A ribosome-bound quality control complex triggers degradation of nascent peptides and signals translation stress.

The conserved transcriptional regulator heat shock factor 1 (Hsf1) is a key sensor of proteotoxic and other stress in the eukaryotic cytosol. We surveyed Hsf1 activity in a genome-wide loss-of-function library in Saccaromyces cerevisiae as well as ~78,000 double mutants and found Hsf1 activity to be modulated by highly diverse stresses. These included disruption of a ribosome-bound complex we named the Ribosome Quality Control Complex (RQC) comprising the Ltn1 E3 ubiquitin ligase, two highly conserved but poorly characterized proteins (Tae2 and Rqc1), and Cdc48 and its cofactors. Electron microscopy and biochemical analyses revealed that the RQC forms a stable complex with 60S ribosomal subunits containing stalled polypeptides and triggers their degradation. A negative feedback loop regulates the RQC, and Hsf1 senses an RQC-mediated translation-stress signal distinctly from other stresses. Our work reveals the range of stresses Hsf1 monitors and elucidates a conserved cotranslational protein quality control mechanism.

Translation from unconventional 5' start sites drives tumour initiation.

We are just beginning to understand how translational control affects tumour initiation and malignancy. Here we use an epidermis-specific, in vivo ribosome profiling strategy to investigate the translational landscape during the transition from normal homeostasis to malignancy. Using a mouse model of inducible SOX2, which is broadly expressed in oncogenic RAS-associated cancers, we show that despite widespread reductions in translation and protein synthesis, certain oncogenic mRNAs are spared. During tumour initiation, the translational apparatus is redirected towards unconventional upstream initiation sites, enhancing the translational efficiency of oncogenic mRNAs. An in vivo RNA interference screen of translational regulators revealed that depletion of conventional eIF2 complexes has adverse effects on normal but not oncogenic growth. Conversely, the alternative initiation factor eIF2A is essential for cancer progression, during which it mediates initiation at these upstream sites, differentially skewing translation and protein expression. Our findings unveil a role for the translation of 5' untranslated regions in cancer, and expose new targets for therapeutic intervention.

Machine learning for metabolic engineering: A review.

Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.

The kinase regulator mob1 acts as a patterning protein for stentor morphogenesis.

Morphogenesis and pattern formation are vital processes in any organism, whether unicellular or multicellular. But in contrast to the developmental biology of plants and animals, the principles of morphogenesis and pattern formation in single cells remain largely unknown. Although all cells develop patterns, they are most obvious in ciliates; hence, we have turned to a classical unicellular model system, the giant ciliate Stentor coeruleus. Here we show that the RNA interference (RNAi) machinery is conserved in Stentor. Using RNAi, we identify the kinase coactivator Mob1--with conserved functions in cell division and morphogenesis from plants to humans-as an asymmetrically localized patterning protein required for global patterning during development and regeneration in Stentor. Our studies reopen the door for Stentor as a model regeneration system.

Plastid: nucleotide-resolution analysis of next-generation sequencing and genomics data.

BACKGROUND: Next-generation sequencing (NGS) informs many biological questions with unprecedented depth and nucleotide resolution. These assays have created a need for analytical tools that enable users to manipulate data nucleotide-by-nucleotide robustly and easily. Furthermore, because many NGS assays encode information jointly within multiple properties of read alignments - for example, in ribosome profiling, the locations of ribosomes are jointly encoded in alignment coordinates and length - analytical tools are often required to extract the biological meaning from the alignments before analysis. Many assay-specific pipelines exist for this purpose, but there remains a need for user-friendly, generalized, nucleotide-resolution tools that are not limited to specific experimental regimes or analytical workflows. RESULTS: Plastid is a Python library designed specifically for nucleotide-resolution analysis of genomics and NGS data. As such, Plastid is designed to extract assay-specific information from read alignments while retaining generality and extensibility to novel NGS assays. Plastid represents NGS and other biological data as arrays of values associated with genomic or transcriptomic positions, and contains configurable tools to convert data from a variety of sources to such arrays. Plastid also includes numerous tools to manipulate even discontinuous genomic features, such as spliced transcripts, with nucleotide precision. Plastid automatically handles conversion between genomic and feature-centric coordinates, accounting for splicing and strand, freeing users of burdensome accounting. Finally, Plastid's data models use consistent and familiar biological idioms, enabling even beginners to develop sophisticated analytical workflows with minimal effort. CONCLUSIONS: Plastid is a versatile toolkit that has been used to analyze data from multiple NGS assays, including RNA-seq, ribosome profiling, and DMS-seq. It forms the genomic engine of our ORF annotation tool, ORF-RATER, and is readily adapted to novel NGS assays. Examples, tutorials, and extensive documentation can be found at https://plastid.readthedocs.io .

Ribosome profiling reveals pervasive and regulated stop codon readthrough in Drosophila melanogaster.

Ribosomes can read through stop codons in a regulated manner, elongating rather than terminating the nascent peptide. Stop codon readthrough is essential to diverse viruses, and phylogenetically predicted to occur in a few hundred genes in Drosophila melanogaster, but the importance of regulated readthrough in eukaryotes remains largely unexplored. Here, we present a ribosome profiling assay (deep sequencing of ribosome-protected mRNA fragments) for Drosophila melanogaster, and provide the first genome-wide experimental analysis of readthrough. Readthrough is far more pervasive than expected: the vast majority of readthrough events evolved within D. melanogaster and were not predicted phylogenetically. The resulting C-terminal protein extensions show evidence of selection, contain functional subcellular localization signals, and their readthrough is regulated, arguing for their importance. We further demonstrate that readthrough occurs in yeast and humans. Readthrough thus provides general mechanisms both to regulate gene expression and function, and to add plasticity to the proteome during evolution. DOI: http://dx.doi.org/10.7554/eLife.01179.001.