Classification of transmembrane protein families in the Caenorhabditis elegans genome and identification of human orthologs

The complete genome sequence of the nematode Caenorhabditis elegans provides an excellent basis for studying the distribution and evolution of protein families in higher eukaryotes. Three fundamental questions are as follows: How many paralog clusters exist in one species, how many of these are shared with other species, and how many proteins can be assigned a functional counterpart in other species? We have addressed these questions in a detailed study of predicted membrane proteins in C. elegans and their mammalian homologs. All worm proteins predicted to contain at least two transmembrane segments were clustered on the basis of sequence similarity. This resulted in 189 groups with two or more sequences, containing, in total, 2647 worm proteins. Hidden Markov models (HMMs) were created for each family, and were used to retrieve mammalian homologs from the SWISSPROT, TREMBL, and VTS databases. About one-half of these clusters had mammalian homologs. Putative worm-mammalian orthologs were extracted by use of nine different phylogenetic methods and BLAST. Eight clusters initially thought to be worm-specific were assigned mammalian homologs after searching EST and genomic sequences. A compilation of 174 orthology assignments made with high confidence is presented.     

Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings

Sleep apnoea is a common disorder that is usually diagnosed through expensive studies conducted in sleep laboratories. Sleep apnoea is accompanied by a characteristic cyclic variation in heart rate or other changes in the waveform of the electrocardiogram (ECG). If sleep apnoea could be diagnosed using only the ECG, it could be possible to diagnose sleep apnoea automatically and inexpensively from ECG recordings acquired in the patient's home. This study had two parts. The first was to assess the ability of an overnight ECG recording to distinguish between patients with and without apnoea. The second was to assess whether the ECG could detect apnoea during each minute of the recording. An expert, who used additional physiological signals, assessed each of the recordings for apnoea. Research groups were invited to access data via the world-wide web and submit algorithm results to an international challenge linked to a conference. A training set of 35 recordings was made available for algorithm development, and results from a test set of 35 different recordings were made available for independent scoring. Thirteen algorithms were compared. The best algorithms made use of frequency-domain features to estimate changes in heart rate and the effect of respiration on the ECG waveform. Four of these algorithms achieved perfect scores of 100% in the first part of the study, and two achieved an accuracy of over 90% in the second part of the study.     

Systematic identification and functional screens of uncharacterized proteins associated with eukaryotic ribosomal complexes

Translation regulation is a critical means by which cells control growth, division, and apoptosis. To gain further insight into translation and related processes, we performed multifaceted mass spectrometry-based proteomic screens of yeast ribosomal complexes and discovered an association of 77 uncharacterized yeast proteins with ribosomes. Immunoblotting revealed an EDTA-dependent cosedimentation with ribosomes in sucrose gradients for 11 candidate translation-machinery-associated (TMA) proteins. Tandem affinity purification linked one candidate, LSM12, to the RNA processing proteins PBP1 and PBP4. A second candidate, TMA46, interacted with RBG1, a GTPase that interacts with ribosomes. By adapting translation assays to high-throughput screening methods, we showed that null yeast strains harboring deletions for several of the TMA genes had alterations in protein synthesis rates (TMA7 and TMA19), susceptibility to drugs that inhibit translation (TMA7), translation fidelity (TMA20), and polyribosome profiles (TMA7, TMA19, and TMA20). TMA20 has significant sequence homology with the oncogene MCT-1. Expression of human MCT-1 in the Deltatma20 yeast mutant complemented translation-related defects, strongly implying that MCT-1 functions in translation-related processes. Together these findings implicate the TMA proteins and, potentially, their human homologs, in translation related processes.     

Lung cancer cell identification based on artificial neural network ensembles.

An artificial neural network ensemble is a learning paradigm where several artificial neural networks are jointly used to solve a problem. In this paper, an automatic pathological diagnosis procedure named Neural Ensemble-based Detection (NED) is proposed, which utilizes an artificial neural network ensemble to identify lung cancer cells in the images of the specimens of needle biopsies obtained from the bodies of the subjects to be diagnosed. The ensemble is built on a two-level ensemble architecture. The first-level ensemble is used to judge whether a cell is normal with high confidence where each individual network has only two outputs respectively normal cell or cancer cell. The predictions of those individual networks are combined by a novel method presented in this paper, i.e. full voting which judges a cell to be normal only when all the individual networks judge it is normal. The second-level ensemble is used to deal with the cells that are judged as cancer cells by the first-level ensemble, where each individual network has five outputs respectively adenocarcinoma, squamous cell carcinoma, small cell carcinoma, large cell carcinoma, and normal, among which the former four are different types of lung cancer cells. The predictions of those individual networks are combined by a prevailing method, i.e. plurality voting. Through adopting those techniques, NED achieves not only a high rate of overall identification, but also a low rate of false negative identification, i.e. a low rate of judging cancer cells to be normal ones, which is important in saving lives due to reducing missing diagnoses of cancer patients.     

A neural network method for identification of RNA-interacting residues in protein

Identification of the most putative RNA-interacting residues in protein is an important and challenging problem in a field of molecular recognition. Structural analysis of protein-RNA complexes reveals a strong correlation between interaction residues and their structure. Building on this viewpoint, we have developed a neural network predictor to correctly identify residues involved in protein-RNA interactions from protein sequence and its structural information. The system has been exhaustedly cross-validated with various strategies differing in input encoding, amount of input information, and network architectures. In addition, we have evaluated performance among functional subsets of complexes. Finally, to reflect the properties of protein-RNA complexes in our dataset, two kinds of post-processing method are adopted. The experimental result shows that our system yields not-trivial performance although the residues in interaction sites are too scarce.     

Comparative genomics on FGF7, FGF10, FGF22 orthologs, and identification of fgf25

FGF family members are key molecules for the integrome network in the fields of oncology and regenerative medicine. Based on the comparative genomics on the CCND1-ORAOV1-FGF19-FGF4 locus, we demonstrated that rodent Fgf15 is the ortholog of human FGF19 in 2003. FGF7 (KGF), FGF10, and FGF22 constitute a subfamily among FGF family members. Here, comparative genomics analyses and comparative proteomics analyses on FGF7, FGF10, and FGF22 orthologs were performed. Chicken fgf22, zebrafish fgf22 and fgf25 genes, consisting of three exons, were identified within AC150066.1, BX927243.9 and CR854981.2 genome sequences, respectively. Zebrafish fgf22 (207 aa) showed 46.9%, 48.6% and 53.5% total amino-acid identity with human FGF7, FGF10 and FGF22, respectively. Zebrafish fgf25 (186 aa) showed 39.2%, 52.9% and 45.9% total amino-acid identity with human FGF7, FGF10 and FGF22, respectively. Phylogenetic analyses revealed that zebrafish fgf25 belongs to the FGF10 ortholog group. Zebrafish fgf25 was a novel FGF family member generated by the duplication of fgf10. FGF10-MRPS30-HCN1 locus at human chromosome 5p12 and FGF22-POLRMT-HCN2 locus at 19p13.3 were paralogous regions within the human genome. FGF7 mRNA was expressed in fetal heart, placenta, lung, kidney, and blood vessels. FGF10 mRNA was expressed in fetal lung, placenta, and uterus. FGF22 mRNA was expressed in hippocampus and ovarian fibrotheoma. FGF7 promoter with bHLH biding site and CCAAT box and FGF10 promoter with double bHLH biding sites were conserved well, while FGF22 promoter was significantly divergent. This is the first report on fgf25 gene and also on the comparative integromics analyses of FGF7, FGF10 and FGF22 orthologs.     

Systematic identification of pharmacogenomics information from clinical trials

Recent progress in high-throughput genomic technologies has shifted pharmacogenomic research from candidate gene pharmacogenetics to clinical pharmacogenomics (PGx). Many clinical related questions may be asked such as ‘what drug should be prescribed for a patient with mutant alleles?’ Typically, answers to such questions can be found in publications mentioning the relationships of the gene–drug–disease of interest. In this work, we hypothesize that ClinicalTrials.gov is a comparable source rich in PGx related information. In this regard, we developed a systematic approach to automatically identify PGx relationships between genes, drugs and diseases from trial records in ClinicalTrials.gov. In our evaluation, we found that our extracted relationships overlap significantly with the curated factual knowledge through the literature in a PGx database and that most relationships appear on average 5 years earlier in clinical trials than in their corresponding publications, suggesting that clinical trials may be valuable for both validating known and capturing new PGx related information in a more timely manner. Furthermore, two human reviewers judged a portion of computer-generated relationships and found an overall accuracy of 74% for our text-mining approach. This work has practical implications in enriching our existing knowledge on PGx gene–drug–disease relationships as well as suggesting crosslinks between ClinicalTrials.gov and other PGx knowledge bases.     

基于运动量的神经网络心率预测器的设计及对比研究

目的 利用神经网络建立有效的基于运动量的心率预测模型,分析运动量与心率变化之间的关系.方法 通过对运动量信号进行不同分析（预处理）,并采用不同的神经网络的结构及学习算法,单步或多步预测方式建立了6个预测模型,然后利用采集到的真实数据进行测试,并对各模型结构框架及预测结果进行了对比.结果 建立的模型平均预测误差均保持在一个很小的范围内.结论 利用神经网络建立心率预测模型可有效地反映运动量如何影响心率变化.对比结果表明,在单步预测中,利用神经网络拓扑增强技术（neuro-evolution of augmenting topologies,NEAT）建立的心率预测模型可达到最佳的预测效果,而多步预测利用Adams-Bashforth技术得到的预测结果是最好的.     

Structural and functional comparison of native pentameric, denatured monomeric and biotinylated C-reactive protein

There are many controversies surrounding the biological activities of native C-reactive protein (nCRP) and its various modified forms such as monomerized and biotinylated CRP (mCRP and bCRP). No simple methods have been described to distinguish among these forms. By adapting established electrophoresis methods, we have developed a useful quality control method with which we have investigated the structural and functional characteristics of these forms of CRP. Under all electrophoresis conditions, biotinylation altered the electrophoretic mobility of CRP. nCRP was sensitive to sodium dodecyl sulphate (SDS)-induced monomerization, and only mCRP was susceptible to digestion by trypsin or neutrophil-derived serine proteases. bCRP and mCRP but not nCRP bound to cells, suggesting that chemical modification by biotin and denaturation had altered the structural integrity of CRP. Neither nCRP nor mCRP had the ability to induce secretion of chemokines, nor did they increase intracellular adhesion molecule 1 (ICAM-1) expression in endothelial cells.     

基于心电信号的睡眠阶段的辨识

本研究以睡眠中的心电图信号为基础,计算出心率变异性的时域和频域参数,根据自律神经系统的变化与心率变异性的关联性,对睡眠的不同阶段进行辨识,从而实现睡眠质量监测的居家化。睡眠质量辨别算法以监督式倒传递类神经网络为核心,通过SDNN、RMSSD、SDSD、NN50、p NN50、HFnorm、VLF百分比、5 min TP等8个特征值,进行睡眠的5个阶段的辨别。实验通过686组数据测试发现,隐藏层神经元数目为30,性能目标为40,为最佳参数设定,其中对睡眠中Stage1阶段的识别率可达93.33%。     

基于网络药理学和蛋白模块分析淫羊藿治疗骨关节炎的作用与机制

文题释义：网络药理学：是基于系统生物学的理论,对生物系统的网络分析、选取特定信号节点进行多靶点药物分子设计的新学科。网络药理学强调对信号通路的多途径调节,提高药物的治疗效果,降低毒副作用,从而提高新药临床试验的成功率,节省药物的研发费用。 淫羊藿：为小檗科多年生草本植物,其性温,味辛,归肝经、肾经,传统中医药理论认为淫羊藿具有补肾壮阳、强筋健骨、祛除风湿痹痛等功效,临床上广泛将其应用于骨关节炎疾病的治疗。淫羊藿参与调控骨关节炎的信号通路主要有白细胞介素受体通路、肿瘤坏死因子信号通路、Toll样受体信号通路、T细胞受体信号通路、NF-κB信号通路、破骨细胞分化通路(RANK/RANKL/OPG)。 背景：现代中药药理学研究表明淫羊藿苷在骨关节炎方面有着非常积极的作用。由于淫羊藿所含化学成分较为复杂,且在分子水平上治疗骨关节炎的机制尚不明确,因此利用网络药理学从分子水平来解释淫羊藿治疗骨关节炎的潜在化学成分及作用机制,可为后期药物研发及疾病治疗提供一定理论依据。 目的：采用网络药理学方法探讨淫羊藿治疗骨关节炎的分子机制。 方法：通过TCMSP数据库筛选淫羊藿的药效成分,通过TCMSP、Swiss Target Prediction和STITCH数据库预测淫羊藿药效成分的调控靶点,通过OMIM、GeneCards和TTD数据库预测治疗骨关节炎的靶点,将淫羊藿药效靶点与骨关节炎治疗靶点取交集,得到淫羊藿治疗骨关节炎的靶点,并构建“药物-成分-靶点-疾病”网络。通过STRING数据库分析蛋白互作关系,运用DAVID数据库分析蛋白模块的相关信号通路和功能。 结果与结论：①筛选得到23个淫羊藿药效成分,共预测得到230个淫羊藿药效靶点和1 221个骨关节炎的治疗靶点,取交集后得到95个淫羊藿治疗骨关节炎的靶点,蛋白互作分析提示JUN、AKT1、RELA、MAPK1、IL6、CXCL8、MAPK8、MAPK14、FOS、IL1B为蛋白互作网络中的核心靶点;②关键蛋白模块主要涉及白细胞介素受体通路、肿瘤坏死因子信号通路、Toll样受体信号通路、T细胞受体信号通路、NF-κB信号通路和破骨细胞分化通路,可能通过调控细胞增殖与凋亡、免疫细胞及免疫反应、炎症因子及炎症反应、脂多糖的细胞反应等多种生物过程发挥治疗骨关节炎的作用。 ORCID: 0000-0002-2572-0229(章晓云) 中国组织工程研究杂志出版内容重点：组织构建;骨细胞;软骨细胞;细胞培养;成纤维细胞;血管内皮细胞;骨质疏松;组织工程     

基于动态功能连接分析的青少年吸烟者大脑静息态默认模式网络

采集30名青少年吸烟者和相匹配的30名非吸烟者的静息态功能性磁共振成像数据。通过独立成分分析、滑动窗相关法和k-均值聚类分析探讨青少年吸烟者的默认模式网络的动态功能连接变化。同时,使用斯皮尔曼相关分析评估动态功能连接指标（时间分数、平均停留时间和转换次数）与吸烟统计学数据（烟龄、包年和FTND）的关系。结果显示,相比于非吸烟组,青少年吸烟者大脑的默认模式网络的功能连接性降低,进一步的相关性分析表明动态功能连接指标中的平均停留时间与烟龄呈显著性正相关。此项研究从动态功能网络连接的角度分析吸烟对青少年吸烟者默认模式网络的影响,可以为进一步探索青少年吸烟成瘾机制提供新的视角。     

静息态脑功能网络的小世界属性研究

人脑是一个复杂的网络,不同的功能区域相互作用、相互协调。本文综述了静息态脑功能网络的小世界属性研究方法,首先介绍了基于图论的复杂网络模型的基本概念和特征度量,然后介绍了基于功能磁共振成像数据构建脑功能网络的方法,最后分析了构建的脑功能网络的小世界属性。     

A similarity network approach for the analysis and comparison of protein sequence/structure sets

A set of proteins is a complex system whose elements are interrelated on the concept of sequence- and structure-based similarity. Here, we applied a similarity network-based methodology for the representation and analysis of protein sequences and structures sets using a non-redundant set of 311 proteins and three different information criteria based on sequence-derived features, sequence local alignment and structural alignment. A wide set of measurements, like network degree, clustering coefficient, characteristic path length and vertex centrality were utilized to characterize the networks’ topology. Protein similarity networks were found medium or highly interconnected and the existence of both clusters and random edges classified their fully connected versions as Small World Networks (SWNs). The SWN architecture was able to host the continuous similarity transition among proteins and model the protein information flow during evolution. Recently reported ancestral elements, like the α/β class and certain folds, were remarkably found to act as hubs in the networks. Additionally, the moderate information value of sequence-derived features when used for fold and class assignment was shown on a network basis. The methodology described here can be applied for the analysis of other complex systems which consist of interrelated elements and a certain information flow.     

基于协同智能的膜蛋白相互作用及其网络特性的研究

膜蛋白及其相互作用网络已经成为现在生物信息学领域研究的一个新方向。本文提出了一种基于协同智能的膜蛋白相互作用及其网络的研究方法。这里所说的协同智能指的是谱分析、模糊K近邻(KNN)算法等方法的集成。我们将生物系统看作是一系列的计算组件,这些组件之间在外界环境下相互作用可以根据要求构造出膜蛋白相互作用网络。基于以上所提出的方法,可以更加深入地发现膜蛋白相互作用及其网络具有一定的动态和集成特性,比如小世界网络、无标度分布以及分层模块结构。这些性质类似于其他已经得到广泛研究过的蛋白质相互作用及其网络。针对这些膜蛋白相互作用及其网络特性的研究将有助于以后更深入开展一些相关生物和药物的研究。     

基于功能连接的屈光参差性弱视视觉网络损伤研究

目的基于功能连接算法分析屈光参差性弱视和正常对照组的功能性磁共振成像(functional magnetic resonance imaging,fMRI)数据,通过对比弱视患者脑部自发活动的视觉网络功能连接异常,揭示其视觉损伤机制。方法在闭眼静息条件下采集21名弱视成年人和21名正常对照组的fMRI数据,进行去噪、校正等预处理后,计算初级视觉网络、高级视觉网络和视觉空间网络3个典型的视觉网络内和网络间的功能连接系数,并采用双样本t检验进行统计分析。结果屈光参差性弱视与正常对照组在初级视觉网络中差异无统计学意义,在高级视觉网络和视觉空间网络中差异具有统计学意义,并且它们网络之间的功能连接差异也无统计学意义。结论基于功能连接的静息态功能磁共振分析是有效的非侵入式脑区功能性连接异常研究方法,能够反映屈光参差性弱视不同视觉网络的自发脑部活动脑区异常表现,对于弱视致病机制模型研究和提出新的治疗方法都有重要启发。