Limits...
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.

Show MeSH

Related in: MedlinePlus

Evaluation of the gene expression RF regression model. (A) Scatterplot of observed and predicted values of gene expression change between Ter119− and Ter119+ cells of differentially regulated GATA-1 target genes. Red and green dots refer to values correctly predicted as decreasing or increasing mRNA levels, respectively. Blue and cyan dots refer to GATA-1 target genes with inverted predicted values. (B) Variable importance measures (%IncMSE) in predicting gene expression changes between Ter119− and Ter119+ erythroid cells. Only the 15 top ranked features are plotted. (C) Dendrogram showing clusters of GATA-1 differentially regulated target genes according to the RF calculated proximity values. (D) Barplot of corresponding gene expression fold change values of the proximity clustered GATA-1 target genes. (E) Heatmap illustrating the mean TGS values of the different occupancy profiles within the genes composing each cluster of GATA-1 differentially regulated target genes. (F) Tables showing the most highly enriched gene ontologies identified for each of the three GATA-1 target gene clusters.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3643580&req=5

gkt167-F5: Evaluation of the gene expression RF regression model. (A) Scatterplot of observed and predicted values of gene expression change between Ter119− and Ter119+ cells of differentially regulated GATA-1 target genes. Red and green dots refer to values correctly predicted as decreasing or increasing mRNA levels, respectively. Blue and cyan dots refer to GATA-1 target genes with inverted predicted values. (B) Variable importance measures (%IncMSE) in predicting gene expression changes between Ter119− and Ter119+ erythroid cells. Only the 15 top ranked features are plotted. (C) Dendrogram showing clusters of GATA-1 differentially regulated target genes according to the RF calculated proximity values. (D) Barplot of corresponding gene expression fold change values of the proximity clustered GATA-1 target genes. (E) Heatmap illustrating the mean TGS values of the different occupancy profiles within the genes composing each cluster of GATA-1 differentially regulated target genes. (F) Tables showing the most highly enriched gene ontologies identified for each of the three GATA-1 target gene clusters.

Mentions: Of the 3651 genes identified as GATA-1 target genes, 321 genes are upregulated by >2-fold with differentiation, 1941 genes are downregulated by >2-fold and 1390 genes show <2-fold variation between Ter119− and Ter119+ erythroid cells (45). As both GATA-1 occupancy and the epigenetic landscape are involved in the regulation of GATA-1 differentially expressed target genes (2258 genes), we integrated all of the available information (Table 1) to model the changes in their expression levels during erythroid differentiation (Supplementary Methods). This approach resulted in a remarkably highly predictive model (R2 = 0.62, r = 0.8, Figure 5A) of differential gene expression profiles by the binding signals of the four TFs, nine histone modifications, RNA pol II and DNA methylation levels measured in Ter119− and Ter119+ cells. The most predictive feature of changes in gene expression during erythroid differentiation is the change in the levels of the H3K79me2 elongation mark (Figure 5B and Supplementary Table S4), in accordance with the findings of Wong et al. (45). Changes in H3K4 methylation levels closely followed, whereas changes in GATA-1 occupancy were found to be in a group of almost equal ranking comprising H3K9Ac, RNApolII and H4K16Ac. It is interesting to note that the most predictive features (H3K79me2 and H3K4 methylation) can be, at least in part, associated with GATA-1 itself, as shown earlier in the text. This observation further consolidates the notion that part of the GATA-1 regulatory function is exerted through the modulation of the epigenetic landscape of its target genes.Figure 5.


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)

Evaluation of the gene expression RF regression model. (A) Scatterplot of observed and predicted values of gene expression change between Ter119− and Ter119+ cells of differentially regulated GATA-1 target genes. Red and green dots refer to values correctly predicted as decreasing or increasing mRNA levels, respectively. Blue and cyan dots refer to GATA-1 target genes with inverted predicted values. (B) Variable importance measures (%IncMSE) in predicting gene expression changes between Ter119− and Ter119+ erythroid cells. Only the 15 top ranked features are plotted. (C) Dendrogram showing clusters of GATA-1 differentially regulated target genes according to the RF calculated proximity values. (D) Barplot of corresponding gene expression fold change values of the proximity clustered GATA-1 target genes. (E) Heatmap illustrating the mean TGS values of the different occupancy profiles within the genes composing each cluster of GATA-1 differentially regulated target genes. (F) Tables showing the most highly enriched gene ontologies identified for each of the three GATA-1 target gene clusters.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

gkt167-F5: Evaluation of the gene expression RF regression model. (A) Scatterplot of observed and predicted values of gene expression change between Ter119− and Ter119+ cells of differentially regulated GATA-1 target genes. Red and green dots refer to values correctly predicted as decreasing or increasing mRNA levels, respectively. Blue and cyan dots refer to GATA-1 target genes with inverted predicted values. (B) Variable importance measures (%IncMSE) in predicting gene expression changes between Ter119− and Ter119+ erythroid cells. Only the 15 top ranked features are plotted. (C) Dendrogram showing clusters of GATA-1 differentially regulated target genes according to the RF calculated proximity values. (D) Barplot of corresponding gene expression fold change values of the proximity clustered GATA-1 target genes. (E) Heatmap illustrating the mean TGS values of the different occupancy profiles within the genes composing each cluster of GATA-1 differentially regulated target genes. (F) Tables showing the most highly enriched gene ontologies identified for each of the three GATA-1 target gene clusters.
Mentions: Of the 3651 genes identified as GATA-1 target genes, 321 genes are upregulated by >2-fold with differentiation, 1941 genes are downregulated by >2-fold and 1390 genes show <2-fold variation between Ter119− and Ter119+ erythroid cells (45). As both GATA-1 occupancy and the epigenetic landscape are involved in the regulation of GATA-1 differentially expressed target genes (2258 genes), we integrated all of the available information (Table 1) to model the changes in their expression levels during erythroid differentiation (Supplementary Methods). This approach resulted in a remarkably highly predictive model (R2 = 0.62, r = 0.8, Figure 5A) of differential gene expression profiles by the binding signals of the four TFs, nine histone modifications, RNA pol II and DNA methylation levels measured in Ter119− and Ter119+ cells. The most predictive feature of changes in gene expression during erythroid differentiation is the change in the levels of the H3K79me2 elongation mark (Figure 5B and Supplementary Table S4), in accordance with the findings of Wong et al. (45). Changes in H3K4 methylation levels closely followed, whereas changes in GATA-1 occupancy were found to be in a group of almost equal ranking comprising H3K9Ac, RNApolII and H4K16Ac. It is interesting to note that the most predictive features (H3K79me2 and H3K4 methylation) can be, at least in part, associated with GATA-1 itself, as shown earlier in the text. This observation further consolidates the notion that part of the GATA-1 regulatory function is exerted through the modulation of the epigenetic landscape of its target genes.Figure 5.

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