Results [32’406]
The transcriptional landscape of the developing cerebellum
To identify Atoh1 target genes, we first analyzed the transcriptional landscape of
the developing cerebellum in Atoh1 wildtype (Atoh1+/+) and knockout (Atoh1-/-)
mice using the Illumina-based RNA-Seq technology (mRNA-seq). We enriched
for poly(A) RNA in E18.5 Atoh1+/+ and Atoh1-/- cerebellar anlage tissue;
generated double-stranded cDNA using random hexamers and prepared
sequencing libraries according to the Illumnia protocol. To increase the poly(A)
RNA yield and decrease biological variation, we pooled three cerebellar anlagen
prior to poly(A) enrichment (see Extended Experimental Procedures for details).
Each library was sequenced twice, each from both ends (pair-end, 36mer reads)
to increase the detection sensitivity as well as judge the reproducibility of the
technique. The sequences were aligned against the mouse genome (mm9) as
well as the junctionome using SOAP (v2.18) (Li et al., 2008) (see Extended
Experimental Procedures for details).
We obtained 48.8 and 43.3 million mappable reads for the Atoh1+/+ and Atoh1-/samples, respectively (Supplement Table 1). These results were comparable to
other published mRNA-seq results (Pan et al., 2008; Berger et al., 2010). All
primary sequence read data for both replicates of the two tissue RNAs have
been submitted to the National Center for Biotechnology Information (NCBI)
short-read archive (accession number _____________). The reproducibility of
the replicates was extremely high with a correlation coefficient of r=0.99 and
r=0.98 for the Atoh1+/+ and Atoh1-/- samples, respectively (Figure 1a). Our
mRNA-seq data was not biased towards the 3’ end of the transcripts, as the
whole length of the transcripts was equally detected (Supplement Figure 1a).
Summing the replicates over an entire transcriptome showed that the majority of
reads mapped to known exons (68%); providing more than 30-fold coverage for
each nucleotide in exonic regions (Supplement Table 2). 15% of the reads were
within introns and 18% fell in the large intergenic territory (Supplement Table 2).
While the intronic reads might include unmapped exons, the intergenic fraction
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might include unmapped genes, as well as longer UTRs of existing transcripts.
Comparing our Atoh1+/+ RNA-seq data to published microarray datasets in the
BioGPS database, revealed that our Atoh1+/+ transcript signature was highly
correlated with brain specific microarray datasets with a correlation factor of
r=0.73 (Figure 1b) (Wu et al., 2009). Among the other dataset groups, only the
spinal cord showed a similar coefficient (0.71) while any other tissue, such as
eye (0.55), immune system (0.45), or epidermis (0.44), was significantly less
correlated (Figure 1b).
We analyzed the genomic location of Atoh1, which transcript should not be
detected in the Atoh1-/- mRNA-seq, as well as two different transcripts, which
should be either differentially expressed, such as Barhl1, or not, such as tubulin,
a ubiquitous expressed gene. The raw reads in the wildtype as well as the
knockout samples were equally distributed in the case of tubulin, whereas we
could see a dramatic decrease in the expression level in the Atoh1-/- sample at
the Barhl1 locus (Figure 1c). The coding sequence of Atoh1 was completely
missing in the Atoh1-/- sample but not in the wildtype samples (Figure 1c).
Interestingly the 5’-UTR was still detectable in the knockout as only the coding
sequence of Atoh1 was removed (Ben-Arie et al., 1997).
Using the R package edgeR in combination with a new metric selection: Reads
Per Selected Region (RPSR), which enabled us to better analyze the large
amount of data and correct for the mRNA-seq length bias, we created a
differentially expressed (DE) transcript list (Supplement Figure 1b-c,
Supplement Table S3, see Extended Experimental Procedures for
details)(Oshlack and Wakefield 2009). With an adjusted p-Value cutoff of 0.01,
we could identify 4064 DE trasncripts. We classified these transcripts based on
two properties: a) their abundance, which was based on their reads per kilobase
of exon model per million mapped reads (RPKM) count [high (RPKM≥10, i.e.
Tubulin), medium (5≤RPKM<10, i.e. Barhl1) and low (RPKM<5, i.e. Atoh1)] and
b) their DE value, which was based on the fold change (FC) [high (FC≥2.0),
medium (1.5≤FC<2.0) and low (FC<1.5)] (Figure 1d). Since the two tissues we
used for the transcriptional analysis, E18.5 Atoh1+/+ and Atoh1-/- cerebella
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anlage, are very similar, as only the small precursor pool is changed by the
absence of Atoh1, we were not surprised that the majority of transcripts (XX%)
were highly expressed, but only slightly changed in Atoh1-/- (5X%) (Figure 1e, red
bars). To evaluate if our mRNA-seq efforts were adequate to reliably detect these
subtle differences, we performed a saturation test, in which we used subsets of
the raw data to re- generate the DE transcript list and compared how many DE
transcripts could be recovered. We recovered all high and medium-expressing
transcripts, no matter how small the expression difference was (Figure 1e, red
and blue lines) although increasing the sequencing depth would recover
additional transcripts with very low abundance (RPKM<5) and small fold-change
differences (FC<1.5) (Figure 1e, black lines). In a Bland–Altman plot using our
DE transcript list, it was apparent that most of the significant changes (red) are
down-regulated transcripts in Atoh1-/- consistent with the notion that Atoh1 is a
transcriptional activator (Figure 1f). Less than 30% were up-regulated, which
might be due to secondary effects during development (Figure 1f).
Using mRNA-seq we indentified the transcriptional landscape of the developing
cerebellum in E18.5 Atoh1+/+ and Atoh1-/- mice, and developed a DE transcript
list for the Atoh1-/- cerebella anlage. To further investigate the developmental
identity of the developing cerebellum, we wanted to combine the transcriptional
signature with epigenetic marks.
Post-natal cerebella Histone signatures
To globally assess the epigenetic signature of the developing cerebellum, we
performed chromatin immunoprecipitation (ChIP) following massive parallel
sequencing using Histone H3 Lysine 4 methylation marks (Histone-seq). We
chose Histone H3 Lysine 4 monomethylation (H3K4me1) and trimethylation
(H3K4me3) to investigate if the identified transcripts are actively transcripted.
H3K4me1 was shown to be a dominant mark for active distal regulatory regions,
whereas at the same time H3K4me3 is present at active promoter regions,
although these methylation marks might overlap depending on the cellular
context (Heintzman et al. 2007; Barski et al. 2007; Wang et al. 2008; Boyle et al.
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2008, Robertson et al. 2009). Single nucleosome chromatin was prepared from
individual post-natal day 5 (P5) cerebella for ChIP, as at this stage the majority of
cells express Atoh1 and therefore the result will reflect the methylation state in
Atoh1-positive cells (see Extended Experimental Procedures for
details)(Robertson et al. 2009). In addition to two Histone-seq experiments for
each H3K4me1 and H3K4me3 mark, we included an IgG negative control. The
uniquely mapped reads were processed using MACS 1.3.5 (Supplement Table
S1)(Zhang et al., 2008). Our seq data was very robust as the biological replicates
were highly similar (H3K4me1 r=0.85; H3K4me3 r=0.95) and a saturation test
revealed, that all regions with 20-fold enrichment could be recovered (Figure 2a,
Supplement Figure 2a, Zhang et al., 2008). While the control showed almost no
distinct peaks, the H3K4me1-positive regions are mostly distinct from the
H3K4me3 peaks, arguing that in the P5 cerebellum, these marks might be more
exclusive than in other tissues (Figure 2a). H3K4me1 is closely associated with
genomic enhancers, which was reflected in the genomic distribution of the peaks
falling into intragenic (55%) and intergenic (41%) and not promoter (3%) and
exonic regions (<0.9%) (Supplement Table 2). H3K4me3 regions, on the other
hand, were higher represented in promoter regions (24%) and less enriched in
intergenic regions (15%) (Supplement Table 2).
If these marks correlate with active transcription, our Histone-seq data should
overlap with the recently published profile of p300 (Vise et al., 2009). p300 is an
acetyltransferase and transcriptional coactivator, which reliable predicts
enhancer/promoter specific transcription in the developing mouse nervous
system (Vise et al., 2009). As our Histone-seq data can only be a subset of the
p300 binding data, we analyzed the Histone-seq regions in respect to the p300
regions. We were able to detect a broad enrichment of H3K4me1 peaks around
the p300 regions arguing that these regions are transcriptional active (Figure 2b).
Not only H3K4me1, but also H3K4me3 regions were found near p300 positive
genomic regions, while a genomic control pool was not enriched (Figure 2b).
These results argue that both our H3K4me1 as well as H3K4me3 regions are
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actively involved in transcript at P5. Therefore, our identified cerebellar
transcripts should hold these marks.
To test this hypothesis, we normalized each transcript in the genome to 3 kb,
with the first nucleotide being the transcriptional start site (TSS) and the 3000th
nucleotide being the transcriptional end site (TES) and included 1 kb of upstream
and downstream sequence. As seen in Figure 1c, there was an overall
enrichment of our histone marks in transcripts, detected in the Atoh1+/+
cerebellum (green lines) as compared to genomic background (black lines)
(Figure 2c). The bottom 1000 transcripts (blue lines) corresponding to transcripts
not detected within the E18.5 cerebellar anlage tissue were not enriched,
whereas the top 1000 transcripts (red lines), the highest expressed transcripts,
possessed even stronger histone methylation marks (Figure 2c). Although the
transcriptional signature was generated at E18.5 – due to the lethality of Atoh1
knockout mice at birth – and the epigenetic signatures were generated with P5
cerebella, these results demonstrated that both tissues were very similar in their
transcriptional activity. This correlation was even more prominent, when we
analyzed the methylation status of the TSS of all transcripts (Supplement Figure
3). While there was little correlation (r=0.36) of all transcripts, it increased
drastically to r=0.84, if the methylation state of the DE transcripts was analyzed
(Supplement Figure 3).
It is interesting to note, although the H3K4me1 signature was widely distributed
throughout the genomic region (gene body as well as up- and downstream
regions), the mark was missing at the TSS, where the transcriptional machinery
is bound (Figure 2c). On the other hand, the highest enrichment of H3K4me3
marks was roughly one nucleosome (around 154 nts) upstream and downstream
of the TSS, after which it sharply dropped off (Figure 2c).
Using Histone H3 Lysine 4 mono- and trimethylation, we established an
epigenetic landscape in the developing cerebellum. In combination with the
transcriptional landscape we revealed that most transcripts, expressed in the
cerebellar anlage at E18.5 have active H3K4 methylation marks persisting during
early postnatal cerebellar development. Transcripts, directly regulated by Atoh1,
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should not only be expressed in the tissue (cerebellar transcriptome signature)
and their genomic region should participate in active transcription (cerebella
histone signatures), but also be bound by Atoh1 in vivo.
Atoh1 genomic binding signature
Since the commercially available antibodies were not suitable to reliable
immunoprecipitate endogenous Atoh1 in our hands, we generated an Atoh1
knock-in mouse model, with a triple FLAG tag attached to its C-terminus
(Atoh1FLAG/FLAG) (Flora et al., 2009). To overcome the limited volume of starting
material, as Atoh1 is expressed only in relatively small progenitor population, and
considering the P5 Histone signatures were correlated with the transcriptome
signature, we pooled four P5 cerebella for an Atoh1 ChIP-seq experiment
(r=0.77)(see Materials and Methods for details). We performed two independent
experiments as well as a negative control, where we interchanged the cerebella
tissue (CB) with forebrain tissue (FB), a tissue not expressing Atoh1. Both
experiments were highly reproducible (Figure 2a). We analyzed the Atoh1 ChIPseq data in the same fashion as the Histone-seq data and identified 19’227
putative Atoh1 binding regions (Supplement Table 4). We could reliable detect all
regions with a 40-fold enrichment (Supplement Figure 2). We chose 30 positive
binding regions and another 30 genomic regions, which were not found to be
enriched for further validation using ChIP followed by quantitative PCR (ChIPqPCR) (Chahrour et al., 2008). We validated 28 of the positive regions by ChIPqPCR and moreover did not detect any of the negative regions to be enriched
(Supplement Figure 4). We were pleased to see that our indentified regions were
highly conserved compared to the genomic background, arguing that these might
be putative regulatory elements (Supplement Figure 4). We chose to closer look
at the known target genes, Atoh1 and Barhl1. In addition to be able to validate
both identified regulatory elements of Atoh1 in its downstream enhancer, we
identified another Atoh1 binding region XX nts upstream of the TSS (Supplement
Figure 5). In the case of the BarH-like homeobox gene Barhl1, we could validate
the known enhancer in the 3’ UTR and identified three additional Atoh1 binding
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regions; XX and YY nts upstream of the TSS and ZZ nts downstream of the TES
(Supplement Figure 5).
To better understand how Atoh1 functions in vivo, we compared its binding
signature to the Histone signatures. The comparison of the heat maps indicated
most Atoh1-positive regions might hold a H3K4me1 mark, and only a small
portion a H3K4me3 mark (Figure 2a). This notion was supported by the
annotation analysis of the Atoh1-positive genomic loci, which fell into intragenic
(43%) and intergenic (49%) and not into promoters (5%), very similar to the
H3K4me1 signature (Supplement Table 2). In a direct comparison, Atoh1positive regions were correlative with the H3K4me3 (r=0.61) but highly correlative
with the enhancer mark, H3K4me1 (r=0.96) (Supplement Figure 3). Therefore it
was not surprising that the Atoh1-positve regions were also transcriptional active
as judged by the overlap of Atoh1-, p300-positive regions (Figure 2b).
As Atoh1 is one of the key transcriptions factors involved in cerebella
development, we suspected that the expressed transcripts, identified by mRNAseq, should not only be transcriptionally active but should also be enriched in
Atoh1 target regions. Towards this end, we analyzed the mRNA-seq transcripts
in respect to Atoh1 binding rather than Histone binding. As evident in Figure 2c,
the TOP1000 transcripts (red) were enriched in Atoh1 binding over the whole
gene body, while the bottom 1000 genes (black) were not (Figure 2c).
Although Atoh1 did bind to promoter regions, it was also enriched at the TES
regions of the TOP1000 transcripts (Figure 2c). This finding is of particular
interest since also H3K4me1 binding is enriched near the TES as well as the fact
that the two known target genes, Atoh1 and Barhl1 have a similar binding pattern
(compare (i) and (iii) in Figure 2c, Supplementary Figure 5).
Using Atoh1 ChIP-seq, we established the Atoh1 binding signature in the postnatal cerebellum, identifying 19’227 genomic regions. Moreover, we determined
that these regions are transcriptionally active as seen by the high p300
correlation and although Atoh1 binding regions fall within promoter regions of
highly expressed transcripts, Atoh1 mainly binds enhancer regions as evident by
the high H3K4me1 correlation making a gene annotation difficult.
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AtEAM characterizes the Atoh1 genomic signature
Atoh1 is a basic helix-loop-helix transcription factor, which has been shown to
bind to a specific DNA motif called E-Box with a consensus CANNTG motif
(Murre et al., 1989; Helms et al., 2000). Using the cis-regulatory element
annotation system (CEAS), we could show that only 8% (or 1544) regions did not
posses an E-Box (Figure 3a), while most possessed anything between 1 and 4
E-Boxes [1=3097 (16%); 2=4619 (24%); 3=4200 (21%); 4=2738 (14%); 5+=3029
(15%)] (Figure 3a). This strongly argued specific Atoh1 binding to the 19’227
identified genomic regions. Taking advantage of the newly identfied Atoh1-bound
sequences, we identified a 10mer palindromic sequence using a de-novo motif
finding algorithm (Figure 3b)(see Extended Experimental Procedures for details).
As this novel motif included an E-Box at its core positions but was identified
through Atoh1-specific sequences, we termed it Atoh1 E-Box Associated Motif, in
short AtEAM. Most Atoh1 regions possessed one or two [1=8070 (42%); 2=3275
(17%); 3+=1048 (5%)] (Figure 3a). As over half (53%) of the AtEAMs were
conserved in mammals, we might have identified an Atoh1 specific motif. The
high affinity of Atoh1 towards this novel motif was also recapitulated in the
distribution of the motif in respect to the summit of the Atoh1 binding regions.
While the E-Box motif was broadly distributed with a 200 nt window around the
summit, the AtEAM concentrated within a 75mer window (Supplementary Figure
6). Multiple regions contained not only an AtEAM but in close proximity another
E-Box, arguing that Atoh1 might act by high and low affinity binding
(Supplementary Figure 6).
We next assessed whether using our in vivo identified AtEAM is superior in the
identification of Atoh1 binding regions to computational approaches by analyzing
the E-Box and AtEAM prevalence within the Atoh1 signature as well as genome
wide (Figure 3b). Our novel AtEAM was 17.9 fold enriched in the Atoh1 signature
as compared to a randomized control, whereas the E-Box motif was only 5.4 fold
enriched (Figure 3b). Analysis of the whole mouse genome revealed 3.61% of all
AtEAMs (only 240’501 in total) were bound by Atoh1 in the cerebellum in vivo,
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compared to only 1.12% of all E-Box Motifs (1’274’088 in total) (Figure 3b). This
suggested our identified AtEAM was much more likely to discover a true Atoh1
target region than by E-Box based computational approach, assuming that the
AtEAM is the true Atoh1 binding motif.
We performed electrophoretic mobility shift assays (EMSA) in a neuroblastoma
cell line (N2A) with a labeled 30mer oligonucleotide harboring a centered AtEAM
to test the binding affinity of Atoh1. The EMSA demonstrated the ability of Atoh1
to bind to the AtEAM (Figure 3c, compare lane 1 and 2). This ability was specific,
as incubation of the lysate with an antibody against the FLAG tagged Atoh1
resulted in a supershift, while incubation of a non-labeled competitor oligo
abolished the interaction (Figure 3c, lanes 3 and 4). Moreover a mutated AtEAM
(in position 1 & 9) leaving the core E-Box intact resulted in a weaken but still very
strong interaction (Figure 3c, lane 5). This experiment demonstrated the high
affinity of Atoh1 towards our motif. We repeated the EMSA using Ascl1, which is
a related bHLH transcription factor, not expressed in the cerebellum to test for
selectivity (Nakada et al., 2003). Although Ascl1 could bind to AtEAM, the
interaction was at least one magnitude weaker than the Atoh1-AtEAM interaction
(Figure 3c, compare lane 2 with 6). This interaction was specific as it was
supershifted with the antibody (lane 7), competed by unlabeled oligo (lane 8) and
not competed with the mutated AtEAM oligo (lane 9), but was most likely due to
the ability of Ascl1 to bind a generic E-Box.
To better assess the specificity of the Atoh1-AtEAM interaction and its selectivity
we constructed a luciferase reporter, with only one AtEAM motif in a 30mer DNA
stretch in front of a minimal promoter. To more closely resemble the in vivo
situation, we used the Daoy medulablastoma cell line and transfected only small
amounts of reporter in combination with three closely related bHLH transcription
factors, Atoh1 itself, Ascl1 or Ngn1 (Figure 3d). With increasing concentrations of
Atoh1, the luciferase reporter was activated to roughly 2-fold (Figure 3d, green).
This activation was AtEAM specific, as the mismatched AtEAM was not activated
even by the highest dose (Figure 3d, green). In contrast, Ascl1 and Ngn1 failed
to activate the reporter (Figure 3d, red and purple). To further investigate the
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specificity, we performed mutation analysis, in which we introduced point
mutations for individual nucleotides with the exception of the E-Box core. Atoh1
was still able to activate the reporter constructs baring one mismatch, although
the activation was weaker than in the original non-mutated reporter (Figure 3e).
Reporter constructs harboring two point mutations, either consecutive or not,
almost abolished the Atoh1 activation (Figure 3f). Atoh1 not only activated these
reporter constructs but also 15 original Atoh1 binding regions cloned in front of a
minimal promoter (Figure 3f). The fragments (all between 200 and 300 ntsin size)
were grouped into no AtEAM, one AtEAM, one AtEAM with 1 mismatch (1MM)
and one AtEAM with 2 mismatches (2MM). While fragments with no AtEAM or
2MM AtEAM failed to get activated by Atoh1, we saw a robust induction if one
AtEAM with or without 1MM was present (Figure 3f). Interestingly, additional EBoxes within the original sequence had little effect on the overall induction as
seen by the similar induction patterns (Figure 3f).
Using the Atoh1 binding regions in combination with electrophoretic mobility shift
and luciferase reporter assays, we discovered AtEAM, a novel 10mer
palindromic DNA binding motif. It is highly selective for Atoh1 and is so abundant
in the Atoh1 genomic signature that out of five randomly chosen regions, three
will contain at least one AtEAM.
Atoh1 targetome
As Atoh1 was mainly bound to enhancers, sometimes far away from any TSS,
we decided to use another approach to generate a meaningful Atoh1 taregt list.
We combined three attributes, which every transcript should hold if regulated by
Atoh1: first, it should be expressed in the developing cerebellum in an Atoh1dependent manner; second, Atoh1 should bind to the genomic location of the
transcript and third, the genomic region should be transcriptionally active. To
address these three attributes, we established an Atoh1 transcriptome, by ranked
each individual transcript in the genome by their differential expression; an Atoh1
cistrome, by ranking each transcript by their binding affinity to Atoh1 and an
Atoh1 epigenome, by ranking the transcripts by their transcriptional activity as
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measured by Histone H3K4 methylation (Supplement Tables 3, 4, 5,
respectively). We combined these attributes using the rank product method,
which more closely modeled the biological circumstances, to create an Atoh1
target gene list for all transcripts in the mouse genome, the AToh1 targetome
(Supplement Table 6)(see Extended Experimental Procedures for details)
(Breitling et al., 2008). We identified 633 transcripts with a p-Value of less than
0.01, which corresponded to 601 genes. Among these genes were the known
target genes, Atoh1, Barhl1 and Gli2, but also crucial differentiation genes, such
as neurogenic differentiation 1, 2 and 6 (Neurod1, Neurod2 and Neurod6) (REF).
To better understand the Atoh1 targetome, we analyzed the 601 genes in four
different ways: first, by their knockout phenotypes; second by their involvement in
cellular processes; third, by there affiliation to developmental pathways; and
fourth, by their ability to influence cerebella development.
The Atoh1 targetome is enriched in Atoh1 knockout phenotypes
To validate the Atoh1 targetome, consisting of 601 genes, we first examined if
the identified genes are associated with the same knockout phenotypes as Atoh1
itself. We used the Mouse Genome Database phenotypes to identify genes,
which were reported to have an ‘abnormal cerebellar granule layer’
(MP:0000886) and ‘abnormal cerebellum development’ phenotype (MP:0000854)
(http://www.informatics.jax.org)(Blake et al., 2008). These groups included 125
and 195 genotypes with 89 and 114 genes associated, respectively. Within our
601 Atoh1 target genes, 211 had a published knockout of which 15 were
categorized as having an ‘abnormal cerebellar granule layer’ phenotype (pValue=2.61E-17) and 16 to have an ‘abnormal cerebellum development’
phenotype (p-Value=1.06E-16) as compared to a random list (Figure 4a,
Supplement Table 7). These two phenotypes included 22 genes, which we
validated by ChIP-PCR in P5, Atoh1FLAG/FLAG cerebella (CB) in vivo using the
forebrain (FB) as negative control tissue (Figure 4b). We analyzed the
expression pattern of six genes, Atoh1, Barhl1, Ccnd2, Pax6, NfiA and Zfp238 in
Atoh1+/+ and Atoh1-/- E18.5 cerebella anlage (Figure 4c). All six genes were
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expressed in the external granual cell layer at E18.5 and their expression was
lost in the Atoh1 knockout (Figure 4c). These data strongly supports the notion,
that we could identify Atoh1 target genes using our Atoh1 targetome, which
combined a transcriptome, cistrome and epigenome analysis.
Atoh1 regulates a diverse set of cellular processes.
Having established the Atoh1 targetome, with the 601 highly likely Atoh1 target
genes, we used online databases to further study the function of Atoh1 in the cell
(see Extended Experimental Procedures for details). We were not surprised to
find that 108 genes were associated with transcription (p-Value=1.58E-09) and
35 with cell cylce (p-Value=4.87E-04)(Supplement Table 8). These two
processes cannot be clearly separated as a transcription factor might activate
transcription and at the same time be involved in cell cycle regulation. One
example is Gli2, a zinc finger transcription factor involved in cell cycle regulation
(Flora et al., 2009). Transcription and cell cycle strongly influence other cellular
components, which were also identified in our targetome, such as chromosomal
organization (27 genes, p-Value=2.11E-06) and cytoskeleton organization (32
genes, p-Value=7.42E-07). Additional enriched categories were; Ribonucleopore
complex genes such as ribosomes (23 genes, p-Value=4.09E-08); RNA
processing, such as splicing (18 genes, p-Value=5.85E-05); Metabolic processes
(30 genes, p-Value=3.09E-04), including lipid and carbohydrate metabolism as
well as genes associated with the Mitochondrion, such as mitoribosomes (41, pValue=5.07E-03) (Supplement Table 8). As with the knockout analysis, we
validated several of these genes by ChIP-PCR (Supplement Figure S7).
Atoh1 conveys competence to granule cell precursors to respond to external
stimuli
The granule cell precursors are exposed to a multitude of external signals, which
results in the orderly development of the cerebellum (Behesti and Marino, 2008).
We explored the possibility that Atoh1 might regulate components of different
signaling pathways to help guide the cell through development. Therefore we
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conducted a literature based pathway analysis (see Extended Experimental
Procedures for details). We could assign 100 genes to several pathways,
including some genes, which were part of multiple pathways; i.e. Ccnd2, which is
involved in sonic hedgehog and wnt signaling (Kenney and Rowitch, 2000;
Rulifson et al., 2007) (Supplement Table 8). We were surprised about the extend
of which Atoh1 seems to influence signaling pathways as we could identify 15
genes belonging to sonic hedgehog, 15 genes to Notch, 14 genes to TGF-beta
and 6 genes to retinoic acid signaling as well as 14 genes associated with wnt
(Supplement Table 8). Moreover, we identified MAP kinase signaling cascades
as a potential new signaling mechanism through which cerebella development
might be influences as 19 genes were associated with ERK signaling, 14 with
NF-kappa B and 12 with JNK signaling, but we did not identify genes involved in
p38 MAP kinase signaling (Supplement Table 8). Using ChIP-PCR, we validated
a subset of these genes (Supplement Figure S7). We noticed that the signaling
pathways could be roughly grouped into proliferation, differentiation and
migration with some overlap, as Notch signaling is involved in proliferation as
well as differentiation (REF). This led us to investigate if Atoh1 is not only
involved in granule cell precursors proliferation (Flora et al., 2009) and
differentiation (Ben Arie et al., 1997), but also migration.
Atoh1 directly influences granule cell precursors migration in vivo
During post-natal development of the cerebellum, the granule cell precursors
undergo a rapid clonal expansion, start their differentiation program and migrate
inwards. The Atoh1 targetome reflected these key features, not only by their
signaling pathway affiliation (see above) but also by their gene ontology, as the
three terms, proliferation (GO: 0008283 / 0042127 / 0008284; 35 genes; pValue=9.81E-07), differentiation (0030154 / 0030182 / 0045664 / 0000904 /
0048667; 74 genes; p-Value=7.53E-05) and migration (0016477 / 0030334; 21
genes; p-Value=3.41E-05) were enriched within our 601 target list (Supplement
Table 8). Interestingly, Atoh1 might play a direct role in granule cell precursors
migration as the two receptors, Plxn2b and Cxcr4, which were shown to be
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involved cerebella development were identified as Atoh1 target genes
(Supplement Table 8)(Deng et al., 2007; Zou et al., 1998). In addition three
members of the Semaphorin family, Sema6a, Sema6c, Sema7a were among the
migratory genes, of which Sema6a was shown to be involved in granule cell
migration (Renaud et al., 2008). We further investigated this possibility by
validating a subset of these genes by ChIP-PCR (Figure 6a). Furthermore, we
could show that, these genes were highly expressed in the external granual layer
in wildtype mice, while their expression is vanished in Atoh1 knockout mice
(Figure 6b).
To establish a direct connection between Atoh1 function and granule cell
migration, we used another Atoh1 mouse model, in which Atoh1 is flanked by
LoxP sites and therefore can be deleted using the Cre/LoxP system (Shroyer et
al., 2007). To investigate if a granual cell still migrates, after loosing Atoh1 we
cultured Atoh1+/+ and Atoh1 floxed (Atoh1Flox/Flox) P0 cerebella ex vivo. We
knocked out Atoh1 using a CMV-cre-IRES-GFP containing virus and analyzed
the infected cells two days later by immunofluorescence using a GFP antibody.
Using this ex vivo approach, we could be sure to only infect granule cells, which
resided within the external granule layer, the location of the cycling precursors. In
the wildtype scenario, we detected GFP-positive cells within the external granule
layer – Atoh1-positive, cycling precursors – as well as migrating cells (Figure 6c).
In the Atoh1Flox/Flox cerebellum, the GFP-positive cells resided in the outer granule
cell layer and failed to migrate (Figure 6c, arrow). The GFP-positive cells most
likely did not trans-differentiate or die due to loss of Atoh1, as previously shown
(Flora et al., 2009). As Atoh1 activates a variety of genes, which fall into very
different – sometime opposing categories, such as proliferation, differentiation
and migration, Atoh1 might make the cell competent to react to and modulate
different developmental stimuli, which is underlined by the diversity of gene
groups we could identify (see above). If this were true, Atoh1 target genes should
be able to take over a subset of Atoh1 functions to allow the cell to terminally
differentiate. We investigated this hypothesis by repeating the ex vivo migration
assay at a later time point (Figure 6d). Using P6 cerebellar, wildtype infected
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cells migrated inwards similar to the P0 data (Figure 6d). Interestingly, deleting
Atoh1 at this stage had no influence on cell migration as the Atoh1-floxed
cerebella showed the same phenotype as the wildtype (Figure 6d).
We could demonstrate that the Atoh1 targetome is enriched in genes, which are
involved in the three key steps of granule cell development, proliferation,
differentiation and migration. We showed evidence that Atoh1 might be a key
competence factor, which allows a cell not only to undergo the transition between
proliferating precursor to post-mitotic differentiation but also allows the cell to
respond to migratory stimuli.
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