Neurotranscriptome profiles of four zebrafish strains

Zebrafish is a model system being used in a variety of basic research and biomedical studies. Understanding the neurotranscriptomic architecture will greatly facilitate and enhance interpretation of research projects. Studies have reported that there are strain and sex-specific behavioral variation particulary in response to stress and anxiety-inducing scenarios. Capitalizing on previously documented behavioral variation by strains and sex of zebrafish, this study seeks to understand the neurotranscriptomic mechanisms potentially underlying this variation. Through RNA-sequencing (4 biological replicates per strain further subdivided into 2 biological replicates per sex) we analyzed the whole-brain transcriptomic profiles of four strains of zebrafish and relate transcriptional differences to phenotypic differences (e.g. behavioral or morphological) of the strains. Using a balanced block design, all 16 samples were multiplexed and run across 16 lanes on an Illumina GAIIx. Resulting reads (approximately 52 million reads per biological replicate) were aligned to the Zv9 genome build. We subsequently performed differential gene expression analysis and weighted gene coexpression network analysis to identify genes and gene networks associated with a phenotype. The goal of the study is to identify neurotranscriptomic mechanisms underlying phenotypic (e.g. morphological, behavioral) variation in zebrafish. Overall design: Through RNA-sequencing we quantified whole-brain transcriptome levels of protein-coding genes for four strains of zebrafish (AB, Scientific Hatcheries, High Stationary Behavior, and Low Stationary Behavior). Each line has 4 biological replicates (2 biological replicates for each sex). Each biological replicate is comprised of a pool of 10 same-sex and age-matched individuals. Using a balanced block design, the samples were mulitplexed and run across 16 lanes on an Illumina GAIIx. Reads that passed default quality control filters were aligned using GSNAP and quantified with HTSEQ. We used edgeR and WGCNA for subsequent differential gene expression and network analyses. qRT–PCR validation was performed using SYBR Green assays

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Wong RY, Godwin J