Identifying miRNA-disease association based on integrating miRNA topological similarity and functional similarity

Background: MicroRNAs (miRNAs) are a significant type of non-coding RNAs, which usually were encoded by endogenous genes with about ~22 nt nucleotides. Accumulating biological experiments have shown that miRNAs have close associations with various human diseases. Although traditional experimental methods achieve great successes in miRNA-disease interaction identification, these methods also have some limitations. Therefore, it is necessary to develop computational method to predict miRNA-disease interactions. Methods: Here, we propose a computational framework (MDVSI) to predict interactions between miRNAs and diseases by integrating miRNA topological similarity and functional similarity. Firstly, the CosRA index is utilized to measure miRNA similarity based on network topological feature. Then, in order to enhance the reliability of miRNA similarity, the functional similarity and CosRA similarity are integrated based on linear weight method. Further, the potential miRNA-disease associations are predicted by using recommendation method. In addition, in order to overcome limitation of recommendation method, for new disease, a new strategy is proposed to predict potential interactions between miRNAs and new disease based on disease functional similarity. Results: To evaluate the performance of different methods, we conduct ten-fold cross validation and de novo test in experiment and compare MDVSI with two the-state-of-art methods. The experimental result shows that MDVSI achieves an AUC of 0.91, which is at least 0.012 higher than other compared methods. Conclusions: In summary, we propose a computational framework (MDSVI) for miRNA-disease interaction prediction. The experiment results demonstrate that it outperforms other the-state-of-the-art methods. Case study shows that it can effectively identify potential miRNA-disease interactions.     

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．     

The impact of urbanization on taxonomic and functional similarity among bird communities

Aim To assess spatial relationships between avian community similarity and level of urbanization. We tested the following hypotheses for taxonomic similarity: Hypothesis A – the decline in taxonomic similarity with distance is stronger for the least urbanized locations compared to the most urbanized locations; Hypothesis B – the converse of Hypothesis A; and Hypothesis C – the decline in taxonomic similarity with distance is stronger for the most and least urbanized locations compared to locations with intermediate levels of urbanization. We also determined if increasing urbanization led to increased functional similarity within bird communities. Location South‐eastern Australia. Methods Bird species occurrence and density were sampled across 18 towns and 72 neighbourhoods occupying a spatial gradient of up to 882 km. We calculated pairwise values in taxonomic similarity among each town and neighbourhood using the Sørenson coefficient and a similarity measure that accounts for differences in species richness among locations. These values were plotted against pairwise distances among towns and neighbourhoods using linear regression to measure similarity–distance relationships. Neighbourhoods were categorized into four levels of urban development based on dwelling density, urban intensity, vegetation cover, or the density of native, nectar‐rich plants. Variation in bird species density across neighbourhoods and frequency of occurrence across broad habitat types (habitat specialization) was used to assess functional similarity of bird communities in each neighbourhood. Results Among the 18 towns, the decline in taxonomic similarity with distance was weak and significantly less than among regional bird communities that occurred within a 1° grid square around each town. Among the 72 neighbourhoods, similarity–distance relationships differed substantially depending on the level of urban development. Generally, the strongest decline in similarity with distance was for neighbourhoods with the highest and lowest dwelling density, urban intensity and vegetation cover, supporting Hypothesis C. The functional similarity of bird communities increased significantly with dwelling density, and decreased significantly with an increasing density of nectar plants. Main conclusions At the town level, urbanization appears to homogenize regional bird communities. Among neighbourhoods, similarity–distance relationships are substantially influenced by the level of urban development, and increasing urbanization leads to greater functional similarity within bird communities.     

基于功能性状的外来植物入侵预测模型框架构建

预测外来植物的潜在入侵性已成为生物多样性保护研究的重要内容,外来植物与乡土物种间的亲缘关系是预测外来植物能否成功入侵的一个重要途径。然而,达尔文归化难题却预测了两种截然不同的结果(即达尔文归化假说和预适应假说)。该研究解析了达尔文归化难题的内涵,提出了基于功能性状的外来植物与乡土群落间的相似性关系应该是进行外来植物入侵预测的重要切入点,而功能性状的种间分化与种内变异可能是外来植物成功入侵的两种不同生态策略。在此基础上,该研究还通过物种功能性状的多维超体积构建了外来植物与乡土群落间的相似性,提出了基于这种相似性的外来植物入侵预测的研究框架和基本流程。该模型框架的建立有助于理解外来植物的入侵机制,对外来植物的潜在入侵性预测提供了理论依据。当然,要实现外来植物能否成功入侵的准确预测,不仅依赖于功能性状的选择,还要考虑入侵的生境依赖性、空间尺度的重要性以及乡土群落的可入侵性等,未来的研究重点是通过控制实验对该模型进行验证和进一步完善。     

Crystal structures and proposed structural/functional classification of three protozoan proteins from the isochorismatase superfamily

We have determined the crystal structures of three homologous proteins from the pathogenic protozoans Leishmania donovani, Leishmania major, and Trypanosoma cruzi. We propose that these proteins represent a new subfamily within the isochorismatase superfamily (CDD classification cd004310). Their overall fold and key active site residues are structurally homologous both to the biochemically well-characterized N-carbamoylsarcosine-amidohydrolase, a cysteine hydrolase, and to the phenazine biosynthesis protein PHZD (isochorismase), an aspartyl hydrolase. All three proteins are annotated as mitochondrial-associated ribonuclease Mar1, based on a previous characterization of the homologous protein from L. tarentolae. This would constitute a new enzymatic activity for this structural superfamily, but this is not strongly supported by the observed structures. In these protozoan proteins, the extended active site is formed by inter-subunit association within a tetramer, which implies a distinct evolutionary history and substrate specificity from the previously characterized members of the isochorismatase superfamily. The characterization of the active site is supported crystallographically by the presence of an unidentified ligand bound at the active site cysteine of the T. cruzi structure.     

Predicting kinetic constants of protein-protein interactions based on structural properties

Elucidating kinetic processes of protein–protein interactions (PPI) helps to understand how basic building blocks affect overall behavior of living systems. In this study, we used structure‐based properties to build predictive models for kinetic constants of PPI. A highly diverse PPI dataset, protein–protein kinetic interaction data and structures (PPKIDS), was built. PPKIDS contains 62 PPI with complex structures and kinetic constants measured experimentally. The influence of structural properties on kinetics of PPI was studied using 35 structure‐based features, describing different aspects of complex structures. Linear models for the prediction of kinetic constants were built by fitting with selected subsets of structure‐based features. The models gave correlation coefficients of 0.801, 0.732, and 0.770 for k_off, k_on, and K_d, respectively, in leave‐one‐out cross validations. The predictive models reported here use only protein complex structures as input and can be generally applied in PPI studies as well as systems biology modeling. Our study confirmed that different properties play different roles in the kinetic process of PPI. For example, k_on was affected by overall structural features of complexes, such as the composition of secondary structures, the change of translational and rotational entropy, and the electrostatic interaction; while k_off was determined by interfacial properties, such as number of contacted atom pairs per 100 Ų. This information provides useful hints for PPI design. Proteins 2010;79:720–734. © 2010 Wiley‐Liss, Inc.     

MCLPMDA: A novel method for miRNA‐disease association prediction based on matrix completion and label propagation

MiRNAs are a class of small non‐coding RNAs that are involved in the development and progression of various complex diseases. Great efforts have been made to discover potential associations between miRNAs and diseases recently. As experimental methods are in general expensive and time‐consuming, a large number of computational models have been developed to effectively predict reliable disease‐related miRNAs. However, the inherent noise and incompleteness in the existing biological datasets have inevitably limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA‐disease association prediction based on matrix completion and label propagation. Specifically, our method first reconstructs a new miRNA/disease similarity matrix by matrix completion algorithm based on known experimentally verified miRNA‐disease associations and then utilizes the label propagation algorithm to reliably predict disease‐related miRNAs. As a result, MCLPMDA achieved comparable performance under different evaluation metrics and was capable of discovering greater number of true miRNA‐disease associations. Moreover, case study conducted on Breast Neoplasms further confirmed the prediction reliability of the proposed method. Taken together, the experimental results clearly demonstrated that MCLPMDA can serve as an effective and reliable tool for miRNA‐disease association prediction.     

Recurrent use of evolutionary importance for functional annotation of proteins based on local structural similarity

The annotation of protein function has not kept pace with the exponential growth of raw sequence and structure data. An emerging solution to this problem is to identify 3D motifs or templates in protein structures that are necessary and sufficient determinants of function. Here, we demonstrate the recurrent use of evolutionary trace information to construct such 3D templates for enzymes, search for them in other structures, and distinguish true from spurious matches. Serine protease templates built from evolutionarily important residues distinguish between proteases and other proteins nearly as well as the classic Ser-His-Asp catalytic triad. In 53 enzymes spanning 33 distinct functions, an automated pipeline identifies functionally related proteins with an average positive predictive power of 62%, including correct matches to proteins with the same function but with low sequence identity (the average identity for some templates is only 17%). Although these template building, searching, and match classification strategies are not yet optimized, their sequential implementation demonstrates a functional annotation pipeline which does not require experimental information, but only local molecular mimicry among a small number of evolutionarily important residues.     

Brain functional network modeling and analysis based on fMRI: a systematic review

In recent years, the number of patients with neurodegenerative diseases (i.e., Alzheimer’s disease, Parkinson’s disease, mild cognitive impairment) and mental disorders (i.e., depression, anxiety and schizophrenia) have increased dramatically. Researchers have found that complex network analysis can reveal the topology of brain functional networks, such as small-world, scale-free, etc. In the study of brain diseases, it has been found that these topologies have undergoed abnormal changes in different degrees. Therefore, the research of brain functional networks can not only provide a new perspective for understanding the pathological mechanism of neurological and psychiatric diseases, but also provide assistance for the early diagnosis. Focusing on the study of human brain functional networks, this paper reviews the research results in recent years. First, this paper introduces the background of the study of brain functional networks under complex network theory and the important role of topological properties in the study of brain diseases. Second, the paper describes how to construct a brain functional network using neural image data. Third, the common methods of functional network analysis, including network structure analysis and disease classification, are introduced. Fourth, the role of brain functional networks in pathological study, analysis and diagnosis of brain functional diseases is studied. Finally, the paper summarizes the existing studies of brain functional networks and points out the problems and future research directions.     

Unrevealed structural requirements for auxin-like molecules by theoretical and experimental evidences

An computational-biostatistical approach, supported by ab initio optimizations of auxin-like molecules, was used to find biologically meaningful relationships between quantum chemical variables and fresh bioassay's data. It is proven that the auxin-like recognition requires different molecular assembling states. We suggest that the carboxyl group is not the determining factor in explaining the biological auxin-like conduct. The biological effects depends essentially on the chemical condition of the ring system. The aim to find active molecules (quantum objects) via statistical grouping-analysis of molecular quantum similarity measures was verified by bioactivity assays. Next, this approach led to the discovery of a non-carboxylated active auxin-like molecule (2,6-dibromo-phenol). This is the first publication on structure activity relationship of auxin-like molecules, which relies on highly standardized bioassays of different auxins screened in parallel as well as analysed by multi-dimensional scaling.     

基于系统发育及功能性状的不同坡向荒漠植物群落构建研究——以博湖县沙化封禁保护区为例

荒漠植物群落构建机制的研究有助于荒漠生态系统植物资源的保护及系统平衡稳定的维持。基于系统发育与功能性状相结合的方法,以博湖县沙化封禁保护区植物群落为研究对象,在研究区内纵向沙垄的不同坡向上(丘间、阴坡、阳坡)设置样方,进行植物群落物种功能性状和土壤因子的调查与测定,通过检验植物群落物种功能性状的系统发育信号,分析不同坡向植物群落物种系统发育结构和功能结构的表现模式,利用主成分分析(PCA)和线性回归模型(Linear regression model)筛选出影响物种共存的环境因子,进而揭示研究区干旱荒漠生态系统植物群落物种共存的驱动因素。结果表明：(1)研究区植被主要以耐旱的灌木和藜科草本植物为优势种;不同坡向土壤因子具有异质性,丘间、阴坡土壤养分、水分更为丰富。(2)研究区样地植物群落物种10个功能性状指标的系统发育信号K值均小于1,说明物种功能性状受系统进化影响较小,物种功能性状未表现出系统发育保守性。(3)不同坡向系统发育结构均趋于发散,限制相似性在植物分布中占主导作用;丘间和阴坡上较丰富的土壤肥力是物种功能结构发散的主要原因,阳坡物种功能结构表现为聚集效应,生境过滤为其驱动因素...     

Identification of core genes in prefrontal cortex and hippocampus of Alzheimer's disease based on mRNA‐miRNA network

Alzheimer''s disease (AD) is a neurodegenerative disorder with cognitive impairment and abnormal mental behaviour. There is currently no effective cure. The development of early diagnostic markers and the mining of potential therapeutic targets are one of the important strategies. This study aimed to explore potential biomarkers or therapeutic targets related to AD in the hippocampus and prefrontal cortex, two brain regions highly related to AD. Differentially expressed genes and miRNAs between AD patients and healthy controls were obtained from the Gene Expression Omnibus database. The mRNA‐miRNA network was constructed and key genes involved in AD were screened out by protein–protein interaction analysis, and were subsequently verified by independent datasets and qPCR in an AD mouse model. Our findings showed that six hub genes including CALN1, TRPM7, ATR, SOCS3, MOB3A and OGDH were believed to be involved in the pathogenesis of AD. Western blot analysis further determined that CALN1, ATR and OGDH were the possible biomarkers and therapeutic targets for AD. In addition, 6 possible miRNAs biomarkers have also been verified by qPCR on AD animal models. Our findings may benefit clinical diagnosis and early prevention of AD.     

蛋白质序列的六维表示与相似性分析

给出了蛋白质序列的一种六维表示方法,根据这种表示方法有3种不同表示形式,利用这3种形式来构造距离矩阵的信息熵,然后通过信息熵向量的欧式距离、夹角来比较序列之间的相似性。     

Recombinant subunits as tools for the structural and functional characterization of Echinococcus granulosus antigen B

Antigen B (AgB) is a major protein component of the Echinococcus granulosus metacestode. It is oligomeric and this raises several questions regarding the subunit structure and composition of AgB. Several genes that encode different AgB subunits have been identified, and some of these have been cloned and expressed to produce recombinant subunits. The study of these recombinant subunits may provide new insights into the structure, physical-chemical properties, and functional aspects of AgB. Like native AgB, the AgB8/1, AgB8/2, and AgB8/3 recombinant subunits produced in our laboratory form 120-160 kDa oligomers that have stable secondary structures, are strongly antigenic and immunogenic, and selectively bind hydrophobic compounds. Here, we review these results and discuss their implications for the elucidation of the structure and function of AgB. This includes a possible role for AgB in host-parasite interactions.     

Predicting CO2 adsorption and reactivity on transition metal surfaces using popular density functional theory methods

ABSTRACT

In this work, with Ni (110) as a model catalyst surface and CO₂ as an adsorbate, a performance study of Density Functional Theory methods (functionals) is performed. CO being a possible intermediate in CO₂ conversion reactions, binding energies of both, CO₂ and CO, are calculated on the Ni surface and are compared with experimental data. OptPBE-vdW functional correctly predicts CO₂ binding energy on Ni (?62?kJ/mol), whereas CO binding energy is correctly predicted by the rPBE-vdW functional (?138?kJ/mol). The difference in computed adsorption energies by different functionals is attributed to the calculation of gas phase CO₂. Three alternate reaction systems based on a different number of C=O double bonds present in the gas phase molecule are considered to replace CO₂. The error in computed adsorption energy is directly proportional to the number of C=O double bonds present in the gas phase molecule. Additionally, both functionals predict similar carbon–oxygen activation barrier (40?kJ/mol) and equivalent C1s shifts for probe species (?2.6?eV for CCH₃ and +1.5?eV CO₃^?), with respect to adsorbed CO₂. Thus, by including a correction factor of 28?kJ/mol for the computed CO₂ gas phase energy, we suggest using rPBE-vdW functional to investigate CO₂ conversion reactions on different metals.     

Enhanced functional and structural domain assignments using remote similarity detection procedures for proteins encoded in the genome of<Emphasis Type="Italic">Mycobacterium tuberculosis</Emphasis> H37Rv

The sequencing of theMycobacterium tuberculosis (MTB) H37Rv genome has facilitated deeper insights into the biology of MTB, yet the functions of many MTB proteins are unknown. We have used sensitive profile-based search procedures to assign functional and structural domains to infer functions of gene products encoded in MTB. These domain assignments have been made using a compendium of sequence and structural domain families. Functions are predicted for 78% of the encoded gene products. For 69% of these, functions can be inferred by domain assignments. The functions for the rest are deduced from their homology to proteins of known function. Superfamily relationships between families of unknown and known structures have increased structural information by ∼ 11%. Remote similarity detection methods have enabled domain assignments for 1325 ‘hypothetical proteins’. The most populated families in MTB are involved in lipid metabolism, entry and survival of the bacillus in host. Interestingly, for 353 proteins, which we refer to as MTB-specific, no homologues have been identified. Numerous, previously unannotated, hypothetical proteins have been assigned domains and some of these could perhaps be the possible chemotherapeutic targets. MTB-specific proteins might include factors responsible for virulence. Importantly, these assignments could be valuable for experimental endeavors. The detailed results are publicly available at http://hodgkin.mbu.iisc.ernet.in/∼dots. An erratum to this article is available at .     

Mechanisms of Gynostemma pentaphyllum against non‐alcoholic fibre liver disease based on network pharmacology and molecular docking

As a progressive chronic disease, the effective treatment for non‐alcoholic fibre liver disease (NAFLD) has not yet been thoroughly explored at the moment. The widespread use of Gynostemma pentaphyllum (Thunb) for its anti‐insulin resistance effect indicates that potential therapeutic value may be found in Thunb for NAFLD. Hence, this research aims to discover the latent mechanism of Thunb for NAFLD treatment. To achieve the goal of discovering the latent mechanism of Thunb for NAFLD treatment, molecular docking strategy integrated a network phamacology was adopted in the exploration. We acquire Thunb compounds with activeness from TCMSP database. We collect the putative targets of Thunb and NAFLD to generate the network. Key targets and mechanism are screened by PPI analysis, GO and KEGG pathway enrichment analyses. Molecular docking simulation is introduced into the study as assessment method. Through network analysis and virtual screening based on molecular docking, 2 targets (AKT 1 and GSK3B) are identified as key therapeutic targets with satisfying binding affinity. Main mechanism is believed to be the biological process and pathway related to insulin resistance according to the enrichment analyses outcomes. Particularly, the P13K–AKT signalling pathway is recognized as a key pathway of the mechanism. In conclusion, the study shows that Thunb could be a potential treatment against NAFLD and may suppress insulin resistance through the P13K–AKT signalling pathway. The result of the exploration provides a novel perspective for approaching experimental exploration.     

Seed size,number and strategies in annual plants: a comparative functional analysis and synthesis

Background and AimsPlants depend fundamentally on establishment from seed. However, protocols in trait-based ecology currently estimate seed size but not seed number. This can be rectified. For annuals, seed number should simply be a positive function of vegetative biomass and a negative function of seed size.MethodsUsing published values of comparative seed number as the ‘gold standard’ and a large functional database, comparative seed yield and number per plant and per m² were predicted by multiple regression. Subsequently, ecological variation in each was explored for English and Spanish habitats, newly calculated C-S-R strategies and changed abundance in the British flora.Key ResultsAs predicted, comparative seed mass yield per plant was consistently a positive function of plant size and competitive ability, and largely independent of seed size. Regressions estimating comparative seed number included, additionally, seed size as a negative function. Relationships differed numerically between regions, habitats and C-S-R strategies. Moreover, some species differed in life history over their geographical range. Comparative seed yield per m² was positively correlated with FAO crop yield, and increasing British annuals produced numerous seeds. Nevertheless, predicted values must be viewed as comparative rather than absolute: they varied according to the ‘gold standard’ predictor used. Moreover, regressions estimating comparative seed yield per m² achieved low precision.ConclusionsFor the first time, estimates of comparative seed yield and number for >800 annuals and their predictor equations have been produced and the ecological importance of these regenerative traits has been illustrated. ‘Regenerative trait-based ecology’ remains in its infancy, with work needed on determinate vs. indeterminate flowering (‘bet-hedging’), C-S-R methodologies, phylogeny, comparative seed yield per m² and changing life history. Nevertheless, this has been a positive start and readers are invited to use estimates for >800 annuals, in the Supplementary data, to help advance ‘regenerative trait-based ecology’ to the next level.     

Calculation of pKas in RNA: on the structural origins and functional roles of protonated nucleotides

pK(a) calculations based on the Poisson-Boltzmann equation have been widely used to study proteins and, more recently, DNA. However, much less attention has been paid to the calculation of pK(a) shifts in RNA. There is accumulating evidence that protonated nucleotides can stabilize RNA structure and participate in enzyme catalysis within ribozymes. Here, we calculate the pK(a) shifts of nucleotides in RNA structures using numerical solutions to the Poisson-Boltzmann equation. We find that significant shifts are predicted for several nucleotides in two catalytic RNAs, the hairpin ribozyme and the hepatitis delta virus ribozyme, and that the shifts are likely to be related to their functions. We explore how different structural environments shift the pK(a)s of nucleotides from their solution values. RNA structures appear to use two basic strategies to shift pK(a)s: (a) the formation of compact structural motifs with structurally-conserved, electrostatic interactions; and (b) the arrangement of the phosphodiester backbone to focus negative electrostatic potential in specific regions.     

On the structure of hisH: protein structure prediction in the context of structural and functional genomics

We predict a structure of the glutamine amidotransferase subunit (hisH) of imidazole glycerol phosphate synthase (IGPS) which catalyzes the fifth step of the histidine biosynthesis in Escherichia coli. The model is constructed using an energy-based threading program augmented by a multiple sequence to structure profile analysis. In developing our model we identified a conserved core region within hisH and a variable domain which is the likely site of interaction with the synthase subunit (hisF) of IGPS. Information available from structural and functional genomics studies was used to improve the structure prediction, to discuss parallels between histidine biosynthesis and other amino acid and nucleotide metabolic pathways, and to better understand the protein-protein interactions between the hisH and hisF domains of IGPS. This work allows us to develop a preliminary model for the structure of the entire IGPS holoenzyme.