Prediction of plant complex traits via integration of multi-omics data

Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops

Abiotic stresses such as drought, heat, cold, salinity and flooding significantly impact plant growth, development and productivity. As the planet has warmed, these abiotic stresses have increased in frequency and intensity, affecting the global food supply and making it imperative to develop stress-resilient crops. In the past 20 years, the development of omics technologies has contributed to the growth of datasets for plants grown under a wide range of abiotic environments. Integration of these rapidly growing data using machine-learning (ML) approaches can complement existing breeding efforts by providing insights into the mechanisms underlying plant responses to stressful conditions, which can be used to guide the design of resilient crops. In this review, we introduce ML approaches and provide examples of how researchers use these approaches to predict molecular activities, gene functions and genotype responses under stressful conditions. Finally, we consider the potential and challenges of using such approaches to enable the design of crops that are better suited to a changing environment.This article is part of the theme issue 'Crops under stress: can we mitigate the impacts of climate change on agriculture and launch the 'Resilience Revolution'?'.

PlantGPT: An Arabidopsis-Based Intelligent Agent that Answers Questions about Plant Functional Genomics

Research into plant gene function is crucial for developing strategies to increase crop yields. The recent introduction of large language models (LLMs) offers a means to aggregate large amounts of data into a queryable format, but the output can contain inaccurate or false claims known as hallucinations. To minimize such hallucinations and produce high-quality knowledge-based outputs, the abstracts of over 60 000 plant research articles are compiled into a Chroma database for retrieval-augmented generation (RAG). Then linguistic data are used from 13 993 Arabidopsis (Arabidopsis thaliana) phenotypes and 23 323 gene functions to fine-tune the LLM Llama3-8B, producing PlantGPT, a virtual expert in Arabidopsis phenotype-gene research. By evaluating answers to test questions, it is demonstrated that PlantGPT outperforms general LLMs in answering specialized questions. The findings provide a blueprint for functional genomics research in food crops and demonstrate the potential for developing LLMs for plant research modalities. To provide broader access and facilitate adoption, the online tool http://www.plantgpt.icu is developed, which will allow researchers to use PlantGPT in their scientific investigations.

Machine Learning-Based identification of resistance genes associated with sunflower broomrape

BACKGROUND

Sunflowers (Helianthus annuus L.), a vital oil crop, are facing a severe challenge from broomrape (Orobanche cumana), a parasitic plant that seriously jeopardizes the growth and development of sunflowers, limits global production and leads to substantial economic losses, which urges the development of resistant sunflower varieties.

RESULTS

This study aims to identify resistance genes from a comprehensive transcriptomic profile of 103 sunflower varieties based on gene expression data and then constructs predictive models with the key resistant genes. The least absolute shrinkage and selection operator (LASSO) regression and random forest feature importance ranking method were used to identify resistance genes. These genes were considered as biomarkers in constructing machine learning models with Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Logistic Regression (LR), and Gaussian Naive Bayes (GaussianNB). The SVM model constructed with the 24 key genes selected by the LASSO method demonstrated high classification accuracy (0.9514) and a robust AUC value (0.9865), effectively distinguishing between resistant and susceptible varieties based on gene expression data. Furthermore, we discovered a correlation between key genes and differential metabolites, particularly jasmonic acid (JA).

CONCLUSION

Our study highlights a novel perspective on screening sunflower varieties for broomrape resistance, which is anticipated to guide future biological research and breeding strategies.

Multi-omics integration uncovers the zinc metabolic regulatory network in the hyperaccumulating ecotype of Sedum alfredii Hance

Plants require a fine balance of zinc (Zn) for proper growth and development. This fine-tuning of Zn metabolism is tightly regulated and often challenging task for plants. Hyperaccumulating ecotype of Sedum alfredii Hance, a Zn hyperaccumulator form the Crassulaceae family, offers a unique model to study Zn homeostasis. To date, their complex molecular mechanisms underlying Zn regulation remain largely unknown. Here, we present a large-scale comparative investigation of Zn homeostasis networks in Zn hyperaccumulating and non-hyperaccumulating ecotypes of S. alfredii. By integrating transcriptomics, proteomics, metabolomics, and ionomics, we uncovered that transcriptional and translational changes play critical roles in maintaining Zn homeostasis. These adaptations include enhanced photosynthetic efficiency, improved Zn ion binding in shoots, and increased antioxidative capacities. Additionally, carbon and sulfur metabolic pathways were found to respond significantly to Zn treatment. Key components of the tricarboxylic acid (TCA) cycle, along with stress-related amino acids, fatty acids, sugars, antioxidants, and Zn-binding phenolics, were coordinately modulated under Zn exposure. This multi-omics integration provides novel insights into the functional genomics and metabolic adaptations of the Zn hyperaccumulator S. alfredii and will facilitate biotechnological applications of Zn hyperaccumulation traits for biofortification, phytoremediation and food crop safety.