J2E erythrocytic differentiation (EPO)

Time course ID: mouse_J2E
Sample provider: Peter Klinken and Louise Winteringham

Introduction

Erythropoietin (Epo) is the hormone, which regulates red blood cell production1. It is produced primarily in the kidney, and binds to Epo receptors (Epor) on the surface of immature erythroid cells in the bone marrow, thereby initiating the final stages of red cell maturation[1,2]. Following binding of Epo to its cognate receptor, a series of intracellular signaling cascades are activated, including stimulation of the JAK/STAT and ras/MAP kinase pathways[3,4]. This leads to enhanced cell division, followed by terminal differentiation which is characterized by the production of hemoglobin. In addition, morphological changes occur involving a reduction in cell size, nuclear condensation, and eventually extrusion of the nucleus to produce reticulocytes. Mature red blood cells (erythrocytes) containing large amounts of hemoglobin then circulate around the body transporting oxygen and carbon dioxide [5].

Samples

J2E model of Erythocytic differentiation

J2E cells are murine fetal liver cells that have been immortalised with the J2 retrovirus. J2E cells retain the capacity to respond to Epo by terminally differentiating and synthesizing hemoglobin6. The mouse J2E cell line responds to Epo by activating the JAK/STAT and ras/MAP kinase pathways7, as well as a novel Lyn-signaling cascade that we identified8. As a consequence of exposure to Epo, the cells undergo a burst of proliferation, followed by entry into the terminally differentiated state by synthesizing hemoglobin and changing morphologically9. These cells, therefore, provide a very good model for normal erythroid maturation in response to Epo.

J2E cells are maintained in DMEM (Gibco) 5% FCS (Bovogen Biologicals) at 370C and 5% CO2. Cell density is kept at 5-8 X105 cells/ml. Cells were induced with 5U/ml of Epo (Eprex®) (Jannsen). At least 1 X 107 cells were collected for RNA at 0min, 15min, 30min, 45min,1h, 1h 20min, 1h 40min, 2h, 2h 30min,3h, 3h 30min, 4h, 6h, 12h, 24h and 48h.

Key marker for differentiation

Enumeration of benzidine positive cells, as an indication of hemoglobin synthesis, was carried out to monitor differentiation. The time course of Epo-induced differentiation of J2E cells shows that hemoglobin production increases markedly 24-48h after stimulation (Figure 1).

Figure 1. Benzidine positive cells were enumerated at each time point. Three biological replicates were analysed.

Quality control

Expression of the following genes was assessed to determine the validity of this cell line as a model of Epo-induced erythroid differentiation (Figure 2). All these genes are required for normal erythroid differentiation.

Epor mediates epo-induced proliferation and differentiation [10]
Alas2 is the rate limiting enzyme for the Heme biosynthesis pathway [11]
Hbb-b1 hemoglobin, adult beta major chain required for oxygen transport [12]
Gata-1 is an essential transcription factor for erythroid development [13, 14]
Klf1 is a key transcriptional regulator for erythroid development [15]
Nfe2 regulates erythroid maturation [16]

Figure 2. Expression of key genes associated with erythroid differentiation. TPM: Tags per million.

References

[1] Bunn HF. Erythropoietin. Cold Spring Harbor perspectives in medicine 2013; 3: a011619.

[2] Koury MJ, Koury ST, Bondurant MC, Graber SE. Correlation of the molecular and anatomical aspects of renal erythropoietin production. Contributions to nephrology 1989; 76: 24-29; discussion 30-22.

[3] Richmond TD, Chohan M, Barber DL. Turning cells red: signal transduction mediated by erythropoietin. Trends Cell Biol 2005; 15: 146-155.

[4] Ingley E. Integrating novel signaling pathways involved in erythropoiesis. IUBMB Life 2012; 64: 402-410.

[5] Palis J. Primitive and definitive erythropoiesis in mammals. Frontiers in physiology 2014; 5: 3.

[6] Klinken SP, Nicola NA, Johnson GR. In vitro-derived leukemic erythroid cell lines induced by a raf- and myc-containing retrovirus differentiate in response to erythropoietin. Proc Natl Acad Sci U S A 1988; 85: 8506-8510.

[7] Tilbrook PA, Bittorf T, Callus BA, Busfield SJ, Ingley E, Klinken SP. Regulation of the erythropoietin receptor and involvement of JAK2 in differentiation of J2E erythroid cells. Cell Growth Differ 1996; 7: 511-520.

[8] Tilbrook PA, Ingley E, Williams JH, Hibbs ML, Klinken SP. Lyn tyrosine kinase is essential for erythropoietin-induced differentiation of J2E erythroid cells. EMBO J 1997; 16: 1610-1619.

[9] Busfield SJ, Klinken SP. Erythropoietin-induced stimulation of differentiation and proliferation in J2E cells is not mimicked by chemical induction. Blood 1992; 80: 412-419.

[10] Lodish HF, Hilton DJ, Klingmuller U, Watowich SS, Wu H. The erythropoietin receptor: biogenesis, dimerization, and intracellular signal transduction. Cold Spring Harb Symp Quant Biol 1995; 60: 93-104.

[11] Meguro K, Igarashi K, Yamamoto M, Fujita H, Sassa S. The role of the erythroid-specific delta-aminolevulinate synthase gene expression in erythroid heme synthesis. Blood 1995; 86: 940-948.

[12] Stamatoyannopoulos G. Control of globin gene expression during development and erythroid differentiation. Exp Hematol 2005; 33: 259-271.

[13] Tsai SF, Martin DI, Zon LI, D'Andrea AD, Wong GG, Orkin SH. Cloning of cDNA for the major DNA-binding protein of the erythroid lineage through expression in mammalian cells. Nature 1989; 339: 446-451.

[14] Whitelaw E, Tsai SF, Hogben P, Orkin SH. Regulated expression of globin chains and the erythroid transcription factor GATA-1 during erythropoiesis in the developing mouse. Mol Cell Biol 1990; 10: 6596-6606.

[15] Miller IJ, Bieker JJ. A novel, erythroid cell-specific murine transcription factor that binds to the CACCC element and is related to the Kruppel family of nuclear proteins. Mol Cell Biol 1993; 13: 2776-2786.

[16] Andrews NC, Erdjument-Bromage H, Davidson MB, Tempst P, Orkin SH. Erythroid transcription factor NF-E2 is a haematopoietic-specific basic- leucine zipper protein. Nature 1993; 362: 722-728.

Beginning of non-public section

Paper outline

Regulation of erythroid differentiation (J2E murine line induced with Epo)

Fig 1: Define clusters of genes based on their expression pattern - this will identify key genes required at different stages of the time course – for example. 1. Late response on 2. Late response off 3. Early response on 4. Early response off 5. Steady increase 6. Steady decrease 7. Pulsed up 8. Pulsed down We can then ask the question; Are there particular functional groups enriched within a cluster? eg transcription factors, cell cycle , enzyme , globin, cytoskeletal etc. Does function correlate with expression? We know this is the case at least for genes like globins , heme enzymes and cytoskeletal genes (in this TC these would fit into cluster 1) (see October Fantom presentation) – can we identify other key genes? This initial analysis will provide a framework to investigate the effect of different transcription factors, enhancers and epigenetic genes.

These data can be compared to the microarray expression data of Merryweather-Clarke et al. Blood, 31 March 31 2011 and Novershtern et al Cell 144, 296–309, January 21, 2011

Fig 2 a) general TF motif enrichment – show correlation with TF expression. b) Specifically, show enrichment of TF binding sites for TFs identified in Fig1 as being important in this TC. What are the downstream genes – can we define specific key networks? c) Are any of the TFs or networks enriched in all inductions (ie other cell types) or all haemopoietic TCs (refer to main paper) ie general TF networks vs haemopoietic TF networks vs erythroid specific networks – including nontransformed cells. d) Is there a correlation between induction with a physiological agent vs chemical? If this analysis identifies novel molecules ie that do not have a known role in erythropoiesis we can validate using shRNAs to knock down or we can over express them.

Fig 3 Can we apply a similar strategy to enhancer usage? a) Enhancer motif enrichment - Can we identify early response enhancers vs late response enhancers? Does this correlate with expression? Does this correlate with TF motif enrichment? b) Can we identify “”enhancers” for pioneer transcription factors ? we can validate these using luciferase assays, ChIP and 3C assay.

Fig 4 What role do epigenetic genes play in this process? The earlier time points may provide some interesting data here. Is there a general down/up regulation or do particular classes of epigenetic genes (eg methylases, dnmts, acetylases HDACs etc) behave co-ordinately. Can this be correlated with gene expression. (A good list of candidate genes can be found in this paper (Miremadi et al Human Molecular Genetics, 2007, Vol. 16)).

This can then be validated using ChIP analysis of specific enhancers and/or promoters to look at specific marks.

Fig 5 How do gene expression patterns during differentiation correlate with leukemia? eg does a gene expression/TF motif enrichment/enhancer pattern at 12hrs or 24hrs look the same as an AML pattern. One feature of AML is a block in differentiation – can we identify a time point and therefore key molecules that might contribute to the cancer.

For general outline of haemopoiesis the following papers might be helpful Orkin and Zon Cell 132, 631–644, February 22, 2008 Hattangadi et al Blood 118, December 8 2011

FANTOM groups involved in the analysis of this time-course

Some groups have expressed an interest in working on aspects this TC, including:

Albin Sandelin, Robin Andersson
Martin Taylor
Michael Rehli
Andreas Lennartson

Zenbu configurations and status

J2E erythroblastic leukemia response to erythropoietin(COMPLETED 18 time points in triplicate)
K562 erythroblastic leukemia response to hemin(COMPLETED 18 time points in triplicate)
K562 erythroblastic leukemia response to hemin timecourse, October 26th 2012

Gene expression profiles

Gene and CAGE cluster expression for the J2E series
- https://fantom5-collaboration.gsc.riken.jp/webdav/home/arner/timecourse/time_course_main_paper_freeze_feb2013/qc_release_130226/mouse_J2E/expression_tables/

Known gene expression for the K562 series (RefSeq based promoters)
- TPM
- tagcount

MARA based network results

https://fantom5-collaboration.gsc.riken.jp/webdav/home/arner/timecourse/time_course_main_paper_freeze_feb2013/qc_release_130226/mouse_J2E/mara/

Self-organising maps

File:Hemin.pdf for hemin response timecourse

TSS Switching (Switch Engine)

TSS dynamics plots can be found here:Media:K562leukemia_hemin.plots.pdf (it is recommended to download these files instead of viewing in browser as they are large in size)
These figures only show those genes for which Switch Engine has detected TSS switching to occur. For now, only the first and last time points are being compared to define a switch.
- One gene per page, RefSeq and Gene Symbol identifiers on top. Each panel is a separate TSS associated with the gene. TPM expression on y-axis and time on x-axis. Colored points correspond to specific expression, with colors referring to different replicates (key on top). Replicates with a * next to the name are those that have missing data from the currently available release of the DPI clustered and normalized TPM matrix. Such missing data were replaced by imputed values. Black lines are the mean trajectories across replicates. Purple vertical bars correspond to the time points being compared for switch definition. Magnitude of switch is expressed in each panel under TSS identifier with approximate 95% confidence interval in brackets. Note: some confidence intervals may not be accurate or may be missing due to the small sample size. TSS identifiers colored GREEN show an increasing TSS, those colored RED show decreasing TSS, WHITE show no statistically significant change.
- TSS switch defined as the simultaneous presence of one or more increasing, together with one or more decreasing TSSs per gene. At least one increase and at least one decrease has to be statistically significant (alpha approximately 5%). At least one of the two time points being compared has to be greater than 5 tpm. At least 2 replicates have to agree to call an increasing/decreasing trajectory. TSSs have to be at least 250 base pairs away from one another to call a switch.
A summary file can be found here: Media:K562leukemia_hemin.summary.xls
- The file contains the following columns: RefSeq ID, Gene Symbol, TSS ID, X{value}: mean tpm expression at {value} time point, Delta: change in tpm expression from first to last time point, FC: fold change in tpm expression from first to last time point, DeltaSum: net change in tpm expression for this gene (summed across all TSS in this gene), Balance: ratio of increasing to decreasing tpm expression TSSs per gene (e.g. a balance value of 1 means that the total increase in tpm expression in increasing TSSs is equal to the total decrease in tpm expression in decreasing TSSs), Chromosome, Strand, Start position of TSS cluster, Stop position of TSS cluster, DBTSS: distance between TSSs relative to NA value.
A list of genes in which TSS switching occurs can be found here: Media:K562leukemia_hemin.genelist.symbol.doc and Media:K562leukemia_hemin.genelist.refseq.doc
BED file with regions corresponding to TSS clusters involved in TSS switching can be found here: Media:K562leukemia_hemin.switchinglist.bed.xls (please remove .xls extension after downloading)