Evidence map›Paper›PMID 17617911›Full record

ArticleBMC genomics2007

Inference of transcription modification in long-live yeast strains from their expression profiles.

Chao Cheng, Paola Fabrizio, Huanying Ge, Valter D Longo, Lei M Li

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
1.2field-weighted citation impact, top 26% 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

19 citing papers in PubMed, 35 citations in OpenAlex.

  1. Article
  2. Review
  3. Emerging Omics Approaches in Aging Research.Antioxidants & redox signaling · 2018
    Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Computational biology for ageing.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2011
    Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. A molecular mechanism of chronological aging in yeast.Cell cycle (Georgetown, Tex.) · 2009
    Article
  16. Identification of longevity genes with systems biology approaches.Advances and applications in bioinformatics and chemistry : AABC · 2009
    Article
  17. Article
  18. Article
  19. 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

5 authors at 1 institution in 1 country.

Chao ChengMolecular and Computational biology program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089-2910, USA. lilei@usc.edu
Paola Fabrizio
Huanying Ge
Valter D Longo
Lei M Li
University of Southern California · US

Funding

Implications of haplotype structure in the human genomeP50HG002790 · NHGRI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI TAVARE, SIMON · 2003 to 2013
$29.9M
Detection and Understanding of Expression DifferentiatioR01GM075308 · NIGMS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI LI, LEI · 2005 to 2009
$1.1M
NHGRI NIH HHS P50 HG002790NIGMS NIH HHS R01 GM075308NIGMS NIH HHS R01 GM75308-01
6 · The paper itself

Abstract

backgroundThree kinases: Sch9, PKA and TOR, are suggested to be involved in both the replicative and chronological ageing in yeast. They function in pathways whose down-regulation leads to life span extension. Several stress response proteins, including two transcription factors Msn2 and Msn4, mediate the longevity extension phenotype associated with decreased activity of either Sch9, PKA, or TOR. However, the mechanisms of longevity, especially the underlying transcription program have not been fully understood.

resultsWe measured the gene expression profiles in wild type yeast and three long-lived mutants: sch9Delta, ras2Delta, and tor1Delta. To elucidate the transcription program that may account for the longevity extension, we identified the transcription factors that are systematically and significantly associated with the expression differentiation in these mutants with respect to wild type by integrating microarray expression data with motif and ChIP-chip data, respectively. Our analysis suggests that three stress response transcription factors, Msn2, Msn4 and Gis1, are activated in all the three mutants. We also identify some other transcription factors such as Fhl1 and Hsf1, which may also be involved in the transcriptional modification in the long-lived mutants.

conclusionCombining microarray expression data with other data sources such as motif and ChIP-chip data provides biological insights into the transcription modification that leads to life span extension. In the chronologically long-lived mutant: sch9Delta, ras2Delta, and tor1Delta, several common stress response transcription factors are activated compared with the wild type according to our systematic transcription inference.

Indexed as

Gene Expression ProfilingGene Expression Regulation, FungalTranscription, GeneticAmino Acid MotifsBase SequenceChromatin ImmunoprecipitationGene Regulatory NetworksGenes, FungalLongevityMolecular Sequence DataSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsSequence Analysis, DNATranscription FactorsSaccharomyces cerevisiae ProteinsTranscription Factors

Identifiers

PMID17617911
PMCPMC1949827
OpenAlexW2008464306

What OpenQuestion holds

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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.