Metal-ion transporter SLC39A8 is required for brain manganese uptake and accumulation.

Manganese (Mn) is an essential nutrient, but is toxic in excess. Whole-body Mn levels are regulated in part by the metal-ion influx transporter SLC39A8, which plays an essential role in the liver by reclaiming Mn from bile. Physiological roles of SLC39A8 in Mn homeostasis in other tissues, however, remain largely unknown. To screen for extrahepatic requirements for SLC39A8 in tissue Mn homeostasis, we crossed Slc39a8-inducible global-KO (Slc39a8 iKO) mice with Slc39a14 KO mice, which display markedly elevated blood and tissue Mn levels. Tissues were then analyzed by inductively coupled plasma-mass spectrometry to determine levels of Mn. Although Slc39a14 KO; Slc39a8 iKO mice exhibited systemic hypermanganesemia and increased Mn loading in the bone and kidney due to Slc39a14 deficiency, we show Mn loading was markedly decreased in the brains of these animals, suggesting a role for SLC39A8 in brain Mn accumulation. Levels of other divalent metals in the brain were unaffected, indicating a specific effect of SLC39A8 on Mn. In vivo radiotracer studies using 54Mn in Slc39a8 iKO mice revealed that SLC39A8 is required for Mn uptake by the brain, but not most other tissues. Furthermore, decreased 54Mn uptake in the brains of Slc39a8 iKO mice was associated with efficient inactivation of Slc39a8 in isolated brain microvessels but not in isolated choroid plexus, suggesting SLC39A8 mediates brain Mn uptake via the blood-brain barrier. These findings establish SLC39A8 as a candidate therapeutic target for mitigating Mn uptake and accumulation in the brain, the primary organ of Mn toxicity.

Apelin-13 Improves Cognitive Impairment and Repairs Hippocampal Neuronal Damage by Activating PGC-1α/PPARγ Signaling.

Alzheimer's disease (AD) is a complex neurodegenerative disease that is prevalent around the world. Both Apelin-13 and proliferator-activated receptor-γ (PPARγ)/PPARγ co-activator 1α (PGC-1α) are regarded as candidate targets for treating AD. The investigation examined whether Apelin-13 exerts neuroprotective effects via PGC-1α/PPARγ signaling. In this study, Apelin-13 improved cognitive deficits in AD mice, while SR-18,292 (a PGC-1α inhibitor) interfered with the therapeutic effects of Apelin-13. Mechanistically, Apelin-13, PGC-1α and PPARγ were decreased in AD mice and oxygen-glucose deprivation (OGD)-induced neuronal cells. Apelin-13 bound to PGC-1α and negatively regulated the expression of PGC-1α and PPARγ. In turn, PGC-1α accelerated the accumulation of Apelin-13 and PPARγ. Additionally, neuronal apoptosis was inhibited, and the abundance of apoptosis-related proteins (Bax, Bcl-2, and cleaved caspase 3) was induced. The content of reactive oxygen species (ROS), malondialdehyde (MDA) and superoxide dismutase (SOD) fluctuated. The level of inflammatory factors (interleukin-6, IL-6, IL-10, tumor necrosis factor-α, TNF-α) was regulated. In short, Apelin-13 exerted anti-apoptosis, anti-oxidant stress and anti-inflammatory effects. Interestingly, PGC-1α silencing promoted neuronal apoptosis, oxidant stress and inflammation, and overexpression of PGC-1α exhibited the opposite. More importantly, inhibition of PGC-1α attenuated Apelin-13-enhanced cognitive impairment and neuronal damage. Therefore, our findings suggested that Apelin-13 exerted neuroprotective effects in part via the PGC-1α/PPARγ pathway.

A Model-Driven Channel Estimation Method for Millimeter-Wave Massive MIMO Systems.

Aiming at the problem of low estimation accuracy under a low signal-to-noise ratio due to the failure to consider the "beam squint" effect in millimeter-wave broadband systems, this paper proposes a model-driven channel estimation method for millimeter-wave massive MIMO broadband systems. This method considers the "beam squint" effect and applies the iterative shrinkage threshold algorithm to the deep iterative network. First, the millimeter-wave channel matrix is transformed into a transform domain with sparse features through training data learning to obtain a sparse matrix. Secondly, a contraction threshold network based on an attention mechanism is proposed in the phase of beam domain denoising. The network selects a set of optimal thresholds according to feature adaptation, which can be applied to different signal-to-noise ratios to achieve a better denoising effect. Finally, the residual network and the shrinkage threshold network are jointly optimized to accelerate the convergence speed of the network. The simulation results show that the convergence speed is increased by 10% and the channel estimation accuracy is increased by 17.28% on average under different signal-to-noise ratios.

Wireless Channel Prediction of GRU Based on Experience Replay and Snake Optimizer.

Aiming at the problem of poor prediction accuracy of Channel State Information (CSI) caused by fast time-varying channels in wireless communication systems, this paper proposes a gated recurrent network based on experience replay and Snake Optimizer for real-time prediction in real-world non-stationary channels. Firstly, a two-channel prediction model is constructed by gated recurrent unit, which adapts to the real and imaginary parts of CSI. Secondly, we use the Snake Optimizer to find the optimal learning rate and the number of hidden layer elements to build the model. Finally, we utilize the experience pool to store recent historical CSI data for fast learning and complete learning. The simulation results show that, compared with LSTM, BiLSTM, and BiGRU, the gated recurrent network based on experience replay and Snake Optimizer has better performance in the optimization ability and convergence speed. The prediction accuracy of the model is also significantly improved under the dynamic non-stationary environment.

Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning.

In order to solve the problem wherein too many base station antennas are deployed in a massive multiple-input-multiple-output system, resulting in high overhead for downlink channel state information feedback, this paper proposes an uplink-assisted channel feedback method based on deep learning. The method applies the reciprocity of the uplink and downlink, uses uplink channel state information in the base station to help users give feedback on unknown downlink information, and compresses and restores the channel state information. First, an encoder-decoder structure is established. The encoder reduces the network depth and uses multi-resolution convolution to increase the accuracy of channel state information extraction while reducing the number of computations relating to user equipment. Afterward, the channel state information is compressed to reduce feedback overhead in the channel. At the decoder, with the help of the reciprocity of the uplink and downlink, the feature extraction of the uplink's magnitudes is carried out, and the downlink channel state information is integrated into a channel state information feature matrix, which is restored to its original size. The simulation results show that compared with CSINet, CRNet, CLNet, and DCRNet, indoor reconstruction precision was improved by an average of 16.4%, and outside reconstruction accuracy was improved by an average of 21.2% under all compressions.

18ß-Glycyrrhetinic acid acts through hepatocyte nuclear factor 4 alpha to modulate lipid and carbohydrate metabolism.

Hepatocyte nuclear factor 4 alpha (HNF4α) regulates the expression of essential genes involved in very-low-density lipoprotein (VLDL) homeostasis and gluconeogenesis.　18ß-glycyrrhetinic acid (GA) is an active ingredient of Glycyrrhiza uralensis an herbal medicine used for treating liver aliments. In this study, we established that GA functions as a partial antagonist of HNF4α through HNF4α-driven reporter luciferase assay and co-immunoprecipitation experiments with co-activator PGC1α. By virtual docking and site-directed mutagenesis analysis, we confirmed that serine 190 and arginine 235 of HNF4α are both essential for GA to exert its antagonistic action on HNF4α. Importantly, GA suppressed the expression of HNF4α target genes such as apolipoprotein B (ApoB), microsomal triglyceride transfer protein (MTP) and phospholipase A2 G12B (PLA2G12B) modulating hepatic VLDL secretion in mice fed on a high fat diet. In addition, GA also suppressed gluconeogenesis and ameliorated glucose intolerance via down-regulating the expression of HNF4α target genes glucose-6-phosphatase (G6pc) and phosphoenolpyruvate carboxykinase (Pepck). Furthermore, GA significantly lowered blood glucose and improved insulin resistance in db/db mice. In all, we established that GA acts as a partial HNF4α antagonist modulating lipid and carbohydrate metabolism.

TIR-only protein RBA1 recognizes a pathogen effector to regulate cell death in Arabidopsis.

Detection of pathogens by plants is mediated by intracellular nucleotide-binding site leucine-rich repeat (NLR) receptor proteins. NLR proteins are defined by their stereotypical multidomain structure: an N-terminal Toll-interleukin receptor (TIR) or coiled-coil (CC) domain, a central nucleotide-binding (NB) domain, and a C-terminal leucine-rich repeat (LRR). The plant innate immune system contains a limited NLR repertoire that functions to recognize all potential pathogens. We isolated Response to the bacterial type III effector protein HopBA1 (RBA1), a gene that encodes a TIR-only protein lacking all other canonical NLR domains. RBA1 is sufficient to trigger cell death in response to HopBA1. We generated a crystal structure for HopBA1 and found that it has similarity to a class of proteins that includes esterases, the heme-binding protein ChaN, and an uncharacterized domain of Pasteurella multocida toxin. Self-association, coimmunoprecipitation with HopBA1, and function of RBA1 require two previously identified TIR-TIR dimerization interfaces. Although previously described as distinct in other TIR proteins, in RBA1 neither of these interfaces is sufficient when the other is disrupted. These data suggest that oligomerization of RBA1 is required for function. Our identification of RBA1 demonstrates that "truncated" NLRs can function as pathogen sensors, expanding our understanding of both receptor architecture and the mechanism of activation in the plant immune system.

Aging Reduces an ERRalpha-Directed Mitochondrial Glutaminase Expression Suppressing Glutamine Anaplerosis and Osteogenic Differentiation of Mesenchymal Stem Cells.

Aging deteriorates osteogenic capacity of mesenchymal stem/stromal cells (MSCs), contributing to imbalanced bone remodeling and osteoporosis. Glutaminase (Gls) catabolizes glutamine into glutamate at the first step of mitochondrial glutamine (Gln)-dependent anaplerosis which is essential for MSCs upon osteogenic differentiation. Estrogen-related receptor α (ERRα) regulates genes required for mitochondrial function. Here, we found that ERRα and Gls are upregulated by osteogenic induction in human MSCs (hMSCs). In contrast, osteogenic differentiation capacity and glutamine consumption of MSCs, as well as ERRα, Gls and osteogenic marker genes are significantly reduced with age. We demonstrated that ERRα binds to response elements on Gls promoter and affects glutamine anaplerosis through transcriptional induction of Gls. Conversely, mTOR inhibitor rapamycin, ERRα inverse agonist compound 29 or Gls inhibitor BPTES leads to reduced Gln anaplerosis and deteriorated osteogenic differentiation of hMSCs. Importantly, overexpression of ERRα or Gls restored impairment by these inhibitors. Finally, we proved that compensated ERRα or Gls expression indeed potentiated Gln anaplerosis and osteogenic capability of elderly mice MSCs in vitro. Together, we establish that Gls is a novel ERRα target gene and ERRα/Gls signaling pathway plays an important role in osteogenic differentiation of MSCs, providing new sights into novel regenerative therapeutics development. Our findings suggest that restoring age-related mitochondrial Gln-dependent anaplerosis may be beneficial for degenerative bone disorders such as osteoporosis. Stem Cells 2017;35:411-424.

Hen egg yolk phosvitin stimulates osteoblast differentiation in the absence of ascorbic acid.

BACKGROUND: Egg yolk phosvitin, one of the most highly phosphorylated extracellular matrix proteins known in nature, has a strong calcium binding and reducing capacity. Here, we investigated the effects of phosvitin on osteoblast differentiation and osteogenic gene expression in cultured mouse osteoblastic MC3T3-E1 cells by using alkaline phosphatase activity analysis, alizarin red S staining and real-time PCR assay. RESULTS: Alkaline phosphatase activity and alizarin red S staining analyses demonstrated no significant difference between differentiating MC3T3-E1 cells cultured in the presence of phosvitin and those cultured in the presence of ascorbic acid after 21 days of differentiation. Our real-time PCR assay also indicated the two groups were similar in the expression of the osteogenic gene markers, collagen type I, osteocalcin, runt-related transcription factor 2, and bone morphogenetic protein-2. CONCLUSION: Our findings indicate that phosvitin plays a similar role to that of ascorbic acid in osteoblast differentiation and mineralisation. © 2017 Society of Chemical Industry.

A Data Augmentation Method for Motor Imagery EEG Signals Based on DCGAN-GP Network.

Motor imagery electroencephalography (EEG) signals have garnered attention in brain-computer interface (BCI) research due to their potential in promoting motor rehabilitation and control. However, the limited availability of labeled data poses challenges for training robust classifiers. In this study, we propose a novel data augmentation method utilizing an improved Deep Convolutional Generative Adversarial Network with Gradient Penalty (DCGAN-GP) to address this issue. We transformed raw EEG signals into two-dimensional time-frequency maps and employed a DCGAN-GP network to generate synthetic time-frequency representations resembling real data. Validation experiments were conducted on the BCI IV 2b dataset, comparing the performance of classifiers trained with augmented and unaugmented data. Results demonstrated that classifiers trained with synthetic data exhibit enhanced robustness across multiple subjects and achieve higher classification accuracy. Our findings highlight the effectiveness of utilizing a DCGAN-GP-generated synthetic EEG data to improve classifier performance in distinguishing different motor imagery tasks. Thus, the proposed data augmentation method based on a DCGAN-GP offers a promising avenue for enhancing BCI system performance, overcoming data scarcity challenges, and bolstering classifier robustness, thereby providing substantial support for the broader adoption of BCI technology in real-world applications.

Electroencephalographic Signal Data Augmentation Based on Improved Generative Adversarial Network.

EEG signals combined with deep learning play an important role in the study of human-computer interaction. However, the limited dataset makes it challenging to study EEG signals using deep learning methods. Inspired by the GAN network in image generation, this paper presents an improved generative adversarial network model L-C-WGAN-GP to generate artificial EEG data to augment training sets and improve the application of BCI in various fields. The generator consists of a long short-term memory (LSTM) network and the discriminator consists of a convolutional neural network (CNN) which uses the gradient penalty-based Wasserstein distance as the loss function in model training. The model can learn the statistical features of EEG signals and generate EEG data that approximate real samples. In addition, the performance of the compressed sensing reconstruction model can be improved by using augmented datasets. Experiments show that, compared with the existing advanced data amplification techniques, the proposed model produces EEG signals closer to the real EEG signals as measured by RMSE, FD and WTD indicators. In addition, in the compressed reconstruction of EEG signals, adding the new data reduces the loss by about 15% compared with the original data, which greatly improves the reconstruction accuracy of the EEG signals' compressed sensing.

Exploring the effects of the dietary fiber compound mediated by a longevity dietary pattern on antioxidation, characteristic bacterial genera, and metabolites based on fecal metabolomics.

BACKGROUND: Age-related dysbiosis of the microbiota has been linked to various negative health outcomes. This study aims to investigate the effects of a newly discovered dietary fiber compound (DFC) on aging, intestinal microbiota, and related metabolic processes. The DFC was identified through in vitro fermentation screening experiments, and its dosage and composition were determined based on a longevity dietary pattern. METHODS: Aged SPF C57BL/6 J mice (65 weeks old) and young mice (8 weeks old) were divided into three groups: a subgroup without dietary fiber (NDF), a low DFC dose subgroup (LDF, 10% DFC), and a high DFC dose subgroup (HDF, 20% DFC). The total antioxidant capacity (T-AOC), total superoxide dismutase (T-SOD) activity, malondialdehyde (MDA) content, and glutathione peroxidase (GSH-Px) activity in liver and serum samples of the mice were measured according to the manufacturer's protocol. The expression levels of characteristic bacterial genera and fecal metabolite concentrations in mice were determined using quantitative real-time PCR (qPCR) and nuclear magnetic resonance hydrogen spectroscopy (1H NMR). Metabolomics analysis was further conducted to identify biological functions and potential pathways related to aging. RESULTS: After an 8-weeks dietary intervention, DFC supplementation significantly attenuated age-related weight loss, organ degeneration, and oxidative stress. And promoted the growth of Lactobacillus and Bifidobacterium and inhibited the growth of Escherichia coli (E. coli) and Bacteroides (p < 0.05) in the intestinal tracts of aged mice. Metabolomic analysis identified glycolipid and amino acid metabolic pathway biomarkers associated with aging that were differentially regulated by DFC consumption. Correlation analysis between the identified microbial flora and the biomarkers revealed potential mechanistic links between altered microbial composition and metabolic activity with aging markers. CONCLUSIONS: In conclusion, this study revealed an important mechanism by which DFC consumption impacts healthspan and longevity, shedding light on optimizing dietary fiber or developing fiber-based interventions to improve human health.

Rational design of a setaria-like NiTe2/MoS2 semi-coherent heterogeneous interface for enhancing diffusion kinetics in potassium-ion batteries.

Constructing unique heterostructures is a highly effective approach for enhancing the K+ storage capability of transition metal selenides. Such structures generate internal electric fields that significantly reduce the charge transfer activation energy. However, achieving a flawless interfacial region that maintains the optimal energy level gradient and degree of lattice matching remains a considerable challenge. In this study, we synthesised Setaria-like NiTe2/MoS2@C heterogeneous interfaces at which three-dimensional MoS2 nanosheets are evenly embedded in NiTe2 nanorods to form stabilised heterojunctions. The NiTe2/MoS2 heterojunctions display distinctive electronic configurations and several active sites owing to their low lattice misfits (δ = 13 %), strong electric fields, and uniform carbon shells. A NiTe2/MoS2@C anode in a potassium-ion battery (KIB) exhibited an impressive reversible capacity of 125.8 mAh/g after 1000 cycles at a rate of 500 mA g-1 and a stable reversible capacity of 111.7 mAh/g even after 3000 cycles at 1000 mA g-1. Even the NiTe2/MoS2@C//perylene tetracarboxylic dianhydride full battery configuration maintained a significant reversible capacity of 92.4 mAh/g after 100 cycles at 200 mA g-1, highlighting its considerable potential for application in KIBs. Calculations further revealed that the well-designed NiTe2/MoS2 heterojunction significantly promotes K+ ion diffusion.

Difunctional Ag nanoparticles with high lithiophilic and conductive decorate on core-shell SiO2 nanospheres for dendrite-free lithium metal anodes.

Lithium metal is an attractive and promising anode material due to its high energy density and low working potential. However, the uncontrolled growth of lithium dendrites during repeated plating and stripping processes hinders the practical application of lithium metal batteries, leading to low Coulombic efficiency, poor lifespan, and safety concerns. In this study, we synthesized highly lithiophilic and conductive Ag nanoparticles decorated on SiO2 nanospheres to construct an optimized lithium host for promoting uniform Li deposition. The Ag nanoparticles not only act as lithiophilic sites but also provide high electrical conductivity to the Ag@SiO2@Ag anode. Additionally, the SiO2 layer serves as a lithiophilic nucleation agent, ensuring homogeneous lithium deposition and suppressing the growth of lithium dendrites. Theoretical calculations further confirm that the combination of Ag nanoparticles and SiO2 effectively enhances the adsorption ability of Ag@SiO2@Ag with Li+ ions compared to pure Ag and SiO2 materials. As a result, the Ag@SiO2@Ag coating, with its balanced lithiophilicity and conductivity, demonstrates excellent electrochemical performance, including high Coulombic efficiency, low polarization voltage, and long cycle life. In a full lithium metal cell with LiFePO4 cathode, the Ag@SiO2@Ag anode exhibits a high capacity of 133.1 and 121.4 mAh/g after 200 cycles at rates of 0.5 and 1C, respectively. These results highlight the synergistic coupling of lithiophilicity and conductivity in the Ag@SiO2@Ag coating, providing valuable insights into the field of lithiophilic chemistry and its potential for achieving high-performance batteries in the next generation.

An pair of an atypical NLR encoding genes confer Asian soybean rust resistance in soybean.

Asian soybean rust (ASR), caused by Phakopsora pachyrhizi, is a devastating disease that is present in all major soybean-producing regions. The limited availability of resistant germplasm has resulted in a scarcity of commercial soybean cultivars that are resistant to the disease. To date, only the Chinese soybean landrace SX6907 has demonstrated an immune response to ASR. In this study, we present the isolation and characterization of Rpp6907-7 and Rpp6907-4, a gene pair that confer broad-spectrum resistance to ASR. Rpp6907-7 and Rpp6907-4 encode atypic nucleotide-binding leucine-rich repeat (NLR) proteins that are found to be required for NLR-mediated immunity. Genetic analysis shows that only Rpp6907-7 confers resistance, while Rpp6907-4 regulates Rpp6907-7 signaling activity by acting as a repressor in the absence of recognized effectors. Our work highlights the potential value of using Rpp6907 in developing resistant soybean cultivars.

A BAC based physical map and genome survey of the rice false smut fungus Villosiclava virens.

BACKGROUND: Rice false smut caused by Villosiclava virens is a devastating fungal disease that spreads in major rice-growing regions throughout the world. However, the genomic information for this fungal pathogen is limited and the pathogenic mechanism of this disease is still not clear. To facilitate genetic, molecular and genomic studies of this fungal pathogen, we constructed the first BAC-based physical map and performed the first genome survey for this species. RESULTS: High molecular weight genomic DNA was isolated from young mycelia of the Villosiclava virens strain UV-8b and a high-quality, large-insert and deep-coverage Bacterial Artificial Chromosome (BAC) library was constructed with the restriction enzyme HindIII. The BAC library consisted of 5,760 clones, which covers 22.7-fold of the UV-8b genome, with an average insert size of 140 kb and an empty clone rate of lower than 1%. BAC fingerprinting generated successful fingerprints for 2,290 BAC clones. Using the fingerprints, a whole genome-wide BAC physical map was constructed that contained 194 contigs (2,035 clones) spanning 51.2 Mb in physical length. Bidirectional-end sequencing of 4,512 BAC clones generated 6,560 high quality BAC end sequences (BESs), with a total length of 3,030,658 bp, representing 8.54% of the genome sequence. Analysis of the BESs revealed general genome information, including 51.52% GC content, 22.51% repetitive sequences, 376.12/Mb simple sequence repeat (SSR) density and approximately 36.01% coding regions. Sequence comparisons to other available fungal genome sequences through BESs showed high similarities to Metarhizium anisopliae, Trichoderma reesei, Nectria haematococca and Cordyceps militaris, which were generally in agreement with the 18S rRNA gene analysis results. CONCLUSION: This study provides the first BAC-based physical map and genome information for the important rice fungal pathogen Villosiclava virens. The BAC clones, physical map and genome information will serve as fundamental resources to accelerate the genetic, molecular and genomic studies of this pathogen, including positional cloning, comparative genomic analysis and whole genome sequencing. The BAC library and physical map have been opened to researchers as public genomic resources (http://gresource.hzau.edu.cn/resource/resource.html).

EEG Emotion Recognition by Fusion of Multi-Scale Features.

Electroencephalogram (EEG) signals exhibit low amplitude, complex background noise, randomness, and significant inter-individual differences, which pose challenges in extracting sufficient features and can lead to information loss during the mapping process from low-dimensional feature matrices to high-dimensional ones in emotion recognition algorithms. In this paper, we propose a Multi-scale Deformable Convolutional Interacting Attention Network based on Residual Network (MDCNAResnet) for EEG-based emotion recognition. Firstly, we extract differential entropy features from different channels of EEG signals and construct a three-dimensional feature matrix based on the relative positions of electrode channels. Secondly, we utilize deformable convolution (DCN) to extract high-level abstract features by replacing standard convolution with deformable convolution, enhancing the modeling capability of the convolutional neural network for irregular targets. Then, we develop the Bottom-Up Feature Pyramid Network (BU-FPN) to extract multi-scale data features, enabling complementary information from different levels in the neural network, while optimizing the feature extraction process using Efficient Channel Attention (ECANet). Finally, we combine the MDCNAResnet with a Bidirectional Gated Recurrent Unit (BiGRU) to further capture the contextual semantic information of EEG signals. Experimental results on the DEAP dataset demonstrate the effectiveness of our approach, achieving accuracies of 98.63% and 98.89% for Valence and Arousal dimensions, respectively.

A mouse model characterizes the roles of ZIP8 in systemic iron recycling and lung inflammation and infection.

ZIP8 (SLC39A8) is a transmembrane divalent metal ion importer that is most highly expressed in the lung and is inducible by inflammatory stimuli. In addition to zinc and manganese, ZIP8 can transport iron, but its specific roles in iron regulation during homeostatic and pathologic processes remain poorly understood. Using a novel global inducible ZIP8 knockout (KO) mouse, we analyzed the role of ZIP8 in steady-state iron homeostasis and during inflammation and infection. We observed an unexpected phenotype of elevated spleen iron levels and decreased serum iron in ZIP8 KO mice, suggesting that ZIP8 plays a role in iron recycling. We also showed that ZIP8 is expressed on lung distal airspace epithelial cells and transports iron from the airway into lung tissue. LPS-induced inflammation induced ZIP8 expression in the lung, but ZIP8 deletion had no detrimental effect on the severity of LPS-induced acute lung injury or on the outcomes of Klebsiella pneumoniae lung infection. Thus, ZIP8 plays a role in systemic iron homeostasis but does not modulate the severity of inflammatory lung injury or the host defense against a common bacterial cause of pneumonia.

γ-Aminobutyric acid treatment induced chilling tolerance in postharvest kiwifruit (Actinidia chinensis cv. Hongyang) via regulating ascorbic acid metabolism.

The effect of γ-Aminobutyric acid (GABA) treatment on ascorbic acid (AsA) metabolism and chilling injury in postharvest kiwifruit was studied. The results revealed that kiwifruit treated with GABA displayed higher chilling tolerance and better quality maintenance as compared to the controls. Higher AsA was observed in GABA-treated fruit which was beneficial to cell membrane protection and damage alleviation against chilling mediated oxidative stress. Gene expression analysis found the increased expression of AsA anabolic and regenerative genes and down-regulation of its catabolic genes together could contribute to the elevation of AsA levels in kiwifruit after GABA treatment. In addition, the transcripts of several candidate transcription factors such as bHLHs and HZ1 involved in AsA biosynthesis were also enhanced by GABA treatment. Collectively, our results indicated that GABA induced chilling tolerance in postharvest kiwifruit due to the higher AsA content by positively regulating ascorbate metabolic genes and candidate transcription factors.