Bayesian prediction of RNA translation from ribosome profiling

LncRNA-encoded peptides in cancer

Long non-coding RNAs (lncRNAs), once considered transcriptional noise, have emerged as critical regulators of gene expression and key players in cancer biology. Recent breakthroughs have revealed that certain lncRNAs can encode small open reading frame (sORF)-derived peptides, which are now understood to contribute to the pathogenesis of various cancers. This review synthesizes current knowledge on the detection, functional roles, and clinical implications of lncRNA-encoded peptides in cancer. We discuss technological advancements in the detection and validation of sORFs, including ribosome profiling and mass spectrometry, which have facilitated the discovery of these peptides. The functional roles of lncRNA-encoded peptides in cancer processes such as gene transcription, translation regulation, signal transduction, and metabolic reprogramming are explored in various types of cancer. The clinical potential of these peptides is highlighted, with a focus on their utility as diagnostic biomarkers, prognostic indicators, and therapeutic targets. The challenges and future directions in translating these findings into clinical practice are also discussed, including the need for large-scale validation, development of sensitive detection methods, and optimization of peptide stability and delivery.

Rp3: Ribosome profiling-assisted proteogenomics improves coverage and confidence during microprotein discovery

There has been a dramatic increase in the identification of non-canonical translation and a significant expansion of the protein-coding genome. Among the strategies used to identify unannotated small Open Reading Frames (smORFs) that encode microproteins, Ribosome profiling (Ribo-Seq) is the gold standard for the annotation of novel coding sequences by reporting on smORF translation. In Ribo-Seq, ribosome-protected footprints (RPFs) that map to multiple genomic sites are removed since they cannot be unambiguously assigned to a specific genomic location. Furthermore, RPFs necessarily result in short (25-34 nucleotides) reads, increasing the chance of multi-mapping alignments, such that smORFs residing in these regions cannot be identified by Ribo-Seq. Moreover, it has been challenging to identify protein evidence for Ribo-Seq. To solve this, we developed Rp3, a pipeline that integrates proteogenomics and Ribosome profiling to provide unambiguous evidence for a subset of microproteins missed by current Ribo-Seq pipelines. Here, we show that Rp3 maximizes proteomics detection and confidence of microprotein-encoding smORFs.

Next generation sequencing technologies to address aberrant mRNA translation in cancer

In this review, we explore the transformative impact of next generation sequencing technologies in the realm of translatomics (the study of how translational machinery acts on a genome-wide scale). Despite the expectation of a direct correlation between mRNA and protein content, the complex regulatory mechanisms that affect this relationship remark the limitations of standard RNA-seq approaches. Then, the review characterizes crucial techniques such as polysome profiling, ribo-seq, trap-seq, proximity-specific ribosome profiling, rnc-seq, tcp-seq, qti-seq and scRibo-seq. All these methods are summarized within the context of cancer research, shedding light on their applications in deciphering aberrant translation in cancer cells. In addition, we encompass databases and bioinformatic tools essential for researchers that want to address translatome analysis in the context of cancer biology.

Deep learning to decode sites of RNA translation in normal and cancerous tissues

The biological process of RNA translation is fundamental to cellular life and has wide-ranging implications for human disease. Yet, accurately delineating the variation in RNA translation represents a significant challenge. Here, we develop RiboTIE, a transformer model-based approach to map global RNA translation. We find that RiboTIE offers unparalleled precision and sensitivity for ribosome profiling data. Application of RiboTIE to normal brain and medulloblastoma cancer samples enables high-resolution insights into disease regulation of RNA translation.

A hidden translatome in tumors-the coding lncRNAs

Long noncoding RNAs (lncRNAs) have been extensively identified in eukaryotic genomes and have been shown to play critical roles in the development of multiple cancers. Through the application and development of ribosome analysis and sequencing technologies, advanced studies have discovered the translation of lncRNAs. Although lncRNAs were originally defined as noncoding RNAs, many lncRNAs actually contain small open reading frames that are translated into peptides. This opens a broad area for the functional investigation of lncRNAs. Here, we introduce prospective methods and databases for screening lncRNAs with functional polypeptides. We also summarize the specific lncRNA-encoded proteins and their molecular mechanisms that promote or inhibit cancerous. Importantly, the role of lncRNA-encoded peptides/proteins holds promise in cancer research, but some potential challenges remain unresolved. This review includes reports on lncRNA-encoded peptides or proteins in cancer, aiming to provide theoretical basis and related references to facilitate the discovery of more functional peptides encoded by lncRNA, and to further develop new anti-cancer therapeutic targets as well as clinical biomarkers of diagnosis and prognosis.

Shining in the dark: the big world of small peptides in plants

Small peptides represent a subset of dark matter in plant proteomes. Through differential expression patterns and modes of action, small peptides act as important regulators of plant growth and development. Over the past 20 years, many small peptides have been identified due to technical advances in genome sequencing, bioinformatics, and chemical biology. In this article, we summarize the classification of plant small peptides and experimental strategies used to identify them as well as their potential use in agronomic breeding. We review the biological functions and molecular mechanisms of small peptides in plants, discuss current problems in small peptide research and highlight future research directions in this field. Our review provides crucial insight into small peptides in plants and will contribute to a better understanding of their potential roles in biotechnology and agriculture.

Discovering microproteins: making the most of ribosome profiling data

Building a reference set of protein-coding open reading frames (ORFs) has revolutionized biological process discovery and understanding. Traditionally, gene models have been confirmed using cDNA sequencing and encoded translated regions inferred using sequence-based detection of start and stop combinations longer than 100 amino-acids to prevent false positives. This has led to small ORFs (smORFs) and their encoded proteins left un-annotated. Ribo-seq allows deciphering translated regions from untranslated irrespective of the length. In this review, we describe the power of Ribo-seq data in detection of smORFs while discussing the major challenge posed by data-quality, -depth and -sparseness in identifying the start and end of smORF translation. In particular, we outline smORF cataloguing efforts in humans and the large differences that have arisen due to variation in data, methods and assumptions. Although current versions of smORF reference sets can already be used as a powerful tool for hypothesis generation, we recommend that future editions should consider these data limitations and adopt unified processing for the community to establish a canonical catalogue of translated smORFs.

Advances in peptides encoded by non-coding RNAs: A cargo in exosome

The metastasis of malignant tumors determines patient prognosis. This is the main reason for the poor prognosis of patients with cancer and the most challenging aspect of treating malignant tumors. Therefore, it is important to identify early tumor markers and molecules that can predict patient prognosis. However, there are currently no molecular markers with good clinical accuracy and specificity. Many non-coding RNA (ncRNAs)have been identified, which can regulate the process of tumor development at multiple levels. Interestingly, some ncRNAs are translated to produce functional peptides. Exosomes act as signal carriers, are encapsulated in nucleic acids and proteins, and play a messenger role in cell-to-cell communication. Recent studies have identified exosome peptides with potential diagnostic roles. This review aims to provide a theoretical basis for ncRNA-encoded peptides or proteins transported by exosomes and ultimately to provide ideas for further development of new diagnostic and prognostic cancer markers.

Tracing Translational Footprint by Ribo-Seq: Principle, Workflow, and Applications to Understand the Mechanism of Human Diseases

RNA-seq has been widely used as a high-throughput method to characterize transcript dynamic changes in a broad context, such as development and diseases. However, whether RNA-seq-estimated transcriptional dynamics can be translated into protein level changes is largely unknown. Ribo-seq (Ribosome profiling) is an emerging technology that allows for the investigation of the translational footprint via profiling ribosome-bounded mRNA fragments. Ribo-seq coupled with RNA-seq will allow us to understand the transcriptional and translational control of the fundamental biological process and human diseases. This review focuses on discussing the principle, workflow, and applications of Ribo-seq to study human diseases.

Analysis of myocardial cellular gene expression during pressure overload reveals matrix based functional intercellular communication

To identify cellular mechanisms responsible for pressure overload triggered heart failure, we isolated cardiomyocytes, endothelial cells, and fibroblasts as most abundant cell types from mouse hearts in the subacute and chronic stages after transverse aortic constriction (TAC) and performed RNA-sequencing. We detected highly cell-type specific transcriptional responses with characteristic time courses and active intercellular communication. Cardiomyocytes after TAC exerted an early and sustained upregulation of inflammatory and matrix genes and a concomitant suppression of metabolic and ion channel genes. Fibroblasts, in contrast, showed transient early upregulation of inflammatory and matrix genes and downregulation of angiogenesis genes, but sustained induction of cell cycle and ion channel genes during TAC. Endothelial cells transiently induced cell cycle and extracellular matrix genes early after TAC, but exerted a long-lasting upregulation of inflammatory genes. As we found that matrix production by multiple cell types triggers pathological cellular responses, it might serve as a future therapeutic target.

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TAC induces matrix and growth, but reduces contraction genes in cardiomyocytes

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TAC induces genes related to matrix, inflammation, and cell cycle in endothelial cells

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TAC induces matrix and inflammation, but reduces angiogenesis genes in fibroblasts

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Matrix proteins trigger growth, proliferation, and migration in cardiac cells

Unannotated proteins expand the MHC-I-restricted immunopeptidome in cancer

Tumor-associated epitopes presented on MHC-I that can activate the immune system against cancer cells are typically identified from annotated protein-coding regions of the genome, but whether peptides originating from novel or unannotated open reading frames (nuORFs) can contribute to antitumor immune responses remains unclear. Here we show that peptides originating from nuORFs detected by ribosome profiling of malignant and healthy samples can be displayed on MHC-I of cancer cells, acting as additional sources of cancer antigens. We constructed a high-confidence database of translated nuORFs across tissues (nuORFdb) and used it to detect 3,555 translated nuORFs from MHC-I immunopeptidome mass spectrometry analysis, including peptides that result from somatic mutations in nuORFs of cancer samples as well as tumor-specific nuORFs translated in melanoma, chronic lymphocytic leukemia and glioblastoma. NuORFs are an unexplored pool of MHC-I-presented, tumor-specific peptides with potential as immunotherapy targets.

Evolutionary Characterization of the Short Protein SPAAR

Microproteins (<100 amino acids) are receiving increasing recognition as important participants in numerous biological processes, but their evolutionary dynamics are poorly understood. SPAAR is a recently discovered microprotein that regulates muscle regeneration and angiogenesis through interactions with conserved signaling pathways. Interestingly, SPAAR does not belong to any known protein family and has known homologs exclusively among placental mammals. This lack of distant homology could be caused by challenges in homology detection of short sequences, or it could indicate a recent de novo emergence from a noncoding sequence. By integrating syntenic alignments and homology searches, we identify SPAAR orthologs in marsupials and monotremes, establishing that SPAAR has existed at least since the emergence of mammals. SPAAR shows substantial primary sequence divergence but retains a conserved protein structure. In primates, we infer two independent evolutionary events leading to the de novo origination of 5' elongated isoforms of SPAAR from a noncoding sequence and find evidence of adaptive evolution in this extended region. Thus, SPAAR may be of ancient origin, but it appears to be experiencing continual evolutionary innovation in mammals.

The C. elegans pseudogene sspt-16 (F55A3.7) is required to safeguard germ cells against reprogramming

We recently identified FAcilitates Chromatin Transcription (FACT) as a reprogramming barrier of transcription factor (TF) mediated conversion of germ cells into neurons in C. elegans. FACT is a conserved heterodimer consisting of SPT16 and SSRP1 in mammals. Duplication events during evolution in C. elegans generated two SSRP1 homologs named HMG-3 and HMG-4, while SPT-16 is the only homolog of SPT16. Yet, the pseudogene F55A3.7 has nearly complete nucleotide sequence homology to the spt-16 gene. However, F55A3.7 lacks some spt-16 exons and DNA pieces so we named it sspt-16 (short spt-16). Surprisingly, the deletion mutant ok1829, which affects only the sspt-16 pseudogene, shows similar germ cell reprogramming effects as described previously for FACT-depleted animals. We examined whether lack of sspt-16 affects other genes or chromatin accessibility, which may explain the permissiveness for germ cell reprogramming.

RiboA: a web application to identify ribosome A-site locations in ribosome profiling data

Background

Translation is a fundamental process in gene expression. Ribosome profiling is a method that enables the study of transcriptome-wide translation. A fundamental, technical challenge in analyzing Ribo-Seq data is identifying the A-site location on ribosome-protected mRNA fragments. Identification of the A-site is essential as it is at this location on the ribosome where a codon is translated into an amino acid. Incorrect assignment of a read to the A-site can lead to lower signal-to-noise ratio and loss of correlations necessary to understand the molecular factors influencing translation. Therefore, an easy-to-use and accurate analysis tool is needed to accurately identify the A-site locations.

Results

We present RiboA, a web application that identifies the most accurate A-site location on a ribosome-protected mRNA fragment and generates the A-site read density profiles. It uses an Integer Programming method that reflects the biological fact that the A-site of actively translating ribosomes is generally located between the second codon and stop codon of a transcript, and utilizes a wide range of mRNA fragment sizes in and around the coding sequence (CDS). The web application is containerized with Docker, and it can be easily ported across platforms.

Conclusions

The Integer Programming method that RiboA utilizes is the most accurate in identifying the A-site on Ribo-Seq mRNA fragments compared to other methods. RiboA makes it easier for the community to use this method via a user-friendly and portable web application. In addition, RiboA supports reproducible analyses by tracking all the input datasets and parameters, and it provides enhanced visualization to facilitate scientific exploration. RiboA is available as a web service at https://a-site.vmhost.psu.edu/. The code is publicly available at https://github.com/obrien-lab/aip_web_docker under the MIT license.

LncRNA-Encoded Peptide: Functions and Predicting Methods

Long non-coding RNA (lncRNA) was originally defined as the representative of the non-coding RNAs and unable to encode. However, recent reports suggest that some lncRNAs actually contain open reading frames that encode peptides. These coding products play important roles in the pathogenesis of many diseases. Here, we summarize the regulatory pathways of mammalian lncRNA-encoded peptides in influencing muscle function, mRNA stability, gene expression, and so on. We also address the promoting and inhibiting functions of the peptides in different cancers and other diseases. Then we introduce the computational predicting methods and data resources to predict the coding ability of lncRNA. The intention of this review is to provide references for further coding research and contribute to reveal the potential prospects for targeted tumor therapy.

A Multi-Network Comparative Analysis of Transcriptome and Translatome Identifies Novel Hub Genes in Cardiac Remodeling

Our understanding of the transition from physiological to pathological cardiac hypertrophy remains elusive and largely based on reductionist hypotheses. Here, we profiled the translatomes of 15 mouse hearts to provide a molecular blueprint of altered gene networks in early cardiac remodeling. Using co-expression analysis, we showed how sub-networks are orchestrated into functional modules associated with pathological phenotypes. We discovered unappreciated hub genes, many undocumented for their role in cardiac hypertrophy, and genes in the transcriptional network that were rewired in the translational network, and associated with semantically different subsets of enriched functional terms, such as Fam210a, a novel musculoskeletal modulator, or Psmd12, implicated in protein quality control. Using their correlation structure, we found that transcriptome networks are only partially reproducible at the translatome level, providing further evidence of post-transcriptional control at the level of translation. Our results provide novel insights into the complexity of the organization of in vivo cardiac regulatory networks.

RiboDiPA: a novel tool for differential pattern analysis in Ribo-seq data

Ribosome profiling, also known as Ribo-seq, has become a popular approach to investigate regulatory mechanisms of translation in a wide variety of biological contexts. Ribo-seq not only provides a measurement of translation efficiency based on the relative abundance of ribosomes bound to transcripts, but also has the capacity to reveal dynamic and local regulation at different stages of translation based on positional information of footprints across individual transcripts. While many computational tools exist for the analysis of Ribo-seq data, no method is currently available for rigorous testing of the pattern differences in ribosome footprints. In this work, we develop a novel approach together with an R package, RiboDiPA, for Differential Pattern Analysis of Ribo-seq data. RiboDiPA allows for quick identification of genes with statistically significant differences in ribosome occupancy patterns for model organisms ranging from yeast to mammals. We show that differential pattern analysis reveals information that is distinct and complimentary to existing methods that focus on translational efficiency analysis. Using both simulated Ribo-seq footprint data and three benchmark data sets, we illustrate that RiboDiPA can uncover meaningful pattern differences across multiple biological conditions on a global scale, and pinpoint characteristic ribosome occupancy patterns at single codon resolution.

RiboToolkit: an integrated platform for analysis and annotation of ribosome profiling data to decode mRNA translation at codon resolution

Ribosome profiling (Ribo-seq) is a powerful technology for globally monitoring RNA translation; ranging from codon occupancy profiling, identification of actively translated open reading frames (ORFs), to the quantification of translational efficiency under various physiological or experimental conditions. However, analyzing and decoding translation information from Ribo-seq data is not trivial. Although there are many existing tools to analyze Ribo-seq data, most of these tools are designed for specific or limited functionalities and an easy-to-use integrated tool to analyze Ribo-seq data is lacking. Fortunately, the small size (26–34 nt) of ribosome protected fragments (RPFs) in Ribo-seq and the relatively small amount of sequencing data greatly facilitates the development of such a web platform, which is easy to manipulate for users with or without bioinformatic expertise. Thus, we developed RiboToolkit (http://rnabioinfor.tch.harvard.edu/RiboToolkit), a convenient, freely available, web-based service to centralize Ribo-seq data analyses, including data cleaning and quality evaluation, expression analysis based on RPFs, codon occupancy, translation efficiency analysis, differential translation analysis, functional annotation, translation metagene analysis, and identification of actively translated ORFs. Besides, easy-to-use web interfaces were developed to facilitate data analysis and intuitively visualize results. Thus, RiboToolkit will greatly facilitate the study of mRNA translation based on ribosome profiling.

Accurate detection of short and long active ORFs using Ribo-seq data

MOTIVATION

Ribo-seq, a technique for deep-sequencing ribosome-protected mRNA fragments, has enabled transcriptome-wide monitoring of translation in vivo. It has opened avenues for re-evaluating the coding potential of open reading frames (ORFs), including many short ORFs that were previously presumed to be non-translating. However, the detection of translating ORFs, specifically short ORFs, from Ribo-seq data, remains challenging due to its high heterogeneity and noise.

RESULTS

We present ribotricer, a method for detecting actively translating ORFs by directly leveraging the three-nucleotide periodicity of Ribo-seq data. Ribotricer demonstrates higher accuracy and robustness compared with other methods at detecting actively translating ORFs including short ORFs on multiple published datasets across species inclusive of Arabidopsis, Caenorhabditis elegans, Drosophila, human, mouse, rat, yeast and zebrafish.

AVAILABILITY AND IMPLEMENTATION

Ribotricer is available at https://github.com/smithlabcode/ribotricer. All analysis scripts and results are available at https://github.com/smithlabcode/ribotricer-results.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Global sequence features based translation initiation site prediction in human genomic sequences

Gene prediction has been increasingly important in genome annotation due to advancements in sequencing technology. Genome annotation further helps in determining the structure and function of these genes. Translation initiation site prediction (TIS) in human genomic sequences is one of the fundamental and essential steps in gene prediction. Thus, accurate prediction of TIS in these sequences is highly desirable. Although many computational methods were developed for this problem, none of them focused on finding these sites in human genomic sequences. In this paper, a new TIS prediction method is proposed by incorporating global sequence based features. Support vector machine is used to assess the prediction power of these features. The proposed method achieved accuracy of above 90% when tested for genomic as well as cDNA sequences. The experimental results indicate that the method works well for both genomic and cDNA sequences. The method can be integrated into gene prediction system in future.

Control of translation by eukaryotic mRNA transcript leaders-Insights from high-throughput assays and computational modeling

Eukaryotic gene expression is tightly regulated during translation of mRNA to protein. Mis-regulation of translation can lead to aberrant proteins which accumulate in cancers and cause neurodegenerative diseases. Foundational studies on model genes established fundamental roles for mRNA 5' transcript leader (TL) sequences in controlling ribosome recruitment, scanning, and initiation. TL cis-regulatory elements and their corresponding trans-acting factors control cap-dependent initiation under unstressed conditions. Under stress, cap-dependent initiation is suppressed, and specific mRNA structures and sequences promote translation of stress-responsive transcripts to remodel the proteome. In this review, we summarize current knowledge of TL functions in translation initiation. We focus on insights from high-throughput analyses of ribosome occupancy, mRNA structure, RNA Binding Protein occupancy, and massively parallel reporter assays. These data-driven approaches, coupled with computational analyses and modeling, have paved the way for a comprehensive understanding of TL functions. Finally, we will discuss areas of future research on the roles of mRNA sequences and structures in translation. This article is categorized under: Translation > Translation Mechanisms RNA Evolution and Genomics > Computational Analyses of RNA RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems.

Principles and innovative technologies for decrypting noncoding RNAs: from discovery and functional prediction to clinical application

Noncoding RNAs (ncRNAs) are a large segment of the transcriptome that do not have apparent protein-coding roles, but they have been verified to play important roles in diverse biological processes, including disease pathogenesis. With the development of innovative technologies, an increasing number of novel ncRNAs have been uncovered; information about their prominent tissue-specific expression patterns, various interaction networks, and subcellular locations will undoubtedly enhance our understanding of their potential functions. Here, we summarized the principles and innovative methods for identifications of novel ncRNAs that have potential functional roles in cancer biology. Moreover, this review also provides alternative ncRNA databases based on high-throughput sequencing or experimental validation, and it briefly describes the current strategy for the clinical translation of cancer-associated ncRNAs to be used in diagnosis.

The small peptide world in long noncoding RNAs

Long noncoding RNAs (lncRNAs) are a group of transcripts that are longer than 200 nucleotides (nt) without coding potential. Over the past decade, tens of thousands of novel lncRNAs have been annotated in animal and plant genomes because of advanced high-throughput RNA sequencing technologies and with the aid of coding transcript classifiers. Further, a considerable number of reports have revealed the existence of stable, functional small peptides (also known as micropeptides), translated from lncRNAs. In this review, we discuss the methods of lncRNA classification, the investigations regarding their coding potential and the functional significance of the peptides they encode.

De novo annotation and characterization of the translatome with ribosome profiling data

By capturing and sequencing the RNA fragments protected by translating ribosomes, ribosome profiling provides snapshots of translation at subcodon resolution. The growing needs for comprehensive annotation and characterization of the context-dependent translatomes are calling for an efficient and unbiased method to accurately recover the signal of active translation from the ribosome profiling data. Here we present our new method, RiboCode, for such purpose. Being tested with simulated and real ribosome profiling data, and validated with cell type-specific QTI-seq and mass spectrometry data, RiboCode exhibits superior efficiency, sensitivity, and accuracy for de novo annotation of the translatome, which covers various types of ORFs in the previously annotated coding and non-coding regions. As an example, RiboCode was applied to assemble the context-specific translatomes of yeast under normal and stress conditions. Comparisons among these translatomes revealed stress-activated novel upstream and downstream ORFs, some of which are associated with translational dysregulations of the annotated main ORFs under the stress conditions.

Selective ribosome profiling to study interactions of translating ribosomes in yeast

A number of enzymes, targeting factors and chaperones engage ribosomes to support fundamental steps of nascent protein maturation, including enzymatic processing, membrane targeting and co-translational folding. The selective ribosome profiling (SeRP) method is a new tool for studying the co-translational activity of maturation factors that provides proteome-wide information on a factor's nascent interactome, the onset and duration of binding and the mechanisms controlling factor engagement. SeRP is based on the combination of two ribosome-profiling (RP) experiments, sequencing the ribosome-protected mRNA fragments from all ribosomes (total translatome) and the ribosome subpopulation engaged by the factor of interest (factor-bound translatome). We provide a detailed SeRP protocol, exemplified for the yeast Hsp70 chaperone Ssb (stress 70 B), for studying factor interactions with nascent proteins that is readily adaptable to identifying nascent interactomes of other co-translationally acting eukaryotic factors. The protocol provides general guidance for experimental design and optimization, as well as detailed instructions for cell growth and harvest, the isolation of (factor-engaged) monosomes, the generation of a cDNA library and data analysis. Experience in biochemistry and RNA handling, as well as basic programing knowledge, is necessary to perform SeRP. Execution of a SeRP experiment takes 8-10 working days, and initial data analysis can be completed within 1-2 d. This protocol is an extension of the originally developed protocol describing SeRP in bacteria.

Monitoring Cell-Type-Specific Gene Expression Using Ribosome Profiling In Vivo During Cardiac Hemodynamic Stress

Supplemental Digital Content is available in the text.

Rationale:

Gene expression profiles have been mainly determined by analysis of transcript abundance. However, these analyses cannot capture posttranscriptional gene expression control at the level of translation, which is a key step in the regulation of gene expression, as evidenced by the fact that transcript levels often poorly correlate with protein levels. Furthermore, genome-wide transcript profiling of distinct cell types is challenging due to the fact that lysates from tissues always represent a mixture of cells.

Objectives:

This study aimed to develop a new experimental method that overcomes both limitations and to apply this method to perform a genome-wide analysis of gene expression on the translational level in response to pressure overload.

Methods and Results:

By combining ribosome profiling (Ribo-seq) with a ribosome-tagging approach (Ribo-tag), it was possible to determine the translated transcriptome in specific cell types from the heart. After pressure overload, we monitored the cardiac myocyte translatome by purifying tagged cardiac myocyte ribosomes from cardiac lysates and subjecting the ribosome-protected mRNA fragments to deep sequencing. We identified subsets of mRNAs that are regulated at the translational level and found that translational control determines early changes in gene expression in response to cardiac stress in cardiac myocytes. Translationally controlled transcripts are associated with specific biological processes related to translation, protein quality control, and metabolism. Mechanistically, Ribo-seq allowed for the identification of upstream open reading frames in transcripts, which we predict to be important regulators of translation.

Conclusions:

This method has the potential to (1) provide a new tool for studying cell-specific gene expression at the level of translation in tissues, (2) reveal new therapeutic targets to prevent cellular remodeling, and (3) trigger follow-up studies that address both, the molecular mechanisms involved in the posttranscriptional control of gene expression in cardiac cells, and the protective functions of proteins expressed in response to cellular stress.

m6A-mRNA methylation regulates cardiac gene expression and cellular growth

Conceptually similar to modifications of DNA, mRNAs undergo chemical modifications, which can affect their activity, localization, and stability. The most prevalent internal modification in mRNA is the methylation of adenosine at the N6-position (m6A). This returns mRNA to a role as a central hub of information within the cell, serving as an information carrier, modifier, and attenuator for many biological processes. Still, the precise role of internal mRNA modifications such as m6A in human and murine-dilated cardiac tissue remains unknown. Transcriptome-wide mapping of m6A in mRNA allowed us to catalog m6A targets in human and murine hearts. Increased m6A methylation was found in human cardiomyopathy. Knockdown and overexpression of the m6A writer enzyme Mettl3 affected cell size and cellular remodeling both in vitro and in vivo. Our data suggest that mRNA methylation is highly dynamic in cardiomyocytes undergoing stress and that changes in the mRNA methylome regulate translational efficiency by affecting transcript stability. Once elucidated, manipulations of methylation of specific m6A sites could be a powerful approach to prevent worsening of cardiac function.

Ribosome profiling analysis of eEF3-depleted Saccharomyces cerevisiae

In addition to the standard set of translation factors common in eukaryotic organisms, protein synthesis in the yeast Saccharomyces cerevisiae requires an ABCF ATPase factor eEF3, eukaryotic Elongation Factor 3. eEF3 is an E-site binder that was originally identified as an essential factor involved in the elongation stage of protein synthesis. Recent biochemical experiments suggest an additional function of eEF3 in ribosome recycling. We have characterised the global effects of eEF3 depletion on translation using ribosome profiling. Depletion of eEF3 results in decreased ribosome density at the stop codon, indicating that ribosome recycling does not become rate limiting when eEF3 levels are low. Consistent with a defect in translation elongation, eEF3 depletion causes a moderate redistribution of ribosomes towards the 5′ part of the open reading frames. We observed no E-site codon- or amino acid-specific ribosome stalling upon eEF3 depletion, supporting its role as a general elongation factor. Surprisingly, depletion of eEF3 leads to a relative decrease in P-site proline stalling, which we hypothesise is a secondary effect of generally decreased translation and/or decreased competition for the E-site with eIF5A.

PausePred and Rfeet: webtools for inferring ribosome pauses and visualizing footprint density from ribosome profiling data

The process of translation is characterized by irregularities in the local decoding rates of specific mRNA codons. This includes the occurrences of long pauses that can take place when ribosomes decode certain peptide sequences, encounter strong RNA secondary structures, or decode "hungry" codons. Examples are known where such pausing or stalling is used for regulating protein synthesis. This can be achieved at the level of translation via direct alteration of ribosome progression through mRNA or by altering mRNA stability via NoGo decay. Ribosome pausing has also been implicated in the cotranslational folding of proteins. Ribosome profiling data often are used for inferring the locations of ribosome pauses. However, no dedicated online software is available for this purpose. Here we present PausePred (https://pausepred.ucc.ie/), which can be used to infer ribosome pauses from ribosome profiling (Ribo-seq) data. Peaks of ribosome footprint density are scored based on their magnitude relative to the background density within the surrounding area. The scoring allows the comparison of peaks across the transcriptome or genome. In addition to the score, PausePred reports the coordinates of the pause, the footprint density at the pause site, and the surrounding nucleotide sequence. The pauses can be visualized in the context of Ribo-seq and RNA-seq density plots generated for specific transcripts or genomic regions with the Rfeet tool. PausePred does not require input on the location of protein coding ORFs (although gene annotations can be optionally supplied). As a result, it can be used universally and its output does not depend on ever evolving annotations.

RibORF: Identifying Genome-Wide Translated Open Reading Frames Using Ribosome Profiling

Ribosome profiling identifies RNA fragments associated with translating ribosomes. The technology provides an opportunity to examine genome-wide translation events at single-nucleotide resolution and in an unbiased manner. Here I present a computational pipeline named RibORF to systematically identify translated open reading frames (ORFs), based on read distribution features representing active translation, including 3-nt periodicity and uniformness across codons. Analyses using the computational tool revealed pervasive translation in putative 'noncoding' regions, such as lncRNAs, pseudogenes, and 5'UTRs. The computational tool is useful for studying functional roles of non-canonical translation events in various biological processes. © 2018 by John Wiley & Sons, Inc.

riboWaltz: Optimization of ribosome P-site positioning in ribosome profiling data

Ribosome profiling is a powerful technique used to study translation at the genome-wide level, generating unique information concerning ribosome positions along RNAs. Optimal localization of ribosomes requires the proper identification of the ribosome P-site in each ribosome protected fragment, a crucial step to determine the trinucleotide periodicity of translating ribosomes, and draw correct conclusions concerning where ribosomes are located. To determine the P-site within ribosome footprints at nucleotide resolution, the precise estimation of its offset with respect to the protected fragment is necessary. Here we present riboWaltz, an R package for calculation of optimal P-site offsets, diagnostic analysis and visual inspection of ribosome profiling data. Compared to existing tools, riboWaltz shows improved accuracies for P-site estimation and neat ribosome positioning in multiple case studies. riboWaltz was implemented in R and is available as an R package at https://github.com/LabTranslationalArchitectomics/RiboWaltz.

Shoelaces: an interactive tool for ribosome profiling processing and visualization

Background

The emergence of ribosome profiling to map actively translating ribosomes has laid the foundation for a diverse range of studies on translational regulation. The data obtained with different variations of this assay is typically manually processed, which has created a need for tools that would streamline and standardize processing steps.

Results

We present Shoelaces, a toolkit for ribosome profiling experiments automating read selection and filtering to obtain genuine translating footprints. Based on periodicity, favoring enrichment over the coding regions, it determines the read lengths corresponding to bona fide ribosome protected fragments. The specific codon under translation (P-site) is determined by automatic offset calculations resulting in sub-codon resolution. Shoelaces provides both a user-friendly graphical interface for interactive visualisation in a genome browser-like fashion and a command line interface for integration into automated pipelines. We process 79 libraries and show that studies typically discard excessive amounts of quality data in their manual analysis pipelines.

Conclusions

Shoelaces streamlines ribosome profiling analysis offering automation of the processing, a range of interactive visualization features and export of the data into standard formats. Shoelaces stores all processing steps performed in an XML file that can be used by other groups to exactly reproduce the processing of a given study. We therefore anticipate that Shoelaces can aid researchers by automating what is typically performed manually and contribute to the overall reproducibility of studies. The tool is freely distributed as a Python package, with additional instructions, tutorial and demo datasets available at https://bitbucket.org/valenlab/shoelaces.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4912-6) contains supplementary material, which is available to authorized users.

The exon-intron gene structure upstream of the initiation codon predicts translation efficiency

Introns in mRNA leaders are common in complex eukaryotes, but often overlooked. These introns are spliced out before translation, leaving exon-exon junctions in the mRNA leaders (leader EEJs). Our multi-omic approach shows that the number of leader EEJs inversely correlates with the main protein translation, as does the number of upstream open reading frames (uORFs). Across the five species studied, the lowest levels of translation were observed for mRNAs with both leader EEJs and uORFs (29%). This class of mRNAs also have ribosome footprints on uORFs, with strong triplet periodicity indicating uORF translation. Furthermore, the positions of both leader EEJ and uORF are conserved between human and mouse. Thus, the uORF, in combination with leader EEJ predicts lower expression for nearly one-third of eukaryotic proteins.

Improved Ribo-seq enables identification of cryptic translation events

Ribosome profiling has been used to predict thousands of short open reading frames (sORFs) in eukaryotic cells, but it suffers from substantial levels of noise. PRICE (https://github.com/erhard-lab/price) is a computational method that models experimental noise to enable researchers to accurately resolve overlapping sORFs and noncanonical translation initiation. We experimentally validated translation using major histocompatibility complex class I (MHC I) peptidomics and observed that sORF-derived peptides efficiently enter the MHC I presentation pathway and thus constitute a substantial fraction of the antigen repertoire.

Scikit-ribo Enables Accurate Estimation and Robust Modeling of Translation Dynamics at Codon Resolution

Ribosome profiling (Ribo-seq) is a powerful technique for measuring protein translation; however, sampling errors and biological biases are prevalent and poorly understood. Addressing these issues, we present Scikit-ribo (https://github.com/schatzlab/scikit-ribo), an open-source analysis package for accurate genome-wide A-site prediction and translation efficiency (TE) estimation from Ribo-seq and RNA sequencing data. Scikit-ribo accurately identifies A-site locations and reproduces codon elongation rates using several digestion protocols (r = 0.99). Next, we show that the commonly used reads per kilobase of transcript per million mapped reads-derived TE estimation is prone to biases, especially for low-abundance genes. Scikit-ribo introduces a codon-level generalized linear model with ridge penalty that correctly estimates TE, while accommodating variable codon elongation rates and mRNA secondary structure. This corrects the TE errors for over 2,000 genes in S. cerevisiae, which we validate using mass spectrometry of protein abundances (r = 0.81), and allows us to determine the Kozak-like sequence directly from Ribo-seq. We conclude with an analysis of coverage requirements needed for robust codon-level analysis and quantify the artifacts that can occur from cycloheximide treatment.

Discovering Putative Peptides Encoded from Noncoding RNAs in Ribosome Profiling Data of Arabidopsis thaliana

Most noncoding RNAs are considered by their expression at low levels and as having a limited phylogenetic distribution in the cytoplasm, indicating that they may be only involved in specific biological processes. However, recent studies showed the protein-coding potential of ncRNAs, indicating that they might be a source of some special proteins. Although there are increasing noncoding RNAs identified to be able to code proteins, it is challenging to distinguish coding RNAs from previously annotated ncRNAs, and to detect the proteins from their translation. In this article, we designed a pipeline to identify these noncoding RNAs in Arabidopsis thaliana from three NCBI GEO data sets with coding potential and predict their translation products. 31 311 noncoding RNAs were predicted to be translated into peptides, and they showed lower conservation rate than common proteins. In addition, we built an interaction network between these peptides and annotated Arabidopsis proteins using BIPS, which included 69 peptides from noncoding RNAs. Peptides in the interaction network showed different characteristics from other noncoding RNA-derived peptides, and they participated in several crucial biological processes, such as photorespiration and stress-responses. All the information of putative ncPEPs and their interaction with proteins predicted above are finally integrated in a database, PncPEPDB ( http://bis.zju.edu.cn/PncPEPDB ). These results showed that peptides derived from noncoding RNAs may play important roles in noncoding RNA regulation, which provided another hypothesis that noncoding RNA may regulate the metabolism via their translation products.