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The hierarchical structural architecture of inflammasomes, supramolecular inflammatory machines.

ABSTRACT: Inflammasomes are caspase-1 activating, molecular inflammatory machines that proteolytically mature pro-inflammatory cytokines and induce pyroptotic cell death during innate immune responses. Recent structural studies of proteins that constitute inflammasomes have yielded fresh insights into their assembly mechanisms. In particular, these include a crystal structure of the CARD-containing NOD-like receptor NLRC4, the crystallographic and electron microscopy (EM) studies of the dsDNA sensors AIM2 and IFI16, and of the regulatory protein p202, and the cryo-EM filament structure of the PYD domain of the inflammasome adapter ASC. These data suggest inflammasome assembly that starts with ligand recognition and release of autoinhibition followed by step-wise rounds of nucleated polymerization from the sensors to the adapters, then to caspase-1. In this elegant manner, inflammasomes form by an 'all-or-none' cooperative mechanism, thereby amplifying the activation of caspase-1. The dense network of filamentous structures predicted by this model has been observed in cells as micron-sized puncta.

SUBMITTER: Hauenstein AV

PROVIDER: S-EPMC4476925 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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The hierarchical structural architecture of inflammasomes, supramolecular inflammatory machines.

Hauenstein Arthur V AV Zhang Liman L Wu Hao H

Current opinion in structural biology 20150413

Inflammasomes are caspase-1 activating, molecular inflammatory machines that proteolytically mature pro-inflammatory cytokines and induce pyroptotic cell death during innate immune responses. Recent structural studies of proteins that constitute inflammasomes have yielded fresh insights into their assembly mechanisms. In particular, these include a crystal structure of the CARD-containing NOD-like receptor NLRC4, the crystallographic and electron microscopy (EM) studies of the dsDNA sensors AIM2 ...[more]

PMID: 25881155

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Project description:BackgroundRecent advances in genome technologies and the subsequent collection of genomic information at various molecular resolutions hold promise to accelerate the discovery of new therapeutic targets. A critical step in achieving these goals is to develop efficient clinical prediction models that integrate these diverse sources of high-throughput data. This step is challenging due to the presence of high-dimensionality and complex interactions in the data. For predicting relevant clinical outcomes, we propose a flexible statistical machine learning approach that acknowledges and models the interaction between platform-specific measurements through nonlinear kernel machines and borrows information within and between platforms through a hierarchical Bayesian framework. Our model has parameters with direct interpretations in terms of the effects of platforms and data interactions within and across platforms. The parameter estimation algorithm in our model uses a computationally efficient variational Bayes approach that scales well to large high-throughput datasets.ResultsWe apply our methods of integrating gene/mRNA expression and microRNA profiles for predicting patient survival times to The Cancer Genome Atlas (TCGA) based glioblastoma multiforme (GBM) dataset. In terms of prediction accuracy, we show that our non-linear and interaction-based integrative methods perform better than linear alternatives and non-integrative methods that do not account for interactions between the platforms. We also find several prognostic mRNAs and microRNAs that are related to tumor invasion and are known to drive tumor metastasis and severe inflammatory response in GBM. In addition, our analysis reveals several interesting mRNA and microRNA interactions that have known implications in the etiology of GBM.ConclusionsOur approach gains its flexibility and power by modeling the non-linear interaction structures between and within the platforms. Our framework is a useful tool for biomedical researchers, since clinical prediction using multi-platform genomic information is an important step towards personalized treatment of many cancers. We have a freely available software at: http://odin.mdacc.tmc.edu/~vbaladan.

Dataset Information

The hierarchical structural architecture of inflammasomes, supramolecular inflammatory machines.

Publications

The hierarchical structural architecture of inflammasomes, supramolecular inflammatory machines.

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