Signalling

We have applied the logical modelling framework to the regulatory network controlling T-lymphocyte specification. This process involves cross-regulations between specific T-cell regulatory factors with factors driving alternative differentiation pathways, which remain accessible during the early steps of thymocyte development. Many transcription factors needed for T-cell specification are required in other hematopoietic differentiation pathways, and are combined in a fine-tuned, time-dependent fashion to achieve T-cell commitment.
Using the software GINsim, we integrated current knowledge into a dynamical model, which recapitulates the main developmental steps from early progenitors entering the thymus up to T-cell commitment, as well as the impact of various documented environmental and genetic perturbations. Our model analysis further enabled the identification of several knowledge gaps.

The associated notebook can be loaded using the CoLoMoTo notebook docker image (see http://www.colomoto.org/notebook).

Jupyter Notebook: Tdev_notebook_2nov2019.ipynb

Understanding the etiology of metastasis is very important in clinical perspective, since it is estimated that metastasis accounts for 90% of cancer patient mortality. Metastasis results from a sequence of multiple steps including invasion and migration. The early stages of metastasis are tightly controlled in normal cells and can be drastically affected by malignant mutations; therefore, they might constitute the principal determinants of the overall metastatic rate even if the later stages take long to occur. To elucidate the role of individual mutations or their combinations affecting the metastatic development, a logical model has been constructed that recapitulates published experimental results of known gene perturbations on local invasion and migration processes, and predict the effect of not yet experimentally assessed mutations. The model has been validated using experimental data on transcriptome dynamics following TGF-β-dependent induction of Epithelial to Mesenchymal Transition in lung cancer cell lines. A method to associate gene expression profiles with different stable state solutions of the logical model has been developed for that purpose. In addition, we have systematically predicted alleviating (masking) and synergistic pairwise genetic interactions between the genes composing the model with respect to the probability of acquiring the metastatic phenotype. We focused on several unexpected synergistic genetic interactions leading to theoretically very high metastasis probability. Among them, the synergistic combination of Notch overexpression and p53 deletion shows one of the strongest effects, which is in agreement with a recent published experiment in a mouse model of gut cancer. The mathematical model can recapitulate experimental mutations in both cell line and mouse models. Furthermore, the model predicts new gene perturbations that affect the early steps of metastasis underlying potential intervention points for innovative therapeutic strategies in oncology.

Based on an exhaustive curation of the existing literature and using the software CellDesigner, we have built and annotated a comprehensive molecular map for the FceRI and FcgRIIb signalling pathways, which play a key role in mast cell activation in mammals. Using this map and the logical modelling software GINsim, we have derived a logical model recapitulating the most salient features of mast cell activation. This model can be used to explore the dynamical properties of the system and its responses to different stimuli, in normal or mutant conditions. For more details, see [1].

References

VEGF (also called PDGF or PVF) pathway participates in different developmental processes, including border cell migration, hemocyte migration and survival, thorax closure during metamorphosis, the rotation and dorsal closure of the male terminalia. and embryonic salivary gland tissue migration. The ability of PVR to activate the MAP-kinase pathway is important for control of cell growth and differentiation in other tissues. Three genes in the Drosophila genome code for PVR ligands: PVF1, PVF2, and PVF3. Binding of one of the ligands (PVF1, 2 or 3) to the receptor PVR triggers the canonical DRK/SOS/RAS/RAF/DSOR1/RL pathway ([1];[2]; [3]; [4]; [5]). DOF is needed to assemble the PVR receptor and allow it to auto-phosphorylate, likely as an adaptor that links the receptor to RAS pathway. DOF is a cytoplasmic protein which is expressed ubiquitously only in cells that express the FGF receptors. It contains an ankyrin repeat, a coiled-coil structure and many tyrosines within environments that suggest that if phosphorylated they act as binding sites for the SH2 domains of proteins such as DRK or CSW ([6]). The SH2-domain-containing protein DRK recruits the guanine nucleotide exchange factor, Son of sevenless (SOS), to catalyze the exchange of GDP bound to RAS for GTP, thereby activating RAS with the help of activated KSR. RAS promotes the activation of RAF, leading to the activation of DSOR1, and ultimately to that of the MAP kinase Rolled (RL ). Rolled can activate transcription, both through inactivation of transcriptional co-repressors such as AOP, as well as through the activation of transcription factors such as the ETS-domain- containing protein Pointed (PNT) ([7]; [8]). The activation of PNT is a major output of the pathway. It is either phosphorylated by MAP kinase to produce an active transcriptional activator (PointedP2), or transcriptionally induced by MAP kinase to produce a constitutive transcriptional activator (PointedP1). Sprouty (STY) acts downstream of the receptor, but upstream of RAS1 and RAF, by recruiting GAP1 and blocking the ability of DRK to bind to its positive effector. We have considered three typical initial states corresponding to i. ligands binding in wild-type signalling enabling situation (VEGF_signalling), ii. ligand binding in the presence of the inhibitor Sprouty (Sprouty_inhibition), iii. absence of ligand (No_signalling).

References

Processing of HH ligand The precursor of HH is auto-catalytically cleaved to produce an N-terminal (HH-N) and a C- terminal (HH-C) fragments ([1]; [2]). A cholesterol moiety is covalently attached to the last amino acid of HH-N to create HH-Np, that is responsible for the biological activities of HH proteins ([1]; [2]; [3]). The N-terminal region of HH-Np is further modified by addition of palmitate that is essential for its signalling activity ([4]; [5]; [6]; [7]; [8]; [9]). We model these aspects by an AND rule (combining inputs from DLP, IHOG, Rasp, DISP, SHF, Lipophorin, BOI and DALLY) attached to the component representing the secreted HH molecule, denoted Hh in our model. HH Signalling Two integral membrane proteins are involved in HH signal reception: Patched and Smoothened. HH binding to its receptor Patched (PTC) relieves PTC-mediated repression of Smoothened (SMO), a serpentine-like membrane protein required for HH signalling ([10]; [11]). This allows SMO stabilisation, activation, and phosphorylation by Shaggy (SGG), and downstream signalling through the formation of a protein complex including the serine threonine kinase Fused (FU), the kinesin-like protein Costa (COS), and the protein Suppressor of Fused (SU(FU)), ultimately controlling the post-translational processing of the protein Cubitus interruptus (CI) ([12]). In the absence of HH, COS binds CI directly and sequesters it in the cytoplasm with the help of SUFU. The recruitment of different kinases (Casein kinase 1 alpha, Shaggy, Protein kinase A) then leads to the phosphorylation of CI and to its proteolysis by SLMB. The resulting truncated protein (CI_rep) is released and enters the nucleus, where it has a transcriptional repressing activity. Recent evidence further indicates that SMO is inhibited by TOW, which tentatively mediates the effect of PTC on SMO ([13]; [14]). Following SMO activation, the transcription factor CI is phosphorylated and translocated into the nucleus in its entire form, which plays a transcriptional activatory role (CI_act). In the model, a cascade of inhibitions, from HH on PTC, and from PTC on SMO, implements the indirect positive action of HH on SMO. A protein complex including CI, COS, and FU, phosphorylates and thereby inhibits SU(FU), ultimately favouring the CI activatory form and its translocation into the nucleus. We model the roles of the kinases (SGG, PKA, and CK1a), COS and SU(FU) (both needed to recruit the kinases) in the processing of CI in terms of inhibitory interactions on CI_act and activatory interactions on CI_rep ([15]; [16]). Complexes are represented implicitly (they are formed as soon as the components are synthesised or activated), while logical rules define component activity requirements to form CI_act versus CI_rep forms. To explore the dynamic of the pathway, we define two initial states to simulate the presence and the absence of signalling. On one hand, the non binding of HH (level expression 0) triggers a series of signalling cascades that lead to the activation of several kinases (for example SGG, PKA, CK1a, ...) at level of expression 1, which will permit the formation of CI repressor (expressed at level 1), which in turn will inhibit the targets. On the other hand, the presence of HH (level of expression 1) leads to a stable state corresponding to the signalling conditions leading to the formation of CI activator that will activate the targets node (level of expression 1).

References