Cargando…

Pattern Analysis of Organellar Maps for Interpretation of Proteomic Data

Localization of organelle proteins by isotope tagging (LOPIT) maps are a coordinate-directed representation of proteome data that can aid in biological interpretation. Analysis of organellar association for proteins as displayed using LOPIT is evaluated and interpreted for two types of proteomic dat...

Descripción completa

Detalles Bibliográficos
Autores principales: Burton, Jordan B., Carruthers, Nicholas J., Hou, Zhanjun, Matherly, Larry H., Stemmer, Paul M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9149908/
https://www.ncbi.nlm.nih.gov/pubmed/35645376
http://dx.doi.org/10.3390/proteomes10020018
_version_ 1784717306286309376
author Burton, Jordan B.
Carruthers, Nicholas J.
Hou, Zhanjun
Matherly, Larry H.
Stemmer, Paul M.
author_facet Burton, Jordan B.
Carruthers, Nicholas J.
Hou, Zhanjun
Matherly, Larry H.
Stemmer, Paul M.
author_sort Burton, Jordan B.
collection PubMed
description Localization of organelle proteins by isotope tagging (LOPIT) maps are a coordinate-directed representation of proteome data that can aid in biological interpretation. Analysis of organellar association for proteins as displayed using LOPIT is evaluated and interpreted for two types of proteomic data sets. First, test and control group protein abundances and fold change data obtained in a proximity labeling experiment are plotted on a LOPIT map to evaluate the likelihood of true protein interactions. Selection of true positives based on co-localization of proteins in the organellar space is shown to be consistent with carboxylase enrichment which serves as a positive control for biotinylation in streptavidin affinity selected proteome data sets. The mapping in organellar space facilitates discrimination between the test and control groups and aids in identification of proteins of interest. The same representation of proteins in organellar space is used in the analysis of extracellular vesicle proteomes for which protein abundance and fold change data are evaluated. Vesicular protein organellar localization patterns provide information about the subcellular origin of the proteins in the samples which are isolates from the extracellular milieu. The organellar localization patterns are indicative of the provenance of the vesicular proteome origin and allow discrimination between proteomes prepared using different enrichment methods. The patterns in LOPIT displays are easy to understand and compare which aids in the biological interpretation of proteome data.
format Online
Article
Text
id pubmed-9149908
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-91499082022-05-31 Pattern Analysis of Organellar Maps for Interpretation of Proteomic Data Burton, Jordan B. Carruthers, Nicholas J. Hou, Zhanjun Matherly, Larry H. Stemmer, Paul M. Proteomes Article Localization of organelle proteins by isotope tagging (LOPIT) maps are a coordinate-directed representation of proteome data that can aid in biological interpretation. Analysis of organellar association for proteins as displayed using LOPIT is evaluated and interpreted for two types of proteomic data sets. First, test and control group protein abundances and fold change data obtained in a proximity labeling experiment are plotted on a LOPIT map to evaluate the likelihood of true protein interactions. Selection of true positives based on co-localization of proteins in the organellar space is shown to be consistent with carboxylase enrichment which serves as a positive control for biotinylation in streptavidin affinity selected proteome data sets. The mapping in organellar space facilitates discrimination between the test and control groups and aids in identification of proteins of interest. The same representation of proteins in organellar space is used in the analysis of extracellular vesicle proteomes for which protein abundance and fold change data are evaluated. Vesicular protein organellar localization patterns provide information about the subcellular origin of the proteins in the samples which are isolates from the extracellular milieu. The organellar localization patterns are indicative of the provenance of the vesicular proteome origin and allow discrimination between proteomes prepared using different enrichment methods. The patterns in LOPIT displays are easy to understand and compare which aids in the biological interpretation of proteome data. MDPI 2022-05-23 /pmc/articles/PMC9149908/ /pubmed/35645376 http://dx.doi.org/10.3390/proteomes10020018 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Burton, Jordan B.
Carruthers, Nicholas J.
Hou, Zhanjun
Matherly, Larry H.
Stemmer, Paul M.
Pattern Analysis of Organellar Maps for Interpretation of Proteomic Data
title Pattern Analysis of Organellar Maps for Interpretation of Proteomic Data
title_full Pattern Analysis of Organellar Maps for Interpretation of Proteomic Data
title_fullStr Pattern Analysis of Organellar Maps for Interpretation of Proteomic Data
title_full_unstemmed Pattern Analysis of Organellar Maps for Interpretation of Proteomic Data
title_short Pattern Analysis of Organellar Maps for Interpretation of Proteomic Data
title_sort pattern analysis of organellar maps for interpretation of proteomic data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9149908/
https://www.ncbi.nlm.nih.gov/pubmed/35645376
http://dx.doi.org/10.3390/proteomes10020018
work_keys_str_mv AT burtonjordanb patternanalysisoforganellarmapsforinterpretationofproteomicdata
AT carruthersnicholasj patternanalysisoforganellarmapsforinterpretationofproteomicdata
AT houzhanjun patternanalysisoforganellarmapsforinterpretationofproteomicdata
AT matherlylarryh patternanalysisoforganellarmapsforinterpretationofproteomicdata
AT stemmerpaulm patternanalysisoforganellarmapsforinterpretationofproteomicdata