Metabolic engineering has created several Escherichia coli biocatalysts for production of biofuels and other useful molecules. However, the inability of these biocatalysts to directly use polymeric substrates necessitates costly pretreatment and enzymatic hydrolysis prior to fermentation. Consolidated bioprocessing has the potential to simplify the process by combining enzyme production, hydrolysis, and fermentation into a single step but requires a fermenting organism to multitask by producing both necessary enzymes and target molecules. We demonstrate here a binary strategy for consolidated bioprocessing of xylan, a complex substrate requiring six hemicellulases for complete hydrolysis. An integrated modular approach was used to design the two strains to function cooperatively in the process of transforming xylan into ethanol. The first strain was engineered to coexpress two hemicellulases. Recombinant enzymes were secreted to the growth medium by a method of lpp deletion with over 90% efficiency. Secreted enzymes hydrolyzed xylan into xylooligosaccharides, which were taken in by the second strain, designed to use the xylooligosaccharides for ethanol production. Cocultivation of the two strains converted xylan hemicellulose to ethanol with a yield about 55% of the theoretical value. Inclusion of other three hemicellulases improved the ethanol yield to 70%. Analysis of the culture broth showed that xylooligosaccharides with four or more xylose units were not utilized, suggesting that improving the use of higher xyloogligomers should be the focus in future efforts. This is the first demonstration of an engineered binary culture for consolidated bioprocessing of xylan. The modular design should allow the strategy to be adopted for a broad range of biofuel and biorefinery products.
A novel hemicellulase-producing fungal strain was isolated from a local soil sample. The organism is identified as Aspergillus fumigatus based on ribosomal RNA analyses. The Aspergillus strain, designated as 2NB, produces both enzymes acting on xylan backbone (xylanase and β-xylosidase), and those acting on side chains (or accessory enzymes) notably α-arabinofuranosidase and acetyl-xylan esterase. The Asperigillus hemicellulases are characterized as having relatively low xylanase and β-xylosidase activities but high side chain removal activities. The activity ratio of side-chain acting enzymes to xylanase is higher than that of the Multifect enzyme, a commercial hemicellulase product. The potential of the novel hemicellulases in lignocelluloses bioprocessing was demonstrated with alkaline-pretreated switchgrass as lignocellulose substrate with hemicellulase supplemented with a ratio of xylanase activity to filter paper unit of 2:1. Supplement of Aspergillus hemicellulases to commercial cellulases significantly enhanced the hydrolysis of lignocellulose, achieving a 94% hydrolysis yield based on reducing sugar measurement, compared to 60% when no hemicellulase or 75% when Multifect enzyme was used under otherwise identical conditions. The significant improvement resulting from supplementing a hemicellulase mix with high side-chain removal activities suggests the importance of accessory hemicellulases in lignocellulose processing.
Although expression profiling of various diseases to identify interesting genes is a well-established methodology, it still faces many challenges. Labs often have difficulty reproducing results on different microarray platforms. Microarray manufacturers use different clones to represent similar genes on various platforms. Consequently, researchers struggle to integrate data published in literature and databases. Even results from identical microarray platforms may not correlate due to technical variability between labs. We seek some degree of congruity between the same microarray platforms implemented at multiple test sites. We analyze two prostate cancer datasets from commercially synthesized oligonucleotide arrays (Affymetrix HG-U95v2). Our analysis focuses on determining reproducibility in identifying differentially expressed genes using fold change and t-tests. We use p-values to compare specificity and sensitivity of the methods applied to each dataset. Findings indicate that, even though both datasets use the same microarray platform, differences in experimental design and test conditions result in variations when detecting differentially expressed genes.
Many methods have been proposed to identify differentially expressed genes in diseased tissues. The performance of the method is closely related to the evaluation metric. We examine several error estimation algorithms (i.e., cross validation, bootstrap, resubstitution, and resubstitution with bolstering) for three classifiers (i.e., support vector machine, Fisher's discriminant, and signed distance function). To control the classifier's data-overfitting problem, usually caused by small sample size for many real datasets, we generate synthetic datasets based on real data. This way, we can monitor sample size impact when evaluating the metrics. We find that resubstitution with bolstering has the best result, especially with respect to computational efficiency. However, classical bolstering tends to bias in high dimensions. Thus, we further investigate ways to reduce bolstering estimation bias without increasing computational intensity. Results of our investigation indicate that the estimator tends to become unbiased as the sample size increases. We also find that modified bolstering is the best among all metrics in terms of estimation accuracy and computational efficiency.
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