Glycomics meets artificial intelligence – Potential of glycan analysis for identification of seropositive and seronegative rheumatoid arthritis patients revealed

Highlights

• 100 serum samples from healthy people and patients with rheumatoid arthritis (Ra) were analyzed.

• Standard immunoassays and glycan detection based on application of lectins were performed.

• A dataset containing 2000 data points was data mined using artificial neural networks.

• Glycan analysis helped to correctly identify RA patients from healthy individuals.

Abstract

In this study, one hundred serum samples from healthy people and patients with rheumatoid arthritis (RA) were analyzed. Standard immunoassays for detection of 10 different RA markers and analysis of glycan markers on antibodies in 10 different assay formats with several lectins were applied for each serum sample. A dataset containing 2000 data points was data mined using artificial neural networks (ANN). We identified key RA markers, which can discriminate between healthy people and seropositive RA patients (serum containing autoantibodies) with accuracy of 83.3%. Combination of RA markers with glycan analysis provided much better discrimination accuracy of 92.5%. Immunoassays completely failed to identify seronegative RA patients (serum not containing autoantibodies), while glycan analysis correctly identified 43.8% of these patients. Further, we revealed other critical parameters for successful glycan analysis such as type of a sample, format of analysis and orientation of captured antibodies for glycan analysis.

Introduction

Rheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by joint inflammation, swelling and synovium destruction [1,2]. In RA, the immune complexes are present in serum including autoantibodies antiCCPs (anti-cyclic citrullinated peptide antibodies) and RFs (rheumatoid factors) for seropositive RA patients (S+ RA). In seronegative RA patients (S− RA, about one-third of RA patients) the serological level of these markers is very low or can be absent. Moreover, the disease of S− RA patients is more severe comparing to S+ RA patients, when taking into account disease activity and joint damage [3]. Treatment of S− RA patients can be successful if identified at an early stage and thus, novel and reliable methods applicable for diagnostic and prognostic purposes of S− RA disease should be available [3]. Additionally, seropositive and seronegative RA disorder although similar in the phenotype might represent two different diseases with different pathogenesis, disease development and prognosis [2].

Glycomics is an emerging scientific field with a huge impact on biomedicine [4]. Glycoconjugates are in vivo involved in a diverse range of cellular events and changes in their structure are associated with different disease development and progression. RA associated changes of a biantennary complex type N-glycan on Asn297 in the Cy2 domain on each Fc constant fragment of an antibody involve degalactosylation and desialylation. As a result the underlying N-acetylglucosamine (GlcNAc) residues are more accessible to mannose-binding protein, activating the complement via an alternative lectin pathway [5]. Moreover, GlcNAc inhibits the binding of C1q (part of a complex activating the complement via a classical pathway) [6]. AntiCCPs were shown to acquire a pro-inflammatory glycoprofile (removal of galactose residues) three months prior to RA diagnosis. Changes in fucosylation prior to the onset of RA were also observed [7]. The glycans also control the thermodynamic stability and maintain the quaternary structure of the Fc domain. Fcγ receptors, expressed on leukocytes (including macrophages, NK cells, lymphocytes, eosino- and neutrophils) require IgG Fc glycans for optimal binding to all three classes of their receptors (RI-III, e.g. CD64, CD32 and CD16) [8]. For a reliable glycan analysis during RA development and progression, expensive and time-consuming methods are employed including capillary electrophoresis or MALDI-TOF-MSⁿ and/or their combination [9].

An alternative to instrumental-based analytical approach is to apply a panel of lectins as natural glycocode decipherers for glycan analysis [10]. Recently we published a series of papers focused on an alternative IgG glycoprofiling of RA patients serum samples [11] and isolated IgGs [12] using electrochemical biosensors integrated with lectins. The aim of this study was to investigate if glycan analysis with or without standard immunoassays can be applied to successfully discriminate between non-RA and S− RA patients. Additionally the same approach was also tested to discriminate between non-RA and S+ RA patients. Data mining was performed using artificial neural network (ANN) and L1 regularized logistic regression model. Glycan analysis of serum samples or antibodies isolated from serum samples was performed using lectin microarray (MA) or enzyme-linked lectin-binding assay (ELLBA).

Recently, machine learning algorithms (MLA) and cloud computing gained an attention in healthcare services for automated and more precise diagnostics with a potential to decrease the financial burden in the healthcare sector. ANN, applicable to pattern recognition tasks, diagnostics and data classification using a learning process [13], was successfully used for RA diagnostics using a panel of inflammatory cytokines expressed in serum samples [14]. It was also already shown that ANN-based algorithms could be used to quantify joint space width for RA assessment [15]. A novel method for predicting the clinical response to a chimeric anti-TNF monoclonal antibody drug infliximab based on machine-learning algorithm was also recently developed [16], together with hybrid clustering-classification ANN for solving diagnostic tasks of RA [17]. The main aim of the study was to combine machine learning algorithms with glycomic studies applied for analysis of human sera of RA patients. The ANN results are summarized in a form of 2 × 2 confusion matrix with all parameters defined in Fig. S1.