Urinary peptidomics and bioinformatics for the detection of diabetic kidney disease
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/225069 |
Resumo: | The aim of this study was to establish a peptidomic profle based on LC-MS/MS and random forest (RF) algorithm to distinguish the urinary peptidomic scenario of type 2 diabetes mellitus (T2DM) patients with diferent stages of diabetic kidney disease (DKD). Urine from 60 T2DM patients was collected: 22 normal (stage A1), 18 moderately increased (stage A2) and 20 severely increased (stage A3) albuminuria. A total of 1080 naturally occurring peptides were detected, which resulted in the identifcation of a total of 100 proteins, irrespective of the patients’ renal status. The classifcation accuracy showed that the most severe DKD (A3) presented a distinct urinary peptidomic pattern. Estimates for peptide importance assessed during RF model training included multiple fragments of collagen and alpha-1 antitrypsin, previously associated to DKD. Proteasix tool predicted 48 proteases potentially involved in the generation of the 60 most important peptides identifed in the urine of DM patients, including metallopeptidases, cathepsins, and calpains. Collectively, our study lightened some biomarkers possibly involved in the pathogenic mechanisms of DKD, suggesting that peptidomics is a valuable tool for identifying the molecular mechanisms underpinning the disease and thus novel therapeutic targets. |
id |
UFRGS-2_5a3ecd35e5e92725ac85a6151e6bb3c6 |
---|---|
oai_identifier_str |
oai:www.lume.ufrgs.br:10183/225069 |
network_acronym_str |
UFRGS-2 |
network_name_str |
Repositório Institucional da UFRGS |
repository_id_str |
|
spelling |
Brondani, Letícia de AlmeidaSoares, Ariana AguiarRecamonde-Mendoza, MarianaDall'Agnol, AngélicaCamargo, Joiza LinsMonteiro, Karina MarianteSilveiro, Sandra Pinho2021-08-04T04:48:29Z20202045-2322http://hdl.handle.net/10183/225069001127980The aim of this study was to establish a peptidomic profle based on LC-MS/MS and random forest (RF) algorithm to distinguish the urinary peptidomic scenario of type 2 diabetes mellitus (T2DM) patients with diferent stages of diabetic kidney disease (DKD). Urine from 60 T2DM patients was collected: 22 normal (stage A1), 18 moderately increased (stage A2) and 20 severely increased (stage A3) albuminuria. A total of 1080 naturally occurring peptides were detected, which resulted in the identifcation of a total of 100 proteins, irrespective of the patients’ renal status. The classifcation accuracy showed that the most severe DKD (A3) presented a distinct urinary peptidomic pattern. Estimates for peptide importance assessed during RF model training included multiple fragments of collagen and alpha-1 antitrypsin, previously associated to DKD. Proteasix tool predicted 48 proteases potentially involved in the generation of the 60 most important peptides identifed in the urine of DM patients, including metallopeptidases, cathepsins, and calpains. Collectively, our study lightened some biomarkers possibly involved in the pathogenic mechanisms of DKD, suggesting that peptidomics is a valuable tool for identifying the molecular mechanisms underpinning the disease and thus novel therapeutic targets.application/pdfengScientific reports. London. Vol. 10 (2020), 1242, 11 p.Nefropatias diabéticasBiologia computacionalDiagnósticoBiomarcadoresUrinaUrinary peptidomics and bioinformatics for the detection of diabetic kidney diseaseEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001127980.pdf.txt001127980.pdf.txtExtracted Texttext/plain53721http://www.lume.ufrgs.br/bitstream/10183/225069/2/001127980.pdf.txtee50304d72e597ec3d7d154fba5c9059MD52ORIGINAL001127980.pdfTexto completo (inglês)application/pdf1955951http://www.lume.ufrgs.br/bitstream/10183/225069/1/001127980.pdf1c3dd90b9429eed462369d83826da040MD5110183/2250692023-08-13 03:43:59.014398oai:www.lume.ufrgs.br:10183/225069Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2023-08-13T06:43:59Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Urinary peptidomics and bioinformatics for the detection of diabetic kidney disease |
title |
Urinary peptidomics and bioinformatics for the detection of diabetic kidney disease |
spellingShingle |
Urinary peptidomics and bioinformatics for the detection of diabetic kidney disease Brondani, Letícia de Almeida Nefropatias diabéticas Biologia computacional Diagnóstico Biomarcadores Urina |
title_short |
Urinary peptidomics and bioinformatics for the detection of diabetic kidney disease |
title_full |
Urinary peptidomics and bioinformatics for the detection of diabetic kidney disease |
title_fullStr |
Urinary peptidomics and bioinformatics for the detection of diabetic kidney disease |
title_full_unstemmed |
Urinary peptidomics and bioinformatics for the detection of diabetic kidney disease |
title_sort |
Urinary peptidomics and bioinformatics for the detection of diabetic kidney disease |
author |
Brondani, Letícia de Almeida |
author_facet |
Brondani, Letícia de Almeida Soares, Ariana Aguiar Recamonde-Mendoza, Mariana Dall'Agnol, Angélica Camargo, Joiza Lins Monteiro, Karina Mariante Silveiro, Sandra Pinho |
author_role |
author |
author2 |
Soares, Ariana Aguiar Recamonde-Mendoza, Mariana Dall'Agnol, Angélica Camargo, Joiza Lins Monteiro, Karina Mariante Silveiro, Sandra Pinho |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Brondani, Letícia de Almeida Soares, Ariana Aguiar Recamonde-Mendoza, Mariana Dall'Agnol, Angélica Camargo, Joiza Lins Monteiro, Karina Mariante Silveiro, Sandra Pinho |
dc.subject.por.fl_str_mv |
Nefropatias diabéticas Biologia computacional Diagnóstico Biomarcadores Urina |
topic |
Nefropatias diabéticas Biologia computacional Diagnóstico Biomarcadores Urina |
description |
The aim of this study was to establish a peptidomic profle based on LC-MS/MS and random forest (RF) algorithm to distinguish the urinary peptidomic scenario of type 2 diabetes mellitus (T2DM) patients with diferent stages of diabetic kidney disease (DKD). Urine from 60 T2DM patients was collected: 22 normal (stage A1), 18 moderately increased (stage A2) and 20 severely increased (stage A3) albuminuria. A total of 1080 naturally occurring peptides were detected, which resulted in the identifcation of a total of 100 proteins, irrespective of the patients’ renal status. The classifcation accuracy showed that the most severe DKD (A3) presented a distinct urinary peptidomic pattern. Estimates for peptide importance assessed during RF model training included multiple fragments of collagen and alpha-1 antitrypsin, previously associated to DKD. Proteasix tool predicted 48 proteases potentially involved in the generation of the 60 most important peptides identifed in the urine of DM patients, including metallopeptidases, cathepsins, and calpains. Collectively, our study lightened some biomarkers possibly involved in the pathogenic mechanisms of DKD, suggesting that peptidomics is a valuable tool for identifying the molecular mechanisms underpinning the disease and thus novel therapeutic targets. |
publishDate |
2020 |
dc.date.issued.fl_str_mv |
2020 |
dc.date.accessioned.fl_str_mv |
2021-08-04T04:48:29Z |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/225069 |
dc.identifier.issn.pt_BR.fl_str_mv |
2045-2322 |
dc.identifier.nrb.pt_BR.fl_str_mv |
001127980 |
identifier_str_mv |
2045-2322 001127980 |
url |
http://hdl.handle.net/10183/225069 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Scientific reports. London. Vol. 10 (2020), 1242, 11 p. |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFRGS instname:Universidade Federal do Rio Grande do Sul (UFRGS) instacron:UFRGS |
instname_str |
Universidade Federal do Rio Grande do Sul (UFRGS) |
instacron_str |
UFRGS |
institution |
UFRGS |
reponame_str |
Repositório Institucional da UFRGS |
collection |
Repositório Institucional da UFRGS |
bitstream.url.fl_str_mv |
http://www.lume.ufrgs.br/bitstream/10183/225069/2/001127980.pdf.txt http://www.lume.ufrgs.br/bitstream/10183/225069/1/001127980.pdf |
bitstream.checksum.fl_str_mv |
ee50304d72e597ec3d7d154fba5c9059 1c3dd90b9429eed462369d83826da040 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 |
repository.name.fl_str_mv |
Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS) |
repository.mail.fl_str_mv |
|
_version_ |
1801225030334414848 |