Analysis of microRNA and Gene Expression Profiles in Multiple Sclerosis: Integrating Interaction Data to Uncover Regulatory Mechanisms

Multiple sclerosis (MS) is a common neurological disability of the central nervous system. Immune-modulatory therapy with interferon-β (IFN-β) has been used as a first-line treatment to prevent relapses in MS patients. While the therapeutic mechanism of IFN-β has not been fully elucidated, the data of microarray experiments that collected longitudinal gene expression profiles to evaluate the long-term response of IFN-β treatment have been analyzed using statistical methods that were incapable of dealing with such data. In this study, the GeneRank method was applied to generate weighted gene expression values and the monotonically expressed genes (MEGs) for both IFN-β treatment responders and nonresponders were identified. The proposed procedure identified 13 MEGs for the responders and 2 MEGs for the nonresponders, most of which are biologically relevant to MS. Our work here provides some useful insight into the mechanism of IFN-β treatment for MS patients. A full understanding of the therapeutic mechanism will enable a more personalized treatment strategy possible.

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Alterations in neuronal gene expression profiles in response to experimental demyelination and axonal transection

Multiple Sclerosis Journal ◽

10.1177/1352458509357063 ◽

2010 ◽

Vol 16 (3) ◽

pp. 303-316 ◽

Cited By ~ 1

Author(s):

G. Lovas ◽

JA Nielsen ◽

KR Johnson ◽

LD Hudson

Keyword(s):

Gene Expression ◽

Multiple Sclerosis ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Neurological Deficits ◽

Activating Transcription Factor 3 ◽

Post Intervention ◽

Neuronal Gene ◽

Activating Transcription Factor ◽

Axonal Transection

The main pathological features of multiple sclerosis, demyelination and axonal transection, are considered to cause reversible and irreversible neurological deficits, respectively. This study aimed to separately analyze the effects of these pathological hallmarks on neuronal gene expression in experimental paradigms. The pontocerebellar pathway was targeted with either lysolecithin-induced chemical demyelination or a complete pathway transection (axonal transection) in rats. Transcriptional changes in the pontocerebellar neurons were investigated with microarrays at days 4, 10 and 37 post-intervention, which was confirmed by immunohistochemistry on protein level. A common as well as unique set of injury-response genes was identified. The increased expression of activating transcription factor 3 (Atf3) and thyrotropin-releasing hormone (Trh) in both injury paradigms was validated by immunohistochemistry. The expression of Atf3 in a patient with Marburg’s variant of multiple sclerosis was also detected, also confirming the activation of the Atf3 pathway in a human disease sample. It was concluded that this experimental approach may be useful for the identification of pathways that could be targeted for remyelinative or neuroprotective drug development.

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GENE FUNCTION PREDICTION BY A COMBINED ANALYSIS OF GENE EXPRESSION DATA AND PROTEIN-PROTEIN INTERACTION DATA

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720005001612 ◽

2005 ◽

Vol 03 (06) ◽

pp. 1371-1389 ◽

Cited By ~ 11

Author(s):

GUANGHUA XIAO ◽

WEI PAN

Keyword(s):

Gene Expression ◽

Protein Interaction ◽

Gene Function ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Function Prediction ◽

Protein Interaction Data ◽

Interaction Data ◽

Gene Function Prediction ◽

Protein Protein Interaction

Prediction of biological functions of genes is an important issue in basic biology research and has applications in drug discoveries and gene therapies. Previous studies have shown either gene expression data or protein-protein interaction data alone can be used for predicting gene functions. In particular, clustering gene expression profiles has been widely used for gene function prediction. In this paper, we first propose a new method for gene function prediction using protein-protein interaction data, which will facilitate combining prediction results based on clustering gene expression profiles. We then propose a new method to combine the prediction results based on either source of data by weighting on the evidence provided by each. Using protein-protein interaction data downloaded from the GRID database, published gene expression profiles from 300 microarray experiments for the yeast S. cerevisiae, we show that this new combined analysis provides improved predictive performance over that of using either data source alone in a cross-validated analysis of the MIPS gene annotations. Finally, we propose a logistic regression method that is flexible enough to combine information from any number of data sources while maintaining computational feasibility.

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