基于语义的疾病表型相似性

已知一种药物可用于治疗某疾病,则该药物可能对与该疾病具有相似表型的其他疾病有疗效。因此,大规模地计算疾病表型相似性可辅助发现的疾病新的治疗方法。我们从OMIM下载了3742种疾病的表型信息,从Mesh词库下载13721个关联解剖学和疾病症状的注释词。我们将以上的Mesh词逐一在3742种疾病的表型信息文本中搜索,得到每种疾病涉及的Mesh词汇列表,进而基于语义分析的方法系统地计算了疾病表型的两两相似性矩阵。我们发现疾病关联生物通路最多的有肿瘤生物通路,胰岛素信号通路,肥大心肌病通路和细胞粘附通路等。随疾病对表型相似度的增加,其更涉及相同KEGG生物通路的概率亦增加,证明了本文方法的可靠性。疾病表型相似性可作为疾病在基因水平相似性的补充,有望为药物发现研究提供一条新途径。     

基于图自编码器和协同训练预测miRNA[]与疾病的关联

近年来,越来越多的生物学实验研究表明,microRNA (miRNA)在人类复杂疾病的发展中发挥着重要作用。因此,预测miRNA与疾病之间的关联有助于疾病的准确诊断和有效治疗。由于传统的生物学实验是一种昂贵且耗时的方式,于是许多基于生物学数据的计算模型被提出来预测miRNA与疾病的关联。本研究提出了一种端到端的深度学习模型来预测miRNA-疾病关联关系,称为MDAGAC。首先,通过整合疾病语义相似性,miRNA功能相似性和高斯相互作用谱核相似性,构建miRNA和疾病的相似性图。然后,通过图自编码器和协同训练来改善标签传播的效果。该模型分别在miRNA图和疾病图上建立了两个图自编码器,并对这两个图自编码器进行了协同训练。miRNA图和疾病图上的图自编码器能够通过初始关联矩阵重构得分矩阵,这相当于在图上传播标签。miRNA-疾病关联的预测概率可以从得分矩阵得到。基于五折交叉验证的实验结果表明,MDAGAC方法可靠有效,优于现有的几种预测miRNA-疾病关联的方法。     

斑马鱼,人类疾病研究的理想模式动物

斑马鱼作为一种理想的模式动物已有广泛的应用,而其基因组测序工程的完成和斑马鱼的基因与人类基因高度的相似性,使得斑马鱼在人类疾病的研究中体现着重要的价值.本文从斑马鱼在几种人类重大疾病研究中的运用的角度综述了斑马鱼作为一种重要的模式动物对人类疾病研究的贡献.     

NELDA：基于网络嵌入的lncRNA-疾病关联关系预测

目的 长非编码RNA（lncRNAs）参与多种重要的生物学过程并与各种人类疾病密切相关,因此,lncRNA-疾病关联预测研究有助于疾病的诊断、治疗和在分子水平理解人类疾病的发生发展机制。目前,大多数lncRNA-疾病关联预测方法倾向于浅层整合lncRNA和疾病的相关信息,忽略网络拓扑结构中的深层嵌入特征;另外通过随机选取lncRNA-疾病非关联对构建负样本训练集合,影响预测方法的鲁棒性。方法 本文提出一种基于网络嵌入的NELDA方法,预测潜在的lncRNA-疾病关联关系。NELDA首先利用lncRNA 表达谱、疾病本体论和已知的lncRNA-疾病关联关系,构建lncRNA相似性网络、疾病相似性网络和lncRNA-疾病关联网络。然后,通过设计4个深度自编码器分别从lncRNA/疾病的相似性网络、lncRNA-疾病关联网络学习lncRNA和疾病的低维网络嵌入特征。串联lncRNA和疾病的相似性网络嵌入特征及lncRNA和疾病的关联网络嵌入特征,分别输入两个支持向量机分类器预测lncRNA-疾病关联。最后,采用加权融合策略融合两个支持向量机分类器的预测结果,给出lncRNA-疾病关联关系的最终预测结果。另外,根据已知的lncRNA-疾病关联对和疾病语义相似性,设计一种负样本选取策略构建可信度相对较高的lncRNA-疾病非关联对样本集,用以改善分类器的鲁棒性,该策略通过设计一种打分函数为每对lncRNA-疾病进行打分,选取得分较低的lncRNA-疾病对作为lncRNA-疾病非关联对样本（即负样本）。结果 十折交叉验证实验结果表明：NELDA能够有效预测lncRNA-疾病关联关系,其AUC达到0.982 7,比现有LDASR和 LDNFSGB方法分别提高了0.062 7和0.020 7。另外,负样本选取策略与决策级加权融合策略能够有效改善NELDA预测性能。胃癌和乳腺癌案例研究中,29/40（72.5%）预测的与胃癌和乳腺癌关联lncRNAs,在近期文献和公共数据库中能够发现相关的支撑证据。结论 这些实验结果表明,NELDA是一种有效的lncRNA-疾病关联关系预测方法,具有挖掘潜在lncRNA-疾病关联关系的能力。     

基于节点-模块置信度及局部模块度双重约束挖掘前列腺癌候选疾病模块

前列腺癌病因及发病机理研究有助于前列腺癌预防和治疗.目前,前列腺癌生化试验研究方法成本高、耗时,而基于网络计算方法容易受基因表达谱数据不完整、噪声高及实验样本数量少等约束.为此,本文提出一种基于节点-模块置信度及局部模块度的双重约束算法(命名为NMCOM),挖掘前列腺癌候选疾病模块.NMCOM算法不依赖基因表达谱数据,采用候选基因与致病表型之间一致性得分,候选基因与致病基因之间语义相似性得分融合排序策略,选取起始节点,并基于节点-模块置信度及局部模块度双重约束挖掘前列腺癌候选疾病模块.通过对挖掘出的模块进行富集分析,最终得到18个有显著意义的候选疾病基因模块.与单一打分排序方法及随机游走重开始方法相比,NMCOM融合排序策略的平均排名比小、AUC值大,且挖掘出结果明显优于其他模块挖掘算法,模块生物学意义显著.NMCOM算法不仅能准确有效地挖掘前列腺癌候选疾病模块,且可扩展挖掘其他疾病候选模块.     

尿蛋白质组学在疾病标志物研究中的应用

尿液是重要的疾病标志物来源. 本文介绍了当前尿蛋白质组学的研究进展和尿液中疾病标志物研究的主要问题, 并对未来的发展进行了展望. 由于实际的临床问题通常是对症状相似的多种疾病进行鉴别诊断, 仅仅比较某一种疾病组和健康人对照组的尿蛋白质组差异不足以找到具有诊断能力的标志物. 另外, 尿蛋白质组在个体间和同一个体的不同生理条件下的变化也为疾病标志物的寻找带来了困难. 本文提出, 进行正常人群个体间和不同生理条件下尿蛋白质变化范围的研究可以为鉴定疾病标志物提供参考标准, 从而帮助研究者发现由疾病、而不是生理学差异引起的蛋白的变化. 比较蛋白在血浆和尿液中丰度的变化可以揭示肾脏的生理学功能和发现疾病标志物. 最后提出, 建立一个数据共享平台, 收集和整合已有的疾病标志物研发成果, 将大大推动尿蛋白质组研究的发展.     

转基因果蝇模型与多聚谷氨酰胺疾病

多聚谷氨酰胺(polyglutamine,PolyQ)疾病是由CAG三核苷酸异常重复扩增并编码多聚谷氨酰胺链而致病的一类疾病.目前,已经发现了9种PolyQ疾病,临床表现为成年起病,缓慢进展性的神经系统功能障碍.近年来许多研究者用转基因动物模型(小鼠、果蝇、斑马鱼等)研究神经退行性疾病的表型、发病机制及治疗,其中,果蝇以其独特的分子遗传学优势成为研究PolyQ疾病的理想模式生物.本文就运用转基因果蝇研究PolyQ疾病的表型及发病机制作一综述.     

野生鸟类传染性疾病研究进展

由于具有独特的飞行能力和极强的地理扩散能力,鸟类活动为某些传染性疾病的快速传播和扩散带来了潜在风险.自20世纪以来,以禽霍乱、禽波特淋菌病、西尼罗河热、禽流感等为代表的鸟类疾病频繁暴发,导致为数众多的野生鸟类、家禽甚至人类死亡,给社会造成巨大的经济损失.因此,有关鸟类传染性疾病的研究已引起了国内外学者的广泛关注.从鸟类传染性疾病的生态学特征、疾病对鸟类与人类社会的影响、鸟类对疾病的传播、鸟类疾病的监测、预警和防控等方面对野生鸟类的传染性疾病研究进展进行了综述.不同疾病导致的鸟类死亡量、易感物种数量、暴发频率和地理扩散等特征差异显著.20世纪以来,疾病已成为全球生物多样性的七大威胁因子之一.疾病可能造成鸟类大量死亡,从而对鸟类种群,特别是濒危鸟类种群造成严重影响.其中,人畜共患病还会导致家禽家畜甚至人类的死亡,从而对社会产生严重的影响.野生鸟类作为多种疾病传播的媒介,其移动和迁徙可能会导致疾病的传播与扩散.开展全面的监测活动和建立疾病预警体系,对于疾病的防控具有重要意义.     

蝗虫类似人脑的异常现象为治疗偏头痛等疾病指明新方向

昆虫和偏头痛、中风及癫痫患者头部异常时存在的相似性为开发新药物疗法指明了方向。加拿大皇后大学（Queen’s University）研究蝗虫的生物学家发现这些疾病的人脑异常存在共同现象：脑部功能受干扰,神经细胞关闭。这种现象在蝗虫暴露于类似于高温或者缺氧等极端条件时也有发生。     

整合素连接激酶与肾小球疾病

目前肾小球疾病系病因及发病机理尚不明确,许多细胞因子均参与了肾小球疾病的发生与发展.其中整合素连接激酶(integrin-linkedkinase,ILK)在肾脏疾病的发生发展过程起了一定的作用.它是一种丝氨酸/苏氨酸蛋白激酶,参与多种信号传导通路,在调节细胞凋亡、胚胎发生、肿瘤发生等过程中起重要作用,近期研究发现它与肾小球疾病密切相关.本文就ILK在肾小球疾病中的研究新进展作一综述.     

Annotating Diseases Using Human Phenotype Ontology Improves Prediction of Disease-Associated Long Non-coding RNAs

Recently, many long non-coding RNAs (lncRNAs) have been identified and their biological function has been characterized; however, our understanding of their underlying molecular mechanisms related to disease is still limited. To overcome the limitation in experimentally identifying disease–lncRNA associations, computational methods have been proposed as a powerful tool to predict such associations. These methods are usually based on the similarities between diseases or lncRNAs since it was reported that similar diseases are associated with functionally similar lncRNAs. Therefore, prediction performance is highly dependent on how well the similarities can be captured. Previous studies have calculated the similarity between two diseases by mapping exactly each disease to a single Disease Ontology (DO) term, and then use a semantic similarity measure to calculate the similarity between them. However, the problem of this approach is that a disease can be described by more than one DO terms. Until now, there is no annotation database of DO terms for diseases except for genes. In contrast, Human Phenotype Ontology (HPO) is designed to fully annotate human disease phenotypes. Therefore, in this study, we constructed disease similarity networks/matrices using HPO instead of DO. Then, we used these networks/matrices as inputs of two representative machine learning-based and network-based ranking algorithms, that is, regularized least square and heterogeneous graph-based inference, respectively. The results showed that the prediction performance of the two algorithms on HPO-based is better than that on DO-based networks/matrices. In addition, our method can predict 11 novel cancer-associated lncRNAs, which are supported by literature evidence.     

A Framework for Annotating Human Genome in Disease Context

Identification of gene-disease association is crucial to understanding disease mechanism. A rapid increase in biomedical literatures, led by advances of genome-scale technologies, poses challenge for manually-curated-based annotation databases to characterize gene-disease associations effectively and timely. We propose an automatic method-The Disease Ontology Annotation Framework (DOAF) to provide a comprehensive annotation of the human genome using the computable Disease Ontology (DO), the NCBO Annotator service and NCBI Gene Reference Into Function (GeneRIF). DOAF can keep the resulting knowledgebase current by periodically executing automatic pipeline to re-annotate the human genome using the latest DO and GeneRIF releases at any frequency such as daily or monthly. Further, DOAF provides a computable and programmable environment which enables large-scale and integrative analysis by working with external analytic software or online service platforms. A user-friendly web interface (doa.nubic.northwestern.edu) is implemented to allow users to efficiently query, download, and view disease annotations and the underlying evidences.     

GIMDA: Graphlet interaction‐based MiRNA‐disease association prediction

MicroRNAs (miRNAs) have been confirmed to be closely related to various human complex diseases by many experimental studies. It is necessary and valuable to develop powerful and effective computational models to predict potential associations between miRNAs and diseases. In this work, we presented a prediction model of Graphlet Interaction for MiRNA‐Disease Association prediction (GIMDA) by integrating the disease semantic similarity, miRNA functional similarity, Gaussian interaction profile kernel similarity and the experimentally confirmed miRNA‐disease associations. The related score of a miRNA to a disease was calculated by measuring the graphlet interactions between two miRNAs or two diseases. The novelty of GIMDA lies in that we used graphlet interaction to analyse the complex relationships between two nodes in a graph. The AUCs of GIMDA in global and local leave‐one‐out cross‐validation (LOOCV) turned out to be 0.9006 and 0.8455, respectively. The average result of five‐fold cross‐validation reached to 0.8927 ± 0.0012. In case study for colon neoplasms, kidney neoplasms and prostate neoplasms based on the database of HMDD V2.0, 45, 45, 41 of the top 50 potential miRNAs predicted by GIMDA were validated by dbDEMC and miR2Disease. Additionally, in the case study of new diseases without any known associated miRNAs and the case study of predicting potential miRNA‐disease associations using HMDD V1.0, there were also high percentages of top 50 miRNAs verified by the experimental literatures.     

Precise Disease Severity Assessment for False Smut Disease of Rice

False smut is a disease of rice inflorescence. The existing systems of disease severity assessment for rice false smut disease are not very sensitive as the ball quality and also the impact of false smut on filled grain number and grain‐filling were not under consideration. Here, a precise assessment method to evaluate the severity of the disease was developed. The ‘yield representative’ (YR) based on ‘mean floret wt.’ and ‘filled grain %’ was simulated for the precise disease severity assessment of rice false smut disease. The single floret weight envisages major yield components irrespective of the type of panicle. Correlation between YR and major yield attributes was studied, and it was observed that YR had significant correlation with ‘filled grain %’ (0.77–0.95) and ‘single spikelet weight’ (0.88–0.98). Significant negative correlation of YR was observed with the chaff percentage, false smut ball number and ball weight. This YR‐based methodology was utilized to assess the disease severity in nine rice cultivars. The disease severity in those nine cultivars was evaluated by the already existing methodologies also. The disease severity measured by the present technique was compared with the disease severity assessed by the old methodologies. The procedure adapted here for disease severity measurement was found to be more informative and useful. It was observed that in case of mild infection, the false smut was enhancing yield attributes. The high‐yielding rice varieties Savitri and Gayatri were found to be tolerant to false smut and may be used as resistant donors.     

Neurochemical and Neurogenetic Correlates of Parkinson's Disease

Abstract: We discuss neurochemical and neurogenetic correlates of Parkinson's disease (PD) based on the recent progress in the study of its etiology and pathogenesis. Nigral degeneration with the presence of Lewy bodies in the remaining neurons is the pathologic hallmark of PD, and the resultant loss of striatal dopamine is responsible for most of the clinical manifestations. Although the primary cause is still unknown, mitochondrial respiratory failure and oxidative stress appear to be two major contributors to the nigral cell death. Many endogenous and exogenous compounds with structural similarity to MPTP have been postulated as potential neurotoxins inducing nigral cell death in PD, but there is little evidence of accumulation of such compounds in the nigra. Genetic influence has increasingly been recognized as an important risk factor for PD. In this respect, genetic linkage analysis and molecular cloning of the disease genes in familial parkinsonism are of utmost importance today. Recently, the disease gene for one of the autosomal dominant forms of familial PD was identified, and we cloned the gene for an autosomal recessive type of familial parkinsonism that had been mapped to the long arm of chromosome 6 by our group. Information obtained on familial parkinsonism will contribute to the studies on sporadic PD as well.     

全身型幼年特发性关节炎与经典自身炎症疾病的比较

幼年特发性关节炎(JIA)是一组病因不明、发病年龄小于16周岁、关节炎持续6周或6周以上疾病的统称,分为七种亚型。其中,全身型JIA的临床表现和发病机制与其他亚型明显不同,目前普遍认为其是一种自身炎症性疾病,非自身免疫性疾病。全身型JIA的临床表现与经典自身炎症性疾病具有很高的相似度,而二者的发病机制不仅有相似点,如促炎因子增加和相关信号通路活化,亦有不同点。自身炎症性疾病有明确与固有免疫系统相关的致病基因及家族史,而全身型JIA的致病基因目前尚无定论,也无明显家族遗传性,这是二者最大的区别。本文主要从临床症状特征、发病机制和家族史方面总结和比较了全身型幼年特发性关节炎与经典自身炎症疾病。     

Development of a Multi-Biomarker Disease Activity Test for Rheumatoid Arthritis

Background

Disease activity measurement is a key component of rheumatoid arthritis (RA) management. Biomarkers that capture the complex and heterogeneous biology of RA have the potential to complement clinical disease activity assessment.

Objectives

To develop a multi-biomarker disease activity (MBDA) test for rheumatoid arthritis.

Methods

Candidate serum protein biomarkers were selected from extensive literature screens, bioinformatics databases, mRNA expression and protein microarray data. Quantitative assays were identified and optimized for measuring candidate biomarkers in RA patient sera. Biomarkers with qualifying assays were prioritized in a series of studies based on their correlations to RA clinical disease activity (e.g. the Disease Activity Score 28-C-Reactive Protein [DAS28-CRP], a validated metric commonly used in clinical trials) and their contributions to multivariate models. Prioritized biomarkers were used to train an algorithm to measure disease activity, assessed by correlation to DAS and area under the receiver operating characteristic curve for classification of low vs. moderate/high disease activity. The effect of comorbidities on the MBDA score was evaluated using linear models with adjustment for multiple hypothesis testing.

Results

130 candidate biomarkers were tested in feasibility studies and 25 were selected for algorithm training. Multi-biomarker statistical models outperformed individual biomarkers at estimating disease activity. Biomarker-based scores were significantly correlated with DAS28-CRP and could discriminate patients with low vs. moderate/high clinical disease activity. Such scores were also able to track changes in DAS28-CRP and were significantly associated with both joint inflammation measured by ultrasound and damage progression measured by radiography. The final MBDA algorithm uses 12 biomarkers to generate an MBDA score between 1 and 100. No significant effects on the MBDA score were found for common comorbidities.

Conclusion

We followed a stepwise approach to develop a quantitative serum-based measure of RA disease activity, based on 12-biomarkers, which was consistently associated with clinical disease activity levels.     

平山病的过屈位MRI 表现及其临床价值

目的：探讨平山病的过屈位颈椎MRI特征性影像学表现及其临床诊断价值。方法：总结分析经临床证实的5 例平山病患者 的临床及MRI资料,并结合相关文献报道进行回顾性分析。所有患者均行常规生化检查,脑脊液检查,肌电图检查及肌肉活检。结 果：5 例均为青少年男性,呈单侧上肢远端无力伴萎缩,其中1 例患者累及另一侧,尺侧肌萎缩明显,上肢呈斜坡样改变,均无感觉 障碍和锥体束征;肌电图检查显示神经源性改变,提示受损节段多在下颈髓前角细胞。屈颈MRI检查均可见下颈髓前移、硬脊膜 外间隙增宽,可见迂曲条状血管流空影。结论：平山病的过屈位MRI表现具有一定的特征性,对平山病的诊断具有重要价值。     

平山病的过屈位MRI表现及其临床价值

目的：探讨平山病的过屈位颈椎MRI特征性影像学表现及其临床诊断价值。方法：总结分析经临床证实的5例平山病患者的临床及MRI资料,并结合相关文献报道进行回顾性分析。所有患者均行常规生化检查,脑脊液检查,肌电图检查及肌肉活检。结果：5例均为青少年男性,呈单侧上肢远端无力伴萎缩,其中1例患者累及另一侧,尺侧肌萎缩明显,上肢呈斜坡样改变,均无感觉障碍和锥体束征;肌电图检查显示神经源性改变,提示受损节段多在下颈髓前角细胞。屈颈MRI检查均可见下颈髓前移、硬脊膜外间隙增宽,可见迂曲条状血管流空影。结论：平山病的过屈位MRI表现具有一定的特征性,对平山病的诊断具有重要价值。