METHODS: Based on the EM transcriptomic datasets GSE7305 and GSE23339, as well as the IBD transcriptomic datasets GSE87466 and GSE126124, differential gene analysis was performed using the limma package in the R environment. Co-expressed differentially expressed genes were identified, and a protein-protein interaction (PPI) network for the differentially expressed genes was constructed using the 11.5 version of the STRING database. The MCODE tool in Cytoscape facilitated filtering out protein interaction subnetworks. Key genes in the PPI network were identified through two topological analysis algorithms (MCC and Degree) from the CytoHubba plugin. Upset was used for visualization of these key genes. The diagnostic value of gene expression levels for these key genes was assessed using the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) The CIBERSORT algorithm determined the infiltration status of 22 immune cell subtypes, exploring differences between EM and IBD patients in both control and disease groups. Finally, different gene expression trends shared by EM and IBD were input into CMap to identify small molecule compounds with potential therapeutic effects.
RESULTS: 113 differentially expressed genes (DEGs) that were co-expressed in EM and IBD have been identified, comprising 28 down-regulated genes and 86 up-regulated genes. The co-expression differential gene of EM and IBD in the functional enrichment analyses focused on immune response activation, circulating immunoglobulin-mediated humoral immune response and humoral immune response. Five hub genes (SERPING1、VCAM1、CLU、C3、CD55) were identified through the Protein-protein Interaction network and MCODE.High Area Under the Curve (AUC) values of Receiver Operating Characteristic (ROC) curves for 5hub genes indicate the predictive ability for disease occurrence.These hub genes could be used as potential biomarkers for the development of EM and IBD. Furthermore, the CMap database identified a total of 9 small molecule compounds (TTNPB、CAY-10577、PD-0325901 etc.) targeting therapeutic genes for EM and IBD.
DISCUSSION: Our research revealed common pathogenic mechanisms between EM and IBD, particularly emphasizing immune regulation and cell signalling, indicating the significance of immune factors in the occurence and progression of both diseases. By elucidating shared mechanisms, our study provides novel avenues for the prevention and treatment of EM and IBD.
METHODS AND RESULTS: We investigated steady-state messenger RNA levels of 84 histone-modifying enzymes and related regulators in colony-stimulating factor-1 differentiated primary human macrophages using quantitative polymerase chain reaction. IFN-γ or IL-4 treatment for 6-48 h changed 11 mRNAs significantly. IFN-γ increased CIITA, KDM6B, and NCOA1, and IL-4 also increased KDM6B by 6 h. However, either cytokine decreased AURKB, ESCO2, SETD6, SUV39H1, and WHSC1, whereas IFN-γ alone decreased KAT2A, PRMT7, and SMYD3 mRNAs only after 18 h, which coincided with decreased cell proliferation. Rendering macrophages quiescent by growth factor starvation or adenovirus-mediated overexpression of p27(kip1) inhibited expression of AURKB, ESCO2, SUV39H1, and WHSC1, and mRNA levels were restored by overexpressing the S-phase transcription factor E2F1, implying their expression, at least partly, depended on proliferation. However, CIITA, KDM6B, NCOA1, KAT2A, PRMT7, SETD6, and SMYD3 were regulated independently of effects on proliferation. Silencing KDM6B, the only transcriptional activator upregulated by both IFN-γ and IL-4, pharmacologically or with short hairpin RNA, blunted a subset of responses to each cytokine.
CONCLUSION: These findings demonstrate that IFN-γ or IL-4 can regulate the expression of histone acetyl transferases and histone methyl transferases independently of effects on proliferation and that upregulation of the histone demethylase, KDM6B, assists phenotypic polarization by both cytokines.
METHODS: Participants were recruited in Intensive Care Units (ICUs) from multiple UK hospitals, including fifty-nine patients with abdominal sepsis, eighty-four patients with pulmonary sepsis, forty-two SIRS patients with Out-of-Hospital Cardiac Arrest (OOHCA), sampled at four time points, in addition to thirty healthy control donors. Multiple clinical parameters were measured, including SOFA score, with many differences observed between SIRS and sepsis groups. Differential gene expression analyses were performed using microarray hybridization and data analyzed using a combination of parametric and non-parametric statistical tools.
RESULTS: Nineteen high-performance, differentially expressed mRNA biomarkers were identified between control and combined SIRS/Sepsis groups (FC>20.0, p<0.05), termed 'indicators of inflammation' (I°I), including CD177, FAM20A and OLAH. Best-performing minimal signatures e.g. FAM20A/OLAH showed good accuracy for determination of severe, systemic inflammation (AUC>0.99). Twenty entities, termed 'SIRS or Sepsis' (S°S) biomarkers, were differentially expressed between sepsis and SIRS (FC>2·0, p-value<0.05).
DISCUSSION: The best performing signature for discriminating sepsis from SIRS was CMTM5/CETP/PLA2G7/MIA/MPP3 (AUC=0.9758). The I°I and S°S signatures performed variably in other independent gene expression datasets, this may be due to technical variation in the study/assay platform.