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Rare and common epilepsies converge on a shared gene regulatory network providing opportunities for novel antiepileptic drug discovery

Overview of attention for article published in Genome Biology, December 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#40 of 4,467)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Citations

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72 Dimensions

Readers on

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140 Mendeley
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1 CiteULike
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Title
Rare and common epilepsies converge on a shared gene regulatory network providing opportunities for novel antiepileptic drug discovery
Published in
Genome Biology, December 2016
DOI 10.1186/s13059-016-1097-7
Pubmed ID
Authors

Andree Delahaye-Duriez, Prashant Srivastava, Kirill Shkura, Sarah R. Langley, Liisi Laaniste, Aida Moreno-Moral, Bénédicte Danis, Manuela Mazzuferi, Patrik Foerch, Elena V. Gazina, Kay Richards, Steven Petrou, Rafal M. Kaminski, Enrico Petretto, Michael R. Johnson

Abstract

The relationship between monogenic and polygenic forms of epilepsy is poorly understood and the extent to which the genetic and acquired epilepsies share common pathways is unclear. Here, we use an integrated systems-level analysis of brain gene expression data to identify molecular networks disrupted in epilepsy. We identified a co-expression network of 320 genes (M30), which is significantly enriched for non-synonymous de novo mutations ascertained from patients with monogenic epilepsy and for common variants associated with polygenic epilepsy. The genes in the M30 network are expressed widely in the human brain under tight developmental control and encode physically interacting proteins involved in synaptic processes. The most highly connected proteins within the M30 network were preferentially disrupted by deleterious de novo mutations for monogenic epilepsy, in line with the centrality-lethality hypothesis. Analysis of M30 expression revealed consistent downregulation in the epileptic brain in heterogeneous forms of epilepsy including human temporal lobe epilepsy, a mouse model of acquired temporal lobe epilepsy, and a mouse model of monogenic Dravet (SCN1A) disease. These results suggest functional disruption of M30 via gene mutation or altered expression as a convergent mechanism regulating susceptibility to epilepsy broadly. Using the large collection of drug-induced gene expression data from Connectivity Map, several drugs were predicted to preferentially restore the downregulation of M30 in epilepsy toward health, most notably valproic acid, whose effect on M30 expression was replicated in neurons. Taken together, our results suggest targeting the expression of M30 as a potential new therapeutic strategy in epilepsy.

X Demographics

X Demographics

The data shown below were collected from the profiles of 44 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 140 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 1%
Czechia 1 <1%
Ireland 1 <1%
Luxembourg 1 <1%
Unknown 135 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 24%
Researcher 26 19%
Student > Bachelor 15 11%
Student > Master 11 8%
Other 8 6%
Other 17 12%
Unknown 29 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 29 21%
Agricultural and Biological Sciences 25 18%
Neuroscience 21 15%
Medicine and Dentistry 15 11%
Computer Science 4 3%
Other 10 7%
Unknown 36 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 242. 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 24 April 2018.
All research outputs
#154,688
of 25,371,288 outputs
Outputs from Genome Biology
#40
of 4,467 outputs
Outputs of similar age
#3,254
of 420,674 outputs
Outputs of similar age from Genome Biology
#2
of 62 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done particularly well, scoring higher than 99% 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 420,674 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 62 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.