iQuantitator: A tool for protein expression inference using iTRAQ

<p>Abstract</p> <p>Background</p> <p>Isobaric Tags for Relative and Absolute Quantitation (iTRAQ™) [Applied Biosystems] have seen increased application in differential protein expression analysis. To facilitate the growing need to analyze iTRAQ data, especially for cases involving multiple iTRAQ experiments, we have developed a modeling approach, statistical methods, and tools for estimating the relative changes in protein expression under various treatments and experimental conditions.</p> <p>Results</p> <p>This modeling approach provides a unified analysis of data from multiple iTRAQ experiments and links the observed quantity (reporter ion peak area) to the experiment design and the calculated quantity of interest (treatment-dependent protein and peptide fold change) through an additive model under log transformation. Others have demonstrated, through a case study, this modeling approach and noted the computational challenges of parameter inference in the unbalanced data set typical of multiple iTRAQ experiments. Here we present the development of an inference approach, based on hierarchical regression with batching of regression coefficients and Markov Chain Monte Carlo (MCMC) methods that overcomes some of these challenges. In addition to our discussion of the underlying method, we also present our implementation of the software, simulation results, experimental results, and sample output from the resulting analysis report.</p> <p>Conclusion</p> <p>iQuantitator's process-based modeling approach overcomes limitations in current methods and allows for application in a variety of experimental designs. Additionally, hypertext-linked documents produced by the tool aid in the interpretation and exploration of results.</p

Reproducibility of differential proteomic technologies in CPTAC fractionated xenografts

The NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) employed a pair of reference xenograft proteomes for initial platform validation and ongoing quality control of its data collection for The Cancer Genome Atlas (TCGA) tumors. These two xenografts, representing basal and luminal-B human breast cancer, were fractionated and analyzed on six mass spectrometers in a total of 46 replicates divided between iTRAQ and label-free technologies, spanning a total of 1095 LC-MS/MS experiments. These data represent a unique opportunity to evaluate the stability of proteomic differentiation by mass spectrometry over many months of time for individual instruments or across instruments running dissimilar workflows. We evaluated iTRAQ reporter ions, label-free spectral counts, and label-free extracted ion chromatograms as strategies for data interpretation (source code is available from http://homepages.uc.edu/~wang2x7/Research.htm). From these assessments, we found that differential genes from a single replicate were confirmed by other replicates on the same instrument from 61 to 93% of the time. When comparing across different instruments and quantitative technologies, using multiple replicates, differential genes were reproduced by other data sets from 67 to 99% of the time. Projecting gene differences to biological pathways and networks increased the degree of similarity. These overlaps send an encouraging message about the maturity of technologies for proteomic differentiation

Genome-scale Precision Proteomics Identifies Cancer Signaling Networks and Therapeutic Vulnerabilities

Mass spectrometry (MS) based-proteomics technology has been emerging as an indispensable tool for biomedical research. But the highly diverse physical and chemical properties of the protein building blocks and the dramatic human proteome complexity largely limited proteomic profiling depth. Moreover, there was a lack of high-throughput quantitative strategies that were both precise and parallel to in-depth proteomic techniques. To solve these grand challenges, a high resolution liquid chromatography (LC) system that coupled with an advanced mass spectrometer was developed to allow genome-scale human proteome identification. Using the combination of pre-MS peptide fractionation, MS2-based interference detection and post-MS computational interference correction, we enabled precise proteome quantification with isobaric labeling. We then applied these advanced proteomics tools for cancer proteome analyses on high grade gliomas (HGG) and rhabdomyosarcomas (RMS). Using systems biology approaches, we demonstrated that these newly developed proteomic analysis pipelines are able to (i) define human proteotypes that link oncogenotypes to cancer phenotypes in HGG and to (ii) identify therapeutic vulnerabilities in RMS. Development of high resolution liquid chromatography is essential for improving the sensitivity and throughput of mass spectrometry-based proteomics to genome-scale. Here we present systematic optimization of a long gradient LC-MS/MS platform to enhance protein identification from a complex mixture. The platform employed an in-house fabricated, reverse phase long column (100 µm x 150 cm, 5 µm C18 beads) coupled with Q Exactive MS. The column was capable of achieving a peak capacity of approximately 700 in a 720 min gradient of 10-45% acetonitrile. The optimal loading amount was about 6 micrograms of peptides, although the column allowed loading as many as 20 micrograms. Gas phase fractionation of peptide ions further increased the number of peptides identified by ~10%. Moreover, the combination of basic pH LC pre-fractionation with the long gradient LC-MS/MS platform enabled the identification of 96,127 peptides and 10,544 proteins at 1% protein false discovery rate in a postmortem brain sample of Alzheimer’s disease. As deep RNA sequencing of the same specimen suggested that ~16,000 genes were expressed, current analysis covered more than 60% of the expressed proteome. Isobaric labeling quantification by mass spectrometry has emerged as a powerful technology for multiplexed large-scale protein profiling, but measurement accuracy in complex mixtures is confounded by the interference from co-isolated ions, resulting in ratio compression. Here we report that the ratio compression can be essentially resolved by the combination of pre-MS peptide fractionation, MS2-based interference detection and post-MS computational interference correction. To recapitulate the complexity of biological samples, we pooled tandem mass tag (TMT) labeled E. coli peptides at 1 : 3 : 10 ratios, and added in ~20-fold more rat peptides as background, followed by the analysis of two dimensional liquid chromatography-MS/MS. Systematic investigation indicated that the quantitative interference was impacted by LC fractionation depth, MS isolation window and peptide loading amount. Exhaustive fractionation (320 x 4 h) can nearly eliminate the interference and achieve results comparable to the MS3-based method. Importantly, the interference in MS2 scans can be estimated by the intensity of contaminated y1 product ions, and we thus developed an algorithm to correct reporter ion ratios of tryptic peptides. Our data indicated that intermediate fractionation (40 x 2 h) and y1 ion-based correction allowed accurate and deep TMT protein profiling, which represents a straightforward and affordable strategy in isobaric labeling proteomics High throughput omics approaches provide an unprecedented opportunity for dissecting molecular mechanisms in cancer biology. Here we present deep profiling of whole proteome, phosphoproteome and transcriptome in two high-grade glioma mouse models driven by mutated receptor tyrosine kinase (RTK) oncogenes, platelet-derived growth factor receptor alpha (PDGFRA) and neurotrophic receptor tyrosine kinase 1 (NTRK1), analyzing 13,860 proteins (11,941 genes) and 30,431 phosphosites by mass spectrometry. Systems biology approaches identified numerous functional modules and master regulators, including 41 kinases and 26 transcription factors. Pathway activity computation and mouse survival curves indicate the NTRK1 mutation induces a higher activation of AKT targets, drives a positive feedback loop to up-regulate multiple other RTKs, and shows higher oncogenic potency than the PDGFRA mutation. Further integration of the mouse data with human HGG transcriptome data determines shared regulators of invasion and stemness. Thus, multi-omics integrative profiling is a powerful avenue to characterize oncogenic activity. There is growing emphasis on personalizing cancer therapy based on somatic mutations identified in patient’s tumors. Among pediatric solid tumors, RAS pathway mutations in rhabdomyosarcoma are the most common potentially actionable lesions. Recent success targeting CDK4/6 and MEK in RAS mutant adult cancers led our collaborator Dr. Dyer’s group to test this approach for rhabdomyosarcoma. They achieved synergistic killing of RAS mutant rhabdomyosarcoma tumor cells by combining MEK and CDK4/6 inhibitors in culture but failed to achieve efficacy in vivo using orthotopic patient derived xenografts (O-PDXs). To determine how rhabdomyosarcomas evade targeting of CDK4/6 and MEK, we collaborated to perform large-scale deep proteomic, phosphoproteomic, and epigenomic profiling of RMS tumors. Integrative analysis of these omics data detected that RMS tumor cells rapidly compensate and overcome CDK4/6 and MEK combination therapy through 6 myogenic signal transduction pathways including WNT, HH, BMP, Adenyl Cyclase, P38/MAPK and PI3K. While it is not feasible to target each of these signal transduction pathways simultaneously in RMS, we discovered that they require the HSP90 chaperone to sustain the complex developmental signal transduction milieu. We achieved specific and synergistic killing of RMS cells using sub-therapeutic concentrations of an HSP90 inhibitor (ganetespib) in combination with conventional chemotherapy used for recurrent RMS. These effects were seen in the most aggressive recurrent RMS orthotopic patient derived xenografts irrespective of RAS pathway perturbations, histologic or molecular classification. Thus, multi-omics integrative cancer profiling using our newly developed tools is powerful to identify core signaling transduction networks, tumor vulnerability (master regulators) for novel cancer therapy

Mass Spectrometric Proteomics

As suggested by the title of this Special Issue, liquid chromatography-mass spectrometry plays a pivotal role in the field of proteomics. Indeed, the research and review articles&nbsp; published in the Issue clearly evidence how the data produced by this sophisticated methodology may promote impressive advancements in this area. From among the topics discussed in the Issue, a few point to the development of&nbsp; new procedures for the&nbsp; optimization of the experimental conditions that should be applied&nbsp; for the identification of proteins present in complex mixtures.&nbsp; Other applications&nbsp; described in these articles show&nbsp; the huge potential of&nbsp; these strategies in the protein profiling of organs and&nbsp; range from&nbsp; to the study of post-translational tissue modifications to the investigation of the molecular mechanisms behind human disorders and the identification of potential biomarkers of these diseases

Tissue Proteomes: Quantitative Mass Spectrometry of Murine Liver and Ovarian Endometrioma

A human genome contains more than 20 000 protein-encoding genes. A human proteome, instead, has been estimated to be much more complex and dynamic. The most powerful tool to study proteins today is mass spectrometry (MS). MS based proteomics is based on the measurement of the masses of charged peptide ions in a gas-phase. The peptide amino acid sequence can be deduced, and matching proteins can be found, using software to correlate MS-data with sequence database information. Quantitative proteomics allow the estimation of the absolute or relative abundance of a certain protein in a sample. The label-free quantification methods use the intrinsic MS-peptide signals in the calculation of the quantitative values enabling the comparison of peptide signals from numerous patient samples. In this work, a quantitative MS methodology was established to study aromatase overexpressing (AROM+) male mouse liver and ovarian endometriosis tissue samples. The workflow of label-free quantitative proteomics was optimized in terms of sensitivity and robustness, allowing the quantification of 1500 proteins with a low coefficient of variance in both sample types. Additionally, five statistical methods were evaluated for the use with label-free quantitative proteomics data. The proteome data was integrated with other omics datasets, such as mRNA microarray and metabolite data sets. As a result, an altered lipid metabolism in liver was discovered in male AROM+ mice. The results suggest a reduced beta oxidation of long chain phospholipids in the liver and increased levels of pro-inflammatory fatty acids in the circulation in these mice. Conversely, in the endometriosis tissues, a set of proteins highly specific for ovarian endometrioma were discovered, many of which were under the regulation of the growth factor TGF-β1. This finding supports subsequent biomarker verification in a larger number of endometriosis patient samples.Siirretty Doriast

Serum proteomics to detect early changes in type 1 diabetes and carotid atherosclerosis

The detection of early markers is the key issue in predicting the outcome of inflammatory diseases such as type 1 diabetes and atherosclerosis. Whilst biochemical testing approaches have improved prediction of inflammatory diseases, validated biomarkers with better diagnostic specificities are still needed. Currently, majority of the disease-related proteomics studies have focused on their endpoints. The work presented in this thesis includes the first comprehensive proteomics analyses on serum samples collected from two unique Finnish longitudinal cohorts, namely The Diabetes Prediction and Prevention Project (DIPP) and The Cardiovascular Risk in Young Finns Study (YFS), to identify early markers associated with type 1 diabetes and carotid atherosclerosis. Using mass spectrometry (MS)-based quantitative serum proteomics, profiling was carried out to the study temporal variation in pre-diabetic samples and early markers of plaque formation with the T1D and YFS cohorts, respectively. The analyses revealed consistent differences in the abundance of a number of proteins in subjects having an ongoing asymptomatic changes, several of which are functionally relevant to the disease process. Taken together, the discovered markers are candidates for further validation studies in an independent cohorts and may be used to characterize an increased risk, progression and early onset of these diseases.Tyypin 1 diabeteksen ja ateroskleroosin kehittymiseen liittyvät varhaiset muutokset seerumiproteomissa Yksi keskeinen haaste tulehduksellisten sairauksien, kuten tyypin 1 diabeteksen ja ateroskleroosin, ennustamisessa on varhaisten tautimarkkerien löytäminen. Vaikka erilaiset biokemialliset testit ovat jo parantaneet tulehdusperäisten sairauksien ennustamista, uusia tarkempia biomarkkereita tarvitaan edelleen. Tästä huolimatta monissa näiden alojen proteomiikkatöissä on nykyisin keskitytty sairastumishetken tutkimiseen. Tämän väitöskirjatyön aikana olemme tehneet laajamittaiset proteomiikka-analyysit seeruminäytteille, jotka on kerätty osana kahta ainutlaatuista suomalaista seurantatutkimusta: DIPP-tutkimusta (tyypin 1 diabeteksen ennustaminen ja ennaltaehkäisy) ja YFS-tutkimusta (sydän- ja verisuonitautien riski nuorilla suomalaisilla). Näissä tutkimuksissa seerumiproteomiikkaa hyödynnettiin ensimmistä kertaa varhaisten tyypin 1 diabetes- ja ateroskleroosimarkkerien etsimiseen. Tutkimme tyypin 1 diabeteksen kehittymiseen ja ateroskleroottisten plakkien muodostumiseen liittyviä muutoksia seerumin proteomiprofiileissa massaspektrometriaan perustuvan kvantitatiivisen proteomiikan avulla. Nämä analyysit paljastivat johdonmukaisia eroja lukuisissa proteiineissa myöhemmin sairastuneiden oireettomien henkilöiden ja terveinä pysyneiden kontrollien välillä. Monet näistä proteiineista saattavat myös liittyä olennaisesti tautien kehittymiseen. Tutkimuksissamme löydetyt markkerit tarjoavat lähtökohdan tuleville validointitutkimuksille, ja niitä voitaisiin tulevaisuudessa käyttää yksilön kohonneen sairastumisriskin, taudin etenemisen sekä taudin varhaisen puhkeamisen kartoittamiseen

QUANTITATIVE CHARACTERIZATION OF PROTEINS AND POST-TRANSLATIONAL MODIFICATIONS IN COMPLEX PROTEOMES USING HIGH-RESOLUTION MASS SPECTROMETRY-BASED PROTEOMICS

Mass spectrometry-based proteomics is focused on identifying the entire suite of proteins and their post-translational modifications (PTMs) in a cell, organism, or community. In particular, quantitative proteomics measures abundance changes of thousands of proteins among multiple samples and provides network-level insight into how biological systems respond to environmental perturbations. Various quantitative proteomics methods have been developed, including label-free, metabolic labeling, and isobaric chemical labeling. This dissertation starts with systematic comparison of these three methods, and shows that isobaric chemical labeling provides accurate, precise, and reproducible quantification for thousands of proteins. Based on these results, we applied this approach to characterizing the proteome of Arabidopsis seedlings treated with Strigolactones (SLs), a new class of plant hormones that modulate various developmental processes. Our study reveals that SLs regulate the expression of a range of proteins that have not been assigned to SL pathways, which provides novel targets for follow-up genetic and biochemical characterization of SL signaling. The same approach was also used to measure how elevated temperature impacts the physiology of individual microbial groups in an acid mine drainage (AMD) microbial community, and shows that related organisms differed in their abundance and functional responses to temperature. Elevated temperature repressed carbon fixation by two Leptospirillum genotypes, whereas carbon fixation was significantly up-regulated at higher temperature by a third member of this genus. Further, we developed a new proteomic approach that harnessed high-resolution mass spectrometry and supercomputing for direct identification and quantification of a broad range of PTMs from an AMD microbial community. We find that PTMs are extraordinarily diverse between different growth stages and highly divergent between closely related bacteria. The findings of this study motivate further investigation of the role of PTMs in the ecology and evolution of microbial communities. Finally, a computational approach has been developed to improve the sensitivity of phosphopeptide identification. Overall, the research presented in the dissertation not only reveals biological insights with existing quantitative proteomics methods, but also develops novel methodologies that open up new avenues in studying PTMs of proteins (e.g. PTM cross-talk)

Proteomic and Metabolomic Analyses of Biological Systems with Liquid Chromatography Tandem Mass Spectrometry and Ion Mobility Mass Spectrometry

Proteomic and metabolomic analyses provide information about altered metabolic processes in plants and animals. This information can be used to assess the impact of toxicant exposure or diseases on biological systems. In this dissertation, development of an ion mobility spectrometry - mass spectrometry (IMS-MS) strategy has been evaluated in addition to utilizing traditional liquid chromatography tandem mass spectrometry (LC-MS/MS) techniques for metabolite and protein analysis. Traditional LC-MS/MS techniques have been applied to the study of: 1) the molecular mechanisms responsible for altered microvasculature function as a result of pulmonary TiO2 exposure in rats; and 2) the effectiveness of intracerebroventricular (ICV) injection of streptozotocin (STZ) to mimic early metabolic changes found in tauopathies in mice exhibiting P301L or human wild-type tau.;Inhalation exposure to TiO2 nanoparticles (NPs) has been shown to produce a pulmonary inflammatory response, to induce oxidative and nitrosative biomarkers in the systemic microcirculation, and to influence downstream microvascular dysfunction. However, the molecular mechanisms relating pulmonary inflammation with the systemic microvascular dysfunction in addition to differences in responses of vascular tissues have yet to be fully elucidated. The first study examined plasma from rats exposed to TiO2 NPs. A total of 58 proteins were identified by at least 2 unique peptides and found in at least 3 samples, and 23 metabolites were identified through ChemSpider matches and filtered for endogenous compounds. The compounds were then analyzed by principal component analysis (PCA) and 29 proteins and 18 metabolites were found to contribute most to the separation in PC1. These proteins and metabolites were then input into Ingenuity Pathway Analysis (IPA). IPA revealed 13 canonical pathways as being significant (p ≤ 0.05) based on the input proteins, but none were found to be significantly up or down regulated (z ≥ |2|) based on fold differences of the input proteins.;The second study examining plasma, aorta, and small resistance vasculature tissue from rats exposed to TiO2 NPs was performed in order to gain further insight into the pathway activation mechanisms as well as to determine if responses differ based on tissue structural and functional differences. Congruent with the previous study, acute phase response signaling, LXR/RXR activation, and FXR/RXR activation emerge as being significant pathways (p ≤ 0.05) in the aorta and plasma; however, none were found to be significantly up or down regulated (z \u3e |2|). ILK signaling, D-myo-inositol (1,3,4)-trisphosphate biosynthesis, and 1D-myo-inositol hexakisphosphate biosynthesis II (mammalian) pathways are observed to be significant (p ≤ 0.05) in the vasculature, but none were found to be significantly up or down regulated (z ≥ |2|).;P301L and human wild-type (WT) tau mice were administered an intracerebroventricular (ICV) injection of control vehicle buffer (Veh) or streptozotocin (STZ) to mimic early metabolic changes found in tauopathies, including Alzheimer\u27s disease and frontotemporal dementia. Brain hemispheres were analyzed from 6 sample cohorts: Control (CT)-Veh, CT-STZ, P301L-Veh, P301L-STZ, WT-Veh, and WT-STZ. Bottom-up proteomic analyses were utilized to identify differentially abundant proteins within the brain proteome and the biopathways altered as a result of ICV-STZ treatment. An ANOVA was used to determine the top 50 significant proteins among the 6 sample cohorts. Biopathway analysis of the top 50 proteins revealed 49 biological pathways as being significant (p ≤ 0.05), but none were significantly up or down-regulated (z ≥ |2|) among the cohorts. 14-3-3 Mediated Signaling was found to be the most significant pathway among the cohorts. Protein Kinase A Signaling pathway was also found to be significant and had an associated z-score, although it was not found to be significantly up or down-regulated in any of the comparisons. Proteome and pathway changes were observed as a result of ICV-STZ administration; however, none were found to be significantly up or down-regulated. (Abstract shortened by ProQuest.)