Recombinant murine cytomegalovirus vector activates human monocyte-derived dendritic cells in a NF-kappaB dependent pathway

Uncovering the Anticancer Potential of Murine Cytomegalovirus against Human Colon Cancer Cells

Human cytomegalovirus (HCMV) components are often found in tumors, but the precise relationship between HCMV and cancer remains a matter of debate. Pro-tumor functions of HCMV were described in several studies, but an association between HCMV seropositivity and reduced cancer risk was also evidenced, presumably relying on recognition and killing of cancer cells by HCMV-induced lymphocytes. This study aimed at deciphering whether CMV influences cancer development in an immune-independent manner. Using immunodeficient mice, we showed that systemic infection with murine CMV (MCMV) inhibited the growth of murine carcinomas. Surprisingly, MCMV, but not HCMV, also reduced human colon carcinoma development in vivo. In vitro, both viruses infected human cancer cells. Expression of human interferon-β (IFN-β) and nuclear domain (ND10) were induced in MCMV-infected, but not in HCMV-infected human colon cancer cells. These results suggest a decreased capacity of MCMV to counteract intrinsic defenses in the human cellular host. Finally, immunodeficient mice receiving peri-tumoral MCMV therapy showed a reduction of human colon cancer cell growth, albeit no clinical sign of systemic virus dissemination was evidenced. Our study, which describes a selective advantage of MCMV over HCMV to control human colon cancer, could pave the way for the development of CMV-based therapies against cancer.

Presentation of Cryptic Peptides by MHC Class I Is Enhanced by Inflammatory Stimuli

Cytolytic T cells eliminate infected or cancer cells by recognizing peptides presented by MHC class I molecules on the cell surface. The antigenic peptides are derived primarily from newly synthesized proteins including those produced by cryptic translation mechanisms. Previous studies have shown that cryptic translation can be initiated by distinct mechanisms at non-AUG codons in addition to conventional translation initiated at the canonical AUG start codon. In this study, we show that presentation of endogenously translated cryptic peptides is enhanced by TLR signaling pathways involved in pathogen recognition as well as by infection with different viruses. This enhancement of cryptic peptides was caused by proinflammatory cytokines, secreted in response to microbial infection. Furthermore, blocking these cytokines abrogated the enhancement of cryptic peptide presentation in response to infection. Thus, presentation of cryptic peptides is selectively enhanced during inflammation and infection, which could allow the immune system to detect intracellular pathogens that might otherwise escape detection because of inhibition of conventional host translation mechanisms.

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.

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.