A generalized HIV vaccine design strategy for priming of broadly neutralizing antibody responses.
Jon M SteichenYing-Cing LinColin Havenar-DaughtonSimone PecettaGabriel OzorowskiJordan R WillisLaura ToyDevin SokAlessia LiguoriSven KratochvilJonathan L TorreOleksandr KalyuzhniyEleonora MelziDaniel W KulpSebastian RämischXiaozhen HuSteffen M BernardErik GeorgesonNicole PhelpsYumiko AdachiMichael KubitzElise LandaisJeffrey C UmotoyAmanda M RobinsonBryan BrineyIan A WilsonDennis R BurtonAndrew B WardShane CrottyFacundo D BatistaWilliam R SchiefPublished in: Science (New York, N.Y.) (2019)
Vaccine induction of broadly neutralizing antibodies (bnAbs) to HIV remains a major challenge. Germline-targeting immunogens hold promise for initiating the induction of certain bnAb classes; yet for most bnAbs, a strong dependence on antibody heavy chain complementarity-determining region 3 (HCDR3) is a major barrier. Exploiting ultradeep human antibody sequencing data, we identified a diverse set of potential antibody precursors for a bnAb with dominant HCDR3 contacts. We then developed HIV envelope trimer-based immunogens that primed responses from rare bnAb-precursor B cells in a mouse model and bound a range of potential bnAb-precursor human naïve B cells in ex vivo screens. Our repertoire-guided germline-targeting approach provides a framework for priming the induction of many HIV bnAbs and could be applied to most HCDR3-dominant antibodies from other pathogens.
Keyphrases
- antiretroviral therapy
- hiv positive
- hiv testing
- hiv infected
- human immunodeficiency virus
- hepatitis c virus
- hiv aids
- men who have sex with men
- endothelial cells
- mouse model
- south africa
- cancer therapy
- dna repair
- induced pluripotent stem cells
- dengue virus
- genome wide
- dna damage
- big data
- human health
- single cell
- drug delivery
- machine learning
- artificial intelligence
- dna methylation
- zika virus