Gene expression profiling of the left ventricles in a rat model of intrinsic aerobic running capacity

Soon Jin Lee1, Justin A. Ways1, John C. Barbato2, David Essig1, Krista Pettee1, Sarah J. DeRaedt1, Siming Yang1, David A. Weaver1, Lauren G. Koch1 and George T. Cicila1

1 Department of Physiology and Cardiovascular Genomics, Medical University of Ohio, Toledo; and 2 Department of Cell Biology, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, Ohio


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Our previous work found DA rats superior for intrinsic aerobic running capacity (ARC) and several cardiac function indexes compared with Copenhagen (COP) rats, and identified ARC quantitative trait loci (QTLs) on rat chromosomes 16 (RNO16) and 3 (RNO3). The purpose of this study was to use these inbred rat strains as a genetic substrate for differential cardiac gene expression to identify candidate genes for the observed ARC QTLs. RNA expression was examined globally in left ventricles of 15-wk-old DA, F1(COP x DA), and COP rats using microarrays to identify candidate genes for ARC QTLs. We identified 199 differentially expressed probe sets and determined their chromosomal locations. Six differentially expressed genes and expressed sequence tags (ESTs) mapped near ARC QTL regions, including PDZ and LIM domain 3 (Pdlim3). Differential expression of these genes/ESTs was confirmed by quantitative RT-PCR. The Ingenuity Pathways program identified 13 biological networks containing 50 (of the 199) differentially expressed probe sets and 85 additional genes. Four of these eighty-five genes mapped near ARC QTL-containing regions, including insulin receptor substrate 2 (Irs2) and acyl-CoA sythetase long-chain family member 1 (Acsl1). Most (148/199) differentially expressed probe sets showed left ventricular expression patterns consistent with the alleles exerting additive effects, i.e., F1(COP x DA) rat RNA expression was intermediate between DA and COP rats. This study identified several potential ARC QTL candidate genes and molecular networks, one of them related to energy expenditure involving Pik3r1 mRNA expression that may, in part, explain the observed strain differences in ARC and cardiac performance.

quantitative RT-PCR; PDZ and LIM domain 3 (Pdlim3); insulin receptor substrate 2 (Irs2); phosphatidylinositol 3-kinase, regulatory subunit 1 (Pik3r1); acyl-CoA sythetase long-chain family member 1 (Acsl1); endurance running; heart


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
AEROBIC RUNNING CAPACITY (ARC) tests are commonly used to assess cardiorespiratory function and as a general test of overall physical health status in humans. Genetic components for this complex trait in humans have been suggested by association studies of both twins and athletes (e.g., Ref. 40). Recently, genome-wide linkage analyses have identified several chromosomal regions associated with exercise capacity, cardiac output, and stroke volume in humans (39, 42). Studies with humans and animal models suggest that, not only is the aerobic capacity trait heritable, it is also a strong predictor of disease risk (34, 49). ARC can be divided, conceptually and functionally, into two major heritable components, intrinsic and adaptational (e.g., responses to training), with this study focusing on the intrinsic component.

Animal models have been used to study the genetic components of ARC (3, 20, 24, 25, 29, 30, 47, 49). Our previous studies established the inbred Copenhagen (COP; a low-performing strain) and DA (a high-performing strain) as phenotypically divergent rat strains useful in identifying the gene(s) responsible for heritable differences in intrinsic ARC (3). A strong, positive association of ARC with isolated heart performance (r = 0.86) was also observed in 11 rat strains, again with DA having the highest isolated cardiac output and COP having a low isolated cardiac output (3). These two strains were also divergent for a number of phenotypes related to intrinsic cardiac performance (6, 25, 46), implicating functional differences in the heart as a likely contributor to ARC. A genome scan using a segregating intercross population bred from these strains identified at least three distinct ARC quantitative train loci (QTLs) on rat chromosomes 3 and 16 (RNO3 and RNO16, respectively; Ref. 47).

Knowledge of model organism genome sequences (e.g., Ref. 9) and the ability to study gene expression globally using microarrays (32) now allows searching for candidate genes responsible, in part, for strain differences in complex traits such as ARC, using functional genomics approaches, where genotype and phenotype are studied simultaneously (reviewed by Refs. 27 and 38). Such an approach, expression profiling a congenic strain in concert with the cognate parental inbred strain, identified cd36 as a candidate gene for an insulin resistance QTL in the spontaneously hypertensive (SHR) rat model (1).

We used a functional genomics approach to identify candidate genes that may be responsible, in part, for the heritable differences in ARC observed in the DA and COP strains. We hypothesize that gene(s) underlying the strain differences in ARC and cardiac performance may be 1) differentially expressed in key organs/tissues, or present in a molecular network or pathway containing differentially expressed genes, and 2) map to an ARC QTL-containing region. Such genes make superior candidates to explain, at least in part, the strain differences in intrinsic ARC and cardiac performance observed between inbred DA and COP rats. Identifying the genes underlying genetically determined differences in ARC and cardiac performance may define new risk factors for cardiovascular disorders and lead to novel ways of assessing cardiac fitness.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Animals
Male inbred DA and COP rats were initially purchased from Harlan Sprague-Dawley (Indianapolis, IN) and maintained in a breeding colony at the Medical University of Ohio under specific pathogen-free conditions. Rats were provided with pelleted rat chow (diet 5001; Purina Mills, Richmond, IN) and water ad libitum. Rats were maintained on a 12:12-h light-dark cycle, with the light cycle occurring during daytime. F1(COP x DA) rats were bred from the same colony. All procedures were carried out with the approval of our Institutional Animal Care and Use Committee and were conducted in accordance with the National Research Council’s guidelines.

Phenotyping and RNA Preparation
ARC was estimated in male DA (n = 4), COP (n = 4), and F1(COP x DA) (n = 4) rats by treadmill run to exhaustion tests, conducted essentially as described (3). The first week consisted of introducing 10-wk-old rats to the treadmill (model Exer-4; Columbus Instruments, Columbus, OH) for a gradually increasing duration each day, so that the rats received sufficient treadmill education to run for 5 min at a speed of 10 m/min on a 15° slope. The following week, each 11-wk-old rat was evaluated for maximal endurance running capacity on 5 consecutive days. Daily endurance trials were performed at about the same time (between 10 AM and noon) using a constant slope of 15° and a 10-m/min starting velocity, with velocity increasing by 1 m/min every 2 min. Each rat was run until exhaustion, operationally defined as the third time a rat willingly slid onto the shock grid and sustain 2 s of shock rather than run. At this point, current to the grid was stopped and the rat removed from the treadmill.

Rats were killed by pentobarbital overdose at 15 wk of age, and their body and heart weights were measured. Left ventricles were collected by cutting the atria and major blood vessels from the ventricles, along with the top portion of the left ventricle and septum to avoid contamination. The right ventricle was then bisected and cut away proximal to the left ventricle and septum.

Left ventricle samples were processed immediately for total RNA isolation as described (28). Briefly, left ventricles were homogenized in a guanidine thiocyanate-phenol solution (Ultraspec; Biotecx Laboratories, Houston, TX), extracted with chloroform, and isolated using RNA Tack Resin (Biotecx Laboratories). Total RNA was further purified by ethanol precipitation and absorption to a column (RNeasy; Qiagen, Valencia, CA). Left ventricular RNA integrity was assessed by 1) electrophoretic size fractionation on 1% agarose gels under denaturing conditions and 2) hybridization of newly made cRNA probes to TestChips (Affymetrix).

Gene Expression Profiling
Oligonucleotide microarray analysis was performed using the Affymetrix Rat Genome U34 array set [GeneChips U34A, U34B, and U34C: a total of 26,379 probe sets, ~7,000 known genes, and 1,000 expressed sequence tag (EST) clusters for U34A, and 8,000 ESTs each for -B and -C chips] according to the manufacturer’s protocol. Double-stranded cDNA was synthesized from 15 µg of total RNA from each rat (4 rats/strain) using RT (SuperScript II; Invitrogen, Carlsbad, CA) and T7(dT)24 as the oligonucleotide primer. Biotinylated cRNA was synthesized using a kit (Enzo Bioarray High Yield RNA Transcript Labeling Kit; Enzo Diagnostics, Farmingdale, NY), and cRNAs were purified on columns (RNeasy mini kit, Qiagen). Each cRNA was fragmented according to the protocol in the Affymetrix GeneChip Expression Analysis manual, with quality assessed by hybridization of a 5-µg aliquot to a test chip (TestChip3, Affymetrix). Fragmented cRNA probe (5 µg) was then hybridized to each of a set of three rat GeneChips (U34A, -B, and -C; Affymetrix). Hybridization, washing, staining with streptavidin-phycoerythrin, and scanning were performed at the Medical University of Ohio Genomics Core Facility according to the manufacturer’s (Affymetrix) instructions. Complete transcription and hybridization were validated using bacterial sequences as an external control, as well as several "housekeeping" genes as internal controls.

Analysis of Microarray Data
Identification of differentially expressed probe sets.
Absolute and comparative analyses of data were conducted using the default settings of the Affymetrix software (Microarray Suite, MAS-5.0), which uses multiple statistical algorithms (31). Microarray images were scaled to an average hybridization intensity of 150 to normalize signals between individual chips. This data was deposited in the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/projects/geo/) as series GSE1795. Data from four different DA rats were compared with data from four separate COP rats (i.e., a 4 x 4 matrix comparison) using Data Mining Tool (DMT 3.0, Affymetrix) software. Student’s t-test, with P < 0.05 as the criterion for significance, was used to initially identify probe sets showing significant strain differences in expression where absolute expression values (signals) from four biological replicates/strains were compared between two strains. Probe sets with signal log ratios greater than 0.38 or less than –0.38 (i.e., a 1.3-fold change) were then selected. We did not include probe sets whose hybridization signals were below background levels, i.e., called "absent" for three of four rats in both strains. Reproducibility was assured by choosing probe sets 1) differentially expressed in >50% of the 4 x 4 matrix comparisons, selecting for consistency of differential calls, and 2) whose signals among the four rats/strains had a standard deviation ≤25%.

Cluster analysis.
Rat GeneChip sets were also hybridized with cRNA prepared from the left ventricles of four F1(COP x DA) rats and analyzed as described above. Probe sets having similar expression patterns in the left ventricles of DA, COP, and F1(COP x DA) rats were identified using a correlation coefficient algorithm (Data Mining Tool, Affymetrix) which utilized a nearest neighbor approach to identify probe sets with similar expression patterns, with the correlation coefficient threshold set at 0.98 for seed pattern and 0.90 for cluster, and 1,000 as the maximum number of probe sets included in the seeding set.

Network analysis of differentially expressed genes/ESTs.
Ingenuity Pathways Analysis (Ingenuity Systems, Mountain View, CA) was used to identify relevant biological networks (http://www.Ingenuity.com). This application was used to query a proprietary database for interactions between our set of differentially expressed left ventricle genes/ESTs and all other genes stored in the knowledge base to generate a set of networks having a network size of 35 genes/proteins. Ingenuity Pathways Analysis software computed a score for each network according to the fit of the set of supplied focus genes (here, differentially expressed). These scores, derived from P values, indicate the likelihood of focus genes found together in a network by chance. A score >2 indicates a ≥99% confidence that a focus gene network was not generated by chance alone.

Chromosomal Location of Genes
Chromosomal locations of probe sets were determined primarily by comparing their sequences with the rat genomic sequence database (build 2, version 1) at the National Center for Biotechnology Information (NCBI) web site (http://www.ncbi.nlm.nih.gov) using the Basic Local Alignment Search Tool (BLAST) program. The BLAST program was used to identify the orthologous location in the mouse genome (mouse genomic sequence database; build 30, NCBI) for probe sets not mapping in the rat genome. Comparative mapping information for orthologous regions of the mouse and rat genomes was also obtained from the Ensembl (http://www.ensembl.org/), Rat Genome Database (http://rgd.mcw.edu/), and Mouse Genome Informatics (http://www.informatics.jax.org/) sites.

Radiation hybrid (RH) mapping of two ESTs and two microsatellite markers was performed using specific primers (Supplemental Table S1; available at the Physiological Genomics web site)1 as described (28). Each primer set was used to amplify DNA from a rat-hamster hybrid cell line panel (Research Genetics; Huntsville, AL) in duplicate, with additional PCR performed to resolve conflicting results. Retention patterns for each marker in the panel were submitted to the RH map server at the Rat Genome Database (http://rgd.mcw.edu/RHMAPSERVER/), with logarithm of the odds (LOD) = 14 as the threshold for linkage.

Quantitative Real-Time RT-PCR
First-strand cDNAs were synthesized with total RNA (5 µg/reaction) using RT (SuperScript II, Invitrogen) and oligo(dT)20 primers. Gene/EST-specific primers were designed (Supplemental Table S1) using Primer Express software (version 1.5; Applied Biosystems, Foster City, CA) to amplify 75- to 100-bp PCR products, with design based on two criteria. One, primer pairs were designed from sequences unique to each gene/EST as determined by BLAST search of the nonredundant GenBank database. Two, primer pairs were designed from gene/EST regions containing the Rat GeneChip probe set sequences (Affymetrix). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), a gene whose expression in DA and COP rats did not significantly differ in the microarray experiments, was used to normalize data. Quantitative real-time RT-PCR (qRT-PCR) was performed essentially according to the protocol of Johnson et al. (23) using an ABI Prism 5700 thermal cycler and Sequence Detection System (using version 1.6 software), with SYBER Green PCR Master Mix (Applied Biosystems).

Specifically, qRT-PCR was performed on five replicates of each rat cDNA sample in a 25-µl reaction volume containing varying dilutions of template cDNA, gene-specific primers (0.2–0.4 µM, optimized for each primer pair; Supplemental Table S1), and SYBER Green PCR Master Mix. PCR product was amplified using the following program: 95°C for 3 min followed by 40 cycles of 95°C for 30 s and 60°C for 30 s. Melt-curve analysis was performed immediately following amplification by increasing the temperature in 0.5°C increments, starting at 55°C for 80 cycles of 10 s each, to confirm amplification of a single PCR product. Standard curves for each PCR primer set were constructed using serial dilutions of cDNA samples, producing median PCR product amounts in preliminary experiments. "No-template" controls were included to ensure amplification specificity. Left ventricular RNA expression levels were calculated for each gene/EST using standard curves constructed for each PCR primer pair and then normalized for GAPDH expression levels. RNA expression levels are presented as the fold change in expression in the left ventricles of COP and F1(COP x DA) compared with DA rats.

Statistical Analysis
Significance was evaluated using one-way ANOVA followed by the Fisher protected least significant difference modification of the t-test as the post hoc test (StatView 5.0.1; Abacus Concepts, Berkley, CA). The threshold for significance was set at P < 0.05.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Validating the Phenotypes of Rats Used As Source of Left Ventricle RNA
Male DA, COP, and F1(COP x DA) rats to be used as a source of left ventricular RNA for microarray experiments were first phenotyped for ARC (measured as best distance run to exhaustion), heart weight, body weight, and relative heart weight (heart wt-to-body wt ratio) (Table 1). As previously reported (3, 25, 47), DA rats had significantly longer best distance run to exhaustion as well as increased heart weight and relative heart weight, but not body weight, values compared with COP rats. F1(COP x DA) rat ARC values were intermediate to those of DA and COP rats, as previously reported (47). Heart weight and relative heart weight values for F1(COP x DA) rats were also intermediate compared with those of DA and COP rats. Significant differences in heart weight and relative heart weight were also observed between DA and COP rats, consistent with previous results (25).


View this table:
[in this window]
[in a new window]
 
Table 1. Comparison of aerobic running capacity, body wt, and heart wt

 
Gene Expression Profiling of Left Ventricles
RNA expression was examined globally in the left ventricles of DA, F1(COP x DA), and COP rats using an oligonucleotide microarray assay. Data were analyzed initially using the MAS-5.0 (Affymetrix) program, and sixteen pairwise comparisons (4 x 4 matrix comparison) were performed using Data Mining Tool version 3.0 (Affymetrix).

Overall Gene Detection and Data Validation
The percentage of probe sets corresponding to expressed transcripts on average was 31.1% for DA rats, 32.7% for COP rats, and 36.2% for the F1(COP x DA) rats. These differences were not statistically significant. The low variability seen in chip data comparisons validated the array analysis, with the average co-variation (CV) between replicates <20% for the majority of genes/ESTs identified as differentially expressed.

Identification of Differentially Expressed Probe Sets in Left Ventricles of DA (High-ARC Strain) Compared with COP (Low-ARC Strain)
Differentially expressed probe sets were identified using sixteen pairwise comparisons (4 x 4 matrix comparison of left ventricular expression data from 4 DA rats with data from 4 COP rats). We used stringent selection criteria, as described in MATERIALS AND METHODS, to ensure that differential gene expression identified would be reproducible in four biological replicates for both inbred strains.

We identified 199 differentially expressed probe sets with the microarray analysis, with 104 probe sets showing higher expression levels and 95 probe sets showing lower expression levels in COP rats compared with DA rats (Supplemental Table S2). After elimination of 28 loci represented by multiple probe sets (25 in duplicate and 3 in triplicate), microarray analysis identified 168 genes/ESTs differentially expressed in the left ventricles of DA compared with COP rats (Supplemental Table S2).

Identifying the Chromosomal Locations of Probe Sets Differentially Expressed in the Left Ventricles of DA and COP Rats
Chromosomal locations of differentially expressed probe sets were determined by comparison of the DNA sequences used to design the probe sets with the Rat Genome Database (build 2, version 1) at the NCBI site using the BLAST program. Due to imprecision of QTL localization (11, 21), probe sets mapping to RNO16, where most of the chromosome was covered by LOD plots above the "suggestive" threshold, or the proximal portion of RNO3 (p-terminus to D3Rat31) were considered in ARC QTL-containing intervals. Eight differentially expressed probe sets had sequences mapping to ARC QTL-containing intervals.

Chromosomal locations of probe sets not mapped using the Rat Genome Database were inferred by comparison of their DNA sequences with the mouse genome database (build 30, NCBI) using the BLAST program. One EST, AI072658, mapped to a mouse chromosome 2 region orthologous to the RNO3 p-terminus, with radiation hybrid mapping confirming this placement (Supplemental Fig. S1), proximal to microsatellite markers defining the location of the RNO3 ARC QTL (47). Thus nine differentially expressed probe set sequences mapped to ARC QTL-containing intervals (Table 2). However, sequences used to design two probe sets (S69874 and AI231572) had high similarity to sequences in multiple genomic locations.


View this table:
[in this window]
[in a new window]
 
Table 2. Genes/ESTs identified as ARC candidates

 
Confirming the Differential Expression of Genes Using qRT-PCR
qRT-PCR, using GAPDH expression levels as an internal control, was used to confirm the differential expression for these nine genes observed in the microarray analysis (Table 3). Values for expression in the left ventricles of F1(COP x DA) rats, whose ARC are intermediate to those of the parental, DA, and COP strains, are also presented for comparison. Differential expression was confirmed for all but one EST, AA924103, in the left ventricles of COP compared with DA rats, with six genes/ESTs showing significant differences (P < 0.05; Table 3A). Left ventricular expression for four of the remaining eight genes/ESTs (U68544, AI231572, AI072166, and AA818129) was intermediate in F1(COP x DA) rats compared with that observed in the inbred, DA, and COP strains from which they were bred. DA and F1(COP x DA) rats showed similar levels of expression for the AA800318 and AI072166 ESTs, while COP and F1(COP x DA) rats showed similar expression levels for the AI072238 and AI072658 ESTs. EST AA924103 did not follow any of the aforementioned patterns.


View this table:
[in this window]
[in a new window]
 
Table 3. Validation of differential left ventricular gene expression by quantitative real-time RT-PCR

 
qRT-PCR was also used to validate differential left ventricular expression for seven genes/ESTs, AA849518, AI008865, U50412, AA059931, AI176584, AA891242, and AI234849, that showed large differences in expression in the microarray experiments but did not map near ARC QTLs (Table 3B). qRT-PCR confirmed differential expression for five of these seven genes/ESTs.

Identification of Gene Expression Cluster Patterns
The 199 differentially expressed probe sets were analyzed for clustering pattern among DA, COP, and F1(COP x DA) rats based on their left ventricular expression levels. Three major gene expression patterns were identified using the correlation coefficient algorithm (Data Mining Tool, Affymetrix) with clusters 1 [DA > F1(COP x DA) > COP] and 2 [COP > F1(COP x DA) > DA] containing the most probe sets (148 of 199; Supplemental Table S2). The 199 differentially expressed probe sets listed in Supplemental Table S2 are divided into four sections (clusters 1, 2, and 3 and "other clusters," which contains the remaining probe sets) reflecting the results of the correlation coefficient clustering using the parameter described in MATERIALS AND METHODS.

Identification of Additional Candidate Genes for ARC QTL Using Network Analysis
The Ingenuity Pathways Analysis program (Ingenuity Systems) identified 13 biological networks (Supplemental Table S3) containing 50 differentially expressed probe sets, with the 3 largest networks collectively containing 40 differentially expressed genes/ESTs (Supplemental Table S3). Table 4 describes the probable functions/pathways associated with the three largest networks identified by the Ingenuity Pathways program, as well as the genes in each network associated with the function/pathway.


View this table:
[in this window]
[in a new window]
 
Table 4. Probable functions for 3 networks

 
Chromosomal locations were determined for the 85 genes belonging to these networks that were not among the 199 differentially expressed probe sets. Four genes, insulin receptor substrate 2 (Irs2), acyl-CoA synthetase long-chain family member 1 (Acsl1), small nuclear RNA activating complex polypeptide 4 (Snapc4) and Nmyc (and STAT) interactor (Nmi) mapped to ARC QTL-containing regions on RNO3 and RNO16 and thus are potential ARC QTL candidate genes (Table 2B).


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Microarray analysis was used to examine gene expression patterns globally in the left ventricles of DA, COP, and F1(COP x DA) rats. Sequences of 9 differentially expressed probe sets mapped to RNO16 and the proximal portion of RNO3 (Table 2), where our previous studies identified ARC QTLs (47). While such genes/ESTs have potential as candidates, based on their 1) differential expression in a biologically relevant tissue and at a biologically relevant time point and 2) location near known ARC QTLs, additional evidence is required.

Only a small fraction of probe sets, 199 of ~27,000 interrogated, were differentially expressed in the left ventricles of 15-wk-old male DA and COP rats. A relatively low threshold (1.3-fold change) was used for determining differential expression due to the expectation that many QTLs exert small effects; however, the reproducibility of these modest changes in expression were ensured by the stringent criteria used. Differential left ventricular RNA expression was confirmed for 13 of 16 genes/ESTs examined (Table 3), reflecting well on data reproducibility.

Most differentially expressed probe sets grouped into two clusters, based on their expression patterns in DA, F1(COP x DA), and COP rat left ventricles (Supplemental Table S2), with Fi(COP x DA) rats showing intermediate expression levels between the two parental strains. This suggests that the effects of the alleles on ARC are additive. Most of the 9 differentially expressed probe sets mapping to ARC QTL-containing chromosomal regions belong to these two clusters. Two genes/ESTs (U68544 and AI231572) were in cluster 1, where DA rat left ventricular expression was greater than that of COP rats, and four genes/ESTs (AA800318, AA818129, AI072238, and AI072658) were in cluster 2, where COP rat left ventricular expression was greater compared with DA rat.

Eliminating Genes/ESTs from Consideration as ARC Candidate Genes
S69874 (Fabp5) was initially considered a candidate based on its differential expression (Table 3A) in a key tissue (left ventricle), having Fabp5-like sequences near the RNO3 ARC QTL, and its biological relevance. However, radiation hybrid and comparative mapping, as well as the lack of EST sequences corresponding to the predicted coding regions in the RNO3 site, eliminated Fabp5 as a candidate gene (see Supplemental Material). Similarly, AI231572 (Hbld2) is an unlikely candidate, as the RNO16 sequence (with the highest BLAST score) was intron-less, unlike the intron-containing sequence on RNO17 orthologous to the mouse and human Hbld2 genes. AA924103 was also eliminated as a candidate, as qRT-PCR did not confirm its differential expression. Thus six genes/ESTs were differentially expressed in a key tissue, the heart, and map to ARC QTL-containing chromosomal intervals.

Using Network Analysis to Identify ARC Candidate Genes
Genes interacting with the 199 probe sets differentially expressed in the left ventricles of DA, compared with COP rats (Supplemental Table S3) were also used to identify potential candidate genes for ARC QTLs. We reasoned that while allelic differences in some gene(s) affecting ARC would not alter RNA expression and thus be undetected by microarray assay, such mutations could affect the expression of downstream genes. Thus candidate genes for ARC QTL might also be found among genes interacting with differentially expressed genes/ESTs. Molecular networks containing the differentially expressed genes/ESTs were identified using the Ingenuity Pathways program, and the chromosomal locations of additional nondifferentially expressed genes belonging to these gene networks were determined, with four genes/ESTs mapping to ARC QTL-containing regions (Table 2B).

Biological Relevance of Putative Candidate Genes/ESTs to ARC
Genes differentially expressed and mapping to ARC QTL-containing intervals.
PDZ and LIM domain 3 (Pdlim3) is a member of the PDZ-LIM protein family. PDZ-LIM proteins, through binding of their PDZ domains to {alpha}2-actinin, localize to Z lines of muscle. However, while Pdlim3 knockout mice have normal skeletal muscle development and structure, they become cardiomyopathic (36), demonstrating the importance of Pdlim3 in cardiac development and function. Peptidyl-prolyl-cis-trans-isomerase (Ppif) is a component of the mitochondrial permeability transition pore (5) whose activation is believed important in inducing death by both apoptosis and necrosis (17, 26). Anaphase promoting complex 2 (Anapc2) encodes one of two subunits responsible for the ubiquitin-ligase activity of the anaphase promoting complex and thus the ubiquitin-mediated destruction of cell cycle regulatory proteins, such as securin and cyclins, shortly before anaphase (51). Targeted deletion of Anapc2 in mouse livers led to unscheduled re-entry of normally quiescent hepatocytes into the cell cycle (48). Serping1 encodes C1 inhibitor protein (C1INH), a member of the serine proteinase inhibitor gene family that regulates all three pathways of complement activation (i.e., the classical, alternative, and contact). C1INH protected ischemic myocardium from reperfusion injury in animal models and in patients with acute myocardial infarctions (12). Because the remaining differentially expressed probe sets mapping to ARC QTL-containing chromosomal intervals (AI072658 and AA818129) are ESTs, their function and relevance to ARC remain unknown.

Genes identified in molecular networks and mapping to ARC QTL-containing intervals .
Acyl-CoA synthetase long-chain family member 1 (Acsl1) gene encodes one of several isoforms catalyzing ligation of long-chain fatty acids to CoA, the first step for fatty acid utilization in mammals. The resulting acyl-CoA products are primary substrates for energy production by ß-oxidation in the heart and other tissues and the synthesis of cholesteryl esters, as well as functioning as signaling molecules (reviewed by Refs. 8, 13). The Nmi protein has been shown to potentiate STAT-dependent transcription and augment coactivator protein recruitment to at least some members of a group of sequence-specific transcription factors (52). Snapc4 (small nuclear RNA activating complex, polypeptide 4) encodes the 190-kDa subunit of SNAPc involved in the nucleation of both RNA polymerase II and III transcription initiation complexes (33).

A fourth gene, insulin receptor substrate 2 (Irs2), maps below the more distal RNO16 ARC QTL and is of particular interest. In the present study, 15-fold higher phosphatidylinositol 3-kinase, regulatory subunit 1 (Pik3r1) RNA expression was observed in the left ventricles of DA compared with COP rats (Table 3B). Previous studies indicated that phosphorylated Irs2 protein interacts with Pik3r1 protein (35, 45). Zabolotny et al. (50) found reduced Irs2 protein phosphorylation in muscle following intramuscular injection of insulin in leukocyte antigen-related transgenic mice, a model of insulin resistance, causing decreased Pik3r1 kinase activity and, ultimately, decreased glucose uptake. Higher Pik3r1 mRNA levels observed in the left ventricles of DA rats suggest a more efficient cardiac glucose uptake, which could lead to incrementally better ARC and/or cardiac performance in this strain, particularly at later stages of the ramped endurance test. Recently, IRS2 was proposed as a candidate gene for a baseline maximal power output QTL on human chromosome 13q33 (42) near the IRS2 locus, orthologous to the more distal rat RNO16 ARC QTL identified in our previous study (47).

Insulin resistance, and thus abnormal glucose homeostasis, is associated with cardiovascular disease (15, 41). Studies of vasculature of obese rat (22) and human skeletal muscle biopsy samples from obese nondiabetic individuals demonstrated that insulin stimulation of the phosphoinositide 3-kinase (PI3K) pathway was dramatically reduced (10). A similar mechanism may operate in our rat model, i.e., the reduced activity of PI3K pathway, as is suggested by the 15-fold lower levels of Pik3r1 mRNA in COP rat left ventricles, may contribute to the lower ARC performance of COP compared with DA rats.

Highly differentially expressed genes and the ARC phenotype.
Although PI3K is most commonly associated with insulin signaling and glucose uptake, it is also a component of the Janus kinase (Jak)/STAT (and other) signaling pathways (19, 43). Stat3 (signal transducer and activator of transcription 3) is another differentially expressed gene present in a molecular network (Tables 3 and 4, Supplemental Table S3). Both Stat3 and Pik3r1 have elevated RNA expression levels in DA rat left ventricles (Table 3, Supplemental Table S3) and interact with Jak proteins (2, 19, 43) including Jak3 (Janus kinase 3), which is located on RNO16 near an ARC QTL. The Jak/STAT pathway has been associated with cardiac hypertrophy, apoptosis, angiotensin signaling, ischemia-reperfusion injury, and preconditioning (44). Thus differences in the Jak/STAT signaling pathway could lead to altered cardiac function, which may explain, in part, strain differences in ARC observed between DA and COP rats. While not part of the networks identified by the Ingenuity Pathyways program (Supplemental Table S3), Tsc1 (tuberous sclerosis complex 1) maps to the proximal portion of RNO3 and is a downstream participant in insulin/PI3K signaling. The tuberous sclerosis complex regulates insulin/PI3K signaling by inhibiting S6K protein action on insulin receptor substrate proteins (18) and is implicated in processes involved in regulating tissue size and mass (reviewed by Ref. 37).

While the rat ortholog of human myosin light chain 2a gene (MLC2a) does not map to an ARC QTL-containing interval, its differential expression in DA compared with COP rats may explain, in part, the observed strain differences in ARC and contractility (3, 6, 25, 47). Indeed, decreased myosin heavy chain {alpha}/ß-isoform expression ratio (for both RNA and protein) was associated with decreased cardiac contractility in Buffalo rats, another low-performing inbred strain, compared with DA rats (4).

Metabolic pathways/molecular networks that may be responsible for strain differences in ARC between COP and DA rats.
The Ingenuity Pathways program was used to identify gene networks (NW) that contained 168 genes and ESTs differentially expressed in DA and COP rat left ventricles. Potential molecular mechanisms operating in this model that are responsible for the strain differences in ARC were sought based on 1) examination of networks containing candidate genes that map to ARC QTL-containing intervals (see above) and 2) the most probable functions assigned to the three largest networks, containing 104 different genes, by the Ingenuity Pathways program (Table 4). These functional groups, and the candidate genes contained within, can be classified into those involving 1) energy expenditure, especially lipid and glucose metabolism, and 2) regulation of cell/tissue growth and development.

Lipid metabolism and endocrine system/metabolic disorders are the top functions listed for NW1 (Table 4). Two members of this network, Acsl1 and Irs2, are involved in lipid metabolism and map to ARC QTL-containing intervals. Irs2, along with Nmi, Tsc1, and Jak3 (the latter 2 map to ARC QTL-containing intervals, but not in the Ingenuity Pathways networks), encodes downstream components of the insulin receptor/PI3K signaling pathway that are key in regulating glucose and fat metabolism, and thus energy expenditure, which can affect ARC. Pik3r1 and Stat3, while not mapping to ARC QTL-containing regions, are differentially expressed in this model and key members of this pathway.

The most probable functions of NW2 are 1) immune, lymphatic, and hematological system development and function and 2) cell cycle regulation. The most probable functions of NW3 are the regulation of 1) cellular death and development and 2) gene expression (Table 4). We should consider these two networks, as well as their most probable functions, together for two reasons. Nmi, which maps to an ARC QTL-containing interval, is present in both networks. The majority of the differentially expressed genes in both networks (12/13 in NW2, and 8/13 in NW3) were expressed at higher levels in the left ventricles of COP rats compared with DA rats (Table 4). Indeed, most of the genes in NW2 and NW3 expressed at higher levels in COP rats are in cluster 2 (Supplemental Tables S2 and S3), suggesting that these genes may be coordinately regulated. These genes are involved in the regulation of cell growth, cell death, and development and may be responsible for the greater heart weight observed for DA rats compared with COP rats shown in this study (Table 1) and our previous studies (3, 25, 47). Indeed, these strain differences in cardiac mass are observed in male rats at 7 wk and 10 mo of age (unpublished observations). Several other genes that map to ARC QTL-containing regions may also be involved in the processes of gene expression regulation (Snapc4), cell cycle regulation (Anapc2), cell growth (Tsc1), and cell death (Csp3 and Ppif1). Aside from differences in size, DA and COP rat hearts were shown to differ for a number of measures of cardiac performance (3, 6, 25, 46, 47), with the DA rats uniformly showing significantly greater values. Pdlim3 (which maps to an ARC QTL-containing interval) and Mlc2a are both highly differentially expressed in this model and both key cardiac muscle components that may influence cardiac contractility and performance, and thus ARC.

Limits to Using Functional Genomic Approaches to Identify Candidate Genes
Proving that any of the above-described candidate genes are causative, in part, of an ARC QTL will require identification of strain-specific allelic differences and altered protein expression in a key organ, such as the left ventricle. Eventually, demonstration that replacement of the variant nucleotide results in phenotypic alteration will be required to prove a candidate gene causative, in part, for the complex trait (16).

We acknowledge limitations in using microarray data to identify candidate genes for QTL, as elegantly reviewed by Pravenec et al. (38). We studied only one time point and only one tissue, which may or may not be when, or where, critical ARC molecular events occur. However, this is an appropriate age for studying expression differences, as this was when final physical measurements of F2(COP x DA) rats used to identify the ARC QTLs were taken (47), and it is intermediate to when ARC and intrinsic cardiac performance were initially measured in DA and COP rats (3). However, other genes, expressed at different time points, may exert important influences on ARC. Indeed, several studies of blood pressure and related traits showed temporal differences in QTL identification (e.g., Refs. 7, 14).

ARC is a complex trait, dependent on the interplay of numerous tissues and organs, and the expression differences observed in this study may not arise solely from differences in myocardial mRNA expression. Finally, potential ARC candidate genes may be missed for the following technical reasons: 1) expression of all rat genes could not be interrogated because they are not all present on the oligonucleotide microarray chips, and 2) the functions of most genes/ESTs remain poorly understood or unknown and are not present in available molecular network/pathway databases.


    GRANTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
This work was supported by National Heart, Lung, and Blood Institute Grant HL-67276 to G. T. Cicila, S. J. Lee, and L. G. Koch.


    ACKNOWLEDGMENTS
 
We thank Dr. Steven L. Britton for useful discussion and suggestions.

Present address of L. G. Koch: Dept. of Physical Medicine and Rehabilitation, Univ. of Michigan, Ann Arbor, MI 48109.


    FOOTNOTES
 
Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).

Address for reprint requests and other correspondence: S. J. Lee, Dept. of Physiology and Cardiovascular Genomics, Medical Univ. of Ohio, 3035 Arlington Ave., Toledo, OH, 43614 (e-mail: sjlee{at}meduohio.edu).

1 The Supplemental Material for this article (Supplemental Fig. S1 and Supplemental Tables S1–S3) is available online at http://physiolgenomics.physiology.org/cgi/content/full/00251.2004/DC1. Back


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 

  1. Aitman TJ, Glazier AM, Wallace CA, Cooper LD, Norsworthy PJ, Wahid FN, Al-Majali KM, Trembling PM, Mann CJ, Shoulders CC, Graf D, St. Lezin E, Kurtz TW, Kren V, Pravenec M, Ibrahimi A, Abumrad NA, Stanton LW, and Scott J. Identification of Cd36 (Fat) as an insulin-resistance gene causing defective fatty acid and glucose metabolism in hypertensive rats. Nat Genet 21: 76–83, 1999.[CrossRef][ISI][Medline]
  2. Al-Shami A and Naccache PH. Granulocyte-macrophage colony-stimulating factor-activated signaling pathways in human neutrophils. Involvement of Jak2 in the stimulation of phosphatidylinositol 3-kinase. J Biol Chem 274: 5333–5338, 1999.[Abstract/Free Full Text]
  3. Barbato JC, Koch LG, Darvish A, Cicila GT, Metting PJ, and Britton SL. Spectrum of aerobic exercise endurance running performance in eleven inbred strains of rats. J Appl Physiol 85: 530–536, 1998.[Abstract/Free Full Text]
  4. Barbato JC, Lee SJ, Koch LG, and Cicila GT. Myocardial function in rat genetic models of low and high aerobic running capacity. Am J Physiol Regul Integr Comp Physiol 282: R721–R726, 2002.[Abstract/Free Full Text]
  5. Basso E, Fante L, Fowlkes J, Petronilli V, Forte MA, and Bernardi P. Properties of the permeability transition pore in mitochondria devoid of cyclophilin D. J Biol Chem 280: 18558–18561, 2005.[Abstract/Free Full Text]
  6. Chen J, Feller GM, Barbato JC, Periyasamy S, Xie ZJ, Koch LG, Shapiro JI, and Britton SL. Cardiac performance in inbred rat genetic models of low and high running capacity. J Physiol 535: 611–617, 2001.[Abstract/Free Full Text]
  7. Clark JS, Jeffs B, Davidson AO, Lee WK, Anderson NH, Bihoreau MT, Brosnan MJ, Devlin AM, Kelman AW, Lindpaintner K, and Dominiczak AF. Quantitative trait loci in genetically hypertensive rats: possible sex specificity. Hypertension 28: 898–906, 1996.[Abstract/Free Full Text]
  8. Coleman RA, Lewin TM, and Muoio DM. Physiological and nutritional regulation of enzymes of triacylglcerol synthesis. Annu Rev Nutr 20: 77–103, 2000.[CrossRef][ISI][Medline]
  9. Consortium RGSP. Genome sequence of the Brown Norway rat yields insights into mammalian evolution. Nature 428: 493–521, 2004.[CrossRef][ISI][Medline]
  10. Cusi K, Maezono K, Osman A, Pendergrass M, Patti ME, Pratipanawatr T, DeFronzo RA, Kahn CR, and Mandarino LJ. Insulin resistance differentially affects the PI 3-kinase- and MAP kinase-mediated signaling in human muscle. J Clin Invest 105: 311–320, 2000.[Abstract/Free Full Text]
  11. Darvasi A, Weinreb A, Minke V, Weller JI, and Soller M. Detecting marker-QTL linkage and estimating QTL gene effect and map location using a saturated genetic map. Genetics 134: 943–951, 1993.[Abstract/Free Full Text]
  12. de Zwaan C, van Dieijen-Visser MP, and Hermens WT. Prevention of cardiac cell injury during acute myocardial infarction. Am J Cardiovasc Drugs 3: 245–251, 2003.[Medline]
  13. Faergeman NJ and Knudsen J. Role of long-chain fatty acyl-CoA esters in the regulation of metabolism and in cell signalling. Biochem J 323: 1–12, 1997.[ISI][Medline]
  14. Garrett MR, Dene H, and Rapp JP. Time-course genetic analysis of albuminuria in Dahl salt-sensitive rats on low-salt diet. J Am Soc Nephrol 14: 1175–1187, 2003.[Abstract/Free Full Text]
  15. Ginsberg HN and Huang LS. The insulin resistance syndrome: impact on lipoprotein metabolism and atherothrombosis. J Cardiovasc Risk 7: 325–331, 2000.[ISI][Medline]
  16. Glazier AM, Nadeau JH, and Aitman TJ. Finding genes that underlie complex traits. Science 298: 2345–2349, 2002.[Abstract/Free Full Text]
  17. Halestrap AP, Kerr PM, Javadov S, and Woodfield KY. Elucidating the molecular mechanism of the permeability transition pore and its role in reperfusion injury of the heart. Biochim Biophys Acta 1366: 79–94, 1998.[ISI][Medline]
  18. Harrington LS, Findlay GM, Gray A, Toldacheva T, Wigfield S, Rebholz H, Barnett J, Leslie NR, Cheng S, Shepherd PR, Gout I, Downes CP, and Lamb RF. The TSC1–2 tumor suppressor controls insulin-PI3K signaling via regulation of IRS proteins. J Cell Biol 166: 213–233, 2004.[Abstract/Free Full Text]
  19. Heim MH. The Jak-STAT pathway: cytokine signalling from the receptor to the nucleus. J Recept Signal Transduct Res 19: 75–120, 1999.[ISI][Medline]
  20. Hoit BD, Kiatchoosakun S, Restivo J, Kirkpatrick D, Olszens K, Shao H, Pao YH, and Nadeau JH. Naturally occurring variation in cardiovascular traits among inbred mouse strains. Genomics 79: 679–685, 2002.[CrossRef][ISI][Medline]
  21. Hyne V, Kearsey MJ, Pike DJ, and Snape JW. QTL analysis: unreliability and bias in estimation procedures. Mol Breeding 1: 273–282, 1995.[ISI]
  22. Jiang ZY, Lin YW, Clemont A, Feener EP, Hein KD, Igarashi M, Yamauchi T, White MF, and King GL. Characterization of selective resistance to insulin signaling in the vasculature of obese Zucker (fa/fa) rats. J Clin Invest 104: 447–457, 1999.[Abstract/Free Full Text]
  23. Johnson MR, Wang K, Smith JB, Heslin MJ, and Diasio RB. Quantitation of dihydropyrimidine dehydrogenase expression by real-time reverse transcription polymerase chain reaction. Anal Biochem 278: 175–184, 2000.[CrossRef][ISI][Medline]
  24. Koch LG and Britton SL. Artificial selection for intrinsic aerobic endurance running capacity in rats. Physiol Genomics 5: 45–52, 2001.[Abstract/Free Full Text]
  25. Koch LG, Britton SL, Barbato JC, Rodenbaugh DW, and DiCarlo SE. Phenotypic differences in cardiovascular regulation in inbred rat models of aerobic capacity. Physiol Genomics 1: 63–69, 1999.[ISI][Medline]
  26. Kroemer G, Dallaporta B, and Resche-Rigon M. The mitochondrial death/life regulator in apoptosis and necrosis. Annu Rev Physiol 60: 619–642, 1998.[CrossRef][ISI][Medline]
  27. Lee SJ and Cicila GT. Functional genomics in rat models of hypertension: using differential expression and congenic strains to identify and evaluate candidate genes. Crit Rev Eukaryot Gene Expr 12: 297–316, 2002.[CrossRef][ISI][Medline]
  28. Lee SJ, Liu J, Qi N, Guarnera RA, Lee SY, and Cicila GT. Use of a panel of congenic strains to evaluate differentially expressed genes as candidate genes for blood pressure QTL. Hypertens Res 26: 75–87, 2003.[CrossRef][ISI][Medline]
  29. Lerman I, Harrison BC, Freeman K, Hewett TE, Allen DI, Robbins J, and Leinwand L. Genetic variability in forced and voluntary endurance exercise performance in seven inbred mouse strains. J Appl Physiol 92: 2245–2255, 2002.[Abstract/Free Full Text]
  30. Lightfoot JT, Turner MJ, Debate KA, and Kleeberger SR. Interstrain variation in murine aerobic capacity. Med Sci Sports Exerc 33: 2053–2057, 2001.[CrossRef][ISI][Medline]
  31. Liu WM, Mei R, Di X, Ryder TB, Hubbell E, Dee S, Webster TA, Harrington CA, Ho MH, Baid J, and Smeekens SP. Analysis of high density expression microarrays with signed-rank call algorithms. Bioinformatics 18: 1593–1599, 2002.[Abstract/Free Full Text]
  32. Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H, and Brown EL. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol 14: 1675–1680, 1996.[CrossRef][ISI][Medline]
  33. Ma B and Hermandez N. A map of protein-protein contacts within the small nuclear RNA-activating protein complex SNAPc. J Biol Chem 276: 5027–5035, 2001.[Abstract/Free Full Text]
  34. Myers J, Prakash M, Froelicher V, Do D, Partington S, and Atwood JE. Exercise capacity and mortality among men referred for exercise testing. N Engl J Med 346: 793–801, 2002.[Abstract/Free Full Text]
  35. Myers MG Jr and White M. Insulin signal transduction and IRS proteins. Annu Rev Pharmacol Toxicol A36: 615–658, 1996.[CrossRef][ISI][Medline]
  36. Pashmforoush M, Pomiès P, Peterson K, Kubalak S, Ross J Jr, Hefti A, Aebi U, Beckerle M, and Chien K. Adult mice deficient in actinin-associated LIM-domain protein reveal a developmental pathway for right ventricular cardiomyopathy. Nat Med 7: 591–597, 2001.[CrossRef][ISI][Medline]
  37. Potter CJ, Pedraza LG, Huang H, and Xu T. The tuberous sclerosis complex (TSC) pathway and mechanism of size control. Biochem Soc Trans 31: 584–586, 2003.[CrossRef][ISI][Medline]
  38. Pravenec M, Wallace C, Aitman TJ, and Kurtz TW. Gene expression profiling in hypertension research: a critical perspective. Hypertension 41: 3–8, 2003.[Abstract/Free Full Text]
  39. Rankinen T, Ann P, Perusse L, Rice T, Chagnon YC, Gagnon J, Leon AS, Skinner JS, Wilmore JH, Rao DC, and Bouchard C. Genome-wide linkage scan for exercise stroke volume and cardiac output in the HERITAGE Family Study. Physiol Genomics 10: 57–62, 2002.[Abstract/Free Full Text]
  40. Rankinen T, Perusse L, Rauramaa R, Rivera MA, Wolfarth B, and Bouchard C. The human gene map for performance and health-related fitness phenotypes. Med Sci Sports Exerc 33: 855–867, 2001.[ISI][Medline]
  41. Reaven GM. Insulin resistance and human disease: a short history. J Basic Clin Physiol Pharmacol 9: 387–406, 1998.[Medline]
  42. Rico-Sanz J, Rankinen T, Rice T, Leon AS, Skinner JS, Wilmore JH, Rao DC, and Bouchard C. Quantitative trait loci for maximal exercise capacity phenotypes and their responses to training in the HERITAGE Family Study. Physiol Genomics 16: 256–260, 2004.[Abstract/Free Full Text]
  43. Ruscher K, Freyer D, Karsch M, Isaev N, Megow D, Sawitzki B, Priller J, Dirnagl U, and Meisel A. Erythropoietin is a paracrine mediator of ischemic tolerance in the brain: evidence from an in vitro model. J Neurosci 22: 10291–10301, 2002.[Abstract/Free Full Text]
  44. Smith RM, Suleman N, Lacerda L, Opie LH, Akira S, Chien KR, and Sack MN. Genetic depletion of cardiac myocyte STAT-3 abolishes classical preconditioning. Cardiovasc Res 63: 611–616, 2004.[CrossRef][ISI][Medline]
  45. Virkamaki A, Ueki K, and Kahn CR. Protein-protein interaction in insulin signaling and the molecular mechanisms of insulin resistance. J Clin Invest 103: 931–943, 1999.[Free Full Text]
  46. Walker JP, Barbato JC, and Koch LG. Cardiac adenosine production in rat genetic models of low and high exercise capacity. Am J Physiol Regul Integr Comp Physiol 283: R168–R173, 2002.[Abstract/Free Full Text]
  47. Ways JA, Cicila GT, Garrett MR, and Koch LG. A genome scan for loci associated with aerobic running capacity in rats. Genomics 80: 13–20, 2002.[CrossRef][ISI][Medline]
  48. Wirth KG, Ricci R, Giménez-Abián JF, Taghybeeglu S, Kudo NR, Jochum W, Vasseur-Cognet M, and Nasmyth K. Loss of the anaphase-promoting complex in quiescent cells causes unscheduled hepatocyte proliferation. Genes Dev 18: 88–98, 2004.[Abstract/Free Full Text]
  49. Wisloff U, Najjar SM, Ellingsen O, Haram PM, Swoap S, Al-Share Q, Fernstrom M, Rezaei K, Lee SJ, Koch LG, and Britton SL. Cardiovascular risk factors emerge after artificial selection for low aerobic capacity. Science 307: 418–420, 2005.[Abstract/Free Full Text]
  50. Zabolotny JM, Kim YB, Peroni OD, Kim JK, Pani MA, Boss O, Klaman LD, Kamatkar S, Shulman GI, Kahn BB, and Neel BG. Overexpression of the LAR (leukocyte antigen-related) protein-tyrosine phosphatase in muscle causes insulin resistance. Proc Natl Acad Sci USA 98: 5187–5192, 2001.[Abstract/Free Full Text]
  51. Zachariae W and Nasmyth K. Whose end is destruction: cell division and the anaphase-promoting complex. Genes Dev 13: 2039–2058, 1999.[Free Full Text]
  52. Zhu M, John S, Berg M, and Leonard WJ. Functional association of Nmi with Stat5 and Stat1 in IL-2- and IFN{gamma}-mediated signaling. Cell 96: 121–130, 1999.[CrossRef][ISI][Medline]