Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.10.01.755987

Genome-wide structural variants uncover strain-specific genes and evolutionary genomic determinants of a fungal maize pathogen

Abstract

Genomic structural variants (SVs) are important sources of genetic diversity and contribute to the adaptation and evolution of plant-pathogenic fungi. Colletotrichum graminicola, the causal agent of maize (Zea mays L.) anthracnose, causes significant yield losses and serves as an important model for genetic studies. To investigate genomic plasticity in this pathogen, long-read genome assemblies were generated for four C. graminicola field strains collected in France, Croatia, Brazil, and Argentina and compared with the chromosome-level reference genome from the USA. Comparative analyses revealed extensive structural variation across core chromosomes, including large-scale translocations, inversions, duplications, and insertions and deletions, whereas minichromosomes showed markedly reduced collinearity among strains. Secreted proteins and predicted effectors were located significantly closer to regions affected by SVs. Approximately 174 kb of segmental duplications lacking genes were shared between core chromosome 04 and minichromosomes 11 and 13 in the reference genome but were absent from the other field strains, revealing a strain-specific structural link between core and minichromosomes. Protein clustering identified 733 candidate strain-specific genes across the reference genome. Approximately 72% of the genes annotated on the minichromosomes were strain-specific, indicating that most of their annotated genes lacked orthologous gene models in the other strains. Of the 733 candidate strain-specific genes, 492 retained homologous genomic sequences in at least one other strain and many also showed evidence of gene disruption. This pattern is consistent with lineage-specific pseudogenization and suggests that apparent gene presence-absence variation may arise through several evolutionary processes, including gene gain, gene loss and loss of gene function. Insertions and deletions in potentially regulatory flanking regions were associated with plant-pathogen interaction genes. In addition, interpretable machine learning analyses revealed a co-occurrence of duplications and LTR retrotransposons in genomic regions associated with secreted genes. This study shows that structural variation, transposable elements, and lineage-specific pseudogenization are important features of genome evolution in C. graminicola and identifies associations between genomic variation and genes involved in plant-pathogen interactions.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Becerra, S., Garcia-Rodriguez, P., Martin, J., Rodriguez-Monaco, L., Gutierrez-Sanchez, S., Cuevas-Fernandez, F. B., Rogerio, F., Boufleur, T. R., Vicente-Villardon, J. L., Faino, L., Baroncelli, R., Sukno, S. A., Thon, M. R.. 2026-10-07. Genome-wide structural variants uncover strain-specific genes and evolutionary genomic determinants of a fungal maize pathogen. https://doi.org/10.64898/2026.10.01.755987

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Somatic variant detection using an individual-specific pangenome reference: a case study of a pediatric acute myeloid leukemia patient

Background: Reference bias can cause false or missed somatic variant calls, also in clinically relevant genes. Pangenomes aim to reduce this bias, but evidence of their diagnostic benefit is lacking. We tested how an individual-specific pangenome affects somatic variant calling in bulk samples and single cells. Results: We built an individual-specific pangenome from Nanopore assemblies of normal hematopoietic stem and progenitor cells of a child with acute myeloid leukemia. We aligned short-read whole-genome sequencing data of bulk and single-cell samples to hg38 and to the pangenome, and called somatic variants with standard downstream tools after anchoring pangenome alignments to hg38. Single nucleotide variants were mostly shared, while indel calls unique to the pangenome were fewer than those unique to hg38, mostly in repeats. Both approaches detected NUP98-NSD1 and FLT3 internal tandem duplication (FLT3-ITD) drivers. The pangenome-based phylogeny showed more parallel branching in comparison to hg38-based tree. The pangenome removed three false-positive PRSS1 variants and, in single cells, frameshift insertion calls next to polyG and polyC repeats. However, surjection to hg38 coordinates split a WT1 driver indel call in a low-complexity region into two separate calls. Validation showed that all somatic structural variant calls, except NUP98-NSD1 and FLT3-ITD, were germline variants or artifacts. Conclusions: The use of individual-specific pangenome led to fewer false-positive indel and driver calls, mainly in repeats and single cells, and changed the branching of the inferred phylogeny. However, surjection artifacts and false-positive somatic structural variants show that pangenome-based results need read-level inspection and validation before clinical use.

genomics↗

Molecule-resolved inference reveals the spatial organization of transcriptional programs

Transcriptional programs encode cellular identity and physiological activity, linking gene regulation to cellular phenotypes. Although spatial transcriptomics measures individual RNA molecules across intact tissues, most analyses aggregate them into cells or pixels, obscuring molecular level functional dynamics. We introduce FICTURE2, a statistical model that infers the transcriptional program from which each individual molecule is generated, using gene identity and spatial context alone without segmentation or binning. FICTURE2 learns programs with distinct spatial distributions, ranging from millimeter-scale continuous transition to micron-scale subcellular localization, the coexisting patterns of which then reveal both cellular identity and physiological activity with unified interpretations. FICTURE2 supports both unsupervised and reference-guided inference and is orders of magnitude more efficient than alternative approaches, enabling systematic analysis of large-scale spatial datasets as we demonstrate in data across diverse tissue types profiled by various spatial technologies.

genomics↗

Shared genomic signatures of Arctic adaptation among distantly related plant lineages

Extreme environments impose strong selection pressures that can drive convergent adaptation among distantly related species. Arctic plants survive low temperatures, 24-hour light in summer and darkness in winter, short growing seasons, limited precipitation, nutrient-poor soils, and extreme seasonal variation. To test if these conditions are associated with shared genomic signatures, we generated a chromosome-level genome assembly for Oxyria digyna and compared it with existing reference genomes for three distantly related Arctic species (Cochlearia groenlandica, Draba nivalis, Dryas octopetala) and for 16 non-Arctic species from the same families. We identified 214 orthogroups with signatures of positive selection in all Arctic species, 1.6 times the number shared among the non-Arctic species. Although no gene ontology (GO) categories remained significantly enriched after multiple-testing correction, descriptive annotations included genes involved in general functions including protein folding, glycolysis, transcription, and oxidative-stress responses. Orthogroups showing copy number variation were associated with biotic interactions, sexual reproduction, and nutrient acquisition, suggesting that species may respond to biotic and nutrient stresses by shifts in gene copy number. Arctic and alpine genomes contained lower proportions of some repetitive elements, including terminal inverted repeats, and smaller regions of centromeric repeats than non-Arctic genomes. Overall, we see shared genes under positive selection, similar functions of genes with high copy number variation, and lower counts of some repetitive elements in the Arctic plant genomes. Our findings reveal a shared genomic signature of Arctic adaptation across deeply diverged plant lineages, suggesting that evolution in extreme environments repeatedly targets similar genomic features.

genomics↗