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Cited by 234Genomics and Chromatin DynamicsGenetic Associations and EpidemiologyGenomics and Rare Diseases

Saturation mutagenesis of twenty disease-associated regulatory elements at single base-pair resolution

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Kircher, Martin, Xiong, Chenling, Martin, Beth, Schubach, Max, Inoue, Fumitaka, Bell, Robert J. A., Costello, Joseph F., Shendure, Jay +1 more·2019·Nature Communications
DOI 10.1038/s41467-019-11526-wPMC PMC6687891
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Abstract

The majority of common variants associated with common diseases, as well as an unknown proportion of causal mutations for rare diseases, fall in noncoding regions of the genome. Although catalogs of noncoding regulatory elements are steadily improving, we have a limited understanding of the functional effects of mutations within them. Here, we perform saturation mutagenesis in conjunction with massively parallel reporter assays on 20 disease-associated gene promoters and enhancers, generating functional measurements for over 30,000 single nucleotide substitutions and deletions. We find that the density of putative transcription factor binding sites varies widely between regulatory elements, as does the extent to which evolutionary conservation or integrative scores predict functional effects. These data provide a powerful resource for interpreting the pathogenicity of clinically observed mutations in these disease-associated regulatory elements, and comprise a rich dataset for the further development of algorithms that aim to predict the regulatory effects of noncoding mutations.

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  • Enformer's predictions are not limited to conserved enhancers because it does not rely explicitly on nucleotide conservation statistics.

    “by not relying explicitly on nucleotide conservation statistics, as the majority of tools do25, its predictions are not limited to conserved enhancers, which comprise a small proportion of all enhancers30”

    From Effective gene expression prediction from sequence by integrating long-range interactions (2021) · cites this paper as [25]

    In Results, under “Computational tools predict expression effects poorly” (page not provided), the paper says conservation was informative for only a subset of its studied regulatory elements. It does not establish that conserved enhancers are a small proportion of all enhancers. Nor does it assess Enformer or whether its predictions extend to unconserved enhancers. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • DeepLiver predictions outperform other methods tested by Kircher et al. (2019) in the HepG2 enhancers.

    “DeepLiver predictions outperform other methods tested by Kircher et al. (2019)60 in the HepG2 enhancers.”

    From Enhancer grammar of liver cell types and hepatocyte zonation states (2022) · cites this paper as Kircher et al. (2019)60

    The Results section (‘Computational tools predict expression effects poorly’; page unavailable) compares several prediction methods, and the Methods section (‘Annotation and downstream analysis’; page unavailable) identifies HepG2 elements used in those comparisons. The paper does not evaluate DeepLiver, so it cannot establish that DeepLiver outperforms those methods in HepG2 enhancers. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • DeepLiver in silico mutagenesis was validated by calculating the correlation between predicted mutation effects and experimental saturation mutagenesis data on six enhancers from earlier studies (three in vivo and three HepG2).

    “To validate DeepLiver in silico mutagenesis, we calculated the correlation between the effect of the mutations predicted by DeepLiver and experimental saturation mutagenesis data on six enhancers from earlier studies (three enhancers from in vivo studies and three enhancers from HepG2)14,60.”

    From Enhancer grammar of liver cell types and hepatocyte zonation states (2022) · cites this paper as [60]

    The Results section (“SORT1-associated enhancer,” page unavailable) reports experimental saturation-mutagenesis measurements for a SORT1 enhancer in HepG2 cells, supporting the citation as a source of such data. The passages do not establish that this paper supplied data for three distinct HepG2 enhancers. The DeepLiver correlation is described as the citing authors’ own validation, not as an analysis performed by this source. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • A major advantage of MPRAs is that sequences to be tested can simply be synthesized, enabling straightforward saturation mutagenesis and programming synthetic enhancers.

    “A major advantage of MPRAs is that sequences to be tested can simply be synthesized, enabling straightforward saturation mutagenesis [G] of enhancers67,184,185 as well as programming synthetic enhancers to inform modelling of their properties186,187.”

    From Towards a comprehensive catalogue of validated and target-linked human enhancers (2020) · cites this paper as [185]

    The Results section, “Construction of saturation mutagenesis libraries,” confirms that the authors used MPRAs to test mutations in regulatory elements, including enhancers. But their Methods describe generating sequence variants by error-prone PCR, not by synthesizing the sequences to be tested. The separate claim about programming synthetic enhancers is attached to other citations in the citing sentence. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Error-prone PCR tends to produce single-nucleotide substitutions, limiting its utility as it cannot produce the full repertoire of amino acid substitutions at each codon.

    “However, this tends to produce single-nucleotide substitutions, limiting its utility as this cannot produce the full repertoire of amino acid substitutions at each codon (45, 46).”

    From Massively parallel functional genomic assays in endocrinology: from promise to delivery (2026) · cites this paper as [46]

    The Results section ('Construction of saturation mutagenesis libraries'; page unavailable) says error-prone PCR introduced fewer than one change per 100 bp and produced libraries covering nearly all possible single-nucleotide variants. It does not address amino-acid substitutions at codons or say that error-prone PCR cannot produce their full repertoire; the experiments concern noncoding regulatory elements. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Kircher, M., Xiong, C., Martin, B., Schubach, M., Inoue, F., Bell, R. J. A., Costello, J. F., Shendure, J., & Ahituv, N.
Affiliations
  • University of Washington (US)
  • University of California, San Francisco (US)
  • Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
  • Howard Hughes Medical Institute (US)
Corresponding author
  • Kircher, Martin
  • Shendure, Jay
  • Ahituv, Nadav
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2019-08-08
Volume
10
Issue
1
Pages
3583-3583
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-019-11526-w
PMC
PMC6687891
OpenAlex
W2967059818
Cited by
234
References
10
Citations by year
2026: 25 · 2025: 43 · 2024: 43 · 2023: 32 · 2022: 29 · 2021: 31 · 2020: 25 · 2019: 6
FWCI
7.49
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • eScholarship (California Digital Library) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
Funders
  • Berlin Institute of Health (BIH), Berlin, Germany
  • U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (1R01HG006768, 1UM1HG009408, 1R01HG009136)
  • U.S. Department of Health & Human Services | NIH | National Cancer Institute (1R01CA197139)
  • Howard Hughes Medical Institute
  • U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (1U01MH116438)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
Keywords
saturation mutagenesis, non-coding regulatory elements, disease-associated mutations, promoter, enhancers, single nucleotide substitution, deletion mutations, transcription factor binding sites, evolutionary conservation, variant pathogenicity, massively parallel reporter assay
MeSH terms
Cell Line, Cloning, Molecular, Disease, Humans, Genomic Library, Genome, Human, Mutagenesis, Computational Biology, Polymorphism, Single Nucleotide, Regulatory Elements, Transcriptional, High-Throughput Nucleotide Sequencing

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