biostudies-arrayexpressAlexandra LollDanio reriohttps://www.ebi.ac.uk/biostudies/studies/E-MTAB-16760The experiment investigates how the artificial sweetener sucralose affects gene expression in the teleost fish Danio rerio in order to derive mechanistic information relevant for environmental risk assessment. The main test including transcriptomics, was conducted with 10 µg/L and 100 mg/L as test concentrations. Pooled animals from each exposure group and corresponding controls were sampled after 96 hours for whole-animal RNA extraction, and poly(A)-enriched RNA was sequenced (paired-end, 150 bp) on an Illumina NovaSeq platform, followed by alignment to the Danio rerio reference genome and differential expression analysis using a DESeq2-based workflow with independent hypothesis weighting, data-driven log2 fold-change thresholds, and multiple-testing correction to define robust sets of differentially expressed genes per substance and concentration. Overrepresentation analysis of Gene Ontology terms using a common detected-gene background then mapped these gene-level changes to biological processes.biostudies-arrayexpressLibrary Construction - Only samples showing RIN values of at least 8 were proceeded to library preparation. Poly(A)-enriched, strand-specific mRNA libraries were then generated by the sequencing provider (Novogene) using a standard Illumina-compatible workflow comprising poly(A) selection, RNA fragmentation, first- and second-strand cDNA synthesis, adapter ligation, PCR amplification and size selection, and the resulting libraries were sequenced as 150 bp paired-end reads on an Illumina NovaSeq 6000 platform at a target depth of approximately 30 million read pairs per sample.Sample Treatment - Sucralose (CAS 56038-13-2, purity 98%) was purchased from abcr. Test solutions of 10 µg/L and 100 mg/L were prepared with copper reduced tap water. 7.5 mL of each test solution were transferred to three test vessels (replicates) and analogously 7.5 mL of water were used as control samples. 15 freshly fertilized eggs were transferred to the test vessels. Incubation was carried out in accordance with OECD 236 at 27±1 °C and a light/dark cycle of 14:10 hours.Sequencing - Sequencing was conducted by Novogene. Paired-end sequencing was performed using Illumina’s NovaSeq 6000 (Illumina Inc., San Diego, CA, USA). The read length was 150 base pairs.Nucleic Acid Extraction - Total RNA and protein were extracted according to the NucleoSpin® RNA/Protein extraction kit (NucleoSpin® RNA/Protein, Macherey-Nagel GmbH & Co. KG, Düren, Germany). RNA quantification (RNA = 50 ng/µl) was performed using a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA, USA), and quality control was conducted using the 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). The samples were stored at -80 °C until mRNA sequencing.Sample Collection - For each sample, 10 zebrafish embryos were randomly transferred from the test vessel into a tube. After adding the lysis buffer (NucleoSpin® RNA/Protein, Macherey-Nagel GmbH & Co. KG, Düren, Germany), lysis was performed according to the manufacturer's protocol for the NucleoSpin® RNA/Protein extraction kit. Homogenization was conducted at 5 m/s for 45s with FastPrep-24 (M.P. Biomedicals, Irvine, CA, USA) at room temperature.OrganizationMINSEQE ScoreAssays and DataProcessed DataMAGE-TAB FilesSequence Alignment - adapter sequences were removed using Trimomatic, and alignment of the sequences to the Danio rerio reference genome (GRCz11) was performed using the STAR aligner (v.2.5.2a), followed by read counting with featureCounts (v1.5.0-p1) to obtain count per gene tables.Data Transformation - Normalization and data transformation followed a DESeq2-based RNA‑seq workflow: raw gene-level count matrices from STAR alignments were imported and merged, then low-abundance genes were removed using a counts-per-million–based relevance threshold applied per experimental condition to restrict analysis to robustly detected features. Size factors and gene-wise dispersions were estimated with DESeq2’s default negative binomial modeling, and Wald tests were performed in a design including exposure condition as the main factor to obtain normalized counts and test statistics for each contrast. Log2 fold changes were then shrunk using apeglm to stabilize effect-size estimates for low-count or highly variable genes, and multiple testing was controlled by combining independent hypothesis weighting with Benjamini–Hochberg adjustment to obtain adjusted p values and q values; genes were considered differentially expressed when the adjusted p value was below 0.05 and the absolute log2 fold change exceeded a data-driven cutoff defined as the upper 30th percentile of the absolute log2 fold-change distribution, with quality-control diagnostics (normalization and dispersion plots, p value and log2 fold-change distributions, sample distance matrices, and PCA/t‑SNE) used to verify normalization performance.UnknownTranscriptomicsGenomicsProteomicssee manuscriptFastPrep‑24 homogenizer (MP Biomedicals), operated at 5 m/s for 10 seconds.NucleoSpin RNA/Protein kit lysis buffer (350 µL per tube; Macherey‑Nagel).Dell PowerEdge R750; CPU: 2 × Intel Xeon Gold 6346 (32 Cores / 64 Threads); RAM: 188 GBIllumina NovaSeq 6000RNA-seq of coding RNADanio rerioSebastian EilebrechtAlexandra LollfalsemRNA-Seq of Danio rerio exposed to different concentrations of sucralose against untreated control groupsThe experiment investigates how the artificial sweetener sucralose affects gene expression in the teleost fish Danio rerio in order to derive mechanistic information relevant for environmental risk assessment. The main test including transcriptomics, was conducted with 10 µg/L and 100 mg/L as test concentrations. Pooled animals from each exposure group and corresponding controls were sampled after 96 hours for whole-animal RNA extraction, and poly(A)-enriched RNA was sequenced (paired-end, 150 bp) on an Illumina NovaSeq platform, followed by alignment to the Danio rerio reference genome and differential expression analysis using a DESeq2-based workflow with independent hypothesis weighting, data-driven log2 fold-change thresholds, and multiple-testing correction to define robust sets of differentially expressed genes per substance and concentration. Overrepresentation analysis of Gene Ontology terms using a common detected-gene background then mapped these gene-level changes to biological processes.2026-04-30T00:00:00Z2026-04-30T01:01:51.698Z2026-03-17T12:09:31.911ZE-MTAB-16760ERP190874EFO_0002944EFO_0004170EFO_0004917EFO_0005518EFO_0003816EFO_0003738EFO_0004184EFO_0003969