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Whole genome assessment of the retinal response to diabetes reveals a progressive neurovascular inflammatory response.

ABSTRACT: BACKGROUND: Despite advances in the understanding of diabetic retinopathy, the nature and time course of molecular changes in the retina with diabetes are incompletely described. This study characterized the functional and molecular phenotype of the retina with increasing durations of diabetes. RESULTS: Using the streptozotocin-induced rat model of diabetes, levels of retinal permeability, caspase activity, and gene expression were examined after 1 and 3 months of diabetes. Gene expression changes were identified by whole genome microarray and confirmed by qPCR in the same set of animals as used in the microarray analyses and subsequently validated in independent sets of animals. Increased levels of vascular permeability and caspase-3 activity were observed at 3 months of diabetes, but not 1 month. Significantly more and larger magnitude gene expression changes were observed after 3 months than after 1 month of diabetes. Quantitative PCR validation of selected genes related to inflammation, microvasculature and neuronal function confirmed gene expression changes in multiple independent sets of animals. CONCLUSION: These changes in permeability, apoptosis, and gene expression provide further evidence of progressive retinal malfunction with increasing duration of diabetes. The specific gene expression changes confirmed in multiple sets of animals indicate that pro-inflammatory, anti-vascular barrier, and neurodegenerative changes occur in tandem with functional increases in apoptosis and vascular permeability. These responses are shared with the clinically documented inflammatory response in diabetic retinopathy suggesting that this model may be used to test anti-inflammatory therapeutics.

SUBMITTER: Brucklacher RM

PROVIDER: S-EPMC2442612 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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Whole genome assessment of the retinal response to diabetes reveals a progressive neurovascular inflammatory response.

Brucklacher Robert M RM Patel Kruti M KM VanGuilder Heather D HD Bixler Georgina V GV Barber Alistair J AJ Antonetti David A DA Lin Cheng-Mao CM LaNoue Kathryn F KF Gardner Thomas W TW Bronson Sarah K SK Freeman Willard M WM

BMC medical genomics 20080613

<h4>Background</h4>Despite advances in the understanding of diabetic retinopathy, the nature and time course of molecular changes in the retina with diabetes are incompletely described. This study characterized the functional and molecular phenotype of the retina with increasing durations of diabetes.<h4>Results</h4>Using the streptozotocin-induced rat model of diabetes, levels of retinal permeability, caspase activity, and gene expression were examined after 1 and 3 months of diabetes. Gene exp ...[more]

PMID: 18554398

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Project description:Lifestyle and genetic factors play a large role in the development of Type 2 Diabetes (T2D). Despite the important role of genetic factors, genetic information is not incorporated into the clinical assessment of T2D risk. We assessed and compared Whole Genome Regression methods to predict the T2D status of 5,245 subjects from the Framingham Heart Study. For evaluating each method we constructed the following set of regression models: A clinical baseline model (CBM) which included non-genetic covariates only. CBM was extended by adding the first two marker-derived principal components and 65 SNPs identified by a recent GWAS consortium for T2D (M-65SNPs). Subsequently, it was further extended by adding 249,798 genome-wide SNPs from a high-density array. The Bayesian models used to incorporate genome-wide marker information as predictors were: Bayes A, Bayes Cπ, Bayesian LASSO (BL), and the Genomic Best Linear Unbiased Prediction (G-BLUP). Results included estimates of the genetic variance and heritability, genetic scores for T2D, and predictive ability evaluated in a 10-fold cross-validation. The predictive AUC estimates for CBM and M-65SNPs were: 0.668 and 0.684, respectively. We found evidence of contribution of genetic effects in T2D, as reflected in the genomic heritability estimates (0.492±0.066). The highest predictive AUC among the genome-wide marker Bayesian models was 0.681 for the Bayesian LASSO. Overall, the improvement in predictive ability was moderate and did not differ greatly among models that included genetic information. Approximately 58% of the total number of genetic variants was found to contribute to the overall genetic variation, indicating a complex genetic architecture for T2D. Our results suggest that the Bayes Cπ and the G-BLUP models with a large set of genome-wide markers could be used for predicting risk to T2D, as an alternative to using high-density arrays when selected markers from large consortiums for a given complex trait or disease are unavailable.

Project description:BackgroundInflammatory breast cancer (IBC) has a highly invasive and metastatic phenotype. However, little is known about its genetic drivers. To address this, we report the largest cohort of whole-genome sequencing (WGS) of IBC cases.MethodsWe performed WGS of 20 IBC samples and paired normal blood DNA to identify genomic alterations. For comparison, we used 23 matched non-IBC samples from the Cancer Genome Atlas Program (TCGA). We also validated our findings using WGS data from the International Cancer Genome Consortium (ICGC) and the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We examined a wide selection of genomic features to search for differences between IBC and conventional breast cancer. These include (i) somatic and germline single-nucleotide variants (SNVs), in both coding and non-coding regions; (ii) the mutational signature and the clonal architecture derived from these SNVs; (iii) copy number and structural variants (CNVs and SVs); and (iv) non-human sequence in the tumors (i.e., exogenous sequences of bacterial origin).ResultsOverall, IBC has similar genomic characteristics to non-IBC, including specific alterations, overall mutational load and signature, and tumor heterogeneity. In particular, we observed similar mutation frequencies between IBC and non-IBC, for each gene and most cancer-related pathways. Moreover, we found no exogenous sequences of infectious agents specific to IBC samples. Even though we could not find any strongly statistically distinguishing genomic features between the two groups, we did find some suggestive differences in IBC: (i) The MAST2 gene was more frequently mutated (20% IBC vs. 0% non-IBC). (ii) The TGF β pathway was more frequently disrupted by germline SNVs (50% vs. 13%). (iii) Different copy number profiles were observed in several genomic regions harboring cancer genes. (iv) Complex SVs were more frequent. (v) The clonal architecture was simpler, suggesting more homogenous tumor-evolutionary lineages.ConclusionsWhole-genome sequencing of IBC manifests a similar genomic architecture to non-IBC. We found no unique genomic alterations shared in just IBCs; however, subtle genomic differences were observed including germline alterations in TGFβ pathway genes and somatic mutations in the MAST2 kinase that could represent potential therapeutic targets.

Dataset Information

Whole genome assessment of the retinal response to diabetes reveals a progressive neurovascular inflammatory response.

Publications

Whole genome assessment of the retinal response to diabetes reveals a progressive neurovascular inflammatory response.

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