Proteomics Combined with RNA Sequencing to Screen Biomarkers of Sepsis

Abstract

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.

WITHDRAWN: Identification of platelets related THBS1 as a critical gene in sepsis related thrombocytopenia via an integrated bioinformatic analysis

“…Considering the life-threating and lacking of effective treatment modality property of sepsis, novel therapeutic targets should be explored using the current integrated bioinformatics analyses [7]. Furthermore, considering the 24-hour SAT was employed as prognostic indicator for sepsis[8], platelet related genes could be served as an intervene for sepsis.…”

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

“…CELA3A has little elastolytic activity and might play a critical role in intestinal transport and cholesterol metabolism. , We found that CELA3A expression was increased significantly in the drainage fluid of AL patients. With respect to serum levels, although AMY2A, RETN, and CELA3A in serum were correlated with acute pancreatitis, sepsis, and chronic pancreatitis, − there is no literature indicating that AMY2A, RETN, and CELA3A in serum were associated with AL in patients who underwent laparoscopic low anterior resection for rectal cancer. Correspondingly, AMY2A, RETN, and CELA3A expression was induced by LPS stimulation.…”

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

“…From the 1312 quantified proteins, RFE-SHAP analysis was able to identify a panel of 18 with high accuracy and separating power that indicates they would provide a good starting point for investigation as potential protein biomarkers. In fact, many of these proteins have already been identified and studied as potential sepsis markers or markers for infection and inflammation 36 – 45 . The majority of the 18 proteins were higher in abundance in the more severe subphenotype, such as CD44 antigen (CD44), Complement C1r subcomponent-like protein (C1RL), Beta-1,4-galactosyltransferase 1 (B4GT1), and Lipopolysaccharide-binding protein (LBP).…”