表达蛋白质质谱分析

对基因编码的蛋白质进行系统分析可以为注释基因组信息和研究疾病发生机理提供参考.质谱因其高通量、高灵敏度和高精度等特点成为蛋白质表达谱研究的核心技术.过去10年,质谱技术的发展大大促进了蛋白质表达谱的研究.本文综述了蛋白质表达谱的定性和定量研究进展,并展望了进一步的研究方向.     

硒蛋白的分子生物学及与疾病的关系*

硒蛋白是微量元素硒在体内存在和发挥生物功能的主要形式。因硒蛋白的活性中心硒代半胱氨酸由传统终止码TGA编码,故从基因组中预测硒蛋白以及用基因工程技术表达硒蛋白均很困难。有关硒抗氧化、对癌症、神经退行性疾病和病毒作用的报导较多,但结论并不一致。本文综述了硒蛋白基因预测、蛋白质表达调控以及硒和硒蛋白对癌症、神经退行性疾病和病毒的作用及机制等方面的近期进展,研究提高硒蛋白生物信息学预测准确率和基因工程表达量的方法,分析了解硒蛋白与疾病发生发展的关系和机制,探索不同硒蛋白作为预防药物开发、作为癌症治疗和药物筛选靶标的可能性。     

检测核酸表观遗传修饰的测序方法

生物个体中的所有体细胞享有相同的遗传信息,但具有不同的RNA表达亚群和蛋白组,在特定的时间,实际上只有部分基因被表达并执行其功能.近年来,表观遗传学研究的突破在一定程度上帮助人们理解了基因表达的调控. DNA、 RNA和蛋白质这3类生物大分子在合成后都会进行化学修饰,这些修饰几乎涉及所有生物过程的调控.迄今,已经在DNA和RNA中分别鉴定出超过17种和160种化学修饰,对DNA和RNA修饰的各种生物学功能的研究兴趣推动了表观基因组学和表观转录组学前沿领域的发展.开发化学和生物学工具来检测基因组或转录组中的特定修饰是表观基因组学和表观转录组学研究的关键,本文综述了一些常见的核酸修饰的高通量测序方法,提出现有方法中的一些瓶颈以及可能的创新方法.     

不相交主成分分析(PCA)和遗传算法(GA)用于差异表达基因的识别

建立了一种基于不相交主成分分析(Disjoint PCA)和遗传算法(GA)的特征变量选择方法, 并用于从基因表达谱(Gene expression profiles)数据中识别差异表达的基因. 在该方法中, 用不相交主成分分析评估基因组在区分两类不同样品时的区分能力; 用GA寻找区分能力最强的基因组; 所识别基因的偶然相关性用统计方法评估. 由于该方法考虑了基因间的协同作用更接近于基因的生物过程, 从而使所识别的基因具有更好的差异表达能力. 将该方法应用于肝细胞癌(HCC)样品的基因芯片数据分析, 结果表明, 所识别的基因具有较强的区分能力, 优于常用的基因芯片显著性分析(Significance analysis of microarrays, SAM)方法.     

糖苷配合物与DNA作用机理的电化学研究进展

本文综述了糖苷配合物的合成、表征、生物功能及糖化学研究现状，并对糖苷功能配合物与DNA的作用机理及生物功能的电分析化学研究进行了探讨。着重讨论了应用电分析化学、谱学方法、单晶X-射线衍射分析来研究糖苷功能配合物。引用文献39篇。     

基于氨基酸模糊聚类分析的跨膜区域预测

跨膜蛋白在进化过程中，序列保守性较差，即使是同源蛋白序列的一致性程度也较低，因而在跨膜区预测算法中，通过序列的一致性程度来选取训练集并不能有效地消除预测结果对训练集的过度适应性．本文提出了一种基于氨基酸模糊聚类分析的预测算法，通过氨基酸在各个区域分布的相似性程度进行模糊聚类，从而根据一类氨基酸的分布特性而不是各个氨基酸的分布特性进行跨膜区预测．结果表明，该方法能在一定程度上消除训练集的选取对测试结果的影响，提高跨膜蛋白拓扑结构预测的准确度，特别是提高对目前知之甚少的跨膜蛋白的预测准确度．     

差异蛋白质组学在水生动物免疫研究中的应用

差异蛋白质组学是指依据蛋白质样品在不同时期、不同组织、不同状态或不同外界条件下表达不同,来筛选、鉴定蛋白质,可以通过对差异蛋白质的分析鉴定研究编码该差异蛋白的基因,扩展对该基因功能的研究,也可以结合功能蛋白质组学研究,发现新的蛋白质,完善已有的蛋白质组数据库.     

含磷糖生物缀合物的合成

糖生物缀合物具有许多重要的生物功能.因而,建立化学合成各种新型糖类缀合物的方法,进一步研究其结构与生物功能之间的关系,对于探索其生物学功能具有十分重要的理论意义和实际应用价值.本文通过改进的化学方法合成了56种结构新颖的糖基-1-磷酰氨基酸甲酯衍生物,所有目标化合物的结构均被NMR和MS等方法确认,并研究了它们NMR谱学特征和在质谱中的裂解规律.     

基于气相色谱-质谱联用技术的高通量细胞表型分析方法

研究开发了一种基于96孔板培养和气相色谱-质谱联用(GC-MS)技术的高通量细胞表型分析方法。该方法分别以48种物质作为唯一能源对大肠杆菌进行培养,利用GC-MS研究野生型和yfcC基因改造大肠杆菌对各物质的分解代谢情况,实现高通量的细胞表型分析。结果显示,野生型和yfcC基因过表达大肠杆菌对14种物质的代谢能力有显著差异,yfcC基因过表达大肠杆菌对甘氨酸和柠檬酸的代谢能力明显强于野生型大肠杆菌,而对其他物质的代谢能力较弱,我们推测可能是由于yfcC基因促进乙醛酸代谢,导致yfcC过表达菌株对甘氨酸的代谢能力较强;野生型和yfcC基因敲除大肠杆菌间分解有显著差异的共16种物质,其中yfcC基因敲除大肠杆菌对丙氨酸、乳糖、肌醇和柠檬酸的代谢能力较强。该方法简单、高效,可以为未知基因功能研究提供更多代谢功能相关的参考数据。     

低同源性蛋白质结构预测

基因组计划在实施产生了大量的DNA序列信息,如何有效地利用这些信息来研究基因的产物－蛋白质的结构与功能成为引入注目的研究领域,同源蛋白质结构预测及蛋白质折工识别是在基因组水平上进行蛋白质结构预测的有效方法,酵母基因组中约有50％的基因可以通过这类方法来确定其表面产物蛋白质的结构[1],但是目前所采用的方法在低同源性蛋白质的结构预测方面尚存在较大困难。     

NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic similarity

Gene Ontology (GO) provides GO annotations (GOA) that associate gene products with GO terms that summarize their cellular, molecular and functional aspects in the context of biological pathways. GO Consortium (GOC) resorts to various quality assurances to ensure the correctness of annotations. Due to resources limitations, only a small portion of annotations are manually added/checked by GO curators, and a large portion of available annotations are computationally inferred. While computationally inferred annotations provide greater coverage of known genes, they may also introduce annotation errors (noise) that could mislead the interpretation of the gene functions and their roles in cellular and biological processes. In this paper, we investigate how to identify noisy annotations, a rarely addressed problem, and propose a novel approach called NoisyGOA. NoisyGOA first measures taxonomic similarity between ontological terms using the GO hierarchy and semantic similarity between genes. Next, it leverages the taxonomic similarity and semantic similarity to predict noisy annotations. We compare NoisyGOA with other alternative methods on identifying noisy annotations under different simulated cases of noisy annotations, and on archived GO annotations. NoisyGOA achieved higher accuracy than other alternative methods in comparison. These results demonstrated both taxonomic similarity and semantic similarity contribute to the identification of noisy annotations. Our study shows that annotation errors are predictable and removing noisy annotations improves the performance of gene function prediction. This study can prompt the community to study methods for removing inaccurate annotations, a critical step for annotating gene and pathway functions.     

Gene function classification using NCI-60 cell line gene expression profiles

Gene expression patterns from NCI's panel of 60 cell lines were used to train a Neural Network model for classifying genes to pathways. The model assigns probabilities to each gene for each of the 21 modeled pathways assigned by the Kyoto Encyclopedia of Genes and Genomes. Cross-validation of the model showed that 10 of the 21 pathways exhibited good performance in statistical significance and accuracy. The model was designed to output gene probabilities that could be screened for higher probabilities resulting in higher confidence in classification though yielding fewer genes per pathway. The model was deployed on 5798 genes and our approach allowed us to ascertain the most relevant genes above an estimated background. Eight pathways were identified with both good cross-validation and significant numbers above background, TCA Cycle, Oxidative Phosphorylation, Porphyrin Biosynthesis, Ribosome, Polymerases, Proteasome, Cell Cycle, and Cell Adhesion. Gene Ontology (GO) annotation was used for additional validation of gene classification results. A total of 551 GO annotated genes and 468 unannotated genes were classified to the 8 pathways. The primary and secondary classifications of genes revealed known pathway relationships and provide the potential for discovering new pathway relationships.     

PK-means: A new algorithm for gene clustering

Microarray technology has been widely applied in study of measuring gene expression levels for thousands of genes simultaneously. Gene cluster analysis is found useful for discovering the function of gene because co-expressed genes are likely to share the same biological function. K-means is one of well-known clustering methods. However, it is sensitive to the selection of an initial clustering and easily becoming trapped in a local minimum. Particle-pair optimizer (PPO) is a variation on the traditional particle swarm optimization (PSO) algorithm, which is stochastic particle-pair based optimization technique that can be applied to a wide range of problems. In this paper we bridges PPO and K-means within the algorithm PK-means for the first time. Our results indicate that PK-means clustering is generally more accurate than K-means and Fuzzy K-means (FKM). PK-means also has better robustness for it is less sensitive to the initial randomly selected cluster centroids. Finally, our algorithm outperforms these methods with fast convergence rate and low computation load.     

Rival penalized competitive learning (RPCL): a topology-determining algorithm for analyzing gene expression data

DNA arrays have become the immediate choice in the analysis of large-scale expression measurements. Understanding the expression pattern of genes provide functional information on newly identified genes by computational approaches. Gene expression pattern is an indicator of the state of the cell, and abnormal cellular states can be inferred by comparing expression profiles. Since co-regulated genes, and genes involved in a particular pathway, tend to show similar expression patterns, clustering expression patterns has become the natural method of choice to differentiate groups. However, most methods based on cluster analysis suffer from the usual problems (i) dead units, and (ii) the problem of determining the correct number of clusters (k) needed to classify the data. Selecting the k has been an open problem of pattern recognition and statistics for decades. Since clustering reveals similar patterns present in the data, fixing this number strongly influences the quality of the result. While there is no theoretical solution to this problem, the number of clusters can be decided by a heuristic clustering algorithm called rival penalized competitive learning (RPCL). We present a novel implementation of RPCL that transforms the correct number of clusters problem to the tractable problem of clustering based on the degree of similarity. This is biologically significant since our implementation clusters functionally co-regulated genes and genes that present similar patterns of expression. This new approach reveals potential genes that are co-involved in a biological process. This implementation of the RPCL algorithm is useful in differentiating groups involved in concerted functional regulation and helps to progressively home into patterns, which are closely similar.     

Biological fingerprinting analysis of the interactome of a kinase inhibitor in human plasma by a chemiproteomic approach

In this study, a gel free chemiproteomic method based on chromatography was developed and applied for the biological fingerprinting analysis of complex biological system. p-Aminobenzamidine (ABA), an inhibitor of trypsin-like serine proteases, was immobilized for characterizing their interacting proteins in human plasma. By the proteomic analysis method, 214 proteins were identified with obvious affinity to the immobilized ABA. By searching the sequences of above proteins with consensus patterns of the two active sites, seven proteins belong to trypsin-like serine protease group were found. Based on the Gene Ontology annotation, the identified trypsin-like serine proteases have the function of catalytic activity and calcium ion binding, and are mainly involved in the biological process of blood coagulation. Eight more other proteins related to calcium ion binding and blood coagulation were found. Nearly all of these proteins cannot be identified by directly analyzing the plasma sample demonstrating the chemiproteomics a useful approach to characterize interacting proteins in the low abundance range.     

Bioinformatics Assisted Gene Discovery and Annotation of Human Genome

IntroductionBioinformatics is coming with Human GenomeProject(HGP) . With the explosions of sequence,structural and other information available ,bioinformatics is playing an increasingly large rolein the study of fundamental biomedical problems.At present and in the near future,it is the mainchallenge for bioinformatists to make gene dicoveryand annotation of human genome sequences whenthe vast amount of data is produced by the highthroughput genome sequencing in a large scale andautomated m…     

Gene expression profiling of tumor-associated macrophages after exposure to single-dose irradiation

Radiotherapy (RT) is a common cancer treatment approach that accounts for nearly 50% of patient treatment; however, tumor relapse after radiotherapy is still a major issue. To study the crucial role of tumor-associated macrophages (TAMs) in the regulation of tumor progression post-RT, microarray experiments comparing TAM gene expression profiles between unirradiated and irradiated tumors were conducted to discover possible roles of TAMs in initiation or contribution to tumor recurrence following RT, taking into account the relationships among gene expression, tumor microenvironment, and immunology. A single dose of 25 Gy was given to TRAMP C-1 prostate tumors established in C57/B6 mice. CD11b-positive macrophages were extracted from the tumors at one, two and three weeks post-RT. Gene ontology (GO) term analysis using the DAVID database revealed that genes that were differentially expressed at one and two weeks after irradiation were associated with biological processes such as morphogenesis of a branching structure, tube development, and cell proliferation. Analysis using Short Time-Series Expression Miner (STEM) revealed the temporal gene expression profiles and identified 13 significant patterns in four main groups of profiles. The genes in the upregulated temporal profile have diverse functions involved in the intracellular signaling cascade, cell proliferation, and cytokine-mediated signaling pathway. We show that tumor irradiation with a single 25-Gy dose can initiate a time-series of differentially expressed genes in TAMs, which are associated with the immune response, DNA repair, cell cycle arrest, and apoptosis. Our study helps to improve our understanding of the function of the group of genes whose expression changes temporally in an irradiated tumor microenvironment.     

Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data

Gene expression data are characterized by thousands even tens of thousands of measured genes on only a few tissue samples. This can lead either to possible overfitting and dimensional curse or even to a complete failure in analysis of microarray data. Gene selection is an important component for gene expression-based tumor classification systems. In this paper, we develop a hybrid particle swarm optimization (PSO) and tabu search (HPSOTS) approach for gene selection for tumor classification. The incorporation of tabu search (TS) as a local improvement procedure enables the algorithm HPSOTS to overleap local optima and show satisfactory performance. The proposed approach is applied to three different microarray data sets. Moreover, we compare the performance of HPSOTS on these datasets to that of stepwise selection, the pure TS and PSO algorithm. It has been demonstrated that the HPSOTS is a useful tool for gene selection and mining high dimension data.     

Identifying condition specific key genes from basal-like breast cancer gene expression data

Mining patterns of co-expressed genes across the subset of conditions help to narrow down the search space for the analysis of gene expression data. Identifying conditions specific key genes from the large-scale gene expression data is a challenging task. The conditions specific key gene signifies functional behavior of a group of co-expressed genes across the subset of conditions and can be act as biomarkers of the diseases. In this paper, we have propose a novel approach for identification of conditions specific key genes from Basal-Like Breast Cancer (BLBC) disease using biclustering algorithm and Gene Co-expression Network (GCN). The proposed approach is a two-stage approach. In the first stage, significant biclusters have been extracted with the help of ‘runibic’ biclustering algorithm. The second stage identifies conditions specific key genes from the extracted significant biclusters with the help of GCN. By using difference matrix and gene correlation matrix, we have constructed biologically meaningful and statistically strong GCN. Also, presented the proposed approach with the help of a process diagram and demonstrated the procedure with an example of bicluster number 93 (Bic93). From the experimental results, we observed that 95% and 85% of the extracted biclusters are found to be biologically significant at the p-values less than 0.05 and 0.01 respectively. We have compared proposed approach with the Weighted Gene Co-expression Network Analysis (WGCNA) based approach. From the comparison, our approach has performed effectively and extracted biologically significant biclusters. Also, identified conditions specific key genes which cannot be extracted using the WGCNA based approach. Some of the important identified known key genes are PIK3CA, SHC3, ERBB2, SHC4, PTOV1, STAG1, ZNF215 etc. These key genes can be used as a diagnostic and prognostic biomarker for the BLBC disease after the rigorous analysis. The identified conditions specific key genes can be helpful to reduce the analysis time and increase the accuracy of further research such as biomarker identification, drug target discovery etc.     

In situ electroporation of surface-bound siRNAs in microwell arrays

Gene silencing using RNA interference (RNAi) has become a prominent biological tool for gene annotation, pathway analysis, and target discovery in mammalian cells. High-throughput screens conducted using whole-genome siRNA libraries have uncovered rich sets of new genes involved in a variety of biological processes and cellular models of disease. However, high-throughput RNAi screening is not yet a mainstream tool in life science research because current screening platforms are expensive and onerous. Miniaturizing the RNAi screening platform to reduce cost and increase throughput will enable its widespread use and harness its potential for rapid genome annotation. With this aim, we have combined semi-conductor microfabrication and nanolitre dispensing techniques to develop miniaturized electroporation-ready microwell arrays loaded with siRNA molecules in which multiplexed gene knockdown can be achieved. Arrays of microwells are created using high-aspect ratio biocompatible photoresists on optically transparent and conductive Indium-Tin Oxide (ITO) substrates with integrated micro-electrodes to enable in situ electroporation. Non-contact inkjet microarraying allows precise dispensing of nanolitre volumes into the microwell structures. We have achieved parallel electroporation of multiple mammalian cells cultured in these microwell arrays and observed efficient knockdown of genes with surface-bound, printed siRNAs. Further integration of microfabrication and non-contact nanolitre dispensing techniques described here may enable single-substrate whole-genome siRNA screening in mammalian cells.