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Data-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases

BACKGROUND: Rheumatic heart disease (RHD) remains a major source of morbidity and mortality in developing countries. A deeper insight into the pathogenetic mechanisms underlying RHD could provide opportunities for drug repurposing, guide recommendations for secondary penicillin prophylaxis, and/or i...

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Autores principales: Salie, M. Taariq, Yang, Jing, Ramírez Medina, Carlos R., Zühlke, Liesl J., Chishala, Chishala, Ntsekhe, Mpiko, Gitura, Bernard, Ogendo, Stephen, Okello, Emmy, Lwabi, Peter, Musuku, John, Mtaja, Agnes, Hugo-Hamman, Christopher, El-Sayed, Ahmed, Damasceno, Albertino, Mocumbi, Ana, Bode-Thomas, Fidelia, Yilgwan, Christopher, Amusa, Ganiyu A., Nkereuwem, Esin, Shaboodien, Gasnat, Da Silva, Rachael, Lee, Dave Chi Hoo, Frain, Simon, Geifman, Nophar, Whetton, Anthony D., Keavney, Bernard, Engel, Mark E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8939134/
https://www.ncbi.nlm.nih.gov/pubmed/35317720
http://dx.doi.org/10.1186/s12014-022-09345-1
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author Salie, M. Taariq
Yang, Jing
Ramírez Medina, Carlos R.
Zühlke, Liesl J.
Chishala, Chishala
Ntsekhe, Mpiko
Gitura, Bernard
Ogendo, Stephen
Okello, Emmy
Lwabi, Peter
Musuku, John
Mtaja, Agnes
Hugo-Hamman, Christopher
El-Sayed, Ahmed
Damasceno, Albertino
Mocumbi, Ana
Bode-Thomas, Fidelia
Yilgwan, Christopher
Amusa, Ganiyu A.
Nkereuwem, Esin
Shaboodien, Gasnat
Da Silva, Rachael
Lee, Dave Chi Hoo
Frain, Simon
Geifman, Nophar
Whetton, Anthony D.
Keavney, Bernard
Engel, Mark E.
author_facet Salie, M. Taariq
Yang, Jing
Ramírez Medina, Carlos R.
Zühlke, Liesl J.
Chishala, Chishala
Ntsekhe, Mpiko
Gitura, Bernard
Ogendo, Stephen
Okello, Emmy
Lwabi, Peter
Musuku, John
Mtaja, Agnes
Hugo-Hamman, Christopher
El-Sayed, Ahmed
Damasceno, Albertino
Mocumbi, Ana
Bode-Thomas, Fidelia
Yilgwan, Christopher
Amusa, Ganiyu A.
Nkereuwem, Esin
Shaboodien, Gasnat
Da Silva, Rachael
Lee, Dave Chi Hoo
Frain, Simon
Geifman, Nophar
Whetton, Anthony D.
Keavney, Bernard
Engel, Mark E.
author_sort Salie, M. Taariq
collection PubMed
description BACKGROUND: Rheumatic heart disease (RHD) remains a major source of morbidity and mortality in developing countries. A deeper insight into the pathogenetic mechanisms underlying RHD could provide opportunities for drug repurposing, guide recommendations for secondary penicillin prophylaxis, and/or inform development of near-patient diagnostics. METHODS: We performed quantitative proteomics using Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectrometry (SWATH-MS) to screen protein expression in 215 African patients with severe RHD, and 230 controls. We applied a machine learning (ML) approach to feature selection among the 366 proteins quantifiable in at least 40% of samples, using the Boruta wrapper algorithm. The case–control differences and contribution to Area Under the Receiver Operating Curve (AUC) for each of the 56 proteins identified by the Boruta algorithm were calculated by Logistic Regression adjusted for age, sex and BMI. Biological pathways and functions enriched for proteins were identified using ClueGo pathway analyses. RESULTS: Adiponectin, complement component C7 and fibulin-1, a component of heart valve matrix, were significantly higher in cases when compared with controls. Ficolin-3, a protein with calcium-independent lectin activity that activates the complement pathway, was lower in cases than controls. The top six biomarkers from the Boruta analyses conferred an AUC of 0.90 indicating excellent discriminatory capacity between RHD cases and controls. CONCLUSIONS: These results support the presence of an ongoing inflammatory response in RHD, at a time when severe valve disease has developed, and distant from previous episodes of acute rheumatic fever. This biomarker signature could have potential utility in recognizing different degrees of ongoing inflammation in RHD patients, which may, in turn, be related to prognostic severity. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12014-022-09345-1.
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spelling pubmed-89391342022-03-23 Data-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases Salie, M. Taariq Yang, Jing Ramírez Medina, Carlos R. Zühlke, Liesl J. Chishala, Chishala Ntsekhe, Mpiko Gitura, Bernard Ogendo, Stephen Okello, Emmy Lwabi, Peter Musuku, John Mtaja, Agnes Hugo-Hamman, Christopher El-Sayed, Ahmed Damasceno, Albertino Mocumbi, Ana Bode-Thomas, Fidelia Yilgwan, Christopher Amusa, Ganiyu A. Nkereuwem, Esin Shaboodien, Gasnat Da Silva, Rachael Lee, Dave Chi Hoo Frain, Simon Geifman, Nophar Whetton, Anthony D. Keavney, Bernard Engel, Mark E. Clin Proteomics Research BACKGROUND: Rheumatic heart disease (RHD) remains a major source of morbidity and mortality in developing countries. A deeper insight into the pathogenetic mechanisms underlying RHD could provide opportunities for drug repurposing, guide recommendations for secondary penicillin prophylaxis, and/or inform development of near-patient diagnostics. METHODS: We performed quantitative proteomics using Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectrometry (SWATH-MS) to screen protein expression in 215 African patients with severe RHD, and 230 controls. We applied a machine learning (ML) approach to feature selection among the 366 proteins quantifiable in at least 40% of samples, using the Boruta wrapper algorithm. The case–control differences and contribution to Area Under the Receiver Operating Curve (AUC) for each of the 56 proteins identified by the Boruta algorithm were calculated by Logistic Regression adjusted for age, sex and BMI. Biological pathways and functions enriched for proteins were identified using ClueGo pathway analyses. RESULTS: Adiponectin, complement component C7 and fibulin-1, a component of heart valve matrix, were significantly higher in cases when compared with controls. Ficolin-3, a protein with calcium-independent lectin activity that activates the complement pathway, was lower in cases than controls. The top six biomarkers from the Boruta analyses conferred an AUC of 0.90 indicating excellent discriminatory capacity between RHD cases and controls. CONCLUSIONS: These results support the presence of an ongoing inflammatory response in RHD, at a time when severe valve disease has developed, and distant from previous episodes of acute rheumatic fever. This biomarker signature could have potential utility in recognizing different degrees of ongoing inflammation in RHD patients, which may, in turn, be related to prognostic severity. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12014-022-09345-1. BioMed Central 2022-03-22 /pmc/articles/PMC8939134/ /pubmed/35317720 http://dx.doi.org/10.1186/s12014-022-09345-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Salie, M. Taariq
Yang, Jing
Ramírez Medina, Carlos R.
Zühlke, Liesl J.
Chishala, Chishala
Ntsekhe, Mpiko
Gitura, Bernard
Ogendo, Stephen
Okello, Emmy
Lwabi, Peter
Musuku, John
Mtaja, Agnes
Hugo-Hamman, Christopher
El-Sayed, Ahmed
Damasceno, Albertino
Mocumbi, Ana
Bode-Thomas, Fidelia
Yilgwan, Christopher
Amusa, Ganiyu A.
Nkereuwem, Esin
Shaboodien, Gasnat
Da Silva, Rachael
Lee, Dave Chi Hoo
Frain, Simon
Geifman, Nophar
Whetton, Anthony D.
Keavney, Bernard
Engel, Mark E.
Data-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases
title Data-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases
title_full Data-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases
title_fullStr Data-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases
title_full_unstemmed Data-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases
title_short Data-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases
title_sort data-independent acquisition mass spectrometry in severe rheumatic heart disease (rhd) identifies a proteomic signature showing ongoing inflammation and effectively classifying rhd cases
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8939134/
https://www.ncbi.nlm.nih.gov/pubmed/35317720
http://dx.doi.org/10.1186/s12014-022-09345-1
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