Rapid and efficient production of L-lactate from xylose using electrodialysis culture-associated product separation

L-Lactate was produced from xylose using electrodialysis culture (ED-C)-associated product separation. In a medium containing 50 g xylose/l, the ED-C was completed in only 32 h (i.e. less than half the time taken by the control culture, without electrodialysis). At 80 g xylose/l, the control culture was unable to consume more than 50 g xylose/1, whereas the ED-C showed increased xylose consumption and was completed by 45 h. The maximum rate of lactate production in the ED-C was higher than that in the control culture. ED-C was also carried out (at 80 g initial xylose/ l) with a supply of fresh xylose-free medium. This ED-C was completed within 30 h, which represents a reduction in fermentation time of 15 h when compared to ED-C without addition of xylose-free medium. Thus, rapid production of L-lactate was achieved by using ED-C which supplied fresh xylose-free medium.

High-speed conversion of xylose to l-lactate by electrodialysis bioprocess

Conversion of xylose to l-lactate was carried out by Lactococcus lactis IO-1 using an electrodialysis bioprocess (ED-BP). At 50 g l -1 xylose, the ED-BP was already complete in half the time (32 h) taken by the control culture without electrodialysis (>60 h). At 80 g l -1 xylose, the control culture was unable to consume >50 g l -1 xylose, whereas the ED-BP consumed 75 g l -1 xylose in 45 h. Thus, the simultaneous removal of lactate and acetate by ED-BP was associated with high-speed l-lactate production, increased xylose consumption and an increased l-lactate production. Copyright (C) 1998 Elsevier Science B.V.

Prior knowledge transfer across transcriptional data sets and technologies using compositional statistics yields new mislabelled ovarian cell line

Here, we describe gene expression compositional assignment (GECA), a powerful, yet simple method based on compositional statistics that can validate the transfer of prior knowledge, such as gene lists, into independent data sets, platforms and technologies. Transcriptional profiling has been used to derive gene lists that stratify patients into prognostic molecular subgroups and assess biomarker performance in the pre-clinical setting. Archived public data sets are an invaluable resource for subsequent in silico validation, though their use can lead to data integration issues. We show that GECA can be used without the need for normalising expression levels between data sets and can outperform rank-based correlation methods. To validate GECA, we demonstrate its success in the cross-platform transfer of gene lists in different domains including: bladder cancer staging, tumour site of origin and mislabelled cell lines. We also show its effectiveness in transferring an epithelial ovarian cancer prognostic gene signature across technologies, from a microarray to a next-generation sequencing setting. In a final case study, we predict the tumour site of origin and histopathology of epithelial ovarian cancer cell lines. In particular, we identify and validate the commonly-used cell line OVCAR-5 as non-ovarian, being gastrointestinal in origin. GECA is available as an open-source R package.