A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Incorporating other data sources with HHPID in the analysis of HIV perturbation in host cell line <s> We have carried out automated extraction of explicit and implicit biomedical knowledge from publicly available gene and text databases to create a gene-to-gene co-citation network for 13,712 named human genes by automated analysis of titles and abstracts in over 10 million MEDLINE records. The associations between genes have been annotated by linking genes to terms from the medical subject heading (MeSH) index and terms from the gene ontology (GO) database. The extracted database and accompanying web tools for gene-expression analysis have collectively been named 'PubGene'. We validated the extracted networks by three large-scale experiments showing that co-occurrence reflects biologically meaningful relationships, thus providing an approach to extract and structure known biology. We validated the applicability of the tools by analyzing two publicly available microarray data sets. <s> BIB001 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Incorporating other data sources with HHPID in the analysis of HIV perturbation in host cell line <s> MicroRNAs (miRNAs) are ~22-nt RNA segments that are involved in the regulation of protein expression primarily by binding to one or more target sites on an mRNA transcript and inhibiting translation. MicroRNAs are likely to factor into multiple developmental pathways, multiple mechanisms of gene regulation, and underlie an array of inherited disease processes and phenotypic determinants. Several computational programs exist to predict miRNA targets in mammals, fruit flies, worms, and plants. However, to date, there is no systematic collection and description of miRNA targets with experimental support. We describe a database, TarBase, which houses a manually curated collection of experimentally tested miRNA targets, in human/mouse, fruit fly, worm, and zebrafish, distinguishing between those that tested positive and those that tested negative. Each positive target site is described by the miRNA that binds it, the gene in which it occurs, the nature of the experiments that were conducted to test it, the sufficiency of the site to induce translational repression and/or cleavage, and the paper from which all these data were extracted. Additionally, the database is functionally linked to several other useful databases such as Gene Ontology (GO) and UCSC Genome Browser. TarBase reveals significantly more experimentally supported targets than even recent reviews claim, thereby providing a comprehensive data set from which to assess features of miRNA targeting that will be useful for the next generation of target prediction programs. TarBase can be accessed at http://www.diana.pcbi.upenn.edu/tarbase. <s> BIB002 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Incorporating other data sources with HHPID in the analysis of HIV perturbation in host cell line <s> Brass AL, Dykxhoorn DM, Benita Y, et al. Science . 2008;319(5865):921–926 PURPOSE OF THE STUDY. The HIV genome encodes only 15 proteins and, therefore, must use multiple host-cell collaborators for successful replication and transmission. Required host-derived proteins include CD4 as the primary virus receptor and chemokine receptors as coreceptors. This study identified multiple other host proteins required for HIV activity. METHODS. Human cells known to be susceptible to HIV were exposed in vitro to HIV. Using small interfering RNAs able to inhibit each known gene in the human genome 1 at a time, the investigators tested whether HIV could establish an infection and copy itself. HIV dependence on >21 000 human genes was examined. RESULTS. More than 250 human genes were identified to be required for efficient HIV replication. Termed “HIV-dependency factors,” the products of these genes are known to participate in a broad array of cellular functions and implicate unsuspected pathways in the virus life cycle. CONCLUSIONS. The extraordinary dependence of HIV on human host proteins for efficient transmission and replication provides many new potential targets for antiretroviral therapy. REVIEWER COMMENTS. An example of targeting host proteins is the use of chemokine receptor 5 (CCR5) inhibitors. Many people with CCR5 deficiency are very resistant to HIV infection yet have limited if any clinical consequences. Maraviroc CCR5 inhibitor is approved for treatment for HIV infection. This study identified many more such potential targets. <s> BIB003 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Incorporating other data sources with HHPID in the analysis of HIV perturbation in host cell line <s> Human Immunodeficiency Viruses (HIV-1 and HIV-2) rely upon host-encoded proteins to facilitate their replication. Here, we combined genome-wide siRNA analyses with interrogation of human interactome databases to assemble a host-pathogen biochemical network containing 213 confirmed host cellular factors and 11 HIV-1-encoded proteins. Protein complexes that regulate ubiquitin conjugation, proteolysis, DNA-damage response, and RNA splicing were identified as important modulators of early-stage HIV-1 infection. Additionally, over 40 new factors were shown to specifically influence the initiation and/or kinetics of HIV-1 DNA synthesis, including cytoskeletal regulatory proteins, modulators of posttranslational modification, and nucleic acid-binding proteins. Finally, 15 proteins with diverse functional roles, including nuclear transport, prostaglandin synthesis, ubiquitination, and transcription, were found to influence nuclear import or viral DNA integration. Taken together, the multiscale approach described here has uncovered multiprotein virus-host interactions that likely act in concert to facilitate the early steps of HIV-1 infection. <s> BIB004 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Incorporating other data sources with HHPID in the analysis of HIV perturbation in host cell line <s> Human immunodeficiency virus (HIV)-1 depends on the host cell machinery to support its replication. To discover cellular factors associated with HIV-1 replication, we conducted a genome-scale siRNA screen, revealing more than 311 host factors, including 267 that were not previously linked to HIV. Surprisingly, there was little overlap between these genes and the HIV dependency factors described recently. However, an analysis of the genes identified in both screens revealed overlaps in several of the associated pathways or protein complexes, including the SP1/mediator complex and the NF-kappaB signaling pathway. cDNAs for a subset of the identified genes were used to rescue HIV replication following knockdown of the cellular mRNA providing strong evidence that the following six genes are previously uncharacterized host factors for HIV: AKT1, PRKAA1, CD97, NEIL3, BMP2K, and SERPINB6. This study highlights both the power and shortcomings of large scale loss-of-function screens in discovering host-pathogen interactions. <s> BIB005 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Incorporating other data sources with HHPID in the analysis of HIV perturbation in host cell line <s> MicroRNAs (miRNAs) are an important class of small noncoding RNAs capable of regulating other genes’ expression. Much progress has been made in computational target prediction of miRNAs in recent years. More than 10 miRNA target prediction programs have been established, yet, the prediction of animal miRNA targets remains a challenging task. We have developed miRecords, an integrated resource for animal miRNA–target interactions. The Validated Targets component of this resource hosts a large, high-quality manually curated database of experimentally validated miRNA–target interactions with systematic documentation of experimental support for each interaction. The current release of this database includes 1135 records of validated miRNA–target interactions between 301 miRNAs and 902 target genes in seven animal species. The Predicted Targets component of miRecords stores predicted miRNA targets produced by 11 established miRNA target prediction programs. miRecords is expected to serve as a useful resource not only for experimental miRNA researchers, but also for informatics scientists developing the next-generation miRNA target prediction programs. The miRecords is available at http:// miRecords.umn.edu/miRecords. <s> BIB006 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Incorporating other data sources with HHPID in the analysis of HIV perturbation in host cell line <s> Motivation: Prediction of microRNA (miRNA) target mRNAs using machine learning approaches is an important area of research. However, most of the methods suffer from either high false positive or false negative rates. One reason for this is the marked deficiency of negative examples or miRNA-non target pairs. Systematic identification of non-target mRNAs is still not addressed properly, and therefore, current machine learning approaches are compelled to rely on artificially generated negative examples for training. Results: In this paper we have identified ∼300 tissue specific negative examples using a novel approach that involves expression profiling of both miRNAs and mRNAs, miRNA-mRNA structural interactions and seed site conservation. The newly generated negative examples are validated with pSILAC data set (Selbach et al., 2008) that elucidate the fact that the identified non-targets are indeed non-targets.These high throughput tissue specific negative examples and a set of experimentally verified positive examples are then used to build a system called TargetMiner, a support vector machine (SVM) based classifier. In addition to assessing the prediction accuracy on cross-validation experiments, TargetMiner has been validated with a completely independent experimental test data set. Our method outperforms 10 existing target prediction algorithms and provides a good balance between sensitivity and specificity that is not reflected in the existing methods. We achieve a significantly higher sensitivity and specificity of 69% and 67.8% based on a pool of 90 feature set and 76.5% and 66.1% using a set of 30 selected feature set on the completely independent test data set. In order to establish the effectiveness of the systematically generated negative examples, the SVM is trained using a different set of negative data generated using the method in (Yousef et al., 2007). A significantly higher false positive rate (70.6%) is observed when tested on the independent set, while all other factors are kept the same. Again, when an existing method (NBmiRTar) is executed with the our proposed negative data, we observe an improvement in its performance. These clearly establish the effectiveness of the proposed approach of selecting the negative examples systematically. Availability: TargetMiner is now available as an online tool at www.isical.ac.in/∼bioinfo miu Supplementary materials: Supplementary materials are available at www.isical.ac.in/∼bioinfo miu/Download.html <s> BIB007 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Incorporating other data sources with HHPID in the analysis of HIV perturbation in host cell line <s> Very little is known to date about the regulation protocol between transcription factors (TFs), genes and microRNAs (miRNAs) associated with diseases in various organisms. In this paper, we focus on finding the activity of miRNAs through the HIV-1 regulatory pathway in humans at the systems level. For this, we integrate and study the characteristics of the interaction information between HIV-1 and human proteins obtained from literature and prediction analysis. This information, realized in the form of a bipartite network, is subsequently mined with an exhaustive graph search technique to identify the strong significant biclusters, which are effectively the bicliques. They are unified further to form the core bipartite subnetwork. Many of the known HIV-1 associated kinase proteins (including LCK) are found in this core module. From this, the secondary significant proteins are identified by mapping these gateway proteins to the human protein-protein interaction network. Finally, these proteins are mapped onto the TF-to-miRNA and miRNA-to-gene regulatory networks derived from a couple of current studies to obtain a global view of the HIV-1 mediated TF-gene-miRNA inter-regulatory network. Interestingly, a few miRNAs participating in this pathway at the secondary level are found to have oncogenic involvement. <s> BIB008 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Incorporating other data sources with HHPID in the analysis of HIV perturbation in host cell line <s> BACKGROUND ::: AIDS is one of the most devastating diseases in human history. Decades of studies have revealed host factors required for HIV infection, indicating that HIV exploits host processes for its own purposes. HIV infection leads to AIDS as well as various comorbidities. The associations between HIV and human pathways and diseases may reveal non-obvious relationships between HIV and non-HIV-defining diseases. ::: ::: ::: PRINCIPAL FINDINGS ::: Human biological pathways were evaluated and statistically compared against the presence of HIV host factor related genes. All of the obtained scores comparing HIV targeted genes and biological pathways were ranked. Different rank results based on overlapping genes, recovered virus-host interactions, co-expressed genes, and common interactions in human protein-protein interaction networks were obtained. Correlations between rankings suggested that these measures yielded diverse rankings. Rank combination of these ranks led to a final ranking of HIV-associated pathways, which revealed that HIV is associated with immune cell-related pathways and several cancer-related pathways. The proposed method is also applicable to the evaluation of associations between other pathogens and human pathways and diseases. ::: ::: ::: CONCLUSIONS ::: Our results suggest that HIV infection shares common molecular mechanisms with certain signaling pathways and cancers. Interference in apoptosis pathways and the long-term suppression of immune system functions by HIV infection might contribute to tumorigenesis. Relationships between HIV infection and human pathways of disease may aid in the identification of common drug targets for viral infections and other diseases. <s> BIB009
Besides the study of the network built from HHPID data set, the key to deriving new understanding from the HHPID lies in its integration with other data sources. For example, Chen et al. BIB009 analysed the association between HIV-1 infection and human pathways of diseases that may lead to the identification of common drug targets for viral infections and other diseases. In this work, they identified links between HIV-1 infection and other human pathways of disease through several approaches. They primarily investigated the overlap of human genes involved in AIDS and other disease pathways. They gathered 220 human pathways in disease from the Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp/kegg/) and statistically compared with three sets of HIV-1 host factors identified from three systematic screening studies BIB003 BIB004 BIB005 . The evaluation of associations between HIV-1 host factors and KEGG pathways is based on (i) overlap of human genes involved in AIDS and other pathways, (ii) co-expression profiles and (iii) common interaction partners in a human PPI network. In particular, the human pathways are ranked based on these evaluation scores. The top 10 KEGG disease/pathways that emerge from the final ranking list are 'Pancreatic cancer', T-cell receptor signalling pathway', 'Acute myeloid leukaemia', 'B cell receptor signalling pathway', 'small cell lung cancer', 'chronic myeloid leukaemia', 'Adipocytokine signalling pathway', 'toll-like receptor signalling pathway', 'chemokine signalling pathway' and 'apoptosis'. Figure 12 shows the proportion of HIV-1 interacting human proteins that are associated with these pathways. It can be seen that most of the HIV-1 interacting proteins are involved in each pathway. So, it is important to investigate whether these HIV-1 interacting proteins have different characteristics from HIV-1 non-interacting proteins that also appear in these pathways. For this, in Figure 13 we plot a scatter diagram showing the degree versus betweenness centrality of HIV-1 interacting proteins and HIV-1 non-interacting proteins involved in each of the pathways. Interestingly, we find a significant correlation between degree and betweenness centrality of HIV-1 interacting proteins than HIV-1 non-interacting counterparts. It is also noticeable that in most of the pathways, HIV-1 interacting proteins have more tendency of being 'hubs' or 'bottlenecks' than the non-interacting ones. So, it can be stated that HIV-1 makes some sort of perturbation in the disease-related pathways that affect the topological properties of the pathway-related proteins in the context of the whole human PPI network. In another work, Bandyopadhyay et al.  analysed a timecourse gene expression data for identifying active pathways of connected proteins that were significantly under/overexpressed in the different stages of latent HIV-1 replication cycle. The differentially expressed genes were traced in the human protein interaction data set [HPRD] BIB001 for creating an integrated network. Some significant 'activated modules' of connected proteins at different stages of virus reactivation cycle were identified through expression clustering. In summary, this work permitted the identification of several human proteins whose expression levels were associated with either the early induction or the latent phase of viral replication. In another study BIB008 , several data sources are integrated with the existing information catalogued in the HHPID to find out the activity of miRNAs through the HIV-1 regulatory pathway in human. For this they used two TF-miRNA regulation information from two data sources BIB002 BIB006 and one putative database BIB007 . An exhaustive search was conducted to find maximal bicliques from the HIV-1-human bipartite network constructed from the HHPID database. These bicliques represent strong interaction modules consisting of a set of HIV-1 proteins and a set of human proteins. These modules are further investigated to uncover their involvement in possible oncogenic pathways. The significant modules discovered in this study are established as gateways to transmit the immunodeficiency signal within the complex regulatory network of non-coding and coding genes within an organism.