RESUMO

NK cells are innate lymphoid cells that exert a key role in immune surveillance through the recognition and elimination of transformed cells and viral, bacterial, and protozoan pathogen-infected cells without prior sensitization. Elucidating when and how NK cell-induced intracellular microbial cell death functions in the resolution of infection and host inflammation has been an important topic of investigation. NK cell activation requires the engagement of specific activating, co-stimulatory, and inhibitory receptors which control positively and negatively their differentiation, memory, and exhaustion. NK cells secrete diverse cytokines, including IFN-γ, TNF-α/ß, CD95/FasL, and TRAIL, as well as cytoplasmic cytotoxic granules containing perforin, granulysin, and granzymes A and B. Paradoxically, NK cells also kill other immune cells like macrophages, dendritic cells, and hyper-activated T cells, thus turning off self-immune reactions. Here we first provide an overview of NK cell biology, and then we describe and discuss the life-death signals that connect the microbial pathogen sensors to the inflammasomes and finally to cell death signaling pathways. We focus on caspase-mediated cell death by apoptosis and pro-inflammatory and non-caspase-mediated cell death by necroptosis, as well as inflammasome- and caspase-mediated pyroptosis.

Assuntos

Infecções/imunologia , Inflamassomos/metabolismo , Células Matadoras Naturais/fisiologia , Animais , Caspases/metabolismo , Morte Celular , Interações Hospedeiro-Patógeno , Humanos , Imunidade Inata , Vigilância Imunológica , Espaço Intracelular , Receptores de Reconhecimento de Padrão/metabolismo , Transdução de Sinais

RESUMO

With multiple omics strategies being applied to several cancer genomics projects, researchers have the opportunity to develop a rational planning of targeted cancer therapy. The investigation of such numerous and diverse pharmacogenomic datasets is a complex task. It requires biological knowledge and skills on a set of tools to accurately predict signaling network and clinical outcomes. Herein, we describe Web-based in silico approaches user friendly for exploring integrative studies on cancer biology and pharmacogenomics. We briefly explain how to submit a query to cancer genome databases to predict which genes are significantly altered across several types of cancers using CBioPortal. Moreover, we describe how to identify clinically available drugs and potential small molecules for gene targeting using CellMiner. We also show how to generate a gene signature and compare gene expression profiles to investigate the complex biology behind drug response using Connectivity Map. Furthermore, we discuss on-going challenges, limitations and new directions to integrate molecular, biological and epidemiological information from oncogenomics platforms to create hypothesis-driven projects. Finally, we discuss the use of Patient-Derived Xenografts models (PDXs) for drug profiling in vivo assay. These platforms and approaches are a rational way to predict patient-targeted therapy response and to develop clinically relevant small molecules drugs.