RESUMO
Ulcerative colitis (UC) is an idiopathic, relapsing inflammatory disorder of the colonic mucosa. Pyroptosis contributes significantly to UC. However, the molecular mechanisms of UC remain unexplained. Herein, using transcriptome data and animal experimental validation, we sought to explore pyroptosis-related molecular mechanisms, signature genes, and potential drugs in UC. Gene profiles (GSE48959, GSE59071, GSE53306, and GSE94648) were selected from the Gene Expression Omnibus (GEO) database, which contained samples derived from patients with active and inactive UC, as well as health controls. Gene Set Enrichment Analysis (GSEA), Weighted Gene Co-expression Network Analysis (WGCNA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on microarrays to unravel the association between UC and pyroptosis. Then, differential expressed genes (DEGs) and pyroptosis-related DEGs were obtained by differential expression analyses and the public database. Subsequently, pyroptosis-related DEGs and their association with the immune infiltration landscape were analyzed using the CIBERSORT method. Besides, potential signature genes were selected by machine learning (ML) algorithms, and then validated by testing datasets which included samples of colonic mucosal tissue and peripheral blood. More importantly, the potential drug was screened based on this. And these signature genes and the drug effect were finally observed in the animal experiment. GSEA and KEGG enrichment analyses on key module genes derived from WGCNA revealed a close association between UC and pyroptosis. Then, a total of 20 pyroptosis-related DEGs of UC and 27 pyroptosis-related DEGs of active UC were screened. Next, 6 candidate genes (ZBP1, AIM2, IL1ß, CASP1, TLR4, CASP11) in UC and 2 candidate genes (TLR4, CASP11) in active UC were respectively identified using the binary logistic regression (BLR), least absolute shrinkage and selection operator (LASSO), random forest (RF) analysis and artificial neural network (ANN), and these genes also showed high diagnostic specificity for UC in testing sets. Specially, TLR4 was elevated in UC and further elevated in active UC. The results of the drug screen revealed that six compounds (quercetin, cyclosporine, resveratrol, cisplatin, paclitaxel, rosiglitazone) could target TLR4, among which the effect of quercetin on intestinal pathology, pyroptosis and the expression of TLR4 in UC and active UC was further determined by the murine model. These findings demonstrated that pyroptosis may promote UC, and especially contributes to the activation of UC. Pyroptosis-related DEGs offer new ideas for the diagnosis of UC. Besides, quercetin was verified as an effective treatment for pyroptosis and intestinal inflammation. This study might enhance our comprehension on the pathogenic mechanism and diagnosis of UC and offer a treatment option for UC.
RESUMO
Elevated atmospheric CO2 concentrations [CO2] potentially alter carbon (C) and phosphorus (P) cycles in terrestrial ecosystems. Although numerous field experiments and a few meta-analyses have been conducted, it is still largely unclear how the P cycle affects plant biomass responses under elevated [CO2] globally. Here, we conducted a global synthesis by analyzing 111 studies on the responses of above- and belowground P cycling to elevated [CO2], to examine how changes in the P cycle affect the plant biomass response to elevated [CO2]. Our results show that elevated [CO2] significantly increased plant aboveground biomass (+13 %), stem biomass (+4 %), leaf biomass (+11 %), belowground biomass (+12 %), and the root: shoot ratio (+7 %). Effects of elevated [CO2] on aboveground biomass, belowground biomass, and root: shoot ratio were best explained by plant P uptake. In addition, elevated [CO2]-induced changes in the aboveground P pool, leaf P pool, and leaf P concentration were modulated by ecological drivers, such as ΔCO2, experimental duration, and aridity index. Our findings highlight the importance of plant P uptake for both above- and belowground plant biomass responses under elevated [CO2], which should be considered in future biosphere models to improve predictions of terrestrial carbon-climate feedbacks.