Evolutionary Dynamics in Structured Populations Under Strong Population Genetic Forces

Elevated HIV Viral Load is Associated with Higher Recombination Rate In Vivo

HIV's exceptionally high recombination rate drives its intrahost diversification, enabling immune escape and multidrug resistance within people living with HIV. While we know that HIV's recombination rate varies by genomic position, we have little understanding of how recombination varies throughout infection or between individuals as a function of the rate of cellular coinfection. We hypothesize that denser intrahost populations may have higher rates of coinfection and therefore recombination. To test this hypothesis, we develop a new approach (recombination analysis via time series linkage decay or RATS-LD) to quantify recombination using autocorrelation of linkage between mutations across time points. We validate RATS-LD on simulated data under short read sequencing conditions and then apply it to longitudinal, high-throughput intrahost viral sequencing data, stratifying populations by viral load (a proxy for density). Among sampled viral populations with the lowest viral loads (<26,800 copies/mL), we estimate a recombination rate of 1.5×10-5 events/bp/generation (95% CI: 7×10-6 to 2.9×10-5), similar to existing estimates. However, among samples with the highest viral loads (>82,000 copies/mL), our median estimate is approximately 6 times higher. In addition to co-varying across individuals, we also find that recombination rate and viral load are associated within single individuals across different time points. Our findings suggest that rather than acting as a constant, uniform force, recombination can vary dynamically and drastically across intrahost viral populations and within them over time. More broadly, we hypothesize that this phenomenon may affect other facultatively asexual populations where spatial co-localization varies.

Elevated HIV viral load is associated with higher recombination rate in vivo

HIV's exceptionally high recombination rate drives its intra-host diversification, enabling immune escape and multi-drug resistance within people living with HIV. While we know that HIV's recombination rate varies by genomic position, we have little understanding of how recombination varies throughout infection or between individuals as a function of the rate of cellular coinfection. We hypothesize that denser intra-host populations may have higher rates of coinfection and therefore recombination. To test this hypothesis, we develop a new approach (Recombination Analysis via Time Series Linkage Decay, or RATS-LD) to quantify recombination using autocorrelation of linkage between mutations across time points. We validate RATS-LD on simulated data under short read sequencing conditions and then apply it to longitudinal, high-throughput intra-host viral sequencing data, stratifying populations by viral load (a proxy for density). Among sampled viral populations with the lowest viral loads (< 26,800 copies/mL), we estimate a recombination rate of 1.5 × 10-5 events/bp/generation (95% CI: 7 × 10-6 - 2.9 × 10-5), similar to existing estimates. However, among samples with the highest viral loads (> 82,000 copies/mL), our median estimate is approximately 6 times higher. In addition to co-varying across individuals, we also find that recombination rate and viral load are associated within single individuals across different time points. Our findings suggest that rather than acting as a constant, uniform force, recombination can vary dynamically and drastically across intra-host viral populations and within them over time. More broadly, we hypothesize that this phenomenon may affect other facultatively asexual populations where spatial co-localization varies.

A theory of oligogenic adaptation of a quantitative trait

Rapid phenotypic adaptation is widespread in nature, but the underlying genetic dynamics remain controversial. Whereas population genetics envisages sequential beneficial substitutions, quantitative genetics assumes a collective response through subtle shifts in allele frequencies. This dichotomy of a monogenic and a highly polygenic view of adaptation raises the question of a middle ground, as well as the factors controlling the transition. Here, we consider an additive quantitative trait with equal locus effects under Gaussian stabilizing selection that adapts to a new trait optimum after an environmental change. We present an analytical framework based on Yule branching processes to describe how phenotypic adaptation is achieved by collective changes in allele frequencies at the underlying loci. In particular, we derive an approximation for the joint allele-frequency distribution conditioned on the trait mean as a comprehensive descriptor of the adaptive architecture. Depending on the model parameters, this architecture reproduces the well-known patterns of sequential, monogenic sweeps, or of subtle, polygenic frequency shifts. Between these endpoints, we observe oligogenic architecture types that exhibit characteristic patterns of partial sweeps. We find that a single compound parameter, the population-scaled background mutation rate Θbg, is the most important predictor of the type of adaptation, while selection strength, the number of loci in the genetic basis, and linkage only play a minor role.

Agent-based methods facilitate integrative science in cancer

In this opinion, we highlight agent-based modeling as a key tool for exploration of cell-cell and cell-environment interactions that drive cancer progression, therapeutic resistance, and metastasis. These biological phenomena are particularly suited to be captured at the cell-scale resolution possible only within agent-based or individual-based mathematical models. These modeling approaches complement experimental work (in vitro and in vivo systems) through parameterization and data extrapolation but also feed forward to drive new experiments that test model-generated predictions.

Microbial experimental evolution in a massively multiplexed and high-throughput era

Experimental evolution with microbial model systems has transformed our understanding of the basic rules underlying ecology and evolution. Experiments leveraging evolution as a central feature put evolutionary theories to the test, and modern sequencing and engineering tools then characterized the molecular basis of adaptation. As theory and experimentations refined our understanding of evolution, a need to increase throughput and experimental complexity has emerged. Here, we summarize recent technologies that have made high-throughput experiments practical and highlight studies that have capitalized on these tools, defining an exciting new era in microbial experimental evolution. Multiple research directions previously limited by experimental scale are now accessible for study and we believe applying evolutionary lessons from in vitro studies onto these applied settings has the potential for major innovations and discoveries across ecology and medicine.

The role of spatial structure in multi-deme models of evolutionary rescue

Genetic variation and population sizes are critical factors for successful adaptation to novel environmental conditions. Gene flow between sub-populations is a potent mechanism to provide such variation and can hence facilitate adaptation, for instance by increasing genetic variation or via the introduction of beneficial variants. On the other hand, if gene flow between different habitats is too strong, locally beneficial alleles may not be able to establish permanently. In the context of evolutionary rescue, intermediate levels of gene flow are therefore often optimal for maximizing a species chance for survival in metapopulations without spatial structure. To which extent and under which conditions gene flow facilitates or hinders evolutionary rescue in spatially structured populations remains unresolved. We address this question by studying the differences between evolutionary rescue in the island model and in the stepping stone model in a gradually deteriorating habitat. We show that evolutionary rescue is modulated by the rate of gene flow between different habitats, which in turn depends strongly on the spatial structure and the pattern of environmental deterioration. We use these insights to show that in many cases spatially structured models can be translated into a simpler island model using an appropriately scaled effective migration rate.

Repeated origins, widespread gene flow, and allelic interactions of target-site herbicide resistance mutations

Causal mutations and their frequency in agricultural fields are well-characterized for herbicide resistance. However, we still lack understanding of their evolutionary history: the extent of parallelism in the origins of target-site resistance (TSR), how long these mutations persist, how quickly they spread, and allelic interactions that mediate their selective advantage. We addressed these questions with genomic data from 19 agricultural populations of common waterhemp (Amaranthus tuberculatus), which we show to have undergone a massive expansion over the past century, with a contemporary effective population size estimate of 8 x 10⁷. We found variation at seven characterized TSR loci, two of which had multiple amino acid substitutions, and three of which were common. These three common resistance variants show extreme parallelism in their mutational origins, with gene flow having shaped their distribution across the landscape. Allele age estimates supported a strong role of adaptation from de novo mutations, with a median age of 30 suggesting that most resistance alleles arose soon after the onset of herbicide use. However, resistant lineages varied in both their age and evidence for selection over two different timescales, implying considerable heterogeneity in the forces that govern their persistence. Two such forces are intra- and inter-locus allelic interactions; we report a signal of extended haplotype competition between two common TSR alleles, and extreme linkage with genome-wide alleles with known functions in resistance adaptation. Together, this work reveals a remarkable example of spatial parallel evolution in a metapopulation, with important implications for the management of herbicide resistance.

SIV Evolutionary Dynamics in Cynomolgus Macaques during SIV-Mycobacterium tuberculosis Co-Infection

Co-infection with Mycobacterium tuberculosis (Mtb) and human immunodeficiency virus (HIV) is a worldwide public health concern, leading to worse clinical outcomes caused by both pathogens. We used a non-human primate model of simian immunodeficiency virus (SIV)-Mtb co-infection, in which latent Mtb infection was established prior to SIV_mac251 infection. The evolutionary dynamics of SIV env was evaluated from samples in plasma, lymph nodes, and lungs (including granulomas) of SIV-Mtb co-infected and SIV only control animals. While the diversity of the challenge virus was low and overall viral diversity remained relatively low over 6–9 weeks, changes in viral diversity and divergence were observed, including evidence for tissue compartmentalization. Overall, viral diversity was highest in SIV-Mtb animals that did not develop clinical Mtb reactivation compared to animals with Mtb reactivation. Among lung granulomas, viral diversity was positively correlated with the frequency of CD4+ T cells and negatively correlated with the frequency of CD8+ T cells. SIV diversity was highest in the thoracic lymph nodes compared to other sites, suggesting that lymphatic drainage from the lungs in co-infected animals provides an advantageous environment for SIV replication. This is the first assessment of SIV diversity across tissue compartments during SIV-Mtb co-infection after established Mtb latency.

Understanding patterns of HIV multi-drug resistance through models of temporal and spatial drug heterogeneity

Triple-drug therapies have transformed HIV from a fatal condition to a chronic one. These therapies should prevent HIV drug resistance evolution, because one or more drugs suppress any partially resistant viruses. In practice, such therapies drastically reduced, but did not eliminate, resistance evolution. In this article, we reanalyze published data from an evolutionary perspective and demonstrate several intriguing patterns about HIV resistance evolution - resistance evolves (1) even after years on successful therapy, (2) sequentially, often via one mutation at a time and (3) in a partially predictable order. We describe how these observations might emerge under two models of HIV drugs varying in space or time. Despite decades of work in this area, much opportunity remains to create models with realistic parameters for three drugs, and to match model outcomes to resistance rates and genetic patterns from individuals on triple-drug therapy. Further, lessons from HIV may inform other systems.

Suppression gene drive in continuous space can result in unstable persistence of both drive and wild-type alleles

Rapid evolutionary processes can produce drastically different outcomes when studied in panmictic population models vs. spatial models. One such process is gene drive, which describes the spread of "selfish" genetic elements through a population. Engineered gene drives are being considered for the suppression of disease vectors or invasive species. While laboratory experiments and modelling in panmictic populations have shown that such drives can rapidly eliminate a population, it remains unclear if these results translate to natural environments where individuals inhabit a continuous landscape. Using spatially explicit simulations, we show that the release of a suppression drive can result in what we term "chasing" dynamics, in which wild-type individuals recolonize areas where the drive has locally eliminated the population. Despite the drive subsequently reconquering these areas, complete population suppression often fails to occur or is substantially delayed. This increases the likelihood that the drive is lost or that resistance evolves. We analyse how chasing dynamics are influenced by the type of drive, its efficiency, fitness costs, and ecological factors such as the maximal growth rate of the population and levels of dispersal and inbreeding. We find that chasing is more common for lower efficiency drives when dispersal is low and that some drive mechanisms are substantially more prone to chasing behaviour than others. Our results demonstrate that the population dynamics of suppression gene drives are determined by a complex interplay of genetic and ecological factors, highlighting the need for realistic spatial modelling to predict the outcome of drive releases in natural populations.

Rapid parallel adaptation despite gene flow in silent crickets

Gene flow is predicted to impede parallel adaptation via de novo mutation, because it can introduce pre-existing adaptive alleles from population to population. We test this using Hawaiian crickets (Teleogryllus oceanicus) in which 'flatwing' males that lack sound-producing wing structures recently arose and spread under selection from an acoustically-orienting parasitoid. Morphometric and genetic comparisons identify distinct flatwing phenotypes in populations on three islands, localized to different loci. Nevertheless, we detect strong, recent and ongoing gene flow among the populations. Using genome scans and gene expression analysis we find that parallel evolution of flatwing on different islands is associated with shared genomic hotspots of adaptation that contain the gene doublesex, but the form of selection differs among islands and corresponds to known flatwing demographics in the wild. We thus show how parallel adaptation can occur on contemporary timescales despite gene flow, indicating that it could be less constrained than previously appreciated.

Global adaptation complicates the interpretation of genome scans for local adaptation

Spatially varying selection promotes variance in allele frequencies, increasing genetic differentiation between the demes of a metapopulation. For that reason, outliers in the genome‐wide distribution of summary statistics measuring genetic differentiation, such as F_ST, are often interpreted as evidence for alleles that contribute to local adaptation. However, theoretical studies have shown that in spatially structured populations the spread of beneficial mutations with spatially uniform fitness effects can also induce transient genetic differentiation. In recent years, numerous empirical studies have suggested that such species‐wide, or global, adaptation makes a substantial contribution to molecular evolution. In this perspective, we discuss how commonly such global adaptation may influence the genome‐wide distribution of F_ST and generate genetic differentiation patterns, which could be mistaken for local adaptation. To illustrate this, we use forward‐in‐time population genetic simulations assuming parameters for the rate and strength of beneficial mutations consistent with estimates from natural populations. We demonstrate that the spread of globally beneficial mutations in parapatric populations may frequently generate F_ST outliers, which could be misinterpreted as evidence for local adaptation. The spread of beneficial mutations causes selective sweeps at flanking sites, so in some cases, the effects of global versus local adaptation may be distinguished by examining patterns of nucleotide diversity within and between populations in addition to F_ST. However, when local adaptation has been only recently established, it may be much more difficult to distinguish from global adaptation, due to less accumulation of linkage disequilibrium at flanking sites. Through our discussion, we conclude that a large fraction of F_ST outliers that are presumed to arise from local adaptation may instead be due to global adaptation.

Recurrent evolution of high virulence in isolated populations of a DNA virus

Hosts and viruses are constantly evolving in response to each other: as a host attempts to suppress a virus, the virus attempts to evade and suppress the host's immune system. Here, we describe the recurrent evolution of a virulent strain of a DNA virus, which infects multiple Drosophila species. Specifically, we identified two distinct viral types that differ 100-fold in viral titer in infected individuals, with similar differences observed in multiple species. Our analysis suggests that one of the viral types recurrently evolved at least four times in the past ~30,000 years, three times in Arizona and once in another geographically distinct species. This recurrent evolution may be facilitated by an effective mutation rate which increases as each prior mutation increases viral titer and effective population size. The higher titer viral type suppresses the host-immune system and an increased virulence compared to the low viral titer type.

Drug Resistance Evolution in HIV in the Late 1990s: Hard Sweeps, Soft Sweeps, Clonal Interference and the Accumulation of Drug Resistance Mutations

The evolution of drug resistance in pathogens such as HIV is an important and widely known example in the field of evolutionary medicine. Here, we focus on a unique data set from the late 1990s with multiple viral sequences from multiple time points in 118 patients. We study patterns of evolutionary dynamics in the viral populations in these patients who were treated with Reverse Transcriptase Inhibitors and Protease Inhibitors in the late 1990s. Specifically, we aim to visualize and analyze examples of population genetic processes such as selective sweeps and clonal interference. The figures and descriptions in this paper can be used in evolution and population genetics classes. We show and analyze a wide variety of patterns, specifically: soft sweeps, hard sweeps, softening sweeps and hardening sweeps, simultaneous sweeps, accumulation of mutations and clonal interference.