The "Protective Immunity from T cells to COVID-19 in Health workers" (PITCH) study examined how the immune system responds to COVID-19 in 78 healthcare workers who had experienced either symptomatic or asymptomatic disease (66 vs 12). An additional 8 patients who experienced severe disease were included for comparison.

Blood samples were taken monthly from 1–6 months post infection to examine different elements of the immune response. This included different types of antibodies—such as Spike-specific and Nucleocapsid-specific antibodies which are produced to target different parts of the virus, alongside B cells, which manufacture antibodies and keep the body's memory of the disease, and several types of T cell.

The researchers used a new machine learning approach—nicknamed SIMON—to identify detailed patterns in the data and to see if initial disease severity and the early immune response could predict longer-term immunity.

They found an early immune signature, detectable one month post infection and linked to both cellular and antibody immunity, which predicted the strength of immune response measured at 6 months post infection. This is the first time that such a signature has been found and improves understanding of the development of lasting immunity. When serum samples (containing antibodies) obtained at 1- and 6-months post infection were tested, the majority of samples from people who produced a weak immune response signature failed to show any neutralizing antibodies against the Alpha variant, with none mounting a neutralizing antibody response against the Beta variant. This raises the possibility that the immune memory of these individuals does not provide sufficient protection to prevent reinfection by these variants.

Regards

John
Editorial Assistant
Immunogenetics Open Access