Biology subjects

Delude, C.

Publications and source records attributed to Delude, C..

3 recordsLinked to original sources

bioRxiv2025

A pangenomics-enabled platform for the high-throughput discovery of antifungal resistance factors in crop pathogens

The rise of antifungal resistance is a global challenge for both human health and food security, because resistance emergence easily outpaces the antifungal development pipeline. Furthermore, resistance arises often in parallel and through alternative mechanisms creating challenges to predict emergence. In agriculture, where vast areas are sprayed by diverse cocktails, antifungal resistance gains are particularly complex. Despite broad efforts, knowledge of resistance mechanisms is often limited to model genotypes and empirical evidence from the field is lacking. Here, we define and validate a high- throughput pipeline for antifungal resistance discovery informed by emerging resistance gains at continental scale. We analyzed a thousand-genome European diversity panel of the major wheat pathogen Zymoseptoria tritici and assessed resistance levels against over 29 fungicides covering all major classes. We optimized high-throughput phenotyping assays to comprehensively capture emerging resistance phenotypes. Pangenome-informed genotyping techniques revealed a total of 2192 genes associated with antifungal resistance. This expands by an order of magnitude the current knowledge and establishes a refined atlas of resistance mutations. We generated mutants to recapitulate several of the discovered resistance factors. Hence, our approach captures in-field resistance gains across Europe for all major fungicide classes and can define exact molecular targets. Broad knowledge of resistance gains will guide more sustainable fungicide development pipelines.

microbiology↗

bioRxiv2025

A large European diversity panel reveals complex azole fungicide resistance gains of a major wheat pathogen

Fungicide resistance in crop pathogens poses severe challenges to sustainable agriculture. Demethylation inhibitors (DMIs) are critical for controlling crop diseases but face rapid resistance gains in the field. Even though the main molecular basis of resistance is well established, field surveys have repeatedly revealed alternative resistance mechanisms. The European continent in particular has seen rapid and heterogeneous gains in azole resistance in the past decades. Here, we establish a large genome panel to dissect the genetic architecture of emerging resistance in the major wheat pathogen Zymoseptoria tritici. The European diversity panel spans 15 sampling years and 27 countries for a total of 1394 sequenced and phenotyped strains. Using two complementary assays to quantify resistance levels of each strain, we captured fine-grained shifts in DMI resistance over space and time. We conducted genome-wide association studies based on a comprehensive set of genotyping approaches for six DMIs. We mapped a total of 21,220 genetic variants and 158 genes linked to resistance. The substantial scope in genetic mechanisms underpinning DMI resistance significantly expands our mechanistic understanding how continent-wide resistance arises in fungal pathogens over time. Diversification of the Cyp51 coding sequence was particularly striking with new resistant haplotypes emerging with complex configurations and geographic patterns. This study provides expansive new insights into fungicide resistance gains of crop pathogens relevant for future resistance management strategies.

microbiology↗

bioRxiv2023

Improved gene annotation of the fungal wheat pathogen Zymoseptoria tritici based on combined Iso-Seq and RNA-Seq evidence

Despite large omics datasets, the establishment of a reliable gene annotation is still challenging for eukaryotic genomes. Here, we used the reference genome of the major fungal wheat pathogen Zymoseptoria tritici (isolate IPO323) as a case study to develop methods to improve eukaryotic gene prediction. Four previous IPO323 annotations identified 10,933 to 13,260 gene models, but only one third of these coding sequences (CDS) have identical structures. To resolve these discrepancies and improve gene models, we generated full-length transcripts using long-read sequencing. This dataset was used together with other evidence (RNA-Seq transcripts and protein sequences) to generate novel ab initio gene models. The selection of the best structure among novel and existing gene models was performed according to transcript and protein evidence using InGenAnnot, a novel bioinformatics suite. Overall, 13,414 re-annotated gene models (RGMs) were predicted, including 671 new genes among which 53 encoded effector candidates. This process corrected many of the errors (15%) observed in previous gene models (coding sequence fusions, false introns, missing exons). While fungal genomes have poor annotations of untranslated regions (UTRs), our Iso-Seq long-read sequences outlined 5 and 3UTRs for 73% of the RGMs. Alternative transcripts were identified for 13% of RGMs, mostly due to intron retention (75%), likely corresponding to unprocessed pre-mRNAs. A total of 353 genes displayed alternative transcripts with combinations of previously predicted or novel exons. Long non-coding transcripts (lncRNAs) and double-stranded RNAs from two fungal viruses were also identified. Most lncRNAs corresponded to antisense transcripts of genes (52%). lncRNAs that were up or down regulated during infection were enriched in antisense transcripts (70%), suggesting their involvement in the control of gene expression. Our results showed that combining different ab initio gene predictions and evidence-driven curation using InGenAnnot improved the quality of gene annotations of a compact eukaryotic genome. Our analysis also provided new insights into the transcriptional landscape of Z. tritici, helping develop an increasingly complex picture of its biology.

genomics↗