Genomic epidemiological models describe pathogen evolution across fitness valleys

Genomics is fundamentally changing epidemiological research. However, exploring hypotheses about pathogen evolution in different epidemiological contexts poses new challenges. Models intertwining pathogen epidemiology and genomic evolution can help understand processes such as the emergence of novel pathogen genotypes with higher transmission or resistance to treatment. In this work, we present Opqua, a computational framework for flexible simulation of pathogen epidemiology and evolution. We use Opqua to study determinants of evolution across fitness valleys. We confirm that competition can limit evolution in high transmission environments and find that low transmission, host mobility, and complex pathogen life cycles facilitate reaching new adaptive peaks through population bottlenecks and decoupling selective pressures. The results show the potential of genomic epidemiological modeling as a tool in infectious disease research.

Host–Pathogen Evolution and Coevolution in Avian Systems

The significance of studying birds and their pathogens goes far beyond the applied conservation or epidemiological implications of their interactions. Evidence suggests that avian host–pathogen systems can be used to test fundamental theoretical predictions about adaptive evolution and coevolution in natural populations. This chapter highlights recent advances in the field of bird–pathogen evolution and coevolution, how these advances have come about, and future directions of research. Further, it shows that, while there is a growing body of work that provides support for both avian host and pathogen evolution, evidence for their antagonistic coevolution, the process of adaptation and counter-adaptation in response to the reciprocal selection pressures that they impose on each other, remains rare. Rigorously demonstrating the processes of evolution and coevolution is complex in natural populations and doing so necessarily requires borrowing methodological approaches from a range of disciplines to fully characterize phenotypic change, its genetic and mechanistic basis, as well as its adaptive benefits. Overcoming the challenge of such a task will, however, generate important insights into a range of processes, from disease transmission dynamics and pathogenesis to the maintenance of biodiversity.

Non-pharmaceutical interventions and the emergence of pathogen variants

Non-pharmaceutical interventions (NPIs), such as social distancing and contact tracing, have been widely implemented during the COVID-19 pandemic. In addition to playing an important role in suppressing transmission, NPIs influence pathogen evolution by mediating mutation supply and altering the strength of selection for novel variants. However, it is unclear how NPIs might affect the emergence of novel variants of concern that are able to escape pre-existing immunity (partially or fully), are more transmissible, or cause greater mortality. Here, we analyse a stochastic two-strain epidemiological model to determine how the strength of NPIs affects the emergence of variants with similar or contrasting life-history characteristics to the wildtype. We show that, while stronger and timelier NPIs generally reduce the likelihood of variant emergence, it is possible for more transmissible variants with high cross immunity to have a greater probability of emerging at intermediate levels of NPIs. However, since one cannot predict the characteristics of a variant, the best strategy to prevent emergence is likely to be implementation of strong, timely NPIs.

Draft Genome Sequences of Two Xanthomonas fragariae Strains

Xanthomonas fragariae is the causal agent of angular leaf spot of strawberry. Short-read sequences were generated for two X. fragariae strains with different virulence phenotypes on the Illumina HiSeq 2000 platform. The genome sequences will contribute to a better understanding of pathogen evolution and the genes contributing to virulence in X. fragariae .

Epidemic spreading under pathogen evolution

Battling a widespread pandemic is an arms race between our mitigation efforts, e.g., social distancing or vaccination, and the pathogen's evolving persistence. This is being observed firsthand during the current COVID-19 crisis, as novel mutations are constantly challenging our global vaccination race. To address this, we introduce here a general framework for epidemic spreading under pathogen evolution, which shows that mutations can fundamentally alter the projection of the spread. Specifically, we detect a new pandemic phase - the mutated phase - in which, despite the fact that the pathogen is initially non-pandemic (R0 < 1), it may still spread due to the emergence of a critical mutation. The boundaries of this phase portray a balance between the epidemic and the evolutionary time-scales. If the mutation rate is too low, the pathogen prevalence decays prior to the appearance of a critical mutation. On the other hand, if mutations are too rapid, the pathogen evolution becomes volatile and, once again, it fails to spread. Between these two extremes, however, a broad range of conditions exists in which an initially sub-pandemic pathogen will eventually gain prevalence. This is especially relevant during vaccination, which creates, as it progresses, increasing selection pressure towards vaccine-resistance. To overcome this, we show that vaccination campaigns must be accompanied by fierce mitigation efforts, to suppress the potential rise of a resistant mutant strain.

Resurrection of Wheat Cultivar PBW343 Using Marker-Assisted Gene Pyramiding for Rust Resistance

Wheat variety PBW343, released in India in 1995, became the most widely grown cultivar in the country by the year 2000 owing to its wide adaptability and yield potential. It initially succumbed to leaf rust, and resistance genes Lr24 and Lr28 were transferred to PBW343. After an unbroken reign of about 10 years, the virulence against gene Yr27 made PBW343 susceptible to stripe rust. Owing to its wide adaptability and yield potential, PBW343 became the prime target for marker-assisted introgression of stripe rust resistance genes. The leaf rust-resistant versions formed the base for pyramiding stripe rust resistance genes Yr5, Yr10, Yr15, Yr17, and Yr70, in different introgression programs. Advanced breeding lines with different gene combinations, PBW665, PBW683, PBW698, and PBW703 were tested in national trials but could not be released as varieties. The genes from alien segments, Aegilops ventricosa (Lr37/Yr17/Sr38) and Aegilops umbellulata (Lr76/Yr70), were later pyramided in PBW343. Modified marker-assisted backcross breeding was performed, and 81.57% of the genetic background was recovered in one of the selected derivative lines, PBW723. This line was evaluated in coordinated national trials and was released for cultivation under timely sown irrigated conditions in the North Western Plain Zone of India. PBW723 yields an average of 58.0 qtl/ha in Punjab with high potential yields. The genes incorporated are susceptible to stripe rust individually, but PBW723 with both genes showed enhanced resistance. Three years post-release, PBW723 occupies approximately 8–9% of the cultivated area in the Punjab state. A regular inflow of diverse resistant genes, their rapid mobilization to most productive backgrounds, and keeping a close eye on pathogen evolution is essential to protect the overall progress for productivity and resistance in wheat breeding, thus helping breeders to keep pace with pathogen evolution.

How do pathogens evolve novel virulence activities?

We consider the state of knowledge on pathogen evolution of novel virulence activities, broadly defined as anything that increases pathogen fitness with the consequence of causing disease in either the qualitative or quantitative senses, including adaptation of pathogens to host immunity and physiology, host species, genotypes, or tissues, or the environment. The evolution of novel virulence activities as an adaptive trait is based on the selection exerted by hosts on variants that have been generated de novo or arrived from elsewhere. In addition, the biotic and abiotic environment a pathogen experiences beyond the host may influence pathogen virulence activities. We consider pathogen evolution in the context of host-pathogen evolution, host range expansion, and external factors that can mediate pathogen evolution. We then discuss the mechanisms by which pathogens generate and recombine the genetic variation that leads to novel virulence activities, including DNA point mutation, transposable element activity, gene duplication and neofunctionalization, and genetic exchange. In summary, if there is an (epi)genetic mechanism that can create variation in the genome, it will be used by pathogens to evolve virulence factors. Our knowledge of virulence evolution has been biased by pathogen evolution in response to major gene resistance, leaving other virulence activities underexplored. Understanding the key driving forces that give rise to novel virulence activities, and the integration of evolutionary concepts and methods with mechanistic research on plant–microbe interactions, can help inform crop protection.

The Three Ts of Pathogen Evolution During Zoonotic Emergence

When novel zoonotic diseases like Sars-CoV-2 emerge, they are likely to be poorly adapted to humans. Effective control measures will suppress transmission before significant evolution can occur, but extended transmission in human populations allows time for selection pressures to act. In this review, we discuss these selection pressures with the aim of better understanding the factors shaping both transmission and virulence in zoonotic pathogens as they become established. We discuss how selection pressures during epidemics of emerging zoonotic disease are determined by the three Ts: trade-offs, transmission, and time scales. In short, virulence and transmission may trade-off, but transmission is likely to be favored by selection early in emergence. However, the relative selection pressures on transmission and virulence shift depending on the time scale of the epidemic. Predicting pathogen evolution in zoonoses therefore depends critically on understanding both the trade-offs of transmission-improving mutations and the time scales of selection.