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A computational modeling of invadopodia protrusion into an extracellular matrix fiber network.

ABSTRACT: Invadopodia are dynamic actin-rich membrane protrusions that have been implicated in cancer cell invasion and metastasis. In addition, invasiveness of cancer cells is strongly correlated with invadopodia formation, which are observed during extravasation and colonization of metastatic cancer cells at secondary sites. However, quantitative understanding of the interaction of invadopodia with extracellular matrix (ECM) is lacking, and how invadopodia protrusion speed is associated with the frequency of protrusion-retraction cycles remains unknown. Here, we present a computational framework for the characterization of invadopodia protrusions which allows two way interactions between intracellular branched actin network and ECM fibers network. We have applied this approach to predicting the invasiveness of cancer cells by computationally knocking out actin-crosslinking molecules, such as α-actinin, filamin and fascin. The resulting simulations reveal distinct invadopodia dynamics with cycles of protrusion and retraction. Specifically, we found that (1) increasing accumulation of MT1-MMP at tips of invadopodia as the duration of protrusive phase is increased, and (2) the movement of nucleus toward the leading edge of the cell becomes unstable as duration of the retractile phase (or myosin turnover time) is longer than 1 min.

SUBMITTER: Kim MC

PROVIDER: S-EPMC8786978 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background Rheumatoid arthritis (RA) is a chronic inflammatory disease, characterized by joint destruction and perpetuated by the synovial membrane (SM). In the inflamed SM, activated synovial fibroblasts (SFB) form the major cell type promoting development and progression of the disease by an abnormal expression/secretion of pro-inflammatory cytokines, tissue-degrading enzymes resulting in a predominant degradation of the extra-cellular matrix (ECM), and collagens causing joint fibrosis. We developed a new procedure, based on human knowledge and formal concept analysis (FCA), to simulate and analyze the temporal behaviour of regulatory and signaling networks. It was applied to a regulatory network (containing 18 genes from 5 functional groups) representing ECM formation and destruction in TGFβ - and TNFα -stimulated SFB. Results For the modelling of SFB-controlled ECM turnover in rheumatic diseases, Boolean network architecture was used as well as extensive literature information and revision by experimental gene expression data from stimulated SFB. In course of revision, the additional experimental information resulted in different biologically reasonable changes, yielding two Boolean networks that describe TGFβ and TNFα effects, respectively. The final simulations were further analyzed by the attribute exploration algorithm of FCA, integrating again the observed time series in a more fine-grained and automated manner. The generated temporal rules clearly reveal subtle regulatory relationships between different genes, co-expression patterns and converse gene expression regulation in rheumatic diseases. Conclusion The developed Boolean network based method for the dynamical analysis of regulatory and signaling networks represents a reliable systems biological solution for the improved understanding of complex regulatory pathways and the interactions among different genes in disease. The resulting knowledge base can be used for further analysis of the ECM system in human fibroblasts and may be queried to predict the functional consequences of observed (e.g. in diseases as RA) or hypothetical (e.g. for therapeutic purposes) gene expression disturbances.

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A computational modeling of invadopodia protrusion into an extracellular matrix fiber network.

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