Biology subjects

Siepe, I.

Publications and source records attributed to Siepe, I..

3 recordsLinked to original sources

bioRxiv2026

Standing genetic variation buffers field populations of Zymoseptoria tritici against seasonal and fungicide selection

Zymoseptoria tritici is a fungal wheat pathogen whose exceptionally large effective population sizes and frequent sexual recombination enable rapid adaptation and the breakdown of disease control strategies, yet the relative contributions of demographic turnover and fungicide selection to within-season genomic change remain unresolved at the field scale. We analysed whole-genome sequences from five wheat fields sampled during epidemic progression, including paired untreated and fungicide-treated populations, to separate seasonal demographic change from fungicide effects on genome-wide diversity allowing us to compare minor allele frequency spectra and diversity statistics to disentangle these effectswithin individual fields. Field populations were locally differentiated yet nested within the broader European gene pool; within-season demographic turnover consistently shifted allele frequency spectra towards more shared, common alleles; whereas nucleotide diversity and adaptive potential remained largely unchanged. Seasonal demographic turnover accounted for most short-term genomic change, while fungicide effects were comparatively small, field-specific and acted primarily on pre-existing resistance alleles and standing genetic variation. Our results show that short-term adaptation is driven primarily by the redistribution rather than depletion of standing genetic variation, highlighting pathogen population biology as a key determinant of disease-control durability and emphasizing the value of population-informed genomic surveillance.

evolutionary biology↗

bioRxiv2025

Zymoseptoria tritici show local differences in within-field diversity and effector variation

Zymoseptoria tritici is a cosmopolitan hemibiotrophic wheat pathogen with a high mutation rate and a mixed reproduction system, leading to challenges in traditional farming management. For successful integration of pest management, especially for early diagnostics of new aggressive or fungicide-resistant lineages, it is critical to understand population diversity in the field. We look at whole-genome sequence data for three datasets to differentiate within field diversity in fields of similar size: one dataset from a newly sampled field population from the United Kingdom and two publicly available datasets from fields from the United States and Switzerland. Inspection of population structure and diversity features, such as minor allele frequency distribution and clonality, show no within-field structure, the most abundant SNPs are present in low frequency, and European fields have higher clonality. Knowing that effectors play particularly important roles in (a)virulence, we specifically assess effector diversity characteristics. Whereas on a whole-genome scale, we can see separation of the populations at the regional scale, we do not find such separation for the effectors. Moreover, we find that multiple effector haplotypes can be found interspersed within the field and even occur within what has been considered clonal isolates or isolates from a single lesion. Our analyses highlight that within-field Z. tritici diversity is higher than previously reported. Our finding that multiple effector haplotypes can be found within a single lesion might explain the large resistance gene-breaking potential of Z. tritici.

microbiology↗

bioRxiv2019

LiP-Quant, an automated chemoproteomic approach to identify drug targets in complex proteomes

Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in developing optimized small-molecule compounds. Current unbiased approaches cannot directly pinpoint the interaction surfaces between ligands and protein targets. To address his limitation we have developed a new drug target deconvolution approach based on limited proteolysis coupled with mass spectrometry that works across species including human cells (LiP-Quant). LiP-Quant features an automated data analysis pipeline and peptide-level resolution for the identification of any small-molecule binding sites, Here we demonstrate drug target identification by LiP-Quant across compound classes, including compounds targeting kinases and phosphatases. We demonstrate that LiP-Quant estimates the half maximal effective concentration (EC50) of compound binding sites in whole cell lysates. LiP-Quant identifies targets of both selective and promiscuous drugs and correctly discriminates drug binding to homologous proteins. We finally show that the LiP-Quant technology identifies targets of a novel research compound of biotechnological interest.

systems biology↗