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Mechanistic insights into transcription factor cooperativity and its impact on protein-phenotype interactions.

ABSTRACT: Recent high-throughput transcription factor (TF) binding assays revealed that TF cooperativity is a widespread phenomenon. However, a global mechanistic and functional understanding of TF cooperativity is still lacking. To address this, here we introduce a statistical learning framework that provides structural insight into TF cooperativity and its functional consequences based on next generation sequencing data. We identify DNA shape as driver for cooperativity, with a particularly strong effect for Forkhead-Ets pairs. Follow-up experiments reveal a local shape preference at the Ets-DNA-Forkhead interface and decreased cooperativity upon loss of the interaction. Additionally, we discover many functional associations for cooperatively bound TFs. Examination of the link between FOXO1:ETV6 and lymphomas reveals that their joint expression levels improve patient clinical outcome stratification. Altogether, our results demonstrate that inter-family cooperative TF binding is driven by position-specific DNA readout mechanisms, which provides an additional regulatory layer for downstream biological functions.

SUBMITTER: Ibarra IL

PROVIDER: S-EPMC6949242 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Mechanistic insights into transcription factor cooperativity and its impact on protein-phenotype interactions.

Ibarra Ignacio L IL Hollmann Nele M NM Klaus Bernd B Augsten Sandra S Velten Britta B Hennig Janosch J Zaugg Judith B JB

Nature communications 20200108 1

Recent high-throughput transcription factor (TF) binding assays revealed that TF cooperativity is a widespread phenomenon. However, a global mechanistic and functional understanding of TF cooperativity is still lacking. To address this, here we introduce a statistical learning framework that provides structural insight into TF cooperativity and its functional consequences based on next generation sequencing data. We identify DNA shape as driver for cooperativity, with a particularly strong effec ...[more]

PMID: 31913281

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Project description:Allostery pervades macromolecular function and drives cooperative binding of ligands to macromolecules. To decipher the mechanisms of cooperative ligand binding it is necessary to define at a microscopic level the structural and thermodynamic consequences of binding of each ligand to its allosterically coupled site(s). However, dynamic sampling of alternative conformations (microstates) in allosteric molecules complicates interpretation of both structural and thermodynamic data. Isothermal titration calorimetry has the potential to directly quantify the thermodynamics of allosteric interactions, but usually falls short of enabling mechanistic insight. This is because 1) its measurements reflect the sum of overlapping caloric processes involving binding-linked population shifts within and between microstates, and 2) data are generally fit with phenomenological binding polynomials that are underdetermined. Nevertheless, temperature-dependent binding data have the potential to resolve overlapping thermodynamic processes, while mechanistically constrained models enable hypothesis testing and identification of informative parameters. We globally fit temperature-dependent isothermal titration calorimetry data for binding of 11 tryptophan ligands to the homo-undecameric trp RNA-binding Attenuation Protein from Bacillus stearothermophilus using nearest-neighbor statistical thermodynamic models. This approach allowed us to distinguish alternative nearest-neighbor interaction models, and quantifies the thermodynamic contribution of neighboring ligands to individual binding sites. We also perform conventional Hill equation modeling and illustrate how comparatively limited it is in quantitative or mechanistic value. This work illustrates the potential of mechanistically constrained global fitting of binding data to yield the microscopic thermodynamic parameters essential for deciphering mechanisms of cooperativity in a wide range of ligand-regulated homo-oligomeric assemblies.

Dataset Information

Mechanistic insights into transcription factor cooperativity and its impact on protein-phenotype interactions.

Publications

Mechanistic insights into transcription factor cooperativity and its impact on protein-phenotype interactions.

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