Prediction of human genes' regulatory functions based on proteinprotein interaction network

In systems biology, regulatory pathway is one of the most important research areas. However, regulatory pathway is so complicated that we still poorly understand this system. On the other hand, with rapid accumulated information on different organisms, it becomes more and more possible to in-depth investigate regulatory pathway. To understand regulatory pathway well, figuring out the components of each pathway is the most important step. In this study, a network- based method was proposed to classify human genes into corresponding pathways. The information of protein-protein interactions retrieved from STRING was used to construct a network and jackknife test was employed to evaluate the method. As a result, the first order prediction accuracy was 87.91%, indicating that interactive proteins always have similar biological regulatory functions. By comparing the predicted results obtained from other methods based on blast and amino acid composition, respectively, it implies that our prediction method is quite promising that may provide an opportunity to understand this complicated pathway system well.     

Predicting protein phenotypes based on protein-protein interaction network

Background

Identifying associated phenotypes of proteins is a challenge of the modern genetics since the multifactorial trait often results from contributions of many proteins. Besides the high-through phenotype assays, the computational methods are alternative ways to identify the phenotypes of proteins.

Methodology/Principal Findings

Here, we proposed a new method for predicting protein phenotypes in yeast based on protein-protein interaction network. Instead of only the most likely phenotype, a series of possible phenotypes for the query protein were generated and ranked acording to the tethering potential score. As a result, the first order prediction accuracy of our method achieved 65.4% evaluated by Jackknife test of 1,267 proteins in budding yeast, much higher than the success rate (15.4%) of a random guess. And the likelihood of the first 3 predicted phenotypes including all the real phenotypes of the proteins was 70.6%.

Conclusions/Significance

The candidate phenotypes predicted by our method provided useful clues for the further validation. In addition, the method can be easily applied to the prediction of protein associated phenotypes in other organisms.     

Quantitative analysis of protein interaction network dynamics in yeast

Many cellular functions are mediated by protein–protein interaction networks, which are environment dependent. However, systematic measurement of interactions in diverse environments is required to better understand the relative importance of different mechanisms underlying network dynamics. To investigate environment‐dependent protein complex dynamics, we used a DNA‐barcode‐based multiplexed protein interaction assay in Saccharomyces cerevisiae to measure in vivo abundance of 1,379 binary protein complexes under 14 environments. Many binary complexes (55%) were environment dependent, especially those involving transmembrane transporters. We observed many concerted changes around highly connected proteins, and overall network dynamics suggested that “concerted” protein‐centered changes are prevalent. Under a diauxic shift in carbon source from glucose to ethanol, a mass‐action‐based model using relative mRNA levels explained an estimated 47% of the observed variance in binary complex abundance and predicted the direction of concerted binary complex changes with 88% accuracy. Thus, we provide a resource of yeast protein interaction measurements across diverse environments and illustrate the value of this resource in revealing mechanisms of network dynamics.     

Establishment of optimal regulatory network of colorectal cancer based on p42.3 protein

Objective: to establish regulatory network of colorectal cancer involving p42.3 protein and to provide theoretical evidence for deep functional exploration of p42.3 protein in the onset and development of colorectal cancer. Methods: with protein similarity algorithm, reference protein set of p42.3 cell apoptosis was built according to structural features of p42.3. GO and KEGG databases were used to establish regulatory network of tumor cell apoptosis involving p42.3; meanwhile, the largest possible working pathway that involves p42.3 protein was screened out based on Bayesian network theory. Besides, GO and KEGG were used to build regulatory network on early diagnosis gene markers for colorectal cancer including WWOX, K-ras, COX-2, p53, APC, DCC and PTEN, at the same time, a regulatory network of colorectal cancer cell apoptosis which involves p42.3 was established. Results: cell apoptotic regulatory network that p42.3 participates in primarily consists of Bcl-2 family genes and the largest possible pathway is p42.3 → FKBP → Bcl-2 centered as FKBP protein. Combined with colorectal cancer regulatory network that involves early diagnosis gene markers, it can be predicted that p42.3 is most likely to regulate the colorectal cancer cell apoptosis through FKBP → Bcl-2 → Bax → caspase-9 → caspase-3 pathway. Conclusion: the colorectal cancer apoptosis network based on p42.3 established in the study provides theoretical evidence for deep exploration of p42.3 regulatory mechanism and molecular targeting treatment of colorectal cancer.     

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。     

Identifying protein complexes based on density and modularity in protein-protein interaction network

Background

Identifying protein complexes is crucial to understanding principles of cellular organization and functional mechanisms. As many evidences have indicated that the subgraphs with high density or with high modularity in PPI network usually correspond to protein complexes, protein complexes detection methods based on PPI network focused on subgraph's density or its modularity in PPI network. However, dense subgraphs may have low modularity and subgraph with high modularity may have low density, which results that protein complexes may be subgraphs with low modularity or with low density in the PPI network. As the density-based methods are difficult to mine protein complexes with low density, and the modularity-based methods are difficult to mine protein complexes with low modularity, both two methods have limitation for identifying protein complexes with various density and modularity.

Results

To identify protein complexes with various density and modularity, including those have low density but high modularity and those have low modularity but high density, we define a novel subgraph's fitness, f _ρ , as f _ρ = (density) ^ρ *(modularity)^1-ρ, and propose a novel algorithm, named LF_PIN, to identify protein complexes by expanding seed edges to subgraphs with the local maximum fitness value. Experimental results of LF-PIN in S.cerevisiae show that compared with the results of fitness equal to density (ρ = 1) or equal to modularity (ρ = 0), the LF-PIN identifies known protein complexes more effectively when the fitness value is decided by both density and modularity (0<ρ<1). Compared with the results of seven competing protein complex detection methods (CMC, Core-Attachment, CPM, DPClus, HC-PIN, MCL, and NFC) in S.cerevisiae and E.coli, LF-PIN outperforms other seven methods in terms of matching with known complexes and functional enrichment. Moreover, LF-PIN has better performance in identifying protein complexes with low density or with low modularity.

Conclusions

By considering both the density and the modularity, LF-PIN outperforms other protein complexes detection methods that only consider density or modularity, especially in identifying known protein complexes with low density or low modularity.

     

Prediction of body fluids where proteins are secreted into based on protein interaction network

Determining the body fluids where secreted proteins can be secreted into is important for protein function annotation and disease biomarker discovery. In this study, we developed a network-based method to predict which kind of body fluids human proteins can be secreted into. For a newly constructed benchmark dataset that consists of 529 human-secreted proteins, the prediction accuracy for the most possible body fluid location predicted by our method via the jackknife test was 79.02%, significantly higher than the success rate by a random guess (29.36%). The likelihood that the predicted body fluids of the first four orders contain all the true body fluids where the proteins can be secreted into is 62.94%. Our method was further demonstrated with two independent datasets: one contains 57 proteins that can be secreted into blood; while the other contains 61 proteins that can be secreted into plasma/serum and were possible biomarkers associated with various cancers. For the 57 proteins in first dataset, 55 were correctly predicted as blood-secrete proteins. For the 61 proteins in the second dataset, 58 were predicted to be most possible in plasma/serum. These encouraging results indicate that the network-based prediction method is quite promising. It is anticipated that the method will benefit the relevant areas for both basic research and drug development.     

Fundamentals of protein interaction network mapping

Studying protein interaction networks of all proteins in an organism (“interactomes”) remains one of the major challenges in modern biomedicine. Such information is crucial to understanding cellular pathways and developing effective therapies for the treatment of human diseases. Over the past two decades, diverse biochemical, genetic, and cell biological methods have been developed to map interactomes. In this review, we highlight basic principles of interactome mapping. Specifically, we discuss the strengths and weaknesses of individual assays, how to select a method appropriate for the problem being studied, and provide general guidelines for carrying out the necessary follow‐up analyses. In addition, we discuss computational methods to predict, map, and visualize interactomes, and provide a summary of some of the most important interactome resources. We hope that this review serves as both a useful overview of the field and a guide to help more scientists actively employ these powerful approaches in their research.     

Enrichment of pinolenic acid from pine nut oil via lipase-catalyzed ethanolysis with an immobilized Candida antarctica lipase

Abstract

Pinolenic acid (PLA) enrichment as an ethyl ester from pine nut oil was successfully accomplished in a batch reactor by lipase-catalyzed ethanolysis using Novozym 435 lipase from Candida antarctica as a biocatalyst. PLA is predominantly an sn-3 substituent of the pine nut oil triacylglycerol (TAG), where it accounts for about 39 mol% of the fatty acids esterified at that position. In the presence of ethanol, Novozym 435 exhibited sn-3 regiospecificity with respect to the TAG of pine nut oil. The effect of the molar ratio of reactants on PLA enrichment by ethanolysis was investigated. The molar ratios of pine nut oil to ethanol were varied from 1:20 to 1:100. A fatty acid ethyl ester (FAEE) fraction with higher PLA content was obtained in the early stage of the reaction, although the yield of PLA was small. However, the PLA content of the FAEEs decreased with increasing reaction time, while the yield of PLA increased. The molar ratio of pine nut oil to ethanol that produced the optimum content and yield of PLA in FAEEs was 1:80.     

根据蛋白质互作网络预测乳腺癌相关蛋白质的细致功能

乳腺癌是最为常见的恶性肿瘤之一。已有的关于乳腺癌相关蛋白质的功能注释比较宽泛, 制约了乳腺癌的后续研究工作。对于已知部分功能的乳腺癌相关蛋白质, 提出了一种结合Gene Ontology功能先验知识和蛋白质互作的方法, 通过构建功能特异的局部相互作用网络来预测乳腺癌相关蛋白质的细致功能。结果显示该方法能够以很高的精确率为乳腺癌相关蛋白质预测更为精细的功能。预测的相关蛋白质的功能对于指导实验研究乳腺癌的分子机制具有重要的价值。     

根据蛋白质互作网络预测前列腺癌相关蛋白质的细致功能

对于功能部分已知的前列腺癌相关蛋白质,提出了一种基于Gent Ontology的功能特异的子网构建方法来细化其功能注释。结果显示该方法能够以很高的精确率为前列腺癌相关蛋白质预测更为精细的功能。预测的相关蛋白质的功能对于指导实验研究前列腺癌的分子机制具有重要的价值。     

Dynamical analysis of yeast protein interaction network during the sake brewing process

Proteins interact with each other for performing essential functions of an organism. They change partners to get involved in various processes at different times or locations. Studying variations of protein interactions within a specific process would help better understand the dynamic features of the protein interactions and their functions. We studied the protein interaction network of Saccharomyces cerevisiae (yeast) during the brewing of Japanese sake. In this process, yeast cells are exposed to several stresses. Analysis of protein interaction networks of yeast during this process helps to understand how protein interactions of yeast change during the sake brewing process. We used gene expression profiles of yeast cells for this purpose. Results of our experiments revealed some characteristics and behaviors of yeast hubs and non-hubs and their dynamical changes during the brewing process. We found that just a small portion of the proteins (12.8 to 21.6%) is responsible for the functional changes of the proteins in the sake brewing process. The changes in the number of edges and hubs of the yeast protein interaction networks increase in the first stages of the process and it then decreases at the final stages.     

酿酒酵母与地衣芽孢杆菌相互作用及基于蛋白组学的作用机制分析

【目的】为解析酱香型白酒发酵过程中群体微生物的酿造特征,研究酱香型白酒酿造中贡献特征风味的地衣芽孢杆菌和贡献酒精的酿酒酵母之间的相互作用。【方法】通过构建酿酒酵母纯培养及与地衣芽孢杆菌共培养发酵体系,比较不同培养体系中的生物量、乙醇产量及有机酸产量差异,并从蛋白组学角度加以分析和认识二者之间的相互作用。【结果】在共培养体系中,酿酒酵母抑制地衣芽孢杆菌生长,其自身生长不受地衣芽孢杆菌的影响,然而代谢产物却发生变化,其中乙醇及有机酸中的丙酮酸、苹果酸、乳酸、琥珀酸及酒石酸的最高产量分别高出其纯培养的11.8%、56.8%、36.3%、24.3%、48.2%及27.7%,而柠檬酸的最高产量低于其纯培养的35.1%;蛋白组分析显示,地衣芽孢杆菌诱导酿酒酵母胞内69个蛋白差异表达(>2倍),质谱鉴定出24个,主要功能为参与糖酵解过程、乙醇代谢过程、细胞壁稳定性调控及应激反应等。糖酵解和乙醇代谢途径相关蛋白对酿酒酵母混合培养条件下的代谢变化起重要作用,其余蛋白可能与微生物相互作用时的防御和适应性相关。【结论】在混合培养发酵体系中地衣芽孢杆菌能够影响酿酒酵母的乙醇及有机酸代谢,这对于白酒品质调控及微生物间相互作用都具有重要意义。蛋白组学结果为从分子层面深入认识酿酒酵母与地衣芽孢杆菌之间的相互作用提供理论基础,有利于促进酱香型白酒发酵过程中群体微生物酿造特征的解析。