Antigenic evolution of viruses in host populations

Evolutionary stability of antigenically escaping viruses

Antigenic variation is the main immune escape mechanism for RNA viruses like influenza or SARS-CoV-2. While high mutation rates promote antigenic escape, they also induce large mutational loads and reduced fitness. It remains unclear how this cost-benefit trade-off selects the mutation rate of viruses. Using a traveling wave model for the coevolution of viruses and host immune systems in a finite population, we investigate how immunity affects the evolution of the mutation rate and other nonantigenic traits, such as virulence. We first show that the nature of the wave depends on how cross-reactive immune systems are, reconciling previous approaches. The immune-virus system behaves like a Fisher wave at low cross-reactivities, and like a fitness wave at high cross-reactivities. These regimes predict different outcomes for the evolution of nonantigenic traits. At low cross-reactivities, the evolutionarily stable strategy is to maximize the speed of the wave, implying a higher mutation rate and increased virulence. At large cross-reactivities, where our estimates place H3N2 influenza, the stable strategy is to increase the basic reproductive number, keeping the mutation rate to a minimum and virulence low.

The influence of immune cells on the existence of virus quasi-species

This article investigate a nonlocal reaction-diffusion system of equations modeling virus distribution with respect to their genotypes in the interaction with the immune response. This study demonstrates the existence of pulse solutions corresponding to virus quasi-species. The proof is based on the Leray-Schauder method, which relies on the topological degree for elliptic operators in unbounded domains and a priori estimates of solutions. Furthermore, linear stability analysis of a spatially homogeneous stationary solution identifies the critical conditions for the emergence of spatial and spatiotemporal structures. Finally, numerical simulations are used to illustrate nonlinear dynamics and pattern formation in the nonlocal model.

Evolutionary implications of SARS-CoV-2 vaccination for the future design of vaccination strategies

Once the first SARS-CoV-2 vaccine became available, mass vaccination was the main pillar of the public health response to the COVID-19 pandemic. It was very effective in reducing hospitalizations and deaths. Here, we discuss the possibility that mass vaccination might accelerate SARS-CoV-2 evolution in antibody-binding regions compared to natural infection at the population level. Using the evidence of strong genetic variation in antibody-binding regions and taking advantage of the similarity between the envelope proteins of SARS-CoV-2 and influenza, we assume that immune selection pressure acting on these regions of the two viruses is similar. We discuss the consequences of this assumption for SARS-CoV-2 evolution in light of mathematical models developed previously for influenza. We further outline the implications of this phenomenon, if our assumptions are confirmed, for the future design of SARS-CoV-2 vaccination strategies.

Threat, challenges, and preparedness for future pandemics: A descriptive review of phylogenetic analysis based predictions

For centuries the world has been confronted with many infectious diseases, with a potential to turn into a pandemic posing a constant threat to human lives. Some of these pandemics occurred due to the emergence of new disease or re-emergence of previously known diseases with a few mutations. In such scenarios their optimal prevention and control options were not adequately developed. Most of these diseases are highly contagious and for their timely control, knowledge about the pathogens and disease progression is the basic necessity. In this review, we have presented a documented chronology of the earlier pandemics, evolutionary analysis of the infectious disease with pandemic potential, the role of RNA, difficulties in controlling pandemics, and the likely pathogens that could trigger future pandemics. In this study, the evolutionary history of the pathogens was identified by carrying out phylogenetic analysis. The percentage similarity between different infectious diseases is critically analysed for the identification of their correlation using online sequence matcher tools. The Baltimore classification system was used for finding the genomic nature of the viruses. It was observed that most of the infectious pathogens rise from their animal hosts with some mutations in their genome composition. The phylogenetic tree shows that the single-stranded RNA diseases have a common origin and many of them are having high similarity percentage. The outcomes of this study will help in the identification of potential pathogens that can cause future pandemics. This information will be helpful in the development of early detection techniques, devising preventive mechanism to limit their spread, prophylactic measures, Infection control and therapeutic options, thereby, strengthening our approach towards global preparedness against future pandemics.

Inferring the intrinsic mutational fitness landscape of influenzalike evolving antigens from temporally ordered sequence data

There still are no effective long-term protective vaccines against viruses that continuously evolve under immune pressure such as seasonal influenza, which has caused, and can cause, devastating epidemics in the human population. To find such a broadly protective immunization strategy, it is useful to know how easily the virus can escape via mutation from specific antibody responses. This information is encoded in the fitness landscape of the viral proteins (i.e., knowledge of the viral fitness as a function of sequence). Here we present a computational method to infer the intrinsic mutational fitness landscape of influenzalike evolving antigens from yearly sequence data. We test inference performance with computer-generated sequence data that are based on stochastic simulations mimicking basic features of immune-driven viral evolution. Although the numerically simulated model does create a phylogeny based on the allowed mutations, the inference scheme does not use this information. This provides a contrast to other methods that rely on reconstruction of phylogenetic trees. Our method just needs a sufficient number of samples over multiple years. With our method, we are able to infer single as well as pairwise mutational fitness effects from the simulated sequence time series for short antigenic proteins. Our fitness inference approach may have potential future use for the design of immunization protocols by identifying intrinsically vulnerable immune target combinations on antigens that evolve under immune-driven selection. In the future, this approach may be applied to influenza and other novel viruses such as SARS-CoV-2, which evolves and, like influenza, might continue to escape the natural and vaccine-mediated immune pressures.

Antigenic waves of virus-immune coevolution

The evolution of many microbes and pathogens, including circulating viruses such as seasonal influenza, is driven by immune pressure from the host population. In turn, the immune systems of infected populations get updated, chasing viruses even farther away. Quantitatively understanding how these dynamics result in observed patterns of rapid pathogen and immune adaptation is instrumental to epidemiological and evolutionary forecasting. Here we present a mathematical theory of coevolution between immune systems and viruses in a finite-dimensional antigenic space, which describes the cross-reactivity of viral strains and immune systems primed by previous infections. We show the emergence of an antigenic wave that is pushed forward and canalized by cross-reactivity. We obtain analytical results for shape, speed, and angular diffusion of the wave. In particular, we show that viral-immune coevolution generates an emergent timescale, the persistence time of the wave's direction in antigenic space, which can be much longer than the coalescence time of the viral population. We compare these dynamics to the observed antigenic turnover of influenza strains, and we discuss how the dimensionality of antigenic space impacts the predictability of the evolutionary dynamics. Our results provide a concrete and tractable framework to describe pathogen-host coevolution.

An evolution-based high-fidelity method of epistasis measurement: Theory and application to influenza

Linkage effects in a multi-locus population strongly influence its evolution. The models based on the traveling wave approach enable us to predict the average speed of evolution and the statistics of phylogeny. However, predicting statistically the evolution of specific sites and pairs of sites in the multi-locus context remains a mathematical challenge. In particular, the effects of epistasis, the interaction of gene regions contributing to phenotype, is difficult to predict theoretically and detect experimentally in sequence data. A large number of false-positive interactions arises from stochastic linkage effects and indirect interactions, which mask true epistatic interactions. Here we develop a proof-of-principle method to filter out false-positive interactions. We start by demonstrating that the averaging of haplotype frequencies over multiple independent populations is necessary but not sufficient for epistatic detection, because it still leaves high numbers of false-positive interactions. To compensate for the residual stochastic noise, we develop a three-way haplotype method isolating true interactions. The fidelity of the method is confirmed analytically and on simulated genetic sequences evolved with a known epistatic network. The method is then applied to a large sequence database of neurominidase protein of influenza A H1N1 obtained from various geographic locations to infer the epistatic network responsible for the difference between the pre-pandemic virus and the pandemic strain of 2009. These results present a simple and reliable technique to measure epistatic interactions of any sign from sequence data.

Interactions between genomic sites create a fitness landscape. The knowledge of topology and strength of interactions is vital for predicting the escape of viruses from drugs and immune response and their passing through fitness valleys. Many efforts have been invested into measuring these interactions from DNA sequence sets. Unfortunately, reproducibility of the results remains low due partly to a very small fraction of interaction pairs and partly to stochastic linkage noise masking true interactions. Here we propose a method to separate stochastic linkage and indirect interactions from epistatic interactions and apply it to influenza virus sequence data.

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.

An Evolutionary Model of Progression to AIDS

The time to the onset of AIDS symptoms in an HIV infected individual is known to correlate inversely with viremia and the level of immune activation. The correlation exists against the background of strong individual fluctuations demonstrating the existence of hidden variables depending on patient and virus parameters. At the moment, prognosis of the time to AIDS based on patient parameters is not possible. In addition, it is of paramount importance to understand the reason of progression to AIDS in untreated patients to be able to learn to control it by means other than anti-retroviral therapy. Here we develop a mechanistic mathematical model to predict the speed of progression to AIDS in individual untreated patients and patients treated with suboptimal therapy, based on a single-time measurement of several virological and immunological parameters. We show that the gradual increase in virus fitness during a chronic infection causes slow gradual depletion of CD4 T cells. Using the existing evolution models of HIV, we obtain general expressions predicting the time to the onset of AIDS symptoms in terms of the patient parameters, for low-viremia and high-viremia patients separately. We show that the evolution model of AIDS fits the existing data on virus-time correlations better than the alternative model of the deregulation of homeostatic response.

Immuno-epidemiology and pathophysiology of coronavirus disease 2019 (COVID-19)

Occasional zoonotic viral attacks on immunologically naive populations result in massive death tolls that are capable of threatening human survival. Currently, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the infectious agent that causes coronavirus disease (COVID-19), has spread from its epicenter in Wuhan China to all parts of the globe. Real-time mapping of new infections across the globe has revealed that variable transmission patterns and pathogenicity are associated with differences in SARS-CoV-2 lineages, clades, and strains. Thus, we reviewed how changes in the SARS-CoV-2 genome and its structural architecture affect viral replication, immune evasion, and transmission within different human populations. We also looked at which immune dominant regions of SARS-CoV-2 and other coronaviruses are recognized by Major Histocompatibility Complex (MHC)/Human Leukocyte Antigens (HLA) genes and how this could impact on subsequent disease pathogenesis. Efforts were also placed on understanding immunological changes that occur when exposed individuals either remain asymptomatic or fail to control the virus and later develop systemic complications. Published autopsy studies that reveal alterations in the lung immune microenvironment, morphological, and pathological changes are also explored within the context of the review. Understanding the true correlates of protection and determining how constant virus evolution impacts on host-pathogen interactions could help identify which populations are at high risk and later inform future vaccine and therapeutic interventions.

A comprehensive global perspective on phylogenomics and evolutionary dynamics of Small ruminant morbillivirus

A string of complete genome sequences of Small ruminant morbillivirus (SRMV) have been reported from different parts of the globe including Asia, Africa and the Middle East. Despite individual genome sequence-based analysis, there is a paucity of comparative genomic and evolutionary analysis to provide overarching and comprehensive evolutionary insights. Therefore, we first enriched the existing database of complete genome sequences of SRMVs with Pakistan-originated strains and then explored overall nucleotide diversity, genomic and residue characteristics, and deduced an evolutionary relationship among strains representing a diverse geographical region worldwide. The average number of pairwise nucleotide differences among the whole genomes was found to be 788.690 with a diversity in nucleotide sequences (0.04889 ± S.D. 0.00468) and haplotype variance (0.00001). The RNA-dependent-RNA polymerase (L) gene revealed phylogenetic relationship among SRMVs in a pattern similar to those of complete genome and the nucleoprotein (N) gene. Therefore, we propose another useful molecular marker that may be employed for future epidemiological investigations. Based on evolutionary analysis, the mean evolution rate for the complete genome, N, P, M, F, H and L genes of SRMV was estimated to be 9.953 × 10^–4, 1.1 × 10^–3, 1.23 × 10^–3, 2.56 × 10^–3, 2.01 × 10^–3, 1.47 × 10^–3 and 9.75 × 10^–4 substitutions per site per year, respectively. A recombinant event was observed in a Pakistan-originated strain (KY967608) revealing Indian strains as major (98.1%, KR140086) and minor parents (99.8%, KT860064). Taken together, outcomes of the study augment our knowledge and current understanding towards ongoing phylogenomic and evolutionary dynamics for better comprehensions of SRMVs and effective disease control interventions.

Phylodynamic theory of persistence, extinction and speciation of rapidly adapting pathogens

Rapidly evolving pathogens like influenza viruses can persist by changing their antigenic properties fast enough to evade the adaptive immunity, yet they rarely split into diverging lineages. By mapping the multi-strain Susceptible-Infected-Recovered model onto the traveling wave model of adapting populations, we demonstrate that persistence of a rapidly evolving, Red-Queen-like state of the pathogen population requires long-ranged cross-immunity and sufficiently large population sizes. This state is unstable and the population goes extinct or 'speciates' into two pathogen strains with antigenic divergence beyond the range of cross-inhibition. However, in a certain range of evolutionary parameters, a single cross-inhibiting population can exist for times long compared to the time to the most recent common ancestor ([Formula: see text]) and gives rise to phylogenetic patterns typical of influenza virus. We demonstrate that the rate of speciation is related to fluctuations of [Formula: see text] and construct a 'phase diagram' identifying different phylodynamic regimes as a function of evolutionary parameters.

Multi-Lineage Evolution in Viral Populations Driven by Host Immune Systems

Viruses evolve in the background of host immune systems that exert selective pressure and drive viral evolutionary trajectories. This interaction leads to different evolutionary patterns in antigenic space. Examples observed in nature include the effectively one-dimensional escape characteristic of influenza A and the prolonged coexistence of lineages in influenza B. Here, we use an evolutionary model for viruses in the presence of immune host systems with finite memory to obtain a phase diagram of evolutionary patterns in a two-dimensional antigenic space. We find that, for small effective mutation rates and mutation jump ranges, a single lineage is the only stable solution. Large effective mutation rates combined with large mutational jumps in antigenic space lead to multiple stably co-existing lineages over prolonged evolutionary periods. These results combined with observations from data constrain the parameter regimes for the adaptation of viruses, including influenza.