Differential translation of mRNA isoforms underlies oncogenic activation of cell cycle kinase Aurora A

  1. Roberta Cacioppo  Is a corresponding author
  2. Hesna Begum Akman
  3. Taner Tuncer
  4. Ayse Elif Erson-Bensan
  5. Catherine Lindon  Is a corresponding author
  1. Department of Pharmacology, University of Cambridge, United Kingdom
  2. Department of Biological Sciences, Orta Dogu Teknik Universitesi, Turkey
  3. Department of Biology, Ondokuz Mayis Universitesi, Turkey

Abstract

Aurora Kinase A (AURKA) is an oncogenic kinase with major roles in mitosis, but also exerts cell cycle- and kinase-independent functions linked to cancer. Therefore, control of its expression, as well as its activity, is crucial. A short and a long 3′UTR isoform exist for AURKA mRNA, resulting from alternative polyadenylation (APA). We initially observed that in triple-negative breast cancer, where AURKA is typically overexpressed, the short isoform is predominant and this correlates with faster relapse times of patients. The short isoform is characterized by higher translational efficiency since translation and decay rate of the long isoform are targeted by hsa-let-7a tumor-suppressor miRNA. Additionally, hsa-let-7a regulates the cell cycle periodicity of translation of the long isoform, whereas the short isoform is translated highly and constantly throughout interphase. Finally, disrupted production of the long isoform led to an increase in proliferation and migration rates of cells. In summary, we uncovered a new mechanism dependent on the cooperation between APA and miRNA targeting likely to be a route of oncogenic activation of human AURKA.

eLife assessment

In this important study, the authors provide compelling evidence that the interplay between alternative polyadenylation (APA) of mRNA encoding Aurora Kinase A (AURKA) and hsa-let-7a miRNA governs AURKA protein levels. The authors show that short 3'UTR isoform of mRNA encoding AURKA is efficiently translated throughout the cell cycle, while the long 3'UTR isoform is suppressed by hsa-let-7a miRNA in a cell cycle-dependent manner. These findings delineate post-transcriptional mechanisms regulating AURKA expression that may be implicated in increase in AURKA protein that is frequently observed across a variety of cancers.

https://doi.org/10.7554/eLife.87253.2.sa0

Introduction

Aurora Kinase A (AURKA) is a critical positive regulator of the mitotic phase of the cell cycle (Willems et al., 2018). AURKA also plays additional cancer-promoting roles in cell proliferation, survival, migration, and cancer stem cell phenotypes, some of which in interphase and in a kinase-independent manner (Naso et al., 2021). AURKA expression follows a strict cell cycle-dependent pattern, with both protein and mRNA levels extremely low in G1 phase, increasing in S phase, and peaking at G2 phase until mitosis (Cacioppo and Lindon, 2022). High expression of AURKA is strongly associated with cancer progression, drug resistance, and poor prognosis, justifying why oncogenic AURKA represents a renowned target of anticancer drugs (Du et al., 2021), and making evident that oncogenic roles of AURKA are prompted by its highly sustained levels of expression.

AURKA overexpression in human cancers is known to be caused by elevated gene copy number, enhanced transcription, or increased protein stability. Dysregulation of translation is also linked to disease and contributions of dysregulated translation to cancer phenotypes are increasingly reported (Kovalski et al., 2022; Modelska et al., 2015). Despite some modest evidence suggesting that modulation of AURKA translation is relevant in disease (Dobson et al., 2013; Lai et al., 2017), control of AURKA expression at the level of translation is widely understudied compared with control of its transcription and mRNA processing (Cacioppo and Lindon, 2022). For example, it is not clear whether AURKA mRNA undergoes translational activation and/or inhibition during the cell cycle, and the precise timing, extent, or regulators of these processes remain unexplored.

The process of cleavage of the 3′end of precursor mRNAs (pre-mRNAs) and concomitant addition of a poly(A) tail represents one key event aiding the maturation of mRNAs, termed cleavage, and polyadenylation (C/P) (Gruber and Zavolan, 2019). The cleavage site is typically preceded by a polyadenylation signal (PAS), located 10–30 nucleotides (nt) upstream, and by UGUA and U-rich motifs, whereas it is typically followed by U- and GU-rich motifs. Altogether, these elements constitute the C/P site (Gruber et al., 2016). Most human pre-mRNAs contain multiple C/P sites (Derti et al., 2012), enabling alternative cleavage and polyadenylation (APA) and, thus, distinct expression of transcript isoforms for the same gene. A search using PolyA_DB (Wang et al., 2018) indicates the presence of two C/P sites with canonical PASs (AATAAA) on AURKA 3′ untranslated region (3′UTR) (Figure 1A). This fostered our hypothesis that AURKA mRNA could be subjected to tandem 3′UTR APA, resulting in two 3′UTR isoforms that differ in length. It is currently unknown which AURKA PAS is preferentially used in which cellular context or whether a 3′UTR isoform switch can be modulatable.

Increased short/long ratio (SLR) of Aurora Kinase A (AURKA) alternative polyadenylation (APA) isoforms in triple-negative breast cancer (TNBC).

(A) AURKA transcript isoforms (USCS Genome Browser). AURKA gene is located on (-) strand. (B) Median and range of SLR values for AURKA 3′UTR obtained using APADetect. Mann–Whitney test; ****p<0.0001. (C), (D) RT-qPCR analysis of SLR of AURKA 3′UTR in TNBC cell lines (C) and patient samples (D). SDHA used as reference gene. TN, tissue number. (E) Relapse-free survival rates of TNBC patients with high (highest 25%) or low (lowest 25%) AURKA SLRs. p-value determined by log-rank test.

APA is involved in most cellular processes and is often altered in cancer (Gruber and Zavolan, 2019). Clinically, human cancers are characterized by unique profiles of alternative 3′UTRs that can be exploited for classification of distinct cancer subtypes (Singh et al., 2009; Wang et al., 2020), and associations between 3′UTR shortening and poor prognosis (Lembo et al., 2012) or drug sensitivity Xiang et al., 2018 have been detected. At the molecular level, a strong positive association between expression of short 3′UTRs, increased protein levels, and proliferative states has been frequently reported (Sandberg et al., 2008; Mayr and Bartel, 2009; Masamha et al., 2014; Xia et al., 2014; Pieraccioli et al., 2022). Such genome-wide 3′UTR shortening sustains cancer cell behavior by removing repressor sequence elements from the 3′UTR of oncogenic mRNAs, for example, microRNA (miRNA) binding sites (Sandberg et al., 2008; Mayr and Bartel, 2009; Masamha et al., 2014), or alternatively by inactivating tumor suppressors through suppression of their expression (Lee et al., 2018; Park et al., 2018).

The role of miRNAs in regulating cell cycle genes and the relevance of this regulation in cancer are well understood (Bueno and Malumbres, 2011; Ghafouri-Fard et al., 2020). Few miRNAs have been pointed to as regulators of AURKA mRNA but, importantly, reported cases of miRNA targeting of AURKA occur in those cancers where AURKA overexpression is a promoting factor or a marker of poor prognosis (Fadaka et al., 2020; Zhang et al., 2020; Yuan et al., 2019; Ma et al., 2015). Regardless, none of these studies consider the existence of distinct AURKA 3′UTR isoforms in their experimental design of targeting assessment. The hsa-let-7 miRNA family comprises 11 closely related genes that map in chromosomal regions that are typically deleted in human tumors and, given their pathogenic downregulation in cancer, they are classified as tumor suppressors (Bueno and Malumbres, 2011; Johnson et al., 2007). Roles for hsa-let-7a in breast tumor growth and metastasis have been proposed (Thammaiah and Jayaram, 2016; Shi et al., 2020) and a correlation between hsa-let-7a expression and clinical variables has been detected in triple-negative breast cancer (TNBC) (Avery-Kiejda et al., 2014; Turashvili et al., 2018).

AURKA was classified within the TNBC subtype with the highest median index of 3′UTR shortening events (Wang et al., 2020; Akman et al., 2015), and also undergoes 3′UTR shortening in poor-prognosis patients of breast and lung cancer (Lembo et al., 2012). Importantly, AURKA overexpression in TNBC represents a marker of early recurrence, poor prognosis, and shorter overall survival (Xu et al., 2013; Jalalirad et al., 2021). However, the correlation between AURKA PAS usage, protein expression, and pathological cell behavior has not been explored for this or other biological contexts, nor at the molecular level. In this study, we uncover a molecular mechanism leveraging the cellular ratio of APA isoforms and their different translational program during the cell cycle to control acquisition of AURKA oncogenic potential.

Results

Increased short/long ratio of AURKA APA isoforms in TNBC

In a preliminary study using the APADetect in silico tool, we analyzed publicly available microarray data to identify changes in AURKA 3′UTR isoform abundance in tissues (Akman et al., 2015). 520 comparable datasets for TNBC samples came from GSE31519 (Rody et al., 2011) and 65 histologically normal epithelium and cancer-free prophylactic mastectomy patients were used: 32 from GSE20437 (Graham et al., 2010), 12 from GSE9574 (Tripathi et al., 2008), 7 from GSE3744 (Richardson et al., 2006), 6 from GSE6883 (Liu et al., 2007), 5 from GSE26910 (Planche et al., 2011), and 3 from GSE21422 (Kretschmer et al., 2011). The analysis revealed increased short/long ratio (SLR) of AURKA 3′UTR isoforms in TNBC compared to normal breast tissues (Figure 1B). Higher SLR was confirmed by RT-qPCR in multiple TNBC cell lines (Figure 1C). Furthermore, RT-qPCR analysis of normal and TNBC patient cDNAs from Origene Breast Cancer cDNA array IV (BCRT504) also showed higher AURKA SLR in TNBC samples compared to normal (Figure 1D). In addition, the shortening of AURKA 3′UTR correlated with faster relapse times in TNBC patients (clinical data from Rody et al., 2011; Figure 1E). These results therefore suggest a potential oncogenic role of AURKA APA in breast cancer worth further investigations.

AURKA shows 3′UTR isoform-dependent protein expression

To probe AURKA APA isoform-dependent protein expression, we developed a single-cell expression sensor suitable for experiments in live cell. The construct independently expresses Venus and mCherry fluorescent proteins via a constitutive bi-directional promoter (Figure 2A). The coding sequence (CDS) of Venus is flanked by AURKA UTRs, whereas that of mCherry lacks regulatory regions and is therefore used to normalize for transfection efficiency. To test for APA-sensitive expression, we alternatively mutated the distal (d) or proximal (p) PAS on the reporter 3′UTR, to generate different 3′UTR isoforms (SHORT and LONG, respectively). Constructs lacking AURKA UTRs (Δ) and expressing AURKA wild-type UTRs (WT) were used as controls.

Figure 2 with 1 supplement see all
Aurora Kinase A (AURKA) shows 3′UTR isoform-dependent protein expression.

(A) Top: UTR-dependent protein expression reporters. Venus coding sequence (CDS) is flanked by AURKA 5′UTR and 3′UTR, WT or polyadenylation signal (PAS)-mutated. Bottom: representative snapshots of transfected U2OS cells. Scale bar 50 µm. (B) Mean and SEM of median Venus/mCherry mean fluorescence intensity (MFI) ratios from transfected U2OS cells from three biological replicates. n ≥ 129 cells per condition. Ordinary one-way ANOVA with Tukeys multiple-comparisons test. (C) 3′RACE of endogenous AURKA APA isoforms. (D) RT-qPCR of endogenous AURKA short/long ratio (SLR) in U2OS cells. Long isoform abundance plotted as fold change over total AURKA mRNA. 18S rRNA used as reference target. (E) Same as (B) but in MCF10A (left) and RPE1 (right) cells. n ≥ 55 cells per condition. Unpaired t-test. ns, not significant; *p<0.05; **p<0.01; ***p<0.001.

We initially assessed the efficiency of the promoter bidirectionality. Correlation between Venus and mCherry expression was strongly maintained at the level of both fluorescence intensity (Figure 2—figure supplement 1A) and mRNA abundance (Figure 2—figure supplement 1B). Promoter strength was, however, not equal in both directions since fewer copies of mCherry mRNA were transcribed compared to Venus mRNA (Figure 2—figure supplement 1B), despite mCherry fluorescence intensity being generally higher than that of Venus (Figure 2A and B, see Δ). We further assessed that mRNA of both mCherry and Venus was stable over time (Figure 3B, see Δ), and fluorescence of both proteins was stable over time and over different cell cycle stages (Figure 2—figure supplement 1C and D). Considering the short maturation time and long half-life of both Venus and mCherry proteins (Shaner et al., 2004; Nagai et al., 2002), the assay allows to reliably measure effects of UTRs on reporter protein levels at any given time and regardless of cell cycle phases. As positive control of our assay, we recapitulated the higher protein expression from the short 3′UTR APA isoform of CDC6 mRNA, which has previously been observed (Akman et al., 2012; Figure 2—figure supplement 1E).

Aurora Kinase A (AURKA) alternative polyadenylation (APA) isoforms are translated with different efficiency.

(A, B) RT-qPCR of reporter mRNAs abundance (A) and decay rate (B) from transfected U2OS cells. mCherry mRNA used as reference target. Ordinary one-way ANOVA with Tukey’s multiple-comparisons test; ns, not significant. (C) Decay rate of endogenous AURKA mRNA as in (B). 18S rRNA used as reference target. Abundance of long isoform plotted as fold change over total AURKA mRNA. (D) Design of the nascent chain immunoprecipitation (NC IP) reporters and assay. (E) Mean and SEM of median FlagVenus/mCherry mean fluorescence intensity (MFI) ratios from transfected U2OS cells from two biological replicates. n ≥ 160 cells per condition. Unpaired t-test; **p<0.005. (F), (G) Immunoblots of NC IP fractions using Δ (F) or Flag-Δ (G) reporter. mCherry used as negative control. (H) RT-qPCR of eluted reporter mRNAs. Results representative of three biological replicates.

Addition of AURKA UTRs to Venus CDS significantly increased protein expression (Figure 2A and B), likely due to the role of 5′UTR in facilitating translation (Hinnebusch et al., 2016). We found that the SHORT reporter generates significantly more protein compared to the LONG (Figure 2A and B). Moreover, similar protein expression levels from the WT and LONG reporters suggest that AURKA WT 3′UTR is processed with a preference for dPAS in U2OS cells. Accordingly, we could detect both endogenous AURKA APA isoforms in U2OS cells by 3′RACE (Figure 2C) and confirmed by RT-qPCR that AURKA long isoform is prevalent (~60% of total AURKA mRNA) (Figure 2D). The different SLR observed between U2OS cells, normal breast tissues, and TNBC cell lines and tissues (Figure 1B–D) indicates that AURKA 3′UTR isoform prevalence is dependent on cell type. In addition, the quantitative difference in reporter protein expression by the isoforms also varied among cell types, suggesting cell-specific regulation (Figure 2B and E, SHORT vs. LONG). In sum, these results provide evidence for the first time of a role for APA in controlling AURKA protein expression.

AURKA APA isoforms are translated with different efficiency

We next investigated the basis of the different protein expression between AURKA APA isoforms. Following transfection of U2OS cells with the constructs in Figure 2A, we first quantified the abundance of reporter mRNA isoforms (Figure 3A). We then assessed the isoforms decay rate by quantifying reporter mRNAs at multiple time points following arrest of transcription by actinomycin D (ActD) (Figure 3B). We observed that while Venus mRNA lacking UTRs was highly stable, reporter mRNA levels decreased at faster rate when carrying UTRs (Figure 3B), indicating that the assay reports on UTR-dependent effects on mRNA stability. Both the abundance and stability of the SHORT and LONG reporter isoforms were similar (Figure 3A and B). We additionally found that the two endogenous AURKA 3′UTR isoforms also have similar decay rates, albeit decaying at a higher rate compared to the reporter mRNAs (Figure 3C), suggesting that features present in AURKA CDS might influence mRNA stability (Narula et al., 2019).

Because the reporter APA isoforms share similar abundance and stability, we wondered whether they undergo different translational regulation instead. To this aim, we adapted a biochemical translation efficiency (TE) assay from Williams et al., 2022, which required addition of a 3XFlag tag to the N-terminus of Venus (FlagVenus) in our reporter constructs, and called this nascent chain immunoprecipitation (NC IP) assay (Figure 3D). We first assessed that addition of the 3XFlag tag did not alter Venus expression (Figure 3E). In our NC IP assay, anti-Flag beads were used to immunoprecipitate nascent FlagVenus chains from ribosomes stalled by treatment with cycloheximide (CHX). Ribosome-mRNA complexes were eluted from the IP-immobilized nascent chains using puromycin, which causes release of nascent chains from ribosomes (Aviner, 2020); RNA was then purified from the elution fraction and quantified by RT-qPCR to provide a measure of the amount of reporter mRNA undergoing translation. All fractions were then blotted for FlagVenus and mCherry (negative control) proteins to monitor their presence at different steps of the experiment. As expected, elution with puromycin retained FlagVenus on the beads, whereas mCherry, as well as untagged Venus, are lost in the flow-through (Figure 3G, left, and F). No reporter mRNA could be detected in the elution fraction when untagged Venus was used (data not shown). Alternatively, purified 3XFlag peptide was used to elute the nascent chain-ribosome-mRNA complexes from the beads following IP (Figure 3G, right). RT-qPCR quantification of the FlagVenus nascent chain-cognate reporter mRNAs revealed about twice more copies of Flag-S mRNA compared to Flag-L mRNA, regardless of the elution method (Figure 3H). This indicates that Flag-S mRNA is translated with higher efficiency than Flag-L mRNA. These results show that APA controls AURKA protein expression mainly via differential translational regulation of the 3′UTR isoforms.

Translation rate of AURKA APA isoforms follows different cell cycle periodicity

Given the known cell cycle-dependent expression of AURKA (Cacioppo and Lindon, 2022), we tested whether differential translational efficiency of AURKA mRNA isoforms might contribute to this regulation. To avoid perturbation of translation provoked by classical cell cycle synchronization methods (Anda and Grallert, 2019), we used a live-cell fluorescence-based translation rate measurement assay in conjunction with a CDK2 activity sensor (Spencer et al., 2013) for in silico cell cycle synchronization. We developed our assay of ‘translation rate imaging by rate of protein stabilization’ (TRIPS) based on a previously introduced reporter system (Han et al., 2014; Tanenbaum et al., 2015). Our bidirectional promoter construct was modified to express superfolder GFP (sfGFP) fused to a mutated Echerichia coli dihydrofolate reductase (DHFR-Y100I) destabilizer domain (DHFR-sfGFP), which is continuously degraded unless the stabilizer molecule trimethoprim (TMP) is added. Addition of TMP leads to an increase of sfGFP signal over time and, given the sfGFP short maturation time (Pédelacq et al., 2006), the accumulation rate of sfGFP reflects DHFR-sfGFP protein synthesis rate (Figure 4A, left, Figure 4—figure supplement 1A). The ratio of the median of single-cell mCherry-normalized sfGFP signals at 2 hr to that at 0 hr of TMP treatment was therefore used as read-out for bulk translation rate. In accordance with our assay being designed to measure translation rate, sfGFP signal could not increase under TMP treatment in the presence of translation inhibitor CHX (Figure 4B). We also ensured that the increase in sfGFP signal was TMP-dependent (Figure 4—figure supplement 1B) and that TMP treatment affected neither mCherry expression (Figure 4—figure supplement 1C) nor DHFR-sfGFP mRNA abundance (Figure 4C). To probe translation rate at different cell cycle phases, we used the CDK2 activity sensor (Spencer et al., 2013) stably expressed in our U2OS cell line (U2OSCDK2) (Figure 4—figure supplement 1A) and called this assay ‘cell cycle-dependent TRIPS’ (C-TRIPS) (Figure 4A, right).

Figure 4 with 1 supplement see all
Translation rate of Aurora Kinase A (AURKA) alternative polyadenylation (APA) isoforms follows different cell cycle periodicity.

(A) Design of the translation rate imaging by rate of protein stabilization (TRIPS) reporters and assay (left) and CDK2 activity sensor (right). (B) Mean and SEM of median sfGFP/mCherry mean fluorescence intensity (MFI) ratios from U2OS cells transfected with TRIPS-Δ and imaged at 0 hr and 2 hr of 50 µM trimethoprim (TMP) treatment, with or without 0.1 mg/ml cycloheximide (CHX), from three biological replicates. Baseline at 0 hr. n ≥ 55 cells per condition. (C) RT-qPCR of sfGFP mRNA from U2OS cells transfected with TRIPS-Δ, at 0 hr and 2 hr of 50 µM TMP treatment. mCherry mRNA used as reference target. RNA extracts at 0 hr of treatment used as reference sample. (D) TRIPS (left) and C-TRIPS (right) assays in transfected U2OSCDK2 cells. n ≥ 200 cells per condition. Left: mean and SEM from three biological replicates. Right: median and 95% CI of pooled data from left. Kruskal–Wallis with Dunnett’s multiple-comparisons test. (E) RT-qPCR of endogenous AURKA mRNA in U2OS cells. 18S rRNA used as reference target. Baseline at G1/S. (F) Endogenous AURKA long isoform as in (E) plotted as percentage of total AURKA mRNA. (D) Left, (E, F) Ordinary one-way ANOVA with Tukey’s multiple-comparisons test. ns, not significant; *p<0.05; **p<0.01; ***p<0.0005; ****p<0.0001.

To test the translation rate of the individual AURKA APA isoforms, we flanked DHFR-sfGFP CDS with AURKA PAS-mutated UTRs (TRIPS-L, TRIPS-S) (Figure 4A, left). We found that our TRIPS assay could recapitulate the difference in translation efficiency of the isoforms previously observed (Figures 3H and 4D, left). Importantly, expression of the CDK2 activity sensor did not affect cellular translation as the different translation rate of AURKA 3′UTR isoforms could be reproduced in U2OS cells lacking the sensor (Figure 4—figure supplement 1D). Following measurements of bulk translation rates (Figure 4D, left), we then binned single-cell translation rate values into three intervals of CDK2 activity (Figure 4D, right). Results of our C-TRIPS assay revealed that, while TRIPS-Δ is translated constantly during the cell cycle, translation rate of TRIPS-L is regulated in the cell cycle. This isoform showed lower translation rate in G1 and S and an enhanced rate at G2, consistent with the increase in both AURKA mRNA and protein levels that occurs in preparation for mitosis (Cacioppo and Lindon, 2022). By contrast, TRIPS-S was translated constantly through the cell cycle and at a maximal rate already in G1 (Figure 4D, right), indicating that this isoform is insensitive to cell cycle regulation of AURKA translation rate.

Furthermore, we quantified abundance of endogenous AURKA APA isoforms at different stages of the cell cycle by performing RT-qPCR following synchronization in G1/S, G2, or M phases (Figure 4E). The expected changes in AURKA mRNA abundance following each treatment represent a positive control for the synchronization. However, abundance of the long isoform changed quite concomitantly with changes in total AURKA mRNA levels (Figure 4F). This suggests that the same ratio of 3′UTR isoforms is rather maintained throughout the cell cycle and that AURKA APA is not cell cycle regulated.

These results not only provide strong, independent validation of our finding that elements present in 5′ and 3′ UTR of AURKA enable translational activation but additionally indicate that elements present on the long 3′UTR might account for its different pattern of translation during interphase as lack of these on the short 3′UTR allow escape from cell cycle phase-dependent translation.

Translational periodicity of long 3′UTR isoform is regulated by hsa-let-7a miRNA

Among known post-transcriptional regulators, miRNAs are widely recognized as molecular regulators of both mRNA stability and translation (Jonas and Izaurralde, 2015). We interrogated miRDB (https://mirdb.org/) (Chen and Wang, 2020) to search for miRNAs that could be involved in the differential regulation of the two AURKA mRNA isoforms and selected hsa-let-7a miRNA (Figure 5A) given its widely established tumor-suppressor role of in TNBC. We assessed the hsa-let-7a targeting of AURKA 3′UTR by co-transfecting our AURKA UTR-dependent protein expression reporters (Figure 2A) with hsa-let-7a or a negative control miRNA that does not have any target in the human genome. As positive control of the assay, we cloned Myeloid Zinc Finger1 (MZF1) 3′UTR downstream Venus CDS in our Δ reporter and could reproduce the previously reported targeting of MZF1 3′UTR by hsa-let-7a (Tvingsholm et al., 2018; Figure 5B). Protein expression from the LONG reporter mRNA was reduced by hsa-let-7a, whereas that from the SHORT mRNA and from a LONG mRNA that lacks the hsa-let-7a binding site (Δlet7a) was not (Figure 5B). Also, the loss of hsa-let-7a targeting was sufficient to increase protein expression from the LONG reporter mRNA (compare LONG + NC vs. Δlet7a + NC). To confirm that altered expression was due to the lack of hsa-let-7a targeting and not an effect of the mutation itself, we also observed an increase in protein expression when we co-transfected our LONG reporter and an inhibitor of hsa-let-7a (anti-let7a) (Figure 5C).

Translational periodicity of long 3′UTR isoform is regulated by hsa-let-7a miRNA.

(A) Complementarity of hsa-let-7a binding to Aurora Kinase A (AURKA) 3′UTR. (B) Mean and SEM of median Venus/mCherry mean fluorescence intensity (MFI) ratios from U2OS cells co-transfected with 250 nM hsa-let-7a or a negative control (NC) miRNA from three biological replicates. n ≥ 182 cells per condition. Unpaired t-test. (C) Same as (B) but co-transfecting 300 nM anti-let-7a or NC. n ≥ 94 cells per condition. Unpaired t-test. (D) RT-qPCR of reporter mRNAs abundance from U2OS cells transfected as (B), at 8 hr of 10 μg/ml ActD. mCherry mRNA used as reference target. Ordinary one-way ANOVA with Tukey’s multiple-comparisons test. (E) Translation rate imaging by rate of protein stabilization (TRIPS) (left) and C-TRIPS (right) assays in transfected U2OSCDK2 cells. n ≥ 162 cells per condition Left: mean and SEM from three biological replicates. Ordinary one-way ANOVA with Dunnett’s multiple-comparisons test vs. NC. Right: median and 95% CI of pooled data from left. Kruskal–Wallis with Dunnett’s multiple-comparisons test vs. NC of the respective phase. ns, not significant; *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001.

In order to assess the role of hsa-let-7a in controlling decay rate of the target mRNA, we next co-transfected our Venus reporters and either hsa-let-7a or negative control miRNA and quantified reporter mRNA abundance after 8 hr of ActD treatment. We found that stability of the LONG reporter mRNA was significantly reduced by hsa-let-7a, whereas that of the SHORT reporter mRNA was unaltered. Additionally, mutation of the hsa-let-7a binding site slightly increased reporter mRNA stability (compare LONG + NC vs. Δlet7a + NC) (Figure 5D).

We then performed our C-TRIPS assay co-transfecting the TRIPS-L reporter and hsa-let-7a or control miRNA and found that hsa-let-7a reduced both bulk translation rate (Figure 5E, left) and translation rate at all interphase stages (Figure 5E, right) of the long 3′UTR. Furthermore, we asked whether loss of hsa-let-7a targeting is sufficient to cause loss of translational regulation of the long isoform during the cell cycle. For this, we performed the C-TRIPS assay using a TRIPS-L reporter with mutations in the hsa-let-7a binding site (TRIPS-Δlet7a) or, alternatively, co-transfecting the TRIPS-L reporter and the hsa-let-7a inhibitor anti-let7a. Interestingly, in both cases, loss of hsa-let-7a targeting only increased translation rate in G1 and S, but not G2 (Figure 5E, right), suggesting that the targeting in G2 is not likely to occur unless in conditions of excess hsa-let-7a.

In conclusion, our results show that hsa-let-7a only silences AURKA long 3′UTR isoform by both promoting mRNA degradation and reducing translation rate, and that hsa-let-7a targeting is responsible for the cell cycle-dependent translational regulation of AURKA long 3′UTR isoform.

Increased AURKA short/long ratio is sufficient to disrupt cell behavior

Having established that APA plays a role in regulating AURKA expression, we tested the idea that AURKA APA directly contributes to cancer cell behavior by performing genome editing to alter AURKA APA in wild-type U2OS cells. We used Cas9D10A-mediated double-nicking strategy and mutated the endogenous dPAS on AURKA 3′UTR with the aim of silencing expression of AURKA long 3′UTR isoform (Figure 6A). Two mutant clones with disrupted dPAS site were obtained (ΔdPAS#1, ΔdPAS#2) (Figure 6—figure supplement 1A) and were used for subsequent functional analyses. Qualitative assessment of AURKA 3′UTR isoforms ratio in these clones by 3′RACE found the long 3′UTR isoform to be undetectable in the mutated cell lines (Figure 6B), indicating that the genetic editing successfully prevents usage of the dPAS site for cleavage and polyadenylation.

Figure 6 with 1 supplement see all
Increased Aurora Kinase A (AURKA) short/long ratio is sufficient to disrupt cell behavior.

(A) Design of CRISPR editing. Nucleotide substitutions in red. (B) 3′RACE of endogenous AURKA alternative polyadenylation (APA) isoforms. (C), (D) Western blot after G1/S enrichment (C) and with or without 6 hr treatment with 0.1 mg/ml cycloheximide (CHX) (D). Blots representative of three biological replicates. (E) CCK8 assay. A nonparental WT U2OS cell line used as negative control. Ordinary one-way ANOVA with Dunnett’s multiple-comparisons test vs. WT; **p<0.005; ***p<0.001. (F) Left: representative images of cells grown in soft agar. Right: mean number of clones and SEM of three biological replicates. (G) Measurement of migration rate. (F, G) Ordinary one-way ANOVA with Dunnett’s multiple-comparisons test vs. WT; *p<0.05; **p<0.01.

We then examined expression of AURKA in the mutated cell lines by immunoblot of extracts from cell populations enriched for the G1/S phase of the cell cycle, where AURKA expression is the lowest in unmodified cells. We observed AURKA was expressed at higher levels in ΔdPAS#1 and ΔdPAS#2 cells compared to WT cells (Figure 6C, Figure 6—figure supplement 1B). AURKA expression in G1/S was reduced in the mutated cell lines when treated with CHX, indicating that translation of the short isoform is active in this phase (Figure 6D). Because AURKA overexpression is a common feature of cancer, we interrogated the mutated cell lines for changes in cancer-relevant behavior. Consistent with a role of AURKA overexpression in accelerating the cell cycle and favoring cell proliferation, we found a higher rate of proliferation of ΔdPAS#1 and ΔdPAS#2 cells compared to WT cells using the CCK8 assay to measure metabolic activity (Figure 6E). Additionally, we assessed the ability of anchorage-independent growth, which closely correlates with tumorigenicity in animal models by growing cells in soft agar. ΔdPAS#1 and ΔdPAS#2 cells resulted more capable to survive and grow in the absence of anchorage to their neighboring cells compared to WT cells (Figure 6F). AURKA also regulates organization of microtubules required for cellular migration and also enhances migration of tumor cells through several pathways. For example, AURKA activates the Cofilin-F-Actin pathway leading to breast cancer metastases (Willems et al., 2018). We were therefore prompted to assess the motility of ΔdPAS#1 and ΔdPAS#2 cells in a 2D cell migration assay. Our result shows a higher rate of migration of ΔdPAS#1 and ΔdPAS#2 cells compared to WT cells (Figure 6G).

In summary, our results show that AURKA overexpression caused by a disruption in the SLR of APA isoforms in favor of the short isoform contributes to cancer-like cell behavior.

Discussion

In this study, we describe for the first time a molecular mechanism involving the dysregulation of APA and the differential targeting of AURKA APA isoforms by hsa-let-7a, a tumor suppressor miRNA, that is sufficient for the oncogenic activation of AURKA at the post-transcriptional level. We also shed light on the cell cycle-dependent regulation of AURKA translation and introduce novel and improved methods for measurements of post-transcriptional gene expression of individual mRNAs of interest.

As a consequence of tandem 3′UTR APA, a short and a long 3′UTR isoform are generated for AURKA mRNA. The SLR of these isoforms is cell-type defined and is not dependent on the cell cycle, indicating that cell-type-specific/cell cycle-independent factors are involved in establishing the SLR. This is unexpected given that periodic regulation of APA may be a characteristic of many cell cycle genes (Dominguez et al., 2016). Because protein expression differs from the two isoforms, AURKA SLR is a crucial element defining AURKA expression levels. Ours and other studies detected increased SLR of AURKA APA isoforms in TNBC and found this correlates with worse disease-free survival (Wang et al., 2020; Akman et al., 2015). Here, we reveal insights into the molecular basis for the correlation by showing that the increased and cell cycle-independent translation rate of the short isoform can lead to marked overexpression of AURKA in interphase. Our results support the hypothesis that deregulation of expression by disruption of APA is sufficient to drive AURKA oncogenic properties, such as promoting increased proliferation and migration rate. Whether this is also sufficient to drive cancer-cell transformation remains to be explored. It has been known for many years that overexpression of AURKA induces mitotic defects, aneuploidy, as well as acceleration of the cell cycle, epithelial-to-mesenchymal transition and migration (Willems et al., 2018; Zhou et al., 1998), whilst the cellular background for AURKA’s transforming potential is also of importance (Asteriti et al., 2010). Nonetheless, the significance of AURKA overexpression specifically in G1 for the exertion of potential oncogenic functions in this phase represents an emerging field of research (Naso et al., 2021; Abdelbaki et al., 2020; Bertolin and Tramier, 2020). It is possible that normal AURKA functions are exerted at low levels of expression in G1 become oncogenic at high levels of expression. This would not be surprising given how the roles played by AURKA in G1 revolve around regulation of transcription, mitochondria fitness, and cellular metabolism.

Our work does not research the cause of the disrupted APA in TNBC. This could be concomitant to a global 3′UTR shortening, for example, due to altered expression in cancer of C/P factors (Gruber and Zavolan, 2019; Wang et al., 2020), or of their regulators (Pieraccioli et al., 2022), or even altered RNA Pol II elongation dynamics (Mitschka and Mayr, 2022). However, these phenomena have not been extensively explored in TNBC (Miles et al., 2016). Alternatively, disrupted APA could represent an AURKA gene-specific phenomenon due to the presence of single-nucleotide polymorphisms (SNPs) on the 3′UTR either on the C/P site or in proximity that could affect PAS choice by the C/P machinery. Finally, an mRNA-dependent mechanism whereby the short 3′UTR isoform itself regulates AURKA protein function or localization throughout or at specific phases of the cell cycle, licensing oncogenic advantage, should not be excluded (Mitschka and Mayr, 2022).

The periodicity of AURKA expression is an important requirement for correct progression through the cell cycle. For example, ubiquitin-mediated proteolysis is a critical pathway for irreversible AURKA inactivation, which must occur for ordered transition to interphase following mitosis (Abdelbaki et al., 2020; Lindon et al., 2015). One important but unanswered question is whether and to what extent translation regulation accounts for the increase and decrease of AURKA expression during the cell cycle. Implementation of our TRIPS assay, which measures protein synthesis rate independent of changes in mRNA abundance, allowed detection of active regulatory mechanisms of translation occurring at different cell cycle phases. This new evidence integrates well with the notion that transcriptional, post-transcriptional, and post-translational mechanisms all combine to provide AURKA gene with a characteristic pattern of expression (Cacioppo and Lindon, 2022). Our work shows that translation of AURKA is regulated by hsa-let-7a miRNA. Because we could still detect active protein synthesis from the long isoform under hsa-let-7a overexpression using our TRIPS assay, and we could also immunoprecipitate tagged nascent chains from the long isoform in U2OS cells, where hsa-let-7a is expressed, it is unlikely that hsa-let-7a blocks translation at the level of initiation, but it rather slows down the rate of translation elongation. This is in accordance with a study showing that hsa-let-7a co-sediments with actively translating polyribosomes (Nottrott et al., 2006), a generally proved mechanism of miRNA action (Tat et al., 2016). In addition, because we observed that hsa-let-7a can also control the decay rate of the long isoform, it is possible that a reduction in translation elongation rate may be required to mediate degradation of the mRNA (Biasini et al., 2021). We also show that the differential hsa-let-7a targeting through the cell cycle is a mechanism responsible for AURKA periodic translational control. Based on evidence from our C-TRIPS assays to assess the temporal hsa-let-7a targeting of the long isoform at different phases of the cell cycle, and on the published evidence that hsa-let-7a levels are constant during the cell cycle of human cancer cells as well as untransformed fibroblasts (Grolmusz et al., 2016), we propose that (i) hsa-let-7a targeting is productive in G1 and S, and is therefore responsible for the low AURKA translation rate in these phases; and (ii) the targeting is not occurring in G2 phase, except in excess of hsa-let-7a, possibly because hsa-let-7a overexpression saturates sequestering factors that prevent its binding to the long isoform. Further investigations will be required to understand this mechanism in more detail.

The characterization of gene-specific post-transcriptional dynamics is desirable for a complete understanding of gene expression regulation. Here, we have developed transient single-cell and biochemical assays to rapidly study mRNA-specific gene expression in a way that measures post-transcriptional events exclusively. Importantly, the assays can be used to test the effect of regulators such as therapeutic miRNAs or drugs on protein expression, mRNA processing, and translation of selected genes.

In conclusion, our study reveals a strong cooperation between APA and miRNA targeting in controlling gene expression dynamics of AURKA and its oncogenic potential. It also provides a workflow to assess the role of mRNA-specific post-transcriptional processing and regulators. Our work additionally highlights a molecular mechanism that could represent an actionable target of RNA-based therapeutics.

Materials and methods

In silico analysis of APA in TNBC

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Publicly available CEL files and associated metadata of microarray results were downloaded from NCBI Gene Expression Omnibus (GEO) repository. APADetect tool (Akman et al., 2015) was used to detect and quantify APA events in TNBC patients and normal breast tissue. CEL files of Human Genome U133A (HGU133A, GPL96) and U133 Plus 2.0 Arrays (HGU133Plus2, GPL570) were analyzed to identify intensities of probes that were grouped based on poly(A) site locations extracted from PolyA_DB (Zhang et al., 2005). Mean signal intensities of proximal and distal probe sets for AURKA were calculated and used as indicators of ‘short’ and ‘long’ AURKA 3′UTR isoforms’ abundance. The ratio of proximal probe set mean (S) to the distal probe set (L) is defined as short to long ratio (SLR). SLR values were subjected to significance analysis of microarrays (SAM), as implemented by the TM4 Multiple Array Viewer tool (Saeed et al., 2003), for statistical significance after log normalization.

Molecular cloning

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The following UTR sequences were obtained by gene synthesis (Genewiz from Azenta Life Sciences, European Genomics Headquarters, Germany): AURKA WT 5′UTR and 3′UTR (769 bp) (NM_003600.4), MZF1 3′UTR (NM_003422.3), AURKA individual PAS-mutated 3′UTRs (AATAAA>AATCCC). CDC6 3′UTRs were from Akman et al., 2012. For Δ reporter, mCherry and Venus ORFs were inserted into the MCSs of pBI-CMV1 (631630, Clontech, TakaraBio). WT, SHORT and LONG reporters were generated by assembly (NEBuilder HiFi DNA Assembly Cloning Kit, E5520S, NEB) of AURKA 5′UTR, Venus ORF and AURKA wt, short (dPAS-mutated) or long (pPAS-mutated) 3′UTR, and insertion into pBI-CMV1-mCherry. CDC6_L, CDC6_S, and MZF1 reporters were generated by insertion of CDC6 long or short 3′UTR, and MZF1 3′UTR downstream Venus CDS in Δ reporter. LONG-Δlet7a was generated by site-directed mutagenesis of LONG reporter with the following forward and reverse primers: 5′-CACGCACCATTTAGGGATTTGCTTG-3′ and 5′AGCACGTGTTCCTATTTTTCACACTC-3′. Flag-Δ was generated by insertion of 3XFlag-Venus ORF into pBI-CMV1-mCherry. Flag-S and Flag-L reporters were generated by assembly of AURKA 5′UTR, 3XFlag-Venus ORF and AURKA short (dPAS-mutated) or long (pPAS-mutated) 3′UTR, and insertion into pBI-CMV1-mCherry. For TRIPS reporters, the DHFR-sfGFP ORF was PCR amplified from pHR-DHFRY100I-sfGFP-NLS-P2A-NLS-mCherry-P2A_Emi1 5' and 3'UTR plasmid, a gift from Ron Vale (Addgene plasmid #67930), and inserted into pBI-CMV1-mCherry (TRIPS-Δ) or assembled with AURKA 5′UTR and AURKA wt 3′UTR (TRIPS-WT), short 3′UTR (TRIPS-S), or long 3′UTR (TRIPS-L) before insertion. TRIPS-Δlet7a was generated by site-directed mutagenesis of TRIPS-L reporter with the primers above. NEB 5-alpha Competent E. coli (High Efficiency) (C2987I, NEB) was used.

Cell lines and drug treatments

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Human U2OS, U2OSCDK2, BT20, HCC1143, HCC1937, MDA-MB-157, MDA-MB-231, and MDA-MB-468 cell lines were cultured in DMEM (41966029, Thermo Fisher) supplemented with 10% FBS (F9665, Sigma), 200 μM GlutaMAX-1 (35050061, Thermo Fisher), 100 U/ml penicillin (15140122, Thermo Fisher), 100 μg/ml streptomycin (15140122, Thermo Fisher), and 250 ng/ml fungizone (15290026, Thermo Fisher) at 37°C with 5% CO2. U2OSCDK2 cells cultures were supplemented with 500 µg/ml G-418 (G8168, Sigma). Human MCF10A were cultured in filtered DMEM-F12 (31331093, Thermo Fisher) supplemented with 10% FBS, 20 ng/ml EGF (AF-100-15, Peprotech), 0.5 mg/ml hydrocortisone (H4001, Sigma), 100 ng/ml cholera toxin (C8052, Sigma), 10 µg/ml insulin (I9278, Sigma), 100 U/ml penicillin, 100 μg/ml streptomycin, and 250 ng/ml fungizone at 37°C with 5% CO2. Human RPE1 cells were cultured as previously described (Grant et al., 2018). Breast cancer cell lines were purchased from DSMZ (Germany) or ATCC (USA) with authentication certificates including STR profiling; all cell lines used in the study were free of mycoplasma contamination. Cell populations were enriched for G1/S phase by incubating with 2.5 mM thymidine (T1895, Sigma) for 24 hr, for G2 phase by incubating with 10 μM RO3306 (4181, Tocris Bioscience) for 16 hr, for M phase by incubating with 10 μM S-trityl l-cysteine (STLC) (2191/50, Tocris Bioscience) for 16 hr, and mitotic cells were then collected by shake-off. CHX (239763, Sigma), TMP (92131, Sigma), and DMSO (sc-358801, Insight Biotech) were used as indicated in the figure legends.

Transfections

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U2OS and RPE1 cells (5 × 106) were electroporated (MPK5000, Neon Transfection System, Invitrogen) using 1150 V pulse voltage, 30 ms pulse width, and two pulses. U2OSCDK2 and MCF10A cells (4 × 104) were transfected using Lipofectamine 3000 Transfection Reagent (L3000001, Thermo Fisher) according to the manufacturer’s instructions. MISSION microRNA Mimic hsa-let-7a (HMI0003, Sigma), miRNA Mimic Negative Control (ABM-MCH00000, abm), and Anti-miR miRNA Inhibitor (AM17000, Thermo Fisher) were co-transfected by Lipofectamine RNAiMAX Transfection Reagent (13778100, Thermo Fisher) according to the manufacturer’s instructions. All analyses were carried out 24 hr post transfection.

Live-cell fluorescence microscopy

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Live-cell microscopy was performed using Olympus IX81 motorized inverted microscope, Orca CCD camera (Hamamatsu Photonics, Japan), motorized stage (Prior Scientific, Cambridge, UK), and 37°C incubation chamber (Solent Scientific, Segensworth, UK) fitted with appropriate filter sets and a 40× NA 1.42 oil objective. Images were collected in the 490 nm (Venus, sfGFP), 550 nm (mCherry), and 435 nm (CFP) channels using Micro-Manager software (Edelstein et al., 2014). Image analysis was performed using a customized plug-in tool in ImageJ (Schindelin et al., 2012), which calculates mean fluorescence intensity (MFI) by measuring average, background-subtracted gray values over regions of interest (ROIs) of defined diameter around manually selected points in the cell.

Western blot

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Western blot was performed as previously described (Abdelbaki et al., 2020). PageRuler Pre-stained Protein Ladder (26616, Thermo Fisher) was used. Primary antibodies were mouse anti-AURKA (1:1000; Clone 4/IAK1, BD Transduction Laboratories), rabbit anti-GFP (1:5000; ab290, Abcam), rabbit anti-GAPDH (1:4000; 2118S, CST), rabbit anti-mCherry (1:1000; ab167453, Abcam), mouse anti-Flag M2 (1:1000; F1804, Sigma). Secondary antibodies were rabbit (P044801-2) or mouse (P044701-2) HRP-conjugated (Dako, Agilent), used at 1:10000 dilution, and detection was performed via Immobilon Western Chemiluminescent HRP Substrate (WBKLS0100, Millipore) on an Odyssey Fc Dual-Mode Imaging System (LI-COR Biosciences).

RNA extraction and RT-qPCR

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RNA extracts were collected using Total RNA miniprep kit (T2010S, NEB). DNA was in-column digested with DNase I. Aliquots were stored with 5 μM EDTA at –20°C for a week or flash-frozen in dry ice and transferred at –80°C. A micro-volume spectrophotometer (NanoDrop Lite, VWR) was used to assess A260/A280 ratios of ~2.0 and A260/A230 ratios of 2.0–2.2. RT-qPCR was performed using Luna Universal One-Step RT-qPCR Kit (E3005S, NEB). 20 µl reactions using 200 nM primers and <100 ng RNA were run on ABI StepOnePlus Real Time PCR system following the manufacturer’s instructions. Primers were designed at Eurofins Genomics. ΔCt or ΔΔCt method was used for relative quantifications accounting for the primer pairs amplification efficiency. Three technical replicates were performed in each biological replicate. Results shown as mean and SEM of three biological replicates. Assessment of DNA contamination, sequences of primers, validation of amplification efficiency of primer pairs, and RT-qPCR reaction conditions are provided (Appendix 1-figures 1-11, Appendix 1-table 1, Appendix 1-table 2). Checklist of MIQE guidelines (Bustin et al., 2009) can be found in Supplementary file 1.

mRNA decay measurement

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Cells were treated with 10 μg/ml actinomycin D (ActD) (10043673, Fisher Scientific) and RNA was isolated from cells at the indicated time points after inhibition of transcription. Target mRNA was quantified by RT-qPCR using the ΔΔCt method with indicated reference targets and corresponding RNA extracts at 0 hr of ActD treatment as reference sample. Mean and SEM of three biological replicates are shown at each time point.

Nascent chain immunoprecipitation

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Transfected cells were treated with 0.1 mg/ml CHX for 15 min, then washed, centrifuged, and resuspended in ice-cold lysis buffer (100 mM Tris-HCl pH 7.5 [BP1757-100, Fisher Scientific], 500 mM LiCl [L7026, Sigma], 10 mM EDTA [10458654, Invitrogen], 0.1 mg/ml CHX, 0.1% Triton X-100 [28817.295, VWR], 100 U/ml RNasIn [3335399001, Merck], and cOmplete EDTA-free protease inhibitor cocktail [11836170001, Roche]) and incubated 15 min on ice. Lysates were cleared by centrifugation and supernatant was incubated with anti-Flag M2 magnetic beads (M8823, Sigma) overnight at 4°C rotating. The bound fraction was washed twice (10 mM Tris-HCl pH 7.5, 600 mM LiCl, 1 mM EDTA, 100 U/ml RNasin, 0.1 mg/ml CHX). Followed elution with 10 mM Tris-HCl pH 7.5, 600 mM LiCl, 1 mM EDTA, 100 U/ml RNAsIn, 0.1 mg/ml puromycin (J67236.XF, Alfa Aesar), or with 3XFLAG peptide buffer (F4799, Sigma), for 30 min rotating at 4°C. RNA was purified (Monarch RNA Cleanup Kit, T2040L, NEB) from fractions and samples were stored as above. Aliquots of each fraction were mixed 1:1 with NuPAGE LDS Sample Buffer 4X (NP0007, Invitrogen) and 10 mM DTT (10197777001, Sigma), boiled 3 min at 95°C and stored at –20°C.

3′RACE

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cDNA synthesis was performed using the Transcriptor Reverse Transcriptase (3531317001, Roche) with an oligo-dT anchor primer (5′-GACCACGCGTATCGATGTCGACTTTTTTTTTTTTTTTTV-3′). In the first round of PCR, an AURKA-specific forward primer (5′-TCCATCTTCCAGGAGGACCACTCTCTG-3′) was used with a reverse primer for the oligo-dT anchor sequence (Anchor_R: 5′-GACCACGCGTATCGATGTCGAC-3′). In the second round of PCR (nested), a new AURKA-specific forward primer (5′-CGGGATCCATATCACGGGTTGAATTCACATTC-3′) was used with Anchor_R. Nested PCR was performed using a 1:10 dilution of first PCR product as template. PCR product was visualized in agarose gels and imaged as above.

Generation of ΔdPAS cell lines

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Two guide RNAs (gRNA1: 5′-GGCCAACAATGAACAGATGG-3′ and gRNA2: 5′-GAGCAGGGGCTGAGAGGAGC-3′) were cloned into AIO-GFP (Chiang et al., 2016), a gift from Steve Jackson (Addgene plasmid #74119). The donor DNA template for homology-directed repair (HDR) was cloned into a separate vector expressing mRuby (mRuby-HDR). AIO-GFP-gRNAs and mRuby-HDR were co-transfected into U2OS cells and 48 hr after transfection GFP+mRuby+ cells were sorted at single-cell density into multiple 96-well plates for clonal expansion. Cell populations were individually screened for dPAS mutation by touch-down PCR. Mutants were confirmed by Sanger sequencing of the genomic locus.

Cell counting kit-8 (CCK-8) assay

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Cells were seeded into 96-well plates at a density of 4000 cells/well. CCK-8 (96992, Merck) was used according to the manufacturer’s instructions and measurements were performed at the indicated time points. The OD450 value was determined using a CLARIOstar Plus microplate reader (BMG LABTECH). Mean and SEM of three biological replicates shown for each time point.

Colony formation assay in soft agar

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A 0.6% base agarose was prepared in 6-well plates, and cells were seeded at density of 15,000 cells per well prior mixing with agarose to a final agarose concentration of 0.3%. Fresh medium was added every 3 d. Colonies were imaged and counted after 10 d using phase contrast microscopy under 4× magnification.

Migration assay

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A suspension of 50,000 cells was added to each well of a Culture-Insert 2 Well (IB-80209, Thistle Scientific Ltd) and grown into a monolayer. After insert removal, cells were washed and serum-free medium was added. At indicated time points, images of three different fields were acquired for every condition using phase contrast microscopy under 10× magnification. The percentage of cell-free area was calculated using Wound_healing_size_tool plugin (Suarez-Arnedo et al., 2020) in ImageJ and is shown as mean of three fields and SEM for three biological replicates.

Statistical analyses

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GraphPad Prism 9 (version 9.5.0; GraphPad Software Inc) and Microsoft Excel (version 16.72; Microsoft Corporation) were used to analyze data, generate graphs, and perform statistical analyses. Statistical parameters, including the sample size, the statistical test used, statistical significance (p-value), and the number of biological replicates, are reported in the figure legends or in the ‘Materials and methods.’.

Materials availability

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Materials from this study are available from the corresponding author upon reasonable request.

Appendix 1

Appendix 1—table 1
Sequences of primers and targets of primer pairs used in RT-qPCR.
Primer pairSequenceTarget
mCherry_F mCherry_RCCGACATCCCCGACTACTTGAAGC CACCTTGTAGATGAACTCGCCGTCCmCherry ORF.
3′UTR_F 3′UTR_RGCCCGACAACCACTACCTGAGCTAC GCTCAAGGATTTCTCCCCCTGCACReporter Venus mRNAs containing AURKA 3′UTR.
Venus_F Venus_RCTGACCCTGAAGCTGATCT GCATGGCGGACTTGAAGAAGVenus ORF.
sfGFP_F sfGFP_RGGCCCTGTCCTTTTACCAGACAACC CATCCATGCCATGTGTAATCCCAGCDHFR-sfGFP ORF.
18S_F 18S_RCTCAACACGGGAAACCTCAC CGCTCCACCAACTAAGAACG18S ribosomal RNA* (NR_146146.1).
AURKA_Long_F AURKA_Long_RGGCGAAGCCTGGTAAAGCTG GCCTCTTCTGTATCCCAAGCAAATCCAll AURKA 5’UTR splice variants and only long 3′UTR APA isoform. Primer pair anneals to AURKA exon XI.
AURKA_Total_F AURKA_Total_RTGTAACAGAGGGAGCCAGGGACC TGATGAATTTGCTGTGATCCAGGGGTGAll AURKA 5′UTR splice variants and both 3′UTR APA isoforms.
Primer pair anneals to AURKA exon XI.
AURKA_SLR_F AURKA_SLR_Short_RTGCTAGGCATGGTGTCTTCA AACAGCTTTACCAGGCTTCGAll AURKA 5′UTR splice variants and both 3′UTR APA isoforms. Primer pair anneals to AURKA exon XI.
AURKA_SLR_F AURKA_SLR_Long_RTGCTAGGCATGGTGTCTTCA AGAAACCCAATCAGGCCTACAll AURKA 5′UTR splice variants and only long 3′UTR APA isoform. Primer pair anneals to AURKA exon XI.
SDHA_F SDHA_RTGGGAACAAGAGGGCATCTG CCACCACTGCATCAAATTCAAll SDHA transcript variants (NM_004168.4, NM_001294332.2, NM_001330758.2). Primer pair anneals to SDHA cDNA exons II–III.
  1. *

    Primer sequences from Lin et al., 2016.

  2. For accession numbers see Cacioppo and Lindon, 2022.

Appendix 1—figure 1
Assessment of DNA contamination of RNA extracts.

(A) Lack of genomic DNA (gDNA) contamination in RNA samples was assessed by PCR using Aurora Kinase A (AURKA)-specific primers. RNA extracts from U2OS cells electroporated with the indicated reporters (Figure 2A) were used as template for the amplification reaction. An RNA extract from mock transfected U2OS cells used as negative control. gDNA from untransfected U2OS cells used as positive control. (B) Lack of plasmid DNA contamination in RNA samples was assessed by PCR using mCherry specific primers. RNA extracts from U2OS cells electroporated with the indicated reporters (Figure 2A) were used as template for the amplification reaction. An RNA extract from mock transfected U2OS cells and gDNA from untransfected U2OS cells used as negative controls. pBI-mCherry plasmid (pBI-mCh) used as positive control template.

Appendix 1—figure 2
Predicted targets on human genome of primer pairs used in this study.

Analysis performed using UCSC In-Silico PCR tool (https://genome.ucsc.edu/cgi-bin/hgPcr). Genomic PCR product of SDHA primer pairs is above >850 bp length set for the in silico search.

Appendix 1—figure 3
Validation of primer pairs used in RT-qPCR.

Standard curve, melt curve, and amplification plot of amplification reactions of serial dilutions (1:5) are shown. Sample reactions were loaded on gel to validate amplicon size. NTC, non-template control.

Appendix 1—figure 4
Validation of primer pairs used in RT-qPCR.

Standard curve, melt curve, and amplification plot of amplification reactions of serial dilutions (1:5) are shown. Sample reactions were loaded on gel to validate amplicon size. NTC, non-template control.

Appendix 1—figure 5
Validation of primer pairs used in RT-qPCR.

Standard curve, melt curve, and amplification plot of amplification reactions of serial dilutions (1:5) are shown. Sample reactions were loaded on gel to validate amplicon size. The target was also amplified from RNA extracts of cells transfected with the pBI-mCherry construct as neg. control (mCh). NTC, non-template control.

Appendix 1—figure 6
Validation of primer pairs used in RT-qPCR.

Standard curve, melt curve, and amplification plot of amplification reactions of serial dilutions (1:5) are shown. NTC, non-template control.

Appendix 1—figure 7
Validation of primer pairs used in RT-qPCR.

Standard curve, melt curve, and amplification plot of amplification reactions of serial dilutions (1:5) are shown. Sample reactions were loaded on gel to validate amplicon size. NTC, non-template control.

Appendix 1—figure 8
Validation of primer pairs used in RT-qPCR.

Standard curve, melt curve, and amplification plot of amplification reactions of serial dilutions (1:5) are shown. Sample reactions were loaded on gel to validate amplicon size. NTC, non-template control.

Appendix 1—figure 9
Validation of primer pairs used in RT-qPCR.

Standard curve, melt curve, and amplification plot of amplification reactions of serial dilutions (1:5) are shown. Sample reactions were loaded on gel to validate amplicon size. NTC, non-template control.

Appendix 1—figure 10
Validation of primer pairs used in RT-qPCR.

Standard curve, melt curve, and amplification plot of amplification reactions of serial dilutions (1:5) are shown. Sample reactions were loaded on gel to validate amplicon size. NTC, non-template control.

Appendix 1—figure 11
Validation of primer pairs used in RT-qPCR.

Standard curve, melt curve, and amplification plot of amplification reactions of serial dilutions (1:5) are shown. Sample reactions were loaded on gel to validate amplicon size. NTC, non-template control.

Appendix 1—table 2
Reaction conditions and thermocycling parameters for one-step RT-qPCR.
ReagentVolume
Luna Universal One-Step Reaction Mix (2×)10 µl
Luna WarmStart RT Enzyme Mix (20×)1 µl
Forward primer (10 µM)0.4 µl
Reverse primer (10 µM)0.4 µl
Template RNA<100 ng
Nuclease-free waterUp to 20 µl
MicroAmp Fast Optical 96-Well Reaction Plate with Barcode, 0.1 ml (Thermo Fisher, 4346906)
Cycling stepTemperature (°C)TimeCycles
Reverse transcription5510 min1
Initial denaturation951 min1
Denaturation extension95
–60
10 s
1 min
40
Melt curve60–95-1

Data availability

The data underlying this article are available in the article and in its supporting files.

The following previously published data sets were used
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Peer review

Reviewer #1 (Public Review):

In this article, Cacioppo et al., report on a previously unappreciated mechanism of the regulation of Aurora Kinase A (AURKA) protein levels that is orchestrated via coordinated action of alternative polyadenylation of AURKA mRNA and hsa-let-7a miRNA. Moreover, it is proposed that this mechanism may play a major role in neoplasia. In support of their model, the authors demonstrate that short-to-long 3'UTR AURKA mRNA isoform ratio is elevated in triple negative breast cancer patients where it correlates with poor prognosis. The authors further generated reporters suitable for single cell live imaging that express different 3'UTR variants, which revealed highly variable ratios of short and long 3'UTR AURKA isoforms across different cell lines. This was followed by actinomycin D chase and nascent chain immunoprecipitation assays in U2OS osteosarcoma cells to demonstrate that while short and long 3'UTR AURKA isoforms have comparable stability, short 3'UTR AURKA isoforms appear to exhibit higher ribosome association which is indicative of higher translation activity. Furthermore, using an additional reporter assay which takes advantage of trimethoprim-based stabilization of highly unstable E. coli dihydrofolate reductase mutants Cacioppo et al., provide evidence that in contrast to the short 3'UTR AURKA mRNA isoform which appears to be constitutively translated throughout the cell cycle, long 3'UTR AURKA mRNA isoform is preferentially translated in the G2 phase. Further evidence is provided that suppression of long 3'UTR AURKA mRNA isoform is at least in part mediated by hsa-let-7a miRNA. Finally, the authors provide evidence that disrupting the expression of long 3'UTR AURKA mRNA isoform using CRISPR-based strategy, leads to overexpression of AURKA driven by the short 3'UTR isoform which is paralleled by an increase in cancer-related phenotypes.

Strengths: Overall it was thought that this study is of potentially broad interest inasmuch as it delineates a hitherto unappreciated mechanisms of regulation of AURKA protein levels, whereby AURKA is emerging as one of the major factors in neoplasia, including resistance to anti-cancer treatments. In general, it was thought that the author's conclusions were sufficiently supported by provided data. It was also thought that this study incorporates innovative methodology including single-cell expression sensors coupled with live cell microscopy and an assay to study translation in different phases of cell cycle without need for cell synchronization.

Weaknesses: Several relatively minor issues were observed regarding methodology and data interpretation. Namely, some inconsistencies between the models and/or cell lines that were used throughout the manuscript were noted. For instance, key experiments were performed almost exclusively in U2OS osteosarcoma cells, whereby triple negative breast cancer patient data were used to set the scientific foundation of the study. Considering potential differences in alternative polyadenylation between cell and tissue types, it was thought that investigation across the broader compendium of cell lines may be required for generalization of findings observed in U2OS cells. It was also found that the precise mechanisms underpinning the role of hsa-let-7a miRNA in regulation of AURKA protein levels remain largely obscure.

https://doi.org/10.7554/eLife.87253.2.sa1

Reviewer #2 (Public Review):

Cacioppo et al describe a mechanism of translation regulation of Aurora A, which is dependent on alternative polyadenylation. They suggest that altered expression of the resulting isoforms in cancers is at least partly responsible for elevated Aurora A levels, which in turn is known to indicate poor prognosis.

The authors exploit publicly available databases and patient data to highlight the correlation of increased abundance of the SHORT isoform (relative to the LONG one) and poor patient survival in TNBC, as well as breast and lung cancer.

In their thorough mechanistic study they use a number of reporters to assess the impact of alternative polyadenylation on mRNA stability and translation efficiency and explore whether this process accounts for cell-cycle-regulated expression of Aurora A. These reporters are carefully controlled and well explained. I particularly commend the authors for the clear graphical presentations of the reporters (eg fig 2A, fig 3D, fig 4A). Rigorous control experiments are performed to make sure that the reporters work and "report" what they are meant to do, and to show that previous findings can be reproduced in experiments based on the reporters (eg higher protein expression from the short 3' UTR APA isoform of CDC6 mRNA, targeting of MZF1 3'UTR by hsa-let-7a).

They show that translation of the longer isoform is subject to suppression by hsa-let-7a, while the shorter isoform is not. They attribute cell-cycle regulated expression of Aurora A at least in part to the suppression of translation of the LONG isoform in G1 and S.

In Figure 6 they address whether the APA-based regulatory mechanism alters Aurora A levels sufficiently to confer features associated with oncogenic transformation and overexpression of Aurora A. These data nicely tie together the observations in databases and the mechanistic part of the study.

The logic is clear and the conclusions are well supported by the data.

The authors state themselves that the impact of translation regulation on Aurora A levels in the cell cycle is an important but unanswered question. The evidence that suppression of translation of the LONG transcript contributes to the cell-cycle regulation of Aurora A is convincing, but the extent could be explored further. I wonder whether published genome-wide studies (eg PMCID 4548207, PMC3959127) have relevant data on the translation rate of Aurora A in the cell cycle.

In the paper this question is addressed in cells enriched in G1/S (Fig 6) and using the reporters (Fig 5). Having generated the ΔdPAS mutants, Aurora A levels could be easily assessed in each cell-cycle phase. The best way to do this would be sorting followed by immunoblotting.

The fact that Aurora A levels are reduced by a 6h treatment with 0.1 mg/ml CHX (Fig 6D) is interpreted as "AURKA expression in G1/S was reduced in the mutated cell lines when treated with CHX, indicating that translation of the short isoform is active in this phase" It is rather expected that using a translation inhibitor will stop the accumulation of a protein and so this experiment does not add much. A better approach to address the effect of the mutations on translation would be to add a proteasome inhibitor and follow accumulation of Aurora A, preferably not only in G1/S but also in other cell-cycle phases. Accumulation of the protein in this experiment would better reflect translation rates.

https://doi.org/10.7554/eLife.87253.2.sa2

Reviewer #3 (Public Review):

Summary:

This manuscript sheds light on the cell cycle-dependent post-transcriptional regulation of the oncogenic kinase AURKA. AURKA mRNA is subjected to alternative polyadenylation (APA), resulting in a short and a long 3'UTR isoform. While the ratio long/short isoform is important for AURKA expression and might impact cancer development, it is not unclear how this is regulated throughout the cell cycle. Translation and decay rate of the long isoform only are targeted by let-7a miRNA and in a cell-cycle dependent manner. In contrast, the short isoform is translated highly and constantly throughout interphase. Finally, depletion of the long isoform led to an increase in proliferation and migration rates of cells. In Triple Negative Breast Cancer, where AURKA is typically overexpressed, the short isoform is predominant and its expression correlates with faster relapse times of patients, suggesting that this mechanism might play an important role in this cancer.

Originality and novelty:

The originality of this work is to show the cooperation between APA and miRNA-targeting in controlling gene expression dynamics of AURKA during cell cycle. To investigate this mechanism, the authors have developed an interesting transient single-cell and biochemical assay to rapidly study mRNA-specific gene expression in a way that measures post-transcriptional events. This manuscript puts an emphasis on the cell cycle dependent expression control of AURKA at the translation level. However, the magnitude of the changes in mRNA levels throughout the cell cycle is even greater than that of the changes in translation. Therefore, it remains unclear whether translation really is that important in controlling AURKA expression during the cell cycle. Moreover, (i) AURKA regulation by miRNA is already known (Fadaka et al., Oncotarget 2020, Zhang et al., Arch Med Sci 2020, Yuan et al., Technol Cancer Res Treat 2019, Ma et al., Oncotarget 2015), (ii) the concept of cooperation between APA and translation already is not new (Sandberg et al., Science 2008, Mayr and Bartel, Cell 2009, Masamha et al. Nature 2015), and (iii) previous transcriptome-wide studies already suggested a cell-cycle dependent control of AURKA at the translation level (Tanenbaun et al., eLife 2015, translation efficiency ratio G2/G1 = 1.59) as well as the mRNA level (Krenning et al., eLife 2022). The impact of this manuscript could be increased by investigating (i) the mechanism of cell cycle-dependent regulation by let7a expression (i.e is there changes in let7a expression or activity during the cell cycle in this model) and (ii) the origin of AURKA APA dysregulation in cancer (could it be modulated by CFIm25? (Masamha et al. Nature 2015, Tamaddon et al. Sci Rep 2020)).

https://doi.org/10.7554/eLife.87253.2.sa3

Author response

We thank the editors and reviewers for their assessment of our manuscript, and their agreement that we present compelling evidence for post-transcriptional regulation of AURKA through the 3’UTR.

In response to Reviewer 1, we acknowledge that much of our study is performed exclusively in U2OS cells, and that study of alternative polyadenylation in additional cell lines would serve to further generalize our findings. However, as U2OS are a well-known model cell line for cell cycle studies we believe our demonstration of cell cycle regulation of AURKA through its 3’UTR offers a depth of understanding that is perhaps of greater interest than confirming the existence of alternative AURKA 3’UTRs in additional cell lines, using our methods. We note that the recent rapid growth in RNA seq data resources allows easy confirmation of the broad existence of alternative polyadenylation events on a genome-wide scale. For example, AURKA-specific data extracted from a recent benchmark study of Nanopore long read RNA sequencing (Chen et al., 2021) clearly shows the existence of two distinct AURKA 3’UTRs differentially expressed between a number of different cancer cell lines. In addition, a recent study investigating the landscape of APA at single-cell resolution detected AURKA APA isoforms in HeLa and MDA-MB-468 cell lines (Wang et al., 2022). Their study further identifies AURKA among genes showing negative correlation between generalized distal polyA site usage index (gDPAU) and expression levels, meaning preference to use the proximal polyA site when expression levels increase, and include AURKA in the gene cluster showing slight increase in usage of the distal polyA site from G1 to M phase (Wang et al., 2022). Both studies are in support of the evidence presented in our manuscript.

We agree with Reviewer 2 that better information on translation rates would improve our understanding of the impact of translation regulation on AURKA levels. Some insight on the translation rate of AURKA in the cell cycle can be derived from inspection of the ribosome profiling dataset published by Tanenbaum et al., 2015. From their analysis, translation efficiency of AURKA mRNA in G2 is 1.59 times that in G1 and in G1 it is 0.69 times that in M phase, whilst in G2 it is 1.10 times higher than in M. Such data reveal a reversible increase in translation of AURKA mRNA, alongside other mitotic regulators, in preparation for M phase (Tanenbaum et al., 2015). These results are in accordance with our findings that translation rates contribute modestly to cell cycle changes in AURKA levels in normal cells, and we concur with Reviewer 3’s comment that the contribution of increased translation rate to AURKA levels at mitosis is less than the change in mRNA levels at this point in the cell cycle.

We think the significance of the regulatory mechanism we describe lies rather in the large effect it has on AURKA levels in interphase (when AURKA expression is normally repressed at both mRNA and translation rate). We hypothesise that it is interphase regulation that may be relevant to roles of AURKA in cancer (and to the association of APA with cancer) (Bertolin and Tramier, 2020; Naso et al., 2021). It is indeed the case that (i) AURKA regulation by miRNA, (ii) cooperation between APA and translation and (iii) cell-cycle dependent control of AURKA at the translation level, are already known. We believe the novelty of our study lies in drawing together these elements to provide new insight into AURKA regulation, using tools that allow similar investigation of other APA events, and contributing new ideas for future therapeutic interventions for disease proteins regulated via APA.

https://doi.org/10.7554/eLife.87253.2.sa4

Article and author information

Author details

  1. Roberta Cacioppo

    Department of Pharmacology, University of Cambridge, Cambridge, United Kingdom
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing
    For correspondence
    rc781@cam.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3048-9444
  2. Hesna Begum Akman

    1. Department of Pharmacology, University of Cambridge, Cambridge, United Kingdom
    2. Department of Biological Sciences, Orta Dogu Teknik Universitesi, Ankara, Turkey
    Contribution
    Conceptualization, Software, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  3. Taner Tuncer

    Department of Biology, Ondokuz Mayis Universitesi, Samsun, Turkey
    Contribution
    Software
    Competing interests
    No competing interests declared
  4. Ayse Elif Erson-Bensan

    Department of Biological Sciences, Orta Dogu Teknik Universitesi, Ankara, Turkey
    Contribution
    Conceptualization, Software, Investigation, Methodology, Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7398-9313
  5. Catherine Lindon

    Department of Pharmacology, University of Cambridge, Cambridge, United Kingdom
    Contribution
    Conceptualization, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    acl34@cam.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3554-2574

Funding

David James Trust

  • Roberta Cacioppo

Biotechnology and Biological Sciences Research Council (BB/R004137/1)

  • Roberta Cacioppo

Scientific and Technological Research Council of Turkey (112S478)

  • Hesna Begum Akman
  • Taner Tuncer
  • Ayse Elif Erson-Bensan

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank past and present members of Lindon Lab for enriching discussions throughout the study. We are grateful to Chiara Marcozzi for advice on CRISPR/Cas9 and to Tim Weil, Adrien Rousseau, and Francesco Nicassio for insightful comments. Cartoon figures were created using https://www.biorender.com/. This work was supported by Biotechnology and Biological Sciences Research Council (BBSRC) (grant no. BB/R004137/1) to CL. RC is supported by David James Studentship from the Department of Pharmacology.

Senior Editor

  1. Jonathan A Cooper, Fred Hutchinson Cancer Center, United States

Reviewing Editor

  1. Ivan Topisirovic, Jewish General Hospital, Canada

Version history

  1. Preprint posted: March 13, 2023 (view preprint)
  2. Sent for peer review: March 13, 2023
  3. Preprint posted: May 2, 2023 (view preprint)
  4. Version of Record published: June 29, 2023 (version 1)

Cite all versions

You can cite all versions using the DOI https://doi.org/10.7554/eLife.87253. This DOI represents all versions, and will always resolve to the latest one.

Copyright

© 2023, Cacioppo et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Roberta Cacioppo
  2. Hesna Begum Akman
  3. Taner Tuncer
  4. Ayse Elif Erson-Bensan
  5. Catherine Lindon
(2023)
Differential translation of mRNA isoforms underlies oncogenic activation of cell cycle kinase Aurora A
eLife 12:RP87253.
https://doi.org/10.7554/eLife.87253.2

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https://doi.org/10.7554/eLife.87253

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