The HIV Nef protein recruits the polycomb protein Eed and mimics an integrin receptor signal for reasons that are not entirely clear. Here we demonstrate that Nef and Eed complex with the integrin effector paxillin to recruit and activate TNFα converting enzyme (TACE alias ADAM 17) and its close relative ADAM10. The activated proteases cleaved proTNFα and were shuttled into extracellular vesicles (EVs). Peripheral blood mononuclear cells that ingested these EVs released TNFα. Analyzing the mechanism, we found that Pak2, an established host cell effector of Nef, phosphorylated paxillin on Ser272/274 to induce TACE-paxillin association and shuttling into EVs via lipid rafts. Conversely, Pak1 phosphorylated paxillin on Ser258, which inhibited TACE association and lipid raft transfer. Interestingly, melanoma cells used an identical mechanism to shuttle predominantly ADAM10 into EVs. We conclude that HIV-1 and cancer cells exploit a paxillin/integrin-controlled mechanism to release TACE/ADAM10-containing vesicles, ensuring better proliferation/growth conditions in their microenvironment.

Research on extracellular vesicles (EVs) is a new and emerging field that is rapidly growing. Many features of these structures still need to be described and discovered. This concerns their biogenesis, their release and cellular entrance mechanisms, as well as their functions, particularly in vivo. Hence our knowledge on EV is constantly evolving and sometimes changing. In our review we summarize the most important facts of our current knowledge about extracellular vesicles and described some of the assumed functions in the context of cancer and HIV infection.

The Asia Oceania Human Proteome Organisation has embarked on a Membrane Proteomics Initiative with goals of systematic comparison of strategies for analysis of membrane proteomes and discovery of membrane proteins. This multi-laboratory project is based on analysis of a subcellular fraction from mouse liver that contains endoplasmic reticulum and other organelles. Here we present the strategy used for preparation and initial characterisation of the membrane sample, including validation that the carbonate-washing step enriches for integral and lipid-anchored membrane proteins. Analysis of seventeen independent datasets from five types of proteomic workflows is in progress.

Cellular phenotypes are established and controlled by complex and precisely orchestrated molecular networks. In cancer, mutations and dysregulations of multiple molecular factors perturb the regulation of these networks and lead to malignant transformation. High-throughput technologies are a valuable source of information to establish the complex molecular relationships behind the emergence of malignancy, but full exploitation of this massive amount of data requires bioinformatics tools that rely on network-based analyses. In this report we present the Virtual Melanoma Cell, an online tool developed to facilitate the mining and interpretation of high-throughput data on melanoma by biomedical researches. The platform is based on a comprehensive, manually generated and expert-validated regulatory map composed of signaling pathways important in malignant melanoma. The Virtual Melanoma Cell is a tool designed to accept, visualize and analyze user-generated datasets. It is available at: https://www.vcells.net/melanoma. To illustrate the utilization of the web platform and the regulatory map, we have analyzed a large publicly available dataset accounting for anti-PD1 immunotherapy treatment of malignant melanoma patients.

Macrophages (Mϕs) are key players in the coordination of the lifesaving or detrimental immune response against infections. The mechanistic understanding of the functional modulation of Mϕs by pathogens and pharmaceutical interventions at the signal transduction level is still far from complete. The complexity of pathways and their cross-talk benefits from holistic computational approaches. In the present study, we reconstructed a comprehensive, validated, and annotated map of signal transduction pathways in inflammatory Mϕs based on the current literature. In a second step, we selectively expanded this curated map with database knowledge. We provide both versions to the scientific community via a Web platform that is designed to facilitate exploration and analysis of high-throughput data. The platform comes preloaded with logarithmic fold changes from 44 data sets on Mϕ stimulation. We exploited three of these data sets-human primary Mϕs infected with the common lung pathogens ,, or -in a case study to show how our map can be customized with expression data to pinpoint regulated subnetworks and druggable molecules. From the three infection scenarios, we extracted a regulatory core of 41 factors, including TNF, CCL5, CXCL10, IL-18, and IL-12 p40, and identified 140 drugs targeting 16 of them. Our approach promotes a comprehensive systems biology strategy for the exploitation of high-throughput data in the context of Mϕ signal transduction. In conclusion, we provide a set of tools to help scientists unravel details of Mϕ signaling. The interactive version of our Mϕ signal transduction map is accessible online at https://vcells.net/macrophage.

In this paper, we combine kinetic modelling and patient gene expression data analysis to elucidate biological mechanisms by which melanoma becomes resistant to the immune system and to immunotherapy. To this end, we systematically perturbed the parameters in a kinetic model and performed a mathematical analysis of their impact, thereby obtaining signatures associated with the emergence of phenotypes of melanoma immune sensitivity and resistance. Our phenotypic signatures were compared with published clinical data on pretreatment tumor gene expression in patients subjected to immunotherapy against metastatic melanoma. To this end, the differentially expressed genes were annotated with standard gene ontology terms and aggregated into metagenes. Our method sheds light on putative mechanisms by which melanoma may develop immunoresistance. Precisely, our results and the clinical data point to the existence of a signature of intermediate expression levels for genes related to antigen presentation that constitutes an intriguing resistance mechanism, whereby micrometastases are able to minimize the combined anti-tumor activity of complementary responses mediated by cytotoxic T cells and natural killer cells, respectively. Finally, we computationally explored the efficacy of cytokines used as low-dose co-adjuvants for the therapeutic anticancer vaccine to overcome tumor immunoresistance.

Dendritic cells (DCs) are professional antigen-presenting cells that induce and regulate adaptive immunity by presenting antigens to T cells. Due to their coordinative role in adaptive immune responses, DCs have been used as cell-based therapeutic vaccination against cancer. The capacity of DCs to induce a therapeutic immune response can be enhanced by re-wiring of cellular signalling pathways with microRNAs (miRNAs). Methods: Since the activation and maturation of DCs is controlled by an interconnected signalling network, we deploy an approach that combines RNA sequencing data and systems biology methods to delineate miRNA-based strategies that enhance DC-elicited immune responses. Results: Through RNA sequencing of IKKβ-matured DCs that are currently being tested in a clinical trial on therapeutic anti-cancer vaccination, we identified 44 differentially expressed miRNAs. According to a network analysis, most of these miRNAs regulate targets that are linked to immune pathways, such as cytokine and interleukin signalling. We employed a network topology-oriented scoring model to rank the miRNAs, analysed their impact on immunogenic potency of DCs, and identified dozens of promising miRNA candidates, with miR-15a and miR-16 as the top ones. The results of our analysis are presented in a database that constitutes a tool to identify DC-relevant miRNA-gene interactions with therapeutic potential ( https://www.synmirapy.net/dc-optimization ). Conclusions: Our approach enables the systematic analysis and identification of functional miRNA-gene interactions that can be experimentally tested for improving DC immunogenic potency.

Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types by combining proteogenomics with phenotypic and functional analyses. By using an optimized computational approach, we discovered a large number of novel tumor-specific and tumor-associated antigens including shared common target candidates. To create a pipeline for the identification of neoantigens in our cohort, we combined deep DNA and RNA sequencing with MS-based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity. In fact, we could detect a broad variety of non-wild type HLA-binding peptides in the majority of patients and confirmed the immunogenicity of 24 neoantigens. Most interestingly, the majority of total and immunogenic neoantigens originated from variants identified in the RNA dataset, illustrating the importance of RNA as a still understudied source of cancer antigens. Moreover, the amount of these mainly RNA-based immunogenic neoantigens correlated positively with overall CD8+ tumor-infiltrating T cells. This study therefore underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates.