BMSCs improve TNBS-induced colitis in rats by inducing Treg differentiation by expressing PD-L1

Quercetin ameliorates ulcerative colitis by restoring the balance of M2/M1 and repairing the intestinal barrier via downregulating cGAS‒STING pathway

Macrophage polarization is closely associated with the pathogenesis of ulcerative colitis (UC). Quercetin, a flavonoid, has shown promise as a treatment for inflammatory diseases, but its specific mechanism of action remains unclear. This study investigates whether quercetin can regulate intestinal macrophage polarization and promote intestinal tissue repair via the cGAS-STING pathway for the treatment of UC. In vivo, mice with 3% DSS-induced UC were intraperitoneally injected with quercetin and RU.521 for 7 days, following which their general conditions and corresponding therapeutic effects were assessed. The impact of interferon-stimulated DNA (ISD) and quercetin on macrophage polarization and the cGAS-STING pathway was investigated using RAW264.7 cells and bone marrow-derived macrophages (BMDMs) in vitro. The results demonstrated that ISD induced M1 macrophage polarization and activated the cGAS-STING pathway in vitro, while quercetin reversed ISD's inflammatory effects. In vivo, quercetin suppressed the cGAS-STING pathway in the intestinal macrophages of DSS-induced UC mice, which reduced M1 macrophage polarization, increased M2 polarization, and facilitated intestinal barrier repair in UC. Taken together, these findings provide new insights into the mechanisms via which quercetin could be used to treat UC.

Therapeutic and immunomodulatory potentials of mesenchymal stromal/stem cells and immune checkpoints related molecules

Mesenchymal stromal/stem cells (MSCs) are used in many studies due to their therapeutic potential, including their differentiative ability and immunomodulatory properties. These cells perform their therapeutic functions by using various mechanisms, such as the production of anti-inflammatory cytokines, growth factors, direct cell-to-cell contact, extracellular vesicles (EVs) production, and mitochondrial transfer. However, mechanisms related to immune checkpoints (ICPs) and their effect on the immunomodulatory ability of MSCs are less discussed. The main function of ICPs is to prevent the initiation of unwanted responses and to regulate the immune system responses to maintain the homeostasis of these responses. ICPs are produced by various types of immune system regulatory cells, and defects in their expression and function may be associated with excessive responses that can ultimately lead to autoimmunity. Also, by expressing different types of ICPs and their ligands (ICPLs), tumor cells prevent the formation and durability of immune responses, which leads to tumors' immune escape. ICPs and ICPLs can be produced by MSCs and affect immune cell responses both through their secretion into the microenvironment or direct cell-to-cell interaction. Pre-treatment of MSCs in inflammatory conditions leads to an increase in their therapeutic potential. In addition to the effect that inflammatory environments have on the production of anti-inflammatory cytokines by MSCs, they can increase the expression of various types of ICPLs. In this review, we discuss different types of ICPLs and ICPs expressed by MSCs and their effect on their immunomodulatory and therapeutic potential.

Utility of four machine learning approaches for identifying ulcerative colitis and Crohn's disease

Objective

Peripheral blood routine parameters (PBRPs) are simple and easily acquired markers to identify ulcerative colitis (UC) and Crohn's disease (CD) and reveal the severity, whereas the diagnostic performance of individual PBRP is limited. We, therefore used four machine learning (ML) models to evaluate the diagnostic and predictive values of PBRPs for UC and CD.

Methods

A retrospective study was conducted by collecting the PBRPs of 414 inflammatory bowel disease (IBD) patients, 423 healthy controls (HCs), and 344 non-IBD intestinal diseases (non-IBD) patients. We used approximately 70 % of the PBRPs data from both patients and HCs for training, 30 % for testing, and another group for external verification. The area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnosis and prediction performance of these four ML models.

Results

Multi-layer perceptron artificial neural network model (MLP-ANN) yielded the highest diagnostic performance than the other three models in six subgroups in the training set, which is helpful for discriminating IBD and HCs, UC and CD, active CD and remissive CD, active UC and remissive UC, non-IBD and HCs, and IBD and non-IBD with the AUC of 1.00, 0.988, 0.942, 1.00, 0.986, and 0.97 in the testing set, as well as the AUC of 1.00, 1.00, 0.773, 0.904, 1.00 and 0.992 in the external validation set.

Conclusion

PBRPs-based MLP-ANN model exhibited good performance in discriminating between UC and CD and revealing the disease activity; however, a larger sample size and more models need to be considered for further research.