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Validation Report: Zhu/Dann T Cell Perturb-seq vs DisMech Autoimmune Entries

Date: 2026-02-14 Pipeline: Zhu, Dann, Ota, Pritchard, Marson et al. (bioRxiv Dec 2025) Method: Genome-scale CRISPRi Perturb-seq in primary CD4+ T cells; 111 regulator clusters tested for enrichment of GWAS disease genes (Open Targets, genetic_evidence >= 0.1)

Summary

Metric Value
Diseases evaluated 12 (of 14 tested; autoimmune umbrella excluded, atopic eczema not in KB)
Significant cluster-disease pairs (FDR < 0.05) 146
Unique GWAS genes tested 408
Genes confirmed in DisMech 11 (2.7%)
Genes novel to DisMech 405 (99.3%)

Per-Disease Confirmation Rates

Disease DisMech Genes Pairs Confirmed Novel Rate
Type 1 diabetes 10 5 3 34 8.1%
Hashimoto's thyroiditis 4 7 2 37 5.1%
Multiple sclerosis 6 14 3 65 4.4%
SLE 11 11 2 65 3.0%
Asthma 6 13 3 110 2.7%
Rheumatoid arthritis 9 15 3 139 2.1%
Psoriasis 5 9 1 59 1.7%
Ulcerative colitis 4 16 1 96 1.0%
IBD (merged) 10 17 1 156 0.6%
Crohn's disease 6 17 0 98 0.0%
Ankylosing spondylitis 3 15 0 74 0.0%
Celiac disease 4 7 0 27 0.0%

Confirmed Genes

These DisMech-curated genes were rediscovered by the T cell Perturb-seq pipeline:

Gene Confirmed In Role
PTPN22 RA, Hashimoto's, T1D, SLE (12 pairs) Shared autoimmune susceptibility; T cell signaling
CTLA4 RA, Hashimoto's, T1D (5 pairs) T cell co-inhibition; immune checkpoint
IL2RA MS, T1D (5 pairs) IL-2 receptor alpha; Treg homeostasis
IL4 Asthma (4 pairs) Th2 cytokine; allergic inflammation
STAT4 RA, SLE (2 pairs) Th1/Th17 signaling
IL13 Asthma (2 pairs) Th2 cytokine; allergic inflammation
IL23R IBD (2 pairs) Th17 differentiation
IL7R MS (2 pairs) T cell homeostasis
ORMDL3 Asthma (1 pair) ER stress, sphingolipid metabolism
TNFAIP3 Psoriasis (1 pair) NF-kB negative regulation (A20)
TNFRSF1A MS (1 pair) TNF receptor; neuroinflammation

Interpretation

Why is the confirmation rate so low?

The 2.7% rate does not indicate contradiction. It reflects a gene coverage gap in DisMech:

  1. DisMech has 3-11 genes per disease (median ~6), curated from classic GWAS papers and textbook genetics. These are the "greatest hits" -- HLA loci, PTPN22, IL23R, NOD2, etc.

  2. The pipeline tests hundreds of genes per disease from Open Targets (genetic_evidence_score >= 0.1), including many more recent GWAS discoveries, rare variant associations, and genes with modest effect sizes.

  3. The "novel" genes are mostly real. EGR2, BACH2, IRF4, STAT3, TNFAIP3, IKZF1, CD28, GATA3, SMAD3 are well-established autoimmune risk genes in the literature. They are "novel" only in the sense that DisMech hasn't curated them yet.

What does DisMech confirm well?

The confirmed genes cluster into functional categories: - T cell signaling: PTPN22, CTLA4, IL2RA, IL7R, CD28 (partial) - Cytokines: IL4, IL13, IL23R - Transcription factors: STAT4

These are exactly the genes where DisMech has the strongest mechanistic documentation.

What does DisMech miss?

The top "novel" genes (appearing in 7+ diseases) reveal systematic gaps:

Gene #Diseases Function Gap Type
EGR2 12 T cell anergy/tolerance Known risk gene, not curated
BACH2 12 Treg/effector balance, class switch Known risk gene, curated only in T1D
ETS1 11 T cell development, Th17 Known risk gene, not curated
IRF4 10 Plasma cell differentiation, Th17 Known risk gene, curated only in Vitiligo
STAT3 8 Th17 signaling, acute phase Known risk gene, not curated
IKZF1 9 Lymphocyte development (Ikaros) Known risk gene, not curated
CD28 9 T cell co-stimulation Known risk gene, not curated
GATA3 7 Th2 master regulator Known risk gene, not curated
SMAD3 9 TGF-beta signaling, Treg Known risk gene, not curated
IL10 8 Anti-inflammatory cytokine Known risk gene, not curated

Actionable Findings

1. High-Priority Gene Additions

These genes should be added to the genetic sections of multiple dismech entries. Ranked by number of diseases where they appear as GWAS hits downstream of T cell regulators:

Tier 1 (10+ diseases): EGR2, BACH2, IL2RA, IL7R, TNFAIP3, ETS1, ADO, IRF4 Tier 2 (7-9 diseases): IKZF1, STAT3, CD28, SMAD3, SATB1, NFKBIA, GATA3, CD6, IRF8, IL10, RASGRP1, FOXO1, PTPN22 Tier 3 (5-6 diseases): SOCS1, MAP3K8, RIPK2, BATF, IL2, FAS, BCL2L11, SH2B3

(*) Already curated in some diseases but missing from others.

2. Missing Disease: Atopic Dermatitis/Eczema

Atopic eczema was tested in the pipeline (16 significant pairs) but we have no KB entry. This is a high-priority curation target given: - Cluster 79 (Th17; IRF4, BATF, STAT3): OR=38.1 - Cluster 110: OR=24.3 - Strong overlap with asthma genetics

3. Cluster 79 (Th17 Differentiation) is the Standout

This 6-gene cluster (IRF4, BATF, STAT3, JUNB, IPMK, NUP188) shows the strongest enrichment across diseases: - Crohn's: OR=58.2 - Atopic eczema: OR=38.1 - Psoriasis: OR=26.9 - IBD: OR=24.0 - MS: OR=16.9 - Autoimmune (general): OR=9.0

DisMech should ensure Th17 differentiation is prominently featured in the pathophysiology of Crohn's, Psoriasis, and Ankylosing Spondylitis.

4. Schema Gap: Gene-Disease Specificity

Many genes (PTPN22, IL2RA, BACH2) are shared across autoimmune diseases but DisMech only records them in 1-4 entries. The pipeline data provides evidence for which diseases each gene is relevant to, with enrichment statistics. Consider: - Systematic addition of pleiotropic autoimmune genes across all relevant entries - Recording the T cell regulatory context (which cluster, which condition)

Methods

Data Sources

  • Zhu/Dann enrichment results: cluster_autoimmune_enrichment_results.suppl_table.csv from GitHub
  • Disease gene sets: Open Targets Platform, genetic_evidence_score >= 0.1
  • dismech entries: 12 autoimmune disorder YAML files from kb/disorders/

Matching Logic

  • Gene symbols from enrichment intersecting_genes matched against dismech genetic[].name and pathophysiology[].genes[].name
  • Exact string match (case-sensitive)
  • IBD umbrella mapped to union of Crohn's Disease + Ulcerative Colitis genes

Limitations

  • Only matches gene symbols in structured fields; does not search free-text descriptions
  • DisMech uses some non-standard gene names (e.g., "HLA-B27" vs "HLA-B", "BCR-ABL1")
  • Does not assess whether DisMech's pathophysiology narratives describe the gene's mechanism
  • The pipeline's gene sets come from Open Targets (which includes GWAS + rare variant evidence), not just the T cell Perturb-seq results directly

Data Files

  • Validation script: scripts/validate_tcell_clusters.py
  • Enrichment data: docs/assets/zhu_dann_2025/cluster_autoimmune_enrichment_results.csv
  • Cluster annotations: docs/assets/zhu_dann_2025/clustering_results_and_annotations.csv