Transcriptional Profiling of Immune-Related Genes in Leishmania infantum-Infected Mice: Identification of Potential Biomarkers of Infection and Progression of Disease

Abstract

Leishmania spp. is a protozoan parasite that affects millions of people around the world. At present, there is no effective vaccine to prevent leishmaniases in humans. A major limitation in vaccine development is the lack of precise understanding of the particular immunological mechanisms that allow parasite survival in the host. The parasite-host cell interaction induces dramatic changes in transcriptome patterns in both organisms, therefore, a detailed analysis of gene expression in infected tissues will contribute to the evaluation of drug and vaccine candidates, the identification of potential biomarkers, and the understanding of the immunological pathways that lead to protection or progression of disease. In this large-scale analysis, differential expression of 112 immune-related genes has been analyzed using high-throughput qPCR in spleens of infected and naïve Balb/c mice at four different time points. This analysis revealed that early response against Leishmania infection is characterized by the upregulation of Th1 markers and M1-macrophage activation molecules such as Ifng, Stat1, Cxcl9, Cxcl10, Ccr5, Cxcr3, Xcl1, and Ccl3. This activation doesn't protect spleen from infection, since parasitic burden rises along time. This marked difference in gene expression between infected and control mice disappears during intermediate stages of infection, probably related to the strong anti-inflammatory and immunosuppresory signals that are activated early upon infection (Ctla4) or remain activated throughout the experiment (Il18bp). The overexpression of these Th1/M1 markers is restored later in the chronic phase (8 wpi), suggesting the generation of a classical "protective response" against leishmaniasis. Nonetheless, the parasitic burden rockets at this timepoint. This apparent contradiction can be explained by the generation of a regulatory immune response characterized by overexpression of Ifng, Tnfa, Il10, and downregulation Il4 that counteracts the Th1/M1 response. This large pool of data was also used to identify potential biomarkers of infection and parasitic burden in spleen, on the bases of two different regression models. Given the results, gene expression signature analysis appears as a useful tool to identify mechanisms involved in disease outcome and to establish a rational approach for the identification of potential biomarkers useful for monitoring disease progression, new therapies or vaccine development.

Keywords: Leishmania infantum; biomarkers; high-throughput qPCR; immune responses; regression models; transcriptional profiling.

Figure 1

Evolution of parasite burden in spleen (A) and liver (B) of infected mice (n = 24). Mice were inoculated with 1 × 10⁶ promastigotes i.v. Spleen and liver parasite load were determined on week 1 (n = 6), week 2 (n = 6), week 4 (n = 6) and week 8 (n = 6) post-infection by limiting dilution assay and expressed as log₁₀ of the average parasite load per gram of tissue. Evolution of spleen weight in infected (n = 24) vs. control mice (n = 23) over the course of infection (C). The bars represent the weight in grams in infected (black bars) and non-infected control mice (white bars) at 1, 2, 4, and 8 weeks post-infection. Statistically significant differences are indicated (*p ≤ 0.05; **p ≤ 0.01).

Figure 2

Differential gene-expression of the 112 analyzed genes in infected (n = 6) vs. control mice (n = 5), 1 wpi. The x-axis represents log₂ of expression fold-change between infected and non-infected mice; the y-axis corresponds to the statistical significance, expressed as the negative logarithm of p-values. The red horizontal line indicates the cut-off for the statistical significance p = 0.05. Black vertical lines represent the log₂ FC of −0.6 and 06 (corresponding to FC −1.5 and 1.5 respectively) used as biological threshold to identify differentially expressed genes. The negative values correspond to down-regulated genes (indicated in blue) and the positive values are the up-regulated genes (indicated in red). Black and gray dots represent non-differentially expressed genes.

Figure 3

Differential gene-expression of the 112 analyzed genes in infected (n = 6) vs. control mice (n = 6), 2 wpi. The x-axis represents log₂ of expression fold-change between infected and non-infected mice; the y-axis corresponds to the statistical significance, expressed as the negative logarithm of p-values. The red horizontal line indicates the cut-off for the statistical significance p = 0.05. Black vertical lines represent the log₂ FC of −0.6 and 0.6 (corresponding to FC −1.5 and 1.5 respectively) used as biological threshold to identify differentially expressed genes. The negative values correspond to down-regulated genes (indicated in blue) and the positive values are the up-regulated genes (indicated in red). Black and gray dots represent non-differentially expressed genes.

Figure 4

Differential gene-expression of the 112 analyzed genes in infected (n = 6) vs. control mice (n = 6), 4 wpi. The x-axis represents log₂ of expression fold-change between infected and non-infected mice; the y-axis corresponds to the statistical significance, expressed as the negative logarithm of p-values. The red horizontal line indicates the cut-off for the statistical significance p = 0.05. Black vertical lines represent the log₂ FC of −0.6 and 0.6 (corresponding to FC −1.5 and 1.5 respectively) used as biological threshold to identify differentially expressed genes. The negative values correspond to down-regulated genes (indicated in blue) and the positive values are the up-regulated genes (indicated in red). Black and gray dots represent non-differentially expressed genes.

Figure 5

Differential gene-expression of the 112 analyzed genes in infected (n = 6) vs. control mice (n = 6), 8 wpi. The x-axis represents log₂ of expression fold-change between infected and non-infected mice; the y-axis corresponds to the statistical significance, expressed as the negative logarithm of p-values. The red horizontal line indicates the cut-off for the statistical significance p = 0.05. Black vertical lines represent the log₂ FC of −0.6 and 0.6 (corresponding to FC −1.5 and 1.5 respectively) used as biological threshold to identify differentially expressed genes. The negative values correspond to down-regulated genes (indicated in blue) and the positive values are the up-regulated genes (indicated in red). Black and gray dots represent non-differentially expressed genes.

Figure 6

Relative gene expression of Th1, Th2 and immunoregulatory markers in spleens along infection. The y-axis represents log2 of expression fold-change for each indicated gene, that is the ratio between the average gene expression in the infected group and non-infected-control mice. The x-axis represents time after infection: 1, 2, 4, and 8 wpi. Solid black bars indicate statistically significant differences with p ≤ 0.05.