{"database": "metadata", "table": "run_metadata", "rows": [[28993, "SRR26936238", "SRX22630100", "SRS19628582", "SRP473892", "PRJNA1044439", "Social stress in fathers affects sperm small RNA and offspring transcriptome profiles", "GSE248535", "Transcriptome Analysis", "Environmental changes may affect paternal condition and following generations but the underlying mechanisms are poorly understood. Male male competition induces a physiological stress response and affects male hormone levels  ejaculate traits and development in their offspring. Here we investigated the role of sperm mediated small RNAs in the transmission of male condition to the next generation. We exposed male zebrafish Danio rerio to high and low male male competition environments for two weeks and collected sperm samples at the end. We also performed IVFs using a split clutch design to distinguish between paternal and maternal effects and collected embryos at 24 hours to test for differentially expressed genes and transposable elements TEs at this key developmental stage. We sequenced micro  mi and Piwi interacting piRNAs in sperm and the full transcriptome in the embryos and ran differential expression analyses. We identified differentially expressed sperm mi  and piRNAs  with the strongest effects observed in sperm of males switching from high to low competition environments. We identified 612 differentially expressed genes in the embryos. These results confirm that the social environment does not only affect males but also the molecular ecology of their sperm and the gene expression in their offspring suggesting a putative role of sRNAs. Overall design: This data deposit includes the small RNA seq data for this research. Male zebrafish were subject to high and low social stress environments  with sperm samples collected  RNA extracted and sent for small RNA sequencing.", null, "pubmed:40121340", null, "Hb 5", "GSM7916499", null, "tissue:sperm|cell line:sperm|genotype:WT|treatment:High2|geo loc name:missing|collection date:missing", "Hb 5", "Reads were trimmed  aligned with PatMan and then count matrices assembled before DESeq2 analyses Assembly: GRCz10 Supplementary files format and content: piRNA raw counts.csv  raw counts of a subset of 24 out of the 40 samples that were analysed. These 24 were chosen due to noise observed in PCA clustering. Supplementary files format and content: mirna raw counts.csv  raw counts for 39/40 samples as one sample failed to progress through our pipeline to the same high quality as the other 39  so this sample was omitted from analyses.", "sperm", null, "Total RNA extraction New England BioLabs kit NEBNext\u00aeMultiplex Small RNA Library Prep Set for Illumina\u00ae Set 1 and 2 NEB #E7300 and NEB #E7580 small RNA library prep for Illumina", null, "cell line:sperm|genotype:WT|treatment:High2", "GSM7916499", "GSM7916499: Hb 5; Danio rerio; ncRNA Seq", "GSM7916499 r1", "GSM7916499", "1", "Total RNA extraction New England BioLabs kit NEBNext\u00aeMultiplex Small RNA Library Prep Set for Illumina\u00ae Set 1 and 2 NEB #E7300 and NEB #E7580 small RNA library prep for Illumina", null, "ncRNA-Seq", "TRANSCRIPTOMIC", "size fractionation", "SINGLE", "ILLUMINA", "Illumina HiSeq 2500", null, "SRP473892", null, null, "12235X24_160303_D00294_0224_AC95NJANXX_6.cutadapt.se.19_100.fastq.gz", "fastq", 1043060550.0, 35761504.0, "GSM7916499 r1", "0:29.17", "A:217942105;C:216398395;G:316456102;T:292217159;N:46789", 29, null, null, null, 217942105, 216398395, 316456102, 292217159, 46789, "SRX22630100", "SRS19628582", "SRA1756943", "University of East Anglia", "University of East Anglia", 1, 0.82114, null, 0.20881, null, 0.85622, null, 0.68486, null, 19, null, "B", null, "usable mapping rate", "illumina", "hiseq_era", "unknown", "size_fractionation", "nebnext", "bulk", "unknown", "unknown", null, "United Kingdom", "2023-11-23", "Undetermined", "Embryo", "Cell Line", "Cell Line"]], "columns": ["rowid", "run.accession", "experiment.accession", "sample.accession", "study.accession", "bioproject", "study.title", "study.alias", "study.type", "study.abstract", "study.attributes", "study.PMIDs", "sample.description", "sample.title", "sample.alias", "sample.centername", "sample.attributes", "GEOsample.title", "GEOsample.dataprocessing", "GEOsample.source", "GEOsample.treatmentprotocol", "GEOsample.extractprotocol", "GEOsample.growthprotocol", "GEOsample.characteristics", "GEOsample.accession", "experiment.title", "experiment.alias", "experiment.library_name", "experiment.design_description", "experiment.library_construction_protocol", "experiment.attributes", "experiment.library_strategy", "experiment.library_source", "experiment.library_selection", "experiment.library_layout", "experiment.platform", "experiment.instrument_model", "experiment.spot_descriptor", "experiment.study_ref", "run.title", "run.attributes", "run.filename", "run.semantic_name", "run.total_bases", "run.total_spots", "run.alias", "run.read_lengths", "run.base_counts", "run.r1_length", "run.r2_length", "run.r3_length", "run.r4_length", "run.Acount", "run.Ccount", "run.Gcount", "run.Tcount", "run.Ncount", "run.experiment", "run.pool_member", "submission.accession", "submission.srasource", "submission.bioprojectsource", "seqdetective.n_mates", "seqdetective.mapping_rate.mate1", "seqdetective.mapping_rate.mate2", "seqdetective.nofeature_rate.mate1", "seqdetective.nofeature_rate.mate2", "seqdetective.sparsity.mate1", "seqdetective.sparsity.mate2", "seqdetective.pos_strand_rate.mate1", "seqdetective.pos_strand_rate.mate2", "seqdetective.readlen.mate1", "seqdetective.readlen.mate2", "seqdetective.judgement.mate1", "seqdetective.judgement.mate2", "seqdetective.judgement.reason", "platform_family", "instrument_generation", "read_bias", "selection_class", "prep_kit", "sc_or_bulk", "tech_class", "technology", "tech_variant", "submission.bioprojectsource.country", "earliest_date", "devstage_curation", "devstage_curation_coarse", "tissue_curation", "tissue_curation_coarse"], "primary_keys": ["rowid"], "primary_key_values": ["28993"], "units": {}, "query_ms": 9.432132002984872}