A distinct influenza infection signature in the blood transcriptome of patients with severe community-acquired pneumonia

Abstract

IntroductionDiagnosis of severe influenza pneumonia remains challenging because of a lack of correlation between the presence of influenza virus and clinical status. We conducted gene-expression profiling in the whole blood of critically ill patients to identify a gene signature that would allow clinicians to distinguish influenza infection from other causes of severe respiratory failure, such as bacterial pneumonia, and noninfective systemic inflammatory response syndrome.MethodsWhole-blood samples were collected from critically ill individuals and assayed on Illumina HT-12 gene-expression beadarrays. Differentially expressed genes were determined by linear mixed-model analysis and overrepresented biological pathways determined by using GeneGo MetaCore.ResultsThe gene-expression profile of H1N1 influenza A pneumonia was distinctly different from those of bacterial pneumonia and systemic inflammatory response syndrome. The influenza gene-expression profile is characterized by upregulation of genes from cell-cycle regulation, apoptosis, and DNA-damage-response pathways. In contrast, no distinctive gene-expression signature was found in patients with bacterial pneumonia or systemic inflammatory response syndrome. The gene-expression profile of influenza infection persisted through 5 days of follow-up. Furthermore, in patients with primary H1N1 influenza A infection in whom bacterial co-infection subsequently developed, the influenza gene-expression signature remained unaltered, despite the presence of a superimposed bacterial infection.ConclusionsThe whole-blood expression-profiling data indicate that the host response to influenza pneumonia is distinctly different from that caused by bacterial pathogens. This information may speed the identification of the cause of infection in patients presenting with severe respiratory failure, allowing appropriate patient care to be undertaken more rapidly.

What was old is new again: using the host response to diagnose infectious disease

“…This signature had a prominent representation of interferon-induced genes, as had been previously reported for other viral classifiers [80,81]. However, these authors could not discriminate between bacterial infection and noninfectious SIRS [83]. Bacterial signatures have previously been described in comparison to healthy controls [72,80,143,145], as well as in relation to noninfectious SIRS for a broader ICU population.…”

“…However, the more relevant testing population would include patients with respiratory illness from noninfectious causes as well as those with bacterial/viral co-infection. A study of gene expression profiles of adult ICU patients with influenza A (H1N1) pneumonia, bacterial pneumonia and respiratory compromise from noninfectious SIRS delineated a 29-gene classifier of viral infection that discriminated viral from bacterial infection and noninfectious SIRS [83]. This signature had a prominent representation of interferon-induced genes, as had been previously reported for other viral classifiers [80,81].…”

“…Furthermore, gene expression changes describing infectious states can be translated from model organisms to humans [76][77][78][79]. Thus far, transcriptomic classifiers to diagnose sepsis [76] and provide pathogen-specific information [78,[80][81][82][83] have been reported, but need validation and refinement in a broader population prior to clinical application.…”

Increased abundance of ADAM9 transcripts in the blood is associated with tissue damage

“…The abundance of ADAM9 RNA measured by microarrays in human blood samples was significantly increased as compared to uninfected controls in subjects with sepsis [ iFigure/ GSE28750] 21 & [ iFigure/ GSE29536] 22 , in subjects with bacterial and influenza pneumonia [ iFigure/ GSE34205] 23 , [ iFigure/ GSE40012] 24 , in subjects with respiratory syncytial virus (RSV) infection [ iFigure/ GSE34205] 23 & [ iFigure/ GSE17156] 23 and in subjects with tuberculosis [ iFigure/ GSE19439] 25 & [ iFigure/ GSE34608] 26 . Aggregated findings were reported in the form of flow charts that were generated using google docs presentations, with links to the source interactive graphs systematically provided as hyperlinks ( Figure 2, Supplementary Figure 2 and Table 1).…”

Genomic Circuitry Underlying Immunological Response to Pediatric Acute Respiratory Infection

“…Distinguishing different viral infections with whole blood or peripheral blood mononuclear cell (PBMC) transcriptomics has been a major goal (Bermejo-Martin et al, 2010; Herberg et al, 2013), and some datasets have been refined into signatures capable of identifying acute influenza (Berdal et al, 2011; Davenport et al, 2015; Ioannidis et al, 2012; Woods et al, 2013) or distinguishing between bacterial versus viral infections (Herberg et al, 2016; Parnell et al, 2012; Suarez et al, 2015; Tsalik et al, 2016), between different viral infections (Mejias et al, 2013; Statnikov et al, 2010; Zaas et al, 2009; Zhai et al, 2015), or both (Hu et al, 2013; Ramilo et al, 2007; Sweeney et al, 2016; Tsalik et al, 2016). Many studies highlight the role of interferon-stimulated genes (ISGs) (Zhai et al, 2015) as key upregulated genes consistent with the important role of interferon and STAT1/2 signaling in viral infection.…”