Pateamine A-sensitive ribosome profiling reveals the scope of translation in mouse embryonic stem cells

mTOR pathway occupies a central role in the emergence of latent cancer cells

The current focus in oncology research is the translational control of cancer cells as a major mechanism of cellular plasticity. Recent evidence has prompted a reevaluation of the role of the mTOR pathway in cancer development leading to new conclusions. The mechanistic mTOR inhibition is well known to be a tool for generating quiescent stem cells and cancer cells. In response to mTOR suppression, quiescent cancer cells dynamically change their proteome, triggering alternative non-canonical translation mechanisms. The shift to selective translation may have clinical relevance, since quiescent tumor cells can acquire new phenotypical features. This review provides new insights into the patterns of mTOR functioning in quiescent cancer cells, enhancing our current understanding of the biology of latent metastasis.

Micropeptides translated from putative long non-coding RNAs

Long non-coding RNAs (lncRNAs) transcribed in mammals and eukaryotes were thought to have no protein coding capability. However, recent studies have suggested that plenty of lncRNAs are mis-annotated and virtually contain coding sequences which are translated into functional peptides by ribosomal machinery, and these functional peptides are called micropeptides or small peptides. Here we review the rapidly advancing field of micropeptides translated from putative lncRNAs, describe the strategies for their identification, and elucidate their critical roles in many fundamental biological processes. We also discuss the prospects of research in micropeptides and the potential applications of micropeptides.

Translation complex stabilization on messenger RNA and footprint profiling to study the RNA responses and dynamics of protein biosynthesis in the cells

During protein biosynthesis, ribosomes bind to messenger (m)RNA, locate its protein-coding information, and translate the nucleotide triplets sequentially as codons into the corresponding sequence of amino acids, forming proteins. Non-coding mRNA features, such as 5' and 3' untranslated regions (UTRs), start sites or stop codons of different efficiency, stretches of slower or faster code and nascent polypeptide interactions can alter the translation rates transcript-wise. Most of the homeostatic and signal response pathways of the cells converge on individual mRNA control, as well as alter the global translation output. Among the multitude of approaches to study translational control, one of the most powerful is to infer the locations of translational complexes on mRNA based on the mRNA fragments protected by these complexes from endonucleolytic hydrolysis, or footprints. Translation complex profiling by high-throughput sequencing of the footprints allows to quantify the transcript-wise, as well as global, alterations of translation, and uncover the underlying control mechanisms by attributing footprint locations and sizes to different configurations of the translational complexes. The accuracy of all footprint profiling approaches critically depends on the fidelity of footprint generation and many methods have emerged to preserve certain or multiple configurations of the translational complexes, often in challenging biological material. In this review, a systematic summary of approaches to stabilize translational complexes on mRNA for footprinting is presented and major findings are discussed. Future directions of translation footprint profiling are outlined, focusing on the fidelity and accuracy of inference of the native in vivo translation complex distribution on mRNA.

Translational control of stem cell function

Stem cells are characterized by their ability to self-renew and differentiate into many different cell types. Research has focused primarily on how these processes are regulated at a transcriptional level. However, recent studies have indicated that stem cell behaviour is strongly coupled to the regulation of protein synthesis by the ribosome. In this Review, we discuss how different translation mechanisms control the function of adult and embryonic stem cells. Stem cells are characterized by low global translation rates despite high levels of ribosome biogenesis. The maintenance of pluripotency, the commitment to a specific cell fate and the switch to cell differentiation depend on the tight regulation of protein synthesis and ribosome biogenesis. Translation regulatory mechanisms that impact on stem cell function include mTOR signalling, ribosome levels, and mRNA and tRNA features and amounts. Understanding these mechanisms important for stem cell self-renewal and differentiation may also guide our understanding of cancer grade and metastasis.

Quantitative Comparisons of Translation Activity by Ribosome Profiling with Internal Standards

Ribosome profiling is a genome-wide approach to map the positions of ribosomes on messenger RNAs. The abundance of ribosome-protected fragments can be used within condition to compare relative translation activities between different transcripts and between distinct conditions for the same transcript. A unified and routine method is currently lacking, however, to normalize between conditions for differences in global translation levels. Here we describe experimental and computational methods to use an orthogonal species spike-in, or internal standard, to enable absolute comparisons of translation activity between conditions. This simple modification of standard ribosome profiling provides a robust approach for accurately interpreting the effects of diverse genetic, chemical, and environmental perturbations of translation.

The computational approaches of lncRNA identification based on coding potential: Status quo and challenges

Long noncoding RNAs (lncRNAs) make up a large proportion of transcriptome in eukaryotes, and have been revealed with many regulatory functions in various biological processes. When studying lncRNAs, the first step is to accurately and specifically distinguish them from the colossal transcriptome data with complicated composition, which contains mRNAs, lncRNAs, small RNAs and their primary transcripts. In the face of such a huge and progressively expanding transcriptome data, the in-silico approaches provide a practicable scheme for effectively and rapidly filtering out lncRNA targets, using machine learning and probability statistics. In this review, we mainly discussed the characteristics of algorithms and features on currently developed approaches. We also outlined the traits of some state-of-the-art tools for ease of operation. Finally, we pointed out the underlying challenges in lncRNA identification with the advent of new experimental data.

Minority Gene Expression Profiling: Probing the Genetic Signatures of Pathogenesis Using Ribosome Profiling

Minority Gene Expression Profiling (MGEP) refers to a scenario where the expression profiles of specific genes of interest are concentrated in a small cellular pool that is embedded within a larger, non-expressive pool. An example of this is the analysis of disease-related genes within sub-populations of blood or biopsied tissues. These systems are characterized by low signal-to-noise ratios that make it difficult, if not impossible, to uncover the desired signatures of pathogenesis in the absence of lengthy, and often problematic, technical manipulations. We have adapted ribosome profiling (RP) workflows from the Illumina to the Ion Proton platform and used them to analyze signatures of pathogenesis in an MGEP model system consisting of human cells eliciting <3% productive dengue infection. We find that RP is powerful enough to identify relevant responses of differentially expressed genes, even in the presence of significant noise. We discuss how to deal with sources of unwanted variation, and propose ways to further improve this powerful approach to the study of pathogenic signatures within MGEP systems.

When Long Noncoding Becomes Protein Coding

Recent advancements in genetic and proteomic technologies have revealed that more of the genome encodes proteins than originally thought possible. Specifically, some putative long noncoding RNAs (lncRNAs) have been misannotated as noncoding. Numerous lncRNAs have been found to contain short open reading frames (sORFs) which have been overlooked because of their small size. Many of these sORFs encode small proteins or micropeptides with fundamental biological importance. These micropeptides can aid in diverse processes, including cell division, transcription regulation, and cell signaling. Here we discuss strategies for establishing the coding potential of putative lncRNAs and describe various functions of known micropeptides.

Long Non-coding RNA in Plants in the Era of Reference Sequences

The discovery of non-coding RNAs (ncRNAs), and the subsequent elucidation of their functional roles, was largely delayed due to the misidentification of non-protein-coding parts of DNA as "junk DNA," which forced ncRNAs into the shadows of their protein-coding counterparts. However, over the past decade, insight into the important regulatory roles of ncRNAs has led to rapid progress in their identification and characterization. Of the different types of ncRNAs, long non-coding RNAs (lncRNAs), has attracted considerable attention due to their mRNA-like structures and gene regulatory functions in plant stress responses. While RNA sequencing has been commonly used for mining lncRNAs, a lack of widespread conservation at the sequence level in addition to relatively low and highly tissue-specific expression patterns challenges high-throughput in silico identification approaches. The complex folding characteristics of lncRNA molecules also complicate target predictions, as the knowledge about the interaction interfaces between lncRNAs and potential targets is insufficient. Progress in characterizing lncRNAs and their targets from different species may hold the key to efficient identification of this class of ncRNAs from transcriptomic and potentially genomic resources. In wheat and barley, two of the most important crops, the knowledge about lncRNAs is very limited. However, recently published high-quality genomes of these crops are considered as promising resources for the identification of not only lncRNAs, but any class of molecules. Considering the increasing demand for food, these resources should be used efficiently to discover molecular mechanisms lying behind development and a/biotic stress responses. As our understanding of lncRNAs expands, interactions among ncRNA classes, as well as interactions with the coding sequences, will likely define novel functional networks that may be modulated for crop improvement.

Ribosome Profiling: Global Views of Translation

The translation of messenger RNA (mRNA) into protein and the folding of the resulting protein into an active form are prerequisites for virtually every cellular process and represent the single largest investment of energy by cells. Ribosome profiling-based approaches have revolutionized our ability to monitor every step of protein synthesis in vivo, allowing one to measure the rate of protein synthesis across the proteome, annotate the protein coding capacity of genomes, monitor localized protein synthesis, and explore cotranslational folding and targeting. The rich and quantitative nature of ribosome profiling data provides an unprecedented opportunity to explore and model complex cellular processes. New analytical techniques and improved experimental protocols will provide a deeper understanding of the factors controlling translation speed and its impact on protein function and cell physiology as well as the role of ribosomal RNA and mRNA modifications in regulating translation.

Bioinformatics and Translation Elongation

Codon usage depends on mutation bias, tRNA-mediated selection, and the need for high efficiency and accuracy in translation. One codon in a synonymous codon family is often strongly over-used, especially in highly expressed genes, which often leads to a high dN/dS ratio because dS is very small. Many different codon usage indices have been proposed to measure codon usage and codon adaptation. Sense codon could be misread by release factors and stop codons misread by tRNAs, which also contribute to codon usage in rare cases. This chapter outlines the conceptual framework on codon evolution, illustrates codon-specific and gene-specific codon usage indices, and presents their applications. A new index for codon adaptation that accounts for background mutation bias (Index of Translation Elongation) is presented and contrasted with codon adaptation index (CAI) which does not consider background mutation bias. They are used to re-analyze data from a recent paper claiming that translation elongation efficiency matters little in protein production. The reanalysis disproves the claim.

Altered translation initiation of Gja1 limits gap junction formation during epithelial-mesenchymal transition

Epithelial-mesenchymal transition (EMT) is activated during development, wound healing, and pathologies including fibrosis and cancer metastasis. Hallmarks of EMT are remodeling of intercellular junctions and adhesion proteins, including gap junctions. The GJA1 mRNA transcript encoding the gap junction protein connexin43 (Cx43) has been demonstrated to undergo internal translation initiation, yielding truncated isoforms that modulate gap junctions. The PI3K/Akt/mTOR pathway is central to translation regulation and is activated during EMT, leading us to hypothesize that altered translation initiation would contribute to gap junction loss. Using TGF-β-induced EMT as a model, we find reductions in Cx43 gap junctions despite increased transcription and stabilization of Cx43 protein. Biochemical experiments reveal suppression of the internally translated Cx43 isoform, GJA1-20k in a Smad3 and ERK-dependent manner. Ectopic expression of GJA1-20k does not halt EMT, but is sufficient to rescue gap junction formation. GJA1-20k localizes to the Golgi apparatus, and using superresolution localization microscopy we find retention of GJA1-43k at the Golgi in mesenchymal cells lacking GJA1-20k. NativePAGE demonstrates that levels of GJA1-20k regulate GJA1-43k hexamer oligomerization, a limiting step in Cx43 trafficking. These findings reveal alterations in translation initiation as an unexplored mechanism by which the cell regulates Cx43 gap junction formation during EMT.

Translation of noncoding RNAs: Focus on lncRNAs, pri-miRNAs, and circRNAs

Mammalian genome is pervasively transcribed, producing large number of noncoding RNAs (ncRNAs), including long noncoding RNAs (lncRNAs), primary miRNAs (pri-miRNA), and circular RNAs (circRNAs). The translation of these ncRNAs has long been overlooked. Increasing studies, however, based on ribosome profiling in various organisms provide important clues to unanticipated translation potential of lncRNAs. Moreover, a few functional peptides encoded by lncRNAs and pri-miRNAs underline the significance of their translation. Recently, several novel researches also evidence the translation of endogenous circRNAs. Given the functional significance exemplified by peptides translated by some ncRNAs and their pervasive translation, it is not too far-fetched to image that abnormal translation of ncRNAs may contribute to human diseases. Through challenging, deciphering ncRNA translation is required for comprehensive understanding of biology and medicine. In this review, we firstly present evidence concerning translation potential of lncRNAs and go on to introduce a few functional short peptides encoded by lncRNAs. Then, salient observations showing translation of pri-miRNAs and circRNAs are described in detail. We end by discussing the impact of ncRNA translation beyond producing peptides and referring briefly to the potential role of abnormal ncRNA translation in human diseases.

Transcriptome-wide measurement of translation by ribosome profiling

Translation is one of the fundamental processes of life. It comprises the assembly of polypeptides whose amino acid sequence corresponds to the codon sequence of an mRNA's ORF. Translation is performed by the ribosome; therefore, in order to understand translation and its regulation we must be able to determine the numbers and locations of ribosomes on mRNAs in vivo. Furthermore, we must be able to examine their redistribution in different physiological contexts and in response to experimental manipulations. The ribosome profiling method provides us with an opportunity to learn these locations, by sequencing a cDNA library derived from the short fragments of mRNA covered by the ribosome. Since its original description, the ribosome profiling method has undergone continuing development; in this article we describe the method's current state. Important improvements include: the incorporation of sample barcodes to enable library multiplexing, the incorporation of unique molecular identifiers to enable to removal of duplicated sequences, and the replacement of a gel-purification step with the enzymatic degradation of unligated linker.

The Growing Toolbox for Protein Synthesis Studies

Protein synthesis stands at the last stage of the central dogma of molecular biology, providing a final regulatory layer for gene expression. Reacting to environmental cues and internal signals, the translation machinery can quickly tune the translatome from a pre-existing pool of RNAs, before the transcriptome changes. Although the translation reaction itself has been known since the 1950s, the quantitative or even qualitative measurement of its efficacy in cells has posed experimental and analytic hurdles. In this review, we outline the array of state-of-the-art methods that have emerged to tackle the hidden aspects of translational control.

Impact of MicroRNAs in the Cellular Response to Hypoxia

In mammalian cells, hypoxia, or inadequate oxygen availability, regulates the expression of a specific set of MicroRNAs (MiRNAs), termed "hypoxamiRs." Over the past 10 years, the appreciation of the importance of hypoxamiRs in regulating the cellular adaptation to hypoxia has grown dramatically. At the cellular level, each hypoxamiR, including the master hypoxamiR MiR-210, can simultaneously regulate expression of multiple target genes in order to fine-tune the adaptive response of cells to hypoxia. This review addresses the complex molecular regulation of MiRNAs in both physiological and pathological conditions of low oxygen adaptation and the multiple functions of hypoxamiRs in various hypoxia-associated biological processes, including apoptosis, survival, proliferation, angiogenesis, inflammation, and metabolism. From a clinical perspective, we also discuss the potential use of hypoxamiRs as new biomarkers and/or therapeutic targets in cancer and aging-associated diseases including cardiovascular and fibroproliferative disorders.

Bioinformatics and Drug Discovery

Bioinformatic analysis can not only accelerate drug target identification and drug candidate screening and refinement, but also facilitate characterization of side effects and predict drug resistance. High-throughput data such as genomic, epigenetic, genome architecture, cistromic, transcriptomic, proteomic, and ribosome profiling data have all made significant contribution to mechanismbased drug discovery and drug repurposing. Accumulation of protein and RNA structures, as well as development of homology modeling and protein structure simulation, coupled with large structure databases of small molecules and metabolites, paved the way for more realistic protein-ligand docking experiments and more informative virtual screening. I present the conceptual framework that drives the collection of these high-throughput data, summarize the utility and potential of mining these data in drug discovery, outline a few inherent limitations in data and software mining these data, point out news ways to refine analysis of these diverse types of data, and highlight commonly used software and databases relevant to drug discovery.

Insights into the mechanisms of eukaryotic translation gained with ribosome profiling

The development of Ribosome Profiling (RiboSeq) has revolutionized functional genomics. RiboSeq is based on capturing and sequencing of the mRNA fragments enclosed within the translating ribosome and it thereby provides a ‘snapshot’ of ribosome positions at the transcriptome wide level. Although the method is predominantly used for analysis of differential gene expression and discovery of novel translated ORFs, the RiboSeq data can also be a rich source of information about molecular mechanisms of polypeptide synthesis and translational control. This review will focus on how recent findings made with RiboSeq have revealed important details of the molecular mechanisms of translation in eukaryotes. These include mRNA translation sensitivity to drugs affecting translation initiation and elongation, the roles of upstream ORFs in response to stress, the dynamics of elongation and termination as well as details of intrinsic ribosome behavior on the mRNA after translation termination. As the RiboSeq method is still at a relatively early stage we will also discuss the implications of RiboSeq artifacts on data interpretation.

RiboProfiling: a Bioconductor package for standard Ribo-seq pipeline processing

The ribosome profiling technique (Ribo-seq) allows the selective sequencing of translated RNA regions. Recently, the analysis of genomic sequences associated to Ribo-seq reads has been widely employed to assess their coding potential. These analyses led to the identification of differentially translated transcripts under different experimental conditions, and/or ribosome pausing on codon motifs. In the context of the ever-growing need for tools analyzing Ribo-seq reads, we have developed 'RiboProfiling', a new Bioconductor open-source package. 'RiboProfiling' provides a full pipeline to cover all key steps for the analysis of ribosome footprints. This pipeline has been implemented in a single R workflow. The package takes an alignment (BAM) file as input and performs ribosome footprint quantification at a transcript level. It also identifies footprint accumulation on particular amino acids or multi amino-acids motifs. Report summary graphs and data quantification are generated automatically. The package facilitates quality assessment and quantification of Ribo-seq experiments. Its implementation in Bioconductor enables the modeling and statistical analysis of its output through the vast choice of packages available in R. This article illustrates how to identify codon-motifs accumulating ribosome footprints, based on data from Escherichia coli.