Non-linear transcriptional responses to gradual modulation of transcription factor dosage

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. 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 modulatecisgene 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 oftransgenes, 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.

Transcriptome-wide characterization of genetic perturbations

“…Another area of significant focus in cell biology and human genetics is in generating datasets with engineered or natural variation dosage (i.e. “allelic series”) to study dosage-response relationships, which can yield insight into gene regulation and guide therapeutic design (Collins et al 2022; Domingo et al 2024). Traditional analytic methods struggle to compare the effects of strong to weak perturbations in these datasets as genuine response differences may be conflated with difference in signal-to-noise ratio.…”

Programmable promoter editing for precise control of transgene expression

“…Subtle changes in gene expression can generate diverging cell fates. 1 – 6 Overexpression of endogenous and synthetic genes drives and redirects native processes and can augment native cellular functions. 7 – 10 However, identifying which transgenes have these subtle effects requires fine-tuned control, and implementing control over dosage-sensitive regimes remains a challenge.…”

“… 7 – 10 However, identifying which transgenes have these subtle effects requires fine-tuned control, and implementing control over dosage-sensitive regimes remains a challenge. 1 , 11 In particular, non-linear effects of gene expression can confound inference of positive and negative regulation of phenotypes. 1 , 2 , 12 , 13 Tools that support fine-scale titration of expression reveal non-monotonic relationships between expression of regulators and phenotypes.…”

“… 1 , 11 In particular, non-linear effects of gene expression can confound inference of positive and negative regulation of phenotypes. 1 , 2 , 12 , 13 Tools that support fine-scale titration of expression reveal non-monotonic relationships between expression of regulators and phenotypes. 1 , 2 , 12 While useful for identifying linear regulators, large-scale screening tools such as CRISPR-based knockout, knockdown, and activation often do not provide sufficient resolution to find regulators with non-linear relationships to phenotypes.…”

“… 1 , 2 , 12 , 13 Tools that support fine-scale titration of expression reveal non-monotonic relationships between expression of regulators and phenotypes. 1 , 2 , 12 While useful for identifying linear regulators, large-scale screening tools such as CRISPR-based knockout, knockdown, and activation often do not provide sufficient resolution to find regulators with non-linear relationships to phenotypes. Such CRISPR-based screening does not predict how overexpression of transgenes influences cellular behaviors.…”