Project description:Muconic acid production from engineered Corynebacterium glutamicum. Gene expression analysis in the pathway redesigned Corynebacterium glutamicum
Project description:Muconic acid (MA) is a valuable compound for adipic acid production, which is a precursor for the synthesis of various polymers such as plastics, coatings, and nylons. Although MA biosynthesis has been previously reported in several bacteria, the engineered strains were not satisfactory owing to low MA titers. Here, we generated an engineered Corynebacterium cell factory to produce a high titer of MA through 3-dehydroshikimate (DHS) conversion to MA, with heterologous expression of foreign protocatechuate (PCA) decarboxylase genes. To accumulate key intermediates in the MA biosynthetic pathway, aroE (shikimate dehydrogenase gene), pcaG/H (PCA dioxygenase alpha/beta subunit genes) and catB (chloromuconate cycloisomerase gene) were disrupted. To accomplish the conversion of PCA to catechol (CA), a step that is absent in Corynebacterium, a codon-optimized heterologous PCA decarboxylase gene was expressed as a single operon under the strong promoter in a aroE-pcaG/H-catB triple knock-out Corynebacterium strain. This redesigned Corynebacterium, grown in an optimized medium, produced about 38 g/L MA and 54 g/L MA in 7-L and 50-L fed-batch fermentations, respectively. These results show highest levels of MA production demonstrated in Corynebacterium, suggesting that the rational cell factory design of MA biosynthesis could be an alternative way to complement petrochemical-based chemical processes.
Project description:3-Dehydroshikimate (DHS) is a useful starting metabolite for the biosynthesis of muconic acid (MA) and shikimic acid (SA), which are precursors of various valuable polymers and drugs. Although DHS biosynthesis has been previously reported in several bacteria, the engineered strains were far from satisfactory, due to their low DHS titers. Here, we created an engineered Escherichia coli cell factory to produce a high titer of DHS as well as an efficient system for the conversion DHS into MA. First, the genes showing negative effects on DHS accumulation in E. coli, such as tyrR (tyrosine dependent transcriptional regulator), ptsG (glucose specific sugar: phosphoenolpyruvate phosphotransferase), and pykA (pyruvate kinase 2), were disrupted. In addition, the genes involved in DHS biosynthesis, such as aroB (DHQ synthase), aroD (DHQ dehydratase), ppsA (phosphoenolpyruvate synthase), galP (D-galactose transporter), aroG (DAHP synthase), and aroF (DAHP synthase), were overexpressed to increase the glucose uptake and flux of intermediates. The redesigned DHS-overproducing E. coli strain grown in an optimized medium produced ~117 g/L DHS in 7-L fed-batch fermentation, which is the highest level of DHS production demonstrated in E. coli. To accomplish the DHS-to-MA conversion, which is originally absent in E. coli, a codon-optimized heterologous gene cassette containing asbF, aroY, and catA was expressed as a single operon under a strong promoter in a DHS-overproducing E. coli strain. This redesigned E. coli grown in an optimized medium produced about 64.5 g/L MA in 7-L fed-batch fermentation, suggesting that the rational cell factory design of DHS and MA biosynthesis could be a feasible way to complement petrochemical-based chemical processes.
Project description:Background: With the growing availability of entire genome sequences, an increasing number of scientists can exploit oligonucleotide microarrays for genome-scale expression studies. While probe-design is a major research area, relatively little work has been reported on the optimization of microarray protocols. Results: As shown in this study, suboptimal conditions can have considerable impact on biologically relevant observations. For example, deviation from the optimal temperature by one degree Celsius lead to a loss of 44% of differentially expressed genes identified. While genes from thousands of Gene Ontology categories were affected, transcription factors and other low-copy-number regulators were disproportionately lost. Calibrated protocols are thus required in order to take full advantage of the large dynamic range of microarrays. For an objective optimization of protocols we introduce an approach that maximizes the amount of information obtained per experiment. A comparison of two typical samples is sufficient for this calibration. We ensure, however, that optimization results are independent of the samples and the specific measures used for calibration. Both simulations and spike-in experiments confirm an unbiased determination of generally optimal experimental conditions. Conclusions: Well calibrated hybridization conditions are thus easily achieved and necessary for the efficient detection of differential expression. They are essential for the sensitive profiling of low-copy-number molecules. This is particularly critical for studies of transcription factor expression, or the inference and study of regulatory networks. Supporting material, including source code and data, is available at http://bioinf.boku.ac.at/pub/optMA2010/. Optimization of hybridization temperature via an assessment of differential expression between two samples (male vs female Drosophila melanogaster) in 6 technical replicates (3 regular + 3 dye-swaps) for hybridizations at different temperatures (in two batches of 50, 52, 54, and 56; and 47, 49, 50, and 51 degree Celsius, with the repeated hybridization at 50 degree Celsius serving to demonstrate batch-to-batch stability).
Project description:We applied the high-throughput interaction assay SORTCERY to measure thousands of protein-peptide binding affinities and used the data to parameterize models of the peptide-binding landscape for three members of the Bcl-2 family of proteins. We applied the models to design peptides that bound with high affinity and specificity to just one of Bcl-xL, Mcl-1, or Bfl-1. We designed additional peptides that bound selectively to two out of three of these proteins. The raw data provided are the multiplexed fastq files that serve as inputs to our analysis pipeline. Additional detail is available in our corresponding publication and at the following github repository. https://github.com/KeatingLab/sortcery_design