Link, V. M. | bioRxiv Science

bioRxiv2018

Diverse motif ensembles specify non-redundant DNA binding activities of AP-1 family members in macrophages

Mechanisms by which members of the AP-1 family of transcription factors play both redundant and non-redundant biological roles despite recognizing the same DNA sequence remain poorly understood. To address this question, we investigated the molecular functions and genome-wide DNA binding patterns of AP-1 family members in macrophages. ChIP-sequencing showed overlapping and distinct binding profiles for each factor that were remodeled following TLR4 ligation. Development of a machine learning approach that jointly weighs hundreds of DNA recognition elements yielded dozens of motifs predicted to drive factor-specific binding profiles. Machine learning-based predictions were confirmed by analysis of the effects of mutations in genetically diverse mice and by loss of function experiments. These findings provide evidence that non-redundant genomic locations of different AP-1 family members in macrophages largely result from collaborative interactions with diverse, locus-specific ensembles of transcription factors and suggest a general mechanism for encoding functional specificities of their common recognition motif.

genomics↗

bioRxiv2018

MARGE: Mutation Analysis for Regulatory Genomic Elements

Cell-specific patterns of gene expression are determined by combinatorial actions of sequence-specific transcription factors at cis-regulatory elements. Studies indicate that relatively simple combinations of lineage-determining transcription factors (LDTFs) play dominant roles in the selection of enhancers that establish cell identities and functions. LDTFs require collaborative interactions with additional transcription factors to mediate enhancer function, but the identities of these factors are often unknown. We have shown that natural genetic variation between individuals has great utility for discovering collaborative transcription factors. Here, we introduce MARGE (Mutation Analysis of Regulatory Genomic Elements), the first publicly available suite of software tools that integrates genome-wide genetic variation with epigenetic data to identify collaborative transcription factor pairs. MARGE is optimized to work with chromatin accessibility assays (such as ATAC-seq or DNase I hypersensitivity), as well as transcription factor binding data collected by ChlP-seq. Herein, we provide investigators with rationale for each step in the MARGE pipeline and key differences for analysis of datasets with different experimental designs. We demonstrate the utility of MARGE using mouse peritoneal macrophages, liver cells, and human lymphoblastoid cells. MARGE provides a powerful tool to identify combinations of cell type-specific transcription factors while simultaneously interpreting functional effects of non-coding genetic variation.

bioinformatics↗