Anti-Factor XII Autoantibodies in Patients with Recurrent Pregnancy Loss Recognize the Second Epidermal Growth Factor–Like Domain

Summary

Background Factor XII (FXII) deficiency and autoantibodies that bind to FXII (anti-FXII) have been described in patients with adverse pregnancy outcomes, including recurrent pregnancy loss. It has been reported that FXII functions not only as a coagulation protein but also as a growth factor.

Objectives We studied the association between anti-FXII and the epidermal growth factor (EGF) system in patients with recurrent pregnancy loss.

Patients/Methods We used synthetic peptides that span the second EGF-like domain in the heavy chain of FXII (EGF2) in inhibition and direct binding studies to determine if anti-FXII antibodies recognize EGF2. Furthermore, we examined whether anti-FXII antibodies, which recognize EGF2, also recognize recombinant EGF and heparin-binding EGF-like growth factor (HB-EGF).

Results Among 100 patients with recurrent pregnancy loss, the plasma of 23 patients (23.0%) recognized the synthetic peptide ASQ41, which covers EGF2. Among the 23 anti-ASQ41-positive patients, plasma samples from 13 patients (56.5%) recognized the 22-residue segment C-terminal half of ASQ41. Among the 23 anti-ASQ41-positive patients, the plasma of 17 patients (73.9%) recognized recombinant human EGF. Affinity-purified anti-FXII antibodies, which recognize ASQ41, also recognized recombinant EGF family proteins such as EGF and HB-EGF.

Conclusions The autoantibodies in patients with recurrent pregnancy loss recognized the EGF2 domain in FXII and other proteins of the EGF family. Since proteins in the EGF family play an important role in normal pregnancy, autoantibody-associated disruption of the EGF system may cause pregnancy loss.

Keywords

factor XII - pregnancy loss - recurrent pregnancy loss - epidermal growth factor - heparin-binding EGF-like growth factor

Authors' Contributions

Study concept and design: Y. Sato and T. Sugi; acquisition of data: Y. Sato, T. Sugi, and R. Sakai; analysis and interpretation of data: Y. Sato and T. Sugi; patient inclusion and management: Y. Sato and T. Sugi; drafting of the manuscript: Y. Sato and T. Sugi; study supervisor: T. Sugi.

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