A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Introduction <s> During progression of the Acquired Immune Deficiency Syndrome (AIDS), the human immunodeficiency virus type 1 (HIV-1) is harbored in CD4+ T cells, which act as the primary reservoir for the virus. In vitro, HIV-1 requires activated T cells for a productive infection; however, in vivo, the number of circulating T cells in the activated state that are potential targets for HIV-1 infection is low. We have investigated the ability of HIV-1 to infect resting T cells, and the consequences of such an infection. T cell activation was not required for HIV-1 infection; however, viral DNA was unable to integrate in resting T cells and was maintained extrachromosomally. Subsequent T cell activation allowed integration of extrachromosomal forms and led to a productive viral life cycle. Extrachromosomal forms of viral DNA were found to persist for several weeks after infection of resting T cells and, following T cell activation, these forms maintained their ability to integrate and act as a template for infectious virus. Several lines of evidence, including temporal analysis of HIV-1 replication and analysis of an HIV-1 integrase deletion mutant, indicated that extra-chromosomal HIV-1 DNA genomes were transcriptionally active. These results are compatible with a model whereby HIV-1 can persist in a non-productive extra-chromosomal state in resting T cells until subsequent antigen-induced or mitogen-induced T cell activation, virus integration and release. Thus agents that induce T cell activation may control the rate of HIV-1 replication and spread during AIDS progression. <s> BIB001 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Introduction <s> The human AIDS viruses human immunodeficiency virus type 1 (HIV-1) and type 2 (HIV-2) represent cross-species (zoonotic) infections. Although the primate reservoir of HIV-2 has been clearly identified as the sooty mangabey (Cercocebus atys), the origin of HIV-1 remains uncertain. Viruses related to HIV-1 have been isolated from the common chimpanzee (Pan troglodytes), but only three such SIVcpz infections have been documented, one of which involved a virus so divergent that it might represent a different primate lentiviral lineage. In a search for the HIV-1 reservoir, we have now sequenced the genome of a new SIVcpzstrain (SIVcpzUS) and have determined, by mitochondrial DNA analysis, the subspecies identity of all known SIVcpz-infected chimpanzees. We find that two chimpanzee subspecies in Africa, the central P. t. troglodytes and the eastern P. t. schweinfurthii, harbour SIVcpz and that their respective viruses form two highly divergent (but subspecies-specific) phylogenetic lineages. All HIV-1 strains known to infect man, including HIV-1 groups M, N and O, are closely related to just one of these SIVcpz lineages, that found in P. t. troglodytes. Moreover, we find that HIV-1 group N is a mosaic of SIVcpzUS- and HIV-1-related sequences, indicating an ancestral recombination event in a chimpanzee host. These results, together with the observation that the natural range of P. t. troglodytes coincides uniquely with areas of HIV-1 group M, N and O endemicity, indicate that P. t. troglodytes is the primary reservoir for HIV-1 and has been the source of at least three independent introductions of SIVcpz into the human population. <s> BIB002 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Introduction <s> RNA interference (RNAi) is the process by which double-stranded RNA (dsRNA) directs sequence-specific degradation of messenger RNA in animal and plant cells. In mammalian cells, RNAi can be triggered by 21-nucleotide duplexes of small interfering RNA (siRNA). Here we describe inhibition of early and late steps of HIV-1 replication in human cell lines and primary lymphocytes by siRNAs targeted to various regions of the HIV-1 genome. We demonstrate that synthetic siRNA duplexes or plasmid-derived siRNAs inhibit HIV-1 infection by specifically degrading genomic HIV-1 RNA, thereby preventing formation of viral complementary-DNA intermediates. These results demonstrate the utility of RNAi for modulating the HIV replication cycle and provide evidence that genomic HIV-1 RNA, as it exists within a nucleoprotein reverse-transcription complex, is amenable to siRNA-mediated degradation. <s> BIB003 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Introduction <s> Viruses that induce chronic infections can evade immune responses. HIV is a prototype of this class of pathogen. Not only does it mutate rapidly and make its surface components difficult to access by neutralizing antibodies, but it also creates cellular hideouts, establishes proviral latency, removes cell-surface receptors and destroys immune effectors to escape eradication. A better understanding of these strategies might lead to new approaches in the fight against AIDS. <s> BIB004 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Introduction <s> The cross-species transmission of lentiviruses from African primates to humans has selected viral adaptations which have subsequently facilitated human-to-human transmission. HIV adapts not only by positive selection through mutation but also by recombination of segments of its genome in individuals who become multiply infected. Naturally infected nonhuman primates are relatively resistant to AIDS-like disease despite high plasma viral loads and sustained viral evolution. Further understanding of host resistance factors and the mechanisms of disease in natural primate hosts may provide insight into unexplored therapeutic avenues for the prevention of AIDS. <s> BIB005 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Introduction <s> Retroviruses make a long and complex journey from outside the cell to the nucleus in the early stages of infection, and then an equally long journey back out again in the late stages of infection. Ongoing efforts are identifying an enormous array of cellular proteins that are used by the viruses in the course of their travels. These host factors are potential new targets for therapeutic intervention. <s> BIB006 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Introduction <s> The ‘Human Immunodeficiency Virus Type 1 (HIV-1), Human Protein Interaction Database’, available through the National Library of Medicine at www.ncbi.nlm.nih.gov/RefSeq/HIVInteractions, was created to catalog all interactions between HIV-1 and human proteins published in the peer-reviewed literature. The database serves the scientific community exploring the discovery of novel HIV vaccine candidates and therapeutic targets. To facilitate this discovery approach, the following information for each HIV-1 human protein interaction is provided and can be retrieved without restriction by web-based downloads and ftp protocols: Reference Sequence (RefSeq) protein accession numbers, Entrez Gene identification numbers, brief descriptions of the interactions, searchable keywords for interactions and PubMed identification numbers (PMIDs) of journal articles describing the interactions. Currently, 2589 unique HIV-1 to human protein interactions and 5135 brief descriptions of the interactions, with a total of 14 312 PMID references to the original articles reporting the interactions, are stored in this growing database. In addition, all protein–protein interactions documented in the database are integrated into Entrez Gene records and listed in the ‘HIV-1 protein interactions’ section of Entrez Gene reports. The database is also tightly linked to other databases through Entrez Gene, enabling users to search for an abundance of information related to HIV pathogenesis and replication. <s> BIB007 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Introduction <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> BIB008 </s> A review ofin silicoapproaches for analysis and prediction of HIV-1-human protein–protein interactions <s> Introduction <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> BIB009
Since 1980s, rapid progression has been noted in the field of HIV/AIDS research. HIV belongs to a special class of viruses called retroviruses. Within this class, HIV is placed in the subgroup of lentiviruses. HIV-1 virus contains a single-stranded RNA genome, which codes for primarily 19 proteins; thus, it mostly relies on human cellular functions for most activities during its life cycle. The RNA genome, consisting of seven structural landmarks (LTR, TAR, RRE, PE, SLIP, CRS and INS) and nine genes (gag, pol, env, tat, rev, nef, vif, vpr and vpu), encodes 19 proteins. Among these genes, gag, pol and env contain information needed to make the structural proteins for new virus particles BIB004 . One of the important areas of HIV research is the detailed understanding of HIV biology, its evolution and origins BIB004 BIB005 BIB006 BIB002  BIB001 and the process of replication in host cell. Like all other viruses, HIV-1 must hijack the host cellular machinery for increasing the production of viral genomic material. Initially, HIV-1 virus attaches the envelop protein GP120, which is embedded in the outer membrane of the HIV virion, to the human receptor protein CD4 Tþ cells. To pass through the cell membrane, binding to a second receptor is also required. Two different coreceptors are also involved in the binding process, namely, CCR5, a chemokine receptor that serves as a coreceptor early in the infection and chemokine receptor (CXCR4) that serves as a coreceptor later. After entering the cell, an enzyme called Reverse Transcriptase is responsible for initiating the reverse transcription of a single stranded viral RNA to produce a double stranded viral DNA. After that, the viral DNA enters into the CD4 cell nucleus and integrates into the cell DNA. An enzyme called integrase acts as a key regulator in this process. After that, long chain of HIV proteins is created through transcription and translation by using the machinery of the host CD4 cell. An HIV enzyme called protease cuts up the long chain to produce smaller HIV proteins, which combine with HIV RNA to form a new virus particle. Viral proteins interact with human proteins at every vital stage of the HIV life cycle and form a complex network of molecular events, including virus-host protein-protein interactions (PPIs) BIB008 BIB003 . This intricate network of host and viral proteins are thus considered as an essential source for understanding the inherent mechanism of viral replication cycle BIB009 . In silico approaches have been developed as an alternative for conducting research at the system level. Most of the approaches primarily focus on the analysis of HIV-1-human PPI network, which is constructed from the available existing information catalogued in the HIV-1-Human Protein Interaction Database (HHPID) BIB007 , published in 2009. There are several approaches that use this database to uncover various relationships between the viral proteins, between the virus and the host and ultimately between the various host proteins by exploring diverse methodologies. This valuable information remains in different published literature in an incoherent way. For an individual researcher, it is difficult to efficiently access this information and also time-consuming to acquire an overview in this area. Thus, it would be beneficial to collect all the works and compare different approaches in a single article. Here we provide a comprehensive survey of various computational approaches that mainly focus on this HIV-1-human PPI network. To retain the computational flavour we restrict our analysis at the systems level. Despite being overwhelmed with dozens of experimental and computational results, our aim is to provide a complete view of different works on computational analysis of the HIV-1-human PPI network, constructed from HHPID data set. We broadly divide all these approaches in two categories: network analysis at systems level and prediction of novel PPIs between HIV-1 and human proteins using various computational approaches. Figure 1 shows the classification of the system-level approaches in these two categories and provides a simple overview of all the approaches with year of publication. Since 2006, a number of researchers have provided their valuable opinions and shed light on different issues for uncovering the inherent relationship between HIV-1 and human proteins. Here we try to review most of these works and accumulate their results to give a simplified view.