Detecting translational regulation by change point analysis of ribosome profiling data sets

ORFeus: a computational method to detect programmed ribosomal frameshifts and other non-canonical translation events

BACKGROUND

In canonical protein translation, ribosomes initiate translation at a specific start codon, maintain a single reading frame throughout elongation, and terminate at the first in-frame stop codon. However, ribosomal behavior can deviate at each of these steps, sometimes in a programmed manner. Certain mRNAs contain sequence and structural elements that cause ribosomes to begin translation at alternative start codons, shift reading frame, read through stop codons, or reinitiate on the same mRNA. These processes represent important translational control mechanisms that can allow an mRNA to encode multiple functional protein products or regulate protein expression. The prevalence of these events remains uncertain, due to the difficulty of systematic detection.

RESULTS

We have developed a computational model to infer non-canonical translation events from ribosome profiling data.

CONCLUSION

ORFeus identifies known examples of alternative open reading frames and recoding events across different organisms and enables transcriptome-wide searches for novel events.

Fungal strain improvement for efficient cellulase production and lignocellulosic biorefinery: Current status and future prospects

Lignocellulosic biomass (LCB) has been recognized as a valuable carbon source for the sustainable production of biofuels and value-added biochemicals. Crude enzymes produced by fungal cell factories benefit economic LCB degradation. However, high enzyme production cost remains a great challenge. Filamentous fungi have been widely used to produce cellulolytic enzymes. Metabolic engineering of fungi contributes to efficient cellulase production for LCB biorefinery. Here the latest progress in utilizing fungal cell factories for cellulase production was summarized, including developing genome engineering tools to improve the efficiency of fungal cell factories, manipulating promoters, and modulating transcription factors. Multi-omics analysis of fungi contributes to identifying novel genetic elements for enhancing cellulase production. Furthermore, the importance of translation regulation of cellulase production are emphasized. Efficient development of fungal cell factories based on integrative strain engineering would benefit the overall bioconversion efficacy of LCB for sustainable bioproduction.

Modeling transport of extended interacting objects with drop-off phenomenon

We study a deterministic framework for important cellular transport phenomena involving a large number of interacting molecules called the excluded flow of extended interacting objects with drop-off effect (EFEIOD). This model incorporates many realistic features of biological transport process including the length of biological "particles" and the fact that they can detach along the biological 'tracks'. The flow between the consecutive sites is unidirectional and is described by a "soft" simple exclusion principle and by repelling or attracting forces between neighboring particles. We show that the model admits a unique steady-state. Furthermore, if the parameters are periodic with common period T, then the steady-state profile converge to a unique periodic solution of period T. Simulations of the EFEIOD demonstrate several non-trivial effects of the interactions on the system steady-state profile. For example, detachment rates may help in increasing the steady-state flow by alleviating traffic jams that can exist due to several reasons like bottleneck rate or interactive forces between the particles. We also analyze the special case of our model, when there are no forces exerted by neighboring particles, and called it as the ribosome flow model of extended objects with drop-off effect (RFMEOD), and study the sensitivity of its steady-state to variations in the parameters.

Luciferase-based reporter system for in vitro evaluation of elongation rate and processivity of ribosomes

The elongation step of translation is a key contributor to the abundance, folding and quality of proteins and to the stability of mRNA. However, control over translation elongation has not been thoroughly investigated. In this study, a Renilla-firefly luciferase fusion reporter system was further developed to investigate the in vitro elongation rate and processivity of ribosomes independent of the initiation and termination steps. The reporter mRNA was constructed to contain a single ORF encoding in-frame Renilla luciferase, a specific domain moiety and firefly luciferase. Such a reporter structure enables the quantitative and individual evaluation of the synthesis of a specific domain. As a proof of principle, the synthesis of three protein domains of different lengths and structures was analyzed. Using a cell-free translation assay, both the elongation rate and processivity of ribosomes were shown to vary depending on the domain synthesized. Additionally, a stalling sequence consisting of ten rare arginine codons notably reduced the elongation rate and the processivity of the ribosomes. All these results are consistent with the previously known dynamics of elongation in vivo. Overall, the methodology presented in this report provides a framework for studying aspects that contribute to the elongation step of translation.

Ribosome elongating footprints denoised by wavelet transform comprehensively characterize dynamic cellular translation events

Translation is dynamically regulated during cell development and stress response. In order to detect actively translated open reading frames (ORFs) and dynamic cellular translation events, we have developed a computational method, RiboWave, to process ribosome profiling data. RiboWave utilizes wavelet transform to denoise the original signal by extracting 3-nt periodicity of ribosomes and precisely locate their footprint denoted as Periodic Footprint P-site (PF P-site). Such high-resolution footprint is found to capture the full track of actively elongating ribosomes, from which translational landscape can be explicitly characterized. We compare RiboWave with several published methods, like RiboTaper, ORFscore and RibORF, and found that RiboWave outperforms them in both accuracy and usage when defining actively translated ORFs. Moreover, we show that PF P-site derived by RiboWave shows superior performance in characterizing the dynamics and complexity of cellular translatome by accurately estimating the abundance of protein levels, assessing differential translation and identifying dynamic translation frameshift.

Computational resources for ribosome profiling: from database to Web server and software

Ribosome profiling is emerging as a powerful technique that enables genome-wide investigation of in vivo translation at sub-codon resolution. The increasing application of ribosome profiling in recent years has achieved remarkable progress toward understanding the composition, regulation and mechanism of translation. This benefits from not only the awesome power of ribosome profiling but also an extensive range of computational resources available for ribosome profiling. At present, however, a comprehensive review on these resources is still lacking. Here, we survey the recent computational advances guided by ribosome profiling, with a focus on databases, Web servers and software tools for storing, visualizing and analyzing ribosome profiling data. This review is intended to provide experimental and computational biologists with a reference to make appropriate choices among existing resources for the question at hand.

Translational regulation contributes to the secretory response of chondrocytic cells following exposure to interleukin-1β

Osteoarthritis is a chronic disease characterized by the loss of articular cartilage in synovial joints through a process of extracellular matrix destruction that is strongly associated with inflammatory stimuli. Chondrocytes undergo changes to their protein translational capacity during osteoarthritis, but a study of how disease-relevant signals affect chondrocyte protein translation at the transcriptomic level has not previously been performed. In this study, we describe how the inflammatory cytokine interleukin 1-β (IL-1β) rapidly affects protein translation in the chondrocytic cell line SW1353. Using ribosome profiling we demonstrate that IL-1β induced altered translation of inflammatory-associated transcripts such as NFKB1, TNFAIP2, MMP13, CCL2, and CCL7, as well as a number of ribosome-associated transcripts, through differential translation and the use of multiple open reading frames. Proteomic analysis of the cellular layer and the conditioned media of these cells identified changes in a number of the proteins that were differentially translated. Translationally regulated secreted proteins included a number of chemokines and cytokines, underlining the rapid, translationally mediated inflammatory cascade that is initiated by IL-1β. Although fewer cellular proteins were found to be regulated in both ribosome profiling and proteomic data sets, we did find increased levels of SOD2, indicative of redox changes within SW1353 cells being modulated at the translational level. In conclusion, we have produced combined ribosome profiling and proteomic data sets that provide a valuable resource in understanding the processes that occur during cytokine stimulation of chondrocytic cells.

Cell-Type-Specific Profiling of Alternative Translation Identifies Regulated Protein Isoform Variation in the Mouse Brain

Alternative translation initiation and stop codon readthrough in a few well-studied cases have been shown to allow the same transcript to generate multiple protein variants. Because the brain shows a particularly abundant use of alternative splicing, we sought to study alternative translation in CNS cells. We show that alternative translation is widespread and regulated across brain transcripts. In neural cultures, we identify alternative initiation on hundreds of transcripts, confirm several N-terminal protein variants, and show the modulation of the phenomenon by KCl stimulation. We also detect readthrough in cultures and show differential levels of normal and readthrough versions of AQP4 in gliotic diseases. Finally, we couple translating ribosome affinity purification to ribosome footprinting (TRAP-RF) for cell-type-specific analysis of neuronal and astrocytic translational readthrough in the mouse brain. We demonstrate that this unappreciated mechanism generates numerous and diverse protein isoforms in a cell-type-specific manner in the brain.

Following Ribosome Footprints to Understand Translation at a Genome Wide Level

Protein translation is a key step in gene expression. The development of Ribosome Profiling has allowed the global analysis of this process at sub-codon resolution. In the last years the method has been applied to several models ranging from bacteria to mammalian cells yielding a surprising amount of insight on the mechanism and the regulation of translation. In this review we describe the key aspects of the experimental protocol and comment on the main conclusions raised in different models.

Leveraging genome-wide datasets to quantify the functional role of the anti-Shine-Dalgarno sequence in regulating translation efficiency

Studies dating back to the 1970s established that sequence complementarity between the anti-Shine–Dalgarno (aSD) sequence on prokaryotic ribosomes and the 5′ untranslated region of mRNAs helps to facilitate translation initiation. The optimal location of aSD sequence binding relative to the start codon, the full extents of the aSD sequence and the functional form of the relationship between aSD sequence complementarity and translation efficiency have not been fully resolved. Here, we investigate these relationships by leveraging the sequence diversity of endogenous genes and recently available genome-wide estimates of translation efficiency. We show that—after accounting for predicted mRNA structure—aSD sequence complementarity increases the translation of endogenous mRNAs by roughly 50%. Further, we observe that this relationship is nonlinear, with translation efficiency maximized for mRNAs with intermediate levels of aSD sequence complementarity. The mechanistic insights that we observe are highly robust: we find nearly identical results in multiple datasets spanning three distantly related bacteria. Further, we verify our main conclusions by re-analysing a controlled experimental dataset.

Beyond Read-Counts: Ribo-seq Data Analysis to Understand the Functions of the Transcriptome

By mapping the positions of millions of translating ribosomes in the cell, ribosome profiling (Ribo-seq) has established its role as a powerful tool to study gene expression. Several laboratories have introduced modifications to the experimental protocol and expanded the repertoire of biochemical methods to study translation transcriptome-wide. However, the diversity of protocols highlights a need for standardization. At the same time, different computational analysis strategies have used Ribo-seq data to identify the set of translated sequences with high confidence. In this review we present an overview of such methodologies, outlining their assumptions, data requirements, and availability. At the interface between RNA and proteins, Ribo-seq can complement data from multiple omics approaches, zooming in on the central role of translation in the molecular cell.

Integrated Translatome and Proteome: Approach for Accurate Portraying of Widespread Multifunctional Aspects of Trichoderma

Genome-wide studies of transcripts expression help in systematic monitoring of genes and allow targeting of candidate genes for future research. In contrast to relatively stable genomic data, the expression of genes is dynamic and regulated both at time and space level at different level in. The variation in the rate of translation is specific for each protein. Both the inherent nature of an mRNA molecule to be translated and the external environmental stimuli can affect the efficiency of the translation process. In biocontrol agents (BCAs), the molecular response at translational level may represents noise-like response of absolute transcript level and an adaptive response to physiological and pathological situations representing subset of mRNAs population actively translated in a cell. The molecular responses of biocontrol are complex and involve multistage regulation of number of genes. The use of high-throughput techniques has led to rapid increase in volume of transcriptomics data of Trichoderma. In general, almost half of the variations of transcriptome and protein level are due to translational control. Thus, studies are required to integrate raw information from different “omics” approaches for accurate depiction of translational response of BCAs in interaction with plants and plant pathogens. The studies on translational status of only active mRNAs bridging with proteome data will help in accurate characterization of only a subset of mRNAs actively engaged in translation. This review highlights the associated bottlenecks and use of state-of-the-art procedures in addressing the gap to accelerate future accomplishment of biocontrol mechanisms.

A deterministic mathematical model for bidirectional excluded flow with Langmuir kinetics

In many important cellular processes, including mRNA translation, gene transcription, phosphotransfer, and intracellular transport, biological "particles" move along some kind of "tracks". The motion of these particles can be modeled as a one-dimensional movement along an ordered sequence of sites. The biological particles (e.g., ribosomes or RNAPs) have volume and cannot surpass one another. In some cases, there is a preferred direction of movement along the track, but in general the movement may be bidirectional, and furthermore the particles may attach or detach from various regions along the tracks. We derive a new deterministic mathematical model for such transport phenomena that may be interpreted as a dynamic mean-field approximation of an important model from mechanical statistics called the asymmetric simple exclusion process (ASEP) with Langmuir kinetics. Using tools from the theory of monotone dynamical systems and contraction theory we show that the model admits a unique steady-state, and that every solution converges to this steady-state. Furthermore, we show that the model entrains (or phase locks) to periodic excitations in any of its forward, backward, attachment, or detachment rates. We demonstrate an application of this phenomenological transport model for analyzing ribosome drop off in mRNA translation.

Ribo-seq enlightens codon usage bias

Codon usage is biased between lowly and highly expressed genes in a genome-specific manner. This universal bias has been well assessed in some unicellular species, but remains problematic to assess in more complex species. We propose a new method to compute codon usage bias based on genome wide translational data. A new technique based on sequencing of ribosome protected mRNA fragments (Ribo-seq) allowed us to rank genes and compute codon usage bias with high precision for a great variety of species, including mammals. Genes ranking using Ribo-Seq data confirms the influence of the tRNA pool on codon usage bias and shows a decreasing bias in multicellular species. Ribo-Seq analysis also makes possible to detect preferred codons without information on genes function.

Isolation of Polysomal RNA for Analyzing Stress-Responsive Genes Regulated at the Translational Level in Plants

Alteration of gene expression is an essential mechanism, which allows plants to respond and adapt to adverse environmental conditions. Transcriptome and proteome analyses in plants exposed to abiotic stresses revealed that protein levels are not correlated with the changes in corresponding mRNAs, indicating regulation at translational level is another major regulator for gene expression. Analysis of translatome, which refers to all mRNAs associated with ribosomes, thus has the potential to bridge the gap between transcriptome and proteome. Polysomal RNA profiling and recently developed ribosome profiling (Ribo-seq) are two main methods for translatome analysis at global level. Here, we describe the classical procedure for polysomal RNA isolation by sucrose gradient ultracentrifugation followed by highthroughput RNA-seq to identify genes regulated at translational level. Polysomal RNA can be further used for a variety of downstream applications including Northern blot analysis, qRT-PCR, RNase protection assay, and microarray-based gene expression profiling.

Accurate Recovery of Ribosome Positions Reveals Slow Translation of Wobble-Pairing Codons in Yeast

Ribosome profiling quantitatively captures ribosome locations during translation. The resulting profiles of ribosome locations are widely used to study translational speed. However, an accurate estimation of the ribosome location depends on identifying the A-site from ribosome profiling reads, a problem that was previously unsolved. Here, we propose a novel method to estimate the ribosome A-site positions from high-coverage ribosome profiling reads. Our model allows more reads to be used, accurately explains the 3-nt periodicity of ribosome profiling reads from various lengths, and recovers consistent ribosome positions across different lengths. Our recovered ribosome positions are correctly highly skewed toward a single frame within a codon. They retain subcodon resolution and enable detection of off-frame translational events, such as frameshifts. Our method improves the correlation with other estimates of codon decoding time. Furthermore, the refined profiles show that yeast wobble-pairing codons are translated slower than their synonymous Watson-Crick-pairing codons. These results provide evidence that protein synthetic rate can be tuned by codon usage bias.

Predictive biophysical modeling and understanding of the dynamics of mRNA translation and its evolution

mRNA translation is the fundamental process of decoding the information encoded in mRNA molecules by the ribosome for the synthesis of proteins. The centrality of this process in various biomedical disciplines such as cell biology, evolution and biotechnology, encouraged the development of dozens of mathematical and computational models of translation in recent years. These models aimed at capturing various biophysical aspects of the process. The objective of this review is to survey these models, focusing on those based and/or validated on real large-scale genomic data. We consider aspects such as the complexity of the models, the biophysical aspects they regard and the predictions they may provide. Furthermore, we survey the central systems biology discoveries reported on their basis. This review demonstrates the fundamental advantages of employing computational biophysical translation models in general, and discusses the relative advantages of the different approaches and the challenges in the field.

Ribosome Footprint Profiling of Translation throughout the Genome

Ribosome profiling has emerged as a technique for measuring translation comprehensively and quantitatively by deep sequencing of ribosome-protected mRNA fragments. By identifying the precise positions of ribosomes, footprinting experiments have unveiled key insights into the composition and regulation of the expressed proteome, including delineating potentially functional micropeptides, revealing pervasive translation on cytosolic RNAs, and identifying differences in elongation rates driven by codon usage or other factors. This Primer looks at important experimental and analytical concerns for executing ribosome profiling experiments and surveys recent examples where the approach was developed to explore protein biogenesis and homeostasis.

Modeling and gene knockdown to assess the contribution of nonsense-mediated decay, premature termination, and selenocysteine insertion to the selenoprotein hierarchy

The expression of selenoproteins, a specific group of proteins that incorporates selenocysteine, is hierarchically regulated by the availability of Se, with some, but not all selenoprotein mRNA transcripts decreasing in abundance with decreasing Se. Selenocysteine insertion into the peptide chain occurs during translation following recoding of an internal UGA stop codon. There is increasing evidence that this UGA recoding competes with premature translation termination, which is followed by nonsense-mediated decay (NMD) of the transcript. In this study, we tested the hypothesis that the susceptibility of different selenoprotein mRNAs to premature termination during translation and differential sensitivity of selenoprotein transcripts to NMD are major factors in the selenoprotein hierarchy. Selenoprotein transcript abundance was measured in Caco-2 cells using real-time PCR under different Se conditions and the data obtained fitted to mathematical models of selenoprotein translation. A calibrated model that included a combination of differential sensitivity of selenoprotein transcripts to NMD and different frequency of non-NMD related premature translation termination was able to fit all the measurements. The model predictions were tested using SiRNA to knock down expression of the crucial NMD factor UPF1 (up-frameshift protein 1) and selenoprotein mRNA expression. The calibrated model was able to predict the effect of UPF1 knockdown on gene expression for all tested selenoproteins, except SPS2 (selenophosphate synthetase), which itself is essential for selenoprotein synthesis. These results indicate an important role for NMD in the hierarchical regulation of selenoprotein mRNAs, with the exception of SPS2 whose expression is likely regulated by a different mechanism.

Exploring Ribosome Positioning on Translating Transcripts with Ribosome Profiling

Recent technological advances (e.g., microarrays and massively parallel sequencing) have facilitated genome-wide measurement of many aspects of gene regulation. Ribosome profiling is a high-throughput sequencing method used to measure gene expression at the level of translation. This is accomplished by quantifying both the number of translating ribosomes and their locations on mRNA transcripts. The inventors of this approach have published several methods papers detailing its implementation and addressing the basics of ribosome profiling data analysis. Here we describe our lab's procedure, which differs in some respects from those published previously. In addition, we describe a data analysis pipeline, Ribomap, for ribosome profiling data. Ribomap allocates sequence reads to alternative mRNA isoforms, normalizes sequencing bias along transcripts using RNA-seq data, and outputs count vectors of per-codon ribosome occupancy for each transcript.

Detecting actively translated open reading frames in ribosome profiling data

RNA-sequencing protocols can quantify gene expression regulation from transcription to protein synthesis. Ribosome profiling (Ribo-seq) maps the positions of translating ribosomes over the entire transcriptome. We have developed RiboTaper (available at https://ohlerlab.mdc-berlin.de/software/), a rigorous statistical approach that identifies translated regions on the basis of the characteristic three-nucleotide periodicity of Ribo-seq data. We used RiboTaper with deep Ribo-seq data from HEK293 cells to derive an extensive map of translation that covered open reading frame (ORF) annotations for more than 11,000 protein-coding genes. We also found distinct ribosomal signatures for several hundred upstream ORFs and ORFs in annotated noncoding genes (ncORFs). Mass spectrometry data confirmed that RiboTaper achieved excellent coverage of the cellular proteome. Although dozens of novel peptide products were validated in this manner, few of the currently annotated long noncoding RNAs appeared to encode stable polypeptides. RiboTaper is a powerful method for comprehensive de novo identification of actively used ORFs from Ribo-seq data.

RiboAbacus: a model trained on polyribosome images predicts ribosome density and translational efficiency from mammalian transcriptomes

Fluctuations in mRNA levels only partially contribute to determine variations in mRNA availability for translation, producing the well-known poor correlation between transcriptome and proteome data. Recent advances in microscopy now enable researchers to obtain high resolution images of ribosomes on transcripts, providing precious snapshots of translation in vivo. Here we propose RiboAbacus, a mathematical model that for the first time incorporates imaging data in a predictive model of transcript-specific ribosome densities and translational efficiencies. RiboAbacus uses a mechanistic model of ribosome dynamics, enabling the quantification of the relative importance of different features (such as codon usage and the 5′ ramp effect) in determining the accuracy of predictions. The model has been optimized in the human Hek-293 cell line to fit thousands of images of human polysomes obtained by atomic force microscopy, from which we could get a reference distribution of the number of ribosomes per mRNA with unmatched resolution. After validation, we applied RiboAbacus to three case studies of known transcriptome-proteome datasets for estimating the translational efficiencies, resulting in an increased correlation with corresponding proteomes. RiboAbacus is an intuitive tool that allows an immediate estimation of crucial translation properties for entire transcriptomes, based on easily obtainable transcript expression levels.