Nonlinear transcriptional responses to gradual modulation of transcription factor dosage

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 in human-derived K562 cells. 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 nonlinear, 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 nonlinear 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.

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Publication Details

Journal
eLife
Published
2026-01-14
DOI
https://doi.org/10.7554/elife.100555.3
Primary Topic
Single-cell and spatial transcriptomics
Type
article
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article

Nonlinear transcriptional responses to gradual modulation of transcription factor dosage

Marcello Ziosi, Mariia Minaeva, Neville E. Sanjana, Samuel Ghatan et al.
eLife
Single-cell and spatial transcriptomics
article

Nonlinear transcriptional responses to gradual modulation of transcription factor dosage

Marcello Ziosi, Mariia Minaeva, Neville E. Sanjana, Samuel Ghatan, Júlia Domingo, John A Morris
article en

Abstract

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 in human-derived K562 cells. 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 nonlinear, 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 nonlinear 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.

eLifeVol. 13
Science for Life Laboratory (SE), New York Genome Center (US), New York University (US), KTH Royal Institute of Technology (SE)
Openalex Percentile: Top 89%
Single-cell and spatial transcriptomics
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