Calibration and Validation of Simulation Parameters for Maize Straw Based on Discrete Element Method and Genetic Algorithm-Backpropagation.

There is a significant difference between the simulation effect and the actual effect in the design process of maize straw-breaking equipment due to the lack of accurate simulation model parameters in the breaking and processing of maize straw. This article used a combination of physical experiments, virtual simulation, and machine learning to calibrate the simulation parameters of maize straw. A bimodal-distribution discrete element model of maize straw was established based on the intrinsic and contact parameters measured via physical experiments. The significance analysis of the simulation parameters was conducted via the Plackett-Burman experiment. The Poisson ratio, shear modulus, and normal stiffness of the maize straw significantly impacted the peak compression force of the maize straw and steel plate. The steepest-climb test was carried out for the significance parameter, and the relative error between the peak compression force in the simulation test and the peak compression force in the physical test was used as the evaluation index. It was found that the optimal range intervals for the Poisson ratio, shear modulus, and normal stiffness of the maize straw were 0.32-0.36, 1.24 × 108-1.72 × 108 Pa, and 5.9 × 106-6.7 × 106 N/m3, respectively. Using the experimental data of the central composite design as the dataset, a GA-BP neural network prediction model for the peak compression force of maize straw was established, analyzed, and evaluated. The GA-BP prediction model's accuracy was verified via experiments. It was found that the ideal combination of parameters was a Poisson ratio of 0.357, a shear modulus of 1.511 × 108 Pa, and a normal stiffness of 6.285 × 106 N/m3 for the maize straw. The results provide a basis for analyzing the damage mechanism of maize straw during the grinding process.

EPA and DHA Enhance CACT Promoter Activity by GABP/NRF2.

Carnitine-acylcarnitine translocase (CACT) is a nuclear-encoded mitochondrial carrier that catalyzes the transfer of long-chain fatty acids across the inner mitochondrial membrane for ß-oxidation. In this study, we conducted a structural and functional characterization of the CACT promoter to investigate the molecular mechanism underlying the transcriptional regulation of the CACT gene by n-3 PUFA, EPA and DHA. In hepatic BRL3A cells, EPA and DHA stimulate CACT mRNA and protein expression. Deletion promoter analysis using a luciferase reporter gene assay identified a n-3 PUFA response region extending from -202 to -29 bp. This region did not contain a response element for PPARα, a well-known PUFA-responsive nuclear receptor. Instead, bioinformatic analysis revealed two highly conserved GABP responsive elements within this region. Overexpression of GABPα and GABPß subunits, but not PPARα, increased CACT promoter activity, more remarkably upon treatment with EPA and DHA. ChIP assays showed that n3-PUFA enhanced the binding of GABPα to the -202/-29 bp sequence. Furthermore, both EPA and DHA induced nuclear accumulation of GABPα. In conclusion, our findings indicate that the upregulation of CACT by n3-PUFA in hepatic cells is independent from PPARα and could be mediated by GABP activation.

Enhancing urban blue-green landscape quality assessment through hybrid genetic algorithm-back propagation (GA-BP) neural network approach: a case study in Fucheng, China.

This study employs an artificial neural network optimization algorithm, enhanced with a Genetic Algorithm-Back Propagation (GA-BP) network, to assess the service quality of urban water bodies and green spaces, aiming to promote healthy urban environments. From an initial set of 95 variables, 29 key variables were selected, including 17 input variables, such as water and green space area, population size, and urbanization rate, six hidden layer neurons, such as patch number, patch density, and average patch size, and one output variable for the comprehensive value of blue-green landscape quality. The results indicate that the GA-BP network achieves an average relative error of 0.94772%, which is superior to the 1.5988% of the traditional BP network. Moreover, it boasts a prediction accuracy of 90% for the comprehensive value of landscape quality from 2015 to 2022, significantly outperforming the BP network's approximate 70% accuracy. This method enhances the accuracy of landscape quality assessment but also aids in identifying crucial factors influencing quality. It provides scientific and objective guidance for future urban landscape structure and layout, contributing to high-quality urban development and the creation of exemplary living areas.

An interval water demand prediction method to reduce uncertainty: A case study of Sichuan Province, China.

Effective prediction of water demand is a prerequisite for decision makers to achieve reliable management of water supply. Currently, the research on water demand prediction focuses on point prediction method. In this study, we constructed a GA-BP-KDE hybrid interval water demand prediction model by combining non-parametric estimation and point prediction. Multiple metaheuristic algorithms were used to optimize the Back-Propagation Neural Network (BP) and Kernel Extreme Learning Machine (KELM) network structures. The performance of the water demand point prediction models was compared by the Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Kling-Gupta Efficiency (KGE), computation time, and fitness convergence curves. The kernel density estimation method (KDE) and the normal distribution method were used to fit the distribution of errors. The probability density function with the best fitting degree was selected based on the index G. The shortest confidence interval under 95% confidence was calculated according to the asymmetry of the error distribution. We predicted the impact indicator values for 2025 using the exponential smoothing method, and obtained water demand prediction intervals for various water use sectors. The results showed that the GA-BP model was the optimal model as it exhibited the highest computational efficiency, algorithmic stability, and prediction accuracy. The three prediction intervals estimated after adjusting the KDE bandwidth parameter covered most of the sample points in the test set. The prediction intervals of the four water use sectors were evaluated as F values of 1.6845, 1.3294, 1.6237, and 1.3600, which indicates high accuracy and quality of the prediction intervals. The mixed water demand interval prediction based on GA-BP-KDE reduces the uncertainty of the point prediction results and can provide a basis for water resource management by decision makers.

Multivariate Regression in Conjunction with GA-BP for Optimization of Data Processing of Trace NO Gas Flow in Active Pumping Electronic Nose.

Exhaled nitric oxide trace gas at the ppb level is a biomarker of human airway inflammation. To detect this, we developed a method for the collection of active pumping electronic nose bionic chamber gas. An optimization algorithm based on multivariate regression (MR) and genetic algorithm-back propagation (GA-BP) was proposed to improve the accuracy of trace-level gas detection. An electronic nose was used to detect NO gas at the ppb level by substituting breathing gas with a sample gas. The impact of the pump suction flow capacity variation on the response of the electronic nose system was determined using an ANOVA. Further, the optimization algorithm based on MR and GA-BP was studied for flow correction. The results of this study demonstrate an increase in the detection accuracy of the system by more than twofold, from 17.40%FS before correction to 6.86%FS after correction. The findings of this research lay the technical groundwork for the practical application of electronic nose systems in the daily monitoring of FeNO.

Research on the Prediction Model of Engine Output Torque and Real-Time Estimation of the Road Rolling Resistance Coefficient in Tracked Vehicles.

Road parameter identification is of great significance for the active safety control of tracked vehicles and the improvement of vehicle driving safety. In this study, a method for establishing a prediction model of the engine output torques in tracked vehicles based on vehicle driving data was proposed, and the road rolling resistance coefficient f was further estimated using the model. First, the driving data from the tracked vehicle were collected and then screened by setting the driving conditions of the tracked vehicle. Then, the mapping relationship between the engine torque Te, the engine speed ne, and the accelerator pedal position ß was obtained by a genetic algorithm-backpropagation (GA-BP) neural network algorithm, and an engine output torque prediction model was established. Finally, based on the vehicle longitudinal dynamics model, the recursive least squares (RLS) algorithm was used to estimate the f. The experimental results showed that when the driving state of the tracked vehicle satisfied the set driving conditions, the engine output torque prediction model could predict the engine output torque T^e in real time based on the changes in the ne and ß, and then the RLS algorithm was used to estimate the road rolling resistance coefficient f^. The average coefficient of determination R of the T^e was 0.91, and the estimation accuracy of the f^ was 98.421%. This method could adequately meet the requirements for engine output torque prediction and real-time estimation of the road rolling resistance coefficient during tracked vehicle driving.

Not Just a Cycle: Three gab Genes Enable the Non-Cyclic Flux Toward Succinate via GABA Shunt in 'Candidatus Liberibacter asiaticus'-Infected Citrus.

Although the mitochondria retain all required enzymes for an intact tricarboxylic acid (TCA) cycle, plants might shift the cyclic flux from the TCA cycle to an alternative noncyclic pathway via γ-aminobutyric acid (GABA) shunt under specific physiological conditions. We hypothesize that several genes may ease this noncyclic flux and contribute to the citrus response to the phytopathogenic bacterium 'Candidatus Liberibacter asiaticus', the causal agent of Huanglongbing in citrus. To test this hypothesis, we used multiomics techniques (metabolomics, fluxomics, and transcriptomics) to investigate the potential roles of putative gab homologies from Valencia sweet orange (Citrus sinensis). Our findings showed that 'Ca. L. asiaticus' significantly increased the endogenous GABA and succinate content but decreased ketoglutarate in infected citrus plants. Citrus genome harbors three putative gab genes, including amino-acid permease (also known as GABA permease; CsgabP), GABA transaminase (CsgabT), and succinate-semialdehyde dehydrogenase (also known as GABA dehydrogenase; CsgabD). The transcript levels of CsgabP, CsgabT, and CsgabD were upregulated in citrus leaves upon the infection with 'Ca. L. asiaticus' and after the exogenous application of GABA or deuterium-labeled GABA isotope (GABA-D6). Moreover, our finding showed that exogenously applied GABA is quickly converted to succinate and fed into the TCA cycle. Likewise, the fluxomics study showed that GABA-D6 is rapidly metabolized to succinate-D4. Our work proved that GABA shunt and three predicated gab genes from citrus, support the upstream noncyclic flux toward succinate rather than an intact TCA cycle and contribute to citrus defense responses to 'Ca. L. asiaticus'.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Artificial neural network for cytocompatibility and antibacterial enhancement induced by femtosecond laser micro/nano structures.

The failure of orthopedic and dental implants is mainly caused by biomaterial-associated infections and poor osseointegration. Surface modification of biomedical materials plays a significant role in enhancing osseointegration and anti-bacterial infection. In this work, a non-linear relationship between the micro/nano surface structures and the femtosecond laser processing parameters was successfully established based on an artificial neural network. Then a controllable functional surface with silver nanoparticles (AgNPs) to was produced to improve the cytocompatibility and antibacterial properties of biomedical titanium alloy. The surface topography, wettability, and Ag+ release were carefully investigated. The effects of these characteristics on antibacterial activity and cytocompatibilty were also evaluated. Results show that the prepared surface is hydrophobic, which can prevent the burst release of Ag+ in the initial stage. The prepared surface also shows both good cytocompatibility toward the murine calvarial preosteoblasts MC3T3-E1 cells (derived from Mus musculus (mouse) calvaria) and good antibacterial effects against Gram-negative (E. coli) and Gram-positive (S. aureus) bacteria, which is caused by the combined effect of appropriate micro/nano-structured feature and reasonable Ag+ release rate. We do not only clarify the antibacterial mechanism but also demonstrate the possibility of balancing the antibacterial and osteointegration-promoting properties by micro/nano-structures. The reported method offers an effective strategy for the patterned surface modification of implants.

Monitoring and Prediction of Glacier Deformation in the Meili Snow Mountain Based on InSAR Technology and GA-BP Neural Network Algorithm.

The morphological changes in mountain glaciers are effective in indicating the environmental climate change in the alpine ice sheet. Aiming at the problems of single monitoring index and low prediction accuracy of mountain glacier deformation at present, this study takes Meili Mountain glacier in western China as the research object and uses InSAR technology to construct the mountain glacier deformation time series and 3D deformation field from January 2020 to December 2021. The relationship between glacier deformation and elevation, slope, aspect, glacier albedo, surface organic carbon content, and rainfall was revealed by grey correlation analysis. The GA-BP neural network prediction model is established from the perspective of multiple factors to predict the deformation of Meili Mountain glacier. The results showed that: The deformation of Meili Mountain glacier has obvious characteristics of spatio-temporal differentiation; the cumulative maximum deformation quantity of glaciers in the study period is -212.16 mm. After three-dimensional decomposition, the maximum deformation quantity of glaciers in vertical direction, north-south direction and east-west direction is -125.63 mm, -77.03 mm, and 107.98 mm, respectively. The average annual deformation rate is between -94.62 and 75.96 mm/year. The deformation of Meili Mountain glacier has a gradient effect, the absolute value of deformation quantity is larger when the elevation is below 4500 m, and the absolute value of deformation quantity is smaller when it is above 4500 m. The R2, MAPE, and RMSE of the GA-BP neural network to predict the deformation of Meili glacier are 0.86, 1.12%, and 10.38 mm, respectively. Compared with the standard BP algorithm, the prediction accuracy of the GA-BP neural network is significantly improved, and it can be used to predict the deformation of mountain glaciers.

Investigation of the Temperature Compensation of Piezoelectric Weigh-In-Motion Sensors Using a Machine Learning Approach.

Piezoelectric ceramics have good electromechanical coupling characteristics and a high sensitivity to load. One typical engineering application of piezoelectric ceramic is its use as a signal source for Weigh-In-Motion (WIM) systems in road traffic monitoring. However, piezoelectric ceramics are also sensitive to temperature, which affects their measurement accuracy. In this study, a new piezoelectric ceramic WIM sensor was developed. The output signals of sensors under different loads and temperatures were obtained. The results were corrected using polynomial regression and a Genetic Algorithm Back Propagation (GA-BP) neural network algorithm, respectively. The results show that the GA-BP neural network algorithm had a better effect on sensor temperature compensation. Before and after GA-BP compensation, the maximum relative error decreased from about 30% to less than 4%. The sensitivity coefficient of the sensor reduced from 1.0192 × 10-2/°C to 1.896 × 10-4/°C. The results show that the GA-BP algorithm greatly reduced the influence of temperature on the piezoelectric ceramic sensor and improved its temperature stability and accuracy, which helped improve the efficiency of clean-energy harvesting and conversion.

Optimal Underwater Acoustic Warfare Strategy Based on a Three-Layer GA-BP Neural Network.

A defense platform is usually based on two methods to make underwater acoustic warfare strategy decisions. One is through Monte-Carlo method online simulation, which is slow. The other is by typical empirical (database) and typical back-propagation (BP) neural network algorithms based on genetic algorithm (GA) optimization, which is less accurate and less robust. Therefore, this paper proposes a method to build an optimal underwater acoustic warfare feedback system using a three-layer GA-BP neural network and dropout processing of the neural network to prevent overfitting, so that the three-layer GA-BP neural network has adequate memory capability while still having suitable generalization capability. This method improves the accuracy and stability of the defense platform in making underwater acoustic warfare strategy decisions, thus increasing the survival probability of the defense platform in the face of incoming torpedoes. This paper uses the optimal underwater acoustic warfare strategies corresponding to incoming torpedoes with different postures as the sample set. Additionally, it uses a three-layer GA-BP neural network with an overfitting treatment for training. The prediction results have less error than the typical single-layer GA-BP neural network, and the survival probability of the defense platform improves by 6.15%. This defense platform underwater acoustic warfare strategy prediction method addresses the impact on the survival probability of the defense platform due to the decision speed and accuracy.

A cis-regulatory element promoting increased transcription at low temperature in cultured ectothermic Drosophila cells.

BACKGROUND: Temperature change affects the myriad of concurrent cellular processes in a non-uniform, disruptive manner. While endothermic organisms minimize the challenge of ambient temperature variation by keeping the core body temperature constant, cells of many ectothermic species maintain homeostatic function within a considerable temperature range. The cellular mechanisms enabling temperature acclimation in ectotherms are still poorly understood. At the transcriptional level, the heat shock response has been analyzed extensively. The opposite, the response to sub-optimal temperature, has received lesser attention in particular in animal species. The tissue specificity of transcriptional responses to cool temperature has not been addressed and it is not clear whether a prominent general response occurs. Cis-regulatory elements (CREs), which mediate increased transcription at cool temperature, and responsible transcription factors are largely unknown. RESULTS: The ectotherm Drosophila melanogaster with a presumed temperature optimum around 25 °C was used for transcriptomic analyses of effects of temperatures at the lower end of the readily tolerated range (14-29 °C). Comparative analyses with adult flies and cell culture lines indicated a striking degree of cell-type specificity in the transcriptional response to cool. To identify potential cis-regulatory elements (CREs) for transcriptional upregulation at cool temperature, we analyzed temperature effects on DNA accessibility in chromatin of S2R+ cells. Candidate cis-regulatory elements (CREs) were evaluated with a novel reporter assay for accurate assessment of their temperature-dependency. Robust transcriptional upregulation at low temperature could be demonstrated for a fragment from the pastrel gene, which expresses more transcript and protein at reduced temperatures. This CRE is controlled by the JAK/STAT signaling pathway and antagonizing activities of the transcription factors Pointed and Ets97D. CONCLUSION: Beyond a rich data resource for future analyses of transcriptional control within the readily tolerated range of an ectothermic animal, a novel reporter assay permitting quantitative characterization of CRE temperature dependence was developed. Our identification and functional dissection of the pst_E1 enhancer demonstrate the utility of resources and assay. The functional characterization of this CoolUp enhancer provides initial mechanistic insights into transcriptional upregulation induced by a shift to temperatures at the lower end of the readily tolerated range.

FGFR4 promotes nuclear localization of GABP to inhibit cell apoptosis in uterine leiomyosarcoma.

Fibroblast growth factor receptor 4 (FGFR4) has been indicated as a potential "oncogene" in various types of cancer. However, the effects and underlying mechanisms of FGFR4 on uterine leiomyosarcoma (ULMS) progression remain unclear. In this study, we firstly discovered that FGFR4 was upregulated in ULMS specimens and cell lines and closely associated with poor prognosis of ULMS patients. Cell viability and apoptosis assays showed that FGFR4 deletion inhibited cell proliferation and promoted cell apoptosis. Moreover, FGFR4 silence increased cytoplasmic GABP (GA binding protein) expression, while it decreased the nuclear GABP level to inhibit nuclear localization of GABP. Mechanistically, the inhibition ability of FGFR4 silence on nuclear localization of GABP was mediated via mammalian Ste20-like kinases 1 (MST1) activation, which could promote phosphorylation of large tumor suppressor 1 (LATS1) to reduce nuclear localization of GABP. Gain- and loss-of-functional assays indicated that FGFR4 promoted nuclear localization of GABP to inhibit cell apoptosis in ULMS. In conclusion, our findings indicated that FGFR4 inhibited cell apoptosis in ULMS via the promotion of MST1/LATS1-mediated GABP nuclear localization, shedding light on the underlying mechanism of FGFR4-induced ULMS progression.

How an increase in the copy number of HSV-1 during latency can cause Alzheimer's disease: the viral and cellular dynamics according to the microcompetition model.

Numerous studies observed a link between the herpes smplex virus-1 (HSV-1) and Alzheimer's disease. However, the exact viral and cellular dynamics that lead from an HSV-1 infection to Alzheimer's disease are unknown. In this paper, we use the microcompetition model to formulate these dynamics by connecting seemingly unconnected observations reported in the literature. We concentrate on four pathologies characteristic of Alzheimer's disease. First, we explain how an increase in the copy number of HSV-1 during latency can decrease the expression of BECN1/Beclin1, the degradative trafficking protein, which, in turn, can cause a dysregulation of autophagy and Alzheimer's disease. Second, we show how an increase in the copy number of the latent HSV-1 can decrease the expression of many genes important for mitochondrial genome metabolism, respiratory chain, and homeostasis, which can lead to oxidative stress and neuronal damage, resulting in Alzheimer's disease. Third, we describe how an increase in this copy number can reduce the concentration of the NMDA receptor subunits NR1 and NR2b (Grin1 and Grin2b genes), and brain derived neurotrophic factor (BDNF), which can cause an impaired synaptic plasticity, Aß accumulation and eventually Alzheimer's disease. Finally, we show how an increase in the copy number of HSV-1 in neural stem/progenitor cells in the hippocampus during the latent phase can lead to an abnormal quantity and quality of neurogenesis, and the clinical presentation of Alzheimer's disease. Since the current understanding of the dynamics and homeostasis of the HSV-1 reservoir during latency is limited, the proposed model represents only a first step towards a complete understanding of the relationship between the copy number of HSV-1 during latency and Alzheimer's disease.

How microcompetition with latent viruses can cause α synuclein aggregation, mitochondrial dysfunction, and eventually Parkinson's disease.

The cause of most Parkinson's disease cases is unknown. However, it is well documented that mitochondrial dysfunction and misfolded α synuclein aggregation are important cellular abnormalities associated with the disease. In this paper, we use the microcompetition model to show how latent viruses, which infect the central and peripheral nervous systems, can cause the observed mitochondrial dysfunction and excess α synuclein aggregation, and eventually, Parkinson's disease.

GA-Binding Protein Alpha Is Involved in the Survival of Mouse Embryonic Stem Cells.

Ets-related transcription factor GA-binding protein alpha (GABPα), which is encoded by Gabpa, is expressed in a variety of cell types and is involved in cellular functions such as cell cycle regulation, apoptosis, and differentiation. Here, we generated Gabpa conditional knockout embryonic stem cells (ESCs) and characterized its cellular phenotypes. Disruption of Gabpa revealed that the proliferation of Gabpa-null ESCs was drastically repressed and cells started to die within 2 days. The repressed proliferation of Gabpa-null ESCs was recovered by artificially forced expression of GABPα. Expression analysis showed that p53 mRNA levels were comparable; however, p53 target genes, including Cdkn1a/p21, Mdm2, and Gadd45a, were upregulated and cell cycle-related genes, including Cyclin D1/D2 and Cyclin E1/E2, were downregulated in Gabpa-null ESCs. Interestingly, p53 and cleaved Caspase3 expressions were enhanced in the cells and reduced proliferation as well as cell death of Gabpa-null ESCs were rescued by either transfection of p53 RNAi or treatment of the p53 inhibitor pifithrin-α. These results suggest that GABPα inhibits the accumulation of p53 and is involved in the proliferation and survival of ESCs. Stem Cells 2017;35:2229-2238.

Human Lineage-Specific Transcriptional Regulation through GA-Binding Protein Transcription Factor Alpha (GABPa).

A substantial fraction of phenotypic differences between closely related species are likely caused by differences in gene regulation. While this has already been postulated over 30 years ago, only few examples of evolutionary changes in gene regulation have been verified. Here, we identified and investigated binding sites of the transcription factor GA-binding protein alpha (GABPa) aiming to discover cis-regulatory adaptations on the human lineage. By performing chromatin immunoprecipitation-sequencing experiments in a human cell line, we found 11,619 putative GABPa binding sites. Through sequence comparisons of the human GABPa binding regions with orthologous sequences from 34 mammals, we identified substitutions that have resulted in 224 putative human-specific GABPa binding sites. To experimentally assess the transcriptional impact of those substitutions, we selected four promoters for promoter-reporter gene assays using human and African green monkey cells. We compared the activities of wild-type promoters to mutated forms, where we have introduced one or more substitutions to mimic the ancestral state devoid of the GABPa consensus binding sequence. Similarly, we introduced the human-specific substitutions into chimpanzee and macaque promoter backgrounds. Our results demonstrate that the identified substitutions are functional, both in human and nonhuman promoters. In addition, we performed GABPa knock-down experiments and found 1,215 genes as strong candidates for primary targets. Further analyses of our data sets link GABPa to cognitive disorders, diabetes, KRAB zinc finger (KRAB-ZNF), and human-specific genes. Thus, we propose that differences in GABPa binding sites played important roles in the evolution of human-specific phenotypes.

The heteromeric transcription factor GABP activates the ITGAM/CD11b promoter and induces myeloid differentiation.

The heteromeric transcription factor GA-binding protein (GABP) consists of two subunits, the alpha subunit (GABPA) carrying the DNA-binding ETS domain, and the beta subunit (GABPB1) harbouring the transcriptional activation domain. GABP is involved in haematopoietic stem cell maintenance and differentiation of myeloid and lymphoid lineages in mice. To elucidate the molecular function of GABP in human haematopoiesis, the present study addressed effects of ectopic overexpression of GABP focussing on the myeloid compartment. Combined overexpression of GABPA and GABPB1 caused a proliferation block in cell lines and drastically reduced the colony-forming capacity of murine lineage-negative cells. Impaired proliferation resulted from perturbed cellular cycling and induction of myeloid differentiation shown by surface markers and myelomonocytic morphology of U937 cells. Depending on the dosage and functional integrity of GABP, ITGAM expression was induced. ITGAM encodes CD11b, the alpha subunit of integrin Mac-1, whose beta subunit, ITGB2/CD18, was already described to be regulated by GABP. Finally, Shield1-dependent proteotuning, luciferase reporter assays and chromatin immunoprecipitation showed that GABP activates the ITGAM/CD11b promoter via three binding sites close to the translational start site. In conclusion, the present study supports the crucial role of GABP in myeloid cell differentiation and identified ITGAM/CD11b as a novel GABP target gene.

Nuclear respiratory factor 2 regulates the transcription of AMPA receptor subunit GluA2 (Gria2).

Neuronal activity is highly dependent on energy metabolism. Nuclear respiratory factor 2 (NRF-2) tightly couples neuronal activity and energy metabolism by transcriptionally co-regulating all 13 subunits of an important energy-generating enzyme, cytochrome c oxidase (COX), as well as critical subunits of excitatory NMDA receptors. AMPA receptors are another major class of excitatory glutamatergic receptors that mediate most of the fast excitatory synaptic transmission in the brain. They are heterotetrameric proteins composed of various combinations of GluA1-4 subunits, with GluA2 being the most common one. We have previously shown that GluA2 (Gria2) is transcriptionally regulated by nuclear respiratory factor 1 (NRF-1) and specificity protein 4 (Sp4), which also regulate all subunits of COX. However, it was not known if NRF-2 also couples neuronal activity and energy metabolism by regulating subunits of the AMPA receptors. By means of multiple approaches, including electrophoretic mobility shift and supershift assays, chromatin immunoprecipitation, promoter mutations, real-time quantitative PCR, and western blot analysis, NRF-2 was found to functionally regulate the expression of Gria2, but not of Gria1, Gria3, or Gria4 genes in neurons. By regulating the GluA2 subunit of the AMPA receptor, NRF-2 couples energy metabolism and neuronal activity at the transcriptional level through a concurrent and parallel mechanism with NRF-1 and Sp4.

Specific γ-aminobutyric acid decomposition by gabP and gabT under neutral pH in recombinant Corynebacterium glutamicum.

OBJECTIVES: Corynebacterium glutamicum that expresses the exogenous L-glutamate decarboxylase (GAD) gene can synthesize γ-aminobutyric acid (GABA). To prevent GABA decomposition in the recombinant C. glutamicum GAD strain, GABA uptake and the GABA shunt pathway were blocked. RESULTS: GABA uptake is catalyzed by GABA permease encoded by gabP. The first reaction of the GABA shunt pathway is catalyzed by the GABA transaminase encoded by gabT. Initially, the effects of pH on GABA decomposition in recombinant C. glutamicum co-expressing two GAD genes (gadB1 and gadB2) were analyzed, demonstrating that GABA could be decomposed under neutral pH. Next, the gabP and gabT were individually deleted, and the GABA production of the related GAD strains was investigated by controlling the pH of the final fermentation stage at a neutral state. During this stage, the GABA concentration of the gabT-deleted GAD strain decreased from 23.9 ± 1.8 to 17.7 ± 0.7 g/l. However, the GABA concentration of the gabP-deleted GAD strain remained at 18.6-19.4 g/l. CONCLUSION: This study demonstrated that GABA was decomposed under neutral pH and that the deletion of gabP could effectively alleviate GABA decomposition in C. glutamicum.