Program

PO1-6-15

Interaction between Ca2+ and cell adhesion molecules during cell migration

[Speaker] Di Li:1
[Co-author] Fei-Lin Chen:1, Ting-Xuan Lu:1, Jean-San Chia:1,3, Feng-Chiao Tsai:1,2
1:Department of Pharmacology, National Taiwan University, College of Medicine, No. 1, Section 1, Ren-Ai Road, Taipei 100, Taiwan, 2:Department of Internal Medicine, National Taiwan University Hospital, No. 7 Chung-Shan South Road, Taipei 100, Taiwan, 3:Department of Immunology, National Taiwan University, College of Medicine, No. 1, Section 1, Ren-Ai Road, Taipei 100, Taiwan

Ca2+ signaling is involved in many important physiological activities including cell migration, but how Ca2+ regulates cell migration has remained elusive. Previous literature reports showed that store-operated Ca2+ entry (SOCE) might promote or inhibit cancer metastasis in different cancer types. Our data also indicated that such differences stemmed from different cell-matrix adhesion strengths in different cancer types, because SOCE increased or decreased cell motility depending on the basal strengths of cell-matrix adhesion forces. We thus hypothesized that SOCE interacted with cell-matrix adhesion to modulate cancer cell migration.
To verify our hypothesis, we established a mathematical heat map model to describe how different activities of SOCE and cell-matrix adhesion interacted to produce corresponding cell motilities. Then we overexpressed or knocked-down the key adhesion molecule PXN and key SOCE molecule STIM1 in migrating cancer cells and measured their motilities. Indeed, our experimental results matched the prediction from the mathematical model, supporting the interaction between SOCE and cell-matrix adhesion. We are currently working on two directions: First, we will elucidate the molecular mechanisms how SOCE and cell-matrix adhesion interact, specifically whether the interaction is directly through Ca2+-modulated adhesion forces or indirectly through Ca2+-mediated cell differentiation. Second, we will examine whether our mathematical model could be employed to predict cancer metastasis and patient prognosis. These approaches will improve our understanding of cancer cell migration so novel therapeutic strategies can be developed accordingly.
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