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Cells responds to diverse stimuli by changing the levels of specific effector proteins. These changes are usually examined using high throughput RNA sequencing data (RNA-Seq); transcriptional regulation is generally assumed to directly influence protein abundances. However, the correlation between RNA-Seq and proteomics data is in general quite limited owing to differences in protein stability and translational regulation. Here we perform RNA-Seq, ribosome profiling and proteomics analyses in baker's yeast cells grown in rich media and oxidative stress conditions to examine gene expression regulation at various levels. With the exception of a small set of genes involved in the maintenance of the redox state, which are regulated at the transcriptional level, modulation of protein expression is largely driven by changes in the relative ribosome density across conditions. The majority of shifts in mRNA abundance are compensated by changes in the opposite direction in the number of translating ribosomes and are predicted to result in no net change at the protein level. We also identify a subset of mRNAs which is likely to undergo specific translational repression during stress and which includes cell cycle control genes. The study suggests that post-transcriptional buffering of gene expression may be more common than previously anticipated. The work was funded by grants BFU2015–65235-P, BFU2015-68351-P and BFU2016-80039-R, from Ministerio de Economía e Innovación (Spanish Government) - FEDER (EU), and from grant PT17/0009/0014 from Instituto de Salud Carlos III – FEDER. We also received funding from the “Maria de Maeztu” Programme for Units of Excellence in R&D (MDM-2014-0370) and from Agència de Gestió d’Ajuts Universitaris i de Recerca Generalitat de Catalunya (AGAUR), grant number 2014SGR1121, 2014SGR0974, 2017SGR01020 and, predoctoral fellowship (FI) to W.B. We also acknowledge support from the EU Erasmus Programme to T.T.

De novo gene origination has been recently established as an important mechanism for the formation of new genes. In organisms with a large genome, intergenic and intronic regions provide plenty of raw material for new transcriptional events to occur, but little is know about how de novo transcripts originate in more densely-packed genomes. Here, we identify 213 de novo originated transcripts in Saccharomyces cerevisiae using deep transcriptomics and genomic synteny information from multiple yeast species grown in two different conditions. We find that about half of the de novo transcripts are expressed from regions which already harbor other genes in the opposite orientation; these transcripts show similar expression changes in response to stress as their overlapping counterparts, and some appear to translate small proteins. Thus, a large fraction of de novo genes in yeast are likely to co-evolve with already existing genes., Genome-wide studies of de novo genes have tended to focus on genomic open reading frames (ORFs). Here, Blevins et al. use deep transcriptomics and synteny information to identify de novo transcripts in the yeast Saccharomyces cerevisiae, many of which are expressed from the alternative DNA strand.

Evidence has accumulated that some genes originate directly from previously non-genic sequences, orde novo, rather than by the duplication or fusion of existing genes. However, howde novogenes emerge and eventually become functional is largely unknown. Here we perform the first study onde novogenes that uses transcriptomics data from eleven different yeast species, all grown identically in both rich media and in oxidative stress conditions. The genomes of these species are densely-packed with functional elements, leaving little room for the co-option of genomic sequences into new transcribed loci. Despite this, we find that at least 213 transcripts (~5%) have arisende novoin the past 20 million years of evolution of baker’s yeast-or approximately 10 new transcripts every million years. Nearly half of the total newly expressed sequences are generated from regions in which both DNA strands are used as templates for transcription, explaining the apparent contradiction between the limited ‘empty’ genomic space and high rate ofde novogene birth. In addition, we find that 40% of thesede novotranscripts are actively translated and that at least a fraction of the encoded proteins are likely to be under purifying selection. This study shows that even in very highly compact genomes,de novotranscripts are continuously generated and can give rise to new functional protein-coding genes.

The highly conserved 5’–3’ exonuclease Xrn1 regulates gene expression in eukaryotes by coupling nuclear DNA transcription to cytosolic mRNA decay. By integrating transcriptome-wide analyses of translation with biochemical and functional studies, we demonstrate an unanticipated regulatory role of Xrn1 in protein synthesis. Xrn1 promotes translation of a specific group of transcripts encoding membrane proteins. Xrn1-dependence for translation is linked to poor structural RNA contexts for translation initiation, is mediated by interactions with components of the translation initiation machinery and correlates with an Xrn1-dependence for mRNA localization at the endoplasmic reticulum, the translation compartment of membrane proteins. Importantly, for this group of mRNAs, Xrn1 stimulates transcription, mRNA translation and decay. Our results uncover a crosstalk between the three major stages of gene expression coordinated by Xrn1 to maintain appropriate levels of membrane proteins., The exonuclease Xrn1 mediates crosstalk between transcription and mRNA decay in yeast. Here the authors demonstrate that Xrn1 promotes translation of mRNAs encoding membrane proteins, coupling transcription, translation, and mRNA decay.

Cells respond to changes in the environment by modifying the concentration of specific proteins. Paradoxically, the cellular response is usually examined by measuring variations in transcript abundance by high throughput RNA sequencing (RNA-Seq), instead of directly measuring protein concentrations. This happens because RNA-Seq-based methods provide better quantitative estimates, and more extensive gene coverage, than proteomics-based ones. However, variations in transcript abundance do not necessarily reflect changes in the corresponding protein abundance. How can we close this gap? Here we explore the use of ribosome profiling (Ribo-Seq) to perform differentially gene expression analysis in a relatively well-characterized system, oxidative stress in baker’s yeast. Ribo-Seq is an RNA sequencing method that specifically targets ribosome-protected RNA fragments, and thus is expected to provide a more accurate view of changes at the protein level than classical RNA-Seq. We show that gene quantification by Ribo-Seq is indeed more highly correlated with protein abundance, as measured from mass spectrometry data, than quantification by RNA-Seq. The analysis indicates that, whereas a subset of genes involved in oxidation-reduction processes is detected by both types of data, the majority of the genes that happen to be significant in the RNA-Seq-based analysis are not significant in the Ribo-Seq analysis, suggesting that they do not result in protein level changes. The results illustrate the advantages of Ribo-Seq to make inferences about changes in protein abundance in comparison with RNA-Seq.

Positive-strand RNA viruses have evolved multiple strategies to not only circumvent the hostile decay machinery but to trick it into being a priceless collaborator supporting viral RNA translation and replication. In this review, we describe the versatile interaction of positive-strand RNA viruses and the 5'-3' mRNA decay machinery with a focus on the viral subversion of decapping activators. This highly conserved viral trickery is exemplified with the plant Brome mosaic virus, the animal Flock house virus and the human hepatitis C virus. This work was supported by the Spanish Ministry of Economy and Competitiveness through/ngrant BFU 2013-44629-R and the “Maria de Maeztu” Programme for Units of Excellence in R&D (MDM-2014-0370)

Every year three million people die as a result of bacterial infections, and this number may further increase due to resistance to current antibiotics. These antibiotics target almost all essential bacterial processes, leaving only a few new targets for manipulation. The host proteome has many more potential targets for manipulation in order to control bacterial infection, as exemplified by the observation that inhibiting the host kinase Akt supports the elimination of different intracellular bacteria including Salmonella and M. tuberculosis. If host kinases are involved in the control of bacterial infections, phosphatases could be as well. Here we present an integrated small interference RNA and small molecule screen to identify host phosphatase-inhibitor combinations that control bacterial infection. We define host phosphatases inhibiting intracellular growth of Salmonella and identify corresponding inhibitors for the dual specificity phosphatases DUSP11 and 27. Pathway analysis places many kinases and phosphatases controlling bacterial infection in an integrated pathway centered around Akt. This network controls host cell metabolism, survival, and growth and bacterial survival and reflect a natural host cell response to bacterial infection. Inhibiting two enzyme classes with opposite activities-kinases and phosphatases-may be a new strategy to overcome infections by antibiotic-resistant bacteria.