Improving drought adaptation in wheat requires robust physiological traits that can be genetically dissected and translated into breeding tools. Carbon isotope composition (δ13C) is a stable proxy for intrinsic water-use efficiency (iWUE) and enables the identification of genetic factors underlying drought-stress responses. In this study, δ13C was measured in the Bavarian MAGIC Wheat (BMW) population, and quantitative trait locus (QTL) mapping at the drought-prone Morgenrot field site identified six significant loci on chromosomes 1D, 2B, 4B, 4D, 6A, and 6B. Based on phenotypic extremes at this site, eight genetically diverse lines with contrasting δ13C/iWUE values (four high δ13C and four low δ13C) were selected for detailed characterization. These extreme lines were evaluated under controlled greenhouse conditions using high-throughput phenotyping under both well-watered and drought-stress treatments.Transcriptomic analyses revealed pronounced drought-induced expression changes, with enrichment of genes involved in abscisic acid signaling, stress perception, stomatal regulation, and energy metabolism. To identify robust molecular targets, QTL mapping results were integrated with differential gene expression (DGE) and weighted gene co-expression network analysis in a multi-layer framework. This approach prioritized 18 high-confidence candidate genes located mainly on chromosomes 1D, 4B, 4D, 6A, and 6B. These genes are embedded in regulatory networks dominated by transcription factors such as WRKY and NAC. Collectively, this study provides genetically anchored candidate genes for iWUE and drought adaptation, offering valuable resources for marker-assisted selection in wheat breeding.
Field capacity (FC) is a key parameter for smart irrigation, but it still requires manual measurement, which limits technological progress. Enhancing the in-situ intelligent analysis capability of soil moisture sensors is crucial for advancing this field. Among 11 common tree-based models, Categorical Boosting (CatBoost) achieved the best performance in FC simulation. Therefore, CatBoost combined with Particle Swarm Optimization (PSO) was employed for parameter optimization, resulting in a data-driven intelligent online FC measurement method. This approach dynamically characterizes soil moisture recession curves at various depths using sensor data, while improving temporal resolution and reducing the complexity of FC estimation. Using a dataset comprising 118 farmland soil moisture monitoring points from major grain-producing regions in China, the proposed method was validated through simulation. The simulation errors were compared with those from other emerging integrated algorithm models, and the results demonstrated that the proposed method achieved superior performance. When applied to FC simulation, the model achieves a remarkably high coefficient of determination (R2) of 0.99, with mean absolute error (MAE) ranging from 0.41% to 1.35%, mean squared error (MSE) from 0.63% to 7.27%, and root mean squared error (RMSE) from 0.80% to 2.64%, respectively. These results confirm that integrating AI with soil sensors enables accurate in-situ FC estimation. In practice, this method enables dynamic adjustment of irrigation thresholds, supporting automated decision-making and precision water management in modern agriculture.
The location and evolution of the pressure losses across sand media filters commonly used in microirrigation have not been extensively studied. However, understanding these patterns may allow identifying feasible redesign strategies that reduce energy consumption and, therefore, enhance the sustainability of this irrigation equipment. The pressure loss in the main regions (diffuser, upper and lower bed media, underdrain and collector) of three media filters with different underdrain types (collector arms, inserted domes and porous media) was measured in filtration operation with two sand bed heights (0.2 and 0.3 m) and two filtration velocities (30 and 60 m/h). Each combination of filter, bed height and filtration velocity was tested for 250 h using reclaimed effluents. Higher pressure losses were observed at the greatest filtration velocity and the deepest media bed. Overall, the highest pressure loss was observed in the sand bed, with the diffuser showing the second largest loss. The evolution of pressure loss throughout each filtration cycle showed a progressive increase across the media, attributed to the accumulation of retained solids. New designs of diffusers are advisable to reduce pressure loss in both the inserted domes and porous underdrain filters, while for the arm collector filters a redesign of the underdrain should be prioritized.
Brassica napus is an economically important oil crop with significant levels of gene presence absence variation (PAV) between individuals, providing a source of genetic diversity that can be applied for crop improvement. Graph pangenomes are a new format for representing pangenomes and can be used to identify and explore PAV for use in breeding to accelerate crop improvement. Here we present a graph pangenome constructed from ten high-quality B. napus genome assemblies. The pangenome is hosted and visualized online with the pangenome analyzer with chromosomal exploration (Panache) tool. This public resource provides a comprehensive map of genome variability in B. napus that can be applied to accelerate the breeding of this important crop.
Leaf and fruit size are the crucial fitness characters for plant evolution and the agronomic traits for crop yield and quality improvement in watermelon. However, the underlying genes and molecular mechanisms for regulating fruit size and biomass remain elusive. Here, we identified a Citrullus lanatus UDP-rhamnose (Rha)/UDP-galactose (Gal) transporter ClURGT4, which is localized in the Golgi apparatus and has the dual functions of transporting UDP-Gal and UDP-Rha. Loss-of-function clurgt4 mutants resulted in decreased biomass and reduced Gal, Rha, and galacturonic acid (GalA) in cell wall components. Microscopical analysis showed that ClURGT4 promoted leaf and fruit size by modulating cell expansion. Proteomic analysis revealed that several cell wall metabolism-related proteins were changed in clurgt4 mutants. Moreover, protein glycosylation was changed in the mutants, and several of the differentially glycosylated proteins were related to cell wall metabolism. These findings elucidated that ClURGT4 might control leaf and fruit size by affecting cell wall metabolism and provided a novel case for comprehensively revealing the regulatory network of watermelon biomass.
Rapidly urbanizing megaregions are pivotal for achieving land-based climate-mitigation targets, yet the mechanisms governing their ecosystem carbon dynamics remain poorly understood. In this study, we quantify the spatiotemporal evolution of carbon storage in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a globally recognized example of rapid coastal urbanization, and diagnose the drivers behind an emerging regional "carbon deficit". We couple a patch-generating land-use simulation (PLUS) model, a process-based carbon accounting model (InVEST-C), and interpretable machine learning (SHapley Additive exPlanations) to (i) reconstruct land-use and carbon changes from 2000 to 2020 and (ii) project carbon trajectories under three contrasting development pathways to 2030. Total ecosystem carbon storage decreased from 5.45 & times; 108 Mg in 2000 to 5.11 & times; 108 Mg in 2020, with the loss rate during 2010-2020 being 18 times higher than during 2000-2010, coinciding with the phase of megaregional integration. Scenario analysis shows that a business-as-usual "natural development" pathway, which extends 2010-2020 land-use trends without additional policy constraints, would lead to a further loss of 2.79 & times; 107Mg by 2030. In contrast, a policy-driven sustainable development pathway, which constrains construction-land expansion through urban growth boundaries and ecological conservation redlines and actively promotes ecological restoration, could avoid more than 83% of this potential decline. SHAP-based interpretation reveals that topography provides the essential natural capital for carbon storage, whereas population pressure is the decisive driver of loss. We further identify a strong non-linear interaction in which the ecological cost of urban expansion is greatly amplified on steep, ecologically fragile slopes. Our findings provide a transferable framework for diagnosing carbon deficits in rapidly urbanizing megaregions and deliver spatially explicit evidence to support strict ecological conservation redlines (ECRs) and urban growth boundaries (UGBs) that reconcile urban development with climate and ecological resilience.
Reactive nitrogen (Nr) losses to the air (ammonia [NH3], nitrous oxide [N2O]) and to water (nitrate [NO3-] leaching) have been reported for Canadian agricultural systems. However, there is very little information on N use efficiency (NUE) and reactive N losses for major crop types. The objectives of this study were to evaluate the NUE and reactive N losses for spring wheat, grain corn, and canola from 1981 to 2021 using the Canadian Agricultural Nitrogen Budget for Reactive N (CANBNr) model. The provincial averages of residual soil N (RSN) at harvest increased by over 3.8-fold for spring wheat, 2.7-fold for canola (grown in Alberta, Saskatchewan and Manitoba), but decreased by 10.8% for corn (mainly grown in Ontario and Quebec) from 1981 to 1985 to 2017-2021. The RSN was higher in Ontario and Quebec than in the Prairie provinces (Alberta, Saskatchewan and Manitoba). The NUE decreased from 1981 to 1985 to 2017-2021 for spring wheat and canola whereas it increased from 40.2% to 58.4% for corn. Over the same period, reactive N losses increased by 86.9% for spring wheat and 65.8% for canola, whereas it decreased by 6.8% for corn. Over the 41-year period, reactive N losses for corn were higher than for spring wheat and canola due in part to higher N inputs and greater precipitation in corn-production regions in Ontario and Quebec compared to spring wheat production in the Prairie provinces. Management practices should first target corn to reduce N losses to the environment while improving NUE and maintaining yields for all three crops.
As pressure on global food security mounts, controlled-environment agriculture (CEA) demands innovations in monitoring and control. This review surveys computer-vision methods for greenhouse pests and diseases, covering foundations, core techniques, and applications, highlighting early detection, multimodal fusion, and decision support. We trace the evolution from traditional machine vision to AI-driven methods, spanning image acquisition and preprocessing, feature extraction, and semantic understanding. Deep-learning-based pest and disease recognition improves accuracy under controlled conditions, and enables earlier detection of small-target pests and incipient diseases. Multimodal fusion and transfer learning further improve model generalization in complex greenhouse scenes. We also explore computer vision-driven intelligent decision-making systems, including disease and pest assessment, precision intervention, and optimization of integrated prevention and control strategies. Case studies indicate that AI-based vision systems can provide pest and disease warnings several days in advance, which reduces pesticide use and improves production efficiency. However, bottlenecks such as insufficient environmental adaptability, difficulties in learning from small samples, and limited computational resources still hinder broader implementation. Future research should prioritize multisource heterogeneous data fusion, lightweight model design, and the integration of agronomic knowledge. Together, these directions can support monitoring from phenotypes to mechanisms and from individual plants to populations.
Senecavirus A (SVA), a member of the Picornaviridae family, is significance not only as an emerging pathogen in the swine industry but also as a potent oncolytic virus that causes neuroendocrine malignancies. For efficient reproduction, SVA deploys sophisticated tactics to hijack the host translational apparatus and initiate viral protein production via its internal ribosome entry site (IRES) element, although the underlying regulatory processes remain incompletely elucidated. In this study, we identified human antigen R (HuR), an established RNA-binding post-transcriptional regulator, as an upregulator of SVA IRES-driven translation and replication. We show that, upon SVA infection, HuR translocates from the nucleus to the cytoplasm, wherein it specifically promotes viral IRES-mediated translation by facilitating the formation of translation initiation complexes on the viral IRES. We also identified HuR's C-terminal RNA recognition motif as perhaps the most important domain associated with this process. Further mechanistic analyses revealed that the viral proteins VP1, 2AB, and 3Cpro synergistically promote the nucleocytoplasmic translocation of HuR via different mechanisms. Specifically, whereas VP1 physically interacts with HuR, thereby promoting its cytoplasmic localization, 2AB and 3Cpro degrade the nucleoporin 62 and nucleoporin 214 via an autophagy-dependent pathway, thereby resulting in a disruption of nuclear pore complex permeability. Intriguingly, we also show that HuR may represent a conserved factor governing translation and infection in other IRES-containing picornaviruses and flaviviruses. Collectively, our findings in this study reveal a novel regulatory function of HuR in the initiation of IRES-driven translation, thereby highlighting its potential utility as a therapeutic target for broad-spectrum antiviral strategies.
Effector proteins secreted by pathogens play critical roles in suppressing host immunity and facilitating infection. In this study, we identified and characterized the putative disulfide-isomerase effector protein Pb001683 from the clubroot pathogen Plasmodiophora brassicae, a major threat to cruciferous crops. Transgenic plants overexpressing Pb001683 exhibited increased susceptibility to P. brassicae, supporting its role in pathogenicity. Subcellular localization analysis showed that Pb001683 was localized in the endoplasmic reticulum (ER) and nucleus in host cells. Co-immunoprecipitation coupled with mass spectrometry (CoIP-MS) analysis identified 353 candidate host proteins associated with Pb001683, suggesting a broad network of potential host targets. Gene Ontology (GO) enrichment indicated that these proteins were enriched in processes such as carboxylic acid metabolic process and oxoacid metabolic process. The interaction between Pb001683 and BrCYP83A1 was further validated using the split-luciferase assay. In addition, reactive oxygen species (ROS) assays showed that Pb001683-overexpressing plants exhibited elevated ROS levels compared with the control. Collectively, these findings provide new insights into the molecular mechanisms of clubroot pathogenesis and highlight potential strategies for enhancing crop resistance by targeting effector-host interactions.
Organic fertilizer application is a key strategy for reducing soil organic carbon (SOC) loss and achieving sustainable agriculture. In this study, a 91-day laboratory incubation experiment was conducted to evaluate soil carbon balance, defined by cumulative CO2 emissions, changes in SOC during incubation, and carbon inputs, for soils amended with compost manure (M), raw slurry (S), and digestate (D), with inorganic fertilizer (IF) and no fertilizer (NF) as references. Two composite indices were developed to standardize carbon decomposition rate and retention efficiency. All organic amendments increased SOC (p < 0.05), whereas CO2 emissions did not increase linearly with carbon input, following the order S > D > M. In M, both CO2 emissions and SOC accumulation were suppressed despite high mineral N availability and sustained N2O emissions, consistent with anaerobic microsite formation that limited decomposition and resulted in the lowest carbon retention efficiency. In treatment S and D, 81.1% and 98.0% of the applied carbon was either emitted as CO2 or retained as SOC, respectively. The highest carbon retention efficiency with the lowest carbon input was achieved in D, likely due to the compositional changes induced by prior anaerobic digestion. Variations in nitrate, ammonium, and cumulative N2O emissions explained the composite indices more effectively than changes in SOC fractions, indicating that nitrogen dynamics are closely associated with carbon balance when comparing fertilizers with different characteristics. Overall, integrating the decomposition process with soil feedback signals provides a more informative assessment of organic fertilizer performance than relying solely on SOC or CO2 measurements.
Precision spraying is a key technology in modern agriculture to enhance crop productivity, reduce resource waste, and promote environmental friendliness. As an important method of precision spraying, prescription variable-rate spray control technology is able to realize on-demand precision spraying according to the growth conditions and presence of pests, diseases, and weeds, thereby contributing to water savings, pesticide reduction and efficiency improvement. However, it still faces challenges including low positioning accuracy of spraying, poor variable control precision, and system instability. This study proposed a multi-nozzle position dynamic calculation method and a real-time prescription analysis algorithm. A prescription variable-rate spray control system was developed using a hybrid mode, achieving independent nozzle control for prescription-based spraying. Experimental evaluations were conducted on lateral and longitudinal positioning response distances, deposition stabilization distance, spray volume accuracy, and spray uniformity. The results showed that the system achieved a lateral response distance of 0.10 m and a longitudinal response distance of 0.20 m through prescription positioning analysis. The deposition stabilization distance reached 0.50 m in the lateral direction and 0.40 m in the longitudinal direction. The maximum relative error of spray flow rate was 1.80%, and the maximum coefficient of variation (CV) of a single nozzle's flow rate was 3.10%. The CV of the deposition uniformity was 10.69%. This system realized precise variable-rate spray control based on prescription maps, promoting the advancement of environmentally friendly and efficient plant protection technologies.
Endophytic fungi are common in plants and are defined as fungi that inhabit the living tissues of their hosts for part or all of their life cycle without causing disease symptoms in the plant. These fungi can adopt diverse lifestyles, functioning as mutualistic symbionts, commensal organisms, transient colonizers, or dormant saprophytes. It is important to investigate the endophytic communities in laurel plants, as they hold the potential to serve as biological control agents to inhibit plant pathogens, enhancers of the plant's defence mechanisms against pathogens and environmental stressors, or as potential substitutes for active plant compounds in medicinal development. Endophytic isolates obtained from plants of the Lauraceae family were investigated, resulting in the identification of three novel species. These isolates were collected from healthy leaves of Cinnamomum loureiroi, Litsea glutinosa, and Persea americana, and were characterized based on their morphology and multilocus gene sequencing. L. glutinosa, C. loureiroi, and P. americana were the sources of three newly discovered endophytic species: Colletotrichum litseae, Diaporthe lauraceicola, and D. perseicola. Among them, D. perseicola was assigned to the D. sojae species complex, while D. lauraceicola was placed within the D. arecae species complex. Particularly within the fungal genera Diaporthe and Colletotrichum, where species delineation remains complex and often challenging, the addition of new species can improve the resolution of closely related species, thereby reducing taxonomic confusion.
The continuous expansion of pig industry has triggered widespread concern regarding the emission of pollutants from pig excrement on farms. Air pollution, heavy metal (HM), and pathogens caused by pig production have become crucial issues that restrict the sustainable development of pig industry. This review summarizes the classifications and causes of pollutants in pig manure and discusses the effects of dietary nutritional levels, dietary types, and feed additives on the pollutants from pig manure. In summary, low-protein diets containing balanced amino acids can reduce fecal nitrogen content, while the precise supply of minerals can decrease HM emissions. Non-corn and non-soybean diets simultaneously reduce nitrogen and phosphorus emissions, and greenhouse gas production. In addition, functional additives such as probiotics, enzymes, plant extracts, and acidifiers can reduce antibiotic residues and malodor in manure through microbial metabolism, HM complexation, and improvement of gastrointestinal pH. In the future, it will be necessary to analyze the interaction network among nutrition, hosts and microorganisms. It will also be necessary to develop regional adaptation schemes for feed resources and establish a comprehensive evaluation system that links environmental benefits and breeding costs. This will encourage the industrial application of technologies that reduce emissions of fecal pollutants.
Graphical pangenomes and orthologous gene clustering visualisations offer powerful tools to analyse, validate, and extract genomic variations within a species. For crop genomics, integrating these models and algorithms into complex and large genomes is essential to unlock the potential of genomic technology. Barley (Hordeum vulgare L.) is an important crop for the global malting industry, and faces increasing challenges from climate change and rising production demands. In this study, we integrated pangenome graph and orthologous gene clustering tools and applied them to multiple high-quality barley genome assemblies. This approach revealed a detailed and compact representation of genetic diversity, capturing variation from base-pair level to large structural rearrangements across the entire barley pangenome. Using pangenome graph and gene clustering analyses, we identified substantial structural variation (SV) between barley cultivars and landraces and characterised presence–absence variation (PAV) and copy number variation (CNV) patterns. Through hierarchical edge bundles, network plots, and sequence tube maps of genes controlling row-type, we identified haplotype blocks defined by single-nucleotide polymorphisms (SNPs) and InDels. Linearised genome graph visualisation via the Panache pangenome browser further enabled exploration of PAV regions. Together, these visualisation approaches demonstrate how multiple levels of resolution in a graphical pangenome can provide a unified view of genomic variation. The genomic resources and tools developed here expand the toolkit for barley breeding and genetic improvement.
Novel fertilizers have emerged as viable alternatives to conventional fertilizers, effectively enhancing agricultural productivity-especially in paddy soils. However, their application may introduce emerging contaminants including antibiotic resistance genes (ARGs). In this study, we systematically evaluated the impacts of three types of fertilizers-conventional chemical (CC), organic-inorganic composite (OI), and slow-and controlled-release (SC) fertilizers-on soil fertility, microbial communities, and ARGs in paddy fields of Xiong'an New Area, China. SC had the greatest effect on soil fertility, increasing nitrogen, phosphorus, and organic matter levels by 5.25 %-23.15 %. The abundance of ARGs varied with different treatments. Higher levels of ARGs, at 0.28 copies per 16S rRNA gene copy, were found in the surface soils of the CC and OI treatments, whereas lower levels, at 0.26 copies per 16S rRNA gene copy, were observed in the rhizosphere soil of the OI treatment. OI showed the highest abundance of top five subclasses ARGs in rhizosphere soil, indicating enhanced risk of ARG generation, especially in the rhizosphere. Additionally, OI resulted in higher microbial diversities, with key genera including Streptomyces (4.3 %-5.2 %), Bradyrhizobium (2.4 %-2.9 %), and Sphingomonas (2.1 %-2.7 %) showing positive correlattions with most ARGs. Redundancy analysis, network plots, and Mantel test indicated that nutrients explained 58.45 % of the variation in ARGs followed by soil properties. This study demonstrates that SC fertilizers represent a promising alternative for sustainable agriculture, effectively enhancing soil fertility while minimizing ARG dissemination risks. Our findings underscore the importance of implementing stringent contaminant management protocols when substituting conventional fertilizers with novel alternatives.
A novel, rapid, and efficient technique for removing phoxim residues from grapes was developed using microbubbles plasma-activated water (mbPAW). The mbPAW system was generated by utilizing a non-thermal plasma jet as the working gas of the Venturi tube. The phoxim residues in the grapes were quantified using high-performance liquid chromatography. Results indicated that the mbPAW treatment significantly enhanced the removal efficiency of the phoxim residues in grapes (92.82 %) compared with plasma-activated water (PAW) treatment (73.60 %) and microbubble generator without the plasma (mbW) treatment (13.56 %). The improved decontamination efficacy of mbPAW was attributed to its stronger oxidation capability and acidic environment, particularly the increased concentration of hydroxyl radicals, which facilitated phoxim removal from the grapes. Notably, LC-Q-TOF analysis revealed identical degradation products of phoxim (diethyl (Z)-(((cyano (phenyl)methylene)amino)oxy)phosphonate and (Z)-N-hydroxybenzimidoyl cyanide) in both the systems, confirming consistent degradation pathways. Crucially, post-treatment quality assessments revealed no statistically significant differences in grape physicochemical properties, including color, firmness, sugar content, vitamin C concentration, and superoxide dismutase activity. This study establishes mbPAW as a green, residue-free strategy for pesticide decontamination in horticultural products, offering high removal efficiency with minimal adverse impacts on produce quality.
Evaluating soil quality and diagnosing obstacles are critical for improving the ecological environment and maintaining soil productivity. Despite its importance, research on fluvo-aquic paddy soil quality in the Jianghan Plain, China, remains limited, even though the region is experiencing significant degradation due to intensified mechanization and inappropriate human activities. These factors have led to soil quality deterioration in specific areas. In this study, a comprehensive evaluation framework was developed to assess the soil quality of fluvoaquic paddy soils in the Jianghan Plain. Principal component analysis was employed to select key physical and chemical indicators and establish a minimum data set (MDS). The cultivated-layer soil quality index (CLSQI) and the soil obstacle factor model were essential tools for evaluating cultivated-layer soil quality (CLSQ) and identifying its degradation characteristics. The findings revealed that the MDS consisted of six critical indicators: bulk density, organic matter, cation exchange capacity, pH, available phosphorus, and soil compactness. The correlation and Nash efficiency coefficients between CLSQI-MDS and CLSQI-TDS (total data set) were 0.693 and 0.124, respectively, indicating that MDS indicators can effectively replace the total data set (TDS) in evaluating CLSQ evaluation in fluvo-aquic paddy soils. The primary obstacles identified were low organic matter and cation exchange capacity levels, low pH values, and high soil compactness. Mechanical tillage practices and improper fertilization methods were identified as the primary causes of these issues. This study offers a theoretical foundation and empirical data for understanding the degradation characteristics of the cultivated layer in fluvoaquic paddy soils. Moreover, it offers practical recommendations for improving cultivated-layer soil quality in the Jianghan Plain.
Microbial biofilms are surface aggregates of microorganisms encapsulated within a self-secreted polymeric matrix. These aggregates grow, colonize, and thrive on various surfaces in diverse ecosystems, from terrestrial and aquatic to within the human body and on medical implants. Due to their compositional heterogeneity, biofilms exhibit varied physicochemical features, providing an arsenal of desirable properties. As symbiotic structures, microbial biofilms are important players in ecosystems, participating in nutrient replenishment, biogeochemical cycles, and soil structure stabilization. With such potential, especially in enhancing soil integrity and mitigating climate change, it is essential to study various aspects of these films’ structure, behavior, and interactions of the constituting microbial communities with soil. Although complex biofilm-soil interactions have been extensively studied, a comprehensive investigation integrating various aspects is still lacking. This review discusses different facets of microbial biofilms concerning microbial diversity, structural and compositional variations, influence on soil structure and aggregation, impact on soil fertility, and involvement in different biophysical processes. Furthermore, this review also highlights the impacts of climate change, challenges and limitations faced at different levels with respect to biofilms, and suggests the directions of future research on agricultural resources to address these limitations. The primary aim of this article is to emphasize how microbial biofilms benefit the resilience and sustainability of agricultural and food systems.
Gut microbiota plays a pivotal role in protecting lipid metabolism and mitigating the risk of metabolic diseases. Bioactive dietary fibers, notably pectin, have been substantiated to exert extensive health benefits against metabolic diseases through the modification of gut microbiota. However, the mechanism underlying tomato pectin (TP)-mediated regulation of hepatic lipid metabolism and gut microbiota remains unclear. The aim of this research was to investigate the correlation between gut microbiota and metabolic adaptations observed in mice supplemented with TP while being fed a high-fat diet (HFD). The findings in our research revealed that TP supplementation alleviated hepatic steatosis and lipid metabolism disorders induced by HFD. Furthermore, TP administration increased gut microbiota diversity, promoting the abundance of beneficial bacteria (e.g.,Bacteroides, Alistipes, and Alloprevotella) while inhibiting the harmful or conditionally pathogenic bacterial populations (e.g., specific taxa within the Firmicutes phylum and Desulfobacterota). Additionally, TP administration in HFD-fed mice improved the integrity of the intestinal barrier by maintaining the expression of tight junction proteins (Claudin-1, ZO-1, Occludin), mucins (MUC1), and antimicrobial peptides (Defb1, Reg3 beta) compared with the HFD group. Besides, the antibiotic treatment showed an improvement in hepatic lipid metabolism disorders, and the intestinal barrier function of TP was contingent upon the gut microbiota. Overall, our findings demonstrate that TP exerts notable impacts on the gut microbiota composition in this HFD mouse model. Moreover, the improvement of hepatic steatosis and intestinal barrier function is mediated by TP-induced alterations in gut microbiota structure.
