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. 2022 Mar 22;19(1):7.
doi: 10.1186/s12014-022-09345-1.

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

M Taariq Salie #  1 , Jing Yang #  2 , Carlos R Ramírez Medina  3 , Liesl J Zühlke  4 , Chishala Chishala  5 , Mpiko Ntsekhe  5 , Bernard Gitura  6 , Stephen Ogendo  7 , Emmy Okello  8 , Peter Lwabi  8 , John Musuku  9 , Agnes Mtaja  9 , Christopher Hugo-Hamman  4   10 , Ahmed El-Sayed  11 , Albertino Damasceno  12 , Ana Mocumbi  13   14 , Fidelia Bode-Thomas  15 , Christopher Yilgwan  15 , Ganiyu A Amusa  16 , Esin Nkereuwem  15 , Gasnat Shaboodien  17 , Rachael Da Silva  18 , Dave Chi Hoo Lee  18 , Simon Frain  2 , Nophar Geifman  19 , Anthony D Whetton  20 , Bernard Keavney #  2   21 , Mark E Engel #  22 ; RHDGen Network Consortium
Affiliations

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

M Taariq Salie et al. Clin Proteomics. .

Abstract

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.

Keywords: Adiponectin; Biomarker; Complement component C7; Fibulin-1; Inflammatory response; Rheumatic heart disease.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
a Boxplot representing the permutation importance of the 56 proteins (from 215 cases; 230 controls) found to be significant by the Boruta algorithm. UniProt IDs are presented in Table 2. b Cumulative AUC for Boruta-identified biomarkers calculated from logistic regression analysis
Fig. 2
Fig. 2
Functionally grouped networks of enriched pathways from ClueGO. For the full enrichment analysis results see Additional file 1: Table S4
See this image and copyright information in PMC

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