Optimizing multiple sclerosis diagnosis: gene expression and genomic association

Abstract

ObjectiveThe diagnosis of multiple sclerosis (MS) at disease onset is sometimes masqueraded by other diagnostic options resembling MS clinically or radiologically (NonMS). In the present study we utilized findings of large-scale Genome-Wide Association Studies (GWAS) to develop a blood gene expression-based classification tool to assist in diagnosis during the first demyelinating event.MethodsWe have merged knowledge of 110 MS susceptibility genes gained from MS GWAS studies together with our experimental results of differential blood gene expression profiling between 80 MS and 31 NonMS patients. Multiple classification algorithms were applied to this cohort to construct a diagnostic classifier that correctly distinguished between MS and NonMS patients. Accuracy of the classifier was tested on an additional independent group of 146 patients including 121 MS and 25 NonMS patients.ResultsWe have constructed a 42 gene-transcript expression-based MS diagnostic classifier. The overall accuracy of the classifier, as tested on an independent patient population consisting of diagnostically challenging cases including NonMS patients with positive MRI findings, achieved a correct classification rate of 76.0 ± 3.5%.InterpretationThe presented diagnostic classification tool complements the existing diagnostic McDonald criteria by assisting in the accurate exclusion of other neurological diseases at presentation of the first demyelinating event suggestive of MS.

No differential gene expression for CD4+ T cells of MS patients and healthy controls

“…Gurevich et al reported a gene expression panel for CD4 þ T cells consisting of 42 genes as a good indicator of disease status. 11 When we perform a principal component analysis using the genes in that panel (we detected 25 out of 42 reported genes in our data) we did not observe the strong clustering of MS patients observed by Gurevich et al We note that not all genes assessed by Gurevich et al were represented in our dataset, possibly the addition of those genes in the panel will improve the clustering. Furthermore, the MS patients in our study are relatively benign in their disease course; including patients with a more aggressive disease course might improve the clustering power of this gene panel.…”

“…In addition, studies focusing on activated CD4 þ T cells or CD4 þ T cells from MS patients and healthy controls reported gene panels that may serve as a biomarker for MS activity and treatment response. 10,11 The majority of young MS patients are treated with immunomodulatory drugs, which is likely to affect the gene expression of immune cells. In our study, we aimed to identify differential gene expression of CD4 þ T cells between MS patients and healthy controls.…”

“…In addition, studies focusing on activated CD4 + T cells or CD4 + T cells from MS patients and healthy controls reported gene panels that may serve as a biomarker for MS activity and treatment response. 10,11…”

Inferring Multiple Sclerosis Stages from the Blood Transcriptome via Machine Learning

“…Although several studies have explored gene expression patterns from blood in MS using traditional statistical analyses, 9 , 10 , 11 only a couple of reports have attempted to apply machine learning to blood transcriptomics and were limited to discrimination between the RR MS form and controls. 22 In this context, our study, which also included PBMC transcriptomes from the progressive forms of MS, responds to the unmet clinical need of potential predictive biomarkers for distinct MS courses.…”

Host cell virus entry mechanisms enhance anti-JCV-antibody switch in natalizumab-treated multiple sclerosis patients

“…Probe synthesis using 3 μg total RNA was performed by the two-cycle RNA amplification kit protocol and in vitro transcription was performed with the GeneChip IVT Labeling Kit (both Affymetrix Inc., Santa Clara, CA, USA). The biotin-labeled IVT-RNA was hybridized to a Genechip array (HU133A-2, including 22,000 probes corresponding to 14,500 human genes), washed in a GeneChip Fluidics Station 450 (Hewlett Packard, Palo Alto, CA, USA), and scanned using GeneArray TM scanner G2500A (Hewlett Packard) according to the manufacturer's protocol, as previously described (Gurevich et al 2015).…”