Relative quantification: characterization of bias, variability and fold changes in mass spectrometry data from iTRAQ-labeled peptides

zMAP toolset: model-based analysis of large-scale proteomic data via a variance stabilizing z-transformation

Isobaric labeling-based mass spectrometry (ILMS) has been widely used to quantify, on a proteome-wide scale, the relative protein abundance in different biological conditions. However, large-scale ILMS data sets typically involve multiple runs of mass spectrometry, bringing great computational difficulty to the integration of ILMS samples. We present zMAP, a toolset that makes ILMS intensities comparable across mass spectrometry runs by modeling the associated mean-variance dependence and accordingly applying a variance stabilizing z-transformation. The practical utility of zMAP is demonstrated in several case studies involving the dynamics of cell differentiation and the heterogeneity across cancer patients.

Proteome of the 2-h in vivo Formed Acquired Enamel Pellicle of Adolescents with Erosive Tooth Wear, Caries, or Sound

INTRODUCTION

Acquired pellicle (AP) acts as a membrane preventing acids from coming into direct contact with the tooth. Possibly, individuals with different dental health status present changes in its composition that could disrupt this function. Thus, the aim of this study was to compare the protein composition of the AP in adolescents with erosive tooth wear (ETW), caries, or sound.

METHODS

Calibrated examiners in BEWE index and ICDAS-merged Epi criteria assessed ETW and caries in a sample of 454 systemically healthy adolescents aged 12-15 years old. Thirty subjects from that sample were selected for this study: ETW group (n = 10; total BEWE ≥9 and absence of dentinal caries lesions); caries group (n = 10; total BEWE <9 and with at least one dentinal caries lesion), and sound group (n = 10; total BEWE <9 and absence of dentinal caries lesions). Two-hour-formation AP samples were taken from buccal, occlusal/incisal, palatal/lingual tooth surfaces. Protein composition was analysed by liquid chromatography-tandem mass spectrometry. Using mean reporter ion values, relative abundances of proteins were compared among the three groups to calculate for fold changes. Twofold protein increases or decreases were reported (t test, p < 0.05). Gene Ontology (GO) of included proteins was assigned.

RESULTS

Mean age of participants was 13.1 ± 1.14 years and 56.6% were females. The prevalence of ETW was of 66.6% and of dentinal caries of 33.3%. The GO analyses showed that the majority of detected proteins were stress response related. The ETW group disclosed upregulated relative abundance of antileukoprotease (2.85-fold in ETW vs. sound and 2.34-fold in ETW group vs. caries group); histatin (2.42-fold in ETW group vs. sound group and 2.20-fold in ETW group vs. caries group), and prolactin-induced protein (2.30-fold in ETW group vs. sound group and 2.06-fold in ETW group vs. caries group) (p < 0.05). Hemoglobin subunits alpha (HBA) and beta (HBB) showed decreased relative abundances in the ETW and caries groups when compared to the sound group (HBA: 0.42-fold in ETW group and 0.40-fold in caries group; HBB: 0.45-fold in ETW group and 0.38-fold in caries group; p < 0.05).

CONCLUSION

AP from individuals with ETW showed differences when compared to other dental conditions, with relative abundance increasing of some stress response-associated proteins in ETW and a decrease in proteins related to salivary protection against acid challenges.

Data-Independent Acquisition: A Milestone and Prospect in Clinical Mass Spectrometry-Based Proteomics

Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given m/z range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.

Clinical application of serum-based proteomics technology in human tumor research

The rapid development of proteomics technology in the past decades has led to further human understanding of tumor research, and in some ways, the technology plays a very important supporting role in the early detection of tumors. Human serum has been shown to contain a variety of proteins closely related to life activities, and the dynamic change in proteins can often reflect the physiological and pathological conditions of the body. Serum has the advantage of easy extraction, so the application of proteomics technology in serum has become a hot spot and frontier area for the study of malignant tumors. However, there are still many difficulties in the standardized use of proteomic technologies, which inevitably limit the clinical application of proteomic technologies due to the heterogeneity of human proteins leading to incomplete whole proteome populations, in addition to most serum protein markers being now not highly specific in aiding the early detection of tumors. Nevertheless, further development of proteomics technologies will greatly increase our understanding of tumor biology and help discover more new tumor biomarkers with specificity that will enable medical technology.

A conserved guided entry of tail-anchored pathway is involved in the trafficking of a subset of membrane proteins in Plasmodium falciparum

Tail-anchored (TA) proteins are defined by the absence of N-terminus signal sequence and the presence of a single transmembrane domain (TMD) proximal to their C-terminus. They play fundamental roles in cellular processes including vesicular trafficking, protein translocation and quality control. Some of the TA proteins are post-translationally integrated by the Guided Entry of TA (GET) pathway to the cellular membranes; with their N-terminus oriented towards the cytosol and C-terminus facing the organellar lumen. The TA repertoire and the GET machinery have been extensively characterized in the yeast and mammalian systems, however, they remain elusive in the human malaria parasite Plasmodium falciparum. In this study, we bioinformatically predicted a total of 63 TA proteins in the P. falciparum proteome and revealed the association of a subset with the P. falciparum homolog of Get3 (PfGet3). In addition, our proximity labelling studies either definitively identified or shortlisted the other eligible GET constituents, and our in vitro association studies validated associations between PfGet3 and the corresponding homologs of Get4 and Get2 in P. falciparum. Collectively, this study reveals the presence of proteins with hallmark TA signatures and the involvement of evolutionary conserved GET trafficking pathway for their targeted delivery within the parasite.

Tail-anchored (TA) membrane proteins are known to play essential cellular functions in the eukaryotes. These proteins are trafficked to their respective destinations by post-translational translocation pathways that are evolutionarily conserved from yeast to human. However, they remain unidentified in the malaria parasite Plasmodium falciparum. We have used bioinformatic prediction algorithms in conjunction with functional validation studies to identify the candidate TA repertoire and some of the homologs of the trafficking machinery in P. falciparum. Initially, we predicted the presence of 63 putative TA proteins localized to distinct compartments within this parasite, including a few confirmed TA homologs in other eukaryotic systems. We then identified and characterized PfGet3 as a central component in the Guided-Entry of TA (GET) translocation machinery, and our bacterial co-expression and pulldown assays with two selected recombinant TA proteins, PfBOS1 and PfUSE1, showed co-association with PfGet3. We also identified PfGet2 and PfGet4 as the other two components of the GET machinery in P. falciparum using proximity biotinylation followed by mass spectrometry. Interestingly, we also found six TA proteins in the parasite enriched in this fraction. We further validated the direct interactions between a few TA candidates, PfGet4 and PfGet2 with PfGet3 using recombinant-based pulldown studies. In conclusion, this study classified a subset of membrane proteins with the TA nomenclature and implicated a previously unidentified GET pathway for their translocation in this apicomplexan parasite.

Global Proteomic Analysis of Listeria monocytogenes' Response to Linalool

Listeria monocytogenes (LM) is one of the most serious foodborne pathogens. Listeriosis, the disease caused by LM infection, has drawn attention worldwide because of its high hospitalization and mortality rates. Linalool is a vital constituent found in many essential oils; our previous studies have proved that linalool exhibits strong anti-Listeria activity. In this study, iTRAQ-based quantitative proteomics analysis was performed to explore the response of LM exposed to linalool, and to unravel the mode of action and drug targets of linalool against LM. A total of 445 differentially expressed proteins (DEPs) were screened out, including 211 up-regulated and 234 down-regulated proteins which participated in different biological functions and pathways. Thirty-one significantly enriched gene ontology (GO) functional categories were obtained, including 12 categories in “Biological Process”, 10 categories in “Cell Component”, and 9 categories in “Molecular Function”. Sixty significantly enriched biological pathways were classified, including 6 pathways in “Cell Process”, 6 pathways in “Environmental Information Processing”, 3 pathways in “Human Disease”, 40 pathways in “Metabolism”, and 2 pathways in “Organic System”. GO and Kyoto Encyclopedia of Genes (KEGG) enrichment analysis together with flow cytometry data implied that cell membranes, cell walls, nucleoids, and ribosomes might be the targets of linalool against LM. Our study provides good evidence for the proteomic analysis of bacteria, especially LM, exposed to antibacterial agents. Further, those drug targets discovered by proteomic analysis can provide theoretical support for the development of new drugs against LM.

Proteomes Are of Proteoforms: Embracing the Complexity

Proteomes are complex-much more so than genomes or transcriptomes. Thus, simplifying their analysis does not simplify the issue. Proteomes are of proteoforms, not canonical proteins. While having a catalogue of amino acid sequences provides invaluable information, this is the Proteome-lite. To dissect biological mechanisms and identify critical biomarkers/drug targets, we must assess the myriad of proteoforms that arise at any point before, after, and between translation and transcription (e.g., isoforms, splice variants, and post-translational modifications [PTM]), as well as newly defined species. There are numerous analytical methods currently used to address proteome depth and here we critically evaluate these in terms of the current 'state-of-the-field'. We thus discuss both pros and cons of available approaches and where improvements or refinements are needed to quantitatively characterize proteomes. To enable a next-generation approach, we suggest that advances lie in transdisciplinarity via integration of current proteomic methods to yield a unified discipline that capitalizes on the strongest qualities of each. Such a necessary (if not revolutionary) shift cannot be accomplished by a continued primary focus on proteo-genomics/-transcriptomics. We must embrace the complexity. Yes, these are the hard questions, and this will not be easy…but where is the fun in easy?

Cold acclimation induces life stage-specific responses in the cardiac proteome of western painted turtles (Chrysemys picta bellii): implications for anoxia tolerance

Western painted turtles (Chrysemys picta bellii) are the most anoxia-tolerant tetrapod. Survival time improves at low temperature and during ontogeny, such that adults acclimated to 3°C survive far longer without oxygen than either warm-acclimated adults or cold-acclimated hatchlings. As protein synthesis is rapidly suppressed to save energy at the onset of anoxia exposure, this study tested the hypothesis that cold acclimation would evoke preparatory changes in protein expression to support enhanced anoxia survival in adult but not hatchling turtles. To test this, adult and hatchling turtles were acclimated to either 20°C (warm) or 3°C (cold) for 5 weeks, and then the heart ventricles were collected for quantitative proteomic analysis. The relative abundance of 1316 identified proteins was compared between temperatures and developmental stages. The effect of cold acclimation on the cardiac proteome was only evident in the context of an interaction with life stage, suggesting that ontogenic differences in anoxia tolerance may be predicated on successful maturation of the heart. The main differences between the hatchling and adult cardiac proteomes reflect an increase in metabolic scope with age that included more myoglobin and increased investment in both aerobic and anaerobic energy pathways. Mitochondrial structure and function were key targets of the life stage- and temperature-induced changes to the cardiac proteome, including reduced Complex II proteins in cold-acclimated adults that may help down-regulate the electron transport system and avoid succinate accumulation during anoxia. Therefore, targeted cold-induced changes to the cardiac proteome may be a contributing mechanism for stage-specific anoxia tolerance in turtles.

DeepLigand: accurate prediction of MHC class I ligands using peptide embedding

Motivation

The computational modeling of peptide display by class I major histocompatibility complexes (MHCs) is essential for peptide-based therapeutics design. Existing computational methods for peptide-display focus on modeling the peptide-MHC-binding affinity. However, such models are not able to characterize the sequence features for the other cellular processes in the peptide display pathway that determines MHC ligand selection.

Results

We introduce a semi-supervised model, DeepLigand that outperforms the state-of-the-art models in MHC Class I ligand prediction. DeepLigand combines a peptide language model and peptide binding affinity prediction to score MHC class I peptide presentation. The peptide language model characterizes sequence features that correspond to secondary factors in MHC ligand selection other than binding affinity. The peptide embedding is learned by pre-training on natural ligands, and can discriminate between ligands and non-ligands in the absence of binding affinity prediction. Although conventional affinity-based models fail to classify peptides with moderate affinities, DeepLigand discriminates ligands from non-ligands with consistently high accuracy.

Availability and implementation

We make DeepLigand available at https://github.com/gifford-lab/DeepLigand.

Supplementary information

Supplementary data are available at Bioinformatics online.

Quantitative proteomics revealed C6orf203/MTRES1 as a factor preventing stress-induced transcription deficiency in human mitochondria

Maintenance of mitochondrial gene expression is crucial for cellular homeostasis. Stress conditions may lead to a temporary reduction of mitochondrial genome copy number, raising the risk of insufficient expression of mitochondrial encoded genes. Little is known how compensatory mechanisms operate to maintain proper mitochondrial transcripts levels upon disturbed transcription and which proteins are involved in them. Here we performed a quantitative proteomic screen to search for proteins that sustain expression of mtDNA under stress conditions. Analysis of stress-induced changes of the human mitochondrial proteome led to the identification of several proteins with poorly defined functions among which we focused on C6orf203, which we named MTRES1 (Mitochondrial Transcription Rescue Factor 1). We found that the level of MTRES1 is elevated in cells under stress and we show that this upregulation of MTRES1 prevents mitochondrial transcript loss under perturbed mitochondrial gene expression. This protective effect depends on the RNA binding activity of MTRES1. Functional analysis revealed that MTRES1 associates with mitochondrial RNA polymerase POLRMT and acts by increasing mitochondrial transcription, without changing the stability of mitochondrial RNAs. We propose that MTRES1 is an example of a protein that protects the cell from mitochondrial RNA loss during stress.

Sum of peak intensities outperforms peak area integration in iTRAQ protein expression measurement by LC-MS/MS using a TripleTOF 5600+ platform

In the field of quantitative proteomics, the Isobaric Tags for Relative and Absolute Quantitation (iTRAQ) technology has demonstrated efficacy for proteome monitoring despite its lack of a consensus for data handling. In the present study, after peptide and protein identification, we compared the widespread quantitation method based on the calculation of MS/MS reporter ion peaks areas ratios (ProteinPilot) to the alternative method based on the calculation of ratios of the sum of peak intensities (jTRAQx [Quant]) and we processed output data with the in-house Customizable iTRAQ Ratios Calculator (CiR-C) algorithm. Quantitation based on peak area ratios displayed no significant linear correlation with Western blot quantitation. In contrast, quantitation based on the sum of peak intensities displayed a significant linear association with Western blot quantitation (non-zero slope; Pearson correlation coefficient test, r = 0.296, P=0.010**) with an average bias of 0.087 ± 0.500 and 95% Limits of Agreement from −0.893 to 1.068. We proposed the Mascot-jTRAQx-CiR-C strategy as a simple yet powerful data processing adjunct to the iTRAQ technology.

Quantitative Early Auxin Root Proteomics Identifies GAUT10, a Galacturonosyltransferase, as a Novel Regulator of Root Meristem Maintenance

Auxin induces rapid gene expression changes throughout root development. How auxin-induced transcriptional responses relate to changes in protein abundance is not well characterized. This report identifies early auxin responsive proteins in roots at 30 min and 2 h after hormone treatment using a quantitative proteomics approach in which 3,514 proteins were reliably quantified. A comparison of the >100 differentially expressed proteins at each the time point showed limited overlap, suggesting a dynamic and transient response to exogenous auxin. Several proteins with established roles in auxin-mediated root development exhibited altered abundance, providing support for this approach. While novel targeted proteomics assays demonstrate that all six auxin receptors remain stable in response to hormone. Additionally, 15 of the top responsive proteins display root and/or auxin response phenotypes, demonstrating the validity of these differentially expressed proteins. Auxin signaling in roots dictates proteome reprogramming of proteins enriched for several gene ontology terms, including transcription, translation, protein localization, thigmatropism, and cell wall modification. In addition, we identified auxin-regulated proteins that had not previously been implicated in auxin response. For example, genetic studies of the auxin responsive protein galacturonosyltransferase 10 demonstrate that this enzyme plays a key role in root development. Altogether these data complement and extend our understanding of auxin response beyond that provided by transcriptome studies and can be used to uncover novel proteins that may mediate root developmental programs.

Proteomes of Leaf-Growing Zones in Rice Genotypes with Contrasting Drought Tolerance

Plants require a distinctive cohort of enzymes to coordinate cell division and expansion. Proteomic analysis now enables interrogation of immature leaf bases where these processes occur. Hence, proteins in tissues sampled from leaves of a drought-tolerant rice (IAC1131) are investigated to provide insights into the effect of soil drying on gene expression relative to the drought-sensitive genotype Nipponbare. Shoot growth zones are dissected to estimate the proportion of dividing cells and extract protein for subsequent tandem mass tags quantitative proteomic analysis. Gene ontology annotations of differentially expressed proteins provide insights into responses of Nipponbare and IAC1131 to drought. Soil drying does not affect the percentage of mitotic cells in IAC1131. More than 800 proteins across most functional categories increase in drought (and decrease on rewatering) in IAC1131, including proteins involved in "organizing the meristem" and "new cell formation". On the other hand, the percentage of dividing cells in Nipponbare is severely impaired during drought and fewer than 200 proteins respond in abundance when growing zones undergo a drying cycle. Remarkably, the proteomes of the growing zones of each genotype respond in a highly distinctive manner, reflecting their contrasting drought tolerance even at the earliest stages of leaf development.

Effect of disease-associated SLC9A9 mutations on protein-protein interaction networks: implications for molecular mechanisms for ADHD and autism

Na⁺/H⁺ Exchanger 9 (NHE9) is an endosomal membrane protein encoded by the Solute Carrier 9A, member 9 gene (SLC9A9). SLC9A9 has been implicated in attention deficit hyperactivity disorder (ADHD), autism spectrum disorders (ASDs), epilepsy, multiple sclerosis and cancers. To better understand the function of NHE9 and the effects of disease-associated variants on protein-protein interactions, we conducted a quantitative analysis of the NHE9 interactome using co-immunoprecipitation and isobaric labeling-based quantitative mass spectrometry. We identified 100 proteins that interact with NHE9. These proteins were enriched in known functional pathways for NHE9: the endocytosis, protein ubiquitination and phagosome pathways, as well as some novel pathways including oxidative stress, mitochondrial dysfunction, mTOR signaling, cell death and RNA processing pathways. An ADHD-associated mutation (A409P) significantly altered NHE9's interactions with a subset of proteins involved in caveolae-mediated endocytosis and MAP2K2-mediated downstream signaling. An ASD nonsense mutation in SLC9A9, R423X, produced no-detectable amount of NHE9, suggesting the overall loss of NHE9 functional networks. In addition, seven of the NHE9 interactors are products of known autism candidate genes (Simons Foundation Autism Research Initiative, SFARI Gene) and 90% of the NHE9 interactome overlap with SFARI protein interaction network PIN (p < 0.0001), supporting the role of NHE9 interactome in ASDs molecular mechanisms. Our results provide a detailed understanding of the functions of protein NHE9 and its disrupted interactions, possibly underlying ADHD and ASDs. Furthermore, our methodological framework proved useful for functional characterization of disease-associated genetic variants and suggestion of druggable targets.

Univariate and classification analysis reveals potential diagnostic biomarkers for early stage ovarian cancer Type 1 and Type 2

Biomarkers for early detection of ovarian tumors are urgently needed. Tumors of the ovary grow within cysts and most are benign. Surgical sampling is the only way to ensure accurate diagnosis, but often leads to morbidity and loss of female hormones. The present study explored the deep proteome in well-defined sets of ovarian tumors, FIGO stage I, Type 1 (low-grade serous, mucinous, endometrioid; n = 9), Type 2 (high-grade serous; n = 9), and benign serous (n = 9) using TMT-LC-MS/MS. Data are available via ProteomeXchange with identifier PXD010939. We evaluated new bioinformatics tools in the discovery phase. This innovative selection process involved different normalizations, a combination of univariate statistics, and logistic model tree and naive Bayes tree classifiers. We identified 142 proteins by this combined approach. One biomarker panel and nine individual proteins were verified in cyst fluid and serum: transaldolase-1, fructose-bisphosphate aldolase A (ALDOA), transketolase, ceruloplasmin, mesothelin, clusterin, tenascin-XB, laminin subunit gamma-1, and mucin-16. Six of the proteins were found significant (p < .05) in cyst fluid while ALDOA was the only protein significant in serum. The biomarker panel achieved ROC AUC 0.96 and 0.57 respectively. We conclude that classification algorithms complement traditional statistical methods by selecting combinations that may be missed by standard univariate tests. SIGNIFICANCE: In the discovery phase, we performed deep proteome analyses of well-defined histology subgroups of ovarian tumor cyst fluids, highly specified for stage and type (histology and grade). We present an original approach to selecting candidate biomarkers combining several normalization strategies, univariate statistics, and machine learning algorithms. The results from validation of selected proteins strengthen our prior proteomic and genomic data suggesting that cyst fluids are better than sera in early stage ovarian cancer diagnostics.

Differential protein expression and post-translational modifications in metronidazole-resistant Giardia duodenalis

Background

Metronidazole (Mtz) is the frontline drug treatment for multiple anaerobic pathogens, including the gastrointestinal protist, Giardia duodenalis. However, treatment failure is common and linked to in vivo drug resistance. In Giardia, in vitro drug-resistant lines allow controlled experimental interrogation of resistance mechanisms in isogenic cultures. However, resistance-associated changes are inconsistent between lines, phenotypic data are incomplete, and resistance is rarely genetically fixed, highlighted by reversion to sensitivity after drug selection ceases or via passage through the life cycle. Comprehensive quantitative approaches are required to resolve isolate variability, fully define Mtz resistance phenotypes, and explore the role of post-translational modifications therein.

Findings

We performed quantitative proteomics to describe differentially expressed proteins in 3 seminal Mtz-resistant lines compared to their isogenic, Mtz-susceptible, parental line. We also probed changes in post-translational modifications including protein acetylation, methylation, ubiquitination, and phosphorylation via immunoblotting. We quantified more than 1,000 proteins in each genotype, recording substantial genotypic variation in differentially expressed proteins between isotypes. Our data confirm substantial changes in the antioxidant network, glycolysis, and electron transport and indicate links between protein acetylation and Mtz resistance, including cross-resistance to deacetylase inhibitor trichostatin A in Mtz-resistant lines. Finally, we performed the first controlled, longitudinal study of Mtz resistance stability, monitoring lines after cessation of drug selection, revealing isolate-dependent phenotypic plasticity.

Conclusions

Our data demonstrate understanding that Mtz resistance must be broadened to post-transcriptional and post-translational responses and that Mtz resistance is polygenic, driven by isolate-dependent variation, and is correlated with changes in protein acetylation networks.

Proteogenomic Analyses Revealed Favorable Metabolism Pattern Alterations in Rotifer Brachionus plicatilis Fed with Selenium-rich Chlorella

Organoselenium have garnered attention because of their potential to be used as ingredients in new anti-aging and antioxidation medicines and food. Rotifers are frequently used as a model organism for aging research. In this study, we used Se-enriched Chlorella (Se- Chlorella), a novel organoselenium compound, to feed Brachionus plicatilis to establish a rotifer model with a prolonged lifespan. The results showed that the antioxidative effect in Se-enriched rotifer was associated with an increase in guaiacol peroxidase (GPX) and catalase (CAT). The authors then performed the first proteogenomic analysis of rotifers to understand their possible metabolic mechanisms. With the de novo assembly of RNA-Seq reads as the reference, we mapped the proteomic output generated by iTRAQ-based mass spectrometry. We found that the differentially expressed proteins were primarily involved in antireactive oxygen species (ROS) and antilipid peroxidation (LPO), selenocompound metabolism, glycolysis, and amino acid metabolisms. Furthermore, the ROS level of rotifers was diminished after Se- Chlorella feeding, indicating that Se- Chlorella could help rotifers to enhance their amino acid metabolism and shift the energy generating metabolism from tricarboxylic acid cycle to glycolysis, which leads to reduced ROS production. This is the first report to demonstrate the anti-aging effect of Se- Chlorella on rotifers and to provide a possible mechanism for this activity. Thus, Se- Chlorella is a promising novel organoselenium compound with the potential to prolong human lifespans.

Quality Assessments of Long-Term Quantitative Proteomic Analysis of Breast Cancer Xenograft Tissues

Clinical proteomics requires large-scale analysis of human specimens to achieve statistical significance. We evaluated the long-term reproducibility of an iTRAQ (isobaric tags for relative and absolute quantification)-based quantitative proteomics strategy using one channel for reference across all samples in different iTRAQ sets. A total of 148 liquid chromatography tandem mass spectrometric (LC-MS/MS) analyses were completed, generating six 2D LC-MS/MS data sets for human-in-mouse breast cancer xenograft tissues representative of basal and luminal subtypes. Such large-scale studies require the implementation of robust metrics to assess the contributions of technical and biological variability in the qualitative and quantitative data. Accordingly, we derived a quantification confidence score based on the quality of each peptide-spectrum match to remove quantification outliers from each analysis. After combining confidence score filtering and statistical analysis, reproducible protein identification and quantitative results were achieved from LC-MS/MS data sets collected over a 7-month period. This study provides the first quality assessment on long-term stability and technical considerations for study design of a large-scale clinical proteomics project.

Data for iTRAQ-based quantification of the cardiac proteome of rainbow trout (Oncorhynchus mykiss) at rest and with exercise training

This data article presents the first description of the rainbow trout cardiac ventricle at the level of the proteome, with more than 700 proteins identified and quantified using isobaric tags for relative and absolute quantitation (iTRAQ) and LC-MS/MS. The abundances of these proteins were compared across 4 durations of moderate exercise training (0, 4, 7, and 14 d), and a total of 107 proteins were differentially abundant during the course of the training program. The differentially abundant proteins are presented here grouped by functional classification. In the research article associated with this data [1], the temporal changes in the cardiac proteome are discussed in the context of cardiac remodelling and development of a trained heart phenotype.

The Human Proteome Organization-Proteomics Standards Initiative Quality Control Working Group: Making Quality Control More Accessible for Biological Mass Spectrometry

To have confidence in results acquired during biological mass spectrometry experiments, a systematic approach to quality control is of vital importance. Nonetheless, until now, only scattered initiatives have been undertaken to this end, and these individual efforts have often not been complementary. To address this issue, the Human Proteome Organization-Proteomics Standards Initiative has established a new working group on quality control at its meeting in the spring of 2016. The goal of this working group is to provide a unifying framework for quality control data. The initial focus will be on providing a community-driven standardized file format for quality control. For this purpose, the previously proposed qcML format will be adapted to support a variety of use cases for both proteomics and metabolomics applications, and it will be established as an official PSI format. An important consideration is to avoid enforcing restrictive requirements on quality control but instead provide the basic technical necessities required to support extensive quality control for any type of mass spectrometry-based workflow. We want to emphasize that this is an open community effort, and we seek participation from all scientists with an interest in this field.

A rapid and sensitive detection of D-Aspartic acid in Crystallin by chiral derivatized liquid chromatography mass spectrometry

A method for the determination of D-Aspartic acid (D-Asp) and its D/L ratio in peptides and proteins has been developed. This method was carried out with good separation of the D/L chiral peptide pairs by combination of a chiral derivatization and an ADME column separation. Furthermore, a cationic derivatization reagent, DBD-Py-NCS, increased the sensitivity of the ESI-MS/MS detection. To confirm the comprehensive peptide analysis, synthesized α-Crystallin tryptic peptides, which included D-Asp residues, were analyzed. The 5 pairs of D/L-Asp that included peptide diastereomers were well separated. Their peak resolutions were more than 1.5 and the results were reproducible (RSD<0.05, n=5). As an application of this method, we analyzed the α-Crystallin standard and UV irradiated α-Crystallin. After trypsin digestion and DBD-Py-NCS derivatization, the tryptic peptide derivatives were applied to LC-MS/MS. Based on the results of peptide sequence identification, almost all the tryptic peptides of the αA- and αB-Crystallin homologous subunits of α-Crystallin were detected as DBD-Py NCS derivatives. However, there was no D-Asp residue in the standard proteins. In the case of the UV irradiated α-Crystallin, Asp⁷⁶ and Asp⁸⁴ in the αA-Crystallin and Asp⁹⁶ in αB-Crystallin were racemized to D-Asp. These results show that this proposed chiral peptide LC-MS/MS method using chiral derivatization provides a rapid and sensitive analysis for post translational Asp racemization sites in aging proteins.

On the Statistical Significance of Compressed Ratios in Isobaric Labeling: A Cross-Platform Comparison

Isobaric labeling is gaining popularity in proteomics due to its multiplexing capacity. However, copeptide fragmentation introduces a bias that undermines its accuracy. Several strategies have been shown to partially and, in some cases, completely solve this issue. However, it is still not clear how ratio compression affects the ability to identify a protein's change of abundance as statistically significant. Here, by using the "two proteomes" approach (E. coli lysates with fixed 2.5 ratios in the presence or absence of human lysates acting as the background interference) and manipulating isolation width values, we were able to model isobaric data with different levels of accuracy and precision in three types of mass spectrometers: LTQ Orbitrap Velos, Impact, and Q Exactive. We determined the influence of these variables on the statistical significance of the distorted ratios and compared them to the ratios measured without impurities. Our results confirm previous findings1-4 regarding the importance of optimizing acquisition parameters in each instrument in order to minimize interference without compromising precision and identification. We also show that, under these experimental conditions, the inclusion of a second replicate increases statistical sensitivity 2-3-fold and counterbalances to a large extent the issue of ratio compression.

Quantitative Proteomics of Vestibular Schwannoma Cerebrospinal Fluid

This pilot study aimed to identify candidate proteins for future study that are differentially expressed in vestibular schwannoma (VS) cerebrospinal fluid (CSF) and to compare such proteins with those previously identified in perilymph and specimen secretions. CSF was collected intraoperatively prior to removal of untreated sporadic VS (3 translabyrinthine, 3 middle cranial fossa approaches) and compared with reference CSF samples. After proteolytic digestion and iTRAQ labeling, tandem mass spectrometry with ProteinPilot was used to identify candidate proteins. Of the 237 proteins detected, 13 were dysregulated in ≥3 of the 6 VS patients versus controls, and 13 were dysregulated (12 up, 1 down) in samples from patients with class D versus class B hearing. Four perilymph proteins of interest were dysregulated in ≥1 VS CSF samples. Thus, 26 candidate VS CSF biomarkers were identified that should be considered in future VS biomarker and tumor pathophysiology investigations.

Complementary Post Transcriptional Regulatory Information is Detected by PUNCH-P and Ribosome Profiling

Two novel approaches were recently suggested for genome-wide identification of protein aspects synthesized at a given time. Ribo-Seq is based on sequencing all the ribosome protected mRNA fragments in a cell, while PUNCH-P is based on mass-spectrometric analysis of only newly synthesized proteins. Here we describe the first Ribo-Seq/PUNCH-P comparison via the analysis of mammalian cells during the cell-cycle for detecting relevant differentially expressed genes between G1 and M phase. Our analyses suggest that the two approaches significantly overlap with each other. However, we demonstrate that there are biologically meaningful proteins/genes that can be detected to be post-transcriptionally regulated during the mammalian cell cycle only by each of the approaches, or their consolidation. Such gene sets are enriched with proteins known to be related to intra-cellular signalling pathways such as central cell cycle processes, central gene expression regulation processes, processes related to chromosome segregation, DNA damage, and replication, that are post-transcriptionally regulated during the mammalian cell cycle. Moreover, we show that combining the approaches better predicts steady state changes in protein abundance. The results reported here support the conjecture that for gaining a full post-transcriptional regulation picture one should integrate the two approaches.

Induction of virulence factors in Giardia duodenalis independent of host attachment

Giardia duodenalis is responsible for the majority of parasitic gastroenteritis in humans worldwide. Host-parasite interaction models in vitro provide insights into disease and virulence and help us to understand pathogenesis. Using HT-29 intestinal epithelial cells (IEC) as a model we have demonstrated that initial sensitisation by host secretions reduces proclivity for trophozoite attachment, while inducing virulence factors. Host soluble factors triggered up-regulation of membrane and secreted proteins, including Tenascins, Cathepsin-B precursor, cystatin, and numerous Variant-specific Surface Proteins (VSPs). By comparison, host-cell attached trophozoites up-regulated intracellular pathways for ubiquitination, reactive oxygen species (ROS) detoxification and production of pyridoxal phosphate (PLP). We reason that these results demonstrate early pathogenesis in Giardia involves two independent host-parasite interactions. Motile trophozoites respond to soluble secreted signals, which deter attachment and induce expression of virulence factors. Trophozoites attached to host cells, in contrast, respond by up-regulating intracellular pathways involved in clearance of ROS, thus anticipating the host defence response.

Dissecting the iTRAQ Data Analysis

In the era of large-scale quantitative biology, mass spectrometry-based quantitative proteomics is progressively becoming indispensable for gaining insights into the biological systems at molecular level. Various quantitative study designs rely on chemical tagging approaches to study disease, stress, or drug response and temporal studies aiming at disease/developmental progression in a biological system. Isobaric tags for relative and absolute quantitation (iTRAQ) is one of the most popular chemical labeling techniques which allows four, six, or eight samples to be multiplexed in a single run. As the iTRAQ tag has a balancer group to equalize all states of a labeled peptide to same mass, the differentially labeled iTRAQ peptides are mixed before chromatography and elute as a single combined peak in MS. This enhances the peptide signal and quantitation is performed during MS/MS along with sequencing, where reporter ions of different masses are released to give relative quantitation. Known amount of a spiked-in protein can also help in absolute quantitation of the proteins in a sample.

Proteomic analysis of early-stage embryos: implications for egg quality in hapuku (Polyprion oxygeneios)

In order to develop biomarkers that may help predict the egg quality of captive hapuku (Polyprion oxygeneios) and provide potential avenues for its manipulation, the present study (1) sequenced the proteome of early-stage embryos using isobaric tag for relative and absolute quantification analysis, and (2) aimed to establish the predictive value of the abundance of identified proteins with regard to egg quality through regression analysis. Egg quality was determined for eight different egg batches by blastomere symmetry scores. In total, 121 proteins were identified and assigned to one of nine major groups according to their function/pathway. A mixed-effects model analysis revealed a decrease in relative protein abundance that correlated with (decreasing) egg quality in one major group (heat-shock proteins). No differences were found in the other protein groups. Linear regression analysis, performed for each identified protein separately, revealed seven proteins that showed a significant decrease in relative abundance with reduced blastomere symmetry: two correlates that have been named in other studies (vitellogenin, heat-shock protein-70) and a further five new candidate proteins (78 kDa glucose-regulated protein, elongation factor-2, GTP-binding nuclear protein Ran, iduronate 2-sulfatase and 6-phosphogluconate dehydrogenase). Notwithstanding issues associated with multiple statistical testing, we conclude that these proteins, and especially iduronate 2-sulfatase and the generic heat-shock protein group, could serve as biomarkers of egg quality in hapuku.

iPQF: a new peptide-to-protein summarization method using peptide spectra characteristics to improve protein quantification

MOTIVATION

Isobaric labelling techniques such as iTRAQ and TMT are popular methods for relative protein abundance estimation in proteomic studies. However, measurements are assessed at the peptide spectrum level and exhibit substantial heterogeneity per protein. Hence, clever summarization strategies are required to infer protein ratios. So far, current methods rely exclusively on quantitative values, while additional information on peptides is available, yet it is not considered in these methods.

METHODS

We present iPQF ( I: sobaric P: rotein Q: uantification based on F: eatures) as a novel peptide-to-protein summarization method, which integrates peptide spectra characteristics as well as quantitative values for protein ratio estimation. We investigate diverse features characterizing spectra reliability and reveal significant correlations to ratio accuracy in spectra. As a result, we developed a feature-based weighting of peptide spectra.

RESULTS

A performance evaluation of iPQF in comparison to nine different protein ratio inference methods is conducted on five published MS2 and MS3 datasets with predefined ground truth. We demonstrate the benefit of using peptide feature information to improve protein ratio estimation. Compared to purely quantitative approaches, our proposed strategy achieves increased accuracy by addressing peptide spectra reliability.

AVAILABILITY AND IMPLEMENTATION

The iPQF algorithm is available within the established R/Bioconductor package MSnbase (version ≥ 1.17.8).

CONTACT

renardB@rki.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An Efficient Approach to Evaluate Reporter Ion Behavior from MALDI-MS/MS Data for Quantification Studies Using Isobaric Tags

Protein quantification, identification, and abundance determination are important aspects of proteome characterization and are crucial in understanding biological mechanisms and human diseases. Different strategies are available to quantify proteins using mass spectrometric detection, and most are performed at the peptide level and include both targeted and untargeted methodologies. Discovery-based or untargeted approaches oftentimes use covalent tagging strategies (i.e., iTRAQ, TMT), where reporter ion signals collected in the tandem MS experiment are used for quantification. Herein we investigate the behavior of the iTRAQ 8-plex chemistry using MALDI-TOF/TOF instrumentation. The experimental design and data analysis approach described is simple and straightforward, which allows researchers to optimize data collection and proper analysis within a laboratory. iTRAQ reporter ion signals were normalized within each spectrum to remove peptide biases. An advantage of this approach is that missing reporter ion values can be accepted for purposes of protein identification and quantification without the need for ANOVA analysis. We investigate the distribution of reporter ion peak areas in an equimolar system and a mock biological system and provide recommendations for establishing fold-change cutoff values at the peptide level for iTRAQ data sets. These data provide a unique data set available to the community for informatics training and analysis.

Stable isotope labelling methods in mass spectrometry-based quantitative proteomics

Mass-spectrometry based proteomics has evolved as a promising technology over the last decade and is undergoing a dramatic development in a number of different areas, such as; mass spectrometric instrumentation, peptide identification algorithms and bioinformatic computational data analysis. The improved methodology allows quantitative measurement of relative or absolute protein amounts, which is essential for gaining insights into their functions and dynamics in biological systems. Several different strategies involving stable isotopes label (ICAT, ICPL, IDBEST, iTRAQ, TMT, IPTL, SILAC), label-free statistical assessment approaches (MRM, SWATH) and absolute quantification methods (AQUA) are possible, each having specific strengths and weaknesses. Inductively coupled plasma mass spectrometry (ICP-MS), which is still widely recognised as elemental detector, has recently emerged as a complementary technique to the previous methods. The new application area for ICP-MS is targeting the fast growing field of proteomics related research, allowing absolute protein quantification using suitable elemental based tags. This document describes the different stable isotope labelling methods which incorporate metabolic labelling in live cells, ICP-MS based detection and post-harvest chemical label tagging for protein quantification, in addition to summarising their pros and cons.

Proteomic analysis of posttranslational modifications using iTRAQ in Leishmania

iTRAQ is a high coverage quantitative proteomics technique identifies and quantitates abundance changes of multiple (up to eight) distinct protein samples. To date, one iTRAQ-MS/MS assay can identify up to quarter of cells proteome. Each of the eight tags covalently binds to the N-terminus as well as arginine and lysine side chains of peptides, enabling labeling of the entire peptide population in each sample. Following tagging, the various protein samples are mixed and subjected to LC-MS/MS analysis. In the first round identical peptides from the different protein populations focus in a single pick. Subsequently, sequence of each peptide is determined. The tags whose m/z is similar to that of natural amino acids are used to determine relative abundance. To date, iTRAQ enabled identification of almost 2,000 Leishmania proteins. Here, we provide protocols for protein abundance changes and for phosphoproteomics analysis in Leishmania parasites.

Isobaric labeling-based relative quantification in shotgun proteomics

Mass spectrometry plays a key role in relative quantitative comparisons of proteins in order to understand their functional role in biological systems upon perturbation. In this review, we review studies that examine different aspects of isobaric labeling-based relative quantification for shotgun proteomic analysis. In particular, we focus on different types of isobaric reagents and their reaction chemistry (e.g., amine-, carbonyl-, and sulfhydryl-reactive). Various factors, such as ratio compression, reporter ion dynamic range, and others, cause an underestimation of changes in relative abundance of proteins across samples, undermining the ability of the isobaric labeling approach to be truly quantitative. These factors that affect quantification and the suggested combinations of experimental design and optimal data acquisition methods to increase the precision and accuracy of the measurements will be discussed. Finally, the extended application of isobaric labeling-based approach in hyperplexing strategy, targeted quantification, and phosphopeptide analysis are also examined.

iTRAQ quantitative clinical proteomics revealed role of Na(+)K(+)-ATPase and its correlation with deamidation in vascular dementia

Dementia is a major public health burden characterized by impaired cognition and loss of function. There are limited treatment options due to inadequate understanding of its pathophysiology and underlying causative mechanisms. Discovery-driven iTRAQ-based quantitative proteomics techniques were applied on frozen brain samples to profile the proteome from vascular dementia (VaD) and age-matched nondementia controls to elucidate the perturbed pathways contributing to pathophysiology of VaD. The iTRAQ quantitative data revealed significant up-regulation of protein-l-isoaspartate O-methyltransferase and sodium-potassium transporting ATPase, while post-translational modification analysis suggested deamidation of catalytic and regulatory subunits of sodium-potassium transporting ATPase. Spontaneous protein deamidation of labile asparagines, generating abnormal l-isoaspartyl residues, is associated with cell aging and dementia due to Alzheimer's disease and may be a cause of neurodegeneration. As ion channel proteins play important roles in cellular signaling processes, alterations in their function by deamidation may lead to perturbations in membrane excitability and neuronal function. Structural modeling of sodium-potassium transporting ATPase revealed the close proximity of these deamidated residues to the catalytic site during E2P confirmation. The deamidated residues may disrupt electrostatic interaction during E1 phosphorylation, which may affect ion transport and signal transduction. Our findings suggest impaired regulation and compromised activity of ion channel proteins contribute to the pathophysiology of VaD.

The necrotrophic effector protein SnTox3 re-programs metabolism and elicits a strong defence response in susceptible wheat leaves

BACKGROUND

The fungus Stagonospora nodorum is a necrotrophic pathogen of wheat. It causes disease by secreting proteinaceous effectors which interact with proteins encoded by dominant susceptibility genes in the host. The outcome of these interactions results in necrosis, allowing the fungus to thrive on dead plant material. The mechanisms of these effectors though are poorly understood. In this study, we undertake a comprehensive transcriptomics, proteomic and metabolomic approach to understand how a susceptible wheat cultivar responds to exposure to the Stagonospora nodorum effector protein SnTox3.

RESULTS

Microarray and proteomic studies revealed that SnTox3 strongly induced responses consistent with those previously associated with classical host defence pathways including the expression of pathogenicity-related proteins and the induction of cell death. Collapse of the photosynthetic machinery was also apparent at the transcriptional and translational level. SnTox3-infiltrated wheat leaves also showed a strong induction of enzymes involved in primary metabolism consistent with increases in hexoses, amino acids and organic acids as determined by primary metabolite profiling. Methionine and homocysteine metabolism was strongly induced upon exposure to SnTox3. Pathogenicity in the presence of homocysteine was inhibited confirming that the compound has a role in plant defence. Consistent with the strong defence responses observed, secondary metabolite profiling revealed the induction of several compounds associated with plant defence, including the phenylpropanoids chlorogenic acid and feruloylquinic acid, and the cyanogenic glucoside dhurrin. Serotonin did not accumulate subsequent to SnTox3 infiltration.

CONCLUSIONS

These data support the theory that the SnTox3 effector protein elicits a host cell death response to facilitate the pathogen's necrotrophic infection cycle. Our data also demonstrate that the mechanism of SnTox3 appears distinct from the previously characterised Stagonospora nodorum effector SnToxA. Collectively, this comprehensive analysis has advanced our understanding of necrotrophic effector biology and highlighted the complexity of effector-triggered susceptibility.

Identification of putative biomarkers for HIV-associated neurocognitive impairment in the CSF of HIV-infected patients under cART therapy determined by mass spectrometry

We identified and measured proteins in the cerebral spinal fluid (CSF) involved in HIV-associated neurological disorders. Protein levels were determined by mass spectrometry (MS) in pooled CSF taken from three patient groups (human immunodeficiency virus (HIV)-1-infected patients that developed HIV-associated neurocognitive disorders (HANDs), HIV-1-infected patients without HAND, and healthy controls). Pools were generated from 10 patients each per group. CSF from individual patient groups were digested with trypsin and separately labeled using with isobaric tags for relative and absolute quantitation (iTRAQ). After combining all samples in one, peptides were extensively fractionated by offline two-dimensional separation and identified by tandem MS. One hundred and ninety three proteins were deemed to be interpretable for quantitation based on permutation tests with a 95 % confidence interval with a p value ≤ 0.05. Using a cutoff of 1.5-fold for upregulation and 0.6 for downregulation, 16 proteins were differentially expressed in HIV + HAND (reporter p value ≤0.05) with seven of them previously described as HIV-interacting proteins: endoplasmin, mitochondrial damage mediator-BH3-interacting domanin death agonist, orosomucoid, apolipoprotein E, metalloproteinase inhibitor 2, peroxiredoxin-2, and the nuclear protein, ruvB-like 2. Several previously unidentified proteins with possible neurological implication in HIV patients include forming-binding protein 1, C-reactive protein, leukocyte-associated immunoglobulin receptor 1, renin receptor, mediator of RNA polymerase II transcription subunit 14, multimerin-2, alpha-N-acetylglucosaminidase, caldesmon, and cadherin EGF LAG G-type receptor. Our results suggest that not only a few but possibly a combination of biomarkers that are highly correlated can predict neurocognitive status in HIV-infected patients and might be involved in monocyte or macrophage activation.

General statistical framework for quantitative proteomics by stable isotope labeling

The combination of stable isotope labeling (SIL) with mass spectrometry (MS) allows comparison of the abundance of thousands of proteins in complex mixtures. However, interpretation of the large data sets generated by these techniques remains a challenge because appropriate statistical standards are lacking. Here, we present a generally applicable model that accurately explains the behavior of data obtained using current SIL approaches, including (18)O, iTRAQ, and SILAC labeling, and different MS instruments. The model decomposes the total technical variance into the spectral, peptide, and protein variance components, and its general validity was demonstrated by confronting 48 experimental distributions against 18 different null hypotheses. In addition to its general applicability, the performance of the algorithm was at least similar than that of other existing methods. The model also provides a general framework to integrate quantitative and error information fully, allowing a comparative analysis of the results obtained from different SIL experiments. The model was applied to the global analysis of protein alterations induced by low H₂O₂ concentrations in yeast, demonstrating the increased statistical power that may be achieved by rigorous data integration. Our results highlight the importance of establishing an adequate and validated statistical framework for the analysis of high-throughput data.

Novel and cost-effective 6-plex isobaric tagging reagent, DiART, is effective for identification and relative quantification of complex protein mixtures using PQD fragmentation

Deuterium isobaric Amine Reactive Tag (DiART) reagents facilitate relative quantification during proteomic analysis in a functionally similar manner to commercially available isobaric tag for relative and absolute quantitation (iTRAQ) and tandem mass tag (TMT) reagents. In contrast to iTRAQ and TMT, DiART reagents incorporate deuterium isotopes which significantly reduce the number of required synthesis steps and hence have potential to significantly reduce reagent production cost. We examined the capability of DiART for performing quantitative proteomic experiments using a linear ion-trap mass spectrometer with Pulsed Q Dissociation (PQD) fragmentation. Using a synthetic peptide tagged with DiART reagent, we observed a precise mass shift of 144.79 Da on the triply charged precursor ion, which shows complete derivatization of the N-terminus and ε-amino group of lysine. A DiART tagged tryptic digest of bovine serum albumin showed a sequence coverage of 57.99% which was very comparable to that showed by iTRAQ, 54.77%. Furthermore, a ten protein mixture tagged with DiART reagents and mixed in 1:1:1:1:1:1 exhibited < 15% error, whereas a linear trend (slope of 1.085) was observed when tagged proteins were mixed in the ratio 2:1:2:4:10:14 and plotted against theoretical ratios. Finally, when complex cell-wall protein mixtures from the model fungus A. nidulans were tagged with DiART reagents and mixed in different ratios, they exhibited similar trends. We conclude that DiART reagents are capable of performing quantitative proteomic experiments using PQD on a linear ion trap mass spectrometer.

Quantitative proteomic analysis of human lung tumor xenografts treated with the ectopic ATP synthase inhibitor citreoviridin

ATP synthase is present on the plasma membrane of several types of cancer cells. Citreoviridin, an ATP synthase inhibitor, selectively suppresses the proliferation and growth of lung cancer without affecting normal cells. However, the global effects of targeting ectopic ATP synthase in vivo have not been well defined. In this study, we performed quantitative proteomic analysis using isobaric tags for relative and absolute quantitation (iTRAQ) and provided a comprehensive insight into the complicated regulation by citreoviridin in a lung cancer xenograft model. With high reproducibility of the quantitation, we obtained quantitative proteomic profiling with 2,659 proteins identified. Bioinformatics analysis of the 141 differentially expressed proteins selected by their relative abundance revealed that citreoviridin induces alterations in the expression of glucose metabolism-related enzymes in lung cancer. The up-regulation of enzymes involved in gluconeogenesis and storage of glucose indicated that citreoviridin may reduce the glycolytic intermediates for macromolecule synthesis and inhibit cell proliferation. Using comprehensive proteomics, the results identify metabolic aspects that help explain the antitumorigenic effect of citreoviridin in lung cancer, which may lead to a better understanding of the links between metabolism and tumorigenesis in cancer therapy.

Defining, comparing, and improving iTRAQ quantification in mass spectrometry proteomics data

The purpose of this study was to generate a basis for the decision of what protein quantities are reliable and find a way for accurate and precise protein quantification. To investigate this we have used thousands of peptide measurements to estimate variance and bias for quantification by iTRAQ (isobaric tags for relative and absolute quantification) mass spectrometry in complex human samples. A549 cell lysate was mixed in the proportions 2:2:1:1:2:2:1:1, fractionated by high resolution isoelectric focusing and liquid chromatography and analyzed by three mass spectrometry platforms; LTQ Orbitrap Velos, 4800 MALDI-TOF/TOF and 6530 Q-TOF. We have investigated how variance and bias in the iTRAQ reporter ions data are affected by common experimental variables such as sample amount, sample fractionation, fragmentation energy, and instrument platform. Based on this, we have suggested a concept for experimental design and a methodology for protein quantification. By using duplicate samples in each run, each experiment is validated based on its internal experimental variation. The duplicates are used for calculating peptide weights, unique to the experiment, which is used in the protein quantification. By weighting the peptides depending on reporter ion intensity, we can decrease the relative error in quantification at the protein level and assign a total weight to each protein that reflects the protein quantitation confidence. We also demonstrate the usability of this methodology in a cancer cell line experiment as well as in a clinical data set of lung cancer tissue samples. In conclusion, we have in this study developed a methodology for improved protein quantification in shotgun proteomics and introduced a way to assess quantification for proteins with few peptides. The experimental design and developed algorithms decreased the relative protein quantification error in the analysis of complex biological samples.