Abstract
Adipocytes contribute to metabolic disorders such as obesity, diabetes, and atherosclerosis. Prior characterizations of the transcriptional network driving adipogenesis overlook 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 RE-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. Glucocorticoid receptor activates transcription by inducing RNA polymerase pause release while SP and AP1 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.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
In the revision we compare inferred transcription factor (TF) binding from our network to published ChIP-seq data to molecularly validate the trans-edges in our network. We performed knocked down Twist2 with two separate siRNAs and performed RNA-seq at several time points after induced adipogenesis in the 3T3-L1 system. These results confirmed the inferred cis edges in our network and validated the conclusion that Twist2 is a transcriptional repressor in this context. We also validated the approach to identify genes regulated predominantly by a single TF and the use of compartment modeling to determine which step the TF regulates. We treated a leukemia cell line with dexamethasone to activate the glucocorticoid receptor and confirmed that GR regulates RNA polymerase II pause release. We also performed validation that Twist2 regulates the adipogenic process by depleting and over-expressing Twist2 in the 3T3-L1 system and measuring lipid deposition, which is a measure of adipogenesis. We also performed experiments in Twist2 knockout mice. First, we cultured preadipocytes from Twist2-/+ KO and induced adipogenesis. We found that lipid deposition in the cells was modulated in the heterozygotes. Lastly, we phenotypically characterized Twist2-/+ and Twist2-/- mice. Twist2-/+ have an absence of subcutaneous white adipose tissue and Twist2-/- mice have a near absence of lipid droplets in their brown adipose tissue.
