Mechanism of Action
Understand how a treatment affects biological pathways and molecular processes. Cortex performs differential expression, pathway enrichment, transcription factor activity inference, and cross-condition comparison on your treated-vs-control omics data. The analysis produces a complete mechanistic picture: from individual gene changes through pathway-level effects to master regulator identification, with every finding grounded in statistical evidence.
Deliverables
Affected pathways ranked by enrichment
GSEA enrichment across Hallmark, KEGG, and Reactome gene sets identifies which biological processes are activated or suppressed by treatment. For the IL-4/IL-13/IL-33 study, 38 significant pathways were identified: TNFα/NF-κB as the top IL-33 pathway (NES 2.27 in mast cells) and IL2/STAT5 as the top IL-4/IL-13 pathway (NES 2.01 in eosinophils). Cross-condition comparison reveals how different treatments engage different biological programs.
Dose-response patterns
Transcription factor activity inference (via decoupler with CollecTRI regulons) identifies the upstream regulators driving observed expression changes. STAT6 emerged as the master regulator in IL-4/IL-13-treated eosinophils (score 19.4/13.6), while NFKB1 dominated IL-33 responses in both cell types (score 52.3/53.3). MEIS2 was identified as a novel regulator, a finding that could not have been predicted from pathway analysis alone.
Cross-pathway interaction mapping
Systematic comparison across all treatment conditions reveals which effects are shared versus condition-specific. The key finding, that IL-33 drives the broadest response via NF-κB/AP-1 rather than STAT6, overturns the assumption that all type-2 cytokines signal through similar pathways. Over 90% of DEGs were cell-type-specific, demonstrating why cell-type resolution is essential for MoA studies.
Identify on-target and off-target effects to inform lead optimization or safety assessment. Focus on treatments that engage intended pathways while monitoring for unexpected transcription factor activation or off-target pathway effects.
IL-4/IL-13/IL-33 mechanism of action in eosinophils and mast cells
| Cytokine | Eos (up/down) | MC (up/down) |
|---|---|---|
| IL-33 | 850 (617↑ 233↓) | 1334 (964↑ 370↓) |
| IL-4 | 664 (481↑ 183↓) | 646 (411↑ 235↓) |
| IL-13 | 376 (315↑ 61↓) | 402 (306↑ 96↓) |
| Pathway | Eos IL-4 | Eos IL-13 | Eos IL-33 | MC IL-4 | MC IL-13 | MC IL-33 |
|---|---|---|---|---|---|---|
| TNFα/NF-κB | n/a | n/a | 1.83 | n/a | n/a | 2.27 |
| IL2/STAT5 | 1.89 | 2.01 | n/a | n/a | n/a | n/a |
| Allograft rejection | n/a | n/a | n/a | 2.09 | 2.26 | n/a |
FLUOXETINE NSCLC MECHANISM (GSE200209)
ATF4 (published mechanism) is NOT significant (padj=0.456). SREBF1 is the true driver (#1/688, diff=1.723, padj=0.024). ATF4 mRNA is also unchanged (log2FC=-0.108, padj=0.665). This overturns the published mechanism.
MYC paradox: MYC mRNA unchanged but MYC_TARGETS_V1 NES=-3.615, suggesting post-translational suppression. ER stress not significant (NES=0.963, FDR=0.574). Cholesterol biosynthesis is the dominant program (NES=2.936).
| Gene | log2FC | Pathway |
|---|---|---|
| HMGCS1 | +2.62 | Cholesterol biosynthesis |
| INSIG1 | +2.37 | Cholesterol homeostasis |
| FADS2 | +2.23 | Fatty acid desaturation |
| MSMO1 | +2.08 | Sterol biosynthesis |
| SCD | +1.81 | Lipid desaturation |
ATG7 ALZHEIMER'S DOUBLE VULNERABILITY (GSE286094-GSE286095)
UPR impairment (ATF6 -1.27) and ferroptosis activation (NES +1.94) are anti-correlated: ATG7-knockout microglia face simultaneous protein quality control collapse and oxidative vulnerability. 8 growth/survival pathways collapsed, with only p53 and TNFa remaining active (DAM state).
| Pathway | NES | Direction |
|---|---|---|
| Ribosome | +2.89 | UP |
| Translation | +2.71 | UP |
| IFN response | +2.34 | UP |
| Protein processing in ER | -2.64 | DOWN |
| Antigen presentation | -2.31 | DOWN |
| N-glycan biosynthesis | -2.22 | DOWN |
Methodology
DESeq2 differential expression per condition
Each treatment condition is analyzed independently using DESeq2 with donor-level covariates. For the 62-sample dataset (24 eosinophil, 38 mast cell), this produced condition-specific DEG lists: from IL-33's 1,334 mast cell DEGs down to IL-13's 376 eosinophil DEGs.
GSEA pathway enrichment across gene set collections
Gene Set Enrichment Analysis runs across Hallmark, KEGG, and Reactome collections for each condition. The analysis identified 38 significant pathways with condition-specific enrichment patterns, revealing that IL-33 and IL-4/IL-13 engage fundamentally different biological programs.
Transcription factor activity inference
Decoupler with CollecTRI regulons infers upstream transcription factor activities from the observed expression changes. This identified STAT6, NFKB1, AP-1, and the novel regulator MEIS2 as the key drivers of the observed transcriptional responses.
Gene regulatory network construction
Condition-specific regulatory edges are constructed from TF-target relationships, producing networks that show how master regulators propagate their signals through downstream gene programs.
Cross-condition concordance metrics
Systematic comparison across all conditions and cell types quantifies shared versus unique effects. Concordance metrics (Pearson, Spearman, Jaccard) measure the degree of overlap, revealing that over 90% of DEGs are cell-type-specific.
Target personas
Pharmacologist
Understand treatment mechanisms at the pathway and transcription factor level to inform pharmacological models.
MoA scientist
Get a complete mechanistic picture from gene-level changes through master regulator identification.
Lead optimization team
Compare MoA profiles across compounds or conditions to guide optimization toward desired biological effects.
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