Zinc-Chelation Contributes to the Anti-Angiogenic Effect of Ellagic Acid on Inhibiting MMP-2 Activity, Cell Migration and Tube Formation

Ellagic acid (EA), a dietary polyphenolic compound, has been demonstrated to exert anti-angiogenic effect but the detailed mechanism is not yet fully understood. The aim of this study was to investigate whether the zinc chelating activity of EA contributed to its anti-angiogenic effect.The matrix metalloproteinases-2 (MMP-2) activity, a zinc-required reaction, was directly inhibited by EA as examined by gelatin zymography, which was reversed dose-dependently by adding zinc chloride. In addition, EA was demonstrated to inhibit the secretion of MMP-2 from human umbilical vein endothelial cells (HUVECs) as analyzed by Western blot method, which was also reversed by the addition of zinc chloride. Reversion-inducing cysteine-rich protein with Kazal motifs (RECK), known to down-regulate the MMP-2 activity, was induced by EA at both the mRNA and protein levels which was correlated well with the inhibition of MMP-2 activity. Interestingly, zinc chloride could also abolish the increase of EA-induced RECK expression. The anti-angiogenic effect of EA was further confirmed to inhibit matrix-induced tube formation of endothelial cells. The migration of endothelial cells as analyzed by transwell filter assay was suppressed markedly by EA dose-dependently as well. Zinc chloride could reverse these two effects of EA also in a dose-dependent manner. Since magnesium chloride or calcium chloride could not reverse the inhibitory effect of EA, zinc was found to be involved in tube formation and migration of vascular endothelial cells.Together these results demonstrated that the zinc chelation of EA is involved in its anti-angiogenic effects by inhibiting MMP-2 activity, tube formation and cell migration of vascular endothelial cells. The role of zinc was confirmed to be important in the process of angiogenesis

Modeling Preferential Recruitment for Respondent-Driven Sampling

Respondent-driven sampling (RDS) is a network sampling methodology used worldwide to sample key populations at high risk for HIV/AIDS who often practice stigmatized/illegal behaviors and are not typically reachable by conventional sampling techniques. In RDS, study participants recruit their peers to enroll, resulting in a sampling mechanism that is unknown to researchers. Current estimators for RDS data require many assumptions about the sampling process, including that recruiters choose people from their network uniformly at random to participate in the study. However, this is likely not true in practice. We believe that people recruit based on observable covariates, such as age, frequency of interaction, geography, socioeconomic status, or social capital.To model preferential recruitment, I develop a sequential two-sided rational-choice framework, referred to as the RCPR model. At each wave of recruitment, each recruiter has a utility for selecting each peer, and symmetrically each peer has a utility for being recruited by each recruiter. Each person also has utilities for selecting themself (not recruiting or not participating). People in the network behave in a way that maximizes their utility given the constraints of the network and the restrictions on recruitment. Although a person's utility is not observed, it can be modeled as a linear combination of observable nodal or dyadic covariates plus unobserved pair-specific heterogeneities. This framework allows generative probabilistic network models to be created for the RDS recruitment process. The models can incorporate observable characteristics of the population and have interpretable parameters. It greatly increases the sophistication of the modeling of the RDS sampling mechanism. Inference can be made about the preference coefficients by maximizing the likelihood of the observed recruitment chain given the observed covariates. As the likelihood is computationally intractable, I develop a Bayesian framework where inference is made feasible by approximating the posterior distribution of the preference coefficients via a Markov chain Monte Carlo algorithm. Each update step samples new values of the preference coefficients and utilities via Metropolis-Hastings, subject to constraints. New prevalence estimates can be calculated be generating many recruitment chains from the population using the RCPR coefficients, then directly obtaining the first-order and second-order inclusion probabilities. This framework allows the incorporation of covariates we think effect recruitment into the sample weights

Modeling Preferential Recruitment for Respondent-Driven Sampling

Respondent-driven sampling (RDS) is a network sampling methodology used worldwide to sample key populations at high risk for HIV/AIDS who often practice stigmatized/illegal behaviors and are not typically reachable by conventional sampling techniques. In RDS, study participants recruit their peers to enroll, resulting in a sampling mechanism that is unknown to researchers. Current estimators for RDS data require many assumptions about the sampling process, including that recruiters choose people from their network uniformly at random to participate in the study. However, this is likely not true in practice. We believe that people recruit based on observable covariates, such as age, frequency of interaction, geography, socioeconomic status, or social capital.To model preferential recruitment, I develop a sequential two-sided rational-choice framework, referred to as the RCPR model. At each wave of recruitment, each recruiter has a utility for selecting each peer, and symmetrically each peer has a utility for being recruited by each recruiter. Each person also has utilities for selecting themself (not recruiting or not participating). People in the network behave in a way that maximizes their utility given the constraints of the network and the restrictions on recruitment. Although a person's utility is not observed, it can be modeled as a linear combination of observable nodal or dyadic covariates plus unobserved pair-specific heterogeneities. This framework allows generative probabilistic network models to be created for the RDS recruitment process. The models can incorporate observable characteristics of the population and have interpretable parameters. It greatly increases the sophistication of the modeling of the RDS sampling mechanism. Inference can be made about the preference coefficients by maximizing the likelihood of the observed recruitment chain given the observed covariates. As the likelihood is computationally intractable, I develop a Bayesian framework where inference is made feasible by approximating the posterior distribution of the preference coefficients via a Markov chain Monte Carlo algorithm. Each update step samples new values of the preference coefficients and utilities via Metropolis-Hastings, subject to constraints. New prevalence estimates can be calculated be generating many recruitment chains from the population using the RCPR coefficients, then directly obtaining the first-order and second-order inclusion probabilities. This framework allows the incorporation of covariates we think effect recruitment into the sample weights

Cognitive Processing Load as a Function of Embedding in Conjoined Cognitions

141 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1973.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Kaposi's Sarcoma-Associated Herpesvirus-Encoded LANA Recruits Topoisomerase IIβ for Latent DNA Replication of the Terminal Repeats

The latency-associated nuclear antigen (LANA) encoded by Kaposi's sarcoma-associated herpesvirus (KSHV) plays a major role in maintaining latency and is critical for the perpetual segregation of viral episomes to the progeny nuclei of newly divided cells. LANA binds to KSHV terminal repeat (TR) DNA and tethers the viral episomes to host chromosomes through the association of chromatin-bound cellular proteins. TR elements serve as potential origin sites of KSHV replication and have been shown to play important roles in latent DNA replication and transcription of adjacent genes. Affinity chromatography and proteomics analysis using KSHV TR DNA and the LANA binding site as the affinity column identified topoisomerase IIβ (TopoIIβ) as a LANA-interacting protein. Here, we show that TopoIIβ forms complexes with LANA that colocalize as punctuate bodies in the nucleus of KSHV-infected cells. The specific TopoIIβ binding region of LANA has been identified to its N terminus and the first 32 amino acid residues containing the nucleosome-binding region crucial for binding. Moreover, this region could also act as a dominant negative to disrupt association of TopoIIβ with LANA. TopoIIβ plays an important role in LANA-dependent latent DNA replication, as addition of ellipticine, a selective inhibitor of TopoII, negatively regulated replication mediated by the TR. DNA break labeling and chromatin immunoprecipitation assay using biotin-16-dUTP and terminal deoxynucleotide transferase showed that TopoIIβ mediates a transient DNA break on viral DNA. These studies confirm that LANA recruits TopoIIβ at the origins of latent replication to unwind the DNA for replication