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pmid:41552362
Identifying subtype-specific molecular pathways in Crohn's disease through RNA-seq and protein-protein interaction network analysis.
Sree Ishan Kolukula
Journal of translational autoimmunity · 2026 doi:10.1016/j.jtauto.2025.100337
Paper
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Experiment
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Data
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Analysis
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Complete file
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Anatomical labels like Ileal Crohn's Disease (ICD) and Colonic Crohn's Disease (CCD) do not capture the molecular heterogeneity which contributes to trial and error therapy. This trial and error pattern costs patients who switch biologics higher annual expenses. We analyzed bulk RNA-seq from 2353 biopsies across two independent data sets (GSE193677 and GSE57945) using a standardized pipeline. Principal component analysis confirmed clear molecular separation between ICD and CCD samples. Differential expression modeling (DESeq2, FDR ≤ 0.05) identified the top 300 differentially expressed genes (DEGs) across subtype specific signatures. Pathway analysis confirmed known subtype biology, with ICD driven by autophagy-related processes and CCD by immune activation pathways. Subtype-specific PPI networks diverged sharply, with CKB driving barrier-related processes in ICD and SPP1 coordinating immune activation in CCD. Known CD susceptibility genes (e.g., NOD2, ATG16L1, IL23R) were recovered within leading-edge sets, supporting construct validity. Proteomic validation using ProteomeXchange PXD012284 confirmed concordant enrichment of ICD-associated autophagy and lysosomal modules and CCD-associated innate immune pathways at the protein level. Single-cell transcriptomic validation further localized leading-edge genes to epithelial lineages in ICD and to myeloid and glial populations in CCD, supporting cellular specificity of subtype programs. Together, these results indicate that ICD and CCD are biologically distinct at the transcriptome, proteome, and network levels. 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We analyzed bulk RNA-seq from 2353 biopsies across two independent da", "type-associated genes. Together, integrating RNA-seq with PPI mapping, proteomic validation, and", "ed differences between ileal and colonic CD. RNA-seq analysis typically follows a standardized co"], "order_basis": "paper_text_order:30", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["nfirm corresponding protein-level trends and single-cell RNA-seq data to verify cell-type specificity. The fu", "l-type-specific signals.Fig. 1 Complementary single-cell RNA-seq (scRNA-seq) data were obtained from Kong et", "ular or cellular consequences, proteomic and scRNA-seq data were used to validate whether key pathw", "ls.Fig. 1 Complementary single-cell RNA-seq (scRNA-seq) data were obtained from Kong et al. which p"], "order_basis": "paper_text_order:2525", "id": "assay:2"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Differential expression analysis was performed using DESeq2, which models RNA-seq count data with a negative binomial distribution, to estimate differential gene expression between subtypes (Love et al. 2014)."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}