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1.
iScience ; 26(12): 108491, 2023 Dec 15.
Article in English | MEDLINE | ID: mdl-38094248

ABSTRACT

Foxp3 acetylation is essential to regulatory T (Treg) cell stability and function, but pharmacologically increasing it remains an unmet challenge. Here, we report that small-molecule compounds that inhibit TIP60, an acetyltransferase known to acetylate Foxp3, unexpectedly increase Foxp3 acetylation and Treg induction. Utilizing a dual experimental/computational approach combined with a newly developed FRET-based methodology compatible with flow cytometry to measure Foxp3 acetylation, we unraveled the mechanism of action of these small-molecule compounds in murine and human Treg induction cell cultures. We demonstrate that at low-mid concentrations they activate TIP60 to acetylate P300, a different acetyltransferase, which in turn increases Foxp3 acetylation, thereby enhancing Treg cell induction. These results reveal a potential therapeutic target relevant to autoimmunity and transplant.

2.
Sci Rep ; 12(1): 4237, 2022 03 10.
Article in English | MEDLINE | ID: mdl-35273299

ABSTRACT

The molecular signaling pathways that orchestrate angiogenesis have been widely studied, but the role of biophysical cues has received less attention. Interstitial flow is unavoidable in vivo, and has been shown to dramatically change the neovascular patterns, but the mechanisms by which flow regulates angiogenesis remain poorly understood. Here, we study the complex interactions between interstitial flow and the affinity for matrix binding of different chemokine isoforms. Using a computational model, we find that changing the matrix affinity of the chemokine isoform can invert the effect of interstitial flow on angiogenesis-from preferential growth in the direction of the flow when the chemokine is initially matrix-bound to preferential flow against the flow when it is unbound. Although fluid forces signal endothelial cells directly, our data suggests a mechanism for the inversion based on biotransport arguments only, and offers a potential explanation for experimental results in which interstitial flow produced preferential vessel growth with and against the flow. Our results point to a particularly intricate effect of interstitial flow on angiogenesis in the tumor microenvironment, where the vessel network geometry and the interstitial flow patterns are complex.


Subject(s)
Endothelial Cells , Extracellular Fluid , Extracellular Fluid/physiology , Humans , Morphogenesis , Neovascularization, Pathologic/pathology , Tumor Microenvironment
3.
J R Soc Interface ; 15(146)2018 09.
Article in English | MEDLINE | ID: mdl-30185542

ABSTRACT

Angiogenesis, the growth of capillaries from pre-existing ones, plays a key role in cancer progression. Tumours release tumour angiogenic factors (TAFs) into the extracellular matrix (ECM) that trigger angiogenesis once they reach the vasculature. The neovasculature provides nutrients and oxygen to the tumour. In the ECM, the interstitial fluid moves driven by pressure differences and may affect the distribution of tumour TAFs, and, in turn, tumour vascularization. In this work, we propose a hybrid mathematical model to investigate the influence of fluid flow in tumour angiogenesis. Our model shows the impact of interstitial flow in a time-evolving capillary network using a continuous approach. The flow model is coupled to a model of angiogenesis that includes tip endothelial cells, filopodia, capillaries and TAFs. The TAF transport equation considers not only diffusive mechanisms but also the convective transport produced by interstitial flow. Our simulations predict a significant alteration of the new vascular networks, which tend to grow more prominently against the flow. The model suggests that interstitial flow may produce increased tumour malignancies and hindered treatments.


Subject(s)
Angiogenesis Inducing Agents/metabolism , Extracellular Matrix/metabolism , Models, Biological , Neoplasms/pathology , Neovascularization, Pathologic , Cell Proliferation , Computer Simulation , Disease Progression , Endothelial Cells/pathology , Endothelium, Vascular/pathology , Humans , Neoplasms/metabolism , Oxygen/metabolism , Vascular Endothelial Growth Factor A/metabolism
4.
J R Soc Interface ; 14(126)2017 01.
Article in English | MEDLINE | ID: mdl-28100829

ABSTRACT

Cancerous tumours have the ability to recruit new blood vessels through a process called angiogenesis. By stimulating vascular growth, tumours get connected to the circulatory system, receive nutrients and open a way to colonize distant organs. Tumour-induced vascular networks become unstable in the absence of tumour angiogenic factors (TAFs). They may undergo alternating stages of growth, regression and regrowth. Following a phase-field methodology, we propose a model of tumour angiogenesis that reproduces the aforementioned features and highlights the importance of vascular regression and regrowth. In contrast with previous theories which focus on vessel remodelling due to the absence of flow, we model an alternative regression mechanism based on the dependency of tumour-induced vascular networks on TAFs. The model captures capillaries at full scale, the plastic dynamics of tumour-induced vessel networks at long time scales, and shows the key role played by filopodia during angiogenesis. The predictions of our model are in agreement with in vivo experiments and may prove useful for the design of antiangiogenic therapies.


Subject(s)
Angiogenesis Inducing Agents/metabolism , Models, Biological , Neoplasm Proteins/metabolism , Neoplasms , Neovascularization, Pathologic , Animals , Humans , Neoplasms/blood supply , Neoplasms/metabolism , Neoplasms/pathology , Neovascularization, Pathologic/metabolism , Neovascularization, Pathologic/pathology , Neovascularization, Pathologic/physiopathology
5.
Proc Natl Acad Sci U S A ; 113(48): E7663-E7671, 2016 11 29.
Article in English | MEDLINE | ID: mdl-27856758

ABSTRACT

Recently, mathematical modeling and simulation of diseases and their treatments have enabled the prediction of clinical outcomes and the design of optimal therapies on a personalized (i.e., patient-specific) basis. This new trend in medical research has been termed "predictive medicine." Prostate cancer (PCa) is a major health problem and an ideal candidate to explore tissue-scale, personalized modeling of cancer growth for two main reasons: First, it is a small organ, and, second, tumor growth can be estimated by measuring serum prostate-specific antigen (PSA, a PCa biomarker in blood), which may enable in vivo validation. In this paper, we present a simple continuous model that reproduces the growth patterns of PCa. We use the phase-field method to account for the transformation of healthy cells to cancer cells and use diffusion-reaction equations to compute nutrient consumption and PSA production. To accurately and efficiently compute tumor growth, our simulations leverage isogeometric analysis (IGA). Our model is shown to reproduce a known shape instability from a spheroidal pattern to fingered growth. Results of our computations indicate that such shift is a tumor response to escape starvation, hypoxia, and, eventually, necrosis. Thus, branching enables the tumor to minimize the distance from inner cells to external nutrients, contributing to cancer survival and further development. We have also used our model to perform tissue-scale, personalized simulation of a PCa patient, based on prostatic anatomy extracted from computed tomography images. This simulation shows tumor progression similar to that seen in clinical practice.


Subject(s)
Prostatic Neoplasms/pathology , Cell Proliferation , Humans , Kallikreins/blood , Male , Models, Biological , Prostate-Specific Antigen/blood , Prostatic Neoplasms/blood
6.
PLoS One ; 11(2): e0149422, 2016.
Article in English | MEDLINE | ID: mdl-26891163

ABSTRACT

We present a mathematical model for vascular tumor growth. We use phase fields to model cellular growth and reaction-diffusion equations for the dynamics of angiogenic factors and nutrients. The model naturally predicts the shift from avascular to vascular growth at realistic scales. Our computations indicate that the negative regulation of the Delta-like ligand 4 signaling pathway slows down tumor growth by producing a larger density of non-functional capillaries. Our results show good quantitative agreement with experiments.


Subject(s)
Models, Theoretical , Neoplasms/pathology , Neovascularization, Pathologic , Algorithms , Cell Proliferation , Computer Simulation , Humans , Intracellular Signaling Peptides and Proteins/metabolism , Membrane Proteins/metabolism , Neoplasms/metabolism , Signal Transduction , Tumor Burden
7.
Int J Numer Method Biomed Eng ; 29(10): 1015-37, 2013 Oct.
Article in English | MEDLINE | ID: mdl-23653256

ABSTRACT

Tumor angiogenesis, the growth of new capillaries from preexisting ones promoted by the starvation and hypoxia of cancerous cell, creates complex biological patterns. These patterns are captured by a hybrid model that involves high-order partial differential equations coupled with mobile, agent-based components. The continuous equations of the model rely on the phase-field method to describe the intricate interfaces between the vasculature and the host tissue. The discrete equations are posed on a cellular scale and treat tip endothelial cells as mobile agents. Here, we put the model into a coherent mathematical and algorithmic framework and introduce a numerical method based on isogeometric analysis that couples the discrete and continuous descriptions of the theory. Using our algorithms, we perform numerical simulations that show the development of the vasculature around a tumor. The new method permitted us to perform a parametric study of the model. Furthermore, we investigate different initial configurations to study the growth of the new capillaries. The simulations illustrate the accuracy and efficiency of our numerical method and provide insight into the dynamics of the governing equations as well as into the underlying physical phenomenon.


Subject(s)
Endothelial Cells/pathology , Neoplasms/blood supply , Neovascularization, Pathologic/pathology , Algorithms , Computer Simulation , Humans
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