Rqc1 and other yeast proteins containing highly positively charged sequences are not targets of the RQC complex

Massively parallel identification of sequence motifs triggering ribosome-associated mRNA quality control

Decay of mRNAs can be triggered by ribosome slowdown at stretches of rare codons or positively charged amino acids. However, the full diversity of sequences that trigger co-translational mRNA decay is poorly understood. To comprehensively identify sequence motifs that trigger mRNA decay, we use a massively parallel reporter assay to measure the effect of all possible combinations of codon pairs on mRNA levels in S. cerevisiae. In addition to known mRNA-destabilizing sequences, we identify several dipeptide repeats whose translation reduces mRNA levels. These include combinations of positively charged and bulky residues, as well as proline-glycine and proline-aspartate dipeptide repeats. Genetic deletion of the ribosome collision sensor Hel2 rescues the mRNA effects of these motifs, suggesting that they trigger ribosome slowdown and activate the ribosome-associated quality control (RQC) pathway. Deep mutational scanning of an mRNA-destabilizing dipeptide repeat reveals a complex interplay between the charge, bulkiness, and location of amino acid residues in conferring mRNA instability. Finally, we show that the mRNA effects of codon pairs are predictive of the effects of endogenous sequences. Our work highlights the complexity of sequence motifs driving co-translational mRNA decay in eukaryotes, and presents a high throughput approach to dissect their requirements at the codon level.

Riboformer: a deep learning framework for predicting context-dependent translation dynamics

Translation elongation is essential for maintaining cellular proteostasis, and alterations in the translational landscape are associated with a range of diseases. Ribosome profiling allows detailed measurements of translation at the genome scale. However, it remains unclear how to disentangle biological variations from technical artifacts in these data and identify sequence determinants of translation dysregulation. Here we present Riboformer, a deep learning-based framework for modeling context-dependent changes in translation dynamics. Riboformer leverages the transformer architecture to accurately predict ribosome densities at codon resolution. When trained on an unbiased dataset, Riboformer corrects experimental artifacts in previously unseen datasets, which reveals subtle differences in synonymous codon translation and uncovers a bottleneck in translation elongation. Further, we show that Riboformer can be combined with in silico mutagenesis to identify sequence motifs that contribute to ribosome stalling across various biological contexts, including aging and viral infection. Our tool offers a context-aware and interpretable approach for standardizing ribosome profiling datasets and elucidating the regulatory basis of translation kinetics.

Transcriptional profile of ribosome-associated quality control components and their associated phenotypes in mammalian cells

During protein synthesis, organisms detect translation defects that induce ribosome stalling and result in protein aggregation. The Ribosome-associated Quality Control (RQC) complex, comprising TCF25, LTN1, and NEMF, is responsible for identifying incomplete protein products from unproductive translation events, targeting them for degradation. Although RQC disruption causes adverse effects on vertebrate neurons, data regarding mRNA/protein expression and regulation across tissues are lacking. Employing high-throughput methods, we analyzed public datasets to explore RQC gene expression and phenotypes. Our findings revealed widespread expression of RQC components in human tissues; however, silencing of RQC yielded only mild negative effects on cell growth. Notably, TCF25 exhibited elevated mRNA levels that were not reflected in the protein content. We experimentally demonstrated that this disparity arose from post-translational protein degradation by the proteasome. Additionally, we observed that cellular aging marginally influenced RQC expression, leading to reduced mRNA levels in specific tissues. Our results suggest the necessity of RQC expression in all mammalian tissues. Nevertheless, when RQC falters, alternative mechanisms seem to compensate, ensuring cell survival under nonstress conditions.

Massively parallel identification of sequence motifs triggering ribosome-associated mRNA quality control

Decay of mRNAs can be triggered by ribosome slowdown at stretches of rare codons or positively charged amino acids. However, the full diversity of sequences that trigger co-translational mRNA decay is poorly understood. To comprehensively identify sequence motifs that trigger mRNA decay, we use a massively parallel reporter assay to measure the effect of all possible combinations of codon pairs on mRNA levels in S. cerevisiae. In addition to known mRNA-destabilizing sequences, we identify several dipeptide repeats whose translation reduces mRNA levels. These include combinations of positively charged and bulky residues, as well as proline-glycine and proline-aspartate dipeptide repeats. Genetic deletion of the ribosome collision sensor Hel2 rescues the mRNA effects of these motifs, suggesting that they trigger ribosome slowdown and activate the ribosome-associated quality control (RQC) pathway. Deep mutational scanning of an mRNA-destabilizing dipeptide repeat reveals a complex interplay between the charge, bulkiness, and location of amino acid residues in conferring mRNA instability. Finally, we show that the mRNA effects of codon pairs are predictive of the effects of endogenous sequences. Our work highlights the complexity of sequence motifs driving co-translational mRNA decay in eukaryotes, and presents a high throughput approach to dissect their requirements at the codon level.

Riboformer: A Deep Learning Framework for Predicting Context-Dependent Translation Dynamics

Translation elongation is essential for maintaining cellular proteostasis, and alterations in the translational landscape are associated with a range of diseases. Ribosome profiling allows detailed measurement of translation at genome scale. However, it remains unclear how to disentangle biological variations from technical artifacts and identify sequence determinant of translation dysregulation. Here we present Riboformer, a deep learning-based framework for modeling context-dependent changes in translation dynamics. Riboformer leverages the transformer architecture to accurately predict ribosome densities at codon resolution. It corrects experimental artifacts in previously unseen datasets, reveals subtle differences in synonymous codon translation and uncovers a bottleneck in protein synthesis. Further, we show that Riboformer can be combined with in silico mutagenesis analysis to identify sequence motifs that contribute to ribosome stalling across various biological contexts, including aging and viral infection. Our tool offers a context-aware and interpretable approach for standardizing ribosome profiling datasets and elucidating the regulatory basis of translation kinetics.

Expression and purification of soluble recombinant β-lactamases using Escherichia coli as expression host and pET-28a as cloning vector

Background

Due to its high expression capability, recombination of Escherichia coli and pET vector has become the bioengineering preferred expression system. Because β-lactamases mediate bacterial antimicrobial resistance, these enzymes have a substantial clinical impact. Using the E. coli expression system, several kinds of β-lactamases have been produced. However, previous studies have been focused on characterizing target β-lactamases, and the effects of cultivation and induction conditions on the expression efficiency of target enzymes were not addressed.

Results

Using pET-28a as the cloning vector and E. coli BL21(DE3) as the expression host, this study originally elucidated the effects of IPTG concentration, culture temperature, induction time, and restriction sites on recombinant β-lactamase expression. Moreover, the effects of the target protein length and the 6 × His-tag fusion position on enzyme purification were also explored, and consequently, this study yielded several important findings. (i) Only the signal peptide–detached recombinant β-lactamase could exist in a soluble form. (ii) Low-temperature induction was beneficial for soluble β-lactamase expression. (iii) The closer to the rbs the selected restriction site was, the more difficult it was to express soluble β-lactamase. (iv) The short-chain recombinant protein and the protein with His-tag fused at its C-terminus showed high affinity to the Ni²⁺ column.

Conclusions

Based on our findings, researchers can easily design an effective program for the high production of soluble recombinant β-lactamases to facilitate other related studies.

Ageing exacerbates ribosome pausing to disrupt cotranslational proteostasis

Ageing is accompanied by a decline in cellular proteostasis, which underlies many age-related protein misfolding diseases^1,2. Yet, how ageing impairs proteostasis remains unclear. As nascent polypeptides represent a substantial burden on the proteostasis network³, we hypothesized that altered translational efficiency during ageing could help to drive the collapse of proteostasis. Here we show that ageing alters the kinetics of translation elongation in both Caenorhabditis elegans and Saccharomyces cerevisiae. Ribosome pausing was exacerbated at specific positions in aged yeast and worms, including polybasic stretches, leading to increased ribosome collisions known to trigger ribosome-associated quality control (RQC)^4-6. Notably, aged yeast cells exhibited impaired clearance and increased aggregation of RQC substrates, indicating that ageing overwhelms this pathway. Indeed, long-lived yeast mutants reduced age-dependent ribosome pausing, and extended lifespan correlated with greater flux through the RQC pathway. Further linking altered translation to proteostasis collapse, we found that nascent polypeptides exhibiting age-dependent ribosome pausing in C. elegans were strongly enriched among age-dependent protein aggregates. Notably, ageing increased the pausing and aggregation of many components of proteostasis, which could initiate a cycle of proteostasis collapse. We propose that increased ribosome pausing, leading to RQC overload and nascent polypeptide aggregation, critically contributes to proteostasis impairment and systemic decline during ageing.

Timer-based proteomic profiling of the ubiquitin-proteasome system reveals a substrate receptor of the GID ubiquitin ligase

Selective protein degradation by the ubiquitin-proteasome system (UPS) is involved in all cellular processes. However, the substrates and specificity of most UPS components are not well understood. Here we systematically characterized the UPS in Saccharomyces cerevisiae. Using fluorescent timers, we determined how loss of individual UPS components affects yeast proteome turnover, detecting phenotypes for 76% of E2, E3, and deubiquitinating enzymes. We exploit this dataset to gain insights into N-degron pathways, which target proteins carrying N-terminal degradation signals. We implicate Ubr1, an E3 of the Arg/N-degron pathway, in targeting mitochondrial proteins processed by the mitochondrial inner membrane protease. Moreover, we identify Ylr149c/Gid11 as a substrate receptor of the glucose-induced degradation-deficient (GID) complex, an E3 of the Pro/N-degron pathway. Our results suggest that Gid11 recognizes proteins with N-terminal threonines, expanding the specificity of the GID complex. This resource of potential substrates and relationships between UPS components enables exploring functions of selective protein degradation.