ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein-DNA Interaction Hotspots.

Protein-DNA interactions are pivotal to various cellular processes. Precise identification of the hotspot residues for protein-DNA interactions holds great significance for revealing the intricate mechanisms in protein-DNA recognition and for providing essential guidance for protein engineering. Aiming at protein-DNA interaction hotspots, this work introduces an effective prediction method, ESPDHot based on a stacked ensemble machine learning framework. Here, the interface residue whose mutation leads to a binding free energy change (ΔΔG) exceeding 2 kcal/mol is defined as a hotspot. To tackle the imbalanced data set issue, the adaptive synthetic sampling (ADASYN), an oversampling technique, is adopted to synthetically generate new minority samples, thereby rectifying data imbalance. As for molecular characteristics, besides traditional features, we introduce three new characteristic types including residue interface preference proposed by us, residue fluctuation dynamics characteristics, and coevolutionary features. Combining the Boruta method with our previously developed Random Grouping strategy, we obtained an optimal set of features. Finally, a stacking classifier is constructed to output prediction results, which integrates three classical predictors, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network (ANN) as the first layer, and Logistic Regression (LR) algorithm as the second one. Notably, ESPDHot outperforms the current state-of-the-art predictors, achieving superior performance on the independent test data set, with F1, MCC, and AUC reaching 0.571, 0.516, and 0.870, respectively.

Metabolic profile of Phellodendron amurense Rupr. in vivo of rat and its metabolomic study on intervention in rheumatoid arthritis.

To analyze the metabolites (blood, urine and feces) in normal rats after intragastric administration of the decoction of Phellodendri Amurensis Cortex (PAC) and to map the metabolic profile of PAC in vivo of rat; meanwhile, to evaluate the anti-rheumatoid arthritis (RA) effect of PAC by blood metabolomics technique and to explore its mechanism. Performing on UPLC-Q-TOF-MS technology with a Waters ACQUITY UPLC BEH-C18 column (100 mm × 2.1 mm, 1.7 µm), the mobile phase was acetonitrile-0.1% formic acid aqueous solution (gradient elution). Prior to and following the administration of the decoction of PAC, the samples of blood, urine, and fecal were collected from the rats, in the positive ion mode, pharmacogenic metabolites in each biological sample were identified according to the accurate mass, fragment ions, retention time, metabolic reaction type, comparison of reference substance and retrieval of Pub Med database; The adjuvant-type arthritis (AA) rat model was established, and blood metabonomics method was used to study the improvement effect of rheumatoid arthritis after drug intervention with PAC, and its mechanism was preliminarily explored through analysis of metabolic pathway. A total of 72 exogenous components were identified, including 17 prototype components and 55 metabolites; 14 biomarkers were screened by blood metabolomics techniques combined with multivariate statistical analysis, and PAC significantly improved symptoms of rheumatoid arthritis in rats, and the metabolic pathway analysis mainly involves 5 metabolic pathways. The components in the aqueous decoction of PAC mainly undergo phase I metabolic reactions in rats, such as oxidation, reduction, dehydrogenation, demethylation, and phase II metabolic reactions, such as acetylation, glucuronidation, methylation; PAC has anti-rheumatoid arthritis effects, and its mechanism of action may be related to biosynthesis of aminoacyl-tRNA, metabolism of phenylalanine, metabolism of tryptophan, degradation of valine, leucine and isoleucine and biosynthesis of pantothenic acid and coenzyme A, providing a scientific basis for the study of the pharmacodynamic substances and the action mechanism of PAC against RA.

Towards a Safer Future: Enhancing Vaccine Development to Combat Animal Coronaviruses.

Coronaviruses (CoVs) are a large class of positively stranded RNA viruses that pose a significant threat to public health, livestock farming, and wild animals. These viruses have the ability to cross species barriers and cause devastating epidemics. Animals are considered to be intermediate hosts for many coronaviruses, and many animal coronaviruses also have the potential for cross-species transmission to humans. Therefore, controlling the epidemic transmission of animal coronaviruses is of great importance to human health. Vaccination programs have proven to be effective in controlling coronaviruses infections, offering a cost-effective approach to reducing morbidity and mortality, so the re-emergence of lethal coronaviruses emphasizes the urgent need for the development of effective vaccines. In this regard, we explore the progress in animal coronavirus vaccine development, covering the latest taxonomy of the main animal coronaviruses, spillover events, diverse vaccine development platforms, potential main targets for animal coronavirus vaccine development, and primary challenges facing animal coronavirus vaccines. We emphasize the urgent need to create a "dual-effect" vaccine capable of eliciting both cellular and humoral immune responses. The goal is to highlight the contributions of veterinary scientists in this field and emphasize the importance of interdisciplinary collaboration between the veterinary and medical communities. By promoting communication and cooperation, we can enhance the development of novel and super vaccines to combat human and animal coronavirus infections in the future.

An asynchronous response fluorescence sensor combines machine learning theory to qualitatively and quantitatively detect tetracyclines.

Excess use of tetracyclines poses significant health risks arising from animal-derived foods, meaning simple and sensitive methods to detect tetracyclines would be beneficial given current laboratory methods are complex and expensive. Herein, we describe an asynchronous response fluorescence sensor constructed based on Zn-based metal-organic framework and Ru(bpy)32+ (denoted as Ru@Zn-BTEC) for the qualitative and quantitative detection of tetracyclines in foods. Under excitation at 365 nm, the sensor emitted red fluorescence at 609 nm. When tetracyclines were present, these molecules aggregated in the Ru@Zn-BTEC framework, causing green fluorescence emission at 528 nm. The developed sensing system accurately distinguished the different categories of tetracyclines with a classifier accuracy of 94 %. The Ru@Zn-BTEC sensor demonstrated a detection limit of 0.012 µM and satisfactory recovery (87.81 %-113.84 %) for tetracyclines in food samples. This work provides a pathway for constructing asynchronous response fluorescence sensors for food analysis.

A pH-regulated fluorescence covalent organic framework for quantitative water content detection in methanol.

In this paper, we designed and synthesized a two-dimensional fluorescent covalent organic framework (TAPB-DMTP-COF) for the precise determination of H2O content in methanol. The COF was synthesized using two typical monomers by grinding method, which significantly reduced the synthesis time. By adjusting the pH value of the COF suspension to 4.0, the portion of the COF material structure is disrupted, thereby mitigating π-π stacking and resolving the aggregation-caused quenching (ACQ) effect. Consequently, the non-fluorescent TAPB-DMTP-COF exhibited blue-purple fluorescence emission in methanol. At the same time, it is observed that in the presence of H2O, there is a red shift in the maximum fluorescence emission peak of TAPB-DMTP-COF, which correlates with the H2O content within a specific range. Notably, this redshift demonstrates a linear relationship with H2O content from 4% to 80% in methanol. Our work presents novel insights for efficient analysis and detection of H2O content in methanol and could be used for H2O detection in other organic solvents.

PMSPcnn: Predicting protein stability changes upon single point mutations with convolutional neural network.

Protein missense mutations and resulting protein stability changes are important causes for many human genetic diseases. However, the accurate prediction of stability changes due to mutations remains a challenging problem. To address this problem, we have developed an unbiased effective model: PMSPcnn that is based on a convolutional neural network. We have included an anti-symmetry property to build a balanced training dataset, which improves the prediction, in particular for stabilizing mutations. Persistent homology, which is an effective approach for characterizing protein structures, is used to obtain topological features. Additionally, a regression stratification cross-validation scheme has been proposed to improve the prediction for mutations with extreme ΔΔG. For three test datasets: Ssym, p53, and myoglobin, PMSPcnn achieves a better performance than currently existing predictors. PMSPcnn also outperforms currently available methods for membrane proteins. Overall, PMSPcnn is a promising method for the prediction of protein stability changes caused by single point mutations.

Insights into Activation Dynamics and Functional Sites of Inwardly Rectifying Potassium Channel Kir3.2 by an Elastic Network Model Combined with Perturbation Methods.

The inwardly rectifying potassium channel Kir3.2, a member of the inward rectifier potassium (Kir) channel family, exerts important biological functions through transporting potassium ions outside of the cell, during which a large-scale synergistic movement occurs among its different domains. Currently, it is not fully understood how the binding of the ligand to the Kir3.2 channel leads to the structural changes and which key residues are responsible for the channel gating and allosteric dynamics. Here, we construct the Gaussian network model (GNM) of the Kir3.2 channel with the secondary structure and covalent interaction information considered (sscGNM), which shows a better performance in reproducing the channel's flexibility compared with the traditional GNM. In addition, the sscANM-based perturbation method is used to simulate the channel's conformational transition caused by the activator PIP2's binding. By applying certain forces to the PIP2 binding pocket, the coarse-grained calculations generate the similar conformational changes to the experimental observation, suggesting that the topology structure as well as PIP2 binding are crucial to the allosteric activation of the Kir3.2 channel. We also utilize the sscGNM-based thermodynamic cycle method developed by us to identify the key residues whose mutations significantly alter the channel's binding free energy with PIP2. We identify not only the residues important for the specific binding but also the ones critical for the allosteric transition coupled with PIP2 binding. This study is helpful for understanding the working mechanism of Kir3.2 channels and can provide important information for related drug design.

The spatial transcriptomic landscape of human gingiva in health and periodontitis.

The gingiva is a key oral barrier that protects oral tissues from various stimuli. A loss of gingival tissue homeostasis causes periodontitis, one of the most prevalent inflammatory diseases in humans. The human gingiva exists as a complex cell network comprising specialized structures. To understand the tissue-specific pathophysiology of the gingiva, we applied a recently developed spatial enhanced resolution omics-sequencing (Stereo-seq) technique to obtain a spatial transcriptome (ST) atlas of the gingiva in healthy individuals and periodontitis patients. By utilizing Stereo-seq, we identified the major cell types present in the gingiva, which included epithelial cells, fibroblasts, endothelial cells, and immune cells, as well as subgroups of epithelial cells and immune cells. We further observed that inflammation-related signalling pathways, such as the JAK-STAT and NF-κB signalling pathways, were significantly upregulated in the endothelial cells of the gingiva of periodontitis patients compared with those of healthy individuals. Additionally, we characterized the spatial distribution of periodontitis risk genes in the gingiva and found that the expression of IFI16 was significantly increased in endothelial cells of inflamed gingiva. In conclusion, our Stereo-seq findings may facilitate the development of innovative therapeutic strategies for periodontitis by mapping periodontitis-relevant genes and pathways and effector cells.

Chinese expert consensus on imaging diagnosis of drug-resistant pulmonary tuberculosis.

Tuberculosis (TB) remains one of the major infectious diseases in the world with a high incidence rate. Drug-resistant tuberculosis (DR-TB) is a key and difficult challenge in the prevention and treatment of TB. Early, rapid, and accurate diagnosis of DR-TB is essential for selecting appropriate and personalized treatment and is an important means of reducing disease transmission and mortality. In recent years, imaging diagnosis of DR-TB has developed rapidly, but there is a lack of consistent understanding. To this end, the Infectious Disease Imaging Group, Infectious Disease Branch, Chinese Research Hospital Association; Infectious Diseases Group of Chinese Medical Association of Radiology; Digital Health Committee of China Association for the Promotion of Science and Technology Industrialization, and other organizations, formed a group of TB experts across China. The conglomerate then considered the Chinese and international diagnosis and treatment status of DR-TB, China's clinical practice, and evidence-based medicine on the methodological requirements of guidelines and standards. After repeated discussion, the expert consensus of imaging diagnosis of DR-PB was proposed. This consensus includes clinical diagnosis and classification of DR-TB, selection of etiology and imaging examination [mainly X-ray and computed tomography (CT)], imaging manifestations, diagnosis, and differential diagnosis. This expert consensus is expected to improve the understanding of the imaging changes of DR-TB, as a starting point for timely detection of suspected DR-TB patients, and can effectively improve the efficiency of clinical diagnosis and achieve the purpose of early diagnosis and treatment of DR-TB.

Core-genome-mediated promising alternative drug and multi-epitope vaccine targets prioritization against infectious Clostridium difficile.

Prevention of Clostridium difficile infection is challenging worldwide owing to its high morbidity and mortality rates. C. difficile is currently being classified as an urgent threat by the CDC. Devising a new therapeutic strategy become indispensable against C. difficile infection due to its high rates of reinfection and increasing antimicrobial resistance. The current study is based on core proteome data of C. difficile to identify promising vaccine and drug candidates. Immunoinformatics and vaccinomics approaches were employed to construct multi-epitope-based chimeric vaccine constructs from top-ranked T- and B-cell epitopes. The efficacy of the designed vaccine was assessed by immunological analysis, immune receptor binding potential and immune simulation analyses. Additionally, subtractive proteomics and druggability analyses prioritized several promising and alternative drug targets against C. difficile. These include FMN-dependent nitroreductase which was prioritized for pharmacophore-based virtual screening of druggable molecule databases to predict potent inhibitors. A MolPort-001-785-965 druggable molecule was found to exhibit significant binding affinity with the conserved residues of FMN-dependent nitroreductase. The experimental validation of the therapeutic targets prioritized in the current study may worthy to identify new strategies to combat the drug-resistant C. difficile infection.

Transcriptomic and metabolomic analyses reveals keys genes and metabolic pathways in tea (Camellia sinensis) against six-spotted spider mite (Eotetranychus Sexmaculatus).

BACKGROUND: Six-spotted spider mite (Eotetranychus sexmaculatus) is one of the most damaging pests of tea (Camellia sinensis). E. sexmaculatus causes great economic loss and affects tea quality adversely. In response to pests, such as spider mites, tea plants have evolved resistance mechanisms, such as expression of defense-related genes and defense-related metabolites. RESULTS: To evaluate the biochemical and molecular mechanisms of resistance in C. sinensis against spider mites, "Tianfu-5" (resistant to E. sexmaculatus) and "Fuding Dabai" (susceptible to E. sexmaculatus) were inoculated with spider mites. Transcriptomics and metabolomics based on RNA-Seq and liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) technology were used to analyze changes in gene expression and metabolite content, respectively. RNA-Seq data analysis revealed that 246 to 3,986 differentially expressed genes (DEGs) were identified in multiple compared groups, and these DEGs were significantly enriched in various pathways, such as phenylpropanoid and flavonoid biosynthesis, plant-pathogen interactions, MAPK signaling, and plant hormone signaling. Additionally, the metabolome data detected 2,220 metabolites, with 194 to 260 differentially abundant metabolites (DAMs) identified in multiple compared groups, including phenylalanine, lignin, salicylic acid, and jasmonic acid. The combined analysis of RNA-Seq and metabolomic data indicated that phenylpropanoid and flavonoid biosynthesis, MAPK signaling, and Ca2+-mediated PR-1 signaling pathways may contribute to spider mite resistance. CONCLUSIONS: Our findings provide insights for identifying insect-induced genes and metabolites and form a basis for studies on mechanisms of host defense against spider mites in C. sinensis. The candidate genes and metabolites identified will be a valuable resource for tea breeding in response to biotic stress.

Epidemiological Investigation of Yak (Bos grunniens) Fascioliasis in the Pastoral Area of Qinghai-Tibet Plateau, China.

The objective of this investigation was to understand the epidemiology of fascioliasis in yaks in the alpine pastoral areas of the Qinghai-Tibet Plateau, China. The prevalence of Fasciola hepatica infection was estimated by examining eggs in the feces of yaks and by autopsy after the slaughter. Yaks were sampled from a total of 16 representative counties in Qinghai province, and risk factors were assessed based on regional and age characteristics. Fecal samples were obtained from 1542 yaks aged 0-1 (<1 year old), 1-2 (≥1 year old and <3 years old), and over 3 years (≥3 years old). In addition, 242 yaks over 3 years old who had not undergone fecal examinations were randomly selected for autopsy. A total of 267 fecal samples were positive for Fasciola spp. eggs. The average infection rate was 17.32% (0-60.61%), and the average infection intensity was 51.9 eggs per gram (epg) of feces, with intensities ranging from 18 to 112 epg. In Maduo, Dari, Zhiduo, Chengduo, and Datong counties, the Fasciola spp. eggs infection rate was zero. Fasciola spp. adult flukes were detected in 66 out of 242 yaks at autopsy, with a total infection rate of 27.27% and an average infection intensity of 21.2 (adult worms), with intensities ranging from 3 to 46 worms. Logistic regression model analysis showed that age was a significant risk factor for yak infection with Fasciola spp. In addition, the risk varied between regions: Haiyan, Gangcha, Duran, and Wulan were all high-risk areas for yak infection with Fasciola spp. The spatial distribution of the Fasciola spp. infection rate in each region showed a very weak negative correlation (Moran's I = -0.062), Duran formed a spatial distribution of high-low clusters with surrounding areas, and Datong formed a low-high clustering distribution characteristic with the surrounding areas. This investigation revealed that the infection rate of Fasciola spp. in yaks was higher on the Qinghai-Tibet Plateau. Increasing age was a risk factor for infection with Fasciola spp.; different regions also have a different risk of Fasciola spp. infection. Only two regions showed clustering characteristics in the spatial distribution of infection rates. These findings extend the epidemiological information on Fasciola spp. infection in yaks and provide baseline data for the execution of control measures against Fasciola spp. infection.

Vaccinomics-aided next-generation novel multi-epitope-based vaccine engineering against multidrug resistant Shigella Sonnei: Immunoinformatics and chemoinformatics approaches.

Shigella sonnei is a gram-negative bacterium and is the primary cause of shigellosis in advanced countries. An exceptional rise in the prevalence of the disease has been reported in Asia, the Middle East, and Latin America. To date, no preventive vaccine is available against S. sonnei infections. This pathogen has shown resistances towards both first- and second-line antibiotics. Therefore, an effective broad spectrum vaccine development against shigellosis is indispensable. In the present study, vaccinomics-aided immunoinformatics strategies were pursued to identify potential vaccine candidates from the S. sonnei whole proteome data. Pathogen essential proteins that are non-homologous to human and human gut microbiome proteome set, are feasible candidates for this purpose. Three antigenic outer membrane proteins were prioritized to predict lead epitopes based on reverse vaccinology approach. Multi-epitope-based chimeric vaccines was designed using lead B- and T-cell epitopes combined with suitable linker and adjuvant peptide sequences to enhance immune responses against the designed vaccine. The SS-MEVC construct was prioritized based on multiple physicochemical, immunological properties, and immune-receptors docking scores. Immune simulation analysis predicted strong immunogenic response capability of the designed vaccine construct. The Molecular dynamic simulations analysis ensured stable molecular interactions of lead vaccine construct with the host receptors. In silico restriction and cloning analysis predicted feasible cloning capability of the SS-MEVC construct within the E. coli expression system. The proposed vaccine construct is predicted to be more safe, effective and capable of inducing robust immune responses against S. sonnei infections and may be worthy of examination via in vitro/in vivo assays.

Vaccinomics-based next-generation multi-epitope chimeric vaccine models prediction against Leishmania tropica - a hierarchical subtractive proteomics and immunoinformatics approach.

Leishmania tropica is a vector-borne parasitic protozoa that is the leading cause of leishmaniasis throughout the global tropics and subtropics. L. tropica is a multidrug-resistant parasite with a diverse set of serological, biochemical, and genomic features. There are currently no particular vaccines available to combat leishmaniasis. The present study prioritized potential vaccine candidate proteins of L. tropica using subtractive proteomics and vaccinomics approaches. These vaccine candidate proteins were downstream analyzed to predict B- and T-cell epitopes based on high antigenicity, non-allergenic, and non-toxic characteristics. The top-ranked overlapping MHC-I, MHC-II, and linear B-cell epitopes were prioritized for model vaccine designing. The lead epitopes were linked together by suitable linker sequences to design multi-epitope constructs. Immunogenic adjuvant sequences were incorporated at the N-terminus of the model vaccine constructs to enhance their immunological potential. Among different combinations of constructs, four vaccine designs were selected based on their physicochemical and immunological features. The tertiary structure models of the designed vaccine constructs were predicted and verified. The molecular docking and molecular dynamic (MD) simulation analyses indicated that the vaccine design V1 demonstrated robust and stable molecular interactions with toll-like receptor 4 (TLR4). The top-ranked vaccine construct model-IV demonstrated significant expressive capability in the E. coli expression system during in-silico restriction cloning analysis. The results of the present study are intriguing; nevertheless, experimental bioassays are required to validate the efficacy of the predicted model chimeric vaccine.

Unravelling the genomic features, phylogeny and genetic basis of tooth ontogenesis in Characiformes through analysis of four genomes.

Characiformes is a diverse and evolutionarily significant order of freshwater fish encompassing over 2,300 species. Despite its diversity, our understanding of Characiformes' evolutionary relationships and adaptive mechanisms is limited due to insufficient genome sequences. In this study, we sequenced and assembled the genomes of four Characiformes species, three of which were chromosome-level assemblies. Our analyses revealed dynamic changes in gene family evolution, repeat sequences and variations in chromosomal collinearity within these genomes. With the assembled genomes, we were not only able to elucidate the evolutionary relationship of the four main orders in Otophysi but also indicated Characiformes as the paraphyletic group. Comparative genomic analysis with other available fish genomes shed light on the evolution of genes related to tooth development in Characiformes. Notably, variations in the copy number of secretory calcium-binding phosphoproteins (SCPP) genes were observed among different orders of Otophysi, indicating their potential contribution to the diversity of tooth types. Our study offers invaluable genome sequences and novel insights into Characiformes' evolution, paving the way for further genomic and evolutionary research in fish.

A phthalocyanine-based porous organic polymer for a lithium-ion battery anode.

Porous organic polymers (POPs) are a novel class of polymeric materials with high flexibility and designability for building structures. Herein, a phthalocyanine-based porous organic polymer (PcPOP) was constructed in situ on copper foil from H2Pc(ethynyl)4 [Pc(ethynyl)4 = 2(3),9(10),16(17),23(24)-tetra(ethynyl)phthalocyanine] by the coupling reaction. Benefiting from the uniformly distributed electron-rich nitrogen atoms in the Pc structure and the sp-hybridized carbons in the acetylenic linkage, Li intercalation in the porous organic polymer would be improved and stabilized. As a result, PcPOP showed remarkable electrochemical performance in lithium-ion batteries as the anode, including high specific capacity (a charge capacity of 1172 mA h g-1 at a current density of 150 mA g-1) and long cycling stability (a reversible capacity of 960.1 mA h g-1 can be achieved even after 600 cycles at a current density of 1500 mA g-1). The result indicates that the intrinsic doping of electron-rich sites of the building molecules is beneficial for the electrochemical performance of the porous organic polymer.

The application of HER2 and CD47 CAR-macrophage in ovarian cancer.

BACKGROUND: The chimeric antigen receptor (CAR)-T therapy has a limited therapeutic effect on solid tumors owing to the limited CAR-T cell infiltration into solid tumors and the inactivation of CAR-T cells by the immunosuppressive tumor microenvironment. Macrophage is an important component of the innate and adaptive immunity, and its unique phagocytic function has been explored to construct CAR macrophages (CAR-Ms) against solid tumors. This study aimed to investigate the therapeutic application of CAR-Ms in ovarian cancer. METHODS: In this study, we constructed novel CAR structures, which consisted of humanized anti-HER2 or CD47 scFv, CD8 hinge region and transmembrane domains, as well as the 4-1BB and CD3ζ intracellular domains. We examined the phagocytosis of HER2 CAR-M and CD47 CAR-M on ovarian cancer cells and the promotion of adaptive immunity. Two syngeneic tumor models were used to estimate the in vivo antitumor activity of HER2 CAR-M and CD47 CAR-M. RESULTS: We constructed CAR-Ms targeting HER2 and CD47 and verified their phagocytic ability to ovarian cancer cells in vivo and in vitro. The constructed CAR-Ms showed antigen-specific phagocytosis of ovarian cancer cells in vitro and could activate CD8+ cytotoxic T lymphocyte (CTL) to secrete various anti-tumor factors. For the in vivo model, mice with human-like immune systems were used. We found that CAR-Ms enhanced CD8+ T cell activation, affected tumor-associated macrophage (TAM) phenotype, and led to tumor regression. CONCLUSIONS: We demonstrated the inhibition effect of our constructed novel HER2 CAR-M and CD47 CAR-M on target antigen-positive ovarian cancer in vitro and in vivo, and preliminarily verified that this inhibitory effect is due to phagocytosis, promotion of adaptive immunity and effect on tumor microenvironment.

Lactiplantibacillus plantarum LPJZ-658 Improves Non-Alcoholic Steatohepatitis by Modulating Bile Acid Metabolism and Gut Microbiota in Mice.

Non-alcoholic steatohepatitis (NASH) is one of the most prevalent diseases worldwide; it is characterized by hepatic lipid accumulation, inflammation, and progressive fibrosis. Here, a Western diet combined with low-dose weekly carbon tetrachloride was fed to C57BL/6J mice for 12 weeks to build a NASH model to investigate the attenuating effects and possible mechanisms of Lactiplantibacillus plantarum LPJZ-658. Hepatic pathology, lipid profiles, and gene expression were assessed. The metabolomic profiling of the serum was performed. The composition structure of gut microbiota was profiled using 16s rRNA sequencing. The results show that LPJZ-658 treatment significantly attenuated liver injury, steatosis, fibrosis, and inflammation in NASH mice. Metabolic pathway analysis revealed that several pathways, such as purine metabolism, glycerophospholipid metabolism, linoleic acid metabolism, and primary bile acid biosynthesis, were associated with NASH. Notably, we found that treatment with LPJZ-658 regulated the levels of bile acids (BAs) in the serum. Moreover, LPJZ-658 restored NASH-induced gut microbiota dysbiosis. The correlation analysis deduced obvious interactions between BAs and gut microbiota. The current study indicates that LPJZ-658 supplementation protects against NASH progression, which is accompanied by alternating BA metabolic and modulating gut microbiota.

Dynamic Insights into the Self-Activation Pathway and Allosteric Regulation of the Orphan G-Protein-Coupled Receptor GPR52.

Within over 800 members of G-protein-coupled receptors, there are numerous orphan receptors whose endogenous ligands are largely unknown, providing many opportunities for novel drug discovery. However, the lack of an in-depth understanding of the intrinsic working mechanism for orphan receptors severely limits the related rational drug design. The G-protein-coupled receptor 52 (GPR52) is a unique orphan receptor that constitutively increases cellular 5'-cyclic adenosine monophosphate (cAMP) levels without binding any exogenous agonists and has been identified as a promising therapeutic target for central nervous system disorders. Although recent structural biology studies have provided snapshots of both active and inactive states of GPR52, the mechanism of the conformational transition between these states remains unclear. Here, an acceptable self-activation pathway for GPR52 was proposed through 6 µs Gaussian accelerated molecular dynamics (GaMD) simulations, in which the receptor spontaneously transitions from the active state to that matching the inactive crystal structure. According to the three intermediate states of the receptor obtained by constructing a reweighted potential of mean force, how the allosteric regulation occurs between the extracellular orthosteric binding pocket and the intracellular G-protein-binding site is revealed. Combined with the independent gradient model, several important microswitch residues and the allosteric communication pathway that directly links the two regions are both identified. Transfer entropy calculations not only reveal the complex allosteric signaling within GPR52 but also confirm the unique role of ECL2 in allosteric regulation, which is mutually validated with the results of GaMD simulations. Overall, this work elucidates the allosteric mechanism of GPR52 at the atomic level, providing the most detailed information to date on the self-activation of the orphan receptor.

The complete chloroplast genome of Rubus pinfaensis, an edible and medicinal dual-purpose plant.

Rubus pinfaensis H. Lév. & Vaniot is of great importance in the phylogeny and evolution amongst Rosaceae, genus Rubus L. plants. The chloroplast genome of R. pinfaensis was reported in this study, which is 155,523 bp in size, with an average GC content of 37.13%. The complete chloroplast genome has a typical quadripartite structure, including a large single copy (LSC) region (85,211 bp) and a small single copy (SSC) region (18,718 bp), which were separated a pair of inverted repeats (IRs, 25,797 bp). This plastome contained 129 different genes (112 unique), including 85 protein-coding genes (79 unique), 36 tRNA genes (29 unique), and 8 rRNA genes (4 unique). The chloroplast genome of R. pinfaensis has completed that will be based on the phylogeny and genomic studies in the family Rosaceae, genus Rubus L.