Kinetic Study of Glucosamine Production Using Aspergillus sydowii BCRC 31742 under Solid-State Fermentation.

In the present study, we aimed to obtain a high yield and productivity for glucosamine using a low-cost solid-state culture with Aspergillus sydowii BCRC 31742. The fermentation conditions, such as inoculum biomass, moisture content, and supplemental volume and mineral salt, were chosen to achieve high productivity of glucosamine (GlcN). When the initial supplemental volume used was 3 mL/g substrate, the yield and productivity of GlcN were 48.7 mg/gds and 0.69 mg/gds·h, respectively. This result will be helpful for the industrialization of the process.

Proteomic analysis of stachyose contribution to the growth of Lactobacillus acidophilus CICC22162.

Stachyose is a functional oligosaccharide, acting as a potential prebiotic for colonic fermentation. To understand the mechanism of how stachyose promotes the growth of probiotic bacterium, we analyzed the differences of the proteome of Lactobacillus acidophilus grown on stachyose or glucose. By a combination of two-dimensional electrophoresis and mass spectrometry analysis, we observed 16 proteins differentially abundant under these two conditions and identified 9 protein spots. Six of these proteins were highly abundant when stachyose was used as the sole carbon source. They included the phosphotransferase system, the energy coupling factor (ECF) transporter and the mannose-6-phosphate isomerase, involved in the uptake and catabolism of stachyose in Lactobacillus acidophilus CICC22162. Supportively, these observations were validated by quantitative RT-PCR analysis and enzymatic activity determination. Positive correlation was found between the content of the proteins and their mRNA levels. Additionally, we explored the recognition mechanism for stachyose binding to the newly identified ECF transporter by MD simulations and free energy analysis. Taken together, these results provide new insights into the mechanism of stachyose in promoting the growth of probiotic bacterium.

DNetDB: The human disease network database based on dysfunctional regulation mechanism.

Disease similarity study provides new insights into disease taxonomy, pathogenesis, which plays a guiding role in diagnosis and treatment. The early studies were limited to estimate disease similarities based on clinical manifestations, disease-related genes, medical vocabulary concepts or registry data, which were inevitably biased to well-studied diseases and offered small chance of discovering novel findings in disease relationships. In other words, genome-scale expression data give us another angle to address this problem since simultaneous measurement of the expression of thousands of genes allows for the exploration of gene transcriptional regulation, which is believed to be crucial to biological functions. Although differential expression analysis based methods have the potential to explore new disease relationships, it is difficult to unravel the upstream dysregulation mechanisms of diseases. We therefore estimated disease similarities based on gene expression data by using differential coexpression analysis, a recently emerging method, which has been proved to be more potential to capture dysfunctional regulation mechanisms than differential expression analysis. A total of 1,326 disease relationships among 108 diseases were identified, and the relevant information constituted the human disease network database (DNetDB). Benefiting from the use of differential coexpression analysis, the potential common dysfunctional regulation mechanisms shared by disease pairs (i.e. disease relationships) were extracted and presented. Statistical indicators, common disease-related genes and drugs shared by disease pairs were also included in DNetDB. In total, 1,326 disease relationships among 108 diseases, 5,598 pathways, 7,357 disease-related genes and 342 disease drugs are recorded in DNetDB, among which 3,762 genes and 148 drugs are shared by at least two diseases. DNetDB is the first database focusing on disease similarity from the viewpoint of gene regulation mechanism. It provides an easy-to-use web interface to search and browse the disease relationships and thus helps to systematically investigate etiology and pathogenesis, perform drug repositioning, and design novel therapeutic interventions.Database URL: http://app.scbit.org/DNetDB/ #.