Non-linear transcriptional responses to gradual modulation of transcription factor dosage.
Júlia DomingoMariia MinaevaJohn A MorrisSam GhatanMarcello ZiosiNeville E SanjanaTuuli LappalainenPublished in: bioRxiv : the preprint server for biology (2024)
Genomic loci associated with common traits and diseases are typically non-coding and likely impact gene expression, sometimes coinciding with rare loss-of-function variants in the target gene. However, our understanding of how gradual changes in gene dosage affect molecular, cellular, and organismal traits is currently limited. To address this gap, we induced gradual changes in gene expression of four genes using CRISPR activation and inactivation. Downstream transcriptional consequences of dosage modulation of three master trans-regulators associated with blood cell traits (GFI1B, NFE2, and MYB) were examined using targeted single-cell multimodal sequencing. We showed that guide tiling around the TSS is the most effective way to modulate cis gene expression across a wide range of fold-changes, with further effects from chromatin accessibility and histone marks that differ between the inhibition and activation systems. Our single-cell data allowed us to precisely detect subtle to large gene expression changes in dozens of trans genes, revealing that many responses to dosage changes of these three TFs are non-linear, including non-monotonic behaviours, even when constraining the fold-changes of the master regulators to a copy number gain or loss. We found that the dosage properties are linked to gene constraint and that some of these non-linear responses are enriched for disease and GWAS genes. Overall, our study provides a straightforward and scalable method to precisely modulate gene expression and gain insights into its downstream consequences at high resolution.
Keyphrases
- genome wide
- gene expression
- copy number
- dna methylation
- single cell
- transcription factor
- mitochondrial dna
- genome wide identification
- rna seq
- high resolution
- high throughput
- oxidative stress
- stem cells
- mass spectrometry
- dna binding
- crispr cas
- machine learning
- chronic pain
- single molecule
- big data
- data analysis
- genome editing