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Abstract Background Sepsis is a life-threatening disease with a poor prognosis, and metabolic disorders play a crucial role in its development. This study aims to identify key metabolites that may be associated with the accurate diagnosis and prognosis of sepsis. Methods Septic patients and healthy individuals were enrolled to investigate metabolic changes using non-targeted liquid chromatography-high-resolution mass spectrometry metabolomics. Machine learning algorithms were subsequently employed to identify key differentially expressed metabolites (DEMs). Prognostic-related DEMs were then identified using univariate and multivariate Cox regression analyses. The septic rat model was established to verify the effect of phenylalanine metabolism-related gene MAOA on survival and mean arterial pressure after sepsis. Results A total of 532 DEMs were identified between healthy control and septic patients using metabolomics. The main pathways affected by these DEMs were amino acid biosynthesis, phenylalanine metabolism, tyrosine metabolism, glycine, serine and threonine metabolism, and arginine and proline metabolism. To identify sepsis diagnosis-related biomarkers, support vector machine (SVM) and random forest (RF) algorithms were employed, leading to the identification of four biomarkers. Additionally, analysis of transcriptome data from sepsis patients in the GEO database revealed a significant up-regulation of the phenylalanine metabolism-related gene MAOA in sepsis. Further investigation showed that inhibition of MAOA using the inhibitor RS-8359 reduced phenylalanine levels and improved mean arterial pressure and survival rate in septic rats. Finally, using univariate and multivariate cox regression analysis, six DEMs were identified as prognostic markers for sepsis. Conclusions This study employed metabolomics and machine learning algorithms to identify differential metabolites that are associated with the diagnosis and prognosis of sepsis patients. Unraveling the relationship between metabolic characteristics and sepsis provides new insights into the underlying biological mechanisms, which could potentially assist in the diagnosis and treatment of sepsis. Trial registration This human study was approved by the Ethics Committee of the Research Institute of Surgery (2021–179) and was registered by the Chinese Clinical Trial Registry (Date: 09/12/2021, ChiCTR2200055772).

Abstract Excessive mitochondrial fission following ischemia and hypoxia relies on the formation of contacts between the endoplasmic reticulum and mitochondria (ER‐Mito); however, the specific mechanisms behind this process remain unclear. Confocal microscopy and time course recording are used to investigate how ischemia and hypoxia affect the activation of dynamin‐related protein 1 (Drp1), a protein central to mitochondrial dynamics, ER‐Mito interactions, and the consequences of modifying the expression of Drp1, shroom (Shrm) 4, and inverted formin (INF) 2 on ER‐Mito contact establishment. Both Drp1 activation and ER‐Mito contact initiation cause excessive mitochondrial fission and dysfunction under ischemic‐hypoxic conditions. The activated form of Drp1 aids in ER‐Mito contact initiation by recruiting Shrm4 and promoting actin bundling between the ER and mitochondria. This process relies on the structural interplay between INF2 and scattered F‐actin on the ER. This study uncovers new roles of cytoplasmic Drp1, providing valuable insights for devising strategies to manage mitochondrial imbalances in the context of ischemic‐hypoxic injury.

Background: The precise diagnostic and prognostic biological markers were needed in immunotherapy for sepsis. Considering the role of necroptosis and immune cell infiltration in sepsis, differentially expressed necroptosis-related genes (DE-NRGs) were identified, and the relationship between DE-NRGs and the immune microenvironment in sepsis was analyzed.Methods: Machine learning algorithms were applied for screening hub genes related to necroptosis in the training cohort. CIBERSORT algorithms were employed for immune infiltration landscape analysis. Then, the diagnostic value of these hub genes was verified by the receiver operating characteristic (ROC) curve and nomogram. In addition, consensus clustering was applied to divide the septic patients into different subgroups, and quantitative real-time PCR was used to detect the mRNA levels of the hub genes between septic patients (SP) (n = 30) and healthy controls (HC) (n = 15). Finally, a multivariate prediction model based on heart rate, temperature, white blood count and 4 hub genes was established.Results: A total of 47 DE-NRGs were identified between SP and HC and 4 hub genes (BACH2, GATA3, LEF1, and BCL2) relevant to necroptosis were screened out via multiple machine learning algorithms. The high diagnostic value of these hub genes was validated by the ROC curve and Nomogram model. Besides, the immune scores, correlation analysis and immune cell infiltrations suggested an immunosuppressive microenvironment in sepsis. Septic patients were divided into 2 clusters based on the expressions of hub genes using consensus clustering, and the immune microenvironment landscapes and immune function between the 2 clusters were significantly different. The mRNA levels of the 4 hub genes significantly decreased in SP as compared with HC. The area under the curve (AUC) was better in the multivariate prediction model than in other indicators.Conclusion: This study indicated that these necroptosis hub genes might have great potential in prognosis prediction and personalized immunotherapy for sepsis.

Hypotension resuscitation is an important principle for the treatment after trauma. Current hypotensive resuscitation strategies cannot obtain an ideal outcome for remote regions. With the uncontrolled hemorrhagic shock (UHS) model in rats, the effects of norepinephrine (NE) on the tolerance time of hypotensive resuscitation, blood loss, vital organ functions, and animal survival were observed. Before bleeding was controlled, only the LR infusion could effectively maintain the MAP to 50–60 mmHg for 1 h, while the MAP gradually decreased with prolonging time, even with increasing infusion volume. Low-dose NE during hypotensive resuscitation prolonged the hypotensive tolerance time to 2–3 h, and the effect of 0.3 μg/kg/min NE was the best. Further studies showed that 0.3 μg/kg/min NE during hypotensive resuscitation significantly lightened the damage of organ function induced by UHS via protecting mitochondrial function, while the LR infusion did not. At the same time, NE administration improved Hb content, DO2, and VO2, and restored liver and kidney blood flow. The survival results showed that low-dose NE administration increased the survival rate and prolonged the survival time. Together, low-dose NE during hypotensive resuscitation was suitable for the early treatment of UHS, which can strive for the golden window of emergency treatment for serious trauma patients by reducing blood loss and protecting vital organ functions.