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

TL;DR

This paper provides RNA-seq data from maize plants showing how different genotypes respond to a fungus under varying water conditions.

Contribution

The study introduces a new RNA-seq dataset capturing genotype-specific transcriptomic responses to mycorrhizal fungi under drought and well-watered conditions.

Findings

Transcriptomic responses of maize genotypes to Funneliformis mosseae were characterized under contrasting water conditions.

RNA-seq data is publicly available for differential gene expression and pathway analysis in drought-related studies.

The dataset supports integrative studies combining transcriptomic and phenotypic data in plant–microbe interactions.

Abstract

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.

Full text

Specifications TableSubjectBiologySpecific subject areaPlant–microbe interactions; drought stress; RNA sequencingType of dataRaw RNA-seq reads (FASTQ files in fastq.gz format); metadataData collectionMaize genotypes grown under controlled soil moisture conditions (60 % and 30 %), with or without F. mosseae inoculation)Data source locationGreenhouse experiment, HUN-REN Biological Research Centre, Szeged, HungaryData accessibilityRepository name: NCBI SRAData identification number: PRJNA1267826Direct URL to data:https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1267826/Instructions for accessing these data: The RNA sequencing data generated in this study are publicly available in the NCBI Sequence Read Archive (SRA) under BioProject accession PRJNA1267826. The dataset includes raw Illumina RNA-seq reads in fastq.gz format and associated metadata for all biological replicates and experimental conditions. Data can be accessed via the NCBI SRA web interface or downloaded using the SRA ToolkitRelated research articleVirág et al., Frontiers in Plant Science (2026), Genotype-specific responses of maize plants to Funneliformis mosseae under drought stress: phenomic and transcriptomic insights." Frontiers in Plant Science 16 (2025): 1723,031. [1]

Value of the Data

1

• •The dataset provides high-quality RNA-seq profiles from maize genotypes with contrasting drought tolerance grown under controlled water availability and mycorrhizal colonization.
• •It enables comparative transcriptomic analyses across genotypes, water regimes, and symbiotic conditions using a unified experimental design.
• •The data are suitable for the study of the symbiosis-associated gene expression patterns.
• •The dataset can be used for gene set enrichment, co-expression network construction, and pathway-level analyses.
• •The publicly available raw reads facilitate meta-analyses and cross-study comparisons of drought and plant–microbe interaction responses in cereal crops.

Background

2

The dataset was compiled to support a controlled experimental investigation of maize responses to arbuscular mycorrhizal fungal (AMF) colonization under contrasting water availability conditions. The theoretical background of the dataset lies in plant stress physiology and plant–microbe interaction research, where transcriptomic profiling is widely used to capture genotype-dependent molecular responses to abiotic stress and symbiotic interactions. In particular, maize genotypes with contrasting drought tolerance provide a suitable system for examining how host genetic background influences transcriptional responses to drought and AMF colonization. Previous studies have demonstrated that drought stress induces extensive transcriptional reprogramming in maize, affecting pathways related to photosynthesis, hormone signaling, primary metabolism, and root development, while AMF colonization can modulate these responses in a genotype-dependent manner [[2], [3], [4]].

Methodologically, the dataset was generated using standardized greenhouse cultivation, controlled soil moisture regimes, and a high-throughput phenotyping platform to ensure reproducible growth conditions across treatments. RNA sequencing was selected as an unbiased approach to quantify genome-wide gene expression changes across genotypes, water regimes, and mycorrhizal status. A 3′-end–focused RNA-seq library preparation strategy was applied to enable robust gene-level expression quantification across multiple biological replicates.

This data article complements the associated research article by providing unrestricted access to the raw RNA-seq reads and metadata, enabling independent reanalysis, validation of reported analyses, and reuse of the data in comparative or integrative transcriptomic studies beyond the scope of the original publication.

Data Description

3

The dataset is deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession PRJNA1267826 and consists of raw Illumina RNA-seq data in fastq.gz format and associated metadata files. The repository is organized as follows (Table 1). For a detailed description of the experimental conditions for each BioSample identifier, see Table 2.

• (i)BioProject level; Contains all sequencing data associated with the study, including links to BioSamples, experiments, and sequencing runs.
• (ii)BioSample records; Individual BioSample accessions correspond to leaf tissue samples collected from maize plants. Each BioSample is annotated with metadata describing: Genotype (K1, K2, or KH), Water regime (60 % or 30 % soil moisture content), Mycorrhizal status (with or without F. mosseae), Biological replicate identifier. Table 1. Organization of the repository of the RNA-Seq dataset.Table 1 dummy alt textData levelContentDescriptionBioProjectPRJNA1267826Complete project containerBioSampleIndividual samplesSAMN48732076 - SAMN48732108Genotype and treatment metadataTable 2Experimental design and sample IDs deposited in the NCBI database.Table 2 dummy alt textGenotypeSoil moistureAMF statusBiological replicatesNCBI Bioproject IDNCBI Biosample IDsK160 %M−3PRJNA1267826SAMN48732076SAMN48732077SAMN48732078K160 %M+3PRJNA1267826SAMN48732082SAMN48732083SAMN48732084K130 %M−3PRJNA1267826SAMN48732079SAMN48732080SAMN48732081K130 %M+3PRJNA1267826SAMN48732085SAMN48732086SAMN48732087K260 %M−3PRJNA1267826SAMN48732088SAMN48732089SAMN48732090K260 %M+3PRJNA1267826SAMN48732091SAMN48732092SAMN48732093K230 %M+3PRJNA1267826SAMN48732094SAMN48732095SAMN48732096KH60 %M−3PRJNA1267826SAMN48732097SAMN48732098SAMN48732099KH60 %M+3PRJNA1267826SAMN48732103SAMN48732104SAMN48732105KH30 %M−3PRJNA1267826SAMN48732100SAMN48732101SAMN48732102KH30 %M+3PRJNA1267826SAMN48732106SAMN48732107SAMN48732108Abbreviations: M-, no F. mosseae colonized; M+, F. mosseae colonized; K1, drought-tolerant genotype; K2, drought-sensitive genotype; KH, their hybrid.

Experimental Design, Materials and Methods

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Plant material and growth conditions

4.1

Seeds of the maize K1 inbred lines, characterized by drought tolerance, and K2, which is drought sensitive, along with their KH hybrid, were pre-germinated for 48 h before sowing. For each genotype and treatment, eight germinated seeds were planted into Plexiglass columns (400 mm in height and 90 mm in diameter) equipped with radio tags and enclosed within PVC sleeves. The columns were packed with a sterilized growth medium consisting of peat soil and sand mixed at an 80:20 ratio and amended with 6 g of a controlled-release fertilizer (Osmocote). The dry mass of the substrate per column was approximately 1050 g.

Plants were cultivated in an automated, greenhouse-based modular phenotyping platform. Daytime and nighttime temperatures were set to 24 °C and 20 °C, respectively, while relative humidity was maintained between 60 % and 80 %. Illumination followed a 14 h light / 10 h dark cycle, providing an average photosynthetic photon flux density of about 400 µmol m⁻² s⁻¹. Soil water availability was automatically controlled by the phenotyping system and maintained at either 60 % (well-watered) or 30 % (drought-stressed) moisture levels.

Mycorrhizal inoculation and colonization assessment

4.2

Inoculation with arbuscular mycorrhizal fungi was carried out at the time of sowing using a F. mosseae BEG12 preparation composed of spores, extraradical hyphae, mycorrhiza-colonized root fragments, and a sterilized sand carrier. Each plant received 50 g of inoculum, corresponding to an estimated density of 30 spores per gram. Control plants without mycorrhizal association were supplied with the same quantity of autoclaved inoculum, complemented by a microbial wash (<20 µm) to ensure comparable background microbial communities across treatments.

The extent of mycorrhizal colonization was evaluated before harvest. Root samples were cleared in 10 % KOH and subsequently stained using the vinegar–ink method described by Vierheilig et al [5]. Colonization frequency was quantified via the gridline intersect technique by scoring 50 root segments per sample under a stereomicroscope. Colonization rate is presented in the related research by Virág et al. [1]

Tissue sampling and RNA extraction

4.3

Leaf material was collected from maize plants at the V6–V8 developmental stage (BBCH 16–18), representing an early transition from vegetative growth toward reproductive development before anthesis. Harvested tissues were snap-frozen in liquid nitrogen and stored at −80 °C until further use. Frozen samples were homogenized into a fine powder with a mortar and pestle under liquid nitrogen. Total RNA was isolated from approximately 50 µg of ground tissue using the Quick-RNA™ Plant Miniprep Kit (Zymo Research) following the manufacturer's protocol. RNA quantity was determined with the Equalbit BR RNA Assay Kit, while RNA integrity was evaluated using a LabChip GX Touch Nucleic Acid Analyzer with the corresponding RNA assay reagents. Only samples that satisfied quality control criteria were advanced to library construction.

RNA-seq library preparation and sequencing

4.4

Gene expression libraries were prepared using the QuantSeq 3′ mRNA-Seq Library Prep Kit FWD for Illumina (Lexogen), which generates strand-specific libraries targeting the 3′ end of polyadenylated transcripts. Library concentrations were quantified using the Equalbit 1× dsDNA HS Assay Kit, and fragment size distributions were assessed by capillary electrophoresis using a LabChip GX Touch system. Libraries were pooled and sequenced on an Illumina NovaSeq X Plus platform using single-end 75 bp sequencing chemistry. Approximately 22–24 million reads were generated per library.

Limitations

The dataset is based on RNA sequencing of leaf tissues collected from maize plants grown under controlled greenhouse conditions. As a result, the data represent transcriptomic profiles from a single tissue type and do not capture gene expression patterns in other organs such as roots, stems, or reproductive tissues. Sampling was performed at a single developmental stage.

The RNA-seq libraries were generated using a 3′-end–focused sequencing protocol (QuantSeq, which enables robust gene-level expression quantification but does not provide full-length transcript coverage. Consequently, the dataset is not suitable for analyses of alternative splicing, transcript isoform structure, or sequence variation within transcripts.

Each experimental condition includes three biological replicates, which is sufficient for standard transcriptomic profiling but may limit statistical power for detecting low-abundance transcripts or subtle expression differences. In addition, although soil moisture content and environmental parameters were tightly controlled, minor biological variability among plants may remain inherent to greenhouse-based experiments.

Ethics Statement

The authors have read the ethical requirements for publication in Data in Brief and confirm that the current work does not involve human subjects, animal experiments, or any data collected from social media platforms.

CRediT authorship contribution statement

Eszter Virág: Data curation, Writing – review & editing, Writing – original draft. Zoltán Zombori: Visualization, Investigation. Géza Hegedűs: Software. Györgyi Ferenc: Visualization, Investigation. Dénes Dudits: Conceptualization, Methodology. Katalin Posta: Conceptualization, Methodology, Supervision.