R-3 Repository :: Browsing by Author "Qutub, Amina"

Browsing by Author "Qutub, Amina"

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Antiangiogenic Factor Receptor PlexinD1 is Required for Proper Formation of the Periocular Vasculature and Establishment of Corneal Avascularity
(2015-08-03) Kwiatkowski, Sam C; Matthews, Kathleen; McNew, James; Stewart, Charles; Qutub, Amina
The cornea is an avascular component of the visual system located in the anterior eye. Avascularity of the cornea is critical for proper vision since the cornea functions by transmitting light into the eye. Impaired vision from loss of avascularity may occur as a result of tissue damage which induces corneal neovascularization from the highly vascularized tissues of the anterior eye. Neovascularization in adult corneas is regulated by secreted pro- and anti-angiogenic factors. These factors function by maintaining corneal avascularity under healthy conditions while permitting neovascularization in damaged corneas. Several pro- and anti-angiogenic factors that function to maintain corneal avascularity during adult life have been identified, however little is known about how pro- and anti-angiogenic factors function to establish avascularity during corneal development. The purpose of this work was to study the role of pro- and anti-angiogenic factors during corneal development. First, the spatial and temporal expression patterns of numerous secreted pro- and anti-angiogenic factors were determined in the anterior eye during avian corneal development using semi-quantitative RT-PCR and RNA in situ hybridization. These techniques were also used to show that known receptors for secreted pro- and anti-angiogenic factors were simultaneously expressed in angioblasts and blood vessels located in the developing anterior eye. These experiments suggested that pro- and anti-angiogenic factor signaling mechanisms may contribute to the patterning of periocular vasculature and establishment of corneal avascularity. Next, I exemplified the role of pro- and anti-angiogenic factors during avian corneal development by using shRNA to knock down the expression of PlexinD1, an antiangiogenic factor receptor expressed in periocular angioblasts and blood vessels. Knockdown of PlexinD1 resulted in multiple patterning defects of the developing periocular vasculature including corneal neovascularization. These phenotypes implicated PlexinD1 as a critical component of the genetic mechanisms that establish corneal avascularity and were suggestive of the role that other pro- and anti-angiogenic factors may play during anterior eye development. These results demonstrate how pro- and antiangiogenic factors are used to simultaneously promote vascularization of the anterior eye and corneal avascularity during development. This information may lead to the creation of novel therapeutic treatments for vascular patterning abnormalities in the anterior eye and corneal neovascularization.
Data-driven Discovery of Proteomic Hallmarks in Acute Myeloid Leukemia
(2018-03-13) Hu, Chenyue; Qutub, Amina
Acute Myeloid Leukemia (AML) poses a unique medical challenge due to its largely unknown risk factors, and heterogeneous response to treatment. While hallmarks of AML cells have been observed qualitatively, and cytogenetics and genetic profiling help to broadly stratify AML patients, the variability inherent in the disease has yet to be well understood and systematically classified. Advances in proteomic techniques in the last decade have generated data with the potential to quantify cancer hallmarks and inform personalized therapy. However, several computational challenges exist when interpreting this data: (1) When applying cluster analysis, a common technique in pattern recognition, the determination of optimal cluster numbers is computationally costly for large datasets, and clustering optimization is often inconvenient to implement. (2) The unique regulation of proteins necessitates the integration of protein functions and protein interactions into the pattern discovery process in order to generate biologically meaningful insights, as opposed to treating proteins as independent entities. The goal of this study is to develop computational tools and paradigms for quantifying proteomic hallmarks in cancer, and to apply such paradigm to identify and characterize proteomic patterns in AML that inform therapy and drug development. To address challenge (1), I first developed a stability-based cluster validation algorithm, Progeny Clustering, which is exceptionally efficient in computing due to its new sampling method to reconstruct cluster identities. The method was shown successful and robust when applied to six datasets, and it was implemented and released as an R package progenyClust. Despite its computational efficiency, Progeny Clustering needs to couple with an existing algorithm for implementation, an inconvenience in practice and a drawback of most validation methods. Therefore, I then designed a new clustering algorithm based on the framework of symmetric non-negative matrix factorization, Shrinkage clustering, that simultaneously finds the optimal number of clusters while partitioning the data. The algorithm was shown to perform with superior speeds and high accuracy across multiple simulated and actual data compared to some commonly used algorithms. To address challenge (2), I developed a multi-layer computational paradigm, meta-Galaxy analysis. In contrast to traditional analysis methods that examine individual proteins and pathways, meta-Galaxy analysis combines individual proteins into groups of functionally related proteins, recognizes the patterns of expression within a functional group, determines constellations of correlated functional patterns and signatures of correlated constellations in order to obtain a cohesive understanding of the proteomic heterogeneities and hallmarks. Applied to the proteomic profiling of 205 AML patients and 111 leukemia cell lines, meta-Galaxy analysis identifies and characterizes 154 functional patterns based on common pathways, 11 constellations correlating functional patterns and 13 signatures that stratify patients' outcome. The proteomic patterns also reveal drastic differences between fresh and cryopreserved samples, limited similarities between primary samples and cell lines, and little overlap between proteomic signatures and cytogenetics and genetic mutations. The findings together provide a knowledge base for proteomic patterns in AML, a guide to leukemia cell line selection, and a broadly applicable computational paradigm for quantifying expression heterogeneities and hallmarks.
Evaluation of Valvular Endothelial Cell Hemostatic Behavior in Native Valves and Novel Co-culture Models
(2014-12-03) Balaoing, Liezl Rae; Grande-Allen, Kathryn J; Moake, Joel L; Kiang, Ching-Hwa; Qutub, Amina; Harrington, Daniel
The endothelial cell-mediated process of hemostasis is critical in all living heart valve tissues. As these tissues undergo changes with age and disease, the ability for valvular endothelial cells (VECs) to manage anti- and pro-thrombotic mechanisms may also change. Furthermore, degeneration- and thrombosis-related failures in artificial valves emphasizes the need to understand the anti-thrombotic mechanisms of VECs in order to develop effective strategies to endothelialize implants and tissue-engineered heart valves. Therefore, a study was performed to evaluate the regulation and function of von Willebrand Factor (VWF), ADAMTS-13 (VWF cleaving enzyme), and other thrombotic and anti-thrombotic mediators secreted from VECs from different aged valves. This work identified age-related differences in VEC hemostatic protein regulation, and an increased capacity of specific proteins to aggregate within regions of elderly valves, which are known to have age-associated loss of extracellular matrix (ECM) organization that are linked to calcific aortic valve disease. With the knowledge that ECM can influence hemostasis, we then studied changes in VEC hemostatic regulation using synthetic culture conditions that modulated substrate stiffness and adhesive ligands. RKRLQVQLSIRT (RKR), a syndecan binding cell adhesive peptide derived from laminin-α1 G-domain, was optimal for promoting strong VEC adhesion and balanced hemostatic function on hydrogel constructs of various stiffness in comparison to the commonly used integrin binding peptide RGDS. Next, to evaluate interactions between valve cells, magnetic levitation technology was used to co-culture VECs with valvular interstitial cells (VICs) in a 3D scaffoldless aortic valve co-culture (AVCC). The cell-based AVCC design allowed for synthesis of multiple constructs within a few hours. AVCCs had regional localization of CD31 positive VECs at construct surface. Cells in the AVCC interior (including VECs) expressed low levels of α-smooth muscle actin (αSMA), suggesting maintenance of quiescent VIC phenotype, but potential endothelial to mesenchymal differentiation in interior-localized VECs. In addition, AVCCs produced ECM and expressed hemostatic proteins such as endothelial nitric oxide synthase (eNOS) and VWF. In light of the VEC localization within the AVCC potentially affecting healthy phenotype, a more physiologically organized and customizable scaffold model was needed for further evaluation of direct interactions between VECs and VICs. Therefore, previous RKR-functionalization work was combined with strategies for VIC encapsulation in biofunctionalized-MMP degradable hydrogels to develop a 3D adhesive ligand localized hydrogel scaffold for an endothelialized aortic valve co-culture model. The resulting hydrogel-based endothelialized aortic valve model (HEAVM) promoted the formation of a stable VEC monolayer at the scaffold surface, and supported the maintenance of VIC quiescent phenotypes within the scaffold, thereby mimicking physiological valve cell organization in aortic valves. Platelet adhesion and nitric oxide functional assays confirmed healthy VEC cell behavior, while immunohistochemistry and qRT-PCR were used to asses VIC and VEC phenotype and extracellular matrix (ECM) production. Overall, by utilizing principles from cell and extracellular matrix biology, biomechanics, and biomaterials, this work was able to improve the understanding of the VEC roles in valve homeostasis and the pathogenesis of valvular disease. Furthermore, new biomaterial-based models were designed to enhance the field’s understanding of VEC functions and communication with VICs. The knowledge learned from these models may be applied to future evaluation of various valve diseases, as well as endothelialization strategies for valve implants.
Evolution-informed modeling improves outcome prediction for cancers
(Wiley, 2016) Liu, Li; Chang, Yung; Yang, Tao; Noren, David P.; Long, Byron; Kornblau, Steven; Qutub, Amina; Ye, Jieping; Bioengineering
Despite wide applications of high-throughput biotechnologies in cancer research, many biomarkers discovered by exploring large-scale omics data do not provide satisfactory performance when used to predict cancer treatment outcomes. This problem is partly due to the overlooking of functional implications of molecular markers. Here, we present a novel computational method that uses evolutionary conservation as prior knowledge to discover bona fide biomarkers. Evolutionary selection at the molecular level is nature's test on functional consequences of genetic elements. By prioritizing genes that show significant statistical association and high functional impact, our new method reduces the chances of including spurious markers in the predictive model. When applied to predicting therapeutic responses for patients with acute myeloid leukemia and to predicting metastasis for patients with prostate cancers, the new method gave rise to evolution-informed models that enjoyed low complexity and high accuracy. The identified genetic markers also have significant implications in tumor progression and embrace potential drug targets. Because evolutionary conservation can be estimated as a gene-specific, position-specific, or allele-specific parameter on the nucleotide level and on the protein level, this new method can be extended to apply to miscellaneous ﾓomicsﾔ data to accelerate biomarker discoveries.
Proteomic Profiling Identifies Distinct Protein Patterns in Acute Myelogenous Leukemia CD34+CD38- Stem-Like Cells
(Public Library of Science, 2013) Kornblau, Steven M.; Qutub, Amina; Yao, Hui; York, Heather; Qiu, Yi Hua; Graber, David; Ravandi, Farhad; Cortes, Jorge; Andreeff, Michael; Zhang, Nianxiang; Coombes, Kevin R.; Bioengineering
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, HIF1?, 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.

Browsing by Author "Qutub, Amina"

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