抗rHu-IFN-αMcAb亲和常数的测定

采用ELISA间接法检测抗原－抗体反应系统中游离抗体量的方法，对本实验室制备的5系抗rHu－IFN－αMcAb进行了亲和力测定，获得比较准确的McAb亲和常数。     

不同表达谱芯片分析方法在药效机制研究中的应用

Affymetrix表达谱芯片技术是一种研究基因在不同条件下表达变化的高通量分析技术,当前在深度动态挖掘药物作用机制方面的芯片分析方法的应用研究仍比较少。以药物表达谱芯片数据为研究对象,运用不同的算法对芯片数据进行预处理,使用t检验和基因芯片显著性分析的方法筛选差异表达的基因,利用凝集型层次聚类和CPP-SOM聚类的方法对差异表达的基因进行聚类分析,最后运用两类富集分析工具DAVID和GSEA对药效可能涉及的信号通路、生物学过程进行相关生物学功能的富集。结果表明,RMA算法处理药物芯片数据优于MAS5.0算法和GCRMA算法;CPP-SOM聚类方法挖掘的数据信息更丰富;GSEA富集分析工具更适合用于药效机制的研究。本研究为新药研发提供算法支持。     

Signal enhancement of surface plasmon resonance-based immunoassays for the allergen detection

A surface plasmon resonance (SPR) biosensor was used to determine the recombinant group 1 house dust mite allergen (rDer f1) in both HBS-EP buffer and fetal bovine serum (FBS). The monoclonal antibody was immobilized onto the CM5 sensor chip surface using an amine coupling method. The procedures of antibody immobilization and the subsequent primary and enhanced immunoassay were monitored in real time. The sensitivity for rDer f1 detection was remarkably improved by using intact polyclonal antibody as signal amplifying agent. Using this signal enhanced SPR immunosensor, rDer f1 in HBS-EP buffer and FBS was detected at a concentration of 15.4 and 32.1 ng/ml, respectively. The result demonstrates that SPR biosensor is a simple and reliable method for allergen detection.     

三维微阵列数据的多目标进化聚类

聚类技术广泛应用于微阵列数据分析中。在基因-样本-时间GST微阵列数据矩阵中,挖掘三雏聚类成为当前的热门研究课题。3D聚类过程经常需要对多个相互冲突的目标进行优化,而且进化算法以其强大的探寻能力成为高维搜索空间中非常有效的搜索方法。本文基于多目标进化计算方法提出一个新的3D聚类算法MOE-TC,以挖掘GST数据中的3D聚类。现实微阵列数据上的实验验证结果充分说明了本文算法的有效性。     

Gene subset selection in microarray data using entropic filtering for cancer classification

Abstract: In this work an entropic filtering algorithm (EFA) for feature selection is described, as a workable method to generate a relevant subset of genes. This is a fast feature selection method based on finding feature subsets that jointly maximize the normalized multivariate conditional entropy with respect to the classification ability of tumours. The EFA is tested in combination with several machine learning algorithms on five public domain microarray data sets. It is found that this combination offers subsets yielding similar or much better accuracies than using the full set of genes. The solutions obtained are of comparable quality to previous results, but they are obtained in a maximum of half an hour computing time and use a very low number of genes.     

Proteomics integrated with Escherichia coli vector-based vaccines and antigen microarrays reveals the immunogenicity of a surface sialidase-like protein of Propionibacterium acnes

Proteomics is a powerful tool for the identification of proteins, which provides a basis for rational vaccine design. However, it is still a highly technical and time‐consuming task to examine a protein's immunogenicity utilizing traditional approaches. Here, we present a platform for effectively evaluating protein immunogenicity and antibody detection. A tetanus toxin C fragment (Tet‐c) was used as a representative antigen to establish this platform. A cell wall‐anchoring sialidase‐like protein (SLP) of Propionibacterium acnes was utilized to assess the efficacy of this platform. We constructed an Escherichia coli vector‐based vaccine by overexpressing Tet‐c or SLP in E. coli and utilized an intact particle of E. coli itself as a vaccine (E. coli Tet‐c or SLP vector). After ultraviolet (UV) irradiation, the E. coli vector‐based vaccines were administered intranasally into imprinting control region mice without adding exogenous adjuvants. For antibody detection, we fabricated antigen microarrays by printing with purified recombinant proteins including Tet‐c and SLP. Our results demonstrated that detectable antibodies were elicited in mice 6 weeks after intranasal administration of UV‐irradiated E. coli vector‐based vaccines. The antibody production of Tet‐c and SLP was significantly elevated after boosting. Notably, the platform with main benefits of using E. coli itself as a vaccine carrier provides a critical template for applied proteomics aimed at screening novel vaccine targets. In addition, the novel immunogenic SLP potentially serves as an antigen candidate for the development of vaccines targeting P. acnes‐associated diseases.     

Screening of serum samples from Wegener's granulomatosis patients using antibody microarrays

Wegener's Granulomatosis (WG) is an idiopathic granulomatosis autoimmune vasculitis that primarily affects small vessels and is associated with glomerulonephritis and pulmonary granulomatous vasculitis. Anti‐neutrophil cytoplasmic auto‐antibodies (cANCA) against proteinase‐3 are used to identify WG, but ANCA titers are not present in some patients with the localized disease. The objective of this study was to develop an antibody array to help identify protein expression patterns in serum from patients with WG as compared to normals. The arrays were tested for limits of detection, background, and cross reactivity using standard proteins. The arrays were hybridized with either normal patient serum (n = 30) or with serum samples from a population of WG patients (n = 26) that were age and sex matched. Data analysis and curve fitting of the standard dilution series calculated r² values and determined a sensitivity of <50 pg/mL for the majority of proteins. A total of 24 proteins were assessed. Several statistically significant increases (p<0.05) were seen in the expression of: angiotensin converting enzyme‐I, IFN‐γ, IL‐8, s‐ICAM‐1 and s‐VCAM in WG patients as compared to controls. Utilizing the antibody microarray technology has led to the identification of potential biomarkers of vascular injury in the serum of WG patients.     

Proteomic expression profiling of thyroid neoplasms

Thyroid cancer is the most common endocrine neoplasm with multiple histologic subtypes, each associated with different treatments and outcomes. Differentiating benign neoplasms such as follicular adenomas from malignant entities such as follicular carcinomas and papillary carcinoma can be challenging. To define the proteomic profile of different thyroid tumors, we screened an antibody array of 330 features against five thyroid neoplasms: follicular adenoma, follicular carcinoma, papillary carcinoma, anaplastic carcinoma, and medullary carcinoma as well as normal thyroid epithelium. Eight candidate biomarkers; c-erbB-2, Stat5a, Annexin IV, IL-11, RARα, FGF7, Caspase 9, and phospho-c-myc were identified as differentially expressed on the antibody array, and validated with immunohistochemistry on tissue microarrays, with a total of 144 samples of the same variety of thyroid neoplasms. Analysis revealed c-erbB-2, Annexin IV, and Stat5a have potential clinical utility to differentiate follicular adenoma, follicular carcinoma, and papillary carcinoma from each other. By using an antibody array as a discovery platform and a tissue microarray as a first step in validation on a large number of specimens, we have identified new markers that have potential utility in the diagnosis of thyroid neoplasms.     

影响RNA芯片探针杂交效率的因素分析

RNA芯片作为研究ncRNA的主要技术之一,已受到广泛关注。然而因RNA芯片中靶序列的单链特征,使得RNA芯片的探针设计比DNA复杂。本研究对加强型绿色荧光蛋白（EGFP）RNA序列进行覆瓦式探针设计,制作了RNA原位合成芯片,研究探针／靶序列杂交双链△G和Tm、靶序列二级结构以及探针的末端碱基对RNA芯片杂交信号强度的影响。结果表明,探针／靶序列杂交双链△G和Tm相同的探针间的杂交信号存在较大差异,探针／靶序列杂交双链△G和,Tm与RNA芯片杂交信号之间未发现明显的规律性;靶序列二级结构的探针PT-sc参数与芯片杂交信号强度间呈较好的线性关系;探针5’末端碱基组成对芯片杂交信号强度也有影响,5’末端碱基为6／C的探针杂交信号总体上高于其邻近的5’末端碱基为A／T的探针的杂交信号,为RNA芯片的探针筛选提供一定的理论基础。     

基于简单统计排名模型的差异表达基因识别

不同实验条件下差异表达基因(DEGs)的识别是微阵列数据分析的主要目标之一,针对分析结果中具有高排名的基因往往表现出较低差异表达水平的缺点,提出了一种基于简单统计排名模型的差异表达基因识别算法MRP(Matrix rank product)。算法可直接处理基因芯片原始数据,排除了数据预处理方法对算法的干扰;另外,通过对基因芯片数据形成的矩阵进行整体排序计算,得到具有高准确度的差异表达性排名结果。