Literature.

<p><i>SA = Staphylococcus aureus, SP = Streptococcus pneumoniae, EF = Enterococcus faecalis, PA = Pseudomonas aeruginosa, KP = Klebsiella pneumoniae, EC = Escherichia coli.</i></p

Inclusion flow diagram.

<p>The initial search resulted in 837 hits. Fifty-nine were selected based on title and abstract. Full text was read and references were checked for additional hits. This resulted in ten additional hits. Thirty papers were included based on the full text.</p

Interaction plot.

<p>The six investigated pathogenic bacteria are plotted on both sides, with gram-positive bacteria on the left and gram-negative on the right. All the metabolites for which convincing evidence on production by at least one of the bacteria was available (as indicated by a green cell in Tables S1 to S9 in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1003311#ppat.1003311.s001" target="_blank">Text S1</a>) were included in the figure and connected with a line to all bacteria known to produce a particular metabolite. The stronger the available evidence for the production of a metabolite by one strain of bacteria, the closer the metabolite is situated to the pathogen. Four zones of interest are highlighted. The blue zone in the middle indicates metabolites that are (almost) always produced by all pathogens and are therefore candidate markers with a high sensitivity that might thus qualify for the exclusion of infection (high negative predictive value). The three red zones indicate metabolites that are produced by only or mainly one strain of bacteria; these are possibly volatile biomarkers specific for a pathogen with a very high positive predictive value.</p

The Impact of Allergic Rhinitis and Asthma on Human Nasal and Bronchial Epithelial Gene Expression

<div><p>Background</p><p>The link between upper and lower airways in patients with both asthma and allergic rhinitis is still poorly understood. As the biological complexity of these disorders can be captured by gene expression profiling we hypothesized that the clinical expression of rhinitis and/or asthma is related to differential gene expression between upper and lower airways epithelium.</p><p>Objective</p><p>Defining gene expression profiles of primary nasal and bronchial epithelial cells from the same individuals and examining the impact of allergic rhinitis with and without concomitant allergic asthma on expression profiles.</p><p>Methods</p><p>This cross-sectional study included 18 subjects (6 allergic asthma and allergic rhinitis; 6 allergic rhinitis; 6 healthy controls). The estimated false discovery rate comparing 6 subjects per group was approximately 5%. RNA was extracted from isolated and cultured epithelial cells from bronchial brushings and nasal biopsies, and analyzed by microarray (Affymetrix U133+ PM Genechip Array). Data were analysed using R and Bioconductor Limma package. For gene ontology GeneSpring GX12 was used.</p><p>Results</p><p>The study was successfully completed by 17 subjects (6 allergic asthma and allergic rhinitis; 5 allergic rhinitis; 6 healthy controls). Using correction for multiple testing, 1988 genes were differentially expressed between healthy lower and upper airway epithelium, whereas in allergic rhinitis with or without asthma this was only 40 and 301 genes, respectively. Genes influenced by allergic rhinitis with or without asthma were linked to lung development, remodeling, regulation of peptidases and normal epithelial barrier functions.</p><p>Conclusions</p><p>Differences in epithelial gene expression between the upper and lower airway epithelium, as observed in healthy subjects, largely disappear in patients with allergic rhinitis with or without asthma, whilst new differences emerge. The present data identify several pathways and genes that might be potential targets for future drug development.</p></div

Comparison of breathprints of patients with RA (<i>circles</i>) versus controls (<i>triangles</i>).

<p>(A) Two-dimensional principal component plot showing the discrimination of breathprints of patients with RA and controls. Accuracy of 71% (P = 0.003). (B). Receiver operator characteristics (ROC) curve for RA vs controls with line of identity of the breathprint discriminant function (representing PC 1). AUC reached 0.75.</p

VOCs (metabolites) found in significantly different concentrations in RA patients versus controls and RA versus PsA patients.

<p>VOCs (metabolites) found in significantly different concentrations in RA patients versus controls and RA versus PsA patients.</p

Cross-validation values for the discrimination between patients with RA, PsA and controls.

<p>Cross-validation values for the discrimination between patients with RA, PsA and controls.</p

Characteristics of the study population.

<p>Characteristics of the study population.</p

Comparison of breathprints of patients with PsA (<i>squares</i>) versus controls (<i>triangles</i>).

<p>(A) Two-dimensional principal component plot showing the discrimination of breathprints of patients with PsA and controls. Accuracy of 69% (P = 0.014). (B) Receiver operator characteristics (ROC) curves for PsA vs. controls with line of the breathprint discriminant function (representing PC 1 and 4). AUC was 0.77.</p

Comparison of breathprints of patients with RA (<i>circles</i>) versus PsA (<i>squares</i>).

<p>(A) Two-dimensional principal component plot showing the discrimination of breathprints of patients with RA and PsA. Accuracy of 69% (P = 0.026). (B) Receiver operator characteristics (ROC) curves for PsA vs. controls with line of the breathprint discriminant function (representing PC 4). AUC was 0.72.</p