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The dynamics of early-state transcriptional changes and aggregate formation in a Huntington’s disease cell model

Overview of attention for article published in BMC Genomics, May 2017
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  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

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Title
The dynamics of early-state transcriptional changes and aggregate formation in a Huntington’s disease cell model
Published in
BMC Genomics, May 2017
DOI 10.1186/s12864-017-3745-z
Pubmed ID
Authors

Martijn van Hagen, Diewertje G. E. Piebes, Wim C. de Leeuw, Ilona M. Vuist, Willeke M. C. van Roon-Mom, Perry D. Moerland, Pernette J. Verschure

Abstract

Huntington's disease (HD) is a fatal neurodegenerative disorder caused by a CAG expansion in the Huntingtin (HTT) gene. Proteolytic cleavage of mutant huntingtin (Htt) protein with an expanded polyglutamine (polyQ) stretch results in production of Htt fragments that aggregate and induce impaired ubiquitin proteasome, mitochondrial functioning and transcriptional dysregulation. To understand the time-resolved relationship between aggregate formation and transcriptional changes at early disease stages, we performed temporal transcriptome profiling and quantification of aggregate formation in living cells in an inducible HD cell model. Rat pheochromocytoma (PC12) cells containing a stably integrated, doxycycline-inducible, eGFP-tagged N-terminal human Htt fragment with an expanded polyQ domain were used to analyse gene expression changes at different stages of mutant Htt aggregation. At earliest time points after doxycycline induction no detectable aggregates and few changes in gene expression were observed. Aggregates started to appear at intermediate time points. Aggregate formation and subsequent enlargement of aggregates coincided with a rapid increase in the number of differentially expressed (DE) genes. The increase in number of large aggregates coincided with a decrease in the number of smaller aggregates whereas the transcription profile reverted towards the profile observed before mutant Htt induction. Cluster-based analysis of the 2,176 differentially expressed genes revealed fourteen distinct clusters responding differently over time. Functional enrichment analysis of the two major gene clusters revealed that genes in the up-regulated cluster were mainly involved in metabolic (antioxidant activity and cellular ketone metabolic processes) and genes in the down-regulated cluster in developmental processes, respectively. Promoter-based analysis of the identified gene clusters resulted in identification of a transcription factor network of which several previously have been linked to HD. We demonstrate a time-resolved relationship between Htt aggregation and changes in the transcriptional profile. We identified two major gene clusters showing involvement of (i) mitochondrial dysfunction and (ii) developmental processes implying cellular homeostasis defects. We identified novel and known HD-linked transcription factors and show their interaction with known and predicted regulatory proteins. Our data provide a novel resource for hypothesis building on the role of transcriptional key regulators in early stages of HD and possibly other polyQ-dependent diseases.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 45 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 16%
Student > Bachelor 7 16%
Student > Ph. D. Student 7 16%
Researcher 5 11%
Student > Doctoral Student 2 4%
Other 7 16%
Unknown 10 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 20%
Agricultural and Biological Sciences 6 13%
Medicine and Dentistry 5 11%
Neuroscience 3 7%
Psychology 2 4%
Other 5 11%
Unknown 15 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 26 December 2017.
All research outputs
#13,030,962
of 23,312,088 outputs
Outputs from BMC Genomics
#4,469
of 10,742 outputs
Outputs of similar age
#146,077
of 310,981 outputs
Outputs of similar age from BMC Genomics
#86
of 208 outputs
Altmetric has tracked 23,312,088 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,742 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 57% 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 310,981 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 208 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 58% of its contemporaries.