High-throughput data and modeling reveal insights into the mechanisms of cooperative DNA-binding by transcription factor proteins (ETS1 and RUNX1). High-throughput data and modeling reveal insights into the mechanisms of cooperative DNA-binding by transcription factor proteins (ETS1 and RUNX1)

Cooperative DNA-binding by transcription factor (TF) proteins is critical for gene expression regulation in eukaryotes. In the human genome, many regulatory regions contain TF binding sites in close proximity to each other, which can facilitate cooperative interactions. However, binding site proximity does not necessarily imply cooperative binding, as two TFs could also bind independently to each of their neighboring target sites. Currently, the rules that drive cooperative versus independent binding of TFs to neighboring sites are not well understood. In addition, it is oftentimes difficult to infer direct cooperativity between TFs from existing DNA-binding data. Here, we show that in vitro binding assays using DNA libraries of a few thousand genomic sequences with putative cooperative TF binding events can be used to develop accurate models of cooperativity and to gain insights into cooperative binding mechanisms. Using human factors ETS1 and RUNX1 as our case study, we show that the distance and orientation between ETS1 sites are critical determinants of cooperative ETS1-ETS1 binding, while cooperative ETS1- RUNX1 interactions show more flexibility in distance and orientation and can be accurately predicted based on binding affinity and the sequence/shape features of the binding sites. The approach described here, combining custom experimental design with machine learning modeling, can be easily applied to study the cooperative DNA-binding patterns of any TFs. Overall design: We performed cooperative gcPBM (genome-context protein binding microarray) experiments for recombinant, full-length, human transcription factors Ets1 and Runx1. Briefly, the PBMs involved binding of GST-tagged Ets1 and/or GST-tagged Runx1 to double-stranded 4x180k Agilent microarrays in order to identify Ets1-Runx1 cooperative binding events. Each genomic DNA sequence represented on the array is present in 8 replicate spots, four for each orientation. We report the PBM signal intensity of Ets1 and/or Runx1 for each spot.

转录因子（transcription factor, TF）蛋白的协同DNA结合对于真核生物的基因表达调控至关重要。在人类基因组中，诸多调控区域内存在彼此邻近的转录因子结合位点，这为协同相互作用提供了基础。然而，结合位点的邻近性并不必然意味着协同结合——两种转录因子也可能独立结合于各自相邻的靶位点。目前，驱动转录因子对邻近位点采取协同还是独立结合的规则仍未被充分阐明。此外，从现有DNA结合数据中推断转录因子间的直接协同作用往往颇具挑战。
在此研究中，我们证实，利用包含数千条基因组序列的DNA文库开展体外结合实验（该文库涵盖推定的转录因子协同结合事件），可用于构建精准的协同结合模型，并深入解析协同结合的分子机制。我们以人类转录因子ETS1和RUNX1作为案例研究对象，结果显示：ETS1结合位点之间的距离与方向是决定ETS1-ETS1协同结合的关键因素；而ETS1-RUNX1的协同相互作用在距离与方向上则表现出更高的灵活性，且可基于结合亲和力以及结合位点的序列/结构特征进行精准预测。
本研究所述方法将定制化实验设计与机器学习建模相结合，可轻松推广至任意转录因子的协同DNA结合模式研究。
整体实验设计：我们针对重组全长人类转录因子Ets1与Runx1开展了协同gcPBM（基因组背景蛋白结合微阵列，genome-context protein binding microarray）实验。简言之，本实验采用双链4×180k安捷伦（Agilent）微阵列，使GST标签化的Ets1和/或GST标签化的Runx1与之结合，以鉴定Ets1-Runx1的协同结合事件。阵列上的每条基因组DNA序列均设置8个重复斑点，每种方向各4个。我们报告了每个斑点对应的Ets1和/或Runx1的PBM信号强度。