Transcriptomic analysis of gene signatures associated with sickle pain

Abstract

Pain is a hallmark feature of sickle cell disease (SCD). Recurrent and unpredictable acute pain due to vaso-oclussive crises (VOC) is unique to SCD; and can be superimposed on chronic pain. To examine the mechanisms underlying pain in SCD, we performed RNA sequencing of dorsal root ganglion (DRG) of transgenic sickle mice and their age-matched control mice expressing normal human hemoglobin A, at 2 and 5 months of age. Sickle and control mice of both ages were equally divided into hypoxia/reoxygenation (to simulate VOC) and normoxia treatment, resulting in eight groups of mice. Each group had at least six mice. RNA isolated from the DRG was sequenced and paired-end 50 bp sequencing data were generated using Illumina's HiSeq 2000. This large dataset can serve as a resource for examining transcriptional changes in the DRG that are associated with age and hypoxia/reoxygenation associated signatures of nociceptive mechanisms underlying chronic and acute pain, respectively.

Figure 1. Overview of the study design.

Two transgenic mouse models—HbSS-BERK (sickle) and HbAA-BERK (control) and two age groups (2 and 5 months old) of each genotype were examined. Each age group/genotype was either treated with hypoxia/reoxygenation (H/R) or treated at room air (normoxia). Each treatment was performed on at least 6 mice. RNA was isolated from the DRG of each mouse and sequenced to obtain gene profile associated with both genotypes, treatments, age and pain status. Subsequent mapping of the sequencing reads provides whole transcriptomic status of individual samples. Differential gene expression following between group comparisons is undertaken to provide insight into the genetic signatures of chronic and VOC-evoked pain. Since these sickle mice recapitulate the features of chronic pain in patients with SCD, which increases with age and the H/R evoked hyperalgesia mechanistically mimics pain due to VOC, this study design is expected to identify mechanisms involved in these sickle pain conditions.

Figure 2. Robustness and quality of RNA sequencing run and processed data.

Each sub-figure illustrates the results for the following quality metric: (a) read quality of both runs in paired-end sequencing based on Phred quality score, (b) percentage of reads aligned to UCSC mm10 mouse reference genome, (c) average insert length in base pairs measured after alignment, and (d) number of reads in each paired-end runs. Each box plot displays the range and distribution of a quality metric computed for sequencing sample runs. The horizontal line is the median, the top of the box is the upper quartile, and the bottom of the box is the lower quartile.

Figure 3. RT-qPCR validation and example of identification of genetic markers.

The RT-qPCR expression data provides validation for genes that were found to be differentially expressed in the RNA-seq data at two different ages of sickle and control mice under two different treatment conditions. (a), (b) and (c) show qPCR validation for differentially expressed SPRR1A, SerpinA1e and SerpinA3g genes, respectively.