Biology subjects

Nowicki, M.

Publications and source records attributed to Nowicki, M..

4 recordsLinked to original sources

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↗

bioRxiv2025

Chromosome-Scale Assemblies of Flowering Dogwood Cultivars Enable Identification of Candidate Genes Regulating Anthocyanin Biosynthesis in Leaves and Bracts

O_LIThe North American-native ornamental tree, flowering dogwood (Cornus florida L.), has a showy bract display that can range in color from white to pink to deep red. Although many trees have white bracts, there is consumer demand for novel pigmentation in the bracts combined with other traits of interest. Because the genetic basis of all traits in flowering dogwood is unknown, combining them using traditional breeding efforts is time, labor, and space-intensive. C_LIO_LIWe developed foundational genomic resources to establish marker-assisted selection within flowering dogwood breeding. We generated diploid, chromosome-scale, annotated genome assemblies for one pink-bracted and red-leafed tree and one white-bracted and green-leafed tree. Additionally, a phenotyping protocol for bract color and presence/absence diagnostic SNPs for bract and leaf color were established. C_LIO_LIWe leveraged these resources to evaluate linkage associations and differential gene expression related to anthocyanin biosynthesis to identify candidate genes regulating bract and leaf pigmentation. Within a 14Mb locus we identified 14 anthocyanin-related candidate genes. Two genes, with MYB (g19533) and RING finger (g19556) binding domains, had both differential gene expression and variants with the expected segregation pattern. C_LIO_LIThese resources will be valuable in combining pink-red bracts with other traits to advance flowering dogwood breeding. C_LI

genomics↗

bioRxiv2025

Genetic regulation of anthocyanin biosynthesis in Cornus species: The roles of R2R3-MYB transcription factors

Flowering dogwood (Cornus florida L.) and Asian dogwood (C. kousa F. Buerger ex Hance) are popular deciduous ornamental trees native to a wide range of the eastern and southeastern United States and East Asia, respectively. Anthocyanin pigments enhance desirable pink or dark red colored bracts in dogwoods. Although anthocyanin biosynthesis is one of the best-studied biological processes in nature, genomic and genetic resources to understand the molecular regulation of its synthesis in dogwoods are still lacking. Two classes of genes control anthocyanin production; both structural genes and MYB transcription factors may function as positive or negative regulators of anthocyanin biosynthesis. To reveal the molecular mechanisms that govern color production in ornamental dogwoods, mature bracts of three cultivars of C. florida (white bracts: Cloud Nine; red bracts: Cherokee Brave and Cherokee Chief) and two cultivars of C. kousa (light green bracts: Greensleeves and mid-tone pink bracts Rosy Teacups) were sampled when color was maximally visible. Differential gene expression analysis of the RNAseq data identified 1,156 differentially expressed genes in C. florida and 1,396 in C. kousa. Phylogenetic analysis with functional orthologues in other plants grouped the candidate R2R3-MYB identified in this study into two distinct subgroups. CfMYB2, CfMYB3, and CkMYB2 belonged to subgroup 4, whereas CfMYB1 80 and CkMYB1 clustered in subgroup 5. The former repress anthocyanin and proanthocyanidin synthesis in flowering and Asian dogwoods, whereas the latter increase it. Our study contributes to understanding processes behind anthocyanin production and lays foundation to future development of molecular markers for faster development of desirable red-bracted dogwoods.

biochemistry↗

bioRxiv2024

Ligand Identification using Deep Learning

MotivationAccurately identifying ligands plays a crucial role in the process of structure-guided drug design. Based on density maps from X-ray diffraction or cryogenic-sample electron microscopy (cryoEM), scientists verify whether small-molecule ligands bind to active sites of interest. However, the interpretation of density maps is challenging, and cognitive bias can sometimes mislead investigators into modeling fictitious compounds. Ligand identification can be aided by automatic methods, but existing approaches are available only for X-ray diffraction and are based on iterative fitting or feature-engineered machine learning rather than end-to-end deep learning. ResultsHere, we propose to identify ligands using a deep learning approach that treats density maps as 3D point clouds. We show that the proposed model is on par with existing machine learning methods for X-ray crystallography while also being applicable to cryoEM density maps. Our study demonstrates that electron density map fragments can aid the training of models that can later be applied to cryoEM structures but also highlights challenges associated with the standardization of electron microscopy maps and the quality assessment of cryoEM ligands. AvailabilityCode and model weights are available on GitHub at https://github.com/jkarolczak/ligands-classification. Datasets used for training and testing are hosted at Zenodo: 10.5281/zenodo.10908325. An accompanying ChimeraX bundle is available at https://github.com/wtaisner/chimerax-ligand-recognizer. Contact: dariusz.brzezinski@cs.put.poznan.pl Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗