Pluralistic and stochastic gene regulation: examples, models and consistent theory

Authors

Elisa N. Salas, University of Nebraska-Lincoln
Jiang Shu, University of Nebraska-Lincoln
Matyas F. Cserhati, University of Nebraska-Lincoln
Donald P. Weeks, University of Nebraska-LincolnFollow
Istvan Ladunga, University of Nebraska at LincolnFollow

ORCID IDs

Istvan Ladunga

Date of this Version

2016

Citation

Nucleic Acids Research, 2016 1 doi: 10.1093/nar/gkw042

Comments

This is an Open Access article distributed under the terms of the Creative Commons Attribution License.

Abstract

We present a theory of pluralistic and stochastic gene regulation. To bridge the gap between empirical studies and mathematical models, we integrate pre-existing observations with our meta-analyses of the ENCODE ChIP-Seq experiments. Earlier evidence includes fluctuations in levels, location, activity, and binding of transcription factors, variable DNA motifs, and bursts in gene expression. Stochastic regulation is also indicated by frequently subdued effects of knockout mutants of regulators, their evolutionary losses/gains and massive rewiring of regulatory sites. We report wide-spread pluralistic regulation in ≈800 000 tightly co-expressed pairs of diverse human genes. Typically, half of ≈50 observed regulators bind to both genes reproducibly, twice more than in independently expressed gene pairs. We also examine the largest set of co-expressed genes, which code for cytoplasmic ribosomal proteins. Numerous regulatory complexes are highly significant enriched in ribosomal genes compared to highly expressed non-ribosomal genes. We could not find any DNA-associated, strict sense master regulator. Despite major fluctuations in transcription factor binding, our machine learning model accurately predicted transcript levels using binding sites of 20+ regulators. Our pluralistic and stochastic theory is consistent with partially random binding patterns, redundancy, stochastic regulator binding, burst-like expression, degeneracy of binding motifs and massive regulatory rewiring during evolution.