Meta-Analysis of Transcriptomic Datasets Quantifies Differential Gene Expression, Affected Pathways, and Predicted Drugs from Hantavirus-Infected Human Material

Abstract

Background We performed a meta-analysis that consisted of re-analyzing publicly available RNA-sequencing datasets across representative species within Orthohantavirus and across host cell types to identify differentially expressed genes associated with hantavirus infection. In addition, we determined signaling pathways that were significantly perturbed in human tissue, as well as predicted existing drugs that could be repurposed to potentially reduce the effects of acute hantavirus infection. Methods NCBI-GEO and ENA databases were searched to find datasets for this analysis, then processed using the Automated Reproducible MOdular Workflow for Preprocessing and Differential Analysis of RNA-seq Data (ARMOR) to identify differentially expressed genes (DEGs). The results from this analysis were then processed using signaling pathway impact analysis (SPIA) to identify enriched signaling pathways. Finally, that data was processed using the Pathways2Targets program to identify existing therapeutics that target the impacted cellular pathways. Results Our results include affirming the roles of the previously identified interferon pathway and the MYBL1 and CCR7 genes, as well as newly identified genes, including SLC27A3, TSEN34 and neuron associated NOG and AMIGO1. We predicted dinaciclib, alvodicib, and ruxolitinib, among many other drugs, that could be useful as potential HFRS and HCPS treatments. Conclusion These data can be used in the design of future wet lab experiments to better characterize the human transcriptional response to infection, as well as to improve host-based diagnostics and/or therapeutics to reduce the severity of hantavirus infection.