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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 Landscapeof the Mammalian Cell CycleCraig 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, USA2Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94158, USA3Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA4Department 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

SUMMARY

Gene regulation during cell-cycle progression is anintricately choreographed process, ensuring accu-rate DNA replication and division. However, thetranslational landscape of gene expression underly-ing cell-cycle progression remains largely unknown.Employing genome-wide ribosome profiling, weuncover widespread translational regulation ofhundreds of mRNAs serving as an unexpectedmechanism for gene regulation underlying cell-cycleprogression. A striking example is the S phase trans-lational regulation of RICTOR, which is associatedwith cell cycle-dependent activation of mammaliantarget of rapamycin complex 2 (mTORC2) signalingand accurate cell-cycle progression.We further iden-tified unappreciated coordination in translationalcontrol of mRNAs within molecular complexes dedi-cated to cell-cycle progression, lipid metabolism,and genome integrity. This includes the majority ofmRNAs comprising the cohesin and condensin com-plexes responsible for maintaining genome organi-zation, which are coordinately translated duringspecific cell cycle phases via their 50 UTRs. Our find-ings illuminate the prevalence and dynamic nature oftranslational regulation underlying the mammaliancell cycle.

INTRODUCTION

During cell division, exquisite temporal control of protein expres-

sion 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 para-

digm that has emerged is that rapid, dynamic, and fine-tuned re-

programming 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 dur-

ing the cell cycle is required for progression to subsequent

phases (Peters, 2006). Systems-level mass spectrometry ap-

proaches are also beginning to elucidate targets of the ubiqui-

tin-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 expres-

sion 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 mamma-

lian cell cycle has generally focused on global reductions in pro-

tein 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 duringmitosis (Qin and Sar-

now, 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 impor-

tance 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 transla-

tional 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. More-

over, we identify surprising, coordinate regulation in translational

control of functionally related sets of mRNAs, suggesting a reg-

ulatory 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 transcript-

specific translational control in the execution of distinct steps

of the cell cycle.

Molecular Cell 52, 1–9, November 21, 2013 ª2013 Elsevier Inc. 1

mailto:[email protected]

mailto:[email protected]


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 givenmRNA 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)

Molecular Cell

The Translational Landscape during the Cell Cycle

2 Molecular Cell 52, 1–9, November 21, 2013 ª2013 Elsevier Inc.


Molecular Cell



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 transcrip-

tome of each phase of the cell cycle. This data revealed a signif-

icant 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 regu-

lation during cell-cycle progression. We therefore developed a

computational framework for determining the statistical signifi-

cance of relative ribosome occupancy of mRNAs within individ-

ual 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 Exper-

imental 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-spe-

cific 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 transcript-

specific translational control during the cell cycle are indepen-

dent 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 in-

dividual 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

(D) Density plots of the nominal p value of ribosome occupancy for each gene betw

given region, where red and blue are the most and least dense, respectively).

(E) Unsupervised hierarchical clustering of mRNA translation across the phases o

between any two phases in direct comparisons. Genes and cell cycle phases

centered translational efficiency by gene, scales and colors indicate the directio

Figure S1 and Tables S1 and S2.

exhibit increased or decreased translation in all phases of the

cell cycle, suggesting that they may share a mechanism to main-

tain high or low levels of translation throughout the cell cycle (Fig-

ure 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 spe-

cific genes within each cell cycle phase, we also identified a large

number of mRNAs that undergo significant changes in transla-

tion 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 exclu-

sively 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 establish-

ing the mitotic spindle checkpoint. Overall, these data demon-

strate that systematic and multifaceted translational control of

gene expression exists during progression through the mamma-

lian 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 parame-

ters of cell cycle-dependent translational regulation, we exam-

ined specific structural characteristics of 50 UTRs, including their

length, percent of G + C content (%G+C), and minimum free en-

ergy (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 bothG1 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 occu-

pancy 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 con-

trol (see below).

The translational dynamics of key cell-cycle regulators are still

poorly understood. We therefore assessed the pattern of statis-

tically significant translational regulation among bona fide cell-

cycle regulatory genes during each phase of the cell cycle (Fig-

ure 2A) (Subramanian et al., 2005). This analysis uncovered

een any two phases of the cell cycle (colors designate the density of genes in a

f the cell cycle using the 353 differentially translationally regulated transcripts

are clustered based on the level of normalized ribosome occupancy (mean-

n and magnitude of mRNA translation; S is S phase, M is Mitosis). See also


C

RenillaFirefly5’UTR

RICTOR

NUAK2

Test ControlB

D

Relative translation0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 3 5 7

RICTOR

NUAK2

G1S phaseMitosis

A

<1e-10N.S.

Significance (q value)

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

G1 S phase

Mitosis

*

<1e-10IncreasedDecreased

8 12 240

10

20

30

40

50

60wildtypeRictor-/-

Perc

ent o

f cel

ls in

Mito

sis

Hours post thymidine

total PKCα

total AKT

pAKT S473

pPKCα S657

RICTOR

Tubulin

pHistone H3 S10

G1 S phase

Mitosis

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 sig-

nificance level of increased or decreased trans-

lational 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 trans-

lation 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 (AKT-

S473, 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.

Molecular Cell



extensive translational regulation among these genes, high-

lighting the prevalence of translational control during cell-cycle

progression. For example, we observed high levels of ribosome

occupancy in G1 and S phase for CCNE2, a cyclin known to be

translationally regulated. On the contrary, our data revealed low

levels of ribosome occupancy of E2F1, also during G1 and S

phase, which is consistent with cell cycle-dependent miRNA-

mediated regulation of E2F1 (Pulikkan et al., 2010). Furthermore,

this analysis identified translational regulation among several

cell-cycle mediators, including PLK1 and BUB1, two mitotic

checkpoint kinases. Notably, RICTOR, the defining subunit of


mTOR complex 2 (mTORC2) (Laplante

and Sabatini, 2012), displayed a signifi-

cant change in the level of ribosome oc-

cupancy, with an almost 3-fold increase

from G1 to S phase and a greater than

13-fold decrease fromS phase tomitosis.

Our findings further show an accumula-

tion of RICTOR protein, but not other

components of mTORC2, that mirrors

this pattern of ribosome occupancy (Fig-

ures 2A, 2C, and S2B), which suggests

that RICTOR protein accumulation is, at

least in part, mediated by translational

control. To further investigate the mecha-

nisms responsible for cell cycle-depen-

dent translational control of RICTOR, we

assessed the activity of its 50 UTR. Strik-ingly, the 50 UTR of the RICTOR mRNA

is sufficient to direct increased protein

expression during S phase, which then

decreases upon entry into mitosis, a

pattern consistent with the cell cycle-

dependent ribosome occupancy we

observed (Figures 2A, 2B, and S2A). We

compared the translation directed by the RICTOR 50 UTR to

the 50 UTR of an mRNA that exhibits a distinct pattern of cell-

cycle phase-specific translation, NUAK2 (Figure 2B). In our

ribosome profiling experiments, NUAK2 showed a significant

increase in ribosome occupancy during mitosis compared to

either G1 or S phase (q values = 9.5 3 10�4 and 1.3 3 10�3,

respectively; Table S2). RICTOR and NUAK2 50 UTRs promote

unique patterns of translation in the luciferase reporter assay,

suggesting that translational regulation of these mRNAs is

specific and the patterns of translation we observe are not due

to global changes in protein synthesis (Figure 2B).

Molecular Cell



These data suggest that regulation of RICTOR mRNA transla-

tion 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 RICTORprotein 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 depen-

dent on RICTOR, since it does not occur in Rictor�/� mouse em-

bryonic 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 sug-

gests that the RICTOR-dependent phosphorylation of AKT at

S473 during S phase is independent from the induction of up-

streamPI3K 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). Phosphoryla-

tion of p27 by AKT inhibits its activity, thereby promoting cell-

cycle 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), associ-

ated with a marked decrease in S phase cells compared to wild-

type (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). Further-

more, 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 path-

ways, 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 cell-

cycle phase-specific manner (Figure 4A). mRNAs from compo-

nents of both the condensin and cohesin complexes exhibit rela-

tively 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 chro-

mosomes 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 molec-

ular 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 transla-

tional 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 con-

trary, the 50 UTR from RANBP1, a control mRNA whose pattern

of ribosome occupancy is distinct from components of the con-

densin 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 theNIPBLmRNA, a member of the cohesin

complex, shifts from the primary initiation codon of the coding

region to an upstream open reading frame (uORF) 59 nucleo-

tides upstream of the primary initiation codon in a highly evolu-

tionarily 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 trans-

lation 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 unex-

pected 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 co-

dons, 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.


A B

C D

G1 S phase

Mitosis

G1S phaseMitosis

Cohesin andCondensin

Metabolism

NuclearTransport

DNARepair

NUP107 NUP205

NUP188 NUP93

NUP133

TPR KPNB1

NUP155

ST6GALNAC6 STS AGPAT1 NEU3 ECHS1 ADM ETNK1 ALG1 CPT1A

G1 S phase

Mitosis

SDHC

PDHA2 IDH3A

PCK1

FH IDH1 ACO2 PDK2 PDK3

G1 S phase

Mitosis

<1e-10

N.S.

Significance (q value)<1e-10

IncreasedDecreased

LipidMetabolism

TCA Cycle

G1 S phase

Mitosis

MSH2

POLA1 MRE11A

RAD50 LIG4

MSH6 MSH3 RECQL ERCC4 UBE2V1

DNA Repair

NuclearTransport

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 rR 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 lipidmetabolism 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.

Molecular Cell



DISCUSSION

Our work delineates the unexpected magnitude and dynamic

nature of translational regulation during the mammalian cell cy-

cle. 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 unex-

pected translational regulation of a key component of the

mTOR pathway, a key regulator of cell growth. RICTOR be-

comes 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


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 com-

plex components, highlighting the specificity in RICTOR 50

UTR translational activation in controlling mTOR signaling during

cell-cycle progression. Elucidating the precisemechanisms gov-

erning 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 core-

gulation 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 specif-

ically translationally activated during S phase. Notably, multiple

orthogonal DNA repair pathways are controlled by translation,

suggesting a critical regulatory mechanism that maintains the

RenillaFirefly5’UTR

STAG1

SMC2

SMC3

SMC4

RANBP1

Test Control

Cohesin complex

-2

-1

0

1

2

3

G1 S phase Mitosis

SMC3

NIPBL

STAG1

WAPAL

Tran

slat

iona

l effi

cien

cy

p = 0.0015

A

-2

-1

0

1

2

3

G1 S phase Mitosis

SMC2

SMC4

Condensin complexTr

ansl

atio

nal e

ffici

ency

p = 0.027

B

C

SMC2 SMC4 SMC3 STAG1 NIPBL RANBP10.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4R

elat

ive

trans

latio

n

G1

S ph

ase

Mito

sis

WAPAL

88,194,980 88,197,970

1.0

0.5

0.0

Conservation

NIPBL

Rib

osom

e-bo

und

Rea

ds

36,954,000

1.0

0.5

0.0

36,953,650

Conservation

AUG

uORF

NIPBL

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




Molecular Cell



fidelity of the genome, adding a robust level of protection in

safeguarding the genome. This may also be true for protein com-

plexes that are responsible for organizing the higher-order struc-

ture of chromosomes, which also show coregulated patterns of

translational control. The primary role of the cohesin and con-

densin 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 disrup-

tion of the cohesin complex impairs ribosomal RNA transcrip-

tion, 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 neurode-

velopmental 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 inNIPBL translation and is associated with a cohe-

sinopathy known as Cornelia de Lange Syndrome, suggesting

that alterations in translational regulation may underlie this hu-

man 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 be-

tween 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 dur-

ing fundamental cellular programs, such as the mammalian cell

cycle. The magnitude of this translational regulation, involving

hundreds ofmRNAs, 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.

EXPERIMENTAL PROCEDURES

Tissue Culture and Cell-Cycle Analysis

HeLa cells andMEFs were cultured under normal growth conditions. Synchro-

nization 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 stain-

ing with 40 mg/ml propidium iodide or with the Click-iT EdU Flow Cytometry

Assay (Life Technologies).

Library Preparation and Sequencing

Next-generation sequencing libraries were prepared as described previously

(Hsieh et al., 2012). Briefly, synchronized cells were treated with cyclohexi-

mide, lysed, and split into pools for isolating total mRNA and ribosome-bound


mRNA. Ribosome-protected mRNA fragments were isolated by centrifuga-

tion. Total mRNA was alkaline fragmented and size selected. Both samples

were processed for small RNA library sequencing. Libraries from two biolog-

ical 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). Transla-

tional 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 simi-

larly 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 ex-

pected 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 fromGencode 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 pGL3-

T7. RNAs were in vitro transcribed using the T7 MEGAscript Kit (Life Technol-

ogies). 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 phos-

pho-T308, total PKCa, and PROTOR-2 (Cell Signaling Technology); PKCa

phospho-S657 (Santa Cruz); PROTOR-1 (GeneTex); SIN1 (Bethyl Labora-

tories); 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 acces-

sion number SRA099816.

SUPPLEMENTAL INFORMATION

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.

ACKNOWLEDGMENTS

We thankMaria Barna andmembers of the Ruggero lab for critical insights dur-

ing 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 & Lym-

phoma Society Scholar.


Molecular Cell



Received: May 14, 2013

Revised: August 5, 2013

Accepted: September 17, 2013

Published: October 10, 2013

REFERENCES

Alessi, D.R., Andjelkovic, M., Caudwell, B., Cron, P., Morrice, N., Cohen, P.,

and Hemmings, B.A. (1996). Mechanism of activation of protein kinase B by in-

sulin and IGF-1. EMBO J. 15, 6541–6551.

Alessi, D.R., James, S.R., Downes, C.P., Holmes, A.B., Gaffney, P.R., Reese,

C.B., and Cohen, P. (1997). Characterization of a 3-phosphoinositide-depen-

dent protein kinase which phosphorylates and activates protein kinase

Balpha. Curr. Biol. 7, 261–269.

Antonin, W., Ellenberg, J., and Dultz, E. (2008). Nuclear pore complex assem-

bly through the cell cycle: regulation and membrane organization. FEBS Lett.

582, 2004–2016.

Borck, G., Zarhrate, M., Cluzeau, C., Bal, E., Bonnefont, J.P., Munnich, A.,

Cormier-Daire, V., and Colleaux, L. (2006). Father-to-daughter transmission

of Cornelia de Lange syndrome caused by amutation in the 50 untranslated re-

gion of the NIPBL Gene. Hum. Mutat. 27, 731–735.

Bose, T., Lee, K.K., Lu, S., Xu, B., Harris, B., Slaughter, B., Unruh, J., Garrett,

A., McDowell, W., Box, A., et al. (2012). Cohesin proteins promote ribosomal

RNA production and protein translation in yeast and human cells. PLoS

Genet. 8, e1002749.

Cho, R.J., Huang, M., Campbell, M.J., Dong, H., Steinmetz, L., Sapinoso, L.,

Hampton, G., Elledge, S.J., Davis, R.W., and Lockhart, D.J. (2001).

Transcriptional regulation and function during the human cell cycle. Nat.

Genet. 27, 48–54.

Fan, H., and Penman, S. (1970). Regulation of protein synthesis in mammalian

cells. II. Inhibition of protein synthesis at the level of initiation during mitosis.

J. Mol. Biol. 50, 655–670.

Gandhi, R., Gillespie, P.J., and Hirano, T. (2006). Human Wapl is a cohesin-

binding protein that promotes sister-chromatid resolution in mitotic prophase.

Curr. Biol. 16, 2406–2417.

Gruber, A.R., Lorenz, R., Bernhart, S.H., Neubock, R., and Hofacker, I.L.

(2008). The Vienna RNA websuite. Nucleic Acids Res. 36(Web Server issue),

W70–W74.

Hirano, T. (2012). Condensins: universal organizers of chromosomes with

diverse functions. Genes Dev. 26, 1659–1678.

Hsieh, A.C., Liu, Y., Edlind, M.P., Ingolia, N.T., Janes, M.R., Sher, A., Shi, E.Y.,

Stumpf, C.R., Christensen, C., Bonham, M.J., et al. (2012). The translational

landscape of mTOR signalling steers cancer initiation and metastasis.

Nature 485, 55–61.

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.

Jackman, J., and O’Connor, P.M. (2001). Methods for synchronizing cells at

specific stages of the cell cycle. Curr. Protoc. Cell Biol. Chapter 8, 3.

Kim,W., Bennett, E.J., Huttlin, E.L., Guo, A., Li, J., Possemato, A., Sowa, M.E.,

Rad, R., Rush, J., Comb, M.J., et al. (2011). Systematic and quantitative

assessment of the ubiquitin-modified proteome. Mol. Cell 44, 325–340.

Konrad, C.G. (1963). Protein Synthesis and Rna Synthesis during Mitosis in

Animal Cells. J. Cell Biol. 19, 267–277.

Kueng, S., Hegemann, B., Peters, B.H., Lipp, J.J., Schleiffer, A., Mechtler, K.,

and Peters, J.M. (2006). Wapl controls the dynamic association of cohesin with

chromatin. Cell 127, 955–967.

Lai, M.C., Chang,W.C., Shieh, S.Y., and Tarn,W.Y. (2010). DDX3 regulates cell

growth through translational control of cyclin E1. Mol. Cell. Biol. 30, 5444–

5453.

Laplante, M., and Sabatini, D.M. (2012). mTOR signaling in growth control and

disease. Cell 149, 274–293.

Merbl, Y., Refour, P., Patel, H., Springer, M., and Kirschner, M.W. (2013).

Profiling of ubiquitin-like modifications reveals features of mitotic control.

Cell 152, 1160–1172.

Olshen, A.B., 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. Published online September 18, 2013. http://dx.doi.org/10.

1093/bioinformatics/btt533.

Peters, J.M. (2006). The anaphase promoting complex/cyclosome: a machine

designed to destroy. Nat. Rev. Mol. Cell Biol. 7, 644–656.

Pulikkan, J.A., Dengler, V., Peramangalam, P.S., Peer Zada, A.A., Muller-

Tidow, C., Bohlander, S.K., Tenen, D.G., and Behre, G. (2010). Cell-cycle regu-

lator E2F1 and microRNA-223 comprise an autoregulatory negative feedback

loop in acute myeloid leukemia. Blood 115, 1768–1778.

Qin, X., and Sarnow, P. (2004). Preferential translation of internal ribosome en-

try site-containing mRNAs during the mitotic cycle in mammalian cells. J. Biol.

Chem. 279, 13721–13728.

Shin, I., Yakes, F.M., Rojo, F., Shin, N.Y., Bakin, A.V., Baselga, J., and Arteaga,

C.L. (2002). PKB/Akt mediates cell-cycle progression by phosphorylation of

p27(Kip1) at threonine 157 and modulation of its cellular localization. Nat.

Med. 8, 1145–1152.

Shiota, C., Woo, J.T., Lindner, J., Shelton, K.D., and Magnuson, M.A. (2006).

Multiallelic disruption of the rictor gene in mice reveals that mTOR complex

2 is essential for fetal growth and viability. Dev. Cell 11, 583–589.

Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L.,

Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., and

Mesirov, J.P. (2005). Gene set enrichment analysis: a knowledge-based

approach for interpreting genome-wide expression profiles. Proc. Natl.

Acad. Sci. USA 102, 15545–15550.

Whitfield,M.L., Sherlock, G., Saldanha, A.J., Murray, J.I., Ball, C.A., Alexander,

K.E., Matese, J.C., Perou, C.M., Hurt, M.M., Brown, P.O., and Botstein, D.

(2002). Identification of genes periodically expressed in the human cell cycle

and their expression in tumors. Mol. Biol. Cell 13, 1977–2000.

Wood, A.J., Severson, A.F., and Meyer, B.J. (2010). Condensin and cohesin

complexity: the expanding repertoire of functions. Nat. Rev. Genet. 11,

391–404.


http://dx.doi.org/10.1093/bioinformatics/btt533

http://dx.doi.org/10.1093/bioinformatics/btt533

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

p<0.001

p<0.0001

**

***

G1

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S-phase

1

10

100

1000

5' U

TR

length

(bp)

10000**

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Ba

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round

Ba

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round

Hig

h

Low

Hig

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Low

C

A

0 200 400 600 800 1000

PI intensity

0

20

40

60

80

100

% o

f M

ax

G1 82%S-phase 89%Mitosis 73%

-1.75 1.75

0

Genes (

n=

1255)

P51$Translation

G1 S M G1 S M

B

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. 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

Figure S2

A

B

RICTOR 5’ UTR sequence:

GUUUCCGGUGUUGUGACUGAAACCCGUCAAUAUG

C

D

G1

S-p

hase

Mito

sis

G1

S-p

hase

Mito

sis

p-AKT S473

total AKT

RICTOR: + + +- - -

Tubulin

WT

rictor-/-

0 20 40 60 80 100

p-value = 0.0006

Percent of cells

G1 S-phase Mitosis

SIN1

PROTOR-2

PROTOR-1

Tubulin

G1

S-p

hase

Mito

sis

total AKT

p-AKT T308

p-p27Kip1

total p27Kip1

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. 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.

NIPBL 5’UTR sequence:

GGGCCCGCGGCGAUGCCCCCCCGGUAGCUCGGGCCCGUGGUCGGGUGUUUGUGAGUGU

UUCUAUGUGGGAGAAGGAGGAGGAGGAGGAAGAAGAAGCAACGAUUUGUCUUCUCGGCUG

GUCUCCCCCCGGCUCUACAUGUUCCCCGCACUGAGGAGACGGAAGAGGAGCCGUAGCCAC

CCCCCCUCCCGGCCCGGAUUAUAGUCUCUCGCCACAGCGGCCUCGGCCUCCCCUUGGAUU

CAGACGCCGAUUCGCCCAGUGUUUGGGAAAUGGGAAGUAAUGACAGCUGGCACCUGAACU

AAGUACUUUUAUAGGCAACACCAUUCCAGAAAUUCAGGAUG...

Figure S3

A

WAPAL 3’UTR sequence:

CTGCTTTACCTTTGCTTCAGGTGCTCGGTAATGCTGGAGCTATCCTTAGACAAAGAAAAGTCAAGTC

ATGAAAGAAGTCCTTGAAGATATACCAAGAACATTCATCAGTATCATTCGTGTTTGGATTTTTAA...

Putative amino acid sequence of C-terminal extension:

LLYLCFRCSVMLELSLDKEKSSHERSP*RYTKNIHQYHSCLDR*

B

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.

Documents

The Translational Landscape of the Mammalian Cell Cycle€¦ · activity of key molecular machinery in the cell. Among these translationally controlled networks are mRNAs required