Human Protein-protein Interaction Research Articles

MiRDRN-miRNA disease regulatory network: a tool for exploring disease and tissue-specific microRNA regulatory networks.

BackgroundMicroRNA (miRNA) regulates cellular processes by acting on specific target genes, and cellular processes proceed through multiple interactions often organized into pathways among genes and gene products. Hundreds of miRNAs and their target genes have been identified, as are many miRNA-disease associations. These, together with huge amounts of data on gene annotation, biological pathways, and protein–protein interactions are available in public databases. Here, using such data we built a database and web service platform, miRNA disease regulatory network (miRDRN), for users to construct disease and tissue-specific miRNA-protein regulatory networks, with which they may explore disease related molecular and pathway associations, or find new ones, and possibly discover new modes of drug action.MethodsData on disease-miRNA association, miRNA-target association and validation, gene-tissue association, gene-tumor association, biological pathways, human protein interaction, gene ID, gene ontology, gene annotation, and product were collected from publicly available databases and integrated. A large set of miRNA target-specific regulatory sub-pathways (RSPs) having the form (T, G1, G2) was built from the integrated data and stored, where T is a miRNA-associated target gene, G1 (G2) is a gene/protein interacting with T (G1). Each sequence (T, G1, G2) was assigned a p-value weighted by the participation of the three genes in molecular interactions and reaction pathways.ResultsA web service platform, miRDRN (http://mirdrn.ncu.edu.tw/mirdrn/), was built. The database part of miRDRN currently stores 6,973,875 p-valued RSPs associated with 116 diseases in 78 tissue types built from 207 diseases-associated miRNA regulating 389 genes. miRDRN also provides facilities for the user to construct disease and tissue-specific miRNA regulatory networks from RSPs it stores, and to download and/or visualize parts or all of the product. User may use miRDRN to explore a single disease, or a disease-pair to gain insights on comorbidity. As demonstrations, miRDRN was applied: to explore the single disease colorectal cancer (CRC), in which 26 novel potential CRC target genes were identified; to study the comorbidity of the disease-pair Alzheimer’s disease-Type 2 diabetes, in which 18 novel potential comorbid genes were identified; and, to explore possible causes that may shed light on recent failures of late-phase trials of anti-AD, BACE1 inhibitor drugs, in which genes downstream to BACE1 whose suppression may affect signal transduction were identified.

Identifying potential drug targets in hepatocellular carcinoma based on network analysis and one-class support vector machine

Hepatocellular carcinoma (HCC) is one major cause of cancer-related death worldwide. But now, the systematic therapy for the advanced stages of HCC is rather limited. Thus, the discovery of novel drug targets and thereafter targeted drugs against HCC is continuously needed. In this study, we combined clinical association data, gene expression profiles and manually collected drug target genes with the human protein-protein interaction (PPI) network to establish an in-silico HCC drug target predictor. First, we found drug target genes (DTGs), disease-associated genes (DAGs), prognostic unfavorable genes (PUGs) and cancer up-regulated genes (URGs) have higher degree, betweenness, closeness centrality, while cancer down-regulated genes (DRGs), prognostic favorable genes (PFGs) have lower degrees, in comparison with background genes. Moreover, DTG nodes were shown to be closer to DAG, PUG and URG nodes, but farther away from PFG and DRG nodes. Compared to the background, PFGs and DRGs were shown to have relatively bigger genetic dependency scores, while PUGs and URGs have smaller genetic dependency scores. Finally, based on the observed features of DTGs, we constructed a drug target predictor using one-class support vector machine (one-class SVM). Performance evaluation results suggested our predictor could effectively identify putative drug target genes for further research.

A systematic approach to orient the human protein\u2013protein interaction network

The protein-protein interaction (PPI) network of an organism serves as a skeleton for its signaling circuitry, which mediates cellular response to environmental and genetic cues. Understanding this circuitry could improve the prediction of gene function and cellular behavior in response to diverse signals. To realize this potential, one has to comprehensively map PPIs and their directions of signal flow. While the quality and the volume of identified human PPIs improved dramatically over the last decade, the directions of these interactions are still mostly unknown, thus precluding subsequent prediction and modeling efforts. Here we present a systematic approach to orient the human PPI network using drug response and cancer genomic data. We provide a diffusion-based method for the orientation task that significantly outperforms existing methods. The oriented network leads to improved prioritization of cancer driver genes and drug targets compared to the state-of-the-art unoriented network.

Automated feature engineering improves prediction of protein-protein interactions.

Over the last decade, various machine learning (ML) and statistical approaches for protein-protein interaction (PPI) predictions have been developed to help annotating functional interactions among proteins, essential for our system-level understanding of life. Efficient ML approaches require informative and non-redundant features. In this paper, we introduce novel types of expert-crafted sequence, evolutionary and graph features and apply automatic feature engineering to further expand feature space to improve predictive modeling. The two-step automatic feature-engineering process encompasses the hybrid method for feature generation and unsupervised feature selection, followed by supervised feature selection through a genetic algorithm (GA). The optimization of both steps allows the feature-engineering procedure to operate on a large transformed feature space with no considerable computational cost and to efficiently provide newly engineered features. Based on GA and correlation filtering, we developed a stacking algorithm GA-STACK for automatic ensembling of different ML algorithms to improve prediction performance. We introduced a unified method, HP-GAS, for the prediction of human PPIs, which incorporates GA-STACK and rests on both expert-crafted and 40% of newly engineered features. The extensive cross validation and comparison with the state-of-the-art methods showed that HP-GAS represents currently the most efficient method for proteome-wide forecasting of protein interactions, with prediction efficacy of 0.93 AUC and 0.85 accuracy. We implemented the HP-GAS method as a free standalone application which is a time-efficient and easy-to-use tool. HP-GAS software with supplementary data can be downloaded from: http://www.vinca.rs/180/tools/HP-GAS.php .

Abstract SY42-03: Systematic interrogation of cancer dependencies and synthetic lethal interactions

Abstract Although molecular profiling of human tumors is becoming increasingly routine, the use of this data to guide remains limited to a subset of patients. To complement genome characterization studies, we have used genome scale gain and loss of function approaches to identify genes required for cell survival and transformation. Specifically, we have performed systematic studies to interrogate rare alleles found altered in cancer genomes and used advances in synthetic gene synthesis to prospectively interrogate all possible alleles of known cancer genes. In parallel, we have performed both genome scale RNAi and CRISRP-Cas9 screens in hundreds of cancer cell lines to identify differentially essential genes and the context that specifies gene dependency. Integrating these studies with genome characterization of human tumors and protein-protein interaction databases facilitates the identification of genes and pathways required for the survival of specific cancer subtypes. These studies allow us to define a global cancer dependencies map, which will help further operationalize precision cancer medicine. Citation Format: William C. Hahn. Systematic interrogation of cancer dependencies and synthetic lethal interactions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr SY42-03.

Abstract 1638: Whole-exome sequencing and protein interaction networks to prioritize candidate genes for cutaneous melanoma susceptibility

Abstract Background: Known cutaneous malignant melanoma (CMM) genes account for melanoma risk in less than 40% of melanoma-prone families, suggesting the existence of additional risk genes or other modifiers. Whole exome sequencing (WES) of high-risk families usually results in the identification of a large number of potential disease-causing genes. However, prioritizing these genes and finding the true disease-causing ones have been challenging. We hypothesize that physically interacting proteins might have a similar effect on cell function and therefore, might produce a shared phenotype or increase the risk when mutated. To test this hypothesis, we applied a network approach integrating WES and protein-protein interaction (PPI) data to detect and prioritize rare variants in melanoma-prone families. Methods: We conducted WES analysis on germline DNA of 212 patients from 45 CMM families (2-5 affected people in a family) without known mutations. Variants that were rare (<1% in public and our in-house control datasets), loss-of-function or missense predicted to be deleterious, and showed complete disease co-segregation (present in all affected members within a family) were included in the network analysis. For this analysis, we only focused on genes that interact with known CMM genes (gene list in Goldstein AM, et al. Hum Mol Genet, 2017) which we used as seed proteins. We then applied a degree-aware algorithm (DADA) to rank the set of candidate genes from WES with respect to the set of known CMM genes using human PPI networks. The goal of DADA is to prioritize a candidate set of genes based on their association level with genes known to be related to the disease. Network proximity calculations of the candidates with respect to the seed set were based on Random Walk with Restarts method. We used high quality and scored human proteins interactions collected from InWeb_IM. In the primary analysis, the network contains 625,641 interactions, aggregated from 8 source databases and spanning 87% of reviewed human proteins. We also conducted additional analyses with CMM PPI subnetworks generated using high confidence interaction data from different sources to compare results. Results: The WES analysis described above identified 546 genes that harbored rare variants, likely affect protein function and co-segregated in affected members in CMM families. After applying DADA across all networks, the rankings of these genes were fairly consistent. Top genes included both known CMM genes (such as MC1R, CDKN2B, KITLG, and TINF2) and genes that have not yet been associated with CMM susceptibility (such as FOXK1, AR, LEF1, RGL4, PABPC1, CHEK2, CTBP2, and BLM). These results highlight the importance of known CMM genes and their networks in CMM susceptibility, and demonstrate the potential value of network approaches in gene prioritization. Further evaluation of novel genes is in progress. Citation Format: Sally Yepes, Margaret Tucker, Hela Koka, Kristine Jones, Aurelie Vogt, Laurie Burdette, Wen Luo, Bin Zhu, Meredith Yeager, Belynda Hicks, Neal D. Freedman, Stephen J. Chanock, Alisa M. Goldstein, Xiaohong R. Yang. Whole-exome sequencing and protein interaction networks to prioritize candidate genes for cutaneous melanoma susceptibility [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1638.

Prediction of Dengue-Human Protein Interaction Using Artificial Neural Network for Anti-Viral Drug Discovery

Dengue fever is a viral disease transmitted by Aedesaegyti mosquito species. Every year more than 2 million people are getting affected by this disease. Efforts are taken globally to develop a vaccine against dengue. Dengue-Human Protein interaction plays a vital role in disease prediction. Due to the recent advancements in computational biology, effective application of algorithms to analyze Protein-Protein Interaction will get a better insight into the disease. DenvInt is a biological repository which provides detailed information on Protein-Protein Interaction between dengue-human and dengue-mosquito. The main objective of this study is to computationally predict the dengue-human protein interaction that assists to know which human proteins are affected when dengue virus enters into the human body. This study focuses on applying Artificial Neural Network mechanism for predicting dengue human protein interaction. The results show the effectiveness of the features and algorithm in predicting the interaction type which leads to the development of anti-viral drugs.

A novel matrix of sequence descriptors for predicting protein-protein interactions from amino acid sequences.

Protein-protein interactions (PPIs) play an important role in the life activities of organisms. With the availability of large amounts of protein sequence data, PPIs prediction methods have attracted increasing attention. A variety of protein sequence coding methods have emerged, but the training of these methods is particularly time consuming. To solve this issue, we have proposed a novel matrix sequence coding method. Based on deep neural network (DNN) and a novel matrix protein sequence descriptor, we constructed a protein interaction prediction model for predicting PPIs. When performed on human PPIs data, the method achieved an accuracy of 94.34%, a recall of 98.28%, an area under the curve (AUC) of 97.79% and a loss of 23.25%. A non-redundant dataset was used to evaluate this prediction model, and the prediction accuracy is 88.29%. These results indicate that the matrix of sequence (MOS) descriptor can enhance the predictive power of PPIs and reduce training time, which can be a useful complement for future proteomics research. The experimental code and experimental results can be found at https://github.com/smalltalkman/hppi-tensorflow.

An ego network analysis approach identified important biomarkers with an association to progression and metastasis of gastric cancer.

Gastric cancer (GC) is the fifth most common cancer type worldwide. The aim of this study was to identify gastric-related therapeutic indicators on the basis of the ego network analysis. The microarray data related to GC was downloaded from ArrayExpress database. All human protein-protein interaction (PPI) networks were downloaded from the STRING database. Ego genes were identified on the basis of PPI networks and the gene expression in GC, and then co-expression networks (ego networks) were constructed using these ego genes. On the basis of ego networks, the optimal GO terms and genes were predicted by affinity predictions and cold read predictions. Finally, the predicted genes as effective biomarkers for GC were verified by the bioinformatics analysis. The differential expression networks were conducted and comprised of 365 edges and 232 nodes, which resulted in 218 ego genes. Although there was no significant difference in the expression of top ten ego genes among different groups of GC samples, it was eventually confirmed that top three optimal GO terms with highest cool read values were translational termination (cool read value = 0.987), translational elongation (cool read value = 0.986), and macromolecular complex disassembly (cool read value = 0.985) and top five optimal genes were UBA52, RPS27A, MAPK1, UBC, and UBB. UBA52, RPS27A, and MAPK1 were verified by the bioinformatics analysis to be related to the progression and metastasis of GC. An ego network analysis approach is a very effective method for screening GC and the screened genes might be biomarkers for GC diagnosis and treatment.

Topological and functional comparison of community detection algorithms in biological networks

BackgroundCommunity detection algorithms are fundamental tools to uncover important features in networks. There are several studies focused on social networks but only a few deal with biological networks. Directly or indirectly, most of the methods maximize modularity, a measure of the density of links within communities as compared to links between communities.ResultsHere we analyze six different community detection algorithms, namely, Combo, Conclude, Fast Greedy, Leading Eigen, Louvain and Spinglass, on two important biological networks to find their communities and evaluate the results in terms of topological and functional features through Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology term enrichment analysis. At a high level, the main assessment criteria are 1) appropriate community size (neither too small nor too large), 2) representation within the community of only one or two broad biological functions, 3) most genes from the network belonging to a pathway should also belong to only one or two communities, and 4) performance speed. The first network in this study is a network of Protein-Protein Interactions (PPI) in Saccharomyces cerevisiae (Yeast) with 6532 nodes and 229,696 edges and the second is a network of PPI in Homo sapiens (Human) with 20,644 nodes and 241,008 edges. All six methods perform well, i.e., find reasonably sized and biologically interpretable communities, for the Yeast PPI network but the Conclude method does not find reasonably sized communities for the Human PPI network. Louvain method maximizes modularity by using an agglomerative approach, and is the fastest method for community detection. For the Yeast PPI network, the results of Spinglass method are most similar to the results of Louvain method with regard to the size of communities and core pathways they identify, whereas for the Human PPI network, Combo and Spinglass methods yield the most similar results, with Louvain being the next closest.ConclusionsFor Yeast and Human PPI networks, Louvain method is likely the best method to find communities in terms of detecting known core pathways in a reasonable time.

Cross-species alcohol dependence-associated gene networks: Co-analysis of mouse brain gene expression and human genome-wide association data

Genome-wide association studies on alcohol dependence, by themselves, have yet to account for the estimated heritability of the disorder and provide incomplete mechanistic understanding of this complex trait. Integrating brain ethanol-responsive gene expression networks from model organisms with human genetic data on alcohol dependence could aid in identifying dependence-associated genes and functional networks in which they are involved. This study used a modification of the Edge-Weighted Dense Module Searching for genome-wide association studies (EW-dmGWAS) approach to co-analyze whole-genome gene expression data from ethanol-exposed mouse brain tissue, human protein-protein interaction databases and alcohol dependence-related genome-wide association studies. Results revealed novel ethanol-responsive and alcohol dependence-associated gene networks in prefrontal cortex, nucleus accumbens, and ventral tegmental area. Three of these networks were overrepresented with genome-wide association signals from an independent dataset. These networks were significantly overrepresented for gene ontology categories involving several mechanisms, including actin filament-based activity, transcript regulation, Wnt and Syndecan-mediated signaling, and ubiquitination. Together, these studies provide novel insight for brain mechanisms contributing to alcohol dependence.

Connectivity problems on heterogeneous graphs

BackgroundNetwork connectivity problems are abundant in computational biology research, where graphs are used to represent a range of phenomena: from physical interactions between molecules to more abstract relationships such as gene co-expression. One common challenge in studying biological networks is the need to extract meaningful, small subgraphs out of large databases of potential interactions. A useful abstraction for this task turned out to be the Steiner Network problems: given a reference “database” graph, find a parsimonious subgraph that satisfies a given set of connectivity demands. While this formulation proved useful in a number of instances, the next challenge is to account for the fact that the reference graph may not be static. This can happen for instance, when studying protein measurements in single cells or at different time points, whereby different subsets of conditions can have different protein milieu.Results and discussionWe introduce the condition Steiner Network problem in which we concomitantly consider a set of distinct biological conditions. Each condition is associated with a set of connectivity demands, as well as a set of edges that are assumed to be present in that condition. The goal of this problem is to find a minimal subgraph that satisfies all the demands through paths that are present in the respective condition. We show that introducing multiple conditions as an additional factor makes this problem much harder to approximate. Specifically, we prove that for C conditions, this new problem is NP-hard to approximate to a factor of C - epsilon , for every C ge 2 and epsilon > 0, and that this bound is tight. Moving beyond the worst case, we explore a special set of instances where the reference graph grows monotonically between conditions, and show that this problem admits substantially improved approximation algorithms. We also developed an integer linear programming solver for the general problem and demonstrate its ability to reach optimality with instances from the human protein interaction network.ConclusionOur results demonstrate that in contrast to most connectivity problems studied in computational biology, accounting for multiplicity of biological conditions adds considerable complexity, which we propose to address with a new solver. Importantly, our results extend to several network connectivity problems that are commonly used in computational biology, such as Prize-Collecting Steiner Tree, and provide insight into the theoretical guarantees for their applications in a multiple condition setting.

Collective influencers in protein interaction networks

Recent research increasingly shows the relevance of network based approaches for our understanding of biological systems. Analyzing human protein interaction networks, we determined collective influencers (CI), defined as network nodes that damage the integrity of the underlying networks to the utmost degree. We found that CI proteins were enriched with essential, regulatory, signaling and disease genes as well as drug targets, indicating their biological significance. Also by focusing on different organisms, we found that CI proteins had a penchant to be evolutionarily conserved as CI proteins, indicating the fundamental role that collective influencers in protein interaction networks plays for our understanding of regulation, diseases and evolution.

Integrated network analysis of transcriptomic and protein-protein interaction data in taurine-treated hepatic stellate cells.

BACKGROUNDStudies show that the antifibrotic mechanism of taurine may involve its inhibition of the activation and proliferation of hepatic stellate cells (HSCs). Since the molecular mechanism of taurine-mediated antifibrotic activity has not been fully unveiled and is little studied, it is imperative to use “omics” methods to systematically investigate the molecular mechanism by which taurine inhibits liver fibrosis.AIMTo establish a network including transcriptomic and protein-protein interaction data to elucidate the molecular mechanism of taurine-induced HSC apoptosis.METHODSWe used microarrays, bioinformatics, protein-protein interaction (PPI) network, and sub-modules to investigate taurine-induced changes in gene expression in human HSCs (LX-2). Subsequently, all of the differentially expressed genes (DEGs) were subjected to gene ontology function and Kyoto encyclopedia of genes and genomes pathway enrichment analysis. Furthermore, the interactions of DEGs were explored in a human PPI network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software.RESULTSA total of 635 DEGs were identified in taurine-treated HSCs when compared with the controls. Of these, 304 genes were statistically significantly up-regulated, and 331 down-regulated. Most of these DEGs were mainly located on the membrane and extracellular region, and are involved in the biological processes of signal transduction, cell proliferation, positive regulation of extracellular regulated protein kinases 1 (ERK1) and ERK2 cascade, extrinsic apoptotic signaling pathway and so on. Fifteen significantly enriched pathways with DEGs were identified, including mitogen-activated protein kinase (MAPK) signaling pathway, peroxisome proliferators-activated receptor signaling pathway, estrogen signaling pathway, Th1 and Th2 cell differentiation, cyclic adenosine monophosphate signaling pathway and so on. By integrating the transcriptomics and human PPI data, nine critical genes, including MMP2, MMP9, MMP21, TIMP3, KLF10, CX3CR1, TGFB1, VEGFB, and EGF, were identified in the PPI network analysis.CONCLUSIONTaurine promotes the apoptosis of HSCs via up-regulating TGFB1 and then activating the p38 MAPK-JNK-Caspase9/8/3 pathway. These findings enhance the understanding of the molecular mechanism of taurine-induced HSC apoptosis and provide references for liver disorder therapy.

Shedding Light on the Interaction of Human Anti-Apoptotic Bcl-2 Protein with Ligands through Biophysical and in Silico Studies.

Bcl-2 protein is involved in cell apoptosis and is considered an interesting target for anti-cancer therapy. The present study aims to understand the stability and conformational changes of Bcl-2 upon interaction with the inhibitor venetoclax, and to explore other drug-target regions. We combined biophysical and in silico approaches to understand the mechanism of ligand binding to Bcl-2. Thermal shift assay (TSA) and urea electrophoresis showed a significant increase in protein stability upon venetoclax incubation, which is corroborated by molecular docking and molecular dynamics simulations. An 18 °C shift in Bcl-2 melting temperature was observed in the TSA, corresponding to a binding affinity multiple times higher than that of any other reported Bcl-2 inhibitor. This protein-ligand interaction does not implicate alternations in protein conformation, as suggested by SAXS. Additionally, bioinformatics approaches were used to identify deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) of Bcl-2 and their impact on venetoclax binding, suggesting that venetoclax interaction is generally favored against these deleterious nsSNPs. Apart from the BH3 binding groove of Bcl-2, the flexible loop domain (FLD) also plays an important role in regulating the apoptotic process. High-throughput virtual screening (HTVS) identified 5 putative FLD inhibitors from the Zinc database, showing nanomolar affinity toward the FLD of Bcl-2.

Exploiting locational and topological overlap model to identify modules in protein interaction networks

BackgroundClustering molecular network is a typical method in system biology, which is effective in predicting protein complexes or functional modules. However, few studies have realized that biological molecules are spatial-temporally regulated to form a dynamic cellular network and only a subset of interactions take place at the same location in cells.ResultsIn this study, considering the subcellular localization of proteins, we first construct a co-localization human protein interaction network (PIN) and systematically investigate the relationship between subcellular localization and biological functions. After that, we propose a Locational and Topological Overlap Model (LTOM) to preprocess the co-localization PIN to identify functional modules. LTOM requires the topological overlaps, the common partners shared by two proteins, to be annotated in the same localization as the two proteins. We observed the model has better correspondence with the reference protein complexes and shows more relevance to cancers based on both human and yeast datasets and two clustering algorithms, ClusterONE and MCL.ConclusionTaking into consideration of protein localization and topological overlap can improve the performance of module detection from protein interaction networks.

Function Analysis of Human Protein Interactions Based on a Novel Minimal Loop Algorithm

Background: Various properties of Protein-Protein Interaction (PPI) network have been widely exploited to discover the topological organizing principle and the crucial function motifs involving specific biological pathway or disease process. The current motifs of PPI network are either detected by the topology-based coarse grain algorithms, i.e. community discovering, or depended on the limited-accessible protein annotation data derived precise algorithms. However, the identified network motifs are hardly compatible with the well-defined biological functions according to those two types of methods. Method: In this paper, we proposed a minimal protein loop finding method to explore the elementary structural motifs of human PPI network. Initially, an improved article exchange model was designed to search all the independent shortest protein loops of PPI network. Furthermore, Gene Ontology (GO) based function clustering analysis was implemented to identify the biological functions of the shortest protein loops. Additionally, the disease process associated shortest protein loops were considered as the potential drug targets. </P><P> Result: Our proposed method presents the lowest computational complexity and the highest functional consistency, compared to the three other methods. The functional enrichment and clustering analysis for the identified minimal protein loops revealed the high correlation between the protein loops and the corresponding biological functions, particularly, statistical analysis presenting the protein loops with the length less than 4 is closely connected with some disease process, suggesting the potential drug target. Conclusion: Our minimal protein loop method provides a novel manner to precisely define the functional motif of PPI network, which extends the current knowledge about the cooperating mechanisms and topological properties of protein modules composed of the short loops.

SA109 - CROSS-SPECIES ANALYSIS OF ALCOHOL-REGULATED AND DEPENDENCE-ASSOCIATED GENE NETWORKS IN THE VENTRAL TEGMENTAL AREA

Background Despite studies suggesting that Alcohol Dependence (AD) is moderately heritable, Genome-Wide Association Studies (GWAS) on AD lack power for detecting the small contribution of individual genes to risk, and the ability to determine their mechanistic role in this complex disorder. Gene co-expression networks provide information about the mechanistic framework through which genetic variants take their effects on AD. Studying gene expression in human brains can be problematic. However, studies have identified significantly ethanol-associated gene networks in mice. Direct integration of human GWAS and Protein-Protein Interaction (PPI) data with mouse gene expression data has the potential to identify novel associated loci and the mechanistic frameworks through which they function. We therefore believe that this co-analysis will identify such loci and networks, and that these networks will reflect results of other GWAS's. Methods The present analysis used Edge-Weighted dense module searching for Genome-Wide Association Studies (EW-dmGWAS) to co-analyze GWAS data from the Irish Affected Sib-Pair Study of Alcohol Dependence, human PPI data, and acute-ethanol-exposed mouse gene expression data, to identify and prioritize ethanol-regulated gene networks (i.e. modules) with respect to AD risk contribution. Expression data was obtained from the Ventral Tegmental Area (VTA), due to its role in reward pathways, and previous findings of significantly ethanol-regulated networks in this region. To validate our results, we tested modules for overrepresentation of genes with nominal GWAS p-values from an alternative GWAS dataset (Avon Longitudinal Study of Parents and Children; ALSPAC). To determine the functional relatedness of genes within significantly overrepresented modules, we analyzed their functional enrichment using ToppGene. Finally, to specifically assess the prioritization of modules, we examined the association of module scores with ALSPAC p-values. Results Of the 276 significantly alcohol-associated modules, 25 of them contained at least one gene with nominal significance in ALSPAC. One of these modules showed significant overrepresentation of these genes, after correction for multiple testing (p=0.006). This module showed functional enrichment for genes associated with ubiquitination, abnormal glial and muscle cell morphology, and neuron degeneration. Finally, module scores was significantly, negatively associated with ALSPAC p-value (β=-2.05, p=0.003) (meaning higher module scores indicated smaller p-values). Discussion The results indicate that integration of mouse gene expression data and human genetic data via EW-dmGWAS allows identification of novel alcohol-associated gene networks in the VTA, and that the resulting scores successfully prioritize these modules with respect to association with risk for AD. This suggests that these modules are bridging the gap between the disparate results of independent GWAS's, and that some of the relevant mechanisms involve dopaminergic reward pathways relevant to the early stages of AD development. One of these modules was significantly overrepresented with nominally significant genes from that dataset, indicating that it is of particular importance. The functional enrichment of its constituent genes suggests that the Ventral Tegmental Area is associated with AD by way of alcohol-regulated pathways involving neuron degeneration via ubiquitination.

An Efficient Feature Extraction Technique Based on Local Coding PSSM and Multifeatures Fusion for Predicting Protein-Protein Interactions.

Background:Increasing evidence has indicated that protein-protein interactions (PPIs) play important roles in various aspects of the structural and functional organization of a cell. Thus, continuing to uncover potential PPIs is an important topic in the biomedical domain. Although various feature extraction methods with machine learning approaches have enhanced the prediction of PPIs. There remains room for improvement by developing novel and effective feature extraction methods and classifier approaches to identify PPIs.Method:In this study, we proposed a sequence-based feature extraction method called LCPSSMMF, which combined local coding position-specific scoring matrix (PSSM) with multifeatures fusion. First, we used a novel local coding method based on PSSM to build a new PSSM (CPSSM); the advantage of this method is that it incorporated global and local feature extraction, which can account for the interactions between residues in both continuous and discontinuous regions of amino acid sequences. Second, we adopted 2 different feature extraction methods (Local Average Group [LAG] and Bigram Probability [BP]) to capture multiple key feature information by employing the evolutionary information embedded in the CPSSM matrix. Finally, feature vectors were acquired by using multifeatures fusion method.Result:To evaluate the performance of the proposed feature extraction approach, we employed support vector machine (SVM) as a prediction classifier and applied this method to yeast and human PPI datasets. The prediction accuracies of LCPSSMMF were 93.43% and 90.41% on the yeast and human datasets, respectively. Moreover, we also compared the proposed method with the previous sequence-based approaches on the yeast datasets by using the same SVM classifier. The experimental results indicated that the performance of LCPSSMMF significantly exceeded that of several other state-of-the-art methods. It is proven that the LCPSSMMF approach can capture more local and global discriminatory information than almost all previous methods and can function remarkably well in identifying PPIs. To facilitate extensive research in future proteomics studies, we developed a LCPSSMMFSVM server, which is freely available for academic use at http://219.219.62.123:8888/LCPSSMMFSVM.

Involvement of Surfactant Protein D in Ebola Virus Infection Enhancement via Glycoprotein Interaction.

Since the largest 2014–2016 Ebola virus disease outbreak in West Africa, understanding of Ebola virus infection has improved, notably the involvement of innate immune mediators. Amongst them, collectins are important players in the antiviral innate immune defense. A screening of Ebola glycoprotein (GP)-collectins interactions revealed the specific interaction of human surfactant protein D (hSP-D), a lectin expressed in lung and liver, two compartments where Ebola was found in vivo. Further analyses have demonstrated an involvement of hSP-D in the enhancement of virus infection in several in vitro models. Similar effects were observed for porcine SP-D (pSP-D). In addition, both hSP-D and pSP-D interacted with Reston virus (RESTV) GP and enhanced pseudoviral infection in pulmonary cells. Thus, our study reveals a novel partner of Ebola GP that may participate to enhance viral spread.