Evidence map›Paper›PMID 31083661›Full record

ArticlePLoS computational biology2019

The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern.

Saman Amini, Annika Jacobsen, Olga Ivanova, Philip Lijnzaad, Jaap Heringa, Frank C P Holstege, K Anton Feenstra, Patrick Kemmeren

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 45% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Global Molecular Response ofJournal of fungi (Basel, Switzerland) · 2023
    Article
  2. Article
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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

8 authors at 3 institutions in 1 country.

Saman AminiPrincess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
Annika JacobsenCentre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.ORCID 0000-0003-4818-2360
Olga IvanovaCentre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.ORCID 0000-0002-9111-4593
Philip LijnzaadPrincess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
Jaap HeringaCentre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Frank C P HolstegePrincess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
K Anton FeenstraCentre for Integrative Bioinformatics (IBIVU), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.ORCID 0000-0001-6755-9667
Patrick KemmerenPrincess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.ORCID 0000-0003-2237-7354
Vrije Universiteit Amsterdam · NLPrincess Máxima Center · NLUniversity Medical Center Utrecht · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several hypothetical molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in baker's yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases.

Indexed as

Gene Expression RegulationModels, GeneticChromosome InversionComputational BiologyComputer SimulationDatabases, GeneticEpistasis, GeneticGenes, FungalGenetic Association StudiesMutationSaccharomyces cerevisiaeSignal TransductionTranscription FactorsTranscription Factors

Identifiers

PMID31083661
PMCPMC6532943
OpenAlexW2897511701

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.