Sepsis in the critically ill patient: current and emerging management strategies

Trauma diagnostic-related target proteins and their detection techniques

Trauma is a significant health issue that not only leads to immediate death in many cases but also causes severe complications, such as sepsis, thrombosis, haemorrhage, acute respiratory distress syndrome and traumatic brain injury, among trauma patients. Target protein identification technology is a vital technique in the field of biomedical research, enabling the study of biomolecular interactions, drug discovery and disease treatment. It plays a crucial role in identifying key protein targets associated with specific diseases or biological processes, facilitating further research, drug design and the development of treatment strategies. The application of target protein technology in biomarker detection enables the timely identification of newly emerging infections and complications in trauma patients, facilitating expeditious medical interventions and leading to reduced post-trauma mortality rates and improved patient prognoses. This review provides an overview of the current applications of target protein identification technology in trauma-related complications and provides a brief overview of the current target protein identification technology, with the aim of reducing post-trauma mortality, improving diagnostic efficiency and prognostic outcomes for patients.

METTL3 achieves lipopolysaccharide-induced myocardial injury via m6A-dependent stabilization of Myh3 mRNA

Septic cardiomyopathy (SCM) was an important pathological component of severe sepsis and septic shock. N6-methyladenosine (m6A) modification was a common RNA modification in both mRNA and non-coding RNAs and was proved to be involved in sepsis and immune disorders. Therefore, the purpose of this study was to investigate the role and mechanism of METTL3 in lipopolysaccharide-induced myocardial injury. We firstly analyzed the expression changes of various m6A-related regulators in human samples in the GSE79962 data and the Receiver Operating Characteristic curve of significantly changed m6A enzymes, showing that METTL3 had a high diagnostic ability in patients with SCM. Western blotting confirmed the high expression of METTL3 in LPS-treated H9C2 cells, which was consistent with the above results in human samples. In vitro and in vivo, the deficiency of METTL3 could improve the cardiac function, cardiac tissue damage, myocardial cell apoptosis and reactive oxygen species levels in LPS-treated H9C2 cells and LPS-induced sepsis rats, respectively. In addition, we obtained 213 differential genes through transcriptome RNA-seq analysis, and conducted GO enrichment analysis and KEGG pathway analysis through DAVID. We also found that the half-life of Myh3 mRNA was significantly reduced after METTL3 deletion and that Myh3 carried several potential m6A modification sites. In conclusion, we found that downregulation of METTL3 reversed LPS-induced myocardial cell and tissue damage and reduced cardiac function, mainly by increasing Myh3 stability. Our study revealed a key role of METTL3-mediated m6A methylation in septic cardiomyopathy, which may offer a potential mechanism for the therapy of septic cardiomyopathy.

Interaction of acute heart failure and acute kidney injury on in-hospital mortality of critically ill patients with sepsis: A retrospective observational study

BACKGROUND

The present study aimed to evaluate the synergistic impact of acute heart failure (AHF) and acute kidney injury (AKI) on in-hospital mortality in critically ill patients with sepsis.

METHODS

We undertook a retrospective, observational analysis using data acquired from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and eICU Collaborative Research Database (eICU-CRD). The effects of AKI and AHF on in-hospital mortality were examined using a Cox proportional hazards model. Additive interactions were analyzed using the relative extra risk attributable to interaction.

RESULTS

A total of 33,184 patients were eventually included, comprising 20,626 patients in the training cohort collected from the MIMIC-IV database and 12,558 patients in the validation cohort extracted from the eICU-CRD database. After multivariate Cox analysis, the independent variables for in-hospital mortality included: AHF only (HR:1.20, 95% CI:1.02-1.41, P = 0.005), AKI only (HR:2.10, 95% CI:1.91-2.31, P < 0.001), and both AHF and AKI (HR:3.80, 95%CI:13.40-4.24, P < 0.001). The relative excess risk owing to interaction was 1.49 (95% CI:1.14-1.87), the attributable percentage due to interaction was 0.39 (95%CI:0.31-0.46), and the synergy index was 2.15 (95%CI:1.75-2.63), demonstrated AHF and AKI had a strong synergic impact on in-hospital mortality. And the findings in the validation cohort indicated identical conclusions to the training cohort.

CONCLUSION

Our data demonstrated a synergistic relationship of AHF and AKI on in-hospital mortality in critically unwell patients with sepsis.

Proteomics Combined with RNA Sequencing to Screen Biomarkers of Sepsis

Purpose

To screen biomarkers in the serum of patients with sepsis by proteomics combined with RNA sequencing technology, and to find new diagnostic and therapeutic targets for sepsis.

Patients and Methods

Blood samples of 22 sepsis patients (sepsis group) and 10 healthy volunteers (normal group) were collected from January 2019 to December 2020. Data-independent acquisition (DIA) method was employed for protein profiling, RNA sequencing was employed for gene sequencing. Subsequently, quality control and differential analysis (FC≥2; FDR<0.05) of DIA data and RNA sequencing data were performed. Then we identified expression trend-consistent divergence factors by nine-quadrant analysis; subsequent protein-protein interaction (PPI) and gene ontology (GO) functional enrichment analysis of intersection factors was performed, and meta-analysis of targets at transcriptome level was implemented using public datasets. Finally, five Peripheral blood mononuclear cell (PBMC) samples (NC=2; SIRS=1; SEPSIS =2) were collected, and cell localization analysis of core genes was performed by 10× single-cell RNA sequencing (scRNA-seq).

Results

Compared with the normal group, there were 4681 differentially expressed genes and 202 differentially expressed proteins in the sepsis group. Among them, 25 factors were expressed in both proteome and transcriptome, and the analysis of PPI and GO found that they were mainly involved in biological processes such as white blood cell and neutrophil response, inflammatory and immune response. Four core genes GSTO1, C1QA, RETN, and GRN were screened by meta-analysis, all of which were highly expressed in the sepsis group compared with the normal group (P<0.05); scRNA-seq showed the core genes were mainly localized in macrophage cell lines.

Conclusion

The core genes GSTO1, C1QA, RETN and GRN are mainly expressed in macrophages, widely involved in inflammation and immune responses, and are highly expressed in plasma in the sepsis, suggesting that they may become potential research targets for sepsis.

Identification of the key ferroptosis-related genes involved in sepsis progression and experimental validation in vivo

Background: Ferroptosis has a vital role in sepsis, but the mechanism is not known. Understanding the mechanism of ferroptosis during sepsis will aid in developing improved therapeutic strategies.

Methods: We used the Gene Expression Omnibus database and FerrDb database to obtain ferroptosis-related differentially expressed genes (DEGs) between sepsis patients and healthy volunteers (HVs). Analyses of PPI networks, functional enrichment, as well as use of the MCODE algorithm were used to identify key ferroptosis-related DEGs. Expression of key ferroptosis-related DEGs was verified using: GSE57065 and GSE65682 datasets; rats in which ferroptosis was induced with erastin; sepsis-induced acute lung injury (siALI) rats. The effects of acupoint catgut embedding (ACE) on ferroptosis and expression of key ferroptosis-related DEGs in the lungs of siALI rats were also observed. A Cox proportional hazard model was used to verify the effect of key ferroptosis-related DEGs on the survival of sepsis patients. Cytoscape was used to construct ceRNA networks and gene–transcription factor networks.

Results: Between sepsis patients and HVs, we identified 33 ferroptosis-related DEGs. According to analyses of PPI networks and the MCODE algorithm, we obtained four modules, of which the most significant module contained nine ferroptosis-related DEGs. Functional-enrichment analyses showed that four of the nine DEGs were enriched in the MAPK signaling pathway: MAPK14, VEGFA, TGFBR1, and DUSP1. We verified expression of these four genes in GSE57065 and GSE65682 datasets and ferroptosis rats. In addition, expression of these four genes and that of the oxidative-stress indicators GSSG and MDA was upregulated, and glutathione peroxidase-4 (GPX4) expression was downregulated, in siALI rats, but ACE reversed these changes. The Cox proportional hazard model showed that survival of sepsis patients in the high-risk group was shorter than that in the low-risk group. We found that the XIST−hsa-let-7b-5p−TGFBR1/DUSP1 ceRNA network and transcription factor E2F1 may be important regulators of these four DEGs.

Conclusion: Our results suggest that MAPK14, VEGFA, TGFBR1, and DUSP1 may be key regulatory targets of ferroptosis in sepsis, and that ACE pretreatment may be antioxidant treatment for sepsis and alleviate ferroptosis. These findings provide a basis for further ferroptosis-related study in sepsis and provide new targets for its treatment.

Methotrexate Ameliorates Systemic Inflammation and Septic Associated-Lung Damage in a Cecal Ligation and Puncture Septic Rat Model

BACKGROUND

Sepsis is a serious, heterogeneous clinical entity produced by a severe and systemic host inflammatory response to infection. Methotrexate (MTX) is a folate-antagonist that induces the generation of adenosine and also inhibits JAK/STAT pathway; MTX it is widely used as an anti-inflammatory drug to control the immune system.

OBJECTIVE

The aim of this study was to assess the beneficial effects of a single and low dose of MTX in the systemic response and acute lung injury (ALI) induced by sepsis. As in the clinics, we treated our animals with antibiotics and fluids and performed the source control to mimic the current clinic treatment.

METHODS AND MAIN RESULTS

Sepsis was induced in rats by a cecal ligation puncture (CLP) procedure. Six hours after induction of sepsis, we proceeded to the source control; fluids and antibiotics were administered at 6 h and 24 h after CLP. MTX (2.5 mg/Kg) was administered 6 h after the first surgery in one CLP experimental group and to one Sham group. A protective effect of MTX was observed through a significant reduction of pro-inflammatory cytokines and a decrease infiltration of inflammatory cells in the lung. In addition, we found a regulation in adenosine receptor A2aR and the metalloproteinases by MTX.

CONCLUSION

A single, low dose of MTX attenuates sepsis lung-associated damage by decreasing pro-inflammatory response, infiltration of pro-inflammatory cells and avoiding defective tissue lung remodeling.

Artificial Intelligence for Clinical Decision Support in Sepsis

Sepsis is one of the main causes of death in critically ill patients. Despite the continuous development of medical technology in recent years, its morbidity and mortality are still high. This is mainly related to the delay in starting treatment and non-adherence of clinical guidelines. Artificial intelligence (AI) is an evolving field in medicine, which has been used to develop a variety of innovative Clinical Decision Support Systems. It has shown great potential in predicting the clinical condition of patients and assisting in clinical decision-making. AI-derived algorithms can be applied to multiple stages of sepsis, such as early prediction, prognosis assessment, mortality prediction, and optimal management. This review describes the latest literature on AI for clinical decision support in sepsis, and outlines the application of AI in the prediction, diagnosis, subphenotyping, prognosis assessment, and clinical management of sepsis. In addition, we discussed the challenges of implementing and accepting this non-traditional methodology for clinical purposes.