Biology subjects

Lee, T. E.

Publications and source records attributed to Lee, T. E..

5 recordsLinked to original sources

bioRxiv2022

Unraveling the Myotis Morass: Ultraconserved-Element Analysis Reveals Introgression, Cryptic Diversity, and Taxonomic Trouble in the Most Species-rich Bat Genus

Using sequences from 2615 UCE loci and multiple methodologies we inferred phylogenies for the largest genetic dataset of New World Myotis to date. The resulting phylogenetic trees were populated with short branch lengths and widespread conflict, hallmarks consistent with rapid adaptive radiations. The degree of conflict observed in Myotis has likely contributed to difficulties disentangling deeper evolutionary relationships. Unlike earlier phylogenies based on 1-2 gene sequences, this UCE dataset places M. brandtii outside the New World clades. Introgression testing of a small subset of our samples revealed evidence of historical but not contemporary gene flow, suggesting that hybridization occurs less frequently in the Neotropics than the Nearctic. We identified several instances of cryptic lineages within described species as well as several instances of potential taxonomic over-splitting. Evidence from Central and South American localities suggests that diversity in those regions is not fully characterized. In light of the accumulated evidence of the evolutionary complexity in Myotis and our survey of the taxonomic implications from our phylogenies it is apparent that the definition of species and regime of species delimitation need to be re-evaluated for Myotis. This will require substantial collaboration and sample sharing between geneticists and taxonomists to build a system that is both robust and applicable in a genus as diverse as Myotis.

genomics↗

bioRxiv2022

A mechanistic spatiotemporal model for drug resistant infections

Effective public health strategies for preventing infectious diseases require a deep understanding of transmission dynamics. However, epidemiological data alone is often insufficient for guiding public health action, as transmission patterns emerge from complex human-environment interactions. Additionally, collecting empirical data is frequently limited by cost and logistical challenges, especially in low-resource settings. Consequently, health policy increasingly relies on inference models that account for spatial and temporal dimensions, confounding factors, and uncertainty. In this work, we introduce a novel hierarchical mechanistic Bayesian model based on ecological diffusion to improve epidemiological inference for spreading pathogens. This model is specifically designed to reveal underlying spatiotemporal dynamics from empirical data, explicitly accounting for sampling bias and uncertainty. To demonstrate its effectiveness, we apply the model to simulated data of a drug-resistant pathogen and formally compare its performance to state-of-the-art epidemiological regression models. Our findings show that the model accurately identifies transmission patterns and is particularly effective at pinpointing transmission hotspots and predicting spread pathways--critical capabilities for controlling drug-resistant pathogens. While reliable monitoring remains challenging in low-resource environments, this model addresses demographic sampling bias and spatiotemporal complexities, though concentrated sampling in presumed high-risk areas can sometimes distort transmission insights. The proposed framework can inform targeted public health interventions in the early stages of resistance emergence, offering valuable guidance for resource-constrained settings.

bioinformatics↗

bioRxiv2021

Mechanistic within-host models of the asexual Plasmodium falciparum infection: a review and analytical assessment

BackgroundMalaria blood-stage infection length and intensity are important drivers of disease and transmission; however, the underlying mechanisms of parasite growth and the hosts immune response during infection remain largely unknown. Over the last 30 years, several mechanistic mathematical models of malaria parasite within-host dynamics have been published and used in malaria transmission models. MethodsWe identified mechanistic within-host models of parasite dynamics through a review of published literature. For a subset of these, we reproduced model code and compared descriptive statistics between the models using fitted data. Through simulation and model analysis, we compare and discuss key features of the models, including assumptions on growth, immune response components, variant switching mechanisms, and inter-individual variability. ResultsThe assessed within-host malaria models generally replicate infection dynamics in malaria-naive individuals. However, there are substantial differences between the model dynamics after disease onset, and models do not always reproduce late infection parasitemia data used for calibration of the within host infections. Models have attempted to capture the considerable variability in parasite dynamics between individuals by including stochastic parasite multiplication rates; variant switching dynamics leading to immune escape; variable effects of the host immune responses; or via probabilistic events. For models that capture realistic length of infections, model representations of innate immunity explain early peaks in infection density that cause clinical symptoms, and model representations of antibody immune responses control the length of infection. Models differed in their assumptions concerning variant switching dynamics, reflecting uncertainty in the underlying mechanisms of variant switching revealed by recent clinical data during early infection. Overall, given the scarce availability of the biological evidence there is limited support for complex models. ConclusionsOur study suggests that much of the inter-individual variability observed in clinical malaria infections has traditionally been attributed in models to random variability, rather than mechanistic disease dynamics. Thus, we propose that newly developed models should assume simple immune dynamics that minimally capture mechanistic understandings and avoid over-parameterisation and large stochasticity which inaccurately represent unknown disease mechanisms.

bioinformatics↗

bioRxiv2021

The competition dynamics of resistant and sensitive infections

Antimicrobial resistance is a major health problem with complex dynamics. Resistance may occur in an area because treated infections mutated and developed resistance, and the proportion of infections in a population may then increase. We developed a novel and flexible model that captures several features of resistance dynamics and competition. The model is able to account for many antimicrobials and thus can generally explore competition dynamics and their impact on pathogens and bacteria. Unlike simpler models, our nested model allows the population of resistant pathogen to smoothly increase or decrease. Time dependent dynamics are incorporated into difference equations which examines the effects of 12 parameters. This enables us to explicitly include three key competition dynamics: the transmission cost of resistance that occurs between hosts, the fitness cost of resistance that occurs within untreated hosts, and the release of this competition (from the fitness cost) that occurs once a host is treated. For malaria, our results suggest that without competitive release, drug resistance does not emerge. However, once emerged, competitive release has little effect, and the best way to mitigate the spread is to ensure that treatment is very effective.

bioinformatics↗

bioRxiv2017

Conflicting evolutionary histories of the mitochondrial and nuclear genomes in New World Myotis

The rapid diversification of Myotis bats into more than 100 species is one of the most extensive mammalian radiations available for study. Efforts to understand relationships within Myotis have primarily utilized mitochondrial markers and trees inferred from nuclear markers lacked resolution. Our current understanding of relationships within Myotis is therefore biased towards a set of phylogenetic markers that may not reflect the history of the nuclear genome. To resolve this, we sequenced the full mitochondrial genomes of 37 representative Myotis, primarily from the New World, in conjunction with targeted sequencing of 3,648 ultraconserved elements (UCEs). We inferred the phylogeny and explored the effects of concatenation and summary phylogenetic methods, as well as combinations of markers based on informativeness or levels of missing data, on our results. Of the 294 phylogenies generated from the nuclear UCE data, all are significantly different from phylogenies inferred using mitochondrial genomes. Even within the nuclear data, quartet frequencies indicate that around half of all UCE loci conflict with the estimated species tree. Several factors can drive such conflict, including incomplete lineage sorting, introgressive hybridization, or even phylogenetic error. Despite the degree of discordance between nuclear UCE loci and the mitochondrial genome and among UCE loci themselves, the most common nuclear topology is recovered in one quarter of all analyses with strong nodal support. Based on these results, we re-examine the evolutionary history of Myotis to better understand the phenomena driving their unique nuclear, mitochondrial, and biogeographic histories.

evolutionary biology↗