Landscapes of ribosome dwell times and relationship with ... · 1 Landscapes of ribosome dwell...
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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
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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
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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
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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
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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
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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
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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
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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.
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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
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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
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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.
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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
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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
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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".
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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
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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).
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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
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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.
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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
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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
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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
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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
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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
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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)
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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
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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
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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
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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
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(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
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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
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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
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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
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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
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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
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E P A-1-2 +3 +4 +5 +6 +7
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56
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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 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
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 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
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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.
.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
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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.
.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
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
Pearson correlation coefficient
# 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.
.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
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.
.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
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
Pearson correlation coefficient
# o
f g
en
es
FAWT
50
0
50
100
0 200 400CDS position
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
0 500 1000CDS position
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).
.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
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).
.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
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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].
.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
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
q
q
q
q
q
q
q
q
q
q
q
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
q
q
q
q
q
q
q
q
q
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
AL_NaCL FA_NaCl AL_NaIO4 FA_NaIO4
AL_NaCL FA_NaCl AL_NaIO4 FA_NaIO4
tRN
A le
vels
(UM
I per
mill
ion,
Log
2)
q
q
q
q
q
q
q
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
AL_NaCL FA_NaCl AL_NaIO4 FA_NaIO4
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.
.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