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Identification of biological processes that distinguish lethal from non-lethal influenza infection

ABSTRACT: Identification of biological processes that distinguish lethal from non-lethal influenza infection and further use of these signatures in a top-down systems analysis investigating the relative pathogenic contributions of direct viral damage to lung epithelium vs. dysregulated immunity to during lethal influenza infection. For acutely lethal influenza infections, the relative pathogenic contributions of direct viral damage to lung epithelium vs. dysregulated immunity remain unresolved. Here, we take a top-down systems approach to this question. Multigene transcriptional signatures from infected lungs suggested that elevated activation of inflammatory signaling networks distinguished lethal from sublethal infections. Flow cytometry and gene expression analysis involving isolated cell subpopulations from infected lungs showed that neutrophil influx largely accounted for the predictive transcriptional signature. Automated imaging analysis together with these gene expression and flow data identified a chemokine-driven feed-forward circuit involving pro-inflammatory neutrophils potently driven by poorly contained lethal viruses. Consistent with these data, attenuation but not ablation of the neutrophil-driven response increased survival without changing viral spread. These findings establish the primacy of damaging innate inflammation in at least some forms of influenza-induced lethality and provide a roadmap for the systematic dissection of infection-associated pathology.

ORGANISM(S): Mus musculus

PROVIDER: GSE42638 | GEO | 2013/07/03

SECONDARY ACCESSION(S): PRJNA182487

REPOSITORIES: GEO

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Project description:Transcriptomic comparison of 5 cell types during lethal and non-lethal influenza infection and further use of these signatures in a top-down systems analysis investigating the relative pathogenic contributions of direct viral damage to lung epithelium vs. dysregulated immunity during lethal influenza infection. For acutely lethal influenza infections, the relative pathogenic contributions of direct viral damage to lung epithelium vs. dysregulated immunity remain unresolved. Here, we take a top-down systems approach to this question. Multigene transcriptional signatures from infected lungs suggested that elevated activation of inflammatory signaling networks distinguished lethal from sublethal infections. Flow cytometry and gene expression analysis involving isolated cell subpopulations from infected lungs showed that neutrophil influx largely accounted for the predictive transcriptional signature. Automated imaging analysis together with these gene expression and flow data identified a chemokine-driven feed-forward circuit involving pro-inflammatory neutrophils potently driven by poorly contained lethal viruses. Consistent with these data, attenuation but not ablation of the neutrophil-driven response increased survival without changing viral spread. These findings establish the primacy of damaging innate inflammation in at least some forms of influenza-induced lethality and provide a roadmap for the systematic dissection of infection-associated pathology. Multiple mice were either sham infected, infected with the seasonal H1N1 influenza A virus TX91 (10^6PFU), or infected with various sublethal or lethal doses of the mouse pathogenic H1N1 strain PR8. Lung tissues were collected at 48h or 72h post infection. 5 different cell types were purified by flow sorting from lungs of individual animals and then processed to yield total RNA that was used for microarray analysis. The dataset contains 75 microarrays covering 25 experimental conditions with 3 biological replicates. This dataset is linked to a dataset containing 138 microarrays of whole lungs covering 20 experimental conditions.

Project description:Identification of biological processes that distinguish lethal from non-lethal influenza infection and further use of these signatures in a top-down systems analysis investigating the relative pathogenic contributions of direct viral damage to lung epithelium vs. dysregulated immunity to during lethal influenza infection. For acutely lethal influenza infections, the relative pathogenic contributions of direct viral damage to lung epithelium vs. dysregulated immunity remain unresolved. Here, we take a top-down systems approach to this question. Multigene transcriptional signatures from infected lungs suggested that elevated activation of inflammatory signaling networks distinguished lethal from sublethal infections. Flow cytometry and gene expression analysis involving isolated cell subpopulations from infected lungs showed that neutrophil influx largely accounted for the predictive transcriptional signature. Automated imaging analysis together with these gene expression and flow data identified a chemokine-driven feed-forward circuit involving pro-inflammatory neutrophils potently driven by poorly contained lethal viruses. Consistent with these data, attenuation but not ablation of the neutrophil-driven response increased survival without changing viral spread. These findings establish the primacy of damaging innate inflammation in at least some forms of influenza-induced lethality and provide a roadmap for the systematic dissection of infection-associated pathology. Multiple mice were either sham infected, infected with the seasonal H1N1 influenza A virus TX91 (10^6PFU), or infected with various sublethal or lethal doses of the mouse pathogenic H1N1 strain PR8. Lung tissues were collected at various time points (24h, 48h, 72h and 240h post infection) and processed to yield whole lung RNA that was used for microarray analysis. The dataset contains 138 microarrays covering 20 experimental conditions with 7 biological replicates each. As an exception, the alternative non-infectious control condition (Alum treatment) contains 5 biological replicates. This dataset is linked to a dataset comparing the transcriptomes of 5 different cell types isolated from individual lungs of influenza A-infected or control animals (contains 75 microarrays covering 25 experimental conditions).

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Identification of biological processes that distinguish lethal from non-lethal influenza infection

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