Transcriptome sequencing and development of an expression microarray platform for the domestic ferret

<p>Abstract</p> <p>Background</p> <p>The ferret (<it>Mustela putorius furo</it>) represents an attractive animal model for the study of respiratory diseases, including influenza. Despite its importance for biomedical research, the number of reagents for molecular and immunological analysis is restricted. We present here a parallel sequencing effort to produce an extensive EST (expressed sequence tags) dataset derived from a normalized ferret cDNA library made from mRNA from ferret blood, liver, lung, spleen and brain.</p> <p>Results</p> <p>We produced more than 500000 sequence reads that were assembled into 16000 partial ferret genes. These genes were combined with the available ferret sequences in the GenBank to develop a ferret specific microarray platform. Using this array, we detected tissue specific expression patterns which were confirmed by quantitative real time PCR assays. We also present a set of 41 ferret genes with even transcription profiles across the tested tissues, indicating their usefulness as housekeeping genes.</p> <p>Conclusion</p> <p>The tools developed in this study allow for functional genomic analysis and make further development of reagents for the ferret model possible.</p

A new polygenic score for refractive error improves detection of children at risk of high myopia but not the prediction of those at risk of myopic macular degeneration

Background High myopia (HM), defined as a spherical equivalent refractive error (SER) ≤ −6.00 diopters (D), is a leading cause of sight impairment, through myopic macular degeneration (MMD). We aimed to derive an improved polygenic score (PGS) for predicting children at risk of HM and to test if a PGS is predictive of MMD after accounting for SER. Methods The PGS was derived from genome-wide association studies in participants of UK Biobank, CREAM Consortium, and Genetic Epidemiology Research on Adult Health and Aging. MMD severity was quantified by a deep learning algorithm. Prediction of HM was quantified as the area under the receiver operating curve (AUROC). Prediction of severe MMD was assessed by logistic regression. Findings In independent samples of European, African, South Asian and East Asian ancestry, the PGS explained 19% (95% confidence interval 17–21%), 2% (1–3%), 8% (7–10%) and 6% (3–9%) of the variation in SER, respectively. The AUROC for HM in these samples was 0.78 (0.75–0.81), 0.58 (0.53–0.64), 0.71 (0.69–0.74) and 0.67 (0.62–0.72), respectively. The PGS was not associated with the risk of MMD after accounting for SER: OR = 1.07 (0.92–1.24). Interpretation Performance of the PGS approached the level required for clinical utility in Europeans but not in other ancestries. A PGS for refractive error was not predictive of MMD risk once SER was accounted fo

Host gene expression signatures discriminate between ferrets infected with genetically similar H1N1 strains.

Different respiratory viruses induce virus-specific gene expression in the host. Recent evidence, including those presented here, suggests that genetically related isolates of influenza virus induce strain-specific host gene regulation in several animal models. Here, we identified systemic strain-specific gene expression signatures in ferrets infected with pandemic influenza A/California/07/2009, A/Mexico/4482/2009 or seasonal influenza A/Brisbane/59/2007. Using uncorrelated shrunken centroid classification, we were able to accurately identify the infecting influenza strain with a combined gene expression profile of 10 selected genes, independent of the severity of disease. Another gene signature, consisting of 7 genes, could classify samples based on lung pathology. Furthermore, we identified a gene expression profile consisting of 31 probes that could classify samples based on both strain and severity of disease. Thus, we show that expression-based analysis of non-infected tissue enables distinction between genetically related influenza viruses as well as lung pathology. These results open for development of alternative tools for influenza diagnostics

Classification analysis.

<p>Panel A shows the heat map of the 10 genes isolated by the USC algorithm to classify the samples with regards to infectious strain. The samples are denoted by their ID number and the euthanasia day, and are sorted according to strain (and dose for A/Cal/07 infected animals). The asterisks denote gene expression verified by qRT-PCR. Panel B shows the 7 genes that were used to classify the samples based on the cumulative histopathology score. The samples are denoted with ID number and euthanasia day, and are sorted according to cumulative histopathology score (given as numbers under the sample names). Panel C displays the 31 genes required to classify the samples based on infectious strain and histopathology score. The samples are denoted with ID number and euthanasia day, and are sorted according to strain and cumulative histopathology score (given as numbers under the heat map). All samples were correctly classified with regards to strain. The samples used to train the classification algorithm is denoted in black, correctly classified samples in the test set in green and incorrectly classified samples in red.</p

Global gene expression changes.

<p>Panel A shows a heat map of the 25% (7685 probes) most variable genes in the dataset for all samples. Each sample day represents three individual animals, except the control group (CTRL, n = 6). HD and LD indicate high dose and low dose, respectively. D1 through D7 designates the day of euthanasia. Panel B illustrates gene expression profiles of 1997 significantly changed probes with a fold change larger than +/−2 in at least one group when compared to the control group. The average fold change from the three animals within each group is shown. Red designate up-regulated genes, blue down regulated genes, where a more intense color illustrates a more pronounced fold change. The Venn diagram in panel C shows the number of probes up or down regulated after infection by any of the three strains (177 genes), by two of the three strains (228, 131 and 29 genes) and the number of probes aberrantly expressed in a strain specific pattern.</p

Retinal vessel diameter and cardiovascular mortality: Pooled data analysis from two older populations

10.1093/eurheartj/ehm221European Heart Journal28161984-199