Inhibiting XPOT suppresses TNBC growth via downregulating specific tRNA- TTC19 pathway mediating cytokinesis failure

doi:10.21203/rs.3.rs-2723400/v1

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Inhibiting XPOT suppresses TNBC growth via downregulating specific tRNA- TTC19 pathway mediating cytokinesis failure

https://doi.org/10.21203/rs.3.rs-2723400/v1

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Background

Transfer RNAs (tRNAs) are transported to the cytoplasm participating in mRNA translation through carrying cognate aminoacyl amino acids to ribosomes. Accumulating evidence shows that tRNAs play an important role in development and progression of various cancers, including breast cancer. However, it is still poorly clarified whether and how individual tRNA is modulated in triple negative breast cancer (TNBC) progression.

Methods

Clinical value of XPOT was explored in breast cancer cohorts. TNBC cell lines with XPOT silence were used for in vitro and in vivo experiments. RNA-seq, tRNA high throughput sequencing and Protein mass spectrometry of XPOT were performed for exploring the underlying molecular mechanism of XPOT promoting TNBC growth.

Results

XPOT (Exportin-T), a nuclear export protein receptor of tRNAs, was associated with poor prognosis of breast cancer. In vitro experiments showed XPOT knockdown could inhibit TNBC cells proliferation. RNA-seq results indicated that XPOT was involved in the cytokinesis completion of TNBC cells. High throughput sequencing of tRNA revealed that XPOT specifically influenced a subset of tRNA isodecoders nucleo-cytoplasmic trafficking including tRNA-Ala-AGC-10-1. Mechanistically, we found that XPOT preferentially transported nuclear tRNA-Ala-AGC-10-1 to the cytoplasm and drove the translation of TPR Repeat Protein 19 (TTC19) through codon preferential analysis and protein mass spectrometry analysis. TTC19 was also indispensable for cytokinesis and proliferation of TNBC cells.

Conclusions

Together, these findings provide a novel regulatory translation mechanism of preferential tRNA isodecoder nucleo-cytoplasmic transport through XPOT to modulate biological behavior of TNBC. This regulation mode could coordinate the spatial location of specific tRNA and the translation of mRNA, to facilitate TNBC proliferation and progression. Targeting XPOT may serve as a novel potential therapeutic strategy for TNBC.

triple negative breast cancer

XPOT

tRNA nucleo-cytoplasmic transport

mRNA translation

cytokinesis

Triple-negative breast cancer (TNBC), with no estrogen receptor (ER), progesterone receptor (PR), or human epidermal growth factor receptor 2 (HER2) expression, has the highest rate of metastasis, relapse, and death among all the subtypes of breast cancer, accounting for 15–20% of all breast cancer patients ¹. Although large amounts of investigations have been focused on TNBC, concrete biological mechanisms underlying TNBC development and progression are not completely clear till date. Therefore, it is highly demanded to seek the potential molecular mechanisms, identify the effective biomarkers and design individualized targeted therapy for TNBC.

Transfer RNAs (tRNAs), with the length of 73–92 nucleotide, are a class of small RNAs that play key roles in protein translation by carrying cognate aminoacyl amino acids to ribosomes ^{2 3}. Their regulation involves multiple steps, including synthesis, nuclear modification, aminoacylation and spatial transportation ⁴. Different dysfunctions of tRNA have been demonstrated to play oncogenic roles in various cancers including breast cancer. Tavazoie et al. reported that the overexpression of specific tRNA-Glu-UUC can boost the invasive and metastatic capacity of breast cancer by increasing its cognate codon usage for unique gene translation ⁵. Subsequent studies have shown that mounting tRNA^Met and tRNA^Leu abundance can also promote the growth of breast cancer cells ^{6 7}. Increasing tRNA transcripts driven by telomerase reverse transcriptase (TERT) in TNBC samples can lead to upregulated biosynthesis of proliferation-related proteins ⁸. Besides, tRNA^His-derived fragments can promote breast cancer cell proliferation ⁹ and aberrant methylation of cytoplasmic tRNA^His in MCF7 cells leads to its dysfunction ¹⁰. However, the potential roles and underlying mechanisms of tRNA spatial trafficking from nucleus to cytoplasm in breast cancer, especially in TNBC, had not been explored yet.

The transportation of macromolecules, including proteins, nucleic acids, and other molecules, between the nucleus and cytoplasm is a key step in cell signal transduction, which is critical for regulating diverse cell biological functions ¹¹. The transportation process is mainly mediated by the karyopherin-β family, which is composed of importin and exportin proteins. The dysregulated expression or dyslocalization of karyopherin has been found in numerous cancers and contributes to sustained cancer cell proliferation, apoptosis resistance, and immune escape ^{11 12}. Due to the high metabolism and rapid proliferation rate, cancer cells appear to be more susceptible to nuclear transporter inhibitors than normal cells ^{13 14}. Karyopherin superfamilies are considered as extremely promising targets for cancer therapy ¹⁵. XPOT, as one of the important karyopherins members, specialized in nucleocytoplasmic trafficking of tRNAs to participate in protein translation for cell growth and metabolism ^{16 17}. XPOT can mediate the subcellular localization of tRNA and respond to nutritional stress by activating the mTORC1 pathway and inhibiting autophagy in human fibroblasts ¹⁸. The deficiency of the XPOT homologue los1 will cause the decreased nuclear export of tRNA and subsequently the accumulation of tRNA in the nucleus, which can result in DNA damage and abnormal cell cycle regulation in yeast cells ¹⁹. Several studies have revealed XPOT overexpression in mesothelioma, leukemia HL-60 cells and hepatocellular carcinoma ^20–23, while the exact molecular mechanisms of XPOT in these tumors remain elusive, especially whether and how XPOT modulates tumor progression through tRNA nuclear export. Thus, it is of great significance to explore the function and regulatory mechanisms of tRNA mediator-XPOT in TNBC.

In this study, we found that XPOT was associated with poor prognosis in TNBC. In vivo and in vitro experiments showed that the inhibition of XPOT could decrease TNBC growth. According to tRNA high-throughput sequencing analysis, we identified that XPOT knockdown preferentially decreased the nuclear export of tRNA-Ala-AGC-10-1. Further experimental results of protein mass spectrometry, codon priority analysis, and transcriptome sequencing indicated that XPOT knockdown reduced the translation of the cytokinesis gene TTC19. Collectively, our results reveal that XPOT can potentially regulate the cytokinesis and proliferation of TNBC cells by selectively transporting specific nuclear tRNAs to the cytoplasm. Therefore, XPOT, as the key regulator of tRNA transportation, may be not only a promising novel prognostic biomarker, but also a potential therapeutic target for TNBC patients.

Human samples

We obtained 12 paired TNBC and adjacent normal tissues from patients who underwent mastectomy or modified radical mastectomy surgery in the Department of Breast Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University. All patients signed informed consent before operations. Human samples were collected between 2014 and 2015. All patients enrolled in this study were histopathologically and immunohistochemically diagnosed with TNBC. All procedures were approved by the Ethical Review Committee of the World Health Organization (WHO) Collaborating Center for Research in Human Production (authorised by the Shanghai Municipal Government).

Animal Experiments

The experimental animals were fed and housed in conformance with the guidelines of the Institutional Animal Care and Use Committee of East China Normal University. To evaluate the effect of XPOT and TTC19 on tumour growth, female nude mice were randomly divided into 2 groups and 3 groups (n = 6 per group): shNC group vs. shXPOT group; shNC + vector group vs shXPOT + vector group vs shXPOT + oeTTC19 group. We injected a total of 2 × 10⁶ cells stably expressing the targeted gene combined with 50% Matrigel (356234; Corning, China) + 50% PBS into the left back flank of 5-week-old mice to construct a subcutaneous xenograft model. Then, the tumor maximum and minimum diameter was determined every 5 days using calipers once the tumors were macroscopic in size. The tumor volumes were calculated using the following equation: volume = (length × width²) × 0.5. All experimental mice were dissected 30 days after the tumor injection day, and the tumors were acquired and weighed. Finally, the tumors were subjected to formalin fixation, paraffin embedding, and sectioned for IHC analysis.

Bioinformatic Analysis Of Karyopherins Expression In Different Cancer Types

Expression profile of karyopherins in different cancer types comparing with normal tissue was obtained from Oncomine (https://www.oncomine.org/). Besides breast cancer, we also enrolled other cancer types with high incidence like lung cancer, colorectal cancer, gastric cancer, and liver cancer. Several cancer types with low incidence like oligodendroglioma and pleural malignant mesothelioma were also contained.

1. Squamous Cell Lung Carcinoma vs. Normal. Bhattacharjee Lung, Proc Natl Acad Sci U S A, 2001.
2. Colorectal Carcinoma vs. Normal. Hong Colorectal, Clin Exp Metastasis, 2010.
3. Pleural Malignant Mesothelioma vs. Normal. Gordon Mesothelioma, Am J Pathol, 2005.
4. Adrenal Cortex Carcinoma vs. Normal. Giordano Adrenal, Am J Pathol, 2003.
5. Ductal Breast Carcinoma vs. Normal. Richardson Breast 2, Cancer Cell, 2006.
6. Rectal Adenocarcinoma vs. Normal. TCGA Colorectal.
7. Infiltrating Bladder Urothelial Carcinoma vs. Normal. Dyrskjot Bladder 3, Cancer Res, 2004.
8. Granular Renal Cell Carcinoma vs. Normal. Higgins Renal, Am J Pathol, 2003.
9. Gastric Intestinal Type Adenocarcinoma vs. Normal. Chen Gastric, Mol Biol Cell, 2003.
10. Esophageal Squamous Cell Carcinoma vs. Normal. Hu Esophagus, BMC Genomics, 2010.
11. Hepatocellular Carcinoma vs. Normal. Roessler Liver 2, Cancer Res, 2010.
12. Small Cell Lung Carcinoma vs. Normal. Garber Lung, Proc Natl Acad Sci U S A, 2001.
13. Acute Myeloid Leukemia vs. Normal. Andersson Leukemia, Leukemia, 2007.
14. Burkitt's Lymphoma vs. Normal. Basso Lymphoma, Nat Genet, 2005.
15. Oligodendroglioma vs. Normal. Shai Brain, Oncogene, 2003.

Immunohistochemical Staining

TNBC tissue microarrays (TMA) were purchased from Superbiotek (Shanghai, China). In total, 80 paired patient samples were enrolled, including tumor tissue and their counterpart normal tissues. Samples were collected between 2005 and 2012. The average age of this cohort of patients with TNBC was 52 years (range, 18–86 years). The follow-up endpoint was May 2019, and the median survival time was 393.5 months (range, 7 to 180 months). We used TMA to perform immunohistochemical (IHC) staining and IHC score evaluation. The protocol was conducted as previously described ²⁴. XPOT and TTC19 were examined using the corresponding primary antibodies: sc514591 (1:100) (Santa Cruz Biotechnology) and 208751AP (1:50) (Proteintech), respectively. Images were obtained using a fluorescent microscope (Carl Zeiss, Oberkochen, Germany). We evaluated the protein staining score based on the following criteria: a dark brown staining of tumor cells of 81–100% was defined as “++++”; a dark brown staining of tumor cells of 51–80% was defined as “+++”; a dark brown staining of tumor cells of 21–50% was defined as “++”; a dark brown staining of tumor cells of 10–20% was defined as “+”. Two experienced pathologists evaluated the scores, both independently and blindly. We classified tumors staining with “+++” and “++++” into the high expression group, and “+” and “++” into the low expression group, which were utilized for the survival analyses.

Cell Lines Culture And Regents

All human cell lines, including the normal mammary epithelial cell line HBL100 and TNBC cell lines (MDA-MB-231, MDA-MB-436, MDA-MB-468, BT20, HCC1937, and HCC1806) were purchased from the American Type Culture Collection (ATCC). All cells were cultured in the specified medium according to ATCC protocols. The medium was supplemented with 10% foetal bovine serum (FBS) (Life Technologies, Inc., USA) and 1% penicillin/streptomycin (P/S) (Life Technologies, Inc., USA) in an incubator at 37°C with 5% CO₂. MDA-MB-468 cells were cultured in a 37°C/non-CO₂ atmosphere. We tested all cell lines in December 2018 and again every 4 months to verify that mycoplasma was negative by the Shanghai Cancer Institute. Proteasome inhibitor MG132 (133407-82-6; MCE, China) was used as the reagent.

Stable Knockdown Cell Line Construction

We designed and purchased the short harpin RNA targeting the human XPOT sequence and sh-scramble, which were attached to the PLKO-puro lentiviral vector (Gene Pharma, Shanghai, China). Briefly, 4 × 10⁵ MDA-MB-231 and MDA-MB-468 cells were plated in 60-mm dishes. Once the cells were adherent, 1 × 10⁶ of lentivirus was added to the dish after the addition of 5 µg/ml Polybrene (H9268; Sigma-Aldrich, St. Louis, MO). Stable knockdown cells were screened using puromycin (A1113802; Gibco) from 2 µg/ml to 8 µg/ml after transfection for 72 h. The knockdown efficiency was validated by western blotting. The sequence of shXPOT was 5’- GCACAUUCCAUGUGUACUATT-3’.

Cell Transfection

Gene knockdown was performed using Lipofectamine RNAiMAX (Invitrogen, Carlsbad, CA, USA) combined with 150 pmol small interfering RNA (siRNA) constructs or 5 µL of negative control siRNA (Gene Pharma, Shanghai, China). The targeted gene sequences were as follows: siXPOT-1 sense: 5’-GGGACAGUCAUUGAUAGUUTT-3’; antisense: 5’- AACUAUCAAUGACUGUCCCAT-3’; siXPOT-2: sense: 5’- GCACAUUCCAUGUGUACUATT-3’; antisense: 5’- UAGUACACAUGGAAUGUGCTG-3’. siTTC19-2: sense, 5’- GCAGGAGGACAAUGCAAUATT-3’; antisense: 5’- UAUUGCAUUGUCCUCCUGCTT-3’; siTTC19-3, sense: 5’- CUGGCUAUGAAUUCUGCAUTT-3’; antisense: 5’- AUGCAGAAUUCAUAGCCAGTT-3’. TTC19 overexpressing plasmids or their vectors were transfected into XPOT stable knockdown cells using Lipofectamine 2000 (11668–019; Invitrogen). Transfection steps were similar with those used for siRNA transfection.

Stable Overexpression Of Ttc19 Cell Line Construction

shXPOT and shNC MDA-MB-468 cells were infected with pSLenti-EF1a-EGFP-P2A -CMV-TTC19-3Flag lentivirus (OBiO Technology, Shanghai) and its control vector, respectively. Before virus transfection, XPOT knockdown efficiency was determined by western blotting. TTC19 stably overexpressing cells were acquired by blasticidin selection from 1 to 5 µg/ml when no cells were killed. Western blot was used to observe transfection efficiency. These stable cell lines were for the following animal experiments.

Cell Proliferation Detection

5-Ethynyl‐2‐deoxyuridine assay (EdU) was performed according to the protocol of 5‐ethynyl‐2’‐deoxyuridine (EdU)-labelling/detection pack (Beyotime, Shanghai, China). Fluorescence microscopy was used to observe the cells. Cell CCK-8 and cell colony formation assays were conducted as previously described ²⁵.

Cell Cycle Analysis

Cells were transfected with siXPOT or si-TTC19 and scramble RNA for 48 h before incubating with serum-free medium to synchronise cells at the G1/S boundary. After 24 h, the cells were cultured in complete medium for an additional 24 h. The cells were collected and washed twice with cold PBS before incubating with 25 µg/ml RNaseA and 2.5 µg/ml propidium iodide (Dojindo, Kumamoto, Japan) for 30 min in the dark at 4°C, followed by 30 min at 37°C. The stage of the cell cycle was determined using flow cytometry (LSRFortessa; BD Biosciences, USA). Data were analysed using ModFit LT software.

Cell Apoptosis Assay

Cells (3 × 10⁵ cells/well) were seeded in a 6-well plate overnight for attachment. siRNAs or plasmids were added to the cells for 48 h, followed by incubating in serum-free medium for another 24 h. To determine the apoptotic cell ratio, the cells were stained using an annexin V-FITC apoptosis detection kit (Dojindo, Kumamoto, Japan) and examined by flow cytometry for analysis. Data were analysed using FlowJo version 10.0.

Rna Isolation And Quantitative Real Time-pcr (Qrt-pcr)

Total RNA was extracted according to the manufacturer’s instructions using TRIzol reagent (15596018; Thermo Fisher Scientific). RNA (1000 ng) was then converted into cDNA, as previously described. The cycling conditions were as follows: 95°C for 30 s, 40 cycles of 95°C for 5 s, and 60°C for 30 s. The reaction system included 12.5 µL of SYBR Green PCR mix, 1 µL of each primer, and 1 µL of cDNA template in a final volume of 25 µL per reaction (RR820A; Takara, Japan). The 2^−△△Ct method was used to analyse the relative gene expression (target gene expression normalised to the expression of the endogenous control gene). The qRT-PCR experiments were performed in triplicate.

Human Trna Sequencing

We sequenced tRNA genes in the cytoplasm and nucleus of XPOT stably knocked-down TNBC (MDA-MB-46) cells. Total RNA was isolated from the cytoplasm and nucleus of the targeted cells. Before the sequencing experiment, we checked the integrity and quantity of each RNA sample using agarose gel electrophoresis in a NanoDrop ND-1000 instrument. tRNA was isolated from total RNA using the Denaturing Urea Polyacrylamide Gel Electrophoresis (Urea PAGE) method. Briefly, approximately 2 µg total RNA was resolved on 7.5% urea PAGE gels (7M urea) and recovered within a size window of 60–100 nt for tRNA. The “demethylation” section in rtStarTM tRF&tiRNA Pretreatment Kit (AS-FS-005; Arraystar) was used for tRNA m1A&m3C demethylation treatment. A 50-µL demethylation reaction mixture was prepared according to the manufacturer’s protocol and incubated at 37°C for 2 h. Then, 40 µL of nuclease-free water and 10 µL of 5× Stop Buffer were added to terminate the reaction. The demethylated tRNA was purified by phenol-chloroform extraction and ethanol precipitation. Demethylated tRNA was partially hydrolysed according to the Hydro-tRNA seq method, with little modification. tRNA was subjected to limited alkaline hydrolysis in 15 µL of buffer comprised of 10 mM Na₂CO₃ and 10 mM NaHCO₃ (pH 9.7) at 90°C for 7 min. The partially hydrolysed tRNA was dephosphorylated with 10 U calf intestinal alkaline phosphatase (CIP) (M0290L; New England Biolabs) in a 50-µL reaction of 100 mM NaCl, 50 mM Tris-HCl (pH 7.9), 10 mM MgCl₂, 1 mM DTT, 3 mM Na₂CO₃, and 3 mM NaHCO₃, at 37°C for 1 h. The resulting tRNA was purified with TRIzol reagent and then re-phosphorylated with 10 U T4 polynucleotide kinase (M0201L; New England Biolabs) polynucleotide kinase in a 20-µL reaction mixture containing 70 mM Tris-HCl (pH 7.6), 10 mM MgCl₂, 5 mM DTT, and 1 mM ATP at 37°C for 1 h. TRIzol reagent purification was performed once more. Partially hydrolysed tRNA fragments were converted to barcoded small RNA sequencing libraries using the NEBNext® Multiplex Small RNA Library Prep Set for Illumina® (E7300L/E7850L; New England Biolabs), according to the manufacturer’s instructions. The procedures generally included: (1) 3’-adapter ligation; (2) 3’-adapter blocking; (3) 5’-adapter ligation; (4) reverse transcription; (5) PCR amplification; (6) size selection of ∼140–155 bp PCR amplified fragments (corresponding to ∼19-35nt tRNA fragments) using 6% PolyAcrylamide Gel (PAGE). The completed libraries were qualified and quantified using the Agilent 2100 Bioanalyzer. The DNA fragments in well-mixed libraries were denatured with 0.1 M NaOH to generate single-stranded DNA molecules, and loaded onto the reagent cartridge for 75 cycles (NextSeq 500/550 High-Output v2 kit, FC-404-2005; Illumina) at a loading. Comprehensive data and statistical analyses were obtained using the Arraystar tRNA-seq data analysis package.

To semi-quantitatively analyse tRNA, we performed qRT-PCR based on the results of tRNA-seq. tRNA demethylation was performed as described above. The cycling conditions were as follows: 95°C for 30 s, 40 cycles of 95°C for 5 s, and 60°C for 30 s. tRNA primers were purchased from Arraystar (USA).

Cytoplasmic And Nuclear Rna Purification And Trna Qrt-pcr

Cytoplasmic and nuclear RNA were harvested according to the manufacturer’s protocol using the Cytoplasmic and Nuclear RNA Purification Kit (Norgen, Canada). Total cellular RNA was isolated as described previously. tRNA demethylation, reverse transcription, and qPCR were performed in conformance with a previous study ²⁶. These procedures required reagents including a demethylation kit, First-Strand cDNA Synthesis Kit, and SYBR (Arraystar, USA). To semi-quantitatively analyse tRNA, we performed qRT-PCR based on the results of tRNA-seq. tRNA demethylation was performed as described above. The cycling conditions were as follows: 95°C for 10 min, 40 cycles of 95°C for 10 s, and 60°C for 60 s. tRNA (tRNA-Ala-AGC-10-1, tRNA-Ala-AGC-1, tRNA-Ala-AGC-3, tRNA-Thr-AGT-3-1, and tRNA-Gly-CCC-1-1) primers were purchased from Arraystar (USA).

Codon Usage Preference Analysis

Based on the NCBI CCD database, the protein-coding nucleic acid sequence (coding sequence) of the genes (all genes detected in our RNA-seq data) was downloaded, and the frequency of codon usage in each protein-corresponding gene was calculated. Then, the frequency was converted into the frequency of each codon in every 1000 nucleic acid bases. We selected genes with the ability to code protein and codon-GCT usage frequency of more than 0.020, while the ratio of GCT score/GCC score and GCT score/GCA score were both > 1.

Rna Sequencing

Total RNA was extracted from siNC or siXPOT/MDA-MB-468 cells. The Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) was used to examine RNA integrity. RNA integrity number (RIN) ≥ 7 qualified for subsequent analysis. We used the TruSeq Stranded mRNA LT Sample Prep Kit (Illumina, San Diego, CA, USA) to construct the libraries in accordance with the manufacturer’s protocols. Then, we sequenced these libraries on the Illumina sequencing platform (Illumina HiSeq X Ten) and generated 150-bp paired-end reads.

Protein Mass Spectrometry

The reagents used in this experiment were as follows: urea, ammonium bicarbonate, dithiothreitol, iodoacetamide, BCA protein quantitative kit, 10 kD ultrafiltration tube, sequencing grade modified trypsin, C18 micro column, formic acid, acetonitrile, and water. First, we extracted the protein supernatant and measured its concentration. Second, we processed the extracted proteins by reduction and alkylation. Third, we enzymatically hydrolysed the processed protein. We also hydrolysed the processed protease into polypeptides and desalinated the polypeptides. Lastly, the samples were tested using Nano Liquid Chromatography–Orbitrap Mass Spectrometry system. The original data were searched and analysed using Proteome Discoverer 2.3 (Thermo Fisher Scientific) software, and the search engine was sequenced. The human protein database was downloaded from UniProt (https://www.uniprot.org/). Its oxidation process is fixed through the modification of cysteine alkylation, the variable modification of methionine oxidation, the deamidation of asparagine/glutamine, and n-terminal acetyl. The precursor mass tolerance was set to 10 ppm, and the fragment mass tolerance was set to 0.02 Da, allowing the Max missed cleavages to 2. FDR (False discovery rate) was set to ≤ 1%. Data were normalized by shifting the median H/L ratio to 1.

Construction Of Synonymous Codon Mutation Plasmids

To obtain synonymous codon mutations in the TTC19 coding sequence (CDS), CDS contains 13 codon GCT. we constructed wild and codon mutation plasmids from OBiO Technology (Shanghai, China): TTC19 wild plasmid, all GCT changing into GCC mutation plasmid, or GCT changing into GCA mutation plasmid. These plasmids were transfected into XPOT stable knockdown or control cells according to the above transfection protocol. After 72 h, western blotting was performed to confirm the XPOT-related codon-dependent translation of TTC19.

Western Blotting

Total protein was extracted and quantified using a BCA protein assay kit (P0010; Beyotime, China). Proteins (20–30 µg) were separated on 10% SDS-polyacrylamide gels and transferred to polyvinylidene difluoride (PVDF) membranes (Millipore, Billerica, MA, USA). The membranes were incubated in 5% non-fat milk diluted with TBST (0.1% Tween-20 in Tris-buffered saline) to block nonspecific binding. The membranes were incubated with primary antibodies at 4°C overnight, and then incubated with horseradish peroxidase-conjugated secondary antibodies (1:2000 dilution in 1% BSA) at room temperature for 1 h. The following antibodies were used: β-catenin (1:5000) (ab32572; Abcam, Cambridge UK), GAPDH (1:1000) (10494-1-AP; Proteintech), tubulin (1:3000) (ab0049; Abways), XPOT (1:100) (sc514591; Santa Cruz Biotechnologies), and TTC19 (1:50) (20875-1-AP; Proteintech).

Immunofluorescent Staining

To determine the role of XPOT or TTC19 in cytokinesis, cells were transfected with siXPOT, siNC, siTTC19, or siNC for 72 h. Then, 4000 cells were seeded into each well of the 12-well chambers (Ibidi, Martinsried, Germany). After adherence, the cells underwent a series of manipulations: fixing with 4% PFA for 15 min, permeabilizing with 1% TritonX-100 for 5 min, and enclosing with 1% BSA for 5 min at room temperature. Enclosed cells were incubated at 4°C overnight with β-tubulin primary antibodies (1:40) (10094-1-AP; Proteintech), and then incubated with secondary antibody conjugated with Alexa Fluor-488 (1:100) (Invitrogen, USA) for 1 h at room temperature. Nuclei were stained with DAPI (Sigma-Aldrich, USA) for 5 min. Finally, we captured the images using confocal microscopy (LSM 510; Carl Zeiss, Oberkochen, Germany).

Live Cell Image

Live cell imaging time-lapse video microscopy was used to evaluate cell division. Cells were seeded into 12-well plates and transfected with siRNA targeting XPOT or TTC19. After 72 h, the cells were placed in an incubator (37℃ and > 95% humidity) (Nikon, Tokyo, Japan) for the observation of cell division using a Biostation Timelapse system. Differential interference contrast images were captured every 15 min/photo with a 20- objective lens for 10 h continuously. Nikon NIS-Elements software was used to analyse the resulting images.

Rnascope

Cells were fixed with 10% neutral buffered formalin at room temperature for 30 min, and subsequently treated with 70% ethanol for 10 min, 90% ethanol for 10 min, and 100% ethanol for 10 min. RNAscope in situ hybridisation was performed using the RNAscope kit (Advanced Cell Diagnostics, Newark, CA, USA) according to the manufacturer’s instructions. We designed a 1ZZ probe named BA-Hs-tRNA-ala-agc-10-1-1zz-st targeting 2–45 customer-provided sequences (Advanced Cell Diagnostics, Newark, CA). The sequence of tRNA-Ala-AGC-10-1 is “GGGGAATTAGCTCAAGTGGTAGAGCGCTTGCTTAGCACGCAAGAGGTAGTGGGATCGATGCCCACATTCTCCACCA”. Fluorescent images were obtained using a confocal microscope (LSM 510; Carl Zeiss, Oberkochen, Germany). MBF Image J was utilised to analyse the fluorescence intensity.

Bioinformatic Analysis

Survival analysis of breast cancer patients were referenced from The Cancer Genome Atlas (TCGA) and Kaplan-Meier plotter (http://kmplot.com/analysis). Patients in TCGA TNBC cohort were separated into high and low XPOT/TTC19 expression group with cutoff selected by R package "maxstat". We chose the best cutoff for Kaplan-Meier analysis of breast cancer patients from Kaplan-Meier plotter. TCGA and GEO databases (GSE13650) was used to compare XPOT expression levels between triple negative breast cancer tissue and normal tissue.

Graphic And Statistical Analyses

All the data and graphic images were analysed using GraphPad Prism version 8.0 (San Diego, USA). Categorical variables were analysed using Pearson’s Chi-square test or Fisher’s exact test. Two-sided Student’s t-test or one-way analysis of variance (ANOVA) was used to analyse the numerical data comparison. Kaplan-Meier plot and log rank test were used to observe the correlation between TTC19 and XPOT expression and survival. Multivariable cox analysis was used for evaluating relationship between prognosis and potential factors. Error bars denote the mean ± standard deviation (SD). Heatmaps were plotted using R (version 3.6.1). Adobe Illustrator (version 21.0.0.0) was used to organise all the images. Statistical significance was defined as two-side p < 0.05.

High XPOT expression predicts shorter overall survival for TNBC patients

We initially evaluated all 25 kinds of karyopherins across 15 analyses consisting of 16 cancer microarrays datasets through Oncomine data-mining analysis. XPOT was found to be the most obviously upregulated karyopherin transport receptors among 15 cancers (Fig. 1A). The association of six top-ranked karyopherins (KPNA2, XPOT, CSEL1, KPNA1, KPNA4 and TNPO1) in the breast cancer datasets from Oncomine (Fig. 1B) was analyzed with survival by Kaplan-Meier Plotter. Indeed, high expression of XPOT predicted worse overall survival of breast cancer, especially in TNBC (Fig. S1B). Through the analysis of TCGA dataset, GEO dataset (GSE13650), and our Renji Hospital cohort, we found that the expression of XPOT mRNA in TNBC tissues was much higher than that in normal tissues (Fig. 1C-E). These results were further validated by the immunohistochemical analysis of 80 pairs of samples in a TNBC tissue microarrays (TMA) (Fig. 1F-G). Importantly, high XPOT levels were closely associated with poor survival for TNBC in public TCGA dataset (P = 0.044) and TNBC TMA (p = 0.045) (Fig. 1H and 1I). Multivariate cox regression analysis showed that XPOT was an independent prognostic factor for overall survival in TNBC based on the TMA and TCGA data (Fig. S2A). Meanwhile, high expression of XPOT was also related to more aggressive biological characteristics, such as larger tumor size (P = 0.019), more lymph node metastasis (P = 0.014) and higher histological grade (P = 0.034) (Fig. 1J).

Xpot Knockdown Suppresses Tnbc Growth Through Impairing Cytokinesis And Increasing Resistance To Apoptosis

Initially, we detected the expression of XPOT in several TNBC cell lines and a normal control breast epithelium cell line HBL100 by western blotting. The protein expression of XPOT in TNBC cells was relatively higher than that in control cells (Fig. S3A). Based on different expression, two TNBC cells MDA-MB-231 and MDA-MB-468 were chosen for the further experiments. In these two cell lines, XPOT was localized in both cytoplasm and nucleus (Fig. S4). For further functional experiments, depletion of XPOT in MDA-MB-231 and MDA-MB-468 by small interfering RNA was validated by quantitative RT-PCR analysis (Fig. S3B). We observed that the proliferation of MDA-MB-231 and MDA-MB-468 cells was inhibited after XPOT downregulation through CCK8 assay, EdU assay and colony formation assay (Fig. 2A-C). To further verify the impact of XPOT in TNBC growth in vivo, we constructed a lentiviral based stable RNA interference vector using XPOT-siRNA2 interference sequence. Knockdown efficiency of Lenti-shXPOT transfected MDA-MB-231 and MDA-MB-468 cells was shown by western blotting (Fig. 2D), and we found that XPOT knockdown could prominently inhibit tumor growth in the xenograft tumor model established with MDA-MB-468 cells (Fig. 2E-G). Consistently, IHC staining of Ki67 showed tumor cell proliferation was decreased in shXPOT xenografts compared with the controls (Fig. 2H).

To further explore the underlying molecular mechanism of XPOT on the behaviours of TNBC cells, we performed RNA sequencing in MDA-MB-468 cells transfected with siNC and siXPOT. Gene Ontology (GO) analysis of differentially expressed genes (DEGs) showed that genes related with cell mitotic processes, especially those linked to chromosomes and the spindle apparatus, were enriched (Fig. 3A). This result suggested that XPOT may be involved in the process of mitosis. To determine which phase of mitosis XPOT affects, we performed cell cycle analysis and noticed that the percentage of cells in G2/M phase increased significantly after XPOT knockdown in MDA-MB-231 and MDA-MB-468 cells, while the percentage of cells in G0/G1 phase decreased, indicating that the cell cycle was arrested at G2/ M stage after XPOT knockdown (Fig. 3B). Given this, we speculated that XPOT possibly influences the last stage of the cell mitosis. We then performed time-lapse live cell imaging and immunofluorescence analysis to observe the binucleated cells. The results showed that binucleated cells ratio was increased after XPOT interference (Fig. 3C, 3D). Cytokinesis mostly covered the anaphase and telophase of cell cycle, with the feature of binucleated or multinucleated cells mounting. Thus, the above results implied that XPOT might play an essential role in ensuring TNBC cell cytokinesis. In addition, cell apoptosis was detected by flow cytometry and we found that the proportion of apoptotic cells increased after XPOT depletion compared to control cells (Fig. S5). Generally, these findings to some extent manifested that downregulation of XPOT potentially decreased TNBC growth through impairing TNBC cell cytokinesis and promoting apoptosis.

Xpot Preferentially Mediates Trna-ala-agc-10-1 Nuclear Export In Tnbc Cells

XPOT is a nuclear transport receptor responsible for the export of tRNAs from nucleus into cytoplasm. To determine the biological mechanism of XPOT participating in the cytokinesis of TNBC cells, we extracted cytoplasmic and nuclear tRNA from stable XPOT knockdown MDA-MB-468 TNBC cells and control cells (Fig. 4A). Then, tRNA high-throughput sequencing was performed (Fig. 4B). According to tRNA sequencing results, we picked out differential subcellular distribution of tRNAs between XPOT-knockdown cells and control cells according to the ratio of nuclear tRNA/cytoplasmic tRNA (Fig. 4C; differential tRNAs listed in Table 1). The top two tRNAs with upregulated nucleo/cytoplasmic distribution, tRNA-Ala-AGC-10-1 and tRNA-Thr-AGT-3-1, were chosen for further examination. qRT-PCR assay showed that knockdown of XPOT decreased the distribution of tRNA-Ala-AGC-10-1 and tRNA-Thr-AGT-3-1 in the cytoplasm of TNBC MDA-MB-468 and MDA-MB-231 cells (Fig. 4D and 4E), while the total content of these two tRNAs in the two cells remained unchanged (Fig. 4F and 4G). Next, we performed RNAscope to locate endogenous tRNA-Ala-AGC-10-1 in TNBC cells. Compared with control cells, the nuclear/cytoplasmic fluorescence ratio (N/C) in XPOT-knockdown cells was significantly increased for MDA-MB-231 and MDA-MB-468 cells (Fig. 4H). In addition, we mapped all XPOT targeted-tRNA detected in MDA-MB-468 cells to the tRNA database (GtRNAdb; http://gtrnadb.ucsc.edu/) through the result of tRNA-seq, and we found 15 tRNA-Ala-AGC isodecoders in MDA-MB-468 cell line (Appendix.S1), whose sequences were displayed in Fig. S6. To further confirm that XPOT mainly affected the nuclear export of tRNA-Ala-AGC-10-1 rather than other isodecoders, we detected the distribution of the remaining 14 isodecoders using the primers of tRNA-Ala-AGC-3 and tRNA-Ala-AGC-1 by qRT-PCR assay, since primers for tRNA-Ala-AGC-3 and tRNA-Ala-AGC-1 can cover all the remaining isodecoders, as listed on the Arraystar website (https://www.arraystar.com/rtstar-pre-designed-trna-primer-sets/). The results showed the nucleo-cytoplasmic distribution and expression of the remaining 14 isodecoders had no statistical significance (Fig. S7A-C). In order to further verify the tRNA bias of XPOT in tRNA transport during TNBC cells growth, tRNA-Gly-CCC-1-1, a tRNA that remained almost unchanged in the tRNA-sequencing result, was further analysed (Table 1). There were no significant differences in the nuclear/cytoplasmic ratio and total level of tRNA-Gly-CCC-1-1 between XPOT-knockdown and control MDA-MB-231 or MDA-MB-468 cells (Fig. S7D and 7E). Therefore, to some extent, these findings implied that XPOT may preferentially mediate the nucleo-cytoplasmic transportation of tRNA-Ala-AGC-10-1 to play its biological roles.

Table 1

Differential distribution of nucleus and cytoplasmic tRNA isodecoders after XPOT depletion in TNBC cells
tRNA isodecoder	Ratio=\(\frac{\mathbf{S}\mathbf{h}\mathbf{X}\mathbf{P}\mathbf{O}\mathbf{T} \mathbf{N}\mathbf{u}\mathbf{c}/\mathbf{C}\mathbf{y}\mathbf{t}\mathbf{o}}{\mathbf{S}\mathbf{h}\mathbf{N}\mathbf{C} \mathbf{N}\mathbf{u}\mathbf{c}/\mathbf{C}\mathbf{y}\mathbf{t}\mathbf{o}}\)
tRNA-Thr-AGT-3-1	2.042221128
tRNA-Ala-AGC-10-1	1.999167172
tRNA-Glu-TTC-1-1	1.658461909
tRNA-Arg-CCT-1-1	1.524281421
tRNA-Glu-TTC-2-1	1.480488632
tRNA-Cys-GCA-5-1	1.477380601
tRNA-Ile-AAT-3-1	1.455185042
tRNA-Val-CAC-5-1	1.378688966
tRNA-Ser-AGA-5-1	1.322156666
tRNA-Cys-GCA-1-1	1.304067958
tRNA-Leu-TAG-3-1	1.258855669
tRNA-Leu-TAA-2-1	1.209773187
tRNA-Lys-CTT-2-1	1.19750431
tRNA-Gly-CCC-1-1	1.145757275
Nuc: nucleus, Cyto: cytoplasm

The Impact Of Xpot On Ttc19 Translation Is Trna-ala-agc-10-1 Dependent

Due to the degeneracy feature of codons, the anticodon AGC can identify codon GCR (GCT, GCC, GCA). However, the cognate codon of tRNA-Ala-AGC-10-1 is GCT. To explore genes with a high GCT abundance, we performed codon usage preference analysis in all transcribed genes in MDA-MB-468 cells. As a result, 1874 genes were found to have a high content of GCT (GCT score > 0.02; GCT score/GCC score > 1, GCT score/GCA score > 1, Appendix S2). Then, we performed a protein mass spectrometry assay using XPOT-knockdown and control MDA-MB-468 cells to detect the proteins modulated by XPOT and identified 736 downregulated proteins whose transcriptional levels were not altered (Fig. S8).

To identify the functionally related target genes of the tRNA-Ala-AGC-10-1, we took the intersection of 80 cytokinesis genes with codon-GCT enriched genes and genes translationally regulated by XPOT. Interestingly, we found one cytokinesis-related gene, TTC19 (Fig. 5A). Therefore, we postulated that XPOT may affect TTC19 translation and further experiments were performed to validate the hypothesis. First, qRT-PCR assay showed that TTC19 transcript was not affected after XPOT knockdown compared to that in control cells (Fig. 5B). However, western blotting analysis revealed a significant decrease (reduced by 22-fold) in TTC19 protein expression after XPOT knockdown (Fig. 5C) and immunochemistry staining in serial sections from TNBC tissue microarray revealed that TTC19 expression was positively correlated with XPOT expression (Fig. 5D). In terms of degradation, we utilized the protein proteasome inhibitor (MG132) to determine whether XPOT played a role in TTC19 protein degradation. The results showed that TTC19 degradation in XPOT-knockdown cells was comparable to that in control cells (Fig. 5E).

To further verify whether TTC19 expression is GCT dependent we constructed a TTC19 coding sequence (CDS) mutational plasmid in which each cognate codon GCT of tRNA-Ala-AGC-10-1 was replaced by the synonymous codon GCC or GCA (Fig. 5F). MDA-MB-231 and MDA-MB-468 cells were transfected with wild-type TTC19 plasmid or mutational plasmid. TTC19 protein expression decreasing was observed after XPOT knockdown in wild-type TTC19 plasmid transfected cells, while TTC19 expression was not affected by XPOT knockdown in mutational plasmid transfected cells (Fig. 5G). All these data indicated that the cytokinesis gene TTC19 might potentially be translationally regulated by XPOT-mediated tRNA-Ala-AGC-10-1 nuclear export in TNBC cells.

Ttc19 Is Essential For Tnbc Cells Proliferation And Cytokinesis Completion

TTC19 has been identified to be involved in cytokinesis as described in the website of GeneCards (https://www.genecards.org/cgi-bin/carddisp.pl?gene=TTC19& keywords = TTC19), but its exact function in cancers, especially in TNBC, remains unknown. To further determine the role of TTC19 in TNBC, we explored the correlation of TTC19 expression with TNBC survival. The high level of TTC19 mRNA was associated with poor TNBC overall survival by analyzing the TCGA dataset and Kaplan-Meier Plotter (http://kmplot.com/analysis/) (Fig. 6A, B). Through immunohistochemical staining of the TNBC TMA, we found that the expression of TTC19 in TNBCs was higher than that in normal tissues (Fig. 6C-E). Higher protein expression of TTC19 was closely associated with larger tumour size, more lymph node metastasis and higher histological grade (Fig. S9), as well as poor overall survival (Fig. 6F) in TNBC patients. Also, we found that the depletion of TTC19 could phenocopy the effects of XPOT downregulation on cell proliferation and cell apoptosis detected by CCK8, EdU, colony formation assays (Fig. 6H-J) and Annexin V/PI flowmetry assays (Fig. S10).

We then performed mRNA sequencing in siNC and siTTC19 MDA-MB-468 cells. Biological process annotation showed that DEGs were enriched in various cytokinesis related processes (Fig. 7A). By performing cell cycle analysis (Fig. 7B), time-lapse live cell imaging (Fig. 7C), and immunofluorescence analysis (Fig. 7D), we found that TTC9 knock down resulted in cell cycle arrest in the G2/M phase, failure in cytokinesis, and the formation of more binuclear cells in both TNBC MDA-MB-231 and MDA-MB-468 cells. These results might indicate that TTC19 affect the growth of TNBC cells by regulating cytokinesis.

Ttc19 Expression Is Indispensable For Xpot Regulating Tnbc Growth

In order to explore whether the function of XPOT were dependent on TTC19, we overexpressed TTC19 in shXPOT/ MDA-MB-231 and MDA-MB-468 cells by transfected the plasmid of TTC19 or vector control. We observed that TTC19 expression recovered moderately after transfection in XPOT knockdown cells (Fig. S11A). CCK8, Edu, and colony formation assays showed that forced overexpression of TTC19 could partially restore the proliferation of MDA-MB-231 and MDA-MB-468 cells inhibited by XPOT depletion (Fig. 8A-C). TTC19 re-expression also partially decreased the apoptosis of MDA-MB-231 and MDA-MB-468 cells induced by XPOT knockdown (Fig. 8D). Next, we transfected lentivirus-TTC19 or lentivirus-vector into shXPOT/MDA-MB-468 cells to construct stable cell lines and western blotting results demonstrated that TTC19 expression did recover moderately (Fig.S11B). Consistently, TTC19 partially restored the growth of TNBC tumor in a subcutaneous xenograft model (Fig. 8E-H). Therefore, we speculated that TTC19 might serve as the downstream protein of XPOT, and participate in regulating cytokinesis, apoptosis, and proliferation.

XPOT, as a member of importin-β family shutters between nucleus and cytoplasm, is responsible for tRNA nucleo-cytoplasmic transportation ^{17 27}. Our study first elucidated the oncogenic role of XPOT in promoting TNBC development by ensuring the successful completion of cell cytokinesis. tRNA is a key component for protein translation through carrying cognate aminoacyl amino acid to ribosomes ³. We clarified the novel biological function of XPOT through its preferential export of certain isodecoders during tRNA nucleo-cytoplasmic transportation according to tRNA high throughout sequence analysis, and XPOT knockdown could preferentially decrease tRNA-Ala-AGC-10-1 export to the cytoplasmic, rather than other isodecoders of tRNA-Ala-AGC. We further uncovered that XPOT could influence cytokinesis-related gene TTC19 translation by affecting tRNA-Ala-AGC-10-1 nuclear export. We also as the first one revealed that TTC19 potentially be a tumorigenic gene in TNBC growth by guaranteeing cytokinesis finishing. What’s more, we demonstrated that TTC19 is to some extent essential for XPOT functioning of its oncogenic role in TNBC. Taken together, these data disclosed a novel mechanism that XPOT promotes TNBC growth through regulating tRNA-TTC19 pathway to mediate the completion of TNBC cells cytokinesis (Fig. S12). We delineated the novel role of XPOT via its modulated role in protein translation. Knockdown of XPOT or TTC19 could inhibit the growth of TNBC, which indicates that targeting XPOT or TTC19 could be a promising novel therapeutic strategy for TNBC. Previous studies indicated that XPOT is indispensable for maintaining physiological function of normal cells. Herbein et.al. reported that XPOT enhances Nef/eEF1A/tRNA complex transporting to cytoplasm and then increases tRNA binding to mitochondrial cytochrome c release causing apoptosis resistance in primary human macrophages ²⁸. Prætorius-Ibba et.al. observed XPOT knockdown could lead to nuclear accumulation of tRNAs, mTOR activation and autophagosome formation under nutritional stress condition in human fibroblasts ¹⁸. However, the role and underlying mechanism of XPOT in breast cancer especially in TNBC had never been explored. In this study, we found that XPOT overexpression was related to worse survival in TNBC, and inhibition of XPOT could suppress TNBC cell proliferation through in vitro and in vivo experiments. These results suggested that XPOT may act as an oncogenic gene in TNBC. A previous bioinformatics analysis of public database partially supported our results, which reported that high XPOT expression could predict worse breast clinical outcome ²⁹. However, this article did not analyse the effect of XPOT in TNBC. The intrinsic mechanism by which XPOT can enhance malignant behaviours of TNBC demands further exploration. Our study revealed that XPOT knockdown could inhibit TNBC cell proliferation through inducing cytokinesis failure. Cytokinesis starts in the anaphase of the mitosis and its failure can lead to cell growth suppression and apoptosis ^{30 31}. Furthermore, our study revealed the underlying novel mechanism for XPOT promoting TNBC growth through up-regulating downstream cytokinesis gene-TTC19 translation. Additionally, XPOT could also influence the expression of tRNA-Ala-AGC-10-1 related genes (P4HA2, ENY2, ELOVL1 et.al.), which were all regarded as prognostic factors of poor outcome in TNBC ^32–34. All these findings indicate that XPOT may play crucial roles in the progression of TNBC through modulating different biological processes.

In numerous cellular events, translation regulation is an important method of gene expression regulation. Translation regulated pathways are complex and tRNA is the indispensable component for codon decoding during protein translation ^{35 36}. Pavon-Eternod et al. reported that tRNAs were selectively upregulated during breast cancer development, and differential tRNA expression orchestrated crucial oncogenic gene expression for tumour growth. Cancer-related proteins may have high codon usage frequency as their cognate tRNA amounts increase in the cytoplasmic pool ³⁷. Tavazoie et.al demonstrated that total tRNAGluUUC overexpression can enhance EXOSC2 translation to promote metastatic progression of breast cancer ⁵. Our study found a total of 15 tRNA-Ala-AGC isodecoders among 22 isodecoders of tRNA-Ala-AGC in MDA-MB-468 cells through tRNA high throughout sequence analysis, which was consistent with the existing findings that tRNA expression is preferential, tissue- and cell-specific ^{38 39}. As we know that tRNAs should be transported to the cytoplasmic pool from nuclear mainly by XPOT to exert their translational function. Our study to some extent further indicated that isodecoder tRNAs nucelo-cytoplasmic transportation through XPOT was also preferential and selective in TNBC cells. We found XPOT could preferentially affect tRNA-Ala-AGC-10-1, but not other tRNA-Ala-AGC isodecoders export to the cytoplasmic pool through tRNA high throughout sequence and further confirmed by qRT-PCR. In addition, our tRNA-seq results also discovered that XPOT could preferentially facilitate the nucleo-cytoplasmic transport of tRNA-Glu-TTC-1-1 and tRNA-Glu-TTC-2-1 but not other isodecoder of tRNA-Glu-TTC. Intriguingly, our study first disclosed that the nucleocytoplasmic distribution of different tRNA isodecoders is distinctive in our experimental cells after XPOT knockdown. It was reported that even TΨC loop or D loop alteration of the same tRNA could influence the interaction affinity with XPOT in Xenopus oocyte cells ¹⁷. As we know tRNAs have the same anticodon with different genome sequence which are termed as isodecoders ^{40 41}, and the sequence and spatial structure of each tRNA-Ala-AGC isodecoder are distinctive as shown in Figure S6, therefore theoretically different tRNA-Ala-AGC isodecoders maybe have different interaction affinity with XPOT during nucleo-cytoplasmic transportation, and our finding provided directly evidence to support this hypothesis.Therefore, these studies at least partial supported our novel finding that XPOT could preferentially influence specific tRNA isodecoder nuclear export. However, the exact underlying mechanism need in-depth investigation. Interestingly, it was reported that one of tRNA-Ala-AGC isodecoders is reported to be related to OS and RFS of breast cancer by bioinformatics analysis [3] and tRNA-Glu-TTC was also reported to function as oncogenic gene in breast cancer. Based on all these results, we speculate that XPOT might prefer to preferentially influence the export of oncogenic tRNA to the cytoplasm to execute corresponding biological functions.

XPOT possesses the ability to transport different tRNAs under different condition according to the requirement of cell growth or metabolism ⁴². In this study, we found that XPOT ensures TNBC cell cytokinesis completement possibly by regulating cytokinesis-gene TTC19 translation via influencing tRNA-Ala-AGC-10-1 nucleocytoplasmic trafficking. The anticodon-AGC can identify codon GCR (GCT, GCC, GCA) due to the feature of codon degeneracy, but the cognate codon of tRNA-Ala-AGC-10-1 is GCT. tRNA-Ala-AGC-10-1 preferentially identify mRNA enriching in GCT to translate. Through codon usage preference analysis (calculating all MDA-MB-468 expressed genes of which coding sequence is enriched in GCT) and protein mass spectrometry assay, we found out a series of genes both influenced by XPOT and tRNA-Ala-AGC-10-1. Interestingly, given the regulated role of XPOT in cytokinesis, we found out one cytokinesis-related gene-TTC19, and TTC19 abundance change is ranked top-2 among all genes regulated by both XPOT and tRNA-Ala-AGC-10-1. Our experiments demonstrated that XPOT neither influenced TTC19 transcription nor degradation. Meanwhile, the effect of XPOT on TTC19 expression was codon GCT dependent. Collectively, these results partly suggested that XPOT could affect TTC19 translation level depending on tRNA-Ala-AGC-10-1 nuclear transportation to the cytoplasmic pool. Our work might reveal a novel mechanism that XPOT affects the translation of specific gene translation by increasing the nucleocytoplasmic transportation of specific tRNAs to the cytoplasmic pool without influencing tRNA expression. Previous studies supported this point at least partially, which showed cancer-related proteins may have a high frequency of codon usage because of their increased number of homologous tRNAs in the cytoplasmic pool⁴². TTC19 is both related to the regulation of cytokinesis and function of mitochondrial complex III according to the annotation in “The Human Gene Database–GeneCards” (https://www.genecards.org/). There are papers reporting the role for TTC19 in regulation of the respiratory chain complex III ^{43 44}. However, the exactly role and underlying mechanism of TTC19 involved in cytokinesis remains to be elucidated. Our study for the first time demonstrated that TTC19 indeed is a cytokinesis gene regulated by XPOT through tRNA-Ala-AGC-10-1 and might act as an oncogenic function in TNBC.

In summary, our findings uncovered a new XPOT-tRNA-TTC19 pathway to regulate TNBC cytokinesis and development. The XPOT-dependent specific tRNA transportation modulation may provide a novel potential molecular treatment target for TNBC, and it also offers a new perspective on extensive physiological and pathological processes, and the XPOT as the regulator of cytokinesis through TTC19 may be a potential novel prognostic and therapeutic biomarker for TNBC.

Ethics approval and consent to participate

All patient samples were approved by the Ethical Review Committee of the World Health Organization (WHO) Collaborating Center for Research in Human Production (authorised by the Shanghai Municipal Government). All in vivo experiments were followed the Guide for the Care and Use of Laboratory Animals.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://cancergenome.nih.gov/).

Competing interests

The authors declare no potential competing interest.

Funding

This study was funded by National Natural Science Foundation of China (No. 82103695 and 82173115), Clinical Research Plan of Shanghai Hospital Development Center (No. SHDC2020CR3003A), Science and Technology Commission of Shanghai Municipality (No. 20DZ2201600), Shanghai Municipal Key Clinical Specialty, Shanghai ‘Rising Stars of Medical Talent’ Youth Development Program for Outstanding Youth Medical Talents (No. 2018-16), Shanghai Rising-Star Program (No. 22QC1400200), Multidisciplinary Cross Research Foundation of Shanghai Jiao Tong University (No. YG2019QNA28), Clinical Research Innovation Nurturing Fund of Renji Hospital and United Imaging (No. 2021RJLY-002), Nurturing Fund of Renji Hospital (No. PYIII20-09, PY2018-III-15 and RJPY-LX-002)

Author’s contributions

Conceptualization, L.J. Y.W. and Y.L; Methodology, D. H. and Y.X.; Formal Analysis, S.X., W.Y., and Z.S; Investigation, Z.X., J.S., and H.L.; Resources,Z.Z; Writing – Original Draft, D.H. and Y.X.; Writing – Review & Editing, L.J, Y.W. and Z. G.H.; Supervision, L.J.

Acknowledgements

We give sincere appreciation to Analysis and Testing Center, Shanghai Jiao Tong University for providing regents to perform protein mass spectrometry. This study was funded by National Natural Science Foundation of China (No. 82103695 and 82173115), Clinical Research Plan of Shanghai Hospital Development Center (No. SHDC2020CR3003A), Science and Technology Commission of Shanghai Municipality (No. 20DZ2201600), Shanghai Municipal Key Clinical Specialty, Shanghai ‘Rising Stars of Medical Talent’ Youth Development Program for Outstanding Youth Medical Talents (No. 2018-16), Shanghai Rising-Star Program (No. 22QC1400200), Multidisciplinary Cross Research Foundation of Shanghai Jiao Tong University (No. YG2019QNA28), Clinical Research Innovation Nurturing Fund of Renji Hospital and United Imaging (No. 2021RJLY-002), Nurturing Fund of Renji Hospital (No. PYIII20-09, PY2018-III-15 and RJPY-LX-002)

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No competing interests reported.

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Inhibiting XPOT suppresses TNBC growth via downregulating specific tRNA- TTC19 pathway mediating cytokinesis failure

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Abstract

Background

Methods

Results

Conclusions

Figures

Background

Methods

Human samples

Animal Experiments

Bioinformatic Analysis Of Karyopherins Expression In Different Cancer Types

Cell Lines Culture And Regents

Stable Knockdown Cell Line Construction

Cell Transfection

Stable Overexpression Of Ttc19 Cell Line Construction

Cell Proliferation Detection

Cell Cycle Analysis

Cell Apoptosis Assay

Rna Isolation And Quantitative Real Time-pcr (Qrt-pcr)

Human Trna Sequencing

Cytoplasmic And Nuclear Rna Purification And Trna Qrt-pcr

Codon Usage Preference Analysis

Rna Sequencing

Protein Mass Spectrometry

Construction Of Synonymous Codon Mutation Plasmids

Western Blotting

Immunofluorescent Staining

Live Cell Image

Rnascope

Bioinformatic Analysis

Graphic And Statistical Analyses

Results

High XPOT expression predicts shorter overall survival for TNBC patients

Xpot Knockdown Suppresses Tnbc Growth Through Impairing Cytokinesis And Increasing Resistance To Apoptosis

Xpot Preferentially Mediates Trna-ala-agc-10-1 Nuclear Export In Tnbc Cells

The Impact Of Xpot On Ttc19 Translation Is Trna-ala-agc-10-1 Dependent

Ttc19 Is Essential For Tnbc Cells Proliferation And Cytokinesis Completion

Ttc19 Expression Is Indispensable For Xpot Regulating Tnbc Growth

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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