HIV-1 glycoprotein 41 ectodomain induces activation of the CD74 protein-mediated extracellular signal-regulated kinase/mitogen-activated protein kinase pathway to enhance viral infection

The multifaceted roles of the invariant chain CD74--More than just a chaperone

The invariant chain (CD74) is well known for its essential role in antigen presentation by mediating assembly and subcellular trafficking of the MHCII complex. Beyond this, CD74 has also been implicated in a number of processes independent of MHCII. These include the regulation of endosomal trafficking, cell migration and cellular signalling as surface receptor of the pro-inflammatory cytokine macrophage migration inhibitory factor (MIF). In several forms of cancer, CD74 is up-regulated and associated with enhanced proliferation and metastatic potential. In this review, an overview of the diverse biological functions of the CD74 protein is provided with a particular focus on how these may be regulated. In particular, proteolysis of CD74 will be discussed as a central mechanism to control the actions of this important protein at different levels.

HIV-1 Structural Proteins Serve as PAMPs for TLR2 Heterodimers Significantly Increasing Infection and Innate Immune Activation

Immune activation is critical to HIV infection and pathogenesis; however, our understanding of HIV innate immune activation remains incomplete. Recently we demonstrated that soluble TLR2 (sTLR2) physically inhibited HIV-induced NFκB activation and inflammation, as well as HIV-1 infection. In light of these findings, we hypothesized that HIV-1 structural proteins may serve as pathogen-associated molecular patterns (PAMPs) for cellular TLR2 heterodimers. These studies made use of primary human T cells and TZMbl cells stably transformed to express TLR2 (TZMbl-2). Our results demonstrated that cells expressing TLR2 showed significantly increased proviral DNA compared to cells lacking TLR2, and mechanistically this may be due to a TLR2-mediated increased CCR5 expression. Importantly, we show that HIV-1 structural proteins, p17, p24, and gp41, act as viral PAMPs signaling through TLR2 and its heterodimers leading to significantly increased immune activation via the NFκB signaling pathway. Using co-immunoprecipitation and a dot blot method, we demonstrated direct protein interactions between these viral PAMPs and TLR2, while only p17 and gp41 bound to TLR1. Specifically, TLR2/1 heterodimer recognized p17 and gp41, while p24 lead to immune activation through TLR2/6. These results were confirmed using TLR2/1 siRNA knock down assays which ablated p17 and gp41-induced cellular activation and through studies of HEK293 cells expressing selected TLRs. Interestingly, our results show in the absence of TLR6, p24 bound to TLR2 and blocked p17 and gp41-induced activation, thus providing a novel mechanism by which HIV-1 can manipulate innate sensing. Taken together, our results identified, for the first time, novel HIV-1 PAMPs that play a role in TLR2-mediated cellular activation and increased proviral DNA. These findings have important implications for our fundamental understanding of HIV-1 immune activation and pathogenesis, as well as HIV-1 vaccine development.

Probability weighted ensemble transfer learning for predicting interactions between HIV-1 and human proteins

Reconstruction of host-pathogen protein interaction networks is of great significance to reveal the underlying microbic pathogenesis. However, the current experimentally-derived networks are generally small and should be augmented by computational methods for less-biased biological inference. From the point of view of computational modelling, data scarcity, data unavailability and negative data sampling are the three major problems for host-pathogen protein interaction networks reconstruction. In this work, we are motivated to address the three concerns and propose a probability weighted ensemble transfer learning model for HIV-human protein interaction prediction (PWEN-TLM), where support vector machine (SVM) is adopted as the individual classifier of the ensemble model. In the model, data scarcity and data unavailability are tackled by homolog knowledge transfer. The importance of homolog knowledge is measured by the ROC-AUC metric of the individual classifiers, whose outputs are probability weighted to yield the final decision. In addition, we further validate the assumption that only the homolog knowledge is sufficient to train a satisfactory model for host-pathogen protein interaction prediction. Thus the model is more robust against data unavailability with less demanding data constraint. As regards with negative data construction, experiments show that exclusiveness of subcellular co-localized proteins is unbiased and more reliable than random sampling. Last, we conduct analysis of overlapped predictions between our model and the existing models, and apply the model to novel host-pathogen PPIs recognition for further biological research.

Dynamic range of Nef functions in chronic HIV-1 infection

HIV-1 Nef is required for efficient viral replication and pathogenesis. However, the extent to which Nef's functions are maintained in natural sequences during chronic infection, and their clinical relevance, remains incompletely characterized. Relative to a control Nef from HIV-1 strain SF2, HLA class I and CD4 down-regulation activities of 46 plasma RNA Nef sequences derived from unique chronic infected individuals were generally high and displayed narrow dynamic ranges, whereas Nef-mediated virion infectivity, PBMC replication and CD74 up-regulation exhibited broader dynamic ranges. 80% of patient-derived Nefs were active for at least three functions examined. Functional co-dependencies were identified, including positive correlations between CD4 down-regulation and virion infectivity, replication, and CD74 up-regulation, and between CD74 up-regulation and PBMC replication. Nef-mediated virion infectivity inversely correlated with patient CD4(±) T-cell count. Strong functional co-dependencies and the polyfunctional nature of patient-derived Nef sequences suggest a phenotypic requirement to maintain multiple Nef functions during chronic infection.

A novel biclustering approach to association rule mining for predicting HIV-1-human protein interactions

Identification of potential viral-host protein interactions is a vital and useful approach towards development of new drugs targeting those interactions. In recent days, computational tools are being utilized for predicting viral-host interactions. Recently a database containing records of experimentally validated interactions between a set of HIV-1 proteins and a set of human proteins has been published. The problem of predicting new interactions based on this database is usually posed as a classification problem. However, posing the problem as a classification one suffers from the lack of biologically validated negative interactions. Therefore it will be beneficial to use the existing database for predicting new viral-host interactions without the need of negative samples. Motivated by this, in this article, the HIV-1–human protein interaction database has been analyzed using association rule mining. The main objective is to identify a set of association rules both among the HIV-1 proteins and among the human proteins, and use these rules for predicting new interactions. In this regard, a novel association rule mining technique based on biclustering has been proposed for discovering frequent closed itemsets followed by the association rules from the adjacency matrix of the HIV-1–human interaction network. Novel HIV-1–human interactions have been predicted based on the discovered association rules and tested for biological significance. For validation of the predicted new interactions, gene ontology-based and pathway-based studies have been performed. These studies show that the human proteins which are predicted to interact with a particular viral protein share many common biological activities. Moreover, literature survey has been used for validation purpose to identify some predicted interactions that are already validated experimentally but not present in the database. Comparison with other prediction methods is also discussed.