Patterns of genetic variation in populations of infectious agents

Understanding underlying physical mechanism reveals early warning indicators and key elements for adaptive infections disease networks

The study of infectious diseases holds significant scientific and societal importance, yet current research on the mechanisms of disease emergence and prediction methods still face challenging issues. This research uses the landscape and flux theoretical framework to reveal the non-equilibrium dynamics of adaptive infectious diseases and uncover its underlying physical mechanism. This allows the quantification of dynamics, characterizing the system with two basins of attraction determined by gradient and rotational flux forces. Quantification of entropy production rates provides insights into the system deviating from equilibrium and associated dissipative costs. The study identifies early warning indicators for the critical transition, emphasizing the advantage of observing time irreversibility from time series over theoretical entropy production and flux. The presence of rotational flux leads to an irreversible pathway between disease states. Through global sensitivity analysis, we identified the key factors influencing infectious diseases. In summary, this research offers valuable insights into infectious disease dynamics and presents a practical approach for predicting the onset of critical transition, addressing existing research gaps.

SCOTTI: Efficient Reconstruction of Transmission within Outbreaks with the Structured Coalescent

Exploiting pathogen genomes to reconstruct transmission represents a powerful tool in the fight against infectious disease. However, their interpretation rests on a number of simplifying assumptions that regularly ignore important complexities of real data, in particular within-host evolution and non-sampled patients. Here we propose a new approach to transmission inference called SCOTTI (Structured COalescent Transmission Tree Inference). This method is based on a statistical framework that models each host as a distinct population, and transmissions between hosts as migration events. Our computationally efficient implementation of this model enables the inference of host-to-host transmission while accommodating within-host evolution and non-sampled hosts. SCOTTI is distributed as an open source package for the phylogenetic software BEAST2. We show that SCOTTI can generally infer transmission events even in the presence of considerable within-host variation, can account for the uncertainty associated with the possible presence of non-sampled hosts, and can efficiently use data from multiple samples of the same host, although there is some reduction in accuracy when samples are collected very close to the infection time. We illustrate the features of our approach by investigating transmission from genetic and epidemiological data in a Foot and Mouth Disease Virus (FMDV) veterinary outbreak in England and a Klebsiella pneumoniae outbreak in a Nepali neonatal unit. Transmission histories inferred with SCOTTI will be important in devising effective measures to prevent and halt transmission.

We present a new tool, SCOTTI, to efficiently reconstruct transmission events within outbreaks. Our approach combines genetic information from infection samples with epidemiological information of patient exposure to infection. While epidemiological information has been traditionally used to understand who infected whom in an outbreak, detailed genetic information is increasingly becoming available with the steady progress of sequencing technologies. However, many complications, if unaccounted for, can affect the accuracy with which the transmission history is reconstructed. SCOTTI efficiently accounts for several complications, in particular within-patient genetic variation of the infectious organism, and non-sampled patients (such as asymptomatic patients). Thanks to these features, SCOTTI provides accurate reconstructions of transmission in complex scenarios, which will be important in finding and limiting the sources and routes of transmission, preventing the spread of infectious disease.

Phylogenetic and epidemic modeling of rapidly evolving infectious diseases

Epidemic modeling of infectious diseases has a long history in both theoretical and empirical research. However the recent explosion of genetic data has revealed the rapid rate of evolution that many populations of infectious agents undergo and has underscored the need to consider both evolutionary and ecological processes on the same time scale. Mathematical epidemiology has applied dynamical models to study infectious epidemics, but these models have tended not to exploit--or take into account--evolutionary changes and their effect on the ecological processes and population dynamics of the infectious agent. On the other hand, statistical phylogenetics has increasingly been applied to the study of infectious agents. This approach is based on phylogenetics, molecular clocks, genealogy-based population genetics and phylogeography. Bayesian Markov chain Monte Carlo and related computational tools have been the primary source of advances in these statistical phylogenetic approaches. Recently the first tentative steps have been taken to reconcile these two theoretical approaches. We survey the Bayesian phylogenetic approach to epidemic modeling of infection diseases and describe the contrasts it provides to mathematical epidemiology as well as emphasize the significance of the future unification of these two fields.

Unlocking pathogen genotyping information for public health by mathematical modeling

Molecular typing and mathematical modeling have gone through rapid development in the past decade. Both offer new insights into the epidemiology of infectious diseases, thereby contributing to a better understanding of transmission dynamics. Infectious disease surveillance and control benefit from the optimum use of these techniques. In this paper, we review recent developments and propose methods to integrate pathogen ecology and molecular evolution based on their common dependence on the underlying host contact patterns.

Expansion of genetic diversity in randomly mating founder populations of Alternaria brassicicola infecting Cakile maritima in Australia

Founder populations of fungal plant pathogens are expected to have low levels of genetic diversity coupled with further genetic drift due to, e.g., limited host availability, which should result in additional population bottlenecks. This study used microsatellite markers in the interaction between Cakile maritima and the fungal pathogen Alternaria brassicicola to explore genetic expectations associated with such situations. The host, C. maritima, was introduced into Australia approximately 100 years ago, but it is unknown whether the pathogen was already present in Australia, as it has a wide occurrence, or whether it was introduced to Australia on brassicaceous hosts. Eleven A. brassicicola populations were studied, and all showed moderate levels of gene and genotypic diversity. Chi-square tests of the frequencies of mating type alleles, a large number of genotypes, and linkage equilibrium among microsatellite loci all suggest A. brassicicola reproduces sexually. Significant genetic differentiation was found among populations, but there was no evidence for isolation by distance effects. Bayesian analyses identified eight clusters where the inferred clusters did not represent geographical populations but instead consisted of individuals admixed from all populations. Further analysis indicated that fungal populations were more likely to have experienced a recent population expansion than a population bottleneck. It is suggested that A. brassicicola has been introduced into Australia multiple times, potentially increasing the diversity and size of any A. brassicola populations already present there. Combined with its ability to reproduce sexually, such processes appear to have increased the evolutionary potential of the pathogen through recent population expansions.

Genetic diversity in the SIR model of pathogen evolution

We introduce a model for assessing the levels and patterns of genetic diversity in pathogen populations, whose epidemiology follows a susceptible-infected-recovered model (SIR). We model the population of pathogens as a metapopulation composed of subpopulations (infected hosts), where pathogens replicate and mutate. Hosts transmit pathogens to uninfected hosts. We show that the level of pathogen variation is well predicted by analytical expressions, such that pathogen neutral molecular variation is bounded by the level of infection and increases with the duration of infection. We then introduce selection in the model and study the invasion probability of a new pathogenic strain whose fitness (R₀(1+s)) is higher than the fitness of the resident strain (R₀). We show that this invasion probability is given by the relative increment in R₀ of the new pathogen (s). By analyzing the patterns of genetic diversity in this framework, we identify the molecular signatures during the replacement and compare these with those observed in sequences of influenza A.

Epistasis and the selective advantage of sex and recombination

The understanding of the central mechanisms favoring sex and recombination in real populations is one of the fundamental issues in evolutionary biology. Based on a previous stochastic formulation for the study of sex, here we aim to investigate the conditions under which epistasis favors the fixation of the sexual mode of reproduction in a given population. In addition, we try to identify the evolutionary forces which contribute to this process. One considers a finite population model which assumes the existence of a recombination modifier allele that can activate the recombination mechanism. We have found that sex is very little favored in a scenario of antagonistic epistasis, and this advantage only occurs in a narrow range of values of the selection coefficient s_{d} . On the other hand, synergistic epistasis favors recombination in a very broad domain. However, the major mechanism contributing to the spreading of the modifier allele depends on the range of values of s_{d} . At large s_{d} , background selection favors recombination since it increases the efficacy of selection, while at low s_{d} Muller's ratchet is the leading mechanism.

New challenges for mathematical and statistical modeling of HIV and hepatitis C virus in injecting drug users

Injecting drug users are not only driving blood-borne transmission of HIV and hepatitis C virus but also likely drive sexual transmission of HIV in large parts of the world. Mathematical and statistical modeling can provide important insights in these epidemiological processes and on the potential impact of interventions but have been little used to date. This review aims to discuss the potential areas of application of modeling in the field of viral infections in injecting drug users. After reviewing key examples of published modeling work on HIV and hepatitis C virus in injecting drug users, we discuss recent developments in the epidemiology, diagnosis, treatment and prevention of these infections. In particular, new methods for the diagnosis of early HIV infection, new antivirals for a more effective treatment of HIV, hepatitis B and hepatitis C virus infections, new concepts in design and surveillance of interventions for drug users and increasing possibilities of molecular typing of pathogens are changing the questions and decisions for public health policy makers who deal with drug-related infectious diseases. Research including mathematical modeling is needed to understand the impact of new diagnostic tools, new treatment options and combined intervention strategies on the epidemiology of viral infections in injecting drug users. Methodological advances in mathematical modeling are required to adequately approach some of the ensuing research questions. Modeling has much to offer for solving urgent policy questions, but current levels of funding in modeling research are insufficient and need to be scaled up to make better use of these possibilities.

Environmental heterogeneity enhances clonal interference

Clonal interference (CI) is a phenomenon that may be important in several asexual microbes. It occurs when population sizes are large and mutation rates to new beneficial alleles are of significant magnitude. Here we explore the role of gene flow and spatial heterogeneity in selection strength in the adaptation of asexuals. We consider a subdivided population of individuals that are adapting, through new beneficial mutations, and that migrate between different patches. The fitness effect of each mutation depends on the patch and all mutations considered are assumed to be unconditionally beneficial. We find that spatial variation in selection pressure affects the rate of adaptive evolution and its qualitative effects depend on the level of gene flow. In particular, we find that both low migration and high levels of heterogeneity lead to enhanced CI. In contrast, for high levels of migration the rate of fixation of adaptive mutations is higher when environmental heterogeneity is present. In addition, we observe that the level of fitness variation is higher and simultaneous fixation of multiple mutations tends to occur in the regime of low migration rates and high heterogeneity.