Gene expression profiles identify inflammatory signatures in dendritic cells

Abstract

Dendritic cells (DCs) constitute a heterogeneous group of antigen-presenting leukocytes important in activation of both innate and adaptive immunity. We studied the gene expression patterns of DCs incubated with reagents inducing their activation or inhibition. Total RNA was isolated from DCs and gene expression profiling was performed with oligonucleotide microarrays. Using a supervised learning algorithm based on Random Forest, we generated a molecular signature of inflammation from a training set of 77 samples. We then validated this molecular signature in a testing set of 38 samples. Supervised analysis identified a set of 44 genes that distinguished very accurately between inflammatory and non inflammatory samples. The diagnostic performance of the signature genes was assessed against an independent set of samples, by qRT-PCR. Our findings suggest that the gene expression signature of DCs can provide a molecular classification for use in the selection of anti-inflammatory or adjuvant molecules with specific effects on DC activity.

Figure 1. PCA score plot.

Seventy-nine “inflammatory” observations and 36 “non inflammatory” observations (listed in Table 1) used to generate and test the random forest model. Genome-wide gene expression data were collected with DNA-microarray technology and the normalized hybridization signals were analyzed by PCA. A score plot with the first and second principal component axes is shown. Inflamed samples are mostly projected toward negative values of the first PC axis, whereas samples from controls and non inflamed samples are projected toward positive values. INF: Inflamed samples; NINF: non-inflamed samples.

Figure 2. Heat map of the dendritic cell-specific signature.

Genes with different levels of expression in DCs treated with inflammatory stimuli and in those treated with anti-inflammatory molecules. Each column represents a sample and each row represents a gene. Levels of gene expression are indicated on a color scale, with red corresponding to the highest level of expression and blue corresponding to the lowest level. The Log2 ratio is expressed with respect to the mean expression level of each gene.

Figure 3. Sample validation by quantitative real-time PCR.

qRT-PCR confirmation of classifier transcript levels in inflamed samples derived from DCs treated with TLR ligands (LPS, PolyI:C and zymosan) and in non inflamed samples treated with anti-inflammatory stimuli (dexamethasone, vitamin D and IL-10) for 24 h. Reactions were performed in two wells, normalized to 18s rRNA levels. The results in the table are expressed relative to the corresponding level of expression of each transcript in the untreated sample. Data are presented as mean fold changes in classifier gene transcript levels in three independent experiments per group. The columns in the left reflect the pattern of expression as determined by microarray analysis. NI: Non inflamed samples; INF; Inflamed samples.

Figure 4. q-Real-time PCR analysis of gene expression on independent samples for class prediction.

A) Expression levels of 54 genes in DCs treated with the bacteria Listeria monocytogenes (Lm) and Lactobacillus paracasei (Lp) for 24 h. B) DC samples treated with nimesulide and IFNα for 24 h. Comparison with cells treated with dexamethasone (DEX), IL-10 and vitamin D (VitD). Data are presented as mean fold changes in classifier gene transcript levels in three independent experiments per group. NI: Non inflamed samples; INF; Inflamed samples.

Figure 5. Specificity of the genetic signature.

Real-time PCR confirmation of 44 inflammatory signature genes in the DC line D1 and the absence of this signature in MT2 cells. Both cell lines were treated with LPS (10 µg/ml) and 10⁻⁸ M dexamethasone (DEX) for 24 h. Data are presented as mean fold changes in classifier gene transcript levels in three independent experiments per group.

Figure 6. In vivo validation of the DC-specific inflammatory signature.

C57BL/6 mice were treated with LPS and, after 5 h, CD11c+ cells were isolated and tested for the inflammatory signature. Data are presented as mean fold changes in classifier gene transcript levels in three independent experiments.