Project description:Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio. The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for ‘RA’), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways. First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10. Synovial tisssue from healthy joints, OA joints, and RA joints.

Project description:Information about the genes that are preferentially expressed during the course of Alzheimer’s disease (AD) could improve our understanding of the molecular mechanisms involved in the pathogenesis of this common cause of cognitive impairment in older persons, provide new opportunities in the diagnosis, early detection, and tracking of this disorder, and provide novel targets for the discovery of interventions to treat and prevent this disorder. Information about the genes that are preferentially expressed in relationship to normal neurological aging could provide new information about the molecular mechanisms that are involved in normal age-related cognitive decline and a host of age-related neurological disorders, and they could provide novel targets for the discovery of interventions to mitigate some of these deleterious effects. Aim 1. Collect brain samples from three Alzheimer’s Disease Centers (ADCs) for subsequent gene expression profiling. Individuals will be stratified with respect to diagnostic groups (using both clinical and neuropathological criteria), age groups, and APOE genotype. 150 individual brains will be sampled from the Arizona ADC, the Duke University ADC, and the Washington University ADC. Miniscule sample sizes (200 um of sectioned tissue) from six brain regions that are histopathologically or metabolically relevant to AD and aging will be collected, ensuring that this proposal does not deplete the national resource. Frozen and fixed samples will be sent to Phoenix, sectioned in a standardized fashion, and then returned. Aim 2. Tissue heterogeneity will be eliminated prior to expression profiling by performing laser capture microscopy on all brain regions. Aim 3. Expression profile LCM-captured cells on the Affymetrix U133 Plus 2.0 array (~55,000 transcripts), and quickly provide these data to the community at large. Aim 4. Identify pathogenic cascades related to each of the clinico-pathologic correlates using unsupervised and supervised analyses coupled with a hypothesis-driven approach. Aim 5. Validation of the expression correlates at the protein and functional levels. Scientific progress in the last few years has improved our understanding of AD and raised the hope of identifying treatments to halt the progression and prevent the onset of this disorder. For instance, researchers have begun to characterize the cascade of molecular events which lead to the major histopathological features of the disorder: neuritic plaques, which contain extra-cellular deposits of amyloid beta-peptides (Abeta); neurofibrillary tangles, which contain the hyperphosphorylated form of the intracellular, microtubule-associated protein, tau; and a loss of neurons and synapses. These molecular events provide targets for the development of promising new treatments. For example, A-beta has been postulated to trigger a cascade of events that are involved in the pathogenesis of AD. This proposal hopes to provide new information about the genes that are preferentially expressed in the development of AD histopathology, including the over-expression of APP, amyloid-induced neurotoxicity, and hyperphosphorylation of tau, as well as bring clarity to the metabolic abnormalities that seem to play a role in dementia and AD development and pathology. We will perform LCM on 6 brain regions with about 14 biological replicates per brain region. The brain regions are as follows: 1) entorhinal cortex 2) hippocampus 3) medial temporal gyrus 4) posterior cingulate 5) superior frontal gyrus and 6) primary visual cortex. We will collect layer III pyramidal cells from the white matter in each region, isolate total RNA from LCMed cell lysates, and perform double round amplification of each sample for array analysis. Keywords: other

Project description:Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio. The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for ‘RA’), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways. First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio.

Dataset Information

Transcription profiling by array of kidney, liver and spleen across different centers - scien-affy-human-87089

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