Gene regulatory network inference from CRISPR perturbations in primary CD4 + T cells elucidates the genomic basis of immune disease.
Level 5 - mechanism / opinion, no new human data
In vitro bench cell perturbation screen and computational modeling (CEBM Level 5)
PubMed 39395408 · doi:10.1016/j.xgen.2024.100671
What was done
Researchers knocked out 84 genes in primary CD4+ T cells using CRISPR, targeting transcription factors (TFs) associated with inborn errors of immunity as well as non-disease-associated TFs. They developed an inference framework called linear latent causal Bayes (LLCB) to construct gene regulatory networks from the perturbation data and linked perturbed programs to immune genome-wide association study (GWAS) loci.
What was found
The method identified 211 regulatory connections among genes. Mapping these downstream programs to immune GWAS targets revealed that members of the JAK-STAT signaling family are regulated by the epigenetic regulator KMT2A. The abstract does not report specific numerical effect sizes, variance explained, or confidence intervals.
Why it matters
Identifying trans-regulatory networks from natural genetic variation is difficult due to small effect sizes; this framework shows how primary cell CRISPR perturbations combined with causal modeling can map the trans-acting cascades connecting disease-associated risk variants to intracellular signaling pathways.
Limits
The abstract does not state the number of human cell donors, baseline donor characteristics, or the specific cellular activation states tested. The 211 inferred regulatory interactions depend on computational assumptions within the LLCB framework and require downstream experimental validation.
Cited by
- context Alex Marson's laboratory released a functional genomics dataset mapping the single-cell RNA sequencing profiles of 22 million primary human immune cells with CRISPR gene knockouts.