Differentially and Co-expressed Genes in Embryo, Germ-Line and Somatic Tissues of Tribolium castaneum.
Sher Afzal KhanHeather EgglestonKevin M MylesZachary N AdelmanPublished in: G3 (Bethesda, Md.) (2019)
Transcriptomic studies of Tribolium castaneum have led to significant advances in our understanding of co-regulation and differential expression of genes in development. However, previously used microarray approaches have covered only a subset of known genes. The aim of this study was to investigate gene expression patterns of beetle embryo, germ-line and somatic tissues. We identified 12,302 expressed genes and determined differentially expressed up and down-regulated genes among all samples. For example, 1624 and 3639 genes were differentially increased in expression greater than or equal to twofold change (FDR < 0.01) in testis vs. ovary (virgin female) and ovary vs. embryo (0-5 hr), respectively. Of these, many developmental, somatic and germ-line differentially expressed genes were identified. Furthermore, many maternally deposited transcripts were identified, whose expression either decreased rapidly or persisted during embryogenesis. Genes with the largest change in expression were predominantly decreased during early embryogenesis as compared to ovary or were increased in testis compared to embryo. We also identify zygotic genes induced after fertilization. The genome wide variation in transcript regulation in maternal and zygotic genes could provide additional information on how the anterior posterior axis formation is established in Tribolium embryos as compared to Drosophila Together, our data will facilitate studies of comparative developmental biology as well as help identify candidate genes for identifying cis-elements to drive transgenic constructs.
Keyphrases
- genome wide
- gene expression
- genome wide identification
- bioinformatics analysis
- dna methylation
- poor prognosis
- copy number
- genome wide analysis
- healthcare
- binding protein
- body mass index
- machine learning
- long non coding rna
- pregnancy outcomes
- deep learning
- social media
- pregnant women
- weight loss
- rna seq
- weight gain
- preterm birth
- high glucose