A mechanistic model of α-synuclein aggregation and degradation to bridge in vitro and in vivo research on Parkinson’s disease.
Elena Righetti (1,2), Luca Marchetti (1,2), Enrico Domenici (1,2), Federico Reali (1)
(1) Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Italy; (2) University of Trento, Department of Cellular, Computational and Integrative Biology (CIBIO), Italy
Objectives: Parkinson’s disease is a debilitating neurodegenerative disorder characterized by an intricate network of molecular mechanisms [1]. Such a biological complexity translates into the absence of approved treatments able to stop or even slow down neurodegeneration. In this context, promising therapeutic strategies target the presynaptic protein α-synuclein (αsyn) and its toxic aggregates as crucial contributors to disease onset and progression [1, 2]. The kinetics of αsyn aggregation has been extensively studied in test-tube experiments [3, 4], leading to valuable mechanistic insights. However, it is unclear how these results can apply to cell cultures and living systems [4], given the intrinsic limitations of in vitro to in vivo translation. Mathematical modeling of pathological processes can be instrumental in bridging the gap between in vitro and in vivo studies, thus supporting experimental preclinical research and, eventually, therapeutic strategy design [5]. Moving in this direction, our work aims at an in silico representation of disrupted αsyn homeostasis to provide a mechanistic understanding of the molecular hallmarks of Parkinson’s disease.
Methods: Relying on available aggregation models tailored for ad-hoc in vitro experiments [6], we adopted a chemical kinetic representation to develop a preliminary model describing αsyn aggregation as a nucleation-conversion polymerization process [3, 4, 6]. Following an extensive literature review [7], we updated the preliminary model with a modular and incremental approach to account for αsyn synthesis, protein misfolding, and dysfunctional degradation mechanisms associated with the disease. The resulting ordinary differential equation system consisting of mass-action and catalytic reactions was implemented in MATLAB. We calibrated the preliminary and extended models on integrated sets of published data from in vitro aggregation assays and in vivo preclinical measurements. The fitting procedure relied on a multi-start least squares optimization to estimate unknown model parameters. Furthermore, we carried out a local sensitivity analysis to quantify the impact of each parameter on the system dynamics through an AUC-based sensitivity index.
Results: We have developed a mechanistic molecular model of αsyn aggregation and degradation that can simultaneously capture different in vivo-like scenarios of αsyn accumulation, e.g., pH level variations and the presence of lipid vesicles. Informed by sensitivity analysis, the model is used to virtually explore the effect of candidate drugs with various mechanisms of action on either αsyn aggregation or degradation pathways.
Conclusions: The model presented here lies within a quantitative systems pharmacology framework as a building block of an intraneuronal model of Parkinson’s disease. Overall, our results show that mathematical modeling can provide insights into the biological complexity of neurodegeneration. Moreover, it can be a valuable tool to study the effect of drugs currently under investigation and even identify potential pharmacological targets, thus supporting drug discovery and development.
References:
[1] W. Poewe et al., “Parkinson disease,” Nat Rev Dis Primers, vol. 3, no. 1, p. 17013, Dec. 2017.
[2] C. R. Fields, N. Bengoa-Vergniory, and R. Wade-Martins, “Targeting alpha-synuclein as a therapy for Parkinson’s disease,” Front Mol Neurosci, vol. 12, Dec. 2019.
[3] T. P. J. Knowles et al., “An analytical solution to the kinetics of breakable filament assembly.,” Science, vol. 326, no. 5959, pp. 1533–7, Dec. 2009.
[4] G. Meisl, T. P. J. Knowles, and D. Klenerman, “Mechanistic models of protein aggregation across length-scales and time-scales: From the test tube to neurodegenerative disease,” Front Neurosci, vol. 16, Jun. 2022.
[5] P. Bloomingdale et al., “Hallmarks of neurodegenerative disease: A systems pharmacology perspective,” CPT Pharmacometrics Syst Pharmacol, Aug. 2022.
[6] M. Iljina et al., “Kinetic model of the aggregation of alpha-synuclein provides insights into prion-like spreading,” Proc Natl Acad Sci U S A, vol. 113, no. 9, pp. E1206–E1215, Mar. 2016.
[7] E. Righetti, A. Antonello, L. Marchetti, E. Domenici, and F. Reali, “Mechanistic models of α-synuclein homeostasis for Parkinson’s disease: A blueprint for therapeutic intervention,” Front Appl Math Stat, vol. 8, Dec. 2022.