β Cell-specific deletion of Zfp148 improves nutrient-stimulated β cell Ca2+ responses

Insulin secretion from pancreatic β cells is essential for glucose homeostasis. An insufficient response to the demand for insulin results in diabetes. We previously showed that β cell-specific deletion of Zfp148 (β-Zfp148KO) improves glucose tolerance and insulin secretion in mice. Here, we performed Ca2+ imaging of islets from β‑Zfp148KO and control mice fed both a chow and a Western-style diet. β-Zfp148KO islets demonstrated improved sensitivity and sustained Ca2+ oscillations in response to elevated glucose levels. β-Zfp148KO islets also exhibited elevated sensitivity to amino acid-induced Ca2+ influx under low glucose conditions, suggesting enhanced mitochondrial phosphoenolpyruvate-dependent (PEP-dependent), ATP-sensitive K+ channel closure, independent of glycolysis. RNA-Seq and proteomics of β-Zfp148KO islets revealed altered levels of enzymes involved in amino acid metabolism (specifically, SLC3A2, SLC7A8, GLS, GLS2, PSPH, PHGDH, and PSAT1) and intermediary metabolism (namely, GOT1 and PCK2), consistent with altered PEP cycling. In agreement with this, β-Zfp148KO islets displayed enhanced insulin secretion in response to l-glutamine and activation of glutamate dehydrogenase. Understanding pathways controlled by ZFP148 may provide promising strategies for improving β cell function that are robust to the metabolic challenge imposed by a Western diet.

Tsc2, a positional candidate gene underlying a quantitative trait locus for hepatic steatosis

Nonalchoholic fatty liver disease (NAFLD) is the most common cause of liver dysfunction and is associated with metabolic diseases, including obesity, insulin resistance, and type 2 diabetes. We mapped a quantitative trait locus (QTL) for NAFLD to chromosome 17 in a cross between C57BL/6 (B6) and BTBR mouse strains made genetically obese with the Lep(ob/ob) mutation. We identified Tsc2 as a gene underlying the chromosome 17 NAFLD QTL. Tsc2 functions as an inhibitor of mammalian target of rapamycin, which is involved in many physiological processes, including cell growth, proliferation, and metabolism. We found that Tsc2(+/-) mice have increased lipogenic gene expression in the liver in an insulin-dependent manner. The coding single nucleotide polymorphism between the B6 and BTBR strains leads to a change in the ability to inhibit the expression of lipogenic genes and de novo lipogenesis in AML12 cells and to promote the proliferation of Ins1 cells. This difference is due to a different affinity of binding to Tsc1, which affects the stability of Tsc2.

Liver and adipose expression associated SNPs are enriched for association to type 2 diabetes

Genome-wide association studies (GWAS) have demonstrated the ability to identify the strongest causal common variants in complex human diseases. However, to date, the massive data generated from GWAS have not been maximally explored to identify true associations that fail to meet the stringent level of association required to achieve genome-wide significance. Genetics of gene expression (GGE) studies have shown promise towards identifying DNA variations associated with disease and providing a path to functionally characterize findings from GWAS. Here, we present the first empiric study to systematically characterize the set of single nucleotide polymorphisms associated with expression (eSNPs) in liver, subcutaneous fat, and omental fat tissues, demonstrating these eSNPs are significantly more enriched for SNPs that associate with type 2 diabetes (T2D) in three large-scale GWAS than a matched set of randomly selected SNPs. This enrichment for T2D association increases as we restrict to eSNPs that correspond to genes comprising gene networks constructed from adipose gene expression data isolated from a mouse population segregating a T2D phenotype. Finally, by restricting to eSNPs corresponding to genes comprising an adipose subnetwork strongly predicted as causal for T2D, we dramatically increased the enrichment for SNPs associated with T2D and were able to identify a functionally related set of diabetes susceptibility genes. We identified and validated malic enzyme 1 (Me1) as a key regulator of this T2D subnetwork in mouse and provided support for the association of this gene to T2D in humans. This integration of eSNPs and networks provides a novel approach to identify disease susceptibility networks rather than the single SNPs or genes traditionally identified through GWAS, thereby extracting additional value from the wealth of data currently being generated by GWAS.

Heterogeneity of gene expression in stromal fibroblasts of breast carcinomas and normal breast

Abstract #105

Background: The cancer microenvironment plays a critical role in tumor development and progression. Cancer associated fibroblasts (CAF) constitute a significant component of the tumor stroma and participate in reciprocal communication with the tumor cells. Information on differential gene expression specifically in stromal fibroblasts is sparse and data describing the variability of gene expression in CAF and normal fibroblasts (NF) is currently lacking. The purpose of this study was to identify genes differentially expressed in CAF and matched NF and to analyze the heterogeneity of gene expression profiles in the two cell types.
 Materials and methods: Fibroblast cell cultures were established from 6 patients with primary invasive breast cancer. Gene expression profiles were generated using oligonucleotide microarrays (Affymetrix HG-U133 Plus 2.0). Differentially expressed genes were ranked using Empirical Bayes modeling. A cut-off value of 0.005 was chosen for the posterior probability of equivalent expression. Lists of overexpressed genes were generated after eliminating genes with less than two-fold overexpression.
 Results: 17 genes were overexpressed in CAF compared to NF with known functions in paracrine and intracellular signaling, transcription regulation and extracellular matrix production. Using the same posterior probability cut-off, we identified 7 genes which were expressed at least two-fold higher in NF than in CAF. These genes have purported roles in steroid hormone metabolism, transcription, migration and cell signaling. Using semiquantitative RT-PCR and immunohistochemistry, we confirmed the over- and underexpression of a subset of 10 differentially expressed genes. The heterogeneity of gene expression in CAF vs. NF was compared with F-tests to determine variances. The estimated probability of NF gene expression variance being higher than CAF gene expression variance was 0.547 with a 95% confidence interval of 0.543 to 0.551 (p<0.0001), indicating that gene expression is more variable in NF than in CAF. By ranking the q-values of individual genes we identified 3 known genes, which show a significant difference in variance between CAF and NF (p<0.05).
 Conclusion: Altered gene expression in fibroblasts likely contributes to tumor growth and progression by enhancing ECM production, promoting stromal-epithelial paracrine signaling and altering steroid hormone metabolism. The inter-individual heterogeneity of gene expression in NF may indicate that the mammary stroma varies between individuals, supporting the hypothesis that the ability of the stroma to act as a barrier to cancer development and tumor progression may also be variable. Conversely, the heterogeneous gene expression in NF may be a reflection of a relative synchronization and uniformity of gene expression in CAF.

Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 105.

Abdominal obesity in BTBR male mice is associated with peripheral but not hepatic insulin resistance

Insulin resistance is a common feature of obesity. BTBR mice have more fat mass than most other inbred mouse strains. On a chow diet, BTBR mice have elevated insulin levels relative to the C57BL/6J (B6) strain. Male F1 progeny of a B6 x BTBR cross are insulin resistant. Previously, we reported insulin resistance in isolated muscle and in isolated adipocytes in this strain. Whereas the muscle insulin resistance was observed only in male F1 mice, adipocyte insulin resistance was also present in male BTBR mice. We examined in vivo mechanisms of insulin resistance with the hyperinsulinemic euglycemic clamp technique. At 10 wk of age, BTBR and F1 mice had a >30% reduction in whole body glucose disposal primarily due to insulin resistance in heart, soleus muscle, and adipose tissue. The increased adipose tissue mass and decreased muscle mass in BTBR and F1 mice were negatively and positively correlated with whole body glucose disposal, respectively. Genes involved in focal adhesion, actin cytoskeleton, and inflammation were more highly expressed in BTBR and F1 than in B6 adipose tissue. The BTBR and F1 mice have higher levels of testosterone, which may be related to the pathological changes in adipose tissue that lead to systemic insulin resistance. Despite profound peripheral insulin resistance, BTBR and F1 mice retained hepatic insulin sensitivity. These studies reveal a genetic difference in body composition that correlates with large differences in peripheral insulin sensitivity.

A target-selected Apc-mutant rat kindred enhances the modeling of familial human colon cancer

Progress toward the understanding and management of human colon cancer can be significantly advanced if appropriate experimental platforms become available. We have investigated whether a rat model carrying a knockout allele in the gatekeeper gene Adenomatous polyposis coli (Apc) recapitulates familial colon cancer of the human more closely than existing murine models. We have established a mutagen-induced nonsense allele of the rat Apc gene on an inbred F344/NTac (F344) genetic background. Carriers of this mutant allele develop multiple neoplasms with a distribution between the colon and small intestine that closely simulates that found in human familial adenomatous polyposis patients. To distinguish this phenotype from the predominantly small intestinal phenotype found in most Apc-mutant mouse strains, this strain has been designated the polyposis in the rat colon (Pirc) kindred. The Pirc rat kindred provides several unique and favorable features for the study of colon cancer. Tumor-bearing Pirc rats can live at least 17 months, carrying a significant colonic tumor burden. These tumors can be imaged both by micro computed tomography scanning and by classical endoscopy, enabling longitudinal studies of tumor genotype and phenotype as a function of response to chemopreventive and therapeutic regimes. The metacentric character of the rat karyotype, like that of the human and unlike the acrocentric mouse, has enabled us to demonstrate that the loss of the wild-type Apc allele in tumors does not involve chromosome loss. We believe that the Pirc rat kindred can address many of the current gaps in the modeling of human colon cancer.

Cholestasis and hypercholesterolemia in SCD1-deficient mice fed a low-fat, high-carbohydrate diet

Stearoyl-coenzyme A desaturase 1-deficient (SCD1(-/-)) mice have impaired MUFA synthesis. When maintained on a very low-fat (VLF) diet, SCD1(-/-) mice developed severe hypercholesterolemia, characterized by an increase in apolipoprotein B (apoB)-containing lipoproteins and the appearance of lipoprotein X. The rate of LDL clearance was decreased in VLF SCD1(-/-) mice relative to VLF SCD1(+/+) mice, indicating that reduced apoB-containing lipoprotein clearance contributed to the hypercholesterolemia. Additionally, HDL-cholesterol was dramatically reduced in these mice. The presence of increased plasma bile acids, bilirubin, and aminotransferases in the VLF SCD1(-/-) mice is indicative of cholestasis. Supplementation of the VLF diet with MUFA- and PUFA-rich canola oil, but not saturated fat-rich hydrogenated coconut oil, prevented these plasma phenotypes. However, dietary oleate was not as effective as canola oil in reducing LDL-cholesterol, signifying a role for dietary PUFA deficiency in the development of this phenotype. These results indicate that the lack of SCD1 results in an increased requirement for dietary unsaturated fat to compensate for impaired MUFA synthesis and to prevent hypercholesterolemia and hepatic dysfunction. Therefore, endogenous MUFA synthesis is essential during dietary unsaturated fat insufficiency and influences the dietary requirement of PUFA.

Statistical methods for expression quantitative trait loci (eQTL) mapping

Traditional genetic mapping has largely focused on the identification of loci affecting one, or at most a few, complex traits. Microarrays allow for measurement of thousands of gene expression abundances, themselves complex traits, and a number of recent investigations have considered these measurements as phenotypes in mapping studies. Combining traditional quantitative trait loci (QTL) mapping methods with microarray data is a powerful approach with demonstrated utility in a number of recent biological investigations. These expression quantitative trait loci (eQTL) studies are similar to traditional QTL studies, as a main goal is to identify the genomic locations to which the expression traits are linked. However, eQTL studies probe thousands of expression transcripts; and as a result, standard multi-trait QTL mapping methods, designed to handle at most tens of traits, do not directly apply. One possible approach is to use single-trait QTL mapping methods to analyze each transcript separately. This leads to an increased number of false discoveries, as corrections for multiple tests across transcripts are not made. Similarly, the repeated application, at each marker, of methods for identifying differentially expressed transcripts suffers from multiple tests across markers. Here, we demonstrate the deficiencies of these approaches and propose a mixture over markers (MOM) model that shares information across both markers and transcripts. The utility of all methods is evaluated using simulated data as well as data from an F(2) mouse cross in a study of diabetes. Results from simulation studies indicate that the MOM model is best at controlling false discoveries, without sacrificing power. The MOM model is also the only one capable of finding two genome regions previously shown to be involved in diabetes.

Microarray analysis of BeWo and JEG3 trophoblast cell lines: identification of differentially expressed transcripts

Trophoblast cell lines are important research tools used as a surrogate for primary trophoblast cells in the study of placental function. Because the cellular origins of transformed trophoblasts are likely to be diverse, it would be of value to understand the unique and shared phenotypes of the cells on a global scale. We have compared two widely used cell lines, BeWo and JEG3, by microarray analysis in order to identify differentially expressed genes. Results indicated that approximately 2700 genes were differentially expressed between the cell lines, with principal differences observed in the biological processes of response to stress, cell adhesion, signal transduction, and protein and nucleobase metabolisms. These data suggest that BeWo and JEG3 cell lines, and perhaps other trophoblast cell lines, are sufficiently dissimilar from each other such that they will be differentially suited for specific experimental paradigms.

On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles

DNA microarrays provide for unprecedented large-scale views of gene expression and, as a result, have emerged as a fundamental measurement tool in the study of diverse biological systems. Statistical questions abound, but many traditional data analytic approaches do not apply, in large part because thousands of individual genes are measured with relatively little replication. Empirical Bayes methods provide a natural approach to microarray data analysis because they can significantly reduce the dimensionality of an inference problem while compensating for relatively few replicates by using information across the array. We propose a general empirical Bayes modelling approach which allows for replicate expression profiles in multiple conditions. The hierarchical mixture model accounts for differences among genes in their average expression levels, differential expression for a given gene among cell types, and measurement fluctuations. Two distinct parameterizations are considered: a model based on Gamma distributed measurements and one based on log-normally distributed measurements. False discovery rate and related operating characteristics of the methodology are assessed in a simulation study. We also show how the posterior odds of differential expression in one version of the model is related to the ratio of the arithmetic mean to the geometric mean of the two sample means. The methodology is used in a study of mammary cancer in the rat, where four distinct patterns of expression are possible.

The efficiency of pooling mRNA in microarray experiments

In a microarray experiment, messenger RNA samples are oftentimes pooled across subjects out of necessity, or in an effort to reduce the effect of biological variation. A basic problem in such experiments is to estimate the nominal expression levels of a large number of genes. Pooling samples will affect expression estimation, but the exact effects are not yet known as the approach has not been systematically studied in this context. We consider how mRNA pooling affects expression estimates by assessing the finite-sample performance of different estimators for designs with and without pooling. Conditions under which it is advantageous to pool mRNA are defined; and general properties of estimates from both pooled and non-pooled designs are derived under these conditions. A formula is given for the total number of subjects and arrays required in a pooled experiment to obtain gene expression estimates and confidence intervals comparable to those obtained from the no-pooling case. The formula demonstrates that by pooling a perhaps increased number of subjects, one can decrease the number of arrays required in an experiment without a loss of precision. The assumptions that facilitate derivation of this formula are considered using data from a quantitative real-time PCR experiment. The calculations are not specific to one particular method of quantifying gene expression as they assume only that a single, normalized, estimate of expression is obtained for each gene. As such, the results should be generally applicable to a number of technologies provided sufficient pre-processing and normalization methods are available and applied.

Loss of stearoyl-CoA desaturase-1 function protects mice against adiposity

Stearoyl-CoA desaturase (SCD) is a central lipogenic enzyme catalyzing the synthesis of monounsaturated fatty acids, mainly oleate (C18:1) and palmitoleate (C16:1), which are components of membrane phospholipids, triglycerides, wax esters, and cholesterol esters. Several SCD isoforms (SCD1-3) exist in the mouse. Here we show that mice with a targeted disruption of the SCD1 isoform have reduced body adiposity, increased insulin sensitivity, and are resistant to diet-induced weight gain. The protection from obesity involves increased energy expenditure and increased oxygen consumption. Compared with the wild-type mice the SCD1-/- mice have increased levels of plasma ketone bodies but reduced levels of plasma insulin and leptin. In the SCD1-/- mice, the expression of several genes of lipid oxidation are up-regulated, whereas lipid synthesis genes are down-regulated. These observations suggest that a consequence of SCD1 deficiency is an activation of lipid oxidation in addition to reduced triglyceride synthesis and storage.

Mapping baroreceptor function to genome: a mathematical modeling approach

To gain information about the genetic basis of a complex disease such as hypertension, blood pressure averages are often obtained and used as phenotypes in genetic mapping studies. In contrast, direct measurements of physiological regulatory mechanisms are not often obtained, due in large part to the time and expense required. As a result, little information about the genetic basis of physiological controlling mechanisms is available. Such information is important for disease diagnosis and treatment. In this article, we use a mathematical model of blood pressure to derive phenotypes related to the baroreceptor reflex, a short-term controller of blood pressure. The phenotypes are then used in a quantitative trait loci (QTL) mapping study to identify a potential genetic basis of this controller.

On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data

We consider the problem of inferring fold changes in gene expression from cDNA microarray data. Standard procedures focus on the ratio of measured fluorescent intensities at each spot on the microarray, but to do so is to ignore the fact that the variation of such ratios is not constant. Estimates of gene expression changes are derived within a simple hierarchical model that accounts for measurement error and fluctuations in absolute gene expression levels. Significant gene expression changes are identified by deriving the posterior odds of change within a similar model. The methods are tested via simulation and are applied to a panel of Escherichia coli microarrays.

Genetic loci controlling breast cancer susceptibility in the Wistar-Kyoto rat

In this study, the Wistar-Kyoto (WKy) rat was genetically characterized for loci that modify susceptibility to mammary carcinogenesis. We used a genetic backcross between resistant WKy and susceptible Wistar-Furth (WF) rats as a panel for linkage mapping to genetically identify mammary carcinoma susceptibility (Mcs) loci underlying the resistance of the WKy rat. Rats were phenotyped for DMBA-induced mammary carcinomas and genotyped using microsatellite markers. To detect quantitative trait loci (QTL), we analyzed the genome scan data under both parametric and nonparametric distributional assumptions and used permutation tests to calculate significance thresholds. A generalized linear model analysis was also performed to test for interactions between significant QTL. This methodology was extended to identify interactions between the significant QTL and other genome locations. Chromosomes 5, 7, 10, and 14 were found to contain significant QTL, termed Mcs5, Mcs6, Mcs7, and Mcs8, respectively. The WKy alleles of Mcs5, -6, and -8 are associated with mammary carcinoma resistance; the WKy allele of Mcs7 is associated with an increased incidence of mammary cancer. In addition, we identified an interaction between Mcs8 and a region on chromosome 6 termed Mcsm1 (modifier of Mcs), which had no significant main effect on mammary cancer susceptibility in this genetic analysis.