Sesame (Sesamum indicum L.) is widely recognized for its exceptional nutritional properties and is often referred to as the "queen of oilseed crops." However, climate change has resulted in a substantial escalation of environmental stress, posing significant threats to sesame cultivation. These complex stressors, such as salinity, drought, waterlogging, and heat, have collectively reduced yields and severely impacted global sesame production. In 2023, the global market value of sesame was estimated at approximately USD 4.52 billion, underscoring its economic importance and the urgent need for intensified research. One of the major challenges to global sesame productivity is the multifaceted nature of these abiotic stresses. To unravel the underlying molecular mechanisms and stress response networks, various omics techniques, including transcriptomics, metabolomics, and proteomics, have been employed. This study highlights the potential of a multi-omics-driven approach to understand how sesame adapts to environmental challenges through changes in gene expression, metabolic profiles, and protein function. Recent advancements in high-throughput phenotyping, combined with genotyping by sequencing (GBS), have provided deeper insights into stress-resilient traits in sesame. Furthermore, integrating multi-omics data with machine learning (ML) approaches offers promising avenues for developing climate-resilient sesame cultivars, contributing to a sustainable and diversified global oilseed supply in addition to the main marketed oil crops, such as mustard and soybean.
Najar et al. (Sun,) studied this question.