Adipocytes contribute to metabolic disorders such as obesity, diabetes, and atherosclerosis. Prior characterizations of the transcriptional network driving adipogenesis have overlooked transiently acting transcription factors (TFs), genes, and regulatory elements that are essential for proper differentiation. Moreover, traditional gene regulatory networks provide neither mechanistic details about individual regulatory element–gene relationships nor temporal information needed to define a regulatory hierarchy that prioritizes key regulatory factors. To address these shortcomings, we integrate kinetic chromatin accessibility (ATAC-seq) and nascent transcription (PRO-seq) data to generate temporally resolved networks that describe TF binding events and resultant effects on target gene expression. Our data indicate which TF families cooperate with and antagonize each other to regulate adipogenesis. Compartment modeling of RNA polymerase density quantifies how individual TFs mechanistically contribute to distinct steps in transcription. The glucocorticoid receptor activates transcription by inducing RNA polymerase pause release, whereas SP and AP-1 factors affect RNA polymerase initiation. We identify Twist2 as a previously unappreciated effector of adipocyte differentiation. We find that TWIST2 acts as a negative regulator of 3T3-L1 and primary preadipocyte differentiation. We confirm that Twist2 knockout mice have compromised lipid storage within subcutaneous and brown adipose tissue. Previous phenotyping of Twist2 knockout mice and Setleis syndrome Twist2^−/− patients noted deficiencies in subcutaneous adipose tissue. This network inference framework is a powerful and general approach for interpreting complex biological phenomena and can be applied to a wide range of cellular processes.

中文翻译：

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动力学网络将 TWIST2 识别为脂肪生成中的关键调控节点

脂肪细胞会导致代谢紊乱，例如肥胖、糖尿病和动脉粥样硬化。先前对驱动脂肪生成的转录网络的表征忽略了瞬时作用的转录因子 (TF)、基因和对正确分化至关重要的调节元件。此外，传统的基因调控网络既不提供有关单个调控元件-基因关系的机制细节，也不提供定义优先考虑关键调控因素的调控等级所需的时间信息。为了解决这些缺点，我们整合了动力学染色质可及性 (ATAC-seq) 和新生转录 (PRO-seq) 数据，以生成描述 TF 结合事件和对靶基因表达的结果影响的时间分辨网络。我们的数据表明哪些 TF 家族相互合作和对抗以调节脂肪生成。RNA 聚合酶密度的隔室模型量化了单个转录因子如何在机械上促进转录中的不同步骤。糖皮质激素受体通过诱导 RNA 聚合酶暂停释放激活转录，而 SP 和 AP-1 因子影响 RNA 聚合酶启动。我们确定Twist2作为脂肪细胞分化的先前未被重视的效应器。我们发现 TWIST2 充当 3T3-L1 和原代前脂肪细胞分化的负调节因子。我们确认Twist2基因敲除小鼠皮下和棕色脂肪组织内的脂质储存受损。先前对Twist2基因敲除小鼠和 Setleis 综合征Twist2 ^{- / -}患者的表型分析表明皮下脂肪组织存在缺陷。该网络推理框架是一种解释复杂生物现象的强大而通用的方法，可应用于广泛的细胞过程。