ABSTRACT: Purpose: High throughput small RNA-Seq analysis from circulating biofluids is crucial in driving the discovery of non-invasive miRNA biomarkers for TBI. In this study, we focused our analysis on acute post-TBI alterations in plasma miRNA profiles in adult, male Sprague-Dawley rats. We compared the plasma miRNA profiles from naive, sham-operated controls and rats with mild and severe TBI. The aim was to identify a miRNA signature that would distinguish the rats with mTBI from the naive and sham-operated controls. Next, we compared the rats with mild and severe TBIs to investigate if a dose-dependent increase would occur in the plasma miRNA levels with corresponding increase in injury severities. Finally, we analysed if the sham surgery itself results in alterations in circulating miRNA profiles, by comparing the naive and sham-operated controls. Methods: TBI was induced with the lateral fluid percussion injury (FPI) model. Tail-vein plasma was collected at 2 days post-TBI from 31 adult male, Sprague-Dawley rats (5 naive, 8 sham-operated controls, 10 mTBI and 8 sTBI) under inhalation of isoflurane anesthesia. Small RNA-Seq was performed from plasma samples of a subset of 20 rats (5 cases each from naive, sham-operated controls, mTBI and sTBI groups). For each case, RNA was isolated from 200 µL plasma using the miRNeasy serum/plasma kit. The extracted RNA was subjected to qPCR-based quality control (QC). Small RNA library preparation was performed with the QIASeq miRNA library kit for Illumina NGS systems. Single-end sequencing of 75 bp reads was performed at a depth of 12M, with one sample/lane in the Illumina NextSeq 550. One naive plasma sample failed at the library preparation step. Hence, small RNA-Seq was succesful for 19/20 cases. The raw fastq files obtained from small RNA-Seq were first manually inspected with FastQC (v0.11.3) to check the overall quality of the sequencing data. For primary quantification of read counts, the fastq files were then uploaded to the Qiagen Geneglobe Data analysis center (DAC), a freely available web resource to analyze data from Qiagen’s QIASeq NGS library kits (https://geneglobe.qiagen.com/in/analyze/). Mapping was performed in the DAC portal with bowtie, using the rat reference genome RGSC Rnor_6.0. Annotation of miRNAs was performed with miRBase v21. Following primary quantification, differential expression analysis for miRNAs was performed with DESeq2 (v1.22.2) in R environment (v3.5.3). Logistic regression (LR) with feature selection utilizing nested leave-one-out cross-validation was also performed to identify miRNAs (“features”) contributing most to groupwise differences (differences between naïve, sham, mTBI, sTBI, sTBI + mTBI and naïve + sham). Results: A total of 748 miRNAs were detected across all samples, out of which 723 (97%) were expressed in all the four groups (naive, sham-operated, mTBI and sTBI). Based on the differential expression and logistic regression analyses, we identified five miRNA candidates that contributed most to the seperation between the mTBI and naive groups: rno-miR-9a-3p, rno-miR-153-3p, rno-miR-15a-3p, rno-miR-136-3p and rno-miR-434-3p. We validated the small RNA-Seq data for these miRNA candidates with miRCURY reverse transcriptase quantitative PCR (RT-qPCR). We could succesfully validate elevated levels of miR-9a-3p, miR-136-3p and miR-434-3p in the mTBI group in comparison to naive. When analysed from the whole cohort, the elevated levels of these miRNAs were consistently observed in the mTBI group compared to the naive, both with RT-qPCR and droplet digital PCR (ddPCR). Further, all the three miRNAs revealed elevated plasma levels in the sTBI group in comparison to the mTBI. Conclusions: Elevated plasma miRNA signature of miR-9a-3p, miR-136-3p and miR-434-3p distinguishes rats with mTBI from the naive, in the lateral FPI model of TBI. Further, all these miRNAs exhibit a dose-dependent increase in plasma levels with increase in injury severities.
