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GATA-1 genome-wide occupancy associates with distinct epigenetic profiles in mouse fetal liver erythropoiesis.

Papadopoulos GL, Karkoulia E, Tsamardinos I, Porcher C, Ragoussis J, Bungert J, Strouboulis J - Nucleic Acids Res. (2013)

Bottom Line: Our results suggest that GATA-1 associates preferentially with changes of specific epigenetic modifications, such as H4K16, H3K27 acetylation and H3K4 di-methylation.Remarkably, our prediction model explained a high proportion of 62% of variation in gene expression.Hierarchical clustering of the proximity values calculated by the RF model produced a clear separation of upregulated versus downregulated genes and a further separation of downregulated genes in two distinct groups.

View Article: PubMed Central - PubMed

Affiliation: Division of Molecular Oncology, Biomedical Sciences Research Center "Alexander Fleming", Vari GR16672, Greece.

ABSTRACT
We report the genomic occupancy profiles of the key hematopoietic transcription factor GATA-1 in pro-erythroblasts and mature erythroid cells fractionated from day E12.5 mouse fetal liver cells. Integration of GATA-1 occupancy profiles with available genome-wide transcription factor and epigenetic profiles assayed in fetal liver cells enabled as to evaluate GATA-1 involvement in modulating local chromatin structure of target genes during erythroid differentiation. Our results suggest that GATA-1 associates preferentially with changes of specific epigenetic modifications, such as H4K16, H3K27 acetylation and H3K4 di-methylation. Furthermore, we used random forest (RF) non-linear regression to predict changes in the expression levels of GATA-1 target genes based on the genomic features available for pro-erythroblasts and mature fetal liver-derived erythroid cells. Remarkably, our prediction model explained a high proportion of 62% of variation in gene expression. Hierarchical clustering of the proximity values calculated by the RF model produced a clear separation of upregulated versus downregulated genes and a further separation of downregulated genes in two distinct groups. Thus, our study of GATA-1 genome-wide occupancy profiles in mouse primary erythroid cells and their integration with global epigenetic marks reveals three clusters of GATA-1 gene targets that are associated with specific epigenetic signatures and functional characteristics.

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Determination of GATA-1 chromatin occupancy in Ter119βˆ’ and Ter119+ cells by ChIP sequencing. (A) Description of the pipeline followed for the analysis of raw deep sequencing results of GATA-1 ChIP-seq, leading to the identification of occupancy sites (peaks) and potential target genes. (B) Bona fide GATA-1 binding sites as determined by GATA-1 ChIP-seq in Ter119βˆ’ and Ter119+ cells. Scale refers to read counts normalized by the peak calling algorithm (QuEST).
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gkt167-F1: Determination of GATA-1 chromatin occupancy in Ter119βˆ’ and Ter119+ cells by ChIP sequencing. (A) Description of the pipeline followed for the analysis of raw deep sequencing results of GATA-1 ChIP-seq, leading to the identification of occupancy sites (peaks) and potential target genes. (B) Bona fide GATA-1 binding sites as determined by GATA-1 ChIP-seq in Ter119βˆ’ and Ter119+ cells. Scale refers to read counts normalized by the peak calling algorithm (QuEST).

Mentions: To identify genome-wide differential GATA-1–binding patterns during erythroid differentiation in vivo, we performed GATA-1 ChIP on Ter119βˆ’ proerythroblasts and Ter119+ mature erythroid cells fractionated from day E12.5 mouse fetal liver cells, followed by high throughput massive parallel sequencing. ChIPed DNA from Ter119βˆ’ and Ter119+ cells was sequenced in duplicate to generate 18.2 and 15.3 million uniquely mapped sequence reads, respectively (Figure 1A). Using the QuEST peak-calling algorithm (36), we assembled the unique non-redundant sequence reads for each replicate into peaks that identify potential GATA-1 bound regions across the genome. For both samples, we took the union of the peaks of the two replicates, resulting in 9795 and 14 239 peaks for the Ter119βˆ’ and Ter119+ samples, respectively (Figure 1A). Visualization in both the Ter119βˆ’ and Ter119+ data sets of peaks in known GATA-1 target gene loci, such as Ξ²-globin, Gata1, Gata2, Klf1 or Scl/Tal1 gene loci (10,30,40,41), or in the Zbtb7 locus that was recently identified as a GATA-1 gene target (16), provided early validation for our sequencing data (Figure 1B).Figure 1.


GATA-1 genome-wide occupancy associates with distinct epigenetic profiles in mouse fetal liver erythropoiesis.

Papadopoulos GL, Karkoulia E, Tsamardinos I, Porcher C, Ragoussis J, Bungert J, Strouboulis J - Nucleic Acids Res. (2013)

Determination of GATA-1 chromatin occupancy in Ter119βˆ’ and Ter119+ cells by ChIP sequencing. (A) Description of the pipeline followed for the analysis of raw deep sequencing results of GATA-1 ChIP-seq, leading to the identification of occupancy sites (peaks) and potential target genes. (B) Bona fide GATA-1 binding sites as determined by GATA-1 ChIP-seq in Ter119βˆ’ and Ter119+ cells. Scale refers to read counts normalized by the peak calling algorithm (QuEST).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3643580&req=5

gkt167-F1: Determination of GATA-1 chromatin occupancy in Ter119βˆ’ and Ter119+ cells by ChIP sequencing. (A) Description of the pipeline followed for the analysis of raw deep sequencing results of GATA-1 ChIP-seq, leading to the identification of occupancy sites (peaks) and potential target genes. (B) Bona fide GATA-1 binding sites as determined by GATA-1 ChIP-seq in Ter119βˆ’ and Ter119+ cells. Scale refers to read counts normalized by the peak calling algorithm (QuEST).
Mentions: To identify genome-wide differential GATA-1–binding patterns during erythroid differentiation in vivo, we performed GATA-1 ChIP on Ter119βˆ’ proerythroblasts and Ter119+ mature erythroid cells fractionated from day E12.5 mouse fetal liver cells, followed by high throughput massive parallel sequencing. ChIPed DNA from Ter119βˆ’ and Ter119+ cells was sequenced in duplicate to generate 18.2 and 15.3 million uniquely mapped sequence reads, respectively (Figure 1A). Using the QuEST peak-calling algorithm (36), we assembled the unique non-redundant sequence reads for each replicate into peaks that identify potential GATA-1 bound regions across the genome. For both samples, we took the union of the peaks of the two replicates, resulting in 9795 and 14 239 peaks for the Ter119βˆ’ and Ter119+ samples, respectively (Figure 1A). Visualization in both the Ter119βˆ’ and Ter119+ data sets of peaks in known GATA-1 target gene loci, such as Ξ²-globin, Gata1, Gata2, Klf1 or Scl/Tal1 gene loci (10,30,40,41), or in the Zbtb7 locus that was recently identified as a GATA-1 gene target (16), provided early validation for our sequencing data (Figure 1B).Figure 1.

Bottom Line: Our results suggest that GATA-1 associates preferentially with changes of specific epigenetic modifications, such as H4K16, H3K27 acetylation and H3K4 di-methylation.Remarkably, our prediction model explained a high proportion of 62% of variation in gene expression.Hierarchical clustering of the proximity values calculated by the RF model produced a clear separation of upregulated versus downregulated genes and a further separation of downregulated genes in two distinct groups.

View Article: PubMed Central - PubMed

Affiliation: Division of Molecular Oncology, Biomedical Sciences Research Center "Alexander Fleming", Vari GR16672, Greece.

ABSTRACT
We report the genomic occupancy profiles of the key hematopoietic transcription factor GATA-1 in pro-erythroblasts and mature erythroid cells fractionated from day E12.5 mouse fetal liver cells. Integration of GATA-1 occupancy profiles with available genome-wide transcription factor and epigenetic profiles assayed in fetal liver cells enabled as to evaluate GATA-1 involvement in modulating local chromatin structure of target genes during erythroid differentiation. Our results suggest that GATA-1 associates preferentially with changes of specific epigenetic modifications, such as H4K16, H3K27 acetylation and H3K4 di-methylation. Furthermore, we used random forest (RF) non-linear regression to predict changes in the expression levels of GATA-1 target genes based on the genomic features available for pro-erythroblasts and mature fetal liver-derived erythroid cells. Remarkably, our prediction model explained a high proportion of 62% of variation in gene expression. Hierarchical clustering of the proximity values calculated by the RF model produced a clear separation of upregulated versus downregulated genes and a further separation of downregulated genes in two distinct groups. Thus, our study of GATA-1 genome-wide occupancy profiles in mouse primary erythroid cells and their integration with global epigenetic marks reveals three clusters of GATA-1 gene targets that are associated with specific epigenetic signatures and functional characteristics.

Show MeSH
Related in: MedlinePlus