Combinatorial perturbation analysis reveals divergent regulations of mesenchymal genes during epithelial-to-mesenchymal transition

Abstract

Epithelial-to-mesenchymal transition (EMT), a fundamental transdifferentiation process in development, produces diverse phenotypes in different physiological or pathological conditions. Many genes involved in EMT have been identified to date, but mechanisms contributing to the phenotypic diversity and those governing the coupling between the dynamics of epithelial (E) genes and that of the mesenchymal (M) genes are unclear. In this study, we employed combinatorial perturbations to mammary epithelial cells to induce a series of EMT phenotypes by manipulating two essential EMT-inducing elements, namely TGF-β and ZEB1. By measuring transcriptional changes in more than 700 E-genes and M-genes, we discovered that the M-genes exhibit a significant diversity in their dependency to these regulatory elements and identified three groups of M-genes that are controlled by different regulatory circuits. Notably, functional differences were detected among the M-gene clusters in motility regulation and in survival of breast cancer patients. We computationally predicted and experimentally confirmed that the reciprocity and reversibility of EMT are jointly regulated by ZEB1. Our integrative analysis reveals the key roles of ZEB1 in coordinating the dynamics of a large number of genes during EMT, and it provides new insights into the mechanisms for the diversity of EMT phenotypes.

Keywords: Dynamical systems; Regulatory networks.

Fig. 1

Quantification of EMT gene expression in response to TGF-β and ZEB1. a Illustration of perturbations of TGF-β and ZEB1 conditions in this study. Each colored perturbation has a control condition. b Mean expression levels of E-genes and M-genes of eight conditions. Vertical and horizontal bars show standard error of the means for all annotated E or M genes. The colors are matched to a. c Venn diagrams showing the overlap in E (top) and M (bottom) genes, which show significant expression differences in response to TGF-β or ZEB1 treatment relative to their respective control conditions. TGF-β response is in pink while ZEB1 response is in light blue. The number E-genes and M-genes responding to TGF-β or ZEB1 uniquely, as well as those responding to both are listed in the respective part of the Venn diagram. The type of response, activation, or repression, is indicated by directional arrows (up-arrow: activation, down-arrow: repression). The overlap was quantified using the Jaccard index, which is the number of genes differentially expressed by both TGF-β and ZEB1 divided by the total number of differentially expressed genes. d Jaccard indices of E-genes and M-genes for each pair of conditions. Heatmap shows all the Jaccard indices. Lower triangular entries: E-genes. Upper triangular entries: M genes. The Jaccard index for the conditions which differ most between E-genes and M-genes are shown in those cells. Swarm plot shows the differences between the Jaccard indices of E-genes and those of the M-genes for each of the 28 pairs of conditions (p < 0.001 for single value t-test with a null distribution centered at 0)

Fig. 2

Self-organizing maps (SOMs) for E-genes and M-genes and for clustering M-genes. a SOM nodes by frequency of E-genes and M-genes using a visual representation of the final map of EMT genes onto a 10-by-10 grid by SOM. The color of each node indicates the frequency of E-genes (darker-red) and M-genes (darker-blue) in each node. Nodes that are colored black are empty. b Clustering of SOM nodes with predominantly M-gene membership using a visual representation of the final map of EMT genes onto a 10-by-10 grid by SOM. Each of the 38 genes which have a 2:1 or greater ratio of M-genes to E-genes is colored according to the cluster of M-genes it was assigned to by hierarchical clustering (red = M1, green = M2, blue = M3, gray = other)

Fig. 3

Expression of M-gene clusters under TGF-β and ZEB1 regulation. a Boxplot of log fold-change of expression of M1 genes in the eight contrast conditions (see Table 2). The colored region (red) indicates the inter-quartile range of expression while whiskers extend 1.5 times this range on either side. Outliers are indicated by black dots. The purple dotted lines above the plot indicate comparisons between the expression under conditions, specifically, TGF-β vs. WT to DOX vs. DMSO, TGF-β vs. WT to TGF-β+dZEB vs. dZEB and DOX vs. DMSO to DOX+SB vs. SB. A * indicates that distribution of expression is significantly different based on the Mann–Whitney U-test at an alpha of 0.05. b Similar to a but for M2 genes, with a green colored region. c Similar to a, but for M3 genes, with a blue colored region. d A model of EMT-gene regulation based on the expression patterns of M-genes clusters in a–c. Green arrows indicate activation while red arrows indicate repression

Fig. 4

Functional annotations and survival analysis of M clusters. a Bar graphs show groups of related GO terms associated with each cluster of M-genes. Venn diagram shows the overlapped GO terms (p < 0.05, Fisher’s exact test, for selection of GO terms) among the three M clusters. b Top: Differences between high expression and low expression cohorts in terms of median survival months for breast cancer patients. Each group represents one type (cluster) of genes. The colored region indicates the inter-quartile range of expression, while whiskers extend 1.5 times this range on either side. Outliers are indicated by black dots. Single-value t-test was performed with each group of median survival months. Bottom: Kaplan–Meier plots for three representative M3 genes. Survival months and Kaplan–Meier plots were obtained from KM-Plotter.

Fig. 5

Differential cell movement patterns regulated by ZEB1-dependent and ZEB1-independent pathways. a Cell movement trajectories when TGF-β signaling and/or ZEB1 expression is perturbed under eight conditions. Hundred cells were randomly selected for each condition. Each trajectory was centered at its starting position. Scale bar represents a length of 100 μm. b Distributions of four metrics (instantaneous velocity, mean displacement normalized by duration of trajectory, straightness index of the movement, and number of nearest neighbors) shown in letter-value plots for cell trajectories under eight conditions. Statistical significance was obtained using Mann–Whitney U-test. FC fold-change. c Distributions of Spearman correlations coefficients (as a distance measurement) between gene expression and movement metrics for four type of genes (E, M1, M2, and M3) across eight conditions. The colored region indicates the inter-quartile range of expression while whiskers extend 1.5 times this range on either side. Outliers are indicated by black dots. d Scatter plots showing pairwise relationships between correlation coefficients of gene expression and different movement metrics. ***p < 0.001, **p < 0.01, *p < 0.05, N.S.: not significant (p > 0.05), t-test. Significant mark at each box indicates the p value for testing if the mean of the group is significantly different from 0. Significant mark at each horizontal bar indicates the p value for testing if two groups of values are significantly different

Fig. 6

Mathematical modeling of EMT under control of TGF-β and ZEB1. a–d Top diagrams: influence diagrams for gene regulatory networks under four conditions. Lower panels: bifurcation diagram for four types of genes (E, M1, M2, and M3) with respect to exogenous TGF-β and ZEB1 expression under two conditions. Solid curves represent stable steady states. Dashed curves represent unstable steady states. Color gradient represents the position in the EMT spectrum, which is calculated by adding the expression of the three M nodes and subtracting the expression of the E node. a Normal condition. Induced by TGF-β. b ZEB1 KO. Induced by TGF-β. c Normal condition. Induced by ZEB1. d TGF-β-inhibited condition. Induced by ZEB1. e Simulation for expression of VIM and E-cad upon treatment and withdrawal of TGF-β. Top: control. Bottom: ZEB1 KO after TGF-β withdrawal. f Expression of VIM and E-cad analyzed by FACS upon treatment and withdrawal of TGF-β. Top: control. Bottom: ZEB1 knockout induced by DOX. Cells were treated with TGF-β for 2 weeks and then subject to TGF-β withdrawal, and (for the experiment group) to ZEB1 knockout. g Simulations for expression of VIM and E-cad upon treatment and withdrawal of exogenous ZEB1 expression followed by inhibition of TGF-β signaling. h Expression of VIM and E-cad analyzed by FACS upon induction and withdrawal of exogenous ZEB1 by DOX. Exogenous ZEB1 was induced for 1 week and then subject to the withdrawal of the induction signal for 2 weeks, and (for the experiment group) to the inhibition of TGF-β signal by SB431542 (SB) for the same period