Artificial Intelligence Analysis of Celiac Disease Using an Autoimmune Discovery Transcriptomic Panel Highlighted Pathogenic Genes including BTLA
Abstract
:1. Introduction
2. Materials and Methods
2.1. Celiac Disease GSE164883 Dataset
2.2. GEOR Analysis
2.3. Transcriptome Panels
2.4. Gene Set Enrichment Analysis (GSEA)
2.5. Statistical Analyses
2.6. Immunohistochemical Analysis of BTLA in an Independent Series
3. Results
- A conventional analysis using GEO2R highlighted the genes differentially expressed between celiac disease and control.
- Gene set enrichment analysis (GSEA) identified the gene sets (pathways) that were associated with celiac disease, including the autoimmune discovery panel.
- Several Machine learning and artificial neural network analyses predicted celiac disease using the autoimmune discovery panel with high accuracy.
- Celiac disease was characterized by high expression of BTLA both at the gene expression level, and at protein level by immunohistochemistry in a validation series.
3.1. Gene Expression Analysis Using the GEO2R Software
3.2. Gene Set Enrichment Analysis (GSEA)
3.3. Artificial Intelligence Analysis
3.4. Differential Gene Expression of BTLA between Celiac Disease and Control Samples
3.5. Validation of BTLA by Immunohistochemistry in an Independent Series
3.6. Differential Gene Expression of LAG3 between Celiac Disease and Control Samples
4. Discussion
Supplementary Materials
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Age | Sex | Biopsy Location | Diagnosis | Marsh-Oberhuber Classification |
---|---|---|---|---|
70 | Male | Duodenum | Celiac Disease | 3a |
62 | Male | Pylorus/duodenum | Celiac Disease/Chronic gastritis | 2 |
62 | Male | Duodenum | Celiac Disease | 2 |
78 | Female | Duodenum | Celiac Disease | 3b |
59 | Male | Duodenum | Celiac Disease | 3a |
44 | Female | Duodenum | Celiac Disease | 2 |
17 | Female | Duodenum | Celiac Disease | 3b |
56 | Female | Duodenum | Celiac Disease | 3a |
54 | Female | Duodenum | Celiac Disease | 2 |
58 | Female | Duodenum | Celiac Disease | 3b |
61 | Female | Duodenum | Celiac Disease | 3c |
45 | Male | Duodenum | Celiac Disease | 3a |
70 | Female | Duodenum | Celiac Disease | 2 |
40 | Female | Duodenum | Celiac Disease | 3a |
61 | Female | Duodenum | Celiac Disease | 3c |
44 | Female | Duodenum | Celiac Disease | 3a |
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Factors | Pathophysiology | References |
---|---|---|
Dietary gluten | ① Gluten of wheat, rye, and barley. Gliadins and glutenins are rich in proline, which makes them resistant to proteolysis by gastric and pancreatic enzymes. Various long gliadin peptides activate the immune system (“33mer”). Undigested peptides may also affect intestinal microbiota. | [18,19,20,21] |
Genetics | ① Genetic predisposition: HLA-DQ2 and HLA-DQ8 contribute to 20%–40% of the genetic risk. They are class II MHC expressed by antigen-presenting cells (APCs). | [22,23,24] |
② Forty-two non-HLA regions have been associated with celiac disease. It is estimated that they account for 15% of the genetic risk: IL18R1, IL18RAP, STAT4, CD28, CTLA4, ICOS, CCR4, CCR1, CCR2, CCR3, CD3E, IL1R1, IL12A, IL2, IL21, TNFAIP3, ELMO1, PRKCQ, SOCS1, ICOSLG, and IRAK1. These genes belong to cytokine-cytokine receptor activation, JAK-STAT pathway, T-cell receptor signaling, intestinal immune network for IgA production, NF-KB signaling, and cell adhesion molecules. Of note, many of these genes belong to the immune checkpoint and immune-oncology pathway. | [22,23,25,26,27,28] | |
Immune | ① Generation of gluten-specific T-cell responses: presence of gluten-specific CD4-positive T lymphocytes, antibodies against gliadin and de enzyme TG2, and pro-inflammatory cytokines. | [29,30] |
② Generation of autoantibodies: activation and differentiation into plasma cells of gluten-specific and TG2-specific B lymphocytes, generation of autoantibodies that are both circulating and deposited in the mucosa. These autoantibodies are responsible for the increased permeability of the epithelial barrier. | [31,32,33] | |
③ Cytokines in the intestinal mucosal immune system: IFN gamma and IL-21 are produced by gluten-specific CD4-positive T lymphocytes. Secretion of IL-15, IL-18, and inhibition of FOXP3-positive regulatory T lymphocytes (Tregs). | [34,35] | |
④ Intraepithelial lymphocytes (IELs): increased in celiac disease and their amount correlates with mucosal atrophy. IELs display cytotoxic transformation and induce apoptosis of intestinal epithelial cells through FAS-L, perforin, granzyme B, and NKG2D. NKG2D interacts with MICA on epithelial cells. | [36,37,38,39,40,41,42] | |
⑤ Innate immune activation: dysregulation of the production of IL-15 and activation of the innate immune response, including the induction of epithelial stress. | [43,44] | |
Environmental | ① Microorganisms: intestinal dysbiosis (unbalanced intestinal microbiota) and increased prevalence of specific microbial virulence genes isolated from celiac disease patients. | [45,46,47,48,49,50] |
② Others, such as smoking | [51] |
Model | Overall Accuracy (%) | No. Genes (Fields) Used | Most Relevant Genes |
---|---|---|---|
C5 | 100 | 1 | IFNG |
Logistic regression | 100 | 737 | (Refer to Table 3) |
Discriminant | 100 | 737 | - |
LSVM 1 | 100 | 737 | CASP1, IL18, ARPC2, CASP3, KLF4, GBP1, SULT1A1, RNASET2, MIF, and PIGR |
SVM | 100 | 737 | - |
XGBoost linear | 100 | 737 | - |
XGBoost tree | 100 | 737 | - |
CHAID | 100 | 2 | BATF, GBP1 |
C&R tree | 100 | 6 | IFNG |
Random forest 1 | 100 | 737 | CXCL10, PRDM1, GZMB, STAT2, IL12RB1, LAG3, PTPN22, TMEM50B, IFI35, PRDX5, GALC, C1QBP, RIPK2, IFNG, CSF2, STAT5A, TNPO3, IQCB1, and DEXI |
Neural network 1 | 100 | 737 | CXCL2, IL7R, PLCH2, CCL23, MBD2, CSF3R, MUC1, GPR183, CD226, and PNMT |
KNN algorithm | 96 | 737 | - |
Quest | 96 | 6 | STAT1 |
Random trees 1 | 86 | 737 | BTLA, CARD14, CASP10, CCL13, CCL5, CCR7, CXCL10, CXCL9, CXCR6, ELMO1, and EXTL |
Bayesian network | 58 | 737 | - |
Equation for Predicting Celiac Disease |
---|
−0.1765 × AAMP + −0.008 × ABHD6 + −0.1178 × ACKR2 + −1.725 × ACOXL + 0.6231 × ACSL6 + 0.0009441 × ADA + 1.16 × ADAM30 + 0.04882 × ADCY3 + 1.108 × ADCY7 + 0.2923 × AFF3 + −0.5828 × AGAP2 + 0.6009 × AHI1 + 0.3013 × AHR + −0.002197 × AIRE + −0.7633 × ANKRD55 + 0.06059 × ANTXR2 + 0.2416 × APEH + 1.215 × APOBEC3G + −2.377 × ARG1 + −0.2806 × ARHGAP30 + 0.0796 × ARID5B + −0.0168 × ARPC2 + −0.009025 × ATF4 + −0.08039 × ATG16L1 + −0.156 × ATG5 + 0.09123 × ATM + 0.003949 × B2M + 0.02826 × B3GNT2 + −0.2021 × BABAM2 + 1.132 × BACH2 + −0.6567 × BAD + −0.2759 × BANK1 + −0.09905 × BATF + 0.617 × BATF3 + 0.1081 × BCL10 + −0.1113 × BCL3 + 0.2034 × BCL6 + 0.7125 × BID + 0.3596 × BLK + 0.1998 × BLNK + −0.4926 × BORCS5 + −3.589 × BSN + 1.291 × BTK + −1.079 × BTLA + −1.254 × BTNL2 + −0.1576 × C1orf53 + 0.004046 × C1QBP + −49.65 |
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Carreras, J. Artificial Intelligence Analysis of Celiac Disease Using an Autoimmune Discovery Transcriptomic Panel Highlighted Pathogenic Genes including BTLA. Healthcare 2022, 10, 1550. https://doi.org/10.3390/healthcare10081550
Carreras J. Artificial Intelligence Analysis of Celiac Disease Using an Autoimmune Discovery Transcriptomic Panel Highlighted Pathogenic Genes including BTLA. Healthcare. 2022; 10(8):1550. https://doi.org/10.3390/healthcare10081550
Chicago/Turabian StyleCarreras, Joaquim. 2022. "Artificial Intelligence Analysis of Celiac Disease Using an Autoimmune Discovery Transcriptomic Panel Highlighted Pathogenic Genes including BTLA" Healthcare 10, no. 8: 1550. https://doi.org/10.3390/healthcare10081550