Genetic regulatory networks programming hematopoietic stem cells and erythroid lineage specification
Erythroid cell production results from passage through cellular hierarchies dependent on differential gene expression under the control of transcription factors responsive to changing niches. We have constructed Genetic Regulatory Networks (GRNs) describing this process, based predominantly on mouse...
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Veröffentlicht in: | Developmental biology 2006-06, Vol.294 (2), p.525-540 |
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description | Erythroid cell production results from passage through cellular hierarchies dependent on differential gene expression under the control of transcription factors responsive to changing niches. We have constructed Genetic Regulatory Networks (GRNs) describing this process, based predominantly on mouse data. Regulatory network motifs identified in
E. coli and yeast GRNs are found in combination in these GRNs. Feed-forward motifs with autoregulation generate forward momentum and also control its rate, which is at its lowest in hematopoietic stem cells (HSCs). The simultaneous requirement for multiple regulators in multi-input motifs (MIMs) provides tight control over expression of target genes. Combinations of MIMs, exemplified by the SCL/LMO2 complexes, which have variable content and binding sites, explain how individual regulators can have different targets in HSCs and erythroid cells and possibly also how HSCs maintain stem cell functions while expressing lineage-affiliated genes at low level, so-called multi-lineage priming. MIMs combined with cross-antagonism describe the relationship between PU.1 and GATA-1 and between two of their target genes, Fli-1 and EKLF, with victory for GATA-1 and EKLF leading to erythroid lineage specification. These GRNs are useful repositories for current regulatory information, are accessible in interactive form via the internet, enable the consequences of perturbation to be predicted, and can act as seed networks to organize the rapidly accumulating microarray data. |
doi_str_mv | 10.1016/j.ydbio.2006.02.051 |
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E. coli and yeast GRNs are found in combination in these GRNs. Feed-forward motifs with autoregulation generate forward momentum and also control its rate, which is at its lowest in hematopoietic stem cells (HSCs). The simultaneous requirement for multiple regulators in multi-input motifs (MIMs) provides tight control over expression of target genes. Combinations of MIMs, exemplified by the SCL/LMO2 complexes, which have variable content and binding sites, explain how individual regulators can have different targets in HSCs and erythroid cells and possibly also how HSCs maintain stem cell functions while expressing lineage-affiliated genes at low level, so-called multi-lineage priming. MIMs combined with cross-antagonism describe the relationship between PU.1 and GATA-1 and between two of their target genes, Fli-1 and EKLF, with victory for GATA-1 and EKLF leading to erythroid lineage specification. 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E. coli and yeast GRNs are found in combination in these GRNs. Feed-forward motifs with autoregulation generate forward momentum and also control its rate, which is at its lowest in hematopoietic stem cells (HSCs). The simultaneous requirement for multiple regulators in multi-input motifs (MIMs) provides tight control over expression of target genes. Combinations of MIMs, exemplified by the SCL/LMO2 complexes, which have variable content and binding sites, explain how individual regulators can have different targets in HSCs and erythroid cells and possibly also how HSCs maintain stem cell functions while expressing lineage-affiliated genes at low level, so-called multi-lineage priming. MIMs combined with cross-antagonism describe the relationship between PU.1 and GATA-1 and between two of their target genes, Fli-1 and EKLF, with victory for GATA-1 and EKLF leading to erythroid lineage specification. These GRNs are useful repositories for current regulatory information, are accessible in interactive form via the internet, enable the consequences of perturbation to be predicted, and can act as seed networks to organize the rapidly accumulating microarray data.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>16626682</pmid><doi>10.1016/j.ydbio.2006.02.051</doi><tpages>16</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Animals Blood Cell Lineage Erythroid lineage Escherichia coli Gene Expression Regulation, Developmental Hematopoiesis Hematopoietic Stem Cells - cytology Hematopoietic Stem Cells - physiology Mice Models, Genetic Network motif Signal Transduction - physiology Stem cell Transcription Factors - metabolism Transcriptional regulatory network |
title | Genetic regulatory networks programming hematopoietic stem cells and erythroid lineage specification |
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