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.

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:Purpose: We utilised high-throughput genomic approache to investigate transcriptional changes in two Neurospora strains (74A [wild-type] and mutant Δmus-52), under different phosphate availability conditions to evaluate the effects of mus-52 deletion in gene transcriptional modulation, and thus, infer its influence regarding metabolic response to changing extracellular levels of inorganic phosphate (Pi). Methods: All N. crassa strains were maintained on Vogel’s minimal medium, pH 5.8, at 30°C. Conidia from each strain were germinated for 5 h at 30°C in an orbital shaker (200 rpm), in low-Pi (10µM) and high-Pi (10mM) media. Total RNA was isolated from both strains and a total of 4 cDNA libraries were sequenced, with their respective biological triplicates corresponding to the paired libraries, using an Illumina HiSeq2000 sequencer, to generate 100 bp paired-end reads. FastQC software was used to visualize the library quality before and after trimming. For quality and sequence filtering, a Phred score lower than 20 was employed to remove sequencing bases from the read ends. Filtered reads were mapped onto the N. crassa genome using Bowtie2 software. The reads count values were obtained and used to calculate the expression variation of the transcripts from different conditions, considering the statistical significance of the differential gene expression. qRT–PCR validation was performed using SYBR Green assays. Results: extracellular Pi availability influenced the expression of genes involved in several biological functions. The main affected gene groups were those associated with integral components of the membrane, such as transport, regulation, and cell signalling pathways, as well as genes involved with the nucleolus and protein synthesis. The absence of mus-52 affected global gene transcription in all conditions tested, highlighting the expression of some specific gene groups, such as transcription factors, kinase proteins, circadian clock-related genes, oxi-reduction balance, phosphate pathways, and general metabolism. Conclusions: The consequences of N. crassa mus-52 disruption to the cell may be deeper than the obvious phenotypic aspect. It may affect canonical and non-canonical pathways, leading to alternative adaptive processes, altering the perception and response to the environmental changes. Overall design: Transcriptional profile of N. crassa strains using RNA-sequencing technique to evaluate the effects of mus-52 deletion in gene transcriptional modulation, and thus, infer its influence regarding metabolic response to changing extracellular levels of inorganic phosphate.

Dataset Information

Hatakeyama2003_MAPK

Publications

Temperature dependence of the epidermal growth factor receptor signaling network can be accounted for by a kinetic model.

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