Commonly expressed key transcriptomic profiles of sepsis in the human circulation and brain via integrated analysis

Identification of immune infiltration and immune-related biomarkers of periprosthetic joint infection

Background

The immune response associated with periprosthetic joint infection (PJI) is an emerging but relatively unexplored topic. The aim of this study was to investigate immune cell infiltration in periprosthetic tissues and identify potential immune-related biomarkers.

Methods

The GSE7103 dataset from the GEO database was selected as the data source. Differentially expressed genes (DEGs) and significant modular genes in weighted correlation network analysis (WGCNA) were identified. Functional enrichment analysis and transcription factor prediction were performed on the overlapping genes. Next, immune-related genes from the ImmPort database were matched. The protein-protein interaction (PPI) analysis was performed to identify hub genes. CIBERSORTx was used to evaluate the immune cell infiltration pattern. Spearman correlation analysis was used to evaluate the relationship between hub genes and immune cells.

Results

A total of 667 DEGs were identified between PJI and control samples, and 1847 PJI-related module genes were obtained in WGCNA. Enrichment analysis revealed that the common genes were mainly enriched in immune and host defense-related terms. TFEC, SPI1, and TWIST2 were the top three transcription factors. Three hub genes, SDC1, MMP9, and IGF1, were identified in the immune-related PPI network. Higher levels of plasma cells, CD4+ memory resting T cells, follicular helper T cells, resting mast cells, and neutrophils were found in the PJI group, while levels of M0 macrophages were lower. Notably, the expression of all three hub genes correlated with the infiltration levels of seven types of immune cells.

Conclusion

The present study revealed immune infiltration signatures in the periprosthetic tissues of PJI patients. SDC1, MMP9, and IGF1 were potential immune-related biomarkers for PJI.

Identification of IL-8 in CSF as a potential biomarker in sepsis-associated encephalopathy

BACKGROUND

Sepsis-associated encephalopathy (SAE) is frequently present at the acute and chronic phase of sepsis, which is characterized by delirium, coma, and cognitive dysfunction. Despite the increased morbidity and mortality of SAE, the pathogenesis of SAE remains unclear. This study aims to discover the potential biomarkers, so as to clear the pathogenesis potentially contributing to the development of SAE and provide new therapeutic strategies for the treatment of SAE.

METHODS

The GSE135838 dataset was obtained from the Gene Expression Omnibus (GEO) database and utilized for analysis the differentially expressed genes (DEGs). The DEGs were analyzed by limma package of R language and the extracellular protein-differentially expressed genes (EP-DEGs) were screened by the Human Protein Atlas (HPA) and UniProt database. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were carried out to analyze the function and pathway of EP-DEGs. STRING, Cytoscape, MCODE and Cytohubba were used to construct a protein-protein interaction (PPI) network and screen key EP-DEGs. Key EP-DEGs levels were detected in the cerebrospinal fluid (CSF) of SAE patients and non-sepsis patients with critical illness. ROC curve was used to evaluate the diagnostic of SAE.

RESULTS

We screened 82 EP-DEGs from DEGs. EP-DEGs were enriched in cytokine-cytokine receptor interaction, IL-17 signaling pathway and NOD-like receptor signaling pathway. We identified 2 key extracellular proteins IL-1B and IL-8. We clinically verified that IL-6 and IL-8 levels were increased in CSF of SAE patients and CSF IL-8 (AUC = 0.882, 95 % CI = 0.775-0.988) had a higher accuracy in the diagnosis of SAE than CSF IL-6 (AUC = 0.824, 95 % CI = 0.686-0.961). Furthermore, we found that the IL-8 levels in CSF might not associated with Glasgow Coma Scale (GCS) scores of SAE patients.

CONCLUSION

IL-8 may be the key extracellular cytokine in the pathogenesis of SAE. Bioinformatics methods were used to explore the biomarkers of SAE and validated the results in clinical samples. Our findings indicate that the IL-8 in CSF might be the potential diagnostic biomarker and therapeutic target in SAE.

SPI1-mediated autophagy of peripheral blood monocyte cells as a mechanism for sepsis based on single-cell RNA sequencing

Autophagy has been documented to participate in immune responses and inflammatory diseases, but the mechanistic actions of monocyte autophagy in sepsis remain largely unknown. This study intends to analyze the mechanism of autophagy of peripheral blood monocyte cells (PBMCs) in sepsis based on single-cell RNA sequencing (scRNA-seq). The scRNA-seq data of PBMC samples from sepsis patients were downloaded from the GEO database, followed by identification of cell marker genes, key pathways and key genes. The bioinformatics analysis showed that the PBMC samples of sepsis patients mainly contained 9 immune cell types, among which three types of monocytes showed significant changes in cell numbers in sepsis patients. Of note, the highest autophagy score was found in the intermediate monocytes. The Annexin signaling pathway was a key pathway for the communication between monocytes and other cells. More importantly, SPI1 was predicted as a key gene in the autophagy phenotype of intermediate monocytes, and SPI1 might suppress ANXA1 transcription. The high expression of SPI1 in sepsis was confirmed by RT-qPCR and Western blot analysis. Dual luciferase reporter gene assay verified that SPI1 could bind to the promoter region of ANXA1. Furthermore, it was found that SPI1 might affect monocyte autophagy in the mouse model of sepsis through regulation of ANXA1. In conclusion, we provide insight into the mechanism underlying the septic potential of SPI1, which enhances monocyte autophagy by inhibiting the transcription of ANXA1 in sepsis.

Emerging biomarkers for the detection of cardiovascular diseases

Background

The prevalence of cardiovascular disease (CVD) has been continuously increasing, and this trend is projected to continue. CVD is rapidly becoming a significant public health issue. Every year there is a spike in hospital cases of CVD, a critical health concern in lower- and middle-income countries. Based on identification of novel biomarkers, it would be necessary to study and evaluate the diagnostic requirements or CVD to expedite early detection.

Main body

The literature review was written using a wide range of sources, such as well-known medical journals, electronic databases, manuscripts, texts, and other writings from the university library. After that, we analysed the specific markers of CVD and compiled a systematic review. A growing body of clinical research aims to identify people who are at risk for cardiovascular disease by looking for biomolecules. A small number of biomarkers have been shown to be useful and reliable in medicine. Biomarkers can be used for a variety of clinical applications, such as predicting heart disease risk, diagnosing disease, or predicting outcomes. As a result of the ability for a single molecule to act as a biomarker, its usefulness in medicine is expected to increase significantly.

Conclusions

Based on assessing the current trends in the application of CVD markers, we discussed and described the requirements for the application of CVD biomarkers in coronary heart disease, cerebrovascular disease, rheumatic heart disease, and other cardiovascular illnesses. Furthermore, the current review focuses on biomarkers for CVD and the procedures that should be considered to establish the comprehensive nature of the expression of biomarkers for cardiovascular illness.

Effects of peripheral administration of lipopolysaccharide on chronic sickness responses in TRPM8-deficient mice

Transient receptor potential melastatin 8 (TRPM8) is a cold-sensing thermoreceptor cation channel; however, its functional role in endotoxin-induced neuroinflammation remains unclear. In the present study, we investigated chronic sickness responses in TRPM8 knockout (KO) mice during lipopolysaccharide (LPS)-induced sepsis. The intraperitoneal administration of 5 mg/kg LPS generated longer-lasting hypothermia in TRPM8 KO mice than in wild-type (WT) mice. TRPM8 KO mice also exhibited longer-lasting declines in locomotor activity, body weight, and food and water intakes than WT mice upon LPS administration. In addition, LPS-induced decreases in the numbers of leucocytes and lymphocytes that persisted for a longer time in TRPM8 KO mice than in WT mice. The present results indicate TRPM8 attenuated chronic sickness responses in endotoxin-induced sepsis.

Comprehensive Genetic Analysis of Tuberculosis and Identification of Candidate Biomarkers

Purpose: The purpose of this study is to use the data in the GEO database to analyze, screen biomarkers that can diagnose tuberculosis, and verification of candidate biomarkers.

Materials and methods: GSE158767 dataset were used to process WGCNA analysis, differential gene analysis, Gene ontology and KEGG analysis, protein-protein network analysis and hub genes analysis. Based on our previous study, the intersect between WGCNA and differential gene analysis could be used as candidate biomarkers. Then, the enzyme-linked immunosorbent assay was used to validate candidate biomarkers, and receiver operating characteristic was used to assess diagnose ability of candidate biomarkers.

Results: A total of 412 differential genes were screened. And we obtained 105 overlapping genes between DEGs and WGCNA. GO and KEGG analysis showed that most of the differential genes were significantly enriched in innate immunity. A total of 15 hub genes were screened, and four of them were verified by Enzyme-linked immunosorbent assay. CCL5 performed well in distinguishing the healthy group from the TB group (AUC = 0.723). And CCL19 performed well in distinguishing the TB group from the ORD groups (AUC = 0.811).

Conclusion: CCL19, C1Qb, CCL5 and HLA-DMB may play important role in tuberculosis, which indicated four genes may become effective biomarkers and could be conveniently used to facilitate the individual tuberculosis diagnosis in Chinese people.