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1 nanoCAGE reveals 5’UTR features that define specific modes of translation of
2 functionally related MTOR-sensitive mRNAs
3 Valentina Gandin1,2,3,4*, Laia Masvidal5*, Laura Hulea1,2*, Simon-Pierre Gravel4,6, Marie
4 Cargnello1,2, Shannon McLaughlan1,2, Yutian Cai1,4, Preetika Balanathan7, Masahiro
5 Morita4,6, Arjuna Rajakumar1, Luc Furic7, Michael Pollak1,2,3, John A. Porco, Jr.8, Julie
6 St-Pierre4,6, Jerry Pelletier2,4,6,, Ola Larsson5** and Ivan Topisirovic1,2,3,4**
7 1Lady Davis Institute, SMBD Jewish General Hospital, Montreal, Canada; Departments
8 of 2Oncology, 3Experimental Medicine, and 4Biochemistry McGill University, Montreal,
9 Canada; 5Department of Oncology-Pathology, Science for Life Laboratory, Karolinska
10 Institutet, Stockholm, Sweden; 6Goodman Cancer Research Centre, McGill University,
11 Montreal, Canada; 7Cancer Program, Biomedicine Discovery Institute and Department of
12 Anatomy & Developmental Biology, Monash University, VIC, Australia, 8Center for
13 Chemical Methodology and Library Development, Boston University, 590
14 Commonwealth Avenue, Boston, Massachusetts 02215, United States
15
16 *equally contributing
17
18 Correspondence: Ola Larsson, Department of Oncology-Pathology, Karolinska Institutet
19 Tomtebodavägen 23A, 171 65 Solna Sweden Tel: +46 (0)8 524 81 228; Email:
20 [email protected] & Ivan Topisirovic, Lady Davis Institute for Medical Research, Office
21 E-445; 3755 Côte Ste-Catherine Road, Montreal, Quebec H3T 1E2, Canada; Tel: (514)
22 340-8222 ext 3146; Email: [email protected]
23 Running title: 5’UTR features of MTOR-sensitive mRNAs.
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24 Key words: nanoCAGE, mRNA translation, MTOR, EIF4E, EIF4A1
25 Abstract
26 The diversity of MTOR-regulated mRNA translation remains unresolved. Whereas 27 ribosome-profiling suggested that MTOR almost exclusively stimulates translation of 28 TOP (terminal oligopyrimidine motif) and TOP-like mRNAs, polysome-profiling 29 indicated that MTOR also modulates translation of mRNAs without 5’TOP motif (non- 30 TOP mRNAs). We demonstrate that in ribosome-profiling studies detection of MTOR- 31 dependent changes in non-TOP mRNA translation was obscured by low sensitivity and 32 methodology biases. Transcription start site profiling using Nano-Cap Analysis of Gene 33 Expression (nanoCAGE) revealed not only do many MTOR-sensitive mRNAs lack 34 5’TOP motif but that 5’UTR features distinguish two functionally, and translationally 35 distinct subsets of MTOR-sensitive mRNAs: i) mRNAs with short 5’UTR enriched for 36 mitochondrial functions, that require EIF4E, but are less EIF4A1-sensitive and ii) long 37 5’UTRs mRNAs encoding proliferation- and survival-promoting proteins, that are both 38 EIF4E- and EIF4A1-sensitive. Selective inhibition of translation of mRNAs harboring 39 long 5’UTRs via EIF4A1 suppression leads to sustained expression of proteins involved 40 in respiration but concomitant loss of those protecting mitochondrial structural integrity, 41 resulting in apoptosis. Conversely, simultaneous suppression of translation of both long 42 and short 5’UTR mRNAs, by MTOR inhibitors, results in metabolic dormancy and a 43 predominantly cytostatic effect. Thus, 5’UTR features define different modes of MTOR- 44 sensitive translation of functionally distinct subsets of mRNAs which may explain the 45 diverse impact of MTOR and EIF4A inhibitors on neoplastic cells. 46
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52 Introduction:
53 Genome-wide gene expression studies have mostly focused on measuring ‘steady-state’
54 mRNA abundance that reflects alterations in transcription and mRNA stability (Piccirillo
55 et al. 2014). Changes in steady-state mRNA abundance do not, however, completely
56 mirror those occurring in the corresponding proteome (Gygi et al. 1999; de Sousa Abreu
57 et al. 2009; Larsson et al. 2012; Kristensen et al. 2013; Ly et al. 2014). Although still
58 debated (Li and Biggin 2015), these studies implicate post-transcriptional mechanisms,
59 including mRNA translation, as key influencers over the proteome (Schwanhausser et al.
60 2011; Vogel and Marcotte 2012; Kristensen et al. 2013; Li et al. 2014; Jovanovic et al.
61 2015).
62
63 Control of translation largely occurs at the initiation step, during which the mRNA is
64 recruited to the ribosome by the eIF4F complex (Hinnebusch 2014). This complex
65 consists of the mRNA cap-binding protein EIF4E, a large scaffolding protein EIF4G and
66 DEAD box helicase EIF4A (Hinnebusch 2014). eIF4F complex assembly is regulated by
67 the mechanistic/mammalian target of rapamycin (mTOR) complex 1 (mTORC1) which
68 phosphorylates and inactivates 4E-binding proteins (EIF4EBP1, 2 and 3) (von Manteuffel
69 et al. 1996; Hara et al. 1997; Burnett et al. 1998; Gingras et al. 1999; Gingras et al. 2001).
70 EIF4EBPs bind to EIF4E thereby preventing EIF4E:EIF4G association (Pause et al.
71 1994a). Phosphorylation of EIF4EBPs leads to their dissociation from EIF4E, allowing
72 EIF4E:EIF4G interaction and eIF4F assembly (Gingras et al. 1999; Gingras et al. 2001).
73 Although EIF4E is required for cap-dependent translation of all nuclear-encoded
74 mRNAs, a subset of mRNAs characterized by long and complex 5’UTRs that encode
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75 proliferation- (e.g. cyclins), survival- (e.g. BCL2 family members) and tumor-promoting
76 proteins (e.g. MYC) are thought to be particularly sensitive to EIF4E (Koromilas et al.
77 1992; Graff et al. 2008; Roux and Topisirovic 2012; Pelletier et al. 2015).
78
79 UTR features dictate translation efficiency (Sonenberg and Hinnebusch 2009). In
80 mammals, mRNAs with long, complex 5’UTRs exhibit a high EIF4A helicase
81 requirement for scanning of 43S complex towards the initiation codon (Svitkin et al.
82 2001). EIF4A processivity, as a single protein, is low (Pause et al. 1994b). EIF4A:EIF4G
83 interaction dramatically increases EIF4A processivity (Garcia-Garcia et al. 2015). EIF4E
84 recruits EIF4A to the eIF4F complex thereby bolstering EIF4A activity (Feoktistova et al.
85 2013). Recent ribosome-profiling studies indicate that the mTORC1/EIF4EBP/EIF4E
86 pathway almost exclusively regulates translation of mRNAs harboring 5’ terminal
87 oligopyrimidine (5’TOP) and related motifs (Hsieh et al. 2012; Thoreen et al. 2012).
88 5’TOP motif consists of a C immediately after the mRNA cap followed by 4-to-15
89 pyrimidines (Meyuhas and Kahan 2014). TOP mRNAs encode translational machinery
90 components, including ribosomal proteins and elongation factors (Meyuhas and Kahan
91 2014).
92
93 The generality of the conclusions that EIF4EBPs are major regulators of TOP mRNA
94 translation has been questioned since, under a number of conditions, translation of TOP
95 mRNAs, albeit MTOR-dependent, is EIF4EBP-independent (Miloslavski et al. 2014).
96 Consistent with this view, the effects of serum deprivation on TOP mRNA translation
97 also appeared to be EIF4E-independent (Shama et al. 1995). In turn, LARP1 and
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98 TIA1/TIAL1 (also known as TIAR) were reported to mediate effects of mTORC1 on
99 TOP mRNAs (Damgaard and Lykke-Andersen 2011; Tcherkezian et al. 2014; Fonseca et
100 al. 2015). Polysome-profiling showed that the mTORC1/EIF4EBP axis also regulates
101 translation of non-TOP mRNAs, including those encoding mitochondrial and survival-
102 and proliferation-promoting proteins (Larsson et al. 2012; Morita et al. 2013). In fact, it
103 appears that ribosome- and polysome-profiling studies provided strikingly different sets
104 of MTOR-sensitive mRNAs.
105
106 Elucidating the precise mechanisms that control mRNA translation also requires accurate
107 knowledge of transcription start sites (TSS). Despite substantial efforts to define TSS
108 using CAGE/nanoCAGE (Consortium et al. 2014) or TSS-seq (Suzuki and Sugano 2003;
109 Suzuki et al. 2015) there are no resources, which provide TSS information for most
110 commonly used model cell lines. Databases such as RefSeq or UCSC are thus routinely
111 used to infer the link between 5’UTR features and translation. These databases are
112 thought to contain numerous errors and for a more accurate understanding of the
113 relationship between the 5’UTR features and translational control both TSS and
114 translation efficiency should be determined in the same cell.
115
116 Results:
117 Simulating the impact of differences in polysome-association on performance of
118 ribosome- and polysome-profiling
119 We compared ribosome- and polysome-profiling for their ability to identify mRNAs
120 undergoing different shifts in translational efficiency. In polysome-profiling, translation
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121 efficiency is directly determined by separating efficiently (commonly defined as mRNAs
122 associated with >3 ribosomes) and not efficiently translated mRNAs (defined as mRNAs
123 associated with 3 or less ribosomes) (Gandin et al. 2014). In contrast, with ribosome-
124 profiling translational efficiency is inferred based on the number of RNA sequencing
125 reads corresponding to ribosome-protected fragments (Gandin et al. 2014; Ingolia 2014;
126 King and Gerber 2014). TOP mRNAs are highly expressed (Fig. 1A-B), engage a
127 considerable proportion of cellular ribosomes when MTOR is active (Levy et al. 1991;
128 Meyuhas and Kahan 2014; Miloslavski et al. 2014) and show large shifts in translational
129 efficiency upon MTOR inhibition, as compared to mRNAs that do not contain 5’TOP
130 motif [non-TOP mRNAs; Sup. Fig. 1 (Levy et al. 1991; Miloslavski et al. 2014)].
131
132 We next determined performance of ribosome- vs. polysome-profiling in identifying
133 differentially translated mRNAs as a function of the size of shifts in translational
134 efficiency using simulation approach. Suitable parameters for simulation were obtained
135 by comparisons of modeled normal distributions of ribosome association converted to
136 sedimentation distances (Fig. 1C) to experimentally determined ribosome associations in
137 insulin vs. insulin+torin1 treated MCF7 cells (Sup. Fig. 2A-C). Using these parameters,
138 we then modeled a range of shifts in control vs. MTOR inhibition conditions (Fig. 1D)
139 and estimated the mean number of ribosomes per mRNA [i.e. ribosome-profiling (Ingolia
140 et al. 2009; Ingolia 2014)] or the proportion of the mRNAs that are associated with >3
141 ribosomes [i.e. polysome-profiling (Larsson et al. 2012)]. We calculated fold-change
142 differences (control vs. MTOR inhibition) for each shift, for both polysome- and
143 ribosome-profiling simulations. Because equal proportions of all shifts were represented
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144 in the simulation, the proportion of each shift among the smallest p-values or largest fold-
145 changes is expected to be the same. Using p-value based ranking, polysome-profiling
146 appeared not to be affected by the size of the shift in polysome association, while
147 ribosome-profiling was biased toward identifying mRNAs that exhibit larger shifts (Fig.
148 1E-G). Similar bias was observed for polysome-profiling when fold-change analysis was
149 employed, but polysome-profiling still outperformed ribosome-profiling (Fig. 1H-I).
150 These analyses were stable inasmuch as modifying the simulation parameters did not
151 alter the outcome (Sup. Fig. 3).
152
153 Using the same approach we compared performance of ribosome- vs. polysome-profiling
154 in identifying changes of non-TOP vs. TOP mRNA translation as a function of MTOR
155 activity. Parameters were determined from experimentally tested TOP and non-TOP
156 mRNAs (Sup. Figs. 1-2). Shifts below sub 80s fractions cannot be modeled using normal
157 distributions, and were thus set to 1 ribosome. While polysome-profiling identified
158 expected equal proportions of TOP and non-TOP mRNAs among genes with the lowest
159 p-values, ribosome-profiling again favored identification of TOP mRNAs as
160 differentially translated (Sup. Fig. 4A). Analysis based on fold-changes showed bias for
161 identification of TOP mRNAs over non-TOP mRNAs as differentially translated for both
162 polysome- and ribosome-profiling, but the bias towards identifying TOP mRNAs as
163 differentially translated was still less pronounced for polysome-profiling (Sup. Fig. 4B-
164 C). Thus ribosome profiling shows a bias which favors identification of mRNAs that
165 exhibit larger shifts in polysomes as translationally regulated, which is the case for TOP
166 mRNAs after modulation of MTOR activity.
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167
168 Gene expression is commonly quantified by relative rather than absolute measurements.
169 Accordingly, changes in each specific mRNA translation is measured in relation to the
170 mean effect on translation of all mRNAs. To account for this we performed simulations
171 using relative translation efficacies and included invariant mRNAs [i.e. those mRNAs
172 that did not appear to change significantly their translation in response to MTOR
173 inhibition in both ribosome- (Hsieh et al. 2012) and polysome-profiling studies (Larsson
174 et al. 2012), hereafter referred to as ‘non-movers’]. Simulation parameters for ribosome
175 association for non-movers were obtained by fitting a normal distribution to the
176 polysome-profile (Sup. Fig. 2A). Changes in translation were then simulated as described
177 above for TOP and non-TOP mRNAs while including non-movers. We assessed how
178 increasing the proportion of TOP mRNAs that changed their translation impacts predicted
179 fold-changes for TOP- and non-TOP mRNAs. This was done before and after
180 normalization by global centering (which is equivalent to an absolute and relative
181 assessment of effects on translation, respectively). For relative measurements, when an
182 increased proportion of TOP mRNAs were differentially translated, there was an
183 underestimation of the fold-changes for both TOP and non-TOP mRNAs (Fig. 1J). This
184 effect was more pronounced for ribosome-profiling e.g. if ~3.5% of all mRNAs are TOP
185 mRNAs and they changed translation, non-TOP mRNAs will show less than a 2-fold
186 difference which is a commonly used threshold.
187
188 As MTOR inhibition also involves a global downregulation of protein synthesis (Roux
189 and Topisirovic 2012), we assessed how this would impact on TOP and non-TOP mRNA
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190 translation. For this simulation we used the parameters identified for abundance and
191 proportions of TOP mRNAs, non-TOP mRNAs and non-movers (Fig. 1A), and gradually
192 introduced a reduction in translation for non-mover mRNAs to simulate reduction in
193 global protein synthesis caused by MTOR inhibition (Fig. 1K). In this case, as above,
194 non-TOP mRNAs exhibited <2 fold-change when ribosome-profiling was simulated.
195 Altogether, methodological features of ribosome-profiling introduce a bias that obscures
196 identification of changes in non-TOP mRNA translation. This is because non-TOP
197 mRNAs show smaller shifts and lower abundance, and are therefore more sensitive to
198 global changes in protein synthesis as compared to TOP mRNAs. This is likely a key
199 factor underpinning the discrepancies between the repertoires of MTOR-regulated
200 mRNAs observed in ribosome vs. polysome-profiling studies.
201
202 Low sensitivity limits the catalogue of MTOR-sensitive mRNAs identified by
203 ribosome-profiling.
204 Optimal RNA-seq depth under control conditions (i.e. vehicle or mock treated cells) is a
205 prerequisite to identify a reduction in translation induced by a treatment (Larsson and
206 Nadon 2013). We observed that less than 2,000 genes consistently had >256 ribosome-
207 profiling counts in the control condition (Sup. Fig. 5A) across the two replicates reported
208 in Hsieh et al. (Hsieh et al. 2012). Indeed, low read counts strongly influenced the signal
209 to variance relationship and the variance strongly increased at <64 reads (Sup. Fig. 5B,
210 Sup. Fig.6A). Using edgeR analysis (see below), we observed that the vast majority of
211 MTOR-sensitive mRNAs did not shift below <64 read threshold upon MTOR inhibition
212 (Fig. 2B). This suggests that only highly abundant mRNAs (e.g. TOP mRNAs [Fig. 1A-
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213 B]) with signals substantially higher than 64 reads under control condition could be
214 identified as translationally suppressed by MTOR inhibitors (Sup. Fig. 5B). This
215 suggests that only abundant mRNAs [e.g. TOP mRNAs (Fig. 1A-B)] would be identified
216 as being translationally suppressed by MTOR inhibitors (MTOR-i) in this study (Hsieh et
217 al. 2012). Indeed, <10% of non-TOP mRNAs, identified as being MTOR-sensitive in
218 polysome-profiling study (Larsson et al. 2012) consistently obtained >256 ribosome-
219 profiling counts in the control condition in Hsieh et al. (Hsieh et al. 2012). Alternative
220 algorithms corroborated these findings (Sup. Fig. 6B). A second ribosome-profiling data
221 set investigating MTOR-sensitive translation (Thoreen et al. 2012) also suffered from this
222 type of low sensitivity and could not identify non-TOP mRNAs as being differentially
223 translated (Sup. Fig. 5C-D, Sup. Fig. 7A-B). Thus, despite the differences in cell lines
224 and conditions that were used in respective studies (Hsieh et al. 2012; Larsson et al. 2012;
225 Thoreen et al. 2012), discrepancies in the observed effects of MTOR-i on the translatome
226 between ribosome- and polysome-profiling studies appear to stem from technical biases
227 and the low sensitivity in ribosome-profiling studies, and our simulation analysis
228 indicates that in addition to TOP mRNAs, MTOR regulates translation of non-TOP
229 mRNAs.
230 nanoCAGE identifies subsets of MTOR-sensitive non-TOP mRNAs that differ in
231 5’UTR features and functions.
232 The above analysis indicates that other 5’UTR features beyond 5’TOP motifs must render
233 mRNA translation MTOR-sensitive. We employed nanoCAGE sequencing (Salimullah et
234 al. 2011) to map Transcription Start Sites (TSS) and 5’UTRs to investigate this
235 hypothesis (Fig. 2A). When we reached a sequencing ‘plateau’, where few additional
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236 genes or TSSs were identified upon additional sequencing (Fig. 2B), we had obtained
237 5’UTR sequence information for ~12,000 RefSeqs, corresponding to ~6,500 genes in
238 MCF7 cells maintained under the control, mock treatment condition used when
239 examining MTOR-sensitive translation by polysome-profiling [Sup. Table 1 (Larsson et
240 al. 2012)]. Examination of a selection of TOP mRNAs showed that nanoCAGE
241 accurately captured TSS including when the TOP motif was suggested by RefSeq to be
242 located downstream of the TSS (Fig. 2C). TSS identified by nanoCAGE confirmed that
243 the most of MTOR-regulated mRNAs identified by polysome-profiling [(Larsson et al.
244 2012), PP242-sensitive] do not contain a 5’TOP motif (~90%, Fig. 2D).
245
246 Determination of 5’UTR lengths using most prevalent peak length (i.e. the length
247 suggested by the position with most RNA-seq reads) or the weighted mean length
248 (allowing all RNA-seq reads to contribute to a mean) revealed ~30% 5’UTRs identified
249 by nanoCAGE were half the length or shorter than those described in the RefSeq
250 database (Fig. 2E-F; Sup. Fig. 8A-B). Among the MTOR-sensitive mRNAs identified by
251 polysome-profiling, the TSS determined by nanoCAGE, but not RefSeq, represented a set
252 of transcripts with extremely short 5’UTRs (<30 nucleotides). This set was enriched in
253 mRNAs encoding proteins with mitochondrial functions (e.g. ATP5O, ATP5G1; Fig. 3A-
254 B) with one exception, RDH13, which had a long 5’UTR. Short 5’UTRs were in stark
255 contrast to cell cycle- and survival-promoting mRNAs with long 5’UTRs [e.g. CCND3,
256 ODC1, MCL1, BIRC5 (survivin) and MYC; Fig. 3C]. mRNAs with short 5’UTR,
257 encoding proteins with mitochondrial function, were enriched (4.7 fold enrichment,
258 p=0.03) for Translation Initiator of Short 5′UTR (TISU) elements (Elfakess et al. 2011)
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259 (Fig. 3D-E). Thus, in addition to TOP mRNAs, MTOR-sensitive mRNAs include non-
260 TOP mRNAs with long 5’UTRs which encode proliferation- and survival-promoting
261 proteins and a subset of mRNAs with short (<30 nucleotides) 5’UTRs that were enriched
262 with TISU elements (Fig. 3A-E).
263 Expression of proteins encoded by a subset of MTOR-sensitive mRNAs harboring
264 short 5’UTRs requires EIF4E, but is largely unaffected by EIF4A1.
265 TISU elements render mRNA translation sensitive to MTOR but not to EIF4A inhibition
266 (Elfakess et al. 2011). We therefore depleted EIF4E or EIF4A1 in MCF7 and HEK293E
267 cells and monitored expression of proteins encoded by MTOR-sensitive mRNAs with
268 either long (CCND3, MCL1, BIRC5, and BCL2) or short (ATP5O, ATP5G1, NDUFS6,
269 and UQCC2) 5’UTRs. Whereas EIF4E depletion decreased expression of both subsets of
270 proteins, reduction in EIF4A1 selectively affected proteins encoded by long 5’UTR
271 mRNAs (Fig. 4A, Sup. Fig. 10A). We replicated these results using the active-site
272 MTOR-i torin1 and two chemically distinct EIF4A inhibitors (EIF4A-i), hippuristanol
273 and silvestrol (Bordeleau et al. 2005; Bordeleau et al. 2006; Bordeleau et al. 2008). As
274 expected, treatment with torin1 abolished mTORC1 signaling (Thoreen et al. 2009;
275 Galicia-Vazquez et al. 2012), whereas hippuristanol slightly induced mTORC1 activity as
276 illustrated by changes in EIF4EBP1 and RPS6 phosphorylation (Sup. Fig. 10B). Torin1
277 increased EIF4E:EIF4EBP1 and reduced EIF4E:EIF4G1 binding. In contrast
278 EIF4E:EIF4G1 association was unaffected by hippuristanol (Sup. Fig. 10C). Thus, as
279 noted following depletion of EIF4E, torin1 reduced expression of proteins encoded by
280 both short and long 5’UTR MTOR-sensitive mRNAs, whereas EIF4A-i selectively
281 downregulated proteins encoded by long 5’UTRs mRNAs (Sup. Fig. 10B). These effects
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282 appeared to be mediated by the EIF4A1 isoform, as EIF4A2 depletion failed to decrease
283 expression of CCND3, which is encoded by a prototypical long 5’UTR mRNA (Sup. Fig.
284 10D). As observed previously, EIF4A1 depletion lead to an increase in EIF4A2 levels
285 (Sup. Fig. 10D) (Galicia-Vazquez et al. 2012). Collectively, these results demonstrate
286 that EIF4A1 regulates expression of proteins encoded by a subset of MTOR-sensitive
287 mRNAs harboring long, but not short 5’UTRs.
288
289 EIF4A1 affects translation of MTOR-sensitive mRNAs with long but not short
290 5’UTRs.
291 Depletion of EIF4E and EIF4A1 did not affect steady-state levels of mRNAs with either
292 long (CCND3, MCL1, BIRC5, and BCL2) or short (ATP5O, ATP5G1, NDUFS6, and
293 UQCC2) 5’UTRs (Fig. 4B). Under these conditions the effects of EIF4E or EIF4A1
294 depletion on global mRNA translation were comparable (Fig. 4C). Whereas EIF4E
295 downregulation decreased translation of both long and short 5’UTR mRNAs, EIF4A1
296 depletion decreased translation of mRNAs with long 5’UTRs, but only marginally altered
297 translation of mRNAs with short 5’UTRs (Fig. 4D, Sup. Fig. 11A). Neither EIF4A1 nor
298 EIF4E depletion altered distribution of MTOR-insensitive mRNAs, e.g. ACTB (beta
299 actin) and GAPDH mRNAs (Fig. 4D, Sup. Fig. 11A). Parallel results were obtained
300 using torin1 and hippuristanol (Sup. Fig. 11B-C).
301
302 To further identify 5’UTR features that render MTOR-sensitive mRNAs largely
303 insensitive to changes in EIF4A1 activity, HEK293E cells were co-transfected with a
304 renilla (control) and a variety of firefly (FF) luciferase reporters. FF reporters included
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305 IRF7 5’UTR [IRF7(5’UTR)-FF], which is long and translated in an EIF4E-dependent
306 manner (Colina et al. 2008) (Fig. 5A), ATP5O 5’UTR [ATP5O(5’UTR)-FF] which
307 contains a 4 nucleotide portion of the TISU element upstream of the initiation codon (Fig.
308 5B) and ATP5O with a complete TISU element including a 5 nucleotide portion distal to
309 the initiation codon [ATP5O(TISU)-FF] (Fig. 5C). ATP5O(5’UTR)-FF therefore harbors
310 a disrupted TISU element whereby the TISU element sequence is substituted with
311 GAGAA (the first 5 nucleotides of FF luciferase ORF) downstream of the initiation
312 codon. EIF4E downregulation strongly decreased expression of all three FF reporters. In
313 contrast, while EIF4A1 depletion strongly repressed luciferase activity of IRF7-FF, it
314 only had a moderate effect on ATP5O(5’UTR)-FF (1.2 fold reduction vs. 6.6 fold
315 observed in EIF4E depleted cells), and did not significantly affect luciferase activity
316 driven by ATP5O(TISU)-FF (Fig. 5A-C). Similar results were obtained with torin1 and
317 hippuristanol (Sup. Fig. 12A-C). As TISU element integrity did not appear to exert a
318 major influence on EIF4A1- or EIF4E-sensitive translation (Fig. 5B-C; Sup. Fig. 12B-
319 C), we also monitored expression of FF luciferase driven by constructs harboring
320 UQCC2 5’UTR which does not contain a TISU and NDUFS6 5’UTR, which, similar to
321 ATP5O(5’UTR)-FF, only contains a portion TISU element upstream of the initiation
322 codon. In both cases results paralleled those observed using the ATP5O reporters (Fig.
323 5D-E). Increasing secondary structure and length by adding a stable stem loop structure
324 (ATP5O(5’UTR)-SL-FF) (Babendure et al. 2006) rendered the reporter both EIF4E and
325 EIF4A1-sensitive (Fig. 5F). Importantly, under all conditions listed above, control renilla
326 luciferase expression was largely insensitive to both EIF4E and EIF4A1 (Sup. Fig. 13-
327 14). These data demonstrate that translation of MTOR-sensitive mRNAs with short
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328 5’UTR is strongly sensitive to EIF4E, but not EIF4A1, while there is no obligatory
329 requirement for an intact TISU element.
330
331 Effects of MTOR-i and EIF4A-i on translation are reflected by changes in
332 mitochondrial functions.
333 mTORC1/EIF4EBP1/EIF4E axis bolsters mitochondrial biogenesis and ATP production
334 by stimulating translation of nuclear-encoded mRNAs with mitochondrial functions,
335 including respiratory chain components e.g. ATP5O (Morita et al. 2013). Here,
336 nanoCAGE identified a subset of MTOR-sensitive mRNAs encoding mitochondria-
337 related proteins characterized by short 5’UTRs (Fig. 3B-D), which is consistent with the
338 findings that mRNAs encoding mitochondrial proteins frequently contain TISU elements
339 (Sinvani et al. 2015). As translation of mRNAs with short 5’UTRs is largely EIF4A-
340 independent, we postulated that short-term EIF4A1 inhibition would have a lesser impact
341 on mitochondrial number and function as compared to MTOR inhibition. To directly
342 compare the effects of MTOR-i vs. EIF4A-i, we selected concentrations of torin1 and
343 silvestrol that comparably suppressed HEK293E cell proliferation (~60% of control; Fig.
344 6A, left panel). After 16h [a time period we previously found to be suitable for detecting
345 the impact of MTOR inhibition on cell mitochondrial content and respiration (Morita et
346 al. 2013)], torin1, but not silvestrol, reduced mitochondrial content (Fig. 6A, right
347 panel).
348
349 In addition, although both torin1 (1.9 fold) and silvestrol (1.4 fold) reduced mitochondrial
350 respiration, the effect of torin1 was more substantial (Fig. 6B). Similar findings were
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351 obtained using siRNA directed against EIF4E or EIF4A1 (Sup. Fig. 15A). Furthermore,
352 whereas EIF4A1 downregulation slightly elevated mitochondrial respiration relative to
353 the control, mitochondrial respiration was dramatically inhibited by EIF4E depletion
354 (Sup. Fig. 15B). Therefore, short-term treatment with MTOR-i suppresses mitochondrial
355 respiration more dramatically than EIF4A-i, which is consistent with the disparity of their
356 effects on translation of mRNAs harboring short 5’UTRs i.e. those encoding proteins
357 involved in mitochondrial respiration and functions.
358 EIF4A-i more dramatically induces apoptosis compared to MTOR-i.
359 Inhibition of the mTORC1/EIF4EBP1/EIF4E axis coincides with downregulation of
360 proteins implicated in mitochondrial respiratory function (e.g. ATP5O, ATP5G1,
361 UQCC2, NDUFS6) as well as in the maintenance of the integrity of the mitochondrial
362 outer membrane (e.g. BCL2, MCL1 and BIRC5) (Fig. 4A). In contrast, suppression of
363 EIF4A1 activity selectively affects proteins that maintain mitochondrial outer membrane
364 integrity (Fig. 4A). This explains why MTOR inhibitors mostly have a cytostatic effect
365 (Morita et al. 2015), and suggests that prolonged EIF4A1 inhibition may trigger
366 mitochondrial mediated apoptosis by disrupting the coordinated expression of proteins
367 with mitochondrial metabolic functions from those regulating mitochondrial structural
368 integrity. We therefore treated HEK293E cells with torin1 or silvestrol for 72h and
369 measured mitochondrial membrane potential using TMRE (tetramethylrhodamine, ethyl
370 ester), a cell-permeant dye that accumulates in active but not depolarized mitochondria
371 (Scaduto and Grotyohann 1999). As a positive control, cells were treated with carbonyl
372 cyanide 4-(trifluoromethoxy)phenylhydrazone (FCCP), a compound that depolarizes
373 mitochondria (Fig. 7A). Exposure to silvestrol (72h) markedly induced depolarization of
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374 mitochondria (Fig. 7A) and this correlated with induction of apoptosis as compared to
375 control or torin1 treated cells (Fig. 7B). Similar results were observed in nutrient-
376 deprived cells depleted of EIF4E or EIF4A1 (Sup. Fig. 15C-D). These observations
377 indicate that suppression of translation of both long and short 5’UTR MTOR-sensitive
378 mRNAs by torin1 yields a predominantly cytostatic outcome, whereas suppression of
379 EIF4A1 function induces apoptosis by exclusively inhibiting translation of mRNAs with
380 long 5’UTRs encoding proteins that maintain mitochondrial structural integrity.
381
382 MTOR inhibition increases autophagy (Kim and Guan 2015) which eliminates
383 depolarized mitochondria (Elmore et al. 2001; Narendra et al. 2008; Twig et al. 2008). To
384 determine whether EIF4A1 inhibition affects autophagy, we monitored autophagic flux
385 by treating HEK293E cells with torin1 or silvestrol in the presence of chloroquine.
386 Chloroquine prevents autophagosome-lysosome fusion causing accumulation of the
387 autophagic markers MAP1LC3A-II (LC3A-II) and MAP1LC3B-II (LC3B-II) (Klionsky
388 et al. 2012). While torin1 induced autophagy as evidenced by elevated levels of both
389 MAP1LC3A-II and MAP1LC3B-II (Fig. 7C), silvestrol prevented accumulation of
390 MAP1LC3A-II and MAP1LC3B-II induced by chloroquine (Fig. 7C). By inhibiting the
391 lysosomal pH gradient, chloroquine impairs mTORC1 activation (Settembre et al.
392 2012) as illustrated by the decrease in the phosphorylation of EIF4EBP1 and RPS6 after
393 16h (Fig. 7C). mTORC1 inhibition by chloroquine was partially reversed by silvestrol
394 (Fig. 7C). This is consistent with induction of mTORC1 in response to EIF4A1 inhibition
395 [Sup. Fig. 10B; (Galicia-Vazquez et al. 2012)]. Therefore, in addition to inducing
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396 mitochondrial depolarization by altering mRNA translation, EIF4A-i prevent induction of
397 autophagy and elimination of depolarized mitochondria.
398
399 Discussion
400 The advantage of ribosome- over polysome-profiling is that ribosome-profiling allows
401 determination of ribosome positioning on mRNA at single-nucleotide resolution (Ingolia
402 et al. 2009). However, there are a number of challenges regarding interpretation of
403 ribosome-profiling (Guttman et al. 2013; Gerashchenko and Gladyshev 2014; King and
404 Gerber 2014; Lareau et al. 2014; Pelechano et al. 2015). The relative performance of
405 ribosome- vs. polysome-profiling to study changes in translation efficiency also remains
406 unclear. mRNAs exhibit different translation properties, in part conferred by their 5’UTR
407 features (Lodish 1974; Pelletier and Sonenberg 1985; Koromilas et al. 1992; Graff et al.
408 2008; Sonenberg and Hinnebusch 2009). We show that ribosome-profiling studies should
409 be interpreted with caution when simultaneously studying subclasses of mRNAs with
410 disparate translation properties. Ribosome-profiling efficiently identifies changes in
411 translation of highly abundant mRNAs that show large shifts in polysome-association
412 (“class A mRNAs”) but is far less sensitive in detecting less abundant mRNAs that shift
413 less (“class B mRNAs”). TOP mRNAs, upon MTOR inhibition, shift from heavy
414 polysomes to sub-polysomal fractions (i.e. “class A mRNAs”), whereas the majority of
415 non-TOP mRNAs transit from intermediate-heavy to light polysomes [i.e. “class B
416 mRNAs” (Sup. Fig. 1]. In contrast, polysome-profiling captures the translational changes
417 in both classes of mRNAs. This may explain discrepancies between the catalogs of
418 MTOR-regulated mRNAs between ribosome- (Hsieh et al. 2012; Thoreen et al. 2012) vs.
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419 polysome-profiling (Larsson et al. 2012) studies. Consistently, while ribosome-profiling
420 led to the conclusion that the mTOR/EIF4EBP1/EIF4E axis exclusively controls TOP and
421 TOP-like mRNA translation (Hsieh et al. 2012), the same group found that EIF4E
422 predominantly affects translation of non-TOP mRNAs implicated in ROS clearance using
423 polysome-profiling (Truitt et al. 2015).
424
425 Length, and plausibly other 5’UTR features, distinguish two separate sub-groups of
426 MTOR-sensitive non-TOP mRNAs which are functionally distinct. Firstly, there are
427 those mRNAs harboring long 5’UTRs that encode survival and proliferation promoting
428 factors and are strongly affected by EIF4E and EIF4A1. Secondly, there are mRNAs that
429 harbor short 5’UTRs/TISU elements, translated in EIF4E but not EIF4A1-sensitive
430 manner, enriched in those encoding mitochondria-related proteins. In mammals, EIF4A1
431 selectively stimulates translation of mRNAs that encode survival-promoting proteins (e.g.
432 BCL2, MCL1 and BIRC5) without altering translation of mitochondria-related short
433 5’UTRs mRNAs (Rubio et al. 2014; Wolfe et al. 2014; Modelska et al. 2015). In yeast,
434 Ded1 (the orthologue of DDX3X in mamals) rather than EIF4A seems to act as a major
435 helicase that resolves secondary 5’UTR structures (Sen et al. 2015). Accordingly, in
436 yeast, changes in EIF4A activity uniformly affect translation of most mRNAs, while
437 modulation of Ded1 primarily affects those with complex 5’UTRs (Sen et al. 2015).
438 Several features that distinguish yeast and mammalian EIF4A may explain these
439 distinctions. This includes differences in the apparent stability of the eIF4F complex, as
440 EIF4E:EIF4G but not EIF4E:EIF4G:EIF4A complexes can be purified from yeast
441 (Merrick 2015). Moreover, MTOR-regulated auxiliary factor EIF4B (Raught et al. 2004)
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442 significantly bolsters RNA-dependent ATPase activity of mammalian, but not yeast
443 EIF4A (Merrick 2015). It is also plausible that helicases thought to be required to unwind
444 exceptionally strong 5’UTRs secondary structures [e.g. DHX29 and DDX3X; (Parsyan et
445 al. 2011)] provide an additional level of diversity to MTOR-dependent translational
446 regulation. However, the signaling mechanisms that regulate DHX29 and DDX3X
447 function remain to be established.
448
449 Translation of TISU mRNAs is 5’ cap-dependent, resistant to glucose deprivation but not
450 to rapamycin, and requires EIF1:EIF4G1 interaction (Sinvani et al. 2015). We
451 demonstrate that MTOR-i or EIF4E depletion, but not inhibition of EIF4A1, suppresses
452 translation of short 5’UTR mRNA (<30 nucleotides). This suggests that EIF4E is key for
453 translation of mRNAs containing short 5’UTRs and TISU elements, which is postulated
454 to occur by facilitating recruitment of EIF4G1, and which does not appear to be affected
455 by EIF4A-i (Sup. Fig. 10C). Moreover, MTOR and EIF4E seem to regulate translation of
456 ATP5O mRNAs independently of the TISU element downstream of the initiation codon,
457 as well as translation of UQCC2 mRNA that comprises a short 5’UTR without a TISU
458 (Fig 5B-D). This suggests that the length of 5’UTR, rather than the presence of a TISU, is
459 the primary factor that determines EIF4E- versus EIF4A1-sensitivity.
460
461 mTORC1 modulates translation of TOP mRNAs via LARP1 (Tcherkezian et al. 2014;
462 Fonseca et al. 2015), whereas synthesis of mitochondrial, proliferation- and survival-
463 promoting proteins is controlled by the EIF4EBP/EIF4E pathway (Larsson et al. 2012;
464 Morita et al. 2013). 5’UTR features further separate non-TOP MTOR-sensitive mRNAs
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465 in two distinctive groups that differ in their EIF4A1-sensitivity. MTOR-i prevent
466 translation of short and long 5’UTR mRNAs diminishing mitochondrial functions,
467 accompanied by metabolic quiescence and cytostasis, whereas selective suppression of
468 translation of a subset of long but not short 5’UTR mRNAs by EIF4A-i leads to
469 mitochondrial depolarization and apoptosis (Fig. 7D). These findings suggest that
470 mTORC1 deploys distinct effectors to modulate translation of a variety of functional
471 groups of mRNAs far beyond TOP mRNAs to regulate cellular homeostasis in response
472 to a variety of extracellular stimuli and intracellular cues.
473
474 In addition to the differential effects of MTOR-i and EIF4A-i on translation, it is likely
475 that these compounds exert opposing metabolic effects. MTOR-i reduce mitochondrial
476 ATP production while suppressing ATP consumption by anabolic processes and
477 stimulating autophagy which facilitates replenishment of a variety of cellular building
478 blocks (Morita et al. 2013; Albert and Hall 2014; Shimobayashi and Hall 2014).
479 Although EIF4A-i may also decrease ATP consumption by the translational machinery,
480 they are likely to stimulate other anabolic processes via increasing mTORC1 activity
481 [Sup. Fig. 10B; (Galicia-Vazquez et al. 2012)]. Contrary to previous reports, the effects
482 of silvestrol on tumor cell viability cannot be accounted for by modulation of p-EIF4E
483 status (Chu et al. 2015). EIF4A-i may therefore avoid compensatory effects seen with
484 MTOR-i, resulting in a more effective precipitation of mitochondrial dysfunction and cell
485 death. Indeed, unlike MTOR-i, silvestrol attenuated autophagy, which is considered a
486 major mechanism for nutrient replenishment and clearance of depolarized mitochondria
487 (Elmore et al. 2001; Narendra et al. 2008; Twig et al. 2008).
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488
489 In conclusion, important 5’UTR mRNA variations determine MTOR-sensitivity. Current
490 databases do not always accurately reflect the 5’UTR features while certain analytical
491 biases currently limit the accuracy of ribosome-profiling techniques for monitoring
492 changes in translational efficiency. Finally, two functionally distinct subgroups of non-
493 TOP MTOR-sensitive mRNAs provide molecular basis for the superior anti-neoplastic
494 effects of EIF4A-i over MTOR-i.
495 Methods:
496 Simulations
497 Distributions of ribosome associations were obtained by sampling from a normal
498 distribution. These were then converted to mean number of ribosomes (equivalent to
499 ribosome-profiling) or proportions of mRNAs associated with >3 ribosomes (equivalent
500 to polysome-profiling) followed by calculation of fold-changes and p-values that were
501 compared across polysome- and ribosome-profiling. Detailed protocols for simulations
502 are provided in the supplement.
503 Preparation of nanoCAGE libraries and analysis of nanoCAGE data
504 NanoCAGE libraries were prepared and analyzed as described (Salimullah et al. 2011)
505 but with a number of modifications that are described in detail in the supplement.
506 Cell culture, inhibitors, antibodies and RNAi
507 HEK293E and MCF7 cells (ATCC) were maintained in Dulbecco modified Eagle
508 medium (DMEM; Wisent, St-Bruno, Quebec, Canada) and RPMI-1640 (Wisent)
509 respectively, supplemented with 10% fetal bovine serum (Wisent), 1%
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510 penicillin/streptomycin (Wisent) and 1% L-Glutamine (Wisent). Where indicated, cells
511 were treated with 250nM torin1 (Tocris, Bristol, United Kingdom), 1μM hippuristanol,
512 25nM silvestrol or the same volume of vehicle (DMSO). Lists of siRNAs, shRNAs
513 antibodies and dilutions that were used in Western blots are provided in the supplement.
514 Polysome profiles and RT-qPCR
515 MCF7 cells were seeded in a 15cm Petri dish, harvested at 80% confluency and lysed in
516 hypotonic lysis buffer (5mM Tris HCl pH7.5, 2.5mM MgCl2, 1.5mM KCl, 100 μg/ml
517 cycloheximide, 2mM DTT, 0.5% Triton, 0.5% Sodium Deoxycholate). Polysome-
518 profiling was carried out as described (Gandin et al. 2014). RT-qPCR was performed as
519 previously described (Miloslavski et al. 2014). Fractions were collected as described in
520 (Gandin et al. 2014) and RNA was extracted using TRIzol according to manufacturer’s
521 instructions. RT and qPCR were performed by SuperScript III Reverse Transcriptase and
522 Fast SYBR Green Mastermix (Invitrogen), respectively. Experiments were done at least
523 in independent duplicates (n=2) whereby every sample was analyzed in a technical
524 triplicate. Analyses were carried out using relative standard curve method as described in
525 http://www3.appliedbiosystems.com/cms/groups/mcb_support/documents/generaldocum
526 ents/cms_040980.pdf. Primers are listed in the supplement.
527 Statistical analysis
528 We performed 1-way or 2-way ANOVA in R (R Core Team 2014) as indicated in the
529 figure legends.
530
531 Data access
532 nanoCAGE data have been deposited at the Gene Expression Omnibus (GSE77033).
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533
534 Acknowledgments:
535 This work is dedicated to the memory of Colin Lister. We thank Alvin Kuate Defo for
536 assistance, Drs. J. Timmons, L. Lindqvist, O. Meyuhas, C. Plessy and R. Dikstein for
537 editing and critical input, suggestions on nanoCAGE sequencing, and TISU element
538 search, respectively. We acknowledge support from Science for Life Laboratory, the
539 Knut and Alice Wallenberg Foundation, the National Genomics Infrastructure funded by
540 the Swedish Research Council, and Uppsala Multidisciplinary Center for Advanced
541 Computational Science for assistance with massively parallel sequencing and access to
542 the UPPMAX computational infrastructure. LDI flow cytometry facility supported in part
543 by MSSS and CFI (223964). This research was supported by the Swedish Research
544 Council, the Swedish Childhood Cancer Foundation, the Swedish Cancer Society, the
545 Cancer Society in Stockholm, the Wallenberg Academy Follows Program and
546 STRATCAN grants (OL), grants from the Canadian Institutes of Health Research (MOP-
547 115195 to IT, MOP-115126 to JP and MOP-106603 to JSt-P), Terry Fox Research
548 Institute (TFF 116128) to IT, JSt-P, and MP, and NIH R01 grant (GM073855, JAP, Jr).
549 Research in the BU-CMD was supported by NIH grant GM067041, Swedish foundation
550 for international cooperation in research and higher education (STINT) OL, IT and LF. IT
551 is a CIHR New Investigator and recipient of FRQS establishment award. J.St-P. is a
552 FRQS scholar. LM is supported by a post-doc fellowship from the Swedish Childhood
553 Cancer Foundation. MC and YC hold fellowships from the CIHR/FRQS training grant in
554 cancer research FRN53888 of the McGill Integrated Cancer Research Training Program.
555 IT is indebted to Prof. R. McInnes for allowing him to focus on science and not nonsense.
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556 Disclosure declarations:
557 Authors have no conflicts to report.
558 Figure legends:
559 Figure 1. Biases in polysome- and ribosome-profiling studies of translation
560 efficiency. (A-B) Mean expression levels and densities according to data from poly(A)+
561 RNA for mRNAs identified as differentially translated by ribosome-profiling (Hsieh et al.
562 2012; Thoreen et al. 2012), which include ~60-70% TOP mRNAs; polysome-profiling
563 (Larsson et al. 2012), which includes ~10% TOP mRNAs; and non-movers. Percentage
564 of mRNAs and mean reads per kilobase per million mapped reads (Rpkm) per subset is
565 indicated. (C) Calculated relationship between number of bound ribosomes and
566 sedimentation in a 5-50% sucrose gradient. (D) Simulated distributions of ribosome
567 association under a condition when MTOR is inhibited (black) and a range of ribosome
568 association under the control condition. (E, H) Proportions of differentially translated
569 mRNAs identified by polysome or ribosome-profiling simulations from analyzed shifts in
570 polysome-association. Genes were ranked by p-value (E) or fold-change (H). (F, I)
571 Observed bias (i.e. obtained percentage of genes from each shift compared to expected
572 percentage) across all shifts. (G). Sums of bias across all shifts for each technology and
573 analysis approach. (F,G,I). Means and standard deviations from 4 replicated simulations
574 are shown. (J-K) Observed absolute or relative fold-changes using polysome or
575 ribosome-profiling for TOP and non-TOP mRNAs under conditions when an increasing
576 proportion of TOP mRNAs change their translation (J) or when global translation is
577 affected (K).
578
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579
580 Figure 2. Transcription start site analysis using nanoCAGE confirms that MTOR
581 regulates translation of non-TOP mRNAs. (A) The nanoCAGE procedure to identify
582 transcription start sites (TSS; BC=barcode; SP=spacer; MS-RanN6=random hexamer;
583 MS-Dir1R/F=primers for semi-suppressive PCR; MS-Dir2R/F=primers for adaptor
584 PCR). (B) A comparison of the number of identified peaks (>5 reads; left) and RefSeq
585 mRNAs (>50 reads; right) under increasing number of sampled nanoCAGE sequencing
586 reads. (C) TSS revealed by nanoCAGE in two TOP mRNAs. The UTR-lengths suggested
587 by the 3 major TSS peaks are indicated together with the 5’UTR region according to
588 RefSeq and the position of the 5’TOP motif. (D). The proportion of MTOR-sensitive (by
589 polysome-profiling) mRNAs with TOP elements according to nanoCAGE. (E-F). A
590 comparison between 5’UTR lengths according to RefSeq and nanoCAGE mean lengths
591 for all RefSeq mRNAs detected (E) and MTOR-sensitive genes (F). Green lines indicate
592 a 2-fold difference in 5’UTR length.
593
594 Figure 3. Mitochondria related transcripts are enriched among MTOR-sensitive
595 mRNAs with extremely short 5’UTRs. (A) Enrichment of biological processes in
596 MTOR-sensitive mRNAs (from polysome-profiling) depending on nanoCAGE or RefSeq
597 5’UTR lengths. Colors represent false discovery rates for enrichments among short (blue)
598 or long (yellow) 5’UTRs. Both mean and largest peak 5’UTR lengths from nanoCAGE
599 were used as input. (B) A table of genes from the mitochondria-related functions
600 (identified in [A]) and their peak 5’UTR lengths as determined by nanoCAGE. (C) A
601 comparison of 5’UTR lengths of MTOR-sensitive mitochondrial function or cell
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602 cycle/survival related genes. (D) Comparison of 5’UTR lengths and the presence of TISU
603 element in the indicated subsets of MTOR-sensitive mRNAs. (E) ATP5O is a subunit of
604 Complex V of the electron transport chain (ATP synthase) and is encoded by mRNA with
605 a putative TISU motif and an extremely short 5’UTR (plot as in Fig. 2C)
606
607 Figure 4. Expression of proteins encoded by mRNAs with long 5’UTR, but not those
608 harboring short 5’UTRs/TISU elements, is dependent on EIF4A1. (A) HEK293E and
609 MCF7 cells were mock transfected or transfected with siRNA targeting EIF4A1 (si-
610 EIF4A1), EIF4E (si-EIF4E) or scrambled control siRNA (Scr). Levels of indicated
611 proteins were determined 48h post-transfection by Western blotting. ACTB served as a
612 loading control. Experiments were carried out in independent triplicate and quantified
613 using densitometry (Sup. Fig 9). (B) Total levels of indicated mRNAs isolated from cells
614 described in (A) were determined by RT-qPCR. Data were log2 transformed and values
615 obtained for indicated mRNAs were normalized to those obtained for ACTB, and to the
616 mean expression per gene. (C) Sub-polyosmal, light polysome and heavy polysome
617 fractions were obtained from cytosolic extracts from cells described in (A) by
618 ultracentrifugation using 5-50% sucrose gradients. During fractionation, UV absorbance
619 at 254nm (Abs 254nm) was continuously monitored to obtain absorbance tracings.
620 Position of 40S and 60S ribosomal subunits, monosome (80S) and polysomes are
621 indicated. (D) Amount of indicated mRNAs in sub-polyosmal, light polysome and heavy
622 polysome fractions isolated from cells described in (C) were determined by RT-qPCR.
623 Experiments in panels (B) and (D) were carried out in independent duplicates each
624 consisting of a triplicate. Data are expressed as a percentage of a given mRNA in each
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625 fraction. Bars represent SD values. P-values from 1-way ANOVAs for heavy polysomes
626 are indicated.
627
628 Figure 5. Translation of reporter mRNAs harboring short 5’UTRs is sensitive to
629 EIF4E, but not EIF4A1. HEK293E cells were transfected with a control (Scr), EIF4E
630 (si-EIF4E) or EIF4A1 (si-EIF4A1) siRNAs and then transfected with firefly (FF)
631 reporters harboring IRF7 5’UTR [IRF7(5’UTR)-FF] (A), ATP5O 5’UTR with a proximal
632 portion of TISU element before the initiation codon [ATP5O(5’UTR)-FF] (B), ATP5O
633 5’UTR with a full TISU element [ATP5O(TISU)-FF] (C), UQCC2 5’UTR
634 [UQCC2(5’UTR)-FF] (D), NDUFS6 5’UTR [NDUFS6(5’UTR)-FF] (E) or ATP5O
635 5’UTR followed by a stem-loop structure [ATP5O(5’UTR)-SL-FF]. As a control, cells
636 were co-transfected with a renilla reporter. Luminescence was monitored 48h post-
637 transfection. Data for firefly luminescence normalized to renilla luminescence were log2
638 transformed and normalized per replicate and to the mean of Scr. (see Sup. Fig. 14). Each
639 experiment was performed in independent triplicates each consisting of 3 replicates and
640 data are shown as a mean +/- SD. RLU, relative light units. Schematics of the FF
641 constructs are provided in the above histograms and the positions of TISU and ORF of FF
642 luciferase are indicated. P-values from 1-way ANOVAs are shown.
643
644 Figure 6. Short-term (16h) MTOR, but not EIF4A1 inhibition, decreases
645 mitochondrial number and respiration. (A) HEK293E cells were treated with torin1
646 (250nM), silvestrol (silv; 25nM) or a vehicle (DMSO) control for 16h. Cells were
647 counted to ensure that used concentrations of torin1 and silvestrol result in comparable
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648 inhibition of proliferation (left panel) and mitochondrial mass was estimated by
649 monitoring mean fluorescence intensity of MitoTracker Green using flow cytometry
650 (right panel). Data were log2 transformed, normalized per replicate and to the mean of
651 DMSO, and are shown as means +/- SD (n=3). (B) HEK293E cells were treated with
652 torin1 (250nM), silvestrol (silv; 25nM) or DMSO for 16h. Mitochondrial respiration was
653 assessed using a Clark electrode and presented as oxygen consumption rate (OCR). Data
654 are shown as means of 4 independent experiments (n=4) +/- SD in linear scale. P-values
655 from 1-way ANOVAs calculated after log2 transformation and normalization per
656 replicate and to DMSO are indicated.
657
658 Figure 7. Prolonged (72h) inhibition of EIF4A1, but not MTOR compromises
659 mitochondrial integrity and leads to apoptosis (A) HEK293E cells were treated with
660 torin1 (250nM), silvestrol (silv; 25nM) or a vehicle (DMSO) for 72h. Mitochondrial
661 membrane potential (MMP) was analyzed by monitoring TMRE fluorescence intensity
662 by flow cytometry. As a control, cells were treated with FCCP (20μM; 10 minutes) to
663 induce mitochondrial membrane depolarization. Left panel: The flow cytometry
664 histogram profiles of unstained (control; black), and TMRE stained cells treated with
665 FCCP (blue), a vehicle (DMSO; red), torin1 (orange) or silvestrol (silv; green). Cells
666 harboring depolarized mitochondria (MMP-) were defined as those with TMRE signal
667 equal of less to TMRE signal observed in FCCP-treated cells. Middle panel: Mean
668 percentage (n=3) of MMP- and MMP+ cells out of total viable cells (DAPI positive dead
669 cells were excluded). Right panel: Overall TMRE emission expressed in arbitrary units
670 (a.u.). Results are presented as mean +/- SD (n=3). (B) HEK293E cells were treated as
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671 described in (A) and apoptosis was measured by flow cytometry using a FITC-Annexin
672 V/PI staining. The fractions (%) of viable (Annexin V-/ PI-), early apoptotic (Annexin
673 V+/ PI-), late apoptotic (Annexin V+/PI low), and dead (Annexin V+/ PI high) cells are
674 shown relative to the total cell population. Results represent means +/- SD (n=3). P-
675 values from 1-way ANOVAs are shown. (C) HEK293E cells were treated with torin1
676 (250nM), silvestrol (silv; 25nM) in the presence or absence of chloroquine (CQ, 50µM)
677 for 4 and 16h. Autophagy induction was monitored by the detection of MAP1LC3A/B
678 lipidation (MAP1LC3A-II and MAP1LC3B-II) using appropriate antibodies. The levels
679 and phosphorylation status of indicated proteins were monitored by Western blotting.
680 ACTB served as a loading control. (D) Schematic representation of the model: In
681 addition to 5’TOP motif (TOP mRNAs), 5’UTR features define two distinct subsets of
682 non-TOP mRNAs whose translation is stimulated by MTOR (mTOR; green): i) mRNAs
683 with long 5’UTRs whose translation is sensitive to both EIF4E (blue) and EIF4A1 (red),
684 which encode pro-survival (e.g. BCL2) and proliferation-promoting (e.g. CCND3)
685 proteins and ii) mRNAs with short 5’UTRs whose translation is sensitive to EIF4E
686 (blue), but not EIF4A1 (red) that encode proteins with mitochondrial functions (e.g.
687 ATP5O). Whereas MTOR-i (mTOR-i;green) suppress translation of both subsets of
688 mRNAs (i and ii) leading to metabolic dormancy and cytostatic effect, EIF4A-i (red)
689 selectively inhibit translation of mRNAs with long 5’UTRs (i) leading to apoptosis.
690
691 References:
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nanoCAGE reveals 5' UTR features that define specific modes of translation of functionally related MTOR-sensitive mRNAs
Valentina Gandin, Laia Masvidal, Laura Hulea, et al.
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