PETALS: Proteomic Evaluation and Topological Analysis of a mutated Locus' Signaling

Gurkan Bebek,Mark R Chance,Vishal Patel

doi:10.1186/1471-2105-11-596

Abstract

BackgroundColon cancer is driven by mutations in a number of genes, the most notorious of which is Apc. Though much of Apc's signaling has been mechanistically identified over the years, it is not always clear which functions or interactions are operative in a particular tumor. This is confounded by the presence of mutations in a number of other putative cancer driver (CAN) genes, which often synergize with mutations in Apc.Computational methods are, thus, required to predict which pathways are likely to be operative when a particular mutation in Apc is observed.ResultsWe developed a pipeline, PETALS, to predict and test likely signaling pathways connecting Apc to other CAN-genes, where the interaction network originating at Apc is defined as a "blossom," with each Apc-CAN-gene subnetwork referred to as a "petal." Known and predicted protein interactions are used to identify an Apc blossom with 24 petals. Then, using a novel measure of bimodality, the coexpression of each petal is evaluated against proteomic (2 D differential In Gel Electrophoresis, 2D-DIGE) measurements from the Apc1638N+/-mouse to test the network-based hypotheses.ConclusionsThe predicted pathways linking Apc and Hapln1 exhibited the highest amount of bimodal coexpression with the proteomic targets, prioritizing the Apc-Hapln1 petal over other CAN-gene pairs and suggesting that this petal may be involved in regulating the observed proteome-level effects. These results not only demonstrate how functional 'omics data can be employed to test in silico predictions of CAN-gene pathways, but also reveal an approach to integrate models of upstream genetic interference with measured, downstream effects.

Highlights

Colon cancer is driven by mutations in a number of genes, the most notorious of which is Apc
As proteins themselves are the mediators of cellular functions, we mapped proteome-level measurements identified through 2 D differential In Gel Electrophoresis (2D-DIGE) to each petal, using mRNAlevel coexpression to quantify the strength of the relationship
Filtering out paths whose (i) average mRNA coexpression was low (r < |0.6|, a significance threshold validated in similar studies [11,17]) and (ii) support of Gene Ontology (GO) annotation association rules based on known signaling pathways and functional annotations [11] was weak (p - value > 0.05), the number of ApcCAN-gene petals was reduced to 24 (Figure 2)

Summary

Introduction

Colon cancer is driven by mutations in a number of genes, the most notorious of which is Apc. Though much of Apc’s signaling has been mechanistically identified over the years, it is not always clear which functions or interactions are operative in a particular tumor. Though much of Apc’s signaling has been mechanistically identified over the years, it is not always clear which functions or interactions are operative in a particular tumor This is confounded by the presence of mutations in a number of other putative cancer driver (CAN) genes, which often synergize with mutations in Apc. Computational methods are, required to predict which pathways are likely to be operative when a particular mutation in Apc is observed. Testing each petal against such functional data correlates gene and protein expression readouts with specific driver gene relationships, thereby allowing the experimenter to identify the petal most likely to be operative in this particular mouse model

Methods

Results

Conclusion