Hard-wired heterogeneity in blood stem cells revealed using a dynamic regulatory network model

Abstract

Motivation: Combinatorial interactions of transcription factors with cis-regulatory elements control the dynamic progression through successive cellular states and thus underpin all metazoan development. The construction of network models of cis-regulatory elements, therefore, has the potential to generate fundamental insights into cellular fate and differentiation. Haematopoiesis has long served as a model system to study mammalian differentiation, yet modelling based on experimentally informed cis-regulatory interactions has so far been restricted to pairs of interacting factors. Here, we have generated a Boolean network model based on detailed cis-regulatory functional data connecting 11 haematopoietic stem/progenitor cell (HSPC) regulator genes.

Results: Despite its apparent simplicity, the model exhibits surprisingly complex behaviour that we charted using strongly connected components and shortest-path analysis in its Boolean state space. This analysis of our model predicts that HSPCs display heterogeneous expression patterns and possess many intermediate states that can act as 'stepping stones' for the HSPC to achieve a final differentiated state. Importantly, an external perturbation or 'trigger' is required to exit the stem cell state, with distinct triggers characterizing maturation into the various different lineages. By focusing on intermediate states occurring during erythrocyte differentiation, from our model we predicted a novel negative regulation of Fli1 by Gata1, which we confirmed experimentally thus validating our model. In conclusion, we demonstrate that an advanced mammalian regulatory network model based on experimentally validated cis-regulatory interactions has allowed us to make novel, experimentally testable hypotheses about transcriptional mechanisms that control differentiation of mammalian stem cells.

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

A blood stem cell regulatory network model grounded on comprehensive cis-regulatory information. (A) Diagram of the haematopoietic gene regulatory network with logical functions between genes (ellipses) encoded directly by explicit transitions (squares). Activating interactions are shown as blue arrows, repressing interactions in red with ’flat heads’. All regulatory information encoded in this model can be found in Supplementary Table S2. (B) UPGMA (Unweighted Pair Group Method with Arithmetic mean) dendrogram based on hamming distance between cell-type–specific gene expression patterns shows that the 11 network model genes are sufficient to uniquely identify each of the cell types considered. Labels indicate the cell type names and the corresponding binary expression patterns. Note that this dendrogram should not be confused with the developmental tree; the latter is shown schematically in grey lines in Figure 3

Fig. 2.

Steady-state analysis and comparison with expression patterns in 10 haematopoietic cell types. (A) The relation between the expression patterns of the 10 major cell types and the steady-states from the network model is shown by means of hierarchical clustering. Cell types are identified by their names. Steady-states are labelled with ‘S’ and two numbers; the first indicates the steady-state (1, 2 or 3) and the second the sub-states within the steady-state (up to 32 for steady-state ‘S-1’). Red, expression present; blue, expression absent. (B) Heterogeneous gene expression observed in single-cell microarray experiments of 12 individual HSPCs for all genes in our network except Erg (from Ramos et al., 2006). Red, expression present; blue, expression absent; magenta, marginal expression. (C) A near-linear correlation of averaged gene expression activity from the 12 single-cell profiles from (B) compared with average gene activity from the modelled HSPC steady-state individually for each of the 10 genes included in (B)

Fig. 3.

Analysis of state transitions. Developmental routes (in grey) between the major cell types in the developmental tree, with corresponding ‘on path’ transitions (leading to mature cell types) observed in the modelled network state space indicated as arrows (in colours; numbers indicate path lengths). The ‘on path’ transitions all start with an external trigger from the HSPC cell-type state; this trigger, or ‘push’, changes the state of one (‘+1’) or more (‘+2’, ‘+3’ and ‘+4’) genes. Similar ‘pushes’ are needed for transitions out of the CD4 and CD8 cell type to their respective activated cell types

Fig. 4.

Gata1 inhibits activity of the Fli1 HSPC enhancer. (A) Co-transfection of the Fli1 enhancer construct with a Gata1 expression vector results in significant reduction of the Fli1 enhancer activity. Co-transfection studies were performed in the HSPC cell line HPC7. The data shown represent the average fold change of four individual experiments, each performed in triplicate. (B) Diagram of the gene regulatory network, compare Figure 1 showing the predicted and experimentally validated inhibition of Fli1 by Gata1 (dashed lines)