Abstract
The cell-context dependency for RNA binding proteins (RBPs) mediated
control of stem cell fate remains to be defined. Here we adapt the
HyperTRIBE method using an RBP fused to a Drosophila RNA editing enzyme
(ADAR) to globally map the mRNA targets of the RBP MSI2 in mammalian
adult normal and malignant stem cells. We reveal a unique MUSASHI-2
(MSI2) mRNA binding network in hematopoietic stem cells that changes
during transition to multipotent progenitors. Additionally, we discover
a significant increase in RNA binding activity of MSI2 in leukemic stem
cells compared with normal hematopoietic stem and progenitor cells,
resulting in selective regulation of MSI2’s oncogenic targets. This
provides a basis for MSI2 increased dependency in leukemia cells
compared to normal cells. Moreover, our study provides a way to measure
RBP function in rare cells and suggests that RBPs can achieve
differential binding activity during cell state transition independent
of gene expression.
Subject terms: RNA, Haematopoietic stem cells, Self-renewal, Cancer
stem cells, Leukaemia
__________________________________________________________________
The identification of mRNA targets for RNA binding proteins (RBP) in
stem cells is difficult due to the limited number of available cells.
Here, as a proof-of-principle, the authors adapt the HyperTRIBE method
to find that an RBP, MSI2, has increased RNA binding in leukemic
compared with normal stem cells for selective regulation of oncogenic
genes.
Introduction
While extensive research has revealed the crucial importance of
transcriptional regulation, the role for post-transcriptional processes
in the function of normal and cancer stem cells remains poorly defined.
RNA binding proteins (RBPs) provide control of mRNA metabolism and
translation of key regulators that mediate stem cells’ self-renewal and
cell fate decisions^[54]1,[55]2. Moreover, mutations and aberrant
expression of RBPs have recently been implicated in multiple types of
cancer, demonstrating the crucial role for RBPs in
tumorigenesis^[56]3–[57]9. However, whether RBPs may have cell-type
specific activity between different cellular states of normal stem cell
differentiation or between normal and transformed contexts is not
known. Understanding cell-specific targets provides a strategy for
identifying unique cancer stem cell dependencies compared with normal
cells, which is the key to developing new therapies.
Studying the molecular function of RBPs, as well as their cell-context
dependency, requires the identification of their direct RNA targets in
each cell type and in specific conditions. Standard approaches have
relied heavily on native or cross-linking immunoprecipitation of RBPs
followed by RNA-sequencing. They have been successfully employed to
study RBP targets in embryonic stem cells, neural stem cells, and
iPSCs, which can be obtained in a large number^[58]10–[59]14. However,
these techniques remain technically challenging for rare cells with
limited input material such as adult stem cells. Here, we address a
critical gap in our understanding of RBP targeting in stem cells. We
adapted a recently developed method, HyperTRIBE^[60]15–[61]17 to
identify direct RBP targets in normal hematopoietic stem cells
(HSCs) and leukemia stem cells (LSCs).
In HyperTRIBE, the catalytic domain of the Drosophila ADAR (Adenosine
Deaminase Acting on RNA enzyme) is fused with an RBP. This fusion
protein leaves a “fingerprint” on the RBP RNA targets by marking the
binding sites with a nearby A-to-G editing event. HyperTRIBE was
originally developed in Drosophila^[62]15,[63]16 and was not yet proven
to work in mammalian systems. We selected MSI2, an RBP previously found
to be essential for maintaining self-renewal in LSCs and to contribute
to normal HSC engraftment and cell fate decisions^[64]18–[65]20, to
demonstrate the feasibility and application of HyperTRIBE in mammalian
stem cells.
In previous studies, MSI2 targets were identified in two independent
AML cell lines (NB4 and K562) using CLIP methods^[66]19,[67]21.
Although these strategies characterized a handful of validated direct
MSI2 mRNA targets, they did not provide a comprehensive map of
endogenous targets in stem cells nor address cell-type specific binding
activity of MSI2. Furthermore, while Msi2 knockout mice exhibit a
modest reduction in blood cells and about 50% reduction in
hematopoietic stem and progenitor cells (HSPCs), depletion of MSI2
severely reduced the frequency and activity of LSCs in both mouse and
human systems. This indicates a significantly higher dependency and
requirement for MSI2 in LSCs and development of
leukemia^[68]20,[69]22–[70]26. The cause for this differential
requirement for MSI2 function in LSCs and HSCs is not known.
In this study, we employ our adapted HyperTRIBE approach to investigate
the cell-type specific requirement of the RBP MSI2 in LSCs and normal
HSPCs. We first demonstrate that HyperTRIBE method efficiently
identifies MSI2 mRNA targets in mammalian cells. We then globally map
MSI2 mRNA binding network in HSCs and reveal MSI2 targeting program
changes during differentiation into multipotent progenitors (MPPs).
Furthermore, we find that RNA binding activity of MSI2 significantly
increases in LSCs compared with normal HSPCs, which results in
selective regulation of MSI2’s oncogenic targets. Overall, this work
suggests that RBPs can achieve cell-context dependent binding activity,
and demonstrates a strategy to study RBP functions in rare cells.
Results
MSI2-HyperTRIBE identifies MSI2 RNA targets in human cells
HyperTRIBE was originally developed to map RBP targets in Drosophila
cells^[71]15–[72]17. In order to measure RBP targets in mammalian
cells, we fused the human MSI2 with the catalytic domain of Drosophila
ADAR (MSI2-ADA) carrying the hyperactive mutant E488Q previously
described to increase editing^[73]27. Codon optimization was performed
to maximize the expression of the fusion protein in human cells. To
control for the background editing, we introduced an E367A catalytic
dead mutation^[74]28,[75]29 in the ADAR domain (MSI2-DCD, Fig. [76]1a,
Supplementary Fig. [77]1a). Overexpression of MSI2-ADA in the human AML
cell line MOLM-13 resulted in a significant increase (over sixfold) in
the number of A->G editing events and edit frequency on RNAs compared
with the empty vector control (MIG) (Fig. [78]1b, c). Overexpressing
the catalytic dead fusion MSI2-DCD did not lead to any increase in edit
sites or frequency (Supplementary Fig. [79]1a, Fig. [80]1b, c),
indicating that MSI2-ADA’s increase in editing events is specifically
due to its deaminase activity. These data suggest that we successfully
adapted Drosophila HyperTRIBE to mammalian RBPs. Importantly, to take
into account the background editing by these controls, when calculating
the actual edit frequency at each site (now referred to as differential
edit frequency or diff.frequency) we subtracted the mean edit frequency
of MSI2-DCD and MIG from the mean edit frequency of MSI2-ADA.
Fig. 1. MSI2-HyperTRIBE identifies MSI2’s direct mRNA targets in a human
leukemia cell line.
[81]Fig. 1
[82]Open in a new tab
a Schematic illustration showing the MSI2 protein fusion with the
catalytic domain of hyperactive ADAR (MSI2-ADA) and the control fusion
of MSI2 with the ADAR dead catalytic domain (MSI2-DCD). b Number of
edit sites on mRNAs in MOLM-13 cells overexpressing MSI2-ADA or
controls MSI2-DCD and empty vector (MIG). Data as means ± SEM of all
the data points in three independent experiments. Two-tailed unpaired
Student t test; *p < 0.05. c Edit frequency on mRNAs in MOLM-13 cells
overexpressing MSI2-ADA or controls MSI2-DCD and empty vector MIG. Only
significant edit frequency (adjusted p < 0.05) are plotted. Data as
means ± SEM of all the data points in three independent experiments.
Unpaired Mann–Whitney test; ****p < 0.0001. d Total number of
MSI2-HyperTRIBE significant edit sites, target genes, and distribution
of sites on the genes in MOLM-13 cells from three HyperTRIBE
experiments. e Illustration of selected window size surrounding edit
sites for de novo motif analysis and the results showing enrichment of
a consensus sequence that matches previously identified MSI2 motif. f
Probability density function (pdf) plot showing the spatial
distribution of distance from edit sites to the nearest MSI2 motifs
found in d (light blue) and from edit sites to nearest NB4 iCLIP peak
(dark yellow). g GSEA analysis shows that top targets found by
MSI2-HyperTRIBE (255 genes with diff.frequency ≥ 0.4) are enriched
among genes that are differentially expressed in MSI2-depleted human
AML cell lines compared with controls (data in Kharas et al.^[83]18).
y-axis shows enrichment score of the 255 geneset. The black bars on the
x-axis show the genes in the MSI2-depleted RNA-seq ranked list, with
log2fc(control/knockdown) value high to low running from left to right.
NES normalized enrichment score.
We next assessed the reproducibility and the effect of overexpressing
the MSI2-HyperTRIBE fusions on global gene expression (GE). Pair-wise
correlation analysis of three independent experiments suggests that the
edit frequency is highly reproducible (Pearson correlation coefficient
r > 0.8, Supplementary Fig. [84]1b–d).
In contrast to CLIP based strategies, we found that the edit frequency
is largely independent of the expression level of the target mRNAs
(Supplementary Fig. [85]1e). Moreover, MSI2 and the fusion
overexpression for 48 h did not lead to any major changes in the
transcriptome of the cells suggesting that forced expression did not
alter mRNA target abundance (Supplementary Fig. [86]1f–h). Overall
these data indicate that the editing activity reflects MSI2 binding and
that it can be used to reliably assess RBP binding.
To assess the accuracy of RNA target identification by the mammalian
HyperTRIBE, we first mapped the binding sites to specific genes and
compared with CLIP strategies. MSI2-HyperTRIBE identified 2056 target
genes marked by 5244 significant edit sites in the human AML cell line
MOLM-13. The majority of sites (~94%) were located in the 3′UTR region
(Fig. [87]1d, Supplementary Data [88]1), which is consistent with
previous studies^[89]21,[90]30. To determine if MSI2-HyperTRIBE
identifies a preferred binding sequence, we performed a de novo motif
search using 200 bp sequences centered at the edit sites. We identified
the known MSI2 binding motif (Fig. [91]1e) and confirmed that it was
enriched within 250 bp of edit sites (Fig. [92]1f, Supplementary
Data [93]2)^[94]31,[95]32. In addition, the editing occurred either on
or near sites that were directly bound by MSI2 as previously
identified by CLIP (Fig. [96]1f)^[97]21. The top 255 genes with the
highest differential frequency of at least 0.4 are positively
correlated with genes upregulated upon MSI2 depletion in four human AML
cell lines^[98]18 (Fig. [99]1g). These targets also correspond to the
top hits with highest number of peaks in our previous MSI2 HITS-CLIP
analysis in the K562 cell line^[100]19, (Supplementary Fig. [101]1i).
Our results demonstrate that MSI2-HyperTRIBE efficiently identified
direct MSI2 binding targets in mammalian cells.
Since multiple sites were found on the same RNA target, we looked to
see if there was a pattern of clustered binding. To decide on a
suitable window size for clustering edit sites, we compared the
enrichment of MSI2 motifs in windows of fixed size around significantly
edited sites (true sites) with windows of the same size around
non-significantly edited sites (background). Using a Fisher’s test, we
determined that ±17 bp is the largest window such that the motif
enrichment was significantly greater around true sites compared with
background. We therefore clustered nearby edit sites falling within
this window size and found that the majority of clusters (87%) contain
only single sites, suggesting that MSI2 binds RNA and then ADAR edits
mainly at these discrete sites (Supplementary Fig. [102]2a, b).
Therefore, the majority of MSI2-HyperTRIBE’s edit sites represent MSI2
binding.
To further rule out the potential of non-specific binding by
MSI2-HyperTRIBE, we performed additional controls using a fusion of
ADAR with MSI2 lacking RNA binding activity, as well as HyperTRIBE with
ADAR domain alone without MSI2. To this end, we overexpressed the
catalytic domain ADAR alone (ADA only) and ADAR fused with MSI2 lacking
both RRMs (RNA Recognition Motifs), RRM(del)MSI2-ADA, or with MSI2
mutated at five amino acids in both RRM domains that are crucial for
RNA binding activity, RRM(mut)MSI2-ADA (Supplementary
Fig. [103]3a)^[104]33. Our analysis found that ADAR alone and the
mutant fusions have low editing frequency and produce only a few
significant edit sites (52 sites for ADA only, 18 for RRM(del)MSI2-ADA
and 20 for RRM(mut)MSI2-ADA) compared with MSI2-ADA fusion (5244
significant sites) (Supplementary Fig. [105]3b–d). These data indicate
that MSI2 and its RRMs provide the cellular binding specificity for
ADAR editing.
Cell-context dependent RNA binding activity of MSI2 in HSPCs
Given that MSI2 is highly expressed in both HSCs and MPPs and that loss
of MSI2 results in a loss of quiescence and reduced
self-renewal^[106]18,[107]19,[108]21, we hypothesized that there could
be differential targets in HSCs compared with MPPs. Thus, we tested if
HyperTRIBE can be applied to HSCs and MPPs by transducing MSI2-ADA,
MSI2-DCD, or empty vector controls into Lin-, Sca1+, c-Kit+ cells
(LSKs) isolated from C57/BJ6 mice. We then transplanted these cells
into lethally irradiated mice and after they were engrafted, long-term
HSCs (LT-HSCs), short-term HSCs (ST-HSCs), multipotent progenitors MPP2
and MPP4 were isolated, followed by RNA-seq (Fig. [109]2a,
Supplementary Fig. [110]4a). We were able to detect 1273 edit sites in
LT-HSCs, 1126 sites in ST-HSCs, 879 and 862 sites in MPP2s and MPP4s,
respectively (Fig. [111]2b). These edit sites represented 856 gene
targets in LT-HSCs, 782 genes in ST-HSCs, 658 genes in MPP2, and 661 in
MPP4 (Fig. [112]2c, Supplementary Data [113]1). Furthermore, despite
equivalent expression of the MSI2-HyperTRIBE fusions, we observed more
edit sites (~1.4–1.5 fold), gene targets (~1.2–1.3 fold), and more
targets marked with at least two sites in HSCs compared with MPPs
(Fig. [114]2b, c, Supplementary Fig. [115]4b–d). These data suggest
that MSI2 binding activity is modestly increased in HSCs compared with
MPPs.
Fig. 2. Cell context MSI2 binding during hematopoietic stem cell
differentiation.
[116]Fig. 2
[117]Open in a new tab
a Schematic illustration of MSI2-HyperTRIBE in HSPCs in vivo. n = 2
independent experiments. b Number of MSI2-HyperTRIBE significant edit
sites and their genic distribution in four compartments of HSPCs. c
Number of target genes with sites (described in b) in HSPCs. d De novo
motif search showing enrichment of MSI2 motif in all four populations
of HSPCs. e Clustering of diff.frequency for target genes across cell
types (left panel). Only genes more significantly edited (beta-binomial
test) in one cell type versus all others are plotted. Relative gene
expression of each target, in same row order as diff.frequency heatmap,
in control cells MIG (middle panel) and for MSI2-ADA overexpressing
cells (right panel). LT:LT-HSC; ST:ST-HSC. f RNA-seq Gene and Drug
Signature analysis for MSI2 targets in LT and ST HSCs (LT-unique,
ST-unique and Shared LT-ST) compared with targets in MPPs (MPP2-unique,
MPP4 unique and Shared MPP2-MPP4). Asterisks indicate FDR < 0.05. g
Differential expression (DEseq2) analysis of MSI2 overexpression in
four HSPCs populations. Red dots represent genes with significant
differential expression in MSI2-DCD versus MIG control. h Editing
occurs on Smad3 mRNAs at three sites in LT-HSC, 0 sites in ST-HSC and
MPP2 and one site in MPP4. Each bar represents one site. i
Representative images of immunofluorescence analysis (IF) showing SMAD3
signal in LT versus ST, MPPs. Scale bar 5 μm. j Quantitation of SMAD3
IF signal from i. n = 125; 45; 130, and 203 cells for LT Msi2 WT; KO;
ST, MPPs Msi2 WT and KO, respectively. Data as mean ± SEM. Unpaired
Student t test, ****p < 0.0001. k Editing occurs on Brcc3 mRNAs only in
LT-HSC and not in other populations. Each bar represents one site. l
Representative IF images showing BRCC3 signal in LT versus ST and MPPs.
Scale bar 5 μm. m Quantitation of BRCC3 IF signal from l in Msi2 WT and
Msi2 K/O. n = 258; 263; 216 and 295 cells for LT Msi2 WT; KO; ST, MPPs
Msi2 WT and KO, respectively. Data as mean ± SEM. Unpaired Student t
test, ****p < 0.0001.
To determine if MSI2’s binding sites were conserved in HSPCs and if
they changed during differentiation, we performed de novo motif
analysis. Similar to the MOLM-13 cells, the same MSI2 motif was found
to be the most enriched in all populations (Fig. [118]2d, Supplementary
Data [119]2). These data confirm that the edit sites marked MSI2
binding sites and demonstrate that HyperTRIBE can identify an RBP’s RNA
targets in limited cell numbers.
We then investigated if and how the MSI2 binding changed when HSCs
differentiated into more committed progenitors. Clustering of gene
targets by differential edit frequency (diff.frequency) across cell
types revealed a group of mRNA targets bound by MSI2 in all four states
of HSPCs with no significant difference in diff.frequency (vs controls)
between populations (beta-binomial test, FDR ≥ 0.1) (Supplementary
Fig. [120]4e). In addition, there are subsets of transcripts that are
bound only in a specific state (unique groups, Fig. [121]2e) with
diff.frequency (vs controls) significantly different in one state
compared with all other states (beta-binomial test, FDR < 0.1; p
value < 0.05). Importantly, we did not observe a similar pattern of
mRNA expression of the targets (middle and right panel, Fig. [122]2e),
suggesting that the majority of differential binding activity at
different states of HSPCs is not simply a consequence of the
differential abundance of mRNA transcripts. These data support the
concept that RBP activity and target engagement depends on cell states.
We then hypothesized that the abundance and target spectrum could also
result in altered biological functions of the shared and specific
targets in HSCs versus those in MPPs. Thus, we performed gene pathway
enrichment analysis using the ENRICHR program^[123]34 for targets
specific and shared in LT and ST-HSC versus targets in MPPs (489 vs
298, Supplementary Figs. [124]4f, [125]5a, Supplementary Data [126]3).
We found that HSC targets are highly enriched for stem cell programs,
such as HSCs, MDS and LSCs; whereas MPP targets are enriched for
lineage-specific programs, such as macrophages, T cells and B cells
(Fig. [127]2f, Supplementary Fig. [128]5b, Supplementary Data [129]3).
In addition, gene ontology (GO molecular functions) analysis indicates
that HSC targets enriches for RNA binding, kinase binding and ubiquitin
ligase activity whereas MPP targets are involved in RNA polII
coactivator binding (Supplementary Fig. [130]5c, d, Supplementary
Data [131]4). These data indicate that MSI2 switches its binding
targets away from HSC-related pathways toward
differentiation-associated pathways as the cells differentiate to MPPs.
Previous studies, using normal and MDS mouse models, found that
inducible overexpression of MSI2 results in the expansion of HSPC
populations^[132]18,[133]21,[134]23,[135]24,[136]35, but the
overexpression impact on specific subsets within the HSPC compartments
remains unclear. Thus, we compared the GE profile of MSI2
overexpression (MSI2-DCD) to control (MIG) in HSCs and in MPPs. MSI2
overexpression resulted in significant changes in the transcriptome in
LT and ST HSCs but not in MPPs, suggesting that MSI2 impacts HSCs
differentially compared with MPPs (Fig. [137]2g). Notably, most of
these genes with expression changes were not direct MSI2 targets (~6%
195 out of 2972 differentially expressed genes in LT; 113 out of 2047
in ST HSCs) (Supplementary Fig. [138]5e). These results suggest that
although HSCs have a modest increase in MSI2 binding compared with
MPPs, it results in a large transcriptional effect. However, this
effect is indirect and likely through its small subset of direct
binding targets in HSCs.
Our previous study found that MSI2 directly controls TGFB signaling
output^[139]19. Based on our MSI2 differential binding activity, we
examined Smad3, a direct target in the TGFB signaling pathway that was
found by HITS-CLIP in K562 cells and has reduced protein abundance in
HSCs upon Msi2 depletion^[140]19. HyperTRIBE identified that MSI2
bound more efficiently to Smad3 transcripts in LT-HSCs than in ST-HSCs,
MPP2, and MPP4 (Fig. [141]2h). This corresponded to a decrease in total
SMAD3 and phosphorylated SMAD3 protein in LT-HSCs but not in ST-HSCs
and MPPs upon Msi2 knockout (Fig. [142]2i, j and Supplementary
Fig. [143]5f, g). In addition, among 21 targets that are more
significantly edited (shown in the heatmap, Fig. [144]2e) in LT-HSCs
versus all other populations, Brcc3 or BRCA1/BRCA2 containing complex
3, has been reported to be mutated in myelodysplasia syndrome (MDS) and
in de novo AML^[145]36,[146]37. These mutations are associated with
clonal hematopoiesis, which suggests that Brcc3 plays a key functional
role in HSCs. Brcc3 is uniquely targeted by MSI2 in LT-HSCs but not in
more committed progenitors (Fig. [147]2k). We therefore chose this
candidate for validation as a novel HSC target. Similar to SMAD3, MSI2
depletion led to significant reduction of BRCC3 abundance in LT-HSCs
but not in ST-HSCs, MPP2s and MPP4s (Fig. [148]2l, m). Of note, the
mRNA level of Smad3^[149]19 or Brcc3 (Supplementary Fig. [150]5h) was
unaffected by MSI2 depletion suggesting that SMAD3 and BRCC3
translation was being controlled specifically in LT-HSCs compared with
ST-HSCs and MPPs. Moreover, LT-HSC have increased BRCC3 protein
abundance without a significant difference in expression Brcc3
transcript compared with ST-HSCs and MPPs (Fig. [151]2m and
Supplementary Fig. [152]5i). The equivalent transcript abundance of
Smad3 was also observed between these two populations (Supplementary
Fig. [153]5i). Overall, our data indicate that despite similar
abundance of MSI2 and its RNA targets, MSI2 can differentially control
its targets’ protein abundance during hematopoietic differentiation.
Increased MSI2 RNA binding activity in LSCs versus HSPCs
Although MSI2 has been demonstrated to play an important role in both
HSPCs and LSCs, it remains unclear why LSCs are more dependent on MSI2
compared with normal cells. Thus, we expressed the MSI2-ADA fusion and
controls in LSCs (c-Kit^hi cells) isolated from quaternary
MLL-AF9-dsRed mice and normal HSPCs (LSKs). Our analysis detected over
12,000 sites located in 2865 genes in LSKs. Strikingly, we observed 2.5
times more edit sites (30,701 vs 12,071 sites) and 1.4 times more
target genes (4162 vs 2865 genes) in LSCs despite a lower expression of
MSI2-ADA fusion and endogenous MSI2 in LSCs compared with LSKs
(Fig. [154]3a, Supplementary Fig. [155]6a, b). In addition, over 60% of
MSI2 targets identified by HyperTRIBE in human leukemia cells are
conserved in murine leukemia (Supplementary Fig. [156]6c, Supplementary
Data [157]1). These data suggest that MSI2 has increased target
engagement in leukemia versus normal cells.
Fig. 3. Increased MSI2 RNA binding activity in LSCs.
[158]Fig. 3
[159]Open in a new tab
a Number of MSI2-HyperTRIBE significant edit sites and their
distribution on genes in LSKs and LSCs. Number of target genes in each
cell type is shown on top of the bars. n = 3. b Overlapping of target
genes in LSKs and LSCs: 2506 shared, 1656 LSC unique targets, and 359
LSK unique targets. c Differential editing of shared sites, represented
by Log2 fold change of diff.frequency in LSCs and in LSKs. d Violin
plot presenting log2 fold change of gene expression in LSCs and LSKs
(overexpressing MIG) of shared targets, LSC unique targets (n = 1651)
and LSK unique targets (n = 359). One-sided Wilcoxon test.
****p < 0.0001. Plot center lines show the median, box limits denote
upper and lower quartiles, whiskers represent 1.5× interquartile range
and individual points show outliers. e Percentage of gene expression
(GE) independent targets in shared, LSC unique and LSK unique target
groups from b. f Clustering of diff.frequency for top gene targets with
diff.frequency of at least 0.6 in LSKs and LSCs (left panel). Only
genes with diff.frequency significantly different (LSC vs LSK,
beta-binomial test), are plotted. Matched number of edit sites for each
target (per row) (the middle panel) and corresponding expression level
in LSKs versus LSCs (right panel). g Total number of significant
RNA-seq Gene and Drug signatures (FDR < 0.05) enriched in LSK and LSC
unique targets. h Top significant RNA-seq Gene and Drug signatures
enriched in LSK unique targets (359 genes) using ENRICHR analysis.
FDR < 0.05 for all indicated pathways. i Top significant RNA-seq Gene
and Drug signatures enriched in LSC unique targets (1656 genes) using
ENRICHR analysis. FDR < 0.05 for all indicated pathways. j Gene
expression (GE) independent RNA-seq Gene and Drug signatures of shared
targets in LSKs and LSCs. Full list of shared target genes in b is
filtered with log2fc (LSC-MIG/LSK-MIG) ≤ 1.2. k GE independent
signature of RNA-seq Gene and Drug signatures of LSC unique and LSK
unique targets. *FDR < 0.05.
To assess the differences in MSI2 binding in LSCs versus normal cells,
we examined the location of editing, the shared and cell-specific
sites. Consistent with our previous results, almost all the edit sites
(~93%) were located in 3′UTR and the MSI2 binding motif was the most
enriched consensus sequence around the edit sites in both LSKs and LSCs
(Fig. [160]3a, Supplementary Fig. [161]6d–f, Supplementary Data [162]1
and [163]2). The vast majority of sites (nearly 80%) and genes (over
87%) marked by MSI2-ADA in LSKs were also found in LSCs, and the number
of targets bound by MSI2 only in LSCs (1656 LSC unique targets) was
approximately five times higher than those bound only in LSKs (359 LSK
unique targets) (Fig. [164]3b, Supplementary Fig. [165]6g,
Supplementary Data [166]1). Moreover, there are more edit sites per
MSI2 target in LSCs compared with LSKs (Supplementary Fig. [167]6h, i)
and at the shared sites, we found that they were edited at higher
frequency in LSCs than in LSKs (Fig. [168]3c). These data suggest that
despite similar expression between normal cells and leukemia cells the
activity of MSI2 is increased in LSCs compared with normal cells.
To assess whether the elevated RNA binding activity of MSI2 in LSCs is
due to higher abundancy of the targets, we carried out differential
expression analysis comparing expression of mRNAs between LSCs and
LSKs. We observed that almost all shared (~94%) and the majority (~69%)
of LSC unique targets have comparable expression in both cell types or
lower expression in LSKs (log2fc LSC/LSK ≤ 0.26 or FDR ≥ 0.05 no
significant difference) whereas the majority (~66%) of LSK-specific
targets were expressed more highly in LSKs (log2fc LSC/LSK ≤ −0.26)
(Fig. [169]3d, e). Thus, RNA transcript abundance could explain a
proportion but not the majority of the differential binding activity in
LSCs.
To determine the significant differences in MSI2 binding in LSCs, we
clustered the differential edit frequency of targets in both cell
types. We observed the elevated editing in LSCs versus LSKs even in the
most highly edited targets (≥0.6 diff.frequency) as shown by an
increase in both diff.frequency and number of edit sites
(Fig. [170]3f). Importantly, for the majority of targets the mRNA
expression could not simply explain this increased editing in leukemia
compared with normal cells (right panel, Fig. [171]3f). Nevertheless,
to further eliminate expression bias, we restricted the clustering to
targets with comparable or lower expression in LSCs (vs LSKs) and still
observed the same pattern of increased RNA binding in LSCs compared
with LSKs (Supplementary Fig. [172]6j). Of note, the overexpression of
MSI2-ADA and MSI2-DCD fusions for this short time course (48 h) did not
result in significant changes in the transcriptome of both cell types
(Supplementary Fig. [173]6k–p). These data suggest that MSI2 binding
activity is elevated in LSCs versus LSKs through mechanisms independent
of mRNA expression.
Next, we wanted to understand how differential RNA binding activity of
MSI2 in LSCs compared with LSKs influences MSI2’s known functional
pathways. Gene pathway analysis by ENRICHR revealed nearly 9 times more
significant pathways enriched in the LSC unique targets versus the LSK
unique targets (900 vs 113, FDR < 0.05) (Fig. [174]3g). Top
LSK-specific signatures include normal embryonic stem cell related
programs, hematopoietic stem cells and progenitors programs, while MSI2
controlled pathways and MLL-AF9 AML leukemia are amongst the most
enriched signatures in LSC-specific targets (Fig. [175]3h, i,
Supplementary Data [176]3). This is in accordance with our previous
study, which demonstrates that MSI2 maintains the mixed-lineage
leukemia (MLL) self-renewal program by controlling the translation of
critical MLL regulated transcription factors such as Hoxa9, Ikzf2 and
Myc in myeloid leukemia^[177]20. In addition, gene ontology (GO
Biological Processes) identified pathways related to RNA metabolism and
protein transport and processing as well as translational regulation in
LSC-specific targets while it did not find any significant biological
processes in the LSK-specific targets (Supplementary Fig. [178]6q and
Supplementary Data [179]4).
To investigate whether this is due to background cell-type specific
expression of the targets, we performed gene enrichment analysis with
only gene-expression (GE) independent targets (log2fc ≤ 0.26 or
FDR ≥ 0.05 no significant difference, shown in Fig. [180]3e) for
Shared, LSK unique and LSC unique groups. We found that the GE
independent shared targets, the majority of which have higher binding
to MSI2 in LSCs versus LSKs, are enriched for both normal HSPC-related
as well as MLL-AF9 leukemia programs (Fig. [181]3j). Remarkably, MSI2
controlled pathways in LSCs and MLL1-HOXA9-MEIS1 leukemia programs were
selectively enriched in GE independent LSC unique targets, which are
expressed at the same or lower level in LSKs (Fig. [182]3k,
Supplementary Data [183]3). Our results reveal that MSI2 not only
enhances its RNA binding activity in LSCs versus LSKs overall, but also
interacts more with genes regulated by the MLL leukemia programs in
LSCs.
Differential regulation of MSI2 targets in LSCs
We then hypothesized that MSI2 differential binding to targets in the
MLL program results in a specific effect on the abundance of the
targets upon MSI2 perturbation in LSCs, compared with LSKs. To test our
hypothesis, we looked at Hoxa9, Ikzf2, and Myc, our previously
established MLL and MSI2 downstream targets as well as key
transcription factors in hematopoiesis and leukemogenesis. We found
that Hoxa9 and Ikzf2 3′UTRs was substantially marked by MSI2-ADA
(Fig. [184]4a, b). Although Myc was previously detected by CLIP and RIP
approaches, we did not find any editing in Myc transcripts in all cell
types in this study. This might be due to the rapid turnover of Myc
mRNAs^[185]9,[186]38,[187]39 and the stable interaction required for
editing or because MSI2 does not actually bind Myc directly. However,
we detected MSI2’s interaction at Myb, a well-known upstream regulator
of Myc and a key transcription factor in hematopoiesis as well as a
driver of MLL related and non-related leukemia^[188]40–[189]45
(Fig. [190]3c).
Fig. 4. Differential control of MSI2 targets in LSCs compared with normal
LSKs.
[191]Fig. 4
[192]Open in a new tab
Diff.frequency at various sites identified by MSI2-HyperTRIBE in Hoxa9
3′UTR (a), Ikzf2 3′UTR (b) and Myb 3′UTR (c) in LSKs and LSCs. Numbers
on the X-axis is the start and end of 3′UTR. Data presented as the mean
values from three independent HyperTRIBE experiments. Significant
difference is determined by beta-binomial test. * adjusted p < 0.1 d
Representative immunoblot images and quantitation showing no
significant change in HOXA9, IKZF2 and MYB protein expression upon Msi2
knockout in LSKs after 3 weeks of pIpC treatment in Msi2 f/f Cre(–) and
Cre(+) mice. Each data point is an independent treated mouse. Data are
presented as mean ± SEM. Two-sided unpaired Student t test.
***p < 0.005. (p = 0.002 for MSI2). e Representative Immunoblot images
and quantitation showing significant decrease in HOXA9, IKZF2 and MYB
protein expression upon Msi2 knockout at 68 h after TAM treatment in
MLL-AF9 Msi2 CreER(+) LSCs. Each data point is an independent treated
mouse. Data are presented as mean ± SEM. N = 6 independent experiments
for HOXA9 and IKZF2, n = 3 independent experiments for MYB. Two-sided
paired Student t test. **p < 0.01, ***p < 0.001; ****p < 0.0001.
(p = 0.000002 for MSI2, p = 0.00015 for HOXA9, p = 0.019 for IKZF2,
p = 0.000019 for MYB). f Schematic depiction of MSI2 elevated RNA
binding and reduction of target protein expression upon MSI2 ablation
in LSCs, but not in LSKs.
We then confirmed the edit sites are indeed regulatory binding sites of
MSI2 by a reporter assay with Hoxa9 and Myb, which have relatively
short 3′UTRs (Supplementary Fig. [193]7a, b). Interestingly, Hoxa9,
Ikzf2, and Myb are less edited in LSKs as demonstrated by the fewer
number of sites and lower differential edit frequency (Fig. [194]4a,
c). Importantly, depletion of Msi2 resulted in a significant reduction
in protein, without changes in mRNA, of Hoxa9, Ikzf2, and Myb, in LSCs
but not in LSKs (Fig. [195]4d, e, Supplementary Fig. [196]7c–e).
Notably, HOXA9, IKZF2, and MYB abundance is modestly higher in LSCs
compared with LSKs (Supplementary Fig. [197]7f). These data indicate
that MSI2 is more required in LSCs to maintain the expression of these
targets. Based on our results, we propose a model in which MSI2
increases interaction with its mRNA targets in LSCs, and therefore MSI2
ablation selectively affects the protein abundance of these targets in
LSCs compared with normal LSKs. These data suggest that the increased
RNA binding activity may explain the enhanced requirement of MSI2 in
LSCs compared with LSKs.
Discussion
Although multiple studies have identified RBP mRNA targets in embryonic
stem cells, pluripotent stem cells and neural stem cells isolated from
embryos, which exist in large quantity^[198]10–[199]14,[200]46, global
mapping of RBP targets in rare cells such as adult normal and cancer
stem cells has been hampered due to limited input material. The
standard methods (RNA-IP and CLIPs including HITS-CLIP, iCLIP, eCLIP
and sCLIP) require typically 5–20 millions of cells^[201]47–[202]50.
The irCLIP method for low input material requires 20,000–100,000
cells^[203]51. However, all of these CLIP methods require cross-linking
and RBP immunoprecipitation (IP) which could result in either lost
targets or the capture of nonspecific targets. In this study, we have
successfully adapted the HyperTRIBE method, originally developed in
Drosophila^[204]15–[205]17, for identification of RBP targets in
mammalian cells. Utilizing our adapted HyperTRIBE method, we have
obtained direct mRNA targets of an RBP in a human AML cell line and in
mouse normal and transformed hematopoietic stem and progenitor cells.
This method uses between 0.5 million cells (for MOLM13) to 360 cells
(for LT-HSC) and does not need any cross-linking, IP, or labeling
steps. We show in all of the cell types used in our study that this
approach accurately captures the known binding motif of MSI2 in stem
cells, an RBP that has been studied in various systems. Moreover, our
data correlate well with previous studies that mapped MSI2 binding
sites using immunoprecipitation techniques and we further validate the
targets by genetic studies.
A-to-I editing by endogenous adenosine deaminase ADAR enzymes exists in
cells to regulate RNA life cycle. This prompts the question whether the
high expression of exogenous ADAR in the RBP-ADAR fusion artificially
affects the expression and processing of target RNAs. We address this
question by analyzing differential expression (DESeq2) for cells
expressing MSI2-ADA compared with those with empty vector (MIG). Our
analysis shows that there is little change in the transcriptome of
MOLM13, LSKs, and LSCs expressing MSI2-ADA after 48 h of transduction.
For in vivo HyperTRIBE in HSPCs, which took 7 weeks for transplantation
and engraftment of cells expressing MSI2-ADA, we observed dramatic
changes in transcriptome of LT-HSC and ST-HSC but not MPP2 and MPP4. Of
the genes significantly changed upon MSI2-ADA expression, the majority
is due to MSI2 overexpression, which is consistent with previous
studies demonstrating a role of MSI2 in HSCs^[206]18,[207]21,[208]23.
Although MSI2 binding sites have previously been identified in cell
lines using alternative approaches, MSI2 binding in HSPCs and LSCs has
never been characterized. Using HyperTRIBE, we are now able to assess
the cell context specific MSI2 binding program for rare cell types
including hematopoietic stem cells, MPPs, and leukemic stem cells.
Importantly, our results demonstrate that RBP–RNA interactions are
highly cell-context dependent even in closely related cell types.
Although previous work has started addressing this question using in
vitro differentiation culture^[209]46,[210]52, extensive and systematic
studies are needed to assess RBP activity in rare cells during fate
switches. Using our optimized HyperTRIBE method, we revealed that MSI2
has differential binding activity at different states of HSPCs and in
LSCs in a target GE independent manner. Moreover, we found that the
enhanced RNA binding activity of MSI2 leads to differential regulation,
e.g., at Hoxa9, Ikzf2, and Myb targets, in LSCs versus LSKs, which
provides a possible explanation for the differential requirement of
MSI2 in leukemia compared with normal hematopoiesis.
Furthermore, it remains to be elucidated (1) how MSI2 achieves more
binding to mRNA targets in LSCs even without upregulating MSI2
expression; and (2) why MSI2 controls protein abundance of its mRNA
targets (e.g., Hoxa9, Ikzf2, and Myb) in LSCs but not in normal HSPCs.
One possibility is that other RBPs that share a similar binding motif
might compete for the same binding sites with MSI2 in LSKs.
Alternatively, post-translational modifications on MSI2 or other RBPs
could result in the increased binding. Moreover, multiple RBP-driven
regulation pathways, including MSI2’s, may coordinate to control
translation process of their shared targets. Cancer cells often alter
or lose multiple pathways and thus might become uniquely dependent on
MSI2 regulation. Therefore, LSCs recruit more MSI2 to its targets
rather than different RBPs as in normal LSKs. As a consequence, the
regulation of the target expression is now more dependent on MSI2.
Regardless of the exact mechanism, our data support a leukemia-specific
role for MSI2 and provide further rationale for targeting MSI2 in
leukemia cells in patients that have equivalent expression of MSI2 as
compared with normal cells. Our data provide a key resource for further
studies on the mechanisms of RBP regulation in rare cells such as stem
cell populations.
Methods
Animal research ethical regulation statement
All animal studies were performed on animal protocols approved by the
Institutional Animal Care and Use Committee (IACUC) at Memorial Sloan
Kettering Cancer Center.
Plasmid constructs
MSI2-ADA fusion was constructed by fusing the human MSI2 CDS to the A-I
deaminase domain of the Drosophila enzyme ADAR containing a hyperactive
mutant E488Q^[211]15, with a linker (the region from Y268 to the
deaminase domain). The inactive ADAR catalytic mutant control MSI2-DCD
was generated by mutating Glutamic acid E367 to Alanine in the
deaminase domain^[212]28,[213]29, using site-directed mutagenesis
(Agilent #200523). Both constructs were codon-optimized for expression
in human cells before gene synthesis and cloning into MSCV-IRES-GFP
(MIG) vector. The sequence of these constructs are provided in the
supplementary information (Supplementary Methods). After Sanger
sequencing, we found that there was additional unexpected mutation,
N495S, in the ADAR catalytic domain of the MSI2-DCD. However, this does
not affect the fusion expression and we confirmed by the data in MOLM13
that the MSI2-DCD containing both E367 and N495S is catalytically
inactive of A-to-I editing. RRM(del)MSI2-ADA was generated by removing
both RRM1 and RRM2 of MSI2. To create RRM(mut)MSI2-ADA, we synthesized
the fusion with RRM1 containing mutations F24A, R62A, F66A and F223A,
F155A mutations on RRM2. To create ADA only construct, we removed MSI2
from the fusion MSI2-ADA. All of the contructs were fused with 2xFlag
tags.
Retroviral production and transductions
Retroviral packaging of all expression constructs was performed in 293T
cells as previously prescribed^[214]53. Retrovirus was kept at 4 °C and
used within 2 weeks of production.
MSI2-HyperTRIBE in MOLM-13 cell line
MOLM-13 cells (obtained from ATCC) were cultured in RPMI 10% FBS
1%L-Glutamine PenStrep. Cells were infected with virus expressing
MSI2-ADA, MSI2-DCD, or MIG controls at 1:1 ratio (v/v) cell: virus at
0.5 million cells per mL (final density). Spinoculation was done with
10 μg/mL polybrene (Millipore #TR-1003-G) at 768 g for 1 h at 32 °C.
Cells were incubated for 48 h and then sorted by flow cytometry for GFP
positive. At least 0.5 million GFP positive cells were used for RNA
extraction and sequencing.
MSI2-HyperTRIBE in HSPCs
Bone marrow cells from 6 to 8-week-old C57BL/6 strain were processed
for c-Kit enrichment by incubation with 50 μl of MACS CD117/c-Kit beads
per mouse and then run on an AutoMACs (Miltenyi Biotec) following the
manufacturer’s instructions. Cells were stained with Lineage antibody
cocktail including CD3 (Fisher #15-0031-83), B220 (ebioscience
#15-0452-83), CD4 (Fisher #5013997), CD8 (ebioscience #15-0081-83),
Gr-1 (ebioscience #15-5931-82), Ter119 (ebioscience #15-5921-83) (all
conjugated with PE-Cy5), CD117-APC-Cy7 (Biolegend #105826),
Sca-1-Pacific Blue (Biolegend #122520), CD150-APC (Biolegend #115910),
and CD48^−PE (Fisher #557485). Lin-Sca^+Kit^+ cells (LSKs) were sorted
using a BD FACS Aria II cell sorter instrument (November 2008 edition)
and BD FACSDiva software (version 8.0.1 2014). Sorted LSKs were grown
overnight in SFEM medium containing 10 ng/ml murine IL-3, 10 ng/ml
IL-6, 50 ng/ml SCF, 10 ng/ml thrombopoietin, and 20 ng/ml FLT3l. Cells
were spinoculated with retrovirus expressing MSI2-ADA, MSI2-DCD, or MIG
controls and 4 μg/mL polybrene on retronectin-coated plates. After 48 h
of transduction, all cells were collected and transplanted into
lethally irradiated C57BL/6 mice (15,000 cells per mouse). Engraftment
was checked after 6 weeks. After 7 weeks of transplantation, mice were
sacrificed and c-Kit enriched bone marrow cells were stained with LSK
markers as described above plus CD48-PE and CD150-APC. Cells were
sorted into four populations GFP positive CD150^+ CD48^−(LT-HSC),
CD150^− CD48^– (ST-HSC or MPP1), CD150^+ CD48^+ (MPP2), and CD150^–
CD48^+ (MPP4). 360–20,000 sorted cells were used for RNA extraction and
sequencing.
MSI2-HyperTRIBE in LSKs and LSCs
LSK cells were obtained and transduced with MSI2-HyperTRIBE constructs
as described above. After 48 h of incubation, cells were sorted for GFP
positive and RNA was extracted for SMARTer library preparation and
RNA-seq.
Quaternary MLL-AF9 leukemia model on Actin-dsRed background mice were
generated as described before^[215]54. Bone marrow cells were infected
with MSI2-HyperTRIBE expressing virus in BMT medium (RPMI 10%FBS
1%L-Glutamine PenStrep supplemented with 10 ng/mL murine IL-3, 10 ng/mL
murine IL-6, 10 ng/mL murine SCF, and 10 ng/mL murine GM-CSF) for 48 h.
LSC-enriched population was isolated by sorting dsRed^+, GFP^+, and
c-Kit-APC-Cy7 high (top 10–12%) for library preparation and RNA-seq.
RNA extraction and sequencing
RNA from cells suspended in Trizol was extracted with chloroform.
Isopropanol and linear acrylamide were added, and the RNA was
precipitated with 75% ethanol. Samples were resuspended in RNase-free
water. For HyperTRIBE in MOLM-13, after PicoGreen quantification and
quality control by Agilent BioAnalyzer, 1 μg RNA input was used for
library preparation (TrueSeq Stranded mRNA LT Sample Prep Kit.
Libraries were run on a HiSeq 4000 in a 50 bp/50 bp paired end run,
using the HiSeq 3000/4000 SBS Kit (Illumina). The average number of
read pairs per sample was 34 million. For HyperTRIBE in HSPCs, after
RiboGreen quantification and quality control by Agilent BioAnalyzer,
0.5 ng total RNA (for eight samples with <0.5 ng, all mass was used)
with RNA integrity numbers ranging from 1 to 9.9 underwent
amplification using the SMART-Seq v4 Ultra Low Input RNA Kit (Clonetech
catalog # 63488), with 12 cycles of amplification. Subsequently, 1–2 ng
of amplified cDNA was used to prepare libraries with the KAPA Hyper
Prep Kit (Kapa Biosystems KK8504) using eight cycles of PCR. Samples
were barcoded and run on a HiSeq 4000 in a 50 bp/50 bp paired end run,
using the HiSeq 3000/4000 SBS Kit (Illumina). An average of 40 million
paired reads were generated per sample and the percent of mRNA bases
per sample ranged from 69 to 82%. For HyperTRIBE in LSKs and LSCs,
after RiboGreen quantification and quality control by Agilent
BioAnalyzer, 2 ng total RNA with RNA integrity numbers ranging from 9.3
to 10 underwent amplification using the SMART-Seq v4 Ultra Low Input
RNA Kit (Clonetech catalog # 63488), with 12 cycles of amplification.
Subsequently, 10 ng of amplified cDNA was used to prepare libraries
with the KAPA Hyper Prep Kit (Kapa Biosystems KK8504) using eight
cycles of PCR. Samples were barcoded and run on a HiSeq 4000 or HiSeq
2500 in High Output mode in a 50 bp/50 bp paired end run, using the
HiSeq 3000/4000 SBS Kit or TruSeq SBS Kit v4 (Illumina). An average of
36 million paired reads were generated per sample and the percent of
mRNA bases per sample ranged from 64 to 77%.
Identification of RNA editing events in RNA-Seq data
We aligned the paired-end RNA-seq reads to human (hg19) or mouse (mm10)
genome using STAR aligner^[216]55. Next we followed the GATK^[217]56
workflow for calling variants in RNA-seq
([218]https://software.broadinstitute.org/gatk/documentation/article?id
=3891) to identify all the mutations in each RNA-seq library. We then
restricted to the mutations within annotated mRNA transcripts, as well
as restricting to A-to-G mutations in transcripts encoded by the
forward strand and T-to-C mutations in transcripts encoded by the
reverse strand. We also filtered out mutations found in the dbSNP
database since they are most likely DNA-level mutations. We then
combined the filtered sets of RNA editing events from all RNA-seq
libraries of the same experiment and counted the number of reads
containing reference (A/T) and alternative (G/C) alleles from each
library at each site.
Statistical test for difference in edit frequencies
We used beta-binomial distribution to model the RNA edit frequencies,
which has also previously been applied to modeling allele frequencies
in RNA-seq reads^[219]57,[220]58. The beta-binomial distribution is the
binomial distribution where the probability of success at each trial is
not fixed, but instead is drawn from the beta distribution. The
probability functions of the binomial distribution and beta
distribution are:
[MATH: Pk∣n,p=nkpk1−pn−k, :MATH]
1
[MATH: πp∣α,β=pα
−11−pβ−1B
α,β. :MATH]
2
Thus the probability density function of the compound distribution, the
beta-binomial distribution, can be represented as
[MATH: fk∣n,α,
β=∫
01Pk∣n,pπp∣α,βdp=∫
01nkpk1−pn−kpα−11−pβ−1B
α,βdp=nkBα,β∫0
1pk+α−11−pn+β−k−1dp=nkBk+α,n+<
/mo>β−kBα,β. :MATH]
3
For convenience, it is common to reparametrize it as:
[MATH: μ=α
α+β, :MATH]
4
[MATH: ρ=1
α+β+1
, :MATH]
5
so that the expectation and variance of the beta-binomial distribution
are:
[MATH: Ek∣n,μ,
ρ=nμ, :MATH]
6
[MATH: Vark∣n,μ,
ρ=nμ1−μ1+n−1ρ. :MATH]
7
In this form, µ corresponds to the estimate of p, and ρ corresponds to
the extent of over-dispersion. Both µ and ρ values are between 0 and 1.
When we use beta-binomial distribution to model the RNA editing events
in RNA-seq, n corresponds to the total number of reads overlapping with
an RNA edit site and k to the number of reads with A-to-G mutations. In
this scenario, the beta-binomial distribution is a better model for
read counts than the binomial distribution since it takes the
variability in mutation frequencies between biological samples into
account. Under the null hypothesis, all samples have equal RNA editing
level, and the edit frequencies are drawn from the same beta
distribution
[MATH:
π(μ
0,ρ) :MATH]
. Under the alternative hypothesis, the samples expressing the MSI2-ADA
fusion protein have a different RNA edit frequency than the control
samples, and the frequencies come from two different beta distributions
[MATH:
π(μ
1,ρ) :MATH]
and
[MATH:
π(μ
2,ρ) :MATH]
. Using the read counts at each RNA edit site from biological
replicates, we maximized the likelihood for both the null and
alternative hypotheses and then computed the p value using a likelihood
ratio test. The p values from all sites were adjusted to control for
false discovery rate (FDR) using a Benjamin–Hochberg correction. The
statistical computation was performed using R packages VGAM (Version
1.1–2) and bbmle (Version 1.0.23.1). Significant sites were determined
by filtering for FDR-adjusted p values, using FDR < 0.05 for MOLM-13,
FDR < 0.01 for LSCs and LSKs and FDR < 0.1 for HSPCs. A target gene is
retained if it has an expression level of at least 5 fpkm and at least
one edit site with a significant differential edit frequency of at
least 0.1 (differential edit frequency is the difference in mean edit
frequency by MSI2-ADA and mean edit frequency by MSI2-DCD and MIG).
Statistical test for differential editing between cell types
For differential editing between HSPC populations, we first identified
all significantly edited genes with a maximum diff.frequency ≥ 0.1. A
gene with a maximum diff.frequency ≥ 0.1 that is significantly edited
in one cell type (ADAR vs controls), but not significantly edited in
the other cell types (ADAR vs controls), is considered a potential
cell-type specific gene target. Next, we obtained the read counts from
all samples (LT, ST, MPP2, MPP4) supporting every A to G and T to C
edit site and tested the significance for cell-type specific edit sites
using the beta-binomial test. Under the null hypothesis, all cell types
have equal RNA editing level, and the edit frequencies are drawn from
the same beta distribution. Under the alternative hypothesis, the cell
type of interest has a different RNA edit frequency than the other cell
types. The difference in edit frequency between cell types is
significant if the FDR-adjusted p < 0.1. For the difference in editing
between LSC and LSK-specific gene targets, we selected genes with a
diff.frequency ≥ 0.6 and fpkm ≥ 5. These gene targets were run through
the beta-binomial test as described above.
Clustering of target genes by edit frequency patterns
After identifying HSPC cell-type specific gene targets using the
beta-binomial test, we filtered for adjusted p < 0.1 and plotted the
maximum diff.frequency value for each gene. The diff.frequencies were
then stacked from lowest to highest diff.frequency in each cell type.
After identifying genes significantly edited between LSCs and LSKs
through the beta-binomial test, genes were filtered by an adjusted
p < 0.05 and fpkm ≥ 5. We obtained the maximum diff.frequency (ADAR vs
MIG/DCD) for each gene that passed the filter and plotted them in a
heatmap with Mcquitty clustering method. GE heatmaps for both HSPCs and
for LSKs and LSCs were created by using DESeq2 to obtain variance
stabilized transformation (VST) of read counts. Then, we calculated the
mean of the VST counts of sample duplicates/triplicates for each gene,
and then performed z-transformation for each gene. Genes in the
expression heatmap match the order of row in the edit frequency
heatmaps.
Motif analysis
For de novo motif discovery, we first extracted sequences extending
100 bp from both sides of each edit site in the 3′UTR and considered
all these windows as the target sequence pool for the HOMER program.
Overlapping sequences were merged into a single sequence. Background
sequences with length 201 bp were randomly selected from 3′UTRs in the
genome that did not overlap with the target sequence pool. We used the
HOMER software to search for enriched motifs of length 6, 7, or 8, and
regional oligomer autonormalization of up to length 3.
To calculate the distance between the MSI2-HyperTRIBE edited site to
the nearest MSI2 motif, we first obtained the genomic coordinates of
exons that contain the HyperTRIBE site. Then we calculated the position
weight matrix (PWM) of HOMER motif results to identify motif sites
within exon sequences. A site was designated as a motif occurrence if
its score was at least 90% of the maximum score; this score was
calculated as the log of the probability of observing the nucleotide
sequence given the motif PWM, divided by the probability of observing
the given sequence at random given the background distribution of
nucleotides, with a sampling correction applied to avoid null
values^[221]59. We then calculated the distance of each edited site to
the nearest motif match.
To find the distance to the nearest iCLIP peak, we then identified the
genomic coordinate of the iCLIP peak nearest to each MSI2-HyperTRIBE
edit site in MOLM-13 cells. NB4 iCLIP data from^[222]21.
MSI2 edit site clustering analysis
To determine a suitable window size for clustering edit sites, we
compared the enrichment of MSI2 motifs in windows of fixed size around
significantly edited sites (“true sites”) compared with windows of the
same size around non-significantly edited sites (“background”). We
performed a Fisher’s test and determined that ±17 bp is the largest
window such that the motif enrichment was significantly greater around
true sites compared with background (p < 0.01).
Differential expression analysis (DESeq2)
Paired-end RNA-seq reads were first processed with Trimmomatic^[223]60
to remove TruSeq adapter sequences and bases with quality scores below
20, and reads with <30 remaining bases were discarded. Trimmed reads
were then aligned to mm9 genome with the STAR spliced-read
aligner^[224]55. For each gene from the RefSeq annotations, the number
of uniquely mapped reads overlapping with the exons was counted with
HTSeq ([225]http://www-huber.embl.de/users/anders/HTSeq/). Read counts
were filtered by keeping all genes with a median read count ≥ 1 or mean
rpkm or fpkm ≥ 1 and then used as input for DESeq2 to evaluate the
difference in read counts of MOLM-13, different mouse HSPC populations,
LSKs and LSCs expressing MSI2-DCD and those expressing MIG control. For
differential expression of targets in LSCs and LSKs, only genes with
fpkm ≥ 5 and edit frequency ≥0.1 were considered. A one-sided Wilcoxon
test was performed to determine the statistical significance between
the log2 fold changes (log2FC) of LSC unique, LSK unique, and shared
targets.
Gene pathway enrichment analysis
Target genes in four populations of HSPCs were overlapped to identify
the common and unique targets between the populations. Target genes
specific for LT and ST HSCs or specific for MPP2 and MPP4 were analyzed
for RNA-seq Gene and Drug signatures and Gene Ontology (molecular
functions and biological processes) using ENRICHR
program^[226]34,[227]61. The same analysis was also done for targets
unique to each population. The ENRICHR combined score was extracted for
significantly enriched pathways and compared between different sets of
targets. For pathway enrichment of GE independent targets, we first are
defined GE independent targets as following. For shared and LSC unique
groups, these are genes that have no significant expression difference
between cell types (FDR ≥ 0.05) or comparable or lower expression in
LSCs versus LSKs (log2FC LSC/LSK ≤ 0.26, equivalent to fold change
LSC/LSK ≤ 1.2, and FDR < 0.05). For LSK unique group, GE independent
targets are genes with no significant expression difference between
cell types (FDR ≥ 0.05) or comparable or lower expression in LSKs
versus LSCs (log2FC LSC/LSK ≤ −0.26, equivalent to fold change
LSK/LSC ≤ 1.2, and FDR < 0.05).
Immunofluorescence
HSCs and MPPs were sorted from primary Msi2 f/f Cre- and Cre+ 6 weeks
after pIpC. Cells were fixed with 1.5% paraformaldehyde, permeabilized
with cold methanol and cytospun onto glass slides. Cells were then
stained on slides with anti-SMAD3 (Cell Signaling Technology, 9523S,
dilution 1:1000), anti-phosphorylated SMAD2/3 (Cell Signaling
Technology, 8685S, dilution 1:1000), or anti-BRCC3 (Novus Biologicals,
NBP1-76831, dilution 1:1000) first and then with secondary antibody
conjugated with rabbit Alexa Fluor 488 (Molecular Probes).
Quantification of the signal intensity of each cells (divided by
surface area) normalized for background staining was done with
AxioVision Rel.4.8.2 (06-2010) software and Zeiss Imager Z2 (Zen 2 Blue
Edition).
Luciferase reporter assay
Original or mutated 3′UTR of murine Hoxa9 and murine c-Myb was cloned
downstream of Renilla luciferase reporter gene in pRL-CMV. MSI2 motifs
in proximity of identified edit sites on Hoxa9 and Myb 3′UTRs were
located by “distance to nearest motif” R script, as described above, in
LSKs and LSCs. All the motifs in Hoxa9 and Myb 3′UTR were mutated. In
the knockdown experiment, pRL-CMV 3′UTR constructs were co-transfected
with firefly luciferase control and MSI2 shRNA or nonspecific shRNA
control (shRNA scr). In the overexpression experiment, pRL-CMV 3′UTR
constructs were co-transfected with firefly luciferase control and MIB
empty vector or vector overexpressing human MSI2. After 48 h of
transfection, expression of renilla and firefly luciferase was
determined by Dual luciferase assay (Promega) following the
manufacturer instructions.
qRT-PCR
Total RNA from sorted cKit-hi MLL-AF9 Msi2 RosaCre ER ± Tamoxifen cells
was isolated using TRIzol (Sigma-Aldrich) and RNAeasy RNA extraction
kit (Qiagen). RNA was reversed transcribed into cDNA with iScript
(BioRad). Quantitative PCR was performed with primers for Msi2 (forward
ACGACTCCCAGCACGACC; reverse GCCAGCTCAGTCCACCGATA), Ikzf2 (forward:
CATCACTCTGCATTTCCAGC; reverse: TGACCTCACCTCAAGCACAC), Myb (forward:
AGATGAAGACAATGTCCTCAAAGCC; reverse: CATGACCAGAGTTCGAGCTGAGAA), and
Hoxa9 (forward: GTAAGGGCATCGCTTCTTCC; reverse: ACAATGCCGAGAATGAGAGC).
Immunoblot analysis
To check the expression of Hoxa9, Ikzf2, and Myb in LSCs, c-Kit^hi (top
10–12%) bone marrow cells (LSCs) from Msi2 f/f Cre-ER- and Msi2 f/f
Cre-ER+ mice were sorted and were left untreated or treated with 600 nM
4-OH Tamoxifen (Sigma-Aldrich) for 68 h in BMT medium. One hundred
thousand cells were collected, washed once with PBS, and then lysed in
1× Laemmli sample buffer (BioRad). LSCs were also sorted from
quaternary MLL-AF9 DsRed leukemia mice, then were transduced with
lentiviral shRNAs against murine Msi2 (sh331 and sh332) or shRNA
against Luciferase. Transduced cells were selected with 2 μg/mL
puromycin. After 72 h of transduction, cells were collected, washed in
PBS and lysed in 1× Laemmli sample buffer. For analysis in LSKs, one
hundred thousand LSK cells from 3 week pIpC treated Msi2 f/f Cre- and
Msi2 f/f Cre+ mice were sorted, washed with PBS and lysed in 1× Laemmli
sample buffer. Cell lysate was run on 4–15% SDS-PAGE gels, transferred
onto nitrocellulose membrane and then probed with antibodies against
MSI2 (Abcam, ab76148, dilution 1:1000), HOXA9 (Abcam, ab140631;
dilution 1:1000), IKZF2 (Santa Cruz, sc-9864, dilution 1:1000), MYB
(Millipore, 05-175, dilution 1:1000), and ACTB (beta-actin-HRP,
dilution 1:30,000) (Sigma-Aldrich, A3854).
Reporting summary
Further information on research design is available in the [228]Nature
Research Reporting Summary linked to this article.
Supplementary information
[229]Supplementary Information^ (15.2MB, pdf)
[230]Peer Review File^ (628.7KB, pdf)
[231]41467_2020_15814_MOESM3_ESM.pdf^ (80.7KB, pdf)
Description of Additional Supplementary Information
[232]Supplementary Data 1^ (1.8MB, xlsx)
[233]Supplementary Data 2^ (2.8MB, xlsx)
[234]Supplementary Data 3^ (4.8MB, xlsx)
[235]Supplementary Data 4^ (859.6KB, xlsx)
[236]Reporting summary^ (242.3KB, pdf)
Source data
[237]Source Data^ (20.2MB, xlsx)
Acknowledgements