靶向斑马鱼VEGF的shRNA表达载体的构建及其对斑马鱼胚胎血管发育的影响

血管内皮生长因子(vascular endothelial growth factor, VEGF)是一种多功能的细胞因子，其主要作用是促进血管内皮细胞增殖和增加血管通透性，是肿瘤及正常组织血管生成的中心调控因素，以VEGF为靶点的肿瘤血管靶向性治疗成为近几年肿瘤治疗的新途径。RNAi是近年来新发展的一项反向遗传学技术，是一种研究基因功能的有力工具。斑马鱼作为一种重要的模式生物，被广泛用于胚胎的分子发育机制、疾病模型的构建以及药物筛选等研究中。然而在斑马鱼中运用RNAi技术进行基因功能研究是一个相对较新的领域，研究资料较少，并且目前进行的斑马鱼RNAi实验中，siRNA大都是通过化学方法或体外转录合成的。体外合成的siRNA在进入体内后会被降解而无法达到持久阻抑基因表达的目的。因此本研究旨在探讨VEGF特异性siRNA表达载体对斑马鱼VEGF基因的沉默作用，通过分析表型及相关细胞因子的变化，阐明VEGF对斑马鱼胚胎血管生成的影响及作用机制。 研究通过计算机辅助设计软件，针对斑马鱼VEGF mRNA不同位点设计合成了4段含siRNA特异序列的DNA单链，经退火，克隆入pSilencer 4.1-CMV neo载体CMV启动子下游，构建了重组质粒pS1-VEGF、pS2-VEGF、pS3-VEGF及pS4-VEGF。 通过显微注射的方法将载体导入1-2细胞期斑马鱼体内，于胚胎发育的48 h采用RT-PCR的方法检测VEGF基因的表达量，研究不同干扰序列对VEGF基因表达的干涉作用。结果显示，针对不同位点的表达载体对VEGF基因表达的抑制效率有显著差异。它们对VEGF mRNA的抑制率分别为80.5%，42.8%，12.5%，40.7%。通过筛选我们得到了一条具有高效抑制作用的载体pS1-VEGF，该载体的相应序列靶向斑马鱼两个主要异构体VEGF165和VEGF121的共有外显子序列。 形态学检测结果显示，注射了pS1-VEGF的胚胎出现了心包膜水肿、血流速度减慢、循环红细胞堆积等症状。定量碱性磷酸酶染色显示，注射pS1-VEGF能够抑制斑马鱼胚胎新生血管的形成，当注射剂量为0.4 ng时，血管生成的抑制率为31.8%。NBT/BCIP血管染色显示，注射该载体后72 h，50%的斑马鱼肠下静脉、节间血管以及其它血管的发育受到不同程度的抑制。随着注射剂量的加大，血管发育受抑制的情况也随之加重，当注射剂量为1 ng时，只有心脏、头部及卵黄有血液循环。对干扰效果的特异性进行了研究，结果表明pS1-VEGF对斑马鱼内源基因胸苷酸合成酶（thymidylate synthase, TS）基因的表达没有明显的抑制作用。针对TS基因的shRNA表达载体及与斑马鱼没有同源性的对照载体对VEGF基因表达也没有明显的抑制作用。浓度梯度实验表明在0-1.2 ng的范围内干扰效果具有剂量依赖性。 以胚胎整体原位杂交的方法检测质粒对VEGF基因受体NRP1基因表达的影响，发现VEGF特异性shRNA表达载体能够引起NRP1基因表达的降低，说明斑马鱼中VEGF所介导的血管生成作用至少在部分上是依赖于NRP通路所调节的。 本研究工作为进一步研究斑马鱼基因功能、VEGF调控网络提供了一个快速、有效的手段，为阐明斑马鱼的血管生成机制提供了新的资料，为采用RNAi技术，以VEGF为靶点，以斑马鱼为模型对肿瘤进行基因治疗研究奠定了基础。

Strand Analysis, a free online program for the computational identification of the best RNA interference (RNAi) targets based on Gibbs free energy

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Emerging trends at the interface of chemistry and biology: Applications to the design of human therapeutics

This article describes recent developments in the design and implementation of various strategies towards the development of novel therapeutics using first principles from biology and chemistry. Strategies for multi-target therapeutics and network analysis with a focus on cancer and HIV are discussed. Methods for gene and siRNA delivery are presented along with challenges and opportunities for siRNA therapeutics. Advances in protein design methodology and screening are described, with a focus on their application to the design of antibody based therapeutics. Future advances in this area relevant to vaccine design are also mentioned.

Soft Computing Approach for Optimization of siRNA Efficiency Prediction for Post-transcriptional Gene Silencing

Post-transcriptional gene silencing by RNA interference is mediated by small interfering RNA called siRNA. This gene silencing mechanism can be exploited therapeutically to a wide variety of disease-associated targets, especially in AIDS, neurodegenerative diseases, cholesterol and cancer on mice with the hope of extending these approaches to treat humans. Over the recent past, a significant amount of work has been undertaken to understand the gene silencing mediated by exogenous siRNA. The design of efficient exogenous siRNA sequences is challenging because of many issues related to siRNA. While designing efficient siRNA, target mRNAs must be selected such that their corresponding siRNAs are likely to be efficient against that target and unlikely to accidentally silence other transcripts due to sequence similarity. So before doing gene silencing by siRNAs, it is essential to analyze their off-target effects in addition to their inhibition efficiency against a particular target. Hence designing exogenous siRNA with good knock-down efficiency and target specificity is an area of concern to be addressed. Some methods have been developed already by considering both inhibition efficiency and off-target possibility of siRNA against agene. Out of these methods, only a few have achieved good inhibition efficiency, specificity and sensitivity. The main focus of this thesis is to develop computational methods to optimize the efficiency of siRNA in terms of “inhibition capacity and off-target possibility” against target mRNAs with improved efficacy, which may be useful in the area of gene silencing and drug design for tumor development. This study aims to investigate the currently available siRNA prediction approaches and to devise a better computational approach to tackle the problem of siRNA efficacy by inhibition capacity and off-target possibility. The strength and limitations of the available approaches are investigated and taken into consideration for making improved solution. Thus the approaches proposed in this study extend some of the good scoring previous state of the art techniques by incorporating machine learning and statistical approaches and thermodynamic features like whole stacking energy to improve the prediction accuracy, inhibition efficiency, sensitivity and specificity. Here, we propose one Support Vector Machine (SVM) model, and two Artificial Neural Network (ANN) models for siRNA efficiency prediction. In SVM model, the classification property is used to classify whether the siRNA is efficient or inefficient in silencing a target gene. The first ANNmodel, named siRNA Designer, is used for optimizing the inhibition efficiency of siRNA against target genes. The second ANN model, named Optimized siRNA Designer, OpsiD, produces efficient siRNAs with high inhibition efficiency to degrade target genes with improved sensitivity-specificity, and identifies the off-target knockdown possibility of siRNA against non-target genes. The models are trained and tested against a large data set of siRNA sequences. The validations are conducted using Pearson Correlation Coefficient, Mathews Correlation Coefficient, Receiver Operating Characteristic analysis, Accuracy of prediction, Sensitivity and Specificity. It is found that the approach, OpsiD, is capable of predicting the inhibition capacity of siRNA against a target mRNA with improved results over the state of the art techniques. Also we are able to understand the influence of whole stacking energy on efficiency of siRNA. The model is further improved by including the ability to identify the “off-target possibility” of predicted siRNA on non-target genes. Thus the proposed model, OpsiD, can predict optimized siRNA by considering both “inhibition efficiency on target genes and off-target possibility on non-target genes”, with improved inhibition efficiency, specificity and sensitivity. Since we have taken efforts to optimize the siRNA efficacy in terms of “inhibition efficiency and offtarget possibility”, we hope that the risk of “off-target effect” while doing gene silencing in various bioinformatics fields can be overcome to a great extent. These findings may provide new insights into cancer diagnosis, prognosis and therapy by gene silencing. The approach may be found useful for designing exogenous siRNA for therapeutic applications and gene silencing techniques in different areas of bioinformatics.

Structure, dynamics, and energetics of siRNA-cationic vector complexation:a molecular dynamics study

The design and synthesis of safe and efficient nonviral vectors for gene delivery has attracted significant attention in recent years. Previous experiments have revealed that the charge density of a polycation (the carrier) plays a crucial role in complexation and the release of the gene from the complex in the cytosol. In this work, we adopt an atomistic molecular dynamics simulation approach to study the complexation of short strand duplex RNA with six cationic carrier systems of varying charge and surface topology. The simulations reveal detailed molecular-level pictures of the structures and dynamics of the RNA-polycation complexes. Estimates for the binding free energy indicate that electrostatic contributions are dominant followed by van der Waals interactions. The binding free energy between the 8(+)polymers and the RNA is found to be larger than that of the 4(+)polymers, in general agreement with previously published data. Because reliable binding free energies provide an effective index of the ability of the polycationic carrier to bind the nucleic acid and also carry implications for the process of gene release within the cytosol, these novel simulations have the potential to provide us with a much better understanding of key mechanistic aspects of gene-polycation complexation and thereby advance the rational design of nonviral gene delivery systems.

Structure and dynamics of multiple cationic vectors-siRNA complexation by all-atomic molecular dynamics simulations

Understanding the molecular mechanism of gene condensation is a key component to rationalizing gene delivery phenomena, including functional properties such as the stability of the gene-vector complex and the intracellular release of the gene. In this work, we adopt an atomistic molecular dynamics simulation approach to study the complexation of short strand duplex RNA with four cationic carrier systems of varying charge and surface topology at different charge ratios. At lower charge ratios, polymers bind quite effectively to siRNA, while at high charge ratios, the complexes are saturated and there are free polymers that are unable to associate with RNA. We also observed reduced fluctuations in RNA structures when complexed with multiple polymers in solution as compared to both free siRNA in water and the single polymer complexes. These novel simulations provide a much better understanding of key mechanistic aspects of gene-polycation complexation and thereby advance progress toward rational design of nonviral gene delivery systems.

The effect of pH on PAMAM dendrimer-siRNA complexation:endosomal considerations as determined by molecular dynamics simulation

Intracellular degradation of genes, most notably within the endo-lysosomal compartment is considered a significant barrier to (non-viral) gene delivery in vivo. Previous reports based on in vitro studies claim that carriers possessing a mixture of primary, secondary and tertiary amines are able to buffer the acidic environment within the endosome, allowing for timely release of their contents, leading to higher transfection rates. In this report, we adopt an atomistic molecular dynamics (MD) simulation approach, comparing the complexation of 21-bp siRNA with low-generation polyamidoamine (PAMAM) dendrimers (G0 and G1) at both neutral and acidic pHs, the latter of which mimics the degradative environment within maturing 'late-endosomes'. Our simulations reveal that the time taken for the dendrimer-gene complex (dendriplex) to reach equilibrium is appreciably longer at low pH and this is accompanied by more compact packaging of the dendriplex, as compared to simulations performed at neutral pH. We also note larger absolute values of calculated binding free energies of the dendriplex at low pH, indicating a higher dendrimer-nucleic acid affinity in comparison with neutral pH. These novel simulations provide a more detailed understanding of low molecular-weight polymer-siRNA behavior, mimicking the endosomal environment and provide input of direct relevance to the "proton sponge theory", thereby advancing the rational design of non-viral gene delivery systems.

Role for cell-to-cell communication in stem cell specification toward pancreatic progenitors: relevance to the design of novel therapies for diabetes.

Thesis (Ph.D.)--University of Washington, 2016-08