Results for “Ribo-seq”

PubMed2022

A custom library construction method for super-resolution ribosome profiling in Arabidopsis.

BACKGROUND: Ribosome profiling, also known as Ribo-seq, is a powerful technique to study genome-wide mRNA translation. It reveals the precise positions and quantification of ribosomes on mRNAs through deep sequencing of ribosome footprints. We previously optimized the resolution of this technique in plants. However, several key reagents in our original method have been discontinued, and thus, there is an urgent need to establish an alternative protocol. RESULTS: Here we describe a step-by-step protocol that combines our optimized ribosome footprinting in plants with available custom library construction methods established in yeast and bacteria. We tested this protocol in 7-day-old Arabidopsis seedlings and evaluated the quality of the sequencing data regarding ribosome footprint length, mapped genomic features, and the periodic properties corresponding to actively translating ribosomes through open resource bioinformatic tools. We successfully generated high-quality Ribo-seq data comparable with our original method. CONCLUSIONS: We established a custom library construction method for super-resolution Ribo-seq in Arabidopsis. The experimental protocol and bioinformatic pipeline should be readily applicable to other plant tissues and species.

3-nt periodicity↗

PubMed2025

High-quality peptide evidence for annotating non-canonical open reading frames as human proteins.

A major scientific drive is to characterize the protein-coding genome as it provides the primary basis for the study of human health. But the fundamental question remains: what has been missed in prior genomic analyses? Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states, with major implications for proteomics, genomics, and clinical science. However, the impact of ncORFs has been limited by the absence of a large-scale understanding of their contribution to the human proteome. Here, we report the collaborative efforts of stakeholders in proteomics, immunopeptidomics, Ribo-seq ORF discovery, and gene annotation, to produce a consensus landscape of protein-level evidence for ncORFs. We show that at least 25% of a set of 7,264 ncORFs give rise to translated gene products, yielding over 3,000 peptides in a pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, we developed an annotation framework for ncORFs and created public tools for researchers through GENCODE and PeptideAtlas. This work will provide a platform to advance ncORF-derived proteins in biomedical discovery and, beyond humans, diverse animals and plants where ncORFs are similarly observed.

GENCODE↗

PubMed2026

Translational reprogramming of TGF-β signaling via TRMT61A-mediated tRNA m1A drives prostatic fibrosis and hyperplasia.

Dysregulation of the epitranscriptomic landscape is closely linked to pathological proliferation, but its specific role in benign prostatic hyperplasia (BPH) remains unclear. Here, we identify the tRNA methyltransferase TRMT61A as a critical driver of BPH progression. We found that TRMT61A and global N1-methyladenosine (m1A) levels are aberrantly upregulated in human BPH tissues. Functionally, TRMT61A knockdown potently suppresses prostate cell proliferation and reduces stromal fibrosis, inducing G1 cell cycle arrest and reversing pathological remodeling both in vitro and in vivo. By integrating ribosome profiling (Ribo-seq) and tRNA-seq, we observed that TRMT61A drives translational reprogramming. TRMT61A preserves the stability of specific tRNA isoacceptors (e.g., tRNA-Leu-CAA), which is required for the efficient decoding of mRNAs containing m1A-dependent codons. Consequently, TRMT61A selectively promotes the translational elongation of the key receptor TGFβR1. This amplifies downstream TGF-β/SMAD signaling and drives epithelial-mesenchymal transition (EMT) without affecting mRNA transcription. In summary, our study reveals how TRMT61A drives BPH progression through TGFβR1 translation, highlighting the therapeutic potential of targeting epitranscriptomic pathways to reverse prostatic hyperplasia and fibrosis.

Male↗

PubMed2026

A Proteogenomic Approach to Discover Novel lncRNA-Derived Microproteins and Their Potential Clinical Utility in Hepatocellular Carcinoma.

Microproteins (i.e., peptides) are increasingly recognized for their functions in versatile biological contexts, but their clinical relevance and utility remain largely unexplored. Proteogenomic approaches can accelerate microprotein discovery in clinical samples by integrating proteomic data with genomics and transcriptomics evidence. However, long noncoding RNA (lncRNA)-derived microproteins (lncPeps) remain largely unidentified, resulting in unmatchable MS/MS spectra. To solve this problem, we have used high-quality Ribo-seq translatomic datasets to generate an extensive database of human liver lncRNA-derived open reading frames (lncORFs), which we subsequently applied to proteomics data of tumor-adjacent normal tissue pairs from hepatocellular carcinoma (HCC) patients. Using the new database, we discovered 104 novel lncPeps, including 46 lncPeps differentially expressed between tumor and nontumor tissues, and 13 lncPeps with significant correlation with prognosis. Remarkably, combining the expression of lncPeps with canonical proteins in a LASSO regression model improved predictive performance for recurrence, increasing the AUC by 0.005 to 0.085 across three recurrence time points. These findings suggest that the discovery of lncPeps contributes to our understanding of the molecular heterogeneity and progression of HCC and broadens the range of potential biomarker candidates and treatment targets for the disease.

Humans↗

PubMed2025

Temporal multiomics gene expression data of human embryonic stem cell-derived cardiomyocyte differentiation.

Human embryonic stem cells (hESCs) serve as a valuable in vitro model for studying early human developmental processes due to their ability to differentiate into all three germ layers. Here, we present a comprehensive multi-omics dataset generated by differentiating hESCs into cardiomyocytes via the mesodermal lineage, collecting samples at 10 distinct time points. We measured mRNA levels by mRNA sequencing (mRNA-seq), translation levels by ribosome profiling (Ribo-seq), and protein levels by quantitative mass spectrometry-based proteomics. Technical validation confirmed high quality and reproducibility across all datasets, with strong correlations between replicates. This extensive dataset provides critical insights into the complex regulatory mechanisms of cardiomyocyte differentiation and serves as a valuable resource for the research community, aiding in the exploration of mammalian development and gene regulation.

Humans↗

PubMed2026

seq2ribo: structure-aware integration of machine learning and simulation to predict ribosome location profiles from RNA sequences.

MOTIVATION: Ribosome dynamics are vital in the process of protein expression. Current methods rely on ribosome profiling (Ribo-seq), RNA-seq profiles, and full genomic context. This restricts their use in de novo sequence design, like messenger RNA (mRNA) vaccines. Simulation-only approaches like the Totally Asymmetric Simple Exclusion Process (TASEP) oversimplify translation by focusing solely on codon elongation times. RESULTS: We present seq2ribo, a hybrid simulation and machine learning framework that predicts ribosome A-site locations using only an mRNA sequence as input. Our method first employs a novel structure-aware TASEP (sTASEP), which models translation using a comprehensive set of fitted parameters that include codon wait times and structural features, such as local angles, base-pairing, and discrete positional buckets. The ribosome locations generated by sTASEP are then processed by a polisher model, which learns to refine the simulated ribosome distributions. seq2ribo provides high-fidelity predictions of ribosome locations across diverse cell types (iPSC, HEK293, LCL, and RPE-1), significantly outperforming baselines. seq2ribo is the first method to achieve meaningful positional correlation with observed ribosome profiles from sequence alone, reaching transcript-level Pearson correlations up to 0.920 and within-transcript shape correlations up to 0.186, where all baselines yield near-zero values on these metrics. seq2ribo also reduces elementwise error by up to 37.7% relative to the sequence-only Translatomer baseline. By adding a task-specific head, seq2ribo achieves Pearson correlations up to 0.732 with experimental translation efficiency (TE) across several cell lines, and up to 0.903 with measured protein expression. By operating from sequence alone, seq2ribo provides a new tool for synthetic biology, enabling the rational design and optimization of mRNA sequences without the need for expression-level data or genomic context. AVAILABILITY: seq2ribo is available at https://github.com/Kingsford-Group/seq2ribo.

Machine Learning↗

PubMed2026

seq2ribo: Structure-aware integration of machine learning and simulation to predict ribosome location profiles from RNA sequences.

MOTIVATION: Ribosome dynamics are vital in the process of protein expression. Current methods rely on ribosome profiling (Ribo-seq), RNA-seq profiles, and full genomic context. This restricts their use in de novo sequence design, like messenger RNA (mRNA) vaccines. Simulation-only approaches like the Totally Asymmetric Simple Exclusion Process (TASEP) oversimplify translation by focusing solely on codon elongation times. RESULTS: We present seq2ribo, a hybrid simulation and machine learning framework that predicts ribosome A-site locations using only an mRNA sequence as input. Our method first employs a novel structure-aware TASEP (sTASEP), which models translation using a comprehensive set of fitted parameters that include codon wait times and structural features, such as local angles, base-pairing, and discrete positional buckets. The ribosome locations generated by sTASEP are then processed by a polisher model, which learns to refine the simulated ribosome distributions. seq2ribo provides high-fidelity predictions of ribosome locations across diverse cell types (iPSC, HEK293, LCL, and RPE-1), significantly outperforming baselines. seq2ribo is the first method to achieve meaningful positional correlation with observed ribosome profiles from sequence alone, reaching transcript-level Pearson correlations up to 0.920 and within-transcript shape correlations up to 0.186, where all baselines yield near-zero values on these metrics. seq2ribo also reduces elementwise error by up to 37.7% relative to the sequence-only Translatomer baseline. By adding a task-specific head, seq2ribo achieves Pearson correlations up to 0.732 with experimental translation efficiency (TE) across several cell lines, and up to 0.903 with measured protein expression. By operating from sequence alone, seq2ribo provides a new tool for synthetic biology, enabling the rational design and optimization of mRNA sequences without the need for expression-level data or genomic context.

Journal Article↗

PubMed2025

OGFOD1 enables AML chemo- and nutrient stress resistance by regulating protein synthesis.

Acute myeloid leukemia (AML) commonly relapses after initial chemotherapy response. We assessed metabolic adaptations in chemoresistant cells in vivo before overt relapse, identifying altered branched-chain amino acid (BCAA) levels in patient-derived xenografts (PDXs) and immunophenotypically identified leukemia stem cells from AML patients. Notably, this was associated with increased BCAA transporter expression with low BCAA catabolism. Restricting BCAAs further reduced chemoresistant AML cells, but relapse still occurred. Among the persisting cells, we found an unexpected increase in protein production. This was accompanied by elevated translation of 2-oxoglutarate- and iron-dependent oxygenase 1 (OGFOD1), a known ribosomal dioxygenase that adjusts the fidelity of tRNA anticodon pairing with coding mRNA. We found that OGFOD1 upregulates protein synthesis in AML, driving disease aggressiveness. Inhibiting OGFOD1 impaired translation processing, decreased protein synthesis and improved animal survival even with chemoresistant AML while sparing normal hematopoiesis. Leukemic cells can therefore persist despite the stress of chemotherapy and nutrient deprivation through adaptive control of translation. Targeting OGFOD1 may offer a distinctive, translation-modifying means of reducing the chemopersisting cells that drive relapse.

Leukemia, Myeloid, Acute↗