Network ontology transcript annotation identifies genetic signals underlying sex determination

2026-04-17 · Source: Machine learning : nature.com subject feeds · Field: Science & Research — Life Sciences & Biology, Mathematics & Computational Sciences, Research Methodology & Innovation · Depth: Expert, long

Summary

Cannabis sativa L. exhibits sex determination influenced by XY chromosomes, hormone regulatory gene networks, and environmental factors, allowing genetically male or female plants to produce opposite-sex flowers under stress. This study analyzed transcriptomic data from cannabis treated with colloidal silver to induce masculinization, building on previous work with silver thiosulfate. Researchers utilized Nota (Network ontology transcript annotation), a multilayer network analysis tool employing Random-Walk-with-Restart, to identify candidate genes involved in sex determination. The analysis revealed genes associated with cell wall morphogenesis and embryogenic tissue homeostasis, suggesting that silver ion treatment disrupts phytohormone signal transduction through metal ion imbalance. These findings demonstrate Nota's effectiveness in enriching the genetic understanding of complex traits in non-model systems like cannabis.

Key takeaway

For AI scientists and plant geneticists studying non-model organisms, this research highlights that environmental stressors like colloidal silver can dramatically alter sex expression in Cannabis sativa by disrupting phytohormone pathways. You should consider integrating multilayer network analysis tools like Nota into your genomic studies to uncover complex gene-trait associations, especially when investigating plastic traits influenced by both genetic and environmental factors. This approach can accelerate the identification of critical regulatory genes.

Key insights

Silver ion treatment in cannabis perturbs phytohormone signaling, inducing sex reversal by altering cell wall and embryogenic tissue genes.

Principles

• Sex expression is governed by genetics, hormones, and environment.
• Abiotic stressors can interrupt phytohormone signaling.
• Multilayer network analysis aids complex trait gene discovery.

Method

Transcriptomic data from colloidal silver-treated cannabis was analyzed using Nota, a Random-Walk-with-Restart multilayer network tool, to identify genes associated with induced masculinization and sex determination.

In practice

• Apply colloidal silver to induce masculinization in cannabis.
• Use Nota for gene-trait association discovery in non-model plants.

Topics

• Cannabis sativa
• Sex Determination
• Transcriptomic Analysis
• Nota
• Multilayer Network Analysis

Best for: AI Scientist, Research Scientist, Data Scientist, Domain Expert

Related on AIssential

• Decoding sequence determinants of gene expression in diverse cellular and disease states — Decima predicts cell-type-specific gene expression from DNA sequence, enabling precise regulatory mechanism and variant effect analysis.
• SEAGAN: domain-Specific and Edge-Aware Graph Attention Network for Dynamic Plant Processes — SEAGAN, an edge-aware graph attention network, accurately identifies biochemical limitation states in plant A-Ci curves.
• Sex-based Network-Specific Differences in Connectomes: A Krakencoder-Based Analysis — Krakencoder analysis shows Default Mode Network perturbations most impact connectome integrity and sex-discriminative information, which is largely retained in intact predictions.
• Why the Human Genome’s Tangled Physicality May Confound AI — The human genome's dynamic, multi-layered regulation defies simple linear models, challenging AI's current sequence-based predictive approaches.
• Discovery of candidate therapeutic targets with Geneformer — Geneformer leverages a transformer model and transfer learning for gene network analysis and therapeutic target discovery with limited data.
• EssentialGIN: a new approach for gene essentiality prediction based on graph isomorphism neural networks — Graph isomorphism networks, by integrating biological data and preserving network topology, significantly improve essential gene prediction accuracy.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Machine learning : nature.com subject feeds.