Relative Protein Quantification Research Articles

In vitro versus cryo-induced capacitation of bovine spermatozoa, part 3: Compositional and molecular changes to the plasma membrane

The aim of this study was to assess the level of membrane cryodamage through the levels of selected capacitation and apoptosis-associated proteins, together with compositional membrane changes in capacitated (CAP), cryopreserved (CRYO) and non-capacitated bovine spermatozoa (CRTL). Sperm kinetic parameters were analyzed by the computer assisted sperm analysis (CASA) while the capacitation patterns were examined with the chlortetracycline (CTC) assay. In the case of DNA integrity, sperm chromatin structure assay and aniline blue staining were used. For the quantification of fatty acid content gas chromatography was performed. Using Western blotting the expression of capacitation (protein kinase C – PKC; phospholipases A2 and Cζ – PLA2, PLCζ; soluble adenylyl cyclase 10 – sAC10) and apoptosis-associated (apoptosis regulator Bax; B-cell lymphoma 2 – Bcl-2; caspase 3) proteins were evaluated. Data indicate a significant decline (p < 0.0001) of sperm kinetic parameters and higher occurrence (p < 0.0001) of DNA fragmentation in the CRYO group. CTC assay revealed a significant increase of acrosome-reacted spermatozoa in the CRYO group when compared to others. Compositional changes in the sperm membrane were visible as a notable decline of docosahexaenoic acid (p < 0.0001) associated with a significant decrease of membrane cholesterol (p < 0.05) and proteins (p < 0.0001) in the CRYO group while the amount of palmitic, stearic, oleic, and linoleic acid increased (p < 0.0001) significantly. Protein expression of all capacitation-associated proteins (PKC, PLA2, PLCζ, sAC10) was significantly down-regulated (p < 0.001; p < 0.0001) in the CRYO group. Relative quantification of apoptosis-associated proteins revealed increased Bax and decreased Bcl-2 levels in the CRYO group, except for caspase-3, which remained without significant changes.

Predicting Peptide Ionization Efficiencies for Electrospray Ionization Mass Spectrometry Using Machine Learning.

Mass spectrometry (MS) is inherently an information-rich technique. In this era of big data, label-free MS quantification for nontargeted studies has gained increasing popularity, especially for complex systems. One of the cornerstones of successful label-free quantification is the predictive modeling of ionization efficiency (IE) based on solutes' physicochemical properties. While many have studied IE modeling for small molecules, there are limited reports on peptide IEs. In this study, we leverage the stoichiometric relationship in trypsin digests of well-characterized monoclonal antibodies (mAbs) to compile a data set of relative ionization efficiencies (RIEs) for 241 peptides. From each peptide's sequence, we computed a set of physiochemical descriptors, which were then used to train machine learning regression models to predict RIEs. Peptides shorter than 20 amino acids had RIEs that were highly correlated to their molecular weight. A random forest (RF) model was able to best predict the RIEs of a test data set with a mean relative error of 23.9%. For larger peptides, a multilayer perceptron (MLP) model improved RIE prediction compared to current best practices, reducing mean relative error from 60.5% to 32.0%. Finally, we also show the application of the RF model in label-free relative protein quantification and improving the quantification of peptide post-translational modifications (PTMs). This approach to predicting peptide IEs from their sequences enables the development of accurate label-free quantification workflows for peptide and protein analysis.

An Early Gestation Plasma Inflammasome in Rural Bangladeshi Women.

Circulating α1-acid glycoprotein (AGP) and C-reactive protein (CRP) are commonly measured to assess inflammation, but these biomarkers fail to reveal the complex molecular biology of inflammation. We mined the maternal plasma proteome to detect proteins that covary with AGP and CRP. In 435 gravida predominantly in <12-week gestation, we correlated the relative quantification of plasma proteins assessed via a multiplexed aptamer assay (SOMAScan®) with AGP and CRP, quantified by immunoassay. We defined a plasma inflammasome as protein correlates meeting a false discovery rate <0.05. We examined potential pathways using principal component analysis. A total of 147 and 879 of 6431 detected plasma proteins correlated with AGP and CRP, respectively, of which 61 overlapped with both biomarkers. Positive correlates included serum amyloid, complement, interferon-induced, and immunoregulatory proteins. Negative correlates were micronutrient and lipid transporters and pregnancy-related anabolic proteins. The principal components (PCs) of AGP were dominated by negatively correlated anabolic proteins associated with gestational homeostasis, angiogenesis, and neurogenesis. The PCs of CRP were more diverse in function, reflecting cell surface and adhesion, embryogenic, and intracellular and extra-hepatic tissue leakage proteins. The plasma proteome of AGP or CRP reveals wide proteomic variation associated with early gestational inflammation, suggesting mechanisms and pathways that merit future research.

Concept of Normativity in Multi-Omics Analysis of Axon Regeneration.

Transcriptomes and proteomes can be normalized with a handful of RNAs or proteins (or their peptides), such as GAPDH, β-actin, RPBMS, and/or GAP43. Even with hundreds of standards, normalization cannot be achieved across different molecular mass ranges for small molecules, such as lipids and metabolites, due to the non-linearity of mass by charge ratio for even the smallest part of the spectrum. We define the amount (or range of amounts) of metabolites and/or lipids per a defined amount of a protein, consistently identified in all samples of a multiple-model organism comparison, as the normative level of that metabolite or lipid. The defined protein amount (or range) is a normalized value for one cohort of complete samples for which intrasample relative protein quantification is available. For example, the amount of citrate (a metabolite) per µg of aconitate hydratase (normalized protein amount) identified in the proteome is the normative level of citrate with aconitase. We define normativity as the amount of metabolites (or amount range) detected when compared to normalized protein levels. We use axon regeneration as an example to illustrate the need for advanced approaches to the normalization of proteins. Comparison across different pharmacologically induced axon regeneration mouse models entails the comparison of axon regeneration, studied at different time points in several models designed using different agents. For the normalization of the proteins across different pharmacologically induced models, we perform peptide doping (fixed amounts of known peptides) in each sample to normalize the proteome across the samples. We develop Regen V peptides, divided into Regen III (SEB, LLO, CFP) and II (HH4B, A1315), for pre- and post-extraction comparisons, performed with the addition of defined, digested peptides (bovine serum albumin tryptic digest) for protein abundance normalization beyond commercial labeled relative quantification (for example, 18-plex tandem mass tags). We also illustrate the concept of normativity by using this normalization technique on regenerative metabolome/lipidome profiles. As normalized protein amounts are different in different biological states (control versus axon regeneration), normative metabolite or lipid amounts are expected to be different for specific biological states. These concepts and standardization approaches are important for the integration of different datasets across different models of axon regeneration.

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry.

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

A Mass Spectrometry Strategy for Protein Quantification Based on the Differential Alkylation of Cysteines Using Iodoacetamide and Acrylamide.

Mass spectrometry has become the most prominent yet evolving technology in quantitative proteomics. Today, a number of label-free and label-based approaches are available for the relative and absolute quantification of proteins and peptides. However, the label-based methods rely solely on the employment of stable isotopes, which are expensive and often limited in availability. Here we propose a label-based quantification strategy, where the mass difference is identified by the differential alkylation of cysteines using iodoacetamide and acrylamide. The alkylation reactions were performed under identical experimental conditions; therefore, the method can be easily integrated into standard proteomic workflows. Using high-resolution mass spectrometry, the feasibility of this approach was assessed with a set of tryptic peptides of human serum albumin. Several critical questions, such as the efficiency of labeling and the effect of the differential alkylation on the peptide retention and fragmentation, were addressed. The concentration of the quality control samples calculated against the calibration curves were within the ±20% acceptance range. It was also demonstrated that heavy labeled peptides exhibit a similar extraction recovery and matrix effect to light ones. Consequently, the approach presented here may be a viable and cost-effective alternative of stable isotope labeling strategies for the quantification of cysteine-containing proteins.

The Effects of Artificial Diets on the Expression of Molecular Marker Genes Related to Honey Bee Health.

Honey bees are commonly used to study metabolic processes, yet the molecular mechanisms underlying nutrient transformation, particularly proteins and their effects on development, health, and diseases, still evoke varying opinions among researchers. To address this gap, we investigated the digestibility and transformation of water-soluble proteins from four artificial diets in long-lived honey bee populations (Apis mellifera ligustica), alongside their impact on metabolism and DWV relative expression ratio, using transcriptomic and protein quantification methods. Diet 2, characterized by its high protein content and digestibility, was selected for further analysis from the other studied diets. Subsequently, machine learning was employed to identify six diet-related molecular markers: SOD1, Trxr1, defensin2, JHAMT, TOR1, and vg. The expression levels of these markers were found to resemble those of honey bees who were fed with Diet 2 and bee bread, renowned as the best natural food. Notably, honey bees exhibiting chalkbrood symptoms (Control-N) responded differently to the diet, underscoring the unique nutritional effects on health-deficient bees. Additionally, we proposed a molecular model to elucidate the transition of long-lived honey bees from diapause to development, induced by nutrition. These findings carry implications for nutritional research and beekeeping, underscoring the vital role of honey bees in agriculture.

ProTInSeq: transposon insertion tracking by ultra-deep DNA sequencing to identify translated large and small ORFs

Identifying open reading frames (ORFs) being translated is not a trivial task. ProTInSeq is a technique designed to characterize proteomes by sequencing transposon insertions engineered to express a selection marker when they occur in-frame within a protein-coding gene. In the bacterium Mycoplasma pneumoniae, ProTInSeq identifies 83% of its annotated proteins, along with 5 proteins and 153 small ORF-encoded proteins (SEPs; ≤100 aa) that were not previously annotated. Moreover, ProTInSeq can be utilized for detecting translational noise, as well as for relative quantification and transmembrane topology estimation of fitness and non-essential proteins. By integrating various identification approaches, the number of initially annotated SEPs in this bacterium increases from 27 to 329, with a quarter of them predicted to possess antimicrobial potential. Herein, we describe a methodology complementary to Ribo-Seq and mass spectroscopy that can identify SEPs while providing other insights in a proteome with a flexible and cost-effective DNA ultra-deep sequencing approach.

Temporal Quantitative Proteomic and Phosphoproteomic Profiling of SH-SY5Y and IMR-32 Neuroblastoma Cells during All-Trans-Retinoic Acid-Induced Neuronal Differentiation.

The human neuroblastoma cell lines SH-SY5Y and IMR-32 can be differentiated into neuron-like phenotypes through treatment with all-trans-retinoic acid (ATRA). After differentiation, these cell lines are extensively utilized as in vitro models to study various aspects of neuronal cell biology. However, temporal and quantitative profiling of the proteome and phosphoproteome of SH-SY5Y and IMR-32 cells throughout ATRA-induced differentiation has been limited. Here, we performed relative quantification of the proteomes and phosphoproteomes of SH-SY5Y and IMR-32 cells at multiple time points during ATRA-induced differentiation. Relative quantification of proteins and phosphopeptides with subsequent gene ontology analysis revealed that several biological processes, including cytoskeleton organization, cell division, chaperone function and protein folding, and one-carbon metabolism, were associated with ATRA-induced differentiation in both cell lines. Furthermore, kinase-substrate enrichment analysis predicted altered activities of several kinases during differentiation. Among these, CDK5 exhibited increased activity, while CDK2 displayed reduced activity. The data presented serve as a valuable resource for investigating temporal protein and phosphoprotein abundance changes in SH-SY5Y and IMR-32 cells during ATRA-induced differentiation.

Exoproteome analysis of Pseudomonas aeruginosa response to high alkane stress.

Microbial biodegradation serves as an effective approach to treat oil pollution. However, the application of such methods for the degrading long-chain alkanes still encounters significant challenges. Comparative proteomics has extensively studied the intracellular proteins of bacteria that degrade short- and medium-chain alkanes, but the role and mechanism of extracellular proteins in many microorganism remain unclear. To enhance our understanding of the roles of extracellular proteins in the adaptation to long-chain alkanes, a label-free LC-MS/MS strategy was applied for the relative quantification of extracellular proteins of Pseudomonas aeruginosa SJTD-1-M (ProteomeXchange identifier PXD014638). 444 alkane-sentitive proteins were acquired and their cell localization analysis was performed using the Pseudomonas Genome Database. Among them, 111 proteins were found to be located in extracellular or Outer Membrane Vesicles (OMVs). The alkane-induced abundance of 11 extracellular or OMV target proteins was confirmed by parallel reaction monitoring (PRM). Furthermore, we observed that the expression levels of three proteins (Pra, PA2815, and FliC) were associated with the carbon chain length of the added alkane in the culture medium. The roles of these proteins in cell mobility, alkane emulsification, assimilation, and degradation were further discussed. OMVs were found to contain a number of enzymes involved in alkane metabolism, fatty acid beta-oxidation, and the TCA cycle, suggesting their potential as sites for facilitated alkane degradation. In this sense, this exoproteome analysis contributes to a better understanding of the role of extracellular proteins in the hydrocarbon treatment process.

Identification of Differentially Expressed Proteins in Rats with Early Subacute Spinal Cord Injury using an iTRAQ-based Quantitative Analysis.

Injuries to the central nervous system (CNS), such as spinal cord injury (SCI), may devastate families and society. Subacute SCI may majorly impact secondary damage during the transitional period between the acute and subacute phases. A range of CNS illnesses has been linked to changes in the level of protein expression. However, the importance of proteins during the early subacute stage of SCI remains unknown. The role of proteins in the early subacute phase of SCI has not been established yet. SCI-induced damage in rats was studied using isobaric tagging for relative and absolute protein quantification (iTRAQ) to identify proteins that differed in expression 3 days after the injury, as well as proteins that did not alter in expression. Differentially expressed proteins (DEPs) were analyzed employing Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to discover the biological processes, cell components, and molecular functions of the proteins. We also performed Gene Set Enrichment Analysis (GSEA) software BP pathway and KEGG analysis on all proteins to further identify their functions. In addition, the first 15 key nodes of a protein-protein interaction (PPI) system were found. During the early subacute stage of SCI, we identified 176 DEPs in total between the control and damage groups, with 114 (64.77%) being up-regulated and 62 (35.23%) being downregulated. As a result of this study, we discovered the most important cellular components and molecular activities, as well as biological processes and pathways, in the early subacute phase of SCI. The top 15 high-degree core nodes were Alb, Plg, F2, Serpina1, Fgg, Apoa1, Vim, Hpx, Apoe, Agt, Ambp, Pcna, Gc, F12, and Gfap. Our study could provide new views on regulating the pathogenesis of proteins in the early subacute phase after SCI, which provides a theoretical basis for exploring more effective therapeutic targets for SCI in the future.

Tissue-based absolute quantification using large-scale TMT and LFQ experiments.

Relative and absolute intensity-based protein quantification across cell lines, tissue atlases and tumour datasets is increasingly available in public datasets. These atlases enable researchers to explore fundamental biological questions, such as protein existence, expression location, quantity and correlation with RNA expression. Most studies provide MS1 feature-based label-free quantitative (LFQ) datasets; however, growing numbers of isobaric tandem mass tags (TMT) datasets remain unexplored. Here, we compare traditional intensity-based absolute quantification (iBAQ) proteome abundance ranking to an analogous method using reporter ion proteome abundance ranking with data from an experiment where LFQ and TMT were measured on the same samples. This new TMT method substitutes reporter ion intensities for MS1 feature intensities in the iBAQ framework. Additionally, we compared LFQ-iBAQ values to TMT-iBAQ values from two independent large-scale tissue atlas datasets (one LFQ and one TMT) using robust bottom-up proteomic identification, normalisation and quantitation workflows.

Statistical Detection of Differentially Abundant Proteins in Experiments with Repeated Measures Designs and Isobaric Labeling.

Repeated measures experimental designs, which quantify proteins in biological subjects repeatedly over multiple experimental conditions or times, are commonly used in mass spectrometry-based proteomics. Such designs distinguish the biological variation within and between the subjects and increase the statistical power of detecting within-subject changes in protein abundance. Meanwhile, proteomics experiments increasingly incorporate tandem mass tag (TMT) labeling, a multiplexing strategy that gains both relative protein quantification accuracy and sample throughput. However, combining repeated measures and TMT multiplexing in a large-scale investigation presents statistical challenges due to unique interplays of between-mixture, within-mixture, between-subject, and within-subject variation. This manuscript proposes a family of linear mixed-effects models for differential analysis of proteomics experiments with repeated measures and TMT multiplexing. These models decompose the variation in the data into the contributions from its sources as appropriate for the specifics of each experiment, enable statistical inference of differential protein abundance, and recognize a difference in the uncertainty of between-subject versus within-subject comparisons. The proposed family of models is implemented in the R/Bioconductor package MSstatsTMT v2.2.0. Evaluations of four simulated datasets and four investigations answering diverse biological questions demonstrated the value of this approach as compared to the existing general-purpose approaches and implementations.

How the West(ern) was won: Solutions for immunoblotting large and small proteins

Relative protein quantification is a well-established technique in the vast majority of molecular biology laboratories. However, western blot standard protocols may not detect proteins of certain sizes. When the molecular weight of protein of interest is out of the 10–250 kDa range, its migration through the gel or transfer to the membrane is compromised, making its detection difficult. Here, we present a set of modifications of the standard working procedure for western blotting based on the experience working with small VCP-interacting protein and MAX-gene-associated protein, whose molecular weights are 8 and 350 kDa, respectively. We expect that these adaptations may help researchers to improve their experiments in a cost-effective manner.

Proteomic-based identification of APCS as candidate protein for diagnosis of patients exhibiting anti-tubercular drug induced liver injury

Traditional markers evaluate anti-tubercular drug-induced liver injury (AT-DILI). However, these markers have certain limitations and studies are in progress to characterize AT-DILI at an early stage. In the present study, 40 patients were categorized and equally distributed into healthy controls, newly diagnosed tuberculosis (TB), TB without hepatotoxicity and TB with hepatotoxicity groups based on their conventional liver function tests. Relative protein quantification was performed on depleted pooled serum samples of each representative group by LC–MS/MS, and validation of shortlisted protein was done by ELISA. Levels of all analysed biochemical parameters showed a statistical increment in the hepatotoxicity group compared to the other three groups, representing AT-DILI. Comparative proteomic analysis between TB with hepatotoxicity versus TB without hepatotoxicity groups highlighted 24 significant differentially expressed proteins, including PROS1, KNG1, CFH, LCAT, APCS and ADIPOQ. Identified proteins were involved in complement activation, triglyceride-rich lipoprotein particle remodelling and pathways comprising complement, coagulation cascades and cholesterol metabolism. Based on functional relevance, the serum amyloid P component (APCS) was shortlisted for validation, and it showed a similar trend as observed in the discovery phase with 100% sensitivity and 87% specificity; however, findings need exploration in larger cohorts.

Quantitative Mass Spectrometry Analysis of Cerebrospinal Fluid Protein Biomarkers in Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common form of dementia, with cerebrospinal fluid (CSF) β-amyloid (Aβ), total Tau, and phosphorylated Tau (pTau) providing the most sensitive and specific biomarkers for diagnosis. However, these diagnostic biomarkers do not reflect the complex changes in AD brain beyond amyloid (A) and Tau (T) pathologies. Here, we report a selected reaction monitoring mass spectrometry (SRM-MS) method with isotopically labeled standards for relative protein quantification in CSF. Biomarker positive (AT+) and negative (AT−) CSF pools were used as quality controls (QCs) to assess assay precision. We detected 62 peptides (51 proteins) with an average coefficient of variation (CV) of ~13% across 30 QCs and 133 controls (cognitively normal, AT−), 127 asymptomatic (cognitively normal, AT+) and 130 symptomatic AD (cognitively impaired, AT+). Proteins that could distinguish AT+ from AT− individuals included SMOC1, GDA, 14-3-3 proteins, and those involved in glycolysis. Proteins that could distinguish cognitive impairment were mainly neuronal proteins (VGF, NPTX2, NPTXR, and SCG2). This demonstrates the utility of SRM-MS to quantify CSF protein biomarkers across stages of AD.

Prolfqua: A Comprehensive R-Package for Proteomics Differential Expression Analysis

Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. There is a large variety of quantification software and analysis tools. Nevertheless, there is a need for a modular, easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures to make applying them to proteomics data, comparing and understanding their differences easy. The prolfqua package integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The package makes integrating new data formats easy. It can be used to model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. The implemented methods allow sensitive and specific differential expression analysis. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard data set. The application programmer interface of prolfqua strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis application development easy and exciting. Finally, the prolfqua R-package is available on GitHub https://github.com/fgcz/prolfqua, distributed under the MIT license. It runs on all platforms supported by the R free software environment for statistical computing and graphics.

No-stain protein labeling as a potential normalization marker for small extracellular vesicle proteins

Western blot analysis of relative protein expression relies on appropriate reference proteins for data normalization. Small extracellular vesicles (sEVs), or exosomes, are increasingly recognized as potential indicators of the physiological state of cells due to their protein composition. Therefore, accurate relative sEVs protein quantification is crucial for disease detection and prognosis applications. Currently, no documented ubiquitous reference proteins are identified for precise normalization of a protein of interest in sEVs. Here we showed the use of total protein staining method for sEVs protein normalization in western blots of samples where conventional housekeeping proteins like β-actin and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) are not always detected in the sEVs western blots. The No-Stain™ Protein Labeling (NSPL) method showed high sensitivity in sEVs-protein labeling and facilitated quantitative evaluation of changes in the expression pattern of the protein of interest. Further, to show the robustness of NSPL for expression analysis, the results were compared with quantitative mass spectroscopy analysis results. Here, we outline a comprehensive method for protein normalization in sEVs that will increase the value of protein expression study of therapeutically significant sEVs.

Super-SILAC Quantitative Proteome Profiling of Zebrafish Larvae.

The super-SILAC approach enables the quantitative proteome profiling of highly complex samples such as biological tissues or whole organisms. In this approach, a super-SILAC mix consisting of heavy isotope-labeled cells representative of the tissue or organism to be analyzed is mixed with the unlabeled samples of interest, such that the labeled proteins act as a spike-in standard, thus allowing the relative quantification of proteins between the samples of interest. In this chapter, we thoroughly describe the protocol to carry out the super-SILAC approach using a common in vivo model such as zebrafish larvae.

Increased levels of cell wall degrading enzymes and peptidases are associated with aggressiveness in a virulent isolate of Pyrenophora teres f. maculata

Pyrenophora teres f. maculata (Ptm) is a fungal pathogen that causes the spot form of net blotch on barley and leads to economic losses in many of the world's barley-growing regions. Isolates of Ptm exhibit varying levels of aggressiveness that result in quantifiable changes in the severity of the disease. Previous research on plant-pathogen interactions has shown that such divergence is reflected in the proteome and secretome of the pathogen, with certain classes of proteins more prominent in aggressive isolates. Here we have made a detailed comparative analysis of the secretomes of two Ptm isolates, GPS79 and E35 (highly and mildly aggressive, respectively) using a proteomics-based approach. The secretomes were obtained in vitro using media amended with barley leaf sections. Secreted proteins therein were harvested, digested with trypsin, and fractionated offline by HPLC prior to LC-MS in a high-resolution instrument to obtain deep coverage of the proteome. The subsequent analysis used a label-free quantitative proteomics approach with relative quantification of proteins based on precursor ion intensities. A total of 1175 proteins were identified, 931 from Ptm and 244 from barley. Further analysis revealed 160 differentially abundant proteins with at least a two-fold abundance difference between the isolates, with the most enriched in the aggressive GPS79 secretome. These proteins were mainly cell-wall (carbohydrate) degrading enzymes and peptidases, with some oxidoreductases and other pathogenesis-related proteins also identified, suggesting that aggressiveness is associated with an improved ability of GPS79 to overcome cell wall barriers and neutralize host defense responses.