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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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Association of GATA-1 occupancy with specific epigenetic events. (A) Heatmap showing the pairwise Pearson correlations between GATA-1 TGS and epigenetic mark TGSs in Ter119− and Ter119+ erythroid cells. (B) Scatterplots of observed and RF regression predicted values of selected histone mark variation between Ter119− and Ter119+ cells. Black dots represent the predicted values of the GATA-1 trained model, whereas gray dots represent the values predicted by the GATA-1/TAL1/KLF1 trained model. (C) Percentage of variation explained (R2) by the GATA-1 (first column) and GATA-1/TAL1/KLF1 (second column) trained RF regression models. Third column refers the percentage of the increase in R2 values between the two models.
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gkt167-F4: Association of GATA-1 occupancy with specific epigenetic events. (A) Heatmap showing the pairwise Pearson correlations between GATA-1 TGS and epigenetic mark TGSs in Ter119− and Ter119+ erythroid cells. (B) Scatterplots of observed and RF regression predicted values of selected histone mark variation between Ter119− and Ter119+ cells. Black dots represent the predicted values of the GATA-1 trained model, whereas gray dots represent the values predicted by the GATA-1/TAL1/KLF1 trained model. (C) Percentage of variation explained (R2) by the GATA-1 (first column) and GATA-1/TAL1/KLF1 (second column) trained RF regression models. Third column refers the percentage of the increase in R2 values between the two models.

Mentions: To characterize the epigenetic landscape of GATA-1 occupied regions, we calculated the linear correlation between TGS scores of GATA-1 target genes and the TGS score calculated for each of the other TF occupancy profiles and epigenetic marks (Figure 4A). Based on this analysis, we observe that GATA-1 occupancy strongly correlates with SCL/TAL-1 binding (RTer119− = 0.53, RTer119+ = 0.49), as has been previously reported (30), whereas a much weaker correlation is observed with KLF1 occupancy profiles (RTer119− = 0.15, RTer119+ =0.07) and PU.1 (RTer119− = 0.1, RTer119+ = 0.08), as also seen by Pilon et al. (32). Furthermore, most of the histone modifications show a considerable correlation with GATA-1 binding (Figure 4A). Interestingly, GATA-1 occupancy correlates highly with the levels of H4K16Ac mark in both early and late stages of erythroid differentiation (RTer119− = 0.49, RTer119+ = 0.58) and with the levels of the enhancer related H3K27Ac and H3K4me1 marks (the latter data were only available for Ter119+ cells) (RTer119− = 0.54, RTer119+ = 0.61 and RTer119− =0.46, RTer119+ = 0.5, respectively). These data are consistent with the observations by Kowalczyk et al. (48), showing that sequences enriched in H3K27Ac are predominantly bound by GATA-1 (and other transcription factors) in erythroid cells. By contrast, we do not find a linear relationship between genome-wide H3K27me3 marks and GATA-1 occupied regions (RTer119− =−0.004, RTer119+ = −0.02). Hence, the association of GATA-1 binding with the H3K27me3 mark seen by Yu et al. (16) in a subset of repressed GATA-1 target genes in mouse erythroleukemic (MEL) cells does not seem to be reflected at the genome-wide level in fetal liver-derived erythroblasts.Figure 4.


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)

Association of GATA-1 occupancy with specific epigenetic events. (A) Heatmap showing the pairwise Pearson correlations between GATA-1 TGS and epigenetic mark TGSs in Ter119− and Ter119+ erythroid cells. (B) Scatterplots of observed and RF regression predicted values of selected histone mark variation between Ter119− and Ter119+ cells. Black dots represent the predicted values of the GATA-1 trained model, whereas gray dots represent the values predicted by the GATA-1/TAL1/KLF1 trained model. (C) Percentage of variation explained (R2) by the GATA-1 (first column) and GATA-1/TAL1/KLF1 (second column) trained RF regression models. Third column refers the percentage of the increase in R2 values between the two models.
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gkt167-F4: Association of GATA-1 occupancy with specific epigenetic events. (A) Heatmap showing the pairwise Pearson correlations between GATA-1 TGS and epigenetic mark TGSs in Ter119− and Ter119+ erythroid cells. (B) Scatterplots of observed and RF regression predicted values of selected histone mark variation between Ter119− and Ter119+ cells. Black dots represent the predicted values of the GATA-1 trained model, whereas gray dots represent the values predicted by the GATA-1/TAL1/KLF1 trained model. (C) Percentage of variation explained (R2) by the GATA-1 (first column) and GATA-1/TAL1/KLF1 (second column) trained RF regression models. Third column refers the percentage of the increase in R2 values between the two models.
Mentions: To characterize the epigenetic landscape of GATA-1 occupied regions, we calculated the linear correlation between TGS scores of GATA-1 target genes and the TGS score calculated for each of the other TF occupancy profiles and epigenetic marks (Figure 4A). Based on this analysis, we observe that GATA-1 occupancy strongly correlates with SCL/TAL-1 binding (RTer119− = 0.53, RTer119+ = 0.49), as has been previously reported (30), whereas a much weaker correlation is observed with KLF1 occupancy profiles (RTer119− = 0.15, RTer119+ =0.07) and PU.1 (RTer119− = 0.1, RTer119+ = 0.08), as also seen by Pilon et al. (32). Furthermore, most of the histone modifications show a considerable correlation with GATA-1 binding (Figure 4A). Interestingly, GATA-1 occupancy correlates highly with the levels of H4K16Ac mark in both early and late stages of erythroid differentiation (RTer119− = 0.49, RTer119+ = 0.58) and with the levels of the enhancer related H3K27Ac and H3K4me1 marks (the latter data were only available for Ter119+ cells) (RTer119− = 0.54, RTer119+ = 0.61 and RTer119− =0.46, RTer119+ = 0.5, respectively). These data are consistent with the observations by Kowalczyk et al. (48), showing that sequences enriched in H3K27Ac are predominantly bound by GATA-1 (and other transcription factors) in erythroid cells. By contrast, we do not find a linear relationship between genome-wide H3K27me3 marks and GATA-1 occupied regions (RTer119− =−0.004, RTer119+ = −0.02). Hence, the association of GATA-1 binding with the H3K27me3 mark seen by Yu et al. (16) in a subset of repressed GATA-1 target genes in mouse erythroleukemic (MEL) cells does not seem to be reflected at the genome-wide level in fetal liver-derived erythroblasts.Figure 4.

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