The challenges of modelling antibody repertoire dynamics in HIV infection
- Univ. of California, Berkeley, CA (United States)
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Antibody affinity maturation by somatic hypermutation of B-cell immunoglobulin variable region genes has been studied for decades in various model systems using well-defined antigens. While much is known about the molecular details of the process, our understanding of the selective forces that generate affinity maturation are less well developed, particularly in the case of a co-evolving pathogen such as HIV. Despite this gap in understanding, high-throughput antibody sequence data are increasingly being collected to investigate the evolutionary trajectories of antibody lineages in HIV-infected individuals. Here, we review what is known in controlled experimental systems about the mechanisms underlying antibody selection and compare this to the observed temporal patterns of antibody evolution in HIV infection. In addition, we describe how our current understanding of antibody selection mechanisms leaves questions about antibody dynamics in HIV infection unanswered. Without a mechanistic understanding of antibody selection in the context of a co-evolving viral population, modelling and analysis of antibody sequences in HIV-infected individuals will be limited in their interpretation and predictive ability.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- AC52-06NA25396
- OSTI ID:
- 1233536
- Report Number(s):
- LA-UR-15-21720
- Journal Information:
- Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences, Vol. 370, Issue 1676; ISSN 0962-8436
- Country of Publication:
- United States
- Language:
- English
Web of Science
Similar Records
Developmental Pathway of the MPER-Directed HIV-1-Neutralizing Antibody 10E8
Focused Evolution of HIV-1 Neutralizing Antibodies Revealed by Structures and Deep Sequencing