Proteomic exploration of potential blood biomarkers in haemophilic arthropathy

Abstract

Background and aims: The pathophysiology of haemophilic arthropathy (HA) is complex and largely undefined. Proteomic analyses provide insights into the intricate mechanisms of the HA.Our study aimed to identify differentially expressed proteins in relation to the severity of HA, explore their pathophysiological roles, and evaluate their potential as HA biomarkers.

Methods: Our cross-sectional observational study encompassed 30 HA patients and 15 healthy subjects. Plasma samples were pooled into three groups of 15 samples from those with severe haemophilic arthropathy (sHA), mild haemophilic arthropathy (mHA) and healthy controls. Proteomic analysis was performed using liquid chromatography-mass spectrometry. The severity of HA was assessed using the World Federation of Haemophilia Physical Examination Score and ultrasonography following the Haemophilia Early Arthropathy Detection with Ultrasound (HEAD-US) guidelines.

Results: A total of 788 proteins were identified, with 97% of the uniquely identified proteins being expressed in all analysed groups. We identified several up and downregulated proteins across the groups that were mainly related to inflammatory and immunity-modulating processes, as well as joint degeneration. We highlighted ten proteins relevant for the development of HA: cathepsin G, endoplasmic reticulum aminopeptidase 2, S100-A9, insulin-like growth factor I, apolipoprotein (a), osteopontin, pregnancy zone protein, cartilage oligomeric matrix protein, CD44, and cadherin-related family member 2.

Conclusion: Our analysis identified several proteins that shed further light on the distinctive pathogenesis of HA and could serve for biomarker research. However, these results need to be validated on a larger patient group.

Keywords: arthropathy; biomarkers; haemophilia; pathophysiology; proteomics.

Figure 1

Study outline depicting the main steps of clinical evaluation and proteomic analysis methods, as well as the study groups. Image created with Biorender. com.

Figure 2

(A) Venn diagram depicting the overlapped, and group‐specific proteins identified in the plasma of analysed patient groups. (B) Proteins specifically detected in sHA and healthy individuals, as well as combinations of mutually expressed proteins in the analysed experimental groups. mHA, mild haemophilic arthropathy; sHA ‐ severe haemophilic arthropathy.

Figure 3

Volcano plot comparing protein expression levels in the plasma of patients with severe haemophilic arthropathy (sHA) and healthy control subjects. The dashed lines on the graph indicate thresholds: first, p‐value ≤ 0.05 (values adjustment on multiple hypothesis testing using the Benjamini & Hochberg method, ‐log10‐transformed) and second, log2‐transformed ratio of severe HA vs control samples higher than 1, or lower than −1. Up‐ and downregulated proteins that meet both thresholds are shown in green and red dots, respectively. The yellow dots represent proteins that did not meet the second threshold. For clarity, full protein names are omitted and are available as supplementary data through the PRIDE database.

Figure 4

Interaction network showing connections between proteins that are statistically significantly (p‐value < 0.05) downregulated (A) and up‐ regulated (B) in the plasma of patients with severe haemophilia (sHA) compared to healthy individuals. The thickness of the grey line depicts the confidence of interaction as predicted by the String 11.5 software. For clarity, full protein names are omitted and are available as supplementary data through the PRIDE database. The lower panels display the analysis of the pathway, that is, the cellular pathways linked to downregulated (panel C; red background) and upregulated (panel D; green background) in sHA compared to healthy individuals. The size of the dot corresponds to the number of proteins ascribed to a certain pathway within the group. The presented molecular pathways are ranked from left to right (lowest to highest) and colour coded according to their EnrichmentScore, i.e. statistical significance. ECM, extracellular matrix; HIF, hypoxia‐inducible factor; PI3K, phosphatidylinositol 3‐kinase; TGF, transforming growth factor; TRP, transient receptor potential.