Biology subjects

Deusch, O.

Publications and source records attributed to Deusch, O..

3 recordsLinked to original sources

bioRxiv2024

Pangenomic context reveals the extent of intraspecific plant NLR evolution

Nucleotide-binding leucine-rich repeat (NLR) proteins are a major component of the plant immune system, which directly or indirectly detect molecular signals of pathogen invasion. Despite their critical role, the processes by which NLR genes diversify remain poorly characterised due to the extraordinary sequence, structural, and regulatory variability of NLRs, even among closely related individuals. To understand the evolution of NLR diversity in Arabidopsis thaliana, we leverage graph-based methods to define pangenomic NLR neighbourhoods in 17 genetically diverse genomes. We integrate full-length transcript and transposable element information to exhaustively annotate all intact and degraded NLRs, enabling exploration of the processes that underpin the birth, death and maintenance of NLR diversity within a species. Our main finding is that many uncorrelated mutational processes create NLR diversity, and that there is no single metric that captures on its own the true extent of NLR structural and sequence variation. This immense diversity in plant immune system diversification allows populations to survive the constant onslaught of pathogens, not unlike vertebrate adaptive immunity, where variation is also generated by a variety of complementary mechanisms, albeit at the level of individuals.

plant biology↗

bioRxiv2020

Model-based prediction of bacterial population dynamics in gastrointestinal infection

The complex interplay of a pathogen with the host immune response and the endogenous microbiome determines the course and outcome of gastrointestinal infection (GI). Expansion of a pathogen within the gastrointestinal tract implies an increased risk to develop systemic infection. Through computational modeling, we aimed to calculate bacterial population dynamics in GI in order to predict infection course and outcome. For the implementation and parameterization of the model, oral mouse infection experiments with Yersinia enterocolitica were used. Our model takes into account pathogen specific characteristics, such as virulence, as well as host properties, such as microbial colonization resistance or immune responses. We were able to confirm the model calculations in these scenarios by experimental mouse infections and show that it is possible to computationally predict the infection course. Far future clinical application of computational modeling of infections may pave the way for personalized treatment and prevention strategies of GI.

microbiology↗

bioRxiv2019

Combining whole genome shotgun sequencing and rDNA amplicon analyses to improve detection of microbe-microbe interaction networks in plant leaves

Microorganisms from all domains of life establish associations with plants. Although some harm the plant, others antagonize pathogens or prime the plant immune system, acquire nutrients, tune plant hormone levels, or perform additional services. Most culture-independent plant microbiome research has focused on amplicon sequencing of 16S rDNA and/or the internal transcribed spacer (ITS) of rDNA loci, but the decreasing cost of high-throughput sequencing has made shotgun metagenome sequencing increasingly accessible. Here, we describe shotgun sequencing of 275 wild Arabidopsis thaliana leaf microbiomes from southwest Germany, with additional bacterial 16S rDNA and eukaryotic ITS1 amplicon data from 176 of these samples. The shotgun data were dominated by bacterial sequences, with eukaryotes contributing only a minority of reads. For shotgun and amplicon data, microbial membership showed weak associations with both site of origin and plant genotype, both of which were highly confounded in this dataset. There was large variation among microbiomes, with one extreme comprising samples of low complexity and a high load of microorganisms typical of infected plants, and the other extreme being samples of high complexity and a low microbial load. We use the metagenome data, which captures the ratio of bacterial to plant DNA in leaves of wild plants, to scale the 16S rDNA amplicon data such that they reflect absolute bacterial abundance. We show that this cost-effective hybrid strategy overcomes compositionality problems in amplicon data and leads to fundamentally different conclusions about microbiome community assembly.

genomics↗