| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
1 Department of Gene Expression, Roslin Institute, Edinburgh, Scotland
2 Medical Research Council Mammalian Genetics Unit, Harwell, England
3 Animal Breeding and Development, Sustainable Livestock Systems, Scottish Agricultural College, Bush Estate, Penicuik, Scotland
4 School of Biological Sciences, University of Edinburgh, Edinburgh, Scotland, United Kingdom
5 University of Ljubljana, Biotechnical Faculty, Zootechnical Department, Domzale, Slovenia
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONMATERIAL AND METHODSRESULTSDISCUSSIONGRANTSREFERENCES |
|---|
Depdc6; congenic
|
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIAL AND METHODSRESULTSDISCUSSIONGRANTSREFERENCES |
|---|
A polygenic mouse obesity model has been developed in Edinburgh by divergent selective breeding that has resulted in strains differing substantially in fat content (5, 19). The mice were selected for high-fat (Fat, F-line) or low-fat (Lean, L-line) content from a genetically highly variable base population for more than 60 generations (19), resulting in lines that differ more that fivefold in fat content and have 22% (F-line) or 4% (L-line) body fat. The lines have subsequently been inbred (5). These lines, originating from a classic selection experiment on fat content, therefore represent a resource for genetic dissection of QTL responsible for the observed selection response. The phenotypic difference most likely arose via selection of several loci of low-to-medium effect, rather than a few single-gene mutations of large effect, and so the genetics of obesity in these lines model the situation in humans, where a large proportion of obesity cases are thought to have a polygenic basis (21).
A genome-wide QTL analysis using the outbred F- and L-lines identified four QTL regions for fat content that were significant at a genome-wide level (8). Further research (6) indicated that variation in the Lep [chromosome (Chr) 4] and Lepr (Chr 6) genes had not contributed to the selection response. We have since further mapped and phenotypically characterized the Fob3 QTL on Chr 15, dissecting it into two smaller-effect QTL, Fob3a and Fob3b (22). In this study, we report the use of the NIA-NIH 15K cDNA array set to identify candidate genes and pathways that are differentially expressed between the parental F-line and a congenic line containing the Fob3b QTL region from the L-line (Fob3b-line). The additive effect of the QTL in the Fob3b-line peaks at 68 cM (P value = 0.006), having an absolute effect of 0.71% ± 0.26 SD on percentage of body fat (Fat%) (22).
Selection lines and microarrays have been used previously to identify candidate genes for quantitative traits (26). Similarly, the use of congenic strains and microarrays in rats has led to the successful identification of Cd36 as an insulin resistance gene (1). Here we report the application of congenic mouse lines derived from selection lines, combined with the application of high-throughput gene expression analysis. Candidate genes are identified by examining differences in gene expression for loci in the QTL region and by statistical tests of groups of genes in pathways to circumvent the problem of multiple statistical testing of expressed sequence tags (ESTs). This analysis highlighted the cholesterol biosynthesis and glycolysis gene pathways as differentially expressed between the F- and congenic Fob3b-lines.
|
MATERIAL AND METHODS |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIAL AND METHODSRESULTSDISCUSSIONGRANTSREFERENCES |
|---|
99.2%), introduced via seven generations of recurrent backcrossing.
Mouse Tissue Collection
Mice were fed ad libitum Rat and Mouse No. 3 diet [Special Diet Services, Essex, UK; digestible crude (dc) oil, 3.8%; dc protein, 20.2%; starches, 33.9%; sugars, 4.4%; digestible energy, 13.8 MJ/kg] from weaning onward and maintained with controlled lighting (12:12-h light-dark cycle) at a temperature of 21 ± 2°C. Mice were weaned at 21 days of age and kept in sex-matched groups in plastic cages. All procedures were authorized and approved by the Home Office (UK) under the Animals (Scientific Procedures) Act 1986. Only female mice from the three lines (F-, L-, and Fob3b-lines) were used in this study. From weaning onward, mice were maintained on the same rack, and cages were randomized in their position on the rack after each cage change to minimize localized environmental effects.
At 98 days of age (±1 day), mice were transferred into individually labeled cages with water but no food for 3 h at 0800. If >10 mice were to be dissected in 1 day, the first 10 would be isolated at 0800 and next 10 at 0900, thus maintaining a 3-h isolation period. For each line, a total of 10 mice from 3 litters were dissected, and the order in which litters were dissected was randomized. These procedures attempted to standardize gene expression differences that might arise between mice because of prekill eating. Each mouse was transported individually from the stock room to the dissecting room and then processed, typically within 5 min. Mice were cervically dislocated and decapitated, and tissues were removed, one at a time. All tissues were transferred to 2-ml Eppendorf tubes, immediately snap-frozen in liquid nitrogen, and then stored at –80°C. All nondisposable dissecting instruments were autoclaved before use, and all disposable instruments used were sterile.
RNA Isolation
RNA extraction was carried out using TRIzol reagent following the manufacturer’s guidelines (Invitrogen, Paisley, UK). Briefly, samples were homogenized, using a Polytron homogenizer (PT100, Kinematica Polytron), for 30 s to 2 min depending on tissue type and volume. This was carried out in "batches" appropriate to the downstream pairwise comparisons. RNA samples were quantified by measuring absorbance at 260 and 280 nm (GeneQuant; Amersham Biosciences, Buckinghamshire, UK), and the integrity of samples was assessed by ethidium bromide staining.
Microarrays
Microarray slides were supplied by the Human Genome Mapping Project (HGMP) Resource Center (Hinxton, UK). Spotted on the slides were 14,938 mouse cDNAs from a clone set prepared by Dr. Minoru Ko’s laboratory (24) at the National Institute on Aging (NIA) of the National Institutes of Health (NIH; Bethesda, MD). Overall, 67% (574 different ESTs) of all the known transcripts in the Fob3b region are represented on the arrays. The 14,938 cDNAs are spotted across two slides [referred to as slide set 1 (1–7,612) and slide set 2 (7,613–14,938)], each containing 7,500 different cDNAs spotted twice on each slide. Clones were sequenced from 5'- and 3'-termini, and the sequences were deposited in GenBank (24). This information is available at the NIA-NIH web site (http://lgsun.grc.nia.nih.gov/cDNA/15k.html). All ESTs are referred to here by their 5'-sequence GenBank accession numbers.
Target Preparation: Array Hybridization, Washing, and Scanning
Targets were prepared with Klenow fragment by the method described in Smith et al. (20). Similarly, procedures for hybridization and slide washing were performed as described previously (20). For liver tissue hybridizations, for each slide set, two self-self (F-line:F-line and Fob3b-line:Fob3b-line) and four cross-line hybridizations (F-line:Fob3b-line) were performed. The self-self hybridizations were performed to examine intraline variability, but analysis indicated that this was at a low level. The data from the self-self hybridizations were also incorporated in the mixed model analysis described below. For brown adipose tissue (BAT), no self-self hybridizations were performed because RNA was limiting. The four cross-line hybridizations are essentially replicates involving two independent pools of RNA for both lines (each pool generated from 5 individual mice, hence a total of 10 mice for each line from 3 different first-parity litters) and dye swaps.
Northern Analysis
Northern analysis was carried out to confirm differential expression and to further characterize ESTs identified from the microarray experiments. Clones representing the differentially expressed ESTs (see Table 2) were obtained from the HGMP (http://www.hgmp.mrc.ac.uk/geneservice/index.shtml). Probes were prepared by extraction from agarose gel slices, following restriction endonuclease digestion of plasmid clones. Probe labeling was performed using the Rediprime II DNA labeling system (Amersham Biosciences), according to the manufacturer’s guidelines. Probes were purified with Sephadex G50 Nick columns (Amersham Biosciences).
Total RNA (10 µg) was loaded onto 1% denaturing formamide agarose gels, and integrity of RNA was examined by ethidium bromide staining. Gels were capillary transferred onto nylon membrane and fixed by UV light. Each Northern blot membrane was prewetted in 50 mM sodium phosphate (pH 7.2) and prehybridized for 1 h at 65°C in 20 ml of 0.5 M sodium phosphate (pH 7.2) containing 7% SDS, 100 mg/ml denatured salmon sperm DNA, and 25 µg/ml denatured tRNA. Hybridization was carried out in glass bottles rotating in a Biometra OV5 (Goettingen, Germany) hybridization oven. Washed membranes were exposed overnight at –80°C to Kodak BIOMAX-MS film (Amersham Biosciences). Where signals were weak, exposure time was extended accordingly.
Data Analysis
The raw data files have been deposited into the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/query/?acc=GSE1558; series GSE1558, platform GPL1341–1342, arrays GSM26714–26733).
The significance of differential expression in liver and BAT between the F- and Fob3b-mouse lines was assessed for all 14,938 ESTs by use of a mixed model procedure coded in SAS (SAS Institute, Cary, NC). This was done for all four experiments, that is, liver cDNAs on the first and second array sets and BAT cDNAs on the first and second array sets. The mixed model is adapted from Kerr and Churchill (10) and is processed in two steps, based on the procedures described by Wolfinger et al. (27). The full model is given in Eq. 1
 | (1) |
 | (2) |
 | (3) |
adglps is the stochastic error for each EST. Because the goal is to assess the EST-specific line (L) effect, the estimates of primary interest are those of the Llg effects. Differences between these effects are tested by use of mixed model-based t-tests of all possible pairwise comparisons within an EST.
Filtering for Significant Differentially Expressed ESTs
ESTs were ranked by P values calculated from the mixed model analysis. For each array, 7,680 independent tests were carried out (1 slide set at a time), so the 
level was corrected with the Bonferroni adjustment to generate a conservative experiment-wide threshold, hence, 0.05/7,680, which reduces the for each test to 0.00000651 (6.51 x 10–6).
This experiment-wide threshold is extremely stringent and may result in many false negatives. To circumvent this problem, we employed Mann-Whitney U-tests to perform pairwise comparisons of the P values of groups of genes. The null hypothesis is that the sums of the ranks of the groups do not differ (allowing for differences in sample sizes). The pairs of exclusive sets of ESTs tested were within or outside specific pathways or within or outside the QTL 95% confidence interval (CI).
Real-Time Quantitative PCR
Real-time quantitative PCR (Q-PCR) was performed to confirm known genes identified as differentially expressed from the array analysis. Design of primers was carried out by use of the Primer3 (16) software, available online at http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi, using sequences that were selected from the Mouse Genome Sequencing Consortium Mouse Assembly (build 30). The rodent mispriming library was chosen as a constraint for improved primer design. Additionally, RNA samples were DNase treated with DNase-free (Ambion) according to the manufacturer’s instructions.
The First-Strand cDNA synthesis kit (Amersham Biosciences) was used for first-strand cDNA synthesis, using RNA pooled from five individual mice. SYBR Green Q-PCR was performed using a kit (Applied Biosystems, CA, USA) on an ABI Prism 7700 Sequence Detector (PerkinElmer) with accompanied software (Sequence Detector v1.6.3). Reactions were carried out on 96-well MicroAmp optical plates and caps (Applied Biosystems). For each reaction, four identical (systematic) replicates were carried out. For each gene examined, identical reactions were processed on the same 96-well plate with primers for the mouse ß-actin gene to normalize for variation in loading between samples (
CT). For each gene, dilution comparisons were performed between the gene under investigation and the control (ß-actin) to establish the working concentration.
|
RESULTS |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIAL AND METHODSRESULTSDISCUSSIONGRANTSREFERENCES |
|---|
Analysis of Chr 15 and the Fob3b QTL Interval
Across the two slides, 145 different ESTs (67 on slide set 1 and 78 on slide set 2) mapping to the Fob3b 95% CI were spotted on the arrays (Fig. 1). The P values of these ESTs were compared with those outside the QTL region (of each slide set) using the Mann-Whitney U-test. The analysis shows little evidence that the ESTs in the 95% CI of the Fob3b QTL region have P values differing from all other ESTs; there is only weak evidence in liver: slide set 2 (P = 0.044; Table 1).
|
|
Characterized genes. Two ESTs corresponding to characterized Chr 15 genes were identified as potential candidates for further investigation. BG074498 corresponds to thiosulfate transferase (Tst) and is downregulated in BAT of the F-line, relative to the Fob3b-line. BG077950 corresponds to squalene epoxidase (Sqle), ranks highly in both tissues, and is upregulated in the F-line (Table 2, top, and Fig. 1). Tst maps just distal to the Fob3b 95% CI, whereas Sqle maps within this interval, which, along with its differential expression and its involvement in the metabolic pathway for cholesterol biosynthesis, makes Sqle a strong positional candidate for Fob3b.
|
Analysis of Sqle and the Cholesterol Biosynthesis Pathway
Given that Sqle was found to be differentially expressed, mapping to the Fob3b region, and is involved in the cholesterol biosynthesis pathway, other genes of this pathway were examined. Of the 13 possible enzymes involved in converting hydroxy-3-methylglutaryl-coenzyme A into cholesterol, 10 had ESTs that were spotted on the arrays, and 8 were upregulated in the F-line (P < 0.05, uncorrected) as summarized in Table 3.
|
Analysis of Enolase 1: the Glycolysis/Gluconeogenesis Pathway
An EST belonging to a characterized gene, Enolase 1 (Eno1), on Chr 2 had a very low P value, ranking as the 14th most significant EST (P = 0.0000068, uncorrected). This gene is involved in the glycolysis/gluconeogenesis pathway. Examination of ESTs matching the genes for enzymes in the glycolysis and gluconeogenesis pathways revealed several points. First, the two gluconeogenesis-specific genes spotted on the arrays are not differentially expressed (P > 0.05; Table 4), suggesting that gluconeogenesis might not be affected by Fob3b. Second, 7 of the 10 genes coding for enzymes involved in glycolysis are spotted on the array, and of these 6 are differentially expressed in BAT (P < 0.05, uncorrected). However, there is no differential expression in liver, the site of gluconeogenesis. We carried out a Mann-Whitney U-test to compare the ranks of the P values for glycolysis and gluconeogenesis ESTs against ESTs outside these pathways. This confirms that ESTs in the glycolysis pathway tend to show more significant P values, especially in BAT (BAT slide set 1, P = 0.00012; liver slide set 2, P = 0.017; Table 1). The gluconeogenesis-specific ESTs and those shared with the glycolysis pathway are not significantly different from the remaining genes.
|
|
|
|
8 kb in size, with a possible 7-kb-long alternative transcript seen in WAT (Fig. 4). Of 16 different tissues examined, expression was observed in all (except the pancreas), with varying degrees of abundance. Generally, BG081526 is downregulated in all L-line and Fob3b-line tissues compared with the F-line tissues, with the lowest amount of expression in L-line tissues. Time series Northern analysis at time points of 28, 49, and 98 days of age indicates an early upregulation of this gene in the F-line relative to both the Fob3b-line and the L-line, which is particularly noticeable in WAT (Fig. 4). Sequence analysis reveals two protein domains of unknown function (DEP domains), leading to the preliminary gene name for the BG081526-associated gene of Depdc6 (Dep domain containing 6). BLAST analysis (nucleotide and translated amino acid sequence) of the BG081526 sequence was performed against the genomic sequences of various species. Depdc6 appears to be a highly conserved gene, with orthologs in the human and rat genomes having 88 and 95% nucleotide similarity, respectively. There is also evidence of homologous sequences in species as diverse as fugu, zebrafish, and fruitfly, but not in the worm Caenorhabditis elegans.
|
|
DISCUSSION |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIAL AND METHODSRESULTSDISCUSSIONGRANTSREFERENCES |
|---|
An ongoing issue related to microarray analysis is the criteria used to determine significance of differentially expressed genes. Under our analysis, each EST was examined in two tissues (liver and BAT), leading to 30,000 tests. Thirteen ESTs were found to meet the conservative Bonferonni experiment-wide 0.05 significance threshold; however, all of these are from uncharacterized genes. And 2,491 tests were significant at the nominal 0.05 threshold, a figure far higher than the 1,500 expected under the null hypothesis (note, however, that this does not represent 2,491 different ESTs, since some were significant in both tissues). However, by grouping the ESTs into pathways and comparing the ranks of their P values with a nonparametric test, we were able to show significant differential expression for these groups of genes, even though at the single gene level the P values do not meet the Bonferonni experiment-wide significance threshold.
Chow-based diets have been shown previously (11) to contain inconsistent proportions of fatty acids among batches. Although we have used a robust statistical analysis and have been able to replicate our results in multiple biological replicates, these potential inconsistencies could affect the gene expression of the metabolic genes in the pathways discussed here. Future analyses can examine the impact of different diets, and batches can be analyzed by gas chromatography for consistency of fatty acids.
Sqle and the Cholesterol Biosynthesis Pathway Genes
When all the genes known to be involved in the cholesterol biosynthesis pathway were examined, 8 of 10 that were spotted on the arrays were significant at the nominal level (P < 0.01). Given that Sqle, which maps within the Fob3b region, is also significantly differentially expressed at the nominal level, the evidence for this pathway being associated with this QTL is compelling. Interestingly, not all the genes were significantly differentially expressed in both tissues, which may indicate that the Fob3b action is different for some genes in this pathway in the two examined tissues.
Sqle catalyses the first oxygenation step in sterol biosynthesis and is thought to strongly influence the flux through the cholesterol biosynthesis pathway. Differences in cholesterol biosynthesis may be associated with the Fob3b obesity QTL in several ways. For instance, the causative genomic alteration(s) resulting in the Fob3b QTL effect (Fob3b allele for simplicity) may act to increase cholesterol biosynthesis in the F-line, resulting in a higher level of cholesterol deposition in adipose cells and increased adiposity (2, 12). Alternatively, the causative Fob3b allele may be acting on increased cholesterol biosynthesis in the F-line, which then acts as an activator of fatty acid synthesis that in turn results in increased obesity (3, 14). Finally, the target of the causative Fob3b allele that leads to obesity may be entirely independent of the cholesterol biosynthesis pathway, and yet the expression of the cholesterol biosynthesis genes are perturbed because they share a common transcription factor. Such a scenario may be demonstrated by the Srebf2 transcription factor, which has been shown to act on the cholesterol biosynthesis genes as well as on fatty acid synthase (Fasn) and stearoyl-coenzyme A desaturase 2 (Scd2), each of which have been shown to give rise to obesity independently (23).
Q-PCR on two genes from the cholesterol biosynthesis pathway, Sqle and Lss, confirmed the results obtained from the array analysis, in that Sqle and Lss were both upregulated in the F-line relative to Fob3b-line (and the L-line). Confirmation was performed using the original pooled samples used for array analysis, and on liver samples from four individual mice for each line. The transcription factor Srebf2 that was absent from the arrays, yet maps to Chr 15, was also investigated for its known regulator properties of the genes involved in cholesterol biosynthesis (13, 17). Differential expression of Srebf2 was observed, indicating that Srebf2 may be responsible for the gene expression differences observed in Sqle, Lss, and the accompanying cholesterol biosynthesis pathway genes.
Additional Differentially Expressed Candidates and Genes
Eno1 was identified as the most significant differentially expressed characterized gene, upregulated in BAT tissue of the F-line relative to the congenic Fob3b-line. Eno1 is involved in the glycolysis pathway. When other genes in the same pathway were examined, it was determined that four additional glycolysis genes (Gpi, Pfk, AldoA, and Tpi) were significant at the nominal level (P < 0.05), bringing the total to six of a possible eight that were spotted on the arrays. None of the eight genes was significantly different in liver tissue, indicating that this may be a pathway that is perturbed in a tissue-specific manner. The individual P values for each of the four genes, Gpi, Pfk, AldoA, and Tpi, are below the genome-wide significance threshold, so each would have been disregarded with the use of stringent selection thresholds; however, when considered together by a nonparametric test as part of the glycolysis pathway, the evidence for their differential expression is strengthened. Increased expression of the glycolysis pathway genes in the F-line could lead to a greater rate of production of the end product, acetyl-CoA, which is a precursor for both cholesterol biosynthesis and fatty acid biosynthesis.
In addition to identifying candidate pathways, a few uncharacterized ESTs were also identified and some verified with follow-up expression studies. We chose the BG081526 EST mapping close to the Fob3b interval on Chr 15 as the most promising uncharacterized candidate to confirm and further characterize, since it was highly differentially expressed in both liver and BAT.
Differential expression of the BG081526 EST, indicated by the array analysis, was confirmed by Northern analysis and shows that the BG081526 EST belongs to an 8-kb transcript whose gene is currently named Depdc6 (DEP domain containing 6). Depdc6 is upregulated in all F-line tissues relative to Fob3b- and L-line tissues, and the differences are apparent from an early stage (28 days). The reported 2 kb of sequence are entirely unique, having no similarity to any gene family, and are highly conserved, while 6 kb of the cDNA sequence remain unidentified. Full gene characterization and further analysis of Depdc6 are ongoing. Although Depdc6 may not be the prime candidate for Fob3b given its map position, it may be interesting to determine why this gene of unknown function is so highly differentially expressed in our lines; it may be that it is a downstream target of the causal Fob3b allele.
Given that, of all the differentially expressed cholesterol biosynthesis pathway genes, Sqle maps within the Fob3b QTL region, this makes Sqle a strong candidate for the Fob3b QTL. It is therefore possible that a genomic alteration of Sqle in the F-line results in an allele that is responsible for the QTL effect. However, given that the confidence interval of Fob3b is still relatively large (26 Mb), it could also be that other linked genes are causal for the Fob3b QTL effect and that expression of Sqle is somehow influenced by the action of this linked QTL. In addition, existence of multiple smaller-effect QTL (7) within the currently mapped Fob3b region resulting in a "cumulative" Fob3b effect cannot be ruled out. Further high-resolution genetic mapping studies combined with detailed expression (RNA and protein) analysis of cholesterol biosynthesis pathway, determination of Sqle polymorphisms between the lines, and possibly transgenic studies (i.e., allele replacement) are needed to finally identify the molecular cause of the Fob3b effect.
|
GRANTS |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIAL AND METHODSRESULTSDISCUSSIONGRANTSREFERENCES |
|---|
|
ACKNOWLEDGMENTS |
|---|
|
FOOTNOTES |
|---|
Address for reprint requests and other correspondence: I. M. Stylianou, The Jackson Laboratory, Bar Harbor, ME 04609 (E-mail: jstylian{at}jax.org).
10.1152/physiolgenomics.00183.2004.
|
REFERENCES |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIAL AND METHODSRESULTSDISCUSSIONGRANTSREFERENCES |
|---|
This article has been cited by other articles:
|
|
 |
|
  I. M. Stylianou, J. P. Affourtit, K. R. Shockley, R. Y. Wilpan, F. A. Abdi, S. Bhardwaj, J. Rollins, G. A Churchill, and B. Paigen Applying Gene Expression, Proteomics and Single-Nucleotide Polymorphism Analysis for Complex Trait Gene Identification Genetics, March 1, 2008; 178(3): 1795 - 1805. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. Chiu, K. Kim, K. A. Haus, G. M. Espinal, L. V. Millon, and C. H. Warden Identification of positional candidate genes for body weight and adiposity in subcongenic mice Physiol Genomics, September 11, 2007; 31(1): 75 - 85. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Ponsuksili, E. Murani, C. Walz, M. Schwerin, and K. Wimmers Pre- and postnatal hepatic gene expression profiles of two pig breeds differing in body composition: insight into pathways of metabolic regulation Physiol Genomics, May 11, 2007; 29(3): 267 - 279. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R. R. Hoopes Jr., F. A. Middleton, S. Sen, P. A. Hueber, R. Reid, D. A. Bushinsky, and S. J. Scheinman Isolation and Confirmation of a Calcium Excretion Quantitative Trait Locus on Chromosome 1 in Genetic Hypercalciuric Stone-Forming Congenic Rats J. Am. Soc. Nephrol., May 1, 2006; 17(5): 1292 - 1304. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 N. M. Morton, V. Densmore, M. Wamil, L. Ramage, K. Nichol, L. Bunger, J. R. Seckl, and C. J. Kenyon A Polygenic Model of the Metabolic Syndrome With Reduced Circulating and Intra-Adipose Glucocorticoid Action Diabetes, December 1, 2005; 54(12): 3371 - 3378. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Liang and B. Ventura Physiological genomics in PG and beyond: July to September 2005 Physiol Genomics, October 17, 2005; 23(2): 119 - 124. [Full Text] [PDF]
|
|
|
|
|
|
J. H. Kim, T. P. Stewart, W. Zhang, H. Y. Kim, P. M. Nishina, and J. K. Naggert Type 2 diabetes mouse model TallyHo carries an obesity gene on chromosome 6 that exaggerates dietary obesity Physiol Genomics, July 14, 2005; 22(2): 171 - 181. [Abstract] [Full Text] [PDF]
|
|
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
