Multi-enzymatic one-pot reduction of dehydrocholic acid to 12-keto-ursodeoxycholic acid with whole-cell biocatalysts.

Ursodeoxycholic acid (UDCA) is a bile acid of industrial interest as it is used as an agent for the treatment of primary sclerosing cholangitis and the medicamentous, non-surgical dissolution of gallstones. Currently, it is prepared industrially from cholic acid following a seven-step chemical procedure with an overall yield of <30%. In this study, we investigated the key enzymatic steps in the chemo-enzymatic preparation of UDCA-the two-step reduction of dehydrocholic acid (DHCA) to 12-keto-ursodeoxycholic acid using a mutant of 7ß-hydroxysteroid dehydrogenase (7ß-HSDH) from Collinsella aerofaciens and 3α-hydroxysteroid dehydrogenase (3α-HSDH) from Comamonas testosteroni. Three different one-pot reaction approaches were investigated using whole-cell biocatalysts in simple batch processes. We applied one-biocatalyst systems, where 3α-HSDH, 7ß-HSDH, and either a mutant of formate dehydrogenase (FDH) from Mycobacterium vaccae N10 or a glucose dehydrogenase (GDH) from Bacillus subtilis were expressed in a Escherichia coli BL21(DE3) based host strain. We also investigated two-biocatalyst systems, where 3α-HSDH and 7ß-HSDH were expressed separately together with FDH enzymes for cofactor regeneration in two distinct E. coli hosts that were simultaneously applied in the one-pot reaction. The best result was achieved by the one-biocatalyst system with GDH for cofactor regeneration, which was able to completely convert 100 mM DHCA to >99.5 mM 12-keto-UDCA within 4.5 h in a simple batch process on a liter scale.

Novel whole-cell biocatalysts with recombinant hydroxysteroid dehydrogenases for the asymmetric reduction of dehydrocholic acid.

Ursodeoxycholic acid is an important pharmaceutical so far chemically synthesized from cholic acid. Various biocatalytic alternatives have already been discussed with hydroxysteroid dehydrogenases (HSDH) playing a crucial role. Several whole-cell biocatalysts based on a 7α-HSDH-knockout strain of Escherichia coli overexpressing a recently identified 7ß-HSDH from Collinsella aerofaciens and a NAD(P)-bispecific formate dehydrogenase mutant from Mycobacterium vaccae for internal cofactor regeneration were designed and characterized. A strong pH dependence of the whole-cell bioreduction of dehydrocholic acid to 3,12-diketo-ursodeoxycholic acid was observed with the selected recombinant E. coli strain. In the optimal, slightly acidic pH range dehydrocholic acid is partly undissolved and forms a suspension in the aqueous solution. The batch process was optimized making use of a second-order polynomial to estimate conversion as function of initial pH, initial dehydrocholic acid concentration, and initial formate concentration. Complete conversion of 72 mM dehydrocholic acid was thus made possible at pH 6.4 in a whole-cell batch process within a process time of 1 h without cofactor addition. Finally, a NADH-dependent 3α-HSDH from Comamonas testosteroni was expressed additionally in the E. coli production strain overexpressing the 7ß-HSDH and the NAD(P)-bispecific formate dehydrogenase mutant. It was shown that this novel whole-cell biocatalyst was able to convert 50 mM dehydrocholic acid directly to 12-keto-ursodeoxycholic acid with the formation of only small amounts of intermediate products. This approach may be an efficient process alternative which avoids the costly chemical epimerization at C-7 in the production of ursodeoxycholic acid.

Differential methylation of TSP50 and mTSP50 genes in different types of human tissues and mouse spermatic cells.

Earlier studies identified human TSP50 as a testis-specific gene that encoded a threonine protease. Most importantly, TSP50 could be a cancer/testis antigen since there was a high frequency of reactivation in breast cancer biopsies. It was also found to be negatively regulated by the p53 gene. To further characterize this gene, we recently examined the DNA methylation patterns of the TSP50 gene promoter in normal human testis, as well as breast tissue and a testicular embryonic carcinoma cell line (HTECCL). Bisulfite genomic sequencing results demonstrated that the promoter exhibited mixed DNA methylation patterns in normal human testis, mainly non-methylation versus slight methylation, which could be attributed to the different stages spermatic cells go through during spermatogenesis. In contrast, it was methylated to a much greater extent in both breast tissue and HTECCL. To find out whether DNA methylation status was related to spermatogenesis stages, we analyzed DNA methylation patterns of the mTSP50 (the mouse ortholog of TSP50) promoter in spermatocytes and spermatozoa isolated from sexually mature mice. The results clearly demonstrated that each group of cells exhibited its preferential DNA methylation pattern that apparently was consistent with the gene expression status observed before. Taken together, our findings suggested that DNA methylation might regulate the TSP50 and mTSP50 gene expressions in different types of tissues and spermatic cells.

Methylation of WTH3, a possible drug resistant gene, inhibits p53 regulated expression.

BACKGROUND: Previous results showed that over-expression of the WTH3 gene in MDR cells reduced MDR1 gene expression and converted their resistance to sensitivity to various anticancer drugs. In addition, the WTH3 gene promoter was hypermethylated in the MCF7/AdrR cell line and primary drug resistant breast cancer epithelial cells. WTH3 was also found to be directly targeted and up regulated by the p53 gene. Furthermore, over expression of the WTH3 gene promoted the apoptotic phenotype in various host cells. METHODS: To further confirm WTH3's drug resistant related characteristics, we recently employed the small hairpin RNA (shRNA) strategy to knockdown its expression in HEK293 cells. In addition, since the WTH3 promoter's p53-binding site was located in a CpG island that was targeted by methylation, we were interested in testing the possible effect this epigenetic modification had on the p53 transcription factor relative to WTH3 expression. To do so, the in vitro methylation method was utilized to examine the p53 transgene's influence on either the methylated or non-methylated WTH3 promoter. RESULTS: The results generated from the gene knockdown strategy showed that reduction of WTH3 expression increased MDR1 expression and elevated resistance to Doxorubicin as compared to the original control cells. Data produced from the methylation studies demonstrated that DNA methylation adversely affected the positive impact of p53 on WTH3 promoter activity. CONCLUSION: Taken together, our studies provided further evidence that WTH3 played an important role in MDR development and revealed one of its transcription regulatory mechanisms, DNA methylation, which antagonized p53's positive impact on WTH3 expression.

Dynamic mechanistic modeling of the multienzymatic one-pot reduction of dehydrocholic acid to 12-keto ursodeoxycholic acid with competing substrates and cofactors.

Ursodeoxycholic acid (UDCA) is a bile acid which is used as pharmaceutical for the treatment of several diseases, such as cholesterol gallstones, primary sclerosing cholangitis or primary biliary cirrhosis. A potential chemoenzymatic synthesis route of UDCA comprises the two-step reduction of dehydrocholic acid to 12-keto-ursodeoxycholic acid (12-keto-UDCA), which can be conducted in a multienzymatic one-pot process using 3α-hydroxysteroid dehydrogenase (3α-HSDH), 7ß-hydroxysteroid dehydrogenase (7ß-HSDH), and glucose dehydrogenase (GDH) with glucose as cosubstrate for the regeneration of cofactor. Here, we present a dynamic mechanistic model of this one-pot reduction which involves three enzymes, four different bile acids, and two different cofactors, each with different oxidation states. In addition, every enzyme faces two competing substrates, whereas each bile acid and cofactor is formed or converted by two different enzymes. First, the kinetic mechanisms of both HSDH were identified to follow an ordered bi-bi mechanism with EBQ-type uncompetitive substrate inhibition. Rate equations were then derived for this mechanism and for mechanisms describing competing substrates. After the estimation of the model parameters of each enzyme independently by progress curve analyses, the full process model of a simple batch-process was established by coupling rate equations and mass balances. Validation experiments of the one-pot multienzymatic batch process revealed high prediction accuracy of the process model and a model analysis offered important insight to the identification of optimum reaction conditions.