Identification of molecular mechanisms causing skin lesions of cutaneous leishmaniasis using weighted gene coexpression network analysis (WGCNA)

Abstract

Leishmaniasis is an infectious disease, caused by a protozoan parasite. Its most common form is cutaneous leishmaniasis, which leaves scars on exposed body parts from bites by infected female phlebotomine sandflies. Approximately 50% of cases of cutaneous leishmaniasis fail to respond to standard treatments, creating slow-healing wounds which cause permanent scars on the skin. We performed a joint bioinformatics analysis to identify differentially expressed genes (DEGs) in healthy skin biopsies and Leishmania cutaneous wounds. DEGs and WGCNA modules were analyzed based on the Gene Ontology function, and the Cytoscape software. Among almost 16,600 genes that had significant expression changes on the skin surrounding Leishmania wounds, WGCNA determined that one of the modules, with 456 genes, has the strongest correlation with the size of the wounds. Functional enrichment analysis indicated that this module includes three gene groups with significant expression changes. These produce tissue-damaging cytokines or disrupt the production and activation of collagen, fibrin proteins, and the extracellular matrix, causing skin wounds or preventing them from healing. The hub genes of these groups are OAS1, SERPINH1, and FBLN1 respectively. This information can provide new ways to deal with unwanted and harmful effects of cutaneous leishmaniasis.

Figure 1

The hierarchical clustering of the genes and samples and a static heatmap of differentially expressed genes (DEGs). This picture shows that there is a clear difference in the expression of the different genes taken from the healthy and diseased samples and that we may reach a meaningful theory by studying them.

Figure 2

Weighted gene co-expression network (WGCNA) identified several modules, represented by different colors. Modules were grouped with closely linked genes and comparable expression profiles. Then identified on the dendrogram using the Dynamic Tree Cut algorithm. Module identification was accomplished with the dynamic tree-cut method, by hierarchically clustering genes using 1-TOM as the distance measure with mergeCutHeight = 0.25 and minModuleSize = 30 for the resulting dendrogram.

Figure 3

Graphical representation of the results and color-coded each association by the correlation value. Differential expressions of Genes in the "yellow" module are most correlated with the "size" and "DTH" traits (size of cutaneous lesions).

Figure 4

Gene Ontology (GO) enrichment analysis results of the genes in the yellow module (https://biit.cs.ut.ee/gplink/l/BrkqggeNSR) In this module, there are genes from CC (Cellular Component), BP (Biological Process) and MF (Molecular Function) groups with a significantly increased expression.

Figure 5

Percentage of immunologic cells. The cell type proportions computed by the "Support Vector Regression model (SVR)" using the sigMatrix_ABIS_S0 reference profile. The percentage of immunologic cells has increased significantly in the tissues infected with parasites. Most of them are Monocytes and T Cells which can play an important role in tissue cytotoxic activities.