ObjectiveSpinal cord injury (SCI) directly impairs the regulatory function of the autonomic nervous system, induces intestinal dysfunction, and significantly reduces patients’ quality of life. Preclinical studies have shown that electroacupuncture (EA) therapy can regulate the brain-gut axis and is used to treat central nervous system diseases such as major depressive disorder, Alzheimer’s disease and Parkinson’s disease. Recent research has established that fecal microbiota transplantation (FMT) from EA-treated SCI rats restored intestinal motility and colonic morphology. However, it remains unclear whether the regulation of gut microbiota by EA therapy directly contributes to neural repair after SCI. This study aims to explore whether gut microbiota mediates the neuroprotective effect of EA in the treatment of SCI and its possible mechanism. MethodsThe study employed RNA transcriptome analysis of spinal cord tissue to characterize gene expression profiles and to identify key signaling pathways following EA treatment for SCI. Hematoxylin-Eosin (HE) staining and Nissl staining were used to observe the morphological changes in spinal cord tissue. Western blot (WB) and enzyme-linked immunosorbent assay (ELISA) were applied to detect the effects of EA on the expression of proteins related to nucleotide-binding domain leucine-rich repeat and pyrin domain-containing receptor 3 (NLRP3) -dependent pyroptosis. Using 16S rDNA sequencing, the study observed alterations in gut microbiota diversity and community composition in SCI rats. Prior to establishing SCI models, rats were pretreated with an antibiotic cocktail to induce gut dysbiosis, and the effects on intestinal function and spinal cord neural repair were evaluated. FMT was performed to investigate the regulatory effects of post-EA FMT on motor function, general status, liver and spleen indices, and NLRP3-mediated pyroptosis in SCI rats. ResultsEA improved motor function and reduced regulated neuronal cell death in SCI rats. Transcriptomic analysis demonstrated the activation of immune- and inflammation-related pathways post-SCI, including NOD-like receptors, nuclear factor-kappa B(NF-κB), and Toll-like receptor (TLR) pathways. EA primarily influenced intestinal inflammation and autoimmune functions. 16S rDNA sequencing illustrated that EA did not alter the diversity of gut microbiota. However, EA altered the gut microbiota composition in SCI rats, increasing Lactobacillus and Akkermansia genera while rebalancing the Firmicutes/Bacteroidetes ratio. Furthermore, depletion of gut microbiota by antibiotics disrupted the intestinal barrier, reduced the expression of intestinal barrier proteins Zonula Occludens-1 (ZO-1) and Occludin, elevated serum lipopolysaccharide-binding protein (LBP) levels, exacerbated spinal cord tissue damage, and hindered motor function recovery in SCI rats. FMT from donors treated with EA reduced LBP levels in the intestine, blood, and spinal cord of rats, inhibited the TLR4 myeloid differentiation primary response protein 88 (MyD88)-NF‑κB pathway and NLRP3-dependent pyroptosis, and improved motor function. On the other hand, FMT treatment resulted in decreased body weight and food intake, whereas FMT using EA-treated donors effectively alleviated these alterations. ConclusionEA effectively alleviated neuroinflammatory responses in rats with SCI, primarily through regulating the gut microbiota and suppressing the NLRP3-dependent pyroptosis signaling pathway.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

ObjectiveSpinal cord injury (SCI) directly impairs the regulatory function of the autonomic nervous system, induces intestinal dysfunction, and significantly reduces patients’ quality of life. Preclinical studies have shown that electroacupuncture (EA) therapy can regulate the brain-gut axis and is used to treat central nervous system diseases such as major depressive disorder, Alzheimer’s disease and Parkinson’s disease. Recent research has established that fecal microbiota transplantation (FMT) from EA-treated SCI rats restored intestinal motility and colonic morphology. However, it remains unclear whether the regulation of gut microbiota by EA therapy directly contributes to neural repair after SCI. This study aims to explore whether gut microbiota mediates the neuroprotective effect of EA in the treatment of SCI and its possible mechanism. MethodsThe study employed RNA transcriptome analysis of spinal cord tissue to characterize gene expression profiles and to identify key signaling pathways following EA treatment for SCI. Hematoxylin-Eosin (HE) staining and Nissl staining were used to observe the morphological changes in spinal cord tissue. Western blot (WB) and enzyme-linked immunosorbent assay (ELISA) were applied to detect the effects of EA on the expression of proteins related to nucleotide-binding domain leucine-rich repeat and pyrin domain-containing receptor 3 (NLRP3) -dependent pyroptosis. Using 16S rDNA sequencing, the study observed alterations in gut microbiota diversity and community composition in SCI rats. Prior to establishing SCI models, rats were pretreated with an antibiotic cocktail to induce gut dysbiosis, and the effects on intestinal function and spinal cord neural repair were evaluated. FMT was performed to investigate the regulatory effects of post-EA FMT on motor function, general status, liver and spleen indices, and NLRP3-mediated pyroptosis in SCI rats. ResultsEA improved motor function and reduced regulated neuronal cell death in SCI rats. Transcriptomic analysis demonstrated the activation of immune- and inflammation-related pathways post-SCI, including NOD-like receptors, nuclear factor-kappa B(NF-κB), and Toll-like receptor (TLR) pathways. EA primarily influenced intestinal inflammation and autoimmune functions. 16S rDNA sequencing illustrated that EA did not alter the diversity of gut microbiota. However, EA altered the gut microbiota composition in SCI rats, increasing Lactobacillus and Akkermansia genera while rebalancing the Firmicutes/Bacteroidetes ratio. Furthermore, depletion of gut microbiota by antibiotics disrupted the intestinal barrier, reduced the expression of intestinal barrier proteins Zonula Occludens-1 (ZO-1) and Occludin, elevated serum lipopolysaccharide-binding protein (LBP) levels, exacerbated spinal cord tissue damage, and hindered motor function recovery in SCI rats. FMT from donors treated with EA reduced LBP levels in the intestine, blood, and spinal cord of rats, inhibited the TLR4 myeloid differentiation primary response protein 88 (MyD88)-NF‑κB pathway and NLRP3-dependent pyroptosis, and improved motor function. On the other hand, FMT treatment resulted in decreased body weight and food intake, whereas FMT using EA-treated donors effectively alleviated these alterations. ConclusionEA effectively alleviated neuroinflammatory responses in rats with SCI, primarily through regulating the gut microbiota and suppressing the NLRP3-dependent pyroptosis signaling pathway.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The current study aimed to clarify the roles of apolipoprotein A5 (ApoA5) and milk fat globule-epidermal growth factor 8 (Mfge8) in regulating myocardial lipid deposition and the regulatory relationship between them. The serum levels of ApoA5 and Mfge8 in obese and healthy people were compared, and the obesity mouse model induced by the high-fat diet (HFD) was established. In addition, primary cardiomyocytes were purified and identified from the hearts of suckling mice. The 0.8 mmol/L sodium palmitate treatment was used to establish the lipid deposition cardiomyocyte model in vitro. ApoA5-overexpressing adenovirus was used to observe its effects on cardiac function and lipids. The expressions of the fatty acid uptake-related molecules and Mfge8 on transcription or translation levels were detected. Co-immunoprecipitation was used to verify the interaction between ApoA5 and Mfge8 proteins. Immunofluorescence was used to observe the co-localization of Mfge8 protein with ApoA5 or lysosome-associated membrane protein 2 (LAMP2). Recombinant rMfge8 was added to cardiomyocytes to investigate the regulatory mechanism of ApoA5 on Mfge8. The results showed that participants in the simple obesity group had a significant decrease in serum ApoA5 levels (P < 0.05) and a significant increase in Mfge8 levels (P < 0.05) in comparison with the healthy control group. The adenovirus treatment successfully overexpressed ApoA5 in HFD-fed obese mice and palmitic acid-induced lipid deposition cardiomyocytes, respectively. ApoA5 reduced the weight of HFD-fed obese mice (P < 0.05), shortened left ventricular isovolumic relaxation time (IVRT), increased left ventricular ejection fraction (LVEF), and significantly reduced plasma levels of triglycerides (TG) and cholesterol (CHOL) (P < 0.05). In myocardial tissue and cardiomyocytes, the overexpression of ApoA5 significantly reduced the deposition of TG (P < 0.05), transcription of fatty acid translocase (FAT/CD36) (P < 0.05), fatty acid-binding protein (FABP) (P < 0.05), and fatty acid transport protein (FATP) (P < 0.05), and protein expression of Mfge8 (P < 0.05), while the transcription levels of Mfge8 were not significantly altered (P > 0.05). In vitro, the Mfge8 protein was captured using ApoA5 as bait protein, indicating a direct interaction between them. Overexpression of ApoA5 led to an increase in co-localization of Mfge8 with ApoA5 or LAMP2 in cardiomyocytes under lipid deposition status. On this basis, exogenous added recombinant rMfge8 counteracted the improvement of lipid deposition in cardiomyocytes by ApoA5. The above results indicate that the overexpression of ApoA5 can reduce fatty acid uptake in myocardial cells under lipid deposition status by regulating the content and cellular localization of Mfge8 protein, thereby significantly reducing myocardial lipid deposition and improving cardiac diastolic and systolic function.
Animals ; Humans ; Mice ; Myocytes, Cardiac/metabolism* ; Obesity/physiopathology* ; Male ; Apolipoprotein A-V/blood* ; Lipid Metabolism/physiology* ; Milk Proteins/blood* ; Myocardium/metabolism* ; Diet, High-Fat ; Antigens, Surface/physiology* ; Mice, Inbred C57BL ; Cells, Cultured ; Female

In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial products of Reduning Injection. The batch reporting rate was estimated according to the report of Reduning Injection in the direct adverse drug reaction(ADR) reporting system of the drug marketing authorization holder of the Center for Drug Reevaluation of the National Medical Products Administration(National Center for ADR Monitoring) from August 2021 to August 2022. According to the batch reporting rate, the samples of Reduning Injection were classified into those with potential risks and those being safe. No processing, random oversampling(ROS), random undersampling(RUS), and synthetic minority over-sampling technique(SMOTE) were then employed to balance the unbalanced data. After the samples were classified according to appropriate sampling methods, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA), uninformative variables elimination(UVE), and genetic algorithm(GA) were respectively adopted to screen the features of spectral data. Then, support vector machine(SVM), logistic regression(LR), k-nearest neighbors(KNN), naive bayes(NB), random forest(RF), and artificial neural network(ANN) were adopted to establish the risk prediction models. The effects of the four feature extraction methods on the accuracy of the models were compared. The optimal method was selected, and bayesian optimization was performned to optimize the model parameters to improve the accuracy and robustness of model prediction. To explore the correlations between potential risks of clinical use and quality test data, TreeNet was employed to identify potential quality parameters affecting the clinical safety of Reduning Injection. The results showed that the models established with the SVM, LR, KNN, NB, RF, and ANN algorithms had the F1 scores of 0.85, 0.85, 0.86, 0.80, 0.88, and 0.85 and the accuracy of 88%, 88%, 88%, 85%, 91%, and 88%, respectively, and the prediction time was less than 5 s. The results indicated that the established models were accurate and efficient. Therefore, near infrared spectroscopy combined with machine learning algorithms can quickly predict the potential risks of clinical use of Reduning Injection in batches. Three key quality parameters that may affect clinical safety were identified by TreeNet, which provided a scientific basis for improving the safety standards of Reduning Injection.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Machine Learning ; Algorithms ; Humans ; Quality Control

To clarify the occurrence and distribution of broad bean wilt virus 2(BBWV2) on Rehmannia glutinosa, this study collected 87 R. glutinosa samples with typical symptoms of viral disease such as chlorosis and crumple from Wenxian county and Wuzhi county in Jiaozuo city, Henan province and Qiaocheng district in Bozhou city, Anhui province. The BBWV2 CP target band was amplified from 37 R. glutinosa samples by RT-PCR technology. The total detection rate reached 42.5%, among which 43.0% was detected in samples from Henan province. The detection rate in samples from Anhui province was 37.5%. 37 BBWV2 CP sequences were obtained by cloning and sequencing of BBWV2 positive samples(data has been submitted to GenBank, accession numbers: PP407959-PP407995), and the sequence analysis of these CP sequences with 91 other BBWV2 isolates in GenBank showed a high genetic diversity with a consistency rate of 70.8%-100%. Meanwhile, phylogenetic analysis showed that BBWV2 could be divided into three groups according to CP sequences, among which the BBWV2 in R. glutinosa isolates obtained in this study were all located in group 3. This study identified the differences in the occurrence, distribution, and genetic diversity of BBWV2 in R. glutinosa from Henan province and Anhui province and provided a theoretical basis for the prevention and control of BBWV2.
Rehmannia/virology* ; Phylogeny ; Plant Diseases/virology* ; China ; Molecular Sequence Data ; Fabavirus/classification*

This study primarily investigates the effect of Liuwei Dihuang Pills on the activation and proliferation of female germline stem cells(FGSCs) in the ovaries and cortex of mice with premature ovarian failure(POF), and how it improves ovarian function. ICR mice were randomly divided into the control group, model group, Liuwei Dihuang Pills group, Liuwei Dihuang Pills double-dose group, and estradiol valerate group. A mouse model of POF was established by intraperitoneal injection of cyclophosphamide. After successful modeling, the mice were treated with Liuwei Dihuang Pills or estradiol valerate for 28 days. Vaginal smears were prepared to observe the estrous cycle and body weight. After the last administration, mice were sacrificed and sampled. Serum levels of estradiol(E_2), follicle-stimulating hormone(FSH), luteinizing hormone(LH), and anti-Müllerian hormone(AMH) were measured by enzyme-linked immunosorbent assay(ELISA). Hematoxylin-eosin(HE) staining was used to observe ovarian morphology and to count follicles at all stages to evaluate ovarian function. Immunohistochemistry was used to detect the expression of mouse vasa homolog(MVH), a marker of ovarian FGSCs. Immunofluorescence staining, using co-labeling of MVH and proliferating cell nuclear antigen(PCNA), was used to detect the expression and localization of specific markers of FGSCs. Western blot was employed to assess the protein expression of MVH, octamer-binding transcription factor 4(Oct4), and PCNA in the ovaries. The results showed that compared with the control group, the model group exhibited disordered estrous cycles, decreased ovarian index, increased atretic follicles, and a reduced number of follicles at all stages. FSH and LH levels were significantly elevated, while AMH and E_2 levels were significantly reduced, indicating the success of the model. After treatment with Liuwei Dihuang Pills or estradiol valerate, hormone levels improved, the number of atretic follicles decreased, and the number of follicles at all stages increased. MVH marker protein and PCNA proliferative protein expression in ovarian tissue also increased. These results suggest that Liuwei Dihuang Pills regulate estrous cycles and hormone disorders in POF mice, promote the proliferation of FGSCs, improve follicular development in POF mice, and enhance ovarian function.
Animals ; Female ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Cell Proliferation/drug effects* ; Mice, Inbred ICR ; Ovary/cytology* ; Primary Ovarian Insufficiency/genetics* ; Follicle Stimulating Hormone/metabolism* ; Humans ; Anti-Mullerian Hormone/blood* ; Antineoplastic Agents/adverse effects* ; Luteinizing Hormone/metabolism* ; Cyclophosphamide/adverse effects*

This study investigated the therapeutic effects and mechanisms of Modified Yiyi Fuzi Baijiang Powder on ulcerative colitis(UC) in rats from the perspective of dampness. SD rats were randomly allocated into six groups(n=10): control, model, mesalazine, and Modified Yiyi Fuzi Baijiang Powder at low(3.96 g·kg~(-1)·d~(-1)), medium(7.92 g·kg~(-1)·d~(-1)), and high(15.84 g·kg~(-1)·d~(-1)) doses. UC was induced in all groups except the control by administration with 3% dextran sulfate sodium(DSS) solution for 7 days. The disease activity index(DAI) was recorded, and the colon tissue was collected for analysis. Histopathological changes were assessed by hematoxylin-eosin staining. Serum levels of D-lactic acid(D-LA) and diamine oxidase(DAO) were measured by ELISA. Immunohistochemistry and PCR were employed to evaluate the expression of aquaporins(AQP3, AQP4) and tight junction proteins [zonula occludens-1(ZO-1) and occludin] at both protein and mRNA levels. Compared with the control group, the model group showed an increased DAI scores(P<0.05), intestinal mucosal damage, elevated serum levels of DAO and D-LA(P<0.05), and decreased expression of AQP3, AQP4, ZO-1, and occludin(P<0.05). Treatment with Modified Yiyi Fuzi Baijiang Powder reduced the DAI scores(P<0.05), lowered the serum levels of D-LA and DAO(P<0.05), and upregulated the expression of AQP3, AQP4, ZO-1, and occludin at both protein and mRNA levels compared with the model group. These findings suggest that Modified Yiyi Fuzi Baijiang Powder exerts therapeutic effects on UC by reducing the intestinal mucosal permeability, promoting colonic mucosal repair, and regulating abnormal intestinal water metabolism, which may involve the upregulation of AQP3 and AQP4 expression.
Animals ; Colitis, Ulcerative/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Rats ; Intestinal Mucosa/metabolism* ; Male ; Aquaporin 3/metabolism* ; Aquaporin 4/metabolism* ; Permeability/drug effects* ; Humans ; Powders ; Intestinal Barrier Function

This article aims to explore the effect and mechanism of Liujunzi Pills on the intestinal microbiota of rats with spleen Qi deficiency syndrome. The raw Rhei Radix et Rhizoma water extract(1 g·mL~(-1)) was used to prepare spleen Qi deficiency rat models. A total of 44 SD male rats were randomly divided into a control group, a model group, Liujunzi Pills groups at high(3.24 g·kg~(-1)), medium(1.62 g·kg~(-1)), low(0.81 g·kg~(-1)) doses, and Shenling Baizhu San(2.50 g·kg~(-1)) group. The drug effect was evaluated by observing the following aspects: spleen index, fecal water content, body weight, and intestinal propulsion index. Gut microbiota analysis and 16S rRNA gene sequencing were conducted on feces. Enzyme-linked immunosorbent assay(ELISA) and UV spectrophotometry were used to detect interleukin-1β(IL-1β) and adenosine triphosphate(ATP) levels in small intestine tissues. Hematoxylin-eosin staining and transmission electron microscopy were employed to observe changes in intestinal pathology and microstructure. The results show that, compared with the control group, fecal moisture content is significantly increased while spleen index, body weight, and intestinal propulsion index are significantly reduced in rats of the model group, indicating the successful establishment of the model. The above symptoms can be improved by both Shenling Baizhu San and Liujunzi Pills. Compared with the control group, in the model group, the gut microbiota abundance is changed with an unbalanced development: the abundance of beneficial bacteria within the Bacteroidetes phylum is reduced, accompanied by a significantly decreased Shannon index, and reduced signal levels of nicotinamide adenine dinucleotide phosphate(NADPH)-related enzymes relevant to mitochondria. However, Liujunzi Pills and Shenling Baizhu San can significantly improve the Bacteroidetes phylum abundance in gut microbiota, microbial diversity, and NADPH activity in the model group. Additionally, compared with the control group, the ATP level is decreased and the IL-1β level is increased in small intestinal tissues of the model group, with shorter small intestinal epithelial villi and decreased mitochondrial number. The above symptoms can be improved by Liujunzi Pills and Shenling Baizhu San. In conclusion, Liujunzi Pills can treat spleen Qi deficiency syndrome by enhancing mitochondrial function to regulate gut microbiota balance and diversity.
Animals ; Gastrointestinal Microbiome/drug effects* ; Drugs, Chinese Herbal/pharmacology* ; Male ; Rats, Sprague-Dawley ; Rats ; Qi ; Spleen/metabolism* ; Splenic Diseases/metabolism* ; Humans ; Interleukin-1beta/genetics* ; Bacteria/drug effects* ; Feces/microbiology* ; Adenosine Triphosphate/metabolism*