A predictive fitness model for influenza

The Molecular Determinants of Antibody Recognition and Antigenic Drift in the H3 Hemagglutinin of Swine Influenza A Virus

Influenza A virus (IAV) of the H3 subtype is an important respiratory pathogen that affects both humans and swine. Vaccination to induce neutralizing antibodies against the surface glycoprotein hemagglutinin (HA) is the primary method used to control disease. However, due to antigenic drift, vaccine strains must be periodically updated. Six of the 7 positions previously identified in human seasonal H3 (positions 145, 155, 156, 158, 159, 189, and 193) were also indicated in swine H3 antigenic evolution. To experimentally test the effect on virus antigenicity of these 7 positions, substitutions were introduced into the HA of an isogenic swine lineage virus. We tested the antigenic effect of these introduced substitutions by using hemagglutination inhibition (HI) data with monovalent swine antisera and antigenic cartography to evaluate the antigenic phenotype of the mutant viruses. Combinations of substitutions within the antigenic motif caused significant changes in antigenicity. One virus mutant that varied at only two positions relative to the wild type had a >4-fold reduction in HI titers compared to homologous antisera. Potential changes in pathogenesis and transmission of the double mutant were evaluated in pigs. Although the double mutant had virus shedding titers and transmissibility comparable to those of the wild type, it caused a significantly lower percentage of lung lesions. Elucidating the antigenic effects of specific amino acid substitutions at these sites in swine H3 IAV has important implications for understanding IAV evolution within pigs as well as for improved vaccine development and control strategies in swine.

IMPORTANCE

A key component of influenza virus evolution is antigenic drift mediated by the accumulation of amino acid substitutions in the hemagglutinin (HA) protein, resulting in escape from prior immunity generated by natural infection or vaccination. Understanding which amino acid positions of the HA contribute to the ability of the virus to avoid prior immunity is important for understanding antigenic evolution and informs vaccine efficacy predictions based on the genetic sequence data from currently circulating strains. Following our previous work characterizing antigenic phenotypes of contemporary wild-type swine H3 influenza viruses, we experimentally validated that substitutions at 6 amino acid positions in the HA protein have major effects on antigenicity. An improved understanding of the antigenic diversity of swine influenza will facilitate a rational approach for selecting more effective vaccine components to control the circulation of influenza in pigs and reduce the potential for zoonotic viruses to emerge.

Forecasting Epidemiological and Evolutionary Dynamics of Infectious Diseases

Mathematical models have been powerful tools in developing mechanistic understanding of infectious diseases. Furthermore, they have allowed detailed forecasting of epidemiological phenomena such as outbreak size, which is of considerable public-health relevance. The short generation time of pathogens and the strong selection they are subjected to (by host immunity, vaccines, chemotherapy, etc.) mean that evolution is also a key driver of infectious disease dynamics. Accurate forecasting of pathogen dynamics therefore calls for the integration of epidemiological and evolutionary processes, yet this integration remains relatively rare. We review previous attempts to model and predict infectious disease dynamics with or without evolution and discuss major challenges facing the development of the emerging science of epidemic forecasting.

Population genetic processes affecting the mode of selective sweeps and effective population size in influenza virus H3N2

Background

Human influenza virus A/H3N2 undergoes rapid adaptive evolution in response to host immunity. Positively selected amino acid substitutions have been detected mainly in the hemagglutinin (HA) segment. The genealogical tree of HA sequences sampled over several decades comprises a long trunk and short side branches, which indicates small effective population size. Various studies have reproduced this unique genealogical structure by modeling recurrent positive selection. However, it has not been clearly demonstrated whether recurrent selective sweeps alone can explain the limited level of genetic diversity observed in the HA of H3N2. In addition, the variation-reducing impacts of other evolutionary processes – background selection and complex demography – relative to that of positive selection have never been explicitly evaluated.

Results

In this paper, using computer simulation of a viral population evolving under recurrent selective sweeps we demonstrate that positive selection alone, if it occurs at a rate estimated by previous studies, cannot lead to such a small effective population size. Genetic hitchhiking fails to completely wipe out pre-existing variation because soft, rather than hard, selective sweeps prevail under realistic parameters of mutation rate and population size. We find that antigenic-cluster-transition substitutions in HA occur as hard sweeps. This indicates that the effective population size under which those mutations arise must be much smaller than the actual population size due to other evolutionary forces before selective sweeps further reduce it. We thus examine the effects of background selection and metapopulation dynamics in reducing the effective population size, using parameter values that reproduce other aspects of molecular evolution in H3N2. When either process is incorporated in recurrent selective sweep simulation, selective sweeps are mostly hard and the observed level of synonymous diversity is obtained with large census population size.

Conclusions

Background selection and metapopulation dynamics have greater variation reducing power than recurrent positive selection under realistic parameters in H3N2. Therefore, these evolutionary processes are likely to play crucial roles in reducing the effective population size of H3N2 viruses and thus explaining the characteristic shape of H3N2 genealogy.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-016-0727-8) contains supplementary material, which is available to authorized users.

Influenza evolution and H3N2 vaccine effectiveness, with application to the 2014/2015 season

Influenza A is a serious disease that causes significant morbidity and mortality, and vaccines against the seasonal influenza disease are of variable effectiveness. In this article, we discuss the use of the pepitope method to predict the dominant influenza strain and the expected vaccine effectiveness in the coming flu season. We illustrate how the effectiveness of the 2014/2015 A/Texas/50/2012 [clade 3C.1] vaccine against the A/California/02/2014 [clade 3C.3a] strain that emerged in the population can be estimated via pepitope In addition, we show by a multidimensional scaling analysis of data collected through 2014, the emergence of a new A/New Mexico/11/2014-like cluster [clade 3C.2a] that is immunologically distinct from the A/California/02/2014-like strains.

Sequence amplification via cell passaging creates spurious signals of positive adaptation in influenza virus H3N2 hemagglutinin

Clinical influenza A virus isolates are frequently not sequenced directly. Instead, a majority of these isolates (~70% in 2015) are first subjected to passaging for amplification, most commonly in non-human cell culture. Here, we find that this passaging leaves distinct signals of adaptation, which can confound evolutionary analyses of the viral sequences. We find distinct patterns of adaptation to Madin-Darby (MDCK) and monkey cell culture absent from unpassaged hemagglutinin sequences. These patterns also dominate pooled datasets not separated by passaging type, and they increase in proportion to the number of passages performed. By contrast, MDCK-SIAT1 passaged sequences seem mostly (but not entirely) free of passaging adaptations. Contrary to previous studies, we find that using only internal branches of influenza virus phylogenetic trees is insufficient to correct for passaging artifacts. These artifacts can only be safely avoided by excluding passaged sequences entirely from subsequent analysis. We conclude that future influenza virus evolutionary analyses should appropriately control for potentially confounding effects of passaging adaptations.

Integrated Omics and Computational Glycobiology Reveal Structural Basis for Influenza A Virus Glycan Microheterogeneity and Host Interactions

Despite sustained biomedical research effort, influenza A virus remains an imminent threat to the world population and a major healthcare burden. The challenge in developing vaccines against influenza is the ability of the virus to mutate rapidly in response to selective immune pressure. Hemagglutinin is the predominant surface glycoprotein and the primary determinant of antigenicity, virulence and zoonotic potential. Mutations leading to changes in the number of HA glycosylation sites are often reported. Such genetic sequencing studies predict at best the disruption or creation of sequons for N-linked glycosylation; they do not reflect actual phenotypic changes in HA structure. Therefore, combined analysis of glycan micro and macro-heterogeneity and bioassays will better define the relationships among glycosylation, viral bioactivity and evolution. We present a study that integrates proteomics, glycomics and glycoproteomics of HA before and after adaptation to innate immune system pressure. We combined this information with glycan array and immune lectin binding data to correlate the phenotypic changes with biological activity. Underprocessed glycoforms predominated at the glycosylation sites found to be involved in viral evolution in response to selection pressures and interactions with innate immune-lectins. To understand the structural basis for site-specific glycan microheterogeneity at these sites, we performed structural modeling and molecular dynamics simulations. We observed that the presence of immature, high-mannose type glycans at a particular site correlated with reduced accessibility to glycan remodeling enzymes. Further, the high mannose glycans at sites implicated in immune lectin recognition were predicted to be capable of forming trimeric interactions with the immune-lectin surfactant protein-D.

Selection of antigenically advanced variants of seasonal influenza viruses

Influenza viruses mutate frequently, necessitating constant updates of vaccine viruses. To establish experimental approaches that may complement the current vaccine strain selection process, we selected antigenic variants from human H1N1 and H3N2 influenza virus libraries possessing random mutations in the globular head of the haemagglutinin protein (which includes the antigenic sites) by incubating them with human and/or ferret convalescent sera to human H1N1 and H3N2 viruses. We also selected antigenic escape variants from human viruses treated with convalescent sera and from mice that had been previously immunized against human influenza viruses. Our pilot studies with past influenza viruses identified escape mutants that were antigenically similar to variants that emerged in nature, establishing the feasibility of our approach. Our studies with contemporary human influenza viruses identified escape mutants before they caused an epidemic in 2014-2015. This approach may aid in the prediction of potential antigenic escape variants and the selection of future vaccine candidates before they become widespread in nature.

Relative rate and location of intra-host HIV evolution to evade cellular immunity are predictable

Human immunodeficiency virus (HIV) evolves within infected persons to escape being destroyed by the host immune system, thereby preventing effective immune control of infection. Here, we combine methods from evolutionary dynamics and statistical physics to simulate in vivo HIV sequence evolution, predicting the relative rate of escape and the location of escape mutations in response to T-cell-mediated immune pressure in a cohort of 17 persons with acute HIV infection. Predicted and clinically observed times to escape immune responses agree well, and we show that the mutational pathways to escape depend on the viral sequence background due to epistatic interactions. The ability to predict escape pathways and the duration over which control is maintained by specific immune responses open the door to rational design of immunotherapeutic strategies that might enable long-term control of HIV infection. Our approach enables intra-host evolution of a human pathogen to be predicted in a probabilistic framework.

Quantifying evolutionary constraints on B-cell affinity maturation

The antibody repertoire of each individual is continuously updated by the evolutionary process of B-cell receptor (BCR) mutation and selection. It has recently become possible to gain detailed information concerning this process through high-throughput sequencing. Here, we develop modern statistical molecular evolution methods for the analysis of B-cell sequence data, and then apply them to a very deep short-read dataset of BCRs. We find that the substitution process is conserved across individuals but varies significantly across gene segments. We investigate selection on BCRs using a novel method that side-steps the difficulties encountered by previous work in differentiating between selection and motif-driven mutation; this is done through stochastic mapping and empirical Bayes estimators that compare the evolution of in-frame and out-of-frame rearrangements. We use this new method to derive a per-residue map of selection, which provides a more nuanced view of the constraints on framework and variable regions.

PREDAC-H3: a user-friendly platform for antigenic surveillance of human influenza a(H3N2) virus based on hemagglutinin sequences

MOTIVATION

Timely surveillance of the antigenic dynamics of the influenza virus is critical for accurate selection of vaccine strains, which is important for effective prevention of viral spread and infection.

RESULTS

Here, we provide a computational platform, called PREDAC-H3, for antigenic surveillance of human influenza A(H3N2) virus based on the sequence of surface protein hemagglutinin (HA). PREDAC-H3 not only determines the antigenic variants and antigenic cluster (grouped for similar antigenicity) to which the virus belongs, based on HA sequences, but also allows visualization of the spatial distribution and temporal dynamics of antigenic clusters of viruses isolated from around the world, thus assisting in antigenic surveillance of human influenza A(H3N2) virus.

AVAILABILITY AND IMPLEMENTATION

It is publicly available from: http://biocloud.hnu.edu.cn/influ411/html/index.php

CONTACTS

: yshu@cnic.org.cn or taijiao@moon.ibp.ac.cn.

Identification of Low- and High-Impact Hemagglutinin Amino Acid Substitutions That Drive Antigenic Drift of Influenza A(H1N1) Viruses

Determining phenotype from genetic data is a fundamental challenge. Identification of emerging antigenic variants among circulating influenza viruses is critical to the vaccine virus selection process, with vaccine effectiveness maximized when constituents are antigenically similar to circulating viruses. Hemagglutination inhibition (HI) assay data are commonly used to assess influenza antigenicity. Here, sequence and 3-D structural information of hemagglutinin (HA) glycoproteins were analyzed together with corresponding HI assay data for former seasonal influenza A(H1N1) virus isolates (1997–2009) and reference viruses. The models developed identify and quantify the impact of eighteen amino acid substitutions on the antigenicity of HA, two of which were responsible for major transitions in antigenic phenotype. We used reverse genetics to demonstrate the causal effect on antigenicity for a subset of these substitutions. Information on the impact of substitutions allowed us to predict antigenic phenotypes of emerging viruses directly from HA gene sequence data and accuracy was doubled by including all substitutions causing antigenic changes over a model incorporating only the substitutions with the largest impact. The ability to quantify the phenotypic impact of specific amino acid substitutions should help refine emerging techniques that predict the evolution of virus populations from one year to the next, leading to stronger theoretical foundations for selection of candidate vaccine viruses. These techniques have great potential to be extended to other antigenically variable pathogens.

Influenza A viruses are characterized by rapid antigenic drift: structural changes in B-cell epitopes that facilitate escape from pre-existing immunity. Consequently, seasonal influenza continues to impose a major burden on human health. Accurate quantification of the antigenic impact of specific amino acid substitutions is a pre-requisite for predicting the fitness and evolutionary outcome of variant viruses. Using assays to attribute antigenic variation to amino acid sequence changes we identify substitutions that contribute to antigenic drift and quantify their impact. We show that substitutions identified as low-impact are a critical component of virus antigenic evolution and by including these, as well as the high-impact substitutions often focused on, the accuracy of predicting antigenic phenotypes of emerging viruses from genotype is doubled.

Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influenza viruses

Human seasonal influenza viruses evolve rapidly, enabling the virus population to evade immunity and reinfect previously infected individuals. Antigenic properties are largely determined by the surface glycoprotein hemagglutinin (HA), and amino acid substitutions at exposed epitope sites in HA mediate loss of recognition by antibodies. Here, we show that antigenic differences measured through serological assay data are well described by a sum of antigenic changes along the path connecting viruses in a phylogenetic tree. This mapping onto the tree allows prediction of antigenicity from HA sequence data alone. The mapping can further be used to make predictions about the makeup of the future A(H3N2) seasonal influenza virus population, and we compare predictions between models with serological and sequence data. To make timely model output readily available, we developed a web browser-based application that visualizes antigenic data on a continuously updated phylogeny.

Influenza infection in human host: challenges in making a better influenza vaccine

Influenza is a ubiquitous infection with a spectrum ranging from mild to severe. The mystery regarding such variability in the clinical spectrum has not been fully unravelled, although a role for the complex interplay among virus characteristics, host immune response and environmental factors has been suggested. Antivirals and current vaccines have a limited role in prophylaxis and treatment because they primarily target surface glycoproteins which undergo antigenic/genetic changes under host immune pressure. Targeting conserved internal proteins could lead the way to a universal vaccine which can be used against various types/subtypes. However, this is on the distant horizon, so in the meantime, developing improved vaccines should be given high priority. In this review, we discuss where the current influenza research stands in terms of vaccines, adjuvants, and how we can better predict the vaccine strains for upcoming influenza seasons by understanding complex phenomena which drive the continuous antigenic evolution.

Persistent Chaos of Measles Epidemics in the Prevaccination United States Caused by a Small Change in Seasonal Transmission Patterns

Epidemics of infectious diseases often occur in predictable limit cycles. Theory suggests these cycles can be disrupted by high amplitude seasonal fluctuations in transmission rates, resulting in deterministic chaos. However, persistent deterministic chaos has never been observed, in part because sufficiently large oscillations in transmission rates are uncommon. Where they do occur, the resulting deep epidemic troughs break the chain of transmission, leading to epidemic extinction, even in large cities. Here we demonstrate a new path to locally persistent chaotic epidemics via subtle shifts in seasonal patterns of transmission, rather than through high-amplitude fluctuations in transmission rates. We base our analysis on a comparison of measles incidence in 80 major cities in the prevaccination era United States and United Kingdom. Unlike the regular limit cycles seen in the UK, measles cycles in US cities consistently exhibit spontaneous shifts in epidemic periodicity resulting in chaotic patterns. We show that these patterns were driven by small systematic differences between countries in the duration of the summer period of low transmission. This example demonstrates empirically that small perturbations in disease transmission patterns can fundamentally alter the regularity and spatiotemporal coherence of epidemics.

Predicting the Mutating Distribution at Antigenic Sites of the Influenza Virus

Mutations of the influenza virus lead to antigenic changes that cause recurrent epidemics and vaccine resistance. Preventive measures would benefit greatly from the ability to predict the potential distribution of new antigenic sites in future strains. By leveraging the extensive historical records of HA sequences for 90 years, we designed a computational model to simulate the dynamic evolution of antigenic sites in A/H1N1. With templates of antigenic sequences, the model can effectively predict the potential distribution of future antigenic mutants. Validation on 10932 HA sequences from the last 16 years showing that the mutated antigenic sites of over 94% of reported strains fell in our predicted profile. Meanwhile, our model can successfully capture 96% of antigenic sites in those dominant epitopes. Similar results are observed on the complete set of H3N2 historical data, supporting the general applicability of our model to multiple sub-types of influenza. Our results suggest that the mutational profile of future antigenic sites can be predicted based on historical evolutionary traces despite the widespread, random mutations in influenza. Coupled with closely monitored sequence data from influenza surveillance networks, our method can help to forecast changes in viral antigenicity for seasonal flu and inform public health interventions.

Dosage and Dose Schedule Screening of Drug Combinations in Agent-Based Models Reveals Hidden Synergies

The fungus Candida albicans is the most common causative agent of human fungal infections and better drugs or drug combination strategies are urgently needed. Here, we present an agent-based model of the interplay of C. albicans with the host immune system and with the microflora of the host. We took into account the morphological change of C. albicans from the yeast to hyphae form and its dynamics during infection. The model allowed us to follow the dynamics of fungal growth and morphology, of the immune cells and of microflora in different perturbing situations. We specifically focused on the consequences of microflora reduction following antibiotic treatment. Using the agent-based model, different drug types have been tested for their effectiveness, namely drugs that inhibit cell division and drugs that constrain the yeast-to-hyphae transition. Applied individually, the division drug turned out to successfully decrease hyphae while the transition drug leads to a burst in hyphae after the end of the treatment. To evaluate the effect of different drug combinations, doses, and schedules, we introduced a measure for the return to a healthy state, the infection score. Using this measure, we found that the addition of a transition drug to a division drug treatment can improve the treatment reliability while minimizing treatment duration and drug dosage. In this work we present a theoretical study. Although our model has not been calibrated to quantitative experimental data, the technique of computationally identifying synergistic treatment combinations in an agent based model exemplifies the importance of computational techniques in translational research.

The population genetics of drug resistance evolution in natural populations of viral, bacterial and eukaryotic pathogens

Drug resistance is a costly consequence of pathogen evolution and a major concern in public health. In this review, we show how population genetics can be used to study the evolution of drug resistance and also how drug resistance evolution is informative as an evolutionary model system. We highlight five examples from diverse organisms with particular focus on: (i) identifying drug resistance loci in the malaria parasite Plasmodium falciparum using the genomic signatures of selective sweeps, (ii) determining the role of epistasis in drug resistance evolution in influenza, (iii) quantifying the role of standing genetic variation in the evolution of drug resistance in HIV, (iv) using drug resistance mutations to study clonal interference dynamics in tuberculosis and (v) analysing the population structure of the core and accessory genome of Staphylococcus aureus to understand the spread of methicillin resistance. Throughout this review, we discuss the uses of sequence data and population genetic theory in studying the evolution of drug resistance.

Genome-Wide Analysis of Evolutionary Markers of Human Influenza A(H1N1)pdm09 and A(H3N2) Viruses May Guide Selection of Vaccine Strain Candidates

Here we analyzed whole-genome sequences of 3,969 influenza A(H1N1)pdm09 and 4,774 A(H3N2) strains that circulated during 2009–2015 in the world. The analysis revealed changes at 481 and 533 amino acid sites in proteins of influenza A(H1N1)pdm09 and A(H3N2) strains, respectively. Many of these changes were introduced as a result of random drift. However, there were 61 and 68 changes that were present in relatively large number of A(H1N1)pdm09 and A(H3N2) strains, respectively, that circulated during relatively long time. We named these amino acid substitutions evolutionary markers, as they seemed to contain valuable information regarding the viral evolution. Interestingly, influenza A(H1N1)pdm09 and A(H3N2) viruses acquired non-overlapping sets of evolutionary markers. We next analyzed these characteristic markers in vaccine strains recommended by the World Health Organization for the past five years. Our analysis revealed that vaccine strains carried only few evolutionary markers at antigenic sites of viral hemagglutinin (HA) and neuraminidase (NA). The absence of these markers at antigenic sites could affect the recognition of HA and NA by human antibodies generated in response to vaccinations. This could, in part, explain moderate efficacy of influenza vaccines during 2009–2014. Finally, we identified influenza A(H1N1)pdm09 and A(H3N2) strains, which contain all the evolutionary markers of influenza A strains circulated in 2015, and which could be used as vaccine candidates for the 2015/2016 season. Thus, genome-wide analysis of evolutionary markers of influenza A(H1N1)pdm09 and A(H3N2) viruses may guide selection of vaccine strain candidates.

Viral O-GalNAc peptide epitopes: a novel potential target in viral envelope glycoproteins

Viral envelope glycoproteins are major targets for antibodies that bind to and inactivate viral particles. The capacity of a viral vaccine to induce virus-neutralizing antibodies is often used as a marker for vaccine efficacy. Yet the number of known neutralization target epitopes is restricted owing to various viral escape mechanisms. We expand the range of possible viral glycoprotein targets, by presenting a previously unknown type of viral glycoprotein epitope based on a short peptide stretch modified with small O-linked glycans. Besides being immunologically active, these epitopes have a high potential for antigenic variation. Thus, sera from patients infected with EBV develop individual IgG responses addressing the different possible glycopeptide glycoforms of one short peptide backbone that reflect individual variations in the course of virus infection. In contrast, in HSV type 2 meningitis patients, CSF antibodies are focussed to only one single glycoform peptide of a major viral glycoprotein. Thus, dependent on the viral disease, the serological response may be variable or constant with respect to the number of targeted peptide glycoforms. Mapping of these epitopes relies on a novel three-step procedure that identifies any reactive viral O-glycosyl peptide epitope with respect to (i) relevant peptide sequence, (ii) the reactive glycoform out of several possible glycopeptide isomers of that peptide sequence, and (iii) possibly tolerated carbohydrate or peptide structural variations at glycosylation sites. In conclusion, the viral O-glycosyl peptide epitopes may be of relevance for development of subunit vaccines and for improved serodiagnosis of viral diseases. Copyright © 2015 John Wiley & Sons, Ltd.

Antigenic Patterns and Evolution of the Human Influenza A (H1N1) Virus

The influenza A (H1N1) virus causes seasonal epidemics that result in severe illnesses and deaths almost every year. A deep understanding of the antigenic patterns and evolution of human influenza A (H1N1) virus is extremely important for its effective surveillance and prevention. Through development of antigenicity inference method for human influenza A (H1N1), named PREDAC-H1, we systematically mapped the antigenic patterns and evolution of the human influenza A (H1N1) virus. Eight dominant antigenic clusters have been inferred for seasonal H1N1 viruses since 1977, which demonstrated sequential replacements over time with a similar pattern in Asia, Europe and North America. Among them, six clusters emerged first in Asia. As for China, three of the eight antigenic clusters were detected in South China earlier than in North China, indicating the leading role of South China in H1N1 transmission. The comprehensive view of the antigenic evolution of human influenza A (H1N1) virus can help formulate better strategy for its prevention and control.

Predicting Carriers of Ongoing Selective Sweeps without Knowledge of the Favored Allele

Methods for detecting the genomic signatures of natural selection have been heavily studied, and they have been successful in identifying many selective sweeps. For most of these sweeps, the favored allele remains unknown, making it difficult to distinguish carriers of the sweep from non-carriers. In an ongoing selective sweep, carriers of the favored allele are likely to contain a future most recent common ancestor. Therefore, identifying them may prove useful in predicting the evolutionary trajectory—for example, in contexts involving drug-resistant pathogen strains or cancer subclones. The main contribution of this paper is the development and analysis of a new statistic, the Haplotype Allele Frequency (HAF) score. The HAF score, assigned to individual haplotypes in a sample, naturally captures many of the properties shared by haplotypes carrying a favored allele. We provide a theoretical framework for computing expected HAF scores under different evolutionary scenarios, and we validate the theoretical predictions with simulations. As an application of HAF score computations, we develop an algorithm (PreCIOSS: Predicting Carriers of Ongoing Selective Sweeps) to identify carriers of the favored allele in selective sweeps, and we demonstrate its power on simulations of both hard and soft sweeps, as well as on data from well-known sweeps in human populations.

Methods for detecting the genomic signatures of natural selection have been heavily studied, and they have been successful in identifying genomic regions under positive selection. However, methods that detect positive selective sweeps do not typically identify the favored allele, or even the haplotypes carrying the favored allele. The main contribution of this paper is the development and analysis of a new statistic (the HAF score), assigned to individual haplotypes. Using both theoretical analyses and simulations, we describe how the HAF scores differ for carriers and non-carriers of the favored allele, and how they change dynamically during a selective sweep. We also develop an algorithm, PreCIOSS, for separating carriers and non-carriers. Our tool has broad applicability as carriers of the favored allele are likely to contain a future most recent common ancestor. Therefore, identifying them may prove useful in predicting the evolutionary trajectory—for example, in contexts involving drug-resistant pathogen strains or cancer subclones.

The effects of a deleterious mutation load on patterns of influenza A/H3N2's antigenic evolution in humans

Recent phylogenetic analyses indicate that RNA virus populations carry a significant deleterious mutation load. This mutation load has the potential to shape patterns of adaptive evolution via genetic linkage to beneficial mutations. Here, we examine the effect of deleterious mutations on patterns of influenza A subtype H3N2's antigenic evolution in humans. By first analyzing simple models of influenza that incorporate a mutation load, we show that deleterious mutations, as expected, act to slow the virus's rate of antigenic evolution, while making it more punctuated in nature. These models further predict three distinct molecular pathways by which antigenic cluster transitions occur, and we find phylogenetic patterns consistent with each of these pathways in influenza virus sequences. Simulations of a more complex phylodynamic model further indicate that antigenic mutations act in concert with deleterious mutations to reproduce influenza's spindly hemagglutinin phylogeny, co-circulation of antigenic variants, and high annual attack rates.

DOI:http://dx.doi.org/10.7554/eLife.07361.001

Each year, up to 15% of the world's population experience symptoms of an influenza infection, also commonly known as flu. The most common culprit is a strain of the virus called influenza type A subtype H3N2. One reason that so many people become infected each year is that this virus evolves rapidly. Within a few years, proteins on the surface of the virus known as antigens become less recognizable to the immune system of a person who has been previously infected. This means that the person can become ill with the virus again because their immune system cannot mount an effective response to the evolved virus strain.

Influenza virus strains evolve rapidly because their genetic material accumulates mutations quickly. Although some of these mutations are beneficial to the virus, other mutations are harmful and reduce the ability of the virus to spread. Sometimes beneficial mutations may occur alongside harmful ones, but it is not known how the harmful mutations affect the evolution of the virus.

Here, Koelle and Rasmussen used computer models of H3N2 influenza to examine the effect of harmful mutations on the evolution of this virus population. The models show that harmful mutations limit how quickly the antigens can evolve. Also, the presence of these harmful mutations effectively acts as a sieve: they allow only large changes in the antigens to establish in the virus population.

The models suggest that there are three routes by which large changes in the antigens on H3N2 viruses may occur. The first is by a single mutation that has a big effect on the antigens in viruses that only carry a few harmful mutations, but these large mutations would not happen very often. Another route may be through more common mutations that have only a small or moderate benefit, which would allow the virus to become more common in the population before it acquires a beneficial mutation with a much greater effect. The third possibility is that a large beneficial mutation may arise in viruses that have many harmful mutations. These harmful mutations may initially limit the ability of the virus to spread, but over time, some of these harmful mutations may then be lost.

Koelle and Rasmussen found that the computer models could recreate the patterns of virus evolution that have been observed in real strains of H3N2. Researchers use predictions of influenza evolution to help them decide which virus strains should be included in flu vaccines each year. Koelle and Rasmussen findings indicate that harmful mutations should be considered when making these predictions.

DOI:http://dx.doi.org/10.7554/eLife.07361.002

The evolution within us

The B-cell immune response is a remarkable evolutionary system found in jawed vertebrates. B-cell receptors, the membrane-bound form of antibodies, are capable of evolving high affinity to almost any foreign protein. High germline diversity and rapid evolution upon encounter with antigen explain the general adaptability of B-cell populations, but the dynamics of repertoires are less well understood. These dynamics are scientifically and clinically important. After highlighting the remarkable characteristics of naive and experienced B-cell repertoires, especially biased usage of genes encoding the B-cell receptors, we contrast methods of sequence analysis and their attempts to explain patterns of B-cell evolution. These phylogenetic approaches are currently unlinked to explicit models of B-cell competition, which analyse repertoire evolution at the level of phenotype, the affinities and specificities to particular antigenic sites. The models, in turn, suggest how chance, infection history and other factors contribute to different patterns of immunodominance and protection between people. Challenges in rational vaccine design, specifically vaccines to induce broadly neutralizing antibodies to HIV, underscore critical gaps in our understanding of B cells' evolutionary and ecological dynamics.

Positive Selection in CD8+ T-Cell Epitopes of Influenza Virus Nucleoprotein Revealed by a Comparative Analysis of Human and Swine Viral Lineages

Numerous experimental studies have demonstrated that CD8(+) T cells contribute to immunity against influenza by limiting viral replication. It is therefore surprising that rigorous statistical tests have failed to find evidence of positive selection in the epitopes targeted by CD8(+) T cells. Here we use a novel computational approach to test for selection in CD8(+) T-cell epitopes. We define all epitopes in the nucleoprotein (NP) and matrix protein (M1) with experimentally identified human CD8(+) T-cell responses and then compare the evolution of these epitopes in parallel lineages of human and swine influenza viruses that have been diverging since roughly 1918. We find a significant enrichment of substitutions that alter human CD8(+) T-cell epitopes in NP of human versus swine influenza virus, consistent with the idea that these epitopes are under positive selection. Furthermore, we show that epitope-altering substitutions in human influenza virus NP are enriched on the trunk versus the branches of the phylogenetic tree, indicating that viruses that acquire these mutations have a selective advantage. However, even in human influenza virus NP, sites in T-cell epitopes evolve more slowly than do nonepitope sites, presumably because these epitopes are under stronger inherent functional constraint. Overall, our work demonstrates that there is clear selection from CD8(+) T cells in human influenza virus NP and illustrates how comparative analyses of viral lineages from different hosts can identify positive selection that is otherwise obscured by strong functional constraint.

IMPORTANCE

There is a strong interest in correlates of anti-influenza immunity that are protective against diverse virus strains. CD8(+) T cells provide such broad immunity, since they target conserved viral proteins. An important question is whether T-cell immunity is sufficiently strong to drive influenza virus evolution. Although many studies have shown that T cells limit viral replication in animal models and are associated with decreased symptoms in humans, no studies have proven with statistical significance that influenza virus evolves under positive selection to escape T cells. Here we use comparisons of human and swine influenza viruses to rigorously demonstrate that human influenza virus evolves under pressure to fix mutations in the nucleoprotein that promote escape from T cells. We further show that viruses with these mutations have a selective advantage since they are preferentially located on the "trunk" of the phylogenetic tree. Overall, our results show that CD8(+) T cells targeting nucleoprotein play an important role in shaping influenza virus evolution.

Evolution of the hemagglutinin expressed by human influenza A(H1N1)pdm09 and A(H3N2) viruses circulating between 2008-2009 and 2013-2014 in Germany

This report describes the evolution of the influenza A(H1N1)pdm09 and A(H3N2) viruses circulating in Germany between 2008-2009 and 2013-2014. The phylogenetic analysis of the hemagglutinin (HA) genes of both subtypes revealed similar evolution of the HA variants that were also seen worldwide with minor exceptions. The analysis showed seven distinct HA clades for A(H1N1)pdm09 and six HA clades for A(H3N2) viruses. Herald strains of both subtypes appeared sporadically since 2008-2009. Regarding A(H1N1)pdm09, herald strains of HA clade 3 and 4 were detected late in the 2009-2010 season. With respect to A(H3N2), we found herald strains of HA clade 3, 4 and 7 between 2009 and 2012. Those herald strains were predominantly seen for minor and not for major HA clades. Generally, amino acid substitutions were most frequently found in the globular domain, including substitutions near the antigenic sites or the receptor binding site. Differences between both influenza A subtypes were seen with respect to the position of the indicated substitutions in the HA. For A(H1N1)pdm09 viruses, we found more substitutions in the stem region than in the antigenic sites. In contrast, in A(H3N2) viruses most changes were identified in the major antigenic sites and five changes of potential glycosylation sites were identified in the head of the HA monomer. Interestingly, we found in seasons with less influenza activity a relatively high increase of substitutions in the head of the HA in both subtypes. This might be explained by the fact that mutations under negative selection are subsequently compensated by secondary mutations to restore important functions e.g. receptor binding properties. A better knowledge of basic evolution strategies of influenza viruses will contribute to the refinement of predictive mathematical models for identifying novel antigenic drift variants.

Fitness Landscapes of Functional RNAs

The notion of fitness landscapes, a map between genotype and fitness, was proposed more than 80 years ago. For most of this time data was only available for a few alleles, and thus we had only a restricted view of the whole fitness landscape. Recently, advances in genetics and molecular biology allow a more detailed view of them. Here we review experimental and theoretical studies of fitness landscapes of functional RNAs, especially aptamers and ribozymes. We find that RNA structures can be divided into critical structures, connecting structures, neutral structures and forbidden structures. Such characterisation, coupled with theoretical sequence-to-structure predictions, allows us to construct the whole fitness landscape. Fitness landscapes then can be used to study evolution, and in our case the development of the RNA world.

A Visualization Tool for Calculating the Genetic Substitution Patterns Between Two Different Groups

We developed simulation tool for influenza virus variation (SimFluVar), an analytics software for calculating genomic variation among members of the influenza virus group. This study is related to computational evolutionary biology and evolutionary bioinformatics. SimFluVar is an analytical tool that can be used to calculate codon substitution patterns of viral genes. Designed to compare a large number of nucleotide sequences, SimFluVar provides precise patterns of codon variations between two viral groups, especially for the influenza virus. SimFluVar also provides useful functions, such as editing and visualization of the result matrix. This new tool can be used to analyze codon variation patterns over time as well as to analyze the genomic differences between viruses obtained from different geographical locations. SimFluVar is developed in C++, and Java RCP is used as a distribution package. SimFluVar, including the associated documentation, manuals, and examples, is publicly available at http://lcbb.snu.ac.kr/simfluvar.

How next-generation sequencing and multiscale data analysis will transform infectious disease management

We should integrate next-generation sequencing data from pathogen specimens with phenotypes from electronic medical records to create quantitative, predictive models of infectious disease. Precision infection control and antimicrobial interventions can address urgent global problems, including healthcare-associated infections and multidrug resistance.

Recent reviews have examined the extent to which routine next-generation sequencing (NGS) on clinical specimens will improve the capabilities of clinical microbiology laboratories in the short term, but do not explore integrating NGS with clinical data from electronic medical records (EMRs), immune profiling data, and other rich datasets to create multiscale predictive models. This review introduces a range of “omics” and patient data sources relevant to managing infections and proposes 3 potentially disruptive applications for these data in the clinical workflow. The combined threats of healthcare-associated infections and multidrug-resistant organisms may be addressed by multiscale analysis of NGS and EMR data that is ideally updated and refined over time within each healthcare organization. Such data and analysis should form the cornerstone of future learning health systems for infectious disease.

Coordinated Evolution of Influenza A Surface Proteins

The surface proteins hemagglutinin (HA) and neuraminidase (NA) of human influenza A virus evolve under selection pressures to escape adaptive immune responses and antiviral drug treatments. In addition to these external selection pressures, some mutations in HA are known to affect the adaptive landscape of NA, and vice versa, because these two proteins are physiologically interlinked. However, the extent to which evolution of one protein affects the evolution of the other one is unknown. Here we develop a novel phylogenetic method for detecting the signatures of such genetic interactions between mutations in different genes – that is, inter-gene epistasis. Using this method, we show that influenza surface proteins evolve in a coordinated way, with mutations in HA affecting subsequent spread of mutations in NA and vice versa, at many sites. Of particular interest is our finding that the oseltamivir-resistance mutations in NA in subtype H1N1 were likely facilitated by prior mutations in HA. Our results illustrate that the adaptive landscape of a viral protein is remarkably sensitive to its genomic context and, more generally, that the evolution of any single protein must be understood within the context of the entire evolving genome.

The fitness of an organism depends on the coordinated function of many genes. Thus, how a mutation in one gene affects fitness often depends on what mutations are present in other genes. This dependence is called “genetic interaction” or “epistasis”. The prevalence and type of such interactions are not well understood. Epistasis can be inferred from time-series sequencing data when a mutation in one gene is observed to facilitate the spread of a mutation in another gene. However, the situation is much more complicated when new combinations of genes are formed by processes such as recombination or reassortment. In such cases, deducing the time and order of genetic changes is difficult. Here, we devise a method to infer pairs of mutations in different genes which closely follow one another in the presence of reassortment. We apply it to evolution of two surface proteins of influenza A virus, hemagglutinin and neuraminidase, which are important targets for the human immune system and drugs. We show that mutations in one of these proteins are often facilitated by prior mutations, or compensated by subsequent mutations, in the other protein. In particular, drug-resistance mutations in neuraminidase were likely made possible by prior mutation in hemagglutinin. Knowledge of such interactions is necessary to fully understand and predict evolution.

Fitness Inference from Short-Read Data: Within-Host Evolution of a Reassortant H5N1 Influenza Virus

We present a method to infer the role of selection acting during the within-host evolution of the influenza virus from short-read genome sequence data. Linkage disequilibrium between loci is accounted for by treating short-read sequences as noisy multilocus emissions from an underlying model of haplotype evolution. A hierarchical model-selection procedure is used to infer the underlying fitness landscape of the virus insofar as that landscape is explored by the viral population. In a first application of our method, we analyze data from an evolutionary experiment describing the growth of a reassortant H5N1 virus in ferrets. Across two sets of replica experiments we infer multiple alleles to be under selection, including variants associated with receptor binding specificity, glycosylation, and with the increased transmissibility of the virus. We identify epistasis as an important component of the within-host fitness landscape, and show that adaptation can proceed through multiple genetic pathways.

Viral evolution: beyond drift and shift

Technological advances have allowed aspects of viral evolution to be explored at unprecedented scales. As a consequence, new quantitative approaches are needed to investigate features of viral evolution that fall outside traditional areas of study, such as antigenic evolution. We examine three areas of viral evolution where tools from disciplines such as statistical physics, topology, and information theory have been used recently as quantitative frameworks for large-scale studies and, in some cases, suggest a novel theoretical approach to a problem. Ongoing interaction among these disciplines with biology is necessary so that experimental researchers can determine which quantitative tools are right for them and quantitative researchers can learn which aspects of viral evolution can be understood and advanced with their approaches.

Spatiotemporal dynamics of Puumala hantavirus associated with its rodent host, Myodes glareolus

Many viruses significantly impact human and animal health. Understanding the population dynamics of these viruses and their hosts can provide important insights for epidemiology and virus evolution. Puumala virus (PUUV) is a European hantavirus that may cause regional outbreaks of hemorrhagic fever with renal syndrome in humans. Here, we analyzed the spatiotemporal dynamics of PUUV circulating in local populations of its rodent reservoir host, the bank vole (Myodes glareolus) during eight years. Phylogenetic and population genetic analyses of all three genome segments of PUUV showed strong geographical structuring at a very local scale. There was a high temporal turnover of virus strains in the local bank vole populations, but several virus strains persisted through multiple years. Phylodynamic analyses showed no significant changes in the local effective population sizes of PUUV, although vole numbers and virus prevalence fluctuated widely. Microsatellite data demonstrated also a temporally persisting subdivision between local vole populations, but these groups did not correspond to the subdivision in the virus strains. We conclude that restricted transmission between vole populations and genetic drift play important roles in shaping the genetic structure and temporal dynamics of PUUV in its natural host which has several implications for zoonotic risks of the human population.

Hemagglutinin Sequence Conservation Guided Stem Immunogen Design from Influenza A H3 Subtype

Seasonal epidemics caused by influenza A (H1 and H3 subtypes) and B viruses are a major global health threat. The traditional, trivalent influenza vaccines have limited efficacy because of rapid antigenic evolution of the circulating viruses. This antigenic variability mediates viral escape from the host immune responses, necessitating annual vaccine updates. Influenza vaccines elicit a protective antibody response, primarily targeting the viral surface glycoprotein hemagglutinin (HA). However, the predominant humoral response is against the hypervariable head domain of HA, thereby restricting the breadth of protection. In contrast, the conserved, subdominant stem domain of HA is a potential "universal" vaccine candidate. We designed an HA stem-fragment immunogen from the 1968 pandemic H3N2 strain (A/Hong Kong/1/68) guided by a comprehensive H3 HA sequence conservation analysis. The biophysical properties of the designed immunogen were further improved by C-terminal fusion of a trimerization motif, "isoleucine-zipper", or "foldon". These immunogens elicited cross-reactive, antiviral antibodies and conferred partial protection against a lethal, homologous HK68 virus challenge in vivo. Furthermore, bacterial expression of these immunogens is economical and facilitates rapid scale-up.

nextflu: real-time tracking of seasonal influenza virus evolution in humans

Summary: Seasonal influenza viruses evolve rapidly, allowing them to evade immunity in their human hosts and reinfect previously infected individuals. Similarly, vaccines against seasonal influenza need to be updated frequently to protect against an evolving virus population. We have thus developed a processing pipeline and browser-based visualization that allows convenient exploration and analysis of the most recent influenza virus sequence data. This web-application displays a phylogenetic tree that can be decorated with additional information such as the viral genotype at specific sites, sampling location and derived statistics that have been shown to be predictive of future virus dynamics. In addition, mutation, genotype and clade frequency trajectories are calculated and displayed.

Availability and implementation: Python and Javascript source code is freely available from https://github.com/blab/nextflu, while the web-application is live at http://nextflu.org.

Contact:tbedford@fredhutch.org

In Silico Prediction and Experimental Confirmation of HA Residues Conferring Enhanced Human Receptor Specificity of H5N1 Influenza A Viruses

Newly emerging influenza A viruses (IAV) pose a major threat to human health by causing seasonal epidemics and/or pandemics, the latter often facilitated by the lack of pre-existing immunity in the general population. Early recognition of candidate pandemic influenza viruses (CPIV) is of crucial importance for restricting virus transmission and developing appropriate therapeutic and prophylactic strategies including effective vaccines. Often, the pandemic potential of newly emerging IAV is only fully recognized once the virus starts to spread efficiently causing serious disease in humans. Here, we used a novel phylogenetic algorithm based on the informational spectrum method (ISM) to identify potential CPIV by predicting mutations in the viral hemagglutinin (HA) gene that are likely to (differentially) affect critical interactions between the HA protein and target cells from bird and human origin, respectively. Predictions were subsequently validated by generating pseudotyped retrovirus particles and genetically engineered IAV containing these mutations and characterizing potential effects on virus entry and replication in cells expressing human and avian IAV receptors, respectively. Our data suggest that the ISM-based algorithm is suitable to identify CPIV among IAV strains that are circulating in animal hosts and thus may be a new tool for assessing pandemic risks associated with specific strains.

Selecting vaccine strains for H3N2 human influenza A virus

H3N2 human influenza A virus causes epidemics of influenza mainly in the winter season in temperate regions. Since the antigenicity of this virus evolves rapidly, several attempts have been made to predict the major amino acid sequence of hemagglutinin 1 (HA1) in the target season of vaccination. However, the usefulness of predicted sequence was unclear because its relationship to the antigenicity was unknown. Here the antigenic model for estimating the degree of antigenic difference (antigenic distance) between amino acid sequences of HA1 was integrated into the process of selecting vaccine strains for H3N2 human influenza A virus. When the effectiveness of a potential vaccine strain for a target season was evaluated retrospectively using the average antigenic distance between the strain and the epidemic viruses sampled in the target season, the most effective vaccine strain was identified mostly in the season one year before the target season (pre-target season). Effectiveness of actual vaccines appeared to be lower than that of the strains randomly chosen in the pre-target season on average. It was recommended to replace the vaccine strain for every target season with the strain having the smallest average antigenic distance to the others in the pre-target season. The procedure of selecting vaccine strains for future epidemic seasons described in the present study was implemented in the influenza virus forecasting system (INFLUCAST) (http://www.nsc.nagoya-cu.ac.jp/~yossuzuk/influcast.html).

Tackling Drug Resistant Infection Outbreaks of Global Pandemic Escherichia coli ST131 Using Evolutionary and Epidemiological Genomics

High-throughput molecular screening is required to investigate the origin and diffusion of antimicrobial resistance in pathogen outbreaks. The most frequent cause of human infection is Escherichia coli, which is dominated by sequence type 131 (ST131)-a set of rapidly radiating pandemic clones. The highly infectious clades of ST131 originated firstly by a mutation enhancing conjugation and adhesion. Secondly, single-nucleotide polymorphisms occurred enabling fluoroquinolone-resistance, which is near-fixed in all ST131. Thirdly, broader resistance through beta-lactamases has been gained and lost frequently, symptomatic of conflicting environmental selective effects. This flexible approach to gene exchange is worrying and supports the proposition that ST131 will develop an even wider range of plasmid and chromosomal elements promoting antimicrobial resistance. To stop ST131, deep genome sequencing is required to understand the origin, evolution and spread of antimicrobial resistance genes. Phylogenetic methods that decipher past events can predict future patterns of virulence and transmission based on genetic signatures of adaptation and gene exchange. Both the effect of partial antimicrobial exposure and cell dormancy caused by variation in gene expression may accelerate the development of resistance. High-throughput sequencing can decode measurable evolution of cell populations within patients associated with systems-wide changes in gene expression during treatments. A multi-faceted approach can enhance assessment of antimicrobial resistance in E. coli ST131 by examining transmission dynamics between hosts to achieve a goal of pre-empting resistance before it emerges by optimising antimicrobial treatment protocols.

Obstruction of adaptation in diploids by recessive, strongly deleterious alleles

Recessive deleterious mutations are common, causing many genetic disorders in humans and producing inbreeding depression in the majority of sexually reproducing diploids. The abundance of recessive deleterious mutations in natural populations suggests they are likely to be present on a chromosome when a new adaptive mutation occurs, yet the dynamics of recessive deleterious hitchhikers and their impact on adaptation remains poorly understood. Here we model how a recessive deleterious mutation impacts the fate of a genetically linked dominant beneficial mutation. The frequency trajectory of the adaptive mutation in this case is dramatically altered and results in what we have termed a "staggered sweep." It is named for its three-phased trajectory: (i) Initially, the two linked mutations have a selective advantage while rare and will increase in frequency together, then (ii), at higher frequencies, the recessive hitchhiker is exposed to selection and can cause a balanced state via heterozygote advantage (the staggered phase), and (iii) finally, if recombination unlinks the two mutations, then the beneficial mutation can complete the sweep to fixation. Using both analytics and simulations, we show that strongly deleterious recessive mutations can substantially decrease the probability of fixation for nearby beneficial mutations, thus creating zones in the genome where adaptation is suppressed. These mutations can also significantly prolong the number of generations a beneficial mutation takes to sweep to fixation, and cause the genomic signature of selection to resemble that of soft or partial sweeps. We show that recessive deleterious variation could impact adaptation in humans and Drosophila.

Geometric Constraints Dominate the Antigenic Evolution of Influenza H3N2 Hemagglutinin

We have carried out a comprehensive analysis of the determinants of human influenza A H3 hemagglutinin evolution. We consider three distinct predictors of evolutionary variation at individual sites: solvent accessibility (as a proxy for protein fold stability and/or conservation), Immune Epitope Database (IEDB) epitope sites (as a proxy for host immune bias), and proximity to the receptor-binding region (as a proxy for one of the functions of hemagglutinin-to bind sialic acid). Individually, these quantities explain approximately 15% of the variation in site-wise dN/dS. In combination, solvent accessibility and proximity explain 32% of the variation in dN/dS; incorporating IEDB epitope sites into the model adds only an additional 2 percentage points. Thus, while solvent accessibility and proximity perform largely as independent predictors of evolutionary variation, they each overlap with the epitope-sites predictor. Furthermore, we find that the historical H3 epitope sites, which date back to the 1980s and 1990s, only partially overlap with the experimental sites from the IEDB, and display similar overlap in predictive power when combined with solvent accessibility and proximity. We also find that sites with dN/dS > 1, i.e., the sites most likely driving seasonal immune escape, are not correctly predicted by either historical or IEDB epitope sites, but only by proximity to the receptor-binding region. In summary, a simple geometric model of HA evolution outperforms a model based on epitope sites. These results suggest that either the available epitope sites do not accurately represent the true influenza antigenic sites or that host immune bias may be less important for influenza evolution than commonly thought.

Darwin and Fisher meet at biotech: on the potential of computational molecular evolution in industry

Background

Today computational molecular evolution is a vibrant research field that benefits from the availability of large and complex new generation sequencing data – ranging from full genomes and proteomes to microbiomes, metabolomes and epigenomes. The grounds for this progress were established long before the discovery of the DNA structure. Specifically, Darwin’s theory of evolution by means of natural selection not only remains relevant today, but also provides a solid basis for computational research with a variety of applications. But a long-term progress in biology was ensured by the mathematical sciences, as exemplified by Sir R. Fisher in early 20th century. Now this is true more than ever: The data size and its complexity require biologists to work in close collaboration with experts in computational sciences, modeling and statistics.

Results

Natural selection drives function conservation and adaptation to emerging pathogens or new environments; selection plays key role in immune and resistance systems. Here I focus on computational methods for evaluating selection in molecular sequences, and argue that they have a high potential for applications. Pharma and biotech industries can successfully use this potential, and should take the initiative to enhance their research and development with state of the art bioinformatics approaches.

Conclusions

This review provides a quick guide to the current computational approaches that apply the evolutionary principles of natural selection to real life problems – from drug target validation, vaccine design and protein engineering to applications in agriculture, ecology and conservation.

Modularity enhances the rate of evolution in a rugged fitness landscape

Biological systems are modular, and this modularity affects the evolution of biological systems over time and in different environments. We here develop a theory for the dynamics of evolution in a rugged, modular fitness landscape. We show analytically how horizontal gene transfer couples to the modularity in the system and leads to more rapid rates of evolution at short times. The model, in general, analytically demonstrates a selective pressure for the prevalence of modularity in biology. We use this model to show how the evolution of the influenza virus is affected by the modularity of the proteins that are recognized by the human immune system. Approximately 25% of the observed rate of fitness increase of the virus could be ascribed to a modular viral landscape.

Modeling infectious disease dynamics in the complex landscape of global health

Despite some notable successes in the control of infectious diseases, transmissible pathogens still pose an enormous threat to human and animal health. The ecological and evolutionary dynamics of infections play out on a wide range of interconnected temporal, organizational, and spatial scales, which span hours to months, cells to ecosystems, and local to global spread. Moreover, some pathogens are directly transmitted between individuals of a single species, whereas others circulate among multiple hosts, need arthropod vectors, or can survive in environmental reservoirs. Many factors, including increasing antimicrobial resistance, increased human connectivity and changeable human behavior, elevate prevention and control from matters of national policy to international challenge. In the face of this complexity, mathematical models offer valuable tools for synthesizing information to understand epidemiological patterns, and for developing quantitative evidence for decision-making in global health.

Scaling laws describe memories of host-pathogen riposte in the HIV population

The enormous genetic diversity and mutability of HIV has prevented effective control of this virus by natural immune responses or vaccination. Evolution of the circulating HIV population has thus occurred in response to diverse, ultimately ineffective, immune selection pressures that randomly change from host to host. We show that the interplay between the diversity of human immune responses and the ways that HIV mutates to evade them results in distinct sets of sequences defined by similar collectively coupled mutations. Scaling laws that relate these sets of sequences resemble those observed in linguistics and other branches of inquiry, and dynamics reminiscent of neural networks are observed. Like neural networks that store memories of past stimulation, the circulating HIV population stores memories of host-pathogen combat won by the virus. We describe an exactly solvable model that captures the main qualitative features of the sets of sequences and a simple mechanistic model for the origin of the observed scaling laws. Our results define collective mutational pathways used by HIV to evade human immune responses, which could guide vaccine design.

The value of monitoring to control evolving populations

Populations can evolve to adapt to external changes. The capacity to evolve and adapt makes successful treatment of infectious diseases and cancer difficult. Indeed, therapy resistance has become a key challenge for global health. Therefore, ideas of how to control evolving populations to overcome this threat are valuable. Here we use the mathematical concepts of stochastic optimal control to study what is needed to control evolving populations. Following established routes to calculate control strategies, we first study how a polymorphism can be maintained in a finite population by adaptively tuning selection. We then introduce a minimal model of drug resistance in a stochastically evolving cancer cell population and compute adaptive therapies. When decisions are in this manner based on monitoring the response of the tumor, this can outperform established therapy paradigms. For both case studies, we demonstrate the importance of high-resolution monitoring of the target population to achieve a given control objective, thus quantifying the intuition that to control, one must monitor.

Time dependence of evolutionary metrics during the 2009 pandemic influenza virus outbreak

With the expansion of DNA sequencing technology, quantifying evolution in emerging viral outbreaks has become an important tool for scientists and public health officials. Although it is known that the degree of sequence divergence significantly affects the calculation of evolutionary metrics in viral outbreaks, the extent and duration of this effect during an actual outbreak remains unclear. We have analyzed how limited divergence time during an early viral outbreak affects the accuracy of molecular evolutionary metrics. Using sequence data from the first 25 months of the 2009 pandemic H1N1 (pH1N1) outbreak, we calculated each of three different standard evolutionary metrics-molecular clock rate (i.e., evolutionary rate), whole gene dN/dS, and site-wise dN/dS-for hemagglutinin and neuraminidase, using increasingly longer time windows, from 1 month to 25 months. For the molecular clock rate, we found that at least three to four months of temporal divergence from the start of sampling was required to make precise estimates that also agreed with long-term values. For whole gene dN/dS, we found that at least two months of data were required to generate precise estimates, but six to nine months were required for estimates to approach their long term values. For site-wise dN/dS estimates, we found that at least six months of sampling divergence was required before the majority of sites had at least one mutation and were thus evolutionarily informative. Furthermore, eight months of sampling divergence was required before the site-wise estimates appropriately reflected the distribution of values expected from known protein-structure-based evolutionary pressure in influenza. In summary, we found that evolutionary metrics calculated from gene sequence data in early outbreaks should be expected to deviate from their long-term estimates for at least several months after the initial emergence and sequencing of the virus.

Can we read the future from a tree?

A new method uses genealogies based on sequence data to predict short-term evolutionary patterns.