To analyse the myogenic transcriptome and identify novel genes involved in muscle development in an in vivo context, we have constructed a muscle specific cDNA library from GFP-expressing myoblasts purified by fluorescent activated cell sorting of transgenic zebrafish embryos. We have generated 153,428 EST sequences from this library that have been clustered into consensi, mapped to the genome assembly Zv6 and analysed for protein homology. Expression analysis of a randomly picked sample of clones using whole mount in situ hybridisation, identified 30 genes that are expressed specifically within the myotome, one third of which represent novel sequences. These genes have been assigned to syn-expression groups. The sequencing of the myoblast enriched cDNA library has significantly increased the number of zebrafish ESTs, facilitating the prediction of new spliced transcripts in the genome assembly and providing a transcriptome of an in vivo myoblast cell.

The mechanisms by which RNA-binding proteins control the translation of subsets of mRNAs are not yet clear. Slf1p and Sro9p are atypical-La motif containing proteins which are members of a superfamily of RNA-binding proteins conserved in eukaryotes. RIP-Seq analysis of these two yeast proteins identified overlapping and distinct sets of mRNA targets, including highly translated mRNAs such as those encoding ribosomal proteins. In paralell, transcriptome analysis of slf1Δ and sro9Δ mutant strains indicated altered gene expression in similar functional classes of mRNAs following loss of each factor. The loss of SLF1 had a greater impact on the transcriptome, and in particular, revealed changes in genes involved in the oxidative stress response. slf1Δ cells are more sensitive to oxidants and RIP-Seq analysis of oxidatively stressed cells enriched Slf1p targets encoding antioxidants and other proteins required for oxidant tolerance. To quantify these effects at the protein level, we used label-free mass spectrometry to compare the proteomes of wild-type and slf1Δ strains following oxidative stress. This analysis identified several proteins which are normally induced in response to hydrogen peroxide, but where this increase is attenuated in the slf1Δ mutant. Importantly, a significant number of the mRNAs encoding these targets were also identified as Slf1p-mRNA targets. We show that Slf1p remains associated with the few translating ribosomes following hydrogen peroxide stress and that Slf1p co-immunoprecipitates ribosomes and members of the eIF4E/eIF4G/Pab1p 'closed loop' complex suggesting that Slf1p interacts with actively translated mRNAs following stress. Finally, mutational analysis of SLF1 revealed a novel ribosome interacting domain in Slf1p, independent of its RNA binding La-motif. Together, our results indicate that Slf1p mediates a translational response to oxidative stress via mRNA-specific translational control.

Proteomics continues to play a critical role in post-genomic science as continued advances in mass spectrometry and analytical chemistry support the separation and identification of increasing numbers of peptides and proteins from their characteristic mass spectra. In order to facilitate the sharing of this data, various standard formats have been, and continue to be, developed. Still not fully mature however, these are not yet able to cope with the increasing number of quantitative proteomic technologies that are being developed.

The transcriptional responses of yeast cells to diverse stresses typically include gene activation and repression. Specific stress defense, citric acid cycle and oxidative phosphorylation genes are activated, whereas protein synthesis genes are coordinately repressed. This view was achieved from comparative transcriptomic experiments delineating sets of genes whose expression greatly changed with specific stresses. Less attention has been paid to the biological significance of 1) consistent, albeit modest, changes in RNA levels across multiple conditions, and 2) the global gene expression correlations observed when comparing numerous genome-wide studies. To address this, we performed a meta-analysis of 1379 microarray-based experiments in yeast, and identified 1388 blocks of RNAs whose expression changes correlate across multiple and diverse conditions. Many of these blocks represent sets of functionally-related RNAs that act in a coordinated fashion under normal and stress conditions, and map to global cell defense and growth responses. Subsequently, we used the blocks to analyze novel RNA-seq experiments, demonstrating their utility and confirming the conclusions drawn from the meta-analysis. Our results provide a new framework for understanding the biological significance of changes in gene expression: 'archetypal' transcriptional blocks that are regulated in a concerted fashion in response to external stimuli.

Non-membrane-bound compartments such as P-bodies (PBs) and stress granules (SGs) play important roles in the regulation of gene expression following environmental stresses. We have systematically and quantitatively determined the protein and mRNA composition of PBs and SGs formed before and after nutrient stress. We find that high molecular weight (HMW) complexes exist prior to glucose depletion that we propose may act as seeds for further condensation of proteins forming mature PBs and SGs. We identify an enrichment of proteins with low complexity and RNA binding domains, as well as long, structured mRNAs that are poorly translated following nutrient stress. Many proteins and mRNAs are shared between PBs and SGs including several multivalent RNA binding proteins that promote condensate interactions during liquid-liquid phase separation. We uncover numerous common protein and RNA components across PBs and SGs that support a complex interaction profile during the maturation of these biological condensates. These interaction networks represent a tuneable response to stress, highlighting previously unrecognized condensate heterogeneity. These studies therefore provide an integrated and quantitative understanding of the dynamic nature of key biological condensates.

The regulation of translation provides a rapid and direct mechanism to modulate the cellular proteome. In eukaryotes, an established model for the recruitment of ribosomes to mRNA depends upon a set of conserved translation initiation factors. Nevertheless, how cells orchestrate and define the selection of individual mRNAs for translation, as opposed to other potential cytosolic fates, is poorly understood. We have previously found significant variation in the interaction between individual mRNAs and an array of translation initiation factors. Indeed, mRNAs can be separated into different classes based upon these interactions to provide a framework for understanding different modes of translation initiation. Here, we extend this approach to include new mRNA interaction profiles for additional proteins involved in shaping the cytoplasmic fate of mRNAs. This work defines a set of seven mRNA clusters, based on their interaction profiles with 12 factors involved in translation and/or RNA binding. The mRNA clusters share both physical and functional characteristics to provide a rationale for the interaction profiles. Moreover, a comparison with mRNA interaction profiles from a host of RNA binding proteins suggests that there are defined patterns in the interactions of functionally related mRNAs. Therefore, this work defines global cytoplasmic mRNA binding modules that likely coordinate the synthesis of functionally related proteins.

Translation initiation factor eIF4E mediates mRNA selection for protein synthesis via the mRNA 5'cap. A family of binding proteins, termed the 4E-BPs, interact with eIF4E to hinder ribosome recruitment. Mechanisms underlying mRNA specificity for 4E-BP control remain poorly understood. Saccharomyces cerevisiae 4E-BPs, Caf20p and Eap1p, each regulate an overlapping set of mRNAs. We undertook global approaches to identify protein and RNA partners of both 4E-BPs by immunoprecipitation of tagged proteins combined with mass spectrometry or next-generation sequencing. Unexpectedly, mass spectrometry indicated that the 4E-BPs associate with many ribosomal proteins. 80S ribosome and polysome association was independently confirmed and was not dependent upon interaction with eIF4E, as mutated forms of both Caf20p and Eap1p with disrupted eIF4E-binding motifs retain ribosome interaction. Whole-cell proteomics revealed Caf20p mutations cause both up and down-regulation of proteins and that many changes were independent of the 4E-binding motif. Investigations into Caf20p mRNA targets by immunoprecipitation followed by RNA sequencing revealed a strong association between Caf20p and mRNAs involved in transcription and cell cycle processes, consistent with observed cell cycle phenotypes of mutant strains. A core set of over 500 Caf20p-interacting mRNAs comprised of both eIF4E-dependent (75%) and eIF4E-independent targets (25%), which differ in sequence attributes. eIF4E-independent mRNAs share a 3' UTR motif. Caf20p binds all tested motif-containing 3' UTRs. Caf20p and the 3'UTR combine to influence ERS1 mRNA polysome association consistent with Caf20p contributing to translational control. Finally ERS1 3'UTR confers Caf20-dependent repression of expression to a heterologous reporter gene. Taken together, these data reveal conserved features of eIF4E-dependent Caf20p mRNA targets and uncover a novel eIF4E-independent mode of Caf20p binding to mRNAs that extends the regulatory role of Caf20p in the mRNA-specific repression of protein synthesis beyond its interaction with eIF4E.

Molecular chaperones play an important role in protein homeostasis and the cellular response to stress. In particular, the HSP70 chaperones in yeast mediate a large volume of protein folding through transient associations with their substrates. This chaperone interaction network can be disturbed by various perturbations, such as environmental stress or a gene deletion. Here, we consider deletions of two major chaperone proteins, SSA1 and SSB1, from the chaperone network in Sacchromyces cerevisiae. We employ a SILAC-based approach to examine changes in global and local protein abundance and rationalise our results via network analysis and graph theoretical approaches. Although the deletions result in an overall increase in intracellular protein content, correlated with an increase in cell size, this is not matched by substantial changes in individual protein concentrations. Despite the phenotypic robustness to deletion of these major hub proteins, it cannot be simply explained by the presence of paralogues. Instead, network analysis and a theoretical consideration of folding workload suggest that the robustness to perturbation is a product of the overall network structure. This highlights how quantitative proteomics and systems modelling can be used to rationalise emergent network properties, and how the HSP70 system can accommodate the loss of major hubs.

The craniofacial disorder mandibulofacial dysostosis Guion-Almeida type is caused by haploinsufficiency of the U5 snRNP gene EFTUD2/SNU114. However, it is unclear how reduced expression of this core pre-mRNA splicing factor leads to craniofacial defects. Here we use a CRISPR-Cas9 nickase strategy to generate a human EFTUD2-knockdown cell line and show that reduced expression of EFTUD2 leads to diminished proliferative ability of these cells, increased sensitivity to endoplasmic reticulum (ER) stress and the mis-expression of several genes involved in the ER stress response. RNA-Seq analysis of the EFTUD2-knockdown cell line revealed transcriptome-wide changes in gene expression, with an enrichment for genes associated with processes involved in craniofacial development. Additionally, our RNA-Seq data identified widespread mis-splicing in EFTUD2-knockdown cells. Analysis of the functional and physical characteristics of mis-spliced pre-mRNAs highlighted conserved properties, including length and splice site strengths, of retained introns and skipped exons in our disease model. We also identified enriched processes associated with the affected genes, including cell death, cell and organ morphology and embryonic development. Together, these data support a model in which EFTUD2 haploinsufficiency leads to the mis-splicing of a distinct subset of pre-mRNAs with a widespread effect on gene expression, including altering the expression of ER stress response genes and genes involved in the development of the craniofacial region. The increased burden of unfolded proteins in the ER resulting from mis-splicing would exceed the capacity of the defective ER stress response, inducing apoptosis in cranial neural crest cells that would result in craniofacial abnormalities during development.

In response to stress, the translation of many mRNAs in yeast can change in a fashion discordant with the general repression of translation. Here, we use machine learning to mine the properties of these mRNAs to determine specific translation control signals. We find a strong association between transcripts acutely translationally repressed under oxidative stress and those associated with the RNA-binding protein Puf3p, a known regulator of cellular mRNAs encoding proteins targeted to mitochondria. Under oxidative stress, a PUF3 deleted strain exhibits more robust growth than wild-type cells and the shift in translation from polysomes to monosomes is attenuated, suggesting puf3Δ cells perceive less stress. In agreement, the ratio of reduced:oxidized glutathione, a major antioxidant and indicator of cellular redox state, is increased in unstressed puf3Δ cells but remains lower under stress. In untreated conditions, Puf3p migrates with polysomes rather than ribosome-free fractions, but this is lost under stress. Finally, reverse transcriptase-polymerase chain reaction (RT-PCR) of Puf3p targets following affinity purification shows Puf3p-mRNA associations are maintained or increased under oxidative stress. Collectively, these results point to Puf3p acting as a translational repressor in a manner exceeding the global translational response, possibly by temporarily limiting synthesis of new mitochondrial proteins as cells adapt to the stress.

Chaperones are fundamental to regulating the heat shock response, mediating protein recovery from thermal-induced misfolding and aggregation. Using the QconCAT strategy and selected reaction monitoring (SRM) for absolute protein quantification, we have determined copy per cell values for 49 key chaperones in Saccharomyces cerevisiae under conditions of normal growth and heat shock. This work extends a previous chemostat quantification study by including up to five Q-peptides per protein to improve confidence in protein quantification. In contrast to the global proteome profile of S. cerevisiae in response to heat shock, which remains largely unchanged as determined by label-free quantification, many of the chaperones are upregulated with an average two-fold increase in protein abundance. Interestingly, eight of the significantly upregulated chaperones are direct gene targets of heat shock transcription factor-1. By performing absolute quantification of chaperones under heat stress for the first time, we were able to evaluate the individual protein-level response. Furthermore, this SRM data was used to calibrate label-free quantification values for the proteome in absolute terms, thus improving relative quantification between the two conditions. This study significantly enhances the largely transcriptomic data available in the field and illustrates a more nuanced response at the protein level.

Defining intracellular protein concentration is critical in molecular systems biology. Although strategies for determining relative protein changes are available, defining robust absolute values in copies per cell has proven significantly more challenging. Here we present a reference data set quantifying over 1800Saccharomyces cerevisiaeproteins by direct means using protein-specific stable-isotope labeled internal standards and selected reaction monitoring (SRM) mass spectrometry, far exceeding any previous study. This was achieved by careful design of over 100 QconCAT recombinant proteins as standards, defining 1167 proteins in terms of copies per cell and upper limits on a further 668, with robust CVs routinely less than 20%. The selected reaction monitoring-derived proteome is compared with existing quantitative data sets, highlighting the disparities between methodologies. Coupled with a quantification of the transcriptome by RNA-seq taken from the same cells, these data support revised estimates of several fundamental molecular parameters: a total protein count of ∼100 million molecules-per-cell, a median of ∼1000 proteins-per-transcript, and a linear model of protein translation explaining 70% of the variance in translation rate. This work contributes a "gold-standard" reference yeast proteome (including 532 values based on high quality, dual peptide quantification) that can be widely used in systems models and for other comparative studies.

We report the release of mzIdentML, an exchange standard for peptide and protein identification data, designed by the Proteomics Standards Initiative. The format was developed by the Proteomics Standards Initiative in collaboration with instrument and software vendors, and the developers of the major open-source projects in proteomics. Software implementations have been developed to enable conversion from most popular proprietary and open-source formats, and mzIdentML will soon be supported by the major public repositories. These developments enable proteomics scientists to start working with the standard for exchanging and publishing data sets in support of publications and they provide a stable platform for bioinformatics groups and commercial software vendors to work with a single file format for identification data.

Proteogenomics has the potential to advance genome annotation through high quality peptide identifications derived from mass spectrometry experiments, which demonstrate a given gene or isoform is expressed and translated at the protein level. This can advance our understanding of genome function, discovering novel genes and gene structure that have not yet been identified or validated. Because of the high-throughput shotgun nature of most proteomics experiments, it is essential to carefully control for false positives and prevent any potential misannotation. A number of statistical procedures to deal with this are in wide use in proteomics, calculating false discovery rate (FDR) and posterior error probability (PEP) values for groups and individual peptide spectrum matches (PSMs). These methods control for multiple testing and exploit decoy databases to estimate statistical significance. Here, we show that database choice has a major effect on these confidence estimates leading to significant differences in the number of PSMs reported. We note that standard target:decoy approaches using six-frame translations of nucleotide sequences, such as assembled transcriptome data, apparently underestimate the confidence assigned to the PSMs. The source of this error stems from the inflated and unusual nature of the six-frame database, where for every target sequence there exists five "incorrect" targets that are unlikely to code for protein. The attendant FDR and PEP estimates lead to fewer accepted PSMs at fixed thresholds, and we show that this effect is a product of the database and statistical modeling and not the search engine. A variety of approaches to limit database size and remove noncoding target sequences are examined and discussed in terms of the altered statistical estimates generated and PSMs reported. These results are of importance to groups carrying out proteogenomics, aiming to maximize the validation and discovery of gene structure in sequenced genomes, while still controlling for false positives.

Proteomic data is a potentially rich, but arguably unexploited, data source for genome annotation. Peptide identifications from tandem mass spectrometry provide prima facie evidence for gene predictions and can discriminate over a set of candidate gene models. Here we apply this to the recently sequenced Aspergillus niger fungal genome from the Joint Genome Institutes (JGI) and another predicted protein set from another A.niger sequence. Tandem mass spectra (MS/MS) were acquired from 1d gel electrophoresis bands and searched against all available gene models using Average Peptide Scoring (APS) and reverse database searching to produce confident identifications at an acceptable false discovery rate (FDR).

The control of gene expression in eukaryotic cells occurs both transcriptionally and post-transcriptionally. Although many genes are now known to be regulated at the translational level, in general, the mechanisms are poorly understood. We have previously presented polysomal gradient and array-based evidence that translational control is widespread in a significant number of genes when yeast cells are exposed to a range of stresses. Here we have re-examined these gene sets, considering the role of UTR sequences in the translational responses of these genes using recent large-scale datasets which define 5' and 3' transcriptional ends for many yeast genes. In particular, we highlight the potential role of 5' UTRs and upstream open reading frames (uORFs).

Regulation of translation is a fundamental facet of the cellular response to rapidly changing external conditions. Specific RNA-binding proteins (RBPs) co-ordinate the translational regulation of distinct mRNA cohorts during stress. To identify RBPs with previously under-appreciated roles in translational control, we used polysome profiling and mass spectrometry to identify and quantify proteins associated with translating ribosomes in unstressed yeast cells and during oxidative stress and amino acid starvation, which both induce the integrated stress response (ISR). Over 800 proteins were identified across polysome gradient fractions, including ribosomal proteins, translation factors, and many others without previously described translation-related roles, including numerous metabolic enzymes. We identified variations in patterns of PE in both unstressed and stressed cells and identified proteins enriched in heavy polysomes during stress. Genetic screening of polysome-enriched RBPs identified the cytosolic aspartate aminotransferase, Aat2, as a ribosome-associated protein whose deletion conferred growth sensitivity to oxidative stress. Loss of Aat2 caused aberrantly high activation of the ISR via enhanced eIF2α phosphorylation and GCN4 activation. Importantly, non-catalytic AAT2 mutants retained polysome association and did not show heightened stress sensitivity. Aat2 therefore has a separate ribosome-associated translational regulatory or 'moonlighting' function that modulates the ISR independent of its aspartate aminotransferase activity.

Leaves of Arabidopsis thaliana transferred from low to high light increase their capacity for photosynthesis, a process of dynamic acclimation. A mutant, gpt2, lacking a chloroplast glucose-6-phosphate/phosphate translocator, is deficient in its ability to acclimate to increased light. Here, we have used a label-free proteomics approach, to perform relative quantitation of 1993 proteins from Arabidopsis wild type and gpt2 leaves exposed to increased light. Data are available via ProteomeXchange with identifier PXD006598. Acclimation to light is shown to involve increases in electron transport and carbon metabolism but no change in the abundance of photosynthetic reaction centers. The gpt2 mutant shows a similar increase in total protein content to wild type but differences in the extent of change of certain proteins, including in the relative abundance of the cytochrome b6f complex and plastocyanin, the thylakoid ATPase and selected Benson-Calvin cycle enzymes. Changes in leaf metabolite content as plants acclimate can be explained by changes in the abundance of enzymes involved in metabolism, which were reduced in gpt2 in some cases. Plants of gpt2 invest more in stress-related proteins, suggesting that their reduced ability to acclimate photosynthetic capacity results in increased stress.

Protein quantitation via mass spectrometry relies on peptide proxies for the parent protein from which abundances are estimated. Owing to the variability in signal from individual peptides, accurate absolute quantitation usually relies on the addition of an external standard. Typically, this involves stable isotope-labeled peptides, delivered singly or as a concatenated recombinant protein. Consequently, the selection of the most appropriate surrogate peptides and the attendant design in recombinant proteins termed QconCATs are challenges for proteome science. QconCATs can now be built in a "a-la-carte" assembly method using synthetic biology: ALACATs. To assist their design, we present "AlacatDesigner", a tool that supports the peptide selection for recombinant protein standards based on the user's target protein. The user-customizable tool considers existing databases, occurrence in the literature, potential post-translational modifications, predicted miscleavage, predicted divergence of the peptide and protein quantifications, and ionization potential within the mass spectrometer. We show that peptide selections are enriched for good proteotypic and quantotypic candidates compared to empirical data. The software is freely available to use either via a web interface AlacatDesigner, downloaded as a Desktop application or imported as a Python package for the command line interface or in scripts.

Birds have played a central role in many biological disciplines, particularly ecology, evolution, and behavior. The chicken, as a model vertebrate, also represents an important experimental system for developmental biologists, immunologists, cell biologists, and geneticists. However, genomic resources for the chicken have lagged behind those for other model organisms, with only 1845 nonredundant full-length chicken cDNA sequences currently deposited in the EMBL databank. We describe a large-scale expressed-sequence-tag (EST) project aimed at gene discovery in chickens (http://www.chick.umist.ac.uk). In total, 339,314 ESTs have been sequenced from 64 cDNA libraries generated from 21 different embryonic and adult tissues. These were clustered and assembled into 85,486 contiguous sequences (contigs). We find that a minimum of 38% of the contigs have orthologs in other organisms and define an upper limit of 13,000 new chicken genes. The remaining contigs may include novel avian specific or rapidly evolving genes. Comparison of the contigs with known chicken genes and orthologs indicates that 30% include cDNAs that contain the start codon and 20% of the contigs represent full-length cDNA sequences. Using this dataset, we estimate that chickens have approximately 35,000 genes in total, suggesting that this number may be a characteristic feature of vertebrates.