BACKGROUND Most humans have been infected with cytomegalovirus (CMV) by midlife without clinical signs of disease. However, in settings in which the immune system is undeveloped or compromised, the virus is not adequately controlled and consequently presents a major infectious cause of both congenital disease during pregnancy as well as opportunistic infection in children and adults. With clear evidence that risk to the fetus varies with gestational age at the time of primary maternal infection, further research on humoral responses to primary CMV infection during pregnancy is needed.METHODS Here, systems serology tools were applied to characterize antibody responses to CMV infection in pregnant and nonpregnant women experiencing either primary or chronic infection.RESULTS Whereas strikingly different antibody profiles were observed depending on infection status, limited differences were associated with pregnancy status. Beyond known differences in IgM responses used clinically for identification of primary infection, distinctions observed in IgA and FcγR-binding antibodies and among antigen specificities accurately predicted infection status. Machine learning was used to define the transition from primary to chronic states and predict time since infection with high accuracy. Humoral responses diverged over time in an antigen-specific manner, with IgG3 responses toward tegument decreasing over time as typical of viral infections, while those directed to pentamer and glycoprotein B were lower during acute and greatest during chronic infection.CONCLUSION In sum, this work provides insights into the antibody response associated with CMV infection status in the context of pregnancy, revealing aspects of humoral immunity that have the potential to improve CMV diagnostics.FUNDING CYMAF consortium and NIH NIAID.
Andrew P. Hederman, Christopher A.L. Remmel, Shilpee Sharma, Harini Natarajan, Joshua A. Weiner, Daniel Wrapp, Catherine Donner, Marie-Luce Delforge, Piera d’Angelo, Milena Furione, Chiara Fornara, Jason S. McLellan, Daniele Lilleri, Arnaud Marchant, Margaret E. Ackerman
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