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. 2024 Jan 25:14:1298041.
doi: 10.3389/fimmu.2023.1298041. eCollection 2023.

Elucidating common pathogenic transcriptional networks in infective endocarditis and sepsis: integrated insights from biomarker discovery and single-cell RNA sequencing

Chen Yi  1 , Haoxiang Zhang  1 , Jun Yang  1 , Dongjuan Chen  2 , Shaofeng Jiang  1
Affiliations

Elucidating common pathogenic transcriptional networks in infective endocarditis and sepsis: integrated insights from biomarker discovery and single-cell RNA sequencing

Chen Yi et al. Front Immunol. .

Abstract

Background: Infective Endocarditis (IE) and Sepsis are two closely related infectious diseases, yet their shared pathogenic mechanisms at the transcriptional level remain unclear. This research gap poses a barrier to the development of refined therapeutic strategies and drug innovation.

Methods: This study employed a collaborative approach using both microarray data and single-cell RNA sequencing (scRNA-seq) data to identify biomarkers for IE and Sepsis. It also offered an in-depth analysis of the roles and regulatory patterns of immune cells in these diseases.

Results: We successfully identified four key biomarkers correlated with IE and Sepsis, namely CD177, IRAK3, RNASE2, and S100A12. Further investigation revealed the central role of Th1 cells, B cells, T cells, and IL-10, among other immune cells and cytokines, in the pathogenesis of these conditions. Notably, the small molecule drug Matrine exhibited potential therapeutic effects by targeting IL-10. Additionally, we discovered two Sepsis subgroups with distinct inflammatory responses and therapeutic strategies, where CD177 demonstrated significant classification value. The reliability of CD177 as a biomarker was further validated through qRT-PCR experiments.

Conclusion: This research not only paves the way for early diagnosis and treatment of IE and Sepsis but also underscores the importance of identifying shared pathogenic mechanisms and novel therapeutic targets at the transcriptional level. Despite limitations in data volume and experimental validation, these preliminary findings add new perspectives to our understanding of these complex diseases.

Keywords: IL-10; immune cells; infective endocarditis; sepsis; single-cell RNA sequencing.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Research flowchart.
Figure 2
Figure 2
DEGs visualization of datasets. (A, B) DEGs from GSE29161. (C, D) DEGs from GSE57065. (E, F) DEGs from GSE95233.
Figure 3
Figure 3
Identification of module genes via WGCNA. (A) Soft threshold analysis in GSE57065. (B) Clustering dendrogram and merging of the gene co-expression modules represented by different colors in GSE57065. (C) Heatmap of the module–trait relationship in GSE57065. (D) Soft threshold analysis in GSE95233. (E) Clustering dendrogram and merging of the gene co-expression modules represented by different colors in GSE95233. (F) Heatmap of the module-trait relationship in GSE95233.
Figure 4
Figure 4
Functional analysis of common genes between DEGs and key modular genes. (A–C) Venn diagram of common genes between DEGs and key modular genes. (D) The PPI network among candidate genes. (E–G) GO analysis. (H) KEGG analysis.
Figure 5
Figure 5
scRNA-seq data analysis. (A) Cells were categorized into 15 clusters. (B) The 15 clusters were further assigned to 6 known cell lineages. (C) Comparison of Cell Distribution between Healthy Control and Sepsis Samples. (D) Percentages of Cells in Healthy Control and Sepsis Samples.
Figure 6
Figure 6
Differential expression and immune infiltration status between sepsis clusters. (A) Unsupervised clustering results, left top - Consensus Matrix(visualizes the frequency with which data points are assigned to the same cluster across multiple clustering runs), left bottom - Consensus Cumulative Distribution Function(CCDF, measures the degree of consistency in the assignment of each data point to different clusters), right top - Delta Area(indicates the change in slope of the CCDF curve), right bottom - Tracking Plot(depicts the dynamic changes of data points across different clusters), based on these visualizations, the optimal number of clusters is 2. (B, C) Visualization of DEGs between sepsis clusters. (D) Differences in abundance of immune cells from blood samples. (E) Differences in expression levels of immune cells from blood samples. * P<0.05, ** P<0.01, *** P<0.001, ns: not significance.
Figure 7
Figure 7
Machine learning screening for key biomarkers. (A–C) Screening of key biomarkers from GSE57065 using Lasso modeling. (D–F) Screening of key biomarkers from GSE95233 using Lasso modeling. (G) RF algorithm shows importance of genes from GSE57065. (H) RF algorithm shows importance of genes from GSE95233. (I) Co-diagnostic genes. (J–L) Screening of key biomarkers from two sepsis clusters samples using Lasso modeling. (M) RF algorithm shows importance of genes from two sepsis clusters samples. (N) Key cluster genes.
Figure 8
Figure 8
Capacity assessment of key biomarkers. (A–D) ROC curve for the Infective Endocarditis dataset (A), two sepsis validation datasets (GSE69063 - B, GSE131761 - C) and sepsis RNA-seq data(GSE185263 - D), left top - CD177, left bottom - IRAK3, right top - RNASE2, right bottom - S100A12. (E) Expression levels of diagnostic genes for GSE29161. (F) Expression levels of diagnostic genes for GSE69063. (G) Expression levels of diagnostic genes for GSE131761. (H) Expression levels of diagnostic genes for GSE185263. (I) Cellular distribution of IRAK3. (J) Cellular distribution of RNASE2. (K) Cellular distribution of S100A12. (L) Differences in expression levels of key biomarkers for scRNA-seq. (M) Common genes for diagnostic and cluster genes. (N) Expression levels of cluster genes for two sepsis clusters samples. (O) ROC curve for sepsis clusters samples, based on CD177. (P) ROC curve for sepsis clusters samples, based on IRAK3. (G) Pathways associated with the action of CD177 in the enrichment results of two sepsis cluster DEGs. * P<0.05, ** P<0.01, *** P<0.001, ns: not significance.
Figure 9
Figure 9
Selection of target proteins. (A) Correlation among diagnostic genes based on GSE29161. (B) Correlation among diagnostic genes based on GSE57065. (C) mRNA-miRNA network. (D) PPI network for diagnostic genes and inflammatory cytokines. (E) Correlation between diagnostic genes and inflammatory cytokines in GSE29161. (F) Correlation between diagnostic genes and inflammatory cytokines in sepsis samples from GSE5706. (G–H) Correlation between diagnostic genes and inflammatory cytokines in sepsis clusters sample(G-sepsis cluster1,H- sepsis cluster2). (I) Expression levels of IL10 for sepsis clusters samples. (J) GSEA analysis of GSE29161. (K) GSEA analysis of GSE57065. * P<0.05, ** P<0.01, *** P<0.001, ns: not significance.
Figure 10
Figure 10
Molecular docking of IL-10 with small molecules. (A) Resveratrol. (B) Curcumin. (C) Matrine. (D) Taurine.
Figure 11
Figure 11
Validation of the reliability of the CD177 as biomarker by qRT-PCR. * P<0.05, ** P<0.01, *** P<0.001, ns: not significance.
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