Construction and Experimental Validation of a Petri Net Model of Wnt/β-Catenin Signaling

Abstract

The Wnt/β-catenin signaling pathway is important for multiple developmental processes and tissue maintenance in adults. Consequently, deregulated signaling is involved in a range of human diseases including cancer and developmental defects. A better understanding of the intricate regulatory mechanism and effect of physiological (active) and pathophysiological (hyperactive) WNT signaling is important for predicting treatment response and developing novel therapies. The constitutively expressed CTNNB1 (commonly and hereafter referred to as β-catenin) is degraded by a destruction complex, composed of amongst others AXIN1 and GSK3. The destruction complex is inhibited during active WNT signaling, leading to β-catenin stabilization and induction of β-catenin/TCF target genes. In this study we investigated the mechanism and effect of β-catenin stabilization during active and hyperactive WNT signaling in a combined in silico and in vitro approach. We constructed a Petri net model of Wnt/β-catenin signaling including main players from the plasma membrane (WNT ligands and receptors), cytoplasmic effectors and the downstream negative feedback target gene AXIN2. We validated that our model can be used to simulate both active (WNT stimulation) and hyperactive (GSK3 inhibition) signaling by comparing our simulation and experimental data. We used this experimentally validated model to get further insights into the effect of the negative feedback regulator AXIN2 upon WNT stimulation and observed an attenuated β-catenin stabilization. We furthermore simulated the effect of APC inactivating mutations, yielding a stabilization of β-catenin levels comparable to the Wnt-pathway activities observed in colorectal and breast cancer. Our model can be used for further investigation and viable predictions of the role of Wnt/β-catenin signaling in oncogenesis and development.

The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern

“…Regulatory effects of two potentially interacting genes (R1 and R2) on two downstream genes (G1 and G1) were simulated using a Petri net approach [42,44,86,87] to recapitulate genetic interaction patterns observed in the gene expression data.…”

“…Unravelling the regulatory network underlying genetic interaction patterns would potentially benefit from application of modeling approaches that allow some degree of quantitativeness to be introduced while still being computationally feasible to exhaustively explore all potential models. In this way, Petri nets may be considered an extension of Boolean modeling that provides more flexibility, in particular by choosing different network edge strengths, without the need to incorporate detailed prior quantitative knowledge [40–44]. Petri net modeling would therefore allow investigation of all possible genetic interaction patterns in an exhaustive and semi-quantitative manner.…”

The Wnt pathway scaffold protein Axin promotes signaling specificity by suppressing competing kinase reactions

“…In cell culture models, treatment with high levels of Wnt ligand can produce ~5-10-fold increases in total β-catenin levels (15, 40), with a corresponding ~5-fold decrease in GSK3β-β-catenin phosphorylation rate (15). Smaller changes can have significant effects in vivo , and transcriptional responses can be detected following ~2-fold changes in β-catenin levels (41). However, the observation that in vivo changes in β-catenin levels and phosphorylation rate can significantly exceed 2-fold suggests that we should be able to identify mechanisms that produce correspondingly larger effects in vitro .…”

Aberrant WNT/CTNNB1 Signaling as a Therapeutic Target in Human Breast Cancer: Weighing the Evidence

“…From these analyses it has been known for a long time that elevated intracellular levels of CTNNB1, a hallmark of active WNT/CTNNB1 signaling, can be detected by immunohistochemistry in a significant (13-77%) proportion of all ductal and lobular breast cancer samples (Jonsson et al, 2000;Karayiannakis et al, 2001;Wong et al, 2002;Ozaki et al, 2005;Prasad et al, 2008a;He et al, 2014;Hou et al, 2018). Care should be taken when performing and interpreting these experiments: Dogma dictates that active WNT/CTNNB1 signaling results in increased nuclear CTNNB1 levels, but those with more hands on experience in the field know that changes in CTNNB1 can be quite subtle and even modest (2-5 fold) increases in the levels of intracellular CTNNB1 can be more than sufficient to robustly activate TCF/LEF target gene expression (Jacobsen et al, 2016).…”