(680d) Identifying Distinct Gene Regulatory Networks Subtending Catecholaminergic Neuronal Subtypes Contributing to Hypertension Development | AIChE

(680d) Identifying Distinct Gene Regulatory Networks Subtending Catecholaminergic Neuronal Subtypes Contributing to Hypertension Development


Park, J. - Presenter, Institute For Systems Biology
Ogunnaike, B. A., University of Delaware
Schwaber, J. S., Thomas Jefferson University
Vadigepalli, R., Thomas Jefferson University

The pathology of hypertension, a chronic disease affecting nearly 50 million adults in the United States, is multifactorial with many environmental and genetic factors and is not completely understood. However, recent physiological studies indicate that neuronal adaptation is a major component of hypertension pathology. We have obtained preliminary results that support our hypothesis that variable synaptic inputs cause transcriptional changes in neurons controlling blood pressure set point leading to hypertension development. Thus, identifying the gene regulatory networks subtending these neuronal changes driving hypertension development will provide greater insight into this disease pathology.

In order to understand the molecular framework of the observed neuronal changes, we developed gene regulatory network models based on our extension of the constrained fuzzy logic (cFL) approach1. The cFL approach converts prior knowledge networks, constrained by literature-based interactions, into refined empirical models that quantitatively describe graded responses of biological relationships using Hill-type functions. The existing published approach considers only binary ON-OFF input values when developing a refined model. However, we have extended this approach by allowing for graded inputs into the network model in order to match the graded input values we observed experimentally in vivo. We applied our cFL-based approach to describe quantitatively gene-to-gene interactions underlying neuronal response of individual neurons to graded synaptic inputs. We developed prior knowledge networks and explored multiple topologies within the logic space of the prior knowledge networks using a genetic algorithm. Through this process we identified potential families of models that fit our experimental data similarly well. Furthermore, we identified interactions that were consistently retained or eliminated across the model families. This analysis elucidated which interactions were more relevant in neuronal behavior that was observed experimentally.   

Using this approach, we modeled a regulatory network of genes involved in neuronal functions associated with blood pressure regulation, with a specific focus on the angiotensin type 1 receptor (AT1R) mediated signaling pathway. The network model was trained against gene expression data collected from 300 individual catecholaminergic neurons obtained from the brainstem of four rats undergoing a pharmacologically induced acute hypertension challenge. Our initial data analysis revealed six distinct neuronal subtypes that aligned with synaptic input-type. Consequently our modeling approach considered the six corresponding data subsets separately and yielded distinct regulatory networks underlying each neuronal subtype. These distinct network structures suggest that the inputs are modulating, or tuning, the gene regulatory networks governing neuronal behavior. This network tuning is apparent when comparing the refined networks across neuronal subtypes. Specifically, the connectivity and sensitivities between TFs and downstream target genes involved in neurotransmitter production and signaling feedback change across subtypes. These gene regulatory models form the foundation for the development of a tissue-level model that incorporates multiple neuronal subtypes. By integrating multiple neuronal subtypes into an encompassing model we will be able to analyze how these neuronal subtypes affect blood pressure set point definition and regulation.

  1. Morris, M. K., Saez-Rodriguez, J., Clarke, D. C., Sorger, P. K., & Lauffenburger, D. a. (2011). Training signaling pathway maps to biochemical data with constrained fuzzy logic: quantitative analysis of liver cell responses to inflammatory stimuli. PLoS computational biology, 7(3)

Research Support:  NIH (NIGMS R01 GM083108; NHLBI R01HL111621; NIAAA 5 T32 AA007463-26)