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Cytokine systems approach demonstrates differences in innate and pro-inflammatory host responses between genetically distinct MERS-CoV isolates.

Selinger C, Tisoncik-Go J, Menachery VD, Agnihothram S, Law GL, Chang J, Kelly SM, Sova P, Baric RS, Katze MG - BMC Genomics (2014)

Bottom Line: Using topological techniques, including persistence homology and filtered clustering, we performed a comparative transcriptional analysis of human Calu-3 cell host responses to the different MERS-CoV strains, with MERS-CoV Eng 1 inducing early kinetic changes, between 3 and 12 hours post infection, compared to MERS-CoV SA 1.Through our genomics-based approach, we found topological differences in the kinetics and magnitude of the host response to MERS-CoV SA 1 and MERS-CoV Eng 1, with differential expression of innate immune and pro-inflammatory responsive genes as a result of IFN, TNF and IL-1α signaling.Predicted activation for STAT3 mediating gene expression relevant for epithelial cell-to-cell adherens and junction signaling in MERS-CoV Eng 1 infection suggest that these transcriptional differences may be the result of amino acid differences in viral proteins known to modulate innate immunity during MERS-CoV infection.

View Article: PubMed Central - PubMed

Affiliation: Department of Microbiology, School of Medicine, University of Washington, Seattle, Washington, USA. csel@uw.edu.

ABSTRACT

Background: The recent emergence of a novel coronavirus in the Middle East (designated MERS-CoV) is a reminder of the zoonotic and pathogenic potential of emerging coronaviruses in humans. Clinical features of Middle East respiratory syndrome (MERS) include atypical pneumonia and progressive respiratory failure that is highly reminiscent of severe acute respiratory syndrome (SARS) caused by SARS-CoV. The host response is a key component of highly pathogenic respiratory virus infection. Here, we computationally analyzed gene expression changes in a human airway epithelial cell line infected with two genetically distinct MERS-CoV strains obtained from human patients, MERS-CoV SA 1 and MERS-CoV Eng 1.

Results: Using topological techniques, including persistence homology and filtered clustering, we performed a comparative transcriptional analysis of human Calu-3 cell host responses to the different MERS-CoV strains, with MERS-CoV Eng 1 inducing early kinetic changes, between 3 and 12 hours post infection, compared to MERS-CoV SA 1. Robust transcriptional changes distinguished the two MERS-CoV strains predominantly at the late time points. Combining statistical analysis of infection and cytokine-stimulated Calu-3 transcriptomics, we identified differential innate responses, including up-regulation of extracellular remodeling genes following MERS-CoV Eng 1 infection and differential pro-inflammatory responses.

Conclusions: Through our genomics-based approach, we found topological differences in the kinetics and magnitude of the host response to MERS-CoV SA 1 and MERS-CoV Eng 1, with differential expression of innate immune and pro-inflammatory responsive genes as a result of IFN, TNF and IL-1α signaling. Predicted activation for STAT3 mediating gene expression relevant for epithelial cell-to-cell adherens and junction signaling in MERS-CoV Eng 1 infection suggest that these transcriptional differences may be the result of amino acid differences in viral proteins known to modulate innate immunity during MERS-CoV infection.

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STAT3 is a predicted regulator mediating the gene constrasts between MERS-CoV Eng 1 and MERS-CoV SA 1. Upstream Regulator Analysis in IPA was used to predict regulators and infer their activation state based on the literature and gene expression of target genes in the data set. STAT3 is the master regulator of a small causal network that postulates differential STAT3 activity in MERS-Co-V SA 1 and MERS-CoV Eng 1 infections. STAT3 activity affects a number of other regulators that explains the downstream gene expression changes in the data set. The color of the lines (edges) signifies the expected direction of effect between two nodes. Blue represents predicted inhibition and orange represents predicted activation. Yellow signifies inconsistency between the gene expression in the data set and the annotated relationship. The color of the node signifies the z-score calculated from the data set. Blue: z-score < -2 and orange: z-score > 2. The downstream genes show gene expression in infected cells relative to mock-infected cells. Green: down-regulated expression and red: upregulated expression).
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Fig5: STAT3 is a predicted regulator mediating the gene constrasts between MERS-CoV Eng 1 and MERS-CoV SA 1. Upstream Regulator Analysis in IPA was used to predict regulators and infer their activation state based on the literature and gene expression of target genes in the data set. STAT3 is the master regulator of a small causal network that postulates differential STAT3 activity in MERS-Co-V SA 1 and MERS-CoV Eng 1 infections. STAT3 activity affects a number of other regulators that explains the downstream gene expression changes in the data set. The color of the lines (edges) signifies the expected direction of effect between two nodes. Blue represents predicted inhibition and orange represents predicted activation. Yellow signifies inconsistency between the gene expression in the data set and the annotated relationship. The color of the node signifies the z-score calculated from the data set. Blue: z-score < -2 and orange: z-score > 2. The downstream genes show gene expression in infected cells relative to mock-infected cells. Green: down-regulated expression and red: upregulated expression).

Mentions: We also identified STAT3 as a predicted upstream regulator at 24 hpi in response to MERS-CoV Eng 1 (z-score = 2.559), with up-regulation of downstream target genes including BCL6, SOCS3, BCL3, IRF7 and PML (Figure 5, Table 3). Through a separate binding motif prediction analysis using JASPAR and PSCAN, we confirmed the STAT3 prediction based on enrichment of STAT3 binding sequences in DE genes at the late time points (Table 3). Conversely, MERS-CoV SA 1 infection resulted in downregulation of these same target genes, suggesting a predicted inhibition or delay in STAT3 regulator activity, though STAT3 did not reach a statistically significant z-score (z-score = 0.364). Among the STAT3 target genes BCL3 had the highest contrasting expression between the viruses. The BCL3 gene was highly up-regulated in response to MERS-CoV Eng 1 (FC = 1.5) and downregulated in response to MERS-CoV SA 1 (FC = -1.66) at 18 hpi. qRT-PCR analysis of BCL3 mRNA expression, and several additional STAT3 target genes, including BCL6, PML, and IRF7, were in agreement with the microarray findings (Additional file 4: Figure S2). Among these genes, IRF7 plays an important role in the cellular antiviral response and Faure and colleagues reported high levels of IRF7 gene expression in BAL cells from a patient that recovered from MERS infection [12].Figure 5


Cytokine systems approach demonstrates differences in innate and pro-inflammatory host responses between genetically distinct MERS-CoV isolates.

Selinger C, Tisoncik-Go J, Menachery VD, Agnihothram S, Law GL, Chang J, Kelly SM, Sova P, Baric RS, Katze MG - BMC Genomics (2014)

STAT3 is a predicted regulator mediating the gene constrasts between MERS-CoV Eng 1 and MERS-CoV SA 1. Upstream Regulator Analysis in IPA was used to predict regulators and infer their activation state based on the literature and gene expression of target genes in the data set. STAT3 is the master regulator of a small causal network that postulates differential STAT3 activity in MERS-Co-V SA 1 and MERS-CoV Eng 1 infections. STAT3 activity affects a number of other regulators that explains the downstream gene expression changes in the data set. The color of the lines (edges) signifies the expected direction of effect between two nodes. Blue represents predicted inhibition and orange represents predicted activation. Yellow signifies inconsistency between the gene expression in the data set and the annotated relationship. The color of the node signifies the z-score calculated from the data set. Blue: z-score < -2 and orange: z-score > 2. The downstream genes show gene expression in infected cells relative to mock-infected cells. Green: down-regulated expression and red: upregulated expression).
© Copyright Policy - open-access
Related In: Results  -  Collection

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Fig5: STAT3 is a predicted regulator mediating the gene constrasts between MERS-CoV Eng 1 and MERS-CoV SA 1. Upstream Regulator Analysis in IPA was used to predict regulators and infer their activation state based on the literature and gene expression of target genes in the data set. STAT3 is the master regulator of a small causal network that postulates differential STAT3 activity in MERS-Co-V SA 1 and MERS-CoV Eng 1 infections. STAT3 activity affects a number of other regulators that explains the downstream gene expression changes in the data set. The color of the lines (edges) signifies the expected direction of effect between two nodes. Blue represents predicted inhibition and orange represents predicted activation. Yellow signifies inconsistency between the gene expression in the data set and the annotated relationship. The color of the node signifies the z-score calculated from the data set. Blue: z-score < -2 and orange: z-score > 2. The downstream genes show gene expression in infected cells relative to mock-infected cells. Green: down-regulated expression and red: upregulated expression).
Mentions: We also identified STAT3 as a predicted upstream regulator at 24 hpi in response to MERS-CoV Eng 1 (z-score = 2.559), with up-regulation of downstream target genes including BCL6, SOCS3, BCL3, IRF7 and PML (Figure 5, Table 3). Through a separate binding motif prediction analysis using JASPAR and PSCAN, we confirmed the STAT3 prediction based on enrichment of STAT3 binding sequences in DE genes at the late time points (Table 3). Conversely, MERS-CoV SA 1 infection resulted in downregulation of these same target genes, suggesting a predicted inhibition or delay in STAT3 regulator activity, though STAT3 did not reach a statistically significant z-score (z-score = 0.364). Among the STAT3 target genes BCL3 had the highest contrasting expression between the viruses. The BCL3 gene was highly up-regulated in response to MERS-CoV Eng 1 (FC = 1.5) and downregulated in response to MERS-CoV SA 1 (FC = -1.66) at 18 hpi. qRT-PCR analysis of BCL3 mRNA expression, and several additional STAT3 target genes, including BCL6, PML, and IRF7, were in agreement with the microarray findings (Additional file 4: Figure S2). Among these genes, IRF7 plays an important role in the cellular antiviral response and Faure and colleagues reported high levels of IRF7 gene expression in BAL cells from a patient that recovered from MERS infection [12].Figure 5

Bottom Line: Using topological techniques, including persistence homology and filtered clustering, we performed a comparative transcriptional analysis of human Calu-3 cell host responses to the different MERS-CoV strains, with MERS-CoV Eng 1 inducing early kinetic changes, between 3 and 12 hours post infection, compared to MERS-CoV SA 1.Through our genomics-based approach, we found topological differences in the kinetics and magnitude of the host response to MERS-CoV SA 1 and MERS-CoV Eng 1, with differential expression of innate immune and pro-inflammatory responsive genes as a result of IFN, TNF and IL-1α signaling.Predicted activation for STAT3 mediating gene expression relevant for epithelial cell-to-cell adherens and junction signaling in MERS-CoV Eng 1 infection suggest that these transcriptional differences may be the result of amino acid differences in viral proteins known to modulate innate immunity during MERS-CoV infection.

View Article: PubMed Central - PubMed

Affiliation: Department of Microbiology, School of Medicine, University of Washington, Seattle, Washington, USA. csel@uw.edu.

ABSTRACT

Background: The recent emergence of a novel coronavirus in the Middle East (designated MERS-CoV) is a reminder of the zoonotic and pathogenic potential of emerging coronaviruses in humans. Clinical features of Middle East respiratory syndrome (MERS) include atypical pneumonia and progressive respiratory failure that is highly reminiscent of severe acute respiratory syndrome (SARS) caused by SARS-CoV. The host response is a key component of highly pathogenic respiratory virus infection. Here, we computationally analyzed gene expression changes in a human airway epithelial cell line infected with two genetically distinct MERS-CoV strains obtained from human patients, MERS-CoV SA 1 and MERS-CoV Eng 1.

Results: Using topological techniques, including persistence homology and filtered clustering, we performed a comparative transcriptional analysis of human Calu-3 cell host responses to the different MERS-CoV strains, with MERS-CoV Eng 1 inducing early kinetic changes, between 3 and 12 hours post infection, compared to MERS-CoV SA 1. Robust transcriptional changes distinguished the two MERS-CoV strains predominantly at the late time points. Combining statistical analysis of infection and cytokine-stimulated Calu-3 transcriptomics, we identified differential innate responses, including up-regulation of extracellular remodeling genes following MERS-CoV Eng 1 infection and differential pro-inflammatory responses.

Conclusions: Through our genomics-based approach, we found topological differences in the kinetics and magnitude of the host response to MERS-CoV SA 1 and MERS-CoV Eng 1, with differential expression of innate immune and pro-inflammatory responsive genes as a result of IFN, TNF and IL-1α signaling. Predicted activation for STAT3 mediating gene expression relevant for epithelial cell-to-cell adherens and junction signaling in MERS-CoV Eng 1 infection suggest that these transcriptional differences may be the result of amino acid differences in viral proteins known to modulate innate immunity during MERS-CoV infection.

Show MeSH
Related in: MedlinePlus