Evidence map›Paper›PMID 38866077›Full record

ArticleMolecular & cellular proteomics : MCP2024

Improved Mass Spectrometry-Based Methods Reveal Abundant Propionylation and Tissue-Specific Histone Propionylation Profiles.

Alessandro Vai, Roberta Noberini, Chiara Ghirardi, Dieggo Rodrigues de Paula, Michele Carminati, Rani Pallavi, Nathália Araújo, Patrick Varga-Weisz, Tiziana Bonaldi

Abstract read
In one paragraph

Article in Molecular & cellular proteomics : MCP, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Review
  9. Review
  10. Article
  11. Article
  12. Article
  13. Review
  14. 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

9 authors.

Alessandro VaiDepartment of Experimental Oncology, European Institute of Oncology (IEO) IRCSS, Milan, Italy.
Roberta NoberiniDepartment of Experimental Oncology, European Institute of Oncology (IEO) IRCSS, Milan, Italy.
Chiara GhirardiDepartment of Experimental Oncology, European Institute of Oncology (IEO) IRCSS, Milan, Italy.
Dieggo Rodrigues de PaulaInternational Laboratory for Microbiome Host Epigenetics, Department of Genetics, Evolution, Microbiology, and Immunology, Institute of Biology, University of Campinas, Campinas, São Paulo, Brazil.
Michele CarminatiDepartment of Experimental Oncology, European Institute of Oncology (IEO) IRCSS, Milan, Italy.
Rani PallaviDepartment of Experimental Oncology, European Institute of Oncology (IEO) IRCSS, Milan, Italy.
Nathália AraújoInternational Laboratory for Microbiome Host Epigenetics, Department of Genetics, Evolution, Microbiology, and Immunology, Institute of Biology, University of Campinas, Campinas, São Paulo, Brazil.
Patrick Varga-WeiszInternational Laboratory for Microbiome Host Epigenetics, Department of Genetics, Evolution, Microbiology, and Immunology, Institute of Biology, University of Campinas, Campinas, São Paulo, Brazil; São Paulo Excellence Chair, Department of Genetics, Evolution, Microbiology, and Immunology, Institute of Biology, University of Campinas, Campinas, São Paulo, Brazil; School of Biological Sciences, University of Essex, Colchester, UK.
Tiziana BonaldiDepartment of Experimental Oncology, European Institute of Oncology (IEO) IRCSS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy. Electronic address: tiziana.bonaldi@ieo.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Histone posttranslational modifications (PTMs) have crucial roles in a multitude of cellular processes, and their aberrant levels have been linked with numerous diseases, including cancer. Although histone PTM investigations have focused so far on methylations and acetylations, alternative long-chain acylations emerged as new dimension, as they are linked to cellular metabolic states and affect gene expression through mechanisms distinct from those regulated by acetylation. Mass spectrometry is the most powerful, comprehensive, and unbiased method to study histone PTMs. However, typical mass spectrometry-based protocols for histone PTM analysis do not allow the identification of naturally occurring propionylation and butyrylation. Here, we present improved state-of-the-art sample preparation and analysis protocols to quantitate these classes of modifications. After testing different derivatization methods coupled to protease digestion, we profiled common histone PTMs and histone acylations in seven mouse tissues and human normal and tumor breast clinical samples, obtaining a map of propionylations and butyrylations found in different tissue contexts. A quantitative histone PTM analysis also revealed a contribution of histone acylations in discriminating different tissues, also upon perturbation with antibiotics, and breast cancer samples from the normal counterpart. Our results show that profiling only classical modifications is limiting and highlight the importance of using sample preparation methods that allow the analysis of the widest possible spectrum of histone modifications, paving the way for deeper insights into their functional significance in cellular processes and disease states.

Indexed as

Breast NeoplasmsHistonesProtein Processing, Post-TranslationalAcetylationAcylationAnimalsFemaleHumansMass SpectrometryMiceOrgan SpecificityProteomicsHistonesepigeneticshistone acylationshistone butyrylationhistone post-translational modificationshistone propionylation

Identifiers

PMID38866077
PMCPMC11277384

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.