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Analysis of the early response to spinal cord injury identified a key role for mTORC1 signaling in the activation of neural stem progenitor cells

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Xenopus laevis are able to regenerate the spinal cord during larvae stages through the activation of neural stem progenitor cells (NSPCs). Here we use high-resolution expression profiling to characterize the early transcriptome changes induced after spinal cord injury, aiming to identify the signals that trigger NSPC proliferation. The analysis delineates a pathway that starts with a rapid and transitory activation of immediate early genes, followed by migration processes and immune response genes, the pervasive increase of NSPC-specific ribosome biogenesis factors, and genes involved in stem cell proliferation. Western blot and immunofluorescence analysis showed that mTORC1 is rapidly and transiently activated after SCI, and its pharmacological inhibition impairs spinal cord regeneration and proliferation of NSPC through the downregulation of genes involved in the G1/S transition of cell cycle, with a strong effect on PCNA. We propose that the mTOR signaling pathway is a key player in the activation of NPSCs during the early steps of spinal cord regeneration.
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ARTICLE OPEN
Analysis of the early response to spinal cord injury identied a
key role for mTORC1 signaling in the activation of neural
stem progenitor cells
Johany Peñailillo
1
, Miriam Palacios
1
, Constanza Mounieres
1
, Rosana Muñoz
1,2
, Paula G. Slater
1
, Elena De Domenico
3,4
,
Ilya Patrushev
3
, Mike Gilchrist
3
and Juan Larraín
1
Xenopus laevis are able to regenerate the spinal cord during larvae stages through the activation of neural stem progenitor cells
(NSPCs). Here we use high-resolution expression proling to characterize the early transcriptome changes induced after spinal cord
injury, aiming to identify the signals that trigger NSPC proliferation. The analysis delineates a pathway that starts with a rapid and
transitory activation of immediate early genes, followed by migration processes and immune response genes, the pervasive
increase of NSPC-specic ribosome biogenesis factors, and genes involved in stem cell proliferation. Western blot and
immunouorescence analysis showed that mTORC1 is rapidly and transiently activated after SCI, and its pharmacological inhibition
impairs spinal cord regeneration and proliferation of NSPC through the downregulation of genes involved in the G1/S transition of
cell cycle, with a strong effect on PCNA. We propose that the mTOR signaling pathway is a key player in the activation of NPSCs
during the early steps of spinal cord regeneration.
npj Regenerative Medicine (2021) 6:68 ; https://doi.org/10.1038/s41536-021-00179-3
INTRODUCTION
Although mammals can regenerate some tissues like the skin,
muscle, bones, and liver, they are unable to efciently regenerate
the central nervous system (CNS). Particularly, regeneration of the
spinal cord is very decient, and spinal cord injury (SCI) results in
loss of sensory, motor, and autonomic functions altering the
autonomy and the life quality of human beings. SCI in mammals
not only produces an immediate damage of neurons, axonal
circuits, and the tissue in the injured area, which is followed by a
second phase that increases the damage and forms a glial scar
that limits the tissue loss, but also acts as a barrier for axonal
growth
15
. In addition, axon regeneration is absent in mammals
6
,
and although neural stem progenitor cells (NSPCs) are activated,
they are mainly fated to astrocytes, and the formation of new
neurons is not observed in vivo
79
.
On the contrary, other species, including planarias, sh,
amphibians and lampreys, have regenerative mechanisms that
allow the anatomical and functional recovery of complex
structures, such as organs, eyes, neural tissue, and appen-
dages
1013
. Amphibians and teleost sh are model organisms
widely used to understand the molecular and cellular mechanisms
involved in spinal cord regeneration
14,15
. Among these, Xenopus
laevis can be highlighted because it has great regenerative
capabilities during larvae stages (regenerative stages, R-stages),
being able to repair severe lesions of the CNS, including the spinal
cord. However, this ability is lost during metamorphosis, and thus,
froglets and adult frogs (non-regenerative stages, NR-stages) are
not able to recover from SCI anymore
16,17
. Previous studies
demonstrated that NSPC expressing Sox2 start to proliferate after
SCI and are required for spinal cord regeneration in R-stage
animals, whereas this is not observed in NR-stage animals, despite
having Sox2
+
cells in their spinal cord
18,19
.
Global studies comparing the transcriptomes deployed after SCI
between R- and NR-stages at 1, 2, and 6 days post-transection
(dpT) showed a fast and massive transcriptome change at 1 dpT in
R-stage
20
. In these animals, enrichment in genes associated with
the cell cycle was detected at 1 and 2 dpT, suggesting that many
changes occur within the rst 24 h after injury, including the
activation of a signal to trigger NSPC proliferation. Based on this,
we envision that a deep study of the transcriptome changes
during the rst day after injury should allow the identication of
the signaling pathways and mechanism involved in NSPC
activation.
Here we use high-resolution expression proling and Gaussian
Process approach
21,22
to identify the transcriptome changes
during the rst 21 h after SCI. The gene expression proles were
classied into early, intermediate, and late, according to their
kinetics. Early modules show a rapid and transitory activation
characteristic of a primary response, with a large component of
immediate early genes (IEGs), meanwhile intermediate and late
modules have a sustained activation representative of a secondary
response. Among the main biological processes present in these
phases, we found a large component of genes associated with
migration and immune response, ribosome biogenesis, and cell
cycle, as well as a downregulation of negative regulators of
mechanistic Target of Rapamycin (mTOR) pathway. Western blot
and immunouorescence analysis conrmed that mTORC1 is
rapidly and transiently activated after SCI, and its pharmacological
inhibition impairs spinal cord regeneration and proliferation of
NSPC. This was conrmed through the evaluation of global
transcriptional changes after inhibition of mTOR pathways,
showing that mTOR targets the expression of genes involved in
transition G1/S of the cell cycle, with a strong effect over genes
such as proliferating cell nuclear antigen (PCNA). We propose that
1
Center for Aging and Regeneration, Departamento de Biología Celular y Molecular, Facultad de Ciencias Biológicas, Ponticia Universidad Católica de Chile, Alameda 340,
Santiago, Chile.
2
Departamento de Tecnología Médica, Facultad de Medicina, Universidad de Chile, Santiago, Chile.
3
The Francis Crick Institute, 1 Midland Road, London, UK.
4
German Center for Neurodegenerative Diseases (DZNE), PRECISE Platform for Genomics and Epigenomics at DZNE, University of Bonn, Bonn, Germany. email: jlarrain@bio.puc.cl
www.nature.com/npjregenmed
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1234567890():,;
the mTOR signaling pathway is a key player in the activation of
NPSCs during the early steps of spinal cord regeneration in X.
laevis.
RESULTS
mRNA sequencing (mRNA-seq) and data consistency
To characterize the genes deployed in the spinal cord of X. laevis
immediately after SCI, high-resolution expression proling
21
was
used to continually evaluate the global transcriptome changes
from 0 to 21 h post-transection (hpT) and were compared to sham
and uninjured animals (Fig. 1a). For transected animals, samples
were isolated synchronously every 30-min between 0 and 2 hpT
and every hour between 2 and 21 hpT. Samples were collected in
six series (Fig. 1a): (i) series 1 and 2 (S1 and S2), correspond to
animals from the same fertilization used for time points between 0
to 2 hpT, including a biological replicate for 0 hpT, (ii) series 3, 4, 5,
and 6 (S3S6) were obtained from different batches of animals to
cover the periods between 0 and 6 hpT, between 6 and 12 hpT;
between 10 and 18 hpT, and between 16 and 21 hpT, respectively.
These last four series also contained samples at 0 hpT and 13
time points overlapping with the subsequent series to facilitate
posterior merge and to evaluate continuity between the different
time series.
Two sets of control samples were also prepared. One of the
controls (Sham" control, blue in Fig. 1a) corresponded to samples
from sham-operated animals, in which only the dorsal skin and
muscle were injured at the same level of the rostro-caudal axes as
the transected animals, but the spinal cord was left uninjured
17
.
This control allowed the exclusion of transcriptional changes in
the spinal cord produced as a consequence of damage to tissues
surrounding the spinal cord. Sham control samples were obtained
in six time series (S1’–S6), in parallel and from the same batches of
animals used to obtain the samples from transected animals, at
equivalent time points, and sharing the 0 h time point (Fig. 1a). For
a second control (Uninjured, green in Fig. 1a), spinal cord tissue
was isolated at the same level of the rostro-caudal axes from sham
and transected animals. This control samples permitted the
elimination of transcriptional changes associated with the normal
progress of metamorphosis during the time lapse analyzed.
Uninjured time series were collected from the same animal batch
in two series at 1-h intervals: one from 0 to 13 h (S7) and the other
one from 12 to 21 h (S8), including 0 h, and 12 and 13 h used as
overlapping points (Fig. 1a).
Illumina RNA-seq polyA+libraries were constructed for the
105 samples collected in the three conditions indicated above. An
average of 28 million paired-end reads per sample were
sequenced with an average of 69% of reads mapped per library
(Table S1) and 43,193 genes detected (95.8% of the transcriptome)
with a mean of 443 gene counts per library. To avoid noise, only
genes having ve or more gene counts detected in at least four
temporal points in some of the three conditions were considered,
resulting in 30,299 genes, which were contemplated for the
following analyses. Using Spearman correlation coefcients to
check the consistency of the data, we found a high degree of
internal consistency for neighboring points within each time
series, and although slightly lower the consistency between series
was still strong. Because of the variation, the batch effect was
corrected, and a better t was observed between the time series
(for more details about data consistency, see the Methods
section).
In summary, high-quality sets of RNA libraries, at contiguous
time points during the rst 21 hpT, were generated. The data
obtained was very reliable based on the quality of the sequencing,
the mapping and gene detection rates, and more importantly the
high degree of internal consistency within and among each time
series supports that the sampling is representative of the
undergoing biological processes.
Gene co-expression networks present in the early response to
SCI
For identication of changes in the transcriptome, the temporal
gene expression proles between transected and uninjured and
between transected and sham conditions were compared using
Gaussian Processes. To do this, two hypotheses were tested for
each comparison, one in which the data of both conditions are
tted to the same temporal prole of gene expression through the
time points measured, and a second hypothesis, in which both
data sets are tted to two different expression proles throughout
the time points measured. To choose between both, the logarithm
of the marginal likelihoods of the two hypotheses were compared
using Bayesian Information Criterion (BIC). A temporal differential
gene expression is assigned when a BIC > 0 is obtained. For a
better visualization of the changes in the transcriptome after SCI, a
two-dimensional histogram was constructed, where the BIC values
of the comparison between transected and uninjured conditions
are shown on the y-axis, and the BIC values of the comparison
between transected and sham on the x-axis (Fig. 1b).
Fig. 1 Early transcriptome changes in response to SCI. a Time-
series samples obtained for mRNA-seq. Spinal cords were isolated in
six time series from transected and sham conditions. Between 0 and
2 h, samples were obtained every 30 min and at 1 h intervals
between 2 and 21 h. Samples for the uninjured condition were
isolated in two time series at 1 h intervals between 0 and 21 h. b2D
histogram for differentially expressed genes. The graph depicts
differential gene expression comparing transected and uninjured
conditions (yaxis) with transected and sham conditions (xaxis). The
light gray square highlights genes with a BIC 10 in both
comparisons, and those on the dark gray square correspond to
genes with a BIC 60.
J. Peñailillo et al.
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To select those genes with higher changes in the levels of
mRNA between the compared conditions, a BIC 10 was
established as a cut-off, which is considered as a very strong
evidence for alternative hypothesis in model selection
23
. The
number of genes with a BIC 10 in the transected against
uninjured comparison (4917 genes) was higher than those
observed in the transected against sham comparison (2269
genes), suggesting that injury of tissues close to the spinal cord
could also elicit a response on the spinal cord, reinforcing the
importance of the sham control. Finally, an intersection of 1850
genes, with BIC 10 in both comparisons, was dened as those
genes that change their expression after a direct damage to spinal
cord and were used for further analysis.
To identify groups of genes that have similar expression proles
in response to SCI, a weighted gene co-expression network
analysis (WGCNA) for the 1850 genes was performed. Using this
approach, 92.3% of the 1850 genes were clustered in 11 co-
expression modules (Table S2). The differences between co-
expression modules are shown in a hierarchical clustering based
on the Euclidean distance calculated from the module eigengene
of each one (Supplementary Fig. S1). Based on module eigengene
and its correlation with the time after lesion, three modules
presented downregulated mRNA levels, and the other eight
modules showed upregulated levels (Fig. 2). According to their
kinetic proles, the eleven modules were organized in: (i) early
upregulated modules (E1, E2), (ii) intermediate modules that were
either upregulated (I1, I2, and I3) or downregulated (D1, D2), and
(iii) late modules that were also upregulated (L1, L2, and L3) or
downregulated (D3).
Genes in modules E1 and E2 showed an upregulation even
before 1 hpT and reached their maximum expression levels at
around 2 hpT (Fig. 2a, b). In both cases, upregulation was
transient, and a decline started around 2 and 8 hpT, but without
returning to basal levels. mRNA levels in intermediate modules
changed (increase or decrease) between 2 and 5 hpT, reaching
stable levels around 46 hpT, except for I2 that shows a transitory
kinetic falling-down around 1112 hpT concomitant with the
activation of late modules (Fig. 2cg). Finally, RNA levels in late
modules only changed at 1112 hpT, and the achieved levels were
maintained (Fig. 2hk). Of note, for the L1 module two phases of
upregulation were appreciated, one at 46 hpT that was
coincident with the intermediate modules, followed by a second
phase that was more similar to the late modules (Fig. 2h).
Interestingly, later time modules contained a larger number of
mRNAs. E1 and E2 modules have an average of 65.5 genes; D1, D2,
I1, I2, and I3 include an average of 147.6 genes in each module;
and the number of genes on the late modules rises to an average
of 206.3 genes. This suggests that changes in mRNA levels were
deployed in a sequential manner, adjusting to a cascade effect,
which would produce a progressive increase in transcriptome
changes during the rst 21 h of the regenerative response.
Gene ontology (GO) enrichment analysis
To identify the main biological processes, molecular functions,
cellular components, and signaling pathways deployed during the
rst 21 h after SCI, a GO and Kyoto Encyclopedia of Genes and
Genomes (KEGG) pathway enrichment analysis was performed to
the 11 co-expression modules. Because several and redundant GO
terms were enriched within some modules, an in-house clustering
method based on semantic similarities between GO terms and
genes associated with them was implemented. From this analysis,
a representative GO term was chosen for each cluster considering
its signicance and the differential expression level of the
associated genes (Fig. 3and Supplementary Tables S3S5). Here
we describe the main biological concepts that emerged from an
integrated examination of the GO terms and KEGG pathway
enrichment analyses.
A detailed examination of the molecular function category from
the GO enrichment analysis showed that all groups enriched in
module E1 are related to transcriptional components (Supple-
mentary Table S3). In addition, 36.7% of the genes in E1
correspond to transcription factors, a large proportion compared
to the other modules in which only an average of 6.5% was
detected. The kinetic prole observed for E1 genes is very similar
to the behavior described for IEGs, which act as rst responders
inducing a transcriptome remodeling in response to a stimulus,
presenting maximal activation levels around 1 h after activation,
and falling very rapidly
24
. Moreover, 48% of the genes in E1 have
already been reported as IEGs, among them some widely studied
as fos, jun, and myc
25
.
Some of the biological processes enriched in modules E1 and E2
are related to the inammatory response, including the activation
and migration of leukocytes and neutrophils (Fig. 3and
Supplementary Table S4). Furthermore, in the KEGG analysis
many of the pathways identied are also related to these
processes (Supplementary Table S6). This includes key signaling
pathways such as tumor necrosis factor and interleukin (IL)-17,
which are known for their pervasive role in triggering an
inammatory response
26,27
, and Toll-like receptor signaling that
could activate an innate immune response
28
. The early upregula-
tion reported for these genes, followed by a fast reduction on
expression levels, suggest that this immune response is rapid,
transient, and tightly controlled.
An enrichment of genes associated with mTOR signaling both in
the biological process and KEGG pathway analyses was found in
module D1 (Fig. 3and Supplementary Tables S4 and S6). The
mRNA levels for deptor (DEP Domain Containing mTOR Interacting
Protein), and tsc2 (TSC Complex Subunit 2), two negative
regulators of mTOR, were rapidly reduced after injury, suggesting
a possible fast activation of mTOR signaling. Moreover, enrich-
ment of biological processes directly regulated by mTOR pathway
such as autophagy, ribosome biogenesis, and starvation
response
29
were found in other intermediate (D2, I2, and I3) and
late (D3) modules as well, suggesting that mTOR could play a key
role during early response after injury. Related to this, a decrease
on mRNA levels of genes involved in positive regulation of
autophagy were also enriched on module D2 (Fig. 3and
Supplementary Tables S4).
Ribosome biogenesis represents a very widespread biological
process throughout the rst 21 h of the response to SCI. In
particular, the components needed for ribosome biosynthesis
represent almost all GO terms and KEGG pathways enriched in
modules I2 and I3 (Fig. 3and Supplementary Tables S3S6). This
GO include genes involved in: ribosome large subunit biogenesis,
rRNA processing, ribonucleoprotein complex assembly, RNA
modication and methyltransferase activity, mRNA, transfer RNA
and small nucleolar RNA binding, and preribosome. Notably,
among these genes, there are several ribosome biogenesis factors
(RBFs) that are important and specic for stem cell homeostasis
and self-renewal
30
.
Intermediate modules contain GO terms related to chromatin
remodeling. Some are upregulated, like ATP-dependent chromatin
remodeling, nuclear matrix, methyltransferase complex, SWI/SNF
complex, and catalytic activity acting on DNA, that are enriched in
module I3 (Fig. 3and Supplementary Tables S4 and S5). And
others enriched in D1 are downregulated, such as regulation of
chromatin organization and regulation of DNA topoisomerase
activity (Fig. 3and Supplementary Table S4). From this, it is
inferred that an important chromatin remodeling starts at around
6 hpT and could be a key process in the response to SCI
31
.
In agreement with the nding about ribosome biosynthesis, an
enrichment of GO terms related to protein synthesis was also
observed in modules L1, L2, and L3 (Supplementary Tables S3S6
and Fig. 3). These groups included GO terms and signaling
pathways, such as transferring of glycosyl groups, misfolded
J. Peñailillo et al.
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protein binding, peptide transporter activity, cytoplasmic ribonu-
cleoprotein granule, endoplasmic reticulum chaperone complex,
protein processing in endoplasmic reticulum, N-glycan biosynth-
esis, and protein export from nucleus.
The GO terms and KEGG pathways DNA replication initiation,
DNA replication origin binding, MCM complex, cell cycle, stem cell
division, and proliferation and signaling pathways regulating
pluripotency of stem cells were enriched in modules L2 and L3
(Fig. 3and Supplementary Tables S3S6). Of note, in module L2, a
concomitant enrichment in genes linked to columnar/cuboidal
epithelial cell development was found, which correspond to the
morphological organization of neural progenitor cells in the
central canal of spinal cord (Fig. 3). Furthermore, genes linked to
neurogenesis, such as nestin and components of the WNT
pathway (wnt7b, fzd2, fzd3, and ctnnb1), were found as well.
These results, together with the activation of important RBFs
involved in stem cell homeostasis and self-renewal, suggest that
activation of NSPC is a key process in spinal cord regeneration.
An enrichment of the biological process related to lymphocyte
and T cell proliferation is observed in module D3, with a signicant
reduction of RNA levels starting at 11 hpT (Fig. 3and
Supplementary Table S4). Among the genes included in these
Fig. 2 Co-expression modules deployed in response to spinal cord injury. WGCNA analysis clustered genes in 11 co-expression modules.
The graphs show the adjusted kinetic prole for each module. Colored dots represent the eigengene modules, continuous lines indicate a
regime-switching model used to explore possible change points, discontinuous lines show a change-point model for each module, and
vertical color bars highlight temporal points at which we observe a change in the direction of gene expression, with more intense bars
indicating a higher probability of change. a,bEarly upregulated modules E1 and E2; ceintermediate upregulated modules I1, I2, and I3; f,g
intermediate downregulated modules D1 and D2; and (hk) late upregulated L1, L2, and L3 and downregulated module D3.
J. Peñailillo et al.
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Fig. 3 Biological process regulated during the early response to spinal cord injury. Gene ontology enrichment analysis was performed for
each co-expression module. The most representative biological processes are described. Color bars associated with each GO term represent
the time period when they showed maximum expression. For downregulated modules, the color bars disappear when the expression levels of
those genes start to decrease. The number of genes in each GO group is also indicated.
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GO terms, we found irs4, which inhibits JAK/Stat signaling
pathway, modulating the immune response
32
; PDE5A (phospho-
diesterase 5A), whose pharmacological inhibition have been
associated with anti-inammatory and neuroprotective effects
33
;
and shh (sonic hedgehog), which has been implicated in
macrophage polarization after SCI, regulating pro- and anti-
inammatory response
34
. This analysis suggests that there is an
active mechanism to control and limit the immune response.
We observe an enrichment in genes associated with regulation
of extrinsic apoptotic signaling pathwayin module E2; however,
Fig. 4 Early activation of mTOR signaling in response to SCI. Activation of mTORC1 was analyzed by immunostaining (ae) and
immunoblotting (f) for p-S6, by immunostaining (gi) against p-4EBP1, and (jl) double immunostaining for Sox2 and pS6. p-S6 levels were
analyzed in transverse cryosections at (a,b) 12 and (c,d) 21 h after transection or sham operation and also in longitudinal cryosections at (e)
21 hpT. fWestern blot for S6 and p-S6 in total protein extracts obtained from spinal cords at 0, 1, 2, and 4 dpT for evaluation of mTOR
signaling pathway activation after injury. See quantication of ve similar blots in Supplementary Fig. S3a. p-4EBP1 levels were analyzed in
transversal cryosections at (g,h) 21 h after transection or sham operation and also in longitudinal cryosections at i21 hpT. Yellow arrow:
meningeal cells, white arrow: motoneurons, *: lateral neurons, vz: ventricular zone, dotted line: injury site or ablation gap. Sox2 and p-S6 were
analyzed in longitudinal cryosections (j), and (k,l) correspond to magnications of (j,k), respectively. White arrows show Sox2
+
cells with
higher levels of p-S6 activation. Immunouorescences were performed in two or three biological replicates. Scale bar =100 μm. All the blots
included in this gure were derived from the same experiment and were processed in parallel.
J. Peñailillo et al.
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these genes showed an early and transitory activation (Fig. 3).
Moreover, we observe a downregulation of casp3 mRNA and
upregulation of bcl2l12 mRNA in late modules D3 and L1,
respectively. Casp3 is associated with apoptosis, meanwhile
bcl2l12 has an anti-apoptotic role (see Supplementary Informa-
tion, Table S2). These transcriptional changes suggest a down-
regulation of apoptotic process during the rst stages of spinal
cord regeneration.
Early activation of the mTORC1 signaling pathway in response
to SCI
mTOR is a signaling pathway that coordinates cell growth and
metabolism and is associated with cancer, regeneration, aging,
and other processes
29
. The bioinformatics analysis described
above suggests that mTOR is activated because a downregulation
of negative regulators, such as tsc2 and deptor, was observed (Fig.
S2). In addition, we detected the upregulation of rps6kb1 and eif4e,
two key components of the ribosome biogenesis and protein
synthesis branches, respectively (Fig. S2).
To evaluate the activation of ribosome biogenesis and cap-
dependent translation, we measured the phosphorylation levels of
S6 (p-S6) and 4EBP1 (p-4EBP1), respectively
29
. p-S6 signal was not
detected in sham operated, nor in uninjured animals, by
immunouorescence analysis (Fig. 4a, c, and data not shown). At
12 hpT, low levels of p-S6 were detected mainly in meningeal cells
and in ventral cells that probably correspond to motoneurons and
Mauthner cells (Fig. 4b). This was followed by the detection of
higher levels at 21 hpT in similar cells, but now also in more lateral
neurons, and the apical pole of ependymal cells in ventricular
zone (Fig. 4d). Longitudinal sections in the region surrounding the
injury site showed the presence of p-S6 in cells lining the
ependymal canal (Fig. 4e) and in cells populating the ablation gap
(Fig. 4l). Western blot analysis conrmed peak levels of p-S6 signal
at 1 dpT and a decrease at 2 and 4 dpT (Fig. 4f and see
Supplementary Fig. S3a).
Similarly, p-4EBP1 signal was absent in sham operated and
uninjured animals (Fig. 4g, data not shown), while detected at 12
and 21 hpT, although it was found mainly in the cells of the
ependymal layer, and in meningeal cells (Fig. 4h, i). To further
evaluate which cells activate mTORC1 signaling, we performed
double immunostaining analysis with Sox2, a marker of NSPC. We
found that many Sox2
+
cells in the ablation gap activate
phosphorylation of S6, and higher levels of p-S6 were observed
closer to the injury site (Fig. 4jl).
Early inhibition of mTORC1 signaling impairs spinal cord
regeneration
To test the function of mTORC1 through spinal cord regenera-
tion, we incubated the animals during the rst 24 hpT with
Torin1 or Rapamycin, two inhibitors of the pathway (Fig. 5a).
Western blot analysis against p-S6 and p-4EBP1 showed a strong
inhibition at 1 dpT (Fig. 5b and see Supplementary Fig. S3b).
Although some activation of the pathway is observed at 2 and 3
dpT, it never attains the levels observed in untreated animals
(Fig. 5b). Following the same inhibitory paradigm (Fig. 5a), we
found a signicantreductionintherecoveryofswimming
compared to controls at 10 and 15 dpT (Fig. 5cand
Supplementary Fig. S3c), suggesting that early mTOR signaling
is necessary for proper spinal cord regeneration. No differences
were found for sham animals treated with Torin1 or Rapamycin
(Supplementary Fig. S3df), in agreement with ndings showing
noeffectofRapamycininmiceswimmingabilitiesinbehavioral
tests
35,36
.
To evaluate a possible mechanism of action to explain the
effects of inhibiting the mTOR pathway, we performed immuno-
uorescence against Sox2 and neurolament (NF), markers of
NSPCs and axons respectively. A clear reduction in the number of
Sox2
+
cells present in the ablation gap was detected at 15 dpT
(Fig. 6ae). In addition, a signicant decrease in the numbers of
axons in the rostral, caudal, and injury site was observed in treated
animals also at 15 dpT (Fig. 6fl). From these results, we
hypothesize that the effects of mTORC1 inhibition in spinal cord
regeneration could be explained at different levels, including (i)
inhibition of NSPC proliferation, (ii) inhibition of intrinsic programs
needed for axon regeneration, (iii) modulation of inammatory
responses to injury, and/or (iv) regulation of cell survival and cell
death after injury.
Fig. 5 Early inhibition of mTOR signaling impairs swimming recovery. a Scheme illustrating the experimental timeline. The mTOR pathway
was inhibited incubating animals with Torin1 1 µM during the rst 24 hpT, and swimming distance was recorded at 10 and 15 dpT. bWestern
blot for S6 and p-S6 in total protein extracts obtained from spinal cords isolated from X. laevis incubated immediately after transection for 24 h
with DMSO (vehicle) and Torin1 (drug) and collected at 24 hpT for vehicle treatment and at 24, 48, and 72 hpT for drug treatment. cDotplot of
swimming distances of animals treated with vehicle (black dots) and 1 µM Torin1 (red dots), measured at 10 and 15 dpT. Data indicate the
mean ± SEM, from three independent experiments. ***P< 0.001, ****P< 0.0001, from a MannWhitney U-test comparing Torin1-treated
animals to control, at each time point. All the blots included in this gure were derived from the same experiment and were processed in
parallel.
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Activation of the mTORC1 signaling pathway is necessary for
proliferation of NSPCs
Based on our previous results demonstrating that activation of
Sox2
+
NSPCs are required for spinal cord regeneration in X.
laevis
19
, we tested the effects of mTOR inhibition in the activation
of NSPC. For this, we rst studied the expression of PCNA, a
marker of DNA replication. Incubation with Torin1 during the rst
21 hpT resulted in a strong reduction in the number of cells
expressing PCNA at 2 dpT (Fig. 7ag). This reduction was mainly
observed in the cells lining the central canal and the injury zone,
J. Peñailillo et al.
8
npj Regenerative Medicine (2021) 68 Published in partnership with the Australian Regenerative Medicine Institute
but the inhibitory effect was less pronounced in the meningeal
cells.
To further study the effects on NSPC proliferation, we provided
a pulse of 5-ethynyl-2-deoxyuridine (EdU) followed by double
immunouorescence against Sox2 and EdU. We found that
mTORC1 inhibition signicantly decreased the number of Sox2
+
cells in the lesion site and sections close to it (Fig. 7hn). In line
with that, we also observed a signicant decrease in the amount
of EdU
+
/Sox2
+
cell in the same zone (Fig. 7o).
Considering that our GO analysis also identied the regulation
of genes involved in apoptosis after injury, we performed terminal
deoxynucleotidyl transferase-mediated dUTP-uorescein nick end
labeling (TUNEL) analysis at 1 dpT. We found that inhibition of
mTOR resulted in increased levels of TUNEL
+
cells (see Supple-
mentary Fig. S4). This result is in line with our bioinformatics
analysis, suggesting a rapid downregulation of cell death after
injury.
These results showed that not only mTORC1 activation is
necessary for proliferation of NSPCs but also the decrease of Sox2
cells close to the lesion site suggest a possible role in cell
migration and cell death.
Effect of mTOR inhibition in early gene expression after SCI
It has been described that mTOR pathway not only regulates
protein synthesis but that its inhibition also affects gene
transcription
37
. Thus, the effect of mTORC1 inhibition in the
transcriptome deployed after SCI was evaluated. For this, animals
were incubated with Torin1 or vehicle during the rst 24 hpT,
spinal cords were isolated at 1, 2, and 4 dpT, and mRNA-seq
analysis was performed. Differential gene expression was calcu-
lated using edgeR; we consider as differentially expressed those
genes with a fold change 2 (upregulated) or 2 (downregulated)
and also having a pvalue 0.01. There were 787, 356, and 212
genes identied as differentially expressed at 1, 2, and 4 dpT,
respectively (see Supplementary Fig. S5ac and Table S7).
We found that a large group of genes related to G1/S transition
of mitotic cell cycle and DNA replication initiation were down-
regulated at 1 dpT in animals treated with Torin1 (Fig. 7p), which is
consistent with the results observed for PCNA and EdU
+
/Sox2
+
(Fig. 7ao). Of note, a negative fold change of 2.7 and 4.1 for
mRNA of both PCNA homeolog genes was observed in the mRNA-
seq analysis, while no differential gene expression was observed
for sox2 gene (data not shown). This is in agreement with the
nding that PCNA protein levels increase at 1 and 2 dpT in animals
without mTORC1 inhibition (Fig. 7q and see Supplementary Fig.
S5d). Furthermore, we also found a signicant upregulation of
casp3 mRNA at 1 dpT (fold change of 2.9, see Supplementary
Table S7), which is in agreement with the TUNEL analysis
described above.
Among the upregulated genes at 1 dpT, we found an
enrichment in genes associated with macrophage chemotaxis
and positive regulation of IL-4 production, suggesting a strong
regulation of the immune response (see Supplementary Informa-
tion Fig. S5e). At 2 and 4 dpT, an enrichment of genes
(upregulated or downregulated) associated with the immune
response and other biological processes was also observed,
suggesting that an early inhibition of mTORC1 could have a
prolonged effect in many process involved in spinal cord
regeneration (see Supplementary Information Fig. S5fi).
All together, these results show that an early and transitory
inhibition of mTORC1 have a negative effect on NSPC prolifera-
tion. We suggest that these could explain the negative effects in
swimming recovery when mTORC1 is inhibited. In addition, this
analysis suggests an early regulation of cell death and immune
response by mTORC1 that could also contribute to the effects on
spinal cord regeneration.
DISCUSSION
Here we performed a high-resolution expression proling analysis
of the rst 21 h after spinal cord transection, which led to the
identication of the transcriptome changes deployed during this
early response, allowing the delineation of the regenerative
program deployed after spinal cord transection, particularly those
steps involved in the activation of NSPC (Fig. 8), and the
identication of the mTORC1 pathway as a key component in
the activation of this process.
Arst step in the response to any insult is the rapid activation of
primary response genes (PRGs)
24
. PRGs are classied into two
groups of genes: IEGs, which reach their peaks within 3060 min
after the stimulus, and delayed early genes with their maximum
expression at about 120 min after stimulus
38
. The change in
expression levels of these genes is characterized by no need of
protein synthesis. IEGs correspond mainly to transcription factors,
commonly activated in response to different stimulus, and
responsible for remodeling the cellular transcriptome, generating
the onset of secondary response genes (SRGs), which induce a
specic response to detected stimulus
25,39
. Interestingly, almost
half of the genes grouped in module E1 correspond to IEGs,
including fos, jun, and myc (Fig. 8), suggesting a role for them in
the regenerative response. Similar results have been reported in
planaria, showing that the activation of IEGs within the rst 30 min
after wounding is necessary for proper regeneration
40
. Further-
more, the Echeverri group recently showed that Fos can have
opposing effects over spinal cord regeneration on axolotl,
suggesting that the presence of different dimerization partners
can modulate distinct regenerative responses
41
. Regarding SRGs,
the intermediate and late modules depicted here have kinetics of
expression with patterns similar to those described for SRG, which
are usually involved in the maintenance of an effective response
to the corresponding stimulus
24,42,43
. Our ndings suggest a
strong conservation of the kinetic reported for these rst
responders, even in a complex process such as spinal cord
regeneration. Future studies are necessary to conrm that genes
in E1 correspond to IEG, specically validating that they do not
need protein synthesis for their activation, as well as to identify
their function in the onset of this regenerative program.
Immediately after 2 hpT, an increase in genes associated with
immune cell migration was detected, suggesting an early role for
innate immune cells. These cells are known to play a role in early
stages of wound healing, and are required for proper regeneration
Fig. 6 Effects of early inhibition of mTOR pathway in Sox2 and neurolament levels. adRepresentative images of transversal cryosections
of the spinal cord at 50 µm rostral (left images) and 50 µm caudal (right images) to the injury site. Samples were obtained at 15 dpT from
animals treated for 24 h with (a,b) vehicle or (c,d) Rapamycin, stained for the NSPC marker Sox2 and Hoechst. eQuantication of the
percentage of Sox2
+
cells over total cells. **P< 0.01, from a ttest comparing Rapamycin to vehicle treatments. n=3 per condition. fk
Representative images of transversal cryosections of the spinal cord, 200 µm rostral (left images), injury site (middle images), and 200 µm
caudal (right images) to the injury site. Samples were obtained at 15 dpT from animals treated for 24 h with (fh) vehicle or (ik) Rapamycin,
stained for the neuronal marker neurolament (NF) and Hoechst. lQuantication of the percentage of spinal cord area positive for NF staining
over spinal cord area, in the injury site as well as 200 µm rostral and caudal to the injury site. ttest. *P< 0.05, **P< 0.01 and ***P< 0.001, from a
ttest comparing Rapamycin to vehicle treatments. n=3 per condition. VZ ventricular zone. Immunouorescences were performed in two or
three biological replicates. Scale bar =100 μm.
J. Peñailillo et al.
9
Published in partnership with the Australian Regenerative Medicine Institute npj Regenerative Medicine (2021) 68
principally through cytokine secretion, and favoring the clearance
of death cells and deposited material
44
. In agreement with the
above, among the intermediate module I1 at around 4 hpT, a big
component of genes associated with chemotaxis and immune
response was detected, suggesting that innate immune response
plays an important and sustained role during the rst few hours
after SCI (Fig. 8), similar to what has been reported in zebrash
45
.
Furthermore, mRNA-seq assays at 1 dpT using Torin1 reported an
increase in genes associated with macrophage migration and
positive regulation of IL-4 production (Fig. S5e), suggesting that
mTORC1 is also involved in the regulation of early innate immune
response.
J. Peñailillo et al.
10
npj Regenerative Medicine (2021) 68 Published in partnership with the Australian Regenerative Medicine Institute
At 56 hpT, we observed an enrichment of components of three
interconnected processes: (1) downregulation of negative regula-
tors of mTOR (tsc2 and deptor, Fig. 8), (2) downregulation of
positive regulators of autophagy (ulk1, Fig. 8), and (3) upregulation
of genes associated with ribosome biogenesis (rps6kb1, RBFs, and
components of EIF4F complex, Fig. 8). These data suggest that the
activation of mTOR promote a cell growth stage that is necessary
for cell cycle entrance
46
, which in our context could be associated
with NSPC activation.
Previously, it has been reported that translational activation is one
of the earliest events in transition from quiescence to activation of
NSPC
47
and mTORC1 is a key regulator of ribosome biogenesis and
protein translation
48,49
. In intermediate modules, an upregulation of
RBFs and protein synthesis was observed, suggesting that this is a
process sustained in time and that mTOR activation is maintained,
which is supported by high levels of p-S6 observed at 21 hpT (Fig.
4dh). In this context, the identied RBFs are involved in different
steps of ribosome biogenesis and several of them have been reported
as necessary for self-renewal in neuroblasts (Fig. 8, highlighted in
bold)
30
, which would support in part the association between these
processes and NSPC activation in our model.
Even more, at 13 hpT, the time at which L2 and L3 modules
reach a peak, we identify an increase in genes associated with
stem cell proliferation and neurogenesis like nestin and compo-
nents of WNT pathway (wnt7b, fzd2, fzd3, and ctnnb1). Also, key
genes involved in transition from G1 to S phase as cdk4, e2f1,
cdk2, and ccnd1 (Fig. 8) were identied, suggesting a successful
progression through cell cycle in NSPC during the rst day after
lesion. This data ts with our previous nding showing that NSPC
start to proliferate between 1 and 2 dpT and are necessary for
spinal cord regeneration
1820
.
In agreement with the model depicted above, we detected an
activation of mTORC1 at 12 hpT, attaining a maximal peak at 1
dpT, and decreasing signicatively with respect to control at 4
dpT. The pathway is mainly activated in NSPC, meningeal cells,
and ventrolateral neurons. This supports a possible activation of
mTORC1 leading to an increased rate of protein synthesis in NSPC,
which could be necessary for NSPC activation
46
, as well as
regulating cap-dependent translation of pro-neurogenic mRNAs
50
.
In line with this, we found that early inhibition of the mTOR
pathway reduce proliferation of NSPCs.
This is further supported with the nding that mTOR inhibition
resulted in the downregulation of genes associated with the cell
cycle G1/S transition at 1 dpT, including genes associated with cell
cycle checkpoint, ccne2 and cdk2, as well as its transcriptional
regulator, e2f1 (Fig. 8). This is in accordance with a previous report
showing that mTORC1 is able to regulate ccnd and ccne,
translationally and transcriptionally, both directly implicated on
the G1/S transition
51
. As a consequence of the latest, a down-
regulation of gene expression levels of PCNA was observed after
mTORC1 inhibition at 1 dpT, which is concomitant with reduced
levels of cells in proliferative state close to injury site at 2 dpT in
animals treated with Torin1 (Fig. 7ag). It is important to consider
that PCNA, a key protein during DNA replication, is signicantly
induced during the rst 2 days of spinal cord regeneration (Fig.
7q). Altogether, our data suggest that mTORC1 plays a key role
during early stages of spinal cord regeneration in X. laevis,
activating NSPC proliferation through translational activation, and
later mediating G1/S transition. This role in NSPC activation
probably explains in part the disruption of functional recovery.
Many questions remain open regarding the role of mTORC1 in
NSPC activation, particularly the identication of the upstream
regulators that trigger mTOR signaling after injury, and the specic
RNAs and/or proteins regulated by this pathway in order to
regulate NSPC proliferation
52
. With respect to upstream regulators
for mTOR signaling, gradients of Ca2
+
and reactive oxygen species
are deployed minutes after tail amputation in Xenopus
53,54
; both
diffusible signals have been previously reported as activators of
phosphoinositide-3 kinase (PI3K)/Akt/mTOR pathway
55,56
, which
could be correlated with its early activation. In addition,
transcriptomic data reported a dual activation of PIK3R3, a
regulatory subunit of PI3K that is associated with proliferation
57
.
This gene showed an early and transitory activation followed by a
late and sustained activation, which was also shared by PI3KR2-
like, another regulatory subunit of PI3K. Also a late down-
regulation of PIK3IP1, a negative regulator of PI3K pathway, was
observed. Thus, a possible role of PI3K upstream of mTOR pathway
during spinal cord regeneration would be interesting to analyze in
future research.
Another question that remains open is about the role of mTORC2
in early stages of spinal cord regeneration. A downregulation of
mSin1 (also known as MAPKAP1, mitogen-activated protein kinase-
associated protein 1) was observed in our transcriptomic data (see
Supplementary Table S2), and an increase in the phosphorylation
levels of S6 protein, which is correlated with an activation of S6K, was
detected experimentally (see Fig. 4). Both observations have been
associated with a reduced activity of mTORC2
58,59
, suggesting low
levels of mTORC2 activity during early stages of SCI in X. laevis,and
consequently that most of the effect of the inhibitors is explained
because of the impact on mTORC1.
Finally, with respect to other implications of mTOR, it is known
that mTORC1 pathway activation promotes axon regeneration
after optic nerve injury
60
. mTORC1 could play a similar role in
Xenopus spinal cord regeneration, implicating a conserved role of
mTOR as a component of the intrinsic mechanism of axon
regeneration, and could explain the decrease in the number of
axons crossing the injury site observed after mTOR inhibition. A
possible role of mTORC1 in axon regeneration could be another
explanation for the diminished swimming recovery, prompting to
the future testing of the effect of this pathway in axonal
regeneration in this model system.
METHODS
Animal husbandry
X. laevis tadpoles were generated by natural mating and cultured as
described previously
17
. Animals were grown until Nieuwkoop and Faber
stages 4951 for experiments. Animal procedures were approved by the
Fig. 7 mTOR inhibition reduces cell proliferation and PCNA levels. afRepresentative images of transversal cryosections of the spinal cord,
100 µm rostral (left images) and 100 µm caudal (right images) to the injury site, obtained at 2 dpT from animals treated with acvehicle or df
Torin1, stained for the proliferation marker PCNA and nuclear marker Hoechst. gQuantication of the percentage of PCNA
+
cells over total
cells. *P< 0.05, from a ttest comparing Torin1 to vehicle treatments. n=3 per condition. hmRepresentative images of transversal
cryosections of the spinal cord, 200 µm rostral (left images) and 200 µm caudal (right images) to the injury site, obtained at 2 dpT from animals
treated with (hj) vehicle or (km) Rapamycin, stained for the NSPC marker Sox2, EdU, and Hoechst. nQuantication of the percentage of
Sox2
+
cells over total cells. *P< 0.05, from a ttest comparing Rapamycin to vehicle treatments. n=3 per condition. oQuantication of the
percentage of EdU
+
/Sox2
+
cells over Sox2
+
cells. *Pvalue < 0.05, from a ttest comparing Rapamycin to vehicle treatments. n=4 per
condition. pThree principal biological process downregulated at 1 dpT after treatment with Torin1, including a large component of genes
related to G1/S transition of cell cycle and DNA replication. qWestern blot for PCNA in total protein extracts obtained from spinal cords
isolated from X. laevis at 0, 1, 2, and 4 dpT, α-tubulin was used as a loading control. Yellow arrow: meningeal cells, vz: ventricular zone. Scale
bar =100 μm. All the blots included in this gure were derived from the same experiment and were processed in parallel.
J. Peñailillo et al.
11
Published in partnership with the Australian Regenerative Medicine Institute npj Regenerative Medicine (2021) 68
Scientic Ethics Committee for the Care of Animals and Environment of the
Ponticia Universidad Católica de Chile.
Experimental conditions and surgical procedures
Three experimental conditions were used: transected, sham, and uninjured
animals. Transected and sham procedures were performed according to
previous publications
17,20
. Briey, animals were anesthetized for 2 min on
0.02% MS222, followed by a dorsal incision at mid-thoracic level, cutting
the skin and muscle to expose the spinal cord for sham animals, and a
second step in which the spinal cord was transected at the same
anatomical level interrupting all ascending and descending axonal tracts
for transected animals. After surgery, animals were left on 0.1× Barth
(8.9 mM NaCl; 102 μM KCl; 238.1 μM NaHCO
3
; 1 mM 4-(2-hydroxyethyl)-1-
piperazine-ethane sulfonic acid; 81.14 μM MgSO
4
; 33.88 μM Ca(NO
3
)
2
;
40.81 μM CaCl
2
, pH 7.6) with antibiotics (100 μg/ml penicillin and 100 μg/
ml streptomycin) until spinal cord isolation, as previously described
17
.
Spinal cord isolation
For each experimental point, 1012 spinal cords were isolated to obtain
enough material for sequencing. For this, a region of 810 mm was isolated
from the spinal cord caudal to the injury site from transected animals and a
similar region from control animals
17,20
. Proper isolation of the spinal cords,
at a precise time, is needed for the success of the time-series experiment.
Thus, sampling for transected and sham conditions were composed of
three steps: (1) 24 h before surgery, animals were accurately selected,
staged to have enough stage 50 tadpoles, and maintained in 0.1× Barth; (2)
at surgery time, animals for each condition were split in six different pools,
which were composed by 2 +2Nanimals each one, where Ncorrespond
to time points in the time series, and surgery was performed to each pool
every 5 min, alternating pools that were transected and sham-operated; (3)
once the proper time for each pool was completed, two spinal cord were
isolated for each one to complete 10 spinal cords (series S1, S1, S2, and
S2) or 12 spinal cords (S3, S4, S5, S6, S3,S4,S5,S6, S7, and S8). These
spinal cords were immediately stored in RNAlater. Uninjured samples were
obtained through a similar 3-step process, but no previous surgery was
performed. For mTOR Inhibition mRNA-seq, animals were selected and
maintained in 0.1× Barth with antibiotics. The next day, animals were
transected and divided into two groups, one was incubated for 24 h in 0.1×
Barth with antibiotics +1 µM Torin1 and the other in 0.1× Barth with
antibiotics +0.05% dimethyl sulfoxide (DMSO). At 1, 2, and 4 dpT, spinal
cords from 15 animals of each group were isolated and stored immediately
in RNAlater.
Library preparation and mRNA-seq
Total RNA extractions were performed using the RNeasy Mini Kit (Qiagen,
MD, USA), including a DNase I treatment to avoid genomic DNA
contamination
20
. RNA concentration and purity and RNA integrity number
were measured using Nanodrop and Bioanalyzer 2100, respectively. PolyA
+RNA-seq libraries were prepared for each time series with the TruSeq
RNA Library Prep Kit v2 (Illumina) using between 120 and 500 ng of total
RNA as starting material. For mTOR inhibition mRNA-seq, libraries were
constructed using 1 µg of total RNA as input. The librariesquality was
determined using Bioanalyzer 2100 and a mean size of 300 bp was
obtained for all libraries. Posteriorly, time-series and mTOR inhibition
libraries were sequenced using the HISeq-2000 and HISeq-4000 platforms
(Illumina).
Bioinformatics analysis and data consistency
Sequencing quality was determined using the FASTQC software
61
, with
96% of the bases having a quality score >Q30 (mean quality score Q37.1;
Table S1). Libraries were mapped to the model transcriptome v9.1 of X.
laevis (Xenbase)
62,63
using Bowtie-RSEM for time-series mRNA-seq
64,65
and
Bowtie2-RSEM for mTOR inhibition mRNA-seq
65,66
, using default para-
meters established by RSEM. Genes with ve or more counts in at least four
temporal points were dened as genes detected consistently in time-series
mRNA-seq.
Reproducibility among time-series mRNA-seq samples was evaluated by
Spearman correlation coefcient over gene counts normalized with
variance stabilizing transformation (VST) method of DESeq2
67
. Gene
counts normalized with VST was also used as input for batch effect
correction using ComBat function included in SVA package, as it has been
demonstrated as a suitable adjust for known batches
68
.
To ensure that the sampling kept an adequate tracking of the biological
processes within each series, and among the different series, the
consistency of the data was evaluated. A high degree of internal
consistency for neighboring points within each time series, in the
Fig. 8 Summary model of early transcriptional response after SCI in X. laevis.In early modules, around 2 hpT, occur a rapid and transitory
activation of IEGs (module E1, green yellow in Figs. 2a and 3) followed by immune response genes (module E2, magenta in Figs. 2b and 3).
Posteriorly, in intermediate modules, around 5 hpT, an increase in genes associated with chromatin remodeling is observed (modules I3, blue
in Figs. 2e and 3), as well as regulation of several biological events, including activation of mTORC1 (module D1, black in Figs. 2f and 3).
Apparently, mTORC1 would act as key regulator over important cellular processes, such as protein synthesis regulation and ribosome
biogenesis (module I3, blue in Figs. 2e and 3) observing a strong upregulation of RBFs linked to self-renewal of neuroblast (highlighted in
bold), and NSPC proliferation (module L2, red in Figs. 2i and 3) and DNA replication (module L3, yellow in Figs. 2j and 3) in late modules,
regulating the G1/S transition.
J. Peñailillo et al.
12
npj Regenerative Medicine (2021) 68 Published in partnership with the Australian Regenerative Medicine Institute
transected condition, was observed using Spearman correlation coef-
cients (0.99 ± 0.006; Supplementary Fig. S6a). The correlations calculated
for neighboring points were very similar to the ones obtained for the
biological replicates at 0 hpT, included in series 1 and 2, which were 0.98
and 0.99, respectively (Supplementary Fig. S6a), indicating that neighbor-
ing points could function as pseudo-replicates in high-resolution time
series
21
. Similar results were observed for correlation coefcient of
neighboring points in sham and uninjured conditions, 0.99 ± 0.007
(Supplementary Fig. S7a) and 0.98 ± 0.017 (Supplementary Fig. S8a),
respectively.
Furthermore, the correlation coefcient between overlapping points was
calculated in different series, and although still strong, they were found to
be slightly lower than those calculated for the biological replicates and
pseudo-replicates (0.96 ± 0.018; Supplementary Fig. S6b). Something
similar was obtained when the 0 hpT time points for each series were
calculated (0.95 ± 0.022; Supplementary Fig. S6b, inset). Considering that
the expression at 0 hpT corresponds to the basal expression level for each
time series, we reasoned that the multiple sampling processes and
different batches of animals contributed to generate a batch effect, which
could explain the differences on gene expression levels for basal and
overlapping points during construction of some gene expression proles
(Supplementary Fig. S9ad). Combat function of the SVA package was
applied to all our data, to correct these batch effects, improving the
correlation coefcients of the basal (0 hpT) and overlapping points, 0.99 ±
0.004 and 0.99 ± 0.003, respectively. After these corrections, a better t was
observed between the time series, especially for those genes particularly
affected by batch effects (Supplementary Fig. S6c and S9a’–d).
In case of sham samples, we observed an improvement in pairwise
correlation for overlapping points after batch effect correction from 0.97 ±
0.014 (Supplementary Fig. S7b) to 0.99 ± 0.003. While for the uninjured
condition, similar results were reported before (0.98 ± 0.012, Supplemen-
tary Fig. S8b) and after (0.98 ± 0.005) batch effect correction, probably
because this condition was just sampled in two time series, and the batch
effect was slightly higher than in the transected condition. Finally, for a
better comparison among conditions, a three-dimensional principal
component analysis plot is included (Supplementary Fig. S8c), showing a
strong difference in differentially expressed genes between the transected
and control conditions, which increases with time, thus validating the
sampling and data processing.
Differential expression analysis
For time-series mRNA-seq, differential gene expression analysis was
performed using Gaussian Processes, as previously reported
21
, over the
list of genes consistently detected. Briey, for each gene, two possible
hypotheses were tested and compared: a null hypothesis in which the
gene expression proles are best tted by one single Gaussian Process
model, non-differential gene expression, and an alternative hypothesis
where the gene expression proles are better tted by two different
Gaussian Process models, temporal differential gene expression. To choose
between both hypotheses, the log marginal likelihood of each one was
compared using BIC, a BIC > 0 indicates a preference for the alternative
hypothesis of two Gaussian Process models. Two different comparisons
were done, transected-vs-sham and transected-vs-uninjured, and were
selected for further analysis those genes with BIC 10 for both
comparisons because these were considered as strongly deployed in
response to SCI. This cut-off has been described previously as a strong
evidence for alternative hypothesis in model selection when BIC is used
23
.
For mTOR inhibition mRNA-seq, differential expression analysis was
performed following a protocol for experiments without replicates using
edgeR
69
. Briey, gene counts were normalized based on library size and
later differential gene expression was tested applying the function
exactTest, using a biological coefcient of variation of 0.2. To determine
differential gene expression, |Log2 (fold change)| 1 and pvalue 0.01
were used as cut-off.
Weighted gene co-expression network analysis
WGCNA
70
was performed over transected data corresponding to genes
differentially expressed with a BIC 10. Initially, a similarity matrix was
calculated for gene expression proles using biweight midcorrelation
(bicor) coefcient, which is less sensible for outliers
71
. Posteriorly, to
determine connection strengths between network nodes, a signed hybrid
adjacency matrix was calculated, based on correlation measures using a
soft-thresholding power equal to 15 (β=15) which was estimated by
pickSoftThreshold function to satised the scale-free topology property of
the co-expression network
70
. Then, adjacency matrix was transformed to a
topological overlap matrix, and its corresponding dissimilarity matrix was
used as input for the hierarchical clustering algorithm to identify co-
expression modules on the network
70
. Posteriorly, a change point analysis
was performed over representative expression prole of each module
using Bayesloop, to determine an approximate time at which gene
expression is changing in each co-expression module
72
.
GO and KEGG pathway enrichment analysis
Protein-coding sequences in the model transcriptome of X. laevis were rst
mapped onto the NCBI non-redundant protein database for mouse,
human, zebrash, chicken, y, and worm using BlastP (-evalue e-5
-max_target_seqs 5)
73
, and InterProScan was used to identify protein
domains
74
. Subsequently, the data were integrated into Blast2GO, and GO
terms were assigned to each gene
75
. An enrichment analysis was
performed to each module using clusterProler
76
, specically for genes
with Module Membership 0.6. A pvalue cut-off of 0.01 in Fishers Exact
Test was chosen for enrichments. To reduce redundancy in the enrichment
analysis of time-series mRNA-seq, GO terms that meet at least one of the
following criteria were chosen: (1) 5 genes in co-expression module are
associated with this, (2) at least 15% of genes in co-expression module are
associated with this, or (3) at least 15% of genes used as background in
enrichment analysis that are associated with this are present in co-
expression module. Finally, GO terms were clustered based on semantic
similarity and genes associated with them, using DBSCAN algorithm, and
representative GO term in each cluster was selected based on the highest
x-value, which was dened as product of Log10 (pvalue) calculated for
GO term and the sum of BIC for genes associated with this,
x-valueGO term ¼LogðpvalueÞGO term ´ΣBICGO term . For mTOR inhibition
mRNA-seq, three GO terms with the biggest gene ratio in each enrichment
were visualized using clusterProler.
For KEGG pathway analysis, protein-coding sequences in model
transcriptome of X. laevis were also mapped onto the NCBI non-
redundant database for human proteins using BlastP (-evalue e-5
-max_target_seqs 5). Posteriorly, these annotations were used to calculate
an enrichment against human KEGG pathway database using clusterPro-
ler and Fishers Exact Test with false discovery rate cut-off of 0.05
76
.
Immunouorescence
Animals were cryosectioned as previously described
17
. Briey, animals
were sacriced using 0.02% MS222 and xed with 4% paraformaldehyde
(PFA) in phosphate-buffered saline (PBS) at room temperature (RT) for 2 h
or at 4 °C overnight (ON). After xation, excess of PFA was removed,
samples were dehydrated in increasing concentration of PBSsucrose
solution (5, 10, and 20%), embedded in optimal cutting temperature
compound, and frozen using liquid nitrogen. Later, samples were
cryosectioned (10 µm) and tissue sections were permeabilized for 10 min
using 0.02% Triton in PBS, blocked for 30 min using bovine serum albumin
(BSA) or goat serum; an incubation with methanol for 10 min at 80 °C was
optionally performed, in order to reduce tissue auto-uorescence (Table
S8), followed by an ON incubation at 4 °C with primary antibodies.
Posteriorly, tissue sections were thoroughly washed with 0.02% Triton in
PBS and incubated with Alexa-conjugated secondary antibodies for 2 h at
RT. Finally, after washing with 0.02% Triton in PBS, nuclei were stained for
5 min using Hoechst (1:10,000, Thermo Fisher Scientic, Waltham, USA) in
0.02% Triton in PBS. Details of antibodies and concentrations used are
reported in Table S8. Immunouorescence images were captured with a
Fluoview FV10i confocal microscope (Olympus). Cell quantication and NF-
stained area was performed using the Fiji/ImageJ software. For Sox2, PCNA,
and Sox2/EdU analysis, we counted the positive cells over total cell
number contained in a dened area of the spinal cord (200 µm rostral and
200 µm caudal to the injury site). For NF analysis, we determined the ratio
between pixels stained for NF and pixels in the total area of spinal cord. In
both cases, statistical differences were calculated using ttest.
TUNEL
Cell death was evaluated in spinal cord longitudinal sections using
DeadEndFluorometric TUNEL System (TUNEL, Promega Corporation, WI,
USA), following the manufacturer instructions. Briey, 24 h after transected
animals were incubated with DMSO or 1 µM Torin1, they were sacriced
and xed with 4% PFA in PBS at 4 °C ON, and samples were dehydrated,
frozen, and cryosectioned (10 µm). Spinal cord sections were
J. Peñailillo et al.
13
Published in partnership with the Australian Regenerative Medicine Institute npj Regenerative Medicine (2021) 68
permeabilized with 20 µg/ml Proteinase K, followed by xation with 4%
PFA, and incubated with the TUNEL mixture for 1 h at 37 °C. Finally,
sections were stained with Hoechst 33342 (1:10,000) for 10 min and
mounted with vectashield (Vector Laboratories, Burlingame, USA). Images
were captured with an Olympus BX51 microscope. The number of TUNEL-
positive nuclei were quantied in a dened area of the spinal cord (300 µm
rostral and 300 µm caudal to the injury site).
Western blot
Spinal cords from 12 animals were isolated at the desired time point and
manually homogenized in RIPA buffer including protease inhibitor. Extracted
proteins were separated by 10% sodium dodecyl sulfatepolyacrylamide gel
electrophoresis, and transferred to polyvinylidene diuoride membranes.
Primary and secondary antibodies were incubated separately in 3% BSA in
TBS-T and revealed with SuperSignal West Pico Chemiluminescent Substrate in
the iBright FL-1000 (Invitrogen). Details of antibodies and concentrations used
are reported in Table S8. All blots or gels were derived from the same
experimentandprocessedinparallel.
mTOR inhibition
Transient mTOR pathway inhibition was performed exposing the animals
for 24 h to 10 µM Rapamycin (553210, Sigma-Aldrich) or 1 µM Torin1
(ab218606, abcam) in 0.1× Barth with antibiotics, while control animals to
0.1 or 0.05% DMSO in 0.1× Barth, respectively, immediately after
transection. After drug exposure, animals were maintained in 0.1× Barth
with antibiotics during the course of the experiments.
Swimming recovery assay
Free swimming distance, in animals treated with mTOR inhibitors, was
evaluated by video-tracking at 10 and 15 dpT as previously reported
19
.
Briey, animals were recorded for 5 min with a video camera and the
swimming paths were tracked by ANY-maze (Stoelting Co, Wood Dale, IL).
Posteriorly, swimming paths were corrected using Resampling Trajectories
of traja (Python package), to avoid overestimations induced by animal
movements that do not generate displacement, and nally statistic
differences between groups were tested using MannWhitney U-test for
non-parametric data.
Proliferation assay
Cell proliferation was evaluated using a pulse-chase assay with thymidine
analog EdU. Thirty-two hpT animals were injected with 1 µL EdU dilution at
a concentration of 5 mg/ml in 0.8× PBS and 16 h later were sacriced, xed,
and processed as previously described
17
.
Reporting summary
Further information on research design is available in the Nature Research
Reporting Summary linked to this article.
DATA AVAILABILITY
mRNA-seq data are available on NCBI Gene Expression Omnibus (GEO) database
repository (accession number: GSE165343; token: ktwxsgagnrcpdkr). Other data or
reagents are available upon request from the corresponding author.
Received: 28 January 2021; Accepted: 30 September 2021;
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ACKNOWLEDGEMENTS
Special thanks to Dasfne Lee-Liu and Fernando Faunes for critical reading of the
manuscript and all members of Larraíns Laboratory for their support. This work was
funded by research grants from: J.P.: CONICYT PhD fellow and Gastos Operacionales
No. 21140993; P.G.S.: FONDECYT No. 3190820; J.L.: FONDECYT No. 1180429, CARE
Chile UC-Centro de Envejecimiento y Regeneración (PFB 12/2007). This work was
supported by the Unidad de Microscopia Avanzada UC (UMA UC).
AUTHOR CONTRIBUTIONS
J.P.: investigation, formal analysis, validation, and manuscript writing. M.P., C.M., R.M.,
E.D.D., I.P.: investigation. P.G.S.: investigation and critical reading of the manuscript.
M.G.: conceptualization and funding acquisition. J.L.: supervision, data analysis,
manuscript writing, and funding acquisition.
COMPETING INTERESTS
The authors declare no competing interests.
ADDITIONAL INFORMATION
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41536-021-00179-3.
Correspondence and requests for materials should be addressed to Juan Larraín.
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Unlike mammals, Xenopus laevis tadpoles have a high regenerative potential. To characterize this regenerative response, we performed single-cell RNA sequencing after tail amputation. By comparing naturally occurring regeneration-competent and -incompetent tadpoles, we identified a previously unrecognized cell type, which we term the regeneration-organizing cell (ROC). ROCs are present in the epidermis during normal tail development and specifically relocalize to the amputation plane of regeneration-competent tadpoles, forming the wound epidermis. Genetic ablation or manual removal of ROCs blocks regeneration, whereas transplantation of ROC-containing grafts induces ectopic outgrowths in early embryos. Transcriptional profiling revealed that ROCs secrete ligands associated with key regenerative pathways, signaling to progenitors to reconstitute lost tissue. These findings reveal the cellular mechanism through which ROCs form the wound epidermis and ensure successful regeneration.