Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder

Protein–protein interactions (PPIs) play essential roles in many biological processes. In protein–protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (IDPs) with unstable structures can promote the promiscuity of hubs and also involve in many disease pathways, so they also could serve as potential drug targets. Moreover, proteins with similar functions measured by semantic similarity of gene ontology (GO) terms tend to interact with each other. Here, the relationship between hub proteins and drug targets based on GO terms and intrinsic disorder was explored. The semantic similarities of GO terms and genes between two proteins, and the rate of intrinsic disorder residues of each protein were extracted as features to characterize the functional similarity between two interacting proteins. Only using 8 feature variables, prediction models by support vector machine (SVM) were constructed to predict PPIs. The accuracy of the model on the PPI data from human hub proteins is as high as 83.72%, which is very promising compared with other PPI prediction models with hundreds or even thousands of features. Then, 118 of 142 PPIs between hubs are correctly predicted that the two interacting proteins are targets of the same drugs. The results indicate that only 8 functional features are fully efficient for representing PPIs. In order to identify new targets from IDP dataset, the PPIs between hubs and IDPs are predicted by the SVM model and the model yields a prediction accuracy of 75.84%. Further research proves that 3 of 5 PPIs between hubs and IDPs are correctly predicted that the two interacting proteins are targets of the same drugs. All results demonstrate that the model with only 8-dimensional features from GO terms and intrinsic disorder still gives a good performance in predicting PPIs and further identifying drug targets.     

含氟高分子基因载体

阳离子高分子被广泛应用为非病毒类基因载体,但这类高分子材料的转染效率与细胞毒性之间通常存在"恶性"关联,即获得高转染效率时往往会伴随严重的细胞毒性.如何制备兼具高效、低毒特点的高分子载体是成功实施基因治疗的关键.含氟高分子是一类具有独特理化性质的高分子,能够在低电荷密度条件下与核酸形成稳定的复合物,从而实现高效、低毒的基因转染.含氟功能基团可帮助阳离子高分子改善复合物稳定性、细胞内吞、内涵体逃逸、胞内核酸释放等多个环节,从而赋予了含氟高分子在基因递送过程中的氟效应.该专论系统地总结了含氟高分子基因载体的研究,介绍了含氟高分子的基因递送性能、作用机理以及在基因治疗、基因编辑中的应用,并对含氟高分子载体的未来发展进行了展望.     

Bipartite network analysis reveals metabolic gene expression profiles that are highly associated with the clinical outcomes of acute myeloid leukemia

Dysregulated and reprogrammed metabolism are one of the most important characteristics of cancer, and exploiting cancer cell metabolism can aid in understanding the diverse clinical outcomes for patients. To investigate the differences in metabolic pathways among patients with acute myeloid leukemia (AML) and differential survival outcomes, we systematically conducted microarray data analysis of the metabolic gene expression profiles from 384 patients available from the Gene Expression Omnibus and Cancer Genome Atlas databases. Pathway enrichment analysis of differentially expressed genes (DEGs) showed that the metabolic differences between low-risk and high-risk patients mainly existed in two pathways: biosynthesis of unsaturated fatty acids and oxidative phosphorylation. Using the gene-pathway bipartite network, 62 metabolic genes were identified from 272 DEGs involved in 88 metabolic pathways. Based on the expression patterns of the 62 genes, patients with shorter overall survival (OS) durations in the training set (hazard ratio (HR) = 1.58, p = 0.038) and in two test sets (HR = 1.69 and 1.56 and p = 0.089 and 0.029, respectively) were well discriminated by hierarchical clustering analysis. Notably, the expression profiles of ALAS2, BCAT1, BLVRB, and HK3 showed distinct differences between the low-risk and high-risk patients. In addition, models for predicting the OS outcome of AML from the 62 gene signatures achieved improved performance compared with previous studies. In conclusion, our findings reveal significant differences in metabolic processes of patients with AML with diverse survival durations and provide valuable information for clinical translation.     

A novel feature selection for RNA-seq analysis

RNA-seq data are challenging existing omics data analytics for its volume and complexity. Although quite a few computational models were proposed from different standing points to conduct differential expression (D.E.) analysis, almost all these methods do not provide a rigorous feature selection for high-dimensional RNA-seq count data. Instead, most or even all genes are invited into differential calls no matter they have real contributions to data variations or not. Thus, it would inevitably affect the robustness of D.E. analysis and lead to the increase of false positive ratios.In this study, we presented a novel feature selection method: nonnegative singular value approximation (NSVA) to enhance RNA-seq differential expression analysis by taking advantage of RNA-seq count data's non-negativity. As a variance-based feature selection method, it selects genes according to its contribution to the first singular value direction of input data in a data-driven approach. It demonstrates robustness to depth bias and gene length bias in feature selection in comparison with its five peer methods. Combining with state-of-the-art RNA-seq differential expression analysis, it contributes to enhancing differential expression analysis by lowering false discovery rates caused by the biases. Furthermore, we demonstrated the effectiveness of the proposed feature selection by proposing a data-driven differential expression analysis: NSVA-seq, besides conducting network marker discovery.     

用积分变换及边界积分方法求解多层地基的静力问题

本文利用积分变换及矩阵递推方法得到了任意n层弹性体平面应变及轴对称问题的Mindlin解。再把此解作为基本解,利用Somigliana关系式,得到计算多层弹性体内部任意点位移的简便方法。利用此法很容易编制程序,且具有较高的计算精度与速度。     

伸缩张量率的抽象表示

本文用“主轴内蕴法”给出右-左伸缩张量 U 和 V 的时间导数 U 和 V 的抽象表示。文中引进所谓“分离技巧”,使能有效地应用张量函数的标准表示。V 的表达式是新的。还给出U 和 V 的两个新关系式。     

Surface miscibility of EPC/DOTAP/DOPE in binary and ternary mixed monolayers

Surface pressure (π)-molecular area (A) curves were used to characterize the packing of pseudo-ternary mixed Langmuir monolayers of egg phosphatidylcholine (EPC), 1,2-dioleoyl-3-trimethylammonium propane (DOTAP) and L-α-dioleoyl phosphatidylethanolamine (DOPE). This pseudo-ternary mixture EPC/DOPE/DOTAP has been successfully employed in liposome formulations designed for DNA non-viral vectors. Pseudo-binary mixtures were also studied as a control. Miscibility behavior was inferred from π-A curves applying the additivity rule by calculating the excess free energy of mixture (ΔG(Exc)). The interaction between the lipids was also deduced from the surface compressional modulus (C(s)(-1)). The deviation from ideality shows dependence on the lipid polar head type and monolayer composition. For lower DOPE concentrations, the forces are predominantly attractive. However, if the monolayer is DOPE rich, the DOTAP presence disturbs the PE-PE intermolecular interaction and the net interaction is then repulsive. The ternary monolayer EPC/DOPE/DOTAP presented itself in two configurations, modulated by the DOPE content, in a similar behavior to the DOPE/DOTAP monolayers. These results contribute to the understanding of the lipid interactions and packing in self-assembled systems associated with the in vitro and in vivo stability of liposomes.     

2010年研究生数学建模竞赛A题综述

第七届全国研究生数学建模竞赛A题是生物信息学中的一个急需解决的问题.虽然有关问题的研究已经经历了十多年,但由于问题的复杂性,人们的认识还很局限,基本的结论大多还以定性的为主,定量的探讨正方兴未艾.对参赛队员来讲解决该问题是一个极大的挑战.研究生们在讨论该问题时,大多直接进行分类.然而对于一个小样本的学习问题,显然这样做是行不通的.所以问题的关键是从数学和生物学角度减少用于分类的特征数目.同时,对于获取的基因标签,需要从临床上或生物学角度找到验证.该问题的求解过程引导研究生们从数学建模走向解决实际问题.