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Sample GSM5425109

Query DataSets for GSM5425109

Status

Public on Jul 12, 2021

Title

PPAR KO-T09-1

Sample type

SRA

Source name

Liver

Organism

Mus musculus

Characteristics

antibody: PPAR
strain: C57BL/6
genotype: Foxa2 KO (Foxa2loxP/loxP;Alfp.Cre)
tissue: Liver
age: 3 months
treatment: T09

Extracted molecule

genomic DNA

Extraction protocol

Snap-frozen mouse liver (100 mg) from wild type, Foxa2-KO, and LXRα-KO mice was used to prepare chromatin. Foxa2‐specific rabbit antiserum (Seven Hills Bioreagents, WRAB‐1,200) and rabbit polyclonal antibody specific to LXRα (Active Motif, 61175) were used for immunoprecipitation. Details about PPAR ChIP antibody: rabbit polyclonal antibody specific to PPARα (Novus Biologicals, NB600-636)
Libraries were prepared according to Illumina's instructions. Briefly, DNAs were blunted with a combination of T4 DNA polymerase, Klenow polymerase, and T4 PNK, then a single 3′-end “A” base was added using Klenow exo (3′-to-5′ exo minus). multiplex adapters were then ligated to the ends of the modified DNA. After adapter ligation DNA was PCR amplified with Broad custom primers for 16 cycles and library fragments of ~280 bp (~150-bp mononucleosome insert plus ~130-bp adaptor and PCR primer sequences) were isolated using Pippin Prep agarose gel. The purified DNA was captured on an Illumina flow cell for cluster generation. All libraries were sequenced on Illumina NextSeq 500.

Library strategy

ChIP-Seq

Library source

genomic

Library selection

ChIP

Instrument model

NextSeq 2000

Description

PPAR_KO-T09_qvalues.bed

Data processing

The reads were aligned to mouse genome (mm10; NCBI Build 38) using BWA (Li and Durbin, 2009). Duplicate reads were removed from the analysis. Reads (phred score > 30) that aligned uniquely were used for subsequent analysis.
ATAC-Seq reads from two biological replicates were merged for each condition (WT Veh-GW, WT Veh-T09, WT GW, WT T09, Foxa2-KO Veh-GW, Foxa2-KO Veh-T09, Foxa2-KO GW, Foxa2-KO T09). PeakSeq algorithm (Rozowsky et al., 2009) was used to identify bound peaks in WT GW against WT GW-Veh (ATAC_WT_GWonVeh), WT T09 against WT T09-Veh (ATAC_WT_T09onVeh), WT GW against Foxa2-KO GW (ATAC_WTonKO_GW), and WT T09 against Foxa2-KO T09 (ATAC_WTonKO_T09).
Foxa2 ChIP-Seq reads from three biological replicates were merged for each condition (WT Veh, WT GW, WT T09, LXRα-KO Veh, LXRα-KO GW, LXRα-KO T09). PeakSeq algorithm (Rozowsky et al., 2009) was used to identify bound peaks against input controls.
LXRα ChIP-Seq reads from three biological replicates were merged for each condition (WT Veh, WT GW, WT T09, Foxa2-KO Veh, Foxa2-KO GW, Foxa2-KO T09). PeakSeq algorithm (Rozowsky et al., 2009) was used to identify bound peaks against input controls.
FXR ChIP-Seq reads from two biological replicates were merged for each condition (WT Veh, WT GW4064, Foxa2-KO Veh, Foxa2-KO GW4064). PeakSeq algorithm (Rozowsky et al., 2009) was used to identify bound peaks against input controls.
PPAR ChIP-Seq details: Reads were aligned to the mouse genome (mm10; NCBI Build) using BWA. Duplicate reads were removed. Reads (phred score > 30) that aligned uniquely were used for subsequent analysis. For PPARa ChIPs, data from two biological replicates were merged for each condition. PeakSeq was used to identify bound peaks in FXR ligand-activated condition against input controls (PPARα FDR 5^, q-value<0.07) and in LXR ligand-activated condition against input controls ( PPARα FDR 5%, q-value<0.07).
RNA-Seq reads from three or four biological replicates were aligned using STAR to mouse genome build mm10. Expression levels were calculated using RSEM. Differential expression analysis of RNA-seq (p-value <0.05) was performed in R using EdgeR package with a Benjamini-Hochberg FDR of 5%.
Genome_build: mm10
Supplementary_files_format_and_content: bed files with peak calls from PeakSeq with the following columns: chromosome, begin coordinate, end coordinate, q-value

Submission date

Jul 06, 2021

Last update date

Jul 12, 2021

Contact name

Irina Bochkis

E-mail(s)

imb3q@virginia.edu

Phone

434-982-6752

Organization name

University of Virginia