Transcription factors (TFs) are key cellular components that control gene expression. They recognize specific DNA sequences, the TF binding sites (TFBSs), and thus are targeted to specific regions of the genome where ...Transcription factors (TFs) are key cellular components that control gene expression. They recognize specific DNA sequences, the TF binding sites (TFBSs), and thus are targeted to specific regions of the genome where they can recruit transcriptional co-factors and/or chromatin regulators to fine-tune spatiotemporal gene regulation. Therefore, the identification of TFBSs in genomic sequences and their subsequent quantitative modeling is of crucial importance for understanding and predicting gene expression. Here, we review how TFBSs can be determined experimentally, how the TFBS models can be constructed in silico, and how they can be optimized by taking into account features such as position interdependence within TFBSs, DNA shape, and/or by introducing state-of-the-art computational algorithms such as deep learning methods. In addition, we discuss the integration of context variables into the TFBS modeling, including nucleosome positioning, chromatin states, methylation patterns, 3D genome architectures, and TF cooperative binding, in order to better predict TF binding under cellular contexts. Finally, we explore the possibilities of combining the optimized TFBS model with technological advances, such as targeted TFBS perturbation by CRISPR, to better understand gene regulation, evolution, and plant diversity.展开更多
Auxin is a key hormone performing a wealth of functions throughout the life cycle of plants. It acts largely by regulating genes at the transcriptional level through a family of transcription factors called auxin resp...Auxin is a key hormone performing a wealth of functions throughout the life cycle of plants. It acts largely by regulating genes at the transcriptional level through a family of transcription factors called auxin response factors (ARFs). Even though all ARF monomers analyzed so far bind a similar DNA sequence, there is evidence that ARFs differ in their target genomic regions and regulated genes. Here, we report the use of position weight matrices (PWMs) to model ARF DNA binding specificity based on published DNA affinity purification sequencing (DAP-seq) data. We found that the genome binding of two ARFs (ARF2 and ARF5/ Monopteros [MP]) differ largely because these two factors have different preferred ARF binding site (ARFbs) arrangements (orientation and spacing). We illustrated why PWMs are more versatile to reliably identify ARFbs than the widely used consensus sequences and demonstrated their power with biochemical experiments in the identification of the regulatory regions o1IAA19, an well-characterized auxin-responsive gene. Finally, we combined gene regulation by auxin with ARF-bound regions and identified specific ARFbs configurations that are over-represented in auxin-upregulated genes, thus deciphering the ARFbs syntax functional for regulation. Our study provides a general method to exploit the potential of genome-wide DNA binding assays and to decode gene regulation.展开更多
文摘Transcription factors (TFs) are key cellular components that control gene expression. They recognize specific DNA sequences, the TF binding sites (TFBSs), and thus are targeted to specific regions of the genome where they can recruit transcriptional co-factors and/or chromatin regulators to fine-tune spatiotemporal gene regulation. Therefore, the identification of TFBSs in genomic sequences and their subsequent quantitative modeling is of crucial importance for understanding and predicting gene expression. Here, we review how TFBSs can be determined experimentally, how the TFBS models can be constructed in silico, and how they can be optimized by taking into account features such as position interdependence within TFBSs, DNA shape, and/or by introducing state-of-the-art computational algorithms such as deep learning methods. In addition, we discuss the integration of context variables into the TFBS modeling, including nucleosome positioning, chromatin states, methylation patterns, 3D genome architectures, and TF cooperative binding, in order to better predict TF binding under cellular contexts. Finally, we explore the possibilities of combining the optimized TFBS model with technological advances, such as targeted TFBS perturbation by CRISPR, to better understand gene regulation, evolution, and plant diversity.
文摘Auxin is a key hormone performing a wealth of functions throughout the life cycle of plants. It acts largely by regulating genes at the transcriptional level through a family of transcription factors called auxin response factors (ARFs). Even though all ARF monomers analyzed so far bind a similar DNA sequence, there is evidence that ARFs differ in their target genomic regions and regulated genes. Here, we report the use of position weight matrices (PWMs) to model ARF DNA binding specificity based on published DNA affinity purification sequencing (DAP-seq) data. We found that the genome binding of two ARFs (ARF2 and ARF5/ Monopteros [MP]) differ largely because these two factors have different preferred ARF binding site (ARFbs) arrangements (orientation and spacing). We illustrated why PWMs are more versatile to reliably identify ARFbs than the widely used consensus sequences and demonstrated their power with biochemical experiments in the identification of the regulatory regions o1IAA19, an well-characterized auxin-responsive gene. Finally, we combined gene regulation by auxin with ARF-bound regions and identified specific ARFbs configurations that are over-represented in auxin-upregulated genes, thus deciphering the ARFbs syntax functional for regulation. Our study provides a general method to exploit the potential of genome-wide DNA binding assays and to decode gene regulation.
