HIV Evolution: Theoretical Framework and Practical Applications

The human immunodeficiency virus (HIV) is one of the most important and interesting organisms to study today. This pathogen causes life-long infection that presently cannot be cured and the infection leads to development of opportunistic diseases and death if not treated. Finding the answers to the questions still remaining about the evolutionary dynamics of the virus may be crucial in order to develop new therapeutics and functional vaccines, as well as to achieve efficient prevention and surveillance of HIV spread. In terms of evolution, the virus has a remarkable ability to accumulate new mutations over short time. Hence, theoretical models can be applied to HIV data from which parameter estimations can be done directly, and consequently detailed inference of the evolutionary history of HIV can be done. In this thesis the evolution of HIV was studied from several different aspects, and both existing as well as newly developed methods were used. The spread dynamics of HIV-1 among injecting drug users (IDUs) in Sweden were studied using genetic viral material from newly diagnosed patients and by comparing clinical and demographic data. We found several old lineages of subtype B that had been present at least since the 1990s and that have continued to spread up until late 2007, and we estimated the rate of spread in these lineages to have been generally slow. There have been additional introductions of subtype B into Sweden but these introductions appear to have caused no or limited spread. An introduction of CRF01_AE from Helsinki to Stockholm caused an outbreak in 2006-2007, probably in a standing social network of IDUs. We estimated the incidence rate to increase with a factor of 12 at the outbreak onset, but time from infection to diagnosis during the outbreak was estimated to be short, indicating a rapid discovery of infected individuals. However, both before and after the outbreak, newly HIV-1 infected individuals seem to have remained undiagnosed for longer time periods than during the outbreak. Within-patient evolutionary rates of HIV were studied in HIV-2 and HIV-1 patients, matched according to viral load, CD4 count, antiretroviral treatment and sampling times. We found that the envelope gene evolved at a faster rate in HIV-2 than in HIV-1 in patients at similar disease stage. The faster rate was more pronounced at synonymous sites, probably a result of factors influencing the replication or mutation rate of the virus. Finally, we investigated the evolutionary dynamics of HIV-1 in an asymptomatic patient during chronic infection. Through high-frequency sampling it was possible to perform detailed analyses of the processes influencing the short-time evolution of HIV-1 (up to months). We found that several subpopulations were present over time, whose fluctuations over longer time periods (~1.5 years) were consistent with a neutral model of evolution. However, signatures of positive selection were observed on the branches connecting the subpopulations. Thus, non-neutral evolution had likely influenced the formation of these subpopulations and is probably acting over longer time periods in chronic infection of HIV-1

The genetic history of Scandinavia from the Roman Iron Age to the present

The authors acknowledge support from the National Genomics Infrastructure in Stockholm funded by Science for Life Laboratory, the Knut and Alice Wallenberg Foundation and the Swedish Research Council, and SNIC/Uppsala Multidisciplinary Center for Advanced Computational Science for assistance with massively parallel sequencing and access to the UPPMAX computational infrastructure. We used resources from projects SNIC 2022/23-132, SNIC 2022/22-117, SNIC 2022/23-163, SNIC 2022/22-299, and SNIC 2021-2-17. This research was supported by the Swedish Research Council project ID 2019-00849_VR and ATLAS (Riksbankens Jubileumsfond). Part of the modern dataset was supported by a research grant from Science Foundation Ireland (SFI), grant number 16/RC/3948, and co-funded under the European Regional Development Fund and by FutureNeuro industry partners.Peer reviewedPublisher PD

Daily Sampling of an HIV-1 Patient with Slowly Progressing Disease Displays Persistence of Multiple env Subpopulations Consistent with Neutrality

The molecular evolution of HIV-1 is characterized by frequent substitutions, indels and recombination events. In addition, a HIV-1 population may adapt through frequency changes of its variants. To reveal such population dynamics we analyzed HIV-1 subpopulation frequencies in an untreated patient with stable, low plasma HIV-1 RNA levels and close to normal CD4+ T-cell levels. The patient was intensively sampled during a 32-day period as well as approximately 1.5 years before and after this period (days −664, 1, 2, 3, 11, 18, 25, 32 and 522). 77 sequences of HIV-1 env (approximately 3100 nucleotides) were obtained from plasma by limiting dilution with 7–11 sequences per time point, except day −664. Phylogenetic analysis using maximum likelihood methods showed that the sequences clustered in six distinct subpopulations. We devised a method that took into account the relatively coarse sampling of the population. Data from days 1 through 32 were consistent with constant within-patient subpopulation frequencies. However, over longer time periods, i.e. between days 1…32 and 522, there were significant changes in subpopulation frequencies, which were consistent with evolutionarily neutral fluctuations. We found no clear signal of natural selection within the subpopulations over the study period, but positive selection was evident on the long branches that connected the subpopulations, which corresponds to >3 years as the subpopulations already were established when we started the study. Thus, selective forces may have been involved when the subpopulations were established. Genetic drift within subpopulations caused by de novo substitutions could be resolved after approximately one month. Overall, we conclude that subpopulation frequencies within this patient changed significantly over a time period of 1.5 years, but that this does not imply directional or balancing selection. We show that the short-term evolution we study here is likely representative for many patients of slow and normal disease progression

A Rare Functional Noncoding Variant at the GWAS-Implicated MIR137/MIR2682 Locus Might Confer Risk to Schizophrenia and Bipolar Disorder

Schizophrenia (SZ) genome-wide association studies (GWASs) have identified common risk variants in >100 susceptibility loci; however, the contribution of rare variants at these loci remains largely unexplored. One of the strongly associated loci spans MIR137 (miR137) and MIR2682 (miR2682), two microRNA genes important for neuronal function. We sequenced ∼6.9 kb MIR137/MIR2682 and upstream regulatory sequences in 2,610 SZ cases and 2,611 controls of European ancestry. We identified 133 rare variants with minor allele frequency (MAF) <0.5%. The rare variant burden in promoters and enhancers, but not insulators, was associated with SZ (p = 0.021 for MAF < 0.5%, p = 0.003 for MAF < 0.1%). A rare enhancer SNP, 1:g.98515539A>T, presented exclusively in 11 SZ cases (nominal p = 4.8 × 10−4). We further identified its risk allele T in 2 of 2,434 additional SZ cases, 11 of 4,339 bipolar (BP) cases, and 3 of 3,572 SZ/BP study controls and 1,688 population controls; yielding combined p values of 0.0007, 0.0013, and 0.0001 for SZ, BP, and SZ/BP, respectively. The risk allele T of 1:g.98515539A>T reduced enhancer activity of its flanking sequence by >50% in human neuroblastoma cells, predicting lower expression of MIR137/MIR2682. Both empirical and computational analyses showed weaker transcription factor (YY1) binding by the risk allele. Chromatin conformation capture (3C) assay further indicated that 1:g.98515539A>T influenced MIR137/MIR2682, but not the nearby DPYD or LOC729987. Our results suggest that rare noncoding risk variants are associated with SZ and BP at MIR137/MIR2682 locus, with risk alleles decreasing MIR137/MIR2682 expression

Towards Estimation of HIV-1 Date of Infection: A Time-Continuous IgG-Model Shows That Seroconversion Does Not Occur at the Midpoint between Negative and Positive Tests

<div><p>Estimating date of infection for HIV-1-infected patients is vital for disease tracking and informed public health decisions, but is difficult to obtain because most patients have an established infection of unknown duration at diagnosis. Previous studies have used HIV-1-specific immunoglobulin G (IgG) levels as measured by the IgG capture BED enzyme immunoassay (BED assay) to indicate if a patient was infected recently, but a time-continuous model has not been available. Therefore, we developed a logistic model of IgG production over time. We used previously published metadata from 792 patients for whom the HIV-1-specific IgG levels had been longitudinally measured using the BED assay. To account for patient variability, we used mixed effects modeling to estimate general population parameters. The typical patient IgG production rate was estimated at <i>r</i> = 6.72[approximate 95% CI 6.17,7.33]×10⁻³ OD-n units day⁻¹, and the carrying capacity at <i>K</i> = 1.84[1.75,1.95] OD-n units, predicting how recently patients seroconverted in the interval ^∧<i>t</i> = (31,711) days. Final model selection and validation was performed on new BED data from a population of 819 Swedish HIV-1 patients diagnosed in 2002–2010. On an appropriate subset of 350 patients, the best model parameterization had an accuracy of 94% finding a realistic seroconversion date. We found that seroconversion on average is at the midpoint between last negative and first positive HIV-1 test for patients diagnosed in prospective/cohort studies such as those included in the training dataset. In contrast, seroconversion is strongly skewed towards the first positive sample for patients identified by regular public health diagnostic testing as illustrated in the validation dataset. Our model opens the door to more accurate estimates of date of infection for HIV-1 patients, which may facilitate a better understanding of HIV-1 epidemiology on a population level and individualized prevention, such as guidance during contact tracing.</p> </div

Comparison of inferred time since seroconversion to serological interval.

<p>(<b>A</b>) The model-inferred time since seroconversion [grey circles with 95% confidence interval as grey lines] from 350 Swedish patients was compared to their known serological intervals [(<i>T₍₊₎,T₍₋₎</i>), blue lines]. When the inferred time since seroconversion did not hit the serological interval, the point estimate and 95% confidence interval is marked in red; 94% of the intervals overlapped. (<b>B</b>) The relative positioning parameter τ measures the normalized position of the inferred time since seroconversion to the serological interval. Values outside this interval are shown in grey at τ = 0 and τ = 1. The relative positioning was biased towards the most recent positive HIV test result at large τ. (<b>C</b>) Distribution of the inferred time since seroconversion, i.e., the times between BED tests and <i>τ</i>-corrected dates of seroconversion.</p

Definitions of dates and time intervals relative to BED testing.

<p>The time since seroconversion (<i>t</i>) is the time from the date a patient seroconverted (<i>T</i>_sc) to when a sample for BED testing was collected (<i>T</i>_BED). We estimate <i>t</i> by a logistic IgG model (Eq. 1) as ^∧<i>t</i>. Date of infection (<i>T</i>_inf) occurred on average 21 days prior to <i>T</i>_sc<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060906#pone.0060906-Fiebig1" target="_blank">[9]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060906#pone.0060906-Cohen1" target="_blank">[10]</a>. When available, the patient history also includes the dates of last negative HIV-1 antibody testing (<i>T</i>₍₋₎) and first positive HIV-1 antibody testing (<i>T</i>₍₊₎), defining the serological interval. Note that <i>T₍₊₎</i> and <i>T_BED</i> may often occur at the same date. To reevaluate national Swedish HIV surveillance data we compared ^∧<i>T</i>_inf with <i>T</i>₍₊₎, resulting in a time difference Δ.</p

Logistic modeling of IgG-capture BED-enzyme immunoassay absorbance as a function of time since seroconversion.

<p>The resulting logistic model is predictive when BED OD-n = (0.07, 1.84), corresponding to 31–711 days. This model describes the typical patient estimated by the SAR mixed effects model (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060906#pone-0060906-t001" target="_blank">Table 1</a>), where parameter values correspond to the whole population.</p

Mixed effects model parameterizations.

<p>Footnotes: 1, random effect included; 0, random effect excluded; −, model did not converge; NA, not applicable.</p

Example from the reevaluation of Swedish IDU surveillance data.

<p>Vertical bars of the rug represent inferred date of infection after model application. The maximal time shift, ±732 days, implied by our SAR based predictive upper time-level (711 days) plus average time from infection to seroconversion (21 days) is indicated by grey zones before and after year 2006. The arrows show the resulting shift, starting at the time of diagnosis and pointing at the inferred date of infection, colored according to the time difference Δ.</p