Resumen de Digging into biologically-driven injury mechanisms in the intervertebral disc: an evidence-based network modelling approach to estimate cell dynamics within complex multicellular systems
Laura Alexa Valentina Baumgartner
Low back pain is responsible for more global disability than any other condition [1]. At least 40% of low back pain cases could be related to intervertebral disc (IVD) failure [2]. The latter is likely caused by an accumulation of microtrauma within the IVD, emerging out of persistent adverse cell behavior and subsequent tissue weakening. Cells respond to microenvironmental stimuli with specific expressions of mRNA, subsequently translated into proteins. Hence, persisting adverse microenvironments lead to catabolic cell responses over time.
The IVD has been investigated over decades at multiple spatial scales (e.g. [3–13]). However, so far, no in silico model exists to explicitly tackle spatial scales lower than the organ/tissue level. This might be partly related to limited methodologies to estimate complex, dynamic cell responses over long periods of time. Accordingly, this work aims to develop a methodology to approximate dynamic cell responses within the complex (heterogenous) multifactorial microenvironments found in the IVD, to investigate the biological mechanisms behind tissue weakening and therefore microtrauma development.
The thesis consists of six chapters: the introduction (Chapter 1) is followed by four self-contained Chapters (2-5) and a final Chapter 6.
Chapter 2 provides the state of the art of research on multiscale regulation of the IVD. It encloses sections 1-3 and 5 of the review article published by the Author of this PhD thesis, in the International Journal of Molecular Sciences [2].
In Chapter 3 two state of the art modeling approaches, agent-based and network modelling, were combined to approximate cell responses in Nucleus Pulposus (NP) multicellular environments, within the IVD. The model simulates 4000 NP cells within a volume of 1mm3, reflecting an average cell density of a human NP [14]. Interleukin 1β (IL1β) immunopositivity, cell viability and protein mRNA expressions were predicted. In particular, the mRNA expressions of the tissue proteins Aggrecan (Agg), Collagen Types I & II and the proteases MMP3 (Metalloproteinase 3) and ADAMTS (A disintegrin and metalloproteinase with thrombospondin motifs) were considered and reflect the cell activity (CA). Microenvironmental stimuli were the cell nutrition-related factors glucose (glc) and lactate (lac), the latter determining the acidity (pH) of the microenvironment. The model was successfully validated and stands for the first in silico model of the IVD that tackles the multicellular level. This chapter was published as a peer reviewed article in Bioinformatics [15].
Chapter 4 focusses on the modeling and simulation of heterogeneous microenvironmental stimuli and cell states (CS) within the previously introduced 3D AB model of the NP. Heterogeneity can emerge due to local proinflammatory cytokine expression within the NP. Thereby, the proinflammatory cytokines IL1β and the tumor necrosis factor α (TNF-α) were simulated, since they are critical in IVD degeneration [16]. The presence of both proinflammatory cytokines led to multicellular environments defined by four CS: non-inflamed, IL1β inflamed, TNF-α inflamed and inflamed for both IL1β&TNF-α. To simulate these multicellular environments, specific experimental research was carried out. Experimental findings showed that TNF-α has a significant catabolic impact on CA, whilst glc did not significantly affect the expression of proinflammatory cytokine mRNA. Biological measurements allowed further developments of the network modeling approach and improved greatly the prediction of CA in terms of differentiation between inflamed and non-inflamed cells and in terms of relative mRNA expressions in different CS. However, TNF-α inflammation within the AB model was overpredicted under early degenerated conditions, indicating that cell nutrition-related factors only are not enough to accurately simulate the proinflammatory environment within the NP. Hence, the effect of mechanical loading was considered in subsequent model developments. The work of this Chapter led to the first IVD multicellular model that allows to approximate and compare CA of different CS within complex (heterogeneous) multifactorial environments. It was published as a peer-reviewed article in Frontiers in Bioengineering and Biotechnology [17].
In Chapter 5, methods were developed to consider the effect of mechanical loads and the chronicity of stimulus exposure within the simulated multicellular environment. Moreover, a mathematical framework was presented to estimate different CA and to obtain interrelated results between different CS. This consisted in a novel top-down and high-level network modeling approach based on ordinary differential equations. To evaluate the model, six months microgravity exposure was simulated and compared to an approximation of daily life on Earth within the same time-period. Additionally, different moving habits such as walking, jogging, sitting, exposure to vibration or carrying heavy weights were simulated over prolonged time periods. Results were in good agreement with literature, including a predicted catabolic change due to sitting with a round back compared to sitting with an active back. Additionally, model predictions provided explanations for contradicting findings due to unloading, as experienced under microgravity or in bedrest studies. Moreover, due to an integration of mechanical loading, it was possible to improve the prediction of proinflammatory environments. Eventually, in this Chapter, a novel top-down high-level network modeling approach and a novel method to integrate CA-specific dose- and time-dependencies were developed. This allowed for the first time in IVD research to estimate combined effects of six key relevant stimuli (glc, pH, mag, freq, TNF-α and IL1β) over time in a multicellular environment of four CS and, hence, approximate cell responses within complex multicellular environments as found in native tissues.
Chapter 6 contains an overall conclusion of the presented work and summarizes the workflow of the thesis development over time, including the most relevant findings at each research stage. Finally, most relevant future research directions were pointed out.
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