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Differentiation

Lymphoid and myeloid cell specification and transdifferentiation

Summary: 

Blood cells are derived from a common set of hematopoietic stem cells, which differentiate into more specific progenitors of the myeloid and lymphoid lineages, ultimately leading to differentiated cells. This developmental process is controlled by a complex regulatory network involving cytokines and their receptors, transcription factors and chromatin remodelers.
Based on public and novel data from molecular genetic experiments (qPCR, western blots, EMSA), along with genome-wide assays (RNA-seq, ChIP-seq), we defined a logical model recapitulating cytokine-induced differentiation of common progenitors, the effect of various reported gene knock-downs, as well as reprogramming of pre-B cells into macrophages induced by ectopic expression of specific transcription factors.

Regulatory graph

Note: This model is also available at BioModels database BioModels ID: 1610240000.

Curation
Submitter: 
C. Chaouiya (D. Thieffry)

miR-9 and timing of neurogenesis (Coolen 2012)

Summary: 

This Boolean model displays the subtle role of miR-9 in the course of neural specification in zebrafish.

Figure 1: Green arrows represent positive regulations, whereas red T arrows represent inhibitory interactions. Dotted lines represent suspected (but not yet molecularly characterized) direct or indirect interactions, which prevent the expression of a gene in the progenitor or neuronal precursor states, such as elavl3/HuC inhibition in progenitors.

The node P denotes a proliferating progenitor state (P=1, N=0). It is defined by the expression of Her6 and/or Zic5. The node N denotes the commitment of a progenitor into a neural precursor (P=0, N=1).
By inhibiting genes with opposite effect on neural differentiation, miR-9 activity generates an ambivalent state (P=0, N=0) poised for responding to both progenitor maintenance and commitment cues.

Model simulations qualitatively recapitulate all the experimental results presented in Coolen et al (submitted), for the wild-type as well as for various mutant situations (including loss-of-functions, ectopic gene expressions, and miR-9 target protection by morpholinos).

Curation
Submitter: 
Chaouiya (Thieffry)

Control of Th1/Th2/Th17/Treg/Tfh/Th9/Th22 cell differentiation

Summary: 

Logical modeling has proven suitable for the dynamical analysis of large signaling and transcriptional regulatory networks. In this context, signaling input components are generally meant to convey external stimuli, or environmental cues. In response to such external signals, cells acquire specific gene expression patterns modeled in terms of attractors (e.g. stable states). The capacity for cells to alter or reprogram their differentiated states upon changes in environmental conditions is referred to as cell plasticity.

In [1], it is presented an extended version of a published logical model of T-helper cell differentiation and plasticity, which accounts for novel cellular subtypes. The model encompasses 20 signaling pathways, a dozen of transcription factors, and about 30 cytokines, amounting to 101 components in total.

Computational methods recently developed to efficiently analyze large models [1] are first used to study static properties of the model (i.e. stables states). Symbolic model checking is then applied to get further insights into reachability properties between Th canonical subtypes upon changes of specific prototypic environmental cues.

The model reproduces novel reported Th subtypes (Tfh, Th9, Th22) and predicts additional Th hybrid subtypes in term of stables states. Using the model checker NuSMV-ARCTL, an abstract view of the dynamics, called reprograming graph, is produced providing a global and synthetic view of Th plasticity. The model is consistent with experimental data showing the polarization of naïve Th cells into the canonical Th subtypes. The model further predicts substancial plasticity of Th subtypes depending on the signalling environment.


References

Curation
Submitter: 
Pedro Monteiro

Specification of vulval precursor cells and cell fusion control in C. elegans

Summary: 

The vulva of Caenorhabditis elegans has been long used as an experimental model of cell differentiation and organogenesis. While it is known that the signaling cascades of Wnt, Ras/MAPK, and NOTCH interact to form a molecular network, there is no consensus regarding its precise topology and dynamical properties. We inferred the molecular network, and developed a multivalued synchronous discrete dynamic model to study its behavior. The model reproduces the patterns of activation reported for the following types of cell: vulval precursor, first fate, second fate, second fate with reversed polarity, third fate, and fusion fate. We simulated the fusion of cells, the determination of the first, second, and third fates, as well as the transition from the second to the first fate. We also used the model to simulate all possible single loss- and gain-of-function mutants, as well as some relevant double and triple mutants. Importantly, we associated most of these simulated mutants to multivulva, vulvaless, egg-laying defective, or defective polarity phenotypes. The model shows that it is necessary for RAL-1 to activate NOTCH signaling, since the repression of LIN-45 by RAL-1 would not suffice for a proper second fate determination in an environment lacking DSL ligands. We also found that the model requires the complex formed by LAG-1, LIN-12, and SEL-8 to inhibit the transcription of eff-1 in second fate cells. Our model is the largest reconstruction to date of the molecular network controlling the specification of vulval precursor cells and cell fusion control in C. elegans. According to our model, the process of fate determination in the vulval precursor cells is reversible, at least until either the cells fuse with the ventral hypoderm or divide, and therefore the cell fates must be maintained by the presence of extracellular signals.

Curation
Submitter: 
Claudine (Nathan Weinstein)

Control of Th1/Th2/Th17/Treg cells differentiation

Summary: 

Alternative cell differentiation pathways are believed to arise from the concerted action of signalling pathways and transcriptional regulatory networks. However, the prediction of mammalian cell differentiation from the knowledge of the presence of specific signals and transcriptional factors is still a daunting challenge. In this respect, the vertebrate hematopoietic system, with its many branching differentiation pathways and cell types, is a compelling case study.

In [1], it is proposed an integrated, comprehensive model of the regulatory network and signalling pathways controlling Th cell differentiation. As most available data are qualitative, it is relied on a logical formalism to perform extensive dynamical analyses. To cope with the size and complexity of the resulting network, it is used an original model reduction approach [2], together with a stable state identification algorithm [3]. To assess the effects of heterogeneous environments on Th cell differentiation, it is performed a systematic series of simulations, considering various prototypic environments.

Consequently, it is identified stable states corresponding to canonical Th1, Th2, Th17 and Treg subtypes, but these were found to coexist with other transient hybrid cell types that co-express combinations of Th1, Th2, Treg and Th17 markers in an environment-dependent fashion. In the process, the logical analysis highlights the nature of these cell types and their relationships with canonical Th subtypes. Finally, this logical model can be used to explore novel differentiation pathways in silico.


References

Curation
Submitter: 
C. Chaouiya

TCR signalisation

Summary: 

Klamt et al. proposed in [1] a Boolean model of the TCR signalling pathway. The model encompasses 40 regulatory components. In this version of the model an auto-regulation has been added on each input.

Analysis

This model has been studied in [2], using novel algorithms for the analysis of feedback circuits and the determination of stable states. The stable state analysis shows seven stable states, listed bellow. Each stable state corresponds to a different input combination, except "111". Indeed, the systems shows an oscilatory behaviour under full activation.


CD45 CD8 TCRlig TCRbind PAGCsk Fyn TCRphos Ikb
0 * 0 0 1 0 0 1
0 * 1 1 0 0 0 1
1 0 0 0 1 0 0 1
1 0 1 1 0 1 1 1
1 1 0 0 1 0 0 1

Table 1. 7 stable states of the model. A "*" means "all possible expression levels (i.e. 0 or 1). The expression level of all other variables is 0.

The feedback circuit analysis shows nine circuits, besides the three auto-activation on the inputs. Only one of these circuits is functional: (ZAP70, cCbl). This negative circuit is functional in presence of LCK and TCRphos which can only be maintained in presence of the three inputs. This circuit drives the oscillatory behaviour observed under full activation.


References

Curation
Submitter: 
Claudine Chaouiya

Control of Th1/Th2 cell differentiation

Summary: 

On the basis of an extensive analysis of the literature, L. Mendoza proposed a logical model encompassing the most crucial regulatory components and the cross-interactions involved in these differentiative decisions (see [1]).

This model encompasses 17 regulatory components.


References

Curation
Submitter: 
Claudine Chaouiya
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