TY - JOUR
T1 - Data-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases
AU - the RHDGen Network Consortium
AU - Salie, M. Taariq
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 Chi Hoo
AU - Frain, Simon
AU - Geifman, Nophar
AU - Whetton, Anthony D.
AU - Keavney, Bernard
AU - Engel, Mark E.
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12/1
Y1 - 2022/12/1
N2 - 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.
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.
KW - Adiponectin
KW - Biomarker
KW - Complement component C7
KW - Fibulin-1
KW - Inflammatory response
KW - Rheumatic heart disease
UR - http://www.scopus.com/inward/record.url?scp=85134015074&partnerID=8YFLogxK
U2 - 10.1186/s12014-022-09345-1
DO - 10.1186/s12014-022-09345-1
M3 - Article
AN - SCOPUS:85134015074
SN - 1542-6416
VL - 19
JO - Clinical Proteomics
JF - Clinical Proteomics
IS - 1
M1 - 7
ER -