Regulating NLRP3 Inflammasome-Induced Pyroptosis via Nrf2: TBHQ Limits Hyperoxia-Induced Lung Injury in a Mouse Model of Bronchopulmonary Dysplasia.

Nuclear factor e2-related factor 2 (Nrf2) plays a key role in cellular resistance to oxidative stress injury. Oxidative stress injury, caused by Nrf2 imbalance, results in increased pyroptosis, DNA damage, and inflammatory activation, which may lead to the arrest of alveolar development and bronchopulmonary dysplasia (BPD) in premature infants under hyperoxic conditions. We established a BPD mouse model to investigate the effects of tert-butylhydroquinone (TBHQ), an Nrf2 activator, on oxidative stress injury, pyroptosis, NLRP3 inflammasome activation, and alveolar development. TBHQ reduced abnormal cell death in the lung tissue of BPD mice and restored the number and normal structure of the alveoli. TBHQ administration activated the Nrf2/heme oxygenase-1 (HO-1) signaling pathway, resulting in the decrease in the following: reactive oxygen species (ROS), activation of the NOD-like receptor pyrin domain containing 3 (NLRP3) inflammasome, and IL-18 and IL-1ß expression and activation, as well as inhibition of pyroptosis. In contrast, after Nrf2 gene knockout in BPD mice, there was more severe oxidative stress injury and cell death in the lungs, there were TUNEL + and NLRP3 + co-positive cells in the alveoli, the pyroptosis was significantly increased, and the development of alveoli was significantly blocked. We demonstrated that TBHQ may promote alveolar development by enhancing Nrf2-induced antioxidation in the lung tissue of BPD mice and that the decrease in the NLRP3 inflammasome and pyroptosis caused by Nrf2 activation may be the underlying mechanism. These results suggest that TBHQ is a promising treatment for lung injury in premature infants with hyperoxia.

Determining modular organization of protein interaction networks by maximizing modularity density.

BACKGROUND: With ever increasing amount of available data on biological networks, modeling and understanding the structure of these large networks is an important problem with profound biological implications. Cellular functions and biochemical events are coordinately carried out by groups of proteins interacting each other in biological modules. Identifying of such modules in protein interaction networks is very important for understanding the structure and function of these fundamental cellular networks. Therefore, developing an effective computational method to uncover biological modules should be highly challenging and indispensable. RESULTS: The purpose of this study is to introduce a new quantitative measure modularity density into the field of biomolecular networks and develop new algorithms for detecting functional modules in protein-protein interaction (PPI) networks. Specifically, we adopt the simulated annealing (SA) to maximize the modularity density and evaluate its efficiency on simulated networks. In order to address the computational complexity of SA procedure, we devise a spectral method for optimizing the index and apply it to a yeast PPI network. CONCLUSIONS: Our analysis of detected modules by the present method suggests that most of these modules have well biological significance in context of protein complexes. Comparison with the MCL and the modularity based methods shows the efficiency of our method.

A hybrid graph-theoretic method for mining overlapping functional modules in large sparse protein interaction networks.

Modular architecture, which encompasses groups of genes/proteins involved in elementary biological functional units, is a basic form of the organisation of interacting proteins. Here, we propose a method that combines the Line Graph Transformation (LGT) and clique percolation-clustering algorithm to detect network modules, which may overlap each other in large sparse PPI networks. The resulting modules by the present method show a high coverage among yeast, fly, and worm PPI networks, respectively. Our analysis of the yeast PPI network suggests that most of these modules have well-biological significance in context of protein localisation, function annotation, and protein complexes.

Identification of functional modules in a PPI network by clique percolation clustering.

Large-scale experiments and data integration have provided the opportunity to systematically analyze and comprehensively understand the topology of biological networks and biochemical processes in cells. Modular architecture which encompasses groups of genes/proteins involved in elementary biological functional units is a basic form of the organization of interacting proteins. Here we apply a graph clustering algorithm based on clique percolation clustering to detect overlapping network modules of a protein-protein interaction (PPI) network. Our analysis of the yeast Sacchromyces cerevisiae suggests that most of the detected modules correspond to one or more experimentally functional modules and half of these annotated modules match well with experimentally determined protein complexes. Our method of analysis can of course be applied to protein-protein interaction data for any species and even other biological networks.