Gene expression patterns in blood leukocytes discriminate patients with acute infections

Abstract

Each infectious agent represents a unique combination of pathogen-associated molecular patterns that interact with specific pattern-recognition receptors expressed on immune cells. Therefore, we surmised that the blood immune cells of individuals with different infections might bear discriminative transcriptional signatures. Gene expression profiles were obtained for 131 peripheral blood samples from pediatric patients with acute infections caused by influenza A virus, Gram-negative (Escherichia coli) or Gram-positive (Staphylococcus aureus and Streptococcus pneumoniae) bacteria. Thirty-five genes were identified that best discriminate patients with influenza A virus infection from patients with either E coli or S pneumoniae infection. These genes classified with 95% accuracy (35 of 37 samples) an independent set of patients with either influenza A, E coli, or S pneumoniae infection. A different signature discriminated patients with E coli versus S aureus infections with 85% accuracy (34 of 40). Furthermore, distinctive gene expression patterns were observed in patients presenting with respiratory infections of different etiologies. Thus, microarray analyses of patient peripheral blood leukocytes might assist in the differential diagnosis of infectious diseases.

Figure 1

Discriminating patients with influenza A virus infection from patients with bacterial infections. (A) Hierarchical clustering of 854 genes obtained from Mann-Whitney rank test comparison (P < .01) between 2 groups: influenza A (Inf A, 11 samples, green rectangle) and bacterial infections (red rectangle) with E coli (E.coli, 6 samples) or S pneumoniae (S.pn, 6 samples). Transformed expression levels are indicated by color scale, with red representing relatively high expression and blue indicating relatively low expression compared to the median expression for each gene across all donors. The black bar indicates IFN-inducible genes (IFN), and the red bar indicates genes involved in protein biosynthesis. Genes are listed in Table S2. (B) A supervised learning algorithm was used to identify 35 genes presenting the highest capacity to discriminate the 2 classes (Tables 1–2 and S3). Leave-one-out cross-validation of the training set with 35 genes classified the samples with 91% accuracy. The predicted class is indicated by light-colored solid rectangles (green for influenza A and red for bacteria). Two patients with bacterial infections were misclassified. (C) The 35 classifier genes thus identified were tested on an independent set of patients (open rectangles), including 7 new patients with influenza A (green), 23 with E coli, and 7 with S pneumoniae (red) infections. The 37 samples in this test set were classified with 95% accuracy (predicted class is indicated by light-colored rectangles). One patient was misclassified and one patient was indeterminate in class prediction (gray box). (D) The 35 classifier genes identified in panel B were tested on an independent set of patients (open squares), including 7 new patients with influenza A (Inf A), and 31 with S aureus infections. The 38 samples were classified with 87% accuracy.

Figure 2

Expression levels of the 35 classifier genes discriminating patients with influenza A infection from patients with bacterial infections. Scaled gene expression values (average difference intensity) are plotted for the 35 classifier genes represented in Figure 1B that discriminate between samples from patients with influenza A (11 samples, green squares) and bacterial infections (6 samples with E coli and 6 samples with S pneumoniae, red diamonds). Each plot represents one sample, lines represent median expression.

Figure 3

Discriminating patients with S aureus infections from patients with E coli infections. (A) Hierarchical clustering of 211 genes obtained from Mann-Whitney rank test comparison (P < .01) between 2 groups: Staphylococcus aureus (S aureus, 10 samples, red rectangle) and Escherichia coli (E coli, 10 samples, blue rectangle) infections. Transformed expression levels are indicated by color scale, with red representing relative high expression and blue indicating relative low expression compared to the median expression for each gene across all donors. Genes are listed in Table S4. (B) A supervised learning algorithm was used to identify 30 genes presenting the highest capacity to discriminate the 2 classes (Table S6). Leave-one-out cross-validation of the training set with 30 classifier genes grouped the samples with 95% accuracy. (C) The 30 classifier genes thus identified were tested on an independent set of patients (open rectangles), including 21 new patients with S aureus and 19 with E coli infections. The 40 samples in this test set were predicted with 85% accuracy (predicted class is indicated by light-colored rectangles). Of these 40 samples, only 2 were misclassified, whereas the class of 4 other samples could not be determined (open rectangles).

Figure 4

Expression levels of the 30 classifier genes discriminating patients with E coli infections from patients with S aureus infections. Scaled gene expression values (average difference intensity) are plotted for the 30 classifier genes represented Figure 3B that discriminate between samples from patients with E coli (10 samples, blue squares) and S aureus infections (10 samples, red diamonds). Each plot represents one sample, lines represent median expression.

Figure 5

Distinctive patterns of gene expression in circulating leukocytes obtained from patients with acute respiratory infections. (A) In addition to the 30 classifier genes found to discriminate S aureus from E coli (Venn diagram, right: Sa from Ec; Figure 2; Table S6), we identified 30 genes that distinguish S aureus from S pneumoniae (Venn diagram, left: Sa from Sp; Figure S2; Table S10) and 45 genes that distinguish E coli from S pneumoniae (Venn diagram, bottom: Ec from Sp; Figure S2; Table S8). Only 3 genes were shared between either of these groups. (B) The 3 groups of genes found to discriminate samples from patients with bacterial infections shown in panel A were merged (102 unique genes, Venn diagram, left) and compared to the classifier genes used to discriminate influenza A from bacterial infections (35 genes, Venn diagram, right; Figure 1; Table S3). No genes were shared between these 2 groups. (C) The 137 classifier genes that discriminate influenza A from bacterial infections and the 3 groups of patients with different bacterial infections were merged and used to generate discriminatory patterns of expression among 27 patients with respiratory infections and 7 healthy volunteers. Values were normalized to the median expression of each gene across all donors. Clustering of conditions partitioned samples into 4 major groups. Four samples belonging to the influenza A group and one from the S aureus formed a distinct subgroup characterized by a mixed signature (*) and are listed in Table 1 (Figure 5C*).

Figure 6

Independent confirmation and validation across microarray platforms. (A) A new set of data obtained from patients with acute influenza (n = 10) and bacterial infection (S aureus: n = 6; S pneumoniae: n = 6) was analyzed using Affymetrix U133 plus 2.0 GeneChips. The original classifier genes found to discriminate influenza A from bacterial infections (35 genes, Venn diagram, right; Figure 1; Table S3) were used to cluster this new set of samples. (B) A subset of 14 samples from patients with acute respiratory infection included in panel A were clustered using the list of 137 transcripts from Figure 5. (C) Another independent set of samples was obtained from a new set of patients with acute influenza (n = 8) or bacterial infection (S aureus: n = 13; S pneumoniae: n = 3) analyzed using Illumina Sentrix Hu6 whole genome BeadChips. Classifier genes used to discriminate influenza A from bacterial infections (35 genes, Venn diagram, right; Figure 1; Table S3) were used to cluster this new set of samples. Transformed expression levels are indicated by color scale, with red representing relative high expression and blue indicating relative low expression compared to the median expression for each gene across all donors.