Abstract
Estrogen receptor alpha (ERα) expression is critical for breast cancer classification, high ERα expression being associated with better prognosis. ERα levels strongly correlate with that of GATA binding protein 3 (GATA3), a major regulator of ERα expression. However, the mechanistic details of ERα–GATA3 regulation remain incompletely understood. Here we combine mathematical modeling with perturbation experiments to unravel the nature of regulatory connections in the ERα–GATA3 network. Through cell population-average, single-cell and single-nucleus measurements, we show that the cross-regulation between ERα and GATA3 amounts to overall negative feedback. Further, mathematical modeling reveals that GATA3 positively regulates its own expression and that ERα autoregulation is most likely absent. Lastly, we show that the two cross-regulatory connections in the ERα–GATA3 negative feedback network decrease the noise in ERα or GATA3 expression. This may ensure robust cell fate maintenance in the face of intracellular and environmental fluctuations, contributing to tissue homeostasis in normal conditions, but also to the maintenance of pathogenic states during cancer progression.
Highlights
Transcription factors (TFs) form regulatory networks playing major roles in cell fate determination, from bacteria to mammalian cells [1]
In accordance with western blots, we found that ER␣ mRNA levels decreased due to GATA binding protein 3 (GATA3) depletion, whereas GATA3 mRNA levels increased in ER␣ siRNA treated cells (Figure 2B)
GATA3 overexpression did not have a significant effect on ER␣ expression (Supplementary Figure S2D–F), possibly indicating that GATA3 may be present at saturating levels from the perspective of ER␣ regulation
Summary
Transcription factors (TFs) form regulatory networks playing major roles in cell fate determination, from bacteria to mammalian cells [1]. The molecular details of transcription in mammalian cells are much less understood and some networks underlying crucial cell fate decisions such as Human Immunodeficiency Virus (HIV) latency [6] or stem cell differentiation [7,8] are still being uncovered. A important area requiring more research is cell fate determination in cancer [9,10]. The discovery and quantitative characterization of cancer cell fate-regulating networks is critically important to fully understand the mechanisms underlying cancer progression and to improve current treatment strategies
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