Evidence map›Paper›PMID 37485750›Full record

ArticleMolecular systems biology2023

Genetic effects on molecular network states explain complex traits.

Matthias Weith, Jan Großbach, Mathieu Clement-Ziza, Ludovic Gillet, María Rodríguez-López, Samuel Marguerat, Christopher T Workman, Paola Picotti, Jürg Bähler, Ruedi Aebersold and 1 more

Abstract read
In one paragraph

Article in Molecular systems biology, 2023. 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
–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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Analysis of Limited Proteolysis-Coupled Mass Spectrometry Data.Molecular & cellular proteomics : MCP · 2025
    Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Gene regulatory networks in disease and ageing.Nature reviews. Nephrology · 2024
    Review
  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

11 authors.

Matthias WeithExcellence Cluster on Cellular Stress Responses in Aging Associated Diseases, University of Cologne, Cologne, Germany.ORCID 0000-0003-0804-4262
Jan GroßbachExcellence Cluster on Cellular Stress Responses in Aging Associated Diseases, University of Cologne, Cologne, Germany.ORCID 0000-0002-9394-5665
Mathieu Clement-ZizaLesaffre Institute for Science and Technology, Lesaffre, Marcq-en-Baroeul, France.ORCID 0000-0003-2763-249X
Ludovic GilletDepartment of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.ORCID 0000-0002-1001-3265
María Rodríguez-LópezInstitute of Healthy Ageing and Department of Genetics, Evolution & Environment, University College London, London, UK.ORCID 0000-0002-2066-0589
Samuel MargueratInstitute of Healthy Ageing and Department of Genetics, Evolution & Environment, University College London, London, UK.
Christopher T WorkmanDepartment of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark.ORCID 0000-0002-2210-3743
Paola PicottiDepartment of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.
Jürg BählerInstitute of Healthy Ageing and Department of Genetics, Evolution & Environment, University College London, London, UK.ORCID 0000-0003-4036-1532
Ruedi AebersoldDepartment of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.
Andreas BeyerExcellence Cluster on Cellular Stress Responses in Aging Associated Diseases, University of Cologne, Cologne, Germany.ORCID 0000-0002-3891-2123

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The complexity of many cellular and organismal traits results from the integration of genetic and environmental factors via molecular networks. Network structure and effect propagation are best understood at the level of functional modules, but so far, no concept has been established to include the global network state. Here, we show when and how genetic perturbations lead to molecular changes that are confined to small parts of a network versus when they lead to modulation of network states. Integrating multi-omics profiling of genetically heterogeneous budding and fission yeast strains with an array of cellular traits identified a central state transition of the yeast molecular network that is related to PKA and TOR (PT) signaling. Genetic variants affecting this PT state globally shifted the molecular network along a single-dimensional axis, thereby modulating processes including energy and amino acid metabolism, transcription, translation, cell cycle control, and cellular stress response. We propose that genetic effects can propagate through large parts of molecular networks because of the functional requirement to centrally coordinate the activity of fundamental cellular processes.

Indexed as

Multifactorial InheritanceSaccharomyces cerevisiae ProteinsPhenotypeSaccharomyces cerevisiaeSignal TransductionSaccharomyces cerevisiae Proteinscomplex traitsnetwork effectsPKA signalingQTL mappingTOR signaling

Identifiers

PMID37485750
PMCPMC10407735

What OpenQuestion holds

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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.