Evidence map›Paper›PMID 28175300›Full record

ArticleHuman molecular genetics2016

Systematic analysis of the gerontome reveals links between aging and age-related diseases.

Maria Fernandes, Cen Wan, Robi Tacutu, Diogo Barardo, Ashish Rajput, Jingwei Wang, Harikrishnan Thoppil, Daniel Thornton, Chenhao Yang, Alex Freitas and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Human molecular genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 2 pooled it
–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

45 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
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  7. Review
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  11. Review
  12. Ageing as a software design flaw.Genome biology · 2023
    Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Age as a risk factor in vasculitis.Seminars in immunopathology · 2022
    Review
  18. Article
  19. Article
  20. 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.

Maria FernandesIntegrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.
Cen WanSchool of Computing, University of Kent, Canterbury, UK.
Robi TacutuIntegrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.
Diogo BarardoIntegrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.
Ashish RajputResearch Group for Computational Systems Biology, German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany.
Jingwei WangIntegrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.
Harikrishnan ThoppilIntegrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.
Daniel ThorntonIntegrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.
Chenhao YangIntegrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.
Alex FreitasSchool of Computing, University of Kent, Canterbury, UK.
João Pedro de MagalhãesIntegrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.

Funding

Wellcome TrustWellcome Trust 104978/Z/14/Z
6 · The paper itself

Abstract

In model organisms, over 2,000 genes have been shown to modulate aging, the collection of which we call the ‘gerontome’. Although some individual aging-related genes have been the subject of intense scrutiny, their analysis as a whole has been limited. In particular, the genetic interaction of aging and age-related pathologies remain a subject of debate. In this work, we perform a systematic analysis of the gerontome across species, including human aging-related genes. First, by classifying aging-related genes as pro- or anti-longevity, we define distinct pathways and genes that modulate aging in different ways. Our subsequent comparison of aging-related genes with age-related disease genes reveals species-specific effects with strong overlaps between aging and age-related diseases in mice, yet surprisingly few overlaps in lower model organisms. We discover that genetic links between aging and age-related diseases are due to a small fraction of aging-related genes which also tend to have a high network connectivity. Other insights from our systematic analysis include assessing how using datasets with genes more or less studied than average may result in biases, showing that age-related disease genes have faster molecular evolution rates and predicting new aging-related drugs based on drug-gene interaction data. Overall, this is the largest systems-level analysis of the genetics of aging to date and the first to discriminate anti- and pro-longevity genes, revealing new insights on aging-related genes as a whole and their interactions with age-related diseases.

Indexed as

Age FactorsAgingAnimalsCaenorhabditis elegansDatabases, Nucleic AcidDrosophilaEvolution, MolecularGenome, HumanHumansLongevityMiceSaccharomyces cerevisiaeSequence Analysis, DNA

Identifiers

PMID28175300
PMCPMC5418736

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.