RESUMEN
The ubiquitous family of dimeric transcription factors AP-1 is made up of Fos and Jun family proteins. It has long been thought to operate principally at gene promoters and how it controls transcription is still ill-understood. The Fos family protein Fra-1 is overexpressed in triple negative breast cancers (TNBCs) where it contributes to tumor aggressiveness. To address its transcriptional actions in TNBCs, we combined transcriptomics, ChIP-seqs, machine learning and NG Capture-C. Additionally, we studied its Fos family kin Fra-2 also expressed in TNBCs, albeit much less. Consistently with their pleiotropic effects, Fra-1 and Fra-2 up- and downregulate individually, together or redundantly many genes associated with a wide range of biological processes. Target gene regulation is principally due to binding of Fra-1 and Fra-2 at regulatory elements located distantly from cognate promoters where Fra-1 modulates the recruitment of the transcriptional co-regulator p300/CBP and where differences in AP-1 variant motif recognition can underlie preferential Fra-1- or Fra-2 bindings. Our work also shows no major role for Fra-1 in chromatin architecture control at target gene loci, but suggests collaboration between Fra-1-bound and -unbound enhancers within chromatin hubs sometimes including promoters for other Fra-1-regulated genes. Our work impacts our view of AP-1.
Asunto(s)
Elementos de Facilitación Genéticos , Regulación Neoplásica de la Expresión Génica , Proteínas Proto-Oncogénicas c-fos/metabolismo , Neoplasias de la Mama Triple Negativas/genética , Sitios de Unión , Línea Celular Tumoral , Cromatina/química , Cromatina/metabolismo , Epigénesis Genética , Antígeno 2 Relacionado con Fos/metabolismo , Humanos , Motivos de Nucleótidos , Regiones Promotoras Genéticas , Proteínas Proto-Oncogénicas c-fos/fisiología , Factor de Transcripción AP-1/metabolismo , Neoplasias de la Mama Triple Negativas/metabolismo , Factores de Transcripción p300-CBP/metabolismoRESUMEN
Characterizing the binding preferences of transcription factors (TFs) in different cell types and conditions is key to understand how they orchestrate gene expression. Here, we develop TFscope, a machine learning approach that identifies sequence features explaining the binding differences observed between two ChIP-seq experiments targeting either the same TF in two conditions or two TFs with similar motifs (paralogous TFs). TFscope systematically investigates differences in the core motif, nucleotide environment and co-factor motifs, and provides the contribution of each key feature in the two experiments. TFscope was applied to > 305 ChIP-seq pairs, and several examples are discussed.