ObjectiveTo explore the typical syndromes and their characteristic of symptoms and signs with high diagnostic value in patients with metabolic syndrome （MS）. MethodsTraditional Chinese medicine （TCM） diagnostic information was collected from 135 MS patients. Syndrome element differentiation and latent class analysis （LCA） were applied to identify the major TCM syndromes in MS patients. Symptoms were analyzed based on the differentiated syndromes， and a binary logistic regression model was constructed to determine symptoms and signs with high diagnostic value. ResultsA total of 135 MS patients were included， involving 163 symptoms and signs with a total frequency of 1749； twenty-three syndrome elements were extracted， 367 times frequency in total， among which 8 syndrome elements occurred ≥10 times with 323 frequencies （88.01% of the total）. These included location-related elements such as kidney （48 times）， spleen （14 times）， and stomach （14 times）， and nature-related elements such as phlegm （71 times）， yin deficiency （64 times）， dampness （57 times）， heat （42 times）， and qi deficiency （13 times）. Based on LCA， the 135 patients were categorized into two groups distinguished by the syndrome elements of dampness and phlegm， forming the "phlegm-dampness syndrome" as the major syndrome type. Nine high-frequency symptoms and signs associated with the phlegm-dampness syndrome were identified，i.e. obesity （39 times）， greasy coating （38 times）， slippery pulse （33 times）， white coating （31 times）， preference for fatty and heavy foods （30 times）， excessive urination （30 times）， fatigue and lack of strength （29 times）， wiry pulse （25 times）， and dark red tongue （25 times）. A binary logistic regression model was constructed combining these nine symptoms and signs with the LCA classification results， ultimately identifying obesity， greasy coating， fatigue and lack of strength， and white coating as independent factors associated with the phlegm-dampness syndrome in MS patients （P＜0.05）. ConclusionThe major TCM syndrome in MS patients is phlegm-dampness syndrome， and obesity， greasy coating， fatigue and lack of strength， and white coating are the typical symptoms and signs for diagnosing phlegm-dampness syndrome in MS patients.

OBJECTIVES: To explore the mechanism of transforming growth factor-β1 (TGF-β1) induce renal fibrosis. METHODS: Renal fibroblast NRK-49F cells treated with and without TGF-β1 were subjected to RNA-seq analysis. DESeq2 was used for analysis. Differentially expressed genes were screened with the criteria of false discovery rate<0.05 and l o g 2 F C >1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for differentially expressed genes. Genes encoding transcription factors were further screened for differential expression genes. Then, the expression of these genes during renal fibrosis was verified using unilateral ureteral obstruction (UUO)-induced mouse renal fibrosis model and a public gene expression dataset (GSE104954). RESULTS: After TGF-β1 treatment for 6, 12 and 24 h, 552, 1209 and 1028 differentially expressed genes were identified, respectively. GO analysis indicated that these genes were significantly enriched in development, cell death, and cell migration. KEGG pathway analysis showed that in the early stage of TGF-β1 induction (TGF-β1 treatment for 6 h), the changes in Hippo, TGF-β and Wnt signaling pathways were observed, while in the late stage of TGF-β1 induction (TGF-β1 treatment for 24 h), the changes of extracellular matrix-receptor interaction, focal adhesion and adherens junction were mainly enriched. Among the 291 up-regulated differentially expressed genes treated with TGF-β1 for 6 h, 13 genes (Snai1, Irf8, Bhlhe40, Junb, Arid5a, Vdr, Lef1, Ahr, Foxo1, Myc, Tcf7, Foxc2, Glis1) encoded transcription factors. Validation in a cell model showed that TGF-β1 induced expression of 9 transcription factors (encoded by Snai1, Irf8, Bhlhe40, Junb, Arid5a, Vdr, Lef1, Myc, Tcf7), while the expression levels of the other 4 genes did not significantly change after TGF-β1 treatment. Validation results in UUO-induced mouse renal fibrosis model showed that Snai1, Irf8, Bhlhe40, Junb, Arid5a, Myc and Tcf7 were up-regulated after UUO, Vdr was down-regulated and there was no significant change in Lef1. Validation based on the GSE104954 dataset showed that IRF8 was significantly overexpressed in the renal tubulointerstitium of patients with diabetic nephropathy or IgA nephropathy, MYC was highly expressed in diabetic nephropathy, and the expressions of the other 7 genes were not significantly different compared with the control group. CONCLUSIONS TGF-β1 induces differentially expressed genes in renal fibroblasts, among which Irf8 and Myc were identified as potential targets of chronic kidney disease and renal fibrosis.
Mice ; Animals ; Humans ; Transforming Growth Factor beta1/metabolism* ; Diabetic Nephropathies/pathology* ; Transcriptome ; Signal Transduction ; Kidney ; Ureteral Obstruction/pathology* ; Fibrosis ; Interferon Regulatory Factors ; Transforming Growth Factor beta/metabolism* ; DNA-Binding Proteins/metabolism* ; Transcription Factors/metabolism*