Biology subjects

Lindtke, D.

Publications and source records attributed to Lindtke, D..

4 recordsLinked to original sources

bioRxiv2023

Temperature effects on the magnitude of the bovine ex vivo immune response towards Mycoplasmopsis bovis

Although cattle are the mammalian species with most global biomass associated with a huge impact on our planet, their immune system remains poorly understood. Notably, the bovine immune system has peculiarities such as an overrepresentation of {gamma}{delta} T cells that requires particular attention, specifically in an infectious context. In line of 3R principles, we developed an ex vivo platform to dissect host-pathogen interactions. The experimental design was based on two independent complementary readouts: firstly, a novel 12-14 color multiparameter flow cytometry assay measuring maturation (modulation of cell surface marker expression) and activation (intracellular cytokine detection) of monocytes, conventional and plasmacytoid dendritic cells, natural killer cells, {gamma}{delta} T cells, B and T cells; secondly, a multiplex immunoassay monitoring bovine chemokine and cytokine secretion levels. The experiments were conducted on fresh primary bovine blood cells exposed to Mycoplasmopsis bovis (M. bovis), a major bovine respiratory pathogen. Besides reaffirming the tight cooperation of the different primary blood cells, we also identified novel key players such as strong IFN-{gamma} secreting NK cells, whose role was so far largely overlooked. Additionally, we compared the host-pathogen interactions at different temperatures, including commonly used 37 {degrees}C, ruminant body temperature (38-38.5 {degrees}C) and fever ([≥]39.5 {degrees}C). Strikingly, working under ruminant physiological temperature influenced the capacity of most immune cell subsets to respond to M. bovis compared to 37 {degrees}C. Under fever-like temperature conditions the immune response was impaired compared to physiological temperature. Our experimental approach, phenotypically delineating the bovine immune system provided a thorough vision of the immune response towards M. bovis and the influence of temperature towards that immune response.

immunology↗

bioRxiv2022

Selection leads to remarkable variability in the outcomes of hybridization across replicate hybrid zones

Hybrid zones have been viewed as an opportunity to see speciation in action. When hybrid zones are replicated, it is assumed that if the same genetic incompatibilities are maintaining reproductive isolation across all instances of secondary contact, those incompatibilities should be identifiable by consistent patterns in the genome. In contrast, changes in allele frequencies due to genetic drift should be idiosyncratic for each hybrid zone. To test this assumption, we simulated 20 replicates of each of 12 hybrid zone scenarios with varied genetic incompatibilities, rates of migration, selection and different starting population size ratios of parental species. We found remarkable variability in the outcomes of hybridization in replicate hybrid zones, particularly with Bateson-Dobzhansky-Muller incompatibilities and strong selection. We found substantial differences among replicates in the overall genomic composition of individuals, including admixture proportions, inter-specific ancestry complement, and number of ancestry junctions. Additionally, we found substantial variation in genomic clines among replicates at focal loci, regardless of locus-specific selection. We conclude that processes other than selection are responsible for some consistent outcomes of hybridization, whereas selection on incompatibilities can lead to genomically widespread and highly variable outcomes. We highlight the challenge of mapping between pattern and process in hybrid zones and call attention to how selection against incompatibilities will commonly lead to variable outcomes. We hope that this study informs future research on replicate hybrid zones and encourages further development of statistical techniques, theoretical models, and exploration of additional axes of variation to understand reproductive isolation.

evolutionary biology↗

bioRxiv2020

Model-based genotype and ancestry estimation for potential hybrids with mixed-ploidy

Non-random mating among individuals can lead to spatial clustering of genetically similar individuals and population stratification. This deviation from panmixia is commonly observed in natural populations. Consequently, individuals can have parentage in single populations or involving hybridization between differentiated populations. Accounting for this mixture and structure is important when mapping the genetics of traits and learning about the formative evolutionary processes that shape genetic variation among individuals and populations. Stratified genetic relatedness among individuals is commonly quantified using estimates of ancestry that are derived from a statistical model. Development of these models for polyploid and mixed-ploidy individuals and populations has lagged behind those for diploids. Here, we extend and test a hierarchical Bayesian model, called entropy, which can use low-depth sequence data to estimate genotype and ancestry parameters in autopolyploid and mixed-ploidy individuals (including sex chromosomes and autosomes within individuals). Our analysis of simulated data illustrated the trade-off between sequencing depth and genome coverage and found lower error associated with low depth sequencing across a larger fraction of the genome than with high depth sequencing across a smaller fraction of the genome. The model has high accuracy and sensitivity as verified with simulated data and through analysis of admixture among populations of diploid and tetraploid Arabidopsis arenosa.

genetics↗

bioRxiv2020

rearrvisr: an R package to detect, classify, and visualize genome rearrangements

The identification of genome rearrangements is of direct relevance for understanding their potential impacts on evolution and disease. However, available methods that detect or visualize rearrangements from deviations in gene order do not map them onto a genome of interest, complicating downstream analysis. In this work, we present rearrvisr, an R package that implements a novel algorithm for the identification and classification of rearrangements. In contrast to other software, it projects rearrangements onto a single genome, facilitating the localization of rearranged regions and estimation of their extent. We show that our tool achieves high precision and recall scores on simulated data, and illustrate the utility of our method by applying it to a data set generated from publicly available Drosophila genomes. The package is freely available from GitHub (https://github.com/dorolin/rearrvisr) and can be installed directly from R.

bioinformatics↗