Stem cell function and stress response are controlled by protein synthesis.

Introduction
Protein synthesis is a fundamental process for all cells, but its precise
regulatory roles in development, stem cells, and cancer are not well understood. We
recently identified post-transcriptional methylation of transfer RNA (tRNA) at
cytosine-5 (m5C) by NSun2 as a novel mechanism to repress global protein
synthesis1,2. Loss of NSun2 causes hypo-methylation of tRNAs,
allowing endonucleolytic cleavage by angiogenin and accumulation of 5’ tRNA
fragments1,3. These fragments repress cap-dependent protein translation4–7.
Correct RNA methylation is essential for development and tissue homeostasis.
Loss-of-function mutations in human NSUN2 cause growth retardation
and neuro-developmental defects including microcephaly1,8–10. In mouse, NSun2-associated microcephaly
can be rescued by inhibiting angiogenin-mediated tRNA-cleavage1. In adult tissues (testis and skin), NSun2 is only expressed
in a sub-population of committed progenitors, where its activity balances
self-renewal and differentiation11,12.
Here, we reveal that the interplay between RNA methylation and translation
shapes stem cell fate. Using skin as a model, we demonstrate that stem cells have
lower protein synthesis than committed cells in both homeostasis and tumourigenesis.
Low translation functionally contributes to maintain stem cells, and is not merely a
consequence of quiescence or cell cycle state. By genetically deleting
NSun2 in a tumour mouse model, we find that protein synthesis
is globally repressed; however, distinct transcripts escape this repression and
establish a translational programme crucial to stimulate stem cell functions.
Unexpectedly, the selective alteration of translation is remarkably effective in
rendering stem cells sensitive to cytotoxic stress.

Results

Stem cells synthesize less protein than their progeny
In skin, the best-characterized stem cell populations reside in the hair
follicle13. Hair follicle stem cells
(HFSC) are periodically activated at the onset of hair growth (anagen), which is
followed by phases of regression (catagen) and rest (telogen) (Extended Data Fig. 1a)14,15. HFSCs located
in the bulge (BG) express the stem cell markers CD34, keratin-19 (K19) and Lgr5
(Fig. 1a)16,17.
To visualize HFSCs and their progeny, we genetically labeled K19- and
Lgr5-expressing bulge stem cells with a tdTomato (tdTom) reporter (Fig. 1a,b; Extended Data Fig. 1a)16,18. To measure global
protein synthesis we quantified incorporation of OP-puromycin (OP-puro) into
nascent proteins (Fig. 1b)19. Protein synthesis was uniformly low in
the interfollicular epidermis (IFE), but highly dynamic in hair follicles
throughout the hair cycle (Extended Data Fig.
1b). In telogen, highly translating cells at the follicle base were
not stem cells, as they were negative for tdTomato (Fig. 1c,d; Extended Data Fig.
1c). In late anagen, OP-puro co-localized with tdTomato in committed
progenitors located in the hair bulb (Fig.
1e,f; Extended Data Fig. 1d;
arrows). The highest translation was displayed above the hair matrix, which
contains committed progenitors that divide a finite number of times before
differentiating (Fig. 1e,f; Extended Data Fig. 1d; arrowheads)20.
Co-labeling of OP-puro with markers for all hair lineages identified the
Henle’s (He) and Huxley’s (Hu) layers of the inner root sheath
(IRS) as the lineages with highest translation (Fig. 1g-k; Extended Data Fig.
1e,f)21,22. Both IRS layers exclusively contain committed and
differentiated cells22.
To fully quantify protein synthesis in distinct epidermal populations, we
flow-sorted bulge stem cells (CD34+/α6+), non-bulge
cells (CD34-/α6+), and differentiated cells
(CD34-/α6-) (Fig.
2a-c)17. To capture epidermal
cells giving rise to the highly translating IRS, we enriched for
OP-purohigh cells (top 2.5% in rate of translation) (Fig. 2b). The selection for high translation
did not perturb the proportion of cell populations found in the epidermis (Extended Data Fig. 2a-d). Quantification of
OP-puro incorporation confirmed that protein synthesis was highest in
differentiated populations in late anagen (Fig.
2d). Translation in bulge stem cells significantly increased from
telogen to anagen (Fig. 2d), suggesting a
correlation between translation rate and stem cell activation.
Next, we focused on HFSCs and their progeny and quantified protein
translation in tdTomato+ cells that were sorted into bulge stem cells, non-bulge
cells, and differentiating cells (Fig.
2e,f). Translation rates significantly increased in bulge HFSCs from
telogen to anagen (Fig. 2e,f). In addition,
the average translation rate increased in differentiating cells in late anagen,
and was around 2-fold higher compared to the background cells (tdTomato-) (Fig. 2d-f; Extended Data Fig. 2e,f). These results were robust to the specific
threshold used to identify cells as highly translating (top 2.5% - 50%) (Extended Data Fig. 3a-c).
Thus, as stem cells proceed into a fully committed progenitor state,
protein translation steadily increases.

Proliferation does not determine protein synthesis
Protein synthesis was highest in growing hair follicles. However,
cellular division alone did not explain translation rates as the greatest
protein synthesis was found in differentiating but non-dividing (Ki67-negative)
cells (Fig. 2g). Although the percentage of
cycling (S/G2/M) cells correlated with increasing translation rates (Extended Data Fig. 3d,e), differentiating
(CD34-/α6-) and non-dividing (G1/G0) cells
represented the population with the highest translation (Extended Data Fig. 3f,g).
To directly test whether protein synthesis was determined by lineage
commitment instead, we measured the translation rate in bulge HFSC and their
offspring (tdTomato+) along the cell cycle. In late anagen, non-cycling (G1/G0)
stem and progenitor cells synthesized significant more protein than their
cycling (S/G2/M) counterparts (Fig. 2h;
Extended Data Fig. 3h). Thus,
increasing translation rates correlated with stem cell commitment and
differentiation rather than proliferation (Extended Data Fig. 4p).

Dividing tumour-initiating cells synthesize low levels of protein
To test whether low protein synthesis simply reflected a quiescent
state, we investigated translation rates in cancer-initiating cells, which
exhibit both high self-renewal and proliferation capacity. We used K5-SOS mice,
which constitutively activate RAS in basal epidermal cells and develop
well-differentiated tumours resembling human squamous tumours23,24.
Undifferentiated progenitors (UP) expressed markers for tumourigenesis
and tumour-initiating cells (Itgβ1, Itgα6, CD44, CD34, PDNP)25–29, and exhibited lower protein synthesis than committed progenitors
(CP) (Fig. 3a,d; Extended Data Fig. 4a-c,f-j). Translation was highest in
supra-basal and differentiating CP (K10+), but absent in terminally
differentiated (TD), non-tumourigenic cells (Fig.
3a,b). In cancer, elevated translation has been associated with
increased proliferation30. However, in
our data high translation was uncoupled from proliferation because both
OP-puro-high and -low cells expressed Ki67 (Fig
3c), and protein synthesis did not correlate with cycling cells
(Fig. 3e).
Thus, similar to normal skin, stem and progenitor cells in tumours
produced less protein than their committed progeny.

Forced reduction of protein synthesis enriches for tumour-initiating
cells
To test whether low translation is cause or consequence of a stem cell
state requires the ability to modulate protein synthesis. An excellent system is
the genetic deletion of the RNA-methyltransferase NSun2. NSun2 modulates global
translation by protecting tRNAs from cleavage1. In normal skin, NSun2 is restricted to distinct hair follicle
populations11 that overlap with
OP-purohigh cells in early and late anagen (Extended Data Fig. 4k,l). NSun2-deletion
delayed HFSC differentiation in adult11
and developing skin (Extended Data Fig.
4m-o). NSun2 is up-regulated in epithelial tumours and homogenously
expressed in mouse and human squamous cell carcinomas (Extended Data Fig. 5a)31,32, and its expression is
restricted to highly translating cells in K5-SOS tumours (Fig. 3f).
We deleted NSun2 in K5-SOS mice, and measured OP-puro
incorporation into the offsprings’ tumours. As expected,
NSun2-ablation reduced protein synthesis in tumours (Fig. 3g-i; Extended Data Fig. 4d,e). K5-SOS/NSun2-/- mice
developed more tumours that appeared earlier, grew larger, and reduced the life
span (Fig. 4a; Extended Data Fig. 5b-d).
NSun2-/- tumours appeared more proliferative; however,
EdU/BrdU pulse-chase experiments revealed that high EdU incorporation reflected
an increased undifferentiated population, but not a faster division rate (Fig 4b,c; Extended Data Fig. 5e,f). NSun2-/- tumours were
poorly differentiated and in a later stage of tumourigenesis as shown by
increased expression of stem cell- and tumour progression-markers (Fig. 4d-i; Extended Data Fig. 5f-j).
To test for the cell-intrinsic, tumour-initiating potential, we injected
NSun2-/- tumour cells subcutaneously into nude mice (Extended Data Fig. 6a). Only
NSun2-/- cancer cells reconstituted the original squamous
tumour with high proliferative potential and elevated levels of Itgβ1 and
PDPN (Extended Data Fig. 6b-f). Thus,
NSun2-deletion enhances self-renewal potential of
tumour-initiating cells in a cell-autonomous manner.
Also in human skin cancer NSun2 expression was inversely correlated with
malignancy, when we compared protein expression levels in normal skin and
cutaneous cancers of increasing TNM stages (Fig.
4j; Extended Data Fig.
6g-m).
These results argue that reduction of translation rates caused by
NSun2-deletion increased the tumour-initiating
population.

tRNA fragments as mediators of a stem cell translational programme
A likely mechanism for translational repression in
NSun2-deficient tumours was that 5’ tRNA fragments
inhibit protein synthesis4. Using RNA
bisulphite-sequencing1,33, we confirmed that in tumours,
NSun2-dependent methylation occurred at most tRNAs (65%), but only at a small
proportion of mRNA exons (2%) and introns (Fig.
5a,b; Extended Data Fig. 7a-e;
Supplementary Table
1,2)1,2,34–39. In the few mRNAs, NSun2-methylation
sites were enriched close to transcriptional start sites, but uncorrelated with
RNA abundances (Extended Data Fig. 7f,g;
Supplementary Table
1,3). In contrast, hypo-methylation of tRNAs directly caused by loss
of NSun2 lead to the accumulation of 5’ tRNA fragments (Fig. 5c,d; Extended Data Fig. 7i-l; Supplementary Table 2,4).
We performed ribosome profiling to evaluate how 5’ tRNA fragments
influenced translation in mouse tumours and patient-derived NSun2-deficient
fibroblasts (Extended Data Fig. 8a,b;
Supplementary Tables
5,6a-c). We verified the high quality of our data by testing for
triplet periodicity of ribosomal footprints40, increased ribosomal density near translation start sites41, and correlation between RNA expression
levels and translation (Extended Data Fig.
8c-j)42.
The distinct translational programme in NSun2-/-
tumours was not driven by transcription, because the changes in protein
synthesis caused by NSun2-removal were decoupled from the
corresponding changes in RNA expression levels (Fig. 5e; Extended Data Fig.
8e). These differences in translation were more likely to be caused by
accumulated 5’ tRNA fragments than by changes in mRNA methylation,
because translation of NSun2-methylated mRNAs remained unaltered (Extended Data Fig. 7h).
In sum, the undifferentiated cellular phenotype of
NSun2-/- tumours was primarily driven by translational, and
not transcriptional changes.

Changes in tRNA methylation impose a distinct translational signature
Accumulation of 5’ tRNA fragments can activate a cap-independent
stress-response programme4; and stress
stimuli can increase ribosomal density in 5’ untranslated regions
(UTR)43,44. Consistent with such a stress response, 5’UTRs
in NSun2-deficient cells showed increased ribosome densities
(Fig. 5f,g; Extended Data Fig. 8k,l; Supplementary Table 7-9).
The increased ribosome density in 5’UTRs is likely due to the occurrence
of up-stream open reading frames (uORFs)41,45. Functionally, uORFs
repress translation by sequestering initiation events or facilitate downstream
re-initiation and translation45–48, which may
explain why protein synthesis of corresponding coding sequences (CDS) remained
unaltered (Fig. 5f,g).
Although the underlying mechanisms are unclear, differential ribosome
density in 5’UTRs should alter protein production of distinct genes.
Indeed, transcripts with increased ribosome density in 5’UTRs were linked
to apoptosis, stress response, cell shape and migration (Fig. 5h-j). In tumours, transcripts with reduced ribosome
density in the CDS were related to differentiation (Fig. 5h,i). Thus, the ribosome profiling data correlated
well with the phenotypic reduction of epidermal differentiation of
NSun2-deficient tumours; and the cell-intrinsic NSun2-controlled translational
programme(s) related to stress responses and cell motility was conserved between
species.
To identify the translational programme that directly depended on RNA
methylation, we performed ribosomal profiling after rescuing
NSun2-/- human fibroblasts with the wild-type or
enzymatically dead constructs of NSun2 (Extended
Data Fig. 9a-d). Modulators of cell adhesion and motility represented
a quarter of translational repressed transcripts that depended on the enzymatic
activity of NSun2 (Extended Data Fig.
9e-g; Supplementary
Tables 10a-c). Consequently, motility and adhesion were
down-regulated but differentiation up-regulated in primary human keratinocytes
when NSun2 was repressed or enzymatic-dead versions over-expressed (Extended Data Fig. 9h-m).
Thus, the undifferentiated stem cell state in
NSun2-deficient tumours was primarily driven by differential
translation of proteins regulating cell migration, adhesion and stress responses
(Extended Data Fig. 10a,b; Supplementary Fig.
1).

Low translating tumour-initiating cells are highly sensitive to anti-cancer
drugs
To test whether the stress-related programme in
NSun2-/- tumours altered their sensitivity to external stress
in vivo, we applied the cytotoxic agent 5-fluorouracil
(5FU). 5FU is commonly used to treat squamous cell carcinomas49. While wild-type tumours only showed a
mild reduction in growth, 5FU-treatment blocked progression of
NSun2-/- tumours (Fig.
6a; Extended Data Fig. 10c,d).
NSun2-/- tumour cells were unable to re-enter the cell
cycle after drug treatment, despite induction of p53 being detectable in all
samples (Fig. 6b,c; Extended Data Fig. 10e,f). We obtained similar results
using cisplatin (Extended Data Fig.
10g-i). 5FU-treated NSun2-/- tumour cell layers were
reduced, and the remaining basal cells (Itgα6) unusually labeled
double-positive for the differentiation marker K10 (Fig. 6d; arrows). Thus, NSun2-deficient
tumours fail to activate survival pathways in response to stress.
Finally, we asked whether the increased sensitivity to 5FU depended on
angiogenin-mediated cleavage of non-methylated tRNAs. We rescued tRNA cleavage
by administering the angiogenin-inhibitor N65828 (AI)1,50. High toxicity
of this drug combination only allowed treatment times up to 7 days.
Nevertheless, NSun2-/- tumours failed to regress and survival
of undifferentiated tumour-initiating cells
(CD34+/α6H) significantly increased when
exposed to both drugs (Fig. 6e,f),
indicating that tRNA fragments reduce the survival of NSun2-/-
tumour-initiating cells.
Thus, combining cytosine-5 RNA methylation inhibitors with conventional
chemotherapeutic agents may provide an effective anti-cancer strategy for solid
tumours (Extended Data Fig. 10j).

Discussion
Similar to the haematopoietic system19, epidermal stem cells produce less protein than their immediate
progenitors, and forced entry into the cell cycle is not sufficient to reverse this
translation repression. Instead, global protein synthesis in normal and tumour cells
is determined by lineage commitment, but not by proliferation.
Here, we identify RNA methylation as an important pathway to modulate global
protein synthesis and cell fate. Both protein synthesis and NSun2 expression are low
in epidermal stem cells, but increase upon commitment to differentiate.
NSun2-mediated methylation protects tRNA from cleavage into non-coding 5’
tRNA fragments, thereby promoting protein translation and differentiation1. External stress stimuli inhibit NSun2
activity1 permitting cleavage into
5’ tRNA fragments, which then decrease protein synthesis in human cells4. Inhibition of post-transcriptional
methylation in squamous tumours promotes stem cell function and tumourigenesis.
However, re-activation of cytosine-5 RNA methylation pathways is required to exit
the specific translation inhibition programme after cytotoxic stress. Thus,
activation of RNA methylation or inhibition of tRNA cleavage is essential for cell
survival of tumour-initiating cells in response to cytotoxic stress
(see
Supplementary
Discussion).

Material and Methods are submitted as supplementary file.

Methods

Transgenic mice
Rosa-CAG-LSL-tdTomato51, K19-CreER52 and Lgr5-CreERT253, NSun2-/- (or homozygous NSun2Gt(D014D11)Wrst)54, and K5-SOS-F (in wa2/wa2
background)55 mutant mice have been
described previously. Balb/C athymic nude mice purchased from Charles River were
used in transplantation experiments. All mice were housed in the Wellcome Trust
- Medical Research Council Cambridge Stem Cell Institute Animal Unit. All mouse
husbandry and experiments were carried out according to the local ethics
committee under the terms of a UK Home Office license PPL80/2231 and
PPL80/2619.
To conditionally induce tdTomato-reporter lines for expression of
Cre-recombinase, Rosa-CAG-LSL-tdTomato mice were crossed with K19-CreER or
Lgr5-CreERT2 mice. To activate CreER, only male mice were treated with two
intra-peritoneal (IP) injections of 50 μl of a Tamoxifen (Sigma-Aldrich)
solution (40mg/ml) in corn oil at post-natal day 15 and 17.
To measure protein synthesis in vivo, mice were
intraperitoneally injected with O-Propargyl-puromycin (Op-puro) (Medchem Source
LLP) at a concentration of 50 mg per kg of body weight dissolved in PBS at pH
6.4–6.6 one hour before sacrifice. Skin samples were collected at Catagen
(postnatal day P17), telogen (P19), early anagen (P25) and late anagen
(P30).
To induce squamous tumours in NSun2-/- background, we
used K5-SOS transgenic mice. These mice express a dominant negative form of Son
of Sevenless (SOS) under control of the keratin-5 (K5) promoter and develop
spontaneous cutaneous tumour with 100% penetrance55. K5-SOS-Fxwa2/wa2 mice were crossed with
NSun2+/- mice carrying a gene trap in the
NSun2 allele (NSun2Gt(D014D11)Wrst). Spontaneous
skin papillomas developed mainly in the tail of K5-SOS-Fxwa2/+ mice two weeks
after birth but they did not develop into malignant squamous cell
carcinomas55.

Histology, tissue and cell stainings, antibodies and imaging
Tissues or tumours were either embedded in OCT and frozen or fixed
overnight with 4% paraformaldehyde, transferred to 70% EtOH and embedded in
paraffin. Samples were then cut at 4 μm (paraffin) or 10 μm
(frozen). Immunofluorescence staining, LacZ and Hematoxylin and Eosin staining
of frozen or paraffin-embedded tissues or cells were performed as described
previously54. For
immunohistochemistry ImmPRESS™ reagents (Vector Labs) or IHC Detection
Kit (Ventana Medical Systems) and DISCOVERY automated IHC staining system
(Ventana Medical Systems) were used.
Primary antibodies were used at the following dilutions: rabbit
polyclonal to RFP (for tdTomato) (1:1000; Rockland inc., 600-401-379), mouse
monoclonal to Dlx3 (1:200, Abnova, H00001747-A01), rabbit polyclonal to K6
(1:200, Abcam, ab24646), mouse monoclonal to Gata3 (1:50, Santa Cruz Biotech,
sc-268), guinea pig polyclonal to K31 and K72 (1:200, Progen, GP-hHa1 and
GP-K6irs2), rabbit polyclonal anti c-maf (1:100, Bethyl, A300-613A), mouse
monoclonal to Lef1 (1:50, Santa Cruz Biotech, sc-81470), goat polyclonal to
P-cadherin (1:100, R&D systems, FAB761A), rabbit monoclonal antibody to Ki67
(1:200; SP6, Vector Labs, VP-RM04), mouse monoclonal anti-mouse Keratin 15
(1:100056), β-Catenin (1:200,
Santa Cruz Biotech, sc-7199), rat monoclonal anti-Itgβ1 (1:200, clone
HMβ1-1, BioLegend, 102203), rabbit polyclonal anti mouse keratin 10
(1:500; Covance, PRB-159P), rabbit polyclonal anti-NSun2 (1:500; Aviva Systems
Biology, ARP48811_P050), rabbit polyclonal anti-human NSun2 (MetA,
1:50057), rat monoclonal
anti-Itgα6 (1:500; GoH3, eBioscience, 14-0495), rat monoclonal anti-CD44
(1:200, IM7, BioLegend, 103004), anti-mouse Podoplanin (1:500, clone 8.1.1,
eBioscience, 14-5381), rat monoclonal anti-BrdU (1:100; Abcam, ab6326), rabbit
polyclonal anti-Laminin alpha 5 (1:100, Abcam, ab75344), mouse monoclonal
anti-cytokeratin 8 (1:100, TROMA-I, DSHB, US), rabbit polyclonal to Slug (1:200,
Cell Signaling, 9585P), chicken polyclonal anti-GFP (1:200, Abcam, ab13970),
rabbit polyclonal anti-p53 (1:100, CM5, Novocastra, NCL-p53-CM5p), and rabbit
anti human Involucrin (SY5 clone, 1:200, Abcam, ab80530). Alexa Fluor 555-,
Alexa Fluor 647- and Alexa Fluor 488-conjugated secondary antibodies (Life
Technologies) were added at a dilution of 1:1000 for 1 hour at room temperature.
Apoptotic cells were visualized staining sections with DeadEnd™
Fluorometric TUNEL System (Promega) or immunostained for rabbit polyclonal
anti-cleaved Caspase3 (1:200, Cell Signaling, 9664). Nuclei were labeled with
DAPI (49,6-diamidino-2-phenylindole) or hematoxylin. Slides were mounted in
glycerol supplemented with Mowiol ® 4-88 mounting medium
(Sigma-Aldrich-Aldrich).
White field images were acquired using an Olympus IX80 microscope and a
DP50 camera. Fluorescence images were acquired either on a Zeiss Axioplan
microscope or using a confocal microscope (Leica TCS SP5) at 1024×1024
dpi resolution. All the images were further processed with Photoshop CS5 (Adobe)
software.

Isolation of mouse keratinocytes from normal skin and skin tumours
To isolate keratinocytes from mouse back skin, shaved skin was floated
on 0.25% Trypsin without EDTA (Life Technologies) for 2 hours at 37°C.
Then the epidermis was scraped off the dermis, and cells were disaggregated by
gentle mincing with a scalpel and pipetting. For back skin in late anagen the
dermis was further minced and digested for 30 minutes at 37°C low-calcium
medium containing 1.25 mg/ml of Collagenase Type I, 0.5 mg/ml of Collagenase
Type II, 0.5 mg/ml of Collagenase Type IV (all from Worthington) and 0.1 mg/ml
of Hyaluronidase (Sigma-Aldrich).
To disaggregate cells from squamous tumours, the tumours were minced
with a scalpel and incubated for 1-2 hours at 37°C in low-calcium medium
containing 1.25 mg/ml of Collagenase Type I, 0.5 mg/ml of Collagenase Type II,
0.5 mg/ml of Collagenase Type IV (all from Worthington) and 0.1 mg/ml of
Hyaluronidase (Sigma-Aldrich). Then pieces were further incubated for another
hour with Trypsin without EDTA (Life Technologies) and cells disaggregated by
scraping with a scalpel blade. Trypsin was inactivated by washing the cell
suspension with low-calcium media containing 10% of FBS (Life Technologies).

In vivo measurement of protein synthesis by flow cytometry
and microscopy
Back skin or cutaneous tumours were collected and further processed for
flow cytometry or histology analysis (described above). For flow cytometry
analysis cells were dissociated as described above. For staining of dissociated
cells or frozen sections, samples were first fixed with 1% paraformaldehyde in
PBS for 15 min on ice. Next samples were washed in PBS, and then permeabilized
in PBS supplemented with 3% fetal bovine serum (Sigma-Aldrich) and 0.1% saponin
(Sigma-Aldrich) for 5 min at room temperature. To conjugate OP-puro to a
fluorochrome, an azide-alkyne cycloaddition was performed using the Click-iT
Cell Reaction Buffer Kit (Life Technologies) and 5 μM of Alexa Fluor 488
or Alexa Fluor 647 conjugated to azide (Life Technologies). After the 30-min
reaction, the cells were washed twice in PBS with 3% fetal bovine serum and 0.1%
saponin and then resuspended in PBS. When indicated cells were further stained
for cell surface markers and DAPI as described in a section below.
To visualize protein synthesis together with antibody staining in skin
or tumour paraffin embedded or frozen sections; frozen sections were first fixed
with 1% paraformaldehyde in PBS for 15 and paraffin sections were first de-waxed
and progressive rehydration sections were then blocked and stained with primary
antibodies overnight at 4°C. The next day sections were washed and
stained with secondary antibodies 1 hour at room temperature. After washes
sections were stained using the Click-iT Cell Reaction Buffer Kit with Alexa
Fluor-647 or 488 azide (Life technologies) as described above.

Quantification of protein synthesis rates
Protein synthesis rates in specific cell populations were calculated by
normalizing the mean of OP-puro signal of each population of interest to the
signal of the whole epidermal or tumour cell preparation, using the following
formula:
The mean of OP-puro incorporation was averaged from several mice
collected in multiple independent experiments. OP-puro fluorescence signal
between experiments was calibrated by including in each run BD rainbow
Calibration particles 8 peaks (BD Bioscience). Samples from PBS-injected mice
were also stained for OP-puro and the fluorescence signal was used to determine
the background signal.

Flow cytometry and cell cycle analysis
Flow cytometry was performed for cells dissociated from normal skin,
skin tumours or cells growing in culture. Cell dissociation from skin or tumours
was performed as described earlier. Cells in culture were trypsinized for 5
minutes before performing the staining. Analysis of specific epidermal or tumour
populations, live cells, or fixed cells previously stained for OP-puro as
indicated in a previous section, were incubated in 2% of BSA with combinations
of antibodies to the following cell-surface markers: PE-Cy7-conjugated CD117
(1:100, clone 2B8, BD Bioscience, 558163), PE-Cy7-conjugated CD31 (1:50,
PE-CAM1, eBioscience, 563651), PE-Cy7-conjugated CD45 (1:100, BD Pharmingen,
552848), PE- or eFluor 450- conjugated Itgα6 (1:500, clone GoH3,
eBioscience, 12-0495 and 48-0495), eFluor 660- or FITC-conjugated CD34 (1:50,
RAM34, eBioscience, 50-0341 and 11-0341), biotinylated CD44 (clone IM7,
BioLegend, 103004) and PE-conjugated Pdpn (eBioscience, 12-5381-82). After
incubation for 30 minutes at 4°C, cells were washed twice in PBS.
Biotinylated antibodies were visualized by incubation with Alexa Fluor 488
conjugated streptavidin (Life Technologies) for 10 minutes. For cell cycle
analysis cells were further stained with DAPI. tdTomato+ cells were detected
using PE-Texas Red channel.
Cells were gated using forward versus side scatter to eliminate debris
and aggregates. Surface markers CD117, CD31 and CD45 were used to gate out
endothelial cells, melanocytes and blood cells when analyzing cell preparations
from skin or tumours. Data acquisition was performed on a BD LSRFortessa™
analyzer (BD Biosciences). Data were analyzed by FlowJo software.

Measurement of tumour growth and tumour treatments
Both male and female mice were used in these experiments. To evaluate
the effect of NSun2 deletion on the formation of skin tumours we measured the
presence (number of tumours), the growth of the tumours, the percentage of mice
with tumours as well the survival of the mice throughout the length of the
experiment (approximately 6-8 weeks). To monitor tumour occurrence and growth
mice were weighed, the number of all tumours formed all over the body were
counted and the growth of each tumour monitored every other day from postnatal
day 16 (earliest time at which K5-SOS-F mice start developing papillomas).
Papillomas in the tail tended to fuse into one covering the whole tail, and
therefore were counted as one tumor from the beginning of the experiments. Other
tumors also developed in ears, mouth, back skin or feed. The growth of each
tumour was monitored by measuring the diameter of the widest area of the tumour
using a precision caliper allowing discriminating size modifications >0.1
mm. When animals have to be treated with drugs, experiments started also at the
third week of age. The end point of the experiments was determined by health
deterioration and casualties or by the length of the treatments when mice were
under a treatment regime. K5-SOS-Fxwa2/+NSun2+/+ (referred as K5-SOS/NSun2+/+)
survived longer than K5-SOS-Fxwa2/+xNSun2-/- (K5-SOS/NSun2-/-) and
K5-SOS-Fxwa2/+xNSun2+/- (K5-SOS/NSun2+/-). All mouse tumour experiments were
carried out according to the local ethics committee under the terms of a UK Home
Office license PPL80/2231 and PPL80/2619. Following these regulations the mean
diameter of a tumour should not normally exceed 1.4 cm (PPL80/2619, 19b 7).
While K5-SOS/NSun2-/- and K5-SOS/NSun2+/- had to be sacrificed before mice
reached two moths of age due to the size and aspect of the tumours and weight
loss or general health deterioration due to excessive tumor burden,
K5-SOS/NSun2+/+ only reached the deterioration state later than two months of
age. For the analysis in Figure 4a we
measured the percentage of mice with tumours for each indicated day. The average
number of tumours in each mouse genotype is shown in Extended Data Figure 5c. Note that data points are shorter
for K5-SOS/NSun2-/- and K5-SOS/NSun2+/- as mice survival was shorter. The
diameter of the tumours was normalized to the size of each mouse (body weight:
BW) in Extended Data Figure 5b to
eliminate genotype variance because K5-SOS/NSun2-/- mice are significantly
smaller than K5-SOS/NSun2+/+.
Cutaneous tumours in transgenic K5-SOS/NSun2+/+ and K5-SOS/NSun2-/- mice
were topically treated with 5-Fluorouracil (5FU) (Efudix 5% Fluorouracil Cream,
Meda Pharmaceuticals) every second day for two weeks. 5FU inhibits thymidylate
synthases leading to the up-regulation of p53 and cell death58. Tumours were also treated with 5FU in
combination with an Angiogenin inhibitor (referred as A.I.) (N65828, NCI,
US)59,60 administered by intraperitoneal injections at 2 mg/kg in PBS pH
7.4 every alternative day to 5FU treatment. Due to the high toxicity of the drug
combination, we were only able to simultaneously treat with 5FU and AI for a
short period of time (up to 7 days). Cisplatin (CDDP) (Sigma) was dissolved in
PBS and injected intraperitoneally at 14 mg/kg every other day. All treatments
started the first cutaneous lesions appeared and the end point was indicated by
the length of the treatment, after which all mice were sacrificed. Control mice
were administered PBS.

BrdU and EdU labeling
To measure proliferation, K5-SOS mice were injected intraperitoneally
with 50 mg of 5-bromo-2′deoxyuridine (BdrU) per kg of body weight, 23
hours later with 20 mg/kg of 5-ethynyl-2′deoxyuridine (EdU). One hour
later mice were sacrificed and tumor samples were processed for histology as
described previously. BrdU was visualized as described in54. EdU was stained with Click-iT® EdU Alexa
Fluor® 488 Imaging Kit (Life Technologies). Images of random areas of the
slide were collected using a confocal microscope (Leica SP5). Numbers of BrdU-
and EdU-positive cells were quantified using Volocity software
(PerkinElmer).

Tumour graft assay
Epidermal keratinocytes from K5-SOS/NSun2+/+ and K5-SOS/NSun2-/-
cutaneous tumors were isolated as described in previous sections. GFP expressing
dermal fibroblasts were isolated from healthy skin of newborn APC-eGFP mice. For
this, skin was first incubated in a 1:1 solution of 5% dispase (BD Biosciences)
at 37ºC for 1 hour. The epidermis was then peeled from the dermis. The
dermis was incubated with of 0.2% collagenase in low-calcium medium for 30
minutes at 37°C to yield a single-cell suspension. The dermis suspension
was filtered through a 70 μm cell strainer. 1x106 of viable
epidermal keratinocytes from K5-SOS/NSun2+/+ or K5-SOS/NSun2-/- tumours were
injected subcutaneously in athymic nude mice. To allow successful engrafting,
the tumour cells were injected with 1x106 of viable GFP dermal
fibroblasts. The GFP-expressing dermal fibroblasts integrated into the dermis
but failed to contribute to tumour formation (Extended Data Fig. 6c). Experiments were done in triplicates. Nude
mice were sacrificed one month after transplantation and tumor size was measured
with a caliper.

RNA extraction and quantitative RT-PCR (qPCR)
Total RNA from mouse skin tumors was prepared using Trizol reagent (Life
Technologies) and double-stranded cDNA was generated with Superscript III
First-Strand Synthesis kit (Life Technologies). Real-time PCR amplification and
analysis was conducted in StepOneTM Real-Time PCR Systems (Applied Biosystems)
using pre-designed probe sets and TaqMan Fast Universal PCR Master Mix
(2×) (Applied Biosystems). The following probes were used to amplify
selected genes NSun2 (Mm00520224_m1), α6 integrin
(Mm01333831_m1), CD34 (Mm00519283_m1), Keratin 10
(Mm03009921_m1) and Involucrin (Mm00515219_s1). GAPDH expression
(4352932E) was used to normalize samples using the ΔCt method.

Mouse SCC and TMA staining and quantification
Mouse squamous cell carcinomas (SCC) were obtained from a TPA/DMBA
chemical induction treatment for 20 weeks and frozen tissues were kindly
provided by Dr. C. Blanpain (Université Libre de Bruxelles, IRIBHM,
Brussels B-1070, Belgium.). Frozen sections were stained as described in the
previous sections.
Tissue microarrays (TMA) for human skin tumours of increased malignancy
according to TNM (Tumour, Node, Metastases) classification were purchased from
Abcam (ab178287 and ab178288). Immunohistochemistry was performed using IHC
Detection Kit (Ventana Medical Systems) and DISCOVERY automated IHC staining
system (Ventana Medical Systems) with a polyclonal antibody for human NSun2
(MetA, 1:100057). Images of each tumour
section were acquired with a Zeiss Axioplan microscope and NSun2 expression
levels were quantified for each individual cell in each tissue (quantified as
average between all cells in all sections) using CellProfiler image
software.

Cell culture, viral infections and siRNA knockdown
Four lines of human dermal fibroblasts were used. Two independent cell
lines of NSun2-/- human dermal fibroblasts were derived from two patients and
referred as NSun2-/- line 1 and line 2 in this study, and one line of NSun2+/-
fibroblasts was derived from the mother of the patients described in61; these three lines were kindly provided
by Dr. J. Gleeson (Laboratory of Pediatric Brain Diseases, Howard Hughes Medical
Institute, The Rockefeller University, New York, NY, USA). NSun2+/+ human dermal
fibroblasts were purchased from Life Technologies (C-013-5C) and were derived
from an age- and gender-matching individual compared to NSun2+/- fibroblasts.
Human fibroblasts were grown in MEM (Invitrogen) supplemented with 20% fetal
bovine serum (FBS) as described previously61. ZHC Human epidermal keratinocytes (Cellworks distributed,
ZHC-1116) were grown in KBM-Gold medium (Lonza). All cells were kept in a
humidified atmosphere at 37°C and 5% CO2.
To rescue expression of NSun2 or express NSun2 catalytically dead
mutants in Nsun2-/- fibroblasts or ZHC keratinocytes, full length human NSun2
(pB-NSun2), inactive mutants C271A (pB-NSun2-C271A), K190M (pB-NSun2-K190M) or
C321A (pB-NSun2-C321A) or the empty vector (pB-empty) were infected via
retrovirus as described previously62. To
knock-down NSun2 expression in human keratinocytes, cells were transfected with
control siRNA (AllStars negative control siRNA (QIAGEN, 1027292) or Human NSun2
siRNA (Flexitube siRNA QIAGEN, SI02655548) using Lipofectamine RNAiMax
transfection reagent (Life Technologies) according to the manufacturer’s
instructions.

Migration assays
For migration analysis in Boyden chambers, human primary keratinocytes
were transfected with siRNAs as described earlier. Transfections were carried
twice every 48 hours and migration assay was performed 24 hours after the second
transfection. Cells were treated with mitomycin C for two hours to arrest cell
cycle progression. After mitomycin C treatment cells were trypsinized and
counted and seeded on Boyden chambers (transwell inserts of 8μm, 24-well
plates, BD Biosciences). 8x104 cells were seeded with KBM growing
medium (Lonza) without hrEGF. Media containing 10 ng/ml of hrEGF (Lonza) was
placed under the transwell inserts as chemoattractant to attract cells. Media
without chemoattractant was placed under the transwell inserts in control
experiments. Cells were allowed to migrate for 6 or 12 hours, after which the
inserts were washed once with PBS, fixed with 4% PFA for 10 min, and cells were
stained with DAPI. Cells from the upper side of the membrane were scratched off
with a cotton bud and wash off with PBS several times. Cells on the bottom side
of the membrane were imaged with a colony scan microscope. Cells were then
quantified with the software CellProfiler.
For motility analysis of human keratinocytes, 104 cells were
seeded in 24-well ImageLock plates (Essen Instruments) in growing medium and
kept for 26 hours at 37°C in 5% CO2. Cell mobility was
recorded with an automated IncuCyte microscope (Essen Instruments). Images were
collected at 15 minutes intervals. 2D-cell migration was analyzed by using the
MTracking function of ImageJ software. 2D-migration tracks were generated by
manually tracing the nucleus of each cell. Migrated distance was obtained by
measuring the linear distance travelled between the first and last position
(after 26 hours) of each tracked cell.

tRNA sequencing library preparation
Small RNA libraries were generated from snap-frozen skin papillomas from
4-weeks old mice. Four independent biological replicates were used. For tRNA
library generation we followed the protocols as in59. Briefly total RNA was extracted using Trizol reagents
(Invitrogen) and treated with DNase (Turbo DNase, Ambion). Ribosomal RNA was
removed with Ribo-zero (Epicentre, Illumina). The remaining RNA fraction was
size-selected using MirVana Isolation Kit (Invitrogen). Using MirVana RNA
purification columns with two sequential filtration steps with different ethanol
concentrations, an RNA fraction highly enriched in RNA species ≤ 200 nt
was obtained. The small RNA fraction (approximately 200 ng) was first treated
with 0.1 M Tris-HCl pH 9.0 and 1 mM EDTA for 30 min at 37°C to
de-aminoacylate mature tRNAs and later T4-PNK (NEB) treated to ensure
phosphorylated 5’ ends and 3’OH ends to proceed with RNA adapter
ligation and library preparation. tRNA libraries were generated using TruSeq
Small RNA Preparation Kit (Illumina). Briefly, 3’adenylated and
5’phosphorylated adapters suitable for Illumina RNA sequencing were
ligated to the small RNA fraction. RNA was reverse-transcribed at 50ºC
for 1 hour (SuperScript III cDNA synthesis kit, Invitrogen), followed by PCR
amplification with Phusion DNA polymerase (Thermo scientific). All samples were
multiplexed and sequenced in HiSeq platform (Illumina).

Bisulphite sequencing library preparation
To generate bisulphite sequencing libraries RNA was prepared and
bisulphite-treated as in63. Briefly RNA
was extracted with Trizol reagents (Invitrogen) from snap-frozen skin tumours
from 4-weeks old mice. Four independent biological replicates were used. Total
RNA was extracted and DNase treated (Turbo DNase, Ambion). Samples were
Ribo-zero treated (Epicentre, Illumina) to deplete them from rRNA. At least 1.5
μg of the remaining RNA fraction was bisulphite-converted as follows: RNA
was mixed in 70 μl of 40% sodium bisulfite pH 5.0 and DNA protection
buffer (EpiTect Bisulfite Kit, Qiagen). The reaction mixture was incubated for
three to four cycles of 5 min at 70ºC followed by 1 hour at 60ºC
and then desalted with Micro Bio-spin 6 chromatography columns (Bio-Rad). RNA
was desulfonated by adding an equal volume of 1 M Tris (pH 9.0) to the reaction
mixture and incubated for 1 h at 37°C, followed by ethanol precipitation.
2’,3’-cyclic phosphate and 5’-hydroxyl termini produced
during the bisulphite/desulfonation reaction were end-repaired with T4 PNK (New
England Biolabs). About 120 ng of bisulphite-converted RNA was used to generate
Bisulphite-seq (BS-Seq) libraries. Because bisulphite treatment and
desulfonation cleaves long RNAs into fragments of about 100 nt, we then used
TruSeq Small RNA preparation kit (Illumina) to generate libraries suitable for
Illumina sequencing as described in59.
Briefly, RNA adapters suitable for Illumina sequencing were ligated to
bisulphite-converted RNAs, reverse-transcribed at 50ºC for 1 hour with
SuperScript III and 2 mM of each dNTP (SuperScript III cDNA synthesis kit,
Invitrogen) followed by PCR amplification. All samples were multiplexed and
sequenced on a HiSeq platform (Illumina).

Preparation of Ribo-seq libraries
Two types of experiments were performed on mouse skin tumours (from
K5-SOS mice) and human dermal fibroblasts samples from each set of conditions:
ribosomal profiling (Ribo-seq) and mRNA-seq. All samples were sequenced using
the HiSeq platform (Illumina). A minimum of three replicates was performed for
each sample. Dermal fibroblasts were grown and infected when indicated as
described in previous sections and with the constructs indicated in each
experiment. For cells or tissue collection, none were pre-treated with
cycloheximide, however cycloheximide was present in the following steps. Cells
were washed with PBS (without cycloheximide) twice and lysis buffer (20 mM
Tris-Cl (pH 7.4), 150 mM NaCl, 5 mM MgCl2, 1 mM DTT (Sigma), 1%
Triton X-100 (Sigma), 25 Units/ml of Turbo DNase I (Life Technologies)
containing 100 μg/ml of cycloheximide (Sigma) was added straight to the
cells. Papillomas were dissected from the mice, snap-frozen in liquid nitrogen
and later homogenized in lysis buffer containing 100 μg/ml of
cycloheximide. Cycloheximide was added to the lysis buffer to arrest translation
elongation while the polysome fraction was being purified and mRNA fragments
were recovered. We then proceeded with ribosome footprint purification without
snap freezing the lysates as indicated in64. All the steps for cell or tissue lysis, nuclease footprinting,
polysome fractionation, mRNA footprint purification and gel size-selection were
performed as indicated in64. Briefly
lysates were further triturated by passing them ten times through a 26-G needle.
Nuclei and debris were removed by centrifugation at 13000 rpm for 10 min.
Supernatant was digested with RNaseI (100U/μl, Ambion) for 45 min at room
temperature. Digestion was blocked with SuperaseIN (Ambion) and lysate was
layered on a 1M sucrose cushion and separated by ultracentrifugation at 45000
rpm in a 70Ti rotor for 9 h at 4°C. Pellets were resuspended in Qiazol
and RNA fragments lower than 200 nt were extracted using miRNeasy kit (Qiagen)
followed by ethanol precipitation. Size selection of footprints with length of
26–34 nt was performed on 15% TBE-urea gel (Invitrogen). Footprints were
3’-dephosphorylated with T4 polynucleotide kinase (10U, NEB). From this
step and to prepare libraries suitable for Illumina sequencing we slightly
modified the original protocol64. mRNA
footprints were then treated with Ribo-zero (Epicentre Illumina) to deplete
rRNA. By using this extra step of rRNA depletion (together with the use of DNA
oligos to deplete rDNA by subtractive hybridization in a later step) we were
able to reduce rRNA contamination to only ~60% of all reads. mRNA
footprints recovered from Ribo-zero were then prepared for Ribo-seq using TruSeq
Small RNA Preparation Kit (Illumina). Briefly, 3’adenylated and
5’phosphorylated adapters suitable for Illumina RNA sequencing were
ligated to the recovered mRNA fragments. RNA was reverse-transcribed at
50ºC for 1 hour (SuperScript III cDNA synthesis kit, Invitrogen),
followed by rDNA depletion by subtractive hybridization (as indicated in the
original protocol) using oligos listed in64 and following the protocol recommendations. Recovered cDNAs were
PCR-amplified with no more than 12 PCR cycles. All samples were multiplexed and
sequenced in HiSeq platform (Illumina).

Preparation of mRNA-seq libraries
mRNA-seq libraries were generated from mouse skin tumours from 4-weeks
old K5-SOS/NSun2+/+ and K5-SOS/NSun2-/- mice, from mouse healthy back skin from
3.5-4-weeks old NSun2+/+ or NSun2-/- mice (without tumours) and from human
dermal fibroblasts (Nsun2+/+, NSun2+/- and NSun2-/-) growing in culture and
infected when indicated. At least four replicates were performed for each
sample. All samples were multiplexed and sequenced using the HiSeq platform
(Illumina). Total RNA was extracted using Trizol (Invitrogen) from cells in
culture or snap-frozen tissues. Total RNA was DNase (Turbo DNase, Ambion) and
Ribo-zero (Epicentre, Illumina) treated. rRNA-depleted RNA was used to generate
mRNA-seq libraries using NEXTflex Directional RNA-seq Kit V2 (Illumina). All
samples were multiplexed and sequenced in HiSeq platform (Illumina).

Next-generation sequence data analyses
For all data analyses, FastQC was used for the initial assessment of the
quality and basic processing of the reads (http://www.bioinformatics.babraham.ac.uk/projects/fastqc).
Sequencing adapters were trimmed from the 5’ and the 3’ ends of
the reads using cutadapt (v1.4.2; https://pypi.python.org/pypi/cutadapt/1.4.2).

RNA bisulphite(BS)-seq analysis
To determine RNA methylation levels in mouse K5-SOS tumours, two
complementary protocols for the analysis of BS-seq data were used: (1)Alignment to the genome. BS-seq reads were
aligned to mouse reference genome (GRCm38/mm10) with
Bismark (http://www.bioinformatics.babraham.ac.uk/projects/bismark;
version 0.13.1; options: ‘--directional –n 0
–l 40’). Methylation levels for all
cytosines with at least coverage of ≥ 5 reads (5X coverage)
in both K5-SOS/NSun2+/+ and K5-SOS/NSun2-/- tumour samples were
inferred with Bismark
‘methylation_extractor’.
Cytosine positions displaying a difference in RNA methylation of at
least 10% between K5-SOS/NSun2+/+ and K5-SOS/NSun2-/- tumour samples
were extracted based on the ENSEMBL (GRCm38, Release 74; www.ensembl.org/info/data/ftp) transcript
annotations and tRNA gene predictions in the mouse (GRCm38/mm10)
reference genome obtained from GtRNAdb (http://lowelab.ucsc.edu/GtRNAdb).

(2)Alignment to representative transcripts.
Sequences for ENSEMBL transcripts and tRNAs were extracted in FASTA
format. All transcript isoforms were considered, and in addition the
longest gene at full length including introns was retained as a
representative sequence to identify RNA methylation sites in
introns. C’s were converted to T’s in the reference
transcript sequences, and in the processed BS-seq reads. Alignment
of converted BS-seq reads against converted transcript sequences
were performed using bowtie (version 1.1.1;
bowtie-bio.sourceforge.net; options ‘–m 500
–v 2 –a –best
–strata’). Following alignments, the reads
that aligned in sense direction were obtained, and the original
transcript sequences and reads were used to compile RNA methylation
(C/(C+T) levels considering only cytosines with at least 5X
coverage. Heatmaps displaying either C or T in the aligned reads at
each cytosine position were generated using custom PERL scripts and
matrix2png (www.chibi.ubc.ca/matrix2png/) for visualization.
Cytosine positions on the heatmaps were reported relative to the
annotated transcriptional start sites of the transcripts.

tRNA-seq data analysis
The abundance of tRNA fragments was determined according to a previously
published protocol described in59.
Adapter-trimmed tRNA-seq reads (> 20 nt and < 200 nt in length)
were mapped to the mouse reference genome (GRCm38/mm10) using
bowtie (version 1.1.1; bowtie-bio.sourceforge.net; options
‘-m1 –v2 –a –best
--strata’) considering only reads that map uniquely to the
genome. To account for the polymerization of CCA-3’-ends onto mature
tRNAs, the remaining unmapped reads were trimmed of CCA[CCA] ends and realigned
using the same options. Annotations were conducted based on tRNA genes predicted
for the mouse reference genome (GRCm38/mm10) and downloaded from GtRNAdb
(http://lowelab.ucsc.edu/GtRNAdb). Reads that exceeded the
annotated tRNA gene start or end by more than 10% were discarded. All distinct
reads, which were shorter than 90% of the annotated tRNA gene length, were
considered as tRNA fragments. Counts per fragments were normalized, and the
differential abundances of fragments processed from the 5’ or 3’
halves of the tRNAs were statistically evaluated using the R/Bioconductor
DESeq package (http://bioconductor.org/packages/release/bioc/html/DESeq.html).
tRNA fragment abundances are given by log2(DESeq-normalized counts).

mRNA-seq and Ribo-seq data analyses
Ribosome profiling data was processed following established
protocols65: The first 5' base
of the adapter-trimmed Ribo-seq reads was removed, as this is usually an
artefact of reverse transcription64. For
removing abundant contamination from digested rRNA present in the libraries, the
reads were aligned to a collection of rRNA sequences obtained from Genbank and
UCSC using bowtie (options: ‘-n 2
--seedlen=23’). Reads aligning to rRNA were discarded, with
the average rRNA contamination per sample being around 60%. Only reads with at
least 24 nt and less than 30 nt length were retained in accordance with the
observed length distribution of ribosome footprints66.
Both the Ribo-seq and mRNA-seq reads were aligned to the human
(GRCh37/hg19) and to the mouse (GRCm38/mm10) reference genomes using
Tophat2 (version: 2.1; options: ‘--read-mismatch
1(2) --max-multihits 1 –GTF’) guided by ENSEMBL gene models
(release 76), allowing for two mismatches per read for human and one mismatch
per read for mouse, and unique alignments only.
To determine mRNA abundance, mRNA-seq read counts for the full
transcript were calculated using htseq-count (http://www-huber.embl.de/HTSeq/doc/overview.html), data sets
were normalized, and the statistical significance of differential expression was
evaluated by using the R/Bioconductor DESeq2 package (https://bioconductor.org/packages/release/bioc/html/DESeq2.html).
To evaluate differences in translation, the following additional
Ribo-seq data analyses and normalizations were performed: (1)Alignment to representative regions. Coding
sequences (CDS) and 5’UTRs were downloaded from ENSEMBL
including ‘protein_coding’ and
‘nonsense-mediated decay’ types of transcripts. Intron
sequences were excluded. Ribo-seq reads, which uniquely aligned to
the genome in the initial alignment step using
Tophat2, were aligned to 5’UTR or CDS
sequences using bowtie (options: ‘-m 1000
–v1’) allowing for multiple mappings to overlapping
regions of the same gene.

(2)Statistical analysis of differential ribosome
footprint densities and normalization. In concordance
with other studies performed in yeast or mammalian cells67,68, we observed a characteristic 5’
‘ramp’ of ribosome footprints at the translation start
site of the CDS for our samples. It has been suggested that these
excess footprints are a result of cycloheximide-inflicted
accumulation of ribosomes64.
To prevent any artifactual bias in our analysis, we followed the
instructions for normalization in69. Read counts were extracted that aligned either to
(1) all full-length CDS (see
Supplementary
tables 6-10) or to (2) all CDS sequences without the
initial 150 nt (50 codons) corresponding to the ribosomal ramp
(see
Supplementary
table 5 and Fig.
5e). For both data sets, statistical tests were performed
with the R/Bioconductor DESeq package. The two sets
of DESeq scaling factors were subsequently used for
normalization of data sets. The DESeq-normalized
counts for all regions were divided by their length in kB to define
ribosome footprint densities.

(3)Analysis of ribosome footprint densities at
5’UTRs. Reads that mapped uniquely to the genome
by using Tophat2, were mapped to the 5’UTR
sequences with bowtie (options: ‘-m 1000
–v1’). Differences in ribosome
footprint densities on the full 5’UTRs were evaluated by
using the DESeq scaling factors obtained from the
analysis of CDS (Supplementary tables 7-9) for normalization, and
DESeq to perform statistical tests for
differences. DESeq-normalized counts for
5’UTR were divided by their length in kB to define
ribosome footprint densities.

(4)Analysis of triplet periodicity. Footprints
of length 28 nt were extracted, since they report with high
precision on the position of the ribosome66. The frequencies of the 5’-starts of
the footprints, which were aligned close to the annotated
translation initiation sites, were aggregated for all genes.

(5)Heat map analysis. For the heat map
analysis in (Extended Data Fig.
8d) we specifically selected the 43,625 representative
and well-annotated protein-coding transcripts from GENECODE that
overlap ENSEMBL transcript structures
(“ensembl_havana”). The positions of the start
codons were obtained from the ENSEMBL “Gene sets” gtf
file (http://www.ensembl.org/info/data/ftp/index.html).
Heat maps of ribosome footprint densities (RPKMs) were generated for
regions +/- 1500 nt around the start codon by using
ngsplot (https://github.com/shenlab-sinai/ngsplot).

If not indicated otherwise Gene ontology categories represent
GOTERM_BP_FAT in DAVID (david.ncifcrf.gov).

Protein extraction and western blot analysis
To extract proteins from squamous tumours, samples were snap-frozen in
liquid nitrogen, transferred to lysis buffer (1% NP-40, 200 mM NaCl, 25 mM
Tris-HCl, pH 8, 1 mM DTT) including protease inhibitor cocktail (Roche) and
homogenized and cleared by centrifugation at 13,000 rpm. To extract proteins
from cells in culture, the same lysis buffer was added to the plate and
scratched the cells from the plate surface, left lysing for 20 min in ice and
cleared by centrifugation. Total protein quantification was performed using BCA
Protein Assay (Thermo fisher). Equal amounts of protein were run in
polyacrylamide gels. Western blotting was performed as described in54. The following primary antibodies were
used for western blot analyses: anti-PSAT (Protein Tech Group, 10501-1-AP),
anti-THBS1 (Santa Cruz, sc-65612), anti-Sesn2 (Protein Tech Group, 10795-1-AP),
anti-Calreticulin (Abcam, ab2907), anti-INHBA (Sigma-Aldrich, SAB1408593),
anti-NSun2 (Aviva Systems Biology, ARP48811_P050), anti-Keratin 19
(Abcam, ab52625), anti-CD44 (IM7, Biolegend, 103004), anti-Bcl10 (H197, Santa
Cruz, sc-5611), anti-Semaphorin3A (SEMA3A) (Abcam, ab23393), anti-PSMD11 (Abcam,
ab66346), anti-SPHK1 (Cell Signaling, 3297), anti-APTX (Abcam, ab31841),
anti-Slug (Cell Signaling, 9585P), anti-Snail (Abcam, ab180714), anti-SOD2
(Abcam, ab13534), anti-CLSPN (Bethyl Laboratories, A300-266A), anti-ZAK (Sigma,
HPA017205), anti-CHAF1B or CAF1 p60 (Abcam, ab180371). Polyclonal
α-Tubulin (Abcam, ab15246) was used as a loading control. Band intensity
was quantified with Image J software.

Statistical methods
Group data are always represented by mean and standard deviation, unless
otherwise indicated in figure legends. To test statistical significance between
samples unpaired two-tailed Student’s t-tests were used. To test for
significance of populations (i.e. stem cells versus differentiated cell
populations) within one sample (mouse) we used the paired Student’s test.
To analyze the differences among group means we used ANOVA. Violin plots were
created using the vioplot package (https://cran.r-project.org/package=vioplot) in R. The outline of
the violin plots represents the kernel probability density of the data at
different values. Violin plots include a marker for the median of the data and a
box indicating the interquartile range70.
Boxplots were created with Prism 6 software. The box extends from the 25th to
75th percentiles and the line in the middle of the box is plotted at the median.
The whiskers show minimum to maximum. Scatter plots, linear regression lines and
coefficient of correlation (r2) were calculated using Prism 6
software by computing non-parametric Spearman correlation and two-tailed P
values.

Sample sizing and collection
At least three samples were used per experimental group and condition.
The number of samples used in each experiment is indicated in all main figures,
in legends of Extended Data figures and in Source Data files.
Samples and experimental animals were randomly assigned to experimental
groups. Sample collection was also assigned randomly. Experimental procedures in
vitro, sample collection and data analysis were performed blindly whenever
possible. Whenever possible automated quantifications were performed using the
appropriate software. Most animal procedures (i.e. mouse treatments) were
performed blindly by individuals unaware of the experimental design.

Extended Data

Supplementary Material
Supplementary DiscussionSupplementary Figure 1 (uncropped Western blots)Supplementary tables