A new class of synthetic retinoid antibiotics effective against bacterial persisters.

A challenge in the treatment of Staphylococcus aureus infections is the high prevalence of methicillin-resistant S. aureus (MRSA) strains and the formation of non-growing, dormant 'persister' subpopulations that exhibit high levels of tolerance to antibiotics and have a role in chronic or recurrent infections. As conventional antibiotics are not effective in the treatment of infections caused by such bacteria, novel antibacterial therapeutics are urgently required. Here we used a Caenorhabditis elegans-MRSA infection screen to identify two synthetic retinoids, CD437 and CD1530, which kill both growing and persister MRSA cells by disrupting lipid bilayers. CD437 and CD1530 exhibit high killing rates, synergism with gentamicin, and a low probability of resistance selection. All-atom molecular dynamics simulations demonstrated that the ability of retinoids to penetrate and embed in lipid bilayers correlates with their bactericidal ability. An analogue of CD437 was found to retain anti-persister activity and show an improved cytotoxicity profile. Both CD437 and this analogue, alone or in combination with gentamicin, exhibit considerable efficacy in a mouse model of chronic MRSA infection. With further development and optimization, synthetic retinoids have the potential to become a new class of antimicrobials for the treatment of Gram-positive bacterial infections that are currently difficult to cure.

Development of fluorescence-linked immunosorbent assay for rapid detection of Staphylococcus aureus.

Staphylococcus aureus is a major pathogen that causes infections and life-threatening diseases. Although antibiotics, such as methicillin, have been used, methicillin-resistant S. aureus (MRSA) causes high morbidity and mortality rates, and conventional detection methods are difficult to be used because of time-consuming process. To control the spread of S. aureus, a development of a rapid and simple detection method is required. In this study, we generated a fluorescent anti-S. aureus antibody, and established a novel fluorescence-linked immunosorbent assay (FLISA)-based S. aureus detection method. The method showed high sensitivity and low limit of detection toward MRSA detection. The assay time for FLISA was 5 h, which was faster than that of conventional enzyme-linked immunosorbent assay (ELISA) or rapid ELISA. Moreover, the FLISA-based detection method was applied to diagnose clinically isolated MRSA samples that required only 5.3 h of preincubation. The FLISA method developed in this study can be widely applied as a useful tool for convenient S. aureus detection. KEY POINTS: • A fluorescence-linked immunosorbent assay-based S. aureus detection method • Simultaneous quantification of a maximum of 96 samples within 5 h • Application of the novel system to diagnosis clinical isolates.

Coalition Formation Game for Cost-Efficient Multiparty Payment Channel in Payment Channel Networks.

Blockchain has introduced a new era for online payment services and its economy with tamper-proof cryptocurrencies. However, blockchain, which is based on global peer-to-peer networks, has its limitations due to payment delays from global consensus and transaction costs for maintenance. Thus, payment channel networks (PCN) have been proposed as one of the most promising off-chain solutions, allowing users to pay directly through payment channels (PC), with minimal blockchain involvement. However, payment delays and cost problems still exist, especially given the large size of the PCN. This study proposes a multiparty payment channel (MPC) that enables multiple users to join the same PC and exchange payment transactions, compared to the legacy PC. To avoid a consensus procedure among users in the PC, we introduce sequential and parallel updates for the PC status. Since increasing the MPC size limits the advantages in terms of the delay and cost, we propose a distributed coalition formation algorithm to form the MPC group, in which each user has the choice to join or leave the group. Simulations show that the proposed algorithm establishes MPCs successfully, considering the trade-off between the payoff gain and the MPC delay cost.

Design and Experiment of Satellite-Terrestrial Integrated Gateway with Dynamic Traffic Steering Capabilities for Maritime Communication.

This study presents the architectural design and implementation of a multi-RAT gateway (MRGW) supporting dual satellite and terrestrial connectivity that enables moving maritime vessels, such as autonomous surface ships, to be connected to multiple radio access networks in the maritime communication environment. We developed an MRGW combining LTE and very-small-aperture terminal (VSAT) access networks to realize access traffic steering, switching, and splitting functionalities between them. In addition, we developed communication interfaces between the MRGW and end-devices connecting to their corresponding radio access networks, as well as between the MRGW and the digital bridge system of an autonomous surface ship, enabling the MRGW to collect wireless channel information from each RAT end-device and provide the collected data to the digital bridge system to determine the optimal navigation route for the autonomous surface ship. Experiments on the MRGW with LTE and VSAT end-devices are conducted at sea near Ulsan city and the Kumsan satellite service center in Korea. Through validation experiments on a real maritime communication testbed, we demonstrate the feasibility of future maritime communication technologies capable of providing the minimum performance necessary for autonomous surface ships or digitized aids to navigation (A to N) systems.

Improved Cattle Disease Diagnosis Based on Fuzzy Logic Algorithms.

The health and productivity of animals, as well as farmers' financial well-being, can be significantly impacted by cattle illnesses. Accurate and timely diagnosis is therefore essential for effective disease management and control. In this study, we consider the development of models and algorithms for diagnosing diseases in cattle based on Sugeno's fuzzy inference. To achieve this goal, an analytical review of mathematical methods for diagnosing animal diseases and soft computing methods for solving classification problems was performed. Based on the clinical signs of diseases, an algorithm was proposed to build a knowledge base to diagnose diseases in cattle. This algorithm serves to increase the reliability of informative features. Based on the proposed algorithm, a program for diagnosing diseases in cattle was developed. Afterward, a computational experiment was performed. The results of the computational experiment are additional tools for decision-making on the diagnosis of a disease in cattle. Using the developed program, a Sugeno fuzzy logic model was built for diagnosing diseases in cattle. The analysis of the adequacy of the results obtained from the Sugeno fuzzy logic model was performed. The processes of solving several existing (model) classification and evaluation problems and comparing the results with several existing algorithms are considered. The results obtained enable it to be possible to promptly diagnose and perform certain therapeutic measures as well as reduce the time of data analysis and increase the efficiency of diagnosing cattle. The scientific novelty of this study is the creation of an algorithm for building a knowledge base and improving the algorithm for constructing the Sugeno fuzzy logic model for diagnosing diseases in cattle. The findings of this study can be widely used in veterinary medicine in solving the problems of diagnosing diseases in cattle and substantiating decision-making in intelligent systems.

A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network.

Locomotion prediction for human welfare has gained tremendous interest in the past few years. Multimodal locomotion prediction is composed of small activities of daily living and an efficient approach to providing support for healthcare, but the complexities of motion signals along with video processing make it challenging for researchers in terms of achieving a good accuracy rate. The multimodal internet of things (IoT)-based locomotion classification has helped in solving these challenges. In this paper, we proposed a novel multimodal IoT-based locomotion classification technique using three benchmarked datasets. These datasets contain at least three types of data, such as data from physical motion, ambient, and vision-based sensors. The raw data has been filtered through different techniques for each sensor type. Then, the ambient and physical motion-based sensor data have been windowed, and a skeleton model has been retrieved from the vision-based data. Further, the features have been extracted and optimized using state-of-the-art methodologies. Lastly, experiments performed verified that the proposed locomotion classification system is superior when compared to other conventional approaches, particularly when considering multimodal data. The novel multimodal IoT-based locomotion classification system has achieved an accuracy rate of 87.67% and 86.71% over the HWU-USP and Opportunity++ datasets, respectively. The mean accuracy rate of 87.0% is higher than the traditional methods proposed in the literature.

Underwater Wireless Sensor Networks with RSSI-Based Advanced Efficiency-Driven Localization and Unprecedented Accuracy.

Deep-sea object localization by underwater acoustic sensor networks is a current research topic in the field of underwater communication and navigation. To find a deep-sea object using underwater wireless sensor networks (UWSNs), the sensors must first detect the signals sent by the object. The sensor readings are then used to approximate the object's position. A lot of parameters influence localization accuracy, including the number and location of sensors, the quality of received signals, and the algorithm used for localization. To determine position, the angle of arrival (AOA), time difference of arrival (TDoA), and received signal strength indicator (RSSI) are used. The UWSN requires precise and efficient localization algorithms because of the changing underwater environment. Time and position are required for sensor data, especially if the sensor is aware of its surroundings. This study describes a critical localization strategy for accomplishing this goal. Using beacon nodes, arrival distance validates sensor localization. We account for the fact that sensor nodes are not in perfect temporal sync and that sound speed changes based on the medium (water, air, etc.) in this section. Our simulations show that our system can achieve high localization accuracy by accounting for temporal synchronisation, measuring mean localization errors, and forecasting their variation. The suggested system localization has a lower mean estimation error (MEE) while using RSSI. This suggests that measurements based on RSSI provide more precision and accuracy during localization.

Generation of a recombinant antibody for sensitive detection of Pseudomonas aeruginosa.

Pseudomonas aeruginosa (P. aeruginosa) is a major pathogen that causes nosocomial infections and often exhibits antibiotic resistance. Therefore, the development of an accurate method for detecting P. aeruginosa is required to control P. aeruginosa-related outbreaks. In this study, we established an enzyme-linked immunosorbent assay method for the sensitive detection of three P. aeruginosa strains, UCBPP PA14, ATCC 27853, and multidrug-resistant ATCC BAA-2108. We produced a recombinant antibody (rAb) against P. aeruginosa V-antigen (PcrV), which is a needle tip protein of the type III secretion system of P. aeruginosa using mammalian cells with high yield and purity, and confirmed its P. aeruginosa binding efficiency. The rAb was paired with commercial anti-P. aeruginosa Ab for a sandwich ELISA, resulting in an antigen-concentration-dependent response with a limit of detection value of 230 CFU/mL. These results suggest that the rAb produced herein can be used for the sensitive detection of P. aeruginosa with a wide range of applications in clinical diagnosis and point-of-care testing.

A selective membrane-targeting repurposed antibiotic with activity against persistent methicillin-resistant Staphylococcus aureus.

Treatment of Staphylococcus aureus infections is complicated by the development of antibiotic tolerance, a consequence of the ability of S. aureus to enter into a nongrowing, dormant state in which the organisms are referred to as persisters. We report that the clinically approved anthelmintic agent bithionol kills methicillin-resistant S. aureus (MRSA) persister cells, which correlates with its ability to disrupt the integrity of Gram-positive bacterial membranes. Critically, bithionol exhibits significant selectivity for bacterial compared with mammalian cell membranes. All-atom molecular dynamics (MD) simulations demonstrate that the selectivity of bithionol for bacterial membranes correlates with its ability to penetrate and embed in bacterial-mimic lipid bilayers, but not in cholesterol-rich mammalian-mimic lipid bilayers. In addition to causing rapid membrane permeabilization, the insertion of bithionol increases membrane fluidity. By using bithionol and nTZDpa (another membrane-active antimicrobial agent), as well as analogs of these compounds, we show that the activity of membrane-active compounds against MRSA persisters positively correlates with their ability to increase membrane fluidity, thereby establishing an accurate biophysical indicator for estimating antipersister potency. Finally, we demonstrate that, in combination with gentamicin, bithionol effectively reduces bacterial burdens in a mouse model of chronic deep-seated MRSA infection. This work highlights the potential repurposing of bithionol as an antipersister therapeutic agent.

DAG-Based Blockchain Sharding for Secure Federated Learning with Non-IID Data.

Federated learning is a type of privacy-preserving, collaborative machine learning. Instead of sharing raw data, the federated learning process cooperatively exchanges the model parameters and aggregates them in a decentralized manner through multiple users. In this study, we designed and implemented a hierarchical blockchain system using a public blockchain for a federated learning process without a trusted curator. This prevents model-poisoning attacks and provides secure updates of a global model. We conducted a comprehensive empirical study to characterize the performance of federated learning in our testbed and identify potential performance bottlenecks, thereby gaining a better understanding of the system.

Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks.

The seamless operation of inter-connected smart devices in Internet of Things (IoT) wireless sensor networks (WSNs) requires consistently available end-to-end routes. However, the sensor nodes that rely on a very limited power source tend to cause disconnection in multi-hop routes due to power shortages in the WSNs, which eventually results in the inefficiency of the overall IoT network. In addition, the density of the available sensor nodes affects the existence of feasible routes and the level of path multiplicity in the WSNs. Therefore, an efficient routing mechanism is expected to extend the lifetime of the WSNs by adaptively selecting the best routes for the data transfer between interconnected IoT devices. In this work, we propose a novel routing mechanism to balance the energy consumption among all the nodes and elongate the WSN lifetime, which introduces a score value assigned to each node along a path as the combination of evaluation metrics. Specifically, the scoring scheme considers the information of the node density at a certain area and the node energy levels in order to represent the importance of individual nodes in the routes. Furthermore, our routing mechanism allows for incorporating non-cooperative nodes. The simulation results show that the proposed work gives comparatively better results than some other experimented protocols.

Caenorhabditis elegans mounts a p38 MAPK pathway-mediated defence to Cutibacterium acnes infection.

Cutibacterium acnes is capable of inducing inflammation in acne and can lead to a chronic prostatic infection. The diverse pathogenicity among different strains of C. acnes has been presented, but simple appropriate animal models for the evaluation of this bacterium are lacking. In this study, the nematode Caenorhabditis elegans was used as an invertebrate infection model. We revealed that C. acnes type strain ATCC 6919 caused lethal infections to C. elegans in solid and liquid culture media (p < .0001). Compared with the strain ATCC 6919, the antibiotic-resistant strain HM-513 was more virulent, resulting in reduced survival (p < .0001). Four different C. acnes strains killed worms with a p value of less than .0001 when provided to C. elegans at 4.8 × 108 CFU/ml. The infection model was also employed to explore host defence responses. An increase in numerous immune effectors in response to C. acnes was detected. We focused on nine C-type lectins, including: clec-13, clec-17, clec-47, clec-52, clec-60, clec-61, clec-70, clec-71 and clec-227. The induced expression of these C-type lectin genes was down-regulated in mutant worms deficient in the p38 mitogen-activated protein kinase (MAPK) pathway. Meanwhile, PMK-1 (MAPK) was phosphorylated and activated at the onset of C. acnes infection. By monitoring the survival of mutant worms, we found that PMK-1, SEK-1 (MAPKK) and TIR-1 (MAPKKK) were critical in responding to C. acnes infection. C. elegans pmk-1 and tir-1 mutants exhibited higher mortality to C. acnes infection (p < .0001). In conclusion, C. elegans serves as a simple and valuable model to study C. acnes virulence and facilitates improvements in understanding of host innate immune responses.

Multi-Objective Optimization of Energy Saving and Throughput in Heterogeneous Networks Using Deep Reinforcement Learning.

Wireless networking using GHz or THz spectra has encouraged mobile service providers to deploy small cells to improve link quality and cell capacity using mmWave backhaul links. As green networking for less CO2 emission is mandatory to confront global climate change, we need energy efficient network management for such denser small-cell heterogeneous networks (HetNets) that already suffer from observable power consumption. We establish a dual-objective optimization model that minimizes energy consumption by switching off unused small cells while maximizing user throughput, which is a mixed integer linear problem (MILP). Recently, the deep reinforcement learning (DRL) algorithm has been applied to many NP-hard problems of the wireless networking field, such as radio resource allocation, association and power saving, which can induce a near-optimal solution with fast inference time as an online solution. In this paper, we investigate the feasibility of the DRL algorithm for a dual-objective problem, energy efficient routing and throughput maximization, which has not been explored before. We propose a proximal policy (PPO)-based multi-objective algorithm using the actor-critic model that is realized as an optimistic linear support framework in which the PPO algorithm searches for feasible solutions iteratively. Experimental results show that our algorithm can achieve throughput and energy savings comparable to the CPLEX.

Simultaneous monitoring of the bioconversion from lysine to glutaric acid by ethyl chloroformate derivatization and gas chromatography-mass spectrometry.

Glutaric acid is a precursor of a plasticizer that can be used for the production of polyester amides, ester plasticizer, corrosion inhibitor, and others. Glutaric acid can be produced either via bioconversion or chemical synthesis, and some metabolites and intermediates are produced during the reaction. To ensure reaction efficiency, the substrates, intermediates, and products, especially in the bioconversion system, should be closely monitored. Until now, high performance liquid chromatography (HPLC) has generally been used to analyze the glutaric acid-related metabolites, although it demands separate time-consuming derivatization and non-derivatization analyses. To substitute for this unreasonable analytical method, we applied herein a gas chromatography - mass spectrometry (GC-MS) method with ethyl chloroformate (ECF) derivatization to simultaneously monitor the major metabolites. We determined the suitability of GC-MS analysis using defined concentrations of six metabolites (l-lysine, cadaverine, 5-aminovaleric acid, 2-oxoglutaric acid, glutamate, and glutaric acid) and their mass chromatograms, regression equations, regression coefficient values (R2), dynamic ranges (mM), and retention times (RT). This method successfully monitored the production process in complex fermentation broth.

5-Aminosalicylic Acid Azo-Coupled with a GPR109A Agonist Is a Colon-Targeted Anticolitic Codrug with a Reduced Risk of Skin Toxicity.

To develop a 5-aminosalicylic acid (5-ASA)-based anticolitic drug with enhanced therapeutic activity, a colon-targeted codrug constituting 5-ASA and a GPR109A agonist was designed. 5-ASA azo-coupled with nicotinic acid (ASA-azo-NA) was synthesized, and the colon specificity and anticolitic effects were evaluated. Approximately 89% of ASA-azo-NA was converted to 5-aminonicotinic acid (5-ANA) and 5-ASA after 24 h of incubation in the cecal contents. 5-ANA was identified as a GPR109A agonist (concentration that gives half-maximal response (EC50): 18 µM) in a cell-based assay. Upon oral gavage of ASA-azo-NA (oral ASA-azo-NA) and sulfasalazine (oral SSZ), a colon-targeted 5-ASA prodrug, cecal accumulation of 5-ASA was comparable, and 5-ANA was barely detectable in the blood, while it was detected up to 62.7 µM with oral 5-ANA. In parallel, oral ASA-azo-NA did not elicit an adverse skin response. In murine macrophage and human colon carcinoma cells, activation of GPR109A by 5-ANA elevated the level of the anti-inflammatory cytokine IL-10, suppressed NF-κB activation, and potentiated the inhibitory activity of 5-ASA on NF-κB. Oral ASA-azo-NA ameliorated rat colitis and was more effective than oral SSZ, which were substantially blunted following cotreatment with the GPR109A antagonist, mepenzolate. In conclusion, ASA-azo-NA is a colon-targeted anticolitic codrug with a reduced risk of skin toxicity induced by the GPR109A agonist, therapeutically surpassing a current 5-ASA-based anti-inflammatory bowel disease drug in a rat colitis model.

In the Model Host Caenorhabditis elegans, Sphingosine-1-Phosphate-Mediated Signaling Increases Immunity toward Human Opportunistic Bacteria.

Sphingosine-1-phophate (S1P) is a sphingolipid-derived signaling molecule that controls diverse cellular functions including cell growth, homeostasis, and stress responses. In a variety of metazoans, cytosolic S1P is transported into the extracellular space where it activates S1P receptors in a concentration-dependent manner. In the free-living nematode Caenorhabditis elegans, the spin-2 gene, which encodes a S1P transporter, is activated during Gram-positive or Gram-negative bacterial infection of the intestine. However, the role during infection of spin-2 and three additional genes in the C. elegans genome encoding other putative S1P transporters has not been elucidated. Here, we report an evolutionally conserved function for S1P and a non-canonical role for S1P transporters in the C. elegans immune response to bacterial pathogens. We found that mutations in the sphingosine kinase gene (sphk-1) or in the S1P transporter genes spin-2 or spin-3 decreased nematode survival after infection with Pseudomonas aeruginosa or Enterococcus faecalis. In contrast to spin-2 and spin-3, mutating spin-1 leads to an increase in resistance to P. aeruginosa. Consistent with these results, when wild-type C. elegans were supplemented with extracellular S1P, we found an increase in their lifespan when challenged with P. aeruginosa and E. faecalis. In comparison, spin-2 and spin-3 mutations suppressed the ability of S1P to rescue the worms from pathogen-mediated killing, whereas the spin-1 mutation had no effect on the immune-enhancing activity of S1P. S1P demonstrated no antimicrobial activity toward P. aeruginosa and Escherichia coli and only minimal activity against E. faecalis MMH594 (40 µM). These data suggest that spin-2 and spin-3, on the one hand, and spin-1, on the other hand, transport S1P across cellular membranes in opposite directions. Finally, the immune modulatory effect of S1P was diminished in C. eleganssek-1 and pmk-1 mutants, suggesting that the immunomodulatory effects of S1P are mediated by the p38 MAPK signaling pathway.

Colon-Targeted Delivery Facilitates the Therapeutic Switching of Sofalcone, a Gastroprotective Agent, to an Anticolitic Drug via Nrf2 Activation.

We investigated if the therapeutic switching of sofalcone (SFC), a gastroprotective agent, to an anticolitic agent is feasible using colon-targeted drug delivery. SFC can activate the anti-inflammatory nuclear factor (erythroid-derived 2)-like 2 (Nrf2)-hemeoxygenase-1 (HO-1) pathway in human colon epithelial cells and murine macrophages. For the efficient treatment of colitis, SFC was coupled with acidic amino acids to yield SFC-aspartic acid (SFC-AA) and SFC-glutamic acid, and their colon targetability and therapeutic effects were assessed as an anticolitic agent in a 2,4-dinitrobenezenesulfonic acid-induced rat colitis model. The SFC derivatives were decoupled up to 72% in the cecal contents but remained stable in the small intestinal contents. Oral gavage of SFC-AA (oral SFC-AA, equivalent to 1.67 mg/kg of SFC) delivered SFC (maximal cecal concentration: 57.36 µM) to the cecum, while no SFC was detected with oral gavage of SFC (oral SFC, 1.67 mg/kg). Moreover, oral SFC-AA (equivalent to 10 mg/kg of SFC) did not afford detectable concentration of SFC in the blood but detected up to 4.64 µM with oral SFC (10 mg/kg), indicating efficient colonic delivery and limited systemic absorption of SFC upon oral SFC-AA. Oral SFC-AA ameliorated colonic damage and inflammation in rat colitis with elevating colonic levels of HO-1 and nuclear Nrf2 protein, and the anticolitic effects of SFC-AA were significantly undermined by an HO-1 inhibitor. At an equivalent dose of SFC, oral SFC-AA but not oral SFC increased colonic HO-1 and nuclear Nrf2 levels, and oral SFC-AA was more effective than oral SFC in treating rat colitis. Moreover, oral SFC-AA was as effective against colitis as oral sulfasalazine being used for the treatment of inflammatory bowel disease. In conclusion, colon-targeted delivery of SFC facilitated the therapeutic switching of the drug to an anticolitic drug via Nrf2 activation.

Experimental Demonstration of MmWave Vehicle-to-Vehicle Communications Using IEEE 802.11ad.

Millimeter wave (mmWave) vehicle-to-vehicle (V2V) communications has received significant attention as one of the key applications in 5G technology, which is called as Giga-V2V (GiV2V). The ultra-wide band of the GiV2V allows vehicles to transfer gigabit data within a few seconds, which can achieve platooning of autonomous vehicles. The platooning process requires the rich data of a 4K dash-camera and LiDAR sensors for accurate vehicle control. To achieve this, 3GPP, a global organization of standards that provides specifications for the 5G mobile technology, is developing a new standard for GiV2V technology by extending the existing specification for device-to-device (D2D) communication. Meanwhile, in the last decade, the mmWave spectrum has been used in the wireless local area network (WLAN) for indoor devices, such as home appliances, based on the IEEE 802.11ad (also known as Wireless Gigabit Alliance (WiGig)) technology, which supports gigabit wireless connectivity of approximately 10 m distance in the 60-GHz frequency spectrum. The WiGig technology has been commercialized and used for various applications ranging from Internet access points to set-top boxes for televisions. In this study, we investigated the applicability of the WiGig technology to the GiV2V communications through experiments on a real vehicular testbed. To achieve this, we built a testbed using commercial off-the-shelf WiGig devices and performed experiments to measure inter-vehicle connectivity on a campus and on city roads with different permitted vehicle speeds. The experimental results demonstrate that disconnections occurred frequently due to the short radio range and the connectivity varied with the vehicle speed. However, the instantaneous throughput was sufficient to exchange large data between moving vehicles in different road environments.

Enhanced isobutanol production from acetate by combinatorial overexpression of acetyl-CoA synthetase and anaplerotic enzymes in engineered Escherichia coli.

Acetic acid is an abundant material that can be used as a carbon source by microorganisms. Despite its abundance, its toxicity and low energy content make it hard to utilize as a sole carbon source for biochemical production. To increase acetate utilization and isobutanol production with engineered Escherichia coli, the feasibility of utilizing acetate and metabolic engineering was investigated. The expression of acs, pckA, and maeB increased isobutanol production by up to 26%, and the addition of TCA cycle intermediates indicated that the intermediates can enhance isobutanol production. For isobutanol production from acetate, acetate uptake rates and the NADPH pool were not limiting factors compared to glucose as a carbon source. This work represents the first approach to produce isobutanol from acetate with pyruvate flux optimization to extend the applicability of acetate. This technique suggests a strategy for biochemical production utilizing acetate as the sole carbon source.

Butyrate-based n-butanol production from an engineered Shewanella oneidensis MR-1.

n-Butanol is considered as the next-generation biofuel, because its physiochemical properties are very similar to fossil fuels and it could be produced by Clostridia under anaerobic culture. Due to the difficulties of strict anaerobic culture, a host which can be used with facultative environment was being searched for n-butanol production. As an alternative, Shewanella oneidensis MR-1, which is known as facultative bacteria, was selected as a host and studied. A plasmid containing adhE2 encoding alcohol dehydrogenase, various CoA transferases (ctfAB, atoAD, pct, and ACT), and acs encoding acetyl-CoA synthetase were introduced and examined to S. oneidensis MR-1 to produce n-butanol. As a result, ctfAB, acs, and adhE2 overexpression in S. oneidensis-pJM102 showed the highest n-butanol production in the presence of 2% of N-acetylglucosamine (NAG), 0.3% of butyrate, and 0.1 mM of IPTG for 96 h under microaerobic condition. When more NAG and butyrate were fed, n-butanol production was enhanced, producing up to 160 mg/L of n-butanol. When metal ions or extra electrons were added to S. oneidensis-pJM102 for n-butanol production, metal ion as electron acceptor or supply of extra electron showed no significant effect on n-butanol production. Overall, we made a newly engineered S. oneidensis that could utilize NAG and butyrate to produce n-butanol. It could be used in further microaerobic condition and electricity supply studies.