Non-linear transcriptional responses to gradual modulation of transcription factor dosage
Open accessAbstract
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 modu…
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Microglial transcription factors often display a nonlinear dose-response relationship.
“these factors often display a nonlinear dose‐response relationship”
From Targeting Microglial Transcriptional Reprogramming as a Therapy Strategy for Alzheimer's Disease (2026) · cites this paper as [83]
Cites
A framework for the interpretation of de novo mutation in human disease
Samocha, K. E., Robinson, E. B., Sanders, S. J., Stevens, C., Sabo, A., McGrath, L. M., Kosmicki, J. A., Rehnström, K., Mallick, S., Kirby, A., et al. (2014) · Nature Genetics · doi:10.1038/ng.3050 · Cited by 979 · see citing works
Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq
Replogle, J. M., Saunders, R. A., Pogson, A. N., Hussmann, J. A., Lenail, A., Guna, A., Mascibroda, L., Wagner, E. J., Adelman, K., Lithwick-Yanai, G., et al. (2022) · Cell · doi:10.1016/j.cell.2022.05.013 · Cited by 751 · see citing works
Systematic differences in discovery of genetic effects on gene expression and complex traits
Mostafavi, H., Spence, J. P., Naqvi, S., & Pritchard, J. K. (2023) · Nature Genetics · doi:10.1038/s41588-023-01529-1 · Cited by 336 · see citing works
The Human Cell Atlas bone marrow single-cell interactive web portal
Hay, S. B., Ferchen, K., Chetal, K., Grimes, H. L., & Salomonis, N. (2018) · Experimental Hematology · doi:10.1016/j.exphem.2018.09.004 · Cited by 256 · see citing works
Phenotype-Specific Enrichment of Mendelian Disorder Genes near GWAS Regions across 62 Complex Traits
Freund, M. K., Burch, K. S., Shi, H., Mancuso, N., Kichaev, G., Garske, K. M., Pan, D. Z., Miao, Z., Mohlke, K. L., Laakso, M., et al. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.08.017 · Cited by 102 · see citing works
SCEPTRE improves calibration and sensitivity in single-cell CRISPR screen analysis
Barry, T., Wang, X., Morris, J. A., Roeder, K., & Katsevich, E. (2021) · Genome Biology · doi:10.1186/s13059-021-02545-2 · Cited by 86 · see citing works
Self-citationPolfus, L. M., Khajuria, R. K., Schick, U. M., Pankratz, N., Pazoki, R., Brody, J. A., Chen, M.-H., Auer, P. L., Floyd, J. S., Huang, J., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.06.016 · Cited by 54 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Design principles of cell-state-specific enhancers in hematopoiesis
Frömel, R., Rühle, J., Bernal Martinez, A., Szu-Tu, C., Pacheco Pastor, F., Martinez-Corral, R., & Velten, L. (2025) · Cell · doi:10.1016/j.cell.2025.04.017 · Cited by 29 · see citing works
Targeting Microglial Transcriptional Reprogramming as a Therapy Strategy for Alzheimer's Disease
Kim, B., Wang, S. S., Kim, J. R., & Kim, J. (2026) · BioEssays · doi:10.1002/bies.70186
- Authors
- Domingo, J., Minaeva, M., Morris, J. A., Ghatan, S., Ziosi, M., Sanjana, N. E., & Lappalainen, T.
- Affiliations
- New York Genome Center (US)
- Science for Life Laboratory (SE)
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2024-12-17
- ISSN
- 2050-084X (ISSN-L)
- OpenAlex
- W4405498103
- Cited by
- 6
- References
- 7
- Citations by year
- 2026: 5 · 2025: 1
- Type
- posted content
- Language
- en
- Open access
- gold
- doi.org
- Locations
- eLife (acceptedVersion, cc-by)
- Funders
- National Institute of Mental Health (R01MH106842)
- National Institute on Aging (R01AG057422)
- National Human Genome Research Institute (DP2HG010099, R01HG012790, K99HG012792)
- National Institute of General Medical Sciences (R01GM122924)
- Knut and Alice Wallenberg Foundation (KAW 2020.0239)
- European Research Council (10.3030/101043238)
- European Molecular Biology Organization (ALTF 345-2021)
- Canadian Institutes of Health Research
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- transcription factor dosage, CRISPR activation, GFI1B, NF-E2, MYB, blood cell traits, chromatin accessibility, histone modifications, disease-associated genes
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review