Degenerate Pax2 and Senseless binding motifs improve detection of low-affinity sites required for enhancer specificity.
Degenerate Pax2 and Senseless binding motifs improve detection of low-affinity sites required for enhancer specificity.
复制标题
DOI：
10.1371/journal.pgen.1007289
复制
发表时间：
2018-04
期刊：
影响因子：
4.5
通讯作者：
Gebelein B
中科院分区：
文献类型：
--
作者：
Zandvakili A;Campbell I;Gutzwiller LM;Weirauch MT;Gebelein B
细胞使用数千个调控序列来募集转录因子（TF）并产生特定的转录结果。由于TF结合简并DNA序列，从背景序列中区分功能性TF结合位点（TFBS）是一个重大挑战。在这里，我们表明，果蝇调控元件，激活表皮生长因子信号需要重叠，低亲和力TFBS竞争TF（Pax2和无意义），以确保细胞和片段特异性活动。然而，测试Pax2和Seneless的可用TF结合模型，揭示了预测这种低亲和力TFBS的可变准确性。为了更好地定义提高准确性的参数，我们开发了一种方法，该方法根据预测的亲和力系统地选择TFBS的子集，以生成数百个位置权重矩阵（PWM）。与直觉相反，我们发现从耗尽高亲和力序列的数据集产生的简并PWM在识别Pax2和无意义TF的低亲和力和高亲和力TFBS方面更准确。两者合计，这些研究结果揭示了TFBS的安排可以限制竞争，而不是协同性和退化模型的TF结合偏好，可以提高生物相关的低亲和力TFBS的识别。虽然生物体中的所有细胞都有一个共同的基因组，但每种细胞类型都必须表达其特定功能所需的基因的适当组合。细胞利用转录因子蛋白激活和抑制基因组的不同部分，这些转录因子蛋白结合被称为增强子的调节区域。我们目前对增强子如何募集转录因子以产生精确的基因激活和抑制有一个不完整的看法。这个问题是复杂的事实，大多数动物含有超过一千种不同的转录因子，每一个通常可以结合多个DNA序列。因此，很难预测哪些转录因子与哪些增强子相互作用。为了深入了解这一过程，我们专注于确定如何在果蝇胚胎中以精确的方式调节激活肝脏样细胞所需基因的增强子。我们证明，这种增强子的比活性依赖于弱和重叠的转录因子结合位点。此外，我们证明，计算模型，包括弱转录因子相互作用产生更好的预测精度。这些结果揭示了DNA序列如何决定增强子活性，以及对预测基因组中转录因子结合位点最有用的策略类型。
Cells use thousands of regulatory sequences to recruit transcription factors (TFs) and produce specific transcriptional outcomes. Since TFs bind degenerate DNA sequences, discriminating functional TF binding sites (TFBSs) from background sequences represents a significant challenge. Here, we show that a Drosophila regulatory element that activates Epidermal Growth Factor signaling requires overlapping, low-affinity TFBSs for competing TFs (Pax2 and Senseless) to ensure cell- and segment-specific activity. Testing available TF binding models for Pax2 and Senseless, however, revealed variable accuracy in predicting such low-affinity TFBSs. To better define parameters that increase accuracy, we developed a method that systematically selects subsets of TFBSs based on predicted affinity to generate hundreds of position-weight matrices (PWMs). Counterintuitively, we found that degenerate PWMs produced from datasets depleted of high-affinity sequences were more accurate in identifying both low- and high-affinity TFBSs for the Pax2 and Senseless TFs. Taken together, these findings reveal how TFBS arrangement can be constrained by competition rather than cooperativity and that degenerate models of TF binding preferences can improve identification of biologically relevant low affinity TFBSs. While all cells in an organism share a common genome, each cell type must express the appropriate combination of genes needed for its specific function. Cells activate and repress different parts of the genome using transcription factor proteins that bind regulatory regions known as enhancers. We currently have an incomplete view of how enhancers recruit transcription factors to yield accurate gene activation and repression. This problem is complicated by the fact that most animals contain over a thousand different transcription factors, and each can generally bind multiple DNA sequences. Thus, it is difficult to predict which transcription factors interact with which enhancers. To gain insights into this process, we focused on determining how an enhancer that activates a gene needed to make liver-like cells is regulated in a precise manner in the fruit-fly embryo. We demonstrate that the specific activity of this enhancer depends on weak and overlapping transcription factor binding sites. Furthermore, we demonstrate that computational models that include weak transcription factor interactions yield better predictive accuracy. These results shed light on how DNA sequences determine enhancer activity and the types of strategies that are most useful for predicting transcription factor binding sites in the genome.
登录
查看更多内容
影响因子：
48
作者：
Huber W;Carey VJ;Gentleman R;Anders S;Carlson M;Carvalho BS;Bravo HC;Davis S;Gatto L;Girke T;Gottardo R;Hahne F;Hansen KD;Irizarry RA;Lawrence M;Love MI;MacDonald J;Obenchain V;Oleś AK;Pagès H;Reyes A;Shannon P;Smyth GK;Tenenbaum D;Waldron L;Morgan M
通讯作者：
Morgan M
影响因子：
11.8
作者：
Goode DK;Obier N;Vijayabaskar MS;Lie-A-Ling M;Lilly AJ;Hannah R;Lichtinger M;Batta K;Florkowska M;Patel R;Challinor M;Wallace K;Gilmour J;Assi SA;Cauchy P;Hoogenkamp M;Westhead DR;Lacaud G;Kouskoff V;Göttgens B;Bonifer C
通讯作者：
Bonifer C
影响因子：
15.3
作者：
John, S;Marais, R;Child, R;Light, Y;Leonard, WJ
通讯作者：
Leonard, WJ
影响因子：
14.9
作者：
Contrino S;Smith RN;Butano D;Carr A;Hu F;Lyne R;Rutherford K;Kalderimis A;Sullivan J;Carbon S;Kephart ET;Lloyd P;Stinson EO;Washington NL;Perry MD;Ruzanov P;Zha Z;Lewis SE;Stein LD;Micklem G
通讯作者：
Micklem G
影响因子：
3.6
作者：
Charlton-Perkins M;Whitaker SL;Fei Y;Xie B;Li-Kroeger D;Gebelein B;Cook T
通讯作者：
Cook T