Integration of biological networks and gene expression data using Cytoscape

Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.     

Site-selective glycosylation of proteins: creating synthetic glycoproteins

In higher organisms, the functions of many proteins are modulated by post-translational modifications (PTMs). Glycosylation is by far the most diverse of the PTM processes. Natural protein production methods typically produce PTM or glycoform mixtures within which function is difficult to dissect or control. Chemical tagging methods allow the precise attachment of multiple glycosylation modifications to bacterially expressed (bare) protein scaffolds, allowing reconstitution of functionally effective mimics of glycoproteins in higher organisms. In this way combining chemical control of PTM with readily available protein scaffolds provides a systematic platform for creating probes of protein-PTM interactions. This protocol describes the modification of Cys residues in proteins using glycomethanethiosulfonates and glycoselenenylsulfides and the modification of azidohomoalanine residues, introduced by Met replacement using auxotrophic Met(-) Escherichia coli strains, with glycoalkynes and the combination of these techniques for the creation of dual-tagged proteins. Each glycosylation procedure outlined in this protocol can be achieved in half a day.     

Imaging mass spectrometry at cellular length scales

Imaging mass spectrometry (IMS) allows the direct investigation of both the identity and the spatial distribution of the molecular content directly in tissue sections, single cells and many other biological surfaces. In this protocol, we present the steps required to retrieve the molecular information from tissue sections using matrix-enhanced (ME) and metal-assisted (MetA) secondary ion mass spectrometry (SIMS) as well as matrix-assisted laser desorption/ionization (MALDI) IMS. These techniques require specific sample preparation steps directed at optimal signal intensity with minimal redistribution or modification of the sample analytes. After careful sample preparation, different IMS methods offer a unique discovery tool in, for example, the investigation of (i) drug transport and uptake, (ii) biological processing steps and (iii) biomarker distributions. To extract the relevant information from the huge datasets produced by IMS, new bioinformatics approaches have been developed. The duration of the protocol is highly dependent on sample size and technique used, but on average takes approximately 5 h.     

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。     

樟子松固沙林林水关系研究进展及对营林实践的指导

中国有着世界上最大面积的人工林, 如何维持人工林的可持续性已成为气候变化背景下需要面对的重大挑战。樟子松(Pinus sylvestris var. mongolica)以其抗旱、抗寒、耐贫瘠等优良特性成为中国北方生态治理中最主要的常绿针叶树种之一, 近70年来发挥了巨大的防风固沙与生态固碳功能。然而, 随着林分的生长与气候变化, 樟子松人工固沙林正经历着越来越严峻的环境胁迫, 部分地区出现了林分“早衰”或死亡的现象, 引起了人们对樟子松固沙林适应与应对气候变化能力的担忧。该文在回溯樟子松基本生物学特征与引种推广历史, 系统总结近年来樟子松林林水关系研究新成果的基础上, 全面分析了樟子松固沙林林水关系存在的主要矛盾, 并提出了基于林水关系相协调的林分经营措施的调整: 由倡导防护功能为主的单一目标向包含林分稳定性、生态固碳功能、可持续发展等多目标平衡方向调整; 由以沙地森林景观培育为主向以良好土壤生境培育为主的方向调整; 由倡导天然更新为主向以人工造林与天然更新相结合的世代更新方向调整。在立足于北方沙地脆弱生境与气候变化客观现实的基础背景下, 应坚持樟子松在固沙林生态系统演化过程中的先锋种与建群种地位, 基于“以水定绿”原则, 采取“隔行带伐+再造林”等方式开展林分密度动态调控, 促进林分向异龄林结构演化, 促进樟子松固沙林生态服务的优质化和生态固碳功能的最大化。     

中国真猛犸象和披毛犀化石14C年代研究新进展

真猛犸象（Mammuthus primigenius）和披毛犀（Coelodonta antiquitatis）是北半球高纬度地区晚更新世动物群的主要成员,其消亡的年代和原因一直是国际学术界关注的热点科学问题。本文对黑龙江青冈县英贤村最新出土的5个真猛犸象和5个披毛犀化石进行了AMS¹⁴C年代测定,结果均大于4万年,部分化石可能已经超出了目前¹⁴C的测定范围。通过整理并对比已公开发表的中国境内两种动物化石的¹⁴C年代学数据,本文认为早期常规¹⁴C测年方法所获得的年代值需要重新考虑其准确性。埋藏地层与最新的AMS¹⁴C测年数据显示,我国真猛犸象化石年代主要集中于MIS3阶段;披毛犀在我国消亡的时间很可能晚于真猛犸象,至少延续到末次冰消期。中国猛犸象-披毛犀动物群化石仍然需要开展更多的年代学研究。     

Protein structure prediction assisted with sparse NMR data in CASP13

CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and ¹⁵N-¹H residual dipolar coupling data, typical of that obtained for ¹⁵N,¹³C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.