Effects of thymosin β4 on a rat model of severe acute pancreatitis

Identification and analysis of chemokine-related and NETosis-related genes in acute pancreatitis to develop a predictive model

Background: Chemokines and NETosis are significant contributors to the inflammatory response, yet there still needs to be a more comprehensive understanding regarding the specific molecular characteristics and interactions of NETosis and chemokines in the context of acute pancreatitis (AP) and severe AP (SAP). Methods: To address this gap, the mRNA expression profile dataset GSE194331 was utilized for analysis, comprising 87 AP samples (77 non-SAP and 10 SAP) and 32 healthy control samples. Enrichment analyses were conducted for differentially expressed chemokine-related genes (DECRGs) and NETosis-related genes (DENRGs). Three machine-learning algorithms were used for the identification of signature genes, which were subsequently utilized in the development and validation of nomogram diagnostic models for the prediction of AP and SAP. Furthermore, single-gene Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed. Lastly, an interaction network for the identified signature genes was constructed. Results: We identified 12 DECRGs and 7 DENRGs, and enrichment analyses indicated they were primarily enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and T cell receptor signaling pathway. Moreover, these machine learning algorithms finally recognized three signature genes (S100A8, AIF1, and IL18). Utilizing the identified signature genes, we developed nomogram models with high predictive accuracy for AP and differentiation of SAP from non-SAP, as demonstrated by area under the curve (AUC) values of 0.968 (95% CI 0.937-0.990) and 0.862 (95% CI 0.742-0.955), respectively, in receiver operating characteristic (ROC) curve analysis. Subsequent single-gene GESA and GSVA indicated a significant positive correlation between these signature genes and the proteasome complex. At the same time, a negative association was observed with the Th1 and Th2 cell differentiation signaling pathways. Conclusion: We have identified three genes (S100A8, AIF1, and IL18) related to chemokines and NETosis, and have developed accurate diagnostic models that might provide a novel method for diagnosing AP and differentiating between severe and non-severe cases.

Double-stranded RNA-dependent kinase PKR activates NF-κB pathway in acute pancreatitis

The activation of transcription factor nuclear factor kappa B (NF-κB) occurs early in acute pancreatitis (AP) simultaneously with intracellular trypsinogen activation. Double-stranded RNA-dependent kinase (PKR) promotes the activation of NF-κB and the production of pro-inflammatory factors including tumor necrosis factor alpha (TNF-α) and interleukin 6 (IL-6). The rat and rat pancreatic AR42J cells were treated by cerulein to establish AP models, showing PKR increased. TNF-α, IL-6 and lactate dehydrogenase (LDH) in AP pancreatic tissues and cerulein-treated AR42J cells increased, while PKR knockdown in AR42J cells reversed cerulein-induced inflammatory response and pancreatic cell injury. In addition, inhibitor of kappa B kinase α (IKKα), phosphorylated P65 (p-P65), P65 increased in cerulein-treated AR42J cells. Meanwhile, in cerulein-treated AR42J cells, interaction between PKR and IKKα, as well as the co-localization and nuclear accumulation of PKR and P65, were detected. Furthermore, cerulein induced the phosphorylation and nuclear translocation of P65, which indicated the activation of NF-κB, while PKR knockdown hindered NF-κB activation to alleviate pancreatic cell injury. In summary, PKR might promote NF-κB activation via facilitating its phosphorylation and nuclear translocation, thus accelerated inflammatory response and pancreatic cell injury in AP, implying a novel molecular target for the treatment of AP.