Pathway crosstalk perturbation network modeling for identification of
connectivity changes induced by diabetic neuropathy and pioglitazone.
Authors de Anda-Jáuregui G, Guo K, McGregor BA, Feldman EL, Hur J
Submitted By Submitted Externally on 1/24/2019
Status Published
Journal BMC systems biology
Year 2019
Date Published 1/1/2019
Volume : Pages 13 : 1
PubMed Reference 30616626
Abstract Aggregation of high-throughput biological data using pathway-based approaches is
useful to associate molecular results to functional features related to the
studied phenomenon. Biological pathways communicate with one another through the
crosstalk phenomenon, forming large networks of interacting processes., In this
work, we present the pathway crosstalk perturbation network (PXPN) model, a
novel model used to analyze and integrate pathway perturbation data based on
graph theory. With this model, the changes in activity and communication between
pathways observed in transitions between physiological states are represented as
networks. The model presented here is agnostic to the type of biological data
and pathway definition used and can be implemented to analyze any type of
high-throughput perturbation experiments. We present a case study in which we
use our proposed model to analyze a gene expression dataset derived from
experiments in a BKS-db/db mouse model of type 2 diabetes mellitus-associated
neuropathy (DN) and the effects of the drug pioglitazone in this condition. The
networks generated describe the profile of pathway perturbation involved in the
transitions between the healthy and the pathological state and the
pharmacologically treated pathology. We identify changes in the connectivity of
perturbed pathways associated to each biological transition, such as rewiring
between extracellular matrix, neuronal system, and G-protein coupled receptor
signaling pathways., The PXPN model is a novel, flexible method used to
integrate high-throughput data derived from perturbation experiments; it is
agnostic to the type of data and enrichment function used, and it is applicable
to a wide range of biological phenomena of interest.