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1.
mBio ; 12(4): e0157221, 2021 08 31.
Article in English | MEDLINE | ID: covidwho-1349194

ABSTRACT

Tissue- and cell-specific expression patterns are highly variable within and across individuals, leading to altered host responses after acute virus infection. Unraveling key tissue-specific response patterns provides novel opportunities for defining fundamental mechanisms of virus-host interaction in disease and the identification of critical tissue-specific networks for disease intervention in the lung. Currently, there are no approved therapeutics for Middle East respiratory syndrome coronavirus (MERS-CoV) patients, and little is understood about how lung cell types contribute to disease outcomes. MERS-CoV replicates equivalently in primary human lung microvascular endothelial cells (MVE) and fibroblasts (FB) and to equivalent peak titers but with slower replication kinetics in human airway epithelial cell cultures (HAE). However, only infected MVE demonstrate observable virus-induced cytopathic effect. To explore mechanisms leading to reduced MVE viability, donor-matched human lung MVE, HAE, and FB were infected, and their transcriptomes, proteomes, and lipidomes were monitored over time. Validated functional enrichment analysis demonstrated that MERS-CoV-infected MVE were dying via an unfolded protein response (UPR)-mediated apoptosis. Pharmacologic manipulation of the UPR in MERS-CoV-infected primary lung cells reduced viral titers and in male mice improved respiratory function with accompanying reductions in weight loss, pathological signatures of acute lung injury, and times to recovery. Systems biology analysis and validation studies of global kinetic transcript, protein, and lipid data sets confirmed that inhibition of host stress pathways that are differentially regulated following MERS-CoV infection of different tissue types can alleviate symptom progression to end-stage lung disease commonly seen following emerging coronavirus outbreaks. IMPORTANCE Middle East respiratory syndrome coronavirus (MERS-CoV) causes severe atypical pneumonia in infected individuals, but the underlying mechanisms of pathogenesis remain unknown. While much has been learned from the few reported autopsy cases, an in-depth understanding of the cells targeted by MERS-CoV in the human lung and their relative contribution to disease outcomes is needed. The host response in MERS-CoV-infected primary human lung microvascular endothelial (MVE) cells and fibroblasts (FB) was evaluated over time by analyzing total RNA, proteins, and lipids to determine the cellular pathways modulated postinfection. Findings revealed that MERS-CoV-infected MVE cells die via apoptotic mechanisms downstream of the unfolded protein response (UPR). Interruption of enzymatic processes within the UPR in MERS-CoV-infected male mice reduced disease symptoms, virus-induced lung injury, and time to recovery. These data suggest that the UPR plays an important role in MERS-CoV infection and may represent a host target for therapeutic intervention.


Subject(s)
Acute Lung Injury/pathology , Apoptosis/physiology , Coronavirus Infections/pathology , Unfolded Protein Response/physiology , Acute Lung Injury/virology , Animals , Cell Line , Endothelial Cells/metabolism , Endothelial Cells/virology , Female , Fibroblasts/metabolism , Fibroblasts/virology , Humans , Male , Mice , Middle East Respiratory Syndrome Coronavirus/immunology
2.
BMC Bioinformatics ; 22(1): 287, 2021 May 29.
Article in English | MEDLINE | ID: covidwho-1257920

ABSTRACT

BACKGROUND: Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets. RESULTS: We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. CONCLUSIONS: Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses.


Subject(s)
Algorithms , Models, Biological , Genomics , Proteins
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