landscapes of ribosome dwell times and relationship with ... · 1 landscapes of ribosome dwell...

Landscapes of ribosome dwell times and relationship with1

aminoacyl-tRNA levels in mammals2

3

Cédric Gobet 1,2, Benjamin Weger1,2, Julien Marquis 2,3, Eva Martin2, Frédéric Gachon *1,2,4, Felix Naef*14

1 Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015,5

Switzerland6

2 Nestlé Research, CH-1015 Lausanne, Switzerland7

3 Current address: Lausanne Genomic Technologies Facility, Université de Lausanne, Lausanne CH-1015, Switzerland8

4 Current address: Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072,9

Australia10

? Corresponding authors11

Abstract12

Protein translation depends on mRNA-specific initiation, elongation and termination13

rates. While ribosome elongation is well studied in bacteria and yeast, less is known in14

higher eukaryotes. Here, we combined ribosome and tRNA profiling to investigate the re-15

lations between ribosome elongation rates, (aminoacyl-) tRNA levels, and codon usage in16

mammals. We modeled codon-specific ribosome dwell times and translation efficiencies from17

ribosome profiling, considering codon-pair interactions between ribosome sites. In mouse18

liver, the model revealed site and codon specific dwell times, as well as codon-pair inter-19

actions clustering by amino acids. While translation efficiencies varied significantly across20

diurnal time and feeding regimen, codon dwell times were highly stable and conserved in21

human. Profiling of tRNA levels correlated with codon usage and several tRNAs were lowly22

aminoacylated, which was conserved in fasted mice. Finally, codons with lowly aminoacy-23

lated tRNAs and high codon usage relative to tRNA abundance exhibited long dwell times.24

Together, these analyses started to reveal complex dependencies between ribosome dwell25

times, tRNA loading, and codon usage in mammals.26

1

.CC-BY-NC-ND 4.0 International licenseavailable under anot certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

The copyright holder for this preprint (which wasthis version posted July 29, 2019. ; https://doi.org/10.1101/551838doi: bioRxiv preprint

https://doi.org/10.1101/551838

http://creativecommons.org/licenses/by-nc-nd/4.0/

Introduction27

Translation dynamically controls gene expression in processes such as development, the cell28

cycle, circadian rhythms, and response to stress [1]. At least three steps underlie protein trans-29

lation: translation initiation, often thought to be rate limiting, elongation, and termination [2].30

Recently, however, elongation has emerged as an important layer to fine-tune gene expression31

(reviewed in [3]). Indeed, variations in elongation rates may influence gene expression [4, 5,32

6, 7, 8] and recent studies showed that alteration of ribosome elongation rates in cancer cells33

influences their proliferation and invasion capabilities [9, 10, 11].34

While the links between translation elongation and gene expression are increasingly studied,35

the determinants of ribosome elongation rates are poorly understood, notably in higher eukary-36

otes. In unicellular organisms, ribosome elongation rates are well explained by tRNA copies and37

expression [12]. This is also reflected evolutionarily, since highly expressed genes are enriched38

for fast codons with high concentrations of tRNAs [13]. Pioneering work in E.coli showed that39

ribosomes elongation rates are different for the codons GAA and GAG [14], decoded by the40

same tRNA. This raises the possibility that elongation rate is not only determined by the con-41

centration of tRNAs, but that codon-anticodon interactions as well as codon context may play42

important roles.43

More recently, the development of ribosome profiling (RP) shed new light on the regulation of44

translation [15], including recently in human tissues [16]. Notably, the possibility to capture the45

positions of translating ribosomes on mRNAs [17] fostered the development of quantitative mod-46

els providing genome-wide insights on key features regulating ribosome elongation rate [18, 19,47

20, 21]. For instance, the properties of amino acids [22], (aminoacyl-) tRNA availability [23, 24,48

25], tRNA modifications [26, 27, 28], secondary structures of mRNAs [29, 30, 31], folding of the49

nascent chain [32], pairs of codons [33, 34], and sterical interactions with the ribosome exit tun-50

nel [35], were shown to influence the local density of ribosomes on transcripts. While RP studies51

have brought new knowledge on translation elongation, these were performed mostly in unicel-52

2



https://doi.org/10.1101/551838


lular organisms and have led to divergent results as highlighted in several meta-analyses [19,53

36]. One reason is that ribosome footprints are sensitive to biases from differences in protocols54

[37, 38, 39, 40, 41], library preparations [21], and data analysis pipelines [42]. Consequently, the55

reported correlations between elongation rates, tRNA abundances, and codon usage frequency56

and bias [43], show inconsistencies. In addition, while codon usage can be precisely estimated,57

it remains difficult to measure tRNA concentrations. Indeed, tRNAs exhibit a high degree of58

modifications and complex secondary structures, which alter cDNA synthesis and biases quan-59

tification by high-throughput sequencing [44]. Thus, improved methods have been proposed to60

quantify tRNAs [45, 46, 47, 9], as well as tRNA aminoacylation levels [48].61

Here, to better establish the determinants of ribosome elongation rate in higher eukaryotes, we62

combined modeling of ribosome profiling data, codon usage analysis, and (aminoacyl-) tRNA63

profiling in mouse liver. In particular, we built a genome-wide statistical model that allowed64

us to identify key features predicting ribosome densities along transcripts, notably the contri-65

butions to the elongation rates of single codons, as well as pairs of codons within and near the66

ribosome E, P and A sites. In yeast, our analysis was able to identify novel features of previously67

reported pairs of adjacent codons that slow down ribosome elongation. In mouse liver, we found68

a wider dynamic range of codon- and amino acid-specific ribosome dwell times (DTs, defined69

as the inverse of the elongation rates, Methods). In addition to single-codon contributions to70

DTs, we identified codon pairs contributing synergistically to the DTs, and notably involving71

codons on the ribosome P and A sites. In mouse liver, the identified contributions of single-72

codon and codon-pair DTs were remarkably stable along the feeding/fasting cycle, and even73

under conditions of prolonged fasting. Moreover, meta-analysis in mammals revealed conserved74

DTs between mouse tissues and human, which were however significantly distinct from those in75

yeast. Finally, we extended a recent tRNA profiling method [9] to quantify (aminoacyl-) tRNA76

levels in liver of ad libitum (AL) fed and fasted mice (FA). tRNA levels correlated with codon77

usage and several tRNAs were lowly aminoacylated, which was conserved in fasted mice. These78

3



https://doi.org/10.1101/551838


data, together with codon usage properties, allowed us to explain some codon-specificity in the79

estimated ribosome DTs.80

Results81

Modeling codon-specific DTs including single- and codon-pair contributions82

Ribosome profiling read counts along transcripts typically show large variations with high and83

low densities of ribosomes. To estimate ribosome DTs in higher eukaryotes from those data, we84

explicitly fit RP read counts on a genome-wide scale, extending previous models [18, 20]. We85

are interested in steady-state translation, and additionally assumed no ribosome drop-off and86

low density of ribosomes per transcript (no traffic jams), such that the probability of finding87

a ribosome at a specific position on an mRNA is proportional to a position-independent gene88

translation flux times a position-dependent ribosome DT. To investigate how the codon context89

determines the DTs, we assumed that DTs depend on the codons translated in the E, P, and90

A sites, as well as surrounding sequences (Fig.S1A). In particular, we modeled DTs additively91

in log-space using single-codon contributions as well as synergistic (non-additive) contributions92

from pairs of codons in the E and P (noted E:P), P and A (P:A) , or E and A (E:A) sites93

(Fig.S1A-B). The additive log-scale model for the DTs is equivalent to modeling the elongation94

rates at any given position with an Arrhenius rate equation, in which the activation energy has95

both single-codon contributions, as well as contributions from pairs of codons. Note that an96

inherent property of such approaches is that the DTs cannot be estimated in absolute units, but97

only relative to each other within each site (shown in log2 in the figure, Methods). Solving this98

model with appropriate noise distributions for RP count data can be conveniently implemented99

as a generalized linear model (GLM), which models the expected read counts as gene specific100

fluxes (gene covariates) multiplied by ribosome DTs (codon covariates) (Methods). The GLM101

uses the 61 sense codon alphabet, and considered positions around the ribosome spanning 40102

4



https://doi.org/10.1101/551838


codons around the E site (Fig.S1A-B). The same model is also applied on RNA-seq experiments103

(when available) to normalize the fluxes per mRNA and attenuate possible technical biases104

affecting the ribosome DTs. While previous models can predict ribosome footprint densities105

[19, 36, 30, 20, 21], our approach stands out as it allows, in addition, to determine globally106

translation elongation parameters (single-codon and codon-pair DTs, and gene specific fluxes)107

directly from the raw reads counts at every position on the coding sequences (CDSs), using108

negative binomial noise model (Fig.S1C-E) (Methods).109

In yeast ribosome DTs anti-correlate with codon usage and show codon-pair110

interactions111

To validate our model, we analyzed two published ribosome profiling datasets in Saccharomyces112

cerevisiae [25, 49], one under normal (WT) [49] conditions and one treated with 3-amino-1,2,4-113

triazol (3-AT), which inhibits the histidine (His) biosynthesis pathway [25] thereby reducing114

loaded His-tRNAs. Both datasets used cycloheximide (CHX) only in the lysis buffer. Our115

model reproduced raw RP read counts along the transcripts with similar accuracy as previous116

methods [19, 36, 30, 20, 21] in both WT and 3-AT conditions (Fig. S2A-B).117

Interestingly, the estimated relative dwell times (DTs) contributions for most codons in WT118

exhibited a two-fold range at the three E, P, and A sites (Fig. 1A-B, left), with some marked119

slower outliers. For instance, one codon (CCG) for proline (Pro) was markedly slow in the A120

and P sites, as expected due to its inefficient peptide bond formation (Discussion). Note that121

although the other three Pro codons are not as extreme, they are still slower than the average122

codon. Then, arginine (Arg) showed long DTs in all three sites. In fact, some Arg codons also123

showed slightly longer DTs in the upstream sequence, highlighting possible interactions of this124

positively charged amino acid with the ribosome exit tunnel (Fig. S2C). On the other hand,125

codons for isoleucine (Ile), leucine (Leu), and valine (Val) were the fastest in the A site (Fig.126

1A-B, left).127

5



https://doi.org/10.1101/551838


As control, we found that the shortage of His in the 3-AT condition resulted in lengthened DTs128

in the P and A sites for both His codons (CAC and CAT) (Fig. 1A-B, right). Interestingly,129

outside the E, P, and A sites, DTs showed a dependency on His codons at around 30 nucleotides130

(positions 11 and 12) downstream of the P-site (Fig. S2C), reflecting queued ribosomes (disomes)131

behind His codons[25]. Moreover, the DTs also displayed signatures of technical biases. Notably,132

the high variation in DTs at position -4, coinciding with the most 5’ nucleotide of the insert,133

was previously shown to reflect biases in library preparation (Fig. S2C) (reviewed in [50]).134

To further validate the biological relevance of our DTs, we compared ribosome DTs in WT135

condition with codon usage weighted by mRNA translation levels, to take into account condition-136

specific demands in codons. Interestingly, we found high negative correlations (R2 = 0.565137

and R2 = 0.495) between the codon usage and the DTs at the P and A sites (Fig. 1C). This138

observation suggests an evolutionary pressure to enrich for fast codons in highly expressed genes,139

and conversely.140

In addition to the single-codon DTs, we probed whether pairs of codons in the ribosome sites141

synergize by analyzing the interaction terms (E:P, P:A, and E:A) (Fig. S2D). We compared142

these predicted DTs with a GFP-reporter experiment in yeast probing for pairs of codons that143

inhibit translation [34]. Indeed, the experimentally determined inhibitory pairs exhibited long144

predicted DTs at the P and A, or E and P ribosome sites (Fig. 1D-E). While for these pairs145

the single-codon DTs were already long, the synergistic interaction terms clearly prolonged the146

DTs of the experimental inhibitory pairs (Fig. 1D-E). Interestingly, though the E:P and P:A147

interactions were not correlated overall, the inhibitory pairs stood out as showing large DT148

contributions in both the E:P and P:A (Fig. 1F). Globally the P:A interaction matrix was149

sparse and not highly structured, but revealed large values and spread for the pairs involving150

codons for Arg or Pro (Fig S2D). Moreover, the net DT contributions for the 612 codon pairs,151

computed by summing the effects (in logs) of single-codon and codon pair interactions, revealed152

Arg in most of the top 50 slowest pairs, making this amino acid potent at decreasing translation153

6



https://doi.org/10.1101/551838


elongation rate. Conversely, Val was contained in most of the fastest pairs.154

Thus, modeling RP data can identify subtle properties of ribosome DTs, such as codon-specific155

and codon-pairs contributions, signatures of sequences outside of the E, P, and A sites, and156

library biases.157

Single-codon and codon-pair DTs in mouse liver cluster by amino acids158

Determinants of translation elongation are less studied in mammals. We and others have previ-159

ously shown that feeding/fasting cycles can regulate translation initiation in mouse liver [51], via160

well described mechanisms, notably through mTOR and GCN2 related nutrient sensor pathways161

(reviewed in [52]). Here, we aimed to extend this analysis to the level of DTs, in particular to162

asses whether perturbed amino acid pools during low nutrient availability can alter DTs. There-163

fore, we applied the above model to our previously 84 ribosome profiling samples harvested in164

WT and circadian clock deficient mice (Bmal1 KO) every 2-4 h around the 24 h day, including165

four biological replicates. As for yeast, our model faithfully captured raw RP read counts along166

transcripts, as reflected by a previously used correlation metric (Fig. S3A-B). Remarkably, DTs167

were very stable across all samples, showing high biological reproducibility and no time or circa-168

dian clock-dependent changes (Fig. S3C-E). Therefore, for the following analyses, we averaged169

the DTs over all the 84 samples.170

Patterns of DTs for the E, P, and A sites showed biologically significant codon and amino acid-171

specificity (Fig.S3C). DTs were strikingly different than in yeast and exhibited a larger dynamic172

range (Fig. 2A-B). In particular, the P and A sites revealed nearly 10-fold range in DTs be-173

tween the fastest and slowest codons, while DTs in the E site were more tightly distributed174

(Fig. 2A-B), presumably reflecting that the DTs are primarily sensitive to aminoacyl-tRNA175

availability (A site) and peptide bond formation (P-A sites). While DTs in the P and A site176

were overall more strongly correlated to each other than with the E site, DTs also showed clear177

site-specificity (Fig. 2A-B). For instance, the four codons for Gly had long DTs in the P site,178

7



https://doi.org/10.1101/551838


E P A E P A

WT 3-ATAA:GCAA:GCCA:GCGA:GCTR:AGAR:AGGR:CGAR:CGCR:CGGR:CGTN:AACN:AATD:GACD:GATC:TGCC:TGTQ:CAAQ:CAGE:GAAE:GAGG:GGAG:GGCG:GGGG:GGTH:CACH:CATI:ATAI:ATCI:ATTL:CTAL:CTCL:CTGL:CTTL:TTAL:TTGK:AAAK:AAGM:ATGF:TTCF:TTTP:CCAP:CCCP:CCGP:CCTS:AGCS:AGTS:TCAS:TCCS:TCGS:TCTT:ACAT:ACCT:ACGT:ACTW:TGGY:TACY:TATV:GTAV:GTCV:GTGV:GTT

E P A E P A

B WT

C R²=0.148 R²=0.495 R²=0.565

dwel

l tim

es (

log2

)

dwel

l tim

es (

log2

)

dwel

l tim

es (

log2

)

3-AT

E P A

codon usage (log2)D

dwel

l tim

es (

log2

), P

+ A

+P

:A

dwell times (log2), P+A dwel

l tim

es (

log2

), E

+ P

+ E

:P

dwell times (log2), E+P dwell times (log2), P:A

dwel

l tim

es (

log2

), E

:P

codon usage (log2)

codon usage (log2)

dwel

l tim

es (

log2

)

dwel

l tim

es (

log2

)

E F-3 -1 1 3-1 10

R

R

RRP

P

H

HH

H

Figure 1: In yeast, ribosome DT anticorrelate with codon usage and show codon-pair interactionsPanels for the single-codon DTs are retrieved from the fit with the P:A interaction. A. DTs (log2, mean centeredper site) for the 61 sense codons in the two conditions (WT/left, 3-AT/right) for the E, P, and A sites. Codons arecolored according to amino acids. DTs with p >= 0.05 are not shown. Relatively fast and slow interactions areshown respectively in dark red and dark blue. B. Heatmap representation of panel (A). Here, DTs with p >= 0.05are set to zero. C. Codon usage weighted by mRNA translation levels correlates with the codon DTs for theE, P, and A sites. Black line: linear fit. D. DTs (log2) for codon-pairs. Total codon-pair DTs (P+A+P:A, i.e.including the interactions P:A) versus the contributions from the single-codons (P+A). Red: Pairs described asinhibitory in [34]. E. DTs (log2) for codon pairs. Total codon-pair DTs (E+P+E:P, i.e. including the interactionsE:P) versus the contributions from the single-codons (E+P). Red: Pairs described as inhibitory in [34]. F. P:A(log2) versus E:P (log2) interactions for all codon-pairs.

8



https://doi.org/10.1101/551838


however, the Gly GGT codon was among the fastest in the E and A sites, while the GGA codon179

was markedly slow in the A site. Strikingly, all three Ile codons had long DTs in the A site180

but very short DTs in the P site (Fig. 2A-B). For the negatively charged glutamate (Glu) and181

aspartate (Asp), all their codons showed long DTs in the P and A sites (Fig. 2A-B). Considering182

a larger window around the ribosome revealed that P and A sites, followed by the E site, showed183

the largest contributions to DTs (Fig. S4A). Upstream and downstream sequences outside the184

(-4,+6) interval did not contribute (Fig. S4A), while codons in the vicinity of the ribosome (-3,185

-2, -1 and +3, +4, +5) exhibited significant variations in DTs, and were correlated between both186

sides. The detected signals at the -4 and +6 positions reflect known ligation biases during the187

library preparation [50].188

Codon-pair DTs revealed a significant influence on translation elongation in mouse liver, with189

P:A interactions showing the widest dynamic range, followed by E:P and E:A (Fig. S4C). Note190

that the interaction matrices are not symmetric, showing codons or amino acid specificity at the191

respective ribosome sites (Fig. 2C and S4B). Intriguingly, the P:A matrix highlighted a striking192

clustering by amino acid for the A site (Fig. 2C), while E:P interactions clustered by amino193

acid in the P site (Fig. S4B). This suggests that P:A codon-pair DTs are determined by amino194

acids through their influence on the peptide bond formation. The clustering by amino acid was195

corroborated by a model selection analysis on the 84 samples, where the alphabet for the DT196

regression coefficients was taken as either the 20 natural amino acids, or the 61 sense codons197

(Fig. S4D). While the preferred alphabet was overall that of the codons, the model with amino198

acid coefficients at the A site for the P:A interaction was preferred to all the other models (Fig.199

S4D). In the case of the E:P interaction, the amino acid alphabet in the P site was considered200

as the best model. Overall, models including the site interactions were preferred to the reduced201

models, emphasizing the importance of codon-pair interactions in determining ribosome DTs in202

mouse liver. The P:A matrix revealed strong positive interactions (ones lengthening the DTs)203

for pairs of bulky (Pro, tryptophan (Trp) and phenylalanine (Phe)) or achiral (Gly) (Fig. 2C204

9



https://doi.org/10.1101/551838


and D) amino acids. As in yeast, Arg codons were frequent among large P:A contributions (Fig.205

2C and D). Surprisingly, the known stalling pair Pro-Pro showed the largest negative interaction206

(Fig. 2C and D), possibly related to eIF5A activity which is known to facilitate otherwise slow207

peptide bond formation for such pairs [53]. Overall, these interactions contributed to the total208

codon-pair DTs for the P and A site by a factor larger than 2 for about one hundred pairs (Fig.209

2D). Summing both single-codon and codon-pair contributions showed that the amino acid pair210

isoleucine-glycine was represented by multiple combinations of codons in the top ten overall211

slowest pairs (Fig. 2D). Similarly the leucine-leucine pair was frequent among the fastest codon212

pairs (Fig. 2D). The E:P matrix was more complex: pairs involving the amino acids Gly, Asp,213

Asparagine (Asn), and Pro in the P site lengthened the total codon-pair DTs (Fig. S4B).214

Unlike in yeast (Fig. 1C), ribosome DTs did not correlate with codon usage in mouse liver (Fig.215

2E), arguing for different evolutionary pressure on translation efficiency.216

Ribosome DT patterns in liver are stable under prolonged fasting217

The above analysis showed highly robust DTs between liver samples collected during the nor-218

mal feeding (night) / fasting (day) cycle (Figure S3C-E). To probe whether ribosome DTs are219

sensitive to longer periods of fasting, we performed new ribosome profiling experiments in mice220

fed either ad libitum (AL) or fasted (FA) for up to 30 hours (Fig. 3A). Since the enrichment221

in ribosome footprints can be sensitive to RP protocols [54], we here used a small RNA-Seq222

protocol with random adapters (UMI) to reduce possible ligation biases and PCR duplicates.223

Moreover, as ribosome dynamics and DTs are affected in ribosome profiling experiments with224

cycloheximide (CHX) in yeast [39, 55], we tested conditions without CHX in the lysis buffer.225

First, we validated the effect of prolonged fasting by analyzing differential ribosome profiling226

signals between AL and FA. Genes related to the Peroxisome Proliferator-Activated Receptor α227

(PPARα) pathway and to fatty acids oxidation were upregulated in FA, presumably to provide228

the energy needs (Fig. 3D). On the contrary, genes related to lipid biosynthesis were downregu-229

10



https://doi.org/10.1101/551838


A B

C

A

E P A

dwel

l tim

es (

log2

)

B

C

DV:GTA

V:GTG

L:CTG

V:GTC

V:GTT

E:GAG

E:GAA

G:GGC

G:GGA

G:GGG

G:GGT

D:GAC

D:GAT

S:AGT

S:AGC

P:CCT

P:CCC

P:CCG

P:CCA

I:ATT

I:ATC

I:ATA

Y:TAC

Y:TAT

N:AAC

N:AAT

T:ACA

T:ACT

T:ACC

H:CAT

H:CAC

L:CTC

R:CGC

L:CTT

F:TTT

F:TTC

K:AAA

W:TGG

C:TGT

C:TGC

A:GCG

A:GCA

A:GCC

A:GCT

M:ATG

T:ACG

Q:CAA

Q:CAG

L:CTA

L:TTG

L:TTA

S:TCA

S:TCT

S:TCC

S:TCG

K:AAG

R:AGA

R:AGG

R:CGT

R:CGA

R:CGG

G:GGAG:GGCG:GGTG:GGGE:GAAW:TGGQ:CAGE:GAGP:CCGP:CCCP:CCTC:TGTL:CTGK:AAGV:GTGM:ATGL:TTGR:CGGI:ATTT:ACTI:ATCL:CTCF:TTTF:TTCY:TATP:CCAR:CGAS:TCTR:CGTA:GCAT:ACCA:GCCK:AAAI:ATAL:CTAL:CTTV:GTCA:GCTS:TCCV:GTAR:CGCY:TACH:CACN:AACR:AGAR:AGGN:AATD:GATD:GACT:ACAQ:CAAT:ACGS:AGTA:GCGS:AGCV:GTTS:TCGC:TGCS:TCAL:TTAH:CAT

−1

0

1

E P AA:GCAA:GCCA:GCGA:GCTR:AGAR:AGGR:CGAR:CGCR:CGGR:CGTN:AACN:AATD:GACD:GATC:TGCC:TGTQ:CAAQ:CAGE:GAAE:GAGG:GGAG:GGCG:GGGG:GGTH:CACH:CATI:ATAI:ATCI:ATTL:CTAL:CTCL:CTGL:CTTL:TTAL:TTGK:AAAK:AAGM:ATGF:TTCF:TTTP:CCAP:CCCP:CCGP:CCTS:AGCS:AGTS:TCAS:TCCS:TCGS:TCTT:ACAT:ACCT:ACGT:ACTW:TGGY:TACY:TATV:GTAV:GTCV:GTGV:GTT

clustered

−2

−1

0

1

2

GE

GEGI

GI

GIGR

RDEP

LLSF SF

LLLLLCSFSC

LR

P

AR²=0.015

dwel

l tim

es (

log2

)

R²=0.032

dwel

l tim

es (

log2

)

codon usage (log2)

R²=0.005

dwel

l tim

es (

log2

)

E

P

A

E

G

GGG

ED

E

EG

DD

DI

Figure 2: Single-codon and codon-pair DTs in mouse liver cluster by amino acidSingle-codon DTs are retrieved from the fit with the P:A interaction. A. DTs (log2, mean centered per site) for theE, P, and A sites averaged over the 84 samples in mouse liver. Codons are colored according to amino acids. DTswith p >= 0.05 are set to zero. Fast and slow DTs (relative to the mean) are shown in dark red and dark blue,respectively. B. Heatmap representation of panel A). DTs with p >= 0.05 are set to zero. C. Interaction matrixfor the pairs P:A (log2). Codons are colored according to amino acids. Codons in both sites are hierarchicallyclustered (euclidean distance matrix, complete linkage algorithm). Fast and slow interactions are shown in darkred and dark blue, respectively (colorbar). D. DTs (log2) for codon pairs. Total codon-pair DTs (P+A+P:A,i.e. including the interactions P:A ) vs. the contributions from the single-codons (P+A). Pairs with interactions> 1.1 or < −1.1 are annotated and colored, respectively, in blue and red. Top 10 slowest and fastest pairs arecolored in green if not already depicted. E. Codon usage weighted by mRNA translation levels does not correlatewith codon DTs for the E, P, and A sites. Black line: linear fit.

11



https://doi.org/10.1101/551838


lated in FA (Fig. 3D), suggesting that animals switched from glucose to fatty acid metabolism in230

FA, as already described [56]. Moreover, Mat1a, Asl and Got1 related to amino acid biosynthesis231

were upregulated in FA (Fig. 3D), presumably in response to perturbed amino acid homeostasis.232

The ribosome profiling data in the new conditions showed a typical tri-repeat nucleotide pattern233

(Fig. 3B), confirming the presence of bona fide translating ribosomes in the FA samples, as well234

as in samples without CHX (NOCHX). In addition, the modified library preparation improved235

the goodness of the fits. Indeed, correlation coefficients introduced above (Fig. S3A-B) were236

now improved (Fig. S5A-B).237

Surprisingly, single-codon DTs in the AL and FA conditions showed no difference in both the238

CHX or NOCHX conditions (Figs. 3C and S5C), and codon-pair DTs very also very similar239

(Fig. S5D). Moreover, these DTs were highly correlated with those described above for the 84240

samples (Fig. S6C-D). Nevertheless, the FA samples showed a reduced dynamic range in the241

DTs, presumably due to variability in ribosome profiling signal quality (Fig. S5A) related to242

the global decrease in translation levels.243

Finally, we probed whether the perturbed metabolic state in FA might lead to differential codon244

usage [24]. Strikingly, when considering the codon usage bias in WT and FA animals, we found245

that most of the codons with a G or C nucleotide at the third position (GC3) were enriched in246

up-regulated transcripts in FA, while codons with an A or T nucleotide were underrepresented247

(Fig. S5E).248

Meta-analysis reveals conserved ribosome DTs in mammals249

To further compare the estimated ribosome DTs, we analyzed published ribosome profiling250

datasets in mouse liver (H: Howard et al.) [57], mouse kidney (CS: Castelo-Szekely et al.) [58],251

and in the human liver cell line Huh7 (L: Lintner et al.) [59]. Despite differences in library252

preparation protocols (Methods), single-codon DTs at the A site were highly correlated between253

the mammalian datasets (0.48 < r < 0.96), including in different tissues (kidney) and human254

12



https://doi.org/10.1101/551838


cells (Fig. S6C, E). However, the mammalian DTs were markedly different from those in yeast255

[19]. Note that the relative contribution to the DTs from the E, P, and A sites vs. that from256

surrounding sites, in particular at positions -4 and +6, differed significantly depending on the257

protocols (Fig. S6A-B). Similar potential biases have been reported previously [54]. Here, we258

found that protocols without cDNA circularization showed highest signals in the P and A sites,259

presumably reflecting ribosome dynamics more faithfully (Fig. S6A-B). Moreover, this was also260

reflected in the codon-pair DTs (P:A), which were more consistent across experiments with that261

protocol (Fig. S6D).262

Together, this meta-analysis highlighted how different library preparations lead to damped RP263

signals in the A and P sites for some protocols, and showed that the codon DT patterns are264

conserved between mouse tissues and mammalian species.265

(Aminoacyl-) tRNA profiles are conserved in fed and fasted mice266

We next asked whether the estimated DTs can be linked with tRNA abundances or loading lev-267

els, which is poorly studied in higher eukaryotes [43]. The chemical modifications and secondary268

structure of tRNAs render them difficult to quantify. A recent hybridization method combined269

with sequencing, which controls specificity using left/right probes and a stringent ligation step,270

allows to bypass the cDNA synthesis to quantify tRNA levels [9]. To measure tRNA abun-271

dances and assess possible links with ribosome DTs in mouse liver, we adapted and optimized272

this method to target all annotated mouse tRNAs (Fig. S7A). Moreover, we quantified the273

fraction of (aminoacyl-) tRNAs using sodium periodate [60], which depletes unloaded tRNAs by274

selective biotinylation of 3’-ends (Fig. S7A). This way, we aimed to quantify the tRNA pools275

available for elongation in the ribosome A site.276

tRNA molecules are encoded by a large number of genes. Therefore, we designed 303 DNA277

probe pairs (left and right) to target all mouse tRNA sequences from the GtRNAdb database278

[61] (Fig. S7A). Our modified protocol yielded a high proportion of specific ligations between279

13



https://doi.org/10.1101/551838


A

AL

FA

4

12

18

4

12

18

−50 −25 0 25 50

0.00

0.02

0.04

0.06

0.00

0.02

0.04

0.06

STARTCODON

−50 −25 0 25 50

0.00

0.02

0.04

0.06

0.00

0.02

0.04

0.06

STOP

CODONno

rmal

ized

fra

ctio

n of

rea

ds

(UM

I, lo

g2)

B

CA:GCAA:GCCA:GCGA:GCTR:AGAR:AGGR:CGAR:CGCR:CGGR:CGTN:AACN:AATD:GACD:GATC:TGCC:TGTQ:CAAQ:CAGE:GAAE:GAGG:GGAG:GGCG:GGGG:GGTH:CACH:CATI:ATAI:ATCI:ATTL:CTAL:CTCL:CTGL:CTTL:TTAL:TTGK:AAAK:AAGM:ATGF:TTCF:TTTP:CCAP:CCCP:CCGP:CCTS:AGCS:AGTS:TCAS:TCCS:TCGS:TCTT:ACAT:ACCT:ACGT:ACTW:TGGY:TACY:TATV:GTAV:GTCV:GTGV:GTT

−2

−1

0

1

2

−1

0

1

FA

AL

D

E

P

A

E

P

A

FA

Lipid oxidation (PPARα targets) Lipid biosynthesis Amino acid biosynthesis

Figure 3: Ribosome DTs are not affected in fasted mice and without cycloheximideSingle-codon DTs are retrieved from the fit with the P:A interaction. A. Livers from mice fed AL or FA wereharvested at ZT4, ZT12, and ZT18. Ribosome profiling was performed without cycloheximide (CHX) in thelysis buffer. B. Normalized fractions of reads of length 32 around the start and stop codons in a window of 100nucleotides genome-wide. Dark blue: AL/NOCHX; dark red: FA/NOCHX. C. DTs (log2, mean centered per site,heatmap) for the E, P, and A sites in AL/NOCHX and FA/NOCHX. Codons are ordered by amino acid. Sidebars: log2 color scale. D. Differential expression of ribosome profiling signals between AL and FA (Methods).Benjamini-Hochberg adjusted p-values (-log10) plotted against averaged log2 fold change between FA and AL.Genes with false discovery rate (FDR) < 1% and absolute log2 fold change > 1 are annotated and colored. Blue:genes in the KEGG "PPARα signal pathway"; Green: KEGG and GO term "lipid biosynthesis"; Orange: KEGG"amino acid biosynthesis".

14



https://doi.org/10.1101/551838


left and right probes, showing target specificity for tRNAs (Fig. S7B). Indeed, mapping of the280

sequencing reads to all possible combinations (3032) of left and right probes showed that more281

than 75% of ligated products belonged to tRNA genes of the same codon (Fig. S7B), and even282

95% were from probe pairs that could be assigned to specific codons with high confidence (Fig.283

S7B) (Methods). We performed further experiments to validate the specificity and evaluated284

the efficiency of DNA ligases (Fig. S7C-D).285

We measured tRNA abundance on mouse livers from the same samples as those used for the ribo-286

some profiling. Specifically, we quantified the total tRNA (control, NaCl) and the (aminoacyl-)287

tRNA (sodium periodate, NaIO4) abundances from the same pieces of liver in two replicates in288

the AL and FA conditions, at three different times during the day (ZT04, ZT12, ZT18) (Fig. 4A,289

Methods). tRNA abundances were highly reproducible (Fig. S7E), exhibited a large dynamic290

range (Fig. 4B), and were significantly correlated with PolIII ChIP-Seq data in mouse liver [62]291

(Fig. S7F). tRNA levels for amino acids encoded by four synonymous codons ("4-codon" box)292

were represented by one dominant highly expressed isoacceptor with a T at the wobble position293

34 (e.g. TGC/Ala, TGG/Pro, TCC/Gly, TAC/Val, TGT/Thr) (Fig. S8A). The tRNA levels294

showed only small variations over the biological conditions, except for mitochondrial tRNAs295

(Fig. S8B). Strikingly though, principal component analysis (PCA) on the four conditions (i.e.296

NaCl/AL, NaCl/FA, NaIO4/AL and NaIO4/FA) showed a clear separation between the control297

and periodate conditions (Fig. 4C), indicating differential loading of the tRNAs. Surprisingly,298

the AL and FA samples were indistinguishable in the total tRNA and (aminoacyl-) tRNA condi-299

tions (Fig. 4C-D), indicating no imbalance of tRNA charging in prolonged fasting and coinciding300

with the stable DTs.301

However, some codons for Asn, Asp, and Ile were lowly aminoacylated, independently of the302

feeding regime (Figs. 4D, S8C). Strikingly, these same codons were found to exhibit the slowest303

DTs in the A site (Fig. 2).304

15



https://doi.org/10.1101/551838


AL

4

12

18

x2

AL FA

FA

4

12

18

x2

A

NaCl NaIO4

NaCl NaIO4

cum

ula

tive

frac

tion

of r

eads

(U

MI)

ordered tRNAs by abundancesNaCl

NaIO4

Serie A

Serie BC D

B

mitochondrial tRNAs"4-codon" boxes

N34N35N36 _amino acidCTT_Lys

GTG−M_His−M

TGC_Ala

TGG_Pro

TCC_Gly

AAT_IleTAC_ValTCG−M_Arg−MGTT_AsnCAT_Met

CTG_GlnGTA_Tyr GTC_AspGAA_Phe TGG−M_Pro−M

TGT_Thr TAG_LeuGAT_Ile GCA−M_Cys−M

AGC_Ala GGG_Pro

CAC_Val GCA_Cys

AAC_ValTTC−M_Glu−M

CAG_LeuGAT−M_Ile−M

AAG_Leu CAT−M_iMet

CCC_Gly TTT−M_Lys−M

TCA−M_Trp−M TTT_Lys

GAA−M_Phe−M GTT−M_Asn−M

GTG_His

TCC−M_Gly−M

AGG_Pro

GCT_Ser

TGA−M_Ser−M

0.0

0.3

0.6

0.9

FA

Figure 4: (Aminoacyl-) tRNA profiling in AL fed and FA miceA. Mice fed AL or FA were sacrificed at ZT4 (fasting duration: 16 h), ZT12 (24 h) and ZT18 (30 h) and liverswere harvested. Each sample was treated with NaCl and sodium periodate (NaIO4). B. Cumulative fraction ofreads for each tRNA, ordered by abundances. Anticodons and amino acids are indicated for the 50 first codons.Blue: four-codon box amino acid; red: mitochondrial tRNAs. C. PCA of the tRNA abundances (log2 UMI).PC1 and PC2 explain 42.2% and 21.3% of the variance, respectively, and separate NaCl from NaIO4 treatment.NaCl (circle), NaIO4 (square), AL (blue), FA (red), replicate 1 (black), replicate 2 (grey). ZT is shown beside thepoints. D. Ratio of tRNA abundances (log2 fold change, averaged over the time points) between the NaCl andNaIO4 for AL fed vs. FA mice (significant changes, p < 0.05 in red). No tRNA showed a significant differencebetween AL and FA (i.e. fell out of the diagonal).

16



https://doi.org/10.1101/551838


Relationship between (aminoacyl-) tRNA levels, codon usage, and dwell times305

To substantiate this observation, we investigated whether variations of single-codon DTs and306

codon usage could be explained by the available tRNA pools. Here, our data showed a significant307

correlation between codon usage and our measured tRNA levels in mouse liver (Fig. 5A),308

extending previous work using POLIII loadings on tRNA genes as proxies [63]. Our analysis309

also highlighted codons with high or low demand (codon usage) compared to the supply (tRNA310

levels), as quantified by the codon balance [64] (Fig. 5A).311

Some codons with the slowest DTs in the A site clearly stood out as having either low codon312

balance or aminoacylation levels. In fact, DTs in the A site did not exhibit a simple correlation313

with tRNA abundances (Fig. 5B), nor with the codon balance (Fig. 5C). However, the slow DTs314

for Glu codons (Fig. 5C) may well result from their low codon balance, hence limiting tRNA315

availability at the A site. Remarkably, codons for Asp, Asn, and Ile, which had particularly316

lowly charged tRNAs, coincided with some of the slowest DTs in the A site (Fig. 5D). However,317

DTs in the A site were overall uncorrelated with tRNA aminoacylation levels. We therefore318

included several effects in a linear model, which uncovered that a linear combination of tRNA319

aminoacylation levels and codon balance captures a significant portion of variation in the A site320

DTs, particularly the long DTs for Glu, Asp, Asn, and Ile codons (Fig. 5E).321

322

Discussion323

We extensively modeled RP datasets and uncovered single-codon and codon-pair DTs determin-324

ing ribosome elongation rates in mammals. These DTs were highly stable across all conditions325

tested. In parallel, we quantified (aminoacyl-) tRNA levels in mouse liver and identified several326

features regulating ribosome elongation, such as aminoacylation levels and the balance between327

tRNA levels and codon usage.328

17



https://doi.org/10.1101/551838


B

D

predicted values

A

C

E

supply > demand

demand > supply

balance

E

DD

NN

I

E

DD

NN

II I K

tRNA levels/codon usagebalance

R²=0.22 p=1.2e-4

E

EDD

K N

I

R²= 0.02 p=0.29

R²=0.01 p=0.39

R²= 0.03 p=0.17

R²= 0.17 p=2.15e-2

Figure 5: Relationship between (aminoacyl-) tRNA levels, codon usage and DTA. Significant correlation (R2 = 0.22, p = 1.2e − 4) between codon usage weighted by mRNA translation levels(wCU, log2) and normalized total tRNA (NaCl) read count for each codon (log2, UMI, averaged over the ALsamples). The gray line shows the first principal component (PC). Orthogonal distance to the PC reflects thebalance between tRNA supply and demand. Codons with positive (resp. negative) balance are colored blue(resp. red). Codons are assigned to their canonical tRNAs (dark-grey) or to wobble tRNAs (light-grey) whereappropriate (Methods). B. DTs in the A site (log2) vs. tRNA levels (NaCl, log2) averaged over the AL samples(R2 = 0.01, p-value= 3.9e − 1). C. Correlation between tRNA levels/codon usage balance and DTs in the Asite (log2) averaged over the AL samples. R2 and p-value are reported for the linear regression (R2 = 0.02,p-value= 2.9e−1). D. Correlation between tRNA uncharged levels (NaCl/NaI04, log2) and DTs in the A site (log2)averaged over the AL samples. R2 and p-value are reported for the linear regression (R2 = 0.03, p-value= 1.7e−1).Positively correlated codons are annotated by their one-letter amino acid. E. Significant correlation(R2 = 0.17,p-value=2.15e− 02) between estimated and predicted DTs in the A site. Prediction uses a linear model with thebalance and uncharged levels, in linear scale, as explanatory variables. In C-E, annotations refer to one-letteramino acid.

18



https://doi.org/10.1101/551838


329

In yeast, our model accurately inferred codon-specific DTs and highlighted mainly Arg and330

Pro as slow in the A and P sites. These amino acids are known for their inefficient peptide331

formation and sterical interactions with the ribosome [65, 66]. A significant negative correla-332

tion was observed between DTs and codon usage, reflecting natural selection for fast codons333

in highly translated genes. While this relationship has been described [67, 36], the found cor-334

relation is, to our knowledge, the highest reported. Moreover, our analysis confirmed recently335

identified inhibitory pairs [34], and deciphered their synergistic effect in addition to the site-336

specific contributions. We showed that the inhibitory pairs lengthened DTs both in the E:P337

and P:A positions, highlighting potentially inefficient translocation of the pair due to wobble338

base-pairing or other mechanisms [34].339

340

In mouse liver, DTs differed significantly from yeast, showing a larger spread and higher341

complexity. Remarkably, DTs were very similar between different tissues, species, and RP pro-342

tocols. Moreover, the DTs were consistent with a peptide motif enriched in stalled ribosome343

sites in mouse embryonic stem cells (mESCs) [68].344

345

We found that the smallest and achiral amino acid Gly exhibited very long DTs (in the346

A and P site) with different magnitudes between the isoacceptors and tissues (i.e. liver and347

kidney). Interestingly, in bacteria, Gly codons are slow, although this effect is still difficult348

to separate from Shine-Dalgarno (SD) dependent stalling [69] or protocol artifacts [70], and is349

therefore debated [71]. As mammals do not use a SD mechanism, our result support an alterna-350

tive hypothesis, such as slow codon-anticodon pairing [72] or inefficient peptide bond formation.351

Pioneering work in E. coli suggested that Gly-tRNAs adopt a particular conformation due to352

the U nucleotide in position 32 and that unmodified U34 on tRNAGlyUCC could decode the four353

Gly codons (a pairing known as superwobbling [73]), but with low efficiency (reviewed in [72]).354

19



https://doi.org/10.1101/551838


While this mechanism was shown in unicellular organisms, our tRNA profiling found tRNAGlyUCC355

as the major Gly isoacceptors and one of the most abundant tRNAs in mouse liver. Further356

work may indicate whether superwobbling occurs in the liver.357

358

The DTs for the acidic amino acids (Asp and Glu) were among the slowest. Glu showed a359

particularly low balance of tRNA levels/codon usage and Asp tRNA was lowly charged. This360

could lead to a shortage of tRNA availability and ribosome stalling [23]. As their codons share361

the same first two bases, competition with near-cognate tRNAs [27], or pairing inefficiency due362

to the wobble mechanism could also explain the long DTs. Indeed, slower elongation would363

allow higher precision in codon-anticodon discrimination [74].364

365

Ile codon DTs were slow in the A site while fast in the P site. Remarkably, the isomeric Leu366

codons were the fastest in the P and A sites, highlighting a structure-independent mechanism.367

Indeed, we showed that Ile-tRNAs were lowly aminoacylated, reducing Ile availability on the368

A site, but other explanations are possible. For instance, since Ile is decoded by three differ-369

ent codons, a suitable pairing mechanism such as inosine or other U34 modifications could be370

used to avoid pairing of the fourth near-cognate codon (Met) and therefore increase the DTs [75].371

372

One of our main findings concerned the contributions of codon-pair interactions towards373

DTs, mainly at the P and A site. At these positions, the ribosome catalyzes the peptide bond374

formation between (aminoacyl-) tRNA in the A site and peptide-tRNA bound to the P site.375

Our analysis revealed that the identity of the amino acid in the A site (acceptor), and not376

the codon, was the best descriptor of those codon pair interactions. Pairs including bulky377

amino acids or Gly in the A site were slow, highlighting their potential inefficiency in peptide378

bond formation. Interestingly the DT for Pro-Pro pairs, known to inefficiently form peptide379

bonds [76], was markedly reduced by the interaction. This observation probably shows the role380

20



https://doi.org/10.1101/551838


of eIF5A in resolving this stalling motif. On the other hand, Gly, Asp, and Glu, which were381

slow in our analysis, were shown by others to require eIF5A for their efficient translation [77, 78].382

383

Other features not included in the model, and which are independent of the codon iden-384

tity, might regulate ribosome elongation. A high number of liver proteins are secreted and385

thereby translated by ribosomes bound to the endoplasmic reticulum via the interaction of sig-386

nal recognition particles with the nascent peptide chain. These interactions are known to stall387

the ribosomes, however, as these appear to be codon independent, we did not detect them in our388

analysis [30]. In addition, chaperone proteins interacting with the nascent peptide can influence389

co-translational folding and subsequent ribosome density on mRNAs [79]. The model could also390

be further extended by including RNA secondary structure and modifications, pseudo-knots391

acting as ribosome roadblocks, and slippery sequences inducing frame shifting [30, 20, 80, 81].392

Recent studies have described ribosome collisions and their relationships with recruitment of393

ribosome quality control and degradation pathways [82, 83]. While these events could happen394

frequently in liver, and thereby bias position-dependent estimation of DTs from standard ribo-395

some profiling, a recent study probing the determinants of collided ribosomes in mouse liver396

showed similar codon dependencies between pausing sites and our DTs [84].397

398

While we found a striking correlation between DTs and codon usage in yeast, the same did399

not hold in mammals. This suggests that biased codon usage in mammals reflects more complex400

evolutionary forces, such as mutation driven GC bias [63]. Nevertheless, the measured tRNA401

abundances showed signatures of adaptation, since tRNA levels correlated with the codon us-402

age. These correlations extended previous results at the transcription level or in highly expressed403

genes [63, 85]. Related to this, one still open challenge is to assign tRNAs to their corresponding404

codons, due to the extended wobble base pairing rules related to tRNA modifications.405

406

21



https://doi.org/10.1101/551838


Surprisingly, tRNA loading was unaffected by prolonged fasting. Several studies in cell lines407

showed that decreasing amino acids in culture media leads to decreased (aminoacyl-) tRNA408

availability and therefore increases ribosome stalling [60, 23]. Moreover, others have shown that409

codon optimality contributes to differential mRNA translation in response to starvation [24].410

While we did not observe this, probably due to the in vivo state, GC3 bias (i.e. GC bias at411

position N3 in codons) was significantly different between genes translated in AL and FA mice412

(Fig. S5E) or also between night and day conditions (not shown). Genes with high GC3 content413

have been shown to provide more targets for methylation than those with low GC3 and to be414

enriched in stress responsive genes [86]. Oxidative stress occurs during fasting/day in mouse and415

correlates with GC3 content. Nevertheless, the reason of the higher GC3 level in FA compared416

to AL still needs to be identified.417

418

Like (aminoacyl-) tRNA levels, DTs were unchanged between AL and FA. We can hypoth-419

esize that after more than 30 hours of starvation, mice compensate the lack of amino acids by420

a large global decrease of translation initiation through mTORC1/GCN2 [87], making tRNA421

availability and translation elongation non limiting. Moreover, since ribosome profiling signals,422

DTs, and tRNAs were measured in relative and not absolute amounts, we cannot exclude a total423

decrease of translation elongation rate, aminoacylation, or tRNA levels.424

425

In conclusion, ribosome DTs, codon usage, tRNA levels, and translation elongation in mam-426

mals do not obey simple relationships. Nevertheless, although a global understanding is still427

missing, we were able to link both tRNA/codon usage balance and aminoacylation levels with428

anomalously slow DTs in the P and A site of the ribosome. Probing different ribosome states429

(e.g. free A site) using RP combined with different drugs [70] or improving the quantification of430

(aminoacyl-) tRNA through nucleotide modification removal [45] will lead to better understand-431

ing of the determinants of translation elongation. Finally, more work is needed to understand the432

22



https://doi.org/10.1101/551838


consequences of changes in ribosome elongation rates for mRNA stability and nascent protein433

folding.434

Methods435

Inference of dwell times and translation fluxes436

Preprocessing of ribosome profiling data437

RP datasets from yeast, mouse, and human were respectively mapped on the sacCer3, mm10 and438

Hg38 genomes using STAR [88] with parameters –seedSearchStartLmax 15. Genomes indexes439

were built using Ensembl transcripts annotations. Adapters were retrieved for the different440

datasets and input as parameter for STAR. In the case of NEXTFlex library, fastqs files were441

parsed and duplicated sequences (UMI and insert) are removed. Sequences were trimmed for442

adapters using fastx_clipper with parameters -Q33 -a TGGAATTCTCGGGTGCCAAGG -l 11 and443

UMIs are removed (4 nucleotides on both sides). Then, the fastq files are mapped using STAR444

with options –seedSearchStartLmax 15. The subsequence BAM files were sorted and indexed.445

Read counting on the coding sequences446

For each protein coding transcript with a CDS larger than 120 nucleotides, reads with zero447

mismatches, unique mapping (nM:i:0 & NH:i:1) and a length between 25 and 40 nucleotides448

were retrieved using samtools view in the respective region. E site position was defined, for449

each read size, in function of the frame on the CDS and pileup plots at the start codon. From450

the E site position, the sequence in the window [-60,+60] nts was reported and incremented by451

one at each new observation. Sequences with a window spanning the start or stop codon were452

removed.453

23



https://doi.org/10.1101/551838


Data filtering454

A sliding window of 120 nucleotides moving 3 by 3 on the CDS of protein coding genes were455

computed and the respective sequences were reported (Figure S1D). This set of sequences is used456

as a reference and their respective number of counts is set to zero. Every time a read occurs at457

one of these sequences, we incremented the count by one (Fig.S1D). Genes with less than 5%458

of positions covered or less than 5 positions observed were discarded. Genes with less than 100459

counts were removed. Sequences containing a stop codon (TAG, TGA, or TAA) or non-unique460

in the coding genome were discarded. Depending on the sample coverage, we monitored about461

5000 genes in mammals.462

Generalized linear model for ribosome profiling count data463

We used a generalized linear model for the observed RP read counts at the different positions464

on the gene CDS. Here, the read counts Yigs at a specific codon position i corresponding to the465

ribosome E site on the CDS of a gene g in sample s were modeled as a negative binomial (NB)466

(i.e. commonly used for overdispersed count data [89]) with mean µigs and dispersion parameter467

θs.468

Yigs ∼ NB (µigs, θ)

E[Yigs] = µigs

Var (Yigs) = µigs +µ2

igs

θs

(1)

24



https://doi.org/10.1101/551838


The mean is further modeled as follows (omitting the sample index) :469

h(µi,g) = fg︸︷︷︸gene

+

20∑k=−20

τ(1)k,c(i+k)︸︷︷︸single

+ τ(2)c(i+s1),c(i+s2)︸︷︷︸

pairs

+ offset(library size and/or RNA-Seq)

with(s1, s2) ∈

(0, 1)for the E:P fit

(0, 2)for the E:A fit

(1, 2)for the P:A fit

(2)

Here, c(i) ∈ {AAA,AAC,AAG, ..., TTT}. h(x) = log(x) the so-called link function that allows470

to express the product of gene flux and DTs (equal to the expected RP density) as a sum in471

log-space. τ(1)k,c(k) denote the contributions of single-codon DTs (in log scale) for the 61 sense472

codons at position k, with k the position relative to the ribosome E site. τ (2)c(i+s1),c(i+s2) denote473

the contributions of codon pair DTs (again in log scale) for the 612 pairs of sense codons at474

positions (s1, s2) relative to the ribosome E site. These codon pair matrices are modeled for the475

sites E:A, E:P, and P:A. fg is the gene-specific translation flux (in log scale). Note that since476

this problem does not have full rank, we must choose some constraints, which means that the477

fit DTs are relative contributions. Here we chose to express these contributions relative to the478

mean for each site. Specifically, we apply the convention:∑

c τ(1)k,c = 0 for all k,

∑c τ

(2)c,c′ = 0479

for all c′, and∑

c′ τ(2)c,c′ = 0 for all c and shifted the gene fluxes accordingly. The offset term(s)480

make the gene fluxes normalized by library size. In addition, when specified in the text, we481

normalized gene translation fluxes by mRNA abundance by fitting the same model to RNA-seq482

data (the DTs then represent sequencing-dependent biases). From the RNA model we predicted483

the expected gene-specific RNA abundance and used it as an offset term.484

In all figures showing DTs, point estimates as returned by glm4 and relatively expressed in log485

are plotted. The reproducibility of those point estimate accross biological samples is shown in486

Fig. S3C.487

25



https://doi.org/10.1101/551838


488

Negative binomial (NB) noise model. θs was taken as a sample specific parameter and was489

empirically estimated from the variance-mean relationship expected for a NB. Specifically, for490

each gene we used pairs of adjacent codons occurring more than once, the rationale being that491

according to our steady-state model, the counts observed on the multiple instances behave as492

replicates (i.e. the counts are sampled from the same NB). For those pairs of codons, we com-493

puted the respective mean and variance of counts and since we assumed that θs only depends494

on the sample, we then estimated θs globally (from all pairs on all genes) by linear regression495

using Eq.1 (Fig.S1 E). The fit was performed using the glm4() function from the R package496

MatrixModels with the noise family negative.binomial(θs) from the MASS package and with497

sparse design matrix option. Sequencing library size is used as an offset. RNA-Seq data is fitted498

(when available) and read counts are then predicted at every positions and used as an offset.499

500

Differential expression in ad libitum fed vs. fasted mice501

Two outlier samples (ZT12/FA/CHX and ZT04/FA/NOCHX) were excluded for the differential502

expression analysis and DT modelling. Statistics were computed using EdgeR [90] comparing503

a model including factors for time, feeding, and drug conditions against a model without the504

feeding term.505

Animal experiments506

Animal studies were approved by the local ethics committee, and all protocols were approved507

by the Service Vétérinaire Cantonal (Lausanne, Switzerland) under license VD3613. 8 weeks508

old male C57BL6/J mice (Charles River Laboratory) are kept under diurnal lighting conditions509

(12-h light, 12-h dark) at a temperature of 21 ◦C +/- 2 ◦C. After a complete night of fasting,510

the mice were kept without access to food for an additional period of up to 24 hours. During511

26



https://doi.org/10.1101/551838


this time period animals were sacrificed every 8 hours starting at ZT4. Control animals were512

kept on ad libitum feeding regimen.513

Ribosome profiling514

Samples preparation for RP was performed as described in [51] except for the conditions without515

cycloheximide (CHX) in which fresh livers were directly lysed in ice-cold lysis buffer without516

CHX and directly flash-frozen in liquid nitrogen. To limit possible bias due to footprint size517

selection related to different conformations of the ribosome [69] [38], a larger band was cut on518

the TBE-gel. Libraries were generated using NEXTflex Small RNA Sequencing Kit v3 (bioo519

scientific) following the manufacturer’s protocol. Samples were pooled based on the Illumina520

indices used. Denaturated pools were spiked with 5% PhiX and clustered (loading equivalent521

to 3 samples per lane) onto a rapid single-end v2 flowcells at a concentration of 8pM. Libraries522

were sequenced on a HiSeq 2500 (Illumina) for 50 cycles.523

(Aminoacyl-) tRNA profiling524

The tRNA profiling protocol was adapted and modified from [9]. We tested the initial protocol525

[9] on mouse liver samples but the results showed a high proportion of unspecific ligations526

between the left and right probes from distinct tRNAs. We solved this issue by inverting the527

order of two steps in the protocol: we performed the pull-down and cleaning on magnetic beads528

before the splint ligation between the two DNA probes on the tRNA (Fig. S7A). Oxidation529

of 3’-tRNA by periodate was adapted from [60]. All the steps were performed under cold and530

acidic conditions to avoid deacylation of the tRNAs before Na periodate oxidation.531

Probe Design532

DNA probes were designed to target all the annotated mouse tRNAs from http://gtrnadb.533

ucsc.edu/. The database contains tRNA gene predictions by tRNAscan-SE [61]. tRNA se-534

quences for Mus musculus (GRCm38/mm10) were downloaded and spliced in silico. The se-535

27



http://gtrnadb.ucsc.edu/



https://doi.org/10.1101/551838


quences were split in the middle of the anticodon in order to design left and right probes. After536

reverse complementation of the sequences, overhangs (for PCR primer binding) and unique537

molecular identifiers (UMIs, 2x6N) were added (right-probe adapter:538

5’-GCACCCGAGAATTCCANNNNNNTGG-3, left-probe adapter:539

5’-NNNNNNGATCGTCGGACTGTAGAACTC-3’). Left probes were ordered with a 5’-phosphate540

to allow ligation with the right probe upon annealing with the corresponding tRNA. The ran-541

dom nucleotides were ordered as «high fidelity wobble »to ensure homogeneous representation542

of the four bases in the UMI and to avoid bias. DNA probes were ordered at MicroSynth AG543

(Switzerland).544

tRNA extraction and oxidation545

50-100 mg of frozen mouse liver tissues were weighted under cold conditions. Beating beads546

were added and the samples were homogenized in 350 µl of cold Qiazol (Qiagen) lysis reagent in547

a TissueLyser (Qiagen) for 2 x 2 min at 20 Hz. Tubes were left 5 min at room temperature. 140548

µl of CHCl3 was added and homogenates were shaken vigorously followed by centrifugation at549

4◦C for 15 min (12’000 x g). The upper aqueous phase was carefully removed and 1 volume (350550

µl) of buffered phenol (Phenol:chloroform:isoamyl alcool, 25:24:1, pH 4.9) was added. Samples551

were mixed and centrifuged for 15 minutes at 4◦C (12’000 x g). Upper phase (300 µl) was552

supplemented with 1 volume (300 µl) of cold isopropanol, precipitated 30 minutes at 4◦C and553

then centrifuged for 15 minutes at 4◦C (12’000 x g). RNA pellets were dried at room temperature554

and re-suspended in 500 µl of Sodium Acetate buffer pH 4.9 (0.2M). Samples were split in two555

tubes (2 x 250µl) for sodium periodate oxidation (NaIO4) or control (NaCl) treatment. 50 µl of556

NaCl (0.3M) or NaIO4 (0.3M) was added and samples were incubated for 30 minutes at room557

temperature. The reaction was then supplemented with 300 µl Ethanol (70%) and loaded on558

a miRNeasy column (Qiagen). tRNA were extracted following the miRNA easy protocol from559

Qiagen. 390 µl Ethanol (100%) was added to the flow through and loaded on a MinElute column560

28



https://doi.org/10.1101/551838


(Qiagen). Columns were washed following the manufacturer’s protocol and RNAs were eluted561

in 15 µl RNase-free H2O.562

Deacylation563

Purified tRNAs (14 µl) supplemented with 6 µl of Tris-HCl (pH 8) were deacylated by heating at564

40◦C for 35 minutes. Reaction was stopped by the addition of 30 µl NaAcetate (0.3 M). RNAs565

were purified using RNA Clean & Concentrator -5 kit (Zymo) according to manufacturer’s566

instructions and eluted in 15 µl RNase-free H2O.567

3’-tRNAs biotinylation568

3’-tRNAs biotinylation was adapted from Pierce RNA 3’-End Biotinylation Kit (Thermo Fisher).569

Deacylated tRNAs were denaturated in 25% DMSO at 85◦C for 5 minutes and directly chilled570

on ice. Biotinylation was performed in a 90 µl reaction with 6 U of T4 ssRNA Ligase (NEB),571

4 µl Biotinylated Cytidine (Thermo Fisher, 1mM), 2 U RNase inhibitor, 9 µl RNase Buffer572

(NEB), 9 µl ATP (NEB, 10mM), 40 µl PEG 800 (50%), and 20 µl denaturated RNAs. The573

reaction was performed overnight at 16◦C. Biotinylated tRNAs were cleaned using RNA Clean574

& Concentrator -5 kit (Zymo) according to manufacturer’s instructions and eluted in 20 µl H20.575

Probes hybridization576

DNA probes were synthesized by Microsynth AG and resuspended at a 100 µM concentration.577

The 606 probes were then mixed at an equimolar ratio (0.15 µM each) and aliquoted for further578

usage. Hybridization of probes was performed in a 300 µl−reaction with 45 µl probes mastermix,579

30 µl hybridization buffer 5x (500µM EGTA, 25mM NaCl, 50mM Tris−HCl), 205 µl RNase-free580

water and 20 µl tRNAs. After a 15 minutes denaturation at 95 ◦C, the mixture was slowly581

cooled down to 55 ◦C (0.2 ◦C/second) and incubated for 30 minutes.582

29



https://doi.org/10.1101/551838


Beads purification583

200 µl of Dynabeads MyOne Streptavidin C1 (Thermo Fisher) were washed following manu-584

facturer’s instructions for RNA usage. 250 µl of beads, re-suspended in washing buffer 2x (10585

mM Tris−HCl, 1 mM EDTA, 2M NaCl), were incubated with 300ul of the resulting RNA-DNA586

hybridization reaction for 40 minutes with gentle rotation. Beads were washed/magnetized three587

times with 1ml of washing buffer (1x) and re-suspended in 300 µl H2O.588

RNA-DNA hybrid ligation589

Bead purified DNA-RNA hybrid on beads were ligated at the anticodon nick by a combination590

of SplintR and T4 DNA ligases (NEB) to minimize ligation efficiency bias. 300 µl of DNA-RNA591

hybrids were splint-ligated with 2.5 U of SplintR DNA ligase and 30µl of SplintR DNA ligase592

buffer (10X, NEB) for 1 hours at 25 ◦C. Then, 10 U of T4 DNA ligase (NEB) and 33µl of T4593

DNA ligase buffer (10x, NEB) were added. Ligation was performed overnight at 16 ◦C.594

RNA digestion595

Beads were magnetized and washed once with washing buffer (1X) to remove any remaining596

ligases. Next, beads were re-suspended in 10µl H2O. 2 U of RNase A (Thermo Fisher) and 10 U597

of RNase H (NEB) with RNase H buffer (10X) (NEB) were added and digestion was performed598

for 30 minutes at 37 ◦C. Elution buffer (5X) was added for a final concentration of 50 mM599

tris pH8, 10 mM EDTA, 1% SDS and samples were incubated at 65 ◦C for 30 minutes with600

intermittent shaking to retrieve ligated DNA probes. Beads were magnetized and supernatant601

extracted. DNA ligated probes were purified using DNA Clean & Concentrator -5 kit (Zymo)602

according to manufacturer’s instructions and eluted in 20 µl RNase-free H2O.603

30



https://doi.org/10.1101/551838


qPCR for quality control and relative concentration estimation604

The relative concentration of the resulting DNA ligated-probes was assessed by quantitative605

PCR (qPCR) using the LightCycler 480 SYBR Green I Master kit (Roche). 3.5 µl of 110 diluted606

samples was used to assemble a 10 µl reaction and run on a Light Cycler 480 II (Roche) with607

the primers CAGAGTTCTACAGTCCGACGAT and TTGGCACCCGAGAATTCCA (match-608

ing each probe’s ends) at a final concentration of 0.3 µM. Cycling conditions consisted of an609

initial denaturation step of 5 min at 95 ◦C followed by 40 cycles of 30 s at 95 ◦C and 45 sat 45610

◦C. The Cp obtained were used to calculate the optimal number of PCR cycles amplification611

required for library amplification, as described previously [51]. The number of required cycles612

were between 13 and 17 depending on the experiments and samples. The quality of the ligation613

was assessed on a Bioanalyzer small RNA chip (Agilent Technologies).614

PCR amplification615

The PCR was designed following Illumina’s recommendation taking advantage of the indexed616

oligos from the TruSeq small RNA kit. A 50 µl-reaction was assembled with Kapa Polymerase617

and 15 ul of DNA ligated probes, and run for the optimal number of PCR cycles calculated as618

described above.619

Library postprocessing620

Amplified libraries were purified with 100 µl AMPure XP beads (Beckman) and eluted in 20621

µl resuspension buffer (10 mM Tris, pH8.0). Libraries were quantified with Picogreen (Life622

Technology) and usually yield 50-400 ng DNA. The libraries size patterns were checked using a623

fragment analyzer (Agilent).624

31



https://doi.org/10.1101/551838


Library Sequencing625

An equimolar library pool was prepared from the individual libraries based on the concentrations626

calculated from Picogreen data. Pools were denaturated with NaOH and neutralized with HT1627

buffer (Illumina) to reach a final concentration of 20pM. Pools were spiked with 10 % PhiX and628

clustered (loading equivalent to 12 samples per lane) onto a rapid single-end flow cell v2 at a629

final concentration of 7pM. Sequencing was performed on a HiSeq 2500 (Illumina) in rapid mode630

for 130 cycles.631

Data Analysis632

To assess the fidelity of left/right probe ligation and efficiency of hybridization, a fasta file with633

all the possible combinations between left and right probes was created. In case two tRNA genes634

share the same left/right probes, they were grouped and annotated accordingly. It led us to a635

total of 68526 sequences. Genome index was generated with STAR with options636

–runMode genomeGenerate –genomeSAindexNbases 3. Fastq files were trimmed for adapters.637

Sequencing reads were aligned against the index with parameters:638

–outFilterScoreMinOverLread 0 –outFilterMatchNminOverLread 0639

–winAnchorMultimapNmax 1000 –outFilterMismatchNmax640

–clip3pNbases 6 –clip5pNbases 6641

–outFilterMultimapNmax 50 –outSAMattributes NM nM NH NM –alignIntronMax 1642

–alignEndsType EndToEnd –seedSearchStartLmax 20 –seedMultimapNmax 100000.643

For each combination, the number of counts were computed and corrected for PCR duplicates644

using both unique molecular identifiers sequences. Reads larger than 60 nucleotides with less645

than 3 mismatches and less than 5 insertions compared to the reference were retained. Combina-646

tions with less than 10 reads were discarded. Reads mapping to combinations of probes coming647

32



https://doi.org/10.1101/551838


from different codons were reassigned in function of the newly ligated sequence. Abundances of648

tRNA coding for the same codon were summed up and normalized by library size using edgeR R649

package [90]. Because tRNA moieties (the two halves of the tRNA) have very similar sequences,650

and since the specificity of the hybrid DNA-RNA around the anticodon is important for the651

ligation, we used the sequence around the anticodon to reassign the ambiguous combinations.652

Data Availability and specificity653

Sequencing data of this study have been submitted to Gene Expression Omnibus (GEO) under654

accession number GSE126384: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126384).655

Datasets and GEO references: Atger Liver (GSE73553, n=84), Howard Liver (SRR826795,656

SRR826796, SRR826797, n=3), Huh Linter (SRR5227294, SRR5227295, SRR5227296, SRR5227303,657

SRR5227304, SRR5227305, n=6), Jan Yeast (SRR1562907, SRR1562909, SRR1562911, SRR1562913,658

n = 4), Kidney Castelo (GSE81283, n=24), Guydosh Yeast (SRR1042865, SRR1042866, SRR1042867,659

n= 1).660

33



https://doi.org/10.1101/551838


Acknowledgments661

We thank Nicolas Bonhoure and Stefan Morgenthaler for useful discussions. Research in the662

Naef laboratory was supported by the EPFL. Some computations were performed on the Vital-It663

computing platform.664

Conflict of interest statement665

CG, BW, EM,and FG were employees of Nestle Institute of Health Sciences SA, CH-1015 Lau-666

sanne, Switzerland.667

Supplementary material668

Table 1. Inferred single-codon DTs669

Table 2. Inferred codon-pair DTs670

Table 3. Ribosome profiling in AL and FA mice w/o CHX671

Table 4. (aminoacyl-) tRNA profiling in AL and FA mice672

Code availability673

Snakefile to model DTs and flux from ribosome profiling data will be made publicly available on674

github upon publication.675

References676

1. Gobet, C. & Naef, F. Ribosome profiling and dynamic regulation of translation in mammals.677

Current Opinion in Genetics and Development 43. issn: 18790380. doi:10.1016/j.gde.678

2017.03.005 (2017).679

34



http://dx.doi.org/10.1016/j.gde.2017.03.005



https://doi.org/10.1101/551838


2. Hinnebusch, A. G. & Lorsch, J. R. The mechanism of eukaryotic translation initiation:680

new insights and challenges. Cold Spring Harbor perspectives in biology 4, a011544. issn:681

1943-0264 (Oct. 2012).682

3. Brule, C. E. & Grayhack, E. J. Synonymous Codons: Choose Wisely for Expression. Trends683

in Genetics 33, 283–297. issn: 01689525 (Apr. 2017).684

4. Presnyak, V. et al. Codon Optimality Is a Major Determinant of mRNA Stability. Cell685

160, 1111–1124. issn: 00928674 (Mar. 2015).686

5. Radhakrishnan, A. et al. The DEAD-Box Protein Dhh1p Couples mRNA Decay and Trans-687

lation by Monitoring Codon Optimality. Cell 0, 1497–1506. issn: 00928674 (2016).688

6. Pelechano, V., Wei, W. & Steinmetz, L. M. Widespread Co-translational RNA Decay Re-689

veals Ribosome Dynamics. Cell 161, 1400–1412. issn: 00928674 (June 2015).690

7. Yu, C.-H. et al. Codon Usage Influences the Local Rate of Translation Elongation to Reg-691

ulate Co-translational Protein Folding. Molecular Cell 59, 744–754. issn: 10972765 (Sept.692

2015).693

8. Chu, D., Kazana, E., Bellanger, N., Singh, T., Tuite, M. F. & von der Haar, T. Translation694

elongation can control translation initiation on eukaryotic mRNAs. The EMBO Journal695

33, 21–34. issn: 02614189 (Jan. 2014).696

9. Goodarzi, H., Nguyen, H. C., Zhang, S., Dill, B. D., Molina, H. & Tavazoie, S. F. Modulated697

Expression of Specific tRNAs Drives Gene Expression and Cancer Progression. Cell 165,698

1416–1427. issn: 00928674 (June 2016).699

10. Loayza-Puch, F. et al. Tumour-specific proline vulnerability uncovered by differential ribo-700

some codon reading. Nature 530, 490–494. issn: 0028-0836 (Feb. 2016).701

11. Rapino, F. et al. Codon-specific translation reprogramming promotes resistance to targeted702

therapy. Nature 558, 605–609. issn: 14764687 (2018).703

35



https://doi.org/10.1101/551838


12. Varenne, S., Buc, J., Lloubes, R. & Lazdunski, C. Translation is a non-uniform process.704

Journal of Molecular Biology 180, 549–576. issn: 00222836 (Dec. 1984).705

13. Quax, T. E., Claassens, N. J., Söll, D. & van der Oost, J. Codon Bias as a Means to706

Fine-Tune Gene Expression. Molecular Cell 59, 149–161. issn: 10972765 (July 2015).707

14. Sørensen, M. A. & Pedersen, S. Absolute in vivo translation rates of individual codons708

in Escherichia coli. The two glutamic acid codons GAA and GAG are translated with a709

threefold difference in rate. Journal of Molecular Biology 222, 265–280. issn: 00222836710

(1991).711

15. Ingolia, N. T., Lareau, L. F. & Weissman, J. S. Ribosome Profiling of Mouse Embryonic712

Stem Cells Reveals the Complexity and Dynamics of Mammalian Proteomes. Cell 147,713

789–802. issn: 00928674 (Nov. 2011).714

16. Heesch, S. V. et al. The Translational Landscape of the Human Heart Resource The Trans-715

lational Landscape of the Human Heart, 1–19 (2019).716

17. Ingolia, N. T. Ribosome Footprint Profiling of Translation throughout the Genome. Cell717

165, 22–33. issn: 00928674 (Mar. 2016).718

18. Pop, C. et al. Causal signals between codon bias, mRNA structure, and the efficiency of719

translation and elongation. Mol Syst Biol 10. doi:10.15252/msb (2014).720

19. O’Connor, P. B. F., Andreev, D. E. & Baranov, P. V. Comparative survey of the relative721

impact of mRNA features on local ribosome profiling read density. Nature Communications722

7, 12915. issn: 2041-1723 (Oct. 2016).723

20. Fang, H., Huang, Y. F., Radhakrishnan, A., Siepel, A., Lyon, G. J. & Schatz, M. C. Scikit-724

ribo Enables Accurate Estimation and Robust Modeling of Translation Dynamics at Codon725

Resolution. Cell Systems. issn: 24054720. doi:10.1016/j.cels.2017.12.007 (2018).726

36



http://dx.doi.org/10.15252/msb

http://dx.doi.org/10.1016/j.cels.2017.12.007

https://doi.org/10.1101/551838


21. Tunney, R., McGlincy, N. J., Graham, M. E., Naddaf, N., Pachter, L. & Lareau, L. F.727

Accurate design of translational output by a neural network model of ribosome distribution.728

Nature Structural and Molecular Biology 25, 577–582. issn: 15459985 (2018).729

22. Charneski, C. A. & Hurst, L. D. Positively charged residues are the major determinants of730

ribosomal velocity. PLoS biology 11, e1001508. issn: 1545-7885 (2013).731

23. Darnell, A. M., Subramaniam, A. R. & O’Shea, E. K. Translational Control through Dif-732

ferential Ribosome Pausing during Amino Acid Limitation in Mammalian Cells. Molecular733

Cell 71, 229–243. issn: 10974164 (2018).734

24. Saikia, M., Wang, X., Mao, Y., Wan, J., Pan, T. & Qian, S.-B. Codon optimality controls735

differential mRNA translation during amino acid starvation. doi:10.1261/rna.058180.736

116.737

25. Guydosh, N. R. & Green, R. Dom34 Rescues Ribosomes in 3 Untranslated Regions. Cell738

156, 950–962 (2014).739

26. Nedialkova, D. D. & Leidel, S. A. Optimization of Codon Translation Rates via tRNA740

Modifications Maintains Proteome Integrity. Cell 161, 1606–1618. issn: 00928674 (June741

2015).742

27. Tuorto, F. et al. The tRNA methyltransferase Dnmt2 is required for accurate polypeptide743

synthesis during haematopoiesis. The EMBO Journal 34, 2350–2362 (2015).744

28. Chou, H.-J., Donnard, E., Gustafsson, H. T., Garber, M. & Rando, O. J. Transcriptome-745

wide Analysis of Roles for tRNA Modifications in Translational Regulation. Molecular Cell746

68, 978–992. issn: 10972765 (Dec. 2017).747

29. Pop, C. et al. Causal signals between codon bias , mRNA structure , and the efficiency of748

translation and elongation. Molecular systems biology, 1–16 (2014).749

37



http://dx.doi.org/10.1261/rna.058180.116



https://doi.org/10.1101/551838


30. Zhang, S., Hu, H., Zhou, J., He, X., Jiang, T. & Zeng, J. Analysis of Ribosome Stalling750

and Translation Elongation Dynamics by Deep Learning. Cell Systems 5, 212–220. issn:751

24054720 (Sept. 2017).752

31. Gorochowski, T. E., Ignatova, Z., Bovenberg, R. a. L. & Roubos, J. A. Trade-offs be-753

tween tRNA abundance and mRNA secondary structure support smoothing of translation754

elongation rate. Nucleic Acids Research 43, 3022–3032. issn: 0305-1048 (Mar. 2015).755

32. Döring, K. et al. Profiling Ssb-Nascent Chain Interactions Reveals Principles of Hsp70-756

Assisted Folding. Cell 170, 298–311. issn: 10974172 (2017).757

33. Harigaya, Y. & Parker, R. The link between adjacent codon pairs and mRNA stability.758

BMC Genomics 18, 364. issn: 1471-2164 (Dec. 2017).759

34. Gamble, C. E., Brule, C. E., Dean, K. M., Fields, S. & Grayhack, E. J. Adjacent Codons Act760

in Concert to Modulate Translation Efficiency in Yeast. Cell 166, 679–690. issn: 00928674761

(July 2016).762

35. Dao Duc, K. & Song, Y. S. The impact of ribosomal interference, codon usage, and exit763

tunnel interactions on translation elongation rate variation. PLOS Genetics 14 (ed Tuller,764

T.) e1007166. issn: 1553-7404 (Jan. 2018).765

36. Gardin, J., Yeasmin, R., Yurovsky, A., Cai, Y., Skiena, S. & Futcher, B. Measurement of766

average decoding rates of the 61 sense codons in vivo. eLife 3. issn: 2050-084X. doi:10.767

7554/eLife.03735. https://elifesciences.org/articles/03735 (Oct. 2014).768

37. Aeschimann, F., Xiong, J., Arnold, A., Dieterich, C. & Großhans, H. Transcriptome-wide769

measurement of ribosomal occupancy by ribosome profiling. Methods 85, 75–89. issn:770

10462023 (Sept. 2015).771

38. Lareau, L. F., Hite, D. H., Hogan, G. J. & Brown, P. O. Distinct stages of the translation772

elongation cycle revealed by sequencing ribosome-protected mRNA fragments. eLife 3,773

e01257. issn: 2050-084X (May 2014).774

38



http://dx.doi.org/10.7554/eLife.03735



https://elifesciences.org/articles/03735

https://doi.org/10.1101/551838


39. Duncan, C. D. S. & Mata, J. Effects of cycloheximide on the interpretation of ribosome775

profiling experiments in Schizosaccharomyces pombe. Scientific Reports. issn: 2045-2322.776

doi:10.1038/s41598-017-10650-1 (2017).777

40. Gerashchenko, M. V., Lobanov, A. V. & Gladyshev, V. N. Genome-wide ribosome profiling778

reveals complex translational regulation in response to oxidative stress. Proceedings of the779

National Academy of Sciences 109, 17394–17399. issn: 0027-8424 (Oct. 2012).780

41. Hussmann, J. a., Patchett, S., Johnson, A., Sawyer, S. & Press, W. H. Understanding781

Biases in Ribosome Profiling Experiments Reveals Signatures of Translation Dynamics in782

Yeast. PLOS Genetics 11 (ed Snyder, M.) e1005732. issn: 1553-7404 (Dec. 2015).783

42. Bartholomäus, A., Del Campo, C. & Ignatova, Z. Mapping the non-standardized biases of784

ribosome profiling. Biological Chemistry 397. issn: 1437-4315. doi:10.1515/hsz-2015-785

0197. https://www.degruyter.com/view/j/bchm.2016.397.issue-1/hsz-2015-786

0197/hsz-2015-0197.xml (Jan. 2016).787

43. Quax, T. E., Claassens, N. J., Söll, D. & van der Oost, J. Codon Bias as a Means to788

Fine-Tune Gene Expression. Molecular Cell 59, 149–161. issn: 10972765 (July 2015).789

44. Orioli, A. tRNA biology in the omics era: Stress signalling dynamics and cancer progression.790

BioEssays 39, 1600158. issn: 02659247 (Mar. 2017).791

45. Zheng, G. et al. Efficient and quantitative high-throughput tRNA sequencing. Nature Meth-792

ods 12, 835–837. issn: 1548-7091 (Sept. 2015).793

46. Gogakos, T., Brown, M., Garzia, A., Meyer, C., Hafner, M. & Tuschl, T. Characterizing794

Expression and Processing of Precursor and Mature Human tRNAs by Hydro-tRNAseq795

and PAR-CLIP. Cell Reports 20, 1463–1475. issn: 22111247 (Aug. 2017).796

47. Cozen, A. E., Quartley, E., Holmes, A. D., Hrabeta-Robinson, E., Phizicky, E. M. & Lowe,797

T. M. ARM-seq: AlkB-facilitated RNAmethylation sequencing reveals a complex landscape798

of modified tRNA fragments. Nature Methods 12, 879–884. issn: 1548-7091 (Sept. 2015).799

39



http://dx.doi.org/10.1038/s41598-017-10650-1

http://dx.doi.org/10.1515/hsz-2015-0197



https://www.degruyter.com/view/j/bchm.2016.397.issue-1/hsz-2015-0197/hsz-2015-0197.xml



https://doi.org/10.1101/551838


48. Evans, M. E., Clark, W. C., Zheng, G. & Pan, T. Determination of tRNA aminoacylation800

levels by high-throughput sequencing. Nucleic Acids Research 45, e133–e133. issn: 0305-801

1048 (Aug. 2017).802

49. Jan, C. H., Williams, C. C. &Weissman, J. S. Principles of ER cotranslational translocation803

revealed by proximity-specific ribosome profiling. Science 346, 1257521–1257521. issn:804

0036-8075 (Nov. 2014).805

50. Ingolia, N. T., Hussmann, J. A. & Weissman, J. S. Ribosome Profiling: Global Views of806

Translation. Cold Spring Harbor Perspectives in Biology, a032698. issn: 1943-0264 (July807

2018).808

51. Atger, F. et al. Circadian and feeding rhythms differentially affect rhythmic mRNA tran-809

scription and translation in mouse liver. Proceedings of the National Academy of Sciences810

112, E6579–E6588. issn: 0027-8424 (Nov. 2015).811

52. González, A. & Hall, M. N. Nutrient sensing and TOR signaling in yeast and mammals.812

The EMBO Journal 36, 397–408. issn: 0261-4189 (Feb. 2017).813

53. Gutierrez, E. et al. eIF5A Promotes Translation of Polyproline Motifs. Molecular Cell 51,814

35–45. issn: 10972765 (July 2013).815

54. Lecanda, A. et al. Dual randomization of oligonucleotides to reduce the bias in ribosome-816

profiling libraries. Methods 107, 89–97. issn: 10959130 (2016).817

55. Gerashchenko, M. V. & Gladyshev, V. N. Translation inhibitors cause abnormalities in818

ribosome profiling experiments. Nucleic Acids Research. issn: 13624962. doi:10.1093/819

nar/gku671 (2014).820

56. Zhang, F., Xu, X., Zhou, B., He, Z. & Zhai, Q. Gene Expression Profile Change and821

Associated Physiological and Pathological Effects in Mouse Liver Induced by Fasting and822

Refeeding. PLoS ONE 6 (ed Androulakis, I. P.) e27553. issn: 1932-6203 (Nov. 2011).823

40



http://dx.doi.org/10.1093/nar/gku671



https://doi.org/10.1101/551838


57. Howard, M. T., Carlson, B. a., Anderson, C. B. & Hatfield, D. L. Translational redefinition824

of UGA codons is regulated by selenium availability. Journal of Biological Chemistry 288,825

19401–19413. issn: 00219258 (2013).826

58. Castelo-Szekely, V., Arpat, A. B., Janich, P. & Gatfield, D. Translational contributions to827

tissue specificity in rhythmic and constitutive gene expression. Genome Biology 18. issn:828

1474760X. doi:10.1186/s13059-017-1222-2 (2017).829

59. Lintner, N. G. et al. Selective stalling of human translation through small-molecule en-830

gagement of the ribosome nascent chain. PLOS Biology 15 (ed Khosla, C.) e2001882. issn:831

1545-7885 (Mar. 2017).832

60. Dittmar, K. A., Sørensen, M. A., Elf, J., Ehrenberg, M. & Pan, T. Selective charging of833

tRNA isoacceptors induced by amino-acid starvation. EMBO reports 6, 151–157. issn:834

1469-221X (Feb. 2005).835

61. Lowe, T. M. & Chan, P. P. tRNAscan-SE On-line: integrating search and context for836

analysis of transfer RNA genes. Nucleic acids research. issn: 13624962. doi:10.1093/nar/837

gkw413 (2016).838

62. Canella, D. et al. A multiplicity of factors contributes to selective RNA polymerase III839

occupancy of a subset of RNA polymerase III genes in mouse liver. Genome Research.840

issn: 10889051. doi:10.1101/gr.130286.111 (2012).841

63. Rudolph, K. L. M. et al. Codon-Driven Translational Efficiency Is Stable across Diverse842

Mammalian Cell States. PLoS Genetics 12, 1–23. issn: 15537404 (2016).843

64. Pechmann, S. & Frydman, J. Evolutionary conservation of codon optimality reveals hidden844

signatures of cotranslational folding. Nature Structural & Molecular Biology 20, 237–243.845

issn: 1545-9993 (Feb. 2013).846

41



http://dx.doi.org/10.1186/s13059-017-1222-2

http://dx.doi.org/10.1093/nar/gkw413



http://dx.doi.org/10.1101/gr.130286.111

https://doi.org/10.1101/551838


65. Woolstenhulme, C. J., Guydosh, N. R., Green, R. & Buskirk, A. R. High-Precision Analysis847

of Translational Pausing by Ribosome Profiling in Bacteria Lacking EFP. Cell Reports 11,848

13–21. issn: 22111247 (Apr. 2015).849

66. Chevance, F. F. V. & Hughes, K. T. Case for the genetic code as a triplet of triplets.850

Proceedings of the National Academy of Sciences 114, 4745–4750. issn: 0027-8424 (May851

2017).852

67. Kanaya, S., Yamada, Y., Kudo, Y. & Ikemura, T. Studies of codon usage and tRNA genes853

of 18 unicellular organisms and quantification of Bacillus subtilis tRNAs: Gene expression854

level and species-specific diversity of codon usage based on multivariate analysis. Gene.855

issn: 03781119. doi:10.1016/S0378-1119(99)00225-5 (1999).856

68. Ingolia, N. T. Ribosome profiling: new views of translation, from single codons to genome857

scale. Nature Reviews Genetics 15, 205–213. issn: 1471-0056 (Mar. 2014).858

69. Mohammad, F., Woolstenhulme, C. J., Green, R. & Buskirk, A. R. Clarifying the Trans-859

lational Pausing Landscape in Bacteria by Ribosome Profiling. Cell Reports 14, 686–694.860

issn: 22111247 (Feb. 2016).861

70. Wu, C. C.-C., Zinshteyn, B., Wehner, K. A. & Green, R. High-Resolution Ribosome Pro-862

filing Defines Discrete Ribosome Elongation States and Translational Regulation during863

Cellular Stress. Molecular Cell 0, 1–12. issn: 10972765 (Jan. 2019).864

71. Rodnina, M. V. The ribosome in action: Tuning of translational efficiency and protein865

folding. Protein Science 25, 1390–1406. issn: 09618368 (Aug. 2016).866

72. Agris, P. F., Eruysal, E. R., Narendran, A., Väre, V. Y. P., Vangaveti, S. & Ranganathan,867

S. V. Celebrating wobble decoding: Half a century and still much is new. RNA Biology 15,868

537–553. issn: 1547-6286 (May 2018).869

73. Alkatib, S. et al. The Contributions of Wobbling and Superwobbling to the Reading of the870

Genetic Code. PLoS Genetics 8 (ed Maraia, R. J.) e1003076. issn: 1553-7404 (Nov. 2012).871

42



http://dx.doi.org/10.1016/S0378-1119(99)00225-5

https://doi.org/10.1101/551838


74. Yang, J. R., Chen, X. & Zhang, J. Codon-by-Codon Modulation of Translational Speed872

and Accuracy Via mRNA Folding. PLoS Biology. issn: 15457885. doi:10.1371/journal.873

pbio.1001910 (2014).874

75. Stadler, M. & Fire, A. Wobble base-pairing slows in vivo translation elongation in meta-875

zoans. RNA 17, 2063–2073. issn: 1355-8382 (Dec. 2011).876

76. Pavlov, M. Y., Watts, R. E., Tan, Z., Cornish, V. W., Ehrenberg, M. & Forster, A. C. Slow877

peptide bond formation by proline and other N-alkylamino acids in translation. Proceedings878

of the National Academy of Sciences 106, 50–54. issn: 0027-8424 (2009).879

77. Lubas, M. et al. eIF5A is required for autophagy by mediating ATG3 translation. doi:10.880

15252/embr.201846072.881

78. Pelechano, V. & Alepuz, P. eIF5A facilitates translation termination globally and pro-882

motes the elongation of many non polyproline-specific tripeptide sequences. Nucleic Acids883

Research 45, 7326–7338. issn: 0305-1048 (July 2017).884

79. Shalgi, R., Hurt, J. A., Krykbaeva, I., Taipale, M., Lindquist, S. & Burge, C. B. Widespread885

Regulation of Translation by Elongation Pausing in Heat Shock. Molecular Cell 49, 439–886

452. issn: 10972765 (Feb. 2013).887

80. Somogyi, P., Jenner, A. J., Brierley, I. & Inglis, S. C. Ribosomal pausing during translation888

of an RNA pseudoknot. Molecular and cellular biology 13, 6931–40. issn: 0270-7306 (1993).889

81. Tholstrup, J., Oddershede, L. B. & Sørensen, M. A. MRNA pseudoknot structures can act890

as ribosomal roadblocks. Nucleic Acids Research. issn: 03051048. doi:10.1093/nar/gkr686891

(2012).892

82. Juszkiewicz, S., Chandrasekaran, V., Lin, Z., Kraatz, S., Ramakrishnan, V. & Hegde, R. S.893

ZNF598 Is a Quality Control Sensor of Collided Ribosomes. Molecular Cell 72, 469–481.894

issn: 10972765 (Nov. 2018).895

43



http://dx.doi.org/10.1371/journal.pbio.1001910



http://dx.doi.org/10.15252/embr.201846072



http://dx.doi.org/10.1093/nar/gkr686

https://doi.org/10.1101/551838


83. Simms, C. L., Yan, L. L. & Zaher, H. S. Ribosome Collision Is Critical for Quality Control896

during No-Go Decay. Molecular Cell 68, 361–373. issn: 10972765 (Oct. 2017).897

84. Arpat, A. B., Liechti, A., De Matos, M., Dreos, R., Janich, P. & Gatfield, D. Transcriptome-898

wide sites of collided ribosomes reveal principles of translational pausing. bioRxiv. doi:10.899

1101/710061 (0).900

85. Dittmar, K. A., Goodenbour, J. M. & Pan, T. Tissue-Specific Differences in Human Trans-901

fer RNA Expression. PLoS Genetics 2, e221. issn: 1553-7390 (Dec. 2006).902

86. Tatarinova, T. V., Alexandrov, N. N., Bouck, J. B. & Feldmann, K. A. GC3biology in corn,903

rice, sorghum and other grasses. BMC Genomics 11. issn: 14712164. doi:10.1186/1471-904

2164-11-308 (2010).905

87. Wolfson, R. L. & Sabatini, D. M. The Dawn of the Age of Amino Acid Sensors for the906

mTORC1 Pathway. Cell Metabolism 26, 301–309. issn: 19327420 (Aug. 2017).907

88. Dobin, A. et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21.908

issn: 13674803 (2013).909

89. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion910

for RNA-seq data with DESeq2. Genome Biology 15, 1–21. issn: 1474760X (2014).911

90. Robinson, M. D., Mccarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for912

differential expression analysis of digital gene expression data. BIOINFORMATICS AP-913

PLICATIONS NOTE 26, 139–140 (2010).914



http://dx.doi.org/10.1101/710061

http://dx.doi.org/10.1101/710061

http://dx.doi.org/10.1101/710061

http://dx.doi.org/10.1186/1471-2164-11-308

http://dx.doi.org/10.1186/1471-2164-11-308

http://dx.doi.org/10.1186/1471-2164-11-308

https://doi.org/10.1101/551838


E P A-1-2 +3 +4 +5 +6 +7

sites-specific codon DT fluxes (F)

codons

E

AP

codon covariates

gene covariatesfast slow

-3 +8 +9 ... +N-N ... -4-5-6

A

C

library biases

sites interactions

exit tunnel interactions

P E

A

B

m1

m1

m2

m2

m2

flux1

dwell time (DT) {-N, ..., +N}

flux1

flux2

flux2

flux2

CDS

EPA+1+N

-1

GGA

GGA

GGA

GGAGGA

GGA

GGA

GGAGGA

GGA

library biases

GGA GAG GAU CCG CUG CUU GAG AGA AGU GGGGAGACGCGAAATUAUAUU

-N

(F2)

(F2)

(F2)

(F1)

(F1)

ribosome footprints

inter-site codon pair DT

rib

oso

me

den

sity

(R

D)

P(F)

F

RD F DT~

CGT CGA CGG AGC GAC GGG CGC AAT AGA AAG GGA GGA ATG GAA AGG

mRNA1 E P A

E P A X +3X

+4X

+5X

+6X

+7X

+8X

+9X

+10X

+11X

+12X

+13X

+15X

+14X

-11X

-10X

-9X

-8X

-7X

-6X

-5X

-4X

-3X

-1X

-2Y flux

56

E P A 2reads count:

CGT CGA CGG AGC GAC GGG CGC AAT AGA AAG GGA GGA ATG GAA AGG CGT CGA CGG AGC GAC GGG CGC AAT AGA AAG GGA GGA ATG GAA AGG





CGT CGA CGG AGC GAC GGG CGC AAT AGA AAG GGA GGA ATG GAA AGG

5600000020

0

D

mRNA1

mRNA10

adapter trimmingmapping

size selectionsite assignment

3-nt CDS parsing and counting

gene selectionmappability

noise estimation (dispersion)

generalized linear model (NB)

sparse design matrix

coefficientsrepresentation

E

Figure S1: Modeling ribosome fluxes and codon-specific DT including codon-pair contributionsA. Ribosome elongation: Snapshot of two different mRNA species (m1,m2) translated with two different fluxes(F1, F2). Zoomed ribosome shows that numerous factors regulate ribosome DTs: 40 codons (+20,-20) aroundthe E site are taken into account in the model to alleviate possible library biases, exit tunnel interactions, andinfluence of upstream/downstream sequences. Codon-pair interactions between the three sites (E, P, A) are alsomodeled. The ribosome densities on the mRNAs are estimated by ribosome profiling, and modeled as genes fluxesmultiplied by DTs. B. Single-codon and codon-pair DTs are visualized in a heatmap, relative to the positionmean. The matrix of codon-pair interactions (E:P, P:A, and E:A) shows all possible combinations of codon pairs.All regression coefficients (gene fluxes and DTs) are inferred genome-wide. C. Bioinformatics pipeline: Sequencingreads are trimmed and mapped to the genome. Reads are first selected based on their size and E sites are assignedfor each read. All annotated CDSs are parsed with a step of 3 nucleotides and number of reads are reported ateach position. Genes with insufficient total read counts and read densities are removed, as well as regions withnon-unique mappability. Dispersion parameters for negative binomial distributions are estimated for each sampleand the GLM is fitted with a sparse design matrix and negative binomial (NB) noise model. DTs are centered(in log2 scale) and represented as shown in Fig. S1B. D. Construction of the data matrix for the GLM. Exampleof a gene CDS with two different positions (dark blue and dark red) covered by 56 and 2 reads, respectively. Theassigned E, P, and A sites are shown. The CDS is parsed 3-by-3 and a matrix is designed with the correspondingposition-dependent codons. E. Mean and variance of measured counts for pairs of codons occurring multiple timeson a gene. The green line shows a Poisson regime with the variance equal to the mean. The red line representsthe estimated fit for a negative binomial distribution (Methods). The dispersion parameter is estimated fromthese fits and used to parameterize the NB used in the GLM, independently for each sample.



https://doi.org/10.1101/551838


A:G

CA

A:G

CC

A:G

CG

A:G

CT

R:A

GA

R:A

GG

R:C

GA

R:C

GC

R:C

GG

R:C

GT

N:A

AC

N:A

AT

D:G

AC

D:G

AT

C:T

GC

C:T

GT

Q:C

AA

Q:C

AG

E:G

AA

E:G

AG

G:G

GA

G:G

GC

G:G

GG

G:G

GT

H:C

AC

H:C

AT

I:ATA

I:AT

CI:AT

TL

:CTA

L:C

TC

L:C

TG

L:C

TT

L:T

TAL

:TT

GK

:AA

AK

:AA

GM

:AT

GF

:TT

CF

:TT

TP

:CC

AP

:CC

CP

:CC

GP

:CC

TS

:AG

CS

:AG

TS

:TC

AS

:TC

CS

:TC

GS

:TC

TT

:AC

AT

:AC

CT

:AC

GT

:AC

TW

:TG

GY

:TA

CY

:TAT

V:G

TAV

:GT

CV

:GT

GV

:GT

T

16

15

14

13

12

11

10

9

8

7

6

5

4

3

−1

−2

−3

−4

−5

−6

−7

−8

−9

−10

−11

−12

−13

−14

−15

−16

−17

−18

−19

−20

−21

−22

−23

EPA

A B

P:C

CG

M:A

TG

C:T

GT

S:A

GC

L:T

TG

R:A

GG

S:T

CG

W:T

GG

Q:C

AG

G:G

GG

R:C

GC

H:C

AC

P:C

CC

V:G

TC

E:G

AG

E:G

AA

D:G

AC

A:G

CA

A:G

CC

A:G

CT

L:C

TG

L:C

TT

V:G

TAV

:GT

TL

:CTA

L:C

TC

I:AT

TL

:TTA

F:T

TC

C:T

GC

G:G

GC

G:G

GA

V:G

TG

P:C

CA

P:C

CT

R:C

GG

R:C

GA

I:ATA

Y:T

AT

D:G

AT

A:G

CG

Q:C

AA

H:C

AT

R:C

GT

G:G

GT

T:A

CT

T:A

CC

Y:T

AC

I:AT

CF

:TT

TN

:AAT

N:A

AC

K:A

AG

K:A

AA

T:A

CG

T:A

CA

S:T

CT

S:T

CC

S:T

CA

S:A

GT

R:A

GA

R:CGAP:CCGW:TGGR:CGGL:CTCP:CCCR:CGTR:AGGR:CGCP:CCAQ:CAAE:GAAG:GGAN:AACN:AATI:ATCS:TCGA:GCTC:TGTS:AGTC:TGCY:TACR:AGAS:TCTL:CTTT:ACAH:CACV:GTCM:ATGV:GTTI:ATTL:TTGQ:CAGF:TTCV:GTGF:TTTI:ATAL:CTAL:TTAK:AAAK:AAGT:ACGY:TATH:CATT:ACTP:CCTG:GGTG:GGGG:GGCD:GATD:GACV:GTAS:TCAE:GAGS:AGCA:GCCT:ACCS:TCCA:GCAL:CTGA:GCG−1

0

1

P

A

−3

−1

1

3

C D

0

1000

2000

3000

4000

5000

0.00 0.25 0.50 0.75 1.00

Pearson correlation coefficient

# o

f g

en

es

200

100

0

100

200

100 200 300 400CDS position

Nu

mb

er

of

co

un

ts

50

0

50

250 500 750CDS position

Nu

mb

er

of

co

un

ts

5

0

5

10

100 200 300 400CDS position

Nu

mb

er

of

co

un

ts

YJR104C, = 0.788ρ

YDR033W, = 0.702ρ

YML094W, = 0.519ρ

WT 3-AT

alphabetical clustered

Figure S2: In yeast ribosome DT anticorrelate with codon usage and exhibit codon-pair interactionsPanels for the single-codon DTs are retrieved from the fit with the P:A interaction. A. Gene-specific Pearsoncorrelation coefficient (ρ) between measured and fitted read counts. Number of genes with ρ larger than x-axisvalues are depicted. WT (green) and 3-AT (blue). B. Measured (black) and fitted (red) read counts for threerepresentative genes. C. Heatmap representation of the DTs (log2, mean centered per site) in a window of 40codons around the E site. Codons are ordered by amino acid and colored accordingly at the top of the heatmap.DTs with p >= 0.05 are not shown(set to zero). D. Interaction matrix for the pairs P:A (log2). Codons arecolored according to amino acid. Codons in both sites are hierarchically clustered based on the euclidean distancematrix and a complete linkage algorithm. Fast and slow interactions are shown respectively in dark red and darkblue (colorbar). DTs with p-value >= 0.05 are set to zero.



https://doi.org/10.1101/551838


E

P

AE

WT AL WT RF KO RF

E P A

Single P:A E:P E:A

A B

C

D E

0

2000

4000

6000

0.00 0.25 0.50 0.75 1.00


# o

f g

en

es

250

0

250

0 200 400 600

Nu

mb

er

of

co

un

ts

Azgp1, = 0.647

500

0

500

0 250 500 750 1000

Nu

mb

er

of

co

un

ts

Calr, = 0.611

200

100

0

100

200

0 500 1000 1500

CDS position

Nu

mb

er

of

co

un

ts

Papss2, = 0.611ρ

ρ

ρ

CDS position

CDS position

Figure S3: Sample specific DTs and correlationsA. Gene-specific Pearson correlation coefficient (ρ) between measured and fitted read counts for four AL samples[51]. Number of genes with ρ larger than x-axis values are depicted. B. Measured (black) and fitted (red) readcounts for three representative genes in one AL sample. C. Mean DTs and standard deviation over the 84 samplesfor the E, P, and A sites. D. DTs (log2, mean centered per site) at the E, P, and A sites for the 84 samplesin the three conditions WT AL (WT ad libitum), WT RF (WT night-restricted feeding), and KO RF (Bmal1KO night-restricted feeding). E. Inter-sample Spearman correlation coefficients for site-specific DTs (single) andcodon-pair DTs for the interactions P:A, E:P, and E:A.



https://doi.org/10.1101/551838


A

0e

+0

0

1e

+0

5

2e

+0

5

3e

+0

5

4e

+0

5

5e

+0

5

E + P + A + PAaE + P + A + PA

E + P + A + PaAE + P + A + PaAaE + P + A + EaAa

E + P + A + EPaE + P + A + EP

E + P + A + EaPE + P + A + EaPaE + P + A + EAaE + P + A + EaA

E + P + A + EAE + P + A

Ea + P + AE + Pa + AE + P + Aa

Ea + Pa + AEa + P + AaE + Pa + Aa

Ea + Pa + Aa

ΔBIC

A:G

CA

A:G

CC

A:G

CG

A:G

CT

R:A

GA

R:A

GG

R:C

GA

R:C

GC

R:C

GG

R:C

GT

N:A

AC

N:A

AT

D:G

AC

D:G

AT

C:T

GC

C:T

GT

Q:C

AA

Q:C

AG

E:G

AA

E:G

AG

G:G

GA

G:G

GC

G:G

GG

G:G

GT

H:C

AC

H:C

AT

I:ATA

I:AT

CI:AT

TL

:CTA

L:C

TC

L:C

TG

L:C

TT

L:T

TAL

:TT

GK

:AA

AK

:AA

GM

:AT

GF

:TT

CF

:TT

TP

:CC

AP

:CC

CP

:CC

GP

:CC

TS

:AG

CS

:AG

TS

:TC

AS

:TC

CS

:TC

GS

:TC

TT

:AC

AT

:AC

CT

:AC

GT

:AC

TW

:TG

GY

:TA

CY

:TAT

V:G

TAV

:GT

CV

:GT

GV

:GT

T

16

15

14

13

12

11

10

9

8

7

6

5

4

3

−1

−2

−3

−4

−5

−6

−7

−8

−9

−10

−11

−12

−13

−14

−15

−16

−17

−18

−19

−20

−21

−22

−23

EPA

D:G

AT

D:G

AC

N:A

AT

N:A

AC

G:G

GA

G:G

GT

G:G

GC

G:G

GG

E:G

AG

E:G

AA

V:G

TAV

:GT

GW

:TG

GY

:TAT

Y:T

AC

A:G

CG

A:G

CA

A:G

CC

V:G

TC

V:G

TT

R:C

GG

R:C

GA

R:C

GT

P:C

CG

L:C

TG

P:C

CC

P:C

CT

P:C

CA

C:T

GT

S:T

CT

S:T

CC

S:T

CA

R:A

GA

S:A

GT

S:A

GC

R:A

GG

L:C

TT

L:C

TC

F:T

TC

H:C

AT

Q:C

AG

Q:C

AA

F:T

TT

L:C

TAR

:CG

CK

:AA

GS

:TC

GK

:AA

AL

:TT

GL

:TTA

C:T

GC

I:AT

CH

:CA

CA

:GC

TI:AT

TT

:AC

GT

:AC

AT

:AC

CT

:AC

TI:ATA

M:A

TG

D:GATG:GGTG:GGGE:GAAG:GGAS:TCGL:TTGL:CTGK:AAGM:ATGQ:CAGP:CCGW:TGGE:GAGV:GTGR:CGGR:AGGR:AGAC:TGTS:AGTK:AAAD:GACR:CGCG:GGCH:CACN:AATN:AACS:AGCT:ACCS:TCCA:GCCF:TTCY:TACT:ACTA:GCTT:ACGA:GCGH:CATR:CGTT:ACAA:GCAQ:CAAP:CCAR:CGAS:TCAV:GTTY:TATC:TGCL:CTCI:ATCP:CCCV:GTCS:TCTI:ATAI:ATTP:CCTV:GTAL:TTAL:CTAF:TTTL:CTT −1

0

1

−2

−1

0

1

2

B

P

E

C D

alphabeticalclustered

Figure S4: Extended analysis of the translation elongation landscape in mouse liverSingle-codon DTs are retrieved from the fit with the P:A interaction. A. Heatmap representation of the DTs(log2, mean centered per site) in a window of 40 codons around the E site. Codons are ordered by amino acid andcolored accordingly. DTs with p >= 0.05 are not shown (set to zero). B. Interaction matrix for E:P interactions(log2). Codons are colored according to amino acid. Codons in both sites are hierarchically clustered based onthe euclidean distance matrix and a complete linkage algorithm. Relatively fast and slow interactions are shownrespectively in dark red and dark blue. DTs with p-value >= 0.05 are not shown (set to zero). C. DT (log2)distributions and boxplots for the three interaction terms P:A, E:P and E:A of the 84 samples in mouse liver. D.Differences of Bayesian Information Criterion (∆BIC) between the model shown and the best model. (∆BIC) iscomputed for each sample and proposed model, in which the alphabet for the DT covariates was taken as eitherthe 20 natural amino acids or the 61 sense codons. A lowercase ’a’ on the right of an uppercase letter indicatesthat the amino acid alphabet was used for this position.



https://doi.org/10.1101/551838


A B

C1 0.9

1

0.82

0.85

1

0.87

0.91

0.89

1

1 0.88

1

0.77

0.79

1

0.84

0.87

0.85

1

AL

CH

X

AL

NO

CH

X

FA C

HX

FA N

OC

HX

AL

CH

X

AL

NO

CH

X

FA C

HX

FA N

OC

HX

P:AE:P

−1

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

1

1 0.87

1

0.74

0.78

1

0.8

0.85

0.81

1

AL

CH

X

AL

NO

CH

X

FA C

HX

FA N

OC

HX

E:A

0

2000

4000

6000

0.00 0.25 0.50 0.75 1.00


# o

f g

en

es

FAWT

50

0

50

100


Nu

mb

er

of co

un

ts

50

0

50

0 250 500 750 1000CDS position

Nu

mb

er

of co

un

ts

qqqqqqqqqq qqqqqqqqqqqqqqqqqqq

5

0

5

10


Nu

mb

er

of co

un

ts

Bcap31, = 0.794ρ

Ppara, = 0.681ρ

Ddx56, = 0.576ρ

NNA or NNTNNG or NNC

CODON N3

Mea

n co

don

usa

ge b

ias

diffe

renc

es (

FA/A

L)

D

E

Figure S5: Ribosome DTs are not affected in fasted mice and with cycloheximideA. Gene-specific Pearson correlation coefficient (ρ) between measured and fitted read counts. Number of geneswith ρ larger than x-axis values are depicted. WT (blue) and FA (green) B. Measured (black) and fitted (red) readcounts for three representative genes in one AL sample. C. DTs in the E (top), P (middle) and A site (bottom)for the CHX vs. NOCHX conditions (left) and AL vs. FA conditions (right). Standard deviation is depicted.D. Pearson correlation coefficient between the different conditions for the interaction terms (log2) E:P, P:A, andE:A. E. Codon usage bias is computed for each gene up- or down-regulated in fasted animals and averaged. Thedifference in codon usage bias is computed between the FA and AL conditions. Codons are colored accordinglyto their nucleotide at the third position (G-C in blue and A-T in red).



https://doi.org/10.1101/551838


A B

C D

A

1 0.2

1

0.12

0.12

1

0.15

0.21

0.41

1

0.15

0.14

0.35

0.73

1

0.11

0.09

0.38

0.78

0.83

1

−1

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

1

Yeast_Jan

Human_Lintner

Kidney_Castelo

Liver_Gobet

Liver_Howard

Liver_Atger

Yeast_Jan

Human_Lintner

Kidney_Castelo

Liver_Gobet

Liver_Howard

Liver_Atger

AP:

1 0.65

1

0.54

0.9

1

0.51

0.68

0.48

1

0.53

0.93

0.96

0.54

1

−0.16

0.2

0.16

0.2

0.26

1

−1

−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

1

Human_Lintner

Liver_Gobet

Liver_Howard

Kidney_Castelo

Liver_Atger

Yeast_Jan

Human_Lintner

Liver_Gobet

Liver_Howard

Kidney_Castelo

Liver_Atger

Yeast_Jan

E

Figure S6: Meta-analysis revealed conserved DT patterns and technical biasesAnalysis of published ribosome profiling datasets in yeast (Yeast Jan) [49], mouse liver (Liver Atger, Liver Howard,Liver Gobet (this paper)) [51, 57], mouse kidney (Kidney Castelo) [58] and in a human hepatocyte cell line (Huh7Lintner) [59]. DTs were inferred for each sample and averaged by condition. A. Spread of the DTs (max-min,log2) at every positions in a window of 40 codons around the E site for studies using small RNA library protocols.Colors show the different datasets. B. Same as (A) for studies using "cDNA circularization" library protocols. C.Correlation for the A site DTs between the different datasets (Pearson coefficient is color coded). D. Same as (C)for P:A interaction. E. DTs in the A site for the Liver Howard, Kidney Castelo and Huh7 Linter datasets vs. DTsin the A site from the RP data in this paper (Liver Gobet, AL and FA).



https://doi.org/10.1101/551838


A

B

0.75

0.80

0.85

0.90

0.95

1.00

frac

tion

of r

eads

codons aminoacids

reassignedcodons

left-right probes matching

SA

ME

Adapter Cytidine

ACC

ACC

ACC

ACC

Biotin

C

N

NN

NNNN

N

NN

NNNNAdapter

UMI

UMI

Cytidine

ACC

ACC

ACC

ACC

C

N

NN

NNNN

N

NN

NNNN

BiotinylationHybridization

AminoAcid

ACC

ACC

ACC

ACC

5'p

ACC

ACC

ACC

ACC

3'OH

5'p

3'- hydrolyse

NaIO4

AminoAcid

ACC

ACC

ACC

ACC

5'p

ACC

ACC

ACC

ACC

5'p

Deacylation

ACC

ACC

ACC

ACC

5'p

ACC

ACC

ACC

ACC

5'p

3'OH

Pull-Down LigationRNA

degradation

Cytidine

ACC

ACC

ACC

ACC

CN

NN

NNNN

N

NN

NNNN

N

NN

NNNN

N

NN

NNNN

C

all probes (UMI per million, Log2)

all p

robe

s (-

Gly

, -P

ro)

(UM

I per

mill

ion,

log2

)

T4

DN

A L

igas

e(U

MI p

er m

illio

n, lo

g2)

T4 and SplintR DNA ligases(UMI per million, Log2)

D

tRNA levels(UMI per million, log2)

tRN

A P

ol3

(log2

)

R²:0.18, p-value= 0.0038E

tRNA levels(ZT04_AL/NaCl, rep 1, log2)

tRN

A l

evel

s(Z

T04

_AL/

NaC

l, re

p 2,

log2

)

F

Figure S7: (aminoacyl-) tRNA profiling in AL and FA mice (bis)A. tRNA profiling protocol. tRNAs were extracted in acidic conditions and uncharged tRNAs were hydrolyzedat 3’-end upon periodate treatment. tRNAs were treated with NaCl in control conditions. Then, tRNAs weredeacylated and biotinylated at their 3’-end. Mix of left and right DNA probes were hybridized to the tRNA poolsand pulled-down on magnetic beads through biotin-streptavidin interactions. Nicks in the anticodon between theleft and right probes were ligated. tRNAs were degraded and DNA probes sequenced after amplification. B. Readswere mapped on every combination of left and right probes. Fraction of reads corresponding to left-right probecombinations belonging to the same codon or amino acid is reported for the 24 samples. The same measure iscomputed after reassignment of the probe combinations (Methods). C. tRNA abundances (log2) at the codon levelfor the control vs. altered conditions in which probes related to tRNAs coding for Pro and Gly were removed.D. tRNA abundances (log2) at the codon level for experiments with T4 or SplintR DNA ligases. Significantdifferences are shown in red. E. tRNA abundances (log2) at the probe level between biological replicates in theNaCl/AL condition at ZT04. F. Correlation between tRNA abundances in control AL vs. RNA polymerase III(POL3) ChIP-Seq signal quantified on the tRNAs gene loci. Data were extracted from the supplementary tableof ref. [62].



https://doi.org/10.1101/551838


A

Cys:ACALeu:TAASer:GGATyr−M:GTA−MThr:CGTArg:TCTLeu:CAASer:AGASer:CGASer−M:GCT−MArg:CCTAla:GGCArg:CCGSeC:TCAArg:TCGIle:TATSer:TGAVal−M:TAC−MGln−M:TTG−MGlu:TTCGly:GCCThr:AGTTrp:CCAHis−M:GTG−MLys:CTTPro:TGGGly:TCCAla:TGCVal:TACIle:AATArg−M:TCG−MGln:CTGPro−M:TGG−MTyr:GTAIle−M:GAT−MCys−M:GCA−MPro:GGGIle:GATLeu:TAGPhe:GAAThr:TGTMet:CATAsn−M:GTT−MHis:GTGPro:AGGPro:CGGGlu:CTCGln:TTGAla:CGCSer:GCTLeu−M:TAG−MLeu−M:TAA−MThr−M:TGT−MAsp−M:GTC−MAla−M:TGC−MAsp:GTCAsn:GTTSer−M:TGA−MGly−M:TCC−MPhe−M:GAA−MTrp−M:TCA−MLys−M:TTT−MGlu−M:TTC−MAla:AGCVal:AACVal:CACiMet:CAT−MLys:TTTCys:GCAGly:CCCLeu:AAGLeu:CAG

AL_NaCL FA_NaCl AL_NaIO4 FA_NaIO4

q

q

qq

q

q

q

q

q

q

q

q

q

q

q

qq

q

q

q

q

q

q

q

04

12

18 04

12

18

04

1218

04

12

18

04

12

18

04

12

18

04

12

18

04

12

18

11

12

13

14

15

GTC Asp

q

q

qq

q

q

q

q

qq

q

q

q

q

q

qq

q

q

q

q

q

q

q

04

12

18 04

12

18

04

12

1804

12

18

04

12

18

0412

18

04

12

18

04

12

18

12

13

14

15

GTT Asn



tRN

A le

vels

(UM

I per

mill

ion,

Log

2)

q

q

q

q

q

q

q

qq

q

q

q

q

q

q

q

q

q

q

q

q

q

q

q

04

12

18

04

12

18

04

12

18

04

12

18

04

12

18

04

12

18

04

12

18

04

12

18

9.5

10.0

10.5

11.0

11.5

TAT Ile


tRN

A le

vels

(UM

I per

mill

ion,

Log

2)

tRN

A le

vels

(UM

I per

mill

ion,

Log

2)

Asn

Asp

Cys

Gln

Glu

His

Lys

Ph

e

Tyr

Ile Ala

Gly

Pro

Th

r

Va

l

Arg

Le

u

Se

r

ATTGTT

ATCGTC

ACAGCACTG

TTGCTC

TTCAT

GGTG

CTT

TTTAAA

GAA

ATAGTA AAT

GATTA

TAG

CCGCGGCTG

CAC

CCCCGCCTC

CAG

GCGGGGGTG

GAG

TCGTGGTTG

TAAC

CAC

GAC

TAC

ACGCCGCCTGCGTC

GTC

TAAG

CAA

CAG

GAG

TAATA

GAC

TAG

ACGAGCTGGATG

A

0.00

0.25

0.50

0.75

1.00

anti−codon

fra

ctio

n n

orm

. U

MI p

er

am

ino

acid G

CTA

N34

B C

Figure S8: (aminoacyl-) tRNA profiling in AL and FA mice (bis)A. Fraction of reads (UMI) per amino acid for the different isoacceptor tRNAs. Colors indicate the nucleotidein position 34 on the tRNA. B. Heatmaps of the normalized tRNA read counts (log2) at the codon level for the24 samples. tRNAs are ordered with hierarchical clustering. Dark blue (high), dark red (low). C. Expressionlevels for three tRNA coding respectively for Asn, Asp, and Ile, showing 5-8 fold differences between control andperiodate-treated conditions.



https://doi.org/10.1101/551838


landscapes of ribosome dwell times and relationship with ... · 1 landscapes of ribosome dwell...

Documents