SED, a normalization free method for DNA microarray data analysis

Production of fructooligosaccharides by Bacillus subtilis natto CCT7712 and their antiproliferative potential

AIMS

Fructooligosaccharides (FOSs) known for their health properties and β-(2→6)-levan-type FOSs have shown prebiotic and immunomodulatory activities that overcome those of commercial β-(2→1)-FOSs, but costs do not favour their use. Moreover, FOSs can reach the bloodstream through the diet, and little is known about their direct effect on cells. The aim of this work was to produce high-content FOSs by Bacillus subtilis natto CCT7712 in a bioreactor using commercial sucrose and to evaluate their antiproliferative effects in OVCAR-3 cells.

METHODS AND RESULTS

FOS production reached 173·60 g l^-1 , 0·2 vvm aeration and uncontrolled pH. Levan-type FOSs, composed of β-(2 → 6) links and mainly GF₃ (6-nystose), were identified using RMN spectroscopy, FT-IR and ESI-MS. FOSs decreased the viability and proliferation of OVCAR-3 cells, and the effects were associated with an increased pro-inflammatory response by the induction of IL-8 and TNF-α, and the repression of ER-β genes. The metabolic profiles showed disruption of cellular homeostasis that can be associated with a decrease in proliferation.

CONCLUSIONS

The high production of levan-type FOSs from B. subtilis natto CCT7712 in a bioreactor was achieved, and they showed antiproliferative potential in OVCAR-3 cells.

SIGNIFICANCE AND IMPACT OF THE STUDY

FOS could be a good target for future therapeutic studies and commercial use.

Normalization using weighted negative second order exponential error functions (NeONORM) provides robustness against asymmetries in comparative transcriptome profiles and avoids false calls

Studies on high-throughput global gene expression using microarray technology have generated ever larger amounts of systematic transcriptome data. A major challenge in exploiting these heterogeneous datasets is how to normalize the expression profiles by inter-assay methods. Different non-linear and linear normalization methods have been developed, which essentially rely on the hypothesis that the true or perceived logarithmic fold-change distributions between two different assays are symmetric in nature. However, asymmetric gene expression changes are frequently observed, leading to suboptimal normalization results and in consequence potentially to thousands of false calls. Therefore, we have specifically investigated asymmetric comparative transcriptome profiles and developed the normalization using weighted negative second order exponential error functions (NeONORM) for robust and global inter-assay normalization. NeONORM efficiently damps true gene regulatory events in order to minimize their misleading impact on the normalization process. We evaluated NeONORM's applicability using artificial and true experimental datasets, both of which demonstrated that NeONORM could be systematically applied to inter-assay and inter-condition comparisons.