Omics studies of nuclear protein aggregates in subcellular fractions reveals co- aggregation of RNA-binding proteins affecting cytosolic pathways

Omics studies of nuclear protein aggregates in subcellular fractions reveals co- aggregation of RNA-binding proteins affecting cytosolic pathways | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Omics studies of nuclear protein aggregates in subcellular fractions reveals co- aggregation of RNA-binding proteins affecting cytosolic pathways Milad Shademan, Sarah Flannery, Erik Bos, Tom Evers, Vahid Sheikhhassani, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5783239/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Disease-associated RNA binding protein (RBP) aggregation is a hallmark of several age-related neurodegenerative diseases. How insoluble RBP aggregates leads to cellular dysfunction is poorly understood. Here, we investigated the molecular mechanisms affected by insoluble PABPN1 aggregates. PABPN1 aggregates are nuclear, but PABPN1 regulates nuclear export of mRNA. To explore the cellular consequences of PABPN1 nuclear aggregates, we performed RNA sequencing and proteomic studies in subcellular fractions in an inducible human muscle cell model. RNA sequencing analyses revealed PABPN1 dysfunction in this cell model associated with reduced endogenous PABPN1 levels. Proteomic analyses revealed that most of the changes driven by PABPN1 nuclear aggregates were in the cytoplasmic fraction, accounting for reduced cell metabolism, muscle cell differentiation and muscle cell biomechanics. Changes in the insoluble fraction were small but enriched for RBPs. We show that sequestration of mRNA in nuclear aggregates is associated with impaired nuclear export of mRNA and reduced translational efficiency. Our study suggests that RBPs nuclear protein aggregates are regulated by both gain-of-function and loss-of-function mechanisms, which is relevant for the development of therapeutics for age-associated protein aggregation diseases. Biological sciences/Genetics Biological sciences/Neuroscience Health sciences/Diseases Health sciences/Pathogenesis Protein aggregates Pathological Protein PABPN1 RNA metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION RNA-binding proteins (RBPs) play an indispensable role in RNA-dependent cellular processes that shape the cell proteome 1 – 3 . RNA metabolic processes, from synthesis to translation in both nuclear and cytosolic compartments, are regulated by versatile ribonucleoprotein complexes 4 , 5 . Dysfunctions of RBPs are associated with many human diseases, with a particularly high prevalence in age-associated neurological and neuromuscular disorders (NMDs). Tissue-specific limited expression levels have also been implicated in the aging of neuromuscular tissues 6 , 7 . Heritable mutations in RBPs cause neuromuscular degenerative diseases 8 , 9 , some of which involve aggregation-prone or modifiers of protein aggregates 10 , 11 . Cytosolic protein aggregates are formed by RPBs such as FUS or HNRNPA1 , which are associated with several NMDs, including amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) 12 – 14 . In addition, RBP aggregates can be nuclear, such as those involving PABPN1 , which causes oculopharyngeal muscular dystrophy (OPMD) 15 . OPMD is a rare (prevalence 1:100,000) autosomal dominant disorder characterized by progressive muscle weakness from midlife onwards 16 . Normal PABPN1 contains an alanine stretch (10 alanine residues) at the N-terminus of the protein, whereas a short alanine expansion (+ 1 to + 8) causes OPMD. The expanded PABPN1 forms nuclear aggregates, which are associated with the disease 17 , though not with disease severity 18 . Although PABPN1 is ubiquitously expressed, OPMD symptoms are primarily restricted to skeletal muscle, suggesting muscle-specific disease mechanisms. The factors involved in PABPN1 aggregation, and the modulation of muscle cell dysfunction remain largely unknown. PABPN1 plays a key role in the processing of mRNAs, including the regulation of poly(A) tail length and alternative polyadenylation (APA) 19 . In disease models with reduced PABPN1 expression levels or those with PABPN1 aggregates, genome-wide APA 20 . PAPBN1 predominantly affects APA at the 3'-UTR of transcripts, with a strong preference for a distal-to-proximal shift that results in shorter and more stable transcripts 21 . In addition, PABPN1 is involved in the nuclear export of mRNAs 22 , 23 . Although PABPN1 functions predominantly in the nucleus, like other RBPs, it shuttles to the cytoplasm. However, PABPN1’s role in the cytoplasm is insufficiently studied, and how the nuclear aggregates cause cell dysfunction is poorly understood. In this study, we investigated the effect of expanded PABPN1 insoluble nuclear aggregates on muscle cell function using an inducible cell model that allowed us to overcome the cytotoxic effect typically associated with PABPN1 aggregates. We analyzed the cell proteome and transcriptome in nuclear, cytoplasmic, and insoluble subcellular fractions and validated the protein networks affected by PABPN1 aggregates. Our study provides insights into the molecular mechanisms by which nuclear aggregates lead to cell dysfunction. MATERIALS AND METHODS Human muscle biopsy PABPN1 constructs and lentivirus production The expanded PABPN1 (Ala16) transgene was cloned into the pCW57-MCS1-2A-MCS2 doxycycline (Dox) inducible lentiviral vector (Addgene plasmid #71782). After the first cloning, the FLAG tag was also fused to the C-terminus of the PABPN1 sequence. Clonings were confirmed by Sanger sequencing. Lentivirus production was performed as detailed in (Carlotti, Bazuine et al. 2004). Cell culture Cells were cultured in growth medium (F10 (Gibco) medium supplemented with 15% FCS, 1 ng/ml bFGF, 10 ng/ml EGF and 0.4 µg/ml Dexamethasone). Cells were propagated in confluence 50–80%. Cell cultures did not reach 100% confluence to avoid spontaneous differentiation. Muscle cell differentiation was done at high confluency (85–95%) in DMEM + 2% horse serum for 3–5 days. The 2417 immortal human muscle cells were transduced with lentiviruses encoding Ala16, and stable cell cultures were created using puromycin selection. The Ala16 transgene was induced with 4 µg/mL doxycycline hydrochloride (D5207, Sigma Aldrich), and DMSO was used for (uninduced) vehicle-treated cells. For high content screening (HCS), cells were seeded in a Nunc 96 well plate; for live cell confocal microscopy, cells were seeded in a µ-Slide 8 Well high ibiTreat (80806, IBIDI) slide. For electron microscopy, cells were seeded in µ-Dish 35 mm, high Grid-500 ibiTreat (81166, IBIDI) dishes. Subcellular fractionation Cell pellets were incubated on ice for 30 minutes with the cytosolic lysis buffer (150mM NaCl, 50mM HEPES (pH 8), 1 mM DTT, and protease inhibitor cocktail (Roche)). The cytosolic supernatant is collected after centrifugation at 3500g, 4°C, for 10 minutes. The remaining pellet was washed with excess PBS and, after additional centrifugation, solubilized in a nuclear lysis buffer (150mM NaCl, 50mM HEPES (pH 8), 0.5% w:v Sodium deoxycholate, 0.1% Triton, 1 mM DTT, and protease inhibitor cocktail). After 15 minutes of incubation on ice, the nuclear supernatant fraction was collected after centrifugation at 5000g, 4°C for 10 minutes. The remaining pellet contained the insoluble fraction and was solubilized in PureLink for RNA extraction or in 2% SDS for protein extraction. Protein concentration from cytosolic and nuclear fractions was determined using the BioRad protein assay (BioRad). Protein extraction and western blot analysis Protein extraction and western blot analysis Bulk protein extraction of soluble and insoluble fractions of a cell pellet was collected from a 12 well o using a lysis buffer (20 mM Tris, pH 7.4, 150 mM NaCl, 5 mM EDTA, 0.1% NP40, and 1 mM DTT and 1x protease inhibitor cocktail). After sonication and centrifugation (1 min, 13000g, at 4°C), the supernatant containing the soluble proteins was transferred to a new tube, and the pellet, containing the insoluble proteins, was washed once in PBS, dissolved in loading buffer, sonicated and spin down before heat inactivation. Insoluble proteins were extracted from the pellet with lysis buffer + 2% SDS. The protein amount was determined in the soluble fraction and the proportional aliquots from the paired insoluble fraction for equal loading on SDS-PAGE. Protein aliquots were separated on 10% SDS-PAGE. Western blotting was carried out with a PVDF membrane. Bulk proteins were visualized with the No-Stain Protein Labeling Reagent (#A44717, ThermoFisher) and imaged using the iBright Imaging System (ThermoFisher). The membrane was blocked with 5% dried milk powder (T145.2, Carl Roth). Primary antibody incubation was carried out at 4 degrees overnight, and secondary antibody incubation at room temperature for one hour. Antibodies are listed in Table S1 . An Odyssey CLx Infrared imaging system (LiCOR, NE. USA) was used to detect the fluorescent signal. Quantification of protein abundance was done using ImageJ. Values were corrected for background and normalized to loading controls. Western blot quantification was carried out with ImageJ. Normalization was made for both the No-Stain and housekeeping signal. Full western blot images are provided in Figure S1 . Mass spectrometry and data analysis Sample aliquots containing 50 µg protein were incubated with 250 Units of Benzonase® Nuclease (Sigma-Aldrich) for 10 minutes at room temperature, then solubilized in 5% SDS. Cysteine residue reduction and alkylation were performed using 10 mM tris(2-carboxyethyl) phosphine and 50 mM iodoacetamide at room temperature for 30 min. Subsequent tryptic digestion was performed by S-trap micro (ProtiFi) according to the manufacturer’s instructions. After elution, peptides were dried by vacuum centrifugation and stored at -20°C for MS analysis. Peptide samples were reconstituted in 3% acetonitrile 0.1% formic acid before MS analysis, then 200 ng were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) using a Dionex Ultimate 3000 UPLC coupled to a Q-Exactive mass spectrometer (Thermo Fisher Scientific). Peptides were trapped on an Acclaim™ PepMap™ 100 C18 HPLC Column (PepMapC18; 300 µm x 5 mm, 5 µm particle size, Thermo Fischer) using solvent A (0.1% Formic Acid in water) at a pressure of 60 bar, and separated on an Easy Spray PepMap RSLC column (75 µm i.d. x 2 µm x 50 mm, 100 Å, Thermo Fisher) at 250 nL/min over a 60 min gradient from 5–35% acetonitrile in 5% DMSO, 0.1% formic acid. MS data was acquired in data-independent acquisition (DIA) mode, with full scan MS spectra acquired in the Orbitrap, from 20 m/z windows over a scan range of 495 to 995 m/z, with an overlap of +/- 2 Daltons, resolution 35000, AGC target 3e6, maximum injection time 55 ms and fragmentation at 28% normalized collision energy. MS/MS spectra were also acquired with a resolution of 17500, AGC target 1e6. Raw MS data were searched (UniProtKB reviewed proteome database UP000005640) using DIA-NN v1.8 in library-free mode with automatic mass accuracy optimization, cross-run normalization enabled, 1 missed cleavage permitted, fixed cysteine carbamidomethylation, and methionine oxidation as a variable modification. DIA-NN output data, containing 6604 annotated proteins, were log2 transformed. For fractionation validation, protein abundance was normalized by centering the median per fraction (cytosolic soluble (C), nuclear soluble (N), and insoluble fraction (Ins) from Dox-induced and vehicle-treated cell cultures with three biological replicates per fraction per genotype). Fractionation efficiency (C vs. N or N vs. Ins.) was assessed by paired differential analysis by two-sample t-test in Perseus Software v2.0.7.0. Ala16 PABPN1 effect per fraction was tested with Dox-induced vs. vehicle-treated samples, with the following inclusion criteria: 1. minimum read > = 1 per sample, 2. minimum read > = 67% (2/3) per group (N = 3). Protein abundance was normalized by median centering per fraction. Differential analysis was performed by two-sample t-test in Perseus Software v2.0.7.0. The expression profiles between fractions were determined by hierarchical clustering with Euclidean distance using Z-scores in Perseus Software v2.0.7.0. Proteomic differential analysis is found in Figure S3 . The correlation between PABPN1 levels and other protein levels was calculated with Pearson correlation in GraphPad Prism 9.3.1. RNA extraction, library preparation, RNA sequencing, and analysis RNA was extracted from cytosolic, nuclear, and insoluble fractions using the PureLink™ RNA Mini Kit (Invitrogen™ 12183018A), according to the manufacturer protocol, continue with on-column DNase treatment PureLink™ DNase Set (Invitrogen™ 12185010). RNAs were stored at -80. RNA integrity was quantified using Qubit and checked on an RNA 6000 Nano Agilent Lab-on-a-Chip kit with Bioanalyzer Systems before cDNA library preparation. The 1C library preparation protocol and RNA sequencing were made as detailed in 24 , using. All reads in FASTQ format were first filtered using Cutadapt (v2.10), removing all remaining adapter sequences. MultiQC program in Python was used for quality control (QC) assessment of FASTQ files. The remaining reads were aligned to the Ensembl transcriptome version 104 using STAR (v2.7.5a), including UMI-based deduplication using UMI-Tools (v1.1.1), generating a transcriptome-based alignment in BAM format. We used the same Ensembl transcript annotation version 104 to create a customized transcript annotation GTF file for all Ensembl annotated transcripts. With the annotation and human transcriptome-based alignment files as input, we quantified the reads at the coding regions using featureCounts (v2.0.1). All analyses were performed using RStudio Software RStudio 2022.02.3 (Build 492) using R Statistical Software (v4.2.3). The APA-shift calculation was made as described in 24 on raw reads counts after exclusion criteria. The ratio between proximal to distal was calculated in R (version 4.3.1) using the equation [log(1 + Proximal) - log(1 + Distal)], and the APA-shift was calculated between induced and uninduced conditions. The APA-shift is in log2: APA-shift > 0 indicates a shift to proximal, and < 0 suggests a shift to distal. APA-shift significance per fraction was calculated with Student’s t -test, corrected for FDR, using two procedures: 1. The APA-shift calculation was done per fraction on raw data N = 3 per fraction and per genotype (exclusion criteria: >6 reads/transcripts). 2a. the APA-shift calculation was made on cytosolic and nuclear fractions N = 6 per genotype (exclusion criteria: >12 reads/transcripts). 2b. the significant transcripts from 2a were sorted per fraction (cytosolic or nuclear) and p-values were recalculated per fractions. Transcript differential expression analysis was made in edgeR Bioconductor package (v3.42.4) 25 . Transcript list excluded read count = < 1 count per sample. TMM normalized read counts were log-transformed. Main variations between samples were assessed unsupervised with the principal component analysis (PCA). Differential expression analysis was calculated with the Empirical Bayes in edgeR with the decideTest and p -value < 0.05 corrected for false discovery rate (FDR). Differential expression analysis was made between fractions (N = 6 per fraction) or between Dox-induced and vehicle-treated cells (N = 3 per fraction). Volcano plot visualization of differential expression was made in https://ggvolcanor.erc.monash.edu/ 26 . Expression profiles across fractions was determined in Perseus Software v2.0.7.0. Hierarchical clustering of Z-scores was made for the 877 overlapping DE transcripts between cytosolic and nuclear fractions using Euclidean distance. RT-qPCR RT-qPCR was conducted on RNA extracted from the Dox-induced and vehicle-treated cells. 500 ng RNA was reverse transcribed for cDNA synthesis using the QuantiTect Reverse Transcription Kit (QIAGEN) and random primers, following the manufacturer’s instructions. Subsequently, qPCR amplification was performed with the QuantiNova SYBR Green kit (QIAGEN) using 5 ng RNA, with technical duplicates, using a standard amplification protocol at a melting temperature of 60°C. Samples with CT values above 35 were excluded from the analysis to eliminate potential noise. The average CT values from the technical duplicates and normalization to the HPRT1 gene were used for ddCT calculation. Two primer sets were used: a primer set to exon 3–4 PABPN1 (ENSG00000100836), amplifies both the Ala16-PABPN1 transgene and the endogenous PABPN1, and the second primer set to the 3’-UTR amplifies only the endogenous transcript. Primer sets were designed with the NCBI Primer design tool ( https://www.ncbi.nlm.nih.gov/tools/primer-blast ), and the primers are listed in Table S2 . Immunofluorescence and staining Cell fixation was performed with 4% Formaldehyde in PBS for 5 minutes. KCl treatment was before to cell fixation was with 1M KCl for 15 minutes, followed by fixation with 4% Formaldehyde for 5 minutes. Subsequently, permeabilization was performed with 1% Triton-X100 for 10 minutes, followed by PBS washing and first antibody incubation for one hour at room temperature, followed by 30 minutes incubation with a fluorophore-conjugated secondary antibody and DAPI. Antibodies are listed in Table S1 . Antibody incubation and washing steps were made with PBS + 0.05%-Triton-X. Cells were left in PBS during imaging. Cellular assays Cellular assays were conducted in vehicle-treated or Dox-induced cell cultures for 4 days. Oligo-dT hybridization : cell cultures were fixed using 3.7% FA for 15 minutes at RT. After two PBS washes, the cells were incubated in protease III and diluted 1:30 in PBS (#322337 Advanced Cell Diagnostics) for 15 minutes at RT. After twice PBS washes cells were incubated in a hybridization buffer (#10369 Cepham Life Sciences) for 15 minutes at RT. Incubation with 5’-Cy5-Oligo-dT12-18 probe (#26-4400-02 Gene Link), diluted 1:1000 in hybridization buffer, was carried out overnight at 40 degrees in a humidified chamber. The following day, washes were carried out at 40 degrees for 5 minutes with 4x, 2x, and 1x SSC buffer and with PBS. Finally, the cells were incubated with Hoechst and kept in PBS during imaging. RNAscope : Spatial localization of a single RNA molecule was carried out in adherent muscle cells using the RNAscope Fluorescent Multiplex Assays kit according to the manufacturer’s protocol (ACD biotech) with the following modifications: the protease solution was diluted 1:30, and the amplifier solutions were diluted 1:2. Ten genes were included in the RNAscope: The positive control probe mix, provided by the company (POLR2A, UBC, and PPIB). The second probe mix included PABPN1 and MYF5. The probes for POLR2A, UBC, PPIB, and MYF5 are from the Human probe set (ACD biotech). The PABPN1 probe is from the mouse set, and the homology between human and mouse is > 95% for the probe regions. All probes were previously demonstrated in human muscle cells 27 . LMB treatment : cells were treated with 37.5 nM LMB (LKT Labs, St. Paul, USA) for 3 hours at 37°C or DMSO (dilution 1:1000) Mock control. Protein synthesis assay the protein synthesis assay kit (Cayman Chemicals #601100) was conducted according to the manufacturer protocol using azido-O-propargyl-puromycin (OPP)-488 (named here OPP). A 30-minute pre-incubation with 20µM cycloheximide was used as a negative control. Hoechst was added after fixation. OPP was imaged with a 488 filter. Mitochondrial activity JC-1 staining was carried out in living cells with JC-1 5µM (final concentration), and Hoechst 1mM final concentration (33342 ThermoFisher) added to the growth medium and incubated for 30 minutes at 37°C. Cells were washed once with PBS and kept in a growth medium during imaging. JC-1 was imaged with 488 and 560nm filters. Glucose uptake assay was conducted in cell cultures treated with Dox and incubated in a differentiation medium for four days. According to the manufacturer protocol, the glucose update was determined with the Glucose Uptake-Glo™ (Promega # J1341). Fluorescence was measured with the SpectraMax iD3 multi-mode microplate reader (Molecular Devices) recorded at 1-second integration. Imaging and image quantification The CellInsight CX7 LZR high-content screening (HCS) platform was used for high-content imaging. The accompanying HCS Platform spot detector and co-localization toolbox (ThermoFisher Scientific) performed a cell-based analysis. Imaging was used to calculate the differentiation index, which was done with a 10x objective covering over 12,000 nuclei per well. Using the co-localization toolbox, the differentiation index was quantified by the percentage of myonuclei without MyHC objects. Imaging for PABPN1 quantification, oligo-dT, OPP, and JC1 was made with a 20x objective, covering at least 5000 nuclei per well. The spot detection toolbox was employed. JC-1, OPP, and RNAscope were analysed from the perinuclear region. Oligo-dT and PABPN1 signals were measured from both nuclear areas. Confocal microscopy imaging: Fixed single nuclei were imaged with a Leica DMi8 with the Andor Dragonfly spinning disc module using a 40x/1.3 or 63x/1.3 oil immersion objective. Identical imaging settings, including exposure time, laser power, the excitation-emission range, and Z-stacks step size, were employed within an experiment. Quantifications of confocal images were carried out in ImageJ. Electron microscopy The embedded OPMD muscle biopsy was reported in 28 . Differentiated cell cultures were fixed in 1.5% glutaraldehyde in 0.1 M Sodium Cacodylate buffer for 2 hours, and successively incubated in 1% Osmium Tetroxide in 0.1 M cacodylate buffer for 1 hour and in 1% Uranyl Acetate in water for 1 hour. The cells were then dehydrated through a series of incubations in Ethanol (70–100%) for 90 minutes and embedded in Epon. The flat embedded cells were sectioned with an ultramicrotome (UC6, Leica, Vienna) using a 35-degree diamond knife (Diatome, Biel, Switzerland) at a nominal section thickness of 90 nm. The sections were transferred to a formvar, and a carbon-coated 1 ×2 mm copper slot grid and stained for 20 minutes with 7% uranyl acetate in water for 10 minutes with lead citrate. EM images were recorded using a Tecnai 12 electron microscope (Thermo Fisher Scientific) with an EAGLE 4k×4k digital camera. Single-cell biomechanics Cell membrane force experiments were performed using a CellHesion 200 instrument (JPK, Berlin, Germany) equipped with a Petri dish heater to maintain a temperature of 37°C. Cantilevers with a cylindrical tip having a 5-micron end radius and nominal spring constants ranging from 0.166–0.179 N m-1 were used (Bruker, SAA-SPH-5UM). The spring constant calibration was performed using the thermal noise method 29 . Cells were approached at a loading rate of 5 µm s − 1 with a maximum force set-point of 0.473 nN. Each cell was indented three times, and 10 cells were probed per condition. Between cell indentations, the substrate was probed to ensure the tip’s cleanliness. Cells were indented above the nuclear region to reduce variability and substrate artifacts. All experiments were analysed using JPK Data Processing to determine the Young’s Modulus of the cells. The Hertz model was used for calculating Young’s modulus, as described in 30 , 31 . This model is valid for small indentation depths and is expressed as follows for a spherical indenter: where F is the indentation force, R is the radius of the indenter, E is Young’s modulus, ν is Poisson’s ratio and d is the indentation depth. Statistical analyses Statistical analyses were conducted in R 4.2.3 and GraphPad Prism 9.3.1. The statistical significance of the APA-shift was assessed by applying the Benjamini-Hochberg method to adjust the p -values for controlling the FDR. Enrichment analysis for the differential abundance proteins in each cluster and DE transcripts in cytosol fraction was conducted within DAVID (v2023q3, https://david.ncifcrf.gov ). The gene network databases Reactome and Gene Ontology were selected for enrichment analysis. Fifty proteins were enriched in the Metabolism of RNA based on the Reactome database and were used for the Gene-Disease enrichment analysis at https://maayanlab.cloud/Enrichr . Reads at the 3′-UTR were visualized using IGV version 2.16.0. Candidates with 3′-UTR lengths above 1 kb were selected for visualization. Linear regression analysis for transcriptome-proteome correlation was made in https://www.statskingdom.com . Ethics Muscle biopsy collection has been approved by the Radboud Medical center ethics committee in accordance with the ethical standards laid down by the 1964 Declaration of Helsinki. The patients signed on informed consent prior to biopsy collection. RESULTS The A16 muscle cell model represents PABPN1 aggregation in OPMD To explore the molecular mechanisms driven by PABPN1 nuclear aggregates, we generated stable cells expressing the Ala16-PABPN1 cDNA under the tetracycline-inducible promoter (here designated A16). Inducible expression overcame the toxic effect of A16-PABPN1 by constitutive expression 32 {Fan, 2001 # 8 }. To address the involvement of skeletal muscle in OPMD, we used human skeletal muscle cells in this study. Western blot confirmed the expression of Ala16-PABPN1 in doxycycline (Dox)-induced muscle cell culture (Fig. 1 A). The accumulation of soluble PABPN1 after Dox induction was 2-fold higher than in vehicle-treated cells (Fig. 1 B). The overexpression level in our muscle cells model is about 10-fold lower than those reported in the OPMD mouse model, A17.1 33,34 . We then confirmed the accumulation of insoluble PABPN1 using fractionation of the soluble and insoluble fractions. Levels of PABPN1 in the insoluble fraction in Dox-induced cells was 6-fold higher than in vehicle-treated cells (Fig. 1 B). The level of insoluble PABPN1 was 3-fold higher than that of the soluble protein (Fig. 1 C). Taken together, overexpression after Dox induction leads to a marked accumulation of PABPN1 in the insoluble fraction. We refer to the Dox-induced cell culture as A16. Immunofluorescence in Dox-treated cells confirmed the presence of nuclear-insoluble Ala16 using KCl treatment, which eliminated most PABPN1 puncta in nuclear speckles (Fig. 1 D). Total PABPN1 fluorescence intensity showed a 1.5- to 2-fold increase in A16 cells compared to vehicle-treated cells (Fig. 1 E, MFI > 0), consistent with Western blot results. PABPN1 intensity in puncta (MFI > 400) was found only in A16 cells (Fig. 1 E). Fluorescence intensity in puncta was unchanged after KCl treatment (Fig. 1 E), indicating that PABPN1 puncta represents the insoluble protein. To assess whether aggregates in the inducible cell model represent the disease, we examined the structure of nuclear aggregates by electron microscopy. We found electron-lucent areas in the A16 myonuclei (Fig. 1 F), as in the OPMD muscle biopsy (Figure S2 ). The electron-lucent area contained short fibrils in the muscle cell model (Fig. 1 G) and longer fibrils in the muscle biopsy (Figure S2 ). This difference is expected considering aggregates develop over the years in OPMD patients, but only after five days in the muscle model. Intranuclear inclusions containing p62 have recently been proposed as a histopathological marker for OPMD 35 . To further assess whether our cell model represents the in vivo condition in OPMD, we performed immunohistochemistry in A16-PABPN1. In cells treated with Dox for 7 days, we found nuclear inclusion of p62 and colocalization with A16-PABPN1 (Fig. 1 H). In vehicle-treated cell cultures and to a lesser extent in cell cultures treated with Dox for 5 days, p62 nuclear export was not detected (Fig. 1 H). This suggests that p62 inclusion in OPMD is associated with PABPN1 nuclear aggregates as a secondary event. The A16 cell culture seems to be a suitable model for studying the molecular mechanisms driven by PABPN1 nuclear aggregates in OPMD. Bulk mRNA and protein profiles are distinguished between subcellular fractions To investigate the impact of PABPN1 aggregates on cellular and molecular processes, we isolated proteins and RNA from the cytosolic, nuclear, and remnants insoluble fractions, and confirmed the fractionation using a Western blot (Figure S3 A). Samples from vehicle-treated or A16 cell cultures were subjected to mass spectrometry (MS) and mRNA sequencing per fraction. We developed a pipeline to compare differences between fractions and vehicle-treated or A16 samples (Figure S3 C). As expected, the cytosolic fraction from both vehicle-treated or A16 samples contained the highest levels of both proteins and mRNA, and the lowest levels were found in the insoluble fraction (Fig. 3 A, Figure S4 A, and S4B). The size of the cytosolic proteome was 10% larger than the nuclear proteome and 33% larger than the insoluble proteome (Figure S4 A). Larger differences were found for mRNA: the cytosolic library size was nearly 10-fold larger than the nuclear fraction and 100-fold larger than the insoluble fraction (Figure S4 B). Since the same amount of total RNA was used for library preparation, this observation suggests that only 1% of the total RNA in the insoluble fraction is mRNA. We then assessed fractionation using the principal component analysis, surprisingly, the variation between fractions exceeded variation between genotypes (Figure S4 C). Moreover, differential analysis of the proteome revealed a clear distinction between the fractions: 94% of the proteins were significantly localized to nuclear or cytosolic fractions, and 71% of the proteins to nuclear or insoluble fractions (Fig. 2 B). To further assessed fractionation we examined subcellular localization of autophagy proteins (ATG), which were highly enriched in the cytosolic fraction, while centromere proteins (CENP) were nuclear, and histone proteins (H2) accumulated in the insoluble fraction (Fig. 2 B). Correlation analysis between fractions showed a higher correlation between nuclear and insoluble fraction proteins than between the cytosolic fraction (Fig. 2 C), indicating that most insoluble proteins are nuclear. In contrast to proteomic analysis, differential analysis of mRNAs showed that only 58% of mRNAs were differentially accumulated in cytosolic or nuclear fractions, and nearly all mRNAs in the insoluble fraction were also nuclear (Fig. 3 D). The correlation of mRNA between the cytosolic and nuclear fractions was higher than with the insoluble fraction (Fig. 2 E), supporting shared material between fractions and nuclear export. Taken together, the proteomic analysis between fractions confirms the fractionation. The differences between fractions dictated normalization for downstream analysis. Protein samples were normalized per fraction (Figure S3 C). For mRNA, nuclear and cytosolic mRNA samples were normalized together (N = 12 samples), but insoluble samples were normalized separately (N = 6) (Figure S3 C). The A16 proteome is enriched by regulators of RNA metabolism of neuromuscular disorders We examined the A16-PABPN1 effect on the proteome, analysis was carried out per fraction, and significance was considered by p < 0.05 corrected for multiple comparisons (Fig. 3 A; Table S3 ). Higher PABPN1 levels in all fractions and unchanged GAPDH levels in the cytosolic fraction were consistent with the Western blot (Fig. 3 A and S2A). PABPN1 fold-change in the nuclear and insoluble fractions was higher than in the cytosolic fraction (12X, 10X, and 5.7X, respectively, Fig. 3 B). Notably, PABPN1 fold-change was highest in the nuclear and insoluble fractions, whereas the most prominent changes in protein abundance were in the cytoplasmic fraction. The abundance of 13.5% of proteins was affected in the cytosolic fraction, but only 5.9% and 1.3% in the nuclear and insoluble fractions, respectively (Figure S5 A). We then explored a pattern across fractions of the A16 proteome (N = 1087 proteins) using Euclidean clustering. Two major clusters were identified (Fig. 3 B and S5B). Cluster-1, with 435 proteins, showed an increase in abundance across all three fractions, similar to PABPN1, whereas cluster-2 (408 proteins) showed an opposite pattern (Fig. 3 C). Proteins in cluster-2 were predominantly enriched for metabolic processes: nucleic acid biosynthesis, mitochondria and glucose metabolism, and pathways regulating cell structure: focal adhesion, cytoskeleton, and stress fiber (Fig. 3 D and Table S4 ). In contrast, cluster-1 proteins were enriched only for processes related to RNA binding proteins (Fig. 3 D). Further a gene-disease association analysis was performed for cluster-1 and six neuromuscular diseases were significantly enriched: OPMD, spinal muscular atrophy (SMA), myotonic dystrophy type 1 (DM-1), granulin-related frontotemporal lobar degeneration (GR-FTLD), inclusion body myositis (IBM), multiple sclerosis (MS), and amyotrophic lateral sclerosis (ALS) (Fig. 3 E). Four diseases are categorized as protein aggregation: OPMD, DM-1, GR-FTLD, IBM, and ALS. Among the disease-associated proteins, a strong positive correlation was found between PABPN1, SRSF1, SRSM2, HNRNPR, and HNRNPA1, but SRSM1, PABPC1, and TNPO1 showed a weak or negative correlation (Fig. 3 F). The expression pattern across fractions confirmed the clustering and showed that PABPN1, HNRNPR, SRSF1, and PABPC1 were enriched in the A16 insoluble fraction (Fig. 3 G). This suggests that common RNA-binding proteins are involved in protein aggregation diseases and specifically accumulate in insoluble aggregates. PABPN1 aggregation leads to reduced PABPN1 mRNA and APA impacting mRNA subcellular localization Aggregation of the expanded PABPN1 is associated with reduced PABPN1 transcripts 24 . Using two sets of primers: one set to the exon region amplifies both the Ala16-PABPN1 transgene and endogenous PABPN1, and the second set to the 3' UTR amplifies only the endogenous transcript (Fig. 4 A), we found reduced endogenous PABPN1 in A16 cells (Fig. 4 B). This further indicates that our cell model is consistent with OPMD and suggests that Ala16 PABPN1 overexpression leads to reduced PABPN1 expression. We then investigated whether the molecular function of PABPN1 is affected by PABPN1 aggregation in this cell model. We calculated the APA shift in the cytosolic and nuclear fractions using two methods. The insoluble fraction was not included in the APA-shift calculation due to the low number of transcripts. The first APA-shift calculation was performed per fraction, resulting in 1151 transcripts with APA-shift in the cytosolic fraction but none in the nuclear fraction (Table S5 ). To verify the p-value, we took a two-step approach: first, we analyzed the cytosolic and nuclear fractions together, resulting in 2793 transcripts with significant APA-shift, among which a shift to proximal was prominent (Fig. 4 C, Table S5 ). A prominent shift to proximal is consistent with results in the A17.1 mouse model 21 , 24 . Subsequently, the significant transcripts with APA-shift (N = 2793) were reanalyzed for APA-shift per fraction. Most of the transcripts were cytosolic (96%), but 990 transcripts showed a significant APA-shift in the nuclear fraction (Fig. 4 D). The identification of transcripts with significant APA shift in the nuclear fraction contrasted with the analysis per fraction of the raw data. We suspected that this difference in results was due to p-value calculation over > 51,000 transcripts compared to 2793 transcripts. To assess the validity of the p-value calculation, we visualized the read counts at the 3' UTR. The PMP22-202 transcript had APA-shift = 4.2 (p-value, FDR 0.015) in the cytosolic fraction but was insignificant in the nuclear fraction. The HSPA4-201 transcript had APA-shift = 1.92 (p-value, FDR 0.045) in the nuclear fraction, but was not significant in the cytosolic fraction. The CLSTN1-201 transcripts had a significant APA shift in both the cytosolic and nuclear fractions: APA-shift = 2.76 (p-value, FDR 0.029) and APA-shift = 2.35 (p-value, FDR 0.042), respectively. This indicates that the p-value calculation made by the two-step approach is correct. Among the 990 nuclear transcripts with APA-shift, the majority (89%) were also cytosolic (Fig. 4 D), suggesting nuclear export and cytosolic accumulation of transcripts with short 3'-UTR. Previous studies have shown that short 3’-UTRs are less efficiently degraded and therefore more stable 36 . Taken together, in our cell model PABPN1 molecular function is impaired and is associated with aggregation and reduced PABPN1 expression levels. The Ala16-PABPN1 showed the most dramatic effect on mRNA levels in the cytosolic fraction: 25% of transcripts (N = 11572) were dysregulated, whereas in the nuclear fraction, only 2.5% (N = 1108) were affected (Fig. 5 A; Table S6). In the insoluble fraction, dysregulation did not pass the p < 0.05, FDR criteria. Considering the unadjusted p-value, 187 transcripts were found, including the PABPN1 isoform (Fig. 5 A). Upregulated transcripts in the insoluble fraction (96%) were exclusively enriched for RNA pathway metabolism (p = 0.027, Bonferroni corrected) and included RBP transcripts associated with protein aggregation such as PABPN1, HNRNPH1, RBM5, and DDX5 (Fig. 5 A). The cDNA library preparation with one amplification cycle representing RNA dynamics in situ 24 , allowed us to study the effect of Ala16-PABPN1 on mRNA dynamics between fractions. The library size of the insoluble fraction was too small for comparative analysis between fractions and was not included. Nuclear export is a highly dynamic process, and under normal conditions (A16 unaffected) only 1% of the nuclear transcripts are localized to the nucleus, with the remaining 99% overlapping with cytosolic transcripts (Fig. 5 B). However, 21% of the A16 dysregulated nuclear transcripts do not overlap with the A16 dysregulated transcripts (Fig. 5 B), suggesting aberrant nuclear export of the dysregulated transcripts. Furthermore, 24% of the cytoplasmic transcripts did not overlap with the nuclear fraction under normal conditions, but 92% of the A16-dysregulated transcripts specifically accumulated in the cytosol (Fig. 5 B). The accumulation of transcripts in the cytoplasmic fraction suggests impaired RNA decay, which is consistent with the enrichment of the RNA decay protein network in the A16 proteome. Of the dysregulated cytosolic transcripts, 42% showed an APA-shift, with a shift to the proximal region (Fig. 5 C). In contrast, only 8% of the nuclear-dysregulated transcripts showed an APA shift (Fig. 5 C). The number of upregulated transcripts with APA shift was significantly higher (3-fold) than the downregulated transcripts (Fig. 5 C), which is consistent with the higher mRNA stability of transcripts with a shift to proximal 21 . Consistent with the RT-qPCR results, we identified a significant downregulation of PABPN1 (iso-201 and iso-202) (Fig. 5 D). PABPN1 isoforms were significantly reduced in the cytosolic fraction (Fig. 5 D), consistent with reduced endogenous PABPN1 protein levels (Figure S3 A). Euclidean clustering was used to identify transcripts whose expression pattern is associated with PABPN1, and we considered the A16 dysregulated transcripts with APA shift. Only two clusters were identified: the blue cluster (343 transcripts) showed an expression level pattern similar to PABPN1, and the red cluster (534 transcripts) showed an opposite pattern (Fig. 5 E). The blue cluster was enriched in pathways related to RNA metabolism and mitochondria, which were also enriched in the A16 proteome. The mitochondrial pathways were upregulated in the A16 proteome and transcriptome (Table S7). RNA metabolism-related pathways correlated with PABPN1 protein up-regulation, but with PABPN1 mRNA down-regulation (Table S7). The translation and cell cycle pathways in the blue cluster and all enriched pathways in the red clusters were not found in the proteome-enriched pathways (Table S7). The paired RNA-protein study design allowed us to investigate whether the effect of A16-PABPN1 on transcript folding directly affects protein folding. We examined a correlation between transcripts in the cytosolic fraction and their proteins in the cytosolic and nuclear fractions. A linear regression over the entire proteome showed very weak direct relationships between protein fold-change and transcript fold-change in both cytoplasmic and nuclear fractions (Figure S6). Only 0.5% and 0.2% of the proteins in the cytosolic and nuclear fractions, respectively, were predicted to show a linear correlation. For the significantly dysregulated transcripts and proteins, the correlation in the nuclear fraction was found to be insignificant (F-test = 0.316), and the fold change of 1.6% of the deregulated nuclear proteins correlated directly with the fold change of the transcripts (Figure S6). In the cytosolic fraction, the linear regression model suggested a weak positive correlation, with 9.9% of protein fold-change directly correlating with transcript fold-change (Figure S6). The accumulation of nuclear and cytosolic proteins is differentially affected by A16-PABPN1. A16-PABPN1 expression impairs mRNA nuclear export and cell metabolism To investigate the impact of the A16 proteome on cell function, we considered pathways identified by proteome enrichment analysis (Fig. 6 A). The oligo-dT-cy5 signal reported alterations in RNA metabolism and mRNA 3'-end processing, and in situ hybridization revealed mRNA nuclear puncta in Ala16 cells (Fig. 6 B). The mRNA signal in Dox-treated cells was 2.5-fold higher than in vehicle-treated cells (Fig. 6 C), and the nuclear-to-perinuclear ratio showed a 5-fold increase in A16 cells compared to vehicle-treated cells (Fig. 6 D). This suggests impaired nuclear export and/or nuclear trafficking. We evaluated the nuclear export of mRNA by leptomycin B (LMB) treatment on oligo-dT-Cy5 localization. A 2-fold increase in nuclear oligo-dT signal was found in LMB-treated vehicle cells, but in Dox-treated cells, the signal was unchanged after LMB treatment (Fig. 6 C and 6 D). To confirm the nuclear accumulation of mRNA in A16 cells, we detected single mRNA molecules for three A16 up-regulated genes: PIPB , UBC , and MYF5 (Fig. 6 E). PABPN1 was used as a control. The RNA single molecule signal was higher for PABPN1, PPIB, UBC , and MYF5 in A16 nuclei (Fig. 6 F). LMB treatment resulted in a higher mRNA signal in vehicle-treated cells, but the signal was unchanged in A16 cells (Fig. 6 F). These results indicate impaired nuclear export in A16 cells. Next, we measured translation efficiency using azido-OPP and found reduced translation efficiency in A16 cell cultures compared to vehicle-treated cell cultures (Fig. 6 G and 6 H). Taken together, in A16 cells, mRNA nuclear entrapment is associated with impaired mRNA nuclear export and reduced translational efficiency. Among the affected pathways, we examined mitochondrial activity and glucose uptake. Mitochondrial activity was significantly reduced in A16 cells compared to vehicle-treated cells (Fig. 7 A and 7 B). Similarly, glucose uptake was significantly reduced in A16 cells, with lower glucose uptake than vehicle-treated cells (Fig. 7 C). The reduced metabolic activity in A16 cells is consistent with the expression pattern of proteome cluster 2. The A16 proteome was also enriched for pathways affecting muscle cell differentiation and cell biomechanics (cytoskeleton, focal adhesion, and stress fiber). Differentiated cells, recognized by multinucleated cells expressing myosin heavy chain (MyHC), were formed in both vehicle-treated and A16 cell cultures (Fig. 7 D), but the percentage of differentiation rate (fusion index) was lower in A16 cell cultures (Fig. 7 E). The reduced cell fusion in A16 cells was associated with reduced cell biomechanics as measured by atomic force microscopy in differentiated cells (Fig. 7 F). Cell stiffness was significantly reduced in A16 cells compared to parental and vehicle-treated cells (Fig. 7 G). This indicates that reduced expression of cytoskeletal and focal adhesion proteins negatively affects cell differentiation and biomechanics. DISCUSSION Protein aggregates are a hallmark of aging and age-related diseases. Protein aggregates disrupt protein homeostasis networks in a feed-forward regulatory loop that affects multiple cellular processes 37 , 38 . Pathogenic protein aggregates are often cytosolic, and these are distinguished from nuclear aggregates by molecular and structural properties 39 . In addition, protein clearance pathways differ between nuclear and cytoplasmic regions 40 . Taken together, insights into the consequences of protein aggregates should be gained in subcellular regions. Here we provide insights into the molecular networks and cellular (dys)functions driven by nuclear aggregates produced by the pathogenic form of PABPN1. In contrast to other studies that elucidate how protein aggregates affect cell function in disease-irrelevant cell types 39 , 41 , we model protein aggregation in muscle cells. Recent studies have highlighted the cell type specificity of proteostasis control and protein aggregation 42 . The aggregates in our human muscle cells share similar features, such as KCl resistance, fibrils in the electron-lucent region, and nuclear sequestration of p62. We also show that PABPN1 molecular function, namely APA suppression, is also affected in this cell model. Together, our cell model is relevant to study the effect of PABPN1 aggregates on muscle cell function. APA and reduced translation efficiency have been reported in cell cultures with reduced PABPN1 expression levels 27 . Consistent with this, we found that PABPN1 levels are reduced in our cell model, in the OPMD mouse model A17.1, and in OPMD muscles 24 . Taken together, we propose that in OPMD, PABPN1 loss-of-function causes APA dysregulation and gain-of-function due to PABPN1 protein aggregation, which sequesters bulk mRNA and induces overexpression of multiple RBPs in the insoluble fraction. Consistent with this model, TDP-43 loss-of-function due to its aggregation has been proposed as a disease mechanism in ALS 43 , which is associated with nuclear accumulation and reduced nuclear export 44 . Although it is not entirely clear how PABPN1 aggregates lead to reduced PABPN1 levels, a previous study suggested that PABPN1 regulates its expression levels through RNA editing: overexpression of PABPN1 led to the accumulation of unspliced PABPN1 transcript resulting in reduced levels of the endogenous PABPN1 mRNA 45 . Here, we show that PABPN1 mRNA is sequestered in the nuclei of A16 cells, resulting in reduced cytoplasmic levels of the mRNA and reduced protein levels. Nuclear insoluble aggregates are a hallmark of OPMD 46 . Therefore, one might expect to see the most dramatic changes in the insoluble fraction. Instead, proteins in the cytoplasmic fraction were most affected in A16 cells compared with the insoluble and nuclear fractions. The nuclear pathways associated with A16-PABPN1 expression primarily affected RNA metabolism. Most interesting RBPs are associated with other neuromuscular diseases and protein aggregation disorders, such as DM-1, GR-FTLD, IBM, and ALS. The accumulation of such RBPs in the insoluble fraction may indicate a depletion of the functional protein and an additional effect on mRNA metabolism beyond APA and polyA tail length. PABPN1 has been implicated in the nuclear export of 23 , and here we show that nuclear export is impaired in A16 cells. In addition, the mRNA surveillance pathway was significantly enriched in the A16 proteome, consistent with an aberrant accumulation of cytoplasmic mRNA in A16 cells. Cytosolic PABPC1 and PABP4 are regulators of the mRNA decay pathway 47 , 48 , and their expression levels were significantly altered in A16 cells. The role of PABPN1 in the cytosol is poorly understood; it has been proposed that PABPN1 may functionally replace PABPC1 in the nonsense-mediated mRNA decay pathway 49 . The A16 enriched cytoplasmic pathways includes proteins involved in cell metabolism energy, and cell structure These proteins were frequently downregulated in A16 cells, suggesting reduced function affected by these pathways. Indeed, we demonstrated reduced mitochondrial activity and glycolysis in A16 cells. Both energy production pathways have been implicated in many neuromuscular diseases, including protein aggregation disorders 50 , 51 . Disruption of protein networks that shape the cell structure is also common in models of protein aggregation disorders 52 – 54 . We demonstrate a significant effect on cell biomechanics that can be translated into reduced contraction. Importantly, the same cytosolic pathways were found to be affected in muscle models with reduced PABPN1 levels 55 . For example, reduced expression of PABPN1 expression affects the expression cytoskeletal proteins, muscle cell fusion, and cell biomechanics 56 . This suggests that reduced levels of PABPN1, or PABPN1 aggregation results in similar cell dysfunction. Our study demonstrates that the A16 proteome affects broad cellular pathways that influence cell metabolism, thereby affecting muscle cell function and biomechanics, and is consistent with growing evidence of a strong link between cellular metabolism and mechanics 57 . Co-localization experiments in PABPN1 nuclear aggregates revealed the sequestering of various proteins, including proteostasis regulators 35 , 46 . We show that p62 is sequestered in PABPN1 aggregates, which is consistent with p62 accumulation in OPMD myonuclei and could account for reduced autophagy in OPMD models 33 , 58 . Although the expression of genes involved in the ubiquitin-proteasome system (UPS) and autophagy have been implicated in OPMD 59 , and proteasomal activity is impaired in OPMD models 6 , 60 , we did not find significant dysregulation of protein networks regulating protein homeostasis. We found p62 co-localization in PABPN1 aggregates later than proteomic changes or mRNA sequestration in PABPN1 aggregates. This suggests that UPS and autophagy dysregulation are secondary in OPMD. Given that UPS and autophagy dysfunction is associated with aging 37 , it is possible that prolonged culture time with PABPN1 nuclear aggregates could affect protein levels of the UPS and autophagy protein levels. Taken together, our data demonstrate that RBPs associated with PABPN1 are involved in protein aggregation diseases, supporting common mechanisms among RBP protein aggregation diseases. Based on our findings, we propose that OPMD pathology involves mRNA sequestration within PABPN1 nuclear aggregates, which affects subsequent mRNA-associated cellular processes such as nuclear export and RNA decay. Furthermore, we propose that PABPN1 loss of function leads to APA dysregulation, which subsequently affects translation, cellular pathways, and cellular mechanics. A better understanding of the role of PABPN1 in the cytosol and its intracellular dynamics may help to develop therapeutic strategies for OPMD and other RBP protein aggregation diseases. Declarations Acknowledgments The study work was supported the argenx and by the PPS Holland Health #21802. Authors are responsible for the accuracy of their funder designation, facilitating compliance with funder requirements. Author contributions Conceptualization: MS, AM, TS, VR Methodology: MS, SF, EB, TE, VS, AM, TS, RF, BMK, VR Software: MS, VR Validation: MS, TE, VS, VR Formal Analysis: MS, TE, VS, VR Investigation: MS, AM, TS, VR Data Curation: MS, SF Visualization: MS, EB, TE, VS, VR Resources: BK, BE, VR Supervision: AM, TS, RF, VR Project Administration: VR Funding Acquisition: VR Writing – Original Draft: MS, EB, TE, VS, VR Writing – Review & Editing: MS, EB, TE, AM, RF, BMK, VR Conflict of interest All authors declare no conflict of interest. Data availability The raw mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD055515. The sequencing data generated in this study have been deposited in the Gene Expression Omnibus (GEO) under accession number GSE277571. The data are publicly available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE277571. Further metadata and related files can be found in the accompanying submission. References Beckmann, B. M., Castello, A. & Medenbach, J. The expanding universe of ribonucleoproteins: of novel RNA-binding proteins and unconventional interactions. Pflügers Archiv - European Journal of Physiology 468, 1029–1040 (2016). https://doi.org/10.1007/s00424-016-1819-4 Hentze, M. W., Castello, A., Schwarzl, T. & Preiss, T. A brave new world of RNA-binding proteins. 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Nature Metabolism 3, 456–468 (2021). https://doi.org/10.1038/s42255-021-00384-w Ishtayeh, H. et al. Oculopharyngeal muscular dystrophy mutations link the RNA-binding protein HNRNPQ to autophagosome biogenesis. Aging Cell 22, e13949 (2023). https://doi.org/https://doi.org/10.1111/acel.13949 Anvar, S. Y. et al. Deregulation of the ubiquitin-proteasome system is the predominant molecular pathology in OPMD animal models and patients. Skeletal Muscle 1, 15 (2011). https://doi.org/10.1186/2044-5040-1-15 Ribot, C. et al. Activation of the ubiquitin-proteasome system contributes to oculopharyngeal muscular dystrophy through muscle atrophy. PLOS Genetics 18, e1010015 (2022). https://doi.org/10.1371/journal.pgen.1010015 Supplementary Table Supplementary Table S5 is not available with this version. Table S5: APA shift analysis Additional Declarations No competing interests reported. Supplementary Files Supplementalinformation.docx TableS3Proteomedifferntialanalysis.xlsx TableS4Proteomeclusterenrichmentanalysis.xlsx TableS6mRNAdifferentialexpressionanalysis.xlsx TableS7mRNAclusterenrichmentanalysis.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5783239","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":400470530,"identity":"11c4dbbd-5ed5-4c6c-bcb0-e55b2a00acb6","order_by":0,"name":"Milad Shademan","email":"","orcid":"","institution":"Leiden University Medical Centre","correspondingAuthor":false,"prefix":"","firstName":"Milad","middleName":"","lastName":"Shademan","suffix":""},{"id":400470531,"identity":"bc45e00f-5951-4dfb-86f1-b035ee197ab6","order_by":1,"name":"Sarah Flannery","email":"","orcid":"","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Flannery","suffix":""},{"id":400470532,"identity":"79ac5dbb-a17d-4ad4-872e-05c6ac884a23","order_by":2,"name":"Erik Bos","email":"","orcid":"","institution":"Leiden University Medical Centre","correspondingAuthor":false,"prefix":"","firstName":"Erik","middleName":"","lastName":"Bos","suffix":""},{"id":400470535,"identity":"5c97f05e-000e-47e2-969a-0dc4a186a4e5","order_by":3,"name":"Tom Evers","email":"","orcid":"","institution":"Leiden University","correspondingAuthor":false,"prefix":"","firstName":"Tom","middleName":"","lastName":"Evers","suffix":""},{"id":400470536,"identity":"d6fbd56c-5e00-47b8-9586-a8a73bc343c8","order_by":4,"name":"Vahid Sheikhhassani","email":"","orcid":"","institution":"Leiden University","correspondingAuthor":false,"prefix":"","firstName":"Vahid","middleName":"","lastName":"Sheikhhassani","suffix":""},{"id":400470537,"identity":"e6ea40a7-10e2-4b0c-be77-3f5c5a266e19","order_by":5,"name":"Alireza Mashaghi","email":"","orcid":"","institution":"Leiden University","correspondingAuthor":false,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Mashaghi","suffix":""},{"id":400470538,"identity":"fecaa713-f9cc-4822-a053-b26e3df1979a","order_by":6,"name":"Benno Kusters","email":"","orcid":"","institution":"Radboud University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Benno","middleName":"","lastName":"Kusters","suffix":""},{"id":400470539,"identity":"66ce0276-9740-4a1c-bb4e-fac6c427f97a","order_by":7,"name":"Baziel Engelen","email":"","orcid":"","institution":"Radboud University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Baziel","middleName":"","lastName":"Engelen","suffix":""},{"id":400470540,"identity":"991539d3-223e-4422-bce2-d61b5a8037f4","order_by":8,"name":"Thom Sharp","email":"","orcid":"","institution":"Bristol University","correspondingAuthor":false,"prefix":"","firstName":"Thom","middleName":"","lastName":"Sharp","suffix":""},{"id":400470541,"identity":"9477483e-66df-44be-9bfc-106cfa654f3c","order_by":9,"name":"Roman Fischer","email":"","orcid":"","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Roman","middleName":"","lastName":"Fischer","suffix":""},{"id":400470542,"identity":"a048acd6-cbf4-48d6-aa9c-675aa64d25c4","order_by":10,"name":"Benedikt M. Kessler","email":"","orcid":"","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Benedikt","middleName":"M.","lastName":"Kessler","suffix":""},{"id":400470543,"identity":"18311d93-49b7-4db2-9a6b-97b506b4cf32","order_by":11,"name":"Vered Raz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAlElEQVRIiWNgGAWjYNCCCgsI/YB4LWckIHQC0ToY20jRYnCA9+HHn/MkEte2H2B7QKQWdmMJyW0SidvOJLAbEKVFsoGNQcIQpOUGA5sEsVqYfyTOIUULPwMbm8TBBpK0MLOxWTYckzDediaxjTgtbOxtzDd/1NjIbjt++JjEB2K0MDDDWYwNRGkYBaNgFIyCUUAEAACIeiq/9PGVtwAAAABJRU5ErkJggg==","orcid":"","institution":"Leiden University Medical Centre","correspondingAuthor":true,"prefix":"","firstName":"Vered","middleName":"","lastName":"Raz","suffix":""}],"badges":[],"createdAt":"2025-01-07 17:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5783239/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5783239/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73700979,"identity":"b51d5713-d723-42ae-b9c3-22f5851cd6d8","added_by":"auto","created_at":"2025-01-13 17:10:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":351598,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInducible A16-PABPN1 forms insoluble aggregates in muscle cells. A. \u003c/strong\u003eWestern blot analysis of PABPN1 abundance in soluble and insoluble fractions in vehicle-treated (V) or Dox-induced cell cultures. GAPDH labels the soluble fraction and HistoneB2 labels the insoluble fraction. No staining is a loading control. \u003cstrong\u003eB. \u003c/strong\u003eBar graphs show PABPN1 fold change (FC) between Dox-induced and vehicle-treated in the soluble and insoluble fractions. \u003cstrong\u003eC. \u003c/strong\u003eBar graphs show PABPN1 FC between soluble and insoluble fractions in vehicle-treated or Dox-induced cell cultures. Means and standard deviations for B and C are from N=4 biological replicates. \u003cstrong\u003eD.\u003c/strong\u003e Confocal images of PABPN1 (red) immunofluorescence in vehicle-treated and Dox-induced cell cultures under proliferating conditions +/- KCl treatment. The scale bar is 20mm. \u003cstrong\u003eE.\u003c/strong\u003eBar graphs show mean nuclear PABPN1 fluorescence intensity with thresholding in a vehicle-treated or Dox-induced cell culture +/- KCl treatment. Mean and standard deviation are from N=3 biological replicates. Two-way ANOVA was used to determine statistical significance; p\u0026lt;0.05 is denoted with *, and p\u0026lt;0.01 is denoted with **. \u003cstrong\u003eF.\u003c/strong\u003e A stitched electron microscopy (EM) image of the vehicle-treated or Dox-induced fused cell. The dashed lines encircle EM fluorescent regions. The scale bar is 10mm. \u003cstrong\u003eG.\u003c/strong\u003e Examples of single myonuclei in Dox-induced fused cells with large (left) and small (right) EM lucent regions and magnification of the lucent region (bottom). Arrowheads indicate fibrils. The scale bar is 500nm. \u003cstrong\u003eH.\u003c/strong\u003e Representative immunofluorescence overlay images of p62-Cy5 (red) and A16-PABPN1-Alex488 (green) in vehicle-treated and Dox-induced cell cultures for 5 or 7 days. Inserts of p62 (gated in red) or A16-PABPN1 (gated in green) are from the gated area (dashed white line). The scale bar is 20mm.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5783239/v1/3eaaffbc468ee0eb2d8b22b0.png"},{"id":73701357,"identity":"36182659-ced4-4991-903a-e55973947199","added_by":"auto","created_at":"2025-01-13 17:18:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":97277,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubcellular fractionation: a workflow and analyses. A. \u003c/strong\u003eFlowchart summarizing the main steps for A16 transcriptome and proteome generation. mRNA analysis is shown in red and protein analysis is shown in green. A detailed flowchart can be found in Figure S3. \u003cstrong\u003eB\u003c/strong\u003e and C Protein analysis. \u003cstrong\u003eD\u003c/strong\u003e and \u003cstrong\u003eE\u003c/strong\u003e mRNA analysis. \u003cstrong\u003eB\u003c/strong\u003eand \u003cstrong\u003eD \u003c/strong\u003eVolcano plots of differential analysis between cytosolic and nuclear fractions (left) or between nuclear and insoluble fractions (right). The number (N) of significant proteins or transcripts is indicated per fraction. The percentage of significant proteins or transcripts from the total is shown per analysis. The black line indicates statistical significance (p\u0026lt;0.05, FDR). Examples of proteins with distinct subcellular localization are marked as autophagy proteins in the cytosol, centromere binding proteins and PABPN1 in the nuclear fraction, and histones in the insoluble fraction. \u003cstrong\u003eC.\u003c/strong\u003eand \u003cstrong\u003eE.\u003c/strong\u003e Heatmap correlation matrix between 18 samples. The correlation scale is indicated.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5783239/v1/3e7703e6991dba212ef0df02.png"},{"id":73700988,"identity":"87246299-8243-40ca-8d2c-4834f32d05b7","added_by":"auto","created_at":"2025-01-13 17:10:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":149824,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe A16 proteome in cell fractions.\u003c/strong\u003e \u003cstrong\u003eA.\u003c/strong\u003e Volcano plots the differential protein abundance in the cytosolic, nuclear, and insoluble fractions. The black line indicates the 5% FDR significance threshold. Higher abundance in A16 is shown in red and lower abundance is shown in blue. PABPN1, GAPDH, and H2BF analyzed by Western blot are indicated. \u003cstrong\u003eB.\u003c/strong\u003e Bar graph of PABPN1 counts (log2). The average fold change is shown per fraction. \u003cstrong\u003eC.\u003c/strong\u003eClustering plot of differentially abundant proteins in the cytosolic, nuclear, and insoluble fractions. The black line represents PABPN1. The number of proteins per cluster is indicated. \u003cstrong\u003eD.\u003c/strong\u003e A bar graph of the pathway enrichment analysis per cluster, the x-axis shows the Bonferroni adjusted p-value. The percentage of A16-enriched proteins is given in parentheses. Functionally related pathways are highlighted with a similar color. The complete enrichment data can be found in Table S6. \u003cstrong\u003eE.\u003c/strong\u003e Protein-disease correlation of the 'Metabolism of RNA' group, p-value is on the x-axis; the associated proteins are labeled. OPMD: oculopharyngeal muscular dystrophy, SMA: spinal muscular atrophy, DM-1: myotonic dystrophy type 1, GR-FTLD: granulin-related frontotemporal lobar degeneration, IBM: Inclusion body myositis, MS: Multiple sclerosis, ALS: Amyotrophic lateral sclerosis). \u003cstrong\u003eF.\u003c/strong\u003e Heatmap of the correlation matrix between disease-associated RNA binding proteins. The Pearson correlation p-value is indicated in the lower half of the matrix (p\u0026lt;0.01 with **; p\u0026lt;0.001 with ***, and p\u0026lt;0.0001 with ****), and the correlation coefficient (r) for significant correlation is indicated in the upper half of the matrix. \u003cstrong\u003eG. \u003c/strong\u003eProtein levels of PABPN1 (red), HNRNPR (blue), SRSF1 (yellow), and PABPC1 (cyan) in the cytosolic, nuclear, and insoluble fractions in vehicle-treated and A16 (highlighted in gray) cell cultures.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5783239/v1/d70d925dffde9f9c8d2ed1ca.png"},{"id":73701359,"identity":"bafff6af-743c-4da4-b47a-f4e879474f3a","added_by":"auto","created_at":"2025-01-13 17:18:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":83061,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAPA shift in A16 cells correlates with A16 overexpression and endogenous downregulation.\u003c/strong\u003e \u003cstrong\u003eA.\u003c/strong\u003e Schematic representation of the wild-type (Ala10) and expanded (Ala16) transgenes and the endogenous PABPN1 primers at the 3' UTR distinguishing the endogenous from the transgene cDNA. The position of the primers is indicated by arrows. Primers to exons 3-4 amplify both the transgene and endogenous PABPN1, and primers to the 3'-UTR amplify only the endogenous transcript. \u003cstrong\u003eB.\u003c/strong\u003e Bar graph of RT-qPCR analysis of PABPN1 levels normalized to HPRT in vehicle-treated (V) and Dox-induced cells. The mean and standard deviation are from N=3. Statistical significance was assessed by ANOVA; p\u0026lt;0.01 is indicated by **. \u003cstrong\u003eC.\u003c/strong\u003e Top: A summary of APA-shift analysis in cytoplasmic and nuclear fractions. Bottom: The volcano plot shows the APA-shift fold change between vehicle-treated and Dox-induced cells. Transcripts with significant proximal APA-shift are in red (N=2793) and distal APA-shift in blue (N=24). The dashed line indicates the 5% FDR significance threshold. \u003cstrong\u003eD.\u003c/strong\u003e A summary of APA-shift analysis per fraction: the APA-shift was recalculated per fraction for the 2793 transcripts in 4C. Venn diagram shows APA-shift transcripts per fraction and the overlap between fractions. Transcript examples are depicted. \u003cstrong\u003eE.\u003c/strong\u003e IGV plots and APA-shift/p-value (FDR) are shown for three transcripts per fraction. Read counts in vehicle-treated cells are shown in gray and Dox-induced cells are shown in red. The y-axis shows the sum of read counts (N=3). PMP22-202 is significant in the cytosolic fraction only, HSPA4-201 is significant in the nuclear fraction only, and CLSTN1-201 is significant in the cytosolic and nuclear fractions. The length of the 3'-UTR is given in kb. P: proximal, D: distal.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5783239/v1/0c3c7fe7cb38c4a0b389e020.png"},{"id":73700980,"identity":"58f2f225-ec6d-4e2b-8436-a9fdf17e889c","added_by":"auto","created_at":"2025-01-13 17:10:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":104815,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe A16 transcriptome in cell fractions. A.\u003c/strong\u003eVolcano plots of differential transcript abundance in the cytosolic, nuclear, and insoluble fractions. The dashed line indicates the 0.05 FDR and p-value significance threshold. Upregulation in the Dox-induced condition is shown in red, and downregulation in blue shows the number of down- or upregulated transcripts. Examples of upregulated transcripts in the insoluble fraction are highlighted. \u003cstrong\u003eB.\u003c/strong\u003e Venn diagrams of the overlap of transcripts in the cytosolic (in green) and nuclear (in blue) fractions in the A16 dysregulated or unaffected transcripts. Numbers represent the percentage of non-overlapping transcripts. Arrows indicate the percentage of overlap of A16-dysregulated transcripts with APA. \u003cstrong\u003eC.\u003c/strong\u003e Bar graph of down- or up-regulated transcripts with APA in cytosolic or nuclear fractions. The percentage is calculated from the total APA shift. Statistical significance was calculated using the chi-squared test; p\u0026lt;0.0001 is indicated with ****. \u003cstrong\u003eD.\u003c/strong\u003e PABPN1 isoform expression changes in cytosolic and nuclear fractions. Full-length PABPN1 isoforms (201, 202) are indicated. The mean and standard deviation are from N=3. Statistical significance was assessed by ANOVA; p\u0026lt;0.001 and p\u0026lt;0.0001 are indicated by *** and ****, respectively. \u003cstrong\u003eE. \u003c/strong\u003eA clustering plot of A16 dysregulated mRNAs with APA shift in cytosolic and nuclear fractions. PABPN1 isoforms are indicated. The number of mRNAs per cluster is indicated.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5783239/v1/1a673092a12621559ac2452f.png"},{"id":73700989,"identity":"dac85528-2db3-4b8f-b367-4f5d7fa0bc4e","added_by":"auto","created_at":"2025-01-13 17:10:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":127968,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHigh throughput analysis of cell function in A16 cell culture. A.\u003c/strong\u003eSchematic of high-throughput cell-based analysis: cells were seeded in a 96-well plate and treated with Dox for 3-4 days, followed by staining for RNA hybridization or cellular metabolism, imaging, and image quantification. \u003cstrong\u003eB.\u003c/strong\u003eImages show oligo-dT-Cy5 (red) hybridization in vehicle-treated and Dox-induced cells. The scale bar is 40μm. Nuclear counterstaining is shown in blue. Corresponding confocal images are shown in the insets. The scale bar is 15μm. \u003cstrong\u003eC. \u003c/strong\u003eOligo-dT intensity in vehicle-treated and Dox-induced cells in mock or LMB (37.5 nm)-treated cell cultures. Bar graph of nuclear oligo-dT fluorescence intensity (FI). D. Bar graph of nuclear to perinuclear oligo-dT FI ratio.\u003cstrong\u003e E.\u003c/strong\u003eImages of RNAscope staining of four genes (PABPN1, MYF5, UBC, PPIB.) in vehicle-treated and Dox-induced cell cultures. The nuclear segmentation is outlined. The scale bar is 10μm. \u003cstrong\u003eF.\u003c/strong\u003e Bar graphs show the number of nuclear spots for PABPN1, MYF5, UBC, and PPIB. \u003cstrong\u003eG. \u003c/strong\u003eBar graphs show the nuclear to perinuclear spot FI ratio for PABPN1, MYF5, UBC, and PPIB. \u003cstrong\u003eH.\u003c/strong\u003eImages show Azido-OPP (green) and nuclear counterstaining in vehicle-treated or Dox-induced cell cultures. The scale bar is 50μm. \u003cstrong\u003eI. \u003c/strong\u003eBar graph of Azido-OPP FI in vehicle-treated and Dox-induced cell cultures. Statistical significance was assessed by ANOVA: p\u0026lt;0.05 and p\u0026lt;0.01 are indicated by * and **, respectively. Means and standard deviations are from N=3 biological replicates.\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5783239/v1/8b2b90ef3e04ce4623dacd6a.png"},{"id":73700991,"identity":"74a2f709-0327-446d-83ac-df3f498eed8a","added_by":"auto","created_at":"2025-01-13 17:10:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":260726,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA16 impaired muscle cell metabolism, differentiation and bio-mechanics. A.\u003c/strong\u003e Images show JC1 fluorescence in vehicle-treated or Dox-induced cell cultures. The monomers are stained green, the activated J-aggregates are stained red, and the image per fluorophore is shown in the grayscale inset. The nuclear counterstain is shown in blue. The scale bar is 40μm. \u003cstrong\u003eB.\u003c/strong\u003e Bar graph of 594/488nm FI ratio in vehicle-treated and Dox-induced cell cultures. \u003cstrong\u003eC.\u003c/strong\u003e Bar graph of glucose uptake assay in vehicle-treated and Dox-induced cell cultures. \u003cstrong\u003eD.\u003c/strong\u003e Images of PABPN1 (red) and MyHC (green) immunofluorescence in vehicle-treated and Dox-induced cell cultures. The scale bar is 40μm. \u003cstrong\u003eE. \u003c/strong\u003eBar graph showing the percentage of differentiation (fusion index) in Dox-induced and vehicle-treated cells. \u003cstrong\u003eF.\u003c/strong\u003eCellHesion image of differentiated cell culture (left). A multinucleated cell is marked by a yellow dashed line. The cantilever is black. (Right) A typical CellHesion measurement curve showing the contact point of the cantilever with the cell. The inset shows a schematic of the cantilever's contact with the cell membrane. \u003cstrong\u003eG. \u003c/strong\u003eMembrane stiffness (Young's modulus) in parental, vehicle-treated, and Dox-induced multinucleated cells. Statistical significance was assessed by ANOVA: p\u0026lt;0.01; p\u0026lt;0.005 or p\u0026lt;0.001 are indicated by **, ***, or ****, respectively. In B, C, and E, the mean and standard deviation are from N=3 biological replicates.\u003c/p\u003e","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5783239/v1/11ed918c64886b7352fc4519.png"},{"id":78375438,"identity":"c332099d-640c-4b69-b884-b8c43011d973","added_by":"auto","created_at":"2025-03-12 14:46:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2857214,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5783239/v1/d130d72e-e43e-499f-90ac-baf8c9b96b5d.pdf"},{"id":73700976,"identity":"eb5c14c1-5ce4-430a-b07e-26e324ab6fe7","added_by":"auto","created_at":"2025-01-13 17:10:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2195171,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-5783239/v1/63aa3d66c43fccc8e9f3734e.docx"},{"id":73701356,"identity":"eecbfbd9-a8ef-4af0-90d1-b9838782a0ca","added_by":"auto","created_at":"2025-01-13 17:18:16","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":784989,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3Proteomedifferntialanalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5783239/v1/237fa35c82561b827eb2a0cf.xlsx"},{"id":73700982,"identity":"2e3c4ec7-bc1d-4880-b768-656673e2e47d","added_by":"auto","created_at":"2025-01-13 17:10:16","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17423,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4Proteomeclusterenrichmentanalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5783239/v1/45dadcd48481778b1094a6a9.xlsx"},{"id":73701358,"identity":"b54cec73-fc6b-4982-896e-c7d32c9ea2a6","added_by":"auto","created_at":"2025-01-13 17:18:17","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":5141030,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6mRNAdifferentialexpressionanalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5783239/v1/6b6a3acf5d80381b3d256847.xlsx"},{"id":73700985,"identity":"eddb6e8b-2971-410f-829f-e18e3a454758","added_by":"auto","created_at":"2025-01-13 17:10:16","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":18849,"visible":true,"origin":"","legend":"","description":"","filename":"TableS7mRNAclusterenrichmentanalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5783239/v1/7939524ef798a422cf67b72b.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Omics studies of nuclear protein aggregates in subcellular fractions reveals co- aggregation of RNA-binding proteins affecting cytosolic pathways","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eRNA-binding proteins (RBPs) play an indispensable role in RNA-dependent cellular processes that shape the cell proteome \u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. RNA metabolic processes, from synthesis to translation in both nuclear and cytosolic compartments, are regulated by versatile ribonucleoprotein complexes \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Dysfunctions of RBPs are associated with many human diseases, with a particularly high prevalence in age-associated neurological and neuromuscular disorders (NMDs). Tissue-specific limited expression levels have also been implicated in the aging of neuromuscular tissues \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Heritable mutations in RBPs cause neuromuscular degenerative diseases \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, some of which involve aggregation-prone or modifiers of protein aggregates \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Cytosolic protein aggregates are formed by RPBs such as \u003cem\u003eFUS or HNRNPA1\u003c/em\u003e, which are associated with several NMDs, including amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) \u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In addition, RBP aggregates can be nuclear, such as those involving \u003cem\u003ePABPN1\u003c/em\u003e, which causes oculopharyngeal muscular dystrophy (OPMD) \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOPMD is a rare (prevalence 1:100,000) autosomal dominant disorder characterized by progressive muscle weakness from midlife onwards \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Normal PABPN1 contains an alanine stretch (10 alanine residues) at the N-terminus of the protein, whereas a short alanine expansion (+\u0026thinsp;1 to +\u0026thinsp;8) causes OPMD. The expanded PABPN1 forms nuclear aggregates, which are associated with the disease \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, though not with disease severity \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Although PABPN1 is ubiquitously expressed, OPMD symptoms are primarily restricted to skeletal muscle, suggesting muscle-specific disease mechanisms. The factors involved in PABPN1 aggregation, and the modulation of muscle cell dysfunction remain largely unknown.\u003c/p\u003e \u003cp\u003ePABPN1 plays a key role in the processing of mRNAs, including the regulation of poly(A) tail length and alternative polyadenylation (APA) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In disease models with reduced PABPN1 expression levels or those with PABPN1 aggregates, genome-wide APA \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. PAPBN1 predominantly affects APA at the 3'-UTR of transcripts, with a strong preference for a distal-to-proximal shift that results in shorter and more stable transcripts \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In addition, PABPN1 is involved in the nuclear export of mRNAs \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Although PABPN1 functions predominantly in the nucleus, like other RBPs, it shuttles to the cytoplasm. However, PABPN1\u0026rsquo;s role in the cytoplasm is insufficiently studied, and how the nuclear aggregates cause cell dysfunction is poorly understood.\u003c/p\u003e \u003cp\u003eIn this study, we investigated the effect of expanded PABPN1 insoluble nuclear aggregates on muscle cell function using an inducible cell model that allowed us to overcome the cytotoxic effect typically associated with PABPN1 aggregates. We analyzed the cell proteome and transcriptome in nuclear, cytoplasmic, and insoluble subcellular fractions and validated the protein networks affected by PABPN1 aggregates. Our study provides insights into the molecular mechanisms by which nuclear aggregates lead to cell dysfunction.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHuman muscle biopsy\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003ePABPN1 constructs and lentivirus production\u003c/h2\u003e \u003cp\u003eThe expanded PABPN1 (Ala16) transgene was cloned into the pCW57-MCS1-2A-MCS2 doxycycline (Dox) inducible lentiviral vector (Addgene plasmid #71782). After the first cloning, the FLAG tag was also fused to the C-terminus of the PABPN1 sequence. Clonings were confirmed by Sanger sequencing. Lentivirus production was performed as detailed in (Carlotti, Bazuine et al. 2004).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eCell culture\u003c/h3\u003e\n\u003cp\u003eCells were cultured in growth medium (F10 (Gibco) medium supplemented with 15% FCS, 1 ng/ml bFGF, 10 ng/ml EGF and 0.4 \u0026micro;g/ml Dexamethasone). Cells were propagated in confluence 50\u0026ndash;80%. Cell cultures did not reach 100% confluence to avoid spontaneous differentiation. Muscle cell differentiation was done at high confluency (85\u0026ndash;95%) in DMEM\u0026thinsp;+\u0026thinsp;2% horse serum for 3\u0026ndash;5 days. The 2417 immortal human muscle cells were transduced with lentiviruses encoding Ala16, and stable cell cultures were created using puromycin selection. The Ala16 transgene was induced with 4 \u0026micro;g/mL doxycycline hydrochloride (D5207, Sigma Aldrich), and DMSO was used for (uninduced) vehicle-treated cells. For high content screening (HCS), cells were seeded in a Nunc 96 well plate; for live cell confocal microscopy, cells were seeded in a \u0026micro;-Slide 8 Well high ibiTreat (80806, IBIDI) slide. For electron microscopy, cells were seeded in \u0026micro;-Dish 35 mm, high Grid-500 ibiTreat (81166, IBIDI) dishes.\u003c/p\u003e\n\u003ch3\u003eSubcellular fractionation\u003c/h3\u003e\n\u003cp\u003eCell pellets were incubated on ice for 30 minutes with the cytosolic lysis buffer (150mM NaCl, 50mM HEPES (pH 8), 1 mM DTT, and protease inhibitor cocktail (Roche)). The cytosolic supernatant is collected after centrifugation at 3500g, 4\u0026deg;C, for 10 minutes. The remaining pellet was washed with excess PBS and, after additional centrifugation, solubilized in a nuclear lysis buffer (150mM NaCl, 50mM HEPES (pH 8), 0.5% w:v Sodium deoxycholate, 0.1% Triton, 1 mM DTT, and protease inhibitor cocktail). After 15 minutes of incubation on ice, the nuclear supernatant fraction was collected after centrifugation at 5000g, 4\u0026deg;C for 10 minutes. The remaining pellet contained the insoluble fraction and was solubilized in PureLink for RNA extraction or in 2% SDS for protein extraction. Protein concentration from cytosolic and nuclear fractions was determined using the BioRad protein assay (BioRad).\u003c/p\u003e\n\u003ch3\u003eProtein extraction and western blot analysis\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eProtein extraction and western blot analysis\u003c/div\u003e \u003cp\u003eBulk protein extraction of soluble and insoluble fractions of a cell pellet was collected from a 12 well o using a lysis buffer (20 mM Tris, pH 7.4, 150 mM NaCl, 5 mM EDTA, 0.1% NP40, and 1 mM DTT and 1x protease inhibitor cocktail). After sonication and centrifugation (1 min, 13000g, at 4\u0026deg;C), the supernatant containing the soluble proteins was transferred to a new tube, and the pellet, containing the insoluble proteins, was washed once in PBS, dissolved in loading buffer, sonicated and spin down before heat inactivation. Insoluble proteins were extracted from the pellet with lysis buffer\u0026thinsp;+\u0026thinsp;2% SDS. The protein amount was determined in the soluble fraction and the proportional aliquots from the paired insoluble fraction for equal loading on SDS-PAGE. Protein aliquots were separated on 10% SDS-PAGE. Western blotting was carried out with a PVDF membrane. Bulk proteins were visualized with the No-Stain Protein Labeling Reagent (#A44717, ThermoFisher) and imaged using the iBright Imaging System (ThermoFisher). The membrane was blocked with 5% dried milk powder (T145.2, Carl Roth). Primary antibody incubation was carried out at 4 degrees overnight, and secondary antibody incubation at room temperature for one hour. Antibodies are listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. An Odyssey CLx Infrared imaging system (LiCOR, NE. USA) was used to detect the fluorescent signal. Quantification of protein abundance was done using ImageJ. Values were corrected for background and normalized to loading controls. Western blot quantification was carried out with ImageJ. Normalization was made for both the No-Stain and housekeeping signal. Full western blot images are provided in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMass spectrometry and data analysis\u003c/h2\u003e \u003cp\u003eSample aliquots containing 50 \u0026micro;g protein were incubated with 250 Units of Benzonase\u0026reg; Nuclease (Sigma-Aldrich) for 10 minutes at room temperature, then solubilized in 5% SDS. Cysteine residue reduction and alkylation were performed using 10 mM tris(2-carboxyethyl) phosphine and 50 mM iodoacetamide at room temperature for 30 min. Subsequent tryptic digestion was performed by S-trap micro (ProtiFi) according to the manufacturer\u0026rsquo;s instructions. After elution, peptides were dried by vacuum centrifugation and stored at -20\u0026deg;C for MS analysis.\u003c/p\u003e \u003cp\u003ePeptide samples were reconstituted in 3% acetonitrile 0.1% formic acid before MS analysis, then 200 ng were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) using a Dionex Ultimate 3000 UPLC coupled to a Q-Exactive mass spectrometer (Thermo Fisher Scientific). Peptides were trapped on an Acclaim\u0026trade; PepMap\u0026trade; 100 C18 HPLC Column (PepMapC18; 300 \u0026micro;m x 5 mm, 5 \u0026micro;m particle size, Thermo Fischer) using solvent A (0.1% Formic Acid in water) at a pressure of 60 bar, and separated on an Easy Spray PepMap RSLC column (75 \u0026micro;m i.d. x 2 \u0026micro;m x 50 mm, 100 \u0026Aring;, Thermo Fisher) at 250 nL/min over a 60 min gradient from 5\u0026ndash;35% acetonitrile in 5% DMSO, 0.1% formic acid. MS data was acquired in data-independent acquisition (DIA) mode, with full scan MS spectra acquired in the Orbitrap, from 20 m/z windows over a scan range of 495 to 995 m/z, with an overlap of +/- 2 Daltons, resolution 35000, AGC target 3e6, maximum injection time 55 ms and fragmentation at 28% normalized collision energy. MS/MS spectra were also acquired with a resolution of 17500, AGC target 1e6. Raw MS data were searched (UniProtKB reviewed proteome database UP000005640) using DIA-NN v1.8 in library-free mode with automatic mass accuracy optimization, cross-run normalization enabled, 1 missed cleavage permitted, fixed cysteine carbamidomethylation, and methionine oxidation as a variable modification. DIA-NN output data, containing 6604 annotated proteins, were log2 transformed. For fractionation validation, protein abundance was normalized by centering the median per fraction (cytosolic soluble (C), nuclear soluble (N), and insoluble fraction (Ins) from Dox-induced and vehicle-treated cell cultures with three biological replicates per fraction per genotype). Fractionation efficiency (C vs. N or N vs. Ins.) was assessed by paired differential analysis by two-sample t-test in Perseus Software v2.0.7.0.\u003c/p\u003e \u003cp\u003eAla16 PABPN1 effect per fraction was tested with Dox-induced vs. vehicle-treated samples, with the following inclusion criteria: 1. minimum read\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;1 per sample, 2. minimum read\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;67% (2/3) per group (N\u0026thinsp;=\u0026thinsp;3). Protein abundance was normalized by median centering per fraction. Differential analysis was performed by two-sample t-test in Perseus Software v2.0.7.0. The expression profiles between fractions were determined by hierarchical clustering with Euclidean distance using Z-scores in Perseus Software v2.0.7.0. Proteomic differential analysis is found in Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e. The correlation between PABPN1 levels and other protein levels was calculated with Pearson correlation in GraphPad Prism 9.3.1.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRNA extraction, library preparation, RNA sequencing, and analysis\u003c/h3\u003e\n\u003cp\u003eRNA was extracted from cytosolic, nuclear, and insoluble fractions using the PureLink\u0026trade; RNA Mini Kit (Invitrogen\u0026trade; 12183018A), according to the manufacturer protocol, continue with on-column DNase treatment PureLink\u0026trade; DNase Set (Invitrogen\u0026trade; 12185010). RNAs were stored at -80. RNA integrity was quantified using Qubit and checked on an RNA 6000 Nano Agilent Lab-on-a-Chip kit with Bioanalyzer Systems before cDNA library preparation. The 1C library preparation protocol and RNA sequencing were made as detailed in \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, using.\u003c/p\u003e \u003cp\u003eAll reads in FASTQ format were first filtered using \u003cem\u003eCutadapt\u003c/em\u003e (v2.10), removing all remaining adapter sequences. \u003cem\u003eMultiQC\u003c/em\u003e program in Python was used for quality control (QC) assessment of FASTQ files. The remaining reads were aligned to the Ensembl transcriptome version 104 using STAR (v2.7.5a), including UMI-based deduplication using UMI-Tools (v1.1.1), generating a transcriptome-based alignment in BAM format. We used the same Ensembl transcript annotation version 104 to create a customized transcript annotation GTF file for all Ensembl annotated transcripts. With the annotation and human transcriptome-based alignment files as input, we quantified the reads at the coding regions using \u003cem\u003efeatureCounts\u003c/em\u003e (v2.0.1). All analyses were performed using RStudio Software RStudio 2022.02.3 (Build 492) using R Statistical Software (v4.2.3).\u003c/p\u003e \u003cp\u003eThe APA-shift calculation was made as described in \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e on raw reads counts after exclusion criteria. The ratio between proximal to distal was calculated in R (version 4.3.1) using the equation [log(1\u0026thinsp;+\u0026thinsp;Proximal) - log(1\u0026thinsp;+\u0026thinsp;Distal)], and the APA-shift was calculated between induced and uninduced conditions. The APA-shift is in log2: APA-shift\u0026thinsp;\u0026gt;\u0026thinsp;0 indicates a shift to proximal, and \u0026lt;\u0026thinsp;0 suggests a shift to distal. APA-shift significance per fraction was calculated with Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test, corrected for FDR, using two procedures: 1. The APA-shift calculation was done per fraction on raw data N\u0026thinsp;=\u0026thinsp;3 per fraction and per genotype (exclusion criteria: \u0026gt;6 reads/transcripts). 2a. the APA-shift calculation was made on cytosolic and nuclear fractions N\u0026thinsp;=\u0026thinsp;6 per genotype (exclusion criteria: \u0026gt;12 reads/transcripts). 2b. the significant transcripts from 2a were sorted per fraction (cytosolic or nuclear) and p-values were recalculated per fractions.\u003c/p\u003e \u003cp\u003eTranscript differential expression analysis was made in edgeR Bioconductor package (v3.42.4) \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Transcript list excluded read count\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;1 count per sample. TMM normalized read counts were log-transformed. Main variations between samples were assessed unsupervised with the principal component analysis (PCA). Differential expression analysis was calculated with the Empirical Bayes in edgeR with the \u003cem\u003edecideTest\u003c/em\u003e and \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 corrected for false discovery rate (FDR). Differential expression analysis was made between fractions (N\u0026thinsp;=\u0026thinsp;6 per fraction) or between Dox-induced and vehicle-treated cells (N\u0026thinsp;=\u0026thinsp;3 per fraction). Volcano plot visualization of differential expression was made in \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ggvolcanor.erc.monash.edu/\u003c/span\u003e\u003cspan address=\"https://ggvolcanor.erc.monash.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003csup\u003e26\u003c/sup\u003e. Expression profiles across fractions was determined in Perseus Software v2.0.7.0. Hierarchical clustering of Z-scores was made for the 877 overlapping DE transcripts between cytosolic and nuclear fractions using Euclidean distance.\u003c/p\u003e\n\u003ch3\u003eRT-qPCR\u003c/h3\u003e\n\u003cp\u003eRT-qPCR was conducted on RNA extracted from the Dox-induced and vehicle-treated cells. 500 ng RNA was reverse transcribed for cDNA synthesis using the QuantiTect Reverse Transcription Kit (QIAGEN) and random primers, following the manufacturer\u0026rsquo;s instructions. Subsequently, qPCR amplification was performed with the QuantiNova SYBR Green kit (QIAGEN) using 5 ng RNA, with technical duplicates, using a standard amplification protocol at a melting temperature of 60\u0026deg;C. Samples with CT values above 35 were excluded from the analysis to eliminate potential noise. The average CT values from the technical duplicates and normalization to the HPRT1 gene were used for ddCT calculation. Two primer sets were used: a primer set to exon 3\u0026ndash;4 PABPN1 (ENSG00000100836), amplifies both the Ala16-PABPN1 transgene and the endogenous PABPN1, and the second primer set to the 3\u0026rsquo;-UTR amplifies only the endogenous transcript. Primer sets were designed with the NCBI Primer design tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/tools/primer-blast\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/tools/primer-blast\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the primers are listed in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImmunofluorescence and staining\u003c/h2\u003e \u003cp\u003eCell fixation was performed with 4% Formaldehyde in PBS for 5 minutes. KCl treatment was before to cell fixation was with 1M KCl for 15 minutes, followed by fixation with 4% Formaldehyde for 5 minutes. Subsequently, permeabilization was performed with 1% Triton-X100 for 10 minutes, followed by PBS washing and first antibody incubation for one hour at room temperature, followed by 30 minutes incubation with a fluorophore-conjugated secondary antibody and DAPI. Antibodies are listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Antibody incubation and washing steps were made with PBS\u0026thinsp;+\u0026thinsp;0.05%-Triton-X. Cells were left in PBS during imaging.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCellular assays\u003c/h2\u003e \u003cp\u003eCellular assays were conducted in vehicle-treated or Dox-induced cell cultures for 4 days.\u003c/p\u003e \u003cp\u003e \u003cem\u003eOligo-dT hybridization\u003c/em\u003e: cell cultures were fixed using 3.7% FA for 15 minutes at RT. After two PBS washes, the cells were incubated in protease III and diluted 1:30 in PBS (#322337 Advanced Cell Diagnostics) for 15 minutes at RT. After twice PBS washes cells were incubated in a hybridization buffer (#10369 Cepham Life Sciences) for 15 minutes at RT. Incubation with 5\u0026rsquo;-Cy5-Oligo-dT12-18 probe (#26-4400-02 Gene Link), diluted 1:1000 in hybridization buffer, was carried out overnight at 40 degrees in a humidified chamber. The following day, washes were carried out at 40 degrees for 5 minutes with 4x, 2x, and 1x SSC buffer and with PBS. Finally, the cells were incubated with Hoechst and kept in PBS during imaging.\u003c/p\u003e \u003cp\u003e \u003cem\u003eRNAscope\u003c/em\u003e: Spatial localization of a single RNA molecule was carried out in adherent muscle cells using the RNAscope Fluorescent Multiplex Assays kit according to the manufacturer\u0026rsquo;s protocol (ACD biotech) with the following modifications: the protease solution was diluted 1:30, and the amplifier solutions were diluted 1:2. Ten genes were included in the RNAscope: The positive control probe mix, provided by the company (POLR2A, UBC, and PPIB). The second probe mix included PABPN1 and MYF5. The probes for POLR2A, UBC, PPIB, and MYF5 are from the Human probe set (ACD biotech). The PABPN1 probe is from the mouse set, and the homology between human and mouse is \u0026gt;\u0026thinsp;95% for the probe regions. All probes were previously demonstrated in human muscle cells \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLMB treatment\u003c/em\u003e: cells were treated with 37.5 nM LMB (LKT Labs, St. Paul, USA) for 3 hours at 37\u0026deg;C or DMSO (dilution 1:1000) Mock control.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eProtein synthesis assay\u003c/strong\u003e \u003cp\u003ethe protein synthesis assay kit (Cayman Chemicals #601100) was conducted according to the manufacturer protocol using azido-O-propargyl-puromycin (OPP)-488 (named here OPP). A 30-minute pre-incubation with 20\u0026micro;M cycloheximide was used as a negative control. Hoechst was added after fixation. OPP was imaged with a 488 filter.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMitochondrial activity\u003c/strong\u003e \u003cp\u003eJC-1 staining was carried out in living cells with JC-1 5\u0026micro;M (final concentration), and Hoechst 1mM final concentration (33342 ThermoFisher) added to the growth medium and incubated for 30 minutes at 37\u0026deg;C. Cells were washed once with PBS and kept in a growth medium during imaging. JC-1 was imaged with 488 and 560nm filters.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGlucose uptake assay\u003c/strong\u003e \u003cp\u003ewas conducted in cell cultures treated with Dox and incubated in a differentiation medium for four days. According to the manufacturer protocol, the glucose update was determined with the Glucose Uptake-Glo\u0026trade; (Promega # J1341). Fluorescence was measured with the SpectraMax iD3 multi-mode microplate reader (Molecular Devices) recorded at 1-second integration.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImaging and image quantification\u003c/h2\u003e \u003cp\u003eThe CellInsight CX7 LZR high-content screening (HCS) platform was used for high-content imaging. The accompanying HCS Platform spot detector and co-localization toolbox (ThermoFisher Scientific) performed a cell-based analysis.\u003c/p\u003e \u003cp\u003eImaging was used to calculate the differentiation index, which was done with a 10x objective covering over 12,000 nuclei per well. Using the co-localization toolbox, the differentiation index was quantified by the percentage of myonuclei without MyHC objects.\u003c/p\u003e \u003cp\u003eImaging for PABPN1 quantification, oligo-dT, OPP, and JC1 was made with a 20x objective, covering at least 5000 nuclei per well. The spot detection toolbox was employed. JC-1, OPP, and RNAscope were analysed from the perinuclear region. Oligo-dT and PABPN1 signals were measured from both nuclear areas.\u003c/p\u003e \u003cp\u003eConfocal microscopy imaging: Fixed single nuclei were imaged with a Leica DMi8 with the Andor Dragonfly spinning disc module using a 40x/1.3 or 63x/1.3 oil immersion objective. Identical imaging settings, including exposure time, laser power, the excitation-emission range, and Z-stacks step size, were employed within an experiment. Quantifications of confocal images were carried out in ImageJ.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eElectron microscopy\u003c/h2\u003e \u003cp\u003eThe embedded OPMD muscle biopsy was reported in \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Differentiated cell cultures were fixed in 1.5% glutaraldehyde in 0.1 M Sodium Cacodylate buffer for 2 hours, and successively incubated in 1% Osmium Tetroxide in 0.1 M cacodylate buffer for 1 hour and in 1% Uranyl Acetate in water for 1 hour. The cells were then dehydrated through a series of incubations in Ethanol (70\u0026ndash;100%) for 90 minutes and embedded in Epon. The flat embedded cells were sectioned with an ultramicrotome (UC6, Leica, Vienna) using a 35-degree diamond knife (Diatome, Biel, Switzerland) at a nominal section thickness of 90 nm. The sections were transferred to a formvar, and a carbon-coated 1 \u0026times;2 mm copper slot grid and stained for 20 minutes with 7% uranyl acetate in water for 10 minutes with lead citrate. EM images were recorded using a Tecnai 12 electron microscope (Thermo Fisher Scientific) with an EAGLE 4k\u0026times;4k digital camera.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell biomechanics\u003c/h2\u003e \u003cp\u003eCell membrane force experiments were performed using a CellHesion 200 instrument (JPK, Berlin, Germany) equipped with a Petri dish heater to maintain a temperature of 37\u0026deg;C. Cantilevers with a cylindrical tip having a 5-micron end radius and nominal spring constants ranging from 0.166\u0026ndash;0.179 N m-1 were used (Bruker, SAA-SPH-5UM). The spring constant calibration was performed using the thermal noise method \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Cells were approached at a loading rate of 5 \u0026micro;m s\u0026thinsp;\u0026minus;\u0026thinsp;1 with a maximum force set-point of 0.473 nN. Each cell was indented three times, and 10 cells were probed per condition. Between cell indentations, the substrate was probed to ensure the tip\u0026rsquo;s cleanliness. Cells were indented above the nuclear region to reduce variability and substrate artifacts. All experiments were analysed using JPK Data Processing to determine the Young\u0026rsquo;s Modulus of the cells. The Hertz model was used for calculating Young\u0026rsquo;s modulus, as described in \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. This model is valid for small indentation depths and is expressed as follows for a spherical indenter:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1736787076.png\"\u003e\u003cbr\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eF\u003c/em\u003e is the indentation force, \u003cem\u003eR\u003c/em\u003e is the radius of the indenter, \u003cem\u003eE\u003c/em\u003e is Young\u0026rsquo;s modulus, \u003cem\u003eν\u003c/em\u003e is Poisson\u0026rsquo;s ratio and \u003cem\u003ed\u003c/em\u003e is the indentation depth.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted in R 4.2.3 and GraphPad Prism 9.3.1. The statistical significance of the APA-shift was assessed by applying the Benjamini-Hochberg method to adjust the \u003cem\u003ep\u003c/em\u003e-values for controlling the FDR.\u003c/p\u003e \u003cp\u003eEnrichment analysis for the differential abundance proteins in each cluster and DE transcripts in cytosol fraction was conducted within DAVID (v2023q3, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The gene network databases Reactome and Gene Ontology were selected for enrichment analysis. Fifty proteins were enriched in the Metabolism of RNA based on the Reactome database and were used for the Gene-Disease enrichment analysis at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/Enrichr\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/Enrichr\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Reads at the 3\u0026prime;-UTR were visualized using IGV version 2.16.0. Candidates with 3\u0026prime;-UTR lengths above 1 kb were selected for visualization.\u003c/p\u003e \u003cp\u003eLinear regression analysis for transcriptome-proteome correlation was made in \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.statskingdom.com\u003c/span\u003e\u003cspan address=\"https://www.statskingdom.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eEthics\u003c/h2\u003e \u003cp\u003eMuscle biopsy collection has been approved by the Radboud Medical center ethics committee in accordance with the ethical standards laid down by the 1964 Declaration of Helsinki. The patients signed on informed consent prior to biopsy collection.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eThe A16 muscle cell model represents PABPN1 aggregation in OPMD\u003c/h2\u003e \u003cp\u003eTo explore the molecular mechanisms driven by PABPN1 nuclear aggregates, we generated stable cells expressing the Ala16-PABPN1 cDNA under the tetracycline-inducible promoter (here designated A16). Inducible expression overcame the toxic effect of A16-PABPN1 by constitutive expression \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e{Fan, 2001 #\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e}. To address the involvement of skeletal muscle in OPMD, we used human skeletal muscle cells in this study. Western blot confirmed the expression of Ala16-PABPN1 in doxycycline (Dox)-induced muscle cell culture (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The accumulation of soluble PABPN1 after Dox induction was 2-fold higher than in vehicle-treated cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The overexpression level in our muscle cells model is about 10-fold lower than those reported in the OPMD mouse model, A17.1 \u003csup\u003e33,34\u003c/sup\u003e. We then confirmed the accumulation of insoluble PABPN1 using fractionation of the soluble and insoluble fractions. Levels of PABPN1 in the insoluble fraction in Dox-induced cells was 6-fold higher than in vehicle-treated cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The level of insoluble PABPN1 was 3-fold higher than that of the soluble protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Taken together, overexpression after Dox induction leads to a marked accumulation of PABPN1 in the insoluble fraction. We refer to the Dox-induced cell culture as A16.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eImmunofluorescence in Dox-treated cells confirmed the presence of nuclear-insoluble Ala16 using KCl treatment, which eliminated most PABPN1 puncta in nuclear speckles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Total PABPN1 fluorescence intensity showed a 1.5- to 2-fold increase in A16 cells compared to vehicle-treated cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, MFI\u0026thinsp;\u0026gt;\u0026thinsp;0), consistent with Western blot results. PABPN1 intensity in puncta (MFI\u0026thinsp;\u0026gt;\u0026thinsp;400) was found only in A16 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Fluorescence intensity in puncta was unchanged after KCl treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE), indicating that PABPN1 puncta represents the insoluble protein.\u003c/p\u003e \u003cp\u003eTo assess whether aggregates in the inducible cell model represent the disease, we examined the structure of nuclear aggregates by electron microscopy. We found electron-lucent areas in the A16 myonuclei (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF), as in the OPMD muscle biopsy (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The electron-lucent area contained short fibrils in the muscle cell model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG) and longer fibrils in the muscle biopsy (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). This difference is expected considering aggregates develop over the years in OPMD patients, but only after five days in the muscle model.\u003c/p\u003e \u003cp\u003eIntranuclear inclusions containing p62 have recently been proposed as a histopathological marker for OPMD \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. To further assess whether our cell model represents the in vivo condition in OPMD, we performed immunohistochemistry in A16-PABPN1. In cells treated with Dox for 7 days, we found nuclear inclusion of p62 and colocalization with A16-PABPN1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). In vehicle-treated cell cultures and to a lesser extent in cell cultures treated with Dox for 5 days, p62 nuclear export was not detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). This suggests that p62 inclusion in OPMD is associated with PABPN1 nuclear aggregates as a secondary event.\u003c/p\u003e \u003cp\u003eThe A16 cell culture seems to be a suitable model for studying the molecular mechanisms driven by PABPN1 nuclear aggregates in OPMD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eBulk mRNA and protein profiles are distinguished between subcellular fractions\u003c/h2\u003e \u003cp\u003eTo investigate the impact of PABPN1 aggregates on cellular and molecular processes, we isolated proteins and RNA from the cytosolic, nuclear, and remnants insoluble fractions, and confirmed the fractionation using a Western blot (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA). Samples from vehicle-treated or A16 cell cultures were subjected to mass spectrometry (MS) and mRNA sequencing per fraction. We developed a pipeline to compare differences between fractions and vehicle-treated or A16 samples (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eC). As expected, the cytosolic fraction from both vehicle-treated or A16 samples contained the highest levels of both proteins and mRNA, and the lowest levels were found in the insoluble fraction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA, and S4B). The size of the cytosolic proteome was 10% larger than the nuclear proteome and 33% larger than the insoluble proteome (Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA). Larger differences were found for mRNA: the cytosolic library size was nearly 10-fold larger than the nuclear fraction and 100-fold larger than the insoluble fraction (Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eB). Since the same amount of total RNA was used for library preparation, this observation suggests that only 1% of the total RNA in the insoluble fraction is mRNA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe then assessed fractionation using the principal component analysis, surprisingly, the variation between fractions exceeded variation between genotypes (Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eC). Moreover, differential analysis of the proteome revealed a clear distinction between the fractions: 94% of the proteins were significantly localized to nuclear or cytosolic fractions, and 71% of the proteins to nuclear or insoluble fractions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). To further assessed fractionation we examined subcellular localization of autophagy proteins (ATG), which were highly enriched in the cytosolic fraction, while centromere proteins (CENP) were nuclear, and histone proteins (H2) accumulated in the insoluble fraction (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Correlation analysis between fractions showed a higher correlation between nuclear and insoluble fraction proteins than between the cytosolic fraction (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), indicating that most insoluble proteins are nuclear.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast to proteomic analysis, differential analysis of mRNAs showed that only 58% of mRNAs were differentially accumulated in cytosolic or nuclear fractions, and nearly all mRNAs in the insoluble fraction were also nuclear (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The correlation of mRNA between the cytosolic and nuclear fractions was higher than with the insoluble fraction (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), supporting shared material between fractions and nuclear export.\u003c/p\u003e \u003cp\u003eTaken together, the proteomic analysis between fractions confirms the fractionation. The differences between fractions dictated normalization for downstream analysis. Protein samples were normalized per fraction (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eC). For mRNA, nuclear and cytosolic mRNA samples were normalized together (N\u0026thinsp;=\u0026thinsp;12 samples), but insoluble samples were normalized separately (N\u0026thinsp;=\u0026thinsp;6) (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eThe A16 proteome is enriched by regulators of RNA metabolism of neuromuscular disorders\u003c/h2\u003e \u003cp\u003eWe examined the A16-PABPN1 effect on the proteome, analysis was carried out per fraction, and significance was considered by p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 corrected for multiple comparisons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA; Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Higher PABPN1 levels in all fractions and unchanged GAPDH levels in the cytosolic fraction were consistent with the Western blot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and S2A). PABPN1 fold-change in the nuclear and insoluble fractions was higher than in the cytosolic fraction (12X, 10X, and 5.7X, respectively, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Notably, PABPN1 fold-change was highest in the nuclear and insoluble fractions, whereas the most prominent changes in protein abundance were in the cytoplasmic fraction. The abundance of 13.5% of proteins was affected in the cytosolic fraction, but only 5.9% and 1.3% in the nuclear and insoluble fractions, respectively (Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eWe then explored a pattern across fractions of the A16 proteome (N\u0026thinsp;=\u0026thinsp;1087 proteins) using Euclidean clustering. Two major clusters were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and S5B). Cluster-1, with 435 proteins, showed an increase in abundance across all three fractions, similar to PABPN1, whereas cluster-2 (408 proteins) showed an opposite pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Proteins in cluster-2 were predominantly enriched for metabolic processes: nucleic acid biosynthesis, mitochondria and glucose metabolism, and pathways regulating cell structure: focal adhesion, cytoskeleton, and stress fiber (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eD and Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). In contrast, cluster-1 proteins were enriched only for processes related to RNA binding proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eFurther a gene-disease association analysis was performed for cluster-1 and six neuromuscular diseases were significantly enriched: OPMD, spinal muscular atrophy (SMA), myotonic dystrophy type 1 (DM-1), granulin-related frontotemporal lobar degeneration (GR-FTLD), inclusion body myositis (IBM), multiple sclerosis (MS), and amyotrophic lateral sclerosis (ALS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Four diseases are categorized as protein aggregation: OPMD, DM-1, GR-FTLD, IBM, and ALS. Among the disease-associated proteins, a strong positive correlation was found between PABPN1, SRSF1, SRSM2, HNRNPR, and HNRNPA1, but SRSM1, PABPC1, and TNPO1 showed a weak or negative correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). The expression pattern across fractions confirmed the clustering and showed that PABPN1, HNRNPR, SRSF1, and PABPC1 were enriched in the A16 insoluble fraction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). This suggests that common RNA-binding proteins are involved in protein aggregation diseases and specifically accumulate in insoluble aggregates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003ePABPN1 aggregation leads to reduced PABPN1 mRNA and APA impacting mRNA subcellular localization\u003c/h2\u003e \u003cp\u003eAggregation of the expanded PABPN1 is associated with reduced \u003cem\u003ePABPN1\u003c/em\u003e transcripts \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Using two sets of primers: one set to the exon region amplifies both the Ala16-PABPN1 transgene and endogenous PABPN1, and the second set to the 3' UTR amplifies only the endogenous transcript (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), we found reduced endogenous PABPN1 in A16 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). This further indicates that our cell model is consistent with OPMD and suggests that Ala16 PABPN1 overexpression leads to reduced PABPN1 expression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe then investigated whether the molecular function of PABPN1 is affected by PABPN1 aggregation in this cell model. We calculated the APA shift in the cytosolic and nuclear fractions using two methods. The insoluble fraction was not included in the APA-shift calculation due to the low number of transcripts. The first APA-shift calculation was performed per fraction, resulting in 1151 transcripts with APA-shift in the cytosolic fraction but none in the nuclear fraction (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). To verify the p-value, we took a two-step approach: first, we analyzed the cytosolic and nuclear fractions together, resulting in 2793 transcripts with significant APA-shift, among which a shift to proximal was prominent (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). A prominent shift to proximal is consistent with results in the A17.1 mouse model \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Subsequently, the significant transcripts with APA-shift (N\u0026thinsp;=\u0026thinsp;2793) were reanalyzed for APA-shift per fraction. Most of the transcripts were cytosolic (96%), but 990 transcripts showed a significant APA-shift in the nuclear fraction (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The identification of transcripts with significant APA shift in the nuclear fraction contrasted with the analysis per fraction of the raw data. We suspected that this difference in results was due to p-value calculation over \u0026gt;\u0026thinsp;51,000 transcripts compared to 2793 transcripts. To assess the validity of the p-value calculation, we visualized the read counts at the 3' UTR. The PMP22-202 transcript had APA-shift\u0026thinsp;=\u0026thinsp;4.2 (p-value, FDR 0.015) in the cytosolic fraction but was insignificant in the nuclear fraction. The HSPA4-201 transcript had APA-shift\u0026thinsp;=\u0026thinsp;1.92 (p-value, FDR 0.045) in the nuclear fraction, but was not significant in the cytosolic fraction. The CLSTN1-201 transcripts had a significant APA shift in both the cytosolic and nuclear fractions: APA-shift\u0026thinsp;=\u0026thinsp;2.76 (p-value, FDR 0.029) and APA-shift\u0026thinsp;=\u0026thinsp;2.35 (p-value, FDR 0.042), respectively. This indicates that the p-value calculation made by the two-step approach is correct. Among the 990 nuclear transcripts with APA-shift, the majority (89%) were also cytosolic (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), suggesting nuclear export and cytosolic accumulation of transcripts with short 3'-UTR. Previous studies have shown that short 3\u0026rsquo;-UTRs are less efficiently degraded and therefore more stable \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Taken together, in our cell model PABPN1 molecular function is impaired and is associated with aggregation and reduced PABPN1 expression levels.\u003c/p\u003e \u003cp\u003eThe Ala16-PABPN1 showed the most dramatic effect on mRNA levels in the cytosolic fraction: 25% of transcripts (N\u0026thinsp;=\u0026thinsp;11572) were dysregulated, whereas in the nuclear fraction, only 2.5% (N\u0026thinsp;=\u0026thinsp;1108) were affected (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA; Table S6). In the insoluble fraction, dysregulation did not pass the p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FDR criteria. Considering the unadjusted p-value, 187 transcripts were found, including the PABPN1 isoform (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Upregulated transcripts in the insoluble fraction (96%) were exclusively enriched for RNA pathway metabolism (p\u0026thinsp;=\u0026thinsp;0.027, Bonferroni corrected) and included RBP transcripts associated with protein aggregation such as PABPN1, HNRNPH1, RBM5, and DDX5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe cDNA library preparation with one amplification cycle representing RNA dynamics in situ \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, allowed us to study the effect of Ala16-PABPN1 on mRNA dynamics between fractions. The library size of the insoluble fraction was too small for comparative analysis between fractions and was not included. Nuclear export is a highly dynamic process, and under normal conditions (A16 unaffected) only 1% of the nuclear transcripts are localized to the nucleus, with the remaining 99% overlapping with cytosolic transcripts (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). However, 21% of the A16 dysregulated nuclear transcripts do not overlap with the A16 dysregulated transcripts (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), suggesting aberrant nuclear export of the dysregulated transcripts. Furthermore, 24% of the cytoplasmic transcripts did not overlap with the nuclear fraction under normal conditions, but 92% of the A16-dysregulated transcripts specifically accumulated in the cytosol (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The accumulation of transcripts in the cytoplasmic fraction suggests impaired RNA decay, which is consistent with the enrichment of the RNA decay protein network in the A16 proteome.\u003c/p\u003e \u003cp\u003eOf the dysregulated cytosolic transcripts, 42% showed an APA-shift, with a shift to the proximal region (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). In contrast, only 8% of the nuclear-dysregulated transcripts showed an APA shift (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The number of upregulated transcripts with APA shift was significantly higher (3-fold) than the downregulated transcripts (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), which is consistent with the higher mRNA stability of transcripts with a shift to proximal \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConsistent with the RT-qPCR results, we identified a significant downregulation of PABPN1 (iso-201 and iso-202) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). PABPN1 isoforms were significantly reduced in the cytosolic fraction (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), consistent with reduced endogenous PABPN1 protein levels (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA). Euclidean clustering was used to identify transcripts whose expression pattern is associated with PABPN1, and we considered the A16 dysregulated transcripts with APA shift. Only two clusters were identified: the blue cluster (343 transcripts) showed an expression level pattern similar to PABPN1, and the red cluster (534 transcripts) showed an opposite pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). The blue cluster was enriched in pathways related to RNA metabolism and mitochondria, which were also enriched in the A16 proteome. The mitochondrial pathways were upregulated in the A16 proteome and transcriptome (Table S7). RNA metabolism-related pathways correlated with PABPN1 protein up-regulation, but with PABPN1 mRNA down-regulation (Table S7). The translation and cell cycle pathways in the blue cluster and all enriched pathways in the red clusters were not found in the proteome-enriched pathways (Table S7).\u003c/p\u003e \u003cp\u003eThe paired RNA-protein study design allowed us to investigate whether the effect of A16-PABPN1 on transcript folding directly affects protein folding. We examined a correlation between transcripts in the cytosolic fraction and their proteins in the cytosolic and nuclear fractions. A linear regression over the entire proteome showed very weak direct relationships between protein fold-change and transcript fold-change in both cytoplasmic and nuclear fractions (Figure S6). Only 0.5% and 0.2% of the proteins in the cytosolic and nuclear fractions, respectively, were predicted to show a linear correlation. For the significantly dysregulated transcripts and proteins, the correlation in the nuclear fraction was found to be insignificant (F-test\u0026thinsp;=\u0026thinsp;0.316), and the fold change of 1.6% of the deregulated nuclear proteins correlated directly with the fold change of the transcripts (Figure S6). In the cytosolic fraction, the linear regression model suggested a weak positive correlation, with 9.9% of protein fold-change directly correlating with transcript fold-change (Figure S6). The accumulation of nuclear and cytosolic proteins is differentially affected by A16-PABPN1.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eA16-PABPN1 expression impairs mRNA nuclear export and cell metabolism\u003c/h2\u003e \u003cp\u003eTo investigate the impact of the A16 proteome on cell function, we considered pathways identified by proteome enrichment analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The oligo-dT-cy5 signal reported alterations in RNA metabolism and mRNA 3'-end processing, and in situ hybridization revealed mRNA nuclear puncta in Ala16 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The mRNA signal in Dox-treated cells was 2.5-fold higher than in vehicle-treated cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), and the nuclear-to-perinuclear ratio showed a 5-fold increase in A16 cells compared to vehicle-treated cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). This suggests impaired nuclear export and/or nuclear trafficking. We evaluated the nuclear export of mRNA by leptomycin B (LMB) treatment on oligo-dT-Cy5 localization. A 2-fold increase in nuclear oligo-dT signal was found in LMB-treated vehicle cells, but in Dox-treated cells, the signal was unchanged after LMB treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). To confirm the nuclear accumulation of mRNA in A16 cells, we detected single mRNA molecules for three A16 up-regulated genes: \u003cem\u003ePIPB\u003c/em\u003e, \u003cem\u003eUBC\u003c/em\u003e, and \u003cem\u003eMYF5\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). PABPN1 was used as a control. The RNA single molecule signal was higher for \u003cem\u003ePABPN1, PPIB, UBC\u003c/em\u003e, and \u003cem\u003eMYF5\u003c/em\u003e in A16 nuclei (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). LMB treatment resulted in a higher mRNA signal in vehicle-treated cells, but the signal was unchanged in A16 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). These results indicate impaired nuclear export in A16 cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we measured translation efficiency using azido-OPP and found reduced translation efficiency in A16 cell cultures compared to vehicle-treated cell cultures (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH). Taken together, in A16 cells, mRNA nuclear entrapment is associated with impaired mRNA nuclear export and reduced translational efficiency.\u003c/p\u003e \u003cp\u003eAmong the affected pathways, we examined mitochondrial activity and glucose uptake. Mitochondrial activity was significantly reduced in A16 cells compared to vehicle-treated cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Similarly, glucose uptake was significantly reduced in A16 cells, with lower glucose uptake than vehicle-treated cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). The reduced metabolic activity in A16 cells is consistent with the expression pattern of proteome cluster 2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe A16 proteome was also enriched for pathways affecting muscle cell differentiation and cell biomechanics (cytoskeleton, focal adhesion, and stress fiber). Differentiated cells, recognized by multinucleated cells expressing myosin heavy chain (MyHC), were formed in both vehicle-treated and A16 cell cultures (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD), but the percentage of differentiation rate (fusion index) was lower in A16 cell cultures (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). The reduced cell fusion in A16 cells was associated with reduced cell biomechanics as measured by atomic force microscopy in differentiated cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF). Cell stiffness was significantly reduced in A16 cells compared to parental and vehicle-treated cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG). This indicates that reduced expression of cytoskeletal and focal adhesion proteins negatively affects cell differentiation and biomechanics.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eProtein aggregates are a hallmark of aging and age-related diseases. Protein aggregates disrupt protein homeostasis networks in a feed-forward regulatory loop that affects multiple cellular processes \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Pathogenic protein aggregates are often cytosolic, and these are distinguished from nuclear aggregates by molecular and structural properties \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. In addition, protein clearance pathways differ between nuclear and cytoplasmic regions \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Taken together, insights into the consequences of protein aggregates should be gained in subcellular regions. Here we provide insights into the molecular networks and cellular (dys)functions driven by nuclear aggregates produced by the pathogenic form of PABPN1. In contrast to other studies that elucidate how protein aggregates affect cell function in disease-irrelevant cell types \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, we model protein aggregation in muscle cells. Recent studies have highlighted the cell type specificity of proteostasis control and protein aggregation \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The aggregates in our human muscle cells share similar features, such as KCl resistance, fibrils in the electron-lucent region, and nuclear sequestration of p62. We also show that PABPN1 molecular function, namely APA suppression, is also affected in this cell model. Together, our cell model is relevant to study the effect of PABPN1 aggregates on muscle cell function.\u003c/p\u003e \u003cp\u003eAPA and reduced translation efficiency have been reported in cell cultures with reduced PABPN1 expression levels \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Consistent with this, we found that PABPN1 levels are reduced in our cell model, in the OPMD mouse model A17.1, and in OPMD muscles \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Taken together, we propose that in OPMD, PABPN1 loss-of-function causes APA dysregulation and gain-of-function due to PABPN1 protein aggregation, which sequesters bulk mRNA and induces overexpression of multiple RBPs in the insoluble fraction. Consistent with this model, TDP-43 loss-of-function due to its aggregation has been proposed as a disease mechanism in ALS \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, which is associated with nuclear accumulation and reduced nuclear export \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Although it is not entirely clear how PABPN1 aggregates lead to reduced PABPN1 levels, a previous study suggested that PABPN1 regulates its expression levels through RNA editing: overexpression of PABPN1 led to the accumulation of unspliced \u003cem\u003ePABPN1\u003c/em\u003e transcript resulting in reduced levels of the endogenous \u003cem\u003ePABPN1\u003c/em\u003e mRNA \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Here, we show that \u003cem\u003ePABPN1\u003c/em\u003e mRNA is sequestered in the nuclei of A16 cells, resulting in reduced cytoplasmic levels of the mRNA and reduced protein levels.\u003c/p\u003e \u003cp\u003eNuclear insoluble aggregates are a hallmark of OPMD \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Therefore, one might expect to see the most dramatic changes in the insoluble fraction. Instead, proteins in the cytoplasmic fraction were most affected in A16 cells compared with the insoluble and nuclear fractions. The nuclear pathways associated with A16-PABPN1 expression primarily affected RNA metabolism. Most interesting RBPs are associated with other neuromuscular diseases and protein aggregation disorders, such as DM-1, GR-FTLD, IBM, and ALS. The accumulation of such RBPs in the insoluble fraction may indicate a depletion of the functional protein and an additional effect on mRNA metabolism beyond APA and polyA tail length. PABPN1 has been implicated in the nuclear export of \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, and here we show that nuclear export is impaired in A16 cells. In addition, the mRNA surveillance pathway was significantly enriched in the A16 proteome, consistent with an aberrant accumulation of cytoplasmic mRNA in A16 cells. Cytosolic PABPC1 and PABP4 are regulators of the mRNA decay pathway \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, and their expression levels were significantly altered in A16 cells. The role of PABPN1 in the cytosol is poorly understood; it has been proposed that PABPN1 may functionally replace PABPC1 in the nonsense-mediated mRNA decay pathway \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe A16 enriched cytoplasmic pathways includes proteins involved in cell metabolism energy, and cell structure These proteins were frequently downregulated in A16 cells, suggesting reduced function affected by these pathways. Indeed, we demonstrated reduced mitochondrial activity and glycolysis in A16 cells. Both energy production pathways have been implicated in many neuromuscular diseases, including protein aggregation disorders \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Disruption of protein networks that shape the cell structure is also common in models of protein aggregation disorders \u003csup\u003e\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. We demonstrate a significant effect on cell biomechanics that can be translated into reduced contraction. Importantly, the same cytosolic pathways were found to be affected in muscle models with reduced PABPN1 levels \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. For example, reduced expression of PABPN1 expression affects the expression cytoskeletal proteins, muscle cell fusion, and cell biomechanics \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. This suggests that reduced levels of PABPN1, or PABPN1 aggregation results in similar cell dysfunction. Our study demonstrates that the A16 proteome affects broad cellular pathways that influence cell metabolism, thereby affecting muscle cell function and biomechanics, and is consistent with growing evidence of a strong link between cellular metabolism and mechanics \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCo-localization experiments in PABPN1 nuclear aggregates revealed the sequestering of various proteins, including proteostasis regulators \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. We show that p62 is sequestered in PABPN1 aggregates, which is consistent with p62 accumulation in OPMD myonuclei and could account for reduced autophagy in OPMD models \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Although the expression of genes involved in the ubiquitin-proteasome system (UPS) and autophagy have been implicated in OPMD \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, and proteasomal activity is impaired in OPMD models \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, we did not find significant dysregulation of protein networks regulating protein homeostasis. We found p62 co-localization in PABPN1 aggregates later than proteomic changes or mRNA sequestration in PABPN1 aggregates. This suggests that UPS and autophagy dysregulation are secondary in OPMD. Given that UPS and autophagy dysfunction is associated with aging \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, it is possible that prolonged culture time with PABPN1 nuclear aggregates could affect protein levels of the UPS and autophagy protein levels.\u003c/p\u003e \u003cp\u003eTaken together, our data demonstrate that RBPs associated with PABPN1 are involved in protein aggregation diseases, supporting common mechanisms among RBP protein aggregation diseases. Based on our findings, we propose that OPMD pathology involves mRNA sequestration within PABPN1 nuclear aggregates, which affects subsequent mRNA-associated cellular processes such as nuclear export and RNA decay. Furthermore, we propose that PABPN1 loss of function leads to APA dysregulation, which subsequently affects translation, cellular pathways, and cellular mechanics. A better understanding of the role of PABPN1 in the cytosol and its intracellular dynamics may help to develop therapeutic strategies for OPMD and other RBP protein aggregation diseases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study work was supported the argenx and by the PPS Holland Health #21802. Authors are responsible for the accuracy of their funder designation, facilitating compliance with funder requirements.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: MS, AM, TS, VR\u003c/p\u003e\n\u003cp\u003eMethodology: MS, SF, EB, TE, VS, AM, TS, RF, BMK, VR\u003c/p\u003e\n\u003cp\u003eSoftware: MS, VR\u003c/p\u003e\n\u003cp\u003eValidation: MS, TE, VS, VR\u003c/p\u003e\n\u003cp\u003eFormal Analysis: MS, TE, VS, VR\u003c/p\u003e\n\u003cp\u003eInvestigation: MS, AM, TS, VR\u003c/p\u003e\n\u003cp\u003eData Curation: MS, SF\u003c/p\u003e\n\u003cp\u003eVisualization: MS, EB, TE, VS, VR\u003c/p\u003e\n\u003cp\u003eResources: BK, BE, VR\u003c/p\u003e\n\u003cp\u003eSupervision: AM, TS, RF, VR\u003c/p\u003e\n\u003cp\u003eProject Administration: VR\u003c/p\u003e\n\u003cp\u003eFunding Acquisition: VR\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; Original Draft: MS, EB, TE, VS, VR\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; Review \u0026amp; Editing: MS, EB, TE, AM, RF, BMK, VR\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData availability\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD055515.\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe sequencing data generated in this study have been deposited in the Gene Expression Omnibus (GEO) under accession number GSE277571. 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PLOS Genetics 18, e1010015 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pgen.1010015\u003c/span\u003e\u003cspan address=\"10.1371/journal.pgen.1010015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Supplementary Table","content":"\u003cp\u003eSupplementary Table S5 is not available with this version.\u003c/p\u003e\u003cp\u003eTable S5: APA shift analysis \u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Protein aggregates, Pathological Protein, PABPN1, RNA metabolism","lastPublishedDoi":"10.21203/rs.3.rs-5783239/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5783239/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDisease-associated RNA binding protein (RBP) aggregation is a hallmark of several age-related neurodegenerative diseases. How insoluble RBP aggregates leads to cellular dysfunction is poorly understood. Here, we investigated the molecular mechanisms affected by insoluble PABPN1 aggregates. PABPN1 aggregates are nuclear, but PABPN1 regulates nuclear export of mRNA. To explore the cellular consequences of PABPN1 nuclear aggregates, we performed RNA sequencing and proteomic studies in subcellular fractions in an inducible human muscle cell model. RNA sequencing analyses revealed PABPN1 dysfunction in this cell model associated with reduced endogenous PABPN1 levels. Proteomic analyses revealed that most of the changes driven by PABPN1 nuclear aggregates were in the cytoplasmic fraction, accounting for reduced cell metabolism, muscle cell differentiation and muscle cell biomechanics. Changes in the insoluble fraction were small but enriched for RBPs. We show that sequestration of mRNA in nuclear aggregates is associated with impaired nuclear export of mRNA and reduced translational efficiency. Our study suggests that RBPs nuclear protein aggregates are regulated by both gain-of-function and loss-of-function mechanisms, which is relevant for the development of therapeutics for age-associated protein aggregation diseases.\u003c/p\u003e","manuscriptTitle":"Omics studies of nuclear protein aggregates in subcellular fractions reveals co- aggregation of RNA-binding proteins affecting cytosolic pathways","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-13 17:10:11","doi":"10.21203/rs.3.rs-5783239/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2e18a573-13bb-4286-9b33-ec3ebf2aeac8","owner":[],"postedDate":"January 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42682803,"name":"Biological sciences/Genetics"},{"id":42682804,"name":"Biological sciences/Neuroscience"},{"id":42682805,"name":"Health sciences/Diseases"},{"id":42682806,"name":"Health sciences/Pathogenesis"}],"tags":[],"updatedAt":"2025-03-12T14:38:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-13 17:10:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5783239","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5783239","identity":"rs-5783239","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}