Regulation of alcohol oxidase 1 (AOX1) promoter and peroxisome biogenesis in different fermentation processes in Pichia pastoris.

Production of recombinant proteins is affected by process conditions, where transcriptional regulation of Pichia pastoris alcohol oxidase 1 (PpAOX1) promoter has been a key factor to influence expression levels of proteins of interest. Here, we demonstrate that the AOX1 promoter and peroxisome biogenesis are regulated based on different process conditions. Two types of GFP-fusion proteins, Ub-R-GFP (short-lived GFP in the cytosol) and GFP-SKL (peroxisomal targeting GFP), were successfully used to characterize the time-course of the AOX1 promoter and peroxisome biogenesis, respectively. The activity of the AOX1 promoter and peroxisome biogenesis was highly subjected to different fermentation process conditions - methanol-limited condition at normoxy (ML), switched feeding of carbon sources (e.g., glucose and methanol) under carbon-limited condition at normoxy (SML), and oxygen-limited (OL) condition. The AOX1 promoter was most active under the ML, but less active under the OL. Peroxisome biogenesis showed a high dependency on methanol consumption. In addition, the proliferation of peroxisomes was inhibited in a medium containing glucose and stimulated in the methanol phase under a carbon-limited fed-batch culture condition. The specific productivity of a monoclonal antibody (qp) under the AOX1 promoter was higher at 86h of induction in the ML than in the OL (0.026 vs 0.020mgg(-1)h(-1)). However, the oxygen-limited condition was a robust process suitable for longer induction (180h) due to high cell fitness. Our study suggests that the maximal production of a recombinant protein is highly dependent on methanol consumption rate that is affected by the availability of methanol and oxygen molecules.

Quantitative methylation profiling in tumor and matched morphologically normal tissues from breast cancer patients.

BACKGROUND: In the present study, we determined the gene hypermethylation profiles of normal tissues adjacent to invasive breast carcinomas and investigated whether these are associated with the gene hypermethylation profiles of the corresponding primary breast tumors. METHODS: A quantitative methylation-specific PCR assay was used to analyze the DNA methylation status of 6 genes (DAPK, TWIST, HIN-1, RASSF1A, RARbeta2 and APC) in 9 normal breast tissue samples from unaffected women and in 56 paired cancerous and normal tissue samples from breast cancer patients. RESULTS: Normal tissue adjacent to breast cancer displayed statistically significant differences to unrelated normal breast tissues regarding the aberrant methylation of the RASSF1A (P = 0.03), RARbeta2 (P = 0.04) and APC (P = 0.04) genes. Although methylation ratios for all genes in normal tissues from cancer patients were significantly lower than in the cancerous tissue from the same patient (P < or = 0.01), in general, a clear correlation was observed between methylation ratios measured in both tissue types for all genes tested (P < 0.01). When analyzed as a categorical variable, there was a significant concordance between methylation changes in normal tissues and in the corresponding tumor for all genes tested but RASSF1A. Notably, in 73% of patients, at least one gene with an identical methylation change in cancerous and normal breast tissues was observed. CONCLUSIONS: Histologically normal breast tissues adjacent to breast tumors frequently exhibit methylation changes in multiple genes. These methylation changes may play a role in the earliest stages of the development of breast neoplasia.

Quantitative assessment of DNA hypermethylation in the inflammatory and non-inflammatory breast cancer phenotypes.

In this study, a comparative quantitative methylation profiling of inflammatory breast cancer (IBC) and non-IBC was set up for the identification of tumor-specific methylation patterns. Methylation ratios of six genes (DAPK, TWIST, HIN-1, RASSF1A, RARbeta2 and APC) were measured in benign breast tissues (n = 9) and in tumor samples from non-IBC (n = 81) and IBC (n = 19) patients using quantitative methylation-specific PCR. Median methylation ratios observed in breast cancer (n = 100) were significantly higher than those observed in benign breast tissues for five of six genes (TWIST, HIN-1, RASSF1A, RARbeta2 and APC). Only one of the individual genes studied, RARbeta2, showed differential methylation ratios in IBC and non-IBC (p = 0.016). Using the maximal methylation ratio observed in benign breast tissue as a threshold, the methylation frequency of two genes, RARbeta2 and APC, was significantly increased in IBC (n = 19) when compared to non-IBC (n = 81): 53 vs. 23% for RARbeta2 (p = 0.012) and 84 vs. 54% for APC (p = 0.017). Using hierarchical clustering, methylation patterns could not classify breast cancers according to their phenotype. The finding of differential frequencies of methylation in IBC and non-IBC for two out of six genes suggests that gene-specific patterns of methylation could provide a basis for molecular classification of IBC. Testing for additional genes could help to define the IBC phenotype based on patterns of aberrant gene promoter methylation.

High-resolution crystal structures of Erythrina cristagalli lectin in complex with lactose and 2'-alpha-L-fucosyllactose and correlation with thermodynamic binding data.

The primary sequence of Erythrina cristagalli lectin (ECL) was mapped by mass spectrometry, and the crystal structures of the lectin in complex with lactose and 2'-alpha-L-fucosyllactose were determined at 1.6A and 1.7A resolution, respectively. The two complexes were compared with the crystal structure of the closely related Erythrina corallodendron lectin (ECorL) in complex with lactose, with the crystal structure of the Ulex europaeus lectin II in complex with 2'-alpha-L-fucosyllactose, and with two modeled complexes of ECorL with 2'-alpha-L-fucosyl-N-acetyllactosamine. The molecular models are very similar to the crystal structure of ECL in complex with 2'-alpha-L-fucosyllactose with respect to the overall mode of binding, with the L-fucose fitting snugly into the cavity surrounded by Tyr106, Tyr108, Trp135 and Pro134 adjoining the primary combining site of the lectin. Marked differences were however noted between the models and the experimental structure in the network of hydrogen bonds and hydrophobic interactions holding the L-fucose in the combining site of the lectin, pointing to limitations of the modeling approach. In addition to the structural characterization of the ECL complexes, an effort was undertaken to correlate the structural data with thermodynamic data obtained from microcalorimetry, revealing the importance of the water network in the lectin combining site for carbohydrate binding.