Identification of Deregulated Pathways, Key Regulators, and Novel miRNA-mRNA Interactions in HPV-Mediated Transformation

Abstract

Next to a persistent infection with high-risk human papillomavirus (HPV), molecular changes are required for the development of cervical cancer. To identify which molecular alterations drive carcinogenesis, we performed a comprehensive and longitudinal molecular characterization of HPV-transformed keratinocyte cell lines. Comparative genomic hybridization, mRNA, and miRNA expression analysis of four HPV-containing keratinocyte cell lines at eight different time points was performed. Data was analyzed using unsupervised hierarchical clustering, integrated longitudinal expression analysis, and pathway enrichment analysis. Biological relevance of identified key regulatory genes was evaluated in vitro and dual-luciferase assays were used to confirm predicted miRNA-mRNA interactions. We show that the acquisition of anchorage independence of HPV-containing keratinocyte cell lines is particularly associated with copy number alterations. Approximately one third of differentially expressed mRNAs and miRNAs was directly attributable to copy number alterations. Focal adhesion, TGF-beta signaling, and mTOR signaling pathways were enriched among these genes. PITX2 was identified as key regulator of TGF-beta signaling and inhibited cell growth in vitro, most likely by inducing cell cycle arrest and apoptosis. Predicted miRNA-mRNA interactions miR-221-3p_BRWD3, miR-221-3p_FOS, and miR-138-5p_PLXNB2 were confirmed in vitro. Integrated longitudinal analysis of our HPV-induced carcinogenesis model pinpointed relevant interconnected molecular changes and crucial signaling pathways in HPV-mediated transformation.

Keywords: CGH; HPV; PITX2; TGF-beta; cervical cancer; mRNA; microRNA; microarray.

Figure 1

Characterization of our longitudinal in vitro model system of human papillomavirus (HPV)-induced transformation. (a) Anchorage-dependent (black) and -independent (red) time points (T) of all four cell lines are shown in relation to the transformation process [16]. MiRNA microarrays of cell line FK18A at T4 and T5 did not pass quality control and were therefore excluded from miRNA analysis. Unsupervised hierarchical cluster results based on (b) DNA copy number, (c) overall mRNA expression, and (d) overall miRNA [16] expression are shown for FK16A, FK16B, FK18A, and FK18B.

Figure 2

Pathway reconstruction. Inferred dynamic networks of the (a) focal adhesion, (b) mTOR signaling, and (c) TGF-beta signaling pathways. Blue circles are the nodes of the network and represent genes. The lines depict which genes’ mRNA expression level at the current time point, denoted by Y_t, affect the genes’ mRNA expression level at a future time point, denoted Y_{t + 1}. Solid and dashed lines indicate whether this relation is of a stimulating (positive) or suppressing (negative) nature, respectively. For plotting purposes, genes with a total edge strength (sum of all absolute in and out connections of the respective gene) <0.25 (or <1 for TGF-beta signaling) were excluded.

Figure 3

PITX2 acts as tumor suppressor in HPV-induced transformation. (a) PITX2 expression in HPV-transformed keratinocyte cell lines. Using our tigaR method, a model without (red continuous line) and with copy number effect (blue discontinuous line) was fitted [17]. Similarity between the fitted models indicates that differential PITX2 expression is not copy number-driven. (b) PITX2 expression in mRNA microarray data obtained from normal HPV-positive cervical epithelium (Normal), high-grade precancerous lesions (CIN2/3), and squamous cell carcinomas (SCC). Boxplots show medians with lower and upper quartiles, and range whiskers. * p < 0.05, ** p < 0.005, according to the Wilcoxon rank-sum test. (c,d) Late FK18B cells (ca. passage 190) were transduced with an empty LeGO-iG2 vector or a LeGO-iG2-PITX2 construct. (c) FACS analysis: Mean and standard deviation of two independent experiments are shown. (d) Western blot analysis. Cells were harvested 72 h after transduction. Cells treated with etoposide were included as positive control for TP53/CDKN1A signaling.

Figure 4

Confirmation of miRNA-mRNA interactions. Predicted miRNA-mRNA interactions (a) miR-221-3p_BRWD3, (b) miR-221-3p_FOS, (c) miR-30a-3p_PECR, and (d) miR-138-5p_PLXNB2 were investigated using mRNA expression analysis and dual-luciferase reporter assays. Left panel: Effects of ectopic expression of the miRNA on the expression level of its predicted mRNA target. Expression levels were determined by qRT-PCR in late passage FK18B cells (ca. passage 190) transfected either with a negative control (cntr#2) or with the respective miRNA mimic. Data was normalized to SNRPA. Mean and standard deviation of two technical replicates are shown. Right panel: Dual-luciferase reporter assay to confirm direct interaction between the miRNA and its predicted target mRNA. HEK293 cells were transiently transfected with a negative control (cntr#1) or the respective miRNA mimic in combination with either an empty pmiRGLO vector, a pmiRGLO construct containing the predicted binding site (pmiRGLO-predicted target-UTR), or a pmiRGLO construct containing a mutated binding site (pmiRGLO-predicted target-UTR_mut). MiRNA, UTR, and binding sites are indicated in the lower panel. Mean and standard deviation of three technical replicates are shown. * p < 0.05, ** p < 0.005., according to the two-sided Student’s t-test.

Figure 5

Effect of miRNAs on adherent and anchorage-independent growth of late passage FK18B cells. Cell viability of late passage FK18B cells upon ectopic expression of miR-221-3p, miR-30a-3p, and miR-138-5p was measured in adherent tissue culture coated plates and ultra-low attachment plates and standardized against miRNA mimic negative control #1. Mean and standard errors of three independent experiments are shown.