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Epigenomic signatures underpinning the axonal regenerative ability of dorsal root ganglia sensory
neurons
Ilaria Palmisano1*§, Matt C. Danzi2,3§, Thomas H.Hutson1, Luming Zhou1, Eilidh McLachlan1, Elisabeth
Serger1, Kirill Shkura4, Prashant K. Srivastava4,5, Arnau Hervera1,.6, Nick O’Neill2, Tong Liu7, Hassen
Dhrif7, Zheng Wang7, Miroslav Kubat8, Stefan Wuchty3,7,9,10, Matthias Merkenschlager11, Liron Levi12,
Evan Elliott12, John L. Bixby2,3, Vance P. Lemmon2,3 and Simone Di Giovanni1*
1. Centre for Restorative Neuroscience, Division of Brain Sciences, Department of Medicine, Imperial
College London, London, UK.
2. The Miami Project to Cure Paralysis, University of Miami, Miami, FL, USA.
3. Center for Computational Science, University of Miami, Miami, FL, USA.
4. Integrative Genomics and Medicine, Division of Brain Sciences, Department of Medicine, Imperial
College London, London, UK.
5. National Heart & Lung Institute, Imperial College London, London, UK.
6. Molecular and Cellular Neurobiotechnology, Institut for Bioengineering of Catalonia (IBEC),
Barcelona, Spain.
7. Department of Computer Science, University of Miami, Miami, FL, USA.
8. Dept. of Electrical & Computer Engineering, University of Miami, Miami, FL, USA.
9. Department of Biology, University of Miami, Miami, FL, USA.
10. Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA.
11. MRC, London Institute of Medical Sciences, Faculty of Medicine, Imperial College London,
London, UK.
12. Faculty of Medicine, Bar Ilan University, Safed, Israel.
§The authors contributed equally to the work
* Correspondence should be addressed to: Simone Di Giovanni, MD, PhD, or Ilaria Palmisano, PhD,
Division of Brain Sciences, Imperial College London, Hammersmith Hospital Campus, Imperial College
London, Du Cane Road, W12 0NN. e.mail: [email protected] or
KEY WORDS
1
Chromatin remodelling, Epigenetics, Open Chromatin, Transcription Factors, Spinal Cord Injury, Axon
Regeneration, Machine learning, CTCF
ABSTRACT
Axonal injury results in regenerative success or failure depending on whether the axon lies in the
peripheral or the central nervous system respectively. Here, we addressed whether epigenetic
signatures in dorsal root ganglia discriminate between regenerative and non-regenerative axonal
injury. We performed RNA, chromatin immunoprecipitation for H3K9ac, H3K27ac, and H3K27me3
and Assay for Transposase-Accessible Chromatin sequencing in DRG after sciatic nerve or dorsal
column axotomy. Distinct histone acetylation and chromatin accessibility signatures correlated with
gene expression following peripheral but not central axonal injury. DNA footprinting analyses
revealed novel transcriptional regulators associated with regenerative ability. Machine learning
algorithms inferred the direction of the majority of gene expression changes. Neuronal conditional
deletion of the chromatin remodeler CTCF impaired nerve regeneration implicating chromatin
organization in the regenerative capacity. Altogether, we offer the first epigenomic map providing
insight into the transcriptional response to injury and into the differential regenerative ability of
sensory neurons.
2
INTRODUCTION
A peripheral nerve injury initiates multiple signalling pathways, converging into the activation of
transcription factors (TF) 1-3, and resulting in the fine tuning of a robust regenerative programme
characterized by the expression of regeneration associated genes (RAGs) 4. In contrast, poor
neuronal regenerative gene expression as well as the presence of glial inhibitory signals lead to
regenerative failure upon axonal injury in the central nervous system (CNS) 5.
The dorsal root ganglia (DRG) sensory neurons allow the direct comparison of molecular
events initiated in the same cell body upon a peripheral vs a central axonal lesion. The DRG
pseudounipolar neurons extend a peripheral regeneration-competent axonal branch that lies in the
peripheral nervous system (PNS) and a central regeneration-incompetent axonal branch that enters
the dorsal columns in the spinal cord 6.
Epigenetic mechanisms play a critical role in gene regulation by controlling the accessibility
of gene promoters and enhancers to TFs 7. These include DNA methylation and hydroxymethylation,
histone post-translational modifications (HPTMs), exchange of histone variants, and binding or
displacement of architectural proteins 8-10. Recent evidence has begun to uncover the importance of
epigenetic processes in regulating the neuronal regenerative transcriptional programme 11-14. Cavalli
cell, finelli JN and pcaf radhika’s papers must be added Nevertheless, it remains to be determined (i)
whether chromatin conformation and accessibility are modified after axonal injury, (ii) whether
chromatin accessibility is associated with other epigenomic reprogramming events, such as histone
modification at cis-regulatory elements, and (iii) whether changes in the transcriptional programme
might be predictive of regenerative success vs failure.
Therefore, we systematically investigated epigenomic signatures in response to axonal
injury. Our study provides the first comprehensive map of epigenomic modifications after axonal
injury and it demonstrates that epigenetic and transcriptional changes discriminate between
regenerative peripheral and non-regenerative central axonal injury. Moreover, it provides new
3
insight into the epigenetic mechanisms that might be harnessed to reprogram neurons into a
regenerative mode.
RESULTS
Overview of the RNAseq, ATACseq, and ChIPseq datasets
Initially, we collected sciatic DRG 24h after an injury of the peripheral branches following a sciatic
nerve axotomy (SNA) vs. sham or after injury of the central branches following a dorsal column
axotomy (DCA) vs. laminectomy (lam). Thereafter, we performed: i) RNAseq 15; ii) ATACseq, in which
the Tn5 transposase exclusively integrates sequencing adapters into open and accessible chromatin,
allowing identification of accessible chromatin regions 16; iii) ChIPseq for H3K9ac 13 and H3K27ac
(marks of active promoters and enhancers) and H3K27me3 (repressive mark) (Figure 1A), which
resulted in a significant DNA enrichment with respect to control IgG (Figure S1A). Pairwise
comparisons of signal within peaks in ATACseq replicate samples indicated high reproducibility
among triplicates (Pearson correlation coefficient threshold >0.7). We also observed strong
reproducibility among replicates in the signal surrounding the differentially accessible (DA)
transcription start sites (TSS+/-1Kb) (Figure S1B). In sham or laminectomy treated animals, ATACseq,
H3K27ac, and H3K9ac signal was identified preferentially at promoters, enhancers, and gene bodies
(Figure 1B and S1C, red), whilst centromeric and telomeric heterochromatin regions were poorly
represented (Figure 1B and S1C, pink and blue). In contrast, H3K27me3 was found selectively
enriched at genomic locations of repressed chromatin, such as centromeric and pericentromeric
regions (red) and depleted at enhancers and promoters of expressed genes (blue). The integration of
chromatin accessibility and ChIPseq signal at the TSS with gene expression data revealed a strong
correlation, with highly expressed genes showing higher levels of chromatin accessibility and
acetylation of H3K27 and H3K9, and lower levels of methylation of H3K27 in comparison to the lower
expressed genes (Figure 1C and D).
Peripheral axotomy induces an increase in chromatin accessibility and in histone acetylation
4
We quantified the changes in chromatin accessibility and histone occupancy upon the two injury
paradigms. Specifically, SNA resulted in a large gain in chromatin accessibility (2,447 more accessible
and 85 less accessible genes, Pvalue<0.05), while few alterations in chromatin accessibility occurred
after DCA (389 more accessible and 106 less accessible genes, Pvalue<0.05) (Figure 2A-B and
Supplementary Data 1). Gene ontology (GO) analysis of the genes showing a gain in chromatin
accessibility after SNA revealed a significant enrichment (Pvalue< 0.01) for several functional
categories related to calcium channel activity, regulation of transcription, chromatin remodelling,
cell signalling, axon transport, and proteasome regulation (Figure 2C and Supplementary Data 2).
Consistent with a more relaxed chromatin conformation upon peripheral injury, H3K9ac and
H3K27ac occupancy was increased on 1,909 and 1,205 TSS regions (Pvalue<0.05) respectively, while
only 64 and 136 TSS regions showed decreased occupancy; 511 TSS regions showed a decreased
H3K27me3 occupancy (Figure 2D). In contrast, the changes after DCA were more modest: the
greatest was a decreased occupancy of H3K27me3 on 587 TSS regions (Figure 2D). A similar pattern
was found at the gene body level (Figure S1D and E), with several genes showing an increased
occupancy of H3K9ac and H3K27ac at both TSS and gene body (Figure S1F). The level of histone
acetylation at the promoter regions correlated with the degree of chromatin accessibility (Figure 2E
and S1G; genes with increased accessibility showed preferential H3ac occupancy: Pearson corr=
0.039 (H3K9ac_SNA); 0.033 (H3K27ac_SNA); 0.01 (H3K27me3_SNA); 0.024 (H3K9ac_DCA); 0.028
(H3K27ac_DCA); -0.072 (H3K27me3_DCA)).
Taken together, our findings imply that peripheral but not central axonal injury promotes a
more relaxed chromatin conformation that may allow the expression of genes involved in
regenerative-associated molecular processes.
Peripheral axotomy induces robust gene expression changes associated with enhanced chromatin
accessibility
5
In line with the ATACseq and ChIPseq findings, the transcriptome analysis revealed that a higher
number of genes were differentially regulated after SNA than after DCA (3,082 genes upon SNA and
830 upon DCA, Pvalue<0.05) (Figure 3A). Interestingly, only a minority of genes (16) were co-
regulated between the two injuries, and over 40% of the genes differentially expressed (DE) upon
DCA showed an opposite regulation after SNA (Figure 3B). In agreement with previous findings 17-19,
GO term enrichment analysis (Pvalue<0.01) of the upregulated genes upon SNA identified several
categories related to transcription, inflammation, developmental and biosynthetic processes, and
injury response, while genes that were downregulated after SNA were enriched for categories
related to ion channels, ion transport, axonogenesis, and synapse (Figure S2A and Supplementary
Data 3). In contrast, DCA-upregulated transcripts mainly belonged to energy metabolism, particularly
related to mitochondrial oxidative phosphorylation (Figure S2A and Supplementary Data 3). Pathway
analysis of the DE genes revealed significant enrichment (Pvalue<0.05) for multiple axonal
regenerative signalling pathways upon SNA, including neurotrophin, JAK-STAT, MAPK, insulin, and
mTOR signalling (Figure S2B and Supplementary Data 3). In contrast, pathways enriched upon DCA
were related to neurological disorders and mitochondrial oxidative metabolism.
The enhanced accessibility of chromatin regions upon SNA correlated with changes in gene
expression (Figure 3C, Fisher’s exact Pvalue=2e-183), while decreased or unchanged chromatin
accessibility was associated with non-DE genes (Fisher’s exact Pvalue=2e-18 and 4e-123). This close
correlation was further supported by the Gene Set Enrichment Analysis (GSEA, see Methods)
showing that genes with gain in accessibility after SNA were enriched for upregulated transcripts
(Figure 3D, Normalized Enrichment Score, NES=1.18, FDR=0.06). In contrast, the few less accessible
genes (85) were not significantly enriched (Figure 3E).
Gene clusters functionally associated to axonal regeneration share similar epigenetic signatures
We next combined the chromatin landscape data with the differential gene expression results upon
SNA or DCA. Across the genome, DE genes were marked for ATACseq and H3K9ac and H3K27ac
6
active histone marks (Figure 4A and S3A), resulting in a correlation between gene expression and
these epigenetic signals (Figure S3B). Specifically, 321 and 305 upregulated genes correlated with an
increased H3K9ac and H3K27ac occupancy at the promoter and gene body regions following SNA
respectively, representing altogether about 35% of the upregulated genes upon SNA; fewer
downregulated genes (252 and 144) showed increased histone acetylation (Figure 4B-C). Following
DCA, we also found 59 upregulated genes that correlated with a decreased occupancy of H3K27me3
(Figure 4B-C). Upregulated genes after SNA with increased histone acetylation occupancy were
enriched for GO categories related to transcription and nucleosome/chromatin assembly,
underlining the importance of chromatin remodelling and gene expression regulations after
regenerative axonal injury (Figure 4D and Supplementary Data 4). Genes known to be associated
with axonal regeneration were well represented among the upregulated transcripts, and they
localised on hyperacetylated chromatin regions more accessible upon SNA (Figure 4E and S3C).
Together, these results indicate that specific gene functional categories share an epigenetic
pattern characterized by a more open and hyperacetylated chromatin configuration.
Enhancers are associated with increased chromatin accessibility following peripheral axonal injury
only
Enhancers are cis-regulatory elements that can mediate gene transcription via long-range
interactions with their target genes. We took advantage of the Encyclopedia of DNA Elements
(ENCODE) collection of more than 200,000 potential mouse enhancers 20, 21 and filtered the list for
those with an overlapping H3K27ac peak in at least one of the tested biological conditions to identify
enhancers active in DRGs (Supplementary Data 5). Differential accessibility analysis revealed that a
higher number of enhancers were more accessible after SNA than DCA (3,677 more accessible after
SNA and 1,620 after DCA, Pvalue<0.05, Figure 5A). Enhancers with gain in chromatin accessibility
upon SNA were about 8 fold more abundant than the ones with decreased accessibility (Figure 5A).
However, while more enhancers had changes in H3K27ac upon SNA than DCA (5,414 after SNA and
7
1,109 after DCA), a higher number of those differentially acetylated enhancers had decreased than
increased H3K27ac signal after SNA (2,168 enhancers with increased and 3,246 with decreased
signal) (Figure 5B). We also found a higher number of enhancers with a decreased H3K27me3
occupancy upon DCA (1,148) versus SNA (687), which might reflect release from Polycomb complex
2 repression (Figure 5B).
We next exploited the enhancer/promoter unit (EPU) annotation from Shen et al. 20,
retrieving information about predicted enhancer-dependent target genes. While many DE genes
were identified as targets of enhancers with differential histone marks, we did not observe a clear
pattern between the regulation of the enhancer and the direction of gene expression change (Figure
5C).
Many of the axonal regenerative pathways identified so far are also involved in neuronal
plasticity during development 22. Therefore, we used a published enhancer activity profiling in the
developing forebrain across a time course from embryonic day 11 to post-natal day 56 22 to assess
how many of these developmentally regulated enhancers were responsive to injury. We found that a
high proportion of injury-responsive neuronal enhancers were developmentally regulated, including
20% of the enhancers showing increased H3K27ac and about 50% increased chromatin accessibility
(Figure 5D-F).
Distinct repertoires of transcription factors bind to accessible promoters and enhancers after
peripheral or central axonal injury
Since promoters and enhancers become more accessible after a peripheral axotomy, we decided to
identify potential TFs that could operate in proximity of accessible DNA cis-regulatory elements. To
this end, we performed a differential TF footprinting analysis using the Bivariate Genomic
Footprinting (BaGfoot) pipeline, which allows the prediction and identification of putative TF
activators or repressors by quantifying the difference in footprint depth (FPD) and flanking
accessibility (FA) between two experimental conditions 23. The analysis (Supplementary Data 6)
8
revealed a set of TFs showing increased occupancy with a deeper footprint and with increased
chromatin accessibility at enhancers and promoters following SNA only (Figure 6A and C, I quadrant,
and 6 E). They included several transcriptional activators acting at DNA motifs recognized by the FOS,
JUN, CREB, and CCAAT/Enhancer binding protein (CEBP) families (Figure 6A and C). Many of these
are well-established factors involved in axon regeneration including after SNA 1-3, 17, 24-26.remove refs
24-26 here Other TFs, including members of the Homeobox (HOX) and the ONECUT family, were
released after SNA resulting in reduced chromatin accessibility in the flanking regions (Figure 6A and
C, III quadrant). Interestingly, only PROP Paired-Like Homeobox (PROP), POU Paired Box (PAX), AT-
Rich Interaction Domain (ARID), and HOX factors were identified as repressors, inducing a decrease
in FA (Figure 6A and C, II quadrant). DCA was characterized by the lack of substantial changes in TF
binding with a tendency for TF repressor recruitment (Figure 6B and D, II quadrant, and 6F). This
provides the first evidence that a regenerative peripheral axonal injury triggers robust transcriptional
activation via the establishment of accessible and dynamic chromatin, while a non-regenerative
central injury is mainly associated with a poor recruitment of transcription regulators.
Next, we asked whether HDAC inhibition leading to increased histone acetylation would
mimic enhanced gene accessibility and expression observed following SNA. Immediately following
DCA, we intraperitoneally injected MS275, a HDAC class I inhibitor previously shown to enhance
axon growth in vitro and to reduce axon retraction in vivo following spinal cord injury (SCI) 27. As
expected, MS275 delivery induced increased H3 K9 and K27 acetylation and changes in chromatin
accessibility that correlated with gene expression (Figure S4A-D and Supplementary Data 7).
Interestingly, MS275 treatment resulted in only a 12% overlap with the SNA-dependent
transcriptional programme (Supplementary Data 8). Indeed, the GO analysis revealed that the
chromatin accessibility changes upon MS275 occurred at different genomic locations compared to
SNA. Increased chromatin accessibility was observed for genes mainly related to neuronal
differentiation, axonogenesis, and nucleoside metabolism. Decreased chromatin accessibility was
found in genes mainly related to chromatin organization, mRNA processing and translation (Figure
9
S4E and Supplementary Data 9). The BaGfoot analysis revealed that MS275 treatment was
associated with the increased or decreased occupancy of several TFs at the level of TSS and
enhancers (Figure S4F and G). However, the TF repertoire was clearly distinct from the one observed
upon SNA (Fig.6A and C). Together these data indicate that SNA-dependent changes in chromatin
accessibility and gene expression are poorly recapitulated by HDAC inhibition following DCA.
To gain further insight into the SNA-dependent genes that are controlled by the identified
TFs, we used the Hidden Markov Model (HMM)-based IdeNtification of Transcription factor
footprints (HINT) pipeline 28. This allows the retrieval of the chromosome coordinates for the
footprints found in the DA regions upon SNA or DCA (Supplementary Data 10). A complementary in
silico enrichment motif analysis performed at the level of the DA regions and of the DE genes
revealed a convergence on similar sets of TFs (Figure S5 and Supplementary Data 11). When
comparing SNA to sham, we found a clear relationship between changes in TF expression and their
footprint signal (Spearman correlation r=0.53; Pvalue=2.523e-8 Figure S6A). However, the
comparison of DCA to control laminectomy did not show any such correlation (Spearman correlation
r=0.02, Pvalue=0.829, Figure S6B), consistently with the BaGfoot analysis. About 60,503 and 33,978
footprints were found at DA regions after SNA and DCA, respectively, and these were associated
with a total of 580 and 486 TFs (Supplementary Data 10), mostly shared between promoters and
enhancers.
We then retrieved the target genes of the footprints found by HINT within DA promoters
and enhancers (see Methods and Supplementary Data 12). About 2,145 and 618 genes were found
associated with footprints identified in DA regions following SNA and DCA respectively. Among
them, a higher number showed an increased occupancy of H3K9ac and H3K27ac, and, as expected,
increased chromatin accessibility upon SNA than DCA, resulting in 478 genes significantly DE
following SNA whilst only 40 genes DE upon DCA (Figure 7A-B). This suggests dissimilar TF
recruitment that differentially regulates the transcriptional programme between peripheral and
central axonal injury.
10
Since DRG contain both neurons and non-neuronal satellite cells, we took advantage of the
single DRG neuron RNAseq analysis performed by Usoskin and colleagues 29 to measure how many of
the identified TFs and DE genes after SNA or DCA were expressed in neurons. Although we were
unable to determine expression levels in DRG cell types other than neurons, as they have not been
profiled with single-cell resolution yet, the large majority of TFs and genes in our analysis was indeed
expressed in neurons (Figure S6C).
Random forest classifier is able to differentiate between upregulated and downregulated genes
following injury based on TF footprints
We next examined whether epigenetic and transcriptional information collected in our datasets
could be used to predict whether genes are upregulated or downregulated after each axonal injury.
To this end, we used the program TEPIC (see online Methods in Supplementary Information) to
generate a score quantifying the effect of each TF on the expression of each gene using HINT
footprints and chromatin accessibility in each injury condition as input, including additional features
for histone occupancy and DNA content at the TSS and gene body. We next subtracted the scores in
the uninjured from the scores in the injured condition for each DE gene (except for the DNA content
features, as those values do not change in response to injury). Finally, we labelled each gene with a
specific class according to its change in expression after injury.
A random forest classifier trained on this input data as part of a five-fold cross-validation was
then able to make out-of-set predictions on whether a gene is upregulated or downregulated with
74% accuracy following peripheral axotomy (75% sensitivity and 72% specificity) and with 77%
accuracy following central axotomy (84% sensitivity and 68% specificity) (Figure 7C). Such high
performance indicates that the random forest algorithm successfully learned patterns in the input
data that explain whether a gene is upregulated or downregulated after axotomy.
In order to resolve which specific features were most informative to predict the direction of
gene expression changes, we employed a feature selection method based on particle swarm
11
optimization (PSO) (see On line Methods in Supplementary Information). Besides the CG content,
the top 10 non-DNA based features most frequently selected from the peripheral axotomy dataset
(Figure 7D) were related to chromatin accessibility, H3K27ac level and several TFs. Most of these
bind the same motifs that were highlighted by the BaGfoot analysis as bound to putative recruited
activators (Figure 6A and C): i.e. TFs that bind to cAMP Response Elements (CRE), and TFs of the AP-1
family. Additional TFs frequently selected by the PSO include Zinc finger and BTB domain containing
7C (ZBTB7C) and members of the ETS variant (ETV) and pre B cell leukemia homeobox (PBX) families
as well as interesting novel putative regulators of axon regeneration such as SOX10 and NANOG.
From the DCA dataset, the PSO algorithm retrieved TFs found in the previous BaGfoot analysis,
mainly associated with a reduced accessibility, as well as additional factors (Figure 7E).
The chromatin organiser CTCF is implicated in the conditioning-dependent DRG regenerative
growth and in the regeneration of DRG sensory axons
The ATACseq experiments allowed the identification of footprints of factors involved in chromatin
remodelling, such as FOXA, ARID3A, Nuclear Factor1 (NF1), and the CCCTC-binding factor (CTCF)
(Supplementary Data 10). Given the role of CTCF in neuronal biology 30, and its wide influence on
both transcription and chromatin organisation 31, we investigated if CTCF signatures would mark
axonal injury-responsive genes with chromatin changes. The in silico motif enrichment analysis
revealed an enrichment of CTCF at the promoter of injury-dependent DE genes (Supplementary Data
11). To better identify possible target genomic regions, we combined our datasets with published
and curated databases of CTCF binding sites (BS) 32, 33. When analysed at the TSS level, the genomic
regions containing CTCF BS were characterized by higher chromatin accessibility (1034 genes) and
higher histone acetylation (728 and 302 genes with increased H3K9ac and H3K27ac occupancy,
respectively) after SNA compared to sham, while this did not occur following DCA (Figure S7A). Since
CTCF BS were also found on active enhancers in DRG (Figure S7B), we then identified target genes
having a CTCF BS at promoters (TSS +-/- 1 Kb) or enhancers (+/- 1Kb) (Supplementary Data 13). We
12
found that 666 and 880 target genes, representing about 50% of the injury-dependent
transcriptional programme, were up and down-regulated upon SNA, respectively (Figure S7C). GO
enrichment analysis of these genes suggests that CTCF might contribute to the regulation of a wide
range of the SNA-dependent transcriptional programme (Figure S7D and Supplementary Data 14).
Interestingly, interrogating a published RNAseq from CTCF-cKO mouse hippocampus (GSE84175)33,
we found that several of these GO categories were affected by the deletion of CTCF, including cell
adhesion, neuron projection, regulation of synaptic transmission, ion transport/binding, and cell
signalling (Figure S7D). CTCF BS were also found in a considerable percentage of the RAGs in our
datasets (compare Figure S7E with Figure 4E). Microccocal nuclease (MNase) digestion assays for
selected RAGs (Cebpd, 2m, Fos, KLf16, and Bdnf) confirmed increased chromatin accessibility upon
SNA (Figure S7F and G).
Next, we assessed whether CTCF was required for conditioning-dependent regenerative
growth of DRG sensory neurons and for axonal regeneration in vivo. CTCF-floxed and WT mice were
injected with an AAV-CreGFP in the sciatic nerve 4 weeks before a sciatic nerve crush (SNC) injury. As
anticipated, CTCF deletion resulted in a strong decrease in the protein expression (Figure 8A and B)
and in a consistent number of neurons with reduced CTCF expression (84.6+14.2% out of the total
number of infected neurons, N=5). Analysis of DRG neurite outgrowth in WT versus CTCF-cKO GFP-
positive neurons plated 24h after injury showed that the conditioning lesion-dependent increase in
neurite outgrowth was significantly reduced in CTCF-cKO neurons (Figure 8C and D). To investigate
the role of CTCF in vivo, sciatic axonal regeneration was assessed at 1, 3 and 7 days following SNC.
We measured the intensity of Stathmin-like 2 (STMN2 or SCG10), marker of regenerating axons 34, at
different distances from the crush site and generated the “regeneration index”, the distance at
which SCG10 intensity is half that at the crush site, as previously reported 34. Data analysis revealed
that the degree of axonal regeneration was significantly reduced up to 7 days (Figure 8E-M).
However, we found no difference between CTCF-cKO and WT in the number of regenerating
retrogradely traced neurons after injection of cholera toxin subunit B (CTB) in the tibialis anterioris
13
and gastrocneus muscles at 28 days post-injury (Figure 8N and O). Consistently, no significant
difference between control and CTCF-cKO mice was observed in locomotion or mechanoception
upon SNC (Fig. S8). Together these data indicate that CTCF is required for conditioning-dependent
DRG regenerative growth and that CTCF deletion delays axonal regeneration after SNC.
Validation of the dataset
We investigated the concordance of our dataset with previously published datasets of various
models of sciatic nerve injury at several time points 35-38 by performing a Spearman correlation
analysis. This showed a positive significant correlation between the differentially expression
signatures upon SNA in our dataset and the majority of the tested comparisons (Fig. S9A, first
column, in red, Pvalues indicated). As expected, upon DCA a negative correlation was found in most
of the comparisons (Fig. S9A, third column, in blue). These results were supported by Fisher’s exact
test showing a strong overlap between up- and downregulated genes in our dataset upon SNA and
the up- and downregulated genes in the tested datasets (Fig. S9B, Fisher’s exact test, Pvalue
indicated).
Interestingly, by applying False Discovery Rate (FDR)-corrected Pvalues to our data, which reduced
the number of DE genes, we lost most of the significant correlations with published datasets
(Fig.S9A, second and forth columns). While the overall pattern of the transcriptional response to the
peripheral lesion remained detectable, DE genes following the central lesion were lost (Fig. S9C),
likely due to the less pronounced degree of differential gene expression after DCA. We were able to
validate differential chromatin accessibility by MNase assay (Fig. S7F-G), gene expression by RT-PCR
(Fig. S9D-E), and H3K27ac promoter occupancy by ChIP followed by RT-PCR (Fig. S9F). Finally, we
compared all published experimentally validated DE genes identified by microarrays or RNAseq
(validation includes RT-PCR, Northern blot and Western blot) in various models of sciatic nerve injury
to genes present in our dataset (Table S1). Indeed, out of 51 entries, 45 genes (88.2%) were
validated by our dataset.
14
DISCUSSION
Our genome-wide combinatorial analysis (Figure S10) a revealed a unique epigenetic
landscape characterized by increased chromatin accessibility and increased acetyl H3K27 and H3K9
occupancy at TSS and body regions of genes related to transcription and regenerative signalling
pathways after SNA but not after DCA. This correlated with prominent or weak changes in gene
expression respectively.
Our data suggests that unique sets of enhancers, including developmentally-regulated
enhancers, respond to axonal injury with changes in H3 acetylation and chromatin accessibility and
may be involved in the activation of the transcriptional programme after peripheral nerve axotomy.
Footprinting analysis of the open chromatin regions showed that the response to axonal
injury is mediated by transcriptional activator recruitment at enhancers and promoters with a more
relaxed chromatin conformation, while only few transcriptional regulators, mainly repressors, are
bound at the compact chromatin regions after central injury. Altogether, this evidence raises the
intriguing hypothesis that a “silent” or poorly dynamic chromatin state might limit axonal
regeneration after injury in the CNS. Importantly, machine learning analysis quantitatively
demonstrated that inclusion of the epigenetic and transcriptional regulatory data is sufficient to
predict the majority of the gene expression changes following axonal injury.
The BaGfoot analysis and the PSO together identified several TF families associated with the
transcriptional response to axonal injury. Several of them have been uncovered previously through
either experimental or computational work and are known to be involved in axonal regeneration or
growth 17, 25, 39. The PSO also disclosed a number of additional novel candidates, such as members of
the ETV family, NANOG, ZBTB7C, and PBX3 upon SNA. A striking feature is the presence of TFs
involved in neuronal differentiation. PBX3 and other family members have been shown to interact
with HOX proteins (identified in the BaGfoot analysis), resulting in higher DNA binding activity and
specificity 40. HOX and NANOG are important regulators of embryonic stem cell and neuronal
15
differentiation during development including in the DRG 41, 42, remove 41 or 42 suggesting that the
axon regeneration programme shares molecular pathways with developmental processes.
HDAC inhibition via MS275 following DCA led to enhanced H3 acetylation and chromatin
accessibility and to differential occupancy of a repertoire of TFs at DA gene locations. However, this
resulted in the activation of a transcriptional programme that only minimally mimics what observed
following SNA.
These data suggest that SNA and HDAC inhibition modulate largely independent molecular
pathways, suggesting that broad HDAC inhibition might not be the best strategy to mimic
conditioning-dependent regeneration of sensory axons.
The identification of BS for the chromatin organiser CTCF at TSS and enhancers of accessible
regenerative genes upon SNA suggested that chromatin organisation might contribute to axonal
regeneration. Indeed, we found that CTCF is required for conditioning lesion-dependent neurite
outgrowth in cultured DRG neurons, and conditional deletion of CTCF in neurons delays axonal
regeneration in vivo following SNC.
The machine learning algorithm suggests that the epigenetic components allow predicting a
significant part but not the entire injury-dependent transcriptional programme. Our data also show
that differential gene expression is not always associated with histone acetylation at gene promoters
and enhancers or with changes in chromatin accessibility. Therefore, it is likely that additional
mechanisms are involved, such as H3K4me2/3, H3K36me3, H3K56ac, H4K8ac, which mark active
promoters/enhancers and gene bodies 43, DNA modifications, such as DNA methylation and
hydroxymethylation 44, 45, as well as RNA-dependent post-transcriptional regulation 46 that have been
described to be partially involved in the regenerative response.
In conclusion, our study provides a map of the chromatin and transcriptional landscape in
DRG after axonal injury offering a novel platform to investigate gene regulatory mechanisms for the
control of axonal signalling and regeneration.
16
Acknowledgments
We would like to acknowledge the start-up funds-Division of Brain Sciences, Imperial College London
(SDG), Wings for Life WFL-UK-07/16 (SDG); Rosetrees Trust A1438 (SDG), the Leverhulme Trust RPG-
2015-092 (SDG), ISRT (SDG), the MRC R/R005311/1 (SDG); WT 099276/Z/12/Z (MM). The research
was supported by the National Institute for Health Research (NIHR) Imperial Biomedical Research
Centre (SDG). The views expressed are those of the author(s) and not necessarily those of the NHS,
the NIHR or the Department of Health. We thank Christopher Sibley, Imperial College of London, for
carefully reviewing the mansucript and for useful discussion and suggestions.
Author contributions
I.P. designed and performed experiments, analysed the data, wrote and edited the manuscript.
M.C.D. analysed the data, wrote and edited the manuscript.
T.H. performed experiments, analysed the data and edited the manuscript.
L.Z., and E.S. performed experiments
E.M. performed experiments, analysed the data, and edited the manuscript.
K.S. analysed data and edited the manuscript
P.K.S. edited the manuscript
A.H. performed experiments, and edited the manuscript
N.O. assisted with the data analysis and edited the manuscript.
H.D., M.K. and S.W. performed the PSO analysis and edited the manuscript.
T.L. and W.Z. performed the random forest analysis and edited the manuscript.
M.M, and E.E. provided the CTCFfloxflox mice and discussed data analysis
L.L. maintained the CTCFfloxflox mice colony
J.L.B. and V.P.L. directed and interpreted experiments and edited the manuscript
S.D.G. designed experiments, wrote and edited the manuscript
17
Competing Interest:
No competing Interests
Figure Legends
Figure 1. ATACseq, ChIPseq and RNAseq datasets
(A) Schematic representation of the experimental design. (B) Heatmaps of the genomic distribution
of ATACseq signal, as Log2(Enrichment vs Background) (left) and H3K9ac, H3K27ac, and H3K27me3
histone mark signal, as Log2(IP vs Input) (right) in the sham control sample. (C) Line plot of the
correlation in the sham sample between chromatin accessibility, histone occupancy of the indicated
histone marks with respect to the TSS (percentile epigenetic signal in the TSS+/-1Kb, mean+s.d. of
N=3 for ATACseq, and N=2 for ChIPseq, independent samples), and the gene expression level
(Log2FPKM, mean+s.d. of N=3 independent samples); genes were divided in quartiles according to
their expression level; Pvalue: 2-way ANOVA, Tukey’s multiple comparison test of the histone mark
and ATAC signal in each quartile compared to the preceding one (ns=not significant) (full statistics in
Supplementary Data 15). (D) Profile of the distribution of ATACseq and the indicated histone mark
signal across the TSS and the gene body; genes were divided in quartiles according to their
expression level.
Figure 2. Peripheral but not central axotomy induces an increased chromatin accessibility and
histone H3 acetylation
(A) Bar plot of the number of genes with differential TSS accessibility upon SNA or DCA (TSS +/- 1Kb;
N=3 independent samples; Pvalue<0.05). (B) Area-proportional Venn diagram of the genes with DA
TSS in SNA and in DCA. (C) Bar chart of the first ranked GO categories of the genes with a gained
accessibility in the TSS upon SNA (BP: Biological Process, Modified Fisher Exact Pvalue<0.01). (D)
Stacked bar plot of the number of genes with a differential occupancy of the indicated histone marks
upon SNA or DCA (TSS +/- 1Kb; N=2 independent samples; Pvalue<0.05). (E) Line plot of the histone
occupancy (peak intensity in the TSS+/-1Kb, mean+s.e.m. of N=2 independent samples) in the genes
18
with higher and lower accessibility after SNA or DCA; Pvalue: 2-way ANOVA, Tukey’s multiple
comparison test (ns= not significant) (full statistics in Supplementary Data 15).
Figure 3. Peripheral but not central axotomy induces a robust transcriptional response in DRG
(A) Bar plot of the number of DE genes in DRG after SNA or DCA (N=3 independent samples;
Pvalue<0.05). (B) Area-proportional Venn diagram of the commonly regulated genes in SNA and in
DCA. (C) Odds ratio analysis of enrichment of genes associated with a higher, lower, or unchanged
chromatin accessibility at the TSS for DE or non-DE genes; the numbers in red represent the Pvalue
given by two-sided Fisher’s exact test (ns= not significant). (D-E) GSEA of genes with gain (D) or loss
(E) of accessibility at the TSS ranked according to the corrected fold change of gene expression (see
Methods), showing the enrichment of the genes with more accessible promoters among
upregulated genes on the left side of the ranked gene list (x axis of the plot) (Kolmogorov Smirnov
test).
Figure 4. Functional gene categories share a similar epigenetic signature
(A) Heatmaps showing ATACseq and ChIPseq signal density for the up and downregulated genes
upon SNA at equivalent genomic regions spanning from 2Kb upstream of the TSS to 2kb downstream
of the Transcription End Site (TES). (B) Bar plot of the number of upregulated and downregulated
genes (N=3 independent samples; Pvalue<0.05) with increased or decreased histone occupancy (TSS
+/- 1Kb, and/or gene body, N=2 independent samples; Pvalue<0.05) in the different conditions. (C)
Odds ratio analysis of the enrichment of genes with differential chromatin accessibility and H3K9ac,
H3K27ac, and H3K27me3 occupancy across each upregulated and downregulated gene cluster in (B);
the numbers in red represent the Pvalue given by two-sided Fisher’s exact test (blank= not
significant). (D) Heatmap showing the GO enrichment analysis of the upregulated and
downregulated genes with differential enrichment for the indicated histone mark (BP:biological
process, Modified Fisher Exact Pvalue<0.05). (E) Heatmap showing the upregulated RAG, ranked by
fold change; in red the ones displaying an increased histone H3K9 or K27 acetylation at the TSS
and/or gene body level are highlighted.
19
Figure 5. Specific enhancers respond to axonal injury
(A) Bar plot of the number of enhancers with differential accessibility in DRG after SNA or DCA (N=3
independent samples; Pvalue<0.05). (B) Bar plot of the number of enhancers with differential
occupancy of the indicated histone marks upon SNA or DCA (N=2 independent samples;
Pvalue<0.05). (C) Bar plot of the number of up and downregulated genes (N=3 independent samples;
Pvalue<0.05) controlled by enhancers displaying a change in the occupancy of the indicated histone
mark. (D-E) Stacked bar plots of the number of enhancers with a differential occupancy of the
indicated histone marks or differential accessibility upon SNA (D) or DCA (E) (N=2 for ChIPseq, N=3
for ATACseq, independent samples; Pvalue<0.05) that are activated across the indicated
developmental stages. (F) Odds ratio analyses of the enrichment of genes with differential chromatin
accessibility, H3K27ac and H3K27me3 occupancy across enhancer clusters in (D-E) (two-sided
Fisher’s exact test).
Figure 6. Footprinting analysis identifies specific clusters of transcription factors after axonal injury
(A-D) BaGfoot plots of the ΔFA (differential flaking accessibility) and ΔFPD (differential footprint
depth) values of the motifs upon SNA (A, C) and DCA (B, D) at the level of TSS (TSS+/-1Kb) (A, B) and
enhancers (C, D). The population median is marked in orange; the bag area (dark blue) represents
the region where 50% of the motifs are located; the fence area (light blue) is the region where the
remaining non-outlier motifs are located. The plots are divided in 4 quadrants (I-IV), and the red
squares depict the statistically significant motifs outside the fence (N=3 independent samples;
Pvalue<0.05). (E-F) Examples of motif-centered aggregation plots used to score differential
footprints for two transcription factors.
Figure 7. Random forest classification differentiates between upregulated and downregulated
genes based on TF footprints
(A) Bar plot of the number of DE genes in DRG after SNA or DCA that are associated to TF footprints
(see On line Methods in Supplementary Information) (N=3 independent samples; Pvalue<0.05). (B)
Stacked bar plot of the number of genes, associated to TF footprints, with a differential occupancy of
20
the indicated histone mark or differential chromatin accessibility upon SNA and DCA (TSS +/- 1Kb;
N=2 for ChIPseq, and N=3 for ATAseq, independent samples; Pvalue<0.05). (C) Receiver Operating
Characteristic (ROC) curves for random forest classification of upregulated vs downregulated genes
after SNA (green line) or DCA (red line). (D-E) Selection frequency of the ten most commonly
selected non-DNA content-derived features for SNA (D) or DCA (E) by the particle swarm
optimization algorithm.
Figure 8. CTCF is required for axonal outgrowth and nerve regeneration
(A) Micrographs showing GFP (green) and CTCF (red) expression in DRG 4 weeks after AAV-Cre-GFP
injection in WT and CTCF-cKO mice. Arrowheads mark GFP positive infected neurons showing
presence or loss of CTCF nuclear staining in WT and CTCF-cKO respectively. Scale bar, 100 m. (B)
Bar graphs of the quantification of CTCF nuclear signal as shown in (A) (mean + s.e.m of N=6 DRGs
from 3 mice; two-sided unpaired Student’s t-test; full statistics in Supplementary Data 15). (C)
Micrographs showing neurite outgrowth in DRG neurons extracted from AAV-Cre-GFP injected WT
and CTCF-cKO mice 24 hours after sham or SNC surgery and plated for 16h. Cre-GFP is showed in
green, III-tubulin in red. Scale bar, 100 m. (D) Bar graphs show quantification of neurite
outgrowth as shown in (C) (mean + s.e.m of N=6 for sham, and N=7 for SNC, mice; 2-way Anova,
Tukey’s multiple comparisons test; full statistics in Supplementary Data 15). (E-L) Representative
micrographs and quantification of SCG10 intensity at the indicated distances from the lesion site in
sciatic nerves from WT and CTCF-cKO mice at 1 (E and F), 3 (G and H) and 7 (I and L) days following
SNC. Asterisk marks the lesion site; scale bar, 1mm. Higher magnification insets display regenerating
axons; scale bar, 05 mm (mean ± s.e.m of N=8 nerves from 4 mice for 1 day; N=6 nerves from 3 mice
for 3 days; N= 6 nerves from 4 wt mice for 7 days; N= 7 nerves from 4 CTCF-cKO mice for 7 days; 2-
way Anova, Sidak’s multiple comparisons test; full statistics in Supplementary Data 15). (M) Bar chart
of the regeneration index in WT and CTCF-cKO mice at 1, 3 and 7 days after SNC (mean + s.e.m of N=
12 nerves from 7 wt mice at 1 day; N=10 nerves from 6 wt mice at 3 days; N=5 nerves from 4 wt
mice at 7 days; N=13 nerves from 7 CTCF-cKO mice at 1 days; N=11 nerves from 6 CTCF-cKO mice at
21
3 days; N=6 nerves from 4 CTCF-cKO mice at 7 days; 2-way Anova, Sidak’s multiple comparisons test;
full statistics in Supplementary Data 15). (N) Micrographs showing GFP (green) and CTB (red) signal
in DRG 4 days after injection of CTB in the tibialis anterioris and gastrocnemius muscle 28 days after
SNC. Arrowheads mark GFP/CTB double positive neurons. Scale bar, 100 m. (O) Bar graphs of the
CTB positive neurons as percentage of GFP expressing neurons (N) (mean + s.e.m of N=4 mice;
ns=not significant; two-sided unpaired Student’s t-test; full statistics in Supplementary Data 15).
Materials and Methods
Animal procedures
Animal work was carried out in accordance to regulations of the UK Home Office. Wild-type C57Bl6/J
(Harlan) and CTCFflox/flox mice (6-10 weeks old) were used. The total number of mice used in the
entire study was 478 WT and 47 genetically modified mice. Always males were used, with the
exception of behaviour tests and the Figure 8N-O where females were used. Animals were
randomized in all experiments, when appropriate.
For all surgeries mice were anesthetized with isofluorane (3% induction, 2% maintenance) and
buprenorphine (0.1mg/kg)/carprophen (5mg/kg) were administered preoperatively as analgesic.
Dorsal column axotomy (DCA), sciatic nerve axotomy (SNA), and sciatic nerve crush (SNC) were
performed as previously reported 15. Briefly, for DCA, the spinal cord was exposed by a T9
laminectomy (~20mm from sciatic DRGs), dura mater was removed and a dorsal hemisection until
the central canal was performed with fine forceps (FST). In selected experiments, MS275 (Bio-
techne, 12.5 mg/Kg) or vehicle (3.2% Tween80, 20% PEG300, 4% DMSO) was injected
intraperitoneally at the time of the injury and 2 h before sacrifice. For the control laminectomy
(LAM) surgery, the dura mater was removed without performing the hemisection. For SNA, the
sciatic nerve was exposed by blunt dissection of the biceps femoris and the gluteus superficialis and
axotomy was carried out with iridectomy scissors (FST) (~20mm from sciatic DRGs). Alternatively,
sciatic nerve was crushed (SNC) by applying pressure in two perpendicular directions for 30sec with
22
Dumont #5 Forceps (FST). The crush site was marked by carbon powder. In control mice (sham) the
sciatic nerve was exposed without axotomy or crush.
For nerve injection of AAV-Cre-GFP (Tebu-bio), after nerve exposure, 3 microliters of viral particles
were injected with a Hamilton syringe slowly, and mice were used after 4 weeks to allow complete
Cre expression. In selected experiments, twenty-eight days after SNC, 2.5 l of 10% CTB were
injected in the gastrocnemius and tibialis anterior and sacrificed 4 days afterwards to trace
regenerating neurons in the DRG, as a measure of muscle reinnervation.
RNAseq
RNAseq upon SNA vs Sham and upon DCA vs Lam has been already published 15. Sciatic DRGs (3
biological replicates, 2 mice/condition) were extracted 24h after DCA injury and MS275 treatment,
and collected in RNAlater (Qiagen) to prevent RNA degradation. RNA extraction was performed as
reported 15. Briefly, tissue was crushed in RLT Lysis buffer (Qiagen), and RNA was isolated with
RNeasy mini kit and DNase on column digestion (Qiagen) following manufacturer’s guidelines. RNA
quality was assessed with Agilent 2100 Bioanalyzer (Agilent). RNA with RIN factor above 8.0 was
used for library preparation. Libraries were generated using the NEBNext poly(A) mRNA magnetic
isolation module and NEBNext Ultra II Directional RNA Library Prep Kit for Illumina according to the
recommended manufacturer protocol. Ligation and library integrity was verified using a Tapestation
and Glowmax station. The NEBNext Multiplex Oligos for Illumina (Dual Index Primers Set 1) adapter
sequences were ligated to each sample in each group to allow for sequencing multiplexing. Sample
libraries ligated with unique adapter sequences, were multiplexed, and sequencing was performed
on an Illumina HiSeq4000, generating 75bp pair-ended reads at Imperial BRC Genomic Facility. An
average of 59 million read pairs were generated for each sample, with a total of 353 million read
pairs generated.
ChIPseq
Sciatic DRGs (2 biological replicates, 10 mice/condition) were extracted as above, 24 hours after
injuries. Chromatin immunoprecipitation (ChIP) was performed according to a previously published
23
protocol 13 (see also Supplementary Note 1). Briefly, DRG pellets were crushed using an automatic
pestle and chemically cross-linked with a 1% formaldehyde solution for 15 minutes at room
temperature. Chromatin was sonicated for 30 min using a Bioruptor (Diagenode) to get 200-800 bp
fragments. Immunoprecipitation was performed overnight with Protein G Dynabeads (Invitrogen)
bound to 10 μg of H3K27ac (Ab4729, Abcam), or H3K9ac (Ab10812, Abcam), or H3K27me3
(C15410195, Diagenode) antibodies. After washing, elution and reverse crosslinking, DNA was
treated with RNAse A and Proteinase K and purified using Qiagen PCR purification columns. Equal
amounts (30 ng) of Input and immunoprecipitated DNA were used for library preparation with the
NEBNext Ultra DNA Library Prep Kit from Illumina (New England BioLabs) following the
manufacturer’s protocol, using a dilution 1:10 of adaptors. Samples were cleaned to remove
unligated adaptors and size selection was performed to select and enrich fragments of 200bp in size.
Libraries were amplified by PCR using multiplex oligo following manufacturer’s protocol. Prior to
sequencing, libraries were run on an Agilent Bioanalyser for size and quality control checking,
quantified using Qbit (ThermoFisher), and multiplexed. Sequencing was performed in an Illumina
HiSeq2000, generating 50bp single ended reads, at the Imperial MRC Genomic Facility. On average,
30 million reads per sample were produced for H3K9ac, 17 million reads per sample for H3K27ac,
and 25 million reads per sample for H3K27me3. This gives a total of 242 million reads for H3K9ac,
132 million reads for H3K27ac, and 200 million reads for H3K27me3. ChiPseq for H3K9ac has been
already published 13.
ATACseq
ATACseq tested the accessibility to Tn5 transposase, which integrates and inserts sequencing
adapters into open chromatin regions (see also Supplementary Note 1). ATACseq was performed
according to a previously published protocol 16. Briefly, sciatic DRGs (3 biological replicates, 1
mouse/condition) were extracted 24 hours after injuries and crushed using an automatic pestle in
Lysis buffer containing 0.1% Igepal (Sigma). Transposition reaction with Nextera Tn5 Transposase
(Illumina, FC-121-1030) was performed on 50000 nuclei. After purification with minElute PCR
24
purification kit (Qiagen), transposed DNA was amplified by 11 cycle PCR using barcoded primers.
Libraries were run on an Agilent Bioanalyser for size and quality control checking, quantified using
Qbit (ThermoFisher), and multiplexed. Sequencing was performed on an Illumina HiSeq4000,
generating 75bp pair-ended reads at Imperial BRC Genomic Facility. On average, 83 million read
pairs were produced per sample for the ATACseq, yielding a total of approximately 1.5 billion read
pairs.
Sequence alignment, differential expression analysis, and identification of ChIP and ATAC peaks
RNAseq analysis was performed as described 15. For ChIPseq analysis, reads were quality checked,
aligned to the mm10 reference genome, and called for peaks using the AQUAS histone ChIPseq
pipeline (https://github.com/kundajelab/chipseq_pipeline) running BWA and MACS2, using Pvalue
<0.01 and then using the IDR naïve overlapping method as described
(https://github.com/kundajelab/chipseq_pipeline). The peak set produced by the AQUAS pipeline
using overlap analysis was used as the peak set for each condition. Genomic bins of 1000bp
upstream and downstream of each TSS for each gene were created using the same gene annotation
as used for the RNAseq data. Read counts per genomic bin (for gene analysis) or peak (for enhancer
analysis) were obtained from the mapped reads using HTSeq-0.6.1 and subsequently, differential
binding testing was conducted in EdgeR-3.8.6. Additional quality examination of aligned reads was
performed using ChIPQC-1.2.2. The cut-off criteria for the differentially occupied regions was set at
Pvalue<0.05. No false discovery rate correction or additional fold change cutoffs were applied in
determining DE genes or differentially occupied regions. The AQUAS pipeline also produced signal
tracks for each sample which plot the –Log10(Pvalue) of enrichment over input control at each
position. ChIP signal distribution plots and heatmaps were generated using NGSplot version 2.47.1 47.
Briefly, the ChIP signal for each condition is plotted along a gene body region either for all genes in
the annotation or a select subset of genes. The enrichment vs input was calculated using the
GenomicAlignment package in R to measure the proportion of reads within each input or ChIP
sample, and then dividing ChIP by input. The expression and differential expression data from the
25
RNAseq experiment were used to select the groups of genes from which to sample the ChIP signal in
order to correlate the ChIP signal with changes in the gene’s expression between biological
conditions.
For ATACseq analysis, quality checking, read alignment, signal track generation, and peak calling
were performed using the Kundaje lab’s ATACseq processing pipeline
(https://github.com/kundajelab/atac_dnase_pipelines) running Bowtie2 and MACS2. The peak set
produced by the pipeline using overlap analysis was used as the peak set for each condition.
Genomic bins of 1000bp upstream and downstream of each TSS for each gene were created using
the same gene annotation as used for the RNAseq data. Read counts per genomic bin (for gene
analysis) or peak (for enhancer analysis) were obtained from the mapped reads using HTSeq-0.6.1
and subsequently, differential accessibility testing was conducted in EdgeR-3.8.6. The enrichment vs
background was calculated using the GenomicAlignment package in R to measure the proportion of
reads within each sample and then dividing by the proportion of the genome spanned by that type
of region. Signal distribution plots and heatmaps were generated using NGSplot version 2.47.1.
Validation of RNAseq differential expression signatures
We compared the gene expression signatures of our dataset upon SNA or DCA against published
datasets. Data for GSE30165 38, GSE21007 37, GSE26350 35, and GSE33175 36, comprising various
models of sciatic nerve injury at several time points, was downloaded from Gene Expression
Omnibus (GSE). The data was pre-processed as probe-level expression and normalised across the
arrays. Quality control tests were performed to determine within and between cluster similarity.
Differential expression analysis was performed using limma 48 R package remove this reference, may
be just provide a web address. To compare these datasets to ours, the ranks of all genes based on
logFC * -log10(P-value) was first calculated. The datasets were then compared using pairwise
Spearman correlation on genes that were significantly expressed (Pvalue<0.05 or FDR<0.05).
In addition, we tested the overlaps between genes that were significantly up- or downregulated
(Pvalue<0.05) between experiments to determine whether the overlaps can be considered
26
significant using Fisher exact test
(https://bioconductor.org/packages/release/bioc/html/GeneOverlap.html).
Transcriptional factor motif enrichment analysis
TF motif enrichment analysis was performed using Analysis of Motif Enrichment (AME) 49 remove
this reference, just provide a web address on the enhancer and TSS (+/-1Kb) regions with
differential chromatin accessibility, and at the level of TSS (+/-1Kb) region of DE genes, using a
Fisher's exact test against the Jaspar core 2016. A shuffled input sequence background was used.
Footprints
ATACseq footprints were generated using HINT 28 for all genomic regions within an ATACseq peak in
that condition. The ATAC footprints setting was used and bias correction was estimated. The
scatterplots showing the change in footprint score vs change in gene expression for the footprinted
TFs was performed using the scripts given in 28 and using the Footprint Score statistic 50 remove this
reference, just provide a web address Differential footprinting analysis was performed using
BaGFoot 23 at the level of promoters and enhancers, separately. The motif database used was
generated by FIMO on the complete mm10 genome with the Jaspar core 2016 motif matrices 51.
remove this reference, just provide a web address. To find TF target genes, the footprints found by
HINT at the level of DA regions were input into GREAT 52 in order to identify the closest genes. The
gene regulatory region was set at -2000/+500 from the TSS. Moreover, if the HINT motifs were inside
enhancer regions (enhancer +/- 05Kb), we retrieved their target genes, identified by ENCODE 21.
Gene Ontology and Pathway analysis
GO and KEGG Pathway analysis were performed using DAVID 6.7 (https://david.ncifcrf.gov/) and
setting all the expressed genes in our dataset as background.
Gene set Enrichment analysis
Pre-ranked GSEA was performed using GSEA software
(http://software.broadinstitute.org/gsea/index.jsp). The DE genes were ranked according to their
combined score “–Log10(Pvalue)*Log2(FC)” (x axis of the GSEA plot). The dataset representing the
27
genes with a more or less accessible promoter region was uploaded to GSEA (with 1000
permutations) to determine if these genes were significantly enriched on either side of the ranked
gene list.
Statistics and Power analysis
All statistical analyses were performed with GraphPad Prism software. Values are presented as
dot blots with individual data points plus bar, with mean ± s.e.m. or s.d., as indicated in figure
legends. All measurements were taken from distinct samples and the same sample was never
measured repeatedly. Statistical analyses were designed using the assumption of normal
distribution, although that assumption was not explicitly tested. Statistical comparisons included
two-sided paired or unpaired Student’s t-test, 1- or 2-way ANOVA followed by Sidak’, Dunnett's or
Tukey's multiple comparison post hoc tests as specified in the figure legends, and one or two-
sided Fisher’s exact test. P values< 0.05 were considered to be statistically significant. The full
statistics is provided in Supplementary Data 15. Sample size was either based upon similar
previously established experimental designs, or calculated using AEEC power calculator, to estimate
the number of replicates required, for a difference of 1.5 and 80% to 90% power assuming a 5%
significance level.
For RNAseq, ATACseq, and ChIPseq sample size calculation and power estimation were carried out
accordingly to an already described pipeline
(http://andre-rendeiro.com/2018/08/10/atacseq_power_analysis) 53. The results are reported in
Supplementary Data 15.
Replication
All attempt of replication were successful.
Blinding
Nerve regeneration was assessed in blind by two independent scientists. VonFrey test was
assessed by 2 experimenters, and one of them was blind to experimental groups.
28
Data and code availability
RNAseq, ChIPseq and ATACseq data have been deposited at the Gene Expression Omnibus (GEO)
under accession ID GSE97090, GSE108806, and GSE132382. All code for processing and analysing
the data presented in this work are available upon request. For detailed information on
experimental design please see the provided Life Sciences Reporting Summary. Uncropped blots
with molecular weight standards are provided in the Supplementary Figure 10.
Accession codes
All the accession codes used for this study are: GSE97090, GSE108806, GSE132382, GSE30165,
GSE21007, GSE26350, and GSE33175.
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