Blood transcriptome changes after stroke in an African American population

Abstract

ObjectiveMolecular diagnostic medicine holds much promise to change point of care treatment. An area where additional diagnostic tools are needed is in acute stroke care, to assist in diagnosis and prognosis. Previous studies using microarray‐based gene expression analysis of peripheral blood following stroke suggests this approach may be effective. Next‐generation sequencing (NGS) approaches have expanded genomic analysis and are not limited to previously identified genes on a microarray chip. Here, we report on a pilot NGS study to identify gene expression and exon expression patterns for the prediction of stroke diagnosis and prognosis.MethodsWe recruited 28 stroke patients and 28 age‐ and sex‐matched hypertensive controls. RNA was extracted from 3 mL blood samples, and RNA‐Seq libraries were assembled and sequenced.ResultsBioinformatical analysis of the aligned RNA data reveal exonic (30%), intronic (36%), and novel RNA components (not currently annotated: 33%). We focused our study on patients with confirmed middle cerebral artery occlusion ischemic stroke (n = 17). On the basis of our observation of differential splicing of gene transcripts, we used all exonic RNA expression rather than gene expression (combined exons) to build prediction models using support vector machine algorithms. Based on model building, these models have a high predicted accuracy rate >90% (spec. 88% sen. 92%). We further stratified outcome based on the improvement in NIHss scores at discharge; based on model building we observe a predicted 100% accuracy rate.Interpretation NGS‐based exon expression analysis approaches have a high potential for patient diagnosis and outcome prediction, with clear utility to aid in clinical patient care.

Assessing the accuracy of blood RNA profiles to identify patients with post-concussion syndrome: A pilot study in a military patient population

“…Based on our previous experiences, we focused our study on exon expression values, rather than whole gene expression values. [ 9 ] The exon expression data were filtered to remove low expressed RNAs and normalized for library size (reads per kilobase per million mapped (RPKM)). The data were further normalized to the trimmed mean value of the filtered data set.…”

“…The resulting data were subjected to one-way ANOVA using PCS diagnosis as the independent variable. To perform cluster analysis and prediction modeling, we relaxed our p-value to an unadjusted p < 0.001, yielding 29 differentially expressed exon values, with an overall fold change that ranged from -10 to -2.1 (parameters based on [ 9 ]). Interestingly, exon expression levels were predominantly decreased in the PCS group compared to controls ( Fig 3A ).…”

“…Our previous study suggests exon expression values to have greater diagnostic utility. [ 9 ] We use whole peripheral blood as a simple biofluid for biomarker assessment, because peripheral blood mononuclear cell isolation procedures affect transcription. [ 17 ] Finally, while previous studies focused on potential biological mechanisms (NFKB and growth factor regulation,[ 10 , 14 ]) we focused on the accuracy of this approach for clinical diagnostic purposes.…”

“…Different disorders or damage to the CNS may impart differential effects on the transcriptome in these cells[ 6 ]. Such an approach shows remarkable sensitivity and specificity for stroke diagnosis and prognosis [ 7 – 9 ]. Recent studies have identified differentially expressed genes in blood following acute concussion, acute sports related concussion, and blast-TBI via microarray technology, [ 10 , 11 ] however prediction modeling was not performed.…”

Profiling of exercise-induced transcripts in the peripheral blood cells of Thoroughbred horses

“…Transcriptome analyses based on DNA microarrays and RNA sequencing provide a snapshot of actively expressed genes and transcripts under various conditions [4, 19, 29] and can characterize all transcriptional phenomena such as several biological processes in cells. DNA microarray technology allows the investigation of gene expression profiles [28] and has enabled the parallel detection of thousands of genes in a single analysis.…”