Epidemiological and clinical consequences of within-host evolution

Within-Host Mathematical Models of Antibiotic Resistance

Mathematical models have been used to study the spread of infectious diseases from person to person. More recently studies are developing within-host modeling which provides an understanding of how pathogens-bacteria, fungi, parasites, or viruses-develop, spread, and evolve inside a single individual and their interaction with the host's immune system.Such models have the potential to provide a more detailed and complete description of the pathogenesis of diseases within-host and identify other influencing factors that may not be detected otherwise. Mathematical models can be used to aid understanding of the global antibiotic resistance (ABR) crisis and identify new ways of combating this threat.ABR occurs when bacteria respond to random or selective pressures and adapt to new environments through the acquisition of new genetic traits. This is usually through the acquisition of a piece of DNA from other bacteria, a process called horizontal gene transfer (HGT), the modification of a piece of DNA within a bacterium, or through. Bacteria have evolved mechanisms that enable them to respond to environmental threats by mutation, and horizontal gene transfer (HGT): conjugation; transduction; and transformation. A frequent mechanism of HGT responsible for spreading antibiotic resistance on the global scale is conjugation, as it allows the direct transfer of mobile genetic elements (MGEs). Although there are several MGEs, the most important MGEs which promote the development and rapid spread of antimicrobial resistance genes in bacterial populations are plasmids and transposons. Each of the resistance-spread-mechanisms mentioned above can be modeled allowing us to understand the process better and to define strategies to reduce resistance.

Duration of antiretroviral therapy impacts the degree of residual SIV infection in the gut in long-term non-progressing Chinese rhesus macaques

The gut is a major reservoir in HIV-infected individuals on antiretroviral therapy (ART) and in long-term non-progressors (LTNPs). Whether ART reduces gut infection and reservoirs in LTNPs is unknown. Herein, SIV-infected LTNP Rhesus macaques were treated with short- or long-term ART, and SIV envelope gp120 sequences obtained from single genome amplification were analyzed before and after ART in peripheral blood and the intestine. Although ART does not eliminate SIV in these LTNPs, a longer ART period dramatically reduces SIV infection in the gut. This study highlights the importance of long-term ART in LTNPs to minimize gut infection and prolong remission.

Characterizing viral within-host diversity in fast and non-equilibrium demo-genetic dynamics

High-throughput sequencing has opened the route for a deep assessment of within-host genetic diversity that can be used, e.g., to characterize microbial communities and to infer transmission links in infectious disease outbreaks. The performance of such characterizations and inferences cannot be analytically assessed in general and are often grounded on computer-intensive evaluations. Then, being able to simulate within-host genetic diversity across time under various demo-genetic assumptions is paramount to assess the performance of the approaches of interest. In this context, we built an original model that can be simulated to investigate the temporal evolution of genotypes and their frequencies under various demo-genetic assumptions. The model describes the growth and the mutation of genotypes at the nucleotide resolution conditional on an overall within-host viral kinetics, and can be tuned to generate fast non-equilibrium demo-genetic dynamics. We ran simulations of this model and computed classic diversity indices to characterize the temporal variation of within-host genetic diversity (from high-throughput amplicon sequences) of virus populations under three demographic kinetic models of viral infection. Our results highlight how demographic (viral load) and genetic (mutation, selection, or drift) factors drive variations in within-host diversity during the course of an infection. In particular, we observed a non-monotonic relationship between pathogen population size and genetic diversity, and a reduction of the impact of mutation on diversity when a non-specific host immune response is activated. The large variation in the diversity patterns generated in our simulations suggests that the underlying model provides a flexible basis to produce very diverse demo-genetic scenarios and test, for instance, methods for the inference of transmission links during outbreaks.

Modelling the effect of within-host dynamics on the diversity of a multi-strain pathogen

Multi-strain pathogens such as Group A Streptococcus, Streptococcus pneumoniae, and Staphylococcus aureus cause millions of infections each year with a substantial health burden. Control of multi-strain pathogens can be complicated by the high strain diversity often observed in endemic settings. It is not well understood how high strain diversity is maintained in populations, given that they compete with each other both directly (within an individual host) and indirectly (via host immunity). Previous modelling studies have investigated how indirect competition affects the prevalence and diversity of strains. However, these studies often make simplifying assumptions about the direct competition that occurs within hosts. Currently, little data is available to validate these assumptions, hence there is a need to clarify how sensitive model outputs are to these assumptions. In this study, we compare the dynamics of multi-strain pathogens under different assumptions about direct competition between strains using an agent-based model. We find that the assumptions made about direct competition can affect the epidemiological dynamics, particularly when there is no long-term immunity following infections and a low rate of importation of non-circulating strains. Our results suggest that while direct and indirect competition can each decrease strain diversity when they act in isolation, they may increase strain diversity when they act together. This finding highlights the importance of examining sensitivity to assumptions about strain competition. In particular, omitting consideration of direct competition can lead to inaccurate estimates of the likely effectiveness of control strategies as changes in strain diversity shift the level of direct strain competition.

A Hierarchical Genotyping Framework Using DNA Melting Temperatures Applied to Adenovirus Species Typing

Known genetic variation, in conjunction with post-PCR melting curve analysis, can be leveraged to provide increased taxonomic detail for pathogen identification in commercial molecular diagnostic tests. Increased taxonomic detail may be used by clinicians and public health decision-makers to observe circulation patterns, monitor for outbreaks, and inform testing practices. We propose a method for expanding the taxonomic resolution of PCR diagnostic systems by incorporating a priori knowledge of assay design and sequence information into a genotyping classification model. For multiplexed PCR systems, this framework is generalized to incorporate information from multiple assays to increase classification accuracy. An illustrative hierarchical classification model for human adenovirus (HAdV) species was developed and demonstrated ~95% cross-validated accuracy on a labeled dataset. The model was then applied to a near-real-time surveillance dataset in which deidentified adenovirus detected patient test data from 2018 through 2021 were classified into one of six adenovirus species. These results show a marked change in both the predicted prevalence for HAdV and the species makeup with the onset of the COVID-19 pandemic. HAdV-B decreased from a pre-pandemic predicted prevalence of up to 40% to less than 5% in 2021, while HAdV-A and HAdV-F species both increased in predicted prevalence.

The source of individual heterogeneity shapes infectious disease outbreaks

There is known heterogeneity between individuals in infectious disease transmission patterns. The source of this heterogeneity is thought to affect epidemiological dynamics but studies tend not to control for the overall heterogeneity in the number of secondary cases caused by an infection. To explore the role of individual variation in infection duration and transmission rate in parasite emergence and spread, while controlling for this potential bias, we simulate stochastic outbreaks with and without parasite evolution. As expected, heterogeneity in the number of secondary cases decreases the probability of outbreak emergence. Furthermore, for epidemics that do emerge, assuming more realistic infection duration distributions leads to faster outbreaks and higher epidemic peaks. When parasites require adaptive mutations to cause large epidemics, the impact of heterogeneity depends on the underlying evolutionary model. If emergence relies on within-host evolution, decreasing the infection duration variance decreases the probability of emergence. These results underline the importance of accounting for realistic distributions of transmission rates to anticipate the effect of individual heterogeneity on epidemiological dynamics.

Antigenic diversity of MASP gene family of Trypanosoma cruzi

Trypanosoma cruzi, the etiological agent of Chagas disease (CD), is a heterogeneous species with high genetic and phenotypic diversity. MASP is the second largest multigene family of T. cruzi. The high degree of polymorphism of the family associated with its location at the surface of infective forms of T. cruzi suggests that MASP participates in mechanisms of host-parasite interaction. In this work, MASP members were divided into 7 subgroups based on protein sequence similarity, and one representative member from each subgroup was chosen to be expressed recombinantly. Immunogenicity of recombinant MASP proteins (rMASP) was investigated using different sera panels from T. cruzi infected mice. To mimic a natural condition in which different MASP members are expressed at the same time in the parasite population, a multiplex bead-based flow cytometry assay was also standardized. Results showed that rMASPs are poorly recognized by sera from mice infected with Colombiana strain, whereas sera from mice infected with CL Brener and Y display high reactivity against the majority of rMASPs tested. Flow cytometry showed that MASP recognition profile changes 10 days after infection. Also, multiplex assay suggests that MASP M1 and M2 are more immunogenic than the other MASP members evaluated that may play an immunodominant role during infection.

Epstein-Barr Virus in Burkitt Lymphoma in Africa Reveals a Limited Set of Whole Genome and LMP-1 Sequence Patterns: Analysis of Archival Datasets and Field Samples From Uganda, Tanzania, and Kenya

Epstein-Barr virus (EBV) is associated with endemic Burkitt lymphoma (eBL), but the contribution of EBV variants is ill-defined. Studies of EBV whole genome sequences (WGS) have identified phylogroups that appear to be distinct for Asian versus non-Asian EBV, but samples from BL or Africa, where EBV was first discovered, are under-represented. We conducted a phylogenetic analysis of EBV WGS and LMP-1 sequences obtained primarily from BL patients in Africa and representative non-African EBV from other conditions or regions using data from GenBank, Sequence Read Archive, or Genomic Data Commons for the Burkitt Lymphoma Genome Sequencing Project (BLGSP) to generate data to support the use of a simpler biomarker of geographic or phenotypic associations. We also investigated LMP-1 patterns in 414 eBL cases and 414 geographically matched controls in the Epidemiology of Burkitt Lymphoma in East African children and minors (EMBLEM) study using LMP-1 PCR and Sanger sequencing. Phylogenetic analysis revealed distinct genetic patterns of African versus Asian EBV sequences. We identified 281 single nucleotide variations (SNVs) in LMP-1 promoter and coding region, which formed 12 unique patterns (A to L). Nine patterns (A, AB, C, D, F, I, J, K and L) predominated in African EBV, of which four were found in 92% of BL samples (A, AB, D, and H). Predominant patterns were B and G in Asia and H in Europe. EBV positivity in peripheral blood was detected in 95.6% of EMBLEM eBL cases versus 79.2% of the healthy controls (odds ratio [OR] =3.83; 95% confidence interval 2.06-7.14). LMP-1 was successfully sequenced in 66.7% of the EBV DNA positive cases but in 29.6% of the controls (ORs ranging 5-11 for different patterns). Four LMP-1 patterns (A, AB, D, and K) were detected in 63.1% of the cases versus 27.1% controls (ORs ranges: 5.58-11.4). Dual strain EBV infections were identified in WGS and PCR-Sanger data. In conclusion, EBV from Africa is phylogenetically separate from EBV in Asia. Genetic diversity in LMP-1 formed 12 patterns, which showed promising geographic and phenotypic associations. Presence of multiple strain infection should be considered in efforts to refine or improve EBV markers of ancestry or phenotype.

Lay Summary

Epstein-Barr virus (EBV) infection, a ubiquitous infection, contributes to the etiology of both Burkitt Lymphoma (BL) and nasopharyngeal carcinoma, yet their global distributions vary geographically with no overlap. Genomic variation in EBV is suspected to play a role in the geographical patterns of these EBV-associated cancers, but relatively few EBV samples from BL have been comprehensively studied. We sought to compare phylogenetic patterns of EBV genomes obtained from BL samples in Africa and from tumor and non-tumor samples from elsewhere. We concluded that EBV obtained from BL in Africa is genetically separate from EBV in Asia. Through comprehensive analysis of nucleotide variations in EBV's LMP-1 gene, we describe 12 LMP-1 patterns, two of which (B and G) were found mostly in Asia. Four LMP-1 patterns (A, AB, D, and F) accounted for 92% of EBVs sequenced from BL in Africa. Our results identified extensive diversity of EBV, but BL in Africa was associated with a limited number of variants identified, which were different from those identified in Asia. Further research is needed to optimize the use of PCR and sequencing to study LMP-1 diversity for classification of EBV variants and for use in epidemiologic studies to characterize geographic and/or phenotypic associations of EBV variants with EBV-associated malignancies, including eBL.

HAMdetector: A Bayesian regression model that integrates information to detect HLA-associated mutations

MOTIVATION

A key process in anti-viral adaptive immunity is that the Human Leukocyte Antigen system (HLA) presents epitopes as Major Histocompatibility Complex I (MHC I) protein-peptide complexes on cell surfaces and in this way alerts CD8+ cytotoxic T-Lymphocytes (CTLs). This pathway exerts strong selection pressure on viruses, favoring viral mutants that escape recognition by the HLA/CTL system. Naturally, such immune escape mutations often emerge in highly variable viruses, e.g. HIV or HBV, as HLA-associated mutations (HAMs), specific to the hosts MHC I proteins. The reliable identification of HAMs is not only important for understanding viral genomes and their evolution, but it also impacts the development of broadly effective anti-viral treatments and vaccines against variable viruses. By their very nature, HAMs are amenable to detection by statistical methods in paired sequence/HLA data. However, HLA alleles are very polymorphic in the human host population which makes the available data relatively sparse and noisy. Under these circumstances, one way to optimize HAM detection is to integrate all relevant information in a coherent model. Bayesian inference offers a principled approach to achieve this.

RESULTS

We present a new Bayesian regression model for the detection of HAMs that integrates a sparsity-inducing prior, epitope predictions, and phylogenetic bias assessment, and that yields easily interpretable quantitative information on HAM candidates. The model predicts experimentally confirmed HAMs as having high posterior probabilities, and it performs well in comparison to state-of-the-art models for several data sets from individuals infected with HBV, HDV, and HIV.

AVAILABILITY

The source code of this software is available at https://github.com/HAMdetector/Escape.jl under a permissive MIT license.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The role of a programmatic immune response on the evolution of pathogen traits

In modelling pathogen evolution during epidemics, it is important to understand the interactions between within-host infection dynamics and between-host pathogen transmission. Multiscale models often assume an immune response that is highly responsive to pathogen dynamics. Empirical evidence, however, suggests that the immune response in acute infections is triggered and programmatic. This leads to somewhat more predictable infection trajectories where transition times and, consequently, the infectious window are non-exponentially distributed. Here, we develop a within-host model where the immune response is triggered by pathogen growth but otherwise develops independently, and use this to obtain analytic expressions for the infectious period and peak pathogen load. This allows us to model the basic reproductive number in terms of explicit functional relationships among within-host traits including the growth rate of the pathogen. We find that the dependence of pathogen load and the infectious window on within-host parameters constrains the evolution of the pathogen growth rate. At low growth rate, selection favours a higher pathogen load and therefore increasing pathogen growth rate. At high growth rates, selection for a longer infectious window trades off against selection against the effects of virulence. At intermediate growth rates the basic reproductive number is relatively insensitive to changes in the growth rate. The resulting "flat" region of the pathogen fitness landscape is due to the stability of the programmatic immune response in clearing the infection.

A runtime alterable epidemic model with genetic drift, waning immunity and vaccinations

We present methods for building a Java Runtime-Alterable-Model Platform (RAMP) of complex dynamical systems. We illustrate our methods by building a multivariant SEIR (epidemic) RAMP. Underlying our RAMP is an individual-based model that includes adaptive contact rates, pathogen genetic drift, waning and cross-immunity. Besides allowing parameter values, process descriptions and scriptable runtime drivers to be easily modified during simulations, our RAMP can used within R-Studio and other computational platforms. Process descriptions that can be runtime altered within our SEIR RAMP include pathogen variant-dependent host shedding, environmental persistence, host transmission and within-host pathogen mutation and replication. They also include adaptive social distancing and adaptive application of vaccination rates and variant-valency of vaccines. We present simulation results using parameter values and process descriptions relevant to the current COVID-19 pandemic. Our results suggest that if waning immunity outpaces vaccination rates, then vaccination rollouts may fail to contain the most transmissible variants, particularly if vaccine valencies are not adapted to deal with escape mutations. Our SEIR RAMP is designed for easy use by others. More generally, our RAMP concept facilitates construction of highly flexible complex systems models of all types, which can then be easily shared as stand-alone application programs.

Comparative Analysis of Within-Host Mutation Patterns and Diversity of Hepatitis C Virus Subtypes 1a, 1b, and 3a

Understanding within-host evolution is critical for predicting viral evolutionary outcomes, yet such studies are currently lacking due to difficulty involving human subjects. Hepatitis C virus (HCV) is an RNA virus with high mutation rates. Its complex evolutionary dynamics and extensive genetic diversity are demonstrated in over 67 known subtypes. In this study, we analyzed within-host mutation frequency patterns of three HCV subtypes, using a large number of samples obtained from treatment-naïve participants by next-generation sequencing. We report that overall mutation frequency patterns are similar among subtypes, yet subtype 3a consistently had lower mutation frequencies and nucleotide diversity, while subtype 1a had the highest. We found that about 50% of genomic sites are highly conserved across subtypes, which are likely under strong purifying selection. We also compared within-host and between-host selective pressures, which revealed that Hyper Variable Region 1 within hosts was under positive selection, but was under slightly negative selection between hosts, which indicates that many mutations created within hosts are removed during the transmission bottleneck. Examining the natural prevalence of known resistance-associated variants showed their consistent existence in the treatment-naïve participants. These results provide insights into the differences and similarities among HCV subtypes that may be used to develop and improve HCV therapies.

The influence of human genetic variation on Epstein-Barr virus sequence diversity

Epstein-Barr virus (EBV) is one of the most common viruses latently infecting humans. Little is known about the impact of human genetic variation on the large inter-individual differences observed in response to EBV infection. To search for a potential imprint of host genomic variation on the EBV sequence, we jointly analyzed paired viral and human genomic data from 268 HIV-coinfected individuals with CD4 + T cell count < 200/mm3 and elevated EBV viremia. We hypothesized that the reactivated virus circulating in these patients could carry sequence variants acquired during primary EBV infection, thereby providing a snapshot of early adaptation to the pressure exerted on EBV by the individual immune response. We searched for associations between host and pathogen genetic variants, taking into account human and EBV population structure. Our analyses revealed significant associations between human and EBV sequence variation. Three polymorphic regions in the human genome were found to be associated with EBV variation: one at the amino acid level (BRLF1:p.Lys316Glu); and two at the gene level (burden testing of rare variants in BALF5 and BBRF1). Our findings confirm that jointly analyzing host and pathogen genomes can identify sites of genomic interactions, which could help dissect pathogenic mechanisms and suggest new therapeutic avenues.

The problem of mediocre generalists: population genetics and eco-evolutionary perspectives on host breadth evolution in pathogens

Many of our theories for the generation and maintenance of diversity in nature depend on the existence of specialist biotic interactions which, in host-pathogen systems, also shape cross-species disease emergence. As such, niche breadth evolution, especially in host-parasite systems, remains a central focus in ecology and evolution. The predominant explanation for the existence of specialization in the literature is that niche breadth is constrained by trade-offs, such that a generalist is less fit on any particular environment than a given specialist. This trade-off theory has been used to predict niche breadth (co)evolution in both population genetics and eco-evolutionary models, with the different modelling methods providing separate, complementary insights. However, trade-offs may be far from universal, so population genetics theory has also proposed alternate mechanisms for costly generalism, including mutation accumulation. However, these mechanisms have yet to be integrated into eco-evolutionary models in order to understand how the mechanism of costly generalism alters the biological and ecological circumstances predicted to maintain specialism. In this review, we outline how population genetics and eco-evolutionary models based on trade-offs have provided insights for parasite niche breadth evolution and argue that the population genetics-derived mutation accumulation theory needs to be better integrated into eco-evolutionary theory.

A Mathematical Framework for Predicting Lifestyles of Viral Pathogens

Despite being similar in structure, functioning, and size, viral pathogens enjoy very different, usually well-defined ways of life. They occupy their hosts for a few days (influenza), for a few weeks (measles), or even lifelong (HCV), which manifests in acute or chronic infections. The various transmission routes (airborne, via direct physical contact, etc.), degrees of infectiousness (referring to the viral load required for transmission), antigenic variation/immune escape and virulence define further aspects of pathogenic lifestyles. To survive, pathogens must infect new hosts; the success determines their fitness. Infection happens with a certain likelihood during contact of hosts, where contact can also be mediated by vectors. Besides structural aspects of the host-contact network, three parameters appear to be key: the contact rate and the infectiousness during contact, which encode the mode of transmission, and third the immunity of susceptible hosts. On these grounds, what can be said about the reproductive success of viral pathogens? This is the biological question addressed in this paper. The answer extends earlier results of the author and makes explicit connection to another basic work on the evolution of pathogens. A mathematical framework is presented that models intra- and inter-host dynamics in a minimalistic but unified fashion covering a broad spectrum of viral pathogens, including those that cause flu-like infections, childhood diseases, and sexually transmitted infections. These pathogens turn out as local maxima of numerically simulated fitness landscapes. The models involve differential and integral equations, agent-based simulation, networks, and probability.

CpG-creating mutations are costly in many human viruses

Mutations can occur throughout the virus genome and may be beneficial, neutral or deleterious. We are interested in mutations that yield a C next to a G, producing CpG sites. CpG sites are rare in eukaryotic and viral genomes. For the eukaryotes, it is thought that CpG sites are rare because they are prone to mutation when methylated. In viruses, we know less about why CpG sites are rare. A previous study in HIV suggested that CpG-creating transition mutations are more costly than similar non-CpG-creating mutations. To determine if this is the case in other viruses, we analyzed the allele frequencies of CpG-creating and non-CpG-creating mutations across various strains, subtypes, and genes of viruses using existing data obtained from Genbank, HIV Databases, and Virus Pathogen Resource. Our results suggest that CpG sites are indeed costly for most viruses. By understanding the cost of CpG sites, we can obtain further insights into the evolution and adaptation of viruses.

Linking influenza virus evolution within and between human hosts

Influenza viruses rapidly diversify within individual human infections. Several recent studies have deep-sequenced clinical influenza infections to identify viral variation within hosts, but it remains unclear how within-host mutations fare at the between-host scale. Here, we compare the genetic variation of H3N2 influenza within and between hosts to link viral evolutionary dynamics across scales. Synonymous sites evolve at similar rates at both scales, indicating that global evolution at these putatively neutral sites results from the accumulation of within-host variation. However, nonsynonymous mutations are depleted between hosts compared to within hosts, suggesting that selection purges many of the protein-altering changes that arise within hosts. The exception is at antigenic sites, where selection detectably favors nonsynonymous mutations at the global scale, but not within hosts. These results suggest that selection against deleterious mutations and selection for antigenic change are the main forces that act on within-host variants of influenza virus as they transmit and circulate between hosts.

Evaluation of A Phylogenetic Pipeline to Examine Transmission Networks in A Canadian HIV Cohort

Keywords: HIV; Canada; molecular phylogenetics; viral evolution; person-to-person transmission inference; transmission network; summary statistics.

Estimating viral bottleneck sizes for FMDV transmission within and between hosts and implications for the rate of viral evolution

RNA viruses exist as populations of closely related genomes, characterized by a high diversity of low-frequency variants. As viral genomes from one population disperse to establish new sites of replication, the fate of these low-frequency variants depends to a large extent on the size of the founding population. Focusing on foot-and-mouth disease virus (FMDV) we conjecture that variants are more likely to be transmitted through wide bottlenecks, but more likely to approach fixation in new populations following narrow bottlenecks; therefore, the longer-term rate of accumulation of 'nearly neutral' variants at high frequencies is likely to be inversely related to the bottleneck size. We examine this conjecture in vivo by estimating bottleneck sizes relating 'parent' and 'daughter' populations observed at different scales ranging from within host to between host (within the same herd, and in different herds) using a previously established method. Within hosts, we find bottleneck sizes to range from 5 to 20 viral genomes between populations transmitted from the pharynx to the serum, and from 4 to 54 between serum and lesion populations. Between hosts, we find bottleneck sizes to range from 2 to 39, suggesting inter-host bottlenecks are of a similar size to intra-host bottlenecks. We establish a statistically significant negative relationship between the probability of genomic consensus level change and bottleneck size, and present a simple sampling model that captures this empirical relationship. We also present a novel in vitro experiment to investigate the impact of bottleneck size on the frequency of mutations within FMDV populations, demonstrate that variant frequency in a population increases more rapidly during small population passages, and provide evidence for positive selection during the passage of large populations.

Direct transmission of within-host Mycobacterium tuberculosis diversity to secondary cases can lead to variable between-host heterogeneity without de novo mutation: A genomic investigation

BACKGROUND

Whole genome sequencing (WGS) has enabled the development of new approaches to track Mycobacterium tuberculosis (Mtb) transmission between tuberculosis (TB) cases but its utility may be challenged by the discovery that Mtb diversifies within hosts. Nevertheless, there is limited data on the presence and degree of within-host evolution.

METHODS

We profiled a well-documented Mtb transmission cluster with three pulmonary TB cases to investigate within-host evolution and describe its impact on recent transmission estimates. We used deep sequencing to track minority allele frequencies (<50·0% abundance) during transmission and standard treatment.

FINDINGS

Pre-treatment (n = 3) and serial samples collected over 2 months of antibiotic treatment (n = 16) from all three cases were analysed. Consistent with the epidemiological data, zero fixed SNP separated all genomes. However, we identified six subclones between the three cases with an allele frequency ranging from 35·0% to 100·0% across sampling intervals. Five subclones were identified within the index case pre-treatment and shared with one secondary case, while only the dominant clone was observed in the other secondary case. By tracking the frequency of these heterogeneous alleles over the two-month therapy, we observed distinct signatures of drift and negative selection, but limited evidence for de novo mutations, even under drug pressure.

INTERPRETATION

We document within-host Mtb diversity in an index case, which led to transmission of minority alleles to a secondary case. Incorporating data on heterogeneous alleles may refine our understanding of Mtb transmission dynamics. However, more evidence is needed on the role of transmission bottleneck on observed heterogeneity between cases.

Linked within-host and between-host models and data for infectious diseases: a systematic review

The observed dynamics of infectious diseases are driven by processes across multiple scales. Here we focus on two: within-host, that is, how an infection progresses inside a single individual (for instance viral and immune dynamics), and between-host, that is, how the infection is transmitted between multiple individuals of a host population. The dynamics of each of these may be influenced by the other, particularly across evolutionary time. Thus understanding each of these scales, and the links between them, is necessary for a holistic understanding of the spread of infectious diseases. One approach to combining these scales is through mathematical modeling. We conducted a systematic review of the published literature on multi-scale mathematical models of disease transmission (as defined by combining within-host and between-host scales) to determine the extent to which mathematical models are being used to understand across-scale transmission, and the extent to which these models are being confronted with data. Following the PRISMA guidelines for systematic reviews, we identified 24 of 197 qualifying papers across 30 years that include both linked models at the within and between host scales and that used data to parameterize/calibrate models. We find that the approach that incorporates both modeling with data is under-utilized, if increasing. This highlights the need for better communication and collaboration between modelers and empiricists to build well-calibrated models that both improve understanding and may be used for prediction.

Coupling multiscale within-host dynamics and between-host transmission with recovery (SIR) dynamics

Multiscale models that link within-host infection to between-host transmission are valuable tools to progress understanding of viral infectious diseases. In this paper, we present two multiscale models that couple within-host infection to a susceptible-infected-recovered (SIR) model. A disease-induced transmission rate bridges the scales from within to between-host. Our stability analysis on the first model (influenza infection) reveals two equilibrium points for the SIR model that describe endemic scenarios where both susceptible and infected cases maintain nonzero population sizes. Consequently, the between-host system has two bifurcations determined by the corresponding basic reproduction number of the within-host and the size of the infected population at the interior equilibrium point. Analysis on the second model (Ebola infection) reveals the limited transient inhibitory effect of antibodies on viral replication, which influences the time window from infection to a potential outbreak. Simulations numerically illustrate our results.

Immune-suppression by OsHV-1 viral infection causes fatal bacteraemia in Pacific oysters

Infectious diseases are mostly explored using reductionist approaches despite repeated evidence showing them to be strongly influenced by numerous interacting host and environmental factors. Many diseases with a complex aetiology therefore remain misunderstood. By developing a holistic approach to tackle the complexity of interactions, we decipher the complex intra-host interactions underlying Pacific oyster mortality syndrome affecting juveniles of Crassostrea gigas, the main oyster species exploited worldwide. Using experimental infections reproducing the natural route of infection and combining thorough molecular analyses of oyster families with contrasted susceptibilities, we demonstrate that the disease is caused by multiple infection with an initial and necessary step of infection of oyster haemocytes by the Ostreid herpesvirus OsHV-1 µVar. Viral replication leads to the host entering an immune-compromised state, evolving towards subsequent bacteraemia by opportunistic bacteria. We propose the application of our integrative approach to decipher other multifactorial diseases that affect non-model species worldwide.

Pacific oyster mortality syndrome is a poorly understood cause of mortality in commercially important oyster species. Here, the authors use multiple infection experiments to show that the syndrome is caused by sequential infection by herpesvirus and opportunistic bacteria.

High-resolution epidemic simulation using within-host infection and contact data

BACKGROUND

Recent epidemics have entailed global discussions on revamping epidemic control and prevention approaches. A general consensus is that all sources of data should be embraced to improve epidemic preparedness. As a disease transmission is inherently governed by individual-level responses, pathogen dynamics within infected hosts posit high potentials to inform population-level phenomena. We propose a multiscale approach showing that individual dynamics were able to reproduce population-level observations.

METHODS

Using experimental data, we formulated mathematical models of pathogen infection dynamics from which we simulated mechanistically its transmission parameters. The models were then embedded in our implementation of an age-specific contact network that allows to express individual differences relevant to the transmission processes. This approach is illustrated with an example of Ebola virus (EBOV).

RESULTS

The results showed that a within-host infection model can reproduce EBOV's transmission parameters obtained from population data. At the same time, population age-structure, contact distribution and patterns can be expressed using network generating algorithm. This framework opens a vast opportunity to investigate individual roles of factors involved in the epidemic processes. Estimating EBOV's reproduction number revealed a heterogeneous pattern among age-groups, prompting cautions on estimates unadjusted for contact pattern. Assessments of mass vaccination strategies showed that vaccination conducted in a time window from five months before to one week after the start of an epidemic appeared to strongly reduce epidemic size. Noticeably, compared to a non-intervention scenario, a low critical vaccination coverage of 33% cannot ensure epidemic extinction but could reduce the number of cases by ten to hundred times as well as lessen the case-fatality rate.

CONCLUSIONS

Experimental data on the within-host infection have been able to capture upfront key transmission parameters of a pathogen; the applications of this approach will give us more time to prepare for potential epidemics. The population of interest in epidemic assessments could be modelled with an age-specific contact network without exhaustive amount of data. Further assessments and adaptations for different pathogens and scenarios to explore multilevel aspects in infectious diseases epidemics are underway.

Multiscale model within-host and between-host for viral infectious diseases

Multiscale models possess the potential to uncover new insights into infectious diseases. Here, a rigorous stability analysis of a multiscale model within-host and between-host is presented. The within-host model describes viral replication and the respective immune response while disease transmission is represented by a susceptible-infected model. The bridging of scales from within- to between-host considered transmission as a function of the viral load. Consequently, stability and bifurcation analyses were developed coupling the two basic reproduction numbers [Formula: see text] and [Formula: see text] for the within- and the between-host subsystems, respectively. Local stability results for each subsystem, including a unique stable equilibrium point, recapitulate classical approaches to infection and epidemic control. Using a Lyapunov function, global stability of the between-host system was obtained. Our main result was the derivation of the [Formula: see text] as an increasing function of [Formula: see text]. Numerical analyses reveal that a Michaelis-Menten form based on the virus is more likely to recapitulate the behavior between the scales than a form directly proportional to the virus. Our work contributes basic understandings of the two models and casts light on the potential effects of the coupling function on linking the two scales.

Emerging Viruses in Bees: From Molecules to Ecology

Emerging infectious diseases arise as a result of novel interactions between populations of hosts and pathogens, and can threaten the health and wellbeing of the entire spectrum of biodiversity. Bees and their viruses are a case in point. However, detailed knowledge of the ecological factors and evolutionary forces that drive disease emergence in bees and other host-pathogen communities is surprisingly lacking. In this review, we build on the fundamental insight that viruses evolve and adapt over timescales that overlap with host ecology. At the same time, we integrate the role of host community ecology, including community structure and composition, biodiversity loss, and human-driven disturbance, all of which represent significant factors in bee virus ecology. Both of these evolutionary and ecological perspectives represent major advances but, in most cases, it remains unclear how evolutionary forces actually operate across different biological scales (e.g., from cell to ecosystem). We present a molecule-to-ecology framework to help address these issues, emphasizing the role of molecular mechanisms as key bottom-up drivers of change at higher ecological scales. We consider the bee-virus system to be an ideal one in which to apply this framework. Unlike many other animal models, bees constitute a well characterized and accessible multispecies assemblage, whose populations and interspecific interactions can be experimentally manipulated and monitored in high resolution across space and time to provide robust tests of prevailing theory.

Stochastic processes constrain the within and between host evolution of influenza virus

The evolutionary dynamics of influenza virus ultimately derive from processes that take place within and between infected individuals. Here we define influenza virus dynamics in human hosts through sequencing of 249 specimens from 200 individuals collected over 6290 person-seasons of observation. Because these viruses were collected from individuals in a prospective community-based cohort, they are broadly representative of natural infections with seasonal viruses. Consistent with a neutral model of evolution, sequence data from 49 serially sampled individuals illustrated the dynamic turnover of synonymous and nonsynonymous single nucleotide variants and provided little evidence for positive selection of antigenic variants. We also identified 43 genetically-validated transmission pairs in this cohort. Maximum likelihood optimization of multiple transmission models estimated an effective transmission bottleneck of 1-2 genomes. Our data suggest that positive selection is inefficient at the level of the individual host and that stochastic processes dominate the host-level evolution of influenza viruses.

Effects of multiple sources of genetic drift on pathogen variation within hosts

Changes in pathogen genetic variation within hosts alter the severity and spread of infectious diseases, with important implications for clinical disease and public health. Genetic drift may play a strong role in shaping pathogen variation, but analyses of drift in pathogens have oversimplified pathogen population dynamics, either by considering dynamics only at a single scale-such as within hosts or between hosts-or by making drastic simplifying assumptions, for example, that host immune systems can be ignored or that transmission bottlenecks are complete. Moreover, previous studies have used genetic data to infer the strength of genetic drift, whereas we test whether the genetic drift imposed by pathogen population processes can be used to explain genetic data. We first constructed and parameterized a mathematical model of gypsy moth baculovirus dynamics that allows genetic drift to act within and between hosts. We then quantified the genome-wide diversity of baculovirus populations within each of 143 field-collected gypsy moth larvae using Illumina sequencing. Finally, we determined whether the genetic drift imposed by host-pathogen population dynamics in our model explains the levels of pathogen diversity in our data. We found that when the model allows drift to act at multiple scales-including within hosts, between hosts, and between years-it can accurately reproduce the data, but when the effects of drift are simplified by neglecting transmission bottlenecks and stochastic variation in virus replication within hosts, the model fails. A de novo mutation model and a purifying selection model similarly fail to explain the data. Our results show that genetic drift can play a strong role in determining pathogen variation and that mathematical models that account for pathogen population growth at multiple scales of biological organization can be used to explain this variation.

Within-Host Evolution of Human Influenza Virus

The rapid global evolution of influenza virus begins with mutations that arise de novo in individual infections, but little is known about how evolution occurs within hosts. We review recent progress in understanding how and why influenza viruses evolve within human hosts. Advances in deep sequencing make it possible to measure within-host genetic diversity in both acute and chronic influenza infections. Factors like antigenic selection, antiviral treatment, tissue specificity, spatial structure, and multiplicity of infection may affect how influenza viruses evolve within human hosts. Studies of within-host evolution can contribute to our understanding of the evolutionary and epidemiological factors that shape influenza virus's global evolution.

Stochastic variation in the initial phase of bacterial infection predicts the probability of survival in D. melanogaster

A central problem in infection biology is understanding why two individuals exposed to identical infections have different outcomes. We have developed an experimental model where genetically identical, co-housed Drosophila given identical systemic infections experience different outcomes, with some individuals succumbing to acute infection while others control the pathogen as an asymptomatic persistent infection. We found that differences in bacterial burden at the time of death did not explain the two outcomes of infection. Inter-individual variation in survival stems from variation in within-host bacterial growth, which is determined by the immune response. We developed a model that captures bacterial growth dynamics and identifies key factors that predict the infection outcome: the rate of bacterial proliferation and the time required for the host to establish an effective immunological control. Our results provide a framework for studying the individual host-pathogen parameters governing the progression of infection and lead ultimately to life or death.

Sick individuals do not all respond to an infection in the same way. One individual may experience mild symptoms and recover easily, while another may suffer devastating illness or even death. A number of factors are often assumed to account for these differences, including the sex, age and genes of the individuals, and differences in the environments the individuals have been exposed to. However, random variations in how an individual’s immune system interacts with the infection could also play an important role in recovery.

Duneau et al. have now studied how genetically identical fruit flies who were raised in the same environment respond to different bacterial infections. This enabled them to develop a mathematical model that describes how a bacterial infection develops in an individual. In an initial phase, the bacteria proliferate freely. If the immune defenses activate in time to control the infection, the number of bacteria in the fly decreases to a constant level and the infection enters a long-term, or chronic, phase. The sooner this happens the more likely it is that the fly will survive. If the immune control happens too late, the infection enters a terminal phase and the fly will die once the number of bacteria increases to a certain level.

The model therefore reveals that the precise time at which the immune system takes control of the bacterial population – termed the “Time to Control” – determines the outcome of the infection. Duneau et al. confirmed this by injecting bacteria into identical flies. A small variation in the Time to Control was sometimes the difference between a fly living or dying. Understanding what controls this apparently random variation is key to understanding infection and potentially developing more efficient treatments for a wide range of diseases – not just those caused by bacteria, but also those caused by viruses and parasites, like HIV and malaria.

High-resolution profiling of linear B-cell epitopes from mucin-associated surface proteins (MASPs) of Trypanosoma cruzi during human infections

BACKGROUND

The Trypanosoma cruzi genome bears a huge family of genes and pseudogenes coding for Mucin-Associated Surface Proteins (MASPs). MASP molecules display a 'mosaic' structure, with highly conserved flanking regions and a strikingly variable central and mature domain made up of different combinations of a large repertoire of short sequence motifs. MASP molecules are highly expressed in mammal-dwelling stages of T. cruzi and may be involved in parasite-host interactions and/or in diverting the immune response.

METHODS/PRINCIPLE FINDINGS

High-density microarrays composed of fully overlapped 15mer peptides spanning the entire sequences of 232 non-redundant MASPs (~25% of the total MASP content) were screened with chronic Chagasic sera. This strategy led to the identification of 86 antigenic motifs, each one likely representing a single linear B-cell epitope, which were mapped to 69 different MASPs. These motifs could be further grouped into 31 clusters of structurally- and likely antigenically-related sequences, and fully characterized. In contrast to previous reports, we show that MASP antigenic motifs are restricted to the central and mature region of MASP polypeptides, consistent with their intracellular processing. The antigenicity of these motifs displayed significant positive correlation with their genome dosage and their relative position within the MASP polypeptide. In addition, we verified the biased genetic co-occurrence of certain antigenic motifs within MASP polypeptides, compatible with proposed intra-family recombination events underlying the evolution of their coding genes. Sequences spanning 7 MASP antigenic motifs were further evaluated using distinct synthesis/display approaches and a large panel of serum samples. Overall, the serological recognition of MASP antigenic motifs exhibited a remarkable non normal distribution among the T. cruzi seropositive population, thus reducing their applicability in conventional serodiagnosis. As previously observed in in vitro and animal infection models, immune signatures supported the concurrent expression of several MASPs during human infection.

CONCLUSIONS/SIGNIFICANCE

In spite of their conspicuous expression and potential roles in parasite biology, this study constitutes the first unbiased, high-resolution profiling of linear B-cell epitopes from T. cruzi MASPs during human infection.

On the principle of host evolution in host-pathogen interactions

In this paper, we use a two-host one pathogen immuno-epidemiological model to argue that the principle for host evolution, when the host is subjected to a fatal disease, is minimization of the case fatality proportion [Formula: see text]. This principle is valid whether the disease is chronic or leads to recovery. In the case of continuum of hosts, stratified by their immune response stimulation rate a, we suggest that [Formula: see text] has a minimum because a trade-off exists between virulence to the host induced by the pathogen and virulence induced by the immune response. We find that the minimization of the case fatality proportion is an evolutionary stable strategy for the host.

Comparative Genome Sequencing Reveals Within-Host Genetic Changes in Neisseria meningitidis during Invasive Disease

Some members of the physiological human microbiome occasionally cause life-threatening disease even in immunocompetent individuals. A prime example of such a commensal pathogen is Neisseria meningitidis, which normally resides in the human nasopharynx but is also a leading cause of sepsis and epidemic meningitis. Using N. meningitidis as model organism, we tested the hypothesis that virulence of commensal pathogens is a consequence of within host evolution and selection of invasive variants due to mutations at contingency genes, a mechanism called phase variation. In line with the hypothesis that phase variation evolved as an adaptation to colonize diverse hosts, computational comparisons of all 27 to date completely sequenced and annotated meningococcal genomes retrieved from public databases showed that contingency genes are indeed enriched for genes involved in host interactions. To assess within-host genetic changes in meningococci, we further used ultra-deep whole-genome sequencing of throat-blood strain pairs isolated from four patients suffering from invasive meningococcal disease. We detected up to three mutations per strain pair, affecting predominantly contingency genes involved in type IV pilus biogenesis. However, there was not a single (set) of mutation(s) that could invariably be found in all four pairs of strains. Phenotypic assays further showed that these genetic changes were generally not associated with increased serum resistance, higher fitness in human blood ex vivo or differences in the interaction with human epithelial and endothelial cells in vitro. In conclusion, we hypothesize that virulence of meningococci results from accidental emergence of invasive variants during carriage and without within host evolution of invasive phenotypes during disease progression in vivo.

Competitive exclusion in a multi-strain immuno-epidemiological influenza model with environmental transmission

In this paper, a multi-strain model that links immunological and epidemiological dynamics across scales is formulated. On the within-host scale, the n strains eliminate each other with the strain having the largest immunological reproduction number persisting. However, on the population scale, we extend the competitive exclusion principle to a multi-strain model of SI-type for the dynamics of highly pathogenic flu in poultry that incorporates both the infection age of infectious individuals and biological age of pathogen in the environment. The two models are linked through the age-since-infection structure of the epidemiological variables. In addition the between-host transmission rate, the shedding rate of individuals infected by strain j and the disease-induced death rate depend on the within-host viral load. The immunological reproduction numbers [Formula: see text] and the epidemiological reproduction numbers [Formula: see text] are computed. By constructing a suitable Lyapunov function, the global stability of the infection-free equilibrium in the system is obtained if all reproduction numbers are smaller or equal to one. If [Formula: see text], the reproduction number of strain j is larger than one, then a single-strain equilibrium, corresponding to strain j exists. This single-strain equilibrium is globally stable whenever [Formula: see text] and [Formula: see text] is the unique maximal reproduction number and all of the reproduction numbers are distinct. That is, the strain with the maximal basic reproduction number competitively excludes all other strains.

Within Host Evolution Selects for a Dominant Genotype of Mycobacterium tuberculosis while T Cells Increase Pathogen Genetic Diversity

Molecular epidemiological assessments, drug treatment optimization, and development of immunological interventions all depend on understanding pathogen adaptation and genetic variation, which differ for specific pathogens. Mycobacterium tuberculosis is an exceptionally successful human pathogen, yet beyond knowledge that this bacterium has low overall genomic variation but acquires drug resistance mutations, little is known of the factors that drive its population genomic characteristics. Here, we compared the genetic diversity of the bacteria that established infection to the bacterial populations obtained from infected tissues during murine M. tuberculosis pulmonary infection and human disseminated M. bovis BCG infection. We found that new mutations accumulate during in vitro culture, but that in vivo, purifying selection against new mutations dominates, indicating that M. tuberculosis follows a dominant lineage model of evolution. Comparing bacterial populations passaged in T cell-deficient and immunocompetent mice, we found that the presence of T cells is associated with an increase in the diversity of the M. tuberculosis genome. Together, our findings put M. tuberculosis genetic evolution in a new perspective and clarify the impact of T cells on sequence diversity of M. tuberculosis.

Recombination-driven generation of the largest pathogen repository of antigen variants in the protozoan Trypanosoma cruzi

Background

The protozoan parasite Trypanosoma cruzi, causative agent of Chagas disease, depends upon a cell surface-expressed trans-sialidase (ts) to avoid activation of complement-mediated lysis and to enhance intracellular invasion. However these functions alone fail to account for the size of this gene family in T. cruzi, especially considering that most of these genes encode proteins lacking ts enzyme activity. Previous whole genome sequencing of the CL Brener clone of T. cruzi identified ~1400 ts variants, but left many partially assembled sequences unannotated.

Results

In the current study we reevaluated the trans-sialidase-like sequences in this reference strain, identifying an additional 1779 full-length and partial ts genes with their important features annotated, and confirming the expression of previously annotated “pseudogenes” and newly annotated ts family members. Multiple EM for Motif Elicitation (MEME) analysis allowed us to generate a model T. cruzi ts (TcTS) based upon the most conserved motif patterns and demonstrated that a common motif order is highly conserved among ts family members. Using a newly developed pipeline for the analysis of recombination within large gene families, we further demonstrate that TcTS family members are undergoing frequent recombination, generating new variants from the thousands of functional and non-functional ts gene segments but retaining the overall structure of the core TcTS family members.

Conclusions

The number and variety as well as high recombination frequency of TcTS family members supports strong evolutionary pressure, probably exerted by immune selection, for continued variation in ts sequences in T. cruzi, and thus for a unique immune evasion mechanism for the large ts gene family.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3037-z) contains supplementary material, which is available to authorized users.

Multiple-genotype infections and their complex effect on virulence

Multiple infections are common. Although in recent years our understanding of multiple infections has increased significantly, it has also become clear that a diversity of aspects has to be considered to understand the interplay between co-infecting parasite genotypes of the same species and its implications for virulence and epidemiology, resulting in high complexity. Here, we review different interaction mechanisms described for multiple infections ranging from competition to cooperation. We also list factors influencing the interaction between co-infecting parasite genotypes and their influence on virulence. Finally, we emphasise the importance of between-host effects and their evolution for understanding multiple infections and their implications.

Crossing the scale from within-host infection dynamics to between-host transmission fitness: a discussion of current assumptions and knowledge

The progression of an infection within a host determines the ability of a pathogen to transmit to new hosts and to maintain itself in the population. While the general connection between the infection dynamics within a host and the population-level transmission dynamics of pathogens is widely acknowledged, a comprehensive and quantitative understanding that would allow full integration of the two scales is still lacking. Here, we provide a brief discussion of both models and data that have attempted to provide quantitative mappings from within-host infection dynamics to transmission fitness. We present a conceptual framework and provide examples of studies that have taken first steps towards development of a quantitative framework that scales from within-host infections to population-level fitness of different pathogens. We hope to illustrate some general themes, summarize some of the recent advances and-maybe most importantly-discuss gaps in our ability to bridge these scales, and to stimulate future research on this important topic.

An immuno-eco-epidemiological model of competition

This paper introduces a novel immuno-eco-epidemiological model of competition in which one of the species is affected by a pathogen. The infected individuals from species one are structured by time-since-infection and the within-host dynamics of the pathogen and the immune response is also modelled. A novel feature of the model is the impact of the species two numbers on the ability of species one to mount an immune response. The within-host model has three equilibria: an extinction equilibrium, pathogen-only equilibrium and pathogen and immune response equilibrium which exists if the immune response reproduction number R0 > 1. The extinction equilibrium is always unstable, the pathogen-only equilibrium is stable if R0 < 1, and the coexistence equilibrium is stable whenever it exists. The between-host competition model has six equilibria: an extinction equilibrium, three disease-free equilibria: species one-only equilibrium, species two-only equilibrium and a disease-free species coexistence equilibrium. There are also two disease-present equilibria: species one-only disease equilibrium and disease coexistence equilibrium. The existence and stability of these equilibria are governed by six reproduction numbers. Results show that for a non-fatal disease, the disease coexistence equilibrium is stable whenever it exists.

Parasite-Parasite Interactions in the Wild: How To Detect Them?

Inter-specific interactions between parasites impact on parasite intra-host dynamics, host health, and disease management. Identifying and understanding interaction mechanisms in the wild is crucial for wildlife disease management. It is however complex because several scales are interlaced. Parasite-parasite interactions are likely to occur via mechanisms at the within-host level, but also at upper levels (host population and community). Furthermore, interactions occurring at one level of organization spread to upper levels through cascade effects. Even if cascade effects are important confounding factors, we argue that we can also benefit from them because upper scales often provide a way to survey a wider range of parasites at lower cost. New protocols and theoretical studies (especially across scales) are necessary to take advantage of this opportunity.

Do mixed infections matter? Assessing virulence of mixed-clone infections in experimental human and murine malaria

BACKGROUND

Malaria parasites within an individual infection often consist of multiple strains (clonal populations) of a single species, which have the potential to interact both with one another, and with the host immune system. Several effects of these interactions have been measured in different parasite systems including competition and mutualism; however, direct observation of these effects in human malaria has been limited by sampling complexities and inherent ethical limitations.

METHODS

Using multiple complementary epidemiological models, we propose a suite of analyses to more fully utilize data from challenge experiments, and re-examine historical human challenge studies with mixed-strain Plasmodium vivax inocula. We then compare these results with murine model systems using mixed-strain Plasmodium yoelii or Plasmodium chabaudi, to explore the utility of these methods in fully utilizing these data, including the first quantitative estimates of effect sizes for mixed-strain parasitemia. These models also provide a method to assess consistency within these animal model systems.

RESULTS

We find that amongst a limited set of P. vivax (incubation time) and P. yoelii infections (time-to-mortality), survival times at a study population-level are intermediate between each single-clone infection, and are not dominated by the more virulent clone; in P. vivax relapses, mixed clone infections also show intermediate survival curves. In these infections, the results strongly suggest that highly virulent clones have their virulence attenuated by the presence of less-virulent clones. The analysis of multiple experiments with P. chabaudi suggests greater nuances in the interactions between strains, and that mortality and time-to-event in mixed-strain infections are both indistinguishable from single infections with the more virulent strain.

CONCLUSIONS

These divergent dynamics support earlier work that suggested drivers of virulence may differ in fundamental ways between malaria species that are reticulocyte-specific and those that readily infect all red blood cell stages which should be studied in greater detail. The effect sizes (magnitude of biological effects) from these analyses are significant, and suggest the potential for important gains in malaria control by greater incorporation of evolutionary epidemiology theory. Moreover, we suggest that using these epidemiological models may generally allow fuller use of data from experimentally challenging animal model experiments.

Within-host evolution decreases virulence in an opportunistic bacterial pathogen

Background

Pathogens evolve in a close antagonistic relationship with their hosts. The conventional theory proposes that evolution of virulence is highly dependent on the efficiency of direct host-to-host transmission. Many opportunistic pathogens, however, are not strictly dependent on the hosts due to their ability to reproduce in the free-living environment. Therefore it is likely that conflicting selection pressures for growth and survival outside versus within the host, rather than transmission potential, shape the evolution of virulence in opportunists. We tested the role of within-host selection in evolution of virulence by letting a pathogen Serratia marcescens db11 sequentially infect Drosophila melanogaster hosts and then compared the virulence to strains that evolved only in the outside-host environment.

Results

We found that the pathogen adapted to both Drosophila melanogaster host and novel outside-host environment, leading to rapid evolutionary changes in the bacterial life-history traits including motility, in vitro growth rate, biomass yield, and secretion of extracellular proteases. Most significantly, selection within the host led to decreased virulence without decreased bacterial load while the selection lines in the outside-host environment maintained the same level of virulence with ancestral bacteria.

Conclusions

This experimental evidence supports the idea that increased virulence is not an inevitable consequence of within-host adaptation even when the epidemiological restrictions are removed. Evolution of attenuated virulence could occur because of immune evasion within the host. Alternatively, rapid fluctuation between outside-host and within-host environments, which is typical for the life cycle of opportunistic bacterial pathogens, could lead to trade-offs that lower pathogen virulence.

Within-host stochastic emergence dynamics of immune-escape mutants

Predicting the emergence of new pathogenic strains is a key goal of evolutionary epidemiology. However, the majority of existing studies have focussed on emergence at the population level, and not within a host. In particular, the coexistence of pre-existing and mutated strains triggers a heightened immune response due to the larger total pathogen population; this feedback can smother mutated strains before they reach an ample size and establish. Here, we extend previous work for measuring emergence probabilities in non-equilibrium populations, to within-host models of acute infections. We create a mathematical model to investigate the emergence probability of a fitter strain if it mutates from a self-limiting strain that is guaranteed to go extinct in the long-term. We show that ongoing immune cell proliferation during the initial stages of infection causes a drastic reduction in the probability of emergence of mutated strains; we further outline how this effect can be accurately measured. Further analysis of the model shows that, in the short-term, mutant strains that enlarge their replication rate due to evolving an increased growth rate are more favoured than strains that suffer a lower immune-mediated death rate (‘immune tolerance’), as the latter does not completely evade ongoing immune proliferation due to inter-parasitic competition. We end by discussing the model in relation to within-host evolution of human pathogens (including HIV, hepatitis C virus, and cancer), and how ongoing immune growth can affect their evolutionary dynamics.

The ongoing evolution of infectious diseases provides a constant health threat. This evolution can either result in the production of new pathogens, or new strains of existing pathogens that escape prevailing drug treatments or immune responses. The latter process, also known as immune escape, is a predominant reason for the persistence of several viruses, including HIV and hepatitis C virus (HCV), in their human host. As a consequence, the within-host emergence of new strains has been the intense focus of modelling studies. However, existing models have neglected important feedbacks that affects this emergence probability. Specifically, once a mutated pathogen arises that spreads more quickly than the initial (resident) strain, it potentially triggers a heightened immune response that can eliminate the mutated strain before it spreads. Our study outlines novel mathematical modelling techniques that accurately quantify how ongoing immune growth reduces the emergence probability of mutated pathogenic strains over the course of an infection. Analysis of this model suggests that, in order to enlarge its emergence probability, it is evolutionary beneficial for a mutated strain to increase its growth rate rather than tolerate immunity by having a lower immune-mediated death-rate. Our model can be readily applied to existing within-host data, as demonstrated with application to HIV, HCV, and cancer dynamics.

Papillomaviruses: Viral evolution, cancer and evolutionary medicine

Papillomaviruses (PVs) are a numerous family of small dsDNA viruses infecting virtually all mammals. PVs cause infections without triggering a strong immune response, and natural infection provides only limited protection against reinfection. Most PVs are part and parcel of the skin microbiota. In some cases, infections by certain PVs take diverse clinical presentations from highly productive self-limited warts to invasive cancers. We propose PVs as an excellent model system to study the evolutionary interactions between the immune system and pathogens causing chronic infections: genotypically, PVs are very diverse, with hundreds of different genotypes infecting skin and mucosa; phenotypically, they display extremely broad gradients and trade-offs between key phenotypic traits, namely productivity, immunogenicity, prevalence, oncogenicity and clinical presentation. Public health interventions have been launched to decrease the burden of PV-associated cancers, including massive vaccination against the most oncogenic human PVs, as well as systematic screening for PV chronic anogenital infections. Anti-PVs vaccines elicit protection against infection, induce cross-protection against closely related viruses and result in herd immunity. However, our knowledge on the ecological and intrapatient dynamics of PV infections remains fragmentary. We still need to understand how the novel anthropogenic selection pressures posed by vaccination and screening will affect viral circulation and epidemiology. We present here an overview of PV evolution and the connection between PV genotypes and the phenotypic, clinical manifestations of the diseases they cause. This differential link between viral evolution and the gradient cancer-warts-asymptomatic infections makes PVs a privileged playground for evolutionary medicine research.

Trade-offs between and within scales: environmental persistence and within-host fitness of avian influenza viruses

Trade-offs between different components of a pathogen's replication and transmission cycle are thought to be common. A number of studies have identified trade-offs that emerge across scales, reflecting the tension between strategies that optimize within-host proliferation and large-scale population spread. Most of these studies are theoretical in nature, with direct experimental tests of such cross-scale trade-offs still rare. Here, we report an analysis of avian influenza A viruses across scales, focusing on the phenotype of temperature-dependent viral persistence. Taking advantage of a unique dataset that reports both environmental virus decay rates and strain-specific viral kinetics from duck challenge experiments, we show that the temperature-dependent environmental decay rate of a strain does not impact within-host virus load. Hence, for this phenotype, the scales of within-host infection dynamics and between-host environmental persistence do not seem to interact: viral fitness may be optimized on each scale without cross-scale trade-offs. Instead, we confirm the existence of a temperature-dependent persistence trade-off on a single scale, with some strains favouring environmental persistence in water at low temperatures while others reduce sensitivity to increasing temperatures. We show that this temperature-dependent trade-off is a robust phenomenon and does not depend on the details of data analysis. Our findings suggest that viruses might employ different environmental persistence strategies, which facilitates the coexistence of diverse strains in ecological niches. We conclude that a better understanding of the transmission and evolutionary dynamics of influenza A viruses probably requires empirical information regarding both within-host dynamics and environmental traits, integrated within a combined ecological and within-host framework.

Recombination is a key driver of genomic and phenotypic diversity in a Pseudomonas aeruginosa population during cystic fibrosis infection

The Cystic Fibrosis (CF) lung harbors a complex, polymicrobial ecosystem, in which Pseudomonas aeruginosa is capable of sustaining chronic infections, which are highly resistant to multiple antibiotics. Here, we investigate the phenotypic and genotypic diversity of 44 morphologically identical P. aeruginosa isolates taken from a single CF patient sputum sample. Comprehensive phenotypic analysis of isolates revealed large variances and trade-offs in growth, virulence factors and quorum sensing (QS) signals. Whole genome analysis of 22 isolates revealed high levels of intra-isolate diversity ranging from 5 to 64 SNPs and that recombination and not spontaneous mutation was the dominant driver of diversity in this population. Furthermore, phenotypic differences between isolates were not linked to mutations in known genes but were statistically associated with distinct recombination events. We also assessed antibiotic susceptibility of all isolates. Resistance to antibiotics significantly increased when multiple isolates were mixed together. Our results highlight the significant role of recombination in generating phenotypic and genetic diversification during in vivo chronic CF infection. We also discuss (i) how these findings could influence how patient-to-patient transmission studies are performed using whole genome sequencing, and (ii) the need to refine antibiotic susceptibility testing in sputum samples taken from patients with CF.