The fetal and adult globin genes in the human β-globin cluster on chromosome 11 are sequentially expressed to achieve normal hemoglobin switching during human development. The pharmacological induction of fetal γ-globin (HBG) to replace abnormal adult sickle βS-globin is a successful strategy to treat sickle cell disease; however the molecular mechanism of γ-gene silencing after birth is not fully understood. Therefore, we performed global gene expression profiling using primary erythroid progenitors grown from human peripheral blood mononuclear cells to characterize gene expression patterns during the γ-globin to β-globin (γ/β) switch observed throughout in vitro erythroid differentiation.
Extensive research has shown the beneficial effect of γ-globin reactivation by pharmacologic methods to induce fetal hemoglobin as a treatment modality for sickle cell patients. One such drug hydroxyurea was approved in 1998 . Numerous laboratories have ongoing efforts to identify additional less toxic agents that induce fetal hemoglobin however few have reach clinical trials [8, 9]. Therefore defining molecular mechanisms of globin gene regulation provides an approach to define specific strategies for γ-globin gene reactivation. With the availability of high throughput genomic methods, research aimed at the discovery of global mechanisms of gene regulation using in vitro models is now feasible  to establish personalized medical therapy .
To properly classify gene function, the transcriptome generated from microarray analysis was analyzed by GO software to: a) address whether or not the data mining correctly identified related genes expressed in the expected cell type using the Kappa score; b) define functional annotation and transcriptome categories based on biological processes, cellular components and molecular function to address the enriched relationships among many genes; and c) reflect the individual GO functional level by over-representation with higher numbers of genes and significant p-values in the GO terms to determine and classify GO groups [39, 40]. We completed a GO clustering analysis for each gene profile subset with > 1.5-fold change in expression during erythroid maturation using the DAVID platform . For interpretation of our analysis, the higher the Kappa score for a given genomic profile, the stronger the agreement with the cell type from which the specimens were extracted; if Kappa = 1, then there is perfect agreement for cell type. The detailed results and corresponding p values for each GO term are shown in Additional file 7: Table S6.
For profile-1 genes, Kappa = 1 was obtained for the GO term hematopoiesis (Enrichment score 15.91, p = 7.7E-10). Profile 2 genes were highly associated with the GO term erythrocyte differentiation, Kappa = 1 (Enrichment score 2.5, p = 1.5E-4). On the other hand, profile-3 genes had a Kappa = 1 for the GO term macromolecular complex subunit organization (Enrichment score 5.27, p = 3.8E-7). This latter term describes a process by which macromolecules aggregate, or disaggregate to reform, disassembly, or alter macromolecular complexes. Reorganization of these complexes has been largely reported in protein expression changes and gene switching produced in cells infected with viruses, bacteria and parasite . We speculate that macromolecule complex reorganization may occur during the γ/β-globin switch.
To further characterize the genes identified in each profile we used over-representation to classify GO groups. In this analysis we investigated two major GO categories where 1) biological processes with 30 subgroups and 2) molecular function with 20 subgroups were identified (p < 0.05). For biological processes, the following subgroups are highlighted for profile-1: 47 genes were over-represented in hemopoiesis (Enrichment score 15.91, p = 7.7E-10) and 85 genes in cell activity (Enrichment score 15.91, p = 1.4E-29). We also observed 121 profile-2 genes related to cellular proliferation (Enrichment score 25.4, p = 1.43E-32) and 8 genes related to heme biosynthesis (Enrichment score 3.27, p = 6.5E-6). Finally, 81 genes in profile-3 were associated with nucleotide metabolism and DNA processing (Enrichment score 5.14, p = 1.1E-6). The GO subgroups identified would be predicted since erythropoiesis involves actively dividing hematopoietic cells, which require heme biosynthesis for normal hemoglobin production.
For the second GO category molecular function, profile-1 genes were over-represented in the hydrolase category including protein tyrosine phosphatase, MAP kinase phosphatase, GTP cyclohydrolase and tyrosine/serine/threonine phosphatase. By contrast, molecular function GO terms for profile-2 genes included iron, heme and oxygen binding proteins; profile-3 genes were related to adenyl nucleotide, ATP and nucleoside binding proteins. In summary, DAVID GO data mining classified profile-1 genes as associated with hematopoiesis while profile-2 genes were related to cell proliferation and erythrocyte differentiation. Finally, profile-3 genes were associated with alteration of a macromolecular complex, or protein switching processes requiring DNA synthesis.
IPA is software that helps researchers model, analyze, and understand complex systems by integrating data from a variety of experimental platforms and providing insight into molecular and chemical interactions, cellular phenotypes, and disease processes. We performed IPA to identify pathways involved in erythropoiesis defined at p < 0.05 and IPA significance value > 1.3.
Moreover, HDAC and erythroid-specific TF interactions are critical for the regulation of the γ-globin gene. For instance, the Ikaros-GATA1-FOG1-HDAC1/NuRD complex is required for silencing the human γ-gene during γ- to β-globin switching . Diminished binding of acetylated NF-E4 to HDAC1 showed activation of γ-globin and inhibition of β-globin in fetal erythroid cells . In addition, inhibition of HDAC3-NCoR (nuclear receptor corepressor) complex activity by the HDAC3-specific inhibitor SCFAD caused displacement of this complex from the γ-globin gene region with the recruitment of RNA polymerase II and upregulation of histones H3 and H4 acetylation status . In contrast, HDAC9 might be recruited by MEF2 (myocyte enhancer factor 2) to the γ-globin gene promoter to mediate γ-globin activation and HbF synthesis during erythroid maturation of K562 cells . Identification of HDACs-containing complex in association with globin gene switching may provide more molecular targets for intervening β-globin gene disorders.
As key deacetyltransferase subunits of multiprotein complexes, they regulate histone affinity for DNA and chromatin accessibility to their cognate binding proteins by compaction of DNA/histone complexes. Their biochemical and molecular characterization significantly affects the deacetyltransferase activity of HDAC-containing complexes. Importantly, the catalytic/noncatalytic and histone/nonhistone effects of HDACs on hematopoietic cells confer their ability to regulate a variety of cellular events in normal and malignant hematopoiesis. HDAC actions are gene or environment specific during hematopoiesis: (1) Different genes regulated by the same HDAC require the recruitment of different coregulators. For instance, HDAC1 has been found in at least three multiprotein complexes, including Sin3, CoREST and NuRD complexes. (2) One HDAC can act as a coactivator or corepressor on different genes and utilize different domains to act on interacting proteins. For instance, HDAC1-containing NuRD/MeCP1 corepressor complexes play an important role in GATA-1-mediated repression of target genes (i.e., GATA-2, γ-globin, c-myc, c-kit and Hes1), which are all required for the proliferation of hematopoietic progenitors. However, during GATA-1-mediated activation of the β-globin gene, the HDAC1/NuRD/MeCP1 complex is still recruited to the GATA-1 sites of the β-globin locus. (3) HDACs act as multifunctional regulators of transcription complex activity. For instance, HDAC1 can be acetylated by histone acetyltransferase p300. Acetylated HDAC1 not only loses its deacetylase activity but also inhibits the deacetylase activity of HDAC2, thereby downregulating the overall deacetylase activity of HDAC1/2-containing complexes, including the NuRD complex.
Although SUMO5 facilities the growth of PML-NBs, there was heterogeneity among NBs co-expressing SUMO2/3 and SUMO5 (Fig. 6A). To differentiate the roles of SUMO5 and SUMO2/3, we used SUMO conjugation analysis and found that SUMO5 enhanced the conjugation of SUMO3 to PML (Fig. 6B), but SUMO3 conjugation promoted deconjugation of SUMO5 from PML (Fig. 6C). The polymeric SUMO chains on high-molecular-weight species of PML contained combinations of SUMO5 and SUMO3 (Fig. S6A, lane 13), reflecting a snapshot of PML-NBs of various sizes from an asynchronous cell population. It was shown that SUMO2/3 conjugation results in recruitment of the ubiquitin E3 ligase RNF4, polyubiquitination of PML and proteasome-dependent degradation of PML15,22. Therefore, we wanted to know if SUMO5 conjugation, which increased SUMO2/3 conjugation, would result in the degradation of PML-NBs. We found that RNF4 indeed mediated the disruption of SUMO5-induced PML-NBs and the dominant negative mutant RNF4(CS) blocked the RNF4-mediated degradation of PML-NBs (Fig. 6D). Furthermore, SUMO5-induced PML-NBs recruited ubiquitin (Fig. 6E) and could be preserved by the proteasome inhibitor MG132 (Fig. S6B). Moreover, RNF4 preferentially interacted with SUMO2 but not SUMO5 (Fig. 6F). These results demonstrate that SUMO5 conjugation of PML increases SUMO2/3 conjugation, which leads to the recruitment of RNF4 and ubiquitin-dependent disintegration of PML-NBs. 2b1af7f3a8