Characterization of Citrullination Sites in Neutrophils and Mast Cells Activated by Ionomycin via Integration of Mass Spectrometry and Machine Learning

Citrullination
is an important post-translational modification
implicated in many diseases including rheumatoid arthritis (RA), Alzheimer’s
disease, and cancer. Neutrophil and mast cells have different expression
profiles for protein-arginine deiminases (PADs), and ionomycin-induced
activation makes them an ideal cellular model to study proteins susceptible
to citrullination. We performed high-resolution mass spectrometry
and stringent data filtration to identify citrullination sites in
neutrophil and mast cells treated with and without ionomycin. We identified
a total of 833 validated citrullination sites on 395 proteins. Several
of these citrullinated proteins are important components of pathways
involved in innate immune responses. Using this benchmark primary
sequence data set, we developed machine learning models to predict
citrullination in neutrophil and mast cell proteins. We show that
our models predict citrullination likelihood with 0.735 and 0.766
AUCs (area under the receiver operating characteristic curves), respectively,
on independent validation sets. In summary, this study provides the
largest number of validated citrullination sites in neutrophil and
mast cell proteins. The use of our novel motif analysis approach to
predict citrullination sites will facilitate the discovery of novel
protein substrates of protein-arginine deiminases (PADs), which may
be key to understanding immunopathologies of various diseases.