsewhere (Joe et al 2000; Remmers 2000; Remmers et al 2002; Wilder et al 2000). Corticosterone levels were determined essentially as de-

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M.N. Potenza et al Table 1. continued

Marker D8Rat16 D8Rat123 D8Rat11 D8Arb119 D9Rat135 D9Rat133 D9Rat126 D9Rat13 D9Rat110 D9Rat1 D10Rat218 D10Rat117 D10Rat45 D10Rat38 D10Rat164 D10Rat28 D10Rat153 D10Rat124 D10Rat142 D10Rat203 D10Rat15 D10Rat11 D10Rat8 D10Rat108 D10Rat135 D11Rat73 D11Mit1 D11Rat6 D11Rat91 D12Rat59 D12Arb8 D12Rat4 D12Rat51 D12Rat76 D12Rat52 D12Rat44 D13Rat7 D13Arb5 D13Arb8 D13Rat126 D13Rat85 D13Rat131 D13Mit4 D13Rat153 D14Rat72 D14Rat77 D14Rat50 D14Rat68 D14Arb10

Table 1. continued

Chromosome

Map Location (cM)

Log Ratio Statistic, Corticosterone

Log Ratio Statistic, Corticosterone/ Serum

8 8 8 8 9 9 9 9 9 9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 11 11 11 11 12 12 12 12 12 12 12 13 13 13 13 13 13 13 13 14 14 14 14 14

57.2 66.3 71.9 77.4 9.1 13.9 37.7 45.5 64.7 79.5 4.6 6.1 21.6 34.0 38.5 47.6 51.0 58.5 66.5 71.1 73.3 77.1 85.9 92.7 94.1 8.2 11.5 19.5 36.5 5.9 11.0 17.1 24.1 33.0 44.3 54.1 1.2 7.9 14.8 19.3 26.1 33.9 40.7 44.1 2.3 6.9 15.9 28.2 43.0

.4 .2 .7 1.2 .9 2.1 4.1 3.7 3.5 .5 2.7 .2 .1 .1 .9 3.1 3.2 5.3 7.4 6.1 6.1 12.1 10.2 3.8 9.1 3.8 4.6 5.9 1.2 .9 7.2 8.0 10.8 2.2 4.6 2.9 .8 1.9 7.6 3.4 2.4 3.4 .4 .1 1.0 2.4 7.1 7.7 .3

.3 .1 .2 .8 .6 1.2 3.5 3.6 2.0 .3 2.4 .2 .6 .8 .8 5.0 5.0 7.0 9.0 7.4 7.5 13.8 12.8 5.0 10.2 3.9 4.7 5.6 1.0 .6 5.3 6.0 8.3 1.8 4.1 3.8 .4 2.3 4.4 2.4 1.3 1.9 .1 .9 .4 1.6 5.3 5.9 .2

scribed above, although a pooled serum control sample was not used as an additional control.

Results General characteristics of the F344 and LEW parental animals and F2 intercross progeny used in the QTL analysis have been described previously (Brodkin et al 1998). F344 animals had significantly higher corticosterone levels than the LEW rats,

Marker D14Rat49 D15Rat55 D15Rat66 D15Rat116 D15Rat96 D15Rat26 D16Rat35 D16Rat67 D16Rat53 D16Rat37 D16Rat15 D17Rat115 D17Rat117 D17Rat15 D17Arb7 D17Rat130 D17Rat154 D18Rat133 D18Rat25 D18Rat17 D18Rat55 D18Rat8 D18Rat76 D19Rat82 D19Rat12 D19Rat35 D19Rat67 D19Rat63 D19Rat58 D20Rat46 D20Rat31 D20Rat34 D20Rat39 D20Mgh1 D20Arb10

Chromosome

Map Location (cM)

Log Ratio Statistic, Corticosterone

Log Ratio Statistic, Corticosterone/ Serum

14 15 15 15 15 15 16 16 16 16 16 17 17 17 17 17 17 18 18 18 18 18 18 19 19 19 19 19 19 20 20 20 20 20 20

64.0 5.5 15.8 25.0 41.9 58.9 5.6 18.1 28.3 38.5 46.7 13.9 19.6 25.5 32.6 40.8 47.5 2.5 11.5 18.3 22.7 43.2 48.9 6.7 20.2 27.8 35.7 39.1 48.8 0 11.5 21.5 30.9 39.0 48.2

1.5 2.2 .4 1.0 2.9 1.1 .2 .5 1.2 5.8 9.5 .4 .5 .6 1.1 1.9 2.6 1.9 4.0 3.3 3.0 .2 .7 2.1 2.2 6.2 3.5 2.1 2.8 1.6 .5 2.1 1.4 2.0 2.1

1.1 4.2 1.2 3.1 2.5 .7 0 .3 .6 3.5 8.3 .4 .8 .4 1.2 1.2 2.8 1.5 2.3 3.0 3.2 .1 .3 3.0 3.1 6.9 3.1 2.2 .9 .8 .4 2.1 .9 2.8 2.7

although the ranges overlapped for the groups (Brodkin et al 1998). The F2 progeny displayed late-afternoon plasma corticosterone levels intermediate between the F344 and LEW parental strains, and the distribution of corticosterone levels within the F2 animals included a majority of animals within the LEW range and relatively few in the F344 range (Brodkin et al 1998). The results of an autosomal genome-wide scan using 178 markers are displayed in Table 1. Two a priori hypothesized trait regions on chromosomes 4 and 10 (Cia3 and Cia5) were those that demonstrated the broadest significance peaks and largest likelihood (log ratio statistics or LRS) scores (Table 1). Chisquared statistics for adjacent loci reaching at least a p ⬍ .05 level of significance for each individual locus are shown in Table 2. The QTL on chromosomes 4 and 10 showed peak LRS values significant at p ⫽ .00007 and .00025, respectively, and each approached significance (were “suggestive”) at the genome-wide level (Lander and Kruglyak 1995; Manly and Olson 1999). The region on chromosome 4 also reached genome-wide significance for corticosterone/serum, a ratio with a pooled serum control in corticosterone measurements to minimize experimental variability in radioimmunoassay determinations. The QTL on chromosomes 4 and 10 accounted for approximately 10% and 9% of the www.elsevier.com/locate/biopsych

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Table 2. Chi-Squared Statistics for Individual Markers Marker D4Rat24 D4Rat226 D4Arb8 D4Rat33 D4Rat108 D4Rat172 D4Rat40 D10Rat142 D10Rat203 D10Rat15 D10Rat11 D10Rat8 D14Rat50 D14Rat68 D16Rat37 D16Rat15

Chromosome

Log Ratio Statistic, Corticosterone

Variance (%)

p

4 4 4 4 4 4 4 10 10 10 10 10 14 14 16 16

19.0 7.1 11.9 8.0 11.3 11.3 12.6 6.9 6.7 6.1 16.6 11.9 7.8 7.4 8.5 11.4

10 4 6 4 6 6 7 4 4 3 9 6 5 4 5 7

.00007 .029 .0027 .018 .0036 .0035 .0019 .031 .036 .047 .00025 .0026 .02 .025 .014 .0034

Results presented represent findings from multipoint analyses keyed to single marker locations. Data presented represent two or more contiguous markers at P ⬍ .05 in a free regression model.

phenotypic variance in corticosterone levels, respectively. Additional regions suggestive of containing QTL for corticosterone were identified on chromosomes 12, 14, and 16 (Tables 1 and 2). The identified region of chromosome 12, accounting for 6% of the variance (LRS ⫽ 10.2, p ⫽ .006 at D12Rat51), overlaps with a previously identified QTL controlling autoimmune uveoretinitis in F344 and LEW rats (Sun et al 1999). To investigate further the potential influence of the QTL within the regions on chromosomes 4 and 10 on corticosterone regulation, congenic animals derived from DA and F344 parental strains were examined (Table 3). A schematic diagram displays the boundaries of the congenic regions for the indicated lines (Figure 1). DA.F344/Arb congenic males with F344 genetic contributions in the regions corresponding to the QTL on chromosomes 4 and 10 demonstrated plasma corticosterone levels different from DA rats and similar to the F344 animals (Table 3). In contrast, a DA.F344(Cia1)/Arb congenic (Remmers et al 2002) associated with a major histocompatability locus on chromosome 20 did not demonstrate corticosterone levels significantly different from those of the DA male rats (Table 3). Although corticosterone levels were numerically greater in

seven of the nine congenic groups targeting the chromosome 4 and 10 regions as compared with the F344 group, these differences were nonsignificant (p ⬎ .1) in all cases except for the comparisons involving the DA.F344(Cia3c)/Arb and DA.F344(Cia3d)/Arb, which reached the p ⫽ .01 and p ⫽ .002 levels, respectively.

Discussion Summary of Findings The current investigation identified genomic contributions to differences in late-afternoon corticosterone levels in two inbred strains of rats. The QTL were in a priori hypothesized regions, and an autosome-wide scan confirmed that the QTL contributing most to differences in corticosterone levels in the two strains were localized to the hypothesized genomic regions. Measurement of late-afternoon corticosterone levels in congenic rats targeting the identified genomic regions provided additional evidence for the importance of these genomic regions in controlling corticosterone levels. The importance of these findings and their relevance for neuropsychiatric disorders are described below. Significance of Current Findings Although one QTL linked to cortisol levels in pigs has been recently reported (Desautes et al 2002), the present study represents the first report of QTL controlling plasma corticosterone levels. The QTL analysis involving the progeny of Meishan and Large White pigs identified a region localized to the end of the long (q) arm of chromosome 7 with significant effects on basal and poststress levels of cortisol (Desautes et al 2002). The gene effect was more robust for poststress levels than for basal levels, accounting for 7.7% and 20.7% of variances, respectively (Desautes et al 2002). The identified region is homologous to the telomeric part of the long arm of human chromosome 14, a locus independent from those identified in the present study and containing the gene encoding plasma corticosteroid-binding globulin, a major determinant of circulating cortisol concentrations (Desautes et al 2002). Thus, the QTL identified in the present study seem distinct from the one identified in pigs by Desautes and colleagues. Although the most robust QTL in the present study reached only suggestive significance at the genome-wide level, they are located in a priori hypothesized regions based on prior QTL studies of stress-related disease susceptibility. Furthermore, data

Table 3. Late-Afternoon Corticosterone Levels in DA/Bkl, F344/NHsd and DA.F344 Congenic Rats

Strain/Line DA/Bkl F344/NHsd DA.F344(Aia2)/Arb DA.F344(Cia3)/Arb DA.F344(Cia3a)/Arb DA.F344(Cia3b)/Arb DA.F344(Cia3c)/Arb DA.F344(Cia3d)/Arb DA.F344(Cia3e)/Arb DA.F344(Cia5)/Arb DA.F344(Cia5a)/Arb DA.F344(Cia1)/Arb

Comparison with DA/Bkl Values

Chromosome

Corticosterone Level, ng/mL

Standard Deviation

No. of Animals

df

␹2

p

4 4 4 4 4 4 4 10 10 20

47.39 95.90 112.73 114.30 126.36 84.70 169.18 182.15 136.24 89.91 111.08 59.25

35.72 50.19 58.49 89.90 52.19 52.26 40.64 46.48 91.06 55.45 62.32 43.65

11 9 8 8 7 8 6 9 4 9 11 10

1, 18 1, 17 1, 17 1, 16 1, 17 1, 15 1, 18 1, 13 1, 18 1, 20 1, 19

6.37 9.16 5.08 14.67 3.44 41.09 53.86 8.00 4.31 8.65 .47

.021 .0076 .038 .0015 .081 .0001 .0001 .014 .052 .0081 .50

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BIOL PSYCHIATRY 2004;55:634 – 641 639 DA.F344(Cia3c)/Arb and DA.F344(Cia3d)/Arb, had higher corticosterone levels than the F344 group. Although the precise mechanism for this finding is not known and requires further direct investigation, it raises the possibility that aspects of the genomic regions encompassed by the Cia3c and Cia3d congenics interact differentially with other regions of the DA genome as compared with their interactions with the corresponding regions of the F344 genome.

Figure 1. DA.F344/Arb congenic lines used in corticosterone analyses. DA.F344/Arb congenic rats were derived by transferring regions of the Fischer (F)344 genome to the Dark Agouti (DA) background by repeated backcross. The regions derived from F344 chromosome 4 (A) and chromosome 10 (B) are indicated by the black filled bars. The open parts of the bars highlight regions encompassing the break points between F344/NHsd and DA/Bkl deoxyribonucleic acid. Representative genomic markers (maps from http://www.niams.nih.gov/rtbc/ratgbase/index.htm) are shown on the F344 parental chromosomes 4 (Chr 4) and 10 (Chr 10). The locations of the markers encompassing the regions identified in the corticosterone genome scan (Table 1), D4Rat24 to D4Rat193 and D10Rat11 to D10Rat135 (indicated by boxed regions marked on the F344 parental chromosomes, marker names in fine print), were deduced from the physical locations of those markers and flanking markers on the rat genome (Ensembl; release 14.2.1, http://www.ensembl.org/Rattus_norvegicus/).

obtained from the congenic animals, demonstrating changes in corticosterone levels from those in the DA parental group after introduction of F344 genomic DNA in the regions of the chromosome 4 and 10 QTL, provide additional support for the involvement of the regions as controlling corticosterone levels. These findings suggest that some similar mechanisms underlie corticosterone differences between F344 versus LEW rats and F344 versus DA rats. Given that the DA.F344(Cia3)/Arb, DA.F344(Cia5)/Arb, DA.F344(Cia5a)/Arb, and DA.F344(Aia2)/ Arb congenic lines, as compared with the DA/Bkl parental line, demonstrate decreased susceptibility to experimental arthritis (Joe et al 2000, 2002; Remmers et al 2002), the findings suggest a role for genetically mediated, impaired corticosterone regulation in the etiology of specific stress-related disorders and raise the possibility that genetic factors mediate other associations between hypocortisolism and stress-related diseases; for example, as described for psychiatric disorders in humans (Heim et al 2000). Interestingly, two of the congenic groups,

Genomic Regions, Candidate Genes, and Implications Quantitative trait loci analyses define relatively large intervals that typically contain multiple candidate genes that might contribute to the phenotype under investigation. The identified regions in this study contain multiple genes, including ones without apparent connections to HPA axis function, stressresponsiveness, or psychiatric illness; however, the regions also include interesting candidate genes previously implicated in these processes. For example, the interval on chromosome 4 identified in the present study has homologous regions in three genomic areas in humans: two on chromosomes 2 and 4 associated with rheumatoid arthritis susceptibility, and a third on chromosome 7 associated with multiple sclerosis, asthma, and Crohn’s disease/ulcerative colitis (Griffiths and Remmers 2001; Remmers et al 2002). The region on chromosome 4 also is similar in location to one identified in studies of alcohol consumption in alcohol-preferring and -nonpreferring rat lines (Bice et al 1998). In the center of this region is the gene encoding neuropeptide Y, a peptide with functional polymorphisms implicated in stressresponsiveness, alcohol dependence, and anxiety and depressive disorders (Antonijevic et al 2000; Heilig and Thorsell 2002; Lappalainen et al 2002; Morgan et al 2002). There also exist within the Cia3 region other candidate genes with potential impact on HPA function; for example, the corticotropin-releasing hormone receptor-2 gene (Crhr-2), a gene with splice variants (Ardati et al 1999) that has been implicated in stress-responsiveness and anxiety in murine gene knock-out experiments (Bale et al 2000). In the region encompassed by Cia5, data also suggest the existence of multiple candidate genes. The Cia5 congenic interval is likely to contain at least two loci related to immune disease susceptibility, one gender-independent and another femalespecific factor (Joe et al 2000, 2002). As with the Cia3 region, areas of the human genome corresponding to Cia5 have been associated with susceptibility for arthritis, multiple sclerosis, and psoriasis (Griffiths and Remmers 2001; Remmers et al 2002). A candidate gene within Cia5 is the corticotropin-releasing hormone receptor-1 gene (Crhr-1), a gene implicated in stressinduced alcohol consumption and drug addiction (Goeders and Clampitt 2002; Sillaber et al 2002). Given the importance of corticotropin-releasing factor in HPA axis function and its proposed role in mental health and substance use disorders (Arborelius et al 1999; Goeders 2002), the potential contributions of Crhr-1 and Crhr-2 to the current findings warrant additional investigation. Study Limitations and Future Directions The extent to which the identified QTL are related to differences in biochemical and behavioral measures of stress- and drug-responsiveness in the F344 and LEW rats requires additional investigation (Kosten and Ambrosio 2002; Nestler et al 1996). These rat lines have been used as models for several stressrelated psychiatric diseases (e.g., addiction [Kosten and Ambrosio 2002; Nestler et al 1996], schizophrenia [Lipska and Weinwww.elsevier.com/locate/biopsych

640 BIOL PSYCHIATRY 2004;55:634 – 641 berger 1996], and depression [Lahmame et al 1997]), and the current findings therefore have significance for multiple psychiatric disorders and provide a possible genetic mechanism that might account for shared risk factors. The demonstration that DA.F344/Arb congenic lines targeting the regions identified in the current QTL analysis display altered corticosterone levels provides not only additional support for the involvement of these genomic regions in corticosterone regulation but also an avenue for future investigations into the relationship between stressresponsiveness, immune system function, and psychiatric symptomatology (Duman et al 2001; Prasad et al 1996; Wilder et al 2000). That is, the availability of congenic animals displaying differences in corticosterone regulation attributable to relatively small regions of genomic DNA should facilitate more precise investigation of corticosterone influences on biochemical and behavioral stress-responsiveness relevant to human health and illness. Limitations of the study include the following. First, we only studied male rats, and future research in female populations is needed. Second, other inbred rat strains might show differences in corticosterone regulation and its relationship to specific behaviors. For example, LR (low-responding) rats, characterized by a low corticosterone response, demonstrate a lower propensity than HR (high-responding) rats, characterized by a high corticosterone response, to self-administration of drugs (Kabbaj et al 2000). This finding is in apparent contrast to the relationship in F344 and LEW rats between corticosterone levels and drug administration, in which low corticosterone levels and high rates of drug self-administration are observed in LEW rats (Kosten and Ambrosio 2002). Although certain QTL might be strain-specific, we speculate that common QTL will influence phenotypes across many strains; however, the complex relationship between corticosterone levels and stress-related behaviors in different rat strains raises the possibility that unique genetic factors influencing distinct aspects of corticosterone regulatory pathways operate in specific animal lines. A third limitation is that the findings relating to corticosterone might not be directly translatable to cortisol. For example, although corticosterone shares with cortisol structural and functional features, there are unique elements to the two compounds and to the synthetic pathways that lead to their production. Given that humans use cortisol, the relevance of the findings from the present study to aspects of human health and disease warrants direct examination. Future experiments could include the examination in stress-related illnesses (including specific psychiatric disorders) of polymorphisms of candidate genes lying within the regions identified in this QTL analysis. Quantification of stress-related measures (e.g., cortisol levels and other HPA axis measures) in conjunction with characterization of allelic variation of the candidate genes could further advance the understanding of the role of the HPA axis in neuropsychiatric disorders. A fourth limitation relates to differences in the animals used in the QTL and congenic experiments. For example, two different suppliers of F344 rats were used in the QTL and congenic experiments. Arguably more important is the use of F344 and LEW rats in the QTL experiments and DA and F344 rat progeny in the congenic experiments. The DA.F344 congenics were used because they were readily available: they had been developed after the identification of the importance of the targeted chromosomal regions in immune responses (Remmers et al 1996). Although the use of different parental strains in the congenic and QTL experiments arguably extends the generalizability of the findings, a better-matched control would have involved F344 and LEW congenics targeting the identified www.elsevier.com/locate/biopsych

M.N. Potenza et al genomic regions. Such strains are presently not available, although efforts are currently under way to create them. Their availability would allow further direct testing of the implication of the identified chromosomal regions in mediating baseline corticosterone levels and stress-related disease susceptibility. On the other hand, the present report has identified that the DA.F344 congenics can be used as primary tools for further genetic dissection and identification of loci controlling corticosterone responses. Despite the limitations of the present study, the findings identify rat QTL underlying differences in late-afternoon corticosterone levels in specific, a priori hypothesized chromosomal regions. As such, the investigation is important in several ways. First, few prior studies have attempted to map neurochemical QTL in inbred strains of rats. Because much work has been devoted to defining the neurochemistry in rat models of psychiatric disorders, this line of research is important in that it circumvents the need to translate phenotypes well described in rats to genetic models in mice. As rat genomics become increasingly well defined and more frequently used, this line of research might become increasingly important. Second, because the significance threshold for targeted QTL analyses is less stringent than for those performed without a priori hypotheses, the use of available genetic information in guiding studies is of importance. Third, the present study highlights the potential of congenic animals in providing additional support for the importance of genomic regions in influencing specific phenotypes. Fourth and most importantly, the findings of specific genomic regions influencing corticosterone levels provide a basis for future investigations into the genetic basis for stress-responsiveness in human health and disease. This work was supported by a Young Investigator Award from the National Alliance for Research in Schizophrenia and Depression (MNP), a Drug Abuse Research Scholar Program in Psychiatry Award from the American Psychiatric Association and the National Institute on Drug Abuse (K12-DA00366; MNP), the Clinician Scientist Training Program (K12-DA00167; MNP), the U.S. Department of Veterans Affairs (the Veterans Affairs Connecticut-Massachusetts Mental Illness Research, Education and Clinical Center [MNP, JG], and the Veterans Affairs Neuroscience and Traumatic Brain Injuries Postdoctoral Fellowship [ESB]), NIDA R01 DA12849 (JG), NIAAA R01 AA11330 (JG), and NIDA P01 DA08227 [EJN]. We thank Eric Londin and Anne Marie Lacobelle for technical assistance. Antonijevic IA, Murck H, Bohlhalter S, Frieboes RM, Holsboer F, Steiger A (2000): Neuropeptide Y promotes sleep and inhibits ACTH and cortisol release in young men. Neuropharmacology 39:1474 –1481. Arborelius L, Owens MJ, Plotsky PM, Nemeroff CB (1999): The role of corticotropin-releasing factor in depression and anxiety disorders. J Endocrinol 160:1–12. Ardati A, Goetschy V, Gottowick J, Henriot S, Valdenaire O, Deuschle U, et al (1999): Human CRF2 alpha and beta splice variants: Phamarcological characterization using radioligand binding and a luciferase gene expression assay. Neuropharmacology 38:441–448. Bale TL, Contarino A, Smith GW, Chan R, Gold LH, Sawchenko PE, et al (2000): Mice deficient for corticotropin releasing hormone receptor-2 display anxiety-like behavior and are hypersensitive to stress. Nat Genet 24:410 – 414. Bice P, Foroud T, Bo R, Castelluccio P, Lumeng L, Li T-K, et al (1998): Genomic screen for QTLs underlying alcohol consumption in the P and NP rat lines. Mamm Genome 9:949 –955.

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