Role of ST6GAL1 and ST6GAL2 in subversion of cellular signaling during enteroaggregative Escherichia coli infection of human intestinal epithelial cell lines

Comprehensive analysis of sialylation-related genes and construct the prognostic model in sepsis

Sepsis, a life-threatening syndrome, continues to be a significant public health issue worldwide. Sialylation is a hot potential marker that affects the surface of a variety of cells. However, the role of genes related to sialylation and sepsis has not been fully explored. Bulk RNA-seq data sets (GSE66099 and GSE65682) were obtained from the open-access databases GEO. The classification of sepsis samples into subtypes was achieved by employing the R package "ConsensusClusterPlus" on the bulk RNA-seq data. Hub genes were discerned through the application of the R package "limma" and univariate regression analysis, with the calculation of risk scores carried out using the R package "survminer". To identify the best learning method and construct a prognostic model, we used 21 different combinations of machine learning, and C-index ranking results of these combinations have been showed. ROC curves, time-dependent ROC curves, and Kaplan-Meier curves were utilized to evaluate the diagnostic accuracy of the model. The R packages "ESTIMATE" and "GSVA" were employed to quantify the fractions of immune cell infiltration in each sample. The bulk RNA-seq samples were categorized into two distinct sepsis subtypes utilizing 14 prognosis-related sialylation genes. A total of 20 differentially expressed genes (DEGs) were identified as being associated with the relationship between sepsis and sialylation. The RSF was used to identify key genes with importance scores higher than 0.01. The nine hub genes (SLA2A1, TMCC2, TFRC, RHAG, FKBP1B, KLF1, PILRA, ARL4A, and GYPA) with the importance values greater than 0.01 was selected for constructing the prognostic model. This research offers some understanding of the relationship between sepsis and sialylation. Besides, it contains one predictive model that might develop into diagnostic biomarkers for sepsis.

Dynamic Interactions between Diarrhoeagenic Enteroaggregative Escherichia coli and Presumptive Probiotic Bacteria: Implications for Gastrointestinal Health

This study delves into the temporal dynamics of bacterial interactions in the gastrointestinal tract, focusing on how probiotic strains and pathogenic bacteria influence each other and human health. This research explores adhesion, competitive exclusion, displacement, and inhibition of selected diarrhoeagenic Escherichia coli (D-EAEC) and potential probiotic strains under various conditions. Key findings reveal that adhesion is time-dependent, with both D-EAEC K2 and probiotic L. plantarum FS2 showing increased adhesion over time. Surprisingly, L. plantarum FS2 outperformed D-EAEC K2 in adhesion and exhibited competitive exclusion and displacement, with inhibition of adhesion surpassing competitive exclusion. This highlights probiotics' potential to slow pathogen attachment when not in competition. Pre-infecting with L. plantarum FS2 before pathogenic infection effectively inhibited adhesion, indicating probiotics' ability to prevent pathogen attachment. Additionally, adhesion correlated strongly with interleukin-8 (IL-8) secretion, linking it to the host's inflammatory response. Conversely, IL-8 secretion negatively correlated with trans-epithelial electrical resistance (TEER), suggesting a connection between tight junction disruption and increased inflammation. These insights offer valuable knowledge about the temporal dynamics of gut bacteria interactions and highlight probiotics' potential in competitive exclusion and inhibiting pathogenic bacteria, contributing to strategies for maintaining gastrointestinal health and preventing infections.