Back

Protective non-neutralizing mAbs Ab94 and Ab81 retain high-affinity and potent Fc-mediated function against SARS-CoV-2 variants from Omicron to XBB1.5

Izadi, A.; Godzwon, M.; Ohlin, M.; Nordenfelt, P.

2023-11-10 immunology Community evaluation
10.1101/2023.09.29.560084 bioRxiv
Show abstract

Antibodies play a central role in the immune defense against SARS-CoV-2. There is substantial evidence supporting that Fc-mediated effector functions of anti-spike antibodies contribute to anti-SARS-Cov-2 immunity. We have previously shown that two non-neutralizing but opsonic mAbs, Ab81 and Ab94, are protective against lethal Wuhan SARS-CoV-2 infection in mice. The protective effect was comparable to a potent neutralizing antibody, Ab59. Here, we hypothesized that, unlike the neutralizing antibodies, non-neutralizing opsonic antibodies would have a higher likelihood of retaining their function to the mutated variants, potentially functioning as broadly protective mAbs. Most of the mutations on the SARS-CoV-2 variants cluster on neutralizing epitopes, leaving other epitopes unaltered. We observed that neutralizing antibodies lost binding to Omicron. In contrast, seven non-neutralizing opsonic antibodies retained nanomolar affinity towards Omicron, BA.2, BA.4, and BA.5. Focusing on the two protective non-neutralizing antibodies Ab81 and Ab94, we showed that they maintain their strong reactivity even to XBB, XBB1.5, and BQ1.1. In the case of Ab94, interestingly, it even has increased affinity towards all variants except for XBB, which is comparable to WT. Finally, we show that Ab94 and Ab81 have potent Fc-mediated functions in vitro against the XBB and BQ1.1 and that combining the mAbs in a cocktail further enhances the effect. These results show that protective non-neutralizing mAbs such as Ab94 and Ab81 can be a viable strategy for anti-SARS-CoV-2 mAb therapies against current and possibly future SARS-CoV-2 variants and that opsonic epitopes could have implications for vaccine design.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.