Post-transcriptional regulation of light-stress responses and predictive modeling in vegetable Solanaceae Crops

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Samarina L., Kozhakhmetova F., Turbekova A., Jantassov S., Syrgaliyev Y., Taukenov A., ...Daha Fazla

Frontiers in Plant Science, cilt.17, 2026 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 17
  • Basım Tarihi: 2026
  • Doi Numarası: 10.3389/fpls.2026.1880044
  • Dergi Adı: Frontiers in Plant Science
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, CAB Abstracts, Directory of Open Access Journals, Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO)
  • Anahtar Kelimeler: abiotic stress, alternative splicing, light signaling, machine learning, small RNAs, Solanaceae, translational control
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Akdeniz Üniversitesi Adresli: Evet

Özet

Vegetable Solanaceae crops—tomato, pepper, eggplant, and potato—are increasingly cultivated in controlled environments where light is managed both as an energy source and as a developmental and stress-regulatory signal. Programmable spectra, photoperiods, and intensities interact with heat, drought, salinity, chilling, and nutrient limitation, generating complex physiological responses that cannot be explained by transcriptional regulation alone. This review highlights post-transcriptional RNA regulation as a key interface linking light perception with stress adaptation in vegetable Solanaceae. We focus on four regulatory layers—alternative splicing, RNA stability and decay, small RNAs pathways, and translational control—that determine which transcripts are processed, stabilized, degraded, or translated under specific environmental histories. Evidence from tomato, pepper, and potato indicates that RNA-level regulation contributes to stress responses, developmental flexibility, and genotype-specific acclimation. However, direct mechanistic links between defined photoreceptor pathways and specific post-transcriptional processes remain limited in Solanaceae; therefore, mechanisms established in Arabidopsis and other model plants are treated here as testable hypotheses rather than confirmed crop mechanisms. We further discuss how machine learning can integrate multi-omics and environmental datasets to identify predictive regulatory modules connecting light regimes with stress resilience and crop performance. Progress in this field will depend on experiments that combine precise light and microclimate monitoring with isoform-resolved transcriptomics, small RNAs/degradome analyses, RNA stability measurements, and translatome profiling. Such integration can transform controlled-environment Solanaceae research from descriptive stress omics to predictive, mechanism-based crop management.