PARP-1 and Ku compete for repair of DNA double strand breaks by distinct NHEJ pathways

Poly(ADP-ribose)polymerase 1 (PARP-1) recognizes DNA strand interruptions in vivo and triggers its own modification as well as that of other proteins by the sequential addition of ADP-ribose to form polymers. This modification causes a release of PARP-1 from DNA ends and initiates a variety of responses including DNA repair. While PARP-1 has been firmly implicated in base excision and single strand break repair, its role in the repair of DNA double strand breaks (DSBs) remains unclear. Here, we show that PARP-1, probably together with DNA ligase III, operates in an alternative pathway of non-homologous end joining (NHEJ) that functions as backup to the classical pathway of NHEJ that utilizes DNA-PKcs, Ku, DNA ligase IV, XRCC4, XLF/Cernunnos and Artemis. PARP-1 binds to DNA ends in direct competition with Ku. However, in irradiated cells the higher affinity of Ku for DSBs and an excessive number of other forms of competing DNA lesions limit its contribution to DSB repair. When essential components of the classical pathway of NHEJ are absent, PARP-1 is recruited for DSB repair, particularly in the absence of Ku and non-DSB lesions. This form of DSB repair is sensitive to PARP-1 inhibitors. The results define the function of PARP-1 in DSB repair and characterize a candidate pathway responsible for joining errors causing genomic instability and cancer

Hollow mesoporous silica nanoparticles for intracellular delivery of fluorescent dye

In this study, hollow mesoporous silica nanoparticles (HMSNs) were synthesized using the sol-gel/emulsion approach and its potential application in drug delivery was assessed. The HMSNs were characterized, by transmission electron microscopy (TEM), Scanning Electron Microscopy (SEM), nitrogen adsorption/desorption and Brunauer-Emmett-Teller (BET), to have a mesoporous layer on its surface, with an average pore diameter of about 2 nm and a surface area of 880 m2/g. Fluorescein isothiocyanate (FITC) loaded into these HMSNs was used as a model platform to assess its efficacy as a drug delivery tool. Its release kinetic study revealed a sequential release of FITC from the HMSNs for over a period of one week when soaked in inorganic solution, while a burst release kinetic of the dye was observed just within a few hours of soaking in organic solution. These FITC-loaded HMSNs was also found capable to be internalized by live human cervical cancer cells (HeLa), wherein it was quickly released into the cytoplasm within a short period of time after intracellular uptake. We envision that these HMSNs, with large pores and high efficacy to adsorb chemicals such as the fluorescent dye FITC, could serve as a delivery vehicle for controlled release of chemicals administered into live cells, opening potential to a diverse range of applications including drug storage and release as well as metabolic manipulation of cells

Robust Statistical Approaches Dealing with High-Dimensional Observational Data

The theme of this dissertation is to develop robust statistical approaches for the high-dimensional observational data. The development of technology makes data sets more accessible than any other time in history. Abundant data leads to numerous appealing findings and at the same time, requires more thoughtful efforts. We are encountered many obstacles when dealing with high-dimensional data. Heterogeneity and complex interaction structure rule out the traditional mean regression method and expect a novel approach to circumvent the complexity and obtain significant conclusions. Missing data mechanism in high-dimensional data is complicated and is hard to manage with existing methods. This dissertation contains three parts to tackle these obstacles: (1) a tree-based method integrated with the domain knowledge to improve prediction accuracy; (2) a tree-based method with linear splits to accommodate the large-scale and highly correlated data set; (3) an integrative analysis method to reduce the dimension and impute the block-wise missing data simultaneously. In the first part of the dissertation, we propose a tree-based method called conditional quantile random forest (CQRF) to improve the screening and intervention of the onset of mentor disorder incorporating with rich and comprehensive electronic medical records (EMR). Our research is motivated by the REactions to Acute Care and Hospitalization (REACH) study, which is an ongoing prospective observational cohort study of the patient with symptoms of a suspected acute coronary syndrome (ACS). We aim to develop a robust and effective statistical prediction method. The proposed approach fully takes the population heterogeneity into account. We partition the sample space guided by quantile regression over the entire quantile process. The proposed CQRF can provide a more comprehensive and accurate prediction. We also provide theoretical justification for the estimate quantile process. In the second part of the dissertation, we apply the proposed CQRF to REACH data set. The predictive analysis derived by the proposed approach shows that for both entire samples and high-risk group, the proposed CQRF provides more accurate predictions compared with other existing and widely used methods. The variable importance scores give a promising result based on the proposed CQRF that the proposed importance scores identify two variables which have been proved to be critical features by the qualitative study. We also apply the proposed CQRF to Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study data set. We show that the proposed approach improves the personalized medicine recommendation compared with existing treatment recommendation method. We also conduct two simulation studies based on the two real data sets. Both simulation studies validate the consistent property of the estimated quantile process. In the second part, we also extend the proposed CQRF with univariate splits to linear splits to accommodate a large number of highly correlated variables. Gene-environment interaction is a widely concerned topic since the traits of complex disease is always difficult to understand, and we are eager to find interventions tailored to individual genetic variations. The proposed approach is applied to a Breast Cancer Family Registry (BCFR) study data set with body mass index (BMI) as the response variable, several nutrition intake factors, and genotype variables. We aim to figure out what kind of genetic variations affect the heterogeneous effect of the environmental factors on BMI. We devise a criterion which measures the relationship between the response variable and gene variants conditioning on the environmental factor to determine the optimal linear combination split. The variable importance score is also calculated by summing up the criterion across all splits in the random forest. We show in the results that top-ranked genes prioritized by the proposed importance scores make the effect of the environmental factors on BMI differently. In the third part, we introduce an integrative analysis approach called generalized integrative principal component analysis (GIPCA). The heterogeneous data types and the presence of block-wise missing data pose significant challenges to the integration of multi-source data and further statistical analyses. There is not literature can easily accommodate data of multiple types with block-wise missing structure. The proposed GIPCA is a low-rank method which conducts the dimension reduction and imputation of block-wise missing data simultaneously to data with multiple types. Both simulation study and real data analysis show that the proposed approach achieves good missing data imputation accuracy and identifies some meaningful signals

Wideband sonar imaging via compressed sensing

Conference Name:OCEANS 2014 - Taipei. Conference Address: Taipei, Taiwan. Time:April 7, 2014 - April 10, 2014.In order to solve the contradiction of high azimuth resolution and wide swath in sonar and radar imaging, narrowband compressed sensing (CS) imaging has been studied in depth. This paper studies the wideband sonar imaging method via CS. Wideband CS imaging model is constructed. Scaling and Doppler frequency shift are considered in the model. The effect of wideband signal to traditional narrowband model is analyzed. Wideband CS imaging method is constructed. A modified 1-norm minimization algorithm is proposed. It is shown that it is possible to apply CS to wideband sonar imaging. Numerical experiments are conducted and demonstrates the analysis

Structural and molecular basis for foot-and-mouth disease virus neutralization by two potent protective antibodies

Foot-and-mouth disease (FMD) is an economically devastating and highly contagious viral disease of cloven-hoofed animals with a global distribution. The causative agent, FMD virus (FMDV) is a small non-enveloped RNA virus, belonging to the Aphthoviruses genus within Picornaviridae family (Tuthill et al., 2010). Control of FMD has been largely reliant on vaccinations with inactivated virus vaccines. However, significant antigenic diversity within FMDV serotypes and inability of the vaccines to induce immune protection for a long duration of time impinge on the efficacy of available vaccines. The roles of neutralizing antibodies (NAbs) as the principal protective components of the immune responses to FMDV vaccination or infection have been well established (Pay and Hingley, 1987; Juleff et al., 2009). Passive immunization of NAbs has also been demonstrated to be effective in curing FMD and many viral diseases (Harmsen et al., 2007; Qiu et al., 2018). A deep understanding of the molecular basis for viral neutralization by antibodies and the identification of key viral epitopes would aid in the development of potent rationally designed broad-spectrum vaccine