CoChat
Sign Up
PreprintCited by 18CRISPR and Genetic EngineeringGenomics and Chromatin DynamicsGene Regulatory Network Analysis

Nonlinear transcriptional responses to gradual modulation of transcription factor dosage

Open access
Domingo, Júlia, Minaeva, Mariia, Morris, John A, Ghatan, Samuel, Ziosi, Marcello, Sanjana, Neville E, Lappalainen, Tuuli·2024·eLife
DOI 10.1101/2024.03.01.582837PMC PMC10925300
Open this paper in CoChat — free account

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 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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1101/2024.03.01.582837

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Experimental manipulations of TF levels or mutagenesis of natural enhancers demonstrate that single TFs cause non-trivial increases and decreases of the activity of TF-bound enhancers.

    “Experimental manipulations of TF levels24,25,26 or mutagenesis of natural enhancers27 demonstrate that single TFs cause non-trivial increases and decreases of the activity of TF-bound enhancers”

    From Design principles of cell-state-specific enhancers in hematopoiesis (2025) · cites this paper as [26]

    The Results section, ‘Trans responses of transcription factor dosage modulation’ (page unavailable), reports that manipulating TF dosage produced substantial increases and decreases in downstream gene expression. It does not establish that these changes were increases or decreases in the activity of TF-bound enhancers; gene expression is the measured outcome. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 45

  1. [1]Maurano M. T. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190– 1195 (2012). 22955828 PMC3771521
  2. [2]GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318– 1330 (2020). 32913098 PMC7737656
  3. [3]Claussnitzer M. A brief history of human disease genetics. Nature 577, 179– 189 (2020). 31915397 PMC7405896
  4. [4]Morris J. A. Discovery of target genes and pathways at GWAS loci by pooled single-cell CRISPR screens. Science 380, eadh7699 (2023). 37141313 PMC10518238
  5. [5]Zschocke J., Byers P. H. & Wilkie A. O. M. Mendelian inheritance revisited: dominance and recessiveness in medical genetics. Nat. Rev. Genet. 24, 442– 463 (2023). 36806206
  6. [6]Backman J. D. Exome sequencing and analysis of 454,787 UK Biobank participants. Nature 599, 628– 634 (2021). 34662886 PMC8596853
  7. [8]Castel S. E. Modified penetrance of coding variants by cis-regulatory variation contributes to disease risk. Nat. Genet. 50, 1327– 1334 (2018). 30127527 PMC6119105
  8. [9]Fahed A. C. Polygenic background modifies penetrance of monogenic variants for tier 1 genomic conditions. Nat. Commun. 11, 3635 (2020). 32820175 PMC7441381
  9. [10]McCaw Z. R. An allelic-series rare-variant association test for candidate-gene discovery. Am. J. Hum. Genet. 110, 1330– 1342 (2023). 37494930 PMC10432147
  10. [11]Sanjana N. E. Genome-scale CRISPR pooled screens. Anal. Biochem. 532, 95– 99 (2017). 27261176 PMC5133192
  11. [12]Collins R. L. A cross-disorder dosage sensitivity map of the human genome. medRxiv (2021). PMC9742861 35917817
  12. [13]Fehrmann R. S. N. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat. Genet. 47, 115– 125 (2015). 25581432
  13. [15]Hart T. High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities. Cell 163, 1515– 1526 (2015). 26627737
  14. [16]Cowley G. S. Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies. Sci Data 1, 140035 (2014). 25984343 PMC4432652
  15. [17]Keren L. Massively Parallel Interrogation of the Effects of Gene Expression Levels on Fitness. Cell vol. 166 1282– 1294.e18 Preprint at 10.1016/j.cell.2016.07.024 (2016). 27545349 · DOI
  16. [18]Jost M. Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs. Nat. Biotechnol. 38, 355– 364 (2020). 31932729 PMC7065968
  17. [19]Hawkins J. S. Mismatch-CRISPRi Reveals the Co-varying Expression-Fitness Relationships of Essential Genes in Escherichia coli and Bacillus subtilis. Cell Syst 11, 523– 535.e9 (2020). 33080209 PMC7704046
  18. [20]Noviello G., Gjaltema R. A. F. & Schulz E. G. CasTuner is a degron and CRISPR/Cas-based toolkit for analog tuning of endogenous gene expression. Nat. Commun. 14, 3225 (2023). 37270532 PMC10239436
  19. [21]Chiarella A. M. Dose-dependent activation of gene expression is achieved using CRISPR and small molecules that recruit endogenous chromatin machinery. Nat. Biotechnol. 38, 50– 55 (2020). 31712774 PMC6954327
  20. [22]Liu W. Dissecting reprogramming heterogeneity at single-cell resolution using scTF-seq. bioRxiv 2024.01.30.577921 (2024) doi: 10.1101/2024.01.30.577921. · DOI
  21. [23]Lalanne J.-B., Parker D. J. & Li G.-W. Spurious regulatory connections dictate the expression-fitness landscape of translation factors. Mol. Syst. Biol. 17, e10302 (2021). 33900014 PMC8073009
  22. [24]Naqvi S. Precise modulation of transcription factor levels identifies features underlying dosage sensitivity. Nat. Genet. (2023) doi: 10.1038/s41588-023-01366-2. PMC10181932 37024583 · DOI · Paper page
  23. [25]Pulice J. L. & Meyerson M. Dosage amplification dictates oncogenic regulation by the NKX2–1 lineage factor in lung adenocarcinoma. bioRxiv (2023) doi: 10.1101/2023.10.26.563996. · DOI
  24. [27]van der Lee R., Correard S. & Wasserman W. W. Deregulated Regulators: Disease-Causing cis Variants in Transcription Factor Genes. Trends Genet. 36, 523– 539 (2020). 32451166
  25. [28]Ulirsch J. C. Systematic Functional Dissection of Common Genetic Variation Affecting Red Blood Cell Traits. Cell 165, 1530– 1545 (2016). 27259154 PMC4893171
  26. [29]Möröy T., Vassen L., Wilkes B. & Khandanpour C. From cytopenia to leukemia: the role of Gfi1 and Gfi1b in blood formation. Blood 126, 2561– 2569 (2015). 26447191 PMC4671106
  27. [31]Jutzi J. S. Altered NFE2 activity predisposes to leukemic transformation and myelosarcoma with AML-specific aberrations. Blood 133, 1766– 1777 (2019). 30755419 PMC6484461
  28. [32]Baker S. J. B-myb is an essential regulator of hematopoietic stem cell and myeloid progenitor cell development. Proc. Natl. Acad. Sci. U. S. A. 111, 3122– 3127 (2014). 24516162 PMC3939923
  29. [33]Doench J. G. Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation. Nat. Biotechnol. 32, 1262– 1267 (2014). 25184501 PMC4262738
  30. [34]Doench J. G. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34, 184– 191 (2016). 26780180 PMC4744125
  31. [35]McKenna A. & Shendure J. FlashFry: a fast and flexible tool for large-scale CRISPR target design. BMC Biol. 16, 74 (2018). 29976198 PMC6033233
  32. [36]Sanson K. R. Optimized libraries for CRISPR-Cas9 genetic screens with multiple modalities. Nat. Commun. 9, 5416 (2018). 30575746 PMC6303322
  33. [37]Legut M. High-Throughput Screens of PAM-Flexible Cas9 Variants for Gene Knockout and Transcriptional Modulation. Cell Rep. 30, 2859– 2868.e5 (2020). 32130891 PMC7558435
  34. [38]Luo Y. New developments on the Encyclopedia of DNA Elements (ENCODE) data portal. Nucleic Acids Res. 48, D882– D889 (2020). 31713622 PMC7061942
  35. [39]Lupo A. KRAB-Zinc Finger Proteins: A Repressor Family Displaying Multiple Biological Functions. Curr. Genomics 14, 268– 278 (2013). 24294107 PMC3731817
  36. [40]Minaeva M., Domingo J., Rentzsch P. & Lappalainen T. Specifying cellular context of transcription factor regulons for exploring context-specific gene regulation programs. NAR Genom Bioinform. 2025 Jan 7; 7(1): lqae178. doi: 10.1093/nargab/lqae178. 39781510 PMC11704787 · DOI
  37. [41]Wang H. Dynamic transcriptomes of human myeloid leukemia cells. Genomics 102, 250– 256 (2013). 23806289
  38. [42]Salvadores M., Fuster-Tormo F. & Supek F. Matching cell lines with cancer type and subtype of origin via mutational, epigenomic, and transcriptomic patterns. Sci Adv 6, (2020). PMC7458440 32937430
  39. [43]Granja J. M. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat. Biotechnol. 37, 1458– 1465 (2019). 31792411 PMC7258684
  40. [44]Jayapal S. R. Down-regulation of Myc is essential for terminal erythroid maturation. J. Biol. Chem. 285, 40252– 40265 (2010). 20940306 PMC3001006
  41. [45]Amberger J. S., Bocchini C. A., Schiettecatte F., Scott A. F. & Hamosh A. OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 43, D789– 98 (2015). 25428349 PMC4383985
  42. [46]Beauchemin H. Gfi1b controls integrin signaling-dependent cytoskeleton dynamics and organization in megakaryocytes. Haematologica 102, 484– 497 (2017). 28082345 PMC5394960
  43. [47]Collins R. L. A structural variation reference for medical and population genetics. Nature 581, 444– 451 (2020). 32461652 PMC7334194
  44. [48]Mohammadi P. Genetic regulatory variation in populations informs transcriptome analysis in rare disease. Science 366, 351– 356 (2019). 31601707 PMC6814274
  45. [49]Dong D. An RNA-informed dosage sensitivity map reflects the intrinsic functional nature of genes. Am. J. Hum. Genet. 110, 1509– 1521 (2023). 37619562 PMC10502852

Cited by

Sorted by citations

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)
Corresponding author
  • Domingo, Júlia
  • Lappalainen, Tuuli
ORCID
Venue
eLife
Publisher
openRxiv
Published
2024-03-04
Volume
13
ISSN
2050-084X (ISSN-L)
DOI
10.1101/2024.03.01.582837
PMC
PMC10925300
OpenAlex
W4392374358
Cited by
18
References
8
Citations by year
2026: 6 · 2025: 4 · 2024: 6 · 2023: 1 · 2022: 1
FWCI
12.40
Type
journal article
Language
en
Open access
Licence
Locations
  • eLife (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health (R01MH106842, R01AG057422, DP2HG010099, R01HG012790, R01GM122924, K99HG012792)
  • European Research Council (101043238)
  • Canadian Institutes of Health Research (Banting Postdoctoral Fellowship to J.A.M.)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords
transcription factor dosage, CRISPR activation, cis-regulation, trans-regulation, 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