Publications

Genome Biol. 2025 Sep 29;26(1):319. doi: 10.1186/s13059-025-03741-0.

ABSTRACT

BACKGROUND: We 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.

RESULTS: We 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.

CONCLUSIONS: DNA 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.

PMID:41024265 | DOI:10.1186/s13059-025-03741-0

F1000Res. 2025 Jul 21;14:714. doi: 10.12688/f1000research.165543.1. eCollection 2025.

ABSTRACT

BACKGROUND: Subcellular localisation is a determining factor of protein function. Mass spectrometry-based correlation profiling experiments facilitate the classification of protein subcellular localisation on a proteome-wide scale. In turn, static localisations can be compared across conditions to identify differential protein localisation events.

METHODS: Here, we provide a workflow for the processing and analysis of subcellular proteomics data derived from mass spectrometry-based correlation profiling experiments. This workflow utilises open-source R software packages from the Bioconductor project and provides extensive discussion of the key processing steps required to achieve high confidence protein localisation classifications and differential localisation predictions. The workflow is applicable to any correlation profiling data and supplementary code is provided to help users adapt the workflow to DDA and DIA data processed with different database softwares.

RESULTS: The workflow is divided into three sections. First we outline data processing using the QFeatures infrastructure to generate high quality protein correlation profiles. Next, protein subcellular localisation classification is carried out using machine learning. Finally, prediction of differential localisation events is covered for dynamic correlation profiling experiments.

CONCLUSIONS: A comprehensive start-to-end workflow for correlation profiling subcellular proteomics experiments is presented. R version: R version 4.5.0 (2025-04-11) Bioconductor version: 3.21.

PMID:40931748 | PMC:PMC12419147 | DOI:10.12688/f1000research.165543.1

Sci Adv. 2025 Aug 29;11(35):eadw9153. doi: 10.1126/sciadv.adw9153. Epub 2025 Aug 27.

ABSTRACT

SUMOylation is essential in plant and animal cells, but it remains unknown how small ubiquitin-like modifier (SUMO) components act in concert to modify specific targets in response to environmental stresses. In this study, we characterize every SUMO component in the Arabidopsis root to create a complete SUMO Cell Atlas in eukaryotes. This unique resource reveals wide spatial variation, where SUMO proteins and proteases have subfunctionalized in both their expression and subcellular localization. During stress, SUMO conjugation is mainly driven by tissue-specific regulation of the SUMO E2-conjugating enzyme. Stress-specific modulation of the SUMO pathway reveals unique combinations of proteases being targeted for regulation in distinct root tissues by salt, osmotic, and biotic signals. Our SUMO Cell Atlas resources reveal how this posttranslational modification (PTM) influences cellular- and tissue-scale adaptations during root development and stress responses. To our knowledge, we provide the first comprehensive study elucidating how multiple stress inputs can regulate an entire PTM system.

PMID:40864707 | DOI:10.1126/sciadv.adw9153

Sci Adv. 2025 Aug 22;11(34):eadt1798. doi: 10.1126/sciadv.adt1798. Epub 2025 Aug 20.

ABSTRACT

Mutations in numerous genes contribute to human cancer, with different oncogenic lesions prevalent in different cancer types. However, the malignant phenotype is simple, characterized by unrestricted cell growth, invasion, and often metastasis. One possible hypothesis explaining this dichotomy is that cancer genes regulate common targets, which then function as master regulators of essential cancer phenotypes. To identify mechanisms that drive the most fundamental feature shared by all tumors-unrestricted cell proliferation-we used a multiomic approach, which identified translation and ribosome biogenesis as common targets of major oncogenic pathways across cancer types. Proteomic analysis of tumors and functional studies of cell cultures established nucleolar and coiled-body phosphoprotein 1 as a key node, whose convergent regulation, both transcriptionally and posttranslationally, is critical for tumor cell proliferation. Our results indicate that lineage-specific oncogenic pathways regulate the same set of targets for growth control, revealing key downstream nodes that could be targeted for therapy or chemoprevention.

PMID:40834066 | DOI:10.1126/sciadv.adt1798

Mol Cell Proteomics. 2025 Aug 7:101047. doi: 10.1016/j.mcpro.2025.101047. Online ahead of print.

ABSTRACT

Subcellular localisation within the proteome fundamentally influences cellular processes, however the development of high-throughput techniques to allow proteome-wide mapping of the cell has proven difficult. Here we present DIA-LOP, an approach capable of high-throughput spatial proteome mapping with in-depth subcellular resolution. This unified framework integrates differential-ultracentrifugation (DC) with ion-mobility-based data-independent acquisition mass spectrometry, alongside data processing using DIA-NN and spatial analysis within the pRoloc bioinformatics pipeline. We obtain the largest DIA-based subcellular proteomics map, with 8242 protein identifications across 13 organellar compartments in U-2 OS cells. Within the same experimental pipeline, we compare DC fractionation with an alternate detergent-based protocol using either DIA or data-dependent acquisition (DDA) mass spectrometry approaches, highlighting the increased subcellular resolution of the DC approach and the increased proteome coverage when DIA is applied. We demonstrate the ability of DIA-LOP to inform clinical studies by identifying and mapping disease related proteins within our osteosarcoma cell model. With impressive coverage and resolution, DIA-LOP provides a straightforward, high-throughput tool for biochemical discovery. This study thus informs potential users of subcellular proteomics strategies that employ biochemical fractionation of the optimal workflows to achieve high proteome coverage and subcellular resolution.

PMID:40783120 | DOI:10.1016/j.mcpro.2025.101047