Isoform-specific sequestration of protein kinase A fine-tunes intracellular signaling during heat stress.

Protein kinase A (PKA) is a conserved kinase crucial for fundamental biological processes linked to growth, development, and metabolism. The PKA catalytic subunit is expressed as multiple isoforms in diverse eukaryotes; however, their contribution to ensuring signaling specificity in response to environmental cues remains poorly defined. Catalytic subunit activity is classically moderated via interaction with an inhibitory regulatory subunit. Here, a quantitative mass spectrometry approach is used to examine heat-stress-induced changes in the binding of yeast Tpk1-3 catalytic subunits to the Bcy1 regulatory subunit. We show that Tpk3 is not regulated by Bcy1 binding but, instead, is deactivated upon heat stress via reversible sequestration into cytoplasmic granules. These "Tpk3 granules" are enriched for multiple PKA substrates involved in various metabolic processes, with the Hsp42 sequestrase required for their formation. Hence, regulated sequestration of Tpk3 provides a mechanism to control isoform-specific kinase signaling activity during stress conditions.

Proteogenomic Gene Structure Validation in the Pineapple Genome.

MD2 pineapple (Ananas comosus) is the second most important tropical crop that preserves crassulacean acid metabolism (CAM), which has high water-use efficiency and is fast becoming the most consumed fresh fruit worldwide. Despite the significance of environmental efficiency and popularity, until very recently, its genome sequence has not been determined and a high-quality annotated proteome has not been available. Here, we have undertaken a pilot proteogenomic study, analyzing the proteome of MD2 pineapple leaves using liquid chromatography-mass spectrometry (LC-MS/MS), which validates 1781 predicted proteins in the annotated F153 (V3) genome. In addition, a further 603 peptide identifications are found that map exclusively to an independent MD2 transcriptome-derived database but are not found in the standard F153 (V3) annotated proteome. Peptide identifications derived from these MD2 transcripts are also cross-referenced to a more recent and complete MD2 genome annotation, resulting in 402 nonoverlapping peptides, which in turn support 30 high-quality gene candidates novel to both pineapple genomes. Many of the validated F153 (V3) genes are also supported by an independent proteomics data set collected for an ornamental pineapple variety. The contigs and peptides have been mapped to the current F153 genome build and are available as bed files to display a custom gene track on the Ensembl Plants region viewer. These analyses add to the knowledge of experimentally validated pineapple genes and demonstrate the utility of transcript-derived proteomics to discover both novel genes and genetic structure in a plant genome, adding value to its annotation.

Loss of regulation of protein synthesis and turnover underpins an attenuated stress response in senescent human mesenchymal stem cells.

Cells respond to stress by synthesizing chaperone proteins that seek to correct protein misfolding and maintain function. However, abrogation of protein homeostasis is a hallmark of aging, leading to loss of function and the formation of proteotoxic aggregates characteristic of pathology. Consequently, discovering the underlying molecular causes of this deterioration in proteostasis is key to designing effective interventions to disease or to maintaining cell health in regenerative medicine strategies. Here, we examined primary human mesenchymal stem cells, cultured to a point of replicative senescence and subjected to heat shock, as an in vitro model of the aging stress response. Multi -omics analysis showed how homeostasis components were reduced in senescent cells, caused by dysregulation of a functional network of chaperones, thereby limiting proteostatic competence. Time-resolved analysis of the primary response factors, including those regulating heat shock protein 70 kDa (HSPA1A), revealed that regulatory control is essentially translational. Senescent cells have a reduced capacity for chaperone protein translation and misfolded protein (MFP) turnover, driven by downregulation of ribosomal proteins and loss of the E3 ubiquitin ligase CHIP (C-terminus of HSP70 interacting protein) which marks MFPs for degradation. This limits the cell's stress response and subsequent recovery. A kinetic model recapitulated these reduced capacities and predicted an accumulation of MFP, a hypothesis supported by evidence of systematic changes to the proteomic fold state. These results thus establish a specific loss of regulatory capacity at the protein, rather than transcript, level and uncover underlying systematic links between aging and loss of protein homeostasis.

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.

Uncertainty-Aware Protein-Level Quantification and Differential Expression Analysis of Proteomics Data with seaMass.

seaMass is an R package for protein-level quantification, normalization, and differential expression analysis of proteomics mass spectrometry data after peptide identification, protein grouping, and feature-level quantification. Using the concept of a blocked experimental design, seaMass can analyze all common discovery proteomics paradigms, including label-free (e.g., Waters Progenesis input), SILAC (e.g., MaxQuant input), isotope labelling (e.g., SCIEX ProteinPilot iTraq and Thermo ProteomeDiscoverer TMT input), and data-independent acquisition (e.g., OpenSWATH-PyProphet input), and is able to scale to study with hundreds of assays or more. By utilizing hierarchical Bayesian modelling, seaMass assesses the quantification reliability of each feature and peptide across assays so that only those in consensus influence the resulting protein group quantification strongly. Similarly, unexplained variation in each individual assay is captured, providing both a metric for quality control and automatic down-weighting of suspect assays. To achieve this, each protein group-level quantification outputted by seaMass is accompanied by the standard deviation of its posterior uncertainty. Moreover, seaMass integrates a flexible differential expression analysis subsystem with false discovery rate control based on the popular MCMCglmm package for Bayesian mixed-effects modelling, and also provides uncertainty-aware principal components analysis. We provide a description for using seaMass to perform an end-to-end analysis using a real dataset associated with a published clinical proteomics study.

Understanding the Role of Yeast Yme1 in Mitochondrial Function Using Biochemical and Proteomics Analyses.

Mitochondrial i-AAA proteinase Yme1 is a multifunctional protein that plays important roles in maintaining mitochondrial protein homeostasis and regulating biogenesis and function of mitochondrial proteins. However, due to the complex interplay of mitochondria and the multifunctional nature of Yme1, how Yme1 affects mitochondrial function and protein homeostasis is still poorly understood. In this study, we investigated how YME1 deletion affects yeast Saccharomyces cerevisiae growth, chronological life span, mitochondrial protein homeostasis and function, with a focus on the mitochondrial oxidative phosphorylation (OXPHOS) complexes. Our results show that whilst the YME1 deleted cells grow poorly under respiratory conditions, they grow similar to wild-type yeast under fermentative conditions. However, the chronological life span is impaired, indicating that Yme1 plays a key role in longevity. Using highly enriched mitochondrial extract and proteomic analysis, we show that the abundances of many mitochondrial proteins are altered by YME1 deletion. Several components of the respiratory chain complexes II, III, IV and V were significantly decreased, suggesting that Yme1 plays an important role in maintaining the level and function of complexes II-V. This result was confirmed using blue native-PAGE and in-solution-based enzyme activity assays. Taken together, this study shows that Yme1 plays an important role in the chronological life span and mitochondrial protein homeostasis and has deciphered its function in maintaining the activity of mitochondrial OXPHOS complexes.

Yeast Protein Kinase A Isoforms: A Means of Encoding Specificity in the Response to Diverse Stress Conditions?

Eukaryotic cells have developed a complex circuitry of signalling molecules which monitor changes in their intra- and extracellular environments. One of the most widely studied signalling pathways is the highly conserved cyclic AMP (cAMP)/protein kinase A (PKA) pathway, which is a major glucose sensing circuit in the yeast Saccharomyces cerevisiae. PKA activity regulates diverse targets in yeast, positively activating the processes that are associated with rapid cell growth (e.g., fermentative metabolism, ribosome biogenesis and cell division) and negatively regulating the processes that are associated with slow growth, such as respiratory growth, carbohydrate storage and entry into stationary phase. As in higher eukaryotes, yeast has evolved complexity at the level of the PKA catalytic subunit, and Saccharomyces cerevisiae expresses three isoforms, denoted Tpk1-3. Despite evidence for isoform differences in multiple biological processes, the molecular basis of PKA signalling specificity remains poorly defined, and many studies continue to assume redundancy with regards to PKA-mediated regulation. PKA has canonically been shown to play a key role in fine-tuning the cellular response to diverse stressors; however, recent studies have now begun to interrogate the requirement for individual PKA catalytic isoforms in coordinating distinct steps in stress response pathways. In this review, we discuss the known non-redundant functions of the Tpk catalytic subunits and the evolving picture of how these isoforms establish specificity in the response to different stress conditions.

Quantitative proteomics and network analysis of SSA1 and SSB1 deletion mutants reveals robustness of chaperone HSP70 network in Saccharomyces cerevisiae.

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.

Puf3p induces translational repression of genes linked to oxidative stress.

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.

Prediction of missed proteolytic cleavages for the selection of surrogate peptides for quantitative proteomics.

Quantitative proteomics experiments are usually performed using proteolytic peptides as surrogates for their parent proteins, inferring protein amounts from peptide-level quantitation. This process is frequently dependent on complete digestion of the parent protein to its limit peptides so that their signal is truly representative. Unfortunately, proteolysis is often incomplete, and missed cleavage peptides are frequently produced that are unlikely to be optimal surrogates for quantitation, particularly for label-mediated approaches seeking to derive absolute values. We have generated a predictive computational tool that is able to predict which candidate proteolytic peptide bonds are likely to be missed by the standard enzyme trypsin. Our cross-validated prediction tool uses support vector machines and achieves high accuracy in excess of 0.94 precision (PPV), with attendant high sensitivity of 0.79, across multiple proteomes. We believe this is a useful tool for selecting candidate quantotypic peptides, seeking to minimize likely loss owing to missed cleavage, which will be a boon for quantitative proteomic pipelines as well as other areas of proteomics. Our results are discussed in the context of recent results examining the kinetics of missed cleavages in proteomic digestion protocols, and show agreement with observed experimental trends. The software has been made available at http://king.smith.man.ac.uk/mcpred .

BioMart Central Portal: an open database network for the biological community.

BioMart Central Portal is a first of its kind, community-driven effort to provide unified access to dozens of biological databases spanning genomics, proteomics, model organisms, cancer data, ontology information and more. Anybody can contribute an independently maintained resource to the Central Portal, allowing it to be exposed to and shared with the research community, and linking it with the other resources in the portal. Users can take advantage of the common interface to quickly utilize different sources without learning a new system for each. The system also simplifies cross-database searches that might otherwise require several complicated steps. Several integrated tools streamline common tasks, such as converting between ID formats and retrieving sequences. The combination of a wide variety of databases, an easy-to-use interface, robust programmatic access and the array of tools make Central Portal a one-stop shop for biological data querying. Here, we describe the structure of Central Portal and show example queries to demonstrate its capabilities.

CONSeQuence: prediction of reference peptides for absolute quantitative proteomics using consensus machine learning approaches.

Mass spectrometric based methods for absolute quantification of proteins, such as QconCAT, rely on internal standards of stable-isotope labeled reference peptides, or "Q-peptides," to act as surrogates. Key to the success of this and related methods for absolute protein quantification (such as AQUA) is selection of the Q-peptide. Here we describe a novel method, CONSeQuence (consensus predictor for Q-peptide sequence), based on four different machine learning approaches for Q-peptide selection. CONSeQuence demonstrates improved performance over existing methods for optimal Q-peptide selection in the absence of prior experimental information, as validated using two independent test sets derived from yeast. Furthermore, we examine the physicochemical parameters associated with good peptide surrogates, and demonstrate that in addition to charge and hydrophobicity, peptide secondary structure plays a significant role in determining peptide "detectability" in liquid chromatography-electrospray ionization experiments. We relate peptide properties to protein tertiary structure, demonstrating a counterintuitive preference for buried status for frequently detected peptides. Finally, we demonstrate the improved efficacy of the general approach by applying a predictor trained on yeast data to sets of proteotypic peptides from two additional species taken from an existing peptide identification repository.

FDRAnalysis: a tool for the integrated analysis of tandem mass spectrometry identification results from multiple search engines.

Confident identification of peptides via tandem mass spectrometry underpins modern high-throughput proteomics. This has motivated considerable recent interest in the postprocessing of search engine results to increase confidence and calculate robust statistical measures, for example through the use of decoy databases to calculate false discovery rates (FDR). FDR-based analyses allow for multiple testing and can assign a single confidence value for both sets and individual peptide spectrum matches (PSMs). We recently developed an algorithm for combining the results from multiple search engines, integrating FDRs for sets of PSMs made by different search engine combinations. Here we describe a web-server and a downloadable application that makes this routinely available to the proteomics community. The web server offers a range of outputs including informative graphics to assess the confidence of the PSMs and any potential biases. The underlying pipeline also provides a basic protein inference step, integrating PSMs into protein ambiguity groups where peptides can be matched to more than one protein. Importantly, we have also implemented full support for the mzIdentML data standard, recently released by the Proteomics Standards Initiative, providing users with the ability to convert native formats to mzIdentML files, which are available to download.

Investigating protein isoforms via proteomics: a feasibility study.

Alternative splicing (AS) and processing of pre-messenger RNAs explains the discrepancy between the number of genes and proteome complexity in multicellular eukaryotic organisms. However, relatively few alternative protein isoforms have been experimentally identified, particularly at the protein level. In this study, we assess the ability of proteomics to inform on differently spliced protein isoforms in human and four other model eukaryotes. The number of Ensembl-annotated genes for which proteomic data exists that informs on AS exceeds 33% of the alternately spliced genes in the human and worm genomes. Examining AS in chicken via proteomics for the first time, we find support for over 600 AS genes. However, although peptide identifications support only a small fraction of alternative protein isoforms that are annotated in Ensembl, many more variants are amenable to proteomic identification. There remains a sizeable gap between these existing identifications (10-52% of AS genes) and those that are theoretically feasible (90-99%). We also compare annotations between Swiss-Prot and Ensembl, recommending use of both to maximize coverage of AS. We propose that targeted proteomic experiments using selected reactions and standards are essential to uncover further alternative isoforms and discuss the issues surrounding these strategies.

Computational approaches to peptide identification via tandem MS.

The peptide identification problem lies at the heart of modern proteomic methodology, from which the presence of a particular protein or proteins in a sample may be inferred. The challenge is to find the most likely amino acid sequence, which corresponds to each tandem mass spectrum that has been collected, and produce some kind of score and associated statistical measure that the putative identification is correct. This approach assumes that the peptide (and parent protein) sequence in question is known and is present in the database which is to be searched, as opposed to de novo methods, which seek to identify the peptide ab initio. This chapter will provide an overview of the methods that common, popular software tools employ to search protein sequence databases to provide the non-expert reader with sufficient background to appreciate the choices they can make. This will cover the approaches used to compare experimental and theoretical spectra and some of the methods used to validate and provide higher confidence in the assignments.

Observations on the detection of b- and y-type ions in the collisionally activated decomposition spectra of protonated peptides.

Tandem mass spectrometric data from peptides are routinely used in an unsupervised manner to infer product ion sequence and hence the identity of their parent protein. However, significant variability in relative signal intensity of product ions within peptide tandem mass spectra is commonly observed. Furthermore, instrument-specific patterns of fragmentation are observed, even where a common mechanism of ion heating is responsible for generation of the product ions. This information is currently not fully exploited within database searching strategies; this motivated the present study to examine a large dataset of tandem mass spectra derived from multiple instrumental platforms. Here, we report marked global differences in the product ion spectra of protonated tryptic peptides generated from two of the most common proteomic platforms, namely tandem quadrupole-time-of-flight and quadrupole ion trap instruments. Specifically, quadrupole-time-of-flight tandem mass spectra show a significant under-representation of N-terminal b-type fragments in comparison to quadrupole ion trap product ion spectra. Energy-resolved mass spectrometry experiments conducted upon test tryptic peptides clarify this disparity; b-type ions are significantly less stable than their y-type N-terminal counterparts, which contain strongly basic residues. Secondary fragmentation processes which occur within the tandem quadrupole-time-of-flight device account for the observed differences, whereas this secondary product ion generation does not occur to a significant extent from resonant excitation performed within the quadrupole ion trap. We suggest that incorporation of this stability information in database searching strategies has the potential to significantly improve the veracity of peptide ion identifications as made by conventional database searching strategies.

Improving sensitivity in proteome studies by analysis of false discovery rates for multiple search engines.

LC-MS experiments can generate large quantities of data, for which a variety of database search engines are available to make peptide and protein identifications. Decoy databases are becoming widely used to place statistical confidence in result sets, allowing the false discovery rate (FDR) to be estimated. Different search engines produce different identification sets so employing more than one search engine could result in an increased number of peptides (and proteins) being identified, if an appropriate mechanism for combining data can be defined. We have developed a search engine independent score, based on FDR, which allows peptide identifications from different search engines to be combined, called the FDR Score. The results demonstrate that the observed FDR is significantly different when analysing the set of identifications made by all three search engines, by each pair of search engines or by a single search engine. Our algorithm assigns identifications to groups according to the set of search engines that have made the identification, and re-assigns the score (combined FDR Score). The combined FDR Score can differentiate between correct and incorrect peptide identifications with high accuracy, allowing on average 35% more peptide identifications to be made at a fixed FDR than using a single search engine.

Analysis of the trypanosome flagellar proteome using a combined electron transfer/collisionally activated dissociation strategy.

The use of electron-transfer dissociation as an alternative peptide ion activation method for generation of protein sequence information is examined here in comparison with the conventional method of choice, collisionally activated dissociation, using a linear ion trapping instrument. Direct comparability between collisionally and electron-transfer-activated product ion data were ensured by employing an activation-switching method during acquisition, sequentially activating precisely the same precursor ion species with each fragmentation method in turn. Sequest (Thermo Fisher Scientific, San Jose, CA) searching of product ion data generated an overlapping yet distinct pool of polypeptide identifications from the products of collisional and electron-transfer-mediated activation products. To provide a highly confident set of protein recognitions, identification data were filtered using parameters that achieved a peptide false discovery rate of 1%, with two or more independent peptide assignments required for each protein. The use of electron transfer dissociation (ETD) has allowed us to identify additional peptides where the quality of product ion data generated by collisionally activated dissociation (CAD) was insufficient to infer peptide sequence. Thus, a combined ETD/CAD approach leads to the recognition of more peptides and proteins than are achieved using peptide analysis by CAD- or ETD-based tandem mass spectrometry alone.

PepSeeker: mining information from proteomic data.

Driven by advances in mass spectrometry and analytical chemistry, coupled with the expanding number of completely sequenced genomes, proteomics is becoming a widely exploited technology for characterizing the proteins found in living systems. As proteomics becomes increasingly more high-throughput there is a parallel need for storage of the large quantities of data generated, to support data exchange and allow further analyses. The capture and storage of such data, along with subsequent release and dissemination, not only aid in sharing of the data throughout the proteomics community but also provide scientific insights into the observations between different laboratories, instruments, and software. Growing numbers of resources offer a range of approaches for the capture, storage, and dissemination of proteomic experimental data reflecting the fact that proteomics has now come of age in the postgenomic era and is delivering large, complex datasets that are rich in information. This chapter demonstrates how one such resource, PepSeeker, can be used to mine useful information from proteomic data, which can then be exploited for peptide identification algorithms via a better understanding of how peptides fragment inside mass spectrometers.

Differential expression of ion channel transcripts in atrial muscle and sinoatrial node in rabbit.

The aim of the study was to identify ion channel transcripts expressed in the sinoatrial node (SAN), the pacemaker of the heart. Functionally, the SAN can be divided into central and peripheral regions (center is adapted for pacemaking only, whereas periphery is adapted to protect center and drive atrial muscle as well as pacemaking) and the aim was to study expression in both regions. In rabbit tissue, the abundance of 30 transcripts (including transcripts for connexin, Na(+), Ca(2+), hyperpolarization-activated cation and K(+) channels, and related Ca(2+) handling proteins) was measured using quantitative PCR and the distribution of selected transcripts was visualized using in situ hybridization. Quantification of individual transcripts (quantitative PCR) showed that there are significant differences in the abundance of 63% of the transcripts studied between the SAN and atrial muscle, and cluster analysis showed that the transcript profile of the SAN is significantly different from that of atrial muscle. There are apparent isoform switches on moving from atrial muscle to the SAN center: RYR2 to RYR3, Na(v)1.5 to Na(v)1.1, Ca(v)1.2 to Ca(v)1.3 and K(v)1.4 to K(v)4.2. The transcript profile of the SAN periphery is intermediate between that of the SAN center and atrial muscle. For example, Na(v)1.5 messenger RNA is expressed in the SAN periphery (as it is in atrial muscle), but not in the SAN center, and this is probably related to the need of the SAN periphery to drive the surrounding atrial muscle.