BACKGROUND: This study is based on deep mining of Ribo-seq data for the identification of lncRNAs that have highly expressed sORFs in HCC. In this paper, dynamic prospects associated with sORFs acting as newly defined tumor-specific epitopes are discussed with possible improvement in strategies for tumor immunotherapy.
OBJECTIVE: Using ribosome profiling to identify and characterize sORFs within lncRNAs in HCC, identify potential therapeutic targets and tumor-specific epitopes applicable for immunotherapy.
METHODS: MetamORF performed the identification of sORFs with deep analysis of the data of ribosome profiling in lncRNAs associated with HCC. The translation efficiency in these molecules was estimated, and epitope prediction was done by pVACbind. Peptide search was done to check the presence of micropeptides translated from these identified sORFs. validated translational activity and identified potential epitopes.
RESULTS: Higher translation efficiency was noted in the case of lncRNAs associated with HCC compared to normal tissues. Of particular note is ORF3418981, which results in the highest expression and has supporting experimental evidence at the protein level. Epitope prediction identified a putative epitope at the C-terminus of ORF3418981.
CONCLUSIONS: This study uncovers the as-yet-unknown potential of lncRNA-derived sORFs as sources of tumor antigens, shifting the research focus from protein-coding genes to non-coding RNAs also in the HCC context. Moreover, this study highlights the contribution of a subset of lncRNAs, especially LINC00152, to the development of tumors and modulation of the immune response by its sORFs.

Regulation of translation is a crucial step in gene expression. Developmental signals and environmental stimuli dynamically regulate translation via upstream small open reading frames (uORFs) and ribosome pausing. Recent studies have revealed many plant genes that are specifically regulated by uORF translation following changes in growth conditions, but ribosome-pausing events are less well understood. In this study, we performed ribosome profiling (Ribo-seq) of etiolated maize (Zea mays) seedlings exposed to light for different durations, revealing hundreds of genes specifically regulated at the translation level during the early period of light exposure. We identified over 400 ribosome-pausing events in the dark that were rapidly released after illumination. These results suggested that ribosome pausing negatively regulates translation from specific genes, a conclusion that was supported by a non-targeted proteomics analysis. Importantly, we identified a conserved nucleotide motif downstream of the pausing sites. Our results elucidate the role of ribosome pausing in the control of gene expression in plants; the identification of the cis-element at the pausing sites provides insight into the mechanisms behind translation regulation and potential targets for artificial control of plant translation.

Multicellular organisms are composed of specialized cell types with distinct proteomes. While recent advances in single-cell transcriptome analyses have revealed differential expression of mRNAs, cellular diversity in translational profiles remains underinvestigated. By performing RNA-seq and Ribo-seq in genetically defined cells in the Drosophila brain, we here revealed substantial post-transcriptional regulations that augment the cell-type distinctions at the level of protein expression. Specifically, we found that translational efficiency of proteins fundamental to neuronal functions, such as ion channels and neurotransmitter receptors, was maintained low in glia, leading to their preferential translation in neurons. Notably, distribution of ribosome footprints on these mRNAs exhibited a remarkable bias toward the 5\' leaders in glia. Using transgenic reporter strains, we provide evidence that the small upstream open-reading frames in the 5\' leader confer selective translational suppression in glia. Overall, these findings underscore the profound impact of translational regulation in shaping the proteomics for cell-type distinction and provide new insights into the molecular mechanisms driving cell-type diversity.

Ribosome profiling is a powerful technique to study translation at a transcriptome-wide level. However, ensuring good data quality is paramount for accurate interpretation, as is ensuring that the analyses are reproducible. We introduce a new Nextflow DSL2 pipeline, riboseq-flow, designed for processing and comprehensive quality control of ribosome profiling experiments. Riboseq-flow is user-friendly, versatile and upholds high standards in reproducibility, scalability, portability, version control and continuous integration. It enables users to efficiently analyse multiple samples in parallel and helps them evaluate the quality and utility of their data based on the detailed metrics and visualisations that are automatically generated. Riboseq-flow is available at https://github.com/iraiosub/riboseq-flow.
Ribosome profiling is a cutting-edge method that provides a detailed view of protein synthesis across the entire set of RNA molecules within cells. To ensure the reliability of such studies, high-quality data and the ability to replicate analyses are crucial. To address this, we present riboseq-flow, a new tool built with Nextflow DSL2, tailored for analysing data from ribosome profiling experiments. This pipeline stands out for its ease of use, flexibility, and commitment to high reproducibility standards. It\'s designed to handle multiple samples simultaneously, ensuring efficient analysis for large-scale studies. Moreover, riboseq-flow automatically generates detailed reports and visual representations to assess the data quality, enhancing researchers\' understanding of their experiments and guiding future decisions. This valuable resource is freely accessible at https://github.com/iraiosub/riboseq-flow.

Accurate and comprehensive annotation of microprotein-coding small open reading frames (smORFs) is critical to our understanding of normal physiology and disease. Empirical identification of translated smORFs is carried out primarily using ribosome profiling (Ribo-seq). While effective, published Ribo-seq datasets can vary drastically in quality and different analysis tools are frequently employed. Here, we examine the impact of these factors on identifying translated smORFs. We compared five commonly used software tools that assess open reading frame translation from Ribo-seq (RibORFv0.1, RibORFv1.0, RiboCode, ORFquant, and Ribo-TISH) and found surprisingly low agreement across all tools. Only ~2% of smORFs were called translated by all five tools, and ~15% by three or more tools when assessing the same high-resolution Ribo-seq dataset. For larger annotated genes, the same analysis showed ~74% agreement across all five tools. We also found that some tools are strongly biased against low-resolution Ribo-seq data, while others are more tolerant. Analyzing Ribo-seq coverage revealed that smORFs detected by more than one tool tend to have higher translation levels and higher fractions of in-frame reads, consistent with what was observed for annotated genes. Together these results support employing multiple tools to identify the most confident microprotein-coding smORFs and choosing the tools based on the quality of the dataset and the planned downstream characterization experiments of the predicted smORFs.

BACKGROUND: Sperm storage capacity (SSC) determines the duration of fertility in hens and is an important reproduction trait that cannot be ignored in production. Currently, the genetic mechanism of SSC is still unclear in hens. Therefore, to explore the genetic basis of SSC, we analyzed the uterus-vagina junction (UVJ) of hens with different SSC at different times after insemination by RNA-seq and Ribo-seq.
RESULTS: Our results showed that 589, 596, and 527 differentially expressed genes (DEGs), 730, 783, and 324 differentially translated genes (DTGs), and 804, 625, and 467 differential translation efficiency genes (DTEGs) were detected on the 5th, 10th, and 15th days after insemination, respectively. In transcription levels, we found that the differences of SSC at different times after insemination were mainly reflected in the transmission of information between cells, the composition of intercellular adhesion complexes, the regulation of ion channels, the regulation of cellular physiological activities, the composition of cells, and the composition of cell membranes. In translation efficiency (TE) levels, the differences of SSC were mainly related to the physiological and metabolic activities in the cell, the composition of the organelle membrane, the physiological activities of oxidation, cell components, and cell growth processes. According to pathway analysis, SSC was related to neuroactive ligand-receptor interaction, histidine metabolism, and PPAR signaling pathway at the transcriptional level and glutathione metabolism, oxidative phosphorylation, calcium signaling pathway, cell adhesion molecules, galactose metabolism, and Wnt signaling pathway at the TE level. We screened candidate genes affecting SSC at transcriptional levels (COL4A4, MUC6, MCHR2, TACR1, AVPR1A, COL1A1, HK2, RB1, VIPR2, HMGCS2) and TE levels(COL4A4, MUC6, CYCS, NDUFA13, CYTB, RRM2, CAMK4, HRH2, LCT, GCK, GALT). Among them, COL4A4 and MUC6 were the key candidate genes differing in transcription, translation, and translation efficiency.
CONCLUSIONS: Our study used the combined analysis of RNA-seq and Ribo-seq for the first time to investigate the SSC and reveal the physiological processes associated with SSC. The key candidate genes affecting SSC were screened, and the theoretical basis was provided for the analysis of the molecular regulation mechanism of SSC.

Bioinformatic studies on small proteins are under-represented due to difficulties in annotation posed by their small size. However, recent discoveries emphasize the functional significance of small proteins in cellular processes including cell signaling, metabolism, and adaptation to stress. In this study, we utilized a Random Forest classifier trained on sequence features, RNA-Seq, and Ribo-Seq data to uncover small proteins (smORFs) in M. tuberculosis. Independent predictions for the exponential and starvation conditions resulted in 695 potential smORFs. We examined the functional implications of these smORFs using homology searches, LC-MS/MS, and ChIP-seq data, testing their expression in diverse growth conditions, and identifying protein domains. We provide evidence that some of these smORFs could be part of operons, or exist as upstream ORFs. This expanded data resource for the proteins of M. tuberculosis would aid in fine-tuning the existing protein and gene regulatory networks, thereby improving system-wide studies. The primary goal of this study was to uncover and characterize smORFs in M. tuberculosis through bioinformatic analysis, shedding light on their functional roles and genomic organization. Further investigation of these potential smORFs would provide valuable insights into the genome organization and functional diversity of the M. tuberculosis proteome.

Intramuscular fat (IMF) in pork holds significant importance for economic performance within the pig industry and dietary calcium supplementation enhances the accumulation of intramuscular fat. Additionally, calcium ions inhibit translation and reduce protein synthesis. However, the mechanism by which calcium regulates IMF deposition in muscle through translation remains largely unknown. In this study, we compared the ribosome profiles of the longissimus dorsi muscles of Duroc × Landrace × Large white pigs from the normal calcium (NC) group or calcium supplement (HC) group by Ribo-seq, and RNA-seq. By integrating multiple-omics analysis, we further discovered 437 genes that were transcriptionally unchanged but translationally altered and these genes were significantly enriched in the oxidative phosphorylation signaling pathway. Furthermore, experimental data showed that inhibiting the expression of COX10 and mtND4L increased triglyceride accumulation in C2C12 cells, providing new targets for intramuscular fat deposition. Finally, this work links dietary calcium, translation regulation and IMF deposition, providing a new strategy for both meat quality and economic performance within the pig industry.

Non-canonical peptides (NCPs) are a class of peptides generated from regions previously thought of as non-coding, such as introns, 5\' untranslated regions (UTRs), 3\' UTRs, and intergenic regions. In recent years, the significance and diverse functions of NCPs have come to light, yet a systematic and comprehensive NCP database remains absent. Here, we developed NCPbook (https://ncp.wiki/ncpbook/), a database of evidence-supported NCPs, which aims to provide a resource for efficient exploration, analysis, and manipulation of NCPs. NCPbook incorporates data from diverse public databases and scientific literature. The current version of NCPbook includes 180,676 NCPs across 29 different species, evidenced by mass spectrometry (MS), ribosome profiling (Ribo-seq), or molecular experiments (ME). These NCPs are distributed across kingdoms, comprising 123,408 from 14 plant species, 56,999 from seven animal species, and 269 from eight microbial species. Furthermore, NCPbook encompasses 9,166 functionally characterized NCPs playing important roles in immunity, stress resistance, growth, and development. Equipped with a user-friendly interface, NCPbook allows users to search, browse, visualize, and retrieve data, making it an indispensable platform for researching NCPs in various plant, animal, and microbial species.

Translational regulation plays the most critical role in gene expression. Ribosome profiling sequencing (Ribo-Seq) is one of the methods to study translation and its regulation. It is a high throughput technology based on deep sequencing, which targets ribosome protected mRNA fragments to produce a \'global snapshot\' of translatome. There has been an annual increase in the number of publications incorporating Ribo-seq technology. Because of its importance, we used PubMed database to conduct a comprehensive bibliometric analysis on Ribo-seq. We identified 2744 published articles that utilized the term \'Ribo-seq\' between 2009 and Jan 2024, and 684 articles that contained both Ribo-seq and RNA-seq terms. Based on keywords correlation analysis, we discovered that the primary focus of Ribo-seq articles lies in the areas of translation, transcriptome, and ribosome in the past few years and other topics such as single-cell ribo-seq and crispr within two years, reflecting current areas of interests in Ribo-seq research. The Ribo-seq data analysis applications were also explored and summarized, providing a guide for researchers to choose corresponding tools for different types of analysis. Overall, we highlighted the advances made by Ribo-seq technologies, and the possibilities of utilizing machine learning models to unravel information from multi-omics data. The integration of Ribo-seq with other omics data, such as RNA-seq, is essential to understand the gene expression in complex biological systems.