Uppsala ChIP Buty Track Settings
 
Uppsala University, Sweden ChIP-chip Na-butyrate time series   (Uppsala ChIP)

This track is part of a parent called 'Uppsala ChIP'. To show other tracks of this parent, go to the Uppsala ChIP configuration page.

Display mode:       Reset to defaults

Type of graph:
Track height: pixels (range: 16 to 128)
Data view scaling: Always include zero: 
Vertical viewing range: min:  max:   (range: 0 to 9)
Transform function:Transform data points by: 
Windowing function: Smoothing window:  pixels
Negate values:
Draw y indicator lines:at y = 0.0:    at y =
Graph configuration help
List subtracks: only selected/visible    all    ()  
dense
 UU H3ac HepG2 0h  Uppsala University, Sweden ChIP-chip (H3ac, HepG2, Butyrate 0h)   schema 
dense
 UU H3ac HepG2 12h  Uppsala University, Sweden ChIP-chip (H3ac, HepG2, Butyrate 12h)   schema 
dense
 UU H4ac HepG2 0h  Uppsala University, Sweden ChIP-chip (H4ac, HepG2, Butyrate 0h)   schema 
dense
 UU H4ac HepG2 12h  Uppsala University, Sweden ChIP-chip (H4ac, HepG2, Butyrate 12h)   schema 
dense
 UU H3ac 0h vs 12h  Uppsala University, Sweden ChIP-chip (H3ac 0h vs. 12h)   schema 
dense
 UU H4ac 0h vs 12h  Uppsala University, Sweden ChIP-chip (H4ac 0h vs. 12h)   schema 
    
Data version: ENCODE May 2006
Data coordinates converted via liftOver from: May 2004 (NCBI35/hg17)

Description

ENCODE regions were investigated by ChIP-chip, analyzing both histone H3 acetylation (H3ac; H3 acetylated lysines 9 and14) and histone H4 acetylation (H4ac; H4 acetylated lysined 5,8,12,16). This analysis was performed using ChIP material obtained from cells that were either untreated or treated with 5mM Na-Butyrate for 12 hours. Na-Butyrate is a histone deacetylase inhibitor (HDACi) that increases bulk levels of acetylated histones. Four tracks presented in the genome browser represent the ChIP-chip signal obtained for either H3ac or H4ac, using cells that were untreated or treated with butyrate: H3ac 0h, H3ac 12h, H4ac 0h, H4ac 12h. Two additional tracks indicate those spots where H3ac or H4ac levels are significantly changed by butyrate treatment.

Methods

Chromatin immunoprecipitation, DNA labelling and array hybridization were exactly as previously described (Rada-Iglesias, et al. 2005). A set of enriched spots was obtained for each of H3ac 0h, H3ac 12h, H4ac 0h and H4ac 12h using the same pre-processing and analysis procedures as in (Rada-Iglesias, et al.). Enriched spots showing different histone acetylation levels between 0h and 12h treatment were then detected through an empirical Bayes method (Smyth). All spots with B-score>0 were either classified as up or down depending on whether the acetylation was increased or decreased. For spots missing all measurements at one of the time points due to filtering, the B-score was instead calculated on un-filtered, print-tip lowess normalized (Yang, et al.) raw data. Enriched spots that were not present in any of the up or down groups were classified as unchanged.

The raw data for this track is available at EBI ArrayExpress, as experiment E-MEXP-693.

Verification

New ChIPs were performed for both H3ac and H4ac, both for untreated cells and cells treated with 5mM Na-butyrate for 12 hours. Furthermore, ChIP was performed in cells that were treated with 5mM Na-butyrate for 15 minutes, 2 hours, 6 hours and 12 hours+6 hours without butyrate. All these ChIP DNAs were analyzed by PCR, including 10 regions were loss of acetylation after 12 hours butyrate treatment was observed in ChIP-chip experiments, two regions where a trend towards increase acetylation was observed, one negative region where no acetylation and no change was observed and three control regions not included in the ENCODE array and covering promoter regions of previously known butyrate-responsive genes.

Credits

These experiments were performed in the Claes Wadelius lab, Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University. The statistical analysis was done at the Linnaeus Centre for Bioinformatics at Uppsala University. Microarrays were produced at the Sanger Institute.

References

Ameur A, Yankovski V, Enroth S, Spjuth O, Komorowski J. The LCB Data Warehouse. Bioinformatics. 2006 Apr 15;22(8):1024-6.

Rada-Iglesias A, Wallerman O, Koch C, Ameur A, Enroth S, Clelland G, Wester K, Wilcox S, Dovey OM, Ellis PD et al. Binding sites for metabolic disease related transcription factors inferred at base pair resolution by chromatin immunoprecipitation and genomic microarrays. Hum Mol Genet. 2005 Nov 15;14(22):3435-47.

Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3:Article3.

Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 2002 Feb 15;30(4):e15.