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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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Location analysis and score distribution of GATA-1 occupancy sites in Ter119− and Ter119+ cells. (A) Venn diagram showing the overlapping and unique GATA-1 target genes identified in Ter119− and Ter119+ erythroid cells. (B) Distribution of GATA-1 TGS and minimum distance from the target gene TSS. Scatterplot of GATA-1 potential target genes identified in both cell populations (each gene is plotted once selecting for the condition with the highest TGS). Horizontal lines define score thresholds for the three classes. Most highly enriched (Class I) target genes are found in the intersection of the two data sets and comprise most of the bibliographically described GATA-1 target genes. (C) GATA-1 potential target genes identified uniquely in Ter119− or Ter119+ cells are shown in the top and bottom scatterplots, respectively. Despite the fact that target genes unique in either Ter119− or Ter119+ cells are poor in Class I genes, they still show a prominent clustering of GATA-1 occupancy sites near the identified target gene TSS. (D) Distribution of GATA-1 potential target genes in classes. Pie chart illustrating the distribution of common genes in three TGS classes and also the presence of a large quota of genes showing dynamic changes in GATA-1 binding throughout erythroid differentiation (ascending and descending).
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gkt167-F3: Location analysis and score distribution of GATA-1 occupancy sites in Ter119− and Ter119+ cells. (A) Venn diagram showing the overlapping and unique GATA-1 target genes identified in Ter119− and Ter119+ erythroid cells. (B) Distribution of GATA-1 TGS and minimum distance from the target gene TSS. Scatterplot of GATA-1 potential target genes identified in both cell populations (each gene is plotted once selecting for the condition with the highest TGS). Horizontal lines define score thresholds for the three classes. Most highly enriched (Class I) target genes are found in the intersection of the two data sets and comprise most of the bibliographically described GATA-1 target genes. (C) GATA-1 potential target genes identified uniquely in Ter119− or Ter119+ cells are shown in the top and bottom scatterplots, respectively. Despite the fact that target genes unique in either Ter119− or Ter119+ cells are poor in Class I genes, they still show a prominent clustering of GATA-1 occupancy sites near the identified target gene TSS. (D) Distribution of GATA-1 potential target genes in classes. Pie chart illustrating the distribution of common genes in three TGS classes and also the presence of a large quota of genes showing dynamic changes in GATA-1 binding throughout erythroid differentiation (ascending and descending).

Mentions: Based on the ±10-kb mapping, 2590 and 2826 potential GATA-1 target genes were identified in the Ter119− and Ter119+ data sets, respectively. The union of the two data sets yielded 3651 potential GATA-1 target genes, of which 1765 genes were common to both Ter119− and Ter119+ data sets, thus giving an intersection of 48.3% (Figure 3A and B). By contrast, 825 (22.6%) and 1061 (29.1%) genes were unique to the Ter119− or Ter119+ cells, respectively (Figure 3C). These data reveal a considerable conservation of GATA-1 target genes throughout erythroid differentiation.Figure 3.


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)

Location analysis and score distribution of GATA-1 occupancy sites in Ter119− and Ter119+ cells. (A) Venn diagram showing the overlapping and unique GATA-1 target genes identified in Ter119− and Ter119+ erythroid cells. (B) Distribution of GATA-1 TGS and minimum distance from the target gene TSS. Scatterplot of GATA-1 potential target genes identified in both cell populations (each gene is plotted once selecting for the condition with the highest TGS). Horizontal lines define score thresholds for the three classes. Most highly enriched (Class I) target genes are found in the intersection of the two data sets and comprise most of the bibliographically described GATA-1 target genes. (C) GATA-1 potential target genes identified uniquely in Ter119− or Ter119+ cells are shown in the top and bottom scatterplots, respectively. Despite the fact that target genes unique in either Ter119− or Ter119+ cells are poor in Class I genes, they still show a prominent clustering of GATA-1 occupancy sites near the identified target gene TSS. (D) Distribution of GATA-1 potential target genes in classes. Pie chart illustrating the distribution of common genes in three TGS classes and also the presence of a large quota of genes showing dynamic changes in GATA-1 binding throughout erythroid differentiation (ascending and descending).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

gkt167-F3: Location analysis and score distribution of GATA-1 occupancy sites in Ter119− and Ter119+ cells. (A) Venn diagram showing the overlapping and unique GATA-1 target genes identified in Ter119− and Ter119+ erythroid cells. (B) Distribution of GATA-1 TGS and minimum distance from the target gene TSS. Scatterplot of GATA-1 potential target genes identified in both cell populations (each gene is plotted once selecting for the condition with the highest TGS). Horizontal lines define score thresholds for the three classes. Most highly enriched (Class I) target genes are found in the intersection of the two data sets and comprise most of the bibliographically described GATA-1 target genes. (C) GATA-1 potential target genes identified uniquely in Ter119− or Ter119+ cells are shown in the top and bottom scatterplots, respectively. Despite the fact that target genes unique in either Ter119− or Ter119+ cells are poor in Class I genes, they still show a prominent clustering of GATA-1 occupancy sites near the identified target gene TSS. (D) Distribution of GATA-1 potential target genes in classes. Pie chart illustrating the distribution of common genes in three TGS classes and also the presence of a large quota of genes showing dynamic changes in GATA-1 binding throughout erythroid differentiation (ascending and descending).
Mentions: Based on the ±10-kb mapping, 2590 and 2826 potential GATA-1 target genes were identified in the Ter119− and Ter119+ data sets, respectively. The union of the two data sets yielded 3651 potential GATA-1 target genes, of which 1765 genes were common to both Ter119− and Ter119+ data sets, thus giving an intersection of 48.3% (Figure 3A and B). By contrast, 825 (22.6%) and 1061 (29.1%) genes were unique to the Ter119− or Ter119+ cells, respectively (Figure 3C). These data reveal a considerable conservation of GATA-1 target genes throughout erythroid differentiation.Figure 3.

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