Computational analysis for infectious diseases surveillance and host-pathogen interactions (in the context of Influenza A viruses)
Influenza A virus (IAV) is a major public health problem responsible for the death of half \na million people every year worldwide. Zoonotic transmissions of the virus from swine and avian \norigin have occurred and can in the worst-case result in pandemics. Prediction of the next \npandemic strain is still a major challenge as mechanisms of antigenic shift and the zoonotic \nnature of influenza viruses are still poorly understood. On the other hand, the current vaccination \nstrategy treating seasonal influenza viruses, given its own problems in efficacy, is not well suited \nto mitigate an impending pandemic. While there are few clinically approved anti-IAV drugs for \ntherapeutic and prophylactic use, these drugs have also been challenged by the emergence of \ndrug resistance, toxicity, adverse effects and low efficacy. Hence, development of a broad range \nanti-IAV drug that could target all of the IAV strains irrespective of their source is critically \nimportant. \nTo discover effective IAV vaccines and alternative anti-IAV therapeutics, it is crucial to \nunderstand how IAVs adapt to different species’ host ranges and vice versa, how different \nspecies hosts respond to different IAV subtypes. Additionally, it is crucial to understand IAVhost interactions and identifying critical viral or host factors that could support the replication of \nthe virus or induce pathologic conditions. Recently, high throughput technologies such as mRNA \nmicroarray-based gene expression, genome-wide siRNA screening, and proteomic analysis are \nproviding in-depth insights into host-pathogen interactions of IAVs. Hence, this study aimed to \ncomputationally investigate IAV-host interactions using three data set (transcriptome, genomewide siRNA screens and interactome) and identify 1) virus and host specific responses, 2) host \ndeterminants in host adaptations and 3) host targets for IAV therapeutics. \nThis thesis contains four main projects. In the first project (chapter 3), we investigated the \nhost gene expression changes of eight IAVs (H1N1/WSN, pH1N1, H5N2/F118, H5N2/F59, \nH5N2/F189, H5N3, H7N9 and H9N2 viruses) in A549 cells at different time points of infections. \nThen we integrated the differentially expressed genes (DEGs) in at least 3 viruses, with 1713 and \n1780 host factors comprehensively curated from 11 siRNA and 13 interactome studies \nrespectively. The integration of the three data set highlighted plausible influenza A virus required \nhost factors (IHFs) that could be targeted against IAVs. The up-regulated IHFs (e.g. TRIM21, \nTRIM26, IRF2, and SAMHD1) might support the replication of IAV through suppression of the innate and adaptive antiviral immune responses. The other up-regulated IHFs could also enhance \nthe replication of IAV at different stages of the virus lifecycle: endocytosis (BTC), prevention of \napoptosis (TIMM17A), nuclear import (JAK2), and translation elongation of viral proteins \n(HEXIM1). Although several of these IHFs have been implicated in other viruses, the detailed \nmechanisms of how several of these up-regulated IHFs could support IAV replication require \nfurther investigation. \nThe second project (chapter 4), explored a comprehensive analysis of host gene \nexpression changes in different IAV infections in different host species. We used host gene \nexpression signatures of cell lines from three species (A549 (human), CEF (chicken), and \nMDCK (canine) in response to six IAVs (H1N1/WSN, H5N2/F59, H5N2/F118, H5N2/F189, \nH5N3 and H9N2 viruses). To compare the expression changes between the three species, we \nperformed comprehensive probe set re-annotation and human ortholog mapping. The result \nshowed that the expression signatures of different IAV isolates in a single species cell type are \nmore similar to each other compared to the expression signatures of a single isolate in different \nspecies’ cell types. The functional annotations (pathways) and the highly expressed cell-specific \nsignatures indicated that IAVs up-regulated host factors could induce virus infectivity (e.g. \nOSBPL1A and ARHGAP21), reduce apoptosis (e.g. MRPS27) and increase cell proliferation (e.g. \nCOPS2) in CEF cells. Conversely, increased antiviral, pro-apoptotic and inflammatory \nsignatures have been identified in A549 cells. Except in H5N3 virus infections, generally IAV \ninfections down-regulates genes associated with cell cycle and metabolic pathways with the \nstrongest effect in MDCK cells, followed by A549 cells in a strain dependent manner, but not in \nCEF cells. Previously our group demonstrated that the replication of the examined viruses was \nsignificantly higher in CEF cells than the other cell types. Thus, we hypothesise that this could \npartially explain the mechanism how infectious LPAI viruses shed by chickens lack the \ninflammatory response and cellular disruption that may lead to disease conditions. \nIn the third project (chapter 5), we used a systems-based approach to investigate changes \nto the transcriptome of primary murine lung macrophages (PMФ) in response to infection with \nthe mouse-adapted H1N1/WSN virus and low pathogenic avian influenza (LPAI) viruses H5N2 \nand H5N3. The results showed that while all viruses induced antiviral responses, the H5N3 \ninfection resulted in higher expression levels of cytokines and chemokines associated with \ninflammatory responses. Previously, our group showed that the LPAI H5N2 and H5N3 were able to infect murine lung macrophages and together with increased expression of inflammatory \nmediators particularly in H5N3 virus could impose threats to human health in the future. \nThe IHFs identified by the IAV genome-wide siRNA screening studies could be used as \npotential anti-IV targets. However, these studies were not consistent (reproducible) mainly due to \nfalse negative results. Hence, in the fourth project (chapter 6), we applied computational gene \nnetwork growing for discovering gene network links that could have been missed by these \nexperimental investigations. Using the known IHFs we compared the network growing function \nof two free tools GeneMANIA and STRING and the commercial IPA for their performance of \nrecovering other known IHFs previously identified from siRNA screens. The result showed that, \ngiven small (~30 genes) or medium (~150 genes) input sets, all three network growing tools \ndetected significantly more known host factors than random human genes with STRING overall \nperforming strongest. Extending the networks with all the three tools significantly improved the \ndetection of GO biological processes of known host factors compared to not growing networks. \nNotably, the rate of identification of true host factors using computational network growing is \nequal or better to doing another experimental siRNA screening study which could also be true \nand applied to other biological pathways/processes. \nFinally, using the known IHFs and the "new IHF candidates" (genes connected to the \nIHFs from the network growing analysis), we predicted computational drug-target interactions \nusing MetaCore. We identified 343 US Food and Drug Administration (FDA) approved drugs \nthat had an inhibitory effect on either the known or new candidate IHFs, of which 258 were new \npredictions. Furthermore, using different criteria, we computationally ranked the 343 FDA \napproved drugs for further experimental validation.
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