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Dynamic Modelling of Pathways to Cellular Senescence Reveals Strategies for Targeted Interventions

Overview of attention for article published in PLoS Computational Biology, August 2014
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  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

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9 X users
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1 Google+ user

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175 Mendeley
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1 CiteULike
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Title
Dynamic Modelling of Pathways to Cellular Senescence Reveals Strategies for Targeted Interventions
Published in
PLoS Computational Biology, August 2014
DOI 10.1371/journal.pcbi.1003728
Pubmed ID
Authors

Piero Dalle Pezze, Glyn Nelson, Elsje G. Otten, Viktor I. Korolchuk, Thomas B. L. Kirkwood, Thomas von Zglinicki, Daryl P. Shanley

Abstract

Cellular senescence, a state of irreversible cell cycle arrest, is thought to help protect an organism from cancer, yet also contributes to ageing. The changes which occur in senescence are controlled by networks of multiple signalling and feedback pathways at the cellular level, and the interplay between these is difficult to predict and understand. To unravel the intrinsic challenges of understanding such a highly networked system, we have taken a systems biology approach to cellular senescence. We report a detailed analysis of senescence signalling via DNA damage, insulin-TOR, FoxO3a transcription factors, oxidative stress response, mitochondrial regulation and mitophagy. We show in silico and in vitro that inhibition of reactive oxygen species can prevent loss of mitochondrial membrane potential, whilst inhibition of mTOR shows a partial rescue of mitochondrial mass changes during establishment of senescence. Dual inhibition of ROS and mTOR in vitro confirmed computational model predictions that it was possible to further reduce senescence-induced mitochondrial dysfunction and DNA double-strand breaks. However, these interventions were unable to abrogate the senescence-induced mitochondrial dysfunction completely, and we identified decreased mitochondrial fission as the potential driving force for increased mitochondrial mass via prevention of mitophagy. Dynamic sensitivity analysis of the model showed the network stabilised at a new late state of cellular senescence. This was characterised by poor network sensitivity, high signalling noise, low cellular energy, high inflammation and permanent cell cycle arrest suggesting an unsatisfactory outcome for treatments aiming to delay or reverse cellular senescence at late time points. Combinatorial targeted interventions are therefore possible for intervening in the cellular pathway to senescence, but in the cases identified here, are only capable of delaying senescence onset.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 175 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 <1%
Germany 1 <1%
France 1 <1%
Italy 1 <1%
Denmark 1 <1%
Unknown 170 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 18%
Researcher 32 18%
Student > Bachelor 23 13%
Student > Master 19 11%
Student > Doctoral Student 9 5%
Other 25 14%
Unknown 35 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 60 34%
Agricultural and Biological Sciences 28 16%
Medicine and Dentistry 10 6%
Engineering 5 3%
Neuroscience 4 2%
Other 27 15%
Unknown 41 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 September 2014.
All research outputs
#6,443,738
of 25,374,917 outputs
Outputs from PLoS Computational Biology
#4,414
of 8,960 outputs
Outputs of similar age
#58,291
of 247,685 outputs
Outputs of similar age from PLoS Computational Biology
#70
of 161 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 50% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 247,685 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 161 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.