# Intricate ribosome composition and translational reprogramming in epithelial–mesenchymal transition

Chloé Morin, Agnès Baudin-Baillieu, Flora Nguyen van Long, Caroline Isaac, Laure Bidou, Hugo Arbes, Pauline François, Roxane M Pommier, Annie Adrait, Akari Saku, et al.

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## To cite this version:

Chloé Morin, Agnès Baudin-Baillieu, Flora Nguyen van Long, Caroline Isaac, Laure Bidou, et al. "Intricate ribosome composition and translational reprogramming in epithelial–mesenchymal transition. Proceedings of the National Academy of Sciences of the United States of America, 2024, 121 (50), pp.e2408114121. 10.1073/pnas.2408114121. hal-04869990

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## Main Manuscript for

# Intricate ribosome composition and translational reprogramming in epithelial-mesenchymal transition

Chloé Morin¹, Agnès Baudin-Baillieu², Flora Nguyen Van Long¹, Caroline Isaac¹, Laure Bidou²,³, Hugo Arbes², Pauline François², Roxane M Pommier⁴, Annie Adrait⁵, Akari Saku¹, Stephanie Gran-Ruaz¹, Camélia Machkouri¹, Christophe Vanbelle⁶, Romain Morichon⁷, Mathieu Boissan⁸,⁹ Frédéric Catez¹, Anthony Ferrari⁴, Anne-Pierre Morel¹⁰, Yohann Couté⁵, Sophie Chat¹¹, Emmanuel Giudice¹¹, Reynald Gillet¹¹, Alain Puisieux¹²,¹³, Caroline Moyret-Lalle¹, Jean-Jacques Diaz¹, Olivier Namy²,<fnref n="14" />,*, Virginie Marcel¹,<fnref n="14" />,<fnref n="15" />,*

¹« Ribosome, Translation and Cancer » team, LabEx DEVweCAN, Institut Convergence Plascan, LYriCAN+, Centre de Recherche en Cancérologie de Lyon, INSERM U1052, CNRS UMR 5286, Centre Léon Bérard, Université de Lyon, Université Claude Bernard Lyon 1, 69008 Lyon, France

²Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France

³Sorbonne Université, 4 Place Jussieu, 75005 Paris, France

⁴Bioinformatics Platform Gilles Thomas, Centre de Recherche en Cancérologie de Lyon, INSERM U1052, CNRS UMR 5286, Centre Léon Bérard, Université de Lyon, Université Claude Bernard Lyon 1, Synergie Lyon Cancer Fondation, 69008 Lyon, France

⁵Univ. Grenoble Alpes, INSERM, CEA, UA13 BGE, CNRS, CEA, FR2048, 38000 Grenoble, France

⁶Cell Imaging Platform, LYriCAN+, Centre de Recherche en Cancérologie de Lyon, INSERM U1052, CNRS UMR 5286, Centre Léon Bérard, Université de Lyon, Université Claude Bernard Lyon 1, 69008 Lyon, France

⁷Cytometry and Imagery platform Saint-Antoine (CISA), SU Centre de Recherche Saint-Antoine, Paris, France

⁸Sorbonne Université, INSERM, Centre de Recherche Saint-Antoine, Paris, France

⁹APHP, Hôpitaux Universitaires Pitié-Salpêtrière-Charles Foix, Laboratoire de Biochimie Endocrinienne et Oncologique, Oncobiologie Cellulaire et Moléculaire, Paris, France

¹⁰« EMT and Cancer Cell Plasticity » team, Centre Léon Bérard, 69008 Lyon, France

¹¹Univ. Rennes, CNRS, Institut de Génétique et Développement de Rennes (IGDR) UMR6290, 35000 Rennes, France

¹²Institut Curie, PSL Research University, Paris, France

¹³Chemical Biology of Cancer Laboratory, CNRS UMR3666, INSERM U1143, Paris, France

<footnote n="14">¹⁴These authors contributed equally</footnote>

<footnote n="15">¹⁵Lead contact</footnote>

<footnote>*Olivier Namy and Virginie Marcel</footnote>
**Author Contributions:** CM, ABB, FNVL, CI, LB, AA, AS, SGR, CMa, CV, RM, MB, FC, APM and SC performed and analyzed experiments. CM, ABB, FNVL, LB, MB, YC, EG, RG, CML, JJD, ON and VM analyzed and interpreted the data. HA, PF, RMP, AF, YC and ON performed bioinformatics analyses. APM and AP provided cellular models. JJD, ON and VM shaped the research question and coordinated the project. CM, FNVL, ON and VM supervised the daily experiments. CM and VM wrote the first draft of the manuscript. All authors contribute to the final version of the manuscript.

**Competing Interest Statement:** The authors declare that there have no competing interests.

**Classification:** Biological Sciences/Genetics

**Keywords:** EMT, ribosome heterogeneity, ribosome profiling, RPL36A, ZEB1
**Abstract**

Epithelial-mesenchymal transition (EMT) involves profound changes in cell morphology, driven by transcriptional and epigenetic reprogramming. However, evidence suggests that translation and ribosome composition also play key roles in establishing pathophysiological phenotypes. Using genome-wide analyses, we reported significant rearrangement of the translational landscape and machinery during EMT. Specifically, a cell line overexpressing the EMT transcription factor ZEB1 displayed alterations in translational reprogramming and fidelity. Furthermore, using riboproteomics we unveiled an increased level of the ribosomal protein RPL36A in mesenchymal ribosomes, indicating precise tuning of ribosome composition. Remarkably, RPL36A overexpression alone was sufficient to trigger the acquisition of mesenchymal features, including a switch in molecular pattern, cell morphology and behavior, demonstrating its pivotal role in EMT. These findings underline the importance of translational reprogramming and fine-tuning of ribosome composition in EMT.

**Significance Statement**

Translation and its machinery appear to be key players in pathophysiological processes. In particular, translational factors have been implicated in cell identity change, such as during epithelial-mesenchymal transition (EMT). However, it remains to be determined whether ribosome components also contribute to the change in cellular identity. Here, we have shown that a single ribosomal protein is responsible for EMT. Not only are ribosome composition and translation modulated during EMT, but overexpression of RPL36A is sufficient to trigger the acquisition of mesenchymal characteristics. This work demonstrates the existence of heterogeneity in ribosome composition during cell identity change, and the unique role of a single ribosomal protein in controlling cell identity.
# Main Text

## Introduction

The epithelial-mesenchymal transition (EMT) is a dynamic and often reversible process of transdifferentiation that generates mesenchymal cells from epithelial ones¹⁻³. Its cellular hallmarks include loss of adhesiveness and cell polarity promoting cell dispersion, and the acquisition of a specific cytoskeletal architecture that enhances cell migration and invasion. Despite the contribution of EMT to normal organ physiology during embryogenesis and adulthood, its aberrant reactivation has been reported in different human diseases⁴.

EMT commitment is driven by deep transcriptional reprogramming coupled to epigenetic events¹⁻³. It relies on an intricate network of EMT-inducing transcription factors (EMT-TFs) including the different ZEB (ZEB1, ZEB2), TWIST (TWIST1, TWIST2) or SNAIL (SNAIL, SLUG, SNAI3) families. EMT-TFs were primarily shown to repress CDH1 (i.e., E-cadherin) transcription and activate VIM (i.e., Vimentin), inducing the loss of cell-cell contacts of epithelial cells and the acquisition of motility properties⁵,⁶. This transcriptional regulation is associated with an epigenetic reprogramming that has been suggested to govern the stability of EMT by defining the residency of cells in a given phenotypic state during the transition⁷. Although changes in gene expression are the driving force of EMT-induced cell plasticity, few studies have assessed the role of translation in EMT³. It was reported that the reactivation of EMT-TFs and mesenchymal markers at the translational level was sufficient to promote EMT. Indeed, IRES (Internal Ribosome Entry Site)-dependent translation of LAMB1 or SNAI1 mRNAs and the specific translation of Dab2 or ILEI mRNAs were shown to trigger EMT³,⁸⁻¹². However, translational reprogramming during EMT remains to be characterized.

Increasing evidence supports the notion that components of the translational apparatus can specifically modulate the translation of some mRNAs, thus contributing to the establishment of different pathophysiological phenotypes. For example, the eukaryotic initiation factor eIF3D, which stabilizes the complex integrating the initiator tRNA and the small 40S ribosomal particle, directly regulates the translation of c-JUN and BTG1 mRNAs to modulate cell growth¹³,¹⁴. Emerging data also indicate that the ribosome itself is an actor of translational regulation, modulation of ribosome composition contributing to the specific translation of particular mRNA subsets¹⁵,¹⁶. Heterogeneity in ribosome composition has been reported at both ribosomal RNA (rRNA) and protein levels, the latter being one of the most extensively investigated mechanisms. In mice, depletion of RPL38 decreases IRES-dependent translation of Hoxa9 mRNA and results in the loss of tissue patterning¹⁷,¹⁸. Interestingly, it was reported that increased ribosome biogenesis promotes EMT¹⁹. Furthermore, components of the translational apparatus promote EMT, as the acquisition of EMT
features is driven by changes in the tRNA repertoire or by decreasing the expression of the
canonical eIF3E factor<sup>20,21</sup>.

However, to date, no study has compared the modulation of transcriptional and translational landscapes in stable epithelial and mesenchymal stages, or the associated changes in ribosome composition. Here, using genome-wide approaches we depict the fine-tunings of both translational landscape and machinery that occur during EMT in adult human mammary cells, as well as the role of ribosomal proteins in EMT commitment.

Results

Translational reprogramming is as important as transcriptional reprogramming in specifying the molecular and cellular phenotype of ZEB1-induced mesenchymal cells

To decipher changes in translation and transcription occurring in mesenchymal cells compared to
epithelial ones, we performed ribosome profiling in an isogenic model of human mammary epithelial
cells (hMEC) overexpressing or not ZEB1, one of the strongest EMT-TF drivers<sup>22</sup>. As previously
described, ZEB1 overexpression was sufficient to promote a complete switch in both cell
morphology and EMT-related molecular features (Fig.S1). Using this stable isogenic model, we
compared (i) steady-state mRNA levels in epithelial and mesenchymal stages, by analyzing total
RNA using RNAseq that reflects changes in transcription level, nuclear-cytoplasmic export and
mRNA stability, thereafter simply referred to total mRNA level; as well as (ii) actively translated
mRNA levels, by analyzing ribosome-protected mRNAs using RIBOseq (i.e., ribosome footprinting
coupled to RNAseq). As previously shown, RIBOseq data mainly detected the 29-mer motif
resulting from the precise mRNA protection by the ribosome, the mapping of which is enriched in
mRNA open-reading frames (Fig.S2A-C)<sup>23</sup>.

Based on biological replicates, read counts were used to calculate log2 fold-changes (FC) and adjusted p-values for RNAseq and RIBOseq data by comparing hMEC-ZEB1 to hMEC. The robustness of RNAseq and RIBOseq log2(FC) values was first verified by comparing them to the ones obtained from polysome profiling (i.e., isolation of mRNAs bound by ribosomes) coupled to medium throughput RT-qPCR, on 90 randomly-chosen genes using 5 independent experiments (Spearman correlation: RNAseq, p < 0.0001 and r² = 0.75; RIBOseq, p < 0.0001 and r² = 0.78) (Fig.S2D-G). We then applied different thresholds as detailed in the decision tree to compare changes in levels of total mRNAs (RNAseq) or ribosome-protected mRNAs (RIBOseq) (Fig.S2H). Using our approach, three main classes of significantly dysregulated mRNAs were identified (Figures 1A-B and Dataset S1).
First, 1,065 mRNAs were significantly and differentially regulated in hMEC-ZEB1 compared to hMEC using both RNAseq and RIBOseq (Figures 1A-B, "BOTH" class, black). These mRNAs corresponded to a concomitant change in levels of total and ribosome-protected mRNAs, suggesting the coupling of transcription/translation for these mRNAs in ZEB1-induced EMT. This mechanism has already been described as a passive buffer effect, where changes in total mRNA levels mechanically affect levels of mRNA translation²⁴. To determine whether these findings were consistent with EMT, overrepresentation analysis in specific biological pathways was performed. As expected, up-regulated mRNAs mostly represented genes involved in EMT and extracellular matrix organization, while down-regulated mRNAs largely corresponded to genes involved in cell junction, differentiation and epidermis development (Fig.S3A and Dataset S2). Overall, the molecular phenotyping data obtained validated the ribosome profiling, and were consistent with the extensive transcriptional reprogramming-driven EMT described in the literature¹,².

In parallel, up to 2,212 mRNAs show a decoupling between their total and ribosome-protected levels in hMEC-ZEB1 compared to hMEC. These data suggest that these mRNAs are regulated at a translational level in ZEB1-induced EMT. Comparison between RNAseq and RIBOseq methodologies unveiled 179 mRNAs that were either significantly up- or down-regulated exclusively by RNAseq ("RNAseq" class) without change in RIBOseq in hMEC-ZEB1 compared to hMEC, suggesting their regulation at the translational level to maintain a basal rate of translation (Figures 1A-B, green). Moreover, 2,033 mRNAs were significantly modified in hMEC-ZEB1 following the RIBOseq technique, without any change in total mRNA level (Figures 1A-B, "RIBOseq" class, pink). Thus, in addition to transcriptional reprogramming, translational reprogramming occurs in mesenchymal cells overexpressing ZEB1 compared to epithelial hMEC.

Next, to determine whether mRNAs regulated at the translational level also contribute to the switch in EMT-related cellular processes, we performed overrepresentation analyses focusing either on the "RNAseq" class or on the "RIBOseq" class. However, due to the small sample size in the "RNAseq" class (n=179), data interpretation was limited (Fig.S3B and Dataset S2). In contrast, in the "RIBOseq" class, most of the enriched pathways (either significantly up- or down-regulated) reflected those identified in the "BOTH" class and were associated with known EMT-related pathways, such as extracellular matrix or development (Figure 1C and Fig.S3A). Moreover, "RIBOseq" and "BOTH" classes encompassed mRNAs of the *EMT hallmark signature*, including several collagens regulated at either transcriptional or translational level (Figure 1D). These data suggest that determining EMT commitment from the *EMT hallmark signature* established solely from transcriptomics data must significantly underestimate the level of EMT induced. Finally, some of the top 10 enriched terms were specifically identified in the "RIBOseq" class, such as metabolism of bile acid and inflammation, which correspond to emerging biological functions involved in EMT
that could be exploited in the future<sup>25-27</sup> (Figure 1C). Overall, these data demonstrate that both
transcription and translation contribute to specifying the molecular phenotype of ZEB1-induced
mesenchymal cells. Interestingly, the number of mRNAs dysregulated at the translational level was
twice as important as the ones dysregulated at the transcriptional level (2,212 vs 1,065;
respectively), indicating that translational reprogramming is at least as important as transcriptional
reprogramming in EMT.

To rule out the possibility that translational reprogramming associated with EMT is specific to ZEB1,
we performed another ribosome profiling in SNAIL-overexpressing hMEC. Similar to ZEB1, SNAIL
overexpression was associated with both coupling and uncoupling in transcription/translation
(Fig.S4). Comparison of differentially regulated mRNAs in hMEC cells overexpressing or not the
EMT-TFs, in each of the three main classes (i.e., BOTH, RNAseq, RIBOseq), revealed that some
mRNAs are similarly or distinctly regulated by ZEB1 and/or SNAIL at the total or ribosome-
protected levels (Fig.S4). These observations support previous reports at the transcriptional level
that ZEB1 and SNAIL both induce EMT, albeit in distinct ways<sup>1-3</sup>. They also demonstrate that the
contribution of translational reprogramming in specifying EMT-related cellular processes is not
restricted to ZEB1, but is likely a shared feature among EMT-TFs.

Altogether, our data show that translational reprogramming occurs in response to the
overexpression of different EMT-TFs and contributes to the molecular and cellular switch occurring
in epithelial cells during EMT. Interestingly, we show for the first time that translational
reprogramming is at least as important as transcriptional reprogramming in ZEB1-induced
mesenchymal cells, with approximately 2,000 mRNAs differentially modulated at the translational
level.

Ribosomes display increased translational defects in ZEB1-induced mesenchymal cells

Having shown that a translational reprogramming occurs in ZEB1-overexpressing mesenchymal
cells, we attempted to identify changes in translational fidelity (i.e., accuracy of mRNA decoding
during protein synthesis). We used a stop codon readthrough reporter assay allowing us to analyze
the recognition of stop codons by ribosomes. Indeed, when a ribosome encounters a stop codon,
translation is either stopped by release factors, or readthrough by a near-cognate tRNA (i.e.,
making 2 out 3 base pairing with the mRNA codon), which causes protein synthesis to continue
beyond the expected termination codon. This specific decoding makes it possible to assess the
level of fidelity of the ribosome by measuring the ability of a near-cognate tRNA to be accepted by
the ribosome<sup>28</sup>. Using a dual-reporter plasmid with both β-galactosidase and luciferase coding
sequences separated by a single in-frame stop codon<sup>29</sup>, we compared the readthrough activity of hMEC overexpressing or not ZEB1. We observed that hMEC exhibited a lower percentage of readthrough (around 2%) compared to hMEC-ZEB1 (around 4%) (Figure 2A), indicating that in hMEC, translation accuracy is higher than in mesenchymal hMEC-ZEB1 cells. Although the RIBOseq phasing is very high, a two-fold increase in readthrough efficiency is not enough to be detected in RIBOseq data.

One possibility to increase UGA stop codon readthrough is to overexpress the corresponding near cognate tRNAs that are naturally incorporated at the stop codon (i.e., Trp, Cys, Lys)<sup>30</sup>. We have verified that the observed increase readthrough efficiency in ZEB1-overexpressing mesenchymal cells were not associated with changes in tRNA expression, as previously described in other conditions<sup>31</sup>. Using total RNA, tRNAome was compared between hMEC overexpressing or not ZEB1 (Figure 2B). Only a few significant changes in tRNA profile were identified impacting different isodecoders, which are not used for stop codon readthrough.

These data demonstrate that readthrough defects are mainly dependent on ribosome activity in our isogenic hMEC cellular models, and that ZEB1-induced mesenchymal cells are less efficient and less prone to recognize stop codons than epithelial ones. This observation indicates that mesenchymal ribosomes display remodelling in their intrinsic activity.

## Ribosome composition is modified in ZEB1-induced mesenchymal cells at ribosomal protein level

Considering the change in translational activity of ZEB1-overexpressing mesenchymal cells, including in translational fidelity, we next investigated changes in ribosome composition. We performed a riboproteome approach, i.e., mass spectrometry (MS)-based quantitative proteomic analysis of purified cytoplasmic ribosomes to highlight any change in the relative amount of ribosomal proteins between hMEC and hMEC-ZEB1 cells<sup>32,33</sup>. Prior to conducting MS-based proteomic characterization, the similarity between protein profiles arising from the five biological replicates of cytoplasmic ribosomes purified from hMEC or hMEC-ZEB1 was verified by 1D SDS PAGE (Fig.S5A-B).

MS-based proteomic approach reproducibly identified and quantified more than 900 different proteins in these purified cytoplasmic ribosomes (Dataset S3). Nevertheless, using the intensity-based absolute quantification (iBAQ) metrics<sup>34</sup>, the enrichment in the 80 cytoplasmic ribosomal proteins ranged from 90.7% and 96.4%, demonstrating the high purity of these ribosome preparations. Among the non-ribosomal proteins, it appeared that 12 of the 13 subunits of the eIF3
complex were significantly lower in ZEB1-overexpressing hMEC compared to hMEC (Figure 3A and Fig.S5C). Of note, the 13th eIF3 complex subunit, eIF3J, was not identified in our MS-based proteomic dataset, consistently to its description as a labile and non-essential subunit in the mammalian eIF3 complex³⁵. Since a slight increase in the protein levels of each eIF3 subunits was observed in hMEC-ZEB1 compared to hMEC cells, it indicates that change in riboproteome is not induced by change in gene expression (Fig.S6). Interestingly, it was previously reported that depletion of the eIF3E subunit promotes EMT commitment, suggesting that our observation may reflect an EMT-dependent remodelling of the eIF3 complex²¹,³⁶.

Among ribosomal proteins, RPL36A alone showed a significant differential abundance in hMEC-ZEB1 compared to hMEC ribosomes (Figures 3A-B). Indeed, a 3-fold increase in the proportion of RPL36A in purified ribosomes was observed in hMEC-ZEB1 cells compared to hMEC. Of note, MS data cannot discriminate RPL36A from its paralog RPL36AL, which differs from RPL36A by a single amino acid, K38R (Uniprot entries: RL36A_HUMAN and RL36L_HUMAN). In the remainder of this section, RPL36A refers to RPL36A and/or RPL36AL. To validate the quantitative proteomic data, we analyzed RPL36A protein levels in both purified ribosomes and ribosome-free fractions by Western blot (Figures 3C-D). As expected, ribosomal proteins were detected only in purified ribosome fractions, and the ribosome associated factor eIF3E was mostly found in the ribosome-free fractions, where its level was lower in hMEC-ZEB1 compared to hMEC. As for our proteomic data, no change in the RPS6 nor in the RPL19 ribosomal proteins was observed between hMEC and hMEC-ZEB1. However, we confirmed the exclusive and significant increase in RPL36A levels in ribosomes purified from ZEB1-overexpressing mesenchymal cells. Overall, these data suggest that ribosomes of hMEC overexpressing or not ZEB1 exhibit different compositions, the stoichiometry of RPL36A being higher in hMEC-ZEB1 purified ribosomes.

This change in ribosome composition may reflect a change in the total levels of RPL36A in response to ZEB1 overexpression. However, similar levels of total RPL36A mRNA and protein were observed in hMEC-ZEB1 and hMEC (Figures 3E-G). Moreover, no significant difference was observed for RPL36A in our ribosome profiling dataset (Dataset S1). These data suggest that the 3-fold increase in the levels of RPL36A in ribosomes purified from ZEB1-overexpressing mesenchymal cells were not linked to similar variation in its protein levels.

Altogether, these data support the existence of ribosome composition remodeling at the protein level in ZEB1-induced mesenchymal cells, which display, at least in part, higher levels of RPL36A in purified ribosomes compared to human mammary epithelial cells.
The ribosomal protein RPL36A triggers epithelial-mesenchymal transition

Before specifically overexpressing a cDNA encoding RPL36A and not RPL36AL to determine whether RPL36A contributes to EMT, we ensured that RPL36A overexpression was associated with the integration of RPL36A within the ribosome using a FLAG-tagged approach. We established a doxycycline inducible HEK293T cell line overexpressing FLAG-tagged RPL36A, and then purified cytoplasmic ribosomes after 72 h of exposure to 1 µg/mL doxycycline (Figures 4A-B). While no FLAG-tagged protein was detected in HEK293T-RPL36A ribosomes in absence of doxycycline, HEK293T-RPL36A ribosomes contained a FLAG-tagged protein in response to doxycycline, indicating that FLAG-tagged RPL36A overexpressed proteins were adequately incorporated within the ribosome. Using the immortalized mammary epithelial MCF10A cell line known to undergo EMT³⁷, we independently established three pairs (C2 to C4) of constitutive MCF10A-empty and - RPL36A cell lines using a lentiviral approach and analyzed them 25 days post-lentiviral infection. Exogenous RPL36A mRNA levels were detected in MCF10A-RPL36A but not in MCF10A-empty clones (Figure 4C). We then assessed a panel of criteria to determine whether RPL36A is involved in EMT.

First, we observed a change in cell morphology and cell dispersion in response to RPL36A overexpression (Figure 4D). Phase contrast imaging showed that MCF10A-RPL36A clones were more isolated and displayed a fibroblast-like morphology compared to MCF10A-empty clones. Interestingly, this switch in cell morphology was already observed 11 days after lentiviral infection (Fig.S7A-C). Using images of Hoechst-stained cells, cell dispersion was quantified using two geometrical models (i.e., Voronoï's diagram and Delaunay's triangulation) (Figure 5 and Fig.S8). MCF10A-RPL36 cell lines displayed significantly larger Voronoï areas and longer Delaunay segments than MCF10A-empty cell lines. These two characteristics are features of a greater cell dispersion. These data indicate that RPL36A promotes loss of cell cohesion and acquisition of cell dispersion, a cellular switch usually associated with EMT.

Second, we observed a change in molecular pattern in response to RPL36A overexpression. MCF10A-RPL36A clones expressed lower levels of epithelial markers (CDH1, OCLN, mir200c) and higher levels of mesenchymal marker (VIM) compared to their paired MCF10A-empty clones, at both mRNA and protein levels, a change supported by immunofluorescent staining (Figures 5A and 6, Fig.S7D-F). In parallel, a significant increase at both mRNA and protein levels in the EMT-TF ZEB1 was observed in response to RPL36A overexpression (Figures 6D, H-I). Furthermore, MCF10A-RPL36 cell lines expressed significantly higher levels of SNAIL and SLUG proteins compared to MCF10A-empty cell lines; this was associated with a decrease in SNAI1 mRNA levels but not in SNAI2 mRNA levels, suggesting a post-transcriptional regulation of SNAI (Figures 6E-F,
H-I). Loss of epithelial markers and acquisition of mesenchymal markers were already observed 11 days post-lentiviral infection (Fig.S7D-F). These data indicate that RPL36A promotes a molecular switch in both EMT markers and EMT-TFs.

Cell proliferation and invasion, two cell behaviors changing during EMT, were then monitored in real-time in response to RPL36A overexpression (Figure 7). MCF10A-RPL36A cell lines showed a significant reduction in cell proliferation compared to MCF10A-empty cell lines (Figures 7A-B). By contrast, RPL36A overexpression promoted cell invasion (Figures 7C-D). This switch in cell behavior is typical of EMT¹,².

Interestingly, a switch in both cell morphology and molecular pattern was observed in response to RPL36A overexpression but not in response to another ribosomal protein (e.g., RPL19) (Fig.S9), demonstrating that cellular and molecular changes are specific of RPL36A. It was reported that RPL36A affects cell sensitivity to antibiotics, a single point mutation being sufficient to promote cycloheximide resistance in a panel of different organisms³⁸,³⁹. However, we observed that RPL36A overexpression promotes change in cell morphology and molecular pattern (Fig.S9), irrespective of the selective antibiotic chosen, demonstrating that RPL36A-dependent EMT induction in MCF10A was specific and did not rely on a particular antibiotic selection mechanism. Furthermore, since methylation of RPL36A at lysine residues was shown to affect ribosome function, we studied four mutant forms of RPL36A⁴⁰. Overexpression of RPL36A-K53A, -K53R, -K38/53/58R and -K38/53/58A induced changes in cell morphology and molecular pattern similar to that observed in response to overexpressing the RPL36A wild-type form (Fig.S10), indicating that the methylation status of RPL36A was not relevant to its ability to promote EMT.

Collectively, these data indicate that RPL36A alone specifically promotes a switch in cell morphology, cell dispersion, cell behavior and molecular pattern. More importantly, RPL36A triggers EMT in the absence of TGF-β, indicating that RPL36A is a strong inducer of EMT by itself.

## Discussion

EMT-related cell plasticity is driven by a multi-layered regulation of gene expression, in particular transcriptional and epigenetic events¹⁻³. To date, some studies have reported a role for translation in reactivating EMT-TF expression and promoting EMT, although no study investigated whether translation is differentially modulated at a genome-wide level in epithelial and mesenchymal cells³. Here, we demonstrated that fine-tuning of the translational landscape and apparatus occurs in mesenchymal mammary cells, the single ribosomal protein RPL36A being sufficient to trigger mammary EMT.
Several years ago, a pioneer study drew the translational landscape of the early phase of a
physiologically-induced EMT in human heart cells using ribosome profiling<sup>41</sup>. In response to 45 min
of TGF-β treatment, approximately 67 mRNAs were significantly differentially regulated at the
translational level, supporting the notion that translational regulation occurs during EMT
commitment. However, compared to the numerous data reporting a huge amount of transcriptional
and epigenetic changes, this pioneer study suggests only a limited change in translation. Since
EMT is a transient and reversible process associated with different metastable stages (i.e.,
epithelial-mesenchymal plasticity, EMP)<sup>2,42</sup>, we aimed at limiting intra-EMT heterogeneity within the
analyzed cell line by first, comparing initial epithelial and final mesenchymal stages and second,
using an isogenic model of complete EMT, induced by the overexpression of the EMT-TF ZEB1 in
hMEC cells. Although this ZEB1-induced EMT might be considered as non-physiological, it is stable
compared to MCF10A cell line, which displays a high plasticity and therefore, a high intra-EMT
heterogeneity<sup>43,44</sup>. As expected, more than 1,000 mRNAs were regulated in our ribosome profiling
at steady-state, recapitulating the deep transcriptional reprogramming reported in EMT<sup>1,2</sup>.
Unexpectedly, our data revealed a change in translational reprogramming as important as that
observed in transcriptional reprogramming; the proportion of mRNAs regulated at these two levels
affecting 1,000-2,000 mRNAs. This notion was supported by two observations. First, similarities
and differences in the translational reprogramming induced by two EMT-TFs, ZEB1 and SNAIL,
reflected observations made at the transcriptional level and reinforced the fact that these two EMT-
TFs induced different EMT programs<sup>1,2</sup>. Second, overrepresentation analysis identified well-known
EMT-related biological processes in translationally differentially regulated mRNAs, suggesting that
translation contributes to the profound phenotypic changes observed during EMT. Interestingly, the
translationally dysregulated mRNAs were involved in biological processes only recently associated
with EMT, such as bile acid metabolism or inflammation<sup>25-27</sup>. Thus, translational reprogramming in
the field of EMT research could uncover novel biological functions associated with EMT and be a
useful approach to characterize and identify EMT heterogeneity<sup>3</sup>. Altogether, these data reiterate
the importance of translational regulation in physiological processes, including in EMT, but also in
human diseases, where EMT-induced resistance to cancer therapies have also been shown to rely
on translational events<sup>3,45,46</sup>.

Several studies recently reported an alteration of the ribosome canonical composition in different
pathophysiological contexts, in particular at the level of ribosomal proteins, while it has never been
investigated in EMT<sup>15,16</sup>. However, the discovery that mesenchymal cells display the highest
readthrough rate, a defect that indicates a modification of translational apparatus<sup>28-30</sup>, supports a
modulation of ribosome composition during EMT. Indeed, we identified a change in composition of
the ribosome, the level of RPL36A between the mammary epithelial hMEC and mesenchymal
hMEC-ZEB1 ribosomes being higher, independently of changes in RPL36A protein levels. Using
cryo-electron microscopy (cryo-EM), we could not identify any notable differences in the overall structures of both 80S ribosomes and 60S large subunits from hMEC and hMEC-ZEB1 cells. Both ribosomal populations have the same 1:1 stoichiometry of RPL36A (Fig.S11). This strongly supports the notion that the increased levels of RPL36A observed in purified ribosomes from ZEB1-induced mesenchymal cells is due to multiple binding sites of RPL36A, some of them being based on dynamic and transitory interactions with the ribosome. Furthermore, a recent study demonstrates that RPL36A is one of the few RPs with the ability to be incorporated into pre-existing cytoplasmic ribosomes independently of the canonical ribosome biogenesis⁴⁷. Indeed, in neurons whose embryonic formation relies on EMT, RPL36A not only shows a short-half-life but also exists at sub-stoichiometric levels within the ribosome; its recruitment within the ribosome occurring at a specific subcellular location. Thus, RPL36A has the ability to contribute to heterogeneity of ribosome composition. Overall, these data support the notion that heterogeneity in ribosome composition and in ribosome intrinsic activity, are novel molecular feature of EMT.

So far, little evidence suggests that ribosomal proteins could contribute to EMT. While ribosomal RPLS7 and RPL22L1 proteins were shown to promote a CDH1/VIM switch in prostate and ovarian cancer cell lines, respectively, RPS5 inhibits fibrosis in hepatic stellate cells⁴⁸⁻⁵⁰. Interestingly, in mouse embryonic stem cells (mESCs), two populations of ribosomes co-exist, RPL10A- and RPS25-containing ribosomes that actively translate distinct pools of mRNAs, respectively encoding extracellular matrix (ECM) proteins and B12 vitamin signaling proteins⁵¹. Another study revealed a significant enrichment in the translation of pro-fibrotic mRNAs, such as *TIMP1* and *CD90* mRNAs, by purifying RPL10A-associated mRNAs as a surrogate of polysome purification (i.e., actively translated mRNAs)⁵². Moreover, it has recently been reported that RPL10A-containing ribosomes decrease during mesoderm differentiation, resulting in a decreased translation of mesoderm regulators⁵³. These data identified RPL10A as a specific translational regulator of ECM and mesoderm regulator, which are key EMT markers. Here, using different immortalized mammary MCF10A cell lines, we revealed that RPL36A is a direct inducer of EMT. Indeed, overexpression of RPL36A specifically induced EMT in the absence of the cytokine TGF-β. This conclusion is based on the demonstration that RPL36A overexpression promotes (i) a molecular switch, affecting both epithelial/mesenchymal markers and EMT-TFs; (ii) the acquisition of a fibroblast-like morphotype, the cells being less cohesive and more dispersed; and (iii) a switch in the proliferation/invasion balance; as recommended by the TEMTIA². Although a loss-of-function experiment would have supported our data, RPL36A knocking-down was ineffective in several cell lines probably due to an auto-regulatory loop preventing the induction of cell death through ribosomal stress (i.e., defect in ribosome biogenesis). Moreover, overexpressed FLAG-tagged RPL36A was detected in the ribosome fractions but not in the ribosome-free ones. However, it remains to be demonstrated that RPL36A overexpression affects the stoichiometry of RPL36A in
ribosomes purified in MCF10A since RPL36A overexpression drastically slows down their growth, preventing the collection of sufficient cells to purify ribosomes. Of note, the biological function of RPL36A in humans remains under investigation. A single study in a panel of cancer cell lines showed that RPL36A knock-down mostly affects translation compared to transcription, the translationally dysregulated mRNAs being involved in cell cycle, organelle organization and nucleotide metabolism, that are key cellular functions for EMT commitment⁵⁴.

Hence, our study demonstrates that a profound translational reprogramming co-occurs with the well-known transcriptional reprogramming in mesenchymal cells. Moreover, it appears that, in addition to translation, change in ribosome composition is a novel molecular feature of EMT, as well as a novel direct inducer of EMT commitment.

## Materials and Methods

**Cellular models and viral infection.** hMEC cell line (Lonza Bioscience, CC-2551) and its derivates were cultured in DMEM F12 media (Gibco, 21331020) supplemented with 10% fetal bovine serum (Capricorn Scientific, FBS-12A), 1% penicillin-streptomycin, 0.01 µg/mL hEGF, 0.5µg/mL hydrocortisone and 280 mU/mL insulin at 37°C in a humidified atmosphere with 5% CO₂. MCF10A (ATCC, CRL-10317™) cells were cultured in DMEM F12 media (Gibco, 21331020) supplemented with 5% horse serum (Gibco, 16050122), 1% penicillin-streptomycin, 1X GlutaMax, 2 mM L-Glutamine (Gibco, 25030024), 0.02 µg/mL hEGF, 0.5 µg/mL hydrocortisone, 300 mU/mL insulin and 100 ng/mL cholera toxin at 37°C in a humidified atmosphere with 5% CO₂.

**Ribosome profiling and tRNAome.** 5.10⁷ and 5.10⁶ hMEC-derived cells were used for RIBOseq and RNAseq, respectively. Stranded Total RNA library preparation kit respectively (Illumina) and next generation sequencing was performed using a Next Seq single read 75. Data were analyzed using the Ribodoc package (see SI Methods). tRNAome has been realized by Arraystar inc, USA.

**Readthrough luciferase reporter assays.** The pAC18 plasmid²⁹ contains a nonsense mutation (UGA) flanked by two F2A self-cleavage sites that separate the luciferase and β-galactosidase coding phases. β-galactosidase and luciferase activities were assayed using Glo lysis buffer (Promega). Readthrough efficacy was estimated by calculating the ratio of luciferase activity to β-galactosidase activity obtained with the mutant construct, with normalization against an in-frame wild-type control construct.
**Ribosome purification.** Cytoplasmic fractions were obtained by mechanical lysis of cells using the PreCellys device (Ozyme) and centrifugation at 750 g for 10 min to pellet nuclei then 12,000 g for 10 min to pellet mitochondria³². Cytoplasmic fractions were loaded onto a 1 M sucrose cushion in buffer (50 mM Tris-HCl pH 7.4; 5 mM MgCl₂; 500 mM KCl; 2 mM DTT) and centrifuged for 2 h at 250,000g. Ribosome pellets were resuspended in buffer (50 mM Tris-HCl pH 7.4; 5 mM MgCl₂; 25 mM KCl).

**MS-based quantitative proteomic analysis of ribosomal fractions.** Five biological replicates of ribosomes were purified from hMEC and hMEC-ZEB1 cell lines before characterization by MS-based proteomic analysis. Only proteins quantified in at least 5 replicates of 1 condition were kept. For each sample, individual iBAQ values were normalized by the sum of iBAQ values coming from ribosomal proteins. Differentially expressed proteins were sorted out using a log2(fold change) cut-off of 1 and an adjusted p-value cut-off of 0.005, allowing to reach an FDR threshold of 1% according to the Benjamini-Hochberg estimator.

**Immunofluorescence.** Cells were grown on glass coverslips and fixed in 4% of paraformaldehyde in 1X PBS before permeabilization with 0.5% Triton X-100 in 1X PBS. Primary antibodies were applied before secondary antibodies labelled with AlexaFluor488 or 555 (Molecular Probes) (Table S1). Coverslips were mounted using the Fluoromount G mounting medium (EMS). Images were acquired on a Zeiss confocal microscope (Zeiss LSM780 NLO) thanks to the Cell Imaging Platform (CRCL, Lyon, France).
**Availability of datasets.** The generated ribosome profiling data are available in the GEO profile (GSE246352). The datasets include the fastq of each sample, a read count matrix for each sample and a matrix summarizing the biological data. The MS-based proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD047409. The cryo-EM maps were deposited with the EMDB under the accession code EMD-19350 and EMD-19351.

**Acknowledgments**

We thank the staff of the platforms that have not been included as co-authors: EDyP Laboratory (Grenoble, France), “Gilles Thomas” Bioinformatics and Cell Imaging Platforms (CRCL, Lyon, France), English editing (Brigitte Manship, CRCL, Lyon, France). This work benefited from access to the IGBMC Strasbourg, an Instruct-ERIC centre. Financial support was provided by Instruct-ERIC (PID 126).

**Funding**

This study was funded by PAIR Sein program (ARC_INCa_LNCC_7625), INCa (PRT-K EMT-CoNCEPT, K17_2018-024; PLBio MARACAS, PLBIO19-178), SIRIC program (INCa-DGOS-INSERM-ITMO cancer_18003, LyRICAN+), ANR (LabEx ANR-10-LABX-0061, DEVweCAN; Institut Convergence ANR-17-CONV-0002, Plascan), Ligue Contre le Cancer Grand Ouest, and by the Fondation pour la Recherche Médicale (Equipe FRM EQU202203014663). Proteomic experiments were partially supported by Agence Nationale de la Recherche under projects ProFI (Proteomics French Infrastructure, ANR-10-INBS-08) and GRAL, a program from the Chemistry Biology Health (CBH) Graduate School of University Grenoble Alpes (ANR-17-EURE-0003). CM and FNVL were recipients of PhD fellowship from Ligue Nationale Contre le Cancer. FNVL received a 4^th^ year PhD fellowship from Fondation pour la Recherche Médicale. HA and PF were supported by ANR grants awarded to ON (Actimeth 19-CE12-0004-02, Rescue_ribosome 17-CE12-0024-01).

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## Figures legends

**Figure 1.** The translational landscape of ZEB1-induced mesenchymal cells is distinct from that of epithelial cells. (A-B) Ribosome profiling. Steady-state mRNA levels and ribosome-protected mRNAs were quantified by RNAseq and RIBOseq, respectively. Using decision tree based on log2 fold-changes (FC) between hMEC and hMEC-ZEB1, and adjusted p-values (Fig.S2H), three main classes were identified for which log2(FC) RIBOseq and RNAseq data are plotted (A). These three classes had the following characteristics (i) total and ribosome-associated levels vary concomitantly ("BOTH", black); (ii) total levels vary independently of ribosome-associated ones ("RNAseq", green); and (iii) ribosome-associated levels vary without change in their total levels ("RIBOseq", pink). The total amount of dysregulated mRNA number in each class is given (B). Mesenchymal hMEC-ZEB1 cells display a higher proportion of mRNAs regulated at the translational level than the transcriptional level. (C) Analysis of gene enrichment. Overrepresentation analysis was performed using "RIBOseq" mRNA lists compared to either hallmark or gene ontology (GO) annotations. The top 10 ranking terms from each list is shown in descending order of adjusted p-value. The RIBO_UP class is enriched in signatures related to epithelial-mesenchymal transition. (D) Dysregulated mRNAs of the *Hallmark EMT signature*. Individual log2(FC) of RNAseq or RIBOseq data are shown for the mRNAs belonging to the *Hallmark EMT signature* and are higher in hMEC-ZEB1 compared to hMEC at the transcriptional/translational levels ("BOTH_UP") and at the translational level only ("RIBOseq_UP").

**Figure 2.** Translational readthrough is higher in ZEB1-induced mesenchymal cells compared to mammary epithelial cells independently of any alteration in the tRNAome. (A) *In cellulo* stop codon readthrough assay. A transient transfection of stop codon readthrough reporter assay was performed to compare translational fidelity of hMEC overexpressing or not ZEB1. Data are shown as box plot (n= 6) and a Student t-test was performed. A significant increase in the percentage of stop codon readthrough was observed in hMEC-ZEB1 compared to hMEC. (B) tRNAome. The repertoire of tRNAs was profiled by tRNAseq in epithelial and ZEB1-overexpressing cells. Data are shown as scatter plot (n=3) and a Pearson's correlation test was performed. No difference in isodecoders used for stop codon readthrough was observed between the two cell lines. Each differentially-expressed tRNA is labelled.

**Figure 3.** Ribosomes of ZEB1-induced mesenchymal hMEC display increased levels of RPL36A compared to epithelial hMEC. (A-B) Riboproteome. Purified ribosomal particles from
hMEC and hMEC-ZEB1 cells were analyzed by mass spectrometry. Differential ratio between hMEC-ZEB1 and hMEC is presented as log2(FC) and -log10(adj.p-value) (A, n=5). Peptide raw counts of RPL36A/RPL36AL are given, where each dot represents a biological replicate and the horizontal bar the median (B, n=5). Compared to epithelial hMEC ribosomes, ZEB1-overexpressing mesenchymal ribosomes display decreased levels in 12 subunits of the eIF3 complex and increased levels of RPL36A. RP: ribosomal protein. (C-D) Validation of riboproteome. A representative Western blot (C) and quantification (D) is shown on purified ribosomal particles and ribosome-free fraction from hMEC-ZEB1 and hMEC cell lines. RPS6 and RPL19 were used as loading control proteins from small and large ribosomal subunits, respectively. RPL36A levels were significantly higher in purified ribosomes of mesenchymal hMEC-ZEB1 than epithelial hMEC (D, n=4). (E-G) Expression of *RPL36A*. Compared to hMEC, hMEC-ZEB1 displayed similar levels of RPL36A at the mRNA (RT-qPCR, E, n=3) and protein levels (F-G). A representative Western blot is shown in (F) and quantification in (G, n=3). Each dot represents a biological replicate (n = 3). Mann-Whitney t tests were performed. Ns: non-significant; *: p<0.05; **: p<0.01.

**Figure 4. RPL36A overexpression promotes a switch in cell morphology. (A-B) Incorporation of overexpressed RPL36A in ribosomal particles. Using HEK293T expressing inducible FLAG-tagged RPL36A protein, composition of purified ribosome was assessed by Western blot. A representative gel is shown in (A) and a quantification of protein levels in (B, n=3). RPS6 and RPL19 were used as loading controls from small and large ribosomal subunits, respectively. FLAG-tagged RPL36A protein was significantly detected only in the doxycycline induced cell line (1 µg/mL for 72 h). (C) Exogenous *RPL36A* mRNA levels. Overexpression of exogenous *RPL36A* was validated 25 days post-infection by RT-qPCR in three pairs of stable MCF10A-empty and -RPL36A cell lines (C2 to C4). Exogenous levels of *RPL36A* mRNA were detected in MCF10A-RPL36A cell lines compared to MCF10A-empty cell lines. (D) Cell morphology. Phase contrast images showing the morphology of pairs of constitutive MCF10A-empty and -RPL36A cell lines 25 days post-infection. While the MCF10A-empty cell lines conserved an epithelial morphology, the MCF10A overexpressing RPL36A cell lines displayed a fibroblast-like morphology. Scale bar = 200 µm (magnification x10). Each dot represents a replicate (n=3). Mann-Whitney (B) or paired-t tests (C) were performed. *: p<0.05.

**Figure 5. RPL36A overexpression promotes switch in cell dispersion. (A)**
Immunofluorescence staining. Representative immunofluorescence images stained for epithelial CDH1 (E-cadherin) and mesenchymal VIM (Vimentin) markers are shown for a single pair of
constitutive MCF10A-empty and -RPL36A cell lines (clone C4, see Fig.S8 for data of 2 additional
pairs of clones). Compared to MCF10A-empty cell line, MCF10A-RPL36A cell line exhibited a loss
of CDH1 protein level and a higher level of VIM protein. Scale bar = 100 µm. (B-C) Cell dispersion.
Cell dispersion was quantified using two geometric models: Voronoï diagram (B) and Delaunay
triangulation (C). Representative images are shown in left panels and quantifications in right panels.
MCF10A-RPL36A cells were significantly more dispersed than MCF10A-empty cells. Each dot
represents a quantification. Paired-t tests were performed. ****: p<0.0001.

**Figure 6. RPL36A overexpression promotes an EMT-related molecular switch. (A-G) EMT-related mRNA levels. Levels of EMT markers (A-C), EMT-TFs (D-F) and miR200c (G) were compared between the three pairs of MCF10A-empty and -RPL36A cell lines by RT-qPCR 25 days post-infection. Compared to MCF10A-empty, constitutive overexpression of RPL36A significantly decreased mRNA levels of epithelial markers (A-B, G), while it increased mRNA levels of the mesenchymal markers VIM and ZEB1 (C-D). (H-I) EMT-related protein levels. Levels of EMT markers and EMT-TFs were compared between the three pairs of MCF10A-empty and -RPL36A cell lines by Western blot. A representative Western blot for each pair of clones is shown in (H) and quantification is given in (I). As for mRNA levels, a decrease in epithelial markers and an increase in mesenchymal markers were observed in MCF10A-RPL36A cell lines compared to MCF10A-empty cells. Each dot represents a replicate (n=4 to n=7). Paired-t tests were performed. Ns: non-significant; *: p<0.05; **: p<0.001; ***: p<0.0001.

**Figure 7. RPL36A overexpression promotes a switch in cell behavior. (A-B) Cell proliferation.
Cell proliferation of MCF10A-empty and -RPL36A cell lines was monitored in real-time. A
representative profile of cell proliferation is shown in (A, mean +/- SEM, n=5) and relative slope
index of each replicate is given in (B, mean +/- SD, n=10). Compared to control, RPL36A
overexpression significantly reduced cell proliferation. (C-D) Cell invasion. Cell invasion of
MCF10A-empty and -RPL36A cell lines was monitored in real-time. A representative profile of cell
invasion is shown in (C, mean +/- SEM, n=2) and relative slope index of each replicate is given in
(D, mean +/- SD, n=5). Compared to control, RPL36A overexpression significantly increased cell
invasion. Each dot represents a replicate. Paired-t tests were performed. *: p<0.05; ****: p<0.0001.
A

<figure><img src="image_3.png" /></figure>

B

<figure><img src="image_4.png" /></figure>

C

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

D

<figure><img src="image_7.png" /></figure>

Genes from "Hallmark_EMT"
A

<figure><img src="image_8.png" /></figure>

B

<figure><img src="image_9.png" /></figure>
A

<figure><img src="image_10.png" /></figure>

B

RPL36A;RPL36AL

<figure><img src="image_11.png" /></figure>

C

Ribosome-free
fraction

RPS6

RPL19

RPL36A

<figure><img src="image_12.png" /></figure>

D

RPL36A protein in ribosome

<figure><img src="image_13.png" /></figure>

E

RPL36A mRNA

<figure><img src="image_14.png" /></figure>

F

<figure><img src="image_15.png" /></figure>

G

RPL36A protein

<figure><img src="image_16.png" /></figure>
A

<figure><img src="image_17.png" /></figure>

B

<figure><img src="image_18.png" /></figure>

C

<figure><img src="image_19.png" /></figure>

D

MCF10A

RPL36A

Empty

C2

<figure><img src="image_20.png" /></figure>

C3

<figure><img src="image_21.png" /></figure>

C4

<figure><img src="image_22.png" /></figure>
<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>
<caption>A</caption>

CDH1

<figure><img src="image_26.png" /><figcaption>B</figcaption></figure>

OCLN

<figure><img src="image_27.png" /><figcaption>C</figcaption></figure>

VIM

<figure><img src="image_28.png" /><figcaption>D</figcaption></figure>

ZEB1

<figure><img src="image_29.png" /><figcaption>E</figcaption></figure>

SNAI1

<figure><img src="image_30.png" /><figcaption>F</figcaption></figure>

SNAI2

<figure><img src="image_31.png" /><figcaption>G</figcaption></figure>

miR200c

<figure><img src="image_32.png" /><figcaption>H</figcaption></figure>

<caption>I</caption>

CDH1

OCLN

<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /></figure>

VIM

<figure><img src="image_35.png" /></figure>

ZEB1

<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /><figcaption>SNAIL/SLUG</figcaption></figure>

<figure><img src="image_38.png" /></figure>
A

Proliferation

<figure><img src="image_39.png" /></figure>

B

<figure><img src="image_40.png" /></figure>

C

Invasion

<figure><img src="image_41.png" /></figure>

D

<figure><img src="image_42.png" /></figure>
**Supporting Information for**

Intricate ribosome composition and translational reprogramming in
epithelial-mesenchymal transition

Chloé Morin, Agnès Baudin-Baillieu, Flora Nguyen Van Long, Caroline Isaac, Laure Bidou, Hugo Arbes, Pauline François, Roxane M Pommier, Annie Adrait, Akari Saku, Stephanie Gran-Ruaz, Camélia Machkouri, Christophe Vanbelle, Romain Morichon, Mathieu Boissan, Frédéric Catez, Anthony Ferrari, Anne-Pierre Morel, Yohann Couté, Sophie Chat, Emmanuel Giudice, Reynald Gillet, Alain Puisieux, Caroline Moyret-Lalle, Jean-Jacques Diaz, Olivier Namy*, Virginie Marcel*

*Email: olivier.namy@i2bc.paris-saclay.fr and virginie.marcel@lyon.unicancer.fr

**This PDF file includes:**

Supporting text

Figures S1 to S11

Table S1

Legends for Datasets S1 to S4

SI References

**Other supporting materials for this manuscript include the following:**

Datasets S1 to S4
## Supporting Information Text

### Materials and Methods

**Cellular models and viral infection.** Primary human mammary epithelial cell line (hMEC, Lonza Bioscience, CC-2551) was immortalized by hTERT overexpression using hygromycin selection¹. From this cell line, additional hMEC models were previously derived using puromycin selection to stably overexpress EMT-TFs, either the murine ZEB1 (hMEC-ZEB1) or the human SNAIL (hMEC-SNAIL), to promote EMT¹. hMEC cell lines were cultured in DMEM F12 media (Gibco, 21331020) supplemented with 10% fetal bovine serum (Capricorn Scientific, FBS-12A), 1% penicillin-streptomycin, 0.01 µg/mL hEGF, 0.5 µg/mL hydrocortisone and 280 mU/mL insulin at 37°C in a humidified atmosphere with 5% CO₂.

MCF10A (ATCC, CRL-10317™) cells were cultured in DMEM F12 media (Gibco, 21331020) supplemented with 5% horse serum (Gibco, 16050122), 1% penicillin-streptomycin, 1X GlutaMax, 2 mM L-Glutamine (Gibco, 25030024), 0.02 µg/mL hEGF, 0.5 µg/mL hydrocortisone, 300 mU/mL insulin and 100 ng/mL cholera toxin at 37°C in a humidified atmosphere with 5% CO₂. They were transduced with different lentiviral particles produced as previously described using HEK293T¹, cultured in DMEM high glucose GlutaMAX (Gibco, 61965026), supplemented with 10% fetal bovine serum (Capricorn Scientific, FBS-12A), 1% penicillin-streptomycin, 1X NEAA, 1 mM sodium pyruvate. Several different cell lines were generated overexpressing either control (empty) and wild-type or mutant ribosomal proteins (RPLs) in a constitutive (pWPIR¹) or inducible (pTRIPZ, Dharmacon) manner. MCF10A derived cellular models were selected and maintained with distinct antibiotics (30 µg/mL hygromycin, 1 µg/mL puromycin or 5 µg/mL blasticidin).

**Ribosome profiling.** Ribosome profiling was performed as previously described². Briefly, for RIBOseq, 5.10⁷ hMEC-derived cells were snap frozen in liquid nitrogen to stop translation. Cells were scraped into lysis buffer (20 mM Tris-HCl pH 7.4; 150 mM NaCl; 5 mM MgCl₂; 1 mM DTT; 1% triton X-100), incubated 10 min on ice and triturated ten times through a 26-gauge needle. The lysate was clarified by centrifugation for 10 min at 16,000 g at 4°C. The supernatant was then digested with turbo DNAse I INVITROGEN 1U/10 µg RNA and RNase I AMBION 15U /OD260 for 1 h at 25°C. Digestion was loaded on a 24% sucrose cushion (24% sucrose; 50 mM Tris-acetate pH7.6; 50 mM NH₄Cl; 12 mM MgCl₂; 1 mM DTT) and centrifuged in a TLA100.3 rotor at 100,000 rpm, 4°C for 2 h 15 min. Ribosomal pellets were resuspended into lysis buffer and RNAs were purified by phenol-chloroform extraction, and suspended in Superase-IN AMBION solution (1 U/µL). 28- to 34-nt ribosome protected fragments (RPF) were then purified from 16% (vol/vol) acrylamide-bisacrylamide (19:1) / 7 M urea gel by passive diffusion. RNAseq was performed in parallel, 5.10⁶ cells were harvested and total RNA extracted using Macherey Nalgene RNeasy kit
including DNAse I treatment. RPFs and total RNA were depleted from ribosomal RNA with the
Ribo-Zero Gold Human kit (Illumina) following the manufacturer's recommendations. Sequencing
libraries were prepared from an equal number of RPFs with the TrueSeq Small RNA and Stranded
Total RNA library preparation kit respectively (Illumina). Next generation sequencing was
performed using a Next Seq single read 75.

Data were analyzed using the Ribodoc package³. Briefly, raw data were first trimmed to remove
the 3' adapter sequence with CutAdapt 1.9.1 and then mapped against the rRNA sequences of
hg38 human genome with Bowtie 1.1.2. These filtered reads were finally mapped against the
complete genome (hg38 human genome) and against the coding sequences only with Bowtie 1.1.2.
The number of reads for each gene was calculated with the featureCounts 1.5.0-p2 program and
normalized with DESeq2 method through SARTools R Package. To represent RNAseq and
RIBOseq profiles, we used the cross-graphic representation where we display log2 fold-changes
(FC) in both data. The data were directly taken from the tables generated by SARTools. The graphic
was done using Bokeh v0.12.9 (https://bokeh.pydata.org/en/0.12.9/). Finally, gene enrichment lists
in specific biological pathways (overrepresentation analysis), were tested using *clusterProfiler*
(v4.0.2) and *msgidbr* (v7.4.1) R packages⁴,⁵. Pathway lists originated from MSigDB (Molecular
Signatures Database): Hallmark (H) gene sets and C5 ontology gene sets derived from the Gene
Ontology resource⁶. Bioinformatic analyses were performed using R (v4.1.0). Dataset are available
on GeoProfile (GSE246352).

**Polysome profiling.** 10⁷ hMECs were seeded in 15 cm in diameter dishes and after 24 h incubated for 15 min with 100 µg/mL cycloheximide (CHX). Cytoplasms were purified in lysis buffer (20 mM KCl; 5 mM MgCl₂; 50 mM Tris-HCl pH 7.4; 4.5% sucrose; 0.5 mM DTE; 100 µg/mL CHX) by mechanical lysis using the PreCellys device (Ozyme) and two rounds of 10 min centrifugation at 1,000 g and 12,000 g to pellet nuclei and then mitochondria. The equivalent of 2 mg of cytoplasmic protein was loaded onto a 10-40% sucrose gradient and sedimented by ultra-centrifugation at 40,000 rpm for 2 h at 4°C using SW41 rotor on L7-55 ultracentrifuge (Beckman). Absorbance profiles and collection of polysomes were performed using an ISCO UA-6 detector at 254 nm. Polysomal fractions were pooled prior RNA extraction for polysome-coupled to medium throughput RT-qPCR.

**Medium throughput RT-qPCR.** mRNA levels in cytoplasm and polysome were quantified by medium throughput real-time qPCR (RT-qPCR) using Master Mix 2X EvaGreen (Bio-Rad) on the BioMarkHD device (Standard BioTools™) as previously described⁷ (Dataset S4). Relative fold-changes (FC) were calculated using the 2-ΔΔCT method⁸, using Human XpressRef Universal Total RNA (Qiagen) as control sample for inter-run normalization and the median of 5 housekeeping genes. Each sample was quantified in technical triplicate.
**Readthrough luciferase reporter assays.** The pAC18 plasmid contains a nonsense mutation (UGA) flanked by two F2A self-cleavage sites that separate the luciferase and β-galactosidase coding phases. The latter being used as an internal control. hMEC-derived cells were seed onto a 96-well plate, 24 h before transfection with the reporter plasmid in the presence of JetPei reagent (Ozyme). Cells were harvested 24 h post-transfection, with trypsin-EDTA (Invitrogen), lysed with Glo lysis buffer (Promega) and β-galactosidase and luciferase activities were assayed. Readthrough efficacy was estimated by calculating the ratio of luciferase activity to β-galactosidase activity obtained with the mutant construct, with normalization against an in-frame wild-type control construct. At least 6 independent biological assays were performed.

**tRNAome.** Total RNA was extracted using Trizol reagent (ThermoFisher). Over 2 µg of total RNA was resolved on urea-polyacrylamide gels and recovered within a size window of 60-100 nt for tRNA. m1A and m3C demethylation of purified tRNA were performed before partial alkaline hydrolysis. Fragments of ~19-35 nt of partially hydrolyzed and re-phosphorylated tRNA were converted to small RNA sequencing libraries using NEBNext® Multiplex Small RNA Library Prep Set for Illumina® kit. These steps have been realized by Arraystar inc, USA.

**Gel staining.** 1D electrophoresis of purified ribosomes was stained after gel prefixation in methanol using either Coomassie blue coloration (0.25% Coomassie Blue R-250 solution) or silver staining (0.1% silver nitrate solution).

**MS-based quantitative proteomic analysis of ribosomal fractions.** Five biological replicates of ribosomes were purified from hMEC and hMEC-ZEB1 cell lines before characterization by mass spectrometry (MS)-based proteomic analysis as described². Briefly, extracted proteins were stacked in the top of a 4-12% NuPAGE gel (Invitrogen) before in-gel digestion using modified trypsin (Promega)⁹. Resulting peptides were analyzed by nanoliquid chromatography coupled to tandem MS (Ultimate 3000 coupled to LTQ-Orbitrap Velos Pro, Thermo Scientific) using a 120-min gradient. Peptides and proteins were identified and quantified using MaxQuant (version 1.5.3.30)¹⁰ and the Uniprot database (*Homo sapiens* taxonomy, March 2017 version), and the frequently observed contaminant database embedded in MaxQuant. Trypsin was chosen as the enzyme and 2 missed cleavages were allowed. Peptide modifications allowed during the search were: carbamidomethyl (C, fixed), acetyl (Protein N-ter, variable) and oxidation (M, variable). Minimum peptide length and minimum number of unique peptides were respectively set to seven amino acids and one. Maximum false discovery rates - calculated by employing a reverse database strategy - were set to 0.01 at peptide and protein levels. Intensity-based absolute quantification (iBAQ) values were calculated from MS intensities of unique+razor peptides and used for statistical analyses
using ProStaR<sup>11,12</sup> (version 1.32.4). Only proteins quantified in at least 5 replicates of 1 condition were kept. For each sample, individual iBAQ values were normalized by the sum of iBAQ values coming from ribosomal proteins. Missing data were imputed using the 2.5-percentile value of each sample before statistical testing using *limma* t-test. Differentially expressed proteins were sorted out using a log2(fold change) cut-off of 1 and an adjusted p-value cut-off of 0.005, allowing to reach an FDR threshold of 1% according to the Benjamini-Hochberg estimator.

**Western blotting.** Proteins were purified using Laemmli (125 mM Tris-HCl pH 6.8; 2% SDS; 200mM DTE; 20% glycerol) supplemented with inhibitors of proteases (PhosSTOP EASYpack, Roche, 04 906 837 001) and phosphatases (Pierce™ Phosphatase inhibitor, Thermo Scientific, #88667) before denaturing at 95°C for 10 min. Protein lysates were quantified with TCA, subjected to SDS-PAGE with the appropriate percentage of acrylamide/bis-acrylamide and blotted onto 0.45 µm nitrocellulose membranes (Amersham™ Protran™, 10600009). Membranes were blocked in TBS solution containing 0.05% Tween 20 and 5% non-fat milk and incubated with primary antibodies overnight (Table S1). Fluorescent secondary antibodies (StarBright Blue 700 Fluorescent Secondary Antibodies, Bio-Rad) were used and signals were visualized using Chemidoc MP and quantified with Image Lab Software (Bio-Rad).

**RNA extraction and RT-qPCR.** Total RNA was extracted using Direct-ZOL RNA purification kit (Ozyme, ZR2073) and quantified with the spectrophotometer NanoDrop 2000 (Thermo Scientific). cDNA was synthetized from 200 ng of purified total RNA using the PrimeScript RT kit (Takara). RT-qPCR was performed using LightCycler® 480 SYBR Green I Master Mix (Roche) using in-house designed primers (Dataset S4). miRNA expression levels were quantified using TaqMan Assay. Briefly, 40 ng of total RNA were retrotranscribed using the TaqMan MicroRNA RT kit and TaqMan MicroRNA Assay 5X primers prior RT-qPCR using the Master Mix TaqMan Universal II without UNG as described by the manufacturer (ThermoFisher, 000502 and 002300). Relative fold-changes were calculated using the 2-ΔΔCT method<sup>8</sup>, using Human XpressRef Universal Total RNA (Qiagen) as control sample for inter-run normalization and *GAPDH* or *RNU44* for miRNA (ThermoFisher, 001094) as housekeeping gene. Each sample was quantified in technical triplicate.

**Cryogenic Electron Microscopy (cryo-EM).** Purified ribosomes resuspended in buffer (50 mM Tris-HCl pH 7.4; 5 mM MgCl<sub>2</sub>; 25 mM KCl; 1 mM DTT) were frozen on Quantifoil grids at 180 nM of 80S final concentration. These grids were then flash-frozen in liquid ethane using a Vitrobot Mark III (FEI), then transferred to a Cs-corrected Titan Krios electron microscope (FEI) operating at 300 kV and equipped with a FEG electron source. Images were automatically recorded using SerialEM<sup>13</sup> under low-dose conditions on a K3 direct electron detector (Gatan) using a defocus
range of -1 to -3 µm, a pixel size of 0.862 Å and a dose per frame of 0.998 e-/Å² for the hMEC ribosome dataset and 1.09 e-/Å² for the hMEC-ZEB1 ribosome dataset. Movies were corrected for the effects of drift and beam-induced motion using MotionCor2 software¹⁴. Contrast transfer function parameters were estimated using Gctf software¹⁵. Electron micrographs showing signs of drift or astigmatism were discarded, resulting in 14,285 micrographs for the hMEC dataset and 10,662 micrographs for the hMEC-ZEB1 dataset. Particles were semi-automatically selected in Cryosparc¹⁶ and subjected to three rounds of 2D classification in order to discard defective particles. This resulted in 194,275 particles for the hMEC dataset and 259,877 particles for the hMEC-ZEB1 dataset. All subsequent data processing was performed in RELION v4.0¹⁷,¹⁸. After initial cryo-EM reconstructions the two datasets were sorted into 10 classes to improve homogeneity. Three distinct populations of ribosomes emerge from this classification: 80S ribosomes (75,568 particles in the hMEC dataset and 137,173 particles in the hMEC-ZEB1 dataset), 60S large ribosomal subunits (52,853 particles in the hMEC and 91,578 particles in the hMEC-ZEB1 datasets), and poorly resolved images (65,854 particles in the hMEC and 31,126 particles in the hMEC-ZEB1 datasets). The 60S large ribosomal subunits and 80S ribosomes were then reconstructed separately and the particles corresponding to the 80S ribosomes were selected for further analysis. We then subtracted the signal of the ribosome from the datasets, using a soft mask (voxel values of 0 inside, 1 outside, extended by 6 pixels with a soft edge of 6 pixels) generated from the previous refinement run. The subtracted datasets were then sorted by a 3D classification without alignment. This enabled the separation of particles containing the tRNAs. As a result, the hMEC dataset was separated into 5 classes: rotated PRE conformation (32,755 particles), 80S with trace of A-site tRNA (18,452 particles), 80S with E-site tRNA (15,688 particles), empty 80S without tRNAs (20,200 particles), and noisy images (3,507 particles). The hMEC-ZEB1 dataset was separated into 5 classes: rotated PRE conformation (49,633 particles); 80S with E-site and trace of A-site tRNA (50,744 particles), empty 80S without tRNAs (20,200 particles), poorly resolved 80S with dissociating SSU (12,460 particles), and noisy images (4,136 particles). The two ribosome populations corresponding to translating 80S, in the rotated PRE conformation with A/P and P/E tRNAs, mRNAs and neosynthesized peptides were then selected for CTF refinement and particle polishing. Finally, the particles were re-transferred to Cryosparc for a final cycle of non-uniform refinement leading to a final resolution of 3.7 Å for the hMEC-ZEB1 and 3.9 Å for the hMEC ribosomes.

**Cell dispersion.** The spatial distribution of cells was characterized and quantified using two geometrical models, namely Voronoï's diagram and Delaunay's triangulation. Voronoï's diagram is the separation of a space into regions based on the distance to a specific set of points, while Delaunay's triangulation is a triangulation of a set of points such that the circumference of each triangle does not contain any other point from the set. These two methods provide some useful information about the disorder and the neighborhood relationships of cells, and were performed on
images of Hoechst-stained cell nuclei using an ImageJ macro. To achieve this, the nuclei were segmented using the Stardist plugin in ImageJ, the Voronoï function was used to generate the Voronoï's diagram, and the Delaunay plugin was used to generate the Delaunay's triangulation<sup>19,20</sup>. The average area in pixels<sup>2</sup> of the cells was calculated for the Voronoï's diagram, and the average distance in pixels was calculated for the Delaunay's triangulation.

**Cell proliferation and invasion.** The rate of cell proliferation and invasion was monitored in real-time using an xCELLigence RTCA device (Agilent), to measure the impedance value of cells within a well. For proliferation assays, cells were plated at 1x10<sup>5</sup> cells per well in 200 µL of culture medium in RTCA E-plate (Agilent, 5469830001). For invasion assays, cells were plated on a 20 µL diluted matrigel slice at 7x10<sup>5</sup> cells per well in 180 µL of serum-free culture medium in RTCA CIM-plate (Agilent, 5665825001), and 180 µL of 10% serum medium were placed in each well of the lower chamber. The plates were incubated at 37°C and 5% CO<sub>2</sub> and monitored on the xCELLigence RTCA system at 15 min time intervals for 4 to 6 days.

**Statistical analyses.** Statistical analyses and graphical representation were performed using GraphPad Prism v9.4.3 software (GraphPad Software, Inc). Correlation was assessed using Pearson's correlation test. To determine significant difference in two biological conditions, non-parametric Mann-Whitney t tests were performed on independent biological replicates and Paired-t tests were used to compare pairs of clones. A p-value < 0.05 was considered to be statistically significant. Histograms show mean and error bars represent standard deviation (SD) or, when indicated, standard error of the mean (SEM).
Supporting Figures S1 to S11

<figure><img src="image_43.png" /><figcaption>**Fig. S1. EMT-related molecular features of hMEC and hMEC-ZEB1 cellular models.** (A) Phase contrast images of cellular morphology. hMECs display cell-to-cell adhesion while hMEC-ZEB1 cells exhibit an isolated fibroblast-like morphology. Scale bar = 200 µm (magnification x10). (B-C) EMT-related mRNA levels. Relative mRNA levels of epithelial (B) and mesenchymal markers (C)</figcaption></figure>
were analyzed by RT-qPCR. Compared to the epithelial hMEC cells, ZEB1-overexpressing hMEC
cells have lost epithelial markers and express higher levels of mesenchymal markers. Each dot
represents a biological replicate analyzed as technical triplicates (n = 3 or 4). (D) EMT-related
protein levels. A representative Western blot gel is shown. Western blot reveals a switch in
CDH1/VIM (E-cadherin/Vimentin) expression between hMEC and hMEC-ZEB1 cells, which
express the mesenchymal VIM marker but not the epithelial CDH1 one. Mann-Whitney tests were
performed. ****: *p-value* < 0.0001; **: *p-value* < 0.01; ns: non-significant. KU80: loading control.
A

<figure><img src="image_44.png" /></figure>

C

<figure><img src="image_45.png" /></figure>

E

<figure><img src="image_46.png" /></figure>

H

<figure><img src="image_47.png" /></figure>

B

<figure><img src="image_48.png" /></figure>

D

<figure><img src="image_49.png" /></figure>

F

<figure><img src="image_50.png" /></figure>

G

<figure><img src="image_51.png" /><figcaption>**Fig. S2. Ribosome profiling approach. (A-C)** Read quality of RIBOseq. In hMEC condition, read distribution is centered on 29-mers, as expected (A). Moreover, an increase and a decrease in read counts occur in the environment of the start (B) and stop (C) codons, respectively. Blue: ORF1; red: ORF2; green: ORF3. (**D-G**) Validation of ribosome profiling using polysome profiling. Using Western blot, purity of cytoplasmic ("Cyto.") compared to nuclear ("Nuc.") fractions was assessed by histone H3 (nucleus) and α-tubulin (cytoplasm) detection (D) prior to performing polysome</figcaption></figure>
profiles (E). Using *de novo* polysome profiles (n=5), values of log2(FC) between hMEC-ZEB1 and hMEC were compared for 90 genes using RNAseq and medium throughput RT-qPCR of cytoplasmic RNA (F) or RIBOseq and medium throughput RT-qPCR of polysomic RNA (G). (H) Decision tree representing the comparison of RNAseq and RIBOseq. Using read counts, fold-changes (FC) between hMEC-ZEB1 and hMEC were determined for RNAseq (n=2) and RIBOseq (n=3). A decision tree was determined based on two values (p-adj and log2(FC)) that identified three main classes of genes: “BOTH”, including genes significantly and biologically dysregulated between hMEC-ZEB1 and hMEC in RIBOseq and RNAseq; “RIBOseq”, including genes significantly and biologically dysregulated between hMEC-ZEB1 and hMEC in RIBOseq but not in RNAseq; “RNAseq”, including genes significantly and biologically dysregulated between hMEC-ZEB1 and hMEC in RNAseq but not in RIBOseq. Other genes correspond to either “non-significant” or “dubious” dysregulation in RIBOseq and/or RNAseq.
<figure><img src="image_52.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_53.png" /></figure>

Fig. S3. Gene enrichment of “BOTH” and “RNAseq” dysregulated mRNA classes identified by ribosome profiling in hMEC-ZEB1 compared to hMEC cells. (A-B) Analysis of gene enrichment. Gene enrichment was performed using hallmark or gene ontology (GO) annotations for dysregulated mRNAs classified either as “BOTH” (A) or “RNAseq” (B). The top 10 ranking terms of each list are shown in descending order of adjusted p-values. Complete datasets of dysregulated mRNAs identified by ribosome profiling between hMEC-ZEB1 and hMEC are given in Dataset S1 and gene enrichment in Dataset S2.
A

BOTH_UP

<figure><img src="image_54.png" /></figure>

B

BOTH_DOWN

<figure><img src="image_55.png" /></figure>

C

RIBOseq_UP

<figure><img src="image_56.png" /></figure>

D

RIBOseq_DOWN

<figure><img src="image_57.png" /></figure>

E

RNAseq_UP

<figure><img src="image_58.png" /></figure>

F

RNAseq_DOWN

<figure><img src="image_59.png" /></figure>

**Fig. S4.** Comparison of dysregulated mRNAs identified by ribosome profiling in hMEC-ZEB1 or hMEC-SNAIL compared to hMEC cells. (A-F) As for hMEC-ZEB1, three classes of dysregulated mRNAs were identified (BOTH, RIBOseq and RNAseq) by ribosome profiling in hMEC-SNAIL compared to hMEC. The identity of enriched mRNAs in hMEC-ZEB1 and hMEC-SNAIL were compared using Venn diagrams for BOTH (A-B), RIBOseq (C-D) and RNAseq (E-F) classes in either up- (A, C, E) or down- (B, D, F) regulated mRNAs. Most mRNAs dysregulated at the transcriptional and translational levels in ZEB1-overexpressing hMEC are similarly dysregulated in SNAIL-overexpressing hMEC.
A

<figure><img src="image_60.png" /></figure>

B

<figure><img src="image_61.png" /></figure>

C

<figure><img src="image_62.png" /></figure>

**Fig. S5. Ribosome purification and mass spectrometry data. (A-B) Protein profiles of purified ribosomes. Five ribosome purifications at 500 mM KCl were processed independently from epithelial hMEC ("E") and mesenchymal hMEC-ZEB1 ("M") cells to perform mass spectrometry. Profiles of 1D separation using both Coomassie blue (A) or silver (B) staining. (C) Peptide raw counts. Mean peptide raw counts of 12 subunits of the eIF3 complex are shown. Each dot represents a biological replicate (n=5) and horizontal bar to median.**
<figure><img src="image_63.png" /><figcaption>**Fig. S6.** Comparison of the 12 subunits of the eIF3 complex between epithelial hMEC and mesenchymal hMEC-ZEB1 cells. Total protein levels were compared using Western blot for the 12 subunits of the eIF3 complex between hMEC and hMEC-ZEB1. A representative gel is shown. A slight increase in total protein level of eIF3 subunits was observed in hMEC-ZEB1 compared to hMEC (n=1 to 2). KDa: kilodalton; Ku80: loading control.</figcaption></figure>
<figure><img src="image_64.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_65.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_66.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_67.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_68.png" /><figcaption>F</figcaption></figure>

<figure><img src="image_69.png" /></figure>

Fig. S7. RPL36A overexpression gradually promotes a switch in cell morphology and EMT-related molecular markers. (A-B) RPL36A overexpression. RPL36A expression levels was compared between the constitutive empty- and RPL36A-overexpressing MCF10A cell lines at the
protein level by Western blot 7 days post-lentiviral infection (A) and at the mRNA level by RT-qPCR
during the first days post-lentiviral infection (B). An increase in both RPL36A protein and exogenous
RPL36A mRNA levels was detected. (C) Monitoring of cell morphology. Pictures in phase contrast
show morphology of both MCF10A-empty and -RPL36A cell lines over time. While the MCF10A-
empty cell line conserves an epithelial morphology, the MCF10A overexpressing RPL36A display
a switch to a fibroblast-like morphology from day 14 onwards. Scale bar = 200 µm (magnification
x10). (D) Immunofluorescence staining. Representative immunofluorescence images of MCF10A-
empty and -RPL36A cell lines from day 11 to day 21 post-lentiviral infection show the staining of
epithelial CDH1 (E-cadherin, red) and mesenchymal VIM (Vimentin, green) markers. Compared to
MCF10A-empty, MCF10A-RPL36A show a complete loss of CDH1 protein expression and a higher
level of VIM protein from day 18. Scale bar = 100 µm (magnification x40). (E-F) Kinetic of EMT-
related mRNA levels. Levels of EMT markers (E) and EMT-TFs (F) were compared between
MCF10A-empty and -RPL36A cell lines during the first days post-lentiviral infection by RT-qPCR.
Compared to MCF10A-empty, constitutive overexpression of RPL36A decreases mRNA levels of
epithelial markers while it increases mRNA levels of mesenchymal markers and all the EMT-TFs
<figure><img src="image_70.png" /><figcaption>**Fig. S8. RPL36A overexpression promotes a switch in cell dispersion. (A)** Immunofluorescence staining. Representative immunofluorescence images stained for epithelial CDH1 (E-cadherin) and mesenchymal VIM (Vimentin) markers are shown for two pairs of constitutive MCF10A-empty and -RPL36A cell lines (clones C2 and C3, see Fig.5 for data of the</figcaption></figure>
third pair of clones). Compared to MCF10A-empty cell lines, MCF10A-RPL36A cell lines exhibited a loss of CDH1 protein level and a higher level of VIM protein. Scale bar = 100 µm. (B-E) Cell dispersion. Cell dispersion was quantified using two geometrical models: Voronoi's diagram (B, D) and Delaunay's triangulation (C, E) for C2 (B-C) and C3 (D-E) pair of clones. Representative images are shown in left panels and quantifications in right panels. MCF10A-RPL36A cells were significantly more dispersed that MCF10A-empty cells. Each dot represents a quantification. Paired-t tests were performed. ****: p<0.0001.
A

Exogenous RPL36A

<figure><img src="image_71.png" /></figure>

B

Exogenous RPL19

<figure><img src="image_72.png" /></figure>

C

Non-Induced MCF10A

<figure><img src="image_73.png" /></figure>

C5
(blasticidin)

C6
(puromycin)

D

Non-Induced MCF10A

<figure><img src="image_74.png" /></figure>

C5
(blasticidin)

C6
(puromycin)

E

CDH1

<figure><img src="image_75.png" /></figure>

F

VIM

<figure><img src="image_76.png" /></figure>

Fig. S9. Overexpression leakage of RPL36A, but not of RPL19, triggers EMT in MCF10A cells independently of the nature of the antibiotic used for cell line selection. (A-B) Exogenous mRNA levels. Levels of exogenous *RPL36A* (A) and *RPL19* (B) were quantified by RT-qPCR at the end of antibiotic selection in basal conditions (i.e., without any doxycycline induction) in the simultaneously derived MCF10A models with inducible overexpression of *RPL36A* and *RPL19*. Compared to empty MCF10A cell lines, leakage occurs in MCF10A-RPL36A and -RPL19 cell lines;
basal levels of exogenous *RPL36A* and *RPL19* mRNAs being detected. (C) Cell morphology. Phase contrast images showing the morphology of MCF10A-empty, -RPL36A and -RPL19 cell lines at the end of the antibiotic selection. While the MCF10A-empty and -RPL19 conserve an epithelial morphology, MCF10A overexpressing RPL36A display a fibroblast-like morphology irrespective of the antibiotic used for the cell line selection. Scale bar = 200 µm (magnification x10). (D) Immunofluorescence staining. Representative immunofluorescence pictures of MCF10A-empty, -RPL36A and -RPL19 cell lines at the end of the antibiotic selection showing the staining of epithelial CDH1 (E-cadherin, red) and mesenchymal VIM (Vimentin, green) markers. Compared to MCF10A-empty and -RPL19 cell lines, the MCF10A-RPL36A cell lines show a loss of CDH1 protein level and a higher level of VIM protein. Scale bar = 100 µm (magnification x40). (E-F) EMT-related mRNA levels. Levels of EMT markers were compared between the three pairs of MCF10A-empty and -RPL36A or -RPL19 cell lines by RT-qPCR. Compared to MCF10A-empty and d-RPL19, constitutive overexpression of RPL36A decreases mRNA levels of epithelial markers (E) while it increases mRNA levels of the mesenchymal markers (F). Each dot represents a replicate.
<figure><img src="image_77.png" /><figcaption>**Fig. S10.** Stable overexpression of RPL36A mutant forms triggers EMT in MCF10A cells. (A) Protein sequences of 4 generated RPL36A mutants. Mutations at a known methylated lysine residue (K) of RPL36A were introduced to either mimic the presence (arginine, R) or absence (alanine, A) of methylation. (B) Exogenous mRNA levels. Levels of exogenous *RPL36A* mutant were quantified by RT-qPCR in the simultaneously derived, stable MCF10A cell lines. Compared to empty MCF10A cell line, exogenous *RPL36A* mRNA was detected in all the MCF10A cell lines</figcaption></figure>
overexpressing a RPL36A form. (C-D) Immunofluorescence staining. Representative immunofluorescence images of MCF10A-empty and -RPL36A wild-type (C) as well as MCF10A-RPL36A mutants (D) show the staining of epithelial CDH1 (E-cadherin, red) and mesenchymal VIM (Vimentin, green) markers. Compared to MCF10A-empty cell line, all the MCF10A cell lines overexpressing the different forms of RPL36A show a loss of CDH1 protein level and a higher level of VIM protein. Scale bar = 100 µm (magnification x40). (E-H) EMT-related mRNA levels. Levels of EMT markers (E-G) and EMT-TF ZEB1 (H) were compared between MCF10A-empty cell line and the different MCF10A-RPL36A mutant by RT-qPCR. Compared to MCF10A-empty, constitutive overexpression of both wild-type and mutant RPL36A decreases mRNA levels of epithelial markers (E-F) while it increases mRNA levels of the mesenchymal markers (G-H). (I) EMT-related protein levels. Levels of EMT protein markers were assessed by Western blot. Compared to MCF10A-empty, a loss of epithelial CDH1 and OCLN proteins and an increase in mesenchymal VIM and ZEB1 proteins was observed in all the MCF10A overexpressing different forms of RPL36A.
A

<figure><img src="image_78.png" /></figure>

B

<figure><img src="image_79.png" /></figure>

**Fig. S11. Cryo-EM electron density maps. (A-B)** Purified 80S ribosomal particles from hMEC (A, 3.9 Å resolution) and hMEC-ZEB1 (B, 3.7 Å resolution) cells were analysed using Cryo-EM. From left to right, front view of the ribosome, 90° rotation to focus on RPL36A (magenta) and atomic model of RPL36A fitted in the electron density map. In both conditions, the ribosomes are mostly observed in a rotated PRE conformation state containing a tRNA in the intermediate A/P state (green), a tRNA in the intermediate P/E state (orange), an mRNA and a neo-synthetized peptide (not highlighted). The large 60S ribosomal subunit is shown in blue, the small 40S ribosomal subunit is represented in yellow.
Supporting Table S1

<table><caption>**Table S1.** List of antibodies used in this study.</caption><thead><tr><td>Gene</td><td>Reference, Brand</td><td>Dilution western blot</td><td>Dilution immunofluorescence</td></tr></thead><tbody><tr><td>α-tubuline</td><td>CP06, Calbiochem</td><td>1/500</td><td>N/A</td></tr><tr><td>CDH1</td><td>810181, BD Biosciences</td><td>1/3,000</td><td>1/100</td></tr><tr><td>eIF3A</td><td>NBP1-18891, Novusbio</td><td>1/1,000</td><td>N/A</td></tr><tr><td>eIF3B</td><td>A301-761A, Bethyl</td><td>1/2,000</td><td>N/A</td></tr><tr><td>eIF3C</td><td>A300-377A, Bethyl</td><td>1/1,000</td><td>N/A</td></tr><tr><td>eIF3D</td><td>pab73778, Covalab</td><td>1/1,000</td><td>N/A</td></tr><tr><td>eIF3E</td><td>ab36766, Abcam</td><td>1/500</td><td>N/A</td></tr><tr><td>eIF3F</td><td>pab70099, Covalab</td><td>1/1,000</td><td>N/A</td></tr><tr><td>eIF3G</td><td>pab75515, Covalab</td><td>1/2,000</td><td>N/A</td></tr><tr><td>eIF3H</td><td>A301-754A, Bethyl</td><td>1/1,000</td><td>N/A</td></tr><tr><td>eIF3I</td><td>646701, Ozyme</td><td>1/1,000</td><td>N/A</td></tr><tr><td>eIF3K</td><td>NB100-93304, Novusbio</td><td>1/2,000</td><td>N/A</td></tr><tr><td>eIF3L</td><td>pab0859-P, Covalab</td><td>1/1,000</td><td>N/A</td></tr><tr><td>eIF3M</td><td>NBP1-56654, Novusbio</td><td>1/1,000</td><td>N/A</td></tr><tr><td>FLAG</td><td>Sigma, F7425</td><td>1/1,000</td><td>N/A</td></tr><tr><td>Histone H3</td><td>ab1791, Abcam</td><td>1/3,000</td><td>N/A</td></tr><tr><td>Ku80</td><td>ab3715, Abcam</td><td>1/2,000</td><td>N/A</td></tr><tr><td>OCLN</td><td>ab31721, Abcam</td><td>1/500</td><td>N/A</td></tr><tr><td>RPS6</td><td>A300-557A, Bethyl</td><td>1/1,000</td><td>N/A</td></tr><tr><td>RPL19</td><td>ab224592, Abcam</td><td>1/500</td><td>N/A</td></tr><tr><td>RPL36A</td><td>Custom, Biotem</td><td>1/2,000</td><td>N/A</td></tr><tr><td>SNAIL/SLUG</td><td>ab85936, Abcam</td><td>1/1,000</td><td>N/A</td></tr><tr><td>VIM</td><td>ab92547, Abcam</td><td>1/500</td><td>1/100</td></tr><tr><td>ZEB1</td><td>HPA027524, Sigma</td><td>1/500</td><td>N/A</td></tr></tbody></table>
## Supporting Datasets S1 to S4

**Dataset S1 (separate file).** Complete datasets of dysregulated mRNA identified by ribosome profiling between hMEC-ZEB1 and hMEC.

**Dataset S2 (separate file).** Complete analysis of gene enrichment of dysregulated mRNA identified by ribosome profiling between hMEC-ZEB1 and hMEC.

**Dataset S3 (separate file).** Complete dataset of proteins identified by mass spectrometry using purified ribosomes from hMEC-ZEB1 and hMEC.

**Dataset S4 (separate file).** Primer sequences.
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