Novel factors in the pathogenesis of psoriasis and potential drug candidates are found with systems biology approach

Abstract

Psoriasis is a multifactorial inflammatory skin disease characterized by increased proliferation of keratinocytes, activation of immune cells and susceptibility to metabolic syndrome. Systems biology approach makes it possible to reveal novel important factors in the pathogenesis of the disease. Protein-protein, protein-DNA, merged (containing both protein-protein and protein-DNA interactions) and chemical-protein interaction networks were constructed consisting of differentially expressed genes (DEG) between lesional and non-lesional skin samples of psoriatic patients and/or the encoded proteins. DEGs were determined by microarray meta-analysis using MetaOMICS package. We used STRING for protein-protein, CisRED for protein-DNA and STITCH for chemical-protein interaction network construction. General network-, cluster- and motif-analysis were carried out in each network. Many DEG-coded proteins (CCNA2, FYN, PIK3R1, CTGF, F3) and transcription factors (AR, TFDP1, MEF2A, MECOM) were identified as central nodes, suggesting their potential role in psoriasis pathogenesis. CCNA2, TFDP1 and MECOM might play role in the hyperproliferation of keratinocytes, whereas FYN may be involved in the disturbed immunity in psoriasis. AR can be an important link between inflammation and insulin resistance, while MEF2A has role in insulin signaling. A controller sub-network was constructed from interlinked positive feedback loops that with the capability to maintain psoriatic lesional phenotype. Analysis of chemical-protein interaction networks detected 34 drugs with previously confirmed disease-modifying effects, 23 drugs with some experimental evidences, and 21 drugs with case reports suggesting their positive or negative effects. In addition, 99 unpublished drug candidates were also found, that might serve future treatments for psoriasis.

Figure 1. PCA biplot numbers on PCA biplot represents studies in Table 1.

Study number placed opposite to quality measure axes are of low quality and should be excluded. No outlier study was detected.

Figure 2. Degree-Fold Change relationship.

Nodes with higher degree has lower fold change of gene expression in all network types. Genes between red lines have higher average degree and are filtered out from network analysis. Remaining nodes in DEG-derived networks have lower average degree and connectivity.

Figure 3. Network motifs with 3 or 4 nodes.

Analysis results of the respective motif can be found in Table 5.

Figure 4. Positive feedback loops and the merged controller sub-network in lesional psoriatic skin.

Individual positive feedback loops with 2, 3 or 4 nodes are shown. Node color is blue if the gene expression is decreased and red if increased. Merged controller sub-network is shown on the top. Node color is proportional with fold change. red line: gene regulatory interaction; blue line: protein-protein interaction; arrow-headed line: activation; bar-headed line: inhibition

Figure 5. Effect of anti-psoriatic drugs on controller network.

Higher number of effective anti-psoriatic drugs act on controller nodes than on other proteins. Totally the targets of 32 effective anti-psoriatic drugs were analyzed (median 10 vs. 1) *p<0.001