Modelling cell migration in 3D micro-environments

UoM administered thesis: Phd

Abstract

Cell migration is a key physiological process which underpins development, wound healing, immune responses and leads to the metastatic onset of diseases such as cancer; understanding cell migration is therefore of paramount importance. Motility has been conceptualised as a cyclic process which requires the coordinated activity of leading edge protrusions and rear retraction. The key regulators of both these events are the Rho GTPases, in particular RhoA, however the signalling leading to and downstream of Rho GTPases is highly complex. Given this inherent complexity, adoption of mathematical modelling approaches can be of great value to formalise knowledge and interrogate systems of interest via the generation of novel, testable predictions. Here, a range of modelling approaches have been combined with imaging assays to elucidate the key signalling events which lead to two important but hitherto understudied areas in physiological 3D environments: the establishment of RhoA driven invasive protrusions at the leading edge; and the potentiation of RhoA mediated retraction at the cell rear. First, a Boolean logical model was assembled based on existing network pathways/models to understand a RhoA-mediated signalling pathway that promotes filopodia formation at the leading edge. Computational simulation of the model predicted an unanticipated feedback loop, whereby Raf/MEK/ERK signalling maintains suppression of Rac1 by inhibiting the Rac-activating Sos1-Eps8-Abi1 complex, allowing RhoA activity to predominate in invasive protrusions. This prediction was subsequently validated experimentally via MEK inhibition and Eps8 knockdown in migration and invasion assays. RhoA driven rear retraction is a highly dynamic process, and other than the requirement for acto-myosin contractility, little is known about the mechanisms that control this, particularly in cells migrating or invading in 3D. In fibrillar matrix, or within gradients of stiffness, cells show marked front-rear polarity, with protrusions at the leading edge and caveolae concentrated at the cell rear. Rather than mediating endocytosis, caveolae sense lower membrane tension and form at the rear of cells, and formation of caveolae in turn promotes the activation of RhoA. RhoA accelerates rear retraction and establishes a positive feedback loop to influence F-actin organisation and non-muscle myosin motor II (NMMII) phosphorylation. Efficient rear movement in stiffnesses of physiological relevance relies on a dynamic, positive feedback mechanism centred on the interplay of caveolae mechanosensing and RhoA signalling properties. Finally, the new data on rear retraction of cells migrating in physiological matrix was extended to a multi-pronged approach built on Boolean logic, ordinary differential equations and stochastic rules. These models, incorporating increasing complexity, make diverse predictions regarding efficient cell rear retraction in 3D environments regarding substrate polarity and RhoA binding partners. Moreover, modelling with these diverse approaches within the same system (3D rear retraction) proved a fruitful comparison of the apparent caveats/benefits of each approach, and gives future general indicators as to the suitability of Boolean/ODE/rule-based methods for other biological situations. In this thesis, the value of combining a mathematical approach with wet lab experimentation has been highlighted, demonstrating the power of computational modelling in providing accurate predictions and understanding of signalling networks. Here, these methods were applied with regards to potential abrogation of harmful cell migration, whereby simple interventions such as inhibition of MEK or caveolae may prevent leading edge protrusion or rear retraction and subsequent whole cell motility. In the future, mathematical modelling could be further developed to build virtual cells and organisms to understand more complex signalling and disease networks.

Details

Original languageEnglish
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Supervisors/Advisors
Award date1 Aug 2018