Host immune transcriptional profiles reflect the variability in clinical disease manifestations in patients with Staphylococcus aureus infections

Abstract

Staphylococcus aureus infections are associated with diverse clinical manifestations leading to significant morbidity and mortality. To define the role of the host response in the clinical manifestations of the disease, we characterized whole blood transcriptional profiles of children hospitalized with community-acquired S. aureus infection and phenotyped the bacterial strains isolated. The overall transcriptional response to S. aureus infection was characterized by over-expression of innate immunity and hematopoiesis related genes and under-expression of genes related to adaptive immunity. We assessed individual profiles using modular fingerprints combined with the molecular distance to health (MDTH), a numerical score of transcriptional perturbation as compared to healthy controls. We observed significant heterogeneity in the host signatures and MDTH, as they were influenced by the type of clinical presentation, the extent of bacterial dissemination, and time of blood sampling in the course of the infection, but not by the bacterial isolate. System analysis approaches provide a new understanding of disease pathogenesis and the relation/interaction between host response and clinical disease manifestations.

Figure 1. The S. aureus infection whole blood transcriptional signature is characterized by over-expression of myeloid lineage transcripts and under-expression of lymphoid lineage transcripts.

A. Statistical group comparison between 22 healthy subjects and 40 patients with acute S. aureus infection (non-parametric test, α = 0.01, Benjamini-Hochberg multiple testing correction, 1.25 fold change) yielded 1,422 differentially expressed transcripts. Transcripts were organized by hierarchical clustering (Spearman) according to similarities in expression profiles. Each row represents a transcript and each column an individual subject. Normalized log ratio levels are indicated by red (over-expressed) or blue (under-expressed), as compared to the median of healthy controls. B. The same 1,422 transcript list and hierarchical clustering were applied to an independent test set of 22 healthy controls and 59 patients with acute S. aureus infection. Sample hierarchical clustering (Spearman) was performed on the 1,422 transcript list in the test set. C. Average modular transcriptional fingerprrint for S. aureus patients as compared to healthy controls in the training set. D. Average modular transcriptional profile for S. aureus patients as compared to healthy controls in the test set. E. Module functional annotations legend. F. Scatter plot comparing module expression between training (x-axis) and test (y-axis) sets. Spearman correlation was applied.

Figure 2. Individual analysis identifies heterogeneous components of the blood signature to S. aureus.

A. Column scatter plot representing the distribution of individual molecular distance to health (MDTH) in healthy controls and S. aureus patients. The list of all transcripts composing the modules was used as reference to calculate individual MDTH (***: p<0.001, Mann-Whitney). Horizontal bars represent the group median. Patients with MDTH within healthy range (n = 25) were categorized as transcriptionally quiescent (TQ) and represented in grey. B. Unsupervised hierarchical clustering of the 10,972 transcripts expressed in at least one of the 143 samples (2-fold normalized, 100 difference in raw data) from the combined training and test sets. C. The modular signature was derived for individual transcriptionally active patients (n = 74) as compared to the median of the healthy control group for the corresponding patient set (training or test). Four major clusters (C1 through C4) of patients were obtained by K-means clustering and reorganized into a single heatmap, with modules in rows and patients in columns. Molecular distance to health for individual samples is represented as a line chart on top of the heatmap. D. Zoom on modules with specific over-expression patterns across the four clusters. E. MDTH and clinical lab measurements distribution by cluster. Five or six-group non-parametric ANOVA (Kruskal-Wallis) with Dunn's post-hoc test was applied. (*: p<0.05, **: p<0.01, ***: p<0.001). F. Bar charts representing the percent distribution of infection localization, clinical presentation and bacterial strain for the five clusters of patients identified.

Figure 3. Specific module subsets correlate with laboratory results.

A. Heatmap representing correlation (Spearman R) between module percent expression in columns and continuous laboratory parameters in rows. Hierarchical clustering (Euclidian distance) was applied in both dimensions. B. Connection network representing correlation between molecular nodes (modules) in blue and clinical nodes (laboratory parameters) in green. Spearman R correlation greater than 0.3 are represented.

Figure 4. Patient signature varies with blood draw index, dissemination and clinical presentation.

A. Patients were organized in four quarters Q1 through Q4 based on blood draw index (ratio draw day/hospitalization duration). A low draw index signifies proximity to hospital admission while a high draw index signifies proximity to discharge. Transcripts differently expressed between the four draw index quarters were selected by non-parametric ANOVA (Kruskal-Wallis, p<0.01, Benjamini-Hochberg false discovery rate) and represented as a heatmap (red, yellow, blue). The same statistical filter was applied at the module level (red, white, blue heatmap below). Individual MDTH was represented above as a line chart. B. Column scatter plot of individual MDTH per blood draw index quartile. Horizontal bars represent group median. Non-parametric ANOVA (Kruskal-Wallis) with Dunn's post-hoc test was applied. C. Non-linear regression model (one-phase decay) of MDTH (left Y-axis) and CRP (right Y-axis) as a function of blood draw index. D. Column scatter plot of individual MDTH per infection localization group. E. Column scatter plot of individual MDTH per clinical presentation group. Horizontal bars represent the median value for each group. Non-parametric ANOVA (Kruskal-Wallis) with Dunn's post-hoc test was applied between patient groups.

Figure 5. The osteoarticular infection signature displays increased blood coagulation.

We compared the transcriptional signatures from patients with pneumonia and patients with osteoarticular infections. To properly balance osteoarticular and pneumonia groups, patients with pneumonia with a draw index less than 0.75 (nine patients) were selected (active disease). Nine patients with osteoarticular infection were selected with matching MDTH so that global quantitative signature was equivalent between the two groups. Nine healthy controls were selected from the training and nine from the test set (18 healthy controls in total) as reference. A. Top left panel: mean module map for the nine patients with osteoarticular infections compared to the 18 healthy controls. Bottom left panel: mean module map for the 9 patients with staphylococcal pneumonia compared to the 18 healthy controls. Top right panel: substraction map of osteoarticular infections minus pneumonia. Only differences greater than 40% are represented. Bottom right panel: Annotation legend for modules identified. B. Heatmap representing genes differentially expressed (t-test, <0.05, no correction) between osteoarticular infections and pneumonia (hierarchical clustering, Pearson). 190 genes were upregulated 1.5-fold or more in osteoarticular infections versus pneumonia and healthy controls. 185 genes were upregulated 1.5-fold or more in pneumonia versus osteoarticular infections and healthy controls. C. Area chart representing PANTHER comparison for pathway enrichment between the two lists from C.