Cell death-associated molecular-pattern molecules: inflammatory signaling and control.

Apoptosis, necroptosis, and pyroptosis are different cellular death programs characterized in organs and tissues as consequence of microbes infection, cell stress, injury, and chemotherapeutics exposure. Dying and death cells release a variety of self-proteins and bioactive chemicals originated from cytosol, nucleus, endoplasmic reticulum, and mitochondria. These endogenous factors are named cell death-associated molecular-pattern (CDAMP), damage-associated molecular-pattern (DAMP) molecules, and alarmins. Some of them cooperate or act as important initial or delayed inflammatory mediators upon binding to diverse membrane and cytosolic receptors coupled to signaling pathways for the activation of the inflammasome platforms and NF-κB multiprotein complexes. Current studies show that the nonprotein thiols and thiol-regulating enzymes as well as highly diffusible prooxidant reactive oxygen and nitrogen species released together in extracellular inflammatory milieu play essential role in controlling pro- and anti-inflammatory activities of CDAMP/DAMP and alarmins. Here, we provide an overview of these emerging concepts and mechanisms of triggering and maintenance of tissue inflammation under massive death of cells.

Using Pharmacogenomic Databases for Discovering Patient-Target Genes and Small Molecule Candidates to Cancer Therapy.

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.

Advances in the integration of optical and mass spectrometry molecular imaging technologies: from omics data to molecular signature discovery.

Many molecular mechanisms in complex biological processes of diseases cannot be fully understood without direct visualization. In the last decades, advances in imaging principles and technologies have expanded our ability to capture and analyze the morphology of tissues and cells' components on conventional widefield optical and fluorescence microscopies and their derivatives confocal and multiphoton fluorescence laser-scanning microscopes, as well as transmission electron microscopes. Innovative imaging technologies are now emerging for constructing fine structural features, precise localization and the dynamic interplay of single and macromolecular assemblies that drive cell growth, differentiation and cell death as well as stromal and chromatin remodeling within many cellular context. The newer super-resolution microscopies capture images with unprecedented sensitivity and clarity allowing the exploration of interactions between individual molecules with a distance resolution as low as 20 nm. But these techniques are not robust enough to quantitate molecules on a genome-wide scale. Mass spectrometry imaging is a high-throughput chemical imaging technique for the identification, quantitation and distribution of proteins, lipids and chemical metabolites at picomol level within a single-cell and complex multicellular tissue. Here we provide an overview on imaging instrumentations and computational platforms to store, data mining, analyze and retrieving genomic, proteomic and immunohistochemistry digital image information which are available for multilevel academic-private collaborations. The expansion of these data sets will lead to a merge picture from it we will retrieve knowledge for more rational-design systems to basic and clinical research in near future.