Cytokines in Hematopoiesis: Specificity and Redundancy in Receptor Function

Socolovsky, Merav; Constantinescu, Stefan N.; Bergelson, Svetlana; Sirotkin, Allen; Lodish, Harvey F.

doi:10.1016/s0065-3233(08)60435-0

Cited by 24 publications

(31 citation statements)

References 221 publications

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“…A ligand-induced receptor homodimer conformational change leads to trans-phosphorylation and activation of Janus kinase 2 (JAK2). 2,3 Erythroid development in JAK2-deficient embryos is arrested earlier and fetal anemia is more severe than that of EpoR Ϫ/Ϫ embryos, indicating JAK2 is essential for signaling downstream of multiple cytokine receptors, in addition to EpoR, that are important for primitive erythroid development. 4,5 Activated JAK2 phosphorylates key tyrosine residues in the EpoR cytoplasmic domain, thereby providing docking sites for SH2 domain-containing downstream signaling molecules.…”

Section: Introductionmentioning

confidence: 99%

“…EpoR activates signal transducers and activators of transcription 5 (Stat5), Ras/mitogen-activated protein kinase (MAPK), and phosphoinositide-3 kinase (PI-3K)/Akt pathways. 2,3 These signaling modules have been implicated in hematopoiesis. Stat5 plays an important role in maintaining a high erythropoietic rate during fetal development and during stress responses in adult mice, as shown by studies using stat5a Ϫ/Ϫ stat5b Ϫ/Ϫ mice.…”

Section: Introductionmentioning

confidence: 99%

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Lnk inhibits erythropoiesis and Epo-dependent JAK2 activation and downstream signaling pathways

Tong

Zhang

Lodish

2005

Blood

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190

186

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lnk inhibits erythropoiesis and Epo-dependent JAK2 activation and downstream signaling pathways

Tong

Zhang

Lodish

2005

Blood

Self Cite

190

186

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“…On the basis of the work of Socolovsky et al, Kina et al, and Chang et al, the parameters Ter119 and CD71 were used to identify events representative of two developmental stages of erythroid precursors (14,15,28). Two applications of the EM algorithm were required to create the necessary training sets, with the first ORIGINAL ARTICLE application used to model events positive and negative for TER-119, which are erythroid and nonerythroid (NE) events respectively.…”

Section: Training Data Selection Using Expectation Maximization (Em) mentioning

confidence: 99%

“…The second application was used to model CD71 positive and negative events, late precursors and very late precursors to red blood cells, respectively. In this work the term late precursor (LP) is used to indicate cells that would be classified as proerythroblasts, basophilic erythroblast, and possibly polychromatic erythroblasts by the gating system described in (14). Very late precursors (VLP) include polychromatic and orthochromatic erythroblasts as classified by the same expert method.…”

Section: Training Data Selection Using Expectation Maximization (Em) mentioning

confidence: 99%

“…AntiTER-119 binds a glycophorin-A associated protein, and has been used to discriminate erythroid precursors from other bone marrow cells in mice. The expression of CD71 (transferrin receptor) is inversely correlated with maturation of erythroid precursors (14,15). To test this hypothesis it was necessary to identify erythroid cells (Ter-1191) from bone marrow and roughly determine their stage of differentiation (CD71 levels), so in addition to classifying events by viability ANNs were used to classify for lineage and developmental stage.…”

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A statistical pattern recognition approach for determining cellular viability and lineage phenotype in cultured cells and murine bone marrow

Quinn

Fisher²,

Capocasale³

et al. 2007

Cytometry Pt A

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Cellular binding of annexin V and membrane permeability to 7-aminoactinomycin D (7AAD) are important tools for studying apoptosis and cell death by flow cytometry. Combining viability markers with cell surface marker expression is routinely used to study various cell lineages. Current classification methods using strict thresholds, or ''gates,'' on the fluorescent intensity of these markers are subjective in nature and may not fully describe the phenotypes of interest. We have developed objective criteria for phenotypic boundary recognition through the application of statistical pattern recognition. This task was achieved using artificial neural networks (ANNs) that were trained to recognize subsets of cells with known phenotypes, and then used to determine decision boundaries based on statistical measures of similarity. This approach was then used to test the hypothesis that erythropoietin (EPO) inhibits apoptosis and cell death in erythroid precursor cells in murine bone marrow. Our method was developed for classification of viability using an in vitro cell system and then applied to an ex vivo analysis of murine late-stage erythroid progenitors. To induce apoptosis and cell death in vitro, an EPO-dependent human leukemic cell line, UT-7 EPO cells were incubated without recombinant human erythropoietin (rhEPO) for 72 h. Five different ANNs were trained to recognize live, apoptotic, and dead cells using a ''known'' subset of the data for training, and a K-fold cross validation procedure for error estimation. The ANNs developed with the in vitro system were then applied to classify cells from an ex vivo study of rhEPO treated mice. Tg197 (human tumor necrosis-a transgenic mice, a model of anemia of chronic disease) received a single s.c. dose of 10,000 U/kg rhEPO and femoral bone marrow was collected 1, 2, 4, and 8 days after dosing. Femoral bone marrow cells were stained with TER-119 PE, CD71 APC enable identification of erythroid precursors, and annexin V FITC and 7AAD to identify the apoptotic and dead cells. During classification forward and side angle light scatter were also input to all pattern recognition systems. Similar decision boundaries between live, apoptotic, and dead cells were consistently identified by the neural networks. The best performing network was a radial basis function multi-perceptron that produced an estimated average error rate of 4.5% AE 0.9%. Using these boundaries, the following results were reached: depriving UT-7 EPO cells of rhEPO induced apoptosis and cell death while the addition of rhEPO rescued the cells in a dose-dependent manner. In vivo, treatment with rhEPO resulted in an increase of live erythroid cells in the bone marrow to 119.8% AE 9.8% of control at the 8 day time point. However, a statistically significant transient increase in TER-119 1 CD71 1 7AAD 1 dead erythroid precursors was observed at the 1 and 2 day time points with a corresponding decrease in TER-119 1 CD71 1 7AAD 2 Annexin V 2 live erythroid precursors, and no change in the number of TER-119 1 C...

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