Nef Alleles from All Major HIV-1 Clades Activate Src-Family Kinases and Enhance HIV-1 Replication in an Inhibitor-Sensitive Manner

The HIV-1 accessory factor Nef is essential for high-titer viral replication and AIDS progression. Nef function requires interaction with many host cell proteins, including specific members of the Src kinase family. Here we explored whether Src-family kinase activation is a conserved property of Nef alleles from a wide range of primary HIV-1 isolates and their sensitivity to selective pharmacological inhibitors. Representative Nef proteins from the major HIV-1 subtypes A1, A2, B, C, F1, F2, G, H, J and K strongly activated Hck and Lyn as well as c-Src to a lesser extent, demonstrating for the first time that Src-family kinase activation is a highly conserved property of primary M-group HIV-1 Nef isolates. Recently, we identified 4-amino substituted diphenylfuropyrimidines (DFPs) that selectively inhibit Nef-dependent activation of Src-family kinases as well as HIV replication. To determine whether DFP compounds exhibit broad-spectrum Nef-dependent antiretroviral activity against HIV-1, we first constructed chimeric forms of the HIV-1 strain NL4-3 expressing each of the primary Nef alleles. The infectivity and replication of these Nef chimeras was indistinguishable from that of wild-type virus in two distinct cell lines (U87MG astroglial cells and CEM-T4 lymphoblasts). Importantly, the 4-aminopropanol and 4-aminobutanol derivatives of DFP potently inhibited the replication of all chimeric forms of HIV-1 in both U87MG and CEM-T4 cells in a Nef-dependent manner. The antiretroviral effects of these compounds correlated with inhibition of Nef-dependent activation of endogenous Src-family kinases in the HIV-infected cells. Our results demonstrate that the activation of Hck, Lyn and c-Src by Nef is highly conserved among all major clades of HIV-1 and that selective targeting of this pathway uniformly inhibits HIV-1 replication

Differential sensitivity of Src-family kinases to activation by SH3 domain displacement

Src-family kinases (SFKs) are non-receptor protein-tyrosine kinases involved in a variety of signaling pathways in virtually every cell type. The SFKs share a common negative regulatory mechanism that involves intramolecular interactions of the SH3 domain with the PPII helix formed by the SH2-kinase linker as well as the SH2 domain with a conserved phosphotyrosine residue in the C-terminal tail. Growing evidence suggests that individual SFKs may exhibit distinct activation mechanisms dictated by the relative strengths of these intramolecular interactions. To elucidate the role of the SH3:linker interaction in the regulation of individual SFKs, we used a synthetic SH3 domain-binding peptide (VSL12) to probe the sensitivity of downregulated c-Src, Hck, Lyn and Fyn to SH3-based activation in a kinetic kinase assay. All four SFKs responded to VSL12 binding with enhanced kinase activity, demonstrating a conserved role for SH3:linker interaction in the control of catalytic function. However, the sensitivity and extent of SH3-based activation varied over a wide range. In addition, autophosphorylation of the activation loops of c-Src and Hck did not override regulatory control by SH3:linker displacement, demonstrating that these modes of activation are independent. Our results show that despite the similarity of their downregulated conformations, individual Src-family members show diverse responses to activation by domain displacement which may reflect their adaptation to specific signaling environments in vivo. © 2014 Moroco et al

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

An integrative approach for a network based meta-analysis of viral RNAi screens.

BACKGROUND: Big data is becoming ubiquitous in biology, and poses significant challenges in data analysis and interpretation. RNAi screening has become a workhorse of functional genomics, and has been applied, for example, to identify host factors involved in infection for a panel of different viruses. However, the analysis of data resulting from such screens is difficult, with often low overlap between hit lists, even when comparing screens targeting the same virus. This makes it a major challenge to select interesting candidates for further detailed, mechanistic experimental characterization. RESULTS: To address this problem we propose an integrative bioinformatics pipeline that allows for a network based meta-analysis of viral high-throughput RNAi screens. Initially, we collate a human protein interaction network from various public repositories, which is then subjected to unsupervised clustering to determine functional modules. Modules that are significantly enriched with host dependency factors (HDFs) and/or host restriction factors (HRFs) are then filtered based on network topology and semantic similarity measures. Modules passing all these criteria are finally interpreted for their biological significance using enrichment analysis, and interesting candidate genes can be selected from the modules. CONCLUSIONS: We apply our approach to seven screens targeting three different viruses, and compare results with other published meta-analyses of viral RNAi screens. We recover key hit genes, and identify additional candidates from the screens. While we demonstrate the application of the approach using viral RNAi data, the method is generally applicable to identify underlying mechanisms from hit lists derived from high-throughput experimental data, and to select a small number of most promising genes for further mechanistic studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13015-015-0035-7) contains supplementary material, which is available to authorized users