Chikungunya virus time course infection of human macrophages reveals intracellular signaling pathways relevant to repurposed therapeutics

Abstract

Background Chikungunya virus (CHIKV) is a mosquito-borne pathogen, within the Alphavirus genus of the Togaviridae family, that causes ~1.1 million human infections annually. CHIKV uses Aedes albopictus and Aedes aegypti mosquitoes as insect vectors. Human infections can develop arthralgia and myalgia, which results in debilitating pain for weeks, months, and even years after acute infection. No therapeutic treatments or vaccines currently exist for many alphaviruses, including CHIKV. Targeting the phagocytosis of CHIKV by macrophages after mosquito transmission plays an important role in early productive viral infection in humans, and could reduce viral replication and/or symptoms. Methods To better characterize the transcriptional response of macrophages during early infection, we generated RNA-sequencing data from a CHIKV-infected human macrophage cell line at eight or 24 hours post-infection (hpi), together with mock-infected controls. We then calculated differential gene expression, enriched functional annotations, modulated intracellular signaling pathways, and predicted therapeutic drugs from these sequencing data. Results We observed 234 pathways were significantly affected 24 hpi, resulting in six potential pharmaceutical treatments to modulate the affected pathways. A subset of significant pathways at 24 hpi includes AGE-RAGE, Fc epsilon RI, Chronic myeloid leukemia, Fc gamma R-mediated phagocytosis, and Ras signaling. We found that the MAPK1 and MAPK3 proteins are shared among this subset of pathways and that Telmisartan and Dasatinib are strong candidates for repurposed small molecule therapeutics that target human processes. The results of our analysis can be further characterized in the wet lab to contribute to the development of host-based prophylactics and therapeutics.

Joint Secondary Transcriptomic Analysis of Non-Hodgkin’s B-Cell Lymphomas Predicts Reliance on Pathways Associated with the Extracellular Matrix and Robust Diagnostic Biomarkers

“…We sorted the results so that drug targets present in multiple signaling pathways would be ranked higher ( Table 6 ). Though Pathways2Targets results are in no way conclusive of drug efficacy for a novel indication, the algorithm provides a short-list of drugs for subsequent validation in the laboratory and has a track record of returning many drugs already in use for a given disease and several novel drug candidates [ 44 - 47 ]. Based on the Pathway2Targets output, we predicted the most relevant existing FDA-approved drugs for other indications that could affect the lymphoma phenotype are Doxycycline, Ocriplasmin, and Collagenase.…”

Pathway2Targets: an open-source pathway-based approach to repurpose therapeutic drugs and prioritize human targets

“…Similarly, the enrichr pathway enrichment software only required a gene symbol, log 2 fold-change values, and FDR-adjusted p -values as input from the DEG list. The statistically significant differentially expressed genes (DEGs; FDR-corrected p -value < 0.05) were then subjected to signaling pathway analysis using the Signal Pathway Impact Analysis (SPIA) algorithm with 3,000 bootstrap replicates to generate a null distribution for each of over 2,000 public signaling pathways ( Tarca et al, 2009 ), as reported previously ( Scott, Jensen & Pickett, 2021 ; Gray et al, 2022 ; Moreno et al, 2022 ; Rapier-Sharman, Clancy & Pickett, 2022 ; Ferrarini et al, 2021 ; Scott et al, 2022 ; Gifford & Pickett, 2022 ). The lists of pathways were derived from publicly available versions of KEGG ( Aoki-Kinoshita & Kanehisa, 2007 ), Reactome ( Jassal et al, 2020 ), Pathway Interaction Database ( Schaefer et al, 2009 ), BioCarta, and Panther ( Mi et al, 2017 ).…”

“…This updated algorithm retrieves additional target information, clinical trial data, automatically fetches the reactome pathway diagrams for the signaling pathways with the highest number of targets, and accepts reactome pathway enrichments generated by the enrichr algorithm ( Xie et al, 2021a ). This additional data and prioritization method are used by the updated algorithm to generate ranked lists of targets and therapeutics that can be applicable to multiple use cases ( Scott, Jensen & Pickett, 2021 ; Gray et al, 2022 ; Moreno et al, 2022 ; Rapier-Sharman, Clancy & Pickett, 2022 ). The entities in these lists can then be evaluated as candidates for condition-specific repurposing efforts based solely on the unique signaling pathway “profile” for the disease/condition of interest.…”

Secondary Analysis of Human Bulk RNA-seq Dataset Suggest Potential Mechanisms and Transcriptional Biomarkers for Letrozole Resistance in Estrogen Positive (ER+) Breast Cancer

“…The Pathway2Targets algorithm was then used to predict and prioritize relevant therapeutic targets from the transcriptomics signatures that could be repurposed in the case of Letrozole resistance, similar to prior work [34][35][36][37]. Briefly, this algorithm identifies known drug targets within each of the significant signaling pathways.…”