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Microarray data of transcriptome shifts in blood cell subsets during S1P receptor modulator therapy.

ABSTRACT: Treatment with fingolimod, a sphingosine-1-phosphate (S1P) receptor modulator, prevents the egress of immune cell subpopulations from lymphoid tissues into the blood. We obtained peripheral blood samples from patients with relapsing multiple sclerosis before the initiation of fingolimod therapy, after one day and after 3 months. To investigate the differential expression induced by the drug, five different cell populations were isolated. We then employed 150 Human Transcriptome Arrays (HTA 2.0) interrogating >245,000 protein-coding and >40,000 non-coding transcript isoforms. After 3 months of treatment, CD4+ and CD8+ T-cells showed huge transcriptome shifts, whereas the profiles of B-cells (CD19+) were slightly altered and those of monocytes (CD14+) and natural killer cells (CD56+) remained unaffected. With >6 million probes for exons and splice junctions, our large HTA 2.0 dataset provides a deep view into alternative splicing patterns in immune cell subsets. Our data may also be useful for comparing the effects on gene expression signatures of novel S1P receptor modulators, which are currently tested in clinical trials for other autoimmune and neurodegenerative diseases.

SUBMITTER: Koczan D

PROVIDER: S-EPMC6057441 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMolecular profiling generates abundance measurements for thousands of gene transcripts in biological samples such as normal and tumor tissues (data points). Given such two-class high-dimensional data, many methods have been proposed for classifying data points into one of the two classes. However, finding very small sets of features able to correctly classify the data is problematic as the fundamental mathematical proposition is hard. Existing methods can find "small" feature sets, but give no hint how close this is to the true minimum size. Without fundamental mathematical advances, finding true minimum-size sets will remain elusive, and more importantly for the microarray community there will be no methods for finding them.ResultsWe use the brute force approach of exhaustive search through all genes, gene pairs (and for some data sets gene triples). Each unique gene combination is analyzed with a few-parameter linear-hyperplane classification method looking for those combinations that form training error-free classifiers. All 10 published data sets studied are found to contain predictive small feature sets. Four contain thousands of gene pairs and 6 have single genes that perfectly discriminate.ConclusionThis technique discovered small sets of genes (3 or less) in published data that form accurate classifiers, yet were not reported in the prior publications. This could be a common characteristic of microarray data, thus making looking for them worth the computational cost. Such small gene sets could indicate biomarkers and portend simple medical diagnostic tests. We recommend checking for small gene sets routinely. We find 4 gene pairs and many gene triples in the large hepatocellular carcinoma (HCC, Liver cancer) data set of Chen et al. The key component of these is the "placental gene of unknown function", PLAC8. Our HMM modeling indicates PLAC8 might have a domain like part of lP59's crystal structure (a Non-Covalent Endonuclease lii-Dna Complex). The previously identified HCC biomarker gene, glypican 3 (GPC3), is part of an accurate gene triple involving MT1E and ARHE. We also find small gene sets that distinguish leukemia subtypes in the large pediatric acute lymphoblastic leukemia cancer set of Yeoh et al.

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Microarray data of transcriptome shifts in blood cell subsets during S1P receptor modulator therapy.

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