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Deep profiling and custom databases improve detection of proteoforms generated by alternative splicing

Alternative pre-mRNA splicing has long been proposed to contribute greatly to proteome complexity. However, the extent to which mature mRNA isoforms are successfully translated into protein remains controversial. Here, we used high-throughput RNA sequencing and mass spectrometry (MS)–based proteomic...

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Autores principales: Agosto, Laura M., Gazzara, Matthew R., Radens, Caleb M., Sidoli, Simone, Baeza, Josue, Garcia, Benjamin A., Lynch, Kristen W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6886501/
https://www.ncbi.nlm.nih.gov/pubmed/31727681
http://dx.doi.org/10.1101/gr.248435.119
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author Agosto, Laura M.
Gazzara, Matthew R.
Radens, Caleb M.
Sidoli, Simone
Baeza, Josue
Garcia, Benjamin A.
Lynch, Kristen W.
author_facet Agosto, Laura M.
Gazzara, Matthew R.
Radens, Caleb M.
Sidoli, Simone
Baeza, Josue
Garcia, Benjamin A.
Lynch, Kristen W.
author_sort Agosto, Laura M.
collection PubMed
description Alternative pre-mRNA splicing has long been proposed to contribute greatly to proteome complexity. However, the extent to which mature mRNA isoforms are successfully translated into protein remains controversial. Here, we used high-throughput RNA sequencing and mass spectrometry (MS)–based proteomics to better evaluate the translation of alternatively spliced mRNAs. To increase proteome coverage and improve protein quantitation, we optimized cell fractionation and sample processing steps at both the protein and peptide level. Furthermore, we generated a custom peptide database trained on analysis of RNA-seq data with MAJIQ, an algorithm optimized to detect and quantify differential and unannotated splice junction usage. We matched tandem mass spectra acquired by data-dependent acquisition (DDA) against our custom RNA-seq based database, as well as SWISS-PROT and RefSeq databases to improve identification of splicing-derived proteoforms by 28% compared with use of the SWISS-PROT database alone. Altogether, we identified peptide evidence for 554 alternate proteoforms corresponding to 274 genes. Our increased depth and detection of proteins also allowed us to track changes in the transcriptome and proteome induced by T-cell stimulation, as well as fluctuations in protein subcellular localization. In sum, our data here confirm that use of generic databases in proteomic studies underestimates the number of spliced mRNA isoforms that are translated into protein and provides a workflow that improves isoform detection in large-scale proteomic experiments.
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spelling pubmed-68865012020-06-01 Deep profiling and custom databases improve detection of proteoforms generated by alternative splicing Agosto, Laura M. Gazzara, Matthew R. Radens, Caleb M. Sidoli, Simone Baeza, Josue Garcia, Benjamin A. Lynch, Kristen W. Genome Res Method Alternative pre-mRNA splicing has long been proposed to contribute greatly to proteome complexity. However, the extent to which mature mRNA isoforms are successfully translated into protein remains controversial. Here, we used high-throughput RNA sequencing and mass spectrometry (MS)–based proteomics to better evaluate the translation of alternatively spliced mRNAs. To increase proteome coverage and improve protein quantitation, we optimized cell fractionation and sample processing steps at both the protein and peptide level. Furthermore, we generated a custom peptide database trained on analysis of RNA-seq data with MAJIQ, an algorithm optimized to detect and quantify differential and unannotated splice junction usage. We matched tandem mass spectra acquired by data-dependent acquisition (DDA) against our custom RNA-seq based database, as well as SWISS-PROT and RefSeq databases to improve identification of splicing-derived proteoforms by 28% compared with use of the SWISS-PROT database alone. Altogether, we identified peptide evidence for 554 alternate proteoforms corresponding to 274 genes. Our increased depth and detection of proteins also allowed us to track changes in the transcriptome and proteome induced by T-cell stimulation, as well as fluctuations in protein subcellular localization. In sum, our data here confirm that use of generic databases in proteomic studies underestimates the number of spliced mRNA isoforms that are translated into protein and provides a workflow that improves isoform detection in large-scale proteomic experiments. Cold Spring Harbor Laboratory Press 2019-12 /pmc/articles/PMC6886501/ /pubmed/31727681 http://dx.doi.org/10.1101/gr.248435.119 Text en © 2019 Agosto et al.; Published by Cold Spring Harbor Laboratory Press http://creativecommons.org/licenses/by-nc/4.0/ This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Method
Agosto, Laura M.
Gazzara, Matthew R.
Radens, Caleb M.
Sidoli, Simone
Baeza, Josue
Garcia, Benjamin A.
Lynch, Kristen W.
Deep profiling and custom databases improve detection of proteoforms generated by alternative splicing
title Deep profiling and custom databases improve detection of proteoforms generated by alternative splicing
title_full Deep profiling and custom databases improve detection of proteoforms generated by alternative splicing
title_fullStr Deep profiling and custom databases improve detection of proteoforms generated by alternative splicing
title_full_unstemmed Deep profiling and custom databases improve detection of proteoforms generated by alternative splicing
title_short Deep profiling and custom databases improve detection of proteoforms generated by alternative splicing
title_sort deep profiling and custom databases improve detection of proteoforms generated by alternative splicing
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6886501/
https://www.ncbi.nlm.nih.gov/pubmed/31727681
http://dx.doi.org/10.1101/gr.248435.119
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