22 research outputs found

    HyperISGylation of Old World Monkey ISG15 in Human Cells

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    BACKGROUND: ISG15 is an Ubiquitin-like protein, highly induced by Type I Interferons. Upon the cooperative activity of specific Ubiquitinating enzymes, ISG15 can be conjugated to its substrates. Increasing evidence points to a role for protein ISGylation in anti-viral and anti-tumoral defense. PRINCIPAL FINDINGS: We identified ISG15 from Old World Monkeys (OWm) as a hyper-efficient protein modifier. Western blot analysis visualized more efficient conjugation of OWmISG15 relative to HuISG15 in human (Hu), monkey and mouse (Mo) cell-lines. Moreover, the substrates of OWmISG15 identified upon Tandem Affinity Purification followed by LC-MS/MS identification largely outnumbered these of HuISG15 itself. Several Ubiquitin-Conjugating enzymes were identified as novel ISGylated substrates. Introduction of a N89D mutation in HuISG15 improved its ISGylation capacity, and additional Q31K/T33A/D133N mutations yielded a HuISG15 variant with an ISGylation efficiency comparable to OWmISG15. Homology modeling and structural superposition situate N89 in the interaction interface with the Activating enzyme. Analysis of the UbE1L residues in this interface revealed a striking homology between OWmUbE1L and HuUbE1, the Activating enzyme of Ubiquitin. In line with this observation, we found efficient activation of AgmISG15, but not HuISG15 or MoISG15, by HuUbE1, thus providing a likely explanation for OWm hyperISGylation. CONCLUSIONS: This study discloses the poor conjugation competence of HuISG15 compared to OWmISG15 and maps the critical determinants for efficient conjugation. HyperISGylation may greatly assist ISGylation studies and may enhance its function as positive regulator of Interferon-related immune responses or as anti-tumoral modulator

    Definition of the interacting interfaces of Apobec3G and HIV-1 Vif using MAPPIT mutagenesis analysis

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    The host restriction factor Apobec3G is a cytidine deaminase that incorporates into HIV-1 virions and interferes with viral replication. The HIV-1 accessory protein Vif subverts Apobec3G by targeting it for proteasomal degradation. We propose a model in which Apobec3G N-terminal domains symmetrically interact via a head-to-head interface containing residues 122 RLYYFW 127. To validate this model and to characterize the Apobec3G–Apobec3G and the Apobec3G–Vif interactions, the mammalian protein–protein interaction trap two-hybrid technique was used. Mutations in the head-to-head interface abrogate the Apobec3G–Apobec3G interaction. All mutations that inhibit Apobec3G–Apobec3G binding also inhibit the Apobec3G–Vif interaction, indicating that the head-to head interface plays an important role in the interaction with Vif. Only the D128K, P129A and T32Q mutations specifically affect the Apobec3G–Vif association. In our model, D128, P129 and T32 cluster at the edge of the head-to-head interface, possibly forming a Vif binding site composed of two Apobec3G molecules. We propose that Vif either binds at the Apobec3G head-to-head interface or associates with an RNA-stabilized Apobec3G oligomer

    MAPPIT analysis of medically relevant protein-protein interactions

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    Identification of binding sites for myeloid differentiation primary response gene 88 (MyD88) and Toll-like receptor 4 in MyD88 adapter-like (Mal)

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    Upon activation, Toll-like receptor 4 (TLR4) binds adapter proteins, including MyD88 (myeloid differentiation primary response gene 88) and Mal (MyD88 adapter-like) for its signal transduction. TLR4 and the adapter proteins each contain a Toll/Il-1 receptor domain (TIR domain). In this study we used random mutagenesis and the mammalian two-hybrid method MAPPIT (mammalian protein-protein interaction trap) to identify mutations in Mal that disrupt its interaction with TLR4 and/or MyD88. Our study shows that four potential binding sites and the AB-loop in the Mal TIR domain all contribute to formation of the TLR4-Mal-MyD88 complex. Mutations in the symmetrical back-to-back Mal homodimer interface affect Mal homodimerization and interaction with MyD88 and TLR4. Our data suggest that Mal dimerization may lead to formation of potential binding platforms on the top and the side of the Mal dimer that bind MyD88 or TLR4. Mutations that affect the interaction of Mal with MyD88 also affect NF-kappa B activation induced by Mal overexpression. In MAPPIT, co-expression of the MyD88 TIR domain enhances Mal dimerization and Mal binding to TLR4. Similarly, co-expression of Mal and the MyD88 TIR domain strongly promotes dimerization of the TLR4 intracellular domain in MAPPIT. The different types of TIR-TIR interactions in the TLR4-Mal-MyD88 complex thus show cooperative binding in MAPPIT. We present plausible models for the TIR-TIR interactions in the TLR4-Mal-MyD88 complex

    Quantitative contamination assessment of Escherichia coli in baby spinach primary production in Spain : effects of weather conditions and agricultural practices

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    A quantitative microbial contamination model of Escherichia colt during primary production of baby spinach was developed. The model included only systematic contamination routes (e.g. soil and irrigation water) and it was used to evaluate the potential impact of weather conditions, agricultural practices as well as bacterial fitness in soil on the E. colt levels present in the crop at harvest. The model can be used to estimate E. colt contamination of baby spinach via irrigation water, via soil splashing due to irrigation water or rain events, and also including the inactivation of E. colt on plants due to solar radiation during a variable time of culturing before harvest. Seasonality, solar radiation and rainfall were predicted to have an important impact on the E. colt contamination. Winter conditions increased E. colt prevalence and levels when compared to spring conditions. As regards agricultural practices, both water quality and irrigation system slightly influenced E. colt levels on baby spinach. The good microbiological quality of the irrigation water (average E. colt counts in positive water samples below 1 log/100 mL) could have influenced the differences observed among the tested agricultural practices (water treatment and irrigation system). This quantitative microbial contamination model represents a preliminary framework that assesses the potential impact of different factors and intervention strategies affecting E. colt concentrations at field level. Taking into account that E. colt strains may serve as a surrogate organism for enteric bacterial pathogens, obtained results on E. colt levels on baby spinach may be indicative of the potential behaviour of these pathogens under defined conditions

    Effect of site-directed mutations in the N-terminal CDA domain of Apobec3G on the interactions with Gagpol and Gag.

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    <p>A: Effect of mutations on the Apobec3G-Gag interaction. Interactions between the Gag prey and the different mutant Apobec3G baits were determined via MAPPIT. The data are expressed as fold induction of luciferase activity after stimulation with Epo. All mutations that disrupt the Apobec3G-Gagpol interaction (Red/Orange in panel C) also disrupt the Apobec3G-Gag interaction. B &C: The effects on the Apobec3G-Apobec3G (B) and Apobec3G-Gagpol (C) interaction were determined via MAPPIT. The residues of the head-to-head interface are directed towards the viewer. B: Effect of mutations on the Apobec3G-Apobec3G interaction (47). C: Effect of mutations on the Apobec3G-Gagpol interaction. The colors in A and B indicate the relative MAPPIT signal of the Apobec3G bait mutants, compared to wild type. Color codes: Red: <25% of WT, orange: <50% of WT, black: >50% of WT (no strong effect). Mutations that disrupt the Apobec3G-Apobec3G interaction (Red/Orange) also disrupt the Apobec3G-Gagpol interaction.</p

    Random Mutagenesis MAPPIT Analysis Identifies Binding Sites for Vif and Gag in Both Cytidine Deaminase Domains of Apobec3G

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    <div><p>The mammalian two-hybrid system MAPPIT allows the detection of protein-protein interactions in intact human cells. We developed a random mutagenesis screening strategy based on MAPPIT to detect mutations that disrupt the interaction of one protein with multiple protein interactors simultanously. The strategy was used to detect residues of the human cytidine deaminase Apobec3G that are important for its homodimerization and its interaction with the HIV-1 Gag and Vif proteins. The strategy is able to identify the previously described head-to-head homodimerization interface in the N-terminal domain of Apobec3G. Our analysis further detects two new potential interaction surfaces in the N-and C-terminal domain of Apobec3G for interaction with Vif and Gag or for Apobec3G dimerization.</p> </div

    Method for the identification of random mutants that disrupt a protein-protein interaction based on MAPPIT.

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    <p>1: A fragment of the MAPPIT bait is randomly mutated via Genemorph® II PCR. 2: The mutated PCR fragments are ligated into the MAPPIT bait vector and the resulting plasmid mutant pool is used to transform E. coli. 3: Plasmid DNA from the resulting colonies is prepared via automated 96-well DNA miniprep. Each 96-well miniprep plate contains DNA from colonies of 72 mutants, 12 wild types and 12 negative controls. The DNA concentration in all DNA samples is normalized to the same concentration. The resulting DNA is used to transfect HEK293T cells via an automated procedure using liquid handling robots. 4: Each bait is co-transfected separately with four different MAPPIT preys and the luciferase reporter. Each bait/prey mixture is used to transfect 8 wells of a 384-well plate with Hek293T cells. One 96-well MAPPIT bait miniprep plate in combination with four MAPPIT preys thus leads to 8 transfected 384 well plates. Each transfected 384 well plate contains a single MAPPIT prey in combination with 6 wild type baits, 6 negative control baits and 36 random bait mutants. 5: From each transfected bait/prey mixture, four wells are stimulated with Epo, the remaining four wells are not stimulated. After overnight Epo stimulation, the luciferase activity is determined via a luminescence reader. The MAPPIT signal is calculated by dividing the signal of the four stimulated wells by the signal of the four unstimulated wells.</p
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