Exercise-Induced Changes in Exhaled NO Differentiates Asthma With or Without Fixed Airway Obstruction From COPD With Dynamic Hyperinflation.

Asthmatic patients with fixed airway obstruction (FAO) and patients with chronic obstructive pulmonary disease (COPD) share similarities in terms of irreversible pulmonary function impairment. Exhaled nitric oxide (eNO) has been documented as a marker of airway inflammation in asthma, but not in COPD. To examine whether the basal eNO level and the change after exercise may differentiate asthmatics with FAO from COPD, 27 normal subjects, 60 stable asthmatics, and 62 stable COPD patients were studied. Asthmatics with FAO (n = 29) were defined as showing a postbronchodilator FEV(1)/forced vital capacity (FVC) ≤70% and FEV(1) less than 80% predicted after inhaled salbutamol (400 μg). COPD with dynamic hyperinflation (n = 31) was defined as a decrease in inspiratory capacity (ΔIC%) after a 6 minute walk test (6MWT). Basal levels of eNO were significantly higher in asthmatics and COPD patients compared to normal subjects. The changes in eNO after 6MWT were negatively correlated with the percent change in IC (r = −0.380, n = 29, P = 0.042) in asthmatics with FAO. Their levels of basal eNO correlated with the maximum mid-expiratory flow (MMEF % predicted) before and after 6MWT. In COPD patients with air-trapping, the percent change of eNO was positively correlated to ΔIC% (rs = 0.404, n = 31, P = 0.024). We conclude that asthma with FAO may represent residual inflammation in the airways, while dynamic hyperinflation in COPD may retain NO in the distal airspace. eNO changes after 6MWT may differentiate the subgroups of asthma or COPD patients and will help toward delivery of individualized therapy for airflow obstruction

PRED_PPI: a server for predicting protein-protein interactions based on sequence data with probability assignment

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions (PPIs) are crucial for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. Given the importance of PPIs, several methods have been developed to detect them. Since the experimental methods are time-consuming and expensive, developing computational methods for effectively identifying PPIs is of great practical significance.</p> <p>Findings</p> <p>Most previous methods were developed for predicting PPIs in only one species, and do not account for probability estimations. In this work, a relatively comprehensive prediction system was developed, based on a support vector machine (SVM), for predicting PPIs in five organisms, specifically humans, yeast, <it>Drosophila</it>, <it>Escherichia coli</it>, and <it>Caenorhabditis elegans</it>. This PPI predictor includes the probability of its prediction in the output, so it can be used to assess the confidence of each SVM prediction by the probability assignment. Using a probability of 0.5 as the threshold for assigning class labels, the method had an average accuracy for detecting protein interactions of 90.67% for humans, 88.99% for yeast, 90.09% for <it>Drosophila</it>, 92.73% for <it>E. coli</it>, and 97.51% for <it>C. elegans</it>. Moreover, among the correctly predicted pairs, more than 80% were predicted with a high probability of ≥0.8, indicating that this tool could predict novel PPIs with high confidence.</p> <p>Conclusions</p> <p>Based on this work, a web-based system, Pred_PPI, was constructed for predicting PPIs from the five organisms. Users can predict novel PPIs and obtain a probability value about the prediction using this tool. Pred_PPI is freely available at <url>http://cic.scu.edu.cn/bioinformatics/predict_ppi/default.html</url>.</p

Predicting Protein Phenotypes Based on Protein-Protein Interaction Network

BACKGROUND: Identifying associated phenotypes of proteins is a challenge of the modern genetics since the multifactorial trait often results from contributions of many proteins. Besides the high-through phenotype assays, the computational methods are alternative ways to identify the phenotypes of proteins. METHODOLOGY/PRINCIPAL FINDINGS: Here, we proposed a new method for predicting protein phenotypes in yeast based on protein-protein interaction network. Instead of only the most likely phenotype, a series of possible phenotypes for the query protein were generated and ranked according to the tethering potential score. As a result, the first order prediction accuracy of our method achieved 65.4% evaluated by Jackknife test of 1,267 proteins in budding yeast, much higher than the success rate (15.4%) of a random guess. And the likelihood of the first 3 predicted phenotypes including all the real phenotypes of the proteins was 70.6%. CONCLUSIONS/SIGNIFICANCE: The candidate phenotypes predicted by our method provided useful clues for the further validation. In addition, the method can be easily applied to the prediction of protein associated phenotypes in other organisms

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences

<p>Abstract</p> <p>Background</p> <p>Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences.</p> <p>Results</p> <p>The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes.</p> <p>Conclusions</p> <p>The improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at <url>http://biomine.ece.ualberta.ca/MODAS/</url>.</p

Effects of Hepatocyte CD14 Upregulation during Cholestasis on Endotoxin Sensitivity

Cholestasis is frequently related to endotoxemia and inflammatory response. Our previous investigation revealed a significant increase in plasma endotoxin and CD14 levels during biliary atresia. We therefore propose that lipopolysacharides (LPS) may stimulate CD14 production in liver cells and promote the removal of endotoxins. The aims of this study are to test the hypothesis that CD14 is upregulated by LPS and investigate the pathophysiological role of CD14 production during cholestasis. Using Western blotting, qRT-PCR, and promoter activity assay, we demonstrated that LPS was associated with a significant increase in CD14 and MD2 protein and mRNA expression and CD14 promoter activity in C9 rat hepatocytes but not in the HSC-T6 hepatic stellate cell line in vitro. To correlate CD14 expression and endotoxin sensitivity, in vivo biliary LPS administration was performed on rats two weeks after they were subjected to bile duct ligation (BDL) or a sham operation. CD14 expression and endotoxin levels were found to significantly increase after LPS administration in BDL rats. These returned to basal levels after 24 h. In contrast, although endotoxin levels were increased in sham-operated rats given LPS, no increase in CD14 expression was observed. However, mortality within 24 h was more frequent in the BDL animals than in the sham-operated group. In conclusion, cholestasis and LPS stimulation were here found to upregulate hepatic CD14 expression, which may have led to increased endotoxin sensitivity and host proinflammatory reactions, causing organ failure and death in BDL rats

Identifying chondroprotective diet-derived bioactives and investigating their synergism

Osteoarthritis (OA) is a multifactorial disease and nutrition is a modifiable factor that may contribute to disease onset or progression. A detailed understanding of mechanisms through which diet-derived bioactive molecules function and interact in OA is needed. We profiled 96 diet-derived, mainly plant-based bioactives using an in vitro model in chondrocytes, selecting four candidates for further study. We aimed to determine synergistic interactions between bioactives that affected the expression of key genes in OA. Selected bioactives, sulforaphane, apigenin, isoliquiritigenin and luteolin, inhibited one or more interleukin-1-induced metalloproteinases implicated in OA (MMP1, MMP13, ADAMTS4, ADAMTS5). Isoliquiritigenin and luteolin showed reactive oxygen species scavenging activity in chondrocytes whereas sulforaphane had no effect and apigenin showed only a weak trend. Sulforaphane inhibited the IL-1/NFκB and Wnt3a/TCF/Lef pathways and increased TGFβ/Smad2/3 and BMP6/Smad1/5/8 signalling. Apigenin showed potent inhibition of the IL-1/NFκB and TGFβ/Smad2/3 pathways, whereas luteolin showed only weak inhibition of the IL-1/NFκB pathway. All four bioactives inhibited cytokine-induced aggrecan loss from cartilage tissue explants. The combination of sulforaphane and isoliquiritigenin was synergistic for inhibiting MMP13 gene expression in chondrocytes. We conclude that dietary-derived bioactives may be important modulators of cartilage homeostasis and synergistic relationships between bioactives may have an anti-inflammatory and chondroprotective role

Adaptive Evolution of the Lactose Utilization Network in Experimentally Evolved Populations of Escherichia coli

Adaptation to novel environments is often associated with changes in gene regulation. Nevertheless, few studies have been able both to identify the genetic basis of changes in regulation and to demonstrate why these changes are beneficial. To this end, we have focused on understanding both how and why the lactose utilization network has evolved in replicate populations of Escherichia coli. We found that lac operon regulation became strikingly variable, including changes in the mode of environmental response (bimodal, graded, and constitutive), sensitivity to inducer concentration, and maximum expression level. In addition, some classes of regulatory change were enriched in specific selective environments. Sequencing of evolved clones, combined with reconstruction of individual mutations in the ancestral background, identified mutations within the lac operon that recapitulate many of the evolved regulatory changes. These mutations conferred fitness benefits in environments containing lactose, indicating that the regulatory changes are adaptive. The same mutations conferred different fitness effects when present in an evolved clone, indicating that interactions between the lac operon and other evolved mutations also contribute to fitness. Similarly, changes in lac regulation not explained by lac operon mutations also point to important interactions with other evolved mutations. Together these results underline how dynamic regulatory interactions can be, in this case evolving through mutations both within and external to the canonical lactose utilization network

FIB patterning of stainless steel for the development of nano-structured stent surfaces for cardiovascular applications

Stent implantation is a percutaneous interventional procedure that mitigates vessel stenosis, providing mechanical support within the artery and as such a very valuable tool in the fight against coronary artery disease. However, stenting causes physical damage to the arterial wall. It is well accepted that a valuable route to reduce in-stent re-stenosis can be based on promoting cell response to nano-structured stainless steel (SS) surfaces such as by patterning nano-pits in SS. In this regard patterning by focused ion beam (FIB) milling offers several advantages for flexible prototyping. On the other hand FIB patterning of polycrystalline metals is greatly influenced by channelling effects and redeposition. Correlative microscopy methods present an opportunity to study such effects comprehensively and derive structure–property understanding that is important for developing improved patterning. In this chapter we present a FIB patterning protocol for nano-structuring features (concaves) ordered in rectangular arrays on pre-polished 316L stainless steel surfaces. An investigation based on correlative microscopy approach of the size, shape and depth of the developed arrays in relation to the crystal orientation of the underlying SS domains is presented. The correlative microscopy protocol is based on cross-correlation of top-view scanning electron microscopy, electron backscattering diffraction, atomic force microscopy and cross-sectional (serial) sectioning. Various FIB tests were performed, aiming at improved productivity by preserving nano-size accuracy of the patterned process. The optimal FIB patterning conditions for achieving reasonably high throughput (patterned rate of about 0.03 mm2/h) and nano-size accuracy in dimensions and shapes of the features are discussed as well

BioDMET: a physiologically based pharmacokinetic simulation tool for assessing proposed solutions to complex biological problems

We developed a detailed, whole-body physiologically based pharmacokinetic (PBPK) modeling tool for calculating the distribution of pharmaceutical agents in the various tissues and organs of a human or animal as a function of time. Ordinary differential equations (ODEs) represent the circulation of body fluids through organs and tissues at the macroscopic level, and the biological transport mechanisms and biotransformations within cells and their organelles at the molecular scale. Each major organ in the body is modeled as composed of one or more tissues. Tissues are made up of cells and fluid spaces. The model accounts for the circulation of arterial and venous blood as well as lymph. Since its development was fueled by the need to accurately predict the pharmacokinetic properties of imaging agents, BioDMET is more complex than most PBPK models. The anatomical details of the model are important for the imaging simulation endpoints. Model complexity has also been crucial for quickly adapting the tool to different problems without the need to generate a new model for every problem. When simpler models are preferred, the non-critical compartments can be dynamically collapsed to reduce unnecessary complexity. BioDMET has been used for imaging feasibility calculations in oncology, neurology, cardiology, and diabetes. For this purpose, the time concentration data generated by the model is inputted into a physics-based image simulator to establish imageability criteria. These are then used to define agent and physiology property ranges required for successful imaging. BioDMET has lately been adapted to aid the development of antimicrobial therapeutics. Given a range of built-in features and its inherent flexibility to customization, the model can be used to study a variety of pharmacokinetic and pharmacodynamic problems such as the effects of inter-individual differences and disease-states on drug pharmacokinetics and pharmacodynamics, dosing optimization, and inter-species scaling. While developing a tool to aid imaging agent and drug development, we aimed at accelerating the acceptance and broad use of PBPK modeling by providing a free mechanistic PBPK software that is user friendly, easy to adapt to a wide range of problems even by non-programmers, provided with ready-to-use parameterized models and benchmarking data collected from the peer-reviewed literature

NFV, an HIV-1 protease inhibitor, induces growth arrest, reduced Akt signalling, apoptosis and docetaxel sensitisation in NSCLC cell lines

HIV-1 protease inhibitor (PI), nelfinavir (NFV) induced growth arrest and apoptosis of NCI-H460 and -H520, A549, EBC-1 and ABC-1 non-small-cell lung cancer (NSCLC) cells in association with upregulation of p21waf1, p27 kip1 and p53, and downregulation of Bcl-2 and matrix metalloproteinase (MMP)-2 proteins. We found that NFV blocked Akt signalling in these cells as measured by Akt kinase assay with glycogen synthase kinase-3α/β (GSK-3α/β) as a substrate. To explore the role of Akt signalling in NFV-mediated growth inhibition of NSCLC cells, we blocked this signal pathway by transfection of Akt small interfering RNA (siRNA) in these cells; transient transfection of Akt siRNA in NCI-H460 cells decreased the level of Bcl-2 protein and slowed their proliferation compared to the nonspecific siRNA-transfected cells. Conversely, forced-expression of Akt partially reversed NFV-mediated growth inhibition of these cells, suggesting that Akt may be a molecular target of NFV in NSCLC cells. Also, we found that inhibition of Akt signalling by NFV enhanced the ability of docetaxel to inhibit the growth of NCI-H460 and -H520 cells, as measured by MTT assay. Importantly, NFV slowed the proliferation and induced apoptosis of NCI-H460 cells present as tumour xenografts in nude mice without adverse systemic effects. Taken together, this family of compounds might be useful for the treatment of individuals with NSCLC