000079846 001__ 79846
000079846 005__ 20200716101427.0
000079846 0247_ $$2doi$$a10.1371/journal.pcbi.1006395
000079846 0248_ $$2sideral$$a112477
000079846 037__ $$aART-2019-112477
000079846 041__ $$aeng
000079846 100__ $$0(orcid)0000-0001-8928-350X$$aEscribano, J.
000079846 245__ $$aBalance of mechanical forces drives endothelial gap formation and may facilitate cancer and immune-cell extravasation
000079846 260__ $$c2019
000079846 5060_ $$aAccess copy available to the general public$$fUnrestricted
000079846 5203_ $$aThe formation of gaps in the endothelium is a crucial process underlying both cancer and immune cell extravasation, contributing to the functioning of the immune system during infection, the unfavorable development of chronic inflammation and tumor metastasis. Here, we present a stochastic-mechanical multiscale model of an endothelial cell monolayer and show that the dynamic nature of the endothelium leads to spontaneous gap formation, even without intervention from the transmigrating cells. These gaps preferentially appear at the vertices between three endothelial cells, as opposed to the border between two cells. We quantify the frequency and lifetime of these gaps, and validate our predictions experimentally. Interestingly, we find experimentally that cancer cells also preferentially extravasate at vertices, even when they first arrest on borders. This suggests that extravasating cells, rather than initially signaling to the endothelium, might exploit the autonomously forming gaps in the endothelium to initiate transmigration.
000079846 536__ $$9info:eu-repo/grantAgreement/EUR/ERC/INSILICO-CELL-ERC-2012-StG-306571$$9info:eu-repo/grantAgreement/ES/MINECO/BES-2013-063684-FPI$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2015-64221-C2-1-R
000079846 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000079846 590__ $$a4.7$$b2019
000079846 592__ $$a2.91$$b2019
000079846 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b6 / 59 = 0.102$$c2019$$dQ1$$eT1
000079846 593__ $$aCellular and Molecular Neuroscience$$c2019$$dQ1
000079846 591__ $$aBIOCHEMICAL RESEARCH METHODS$$b9 / 77 = 0.117$$c2019$$dQ1$$eT1
000079846 593__ $$aComputational Theory and Mathematics$$c2019$$dQ1
000079846 593__ $$aEcology$$c2019$$dQ1
000079846 593__ $$aMolecular Biology$$c2019$$dQ1
000079846 593__ $$aGenetics$$c2019$$dQ1
000079846 593__ $$aModeling and Simulation$$c2019$$dQ1
000079846 593__ $$aEcology, Evolution, Behavior and Systematics$$c2019$$dQ1
000079846 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000079846 700__ $$aChen, M.B.
000079846 700__ $$aMoeendarbary, E.
000079846 700__ $$aCao, X.
000079846 700__ $$aShenoy, V.
000079846 700__ $$0(orcid)0000-0002-9864-7683$$aGarcia-Aznar, J.M.$$uUniversidad de Zaragoza
000079846 700__ $$aKamm, R.D.
000079846 700__ $$aSpill, F.
000079846 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000079846 773__ $$g15, 5 (2019), [15 pp]$$pPLoS Comput. Biol.$$tPLoS computational biology$$x1553-7358
000079846 8564_ $$s2382175$$uhttps://zaguan.unizar.es/record/79846/files/texto_completo.pdf$$yVersión publicada
000079846 8564_ $$s108706$$uhttps://zaguan.unizar.es/record/79846/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000079846 909CO $$ooai:zaguan.unizar.es:79846$$particulos$$pdriver
000079846 951__ $$a2020-07-16-08:46:26
000079846 980__ $$aARTICLE