<HashMap><database>biostudies-arrayexpress</database><scores/><additional><submitter>LUIS ROMERO</submitter><organism>Oryza sativa Japonica Group</organism><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/E-MTAB-15224</full_dataset_link><description>To investigate the effect of H2S on the rice transcriptomic responses to drought stress, we performed transcriptome sequencing analysis in four experimental treatments: rice watered plants (control sample, C), rice plants watered with NaHS solution, as sulfide donor (sulfide treatment, S), rice plants subjected to drought stress (drought, D) and rice plants pretreated with NaHS and subjected to drought stress (S_D). Thus, 25-day-old plants grown in soil under physiological conditions were divided into two batches with one batch irrigated with water and the other with NaHS for 10 days, replacing the NaHS solution every two days. After this period, each batch was subsequently divided into two new batches and subjected to water irrigation or drought for another 15 additional days. At the end of the full treatment, four replicates of each condition were obtained that were processed independently</description><repository>biostudies-arrayexpress</repository><sample_protocol>Growth Protocol - Rice (Oryza sativa L., japonica, Jsendra cultivar, Spain) (Reig-Valiente et al., 2016) was used in this study. The rice growth and treatments were performed as previously (Zhang et al., 2024a). Briefly, germinated seeds were sown in soil under a photoperiod of 12 h of white light (100 μmol m–2 s–1) at 28 C and 12 h of darkness at 28 C, and grown for 25 days under physiological water regime. Then, plants were divided into two batches, one water-irrigated and other treated with 50 μM NaHS (as hydrogen sulfide donor compound) for an additional 10 d. After this period, each batch was subsequently divided into two new batches and subjected to water irrigation or drought for another 15 d. At the end of the whole treatment, four different plant samples were obtained as follows: control (C, water-irrigated plants during all period), sulfide-treated (S, sulfide-treated plants without any drought stress), drought (D, drought-treated plants with no-sulfide treatment), and sulfide-pretreated plus drought (S_D, sulfide-pretreated and after drought subjected plants). Leaves from different plant samples were collected, frozen in liquid nitrogen and stored at -80 C for further analysis.</sample_protocol><sample_protocol>Sample Collection - At the end of the whole treatment, four different plant samples were obtained as follows: control (C, water-irrigated plants during all period), sulfide-treated (S, sulfide-treated plants without any drought stress), drought (D, drought-treated plants with no-sulfide treatment), and sulfide-pretreated plus drought (S_D, sulfide-pretreated and after drought subjected plants). Leaves from different plant samples were collected, frozen in liquid nitrogen and stored at -80 C for further analysis.</sample_protocol><sample_protocol>Sequencing - The library preparations were sequenced on an Illumina platform and paired-end reads were generated. Raw data (raw reads) of fastq format were firstly processed through fastp software. In this step, clean data (clean reads) were obtained by removing reads containing adapter, reads containing ploy-N and low-quality reads from raw data. At the same time, Q20, Q30 and GC content of the clean data were calculated. All the downstream analyses were based on the clean data with high quality. The obtained clean reads were compared with the Oryza sativa japonica reference genome using Hisat2 v2.0.5 software (ensemblplants_oryza_sativa_japonica_ group_irgsp_1_0_gca_001433935_1). Feature Counts v1.5.0-p3 was used to count the reads numbers mapped to each gene. FPKM (Fragments Per Kilobase of transcript sequence per Millions base pairs) of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of two conditions (four biological replicates per condition) was performed using the DESeq2 R package (1.20.0). The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate. Genes with an adjusted P-value &lt; 0.05 and log2 (fold change) >1 found by DESeq2 were assigned as differentially expressed</sample_protocol><sample_protocol>Library Construction - Total RNA was extracted from the leaves of the four different plant samples described above using the Qiagen RNeasy Plant Mini Kit. Four independent biological replicates of each sample were used for RNA-seq performed by Novogene Co. Ltd. Briefly, after previous analyses of RNA quantification, integrity and purity using Agilent 5400 analyzer.  Messenger RNA was purified from total RNA using poly-T oligo-attached magnetic beads. After fragmentation, the first strand cDNA was synthesized using random hexamer primers, followed by the second strand cDNA synthesis using either dUTP for directional library or dTTP for non-directional library. For the non-directional library, it was ready after end repair, A-tailing, adapter ligation, size selection, amplification, and purification. For the directional library, it was ready after end repair, A-tailing, adapter ligation, size selection, USER enzyme digestion, amplification, and purification. The library was checked with Qubit and real-time PCR for quantification and bioanalyzer for size distribution detection.</sample_protocol><sample_protocol>Nucleic Acid Extraction - Total RNA was extracted from the leaves of the four different plant samples described above using the Qiagen RNeasy Plant Mini Kit. Four independent biological replicates of each sample were used for RNA-seq performed by Novogene Co. Ltd</sample_protocol><figure_sub>Organization</figure_sub><figure_sub>MINSEQE Score</figure_sub><figure_sub>Assays and Data</figure_sub><figure_sub>Processed Data</figure_sub><figure_sub>MAGE-TAB Files</figure_sub><data_protocol>Data Transformation - The raw readcount was normalized using the DESeq2 R package (1.20.0) to correct the sequencing depth. DESeq2 uses a model based on the negative binomial distribution. The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate. Genes with an adjusted P-value &lt;=0.05 found by DESeq2 were assigned as differentially expressed.</data_protocol><omics_type>Metabolomics</omics_type><omics_type>Unknown</omics_type><omics_type>Transcriptomics</omics_type><omics_type>Genomics</omics_type><omics_type>Proteomics</omics_type><instrument_platform>Illumina NovaSeq 6000</instrument_platform><study_type>RNA-seq of coding RNA</study_type><species>Oryza sativa Japonica Group</species><pubmed_authors>LUIS ROMERO</pubmed_authors></additional><is_claimable>false</is_claimable><name>Hydrogen sulfide regulation of the transcriptional profile of rice plants under drought stress</name><description>To investigate the effect of H2S on the rice transcriptomic responses to drought stress, we performed transcriptome sequencing analysis in four experimental treatments: rice watered plants (control sample, C), rice plants watered with NaHS solution, as sulfide donor (sulfide treatment, S), rice plants subjected to drought stress (drought, D) and rice plants pretreated with NaHS and subjected to drought stress (S_D). Thus, 25-day-old plants grown in soil under physiological conditions were divided into two batches with one batch irrigated with water and the other with NaHS for 10 days, replacing the NaHS solution every two days. After this period, each batch was subsequently divided into two new batches and subjected to water irrigation or drought for another 15 additional days. At the end of the full treatment, four replicates of each condition were obtained that were processed independently</description><dates><release>2026-07-01T00:00:00Z</release><modification>2026-07-01T01:04:16.039Z</modification><creation>2025-06-13T10:27:31.288Z</creation></dates><accession>E-MTAB-15224</accession><cross_references><ENA>ERP173438</ENA><EFO>EFO_0002944</EFO><EFO>EFO_0004170</EFO><EFO>EFO_0003789</EFO><EFO>EFO_0005518</EFO><EFO>EFO_0003816</EFO><EFO>EFO_0003738</EFO><EFO>EFO_0004184</EFO></cross_references></HashMap>