The Role of Sialic Acid Acetylesterase in the Maintenance of B Cell Self Tolerance

Abstract

Abstract Sialic acid acetylesterase (SIAE) removes 9-O-acetyl moieties from acetylated sialic acids. The B cell receptor (BCR) inhibitory receptor CD22 cannot bind 9-O-acetylated α2-6-linked sialic acid-containing ligands. Therefore, the removal of these moieties by SIAE is important for inhibition of signaling through the BCR by CD22. Previous studies on Siae-deficient mice revealed a role for SIAE in the maintenance of B cell tolerance. Deep sequencing of the SIAE exons in patients from several autoimmune cohorts revealed numerous single nucleotide polymorphisms (SNPs). Fluorometric enzymatic assays revealed that about half of these encode catalytically dead proteins while others have reduced activity. Coimmunoprecipitation studies indicated that mutant SIAE associates with wildtype SIAE in a multimer and acts in a dominant-interfering manner to decrease activity of the wildtype protein. We used monoclonal antibodies against different human SIAE epitopes in quantitative western blotting assays to assess whether variant-encoded SIAE proteins are misfolded and if activity and folding correlate. We found that most catalytically dead, disease-associated variants of SIAE are partially misfolded. Circular Dichroism studies were used to further investigate misfolding of mutant SIAE. Preliminary CD data indicate that mutant SIAE proteins are partially misfolded but retain significant structural integrity. These data are consistent with the finding that mutant SIAE proteins are able to multimerize with wildtype SIAE. We used FPLC to investigate the oligomeric structure of SIAE and found that SIAE exists as a dimer. We compared the results of the enzymatic assays of SIAE variants to the predictions generated by three commonly used algorithms; Polyphen-2, SIFT, and Provean. We found that the predictions of the algorithms were erroneous for between 11% (PolyPhen-2) and 28% (SIFT) of SIAE variants erroneous predictions for a given variant were often made by more than one algorithm, pointing to a need for non-computational predictive methods for the investigation of the effects of SNPs.