Cigarette smoke (CS) predisposes exposed individuals to respiratory infections not only by suppressing immune response but also by enhancing the virulence of pathogenic bacteria. As per our observations, in methicillin-resistant Staphylococcus aureus strain USA300, CS extract (CSE) potentiates biofilm formation via the down-regulation of quorum-sensing regulon accessory gene regulator. Because accessory gene regulator is a global regulator of the staphylococcal virulome, in the present study we sought to identify the effects of CS exposure on staphylococcal gene expression using RNAseq. Comparative analysis of RNAseq profiles revealed the upregulation of important virulence genes encoding surface adhesins (fibronectin-and fibrinogen-binding proteins A and B and clumping factor B) and proteins involved in immune evasion, such as staphylocoagulase, staphylococcal protein A, and nuclease. In concurrence with the RNAseq data, we observed: (1) significant up-regulation of the ability of CSE-exposed USA300 to evade phagocytosis by macrophages and neutrophils, a known function of staphylococcal protein A; and (2) twofold higher (P , 0.001) number of CSE-exposed USA300 escaping neutrophil extracellular trap-mediated killing by neutrophils as a result of CS-mediated induction of nuclease. Importantly, in three different mouse strains, C57BL6/J, Balb/C, and A/J, we observed significantly higher pulmonary bacterial burden in animals infected with CSEexposed USA300 as compared with medium-exposed control USA300. Taken together, these observations indicate that bioactive chemicals in CS induce hypervirulence by augmenting the ability of USA300 to evade bactericidal functions of leukocytes, such as phagocytosis and neutrophil extracellular trap-mediated killing.
A novel implementation of Replica Exchange Statistical Temperature Molecular Dynamics (RESTMD), belonging to a generalized ensemble method and also known as parallel tempering, is presented. Our implementation employs the MapReduce (MR)-based iterative framework for launching RESTMD over high performance computing (HPC) clusters including our testbed system, Cyber-infrastructure for Reconfigurable Optical Networks (CRON) simulating a network-connected distributed system. Our main contribution is a new implementation of STMD plugged into the well-known CHARMM molecular dynamics package as well as the RESTMD implementation powered by the Hadoop that scales out in a cluster and across distributed systems effectively. To address challenges for the use of Hadoop MapReduce, we examined contributing factors on the performance of the proposed framework with various runtime analysis experiments with two biological systems that differ in size and over different types of HPC resources. Many advantages with the use of RESTMD suggest its effectiveness for enhanced sampling, one of grand challenges in a variety of areas of studies ranging from chemical systems to statistical inference. Lastly, with its support for scale-across capacity over distributed computing infrastructure (DCI) and the use of Hadoop for coarse-grained task-level parallelism, MapReduce-based RESTMD represents truly a good example of the next-generation of applications whose provision is increasingly becoming demanded by science gateway projects, in particular, backed by IaaS clouds.
While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread—a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure.
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