Proteomics Combined with RNA Sequencing to Screen Biomarkers of Sepsis

SPINK1 is a Potential Diagnostic and Prognostic Biomarker for Sepsis

Purpose

There are no satisfactory diagnostic biomarkers for sepsis. Accordingly, this study screened biomarkers valuable for sepsis diagnosis and prognosis using data-independent acquisition (DIA) combined with clinical data analysis.

Patients and Methods

Serine protease inhibitor Kazal-type 1 (SPINK1) is a differentially expressed protein that was screened using DIA and bioinformatics in sepsis patients (n = 22) and healthy controls (n = 10). The plasma SPINK1 levels were detected using an enzyme-linked immunosorbent assay (ELISA) in an expanded population (sepsis patients, n = 52; healthy controls, n = 10). The diagnostic value of SPINK1 in sepsis was evaluated using receiver operating characteristic (ROC) curve analysis based on clinical data. The prognostic value of SPINK1 for sepsis was evaluated using correlation and survival analyses.

Results

DIA quality control identified 78 differential proteins (72 upregulated and six downregulated), among which SPINK1 was highly expressed in sepsis. The ELISA results suggested that SPINK1 expression was significantly elevated in the sepsis group (P < 0.05). ROC analysis of SPINK1 yielded an area under the curve (AUC) of 0.9096. Combining SPINK1 with procalcitonin (PCT) for ROC analysis yielded an AUC of 1. SPINK1 expression was positively correlated with the Sequential Organ Failure Assessment (SOFA) score (r = 3497, P = 0.0053) and APACHE II score (r = 3223, P = 0.0106). High plasma SPINK1 protein expression was negatively correlated with the 28-day survival rate of patients with sepsis (P = 0.0149).

Conclusion

The plasma of sepsis patients contained increased SPINK1 protein expression. Combining SPINK1 with PCT might have a high diagnostic value for sepsis. SPINK1 was associated with the SOFA score, APACHE II score, and the 28-day survival rate in patients with sepsis.

Tandem Mass Tag-Based Proteomic Profiling Identifies Biomarkers in Drainage Fluid for Early Detection of Anastomotic Leakage after Rectal Cancer Resection

Anastomotic leakage (AL), one of the most severe complications in rectal surgery, is often diagnosed late because of the low specificity of the clinical symptoms and limitations of current clinical investigations. Identification of patients with early AL remains challenging. Here, we explored the protein expression profiles of AL patients to provide potential biomarkers to identify AL in patients who undergo surgery for rectal cancer. We screened differentially expressed proteins (DEPs) in drainage fluid from AL and non-AL patients using a tandem mass tag method. A total of 248 DEPs, including 98 upregulated and 150 downregulated proteins, were identified between AL and non-AL groups. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses suggested that DEPs were enriched in neutrophil degranulation, bacterial infection, proteolysis, hemostasis, and complement and coagulation cascades. The results of enzyme-linked immunosorbent assay validated that the expression of the top three upregulated DEPs, AMY2A, RETN, and CELA3A, was significantly increased in the drainage fluid of AL patients, compared with that of non-AL patients (AMY2A, P = 0.001; RETN, P < 0.0001; and CELA3A, P = 0.023). Thus, our findings provide several potential biomarkers for the early diagnosis of AL after rectal cancer resection.

Peripheral immune cell death in sepsis based on bulk RNA and single-cell RNA sequencing

Background

Immune cell activation in early sepsis is beneficial to clear pathogens, but immune cell exhaustion during the inflammatory response induces immunosuppression in sepsis. Here, we studied the relationship between immune cell survival status and the prognosis of sepsis patients.

Methods

Sepsis patients admitted to our hospital with a diagnosis time of less than 24 h were recruited. RNA sequencing technologies were used to study functional alterations in various immune cells in peripheral blood mononuclear cells (PBMCs) from sepsis patients. Flow cytometry and electron microscopy were performed to study cell apoptosis and morphological alterations.

Results

A total of 68 sepsis patients with complete data were enrolled and divided into survival (45 patients) and death (23 patients) groups according to their prognosis. Patients in the death group had significantly increased lactic acid levels compared with those in the survival group, but there was no significant difference in other physiological and coagulation functional indicators between the two groups. Bulk RNA sequencing showed that cell death-related pathways and biomarkers were highly enriched and activated in the PBMCs of the death group than that in the survival group. Signs of mitochondrial damage, autophagosomes, cell surface damage and cell surface pore forming were also more pronounced in PBMCs from the death group under electron microscopy. Further single-cell RNA sequencing revealed that cell death occurred mainly in myeloid cells rather than lymphocytes at the early stage of sepsis; cell death patterns of destructive necrosis and pyroptosis were predominant in neutrophils, and apoptosis, autophagy and ferroptosis with less damage to the surroundings were predominant in monocytes.

Conclusion

Cell death mainly occurs in monocytes and neutrophils in the PBMCs of sepsis at the early stage. The study provides a perspective for the immunotherapy of early sepsis targeting immune cell death.

Generalized precursor prediction boosts identification rates and accuracy in mass spectrometry based proteomics

Data independent acquisition mass spectrometry (DIA-MS) has recently emerged as an important method for the identification of blood-based biomarkers. However, the large search space required to identify novel biomarkers from the plasma proteome can introduce a high rate of false positives that compromise the accuracy of false discovery rates (FDR) using existing validation methods. We developed a generalized precursor scoring (GPS) method trained on 2.75 million precursors that can confidently control FDR while increasing the number of identified proteins in DIA-MS independent of the search space. We demonstrate how GPS can generalize to new data, increase protein identification rates, and increase the overall quantitative accuracy. Finally, we apply GPS to the identification of blood-based biomarkers and identify a panel of proteins that are highly accurate in discriminating between subphenotypes of septic acute kidney injury from undepleted plasma to showcase the utility of GPS in discovery DIA-MS proteomics.

Multi-Omics Endotypes in ICU Sepsis-Induced Immunosuppression

It is evident that the admission of some patients with sepsis and septic shock to hospitals is occurring late in their illness, which has contributed to the increase in poor outcomes and high fatalities worldwide across age groups. The current diagnostic and monitoring procedure relies on an inaccurate and often delayed identification by the clinician, who then decides the treatment upon interaction with the patient. Initiation of sepsis is accompanied by immune system paralysis following "cytokine storm". The unique immunological response of each patient is important to define in terms of subtyping for therapy. The immune system becomes activated in sepsis to produce interleukins, and endothelial cells express higher levels of adhesion molecules. The proportions of circulating immune cells change, reducing regulatory cells and increasing memory cells and killer cells, having long-term effects on the phenotype of CD8 T cells, HLA-DR, and dysregulation of microRNA. The current narrative review seeks to highlight the potential application of multi-omics data integration and immunological profiling at the single-cell level to define endotypes in sepsis and septic shock. The review will consider the parallels and immunoregulatory axis between cancer and immunosuppression, sepsis-induced cardiomyopathy, and endothelial damage. Second, the added value of transcriptomic-driven endotypes will be assessed through inferring regulatory interactions in recent clinical trials and studies reporting gene modular features that inform continuous metrics measuring clinical response in ICU, which can support the use of immunomodulating agents.

Screening and identification of the core immune-related genes and immune cell infiltration in severe burns and sepsis

Severe burns often have a high mortality rate due to sepsis, but the genetic and immune crosstalk between them remains unclear. In the present study, the GSE77791 and GSE95233 datasets were analysed to identify immune-related differentially expressed genes (DEGs) involved in disease progression in both burns and sepsis. Subsequently, weighted gene coexpression network analysis (WGCNA), gene enrichment analysis, protein-protein interaction (PPI) network construction, immune cell infiltration analysis, core gene identification, coexpression network analysis and clinical correlation analysis were performed. A total of 282 common DEGs associated with burns and sepsis were identified. Kyoto Encyclopedia of Genes and Genomes pathway analysis identified the following enriched pathways in burns and sepsis: metabolic pathways; complement and coagulation cascades; legionellosis; starch and sucrose metabolism; and ferroptosis. Finally, six core DEGs were identified, namely, IL10, RETN, THBS1, FGF13, LCN2 and MMP9. Correlation analysis showed that some core DEGs were significantly associated with simultaneous dysregulation of immune cells. Of these, RETN upregulation was associated with a worse prognosis. The immune-related genes and dysregulated immune cells in severe burns and sepsis provide potential research directions for diagnosis and treatment.