Expansin is a plant protein family that induces plant cell wall-loosening and cellulose disruption without exerting cellulose-hydrolytic activity. Expansin-like proteins have also been found in other eukaryotes such as nematodes and fungi. While searching for an expansin produced by bacteria, we found that the BsEXLX1 protein from Bacillus subtilis had a structure that was similar to that of a beta-expansin produced by maize. Therefore, we cloned the BsEXLX1 gene and expressed it in Escherichia coli to evaluate its function. When incubated with filter paper as a cellulose substrate, the recombinant protein exhibited both cellulose-binding and cellulose-weakening activities, which are known functions of plant expansins. In addition, evaluation of the enzymatic hydrolysis of filter paper revealed that the recombinant protein also displayed a significant synergism when mixed with cellulase. By comparing the activity of a mixture of cellulase and the bacterial expansin to the additive activity of the individual proteins, the synergistic activity was found to be as high as 240% when filter paper was incubated with cellulase and BsEXLX1, which was 5.7-fold greater than the activity of cellulase alone. However, this synergistic effect was observed when only a low dosage of cellulase was used. This is the first study to characterize the function of an expansin produced by a non-eukaryotic source.

No abstract

Currently, reliable biomarkers that can be used to distinguish rheumatoid arthritis (RA) from other inflammatory diseases are unavailable. To find possible distinctive metabolic patterns and biomarker candidates for RA, we performed global metabolite profiling of synovial fluid samples. Synovial fluid samples from 38 patients with RA, ankylosing spondylitis, Behçet's disease, and gout were analyzed by gas chromatography/time-of-flight mass spectrometry (GC/TOF MS). Orthogonal partial least-squares discriminant and hierarchical clustering analyses were performed for the discrimination of RA and non-RA groups. Variable importance for projection values were determined, and the Wilcoxon-Mann-Whitney test and the breakdown and one-way analysis of variance were conducted to identify potential biomarkers for RA. A total of 105 metabolites were identified from synovial fluid samples. The score plot of orthogonal partial least squares discriminant analysis showed significant discrimination between the RA and non-RA groups. The 20 metabolites, including citrulline, succinate, glutamine, octadecanol, isopalmitic acid, and glycerol, were identified as potential biomarkers for RA. These metabolites were found to be associated with the urea and TCA cycles as well as fatty acid and amino acid metabolism. The metabolomic analysis results demonstrated that global metabolite profiling by GC/TOF MS might be a useful tool for the effective diagnosis and further understanding of RA.

No abstract

Metabolome sampling is one of the most important factors that determine the quality of metabolomics data. The main steps in metabolite sample preparation include quenching and metabolite extraction. Quenching with 60% (v/v) cold methanol at -40 °C has been most commonly used for Saccharomyces cerevisiae, and this method was recently modified as "leakage-free cold methanol quenching" using pure methanol at -40 °C. Boiling ethanol (75%, v/v) and cold pure methanol are the most widely used extraction solvents for S. cerevisiae. In the present study, metabolome sampling protocols, including the above methods, were evaluated by analyzing 110 identified intracellular metabolites of S. cerevisiae using gas chromatography/time-of-flight mass spectrometry. According to our results, fast filtration followed by washing with an appropriate volume of water can minimize the metabolite loss due to cell leakage as well as the contamination by extracellular metabolites. For metabolite extraction, acetonitrile/water mixture (1:1, v/v) at -20 °C was the most effective. These results imply that the systematic evaluation of existing methods and the development of customized methods for each microorganism are critical for metabolome sample preparation to facilitate the reliable and accurate analysis of metabolome.