Please cite this article in press as: Stumpf et al., The Translational Landscape of the Mammalian Cell Cycle, Molecular Cell (2013), http://dx.doi.org/
10.1016/j.molcel.2013.09.018
Molecular Cell
Short Article
The Translational Landscape
of the Mammalian Cell Cycle
Craig R. Stumpf,1,2 Melissa V. Moreno,1,2 Adam B. Olshen,2,3 Barry S. Taylor,2,3,4,* and Davide Ruggero1,2,*
1Department
of Urology, University of California, San Francisco, CA 94158, USA
Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA
3Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA
4Department of Medicine, University of California, San Francisco, CA 94158, USA
*Correspondence: [email protected] (B.S.T.), [email protected] (D.R.)
http://dx.doi.org/10.1016/j.molcel.2013.09.018
2Helen
SUMMARY
Gene regulation during cell-cycle progression is an
intricately choreographed process, ensuring accurate DNA replication and division. However, the
translational landscape of gene expression underlying cell-cycle progression remains largely unknown.
Employing genome-wide ribosome profiling, we
uncover widespread translational regulation of
hundreds of mRNAs serving as an unexpected
mechanism for gene regulation underlying cell-cycle
progression. A striking example is the S phase translational regulation of RICTOR, which is associated
with cell cycle-dependent activation of mammalian
target of rapamycin complex 2 (mTORC2) signaling
and accurate cell-cycle progression. We further identified unappreciated coordination in translational
control of mRNAs within molecular complexes dedicated to cell-cycle progression, lipid metabolism,
and genome integrity. This includes the majority of
mRNAs comprising the cohesin and condensin complexes responsible for maintaining genome organization, which are coordinately translated during
specific cell cycle phases via their 50 UTRs. Our findings illuminate the prevalence and dynamic nature of
translational regulation underlying the mammalian
cell cycle.
INTRODUCTION
During cell division, exquisite temporal control of protein expression in distinct phases of the cell cycle underlies fundamental
checkpoints that ensure accurate completion of chromosome
duplication and segregation of a daughter cell. A central paradigm that has emerged is that rapid, dynamic, and fine-tuned reprogramming of gene expression occurs during specific phases
of the cell cycle. For example, a large number of mRNAs,
including those involved in promoting cell-cycle progression,
are transcriptionally activated in a cell-cycle phase-dependent
manner (Cho et al., 2001; Whitfield et al., 2002). In addition,
degradation of many cell-cycle checkpoint proteins, primarily
through the ubiquitin proteasome pathway, at specific times during the cell cycle is required for progression to subsequent
phases (Peters, 2006). Systems-level mass spectrometry approaches are also beginning to elucidate targets of the ubiquitin-proteasome pathway, as well as cell-cycle-specific patterns
of posttranslational modifications on a large number of proteins
(Kim et al., 2011; Merbl et al., 2013). While these studies have
provided great insight into the highly coordinated gene expression program of the cell cycle, a key step in modulating protein
levels has remained undefined: the regulation of mRNA
translation.
To date, the study of translational control during the mammalian cell cycle has generally focused on global reductions in protein synthesis during mitosis, monitored by a decrease in amino
acid incorporation into proteins (Fan and Penman, 1970; Konrad,
1963). Conversely, a relatively modest number of mRNAs have
been identified as actively translated during mitosis (Qin and Sarnow, 2004). Other studies have primarily investigated regulation
of single mRNAs, such as the translational regulation of cyclin E,
which is required for progression into S phase (Lai et al., 2010).
While limited in scope, these studies both highlight the importance of translational regulation during cell-cycle progression
and underscore the need for an unbiased, genome-wide analysis
of the translational landscape during the mammalian cell cycle.
Here, we have employed ribosome profiling (Ingolia et al.,
2009) to uncover widespread translational regulation during
cell-cycle progression. We defined remarkable levels of translational control of key cell cycle genes. Importantly, among these
mRNAs, we uncovered translational regulation of RICTOR,
which correlates with the cell-cycle phase-specific signaling of
the mammalian target of rapamycin (mTOR) kinase pathway
and suggests an important role in cell-cycle progression. Moreover, we identify surprising, coordinate regulation in translational
control of functionally related sets of mRNAs, suggesting a regulatory mechanism that dictates the coordinate expression and
activity of key molecular machinery in the cell. Among these
translationally controlled networks are mRNAs required for
metabolism, nuclear transport, and DNA repair, the latter of
which ensures genome fidelity. Together, this work highlights
both the prevalence and the dynamic nature of translational
regulation during cell-cycle progression. It further suggests a
multifaceted mechanism for differential regulation of transcriptspecific translational control in the execution of distinct steps
of the cell cycle.
Molecular Cell 52, 1–9, November 21, 2013 ª2013 Elsevier Inc. 1
Please cite this article in press as: Stumpf et al., The Translational Landscape of the Mammalian Cell Cycle, Molecular Cell (2013), http://dx.doi.org/
10.1016/j.molcel.2013.09.018
Molecular Cell
The Translational Landscape during the Cell Cycle
Figure 1. Systematic and Multifaceted Translational Control of Gene Expression during the Mammalian Cell Cycle
(A) The cumulative fraction of ribosome-bound mRNA on all expressed transcripts in each phase of the cell cycle is shown as a function of increasing
ribosome-bound mRNA. The x axis represents the scaled fraction of total ribosome-bound reads, and the y axis represents the fraction of expressed
transcripts.
(B) Representative scatter plots illustrate ribosome occupancy as a function of mRNA abundance (measured as sequencing read counts). The dashed line
represents the expected level of ribosome occupancy given mRNA abundance (see Supplemental Experimental Procedures). mRNAs with statistically significant
translational regulation are those with greater or less than the expected levels of ribosome occupancy given their mRNA expression (FDR < 1%; G1 is gray,
S phase is blue, and mitosis is green).
(C) The total number of genes with significantly increased (black) or decreased (gray) ribosome occupancy is shown, including those unique to a given phase or
shared between multiple phases of the cell cycle.
(legend continued on next page)
2 Molecular Cell 52, 1–9, November 21, 2013 ª2013 Elsevier Inc.
Please cite this article in press as: Stumpf et al., The Translational Landscape of the Mammalian Cell Cycle, Molecular Cell (2013), http://dx.doi.org/
10.1016/j.molcel.2013.09.018
Molecular Cell
The Translational Landscape during the Cell Cycle
RESULTS
To understand the extent and impact of translational regulation
during cell-cycle progression, we employed ribosome profiling
to identify individual mRNAs exhibiting unexpected levels of
ribosome association during each phase of the cell cycle. After
synchronizing human HeLa cells in G1, S phase, or mitosis,
libraries for total mRNA and ribosome-protected RNA fragments
were prepared, sequenced, and analyzed using a sophisticated
statistical framework (see Experimental Procedures) (Figure S1A
available online) (Olshen et al., 2013). We initially characterized
global levels of ribosome occupancy in the expressed transcriptome of each phase of the cell cycle. This data revealed a significant decrease in the overall level of ribosome-bound mRNAs
during mitosis compared to either G1 or S phase, which is
consistent with a decline in cap-dependent translation during
mitosis (Figure 1A) (Fan and Penman, 1970; Konrad, 1963).
Beyond these global changes in translation, our principal aim
was to understand the extent of gene-specific translational regulation during cell-cycle progression. We therefore developed a
computational framework for determining the statistical significance of relative ribosome occupancy of mRNAs within individual phases of the cell cycle. This analysis quantifies levels of
ribosome occupancy higher or lower than those predicted from
transcript abundance (Figure 1B, highlighted points; see Experimental Procedures). This approach enabled us to quantify the
landscape of mRNA translation during a given cell cycle phase
or to directly assess differential translational regulation between
any two phases of the cell cycle.
Strikingly, we observed extensive and dynamic translational
control of individual mRNAs during different phases of the cell
cycle (Figure 1B, Table S1). In total, we identified 1,255 mRNAs,
representing 12% of the expressed transcripts in these cells,
that exhibit levels of ribosome occupancy in any cell cycle phase
higher or lower than those expected (false discovery rate [FDR] <
1%). We next sought to determine how the translation of these
1,255 mRNAs varies among cell cycle phases. Transcript-specific translational regulation was most prevalent in G1 and S
phases (Figure 1C). Indeed, genome-wide, there was far greater
similarity in the level of ribosome occupancy in mRNAs between
G1 and S phase than existed when comparing either of these to
mitosis (Figure 1D). Importantly, these differences in transcriptspecific translational control during the cell cycle are independent from global changes in the levels of protein synthesis that
occur during specific cell cycle phases. Here, we measured
the levels of ribosome association for specific mRNAs relative
to the background level of ribosome association during each individual phase. This is especially relevant during mitosis, when
global protein synthesis levels are lower compared to those of
G1 or S phase (Figure 1A) (Fan and Penman, 1970; Konrad,
1963). Additionally, we identified core groups of mRNAs that
exhibit increased or decreased translation in all phases of the
cell cycle, suggesting that they may share a mechanism to maintain high or low levels of translation throughout the cell cycle (Figure 1C). Moreover, the specific patterns obtained by hierarchical
clustering of the 1,255 translationally regulated mRNAs were not
observed when comparing corresponding transcript expression
levels, indicating that translational regulation is indeed a distinct
regulatory system, uncoupled from transcription, controlling
gene expression during the cell cycle (Figure S1B).
In addition to defining the relative level of translation for specific genes within each cell cycle phase, we also identified a large
number of mRNAs that undergo significant changes in translation between phases of the cell cycle. We identified 353 mRNAs
with a statistically significant change in translation between any
two phases of the cell cycle (FDR < 5%) (Figure 1E, Table S2).
Among these, 112 mRNAs were translationally regulated exclusively between specific phases of the cell cycle. These mRNAs
comprise a number of important cell-cycle regulatory genes,
including CLASP2 and KNTC1, which are involved in establishing the mitotic spindle checkpoint. Overall, these data demonstrate that systematic and multifaceted translational control of
gene expression exists during progression through the mammalian cell cycle.
We next sought to understand the regulatory logic underlying
general changes in translational regulation observed among
specific phases of the cell cycle. To determine certain parameters of cell cycle-dependent translational regulation, we examined specific structural characteristics of 50 UTRs, including their
length, percent of G + C content (%G+C), and minimum free energy (MFE). In the G1 phase of the cell cycle, there is a significant
association between ribosome occupancy and the length of 50
UTRs such that mRNAs with shorter UTRs had high levels of
ribosome occupancy. In both G1 and S phase, there is an inverse
relationship between the G+C content of 50 UTRs and ribosome
occupancy levels. Finally, mRNAs with higher ribosome occupancy in all phases of the cell cycle studied were associated
with higher predicted MFE, revealing that their 50 UTRs possess
less-complex secondary structures (Figure S1C). Together,
these findings suggest that several of the defining features of
50 UTRs may contribute to the translational efficiency of the
mammalian genome within specific phases of the cell cycle.
However, these very general characteristics of the 50 UTR do
not account for all the patterns of translational regulation
observed here. Therefore, it is likely that there are additional
important determinants of transcript-specific translational control (see below).
The translational dynamics of key cell-cycle regulators are still
poorly understood. We therefore assessed the pattern of statistically significant translational regulation among bona fide cellcycle regulatory genes during each phase of the cell cycle (Figure 2A) (Subramanian et al., 2005). This analysis uncovered
(D) Density plots of the nominal p value of ribosome occupancy for each gene between any two phases of the cell cycle (colors designate the density of genes in a
given region, where red and blue are the most and least dense, respectively).
(E) Unsupervised hierarchical clustering of mRNA translation across the phases of the cell cycle using the 353 differentially translationally regulated transcripts
between any two phases in direct comparisons. Genes and cell cycle phases are clustered based on the level of normalized ribosome occupancy (meancentered translational efficiency by gene, scales and colors indicate the direction and magnitude of mRNA translation; S is S phase, M is Mitosis). See also
Figure S1 and Tables S1 and S2.
Molecular Cell 52, 1–9, November 21, 2013 ª2013 Elsevier Inc. 3
Please cite this article in press as: Stumpf et al., The Translational Landscape of the Mammalian Cell Cycle, Molecular Cell (2013), http://dx.doi.org/
10.1016/j.molcel.2013.09.018
Molecular Cell
5’UTR
Test
Control
Firefly
Renilla
RICTOR
NUAK2
Relative translation
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6
3
5
7
G1
S phase
Mitosis
RICTOR
e
as
ph
S
C
M
ito
si
s
NUAK2
G
1
FBXO5
SMC4
KIF15
POLA1
MRE11A
OFD1
SASS6
KIF18A
RINT1
RAD50
AHR
ROCK1
AHCTF1
LIN9
NEDD1
CENPF
RICTOR
CENPC1
PLK1
CCNB1
ASNS
PRIM1
BUB1B
TPX2
PRKDC
E2F5
BUB1
CUL2
EGF
PSMA3
NCAPH
RBL2
DOCK2
KIF23
RACGAP1
SYNE1
KIF4A
KIFAP3
PSMD14
CCNE2
CASC5
KIF11
CENPE
MYO6
ANAPC1
KIF2A
TTK
ANLN
LRPPRC
NUP107
CENPI
SGOL2
NUP133
PSMD1
SMC3
PCM1
CLASP1
SMARCA5
CEP135
HOOK3
CKAP5
RBBP4
STAG1
KIF20A
DYNC1I2
CDC26
RHOF
E2F1
CRIPAK
ARPC4
RUNX3
MRAS
RAC1
RPS27
EVL
APITD1
MYL6B
APOE
B
*
A
G
1
S
p
M has
ito e
si
s
The Translational Landscape during the Cell Cycle
Figure 2. Phase-Dependent Translational
Control of Key Cell-Cycle Regulators,
Including RICTOR and mTOR Signaling
(A) A heatmap of the significance of translational
regulation among cell-cycle progression genes
(asterisk: RICTOR). Shading represents the significance level of increased or decreased translational regulation (red and blue, as indicated).
(B) A diagram describing the 50 UTR luciferase
reporter assay (top panel). 50 UTRs cloned into the
firefly reporter are scaled to length. Capped
mRNAs are transfected into synchronized cells
prior to assaying reporter levels. Levels of translation directed by RICTOR or NUAK2 50 UTRs are
shown: y axis is reporter value relative to cells in
G1 (bottom panel).
(C) Western blots showing RICTOR protein levels
and phosphorylation of mTORC2 targets (AKTS473, PKCa-S657). pHistone H3 is a mitosis
marker. Tubulin is a loading control.
(D) Mitotic progression of thymidine-synchronized
MEFs: y axis is the percent of mitotic cells. Bars
represent the mean ± SD. See also Figure S2.
RICTOR
mTOR complex 2 (mTORC2) (Laplante
and Sabatini, 2012), displayed a significant change in the level of ribosome octotal AKT
cupancy, with an almost 3-fold increase
pPKCα S657
from G1 to S phase and a greater than
total PKCα
13-fold decrease from S phase to mitosis.
Our findings further show an accumulapHistone H3 S10
tion of RICTOR protein, but not other
Tubulin
components of mTORC2, that mirrors
this pattern of ribosome occupancy (Figures 2A, 2C, and S2B), which suggests
60
D
wildtype
that RICTOR protein accumulation is, at
50
Rictor-/least in part, mediated by translational
control. To further investigate the mecha40
nisms responsible for cell cycle-depen30
dent translational control of RICTOR, we
assessed the activity of its 50 UTR. Strik20
ingly, the 50 UTR of the RICTOR mRNA
10
is sufficient to direct increased protein
expression during S phase, which then
Significance (q value)
0
N.S.
decreases upon entry into mitosis, a
<1e-10
<1e-10
8
12
24
Decreased
Increased
pattern consistent with the cell cycleHours post thymidine
dependent ribosome occupancy we
observed (Figures 2A, 2B, and S2A). We
extensive translational regulation among these genes, high- compared the translation directed by the RICTOR 50 UTR to
lighting the prevalence of translational control during cell-cycle the 50 UTR of an mRNA that exhibits a distinct pattern of cellprogression. For example, we observed high levels of ribosome cycle phase-specific translation, NUAK2 (Figure 2B). In our
occupancy in G1 and S phase for CCNE2, a cyclin known to be ribosome profiling experiments, NUAK2 showed a significant
translationally regulated. On the contrary, our data revealed low increase in ribosome occupancy during mitosis compared to
levels of ribosome occupancy of E2F1, also during G1 and S either G1 or S phase (q values = 9.5 3 104 and 1.3 3 103,
phase, which is consistent with cell cycle-dependent miRNA- respectively; Table S2). RICTOR and NUAK2 50 UTRs promote
mediated regulation of E2F1 (Pulikkan et al., 2010). Furthermore, unique patterns of translation in the luciferase reporter assay,
this analysis identified translational regulation among several suggesting that translational regulation of these mRNAs is
cell-cycle mediators, including PLK1 and BUB1, two mitotic specific and the patterns of translation we observe are not due
checkpoint kinases. Notably, RICTOR, the defining subunit of to global changes in protein synthesis (Figure 2B).
Percent of cells in Mitosis
pAKT S473
4 Molecular Cell 52, 1–9, November 21, 2013 ª2013 Elsevier Inc.
Please cite this article in press as: Stumpf et al., The Translational Landscape of the Mammalian Cell Cycle, Molecular Cell (2013), http://dx.doi.org/
10.1016/j.molcel.2013.09.018
Molecular Cell
The Translational Landscape during the Cell Cycle
These data suggest that regulation of RICTOR mRNA translation and its accumulation at the protein level during S phase
could modulate the activity of mTOR during the cell cycle. To
test this hypothesis, we assessed phosphorylation levels of
mTORC2 targets AKT (S473) and PKCa (S657) in lysates from
synchronized cells (Figure 2C). We identified a strong correlation
between the levels of RICTOR protein and the phosphorylation of
these two mTORC2 targets during the S phase of the cell cycle.
Moreover, the phosphorylation of AKT during S phase is dependent on RICTOR, since it does not occur in Rictor/ mouse embryonic fibroblasts (MEFs) (Figure S2C). Upstream signaling
pathways, such as phosphatidylinositol 3-kinase (PI3K)/
PDPK1, can activate AKT by phosphorylating T308 (Alessi
et al., 1996; Alessi et al., 1997). The fact that phosphorylation
of AKT at T308 does not change during the cell cycle further suggests that the RICTOR-dependent phosphorylation of AKT at
S473 during S phase is independent from the induction of upstream PI3K signaling (Figure S2B). Importantly, we also observe
an increase in the phosphorylation of p27, a downstream target
of AKT, specifically during S phase (Figure S2B). Phosphorylation of p27 by AKT inhibits its activity, thereby promoting cellcycle progression (Shin et al., 2002). These findings suggest
that the accumulation of RICTOR protein levels in S phase may
have an important function in cell-cycle progression. Rictor/
MEFs have previously been shown to possess a proliferation
defect (Shiota et al., 2006). We therefore determined when the
proliferation defect in Rictor/ MEFs manifests during the cell
cycle relative to the increase in translation of RICTOR observed
in S phase. Rictor/ MEFs exhibit a defect in progression
through mitosis as evidenced by a lag in the accumulation of
diploid cells after synchronization in S phase (Figure 2D), associated with a marked decrease in S phase cells compared to wildtype (p value = 0.0006, Student’s t test; Figure S2D). Together,
our findings suggest that translational regulation, at least in
part, leads to an increase in RICTOR protein that underlies cell
cycle-dependent mTORC2 activity and is associated with the
progression from S phase to mitosis.
An outstanding question is whether functionally related
groups of genes may be translationally coregulated as a means
by which to simultaneously control the expression of important
mRNA networks. Strikingly, we identified numerous clusters of
functionally related genes among translationally coregulated
mRNAs (Table S3). These include genes central to the control
of metabolism, nuclear transport, and DNA repair (Figure 3A,
Table S3). Among the metabolism genes identified, there was
a particular enrichment of genes involved in lipid metabolism
and the tricarboxylic acid cycle (TCA) cycle (Figure 3B). Furthermore, we identified a significant enrichment of translationally
regulated mRNAs, primarily during G1 and S phase, involved
in nuclear-cytoplasmic transport, including a large number of
core scaffolding components of the nuclear pore complex
(NPC) (Figure 3C). In fact, over 20% of NPC components are
translationally regulated during cell-cycle progression, primarily
during interphase, when the number of NPCs increases
dramatically (Antonin et al., 2008). Furthermore, translationally
regulated genes are components of multiple DNA repair pathways, including mismatch repair and double-strand break
repair pathways (Figure 3D). This suggests that translational
control may be a key contributor in regulating the response to
diverse types of DNA damage that arise during cell-cycle
progression.
Given this striking pattern of translational regulation among
genes involved in DNA repair, we sought to identify cell-cycle
phase-specific translational regulation of mRNAs required for
genome fidelity. Strikingly, the majority of genes comprising
the cohesin and condensin complexes are translated in a cellcycle phase-specific manner (Figure 4A). mRNAs from components of both the condensin and cohesin complexes exhibit relatively high levels of ribosome occupancy during G1 and S phase
that decrease in mitosis (Figure 4A). As these complexes are
loaded onto DNA during S phase or G2 in order to prepare chromosomes for segregation during mitosis (Hirano, 2012; Wood
et al., 2010), the ribosome occupancy we observe is consistent
with the requirement for the condensin and cohesin complexes
to be produced prior to mitosis. We determined that the molecular basis for these translational patterns in gene expression is,
at least in part, through 50 UTR-mediated regulation. For
example, the 50 UTRs of the core components of the condensin
complex, SMC2 and SMC4, direct similar patterns of translational activation during G1 and S phase that decrease in mitosis
and mirror the cell cycle ribosome occupancy profile. Likewise,
the 50 UTRs of SMC3, STAG1, and NIPBL, components of the
cohesin complex, direct translation that is high during G1 and
S phase and decreased during mitosis (Figure 4B). On the contrary, the 50 UTR from RANBP1, a control mRNA whose pattern
of ribosome occupancy is distinct from components of the condensin and cohesin complexes and is functionally unrelated,
does not (Figure 4B).
We further identified unique cell cycle-dependent patterns of
ribosome occupancy within specific regions of transcripts
belonging to the cohesin complex. For example, the density of
bound ribosomes on the NIPBL mRNA, a member of the cohesin
complex, shifts from the primary initiation codon of the coding
region to an upstream open reading frame (uORF) 59 nucleotides upstream of the primary initiation codon in a highly evolutionarily conserved region of the 50 UTR as cells progress
through the cell cycle into mitosis (Figures 4C, left, and S3A).
uORFs often act to decrease the translation of primary open
reading frames, which is consistent with the decrease in translation observed from the NIPBL 50 UTR in the luciferase reporter
assay (Figure 4B). In the case of WAPAL, a gene that promotes
dissociation of cohesin from sister chromatids during mitosis
(Gandhi et al., 2006; Kueng et al., 2006), we identified an unexpected peak of ribosome density downstream of the translation
termination codon in the 30 UTR that is only present during
mitosis (Figure 4C, right). This region of the WAPAL 30 UTR is
highly conserved and contains two additional in-frame stop codons, suggesting that WAPAL could produce a C-terminally
extended protein product during mitosis (Figures 4C, right,
and S3B). Together, these data suggest that components of
the condensin and cohesin complexes utilize multiple modes
of translational regulation to coordinate their expression during
the cell cycle. Furthermore, translational regulation may help
to facilitate the assembly or modulate the activity of large protein
complexes by ensuring that individual components of these
complexes are coordinately expressed.
Molecular Cell 52, 1–9, November 21, 2013 ª2013 Elsevier Inc. 5
Please cite this article in press as: Stumpf et al., The Translational Landscape of the Mammalian Cell Cycle, Molecular Cell (2013), http://dx.doi.org/
10.1016/j.molcel.2013.09.018
Molecular Cell
G1
S phase
Mitosis
Nuclear
Transport
G
1
S
p
M ha
ito se
si
s
B
ST6GALNAC6
STS
AGPAT1
NEU3
ECHS1
ADM
ETNK1
ALG1
CPT1A
Metabolism
Cohesin and
Condensin
FH
IDH1
ACO2
PDK2
PDK3
IDH3A
PDHA2
PCK1
SDHC
C
DNA
Repair
G
1
S
p
M ha
ito se
si
s
Lipid
Metabolism
TCA Cycle
D
G
1
S
p
M ha
ito se
si
s
A
G
1
S
p
M ha
ito se
si
s
The Translational Landscape during the Cell Cycle
NUP133
NUP205
NUP107
TPR
KPNB1
NUP93
NUP188
NUP155
POLA1
MRE11A
MSH2
LIG4
RAD50
MSH6
MSH3
RECQL
ERCC4
UBE2V1
Nuclear
Transport
DNA Repair
Significance (q value)
<1e-10
<1e-10
Decreased N.S.
Increased
Figure 3. Translational Coregulation of Large Molecular Complexes during Cell-Cycle Progression
(A) Among translationally regulated genes during the cell cycle, a network of statistically significant functional enrichments (nodes, nominal p value < 0.05; radius
scaled to the number of genes) and their relatedness (edges, spearman correlation r R 0.3) indicate a highly interconnected set of modules of molecular function.
Groups of nodes closely related by function are highlighted in yellow and labeled. A Venn diagram overlay represents the relative overlap of enriched molecular
function between cell cycle phases (G1 is gray, S phase is blue, and mitosis is green).
(B–D) Heatmaps highlight the significance of translational regulation of genes that define representative functional categories, including lipid metabolism and TCA
cycle (B), nuclear transport (C), and DNA repair (D). Shading represents the significance level of increased or decreased translational regulation (red and blue, as
indicated). See also Table S3.
DISCUSSION
Our work delineates the unexpected magnitude and dynamic
nature of translational regulation during the mammalian cell cycle. We have presented a comprehensive network of interrelated
and coordinately translationally regulated mRNAs underlying
this fundamental biological process. These data suggest that
translational control is a particularly well-suited mechanism for
fine-tuning gene expression during dynamic processes such
as cell-cycle progression. For example, we uncovered unexpected translational regulation of a key component of the
mTOR pathway, a key regulator of cell growth. RICTOR becomes translationally induced specifically upon transitioning
into S phase of the cell cycle. Although we cannot exclude that
other mechanisms, such as control of protein stability, may
also cooperate in modulating RICTOR protein abundance during
the cell cycle, our findings show that accumulation of RICTOR
during S phase modulates mTORC2 signaling to promote the
phosphorylation of specific downstream targets, including AKT
6 Molecular Cell 52, 1–9, November 21, 2013 ª2013 Elsevier Inc.
and PKCa. Phosphorylation of both AKT and PKCa by mTORC2
during S phase is consistent with their roles in promoting cell
growth and proliferation and reveals how this process may be
regulated at the level of translational control. Moreover, we did
not observe overt translational regulation of other mTOR complex components, highlighting the specificity in RICTOR 50
UTR translational activation in controlling mTOR signaling during
cell-cycle progression. Elucidating the precise mechanisms governing translational regulation of RICTOR will be an important
area of future research that may play an important role in
mTORC2 signaling during cell-cycle progression.
One surprising finding from our data is the translational coregulation of the molecular machinery responsible for maintaining
genome integrity. A number of translationally regulated genes
are involved in sensing multiple types of DNA damage, such as
base pair mismatches and double-strand breaks, that are specifically translationally activated during S phase. Notably, multiple
orthogonal DNA repair pathways are controlled by translation,
suggesting a critical regulatory mechanism that maintains the
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10.1016/j.molcel.2013.09.018
Molecular Cell
The Translational Landscape during the Cell Cycle
A
Condensin complex
Cohesin complex
p = 0.027
p = 0.0015
3
Translational efficiency
Translational efficiency
3
2
1
0
SMC2
-1
SMC4
2
1
SMC3
0
STAG1
-1
NIPBL
-2
WAPAL
-2
G1
S phase
Mitosis
G1
B
S phase
Mitosis
Test
Firefly
5’UTR
SMC2
SMC4
SMC3
STAG1
NIPBL
RANBP1
Control
Renilla
Relative translation
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
Conservation
1.0
SMC4
SMC3
STAG1
NIPBL RANBP1
1.0
0.5
0.5
0.0
0.0
NIPBL
WAPAL
Mitosis S phase G1
Ribosome-bound Reads
C
Conservation
SMC2
36,953,650
uORF
36,954,000
88,194,980
88,197,970
A
U
G
Figure 4. Condensin and Cohesion Complex Components Are Coordinately and Translationally Regulated during the Cell Cycle at the Level
of Their 50 UTR
(A) The translational efficiency of components of the condensin (left) and cohesin (right) complexes during each cell cycle phase are indicated (specific genes
mentioned elsewhere are outlined for clarity).
(B) A diagram of the luciferase reporter assay with 50 UTRs scaled to length (left). Levels of translation directed by specific 50 UTRs are indicated: y axis is the
reporter value relative to cells in G1 (right). Bars represent mean ± SD from six replicates.
(C) The level of bound ribosome in the 50 UTR of NIPBL (left) and the 30 UTR of WAPAL (right), with peaks of interest denoted by red arrows (evolutionary
conservation is indicated). G1 is gray, S phase is blue, and mitosis is green. Representative gene features are indicated: uORF is designated by an orange box;
narrow or wide black bars represent UTRs and coding exons, respectively; black lines indicate introns; and arrows indicate the coding strand. Numbers represent
absolute genomic positions. See also Figure S3.
Molecular Cell 52, 1–9, November 21, 2013 ª2013 Elsevier Inc. 7
Please cite this article in press as: Stumpf et al., The Translational Landscape of the Mammalian Cell Cycle, Molecular Cell (2013), http://dx.doi.org/
10.1016/j.molcel.2013.09.018
Molecular Cell
The Translational Landscape during the Cell Cycle
fidelity of the genome, adding a robust level of protection in
safeguarding the genome. This may also be true for protein complexes that are responsible for organizing the higher-order structure of chromosomes, which also show coregulated patterns of
translational control. The primary role of the cohesin and condensin complexes is to package the genome to ensure faithful
segregation of chromosomes during cell division (Hirano, 2012;
Wood et al., 2010). The mRNAs comprising the majority of these
two complexes exhibit high levels of translation during both G1
and S phase that may ensure sufficient, stoichiometric amounts
of the proteins required to package chromosomes prior to their
segregation.
Interestingly, cohesins can promote transcription, and disruption of the cohesin complex impairs ribosomal RNA transcription, thus leading to defects in protein synthesis (Bose et al.,
2012). Most notably, mutations in cohesin genes characterize
an entire class of human disorders termed cohesinopathies.
Cohesinopathies manifest as developmental disorders with
characteristic limb defects, including oligodactyly, and neurodevelopmental delay. These features overlap with the phenotypic
spectrum of ribosomopathies where ribosome components are
found mutated, suggesting a possible relationship between
these two fundamental biological processes. Moreover, it is
notable that a mutation in a uORF in the 50 UTR of NIPBL leads
to a decrease in NIPBL translation and is associated with a cohesinopathy known as Cornelia de Lange Syndrome, suggesting
that alterations in translational regulation may underlie this human disease (Borck et al., 2006). This mutation disrupts a
uORF in the 50 UTR of NIPBL, which could be responsible for
the observed decrease in translation. This finding is consistent
with our studies showing a critical role for the 50 UTR in regulating
the translation of mRNAs belonging to the cohesin complex.
These results also suggest a potential feedback mechanism between the cohesin complex and the translation machinery that
may be of great importance to the etiology of cohesinopathies.
Together, our studies shed light on the unexpected dynamics
of translational control in the regulation of gene expression during fundamental cellular programs, such as the mammalian cell
cycle. The magnitude of this translational regulation, involving
hundreds of mRNAs, suggests that currently unknown regulatory
mechanisms and transcript-specific translational regulators may
endow remarkable specificity to the posttranscriptional gene
expression program that is fundamental for accurate replication
and cell division.
mRNA. Ribosome-protected mRNA fragments were isolated by centrifugation. Total mRNA was alkaline fragmented and size selected. Both samples
were processed for small RNA library sequencing. Libraries from two biological replicates per cell cycle phase were sequenced on an Illumina HiSeq
2000.
Alignment and Analysis of Ribosome Profiling Data
Sequencing reads were processed and aligned to the human genome using
standard procedures (see Supplemental Experimental Procedures). Translational regulation was inferred using an errors-in-variables regression model.
This analytical model estimates p values in each replicate to represent
ribosome-given-mRNA counts lower or higher than those expected. We similarly developed methods for combining p values among replicates within cell
cycle phases and for testing differential translational regulation, all of which
is described in greater detail in the Supplemental Experimental Procedures.
50 UTR Characterizations
Genes were classified as having a level either higher than, lower than, or expected of ribosome occupancy given their mRNA levels within each phase
of the cell cycle as described in the Supplemental Experimental Procedures.
The length and %G+C content of the 50 UTR of each expressed gene was
computed from Gencode version 11, while the predicted minimum free energy
was computed with the Vienna suite; RNAfold version 1.8.4 (Gruber et al.,
2008). The significance of each 50 UTR feature was determined with Wilcoxon
rank sum test statistics.
Luciferase Reporter Assays
50 UTRs from HeLa cDNA were cloned upstream of firefly luciferase in pGL3T7. RNAs were in vitro transcribed using the T7 MEGAscript Kit (Life Technologies). Purified RNA was capped using a vaccinia RNA capping system (New
England Biolabs). Luciferase reporter RNA was transfected at a ratio of 20:1
with a control Renilla reporter RNA using the TransIT-mRNA Transfection Kit
(Mirus Bio). Luciferase levels were read after a 4 hr incubation using the Dual
Luciferase Assay (Promega). Firefly luciferase signal was normalized to
Renilla signal for each sample, averaged, and the signal relative to G1 was
calculated.
Western Blotting
Proteins were analyzed by standard western blotting protocols. Antibodies
used were as follows: RICTOR, AKT phospho-S473, total AKT, AKT phospho-T308, total PKCa, and PROTOR-2 (Cell Signaling Technology); PKCa
phospho-S657 (Santa Cruz); PROTOR-1 (GeneTex); SIN1 (Bethyl Laboratories); phospho-p27 (R&D Systems); total p27 (BD Biosciences); histone H3
phospho-S10 (Millipore); and a-tubulin (Sigma-Aldrich).
ACCESSION NUMBERS
Sequencing data were deposited in the NCBI Short Read Archive under accession number SRA099816.
EXPERIMENTAL PROCEDURES
SUPPLEMENTAL INFORMATION
Tissue Culture and Cell-Cycle Analysis
HeLa cells and MEFs were cultured under normal growth conditions. Synchronization in specific cell cycle phases was achieved by release from thymidine
block (G1 and S phase) and nocodazole treatment (mitosis) as previously
described (Jackman and O’Connor, 2001). Cell-cycle analysis was performed
on cells fixed in 80% ethanol prior to ribonuclease (RNase) digestion and staining with 40 mg/ml propidium iodide or with the Click-iT EdU Flow Cytometry
Assay (Life Technologies).
Supplemental Information includes Supplemental Experimental Procedures,
three figures, and three tables and can be found with this article online at
http://dx.doi.org/10.1016/j.molcel.2013.09.018.
Library Preparation and Sequencing
Next-generation sequencing libraries were prepared as described previously
(Hsieh et al., 2012). Briefly, synchronized cells were treated with cycloheximide, lysed, and split into pools for isolating total mRNA and ribosome-bound
8 Molecular Cell 52, 1–9, November 21, 2013 ª2013 Elsevier Inc.
ACKNOWLEDGMENTS
We thank Maria Barna and members of the Ruggero lab for critical insights during this work and D. Byrd, K. Tong, and C. Milentis for reading the manuscript.
Mark Magnuson provided the Rictor/ MEFs. C.R.S. is supported by NIH/
NRSA (F32CA162634). This work was supported by NIH R01CA140456
(D.R.), NIH R01CA154916 (D.R.), and NIH P30CA82103 (A.B.O.). B.S.T. is a
Prostate Cancer Foundation Young Investigator. D.R. is a Leukemia & Lymphoma Society Scholar.
Please cite this article in press as: Stumpf et al., The Translational Landscape of the Mammalian Cell Cycle, Molecular Cell (2013), http://dx.doi.org/
10.1016/j.molcel.2013.09.018
Molecular Cell
The Translational Landscape during the Cell Cycle
Received: May 14, 2013
Revised: August 5, 2013
Accepted: September 17, 2013
Published: October 10, 2013
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Molecular Cell 52, 1–9, November 21, 2013 ª2013 Elsevier Inc. 9
Molecular Cell, Volume 52
Supplemental Information
The Translational Landscape of the Mammalian Cell Cycle
Craig R. Stumpf, Melissa V. Moreno, Adam B. Olshen, Barry S. Taylor, and Davide Ruggero
Supplemental Materials Figure S1
A
100
G1
82%
S-phase 89%
Mitosis 73%
% of Max
80
60
40
20
0
0
200
400 600
PI intensity
B
800
1000
G1
S
Translation
S
M
M
Genes (n=1255)
G1
-1.75
C
**
10000
***
***
100
10
***
0.0
0.8
1000
***
***
***
-0.2
** p<0.001
*** p<0.0001
-0.4
G1
S-phase
Mitosis
0.6
0.4
-0.6
0.2
Background
Low
High
Background
Low
High
Background
-0.8
Low
High
Background
Low
High
Background
Low
High
Background
Low
0.0
High
Background
Low
High
Background
Low
High
Background
Low
High
1
***
1.0
5' UTR G+C%
5' UTR length (bp)
1.75
0
Figure S1. Related to Figure 1. (A) Flow cytometry histogram plotting the distribution of cells as a function of DNA content measured by propidium iodide (PI) fluorescence intensity. (B) Heat maps representing the translational regulation (left) and mRNA expression levels (right) for the 1255 translationally regulated genes within any phase of the cell cycle. Genes and cell cycle phases are clustered based on the level of normalized ribosome occupancy (mean-­‐centered translational efficiency by gene, left). mRNA expression levels (right) were ordered based on the clustering of translational regulation. Scales and colors indicate the direction and magnitude of mRNA translation or levels, respectively. (C) Box and whisker plots denoting the 5’UTR length (left), %G+C content (middle), and minimum free energy (MFE) (right) for genes with higher, lower, or expected levels of ribosome occupancy. Y-­‐axes are as labeled. Cell cycle phases are colored: Grey, G1; Blue, S-­‐
phase; Green, mitosis. Figure S2
A
RICTOR 5’ UTR sequence:
GUUUCCGGUGUUGUGACUGAAACCCGUCAAUAUG
si
s
ito
ph
M
S-
G
1
as
e
B
SIN1
PROTOR-1
PROTOR-2
p-AKT T308
total AKT
p-p27Kip1
total p27Kip1
Tubulin
si
s
M
ito
si
s
e
as
ito
+
M
-
ph
+
S-
ph
S-
1
G
RICTOR:
G
1
as
e
C
-
+
-
p-AKT S473
total AKT
Tubulin
D
Percent of cells
0
20
40
60
80
100
WT
rictor-/G1
S-phase
Mitosis
p-value = 0.0006
Figure S2. Related to Figure 2. (A) The sequence of the RICTOR 5’UTR. The start codon for the coding region is highlighted in red. (B) Western blots showing the levels of mTORC2 components and the phosphorylation status of AKT and downstream signaling targets. SIN1, PROTOR-­‐1, and PROTOR-­‐2 are components of mTORC2. Phosphorylation of AKT T308 is independent of mTORC2. p27Kip1 is a phosphorylation target of AKT. Tubulin is a loading control. (C) Western blots illustrating the phosphorylation status of AKT in synchronized wildtype and Rictor-­‐/-­‐ MEFs. (D) Cell cycle analysis of asynchronous MEFs with the percentage of cells in each phase of the cell cycle indicated along the Y-­‐axis. The red box highlights the magnitude of the difference between the number of WT and Rictor-­‐/-­‐ MEFs in S-­‐
phase. Probability calculated by Student’s t-­‐test. Cell cycle phases are colored as labeled. Figure S3
A
NIPBL 5’UTR sequence:
GGGCCCGCGGCGAUGCCCCCCCGGUAGCUCGGGCCCGUGGUCGGGUGUUUGUGAGUGU
UUCUAUGUGGGAGAAGGAGGAGGAGGAGGAAGAAGAAGCAACGAUUUGUCUUCUCGGCUG
GUCUCCCCCCGGCUCUACAUGUUCCCCGCACUGAGGAGACGGAAGAGGAGCCGUAGCCAC
CCCCCCUCCCGGCCCGGAUUAUAGUCUCUCGCCACAGCGGCCUCGGCCUCCCCUUGGAUU
CAGACGCCGAUUCGCCCAGUGUUUGGGAAAUGGGAAGUAAUGACAGCUGGCACCUGAACU
AAGUACUUUUAUAGGCAACACCAUUCCAGAAAUUCAGGAUG...
B
WAPAL 3’UTR sequence:
CTGCTTTACCTTTGCTTCAGGTGCTCGGTAATGCTGGAGCTATCCTTAGACAAAGAAAAGTCAAGTC
ATGAAAGAAGTCCTTGAAGATATACCAAGAACATTCATCAGTATCATTCGTGTTTGGATTTTTAA...
Putative amino acid sequence of C-terminal extension:
LLYLCFRCSVMLELSLDKEKSSHERSP*RYTKNIHQYHSCLDR*
Figure S3. Related to Figure 4. (A) The sequence of the NIPBL 5’UTR. Potential AUG start codons are bold. The uORF corresponding to the peak of bound ribosome in Figure 4C is orange. The region of the CC > A mutation in Cornelia de Lange syndrome is underlined. (B) The sequence of the WAPAL 3’UTR immediately downstream of the stop codon. The region corresponding to the peak of ribosome binding is underlined. In-­‐frame stop codons are shown in red. The predicted amino acid sequence of the putative C-­‐terminal extension is shown below. The sequence up to the first in-­‐frame stop codon is red. Supplemental Tables Table S1. Translationally regulated genes from each phase of the cell cycle, related to Figure 1. Table S2. Genes with significant changes in translation between G1 and S-­‐phase, G1 and mitosis, and S-­‐phase and mitosis, related to Figure 1. Table S3. Functional enrichments of genes that are translationally regulated during G1, S-­‐phase, or mitosis, related to Figure 3. Supplemental Experimental Procedures Alignment and analysis of ribosome profiling data Raw single-­‐end sequencing reads were processed in the following manner. First, reads were clipped of the known adaptor sequence (CTGTAGGCACCATCAAT) and remaining 3’ sequence with the FASTX-­‐Toolkit, retaining only reads of 24bp post-­‐
clipping length or longer. Clipped reads were mapped to the human genome assembly (NCBI build 36) with Burrows-­‐Wheeler Alignment (BWA; ver. 0.5.9-­‐r16, default parameters)(Li and Durbin, 2009). Unaligned reads were then mapped to known splice junctions with Tophat (ver. 1.4.1) (Trapnell et al., 2009) using a transcriptome index created from version 11 of the Gencode gene set (Harrow et al., 2006). Provisional merged BAM files (Li et al., 2009) were created from aligned and unaligned reads from either the genome or spliced alignment and read groups were enforced across lanes and experiments. BAM files were then indexed, coordinate sorted, and had alignment metrics determined, all with the Picard suite (http://picard.sourceforge.net/). Only those protein-­‐coding transcripts of levels 1 or 2 of Gencode support (verified or manually curated loci respectively) were used for all subsequent analyses. A unified gene model was created for each gene in which the union of coding sequences across all isoforms of a given gene was collapsed into a single model per gene. Gene-­‐level translational regulation was inferred for only those genes estimated to be expressed in the Hela transcriptome, determined by comparing their observed expression level (calculated as a normalized rpkM expression estimate) to a null distribution of similar expression in randomly selected non-­‐coding regions of the genome with a size distribution sampled from the empirical size distribution (Olshen et al. submitted). In total, 10,814 genes were retained for further analysis. We quantify mRNA translation for genes (ribosome-­‐given-­‐mRNA levels) with a statistical framework called Babel. Briefly, this errors-­‐in-­‐variables regression model, the details of which are presented elsewhere (Olshen et al.), assesses the significance of translational regulation for every gene within and between samples and conditions. For a transcript with a given abundance (mRNA counts), the expected level of ribosome occupancy (RPF counts) is inferred from trimmed least-­‐
squares regression (based on the monotonic relationship typified in Figure 1). We model both mRNA levels and the level of bound ribosome given mRNA abundance by the negative binomial distribution. The over-­‐dispersion of the latter is modeled using an iterative algorithm to prevent over-­‐fitting. We then use a parametric bootstrap based on the modeling described above to estimate a p-­‐value for every gene. It tests formally the null hypothesis that the level of bound ribosome is as expected from mRNA abundance. These p-­‐values are estimated for a one-­‐sided test in which both low and high p-­‐values are of interest; low p-­‐values correspond to higher than expected ribosome-­‐given-­‐mRNA counts and high p-­‐values correspond to the opposite. Since Babel quantifies this mRNA translation (ribosome-­‐given-­‐mRNA levels) as a p-­‐
value in each sample of a given condition, we combine these p-­‐values into a single assessment of the significance of translational regulation in each condition (phase of the cell cycle). Here, we developed an alternative methodology to Fisher’s method first proposed by Edgington (Edgington, 1972). This approach, in which the combined p-­‐value is a function of the arithmetic mean of individual p-­‐values, maintains symmetry and convexity between each of two or more tests of hypotheses and their p-­‐values. It produces a small combined p-­‐value from consistently small p-­‐values of ribosome-­‐given-­‐mRNA levels (and the reverse for two or more large p-­‐values). Once p-­‐values are combined, two-­‐sided p-­‐values can be estimated and genes are considered regulated at the translational level if their corresponding p-­‐values are low after adjusting for multiple comparisons [in this study, those corresponding to a false discovery rate (FDR) less than 1% (Storey, 2002)]. Differences in ribosome occupancy given mRNA levels between conditions are assessed utilizing a statistic based upon using the Gaussian quantile function to transform the within condition p-­‐values into z-­‐statistics. Corresponding p-­‐values are estimated by comparison to the Gaussian distribution and again corrected for multiple comparisons as described above. Significant differentially translationally regulated genes are those corresponding to an FDR < 5% in any of the pair-­‐wise comparisons of cell cycle phases. For visualization purposes, the conventional measure of translational efficiency was used and calculated as previously described (Ingolia et al., 2009). Supplemental References Edgington, E.S. (1972). Additive Method for Combining Probability Values from Independent Experiments. J Psychol 80, 351-­‐&. Harrow, J., Denoeud, F., Frankish, A., Reymond, A., Chen, C.K., Chrast, J., Lagarde, J., Gilbert, J.G., Storey, R., Swarbreck, D., et al. (2006). GENCODE: producing a reference annotation for ENCODE. Genome Biol 7 Suppl 1, S4 1-­‐9. Ingolia, N.T., Ghaemmaghami, S., Newman, J.R., and Weissman, J.S. (2009). Genome-­‐
wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218-­‐223. Li, H., and Durbin, R. (2009). Fast and accurate short read alignment with Burrows-­‐
Wheeler transform. Bioinformatics 25, 1754-­‐1760. Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., and Durbin, R. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078-­‐2079. Olshen, A.O., Hsieh, A.C., Stumpf, C.R., Olshen, R.A., Ruggero, D., and Taylor, B.S. (2013). Assessing gene-­‐level translational control from ribosome profiling. Bioinformatics (in press). Storey, J.D. (2002). A direct approach to false discovery rates. J Roy Stat Soc B 64, 479-­‐498. Trapnell, C., Pachter, L., and Salzberg, S.L. (2009). TopHat: discovering splice junctions with RNA-­‐Seq. Bioinformatics 25, 1105-­‐1111.