Study On Pathogen - Host Interaction Based On Expression

Modern categorization of living microorganisms and discovery of antimicrobial drugs largely lay their foundation and focus on the knowledge of microbial genome structures and the defined molecular events associated with multistep viral life cycles. Recent years have witnessed the increasing recognition of importance of host response to pathogenic infection in characterizing microbial pathogenesis, disease diagnosis and prognosis, as well as novel therapeutic development. Specifically, host transcriptional responses(HTRs) depicted by gene expression profiles using genome-wide DNA microarrays seemed to be one of the most delicate description of the complexity of both pathogenic infection and disease states with practical availability, wide coverage, and excellent discrimination.Comparison of host cell responses to pathogenic microorganisms in a systematic manner would thrive the new understanding of host-pathogen interaction. At transcriptional level, such attempt has first been made by Jenner RG and Young RA by presenting a ―common host response‖ through cell type-based clustering analysis on transcriptional-profiling data from 32 studies that involved 77 different host-pathogen interactions. The same methodology has been used in later attempts, to identify new gene factors and exquisite host cellular defense mechanisms against single pathogen infection. However, such an approach is far away from drawing a comprehensive and systematic view of greater depth that integrates the diverse information of HTRs to infections. This is because the dominant HTR structure detected was related to cell type and lab effects(dissimilarity among expression profiles from different labs), masking the integrity of infection characteristics with respect to individual pathogen in the entire human system. Meanwhile, only the most differentially dysregulated genes are analytical focus that accepts for systematic annotation, which neglects the contribution of each delicate gene-expression change in an integral background. Moreover, due to the diverse quantity and therefore low overlapping rate, the similarity estimation based on the most dysregulated gene set is hardly to achieve or a much less reflection of reality.Herein, we applicated a generic and unbiased HTR characterization method and a rank-based expression profile comparison measurement for analyzing 1225 pairs of pathogen-pathogen HTR similarities. We first explored the relationships between HTR and microbial important clinical and laboratory properties We then identified eight robust ―HTR Communities‖ in the pathogen-pathogen HTR network and annotated them with HTR-related infection attributes and gene expression patterns. Eventually, through the recovery of known and the discovery of new biological associations, we demonstrated the potential of this HTR Community to reveal functional associations among infectious pathogens, and with diseases at an unprecedented host-oriented perspective.The associations that HTR Community established are of robustness and biologically revealing. It highly indicated that the relevance of HTR landscape to traditional microorganism taxonomy, infection, and disease characterization is complex but with limited and differential modes. More specifically, we show that HTR Community can be used to recognize pathogen class with common HTRs(e.g., proteobacteria), discern the pathogenesis of pathogens with close phylogenetic relations(e.g., Streptococcus species), identify HTR representing specific human microbiota and further reflects the degree of host adaptation to exquisitely determine the opportunistic commensal phenotype or invasively pathogenic specie(e.g., oral commensal and pathogenic bacteria), and discover the unknown common and unique HTR of pathogens whose infection cause indistinguishable clinical symptoms(e.g., respiratory viruses). These findings also reveal the potential of HTR Community to validate old and identify new associations among pathogenic infection and non-infectious diseases(e.g., oncogenic infections, asthma, SLE and AD), in which both positive and negative associations are of equal indicative values. Notably, we employ as many as possible the query signatures of external pathogens or disease states to challenge the proposed associations, and the positive results successfully serve as strong evidence to confirm the robustness of HTR Community constitution and the reliability of our findings.Here, we identified interactions between EV71 proteins and cellular proteins to provide network view of EV71 infection. Analysis of the integrated EV71-Human protein interaction network revealed the topological features of the EV71-interacted human proteins(EIPs) and functional pathways related to EV71 infection. Through the overlap analysis of EIPs and other virus interacted proteins, we found that ATP6V0 C may be a broad-spectrum essential host factor, and further validated its essentiality for EV71 infection in vitro. And we mapped EIPs to drug targets to find anti-viral drug candidates. The enrichment of psychotropic and neurological or anti-inflammatory drugs indicated the value of EIPs to serve as host-oriented anti-vrial targets. We further tried to find drugs that inhibit EIPs using gene set enrichment analysis and the public datasets. And the predicted anti-viral agent Tanespimycin were proved to have antiviral activity in the EV71 infection cell model. And these results suggests the power of systems analysis in revealing microbial pathogenesis mechanisms and developing novel therapies.These findings provide the first systems analyses of HTR landscape relations to pathogen infections, identify HTR Communities that differentiate and categorize pathogens at a new host-oriented perspective, and inform its potential to reveal functional associations of infection with disease. And the intergrative analysis of EV71-host protein interaction network provided new methology for developing novel anti-infection therapies.