bioRxivImmunologyPreprint
Rapid acquisition of HIV-1 neutralization breadth in a rhesus V2 apex germline antibody mouse model after a single bolus immunization
Amrit Raj Ghosh, Rumi HabibiD, Nitesh MishraiD, Ryan S. RoarkiD, Madhav AkauliyaiD, Ali A. AlbowaideyiD, Joel D. AlleniD, Khaled AmerehiD, Gabriel AvillioniD, Maria BottermanniD, Bo LiangiD, Namit ChaudharyiD, Sean CallaghaniD, Jonathan DyeiD, Xuduo LiiD, Jordan Renae Ellis-PughiD, Rohan Roy ChowdhuryiD, Nicole E. James, Xiao LiuiD, Laura MaiorinoiD, Paula Maldonado VillavicencioiD, Rebecca NedelleciD, Prabhgun Oberoi, Kirsten J. SowersiD, Young-Hoon ParkiD, , Facundo D. BatistaiD
Abstract
Current vaccine strategies to elicit broadly neutralizing antibodies (bnAbs) against HIV-1 generally propose complex, multi-boost immunization regimens. In rhesus macaques, SHIV infection has been observed to rapidly drive the development of some classes of bnAbs that share structural similarities with those in humans. Here, we generated a knockin mouse model with B cells bearing the unmutated common ancestor (UCA) of the V2 apex-targeted bnAb lineage, V033-a. A single immunization of mice with a germline-targeting native-like trimer was sufficient to recapitulate the ontogeny of the mature rhesus bnAb in knockin mice--including rare, disfavored somatic mutations--leading to the induction of antibodies that exhibited potent neutralization against both autologous and heterologous tier 2 viruses. A boost with Env escape mutant trimers further improved breadth and potency, and cryo-EM structure revealed the structural basis for heterologous neutralization breadth. Non-human primate and mouse models can thus combine with structure to serve as a platform for identifying and confirming immunogens that streamline HIV-vaccination regimens.
- organism: rat
- assay: spatial transcriptomics
- research focus: host-pathogen & immune response
- research focus: cell type annotation
- research focus: clustering
- research focus: trajectory inference
- approach: experimental assay
- approach: deep learning
- Citations
- 2
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