Beever, J. E. | bioRxiv Science

bioRxiv2025

The telomere-to-telomere genome and lifestyle transcriptome profiling of Discula destructiva Redlin provide modern molecular and genomic context to a historical epidemic

Fungal pathogens have dramatically altered forests worldwide, yet the mechanisms underlying their virulence remain poorly understood. From the 1970s to the early 2000s, dogwood anthracnose, caused by Discula destructiva Redlin, devastated flowering and Pacific dogwoods (Cornus florida L. and C. nuttallii Aud., respectively). Despite the impacts of D. destructiva and other phytopathogens on forest ecosystems, genomic resources remain limited, hindering efforts to understand pathogenicity. The goal of this study was to evaluate the historical D. destructiva epidemic through a modern genomics lens by uncovering virulence- associated genes that likely contributed to its rapid spread across native dogwoods. We therefore utilized PacBio HiFi and Proximo Hi-C sequencing to assemble the first telomere-to-telomere, chromosome-scale genome for D. destructiva isolate AS111. The resulting 46.655 Mb assembly comprised eight chromosomes with an overall BUSCO completeness of 97.64%. We also identified 10,373 predicted gene models with an overall BUSCO completeness of 96.45%. To investigate gene expression across distinct life cycle phases, reproductive (sporulating) and vegetative (nonsporulating), we conducted RNA sequencing and identified 240 differentially expressed genes (padj < 0.05). GO enrichment revealed 162 upregulated genes during sporulation linked to plant cell wall degradation and sugar metabolism, whereas 78 downregulated genes were linked to electron carrier activity and redox balance. Among these 240 genes, 117 genes had predicted protein sequences that were also identified as a candidate virulence factor, including signal peptides, carbohydrate-active enzymes (CAZymes), and effectors, highlighting the role of sporulation-associated gene expression in D. destructiva virulence. Together, these findings suggest that the reproductive phase primes D. destructiva for host invasion and ecological persistence, which may influence its success as a forest pathogen. Author SummaryForest pathogens threaten ecosystems worldwide and cause extensive ecological and economic damage. Since the 1970s, native dogwood populations in North America have been devastated by dogwood anthracnose, caused by the exotic fungal pathogen Discula destructiva. Discula destructiva is just one of many destructive fungal pathogens in the order Diaporthales, which includes other noteworthy pathogens that cause chestnut blight (Cryphonectria parasitica) and butternut canker (Ophiognomonia clavigignenti-juglandacearum). Despite the widespread impact of fungal diseases, little is known about the genetic factors that drive their spread and severity. To help address this gap, we generated the first telomere-to-telomere, chromosome- scale genome assembly of D. destructiva and analyzed gene expression across distinct life cycle phases: reproductive (sporulating) and vegetative (nonsporulating) growth. Our findings revealed key sporulation-associated genes and virulence factors that may facilitate host infection and underscore the importance of sporulation in the pathogenicity of D. destructiva. These insights improve our understanding of the mechanisms that drive disease development, influence how fungal pathogens such as D. destructiva establish and persist in forest ecosystems, and provide a foundation for future comparative genomics among other devastating pathogens in Diaporthales.

genomics↗

bioRxiv2024

Optimizing Cost-Effective Gene Expression Phenotyping Approaches in Cattle Using 3' mRNA Sequencing

BackgroundGenetic and genomic selection programs require large numbers of phenotypes observed for animals in shared environments. Direct measurements of phenotypes like meat quality, methane emission, and disease susceptibility are difficult and expensive to measure at scale but are critically important to livestock production. Our work leans on our understanding of the "Central Dogma" of molecular genetics to leverage molecular intermediates as cheaply-measured proxies of organism-level phenotypes. The rapidly declining cost of next-generation sequencing presents opportunities for population-level molecular phenotyping. While the cost of whole transcriptome sequencing has declined recently, its required sequencing depth still makes it an expensive choice for wide-scale molecular phenotyping. We aim to optimize 3' mRNA sequencing (3' mRNA-Seq) approaches for collecting cost-effective proxy molecular phenotypes for cattle from easy-to-collect tissue samples (i.e., whole blood). We used matched 3' mRNA-Seq samples for 15 Holstein male calves in a heat stress trail to identify the 1) best library preparation kit (Takara SMART-Seq v4 3' DE and Lexogen QuantSeq) and 2) optimal sequencing depth (0.5 to 20 million reads/sample) to capture gene expression phenotypes most cost-effectively. ResultsTakara SMART-Seq v4 3' DE outperformed Lexogen QuantSeq libraries across all metrics: number of quality reads, expressed genes, informative genes, differentially expressed genes, and 3' biased intragenic variants. Serial downsampling analyses identified that as few as 8.0 million reads per sample could effectively capture most of the between-sample variation in gene expression. However, progressively more reads did provide marginal increases in recall across metrics. These 3' mRNA-Seq reads can also capture animal genotypes that could be used as the basis for downstream imputation. The 10 million read downsampled groups called an average of 104,386 SNPs and 20,131 INDELs, many of which segregate at moderate minor allele frequencies in the population. ConclusionThis work demonstrates that 3' mRNA-Seq with Takara SMART-Seq v4 3' DE can provide an incredibly cost-effective (<$25/sample) approach to quantifying molecular phenotypes (gene expression) while discovering sufficient variation for use in genotype imputation. Ongoing work is evaluating the accuracy of imputation and the ability of much larger datasets to predict individual animal phenotypes.

genomics↗