Mechanisms of severe acute respiratory syndrome coronavirus-induced acute lung injury

Annotation of long non-coding RNAs expressed in collaborative cross founder mice in response to respiratory virus infection reveals a new class of interferon-stimulated transcripts

The outcome of respiratory virus infection is determined by a complex interplay of viral and host factors. Some potentially important host factors for the antiviral response, whose functions remain largely unexplored, are long non-coding RNAs (lncRNAs). Here we systematically inferred the regulatory functions of host lncRNAs in response to influenza A virus and severe acute respiratory syndrome coronavirus (SARS-CoV) based on their similarity in expression with genes of known function. We performed total RNA-Seq on viral-infected lungs from eight mouse strains, yielding a large data set of transcriptional responses. Overall 5,329 lncRNAs were differentially expressed after infection. Most of the lncRNAs were co-expressed with coding genes in modules enriched in genes associated with lung homeostasis pathways or immune response processes. Each lncRNA was further individually annotated using a rank-based method, enabling us to associate 5,295 lncRNAs to at least one gene set and to predict their potential cis effects. We validated the lncRNAs predicted to be interferon-stimulated by profiling mouse responses after interferon-α treatment. Altogether, these results provide a broad categorization of potential lncRNA functions and identify subsets of lncRNAs with likely key roles in respiratory virus pathogenesis. These data are fully accessible through the MOuse NOn-Code Lung interactive database (MONOCLdb).

Systems approaches to Coronavirus pathogenesis

Coronaviruses comprise a large group of emergent human and animal pathogens, including the highly pathogenic SARS-CoV and MERS-CoV strains that cause significant morbidity and mortality in infected individuals, especially the elderly. As emergent viruses may cause episodic outbreaks of disease over time, human samples are limited. Systems biology and genetic technologies maximize opportunities for identifying critical host and viral genetic factors that regulate susceptibility and virus-induced disease severity. These approaches provide discovery platforms that highlight and allow targeted confirmation of critical targets for prophylactics and therapeutics, especially critical in an outbreak setting. Although poorly understood, it has long been recognized that host regulation of virus-associated disease severity is multigenic. The advent of systems genetic and biology resources provides new opportunities for deconvoluting the complex genetic interactions and expression networks that regulate pathogenic or protective host response patterns following virus infection. Using SARS-CoV as a model, dynamic transcriptional network changes and disease-associated phenotypes have been identified in different genetic backgrounds, leading to the promise of population-wide discovery of the underpinnings of Coronavirus pathogenesis.

Systems analysis of West Nile virus infection

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Emerging/re-emerging mosquito-borne viruses are a significant public health threat.

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There is a need define the virus–host interactions that govern immunity to infection.

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Systems biology provides a comprehensive analysis of the host response.

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High-throughput based assays provide rapid identification of host targets.

Emerging and re-emerging mosquito-borne viruses continue to pose a significant threat to human health throughout the world. Over the past decade, West Nile virus (WNV), Dengue virus (DENV), and Chikungunya virus (CHIKV), have caused annual epidemics of virus-induced encephalitis, hemorrhagic fever\shock syndromes, and arthritis, respectively. Currently, no specific antiviral therapies or vaccines exist for use in humans to combat or prevent these viral infections. Thus, there is a pressing need to define the virus–host interactions that govern immunity and infection outcome. Recent technological breakthroughs in ‘omics’ resources and high-throughput based assays are beginning to accelerate antiviral drug discovery and improve on current strategies for vaccine design. In this review, we highlight studies with WNV and discuss how traditional and systems biological approaches are being used to rapidly identify novel host targets for therapeutic intervention and develop a deeper conceptual understanding of the host response to virus infection.

Attenuation and restoration of severe acute respiratory syndrome coronavirus mutant lacking 2'-o-methyltransferase activity

The sudden emergence of severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002 and, more recently, Middle Eastern respiratory syndrome CoV (MERS-CoV) underscores the importance of understanding critical aspects of CoV infection and pathogenesis. Despite significant insights into CoV cross-species transmission, replication, and virus-host interactions, successful therapeutic options for CoVs do not yet exist. Recent identification of SARS-CoV NSP16 as a viral 2'-O-methyltransferase (2'-O-MTase) led to the possibility of utilizing this pathway to both attenuate SARS-CoV infection and develop novel therapeutic treatment options. Mutations were introduced into SARS-CoV NSP16 within the conserved KDKE motif and effectively attenuated the resulting SARS-CoV mutant viruses both in vitro and in vivo. While viruses lacking 2'-O-MTase activity had enhanced sensitivity to type I interferon (IFN), they were not completely restored in their absence in vivo. However, the absence of either MDA5 or IFIT1, IFN-responsive genes that recognize unmethylated 2'-O RNA, resulted in restored replication and virulence of the dNSP16 mutant virus. Finally, using the mutant as a live-attenuated vaccine showed significant promise for possible therapeutic development against SARS-CoV. Together, the data underscore the necessity of 2'-O-MTase activity for SARS-CoV pathogenesis and identify host immune pathways that mediate this attenuation. In addition, we describe novel treatment avenues that exploit this pathway and could potentially be used against a diverse range of viral pathogens that utilize 2'-O-MTase activity to subvert the immune system.

IMPORTANCE

Preventing recognition by the host immune response represents a critical aspect necessary for successful viral infection. Several viruses, including SARS-CoV, utilize virally encoded 2'-O-MTases to camouflage and obscure their viral RNA from host cell sensing machinery, thus preventing recognition and activation of cell intrinsic defense pathways. For SARS-CoV, the absence of this 2'-O-MTase activity results in significant attenuation characterized by decreased viral replication, reduced weight loss, and limited breathing dysfunction in mice. The results indicate that both MDA5, a recognition molecule, and the IFIT family play an important role in mediating this attenuation with restored virulence observed in their absence. Understanding this virus-host interaction provided an opportunity to design a successful live-attenuated vaccine for SARS-CoV and opens avenues for treatment and prevention of emerging CoVs and other RNA virus infections.

Multi-omic network signatures of disease

To better understand dynamic disease processes, integrated multi-omic methods are needed, yet comparing different types of omic data remains difficult. Integrative solutions benefit experimenters by eliminating potential biases that come with single omic analysis. We have developed the methods needed to explore whether a relationship exists between co-expression network models built from transcriptomic and proteomic data types, and whether this relationship can be used to improve the disease signature discovery process. A naïve, correlation based method is utilized for comparison. Using publicly available infectious disease time series data, we analyzed the related co-expression structure of the transcriptome and proteome in response to SARS-CoV infection in mice. Transcript and peptide expression data was filtered using quality scores and subset by taking the intersection on mapped Entrez IDs. Using this data set, independent co-expression networks were built. The networks were integrated by constructing a bipartite module graph based on module member overlap, module summary correlation, and correlation to phenotypes of interest. Compared to the module level results, the naïve approach is hindered by a lack of correlation across data types, less significant enrichment results, and little functional overlap across data types. Our module graph approach avoids these problems, resulting in an integrated omic signature of disease progression, which allows prioritization across data types for down-stream experiment planning. Integrated modules exhibited related functional enrichments and could suggest novel interactions in response to infection. These disease and platform-independent methods can be used to realize the full potential of multi-omic network signatures. The data (experiment SM001) are publically available through the NIAID Systems Virology (https://www.systemsvirology.org) and PNNL (http://omics.pnl.gov) web portals. Phenotype data is found in the supplementary information. The ProCoNA package is available as part of Bioconductor 2.13.

A decade after SARS: strategies for controlling emerging coronaviruses

Two novel coronaviruses have emerged in humans in the twenty-first century: severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV), both of which cause acute respiratory distress syndrome (ARDS) and are associated with high mortality rates. There are no clinically approved vaccines or antiviral drugs available for either of these infections; thus, the development of effective therapeutic and preventive strategies that can be readily applied to new emergent strains is a research priority. In this Review, we describe the emergence and identification of novel human coronaviruses over the past 10 years, discuss their key biological features, including tropism and receptor use, and summarize approaches for developing broadly effective vaccines.

Bugs in the system

Immunity to respiratory virus infection is governed by complex biological networks that influence disease progression and pathogenesis. Systems biology provides an opportunity to explore and understand these multifaceted interactions based on integration and modeling of multiple biological parameters. In this review, we describe new and refined systems-based approaches used to model, identify, and validate novel targets within complex networks following influenza and coronavirus infection. In addition, we propose avenues for extension and expansion that can revolutionize our understanding of infectious disease processes. Together, we hope to provide a window into the unique and expansive opportunity presented by systems biology to understand complex disease processes within the context of infectious diseases.