FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2950315967> ?p ?o ?g. }

• W2950315967 endingPage "3240.e7" @default.
• W2950315967 startingPage "3228" @default.
• W2950315967 abstract "•Integrated accessible chromatin and transcriptome analysis of human erythropoiesis•Inference of differentiation stage-specific transcription factor activities•Mapping of genetic variants underlying diseases and traits to regulatory regions•Identification of TMCC2 as a regulator in terminal erythropoiesis Human erythropoiesis serves as a paradigm of physiologic cellular differentiation. This process is also of considerable interest for better understanding anemias and identifying new therapies. Here, we apply deep transcriptomic and accessible chromatin profiling to characterize a faithful ex vivo human erythroid differentiation system from hematopoietic stem and progenitor cells. We reveal stage-specific transcriptional states and chromatin accessibility during various stages of erythropoiesis, including 14,260 differentially expressed genes and 63,659 variably accessible chromatin peaks. Our analysis suggests differentiation stage-predominant roles for specific master regulators, including GATA1 and KLF1. We integrate chromatin profiles with common and rare genetic variants associated with erythroid cell traits and diseases, finding that variants regulating different erythroid phenotypes likely act at variable points during differentiation. In addition, we identify a regulator of terminal erythropoiesis, TMCC2, more broadly illustrating the value of this comprehensive analysis to improve our understanding of erythropoiesis in health and disease. Human erythropoiesis serves as a paradigm of physiologic cellular differentiation. This process is also of considerable interest for better understanding anemias and identifying new therapies. Here, we apply deep transcriptomic and accessible chromatin profiling to characterize a faithful ex vivo human erythroid differentiation system from hematopoietic stem and progenitor cells. We reveal stage-specific transcriptional states and chromatin accessibility during various stages of erythropoiesis, including 14,260 differentially expressed genes and 63,659 variably accessible chromatin peaks. Our analysis suggests differentiation stage-predominant roles for specific master regulators, including GATA1 and KLF1. We integrate chromatin profiles with common and rare genetic variants associated with erythroid cell traits and diseases, finding that variants regulating different erythroid phenotypes likely act at variable points during differentiation. In addition, we identify a regulator of terminal erythropoiesis, TMCC2, more broadly illustrating the value of this comprehensive analysis to improve our understanding of erythropoiesis in health and disease. Erythropoiesis describes the process of proliferation and differentiation of hematopoietic stem and progenitor cells (HSPCs) through distinct functionally and morphologically defined stages to produce enucleate reticulocytes. In the circulation, these cells mature further into red blood cells (RBCs), which are the key transporters of oxygen and carbon dioxide for cellular respiration (Nandakumar et al., 2016Nandakumar S.K. Ulirsch J.C. Sankaran V.G. Advances in understanding erythropoiesis: evolving perspectives.Br. J. Haematol. 2016; 173: 206-218Crossref PubMed Scopus (79) Google Scholar, Sankaran and Weiss, 2015Sankaran V.G. Weiss M.J. Anemia: progress in molecular mechanisms and therapies.Nat. Med. 2015; 21: 221-230Crossref PubMed Scopus (165) Google Scholar). In adult humans, approximately 2 million RBCs are produced every second in the bone marrow through the tightly coordinated process of erythropoiesis, making RBCs the most abundant cell type in the human body (Palis, 2014Palis J. Primitive and definitive erythropoiesis in mammals.Front. Physiol. 2014; 5: 3Crossref PubMed Scopus (256) Google Scholar). Congenital as well as acquired defects may lead to various forms of anemia, the lack of sufficient RBCs, causing significant morbidity and mortality (Sankaran and Weiss, 2015Sankaran V.G. Weiss M.J. Anemia: progress in molecular mechanisms and therapies.Nat. Med. 2015; 21: 221-230Crossref PubMed Scopus (165) Google Scholar). Genetic and cell biological approaches have provided important insights into normal RBC differentiation and how this process is perturbed in different blood diseases, such as anemias (Arlet et al., 2014Arlet J.B. Ribeil J.A. Guillem F. Negre O. Hazoume A. Marcion G. Beuzard Y. Dussiot M. Moura I.C. Demarest S. et al.HSP70 sequestration by free α-globin promotes ineffective erythropoiesis in β-thalassaemia.Nature. 2014; 514: 242-246Crossref PubMed Scopus (82) Google Scholar, Giani et al., 2016Giani F.C. Fiorini C. Wakabayashi A. Ludwig L.S. Salem R.M. Jobaliya C.D. Regan S.N. Ulirsch J.C. Liang G. Steinberg-Shemer O. et al.Targeted application of human genetic variation can improve red blood cell production from stem cells.Cell Stem Cell. 2016; 18: 73-78Abstract Full Text Full Text PDF PubMed Scopus (62) Google Scholar, Khajuria et al., 2018Khajuria R.K. Munschauer M. Ulirsch J.C. Fiorini C. Ludwig L.S. McFarland S.K. Abdulhay N.J. Specht H. Keshishian H. Mani D.R. et al.Ribosome levels selectively regulate translation and lineage commitment in human hematopoiesis.Cell. 2018; 173: 90-103.e9Abstract Full Text Full Text PDF PubMed Scopus (186) Google Scholar, Ludwig et al., 2014Ludwig L.S. Gazda H.T. Eng J.C. Eichhorn S.W. Thiru P. Ghazvinian R. George T.I. Gotlib J.R. Beggs A.H. Sieff C.A. et al.Altered translation of GATA1 in Diamond-Blackfan anemia.Nat. Med. 2014; 20: 748-753Crossref PubMed Scopus (205) Google Scholar, Ludwig et al., 2016Ludwig L.S. Khajuria R.K. Sankaran V.G. Emerging cellular and gene therapies for congenital anemias.Am. J. Med. Genet. C. Semin. Med. Genet. 2016; 172: 332-348Crossref PubMed Scopus (5) Google Scholar). Our knowledge of this process has been further enhanced by comprehensive transcriptomic and proteomic profiling of distinct stages of neonatal and adult erythropoiesis (An et al., 2014An X. Schulz V.P. Li J. Wu K. Liu J. Xue F. Hu J. Mohandas N. Gallagher P.G. Global transcriptome analyses of human and murine terminal erythroid differentiation.Blood. 2014; 123: 3466-3477Crossref PubMed Scopus (213) Google Scholar, Gautier et al., 2016Gautier E.F. Ducamp S. Leduc M. Salnot V. Guillonneau F. Dussiot M. Hale J. Giarratana M.C. Raimbault A. Douay L. et al.Comprehensive proteomic analysis of human erythropoiesis.Cell Rep. 2016; 16: 1470-1484Abstract Full Text Full Text PDF PubMed Scopus (138) Google Scholar, Li et al., 2014Li J. Hale J. Bhagia P. Xue F. Chen L. Jaffray J. Yan H. Lane J. Gallagher P.G. Mohandas N. et al.Isolation and transcriptome analyses of human erythroid progenitors: BFU-E and CFU-E.Blood. 2014; 124: 3636-3645Crossref PubMed Scopus (113) Google Scholar, Yan et al., 2018Yan H. Hale J. Jaffray J. Li J. Wang Y. Huang Y. An X. Hillyer C. Wang N. Kinet S. et al.Developmental differences between neonatal and adult human erythropoiesis.Am. J. Hematol. 2018; 93: 494-503Crossref PubMed Scopus (31) Google Scholar). Additional efforts have recorded the accessible chromatin landscape (Buenrostro et al., 2018Buenrostro J.D. Corces M.R. Lareau C.A. Wu B. Schep A.N. Aryee M.J. Majeti R. Chang H.Y. Greenleaf W.J. Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation.Cell. 2018; 173: 1535-1548.e16Abstract Full Text Full Text PDF PubMed Scopus (277) Google Scholar, Corces et al., 2016Corces M.R. Buenrostro J.D. Wu B. Greenside P.G. Chan S.M. Koenig J.L. Snyder M.P. Pritchard J.K. Kundaje A. Greenleaf W.J. et al.Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution.Nat. Genet. 2016; 48: 1193-1203Crossref PubMed Scopus (549) Google Scholar) and epigenetic marks such as histone modifications (Huang et al., 2016Huang J. Liu X. Li D. Shao Z. Cao H. Zhang Y. Trompouki E. Bowman T.V. Zon L.I. Yuan G.C. et al.Dynamic control of enhancer repertoires drives lineage and stage-specific transcription during hematopoiesis.Dev. Cell. 2016; 36: 9-23Abstract Full Text Full Text PDF PubMed Scopus (142) Google Scholar) at select stages and investigated the role of genetic variation and chromatin accessibility in murine cellular models (Behera et al., 2018Behera V. Evans P. Face C.J. Hamagami N. Sankaranarayanan L. Keller C.A. Giardine B. Tan K. Hardison R.C. Shi J. Blobel G.A. Exploiting genetic variation to uncover rules of transcription factor binding and chromatin accessibility.Nat. Commun. 2018; 9: 782Crossref PubMed Scopus (25) Google Scholar). However, we lack a detailed characterization of the accessible chromatin landscape and transcription factor dynamics throughout the entire process of human erythroid differentiation, even though such characterization would facilitate a more comprehensive understanding of gene regulatory dynamics and their relation to human genetic variation and disease. Here, we leverage the assay for transposase accessible chromatin using sequencing (ATAC-seq) (Corces et al., 2016Corces M.R. Buenrostro J.D. Wu B. Greenside P.G. Chan S.M. Koenig J.L. Snyder M.P. Pritchard J.K. Kundaje A. Greenleaf W.J. et al.Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution.Nat. Genet. 2016; 48: 1193-1203Crossref PubMed Scopus (549) Google Scholar, Corces et al., 2017Corces M.R. Trevino A.E. Hamilton E.G. Greenside P.G. Sinnott-Armstrong N.A. Vesuna S. Satpathy A.T. Rubin A.J. Montine K.S. Wu B. et al.An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues.Nat. Methods. 2017; 14: 959-962Crossref PubMed Scopus (762) Google Scholar) to assess eight populations that chart the dynamic open chromatin landscapes of HSPCs undergoing the process of erythroid differentiation. We integrate our data with matched deep transcriptomic profiles, results from genome-wide association studies (GWAS), and mutations resulting in human disease, to facilitate a systems-level understanding of the molecular circuits governing erythropoiesis in human health and disease. We show that our chromatin accessibility profiles yield a detailed description of the regulatory elements and transcription factor dynamics that control gene expression during human erythropoiesis. The intersection with variants identified in GWAS and Mendelian diseases reveal the differential and putative temporal contributions of genetic variants in accessible chromatin in affecting human erythroid traits and pathologies. Overall, our integrated analysis framework provides a comprehensive resource to advance our understanding of gene regulation and will expedite downstream functional and validation studies of regulators, as exemplified by our use of this dataset to identify a role for TMCC2 in terminal human erythropoiesis. To obtain a comprehensive and integrative picture of the chromatin and transcriptional landscape of human adult erythropoiesis, we differentiated CD34+ HSPCs from healthy donors using an established three-phase erythroid differentiation protocol that allows the efficient production of mature enucleated reticulocytes (Giani et al., 2016Giani F.C. Fiorini C. Wakabayashi A. Ludwig L.S. Salem R.M. Jobaliya C.D. Regan S.N. Ulirsch J.C. Liang G. Steinberg-Shemer O. et al.Targeted application of human genetic variation can improve red blood cell production from stem cells.Cell Stem Cell. 2016; 18: 73-78Abstract Full Text Full Text PDF PubMed Scopus (62) Google Scholar, Hu et al., 2013Hu J. Liu J. Xue F. Halverson G. Reid M. Guo A. Chen L. Raza A. Galili N. Jaffray J. et al.Isolation and functional characterization of human erythroblasts at distinct stages: implications for understanding of normal and disordered erythropoiesis in vivo.Blood. 2013; 121: 3246-3253Crossref PubMed Scopus (233) Google Scholar). We then applied flow cytometry-activated cell sorting (FACS) using well-characterized erythroid surface markers CD71, CD235a, CD49d, and Band 3 (encoded by the SLC4A1 gene) across multiple stages of the differentiation process to enrich for cells at different stages of maturation (Hu et al., 2013Hu J. Liu J. Xue F. Halverson G. Reid M. Guo A. Chen L. Raza A. Galili N. Jaffray J. et al.Isolation and functional characterization of human erythroblasts at distinct stages: implications for understanding of normal and disordered erythropoiesis in vivo.Blood. 2013; 121: 3246-3253Crossref PubMed Scopus (233) Google Scholar), validated purity by morphology, and processed each population (P1–P8) using ATAC-seq (FAST-ATAC) and RNA sequencing (RNA-seq) (Smart-seq2) (Figures 1A, 1B, and S1A) (Corces et al., 2016Corces M.R. Buenrostro J.D. Wu B. Greenside P.G. Chan S.M. Koenig J.L. Snyder M.P. Pritchard J.K. Kundaje A. Greenleaf W.J. et al.Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution.Nat. Genet. 2016; 48: 1193-1203Crossref PubMed Scopus (549) Google Scholar, Picelli et al., 2014Picelli S. Faridani O.R. Björklund A.K. Winberg G. Sagasser S. Sandberg R. Full-length RNA-seq from single cells using Smart-seq2.Nat. Protoc. 2014; 9: 171-181Crossref PubMed Scopus (1959) Google Scholar). Each population was processed in three or four replicates using cultured cells from two or three healthy adult human donors, resulting in a total of 28 paired RNA-seq and ATAC-seq libraries. Overall, our ATAC-seq libraries were sequenced to an average depth of 21.5 million aligned reads/sample (mean 75.3 million/population), and the RNA-seq libraries were sequenced to an average depth 26.1 million aligned reads/sample (mean 91.1 million/population) (Tables S1 and S2). To yield a complete accessible chromatin and transcriptional trajectory during human erythroid differentiation, we included published profiles of paired sequencing data from early hematopoietic stem and progenitor populations, including hematopoietic stem cells (HSCs), multipotential progenitors (MPPs), common myeloid progenitors (CMPs), and megakaryocyte erythroid progenitors (MEP)s (Corces et al., 2016Corces M.R. Buenrostro J.D. Wu B. Greenside P.G. Chan S.M. Koenig J.L. Snyder M.P. Pritchard J.K. Kundaje A. Greenleaf W.J. et al.Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution.Nat. Genet. 2016; 48: 1193-1203Crossref PubMed Scopus (549) Google Scholar). Indeed, principal-component analysis on our paired datasets shows a continuous trajectory of erythroid differentiation and high concordance between replicates (Figures 1C, 1D, S1B, and S1C). Population P1 was most similar to myeloid progenitor cells (MyP) that did not commit to the erythroid lineage in our in vitro culture but were included in many downstream analyses. Population P2 was enriched for colony-forming unit-erythroid cells (CFU-E), P3 and P4 for proerythroblasts (ProE1 and ProE2), P5 for basophilic erythroblasts (BasoE), P6 for polychromatic erythroblasts (PolyE), and P7 for orthochromatic erythroblasts (OrthoE), which were further enriched with reticulocytes in P8 (Orth/Ret), as confirmed by morphology as well as comparison with published transcriptional profiles (Yan et al., 2018Yan H. Hale J. Jaffray J. Li J. Wang Y. Huang Y. An X. Hillyer C. Wang N. Kinet S. et al.Developmental differences between neonatal and adult human erythropoiesis.Am. J. Hematol. 2018; 93: 494-503Crossref PubMed Scopus (31) Google Scholar) (Figures S1A and S2A), providing an independent validation for this dataset of human erythroid differentiation. Overall, chromatin accessibility profiles were of high quality (Figure S1D) and across populations, we detected 63,659 variably accessible chromatin peaks and 14,260 differentially expressed genes (Figures 1E and 1F). The majority of identified peaks mapped to distal, intronic, and promoter gene regions, with a comparable distribution with those in previous reports profiling human T lymphocytes (Figures 1G and S1D) (Qu et al., 2015Qu K. Zaba L.C. Giresi P.G. Li R. Longmire M. Kim Y.H. Greenleaf W.J. Chang H.Y. Individuality and variation of personal regulomes in primary human T cells.Cell Syst. 2015; 1: 51-61Abstract Full Text Full Text PDF PubMed Scopus (101) Google Scholar). To characterize transcriptional modules throughout human erythroid differentiation, we used k-means clustering to identify co-regulated genes. Using the gap statistic to determine an appropriate number of clusters (Figure S2B), we identified seven clusters of differentially expressed genes showing a wide range of expression patterns and dynamics (Figure 2A; Table S3). From the Z score-normalized gene expression values, we observed population-specific expression primarily in presumed MyP (cluster k3) and at the latest stages of erythroid differentiation (OrthoE/Ret; cluster k7). Most other gene expression programs were broadly expressed in early progenitor populations (clusters k1 and k2) and throughout erythroid differentiation (clusters k4–k6). To determine the underlying cellular processes governed by these patterns of expression variability, we performed Gene Ontology (GO) analyses to identify biological processes that were enriched within these clusters and that may be involved in regulating erythroid differentiation (Figure 2B; Table S4). RNA, non-coding RNA, and DNA processing-related gene sets were most dominant during the earlier stages of terminal erythroid differentiation (cluster k4). Cell cycle-related processes showed maximum activity around the ProE1/2 stage (cluster k5), consistent with the high proliferative capacity of these cells. Heme biosynthetic and oxygen transport processes showed highest activity at the BasoE and PolyE stage, reflecting the peak of hemoglobin synthesis (cluster k6). Regulation of catabolic processes dominate during the OrthoE/Ret stage, as cellular organelles are being recycled before final maturation into erythrocytes (cluster k7) (Figure 2B). Overall, our transcriptional analysis revealed highly consistent findings with those described in prior reports (An et al., 2014An X. Schulz V.P. Li J. Wu K. Liu J. Xue F. Hu J. Mohandas N. Gallagher P.G. Global transcriptome analyses of human and murine terminal erythroid differentiation.Blood. 2014; 123: 3466-3477Crossref PubMed Scopus (213) Google Scholar, Yan et al., 2018Yan H. Hale J. Jaffray J. Li J. Wang Y. Huang Y. An X. Hillyer C. Wang N. Kinet S. et al.Developmental differences between neonatal and adult human erythropoiesis.Am. J. Hematol. 2018; 93: 494-503Crossref PubMed Scopus (31) Google Scholar). Previous genome-wide analyses of accessible chromatin profiles have revealed a comprehensive repertoire of gene regulatory elements, many of which appear to act in a cell type- and tissue-specific manner (Thurman et al., 2012Thurman R.E. Rynes E. Humbert R. Vierstra J. Maurano M.T. Haugen E. Sheffield N.C. Stergachis A.B. Wang H. Vernot B. et al.The accessible chromatin landscape of the human genome.Nature. 2012; 489: 75-82Crossref PubMed Scopus (1790) Google Scholar). However, we have a limited understanding of the accessibility dynamics of these elements during cellular differentiation. Our dense profiling of erythroid populations at different stages of differentiation using ATAC-seq enables the most comprehensive description to date of the dynamic accessible chromatin landscape throughout human erythropoiesis (Figure 3A). Quantification of Tn5 insertion density around CTCF motifs as previously described (Buenrostro et al., 2013Buenrostro J.D. Giresi P.G. Zaba L.C. Chang H.Y. Greenleaf W.J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position.Nat. Methods. 2013; 10: 1213-1218Crossref PubMed Scopus (3189) Google Scholar) verified high-quality libraries throughout our differentiation system (Figure S3A). Using k-means clustering and gap statistic optimization (Figure S3B), we identify seven clusters of differential accessible peaks, showing similar patterns and dynamics at key erythroid gene loci such as ALAS2 (Figure 3B), similar to what we describe for the transcriptomic profiles described above (Figure 2A). Cluster k1 contained regions of open chromatin showing highest accessibility in HSPCs, which became less accessible during early erythroid differentiation. By contrast, regulatory elements in cluster k2 displayed slower closing dynamics (decreasing accessibility). We also observed profound changes from the ProE1/2 to the OrthoE stage. Some elements were most accessible around the ProE1/2 stage but quickly closed (lost accessibility) in BasoE (cluster k4), whereas cluster k5 contained elements remaining accessible throughout the PolyE stage. Interestingly, we also observed elements showing highest accessibility at the OrthoE stage immediately preceding enucleation. This process has been typically associated with increased nuclear condensation and an anticipated loss of accessibility, also supported by an overall decreasing number of accessibility peaks at late stages of differentiation (Figure 1E) (Zhao et al., 2016Zhao B. Mei Y. Schipma M.J. Roth E.W. Bleher R. Rappoport J.Z. Wickrema A. Yang J. Ji P. Nuclear condensation during mouse erythropoiesis requires caspase-3-mediated nuclear opening.Dev. Cell. 2016; 36: 498-510Abstract Full Text Full Text PDF PubMed Scopus (62) Google Scholar). Of note, the late stage-specific peaks were enriched in genes displaying the highest levels of induction and expression at the OrthoE stage, as exemplified by the TMCC2 locus, a gene highly induced in terminal erythropoiesis whose role in this process has not been previously studied (Figures 6 and 7). Our ATAC-seq dataset enables the use of chromVAR to infer transcription factor (TF) variability and dynamics throughout erythroid development (Schep et al., 2017Schep A.N. Wu B. Buenrostro J.D. Greenleaf W.J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data.Nat. Methods. 2017; 14: 975-978Crossref PubMed Scopus (384) Google Scholar). In brief, chromVAR aggregates accessible regions sharing the same TF motif, then compares the observed accessibility of all peaks containing that TF motif with a background set of peaks normalizing for known technical confounders. Among the most variably accessible sequence motifs determined, we identified important hematopoietic TF motifs such as from SPI1, GATA1, CEBPA, RUNX1, and FOXD4 (Figures 3C and S3C). Notably, we observed profound differences in the accessibility within our sampled populations, including significant loss of inferred activity of erythroid master regulator GATA1 after the BasoE stage (Figures 3D, S3D, and 4). Similar to regions of open chromatin most accessible at the OrthoE stage (Figure 3A), we identified several TF motifs with highest accessibility at this stage (FOSL1, NFE2, and FOXD4). These findings suggest a possible role for these factors during the most terminal stages of erythropoiesis and warrant further investigation, noting that augmenting motif-based inference with true-positive factor binding via chromatin immunoprecipitation sequencing (ChIP-seq) may enable greater specification of individual factors. Overall, our results provide comprehensive insights into chromatin accessibility dynamics and the distinct TF regulatory programs active during human RBC development. GATA1, TAL1, and KLF1 are master transcriptional regulators of hematopoiesis and are essential for erythroid development (Arnaud et al., 2010Arnaud L. Saison C. Helias V. Lucien N. Steschenko D. Giarratana M.C. Prehu C. Foliguet B. Montout L. de Brevern A.G. et al.A dominant mutation in the gene encoding the erythroid transcription factor KLF1 causes a congenital dyserythropoietic anemia.Am. J. Hum. Genet. 2010; 87: 721-727Abstract Full Text Full Text PDF PubMed Scopus (147) Google Scholar, Ludwig et al., 2014Ludwig L.S. Gazda H.T. Eng J.C. Eichhorn S.W. Thiru P. Ghazvinian R. George T.I. Gotlib J.R. Beggs A.H. Sieff C.A. et al.Altered translation of GATA1 in Diamond-Blackfan anemia.Nat. Med. 2014; 20: 748-753Crossref PubMed Scopus (205) Google Scholar, Shivdasani et al., 1995Shivdasani R.A. Mayer E.L. Orkin S.H. Absence of blood formation in mice lacking the T-cell leukaemia oncoprotein tal-1/SCL.Nature. 1995; 373: 432-434Crossref PubMed Scopus (781) Google Scholar). However, their DNA binding and regulatory dynamics in controlling gene expression in a stage-specific manner has not yet been fully resolved. Here, we investigated the relation between ATAC-seq Tn5 insertion density (i.e., chromatin accessibility) and proximity to the canonical GATA1-TAL1 and KLF1 motifs throughout erythroid differentiation. Chromatin accessibility near canonical GATA1 motifs appeared highest in BasoE, consistent with a peak in expression of canonical GATA1 target genes at this stage as predicted using gene set enrichment analysis (GSEA) (Figures 4A and 4B) (Ludwig et al., 2014Ludwig L.S. Gazda H.T. Eng J.C. Eichhorn S.W. Thiru P. Ghazvinian R. George T.I. Gotlib J.R. Beggs A.H. Sieff C.A. et al.Altered translation of GATA1 in Diamond-Blackfan anemia.Nat. Med. 2014; 20: 748-753Crossref PubMed Scopus (205) Google Scholar, Subramanian et al., 2005Subramanian A. Tamayo P. Mootha V.K. Mukherjee S. Ebert B.L. Gillette M.A. Paulovich A. Pomeroy S.L. Golub T.R. Lander E.S. Mesirov J.P. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.Proc. Natl. Acad. Sci. U S A. 2005; 102: 15545-15550Crossref PubMed Scopus (26569) Google Scholar). Interestingly, GATA1 activity declined preceding enucleation, suggesting that its expression becomes dispensable at the final stage of erythroid differentiation. Indeed, using western blot analysis, we documented reduced GATA1 protein levels in late-stage erythroid cells in our in vitro culture system (Figures 4C, 4D, S4A, and S4B), consistent with concomitant downregulation of HSP70 protein levels and its protective role in caspase-3-dependent GATA1 cleavage (Gautier et al., 2016Gautier E.F. Ducamp S. Leduc M. Salnot V. Guillonneau F. Dussiot M. Hale J. Giarratana M.C. Raimbault A. Douay L. et al.Comprehensive proteomic analysis of human erythropoiesis.Cell Rep. 2016; 16: 1470-1484Abstract Full Text Full Text PDF PubMed Scopus (138) Google Scholar, Ribeil et al., 2007Ribeil J.A. Zermati Y. Vandekerckhove J. Cathelin S. Kersual J. Dussiot M. Coulon S. Moura I.C. Zeuner A. Kirkegaard-Sørensen T. et al.Hsp70 regulates erythropoiesis by preventing caspase-3-mediated cleavage of GATA-1.Nature. 2007; 445: 102-105Crossref PubMed Scopus (216) Google Scholar) (Figure S4C). In contrast, KLF1 motif accessibility and target gene expression appeared to increase throughout terminal differentiation, consistent with increasing transcript levels and higher chromatin accessibility in the KLF1 locus throughout the late stages compared with the GATA1 locus (Figures S4D–S4G). These findings support the notion that GATA1 and KLF1 play distinct regulatory roles in orchestrating the erythroid maturation program. As such, human KLF1 mutations result in distinct erythroid phenotypes (Arnaud et al., 2010Arnaud L. Saison C. Helias V. Lucien N. Steschenko D. Giarratana M.C. Prehu C. Foliguet B. Montout L. de Brevern A.G. et al.A dominant mutation in the gene encoding the erythroid transcription factor KLF1 causes a congenital dyserythropoietic anemia.Am. J. Hum. Genet. 2010; 87: 721-727Abstract Full Text Full Text PDF PubMed Scopus (147) Google Scholar, Borg et al., 2010Borg J. Papadopoulos P. Georgitsi M. Gutiérrez L. Grech G. Fanis P. Phylactides M. Verkerk A.J. van der Spek P.J. Scerri C.A. et al.Haploinsufficiency for the erythroid transcription factor KLF1 causes hereditary persistence of fetal hemoglobin.Nat. Genet. 2010; 42: 801-805Crossref PubMed Scopus (299) Google Scholar) compared with GATA1-associated pathologies; the latter are characterized by altered erythroid lineage commitment or impaired differentiation of erythroid progenitors (Campbell et al., 2013Campbell A.E. Wilkinson-White L. Mackay J.P. Matthews J.M. Blobel G.A. Analysis of disease-causing GATA1 mutations in murine gene complementation systems.Blood. 2013; 121: 5218-5227Crossref PubMed Scopus (38) Google Scholar, Crispino and Horwitz, 2017Crispino J.D. Horwitz M.S. GATA factor mutations in hematologic disease.Blood. 2017; 129: 2103-2110Crossref PubMed Scopus (111) Google Scholar, Khajuria et al., 2018Khajuria R.K. Munschauer M. Ulirsch J.C. Fiorini C. Ludwig L.S. McFarland S.K. Abdulhay N.J. Specht H. Keshishian H. Mani D.R. et al.Ribosome levels selectively regulate translation and lineage commitment in human hematopoiesis.Cell. 2018; 173: 90-103.e9Abstract Full Text Full Text PDF PubMed Scopus (186) Google Scholar, Ludwig et al., 2014Ludwig L.S. Gazda H.T. Eng J.C. Eichhorn S.W. Thiru P. Ghazvinian R. George T.I. Gotlib J.R. Beggs A.H. Sieff C.A. et al.Altered translation of GATA1 in Diamond-Blackfan anemia.Nat. Med. 2014; 20: 748-753Crossref PubMed Scopus (205) Google Scholar, Sankaran et al., 2012aSankaran V.G. Ghazvinian R. Do R. Thiru P. Vergilio J.-A. Beggs A.H. Sieff C.A. Orkin S.H. Nathan D.G. Lander E.S. Gazda H.T. Exome sequencing identifies GATA1 mutations resulting in Diamond-Blackfan anemia.J. Clin. Invest. 2012; 122: 2439-2443Crossref PubMed Scopus (256) Google Scholar). We next hypothesized that rare variants disrupting GATA1 motifs would also be most accessible at stages of maximal GATA1 transcriptional activity. Investigating the chromatin accessibility of 11 variants known to disrupt GATA1 regulatory elements, including several in human Mendelian erythroid disorders (Campagna et al., 2014Campagna D.R. de Bie C.I. Schmitz-Abe K. Sweeney M. Sendamarai A.K. Schmidt P.J. Heeney M.M. Yntema H.G. Kannengiesser C. Grandchamp B. et al.X-linked sideroblastic anemia due to ALAS2 intron 1 enhancer element GATA-binding site mutations.Am. J. Hematol. 2014; 89: 31" @default.
• W2950315967 created "2019-06-27" @default.
• W2950315967 creator A5003021419 @default.
• W2950315967 creator A5003686433 @default.
• W2950315967 creator A5004067604 @default.
• W2950315967 creator A5029834857 @default.
• W2950315967 creator A5034746599 @default.
• W2950315967 creator A5042844951 @default.
• W2950315967 creator A5050285238 @default.
• W2950315967 creator A5054483679 @default.
• W2950315967 creator A5055224426 @default.
• W2950315967 creator A5058941404 @default.
• W2950315967 creator A5059831707 @default.
• W2950315967 creator A5077173552 @default.
• W2950315967 creator A5088620852 @default.
• W2950315967 date "2019-06-01" @default.
• W2950315967 modified "2023-10-17" @default.
• W2950315967 title "Transcriptional States and Chromatin Accessibility Underlying Human Erythropoiesis" @default.
• W2950315967 cites W1498148355 @default.
• W2950315967 cites W1602702667 @default.
• W2950315967 cites W1704670422 @default.
• W2950315967 cites W1811026913 @default.
• W2950315967 cites W1963925738 @default.
• W2950315967 cites W1964963969 @default.
• W2950315967 cites W1974742081 @default.
• W2950315967 cites W1992470811 @default.
• W2950315967 cites W1995364119 @default.
• W2950315967 cites W1996359941 @default.
• W2950315967 cites W2014538791 @default.
• W2950315967 cites W2014865621 @default.
• W2950315967 cites W2031211461 @default.
• W2950315967 cites W2034914128 @default.
• W2950315967 cites W2035450824 @default.
• W2950315967 cites W2044354693 @default.
• W2950315967 cites W2051740914 @default.
• W2950315967 cites W2065401691 @default.
• W2950315967 cites W2068767807 @default.
• W2950315967 cites W2073724558 @default.
• W2950315967 cites W2077274633 @default.
• W2950315967 cites W2080312641 @default.
• W2950315967 cites W2080762011 @default.
• W2950315967 cites W2098586987 @default.
• W2950315967 cites W2100681150 @default.
• W2950315967 cites W2108234281 @default.
• W2950315967 cites W2108401821 @default.
• W2950315967 cites W2117757143 @default.
• W2950315967 cites W2130410032 @default.
• W2950315967 cites W2141455745 @default.
• W2950315967 cites W2152505144 @default.
• W2950315967 cites W2153926593 @default.
• W2950315967 cites W2155119455 @default.
• W2950315967 cites W2168865951 @default.
• W2950315967 cites W2169456326 @default.
• W2950315967 cites W2170551349 @default.
• W2950315967 cites W2170770317 @default.
• W2950315967 cites W2171808845 @default.
• W2950315967 cites W2179438025 @default.
• W2950315967 cites W2239249333 @default.
• W2950315967 cites W2256016639 @default.
• W2950315967 cites W2295579317 @default.
• W2950315967 cites W2299883728 @default.
• W2950315967 cites W2325099115 @default.
• W2950315967 cites W2406250479 @default.
• W2950315967 cites W2413629639 @default.
• W2950315967 cites W2418024629 @default.
• W2950315967 cites W2437351093 @default.
• W2950315967 cites W2504220050 @default.
• W2950315967 cites W2514448720 @default.
• W2950315967 cites W2524507363 @default.
• W2950315967 cites W2542038021 @default.
• W2950315967 cites W2550031966 @default.
• W2950315967 cites W2587454956 @default.
• W2950315967 cites W2590143063 @default.
• W2950315967 cites W2750049634 @default.
• W2950315967 cites W2750416916 @default.
• W2950315967 cites W2778060445 @default.
• W2950315967 cites W2793800931 @default.
• W2950315967 cites W2801527818 @default.
• W2950315967 cites W2806575716 @default.
• W2950315967 cites W2952573679 @default.
• W2950315967 cites W2953047957 @default.
• W2950315967 doi "https://doi.org/10.1016/j.celrep.2019.05.046" @default.
• W2950315967 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6579117" @default.
• W2950315967 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31189107" @default.
• W2950315967 hasPublicationYear "2019" @default.
• W2950315967 type Work @default.
• W2950315967 sameAs 2950315967 @default.
• W2950315967 citedByCount "116" @default.
• W2950315967 countsByYear W29503159672019 @default.
• W2950315967 countsByYear W29503159672020 @default.
• W2950315967 countsByYear W29503159672021 @default.
• W2950315967 countsByYear W29503159672022 @default.
• W2950315967 countsByYear W29503159672023 @default.
• W2950315967 crossrefType "journal-article" @default.
• W2950315967 hasAuthorship W2950315967A5003021419 @default.
• W2950315967 hasAuthorship W2950315967A5003686433 @default.
• W2950315967 hasAuthorship W2950315967A5004067604 @default.
• W2950315967 hasAuthorship W2950315967A5029834857 @default.

• next