(680b) Systems Tissue Engineering Approach to Infer Dynamic Transcription Factor Activity Networks Using Large-Scale, Real Time Living Cell Arrays | AIChE

(680b) Systems Tissue Engineering Approach to Infer Dynamic Transcription Factor Activity Networks Using Large-Scale, Real Time Living Cell Arrays


Penalver Bernabe, B. - Presenter, Northwestern University
Weiss, M. S., Northwestern University
Shin, S., Northwestern University
Asztalos, S., University of Illinois at Chicago
Bellis, A. D., Northwestern University
Tonetti, D., University of Illinois at Chicago
Jeruss, J., Northwestern University
Broadbelt, L. J., Northwestern University
Shea, L. D., Northwestern University

Identifying the dynamic activity of critical signaling pathways that lead to a given phenotype and inferring the relationships between those critical signaling pathways are still challenges for the systems biology community. In this work we present a new alternative. Our lab has recently established a technique for large-scale analysis of dynamic signaling pathways within living cell arrays employing lentiviral delivery of transcription factor (TF) reporter constructs, capable of tracking simultaneously the dynamic activities of multiple TFs. We have exploited them to investigate dynamic transcriptional networks of ErbB2 signaling during tissue formation, cancer progression and response to ErbB2-specific therapeutics in 3D hydrogels employing 25 TF reporters in parallel. ErbB2 (HER2/Neu), a member of the EGF receptor family, is overexpressed in a large percentage of aggressive breast cancers, but little is known about its underlying mechanisms or the complete mode of action of the several therapeutic molecules targeting ErbB2 activity.

10A/ErbB2 cells, inducible ErbB2 dimerization MCF10A cells, cultured in Matrigel over 10 days presented different phenotypes and transcription factor activity profiles when treated with EGF or with an ErbB2 dimerization agent (DA). EGF stimulation led to larger spherical structures and generated transient TF reporter activity responses while DA produced larger, highly disorganized structures and targeted more cellular processes stably. DA phenotype required constitutive activation of multiple TFs reporters, such as E2F1, SP1, SRE, STATs and YY1, while EGF effects on TF reporter activity disappeared after 3 days in culture. When DA-activated 10A.ErbB2 cells in 3D culture were treated with lapatinib, larger changes in TF reporter activity were observed at early experimental times in E2F1, ELK1, GATA, P53, RAR and STAT4 reporters, which may be the most direct target processes of lapatinib.

TF networks, represented as a three-level Boolean model, were identified by a combination of protein-DNA interactions (prior knowledge) and an ensemble of inference methods. The most likely active connections (edges) at each experimental time were found by structure optimization through minimization of the difference between the observed data and the response of the Boolean model. Consensus networks showed that cells treated with DA responded with a slow activation of TF reporters up to day 5 in culture, followed by a blossom of TFs that are activated through E2F1, YY1 and STAT3 reporters, with the AP1 reporter being the most important dynamic hub between those days.  EGF response was translated sooner through ELK1 reporter to the rest of the cellular processes, with ELK1, NFkB and SP1 being the most important hubs. In the case of lapatinib, its effects were translated through ELK1, GATA, P53, and RAR reporters, with SP1 and STAT4 being the reporters that were responsible for the modulation of the activity of the majority of rest of the significantly altered reporters. After two days in culture, GATA1, P53 and SP1 reporters inhibited E2F1 reporter activity. Based on our computational analysis, we hypothesized that lapatinib might reduce cell proliferation and induce apoptosis through GATA1. In fact, overexpression of GATA1 in DA-activated 10A.ErbB2 cells produced smaller and less disorganized 3D structures and reduced cell viability to similar levels as lapatinib.

Taken together, living cell arrays are a promising tool that can shed light on the most likely processes responsible for the observed phenotype. Combining living cell arrays with dynamic computational approaches can identify novel biomarkers and potential therapeutic targets to control the final cellular phenotype.