Taizo Hanai

Non-fermentative pathways for synthesis of branched-chain higher alcohols as biofuels

Global energy and environmental problems have stimulated increased efforts towards synthesizing biofuels from renewable resources. Compared to the traditional biofuel, ethanol, higher alcohols offer advantages as gasoline substitutes because of their higher energy density and lower hygroscopicity. In addition, branched-chain alcohols have higher octane numbers compared with their straight-chain counterparts. However, these alcohols cannot be synthesized economically using native organisms. Here we present a metabolic engineering approach using Escherichia coli to produce higher alcohols including isobutanol, 1-butanol, 2-methyl-1-butanol, 3-methyl-1-butanol and 2-phenylethanol from glucose, a renewable carbon source. This strategy uses the host’s highly active amino acid biosynthetic pathway and diverts its 2-keto acid intermediates for alcohol synthesis. In particular, we have achieved high-yield, high-specificity production of isobutanol from glucose. The strategy enables the exploration of biofuels beyond those naturally accumulated to high quantities in microbial fermentation.

Engineered Synthetic Pathway for Isopropanol Production in Escherichia coli

ABSTRACT A synthetic pathway was engineered in Escherichia coli to produce isopropanol by expressing various combinations of genes from Clostridium acetobutylicum ATCC 824, E. coli K-12 MG1655, Clostridium beijerinckii NRRL B593, and Thermoanaerobacter brockii HTD4. The strain with the combination of C. acetobutylicum thl (acetyl-coenzyme A [CoA] acetyltransferase), E. coli atoAD (acetoacetyl-CoA transferase), C. acetobutylicum adc (acetoacetate decarboxylase), and C. beijerinckii adh (secondary alcohol dehydrogenase) achieved the highest titer. This strain produced 81.6 mM isopropanol in shake flasks with a yield of 43.5% (mol/mol) in the production phase. To our knowledge, this work is the first to produce isopropanol in E. coli, and the titer exceeded that from the native producers.

Improvement of isopropanol production by metabolically engineered Escherichia coli using gas stripping

To improve isopropanol production by metabolically engineered Escherichia coli strain TA76, the optimization of fermentation conditions and isopropanol removal by gas stripping were performed. Isopropanol is one of the simplest secondary alcohols, and it can be dehydrated to yield propylene, which is currently derived from petroleum as a monomer for making polypropylene. Initially, using a pH-controlled fed-batch culture with the intermittent addition of glucose, strain TA76 produced 667 mM (40.1g/L) of isopropanol after 60 h, representing 73.2% (mol isopropanol/mol glucose) of the theoretical maximum yield. Because the accumulation of isopropanol drastically reduced production yields, a gas stripping recovery method was incorporated into the fed-batch culture system. Using this approach, strain TA76 produced 2378 mM (143 g/L) of isopropanol after 240 h with a yield of 67.4% (mol/mol). To our knowledge, this titer represents the highest level of isopropanol production by E. coli to date and suggests that strain TA76 has a great potential for commercial fermentative isopropanol production.

Metabolic flux redirection from a central metabolic pathway toward a synthetic pathway using a metabolic toggle switch

Overexpression of genes in production pathways and permanent knockout of genes in competing pathways are often employed to improve production titer and yield in metabolic engineering. However, the deletion of a pathway responsible for growth and cell maintenance has not previously been employed, even if its competition with the production pathway is obvious. In order to optimize intracellular metabolism at each fermentation phase for bacterial growth and production, a methodology employing conditional knockout is required. We constructed a metabolic toggle switch in Escherichia coli as a novel conditional knockout approach and applied it to isopropanol production. The resulting redirection of excess carbon flux caused by interruption of the TCA cycle via switching gltA OFF improved isopropanol production titer and yield up to 3.7 and 3.1 times, respectively. This approach is a useful tool to redirect carbon flux responsible for bacterial growth and/or cell maintenance toward a synthetic production pathway.

Engineering a synthetic pathway in cyanobacteria for isopropanol production directly from carbon dioxide and light.

Production of alternate fuels or chemicals directly from solar energy and carbon dioxide using engineered cyanobacteria is an attractive method to reduce petroleum dependency and minimize carbon emissions. Here, we constructed a synthetic pathway composed of acetyl-CoA acetyl transferase (encoded by thl), acetoacetyl-CoA transferase (encoded by atoAD), acetoacetate decarboxylase (encoded by adc) and secondary alcohol dehydrogenase (encoded by adh) in Synechococcus elongatus strain PCC 7942 to produce isopropanol. The enzyme-coding genes, heterogeneously originating from Clostridium acetobutylicum ATCC 824 (thl and adc), Escherichia coli K-12 MG1655 (atoAD) and Clostridium beijerinckii (adh), were integrated into the S. elongatus genome. Under the optimized production conditions, the engineered cyanobacteria produced 26.5 mg/L of isopropanol after 9 days.

Hidden Markov model-based prediction of antigenic peptides that interact with MHC class II molecules

Elucidating the interaction between major histocompatibility complex (MHC) molecules and antigenic peptides is fundamental to better understanding of the processes involved in immune responses and for the development of innovative immunotherapies. In the present study, hidden Markov models (HMM) were combined with the successive state splitting (SSS) algorithm for optimization of the HMM structure, to predict peptide binders to the human MHC class II molecule HLA-DRB1*0101. The predictive performance of our model (S-HMM) was compared with fully connected HMM and artificial neural network (ANN) methods using the relative operating characteristic (ROC) analysis. The S-HMM predictions had values of ROC > or = 0.85 which was at least as good, or better than the comparison methods. In addition, S-HMM is trained on positive data only and does not require exhaustive data preprocessing, such as peptide alignment. Our results demonstrated that S-HMM combines the high accuracy of predictions with the simplicity of implementation and is therefore useful for analyzing MHC class II binding peptides. In particular the S-HMM may be trained using only positive data and, the preprocessing of training data, such as peptide alignment and the selection of binding cores, is not required in this method.

Analysis of expression profile using fuzzy adaptive resonance theory

MOTIVATION It is well understood that the successful clustering of expression profiles give beneficial ideas to understand the functions of uncharacterized genes. In order to realize such a successful clustering, we investigate a clustering method based on adaptive resonance theory (ART) in this report. RESULTS We apply Fuzzy ART as a clustering method for analyzing the time series expression data during sporulation of Saccharomyces cerevisiae. The clustering result by Fuzzy ART was compared with those by other clustering methods such as hierarchical clustering, k-means algorithm and self-organizing maps (SOMs). In terms of the mathematical validations, Fuzzy ART achieved the most reasonable clustering. We also verified the robustness of Fuzzy ART using noised data. Furthermore, we defined the correctness ratio of clustering, which is based on genes whose temporal expressions are characterized biologically. Using this definition, it was proved that the clustering ability of Fuzzy ART was superior to other clustering methods such as hierarchical clustering, k-means algorithm and SOMs. Finally, we validate the clustering results by Fuzzy ART in terms of biological functions and evidence. AVAILABILITY The software is available at http//www.nubio.nagoya-u.ac.jp/proc/index.html

Specific gene-expression profiles of noncancerous liver tissue predict the risk for multicentric occurrence of hepatocellular carcinoma in hepatitis C virus-positive patients.

BackgroundHepatitis C virus (HCV) infection produces chronic hepatitis, cirrhosis, and, ultimately, hepatocellular carcinoma (HCC). A molecular analysis of the damaged liver tissues infected with HCV may identify specific gene-expression profiles associated with a risk for liver carcinogenesis.MethodsForty patients with HCV-positive HCC were classified into two groups: single nodular HCC group (n = 28) and multicentric HCC group (n = 12). Using a complementary DNA microarray, we compared the gene-expression patterns of the noncancerous liver tissue specimens between the two groups. We also identified the differentially expressed genes related to multicentric recurrence in the liver remnant. We then evaluated whether a specific gene-expression profile can accurately estimate the risk for multicentric hepatocarcinogenesis.ResultsWe selected the 230 differentially expressed genes in the multicentric HCC group. A hierarchical clustering analysis identified a cluster that might be closely associated with the multicentric occurrence of HCC. On the basis of the gene-expression profiling of the 36 genes commonly associated with both multicentric HCC and multicentric recurrence, we created a scoring system to estimate the risk for multicentric hepatocarcinogenesis. The prediction score of patients in the multicentric HCC group with multicentric recurrence (19.9 ± 9.2) was significantly higher (P < .05) than that in the single nodular HCC group without multicentric recurrence (−1.8 ± 12.7).ConclusionsSpecific gene-expression signatures in noncancerous liver tissue may help to accurately predict the risk for developing HCC.

Fuzzy neural network-based prediction of the motif for MHC class II binding peptides

Characterizing the interaction between major histocompatibility complex (MHC) molecules and antigenic peptides is critical for understanding immunity and developing immunotherapies for autoimmune diseases and cancer. To identify the peptide binding motif and predict peptides that bind to the human MHC classII molecule HLA-DR4(*0401), we applied a fuzzy neural network (FNN) capable of extracting the relationship between input and output. Analysis of the peptide binding motif revealed that the hydrophilicity of the position 1 residue located on the N-terminal side of the nonamer (9mer) was the most important variable and that the van der Waals volume and hydrophilicity of the position 6 residue and the hydrophilicity of the position 7 residue were also important variables. The estimation accuracy (A(ROC) value) was high and the binding motif extracted from the FNN agreed with that derived experimentally. This study demonstrates that FNN modeling allows candidate antigenic peptides to be selected without the need for further experiments.

Self-induced metabolic state switching by a tunable cell density sensor for microbial isopropanol production

Chemicals production by engineered microorganisms often requires induction of target gene expression at an appropriate cell density to reduce conflict with cell growth. The lux system in Vibrio fischeri is a well-characterized model for cell density-dependent regulation of gene expression termed quorum sensing (QS). However, there are currently no reports for application of the lux system to microbial chemical production. Here, we constructed a synthetic lux system as a tunable cell density sensor-regulator using a synthetic lux promoter and a positive feedback loop in Escherichia coli. In this system, self-induction of a target gene expression is driven by QS-signal, and its threshold cell density can be changed depending on the concentration of a chemical inducer. We demonstrate auto-redirection of metabolic flux from central metabolic pathways toward a synthetic isopropanol pathway at a desired cell density resulting in a significant increase in isopropanol production.