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Tavares R.,Instituto Nacional Of Cancer Inca | de Miranda Scherer N.,Instituto Nacional Of Cancer Inca | Pauletti B.A.,Laboratorio Of Espectrometria Of Massas | Araujo E.,Federal University of Mato Grosso do Sul | And 6 more authors.
Proteomics | Year: 2014

The mechanism of alternative splicing in the transcriptome may increase the proteome diversity in eukaryotes. In proteomics, several studies aim to use protein sequence repositories to annotate MS experiments or to detect differentially expressed proteins. However, the available protein sequence repositories are not designed to fully detect protein isoforms derived from mRNA splice variants. To foster knowledge for the field, here we introduce SpliceProt, a new protein sequence repository of transcriptome experimental data used to investigate for putative splice variants in human proteomes. Current version of SpliceProt contains 159 719 non-redundant putative polypeptide sequences. The assessment of the potential of SpliceProt in detecting new protein isoforms resulting from alternative splicing was performed by using publicly available proteomics data. We detected 173 peptides hypothetically derived from splice variants, which 54 of them are not present in UniprotKB/TrEMBL sequence repository. In comparison to other protein sequence repositories, SpliceProt contains a greater number of unique peptides and is able to detect more splice variants. Therefore, SpliceProt provides a solution for the annotation of proteomics experiments regarding splice isofoms. The repository files containing the translated sequences of the predicted splice variants and a visualization tool are freely available at http://lbbc.inca.gov.br/spliceprot. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. Source


Tavares R.,Oswaldo Cruz Institute | Tavares R.,Instituto Nacional Of Cancer Inca | Wajnberg G.,Oswaldo Cruz Institute | Wajnberg G.,Instituto Nacional Of Cancer Inca | And 10 more authors.
Journal of Proteomics | Year: 2016

Oligodendrocytes produce and maintain the myelin sheath of axons in the central nervous system. Because misassembled myelin sheaths have been associated with brain disorders such as multiple sclerosis and schizophrenia, recent advances have been made towards the description of the oligodendrocyte proteome. The identification of splice variants represented in the proteome is as important as determining the level of oligodendrocyte-associated proteins. Here, we used an oligodendrocyte proteome dataset deposited in ProteomeXchange to search against a customized protein sequence file containing computationally predicted splice variants. Our approach resulted in the identification of 39 splice variants, including one variant from the GTPase . KRAS gene and another from the human glutaminase gene family. We also detected the mRNA expression of five selected splice variants and demonstrated that a fraction of these have their canonical proteins participating in direct protein-protein interactions. In conclusion, we believe our findings contribute to the molecular characterization of oligodendrocytes and may encourage other research groups working with central nervous system disorders to investigate the biological significance of these splice variants. The splice variants identified in this study may encode proteins that could be targeted in novel treatment strategies and diagnostic methods. Significance: Several disorders of the central nervous system (CNS) are associated with misassembled myelin sheaths, which are produced and maintained by oligodendrocytes (OL). Recently, the OL proteome has been explored to identify key proteins and molecular functions associated with CNS disorders. We developed an innovative approach to select, with a higher level of confidence, a relevant list of splice variants from a proteome dataset and detected the mRNA expression of five selected variants: . EEF1D, . KRAS, . MFF, . SDR39U1, and . SUGT1. We also described splice variants extracted from OL proteome data. Among the splice variants identified, some are from genes previously linked to CNS and related disorders. Our findings may contribute to oligodendrocyte characterization and encourage other research groups to investigate the biological role of splice variants and to improve current treatments and diagnostic methods for CNS disorders. © 2016 Elsevier B.V. Source


Carnielli C.M.,Laboratorio Of Espectrometria Of Massas | Winck F.V.,Laboratorio Of Espectrometria Of Massas | Paes Leme A.F.,Laboratorio Of Espectrometria Of Massas
Biochimica et Biophysica Acta - Proteins and Proteomics | Year: 2015

Proteomics experiments often generate a vast amount of data. However, the simple identification and quantification of proteins from a cell proteome or subproteome is not sufficient for the full understanding of complex mechanisms occurring in the biological systems. Therefore, the functional annotation analysis of protein datasets using bioinformatics tools is essential for interpreting the results of high-throughput proteomics. Although large-scale proteomics data have rapidly increased, the biological interpretation of these results remains as a challenging task. Here we reviewed basic concepts and different programs that are commonly used in proteomics data functional annotation, emphasizing the main strategies focused in the use of gene ontology annotations. Furthermore, we explored the characteristics of some tools developed for functional annotation analysis, concerning the ease of use and typical caveats on ontology annotations. The utility and variations between different tools were assessed through the comparison of the resulting outputs generated for an example of proteomics dataset. © 2014 Elsevier B.V. All rights reserved. Source


Kawahara R.,Laboratorio Of Espectrometria Of Massas | Bollinger J.G.,University of Washington | Rivera C.,Laboratorio Of Espectrometria Of Massas | Ribeiro A.C.P.,Instituto Do Cancer Do Estado Of Sao Paulo | And 3 more authors.
Proteomics | Year: 2016

Head and neck cancers, including oral squamous cell carcinoma (OSCC), are the sixth most common malignancy in the world and are characterized by poor prognosis and a low survival rate. Saliva is oral fluid with intimate contact with OSCC. Besides non-invasive, simple, and rapid to collect, saliva is a potential source of biomarkers. In this study, we build an SRM assay that targets fourteen OSCC candidate biomarker proteins, which were evaluated in a set of clinically-derived saliva samples. Using Skyline software package, we demonstrated a statistically significant higher abundance of the C1R, LCN2, SLPI, FAM49B, TAGLN2, CFB, C3, C4B, LRG1, SERPINA1 candidate biomarkers in the saliva of OSCC patients. Furthermore, our study also demonstrated that CFB, C3, C4B, SERPINA1 and LRG1 are associated with the risk of developing OSCC. Overall, this study successfully used targeted proteomics to measure in saliva a panel of biomarker candidates for OSCC. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. Source


Kawahara R.,Laboratorio Of Espectrometria Of Massas | Meirelles G.V.,Laboratorio Of Espectrometria Of Massas | Heberle H.,University of Sao Paulo | Domingues R.R.,Laboratorio Of Espectrometria Of Massas | And 19 more authors.
Oncotarget | Year: 2015

Targeted proteomics has flourished as the method of choice for prospecting for and validating potential candidate biomarkers in many diseases. However, challenges still remain due to the lack of standardized routines that can prioritize a limited number of proteins to be further validated in human samples. To help researchers identify candidate biomarkers that best characterize their samples under study, a well-designed integrative analysis pipeline, comprising MS-based discovery, feature selection methods, clustering techniques, bioinformatic analyses and targeted approaches was performed using discovery-based proteomic data from the secretomes of three classes of human cell lines (carcinoma, melanoma and non-cancerous). Three feature selection algorithms, namely, Beta-binomial, Nearest Shrunken Centroids (NSC), and Support Vector Machine-Recursive Features Elimination (SVM-RFE), indicated a panel of 137 candidate biomarkers for carcinoma and 271 for melanoma, which were differentially abundant between the tumor classes. We further tested the strength of the pipeline in selecting candidate biomarkers by immunoblotting, human tissue microarrays, label-free targeted MS and functional experiments. In conclusion, the proposed integrative analysis was able to pre-qualify and prioritize candidate biomarkers from discovery-based proteomics to targeted MS. Source

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