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Mapping Mammalian Cell-type-specific Transcriptional Regulatory Networks Using KD-CAGE and ChIP-seq Data in the TC-YIK Cell Line

Overview of attention for article published in Frontiers in Genetics, November 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

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Title
Mapping Mammalian Cell-type-specific Transcriptional Regulatory Networks Using KD-CAGE and ChIP-seq Data in the TC-YIK Cell Line
Published in
Frontiers in Genetics, November 2015
DOI 10.3389/fgene.2015.00331
Pubmed ID
Authors

Marina Lizio, Yuri Ishizu, Masayoshi Itoh, Timo Lassmann, Akira Hasegawa, Atsutaka Kubosaki, Jessica Severin, Hideya Kawaji, Yukio Nakamura, FANTOM consortium, Harukazu Suzuki, Yoshihide Hayashizaki, Piero Carninci, Alistair R. R. Forrest

Abstract

Mammals are composed of hundreds of different cell types with specialized functions. Each of these cellular phenotypes are controlled by different combinations of transcription factors. Using a human non islet cell insulinoma cell line (TC-YIK) which expresses insulin and the majority of known pancreatic beta cell specific genes as an example, we describe a general approach to identify key cell-type-specific transcription factors (TFs) and their direct and indirect targets. By ranking all human TFs by their level of enriched expression in TC-YIK relative to a broad collection of samples (FANTOM5), we confirmed known key regulators of pancreatic function and development. Systematic siRNA mediated perturbation of these TFs followed by qRT-PCR revealed their interconnections with NEUROD1 at the top of the regulation hierarchy and its depletion drastically reducing insulin levels. For 15 of the TF knock-downs (KD), we then used Cap Analysis of Gene Expression (CAGE) to identify thousands of their targets genome-wide (KD-CAGE). The data confirm NEUROD1 as a key positive regulator in the transcriptional regulatory network (TRN), and ISL1, and PROX1 as antagonists. As a complimentary approach we used ChIP-seq on four of these factors to identify NEUROD1, LMX1A, PAX6, and RFX6 binding sites in the human genome. Examining the overlap between genes perturbed in the KD-CAGE experiments and genes with a ChIP-seq peak within 50 kb of their promoter, we identified direct transcriptional targets of these TFs. Integration of KD-CAGE and ChIP-seq data shows that both NEUROD1 and LMX1A work as the main transcriptional activators. In the core TRN (i.e., TF-TF only), NEUROD1 directly transcriptionally activates the pancreatic TFs HSF4, INSM1, MLXIPL, MYT1, NKX6-3, ONECUT2, PAX4, PROX1, RFX6, ST18, DACH1, and SHOX2, while LMX1A directly transcriptionally activates DACH1, SHOX2, PAX6, and PDX1. Analysis of these complementary datasets suggests the need for caution in interpreting ChIP-seq datasets. (1) A large fraction of binding sites are at distal enhancer sites and cannot be directly associated to their targets, without chromatin conformation data. (2) Many peaks may be non-functional: even when there is a peak at a promoter, the expression of the gene may not be affected in the matching perturbation experiment.

X Demographics

X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
United Kingdom 1 2%
Taiwan 1 2%
Japan 1 2%
United States 1 2%
Unknown 43 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 38%
Researcher 7 15%
Student > Bachelor 6 13%
Student > Postgraduate 3 6%
Student > Doctoral Student 2 4%
Other 7 15%
Unknown 5 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 33%
Agricultural and Biological Sciences 15 31%
Medicine and Dentistry 4 8%
Computer Science 3 6%
Engineering 2 4%
Other 1 2%
Unknown 7 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 08 April 2018.
All research outputs
#4,226,936
of 25,464,544 outputs
Outputs from Frontiers in Genetics
#1,273
of 13,727 outputs
Outputs of similar age
#63,775
of 393,079 outputs
Outputs of similar age from Frontiers in Genetics
#5
of 49 outputs
Altmetric has tracked 25,464,544 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,727 research outputs from this source. They receive a mean Attention Score of 3.8. This one has done particularly well, scoring higher than 90% 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 393,079 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 83% of its contemporaries.
We're also able to compare this research output to 49 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 91% of its contemporaries.