One-hour proteome analysis in yeast

Mixed-mode ion exchange-based integrated proteomics technology for fast and deep plasma proteome profiling

Plasma proteome profiling by LC-MS based proteomics has drawn great attention recently for biomarker discovery from blood liquid biopsy. Due to standard multi-step sample preparation could potentially cause plasma protein degradation and analysis variation, integrated proteomics sample preparation technologies became promising solution towards this end. Here, we developed a fully integrated proteomics sample preparation technology for both fast and deep plasma proteome profiling under its native pH. All the sample preparation steps, including protein digestion and two-dimensional fractionation by both mixed-mode ion exchange and high-pH reversed phase mechanism were integrated into one spintip device for the first time. The mixed-mode ion exchange beads design achieved the sample loading at neutral pH and protein digestion within 30 min. Potential sample loss and protein degradation by pH changing could be voided. 1 μL of plasma sample with depletion of high abundant proteins was processed by the developed technology with 12 equally distributed fractions and analyzed with 12 h of LC-MS gradient time, resulting in the identification of 862 proteins. The combination of the Mixed-mode-SISPROT and data-independent MS method achieved fast plasma proteome profiling in 2 h with high identification overlap and quantification precision for a proof-of-concept study of plasma samples from 5 healthy donors. We expect that the Mixed-mode-SISPROT become a generally applicable sample preparation technology for clinical oriented plasma proteome profiling.

Phosphopeptidomics Reveals Differential Phosphorylation States and Novel SxE Phosphosite Motifs of Neuropeptides in Dense Core Secretory Vesicles

Neuropeptides are vital for cell-cell communication and function in the regulation of the nervous and endocrine systems. They are generated by post-translational modification (PTM) steps resulting in small active peptides generated from prohormone precursors. Phosphorylation is a significant PTM for the bioactivity of neuropeptides. From the known diversity of distinct neuropeptide functions, it is hypothesized that the extent of phosphorylation varies among different neuropeptides. To assess this hypothesis, neuropeptide-containing dense core secretory vesicles from bovine adrenal medullary chromaffin cells were subjected to global phosphopeptidomics analyses by liquid chromatography (LC)-mass spectrometry (MS/MS). Phosphopeptides were identified directly by LC-MS/MS and indirectly by phosphatase treatment followed by LC-MS/MS. The data identified numerous phosphorylated peptides derived from neuropeptide precursors such as chromogranins, secretogranins, proenkephalin and pro-NPY. Phosphosite occupancies were observed at high and low levels among identified peptides and many of the high occupancy phosphopeptides represent prohormone-derived peptides with currently unknown bioactivities. Peptide sequence analyses demonstrated SxE as the most prevalent phosphorylation site motif, corresponding to phosphorylation sites of the Fam20C protein kinase known to be present in the secretory pathway. The range of high to low phosphosite occupancies for neuropeptides demonstrates cellular regulation of neuropeptide phosphorylation. Graphical Abstract ᅟ.

Accurate, Sensitive, and Precise Multiplexed Proteomics Using the Complement Reporter Ion Cluster

Quantitative analysis of proteomes across multiple time points, organelles, and perturbations is essential for understanding both fundamental biology and disease states. The development of isobaric tags (e.g., TMT) has enabled the simultaneous measurement of peptide abundances across several different conditions. These multiplexed approaches are promising in principle because of advantages in throughput and measurement quality. However, in practice, existing multiplexing approaches suffer from key limitations. In its simple implementation (TMT-MS2), measurements are distorted by chemical noise leading to poor measurement accuracy. The current state-of-the-art (TMT-MS3) addresses this but requires specialized quadrupole-iontrap-Orbitrap instrumentation. The complement reporter ion approach (TMTc) produces high accuracy measurements and is compatible with many more instruments, like quadrupole-Orbitraps. However, the required deconvolution of the TMTc cluster leads to poor measurement precision. Here, we introduce TMTc+, which adds the modeling of the MS2-isolation step into the deconvolution algorithm. The resulting measurements are comparable in precision to TMT-MS3/MS2. The improved duty cycle and lower filtering requirements make TMTc+ more sensitive than TMT-MS3 and comparable with TMT-MS2. At the same time, unlike TMT-MS2, TMTc+ is exquisitely able to distinguish signal from chemical noise even outperforming TMT-MS3. Lastly, we compare TMTc+ to quantitative label-free proteomics of total HeLa lysate and find that TMTc+ quantifies 7.8k versus 3.9k proteins in a 5-plex sample. At the same time, the median coefficient of variation improves from 13% to 4%. Thus, TMTc+ advances quantitative proteomics by enabling accurate, sensitive, and precise multiplexed experiments on more commonly used instruments.

Nanoliter-Scale Oil-Air-Droplet Chip-Based Single Cell Proteomic Analysis

Single cell proteomic analysis provides crucial information on cellular heterogeneity in biological systems. Herein, we describe a nanoliter-scale oil-air-droplet (OAD) chip for achieving multistep complex sample pretreatment and injection for single cell proteomic analysis in the shotgun mode. By using miniaturized stationary droplet microreaction and manipulation techniques, our system allows all sample pretreatment and injection procedures to be performed in a nanoliter-scale droplet with minimum sample loss and a high sample injection efficiency (>99%), thus substantially increasing the analytical sensitivity for single cell samples. We applied the present system in the proteomic analysis of 100 ± 10, 50 ± 5, 10, and 1 HeLa cell(s), and protein IDs of 1360, 612, 192, and 51 were identified, respectively. The OAD chip-based system was further applied in single mouse oocyte analysis, with 355 protein IDs identified at the single oocyte level, which demonstrated its special advantages of high enrichment of sequence coverage, hydrophobic proteins, and enzymatic digestion efficiency over the traditional in-tube system.

Islet proteomics reveals genetic variation in dopamine production resulting in altered insulin secretion

The mouse is a critical model in diabetes research, but most research in mice has been limited to a small number of mouse strains and limited genetic variation. Using the eight founder strains and both sexes of the Collaborative Cross (C57BL/6J (B6), A/J, 129S1/SvImJ (129), NOD/ShiLtJ (NOD), NZO/HILtJ (NZO), PWK/PhJ (PWK), WSB/EiJ (WSB), and CAST/EiJ (CAST)), we investigated the genetic dependence of diabetes-related metabolic phenotypes and insulin secretion. We found that strain background is associated with an extraordinary range in body weight, plasma glucose, insulin, triglycerides, and insulin secretion. Our whole-islet proteomic analysis of the eight mouse strains demonstrates that genetic background exerts a strong influence on the islet proteome that can be linked to the differences in diabetes-related metabolic phenotypes and insulin secretion. We computed protein modules consisting of highly correlated proteins that enrich for biological pathways and provide a searchable database of the islet protein expression profiles. To validate the data resource, we identified tyrosine hydroxylase (Th), a key enzyme in catecholamine synthesis, as a protein that is highly expressed in β-cells of PWK and CAST islets. We show that CAST islets synthesize elevated levels of dopamine, which suppresses insulin secretion. Prior studies, using only the B6 strain, concluded that adult mouse islets do not synthesize l-3,4-dihydroxyphenylalanine (l-DOPA), the product of Th and precursor of dopamine. Thus, the choice of the CAST strain, guided by our islet proteomic survey, was crucial for these discoveries. In summary, we provide a valuable data resource to the research community, and show that proteomic analysis identified a strain-specific pathway by which dopamine synthesized in β-cells inhibits insulin secretion.

Characterisation of the biological response of Saccharomyces cerevisiae to the loss of an allele of the eukaryotic initiation factor 4A

The translation initiation machinery is emerging as an important target for therapeutic intervention, with potential in the treatment of cancer, viral infections, and muscle wasting. Amongst the targets for pharmacological control of translation initiation is the eukaryotic initiation factor 4A (eIF4A), an RNA helicase that is essential for cap-dependent translation initiation. We set out to explore the system-wide impact of a reduction of functional eIF4A. To this end, we investigated the effect of deletion of TIF1, one of the duplicate genes that produce eIF4A in yeast, through synthetic genetic array interactions and system-wide changes in GFP-tagged protein abundances. We show that there is a biological response to deletion of the TIF1 gene that extends through the proteostasis network. Effects of the deletion are apparent in processes as distributed as chromatin remodelling, ribosome biogenesis, amino acid metabolism, and protein trafficking. The results from this study identify protein complexes and pathways that will make ideal targets for combination therapies with eIF4A inhibitors.

Multiplexed proteome analysis with neutron-encoded stable isotope labeling in cells and mice

We describe a protocol for multiplexed proteomic analysis using neutron-encoded (NeuCode) stable isotope labeling of amino acids in cells (SILAC) or mice (SILAM). This method currently enables simultaneous comparison of up to nine treatment and control proteomes. Another important advantage over traditional SILAC/SILAM is that shorter labeling times are required. Exploiting the small mass differences that correspond to subtle differences in the neutron-binding energies of different isotopes, the amino acids used in NeuCode SILAC/SILAM differ in mass by just a few milliDaltons. Isotopologs of lysine are introduced into cells or mammals, via the culture medium or diet, respectively, to metabolically label the proteome. Labeling time is ∼2 weeks for cultured cells and 3-4 weeks for mammals. The proteins are then extracted, relevant samples are combined, and these are enzymatically digested with lysyl endopeptidase (Lys-C). The resultant peptides are chromatographically separated and then mass analyzed. During mass spectrometry (MS) data acquisition, high-resolution MS¹ spectra (≥240,000 resolving power at m/z = 400) reveal the embedded isotopic signatures, enabling relative quantification, while tandem mass spectra, collected at lower resolutions, provide peptide identities. Both types of spectra are processed using NeuCode-enabled MaxQuant software. In total, the approximate completion time for the protocol is 3-5 weeks.

Improved Precursor Characterization for Data-Dependent Mass Spectrometry

Modern ion trap mass spectrometers are capable of collecting up to 60 tandem MS (MS/MS) scans per second, in theory providing acquisition speeds that can sample every eluting peptide precursor presented to the MS system. In practice, however, the precursor sampling capacity enabled by these ultrafast acquisition rates is often underutilized due to a host of reasons (e.g., long injection times and wide analyzer mass ranges). One often overlooked reason for this underutilization is that the instrument exhausts all the peptide features it identifies as suitable for MS/MS fragmentation. Highly abundant features can prevent annotation of lower abundance precursor ions that occupy similar mass-to-charge (m/z) space, which ultimately inhibits the acquisition of an MS/MS event. Here, we present an advanced peak determination (APD) algorithm that uses an iterative approach to annotate densely populated m/z regions to increase the number of peptides sampled during data-dependent LC-MS/MS analyses. The APD algorithm enables nearly full utilization of the sampling capacity of a quadrupole-Orbitrap-linear ion trap MS system, which yields up to a 40% increase in unique peptide identifications from whole cell HeLa lysates (approximately 53 000 in a 90 min LC-MS/MS analysis). The APD algorithm maintains improved peptide and protein identifications across several modes of proteomic data acquisition, including varying gradient lengths, different degrees of prefractionation, peptides derived from multiple proteases, and phosphoproteomic analyses. Additionally, the use of APD increases the number of peptides characterized per protein, providing improved protein quantification. In all, the APD algorithm increases the number of detectable peptide features, which maximizes utilization of the high MS/MS capacities and significantly improves sampling depth and identifications in proteomic experiments.

Multi-omic Mitoprotease Profiling Defines a Role for Oct1p in Coenzyme Q Production

Mitoproteases are becoming recognized as key regulators of diverse mitochondrial functions, although their direct substrates are often difficult to discern. Through multi-omic profiling of diverse Saccharomyces cerevisiae mitoprotease deletion strains, we predicted numerous associations between mitoproteases and distinct mitochondrial processes. These include a strong association between the mitochondrial matrix octapeptidase Oct1p and coenzyme Q (CoQ) biosynthesis-a pathway essential for mitochondrial respiration. Through Edman sequencing and in vitro and in vivo biochemistry, we demonstrated that Oct1p directly processes the N terminus of the CoQ-related methyltransferase, Coq5p, which markedly improves its stability. A single mutation to the Oct1p recognition motif in Coq5p disrupted its processing in vivo, leading to CoQ deficiency and respiratory incompetence. This work defines the Oct1p processing of Coq5p as an essential post-translational event for proper CoQ production. Additionally, our data visualization tool enables efficient exploration of mitoprotease profiles that can serve as the basis for future mechanistic investigations.

An integrative strategy to identify the entire protein coding potential of prokaryotic genomes by proteogenomics

Accurate annotation of all protein-coding sequences (CDSs) is an essential prerequisite to fully exploit the rapidly growing repertoire of completely sequenced prokaryotic genomes. However, large discrepancies among the number of CDSs annotated by different resources, missed functional short open reading frames (sORFs), and overprediction of spurious ORFs represent serious limitations. Our strategy toward accurate and complete genome annotation consolidates CDSs from multiple reference annotation resources, ab initio gene prediction algorithms and in silico ORFs (a modified six-frame translation considering alternative start codons) in an integrated proteogenomics database (iPtgxDB) that covers the entire protein-coding potential of a prokaryotic genome. By extending the PeptideClassifier concept of unambiguous peptides for prokaryotes, close to 95% of the identifiable peptides imply one distinct protein, largely simplifying downstream analysis. Searching a comprehensive Bartonella henselae proteomics data set against such an iPtgxDB allowed us to unambiguously identify novel ORFs uniquely predicted by each resource, including lipoproteins, differentially expressed and membrane-localized proteins, novel start sites and wrongly annotated pseudogenes. Most novelties were confirmed by targeted, parallel reaction monitoring mass spectrometry, including unique ORFs and single amino acid variations (SAAVs) identified in a re-sequenced laboratory strain that are not present in its reference genome. We demonstrate the general applicability of our strategy for genomes with varying GC content and distinct taxonomic origin. We release iPtgxDBs for B. henselae, Bradyrhizobium diazoefficiens and Escherichia coli and the software to generate both proteogenomics search databases and integrated annotation files that can be viewed in a genome browser for any prokaryote.

Identification of RNA-binding domains of RNA-binding proteins in cultured cells on a system-wide scale with RBDmap

This protocol is an extension to: Nat. Protoc. 8, 491-500 (2013); doi:10.1038/nprot.2013.020; published online 14 February 2013RBDmap is a method for identifying, in a proteome-wide manner, the regions of RNA-binding proteins (RBPs) engaged in native interactions with RNA. In brief, cells are irradiated with UV light to induce protein-RNA cross-links. Following stringent denaturing washes, the resulting covalently linked protein-RNA complexes are purified with oligo(dT) magnetic beads. After elution, RBPs are subjected to partial proteolysis, in which the protein regions still bound to the RNA and those released to the supernatant are separated by a second oligo(dT) selection. After sample preparation and mass-spectrometric analysis, peptide intensity ratios between the RNA-bound and released fractions are used to determine the RNA-binding regions. As a Protocol Extension, this article describes an adaptation of an existing Protocol and offers additional applications. The earlier protocol (for the RNA interactome capture method) describes how to identify the active RBPs in cultured cells, whereas this Protocol Extension also enables the identification of the RNA-binding domains of RBPs. The experimental workflow takes 1 week plus 2 additional weeks for proteomics and data analysis. Notably, RBDmap presents numerous advantages over classic methods for determining RNA-binding domains: it produces proteome-wide, high-resolution maps of the protein regions contacting the RNA in a physiological context and can be adapted to different biological systems and conditions. Because RBDmap relies on the isolation of polyadenylated RNA via oligo(dT), it will not provide RNA-binding information on proteins interacting exclusively with nonpolyadenylated transcripts. Applied to HeLa cells, RBDmap uncovered 1,174 RNA-binding sites in 529 proteins, many of which were previously unknown.

pGlyco 2.0 enables precision N-glycoproteomics with comprehensive quality control and one-step mass spectrometry for intact glycopeptide identification

The precise and large-scale identification of intact glycopeptides is a critical step in glycoproteomics. Owing to the complexity of glycosylation, the current overall throughput, data quality and accessibility of intact glycopeptide identification lack behind those in routine proteomic analyses. Here, we propose a workflow for the precise high-throughput identification of intact N-glycopeptides at the proteome scale using stepped-energy fragmentation and a dedicated search engine. pGlyco 2.0 conducts comprehensive quality control including false discovery rate evaluation at all three levels of matches to glycans, peptides and glycopeptides, improving the current level of accuracy of intact glycopeptide identification. The N-glycoproteome of samples metabolically labeled with ¹⁵N/¹³C were analyzed quantitatively and utilized to validate the glycopeptide identification, which could be used as a novel benchmark pipeline to compare different search engines. Finally, we report a large-scale glycoproteome dataset consisting of 10,009 distinct site-specific N-glycans on 1988 glycosylation sites from 955 glycoproteins in five mouse tissues.

Protein glycosylation is a heterogeneous post-translational modification that generates greater proteomic diversity that is difficult to analyze. Here the authors describe pGlyco 2.0, a workflow for the precise one step identification of intact N-glycopeptides at the proteome scale.

Implementation of Activated Ion Electron Transfer Dissociation on a Quadrupole-Orbitrap-Linear Ion Trap Hybrid Mass Spectrometer

Using concurrent IR photoactivation during electron transfer dissociation (ETD) reactions, i.e., activated ion ETD (AI-ETD), significantly increases dissociation efficiency resulting in improved overall performance. Here we describe the first implementation of AI-ETD on a quadrupole-Orbitrap-quadrupole linear ion trap (QLT) hybrid MS system (Orbitrap Fusion Lumos) and demonstrate the substantial benefits it offers for peptide characterization. First, we show that AI-ETD can be implemented in a straightforward manner by fastening the laser and guiding optics to the instrument chassis itself, making alignment with the trapping volume of the QLT simple and robust. We then characterize the performance of AI-ETD using standard peptides in addition to a complex mixtures of tryptic peptides using LC-MS/MS, showing not only that AI-ETD can nearly double the identifications achieved with ETD alone but also that it outperforms the other available supplemental activation methods (ETcaD and EThcD). Finally, we introduce a new activation scheme called AI-ETD+ that combines AI-ETD in the high pressure cell of the QLT with a short infrared multiphoton dissociation (IRMPD) activation in the low-pressure cell. This reaction scheme introduces no addition time to the scan duty cycle but generates MS/MS spectra rich in b/y-type and c/z^•-type product ions. The extensive generation of fragment ions in AI-ETD+ substantially increases peptide sequence coverage while also improving peptide identifications over all other ETD methods, making it a valuable new tool for hybrid fragmentation approaches.

A Comprehensive Guide for Performing Sample Preparation and Top-Down Protein Analysis

Methodologies for the global analysis of proteins in a sample, or proteome analysis, have been available since 1975 when Patrick O′Farrell published the first paper describing two-dimensional gel electrophoresis (2D-PAGE). This technique allowed the resolution of single protein isoforms, or proteoforms, into single ‘spots’ in a polyacrylamide gel, allowing the quantitation of changes in a proteoform′s abundance to ascertain changes in an organism′s phenotype when conditions change. In pursuit of the comprehensive profiling of the proteome, significant advances in technology have made the identification and quantitation of intact proteoforms from complex mixtures of proteins more routine, allowing analysis of the proteome from the ‘Top-Down’. However, the number of proteoforms detected by Top-Down methodologies such as 2D-PAGE or mass spectrometry has not significantly increased since O’Farrell’s paper when compared to Bottom-Up, peptide-centric techniques. This article explores and explains the numerous methodologies and technologies available to analyse the proteome from the Top-Down with a strong emphasis on the necessity to analyse intact proteoforms as a better indicator of changes in biology and phenotype. We arrive at the conclusion that the complete and comprehensive profiling of an organism′s proteome is still, at present, beyond our reach but the continuing evolution of protein fractionation techniques and mass spectrometry brings comprehensive Top-Down proteome profiling closer.

Direct digestion of living cells via a gel-based strategy for mass spectrometric analysis

A novel sample preparation method was established for proteomic analysis, during which living cells were absorbed into vacuum-dried polyacrylamide gel and directly digested into peptides for subsequent LC-MS/MS assays. As a consequence, both of the steps for cell lysis and protein extraction involved in a conventional digestion method were skipped.

Proteome Speciation by Mass Spectrometry: Characterization of Composite Protein Mixtures in Milk Replacers

The ability of tandem mass spectrometry to determine the primary structure of proteolytic peptides can be exploited to trace back the organisms from which the corresponding proteins were extracted. This information can be important when food products, such as protein powders, can be supplemented with lower-quality starting materials. In order to dissect the origin of proteinaceous material composing a given unknown mixture, a two-step database search strategy for bottom-up nanoscale liquid chromatography-tandem mass spectrometry (nanoLC-MS/MS) data was implemented. A single nanoLC-MS/MS analysis was sufficient not only to determine the qualitative composition of the mixtures under examination, but also to assess the relative percent composition of the various proteomes, if dedicated calibration curves were previously generated. The approach of two-step database search for qualitative analysis and proteome total ion current (pTIC) calculation for quantitative analysis was applied to several binary and ternary mixtures which mimic the composition of milk replacers typically used in calf feeding.

Multi-omics Evidence for Inheritance of Energy Pathways in Red Blood Cells

Each year over 90 million units of blood are transfused worldwide. Our dependence on this blood supply mandates optimized blood management and storage. During storage, red blood cells undergo degenerative processes resulting in altered metabolic characteristics which may make blood less viable for transfusion. However, not all stored blood spoils at the same rate, a difference that has been attributed to variable rates of energy usage and metabolism in red blood cells. Specific metabolite abundances are heritable traits; however, the link between heritability of energy metabolism and red blood cell storage profiles is unclear. Herein we performed a comprehensive metabolomics and proteomics study of red blood cells from 18 mono- and di-zygotic twin pairs to measure heritability and identify correlations with ATP and other molecular indices of energy metabolism. Without using affinity-based hemoglobin depletion, our work afforded the deepest multi-omic characterization of red blood cell membranes to date (1280 membrane proteins and 330 metabolites), with 119 membrane protein and 148 metabolite concentrations found to be over 30% heritable. We demonstrate a high degree of heritability in the concentration of energy metabolism metabolites, especially glycolytic metabolites. In addition to being heritable, proteins and metabolites involved in glycolysis and redox metabolism are highly correlated, suggesting that crucial energy metabolism pathways are inherited en bloc at distinct levels. We conclude that individuals can inherit a phenotype composed of higher or lower concentrations of these proteins together. This can result in vastly different red blood cells storage profiles which may need to be considered to develop precise and individualized storage options. Beyond guiding proper blood storage, this intimate link in heritability between energy and redox metabolism pathways may someday prove useful in determining the predisposition of an individual toward metabolic diseases.

Ethylene glycol improves electrospray ionization efficiency in bottom-up proteomics

Liquid chromatography coupled online to nano-electrospray ionization (nESI) tandem mass spectrometry is the analytical workhorse in the field of proteome research. Dimethyl sulfoxide (DMSO) was recently shown to improve nESI efficiency by a factor of three to ten thus improving the sensitivity and coverage of proteomic experiments. However, relatively few investigations into which solvent additives promote nESI response have been performed at a proteomic scale. Here, we systematically evaluated the concept by screening about 30 compounds with various physico-chemical properties. Detailed further analysis showed that ethylene glycol performed similarly to DMSO and the results indicate that enhancing the nESI response of peptides by simple solvent additives is a valid and promising approach. Ethylene glycol may serve as a viable alternative to DMSO in applications where DMSO has disadvantages. In keeping with nESI theory, the key properties of an effective solvent additive for proteomic applications are a boiling point higher than water, low surface tension, and preferably high polarity for reversed phase LC-MS/MS applications. Graphical Abstract Ethylene glycol substantially improves peptide ionization.

Reannotation of Genomes by Means of Proteomics Data

Omics approaches have become popular in biology as powerful discovery tools, and currently gain in interest for diagnostic applications. Establishing the accurate genome sequence of any organism is easy, but the outcome of its annotation by means of automatic pipelines remains imprecise. Some protein-encoding genes may be missed as soon as they are specific and poorly conserved in a given taxon, while important to explain the specific traits of the organism. Translational starts are also poorly predicted in a relatively important number of cases, thus impacting the protein sequence database used in proteomics, comparative genomics, and systems biology. The use of high-throughput proteomics data to improve genome annotation is an attractive option to obtain a more comprehensive molecular picture of a given organism. Here, protocols for reannotating prokaryote genomes are described based on shotgun proteomics and derivatization of protein N-termini with a positively charged reagent coupled to high-resolution tandem mass spectrometry.

Mitochondrial protein functions elucidated by multi-omic mass spectrometry profiling

Mitochondrial dysfunction is associated with many human diseases, including cancer and neurodegeneration, that are often linked to proteins and pathways that are not well-characterized. To begin defining the functions of such poorly characterized proteins, we used mass spectrometry to map the proteomes, lipidomes, and metabolomes of 174 yeast strains, each lacking a single gene related to mitochondrial biology. 144 of these genes have human homologs, 60 of which are associated with disease and 39 of which are uncharacterized. We present a multi-omic data analysis and visualization tool that we use to find covariance networks that can predict molecular functions, correlations between profiles of related gene deletions, gene-specific perturbations that reflect protein functions, and a global respiration deficiency response. Using this multi-omic approach, we link seven proteins including Hfd1p and its human homolog ALDH3A1 to mitochondrial coenzyme Q (CoQ) biosynthesis, an essential pathway disrupted in many human diseases. This Resource should provide molecular insights into mitochondrial protein functions.

Performance Investigation of Proteomic Identification by HCD/CID Fragmentations in Combination with High/Low-Resolution Detectors on a Tribrid, High-Field Orbitrap Instrument

The recently-introduced Orbitrap Fusion mass spectrometry permits various types of MS2 acquisition methods. To date, these different MS2 strategies and the optimal data interpretation approach for each have not been adequately evaluated. This study comprehensively investigated the four MS2 strategies: HCD-OT (higher-energy-collisional-dissociation with Orbitrap detection), HCD-IT (HCD with ion trap, IT), CID-IT (collision-induced-dissociation with IT) and CID-OT on Orbitrap Fusion. To achieve extensive comparison and identify the optimal data interpretation method for each technique, several search engines (SEQUEST and Mascot) and post-processing methods (score-based, PeptideProphet, and Percolator) were assessed for all techniques for the analysis of a human cell proteome. It was found that divergent conclusions could be made from the same dataset when different data interpretation approaches were used and therefore requiring a relatively fair comparison among techniques. Percolator was chosen for comparison of techniques because it performs the best among all search engines and MS2 strategies. For the analysis of human cell proteome using individual MS2 strategies, the highest number of identifications was achieved by HCD-OT, followed by HCD-IT and CID-IT. Based on these results, we concluded that a relatively fair platform for data interpretation is necessary to avoid divergent conclusions from the same dataset, and HCD-OT and HCD-IT may be preferable for protein/peptide identification using Orbitrap Fusion.

193 nm Ultraviolet Photodissociation Mass Spectrometry for Phosphopeptide Characterization in the Positive and Negative Ion Modes

Advances in liquid chromatography tandem mass spectrometry (LC-MS/MS) have permitted phosphoproteomic analysis on a grand scale, but ongoing challenges specifically associated with confident phosphate localization continue to motivate the development of new fragmentation techniques. In the present study, ultraviolet photodissociation (UVPD) at 193 nm is evaluated for the characterization of phosphopeptides in both positive and negative ion modes. Compared to the more standard higher energy collisional dissociation (HCD), UVPD provided more extensive fragmentation with improved phosphate retention on product ions. Negative mode UVPD showed particular merit for detecting and sequencing highly acidic phosphopeptides from alpha and beta casein, but was not as robust for larger scale analysis because of lower ionization efficiencies in the negative mode. HeLa and HCC70 cell lysates were analyzed by both UVPD and HCD. While HCD identified more phosphopeptides and proteins compared to UVPD, the unique matches from UVPD analysis could be combined with the HCD data set to improve the overall depth of coverage compared to either method alone.

Current Trends and Future Perspectives of State-of-the-Art Proteomics Technologies Applied to Cardiovascular Disease Research

The use of mass spectrometry (MS)-dependent protein research is increasing in the cardiovascular sciences. A major reason for this is the versatility of and ability for MS technologies to accommodate a variety of biological questions such as those pertaining to basic research and clinical applications. In addition, mass spectrometers are becoming easier to operate, and require less expertise to run standard proteomics experiments. Nonetheless, despite the increasing interest in proteomics, many non-expert end users may not be as familiar with the variety of mass spectrometric tools and workflows available to them. We therefore review the major strategies used in unbiased and targeted MS, while providing specific applications in cardiovascular research. Because MS technologies are developing rapidly, it is important to understand the core concepts, strengths and weaknesses. Most importantly, we hope to inspire the further integration of this exciting technology into everyday research in the cardiovascular sciences. (Circ J 2016; 80: 1674-1683).

Six alternative proteases for mass spectrometry-based proteomics beyond trypsin

Protein digestion using a dedicated protease represents a key element in a typical mass spectrometry (MS)-based shotgun proteomics experiment. Up to now, digestion has been predominantly performed with trypsin, mainly because of its high specificity, widespread availability and ease of use. Lately, it has become apparent that the sole use of trypsin in bottom-up proteomics may impose certain limits in our ability to grasp the full proteome, missing out particular sites of post-translational modifications, protein segments or even subsets of proteins. To overcome this problem, the proteomics community has begun to explore alternative proteases to complement trypsin. However, protocols, as well as expected results generated from these alternative proteases, have not been systematically documented. Therefore, here we provide an optimized protocol for six alternative proteases that have already shown promise in their applicability in proteomics, namely chymotrypsin, LysC, LysN, AspN, GluC and ArgC. This protocol is formulated to promote ease of use and robustness, which enable parallel digestion with each of the six tested proteases. We present data on protease availability and usage including recommendations for reagent preparation. We additionally describe the appropriate MS data analysis methods and the anticipated results in the case of the analysis of a single protein (BSA) and a more complex cellular lysate (Escherichia coli). The digestion protocol presented here is convenient and robust and can be completed in ∼2 d.

Proteomic Analysis of Pathogenic Fungi Reveals Highly Expressed Conserved Cell Wall Proteins

We are presenting a quantitative proteomics tally of the most commonly expressed conserved fungal proteins of the cytosol, the cell wall, and the secretome. It was our goal to identify fungi-typical proteins that do not share significant homology with human proteins. Such fungal proteins are of interest to the development of vaccines or drug targets. Protein samples were derived from 13 fungal species, cultured in rich or in minimal media; these included clinical isolates of Aspergillus, Candida, Mucor, Cryptococcus, and Coccidioides species. Proteomes were analyzed by quantitative MS^E (Mass Spectrometry-Elevated Collision Energy). Several thousand proteins were identified and quantified in total across all fractions and culture conditions. The 42 most abundant proteins identified in fungal cell walls or supernatants shared no to very little homology with human proteins. In contrast, all but five of the 50 most abundant cytosolic proteins had human homologs with sequence identity averaging 59%. Proteomic comparisons of the secreted or surface localized fungal proteins highlighted conserved homologs of the Aspergillus fumigatus proteins 1,3-β-glucanosyltransferases (Bgt1, Gel1-4), Crf1, Ecm33, EglC, and others. The fact that Crf1 and Gel1 were previously shown to be promising vaccine candidates, underlines the value of the proteomics data presented here.

Silicon: Potential to Promote Direct and Indirect Effects on Plant Defense Against Arthropod Pests in Agriculture

Silicon has generally not been considered essential for plant growth, although it is well recognized that many plants, particularly Poaceae, have substantial plant tissue concentrations of this element. Recently, however, the International Plant Nutrition Institute [IPNI] (2015), Georgia, USA has listed it as a "beneficial substance". This reflects that numerous studies have now established that silicon may alleviate both biotic and abiotic stress. This paper explores the existing knowledge and recent advances in elucidating the role of silicon in plant defense against biotic stress, particularly against arthropod pests in agriculture and attraction of beneficial insects. Silicon confers resistance to herbivores via two described mechanisms: physical and biochemical/molecular. Until recently, studies have mainly centered on two trophic levels; the herbivore and plant. However, several studies now describe tri-trophic effects involving silicon that operate by attracting predators or parasitoids to plants under herbivore attack. Indeed, it has been demonstrated that silicon-treated, arthropod-attacked plants display increased attractiveness to natural enemies, an effect that was reflected in elevated biological control in the field. The reported relationships between soluble silicon and the jasmonic acid (JA) defense pathway, and JA and herbivore-induced plant volatiles (HIPVs) suggest that soluble silicon may enhance the production of HIPVs. Further, it is feasible that silicon uptake may affect protein expression (or modify proteins structurally) so that they can produce additional, or modify, the HIPV profile of plants. Ultimately, understanding silicon under plant ecological, physiological, biochemical, and molecular contexts will assist in fully elucidating the mechanisms behind silicon and plant response to biotic stress at both the bi- and tri-trophic levels.

Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0

PatternLab for proteomics is an integrated computational environment that unifies several previously published modules for the analysis of shotgun proteomic data. The contained modules allow for formatting of sequence databases, peptide spectrum matching, statistical filtering and data organization, extracting quantitative information from label-free and chemically labeled data, and analyzing statistics for differential proteomics. PatternLab also has modules to perform similarity-driven studies with de novo sequencing data, to evaluate time-course experiments and to highlight the biological significance of data with regard to the Gene Ontology database. The PatternLab for proteomics 4.0 package brings together all of these modules in a self-contained software environment, which allows for complete proteomic data analysis and the display of results in a variety of graphical formats. All updates to PatternLab, including new features, have been previously tested on millions of mass spectra. PatternLab is easy to install, and it is freely available from http://patternlabforproteomics.org.

Capillary zone electrophoresis for bottom-up analysis of complex proteomes

Capillary zone electrophoresis (CZE) is emerging as a useful tool in proteomic analysis. Interest arises from dramatic improvements in performance that result from improvements in the background electrolyte used for the separation, the incorporation of advanced sample injection methods, the development of robust and sensitive electrospray interfaces, and the coupling with Orbitrap mass spectrometers with high resolution and sensitivity. The combination of these technologies produces performance that is rapidly approaching the performance of UPLC-based methods for microgram samples and exceeds the performance of UPLC-based methods for mid- to low nanogram samples. These systems now produce over 10 000 peptide IDs in a single 100-min analysis of the HeLa proteome.