基于关联基因本体论注释的蛋白质相互作用预测

细胞中的生理活动主要是通过蛋白质 - 蛋白质之间的相互作用来调控完成 . 详尽细致的蛋白质 - 蛋白质相互作用网络的解析对于理解细胞中复杂的调控、代谢和信号通路有重要的意义 . 近年来,关于新的蛋白质 - 蛋白质相互作用预测领域进展快速,这里,利用贝叶斯算法结合关联的 GO (Gene Ontology) ,来预测蛋白质的相互作用 . 利用非冗余的蛋白质相互作用数据来观察 GO 对的特性,得到 GO 关联的概率 . 通过阳性的和阴性的标准对照数据证实这个新方法可以很好地区别这两类不同的数据,显示出较好的灵敏度和非常低的假阳性预测率 . 通过与已知的高通量的实验数据比较,这个方法具有灵敏度高、速度快的优点 . 而且,运用这个新方法可以提供一些新的关于细胞内蛋白质之间相互作用的信息,为进一步的实验提供理论依据 .     

Predicting protein subnuclear localization using GO-amino-acid composition features

The nucleus guides life processes of cells. Many of the nuclear proteins participating in the life processes tend to concentrate on subnuclear compartments. The subnuclear localization of nuclear proteins is hence important for deeply understanding the construction and functions of the nucleus. Recently, Gene Ontology (GO) annotation has been used for prediction of subnuclear localization. However, the effective use of GO terms in solving sequence-based prediction problems remains challenging, especially when query protein sequences have no accession number or annotated GO term. This study obtains homologies of query proteins with known accession numbers using BLAST to retrieve GO terms for sequence-based subnuclear localization prediction. A prediction method PGAC, which involves mining informative GO terms associated with amino acid composition features, is proposed to design a support vector machine-based classifier. PGAC yields 55 informative GO terms with training and test accuracies of 85.7% and 76.3%, respectively, using a data set SNL_35 (561 proteins in 9 localizations) with 35% sequence identity. Upon comparison with Nuc-PLoc, which combines amphiphilic pseudo amino acid composition of a protein with its position-specific scoring matrix, PGAC using the data set SNL_80 yields a leave-one-out cross-validation accuracy of 81.1%, which is better than that of Nuc-PLoc, 67.4%. Experimental results show that the set of informative GO terms are effective features for protein subnuclear localization. The prediction server based on PGAC has been implemented at http://iclab.life.nctu.edu.tw/prolocgac.     

O-Glycomic and Proteomic Signatures of Spontaneous and Butyrate-Stimulated Colorectal Cancer Cell Line Differentiation

Gut microbiota of the gastrointestinal tract provide health benefits to the human host via bacterial metabolites. Bacterial butyrate has beneficial effects on intestinal homeostasis and is the preferred energy source of intestinal epithelial cells, capable of inducing differentiation. It was previously observed that changes in the expression of specific proteins as well as protein glycosylation occur with differentiation. In this study, specific mucin O-glycans were identified that mark butyrate-induced epithelial differentiation of the intestinal cell line CaCo-2 (Cancer Coli-2), by applying porous graphitized carbon nano–liquid chromatography with electrospray ionization tandem mass spectrometry. Moreover, a quantitative proteomic approach was used to decipher changes in the cell proteome. It was found that the fully differentiated butyrate-stimulated cells are characterized by a higher expression of sialylated O-glycan structures, whereas fucosylation is downregulated with differentiation. By performing an integrative approach, we generated hypotheses about the origin of the observed O-glycome changes. These insights pave the way for future endeavors to study the dynamic O-glycosylation patterns in the gut, either produced via cellular biosynthesis or through the action of bacterial glycosidases as well as the functional role of these patterns in homeostasis and dysbiosis at the gut–microbiota interface.     

Altered levels of focal adhesion and extracellular matrix-receptor interacting proteins were identified in Hailey-Hailey disease by quantitative iTRAQ proteome analysis

Benign chronic familial pemphigus or Hailey-Hailey disease (HHD, OMIM 169600) is a rare, autosomal dominant blistering skin disorder characterized by suprabasal cell separation (acantholysis) of the epidermis. To date, the proteomic changes in skin lesions from HHD patients has not been reported yet. In this study, a sample of skin lesions from HHD patients was collected for isobaric tags for relative and absolute quantitation to analyze proteome changes compared with unaffected individuals. The 134 differentially expressed proteins were assigned to at least one Gene Ontology term, and 123 annotated proteins with significant matches were assigned to 187 known metabolic or signaling pathways listed in the Kyoto Encyclopedia of Genes and Genomes. Most of the altered proteins in skin lesions of HHD patients were enriched in pathways involved in the PI3K-Akt signaling, focal adhesion, extracellular matrix (ECM)-receptor interaction, and protein digestion and absorption, such as collagen family members, microfibril-associated glycoprotein 4 and plakophilin. The changes of proteins related to cell adhesion, ECM-receptor interaction, and protein folding and glycosylation suggested that strategy targeted to alter cell junction and extracellular microenvironment might provide a potential treatment for HHD.     

Protein–protein interaction and SNP analysis in intraductal papillary mucinous neoplasm

Intraductal papillary mucinous neoplasm (IPMN) is a type of tumor that grows within the pancreatic ducts. It is a progress from hyperplasia to intraductal adenoma (IPMA), to noninvasive carcinoma, and ultimately to invasive carcinoma (IPMC). The objective of this study was to explore the molecular mechanism of the progression from IPMA to IPMC. By using the GSE19650 affymetrix microarray data accessible from Gene Expression Omnibus (GEO) database, we first identified the differentially expressed genes (DEGs) between IPMA and IPMC, followed by the protein–protein interaction and single-nucleotide polymorphism (SNP) analysis of the DEGs. Our study identified thousands of DEGs which involved regulation of cell cycle and apoptosis in this progression from IPMA to IPMC. Protein–protein interaction network construction found that MYC, IL6ST, NR3C1, CREBBP, GATA1 and LRP1 might play an important role in the progression. Furthermore, the SNP analysis confirmed the association between BRAC1 and pancreas cancer. In conclusion, our data provide a comprehensive bioinformatics analysis of genes and pathways which may be involved in the progression of IPMN from IPMA to IPMC.