Cell fate decision

Idiopathic primary myelofibrosis is an age-related clonal neoplastic disorder of
haematopoiesis characterised by a myeloproliferation and myelofibrosis. Recent
evidence suggests that disease onset results from an altered bone marrow
microenvironment, leading to disrupted crosstalk between progenitor haematopoietic
and mesenchymal stem cells populations. 90% of myelofibrosis cases exhibit ectopic
mutations of JAK2, CALR and, or MPL genes which all converge on the activation of
JAK and STAT signaling pathways. Treatments aiming to target STAT overactivity
have been developed; however, disease management is conducted at advanced
stages of the disease and treatments are not effective. A computational description
of how altered microenvironments can lead to dysregulated crosstalk between
haematopoietic and mesenchymal stem cells populations following STAT activation
would increase our knowledge of disease pathology and influence future treatment
protocols. To meet this aim, we have constructed a logical model that accounts for
the myelofibrotic microenvironment following TPO and lTLR signalling, integrated
with JAK-STAT signalling. The model primarily aims to provide a mechanistic
understanding of the dysregulated crosstalk between progenitor HSC’s, MSC’s and
the microenvironment to predict the onset of PMF with, and without the JAK
activation. Wildtype simulations result in 4 cyclic attractors being obtained, all
depending on combination of inputs being modelled. The model predicted that
presence of TPO and lTLR signalling are both required to facilitate disease onset for
wildtype simulations. For simulations involving JAK knock-in mutated scenarios, the
model resulted in 4 fixed point attractors, with the presence of lTLR alone being
sufficient to drive disease progression.

This model provides a generic high-level view of the interplays between NFκB pro-survival pathway, RIP1-dependent necrosis, and the apoptosis pathway in response to death receptor-mediated signals.

Wild type simulations demonstrate robust segregation of cellular responses to receptor engagement. Model simulations recapitulate documented phenotypes of protein knockdowns and enable the prediction of the effects of novel knockdowns. In silico experiments simulate the outcomes following ligand removal at different stages, and suggest experimental approaches to further validate and specialise the model for particular cell types.

This analysis gives specific predictions regarding cross-talks between the three pathways, as well as the transient role of RIP1 protein in necrosis, and confirms the phenotypes of novel perturbations. Our wild type and mutant simulations provide novel insights to restore apoptosis in defective cells. The model analysis expands our understanding of how cell fate decision is made.

The original model focuses on the interplay between three pathways activated in response to the same signal [1].

This model has then been adapted for multiscale analysis [2].

References

Relationships between genetic alterations, such as co-occurrence or mutual exclusivity, are often observed in cancer, where their understanding may provide new insights into etiology and clinical management. In this study, we combined statistical analyses and computational modelling to explain patterns of genetic alterations seen in 178 patients with bladder tumours (either muscle-invasive or non-muscle-invasive). A statistical analysis on frequently altered genes identified pair associations including co-occurrence or mutual exclusivity. Focusing on genetic alterations of protein-coding genes involved in growth factor receptor signalling, cell cycle and apoptosis entry, we complemented this analysis with a literature search to focus on nine pairs of genetic alterations of our dataset, with subsequent verification in three other datasets available publically. To understand the reasons and contexts of these patterns of associations while accounting for the dynamics of associated signalling pathways, we built a logical model. This model was validated first on published mutant mice data, then used to study patterns and to draw conclusions on counter-intuitive observations, allowing one to formulate predictions about conditions where combining genetic alterations benefits tumorigenesis. For example, while CDKN2A homozygous deletions occur in a context of FGFR3 activating mutations, our model suggests that additional PIK3CA mutation or p21CIP deletion would greatly favour invasiveness. Further, the model sheds light on the temporal orders of gene alterations, for example, showing how mutual exclusivity of FGFR3 and TP53 mutations is interpretable if FGFR3 is mutated first. Overall, our work shows how to predict combinations of the major gene alterations leading to invasiveness.

Zhang et al. defined a logical model of the T-LGL survival signaling network to investigate the signaling components that determine the survival of CTL in T-LGL leukemia. Please refer to the supporting publication [1].

References