基于功能一致性和网络拓扑属性预测冠心病致病基因

基于功能一致性利用蛋白质互作网络挖掘潜在的疾病致病基因,对于了解疾病致病机理和改进临床治疗至关重要．基于基因功能一致性和其在蛋白质互作网络中的拓扑属性将基因与疾病之间建立关联,对疾病风险位点内的基因进行了致病风险预测,并通过GO及KEGG功能富集分析方法进一步筛选,预测出新的致病基因．预测出了51个新的冠心病致病基因,分析发现大部分基因参与了冠心病的致病过程．为疾病基因的挖掘提出一个新的思路,从而有助于复杂疾病致病机理的研究．     

基于蛋白质互作网络的COVID-19对肺动脉高压的影响机制

通过构建肺动脉高压差异基因和冠状病毒侵入人体后免疫反应相关基因的互作网络,探索COVID-19对肺动脉高压的影响机制。首先通过Meta分析挖掘肺动脉高压相关差异表达基因;其次通过SARS-CoV侵染人体后的基因表达数据,挖掘主要功能通路;最后构建肺动脉高压差异表达基因和冠状病毒主要功能通路基因的互作网络,挖掘网络的显著功能模块。发现肺动脉高压与血管平滑肌细胞、成纤细胞、T/B细胞免疫过程、转录调节因子通路、Toll样信号通路等密切相关,互作网络发现ITGAM、HBB、VCAM1、IL1R2等基因是COVID-19感染肺动脉高压患者的重要调节基因。通过肺动脉高压与冠状病毒感染机体后蛋白质互作网络探索了COVID-19对肺动脉高压的影响机制,为肺动脉高压感染COVID-19的研究及治疗提供了新思路。     

人类蛋白质结构互作网络——结构域对网络拓扑与蛋白质功能的影响

高通量的蛋白质互作数据与结构域互作数据的出现,使得在蛋白质组学领域内研究人类蛋白质结构互作网络,进一步揭示蛋白质结构与功能间的潜在关系成为可能.蛋白质上广泛分布的结构域被认为是蛋白质结构、功能以及进化的基本功能单元.然而,结合蛋白质的结构信息(例如蛋白质结构域数目、长度和覆盖率等)来研究这些表象后的内部机制仍然面临着挑战.将蛋白质分为单结构域蛋白质与多结构域蛋白质,并进一步结合蛋白质互作信息与结构域互作信息构建了人类蛋白质结构互作网络;通过与人类蛋白质互作网络进行比较,研究了人类蛋白质结构互作网络的特殊结构特征;对于单结构域蛋白质与多结构域蛋白质,分别进行了功能富集分析、功能离散度分析以及功能一致性分析等.结果发现,将结构域互作信息综合考虑进来后,人类蛋白质结构互作网络可以提供更多的单纯的蛋白质互作网络无法提供的细节信息,揭示蛋白质互作网络的复杂性.     

河蟹螺原体非编码RNA及其毒力靶标的多组学系统挖掘*

我国是世界最大水产养殖国,每年甲壳动物因病害造成的经济损失约为70亿元。其中,螺原体(Spiroplasma)是甲壳动物重要的致病菌之一,可造成虾蟹大面积死亡,已列入农业农村部三类疫病。非编码RNA(ncRNA)广泛存在于细菌中,其主要通过碱基配对识别靶标mRNA在转录后水平调节基因的表达,部分ncRNAs通过与蛋白质相互作用而影响蛋白质功能。近年研究表明,细菌ncRNAs在毒力调控中扮演极为重要的角色。为了研究河蟹螺原体ncRNAs在甲壳动物致病中的分子调控作用,需系统筛选鉴定螺原体感染相关的ncRNAs和毒力靶标。通过比较基因组、差异转录组、定量蛋白质组、系统生物学和分子相互作用联合研究得到:整合基因组和转录组挖掘得到河蟹螺原体ncRNAs 共54个;在体内感染和体外培养的不同时期,利用数字基因表达谱分析分别得到11个和28个差异显著ncRNAs;利用4款生物软件预测ncRNAs靶标,取交集得到423个;利用定量蛋白质组检测,鉴定出68个差异毒力蛋白,这些差异毒力蛋白与ncRNAs的30个毒力靶标中的21个相同;利用网络生物学分析得到主要的节点Hub-ncRNA共有6个;利用RNA pull-down、原核链特异性测序和LC-MS/MS综合分析,得到重要节点ncRNA SR05的互作RNA 53个、互作蛋白质120个。相关研究成果,可为诠释河蟹螺原体致病机制及其与宿主相互作用机制奠定基础,为虾蟹该疾病的综合防治提供科学依据。     

基于整合网络和联合策略预测心肌梗死的新致病基因

目的:基于整合网络和联合策略预测心肌梗死的新致病基因.方法:从系统生物学的角度,提出基于蛋白质亚细胞定位信息,构建区域化的蛋白质互作的整合网络;通过疾病风险基因与已知致病基因的功能一致性程度和互作相关性的强度联合筛选的新策略,预测心肌梗死的新致病基因.结果:预测出10个心肌梗死的新致病基因(CCL19、CCL25、COMP、CCL11、CCL7、F2、KLKB1、HTR6、ADRB1、BDKRB2),其中8个基因(CCL 19、CCL25、CCL11、CCL7、F2、KLKB1、ADRB1、BDKRB2)经文献证实与心肌梗死的发生发展有着密切的联系;另外2个基因(COMP、HTR6)尚需实验验证.结论:基于整合网络和联合策略预测出10个心肌梗死的新致病基因,此方法为探索复疾病的致病基因提供了新的思路,有助于阐明复杂疾病的致病机理.     

棉铃虫围食膜的蛋白质组鉴定

【目的】围食膜(peritrophic membrane, PM)是昆虫抵御随食物摄入的病原微生物入侵的第一道天然屏障。本研究旨在鉴定出农业重大害虫棉铃虫Helicoverpa armigera围食膜的总蛋白成分,为进一步揭示昆虫围食膜的形成机制及研发新颖的害虫控制策略奠定基础。【方法】剥离棉铃虫5龄幼虫PM,用三氟甲磺酸(trifluoromethane sulfonic acid, TFMS)处理,采用液质联用技术(LC-MS/MS)鉴定围食膜蛋白质组,然后对鉴定结果进行生物信息学分析。【结果】本研究共鉴定出棉铃虫幼虫围食膜蛋白质169个,是目前鉴定最多的棉铃虫围食膜蛋白。通过GO分析,可以将这些鉴定的蛋白分为细胞组分、分子功能和生物学过程三大类;KEGG富集结果显示,鉴定蛋白可以富集在12条代谢通路中;蛋白互作分析(protein protein interaction, PPI)结果表明,以ACC和CG3011等蛋白为核心可以形成蛋白互作网络。【结论】本研究鉴定了169个棉铃虫幼虫围食膜蛋白质,并对其进行了GO, KEGG和PPI分析,结果有助于人们全面理解昆虫围食膜的分子结构和功能。     

基于网络特征的药物靶蛋白识别

蛋白质很少孤立得发挥作用,往往通过网络中彼此互作来共同行使功能.因此分析药物靶蛋白在生物学网络中的性质将十分有助于从信息学角度理解药物的作用机制.但目前尚无研究对药物靶蛋白在人类蛋白质互作网络中的拓扑特性给与具体的分析和描述.本文首先将药物靶蛋白映射到人类蛋白质互作网络中,进而分析了药物作用靶蛋白在互作网络中的5种拓扑指标,并与互作网络中全蛋白质组集合及非药物靶点集合的拓扑指标进行了对比.结果显示,药物靶蛋白之间具有更高的连通性,信息能够得到更快得传递.基于这些拓扑特征,将互作网络中的所有蛋白进行排序.发现排序在前100位的蛋白中有48个是Drugbank中记录的药物靶点,另外的52个蛋白中有9个蛋白已在TTD,Matador等数据库中被记录为药物靶点,还有部分蛋白通过文献检索被证实为药物靶点.     

GST pull-down筛选冠突曲霉MAT的互作蛋白*

目的: 冠突曲霉(Aspergillus cristatus)是一种同宗结合菌,它的产孢受渗透压调控,与构巢曲霉的光调控产孢机制存在较大差异。冠突曲霉的有性生殖主要受MAT1-1-1和MAT1-2-1调控,但MAT基因对该菌有性生殖的调控机制仍不清楚。期望筛选得到冠突曲霉MAT的互作蛋白,为深入研究冠突曲霉有性产孢机制奠定基础。方法: 利用GST pull-down联合液相色谱-串联质谱(LC-MS/MS)技术筛选可能与冠突曲霉MAT1-1-1和MAT1-2-1互作的蛋白,结合ProteinPilot和冠突曲霉基因组注释结果进行互作蛋白的注释及GO分析,其中互作蛋白SI65_00917和SI65_03348利用RT-qPCR探索它们与有性发育的联系,并利用酵母双杂交技术初步验证它们与MAT蛋白的互作关系。结果: 成功构建了GST-MAT1-1-1、GST-MAT1-2-1表达载体,诱导表达纯化出目的诱饵蛋白,分别利用诱饵蛋白捕获冠突曲霉总蛋白中的互作蛋白,经分析、筛选共鉴定出与MAT1-1-1互作的蛋白56个,与MAT1-2-1互作的蛋白413个。GO分析表明,这些蛋白参与翻译调控、代谢过程、蛋白质转运及蛋白结合等生物学过程,具有核苷酸结合活性、催化活性、蛋白结合活性;RT-qPCR结果表明互作蛋白SI65_00917可能与有性发育相关。酵母双杂交结果表明,SI65_00917蛋白具有自激活作用,可能是转录因子;SI65_03348蛋白与MAT1-1-1、MAT1-2-1在酵母中均有互作。结论: MAT通过与其他蛋白直接或间接的相互作用调控其有性发育过程。     

木薯栽培种ZM-Seaside和花叶变种块根蛋白组学分析

为研究木薯栽培种ZM-Seaside(高产种质)和花叶变种(低产种质)块根产量差异的原因,从农艺性状和蛋白质组学角度对以上2个种质进行分析,为选育高产木薯品种提供理论依据。试验中采用旋光法测定淀粉含量;硝酸银滴定法测定氢氰酸含量;苯酚抽提法提取蛋白质;双向电泳技术分离蛋白质;Delta2D软件确定差异蛋白质点;质谱技术鉴定差异蛋白质点,结合KEGG数据库将其功能分类;利用Western blot技术对部分差异蛋白质进行验证;String在线软件构建蛋白质互作调控网络。结果显示,ZM-Seaside块根淀粉含量为29.18%,显著高于花叶变种的25.83%;两种木薯鲜薯薯肉氢氰酸含量均低于50 mg/kg,属可食用木薯种质。ZM-Seaside干物率为40.28%,显著高于花叶变种的37.16%。以花叶变种块根的全蛋白质为对照,ZM-Seaside的块根存在39个差异蛋白质点,其中上调表达23个,下调表达16个;经质谱技术成功鉴定到其中28个,其功能涉及到碳水化合物和能量代谢(7个)、分子伴侣(8个)、解毒和抗氧化(2个)、蛋白质合成(1个)、结构蛋白(3个)及未知功能蛋白质(7个)。STRING代谢网络显示:热激蛋白Heat shock protein和分子伴侣Molecular chaperone Hsp90-1互作关系最多,是整个互作调控网络的枢纽。推测这2个蛋白质是影响ZM-Seaside和花叶变种块根产量差异的关键蛋白质,这些蛋白质有可能成为选育高产木薯种质的标记蛋白质。     

人类蛋白质互作网络hub蛋白与其结构域关联分析

hub蛋白质作为参与较多互作的“中心蛋白”,在实现蛋白质功能和生命活动中发挥着关键作用．而结构域作为蛋白质上的基本功能区域,决定着蛋白质功能及蛋白质互作的情况．互作网络中hub蛋白质和结构域对于蛋白质功能的实现均起到决定性的作用．对蛋白质互作与结构域的关系分析表明,蛋白质互作与结构域之间存在着密切的联系．对人类蛋白质互作网络中的hub蛋白与结构域进行关联分析,探讨hub蛋白及其互作partner与结构域数目之间的关系．并通过hub蛋白质之间的互作对相应结构域的关系进行进一步的论证．     

An integrated approach to inferring gene-disease associations in humans

One of the most important tasks of modern bioinformatics is the development of computational tools that can be used to understand and treat human disease. To date, a variety of methods have been explored and algorithms for candidate gene prioritization are gaining in their usefulness. Here, we propose an algorithm for detecting gene-disease associations based on the human protein-protein interaction network, known gene-disease associations, protein sequence, and protein functional information at the molecular level. Our method, PhenoPred, is supervised: first, we mapped each gene/protein onto the spaces of disease and functional terms based on distance to all annotated proteins in the protein interaction network. We also encoded sequence, function, physicochemical, and predicted structural properties, such as secondary structure and flexibility. We then trained support vector machines to detect gene-disease associations for a number of terms in Disease Ontology and provided evidence that, despite the noise/incompleteness of experimental data and unfinished ontology of diseases, identification of candidate genes can be successful even when a large number of candidate disease terms are predicted on simultaneously. Availability: www.phenopred.org.     

In silico protein interaction analysis using the global proteome machine database

Experiments to probe for protein-protein interactions are the focus of functional proteomic studies, thus proteomic data repositories are increasingly likely to contain a large cross-section of such information. Here, we use the Global Proteome Machine database (GPMDB), which is the largest curated and publicly available proteomic data repository derived from tandem mass spectrometry, to develop an in silico protein interaction analysis tool. Using a human histone protein for method development, we positively identified an interaction partner from each histone protein family that forms the histone octameric complex. Moreover, this method, applied to the α subunits of the human proteasome, identified all of the subunits in the 20S core particle. Furthermore, we applied this approach to human integrin αIIb and integrin β3, a major receptor involved in the activation of platelets. We identified 28 proteins, including a protein network for integrin and platelet activation. In addition, proteins interacting with integrin β1 obtained using this method were validated by comparing them to those identified in a formaldehyde-supported coimmunoprecipitation experiment, protein-protein interaction databases and the literature. Our results demonstrate that in silico protein interaction analysis is a novel tool for identifying known/candidate protein-protein interactions and proteins with shared functions in a protein network.     

Construction of reliable protein-protein interaction networks with a new interaction generality measure

MOTIVATION: Recent screening techniques have made large amounts of protein-protein interaction data available, from which biologically important information such as the function of uncharacterized proteins, the existence of novel protein complexes, and novel signal-transduction pathways can be discovered. However, experimental data on protein interactions contain many false positives, making these discoveries difficult. Therefore computational methods of assessing the reliability of each candidate protein-protein interaction are urgently needed. RESULTS: We developed a new 'interaction generality' measure (IG2) to assess the reliability of protein-protein interactions using only the topological properties of their interaction-network structure. Using yeast protein-protein interaction data, we showed that reliable protein-protein interactions had significantly lower IG2 values than less-reliable interactions, suggesting that IG2 values can be used to evaluate and filter interaction data to enable the construction of reliable protein-protein interaction networks.     

基于MADS-box诱饵与蛋白质相互作用的拟南芥花瓣发育分子网络拓展

阐明花器官发育调控机理具重要的进化、发育和生态学意义。该文以拟南芥(Arabidopsis thaliana)花瓣发育为例, 整合蛋白质互作、亚细胞定位、基因芯片和基因功能注释等数据库, 通过组建蛋白质互作可信预测模型, 获得拟南芥花瓣蛋白质互作网络, 以含有MADS-box结构域蛋白为诱饵在网络中进行一级拓展, 得到含38个蛋白质和67对互作的拓展网络。基于拓展网络, DAVID基因功能注释表明, 多数蛋白质涉及的生物学过程与花发育调控相关; 提取到19个候选四元互作, 涉及ABCDE模型基因之外的8个基因, 其中含MADS-box结构域的AGL16可能是B类基因新成员或其冗余; SEU、LUH、CHR4、CHR11、CHR17和AT3G04960为拟南芥花瓣AP1-AP3-PI-SEP四聚体的候选靶标基因。研究结果为深入解析拟南芥花瓣发育分子调控网络奠定了基础。     

Prioritizing Disease Candidate Proteins in Cardiomyopathy-Specific Protein-Protein Interaction Networks Based on “Guilt by Association” Analysis

The cardiomyopathies are a group of heart muscle diseases which can be inherited (familial). Identifying potential disease-related proteins is important to understand mechanisms of cardiomyopathies. Experimental identification of cardiomyophthies is costly and labour-intensive. In contrast, bioinformatics approach has a competitive advantage over experimental method. Based on “guilt by association” analysis, we prioritized candidate proteins involving in human cardiomyopathies. We first built weighted human cardiomyopathy-specific protein-protein interaction networks for three subtypes of cardiomyopathies using the known disease proteins from Online Mendelian Inheritance in Man as seeds. We then developed a method in prioritizing disease candidate proteins to rank candidate proteins in the network based on “guilt by association” analysis. It was found that most candidate proteins with high scores shared disease-related pathways with disease seed proteins. These top ranked candidate proteins were related with the corresponding disease subtypes, and were potential disease-related proteins. Cross-validation and comparison with other methods indicated that our approach could be used for the identification of potentially novel disease proteins, which may provide insights into cardiomyopathy-related mechanisms in a more comprehensive and integrated way.     

The knowledge-integrated network biomarkers discovery for major adverse cardiac events

The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein-protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein-protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction.     

Diffusion kernel-based logistic regression models for protein function prediction

Assigning functions to unknown proteins is one of the most important problems in proteomics. Several approaches have used protein-protein interaction data to predict protein functions. We previously developed a Markov random field (MRF) based method to infer a protein's functions using protein-protein interaction data and the functional annotations of its protein interaction partners. In the original model, only direct interactions were considered and each function was considered separately. In this study, we develop a new model which extends direct interactions to all neighboring proteins, and one function to multiple functions. The goal is to understand a protein's function based on information on all the neighboring proteins in the interaction network. We first developed a novel kernel logistic regression (KLR) method based on diffusion kernels for protein interaction networks. The diffusion kernels provide means to incorporate all neighbors of proteins in the network. Second, we identified a set of functions that are highly correlated with the function of interest, referred to as the correlated functions, using the chi-square test. Third, the correlated functions were incorporated into our new KLR model. Fourth, we extended our model by incorporating multiple biological data sources such as protein domains, protein complexes, and gene expressions by converting them into networks. We showed that the KLR approach of incorporating all protein neighbors significantly improved the accuracy of protein function predictions over the MRF model. The incorporation of multiple data sets also improved prediction accuracy. The prediction accuracy is comparable to another protein function classifier based on the support vector machine (SVM), using a diffusion kernel. The advantages of the KLR model include its simplicity as well as its ability to explore the contribution of neighbors to the functions of proteins of interest.     

Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network

Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.     

Categorizing biases in high-confidence high-throughput protein-protein interaction data sets

We characterized and evaluated the functional attributes of three yeast high-confidence protein-protein interaction data sets derived from affinity purification/mass spectrometry, protein-fragment complementation assay, and yeast two-hybrid experiments. The interacting proteins retrieved from these data sets formed distinct, partially overlapping sets with different protein-protein interaction characteristics. These differences were primarily a function of the deployed experimental technologies used to recover these interactions. This affected the total coverage of interactions and was especially evident in the recovery of interactions among different functional classes of proteins. We found that the interaction data obtained by the yeast two-hybrid method was the least biased toward any particular functional characterization. In contrast, interacting proteins in the affinity purification/mass spectrometry and protein-fragment complementation assay data sets were over- and under-represented among distinct and different functional categories. We delineated how these differences affected protein complex organization in the network of interactions, in particular for strongly interacting complexes (e.g. RNA and protein synthesis) versus weak and transient interacting complexes (e.g. protein transport). We quantified methodological differences in detecting protein interactions from larger protein complexes, in the correlation of protein abundance among interacting proteins, and in their connectivity of essential proteins. In the latter case, we showed that minimizing inherent methodology biases removed many of the ambiguous conclusions about protein essentiality and protein connectivity. We used these findings to rationalize how biological insights obtained by analyzing data sets originating from different sources sometimes do not agree or may even contradict each other. An important corollary of this work was that discrepancies in biological insights did not necessarily imply that one detection methodology was better or worse, but rather that, to a large extent, the insights reflected the methodological biases themselves. Consequently, interpreting the protein interaction data within their experimental or cellular context provided the best avenue for overcoming biases and inferring biological knowledge.