Microbial Communities in the Upper Respiratory Tract of Patients with Asthma and Chronic Obstructive Pulmonary Disease

Respiratory infections are well-known triggers of chronic respiratory diseases. Recently, culture-independent tools have indicated that lower airway microbiota may contribute to pathophysiologic processes associated with asthma and chronic obstructive pulmonary disease (COPD). However, the relationship between upper airway microbiota and chronic respiratory diseases remains unclear. This study was undertaken to define differences of microbiota in the oropharynx of asthma and COPD patients relative to those in healthy individuals. To account for the qualitative and quantitative diversity of the 16S rRNA gene in the oropharynx, the microbiomes of 18 asthma patients, 17 COPD patients, and 12 normal individuals were assessed using a high-throughput next-generation sequencing analysis. In the 259,572 total sequence reads, α and β diversity measurements and a generalized linear model revealed that the oropharynx microbiota are diverse, but no significant differences were observed between asthma and COPD patients. Pseudomonas spp. of Proteobacteria and Lactobacillus spp. of Firmicutes were highly abundant in asthma and COPD. By contrast, Streptococcus, Veillonella, Prevotella, and Neisseria of Bacteroidetes dominated in the healthy oropharynx. These findings are consistent with previous studies conducted in the lower airways and suggest that oropharyngeal airway microbiota are important for understanding the relationships between the various parts of the respiratory tract with regard to bacterial colonization and comprehensive assessment of asthma and COPD.     

Effects of N-acetyl-l-cysteine on adhesive strength between breast cancer cell and extracellular matrix proteins after ionizing radiation

Aims: To evaluate the effect of N-acetyl-L-cysteine (LNAC), a common ROS scavenger, on the adhesive affinity between MDA-MB-231 breast cancer cells and extracellular matrix (ECM) proteins after IR.     

Augmenting Microarray Data with Literature-Based Knowledge to Enhance Gene Regulatory Network Inference

Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology.     

Structural basis for the N‐degron specificity of ClpS1 from Arabidopsis thaliana

The N‐degron pathway determines the half‐life of proteins in both prokaryotes and eukaryotes by precisely recognizing the N‐terminal residue (N‐degron) of substrates. ClpS proteins from bacteria bind to substrates containing hydrophobic N‐degrons (Leu, Phe, Tyr, and Trp) and deliver them to the caseinolytic protease system ClpAP. This mechanism is preserved in organelles such as mitochondria and chloroplasts. Bacterial ClpS adaptors bind preferentially to Leu and Phe N‐degrons; however, ClpS1 from Arabidopsis thaliana (AtClpS1) shows a difference in that it binds strongly to Phe and Trp N‐degrons and only weakly to Leu. This difference in behavior cannot be explained without structural information due to the high sequence homology between bacterial and plant ClpS proteins. Here, we report the structure of AtClpS1 at 2.0 Å resolution in the presence of a bound N‐degron. The key determinants for α‐amino group recognition are conserved among all ClpS proteins, but the α3‐helix of eukaryotic AtClpS1 is significantly shortened, and consequently, a loop forming a pocket for the N‐degron is moved slightly outward to enlarge the pocket. In addition, amino acid replacement from Val to Ala causes a reduction in hydrophobic interactions with Leu N‐degron. A combination of the fine‐tuned hydrophobic residues in the pocket and the basic gatekeeper at the entrance of the pocket controls the N‐degron selectivity of the plant ClpS protein.     

Reducing acetylated tau is neuroprotective in brain injury

1. Download : Download high-res image (138KB)
2. Download : Download full-size image

     

A Computational Model of Afterimage Rotation in the Peripheral Drift Illusion Based on Retinal ON/OFF Responses

Human observers perceive illusory rotations after the disappearance of circularly repeating patches containing dark-to-light luminance. This afterimage rotation is a very powerful phenomenon, but little is known about the mechanisms underlying it. Here, we use a computational model to show that the afterimage rotation can be explained by a combination of fast light adaptation and the physiological architecture of the early visual system, consisting of ON- and OFF-type visual pathways. In this retinal ON/OFF model, the afterimage rotation appeared as a rotation of focus lines of retinal ON/OFF responses. Focus lines rotated clockwise on a light background, but counterclockwise on a dark background. These findings were consistent with the results of psychophysical experiments, which were also performed by us. Additionally, the velocity of the afterimage rotation was comparable with that observed in our psychophysical experiments. These results suggest that the early visual system (including the retina) is responsible for the generation of the afterimage rotation, and that this illusory rotation may be systematically misinterpreted by our high-level visual system.     

Structure and Optical Bandgap Relationship of π-Conjugated Systems

In bulk heterojunction photovoltaic systems both the open-circuit voltage as well as the short-circuit current, and hence the power conversion efficiency, are dependent on the optical bandgap of the electron-donor material. While first-principles methods are computationally intensive, simpler model Hamiltonian approaches typically suffer from one or more flaws: inability to optimize the geometries for their own input; absence of general, transferable parameters; and poor performance for non-planar systems. We introduce a set of new and revised parameters for the adapted Su-Schrieffer-Heeger (aSSH) Hamiltonian, which is capable of optimizing geometries, along with rules for applying them to any -conjugated system containing C, N, O, or S, including non-planar systems. The predicted optical bandgaps show excellent agreement to UV-vis spectroscopy data points from literature, with a coefficient of determination , a mean error of −0.05 eV, and a mean absolute deviation of 0.16 eV. We use the model to gain insights from PEDOT, fused thiophene polymers, poly-isothianaphthene, copolymers, and pentacene as sources of design rules in the search for low bandgap materials. Using the model as an in-silico design tool, a copolymer of benzodithiophenes along with a small-molecule derivative of pentacene are proposed as optimal donor materials for organic photovoltaics.     

Cellular Changes in Blood Indicate Severe Respiratory Disease during Influenza Infections in Mice

Influenza A infection is a serious threat to human and animal health. Many of the biological mechanisms of the host-pathogen-interactions are still not well understood and reliable biomarkers indicating the course of the disease are missing. The mouse is a valuable model system enabling us to study the local inflammatory host response and the influence on blood parameters under controlled circumstances. Here, we compared the lung and peripheral changes after PR8 (H1N1) influenza A virus infection in C57BL/6J and DBA/2J mice using virus variants of different pathogenicity resulting in non-lethal and lethal disease. We monitored hematological and immunological parameters revealing that the granulocyte to lymphocyte ratio in the blood represents an early indicator of severe disease progression already two days after influenza A infection in mice. These findings might be relevant to optimize early diagnostic options of severe influenza disease and to monitor successful therapeutic treatment in humans.