Identification of RRM2 as a key ferroptosis-related gene in sepsis.

OBJECTIVE: Sepsis and sepsis-associated organ failure are devastating conditions for which there are no effective therapeutic agent. Several studies have demonstrated the significance of ferroptosis in sepsis. The study aimed to identify ferroptosis-related genes (FRGs) in sepsis, providing potential therapeutic targets. METHODS: The weighted gene co-expression network analysis (WGCNA) was utilized to screen sepsis-associated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore gene functions. Three machine learning methods were employed to identify sepsis-related hub genes. Survival and multivariate Cox regression analysis allowed further screening for the key gene RRM2 associated with prognosis. The immune infiltration analysis of the screened sepsis key genes was performed. Additionally, a cecum ligation and puncture (CLP)-induced mouse sepsis model was constructed to validate the expression of key gene in the sepsis. RESULTS: Six sepsis-associated differentially expressed FRGs (RRM2, RPL7A, HNRNPA1, PEBP1, MYL8B and TXNIP) were screened by WGCNA and three machine learning methods analysis. Survival analysis and multivariate Cox regression analysis showed that RRM2 was a key gene in sepsis and an independent prognostic factor associated with clinicopathological and molecular features of sepsis. Immune cell infiltration analysis demonstrated that RRM2 had a connection to various immune cells, such as CD4 T cells and neutrophils. Furthermore, animal experiment demonstrated that RRM2 was highly expressed in CLP-induced septic mice, and the use of Fer-1 significantly inhibited RRM2 expression, inhibited serum inflammatory factor TNF-α, IL-6 and IL-1ß expression, ameliorated intestinal injury and improved survival in septic mice. CONCLUSION: RRM2 plays an important role in sepsis and may contribute to sepsis through the ferroptosis pathway. This study provides potential therapeutic targets for sepsis.

Robust and Precise Quantitative Real-Time Polymerase Chain Reaction with an Amplification Efficiency-Aware Reaction Kinetics Model.

Quantitative real-time PCR (qPCR) is a method extensively used in nucleic acid testing for plants and animals. During the coronavirus disease 2019 (COVID-19) pandemic, high-precision qPCR analysis was urgently needed since quantitative results obtained from conventional qPCR methods were not accurate and precise, causing misdiagnoses and high rates of false-negative. To achieve more accurate results, we propose a new qPCR data analysis method with an amplification efficiency-aware reaction kinetics model (AERKM). Our reaction kinetics model (RKM) mathematically describes the tendency of the amplification efficiency during the whole qPCR process inferred by biochemical reaction dynamics. Amplification efficiency (AE) was introduced to rectify the fitted data so as to match the real reaction process for individual tests, thus reducing errors. The 5-point 10-fold gradient qPCR tests of 63 genes have been verified. The results of a 0.9% slope bias and an 8.2% ratio bias using AERKM exceed 4.1 and 39.4%, respectively, of the best performance of existing models, which demonstrates higher precision, less fluctuation, and better robustness among different nucleic acids. AERKM also provides a better understanding of the real qPCR process and gives insights into the detection, treatment, and prevention of severe diseases.