{"database": "metadata", "table": "run_metadata", "rows": [[60506, "SRR12338625", "SRX8838541", "SRS7101849", "SRP273930", "PRJNA649208", "A zebrafish model of Granulin deficiency reveals essential roles in myeloid cell differentiation", "GSE155258", "Transcriptome Analysis", "Granulin GRN is a pleiotropic protein involved in inflammation  wound healing  neurodegenerative disease  and tumorigenesis. These roles in human health have prompted research efforts to utilize Granulin in the treatment of rheumatoid arthritis  frontotemporal dementia  and to enhance wound healing. How granulin contributes to each of these diverse biological functions  however  remains largely unknown. Here  we have uncovered a new role for granulin during myeloid cell differentiation. Using a zebrafish model of granulin deficiency  we reveal that myeloid progenitors are unable to terminally differentiate into neutrophils and macrophages in the absence of granulin a grna  and fail to express the myeloid genes cebpa  rgs2  lyz  mpx  mpeg1  mfap4  and apoeb. Pathology studies in combination with RNA sequencing show that in addition to facilitating myeloid cell differentiation  granulin actively inhibits the erythroid program. Moreover  grna deficient myeloid progenitors are incapable of triggering a myelopoiesis emergency response  resulting in decreased recruitment of macrophages to the wound and therefore abnormal healing showing aberrant collagen depositions. Mechanistically  we have performed CUT&RUN for the first time in zebrafish  and identified that Pu.1 directly binds grna enhancers  triggering its expression. Similarly  mammalian granulin is also upregulated in myeloid cells  and its expression is controlled by the myeloid transcription factors PU.1 and IRF8  demonstrating a conserved regulatory mechanism among the zebrafish and mammalian genes. Altogether  our findings uncover a previously unrecognized role for granulin during myeloid cell differentiation  opening a new field of study that will help elucidate how granulin impacts inflammation  wound healing  tumor progression  and neurodegenerative disease. Overall design: Adult grna /  and grna+/+ control fish three fish per condition were subjected to cardiocentesis and kidney dissection as described here. RNA was isolated with RNeasy Qiagen following the manufacturer instructions. Total RNA was assessed for quality using an Agilent Tapestation 4200  and samples with an RNA Integrity Number RIN greater than 8.0 were used to generate RNA sequencing libraries using the TruSeq Stranded mRNA Sample Prep Illumina  San Diego  CA. Samples were processed following manufacturer's instructions  starting with 50 ng of RNA and modifying RNA shear time to five minutes. Resulting libraries were multiplexed and sequenced with 75 basepair bp single reads SR75 to a depth of approximately 20 million reads per sample on an Illumina HiSeq 4000. Samples were demuxltiplexed using bcl2fastq v2.20 Conversion Software Illumina  San Diego  CA.", null, null, null, "WT2 S12", "GSM4698243", null, "source name:grna+/+ control fish|tissue:kidney marrow|genotype:WT", "WT2 S12", "RNASeq data was mapped to Reference Consortium Zebrafish Build 10 UCSC Genome GRCz10/danRer10; Sept 2014 using Olego Wu  Anczukow et al.  2013 featureCounts Liao  Smyth et al.  2014 from subread package is used to compute the raw read counts for each gene. TPM Transcripts Per MillionsLi & Dewey  2011  Pachter  2011 values were computed from the raw read counts using a custom perl script and log2TPM+1 is used to compute the final log reduced expression values. DESeq2 1.26.0 Love  Huber et al.  2014 R package is used to compute differentially expressed genes at 1% false discovery rate. Genome build: Reference Consortium Zebrafish Build 10 UCSC Genome GRCz10/danRer10; Sept 2014 Supplementary files format and content: tab delimited text files include TPM values for each Sample.", "grna+/+ control fish", "Six kidney marrows were used to perform RNA seq. One kidney marrow per sample. Triplicates were used per sample: three grna+/+ and three grna /  siblings", "Zebrafish grna /  and grna+/+ control siblings were anesthetized in tricaine  subjected to cardiocentesis and kidney dissection as previously described Traver et al.  2003. The resulting kidney suspension was gently triturated with a P1000 pipette and filtered with a 30\u00b5m cell strainer. The cell suspension was lysated in RLT buffer and total RNA extraceted using RNeasy Qiagen following the manufacturer instructions. Total RNA was assessed for quality using an Agilent Tapestation 4200  and samples with an RNA Integrity Number RIN greater than 8.0 were used to generate RNA sequencing libraries using the TruSeq Stranded mRNA Sample Prep Illumina  San Diego  CA. Samples were processed following manufacturer\u2019s instructions  starting with 50 ng of RNA and modifying RNA shear time to five minutes.", "Zebrafish Danio rerio were raised as described Westerfield  2000 in a circulating aquarium system Aquaneering at 28\u00b0C and maintained in accordance with ISU and UCSD IACUC guidelines.", "tissue:kidney marrow|genotype:WT", "GSM4698243", "GSM4698243: WT2 S12; Danio rerio; RNA Seq", "GSM4698243", null, "1", "Zebrafish grna /  and grna+/+ control siblings were anesthetized in tricaine  subjected to cardiocentesis and kidney dissection as previously described Traver et al.  2003. The resulting kidney suspension was gently triturated with a P1000 pipette and filtered with a 30\u00b5m cell strainer. The cell suspension was lysated in RLT buffer and total RNA extraceted using RNeasy Qiagen following the manufacturer instructions. Total RNA was assessed for quality using an Agilent Tapestation 4200  and samples with an RNA Integrity Number RIN greater than 8.0 were used to generate RNA sequencing libraries using the TruSeq Stranded mRNA Sample Prep Illumina  San Diego  CA. Samples were processed following manufacturer's instructions  starting with 50 ng of RNA and modifying RNA shear time to five minutes.", "GEO Accession:GSM4698243", "RNA-Seq", "TRANSCRIPTOMIC", "cDNA", "SINGLE", "ILLUMINA", "Illumina HiSeq 4000", null, "SRP273930", null, null, "WT2_S12_L003_R1_001.fastq.gz", "fastq", 1637691700.0, 21548575.0, "GSM4698243 r1", "0:76 1:0", "A:405974612;C:400305204;G:377359541;T:453830617;N:221726", 76, 0, null, null, 405974612, 400305204, 377359541, 453830617, 221726, "SRX8838541", "SRS7101849", "SRA1104643", "GEO", "Boolean, Pediatrics, UCSD", 1, 0.89995, null, 0.07959, null, 0.7429, null, 0.5021, null, 76, null, "B", null, "usable mapping rate", "illumina", "hiseq_era", "unknown", "cdna_unspecified", "trueseq", "bulk", "unknown", "unknown", null, "United States", "2020-07-28", "Undetermined", "Undetermined", "Kidney", "Renal System"]], "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": ["60506"], "units": {}, "query_ms": 5.876430001080735}