Effects of high fat diet (HFD) on obesity and, subsequently, on diabetes are highly variable and modulated by genetics in both humans and rodents. In this report, we characterized the response of Goto-Kakizaki (GK) rats, a spontaneous polygenic model for lean diabetes and healthy Wistar-Kyoto (WKY) controls, to high fat feeding from weaning to 20 weeks of age. Animals fed either normal diet or HFD were sacrificed at 4, 8, 12, 16 and 20 weeks of age and a wide array of physiological measurements were made along with gene expression profiling using Affymetrix gene array chips. Mining of the microarray data identified differentially regulated genes (involved in inflammation, metabolism, transcription regulation, and signaling) in diabetic animals, as well as the response of both strains to HFD. Functional annotation suggested that HFD increased inflammatory differences between the two strains. Chronic inflammation driven by heightened innate immune response was identified to be present in GK animals regardless of diet. In addition, compensatory mechanisms by which WKY animals on HFD resisted the development of diabetes were identified, thus illustrating the complexity of diabetes disease progression.

The Goto-Kakizaki (GK) rat, a polygenic non-obese model of type 2 diabetes, is a useful surrogate for study of diabetes-related changes independent of obesity. GK rats and appropriate controls were killed at 4, 8, 12, 16 and 20 weeks post-weaning and differential muscle gene expression along with body and muscle weights, plasma hormones and lipids, and blood cell measurements were carried out. Gene expression analysis identified 204 genes showing 2-fold or greater differences between GK and controls in at least 3 ages. Array results suggested increased oxidative capacity in GK muscles, as well as differential gene expression related to insulin resistance, which was also indicated by HOMA-IR measurements. In addition, potential new biomarkers in muscle gene expression were identified that could be either a cause or consequence of T2DM. Furthermore, we demonstrate here the presence of chronic inflammation evident both systemically and in the musculature, despite the absence of obesity.

Background

We previously reported reduced prefrontal cortex (PFC) grey matter volume among first-episode, antipsychotic-naïve schizophrenia subjects (SZ) exposed to HSV1 but not among healthy subjects (HS) (Prasad et al. 2007). Independently, rs1051788, an exonic polymorphism of the MHC Class I polypeptide-related sequence B (MICB) gene was associated with HSV1 seropositivity, as well as SZ risk. In this study, we examined whether PFC grey matter changes associated with HSV1 exposure varied against the background of MICB genotypes.

Methods

We examined Caucasian individuals from the sample we studied in our previous report (Prasad et al. 2007) (SZ, n=21 and HS, n=19). Whole-brain voxel-wise analysis of structural MRI scans was conducted using Statistical Parametric Mapping, ver 5 (SPM5). The impact of rs1051788 variation and HSV1 seropositivity on grey matter volumes was examined using regression models on the combined sample of cases and controls, and then within each diagnostic group.

Results

In the combined sample of cases and controls, we observed the main effects of HSV1 seropositivity and genotypes, and a significant joint effect of HSV1 seropositivity and genotype mainly in the PFC. The joint effect was more prominent among cases than among controls.

Discussion

Our observations suggest that rs1051788 and HSV1 seropositivity are associated individually and jointly with reduced PFC grey matter volume. The patterns of these associations differ by diagnostic status, and these factors explain only a “small” portion of the variance in the grey matter volume reductions.

Type 2 diabetes (T2DM) is a heterogeneous group of diseases that is progressive and involves multiple tissues. Goto-Kakizaki (GK) rats are a polygenic model with elevated blood glucose, peripheral insulin resistance, a non-obese phenotype, and exhibit many degenerative changes observed in human T2DM. As part of a systems analysis of disease progression in this animal model, this study characterized the contribution of adipose tissue to pathophysiology of the disease. We sacrificed subgroups of GK rats and appropriate controls at 4, 8, 12, 16 and 20 weeks of age and carried out a gene array analysis of white adipose tissue. We expanded our physiological analysis of the animals that accompanied our initial gene array study on the livers from these animals. The expanded analysis included adipose tissue weights, HbA1c, additional hormonal profiles, lipid profiles, differential blood cell counts, and food consumption. HbA1c progressively increased in the GK animals. Altered corticosterone, leptin, and adiponectin profiles were also documented in GK animals. Gene array analysis identified 412 genes that were differentially expressed in adipose tissue of GKs relative to controls. The GK animals exhibited an age-specific failure to accumulate body fat despite their relatively higher calorie consumption which was well supported by the altered expression of genes involved in adipogenesis and lipogenesis in the white adipose tissue of these animals, including Fasn, Acly, Kklf9, and Stat3. Systemic inflammation was reflected by chronically elevated white blood cell counts. Furthermore, chronic inflammation in adipose tissue was evident from the differential expression of genes involved in inflammatory responses and activation of natural immunity, including two interferon regulated genes, Ifit and Iipg, as well as MHC class II genes. This study demonstrates an age specific failure to accumulate adipose tissue in the GK rat and the presence of chronic inflammation in adipose tissue from these animals.

A fast and efficient method for quantifying photoreceptor density in images obtained with an en-face flood-illuminated adaptive optics (AO) imaging system is described. To improve accuracy of cone counting, en-face images are analyzed over extended areas. This is achieved with two separate semiautomated algorithms: (1) a montaging algorithm that joins retinal images with overlapping common features without edge effects and (2) a cone density measurement algorithm that counts the individual cones in the montaged image. The accuracy of the cone density measurement algorithm is high, with >97% agreement for a simulated retinal image (of known density, with low contrast) and for AO images from normal eyes when compared with previously reported histological data. Our algorithms do not require spatial regularity in cone packing and are, therefore, useful for counting cones in diseased retinas, as demonstrated for eyes with Stargardt’s macular dystrophy and retinitis pigmentosa.