An integrated, directed mass spectrometric approach for in-depth characterization of complex peptide mixtures

Quantitative measurement of the requirement of diverse protein degradation pathways in MHC class I peptide presentation

Peptides from degradation of intracellular proteins are continuously displayed by major histocompatibility complex (MHC) class I. To better understand origins of these peptides, we performed a comprehensive census of the class I peptide repertoire in the presence and absence of ubiquitin-proteasome system (UPS) activity upon developing optimized methodology to enrich for and quantify these peptides. Whereas most class I peptides are dependent on the UPS for their generation, a surprising 30%, enriched in peptides of mitochondrial origin, appears independent of the UPS. A further ~10% of peptides were found to be dependent on the proteasome but independent of ubiquitination for their generation. Notably, clinically achievable partial inhibition of the proteasome resulted in display of atypical peptides. Our results suggest that generation of MHC class I•peptide complexes is more complex than previously recognized, with UPS-dependent and UPS-independent components; paradoxically, alternative protein degradation pathways also generate class I peptides when canonical pathways are impaired.

Nvp63 and nvPIWIL1 Suppress Retrotransposon Activation in the Sea Anemone Nematostella vectensis

The transcription factors p63 and p73 have high similarity to the tumor suppressor protein p53. While the importance of p53 in DNA damage control is established, the functions of p63 or p73 remain elusive. Here, we analyzed nvp63, the cnidarian homologue of p63, that is expressed in the mesenteries of the starlet sea anemone Nematostella vectensis and that is activated in response to DNA damage. We used ultraviolet light (UV) to induce DNA damage and determined the chromatin-bound proteome with quantitative, bottom-up proteomics. We found that genotoxic stress or nvp63 knockdown recruited the protein nvPIWIL1, a homologue of the piRNA-binding PIWI protein family. Knockdown nvPIWIL1 increased protein expression from open reading frames (ORFs) that overlap with class I and II transposable element DNA sequences in the genome of N. vectensis. UV irradiation induced apoptosis, and apoptosis was reduced in the absence of nvp63 but increased with the loss of nvPIWIL1. Loss of nvp63 increased the presence of class I LTR and non-LTR retrotransposon but not of class II DNA transposon-associated protein products. These results suggest that an evolutionary early function of nvp63 might be to control genome stability in response to activation of transposable elements, which induce DNA damage during reintegration in the genome.

Sensitive Screening of New Psychoactive Substances in Serum Using Liquid-Chromatography Quadrupole Time-of-Flight Mass Spectrometry

Analysis of new psychoactive substances (NPS) still pose a challenge for many institutions due to the number of available substances and the constantly changing drug market. Both new and well-known substances keep appearing and disappearing on the market, making it hard to adapt analytical methods in a timely manner. In this study we developed a qualitative screening approach for serum samples by means of liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Samples were measured in data-dependent auto-MS/MS mode and identified by fragment spectra comparison, retention time and accurate mass. Approximately 500 NPS, including 195 synthetic cannabinoids, 180 stimulants, 86 hallucinogens, 26 benzodiazepines and 7 others were investigated. Serum samples were fortified to 1 ng/mL and 10 ng/mL concentrations to estimate approximate limits of identification. Samples were extracted using solid-phase extraction with non-endcapped C18 material and elution in two consecutive steps. Benzodiazepines were eluted in the first step, while substances of other NPS subclasses were distributed among both extracts. To determine limits of identification, both extracts were combined. 96 % (470/492) of investigated NPS were detected in 10 ng/mL samples and 88 % (432/492) were detected in 1 ng/mL samples. Stimulants stood out with higher limits of identification, possibly due to instability of certain methcathinone derivatives. However, considering relevant blood concentrations, the method provided sufficient sensitivity for stimulants as well as other NPS subclasses. Data-dependent acquisition was proven to provide high sensitivity and reliability when combined with an information-dependent preferred list, without losing its untargeted operation principle. Summarizing, the developed method fulfilled its purpose as a sensitive untargeted screening for serum samples and allows uncomplicated expansion of the spectral library to include thousands of targets.

BoxCar increases the depth and reproducibility of diabetic urinary proteome analysis

PURPOSE

Mass spectrometry-based proteomics performs well in high throughput detection of urinary proteins. Nonetheless, protein identification depth and reproducibility remain the challenges in diabetic urinary proteome with high complexity and broad dynamic range, especially for low-abundant proteins. As a new data acquisition strategy, the BoxCar method was reported to benefit for low-abundant protein identification. Whether it is propitious to diabetic samples with high dynamic range proteomes has not been discussed yet. We aimed to apply BoxCar method to diabetic urine sample analysis, and to compare it with standard data dependent acquisition (DDA) method on protein identification in detail.

EXPERIMENTAL DESIGN

We performed seven technical replicates analysis on two urine samples from healthy individuals and diabetic patients to evaluate protein detection of BoxCar and standard DDA methods on single sample. Further comparison of two methods was made on multiple diabetic urine samples.

RESULTS

BoxCar could increase over 20% of identified proteins and performed better quantitative reproducibility than standard DDA method either in single or multiple diabetic urinary samples. BoxCar also improved the detection of low-abundant proteins. Functional enrichment analysis of normal albuminuria or microalbuminuria samples indicated that BoxCar acquired more diabetes-related biological information.

CONCLUSIONS AND CLINICAL RELEVANCE

The study demonstrates that BoxCar could enhance the depth and reproducibility in diabetic urinary proteome analysis, which provides reference for mass spectrometry approach selection in clinical urinary proteomic research.

Targeted Quantification of the Lysosomal Proteome in Complex Samples

In eukaryotic cells, lysosomes play a crucial role in the breakdown of a variety of components ranging from small molecules to complex structures, ascertaining the continuous turnover of cellular building blocks. Furthermore, they act as a regulatory hub for metabolism, being crucially involved in the regulation of major signaling pathways. Currently, ~450 lysosomal proteins can be reproducibly identified in a single cell line by mass spectrometry, most of which are low-abundant, restricting their unbiased proteomic analysis to lysosome-enriched fractions. In the current study, we applied two strategies for the targeted investigation of the lysosomal proteome in complex samples: data-independent acquisition (DIA) and parallel reaction monitoring (PRM). Using a lysosome-enriched fraction, mouse embryonic fibroblast whole cell lysate, and mouse liver whole tissue lysate, we investigated the capabilities of DIA and PRM to investigate the lysosomal proteome. While both approaches identified and quantified lysosomal proteins in all sample types, and their data largely correlated, DIA identified on average more proteins, especially for lower complex samples and longer chromatographic gradients. For the highly complex tissue sample and shorter gradients, however, PRM delivered a better performance regarding both identification and quantification of lysosomal proteins. All data are available via ProteomeXchange with identifier PXDD023278.

A comprehensive spectral assay library to quantify the Escherichia coli proteome by DIA/SWATH-MS

Data-Independent Acquisition (DIA) is a method to improve consistent identification and precise quantitation of peptides and proteins by mass spectrometry (MS). The targeted data analysis strategy in DIA relies on spectral assay libraries that are generally derived from a priori measurements of peptides for each species. Although Escherichia coli (E. coli) is among the best studied model organisms, so far there is no spectral assay library for the bacterium publicly available. Here, we generated a spectral assay library for 4,014 of the 4,389 annotated E. coli proteins using one- and two-dimensional fractionated samples, and ion mobility separation enabling deep proteome coverage. We demonstrate the utility of this high-quality library with robustness in quantitation of the E. coli proteome and with rapid-chromatography to enhance throughput by targeted DIA-MS. The spectral assay library supports the detection and quantification of 91.5% of all E. coli proteins at high-confidence with 56,182 proteotypic peptides, making it a valuable resource for the scientific community. Data and spectral libraries are available via ProteomeXchange (PXD020761, PXD020785) and SWATHAtlas (SAL00222-28).

UHPLC/GC-TOF-MS metabolomics, MTT assay, and molecular docking studies reveal physostigmine as a new anticancer agent from the ethyl acetate and butanol fractions of Kigelia africana (Lam.) Benth. fruit extracts

Kigelia africana plant is widely used as a herbal remedy in preventing the onset and the treatment of cancer-related infections. With the increase in the research interest of the plant, the specific chemical compound or metabolite that confers its anticancer properties has not been adequately investigated. The ethyl acetate and butanol fractions of the fruit extracts were evaluated by 2-(4,5-dimethylthiazol-2-yl)-3,5-diphenyl-2H-tetrazolium bromide assay against four different cell lines, with the ethyl acetate fraction having inhibition concentration values of 0.53 and 0.42 μM against Hep G2 and HeLa cells, respectively. More than 235 phytoconstituents were profiled using UHPLC-TOF-MS, while more than 15 chemical compounds were identified using GC-MS from the fractions. Molecular docking studies revealed that physostigmine, fluazifop, dexamethasone, sulfisomidine, and desmethylmirtazapine could favorably bind at higher binding energies of -8.3, -8.6, -8.2, and -8.1 kcal/mol, respectively, better than camptothecin with a binding energy of -7.9 kcal/mol. The results of this study showed that physostigmine interacted well with topoisomerase IIα and had a high score of pharmacokinetic prediction using absorption, distribution, metabolism, excretion, and toxicity profiles, thereby suggesting that drug design using physostigmine as a base structure could serve as an alternative against the toxic side effects of doxorubicin and camptothecin.

Combining Targeted and Untargeted Data Acquisition to Enhance Quantitative Plant Proteomics Experiments

Most quantitative proteomics experiments either target a limited number of selected proteins for quantification or quantify proteins on a broad scale in an untargeted manner. However, we recently demonstrated that experiments that have both targeted and untargeted components can be particularly advantageous. Using a combined targeted and untargeted liquid chromatography-tandem mass spectrometry data acquisition strategy termed TDA/DDA (shorthand for targeted data acquisition/data-dependent acquisition), which we applied to a model quantitative plant proteomics experiment performed on Arabidopsis, we demonstrated improved quantification of both targeted and untargeted proteins relative to purely untargeted experiments performed using conventional data-dependent acquisition (Hart-Smith et al. Front Plant Sci 8:1669, 2017). This suggests that many quantitative proteomics datasets earmarked for collection using data-dependent acquisition are likely to benefit from the use of TDA/DDA instead.This chapter describes how TDA/DDA liquid chromatography-tandem mass spectrometry methods can be created on commonly used mass spectrometric instrument platforms. It described how, using freely available software, tandem mass spectrometry inclusion lists designed to target proteins of hypothesized interest can be generated. Best practice implementation of these inclusion lists in TDA/DDA strategies is then described. Relative to conventional data-dependent acquisition, the liquid chromatography-tandem mass spectrometry methods created using these guidelines increase the chances of quantifying targeted proteins and can produce widespread improvements in the reproducibility of untargeted protein quantification, without compromising the total numbers of proteins quantified. They are compatible with different quantitative proteomics methodologies, including metabolic labeling, chemical labeling and label-free approaches, and can be used to create tailored assay libraries to aid the interpretation of quantitative proteomics data collected using data-independent acquisition.

Computational solutions in redox lipidomics - Current strategies and future perspectives

The high chemical diversity of lipids allows them to perform multiple biological functions ranging from serving as structural building blocks of biological membranes to regulation of metabolism and signal transduction. In addition to the native lipidome, lipid species derived from enzymatic and non-enzymatic modifications (the epilipidome) make the overall picture even more complex, as their functions are still largely unknown. Oxidized lipids represent the fraction of epilipidome which has attracted high scientific attention due to their apparent involvement in the onset and development of numerous human disorders. Development of high-throughput analytical methods such as liquid chromatography coupled on-line to mass spectrometry provides the possibility to address epilipidome diversity in complex biological samples. However, the main bottleneck of redox lipidomics, the branch of lipidomics dealing with the characterization of oxidized lipids, remains the lack of optimal computational tools for robust, accurate and specific identification of already discovered and yet unknown modified lipids. Here we discuss the main principles of high-throughput identification of lipids and their modified forms and review the main software tools currently available in redox lipidomics. Different levels of confidence for software assisted identification of redox lipidome are defined and necessary steps toward optimal computational solutions are proposed.

Improved Sensitivity and Separations for Phosphopeptides using Online Liquid Chromotography Coupled with Structures for Lossless Ion Manipulations Ion Mobility-Mass Spectrometry

Phosphoproteomics greatly augments proteomics and holds tremendous potential for insights into the modulation of biological systems for various disease states. However, numerous challenges hinder conventional methods in terms of measurement sensitivity, throughput, quantification, and capabilities for confident phosphopeptide and phosphosite identification. In this work, we report the first example of integrating structures for lossless ion manipulations ion mobility-mass spectrometry (SLIM IM-MS) with online reversed-phase liquid chromatography (LC) to evaluate its potential for addressing the aforementioned challenges. A mixture of 51 heavy-labeled phosphopeptides was analyzed with a SLIM IM module having integrated ion accumulation and long-path separation regions. The SLIM IM-MS provided limits of detection as low as 50-100 pM (50-100 amol/μL) for several phosphopeptides, with the potential for significant further improvements. In addition, conventionally problematic phosphopeptide isomers could be resolved following an 18 m SLIM IM separation. The 2-D LC-IM peak capacity was estimated as ∼9000 for a 90 min LC separation coupled to an 18 m SLIM IM separation, considerably higher than LC alone and providing a basis for both improved identification and quantification, with additional gains projected with the future use of longer path SLIM IM separations. Thus, LC-SLIM IM-MS offers great potential for improving the sensitivity, separation, and throughput of phosphoproteomics analyses.

Measurement of In Vivo Protein Binding Affinities in a Signaling Network with Mass Spectrometry

Protein interaction networks play a key role in signal processing. Despite the progress in identifying the interactions, the quantification of their strengths lags behind. Here we present an approach to quantify the in vivo binding of proteins to their binding partners in signaling-transcriptional networks, by the pairwise genetic isolation of each interaction and by varying the concentration of the interacting components over time. The absolute quantification of the protein concentrations was performed with targeted mass spectrometry. The strengths of the interactions, as defined by the apparent dissociation constants, ranged from subnanomolar to micromolar values in the yeast galactose signaling network. The weak homodimerization of the Gal4 activator amplifies the signal elicited by glucose. Furthermore, combining the binding constants in a feedback loop correctly predicted cellular memory, a characteristic network behavior. Thus, this genetic-proteomic binding assay can be used to faithfully quantify how strongly proteins interact with proteins, DNA and metabolites.

Improved Quantitative Plant Proteomics via the Combination of Targeted and Untargeted Data Acquisition

Quantitative proteomics strategies - which are playing important roles in the expanding field of plant molecular systems biology - are traditionally designated as either hypothesis driven or non-hypothesis driven. Many of these strategies aim to select individual peptide ions for tandem mass spectrometry (MS/MS), and to do this mixed hypothesis driven and non-hypothesis driven approaches are theoretically simple to implement. In-depth investigations into the efficacies of such approaches have, however, yet to be described. In this study, using combined samples of unlabeled and metabolically ¹⁵N-labeled Arabidopsis thaliana proteins, we investigate the mixed use of targeted data acquisition (TDA) and data dependent acquisition (DDA) - referred to as TDA/DDA - to facilitate both hypothesis driven and non-hypothesis driven quantitative data collection in individual LC-MS/MS experiments. To investigate TDA/DDA for hypothesis driven data collection, 7 miRNA target proteins of differing size and abundance were targeted using inclusion lists comprised of 1558 m/z values, using 3 different TDA/DDA experimental designs. In samples in which targeted peptide ions were of particularly low abundance (i.e., predominantly only marginally above mass analyser detection limits), TDA/DDA produced statistically significant increases in the number of targeted peptides identified (230 ± 8 versus 80 ± 3 for DDA; p = 1.1 × 10^-3) and quantified (35 ± 3 versus 21 ± 2 for DDA; p = 0.038) per experiment relative to the use of DDA only. These expected improvements in hypothesis driven data collection were observed alongside unexpected improvements in non-hypothesis driven data collection. Untargeted peptide ions with m/z values matching those in inclusion lists were repeatedly identified and quantified across technical replicate TDA/DDA experiments, resulting in significant increases in the percentages of proteins repeatedly quantified in TDA/DDA experiments only relative to DDA experiments only (33.0 ± 2.6% versus 8.0 ± 2.7%, respectively; p = 0.011). These results were observed together with uncompromised broad-scale MS/MS data collection in TDA/DDA experiments relative to DDA experiments. Using our observations we provide guidelines for TDA/DDA method design for quantitative plant proteomics studies, and suggest that TDA/DDA is a broadly underutilized proteomics data acquisition strategy.

Simultaneous Proteomic Discovery and Targeted Monitoring using Liquid Chromatography, Ion Mobility Spectrometry, and Mass Spectrometry

Current proteomic approaches include both broad discovery measurements and quantitative targeted analyses. In many cases, discovery measurements are initially used to identify potentially important proteins (e.g. candidate biomarkers) and then targeted studies are employed to quantify a limited number of selected proteins. Both approaches, however, suffer from limitations. Discovery measurements aim to sample the whole proteome but have lower sensitivity, accuracy, and quantitation precision than targeted approaches, whereas targeted measurements are significantly more sensitive but only sample a limited portion of the proteome. Herein, we describe a new approach that performs both discovery and targeted monitoring (DTM) in a single analysis by combining liquid chromatography, ion mobility spectrometry and mass spectrometry (LC-IMS-MS). In DTM, heavy labeled target peptides are spiked into tryptic digests and both the labeled and unlabeled peptides are detected using LC-IMS-MS instrumentation. Compared with the broad LC-MS discovery measurements, DTM yields greater peptide/protein coverage and detects lower abundance species. DTM also achieved detection limits similar to selected reaction monitoring (SRM) indicating its potential for combined high quality discovery and targeted analyses, which is a significant step toward the convergence of discovery and targeted approaches.

GlycoPep MassList: software to generate massive inclusion lists for glycopeptide analyses

Protein glycosylation drives many biological processes and serves as markers for disease; therefore, the development of tools to study glycosylation is an essential and growing area of research. Mass spectrometry can be used to identify both the glycans of interest and the glycosylation sites to which those glycans are attached, when proteins are proteolytically digested and their glycopeptides are analyzed by a combination of high-resolution mass spectrometry (MS) and tandem mass spectrometry (MS/MS) methods. One major challenge in these experiments is collecting the requisite MS/MS data. The digested glycopeptides are often present in complex mixtures and in low abundance, and the most commonly used approach to collect MS/MS data on these species is data-dependent acquisition (DDA), where only the most intense precursor ions trigger MS/MS. DDA results in limited glycopeptide coverage. Semi-targeted data acquisition is an alternative experimental approach that can alleviate this difficulty. However, due to the massive heterogeneity of glycopeptides, it is not obvious how to expediently generate inclusion lists for these types of analyses. To solve this problem, we developed the software tool GlycoPep MassList, which can be used to generate inclusion lists for liquid chromatography tandem-mass spectrometry (LC-MS/MS) experiments. The utility of the software was tested by conducting comparisons between semi-targeted and untargeted data-dependent analysis experiments on a variety of proteins, including IgG, a protein whose glycosylation must be characterized during its production as a biotherapeutic. When the GlycoPep MassList software was used to generate inclusion lists for LC-MS/MS experiments, more unique glycopeptides were selected for fragmentation. Generally, ∼30 % more unique glycopeptides can be analyzed per protein, in the simplest cases, with low background. In cases where background ions from proteins or other interferents are high, usage of an inclusion list is even more advantageous. The software is freely publically accessible. Graphical abstract Software increases the number of glycopeptides that get selected for MS/MS analysis.

Advanced Precursor Ion Selection Algorithms for Increased Depth of Bottom-Up Proteomic Profiling

Conventional TopN data-dependent acquisition (DDA) LC-MS/MS analysis identifies only a limited fraction of all detectable precursors because the ion-sampling rate of contemporary mass spectrometers is insufficient to target each precursor in a complex sample. TopN DDA preferentially targets high-abundance precursors with limited sampling of low-abundance precursors and repeated analyses only marginally improve sample coverage due to redundant precursor sampling. In this work, advanced precursor ion selection algorithms were developed and applied in the bottom-up analysis of HeLa cell lysate to overcome the above deficiencies. Precursors fragmented in previous runs were efficiently excluded using an automatically aligned exclusion list, which reduced overlap of identified peptides to ∼10% between replicates. Exclusion of previously fragmented high-abundance peptides allowed deeper probing of the HeLa proteome over replicate LC-MS runs, resulting in the identification of 29% more peptides beyond the saturation level achievable using conventional TopN DDA. The gain in peptide identifications using the developed approach translated to the identification of several hundred low-abundance protein groups, which were not detected by conventional TopN DDA. Exclusion of only identified peptides compared with the exclusion of all previously fragmented precursors resulted in an increase of 1000 (∼10%) additional peptide identifications over four runs, suggesting the potential for further improvement in the depth of proteomic profiling using advanced precursor ion selection algorithms.

Discovery Proteomics Identifies a Molecular Link between the Coatomer Protein Complex I and Androgen Receptor-dependent Transcription

Aberrant androgen receptor (AR)-dependent transcription is a hallmark of human prostate cancers. At the molecular level, ligand-mediated AR activation is coordinated through spatial and temporal protein-protein interactions involving AR-interacting proteins, which we designate the “AR-interactome.” Despite many years of research, the ligand-sensitive protein complexes involved in ligand-mediated AR activation in prostate tumor cells have not been clearly defined. Here, we describe the development, characterization, and utilization of a novel human LNCaP prostate tumor cell line, N-AR, which stably expresses wild-type AR tagged at its N terminus with the streptavidin-binding peptide epitope (streptavidin-binding peptide-tagged wild-type androgen receptor; SBP-AR). A bioanalytical workflow involving streptavidin chromatography and label-free quantitative mass spectrometry was used to identify SBP-AR and associated ligand-sensitive cytosolic proteins/protein complexes linked to AR activation in prostate tumor cells. Functional studies verified that ligand-sensitive proteins identified in the proteomic screen encoded modulators of AR-mediated transcription, suggesting that these novel proteins were putative SBP-AR-interacting proteins in N-AR cells. This was supported by biochemical associations between recombinant SBP-AR and the ligand-sensitive coatomer protein complex I (COPI) retrograde trafficking complex in vitro. Extensive biochemical and molecular experiments showed that the COPI retrograde complex regulates ligand-mediated AR transcriptional activation, which correlated with the mobilization of the Golgi-localized ARA160 coactivator to the nuclear compartment of prostate tumor cells. Collectively, this study provides a bioanalytical strategy to validate the AR-interactome and define novel AR-interacting proteins involved in ligand-mediated AR activation in prostate tumor cells. Moreover, we describe a cellular system to study how compartment-specific AR-interacting proteins influence AR activation and contribute to aberrant AR-dependent transcription that underlies the majority of human prostate cancers.

Slx5/Slx8 Promotes Replication Stress Tolerance by Facilitating Mitotic Progression

Loss of minichromosome maintenance protein 10 (Mcm10) causes replication stress. We uncovered that S. cerevisiae mcm10-1 mutants rely on the E3 SUMO ligase Mms21 and the SUMO-targeted ubiquitin ligase complex Slx5/8 for survival. Using quantitative mass spectrometry, we identified changes in the SUMO proteome of mcm10-1 mutants and revealed candidates regulated by Slx5/8. Such candidates included subunits of the chromosome passenger complex (CPC), Bir1 and Sli15, known to facilitate spindle assembly checkpoint (SAC) activation. We show here that Slx5 counteracts SAC activation in mcm10-1 mutants under conditions of moderate replication stress. This coincides with the proteasomal degradation of sumoylated Bir1. Importantly, Slx5-dependent mitotic relief was triggered not only by Mcm10 deficiency but also by treatment with low doses of the alkylating drug methyl methanesulfonate. Based on these findings, we propose a model in which Slx5/8 allows for passage through mitosis when replication stress is tolerable.

Nontargeted identification of peptides and disinfection byproducts in water

A broad range of organic compounds are known to exist in drinking water sources and serve as precursors of disinfection byproducts (DBPs). Epidemiological findings of an association of increased risk of bladder cancer with the consumption of chlorinated water has resulted in health concerns about DBPs. Peptides are thought to be an important category of DBP precursors in water. However, little is known about the actual presence of peptides and their DBPs in drinking water because of their high sample complexity and low concentrations. To address this challenge and identify peptides and non-chlorinated/chlorinated peptide DBPs from large sets of organic compounds in water, we developed a novel high throughput analysis strategy, which integrated multiple solid phase extraction (SPE), high performance liquid chromatography (HPLC) separation, and non-target identification using precursor ion exclusion (PIE) high resolution mass spectrometry (MS). After MS analysis, structures of candidate compounds, particularly peptides, were obtained by searching against the Human Metabolome Database (HMDB). Using this strategy, we successfully detected 625 peptides (out of 17,205 putative compounds) and 617 peptides (out of 13,297) respectively in source and finished water samples. The source and finished water samples had 501 peptides and amino acids in common. The remaining 116 peptides and amino acids were unique to the finished water. From a subset of 30 putative compounds for which standards were available, 25 were confirmed using HPLC-MS analysis. By analyzing the peptides identified in source and finished water, we successfully confirmed three disinfection reaction pathways that convert peptides into toxic DBPs.

Liver lipid metabolism is altered by increased circulating estrogen to androgen ratio in male mouse

Estrogens are suggested to lower the risk of developing metabolic syndrome in both sexes. In this study, we investigated how the increased circulating estrogen-to-androgen ratio (E/A) alters liver lipid metabolism in males. The cytochrome P450 aromatase (P450arom) is an enzyme converting androgens to estrogens. Male mice overexpressing human aromatase enzyme (AROM+ mice), and thus have high circulating E/A, were used as a model in this study. Proteomics and gene expression analyses indicated an increase in the peroxisomal β-oxidation in the liver of AROM+ mice as compared with their wild type littermates. Correspondingly, metabolomic analysis revealed a decrease in the amount of phosphatidylcholines with long-chain fatty acids in the plasma. With interest we noted that the expression of Cyp4a12a enzyme, which specifically metabolizes arachidonic acid (AA) to 20-hydroxy AA, was dramatically decreased in the AROM+ liver. As a consequence, increased amounts of phospholipids having AA as a fatty acid tail were detected in the plasma of the AROM+ mice. Overall, these observations demonstrate that high circulating E/A in males is linked to indicators of higher peroxisomal β-oxidation and lower AA metabolism in the liver. Furthermore, the plasma phospholipid profile reflects the changes in the liver lipid metabolism.

High-Efficiency Recognition and Identification of Disulfide Bonded Peptides in Rat Neuropeptidome Using Targeted Electron Transfer Dissociation Tandem Mass Spectrometry

The main goal of the present study is to develop a method to recognize and identify endogenous intrachain disulfide bonded peptide, which are rarely sequenced in current peptidomics studies. In order to achieve highly efficient detection of these peptides in a neuropeptidome analysis, we alkylated the peptides, mined the raw mass spectrometry data, and then recognized the candidates of untreated disulfide bonded peptides from unalkylated peptide extracts. After removing more than 90% features, targeted electron transfer dissociation fragmentation was performed for detecting and fragmenting disulfide bonded peptides, and even most of them were present in low abundance in the original sample. Diverse endogenous disulfide bonded peptides were then detected and sequenced, opening up new perspectives for comprehensively understanding the response of a neuropeptidome.

The Use of Variable Q1 Isolation Windows Improves Selectivity in LC-SWATH-MS Acquisition

As tryptic peptides and metabolites are not equally distributed along the mass range, the probability of cross fragment ion interference is higher in certain windows when fixed Q1 SWATH windows are applied. We evaluated the benefits of utilizing variable Q1 SWATH windows with regards to selectivity improvement. Variable windows based on equalizing the distribution of either the precursor ion population (PIP) or the total ion current (TIC) within each window were generated by an in-house software, swathTUNER. These two variable Q1 SWATH window strategies outperformed, with respect to quantification and identification, the basic approach using a fixed window width (FIX) for proteomic profiling of human monocyte-derived dendritic cells (MDDCs). Thus, 13.8 and 8.4% additional peptide precursors, which resulted in 13.1 and 10.0% more proteins, were confidently identified by SWATH using the strategy PIP and TIC, respectively, in the MDDC proteomic sample. On the basis of the spectral library purity score, some improvement warranted by variable Q1 windows was also observed, albeit to a lesser extent, in the metabolomic profiling of human urine. We show that the novel concept of "scheduled SWATH" proposed here, which incorporates (i) variable isolation windows and (ii) precursor retention time segmentation further improves both peptide and metabolite identifications.

A mass spectrometric-derived cell surface protein atlas

Cell surface proteins are major targets of biomedical research due to their utility as cellular markers and their extracellular accessibility for pharmacological intervention. However, information about the cell surface protein repertoire (the surfaceome) of individual cells is only sparsely available. Here, we applied the Cell Surface Capture (CSC) technology to 41 human and 31 mouse cell types to generate a mass-spectrometry derived Cell Surface Protein Atlas (CSPA) providing cellular surfaceome snapshots at high resolution. The CSPA is presented in form of an easy-to-navigate interactive database, a downloadable data matrix and with tools for targeted surfaceome rediscovery (http://wlab.ethz.ch/cspa). The cellular surfaceome snapshots of different cell types, including cancer cells, resulted in a combined dataset of 1492 human and 1296 mouse cell surface glycoproteins, providing experimental evidence for their cell surface expression on different cell types, including 136 G-protein coupled receptors and 75 membrane receptor tyrosine-protein kinases. Integrated analysis of the CSPA reveals that the concerted biological function of individual cell types is mainly guided by quantitative rather than qualitative surfaceome differences. The CSPA will be useful for the evaluation of drug targets, for the improved classification of cell types and for a better understanding of the surfaceome and its concerted biological functions in complex signaling microenvironments.

iPhos: a toolkit to streamline the alkaline phosphatase-assisted comprehensive LC-MS phosphoproteome investigation

Background

Comprehensive characterization of the phosphoproteome in living cells is critical in signal transduction research. But the low abundance of phosphopeptides among the total proteome in cells remains an obstacle in mass spectrometry-based proteomic analysis. To provide a solution, an alternative analytic strategy to confidently identify phosphorylated peptides by using the alkaline phosphatase (AP) treatment combined with high-resolution mass spectrometry was provided. While the process is applicable, the key integration along the pipeline was mostly done by tedious manual work.

Results

We developed a software toolkit, iPhos, to facilitate and streamline the work-flow of AP-assisted phosphoproteome characterization. The iPhos tookit includes one assister and three modules. The iPhos Peak Extraction Assister automates the batch mode peak extraction for multiple liquid chromatography mass spectrometry (LC-MS) runs. iPhos Module-1 can process the peak lists extracted from the LC-MS analyses derived from the original and dephosphorylated samples to mine out potential phosphorylated peptide signals based on mass shift caused by the loss of some multiples of phosphate groups. And iPhos Module-2 provides customized inclusion lists with peak retention time windows for subsequent targeted LC-MS/MS experiments. Finally, iPhos Module-3 facilitates to link the peptide identifications from protein search engines to the quantification results from pattern-based label-free quantification tools. We further demonstrated the utility of the iPhos toolkit on the data of human metastatic lung cancer cells (CL1-5).

Conclusions

In the comparison study of the control group of CL1-5 cell lysates and the treatment group of dasatinib-treated CL1-5 cell lysates, we demonstrated the applicability of the iPhos toolkit and reported the experimental results based on the iPhos-facilitated phosphoproteome investigation. And further, we also compared the strategy with pure DDA-based LC-MS/MS phosphoproteome investigation. The results of iPhos-facilitated targeted LC-MS/MS analysis convey more thorough and confident phosphopeptide identification than the results of pure DDA-based analysis.

Ovarian endometriosis signatures established through discovery and directed mass spectrometry analysis

New molecular information on potential therapeutic targets or tools for noninvasive diagnosis for endometriosis are important for patient care and treatment. However, surprisingly few efforts have described endometriosis at the protein level. In this work we enumerate the proteins in patient endometrium and ovarian endometrioma by extensive and comprehensive analysis of minute amounts of cryosectioned tissues in a three-tiered mass spectrometric approach. Quantitative comparison of the tissues revealed 214 differentially expressed proteins in ovarian endometrioma and endometrium. These proteins are reported here as a resource of SRM (selected reaction monitoring) assays that are unique, standardized, and openly available. Pathway analysis of the proteome measurements revealed a potential role for Transforming growth factor β-1 in ovarian endometriosis development. Subsequent mRNA microarray analysis further revealed clear ovarian endometrioma specificity for a subset of these proteins, which was also supported by further in silico studies. In this process two important proteins emerged, Calponin-1 and EMILIN-1, that were additionally confirmed in ovarian endometrioma tissues by immunohistochemistry and Western blotting. This study provides the most comprehensive molecular description of ovarian endometriosis to date and researchers with new molecular methods and tools for high throughput patient screening using the SRM assays.

Improving natural products identification through targeted LC-MS/MS in an untargeted secondary metabolomics workflow

Tandem mass spectrometry is a widely applied and highly sensitive technique for the discovery and characterization of microbial natural products such as secondary metabolites from myxobacteria. Here, a data mining workflow based on MS/MS precursor lists targeting only signals related to bacterial metabolism is established using LC-MS data of crude extracts from shaking flask fermentations. The devised method is not biased toward specific compound classes or structural features and is capable of increasing the information content of LC-MS/MS analyses by directing fragmentation events to signals of interest. The approach is thus contrary to typical auto-MS(2) setups where precursor ions are usually selected according to signal intensity, which is regarded as a drawback for metabolite discovery applications when samples contain many overlapping signals and the most intense signals do not necessarily represent compounds of interest. In line with this, the method described here achieves improved MS/MS scan coverage for low-abundance precursor ions not captured by auto-MS(2) experiments and thereby facilitates the search for new secondary metabolites in complex biological samples. To underpin the effectiveness of the approach, the identification and structure elucidation of two new myxobacterial secondary metabolite classes is reported.

Quantitative proteomics using the high resolution accurate mass capabilities of the quadrupole-orbitrap mass spectrometer

High resolution/accurate mass hybrid mass spectrometers have considerably advanced shotgun proteomics and the recent introduction of fast sequencing capabilities has expanded its use for targeted approaches. More specifically, the quadrupole-orbitrap instrument has a unique configuration and its new features enable a wide range of experiments. An overview of the analytical capabilities of this instrument is presented, with a focus on its application to quantitative analyses. The high resolution, the trapping capability and the versatility of the instrument have allowed quantitative proteomic workflows to be redefined and new data acquisition schemes to be developed. The initial proteomic applications have shown an improvement of the analytical performance. However, as quantification relies on ion trapping, instead of ion beam, further refinement of the technique can be expected.

Carbonylated plasma proteins as potential biomarkers of obesity induced type 2 diabetes mellitus

Protein carbonylation is a common nonenzymatic oxidative post-translational modification, which is often considered as biomarker of oxidative stress. Recent evidence links protein carbonylation also to obesity and type 2 diabetes mellitus (T2DM), though the protein targets of carbonylation in human plasma have not been identified. In this study, we profiled carbonylated proteins in plasma samples obtained from lean individuals and obese patients with or without T2DM. The plasma samples were digested with trypsin, carbonyl groups were derivatized with O-(biotinylcarbazoylmethyl)hydroxylamine, enriched by avidin affinity chromatography, and analyzed by RPC-MS/MS. Signals of potentially modified peptides were targeted in a second LC-MS/MS analysis to retrieve the peptide sequence and the modified residues. A total of 158 unique carbonylated proteins were identified, of which 52 were detected in plasma samples of all three groups. Interestingly, 36 carbonylated proteins were detected only in obese patients with T2DM, whereas 18 were detected in both nondiabetic groups. The carbonylated proteins originated mostly from liver, plasma, platelet, and endothelium. Functionally, they were mainly involved in cell adhesion, signaling, angiogenesis, and cytoskeletal remodeling. Among the identified carbonylated proteins were several candidates, such as VEGFR-2, MMP-1, argin, MKK4, and compliment C5, already connected before to diabetes, obesity and metabolic diseases.

Recent advances in mass spectrometry: data independent analysis and hyper reaction monitoring

New mass spectrometry (MS) methods, collectively known as data independent analysis and hyper reaction monitoring, have recently emerged. These methods hold promises to address the shortcomings of data-dependent analysis and selected reaction monitoring (SRM) employed in shotgun and targeted proteomics, respectively. They allow MS analyses of all species in a complex sample indiscriminately, or permit SRM-like experiments conducted with full high-resolution product ion spectra, potentially leading to higher sequence coverage or analytical selectivity. These methods include MS(E), all-ion fragmentation, Fourier transform-all reaction monitoring, SWATH Acquisition, multiplexed MS/MS, pseudo-SRM (pSRM) and parallel reaction monitoring (PRM). In this review, the strengths and pitfalls of these methods are discussed and illustrated with examples. In essence, the suitability of the use of each method is contingent on the biological questions posed. Although these methods do not fundamentally change the shape of proteomics, they are useful additional tools that should expedite biological discoveries.

Spatial proteomic and phospho-proteomic organization in three prototypical cell migration modes

Background

Tight spatio-temporal signaling of cytoskeletal and adhesion dynamics is required for localized membrane protrusion that drives directed cell migration. Different ensembles of proteins are therefore likely to get recruited and phosphorylated in membrane protrusions in response to specific cues.

Results

Here, we use an assay that allows to biochemically purify extending protrusions of cells migrating in response to three prototypical receptors: integrins, recepor tyrosine kinases and G-coupled protein receptors. Using quantitative proteomics and phospho-proteomics approaches, we provide evidence for the existence of cue-specific, spatially distinct protein networks in the different cell migration modes.

Conclusions

The integrated analysis of the large-scale experimental data with protein information from databases allows us to understand some emergent properties of spatial regulation of signaling during cell migration. This provides the cell migration community with a large-scale view of the distribution of proteins and phospho-proteins regulating directed cell migration.

Proteome-wide profiling of carbonylated proteins and carbonylation sites in HeLa cells under mild oxidative stress conditions

A number of oxidative protein modifications have been well characterized during the past decade. Presumably, reversible oxidative posttranslational modifications (PTMs) play a significant role in redox signaling pathways, whereas irreversible modifications including reactive protein carbonyl groups are harmful, as their levels are typically increased during aging and in certain diseases. Despite compelling evidence linking protein carbonylation to numerous disorders, the underlying molecular mechanisms at the proteome remain to be identified. Recent advancements in analysis of PTMs by mass spectrometry provided new insights into the mechanisms of protein carbonylation, such as protein susceptibility and exact modification sites, but only for a limited number of proteins. Here we report the first proteome-wide study of carbonylated proteins including modification sites in HeLa cells for mild oxidative stress conditions. The analysis relied on our recent strategy utilizing mass spectrometry-based enrichment of carbonylated peptides after DNPH derivatization. Thus a total of 210 carbonylated proteins containing 643 carbonylation sites were consistently identified in three replicates. Most carbonylation sites (284, 44.2%) resulted from oxidation of lysine residues (aminoadipic semialdehyde). Additionally, 121 arginine (18.8%), 121 threonine (18.8%), and 117 proline residues (18.2%) were oxidized to reactive carbonyls. The sequence motifs were significantly enriched for lysine and arginine residues near carbonylation sites (±10 residues). Gene Ontology analysis revealed that 80% of the carbonylated proteins originated from organelles, 50% enrichment of which was demonstrated for the nucleus. Moreover, functional interactions between carbonylated proteins of kinetochore/spindle machinery and centrosome organization were significantly enriched. One-third of the 210 carbonylated proteins identified here are regulated during apoptosis.

Mass spectrometric protein maps for biomarker discovery and clinical research

Among the wide range of proteomic technologies, targeted mass spectrometry (MS) has shown great potential for biomarker studies. To extend the degree of multiplexing achieved by selected reaction monitoring (SRM), we recently developed SWATH MS. SWATH MS is a variant of the emerging class of data-independent acquisition (DIA) methods and essentially converts the molecules in a physical sample into perpetually re-usable digital maps. The thus generated SWATH maps are then mined using a targeted data extraction strategy, allowing us to profile disease-related proteomes at a high degree of reproducibility. The successful application of both SRM and SWATH MS requires the a priori generation of reference spectral maps that provide coordinates for quantification. Herein, we demonstrate that the application of the mass spectrometric reference maps and the acquisition of personalized SWATH maps hold a particular promise for accelerating the current process of biomarker discovery.

Proteomics and the analysis of proteomic data: 2013 overview of current protein-profiling technologies

Mass spectrometry has become a major tool in the study of proteomes. The analysis of proteolytic peptides and their fragment ions by this technique enables the identification and quantitation of the precursor proteins in a mixture. However, deducing chemical structures and then protein sequences from mass-to-charge ratios is a challenging computational task. Software tools incorporating powerful algorithms and statistical methods improved our ability to process the large quantities of proteomics data. Repositories of spectral data make both data analysis and experimental design more efficient. New approaches in quantitative and statistical proteomics make possible a greater coverage of the proteome, the identification of more post-translational modifications, and a greater sensitivity in the quantitation of targeted proteins.

Chronic nicotine treatment impacts the regulation of opioid and non-opioid peptides in the rat dorsal striatum

The chronic use of nicotine, the main psychoactive ingredient of tobacco smoking, alters diverse physiological processes and consequently generates physical dependence. To understand the impact of chronic nicotine on neuropeptides, which are potential molecules associated with dependence, we conducted qualitative and quantitative neuropeptidomics on the rat dorsal striatum, an important brain region implicated in the preoccupation/craving phase of drug dependence. We used extensive LC-FT-MS/MS analyses for neuropeptide identification and LC-FT-MS in conjunction with stable isotope addition for relative quantification. The treatment with chronic nicotine for 3 months led to moderate changes in the levels of endogenous dorsal striatum peptides. Five enkephalin opioid peptides were up-regulated, although no change was observed for dynorphin peptides. Specially, nicotine altered levels of nine non-opioid peptides derived from precursors, including somatostatin and cerebellin, which potentially modulate neurotransmitter release and energy metabolism. This broad but selective impact on the multiple peptidergic systems suggests that apart from the opioid peptides, several other peptidergic systems are involved in the preoccupation/craving phase of drug dependence. Our finding permits future evaluation of the neurochemical circuits modulated by chronic nicotine exposure and provides a number of novel molecules that could serve as potential therapeutic targets for treating drug dependence.

Optimal precursor ion selection for LC-MALDI MS/MS

Background

Liquid chromatography mass spectrometry (LC-MS) maps in shotgun proteomics are often too complex to select every detected peptide signal for fragmentation by tandem mass spectrometry (MS/MS). Standard methods for precursor ion selection, commonly based on data dependent acquisition, select highly abundant peptide signals in each spectrum. However, these approaches produce redundant information and are biased towards high-abundance proteins.

Results

We present two algorithms for inclusion list creation that formulate precursor ion selection as an optimization problem. Given an LC-MS map, the first approach maximizes the number of selected precursors given constraints such as a limited number of acquisitions per RT fraction. Second, we introduce a protein sequence-based inclusion list that can be used to monitor proteins of interest. Given only the protein sequences, we create an inclusion list that optimally covers the whole protein set. Additionally, we propose an iterative precursor ion selection that aims at reducing the redundancy obtained with data dependent LC-MS/MS. We overcome the risk of erroneous assignments by including methods for retention time and proteotypicity predictions. We show that our method identifies a set of proteins requiring fewer precursors than standard approaches. Thus, it is well suited for precursor ion selection in experiments with limited sample amount or analysis time.

Conclusions

We present three approaches to precursor ion selection with LC-MALDI MS/MS. Using a well-defined protein standard and a complex human cell lysate, we demonstrate that our methods outperform standard approaches. Our algorithms are implemented as part of OpenMS and are available under http://www.openms.de.

A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis

Experience from different fields of life sciences suggests that accessible, complete reference maps of the components of the system under study are highly beneficial research tools. Examples of such maps include libraries of the spectroscopic properties of molecules, or databases of drug structures in analytical or forensic chemistry. Such maps, and methods to navigate them, constitute reliable assays to probe any sample for the presence and amount of molecules contained in the map. So far, attempts to generate such maps for any proteome have failed to reach complete proteome coverage. Here we use a strategy based on high-throughput peptide synthesis and mass spectrometry to generate an almost complete reference map (97% of the genome-predicted proteins) of the Saccharomyces cerevisiae proteome. We generated two versions of this mass-spectrometric map, one supporting discovery-driven (shotgun) and the other supporting hypothesis-driven (targeted) proteomic measurements. Together, the two versions of the map constitute a complete set of proteomic assays to support most studies performed with contemporary proteomic technologies. To show the utility of the maps, we applied them to a protein quantitative trait locus (QTL) analysis, which requires precise measurement of the same set of peptides over a large number of samples. Protein measurements over 78 S. cerevisiae strains revealed a complex relationship between independent genetic loci, influencing the levels of related proteins. Our results suggest that selective pressure favours the acquisition of sets of polymorphisms that adapt protein levels but also maintain the stoichiometry of functionally related pathway members.

Multi-parameter systematic strategies for predictive, preventive and personalised medicine in cancer

Cancer is a complex disease that causes the alterations in the levels of gene, RNA, protein and metabolite. With the development of genomics, transcriptomics, proteomics and metabolomic techniques, the characterisation of key mutations and molecular pathways responsible for tumour progression has led to the identification of a large number of potential targets. The increasing understanding of molecular carcinogenesis has begun to change paradigms in oncology from traditional single-factor strategy to multi-parameter systematic strategy. The therapeutic model of cancer has changed from adopting the general radiotherapy and chemotherapy to personalised strategy. The development of predictive, preventive and personalised medicine (PPPM) will allow prediction of response with substantially increased accuracy, stratification of particular patient groups and eventual personalisation of medicine. The PPPM will change the approach to tumour diseases from a systematic and comprehensive point of view in the future. Patients will be treated according to the specific molecular profiles that are found in the individual tumour tissue and preferentially with targeted substances, if available.

Proteomics of Pyrococcus furiosus (Pfu): Identification of Extracted Proteins by Three Independent Methods

Pyrococcus furiosus (Pfu) is an excellent organism to generate reference samples for proteomics laboratories because of its moderately sized genome and very little sequence duplication within the genome. We demonstrated a stable and consistent method to prepare proteins in bulk that eliminates growth and preparation as a source of uncertainty in the standard. We performed several proteomic studies in different laboratories using each laboratory's specific workflow as well as separate and integrated data analysis. This study demonstrated that a Pfu whole cell lysate provides suitable protein sample complexity to not only validate proteomic methods, work flows, and benchmark new instruments but also to facilitate comparison of experimental data generated over time and across instruments or laboratories.

Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics

Selected reaction monitoring on a triple quadrupole mass spectrometer is currently experiencing a renaissance within the proteomics community for its, as yet, unparalleled ability to characterize and quantify a set of proteins reproducibly, completely, and with high sensitivity. Given the immense benefit that high resolution and accurate mass instruments have brought to the discovery proteomics field, we wondered if highly accurate mass measurement capabilities could be leveraged to provide benefits in the targeted proteomics domain as well. Here, we propose a new targeted proteomics paradigm centered on the use of next generation, quadrupole-equipped high resolution and accurate mass instruments: parallel reaction monitoring (PRM). In PRM, the third quadrupole of a triple quadrupole is substituted with a high resolution and accurate mass mass analyzer to permit the parallel detection of all target product ions in one, concerted high resolution mass analysis. We detail the analytical performance of the PRM method, using a quadrupole-equipped bench-top Orbitrap MS, and draw a performance comparison to selected reaction monitoring in terms of run-to-run reproducibility, dynamic range, and measurement accuracy. In addition to requiring minimal upfront method development and facilitating automated data analysis, PRM yielded quantitative data over a wider dynamic range than selected reaction monitoring in the presence of a yeast background matrix because of PRM's high selectivity in the mass-to-charge domain. With achievable linearity over the quantifiable dynamic range found to be statistically equal between the two methods, our investigation suggests that PRM will be a promising new addition to the quantitative proteomics toolbox.