The biological context of HIV-1 host interactions reveals subtle insights into a system hijack

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

The biological context of HIV-1 host interactions reveals subtle insights into a system hijack

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

HIV Protein Sequence Hotspots for Crosstalk with Host Hub Proteins

HIV proteins target host hub proteins for transient binding interactions. The presence of viral proteins in the infected cell results in out-competition of host proteins in their interaction with hub proteins, drastically affecting cell physiology. Functional genomics and interactome datasets can be used to quantify the sequence hotspots on the HIV proteome mediating interactions with host hub proteins. In this study, we used the HIV and human interactome databases to identify HIV targeted host hub proteins and their host binding partners (H2). We developed a high throughput computational procedure utilizing motif discovery algorithms on sets of protein sequences, including sequences of HIV and H2 proteins. We identified as HIV sequence hotspots those linear motifs that are highly conserved on HIV sequences and at the same time have a statistically enriched presence on the sequences of H2 proteins. The HIV protein motifs discovered in this study are expressed by subsets of H2 host proteins potentially outcompeted by HIV proteins. A large subset of these motifs is involved in cleavage, nuclear localization, phosphorylation, and transcription factor binding events. Many such motifs are clustered on an HIV sequence in the form of hotspots. The sequential positions of these hotspots are consistent with the curated literature on phenotype altering residue mutations, as well as with existing binding site data. The hotspot map produced in this study is the first global portrayal of HIV motifs involved in altering the host protein network at highly connected hub nodes

Genome-wide search for the genes accountable for the induced resistance to HIV-1 infection in activated CD4+ T cells: apparent transcriptional signatures, co-expression networks and possible cellular processes

BACKGROUND: Upon co-stimulation with CD3/CD28 antibodies, activated CD4 + T cells were found to lose their susceptibility to HIV-1 infection, exhibiting an induced resistant phenotype. This rather unexpected phenomenon has been repeatedly confirmed but the underlying cell and molecular mechanisms are still unknown. METHODS: We first replicated the reported system using the specified Dynal beads with PHA/IL-2-stimulated and un-stimulated cells as controls. Genome-wide expression and analysis were then performed by using Agilent whole genome microarrays and established bioinformatics tools. RESULTS: We showed that following CD3/CD28 co-stimulation, a homogeneous population emerged with uniform expression of activation markers CD25 and CD69 as well as a memory marker CD45RO at high levels. These cells differentially expressed 7,824 genes when compared with the controls on microarrays. Series-Cluster analysis identified 6 distinct expression profiles containing 1,345 genes as the representative signatures in the permissive and resistant cells. Of them, 245 (101 potentially permissive and 144 potentially resistant) were significant in gene ontology categories related to immune response, cell adhesion and metabolism. Co-expression networks analysis identified 137 “key regulatory” genes (84 potentially permissive and 53 potentially resistant), holding hub positions in the gene interactions. By mapping these genes on KEGG pathways, the predominance of actin cytoskeleton functions, proteasomes, and cell cycle arrest in induced resistance emerged. We also revealed an entire set of previously unreported novel genes for further mining and functional validation. CONCLUSIONS: This initial microarray study will stimulate renewed interest in exploring this system and open new avenues for research into HIV-1 susceptibility and its reversal in target cells, serving as a foundation for the development of novel therapeutic and clinical treatments

Sequence- and Interactome-Based Prediction of Viral Protein Hotspots Targeting Host Proteins: A Case Study for HIV Nef

Virus proteins alter protein pathways of the host toward the synthesis of viral particles by breaking and making edges via binding to host proteins. In this study, we developed a computational approach to predict viral sequence hotspots for binding to host proteins based on sequences of viral and host proteins and literature-curated virus-host protein interactome data. We use a motif discovery algorithm repeatedly on collections of sequences of viral proteins and immediate binding partners of their host targets and choose only those motifs that are conserved on viral sequences and highly statistically enriched among binding partners of virus protein targeted host proteins. Our results match experimental data on binding sites of Nef to host proteins such as MAPK1, VAV1, LCK, HCK, HLA-A, CD4, FYN, and GNB2L1 with high statistical significance but is a poor predictor of Nef binding sites on highly flexible, hoop-like regions. Predicted hotspots recapture CD8 cell epitopes of HIV Nef highlighting their importance in modulating virus-host interactions. Host proteins potentially targeted or outcompeted by Nef appear crowding the T cell receptor, natural killer cell mediated cytotoxicity, and neurotrophin signaling pathways. Scanning of HIV Nef motifs on multiple alignments of hepatitis C protein NS5A produces results consistent with literature, indicating the potential value of the hotspot discovery in advancing our understanding of virus-host crosstalk

Analysis of networks of host proteins in the early time points following HIV transduction

Background: Utilization of quantitative proteomics data on the network level is still a challenge in proteomics data analysis. Currently existing models use sophisticated, sometimes hard to implement analysis techniques. Our aim was to generate a relatively simple strategy for quantitative proteomics data analysis in order to utilize as much of the data generated in a proteomics experiment as possible. Results: In this study, we applied label-free proteomics, and generated a network model utilizing both qualitative, and quantitative data, in order to examine the early host response to Human Immunodeficiency Virus type 1 (HIV-1). A weighted network model was generated based on the amount of proteins measured by mass spectrometry, and analysis of weighted networks and functional sub-networks revealed upregulation of proteins involved in translation, transcription, and DNA condensation in the early phase of the viral life-cycle. Conclusion: A relatively simple strategy for network analysis was created and applied to examine the effect of HIV-1 on host cellular proteome. We believe that our model may prove beneficial in creating algorithms, allowing for both quantitative and qualitative studies of proteome change in various biological and pathological processes by quantitative mass spectrometry.Hungarian Scientific Research Fund [NKFI-6, 125238]; Higher Education Institutional Excellence Programme of the Ministry of Human Capacities in Hungary [GINOP-2.3.3-15-2016-00020]; Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences; NIEHS [ES06694]; NIH/NCI [CA023074]; BIO5 Institute of the University of Arizona; NIH/NCRR [1S10 RR028868-01]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Patterns of HIV-1 Protein Interaction Identify Perturbed Host-Cellular Subsystems

Human immunodeficiency virus type 1 (HIV-1) exploits a diverse array of host cell functions in order to replicate. This is mediated through a network of virus-host interactions. A variety of recent studies have catalogued this information. In particular the HIV-1, Human Protein Interaction Database (HHPID) has provided a unique depth of protein interaction detail. However, as a map of HIV-1 infection, the HHPID is problematic, as it contains curation error and redundancy; in addition, it is based on a heterogeneous set of experimental methods. Based on identifying shared patterns of HIV-host interaction, we have developed a novel methodology to delimit the core set of host-cellular functions and their associated perturbation from the HHPID. Initially, using biclustering, we identify 279 significant sets of host proteins that undergo the same types of interaction. The functional cohesiveness of these protein sets was validated using a human protein-protein interaction network, gene ontology annotation and sequence similarity. Next, using a distance measure, we group host protein sets and identify 37 distinct higher-level subsystems. We further demonstrate the biological significance of these subsystems by cross-referencing with global siRNA screens that have been used to detect host factors necessary for HIV-1 replication, and investigate the seemingly small intersect between these data sets. Our results highlight significant host-cell subsystems that are perturbed during the course of HIV-1 infection. Moreover, we characterise the patterns of interaction that contribute to these perturbations. Thus, our work disentangles the complex set of HIV-1-host protein interactions in the HHPID, reconciles these with siRNA screens and provides an accessible and interpretable map of infection

Associations between HIV and Human Pathways Revealed by Protein-Protein Interactions and Correlated Gene Expression Profiles

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

Computational analysis of protein interaction networks for infectious diseases

Infectious diseases caused by pathogens, including viruses, bacteria and parasites, pose a serious threat to human health worldwide. Frequent changes in the pattern of infection mechanisms and the emergence of multidrug resistant strains among pathogens have weakened the current treatment regimen. This necessitates the development of new therapeutic interventions to prevent and control such diseases. To cater to the need, analysis of protein interaction networks (PINs) has gained importance as one of the promising strategies. The present review aims to discuss various computational approaches to analyse the PINs in context to infectious diseases. Topology and modularity analysis of the network with their biological relevance, and the scenario till date about host-pathogen and intra-pathogenic protein interaction studies were delineated. This would provide useful insights to the research community thereby enabling them to design novel biomedicine against such infectious diseases