Evidence map›Paper›PMID 32187373›Full record

ArticleNucleic acids research2020

Epigenetic engineering of yeast reveals dynamic molecular adaptation to methylation stress and genetic modulators of specific DNMT3 family members.

Alex I Finnegan, Somang Kim, Hu Jin, Michael Gapinske, Wendy S Woods, Pablo Perez-Pinera, Jun S Song

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.3field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 22 citations in OpenAlex.

  1. A Modular and Customizable CRISPR/Cas Toolkit for Epigenome Editing of Cis-regulatory Modules.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Epigenetic Response ofMicroorganisms · 2021
    Article
  8. ExploringMicrobial cell (Graz, Austria) · 2021
    Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Alex I FinneganDepartment of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Somang KimDepartment of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Hu JinDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Michael GapinskeDepartment of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Wendy S WoodsDepartment of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Pablo Perez-PineraCarl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Jun S SongDepartment of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
University of Illinois Urbana-Champaign · USHarvard University · US

Funding

Predicting Transcriptional and Epigenetic Networks in Cancer from Sequencing DataR01CA163336 · NCI · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI Jun S Song · 2012 to 2026
$4.4M
Engineering platforms for editing RNA with single base resolutionR01GM127497 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI PEREZ-PINERA, PABLO · 2018 to 2021
$1.3M
NCI NIH HHS R01 CA163336NIGMS NIH HHS R01 GM127497
6 · The paper itself

Abstract

Cytosine methylation is a ubiquitous modification in mammalian DNA generated and maintained by several DNA methyltransferases (DNMTs) with partially overlapping functions and genomic targets. To systematically dissect the factors specifying each DNMT's activity, we engineered combinatorial knock-in of human DNMT genes in Komagataella phaffii, a yeast species lacking endogenous DNA methylation. Time-course expression measurements captured dynamic network-level adaptation of cells to DNMT3B1-induced DNA methylation stress and showed that coordinately modulating the availability of S-adenosyl methionine (SAM), the essential metabolite for DNMT-catalyzed methylation, is an evolutionarily conserved epigenetic stress response, also implicated in several human diseases. Convolutional neural networks trained on genome-wide CpG-methylation data learned distinct sequence preferences of DNMT3 family members. A simulated annealing interpretation method resolved these preferences into individual flanking nucleotides and periodic poly(A) tracts that rotationally position highly methylated cytosines relative to phased nucleosomes. Furthermore, the nucleosome repeat length defined the spatial unit of methylation spreading. Gene methylation patterns were similar to those in mammals, and hypo- and hypermethylation were predictive of increased and decreased transcription relative to control, respectively, in the absence of mammalian readers of DNA methylation. Introducing controlled epigenetic perturbations in yeast thus enabled characterization of fundamental genomic features directing specific DNMT3 proteins.

Indexed as

DNA MethylationEpigenesis, GeneticCell EngineeringCentromereChromatinDNA (Cytosine-5-)-MethyltransferasesDNA Methyltransferase 3ADNA Methyltransferase 3BGene Knock-In TechniquesGenome, FungalHumansNeural Networks, ComputerSaccharomycetalesS-AdenosylmethionineStress, PhysiologicalTelomereChromatinDNA (Cytosine-5-)-MethyltransferasesDNA Methyltransferase 3ADNA Methyltransferase 3BDNMT3A protein, humanDNMT3L protein, humanS-Adenosylmethionine

Identifiers

PMID32187373
PMCPMC7192628
OpenAlexW3012143441

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.