<HashMap><database>biostudies-arrayexpress</database><scores/><additional><submitter>Camille Stephan-Otto Attolini</submitter><organism>Mus musculus</organism><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/E-MTAB-16295</full_dataset_link><description>Metabolic dysfunction-associated steatohepatitis (MASH) and its progression to hepatocellular carcinoma remain major clinical challenges. Chronic endoplasmic reticulum (ER) stress, induced by sustained high-fat diet (HFD) intake, promotes hepatic inflammation, lipid accumulation, and hepatocellular dysfunction during MASH pathogenesis. While transcriptional responses are well-characterized, the post-transcriptional mechanisms underlying hepatocyte adaptation to chronic ER stress remain poorly understood. Using an integrative approach combining transcriptomics, ribosome profiling, cytoplasmic polyadenylation analysis, and cis-regulatory mapping, we define the post-transcriptional landscape induced by chronic HFD exposure. To delineate the specific role of chronic ER stress, we use a hepatocyte-specific knockout of a key regulator of translational control under prolonged ER stress. We show that ~70% of HFD-induced gene expression changes are modulated at the translational level. A distinct subset of mRNAs - enriched in suboptimal codons and bearing short poly(A) tails under normal diet - becomes selectively activated upon HFD-induced poly(A) tail elongation. These transcripts, associated with cell cycle, immune response, fibrosis, and tissue remodeling, correlate with MASH severity in both murine models and human samples. Their regulation is mediated by cis-elements in the 3'UTR that coordinate polyadenylation and deadenylation. Loss of this adaptive response exacerbates liver damage and tumor burden in HFD-fed mice.</description><repository>biostudies-arrayexpress</repository><sample_protocol>Sequencing - Libraries were pooled and sequenced with HiSeq 2500.</sample_protocol><sample_protocol>Sample Treatment - Male C57BL/6J mice were housed under standard conditions. At 6 weeks of age, mice were randomly assigned to either a high-fat diet (HFD) or a normal diet (ND) and fed HFD or ND for 30 weeks when the experiments were perfomed.</sample_protocol><sample_protocol>Library Construction - RNA libraries were prepared for sequencing using the kit NEBNext® Ultra™ II RNA Library Prep Kit for Illumina® (Cat#E7770), following manufacturer´s instructions and 10-11 cycles of amplification.</sample_protocol><sample_protocol>Sample Collection - A piece of aproximately 30 mm3 of snap frozen murine liver was homogenized in lysis buffer [20 mM Tris-HCl pH 7.8, 10 mM MgCl2, 100 mM KCl, 1% Triton X-100, 2 mM DTT, 100 μg/ml CHX, complete protease inhibitors] using a bead beating grinder.  Lysates were passed through a 26G needle (BD bioscience) for further homogenization, and centrifuged at 4°C 1300xg for 10 min. Supernatants were collected and used to perform Ribosome profiling (RP) and total mRNA isolation, as previously described in Slobodin et al. 2017.</sample_protocol><sample_protocol>Nucleic Acid Extraction - RNA was isolated using TRI-sure reagent (Bioline) according to the manual, using Glycoblue (Ambion) as a carrier.</sample_protocol><figure_sub>Organization</figure_sub><figure_sub>MINSEQE Score</figure_sub><figure_sub>Assays and Data</figure_sub><figure_sub>Processed Data</figure_sub><figure_sub>MAGE-TAB Files</figure_sub><data_protocol>Data Transformation - Processed data includes RPKM values for each sample</data_protocol><data_protocol>Sequence Alignment - Reads were aligned to the mm10 genome using STAR 2.5.2 with default parameters. Counts per genomic feature were computed with the R package Rsubread</data_protocol><omics_type>Unknown</omics_type><omics_type>Transcriptomics</omics_type><omics_type>Genomics</omics_type><omics_type>Proteomics</omics_type><instrument_platform>Illumina HiSeq 2500</instrument_platform><study_type>RNA-seq of total RNA</study_type><species>Mus musculus</species><pubmed_authors>Camille Stephan-Otto Attolini</pubmed_authors></additional><is_claimable>false</is_claimable><name>Posttranscriptional reprogramming controls MASLD progression through chronic ER stress adaptation (RNAseq)</name><description>Metabolic dysfunction-associated steatohepatitis (MASH) and its progression to hepatocellular carcinoma remain major clinical challenges. Chronic endoplasmic reticulum (ER) stress, induced by sustained high-fat diet (HFD) intake, promotes hepatic inflammation, lipid accumulation, and hepatocellular dysfunction during MASH pathogenesis. While transcriptional responses are well-characterized, the post-transcriptional mechanisms underlying hepatocyte adaptation to chronic ER stress remain poorly understood. Using an integrative approach combining transcriptomics, ribosome profiling, cytoplasmic polyadenylation analysis, and cis-regulatory mapping, we define the post-transcriptional landscape induced by chronic HFD exposure. To delineate the specific role of chronic ER stress, we use a hepatocyte-specific knockout of a key regulator of translational control under prolonged ER stress. We show that ~70% of HFD-induced gene expression changes are modulated at the translational level. A distinct subset of mRNAs - enriched in suboptimal codons and bearing short poly(A) tails under normal diet - becomes selectively activated upon HFD-induced poly(A) tail elongation. These transcripts, associated with cell cycle, immune response, fibrosis, and tissue remodeling, correlate with MASH severity in both murine models and human samples. Their regulation is mediated by cis-elements in the 3'UTR that coordinate polyadenylation and deadenylation. Loss of this adaptive response exacerbates liver damage and tumor burden in HFD-fed mice.</description><dates><release>2026-03-05T00:00:00Z</release><modification>2026-03-05T10:03:29.115Z</modification><creation>2025-11-26T00:05:51.185Z</creation></dates><accession>E-MTAB-16295</accession><cross_references><ENA>ERP185651</ENA><EFO>EFO_0002944</EFO><EFO>EFO_0004170</EFO><EFO>EFO_0009653</EFO><EFO>EFO_0004917</EFO><EFO>EFO_0005518</EFO><EFO>EFO_0003816</EFO><EFO>EFO_0004184</EFO><EFO>EFO_0003969</EFO></cross_references></HashMap>