Transition between stochastic evolution and deterministic evolution in the presence of selection: general theory and application to virology

A computational framework for resolving the microbiome diversity conundrum

Recent empirical studies offer conflicting findings regarding the relation between host fitness and the composition of its microbiome, a conflict which we term 'the microbial β- diversity conundrum'. The microbiome is crucial for host wellbeing and survival. Surprisingly, different healthy individuals' microbiome compositions, even in the same population, often differ dramatically, contrary to the notion that a vital trait should be highly conserved. Moreover, gnotobiotic individuals exhibit highly deleterious phenotypes, supporting the view that the microbiome is paramount to host fitness. However, the introduction of almost arbitrarily selected microbiota into the system often achieves a significant rescue effect of the deleterious phenotypes. This is true even for microbiota from soil or phylogenetically distant host species, highlighting an apparent paradox. We suggest several solutions to the paradox using a computational framework, simulating the population dynamics of hosts and their microbiomes over multiple generations. The answers invoke factors such as host population size, the specific mode of microbial contribution to host fitness, and typical microbiome richness, offering solutions to the conundrum by highlighting scenarios where even when a host's fitness is determined in full by its microbiome composition, this composition has little effect on the natural selection dynamics of the population.

Genomic characterization of Alphacoronavirus from Mops condylurus bats in Nigeria

Coronaviruses (CoVs) are responsible for sporadic, epidemic and pandemic respiratory diseases worldwide. Bats have been identified as the reservoir for CoVs. To increase the number of complete coronavirus genomes in Africa and to comprehend the molecular epidemiology of bat Alphacoronaviruses (AlphaCoVs), we used deep metagenomics shotgun sequencing to obtain three (3) near-complete genomes of AlphaCoVs from Mops condylurus (Angolan free-tailed) bat in Nigeria. Phylogenetic and pairwise identity analysis of open reading frame 1ab (ORF1ab), spike (S), envelope (E), membrane (M) and nucleocapsid (N) genes of AlphaCoV in this study to previously described AlphaCoVs subgenera showed that the Nigerian AlphaCoVs may be members of potentially unique AlphaCoV subgenera circulating exclusively in bats in the Molossidae bat family. Recombination events were detected, suggesting the evolution of AlphaCoVs within the Molossidae family. The pairwise identity of the S gene in this study and previously published S gene sequences of other AlphaCoVs indicate that the Nigerian strains may have a genetically unique spike protein that is distantly related to other AlphaCoVs. Variations involving non-polar to polar amino acid substitution in both the Heptad Repeat (HR) regions 1 and 2 were observed. Further monitoring of bats to understand the host receptor use requirements of CoVs and interspecies CoV transmission in Africa is necessary to identify and prevent the potential danger that bat CoVs pose to public health.

Genetic stabilization of attenuated oral vaccines against poliovirus types 1 and 3

Vaccination with Sabin, a live attenuated oral polio vaccine (OPV), results in robust intestinal and humoral immunity and has been key to controlling poliomyelitis. As with any RNA virus, OPV evolves rapidly to lose attenuating determinants critical to the reacquisition of virulence1-3 resulting in vaccine-derived, virulent poliovirus variants. Circulation of these variants within underimmunized populations leads to further evolution of circulating, vaccine-derived poliovirus with higher transmission capacity, representing a significant risk of polio re-emergence. A new type 2 OPV (nOPV2), with promising clinical data on genetic stability and immunogenicity, recently received authorization from the World Health Organization for use in response to circulating, vaccine-derived poliovirus outbreaks. Here we report the development of two additional live attenuated vaccine candidates against type 1 and 3 polioviruses. The candidates were generated by replacing the capsid coding region of nOPV2 with that from Sabin 1 or 3. These chimeric viruses show growth phenotypes similar to nOPV2 and immunogenicity comparable to their parental Sabin strains, but are more attenuated. Our experiments in mice and deep sequencing analysis confirmed that the candidates remain attenuated and preserve all the documented nOPV2 characteristics concerning genetic stability following accelerated virus evolution. Importantly, these vaccine candidates are highly immunogenic in mice as monovalent and multivalent formulations and may contribute to poliovirus eradication.

Constrained disorder principle-based variability is fundamental for biological processes: Beyond biological relativity and physiological regulatory networks

The constrained disorder principle (CDP) defines systems based on their degree of disorder bounded by dynamic boundaries. The principle explains stochasticity in living and non-living systems. Denis Noble described the importance of stochasticity in biology, emphasizing stochastic processes at molecular, cellular, and higher levels in organisms as having a role beyond simple noise. The CDP and Noble's theories (NT) claim that biological systems use stochasticity. This paper presents the CDP and NT, discussing common notions and differences between the two theories. The paper presents the CDP-based concept of taking the disorder beyond its role in nature to correct malfunctions of systems and improve the efficiency of biological systems. The use of CDP-based algorithms embedded in second-generation artificial intelligence platforms is described. In summary, noise is inherent to complex systems and has a functional role. The CDP provides the option of using noise to improve functionality.

Evolution of highly pathogenic H5N1 influenza A virus in the central nervous system of ferrets

Central nervous system (CNS) disease is the most common extra-respiratory tract complication of influenza A virus infections in humans. Remarkably, zoonotic highly pathogenic avian influenza (HPAI) H5N1 virus infections are more often associated with CNS disease than infections with seasonal influenza viruses. Evolution of avian influenza viruses has been extensively studied in the context of respiratory infections, but evolutionary processes in CNS infections remain poorly understood. We have previously observed that the ability of HPAI A/Indonesia/5/2005 (H5N1) virus to replicate in and spread throughout the CNS varies widely between individual ferrets. Based on these observations, we sought to understand the impact of entrance into and replication within the CNS on the evolutionary dynamics of virus populations. First, we identified and characterized three substitutions-PB1 E177G and A652T and NP I119M - detected in the CNS of a ferret infected with influenza A/Indonesia/5/2005 (H5N1) virus that developed a severe meningo-encephalitis. We found that some of these substitutions, individually or collectively, resulted in increased polymerase activity in vitro. Nevertheless, in vivo, the virus bearing the CNS-associated mutations retained its capacity to infect the CNS but showed reduced dispersion to other anatomical sites. Analyses of viral diversity in the nasal turbinate and olfactory bulb revealed the lack of a genetic bottleneck acting on virus populations accessing the CNS via this route. Furthermore, virus populations bearing the CNS-associated mutations showed signs of positive selection in the brainstem. These features of dispersion to the CNS are consistent with the action of selective processes, underlining the potential for H5N1 viruses to adapt to the CNS.

Epidemic spreading under mutually independent intra- and inter-host pathogen evolution

The dynamics of epidemic spreading is often reduced to the single control parameter R₀ (reproduction-rate), whose value, above or below unity, determines the state of the contagion. If, however, the pathogen evolves as it spreads, R₀ may change over time, potentially leading to a mutation-driven spread, in which an initially sub-pandemic pathogen undergoes a breakthrough mutation. To predict the boundaries of this pandemic phase, we introduce here a modeling framework to couple the inter-host network spreading patterns with the intra-host evolutionary dynamics. We find that even in the extreme case when these two process are driven by mutually independent selection forces, mutations can still fundamentally alter the pandemic phase-diagram. The pandemic transitions, we show, are now shaped, not just by R₀, but also by the balance between the epidemic and the evolutionary timescales. If mutations are too slow, the pathogen prevalence decays prior to the appearance of a critical mutation. On the other hand, if mutations are too rapid, the pathogen evolution becomes volatile and, once again, it fails to spread. Between these two extremes, however, we identify a broad range of conditions in which an initially sub-pandemic pathogen can breakthrough to gain widespread prevalence.

Loss and fixation of strongly favoured new variants: Understanding and extending Haldane's result via the Wright-Fisher model

The implications of Haldane's analysis for the fixation of beneficial alleles lies at the heart of much of 'population genetic thinking' and underlies many approaches that have been tailored to the detection of positive selection. Within the framework of a branching process, Haldane gave an approximation for the probability that fixation ultimately occurs when the selective advantage of a beneficial allele is small (≪1). Here, we make no use of branching processes. Rather, we work solely within a finite-population Wright-Fisher framework. We use this framework to analyse where Haldane's result applies, and extend Haldane's analysis. In particular, we present results for: (i) the domain of applicability of Haldane's analysis; (ii) the probability that loss occurs up to a given time; (iii) the probabilities that loss and fixation ultimately occur; (iv) an analytic approximation associated with the probability of loss and fixation ultimately occurring; (v) quantification of the crossover from weak to strong selection; (vi) determination of the number of invasive alleles that have a significant probability (>0.95) of invading a novel population. We note that the results obtained for (ii), (iii) and (iv) hold for an arbitrary initial number of mutations, and for selection that can be arbitrarily strong. Our results have fundamental implications for population and conservation genetics, and open up new avenues to identify traces of historically beneficial alleles through comparative genomics.

Two Years of Evolutionary Dynamics of SARS-CoV-2 in Mexico, With Emphasis on the Variants of Concern

Background

The advance of the COVID-19 pandemic and spread of SARS-CoV-2 around the world has generated the emergence of new genomic variants. Those variants with possible clinical and therapeutic implications have been classified as variants of concern (VOCs) and variants of interest (VOIs).

Objective

This study aims to describe the COVID-19 pandemic and build the evolutionary and demographic dynamics of SARS-CoV-2 populations in Mexico, with emphasis on VOCs.

Methods

30,645 complete genomes of SARS-CoV-2 from Mexico were obtained from GISAID databases up to January 25, 2022. A lineage assignment and phylogenetic analysis was completed, and demographic history for Alpha, Gamma, Delta and Omicron VOCs, and the Mexican variant (B.1.1.519) was performed.

Results

148 variants were detected among the 30,645 genomes analyzed with the Delta variant being the most prevalent in the country, representing 49.7% of all genomes.

Conclusion

The COVID-19 pandemic in Mexico was caused by several introductions of SARS-CoV-2, mainly from the United States of America and Europe, followed by local transmission. Regional molecular epidemiological surveillance must implement to detect emergence, introductions and spread of new variants with biologically important mutations.

Intra- and inter-host evolution of H9N2 influenza A virus in Japanese quail

Influenza A viruses (IAVs) are constantly evolving. Crucial steps in the infection cycle, such as sialic acid (SA) receptor binding on the host cell surface, can either promote or hamper the emergence of new variants. We previously assessed the relative fitness in Japanese quail of H9N2 variant viruses differing at a single amino acid position, residue 216 in the hemagglutinin (HA) viral surface protein. This site is known to modulate SA recognition. Our prior study generated a valuable set of longitudinal samples from quail transmission groups where the inoculum comprised different mixed populations of HA 216 variant viruses. Here, we leveraged these samples to examine the evolutionary dynamics of viral populations within and between inoculated and naïve contact quails. We found that positive selection dominated HA gene evolution, but fixation of the fittest variant depended on the competition mixture. Analysis of the whole genome revealed further evidence of positive selection acting both within and between hosts. Positive selection drove fixation of variants in non-HA segments within inoculated and contact quails. Importantly, transmission bottlenecks were modulated by the molecular signature at HA 216, revealing viral receptor usage as a determinant of transmitted diversity. Overall, we show that selection strongly shaped the evolutionary dynamics within and between quails. These findings support the notion that selective processes act effectively on IAV populations in poultry hosts, facilitating rapid viral evolution in this ecological niche.

Predator-Prey Dynamics of Intra-Host Simian Immunodeficiency Virus Evolution Within the Untreated Host

The dynamic nature of the SIV population during disease progression in the SIV/macaque model of AIDS and the factors responsible for its behavior have not been documented, largely owing to the lack of sufficient spatial and temporal sampling of both viral and host data from SIV-infected animals. In this study, we detail Bayesian coalescent inference of the changing collective intra-host viral effective population size (N_e ) from various tissues over the course of infection and its relationship with what we demonstrate is a continuously changing immune cell repertoire within the blood. Although the relative contribution of these factors varied among hosts and time points, the adaptive immune response best explained the overall periodic dynamic behavior of the effective virus population. Data exposing the nature of the relationship between the virus and immune cell populations revealed the plausibility of an eco-evolutionary mathematical model, which was able to mimic the large-scale oscillations in N_e through virus escape from relatively few, early immunodominant responses, followed by slower escape from several subdominant and weakened immune populations. The results of this study suggest that SIV diversity within the untreated host is governed by a predator-prey relationship, wherein differing phases of infection are the result of adaptation in response to varying immune responses. Previous investigations into viral population dynamics using sequence data have focused on single estimates of the effective viral population size (N_e ) or point estimates over sparse sampling data to provide insight into the precise impact of immune selection on virus adaptive behavior. Herein, we describe the use of the coalescent phylogenetic frame- work to estimate the relative changes in N_e over time in order to quantify the relationship with empirical data on the dynamic immune composition of the host. This relationship has allowed us to expand on earlier simulations to build a predator-prey model that explains the deterministic behavior of the virus over the course of disease progression. We show that sequential viral adaptation can occur in response to phases of varying immune pressure, providing a broader picture of the viral response throughout the entire course of progression to AIDS.

Models of Plant Resistance Deployment

Owing to their evolutionary potential, plant pathogens are able to rapidly adapt to genetically controlled plant resistance, often resulting in resistance breakdown and major epidemics in agricultural crops. Various deployment strategies have been proposed to improve resistance management. Globally, these rely on careful selection of resistance sources and their combination at various spatiotemporal scales (e.g., via gene pyramiding, crop rotations and mixtures, landscape mosaics). However, testing and optimizing these strategies using controlled experiments at large spatiotemporal scales are logistically challenging. Mathematical models provide an alternative investigative tool, and many have been developed to explore resistance deployment strategies under various contexts. This review analyzes 69 modeling studies in light of specific model structures (e.g., demographic or demogenetic, spatial or not), underlying assumptions (e.g., whether preadapted pathogens are present before resistance deployment), and evaluation criteria (e.g., resistance durability, disease control, cost-effectiveness). It highlights major research findings and discusses challenges for future modeling efforts.

The Evolution of Severe Acute Respiratory Syndrome Coronavirus-2 during Pandemic and Adaptation to the Host

Severe Acute Respiratory Syndrome Coronavirus-2 is a zoonotic virus with a possible origin in bats and potential transmission to humans through an intermediate host. When zoonotic viruses jump to a new host, they undergo both mutational and natural selective pressures that result in non-synonymous and synonymous adaptive changes, necessary for efficient replication and rapid spread of diseases in new host species. The nucleotide composition and codon usage pattern of SARS-CoV-2 indicate the presence of a highly conserved, gene-specific codon usage bias. The codon usage pattern of SARS-CoV-2 is mostly antagonistic to human and bat codon usage. SARS-CoV-2 codon usage bias is mainly shaped by the natural selection, while mutational pressure plays a minor role. The time-series analysis of SARS-CoV-2 genome indicates that the virus is slowly evolving. Virus isolates from later stages of the outbreak have more biased codon usage and nucleotide composition than virus isolates from early stages of the outbreak.

Risk of rapid evolutionary escape from biomedical interventions targeting SARS-CoV-2 spike protein

The spike protein receptor-binding domain (RBD) of SARS-CoV-2 is the molecular target for many vaccines and antibody-based prophylactics aimed at bringing COVID-19 under control. Such a narrow molecular focus raises the specter of viral immune evasion as a potential failure mode for these biomedical interventions. With the emergence of new strains of SARS-CoV-2 with altered transmissibility and immune evasion potential, a critical question is this: how easily can the virus escape neutralizing antibodies (nAbs) targeting the spike RBD? To answer this question, we combined an analysis of the RBD structure-function with an evolutionary modeling framework. Our structure-function analysis revealed that epitopes for RBD-targeting nAbs overlap one another substantially and can be evaded by escape mutants with ACE2 affinities comparable to the wild type, that are observed in sequence surveillance data and infect cells in vitro. This suggests that the fitness cost of nAb-evading mutations is low. We then used evolutionary modeling to predict the frequency of immune escape before and after the widespread presence of nAbs due to vaccines, passive immunization or natural immunity. Our modeling suggests that SARS-CoV-2 mutants with one or two mildly deleterious mutations are expected to exist in high numbers due to neutral genetic variation, and consequently resistance to vaccines or other prophylactics that rely on one or two antibodies for protection can develop quickly -and repeatedly- under positive selection. Predicted resistance timelines are comparable to those of the decay kinetics of nAbs raised against vaccinal or natural antigens, raising a second potential mechanism for loss of immunity in the population. Strategies for viral elimination should therefore be diversified across molecular targets and therapeutic modalities.

The evolutionary origin of the universal distribution of mutation fitness effect

An intriguing fact long defying explanation is the observation of a universal exponential distribution of beneficial mutations in fitness effect for different microorganisms. To explain this effect, we use a population model including mutation, directional selection, linkage, and genetic drift. The multiple-mutation regime of adaptation at large population sizes (traveling wave regime) is considered. We demonstrate analytically and by simulation that, regardless of the inherent distribution of mutation fitness effect across genomic sites, an exponential distribution of fitness effects emerges in the long term. This result follows from the exponential statistics of the frequency of the less-fit alleles, f, that we predict to evolve, in the long term, for both polymorphic and monomorphic sites. We map the logarithmic slope of the distribution onto the previously derived fixation probability and demonstrate that it increases linearly in time. Our results demonstrate a striking difference between the distribution of fitness effects observed experimentally for naturally occurring mutations, and the "inherent" distribution obtained in a directed-mutagenesis experiment, which can have any shape depending on the organism. Based on these results, we develop a new method to measure the fitness effect of mutations for each variable residue using DNA sequences sampled from adapting populations. This new method is not sensitive to linkage effects and does not require the one-site model assumptions.

The HIV-1 latent reservoir is largely sensitive to circulating T cells

HIV-1-specific CD8+ T cells are an important component of HIV-1 curative strategies. Viral variants in the HIV-1 reservoir may limit the capacity of T cells to detect and clear virus-infected cells. We investigated the patterns of T cell escape variants in the replication-competent reservoir of 25 persons living with HIV-1 (PLWH) durably suppressed on antiretroviral therapy (ART). We identified all reactive T cell epitopes in the HIV-1 proteome for each participant and sequenced HIV-1 outgrowth viruses from resting CD4+ T cells. All non-synonymous mutations in reactive T cell epitopes were tested for their effect on the size of the T cell response, with a≥50% loss defined as an escape mutation. The majority (68%) of T cell epitopes harbored no detectable escape mutations. These findings suggest that circulating T cells in PLWH on ART could contribute to control of rebound and could be targeted for boosting in curative strategies.

Adapt or Perish: Evolutionary Rescue in a Gradually Deteriorating Environment

We investigate the evolutionary rescue of a microbial population in a gradually deteriorating environment, through a combination of analytical calculations and stochastic simulations. We consider a population destined for extinction in the absence of mutants, which can survive only if mutants sufficiently adapted to the new environment arise and fix. We show that mutants that appear later during the environment deterioration have a higher probability to fix. The rescue probability of the population increases with a sigmoidal shape when the product of the carrying capacity and of the mutation probability increases. Furthermore, we find that rescue becomes more likely for smaller population sizes and/or mutation probabilities if the environment degradation is slower, which illustrates the key impact of the rapidity of environment degradation on the fate of a population. We also show that our main conclusions are robust across various types of adaptive mutants, including specialist and generalist ones, as well as mutants modeling antimicrobial resistance evolution. We further express the average time of appearance of the mutants that do rescue the population and the average extinction time of those that do not. Our methods can be applied to other situations with continuously variable fitnesses and population sizes, and our analytical predictions are valid in the weak-to-moderate mutation regime.

A large effective population size for established within-host influenza virus infection

Strains of the influenza virus form coherent global populations, yet exist at the level of single infections in individual hosts. The relationship between these scales is a critical topic for understanding viral evolution. Here we investigate the within-host relationship between selection and the stochastic effects of genetic drift, estimating an effective population size of infection Ne for influenza infection. Examining whole-genome sequence data describing a chronic case of influenza B in a severely immunocompromised child we infer an Ne of 2.5 × 107 (95% confidence range 1.0 × 107 to 9.0 × 107) suggesting that genetic drift is of minimal importance during an established influenza infection. Our result, supported by data from influenza A infection, suggests that positive selection during within-host infection is primarily limited by the typically short period of infection. Atypically long infections may have a disproportionate influence upon global patterns of viral evolution.

Within-Host Viral Diversity: A Window into Viral Evolution

The evolutionary dynamics of a virus can differ within hosts and across populations. Studies of within-host evolution provide an important link between experimental studies of virus evolution and large-scale phylodynamic analyses. They can determine the extent to which global processes are recapitulated on local scales and how accurately experimental infections model natural ones. They may also inform epidemiologic models of disease spread and reveal how host-level dynamics contribute to a virus's evolution at a larger scale. Over the last decade, advances in viral sequencing have enabled detailed studies of viral genetic diversity within hosts. I review how within-host diversity is sampled, measured, and expressed, and how comparative studies of viral diversity can be leveraged to elucidate a virus's evolutionary dynamics. These concepts are illustrated with detailed reviews of recent research on the within-host evolution of influenza virus, dengue virus, and cytomegalovirus.

A de novo approach to inferring within-host fitness effects during untreated HIV-1 infection

In the absence of effective antiviral therapy, HIV-1 evolves in response to the within-host environment, of which the immune system is an important aspect. During the earliest stages of infection, this process of evolution is very rapid, driven by a small number of CTL escape mutations. As the infection progresses, immune escape variants evolve under reduced magnitudes of selection, while competition between an increasing number of polymorphic alleles (i.e., clonal interference) makes it difficult to quantify the magnitude of selection acting upon specific variant alleles. To tackle this complex problem, we developed a novel multi-locus inference method to evaluate the role of selection during the chronic stage of within-host infection. We applied this method to targeted sequence data from the p24 and gp41 regions of HIV-1 collected from 34 patients with long-term untreated HIV-1 infection. We identify a broad distribution of beneficial fitness effects during infection, with a small number of variants evolving under strong selection and very many variants evolving under weaker selection. The uniquely large number of infections analysed granted a previously unparalleled statistical power to identify loci at which selection could be inferred to act with statistical confidence. Our model makes no prior assumptions about the nature of alleles under selection, such that any synonymous or non-synonymous variant may be inferred to evolve under selection. However, the majority of variants inferred with confidence to be under selection were non-synonymous in nature, and in most cases were have previously been associated with either CTL escape in p24 or neutralising antibody escape in gp41. We also identified a putative new CTL escape site (residue 286 in gag), and a region of gp41 (including residues 644, 648, 655 in env) likely to be associated with immune escape. Sites inferred to be under selection in multiple hosts have high within-host and between-host diversity although not all sites with high between-host diversity were inferred to be under selection at the within-host level. Our identification of selection at sites associated with resistance to broadly neutralising antibodies (bNAbs) highlights the need to fully understand the role of selection in untreated individuals when designing bNAb based therapies.

Metagenomic analysis of an urban resistome before and after wastewater treatment

Determining the effect of wastewater treatment in water resistome is a topic of interest for water quality, mainly under re-use and One-Health perspectives. The resistome, the plasmidome, and the bacterial community composition of samples from influents and treated effluents from a wastewater treatment plant located in Northern Portugal were studied using metagenomic techniques. Wastewater treatment contributed to reduce the abundance of resistance genes and of plasmid replicons, coinciding with a decline in the number of intrinsic resistance genes from Enterobacteriaceae, as well as with a reduction in the relative abundance of Firmicutes and Proteobacteria after treatment. These taxons comprise bacterial pathogens, including those belonging to the ESKAPE group, which encompasses bacteria with the highest risk of acquiring antibiotic resistance, being the most relevant hosts of resistance genes acquired through horizontal gene transfer. Our results support that wastewater treatment efficiently removes the hosts of antibiotic resistance genes and, consequently, the harboured antibiotic resistance genes. Principal component analysis indicates that the resistome and the bacterial composition clustered together in influent samples, while did not cluster in final effluent samples. Our results suggest that wastewater treatment mitigates the environmental dissemination of urban resistome, through the removal of the hosts harbouring mobile resistance genes.

Engineering the Live-Attenuated Polio Vaccine to Prevent Reversion to Virulence

The live-attenuated oral poliovirus vaccine (OPV or Sabin vaccine) replicates in gut-associated tissues, eliciting mucosa and systemic immunity. OPV protects from disease and limits poliovirus spread. Accordingly, vaccination with OPV is the primary strategy used to end the circulation of all polioviruses. However, the ability of OPV to regain replication fitness and establish new epidemics represents a significant risk of polio re-emergence should immunization cease. Here, we report the development of a poliovirus type 2 vaccine strain (nOPV2) that is genetically more stable and less likely to regain virulence than the original Sabin2 strain. We introduced modifications within at the 5' untranslated region of the Sabin2 genome to stabilize attenuation determinants, 2C coding region to prevent recombination, and 3D polymerase to limit viral adaptability. Prior work established that nOPV2 is immunogenic in preclinical and clinical studies, and thus may enable complete poliovirus eradication.

Evolutionary landscapes of Pseudomonas aeruginosa towards ribosome-targeting antibiotic resistance depend on selection strength

It is generally accepted that antibiotic-resistant mutants are selected in a range of concentrations ranging from the minimum inhibitory concentration (MIC) to the mutant preventive concentration. More recently, it has been found that antibiotic-resistant mutants can also be selected at concentrations below MIC, which expands the conditions where this selection may occur. Using experimental evolution approaches followed by whole-genome sequencing, the current study compares the evolutionary trajectories of Pseudomonas aeruginosa in the presence of tobramycin or tigecycline at lethal and sublethal concentrations. Mutants were selected at sublethal concentrations of tigecycline (1/10 and 1/50 MIC), whereas no mutants were selected in the case of tobramycin, indicating that the width of sub-MIC selective windows is antibiotic-specific. In addition, the patterns of evolution towards tigecycline resistance depend on selection strength. Sublethal concentrations of tigecycline select mutants with lower tigecycline MICs and higher MICs to other antibiotics belonging to different structural families than mutants selected under lethal concentrations. This indicates that the strength of the cross-resistance phenotype associated with tigecycline resistance is decoupled from selection strength. Accurate information on the sublethal selection window for each antibiotic of clinical value, including the phenotypes of cross-resistance of mutants selected at each antibiotic concentration, is needed to understand the role of ecosystems polluted with different antibiotic concentrations in the selection of antibiotic resistance. Integration of this information into clinical and environmental safety controls may help to tackle the problem of antibiotic resistance.

Virus epidemics, plant-controlled population bottlenecks and the durability of plant resistance

Plant qualitative resistances to viruses are natural exhaustible resources that can be impaired by the emergence of resistance-breaking (RB) virus variants. Mathematical modelling can help determine optimal strategies for resistance durability by a rational deployment of resistance in agroecosystems. Here, we propose an innovative approach, built up from our previous empirical studies, based on plant cultivars combining qualitative resistance with quantitative resistance narrowing population bottlenecks exerted on viruses during host-to-host transmission and/or within-host infection. Narrow bottlenecks are expected to slow down virus adaptation to plant qualitative resistance. To study the effect of bottleneck size on yield, we developed a stochastic epidemic model with mixtures of susceptible and resistant plants, relying on continuous-time Markov chain processes. Overall, narrow bottlenecks are beneficial when the fitness cost of RB virus variants in susceptible plants is intermediate. In such cases, they could provide up to 95 additional percentage points of yield compared with deploying a qualitative resistance alone. As we have shown in previous works that virus population bottlenecks are at least partly heritable plant traits, our results suggest that breeding and deploying plant varieties exposing virus populations to narrowed bottlenecks will increase yield and delay the emergence of RB variants. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

Resist or perish: Fate of a microbial population subjected to a periodic presence of antimicrobial

The evolution of antimicrobial resistance can be strongly affected by variations of antimicrobial concentration. Here, we study the impact of periodic alternations of absence and presence of antimicrobial on resistance evolution in a microbial population, using a stochastic model that includes variations of both population composition and size, and fully incorporates stochastic population extinctions. We show that fast alternations of presence and absence of antimicrobial are inefficient to eradicate the microbial population and strongly favor the establishment of resistance, unless the antimicrobial increases enough the death rate. We further demonstrate that if the period of alternations is longer than a threshold value, the microbial population goes extinct upon the first addition of antimicrobial, if it is not rescued by resistance. We express the probability that the population is eradicated upon the first addition of antimicrobial, assuming rare mutations. Rescue by resistance can happen either if resistant mutants preexist, or if they appear after antimicrobial is added to the environment. Importantly, the latter case is fully prevented by perfect biostatic antimicrobials that completely stop division of sensitive microorganisms. By contrast, we show that the parameter regime where treatment is efficient is larger for biocidal drugs than for biostatic drugs. This sheds light on the respective merits of different antimicrobial modes of action.

A percolation model of natural selection

A new approach has been proposed and developed: the selection of optimal variants in the evolutionary mutation flow is considered as an analogue of a percolation filter. Interaction of mutations in a series of generations and random processes of drift determine the collective behavior of nodes (individuals - carriers and converters of mutations) and bonds (mutations) in the space of percolation lattice. It is shown that the choice of the development trajectory at the population level depends on the spectrum of supporting and prohibiting mutations under the influence of conjugate deterministic and random factors. From the point of view of the fluctuation-bifurcation process, new concepts of the lower and upper thresholds of the percolation selection grid are defined in the hierarchical structure of speciation. The upper threshold determines the state of self-organized criticality, which, when overcome, leads to irreversible self-organization processes in the population caused by the accumulation of mutations.

Impact of genetic drift, selection and accumulation level on virus adaptation to its host plants

The efficiency of plant major resistance genes is limited by the emergence and spread of resistance-breaking mutants. Modulation of the evolutionary forces acting on pathogen populations constitutes a promising way to increase the durability of these genes. We studied the effect of four plant traits affecting these evolutionary forces on the rate of resistance breakdown (RB) by a virus. Two of these traits correspond to virus effective population sizes (N_e ) at either plant inoculation or during infection. The third trait corresponds to differential selection exerted by the plant on the virus population. Finally, the fourth trait corresponds to within-plant virus accumulation (VA). These traits were measured experimentally on Potato virus Y (PVY) inoculated to a set of 84 pepper doubled-haploid lines, all carrying the same pvr2³ resistance gene, but having contrasting genetic backgrounds. The lines showed extensive variation for the rate of pvr2³ RB by PVY and for the four other traits of interest. A generalized linear model showed that three of these four traits, with the exception of N_e at inoculation, and several pairwise interactions between them had significant effects on RB. RB increased with increasing values of N_e during plant infection or VA. The effect of differential selection was more complex because of a strong interaction with VA. When VA was high, RB increased as the differential selection increased. An opposite relationship between RB and differential selection was observed when VA was low. This study provides a framework to select plants with appropriate virus evolution-related traits to avoid or delay RB.

A novel framework for inferring parameters of transmission from viral sequence data

Transmission between hosts is a critical part of the viral lifecycle. Recent studies of viral transmission have used genome sequence data to evaluate the number of particles transmitted between hosts, and the role of selection as it operates during the transmission process. However, the interpretation of sequence data describing transmission events is a challenging task. We here present a novel and comprehensive framework for using short-read sequence data to understand viral transmission events, designed for influenza virus, but adaptable to other viral species. Our approach solves multiple shortcomings of previous methods for this purpose; for example, we consider transmission as an event involving whole viruses, rather than sets of independent alleles. We demonstrate how selection during transmission and noisy sequence data may each affect naive inferences of the population bottleneck, accounting for these in our framework so as to achieve a correct inference. We identify circumstances in which selection for increased viral transmission may or may not be identified from data. Applying our method to experimental data in which transmission occurs in the presence of strong selection, we show that our framework grants a more quantitative insight into transmission events than previous approaches, inferring the bottleneck in a manner that accounts for selection, both for within-host virulence, and for inherent viral transmissibility. Our work provides new opportunities for studying transmission processes in influenza, and by extension, in other infectious diseases.

Evolutionary footprint of epistasis

Variation of an inherited trait across a population cannot be explained by additive contributions of relevant genes, due to epigenetic effects and biochemical interactions (epistasis). Detecting epistasis in genomic data still represents a significant challenge that requires a better understanding of epistasis from the mechanistic point of view. Using a standard Wright-Fisher model of bi-allelic asexual population, we study how compensatory epistasis affects the process of adaptation. The main result is a universal relationship between four haplotype frequencies of a single site pair in a genome, which depends only on the epistasis strength of the pair defined regarding Darwinian fitness. We demonstrate the existence, at any time point, of a quasi-equilibrium between epistasis and disorder (entropy) caused by random genetic drift and mutation. We verify the accuracy of these analytic results by Monte-Carlo simulation over a broad range of parameters, including the topology of the interacting network. Thus, epistasis assists the evolutionary transit through evolutionary hurdles leaving marks at the level of haplotype disequilibrium. The method allows determining selection coefficient for each site and the epistasis strength of each pair from a sequence set. The resulting ability to detect clusters of deleterious mutation close to full compensation is essential for biomedical applications. These findings help to understand the role of epistasis in multiple compensatory mutations in viral resistance to antivirals and immune response.

Antigenic evolution of viruses in host populations

To escape immune recognition in previously infected hosts, viruses evolve genetically in immunologically important regions. The host’s immune system responds by generating new memory cells recognizing the mutated viral strains. Despite recent advances in data collection and analysis, it remains conceptually unclear how epidemiology, immune response, and evolutionary factors interact to produce the observed speed of evolution and the incidence of infection. Here we establish a general and simple relationship between long-term cross-immunity, genetic diversity, speed of evolution, and incidence. We develop an analytic method fusing the standard epidemiological susceptible-infected-recovered approach and the modern virus evolution theory. The model includes the factors of strain selection due to immune memory cells, random genetic drift, and clonal interference effects. We predict that the distribution of recovered individuals in memory serotypes creates a moving fitness landscape for the circulating strains which drives antigenic escape. The fitness slope (effective selection coefficient) is proportional to the reproductive number in the absence of immunity R₀ and inversely proportional to the cross-immunity distance a, defined as the genetic distance of a virus strain from a previously infecting strain conferring 50% decrease in infection probability. Analysis predicts that the evolution rate increases linearly with the fitness slope and logarithmically with the genomic mutation rate and the host population size. Fitting our analytic model to data obtained for influenza A H3N2 and H1N1, we predict the annual infection incidence within a previously estimated range, (4-7)%, and the antigenic mutation rate of U_b = (5 − 8) ⋅ 10⁻⁴ per transmission event per genome. Our prediction of the cross-immunity distance of a = (14 − 15) aminoacid substitutions agrees with independent data for equine influenza.

Spread of many RNA viruses in a population represents a competition between host immune responses and viral evolution. RNA viruses accumulate mutations in immunologically important regions to escape immune recognition in hosts previously exposed to infection, while the immune system responds by producing new memory cells. Despite recent advances in data collection and their analysis, it remains conceptually unclear how epidemiology, immune response, and evolutionary factors interact to produce the observed speed of evolution and its incidence. By combining the standard epidemiological approach with the modern theory of viral evolution, we predict a general relationship between long-term cross-immunity, antigenic diversity of virus, its evolution speed, infection incidence, and the time to the most recent common ancestor. We apply these theoretical findings to available data on influenza virus to determine two important parameters of its evolution and confirm the model. Current strategies of vaccination against influenza should take into account stochastic fluctuations in fitness effect of mutations predicted by the theory.

Quantifying the impact of a periodic presence of antimicrobial on resistance evolution in a homogeneous microbial population of fixed size

The evolution of antimicrobial resistance generally occurs in an environment where antimicrobial concentration is variable, which has dramatic consequences on the microorganisms' fitness landscape, and thus on the evolution of resistance. We investigate the effect of these time-varying patterns of selection within a stochastic model. We consider a homogeneous microbial population of fixed size subjected to periodic alternations of phases of absence and presence of an antimicrobial that stops growth. Combining analytical approaches and stochastic simulations, we quantify how the time necessary for fit resistant bacteria to take over the microbial population depends on the alternation period. We demonstrate that fast alternations strongly accelerate the evolution of resistance, reaching a plateau for sufficiently small periods. Furthermore, this acceleration is stronger in larger populations. For asymmetric alternations, featuring a different duration of the phases with and without antimicrobial, we shed light on the existence of a minimum for the time taken by the population to fully evolve resistance. The corresponding dramatic acceleration of the evolution of antimicrobial resistance likely occurs in realistic situations, and may have an important impact both in clinical and experimental situations.

Within-patient mutation frequencies reveal fitness costs of CpG dinucleotides and drastic amino acid changes in HIV

HIV has a high mutation rate, which contributes to its ability to evolve quickly. However, we know little about the fitness costs of individual HIV mutations in vivo, their distribution and the different factors shaping the viral fitness landscape. We calculated the mean frequency of transition mutations at 870 sites of the pol gene in 160 patients, allowing us to determine the cost of these mutations. As expected, we found high costs for non-synonymous and nonsense mutations as compared to synonymous mutations. In addition, we found that non-synonymous mutations that lead to drastic amino acid changes are twice as costly as those that do not and mutations that create new CpG dinucleotides are also twice as costly as those that do not. We also found that G→A and C→T mutations are more costly than A→G mutations. We anticipate that our new in vivo frequency-based approach will provide insights into the fitness landscape and evolvability of not only HIV, but a variety of microbes.

Inferring Fitness Effects from Time-Resolved Sequence Data with a Delay-Deterministic Model

A broad range of approaches have considered the challenge of inferring selection from time-resolved genome sequence data. Models describing deterministic changes in allele or haplotype frequency have been highlighted as providing accurate and computationally...

A common challenge arising from the observation of an evolutionary system over time is to infer the magnitude of selection acting upon a specific genetic variant, or variants, within the population. The inference of selection may be confounded by the effects of genetic drift in a system, leading to the development of inference procedures to account for these effects. However, recent work has suggested that deterministic models of evolution may be effective in capturing the effects of selection even under complex models of demography, suggesting the more general application of deterministic approaches to inference. Responding to this literature, we here note a case in which a deterministic model of evolution may give highly misleading inferences, resulting from the nondeterministic properties of mutation in a finite population. We propose an alternative approach that acts to correct for this error, and which we denote the delay-deterministic model. Applying our model to a simple evolutionary system, we demonstrate its performance in quantifying the extent of selection acting within that system. We further consider the application of our model to sequence data from an evolutionary experiment. We outline scenarios in which our model may produce improved results for the inference of selection, noting that such situations can be easily identified via the use of a regular deterministic model.

Quasispecies and virus

Quasispecies theory has been instrumental in the understanding of RNA virus population dynamics because it considered for the first time mutation as an integral part of the replication process. The key influences of quasispecies theory on experimental virology have been: (1) to disclose the mutant spectrum nature of viral populations and to evaluate its consequences; (2) to unveil collective properties of genome ensembles that can render a mutant spectrum a unit of selection; and (3) to identify new vulnerability points of pathogenic RNA viruses on three fronts: the need to apply multiple selective constraints (in the form of drug combinations) to minimize selection of treatment-escape variants, to translate the error threshold concept into antiviral designs, and to construct attenuated vaccine viruses through alterations of viral polymerase copying fidelity or through displacements of viral genomes towards unfavorable regions of sequence space. These three major influences on the understanding of viral pathogens preceded extensions of quasispecies to non-viral systems such as bacterial and tumor cell collectivities and prions. These developments are summarized here.

Evaluating genetic drift in time-series evolutionary analysis

The Wright-Fisher model is the most popular population model for describing the behaviour of evolutionary systems with a finite population size. Approximations have commonly been used but the model itself has rarely been tested against time-resolved genomic data. Here, we evaluate the extent to which it can be inferred as the correct model under a likelihood framework. Given genome-wide data from an evolutionary experiment, we validate the Wright-Fisher drift model as the better option for describing evolutionary trajectories in a finite population. This was found by evaluating its performance against a Gaussian model of allele frequency propagation. However, we note a range of circumstances under which standard Wright-Fisher drift cannot be correctly identified.

Generalization of the Ewens sampling formula to arbitrary fitness landscapes

In considering evolution of transcribed regions, regulatory sequences, and other genomic loci, we are often faced with a situation in which the number of allelic states greatly exceeds the size of the population. In this limit, the population eventually adopts a steady state characterized by mutation-selection-drift balance. Although new alleles continue to be explored through mutation, the statistics of the population, and in particular the probabilities of seeing specific allelic configurations in samples taken from the population, do not change with time. In the absence of selection, the probabilities of allelic configurations are given by the Ewens sampling formula, widely used in population genetics to detect deviations from neutrality. Here we develop an extension of this formula to arbitrary fitness distributions. Although our approach is general, we focus on the class of fitness landscapes, inspired by recent high-throughput genotype-phenotype maps, in which alleles can be in several distinct phenotypic states. This class of landscapes yields sampling probabilities that are computationally more tractable and can form a basis for inference of selection signatures from genomic data. Using an efficient numerical implementation of the sampling probabilities, we demonstrate that, for a sizable range of mutation rates and selection coefficients, the steady-state allelic diversity is not neutral. Therefore, it may be used to infer selection coefficients, as well as other evolutionary parameters from population data. We also carry out numerical simulations to challenge various approximations involved in deriving our sampling formulas, such as the infinite-allele limit and the “full connectivity” assumption inherent in the Ewens theory, in which each allele can mutate into any other allele. We find that, at least for the specific numerical examples studied, our theory remains sufficiently accurate even if these assumptions are relaxed. Thus our framework establishes both theoretical and practical foundations for inferring selection signatures from population-level genomic sequence samples.

Estimating virus effective population size and selection without neutral markers

By combining high-throughput sequencing (HTS) with experimental evolution, we can observe the within-host dynamics of pathogen variants of biomedical or ecological interest. We studied the evolutionary dynamics of five variants of Potato virus Y (PVY) in 15 doubled-haploid lines of pepper. All plants were inoculated with the same mixture of virus variants and variant frequencies were determined by HTS in eight plants of each pepper line at each of six sampling dates. We developed a method for estimating the intensities of selection and genetic drift in a multi-allelic Wright-Fisher model, applicable whether these forces are strong or weak, and in the absence of neutral markers. This method requires variant frequency determination at several time points, in independent hosts. The parameters are the selection coefficients for each PVY variant and four effective population sizes N_e at different time-points of the experiment. Numerical simulations of asexual haploid Wright-Fisher populations subjected to contrasting genetic drift (N_e ∈ [10, 2000]) and selection (|s| ∈ [0, 0.15]) regimes were used to validate the method proposed. The experiment in closely related pepper host genotypes revealed that viruses experienced a considerable diversity of selection and genetic drift regimes. The resulting variant dynamics were accurately described by Wright-Fisher models. The fitness ranks of the variants were almost identical between host genotypes. By contrast, the dynamics of N_e were highly variable, although a bottleneck was often identified during the systemic movement of the virus. We demonstrated that, for a fixed initial PVY population, virus effective population size is a heritable trait in plants. These findings pave the way for the breeding of plant varieties exposing viruses to stronger genetic drift, thereby slowing virus adaptation.

A growing number of experimental evolution studies are using an “evolve-and-resequence” approach to observe the within-host dynamics of pathogen variants of biomedical or ecological interest. The resulting data are particularly appropriate for studying the effects of evolutionary forces, such as selection and genetic drift, on the emergence of new pathogen variants. However, it remains challenging to unravel the effects of selection and genetic drift in the absence of neutral markers, a situation frequently encountered for microbes, such as viruses, due to their small constrained genomes. Using such an approach on a plant virus, we observed that the same set of virus variants displayed highly diverse dynamics in closely related plant genotypes. We developed and validated a method that does not require neutral markers, for estimating selection coefficients and effective population sizes from these experimental evolution data. We found that the viruses experienced considerable diversity in genetic drift regimes, depending on host genotype. Importantly, genetic drift experienced by virus populations was shown to be a heritable plant trait. These findings pave the way for the breeding of plant varieties exposing viruses to strong genetic drift, thereby slowing virus adaptation.

Genetic bottlenecks in intraspecies virus transmission

Ultimately, viral evolution is a consequence of mutations that arise within and spread between infected hosts. The transmission bottleneck determines how much of the viral diversity generated in one host passes to another during transmission. It therefore plays a vital role in linking within-host processes to larger evolutionary trends. Although many studies suggest that transmission severely restricts the amount of genetic diversity that passes between individuals, there are important exceptions to this rule. In many cases, the factors that determine the size of the transmission bottleneck are only beginning to be understood. Here, we review how transmission bottlenecks are measured, how they arise, and their consequences for viral evolution.

Quantitative trait loci in pepper control the effective population size of two RNA viruses at inoculation

Infection of plants by viruses is a complex process involving several steps: inoculation into plant cells, replication in inoculated cells and plant colonization. The success of the different steps depends, in part, on the viral effective population size (Ne), defined as the number of individuals passing their genes to the next generation. During infection, the virus population will undergo bottlenecks, leading to drastic reductions in Ne and, potentially, to the loss of the fittest variants. Therefore, it is crucial to better understand how plants affect Ne. We aimed to (i) identify the plant genetic factors controlling Ne during inoculation, (ii) understand the mechanisms used by the plant to control Ne and (iii) compare these genetic factors with the genes controlling plant resistance to viruses. Ne was measured in a doubled-haploid population of Capsicum annuum. Plants were inoculated with either a Potato virus Y (PVY) construct expressing the green fluorescent protein or a necrotic variant of Cucumber mosaic virus (CMV). Newas assessed by counting the number of primary infection foci on cotyledons for PVY or the number of necrotic local lesions on leaves for CMV. The number of foci and lesions was correlated (r=0.57) and showed a high heritability (h2=0.93 for PVY and h2=0.98 for CMV). The Ne of the two viruses was controlled by both common quantitative trait loci (QTLs) and virus-specific QTLs, indicating the contribution of general and specific mechanisms. The PVY-specific QTL colocalizes with a QTL that reduces PVY accumulation and the capacity to break down a major-effect resistance gene.

The Evolutionary Pathway to Virulence of an RNA Virus

Paralytic polio once afflicted almost half a million children each year. The attenuated oral polio vaccine (OPV) has enabled world-wide vaccination efforts, which resulted in nearly complete control of the disease. However, poliovirus eradication is hampered globally by epidemics of vaccine-derived polio. Here, we describe a combined theoretical and experimental strategy that describes the molecular events leading from OPV to virulent strains. We discover that similar evolutionary events occur in most epidemics. The mutations and the evolutionary trajectories driving these epidemics are replicated using a simple cell-based experimental setup where the rate of evolution is intentionally accelerated. Furthermore, mutations accumulating during epidemics increase the replication fitness of the virus in cell culture and increase virulence in an animal model. Our study uncovers the evolutionary strategies by which vaccine strains become pathogenic and provides a powerful framework for rational design of safer vaccine strains and for forecasting virulence of viruses. VIDEO ABSTRACT.

The Effects of Population Size Histories on Estimates of Selection Coefficients from Time-Series Genetic Data

Many approaches have been developed for inferring selection coefficients from time series data while accounting for genetic drift. These approaches have been motivated by the intuition that properly accounting for the population size history can significantly improve estimates of selective strengths. However, the improvement in inference accuracy that can be attained by modeling drift has not been characterized. Here, by comparing maximum likelihood estimates of selection coefficients that account for the true population size history with estimates that ignore drift by assuming allele frequencies evolve deterministically in a population of infinite size, we address the following questions: how much can modeling the population size history improve estimates of selection coefficients? How much can mis-inferred population sizes hurt inferences of selection coefficients? We conduct our analysis under the discrete Wright–Fisher model by deriving the exact probability of an allele frequency trajectory in a population of time-varying size and we replicate our results under the diffusion model. For both models, we find that ignoring drift leads to estimates of selection coefficients that are nearly as accurate as estimates that account for the true population history, even when population sizes are small and drift is high. This result is of interest because inference methods that ignore drift are widely used in evolutionary studies and can be many orders of magnitude faster than methods that account for population sizes.

Inference of fitness values and putative appearance time points for evolvable self-replicating molecules from time series of occurrence frequencies in an evolution reactor

We have established a translation-coupled RNA replication system within a cell-like compartment, and conducted an experimental evolution of the RNA molecules in the system. Then, we obtained a time series of occurrence frequencies of 91 individual genotypes through random sampling and next-generation sequencing. The time series showed a complex clonal interference and a polymorphic population called the "quasispecies". By fitting a deterministic kinetic model of evolvable simple self-replicators to the time series, we estimated the fitness value and "putative appearance time point" for each of the 91 major genotypes identified, where the putative appearance time point is defined as a certain time point at which a certain mutant genotype is supposed to appear in the deterministic kinetic model. As a result, the kinetic model was well fitted and additionally we confirmed that the estimated fitness values for 11 genotypes were considerably close to the experimentally measured ones (Ichihashi et al., 2015). In this sequel paper, with the theoretical basis of the deterministic kinetic model, we present the details of inference of the fitness values and putative appearance time points for the 91 genotypes. It may be possible to apply this methodology to other self-replicating molecules, viruses and bacteria.

Conflicting Selection Pressures Will Constrain Viral Escape from Interfering Particles: Principles for Designing Resistance-Proof Antivirals

The rapid evolution of RNA-encoded viruses such as HIV presents a major barrier to infectious disease control using conventional pharmaceuticals and vaccines. Previously, it was proposed that defective interfering particles could be developed to indefinitely control the HIV/AIDS pandemic; in individual patients, these engineered molecular parasites were further predicted to be refractory to HIV’s mutational escape (i.e., be ‘resistance-proof’). However, an outstanding question has been whether these engineered interfering particles—termed Therapeutic Interfering Particles (TIPs)—would remain resistance-proof at the population-scale, where TIP-resistant HIV mutants may transmit more efficiently by reaching higher viral loads in the TIP-treated subpopulation. Here, we develop a multi-scale model to test whether TIPs will maintain indefinite control of HIV at the population-scale, as HIV (‘unilaterally’) evolves toward TIP resistance by limiting the production of viral proteins available for TIPs to parasitize. Model results capture the existence of two intrinsic evolutionary tradeoffs that collectively prevent the spread of TIP-resistant HIV mutants in a population. First, despite their increased transmission rates in TIP-treated sub-populations, unilateral TIP-resistant mutants are shown to have reduced transmission rates in TIP-untreated sub-populations. Second, these TIP-resistant mutants are shown to have reduced growth rates (i.e., replicative fitness) in both TIP-treated and TIP-untreated individuals. As a result of these tradeoffs, the model finds that TIP-susceptible HIV strains continually outcompete TIP-resistant HIV mutants at both patient and population scales when TIPs are engineered to express >3-fold more genomic RNA than HIV expresses. Thus, the results provide design constraints for engineering population-scale therapies that may be refractory to the acquisition of antiviral resistance.

A major obstacle to effective antimicrobial therapy campaigns is the rapid evolution of drug resistance. Given the static nature of current pharmaceuticals and vaccines, natural selection inevitably drives pathogens to mutate into drug-resistant variants that can resume productive replication. Further, these drug-resistant mutants transmit across populations, resulting in untreatable epidemics. Recently, a therapeutic strategy was proposed in which viral deletion mutants—termed therapeutic interfering particles (TIPs)—are engineered to only replicate by stealing their missing proteins from full-length viruses in co-infected cells. By stealing essential viral proteins, these engineered molecular parasites have been predicted to reduce viral levels in patients and viral transmission events across populations. Yet, a critical question is whether rapidly mutating viruses like HIV can evolve around TIP control by reducing production of the proteins that TIPs must steal in order to replicate (i.e., by ‘starving’ the TIPs). Here we develop a multi-scale model that tests whether TIP-starving HIV mutants can spread across populations to undermine TIP therapy campaigns at the population-scale. Strikingly, model results show that inherent evolutionary tradeoffs prevent these TIP-resistant HIV mutants from increasing in frequency (i.e., these TIP-resistant HIV mutants are continually outcompeted by TIP-sensitive mutants in both patients and populations). Maintained by natural selection, TIPs may offer a novel therapeutic approach to indefinitely control rapidly evolving viral pandemics.

The role of HIV integration in viral persistence: no more whistling past the proviral graveyard

A substantial research effort has been directed to identifying strategies to eradicate or control HIV infection without a requirement for combination antiretroviral therapy (cART). A number of obstacles prevent HIV eradication, including low-level viral persistence during cART, long-term persistence of HIV-infected cells, and latent infection of resting CD4+ T cells. Mechanisms of persistence remain uncertain, but integration of the provirus into the host genome represents a central event in replication and pathogenesis of all retroviruses, including HIV. Analysis of HIV proviruses in CD4+ lymphocytes from individuals after prolonged cART revealed that a substantial proportion of the infected cells that persist have undergone clonal expansion and frequently have proviruses integrated in genes associated with regulation of cell growth. These data suggest that integration may influence persistence and clonal expansion of HIV-infected cells after cART is introduced, and these processes may represent key mechanisms for HIV persistence. Determining the diversity of host genes with integrants in HIV-infected cells that persist for prolonged periods may yield useful information regarding pathways by which infected cells persist for prolonged periods. Moreover, many integrants are defective, and new studies are required to characterize the role of clonal expansion in the persistence of replication-competent HIV.

HIV-infected cells are frequently clonally expanded after prolonged antiretroviral therapy: implications for HIV persistence

HIV infection is controlled but not eradicated by combination antiretroviral therapy (cART), and persistence during therapy represents a substantial barrier to strategies to eradicate infection. The nature of persistence is uncertain, and a number of mechanisms have been proposed to explain HIV persistence in vivo, including low-level HIV replication, sanctuary sites for HIV-infected cells, and latent HIV residing within long-lived cells. Analysis of residual viraemia and of cell-associated HIV revealed evidence of multiple copies of identical HIV sequences suggesting infected cells can undergo cellular expansion. Recently, analysis of integration sites in HIV-infected cells derived from peripheral blood lymphocytes of patients undergoing long-term cART revealed direct evidence that HIV-infected cells undergo clonal expansion. These studies demonstrated that clonally expanded populations are common in HIV-infected individuals, persist for prolonged periods and increase in frequency during prolonged therapy. Several analyses reported that site of integration may affect persistence, clonal expansion, or both. As such, expanded populations may represent an important source of infectious HIV during cART. Many HIV integrants are defective for replication, however, and additional research is essential to determine to what degree clonally expanded populations represent a reservoir of replication-competent HIV.

Optimal Treatment Strategies in the Context of 'Treatment for Prevention' against HIV-1 in Resource-Poor Settings

An estimated 2.7 million new HIV-1 infections occurred in 2010. `Treatment-for-prevention’ may strongly prevent HIV-1 transmission. The basic idea is that immediate treatment initiation rapidly decreases virus burden, which reduces the number of transmittable viruses and thereby the probability of infection. However, HIV inevitably develops drug resistance, which leads to virus rebound and nullifies the effect of `treatment-for-prevention’ for the time it remains unrecognized. While timely conducted treatment changes may avert periods of viral rebound, necessary treatment options and diagnostics may be lacking in resource-constrained settings. Within this work, we provide a mathematical platform for comparing different treatment paradigms that can be applied to many medical phenomena. We use this platform to optimize two distinct approaches for the treatment of HIV-1: (i) a diagnostic-guided treatment strategy, based on infrequent and patient-specific diagnostic schedules and (ii) a pro-active strategy that allows treatment adaptation prior to diagnostic ascertainment. Both strategies are compared to current clinical protocols (standard of care and the HPTN052 protocol) in terms of patient health, economic means and reduction in HIV-1 onward transmission exemplarily for South Africa. All therapeutic strategies are assessed using a coarse-grained stochastic model of within-host HIV dynamics and pseudo-codes for solving the respective optimal control problems are provided. Our mathematical model suggests that both optimal strategies (i)-(ii) perform better than the current clinical protocols and no treatment in terms of economic means, life prolongation and reduction of HIV-transmission. The optimal diagnostic-guided strategy suggests rare diagnostics and performs similar to the optimal pro-active strategy. Our results suggest that ‘treatment-for-prevention’ may be further improved using either of the two analyzed treatment paradigms.

HIV-1 continues to spread globally. Antiviral treatment cannot cure patients, but it slows disease progression and may prevent HIV transmission by decreasing the amount of transmittable viruses in treated individuals. ‘Treatment-for-prevention’ argues for immediate treatment initiation and may reduce transmission by 96% (CI: 73–99%), according to the results of a large clinical study (HPTN052). In order to ensure long-lasting treatment success, early therapy initiation demands more sophisticated treatment strategies & exceeding funds. However, countries facing the highest HIV burden are among the poorest. Within this work, we provide a mathematical framework that allows assessing different treatment paradigms using optimal control theory together with stochastic modelling of within-host viral dynamics and drug resistance development. We use this framework to compute and evaluate two distinct optimal long-term treatment strategies for resource-constrained settings: (i) a diagnostic-guided and (ii) a pro-active treatment strategy. The cost of a strategy is evaluated from a national economic perspective, valuating a severe patient health status in terms of an economic loss. The optimal strategies are compared with current clinical treatment protocols and no treatment in terms of costs, life expectation and reduction of secondary cases. Our simulations indicate that the pro-active treatment strategy performs comparably to the diagnostic-guided treatment strategy. Both strategies perform better than current clinical protocols, suggesting directions for improvement.

Coxsackievirus B3 replication and pathogenesis

ABSTRACT 

Viruses such as coxsackievirus B3 (CVB3) are entirely host cell-dependent parasites. Indeed, they must cleverly exploit various compartments of host cells to complete their life cycle, and consequently launch disease. Evolution has equipped this pico-rna-virus, CVB3, to use different strategies, including CVB3-induced direct damage to host cells followed by a host inflammatory response to CVB3 infection, and cell death to super-additively promote target organ tissue injury, and dysfunction. In this update, the patho-stratagems of CVB3 are explored from molecular, and systems-level approaches. In summarizing recent developments in this field, we focus particularly on mechanisms by which CVB3 can harness different host cell processes including kinases, host cell-killing and cell-eating machineries, matrix metalloproteinases and miRNAs to promote disease.