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. 2023 Jan 5:13:1096587.
doi: 10.3389/fimmu.2022.1096587. eCollection 2022.

Investigating regulatory patterns of NLRP3 Inflammasome features and association with immune microenvironment in Crohn's disease

Huihuan Wu  1   2 , Ruijie Zeng  1   3 , Xinqi Qiu  4 , Kequan Chen  5 , Zewei Zhuo  1 , Kehang Guo  6 , Yawen Xiang  7 , Qi Yang  1 , Rui Jiang  2 , Felix W Leung  8 , Qizhou Lian  9 , Weihong Sha  1   2 , Hao Chen  1   2
Affiliations

Investigating regulatory patterns of NLRP3 Inflammasome features and association with immune microenvironment in Crohn's disease

Huihuan Wu et al. Front Immunol. .

Abstract

Introduction: Crohn's disease is characterized of dysregulated inflammatory and immune reactions. The role of the NOD-like receptor family, pyrin domain-containing 3 (NLRP3) inflammasome in Crohn's disease remains largely unknown.

Methods: The microarray-based transcriptomic data and corresponding clinical information of GSE100833 and GSE16879 were obtained from the Gene Expression Omnibus (GEO) database. Identification of in the NLRP3 inflammasome-related genes and construction of LASSO regression model. Immune landscape analysis was evaluated with ssGSEA. Classification of Crohn's-disease samples based on NLRP3 inflammasome-related genes with ConsensusClusterPlus. Functional enrichment analysis, gene set variation analysis (GSVA) and drug-gene interaction network.

Results: The expressions of NLRP3 inflammasome-related genes were increased in diseased tissues, and higher expressions of NLRP3 inflammasome-related genes were correlated with generally enhanced immune cell infiltration, immune-related pathways and human leukocyte antigen (HLA)-gene expressions. The gene-based signature showed well performance in the diagnosis of Crohn's disease. Moreover, consensus clustering identified two Crohn's disease clusters based on NLRP3 inflammasome-related genes, and cluster 2 was with higher expressions of the genes. Cluster 2 demonstrated upregulated activities of immune environment in Crohn's disease. Furthermore, four key hub genes were identified and potential drugs were explored for the treatment of Crohn's disease.

Conclusions: Our findings indicate that NLRP3 inflammasome and its related genes could regulate immune cells and responses, as well as involve in the pathogenesis of Crohn's disease from transcriptomic aspects. These findings provide in silico insights into the diagnosis and treatment of Crohn's disease and might assist in the clinical decision-making process.

Keywords: Crohn’s disease; NLRP3 inflammasome; drug; immune landscape; treatment.

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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. The reviewer HY declared a shared parent affiliation with the author, KC, to the handling editor at the time of the review.

Figures

Figure 1
Figure 1
Characteristics and interactions of 25 NLRP3 inflammasome-related genes. (A) Circos plot showing the location of genes in 24 chromosomes. (B) protein-protein interaction networks of DEGs. Red represents positive correlations while blue represents negative correlations.
Figure 2
Figure 2
Expressions of NLRP3 inflammasome-related genes. (A) Box plot demonstrating the overview of gene expressions in the GSE100833 dataset. Blue: healthy (non-inflamed) tissues; Red: diseased (inflamed) tissues. (B) Volcano plot illustrating the differentially expressed genes (DEGs) in the GSE100833 dataset. Green: downregulation. Red: upregulation. Grey: not significantly altered. (C) Heatmap for the DEGs in the GSE100833 dataset. Blue: tissues from healthy control; Red: tissues from patients with Crohn’s disease. ns: P ≥ 0.05; *: P < 0.05; ** P ≤ 0.01; **** P ≤ 0.0001.
Figure 3
Figure 3
Correlations between the expressions of 25 NLRP3 inflammasome-related genes. (A) Correlation plot showing significant correlations between the genes. Purple: positive correlation. Yellow: negative correlation. Circles with deeper colors indicate stronger correlations. (B) Correlation between CARD8 and GBP5 expressions in integrated samples. (C) Correlation between TLR4 and PANX1 expressions in diseased samples.
Figure 4
Figure 4
Construction of LASSO regression model and receiver operating characteristic (ROC) curve. (A) Forest plot showing the odds ratio and 95% confidence intervals for predicting Crohn’s disease of each gene. (B) Log lambda values of the genes corresponding to the minimum cross-validation error point. (C) Selection of genes with non-zero coefficient for construction of model. (D) Coefficients of each gene within the prediction model. (E) Box plot showing the scores of in the disease (red) and control group (blue). (F) ROC curve for prediction of Crohn’s disease. **** P ≤ 0.0001.
Figure 5
Figure 5
NLRP3 inflammasome-related genes and immune cell infiltrations in the immune landscape. (A) Comparison of immune cell infiltration levels between the two groups. (B) Correlation plot showing significant correlations between the genes and immune cell infiltrations. Purple: positive correlation. Yellow: negative correlation. Circles with deeper colors indicate stronger correlations. (C) Correlation between GBP5 expression and activated CD4+ T cell infiltration. (D, E) Violin plots showing the levels of activated CD4+ T cell (D) and GBP5 expression (E) in two groups. (F) Correlation between CARD8 expression and CD56dim natural killer cell infiltration. (G, H) Violin plots showing the levels of CD56dim natural killer cell (G) and CARD8 expression (H) in two groups. R represents Pearson correlation coefficients. *: P < 0.05; **** P ≤ 0.0001.
Figure 6
Figure 6
NLRP3 inflammasome-related genes and immune-related pathways. (A) Comparison of immune-related pathway activations between the two groups. (B) Correlation plot showing significant correlations between the genes and immune-related pathways. Purple: positive correlation. Yellow: negative correlation. Circles with deeper colors indicate stronger correlations. (C) Correlation between GBP5 expression and antigen processing and presentation pathway. (D, E) Violin plots showing the levels of antigen processing and presentation pathway (D) and GBP5 expression (E) in two groups. (F) Correlation between CASP1 expression and TGFβ family member. (G, H) Violin plots showing the levels of TGFβ family member (G) and CASP1 expression (H) in two groups. R represents Pearson correlation coefficients. *: P < 0.05; **** P ≤ 0.0001.
Figure 7
Figure 7
NLRP3 inflammasome-related genes and HLA-related genes. (A) Comparison of HLA-related gene expressions between the two groups. (B) Correlation plot showing significant correlations between the NLRP3 inflammasome- and HLA-related genes. Purple: positive correlation. Yellow: negative correlation. Circles with deeper colors indicate stronger correlations. (C) Correlation between CASP1 expression and antigen processing and presentation pathway. (D, E) Violin plots showing the expressions of HLA-DMA (D) and CASP1 expression (E) in two groups. (F) Correlation between TXN and HLA-DMA expressions (G-H) Violin plots showing the expressions of HLA-DMA (G) and TXN (H) in two groups. R represents Pearson correlation coefficients. ns: P ≥ 0.05; ** P ≤ 0.01; **** P ≤ 0.0001.
Figure 8
Figure 8
NLRP3 inflammasome-related gene-based classification of Crohn’s disease. (A) Unsupervised consensus clustering matrix and optimal clusters. (B) Item-consensus plot showing the relationship between each cluster. (C) Principal component analysis (PCA) based on clustering results. (D) Heatmap and box plots demonstrating expressions of NLRP3 inflammasome-related genes in each cluster. ns: P ≥ 0.05; *: P < 0.05; ** P ≤ 0.01; **** P ≤ 0.0001.
Figure 9
Figure 9
Comparison of immune microenvironment between clusters. (A) Box plot showing immune cell infiltration levels in two clusters. (B) Box plot demonstrating HLA expressions in two clusters. (C) Box plot illustrating immune reaction in two clusters. ns: P ≥ 0.05; *: P < 0.05; ** P ≤ 0.01; *** P ≤ 0.001; **** P ≤ 0.0001.
Figure 10
Figure 10
Pathway enrichment analysis and gene set variation analysis (GSVA) for two Crohn’s disease clusters. (A) The top 10 enriched gene sets by GO pathway (biological process, molecular function and cellular components) and KEGG pathway analysis according to enrichment scores. (B) Heatmap showing enriched pathways for single hub gene through GSVA.
Figure 11
Figure 11
Drug-gene interaction network and potential drug prediction. (A) Network of protein-protein interaction. (B) Drug-interaction prediction of key genes. Four key genes (APP, HSP90AB1, NFKB1 and TLR4) were targeted in the DGIdb database. Nine potential target drugs were predicted from the database. Blue: NLRP3 inflammasome-related genes, orange: non-NLRP3 inflammasome-related genes. the size of the point indicate the drugs interacts with NLRP3 inflammasome-related genes.
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References

    1. D’Silva A, Fox DE, Nasser Y, Vallance JK, Quinn RR, Ronksley PE, et al. . Prevalence and risk factors for fatigue in adults with inflammatory bowel disease: A systematic review with meta-analysis. Clin Gastroenterol Hepatol. (2021) 20(5):995–1009.e7. doi: 10.1016/j.cgh.2021.06.034 - DOI - PubMed
    1. Ng SC, Shi HY, Hamidi N, Underwood FE, Tang W, Benchimol EI, et al. . Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: A systematic review of population-based studies. Lancet (2017) 390:2769–78. doi: 10.1016/S0140-6736(17)32448-0 - DOI - PubMed
    1. Vadstrup K, Alulis S, Borsi A, Gustafsson N, Nielsen A, Wennerström ECM, et al. . Cost burden of crohn’s disease and ulcerative colitis in the 10-year period before diagnosis–a Danish register-based study from 2003–2015. Inflammatory Bowel Dis (2020) 26:1377–82. doi: 10.1093/ibd/izz265 - DOI - PMC - PubMed
    1. Klang E, Barash Y, Soffer S, Shachar E, Lahat A. Trends in inflammatory bowel disease treatment in the past two decades-a high-level text mining analysis of PubMed publications. UEG J (2021) 9(9):1019–26. doi: 10.1002/ueg2.12138 - DOI - PMC - PubMed
    1. Danese S, Argollo M, Le Berre C, Peyrin-Biroulet L. JAK selectivity for inflammatory bowel disease treatment: Does it clinically matter? Gut (2019) 68:1893–9. doi: 10.1136/gutjnl-2019-318448 - DOI - PubMed

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