Modeling the immunological pre-adaptation of HIV-1

Abstract

It is becoming increasingly evident that the evolution of HIV-1 is to a large extent determined by the immunological background of the host. On the population-level this results in associations between specific human leukocyte antigen (HLA) alleles and polymorphic loci of the virus. Furthermore, some HLA alleles that were previously associated with slow progression to AIDS have been shown to lose their protective effect, because HLA-specific immunological escape variants have spread through the population. This phenomenon is known as immunological preadaptation. Apart from adapting to human immune responses, the set-point virus load (SPVL) of HIV-1 is thought to have evolved to values that optimize the population-level fitness of the virus. This suggestion is supported by considerable heritability of the SPVL. Previous modeling studies show that whether or not SPVL optimization is expected to occur depends sensitively on the underlying assumptions with respect to the extent of within-versus between-host selection. Here we use a detailed and semi-realistic multi-level HIV-1 model in which immunological preadaptation and SPVL evolution can emerge from the underlying interactions of the virus with the immune system of the host. This enables us to study the effect of immunological escape on disease progression, and how disease progression may be molded by SPVL evolution. We find that the time to AIDS could decrease significantly (0.5-1.0 years) in a HLA-dependent manner by immunological pre-adaptation over the long-term course of the epidemic (> 100 years). We find that SPVL is not expected to evolve to optimize the population-level fitness of HIV-1, even though high heritability of the SPVL emerges from continual selection of immune-escape mutations.

Microbial Evolution at Multiple Scales

“…Even if within-host selection is based on relatively small differences in within-host replication rate, within-host selection is expected to completely dominate the evolutionary process, selecting strains with high within-host replication rate, but relatively small population-level fitness (Lythgoe et al (2013), see also Figure 2.5 in chapter 2). Similar results were found by van Dorp et al in more complex simulation models of the evolution of HIV through immune escape mutations in heterogeneous host populations (van Dorp et al, 2014(van Dorp et al, , 2020. Surprisingly, these model predictions do not coincide with epidemiological observations.…”

Reconstructing multi-strain pathogen interactions from cross-sectional survey data via statistical network inference

“…Interactions between pathogens or pathogen strains can take various forms: infection by one strain can modify susceptibility to subsequent infection by others, and the simultaneous presence of multiple strains can affect the duration, infectiousness, as well as severity of infection [9,10]. Consequently, pathogen interactions may have far-reaching clinical, epidemiological, and eco-evolutionary implications [11][12][13].…”

Evolution during primary HIV infection does not require adaptive immune selection

“…Within-host HIV models have modeled nucleotide sequences and assumed multistrain phenotypes with fitness distributions ( 6 – 12 ). Particular aspects of HIV biology including recombination ( 13 , 14 ), drug resistance ( 15 ), antibody evolution ( 16 , 17 ), adaptive immunity ( 18 , 19 ), and latency ( 20 ) have been considered. Several general forward simulation packages are available ( 21 – 23 ).…”