Tip cell overtaking occurs as a side effect of sprouting in computational models of angiogenesis

During angiogenesis, endothelial cells compete for the tip position during angiogenesis: a phenomenon named tip cell overtaking. It is still unclear to what extent tip cell overtaking is a side effect of sprouting or to what extent a biological function. To address this question, we studied tip cell overtaking in two existing cellular Potts models of angiogenic sprouting. In these models angiogenic sprouting-like behavior emerges from a small set of plausible cell behaviors and the endothelial cells spontaneously migrate forwards and backwards within sprouts, suggesting that tip cell overtaking might occur as a side effect of sprouting. In accordance with experimental observations, in our simulations the cells' tendency to occupy the tip position can be regulated when two cell lines with different levels of Vegfr2 expression are contributing to sprouting (mosaic sprouting assay), where cell behavior is regulated by a simple VEGF-Dll4-Notch signaling network. Our modeling results suggest that tip cell overtaking occurs spontaneously due to the stochastic motion of cells during sprouting. Thus, tip cell overtaking and sprouting dynamics may be interdependent and should be studied and interpreted in combination. VEGF-Dll4-Notch can regulate the ability of cells to occupy the tip cell position, but only when cells in the simulation strongly differ in their levels of Vegfr2. We propose that VEGF-Dll4-Notch signaling might not regulate which cell ends up at the tip, but assures that the cell that randomly ends up at the tip position acquires the tip cell phenotype.Comment: 20 pages, 6 figures, 4 supplementary figure

The Nuclear Protein Sge1 of Fusarium oxysporum Is Required for Parasitic Growth

Dimorphism or morphogenic conversion is exploited by several pathogenic fungi and is required for tissue invasion and/or survival in the host. We have identified a homolog of a master regulator of this morphological switch in the plant pathogenic fungus Fusarium oxysporum f. sp. lycopersici. This non-dimorphic fungus causes vascular wilt disease in tomato by penetrating the plant roots and colonizing the vascular tissue. Gene knock-out and complementation studies established that the gene for this putative regulator, SGE1 (SIX Gene Expression 1), is essential for pathogenicity. In addition, microscopic analysis using fluorescent proteins revealed that Sge1 is localized in the nucleus, is not required for root colonization and penetration, but is required for parasitic growth. Furthermore, Sge1 is required for expression of genes encoding effectors that are secreted during infection. We propose that Sge1 is required in F. oxysporum and other non-dimorphic (plant) pathogenic fungi for parasitic growth

A local uPAR-plasmin-TGF<i>β</i>1 positive feedback loop in a qualitative computational model of angiogenic sprouting explains the <i>in vitro</i> effect of fibrinogen variants

<div><p>In experimental assays of angiogenesis in three-dimensional fibrin matrices, a temporary scaffold formed during wound healing, the type and composition of fibrin impacts the level of sprouting. More sprouts form on high molecular weight (HMW) than on low molecular weight (LMW) fibrin. It is unclear what mechanisms regulate the number and the positions of the vascular-like structures in cell cultures. To address this question, we propose a mechanistic simulation model of endothelial cell migration and fibrin proteolysis by the plasmin system. The model is a hybrid, cell-based and continuum, computational model based on the cellular Potts model and sets of partial-differential equations. Based on the model results, we propose that a positive feedback mechanism between uPAR, plasmin and transforming growth factor <i>β</i>1 (TGF<i>β</i>1) selects cells in the monolayer for matrix invasion. Invading cells releases TGF<i>β</i>1 from the extracellular matrix through plasmin-mediated fibrin degradation. The activated TGF<i>β</i>1 further stimulates fibrin degradation and keeps proteolysis active as the sprout invades the fibrin matrix. The binding capacity for TGF<i>β</i>1 of LMW is reduced relative to that of HMW. This leads to reduced activation of proteolysis and, consequently, reduced cell ingrowth in LMW fibrin compared to HMW fibrin. Thus our model predicts that endothelial cells in LMW fibrin matrices compared to HMW matrices show reduced sprouting due to a lower bio-availability of TGF<i>β</i>1.</p></div

CPM parameters.

<p>CPM parameters.</p

Overview of the binding and conversion reactions of plasminogen and latent-TGF<i>β</i>1 in relation to fibrin.

<p>Plasminogen (PLG) and latent-TGF<i>β</i>1 (LTGF) do not compete for binding with fibrin, thus fibrin can be unbound (F), bound solely by plasminogen (<i>F</i>_PLG), bound by solely latent-TGF<i>β</i>1 (<i>F</i>_LTGF), or by both (<i>F</i>_PLG,LTGF). Plasminogen reversible binds fibrin (reactions 1A and 1B). Latent-TGF<i>β</i>1 also reversible binds fibrin (reactions 2A, 2B, and 2C). Latent-TGF<i>β</i>1 is released from fibrin by plasmin into the active form (TGF, reactions 3A, 3B, and 3C). Fibrin-bound plasminogen can be converted to fibrin-bound plasmin, either without (<i>F</i>_PLS, reaction 4A) or with (<i>F</i>_{PLS, LTGF}, reaction 4B) co-binding of latent-TGF<i>β</i>1. Reactions 5A and 5B represent fibrinolysis, which can result in the release of latent-TGF<i>β</i>1 (reaction 5B).</p

Model validation experiments.

<p>The sprouting percentage (red curve), the angiogenesis level (blue curve), and the fibrinolysis percentage (green curve), are plotted against changes in (A) the initial concentration of fibrin-bound plasminogen (relative units), (B) the decay rate of uPAR (MCS⁻¹), and (C) the decay rate of PAI-1 (MCS⁻¹). The sprouting percentage is the percentage of simulations (out of a 100 simulations) that have an angiogenesis level larger than zero. The angiogenesis level is a measure that simultaneously reflects sprout depth and sprout count, and the mean angiogenesis level is calculated over all simulations that actually formed sprouts. The fibrinolysis percentage is the percentage of the initial fibrin lattice sites that are invaded by the endothelial cells at MCS 6000. (D) Blocking PAI-1 activity increased endothelial sprouting in 3D fibrin matrices in a biphasic manner. hMVECs were seeded confluently on top of 3D fibrin matrices. Subsequently, the hMVECs were stimulated with the combination of FGF-2/TNF<i>α</i> (bT) with or without 100 U/ml trasylol, 25 ug/ml anti-uPAR antibody H2, control mIgG or anti-PAI-1 antibody MAI-2 (n = 4 independent donors, each in triplicate). 7 days after seeding and stimulation with FGF-2/TNF<i>α</i>, tube length was quantified by using Optimas software and expressed as mm/cm² with error bars expressing standard error of the mean. For statistical analysis a one-way ANOVA with Bonferroni post-hoc test was used. * indicates P < 0.05. Error bars of panels A-C are the standard deviation of 100 runs.</p

TGF<i>β</i>1 experiments.

<p>(A) The sprouting percentage (red curve), the angiogenesis level (blue curve), and the fibrinolysis percentage (green curve), are plotted against changes in the initial concentration of fibrin-bound latent-TGF<i>β</i>1 (relative units). The sprouting percentage is the percentage of simulations (out of a 100 simulations) that have an angiogenesis level larger than zero. The angiogenesis level is a measure that simultaneously reflects sprout depth and sprout count, and the mean angiogenesis level is taken over all simulations that actually formed sprouts. The fibrinolysis percentage is the percentage of the initial fibrin lattice sites that are invaded by the endothelial cells at MCS 6000. (B) Addition of active TGF<i>β</i>1 has a biphasic effect on sprout formation in our model. The sprouting frequency increases for the addition of low doses of TGF<i>β</i>1, but global degradation of the complete endothelial cell monolayer prevents sprout formation at high doses of TGF<i>β</i>1. Error bars are the standard deviation of 100 runs.</p

Spontaneous ‘uPAR-rich’ cell selection in the monolayer by a uPAR-plasmin-TGF<i>β</i>1 positive feedback loop.

<p>All cells in the model (A) express the same level of uPAR (the uPAR concentration in the cells is indicated by the red color) at initialization of a simulation. Local changes in fibrin-cell contact can increase local plasmin concentration (B), resulting in degradation of fibrin and release of active TGF<i>β</i>1 (C). TGF<i>β</i>1 can stimulate uPAR expression (D). The positive feedback loop selects ‘uPAR-rich’ cells in the monolayer (E), with a few cells having high level (red color) and most cells having low levels (blue color).</p