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

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dc.contributor.authorSalie, M T
dc.contributor.authorYang, Jing
dc.contributor.authorRamírez Medina, Carlos R
dc.contributor.authorZühlke, Liesl J
dc.contributor.authorChishala, Chishala
dc.contributor.authorNtsekhe, Mpiko
dc.contributor.authorGitura, Bernard
dc.contributor.authorOgendo, Stephen
dc.contributor.authorOkello, Emmy
dc.contributor.authorLwabi, Peter
dc.contributor.authorMusuku, John
dc.contributor.authorMtaja, Agnes
dc.contributor.authorHugo-Hamman, Christopher
dc.contributor.authorEl-Sayed, Ahmed
dc.contributor.authorDamasceno, Albertino
dc.contributor.authorMocumbi, Ana
dc.contributor.authorBode-Thomas, Fidelia
dc.contributor.authorYilgwan, Christopher
dc.contributor.authorAmusa, Ganiyu A
dc.contributor.authorNkereuwem, Esin
dc.contributor.authorShaboodien, Gasnat
dc.contributor.authorDa Silva, Rachael
dc.contributor.authorLee, Dave C H
dc.contributor.authorFrain, Simon
dc.contributor.authorGeifman, Nophar
dc.contributor.authorWhetton, Anthony D
dc.contributor.authorKeavney, Bernard
dc.contributor.authorEngel, Mark E
dc.date.accessioned2022-04-13T11:23:08Z
dc.date.available2022-04-13T11:23:08Z
dc.date.issued2022-03-22
dc.date.updated2022-03-27T03:10:22Z
dc.description.abstractBackground 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.en_US
dc.identifier.apacitationSalie, M. T., Yang, J., Ramírez Medina, C. R., Zühlke, L. J., Chishala, C., Ntsekhe, M., ... Engel, M. E. (2022). Data-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases. <i>Clinical Proteomics</i>, 19(1), 7. http://hdl.handle.net/11427/36373en_ZA
dc.identifier.chicagocitationSalie, M T, Jing Yang, Carlos R Ramírez Medina, Liesl J Zühlke, Chishala Chishala, Mpiko Ntsekhe, Bernard Gitura, et al "Data-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases." <i>Clinical Proteomics</i> 19, 1. (2022): 7. http://hdl.handle.net/11427/36373en_ZA
dc.identifier.citationSalie, M.T., Yang, J., Ramírez Medina, C.R., Zühlke, L.J., Chishala, C., Ntsekhe, M., Gitura, B. & Ogendo, S. et al. 2022. Data-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases. <i>Clinical Proteomics.</i> 19(1):7. http://hdl.handle.net/11427/36373en_ZA
dc.identifier.ris TY - Journal Article AU - Salie, M T AU - Yang, Jing AU - Ramírez Medina, Carlos R AU - Zühlke, Liesl J AU - Chishala, Chishala AU - Ntsekhe, Mpiko AU - Gitura, Bernard AU - Ogendo, Stephen AU - Okello, Emmy AU - Lwabi, Peter AU - Musuku, John AU - Mtaja, Agnes AU - Hugo-Hamman, Christopher AU - El-Sayed, Ahmed AU - Damasceno, Albertino AU - Mocumbi, Ana AU - Bode-Thomas, Fidelia AU - Yilgwan, Christopher AU - Amusa, Ganiyu A AU - Nkereuwem, Esin AU - Shaboodien, Gasnat AU - Da Silva, Rachael AU - Lee, Dave C H AU - Frain, Simon AU - Geifman, Nophar AU - Whetton, Anthony D AU - Keavney, Bernard AU - Engel, Mark E AB - 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. DA - 2022-03-22 DB - OpenUCT DP - University of Cape Town IS - 1 J1 - Clinical Proteomics KW - Rheumatic heart disease KW - Biomarker KW - Inflammatory response KW - Adiponectin KW - Complement component C7 KW - Fibulin-1 LK - https://open.uct.ac.za PY - 2022 T1 - Data-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases TI - Data-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases UR - http://hdl.handle.net/11427/36373 ER - en_ZA
dc.identifier.urihttps://doi.org/10.1186/s12014-022-09345-1
dc.identifier.urihttp://hdl.handle.net/11427/36373
dc.identifier.vancouvercitationSalie MT, Yang J, Ramírez Medina CR, Zühlke LJ, Chishala C, Ntsekhe M, et al. Data-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases. Clinical Proteomics. 2022;19(1):7. http://hdl.handle.net/11427/36373.en_ZA
dc.language.isoenen_US
dc.language.rfc3066en
dc.publisher.departmentDepartment of Medicineen_US
dc.publisher.facultyFaculty of Health Sciencesen_US
dc.rights.holderThe Author(s)
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.sourceClinical Proteomicsen_US
dc.source.journalissue1en_US
dc.source.journalvolume19en_US
dc.source.pagination7en_US
dc.source.urihttps://clinicalproteomicsjournal.biomedcentral.com/
dc.subjectRheumatic heart diseaseen_US
dc.subjectBiomarkeren_US
dc.subjectInflammatory responseen_US
dc.subjectAdiponectinen_US
dc.subjectComplement component C7en_US
dc.subjectFibulin-1en_US
dc.titleData-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD casesen_US
dc.typeJournal Articleen_US
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