Effects of dietary citrus pulp level on the growth and intestinal health of largemouth bass (Micropterus salmoides).

BACKGROUND: Citrus pulp (CP) is rich in pectin, and studies have shown that pectin possesses antioxidant, anti-inflammatory, and gut microbiota-regulating properties. However, the application of CP in aquafeed is limited. In this study, the effect of dietary inclusion of CP on the intestinal health of largemouth bass (Micropterus salmoides) was investigated. Juveniles of similar size (6.95 ± 0.07 g) were fed isonitrogenous and isoenergetic diets containing different levels of CP (0%, 3%, 6%, 9%, 12%, or 15%) for 58 days. RESULTS: As the level of CP in the feed for largemouth bass increased, the fish's growth performance and intestinal health initially improved and then declined. Adding low doses of CP (≤9%) to the feed had no significant impact on the growth performance of large-mouth black bass, whereas high doses of CP (>9%) significantly reduced their growth performance. Adding 6%, 9%, or 12% of CP to that feed enhanced the expression of genes related to tight junctions, anti-inflammatory activity, anti-apoptotic activity, and antioxidant activity in the intestines of largemouth bass. It reduced intestinal inflammation and improved intestinal nutrient absorption, intestinal mucosal barrier function, and intestinal antioxidant capacity. Moreover, it improved the α-diversity, structure, and function of the intestinal flora. The addition of 6% CP had the most beneficial effect on the intestinal health of largemouth bass. On the other hand, the addition of 15% CP had adverse effects on the intestinal antioxidant capacity and intestinal mucosal barrier function of largemouth bass. CONCLUSION: Adding 6-9% CP to the feed for largemouth bass can improve their intestinal health without having a significant impact on their growth performance. CP could serve as a novel prebiotic and immunostimulant ingredient in aquafeed. © 2023 Society of Chemical Industry.

Novel Clinical mNGS-Based Machine Learning Model for Rapid Antimicrobial Susceptibility Testing of Acinetobacter baumannii.

Multidrug-resistant (MDR) bacteria are important public health problems. Antibiotic susceptibility testing (AST) currently uses time-consuming culture-based procedures, which cause treatment delays and increased mortality. We developed a machine learning model using Acinetobacter baumannii as an example to explore a fast AST approach using metagenomic next-generation sequencing (mNGS) data. The key genetic characteristics associated with antimicrobial resistance (AMR) were selected through a least absolute shrinkage and selection operator (LASSO) regression model based on 1,942 A. baumannii genomes. The mNGS-AST prediction model was accordingly established, validated, and optimized using read simulation sequences of clinical isolates. Clinical specimens were collected to evaluate the performance of the model retrospectively and prospectively. We identified 20, 31, 24, and 3 AMR signatures of A. baumannii for imipenem, ceftazidime, cefepime, and ciprofloxacin, respectively. Four mNGS-AST models had a positive predictive value (PPV) greater than 0.97 for 230 retrospective samples, with negative predictive values (NPVs) of 100% (imipenem), 86.67% (ceftazidime), 86.67% (cefepime), and 90.91% (ciprofloxacin). Our method classified antibacterial phenotypes with an accuracy of 97.65% for imipenem, 96.57% for ceftazidime, 97.64% for cefepime, and 98.36% for ciprofloxacin. The average reporting time of mNGS-based AST was 19.1 h, in contrast to the 63.3 h for culture-based AST, thus yielding a significant reduction of 44.3 h. mNGS-AST prediction results coincided 100% with the phenotypic AST results when testing 50 prospective samples. The mNGS-based model could be used as a rapid genotypic AST approach to identify A. baumannii and predict resistance and susceptibility to antibacterials and could be applicable to other pathogens and facilitate rational antimicrobial usage.

Direct prediction of carbapenem resistance in Pseudomonas aeruginosa by whole genome sequencing and metagenomic sequencing.

Carbapenem resistance is a major concern in the management of antibiotic-resistant Pseudomonas aeruginosa infections. The direct prediction of carbapenem-resistant phenotype from genotype in P. aeruginosa isolates and clinical samples would promote timely antibiotic therapy. The complex carbapenem resistance mechanism and the high prevalence of variant-driven carbapenem resistance in P. aeruginosa make it challenging to predict the carbapenem-resistant phenotype through the genotype. In this study, using whole genome sequencing (WGS) data of 1,622 P. aeruginosa isolates followed by machine learning, we screened 16 and 31 key gene features associated with imipenem (IPM) and meropenem (MEM) resistance in P. aeruginosa, including oprD(HIGH), and constructed the resistance prediction models. The areas under the curves of the IPM and MEM resistance prediction models were 0.906 and 0.925, respectively. For the direct prediction of carbapenem resistance in P. aeruginosa from clinical samples by the key gene features selected and prediction models constructed, 72 P. aeruginosa-positive sputum samples were collected and sequenced by metagenomic sequencing (MGS) based on next-generation sequencing (NGS) or Oxford Nanopore Technology (ONT). The prediction applicability of MGS based on NGS outperformed that of MGS based on ONT. In 72 P. aeruginosa-positive sputum samples, 65.0% (26/40) of IPM-insensitive and 63.2% (24/38) of MEM-insensitive P. aeruginosa were directly predicted by NGS-based MGS with positive predictive values of 0.897 and 0.889, respectively. By the direct detection of the key gene features associated with carbapenem resistance of P. aeruginosa, the carbapenem resistance of P. aeruginosa could be directly predicted from cultured isolates by WGS or from clinical samples by NGS-based MGS, which could assist the timely treatment and surveillance of carbapenem-resistant P. aeruginosa.

The Mpro structure-based modifications of ebselen derivatives for improved antiviral activity against SARS-CoV-2 virus.

The main protease (Mpro or 3CLpro) of SARS-CoV-2 virus is a cysteine enzyme critical for viral replication and transcription, thus indicating a potential target for antiviral therapy. A recent repurposing effort has identified ebselen, a multifunctional drug candidate as an inhibitor of Mpro. Our docking of ebselen to the binding pocket of Mpro crystal structure suggests a noncovalent interaction for improvement of potency, antiviral activity and selectivity. To test this hypothesis, we designed and synthesized ebselen derivatives aimed at enhancing their non-covalent bonds within Mpro. The inhibition of Mpro by ebselen derivatives (0.3 µM) was screened in both HPLC and FRET assays. Nine ebselen derivatives (EBs) exhibited stronger inhibitory effect on Mpro with IC50 of 0.07-0.38 µM. Further evaluation of three derivatives showed that EB2-7 exhibited the most potent inhibition of SARS-CoV-2 viral replication with an IC50 value of 4.08 µM in HPAepiC cells, as compared to the prototype ebselen at 24.61 µM. Mechanistically, EB2-7 functions as a noncovalent Mpro inhibitor in LC-MS/MS assay. Taken together, our identification of ebselen derivatives with improved antiviral activity may lead to developmental potential for treatment of COVID-19 and SARS-CoV-2 infection.

Mitochondrial dysfunction induced by HIF-1α under hypoxia contributes to the development of gastric mucosal lesions.

INTRODUCTION: Hypoxia is an important characteristic of gastric mucosal diseases, and hypoxia-inducible factor-1α (HIF-1α) contributes to microenvironment disturbance and metabolic spectrum abnormalities. However, the underlying mechanism of HIF-1α and its association with mitochondrial dysfunction in gastric mucosal lesions under hypoxia have not been fully clarified. OBJECTIVES: To evaluate the effects of hypoxia-induced HIF-1α on the development of gastric mucosal lesions. METHODS: Portal hypertensive gastropathy (PHG) and gastric cancer (GC) were selected as representative diseases of benign and malignant gastric lesions, respectively. Gastric tissues from patients diagnosed with the above diseases were collected. Portal hypertension (PHT)-induced mouse models in METTL3 mutant or NLRP3-deficient littermates were established, and nude mouse gastric graft tumour models with relevant inhibitors were generated. The mechanisms underlying hypoxic condition, mitochondrial dysfunction and metabolic alterations in gastric mucosal lesions were further analysed. RESULTS: HIF-1α, which can mediate mitochondrial dysfunction via upregulation of METTL3/IGF2BP3-dependent dynamin-related protein 1 (Drp1) N6-methyladenosine modification to increase mitochondrial reactive oxygen species (mtROS) production, was elevated under hypoxic conditions in human and mouse portal hypertensive gastric mucosa and GC tissues. While blocking HIF-1α with PX-478, inhibiting Drp1-dependent mitochondrial fission via mitochondrial division inhibitor 1 (Mdivi-1) treatment or METTL3 mutation alleviated this process. Furthermore, HIF-1α influenced energy metabolism by enhancing glycolysis via lactate dehydrogenase A. In addition, HIF-1α-induced Drp1-dependent mitochondrial fission also enhanced glycolysis. Drp1-dependent mitochondrial fission and enhanced glycolysis were associated with alterations in antioxidant enzyme activity and dysfunction of the mitochondrial electron transport chain, resulting in massive mtROS production, which was needed for activation of NLRP3 inflammasome to aggravate the development of the PHG and GC. CONCLUSIONS: Under hypoxic conditions, HIF-1α enhances mitochondrial dysfunction via Drp1-dependent mitochondrial fission and influences the metabolic profile by altering glycolysis to increase mtROS production, which can trigger NLRP3 inflammasome activation and mucosal microenvironment alterations to contribute to the development of benign and malignant gastric mucosal lesions.

Rapid inference of antibiotic resistance and susceptibility for Klebsiella pneumoniae by clinical shotgun metagenomic sequencing.

OBJECTIVES: The study aimed to develop a genotypic antimicrobial resistance testing method for Klebsiella pneumoniae using metagenomic sequencing data. METHODS: We utilized Lasso regression on assembled genomes to identify genetic resistance determinants for six antibiotics (Gentamicin, Tobramycin, Imipenem, Meropenem, Ceftazidime, Trimethoprim/Sulfamethoxazole). The genetic features were weighted, grouped into clusters to establish classifier models. Origin species of detected antibiotic resistant gene (ARG) was determined by novel strategy integrating "possible species", "gene copy number calculation" and "species-specific kmers". The performance of the method was evaluated on retrospective case studies. RESULTS: Our study employed machine learning on 3928 K. pneumoniae isolates, yielding stable models with AUCs > 0.9 for various antibiotics. GenseqAMR, a read-based software, exhibited high accuracy (AUC 0.926-0.956) for short-read datasets. The integration of a species-specific kmer strategy significantly improved ARG-species attribution to an average accuracy of 96.67%. In a retrospective study of 191 K. pneumoniae-positive clinical specimens (0.68%-93.39% genome coverage), GenseqAMR predicted 84.23% of AST results on average. It demonstrated 88.76%-96.26% accuracy for resistance prediction, offering genotypic AST results with a shorter turnaround time (mean ± SD: 18.34 ± 0.87 hours) than traditional culture-based AST (60.15 ± 21.58 hours). Furthermore, a retrospective clinical case study involving 63 cases showed that GenseqAMR could lead to changes in clinical treatment for 24 (38.10%) cases, with 95.83% (23/24) of these changes deemed beneficial. CONCLUSIONS: In conclusion, GenseqAMR is a promising tool for quick and accurate AMR prediction in Klebsiella pneumoniae, with the potential to improve patient outcomes through timely adjustments in antibiotic treatment.

Predefined-Time Zeroing Neural Networks With Independent Prior Parameter for Solving Time-Varying Plural Lyapunov Tensor Equation.

As an extension of the Lyapunov equation, the time-varying plural Lyapunov tensor equation (TV-PLTE) can carry multidimensional data, which can be solved by zeroing neural network (ZNN) models effectively. However, existing ZNN models only focus on time-varying equations in field of real number. Besides, the upper bound of the settling time depends on the value of ZNN model parameters, which is a conservative estimation for existing ZNN models. Therefore, this article proposes a novel design formula for converting the upper bound of the settling time into an independent and directly modifiable prior parameter. On this basis, we design two new ZNN models called strong predefined-time convergence ZNN (SPTC-ZNN) and fast predefined (FP)-time convergence ZNN (FPTC-ZNN) models. The SPTC-ZNN model has a nonconservative upper bound of the settling time, and the FPTC-ZNN model has excellent convergence performance. The upper bound of the settling time and robustness of the SPTC-ZNN and FPTC-ZNN models are verified by theoretical analyses. Then, the effect of noise on the upper bound of settling time is discussed. The simulation results show that the SPTC-ZNN and FPTC-ZNN models have better comprehensive performance than existing ZNN models.

Study on carbon dioxide emission from reservoirs with different regulation types and its empirical prediction model.

The construction of artificial reservoirs with various regulation types on river is currently an important form of comprehensive utilization of water energy and water resources in river basins. The type of regulation is important in controlling the residence time, which in turn affects the photosynthesis-respiration balance in the water. This process has a significant impact on carbon dioxide (CO2) emissions from reservoirs. In this study, seasonal observations were carried out from September 2020 to July 2021 at five artificial reservoirs in the Qiantang River Basin, eastern China, to reveal the characteristics of CO2 emission from the water-air interface of reservoirs with different regulating types. The results showed that the annual average CO2 emission flux of the studied reservoirs varied significantly, ranging from 4.2 to 155.3 mmol m-2 day-1 with an average of 48.4 mmol m-2 day-1, which also had a significant negative correlation with the hydraulic retention time. While downstream of the dam, the annual average CO2 emission flux was quite high with a range of 105.8 to 543.0 mmol m-2 day-1, averaging 381.6 mmol m-2 day-1. This is mainly due to the release of water with high-concentration CO2 from the bottom of the reservoir. Additionally, using related data of reservoirs around the world, a CO2 emission model with hydraulic retention time, air temperature, and reservoir age as the primary parameters was developed, which was conducive to evaluate reservoir CO2 emissions on a larger scale and provided theoretical support for effective reservoir management.

Chlorophyll maxima layer in a large subtropical reservoir (Xinanjiang Reservoir): Spatial development process and limitation by CO2 and phosphorus.

In marine investigations, the maximum chlorophyll-a (Chla) concentration is often reported to occur at a specific depth below the ocean surface, a phenomenon known as subsurface Chla maxima (SCM). However, SCM has long been overlooked in artificial reservoirs, which may lead to a serious underestimation of the primary productivity level and trophic status of reservoirs. To better understand the temporal and spatial variability of SCM and the mechanisms leading to SCM development, this study conducted a detailed survey in a large subtropical reservoir (Xinanjiang Reservoir, XAJR) from September 2020 to August 2021. The seasonal thermal stratification, in situ variables (WT, pH, DO and Chla), nutrient concentrations (DSi, NO3-, DIP and DCO2), Chla maxima depth and magnitude of the riverine region (S1), transition region (S2) and the central part of the XAJR (S3 and S4) were all thoroughly investigated. Thermal stratification and SCM in XAJR exhibited significant seasonal and spatial heterogeneity. Phytoplankton biomass in the epilimnion was limited by dissolved CO2 from June to October in the warm seasons, while it was primarily limited by phosphorus in the other seasons, according to the nutrient limitation analysis. Along the water column, dissolved CO2 limitation occurred mainly above the SCM layer, and the water column below the SCM layer gradually transitioned from dissolved CO2 limitation to phosphorus limitation. Furthermore, as the thermal stratification developed, the upstream water mass moves along the middle of the water column as density flow toward the reservoir, providing nutrients for the development of the SCM. This research contributes to a better understanding of the temporal and spatial variation of SCM and nutrient supply in deep and large stratified reservoirs.

Identification of Micro Ribonucleic Acids and Their Targets in Response to Plasmodiophora brassicae Infection in Brassica napus.

Clubroot disease, which is caused by the soil-borne pathogen Plasmodiophora brassicae War (P. brassicae), is one of the oldest and most destructive diseases of Brassica and cruciferous crops in the world. Plant microRNAs [micro ribonucleic acids (miRNAs)] play important regulatory roles in several developmental processes. Although the role of plant miRNAs in plant-microbe interaction has been extensively studied, there are only few reports on the specific functions of miRNAs in response to P. brassicae. This study investigated the roles of miRNAs and their targets during P. brassicae infection in a pair of Brassica napus near-isogenic lines (NILs), namely clubroot-resistant line 409R and clubroot-susceptible line 409S. Small RNA sequencing (sRNA-seq) and degradome-seq were performed on root samples of 409R and 409S with or without P. brassicae inoculation. sRNA-seq identified a total of 48 conserved and 72 novel miRNAs, among which 18 had a significant differential expression in the root of 409R, while only one miRNA was differentially expressed in the root of 409S after P. brassicae inoculation. The degradome-seq analysis identified 938 miRNA target transcripts, which are transcription factors, enzymes, and proteins involved in multiple biological processes and most significantly enriched in the plant hormone signal transduction pathway. Between 409R and 409S, we found eight different degradation pathways in response to P. brassicae infection, such as those related to fatty acids. By combining published transcriptome data, we identified a total of six antagonistic miRNA-target pairs in 409R that are responsive to P. brassicae infection and involved in pathways associated with root development, hypersensitive cell death, and chloroplast metabolic synthesis. Our results reveal that P. brassicae infection leads to great changes in miRNA pool and target transcripts. More interestingly, these changes are different between 409R and 409S. Clarification of the crosstalk between miRNAs and their targets may shed new light on the possible mechanisms underlying the pathogen resistance against P. brassicae.

Photosensitive and Photoswitchable TRPA1 Agonists Optically Control Pain through Channel Desensitization.

Transient receptor potential ankyrin 1 (TRPA1) channel, as a nonselective ligand-gated cation channel robustly in dorsal root ganglion sensory neurons, is implicated in sensing noxious stimuli and nociceptive signaling. However, small-molecule tools targeting TRPA1 lack temporal and spatial resolution, limiting their use for validation of TRPA1 as a therapeutic target for pain. In our previous work, we found that 4,4'-(diazene-1,2-diyl)dianiline (AB1) is a photoswitchable TRPA1 agonist, but the poor water solubility and activity hinder its further development. Here, we report a series of specific and potent azobenzene-derived photoswitchable TRPA1 agonists (series 1 and 2) that enable optical control of the TRPA1 channel. Two representative compounds 1g and 2c can alleviate capsaicin-induced pain in the cheek model of mice through channel desensitization but not in TRPA1 knockout mice. Taken together, our findings demonstrate that photoswitchable TRPA1 agonists can be used as pharmacological tools for study of pain signaling.

Highly Efficient Cell Membrane Tracker Based on a Solvatochromic Dye with Near-Infrared Emission.

The cell membrane is composed of a phospholipid bilayer with embedded proteins and maintains cell homeostasis through dynamic changes. An abnormal cell membrane shape could be a sign of unhealthy cells. Probes for subcellular fluorescence imaging that can identify the abnormal plasma membrane and record the dynamic changes are needed. Based on a solvatochromic dye with a near-infrared emission strategy, the amphipathic molecule (E)-2,2'-((4-(2-(4-(dicyanomethylene)-4H-chromen-2-yl)vinyl)phenyl)azanediyl)bis(ethane-1-sulfonic acid) (MRL) contained a hydrophilic sulfo group and a hydrophobic chromone group, which was designed and synthesized for staining the cell membrane and monitoring the morphology of the membranes under different conditions. MRL exhibited an excellent photostability and low cytotoxicity; when cells were incubated with MRL, cell membranes were specifically labeled. MRL is capable of long-term monitoring of the morphological changes of cell membrane.