Elucidating common pathogenic transcriptional networks in infective endocarditis and sepsis: integrated insights from biomarker discovery and single-cell RNA sequencing

Abstract

BackgroundInfective Endocarditis (IE) and Sepsis are two closely related infectious diseases, yet their shared pathogenic mechanisms at the transcriptional level remain unclear. This research gap poses a barrier to the development of refined therapeutic strategies and drug innovation.MethodsThis study employed a collaborative approach using both microarray data and single-cell RNA sequencing (scRNA-seq) data to identify biomarkers for IE and Sepsis. It also offered an in-depth analysis of the roles and regulatory patterns of immune cells in these diseases.ResultsWe successfully identified four key biomarkers correlated with IE and Sepsis, namely CD177, IRAK3, RNASE2, and S100A12. Further investigation revealed the central role of Th1 cells, B cells, T cells, and IL-10, among other immune cells and cytokines, in the pathogenesis of these conditions. Notably, the small molecule drug Matrine exhibited potential therapeutic effects by targeting IL-10. Additionally, we discovered two Sepsis subgroups with distinct inflammatory responses and therapeutic strategies, where CD177 demonstrated significant classification value. The reliability of CD177 as a biomarker was further validated through qRT-PCR experiments.ConclusionThis research not only paves the way for early diagnosis and treatment of IE and Sepsis but also underscores the importance of identifying shared pathogenic mechanisms and novel therapeutic targets at the transcriptional level. Despite limitations in data volume and experimental validation, these preliminary findings add new perspectives to our understanding of these complex diseases.

Whole blood transcriptomics reveals the enrichment of neutrophil activation pathways during erythema nodosum leprosum reaction

“…Remarkably, in the RT-qPCR cohort, we also observed a positive correlation of expression of CD177 with those of S100A8/A9/A12 , where the pattern of increased expression level of one gene matches the other, suggesting an interplay of these neutrophil-associated genes during ENL. Interestingly, several studies have observed a combined increased expression of these genes in different inflammatory contexts, such as sepsis ( 81 ) and COVID-19 ( 82 ), strengthening the correlation among these genes. Nevertheless, the precise mechanistic role of S100 proteins during ENL requires further study.…”

Multidisciplinary Perspectives of Challenges in Infective Endocarditis Complicated by Septic Embolic-Induced Acute Myocardial Infarction

“…By analyzing complex patient data, including clinical, imaging, and laboratory parameters, AI algorithms can help stratify patients based on their risk of developing complications or experiencing adverse outcomes, enabling more personalized and targeted management strategies. Additionally, AI is shedding new light on the pathogenesis of IE and its complications, such as sepsis [ 85 ]. Through the sophisticated analysis of molecular interactions, genetic factors, and environmental influences, AI-driven research is unraveling the intricate mechanisms underlying these diseases, offering novel insights that could inform the development of innovative therapeutic interventions and preventive measures.…”