Illumina RNA-seq data of Genotype-specific responses of maize plants to Funneliformis mosseae

This article presents a publicly available RNA sequencing dataset generated to characterize transcriptomic responses of maize (Zea mays L.) genotypes to arbuscular mycorrhizal fungal (AMF) colonization under contrasting water availability conditions. The dataset underpins a controlled greenhouse experiment involving two maize inbred lines with contrasting drought responses (K1, drought-tolerant; K2, drought-sensitive) and their hybrid (KH), grown under well-watered (60% soil moisture content) and drought-stressed (30% soil moisture content) conditions, with or without inoculation with Funneliformis mosseae (F. mosseae, BEG12). Plants were cultivated in an automated phenotyping system that enabled precise irrigation control and non-destructive monitoring of shoot and root development. AMF inoculation was applied at planting, and mycorrhizal colonization was confirmed microscopically before tissue sampling. Leaf samples were collected at identical developmental stages from three biological replicates per genotype × treatment combination and immediately frozen for RNA isolation. Total RNA was extracted using a column-based purification protocol, and RNA quality and integrity were assessed prior to sequencing library preparation. Gene expression libraries were constructed using the QuantSeq 3' mRNA-Seq Library Prep Kit (Lexogen), which enables strand-specific, 3'-end-focused transcript quantification. Libraries were sequenced on an Illumina NovaSeq X Plus platform using single-end 75 bp reads, generating approximately 22-24 million reads per library. The complete set of raw RNA-seq reads and associated metadata has been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession PRJNA1267826, providing unrestricted public access to the dataset. This dataset enables reuse for a broad range of transcriptomic applications, including differential gene expression analysis, gene set enrichment analysis, hormone- and stress-related pathway exploration, and comparative analyses across maize genotypes, water regimes, or symbiotic conditions. The data can also support integrative studies combining transcriptomic profiles with phenotypic or physiological measurements, as well as meta-analyses of plant-microbe interactions and drought-related transcriptional responses in cereal crops.