Direct and Absolute Quantification of over 1800 Yeast Proteins via Selected Reaction Monitoring

Autorepression of yeast Hsp70 cochaperones by intramolecular interactions involving their J-domains

The 70 kDa heat shock protein (Hsp70) chaperones control protein homeostasis in all ATP-containing cellular compartments. J-domain proteins (JDPs) coevolved with Hsp70s to trigger ATP hydrolysis and catalytically upload various substrate polypeptides in need to be structurally modified by the chaperone. Here, we measured the protein disaggregation and refolding activities of the main yeast cytosolic Hsp70, Ssa1, in the presence of its most abundant JDPs, Sis1 and Ydj1, and two swap mutants, in which the J-domains have been interchanged. The observed differences by which the four constructs differently cooperate with Ssa1 and cooperate with each other, as well as their observed intrinsic ability to bind misfolded substrates and trigger Ssa1's ATPase, indicate the presence of yet uncharacterized intramolecular dynamic interactions between the J-domains and the remaining C-terminal segments of these proteins. Taken together, the data suggest an autoregulatory role to these intramolecular interactions within both type A and B JDPs, which might have evolved to reduce energy-costly ATPase cycles by the Ssa1-4 chaperones that are the most abundant Hsp70s in the yeast cytosol.

Paralogous translation factors target distinct mRNAs to differentially regulate tolerance to oxidative stress in yeast

Translation initiation factor 4G (eIF4G) is an integral component of the eIF4F complex which is key to translation initiation for most eukaryotic mRNAs. Many eIF4G isoforms have been described in diverse eukaryotic organisms but we currently have a poor understanding of their functional roles and whether they regulate translation in an mRNA specific manner. The yeast Saccharomyces cerevisiae expresses two eIF4G isoforms, eIF4G1 and eIF4G2, that have previously been considered as functionally redundant with any phenotypic differences arising due to alteration in eIF4G expression levels. Using homogenic strains that express eIF4G1 or eIF4G2 as the sole eIF4G isoforms at comparable expression levels to total eIF4G, we show that eIF4G1 is specifically required to mediate the translational response to oxidative stress. eIF4G1 binds the mRNA cap and remains associated with actively translating ribosomes during oxidative stress conditions and we use quantitative proteomics to show that eIF4G1 promotes oxidative stress-specific proteome changes. eIF4G1, but not eIF4G2, binds the Slf1 LARP protein which appears to mediate the eIF4G1-dependent translational response to oxidative stress. We show similar isoform specific roles for eIF4G in human cells suggesting convergent evolution of multiple eIF4G isoforms offers significant advantages especially where translation must continue under stress conditions.

The proteomic landscape of genome-wide genetic perturbations

Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.

Novel 15N Metabolic Labeling-Based Large-Scale Absolute Quantitative Proteomics Method for Corynebacterium glutamicum

With fast growth, synthetic biology powers us with the capability to produce high commercial value products in an efficient resource/energy-consuming manner. Comprehensive knowledge of the protein regulatory network of a bacterial host chassis, e.g., the actual amount of the given proteins, is the key to building cell factories for certain target hyperproduction. Many talent methods have been introduced for absolute quantitative proteomics. However, for most cases, a set of reference peptides with isotopic labeling (e.g., SIL, AQUA, QconCAT) or a set of reference proteins (e.g., commercial UPS2 kit) needs to be prepared. The higher cost hinders these methods for large sample research. In this work, we proposed a novel metabolic labeling-based absolute quantification approach (termed nMAQ). The reference Corynebacterium glutamicum strain is metabolically labeled with ¹⁵N, and a set of endogenous anchor proteins of the reference proteome is quantified by chemically synthesized light (¹⁴N) peptides. The prequantified reference proteome was then utilized as an internal standard (IS) and spiked into the target (¹⁴N) samples. SWATH-MS analysis is performed to obtain the absolute expression levels of the proteins from the target cells. The cost for nMAQ is estimated to be less than 10 dollars per sample. We have benchmarked the quantitative performance of the novel method. We believe this method will help with the deep understanding of the intrinsic regulatory mechanism of C. glutamicum during bioengineering and will promote the process of building cell factories for synthetic biology.

A dynamical stochastic model of yeast translation across the cell cycle

Translation is a central step in gene expression, however its quantitative and time-resolved regulation is poorly understood. We developed a discrete, stochastic model for protein translation in S. cerevisiae in a whole-transcriptome, single-cell context. A "base case" scenario representing an average cell highlights translation initiation rates as the main co-translational regulatory parameters. Codon usage bias emerges as a secondary regulatory mechanism through ribosome stalling. Demand for anticodons with low abundancy is shown to cause above-average ribosome dwelling times. Codon usage bias correlates strongly both with protein synthesis rates and elongation rates. Applying the model to a time-resolved transcriptome estimated by combining data from FISH and RNA-Seq experiments, it could be shown that increased total transcript abundance during the cell cycle decreases translation efficiency at single transcript level. Translation efficiency grouped by gene function shows highest values for ribosomal and glycolytic genes. Ribosomal proteins peak in S phase while glycolytic proteins rank highest in later cell cycle phases.

AlacatDesigner─Computational Design of Peptide Concatamers for Protein Quantitation

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.

Estimating Total Quantitative Protein Content in Escherichia coli, Saccharomyces cerevisiae, and HeLa Cells

The continuous improvement of proteomic techniques, most notably mass spectrometry, has generated quantified proteomes of many organisms with unprecedented depth and accuracy. However, there is still a significant discrepancy in the reported numbers of total protein molecules per specific cell type. In this article, we explore the results of proteomic studies of Escherichia coli, Saccharomyces cerevisiae, and HeLa cells in terms of total protein copy numbers per cell. We observe up to a ten-fold difference between reported values. Investigating possible reasons for this discrepancy, we conclude that neither an unmeasured fraction of the proteome nor biases in the quantification of individual proteins can explain the observed discrepancy. We normalize protein copy numbers in each study using a total protein amount per cell as reported in the literature and create integrated proteome maps of the selected model organisms. Our results indicate that cells contain from one to three million protein molecules per µm³ and that protein copy density decreases with increasing organism complexity.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

Construction of à la carte QconCAT protein standards for multiplexed quantification of user-specified target proteins

Background

QconCATs are quantitative concatamers for proteomic applications that yield stoichiometric quantities of sets of stable isotope-labelled internal standards. However, changing a QconCAT design, for example, to replace poorly performing peptide standards has been a protracted process.

Results

We report a new approach to the assembly and construction of QconCATs, based on synthetic biology precepts of biobricks, making use of loop assembly to construct larger entities from individual biobricks. The basic building block (a Qbrick) is a segment of DNA that encodes two or more quantification peptides for a single protein, readily held in a repository as a library resource. These Qbricks are then assembled in a one tube ligation reaction that enforces the order of assembly, to yield short QconCATs that are useable for small quantification products. However, the DNA context of the short construct also allows a second cycle of loop assembly such that five different short QconCATs can be assembled into a longer QconCAT in a second, single tube ligation. From a library of Qbricks, a bespoke QconCAT can be assembled quickly and efficiently in a form suitable for expression and labelling in vivo or in vitro.

Conclusions

We refer to this approach as the ALACAT strategy as it permits à la carte design of quantification standards. ALACAT methodology is a major gain in flexibility of QconCAT implementation as it supports rapid editing and improvement of QconCATs and permits, for example, substitution of one peptide by another.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-021-01135-9.

Factors affecting the rapid changes of protein under short-term heat stress

BACKGROUND

Protein content determines the state of cells. The variation in protein abundance is crucial when organisms are in the early stages of heat stress, but the reasons affecting their changes are largely unknown.

RESULTS

We quantified 47,535 mRNAs and 3742 proteins in the filling grains of wheat in two different thermal environments. The impact of mRNA abundance and sequence features involved in protein translation and degradation on protein expression was evaluated by regression analysis. Transcription, codon usage and amino acid frequency were the main drivers of changes in protein expression under heat stress, and their combined contribution explains 58.2 and 66.4% of the protein variation at 30 and 40 °C (20 °C as control), respectively. Transcription contributes more to alterations in protein content at 40 °C (31%) than at 30 °C (6%). Furthermore, the usage of codon AAG may be closely related to the rapid alteration of proteins under heat stress. The contributions of AAG were 24 and 13% at 30 and 40 °C, respectively.

CONCLUSION

In this study, we analyzed the factors affecting the changes in protein expression in the early stage of heat stress and evaluated their influence.

Comprehensive predictions of secondary structures for comparative analysis in different species

Protein structures are directly linked to biological functions. However, there is a gap of knowledge between the decoded genome and the structure. To bridge the gap, we focused on the secondary structure (SS). From a comprehensive analysis of predicted SS of proteins in different types of organisms, we have arrived at the following findings: The proportions of SS in genomes were different among phylogenic domains. The distributions of strand lengths indicated structural limitations in all of the species. Different from bacteria and archaea, eukaryotes have an abundance of α-helical and random coil proteins. Interestingly, there was a relationship between SS and post-translational modifications. By calculating hydrophobicity moments of helices and strands, highly amphipathic fragments of SS were found, which might be related to the biological functions. In conclusion, comprehensive predictions of SS will provide valuable perspectives to understand the entire protein structures in genomes and will help one to discover or design functional proteins.

High-throughput protein modification quantitation analysis using intact protein MRM and its application on hENGase inhibitor screening

Proteins are widely used as drug targets, enzyme substrates, and biomarkers for numerous diseases. The emerging demand for proteins quantitation has been increasing in multiple fields. Currently, there is still a big gap for high-throughput protein quantitation at intact protein level using label-free method. Here we choose ribonuclease B (RNB) as a model, which is the substrate for human endo-β-N-acetylglucosaminidase (hENGase), a promising drug target for the treatment of N-Glycanase deficiency. Intact proteinlevel multiple reaction monitoring (MRM) methods were initally developed and optimized to quantify RNB and deglycosylated RNB (RNB-deg), with the S/N ratio improved by nearly 20-fold compared to the traditional full MS scan methods. To further increase the throughput making it possible for hENGase inhibitors screen, the protein MRM methods were introduced to the RapidFire-MS/MS system, achieving at least 12-fold throughput improvement. This assay was further optimized into 384-well plate format for compound screening with S/B ratio >37-fold and Z' factor >0.7 that is suitable for high-throughput screening of compound collections with a speed of 2 h per 384-well plate and an ability to screen over 3000 compounds per day at a single concentration dose. This 384-well plate based automated SPE-MS/MS assay is efficient and robust for compound screening and the assay format has a wide applicability to protein targets for other disease models.

Benchmarking accuracy and precision of intensity-based absolute quantification of protein abundances in Saccharomyces cerevisiae

Protein quantification via label-free mass spectrometry (MS) has become an increasingly popular method for predicting genome-wide absolute protein abundances. A known caveat of this approach, however, is the poor technical reproducibility, that is, how consistent predictions are when the same sample is measured repeatedly. Here, we measured proteomics data for Saccharomyces cerevisiae with both biological and inter-batch technical triplicates, to analyze both accuracy and precision of protein quantification via MS. Moreover, we analyzed how these metrics vary when applying different methods for converting MS intensities to absolute protein abundances. We demonstrate that our simple normalization and rescaling approach can perform as accurately, yet more precisely, than methods which rely on external standards. Additionally, we show that inter-batch reproducibility is worse than biological reproducibility for all evaluated methods. These results offer a new benchmark for assessing MS data quality for protein quantification, while also underscoring current limitations in this approach.

Regulation of the conserved 3'-5' exoribonuclease EXOSC10/Rrp6 during cell division, development and cancer

The conserved 3'-5' exoribonuclease EXOSC10/Rrp6 processes and degrades RNA, regulates gene expression and participates in DNA double-strand break repair and control of telomere maintenance via degradation of the telomerase RNA component. EXOSC10/Rrp6 is part of the multimeric nuclear RNA exosome and interacts with numerous proteins. Previous clinical, genetic, biochemical and genomic studies revealed the protein's essential functions in cell division and differentiation, its RNA substrates and its relevance to autoimmune disorders and oncology. However, little is known about the regulatory mechanisms that control the transcription, translation and stability of EXOSC10/Rrp6 during cell growth, development and disease and how these mechanisms evolved from yeast to human. Herein, we provide an overview of the RNA- and protein expression profiles of EXOSC10/Rrp6 during cell division, development and nutritional stress, and we summarize interaction networks and post-translational modifications across species. Additionally, we discuss how known and predicted protein interactions and post-translational modifications influence the stability of EXOSC10/Rrp6. Finally, we explore the idea that different EXOSC10/Rrp6 alleles, which potentially alter cellular protein levels or affect protein function, might influence human development and disease progression. In this review we interpret information from the literature together with genomic data from knowledgebases to inspire future work on the regulation of this essential protein's stability in normal and malignant cells.

A predictive model of gene expression reveals the role of network motifs in the mating response of yeast

Cells use signaling pathways to receive and process information about their environment. These nonlinear systems rely on feedback and feedforward regulation to respond appropriately to changing environmental conditions. Mathematical models describing signaling pathways often lack predictive power because they are not trained on data that encompass the diverse time scales on which these regulatory mechanisms operate. We addressed this limitation by measuring transcriptional changes induced by the mating response in Saccharomyces cerevisiae exposed to different dynamic patterns of pheromone. We found that pheromone-induced transcription persisted after pheromone removal and showed long-term adaptation upon sustained pheromone exposure. We developed a model of the regulatory network that captured both characteristics of the mating response. We fit this model to experimental data with an evolutionary algorithm and used the parameterized model to predict scenarios for which it was not trained, including different temporal stimulus profiles and genetic perturbations to pathway components. Our model allowed us to establish the role of four architectural elements of the network in regulating gene expression. These network motifs are incoherent feedforward, positive feedback, negative feedback, and repressor binding. Experimental and computational perturbations to these network motifs established a specific role for each in coordinating the mating response to persistent and dynamic stimulation.

A proteome-integrated, carbon source dependent genetic regulatory network in Saccharomyces cerevisiae

Integrated regulatory networks can be powerful tools to examine and test properties of cellular systems, such as modelling environmental effects on the molecular bioeconomy, where protein levels are altered in response to changes in growth conditions. Although extensive regulatory pathways and protein interaction data sets exist which represent such networks, few have formally considered quantitative proteomics data to validate and extend them. We generate and consider such data here using a label-free proteomics strategy to quantify alterations in protein abundance for S. cerevisiae when grown on minimal media using glucose, galactose, maltose and trehalose as sole carbon sources. Using a high quality-controlled subset of proteins observed to be differentially abundant, we constructed a proteome-informed network, comprising 1850 transcription factor interactions and 37 chaperone interactions, which defines the major changes in the cellular proteome when growing under different carbon sources. Analysis of the differentially abundant proteins involved in the regulatory network pointed to their significant roles in specific metabolic pathways and function, including glucose homeostasis, amino acid biosynthesis, and carbohydrate metabolic process. We noted strong statistical enrichment in the differentially abundant proteome of targets of known transcription factors associated with stress responses and altered carbon metabolism. This shows how such integrated analysis can lend further experimental support to annotated regulatory interactions, since the proteomic changes capture both magnitude and direction of gene expression change at the level of the affected proteins. Overall this study highlights the power of quantitative proteomics to help define regulatory systems pertinent to environmental conditions.

Nutrient Signaling, Stress Response, and Inter-organelle Communication Are Non-canonical Determinants of Cell Fate

Isogenic cells manifest distinct cellular fates for a single stress; however, the nongenetic mechanisms driving such fates remain poorly understood. Here, we implement a robust multi-channel live-cell imaging approach to uncover noncanonical factors governing cell fate. We show that in response to acute glucose removal (AGR), budding yeast undergoes distinct fates, becoming either quiescent or senescent. Senescent cells fail to resume mitotic cycles following glucose replenishment but remain responsive to nutrient stimuli. Whereas quiescent cells manifest starvation-induced adaptation, senescent cells display perturbed endomembrane trafficking and defective nucleus-vacuole junction (NVJ) expansion. Surprisingly, senescence occurs even in the absence of lipid droplets. Importantly, we identify the nutrient-sensing kinase Rim15 as a key biomarker predicting cell fates before AGR stress. We propose that isogenic yeast challenged with acute nutrient shortage contains determinants influencing post-stress fate and demonstrate that specific nutrient signaling, stress response, trafficking, and inter-organelle biomarkers are early indicators for long-term fate outcomes.

Resveratrol and insulin association reduced alveolar bone loss and produced an antioxidant effect in diabetic rats

BACKGROUND

The present investigation studied the effects of systemic administration of resveratrol (RSV) on the development of experimental periodontitis (EP) and on the release of markers of inflammation, bone metabolism, and oxidative stress in diabetic rats.

METHODS

Seventy-five male rats were divided into five groups: DM+PLAC: Diabetes Mellitus + placebo solution; DM+INS: DM + insulin therapy; DM+RSV: DM + RSV; DM+RSV+INS: DM + RSV and insulin; NDM: non-diabetic. Streptozotocin was used to induce DM and EP was induced by the placement of a ligature at the fist mandibular and the second maxillary molars. Euthanasia occurred 30 days after the initiation of the study and mandible specimens were subjected for morphometric analysis of bone level. Gingival tissues from mandibular molars were collected for quantification of inflammatory and oxidative stress markers by multiplex assay system and ELISA assay, respectively. Maxillary gingival tissues were processed for real-time polymerase chain reaction (real-time PCR) assessment of markers of bone metabolism and oxidative stress.

RESULTS

Morphometric analysis revealed greater bone loss in DM+PLAC and DM+INS in comparison to the other treatments (P < 0.05). RSV used in conjunction with INS reduced the levels of interleukin (IL)-1β, IL-6, IL-17, interferon-gamma (IFN-γ) and superoxide dismutase 1 (SOD) (P < 0.05). RSV alone reduced nicotinamide adenine dinucleotide phosphatase oxidase (NADPH oxidase) levels, in comparison to DM+INS and DM+RSV+INS (P < 0.05). All treatments upregulated mRNA levels for osteoprotegerin (OPG) in comparison to PLAC (P < 0.05). Sirtuin 1 (SIRT) mRNA levels were lower in PLAC when compared to DM+RSV, DM+RSV+INS and NDM (P < 0.05).

CONCLUSION

RSV reduced the progression of EP and the levels of NADPH oxidase. Co-treatment with RSV and insulin reduced the levels of pro-inflammatory factors (either proteins or mRNA) and increased the levels of SOD. The data also demonstrated that treatment with RSV and INS alone or in combination had beneficial effects on bone loss.

Absolute Quantification of Proteins in the Eye of Drosophila melanogaster

Absolute (molar) quantification of proteins determines their molar ratios in complexes, networks, and metabolic pathways. MS Western workflow is employed to determine molar abundances of proteins potentially critical for morphogenesis and phototransduction (PT) in eyes of Drosophila melanogaster using a single chimeric 264 kDa protein standard that covers, in total, 197 peptides from 43 proteins. The majority of proteins are independently quantified with two to four proteotypic peptides with the coefficient of variation of less than 15%, better than 1000-fold dynamic range and sub-femtomole sensitivity. Here, the molar abundance of proteins of the PT machinery and of the rhabdomere, the photosensitive organelle, is determined in eyes of wild-type flies as well as in crumbs (crb) mutant eyes, which exhibit perturbed rhabdomere morphogenesis.

What Room for Two-Dimensional Gel-Based Proteomics in a Shotgun Proteomics World?

Two-dimensional gel electrophoresis was instrumental in the birth of proteomics in the late 1980s. However, it is now often considered as an outdated technique for proteomics—a thing of the past. Although this opinion may be true for some biological questions, e.g., when analysis depth is of critical importance, for many others, two-dimensional gel electrophoresis-based proteomics still has a lot to offer. This is because of its robustness, its ability to separate proteoforms, and its easy interface with many powerful biochemistry techniques (including western blotting). This paper reviews where and why two-dimensional gel electrophoresis-based proteomics can still be profitably used. It emerges that, rather than being a thing of the past, two-dimensional gel electrophoresis-based proteomics is still highly valuable for many studies. Thus, its use cannot be dismissed on simple fashion arguments and, as usual, in science, the tree is to be judged by the fruit.

Proteome Cold-Shock Response in the Extremely Acidophilic Archaeon, Cuniculiplasma divulgatum

The archaeon Cuniculiplasma divulgatum is ubiquitous in acidic environments with low-to-moderate temperatures. However, molecular mechanisms underlying its ability to thrive at lower temperatures remain unexplored. Using mass spectrometry (MS)-based proteomics, we analysed the effect of short-term (3 h) exposure to cold. The C. divulgatum genome encodes 2016 protein-coding genes, from which 819 proteins were identified in the cells grown under optimal conditions. In line with the peptidolytic lifestyle of C. divulgatum, its intracellular proteome revealed the abundance of proteases, ABC transporters and cytochrome C oxidase. From 747 quantifiable polypeptides, the levels of 582 proteins showed no change after the cold shock, whereas 104 proteins were upregulated suggesting that they might be contributing to cold adaptation. The highest increase in expression appeared in low-abundance (0.001-0.005 fmol%) proteins for polypeptides' hydrolysis (metal-dependent hydrolase), oxidation of amino acids (FAD-dependent oxidoreductase), pyrimidine biosynthesis (aspartate carbamoyltransferase regulatory chain proteins), citrate cycle (2-oxoacid ferredoxin oxidoreductase) and ATP production (V type ATP synthase). Importantly, the cold shock induced a substantial increase (6% and 9%) in expression of the most-abundant proteins, thermosome beta subunit and glutamate dehydrogenase. This study has outlined potential mechanisms of environmental fitness of Cuniculiplasma spp. allowing them to colonise acidic settings at low/moderate temperatures.

Matrix-Matched Calibration Curves for Assessing Analytical Figures of Merit in Quantitative Proteomics

Mass spectrometry is a powerful tool for quantifying protein abundance in complex samples. Advances in sample preparation and the development of data-independent acquisition (DIA) mass spectrometry approaches have increased the number of peptides and proteins measured per sample. Here, we present a series of experiments demonstrating how to assess whether a peptide measurement is quantitative by mass spectrometry. Our results demonstrate that increasing the number of detected peptides in a proteomics experiment does not necessarily result in increased numbers of peptides that can be measured quantitatively.

Incorporation of a unified protein abundance dataset into the Saccharomyces genome database

The identification and accurate quantitation of protein abundance has been a major objective of proteomics research. Abundance studies have the potential to provide users with data that can be used to gain a deeper understanding of protein function and regulation and can also help identify cellular pathways and modules that operate under various environmental stress conditions. One of the central missions of the Saccharomyces Genome Database (SGD; https://www.yeastgenome.org) is to work with researchers to identify and incorporate datasets of interest to the wider scientific community, thereby enabling hypothesis-driven research. A large number of studies have detailed efforts to generate proteome-wide abundance data, but deeper analyses of these data have been hampered by the inability to compare results between studies. Recently, a unified protein abundance dataset was generated through the evaluation of more than 20 abundance datasets, which were normalized and converted to common measurement units, in this case molecules per cell. We have incorporated these normalized protein abundance data and associated metadata into the SGD database, as well as the SGD YeastMine data warehouse, resulting in the addition of 56 487 values for untreated cells grown in either rich or defined media and 28 335 values for cells treated with environmental stressors. Abundance data for protein-coding genes are displayed in a sortable, filterable table on Protein pages, available through Locus Summary pages. A median abundance value was incorporated, and a median absolute deviation was calculated for each protein-coding gene and incorporated into SGD. These values are displayed in the Protein section of the Locus Summary page. The inclusion of these data has enhanced the quality and quantity of protein experimental information presented at SGD and provides opportunities for researchers to access and utilize the data to further their research.

Quantification of Proteins and Histone Marks in Drosophila Embryos Reveals Stoichiometric Relationships Impacting Chromatin Regulation

Gene transcription in eukaryotes is regulated through dynamic interactions of a variety of different proteins with DNA in the context of chromatin. Here, we used mass spectrometry for absolute quantification of the nuclear proteome and methyl marks on selected lysine residues in histone H3 during two stages of Drosophila embryogenesis. These analyses provide comprehensive information about the absolute copy number of several thousand proteins and reveal unexpected relationships between the abundance of histone-modifying and -binding proteins and the chromatin landscape that they generate and interact with. For some histone modifications, the levels in Drosophila embryos are substantially different from those previously reported in tissue culture cells. Genome-wide profiling of H3K27 methylation during developmental progression and in animals with reduced PRC2 levels illustrates how mass spectrometry can be used for quantitatively describing and comparing chromatin states. Together, these data provide a foundation toward a quantitative understanding of gene regulation in Drosophila.

Definition of the Minimal Contents for the Molecular Simulation of the Yeast Cytoplasm

The cytoplasm is a densely packed environment filled with macromolecules with hindered diffusion. Molecular simulation of the diffusion of biomolecules under such macromolecular crowding conditions requires the definition of a simulation cell with a cytoplasmic-like composition. This has been previously done for prokaryote cells (E. coli) but not for eukaryote cells such as yeast as a model organism. Yeast proteomics datasets vary widely in terms of cell growth conditions, the technique used to determine protein composition, the reported relative abundance of proteins, and the units in which abundances are reported. We determined that the gene ontology profiles of the most abundant proteins across these datasets are similar, but their abundances vary greatly. To overcome this problem, we chose five mass spectrometry proteomics datasets that fulfilled the following criteria: high internal consistency, consistency with published experimental data, and freedom from GFP-tagging artifacts. Using these datasets, the contents of a simulation cell containing a single 80S ribosome were defined, such that the macromolecular density and the mass ratio of ribosomal-to-cytoplasmic proteins were consistent with experiment and chosen datasets. Finally, multiple tRNAs were added, consistent with their experimentally-determined number in the yeast cell. The resulting composition can be readily used in molecular simulations representative of yeast cytoplasmic macromolecular crowding conditions to characterize a variety of phenomena, such as protein diffusion, protein-protein interactions and biological processes such as protein translation.

Translation factor mRNA granules direct protein synthetic capacity to regions of polarized growth

mRNA localization serves key functions in localized protein production, making it critical that the translation machinery itself is present at these locations. Here we show that translation factor mRNAs are localized to distinct granules within yeast cells. In contrast to many messenger RNP granules, such as processing bodies and stress granules, which contain translationally repressed mRNAs, these granules harbor translated mRNAs under active growth conditions. The granules require Pab1p for their integrity and are inherited by developing daughter cells in a She2p/She3p-dependent manner. These results point to a model where roughly half the mRNA for certain translation factors is specifically directed in granules or translation factories toward the tip of the developing daughter cell, where protein synthesis is most heavily required, which has particular implications for filamentous forms of growth. Such a feedforward mechanism would ensure adequate provision of the translation machinery where it is to be needed most over the coming growth cycle.

Quantitative assay of targeted proteome in tomato trichome glandular cells using a large-scale selected reaction monitoring strategy

BACKGROUND

Glandular trichomes found in vascular plants are called natural cell factories because they synthesize and store secondary metabolites in glandular cells. To systematically understand the metabolic processes in glandular cells, it is indispensable to analyze cellular proteome dynamics. The conventional proteomics methods based on mass spectrometry have enabled large-scale protein analysis, but require a large number of trichome samples for in-depth analysis and are not suitable for rapid and sensitive quantification of targeted proteins.

RESULTS

Here, we present a high-throughput strategy for quantifying targeted proteins in specific trichome glandular cells, using selected reaction monitoring (SRM) assays. The SRM assay platform, targeting proteins in type VI trichome gland cells of tomato as a model system, demonstrated its effectiveness in quantifying multiple proteins from a limited amount of sample. The large-scale SRM assay uses a triple quadrupole mass spectrometer connected online to a nanoflow liquid chromatograph, which accurately measured the expression levels of 221 targeted proteins contained in the glandular cell sample recovered from 100 glandular trichomes within 120 min. Comparative quantitative proteomics using SRM assays of type VI trichome gland cells between different organs (leaves, green fruits, and calyx) revealed specific organ-enriched proteins.

CONCLUSIONS

We present a targeted proteomics approach using the established SRM assays which enables quantification of proteins of interest with minimum sampling effort. The remarkable success of the SRM assay and its simple experimental workflow will increase proteomics research in glandular trichomes.

Extension of the yeast metabolic model to include iron metabolism and its use to estimate global levels of iron-recruiting enzyme abundance from cofactor requirements

Metabolic networks adapt to changes in their environment by modulating the activity of their enzymes and transporters; often by changing their abundance. Understanding such quantitative changes can shed light onto how metabolic adaptation works, or how it can fail and lead to a metabolically dysfunctional state. We propose a strategy to quantify metabolic protein requirements for cofactor‐utilising enzymes and transporters through constraint‐based modelling. The first eukaryotic genome‐scale metabolic model to comprehensively represent iron metabolism was constructed, extending the most recent community model of the Saccharomyces cerevisiae metabolic network. Partial functional impairment of the genes involved in the maturation of iron‐sulphur (Fe‐S) proteins was investigated employing the model and the in silico analysis revealed extensive rewiring of the fluxes in response to this functional impairment, despite its marginal phenotypic effect. The optimal turnover rate of enzymes bearing ion cofactors can be determined via this novel approach; yeast metabolism, at steady state, was determined to employ a constant turnover of its iron‐recruiting enzyme at a rate of 3.02 × 10 ⁻¹¹ mmol·(g biomass) ⁻¹·h  ⁻¹.

Quantitative Proteomics Data in the Public Domain: Challenges and Opportunities

Mass spectrometry based proteomics is no longer only a qualitative discipline, and can be successfully employed to obtain a truly multidimensional view of the proteome. In particular, systematic protein expression profiling is now a routine part of many studies in the field and beyond. The large growth in the number of quantitative studies is accompanied by a trend to share publicly the associated analysis results and the underlying raw data. This trend, established and strongly supported by public repositories such as the PRIDE database at the European Bioinformatics Institute, opens up enormous possibilities to explore the data beyond the original publications, for instance by reusing, reanalyzing, and performing different flavors of meta-analysis studies. To help researchers and scientists realize about this potential, here we describe the mainstream public proteomics resources containing quantitative proteomics data, including the processed analysis results and/or the underlying raw data. We then present and discuss the most important points to consider when attempting to (re)use proteomics data in the public domain. We conclude by highlighting potential pitfalls of (re)using quantitative data and discuss some of our own experiences in this context.

miRTissue: a web application for the analysis of miRNA-target interactions in human tissues

Background

microRNAs act as regulators of gene expression interacting with their gene targets. Current bioinformatics services, such as databases of validated miRNA-target interactions and prediction tools, usually provide interactions without any information about what tissue that interaction is more likely to appear nor information about the type of interactions, causing mRNA degradation or translation inhibition respectively.

Results

In this work, we introduce miRTissue, a web application that combines validated miRNA-target interactions with statistical correlation among expression profiles of miRNAs, genes and proteins in 15 different human tissues. Validated interactions are taken from the miRTarBase database, while expression profiles are downloaded from The Cancer Genome Atlas repository. As a result, the service provides a tissue-specific characterisation of each couple of miRNA and gene together with its statistical significance (p-value). The inclusion of protein data also allows providing the type of interaction. Moreover, miRTissue offers several views for analysing interactions, focusing for example on the comparison between different cancer types or different tissue conditions. All the results are freely downloadable in the most common formats.

Conclusions

miRTissue fills a gap concerning current bioinformatics services related to miRNA-target interactions because it provides a tissue-specific context to each validated interaction and the type of interaction itself. miRTissue is easily browsable allowing the user to select miRNAs, genes, cancer types and tissue conditions. The results can be sorted according to p-values to immediately identify those interactions that are more likely to occur in a given tissue. miRTissue is available at http://tblab.pa.icar.cnr.it/mirtissue.html.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2418-5) contains supplementary material, which is available to authorized users.

Ribosomal flavours: an acquired taste for specific mRNAs?

The regulation of translation is critical in almost every aspect of gene expression. Nonetheless, the ribosome is historically viewed as a passive player in this process. However, evidence is accumulating to suggest that variations in the ribosome can have an important influence on which mRNAs are translated. Scope for variation is provided via multiple avenues, including heterogeneity at the level of both ribosomal proteins and ribosomal RNAs and their covalent modifications. Together, these variations provide the potential for hundreds, if not thousands, of flavours of ribosome, each of which could have idiosyncratic preferences for the translation of certain messenger RNAs. Indeed, perturbations to this heterogeneity appear to affect specific subsets of transcripts and manifest as cell-type-specific diseases. This review provides a historical perspective of the ribosomal code hypothesis, before outlining the various sources of heterogeneity, their regulation and functional consequences for the cell.

Context-dependent prediction of protein complexes by SiComPre

Most cellular processes are regulated by groups of proteins interacting together to form protein complexes. Protein compositions vary between different tissues or disease conditions enabling or preventing certain protein-protein interactions and resulting in variations in the complexome. Quantitative and qualitative characterization of context-specific protein complexes will help to better understand context-dependent variations in the physiological behavior of cells. Here, we present SiComPre 1.0, a computational tool that predicts context-specific protein complexes by integrating multi-omics sources. SiComPre outperforms other protein complex prediction tools in qualitative predictions and is unique in giving quantitative predictions on the complexome depending on the specific interactions and protein abundances defined by the user. We provide tutorials and examples on the complexome prediction of common model organisms, various human tissues and how the complexome is affected by drug treatment.

Archetypal transcriptional blocks underpin yeast gene regulation in response to changes in growth conditions

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.

Scikit-ribo Enables Accurate Estimation and Robust Modeling of Translation Dynamics at Codon Resolution

Ribosome profiling (Ribo-seq) is a powerful technique for measuring protein translation; however, sampling errors and biological biases are prevalent and poorly understood. Addressing these issues, we present Scikit-ribo (https://github.com/schatzlab/scikit-ribo), an open-source analysis package for accurate genome-wide A-site prediction and translation efficiency (TE) estimation from Ribo-seq and RNA sequencing data. Scikit-ribo accurately identifies A-site locations and reproduces codon elongation rates using several digestion protocols (r = 0.99). Next, we show that the commonly used reads per kilobase of transcript per million mapped reads-derived TE estimation is prone to biases, especially for low-abundance genes. Scikit-ribo introduces a codon-level generalized linear model with ridge penalty that correctly estimates TE, while accommodating variable codon elongation rates and mRNA secondary structure. This corrects the TE errors for over 2,000 genes in S. cerevisiae, which we validate using mass spectrometry of protein abundances (r = 0.81), and allows us to determine the Kozak-like sequence directly from Ribo-seq. We conclude with an analysis of coverage requirements needed for robust codon-level analysis and quantify the artifacts that can occur from cycloheximide treatment.

Cell-free synthesis of stable isotope-labeled internal standards for targeted quantitative proteomics

High-sensitivity mass spectrometry approaches using selected reaction monitoring (SRM) or multiple reaction monitoring (MRM) methods are powerful tools for targeted quantitative proteomics-based investigation of dynamics in specific biological systems. Both high-sensitivity detection of low-abundance proteins and their quantification using this technique employ stable isotope-labeled peptide internal standards. Currently, there are various ways for preparing standards, including chemical peptide synthesis, cellular protein expression, and cell-free protein or peptide synthesis. Cell-free protein synthesis (CFPS) or in vitro translation (IVT) systems in particular provide high-throughput and low-cost preparation methods, and various cell types and reconstituted forms are now commercially available. Herein, we review the use of such systems for precise and reliable protein quantification.

Application of targeted mass spectrometry in bottom-up proteomics for systems biology research

The enormous diversity of proteoforms produces tremendous complexity within cellular proteomes, facilitates intricate networks of molecular interactions, and constitutes a formidable analytical challenge for biomedical researchers. Currently, quantitative whole-proteome profiling often relies on non-targeted liquid chromatography-mass spectrometry (LC-MS), which samples proteoforms broadly, but can suffer from lower accuracy, sensitivity, and reproducibility compared with targeted LC-MS. Recent advances in bottom-up proteomics using targeted LC-MS have enabled previously unachievable identification and quantification of target proteins and posttranslational modifications within complex samples. Consequently, targeted LC-MS is rapidly advancing biomedical research, especially systems biology research in diverse areas that include proteogenomics, interactomics, kinomics, and biological pathway modeling. With the recent development of targeted LC-MS assays for nearly the entire human proteome, targeted LC-MS is positioned to enable quantitative proteomic profiling of unprecedented quality and accessibility to support fundamental and clinical research. Here we review recent applications of bottom-up proteomics using targeted LC-MS for systems biology research. SIGNIFICANCE: Advances in targeted proteomics are rapidly advancing systems biology research. Recent applications include systems-level investigations focused on posttranslational modifications (such as phosphoproteomics), protein conformation, protein-protein interaction, kinomics, proteogenomics, and metabolic and signaling pathways. Notably, absolute quantification of metabolic and signaling pathway proteins has enabled accurate pathway modeling and engineering. Integration of targeted proteomics with other technologies, such as RNA-seq, has facilitated diverse research such as the identification of hundreds of "missing" human proteins (genes and transcripts that appear to encode proteins but direct experimental evidence was lacking).

Nonlinear Regression Improves Accuracy of Characterization of Multiplexed Mass Spectrometric Assays

The need for assay characterization is ubiquitous in quantitative mass spectrometry-based proteomics. Among many assay characteristics, the limit of blank (LOB) and limit of detection (LOD) are two particularly useful figures of merit. LOB and LOD are determined by repeatedly quantifying the observed intensities of peptides in samples with known peptide concentrations and deriving an intensity versus concentration response curve. Most commonly, a weighted linear or logistic curve is fit to the intensity-concentration response, and LOB and LOD are estimated from the fit. Here we argue that these methods inaccurately characterize assays where observed intensities level off at low concentrations, which is a common situation in multiplexed systems. This manuscript illustrates the deficiencies of these methods, and proposes an alternative approach based on nonlinear regression that overcomes these inaccuracies. We evaluated the performance of the proposed method using computer simulations and using eleven experimental data sets acquired in Data-Independent Acquisition (DIA), Parallel Reaction Monitoring (PRM), and Selected Reaction Monitoring (SRM) mode. When the intensity levels off at low concentrations, the nonlinear model changes the estimates of LOB/LOD upwards, in some data sets by 20-40%. In absence of a low concentration intensity leveling off, the estimates of LOB/LOD obtained with nonlinear statistical modeling were identical to those of weighted linear regression. We implemented the nonlinear regression approach in the open-source R-based software MSstats, and advocate its general use for characterization of mass spectrometry-based assays.

Unification of Protein Abundance Datasets Yields a Quantitative Saccharomyces cerevisiae Proteome

Protein activity is the ultimate arbiter of function in most cellular pathways, and protein concentration is fundamentally connected to protein action. While the proteome of yeast has been subjected to the most comprehensive analysis of any eukaryote, existing datasets are difficult to compare, and there is no consensus abundance value for each protein. We evaluated 21 quantitative analyses of the S. cerevisiae proteome, normalizing and converting all measurements of protein abundance into the intuitive measurement of absolute molecules per cell. We estimate the cellular abundance of 92% of the proteins in the yeast proteome and assess the variation in each abundance measurement. Using our protein abundance dataset, we find that a global response to diverse environmental stresses is not detected at the level of protein abundance, we find that protein tags have only a modest effect on protein abundance, and we identify proteins that are differentially regulated at the mRNA abundance, mRNA translation, and protein abundance levels.

A quantitative and temporal map of proteostasis during heat shock in Saccharomyces cerevisiae

Temperature fluctuation is a common environmental stress that elicits a molecular response in order to maintain intracellular protein levels. Here, for the first time, we report a comprehensive temporal and quantitative study of the proteome during a 240 minute heat stress, using label-free mass spectrometry. We report temporal expression changes of the hallmark heat stress proteins, including many molecular chaperones, tightly coupled to their protein clients. A notable lag of 30 to 120 minutes was evident between transcriptome and proteome levels for differentially expressed genes. This targeted molecular response buffers the global proteome; fewer than 15% of proteins display significant abundance change. Additionally, a parallel study in a Hsp70 chaperone mutant (ssb1Δ) demonstrated a significantly attenuated response, at odds with the modest phenotypic effects that are observed on growth rate. We cast the global changes in temporal protein expression into protein interaction and functional networks, to afford a unique, time-resolved and quantitative description of the heat shock response in an important model organism.

Deciphering Multifactorial Resistance Phenotypes in Acinetobacter baumannii by Genomics and Targeted Label-free Proteomics

Resistance to β-lactams in Acinetobacter baumannii involves various mechanisms. To decipher them, whole genome sequencing (WGS) and real-time quantitative polymerase chain reaction (RT-qPCR) were complemented by mass spectrometry (MS) in selected reaction monitoring mode (SRM) in 39 clinical isolates. The targeted label-free proteomic approach enabled, in one hour and using a single method, the quantitative detection of 16 proteins associated with antibiotic resistance: eight acquired β-lactamases (i.e. GES, NDM-1, OXA-23, OXA-24, OXA-58, PER, TEM-1, and VEB), two resident β-lactamases (i.e. ADC and OXA-51-like) and six components of the two major efflux systems (i.e. AdeABC and AdeIJK). Results were normalized using "bacterial quantotypic peptides," i.e. peptide markers of the bacterial quantity, to obtain precise protein quantitation (on average 8.93% coefficient of variation for three biological replicates). This allowed to correlate the levels of resistance to β-lactam with those of the production of acquired as well as resident β-lactamases or of efflux systems. SRM detected enhanced ADC or OXA-51-like production and absence or increased efflux pump production. Precise protein quantitation was particularly valuable to detect resistance mechanisms mediated by regulated genes or by overexpression of chromosomal genes. Combination of WGS and MS, two orthogonal and complementary techniques, allows thereby interpretation of the resistance phenotypes at the molecular level.

Quantitating translational control: mRNA abundance-dependent and independent contributions and the mRNA sequences that specify them

Translation rate per mRNA molecule correlates positively with mRNA abundance. As a result, protein levels do not scale linearly with mRNA levels, but instead scale with the abundance of mRNA raised to the power of an ‘amplification exponent’. Here we show that to quantitate translational control, the translation rate must be decomposed into two components. One, TR_mD, depends on the mRNA level and defines the amplification exponent. The other, TR_mIND, is independent of mRNA amount and impacts the correlation coefficient between protein and mRNA levels. We show that in Saccharomyces cerevisiae TR_mD represents ∼20% of the variance in translation and directs an amplification exponent of 1.20 with a 95% confidence interval [1.14, 1.26]. TR_mIND constitutes the remaining ∼80% of the variance in translation and explains ∼5% of the variance in protein expression. We also find that TR_mD and TR_mIND are preferentially determined by different mRNA sequence features: TR_mIND by the length of the open reading frame and TR_mD both by a ∼60 nucleotide element that spans the initiating AUG and by codon and amino acid frequency. Our work provides more appropriate estimates of translational control and implies that TR_mIND is under different evolutionary selective pressures than TR_mD.

MS Western, a Method of Multiplexed Absolute Protein Quantification is a Practical Alternative to Western Blotting

Absolute quantification of proteins elucidates the molecular composition, regulation and dynamics of multiprotein assemblies and networks. Here we report on a method termed MS Western that accurately determines the molar abundance of dozens of user-selected proteins at the subfemtomole level in whole cell or tissue lysates without metabolic or chemical labeling and without using specific antibodies. MS Western relies on GeLC-MS/MS and quantifies proteins by in-gel codigestion with an isotopically labeled QconCAT protein chimera composed of concatenated proteotypic peptides. It requires no purification of the chimera and relates the molar abundance of all proteotypic peptides to a single reference protein. In comparative experiments, MS Western outperformed immunofluorescence Western blotting by the protein detection specificity, linear dynamic range and sensitivity of protein quantification. To validate MS Western in an in vivo experiment, we quantified the molar content of zebrafish core histones H2A, H2B, H3 and H4 during ten stages of early embryogenesis. Accurate quantification (CV<10%) corroborated the anticipated histones equimolar stoichiometry and revealed an unexpected trend in their total abundance.

A global function for transcription factors in assisting RNA polymerase II termination

The role of transcription factors (TFs) on nucleosome positioning, RNA polymerase recruitment, and transcription initiation has been extensively characterized. Here, we propose that a subset of TFs such as Reb1, Abf1, Rap1, and TFIIIB also serve a major function in partitioning transcription units by assisting the Nrd1p-Nab3p-Sen1p Pol II termination pathway.

Dynamic changes in eIF4F-mRNA interactions revealed by global analyses of environmental stress responses

Background

Translation factors eIF4E and eIF4G form eIF4F, which interacts with the messenger RNA (mRNA) 5′ cap to promote ribosome recruitment and translation initiation. Variations in the association of eIF4F with individual mRNAs likely contribute to differences in translation initiation frequencies between mRNAs. As translation initiation is globally reprogrammed by environmental stresses, we were interested in determining whether eIF4F interactions with individual mRNAs are reprogrammed and how this may contribute to global environmental stress responses.

Results

Using a tagged-factor protein capture and RNA-sequencing (RNA-seq) approach, we have assessed how mRNA associations with eIF4E, eIF4G1 and eIF4G2 change globally in response to three defined stresses that each cause a rapid attenuation of protein synthesis: oxidative stress induced by hydrogen peroxide and nutrient stresses caused by amino acid or glucose withdrawal. We find that acute stress leads to dynamic and unexpected changes in eIF4F–mRNA interactions that are shared among each factor and across the stresses imposed. eIF4F–mRNA interactions stabilised by stress are predominantly associated with translational repression, while more actively initiating mRNAs become relatively depleted for eIF4F. Simultaneously, other mRNAs are insulated from these stress-induced changes in eIF4F association.

Conclusion

Dynamic eIF4F–mRNA interaction changes are part of a coordinated early translational control response shared across environmental stresses. Our data are compatible with a model where multiple mRNA closed-loop complexes form with differing stability. Hence, unexpectedly, in the absence of other stabilising factors, rapid translation initiation on mRNAs correlates with less stable eIF4F interactions.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1338-4) contains supplementary material, which is available to authorized users.

MEERCAT: Multiplexed Efficient Cell Free Expression of Recombinant QconCATs For Large Scale Absolute Proteome Quantification

A major challenge in proteomics is the absolute accurate quantification of large numbers of proteins. QconCATs, artificial proteins that are concatenations of multiple standard peptides, are well established as an efficient means to generate standards for proteome quantification. Previously, QconCATs have been expressed in bacteria, but we now describe QconCAT expression in a robust, cell-free system. The new expression approach rescues QconCATs that previously were unable to be expressed in bacteria and can reduce the incidence of proteolytic damage to QconCATs. Moreover, it is possible to cosynthesize QconCATs in a highly-multiplexed translation reaction, coexpressing tens or hundreds of QconCATs simultaneously. By obviating bacterial culture and through the gain of high level multiplexing, it is now possible to generate tens of thousands of standard peptides in a matter of weeks, rendering absolute quantification of a complex proteome highly achievable in a reproducible, broadly deployable system.

High-throughput production of a stable isotope-labeled peptide library for targeted proteomics using a wheat germ cell-free synthesis system

Quantitative proteomic approaches using selected reaction monitoring (SRM) are currently limited by the difficulty in the preparation of reference standards. In this study, we demonstrat the high-throughput production of a reference peptide library using a wheat germ cell-free synthesis system to develop a large-scale SRM assay for targeted proteomics.