CXXC-type zinc finger protein 4 (CXXC4) : Time behavioural study of 3rd order combinations in WNT3A stimulated HEK 293 cells
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
CXXC4 (or Dishevelled DVL-binding protein - inhibition of the DVL and AXIN complex (IDAX)) encodes a CXXC-type zinc finger domain-containing protein that antagonizes the canonical WNT signaling pathway. This domain contains eight conserved cysteine residues that bind to two zinc ions. The encoded protein negatively regulates WNT signaling through interaction with the postsynaptic density protein (PSD95) / Drosophila disc large tumor suppressor (DlgA) / zonula occludens-1 protein (ZO-1) or (PDZ) protein domain of DVL, that is required for the stabilization of the transcriptional co-activator β-catenin. Gujral and MacBeath [1] provides a quantitative, and dynamic study of WNT3A-mediated stimulation of HEK 293 cells, where they record time based expression profiles of several response genes which correlated significantly with proliferation and migration. By monitoring the dynamics of gene expression using self-organizing maps, they identified clusters of genes that exhibit similar expression dynamics and uncovered previously unrecognized positive and negative feedback loops. However, their study depicts/uses singular measurements of individual gene expression at different time snapshots/points to infer the system wide analysis of the pathway. At any particular time point, it is often the case that genes are working synergistically in combinations, even though their expression measurements are singular in nature. Here, I • enumerate and rank all 2415 CXXC4 related 3rd order combinations in a forest of 71C3 combinations using four different sensitivity methods; • show the conserved rankings for CXXC4-X-X combinations, which point to existence of bio- logical synergy of some of these combinations across the different sensitivity methods; and • study the behaviour of some of these combinations related to WNT3A response genes that are ranked by the machine learning search engine (Sinha [2]) in time. Patterns of combinations emerge, some of which have been tested in wet lab, while others require further wet lab analysis.