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Abstract Recombination is central to genetics and to evolution of sexually reproducing organisms. However, obtaining accurate estimates of recombination rates, and of how they vary along chromosomes, continues to be challenging. To advance our ability to estimate recombination rates, we present Hi-reComb, a new method and software for estimation of recombination maps from bulk gamete chromosome conformation capture sequencing (Hi-C). Simulations show that Hi-reComb produces robust, accurate recombination landscapes. With empirical data from sperm of five fish species we show the advantages of this approach, including joint assessment of recombination maps and large structural variants, map comparisons using bootstrap, and workflows with trio phasing vs. Hi-C phasing. With off-the-shelf library construction and a straightforward rapid workflow, our approach will facilitate routine recombination landscape estimation for a broad range of studies and model organisms in genetics and evolutionary biology. Hi-reComb is open-source and freely available at https://github.com/millanek/Hi-reComb. 

Abstract Gene co-expression networks are a widely used tool for summarizing transcriptomic variation between individuals, and for inferring the transcriptional regulatory pathways that mediate genotype–phenotype relationships. However, these co-expression networks must be interpreted with caution, as they can arise from multiple processes. Here, we investigate one such process, using simulations to demonstrate that hybridization and gene flow between populations can greatly modify co-expression networks. Admixture between populations produces correlated expression between genes experiencing linkage disequilibrium. This correlated expression does not reflect functional relationships between genes but rather depends on migration rates and physical linkage on chromosomes. Given the prevalence of gene flow and hybridization between divergent populations in nature, these introgression effects likely represent a significant force in network evolution, even in populations where hybridization is historical rather than contemporary. These findings emphasize the critical importance of considering both evolutionary history and genomic architecture when analyzing gene co-expression networks in natural populations. 

ABSTRACT Many terrestrial ectotherms have gone to great evolutionary lengths to adapt to long cold winters; some have even evolved the ability to tolerate the freezing of most of the extracellular fluid in the body. Now, however, high‐elevation and high‐latitude winters are experiencing an accelerated period of warming. Specialised winter adaptations that promoted fitness in a seasonally frozen environment may soon be superfluous or even maladaptive. We ask whether winter adaptations include changes in immune functions, and whether changing winter conditions could exert disparate effects on populations of a wide‐ranging terrestrial ectotherm, the wood frog (Lithobates sylvaticus). By rearing wood frogs from ancestral winter environments that vary in length and temperature in a common garden, and reciprocally exposing post‐metamorphic frogs to unfrozen and frozen artificial winter conditions in the lab, we were able to decompose transcriptomic differences in ventral skin gene expression into those that were environmentally induced (responsive to temperature) and genetically determined and those that varied as an interaction between the genotype and environment. We found that frogs from harsh ancestral winter environments constitutively upregulated immune processes, including cellular immunity, inflammatory processes and adaptive immune processes, as compared to frogs from mild ancestral winter environments. Further, we saw that the expression of several genes varied in an interaction between the genotype and artificial winter. We suggest that just as winter climates likely served as the selective force resulting in remarkable winter adaptations such as freeze tolerance, they may have also induced constitutive changes in immune gene expression. 

ABSTRACT Helminths infect humans, livestock, and wildlife, yet remain understudied despite their significant impact on public health and agriculture. Because many of the most prevalent helminth‐borne diseases are zoonotic, understanding helminth transmission among wildlife could improve predictions and management of infection risks across species. A key challenge to understanding helminth transmission dynamics in wildlife is accurately and quantitatively tracking parasite load across hosts and environments. Traditional methods, such as visual parasite identification from environmental samples or infected hosts, are time‐consuming, while standard molecular techniques (e.g., PCR and qPCR) often lack the sensitivity to reliably detect lower parasite burdens. These limitations can underestimate the prevalence and severity of infection, hindering efforts to manage infectious diseases. Here, we developed a multiplexed droplet digital PCR (ddPCR) assay to quantify helminth loads in aquatic habitats using 18S rRNA target genes. UsingSchistocephalus solidusand their copepod hosts as a case study, we demonstrate ddPCR's sensitivity and precision. The assay is highly reproducible, reliably detecting target genes at concentrations as low as 1 pg of DNA in lab standards and field samples (multi‐species and eDNA). Thus, we provide a toolkit for quantifying parasite load in intermediate hosts and monitoring infection dynamics across spatio‐temporal scales in multiple helminth systems of concern for public health, agriculture, and conservation biology. 

To understand infectious disease dynamics, we need to understand the inextricably intertwined nature of the ecology and evolution of pathogens and hosts. Epidemiological dynamics of many infectious diseases have highlighted the importance of considering the demographics of the societies in which they spread, particularly with respect to age structure. In addition, the waves of the recent COVID-19 pandemic driven by variant replacements at an unprecedented speed show that it is vital to consider the evolutionary aspects. The classic trade-off theory of virulence addresses aspects of pathogen evolution, but here we explore in more detail the possibility of society-specific evolutionarily stable strategies (ESS) during an unfolding pandemic. Theory posits the existence under some conditions of an ESS representing the evolutionary endpoint of change. By using a demographically realistic model incorporating infection rates that vary with age, we outline which evolutionary scenarios are plausible. Focusing on the rate of infection and duration of infectivity, we ask whether an ESS exists, what characterizes it, and as a result which long-term public-health consequences may be expected. We demonstrate that the ESS of an evolving pathogen depends upon the background age-dependent frailty and mortality rates. Our findings shed important light on the plausible long-term trajectories of highly evolvable novel pathogens. 

Spatial-temporal variation in environmental conditions is ubiquitous in nature. This variation simultaneously impacts survival, reproduction, and movement of individuals and thereby the rate at which metapopulations grow. Using the tools of stochastic demography, the metapopulation growth rate is decomposed into five components corresponding to temporal, spatial, and spatial-temporal variation in fitness and spatial and spatial-temporal covariation in dispersal and fitness. While temporal variation in fitness always reduces the metapopulation growth rate, all other sources of variation can either increase or reduce the metapopulation growth rate. Increases occur either by reducing the impacts of temporal variation or by generating a positive fitness-density covariance where individuals tend to concentrate in higher-quality patches. For example, positive autocorrelations in spatial-temporal variability in fitness generate this positive fitness-density covariance for less dispersive populations but decrease it for highly dispersive populations (e.g., migratory species). Negative autocorrelations in spatial-temporal variability have the opposite effects. Positive covariances between movement and future fitness, on short or long timescales, increase growth rates. These positive covariances can arise in unexpected ways. For example, the win-stay, lose-shift dispersal strategy in negatively autocorrelated environments can generate positive spatial covariances that exceed negative spatial-temporal covariances. This decomposition of the metapopulation growth rate provides a way to quantify the relative importance of fundamental sources of variation for metapopulation persistence. 

A common assumption is that pathogens more readily destabilize their host populations, leading to an elevated risk of driving both the host and pathogen to extinction. This logic underlies many strategies in conservation biology and pest and disease management. Yet, the interplay between pathogens and population stability likely varies across contexts, depending on the environment and traits of both the hosts and pathogens. This context-dependence may be particularly important in natural consumer-host populations where size- and stage-structured competition for resources strongly modulates population stability. Few studies, however, have examined how the interplay between size and stage structure and infectious disease shapes the stability of host populations. Here, we extend previously developed size-dependent theory for consumer-resource interactions to examine how pathogens influence the stability of host populations across a range of contexts. Specifically, we integrate a size- and stage-structured consumer-resource model and a standard epidemiological model of a directly transmitted pathogen. The model reveals surprisingly rich dynamics, including sustained oscillations, multiple steady states, biomass overcompensation, and hydra effects. Moreover, these results highlight how the stage structure and density of host populations interact to either enhance or constrain disease outbreaks. Our results suggest that accounting for these cross-scale and bidirectional feedbacks can provide key insight into the structuring role of pathogens in natural ecosystems while also improving our ability to understand how interventions targeting one may impact the other.