Rapalog-Mediated Repression of Tribbles Pseudokinase 3 Regulates Pre- Mrna Splicing

Author Manuscript Published OnlineFirst on April 3, 2020; DOI: 10.1158/0008-5472.CAN-19-2366 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Rapalog-mediated repression of Tribbles pseudokinase 3 regulates pre- mRNA splicing

Bojana Stefanovska1,2,3,#, Cecile E Vicier1,2,3,#, Thibault Dayris2,4, Vasily Ogryzko5,†,

Veronique Scott1,2,3, Ibrahim Bouakka1,2,3, Suzette Delaloge6, Anna Rocca1,2,3, Olivia Le

Saux7, Olivier Trédan7, Thomas Bachelot7, Fabrice André1,2,6,$ & Olivia Fromigué1,2,3,$,*

1 Inserm, UMR981, Villejuif, F-94805, France

2 Gustave Roussy, Villejuif, F-94805, France

3 Université Paris Sud, Orsay, F-91400, France

4 Inserm, US23, CNRS, UMS3655, Villejuif, F-94805, France

5 CNRS, UMR8126, Villejuif, F-94805, France

6 Department of Medical Oncology, Gustave Roussy, Villejuif, F-94805, France

7 Department of Medical Oncology, Centre Léon Bérard, Lyon F-69008, France

† Deceased

# or $ indicates that both authors contributed equally to this work

* Corresponding author

Running title: Rapalogs regulate pre-mRNA splicing

Keywords: TRB3; mammalian target of rapamycin; mTOR inhibitors; RNA splicing; FKBP

Significance: Independent of mTOR signaling, rapalogs induce cytoxicity by dysregulating spliceosome function via repression of TRIB3, the loss of which may in the long term contribute to therapeutic resistance.

Downloaded from cancerres.aacrjournals.org on September 29, 2021. © 2020 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 3, 2020; DOI: 10.1158/0008-5472.CAN-19-2366 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Financial support: This work was supported by INSERM, Gustave Roussy (taxe d’apprentissage), Opération Parrains Chercheurs, Odyssea, Dassault Foundation, Breast

Cancer Research Foundation, and La Ligue contre le Cancer (Rhone-69, and Allier-03). C.V. was a recipient of a PhD fellowship (Soutien pour la formation à la recherche fondamentale et translationnelle en Cancérologie) from the French National Cancer Institute (INCa-INSERM

Plan Cancer).

Corresponding author: Dr Olivia FROMIGUE ; Inserm UMR981, Gustave Roussy, 39 Rue

Camille Desmoulins, F-94805 Villejuif, France ; Phone (+33) 142113695 ; E-mail: [email protected]

Conflict of Interest: Research grant from Novartis (FA, OT, TB)

Total number of items: 7 figures + 0 table

Supplemental materials : 4 figures + 4 tables

References: 50

Abstract

Rapalogs have become standard of care in patients with metastatic breast, kidney, and neuroendocrine cancers. Nevertheless, tumor escape occurs after several months in most patients, highlighting the need to understand mechanisms of resistance. Using a panel of cancer cell lines, we show that rapalogs downregulate the putative protein kinase TRIB3.

Blood samples of a small cohort of cancer patients treated with rapalogs confirmed downregulation of TRIB3. Downregulation of TRIB3 was mediated by LRRFIP1 independently of mTOR and disrupted its interaction with the spliceosome, where it participated in rapalog-induced deregulation of RNA splicing. Conversely, overexpression of

TRIB3 in a panel of cancer cell lines abolished the cytotoxic effects of rapalogs. These findings identify TRIB3 as a key component of the spliceosome whose repression contributes significantly to the mechanism of resistance to rapalog therapy.

Introduction

Tumor progression is a complex process resulting from the accumulation of genetic alterations like chromosomal rearrangements, mutations, silencing of transcription through methylation, or defects in post-transcriptional modifications such as mRNA splicing, mRNA stabilization, and translational regulation. Thus, tumor cells synthesize and accumulate proteins altered in their structure or function, modifying some signaling pathways. One of the key proteins involved in tumor progression is mTOR (mechanistic Target of Rapamycin), since it integrates signals from nutrients, hormones, and growth factors to regulate cell metabolism, growth, proliferation, and survival (1). The mTOR signaling pathway is hyper- activated in up to 70% of human tumors (2). The macrolide rapamycin, originally identified for its antifungal properties, forms a gain-of-function complex with the immunophilin

FKBP12 (FK506-binding protein 1A, 12 kDa) that acts as an allosteric inhibitor of mTOR (3).

Rapamycin exhibits immunosuppressive properties, but is also used as anti-cancer therapy

(4,5). The development of some derivatives overcame issues related to poor solubility and pharmacokinetics of the original rapamycin molecule. In that way, temsirolimus and everolimus (also known as rapalogs) improved patient outcome in a broad range of cancers.

As illustration, the phase III trials BOLERO-2, RECORD-1, and RADIANT-3 demonstrated that everolimus could improve progression-free survival of patients with metastatic hormone receptor positive breast cancer, metastatic renal cell carcinoma or advanced pancreatic neuroendocrine tumors, respectively (6-8). Rapalogs are currently administered to patients presenting hormone-receptor positive breast cancers, metastatic kidney cancers, and neuroendocrine tumors (9). While the benefit for patients is matter of consensus, resistance to therapy occurs usually after several months of treatment. Resistance can often occur through the activation of mTOR-regulated feedback loops or by the acquisition of new mutations

(10,11). Nevertheless, these mechanisms explain only a few percentage of resistance, and there is a need to identify new mechanisms of treatment escape.

In the present study, we aim to identify new mechanisms of action for rapalogs, and have discovered that TRIB3 encoded by the tribbles pseudokinase-3 gene mediates effects of rapalogs on splicing, in an mTOR-independent manner.

Materials and Methods

Chemicals and reagents. Rapamycin was purchased from Sigma-Aldrich (Lyon, France).

Everolimus (40-O-(2-Hydroxy)ethyl-rapamycin) was purchased from Novartis (Bale,

Switzerland). Rabbit anti-TRIB3, anti-GFP, anti-CDC5L, anti-FKBP25, and mouse anti-

LRRFIP1 antibodies were purchased from Abcam (Cambridge, UK). Mouse anti-β-Actin antibody was purchased from Sigma-Aldrich. Rabbit anti-phospho-Akt, and anti-Akt antibodies were purchased from Cell Signaling (Danvers, MA, USA).

Plasmids. Lentiviral vector for TRIB3 over-expression was generated by Gateway cloning from pDONR223-TRIB3 into pLX303, both purchased from Addgene (Cambridge, MA,

USA). Prof Eperon (University of Leicester, UK), and Dr Itoh (Nagoya City University,

Nagoya, Japan) kindly provided the pTN23 (12,13) and pTRB3-Luc1 reporter vector, respectively. The derived plasmid pTRB3-Luc1 Δ136-145 was generated by the

QuikChange II XL Site-Directed Mutagenesis kit (Stratagene; La Jolla, CA, USA). The mutagenic oligonucleotides were: 5’-TGGAGGCTCTGGCGCCCGGGCC-3’ (forward), and

5’-GGCCCGGGCGCCAGAGCCTCCA-3’ (reverse). The pEZX-PG02-TRIB3 promoter reported plasmid (HPRM13929) was purchased from Tebu-Bio (Le Perray-en-Yvelines,

France). The pCH110 plasmid was purchased from Addgene.

Minigene reporter assay. Cells at 65-70% confluence were co-transfected with pCH110 and pTRB3-Luc1, pEZX-PG02-TRB3 or pTRB3-Luc1 Δ136-145 using Lipofectamine2000 reagent (Thermo Fisher Scientific; Illkirch, France). Transcriptional activity was evaluated using Luciferase Assay Kit (Promega; Charbonnières-les-Bains, France) or Gaussia

Luciferase Flash Assay Kit (Thermo Fisher Scientific) and transfection efficiency by β-gal gene Reporter Assay Kit (Roche; Meylan, France).

Cell culture. All cell lines (except SUM52PE cells, kindly provided by Dr Turner from The

Institute of Cancer Research, London, UK) were purchased from the American Type Culture

Collection (ATCC; LGC Standards; Molsheim, France) between 2007 and 2011. Each ATCC cell line was first amplified to generate a cell master bank. All experiments were carried out from this master bank with cells kept in continuous culture for less than 6 months. No further authentication was performed. HCC827, SKBR3, MKN45, and SUM52PE cells were cultured in GlutaMAX containing RPMI-1640 medium supplemented with 10% heat-inactivated Fetal

Bovine Serum (Thermo Fisher Scientific), 1 mM sodium pyruvate, and 1% HEPES. A375 cells were cultured in GlutaMAX containing Dulbeccos' Modified Eagle's Media (DMEM) medium supplemented with 10% FBS, 1 mM sodium pyruvate, and 1% HEPES.

MDAMB468 cells were cultured in GlutaMAX containing DMEM/Nutrient Mix F-12 medium supplemented with 10% FBS, 1 mM sodium pyruvate, and 1% HEPES. MCF7 and

T47D cells were cultured in DMEM/F12 medium supplemented with 10% FBS, 1 mM sodium pyruvate, and 10 µg/ml insulin. The Human Embryonic Kidney 293T (HEK293T) cells were cultured in GlutaMAX containing DMEM medium supplemented with 10% FBS.

Cells were maintained under mycoplasma free conditions, and regularly tested using the

MycoAlert mycoplasma detection kit (Lonza, Levallois-Perret, France).

Cell transduction. Lentiviral particles containing vector-encoding LRRFIP1, mTOR or scrambled control shRNA were purchased from Santa Cruz Biotechnology (Santa Cruz, CA,

USA). New stable cell lines were established as previously described (14).

Cytotoxicity assay. WST-1 solution (Sigma-Aldrich) was added for the last 2 hrs of cell culture. Formazan dye content was quantified by measuring its absorbance (450 nm).

PBMC and whole blood samples. Peripheral blood mononuclear cells (PBMC) were isolated from blood samples of nine patients with various cancer types (Supplemental Table 1). The protocol used in the study were approved by the Institutional Review Board of Gustave

Roussy. Written informed consent was received from all patients, according to the principles of the Declaration of Helsinki. Blood samples (10 ml) were collected in EDTA tubes, diluted in PBS, layered onto Ficoll in SepMate tubes (Stemcell Technologies; Vancouver, Canada), and centrifuged at 1,200 g for 10 min at RT. The viability of PBMC was evaluated with 1% trypan blue solution. Experiments were conducted with 6 x 106 viable cells per condition.

Whole blood samples were derived from the ongoing clinical trial NCT02536625 (RAPANK;

Supplemental Table 1). Blood samples were collected before and one month after everolimus initiation. Written informed consent was collected from all patients, according to the principles of the Declaration of Helsinki.

RNA extraction. Total RNAs were isolated using TRIzol reagent (Thermo Fisher Scientific) for cell lines or PBMC, and using TRIzol-LS reagent (Thermo Fisher Scientific) for whole blood samples. The quality of RNA samples was checked using Bioanalyzer. Mean values of

RNA integrity numbers (RIN) were 9.1 ± 0.3.

Comparative microarray analyses. Transcriptional profiles were determined using Agilent

SurePrint G3 Human GE 8x60K V2 microarrays (AMADID 39494; Agilent Technologies;

Les Ulis, France), and analyzed with Agilent Feature Extraction software version 10.7.3.1 using the default settings. Raw data files were processed as follows: control probes were systematically removed, and flagged probes were set to NA. Arrays were normalized by quantile normalization. Missing values were inferred using KNN algorithm (package ‘impute’ from R).

RNAseq analysis. Libraries were made using the TruSeq Stranded mRNA Library

Preparation Kit (Illumina, Paris, France). Equimolar pool of libraries were sequenced on a

Illumina HiSeq2500 machine using paired ends reads (2x75bp), and high-output run mode allowing to get 40 millions of raw reads per sample. The quality of reads was assessed using

FastQC. Quality reports were gathered with MultiQC. Abundance estimation was achieved with Salmon (15) with the arguments keepDuplicates --gencode --perfectHash in indexing step:, and numBootstraps 100 --validateMappings --gcBias --seqBias in quantification step.

Paired-end clean reads were mapped to the GRCh38 human reference genome assembly

(version V27). Aggregation was achieved with an in-house script. The whole quantification process and pipeline can be found online on https://bitbucket.org/tdayris/rna-count- salmon/src/master, powered by both Snakemake and SnakemakeWrappers (16). Both differential transcript expression and differential gene expression were assessed with Sleuth

(17) following the SwimmingDownstream guidelines (18). In-house Sleuth scripts are available on https://github.com/tdayris/yawn/tree/master/Scripts/.

Among differentially expressed transcripts, we applied the following filters to keep only splicing variants. First, we identified the transcripts with differential expression status and/or

b value opposing to the ones of their respective gene. Same parameters and thresholds as above were applied for Sleuth analysis to identify differentially expressed targets. Transcripts and genes were then divided into two classes: up-regulated if q-value ≤ 0.001 and b-value ≥ 1; and down-regulated if q-value ≤ 0.001 and b-value < 1. Not differentially expressed genes (q- value > 0.001) were excluded from tables and graphs. Secondly, we identified transcripts sharing the same transcription start site (TSS). Transcripts with different TSS were excluded from tables and graphs.

Specific junction differential coverage was performed on reads mapped against Gencode

GRCH38 version V27 with STAR version 2.7.1, using LeafCutter version 0.2.7 (19). After conversion into Bed4 format with in-house scripts, comparison of sets of splicing regions were achieved using bedtools. Finally, LeafCutter computes Percentage of Spliced In (PSI) values that reflect the proportion of usage of a given junction in relation to all other possible junctions. The dPSI is the difference in this usage proportion between two conditions.

Gene ontology analysis to identify biological processes likely modulated was achieved with

Database for Annotation, Visualization, and Integrated Discovery (DAVID) tools.

Reverse transcription and Polymerase Chain Reaction. Total RNA (1 µg) were reverse transcribed using Superscript-II Reverse Transcriptase (Thermo Fisher Scientific) and random hexamers.

Real-time quantitative Polymerase Chain Reaction (qPCR) was performed on a ViiA7 apparatus with the SYBR-green master mix (Thermo Fisher Scientific). Gene-specific primers sequences are 5’-GTCTTCGCTGACCGTGAGA-3’ and 5’-CAGTCAGCACGCAGGAGTC-

3’ for TRIB3; 5’-CGGCTACCACATCCAAGGAA-3’ and 5’-GCTGGAATTACCGCGGCT-

3’ for 18S. The cycling protocol comprised 45 cycles of 95°C for 15 sec, 60°C for 60 sec and

72°C for 15 sec. The relative amounts of RNA were calculated by the 2-ΔΔCt method.

Semi-quantitative PCR amplification was performed using GoTaq G2 Hot Start Master mix

(Promega), and specific primers designed on the constitutive exons: 5’-

CATAGGATCCAGCTACCGGG-3’ 5’-TGTCTTCTTGGAGCTGACGA-3’ for SIN3B; 5’-

TGAGGACATAGCTCGCCTTT-3’ 5’-ACCATTCCTGATCCCTGTGG-3’ for CCDC50; 5’-

CTGCCAGCATCGACCTTCT-3’ 5’-GTCCTCCGGCCTTCCACT-3’ for DENND1A; 5’-

GGGGCAGATGGTGGATGAG-3’ 5’-AGGCAGTTCTCGTGGGATC-3’ for PSD4; 5’-

TCTTTACGGCTCTGGGGATC-3’ 5’-GCTTGTCACCTGTCTCCTCA-3’ for SLC2A11; 5'-

GTGTTGCTGGCACCAATATCA-3' 5'-GAGGTCGCCCATTTTCACAG-3' for ADAM22.

The cycling protocol comprised 35 cycles of 95°C for 30 sec, 55°C for 30 sec, and 72°C for

30 sec. The PCR products were separated onto 2% agarose gel. Percentage of spliced in (PSI) was calculated as the ratio of the density of the short isoform signal versus that of the sum of the long and short isoform. The difference in PSI (dPSI) was calculated between PSI in rapamycin-treated versus that of the vehicle.

Western blotting assay. Proteins (30 µg) from total cell lysates were resolved by SDS-PAGE as previously described (14).

Immunoprecipitation. Cell lysates were prepared as for western blotting except that

Nonidet-P40 (Igepal) concentration was reduced to 0.1%. Lysates were precleared using recombinant fusion protein A/G (PAG) coupled to magnetic microbeads (Ademtech; Pessac,

France) for 1 hr. Aliquots of 2 mg of protein were incubated ON at +4°C under weak agitation with 40 µl of PAG magnetic beads and 4 µg of indicated antibody. The immunoprecipitated complexes were washed thrice with lysis buffer, aliquoted and stored at -80°C.

Chromatin immunoprecipitation (ChIP). Cells were incubated in 1% formaldehyde for

10 min at RT. After three washes in PBS, cells (2.107) were sonicated for 20 min (30% of maximum power) on ice, then centrifuged at 17,000 g for 2 min at +4°C. DNA shearing efficacy was validated on a 2% agarose gel. Preclearing of ChIP lysates was carried out using

PAG magnetic beads (Ademtech) for 1 hr at +4°C. Specific and non-specific immunoprecipitations were carried out overnight using mouse anti-LRRFIP1 or IgG2a

Isotype Control antibodies (Abcam) at 10 µg/ml. Immune complexes were collected with

PAG magnetic beads for 90 min at +4°C, then washed twice with each of the low salt, high salt, LiCl immune complex wash buffers, and TE buffer. The protein-DNA complexes were reverse cross-linked according to the IPure Kit v2 protocol (Diagenode; Seraing, Belgium).

The recovered DNA was amplified by RT-qPCR using non-overlapping primer pairs of about

70 to 150 bp apart for each region of interest: 5’-CACCCCGCCGAGTACCTC-3’ and 5’-

CACCCCGCCGAGTACCTC-3’ for TRIB3; 5’-ACTCAGTCTGGGTGGAAGGT-3’ and 5’-

CCCATTGCATTTGTTGGGGG-3’ for MYC; 5’-GACATAACCCCCACCCCTTG-3’ and

5’-AAGGAAGCAGCCTCTCAACC-3’ for DNMT3B set1; 5’-

TAAGGGAGGAGCAAGGGAGG-3’ and 5’-TGGGGCTGCAATAATGAGGG-3’ for

DNMT3B set2.

Liquid chromatography-tandem mass spectrometry (LC-MS/MS). For trypsin digestion, the beads with immune complexes were pre-incubated with 7 µL of 15 ng/mL trypsin and ambic at RT for 10 min. Afterward, 25 µL of 50 mM ammonium bicarbonate was added, and the samples were incubated at 37°C for 16 hrs. The supernatants were dried at 56°C by

SpeedVac for 30 min, then resuspended in 20 µL of solution containing 0.05% formic acid and 3% acetonitrile.

The resulting peptide mixtures were analyzed with a qExactive-Orbitrap High Resolution and

Accuracy Mass Spectrometer (HRAM; Thermo Scientific; San Diego, CA, USA) equipped with a nano-electrospray source and coupled to a NanoLC Esay nLC 1000 (Thermo

Scientific). The peptides were separated for 30 min on a gradient of 5 to 90% acetonitrile. The fragmentation voltage was 1.3V. The mass spectrometer acquired successive sets of four scan modes consisting of one full scan MS over the range of 200–2200 m/z followed by three data- dependent MS/MS scans on the three most abundant ions in the full scan. Peak lists (mgf) were generated from the raw data ﬁles using Proteome Discoverer application (Thermo

Scientific) and proteins were identified with MASCOT search engine. Datasets were submitted to the Contaminant Repository for Afﬁnity Puriﬁcation (CRAPome)(20).

Putative candidates were discarded if 1/ the anti-GFP related score was higher than 15% of any anti-TRIB3 related scores, 2/ they were not detected in all the four tested cell lines, 3/ they were described as background contaminants by the CRAPome interface. The protein subfamily functional classification was performed using the bioinformatics tool DAVID. No cutoff was applied before submission of the list to DAVID application.

Promoter analysis for transcription factor binding sites. In silico analysis of the promoter region of the human TRIB3 gene (Gene ID: 57761) was performed using Genomatix software

(Ann Arbor, MI, USA). The region conserved in 8 of 12 orthologs loci and corresponding to

380117-381145 (GRCh38 Chromosome 20 contig NC_000020.11) was selected for the identification of putative promoters, and transcription starting sites (TSS). We focused on the region from 400 bp upstream to 200 bp downstream of the main TSS to select common transcription factor binding sites using the MatInspector tool of the Genomatix software.

Statistical analyses. All quantitative data are presented as mean ± standard deviation (SD) unless otherwise specified. Two-sided Student’s t-tests were used for comparisons of the means of data between two groups. For multiple independent groups, one-way or two-way

ANOVA was used. A level of p <0.05 was considered significant.

Results

Rapamycin reduced TRIB3 gene expression in cancer cell lines and patient samples

We first determined the cell response in a panel of eight cancer cell lines to increasing doses of rapamycin (0.01-1000 nM) using the WST1 colorimetric assay. Rapamycin induced cell cytotoxicity in all tested cell lines in a dose-dependent manner (-13 to -31% in cell viability; p<0.05 vs. vehicle; Supplemental Figure 1A). All the following experiments were performed with 10 nM rapamycin.

Using gene expression arrays, we identified genes whose expression was modulated by rapamycin. The subsequent analyses using |Log2(FC)|>1 and p<0.05 cut-off revealed that

TRIB3 (Tribbles pseudokinase 3) is the only gene down-regulated in common in all tested cell lines treated with rapamycin (Figures 1A-B, Supplemental Table 2). The full list of detected genes is available in the ArrayExpress database at EMBL-EBI

(www.ebi.ac.uk/arrayexpress) under accession number E-MTAB-8727. Quantitative real-time

PCR and western blotting confirmed that rapamycin decreased TRIB3 mRNA (-36 to -62%, p<0.05 vs. vehicle; Figure 1C) and protein (-37 to -71%; Figure 1E) expression level in all tested cell lines. Moreover, everolimus also decreased TRIB3 mRNA expression level in all tested cell lines (-30 to -65%, p<0.05 vs. vehicle; Figure 1D) with the same magnitude as rapamycin.

To confirm the effect of rapamycin or everolimus on TRIB3 expression ex vivo, peripheral blood mononuclear cells (PBMC) collected from patients with various malignancies

(Supplemental Table 1) were incubated for 24 hrs with or without rapamycin or everolimus.

Quantitative real-time PCR confirmed that rapamycin decreased (average -36%; p<0.0002 vs. vehicle; Supplemental Figure 1B) TRIB3 expression level. Accordingly, everolimus also decreased (average -31%; p<0.001 vs. vehicle; Supplemental Figure 1C) TRIB3 expression level.

In order to estimate the effect of everolimus on TRIB3 expression in vivo, whole blood cells were collected from breast cancer patients before and one month after everolimus treatment initiation. (Supplemental Table 1). Quantitative real-time PCR showed that the treatment decreased TRIB3 expression in 10 out of 16 (63%) samples (Figure 1F) leading to an average of -33% vs. beginning of treatment (Figure 1G).

These data suggest that TRIB3 expression is down-regulated by rapalogs, leading to the hypothesis that it could mediate at least part of the activity of this drug family.

TRIB3 over-expression prevents the cytotoxic effects of rapamycin in vitro

In order to evaluate whether TRIB3 expression levels influence the response to rapamycin, we established new cell lines by lentiviral transduction. As expected, the stable integration of the

TRIB3 coding sequence into the cancer cell genome led to a stably increased TRIB3 protein level compared to cells transduced with empty vector (Supplemental Figure 2). TRIB3 overexpression prevented or at least markedly reduced the cytotoxic effect of rapamycin on all tested cancer cell lines by 64% in average (range: -39% to -86%; Figure 2).

TRIB3 interacts with pre-mRNA splicing machinery

Inhibiting mTOR with rapamycin abolishes negative feedback-loop signals and leads to the reactivation of PI3K/Akt pathway (21). Several publications suggest that TRIB3 directly interacts with Akt and inhibits its phosphorylation (22-24). Western blotting analyses confirmed that rapamycin increased (1.79-fold vs. vehicle; Supplemental Figure 2) Akt phosphorylation level on Ser473 and/or Thr308 residues in all tested cell lines. The overexpression of TRIB3 did not significantly interfere with the stimulatory effect of rapamycin.

We therefore assessed other potential mechanisms of action of TRIB3. To characterize the

TRIB3-protein interaction network, we isolated protein complexes by immunoprecipitation

(IP) with antibodies raised against N-term and C-term domains of TRIB3. We identified partner candidates by liquid chromatography tandem mass chromatography (LC-MS/MS) based targeted proteomics. First screening on whole cell lysates resulted with the detection of

350 putative partners of TRIB3 in common between the four tested cell lines (HCC827,

MCF7, MDAMB468, and SUM52PE). We identified 22 functional annotation clusters based on GO categories and KEGG pathway enrichment analyses. The cluster with the highest ES suggested that the majority of the identified partners are related to the pre-mRNA splicing process (Supplemental Figure 3A). We performed a second round of IP on whole cell lysates pretreated with RNase-A to avoid precipitation of proteins present onto RNA molecules but not related to TRIB3-containing complexes. This second screening shortened the list to 209 putative partners for TRIB3 (Supplemental Table 3). Four out of the top 5 clusters with the highest enrichment score still designated the mRNA splicing process (Figure 3A), and 41 of the 209 putative partners mapped with the spliceosome complexes (Supplemental Table 3).

To better identify the spliceosomal components that interact with TRIB3 we performed enrichment analysis using a published gene set (25) compiling 404 spliceosomal proteins identified in previous studies (26,27) and/or reported in the spliceosome database

(http://spliceosomedb.ucsc.edu). We refined this gene set for components that are shared by

the three sources (total 149 hits). Among the 16 identified spliceosomal core proteins, 63%

(10/16) belong to the B or Bact complexes. Ranking TRIB3 putative interactors according to the relative score revealed enrichment in proteins present in the PRP19 complex, and the U2 small nuclear ribonucleoproteins complex (U2 snRNP) at the top of the list (Supplemental

Table 3). Hence we chose CDC5L (Cell Division Cycle 5 Like) and SF3B1 (Splicing Factor

3B Subunit 1A), key components of the splicing machinery (28) to confirm that TRIB3 interacts with the PRP19, and U2 complexes. Reciprocal co-immunoprecipitations, and western blotting analyses confirmed that TRIB3 interacts with CDC5L and SF3B1

(Figures 3B and 3C, respectively), reinforcing the hypothesis that TRIB3 could be involved in pre-mRNA splicing modulation.

These findings led to the hypothesis that rapalogs could modulate splicing via TRIB3 downregulation.

Rapamycin inhibits pre-mRNA splicing process through down-regulation of TRIB3 expression

In order to evaluate splicing efficiency in the presence of rapamycin and the possible contribution of TRIB3 in the pre-mRNA splicing process, we used a double-reporter vector previously reported and validated to assess splicing (13,29). This vector encodes β- galactosidase and luciferase in the same reading frame when splicing occurs. When there is no splicing they are separated by an intronic region containing a stop codon (Figure 3D).

Rapamycin reduced the splicing efficiency up to 55% (p<0.05 vs. vehicle; Figure 3E). For

A375, MCF7, SKBR3, and SUM52PE cell lines, where the splicing is impaired the most, we observed that the overexpression of TRIB3 abolishes or markedly reduces the inhibitory effect of rapamycin on splicing activity (Figure 3F).

These data suggest that rapamycin treatment affected the splicing process, and that TRIB3 expression level could modulate this effect. Since this double-reporter is an artificial system that evaluates particular splicing context, we performed RNAseq analyses to determine whether rapamycin affects splicing genome-wide. The complete list of the detected transcripts is available in the ArrayExpress database at EMBL-EBI under accession number E-MTAB-

8726.

In order to avoid a bias due to transcriptional regulation, we discarded alternative transcripts that do not share the same TSS. We then focused on transcripts with differential or opposing expression level compared to their respective genes (q-value ≤ 0.001). This multi-level analysis allowed us to identify 3161 transcripts with modulated expression level between rapamycin- and vehicle-treated cells (Figure 4A). Among those transcripts, 1365 (43%) were modulated in both pLX-empty and pLX-TRIB3 cells, whereas 844 transcripts (27%) were specifically modulated in pLX-empty cells, and 952 (30%) in pLX-TRIB3 cells. The corresponding genes are listed in Supplemental Table 2, and their intersections are presented in Supplemental Figure 3B. The modulations were consistent between the pLX-empty and pLX-TRIB3 cells: 704 (52%) upregulated transcripts and 660 (48%) downregulated transcripts, and only one transcript was modulated in an opposing sense (Figure 4c).

Interestingly, we observed that the overexpression of TRIB3 led to the modulation of expression of just 16 transcripts in the absence of rapamycin (Figure 4B), encoding for 13 genes (Supplemental Figure 3C), whereas the overexpression of TRIB3 combined with the rapamycin treatment modulated only 65 transcripts (56 genes).

In summary, these data suggest that the overexpression of TRIB3 alone modulated a very limited number of transcripts. The vast majority of the splicing modulations were dependent on the presence of rapamycin and the TRIB3 overexpression prevents some of these splicing modulations.

According to the alternative splicing patterns (Supplemental Figure 3D), we identified skipped exon (SE) as the major pattern followed by alternative first exon (AF), and alternative

3′ splice site (A3), whereas retained intron (RI), mutually exclusive exons (MX), and alternative last exon (AL) occurred with a lower frequency. No difference was detectable in the pattern distribution between the pLX-empty cells and the TRIB3 overexpressing cells

(Supplemental Figure 3E). These results are in agreement with the distribution of alternative splicing events landscape described in reference annotations of the human genome (RefSeq,

Gencode, and EnsEMBL)(30), and indicate that the overexpression of TRIB3 did not induce a significant shift in alternative splicing patterns. To gain insights into the most relevant GO- terms and bio-pathways that underwent alternative splicing under rapamycin treatment, we performed a DAVID functional annotation clustering analysis of the 1,847 impacted genes.

The top four most enriched clusters refer to 1/ cell division, 2/ kinetochore, 3/ sterol biosynthesis and metabolism, and 4/ translation and ribosome (Figure 4D).

To identify the regions affected by splicing modulations we performed a differential splice junction coverage (DJC; Supplemental Table 2). We identified 13,417 genes that contain at least one differential spliced region. Among them 4,182 are in common between the pLX- empty and pLX-TRIB3 cells whereas 8,923 are present only and pLX-TRIB3 cells and 312 only in pLX-empty cells (Supplemental Figure 3F). The intersections of the splicing modulations between the modulation (Up or Down) and the stable cell line modification

(pLX-empty or pLX-TRIB3) are presented in Supplemental Figure 3G. Among the 232 differential spliced regions exhibiting a dPSI ≥ 20% (Supplemental Table 2), we investigated high confident examples by semi quantitative RT-PCR. Differential splicing was confirmed for all tested examples (Figures 5A-B). The prevention mediated by the overexpression of

TRIB3 was also confirmed. For instance, we observed similar splicing modulations by

rapamycin in both pLX-empty and pLX-TRIB3 cells for CCDC50 (Coiled-Coil Domain

Containing 50), PSD4 (Pleckstrin And Sec7 Domain Containing 4), and SLC2A11 (Solute

Carrier Family 2 Member 11). Furthermore, we confirmed a prevention of the splicing modulations by rapamycin in pLX-TRIB3 cells for SIN3B (SIN3 Transcription Regulator

Family Member B), DENND1A (DENN Domain Containing 1A), and ADAM22 (ADAM

Metallopeptidase Domain 22). The variation in dPSI values determined from signal on agarose gels (Figure 5B) were similar to those determined from RNAseq (Figure 5C), confirming that treatment of pLX-empty cells with rapamycin impacts splicing process, and that TRIB3 overexpression could prevent some events.

Altogether, these data suggest that rapamycin modulates the pre-mRNA splicing process, and that part of this effect is dependent on the down-regulation of TRIB3.

Rapamycin down-regulates TRIB3 expression through an mTOR-independent mechanism via LRRFIP1-dependent inhibition of promoter activity

We then assessed the molecular mechanisms involved in the regulation of TRIB3 expression by rapalogs. Since rapamycin is an allosteric mTOR inhibitor, we first investigated the role of this signaling pathway in the regulation of TRIB3 expression. Targeting mTOR with the

ATP-competitive inhibitor Torin-1 dose dependently reduced phosphorylation level of key effectors of mTOR signaling pathway such as Akt or S6RP but did not affect TRIB3 protein level (Figure 6a).

In silico analyses of TRIB3 promoter region (chromosome location 20p13-p12.2; contig

NC_000020.11; GRCh38 reference primary assembly) identified six unique putative promoters, but three of them without assigned transcript. Among the three remaining putative promoters (Supplemental Figure 4A), promoter #1 allowed generating a transcript encoding a truncated protein (60 aa). In contrast, promoter #2 and #3 allowed generating ten putative

encoding transcripts ultimately corresponding to the two major isoforms of TRIB3 protein

(358 aa and 385 aa). A minor truncated cds (130 aa) was also proposed.

A broad spectrum screening for putative transcription factor binding sites in the region covering the promoters #2 and #3 detected 267 matches for sequences corresponding to 109 putative different transcription factors (multiple match score). Applying a threshold adjusted for p value < 0.05 restricted the list to 12 candidates (Supplemental Table 4). Among those 12 candidates, LRRFIP1 (Leucine Rich Repeat In Flightless-1 Interacting Protein 1), also known as GCF2 (GC-Binding Factor 2) was the only DNA-template negative regulator of transcription. ChIP experiments performed on SUM52PE cells, followed by RT-qPCR detected specific LRRFIP1 binding on the TRIB3 promoter region upon rapamycin treatment

(Figure 6B). No LRRFIP1 binding was detectable on nonspecific target sequences, namely the promoters of MYC or DNMT3B (DNA Methyltransferase 3 Beta).

The inhibitory effect of rapamycin on the TRIB3 promoter activity was evaluated using a gene reporter assay. Three vectors were used (Supplemental Figure 4B): pTRB3-Luc1 which contains the LRRFIP1 binding site located upstream of TSS; pTRB3-Luc1 ∆136-145, where the LFFRIP1 binding site was deleted by site-directed mutagenesis (Supplemental Figure 4C), and pEZX-PG02 TRB3 which contains a region upstream to the LRRFIP1 binding site.

Rapamycin reduced (-20 to -32%, p<0.05 vs. vehicle; Figure 6C) luciferase activity in cells transfected with the pTRB3-Luc1 vector but did not affect luciferase activity in cells transfected with the pTRB3-Luc1 ∆136-145 vector or with the pEZX-PG02 TRB3 vector.

To confirm that LRRFIP1 is a key factor for the inhibitory effect of rapamycin on TRIB3 promoter activity, we established new cell lines by lentiviral transduction of shRNA sequences targeting LRRFIP1. As expected, the stable integration of shRNA-LRRFIP1 sequences into the genome of the cell, markedly reduced LRRFIP1 protein level compared to control cells transduced with non-relevant shRNA sequences (-43 to -70% vs. sh-Ctrl;

Figure 6D). Rapamycin reduced luciferase activity in sh-Ctrl cells transfected with the pTRB3-Luc1 vector (-20 to -30%, p<0.05 vs. vehicle; Figure 6E), but did not affect luciferase activity in LRRFIP1-silenced cells. Furthermore, in LRRFIP1 silenced cells rapamycin did not reduce the level of endogenous TRIB3 protein (Figure 6F).

In silico analyses of LRRFIP1 putative partners using the general repository for interaction datasets BioGRID identified FKBP3 (FK506-Binding Protein 3; alias FKBP25), a cis-trans prolylisomerase reported to bind with high affinity FK506 and rapamycin (31,32)

(Supplemental Table 4). Reciprocal co-immunoprecipitation experiments confirmed that

FKBP3 robustly binds to LRRFIP1 in presence of rapamycin (Supplemental Figures 4D-E).

Altogether, these data suggest that rapamycin reduced TRIB3 expression through LRRFIP1- dependent repression of promoter activity in an mTOR-independent manner.

Discussion

In this study, we identified TRIB3 as a target gene of rapamycin in cancer cells. We demonstrated that the rapamycin driven regulation of TRIB3 expression relies rather on its transcriptional regulation via the repressor LRRFIP1 than the inhibition of the mTOR signaling pathway. Moreover, we showed that the overexpression of TRIB3 impairs the cytotoxic effect of rapamycin in vitro. In addition, we identified a novel role of TRIB3 as component of the spliceosome complex. We demonstrated that rapamycin modulates the rate of pre-mRNA splicing in cancer cells and that this effect is prevented if TRIB3 is overexpressed. Overall, these data suggest that rapalogs could have an mTOR-independent inhibitory effect on splicing machinery, and that LRRFIP1/TRIB3 mediate a large part of this effect.

Targeting mTOR with rapamycin triggers feedback-loop signals and re-activation of

PI3K/Akt pathway (21). Du et al. demonstrated that TRIB3 could bind to Akt and interfere with its phosphorylation on Ser473 and Thr308, both required for Akt activation (22). The literature suggests that in fact TRIB3 inhibits cancer initiation by limiting Akt-driven tumorigenesis (33). In addition, there are evidences that TRIB3 can interact with the rapamycin-insensitive mTORC2 complex, necessary for the complete activation of Akt (34).

As expected, rapamycin increased pAkt relative level suggesting an upstream reactivation of

Akt signaling. However, TRIB3 overexpression did not significantly interfere with the rapamycin-induced Akt activation, while it abolished or limited the rapamycin cytotoxic effects. Thus, TRIB3 influences rapalogs action in a mechanism independent of the suppression of the negative-feedback loops and the reactivation of pro-survival Akt.

Schwarzer et al. have demonstrated that in vitro, in absence of nutrients, TRIB3 expression is dependent on the PI3K activity in [PTEN-/-] prostate carcinoma PC3 cells, and that the use of the PI3K inhibitor LY294002 or rapamycin leads to TRIB3 down-regulation (35). In our in vitro cancer cell models, the blockade of mTOR signaling with the ATP-competitive inhibitor

Torin-1 did not modify the expression level of TRIB3 suggesting that the rapalogs-driven down-regulation of TRIB3 is independent from the mTOR pathway. In silico exploration of the TRIB3 promoter sequence identified among several response elements a putative binding site for LRRFIP1. LRRFIP1 is described as a transcriptional repressor overexpressed in cancer cell lines that decreases activity of the epidermal growth factor receptor (EGFR)

(36,37) and other genes (38). Through ChIP we confirmed the interaction of LRRFIP1 with

TRIB3 proximal promoter region in our cancer cell model. Site-directed mutagenesis and genetic modification of our cell lines fully confirmed that LRRFIP1 is a key factor in TRIB3 promoter activity repression by rapamycin. Further in silico analyses of protein-protein association networks (interactome) and corresponding co-immunoprecipitations identified

FKBP3 (FK506-binding protein) as a LRRFIP1 putative partner. FKBP3 belongs to the same family of immunophilin as FKBP12 and has been reported to bind also rapamycin (32).

However, further investigations ought to be carried out to determine whether both FKBP3 and

LRRFIP1 are required for this complex to repress TRIB3 promoter activity, or on the contrary, if the dissociation is required to release LRRFIP1 for binding onto TRIB3 promoter

(Figures 7A, and 7B, respectively). In both models, the binding of LRRFIP1 on the promoter of TRIB3, in proximity of the transcription-starting site is crucial to prevent TRIB3 gene transcription.

Immunoprecipitation of TRIB3 enriched complexes combined to mass spectrometry and additional co-IP suggested the interaction between TRIB3 and some components of the spliceosome, a highly dynamic, multi-megadalton ribonucleoprotein (RNP) complex that catalyzes the process of pre-mRNA splicing (28). The spliceosome is emerging as a potential hallmark of cancer progression (39). Our previous work highlighted that the expression level of some spliceosome components differs between normal and cancer cells (40). Moreover, splicing factors expression level can also be de-regulated in cancer cells and, in some cases, could even act as oncogenes, such as for instance, SRSF1 (41). In addition, mutations in spliceosome coding genes like SF3B1 could be recurrent driver of cancer progression (42).

Finally, all the modifications that lead to the disruption of the splicing process can result in the production of aberrant end-products from cancer-critical genes (reviewed in (43)). The majority of the core spliceosomal factors that interact with TRIB3 are present in the U2, U5,

PRP19, PRP19-related and Bact complexes, thus suggesting that TRIB3 may operate just prior to the catalytic activation of the spliceosome (Figure 3C). In addition, the RNAseq derived analysis indicated no difference between the splicing patterns in control cells and cells overexpressing TRIB3, supporting the hypothesis that TRIB3 is required for the catalytic activation of the spliceosome after the choice of the splice sites. The overexpression of TRIB3

did not markedly influence the splicing efficacy, suggesting that endogenous level of TRIB3 protein is sufficient for optimal function of the spliceosome. In contrast, downregulation of its endogenous level leads to impairment of the spliceosome function.

The present study shows for the first time to our knowledge, that part of the antitumor activity of rapalogs is related to the deregulation of spliceosome activity, independently from mTOR signaling. One publication demonstrated that constitutive mTORC1 activation promotes de novo lipids biogenesis through SRPK2 nuclear translocation that phosphorylates SR proteins and favors the splicing of lipogenic pre-mRNAs. In contrast, mTORC1 inhibition leads to intron retention and subsequent degradation of the lipogenic pre-mRNAs (44). In accordance with these results, we detected that one of the major enriched functional annotations of the transcripts impacted by splicing modulations was the metabolism of lipids. Moreover, we observed rapamycin-induced splicing modulations in the same lipogenic genes. However, in addition to lipid metabolism, our results suggest that rapamycin can induce splicing modulations in genes related to a variety of cellular processes. Thus, we can speculate that both antitumor activity of rapalogs, and the mechanisms of resistance are highly complex.

Among the tested cancer cell lines, we detected two different behaviors in terms of splicing alteration under rapamycin treatment: one cluster of cell lines that is sensitive to rapamycin deregulation of pre-mRNA splicing (A375, MCF7, SKBR3, and SUM52PE) and a second one that is not sensitive (HCC827, MDAMB468, MKN45, and T47D). We hypothesized that this phenomenon could be dependent on the different genetic background of the cancer cell lines.

Among the known alterations, a common one concerns EGFR signaling with either gene amplification or activating mutations for the non-sensitive cells HCC827, MDAMB468, and

MKN45 (45-47). Zhou et al. have reported that a massive reprogramming of alternative splicing occurs upon EGF treatment in HEK293T cells (48). Chettouh et al. demonstrated that

EGFR stimulates splicing factor expression, thus increases mRNA splicing of the insulin

receptors A and B in Hepatocellular carcinoma cells (49). Further investigations are required to evaluate the possible relationship between the alterations in EGFR signaling that could over-activate the splicing machinery, and overcome the resistance or insensitivity upon rapamycin treatment.

In conclusion, the purpose of the present study was to explore novel mechanism of action of rapalogs. We demonstrated that the rapamycin driven downregulation of TRIB3 expression is independent from the mTOR signaling, but relies on its transcriptional regulation via the transcriptional repressor LRRFIP1 (proposed models on Figures 7A-C). We showed that rapalogs modulate pre-mRNA splicing genome-wide. Finally, we demonstrated that the overexpression of TRIB3 prevents the cytotoxic and splicing effects of rapalogs.

We believe our findings have important implications in overcoming the resistance to rapalogs in clinic. We highlighted TRIB3 as a suitable biomarker to assess the efficacy of the rapalogs treatment by monitoring its expression. Moreover, TRIB3 could be suitable as a therapeutic target to synergize with rapalogs. In addition, our research hints at the possibility of combining rapalogs and splicing modulators, like for instance H3B-8800 (50), to improve the outcome of the rapalogs treatment.

Acknowledgments

The authors thank Dr Y Itoh (Nagoya City University, Nagoya, Japan), and Prof IC Eperon

(University of Leicester, Leicester, UK) for providing the pTRB3-Luc1 and pTN23 reporter vectors, respectively. The Authors thank Dr N Turner (The Institute of Cancer Research,

London, UK) for providing the SUM52PE cell line. We thank Dr G Meurice and Mrs N Pata-

Merci (Gustave Roussy Functional Genomics Unit and Bioinformatic Core Facility, UMS

AMMICA, Villejuif, France) for microarray analyses. The Authors thank Mrs A Vouillon

(Gustave Roussy, Proteomic Platform, Villejuif, France), and Mrs C Thirant (INSERM

U1170, Villejuif, France) for their technical help with proteomics and ChIP experiments, respectively.

This work was supported by INSERM, Gustave Roussy (taxe d’apprentissage), Opération

Parrains Chercheurs, Odyssea, Dassault Foundation, Breast Cancer Research Foundation, and

La Ligue contre le Cancer (Rhone-69, and Allier-03). C.V. was a recipient of a PhD fellowship from the French National Cancer Institute (INCa-INSERM Plan Cancer).

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Figure Legends

Figure 1. Rapalogs reduce TRIB3 expression level in cancer cells and patient samples.

(A) Volcano plot of differentially transcribed genes identified by microarray analysis. The

average difference in mRNA expression level (log2 fold-change) for all genes are plotted against the -log10 of the adjusted p-value. Thresholds are shown as dashed lines. (B) Box- and-whisker plot showing the minimum, first quartile, median, third quartile, and maximum fold change in TRIB3 mRNA expression level. (C,D) Expression pattern of TRIB3 mRNA after 24 hrs incubation with 10 nM rapamycin (C) or everolimus (D), assessed by RT-qPCR.

The relative mRNA levels are expressed as log2(fold-change) vs. vehicle. (E) Expression pattern of TRIB3 protein, assessed by Western blot. Actin is used as loading control. (F)

Expression pattern of TRIB3 mRNA in whole blood samples (see characteristics of patients in supplemental Table 1), assessed by RT-qPCR. The relative expression levels are expressed as log2(fold-change). (G) Box-and-whisker plot showing the minimum, first quartile, median, third quartile, and maximum average log2(fold change) in TRIB3 expression level in whole blood cells.

Figure 2. TRIB3 over-expression prevents the rapamycin inhibitory effect on cancer cells viability. Cell viability was determined after 24 hrs incubation in the presence of rapamycin (1-100 nM) or vehicle, assessed by the WST-1 assay. Results are expressed as mean of percentage of inhibition compared to vehicle ± SEM (3 independent experiments; n=8-10 replicates per experiment). a: p<0.05 vs. vehicle, b: p<0.05 vs. pLX-empty vector.

Figure 3. TRIB3 over-expression counterbalances the rapamycin inhibitory effects on pre-mRNA splicing efficiency. (A) Classification of identified proteins with DAVID. The top 5 functional association clusters ranked by the enrichment score (ES) are shown. (B,C)

Whole SUM-52PE cell lysate was subjected to CDC5L, SF3B1 or TRIB3 immunoprecipitation (IP). Anti-GFP antibody served as negative IP control. (D) Schematic representation of the principle of the assay using the pTN23 minigene reporter construct. A

segment of the β-Galactosidase gene and an intervening intron are cloned in-frame with the luciferase coding sequence. If correct splicing occurs both β-galactosidase and luciferase activities are detectable. In case of altered splicing, the stop codon (dotted line) present in the retained intron prevents the expression of luciferase. (E,F) Relative luciferase activity determined after 24 hrs incubation. Results are expressed as fold change ± SD (3 independent experiments; n=4-5 replicates per experiment). *: p<0.05 vs. vehicle.

Figure 4. Rapamycin modulates splicing genome-wide. (A-C) UpSet plot of intersections between sets of shared splicing events (transcripts) in pLX-empty, and pLX-TRIB3 cells incubated for 24 hrs in the presence of 10 nM rapamycin or vehicle. The bar chart on the left indicates the total number of eventThe upper bar chart indicates the intersection size between sets of events shared with one or more condition(s). Dark connected dots indicate which conditions are considered for each intersection. (D) Functional annotation of transcripts with modulated splicing after 24 hrs rapamycin treatment was performed with DAVID. The top 4 functional association clusters ranked by the enrichment score (ES) are shown.

Figure 5. RT-PCR validation of high confidence alternative splicing events identified by

RNAseq. (A, left panel) Diagram of alternative splicing pattern. The arrows represent the primers, boxes represent the exons, and lines represent the introns. (A, right panel) Agarose gel electrophoresis of RT-PCR products from pLX-empty and pLX-TRIB3 cells incubated for

24 hrs with 10 nM rapamycin or vehicle. (B) Graphical comparison in the difference in PSI

(dPSI) between pLX-empty and pLX-TRIB3 cells incubated for 24 hrs with rapamycin or vehicle, assessed by RT-PCR (C) Graphical comparison in dPSI between pLX-empty and pLX-TRIB3 cells incubated for 24 hrs with rapamycin or vehicle, assessed by RNAseq.

Figure 6. Rapamycin reduces TRIB3 expression through a LRRFIP1-dependent repression of promoter activity, independently of mTOR. (A) Expression pattern of indicated proteins after 24 hrs incubation with Torin-1 (2-250 nM), assessed by Western Blot.

Actin is used as loading control. (B) Chromatin immunoprecipitation (ChIP)-qPCR analysis using LRRFIP1 or IgG control antibodies, and primers against TRIB3 promoter region, or

MYC and DNMT3B promoter regions, as negative controls. Results are expressed as Fold enrichment ± SD (n=3 independent ChIP). (C) Relative luciferase activity determined after

24 hrs incubation. Results are expressed as percentage of control after normalization for β- galactosidase expression ± SD (3 independent experiments; n=4-5 replicates per experiment). a: p<0.05 vs. vehicle. (D) Expression pattern of LRRFIP1 protein in cells transduced with shRNA-control or shRNA-LRRFIP1 vector, assessed by Western blot. Actin is used as loading control. (E) Relative luciferase activity determined after 24 hrs incubation. Results are expressed as percentage of control after normalization for β-galactosidase expression± SD

(2 independent experiments; n=4-5 replicates per experiment). a: p<0.05 vs. vehicle, b: p<0.05 vs. sh-Control cells. (F) Expression pattern of LRRFIP1 and TRIB3 proteins in sh-

Control or sh-LRRFIP1 cells after 24 hrs rapamycin treatment (0.1-100 nM), assessed by

Western blot. Actin is used as loading control.

Figure 7. Proposed models suggesting that rapalogs deregulate splicing of cancer cells by down-regulating TRIB3. (A) We propose a first model suggesting that in presence of rapalogs (R) FKBP3 forms a complex with the transcriptional repressor LRRFIP1. The binding site for LRRFIP1 is proximal to the transcription starting-site (TSS), restricting the access of the RNA polymerase II to the promoter and thus leading to reduced transcription of

TRIB3. In an alternative model, we propose that in presence of rapalogs FKBP3 releases the transcriptional repressor LRRFIP1, which in turn inhibits TRIB3 transcription. (B) We

propose that in absence of rapalogs the RNA polymerase II binds to the TRIB3 promoter and allows the transcription of TRIB3 gene. In this scenario, the basal level of TRIB3 is guaranteed, and the spliceosome machinery works properly. (C) When rapalogs are present,

LRRFIP1 reduces the transcription of TRIB3 and the function of the spliceosome machinery is consequently impaired.

Rapalog-mediated repression of Tribbles pseudokinase 3 regulates pre-mRNA splicing

Bojana Stefanovska, Cecile Edith Vicier, Thibault Dayris, et al.

Cancer Res Published OnlineFirst April 3, 2020.

Updated version Access the most recent version of this article at: doi:10.1158/0008-5472.CAN-19-2366

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