A Moonlighting Protein Secreted by a Nasal Microbiome Fortifies the Innate Host Defense Against Bacterial and Viral Infections.

Evidence suggests that the human respiratory tract, as with the gastrointestinal tract, has evolved to its current state in association with commensal microbes. However, little is known about how the airway microbiome affects the development of airway immune system. Here, we uncover a previously unidentified mode of interaction between host airway immunity and a unique strain (AIT01) of Staphylococcus epidermidis, a predominant species of the nasal microbiome. Intranasal administration of AIT01 increased the population of neutrophils and monocytes in mouse lungs. The recruitment of these immune cells resulted in the protection of the murine host against infection by Pseudomonas aeruginosa, a pathogenic bacterium. Interestingly, an AIT01-secreted protein identified as GAPDH, a well-known bacterial moonlighting protein, mediated this protective effect. Intranasal delivery of the purified GAPDH conferred significant resistance against other Gram-negative pathogens (Klebsiella pneumoniae and Acinetobacter baumannii) and influenza A virus. Our findings demonstrate the potential of a native nasal microbe and its secretory protein to enhance innate immune defense against airway infections. These results offer a promising preventive measure, particularly relevant in the context of global pandemics.

HiGlass: web-based visual exploration and analysis of genome interaction maps.

We present HiGlass, an open source visualization tool built on web technologies that provides a rich interface for rapid, multiplex, and multiscale navigation of 2D genomic maps alongside 1D genomic tracks, allowing users to combine various data types, synchronize multiple visualization modalities, and share fully customizable views with others. We demonstrate its utility in exploring different experimental conditions, comparing the results of analyses, and creating interactive snapshots to share with collaborators and the broader public. HiGlass is accessible online at http://higlass.io and is also available as a containerized application that can be run on any platform.

An Efficient Steady-State Analysis Method for Large Boolean Networks with High Maximum Node Connectivity.

Boolean networks have been widely used to model biological processes lacking detailed kinetic information. Despite their simplicity, Boolean network dynamics can still capture some important features of biological systems such as stable cell phenotypes represented by steady states. For small models, steady states can be determined through exhaustive enumeration of all state transitions. As the number of nodes increases, however, the state space grows exponentially thus making it difficult to find steady states. Over the last several decades, many studies have addressed how to handle such a state space explosion. Recently, increasing attention has been paid to a satisfiability solving algorithm due to its potential scalability to handle large networks. Meanwhile, there still lies a problem in the case of large models with high maximum node connectivity where the satisfiability solving algorithm is known to be computationally intractable. To address the problem, this paper presents a new partitioning-based method that breaks down a given network into smaller subnetworks. Steady states of each subnetworks are identified by independently applying the satisfiability solving algorithm. Then, they are combined to construct the steady states of the overall network. To efficiently apply the satisfiability solving algorithm to each subnetwork, it is crucial to find the best partition of the network. In this paper, we propose a method that divides each subnetwork to be smallest in size and lowest in maximum node connectivity. This minimizes the total cost of finding all steady states in entire subnetworks. The proposed algorithm is compared with others for steady states identification through a number of simulations on both published small models and randomly generated large models with differing maximum node connectivities. The simulation results show that our method can scale up to several hundreds of nodes even for Boolean networks with high maximum node connectivity. The algorithm is implemented and available at http://cps.kaist.ac.kr/∼ckhong/tools/download/PAD.tar.gz.

Trichostatin A increases the thermosensitivity of human glioblastoma A172 cells.

Trichostatin A (TSA), histone deacetylase inhibitor, shows a promising therapeutic effect on cancer cells in combination with radiotherapy or chemotherapy. However, little has been reported on the combined treatment of TSA with hyperthermia. Here, we have assessed the effect of TSA/hyperthermia on human glioblastoma A172 cells and found that TSA increases the thermosensitivity of A172 cells, resulting in cellular apoptosis. The underlying mechanism of this effect consists of reduction in the level of phosphorylated STAT3 (Tyr705), a transcription factor required for survival of A172 cells, which leads to down-regulation of STAT3 target genes, cyclin D1 and Bcl-xL. Furthermore, the level of VEGF mRNA was also decreased by TSA/hyperthermia, suggesting the antiangiogenic effect of TSA/hyperthermia on human glioblastoma. Collectively, our results show the role of TSA as a chemical thermosensitizer, suggesting the possible therapeutic application of combined treatment of TSA/hyperthermia on STAT3-dependent tumors.