A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Topological insights into HIV interacting host factors <s> Motivation: Understanding the role of genetics in diseases is one of the most important aims of the biological sciences. The completion of the Human Genome Project has led to a rapid increase in the number of publications in this area. However, the coverage of curated databases that provide information manually extracted from the literature is limited. Another challenge is that determining disease-related genes requires laborious experiments. Therefore, predicting good candidate genes before experimental analysis will save time and effort. We introduce an automatic approach based on text mining and network analysis to predict gene-disease associations. We collected an initial set of known disease-related genes and built an interaction network by automatic literature mining based on dependency parsing and support vector machines. Our hypothesis is that the central genes in this disease-specific network are likely to be related to the disease. We used the degree, eigenvector, betweenness and closeness centrality metrics to rank the genes in the network. ::: ::: Results: The proposed approach can be used to extract known and to infer unknown gene-disease associations. We evaluated the approach for prostate cancer. Eigenvector and degree centrality achieved high accuracy. A total of 95% of the top 20 genes ranked by these methods are confirmed to be related to prostate cancer. On the other hand, betweenness and closeness centrality predicted more genes whose relation to the disease is currently unknown and are candidates for experimental study. ::: ::: Availability: A web-based system for browsing the disease-specific gene-interaction networks is available at: http://gin.ncibi.org ::: ::: Contact: [email protected] <s> BIB001 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Topological insights into HIV interacting host factors <s> DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies. <s> BIB002 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Topological insights into HIV interacting host factors <s> Background: In order to replicate, HIV, like all viruses, needs to invade a host cell and hijack it for its own use, a process that involves multiple protein interactions between virus and host. The HIV-1, Human Protein Interaction Database available at NCBI's website captures this information from the primary literature, containing over 2,500 unique interactions. We investigate the general properties and biological context of these interactions and, thus, explore the molecular specificity of the HIV-host perturbation. In particular, we investigate (i) whether HIV preferentially interacts with highly connected and 'central' proteins, (ii) known phenotypic properties of host proteins inferred from essentiality and disease-association data, and (iii) biological context (molecular function, processes and location) of the host proteins to identify attributes most strongly associated with specific HIV interactions. Results: After correcting for ascertainment bias in the literature, we demonstrate a significantly greater propensity for HIV to interact with highly connected and central host proteins. Unexpectedly, we find there are no associations between HIV interaction and inferred essentiality. Similarly, we find a tendency for HIV not to interact with proteins encoded by genes associated with disease. Crucially, we find that functional categories over-represented in HIV-host interactions are innately enriched for highly connected and central proteins in the host system. Conclusions: Our results imply that HIV's propensity to interact with highly connected and central proteins is a consequence of interactions with particular cellular functions, rather than being a direct effect of network topological properties. The lack of a propensity for interactions with phenotypically essential proteins suggests a selective pressure to minimise virulence in retroviral evolution. Thus, the specificity of HIV-host interactions is complex, and only superficially explained by network properties. <s> BIB003 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Topological insights into HIV interacting host factors <s> Since the 1980s, the rapid progression of the HIV/AIDS pandemic has prompted a major international research effort. As a result, the current knowledge on HIV biology, its evolution, and origins [1–5] exceeds that of many, if not all, other viruses. One of the most important areas of HIV research is the detailed understanding of HIV replication. As with all viruses, HIV must exploit the host’s cellular machinery and metabolism to copy its genetic material, synthesize viral proteins, and assemble new virions. The viral replication cycle is thus dependent on an intricate network of direct and indirect protein interactions: between the viral proteins, between the virus and the host, and ultimately between the various host proteins that constitute the subverted cellular systems. When we also take into account the host’s immune response and intrinsic antiviral factors, there are clearly a large number of host–pathogen relationships that are important to our full understanding of HIV biology. However, until recently [6], this valuable information has remained ‘locked’ in the published literature, making it time-consuming to study by individual researchers and inaccessible to computational analysis, thus hindering the progress of research. Here we highlight new developments in the area of host–pathogen systems biology that in our opinion will provide helpful insights to the HIVresearch community. <s> BIB004 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Topological insights into HIV interacting host factors <s> We have analyzed host cell genes linked to HIV replication that were identified in nine genome-wide studies, including three independent siRNA screens. Overlaps among the siRNA screens were very modest (<7% for any pairwise combination), and similarly, only modest overlaps were seen in pairwise comparisons with other types of genome-wide studies. Combining all genes from the genome-wide studies together with genes reported in the literature to affect HIV yields 2,410 protein-coding genes, or fully 9.5% of all human genes (though of course some of these are false positive calls). Here we report an “encyclopedia” of all overlaps between studies (available at http://www.hostpathogen.org), which yielded a more extensively corroborated set of host factors assisting HIV replication. We used these genes to calculate refined networks that specify cellular subsystems recruited by HIV to assist in replication, and present additional analysis specifying host cell genes that are attractive as potential therapeutic targets. <s> BIB005 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Topological insights into HIV interacting host factors <s> BackgroundThe National Institute of Allergy and Infectious Diseases has launched the HIV-1 Human Protein Interaction Database in an effort to catalogue all published interactions between HIV-1 and human proteins. In order to systematically investigate these interactions functionally and dynamically, we have constructed an HIV-1 human protein interaction network. This network was analyzed for important proteins and processes that are specific for the HIV life-cycle. In order to expose viral strategies, network motif analysis was carried out showing reoccurring patterns in virus-host dynamics.ResultsOur analyses show that human proteins interacting with HIV form a densely connected and central sub-network within the total human protein interaction network. The evaluation of this sub-network for connectivity and centrality resulted in a set of proteins essential for the HIV life-cycle. Remarkably, we were able to associate proteins involved in RNA polymerase II transcription with hubs and proteasome formation with bottlenecks. Inferred network motifs show significant over-representation of positive and negative feedback patterns between virus and host. Strikingly, such patterns have never been reported in combined virus-host systems.ConclusionsHIV infection results in a reprioritization of cellular processes reflected by an increase in the relative importance of transcriptional machinery and proteasome formation. We conclude that during the evolution of HIV, some patterns of interaction have been selected for resulting in a system where virus proteins preferably interact with central human proteins for direct control and with proteasomal proteins for indirect control over the cellular processes. Finally, the patterns described by network motifs illustrate how virus and host interact with one another. <s> BIB006
Besides the biological and ontological analysis of the HHPID data set, there are several works that use this catalogued information for the purpose of analysing the network to gain interesting topological insights. For example, Dickerson et al. BIB003 analysed the HIV-1-human bipartite network based on its topological properties and showed an association of interaction pattern of HIV-1 and human proteins. Their analysis mainly concentrated on the general properties of the HIV-1-human PPI interactions and explored the molecular specificity of the HIV-1-host perturbation. The analysis showed how the HIV-1 proteins have a tendency to interact with highly connected ('hubs') and central ('bottlenecks') proteins. The 'hubs' are proteins that have high degree and 'bottlenecks' are proteins with high betweenness centrality. The degree centrality represents the number of connected edges, i.e. the number of protein interactions measured per node. Betweenness centrality is used to measure the centrality of a node in the network by counting the number of shortest paths that go through that node. In other words, how many shortest paths would increase in length if the node is removed from the network BIB001 . In Pinney et al. BIB004 , ten over-represented biological processes, viz., cell differentiation, intracellular signalling cascade, apoptosis, immune response, regulation of cell cycle, DNA packaging, inflammatory response, protein-DNA complex assembly, chemotaxis and nucleosome assembly in HIV-1 interactions were identified. A direct consequence was observed in BIB003 between degree and betweenness centrality of human proteins and the over-representation of those proteins in fundamental biological processes. The mean degree and betweenness centrality of human proteins associated with the above biological processes were 7.27 Â 10 5 and 7.40 Â 10 5 , whereas these values reduced to 2.63 Â 10 5 and 2.33 Â 10 5 when whole human protein interaction network is considered. Therefore, it is obvious that the proteins involved in fundamental biological processes which are over-represented Review on analysis and prediction of HIV-1-human PPI | 835 in HIV-1 interactions, are more connected and central. In Dijk et al. BIB006 associations between proteins involved in RNA polymerase II transcription with 'hubs', and proteasome formation proteins with 'bottlenecks' were established. In this work, the authors hypothesized that during the evolution of HIV-1, the viral proteins are preferentially attached with central human proteins for direct control and with proteasomal proteins for indirect control over the cellular process. Proteosomal proteins acted as negative regulators for HIV-1 proteins BIB005  . In BIB006 it is demonstrated that in early stage of infection, proteasomal proteins regulate some important antiviral host factors like APOBEC3G/F and CD317. In this study, some proteasomal proteins are marked as significantly important based on high bottleneck (or betweenness centrality) scores and linked with important cellular processes. To investigate the extent to which the proteasomal proteins are associated with crucial and important cellular processes we perform clustering of HIV-interacting proteasomal proteins with Gene Ontology (GO) biological process. For functional annotation clustering, we have used the DAVID bioinformatics resources online service BIB002 . We find almost 75% proteins among all proteasomal proteins are involved in at least one annotation cluster and are therefore linked to crucial and important biological processes. To show the significance of this association we randomly pick 10 sets of non-proteasomal proteins and perform annotation clustering in similar way. We notice only 4-9.6% of proteins are associated with important biological processes. In addition, we collect the first and second interacting neighbours of proteasomal proteins and again perform functional annotation clustering with GO biological processes using DAVID. We find almost 78% proteins of the first neighbours and 76% proteins of the second neighbours are associated with important biological and cellular processes. The clustering results of proteasomal proteins as well as their first and second neighbours are given as supplementary files (S1.xlsx, S2.xlsx and S3.xlsx). This study suggests that proteasome proteins are not only responsible for preventing HIV-1 infection at the early stage of infection but also it shows a better connectivity to the important cellular processes because of its high bottleneck scores. In BIB006 , Dijk et al. find a remarkable over-representation of disease-associated genes among HIV-1 interacting proteins compared with non-interacting ones. In  , it is reported that degree and betweenness centrality of proteins are poorly correlated with disease association. However, it is tempting to investigate the correlation between the degree and betweenness centrality of disease-associated HIV-1-interacting human proteins. For this purpose we collect list of genes associated with different disorder/disease classes from Goh et al.  . They have classified all genetic disorders into 22 primary classes. We study the association of HIV-1 interacting human proteins in these 22 primary disease classes. We plot the degree against the betweenness centrality of all HIV-1 interacting proteins that are associated with specific disease classes. In Figure 7 , a total of 22 scatter plots are shown, clearly signifying some sort of relation between degree and betweenness centrality with certain diseases. While for cancer, the correlation appears to be strong (0.9439), for haematological class it is low (0.4840). This indicates that in case of cancer-associated HIV-1 interacting proteins, the 'hubs' (proteins with high degree) are also often 'bottlenecks' (proteins with high betweenness centrality). Most of the disease classes except 'Hematological' and 'Skeletal' roughly follow an association between degree and betweenness centrality. In Supplementary Table S1 , we have listed all the HIV-1 interacting proteins associated with specific disease classes and their degree and betweenness centrality measured in the corpus of whole human PPI network. To show whether the correlation patterns between degree and betweenness centrality of HIV-1 interacting disease associated proteins are statistically significant with respect to random set of proteins, we have conducted the Wilcoxon Rank Sum test. For this purpose we take 22 sets of random proteins one for each disease class, from associated HIV-1 non-interacting proteins and measure degree and betweeness centrality in the context of whole human PPI network. We repeat this procedure 300 times to get 300 instances of 22 samples. After that, we compute mean degree and betweenness centrality for each sample. We plot mean degree against mean betweenness centrality for all 22 samples. This is shown in Figure 8 . From this figure, it is clear that the correlation is not as strong as it appears for HIV-1 interacting proteins. In fact, the Wilcoxon Rank Sum test shows that there is a significant difference at 5% significance level (Pvalue ¼ 0.0265).