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

Authors: LAPALU, NICOLAS - Université Paris-Saclay; LAMOTHE, LUCIE - Université Paris-Saclay; PETIT, YOHANN - Université Paris-Saclay; GENISSEL, ANNE - Université Paris-Saclay; DELUDE, CAMILLE - Syngenta Crop Protection; FUERTEY, ALICE - Neuchatel University - Switzerland; ABRAHAM, LEEN NANCHIRA - Neuchatel University - Switzerland; SMITH, DAN - Rothamsted Research; KING, ROBERT - Rothamsted Research; RENWICK, ALISON - Curtin University; Goodwin, Stephen; HANE, JAMES - Max Planck Institute For Evolutionary Biology; RUDD, JASON - Rothamsted Research; STUKENBRIOCK, EVA - Max Planck Institute For Evolutionary Biology; CROLL, DANIEL - Neuchatel University - Switzerland; SCALLIET, GABRIEL - Syngenta Crop Protection; LEBRUN, MARC-HENRI - Université Paris-Saclay

Submitted to: Molecular Plant-Microbe Interactions
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/12/2025
Publication Date: 11/18/2025
Citation: Lapalu, N., Lamothe, L., Petit, Y., Genissel, A., Delude, C., Fuertey, A., Abraham, L.N., Smith, D., King, R., Renwick, A., Appertet, M., Sucher, J., Steindorff, A.S., Goodwin, S.B., Kema, G.H.J., Grigoriev, I.V., Hane, J., Rudd, J., Stukenbriock, E., Croll, D., Scalliet, G., Lebrun, M. 2025. Improved gene annotation of the fungal wheat pathogen Zymoseptoria tritici based on combined Iso-Seq and RNA-Seq evidence. Molecular Plant-Microbe Interactions. https://doi.org/10.1094/MPMI-07-25-0077-TA.
DOI: https://doi.org/10.1094/MPMI-07-25-0077-TA

Interpretive Summary: It is now easy to generate genome sequences for many species throughout the kingdom of life, but identifying and annotating all of the genes to predict their likely functions remains a problem for biology, particularly for complex organisms. To test methods for improving annotations of genome sequences, additional data from expressed genes was combined with four previous annotations of the fungus that causes Septoria tritici blotch of wheat. Comparisons among the four previous annotations identified many discrepancies. Re-analyzing these annotations with a new software plus additional data from long sequences of genes that were expressed under multiple conditions identified 13,414 re-annotated gene models, compared to 10,933 for the original annotation. Many of these new genes resulted from splitting genes that were improperly joined previously but many were new. The approach also allowed identification of genes that could have multiple forms, identified regions of the genome that do not code for proteins, and identified sequences for two viruses that infect this fungus. The new, much-improved genome annotation will be invaluable to researchers trying to understand how this fungus infects wheat, and the approach and software can guide annotation of numerous other genomes in the future.

Technical Abstract: 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 13,922 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 3’UTRs 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) were also identified, as well as double-stranded RNAs from two fungal viruses. 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.