Exploring the Regulatory Effect of LPJZ-658 on Copper Deficiency Combined with Sugar-Induced MASLD in Middle-Aged Mice Based on Multi-Omics Analysis.

Globally, metabolic dysfunction-associated steatotic liver disease (MASLD), previously termed nonalcoholic fatty liver disease (NAFLD), is one of the most common liver disorders and is strongly associated with copper deficiency. To explore the potential effects and mechanisms of Lactiplantibacillus plantarum LPJZ-658, copper deficiency combined with a high-sugar diet-induced MASLD mouse model was utilized in this study. We fed 40-week-old (middle-aged) male C57BL/6 mice a copper-deficient and high-sugar diet for 16 weeks (CuDS), with supplementary LPJZ-658 for the last 6 weeks (CuDS + LPJZ-658). In this study, we measured body weight, liver weight, and serum biochemical markers. Lipid accumulation, histology, lipidomics, and sphingolipid metabolism-related enzyme expression were investigated to analyze liver function. Untargeted metabolomics was used to analyze the serum and the composition and abundance of intestinal flora. In addition, the correlation between differential liver lipid profiles, serum metabolites, and gut flora at the genus level was measured. The results show that LPJZ-658 significantly improves abnormal liver function and hepatic steatosis. The lipidomics analyses and metabolic pathway analysis identified sphingolipid, retinol, and glycerophospholipid metabolism as the most relevant metabolic pathways that characterized liver lipid dysregulation in the CuDS group. Consistently, RT-qPCR analyses revealed that the enzymes catalyzing sphingolipid metabolism that were significantly upregulated in the CuDS group were downregulated by the LPJZ-658 treatment. In addition, the serum metabolomics results indicated that the linoleic acid, taurine and hypotaurine, and ascorbate and aldarate metabolism pathways were associated with CuDS-induced MASLD. Notably, we found that treatment with LPJZ-658 partially reversed the changes in the differential serum metabolites. Finally, LPJZ-658 effectively regulated intestinal flora abnormalities and was significantly correlated with differential hepatic lipid species and serum metabolites. In conclusion, we elucidated the function and potential mechanisms of LPJZ-658 in alleviating copper deficiency combined with sugar-induced middle-aged MASLD and hope this will provide possible treatment strategies for improving MASLD.

A united framework modeling of spatial-temporal characteristics for county-level agricultural carbon emission with an application to Hunan in China.

Effectively tackling extreme climate change requires sound knowledge about carbon emissions and their driving forces. Currently, agricultural carbon emission assessment often deals with its inventory, efficiency, determinants, and response independently, which will leave out the complex interactions among its various components, thus there is a lack of comprehensive, scalable, comparable explanations for agricultural carbon emissions. Herein, we introduce an integrated agricultural carbon emission assessment framework (IEDR): Inventory (I) × Efficiency (E) × Determinants (D) × Response (R), which was then applied to an illustration for the county-level agricultural carbon emissions in Hunan Province, China. Results show that: (1) Agricultural carbon emission inventory (ACEI) increased from 20.06 × 106 tC in 2006 to 21.99 × 106 tC in 2014 and decreased to 19.07 × 106 tC by 2020, depicting a fluctuating trend. Meanwhile, there was remarkable spatial heterogeneity, with higher ACEI in the North and South than in the East and West. (2) Agricultural carbon emission efficiency (ACEE) increased from 0.8520 in 2006 to 0.8992 in 2020, depicting a growing trend driven by technological progress. Spatially distributed in contrast to ACEI, regions with higher ACEE were located in the eastern and western areas. (3) ACEI was negatively correlated with ACEE (-0.657), indicating that increasing ACEE is a key strategy for reducing emissions. (4) The natural environment, rural development level, and policy support had critical impacts on ACEE and ACEI. In particular, the cultivated area and rural water affairs development were significant influences on ACEE and ACEI. Given the externalities of carbon emissions and its important public goods characteristics of the atmosphere, local carbon issues are also global concerns. Therefore, the case study of the IEDR model not only validates this theoretical paradigm and realizes regional responsibility for global carbon reduction but also supports and expands the theoretical and empirical corpus in the field of agricultural carbon emissions and efficiency, providing insights and references for other global regions facing similar challenges.

The Role of Microglia with Mitochondrial Dysfunction and Its Therapeutic Prospects in Alzheimer's Disease.

Alzheimer's disease (AD), a primary cause of dementia, is rapidly emerging as one of the most financially taxing, lethal, and burdensome diseases of the 21st century. Increasing evidence suggests that microglia-mediated neuroinflammation plays a key role in both the initiation and progression of AD. Recently, emerging evidence has demonstrated mitochondrial dysfunction, particular in microglia where precedes neuroinflammation in AD. Multiple signaling pathways are implicated in this process and pharmaceutical interventions are potentially involved in AD treatment. In this review, advance over the last five years in the signaling pathways and pharmaceutical interventions are summarized and it is proposed that targeting the signaling pathways in microglia with mitochondrial dysfunction could represent a novel direction for AD treatment.

Focus on negatively regulated NLRs in inflammation and cancer.

Nucleotide-binding and oligomerization structural domain (NOD)-like receptors (NLRs) play an important role in innate immunity as cytoplasmic pattern recognition receptors (PRRs). Over the past decade, considerable progress has been made in understanding the mechanisms by which NLR family members regulate immune system function, particularly the formation of inflammasome and downstream inflammatory signals. However, recent studies have shown that some members of the NLRs, including Nlrp12, NLRX1, and NLRC3, are important in the negative regulation of inflammatory signaling and are involved in the development of various diseases, including inflammatory diseases and cancer. Based on this, in this review, we first summarize the interactions between canonical and non-canonical nuclear factor-κB (NF-κB) signaling pathways that are mainly involved in NLRs, then highlight the mechanisms by which the above NLRs negatively regulate inflammatory signaling responses as well as their roles in tumor progression, and finally summarize the synthetic and natural derivatives with therapeutic effects on these NLRs, which are considered as potential therapeutic agents for overcoming inflammatory diseases.

Development of a manganese complex hyaluronic acid hydrogel encapsulating stimuli-responsive Gambogic acid nanoparticles for targeted Intratumoral delivery.

Gambogic acid is a natural compound with anticancer properties and is effective for many tumors. But its low water solubility and dose-dependent side effects limit its clinical application. This study aims to develop a novel drug delivery system for intratumoral delivery of gambogic acid. In our experimental study, we propose a new method for encapsulating gambogic acid nanoparticles using a manganese composite hyaluronic acid hydrogel as a carrier, designed for targeted drug delivery to tumors. The hydrogel delivery system is synthesized through the coordination of hyaluronic acid-dopamine (HA-DOPA) and manganese ions. The incorporation of manganese ions serves three purposes:1.To form cross-linked hydrogels, thereby improving the mechanical properties of HA-DOPA.2.To monitor the retention of hydrogels in vivo in real-time using magnetic resonance imaging (MRI).3.To activate the body's immune response. The experimental results show that the designed hydrogel has good biosafety, in vivo sustained release effect and imaging tracking ability. In the mouse CT26 model, the hydrogel drug-loaded group can better inhibit tumor growth. Further immunological analysis shows that the drug-loaded hydrogel group can stimulate the body's immune response, thereby better achieving anti-tumor effects. These findings indicate the potential of the developed manganese composite hyaluronic acid hydrogel as an effective and safe platform for intratumoral drug delivery. The amalgamation of biocompatibility, controlled drug release, and imaging prowess positions this system as a promising candidate for tumor treatment.

Developing Next-Generation Live Attenuated Vaccines for Porcine Epidemic Diarrhea Using Reverse Genetic Techniques.

Porcine epidemic diarrhea virus (PEDV) is the etiology of porcine epidemic diarrhea (PED), a highly contagious digestive disease in pigs and especially in neonatal piglets, in which a mortality rate of up to 100% will be induced. Immunizing pregnant sows remains the most promising and effective strategy for protecting their neonatal offspring from PEDV. Although half a century has passed since its first report in Europe and several prophylactic vaccines (inactivated or live attenuated) have been developed, PED still poses a significant economic concern to the swine industry worldwide. Hence, there is an urgent need for novel vaccines in clinical practice, especially live attenuated vaccines (LAVs) that can induce a strong protective lactogenic immune response in pregnant sows. Reverse genetic techniques provide a robust tool for virological research from the function of viral proteins to the generation of rationally designed vaccines. In this review, after systematically summarizing the research progress on virulence-related viral proteins, we reviewed reverse genetics techniques for PEDV and their application in the development of PED LAVs. Then, we probed into the potential methods for generating safe, effective, and genetically stable PED LAV candidates, aiming to provide new ideas for the rational design of PED LAVs.

A new analytical model for bond strength between corroded steel strand and concrete.

Degradation of bond strength due to corrosion of steel strands is of great importance for serviceability of prestressed concrete structures. An analytical model is proposed to demonstrate the effect of corrosion of steel strand on reduction of bond strength. Corrosion expansion force generated by steel strand corrosion before and after corrosion cracking is firstly estimated. Then, the reduced gripping effect of the concrete, change of friction coefficient between the corroded strand and reduction force on the bearing face are considered in calculating the pre-rib extrusion force. Finally, the enhancement of bond strength due to transverse confinement of stirrups is considered and the ultimate bond strength of corroded steel strand is calculated. Comparison of results between the prediction and experimental result shows the proposed model can be used to reasonably evaluate the bond strength. The prediction result of the bond strength model is affected by the degree of strand corrosion, but almost not by the drawing method.

Factors Influencing Iatrogenic Skin Injury in Neonates and Nursing Strategies.

Objective: Iatrogenic skin injury is a common neonatal skin problem that can have a severe impact on the health and life of newborns. The purpose of this study was to explore the factors influencing iatrogenic skin injury in neonates, identify and correct nursing behaviors that may lead to skin damage, thereby reduce the occurrence of skin damage and protect the health of newborns. Methods: The clinical data of 87 neonates with iatrogenic skin injury admitted to the Department of Neonatology of Shangrao People's Hospital, China, between January and June 2022, were retrospectively collected as a research group. The causes of iatrogenic skin injury were statistically analyzed. 50 neonates without iatrogenic skin injury in the same department during the same period were selected as the control group. The general data of the two groups were contracted, and the independent risk factors affecting iatrogenic skin injury in neonates were explored using multivariate Logistic regression. The corresponding nursing strategies were analyzed. Result: (1) Among the 87 neonates with iatrogenic skin injury, the causes included adhesive dressing stripping (41.38%, 36/87), skin scratch during blue light phototherapy (25.29%, 22/87), diaper dermatitis (20.69%, 18/87), and skin pressure redness related to ventilator and continuous positive airway pressure (CPAP) (12.64%, 11/87). (2) The gestational age, birth weight, length of stay, use of noninvasive mechanical ventilation, orotracheal intubation, gastric tube, PICC catheterization, and skin allergy history of the two groups had statistically significant differences (P < .05). (3) The results of multivariate Logistic regression analysis indicated that the length of stay (OR=2.994, 95% CI=1.341~6.686), orotracheal intubation use (OR=0.015, 95% CI=0.004~0.060), and gastric tube use (OR=17.132, 95% CI=5.231~56.108) were independent risk factors of iatrogenic skin injury in neonates (P < .05). Conclusion: Length of stay, orotracheal intubation use, and gastric tube use are independent risk factors for iatrogenic skin injury in neonates. Hospital stays and unnecessary use of orotracheal intubation and gastric tube should be reduced in future clinical management. Attention should be paid to strengthening skin observation and care, keeping skin dry and clean, and preventing iatrogenic skin injury.

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.

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.

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.

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.

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.

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.