Amina A. Qutub

Multiscale models of angiogenesis

This article aims to promote integration of angiogenesis-related models, both integration of the work described herein and many other existing and future models. Integration of molecular mechanisms with cell- and organ- level models allows investigators to study angiogenesis from perspectives in time and space that were once unattainable. As new tools develop for the systematic validation, integration, visualization, and adaptation of these models, the field of angiogenesis heralds an era where modeling becomes an essential component of rigorous experimental design and therapeutic advances.

Reactive Oxygen Species Regulate Hypoxia-Inducible Factor 1α Differentially in Cancer and Ischemia

ABSTRACT In exercise, as well as cancer and ischemia, hypoxia-inducible factor 1 (HIF1) transcriptionally activates hundreds of genes vital for cell homeostasis and angiogenesis. While potentially beneficial in ischemia, upregulation of the HIF1 transcription factor has been linked to inflammation, poor prognosis in many cancers, and decreased susceptibility of tumors to radiotherapy and chemotherapy. Considering HIF1s function, HIF1α protein and its hydroxylation cofactors look increasingly attractive as therapeutic targets. Independently, antioxidants have shown promise in lowering the risk of some cancers and improving neurological and cardiac function following ischemia. The mechanism of how different antioxidants and reactive oxygen species influence HIF1α expression has drawn interest and intense debate. Here we present an experimentally based computational model of HIF1α protein degradation that represents how reactive oxygen species and antioxidants likely affect the HIF1 pathway differentially in cancer and ischemia. We use the model to demonstrate effects on HIF1α expression from combined doses of five potential therapeutically targeted compounds (iron, ascorbate, hydrogen peroxide, 2-oxoglutarate, and succinate) influenced by cellular oxidation-reduction and involved in HIF1α hydroxylation. Results justify the hypothesis that reactive oxygen species work by two opposite ways on the HIF1 system. We also show how tumor cells and cells under ischemic conditions would differentially respond to reactive oxygen species via changes to HIF1α expression over the course of hours to days, dependent on extracellular hydrogen peroxide levels and largely independent of initial intracellular levels, during hypoxia.

Elongation, proliferation & migration differentiate endothelial cell phenotypes and determine capillary sprouting

BackgroundAngiogenesis, the growth of capillaries from preexisting blood vessels, has been extensively studied experimentally over the past thirty years. Molecular insights from these studies have lead to therapies for cancer, macular degeneration and ischemia. In parallel, mathematical models of angiogenesis have helped characterize a broader view of capillary network formation and have suggested new directions for experimental pursuit. We developed a computational model that bridges the gap between these two perspectives, and addresses a remaining question in angiogenic sprouting: how do the processes of endothelial cell elongation, migration and proliferation contribute to vessel formation?ResultsWe present a multiscale systems model that closely simulates the mechanisms underlying sprouting at the onset of angiogenesis. Designed by agent-based programming, the model uses logical rules to guide the behavior of individual endothelial cells and segments of cells. The activation, proliferation, and movement of these cells lead to capillary growth in three dimensions. By this means, a novel capillary network emerges out of combinatorially complex interactions of single cells. Rules and parameter ranges are based on literature data on endothelial cell behavior in vitro. The model is designed generally, and will subsequently be applied to represent species-specific, tissue-specific in vitro and in vivo conditions.Initial results predict tip cell activation, stalk cell development and sprout formation as a function of local vascular endothelial growth factor concentrations and the Delta-like 4 Notch ligand, as it might occur in a three-dimensional in vitro setting. Results demonstrate the differential effects of ligand concentrations, cell movement and proliferation on sprouting and directional persistence.ConclusionThis systems biology model offers a paradigm closely related to biological phenomena and highlights previously unexplored interactions of cell elongation, migration and proliferation as a function of ligand concentration, giving insight into key cellular mechanisms driving angiogenesis.

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

A computational model of intracellular oxygen sensing by hypoxia-inducible factor HIF1α

Hypoxia-inducible factor-1, HIF1, transcriptionally activates over 200 genes vital for cell homeostasis and angiogenesis. We developed a computational model to gain a detailed quantitative understanding of how HIF1 acts to sense oxygen and respond to hypoxia. The model consists of kinetic equations describing the intracellular variation of 17 compounds, including HIF1, iron, prolyl hydroxylase, oxygen, ascorbate, 2-oxoglutarate, von Hippel Lindau protein and associated complexes. We tested an existing hypothesis of a switch-like change in HIF1 expression in response to a gradual decrease in O2 concentration. Our model predicts that depending on the molecular environment, such as intracellular iron levels, the hypoxic response varies considerably. We show HIF1-activated cellular responses can be divided into two categories: a steep, switch-like response to O2 and a gradual one. Discovery of this dual response prompted comparison of two therapeutic strategies, ascorbate and iron supplementation, and prolyl hydroxylase targeting, to predict under what microenvironments either effectively increases HIF1α hydroxylation. Results provide crucial insight into the effects of iron and prolyl hydroxylase on oxygen sensing. The model advances quantitative molecular level understanding of HIF1 pathways - an endeavor that will help elucidate the diverse responses to hypoxia found in cancer, ischemia and exercise.

Module-based multiscale simulation of angiogenesis in skeletal muscle

BackgroundMathematical modeling of angiogenesis has been gaining momentum as a means to shed new light on the biological complexity underlying blood vessel growth. A variety of computational models have been developed, each focusing on different aspects of the angiogenesis process and occurring at different biological scales, ranging from the molecular to the tissue levels. Integration of models at different scales is a challenging and currently unsolved problem.ResultsWe present an object-oriented module-based computational integration strategy to build a multiscale model of angiogenesis that links currently available models. As an example case, we use this approach to integrate modules representing microvascular blood flow, oxygen transport, vascular endothelial growth factor transport and endothelial cell behavior (sensing, migration and proliferation). Modeling methodologies in these modules include algebraic equations, partial differential equations and agent-based models with complex logical rules. We apply this integrated model to simulate exercise-induced angiogenesis in skeletal muscle. The simulation results compare capillary growth patterns between different exercise conditions for a single bout of exercise. Results demonstrate how the computational infrastructure can effectively integrate multiple modules by coordinating their connectivity and data exchange. Model parameterization offers simulation flexibility and a platform for performing sensitivity analysis.ConclusionsThis systems biology strategy can be applied to larger scale integration of computational models of angiogenesis in skeletal muscle, or other complex processes in other tissues under physiological and pathological conditions.

Multiplexed In Situ Immunofluorescence Using Dynamic DNA Complexes

Dynamic DNA complexes are a new class of DNA technologies that can be engineered to function as programmable molecular machines,[1] detectors,[2] logic gates,[3] and chemical amplifiers.[4, 5] A unique feature of these devices is that, instead of purely classical hybridization mechanisms, they harness a process called strand displacement to facilitate the exchange of oligonucleotides between different thermodynamically-stable DNA complexes.[6, 7] As a result, adaptive and/or reconfigurable molecular devices can be created that operate through enzyme-free, isothermal chemical reactions between different oligonucleotide complexes. While improved understanding of strand displacement has opened new opportunities to engineer elaborate reaction networks for molecular computing,[8] a number of important biological applications for these devices have also emerged. Dynamic nucleic acid devices have been adapted for multiplexed in situ detection of proteins and mRNA,[9-11] and engineered to function as dynamic therapeutic devices[12] and molecular delivery vehicles.[13] Overall, such advances suggest dynamic oligonucleotide systems can function robustly within complex cellular environments and provide new molecular detection capabilities that are not available using existing nucleotide technologies.

Abnormal expression of FLI1 protein is an adverse prognostic factor in acute myeloid leukemia

Friend leukemia virus integration 1 (FLI1), an Ets transcription factor family member, is linked to acute myelogenous leukemia (AML) by chromosomal events at the FLI1 locus, but the biologic impact of FLI1 expression on AML is unknown. FLI1 protein expression was measured in 511 newly diagnosed AML patients. Expression was similar in peripheral blood (PB) and BM and higher at diagnosis than at relapse (P = .02). Compared with normal CD34(+) cells, expression in AML was above or below normal in 32% and 5% of patients, respectively. Levels were negatively correlated with an antecedent hematologic disorder (P = .002) but not with age or cytogenetics. Mutated NPM1 (P = .0007) or FLT3-ITD (P < .02) had higher expression. FLI1 levels were negatively correlated with 10 of 195 proteins associated with proliferation and stromal interaction, and positively correlated (R > 0.3) with 19 others. The FLI1 level was not predictive of remission attainment, but patients with low or high FLI1 expression had shorter remission duration (22.6 and 40.3 vs 51.1 weeks, respectively; P = .01) and overall survival (45.2 and 35.4 vs 59.4 weeks, respectively; P = .03). High FLI1 levels were adverse in univariate and multivariate analysis. FLI1 expression is frequently abnormal and prognostically adverse in AML. FLI1 and/or its response genes may be therapeutically targetable to interfere with AML cell biology.

Proteomic Profiling Identifies Distinct Protein Patterns in Acute Myelogenous Leukemia CD34+CD38- Stem-Like Cells

Acute myeloid leukemia (AML) is believed to arise from leukemic stem-like cells (LSC) making understanding the biological differences between LSC and normal stem cells (HSC) or common myeloid progenitors (CMP) crucial to understanding AML biology. To determine if protein expression patterns were different in LSC compared to other AML and CD34+ populations, we measured the expression of 121 proteins by Reverse Phase Protein Arrays (RPPA) in 5 purified fractions from AML marrow and blood samples: Bulk (CD3/CD19 depleted), CD34-, CD34+(CMP), CD34+CD38+ and CD34+CD38-(LSC). LSC protein expression differed markedly from Bulk (n=31 cases, 93/121 proteins) and CD34+ cells (n= 30 cases, 88/121 proteins) with 54 proteins being significantly different (31 higher, 23 lower) in LSC than in either Bulk or CD34+ cells. Sixty-seven proteins differed significantly between CD34+ and Bulk blasts (n=69 cases). Protein expression patterns in LSC and CD34+ differed markedly from normal CD34+ cells. LSC were distinct from CD34+ and Bulk cells by principal component and by protein signaling network analysis which confirmed individual protein analysis. Potential targetable submodules in LSC included the proteins PU.1(SP1), P27, Mcl1, HIF1α, cMET, P53, Yap, and phospho-Stats 1, 5 and 6. Protein expression and activation in LSC differs markedly from other blast populations suggesting that studies of AML biology should be performed in LSC.

INTEGRATION OF ANGIOGENESIS MODULES AT MULTIPLE SCALES: FROM MOLECULAR TO TISSUE

Multiscale modeling has emerged as a powerful approach to interpret and capitalize on the biological complexity underlying blood vessel growth. We present a multiscale model of angiogenesis that heralds the start of a large scale initiative to integrate related biological models. The goal of the integrative project is to better understand underlying biological mechanisms from the molecular level up through the organ systems level, and test new therapeutic strategies. Model methodology includes ordinary and partial differential equations, stochastic models, complex logical rules, and agent-based architectures. Current modules represent blood flow, oxygen transport, growth factor distribution and signaling, cell sensing, cell movement and cell proliferation. Challenges of integration lie in connecting modules that are diversely designed, seamlessly coordinating feedback, and representing spatial and time scales from ligand-receptor interactions and intracellular signaling, to cell-level movement and cell-matrix interactions, to vessel branching and capillary network formation, to tissue level characteristics, to organ system response. We briefly introduce the individual modules, discuss our approach to integration, present initial results from the coordination of modules, and propose solutions to some critical issues facing angiogenesis multiscale modeling and integration.