Biomedical subjects

Sara Hilmer

Publications and source records attributed to Sara Hilmer.

3 recordsLinked to original sources

PubMed2005

Differences in gene expression profiles in dermal fibroblasts from control and patients with age-related macular degeneration elicited by oxidative injury.

The pathogenesis of age-related macular degeneration (AMD) is still unknown but there is growing evidence that a combination of both oxidative injury and genetic factors may play a role. One particle hypothesis proposes that dysregulation of multiple genes in response to an oxidative injury could contribute to the development of AMD. While direct examination of ocular cells from AMD patients is difficult, AMD also appears to have a systemic component. Therefore, as is the case with other central nervous diseases, peripheral sites may also manifest any underlying genetic abnormalities. For the present study, biopsy-derived fibroblasts from 4 patients with the early form and 4 patients with the late form of AMD and 3 age-matched control patients were grown in culture and treated with a nonlethal dose of the oxidative stimulus menadione. Gene expression patterns were quantitatively and qualitatively examined using Human Genome U95A GeneChips (Affymetrix) and verified by real-time PCR analysis. In response to the oxidative injury 755 genes were found to be upregulated at least twofold in one of the patients groups. Cluster analysis of expression profiles detected six patterns of dysregulation initiated by oxidative injury specific for the disease groups (98 genes total). Clusters of genes dysregulated by the sublethal oxidative injury in either early and/or late AMD groups were further categorized by overrepresentation of GO "biological process" categories using Expression Analysis Systematic Explorer (EASE) software. This approach demonstrated that four major functional gene groups including inflammatory/innate immune response, transcriptional regulation, cell cycle, and proliferation were significantly overrepresented (Fisher test ranging from 0.0393 to 0.00018) in both AMD patients groups in response to the oxidative injury. Despite the small number of patients in the study, specific biological and statistical differences in gene expression profiles between control and AMD patients were identified but only in the presence of an environmental stimulus.

Aged↗

PubMed2004

Interactively optimizing signal-to-noise ratios in expression profiling: project-specific algorithm selection and detection p-value weighting in Affymetrix microarrays.

MOTIVATION: The most commonly utilized microarrays for mRNA profiling (Affymetrix) include 'probe sets' of a series of perfect match and mismatch probes (typically 22 oligonucleotides per probe set). There are an increasing number of reported 'probe set algorithms' that differ in their interpretation of a probe set to derive a single normalized 'signal' representative of expression of each mRNA. These algorithms are known to differ in accuracy and sensitivity, and optimization has been done using a small set of standardized control microarray data. We hypothesized that different mRNA profiling projects have varying sources and degrees of confounding noise, and that these should alter the choice of a specific probe set algorithm. Also, we hypothesized that use of the Microarray Suite (MAS) 5.0 probe set detection p-value as a weighting function would improve the performance of all probe set algorithms. RESULTS: We built an interactive visual analysis software tool (HCE2W) to test and define parameters in Affymetrix analyses that optimize the ratio of signal (desired biological variable) versus noise (confounding uncontrolled variables). Five probe set algorithms were studied with and without statistical weighting of probe sets using the MAS 5.0 probe set detection p-values. The signal-to-noise ratio optimization method was tested in two large novel microarray datasets with different levels of confounding noise, a 105 sample U133A human muscle biopsy dataset (11 groups: mutation-defined, extensive noise), and a 40 sample U74A inbred mouse lung dataset (8 groups: little noise). Performance was measured by the ability of the specific probe set algorithm, with and without detection p-value weighting, to cluster samples into the appropriate biological groups (unsupervised agglomerative clustering with F-measure values). Of the total random sampling analyses, 50% showed a highly statistically significant difference between probe set algorithms by ANOVA [F(4,10) > 14, p < 0.0001], with weighting by MAS 5.0 detection p-value showing significance in the mouse data by ANOVA [F(1,10) > 9, p < 0.013] and paired t-test [t(9) = -3.675, p = 0.005]. Probe set detection p-value weighting had the greatest positive effect on performance of dChip difference model, ProbeProfiler and RMA algorithms. Importantly, probe set algorithms did indeed perform differently depending on the specific project, most probably due to the degree of confounding noise. Our data indicate that significantly improved data analysis of mRNA profile projects can be achieved by optimizing the choice of probe set algorithm with the noise levels intrinsic to a project, with dChip difference model with MAS 5.0 detection p-value continuous weighting showing the best overall performance in both projects. Furthermore, both existing and newly developed probe set algorithms should incorporate a detection p-value weighting to improve performance. AVAILABILITY: The Hierarchical Clustering Explorer 2.0 is available at http://www.cs.umd.edu/hcil/hce/ Murine arrays (40 samples) are publicly available at the PEPR resource (http://microarray.cnmcresearch.org/pgadatatable.asp http://pepr.cnmcresearch.org Chen et al., 2004).

Cluster Analysis↗

PubMed2004

Differential expression of immunoregulatory genes in male and female Norway rats following infection with Seoul virus.

Males of many species are more susceptible than females to infections caused by parasites, bacteria, fungi, and viruses. Following inoculation with Seoul virus, male rats have more virus present in target organs and shed virus longer than females. The goal of this study was to test the hypothesis that variation in the expression of genes associated with immune function mediates sex differences in hantavirus infection. Using DNA microarrays, we examined changes in gene expression in lung tissue during the early (when animals are viremic and shedding virus; Day 15 post-inoculation (p.i.)) and late (animals have low levels of infectious virus, but high antibody titers; Day 40 p.i.) phases of infection in adult male and female rats. After normalizing the gene expression levels from infected animals to the gene expression levels from same-sex uninfected controls, our data revealed that 1,813 genes were differentially expressed between the sexes during infection. The expression of key transcriptional factors (e.g., eIF-2 alpha, NF-kappa B, IRF-1, NF-IL-6, and STAT6) and genes that encode for proinflammatory (e.g., TNF alpha R, IL-1R, and IL-1RAcP), antiviral (e.g., IFN gamma R and Mx proteins), T cell (e.g., CD3 and TCR), and Ig superfamily (e.g., IgM, IgG, and MHC class I and II) proteins was higher in females than males. Conversely, males had higher expression of heat shock protein genes (e.g., hsp70) suggesting that cellular stress is elevated in males. These data provide candidate genes and cellular pathways that may underlie sex differences in responses to Seoul virus and possibly other hemorrhagic fever viruses.

Animals↗