Monomeric serum immunoglobulin A (IgA) can contribute to the development of various autoimmune diseases, but the regulation of serum IgA effector functions is not well defined. Here, we show that the two IgA subclasses (IgA1 and IgA2) differ in their effect on immune cells due to distinct binding and signaling properties. Whereas IgA2 acts pro-inflammatory on neutrophils and macrophages, IgA1 does not have pronounced effects. Moreover, IgA1 and IgA2 have different glycosylation profiles, with IgA1 possessing more sialic acid than IgA2. Removal of sialic acid increases the pro-inflammatory capacity of IgA1, making it comparable to IgA2. Of note, disease-specific autoantibodies in patients with rheumatoid arthritis display a shift toward the pro-inflammatory IgA2 subclass, which is associated with higher disease activity. Taken together, these data demonstrate that IgA effector functions depend on subclass and glycosylation, and that disturbances in subclass balance are associated with autoimmune disease.

Glycosylation is a common co- and post-translational protein modification, having a large influence on protein properties like conformation and solubility. Furthermore, glycosylation is an important determinant of efficacy and clearance of biopharmaceuticals such as immunoglobulin G (IgG). Matrix-assisted laser desorption/ionization (MALDI)-time-of-flight (TOF)-mass spectrometry (MS) shows potential for the site-specific glycosylation analysis of IgG at the glycopeptide level. With this approach, however, important information about glycopeptide sialylation is not duly covered because of in-source and metastable decay of the sialylated species. Here, we present a highly repeatable sialic acid derivatization method to allow subclass-specific MALDI-TOF-MS analysis of tryptic IgG glycopeptides. The method, employing dimethylamidation with the carboxylic acid activator 1-ethyl-3-(3-dimethylamino)propyl)carbodiimide (EDC) and the catalyst 1-hydroxybenzotriazole (HOBt), results in different masses for the functionally divergent α2,3- and α2,6-linked sialic acids. Respective lactonization and dimethylamidation leads to their direct discrimination in MS and importantly, both glycan and peptide moieties reacted in a controlled manner. In addition, stabilization allowed the acquisition of fragmentation spectra informative with respect to glycosylation and peptide sequence. This was in contrast to fragmentation spectra of underivatized samples, which were dominated by sialic acid loss. The method allowed the facile discrimination and relative quantitation of IgG Fc sialylation in therapeutic IgG samples. The method has considerable potential for future site- and sialic acid linkage-specific glycosylation profiling of therapeutic antibodies, as well as for subclass-specific biomarker discovery in clinical IgG samples derived from plasma.

The study of N-linked glycosylation has long been complicated by a lack of bioinformatics tools. In particular, there is still a lack of fast and robust data processing tools for targeted (relative) quantitation. We have developed modular, high-throughput data processing software, MassyTools, that is capable of calibrating spectra, extracting data, and performing quality control calculations based on a user-defined list of glycan or glycopeptide compositions. Typical examples of output include relative areas after background subtraction, isotopic pattern-based quality scores, spectral quality scores, and signal-to-noise ratios. We demonstrated MassyTools' performance on MALDI-TOF-MS glycan and glycopeptide data from different samples. MassyTools yielded better calibration than the commercial software flexAnalysis, generally showing 2-fold better ppm errors after internal calibration. Relative quantitation using MassyTools and flexAnalysis gave similar results, yielding a relative standard deviation (RSD) of the main glycan of ~6%. However, MassyTools yielded 2- to 5-fold lower RSD values for low-abundant analytes than flexAnalysis. Additionally, feature curation based on the computed quality criteria improved the data quality. In conclusion, we show that MassyTools is a robust automated data processing tool for high-throughput, high-performance glycosylation analysis. The package is released under the Apache 2.0 license and is freely available on GitHub ( https://github.com/Tarskin/MassyTools ).