Microarray analysis revealing common and distinct functions of promyelocytic leukemia protein (PML) and tumor necrosis factor alpha (TNFα) signaling in endothelial cells

Abstract

Background: Promyelocytic leukemia protein (PML) is a tumor suppressor that is highly expressed in endothelial cells nonetheless its role in endothelial cell biology remains elusive. Tumor necrosis factor alpha (TNFα) is an important cytokine associated with many inflammation-related diseases. We have previously demonstrated that TNFα induces PML protein accumulation. We hypothesized that PML may play a role in TNFα signaling pathway. To identify potential PML target genes and investigate the putative crosstalk between PML's function and TNFα signaling in endothelial cells, we carried out a microarray analysis in human primary umbilical endothelial cells (HUVECs).

Results: We found that PML and TNFα regulate common and distinct genes involved in a similar spectrum of biological processes, pathways and human diseases. More importantly, we found that PML is required for fine-tuning of TNFα-mediated immune and inflammatory responses. Furthermore, our data suggest that PML and TNFα synergistically regulate cell adhesion by engaging multiple molecular mechanisms. Our biological functional assays exemplified that adhesion of U937 human leukocytes to HUVECs is co-regulated by PML and TNFα signaling.

Conclusions: Together, our study identified PML as an essential regulator of TNFα signaling by revealing the crosstalk between PML knockdown-mediated effects and TNFα-elicited signaling, thereby providing novel insights into TNFα signaling in endothelial cells.

Figure 1

Venn diagrams of significantly affected genes. (a) Significantly affected genes following PML knockdown by two independent siRNAs (siP1, siP2) compared to control siRNA (siC) in HUVECs without TNFα treatment. The intersection of both circles (i) is considered significantly affected genes by PML knockdown (designated as “siP.U-siC.U”). (b) Comparison between TNFα responsive genes (“siC.T-siC.U”) and TNFα responsive genes when PML was knocked down (“siP.T-siP.U”). (c) Comparison between PML-knockdown responsive genes in the absence of TNFα treatment (i, “siP.U-siC.U” from a) and in the presence of TNFα treatment (“siP.T-siC.T”). (d) Identification of genes interactively regulated by PML knockdown (two siRNAs, siP1 and siP2) and TNFα treatment. The intersection of both circles (ii) is considered significantly affected interaction genes. Untreated samples, “U”; TNFα treated samples, “T”; comparison between two samples, “-”; interaction effects, “∗”. Numbers in the circles, the number of significantly altered genes (>1.5fold,q < 0.05) by the indicated comparison. Numbers in the outer box, the number of unchanged genes; up-regulation, “up” shown in red; down-regulation, “down” shown in green.

Figure 2

Functional ontology analyses of PML knockdown responsive genes and TNFα treatment affected genes. The identified gene lists were analyzed by the Hyper-Geometric test (q < 0.01) as described in Methods to identify the over-represented terms of (a) GO.BP, (b) GO.MF, (c) GO.CC and (d) KEGG pathways affected by PML knockdown (“kdPML”) and TNFα treatment (“TNFα”). Two parental GO terms were removed to reduce redundancy of the GO term definition. GO.BP, Gene Ontology Biological Process; GO.MF, Gene Ontology Molecular Function; GO.CC, Gene Ontology Cellular Component; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Figure 3

PML function and TNFα signaling are linked to multiple human diseases. The significantly affected genes by PML knockdown and TNFα signaling are significantly associated (q < 0.01) with multiple human diseases analyzed as described in Methods using a Disease Ontology database. The diseases are sorted vertically according to the number of associated significantly altered genes following PML knockdown. The transformed values (−ln(qvalue)) of FDR adjusted p value for each category of diseases are shown in the same graph. The dashed lines indicate −ln(q = 0.01) ≈ 4.6.

Figure 4

The interactive effects of PML knockdown on TNFα signaling-induced inflammatory response. (a and b) Two clusters of genes annotated with inflammation-associated ontology information were identified in a clustering analysis of the log fold change values of significantly affected genes combined with their gene ontology information (GO and KEGG). The affected categories of ontology information and the Fisher exact test results are summarized in the table. “CT-CU”, TNFα effects with control siRNA transfection; “PT-PU”, TNFα effects with PML siRNAs transfected; “PU-CU”, PML knockdown effects; “PT-CT”, PML knockdown effects when TNFα treated; “PxT”, the interaction effects of PML knockdown and TNFα treatment defined as P×T = (PT−PU)−(CT−CU)=(PT−CT)−(PU−CU). GO, Gene Ontology database; BP, biological function; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Figure 5

The effects of PML knockdown and/or TNFα on leukocyte:HUVEC adhesion. (a) Clustering analysis of the log change fold values of the coregulatory network of genes significantly affected by PML knockdown and/or TNFα signaling. Using dynamicTreeCut R package as described in Methods, 4 sub-clusters of significantly altered genes were identified and annotated by color side bars (blue, red, green and purple respectively). Control siRNA, “siC”; two PML siRNAs, “siP1” and “siP2”; vehicle treated samples, “U”; TNFα treated samples, “T”; minus sign of “-”, comparison between two samples; sign of “∗”, interaction effects. (b) Quantification of the adherence of human leukocyte U937 cells on HUVECs transfected with control siRNA (siCtrl) or two independent PML siRNAs (siPML-1 and siPML-2) without or with TNFα treatment. Two-tail unpaired t-tests: ∗, p < 0.05; ∗∗, p < 0.01; ∗∗∗, p < 0.001.