The mechanisms underlying cell fate decisions during differentiation and development have long been a subject of fascination among biologists. It is widely accepted that lineage-restricted transcription factors (TFs) play a crucial role in driving cell differentiation by repressing the old gene expression program while activating new ones at bifurcation points. The balance between antagonistic TFs is a key determinant of cell fate, with the more highly expressed factor silencing the opposing factor (Graf and Enver, 2009; Orkin and Zon, 2008). However, the precise mechanisms, such as how gene silencing and activation are coordinated, and whether factors other than TF levels influence cell fate decisions, are still largely unknown.

A powerful method for studying mechanisms of cell fate decisions is to induce lineage conversions through TF overexpression (Graf, 2002; Zhou and Melton, 2008; Graf and Enver, 2009). An especially efficient system for this purpose is the C/EBPα-induced transdifferentiation of B cells into macrophages, designated here as BMT (Xie et al., 2004 ; Figure 1A). C/EBPα is composed of a bZip DNA-binding domain on its C-terminus and a tripartite transactivation domain on its N-terminus (Figure 1B; Ramberger et al., 2021b). During hematopoiesis, it is most highly expressed in granulocyte-macrophage progenitors (GMPs) (Ohlsson et al., 2016) and its absence inhibits the formation of GMPs, and monocytes/macrophages (Heath et al., 2004; Ma et al., 2014; Zhang et al., 2004). The C/EBPα-induced BMT requires partnership with the TF PU.1, which participates in regulatory networks of both B/T cells and in myeloid cells (Rothenberg, 2014; Laiosa et al., 2006; Heinz et al., 2010; Arinobu et al., 2007; Leddin et al., 2011; Singh et al., 1999).

Figure 1 with 1 supplement see all

Download asset Open asset

Post-translational modifications of TFs can have a significant impact on their structure, subcellular localization, interactions with other proteins and activity (Deribe et al., 2010; Torcal Garcia and Graf, 2021). Recently, specific protein arginine methyltransferases (PRMTs) that effect arginine modifications resulting in asymmetric or symmetric di- or mono-methylation have gained prominence (Wu et al., 2021; Guo et al., 2010; Kawabe et al., 2012; Kowenz-Leutz et al., 2010). In the context of cell fate decisions, Carm1 (Prmt4) is of particular interest as it is crucial for early embryo development, muscle regeneration, adipogenesis, and cancer (Kawabe et al., 2012; Kim et al., 2010; Li et al., 2013; Torres-Padilla et al., 2007; Yadav et al., 2008).

Our study revealed that Carm1-mediated methylation of a specific arginine located in the intrinsically disordered transcription activation domain of C/EBPα plays a critical role in regulating the speed of transdifferentiation induced by this factor. In its mutated or unmethylated form C/EBPα induces BMT at a dramatically increased velocity, initiated by a higher interaction affinity for its partner TF PU.1. This triggers the acceleration of chromatin accessibility changes and the redistribution of PU.1 from B- to M-GREs. The methyltransferase involved in the methylation of C/EBPa is Carm1, and it plays a role in determining the lineage directionality of GMPs.

C/EBPα^R35A accelerates chromatin remodeling at regulatory elements of lineage-restricted genes

To examine the chromatin remodeling accompanying induced gene expression changes during BMT, we conducted an assay for transposase-accessible chromatin sequencing (ATAC-seq) (Buenrostro et al., 2015) of samples collected at multiple timepoints after activation of C/EBPα^WT and C/EBPα^R35A (Figure 2A). Our analysis identified 91,830 regions that showed significant differences in chromatin accessibility throughout wild-type- or mutant-induced BMT, representing in their vast majority TF-bound GREs (Buenrostro et al., 2015). These regions were grouped into (1) faster opening GREs induced by C/EBPα^R35A, with the highest accessibility at 120 hpi (43,429 GREs); (2) faster closing GREs, most accessible at 0 hpi (36,380 GREs); and (3) transiently opening GREs, most accessible at 18hpi (12,021 GREs) that showed little differences between the two conditions (Figure 2—figure supplement 1A,B). PCA of the differential ATAC peaks further confirmed the acceleration of chromatin accessibility changes by C/EBPα^R35A, with the coordinates of the 1–6 hpi C/EBPα^R35A samples resembling those of the 18 hpi C/EBPα^WT sample (Figure 2B).

Figure 2 with 1 supplement see all

Download asset Open asset

Focusing on differentially remodeled promoter regions, we found 14,233 and these were grouped into eight clusters (Figure 2C). Promoter-associated genes in clusters I, II, and III exhibited dramatically accelerated opening dynamics for C/EBPα^R35A, while genes in clusters VII and VIII showed accelerated closing. GO analysis revealed an enrichment of macrophage terms for cluster III, which included the macrophage-restricted genes Itgam and Lyz2 (Figure 2C). C/EBPα^R35A already initiated chromatin opening at promoters and enhancers of Itgam and Lyz2 within 1 hpi, and this increased over time much more rapidly than with C/EBPα^WT cells (Figure 2—figure supplement 1C). Conversely, faster closing promoters in cluster VIII were enriched for B cell-related GO terms and included the B cell genes Cd19, Pax5, and Rag2 (Figure 2C and E). The kinetics of chromatin closing were likewise accelerated at B cell GREs. Strikingly, however, as exemplified for Cd19 and Pax5, while mutant-induced changes occurred already at 1 hpi and progressed from there onward, the same sites showed a transient chromatin opening in wild-type-induced cells (Figure 2—figure supplement 1D).

Our results show that C/EBPα^R35A is more effective than C/EBPα^WT in inducing changes of chromatin closing and opening at B- and M-GREs. However, while the mutant acts immediately to close chromatin, wild-type-induced chromatin closing is preceded by a transient chromatin opening, as is also reflected by a transient increase in gene expression (Figure 1—figure supplement 1F, G). This is in line with the recently described Gata1-induced silencing of the Kit gene enhancer during erythroid cell differentiation, which becomes transiently activated (Vermunt et al., 2023). It is also noteworthy that despite progressive alterations in chromatin accessibility at B- and M-GREs during BMT induction (Figure 2—figure supplement 1D), essentially no changes in gene expression were observed between 6–18 hpi, suggesting that chromatin remodeling is uncoupled from gene expression during this phase of the process.

C/EBPα^35A preferentially interacts with PU.1

To determine if the enhanced ability of the mutant to modify chromatin accessibility was due to differences in DNA binding capacity, we compared nuclear extracts of 293T transfected with C/EBPα^WT and C/EBPα^R35A. An electrophoretic mobility shift assay (EMSA) revealed no significant differences in overall DNA binding affinities between the two C/EBPα forms (Figure 3—figure supplement 1A).

We next explored whether the differences are due instead to the mutant’s preferential interaction with other proteins, performing a de novo motif discovery analyses on differentially accessible GREs. Results in Figure 3A showed that faster and transiently opening GREs were enriched for AP-1/leucine zipper family TF motifs (c-Fos, c-Jun, and JunB), known to heterodimerize with C/EBPα and activate myeloid genes (Cai et al., 2008). In contrast, faster closing GREs were mostly enriched for ETS family TF motifs (Ets1, Fli1, SpiB, and Gabpa), linked to B cell lineage differentiation and function (Eyquem et al., 2004; Hu et al., 2001; Xue et al., 2007). Of note, the Spi1 (PU.1) motif was enriched in both the accelerated chromatin opening and closing groups (Figure 3B and Figure 3—figure supplement 1B), possibly reflecting the known dual roles of PU.1 in B cells and macrophages (Scott et al., 1994; Singh et al., 1999; Heinz et al., 2010; Rothenberg, 2014). However, no PU.1 motif enrichment was seen in the transiently opening group (Figure 3—figure supplement 1C).

Figure 3 with 1 supplement see all

Download asset Open asset

Figure 3—source data 1

Electrophoretic mobility shift assay (EMSA) with 293T cells transfected with either C/EBPα^WT or C/EBPα^R35A incubated with a fluorophore-labeled oligonucleotide containing a palindromic C/EBPα-binding site.

https://cdn.elifesciences.org/articles/83951/elife-83951-fig3-data1-v1.docx

Download elife-83951-fig3-data1-v1.docx

Figure 3—source data 2

Table listing 300 proteins that represent the interactome of C/EBPα showing log2-based differences in biotin labeling with the full length (p42) C/EBPa^R35A protein and the p42 C/EBPα^WT protein as well as p-values for the differences in the corresponding interactions.

https://cdn.elifesciences.org/articles/83951/elife-83951-fig3-data2-v1.docx

Download elife-83951-fig3-data2-v1.docx

Figure 3—source data 3

Co-immunoprecipitation of PU.1 and C/EBPα in 293T cells transfected with either C/EBPα^WT or C/EBPα^R35A and quantification of interaction.

https://cdn.elifesciences.org/articles/83951/elife-83951-fig3-data3-v1.zip

Download elife-83951-fig3-data3-v1.zip

To examine whether C/EBPα^WT and C/EBPα^R35A differentially interact with other proteins, we performed a proximity-dependent biotin identification assay (Figure 3B), which was previously used to identify high confidence C/EBPα interactors (Ramberger et al., 2021a). We used a mouse pre-B cell line expressing doxycycline (Dox)-inducible C/EBPα^WT or C/EBPα^R35A constructs fused with the biotin ligase variant TurboID (Branon et al., 2018). To avoid confounding interactions this cell line contained a double knockout of Cebpa and Cebpb, genes which are rapidly activated by exogenous C/EBPα (Bussmann et al., 2009). After Dox induction for 6 hr and biotin labeling, biotin-labeled proteins were identified by mass spectrometry. A comparison of the high confidence C/EBPα^WT and C/EBPα^R35A interactome revealed that C/EBPα^R35A preferentially interacted with Spi1 (PU.1), showing a 3.6-fold increase in biotin label compared to C/EBPα^WT. In addition, preferential but non-significant interactions of C/EBPα^R35A were observed with Ncor1 and Ebf1 (Figure 3B; Figure 3B, source data 1), which, however, were not further studied. The observation that most shared interactors show a skewing toward the wild-type protein suggests that C/EBPα^WT is expressed at slightly higher levels than the mutant, in line with the detection of moderately reduced levels of the mutant protein by western blot (Figure 4—figure supplement 1C). To further evaluate the interaction between C/EBPα^WT and C/EBPα^R35A with PU.1, we conducted co-immunoprecipitation (Co-IP) experiments with 293T cells co-transfected with PU.1 and either C/EBPα^WT or C/EBPα^R35A. The results showed an approximately twofold increase in the interaction between C/EBPα^R35A and PU.1 compared to C/EBPα^WT and PU.1 (Figure 3C). Finally, performing a proximity ligation assay (PLA) to study the protein’s interaction in live cells showed a significantly higher number of fluorescent nuclear dots for the combination of C/EBPα^R35A with PU.1 than with C/EBPα^WT (Figure 3D), again indicating a higher affinity between the two proteins.

In sum, motif analyses showed an enrichment of the PU.1 motif at both B- and M-GREs that were differentially accessed by the mutant. Moreover, the results obtained by Co-IP, mass spectrometry, and PLA indicate that C/EBPα^R35A shows a 1.5- to 3.5-fold higher affinity for PU.1 compared to the wild-type.

C/EBPα^R35A shows an increased synergy with PU.1 for myeloid gene activation in fibroblasts

Previously, we showed that the combination between C/EBPα and PU.1 is required for converting NIH 3T3 fibroblasts into macrophage-like cells (Feng et al., 2008). To investigate if the synergy between the C/EBPα mutant and PU.1 is altered, we isolated NIH 3T3 cell lines expressing C/EBPα^WT-ER or C/EBPα^R35A-ER. These were subsequently infected or not with a PU.1 retroviral construct, and Mac-1 levels monitored at different times after E2 treatment. The results showed that in the presence of PU.1, C/EBPα^R35A activated Mac-1 more strongly than C/EBPα^WT, while cells infected with PU.1 only showed weak Mac-1 activation and C/EBPα only cells remained negative (Figure 4A, Figure 4—figure supplement 1A). Additionally, cells co-expressing C/EBPα^R35A and PU.1 displayed more striking morphological changes than those with C/EBPα^WT and PU.1 (Figure 4—figure supplement 1B). Cycloheximide treatment experiments showed that C/EBPα^R35A exhibits a similar stability than wild-type protein and under steady-state condition is expressed at 20–30% lower levels (Figure 4—figure supplement 1C). This indicates that the observed gain of function of the mutant cannot simply be explained by an increased stability or expression.

Figure 4 with 1 supplement see all

Download asset Open asset

C/EBPα and PU.1 binding and chromatin accessibility changes at GREs at a selected locus harboring both a B cell- and a macrophage-associated gene

The findings described so far pointed to the possibility that PU.1 acts as a coordinator of the B cell to macrophage conversion. A plausible scenario therefore is that C/EBPα removes PU.1 from B cell GREs and induces its relocation to macrophage GREs. Moreover, through its enhanced interaction with PU.1, C/EBPα^R35A might be more efficient at relocating the factor, explaining the observed acceleration of BMT. To test this hypothesis, we conducted DNA binding experiments by chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) for PU.1 and C/EBPα with primary B cells expressing C/EBPα^WTER and C/EBPα^R35AER 3 hr after E2 induction. The data obtained were then visualized on the UCSC browser in parallel with our ATAC-seq data with cells from 0, 1, 3, 6, 18, and 120 hpi. The main messages of our results are encapsulated by the Cyfip2-Havcr2 locus, a 270 Kb genomic region containing both a B cell- and a macrophage-restricted gene (Figure 5A).

Figure 5 with 1 supplement see all

Download asset Open asset

Of the five genes contained in this locus, two, namely Cyfip2 and Havcr2, become down- and upregulated, respectively, during BMT, with C/EBPα^R35A inducing an acceleration of both processes (Figure 5B). Mining of published data (Mullen et al., 2011) showed that PU.1 binds to four sites 50–150 kb upstream of Cyfip2 in B cells but is essentially absent at these sites in macrophages (highlighted in red). Conversely, both PU.1 and C/EBPα bind to the promoter of Havcr2 and three additional GREs in macrophages but not in B cells (highlighted in green, Figure 5C), showing that PU.1 can bind to both B- and M-GREs while C/EBPα can only bind to M-GREs.

Analyzing the PU.1 binding sites in primary B cells induced by C/EBPα for 3 hr, the factor showed higher occupancy at the B-GREs of C/EBPα^WT cells and a modestly increased binding at the M-GREs of C/EBPα^R35A cells (Figure 5D). Similarly, C/EBPα^WT strongly bound to the B-GREs and weakly to the M-GREs, while C/EBPα^R35A was selectively bound at the M-GREs (Figure 5E, Figure 5—figure supplement 1A). Since the DNA binding data only represent a snapshot in time, we also analyzed our dynamic ATAC-seq data for the Cyfip2-Havcr2 locus and found that the mutant accelerates both the closure of B-GREs and opening of M-GREs (Figure 5—figure supplement 1B). Notably, while C/EBPα^WT triggered a transient and partial opening of B-GREs at 1 hpi and closure at 18 hpi, the mutant induced an immediate and progressive closure (Figure 5F). Similar effects were also seen at GREs of B cell signature genes (Figure 2—figure supplement 1D).

Our findings at the Cyfip2-Havcr2 locus revealed that at 3 hpi both PU.1 and C/EBPα show distinct DNA occupancies at B- and M-GREs in wild-type and mutant expressing cells. These differences can best be explained by the hypothesis that C/EBPα triggers the redistribution of PU.1 bound to B-GREs to M-GREs.

Genome-wide analysis of TF binding and chromatin accessibility kinetics

The finding that the PU.1 redistribution appears to be accelerated in the mutant cells predicts that B cell enhancers show lower levels of PU.1 binding in C/EBPα^R35A-induced cells than in C/EBPα^WT-induced cells. Conversely, myeloid enhancers should show higher PU.1 occupancy. To test this, we conducted a genome-wide analysis of the ChIP-seq data, revealing 4310 regions differentially bound by PU.1 at 3 hpi. These regions were divided into sites bound either Less or More in C/EBPα^R35A cells relative to C/EBPα^WT cells. As seen from the ATAC-seq data, the majority of the observed 1647 sites that were bound Less by PU.1 became inaccessible at 18 hpi in C/EBPα^WT cells, showing a transient opening between 1 and 6 hpi. In contrast, in C/EBPα^R35A cells these sites became already partially closed at 1 hpi and fully inaccessible after 18 hpi (Figure 6A). The opposite was observed for the 2650 sites bound More by PU.1, where chromatin accessibility increased already at 1 hpi in the mutant cells, while in wild-type cells they only opened at 120 hpi (Figure 6D and E). As predicted, genes associated to sites bound Less by PU.1 showed a strong enrichment of B cell-related GO terms while More-bound sites were enriched for myeloid-related GO terms (Figure 6C).

Figure 6 with 1 supplement see all

Download asset Open asset

The redistribution model further predicts that C/EBPα^R35A is released from B cell enhancers already at 3 hpi and is instead enriched at myeloid GREs, while C/EBPα^WT shows a relocation delay. We therefore compared the genome-wide DNA binding profiles of wild-type and mutant C/EBPα proteins. Of 7144 differentially occupied sites, 4352 sites were bound Less by C/EBPα^R35A. Approximately 70% of these started closing at 1 hpi in C/EBPα^R35A cells while in C/EBPα^WT cells they only closed at 120 hpi, after transiently opening between 1 and 6 hpi (Figure 6—figure supplement 1A). The remaining 30% of the sites showed a gradual but heterogeneous increase in accessibility. Finally, about half of the sites bound More by C/EBPα^R35A became more rapidly opened, as expected, while the other half showed a transient opening before closing in both wild-type and mutant cells (Figure 6—figure supplement 1B). GO analysis for the Less C/EBPα^R35A-bound sites showed an association with both B cell and myeloid terms while the rapidly opening More bound sites were associated with myeloid terms (Figure 6—figure supplement 1C).

Finally, we tested sites bound Less by both PU.1 and C/EBPα^R35A and found 783 shared sites, whereas 385 sites were bound More by both PU.1 and mutant C/EBPα. In contrast, no overlap was found between PU.1 bound Less and C/EBPα bound More or vice versa (Figure 6—figure supplement 1D) showing a strong positive correlation in the binding of the two factors. The chromatin accessibility dynamics at these commonly bound regions reflected by and large the observations described for the singly bound factors and showed the expected association with B cell and myeloid-associated GO terms (data not shown).

In conclusion, our genome-wide analyses showed that C/EBPα induces the relocation of PU.1 from B- to M-GREs, in a process accelerated by the mutant. We also observed that C/EBPα^WT transiently opens B-GREs during B cell silencing, while C/EBPα^R35A induces their immediate closing. The finding that the chromatin of B-GREs is more accessible in uninduced C/EBPα^R35A cells suggests a leakiness of the construct and could at least in part explain this finding. However, this poised state does not result in an altered expression of associated B cell genes compared to uninduced C/EBPα^WT cells. Finally, our data showed the progressive closing/opening of B/M-GREs between 1 and 6 hpi, while gene expression during this phase remains essentially unchanged (Figure 1—figure supplement 1F, G; Figure 2—figure supplement 1C, D; Figure 5—figure supplement 1B). These observations suggest that chromatin restructuring can be uncoupled from changes in gene regulation.

Carm1 asymmetrically di-methylates arginine 35, decreasing the affinity of C/EBPα for PU.1

The observed accelerated BMT induced by C/EBPα with an alanine replacement of arginine 35 could either be due to the replacement of a charged amino acid with a hydrophobic residue or to the loss of arginine methylation, or both. To test this, we replaced the arginine with the charged residue lysine in our inducible retroviral vector and expressed it in primary B cells. These cells were then induced with E2 for 1–120 hr and analyzed by RNA-seq and ATAC-seq in comparison to C/EBPα^WT-induced cells. PCA of the data showed an accelerated BMT time course of gene expression and chromatin accessibility (ATAC peaks) for the C/EBPα^R35K-induced cells (Figure 7—figure supplement 1A,B), similar but not identical to those seen with C/EBPα^R35A cells (not shown). This suggests that the C/EBPα^R35A-induced BMT acceleration is not simply due to a lack of charge at the R35 residue.

To explore whether R35 is asymmetrically di-methylated, we used a previously described human B cell to macrophage BMT system (Rapino et al., 2013). For this we generated 4-hydroxytamoxifen (4-OHT)-inducible lines expressing C/EBPα^WT and C/EBPα^R35A fused to ERT-2, called BLaER2 and BLaER2-A, respectively. After treating the cells with 4-OHT for 24 hr and immunoprecipitating C/EBPα followed by western blotting and staining of the blot with an antibody specific for asymmetrically di-methylated arginine-containing proteins, C/EBPα could be detected in BLaER2 but not in BlaER2-A cells, as expected (Figure 7A). Similar results were obtained with transfected 293T cells, showing methylated C/EBPα in the wild-type but not mutant cells (Figure 7B).

Figure 7 with 2 supplements see all

Download asset Open asset

Figure 7—source data 1

Immunoprecipitation (IP) and immunoblotting of C/EBPα and asymmetrically di-methylated arginine (aDMA) containing proteins.

https://cdn.elifesciences.org/articles/83951/elife-83951-fig7-data1-v1.zip

Download elife-83951-fig7-data1-v1.zip

Figure 7—source data 2

Immunoprecipitation (IP) of C/EBPα from 293T cells co-transfected with either C/EBPα^WT-Flag or C/EBPα^R35A-Flag with or without Carm1-HA, followed by immunoblot (IB) with antibodies (Abs) against asymmetrically di-methylated arginine (aDMA), Flag, and HA.

https://cdn.elifesciences.org/articles/83951/elife-83951-fig7-data2-v1.zip

Download elife-83951-fig7-data2-v1.zip

Figure 7—source data 3

Immunoprecipitation (IP) of Carm1 from 293T cells co-transfected with either C/EBPα^WT-Flag or C/EBPα^R35A-Flag with or without Carm1-HA, followed by immunoblot with antibodies against Flag and HA.

https://cdn.elifesciences.org/articles/83951/elife-83951-fig7-data3-v1.zip

Download elife-83951-fig7-data3-v1.zip

To identify which of the Prmts known to asymmetrically methylate arginine is responsible, 293T cells were co-transfected with C/EBPα^WT or C/EBPα^R35A and with either Prmt1, Prmt3, Carm1 (Prmt4), or Prmt6. Only Carm1 induced asymmetric di-methylation of C/EBPα (as detected with an antibody specific for the modification) and that this was restricted to the wild-type protein (Figure 7—figure supplement 2A). These data indicate that Carm1 specifically and asymmetrically di-methylates R35.

We next performed Co-IP experiments to examine whether C/EBPα and Carm1 can interact, using 293T cells co-transfected with the two proteins, and found that both C/EBPα^WT and C/EBPα^R35A are capable of interacting with Carm1 (Figure 7C). Next, we performed a PLA with NIH 3T3 cell derivatives expressing either C/EBPα^WT-ER or C/EBPα^R35A-ER. After 24 hr of induction with E2, cells were stained with antibodies to C/EBPα and Carm1 and a secondary antibody to detect their interaction. This again demonstrated that both forms of C/EBPα can interact with Carm1, with C/EBPα^R35A cells showing a slightly higher signal (Figure 7—figure supplement 2B).

To determine which of the 20 arginines in C/EBPα can be bound by Carm1, we conducted an in vitro methylation assay using 14 synthetic peptides spanning the entire molecule. Only the peptide containing arginine 35 generated a signal (Figure 7—figure supplement 2C). Next, we studied the enzyme’s specificity for selected targets. For this we chose as substrates a 40 amino acid long C/EBPα peptide with either an unmethylated R35, an asymmetrically di-methylated R35, or an alanine replacement. The three peptides were then treated with purified Carm1, Prmt1, Prmt3, Prmt6, or Prmt8 proteins. Only unmethylated R35 peptide could be methylated Carm1, with all other conditions being negative (Figure 7D). To examine if methylation affects the affinity of an R35 containing C/EBPα peptide for PU.1, we analyzed the data from an interactome screen (PRISMA) (Ramberger et al., 2021a). The comparison of a 15 mer with either an unmethylated or an asymmetrically di-methylated R35 showed that PU.1 interacts more strongly with the unmethylated peptide (Figure 7—figure supplement 2F).

In summary, our results indicate that Carm1, but none of several other Prmts tested, induces the asymmetric di-methylation of specifically arginine 35 of C/EBPα and that an unmethylated peptide at R35 has a higher affinity for PU.1 than its methylated form.

Carm1 modulates the directionality of GMP cell differentiation

To examine if Carm1 plays a role in myeloid cell fate specification, we first analyzed its role during myeloid differentiation. During this process, common myeloid progenitors (CMPs) differentiate into granulocyte-macrophage progenitor (GMPs), which further differentiate into granulocytes and monocyte/macrophages (Figure 8A), requiring C/EBPα (Zhang et al., 1997; Zhang et al., 2004; Ohlsson et al., 2016).

Figure 8 with 1 supplement see all

Download asset Open asset

Examining the expression of Carm1 in CMPs, GMPs, granulocytes, and macrophages (Choi et al., 2019) revealed a steady decrease during differentiation (Figure 8—figure supplement 1A). We next sorted GMPs, granulocytes, and monocytes from the bone marrow and measured AsDM-BAF155 as a proxy for Carm1 activity (Wang et al., 2014) relative to total BAF155. The highest Carm1 activity was detected in GMPs, with a 3.5-fold decrease in granulocytes and a further 4.5-fold decrease in monocytes (Figure 8B and Figure 8—figure supplement 1B). This showed that not only the expression of Carm1 but also its activity decreases during myeloid differentiation.

To investigate whether Carm1 activity influences the direction of differentiation of a bipotent precursor, we conducted a colony assay with sorted GMPs in the absence or presence of the Carm1 inhibitor TP064. For this, GMPs were seeded in semisolid medium containing 0, 1, 2.5, or 10 µM TP064 and the number of granulocytic (CFU-G), monocytic (CFU-M), and mixed (CFU-GM) colonies scored after 12 days (Figure 8—figure supplement 1). The results showed a significant dose-dependent reduction of granulocyte colonies and a corresponding increase in monocyte colonies (Figure 8C). The observed bias was not due to a granulocyte-specific toxicity of the inhibitor, as the total number of colonies remained unchanged (Figure 8—figure supplement 1C).

Our findings suggest that Carm1 modulates the directionality of differentiation of GMPs, with low activities increasing the proportion of monocytic colonies while reducing the proportion of granulocytic colonies.

Here, we describe a mechanism by which the speed of C/EBPα-induced BMT can be regulated. It is based on the identification of a C/EBPα missense mutant in a single arginine (R35), located within the unstructured transactivation domain, and which is specifically targeted by the methyltransferase Carm1. As summarized in the diagram of Figure 9, an alanine replacement of R35 or Carm1 inhibition dramatically accelerates the velocity of BMT. This is based on an increased affinity between C/EBPα and its obligate partner PU.1. The C/EBPα and PU.1 combination induces a rapid closing of B cell GREs and opening of macrophage of GREs, further accelerated by the mutant. In addition, C/EBPα is capable of redistributing PU.1 from B cell to macrophage GREs, in a process again enhanced by the mutant, resulting in the coordinated silencing of B cell genes and activation of macrophage genes.

Figure 9

Download asset Open asset

Our data indicate that the interaction affinity between C/EBPα and endogenous PU.1, already expressed in B cells, is rate limiting, as the observed higher affinity of mutant C/EBPα is sufficient to accelerate all subsequent BMT steps. In this scenario, C/EBPα acts as a trigger for PU.1, which serves as a ‘relay’ that couples the concomitant silencing and activation of gene expression (Figure 10). This mechanism avoids the formation of ‘confused’ cells, such as cells that upregulate the macrophage program without silencing the B cell program or vice versa, and therefore ensures the faithful establishment of the macrophage transcriptome. It will be interesting to determine whether the structure of PU.1 changes as it is repurposed from a B cell to a macrophage regulator during BMT, and if so, whether this reflects alternative forms of PU.1, such as recently described in an in vitro study (Xhani et al., 2020). Of note, we have not attempted to integrate into our model the fact that PU.1 is expressed at approximately twice the levels in macrophages than in B cells (Singh et al., 1999).

Figure 10

Download asset Open asset

A mechanism similar to the C/EBPα-induced redistribution (‘theft’) of PU.1 from B- to M-GREs by an incoming TF as described here has recently been described for Gata6-induced reprogramming of ESCs into primitive endoderm (Thompson et al., 2022) and might also be operative during the Oct4-induced reprogramming of Sox2-expressing neural progenitors to induced pluripotent stem cells (Kim et al., 2009). A theft mechanism involving PU.1 has also been reported for T cell differentiation although in this case PU.1 acts as the ‘thief’ by redirecting the Runx1 and Satb1 to novel sites (Hosokawa et al., 2018). Another relevant example is the sequestering of Gata3 by Tbet from Th2 genes and its relocation to Th1 genes during Th1 cell specification (Hertweck et al., 2022). Thus, TF-mediated redistribution by a theft or ‘relay’ mechanism as proposed here may be a more widespread phenomenon governing cell fate decisions during differentiation.

Arginine side chains can also be converted by peptidylarginine deiminases to citrulline residues that cannot be methylated by Carm1 (Cuthbert et al., 2004) and therefore deimination at R35 might be involved in regulating C/EBPα activity. However, against this possibility is our previous finding that R35 is methylated but not citrullinated in myeloid cells (Ramberger et al., 2021b). Another argument is the observed capacity of TP064 to accelerate BMT, as this is a highly specific inhibitor of Carm1 that blocks the substrate binding site of the enzyme (Nakayama et al., 2018).

The location of arginine 35 within the IDR of C/EBPα, together with its ability to regulate the activity of the factor, raises the possibility that C/EBPαR35A generates a locally structured stretch within the IDR that acts as a substrate for PU.1. Alternatively, the IDRs of C/EBPα and PU.1 might engage in weak interactions to form transcriptional condensates Boija et al., 2018 whose properties might differ between wild-type and mutant C/EBPα. The initial inspection of the C/EBPα DNA binding profiles at 3 hpi suggested that C/EBPα^WT and C/EBPα^R35A have different DNA binding preferences for B- and M-GREs. However, the observation of similar ‘preferences’ by PU.1 suggests that they reflect differences in the timing of PU.1:C/EBPα release from B-GREs and binding to M-GREs. The kinetics of chromatin accessibility changes over time at the same sites observed by ATAC-seq further supports this interpretation.

The findings described help explain how C/EBPα and PU.1 co-operate to generate macrophage-like cells from fibroblasts (Feng et al., 2008). Thus, while C/EBPα^WT can bind to closed chromatin and induce a transition to open chromatin, the combination with PU.1 does so faster and this is even further accelerated with C/EBPα^R35A, at speeds exceeding that of other pioneer TFs studied so far (Lerner et al., 2020). Similar to our observations of gene expression during BMT, in this system the differences between the wild-type and the mutant are much more pronounced at an early timepoint, suggesting that the chromatin changes in C/EBPα^WT cells eventually catch up.

Our research has uncovered a close connection between the speed of cell fate choice and directionality during forced and normal myeloid differentiation. Such a link may also have implications for the formation of cancer cells. Thus, Carm1-deficient mice are resistant to MLL-AF9-induced acute myeloid leukemia (Greenblatt et al., 2016; Greenblatt et al., 2018), probably because a critical target protein cannot be methylated by the enzyme. This target might be C/EBPα itself, which had been shown to be required for the formation of MLL-AF9-induced AML (Ye et al., 2015; Ohlsson et al., 2016). Our findings suggest that in GMPs the methylated form of C/EBPα promotes the development of granulocytes, while the unmethylated form drives monocyte formation, consistent with observations with the Carm1 knockout model (Greenblatt et al., 2018). However, further research is needed to determine whether the two forms of C/EBPα exhibit mutually exclusive activities or distinct but overlapping functions.

A role of TF methylation by Carm1 in cell fate directionality has also been described for muscle differentiation of satellite stem cells. Here, asymmetric di-methylation of four arginines in Pax7 results in the recruitment of MLL1/2, activation of Myf5 and subsequent muscle cell specification (Chang et al., 2018; Kawabe et al., 2012). Another example is the requirement of Carm1 for early embryo development, which has been associated with the methylation of H3R26 and BAF155 and have been proposed as drivers of the process (Torres-Padilla et al., 2007; Panamarova et al., 2016). However, it seems possible that there are so far unknown TFs targeted by Carm1 that might play a key role.

In summary, our studies about how a mutant of C/EBPα accelerates BMT has provided substantial new insights into the sequential steps occurring during a TF-driven cell fate conversion. Importantly, they suggest that the balance of two forms of a single TF - one post-translationally modified and the other not - can determine the directionality of a binary cell fate decision. This finding challenges the current view that the relative levels of antagonistic lineage-instructive TFs are primarily decisive for cell fate decisions (Radomska et al., 1998; Graf and Enver, 2009; Torcal Garcia and Graf, 2021). Enzyme-mediated TF modifications that accelerate the speed of transdifferentiation, as described here, may apply more broadly to cell fate decisions during development and differentiation. This might apply regardless of whether these decisions occur gradually, such as in hematopoiesis (Velten et al., 2017), or abruptly, such as in neuronal differentiation (Konstantinides et al., 2022). Further explorations about the relationship between the velocity of a cell fate transition and lineage choice should provide a deeper understanding of the mechanisms that underly developmental disorders, including certain forms of cancer.

The GEO accession number is GSE204746 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE204746).

1. 1. Garcia GT
   2. Kowenz-Leutz E
   3. Tian TV
   4. Klonizakis A
   5. Lerner J
   6. Andrés-Aguayo L
   7. Sapozhnikova V
   8. Berenguer C
   9. Plana-Carmona M
   10. Vila-Casadesús M
   11. Bulteau R
   12. Francesconi M
   13. Peiró S
   14. Mertins P
   15. Zaret KS
   16. Leutz A
   17. Graf T

   (2023) NCBI Gene Expression Omnibus

   ID GSE204746. Arginine methylation of C/EBP⍺ controls the speed of immune cell transdifferentiation.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE204746

1. 1. Arinobu Y
   2. Mizuno S
   3. Chong Y
   4. Shigematsu H
   5. Iino T
   6. Iwasaki H
   7. Graf T
   8. Mayfield R
   9. Chan S
   10. Kastner P
   11. Akashi K

   (2007) Reciprocal activation of GATA-1 and PU.1 marks initial specification of hematopoietic stem cells into Myeloerythroid and Myelolymphoid lineages

   Cell Stem Cell 1:416–427.

   https://doi.org/10.1016/j.stem.2007.07.004

   • PubMed
   • Google Scholar
2. 1. Boija A
   2. Klein IA
   3. Sabari BR
   4. Dall’Agnese A
   5. Coffey EL
   6. Zamudio AV
   7. Li CH
   8. Shrinivas K
   9. Manteiga JC
   10. Hannett NM
   11. Abraham BJ
   12. Afeyan LK
   13. Guo YE
   14. Rimel JK
   15. Fant CB
   16. Schuijers J
   17. Lee TI
   18. Taatjes DJ
   19. Young RA

   (2018) Transcription factors activate genes through the phase-separation capacity of their activation domains

   Cell 175:1842–1855.

   https://doi.org/10.1016/j.cell.2018.10.042

   • PubMed
   • Google Scholar
3. 1. Branon TC
   2. Bosch JA
   3. Sanchez AD
   4. Udeshi ND
   5. Svinkina T
   6. Carr SA
   7. Feldman JL
   8. Perrimon N
   9. Ting AY

   (2018) Efficient proximity labeling in living cells and organisms with Turboid

   Nature Biotechnology 36:880–887.

   https://doi.org/10.1038/nbt.4201

   • PubMed
   • Google Scholar
4. 1. Buenrostro JD
   2. Wu B
   3. Chang HY
   4. Greenleaf WJ

   (2015) ATAC-Seq: A method for assaying Chromatin accessibility genome-wide

   Current Protocols in Molecular Biology 109:21.

   https://doi.org/10.1002/0471142727.mb2129s109

   • PubMed
   • Google Scholar
5. 1. Bussmann LH
   2. Schubert A
   3. Vu Manh TP
   4. De Andres L
   5. Desbordes SC
   6. Parra M
   7. Zimmermann T
   8. Rapino F
   9. Rodriguez-Ubreva J
   10. Ballestar E
   11. Graf T

   (2009) A robust and highly efficient immune cell Reprogramming system

   Cell Stem Cell 5:554–566.

   https://doi.org/10.1016/j.stem.2009.10.004

   • PubMed
   • Google Scholar
6. 1. Cai DH
   2. Wang D
   3. Keefer J
   4. Yeamans C
   5. Hensley K
   6. Friedman AD

   (2008) C/EBPα:AP-1 Leucine Zipper Heterodimers bind novel DNA elements, activate the PU.1 promoter and direct monocyte lineage commitment more Potently than C/EBPα Homodimers or AP-1

   Oncogene 27:2772–2779.

   https://doi.org/10.1038/sj.onc.1210940

   • PubMed
   • Google Scholar
7. 1. Chang NC
   2. Sincennes MC
   3. Chevalier FP
   4. Brun CE
   5. Lacaria M
   6. Segalés J
   7. Muñoz-Cánoves P
   8. Ming H
   9. Rudnicki MA

   (2018) The dystrophin glycoprotein complex regulates the epigenetic activation of muscle stem cell commitment

   Cell Stem Cell 22:755–768.

   https://doi.org/10.1016/j.stem.2018.03.022

   • PubMed
   • Google Scholar
8. 1. Choi J
   2. Baldwin TM
   3. Wong M
   4. Bolden JE
   5. Fairfax KA
   6. Lucas EC
   7. Cole R
   8. Biben C
   9. Morgan C
   10. Ramsay KA
   11. Ng AP
   12. Kauppi M
   13. Corcoran LM
   14. Shi W
   15. Wilson N
   16. Wilson MJ
   17. Alexander WS
   18. Hilton DJ
   19. de Graaf CA

   (2019) Haemopedia RNA-Seq: a database of gene expression during Haematopoiesis in mice and humans

   Nucleic Acids Research 47:D780–D785.

   https://doi.org/10.1093/nar/gky1020

   • PubMed
   • Google Scholar
9. 1. Cox J
   2. Hein MY
   3. Luber CA
   4. Paron I
   5. Nagaraj N
   6. Mann M

   (2014) Accurate Proteome-wide label-free Quantification by delayed normalization and maximal peptide ratio extraction, termed Maxlfq

   Molecular & Cellular Proteomics 13:2513–2526.

   https://doi.org/10.1074/mcp.M113.031591

   • PubMed
   • Google Scholar
10. 1. Cuthbert GL
    2. Daujat S
    3. Snowden AW
    4. Erdjument-Bromage H
    5. Hagiwara T
    6. Yamada M
    7. Schneider R
    8. Gregory PD
    9. Tempst P
    10. Bannister AJ
    11. Kouzarides T

    (2004) Histone Deimination Antagonizes arginine methylation.Cuthbert GL

    Cell 118:545–553.

    https://doi.org/10.1016/j.cell.2004.08.020

    • PubMed
    • Google Scholar
11. 1. Deribe YL
    2. Pawson T
    3. Dikic I

    (2010) Post-Translational modifications in signal integration

    Nature Structural & Molecular Biology 17:666–672.

    https://doi.org/10.1038/nsmb.1842

    • PubMed
    • Google Scholar
12. 1. Di Stefano B
    2. Sardina JL
    3. van Oevelen C
    4. Collombet S
    5. Kallin EM
    6. Vicent GP
    7. Lu J
    8. Thieffry D
    9. Beato M
    10. Graf T

    (2014) C/EBPα poises B cells for rapid Reprogramming into induced Pluripotent stem cells

    Nature 506:235–239.

    https://doi.org/10.1038/nature12885

    • PubMed
    • Google Scholar
13. 1. Di Stefano B
    2. Collombet S
    3. Jakobsen JS
    4. Wierer M
    5. Sardina JL
    6. Lackner A
    7. Stadhouders R
    8. Segura-Morales C
    9. Francesconi M
    10. Limone F
    11. Mann M
    12. Porse B
    13. Thieffry D
    14. Graf T

    (2016) C/EBPα creates elite cells for iPSC Reprogramming by Upregulating Klf4 and increasing the levels of Lsd1 and Brd4.Di

    Nature Cell Biology 18:371–381.

    https://doi.org/10.1038/ncb3326

    • PubMed
    • Google Scholar
14. 1. Dobin A
    2. Davis CA
    3. Schlesinger F
    4. Drenkow J
    5. Zaleski C
    6. Jha S
    7. Batut P
    8. Chaisson M
    9. Gingeras TR

    (2013) STAR: Ultrafast universal RNA-Seq Aligner

    Bioinformatics 29:15–21.

    https://doi.org/10.1093/bioinformatics/bts635

    • PubMed
    • Google Scholar
15. 1. Eyquem S
    2. Chemin K
    3. Fasseu M
    4. Chopin M
    5. Sigaux F
    6. Cumano A
    7. Bories JC

    (2004) The development of early and mature B cells is impaired in mice deficient for the Ets-1 transcription factor

    European Journal of Immunology 34:3187–3196.

    https://doi.org/10.1002/eji.200425352

    • PubMed
    • Google Scholar
16. 1. Feng R
    2. Desbordes SC
    3. Xie H
    4. Tillo ES
    5. Pixley F
    6. Stanley ER
    7. Graf T

    (2008) PU.1 and C/Ebpalpha/beta convert fibroblasts into macrophage-like cells

    PNAS 105:6057–6062.

    https://doi.org/10.1073/pnas.0711961105

    • PubMed
    • Google Scholar
17. 1. Fernandez Garcia M
    2. Moore CD
    3. Schulz KN
    4. Alberto O
    5. Donague G
    6. Harrison MM
    7. Zhu H
    8. Zaret KS

    (2019) Structural features of transcription factors associating with Nucleosome binding

    Molecular Cell 75:921–932.

    https://doi.org/10.1016/j.molcel.2019.06.009

    • PubMed
    • Google Scholar
18. 1. Francesconi M
    2. Di Stefano B
    3. Berenguer C
    4. de Andrés-Aguayo L
    5. Plana-Carmona M
    6. Mendez-Lago M
    7. Guillaumet-Adkins A
    8. Rodriguez-Esteban G
    9. Gut M
    10. Gut IG
    11. Heyn H
    12. Lehner B
    13. Graf T

    (2019) Single cell RNA-Seq identifies the origins of heterogeneity in efficient cell Transdifferentiation and Reprogramming

    eLife 8:e41627.

    https://doi.org/10.7554/eLife.41627

    • PubMed
    • Google Scholar
19. 1. Graf T

    (2002) Differentiation plasticity of hematopoietic cells

    Blood 99:3089–3101.

    https://doi.org/10.1182/blood.v99.9.3089

    • PubMed
    • Google Scholar
20. 1. Graf T
    2. Enver T

    (2009) Forcing cells to change lineages

    Nature 462:587–594.

    https://doi.org/10.1038/nature08533

    • PubMed
    • Google Scholar
21. 1. Greenblatt SM
    2. Liu F
    3. Nimer SD

    (2016) Arginine Methyltransferases in normal and malignant Hematopoiesis

    Experimental Hematology 44:435–441.

    https://doi.org/10.1016/j.exphem.2016.03.009

    • PubMed
    • Google Scholar
22. 1. Greenblatt SM
    2. Man N
    3. Hamard PJ
    4. Asai T
    5. Karl D
    6. Martinez C
    7. Bilbao D
    8. Stathias V
    9. McGrew-Jermacowicz A
    10. Duffort S
    11. Tadi M
    12. Blumenthal E
    13. Newman S
    14. Vu L
    15. Xu Y
    16. Liu F
    17. Schurer SC
    18. McCabe MT
    19. Kruger RG
    20. Xu M
    21. Yang FC
    22. Tenen D
    23. Watts J
    24. Vega F
    25. Nimer SD

    (2018) Carm1 is essential for myeloid Leukemogenesis but Dispensable for normal Hematopoiesis

    Cancer Cell 34:868.

    https://doi.org/10.1016/j.ccell.2018.10.009

    • PubMed
    • Google Scholar
23. 1. Guo Z
    2. Zheng L
    3. Xu H
    4. Dai H
    5. Zhou M
    6. Pascua MR
    7. Chen QM
    8. Shen B

    (2010) Methylation of Fen1 suppresses nearby Phosphorylation and facilitates PCNA binding

    Nature Chemical Biology 6:766–773.

    https://doi.org/10.1038/nchembio.422

    • PubMed
    • Google Scholar
24. 1. Heath V
    2. Suh HC
    3. Holman M
    4. Renn K
    5. Gooya JM
    6. Parkin S
    7. Klarmann KD
    8. Ortiz M
    9. Johnson P
    10. Keller J

    (2004) C/Ebpalpha deficiency results in Hyperproliferation of hematopoietic progenitor cells and disrupts macrophage development in vitro and in vivo

    Blood 104:1639–1647.

    https://doi.org/10.1182/blood-2003-11-3963

    • PubMed
    • Google Scholar
25. 1. Heinz S
    2. Benner C
    3. Spann N
    4. Bertolino E
    5. Lin YC
    6. Laslo P
    7. Cheng JX
    8. Murre C
    9. Singh H
    10. Glass CK

    (2010) Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities

    Molecular Cell 38:576–589.

    https://doi.org/10.1016/j.molcel.2010.05.004

    • PubMed
    • Google Scholar
26. 1. Hertweck A
    2. Vila de Mucha M
    3. Barber PR
    4. Dagil R
    5. Porter H
    6. Ramos A
    7. Lord GM
    8. Jenner RG

    (2022) The Th1 cell lineage-determining transcription factor T-bet suppresses Th2 gene expression by redistributing Gata3 away from Th2 genes

    Nucleic Acids Research 50:4557–4573.

    https://doi.org/10.1093/nar/gkac258

    • PubMed
    • Google Scholar
27. 1. Hosokawa H
    2. Ungerbäck J
    3. Wang X
    4. Matsumoto M
    5. Nakayama KI
    6. Cohen SM
    7. Tanaka T
    8. Rothenberg EV

    (2018) Transcription factor PU.1 represses and activates gene expression in early T cells by redirecting partner transcription factor binding

    Immunity 49:782.

    https://doi.org/10.1016/j.immuni.2018.09.019

    • PubMed
    • Google Scholar
28. 1. Hsiao K
    2. Zegzouti H
    3. Goueli SA

    (2016) Methyltransferase-Glo: a universal, Bioluminescent and Homogenous assay for monitoring all classes of Methyltransferases

    Epigenomics 8:321–339.

    https://doi.org/10.2217/epi.15.113

    • PubMed
    • Google Scholar
29. 1. Hu CJ
    2. Rao S
    3. Ramirez-Bergeron DL
    4. Garrett-Sinha LA
    5. Gerondakis S
    6. Clark MR
    7. Simon MC

    (2001) PU.1/SPI-B regulation of C-REL is essential for mature B cell survival

    Immunity 15:545–555.

    https://doi.org/10.1016/s1074-7613(01)00219-9

    • PubMed
    • Google Scholar
30. 1. Kawabe YI
    2. Wang YX
    3. McKinnell IW
    4. Bedford MT
    5. Rudnicki MA

    (2012) Carm1 regulates Pax7 transcriptional activity through Mll1/2 recruitment during asymmetric satellite stem cell divisions

    Cell Stem Cell 11:333–345.

    https://doi.org/10.1016/j.stem.2012.07.001

    • PubMed
    • Google Scholar
31. 1. Kim JB
    2. Greber B
    3. Araúzo-Bravo MJ
    4. Meyer J
    5. Park KI
    6. Zaehres H
    7. Schöler HR

    (2009) Direct Reprogramming of human neural stem cells by Oct4

    Nature 461:649–653.

    https://doi.org/10.1038/nature08436

    • PubMed
    • Google Scholar
32. 1. Kim D
    2. Lee J
    3. Cheng D
    4. Li J
    5. Carter C
    6. Richie E
    7. Bedford MT

    (2010) Enzymatic activity is required for the in vivo functions of Carm1

    The Journal of Biological Chemistry 285:1147–1152.

    https://doi.org/10.1074/jbc.M109.035865

    • PubMed
    • Google Scholar
33. 1. Konstantinides N
    2. Holguera I
    3. Rossi AM
    4. Escobar A
    5. Dudragne L
    6. Chen YC
    7. Tran TN
    8. Martínez Jaimes AM
    9. Özel MN
    10. Simon F
    11. Shao Z
    12. Tsankova NM
    13. Fullard JF
    14. Walldorf U
    15. Roussos P
    16. Desplan C

    (2022) A complete temporal transcription factor series in the fly visual system

    Nature 604:316–322.

    https://doi.org/10.1038/s41586-022-04564-w

    • PubMed
    • Google Scholar
34. 1. Kowenz-Leutz E
    2. Twamley G
    3. Ansieau S
    4. Leutz A

    (1994) Novel mechanism of C/EBP beta (NF-M) transcriptional control: activation through Derepression

    Genes & Development 8:2781–2791.

    https://doi.org/10.1101/gad.8.22.2781

    • PubMed
    • Google Scholar
35. 1. Kowenz-Leutz E
    2. Pless O
    3. Dittmar G
    4. Knoblich M
    5. Leutz A

    (2010) Crosstalk between C/Ebpbeta Phosphorylation, arginine methylation, and SWI/SNF/mediator implies an indexing transcription factor code

    The EMBO Journal 29:1105–1115.

    https://doi.org/10.1038/emboj.2010.3

    • PubMed
    • Google Scholar
36. 1. Laiosa CV
    2. Stadtfeld M
    3. Graf T

    (2006) Determinants of Lymphoid-myeloid lineage diversification

    Annual Review of Immunology 24:705–738.

    https://doi.org/10.1146/annurev.immunol.24.021605.090742

    • PubMed
    • Google Scholar
37. 1. Leddin M
    2. Perrod C
    3. Hoogenkamp M
    4. Ghani S
    5. Assi S
    6. Heinz S
    7. Wilson NK
    8. Follows G
    9. Schönheit J
    10. Vockentanz L
    11. Mosammam AM
    12. Chen W
    13. Tenen DG
    14. Westhead DR
    15. Göttgens B
    16. Bonifer C
    17. Rosenbauer F

    (2011) HEMATOPOIESIS AND STEM CELLS two distinct auto-regulatory loops operate at the PU.1 locus in B cells and myeloid cells

    Blood 117:2827–2838.

    https://doi.org/10.1182/blood-2010-08-302976

    • PubMed
    • Google Scholar
38. 1. Lerner J
    2. Gomez-Garcia PA
    3. McCarthy RL
    4. Liu Z
    5. Lakadamyali M
    6. Zaret KS

    (2020) Two-parameter mobility assessments discriminate diverse regulatory factor behaviors in Chromatin

    Molecular Cell 79:677–688.

    https://doi.org/10.1016/j.molcel.2020.05.036

    • PubMed
    • Google Scholar
39. 1. Li H
    2. Handsaker B
    3. Wysoker A
    4. Fennell T
    5. Ruan J
    6. Homer N
    7. Marth G
    8. Abecasis G
    9. Durbin R
    10. 1000 Genome Project Data Processing Subgroup

    (2009) The sequence alignment/map format and Samtools

    Bioinformatics (Oxford, England) 25:2078–2079.

    https://doi.org/10.1093/bioinformatics/btp352

    • PubMed
    • Google Scholar
40. 1. Li J
    2. Zhao Z
    3. Carter C
    4. Ehrlich LIR
    5. Bedford MT
    6. Richie ER

    (2013) Coactivator-associated arginine Methyltransferase 1 regulates fetal Hematopoiesis and Thymocyte development

    Journal of Immunology 190:597–604.

    https://doi.org/10.4049/jimmunol.1102513

    • PubMed
    • Google Scholar
41. 1. Liu Z
    2. Tjian R

    (2018) Visualizing transcription factor Dynamics in living cells

    The Journal of Cell Biology 217:1181–1191.

    https://doi.org/10.1083/jcb.201710038

    • PubMed
    • Google Scholar
42. 1. Love MI
    2. Huber W
    3. Anders S

    (2014) Moderated estimation of fold change and dispersion for RNA-Seq data with Deseq2

    Genome Biology 15:550.

    https://doi.org/10.1186/s13059-014-0550-8

    • PubMed
    • Google Scholar
43. 1. Ma O
    2. Hong SH
    3. Guo H
    4. Ghiaur G
    5. Friedman AD

    (2014) Granulopoiesis requires increased C/EBPα compared to Monopoiesis, correlated with elevated Cebpa in immature G-CSF receptor versus M-CSF receptor expressing cells

    PLOS ONE 9:e95784.

    https://doi.org/10.1371/journal.pone.0095784

    • PubMed
    • Google Scholar
44. 1. Martin M

    (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads

    EMBnet.Journal 17:10.

    https://doi.org/10.14806/ej.17.1.200

    • Google Scholar
45. 1. Mullen AC
    2. Orlando DA
    3. Newman JJ
    4. Lovén J
    5. Kumar RM
    6. Bilodeau S
    7. Reddy J
    8. Guenther MG
    9. DeKoter RP
    10. Young RA

    (2011) Master transcription factors determine cell-type-specific responses to TGF-Β signaling

    Cell 147:565–576.

    https://doi.org/10.1016/j.cell.2011.08.050

    • PubMed
    • Google Scholar
46. 1. Nakayama K
    2. Szewczyk MM
    3. Dela Sena C
    4. Wu H
    5. Dong A
    6. Zeng H
    7. Li F
    8. de Freitas RF
    9. Eram MS
    10. Schapira M
    11. Baba Y
    12. Kunitomo M
    13. Cary DR
    14. Tawada M
    15. Ohashi A
    16. Imaeda Y
    17. Saikatendu KS
    18. Grimshaw CE
    19. Vedadi M
    20. Arrowsmith CH
    21. Barsyte-Lovejoy D
    22. Kiba A
    23. Tomita D
    24. Brown PJ

    (2018) TP-064, a potent and selective small molecule inhibitor of Prmt4 for multiple myeloma

    Oncotarget 9:18480–18493.

    https://doi.org/10.18632/oncotarget.24883

    • PubMed
    • Google Scholar
47. 1. Ohlsson E
    2. Schuster MB
    3. Hasemann M
    4. Porse BT

    (2016) The Multifaceted functions of C/EBPα in normal and malignant Haematopoiesis

    Leukemia 30:767–775.

    https://doi.org/10.1038/leu.2015.324

    • PubMed
    • Google Scholar
48. 1. Orkin SH
    2. Zon LI

    (2008) Hematopoiesis: an evolving paradigm for stem cell biology

    Cell 132:631–644.

    https://doi.org/10.1016/j.cell.2008.01.025

    • PubMed
    • Google Scholar
49. 1. Panamarova M
    2. Cox A
    3. Wicher KB
    4. Butler R
    5. Bulgakova N
    6. Jeon S
    7. Rosen B
    8. Seong RH
    9. Skarnes W
    10. Crabtree G
    11. Zernicka-Goetz M

    (2016) BAF Chromatin remodelling complex is an epigenetic regulator of lineage specification in the early mouse embryo

    Development 143:1271–1283.

    https://doi.org/10.1242/dev.131961

    • PubMed
    • Google Scholar
50. 1. Radomska HS
    2. Huettner CS
    3. Zhang P
    4. Cheng T
    5. Scadden DT
    6. Tenen DG

    (1998) CCAAT/enhancer binding protein Α is a regulatory switch sufficient for induction of granulocytic development from Bipotential myeloid Progenitors

    Molecular and Cellular Biology 18:4301–4314.

    https://doi.org/10.1128/MCB.18.7.4301

    • PubMed
    • Google Scholar
51. 1. Ramberger E
    2. Sapozhnikova V
    3. Kowenz-Leutz E
    4. Zimmermann K
    5. Nicot N
    6. Nazarov PV
    7. Perez-Hernandez D
    8. Reimer U
    9. Mertins P
    10. Dittmar G
    11. Leutz A

    (2021a) PRISMA and Bioid disclose a motifs-based Interactome of the intrinsically disordered transcription factor C/EBPα

    IScience 24:102686.

    https://doi.org/10.1016/j.isci.2021.102686

    • PubMed
    • Google Scholar
52. 1. Ramberger E
    2. Suarez-Artiles L
    3. Perez-Hernandez D
    4. Haji M
    5. Popp O
    6. Reimer U
    7. Leutz A
    8. Dittmar G
    9. Mertins P

    (2021b) A universal peptide matrix Interactomics approach to disclose motif-dependent protein binding

    Molecular & Cellular Proteomics 20:100135.

    https://doi.org/10.1016/j.mcpro.2021.100135

    • PubMed
    • Google Scholar
53. 1. Ramírez F
    2. Ryan DP
    3. Grüning B
    4. Bhardwaj V
    5. Kilpert F
    6. Richter AS
    7. Heyne S
    8. Dündar F
    9. Manke T

    (2016) Deeptools2: A next generation web server for deep-sequencing data analysis

    Nucleic Acids Research 44:W160–W165.

    https://doi.org/10.1093/nar/gkw257

    • PubMed
    • Google Scholar
54. 1. Rapino F
    2. Robles EF
    3. Richter-Larrea JA
    4. Kallin EM
    5. Martinez-Climent JA
    6. Graf T

    (2013) C/EBPα induces highly efficient macrophage Transdifferentiation of B lymphoma and leukemia cell lines and impairs their Tumorigenicity

    Cell Reports 3:1153–1163.

    https://doi.org/10.1016/j.celrep.2013.03.003

    • PubMed
    • Google Scholar
55. 1. Raudvere U
    2. Kolberg L
    3. Kuzmin I
    4. Arak T
    5. Adler P
    6. Peterson H
    7. Vilo J

    (2019) G:profiler: a web server for functional enrichment analysis and conversions of Gene lists (2019 update)

    Nucleic Acids Research 47:W191–W198.

    https://doi.org/10.1093/nar/gkz369

    • PubMed
    • Google Scholar
56. 1. Rothenberg EV

    (2014) Transcriptional control of early T and B cell developmental choices

    Annual Review of Immunology 32:283–321.

    https://doi.org/10.1146/annurev-immunol-032712-100024

    • PubMed
    • Google Scholar
57. 1. Schmidl C
    2. Rendeiro AF
    3. Sheffield NC
    4. Bock C

    (2015) Chipmentation: fast, robust, low-input chip-Seq for Histones and transcription factors

    Nature Methods 12:963–965.

    https://doi.org/10.1038/nmeth.3542

    • PubMed
    • Google Scholar
58. 1. Schreiber E
    2. Matthias P
    3. Müller MM
    4. Schaffner W

    (1989) Rapid detection of Octamer binding proteins with 'mini-extracts', prepared from a small number of cells

    Nucleic Acids Research 17:6419.

    https://doi.org/10.1093/nar/17.15.6419

    • PubMed
    • Google Scholar
59. 1. Scott EW
    2. Simon MC
    3. Anastasi J
    4. Singh H

    (1994) Requirement of transcription factor PU.1 in the development of multiple hematopoietic lineages

    Science 265:1573–1577.

    https://doi.org/10.1126/science.8079170

    • PubMed
    • Google Scholar
60. 1. Sergé A
    2. Bertaux N
    3. Rigneault H
    4. Marguet D

    (2008) Dynamic multiple-target tracing to probe Spatiotemporal Cartography of cell membranes

    Nature Methods 5:687–694.

    https://doi.org/10.1038/nmeth.1233

    • PubMed
    • Google Scholar
61. 1. Singh H
    2. DeKoter RP
    3. Walsh JC

    (1999) PU.1, a shared transcriptional regulator of Lymphoid and myeloid cell fatesCold spring harbor Symposia on quantitative biology

    Cold Spring Harb Symp Quant Biol 64:13–20.

    https://doi.org/10.1101/sqb.1999.64.13

    • PubMed
    • Google Scholar
62. 1. Teves SS
    2. An L
    3. Hansen AS
    4. Xie L
    5. Darzacq X
    6. Tjian R

    (2016) A dynamic mode of mitotic Bookmarking by transcription factors

    eLife 5:e22280.

    https://doi.org/10.7554/eLife.22280

    • PubMed
    • Google Scholar
63. 1. Thompson JJ
    2. Lee DJ
    3. Mitra A
    4. Frail S
    5. Dale RK
    6. Rocha PP

    (2022) Extensive Co-binding and rapid redistribution of NANOG and Gata6 during emergence of divergent lineages

    Nature Communications 13:4257.

    https://doi.org/10.1038/s41467-022-31938-5

    • PubMed
    • Google Scholar
64. 1. Tokunaga M
    2. Imamoto N
    3. Sakata-Sogawa K

    (2008) Highly inclined thin illumination enables clear single-molecule imaging in cells

    Nature Methods 5:159–161.

    https://doi.org/10.1038/nmeth1171

    • PubMed
    • Google Scholar
65. 1. Torcal Garcia G
    2. Graf T

    (2021) The transcription factor code: a beacon for Histone Methyltransferase docking

    Trends in Cell Biology 31:792–800.

    https://doi.org/10.1016/j.tcb.2021.04.001

    • PubMed
    • Google Scholar
66. 1. Torres-Padilla ME
    2. Parfitt DE
    3. Kouzarides T
    4. Zernicka-Goetz M

    (2007) Histone arginine methylation regulates Pluripotency in the early mouse embryo

    Nature 445:214–218.

    https://doi.org/10.1038/nature05458

    • PubMed
    • Google Scholar
67. 1. van Oevelen C
    2. Collombet S
    3. Vicent G
    4. Hoogenkamp M
    5. Lepoivre C
    6. Badeaux A
    7. Bussmann L
    8. Sardina JL
    9. Thieffry D
    10. Beato M
    11. Shi Y
    12. Bonifer C
    13. Graf T

    (2015) C/EBPα activates pre-existing and de novo macrophage enhancers during induced pre-B cell Transdifferentiation and Myelopoiesis

    Stem Cell Reports 5:232–247.

    https://doi.org/10.1016/j.stemcr.2015.06.007

    • PubMed
    • Google Scholar
68. 1. Velten L
    2. Haas SF
    3. Raffel S
    4. Blaszkiewicz S
    5. Islam S
    6. Hennig BP
    7. Hirche C
    8. Lutz C
    9. Buss EC
    10. Nowak D
    11. Boch T
    12. Hofmann WK
    13. Ho AD
    14. Huber W
    15. Trumpp A
    16. Essers MAG
    17. Steinmetz LM

    (2017) Human haematopoietic stem cell lineage commitment is a continuous process

    Nature Cell Biology 19:271–281.

    https://doi.org/10.1038/ncb3493

    • PubMed
    • Google Scholar
69. 1. Vermunt MW
    2. Luan J
    3. Zhang Z
    4. Thrasher AJ
    5. Huang A
    6. Saari MS
    7. Khandros E
    8. Beagrie RA
    9. Zhang S
    10. Vemulamada P
    11. Brilleman M
    12. Lee K
    13. Yano JA
    14. Giardine BM
    15. Keller CA
    16. Hardison RC
    17. Blobel GA

    (2023) Gene silencing Dynamics are modulated by transiently active regulatory elements

    Molecular Cell 83:715–730.

    https://doi.org/10.1016/j.molcel.2023.02.006

    • PubMed
    • Google Scholar
70. 1. Wang L
    2. Zhao Z
    3. Meyer MB
    4. Saha S
    5. Yu M
    6. Guo A
    7. Wisinski KB
    8. Huang W
    9. Cai W
    10. Pike JW
    11. Yuan M
    12. Ahlquist P
    13. Xu W

    (2014) Carm1 Methylates Chromatin remodeling factor Baf155 to enhance tumor progression and metastasis

    Cancer Cell 25:21–36.

    https://doi.org/10.1016/j.ccr.2013.12.007

    • PubMed
    • Google Scholar
71. 1. Wu Q
    2. Schapira M
    3. Arrowsmith CH
    4. Barsyte-Lovejoy D

    (2021) Protein arginine methylation: from enigmatic functions to therapeutic targeting

    Nature Reviews. Drug Discovery 20:509–530.

    https://doi.org/10.1038/s41573-021-00159-8

    • PubMed
    • Google Scholar
72. 1. Xhani S
    2. Lee S
    3. Kim HM
    4. Wang S
    5. Esaki S
    6. Ha VLT
    7. Khanezarrin M
    8. Fernandez GL
    9. Albrecht AV
    10. Aramini JM
    11. Germann MW
    12. Poon GMK

    (2020) Intrinsic disorder controls two functionally distinct dimers of the master transcription factor PU.1

    Science Advances 6:eaay3178.

    https://doi.org/10.1126/sciadv.aay3178

    • PubMed
    • Google Scholar
73. 1. Xie H
    2. Ye M
    3. Feng R
    4. Graf T

    (2004) Stepwise reprogramming of B cells into macrophages

    Cell 117:663–676.

    https://doi.org/10.1016/s0092-8674(04)00419-2

    • PubMed
    • Google Scholar
74. 1. Xue HH
    2. Bollenbacher-Reilley J
    3. Wu Z
    4. Spolski R
    5. Jing X
    6. Zhang YC
    7. McCoy JP
    8. Leonard WJ

    (2007) The transcription factor GABP is a critical regulator of B lymphocyte development

    Immunity 26:421–431.

    https://doi.org/10.1016/j.immuni.2007.03.010

    • PubMed
    • Google Scholar
75. 1. Yadav N
    2. Cheng D
    3. Richard S
    4. Morel M
    5. Iyer VR
    6. Aldaz CM
    7. Bedford MT

    (2008) Carm1 promotes adipocyte differentiation by coactivating PPARγ

    EMBO Reports 9:193–198.

    https://doi.org/10.1038/sj.embor.7401151

    • PubMed
    • Google Scholar
76. 1. Ye M
    2. Zhang H
    3. Yang H
    4. Koche R
    5. Staber PB
    6. Cusan M
    7. Levantini E
    8. Welner RS
    9. Bach CS
    10. Zhang J
    11. Krivtsov AV
    12. Armstrong SA
    13. Tenen DG

    (2015) Hematopoietic differentiation is required for initiation of acute myeloid leukemia

    Cell Stem Cell 17:611–623.

    https://doi.org/10.1016/j.stem.2015.08.011

    • PubMed
    • Google Scholar
77. 1. Zhang DE
    2. Zhang P
    3. Wang ND
    4. Hetherington CJ
    5. Darlington GJ
    6. Tenen DG

    (1997) Absence of granulocyte colony-stimulating factor signaling and neutrophil development in CCAAT enhancer binding protein alpha-deficient mice

    PNAS 94:569–574.

    https://doi.org/10.1073/pnas.94.2.569

    • PubMed
    • Google Scholar
78. 1. Zhang P
    2. Iwasaki-Arai J
    3. Iwasaki H
    4. Fenyus ML
    5. Dayaram T
    6. Owens BM
    7. Shigematsu H
    8. Levantini E
    9. Huettner CS
    10. Lekstrom-Himes JA
    11. Akashi K
    12. Tenen DG

    (2004) Enhancement of hematopoietic stem cell Repopulating capacity and self-renewal in the absence of the transcription factor C/EBPα

    Immunity 21:853–863.

    https://doi.org/10.1016/j.immuni.2004.11.006

    • PubMed
    • Google Scholar
79. 1. Zhou Q
    2. Melton DA

    (2008) Extreme Makeover: converting one cell into another

    Cell Stem Cell 3:382–388.

    https://doi.org/10.1016/j.stem.2008.09.015

    • PubMed
    • Google Scholar

Author details

1. Guillem Torcal Garcia

   1. Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
   2. Universitat Pompeu Fabra (UPF), Barcelona, Spain

   Contribution

   Conceptualization, Validation, Investigation, Writing - original draft

   Contributed equally with

   Elisabeth Kowenz-Leutz, Tian V Tian and Antonis Klonizakis

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0843-7296

2. Elisabeth Kowenz-Leutz

   Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany

   Contribution

   Investigation, Methodology

   Contributed equally with

   Guillem Torcal Garcia, Tian V Tian and Antonis Klonizakis

   Competing interests

   No competing interests declared

3. Tian V Tian

   1. Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
   2. Universitat Pompeu Fabra (UPF), Barcelona, Spain
   3. Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain

   Contribution

   Investigation, Methodology

   Contributed equally with

   Guillem Torcal Garcia, Elisabeth Kowenz-Leutz and Antonis Klonizakis

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9906-0980

4. Antonis Klonizakis

   1. Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
   2. Universitat Pompeu Fabra (UPF), Barcelona, Spain

   Contribution

   Data curation, Formal analysis, Investigation

   Contributed equally with

   Guillem Torcal Garcia, Elisabeth Kowenz-Leutz and Tian V Tian

   Competing interests

   No competing interests declared

5. Jonathan Lerner

   Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

6. Luisa De Andres-Aguayo

   1. Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
   2. Universitat Pompeu Fabra (UPF), Barcelona, Spain

   Contribution

   Investigation

   Competing interests

   No competing interests declared

7. Valeriia Sapozhnikova

   Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany

   Contribution

   Investigation

   Competing interests

   No competing interests declared

8. Clara Berenguer

   1. Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
   2. Universitat Pompeu Fabra (UPF), Barcelona, Spain

   Contribution

   Investigation

   Competing interests

   No competing interests declared

9. Marcos Plana Carmona

   1. Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
   2. Universitat Pompeu Fabra (UPF), Barcelona, Spain

   Contribution

   Investigation

   Competing interests

   No competing interests declared

10. Maria Vila Casadesus

    1. Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
    2. Universitat Pompeu Fabra (UPF), Barcelona, Spain
    3. Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain

    Contribution

    Data curation, Formal analysis

    Competing interests

    No competing interests declared

11. Romain Bulteau

    Laboratorie de Biologie et Modélisation de la Cellule, Université de Lyon, Lyon, France

    Contribution

    Formal analysis

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-9128-8809

12. Mirko Francesconi

    Laboratorie de Biologie et Modélisation de la Cellule, Université de Lyon, Lyon, France

    Contribution

    Formal analysis

    Competing interests

    No competing interests declared

13. Sandra Peiro

    Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain

    Contribution

    Discussions

    Competing interests

    No competing interests declared

14. Philipp Mertins

    Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany

    Contribution

    Methodology

    Competing interests

    No competing interests declared

15. Kenneth Zaret

    Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

    Contribution

    Conceptualization

    Competing interests

    No competing interests declared

16. Achim Leutz

    Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany

    Contribution

    Conceptualization, Writing – review and editing

    Contributed equally with

    Thomas Graf

    Competing interests

    No competing interests declared

17. Thomas Graf

    1. Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
    2. Universitat Pompeu Fabra (UPF), Barcelona, Spain

    Contribution

    Conceptualization, Supervision, Investigation, Writing – review and editing

    Contributed equally with

    Achim Leutz

    For correspondence

    Thomas.Graf@crg.eu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2774-4117

• 961

  views

• 122

  downloads

• 2

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.