OBJECTIVE To comprehensively evaluate the quality of Sanzi powder from different batches based on 12 components quantitative analysis combined with chemical pattern recognition and entropy weight-TOPSIS method. METHODS The contents of 12 components in 15 batches of Sanzi powder （No. S1-S15） were determined by HPLC-MS/MS， such as ethyl gallate， gallic acid， ferulic acid， corilagin， genipin-1-O-β-D-gentiobioside， toosendanin， geniposide， caffeic acid， methyl deacetylated coumarinate， tannic acid， rutin， quercetin. Cluster analysis （CA）， principal component analysis （PCA）， and orthogonal partial least squares-discriminant analysis （OPLS-DA） were conducted on the assay results. Using variable importance projection （VIP） value＞1 and P＜0.05 as the evaluation criteria， the quality differential markers in Sanzi powder were screened. The entropy weight method was used to calculate the weight value， and TOPSIS method was used to rank the quality of 15 batches of Sanzi powder from superior to inferior. RESULTS The contents of the 12 components were 13.494-24.292， 2 069.608-3 188.100， 1.410-3.616， 1 065.030-2 630.584， 1 404.704-1 838.078， 101.640-354.268， 9 193.720-14 777.854， 1.240-5.060， 148.028-5 541.990， 4 261.422-5 607.438， 107.560- 195.512， 2.226-4.192 μg/g， respectively. The results of CA， PCA and OPLS-DA indicated that 15 batches of Sanzi powder could be clustered into two groups. Specifically， batches S3， S7， S10 and S15 were grouped into one category， and remaining batches were grouped into one category. VIP values of geniposide， quercetin， caffeic acid， and methyl deacetylated coumarinate were all greater than 1， with corresponding P-values less than 0.05. The results of the entropy weight-TOPSIS analysis revealed that methyl deacetylate exhibited the smallest information entropy and the highest weight. The relative closeness degrees of samples S3， S7， S10 and S15 ranged from 0.789 to 0.973， while the remaining samples ranged from 0.054 to 0.172. CONCLUSIONS The contents of 12 components in Sanzi powder could be determined accurately by using HPLC-MS/MS technology. Methyl deacetylated coumarinate， geniposide， quercetin and caffeic acid were identified as the quality differential markers. It was found that the overall quality of samples S3， S7， S10 and S15 were superior to that of other batches. Notably， the quality of Gardeniae Fructus decoction pieces emerges as a critical factor in ensuring the consistency of the preparation’s quality.

ObjectiveTo explore the mechanism of action of Wuwei Shenqintang in improving pulmonary fibrosis by using ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry （UPLC-Q-TOF-MS） for metabolomic analysis of lung tissue and feces. MethodsA rat model with pulmonary fibrosis was established by intratracheal injection of 5 mg·kg^-1 bleomycin. The successfully modeled rats were randomly divided into a blank group， a model group， a prednisone （3.15 mg·kg^-1） group， and low-dose， medium-dose， and high-dose groups of Wuwei Shenqintang （4.586， 9.172， 18.344 g·kg^-1）. The rats were given intragastric administration once a day for 28 consecutive days. Hematoxylin-eosin （HE） staining was used to measure the pathological changes in lung and colon tissue， and Masson staining was used to detect the degree of pulmonary fibrosis. Enzyme-linked immunosorbent assay （ELISA） was used to detect the expression of interleukin-1β （IL-1β）， IL-6， IL-8， tumor necrosis factor-α （TNF-α）， and secretory immunoglobulin A （SIgA） in bronchoalveolar lavage fluid and intestinal mucus. Immunohistochemistry and reverse transcription quantitative polymerase chain reaction （Real-time PCR） were used to detect the expression of type Ⅰ collagen （Col-Ⅰ）， fibronectin （FN）， and alpha smooth muscle actin （α-SMA） in lung tissue. UPLC-Q-TOF-MS was used to study the changes in the metabolic network of lung tissue and feces in rats with pulmonary fibrosis treated with Wuwei Shenqintang， screen potential biomarkers for the treatment of pulmonary fibrosis by Wuwei Shenqintang， and perform pathway enrichment analysis. ResultsCompared with the blank group， the model group showed extensive inflammatory cell infiltration and continuous fibrotic lesions in lung tissue， colonic mucosal damage， and connective tissue hyperplasia. The expression of IL-6， IL-8， IL-1β， TNF-α， and SIgA in bronchoalveolar lavage fluid and intestinal mucus was significantly increased （P<0.01）. The expression of Col-Ⅰ， FN， and α-SMA proteins and mRNAs in lung tissue was significantly upregulated （P<0.01）. Compared with the model group， the groups of Wuwei Shenqintang exhibited significantly reduced inflammatory infiltration and blue collagen deposition in lung tissue， alleviated colonic damage， decreased expression of IL-6， IL-8， IL-1β， TNF-α， and SIgA in bronchoalveolar lavage fluid and intestinal mucus （P<0.01）， and reduced average absorbance values and mRNA expression of Col-Ⅰ， FN， and α-SMA in lung tissue （P<0.05， P<0.01）， with the prednisone group and the medium-dose and high-dose groups of Wuwei Shenqintang showing the most significant effects. The metabolomics results for lung tissue showed that compared with the blank group， the model group had 19 significantly different compounds （P<0.05， P<0.01）. Wuwei Shenqintang could normalize 17 of these compounds compared with the model group （P<0.05， P<0.01）. Fecal metabolomics results showed that compared with those in the blank group， there were 42 compounds with significant differences in the model group （P<0.05， P<0.01）. Compared with the model control group， Wuwei Shenqintang could normalize 41 of these compounds （P<0.05， P<0.01）. The combined analysis results indicated that Wuwei Shenqintang might inhibit pulmonary fibrosis by regulating the biosynthesis of phenylalanine， tyrosine， and tryptophan as well as the retinol metabolism pathway. ConclusionWuwei Shenqintang can ameliorate pulmonary fibrosis， which may be related to the regulation of the "lung-intestine axis".

ObjectiveTo explore the mechanism of action of Wuwei Shenqintang in improving pulmonary fibrosis by using ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry （UPLC-Q-TOF-MS） for metabolomic analysis of lung tissue and feces. MethodsA rat model with pulmonary fibrosis was established by intratracheal injection of 5 mg·kg^-1 bleomycin. The successfully modeled rats were randomly divided into a blank group， a model group， a prednisone （3.15 mg·kg^-1） group， and low-dose， medium-dose， and high-dose groups of Wuwei Shenqintang （4.586， 9.172， 18.344 g·kg^-1）. The rats were given intragastric administration once a day for 28 consecutive days. Hematoxylin-eosin （HE） staining was used to measure the pathological changes in lung and colon tissue， and Masson staining was used to detect the degree of pulmonary fibrosis. Enzyme-linked immunosorbent assay （ELISA） was used to detect the expression of interleukin-1β （IL-1β）， IL-6， IL-8， tumor necrosis factor-α （TNF-α）， and secretory immunoglobulin A （SIgA） in bronchoalveolar lavage fluid and intestinal mucus. Immunohistochemistry and reverse transcription quantitative polymerase chain reaction （Real-time PCR） were used to detect the expression of type Ⅰ collagen （Col-Ⅰ）， fibronectin （FN）， and alpha smooth muscle actin （α-SMA） in lung tissue. UPLC-Q-TOF-MS was used to study the changes in the metabolic network of lung tissue and feces in rats with pulmonary fibrosis treated with Wuwei Shenqintang， screen potential biomarkers for the treatment of pulmonary fibrosis by Wuwei Shenqintang， and perform pathway enrichment analysis. ResultsCompared with the blank group， the model group showed extensive inflammatory cell infiltration and continuous fibrotic lesions in lung tissue， colonic mucosal damage， and connective tissue hyperplasia. The expression of IL-6， IL-8， IL-1β， TNF-α， and SIgA in bronchoalveolar lavage fluid and intestinal mucus was significantly increased （P<0.01）. The expression of Col-Ⅰ， FN， and α-SMA proteins and mRNAs in lung tissue was significantly upregulated （P<0.01）. Compared with the model group， the groups of Wuwei Shenqintang exhibited significantly reduced inflammatory infiltration and blue collagen deposition in lung tissue， alleviated colonic damage， decreased expression of IL-6， IL-8， IL-1β， TNF-α， and SIgA in bronchoalveolar lavage fluid and intestinal mucus （P<0.01）， and reduced average absorbance values and mRNA expression of Col-Ⅰ， FN， and α-SMA in lung tissue （P<0.05， P<0.01）， with the prednisone group and the medium-dose and high-dose groups of Wuwei Shenqintang showing the most significant effects. The metabolomics results for lung tissue showed that compared with the blank group， the model group had 19 significantly different compounds （P<0.05， P<0.01）. Wuwei Shenqintang could normalize 17 of these compounds compared with the model group （P<0.05， P<0.01）. Fecal metabolomics results showed that compared with those in the blank group， there were 42 compounds with significant differences in the model group （P<0.05， P<0.01）. Compared with the model control group， Wuwei Shenqintang could normalize 41 of these compounds （P<0.05， P<0.01）. The combined analysis results indicated that Wuwei Shenqintang might inhibit pulmonary fibrosis by regulating the biosynthesis of phenylalanine， tyrosine， and tryptophan as well as the retinol metabolism pathway. ConclusionWuwei Shenqintang can ameliorate pulmonary fibrosis， which may be related to the regulation of the "lung-intestine axis".

Microbial communities such as those residing in the human gut are highly diverse and complex, and many with important implications for health and diseases. The effects and functions of these microbial communities are determined not only by their species compositions and diversities but also by the dynamic intra- and inter-cellular states at the transcriptional level. Powerful and scalable technologies capable of acquiring single-microbe-resolution RNA sequencing information in order to achieve a comprehensive understanding of complex microbial communities together with their hosts are therefore utterly needed. Here we report the development and utilization of a droplet-based smRNA-seq (single-microbe RNA sequencing) method capable of identifying large species varieties in human samples, which we name smRandom-seq2. Together with a triple-module computational pipeline designed for the bacteria and bacteriophage sequencing data by smRandom-seq2 in four human gut samples, we established a single-cell level bacterial transcriptional landscape of human gut microbiome, which included 29,742 single microbes and 329 unique species. Distinct adaptive response states among species in Prevotella and Roseburia genera and intrinsic adaptive strategy heterogeneity in Phascolarctobacterium succinatutens were uncovered. Additionally, we identified hundreds of novel host-phage transcriptional activity associations in the human gut microbiome. Our results indicated that smRandom-seq2 is a high-throughput and high-resolution smRNA-seq technique that is highly adaptable to complex microbial communities in real-world situations and promises new perspectives in the understanding of human microbiomes.
Humans ; Gastrointestinal Microbiome/genetics* ; Bacteriophages/physiology* ; High-Throughput Nucleotide Sequencing ; Sequence Analysis, RNA/methods* ; Bacteria/virology*

Computational approaches for predicting drug-target interactions (DTIs) are pivotal in advancing drug discovery. Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local and global network information. To comprehensively consider network information, we propose DHGT-DTI, a novel deep learning-based approach for DTI prediction. Specifically, we capture the local and global structural information of the network from both neighborhood and meta-path perspectives. In the neighborhood perspective, we employ a heterogeneous graph neural network (HGNN), which extends Graph Sample and Aggregate (GraphSAGE) to handle diverse node and edge types, effectively learning local network structures. In the meta-path perspective, we introduce a Graph Transformer with residual connections to model higher-order relationships defined by meta-paths, such as "drug-disease-drug", and use an attention mechanism to fuse information across multiple meta-paths. The learned features from these dual perspectives are synergistically integrated for DTI prediction via a matrix decomposition method. Furthermore, DHGT-DTI reconstructs not only the DTI network but also auxiliary networks to bolster prediction accuracy. Comprehensive experiments on two benchmark datasets validate the superiority of DHGT-DTI over existing baseline methods. Additionally, case studies on six drugs used to treat Parkinson's disease not only validate the practical utility of DHGT-DTI but also highlight its broader potential in accelerating drug discovery for other diseases.

Obesity and metabolic dysfunction-associated steatotic liver disease (MASLD) are linked to numerous chronic conditions, including cardiovascular disease, atherosclerosis, chronic kidney disease, and type II diabetes. Previous research identified the natural flavonoid diosmin, derived from Chrysanthemum morifolium, as a regulator of glucose metabolism. However, its effects on lipid metabolism and underlying mechanisms remained unexplored. The AMP-activated protein kinase (AMPK) pathway serves a critical function in glucose and lipid metabolism. The relationship between diosmin and the AMPK pathway has not been previously documented. This investigation examined diosmin's capacity to reduce lipid content through AMPK pathway activation in hepatoblastoma cell line G2 (HepG2) and 3T3-L1 cells. The study revealed that diosmin inhibits lipogenesis, indicating its potential as an anti-obesity agent in obese mice. Moreover, diosmin demonstrated effective MASLD alleviation in vivo. These findings suggest that diosmin may represent a promising therapeutic candidate for treating obesity and MASLD.
Diosmin/administration & dosage* ; Animals ; AMP-Activated Protein Kinases/genetics* ; Humans ; Non-alcoholic Fatty Liver Disease/enzymology* ; Mice ; Obesity/enzymology* ; Hep G2 Cells ; Male ; 3T3-L1 Cells ; Mice, Inbred C57BL ; Signal Transduction/drug effects* ; Lipid Metabolism/drug effects* ; Chrysanthemum/chemistry* ; Lipogenesis/drug effects*

Objective:To investigate the effect and mechanism of SIRT7 in epithelial mesenchymal transformation (EMT) of pancreatic cancer cells.Methods:The pancreatic cancer cells were divided into siControl, siSIRT7, over-expression SIRT7, siSIRT7+siCOL4A1, and siSIRT7+siSLUG groups using siRNA or plasmid transfection. The proliferation, migration and invasion of pancreatic cancer cells were detected by EdU, wound healing assay and Transwell experiments, respectively. The expression of EMT and cancer stem cell (CSC) markers were detected by quantitative real-time reverse transcription polymerase chain reaction assay (qRT-PCR) and western blot. RNA sequencing (RNA-seq) in SIRT7 knockdown PANC-1 cells was performed to explore the signaling pathways and target genes regulated by SIRT7. Then the target genes directly regulated by SIRT7 were identified with quantitative chromatin immunoprecipitation experiment (q-ChIP) and chromatin immunoprecipitation polymerase chain reaction (ChIP-PCR). The expressions of SIRT7 and target genes were detected by immunohistochemical (IHC) in pancreatic cancer tissues, and the correlation between SIRT7 and target gene expression was analyzed using TCGA dataset. The correlation between expression of SIRT7 or target genes and survival was analyzed on KM-plotter website. Finally, GeneMANIA, STRING and ENCORI were used to predict SIRT7-related proteins and miRNAs.Results:EdU assay showed that the cell proliferation rates in SIRT7-overexpressed PANC-1 [(19.33±0.35)%] and BxPC-3 cells [(17.00±1.89)%] were lower than those in the control group [(31.60±1.37)% and (24.33±0.78)%, respectively, P＜0.05]. The proliferation rates of SIRT7-knockdown PANC-1 [(23.94±1.00)% and (27.08±0.97)%] and BxPC-3 cells [(22.00±1.86)% and (25.96±1.61)%] were higher than those of the siControl group [(11.80±1.86)% and (13.42±1.39)%, respectively, P＜0.05]. In PANC-1 cells, the wound healing assay showed that the relative migration rate of SIRT7-overexpression cells [(76.67±2.74)%] was lower than that of control cells [(100.00±2.13)%, P＜0.05]; the relative migration rate of cells with SIRT7 knockdown [(134.22±4.08)% and (199.82±9.20)%, respectively] was higher than that of siControl group [(102.24±3.13)%, P＜0.05]. Compared with the control group, SIRT7 overexpression decreased the number of migrated BxPC-3 cells (45.66±1.69 vs 28.33±2.62, P＜0.05); while SIRT7 knockdown increased these numbers (65.66±2.86 and 82.00±2.94 versus 33.00±0.81, P＜0.01). Transwell experiment revealed that the number of invaded cells in SIRT7 overexpression groups (16.33±2.05 and 34.66±1.69) was lower than that control groups (54.33±4.64 and 58.66±5.90, P＜0.05); with SIRT7 knockdown, the numbers of invaded PANC-1 (63.66±2.49 and 69.33±3.29) and BxPC-3 cells (134.33±3.09 and 181.66±4.02) were higher than those in control groups (35.33±2.49 and 42.00±0.81, P?0.05). Also, SIRT7 knockdown decreased the expressions of epithelial markers and increased the expressions of mesenchymal and CSC markers. RNA-seq analysis showed that SIRT7 was involved in regulating a variety of cancer-related signaling pathways, including the pancreatic cancer pathway and the EMT pathway. Furthermore, SIRT7 could directly bind to the promoter regions of target genes, such as COL4A1 and SLUG. SIRT7 was negatively correlated with the expression and function of COL4A1 and SLUG in pancreatic cancer cells. The expressions of SIRT7, COL4A1, SLUG and SOX2 were verified in pancreatic cancer tissues by IHC. Finally, SIRT7 was predicted to be associated with many proteins and miRNAs based on GeneMANIA, STRING, and ENCORI online tools. Conclusions:SIRT7 can inhibit the EMT of pancreatic cancer cells through transcriptionally inhibiting the expression of target genes, such as COL4A1 and SLUG. Thus, SIRT7 may serve as a potential tumor suppressor gene in pancreatic cancer.

Objective:To investigate the effect and mechanism of SIRT7 in epithelial mesenchymal transformation (EMT) of pancreatic cancer cells.Methods:The pancreatic cancer cells were divided into siControl, siSIRT7, over-expression SIRT7, siSIRT7+siCOL4A1, and siSIRT7+siSLUG groups using siRNA or plasmid transfection. The proliferation, migration and invasion of pancreatic cancer cells were detected by EdU, wound healing assay and Transwell experiments, respectively. The expression of EMT and cancer stem cell (CSC) markers were detected by quantitative real-time reverse transcription polymerase chain reaction assay (qRT-PCR) and western blot. RNA sequencing (RNA-seq) in SIRT7 knockdown PANC-1 cells was performed to explore the signaling pathways and target genes regulated by SIRT7. Then the target genes directly regulated by SIRT7 were identified with quantitative chromatin immunoprecipitation experiment (q-ChIP) and chromatin immunoprecipitation polymerase chain reaction (ChIP-PCR). The expressions of SIRT7 and target genes were detected by immunohistochemical (IHC) in pancreatic cancer tissues, and the correlation between SIRT7 and target gene expression was analyzed using TCGA dataset. The correlation between expression of SIRT7 or target genes and survival was analyzed on KM-plotter website. Finally, GeneMANIA, STRING and ENCORI were used to predict SIRT7-related proteins and miRNAs.Results:EdU assay showed that the cell proliferation rates in SIRT7-overexpressed PANC-1 [(19.33±0.35)%] and BxPC-3 cells [(17.00±1.89)%] were lower than those in the control group [(31.60±1.37)% and (24.33±0.78)%, respectively, P＜0.05]. The proliferation rates of SIRT7-knockdown PANC-1 [(23.94±1.00)% and (27.08±0.97)%] and BxPC-3 cells [(22.00±1.86)% and (25.96±1.61)%] were higher than those of the siControl group [(11.80±1.86)% and (13.42±1.39)%, respectively, P＜0.05]. In PANC-1 cells, the wound healing assay showed that the relative migration rate of SIRT7-overexpression cells [(76.67±2.74)%] was lower than that of control cells [(100.00±2.13)%, P＜0.05]; the relative migration rate of cells with SIRT7 knockdown [(134.22±4.08)% and (199.82±9.20)%, respectively] was higher than that of siControl group [(102.24±3.13)%, P＜0.05]. Compared with the control group, SIRT7 overexpression decreased the number of migrated BxPC-3 cells (45.66±1.69 vs 28.33±2.62, P＜0.05); while SIRT7 knockdown increased these numbers (65.66±2.86 and 82.00±2.94 versus 33.00±0.81, P＜0.01). Transwell experiment revealed that the number of invaded cells in SIRT7 overexpression groups (16.33±2.05 and 34.66±1.69) was lower than that control groups (54.33±4.64 and 58.66±5.90, P＜0.05); with SIRT7 knockdown, the numbers of invaded PANC-1 (63.66±2.49 and 69.33±3.29) and BxPC-3 cells (134.33±3.09 and 181.66±4.02) were higher than those in control groups (35.33±2.49 and 42.00±0.81, P?0.05). Also, SIRT7 knockdown decreased the expressions of epithelial markers and increased the expressions of mesenchymal and CSC markers. RNA-seq analysis showed that SIRT7 was involved in regulating a variety of cancer-related signaling pathways, including the pancreatic cancer pathway and the EMT pathway. Furthermore, SIRT7 could directly bind to the promoter regions of target genes, such as COL4A1 and SLUG. SIRT7 was negatively correlated with the expression and function of COL4A1 and SLUG in pancreatic cancer cells. The expressions of SIRT7, COL4A1, SLUG and SOX2 were verified in pancreatic cancer tissues by IHC. Finally, SIRT7 was predicted to be associated with many proteins and miRNAs based on GeneMANIA, STRING, and ENCORI online tools. Conclusions:SIRT7 can inhibit the EMT of pancreatic cancer cells through transcriptionally inhibiting the expression of target genes, such as COL4A1 and SLUG. Thus, SIRT7 may serve as a potential tumor suppressor gene in pancreatic cancer.

Background::Liver cancer remains the sixth most commonly diagnosed cancer and the third leading cause of cancer-related deaths worldwide, causing a heavy burden globally. An updated assessment of the global epidemiology of the liver cancer burden that addresses geographical disparities is necessary to better understand and promote healthcare delivery.Methods::Data were extracted from the GLOBOCAN 2022 database, including the number, crude, and age-standardized rates of incidence and mortality at the global, country, continent, and human development index (HDI) regional levels. Age-standardized rates (incidence and mortality) per 100,000 person-years were adjusted based on the Segi-Doll World standard population. The mortality-to-incidence ratios (MIR) for each region and country were calculated. The HDI and gross national income (GNI) for 2022 were obtained, and a Pearson correlation analysis was conducted with the incidence, mortality, and MIR.Results::In 2022, approximately 866,136 new liver cancer cases and 758,725 related deaths were recorded worldwide, with a global MIR of 0.86. Males had a disproportionately higher burden than females across all levels, and the highest burden was observed in the elderly population. Geographically, the regions with the highest incidence rates included Micronesia, Eastern Asia, and Northern Africa, and the regions with the highest mortality rates included Northern Africa, Southeastern Asia, Eastern Asia, and Micronesia. Notably, Mongolia had a strikingly high burden compared to other countries. The highest MIR was observed in North America and the lowest in Africa. Negative associations of HDI and GNI with liver cancer mortality and MIR were identified, irrespective of sex.Conclusions::The current liver cancer burden underscores the presence of remarkable geographic heterogeneity, which is particularly evident across countries with varying HDI levels, highlighting the urgent need to prioritize health accessibility and availability to achieve health inequities.