Large-scale Quantitative Proteomics Research Articles

Rapid modulation of interscapular brown adipose tissue mitochondrial activity by ketosis induced by 1,3-butanediol administration to rats.

Some studies indicate that brown adipose tissue (BAT) represents a promising target in the fight against dysmetabolic diseases, with indications suggesting it as a potential target for the effects of ketone bodies. We investigate whether the elevation of plasma levels of the ketone body β-hydroxybutyrate, achieved through the invivo administration of its precursor 1,3-butanediol (BD) to rats, could impact interscapular BAT (iBAT) mitochondrial biochemistry and functionality. We examined the effects induced by BD within 3 h and after 2 weeks of treatment. A large-scale quantitative proteomics approach, coupling liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis, and Western blot associated with functional studies by respirometry allowed us to evaluate the changes in iBAT mitochondrial protein expression and bioenergetics induced by BD. BD administration increased β-hydroxybutyrate plasma levels, which correlated with an enhancement in iBAT mitochondrial respiration rate, likely due to the activation of the respiratory chain and uncoupling protein-1. The proteomic analysis demonstrated that BD influenced the mitochondrial levels of specific subunits belonging to the five respiratory complexes, uncoupling protein-1, and proteins involved in propanoate metabolism. BD administration also induced lysine β-hydroxybutyrylation of mitochondrial proteins, including specific subunits of the respiratory chain complexes and uncoupling protein-1. Most of the BD-induced effects were observed within 3 h of its administration and persisted/increased after 2 weeks of treatment. In conclusion, by using BD to increase β-hydroxybutyrate levels, we provide evidence supporting the role of β-hydroxybutyrate as a signaling molecule capable of rapidly modulating BAT physiology by acting at the mitochondrial level.

Perspectives and opinions from scientific leaders on the evolution of data-independent acquisition for quantitative proteomics and novel biological applications

The methodology of data-independent acquisition (DIA) within mass spectrometry (MS) was developed into a method of choice for quantitative proteomics, to capture the depth and dynamics of biological systems, and to perform large-scale protein quantification. DIA provides deep quantitative proteome coverage with high sensitivity, high quantitative accuracy, and excellent acquisition-to-acquisition reproducibility. DIA workflows benefited from the latest advancements in MS instrumentation, acquisition/isolation schemes, and computational algorithms, which have further improved data quality and sample throughput. This powerful DIA-MS scan type selects all precursor ions contained in pre-determined isolation windows, and systematically fragments all precursor ions from each window by tandem mass spectrometry, subsequently covering the entire precursor ion m/z range. Comprehensive proteolytic peptide identification and label-free quantification are achieved post-acquisition using spectral library-based or library-free approaches. To celebrate the > 10 years of success of this quantitative DIA workflow, we interviewed some of the scientific leaders who have provided crucial improvements to DIA, to the quantification accuracy and proteome depth achieved, and who have explored DIA applications across a wide range of biology. We discuss acquisition strategies that improve specificity using different isolation schemes, and that reduce complexity by combining DIA with sophisticated chromatography or ion mobility separation. Significant leaps forward were achieved by evolving data processing strategies, such as library-free processing, and machine learning to interrogate data more deeply. Finally, we highlight some of the diverse biological applications that use DIA-MS methods, including large-scale quantitative proteomics, post-translational modification studies, single-cell analysis, food science, forensics, and small molecule analysis.

Systems-wide analysis of glycoprotein conformational changes by limited deglycosylation assay

A new method to probe the conformational changes of glycoproteins on a systems-wide scale, termed limited deglycosylation assay (LDA), is described. The method measures the differential rate of deglycosylation of N-glycans on natively folded proteins by the common peptide:N-glycosidase F (PNGase F) enzyme which in turn informs on their spatial presentation and solvent exposure on the protein surface hence ultimately the glycoprotein conformation. LDA involves 1) protein-level N-deglycosylation under native conditions, 2) trypsin digestion, 3) glycopeptide enrichment, 4) peptide-level N-deglycosylation and 5) quantitative MS-based analysis of formerly N-glycosylated peptides (FNGPs). LDA was initially developed and the experimental conditions optimized using bovine RNase B and fetuin. The method was then applied to glycoprotein extracts from LLC-MK2 epithelial cells upon treatment with dithiothreitol to induce endoplasmic reticulum stress and promote protein misfolding. Data from the LDA and 3D structure analysis showed that glycoproteins predominantly undergo structural changes in loops/turns upon ER stress as exemplified with detailed analysis of ephrin-A5, GALNT10, PVR and BCAM. These results show that LDA accurately reports on systems-wide conformational changes of glycoproteins induced under controlled treatment regimes. Thus, LDA opens avenues to study glycoprotein structural changes in a range of other physiological and pathophysiological conditions relevant to acute and chronic diseases. SignificanceWe describe a novel method termed limited deglycosylation assay (LDA), to probe conformational changes of glycoproteins on a systems-wide scale. This method improves the current toolbox of structural proteomics by combining site and conformational-specific PNGase F enzymatic activity with large scale quantitative proteomics. X-ray crystallography, nuclear magnetic resonance spectroscopy and cryoEM techniques are the major techniques applied to elucidate macromolecule structures. However, the size and heterogeneity of the oligosaccharide chains poses several challenges to the applications of these techniques to glycoproteins. The LDA method presented here, can be applied to a range of pathophysiological conditions and expanded to investigate PTMs-mediated structural changes in complex proteomes.

Biostimulant impacts of Glutacetine® and derived formulations (VNT1 and VNT4) on the bread wheat grain proteome

Nitrogen (N) fertilizer is essential to ensure grain yield and quality in bread wheat. Improving N use efficiency is therefore crucial for wheat grain protein quality. In the present work, we analysed the effects on the winter wheat grain proteome of biostimulants containing Glutacetine® or two derived formulations (VNT1 and 4) when mixed with urea-ammonium-nitrate fertilizer. A large-scale quantitative proteomics analysis of two wheat flour fractions produced a dataset of 4369 identified proteins. Quantitative analysis revealed 9, 39 and 96 proteins with a significant change in abundance after Glutacetine®, VNT1 and VNT4 treatments, respectively, with a common set of 11 proteins that were affected by two different biostimulants. The major effects impacted proteins involved in (i) protein synthesis regulation (mainly ribosomal and binding proteins), (ii) defence and responses to stresses (including chitin-binding protein, heat shock 70 kDa protein 1 and glutathione S-transferase proteins), (iii) storage functions related to gluten protein alpha-gliadins and starch synthase and (iv) seed development with proteins implicated in protease activity, energy machinery, and the C and N metabolism pathways. Altogether, our study showed that Glutacetine®, VNT1 and VNT4 biostimulants positively affected protein composition related to grain quality.Data are available via ProteomeXchange with identifier PXD021513. SignificanceWe performed a large-scale quantitative proteomics study of the total protein extracts from flour samples to determine the effect of Glutacetine®-based biostimulants treatment on the protein composition of bread wheat grain. To our knowledge, only a few studies in the literature have applied proteomic approaches to study bread wheat grains and in particular to investigate the effect of biostimulants on the grain proteome of this cereal crop. In addition, most approaches used fractional extraction of proteins to target reserve proteins followed electrophoresis which leads to low identification rate of proteins. We identified and quantified a large protein dataset of 4369 proteins and determined ontological class of proteins affected by biostimulants treatments. Our proteomics investigation revealed the important role of these new biostimulants in achieving significant changes in protein synthesis regulation, storage functions, protease activity, energy machinery, C and N metabolism pathways and responses to biotic and abiotic stresses in grain.

Assessment of TMT Labeling Efficiency in Large-Scale Quantitative Proteomics: The Critical Effect of Sample pH.

Isobaric labeling via tandem mass tag (TMT) reagents enables sample multiplexing prior to LC–MS/MS, facilitating high-throughput large-scale quantitative proteomics. Consistent and efficient labeling reactions are essential to achieve robust quantification; therefore, embedded in our clinical proteomic protocol is a quality control (QC) sample that contains a small aliquot from each sample within a TMT set, referred to as “Mixing QC.” This Mixing QC enables the detection of TMT labeling issues by LC–MS/MS before combining the full samples to allow for salvaging of poor TMT labeling reactions. While TMT labeling is a valuable tool, factors leading to poor reactions are not fully studied. We observed that relabeling does not necessarily rescue TMT reactions and that peptide samples sometimes remained acidic after resuspending in 50 mM HEPES buffer (pH 8.5), which coincided with low labeling efficiency (LE) and relatively low median reporter ion intensities (MRIIs). To obtain a more resilient TMT labeling procedure, we investigated LE, reporter ion missingness, the ratio of mean TMT set MRII to individual channel MRII, and the distribution of log 2 reporter ion ratios of Mixing QC samples. We discovered that sample pH is a critical factor in LE, and increasing the buffer concentration in poorly labeled samples before relabeling resulted in the successful rescue of TMT labeling reactions. Moreover, resuspending peptides in 500 mM HEPES buffer for TMT labeling resulted in consistently higher LE and lower missing data. By better controlling the sample pH for labeling and implementing multiple methods for assessing labeling quality before combining samples, we demonstrate that robust TMT labeling for large-scale quantitative studies is achievable.

Optimised data-independent acquisition strategy recaptures the classification of early-stage hepatocellular carcinoma based on data-dependent acquisition

Proteomics is increasingly used for exploring disease biomarkers and therapeutic targets. The data-independent acquisition (DIA) method collects all peptide signals in a sample, and provides a convenient way to archive disease-related molecular features for further exploration. In this study, we first established a high-coverage human hepatocellular carcinoma (HCC) spectral library containing 9393 protein groups, 119,903 peptides. Furthermore, we optimised the DIA method with respect to four key parameters: settings for mass spectrometry acquisition, gradient length, amount of sample loading, and length of analytical column. More than 6000 proteins from HepG2 cells could be stably quantified using the optimised one-shot DIA approach with a 2 h gradient time. One-shot DIA identified a similar number of proteins as did multi-fraction data-dependent acquisition (DDA) from the same group of HCC samples, but at a quarter of the total acquisition time. DIA data could recapture the classification results obtained from DDA data, thus paving the way for large-scale, multi-centre proteomics analysis of clinical samples. SignificanceThe organ-specific spectral library for HCC and the optimised 2 h DIA approach met the urgent demands for large-scale quantitative proteomics analysis of HCC clinical samples. Compared with multi-fraction-DDA, the optimised one-shot DIA could reach a similar identification while consuming shorter acquisition time, thus making it possible to analyse thousands of clinical samples.

Connectivity mapping of glomerular proteins identifies dimethylaminoparthenolide as a new inhibitor of diabetic kidney disease

While blocking the renin angiotensin aldosterone system (RAAS) has been the main therapeutic strategy to control diabetic kidney disease (DKD) for many years, 25–30% of diabetic patients still develop the disease. In the present work we adopted a systems biology strategy to analyze glomerular protein signatures to identify drugs with potential therapeutic properties in DKD acting through a RAAS-independent mechanism. Glomeruli were isolated from wild type and type 1 diabetic (Ins2Akita) mice treated or not with the angiotensin-converting enzyme inhibitor (ACEi) ramipril. Ramipril efficiently reduced the urinary albumin/creatine ratio (ACR) of Ins2Akita mice without modifying DKD-associated renal-injuries. Large scale quantitative proteomics was used to identify the DKD-associated glomerular proteins (DKD-GPs) that were ramipril-insensitive (RI-DKD-GPs). The raw data are publicly available via ProteomeXchange with identifier PXD018728. We then applied an in silico drug repurposing approach using a pattern-matching algorithm (Connectivity Mapping) to compare the RI-DKD-GPs’s signature with a collection of thousands of transcriptional signatures of bioactive compounds. The sesquiterpene lactone parthenolide was identified as one of the top compounds predicted to reverse the RI-DKD-GPs’s signature. Oral treatment of 2 months old Ins2Akita mice with dimethylaminoparthenolide (DMAPT, a water-soluble analogue of parthenolide) for two months at 10 mg/kg/d by gavage significantly reduced urinary ACR. However, in contrast to ramipril, DMAPT also significantly reduced glomerulosclerosis and tubulointerstitial fibrosis. Using a system biology approach, we identified DMAPT, as a compound with a potential add-on value to standard-of-care ACEi-treatment in DKD.

Expanding the Perseus Software for Omics Data Analysis With Custom Plugins

The Perseus software provides a comprehensive framework for the statistical analysis of large-scale quantitative proteomics data, also in combination with other omics dimensions. Rapid developments in proteomics technology and the ever-growing diversity of biological studies increasingly require the flexibility to incorporate computational methods designed by the user. Here, we present the new functionality of Perseus to integrate self-made plugins written in C#, R, or Python. The user-written codes will be fully integrated into the Perseus data analysis workflow as custom activities. This also makes language-specific R and Python libraries from CRAN (cran.r-project.org), Bioconductor (bioconductor.org), PyPI (pypi.org), and Anaconda (anaconda.org) accessible in Perseus. The different available approaches are explained in detail in this article. To facilitate the distribution of user-developed plugins among users, we have created a plugin repository for community sharing and filled it with the examples provided in this article and a collection of already existing and more extensive plugins. © 2020 The Authors. Basic Protocol 1: Basic steps for R plugins Support Protocol 1: R plugins with additional arguments Basic Protocol 2: Basic steps for python plugins Support Protocol 2: Python plugins with additional arguments Basic Protocol 3: Basic steps and construction of C# plugins Basic Protocol 4: Basic steps of construction and connection for R plugins with C# interface Support Protocol 4: Advanced example of R Plugin with C# interface: UMAP Basic Protocol 5: Basic steps of construction and connection for python plugins with C# interface Support Protocol 5: Advanced example of python plugin with C# interface: UMAP Support Protocol 6: A basic workflow for the analysis of label-free quantification proteomics data using perseus.

PolySTest: Robust Statistical Testing of Proteomics Data with Missing Values Improves Detection of Biologically Relevant Features

Statistical testing remains one of the main challenges for high-confidence detection of differentially regulated proteins or peptides in large-scale quantitative proteomics experiments by mass spectrometry. Statistical tests need to be sufficiently robust to deal with experiment intrinsic data structures and variations and often also reduced feature coverage across different biological samples due to ubiquitous missing values. A robust statistical test provides accurate confidence scores of large-scale proteomics results, regardless of instrument platform, experimental protocol and software tools. However, the multitude of different combinations of experimental strategies, mass spectrometry techniques and informatics methods complicate the decision of choosing appropriate statistical approaches. We address this challenge by introducing PolySTest, a user-friendly web service for statistical testing, data browsing and data visualization. We introduce a new method, Miss test, that simultaneously tests for missingness and feature abundance, thereby complementing common statistical tests by rescuing otherwise discarded data features. We demonstrate that PolySTest with integrated Miss test achieves higher confidence and higher sensitivity for artificial and experimental proteomics data sets with known ground truth. Application of PolySTest to mass spectrometry based large-scale proteomics data obtained from differentiating muscle cells resulted in the rescue of 10-20% additional proteins in the identified molecular networks relevant to muscle differentiation. We conclude that PolySTest is a valuable addition to existing tools and instrument enhancements that improve coverage and depth of large-scale proteomics experiments. A fully functional demo version of PolySTest and Miss test is available via http://computproteomics.bmb.sdu.dk/Apps/PolySTest.

Cellular Imprinting Proteomics Assay: A Novel Method for Detection of Neural and Ocular Disorders Applied to Congenital Zika Virus Syndrome.

Congenital Zika syndrome was first described due to increased incidence of congenital abnormalities associated with Zika virus (ZIKV) infection. Since the eye develops as part of the embryo central nervous system (CNS) structure, it becomes a specialized compartment able to display symptoms of neurodegenerative diseases and has been proposed as a noninvasive approach to the early diagnosis of neurological diseases. Ocular lesions result from defects that occurred during embryogenesis and can become apparent in newborns exposed to ZIKV. Furthermore, the absence of microcephaly cannot exclude the occurrence of ocular lesions and other CNS manifestations. Considering the need for surveillance of newborns and infants with possible congenital exposure, we developed a method termed cellular imprinting proteomic assay (CImPA) to evaluate the ocular surface proteome specific to infants exposed to ZIKV during gestation compared to nonexposure. CImPA combines surface cells and fluid capture using membrane disks and a large-scale quantitative proteomics approach, which allowed the first-time report of molecular alterations such as neutrophil degranulation, cell death signaling, ocular and neurological pathways, which are associated with ZIKV infection with and without the development of congenital Zika syndrome, CZS. Particularly, infants exposed to ZIKV during gestation and without early clinical symptoms could be detected using the CImPA method. Lastly, this methodology has broad applicability as it could be translated in the study of several neurological diseases to identify novel diagnostic biomarkers. Data are available via ProteomeXchange with identifier PXD014038.

Identification of differentially activated pathways in Phytophthora sojae at the mycelial, cyst, and oospore stages by TMT-based quantitative proteomics analysis

Phytophthora sojae is a widely distributed, destructive oomycete plant pathogen that has been developed as a model for oomycete biology. Given the important but limited reports on the comparison of the sexual and asexual stages in Phytophthora, we performed a large-scale quantitative proteomics study on two key asexual life stages of P. sojae—the mycelium and cyst—as well as on the oospore, which is a key sexual stage. Over 29,631 peptides from 4688 proteins were analyzed. Briefly, 445 proteins, 624 proteins, and 579 proteins were defined as differentially quantified proteins in cyst vs mycelium, oospore vs cyst, and oospore vs mycelium comparisons, respectively (|log2 fold change| > 1 and P < 0.05). Compared to the mycelium and oospore, fatty acid and nitrogen metabolism were specifically induced in cysts. In oospores, the up-regulated proteins focused on RNA transport and protein processing in endoplasmic reticulum, indicating translation, folding, and the secretion of core cellular or stage-specific proteins active in oospores, which might be used for oospore germination. The data presented expand our knowledge of pathways specifically linked to asexual and sexual stages of this pathogen. Biological significanceThe sexual spores (oospores) in oomycetes have thick cell walls and can survive in the soil for years, thus providing a primary source and allowing the reinfection of their host plant in subsequent growing seasons. However, the proteomic study on oospores remains very limited as they are generally considered to be dormant. In the present study, we successfully isolated oospores, and performed a large-scale comparative quantitative proteomics study on this key sexual stage and two representative asexual stages of P. sojae. The results provide an improved understanding of P. sojae biology and suggest potential metabolic targets for disease control at the three different developmental stages in oomycetes.

Accelerating SWATH® Acquisition for Protein Quantitation – Up to 100 Samples per Day

The combination of microflow LC with SWATH Acquisition for large scale quantitative proteomics studies is becoming increasingly more widespread, due to the improved robustness and throughput obtained relative to the traditional nanoflow LC approach. High quality quantitative datasets have been generated using a standard 1 hour gradient, demonstrating large numbers of proteins quantified routinely.The impact of gradient length on protein identification results using data dependent acquisition (DDA) was also explored previously and summarized here. Next, an exploration into the impact of gradient length on proteins quantified using data independent acquisition (DIA) was undertaken to provide researchers expanded workflow options with microflow SWATH acquisition. Using microflow liquid chromatography gradients as short as five minutes (total run time &lt;15 mins), SWATH acquisition parameters were first optimized then used on multiple TripleTOF® 6600 systems to study impact of shortened separations on number of proteins quantified.Testing on multiple instruments with different complex matrices, it was observed that the number of proteins quantified using SWATH acquisition for the fastest gradients was over 1300 proteins for multiple complex matrices from 1 μg protein load. When combined with larger ion libraries, over 2100 proteins were identified and quantified in a 5 min gradient from a complex HEK digest (1 μg). The combination of microflow LC with the high MS/MS acquisition rates of the TripleTOF® 6600 system enables high‐throughput yet comprehensive analysis for proteomics samples, at rates approaching 100 samples per day.

Quantitative proteomics analysis reveals important roles of N-glycosylation on ER quality control system for development and pathogenesis in Magnaporthe oryzae.

Genetic studies have shown essential functions of N-glycosylation during infection of the plant pathogenic fungi, however, systematic roles of N-glycosylation in fungi is still largely unknown. Biological analysis demonstrated N-glycosylated proteins were widely present at different development stages of Magnaporthe oryzae and especially increased in the appressorium and invasive hyphae. A large-scale quantitative proteomics analysis was then performed to explore the roles of N-glycosylation in M. oryzae. A total of 559 N-glycosites from 355 proteins were identified and quantified at different developmental stages. Functional classification to the N-glycosylated proteins revealed N-glycosylation can coordinate different cellular processes for mycelial growth, conidium formation, and appressorium formation. N-glycosylation can also modify key components in N-glycosylation, O-glycosylation and GPI anchor pathways, indicating intimate crosstalk between these pathways. Interestingly, we found nearly all key components of the endoplasmic reticulum quality control (ERQC) system were highly N-glycosylated in conidium and appressorium. Phenotypic analyses to the gene deletion mutants revealed four ERQC components, Gls1, Gls2, GTB1 and Cnx1, are important for mycelial growth, conidiation, and invasive hyphal growth in host cells. Subsequently, we identified the Gls1 N-glycosite N497 was important for invasive hyphal growth and partially required for conidiation, but didn't affect colony growth. Mutation of N497 resulted in reduction of Gls1 in protein level, and localization from ER into the vacuole, suggesting N497 is important for protein stability of Gls1. Our study showed a snapshot of the N-glycosylation landscape in plant pathogenic fungi, indicating functions of this modification in cellular processes, developments and pathogenesis.

Analysis of durum wheat proteome changes under marine and fungal biostimulant treatments using large-scale quantitative proteomics: A useful dataset of durum wheat proteins

Durum wheat requires high nitrogen inputs to obtain the high protein concentration necessary to satisfy pasta and semolina quality criteria. Optimizing plant nitrogen use efficiency is therefore of major importance for wheat grain quality. Here, we studied the impact on grain yield, protein concentration, and for the first time on protein composition of a marine (DPI4913) and a fungal (AF086) biostimulants applied to plant leaves. A large-scale quantitative proteomics analysis of wheat flour samples led to a dataset of 1471 identified proteins. Quantitative analysis of 1391 proteins revealed 26 and 38 proteins with a significantly varying abundance after DPI4913 and AF086 treatment, respectively, with 14 proteins in common. Major effects affected proteins involved in grain technological properties like grain hardness, in storage functions with the gluten protein gamma-gliadin, in regulation processes with transcription regulator proteins, and in stress responses with biotic and abiotic stress defense proteins. The involvement of biostimulants in the abiotic stress response was further suggested by an increase in water-use efficiency for both DPI4913 (15.4%) and AF086 (9.9%) treatments. Overall, our work performed in controlled conditions showed that DPI4913 and AF086 treatments promoted grain yield while maintaining protein concentration, and positively affected protein composition for grain quality.Data are available via ProteomeXchange with identifier PXD012469.

StatTarget: A streamlined tool for signal drift correction and interpretations of quantitative mass spectrometry-based omics data

Large-scale quantitative mass spectrometry-based metabolomics and proteomics study requires the long-term analysis of multiple batches of biological samples, which often accompanied with significant signal drift and various inter- and intra-batch variations. The unwanted variations can lead to poor inter- and intra-day reproducibility, which is a hindrance to discover real significance. The use of quality control samples and data treatment strategies in the quality assurance procedure provides a mechanism to evaluate the quality and remove the analytical variance of the data. The statTarget we developed is a streamlined tool with an easy-to-use graphical user interface and an integrated suite of algorithms specifically developed for the evaluation of data quality and removal of unwanted variations for quantitative mass spectrometry-based omics data. A novel quality control-based random forest signal correction algorithm, which can remove inter- and intra-batch unwanted variations at feature-level was implanted in the statTarget. Our evaluation based on real samples showed the developed algorithm could improve the data precision and statistical accuracy for mass spectrometry-based metabolomics and proteomics data. Additionally, the statTarget offers the streamlined procedures for data imputation, data normalization, univariate analysis, multivariate analysis, and feature selection. To conclude, the statTarget allows user-friendly the improvement of the data precision for uncovering the biologically differences, which largely facilitates quantitative mass spectrometry-based omics data processing and statistical analysis.

Chronic platelet-derived growth factor receptor signaling exerts control over initiation of protein translation in glioma.

Activation of the platelet-derived growth factor receptors (PDGFRs) gives rise to some of the most important signaling pathways that regulate mammalian cellular growth, survival, proliferation, and differentiation and their misregulation is common in a variety of diseases. Herein, we present a comprehensive and detailed map of PDGFR signaling pathways assembled from literature and integrate this map in a bioinformatics protocol designed to extract meaningful information from large-scale quantitative proteomics mass spectrometry data. We demonstrate the usefulness of this approach using a new genetically engineered mouse model of PDGFRα-driven glioma. We discovered that acute PDGFRα stimulation differs considerably from chronic receptor activation in the regulation of protein translation initiation. Transient stimulation activates several key components of the translation initiation machinery, whereas the clinically relevant chronic activity of PDGFRα is associated with a significant shutdown of translational members. Our work defines a step-by-step approach to extract biologically relevant insights from global unbiased phospho-protein datasets to uncover targets for therapeutic assessment.

Extended investigation of tube-gel sample preparation: a versatile and simple choice for high throughput quantitative proteomics

Sample preparation for quantitative proteomics is a crucial step to ensure the repeatability and the accuracy of the results. However, there is no universal method compatible with the wide variety of protein extraction buffers currently used. We have recently demonstrated the compatibility of tube-gel with SDS-based buffers and its efficiency for label-free quantitative proteomics by comparing it to stacking gel and liquid digestion. Here, we investigated the compatibility of tube-gel with alternatives to SDS-based buffers allowing notably the extraction of proteins in various pH conditions. We also explored the use of photopolymerization to extend the number of possibilities, as it is compatible with a wide range of pH and is non-oxidative. To achieve this goal, we compared six extraction buffers in combination with two polymerization conditions to further optimize the tube-gel protocol and evaluate its versatility. Identification and quantitative results demonstrated the compatibility of tube-gel with all tested conditions by overall raising quite comparable results. In conclusion, tube-gel is a versatile and simple sample preparation method for large-scale quantitative proteomics applications. Complete datasets are available via ProteomeXchange with identifier PXD008656.

Down-regulation of RIP3 potentiates cisplatin chemoresistance by triggering HSP90-ERK pathway mediated DNA repair in esophageal squamous cell carcinoma

Receptor interacting protein kinase 3 (RIP3) is a critical regulator of programmed necrotic cell death. Here, we observed that RIP3 was significantly down-regulated in esophageal cancer. And its remaining expression was associated with better response to chemotherapy and prolonged survival. Notably, re-expression of kinase-dead RIP3 also restored cisplatin sensitivity, suggesting that some roles of RIP3 beyond necroptosis may be involved in cisplatin-based chemosensitivity. To investigate the mechanisms, a large-scale quantitative proteomics study was performed after cisplatin treatment in RIP3-knockdown cells. In total, approximately 7000 protein groups were confidently identified, with a false discovery rate of 0.21% at the protein level. Of these proteins, 685 displayed RIP3-dependent changes in abundance. Bioinformatics analyses indicated that DNA repair pathway was stimulated after RIP3 depletion. Functional studies showed that deficient RIP3 upregulated FOSL1 and POLD1 through activation of the HSP90/CDC37 complex and ERK phosphorylation in multiple cell lines. Furthermore, via inhibition of the HSP90/CDC37 complex, ERK and FOSL1 reversed the cisplatin resistance phenotype. These results suggest that RIP3 regulates cisplatin sensitivity through both pronecrotic and non-necrotic functions. RIP3 may be a potential marker for predicting chemosensitivity.

Quantitative proteomics reveals a role of JAZ7 in plant defense response to Pseudomonas syringae DC3000

Jasmonate ZIM-domain (JAZ) proteins are key transcriptional repressors regulating various biological processes. Although many studies have studied JAZ proteins by genetic and biochemical analyses, little is known about JAZ7-associated global protein networks and how JAZ7 contributes to bacterial pathogen defense. In this study, we aim to fill this knowledge gap by conducting unbiased large-scale quantitative proteomics using tandem mass tags (TMT). We compared the proteomes of a JAZ7 knock-out line, a JAZ7 overexpression line, as well as the wild type Arabidopsis plants in the presence and absence of Pseudomonas syringae DC3000 infection. Both pairwise comparison and multi-factor analysis of variance reveal that differential proteins are enriched in biological processes such as primary and secondary metabolism, redox regulation, and response to stress. The differential regulation in these pathways may account for the alterations in plant size, redox homeostasis and accumulation of glucosinolates. In addition, possible interplay between genotype and environment is suggested as the abundance of seven proteins is influenced by the interaction of the two factors. Collectively, we demonstrate a role of JAZ7 in pathogen defense and provide a list of proteins that are uniquely responsive to genetic disruption, pathogen infection, or the interaction between genotypes and environmental factors. SignificanceWe report proteomic changes as a result of genetic perturbation of JAZ7, and the contribution of JAZ7 in plant immunity. Specifically, the similarity between the proteomes of a JAZ7 knockout mutant and the wild type plants confirmed the functional redundancy of JAZs. In contrast, JAZ7 overexpression plants were much different, and proteomic analysis of the JAZ7 overexpression plants under Pst DC3000 infection revealed that JAZ7 may regulate plant immunity via ROS modulation, energy balance and glucosinolate biosynthesis. Multiple variate analysis for this two-factor proteomics experiment suggests that protein abundance is determined by genotype, environment and the interaction between them.

Development of novel tags for enhanced sample multiplexing and application of cPILOT to understand aging mechanisms

Event Abstract Back to Event Development of novel tags for enhanced sample multiplexing and application of cPILOT to understand aging mechanisms Bushra Amin1, Jasmine Daniels2 and Renã A. Robinson1* 1 Vanderbilt University, United States 2 Texas Southern University, United States Large-scale quantitative proteomics can provide a near-global view of cellular protein abundance. To fully leverage mass spectrometry for the elucidation of disease processes high-throughput proteomics is necessary to monitor dynamic changes across samples from varying conditions. One way to do this, is with the use of isobaric tagging strategies, which allow as many as 12 samples to be multiplexed in a single analysis. Through an approach which has dual tagging nature, combines isotopic labeling with isobaric tagging (cPILOT), the capacity of samples doubles. The goal of this study is to improve sample throughput of our combined precursor isotopic labeling and isobaric tagging (cPILOT) approach. The major challenge to making this approach work involve developing amine tagging reagents specific to either N-terminal or lysine amine residues. To overcome this challenge, we are building on previous studies in our laboratory, and developing novel strategies. This presentation will cover preliminary findings to date. Moreover, we have successfully applied cPILOT approach to study metabolic pathways involved in the aging process. cPILOT has enabled us to analyze 14 samples in a single shot for the identification of 2997 and quantification of 1814 protein groups in rabbit liver. The implications of this study will be presented. Keywords: multiplexing, cPILOT, Novel Tags Development, Quantitative proteome analysis, Aging, Peptide-based tags, amine tagging reagents Conference: National Organization for the Professional Advancement of Black Chemists and Chemical Engineers (NOBCChE) 45th Annual Conference , Orlando, Florida, United States, 17 Sep - 20 Sep, 2018. Presentation Type: Oral Presentation Topic: Analytical Chemsitry Citation: Amin B, Daniels J and Robinson RA (2019). Development of novel tags for enhanced sample multiplexing and application of cPILOT to understand aging mechanisms. Front. Chem. Conference Abstract: National Organization for the Professional Advancement of Black Chemists and Chemical Engineers (NOBCChE) 45th Annual Conference . doi: 10.3389/conf.fchem.2018.01.00037 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 09 Oct 2018; Published Online: 17 Jan 2019. * Correspondence: Prof. Renã A Robinson, Vanderbilt University, Nashville, United States, rena.as.robinson@vanderbilt.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Bushra Amin Jasmine Daniels Renã A Robinson Google Bushra Amin Jasmine Daniels Renã A Robinson Google Scholar Bushra Amin Jasmine Daniels Renã A Robinson PubMed Bushra Amin Jasmine Daniels Renã A Robinson Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.