Abstract Body: Background
Subarachnoid Hemorrhage (SAH) accounts for 2-7% of strokes and has high mortality and morbidity. We sought to identify peripheral blood transcriptome changes in the acute SAH phase that associated with 90-day outcome, as measured by the modified Rankin Scale (mRS), to gain insights about potential mechanisms contributing to long term outcome.
Methods
We sequenced the peripheral blood transcriptome of SAH patients within 3 days post ictus and stratified the patients into patients with Good (mRS≤2, n=37) and Poor (mRS≥3, n=23) outcomes at 90-day follow up. We generated the co-expression networks using the Weighted Gene Co-Expression Network Analysis (WGCNA) package to determine modules (groups of co-expressed genes) associated with 90-day SAH outcome. The outcome-significant modules (p<0.05) were further analyzed for their biological relevance using pathway analysis.
Results
We identified two outcome-significant modules, the Pink module and the Purple module. The Pink module was enriched (corrected p<0.05) in Monocyte- and Granulocyte-specific genes, while the Purple module and its hubs were enriched in Monocyte-specific genes. The hub genes are the most interconnected genes in each module, which are therefore potential master regulators. The Pink module was enriched (corrected p<0.05) in Neutrophil Degranulation, an inflammatory pathway which was predicted to be activated in patients with worse outcome, in Histone Modification Signaling, which is involved in epigenetic control of gene expression, and in SUMOylation of transcriptional cofactors, which confers post-transcriptional modifications of these cofactors. The Purple module was enriched in 45 canonical biological pathways, including numerous inflammatory pathways predicted to be activated in SAH patients with worse outcomes, such as Neutrophil Degranulation, Phagosome Formation, IL-8 Signaling, and Macrophage Classical Activation Signaling.

Conclusions
Early peripheral blood changes in the transcriptome architecture following SAH are associated with long-term outcome. The identified genes and networks may guide the search for potential biomarkers of outcome and novel treatment targets.