Flux Analysis of Glucose Metabolism in Rhizopus oryzae for the Purpose of Increasing Lactate Yields

A flux analysis of glucose metabolism in the filamentous fungus Rhizopus oryzae was achieved using a specific radioactivity curve-matching program, TFLUX. Glycolytic and tricarboxylic acid cycle intermediates labeled through the addition of extracellular [U-14C]glucose were isolated and purified for specific radioactivity determinations. This information, together with pool sizes and the rates of glucose utilization and end product production, provided input for flux maps of the metabolic network under two different experimental conditions. Based upon the flux analysis of this system, a mutant of R. oryzae with higher lactate and lower ethanol yields than the parent was sought for and found.     

Esterase Variations between the 14 Syngens of PARAMECIUM AURELIA under Axenic Growth

Under axenic growth all 14 syngens of Paramecium aurelia have 4 types of esterases. The three major types (A, B and cathodal C) vary independently in electrophoretic mobility among the syngens. Using these three esterases, stocks can be keyed to a syngen, except for the groupings 1-3-5 and 7-13. Using 5 other enzymes only syngens 1 and 5 cannot be distinguished. Most syngens differ from each other in 6 out of the 8 enzymes. An axenically-grown stock of Paramecium multimicronucleatum collected in Costa Rica has the same types of esterases as P. aurelia. Two of the types (A and C) are similar in mobility to those found in syngens 7 and 13, but its B esterase differs in mobility from all the known syngens of P. aurelia.     

Airway cellularity, lipid laden macrophages and microbiology of gastric juice and airways in children with reflux oesophagitis

Background and methods

Human metapneumovirus (hMPV) is a recently discovered respiratory virus associated with bronchiolitis, pneumonia, croup and exacerbations of asthma. Since respiratory viruses are frequently detected in patients with acute exacerbations of COPD (AE-COPD) it was our aim to investigate the frequency of hMPV detection in a prospective cohort of hospitalized patients with AE-COPD compared to patients with stable COPD and to smokers without by means of quantitative real-time RT-PCR.

Results

We analysed nasal lavage and induced sputum of 130 patients with AE-COPD, 65 patients with stable COPD and 34 smokers without COPD. HMPV was detected in 3/130 (2.3%) AE-COPD patients with a mean of 6.5 × 10⁵ viral copies/ml in nasal lavage and 1.88 × 10⁵ viral copies/ml in induced sputum. It was not found in patients with stable COPD or smokers without COPD.

Conclusion

HMPV is only found in a very small number of patients with AE-COPD. However it should be considered as a further possible viral trigger of AE-COPD because asymptomatic carriage is unlikely.     

Characterization of PR-10 genes from eight Betula species and detection of Bet v 1 isoforms in birch pollen

Background  

Bet v 1 is an important cause of hay fever in northern Europe. Bet v 1 isoforms from the European white birch (Betula pendula) have been investigated extensively, but the allergenic potency of other birch species is unknown. The presence of Bet v 1 and closely related PR-10 genes in the genome was established by amplification and sequencing of alleles from eight birch species that represent the four subgenera within the genus Betula. Q-TOF LC-MS^E was applied to identify which PR-10/Bet v 1 genes are actually expressed in pollen and to determine the relative abundances of individual isoforms in the pollen proteome.     

Mapping of citrullinated fibrinogen B-cell epitopes in rheumatoid arthritis by imaging surface plasmon resonance

Introduction  

Rheumatoid arthritis (RA) frequently involves the loss of tolerance to citrullinated antigens, which may play a role in pathogenicity. Citrullinated fibrinogen is commonly found in inflamed synovial tissue and is a frequent target of autoantibodies in RA patients. To obtain insight into the B-cell response to citrullinated fibrinogen in RA, its autoepitopes were systematically mapped using a new methodology.     

Flexible Modeling of Epidemics with an Empirical Bayes Framework

Seasonal influenza epidemics cause consistent, considerable, widespread loss annually in terms of economic burden, morbidity, and mortality. With access to accurate and reliable forecasts of a current or upcoming influenza epidemic’s behavior, policy makers can design and implement more effective countermeasures. This past year, the Centers for Disease Control and Prevention hosted the “Predict the Influenza Season Challenge”, with the task of predicting key epidemiological measures for the 2013–2014 U.S. influenza season with the help of digital surveillance data. We developed a framework for in-season forecasts of epidemics using a semiparametric Empirical Bayes framework, and applied it to predict the weekly percentage of outpatient doctors visits for influenza-like illness, and the season onset, duration, peak time, and peak height, with and without using Google Flu Trends data. Previous work on epidemic modeling has focused on developing mechanistic models of disease behavior and applying time series tools to explain historical data. However, tailoring these models to certain types of surveillance data can be challenging, and overly complex models with many parameters can compromise forecasting ability. Our approach instead produces possibilities for the epidemic curve of the season of interest using modified versions of data from previous seasons, allowing for reasonable variations in the timing, pace, and intensity of the seasonal epidemics, as well as noise in observations. Since the framework does not make strict domain-specific assumptions, it can easily be applied to some other diseases with seasonal epidemics. This method produces a complete posterior distribution over epidemic curves, rather than, for example, solely point predictions of forecasting targets. We report prospective influenza-like-illness forecasts made for the 2013–2014 U.S. influenza season, and compare the framework’s cross-validated prediction error on historical data to that of a variety of simpler baseline predictors.