A HIV-1 Tat mutant protein disrupts HIV-1 Rev function by targeting the DEAD-box RNA helicase DDX1

BACKGROUND: Previously we described a transdominant negative mutant of the HIV-1 Tat protein, termed Nullbasic, that downregulated the steady state levels of unspliced and singly spliced viral mRNA, an activity caused by inhibition of HIV-1 Rev activity. Nullbasic also altered the subcellular localizations of Rev and other cellular proteins, including CRM1, B23 and C23 in a Rev-dependent manner, suggesting that Nullbasic may disrupt Rev function and trafficking by intervening with an unidentified component of the Rev nucleocytoplasmic transport complex. RESULTS: To seek a possible mechanism that could explain how Nullbasic inhibits Rev activity, we used a proteomics approach to identify host cellular proteins that interact with Nullbasic. Forty-six Nullbasic-binding proteins were identified by mass spectrometry including the DEAD-box RNA helicase, DDX1. To determine the effect of DDX1 on Nullbasic-mediated Rev activity, we performed cell-based immunoprecipitation assays, Rev reporter assays and bio-layer interferometry (BLI) assays. Interaction between DDX1 and Nullbasic was observed by co-immunoprecipitation of Nullbasic with endogenous DDX1 from cell lysates. BLI assays showed a direct interaction between Nullbasic and DDX1. Nullbasic affected DDX1 subcellular distribution in a Rev-independent manner. Interestingly overexpression of DDX1 in cells not only restored Rev-dependent mRNA export and gene expression in a Rev reporter assay but also partly reversed Nullbasic-induced Rev subcellular mislocalization. Moreover, HIV-1 wild type Tat co-immunoprecipitated with DDX1 and overexpression of Tat could rescue the unspliced viral mRNA levels inhibited by Nullbasic in HIV-1 expressing cells. CONCLUSIONS: Nullbasic was used to further define the complex mechanisms involved in the Rev-dependent nuclear export of the 9 kb and 4 kb viral RNAs. All together, these data indicate that DDX1 can be sequestered by Nullbasic leading to destabilization of the Rev nucleocytoplasmic transport complex and decreased levels of Rev-dependent viral transcripts. The outcomes support a role for DDX1 in maintenance of a Rev nuclear complex that transports viral RRE-containing mRNA to the cytoplasm. To our knowledge Nullbasic is the first anti-HIV protein that specifically targets the cellular protein DDX1 to block Rev’s activity. Furthermore, our research raises the possibility that wild type Tat may play a previously unrecognized but very important role in Rev function. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12977-014-0121-9) contains supplementary material, which is available to authorized users

Advanced methods of source separation applicable to linear-quadratic mixtures

Dans cette thèse, nous nous sommes intéressés à proposer de nouvelles méthodes de Séparation Aveugle de Sources (SAS) adaptées aux modèles de mélange non-linéaires. La SAS consiste à estimer les signaux sources inconnus à partir de leurs mélanges observés lorsqu'il existe très peu d'informations disponibles sur le modèle de mélange. La contribution méthodologique de cette thèse consiste à prendre en considération les interactions non-linéaires qui peuvent se produire entre les sources en utilisant le modèle linéaire-quadratique (LQ). A cet effet, nous avons développé trois nouvelles méthodes de SAS. La première méthode vise à résoudre le problème du démélange hyperspectral en utilisant un modèle linéaire-quadratique. Celle-ci se repose sur la méthode d'Analyse en Composantes Parcimonieuses (ACPa) et nécessite l'existence des pixels purs dans la scène observée. Dans le même but, nous proposons une deuxième méthode du démélange hyperspectral adaptée au modèle linéaire-quadratique. Elle correspond à une méthode de Factorisation en Matrices Non-négatives (FMN) se basant sur l'estimateur du Maximum A Posteriori (MAP) qui permet de prendre en compte les informations a priori sur les distributions des inconnus du problème afin de mieux les estimer. Enfin, nous proposons une troisième méthode de SAS basée sur l'analyse en composantes indépendantes (ACI) en exploitant les Statistiques de Second Ordre (SSO) pour traiter un cas particulier du mélange linéaire-quadratique qui correspond au mélange bilinéaire.In this thesis, we were interested to propose new Blind Source Separation (BSS) methods adapted to the nonlinear mixing models. BSS consists in estimating the unknown source signals from their observed mixtures when there is little information available on the mixing model. The methodological contribution of this thesis consists in considering the non-linear interactions that can occur between sources by using the linear-quadratic (LQ) model. To this end, we developed three new BSS methods. The first method aims at solving the hyperspectral unmixing problem by using a linear-quadratic model. It is based on the Sparse Component Analysis (SCA) method and requires the existence of pure pixels in the observed scene. For the same purpose, we propose a second hyperspectral unmixing method adapted to the linear-quadratic model. It corresponds to a Non-negative Matrix Factorization (NMF) method based on the Maximum A Posteriori (MAP) estimate allowing to take into account the available prior information about the unknown parameters for a better estimation of them. Finally, we propose a third BSS method based on the Independent Component Analysis (ICA) method by using the Second Order Statistics (SOS) to process a particular case of the linear-quadratic mixture that corresponds to the bilinear one

Méthodes avancées de séparation de sources applicables aux mélanges linéaires-quadratiques

In this thesis, we were interested to propose new Blind Source Separation (BSS) methods adapted to the nonlinear mixing models. BSS consists in estimating the unknown source signals from their observed mixtures when there is little information available on the mixing model. The methodological contribution of this thesis consists in considering the non-linear interactions that can occur between sources by using the linear-quadratic (LQ) model. To this end, we developed three new BSS methods. The first method aims at solving the hyperspectral unmixing problem by using a linear-quadratic model. It is based on the Sparse Component Analysis (SCA) method and requires the existence of pure pixels in the observed scene. For the same purpose, we propose a second hyperspectral unmixing method adapted to the linear-quadratic model. It corresponds to a Non-negative Matrix Factorization (NMF) method based on the Maximum A Posteriori (MAP) estimate allowing to take into account the available prior information about the unknown parameters for a better estimation of them. Finally, we propose a third BSS method based on the Independent Component Analysis (ICA) method by using the Second Order Statistics (SOS) to process a particular case of the linear-quadratic mixture that corresponds to the bilinear one.Dans cette thèse, nous nous sommes intéressés à proposer de nouvelles méthodes de Séparation Aveugle de Sources (SAS) adaptées aux modèles de mélange non-linéaires. La SAS consiste à estimer les signaux sources inconnus à partir de leurs mélanges observés lorsqu'il existe très peu d'informations disponibles sur le modèle de mélange. La contribution méthodologique de cette thèse consiste à prendre en considération les interactions non-linéaires qui peuvent se produire entre les sources en utilisant le modèle linéaire-quadratique (LQ). A cet effet, nous avons développé trois nouvelles méthodes de SAS. La première méthode vise à résoudre le problème du démélange hyperspectral en utilisant un modèle linéaire-quadratique. Celle-ci se repose sur la méthode d'Analyse en Composantes Parcimonieuses (ACPa) et nécessite l'existence des pixels purs dans la scène observée. Dans le même but, nous proposons une deuxième méthode du démélange hyperspectral adaptée au modèle linéaire-quadratique. Elle correspond à une méthode de Factorisation en Matrices Non-négatives (FMN) se basant sur l'estimateur du Maximum A Posteriori (MAP) qui permet de prendre en compte les informations a priori sur les distributions des inconnus du problème afin de mieux les estimer. Enfin, nous proposons une troisième méthode de SAS basée sur l'analyse en composantes indépendantes (ACI) en exploitant les Statistiques de Second Ordre (SSO) pour traiter un cas particulier du mélange linéaire-quadratique qui correspond au mélange bilinéaire

A new unsupervised method for hyperspectral image unmixing using a linear-quadratic model

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A map-based NMF approach to hyperspectral image unmixing using a linear-quadratic mixture model

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A second-order blind source separation method for bilinear mixtures

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