Evidence map›Paper›PMID 42134787›Full record

ArticleACS chemical biology2026

Mapping Protein Occupancy on DNA with an Unnatural Cytosine Modification.

Ruiyao Zhu, Christian E Loo, Christina M Hurley, Jared B Parker, Rahul M Kohli

Abstract read
In one paragraph

Article in ACS chemical biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Ruiyao ZhuDepartment of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania19104, United States.ORCID 0009-0000-2778-8932
Christian E LooGraduate Group in Biochemistry, Biophysics, and Chemical Biology, University of Pennsylvania, Philadelphia, Pennsylvania19104, United States.
Christina M HurleyGraduate Group in Biochemistry, Biophysics, and Chemical Biology, University of Pennsylvania, Philadelphia, Pennsylvania19104, United States.ORCID 0000-0002-8821-7233
Jared B ParkerDepartment of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania19104, United States.ORCID 0000-0002-3405-1581
Rahul M KohliDepartment of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania19104, United States.ORCID 0000-0002-7689-5678

Funding

Ultra low-input epigenetic sequencing with combined enzymatic and long-read technologiesR01HG010646 · NHGRI · UNIVERSITY OF PENNSYLVANIA · PI Gang Fang, Rahul Manu Kohli · 2019 to 2026
$3.9M
Predoctoral Training at the Chemistry-Biology InterfaceT32GM133398 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI Ronen Marmorstein, Ernest James Petersson · 2020 to 2026
$2.4M
Advancing Epigenetic Sequencing Through Solid-Phase Enzymatic ApproachesF31HG012892 · NHGRI · UNIVERSITY OF PENNSYLVANIA · PI LOO, CHRISTIAN ETHAN · 2023 to 2024
$95k
NHGRI NIH HHS F31 HG012892NHGRI NIH HHS R01 HG010646NIGMS NIH HHS T32 GM133398
6 · The paper itself

Abstract

The epigenome provides a dynamic layer of gene regulatory control above the static genetic sequence. DNA base modifications are key epigenetic regulators, predominantly found within CpG contexts in mammalian genomes. Working in tandem with these DNA modifications, chromatin-associated proteins and transcription factors further control gene expression. Given the interplay of these factors, concurrent mapping of DNA base modifications with protein-DNA occupancy can greatly aid in interpreting the epigenome. Existing multimodal mapping methods include the use of DNA methyltransferases to mark accessible, protein-unbound DNA in non-CpG contexts. However, such approaches can either confound readouts with native DNA modifications or constrain users to third-generation sequencing approaches. To circumvent these limitations, we explored the possibility of introducing an unnatural DNA base modification, 5-carboxymethylcytosine, as an alternative label for protein occupancy. Here, we report our efforts to rationally engineer non-CpG-specific DNA methyltransferases to take on neomorphic DNA carboxymethyltransferase (CxMTase) activities. We find that DNA carboxymethylation of cytosines in GpC contexts shows broad compatibility with the most widely used epigenetic detection methods and can be used to reliably report on protein occupancy states. Using this approach, we reveal the single-molecule binding patterns of LexA, a master repressor in the bacterial DNA damage (SOS) response, at its self-regulated and endogenously methylated promoter. We thus show that unnatural DNA modifications can uncover novel biological insights and potentiate new approaches to multimodal epigenetic profiling.

Indexed as

CytosineDNADNA MethylationEpigenesis, GeneticCytosineDNA

Identifiers

PMID42134787
PMCPMC13288465

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.