Data_Sheet_1_Toward Modeling Context-Specific EMT Regulatory Networks Using Temporal Single Cell RNA-Seq Data.ZIP

Epithelial-mesenchymal transition (EMT) is well established as playing a crucial role in cancer progression and being a potential therapeutic target. To elucidate the gene regulation that drives the decision making of EMT, many previous studies have been conducted to model EMT gene regulatory circuits (GRCs) using interactions from the literature. While this approach can depict the generic regulatory interactions, it falls short of capturing context-specific features. Here, we explore the effectiveness of a combined bioinformatics and mathematical modeling approach to construct context-specific EMT GRCs directly from transcriptomics data. Using time-series single cell RNA-sequencing data from four different cancer cell lines treated with three EMT-inducing signals, we identify context-specific activity dynamics of common EMT transcription factors. In particular, we observe distinct paths during the forward and backward transitions, as is evident from the dynamics of major regulators such as NF-KB (e.g., NFKB2 and RELB) and AP-1 (e.g., FOSL1 and JUNB). For each experimental condition, we systematically sample a large set of network models and identify the optimal GRC capturing context-specific EMT states using a mathematical modeling method named Random Circuit Perturbation (RACIPE). The results demonstrate that the approach can build high quality GRCs in certain cases, but not others and, meanwhile, elucidate the role of common bioinformatics parameters and properties of network structures in determining the quality of GRCs. We expect the integration of top-down bioinformatics and bottom-up systems biology modeling to be a powerful and generally applicable approach to elucidate gene regulatory mechanisms of cellular state transitions.

上皮-间质转化（EMT）在癌症进展中扮演着至关重要的角色，并被视为一种潜在的诊疗靶点。为阐明驱动EMT决策过程的基因调控机制，众多前期研究已通过对文献中相互作用进行建模，构建了EMT基因调控回路（GRCs）。虽然此方法能够描绘出通用的调控相互作用，但未能捕捉到特定情境下的特征。本研究中，我们探索了结合生物信息学与数学建模方法构建特定情境下EMT GRCs的有效性，该方法可直接从转录组数据中构建。利用来自四个不同癌细胞系在三种EMT诱导信号处理下的时间序列单细胞RNA测序数据，我们识别了常见EMT转录因子的特定情境活性动力学。特别是，我们观察到了正向和反向转化过程中的不同路径，如主要调控因子NF-KB（例如，NFKB2和RELB）以及AP-1（例如，FOSL1和JUNB）的动力学所示。对于每个实验条件，我们系统地采样大量网络模型，并利用名为随机电路扰动（RACIPE）的数学建模方法识别捕获特定情境EMT状态的优化GRC。结果表明，该方法在某些情况下能够构建高质量的GRCs，而在其他情况下则不然，同时阐明了常见生物信息学参数和网络结构性质在决定GRCs质量中的作用。我们预期，自上而下的生物信息学方法与自下而上的系统生物学建模方法的整合，将成为阐明细胞状态转化基因调控机制的强大且普遍适用的方法。