Dataset Information

E-GEOD-29349

ABSTRACT: Microarray data of genome-wide gene expression profile in osmads6-1

PROVIDER: E-GEOD-29349 | ExpressionAtlas | 2014-07-03

REPOSITORIES: ExpressionAtlas

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			Action	DRS
	E-GEOD-29349-analysis-methods.tsv	Tabular
	E-GEOD-29349-atlasExperimentSummary.Rdata	Rdata
	E-GEOD-29349-configuration.xml	Xml
	E-GEOD-29349-percentile-ranks.tsv	Tabular
	E-GEOD-29349.condensed-sdrf.tsv	Tabular

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Project description:Metabolomic Analysis in Secondary Progressive and Benign MS. Preliminary data: We have conducted whole blood cell microarray comparing gene expression profile of 20 SPMS and 13 BMS patients. The data revealed significant increase in pathways involving iron ion binding, oxygen transporter activity and hemoglobin functions. Specifically, the identified down regulated genes are involved in interacting selectively and non-covalently with iron (Fe) ions. Heme has been described as a potent pro-inflammatory molecule that can induce multiple innate immune responses, and cause excessive iron and heme-induced oxidative stress and cell death. In the brain, it causes neurodegeneration. In addition, our microarray preliminary results also show a statistically significant increase in arachidonate 12-lipoxygenase activity in SPMS compared to benign MS. Specific aim. To validate metabolites and lipid biomarkers in serum samples from patients with benign and secondary progressive MS. Our central hypothesis is that the gene expression in various types of multiple sclerosis likely create a systematic metabolomic/lipidomic environment that leads to progression in MS. We plan to compare and contrast metabolomics in SP-MS to BMS patients using the innovative technology at the Metabolomics Research Core at the University of Michigan. By monitoring lipid and metabolite changes in SP and BMS patients, we aim to identify molecular processes and biomarkers of axonal damage for MS. Then, we plan to translate such metabolomic biomarkers to correlate with our rich clinical, immunological and imaging readouts. The goal of this project is to develop a blood-based test to differentiate SP-MS from BMS patients. To accomplish this, we will first identify the gene expression signature associated with of SP-MS. Then, we will correlate these gene expression changes with lipids and metabolite changes in the blood by metabolomic network analysis. Because metabolism is the final fingerprint of functionality and has been implicated in neurodegeneration, metabolomic network analysis can be used for refining the relationships between specific gene expression and metabolites and axonal damage in progressive MS. Ultimately, we hope to develop blood, CSF and stool-based assays, and use this comprehensive profile to predict susceptibility and progression of MS. This is a new and innovative idea, which will hope to lead to future diagnostic and therapeutic development in MS.

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E-GEOD-29349

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