Proteomics Combined with RNA Sequencing to Screen Biomarkers of Sepsis

Screening Therapeutic Core Genes in Sepsis Using Network Pharmacology and Single-Cell RNA Sequencing

Sepsis, a life-threatening condition characterized by a systemic inflammatory response, leads to organ dysfunction and high mortality rates. Honeysuckle, a traditional herbal remedy, has shown promise in attenuating organ damage and inhibiting pro-inflammatory factors in sepsis. However, the underlying molecular mechanisms remain unclear. We employed a multi-omics approach to elucidate honeysuckle's potential therapeutic effects in sepsis. RNA sequencing was performed on blood samples from 22 sepsis patients and 10 healthy controls to identify differentially expressed genes. Network pharmacology was utilized to predict effective ingredients and therapeutic targets of honeysuckle in sepsis. Meta-analysis compared gene expression between sepsis survivors and non-survivors. Single-cell RNA sequencing was employed to localize target gene expression at the cellular level. We identified 1328 differentially expressed genes in sepsis, with 221 upregulated and 1107 downregulated. Network analysis revealed 15 genes linked to 12 honeysuckle components. Four genes-DPP4, CD40LG, BCL2, and TP53-emerged as core therapeutic targets, showing decreased expression in non-survivors but upregulation in survivors. Single-cell analysis demonstrated that these genes were primarily expressed in T cells and other immune cells, suggesting their role in regulating immune response and inflammation. This study uses single-cell RNA sequencing and network analysis to identify DPP4, CD40LG, BCL2, and TP53 as key regulatory targets in sepsis, providing insights into disease mechanisms and potential therapeutic interventions. Network pharmacology analysis suggests possible interactions with honeysuckle compounds, though experimental validation is needed.

Screening and analysis of programmed cell death related genes and targeted drugs in sepsis

OBJECTIVE

This study employed bioinformatics techniques to identify diagnostic genes associated with programmed cell death (PCD) and to explore potential therapeutic agents for the treatment of sepsis.

METHODS

Gene expression profiles from sepsis patients were analyzed to identify differentially expressed genes (DEGs) and hub genes through Weighted Gene Co-expression Network Analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to elucidate the functions of the DEGs. PCD-related genes were cross-referenced with the identified DEGs. Diagnostic genes were selected using Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) methodologies. Single-cell RNA sequencing was utilized to assess gene expression in blood cells, while CIBERSORT was employed to evaluate immune cell infiltration. A transcription factor (TF)-microRNA (miRNA)-hub gene network was constructed, and potential therapeutic compounds were predicted using the Drug Gene Interaction Database (DGIdb). Mendelian Randomization (MR) methods were applied to analyze genome-wide association study (GWAS) data for S100A9, TXN, and GSTO1.

RESULTS

The analysis revealed 2156 PCD-related genes, 714 DEGs, and 1198 hub genes, with 88 genes enriched in immune and cell death pathways. Five pivotal PCD-related genes (IRAK3, S100A9, TXN, NFATC2, and GSTO1) were identified, leading to the construction of a network comprising six transcription factors and 171 microRNAs. Additionally, seven drugs targeting S100A9, TXN, and NFATC2 were identified. MR analysis suggested that a decrease in GSTO1 levels is associated with an increased risk of sepsis, and that sepsis influences the levels of S100A9, TXN, and GSTO1.

CONCLUSIONS

Through bioinformatics approaches, this study successfully identified five genes (IRAK3, S100A9, TXN, NFATC2, and GSTO1) associated with programmed cell death in the context of sepsis. This research identified seven candidate drugs for sepsis treatment and established a methodological framework for predicting biomarkers and drug targets that could be applicable to other diseases.

Identifying signature genes and their associations with immune cell infiltration in spinal cord injury

Background

Early detection of spinal cord injury (SCI) is conducive to improving patient outcomes. In addition, many studies have revealed the role of immune cells in the progression or treatment of SCI. The objective of this study was to identify the early signature genes and clarify how they are related to immune cell infiltration in SCI.

Methods

We analysed and identified early signature genes associated with SCI via bioinformatics analysis of the GSE151371 dataset from the GEO database. These genes were subsequently verified in the GSE33886 dataset and qRT-PCR. Finally, the CIBERSORT algorithm was used to examine the immune cell infiltration in SCI and its relationship with signature genes.

Results

Seven SCI-related signature genes, including ARG1, RETN, BPI, GGH, CCNB1, HIST1H2AC, and HIST1H2BJ, were identified, and their expression was verified via an external validation cohort and qRT-PCR. Moreover, the ROC curves revealed the diagnostic value of these genes. In addition, on the basis of immune cell infiltration analysis, plasma cells, M0 macrophages, activated CD4+ memory T cells, γδ T cells, naive CD4+ T cells, and resting CD4+ memory T cells may participate in the progression of SCI.

Conclusion

This study identified seven early signature genes of SCI that may serve as biomarkers for the early diagnosis of SCI and contribute to our understanding of immune changes during the pathology of SCI.

NOVEL ACTIVE PROTEINS FOR SEPSIS PROGNOSIS REVEALED THROUGH ScRNA-seq AND QUANTITATIVE PROTEOMICS

ABSTRACT

Objective: To uncover critical active proteins influencing sepsis outcomes through multiomics analysis. Methods: This study collected peripheral blood from sepsis patients (NS = 26, SV = 27) and controls (Con = 16). Cellular heterogeneity was assessed using scRNA-seq. Cellular populations were identified through clustering and annotation. Gene set variation analysis was employed to detect pathway alterations in sepsis, while the Viper algorithm estimated protein activity at the single-cell level. Signaling networks were investigated via cell-cell communication analysis. Differentially expressed proteins were identified by DIA proteomics and confirmed through integrated analysis. Prognostic value was evaluated via meta and survival analyses. Results: scRNA-seq of 22,673 features within 34,228 cells identified five cellular clusters and 253 active proteins via Viper, validated by DIA (FC > 2, P < 0.05). Four proteins (SPI1, MEF2A, CBX3, UBTF) with prognostic significance were discovered and mapped onto the cellular landscape. Gene set variation analysis enrichment analysis revealed that the NS group exhibited significant alterations in pathways related to cellular apoptosis and inflammatory responses, while the SV group displayed increased activity in DNA repair and cellular survival pathways. Conclusion: The study's findings advance the understanding of sepsis pathophysiology by linking differentially active proteins to patient prognosis, paving the way for targeted therapeutic strategies.

Single-cell RNA sequencing and transcriptomic analysis reveal key genes and regulatory mechanisms in sepsis

The pathogenesis of sepsis, with a high mortality rate and often poor prognosis, has not been fully elucidated. Therefore, an in-depth study on the pathogenesis of sepsis at the molecular level is essential to identify key sepsis-related genes. The aim of this study was to explore the key genes and potential molecular mechanisms of sepsis using a bioinformatics approach. In addition, key genes with miRNA network correlation analysis and immune infiltration correlation analysis were investigated. The scRNA dataset (GSE167363) and RNA-seq dataset (GSE65682, GSE134347) from GEO database were used for screening out differentially expressed genes using single-cell sequencing and transcriptome sequencing. The analysis of immune infiltration was evaluated by the CIBERSORT method. Key genes and possible mechanisms were identified by WGCNA analysis, GSVA analysis, GSEA enrichment analysis and regulatory network analysis, and miRNA networks associated with key genes were constructed. Nine key genes associated with the development of sepsis, namely IL7R, CD3D, IL32, GPR183, HLA-DPB1, CD81, PEBP1, NCL, and ETS1 were screened, and the specific signaling mechanisms associated with the key genes causing sepsis were predicted. Immune profiling showed immune heterogeneity between control and sepsis samples. A regulatory network of 82 miRNAs, 266 pairs of mRNA-miRNA relationship pairs was also constructed. These nine key genes have the potential to become biomarkers for the diagnosis of sepsis and provide new targets and research directions for the treatment 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.