Mass Spectrometry Proteomics Data Research Articles

Detergent-Insoluble Proteome Analysis Revealed Aberrantly Aggregated Proteins in Human Preeclampsia Placentas.

Preeclampsia (PE) is a placenta disease, featured by hypertension, proteinuria, and other multiorgan dysfunctions, and its etiology is unclear. We and others have shown that intensive endoplasmic reticulum (ER) stress and unfolded protein response (UPR) occur in the PE placenta. In this study, we isolated detergent-insoluble proteins (DIPs) from human placenta tissues, which were enriched with protein aggregates, to characterize the placenta UPR in PE. With data-independent acquisition (DIA) mass spectrometry, we identified 2066 DIPs across all normal (n = 10) and PE (n = 10) placenta samples, among which 110 and 108 DIPs were significantly up- and down-regulated in PE, respectively. Per clustering analysis, differential DIPs could generally distinguish PE from normal placentas. We verified the MS quantitation of endoglin and vimentin by immunoblotting. In addition, we observed that PE placenta tissues have remarkably more endoglin in the cytoplasm. Furthermore, we found that DIPs were evenly distributed across different chromosomes and could be enriched in diversified gene ontology terms, while differential DIPs avoided to distribute on X-chromosome. Significantly up-regulated DIPs in PE were focused on the top functions of lipid metabolism, while 23 of these DIPs could form the top network regulating cellular movement, development, growth, and proliferation. Our results implicate that human PE placentas have disease-relevant differential DIPs, which reflect aberrantly aggregated proteins of placental tissues. The mass spectrometry proteomics data have been deposited to ProteomeXchange consortium with the data set identifier PXD006654, and iProX database (accession number: IPX0000948000).

Feature selection method based on support vector machine and shape analysis for high-throughput medical data

Proteomics data analysis based on the mass-spectrometry technique can provide a powerful tool for early diagnosis of tumors and other diseases. It can be used for exploring the features that reflect the difference between samples from high-throughput mass spectrometry data, which are important for the identification of tumor markers. Proteomics mass spectrometry data have the characteristics of too few samples, too many features and noise interference, which pose a great challenge to traditional machine learning methods. Traditional unsupervised dimensionality reduction methods do not utilize the label information effectively, so the subspaces they find may not be the most separable ones of the data. To overcome the shortcomings of traditional methods, in this paper, we present a novel feature selection method based on support vector machine (SVM) and shape analysis. In the process of feature selection, our method considers not only the interaction between features but also the relationship between features and class labels, which improves the classification performance. The experimental results obtained from four groups of proteomics data show that, compared with traditional unsupervised feature extraction methods (i.e., Principal Component Analysis - Procrustes Analysis, PCA-PA), our method not only ensures that fewer features are selected but also ensures a high recognition rate. In addition, compared with the two kinds of multivariate filter methods, i.e., Max-Relevance Min-Redundancy (MRMR) and Fast Correlation-Based Filter (FCBF), our method has a higher recognition rate.

Identification of Differentially Expressed Splice Variants by the Proteogenomic Pipeline Splicify

Proteogenomics, i.e. comprehensive integration of genomics and proteomics data, is a powerful approach identifying novel protein biomarkers. This is especially the case for proteins that differ structurally between disease and control conditions. As tumor development is associated with aberrant splicing, we focus on this rich source of cancer specific biomarkers. To this end, we developed a proteogenomic pipeline, Splicify, which can detect differentially expressed protein isoforms. Splicify is based on integrating RNA massive parallel sequencing data and tandem mass spectrometry proteomics data to identify protein isoforms resulting from differential splicing between two conditions. Proof of concept was obtained by applying Splicify to RNA sequencing and mass spectrometry data obtained from colorectal cancer cell line SW480, before and after siRNA-mediated downmodulation of the splicing factors SF3B1 and SRSF1. These analyses revealed 2172 and 149 differentially expressed isoforms, respectively, with peptide confirmation upon knock-down of SF3B1 and SRSF1 compared with their controls. Splice variants identified included RAC1, OSBPL3, MKI67, and SYK. One additional sample was analyzed by PacBio Iso-Seq full-length transcript sequencing after SF3B1 downmodulation. This analysis verified the alternative splicing identified by Splicify and in addition identified novel splicing events that were not represented in the human reference genome annotation. Therefore, Splicify offers a validated proteogenomic data analysis pipeline for identification of disease specific protein biomarkers resulting from mRNA alternative splicing. Splicify is publicly available on GitHub (https://github.com/NKI-TGO/SPLICIFY) and suitable to address basic research questions using pre-clinical model systems as well as translational research questions using patient-derived samples, e.g. allowing to identify clinically relevant biomarkers.

REPARATION: ribosome profiling assisted (re-)annotation of bacterial genomes.

Prokaryotic genome annotation is highly dependent on automated methods, as manual curation cannot keep up with the exponential growth of sequenced genomes. Current automated methods depend heavily on sequence composition and often underestimate the complexity of the proteome. We developed RibosomeE Profiling Assisted (re-)AnnotaTION (REPARATION), a de novo machine learning algorithm that takes advantage of experimental protein synthesis evidence from ribosome profiling (Ribo-seq) to delineate translated open reading frames (ORFs) in bacteria, independent of genome annotation (https://github.com/Biobix/REPARATION). REPARATION evaluates all possible ORFs in the genome and estimates minimum thresholds based on a growth curve model to screen for spurious ORFs. We applied REPARATION to three annotated bacterial species to obtain a more comprehensive mapping of their translation landscape in support of experimental data. In all cases, we identified hundreds of novel (small) ORFs including variants of previously annotated ORFs and >70% of all (variants of) annotated protein coding ORFs were predicted by REPARATION to be translated. Our predictions are supported by matching mass spectrometry proteomics data, sequence composition and conservation analysis. REPARATION is unique in that it makes use of experimental translation evidence to intrinsically perform a de novo ORF delineation in bacterial genomes irrespective of the sequence features linked to open reading frames.

Salt stress induces changes in the proteomic profile of micropropagated sugarcane shoots.

Salt stress is one of the most common stresses in agricultural regions worldwide. In particular, sugarcane is affected by salt stress conditions, and no sugarcane cultivar presently show high productivity accompanied by a tolerance to salt stress. Proteomic analysis allows elucidation of the important pathways involved in responses to various abiotic stresses at the biochemical and molecular levels. Thus, this study aimed to analyse the proteomic effects of salt stress in micropropagated shoots of two sugarcane cultivars (CB38-22 and RB855536) using a label-free proteomic approach. The mass spectrometry proteomics data are available via ProteomeXchange with identifier PXD006075. The RB855536 cultivar is more tolerant to salt stress than CB38-22. A quantitative label-free shotgun proteomic analysis identified 1172 non-redundant proteins, and 1160 of these were observed in both cultivars in the presence or absence of NaCl. Compared with CB38-22, the RB855536 cultivar showed a greater abundance of proteins involved in non-enzymatic antioxidant mechanisms, ion transport, and photosynthesis. Some proteins, such as calcium-dependent protein kinase, photosystem I, phospholipase D, and glyceraldehyde-3-phosphate dehydrogenase, were more abundant in the RB855536 cultivar under salt stress. Our results provide new insights into the response of sugarcane to salt stress, and the changes in the abundance of these proteins might be important for the acquisition of ionic and osmotic homeostasis during exposure to salt stress.

Sparse Proteomics Analysis \u2013 a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data

BackgroundHigh-throughput proteomics techniques, such as mass spectrometry (MS)-based approaches, produce very high-dimensional data-sets. In a clinical setting one is often interested in how mass spectra differ between patients of different classes, for example spectra from healthy patients vs. spectra from patients having a particular disease. Machine learning algorithms are needed to (a) identify these discriminating features and (b) classify unknown spectra based on this feature set. Since the acquired data is usually noisy, the algorithms should be robust against noise and outliers, while the identified feature set should be as small as possible.ResultsWe present a new algorithm, Sparse Proteomics Analysis (SPA), based on the theory of compressed sensing that allows us to identify a minimal discriminating set of features from mass spectrometry data-sets. We show (1) how our method performs on artificial and real-world data-sets, (2) that its performance is competitive with standard (and widely used) algorithms for analyzing proteomics data, and (3) that it is robust against random and systematic noise. We further demonstrate the applicability of our algorithm to two previously published clinical data-sets.

Proteomic profiling of HaCaT keratinocytes exposed to skin damaging detergents

The effects of sodium dodecyl sulfate (25 mg/ml) and Triton X-100 (12.5 mg/ml and 25 mg/ml) on the HaCaT immortalized keratinocytes exposed to these surfactants for 48 h were studied. Using shotgun proteomics, a comparative analysis of the proteomic profiles of control and experimental cells after surfactants exposure was carried out. 260 common proteins were identified in control and experimental cells; 33 proteins were found in cells exposed to all three treatments, but not in control cells. These 33 proteins apparently reflect a nonspecific (universal) response of cells to toxic damage by the surfactants. These proteins are associated with activation of cell proliferation, changes in the functional activity of their ER and mitochondria, increased mRNA stability and activation of protein degradation processes in the cells. The possibility of using these proteins as a nonspecific parameter of cell response to cytotoxic damage is discussed. The mass spectrometry proteomics data ("raw", "mgf" and "xml" files) have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD007789 and PXD007776.

Adapting censored regression methods to adjust for the limit of detection in the calibration of diagnostic rules for clinical mass spectrometry proteomic data.

In this paper, we consider the problem of calibrating diagnostic rules based on high-resolution mass spectrometry data subject to the limit of detection. The limit of detection is related to the limitation of instruments in measuring low-concentration proteins. As a consequence, peak intensities below the limit of detection are often reported as missing during the quantification step of proteomic analysis. We propose the use of censored data methodology to handle spectral measurements within the presence of limit of detection, recognizing that those have been left-censored for low-abundance proteins. We replace the set of incomplete spectral measurements with estimates of the expected intensity and use those as input to a prediction model. To correct for lack of information and measurement uncertainty, we combine this approach with borrowing of information through the addition of an individual-specific random effect formulation. We present different modalities of using the above formulation for prediction purposes and show how it may also allow for variable selection. We evaluate the proposed methods by comparing their predictive performance with the one achieved using the complete information as well as alternative methods to deal with the limit of detection.

Informed baseline subtraction of proteomic mass spectrometry data aided by a novel sliding window algorithm.

BackgroundProteomic matrix-assisted laser desorption/ionisation (MALDI) linear time-of-flight (TOF) mass spectrometry (MS) may be used to produce protein profiles from biological samples with the aim of discovering biomarkers for disease. However, the raw protein profiles suffer from several sources of bias or systematic variation which need to be removed via pre-processing before meaningful downstream analysis of the data can be undertaken. Baseline subtraction, an early pre-processing step that removes the non-peptide signal from the spectra, is complicated by the following: (i) each spectrum has, on average, wider peaks for peptides with higher mass-to-charge ratios (m/z), and (ii) the time-consuming and error-prone trial-and-error process for optimising the baseline subtraction input arguments. With reference to the aforementioned complications, we present an automated pipeline that includes (i) a novel ‘continuous’ line segment algorithm that efficiently operates over data with a transformed m/z-axis to remove the relationship between peptide mass and peak width, and (ii) an input-free algorithm to estimate peak widths on the transformed m/z scale.ResultsThe automated baseline subtraction method was deployed on six publicly available proteomic MS datasets using six different m/z-axis transformations. Optimality of the automated baseline subtraction pipeline was assessed quantitatively using the mean absolute scaled error (MASE) when compared to a gold-standard baseline subtracted signal. Several of the transformations investigated were able to reduce, if not entirely remove, the peak width and peak location relationship resulting in near-optimal baseline subtraction using the automated pipeline. The proposed novel ‘continuous’ line segment algorithm is shown to far outperform naive sliding window algorithms with regard to the computational time required. The improvement in computational time was at least four-fold on real MALDI TOF-MS data and at least an order of magnitude on many simulated datasets.ConclusionsThe advantages of the proposed pipeline include informed and data specific input arguments for baseline subtraction methods, the avoidance of time-intensive and subjective piecewise baseline subtraction, and the ability to automate baseline subtraction completely. Moreover, individual steps can be adopted as stand-alone routines.Electronic supplementary materialThe online version of this article (doi:10.1186/s12953-016-0107-8) contains supplementary material, which is available to authorized users.

Comparison of different statistical approaches for urinary peptide biomarker detection in the context of coronary artery disease.

BackgroundWhen combined with a clinical outcome variable, the size, complexity and nature of mass-spectrometry proteomics data impose great statistical challenges in the discovery of potential disease-associated biomarkers. The purpose of this study was thus to evaluate the effectiveness of different statistical methods applied for urinary proteomic biomarker discovery and different methods of classifier modelling in respect of the diagnosis of coronary artery disease in 197 study subjects and the prognostication of acute coronary syndromes in 368 study subjects.ResultsComputing the discovery sub-cohorts comprising {scriptscriptstyle raisebox{1ex}{2}!left/ !raisebox{-1ex}{3}right.} of the study subjects based on the Wilcoxon rank sum test, t-score, cat-score, binary discriminant analysis and random forests provided largely different numbers (ranging from 2 to 398) of potential peptide biomarkers. Moreover, these biomarker patterns showed very little overlap limited to fragments of type I and III collagens as the common denominator. However, these differences in biomarker patterns did mostly not translate into significant differently performing diagnostic or prognostic classifiers modelled by support vector machine, diagonal discriminant analysis, linear discriminant analysis, binary discriminant analysis and random forest. This was even true when different biomarker patterns were combined into master-patterns.ConclusionIn conclusion, our study revealed a very considerable dependence of peptide biomarker discovery on statistical computing of urinary peptide profiles while the observed diagnostic and/or prognostic reliability of classifiers was widely independent of the modelling approach. This may however be due to the limited statistical power in classifier testing. Nonetheless, our study showed that urinary proteome analysis has the potential to provide valuable biomarkers for coronary artery disease mirroring especially alterations in the extracellular matrix. It further showed that for a comprehensive discovery of biomarkers and thus of pathological information, the results of different statistical methods may best be combined into a master pattern that then can be used for classifier modelling.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1390-1) contains supplementary material, which is available to authorized users.

Comparative Ploidy Proteomics of Candida albicans Biofilms Unraveled the Role of the AHP1 Gene in the Biofilm Persistence Against Amphotericin B

Candida albicans is a major fungal pathogen causing lethal infections in immunocompromised patients. C. albicans forms antifungal tolerant biofilms contributing significantly to therapeutic failure. The recently established haploid C. albicans biofilm model provides a new toolbox to uncover the mechanism governing the higher antifungal tolerance of biofilms. Here, we comprehensively examined the proteomics and antifungal susceptibility of standard diploid (SC5314 and BWP17) and stable haploid (GZY792 and GZY803) strains of C. albicans biofilms. Subsequent downstream analyses identified alkyl hydroperoxide reductase 1 (AHP1) as a critical determinant of C. albicans biofilm's tolerance of amphotericin B. At 32 μg/ml of amphotericin B, GZY803 haploid biofilms showed 0.1% of persister population as compared with 1% of the diploid biofilms. AHP1 expression was found to be lower in GZY803 biofilms, and AHP1 overexpression in GZY803 restored the percentage of persister population. Consistently, deleting AHP1 in the diploid strain BWP17 caused a similar increase in amphotericin B susceptibility. AHP1 expression was also positively correlated with the antioxidant potential. Furthermore, C. albicans ira2Δ/Δ biofilms were susceptible to amphotericin B and had a diminished antioxidant capacity. Interestingly, AHP1 overexpression in the ira2Δ/Δ strain restored the antioxidant potential and enhanced the persister population against amphotericin B, and shutting down the AHP1 expression in ira2Δ/Δ biofilms reversed the effect. In conclusion, we provide evidence that the AHP1 gene critically determines the amphotericin B tolerance of C. albicans biofilms possibly by maintaining the persisters' antioxidant capacity. This finding will open up new avenues for developing therapies targeting the persister population of C. albicans biofilms. The mass spectrometry proteomics data are available via ProteomeXchange with identifier PXD004274.

Sex-specific vitamin D effects on blood coagulation among overweight adults.

Overweight adults are at increased risk for cardiovascular disease and vitamin D deficiency, whereas an important feature to vitamin D physiology is its sex dependence. The aim of this study was to examine whether vitamin D status improvement exerts a sexually dimorphic effect on serum proteins associated with cardiovascular risk among overweight adults. Unprocessed serum from age- and BMI-matched men (n = 26) and premenopausal women (n = 24) with vitamin D deficiency and after they achieved sufficiency through a 12-month nutritional intervention was analysed using our previously published depletion-free quantitative proteomics method. Key findings were verified with ELISA. Differentially expressed proteins were subjected to in silico bioinformatics assessment using principal component analysis, hierarchical clustering and Metacore™ pathway analysis. All mass spectrometry proteomic data are available via ProteomeXchange (identifier: PXD003663). A total of 282 proteins were differentially expressed after the intervention between men and women (P-value ≤ 0·05), in which the blood coagulation pathway was significantly enriched. In agreement with the proteomics findings, ELISA measurements showed vitamin K-dependent protein C, von Willebrand factor, fibrinogen gamma chain and multimerin-1 proteins, of relevance to blood coagulation, to be differentially affected (P-value ≤ 0·05) between sexes after vitamin D status correction. This study identified novel protein-level molecular indicators on the sexually dimorphic effect of vitamin D status correction associated with blood coagulation among overweight adults. These sex-mediated vitamin D effects should be factored in the design and interpretation of vitamin D observational and interventional studies testing cardiometabolic outcomes.

The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition.

The ProteomeXchange (PX) Consortium of proteomics resources (http://www.proteomexchange.org) was formally started in 2011 to standardize data submission and dissemination of mass spectrometry proteomics data worldwide. We give an overview of the current consortium activities and describe the advances of the past few years. Augmenting the PX founding members (PRIDE and PeptideAtlas, including the PASSEL resource), two new members have joined the consortium: MassIVE and jPOST. ProteomeCentral remains as the common data access portal, providing the ability to search for data sets in all participating PX resources, now with enhanced data visualization components.We describe the updated submission guidelines, now expanded to include four members instead of two. As demonstrated by data submission statistics, PX is supporting a change in culture of the proteomics field: public data sharing is now an accepted standard, supported by requirements for journal submissions resulting in public data release becoming the norm. More than 4500 data sets have been submitted to the various PX resources since 2012. Human is the most represented species with approximately half of the data sets, followed by some of the main model organisms and a growing list of more than 900 diverse species. Data reprocessing activities are becoming more prominent, with both MassIVE and PeptideAtlas releasing the results of reprocessed data sets. Finally, we outline the upcoming advances for ProteomeXchange.

The proteome of normal human retrobulbar optic nerve and sclera.

The optic nerve is a white matter tract that conveys visual information to the brain. The sclera comprises the white, protective outer layer of the eye. A characterization of the proteome of normal human retrobulbar optic nerve and sclera may facilitate studies of the eye. We conducted a proteomic analysis of optic nerve and sclera from five adults. Proteins were fractionated using SDS-PAGE. After in-gel digestion, peptides were analyzed using LC-MS/MS on an Orbitrap Elite mass spectrometer. We identified 2711 non-redundant proteins in retrobulbar optic nerve and 1945 non-redundant proteins in sclera. Optic nerve proteins included proteins expressed by oligodendrocytes (laminin, proteolipid protein, fibronectin), myelin proteins (myelin basic protein, myelin-associated glycoprotein), and paranodal structural proteins (ankyrin β, spectrin). Sclera included 18 collagen protein chains, small leucine-rich proteoglycans (decorin, biglycan, lumican, keratocan, prolargin, fibromodulin, mimecan), non-collagenous glycoproteins (fibronectin, vitronectin, laminin), extracellular matrix proteins (thrombospondins 1-4, dystroglycan, transgelins 1-3), and integrins alpha-V, alpha-1 and 2, beta-1, -2, and -5. Twenty-one unambiguous alternative splicing protein isoforms were identified in optic nerve and ten unambiguous alternative splicing protein isoforms were identified in sclera. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD001581.

Tiered Human Integrated Sequence Search Databases for Shotgun Proteomics.

The results of analysis of shotgun proteomics mass spectrometry data can be greatly affected by the selection of the reference protein sequence database against which the spectra are matched. For many species there are multiple sources from which somewhat different sequence sets can be obtained. This can lead to confusion about which database is best in which circumstances-a problem especially acute in human sample analysis. All sequence databases are genome-based, with sequences for the predicted gene and their protein translation products compiled. Our goal is to create a set of primary sequence databases that comprise the union of sequences from many of the different available sources and make the result easily available to the community. We have compiled a set of four sequence databases of varying sizes, from a small database consisting of only the ∼20,000 primary isoforms plus contaminants to a very large database that includes almost all nonredundant protein sequences from several sources. This set of tiered, increasingly complete human protein sequence databases suitable for mass spectrometry proteomics sequence database searching is called the Tiered Human Integrated Search Proteome set. In order to evaluate the utility of these databases, we have analyzed two different data sets, one from the HeLa cell line and the other from normal human liver tissue, with each of the four tiers of database complexity. The result is that approximately 0.8%, 1.1%, and 1.5% additional peptides can be identified for Tiers 2, 3, and 4, respectively, as compared with the Tier 1 database, at substantially increasing computational cost. This increase in computational cost may be worth bearing if the identification of sequence variants or the discovery of sequences that are not present in the reviewed knowledge base entries is an important goal of the study. We find that it is useful to search a data set against a simpler database, and then check the uniqueness of the discovered peptides against a more complex database. We have set up an automated system that downloads all the source databases on the first of each month and automatically generates a new set of search databases and makes them available for download at http://www.peptideatlas.org/thisp/ .

Proteomic Studies on the Effects of Lipo-Chitooligosaccharide and Thuricin 17 under Unstressed and Salt Stressed Conditions in Arabidopsis thaliana.

Plants, being sessile organisms, are exposed to widely varying environmental conditions throughout their life cycle. Compatible plant-microbe interactions favor plant growth and development, and help plants deal with these environmental challenges. Microorganisms produce a diverse range of elicitor molecules to establish symbiotic relationships with the plants they associate with, in a given ecological niche. Lipo-chitooligosaccharide (LCO) and Thuricin 17 (Th17) are two such compounds shown to positively influence plant growth of both legumes and non-legumes. Arabidopsis thaliana responded positively to treatment with the bacterial signal compounds LCO and Th17 in the presence of salt stress (up to 250 mM NaCl). Shotgun proteomics of unstressed and 250 mM NaCl stressed A. thaliana rosettes (7 days post stress) in combination with the LCO and Th17 revealed many known, putative, hypothetical, and unknown proteins. Overall, carbon and energy metabolic pathways were affected under both unstressed and salt stressed conditions when treated with these signals. PEP carboxylase, Rubisco-oxygenase large subunit, pyruvate kinase, and proteins of photosystems I and II were some of the noteworthy proteins enhanced by the signals, along with other stress related proteins. These findings suggest that the proteome of A. thaliana rosettes is altered by the bacterial signals tested, and more so under salt stress, thereby imparting a positive effect on plant growth under high salt stress. The roles of the identified proteins are discussed here in relation to salt stress adaptation, which, when translated to field grown crops can be a crucial component and of significant importance in agriculture and global food production. The mass spectrometry proteomics data have been deposited to the ProteomeXchange with identifier PXD004742.

A Proteomic Approach to Lipo-Chitooligosaccharide and Thuricin 17 Effects on Soybean GerminationUnstressed and Salt Stress.

Salt stress is an important abiotic stressor affecting crop growth and productivity. Of the 20 percent of the terrestrial earth’s surface available as agricultural land, 50 percent is estimated by the United Nations Environment Program to be salinized to the level that crops growing on it will be salt-stressed. Increased soil salinity has profound effects on seed germination and germinating seedlings as they are frequently confronted with much higher salinities than vigorously growing plants, because germination usually occurs in surface soils, the site of greatest soluble salt accumulation. The growth of soybean exposed to 40 mM NaCl is negatively affected, while an exposure to 80 mM NaCl is often lethal. When treated with the bacterial signal compounds lipo-chitooligosaccharide (LCO) and thuricin 17 (Th17), soybean seeds (variety Absolute RR) responded positively at salt stress of up to 150 mM NaCl. Shotgun proteomics of unstressed and 100 mM NaCl stressed seeds (48 h) in combination with the LCO and Th17 revealed many known, predicted, hypothetical and unknown proteins. In all, carbon, nitrogen and energy metabolic pathways were affected under both unstressed and salt stressed conditions when treated with signals. PEP carboxylase, Rubisco oxygenase large subunit, pyruvate kinase, and isocitrate lyase were some of the noteworthy proteins enhanced by the signals, along with antioxidant glutathione-S-transferase and other stress related proteins. These findings suggest that the germinating seeds alter their proteome based on bacterial signals and on stress, the specificity of this response plays a crucial role in organ maturation and transition from one stage to another in the plants' life cycle; understanding this response is of fundamental importance in agriculture and, as a result, global food security. The mass spectrometry proteomics data have been deposited to the ProteomeXchange with identifier PXD004106.

Phosphoproteome Characterization of Human Colorectal Cancer SW620 Cell-Derived Exosomes and New Phosphosite Discovery for C-HPP.

Identification of all phosphorylation forms of known proteins is a major goal of the Chromosome-Centric Human Proteome Project (C-HPP). Recent studies have found that certain phosphoproteins can be encapsulated in exosomes and function as key regulators in tumor microenvironment, but no deep coverage phosphoproteome of human exosomes has been reported to date, which makes the exosome a potential source for the new phosphosite discovery. In this study, we performed highly optimized MS analyses on the exosomal and cellular proteins isolated from human colorectal cancer SW620 cells. With stringent data quality control, 313 phosphoproteins with 1091 phosphosites were confidently identified from the SW620 exosome, from which 202 new phosphosites were detected. Exosomal phosphoproteins were significantly enriched in the 11q12.1-13.5 region of chromosome 11 and had a remarkably high level of tyrosine-phosphorylated proteins (6.4%), which were functionally relevant to ephrin signaling pathway-directed cytoskeleton remodeling. In conclusion, we here report the first high-coverage phosphoproteome of human cell-secreted exosomes, which leads to the identification of new phosphosites for C-HPP. Our findings provide insights into the exosomal phosphoprotein systems that help to understand the signaling language being delivered by exosomes in cell-cell communications. The mass spectrometry proteomics data have been deposited to the ProteomeXchange consortium with the data set identifier PXD004079, and iProX database (accession number: IPX00076800).

Dinosaur: A Refined Open-Source Peptide MS Feature Detector.

In bottom-up mass spectrometry (MS)-based proteomics, peptide isotopic and chromatographic traces (features) are frequently used for label-free quantification in data-dependent acquisition MS but can also be used for the improved identification of chimeric spectra or sample complexity characterization. Feature detection is difficult because of the high complexity of MS proteomics data from biological samples, which frequently causes features to intermingle. In addition, existing feature detection algorithms commonly suffer from compatibility issues, long computation times, or poor performance on high-resolution data. Because of these limitations, we developed a new tool, Dinosaur, with increased speed and versatility. Dinosaur has the functionality to sample algorithm computations through quality-control plots, which we call a plot trail. From the evaluation of this plot trail, we introduce several algorithmic improvements to further improve the robustness and performance of Dinosaur, with the detection of features for 98% of MS/MS identifications in a benchmark data set, and no other algorithm tested in this study passed 96% feature detection. We finally used Dinosaur to reimplement a published workflow for peptide identification in chimeric spectra, increasing chimeric identification from 26% to 32% over the standard workflow. Dinosaur is operating-system-independent and is freely available as open source on https://github.com/fickludd/dinosaur.

Mass spectrometry data for in vitro protein profiles in early and late stages of Douglas-fir xylogenesis.

A Douglas-fir tissue culture system was developed [1] that could be induced to differentiate into tracheary elements (fibers) making it possible to monitor xylogenesis in vitro by a proteomics approach. Two proteomes, one from an early and one from a late stage of fiber differentiation process were analyzed and compared. Obtained mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository [2] with the dataset identifiers PXD001484 and DOI:10.6019/ PXD001484 [3].