A system mathematical model of a cell-cell communication network in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a devastating and chronic neurodegenerative disease without any known cure. In the brain and spinal cord of both patients and animal models with ALS, neuroinflammation is a prominent pathological hallmark which is characterized by infiltrating T cells at sites of motor neuron injury. Their presence in mutant Cu(2+)/Zn(2+) superoxide dismutase (mSOD1) induced ALS plays an important role in shifting the response of microglia from neuroprotective to neurotoxic. In order to better understand how these cells and their communication network collectively modulate the disease progression, we have established a mathematical model integrating diverse cells and cytokines. According to the experimental data sets, we first refined this model by identifying a link between TGFβ and M1 microglia which can produce an optimized model to fit data sets better. Then based on this model, parameters were estimated using genetic algorithm. Sensitivity analysis of these parameters identified several factors such as the release rate of IFNγ by T helper 1 (Th1) cells, which may be related to the heterogeneity between the patients with different survival times. Furthermore, the tests on T cell based therapeutic strategies indicated that elimination of Th1 cells is the most effective approach extending survival time. This confirmed the dominant role of Th1 cells in leading the rapid disorder in the later stage of ALS. For the therapies targeting cytokines, injection of IL6 can essentially augment the neuroprotective response and extend the life effectively by elevating the level of IL4, a neuroprotective cytokine, while directly injected IL4 will decay rapidly in the ALS microenvironment and cannot provide a persistent protective effect. On the other hand, in spite of the attractive effect of direct elimination of mSOD1 or self-antigen, it is difficult to implement in CNS. As an alternative, elimination of IFNγ can be chosen as another effective therapy. In the future, if we combine the side effects of different therapies, this model can be used to optimize the therapeutic strategies so that they can effectively improve survival rates and quality of life for patients with ALS.