Abstract
Cannabis sativa L. (marijuana, hemp) is a dioecious angiosperm species currently evolving sex chromosomes. Genetic mechanisms, primarily controlled by an XY chromosome system, appear to dictate sex expression in C. sativa. However, sexual expression is also governed by the interplay of hormone regulatory gene networks, influenced by both genetic and environmental factors. Within the species, some populations exhibit dioecy, monoecy, or a gradient of both. Dioecious individuals produce exclusively male or female flowers, while monoecious plants bear both male and female flowers. However, through interruption of phytohormone signal transduction via abiotic stressors, male and female Cannabis are able to produce flowers of the opposite sex. This mechanism appears to be mediated via long-range signaling cascades enabling alternative cell wall embryo-genesis programming. When the pollen produced by these masculinized XX (genetically female) plants is applied for breeding, the seeds produced all lack a Y chromosome, and are thus considered "feminized" seeds. Previous transcriptomic analysis have identified genes associated with masculinization through the application of phytohormone signal disruption using silver thiosulfate treatment. We used Jack and Nota (Network Ontology Transcript Annotation), tools developed by SciAnno Mosaics and CU Boulder, to analyze transcriptomic data from C. sativa treated with colloidal silver to induce masculinization. Jack streamlined differential gene expression analysis and functional annotation of the resulting transcripts. Using Nota's multilayer network analysis (random walk with restart), we identified candidate sex-determining genes. Nota and Jack operate on the principle of building a reference-free biology, enabling discovery and annotation of gene-trait associations without relying on pre-existing genomic references. Our findings highlight Nota's power to dissect the genetic architecture of complex traits, particularly in systems like C. sativa where sex determination is influenced by multiple factors.