Shadow enhancers mediate trade-offs between transcriptional noise and fidelity

Abstract

Enhancers are stretches of regulatory DNA that bind transcription factors (TFs) and regulate the expression of a target gene. Shadow enhancers are two or more enhancers that regulate the same target gene in space and time and are associated with most animal developmental genes. These multi-enhancer systems can drive more consistent transcription than single enhancer systems. Nevertheless, it remains unclear why shadow enhancer TF binding sites are distributed across multiple enhancers rather than within a single large enhancer. Here, we use a computational approach to study systems with varying numbers of TF binding sites and enhancers. We employ chemical reaction networks with stochastic dynamics to determine the trends in transcriptional noise and fidelity, two key performance objectives of enhancers. This reveals that while additive shadow enhancers do not differ in noise and fidelity from their single enhancer counterparts, sub- and superadditive shadow enhancers have noise and fidelity trade-offs not available to single enhancers. We also use our computational approach to compare the duplication and splitting of a single enhancer as mechanisms for the generation of shadow enhancers and find that the duplication of enhancers can decrease noise and increase fidelity, although at the metabolic cost of increased RNA production. A saturation mechanism for enhancer interactions similarly improves on both of these metrics. Taken together, this work highlights that shadow enhancer systems may exist for several reasons: genetic drift or the tuning of key functions of enhancers, including transcription fidelity, noise and output.

Author summary

During development, cells assume different fates based upon signals, including transcription factor proteins that bind to regions of the DNA called enhancers. Enhancers can interact with promoters to control the transcription of a target gene. Many developmental genes have multiple, seemingly redundant enhancers called shadow enhancers.

When each separate enhancer is bound by distinct transcription factors, shadow enhancers can drive less noisy gene expression than single enhancers. This allows for the buffering of perturbations in the transcription factor inputs. However, under this premise, a single large enhancer bound by distinct transcription factors should also be capable of buffering perturbations. Why then are shadow enhancers so prevalent?

Fletcher et al. developed computational models of enhancer-mediated transcription that vary in the numbers of enhancers and transcription factor binding sites. They analyzed transcriptional properties in systems with and without shadow enhancers. The models revealed that shadow enhancers can provide a wider landscape of possible transcriptional properties. This computational approach enabled a broader exploration of shadow enhancer properties than is feasible experimentally and may guide future experimentation. Given their prevalence in developmental gene regulation, investigation of shadow enhancers may lead to a better understanding on the pathogenicity of certain mutations found in developmental enhancers.