Extensive mass spectrometry-based analysis of the fission yeast proteome: the Schizosaccharomyces pombe PeptideAtlas

Candida albicans: A Comprehensive View of the Proteome

We describe a new release of the Candida albicans PeptideAtlas proteomics spectral resource (build 2024-03), providing a sequence coverage of 79.5% at the canonical protein level, matched mass spectrometry spectra, and experimental evidence identifying 3382 and 536 phosphorylated serine and threonine sites with false localization rates of 1% and 5.3%, respectively. We provide a tutorial on how to use the PeptideAtlas and associated tools to access this information. The C. albicans PeptideAtlas summary web page provides "Build overview", "PTM coverage", "Experiment contribution", and "Data set contribution" information. The protein and peptide information can also be accessed via the Candida Genome Database via hyperlinks on each protein page. This allows users to peruse identified peptides, protein coverage, post-translational modifications (PTMs), and experiments that identify each protein. Given the value of understanding the PTM landscape in the sequence of each protein, a more detailed explanation of how to interpret and analyze PTM results is provided in the PeptideAtlas of this important pathogen. Candida albicans PeptideAtlas web page: https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/buildDetails?atlas_build_id=578.

Candida albicans: a comprehensive view of the proteome

We describe a new release of the Candida albicans PeptideAtlas proteomics spectral resource (build 2024-03), providing a sequence coverage of 79.5% at the canonical protein level, matched mass spectrometry spectra, and experimental evidence identifying 3382 and 536 phosphorylated serine and threonine sites with false localization rates of 1% and 5.3%, respectively. We provide a tutorial on how to use the PeptideAtlas and associated tools to access this information. The C. albicans PeptideAtlas summary web page provides "Build overview", "PTM coverage", "Experiment contribution", and "Dataset contribution" information. The protein and peptide information can also be accessed via the Candida Genome Database via hyperlinks on each protein page. This allows users to peruse identified peptides, protein coverage, post-translational modifications (PTMs), and experiments identifying each protein. Given the value of understanding the PTM landscape in the sequence of each protein, a more detailed explanation of how to interpret and analyse PTM results is provided in the PeptideAtlas of this important pathogen. Candida albicans PeptideAtlas web page: https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/buildDetails?atlas_build_id=578.

Detecting differentially expressed genes from RNA-seq data using fuzzy clustering

A two-group comparison test is generally performed on RNA sequencing data to detect differentially expressed genes (DEGs). However, the accuracy of this method is low due to the small sample size. To address this, we propose a method using fuzzy clustering that artificially generates data with expression patterns similar to those of DEGs to identify genes that are highly likely to be classified into the same cluster as the initial cluster data. The proposed method is advantageous in that it does not perform any test. Furthermore, a certain level of accuracy can be maintained even when the sample size is biased, and we show that such a situation may improve the accuracy of the proposed method. We compared the proposed method with the conventional method using simulations. In the simulations, we changed the sample size and difference between the expression levels of group 1 and group 2 in the DEGs to obtain the desired accuracy of the proposed method. The results show that the proposed method is superior in all cases under the conditions simulated. We also show that the effect of the difference between group 1 and group 2 on the accuracy is more prominent when the sample size is biased.

Spatiotemporal analysis of mRNA-protein relationships enhances transcriptome-based developmental inference

Elucidating the complex relationships between mRNA and protein expression at high spatiotemporal resolution is critical for unraveling multilevel gene regulation and enhancing mRNA-based developmental analyses. In this study, we conduct a single-cell analysis of mRNA and protein expression of transcription factors throughout C. elegans embryogenesis. Initially, cellular co-presence of mRNA and protein is low, increasing to a medium-high level (73%) upon factoring in delayed protein synthesis and long-term protein persistence. These factors substantially affect mRNA-protein concordance, leading to potential inaccuracies in mRNA-reliant gene detection and specificity characterization. Building on the learned relationship, we infer protein presence from mRNA expression and demonstrate its utility in identifying tissue-specific genes and elucidating relationships between genes and cells. This approach facilitates identifying the role of sptf-1/SP7 in neuronal lineage development. Collectively, this study provides insights into gene expression dynamics during rapid embryogenesis and approaches for improving the efficacy of transcriptome-based developmental analyses.

Detection of the Arabidopsis Proteome and Its Post-translational Modifications and the Nature of the Unobserved (Dark) Proteome in PeptideAtlas

This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource (build 2023-10) providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected post-translational modifications (PTMs), and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18,267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins, and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome: the "dark" proteome. This dark proteome is highly enriched for E3 ligases, transcription factors, and for certain (e.g., CLE, IDA, PSY) but not other (e.g., THIONIN, CAP) signaling peptides families. A machine learning model trained on RNA expression data and protein properties predicts the probability that proteins will be detected. The model aids in discovery of proteins with short half-life (e.g., SIG1,3 and ERF-VII TFs) and for developing strategies to identify the missing proteins. PeptideAtlas is linked to TAIR, tracks in JBrowse, and several other community proteomics resources.

Mapping the Arabidopsis thaliana proteome in PeptideAtlas and the nature of the unobserved (dark) proteome; strategies towards a complete proteome

This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected PTMs, and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for building the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome - the 'dark' proteome. This dark proteome is highly enriched for certain ( e.g. CLE, CEP, IDA, PSY) but not other ( e.g. THIONIN, CAP,) signaling peptides families, E3 ligases, TFs, and other proteins with unfavorable physicochemical properties. A machine learning model trained on RNA expression data and protein properties predicts the probability for proteins to be detected. The model aids in discovery of proteins with short-half life ( e.g. SIG1,3 and ERF-VII TFs) and completing the proteome. PeptideAtlas is linked to TAIR, JBrowse, PPDB, SUBA, UniProtKB and Plant PTM Viewer.

Protein Abundance Prediction Through Machine Learning Methods

Proteins are responsible for most physiological processes, and their abundance provides crucial information for systems biology research. However, absolute protein quantification, as determined by mass spectrometry, still has limitations in capturing the protein pool. Protein abundance is impacted by translation kinetics, which rely on features of codons. In this study, we evaluated the effect of codon usage bias of genes on protein abundance. Notably, we observed differences regarding codon usage patterns between genes coding for highly abundant proteins and genes coding for less abundant proteins. Analysis of synonymous codon usage and evolutionary selection showed a clear split between the two groups. Our machine learning models predicted protein abundances from codon usage metrics with remarkable accuracy, achieving strong correlation with experimental data. Upon integration of the predicted protein abundance in enzyme-constrained genome-scale metabolic models, the simulated phenotypes closely matched experimental data, which demonstrates that our predictive models are valuable tools for systems metabolic engineering approaches.

A Trans-Omics Comparison Reveals Common Gene Expression Strategies in Four Model Organisms and Exposes Similarities and Differences between Them

The ultimate goal of gene expression regulation is on the protein level. However, because the amounts of mRNAs and proteins are controlled by their synthesis and degradation rates, the cellular amount of a given protein can be attained by following different strategies. By studying omics data for six expression variables (mRNA and protein amounts, plus their synthesis and decay rates), we previously demonstrated the existence of common expression strategies (CESs) for functionally related genes in the yeast Saccharomyces cerevisiae. Here we extend that study to two other eukaryotes: the yeast Schizosaccharomyces pombe and cultured human HeLa cells. We also use genomic data from the model prokaryote Escherichia coli as an external reference. We show that six-variable profiles (6VPs) can be constructed for every gene and that these 6VPs are similar for genes with similar functions in all the studied organisms. The differences in 6VPs between organisms can be used to establish their phylogenetic relationships. The analysis of the correlations among the six variables supports the hypothesis that most gene expression control occurs in actively growing organisms at the transcription rate level, and that translation plays a minor role. We propose that living organisms use CESs for the genes acting on the same physiological pathways, especially for those belonging to stable macromolecular complexes, but CESs have been modeled by evolution to adapt to the specific life circumstances of each organism.

Analysis of the S. pombe Meiotic Proteome Reveals a Switch from Anabolic to Catabolic Processes and Extensive Post-transcriptional Regulation

Meiotic progression in S. pombe is regulated by stage-specific gene expression and translation, changes in RNA stability, expression of anti-sense transcripts, and targeted proteolysis of regulatory proteins. We have used SILAC labeling to examine the relative levels of proteins in diploid S. pombe cells during meiosis. Among the 3,268 proteins quantified at all time points, the levels of 880 proteins changed at least 2-fold; the majority of proteins showed stepwise increases or decreases during the meiotic divisions, while some changed transiently. Overall, we observed reductions in proteins involved in anabolism and increases in proteins involved in catabolism. We also observed increases in the levels of proteins of the ESCRT-III complex and revealed a role for ESCRT-III components in chromosome segregation and spore formation. Correlation with studies of meiotic gene expression and ribosome occupancy reveals that many of the changes in steady-state protein levels are post-transcriptional.

Sensitive Quantitative Proteomics of Human Hematopoietic Stem and Progenitor Cells by Data-independent Acquisition Mass Spectrometry

Physiological processes in multicellular organisms depend on the function and interactions of specialized cell types operating in context. Some of these cell types are rare and thus obtainable only in minute quantities. For example, tissue-specific stem and progenitor cells are numerically scarce, but functionally highly relevant, and fulfill critical roles in development, tissue maintenance, and disease. Whereas low numbers of cells are routinely analyzed by genomics and transcriptomics, corresponding proteomic analyses have so far not been possible due to methodological limitations. Here we describe a sensitive and robust quantitative technique based on data-independent acquisition mass spectrometry. We quantified the proteome of sets of 25,000 human hematopoietic stem/multipotent progenitor cells (HSC/MPP) and three committed progenitor cell subpopulations of the myeloid differentiation pathway (common myeloid progenitors, megakaryocyte-erythrocyte progenitors, and granulocyte-macrophage progenitors), isolated by fluorescence-activated cell sorting from five healthy donors. On average, 5,851 protein groups were identified per sample. A subset of 4,131 stringently filtered protein groups was quantitatively compared across the 20 samples, defining unique signatures for each subpopulation. A comparison of proteomic and transcriptomic profiles indicated HSC/MPP-specific divergent regulation of biochemical functions such as telomerase maintenance and quiescence-inducing enzymes, including isocitrate dehydrogenases. These are essential for maintaining stemness and were detected at proteome, but not transcriptome, level. The method is equally applicable to almost any rare cell type, including healthy and cancer stem cells or physiologically and pathologically infiltrating cell populations. It thus provides essential new information toward the detailed biochemical understanding of cell development and functionality in health and disease.

A long noncoding (lnc)RNA governs expression of the phosphate transporter Pho84 in fission yeast and has cascading effects on the flanking prt lncRNA and pho1 genes

The expression of the phosphate transporter Pho84 in fission yeast Schizosaccharomyces pombe is repressed in phosphate-rich medium and induced during phosphate starvation. Two other phosphate-responsive genes in S. pombe (pho1 and tgp1) had been shown to be repressed in cis by transcription of a long noncoding (lnc) RNA from the upstream flanking gene, but whether pho84 expression is regulated in this manner is unclear. Here, we show that repression of pho84 is enforced by transcription of the SPBC8E4.02c locus upstream of pho84 to produce a lncRNA that we name prt2 ( pho-repressive transcript 2). We identify two essential elements of the prt2 promoter, a HomolD box and a TATA box, mutations of which inactivate the prt2 promoter and de-repress the downstream pho84 promoter under phosphate-replete conditions. We find that prt2 promoter inactivation also elicits a cascade effect on the adjacent downstream prt (lncRNA) and pho1 (acid phosphatase) genes, whereby increased pho84 transcription down-regulates prt lncRNA transcription and thereby de-represses pho1 Our results establish a unified model for the repressive arm of fission yeast phosphate homeostasis, in which transcription of prt2, prt, and nc-tgp1 lncRNAs interferes with the promoters of the flanking pho84, pho1, and tgp1 genes, respectively.

Tandem mass spectral libraries of peptides and their roles in proteomics research

Proteomics is a rapidly maturing field aimed at the high-throughput identification and quantification of all proteins in a biological system. The cornerstone of proteomic technology is tandem mass spectrometry of peptides resulting from the digestion of protein mixtures. The fragmentation pattern of each peptide ion is captured in its tandem mass spectrum, which enables its identification and acts as a fingerprint for the peptide. Spectral libraries are simply searchable collections of these fingerprints, which have taken on an increasingly prominent role in proteomic data analysis. This review describes the historical development of spectral libraries in proteomics, details the computational procedures behind library building and searching, surveys the current applications of spectral libraries, and discusses the outstanding challenges. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:634-648, 2017.

Ribosome reinitiation can explain length-dependent translation of messenger RNA

Models of mRNA translation usually presume that transcripts are linear; upon reaching the end of a transcript each terminating ribosome returns to the cytoplasmic pool before initiating anew on a different transcript. A consequence of linear models is that faster translation of a given mRNA is unlikely to generate more of the encoded protein, particularly at low ribosome availability. Recent evidence indicates that eukaryotic mRNAs are circularized, potentially allowing terminating ribosomes to preferentially reinitiate on the same transcript. Here we model the effect of ribosome reinitiation on translation and show that, at high levels of reinitiation, protein synthesis rates are dominated by the time required to translate a given transcript. Our model provides a simple mechanistic explanation for many previously enigmatic features of eukaryotic translation, including the negative correlation of both ribosome densities and protein abundance on transcript length, the importance of codon usage in determining protein synthesis rates, and the negative correlation between transcript length and both codon adaptation and 5' mRNA folding energies. In contrast to linear models where translation is largely limited by initiation rates, our model reveals that all three stages of translation-initiation, elongation, and termination/reinitiation-determine protein synthesis rates even at low ribosome availability.

Altered nucleocytoplasmic proteome and transcriptome distributions in an in vitro model of amyotrophic lateral sclerosis

Aberrant nucleocytoplasmic localization of proteins has been implicated in many neurodegenerative diseases. Evidence suggests that cytoplasmic mislocalization of nuclear proteins such as transactive response DNA-binding protein 43 (TDP-43) and fused in sarcoma (FUS) may be associated with neurotoxicity in amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration. This study investigated the changes in nucleocytoplasmic distributions of the proteome and transcriptome in an in vitro model of ALS. After subcellular fractionation of motor neuron-like cell lines expressing wild-type or G93A mutant hSOD1, quantitative mass spectrometry and next-generation RNA sequencing (RNA-seq) were performed for the nuclear and cytoplasmic compartments. A subset of the results was validated via immunoblotting. A total of 1,925 proteins were identified in either the nuclear or cytoplasmic fractions, and 32% of these proteins were quantified in both fractions. The nucleocytoplasmic distribution of 37 proteins was significantly changed in mutant cells with nuclear and cytoplasmic shifts in 13 and 24 proteins, respectively (p<0.05). The proteins shifted towards the nucleus were enriched regarding pathways of RNA transport and processing (Dhx9, Fmr1, Srsf3, Srsf6, Tra2b), whereas protein folding (Cct5, Cct7, Cct8), aminoacyl-tRNA biosynthesis (Farsb, Nars, Txnrd1), synaptic vesicle cycle (Cltc, Nsf), Wnt signalling (Cltc, Plcb3, Plec, Psmd3, Ruvbl1) and Hippo signalling (Camk2d, Plcb3, Ruvbl1) pathways were over-represented in the proteins shifted to the cytoplasm. A weak correlation between the changes in protein and mRNA levels was found only in the nucleus, where mRNA was relatively abundant in mutant cells. This study provides a comprehensive dataset of the nucleocytoplasmic distribution of the proteome and transcriptome in an in vitro model of ALS. An integrated analysis of the nucleocytoplasmic distribution of the proteome and transcriptome demonstrated multiple candidate pathways including RNA processing/transport and protein synthesis and folding that may be relevant to the pathomechanism of ALS.

Quantitative proteomics signature profiling based on network contextualization

Background

We present a network-based method, namely quantitative proteomic signature profiling (qPSP) that improves the biological content of proteomic data by converting protein expressions into hit-rates in protein complexes.

Results

We demonstrate, using two clinical proteomics datasets, that qPSP produces robust discrimination between phenotype classes (e.g. normal vs. disease) and uncovers phenotype-relevant protein complexes. Regardless of acquisition paradigm, comparisons of qPSP against conventional methods (e.g. t-test or hypergeometric test) demonstrate that it produces more stable and consistent predictions, even at small sample size. We show that qPSP is theoretically robust to noise, and that this robustness to noise is also observable in practice. Comparative analysis of hit-rates and protein expressions in significant complexes reveals that hit-rates are a useful means of summarizing differential behavior in a complex-specific manner.

Conclusions

Given qPSP’s ability to discriminate phenotype classes even at small sample sizes, high robustness to noise, and better summary statistics, it can be deployed towards analysis of highly heterogeneous clinical proteomics data.

Reviewers

This article was reviewed by Frank Eisenhaber and Sebastian Maurer-Stroh.

Open peer review

Reviewed by Frank Eisenhaber and Sebastian Maurer-Stroh.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-015-0098-x) contains supplementary material, which is available to authorized users.

A comprehensive Candida albicans PeptideAtlas build enables deep proteome coverage

To provide new and expanded proteome documentation of the opportunistically pathogen Candida albicans, we have developed new protein extraction and analysis routines to provide a new, extended and enhanced version of the C. albicans PeptideAtlas. Two new datasets, resulting from experiments consisting of exhaustive subcellular fractionations and different growing conditions, plus two additional datasets from previous experiments on the surface and the secreted proteomes, have been incorporated to increase the coverage of the proteome. High resolution precursor mass spectrometry (MS) and ion trap tandem MS spectra were analyzed with three different search engines using a database containing allele-specific sequences. This approach, novel for a large-scale C. albicans proteomics project, was combined with the post-processing and filtering implemented in the Trans Proteomic Pipeline consistently used in the PeptideAtlas project and resulted in 49,372 additional peptides (3-fold increase) and 1630 more proteins (1.6-fold increase) identified in the new C. albicans PeptideAtlas with respect to the previous build. A total of 71,310 peptides and 4174 canonical (minimal non-redundant set) proteins (4115 if one protein per pair of alleles is considered) were identified representing 66% of the 6218 proteins in the predicted proteome. This makes the new PeptideAtlas build the most comprehensive C. albicans proteomics resource available and the only large-scale one with detections of individual alleles.

Deletion of Genes Encoding Arginase Improves Use of "Heavy" Isotope-Labeled Arginine for Mass Spectrometry in Fission Yeast

The use of "heavy" isotope-labeled arginine for stable isotope labeling by amino acids in cell culture (SILAC) mass spectrometry in the fission yeast Schizosaccharomyces pombe is hindered by the fact that under normal conditions, arginine is extensively catabolized in vivo, resulting in the appearance of "heavy"-isotope label in several other amino acids, most notably proline, but also glutamate, glutamine and lysine. This "arginine conversion problem" significantly impairs quantification of mass spectra. Previously, we developed a method to prevent arginine conversion in fission yeast SILAC, based on deletion of genes involved in arginine catabolism. Here we show that although this method is indeed successful when (13)C6-arginine (Arg-6) is used for labeling, it is less successful when (13)C6(15)N4-arginine (Arg-10), a theoretically preferable label, is used. In particular, we find that with this method, "heavy"-isotope label derived from Arg-10 is observed in amino acids other than arginine, indicating metabolic conversion of Arg-10. Arg-10 conversion, which severely complicates both MS and MS/MS analysis, is further confirmed by the presence of (13)C5(15)N2-arginine (Arg-7) in arginine-containing peptides from Arg-10-labeled cells. We describe how all of the problems associated with the use of Arg-10 can be overcome by a simple modification of our original method. We show that simultaneous deletion of the fission yeast arginase genes car1+ and aru1+ prevents virtually all of the arginine conversion that would otherwise result from the use of Arg-10. This solution should enable a wider use of heavy isotope-labeled amino acids in fission yeast SILAC.

Nic1 inactivation enables stable isotope labeling with 13C615N4-arginine in Schizosaccharomyces pombe

Stable Isotope Labeling by Amino Acids (SILAC) is a commonly used method in quantitative proteomics. Because of compatibility with trypsin digestion, arginine and lysine are the most widely used amino acids for SILAC labeling. We observed that Schizosaccharomyces pombe (fission yeast) cannot be labeled with a specific form of arginine, (13)C(6) (15)N(4)-arginine (Arg-10), which limits the exploitation of SILAC technology in this model organism. We hypothesized that in the fission yeast the guanidinium group of (13)C(6) (15)N(4)-arginine is catabolized by arginase and urease activity to (15)N1-labeled ammonia that is used as a precursor for general amino acid biosynthesis. We show that disruption of Ni(2+)-dependent urease activity, through deletion of the sole Ni(2+) transporter Nic1, blocks this recycling in ammonium-supplemented EMMG medium to enable (13)C(6) (15)N(4)-arginine labeling for SILAC strategies in S. pombe. Finally, we employed Arg-10 in a triple-SILAC experiment to perform quantitative comparison of G1 + S, M, and G2 cell cycle phases in S. pombe.

Improvement of shotgun proteomics in the negative mode by carbamylation of peptides and ultraviolet photodissociation mass spectrometry

Although acidic peptides compose a substantial portion of many proteomes, their less efficient ionization during positive polarity electrospray ionization (ESI) impedes their detection in bottom-up mass spectrometry workflows. We have implemented a derivatization strategy based on carbamylation which converts basic amine sites (Lys, N-termini) to less basic amides for enhanced analysis in the negative mode. Ultraviolet photodissociation (UVPD) is used to analyze the resulting peptide anions, as demonstrated for tryptic peptides from bovine serum albumin and Halobacterium salinarum in a high throughput liquid chromatography/tandem mass spectrometry (LC/MS/MS) mode. LC/UVPD-MS of a carbamylated H. salinarum digest resulted in 45% more identified peptides and 25% more proteins compared to the unmodified digest analyzed in the negative mode.

Natural genetic variation impacts expression levels of coding, non-coding, and antisense transcripts in fission yeast

Our current understanding of how natural genetic variation affects gene expression beyond well-annotated coding genes is still limited. The use of deep sequencing technologies for the study of expression quantitative trait loci (eQTLs) has the potential to close this gap. Here, we generated the first recombinant strain library for fission yeast and conducted an RNA-seq-based QTL study of the coding, non-coding, and antisense transcriptomes. We show that the frequency of distal effects (trans-eQTLs) greatly exceeds the number of local effects (cis-eQTLs) and that non-coding RNAs are as likely to be affected by eQTLs as protein-coding RNAs. We identified a genetic variation of swc5 that modifies the levels of 871 RNAs, with effects on both sense and antisense transcription, and show that this effect most likely goes through a compromised deposition of the histone variant H2A.Z. The strains, methods, and datasets generated here provide a rich resource for future studies.

Absolute proteome and phosphoproteome dynamics during the cell cycle of Schizosaccharomyces pombe (Fission Yeast)

To quantify cell cycle-dependent fluctuations on a proteome-wide scale, we performed integrative analysis of the proteome and phosphoproteome during the four major phases of the cell cycle in Schizosaccharomyces pombe. In highly synchronized cells, we identified 3753 proteins and 3682 phosphorylation events and relatively quantified 65% of the data across all phases. Quantitative changes during the cell cycle were infrequent and weak in the proteome but prominent in the phosphoproteome. Protein phosphorylation peaked in mitosis, where the median phosphorylation site occupancy was 44%, about 2-fold higher than in other phases. We measured copy numbers of 3178 proteins, which together with phosphorylation site stoichiometry enabled us to estimate the absolute amount of protein-bound phosphate, as well as its change across the cell cycle. Our results indicate that 23% of the average intracellular ATP is utilized by protein kinases to phosphorylate their substrates to drive regulatory processes during cell division. Accordingly, we observe that phosphate transporters and phosphate-metabolizing enzymes are phosphorylated and therefore likely to be regulated in mitosis.

Daily rhythms in the cyanobacterium synechococcus elongatus probed by high-resolution mass spectrometry-based proteomics reveals a small defined set of cyclic proteins

Circadian rhythms are self-sustained and adjustable cycles, typically entrained with light/dark and/or temperature cycles. These rhythms are present in animals, plants, fungi, and several bacteria. The central mechanism behind these "pacemakers" and the connection to the circadian regulated pathways are still poorly understood. The circadian rhythm of the cyanobacterium Synechococcus elongatus PCC 7942 (S. elongatus) is highly robust and controlled by only three proteins, KaiA, KaiB, and KaiC. This central clock system has been extensively studied functionally and structurally and can be reconstituted in vitro. These characteristics, together with a relatively small genome (2.7 Mbp), make S. elongatus an ideal model system for the study of circadian rhythms. Different approaches have been used to reveal the influence of the central S. elongatus clock on rhythmic gene expression, rhythmic mRNA abundance, rhythmic DNA topology changes, and cell division. However, a global analysis of its proteome dynamics has not been reported yet. To uncover the variation in protein abundances during 48 h under light and dark cycles (12:12 h), we used quantitative proteomics, with TMT 6-plex isobaric labeling. We queried the S. elongatus proteome at 10 different time points spanning a single 24-h period, leading to 20 time points over the full 48-h period. Employing multidimensional separation and high-resolution mass spectrometry, we were able to find evidence for a total of 82% of the S. elongatus proteome. Of the 1537 proteins quantified over the time course of the experiment, only 77 underwent significant cyclic variations. Interestingly, our data provide evidence for in- and out-of-phase correlation between mRNA and protein levels for a set of specific genes and proteins. As a range of cyclic proteins are functionally not well annotated, this work provides a resource for further studies to explore the role of these proteins in the cyanobacterial circadian rhythm.

Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells

Mass spectrometry (MS)-based proteomics typically employs multistep sample-preparation workflows that are subject to sample contamination and loss. We report an in-StageTip method for performing sample processing, from cell lysis through elution of purified peptides, in a single, enclosed volume. This robust and scalable method largely eliminates contamination or loss. Peptides can be eluted in several fractions or in one step for single-run proteome analysis. In one day, we obtained the largest proteome coverage to date for budding and fission yeast, and found that protein copy numbers in these cells were highly correlated (R(2) = 0.78). Applying the in-StageTip method to quadruplicate measurements of a human cell line, we obtained copy-number estimates for 9,667 human proteins and observed excellent quantitative reproducibility between replicates (R(2) = 0.97). The in-StageTip method is straightforward and generally applicable in biological or clinical applications.

A Candida albicans PeptideAtlas

Candida albicans public proteomic datasets, though growing steadily in the last few years, still have a very limited presence in online repositories. We report here the creation of a C. albicans PeptideAtlas comprising near 22,000 distinct peptides at a 0.24% False Discovery Rate (FDR) that account for over 2500 canonical proteins at a 1.2% FDR. Based on data from 16 experiments, we attained coverage of 41% of the C. albicans open reading frame sequences (ORFs) in the database used for the searches. This PeptideAtlas provides several useful features, including comprehensive protein and peptide-centered search capabilities and visualization tools that establish a solid basis for the study of basic biological mechanisms key to virulence and pathogenesis such as dimorphism, adherence, and apoptosis. Further, it is a valuable resource for the selection of candidate proteotypic peptides for targeted proteomic experiments via Selected Reaction Monitoring (SRM) or SWATH-MS.

BIOLOGICAL SIGNIFICANCE

This C. albicans PeptideAtlas resolves the previous absence of fungal pathogens in the PeptideAtlas project. It represents the most extensive characterization of the proteome of this fungus that exists up to the current date, including evidence for uncharacterized ORFs. Through its web interface, PeptideAtlas supports the study of interesting proteins related to basic biological mechanisms key to virulence such as apoptosis, dimorphism and adherence. It also provides a valuable resource to select candidate proteotypic peptides for future (SRM) targeted proteomic experiments. This article is part of a Special Issue entitled: Trends in Microbial Proteomics.