Evidence map›Paper›PMID 41024265›Full record

ArticleGenome biology2025

The distinct roles of genome, methylation, transcription, and translation on protein expression in Arabidopsis thaliana resolve the Central Dogma's information flow.

Ziming Zhong, Mark Bailey, Yong-In Kim, Nazanin P Afsharyan, Briony Parker, Louise Arathoon, Xiaowei Li, Chelsea A Rundle, Andrew Behrens, Danny Nedialkova and 5 more

Abstract read
In one paragraph

Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Cooperative Architecture of Mitochondrial Proteome Homeostasis.medRxiv : the preprint server for health sciences · 2026
    Article
  4. Article
  5. Review
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

15 authors.

Ziming Zhong *Genetics Institute, University College London, London, WC1E 6BT, UK.
Mark Bailey *Plant Sciences and the Bioeconomy, Rothamsted Research, Harpenden, AL5 2JQ, UK.
Yong-In Kim *Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, UK.
Nazanin P Afsharyan *Plant Sciences and the Bioeconomy, Rothamsted Research, Harpenden, AL5 2JQ, UK.
Briony ParkerPlant Sciences and the Bioeconomy, Rothamsted Research, Harpenden, AL5 2JQ, UK.
Louise ArathoonGenetics Institute, University College London, London, WC1E 6BT, UK.
Xiaowei LiPlant Sciences and the Bioeconomy, Rothamsted Research, Harpenden, AL5 2JQ, UK.
Chelsea A RundlePlant Sciences and the Bioeconomy, Rothamsted Research, Harpenden, AL5 2JQ, UK.
Andrew BehrensMechanisms of Protein Biogenesis, Max Planck Institute of Biochemistry, Martinsried, 82152, Germany.
Danny NedialkovaMechanisms of Protein Biogenesis, Max Planck Institute of Biochemistry, Martinsried, 82152, Germany.
Gancho SlavovInstitute of Biological Environmental and Rural Sciences (IBERS), Aberystwyth University, Gogerddan, Aberystwyth, UK.
Keywan Hassani-PakPlant Sciences and the Bioeconomy, Rothamsted Research, Harpenden, AL5 2JQ, UK.
Kathryn S Lilley *Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, UK.
Frederica L Theodoulou *Plant Sciences and the Bioeconomy, Rothamsted Research, Harpenden, AL5 2JQ, UK.
Richard Mott *Genetics Institute, University College London, London, WC1E 6BT, UK. r.mott@ucl.ac.uk.

Funding

Biotechnology and Biological Sciences Research Council BB/T002182/1Horizon 2020 Framework Programme 803825-TransTempoFold
6 · The paper itself

Abstract

backgroundWe investigate the flow of genetic information from DNA to RNA to protein as described by the Central Dogma in molecular biology, to determine the impact of intermediate genomic levels on plant protein expression.

resultsWe perform genomic profiling of rosette leaves in two Arabidopsis accessions, Col-0 and Can-0, and assemble their genomes using long reads and chromatin interaction data. We measure gene and protein expression in biological replicates grown in a controlled environment, also measuring CpG methylation, ribosome-associated transcript levels, and tRNA abundance. Each omic level is highly reproducible between biological replicates and between accessions despite their ~1% sequence divergence; the single best predictor of any level in one accession is the corresponding level in the other. Within each accession, gene codon frequencies accurately model both mRNA and protein expression. The effects of a codon on mRNA and protein expression are highly correlated but independent of genome-wide codon frequencies or tRNA levels which instead match genome-wide amino acid frequencies. Ribosome-associated transcripts closely track mRNA levels.

conclusionsDNA codon frequencies and mRNA expression levels are the main predictors of protein abundance. In the absence of environmental perturbation neither gene-body methylation, tRNA abundance nor ribosome-associated transcript levels add appreciable information. The impact of constitutive gene-body methylation is mostly explained by gene codon composition. tRNA abundance tracks overall amino acid demand. However, genetic differences between accessions associate with differential gene-body methylation by inflating differential expression variation. Our data show that the dogma holds only if both sequence and abundance information in mRNA are considered.

Indexed as

ArabidopsisArabidopsis ProteinsDNA MethylationGene Expression Regulation, PlantGenome, PlantProtein BiosynthesisTranscription, GeneticCodonRNA, MessengerRNA, TransferArabidopsis ProteinsCodonRNA, MessengerRNA, TransferCentral DogmaChromatin interactionData-independent acquisitionGene-body methylationGene expressionGenome assemblyLong readsMim-tRNAseqProtein expressionRibosome-associated expressionRNAseq

Identifiers

PMID41024265
PMCPMC12477803

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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.