Defining an essential transcription factor program for naïve pluripotency

Abstract

The gene regulatory circuitry through which pluripotent embryonic stem (ES) cells choose between self-renewal and differentiation appears vast and has yet to be distilled into an executive molecular program. We developed a data-constrained, computational approach to reduce complexity and to derive a set of functionally validated components and interaction combinations sufficient to explain observed ES cell behavior. This minimal set, the simplest version of which comprises only 16 interactions, 12 components, and three inputs, satisfies all prior specifications for self-renewal and furthermore predicts unknown and nonintuitive responses to compound genetic perturbations with an overall accuracy of 70%. We propose that propagation of ES cell identity is not determined by a vast interactome but rather can be explained by a relatively simple process of molecular computation.

Fig. 1. Flexible culture conditions for mouse ES cells allow deduction of possible interactions between pluripotency factors

(A) Under different combinations of LIF, CH, and PD, ES cells show homogeneous expression of Rex1, Oct4, Esrrb, and Nanog. See also Figure S2A-C. Scale bar: 50μm. (B) Gene expression for the set of 17 pluripotency-associated TFs from 5 experiments under different culture conditions. Columns 1-4 and 5-8 are biological replicates in the indicated steady state conditions, while columns 9-23 illustrate the change in gene expression over time from cells in LIF+PD, 2i or LIF+CH switched to 2i+LIF. Gene expression was measured by qRT-PCR and normalized to mean expression level. β-actin was used as an internal control. (C) Examples showing negative correlation between Esrrb and Tcf3, and positive correlation between expression of Esrrb and Klf4 under time series experiments. Pearson correlation coefficient indicated on each plot. (D) The meta-model derived from a Pearson coefficient threshold of 0.7, with the expected gene expression under 2i+LIF conditions. Gene expression is discretized to high (blue) or low (white). Positive regulation indicated by a black arrow, negative regulation indicated by a red circle-headed line. Dashed lines indicate optional interactions, solid lines indicate definite interactions.

Fig. 2. Iteration with experimental observations reduces model complexity

(A) 23 experimental constraints are defined, each with initial (left column) and final (right column) conditions (Section 1K). (B) The meta-model derived from a Pearson correlation threshold of 0.792, constrained to satisfy the expected experimental behavior (panel A). 11 of the possible interactions are present in all possible models, and the remaining possible interactions are shown by dashed grey lines. All of the constraints can be satisfied without including five components of the network (grey).

Fig. 3. Experimental validation of model predictions

(A) Summary of model predictions and experimental validation. Incorrect predictions highlighted with an asterisk. Each box in the “Exp. Validation” column indicates the number of AP+ colonies obtained in a clonal assay. See also Fig. S5. (B) Clonal assay of ES cells in the indicated culture conditions. For each sample the number of AP+ colonies, relative to non-targeting siRNA transfected cells, is indicated. Each bar represents the mean and SEM of at least 4 independent experiments. (C) Rex1GFPd2 flow profile of cells withdrawn from either 2i+LIF or 2i conditions for the indicated time. Cells taken from 2i downregulate the reporter more rapidly. (D) The simplest model of 16 interactions and 12 components required to satisfy all experimental constraints. The complete set of constrained models is shown in Fig. S7.