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. 2022 Aug 22:13:964622.
doi: 10.3389/fimmu.2022.964622. eCollection 2022.

Novel insight on marker genes and pathogenic peripheral neutrophil subtypes in acute pancreatitis

Deyu Zhang  1 , Meiqi Wang  1 , Yang Zhang  1 , Chuanchao Xia  1 , Lisi Peng  1 , Keliang Li  2 , Hua Yin  1 , Shiyu Li  1 , Xiaoli Yang  1 , Xiaoju Su  1 , Haojie Huang  1
Affiliations

Novel insight on marker genes and pathogenic peripheral neutrophil subtypes in acute pancreatitis

Deyu Zhang et al. Front Immunol. .

Abstract

Acute pancreatitis is a common critical and acute gastrointestinal disease worldwide, with an increasing percentage of morbidity. However, the gene expression pattern in peripheral blood has not been fully analyzed. In addition, the mechanism of coronavirus disease 2019 (COVID-19)-induced acute pancreatitis has not been investigated. Here, after bioinformatic analysis with machine-learning methods of the expression data of peripheral blood cells and validation in local patients, two functional gene modules in peripheral blood cells of acute pancreatitis were identified, and S100A6, S100A9, and S100A12 were validated as predictors of severe pancreatitis. Additionally, through a combination analysis of bulk sequencing and single-cell sequencing data of COVID-19 patients, a pivotal subtype of neutrophils with strong activation of the interferon-related pathway was identified as a pivotal peripheral blood cell subtype for COVID-19-induced acute pancreatitis. These results could facilitate the prognostic prediction of acute pancreatitis and research on COVID-19-induced acute pancreatitis.

Keywords: COVID-19; WGCNA; acute pancreatitis; biomarkers; neutrophil; single-cell 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
(A) Volcano plot of peripheral blood of acute pancreatitis datasets (logFC >1, p < 0.05). (B) Heatmap of the top 50 upregulated and downregulated genes with unsupervised clustering analysis. (C) Bar graph of enriched terms across input gene lists, colored by p-values. (D) Terms of the Gene Ontology list, colored by p-values. (E) Protein−protein interaction network of DEGs. (F) Network of the top 10 genes. (G) Enriched score of the top 10 genes.
Figure 2
Figure 2
WGCNA results in different subtypes of acute pancreatitis. (A) Sample dendrogram. (B) The scale plot of WGCNA to identify optimal vector power (cutoff value = 0.8). (C) Sample dendrogram and trait heatmap. (D) Module–trait relationships: every module has its correlation coefficient and corresponding p-value.
Figure 3
Figure 3
(A) Gene Ontology analysis and KEGG pathway analysis with an interaction network in genes from the turquoise module. (B) Gene Ontology analysis and KEGG pathway analysis with an interaction network in genes from the brown module.
Figure 4
Figure 4
(A) Estimation of immune cell subtype infiltration in all samples, divided into three groups (1 = healthy control group, 2 = MAP group, and 3 = MSAP&SAP group). (B) Error plot of random forest analysis (Tree = 1,500). (C) Top 30 genes in random forest analysis. (D) ROC plot of genes with significant diagnostic value (AUC >0.8). **P < 0.01, ***P < 0.001, ****P < 0.0001, ns, P > 0.05.
Figure 5
Figure 5
(A) Bar plot of 10 identified genes with prognostic value in the current datasets. (B) Expression of S100A6, S100A9, and S100A12 in the peripheral blood of our local cohort. (C) ROC plot of S100A6, S100A9, and S100A12 based on the peripheral blood expression of our local cohort. ***P < 0.001.
Figure 6
Figure 6
(A) Common DEGs between the peripheral blood of acute pancreatitis and COVID-19 patients. (B) Gene Ontology and KEGG pathways in the identified common DEGs. (C) Gene Ontology and KEGG pathway interaction network with each term. (D) Gene Ontology and KEGG pathway interaction network with p-values.
Figure 7
Figure 7
(A) Cluster tree at different resolutions. (B) Neutrophil markers in different subtypes of peripheral blood. (C) Identified neutrophil subtypes in the UMAP plot with subtypes. (D) Identified neutrophil subtypes in the UMAP plot with groups.
Figure 8
Figure 8
(A) Neutrophil markers in different subtypes of peripheral blood. (B) Identified neutrophil subtypes in the UMAP plot with subtypes. (C) Cluster tree at different resolutions. (D) Stacked plot of neutrophil subtypes. (E) Heatmap of the top 10 genes in each subtype. (F) Markers of neutrophil degranulation in each subtype.
Figure 9
Figure 9
(A) Single-cell pathway analysis of the five subgroups. (B) The activated interferon-related pathway among all subgroups. (C) Pseudotime locus analysis of the five subgroups sorted by pseudotime. (D) Pseudotime locus analysis of the five subgroups sorted by subgroup. (E) Single-cell metabolism-related pathway analysis in Group 0 between the COVID-19-induced sepsis group and the bacteria-induced sepsis group. (F) The common identified metabolism-related pathways in Group 0. (G) The proportion of the identified subtype from single-cell sequencing data in all neutrophils among acute pancreatitis patients and healthy controls (based on the GSE194331 dataset) (*p < 0.05, ***p < 0.0001).
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