Trans-Proteomic Pipeline Research Articles

Bioinformatics Tools Assist in The Screening of Potential Porcine-Specific Peptide Biomarkers of Gelatin and Collagen For Halal Authentication

Gelatin and collagen are two animal-derived ingredients that are widely used in various industries. Both have distinctive physico-chemical characteristic that made them ingredients of interest for many industrial players to be applied as there are vast arrays of usage in the food, cosmetic and biomedical fields. However, the origin of gelatin and collagen poses ethical and religious concerns, especially for Muslims and Jews who have restrictions on food consumption. Porcine by-products are of concern for religious and health reasons, and there is a demand for precise and reliable detection techniques. The limitation of DNA detection is due to extreme environment in food processing which results in low extractability of DNA. Therefore, peptide-based detection using mass spectrometry is required. However, identify the suitable marker is like searching needle in haystacks. Hence, combination of bioinformatics and mass spectrometry is proposed. This study aims to identify the specific peptide biomarkers by employing bioinformatics technique which can be applied to identify gelatin and collagen sources with the aid of mass spectrometry. In these approach, combination of Petunia Trans-Proteomic Pipeline (TPP, version 5.2.0) and sequence alignment ClustalW were applied to facilitate the MS data (LC-QTOF-MS) and peptide identification. As a result, 69 fasta file of protein sequence from both UniProtKB and NCBInr have been collected, 81 collagen peptides sequence and 118 gelatine peptides has been attainable that have the potential to distinguish different species. In conclusion, in silico protein sequence approaches helps to enable rapid screening of proteotypic peptides that can serve as species biomarkers proficiently.

Improving quantitation accuracy in isobaric-labeling mass spectrometry experiments with spectral library searching and feature-based peptide-spectrum match filter

Isobaric labeling relative quantitation is one of the dominating proteomic quantitation technologies. Traditional quantitation pipelines for isobaric-labeled mass spectrometry data are based on sequence database searching. In this study, we present a novel quantitation pipeline that integrates sequence database searching, spectral library searching, and a feature-based peptide-spectrum-match (PSM) filter using various spectral features for filtering. The combined database and spectral library searching results in larger quantitation coverage, and the filter removes PSMs with larger quantitation errors, retaining those with higher quantitation accuracy. Quantitation results show that the proposed pipeline can improve the overall quantitation accuracy at the PSM and protein levels. To our knowledge, this is the first study that utilizes spectral library searching to improve isobaric labeling-based quantitation. For users to conveniently perform the proposed pipeline, we have implemented the feature-based filter being executable on both Windows and Linux platforms; its executable files, user manual, and sample data sets are freely available at https://ms.iis.sinica.edu.tw/comics/Software_FPF.html. Furthermore, with the developed filter, the proposed pipeline is fully compatible with the Trans-Proteomic Pipeline.

Trans-Proteomic Pipeline: Robust Mass Spectrometry-Based Proteomics Data Analysis Suite.

The Trans-Proteomic Pipeline (TPP) mass spectrometry data analysis suite has been in continual development and refinement since its first tools, PeptideProphet and ProteinProphet, were published 20 years ago. The current release provides a large complement of tools for spectrum processing, spectrum searching, search validation, abundance computation, protein inference, and more. Many of the tools include machine-learning modeling to extract the most information from data sets and build robust statistical models to compute the probabilities that derived information is correct. Here we present the latest information on the many TPP tools, and how TPP can be deployed on various platforms from personal Windows laptops to Linux clusters and expansive cloud computing environments. We describe tutorials on how to use TPP in a variety of ways and describe synergistic projects that leverage TPP. We conclude with plans for continued development of TPP.

Improved Analysis of Cross-Linking Mass Spectrometry Data with Kojak 2.0, Advanced by Integration into the Trans-Proteomic Pipeline.

Fragmentation ion spectral analysis of chemically cross-linked proteins is an established technology in the proteomics research repertoire for determining protein interactions, spatial orientation, and structure. Here we present Kojak version 2.0, a major update to the original Kojak algorithm, which was developed to identify cross-linked peptides from fragment ion spectra using a database search approach. A substantially improved algorithm with updated scoring metrics, support for cleavable cross-linkers, and identification of cross-links between 15N-labeled homomultimers are among the newest features of Kojak 2.0 presented here. Kojak 2.0 is now integrated into the Trans-Proteomic Pipeline, enabling access to dozens of additional tools within that suite. In particular, the PeptideProphet and iProphet tools for validation of cross-links improve the sensitivity and accuracy of correct cross-link identifications at user-defined thresholds. These new features improve the versatility of the algorithm, enabling its use in a wider range of experimental designs and analysis pipelines. Kojak 2.0 remains open-source and multiplatform.

Metaproteomic Analysis of an Oral Squamous Cell Carcinoma Dataset Suggests Diagnostic Potential of the Mycobiome.

Oral squamous cell carcinoma (OSCC) is the most common head and neck malignancy, with an estimated 5-year survival rate of only 40-50%, largely due to late detection and diagnosis. Emerging evidence suggests that the human microbiome may be implicated in OSCC, with oral microbiome studies putatively identifying relevant bacterial species. As the impact of other microbial organisms, such as fungi and viruses, has largely been neglected, a bioinformatic approach utilizing the Trans-Proteomic Pipeline (TPP) and the R statistical programming language was implemented here to investigate not only bacteria, but also viruses and fungi in the context of a publicly available, OSCC, mass spectrometry (MS) dataset. Overall viral, bacterial, and fungal composition was inferred in control and OSCC patient tissue from protein data, with a range of proteins observed to be differentially enriched between healthy and OSCC conditions, of which the fungal protein profile presented as the best potential discriminator of OSCC within the analysed dataset. While the current project sheds new light on the fungal and viral spheres of the oral microbiome in cancer in silico, further research will be required to validate these findings in an experimental setting.

Bioinformatic Analysis to Investigate Metaproteome Composition Using Trans-Proteomic Pipeline.

With evidence emerging that the microbiome has a role in the onset of many human diseases, including cancer, analyzing these microbial communities and their proteins (i.e., the metaproteome) has become a powerful research tool. The Trans-Proteomic Pipeline (TPP) is a free, comprehensive software suite that facilitates the analysis of mass spectrometry (MS) data. By utilizing available microbial proteomes, TPP can identify microbial proteins and species, with an acceptable peptide false-discovery rate (FDR). An application to a publicly available oral cancer dataset is presented as an example to identify the viral metaproteome on the oral cancer invasive tumor front. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Collection of data and resources Basic Protocol 2: Analysis of MS data using TPP Basic Protocol 3: Analysis of TPP output using R in RStudio.

Label-free plasma proteomics for the identification of the putative biomarkers of oral squamous cell carcinoma

Mass spectrometry-based label-free proteomics is becoming the analytical tool of interest to identify and quantitate the biomarkers for cancer. The oral squamous cell carcinoma (OSCC) which is one of the leading cancers worldwide, lacks biomarkers for the early prognosis and diagnosis. The present study profiled plasma proteome of the Indian OSCC human patients using a label-free mass spectrometry proteomics approach. The study first time utilized the three most widely used data analysis software MaxQuant (MQ), Proteome discoverer (PD), and Trans proteomic pipeline (TPP) together for label-free quantitation of the proteins. The study identified 16 proteins which can be used as a signature protein panel for OSCC. The pathway analysis showed predominant involvement of the immune system, hemostasis as the major pathways that are indicative of the disease progression. The network analysis showed maximum interaction for the Fibronectin and C-reactive protein. The study demonstrates that plasma proteins contain signatures that can be used as putative biomarkers for OSCC. Based on the label-free quantitation and the mechanistic analysis C-reactive protein, Carbonic anhydrase-1, and Fibronectin are identified as putative biomarkers of OSCC. Once these findings are validated in the large cohorts these protein signatures can be used as a biomarker for OSCC. SignificanceThe oral squamous cell carcinoma (OSCC) is the eighteenth most prevalent malignancy in the world and ranks second in India. There are no biomarkers that could be indicative of the diseased state. Various studies have been carried out on saliva and tumors of OSCC patients in India, but none of the studies have profiled the plasma. We utilized the label-free approach for the first time on the Indian population in generating the panel of plasma proteins which could be indicative of the diseased state. The study identified Carbonic anhydrase 1, C-reactive protein, and Fibronectin proteins as putative biomarkers for the OSCC. The study obtained the signature panel by utilizing the 3 most widely used software for the label-free quanatitation (LFQ) namely MaxQuant, Proteome Discoverer, and Trans proteomic pipeline. The utilization of 3 software for the LFQ reduced the bias and provided a comprehensive list of proteins. All the differential proteins were mechanistically studied for their biological relevance and the pathway and network analysis were carried out. The study helps us in increasing the understanding of the proteins which are involved in the progression of the diseases. Studying the panel of proteins from all biofluids along with plasma in large cohorts of the population will help in understanding the disease in greater depth and help in identifying the relevant biomarkers for the OSCC.

Discovery of Post-Translational Modifications in Emiliania huxleyi.

Emiliania huxleyi is a cosmopolitan coccolithophore that plays an essential role in global carbon and sulfur cycling, and contributes to marine cloud formation and climate regulation. Previously, the proteomic profile of Emiliania huxleyi was investigated using a three-dimensional separation strategy combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS). The current study reuses the MS/MS spectra obtained, for the global discovery of post-translational modifications (PTMs) in this species without specific enrichment methods. Twenty-five different PTM types were examined using Trans-Proteomic Pipeline (Comet and PeptideProphet). Overall, 13,483 PTMs were identified in 7421 proteins. Methylation was the most frequent PTM with more than 2800 modified sites, and lysine was the most frequently modified amino acid with more than 4000 PTMs. The number of proteins identified increased by 22.5% to 18,780 after performing the PTM search. Compared to intact peptides, the intensities of some modified peptides were superior or equivalent. The intensities of some proteins increased dramatically after the PTM search. Gene ontology analysis revealed that protein persulfidation was related to photosynthesis in Emiliania huxleyi. Additionally, various membrane proteins were found to be phosphorylated. Thus, our global PTM discovery platform provides an overview of PTMs in the species and prompts further studies to uncover their biological functions. The combination of a three-dimensional separation method with global PTM search is a promising approach for the identification and discovery of PTMs in other species.

ProtyQuant: Comparing label-free shotgun proteomics datasets using accumulated peptide probabilities

Comparing multiple label-free shotgun proteomics datasets requires various data processing and formatting steps, including peptide-spectrum matching, protein inference, and quantification. Finally, the compilation of results files into a format that allows for downstream analyses. ProtyQuant performs protein inference and quantification calculations, and combines the results of individual datasets into plain text tables. These are lightweight, human-readable, and easy to import into databases or statistical software. ProtyQuant reads validated pepXML from proteomic workflows such as the Trans-Proteomic Pipeline (TPP), which makes it compatible with many commercial and free search engines. For protein inference and quantification, a modified version of the PIPQ program (He et al. 2016) was integrated. In contrast to simple spectral-counting, PIPQ sums up peptide probabilities. For assigning peptides to proteins, three algorithms are available: Multiple Counting, Equal Division, and Linear Programming. The accumulated peptide probabilities (app) are used for both tasks, protein probability estimation, and quantification. ProtyQuant was tested using a reference dataset for label-free shotgun proteomics, obtained from different concentrations of 48 human UPS proteins spiked into yeast lysate. Compared to ProteinProphet, ProtyQuant detected up to 126 (15%) more proteins in the mixture, applying an equal false positive rate (FPR). Using the app values for label-free quantification showed suitable sensitivity and linearity. Strikingly, the app values represent a realistic measure of ‘Protein Presence,’ an integral concept of protein probability and quantity. ProtyQuant provides a graphical user interface (GUI) and scripts for console-based processing. It is available (GNU GLP v3) for Windows, Linux, and Docker from https://bitbucket.org/lababi/protyquant/. SignificanceIntegrating data from multiple shot-gun proteomics experiments overwhelms non-expert researchers. ProtyQuant complements well-established workflows by aiding the comparison of proteins across samples. Importantly, the probability and abundance of proteins are seen from a holistic point of view. The accumulated peptide probability (app) as an integral measure of ‘Protein Presence’ demonstrated reliable performance for both protein identification and quantification. Using the app as a single measure facilitates the compilation of reports in comparative proteomics.

Interleukin-6 induces immunometabolic reprogramming in human rheumatoid arthritis synovial fibroblasts to promote osteoclastogenesis

Abstract Interleukin-6 (IL-6) plays an important role in progressive bone loss in rheumatoid arthritis (RA). However, the molecular mechanisms through which IL-6 propels RA synovial fibroblasts (RASFs) to contribute to bone loss are not fully understood. Treatment of human RASFs with IL-6 and IL-6 receptor (IL-6/IL-6R, 100 ng/ml each) alone or in combination with M-CSF (2 ng/ml) and RANKL (5 ng/ml) for 9 days resulted in the phenotypic changes to osteoclast-like features as determined by increase in TRAP staining and Dentin pit resorption area, which were comparable to M-CSF/RANKL group. IL-6/IL-6R induced a dose-dependent increase in the expression of transcription factor Ets2 and enhanced its nuclear translocation alone or combinatorially in M-CSF/RANKL-differentiated human RASFs. Chromatin immunoprecipitation (ChIP) analysis of Cathepsin K and B promoters showed Ets2 binding and transcriptional activation upon IL-6/IL-6R stimulation, which was further increased with M-CSF/RANKL. Interestingly, the untargeted proteomics analysis of the conditioned media from activated RASFs using Mass-Spectrometry (MS) technique and Trans-Proteomic Pipeline tool showed the production of 113 analytes unique to IL-6/IL-6R stimulation. Further analysis of the proteomics data using STRING database for pathway analysis showed the impact of IL-6/IL-6R on immunometabolic reprogramming of human RASFs towards osteoclast-like phenotype, which was partly mediated through Ets2. These preliminary studies identified a novel role of IL-6/IL-6R-mediated transsignaling in the metabolic reprogramming in RASFs that could potentially be targeted by inhibiting Ets2 to limit their role in bone resorption in RA.

WinProphet: A User-Friendly Pipeline Management System for Proteomics Data Analysis Based on Trans-Proteomic Pipeline.

Protein and peptide identification and quantitation are essential tasks in proteomics research and involve a series of steps in analyzing mass spectrometry data. Trans-Proteomic Pipeline (TPP) provides a wide range of useful tools through its web interfaces for analyses such as sequence database search, statistical validation, and quantitation. To utilize the powerful functionality of TPP without the need for manual intervention to launch each step, we developed a software tool, called WinProphet, to create and automatically execute a pipeline for proteomic analyses. It seamlessly integrates with TPP and other external command-line programs, supporting various functionalities, including database search for protein and peptide identification, spectral library construction and search, data-independent acquisition (DIA) data analysis, andisobaric labeling and label-free quantitation. WinProphet is a standalone, installation-free tool with graphical interfaces for users to configure, manage, and automatically execute pipelines. The constructed pipelines can be exported as XML files with all of the parameter settings for reusability and portability. The executable files, user manual, and sample data sets of WinProphet are freely available at http://ms.iis.sinica.edu.tw/COmics/Software_WinProphet.html .

PTMProphet: Fast and Accurate Mass Modification Localization for the Trans-Proteomic Pipeline.

Spectral matching sequence database search engines commonly used on mass spectrometry-based proteomics experiments excel at identifying peptide sequence ions, and in addition, possible sequence ions carrying post-translational modifications (PTMs), but most do not provide confidence metrics for the exact localization of those PTMs when several possible sites are available. Localization is absolutely required for downstream molecular cell biology analysis of PTM function in vitro and in vivo. Therefore, we developed PTMProphet, a free and open-source software tool integrated into the Trans-Proteomic Pipeline, which reanalyzes identified spectra from any search engine for which pepXML output is available to provide localization confidence to enable appropriate further characterization of biologic events. Localization of any type of mass modification (e.g., phosphorylation) is supported. PTMProphet applies Bayesian mixture models to compute probabilities for each site/peptide spectrum match where a PTM has been identified. These probabilities can be combined to compute a global false localization rate at any threshold to guide downstream analysis. We describe the PTMProphet tool, its underlying algorithms, and demonstrate its performance on ground-truth synthetic peptide reference data sets, one previously published small data set, one new larger data set, and also on a previously published phosphoenriched data set where the correct sites of modification are unknown. Data have been deposited to ProteomeXchange with identifier PXD013210.

Comparative Analysis of Proteins in Extracellular Vesicles from Quiescent versus Activated Hepatic Stellate Cells

AimsA newly recognized mechanism of cell communication is intercellular trafficking of molecules that are contained within nano‐sized extracellular vesicles (EVs) such as exosomes or microvesicles. EVs mediate cell‐cell transfer of their cargo (miRs, mRNAs, proteins) resulting in functional reprograming of recipient cells. Hepatic stellate cells (HSC) are a normally‐quiescent cell type in the liver but after hepatic injury they transdifferentiate into collagen‐producing myofibroblasts that account for the majority of the fibrotic scar that is deposited during chronic liver injury. Recent studies have shown that EVs from quiescent HSC have anti‐fibrogenic properties whereas those from activated HSC are pro‐fibrogenic. To help understand these distinct properties, proteins in EVs from quiescent versus activated HSC were evaluated by mass spectrometry.MethodsNanoparticle tracking and Western blot confirmed the presence of EVs after differential ultracentrifugation of serum‐free conditioned medium from quiescent (Day 1–4) or activated (passage 1 (P1)) primary mouse HS. Five separate D4 EV preparations and three separate P1 EV preparations were individually subjected to mass spectrometry. Data were analyzed using Mascot (Matrix Science, London, UK) while MS/MS based peptide and protein identifications were validated with Scaffold (Proteome Software Inc., Portland, OR). Peptide identification was accepted if there was > 95% probability of FDR < 1.0% by the Peptide Prophet algorithm with Scaffold delta‐mass correction. Protein identifications were accepted if there was >99.0% probability of FDR < 1.0% with at least 2 constituent peptides identified. Protein probabilities were assigned by the Protein Prophet algorithm.Results46 proteins were identified in at least three of five D4 EV samples while 337 proteins were identified in three P1 EV samples. D4 EVs contained 19 specific proteins while P1 EVs contained 310 specific proteins. There were 27 proteins that were shared between both groups, with 11 of these proteins present at significantly higher levels in D4 EVs compared to P1 EVs. EVs from both groups contained exosomal proteins, but D4 EVs exhibited higher abundance of nucleosome components (histones, 17.4%, 46.7‐fold enrichment) or keratin filaments (15.2%, 57.6‐fold enrichment) while P1 EVs contained more proteins associated with extracellular space (30.9%, 4.0‐fold), proteasome complex (6.8%, 24.3‐fold), basement membrane (6.5%, 14.5‐fold), and collagen trimer (3.9%, 10.0‐fold). KEGG analysis showed 1 unique pathway for D4 EVs, 33 unique pathways for P1 EVs and 4 shared pathways for D4 and P1 EVs.ConclusionsEVs produced by activated HSC contain a protein cargo that is qualitatively and quantitatively different from that of EVs from quiescent HSC. These differences may account for distinct biological responses when each EV population acts on various hepatic cells, thus contributing to their differential pathophysiological or therapeutic outcomes. RC and XL contributed equally to this work.Support or Funding InformationNIH AA023626 & AA025974This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Metaproteomics analysis of microbial diversity of human saliva and tongue dorsum in young healthy individuals

ABSTRACTBackground: The human oral microbiome influences initiation or progression of diseases like caries or periodontitis. Metaproteomics approaches enable the simultaneous investigation of microbial and host proteins and their interactions to improve understanding of oral diseases.Objective: In this study, we provide a detailed metaproteomics perspective of the composition of salivary and tongue microbial communities of young healthy subjects.Design: Stimulated saliva and tongue samples were collected from 24 healthy volunteers, subjected to shotgun nLC-MS/MS and analyzed by the Trans-Proteomic Pipeline and the Prophane tool.Results: 3,969 bacterial and 1,857 human proteins could be identified from saliva and tongue, respectively. In total, 1,971 bacterial metaproteins and 1,154 human proteins were shared in both sample types. Twice the amount of bacterial metaproteins were uniquely identified for the tongue dorsum compared to saliva. Overall, 107 bacterial genera of seven phyla formed the microbiome. Comparative analysis identified significant functional differences between the microbial biofilm on the tongue and the microbiome of saliva.Conclusion: Even if the microbial communities of saliva and tongue dorsum showed a strong similarity based on identified protein functions and deduced bacterial composition, certain specific characteristics were observed. Both microbiomes exhibit a great diversity with seven genera being most abundant.

A Tandem Mass Spectrometry Sequence Database Search Method for Identification of O-Fucosylated Proteins by Mass Spectrometry.

Thrombospondin type 1 repeats (TSRs), small adhesive protein domains with a wide range of functions, are usually modified with O-linked fucose, which may be extended to O-fucose-β1,3-glucose. Collision-induced dissociation (CID) spectra of O-fucosylated peptides cannot be sequenced by standard tandem mass spectrometry (MS/MS) sequence database search engines because O-linked glycans are highly labile in the gas phase and are effectively absent from the CID peptide fragment spectra, resulting in a large mass error. Electron transfer dissociation (ETD) preserves O-linked glycans on peptide fragments, but only a subset of tryptic peptides with low m/ z can be reliably sequenced from ETD spectra compared to CID. Accordingly, studies to date that have used MS to identify O-fucosylated TSRs have required manual interpretation of CID mass spectra even when ETD was also employed. In order to facilitate high-throughput, automatic identification of O-fucosylated peptides from CID spectra, we re-engineered the MS/MS sequence database search engine Comet and the MS data analysis suite Trans-Proteomic Pipeline to enable automated sequencing of peptides exhibiting the neutral losses characteristic of labile O-linked glycans. We used our approach to reanalyze published proteomics data from Plasmodium parasites and identified multiple glycoforms of TSR-containing proteins.

SimPhospho: a software tool enabling confident phosphosite assignment.

MotivationMass spectrometry combined with enrichment strategies for phosphorylated peptides has been successfully employed for two decades to identify sites of phosphorylation. However, unambiguous phosphosite assignment is considered challenging. Given that site-specific phosphorylation events function as different molecular switches, validation of phosphorylation sites is of utmost importance. In our earlier study we developed a method based on simulated phosphopeptide spectral libraries, which enables highly sensitive and accurate phosphosite assignments. To promote more widespread use of this method, we here introduce a software implementation with improved usability and performance.ResultsWe present SimPhospho, a fast and user-friendly tool for accurate simulation of phosphopeptide tandem mass spectra. Simulated phosphopeptide spectral libraries are used to validate and supplement database search results, with a goal to improve reliable phosphoproteome identification and reporting. The presented program can be easily used together with the Trans-Proteomic Pipeline and integrated in a phosphoproteomics data analysis workflow.Availability and implementationSimPhospho is open source and it is available for Windows, Linux and Mac operating systems. The software and its user’s manual with detailed description of data analysis as well as test data can be found at https://sourceforge.net/projects/simphospho/.Supplementary information Supplementary data are available at Bioinformatics online.

StPeter: Seamless Label-Free Quantification with the Trans-Proteomic Pipeline.

Label-free quantification has grown in popularity as a means of obtaining relative abundance measures for proteomics experiments. However, easily accessible and integrated tools to perform label-free quantification have been lacking. We describe StPeter, an implementation of Normalized Spectral Index quantification for wide availability through integration into the widely used Trans-Proteomic Pipeline. This implementation has been specifically designed for reproducibility and ease of use. We demonstrate that StPeter outperforms other state-of-the art packages using a recently reported benchmark data set over the range of false discovery rates relevant to shotgun proteomics results. We also demonstrate that the software is computationally efficient and supports data from a variety of instrument platforms and experimental designs. Results can be viewed within the Trans-Proteomic Pipeline graphical user interfaces and exported in standard formats for downstream statistical analysis. By integrating StPeter into the freely available Trans-Proteomic Pipeline, users can now obtain high-quality label-free quantification of any data set in seconds by adding a single command to the workflow.

Rapid and comprehensive discovery of unreported shellfish allergens using large-scale transcriptomic and proteomic resources

[Extract] Discovery of allergens in various food and inhalant sources is central to our understanding of the molecular mechanisms of allergic reactions. Allergen characterization is the most important underlying factor for better patient management and the design and development of novel immunotherapeutics.1 Of the "Big eight" allergen food groups, shellfish presents a unique challenge in terms of allergen discovery due to the large number and diversity of consumed species, leading to heterogeneity of allergen structure and cross-reactivity among various sources. At present, 31 crustacean allergens have been officially registered in the World Health Organization and International Union of Immunological Societies Allergen Nomenclature Database as compared with 4 mollusk allergens due to pitfalls in current allergy discovery approaches. Cosensitization of patients with crustacean and mollusk allergy is often described; however, the current diagnostic approaches to manage these patients are not based on sufficient molecular knowledge of these shellfish allergens.

Abstract LB-072: Differential plasma proteome analysis in patients with colorectal cancer from Colombia

Abstract Introduction: Colorectal cancer (CRC) is the fourth leading cause of cancer death in Colombia, where mortality rates are increasing. Current screening and diagnosis methods range from non-invasive and not very sensitive and specific, such as occult blood in feces, to highly sensitive and specific but invasive, such as colonoscopy plus biopsy. Furthermore, the carcinoembryonic antigen in plasma, used as patient monitoring and prognostic biomarker, have a low specificity for the disease. Therefore, there is a need to find minimally invasive and accessible biomarkers that have potential as tools for screening, diagnosis or prognosis of CRC with a high sensitivity and specificity. Objective: To explore differences of plasma proteomic profiles between CRC cases and controls from Colombia, and to propose potential biomarkers of the disease. Materials and methods: We applied a bottom-up shotgun proteomics method to explore the plasma proteome profile of 20 CRC cases and 6 controls from Colombia, using RPLC coupled with an ESI-Q-Exactive Orbitrap. Mass spectra were collected in data-dependent mode with one MS precursor scan followed by 15 MS/MS scans. Mass spectra were analysed with X! Tandem, using the Uniprot Human Reference. 469 proteins were selected in Scaffold software after applying a Peptide and Protein Prophet scores of > 99% and > 95%, respectively, with a minimum of 2 peptides. Differential expression analyses were performed with the R package DESeq2 on the spectral count data, which uses a negative binomial GLM fitting and Wald statistics. Results: Using the first two principal components that explained 25% and 16% of the variance, led to separate most of the cases and controls of the study. We did not find significant differences in the only cases analyses (rectum vs. colon; left vs right; invasive vs in situ) after adjusting for multiple tests. When comparing CRC cases with controls we found 14 proteins differentially expressed (FDR < 0.05). Three of these proteins have a fold change of ± 2; LV401 and CD14 were found overexpressed in CRC cases whilst FBLN1 was found overexpressed in controls. Moreover, both supervised and unsupervised cluster analysis confirmed their utility as predictors of the disease. Conclusions: We identified a profile of 14 differentially expressed proteins among CRC cases compared to controls from Colombia. All these proteins have previously been found to be associated with CRC, or other cancer types, since they play an important role in the regulation or structure of the extracellular matrix, in the inflammatory response, in lipid metabolism and in drug resistance. Three of these are strong candidates as disease prognostic biomarkers (LV401, CD14 and FBLN1) according to what has been reported in the literature. Additional validation studies are required in a larger sample, including patients with CRC at different stages of the disease, in order to adequately assess their clinical utility. Citation Format: María Carolina Sanabria-Salas, Ruth Andrea Rodríguez-Castro, Martha Lucía Serrano-López, Gustavo Hernández-Suárez, Myriam Sánchez de Gómez, Adriana Umaña-Pérez. Differential plasma proteome analysis in patients with colorectal cancer from Colombia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-072. doi:10.1158/1538-7445.AM2017-LB-072

Insights from Comparative Serum Proteomic Profiling of Children with Sickle Cell Disease: The Effect of Hydroxyurea and Genotype on Protein Abundance

▪Introduction: Sickle cell disease (SCD) is a multisystem disease, with substantial variation in presentation and clinical course. Many protein biomarkers have been described in serum and plasma of children with homozygous sickle cell disease. In order to understand the effect of hydroxyurea and the hemoglobin SC genotype on serum protein levels, we compared the relative abundance of serum proteins in healthy children and children with SCD.Methods: The relative concentration of 140 different serum proteins was measured using liquid chromatography tandem mass spectrometry. Serum samples of healthy children and of children with SCD were collected at a baseline visit: 30 healthy African American children without sickle cell trait, 30 children with hemoglobin SS genotype, 30 children with hemoglobin SC (HbSC) genotype, and 30 children with hemoglobin SS (HbSS) genotype while adherent to hydroxyurea for at least one year. All groups were matched for age and gender. Type of SCD, hydroxyurea dose, laboratory values prior to starting hydroxyurea and at the time of sample collection were recorded. Disease and control samples were processed in a random block design stratified by disease, sex, and age to minimize the effect of sample preparation and technician processing. Samples were depleted of albumin and immunoglobulins. Tryptic peptides were analyzed on a linear ion-trap (LTQ) mass spectrometer. The acquired data was searched against the Human UniProt database using X!Tandem. Peptide identification and protein assignment were performed using the PeptideProphet and ProteinProphet in the Trans-Proteomic Pipeline. Alignment and quantification were done as described (Lai, X. et. al. 2011). Peptide-level quantitative information was analyzed for each protein using a mixed effects model fit with restricted maximum likelihood estimation to test the differential abundance of each protein. Within each comparison, the false discovery rate was controlled at 5% using the method of Benjamini and Hochberg.Results: Seventy-one serum proteins were significantly different between the children with HbSS disease and the healthy children. In contrast, between children with HbSS disease treated with hydroxyurea and healthy children, only 56 serum proteins were significantly different. Furthermore, in children with HbSC disease, only 25 serum proteins were significantly different compared to healthy children. In assessing the effect of hydroxyurea treatment on serum protein abundance, we found a total of 50 proteins that were significantly different between the 30 children with HbSS disease not prescribed hydroxyurea and the 30 children who were adherent to hydroxyurea (average 24 mg/kg/day) for at least one year (average 3.3 years). We also found a total of 41 proteins that were significantly different in comparing the 30 children with HbSS disease and the 30 children with HbSC disease.Conclusion: Due to the multisystem nature of sickle cell disease, proteins from many pathways are dysregulated, including inflammatory proteins, hemolysis-associated proteins, proteins involved in coagulation and complement regulation and apolipoproteins. Our findings indicate that the increase of HbF might not be the only beneficial effect of hydroxyurea treatment and that some of the clinical improvement might be due to decreased serum protein dysregulation. DisclosuresHulbert:Pfizer, Inc.: Other: spouse employment.